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Sample records for artificial genome model

  1. Impulsive Neural Networks Algorithm Based on the Artificial Genome Model

    Directory of Open Access Journals (Sweden)

    Yuan Gao

    2014-05-01

    Full Text Available To describe gene regulatory networks, this article takes the framework of the artificial genome model and proposes impulsive neural networks algorithm based on the artificial genome model. Firstly, the gene expression and the cell division tree are applied to generate spiking neurons with specific attributes, neural network structure, connection weights and specific learning rules of each neuron. Next, the gene segment duplications and divergence model are applied to design the evolutionary algorithm of impulsive neural networks at the level of the artificial genome. The dynamic changes of developmental gene regulatory networks are controlled during the whole evolutionary process. Finally, the behavior of collecting food for autonomous intelligent agent is simulated, which is driven by nerves. Experimental results demonstrate that the algorithm in this article has the evolutionary ability on large-scale impulsive neural networks

  2. Use of artificial genomes in assessing methods for atypical gene detection.

    Directory of Open Access Journals (Sweden)

    Rajeev K Azad

    2005-11-01

    Full Text Available Parametric methods for identifying laterally transferred genes exploit the directional mutational biases unique to each genome. Yet the development of new, more robust methods--as well as the evaluation and proper implementation of existing methods--relies on an arbitrary assessment of performance using real genomes, where the evolutionary histories of genes are not known. We have used the framework of a generalized hidden Markov model to create artificial genomes modeled after genuine genomes. To model a genome, "core" genes--those displaying patterns of mutational biases shared among large numbers of genes--are identified by a novel gene clustering approach based on the Akaike information criterion. Gene models derived from multiple "core" gene clusters are used to generate an artificial genome that models the properties of a genuine genome. Chimeric artificial genomes--representing those having experienced lateral gene transfer--were created by combining genes from multiple artificial genomes, and the performance of the parametric methods for identifying "atypical" genes was assessed directly. We found that a hidden Markov model that included multiple gene models, each trained on sets of genes representing the range of genotypic variability within a genome, could produce artificial genomes that mimicked the properties of genuine genomes. Moreover, different methods for detecting foreign genes performed differently--i.e., they had different sets of strengths and weaknesses--when identifying atypical genes within chimeric artificial genomes.

  3. Use of Artificial Genomes in Assessing Methods for Atypical Gene Detection.

    Directory of Open Access Journals (Sweden)

    2005-11-01

    Full Text Available Parametric methods for identifying laterally transferred genes exploit the directional mutational biases unique to each genome. Yet the development of new, more robust methods-as well as the evaluation and proper implementation of existing methods-relies on an arbitrary assessment of performance using real genomes, where the evolutionary histories of genes are not known. We have used the framework of a generalized hidden Markov model to create artificial genomes modeled after genuine genomes. To model a genome, "core" genes-those displaying patterns of mutational biases shared among large numbers of genes-are identified by a novel gene clustering approach based on the Akaike information criterion. Gene models derived from multiple "core" gene clusters are used to generate an artificial genome that models the properties of a genuine genome. Chimeric artificial genomes-representing those having experienced lateral gene transfer-were created by combining genes from multiple artificial genomes, and the performance of the parametric methods for identifying "atypical" genes was assessed directly. We found that a hidden Markov model that included multiple gene models, each trained on sets of genes representing the range of genotypic variability within a genome, could produce artificial genomes that mimicked the properties of genuine genomes. Moreover, different methods for detecting foreign genes performed differently-i.e., they had different sets of strengths and weaknesses-when identifying atypical genes within chimeric artificial genomes.

  4. Artificial neural network modelling

    CERN Document Server

    Samarasinghe, Sandhya

    2016-01-01

    This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling. .

  5. Genome-Scale Models

    DEFF Research Database (Denmark)

    Bergdahl, Basti; Sonnenschein, Nikolaus; Machado, Daniel;

    2016-01-01

    An introduction to genome-scale models, how to build and use them, will be given in this chapter. Genome-scale models have become an important part of systems biology and metabolic engineering, and are increasingly used in research, both in academica and in industry, both for modeling chemical...

  6. Construction and characterization of bacterial artificial chromosomes (BACs) containing herpes simplex virus full-length genomes.

    Science.gov (United States)

    Nagel, Claus-Henning; Pohlmann, Anja; Sodeik, Beate

    2014-01-01

    Bacterial artificial chromosomes (BACs) are suitable vectors not only to maintain the large genomes of herpesviruses in Escherichia coli but also to enable the traceless introduction of any mutation using modern tools of bacterial genetics. To clone a herpes simplex virus genome, a BAC replication origin is first introduced into the viral genome by homologous recombination in eukaryotic host cells. As part of their nuclear replication cycle, genomes of herpesviruses circularize and these replication intermediates are then used to transform bacteria. After cloning, the integrity of the recombinant viral genomes is confirmed by restriction length polymorphism analysis and sequencing. The BACs may then be used to design virus mutants. Upon transfection into eukaryotic cells new herpesvirus strains harboring the desired mutations can be recovered and used for experiments in cultured cells as well as in animal infection models. PMID:24671676

  7. Genomic Feature Models

    DEFF Research Database (Denmark)

    Sørensen, Peter; Edwards, Stefan McKinnon; Rohde, Palle Duun

    Whole-genome sequences and multiple trait phenotypes from large numbers of individuals will soon be available in many populations. Well established statistical modeling approaches enable the genetic analyses of complex trait phenotypes while accounting for a variety of additive and non-additive g......Whole-genome sequences and multiple trait phenotypes from large numbers of individuals will soon be available in many populations. Well established statistical modeling approaches enable the genetic analyses of complex trait phenotypes while accounting for a variety of additive and non...... sets of genetic variants. We have applied these approaches to whole genome sequences and a complex trait phenotype resistance to starvation collected on inbred lines from the Drosophila Genome Reference Panel population. We identified a number of genomic features classification schemes (e.g. prior QTL...

  8. Genome Signature of Artificial Selection for High Milk Yield in Holstein Cattle

    Science.gov (United States)

    Artificial selection for high milk yield in Holstein cattle during the past forty years achieved tremendous increases in milk yield but had an unintended consequence of reduced fertility. It was unknown how artificial selection changed the Holstein genome and what genome changes were associated wit...

  9. Economic modeling using artificial intelligence methods

    CERN Document Server

    Marwala, Tshilidzi

    2013-01-01

    This book examines the application of artificial intelligence methods to model economic data. It addresses causality and proposes new frameworks for dealing with this issue. It also applies evolutionary computing to model evolving economic environments.

  10. Plant Growth Models Using Artificial Neural Networks

    Science.gov (United States)

    Bubenheim, David

    1997-01-01

    In this paper, we descrive our motivation and approach to devloping models and the neural network architecture. Initial use of the artificial neural network for modeling the single plant process of transpiration is presented.

  11. Final report. Human artificial episomal chromosome (HAEC) for building large genomic libraries

    Energy Technology Data Exchange (ETDEWEB)

    Jean-Michael H. Vos

    1999-12-09

    Collections of human DNA fragments are maintained for research purposes as clones in bacterial host cells. However for unknown reasons, some regions of the human genome appear to be unclonable or unstable in bacteria. Their team has developed a system using episomes (extrachromosomal, autonomously replication DNA) that maintains large DNA fragments in human cells. This human artificial episomal chromosomal (HAEC) system may prove useful for coverage of these especially difficult regions. In the broader biomedical community, the HAEC system also shows promise for use in functional genomics and gene therapy. Recent improvements to the HAEC system and its application to mapping, sequencing, and functionally studying human and mouse DNA are summarized. Mapping and sequencing the human genome and model organisms are only the first steps in determining the function of various genetic units critical for gene regulation, DNA replication, chromatin packaging, chromosomal stability, and chromatid segregation. Such studies will require the ability to transfer and manipulate entire functional units into mammalian cells.

  12. Ground water modelling in artificial recharge projects

    International Nuclear Information System (INIS)

    Decision-making is strongly based on modelling in two of our water supply projects concerning artificial ground water. The model used is a 2-dimensional flow and transport model using the element method. Due to sharp variations in Finnish soils, the modelling process is based on careful and detailed evaluation of basic hydrogeological data before and during model calibration

  13. Modelling Microwave Devices Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Andrius Katkevičius

    2012-04-01

    Full Text Available Artificial neural networks (ANN have recently gained attention as fast and flexible equipment for modelling and designing microwave devices. The paper reviews the opportunities to use them for undertaking the tasks on the analysis and synthesis. The article focuses on what tasks might be solved using neural networks, what challenges might rise when using artificial neural networks for carrying out tasks on microwave devices and discusses problem-solving techniques for microwave devices with intermittent characteristics.Article in Lithuanian

  14. Complete Genomes of Classical Swine Fever Virus Cloned into Bacterial Artificial Chromosomes

    OpenAIRE

    Rasmussen, Thomas Bruun; Reimann, I; Uttenthal, Åse; De Beer, M.

    2011-01-01

    Complete genome amplification of viral RNA provides a new tool for the generation of modified pestiviruses. We have used our full-genome amplification strategy for generation of amplicons representing complete genomes of classical swine fever virus. The amplicons were cloned directly into a stable single-copy bacterial artificial chromosome (BAC) generating full-length pestivirus DNAs from which infectious RNA transcripts could be also derived. Our strategy allows construction of stable infec...

  15. Psychometric Measurement Models and Artificial Neural Networks

    Science.gov (United States)

    Sese, Albert; Palmer, Alfonso L.; Montano, Juan J.

    2004-01-01

    The study of measurement models in psychometrics by means of dimensionality reduction techniques such as Principal Components Analysis (PCA) is a very common practice. In recent times, an upsurge of interest in the study of artificial neural networks apt to computing a principal component extraction has been observed. Despite this interest, the…

  16. Repetitive genome elements in a European corn borer, Ostrinia nubilalis, bacterial artificial chromosome library were indicated by bacterial artificial chromosome end sequencing and development of sequence tag site markers: implications for lepidopteran genomic research.

    Science.gov (United States)

    Coates, Brad S; Sumerford, Douglas V; Hellmich, Richard L; Lewis, Leslie C

    2009-01-01

    The European corn borer, Ostrinia nubilalis, is a serious pest of food, fiber, and biofuel crops in Europe, North America, and Asia and a model system for insect olfaction and speciation. A bacterial artificial chromosome library constructed for O. nubilalis contains 36 864 clones with an estimated average insert size of >or=120 kb and genome coverage of 8.8-fold. Screening OnB1 clones comprising approximately 2.76 genome equivalents determined the physical position of 24 sequence tag site markers, including markers linked to ecologically important and Bacillus thuringiensis toxin resistance traits. OnB1 bacterial artificial chromosome end sequence reads (GenBank dbGSS accessions ET217010 to ET217273) showed homology to annotated genes or expressed sequence tags and identified repetitive genome elements, O. nubilalis miniature subterminal inverted repeat transposable elements (OnMITE01 and OnMITE02), and ezi-like long interspersed nuclear elements. Mobility of OnMITE01 was demonstrated by the presence or absence in O. nubilalis of introns at two different loci. A (GTCT)n tetranucleotide repeat at the 5' ends of OnMITE01 and OnMITE02 are evidence for transposon-mediated movement of lepidopteran microsatellite loci. The number of repetitive elements in lepidopteran genomes will affect genome assembly and marker development. Single-locus sequence tag site markers described here have downstream application for integration within linkage maps and comparative genomic studies. PMID:19132072

  17. Artificial intelligence model for sustain ability measurement

    International Nuclear Information System (INIS)

    The article analyses the main dimensions of organizational sustain ability, their possible integrations into artificial neural network. In this article authors performing analyses of organizational internal and external environments, their possible correlations with 4 components of sustain ability, and the principal determination models for sustain ability of organizations. Based on the general principles of sustainable development organizations, a artificial intelligence model for the determination of organizational sustain ability has been developed. The use of self-organizing neural networks allows the identification of the organizational sustain ability and the endeavour to explore vital, social, antropogenical and economical efficiency. The determination of the forest enterprise sustain ability is expected to help better manage the sustain ability. (Authors)

  18. Efficient assembly of de novo human artificial chromosomes from large genomic loci

    Directory of Open Access Journals (Sweden)

    Stromberg Gregory

    2005-07-01

    Full Text Available Abstract Background Human Artificial Chromosomes (HACs are potentially useful vectors for gene transfer studies and for functional annotation of the genome because of their suitability for cloning, manipulating and transferring large segments of the genome. However, development of HACs for the transfer of large genomic loci into mammalian cells has been limited by difficulties in manipulating high-molecular weight DNA, as well as by the low overall frequencies of de novo HAC formation. Indeed, to date, only a small number of large (>100 kb genomic loci have been reported to be successfully packaged into de novo HACs. Results We have developed novel methodologies to enable efficient assembly of HAC vectors containing any genomic locus of interest. We report here the creation of a novel, bimolecular system based on bacterial artificial chromosomes (BACs for the construction of HACs incorporating any defined genomic region. We have utilized this vector system to rapidly design, construct and validate multiple de novo HACs containing large (100–200 kb genomic loci including therapeutically significant genes for human growth hormone (HGH, polycystic kidney disease (PKD1 and ß-globin. We report significant differences in the ability of different genomic loci to support de novo HAC formation, suggesting possible effects of cis-acting genomic elements. Finally, as a proof of principle, we have observed sustained ß-globin gene expression from HACs incorporating the entire 200 kb ß-globin genomic locus for over 90 days in the absence of selection. Conclusion Taken together, these results are significant for the development of HAC vector technology, as they enable high-throughput assembly and functional validation of HACs containing any large genomic locus. We have evaluated the impact of different genomic loci on the frequency of HAC formation and identified segments of genomic DNA that appear to facilitate de novo HAC formation. These genomic loci

  19. Artificial Intelligence Software Engineering (AISE) model

    Science.gov (United States)

    Kiss, Peter A.

    1990-01-01

    The American Institute of Aeronautics and Astronautics has initiated a committee on standards for Artificial Intelligence. Presented are the initial efforts of one of the working groups of that committee. A candidate model is presented for the development life cycle of knowledge based systems (KBSs). The intent is for the model to be used by the aerospace community and eventually be evolved into a standard. The model is rooted in the evolutionary model, borrows from the spiral model, and is embedded in the standard Waterfall model for software development. Its intent is to satisfy the development of both stand-alone and embedded KBSs. The phases of the life cycle are shown and detailed as are the review points that constitute the key milestones throughout the development process. The applicability and strengths of the model are discussed along with areas needing further development and refinement by the aerospace community.

  20. Mathematical problems in modeling artificial heart

    Directory of Open Access Journals (Sweden)

    Ahmed N. U.

    1995-01-01

    Full Text Available In this paper we discuss some problems arising in mathematical modeling of artificial hearts. The hydrodynamics of blood flow in an artificial heart chamber is governed by the Navier-Stokes equation, coupled with an equation of hyperbolic type subject to moving boundary conditions. The flow is induced by the motion of a diaphragm (membrane inside the heart chamber attached to a part of the boundary and driven by a compressor (pusher plate. On one side of the diaphragm is the blood and on the other side is the compressor fluid. For a complete mathematical model it is necessary to write the equation of motion of the diaphragm and all the dynamic couplings that exist between its position, velocity and the blood flow in the heart chamber. This gives rise to a system of coupled nonlinear partial differential equations; the Navier-Stokes equation being of parabolic type and the equation for the membrane being of hyperbolic type. The system is completed by introducing all the necessary static and dynamic boundary conditions. The ultimate objective is to control the flow pattern so as to minimize hemolysis (damage to red blood cells by optimal choice of geometry, and by optimal control of the membrane for a given geometry. The other clinical problems, such as compatibility of the material used in the construction of the heart chamber, and the membrane, are not considered in this paper. Also the dynamics of the valve is not considered here, though it is also an important element in the overall design of an artificial heart. We hope to model the valve dynamics in later paper.

  1. Electronic circuits modeling using artificial neural networks

    Directory of Open Access Journals (Sweden)

    Andrejević Miona V.

    2003-01-01

    Full Text Available In this paper artificial neural networks (ANN are applied to modeling of electronic circuits. ANNs are used for application of the black-box modeling concept in the time domain. Modeling process is described, so the topology of the ANN, the testing signal used for excitation, together with the complexity of ANN are considered. The procedure is first exemplified in modeling of resistive circuits. MOS transistor, as a four-terminal device, is modeled. Then nonlinear negative resistive characteristic is modeled in order to be used as a piece-wise linear resistor in Chua's circuit. Examples of modeling nonlinear dynamic circuits are given encompassing a variety of modeling problems. A nonlinear circuit containing quartz oscillator is considered for modeling. Verification of the concept is performed by verifying the ability of the model to generalize i.e. to create acceptable responses to excitations not used during training. Implementation of these models within a behavioral simulator is exemplified. Every model is implemented in realistic surrounding in order to show its interaction, and of course, its usage and purpose.

  2. Artificial neural network models for image understanding

    Science.gov (United States)

    Kulkarni, Arun D.; Byars, P.

    1991-06-01

    In this paper we introduce a new class of artificial neural network (ANN) models based on transformed domain feature extraction. Many optical and/or digital recognition systems based on transformed domain feature extraction are available in practice. Optical systems are inherently parallel in nature and are preferred for real time applications, whereas digital systems are more suitable for nonlinear operations. In our ANN models we combine advantages of both digital and optical systems. Many transformed domain feature extraction techniques have been developed during the last three decades. They include: the Fourier transform (FT), the Walsh Hadamard transform (WHT), the discrete cosine transform (DCT), etc. As an example, we have developed ANN models using the FT and WHT domain features. The models consist of two stages, the feature extraction stage and the recognition stage. We have used back-propagation and competitive learning algorithms in the recognition stage. We have used these ANN models for invariant object recognition. The models have been used successfully to recognize various types of aircraft, and also have been tested with test patterns. ANN models based on other transforms can be developed in a similar fashion.

  3. Multiple models for Rosaceae genomics.

    Science.gov (United States)

    Shulaev, Vladimir; Korban, Schuyler S; Sosinski, Bryon; Abbott, Albert G; Aldwinckle, Herb S; Folta, Kevin M; Iezzoni, Amy; Main, Dorrie; Arús, Pere; Dandekar, Abhaya M; Lewers, Kim; Brown, Susan K; Davis, Thomas M; Gardiner, Susan E; Potter, Daniel; Veilleux, Richard E

    2008-07-01

    The plant family Rosaceae consists of over 100 genera and 3,000 species that include many important fruit, nut, ornamental, and wood crops. Members of this family provide high-value nutritional foods and contribute desirable aesthetic and industrial products. Most rosaceous crops have been enhanced by human intervention through sexual hybridization, asexual propagation, and genetic improvement since ancient times, 4,000 to 5,000 B.C. Modern breeding programs have contributed to the selection and release of numerous cultivars having significant economic impact on the U.S. and world markets. In recent years, the Rosaceae community, both in the United States and internationally, has benefited from newfound organization and collaboration that have hastened progress in developing genetic and genomic resources for representative crops such as apple (Malus spp.), peach (Prunus spp.), and strawberry (Fragaria spp.). These resources, including expressed sequence tags, bacterial artificial chromosome libraries, physical and genetic maps, and molecular markers, combined with genetic transformation protocols and bioinformatics tools, have rendered various rosaceous crops highly amenable to comparative and functional genomics studies. This report serves as a synopsis of the resources and initiatives of the Rosaceae community, recent developments in Rosaceae genomics, and plans to apply newly accumulated knowledge and resources toward breeding and crop improvement. PMID:18487361

  4. Natural - synthetic - artificial!

    DEFF Research Database (Denmark)

    Nielsen, Peter E

    2010-01-01

    The terms "natural," "synthetic" and "artificial" are discussed in relation to synthetic and artificial chromosomes and genomes, synthetic and artificial cells and artificial life.......The terms "natural," "synthetic" and "artificial" are discussed in relation to synthetic and artificial chromosomes and genomes, synthetic and artificial cells and artificial life....

  5. Interaction model of artificial fish in virtual environment

    Institute of Scientific and Technical Information of China (English)

    Meng Xiangsong; Ban Xiaojuan; Yin Yixin

    2008-01-01

    Conventional artificial fish has some shortages on the interaction with environment,other fish,and the animator.This article proposes a multi-tier interaction control model of artificial fish,realizes the interaction model through integration of virtual reality technology and Markov sequence,and provides a virtual marine world to describe the interaction between artificial fish and the virtual environment and the interaction between the artificial fish and the animator.Simulation results show that the interaction model owns not only the basic characteristics of virtual biology,but also has high trueness interaction function.

  6. Efficient manipulation of the human adenovirus genome as an infectious yeast artificial chromosome clone.

    OpenAIRE

    Ketner, G; Spencer, F; Tugendreich, S; C. Connelly; Hieter, P

    1994-01-01

    A yeast artificial chromosome (YAC) containing a complete human adenovirus type 2 genome was constructed, and viral DNA derived from the YAC was shown to be infectious upon introduction into mammalian cells. The adenovirus YAC could be manipulated efficiently using homologous recombination-based methods in the yeast host, and mutant viruses, including a variant that expresses the human analog of the Saccharomyces cerevisiae CDC27 gene, were readily recovered from modified derivatives of the Y...

  7. Building an Artificial Idiotopic Immune Model Based on Artificial Neural Network Ideology

    Directory of Open Access Journals (Sweden)

    Hossam Meshref

    2013-01-01

    Full Text Available In the literature, there were many research efforts that utilized the artificial immune networks to model their designed applications, but they were considerably complicated, and restricted to a few areas that such as computer security applications. The objective of this research is to introduce a new model for artificial immune networks that adopts features from other biological successful models to overcome its complexity such as the artificial neural networks. Common concepts between the two systems were investigated to design a simple, yet a robust, model of artificial immune networks. Three artificial neural networks learning models were available to choose from in the research design: supervised, unsupervised, and reinforcement learning models. However, it was found that the reinforcement model is the most suitable model. Research results examined network parameters, and appropriate relations between concentration ranges and their dependent parameters as well as the expected reward during network learning. In conclusion, it is recommended the use of the designed model by other researchers in different applications such as controlling robots in hazardous environment to save human lives as well as using it on image retrieval in general to help the police department identify suspects.

  8. Artificial duplicate reads in sequencing data of 454 Genome Sequencer FLX System

    Institute of Scientific and Technical Information of China (English)

    Hui Dong; Yangyi Chen; Yan Shen; Shengyue Wang; Guoping Zhao; Weirong Jin

    2011-01-01

    The 454 Genome Sequencer (GS) FLX System is one of the next-generation sequencing systems featured by long reads, high accuracy, and ultra-high throughput.Based on the mechanism of emulsion PCR, a unique DNA template would only generate a unique sequence read after being amplified and sequenced on GS FLX.However,biased amplification of DNA templates might occur in the process of emulsion PCR, which results in production of artificial duplicate reads.Under the condition that each DNA template is unique to another, 3.49%-18.14% of total reads in GS FLX-sequencing data were found to be artificial duplicate reads.These duplicate reads may lead to misunderstanding of sequencing data and special attention should be paid to the potential biases they introduced to the data.

  9. Artificial Intelligence Techniques for Steam Generator Modelling

    CERN Document Server

    Wright, Sarah

    2008-01-01

    This paper investigates the use of different Artificial Intelligence methods to predict the values of several continuous variables from a Steam Generator. The objective was to determine how the different artificial intelligence methods performed in making predictions on the given dataset. The artificial intelligence methods evaluated were Neural Networks, Support Vector Machines, and Adaptive Neuro-Fuzzy Inference Systems. The types of neural networks investigated were Multi-Layer Perceptions, and Radial Basis Function. Bayesian and committee techniques were applied to these neural networks. Each of the AI methods considered was simulated in Matlab. The results of the simulations showed that all the AI methods were capable of predicting the Steam Generator data reasonably accurately. However, the Adaptive Neuro-Fuzzy Inference system out performed the other methods in terms of accuracy and ease of implementation, while still achieving a fast execution time as well as a reasonable training time.

  10. Cloning the simian varicella virus genome in E. coli as an infectious bacterial artificial chromosome

    OpenAIRE

    Gray, Wayne L.; Zhou, Fuchun; Noffke, Juliane; Tischer, B Karsten

    2011-01-01

    Simian varicella virus (SVV) is closely related to human varicella-zoster virus and causes varicella and zoster-like disease in nonhuman primates. In this study, a mini-F replicon was inserted into a SVV cosmid and infectious SVV was generated by co-transfection of Vero cells with overlapping SVV cosmids. The entire SVV genome, cloned as a bacterial artificial chromosome (BAC), was stably propagated upon serial passage in E. coli. Transfection of pSVV-BAC DNA into Vero cells yielded infectiou...

  11. Artificial neural network based modelling of internal combustion engine performance

    OpenAIRE

    Boruah, Dibakor; Thakur, Pintu Kumar; Baruah, Dipal

    2016-01-01

    The present study aims to quantify the applicability of artificial neural network as a black-box model for internal combustion engine performance. In consequence, an artificial neural network (ANN) based model for a four cylinder, four stroke internal combustion diesel engine has been developed on the basis of specific input and output factors, which have been taken from experimental readings for different load and engine speed circumstances. The input parameters that have been used to create...

  12. Artificial Life of Soybean Plant Growth Modeling Using Intelligence Approaches

    OpenAIRE

    Atris Suyantohadi; Mochamad Hariadi; Mauridhi Hery Purnomo

    2010-01-01

    The natural process on plant growth system has a complex system and it has could be developed on characteristic studied using intelligent approaches conducting with artificial life system. The approaches on examining the natural process on soybean (Glycine Max L.Merr) plant growth have been analyzed and synthesized in these research through modeling using Artificial Neural Network (ANN) and Lindenmayer System (L-System) methods. Research aimed to design and to visualize plant growth modeling...

  13. Multiplex sequencing of bacterial artificial chromosomes for assembling complex plant genomes.

    Science.gov (United States)

    Beier, Sebastian; Himmelbach, Axel; Schmutzer, Thomas; Felder, Marius; Taudien, Stefan; Mayer, Klaus F X; Platzer, Matthias; Stein, Nils; Scholz, Uwe; Mascher, Martin

    2016-07-01

    Hierarchical shotgun sequencing remains the method of choice for assembling high-quality reference sequences of complex plant genomes. The efficient exploitation of current high-throughput technologies and powerful computational facilities for large-insert clone sequencing necessitates the sequencing and assembly of a large number of clones in parallel. We developed a multiplexed pipeline for shotgun sequencing and assembling individual bacterial artificial chromosomes (BACs) using the Illumina sequencing platform. We illustrate our approach by sequencing 668 barley BACs (Hordeum vulgare L.) in a single Illumina HiSeq 2000 lane. Using a newly designed parallelized computational pipeline, we obtained sequence assemblies of individual BACs that consist, on average, of eight sequence scaffolds and represent >98% of the genomic inserts. Our BAC assemblies are clearly superior to a whole-genome shotgun assembly regarding contiguity, completeness and the representation of the gene space. Our methods may be employed to rapidly obtain high-quality assemblies of a large number of clones to assemble map-based reference sequences of plant and animal species with complex genomes by sequencing along a minimum tiling path. PMID:26801048

  14. Using Artificial Intelligence Models in System Identification

    OpenAIRE

    Elshamy, Wesam

    2013-01-01

    Artificial Intelligence (AI) techniques are known for its ability in tackling problems found to be unyielding to traditional mathematical methods. A recent addition to these techniques are the Computational Intelligence (CI) techniques which, in most cases, are nature or biologically inspired techniques. Different CI techniques found their way to many control engineering applications, including system identification, and the results obtained by many researchers were encouraging. However, most...

  15. Comparing Neural Networks and ARMA Models in Artificial Stock Market

    Czech Academy of Sciences Publication Activity Database

    Krtek, Jiří; Vošvrda, Miloslav

    2011-01-01

    Roč. 18, č. 28 (2011), s. 53-65. ISSN 1212-074X R&D Projects: GA ČR GD402/09/H045 Institutional research plan: CEZ:AV0Z10750506 Keywords : neural networks * vector ARMA * artificial market Subject RIV: AH - Economics http://library.utia.cas.cz/separaty/2011/E/krtek-comparing neural networks and arma models in artificial stock market.pdf

  16. A Study on Artificial Intelligence IQ and Standard Intelligent Model

    OpenAIRE

    Liu, Feng; Shi, Yong

    2015-01-01

    Currently, potential threats of artificial intelligence (AI) to human have triggered a large controversy in society, behind which, the nature of the issue is whether the artificial intelligence (AI) system can be evaluated quantitatively. This article analyzes and evaluates the challenges that the AI development level is facing, and proposes that the evaluation methods for the human intelligence test and the AI system are not uniform; and the key reason for which is that none of the models ca...

  17. Neuro-Based Artificial Intelligence Model for Loan Decisions

    OpenAIRE

    Shorouq F. Eletter; Saad G. Yaseen; Ghaleb A. Elrefae

    2010-01-01

    Problem statement: Despite the increase in consumer loans defaults and competition in the banking market, most of the Jordanian commercial banks are reluctant to use artificial intelligence software systems for supporting loan decisions. Approach: This study developed a proposed model that identifies artificial neural network as an enabling tool for evaluating credit applications to support loan decisions in the Jordanian Commercial banks. A multi-layer feed-forward neural network with backpr...

  18. A new model of artificial neuron: cyberneuron and its use

    CERN Document Server

    Polikarpov, S V; Rumyantsev, K E; Golubchikov, D M

    2009-01-01

    This article describes a new type of artificial neuron, called the authors "cyberneuron". Unlike classical models of artificial neurons, this type of neuron used table substitution instead of the operation of multiplication of input values for the weights. This allowed to significantly increase the information capacity of a single neuron, but also greatly simplify the process of learning. Considered an example of the use of "cyberneuron" with the task of detecting computer viruses.

  19. Minimal model for genome evolution and growth

    OpenAIRE

    Hsieh, L. C.; Luo, L. F.; Ji, F. M.; Lee, H C

    2002-01-01

    Textual analysis of typical microbial genomes reveals that they have the statistical characteristics of a DNA sequence of a much shorter length. This peculiar property supports an evolutionary model in which a genome evolves by random mutation but primarily grows by random segmental self-copying. That genomes grew mostly by self-copying is consistent with the observation that repeat sequences in all genomes are widespread and intragenomic and intergenomic homologous genes are preponderance ac...

  20. Construction of an infectious clone of canine herpesvirus genome as a bacterial artificial chromosome.

    Science.gov (United States)

    Arii, Jun; Hushur, Orkash; Kato, Kentaro; Kawaguchi, Yasushi; Tohya, Yukinobu; Akashi, Hiroomi

    2006-04-01

    Canine herpesvirus (CHV) is an attractive candidate not only for use as a recombinant vaccine to protect dogs from a variety of canine pathogens but also as a viral vector for gene therapy in domestic animals. However, developments in this area have been impeded by the complicated techniques used for eukaryotic homologous recombination. To overcome these problems, we used bacterial artificial chromosomes (BACs) to generate infectious BACs. Our findings may be summarized as follows: (i) the CHV genome (pCHV/BAC), in which a BAC flanked by loxP sites was inserted into the thymidine kinase gene, was maintained in Escherichia coli; (ii) transfection of pCHV/BAC into A-72 cells resulted in the production of infectious virus; (iii) the BAC vector sequence was almost perfectly excisable from the genome of the reconstituted virus CHV/BAC by co-infection with CHV/BAC and a recombinant adenovirus that expressed the Cre recombinase; and (iv) a recombinant virus in which the glycoprotein C gene was deleted was generated by lambda recombination followed by Flp recombination, which resulted in a reduction in viral titer compared with that of the wild-type virus. The infectious clone pCHV/BAC is useful for the modification of the CHV genome using bacterial genetics, and CHV/BAC should have multiple applications in the rapid generation of genetically engineered CHV recombinants and the development of CHV vectors for vaccination and gene therapy in domestic animals. PMID:16515874

  1. Stochastic Differential Equations in Artificial Pancreas Modelling

    DEFF Research Database (Denmark)

    Duun-Henriksen, Anne Katrine

    Type 1 diabetes accounts for approximately 5% of the total diabetes population. It is caused by the destruction of insulin producing β-cells in the pancreas. Various treatment strategies are available today, some of which include advanced technological devices such as an insulin pump and a contin......Type 1 diabetes accounts for approximately 5% of the total diabetes population. It is caused by the destruction of insulin producing β-cells in the pancreas. Various treatment strategies are available today, some of which include advanced technological devices such as an insulin pump...... of the insulin pump and the CGM has paved the way for a fully automatic treatment regime, the artificial pancreas. The idea is to connect the CGM with the insulin pump via a control algorithm running on e.g. the patients smart phone. The CGM observations are sent to the smart phone and based on this information...... of the system directly. The purpose of this PhD-project was to investigate the potential of SDEs in the artificial pancreas development. Especially, the emerging continuous monitoring of glucose levels makes SDEs highly applicable to this field. The current thesis aims at demonstrating and discussing...

  2. Metaplasticity Artificial Neural Networks Model Application to Radar Detection

    Directory of Open Access Journals (Sweden)

    Diego Andina

    2007-12-01

    Full Text Available Many Artificial Neural Networks design algorithms or learning methods imply the minimization of an error objective function. During learning, weight values are updated following a strategy that tends to minimize the final mean error in the Network performance. Weight values are classically seen as a representation of the synaptic weights in biological neurons and their ability to change its value could be interpreted as artificial plasticity inspired by this biological property of neurons. In such a way, metaplasticity is interpreted in this paper as the ability to change the efficiency of artificial plasticity giving more relevance to weight updating of less frequent activations and resting relevance to frequent ones. Modeling this interpretation in the training phase, the hypothesis of an improved training is tested in the Multilayer Perceptron with Backpropagation case. The results show a much more efficient training maintaining the Artificial Neural Network performance.

  3. Cloning human herpes virus 6A genome into bacterial artificial chromosomes and study of DNA replication intermediates

    OpenAIRE

    Borenstein, Ronen; Frenkel, Niza

    2009-01-01

    Cloning of large viral genomes into bacterial artificial chromosomes (BACs) facilitates analyses of viral functions and molecular mutagenesis. Previous derivations of viral BACs involved laborious recombinations within infected cells. We describe a single-step production of viral BACs by direct cloning of unit length genomes, derived from circular or head-to-tail concatemeric DNA replication intermediates. The BAC cloning is independent of intracellular recombinations and DNA packaging constr...

  4. Neuro-Based Artificial Intelligence Model for Loan Decisions

    Directory of Open Access Journals (Sweden)

    Shorouq F. Eletter

    2010-01-01

    Full Text Available Problem statement: Despite the increase in consumer loans defaults and competition in the banking market, most of the Jordanian commercial banks are reluctant to use artificial intelligence software systems for supporting loan decisions. Approach: This study developed a proposed model that identifies artificial neural network as an enabling tool for evaluating credit applications to support loan decisions in the Jordanian Commercial banks. A multi-layer feed-forward neural network with backpropagation learning algorithm was used to build up the proposed model. Results: Different representative cases of loan applications were considered based on the guidelines of different banks in Jordan, to validate the neural network model. Conclusion: The results indicated that artificial neural networks are a successful technology that can be used in loan application evaluation in the Jordanian commercial banks.

  5. Artificial Neural Networks for Modeling Knowing and Learning in Science.

    Science.gov (United States)

    Roth, Wolff-Michael

    2000-01-01

    Advocates artificial neural networks as models for cognition and development. Provides an example of how such models work in the context of a well-known Piagetian developmental task and school science activity: balance beam problems. (Contains 59 references.) (Author/WRM)

  6. Systems in Science: Modeling Using Three Artificial Intelligence Concepts.

    Science.gov (United States)

    Sunal, Cynthia Szymanski; Karr, Charles L.; Smith, Coralee; Sunal, Dennis W.

    2003-01-01

    Describes an interdisciplinary course focusing on modeling scientific systems. Investigates elementary education majors' applications of three artificial intelligence concepts used in modeling scientific systems before and after the course. Reveals a great increase in understanding of concepts presented but inconsistent application. (Author/KHR)

  7. Artificial Neural Network Model for Friction Stir Processing

    Directory of Open Access Journals (Sweden)

    Syed Muhammed Fahd

    2014-06-01

    Full Text Available Friction stir processing (FSP is an effective means of refining grain size of aluminum alloys. An artificial neural network model (ANN is made for predicting the grain size of alloys which are processed by FSP. The simulated results from the model show how grain size varies with the process parameters.

  8. Plantagora: modeling whole genome sequencing and assembly of plant genomes.

    Directory of Open Access Journals (Sweden)

    Roger Barthelson

    Full Text Available BACKGROUND: Genomics studies are being revolutionized by the next generation sequencing technologies, which have made whole genome sequencing much more accessible to the average researcher. Whole genome sequencing with the new technologies is a developing art that, despite the large volumes of data that can be produced, may still fail to provide a clear and thorough map of a genome. The Plantagora project was conceived to address specifically the gap between having the technical tools for genome sequencing and knowing precisely the best way to use them. METHODOLOGY/PRINCIPAL FINDINGS: For Plantagora, a platform was created for generating simulated reads from several different plant genomes of different sizes. The resulting read files mimicked either 454 or Illumina reads, with varying paired end spacing. Thousands of datasets of reads were created, most derived from our primary model genome, rice chromosome one. All reads were assembled with different software assemblers, including Newbler, Abyss, and SOAPdenovo, and the resulting assemblies were evaluated by an extensive battery of metrics chosen for these studies. The metrics included both statistics of the assembly sequences and fidelity-related measures derived by alignment of the assemblies to the original genome source for the reads. The results were presented in a website, which includes a data graphing tool, all created to help the user compare rapidly the feasibility and effectiveness of different sequencing and assembly strategies prior to testing an approach in the lab. Some of our own conclusions regarding the different strategies were also recorded on the website. CONCLUSIONS/SIGNIFICANCE: Plantagora provides a substantial body of information for comparing different approaches to sequencing a plant genome, and some conclusions regarding some of the specific approaches. Plantagora also provides a platform of metrics and tools for studying the process of sequencing and assembly

  9. Evaluation of Artificial Intelligence Based Models for Chemical Biodegradability Prediction

    Directory of Open Access Journals (Sweden)

    Aleksandar Sabljic

    2004-12-01

    Full Text Available This study presents a review of biodegradability modeling efforts including a detailed assessment of two models developed using an artificial intelligence based methodology. Validation results for these models using an independent, quality reviewed database, demonstrate that the models perform well when compared to another commonly used biodegradability model, against the same data. The ability of models induced by an artificial intelligence methodology to accommodate complex interactions in detailed systems, and the demonstrated reliability of the approach evaluated by this study, indicate that the methodology may have application in broadening the scope of biodegradability models. Given adequate data for biodegradability of chemicals under environmental conditions, this may allow for the development of future models that include such things as surface interface impacts on biodegradability for example.

  10. Natural vs. artificial groundwater recharge, quantification through inverse modeling

    Directory of Open Access Journals (Sweden)

    H. Hashemi

    2012-08-01

    Full Text Available Estimating the change in groundwater recharge from an introduced artificial recharge system is important in order to evaluate future water availability. This paper presents an inverse modeling approach to quantify the recharge contribution from both an ephemeral river channel and an introduced artificial recharge system based on floodwater spreading in arid Iran. The study used the MODFLOW-2000 to estimate recharge for both steady and unsteady-state conditions. The model was calibrated and verified based on the observed hydraulic head in observation wells and model precision, uncertainty, and model sensitivity were analyzed in all modeling steps. The results showed that in a normal year without extreme events the floodwater spreading system is the main contributor to recharge with 80% and the ephemeral river channel with 20% of total recharge in the studied area. Uncertainty analysis revealed that the river channel recharge estimation represents relatively more uncertainty in comparison to the artificial recharge zones. The model is also less sensitive to the river channel. The results show that by expanding the artificial recharge system the recharge volume can be increased even for small flood events while the recharge through the river channel increases only for major flood events.

  11. Natural vs. artificial groundwater recharge, quantification through inverse modeling

    Directory of Open Access Journals (Sweden)

    H. Hashemi

    2013-02-01

    Full Text Available Estimating the change in groundwater recharge from an introduced artificial recharge system is important in order to evaluate future water availability. This paper presents an inverse modeling approach to quantify the recharge contribution from both an ephemeral river channel and an introduced artificial recharge system based on floodwater spreading in arid Iran. The study used the MODFLOW-2000 to estimate recharge for both steady- and unsteady-state conditions. The model was calibrated and verified based on the observed hydraulic head in observation wells and model precision, uncertainty, and model sensitivity were analyzed in all modeling steps. The results showed that in a normal year without extreme events, the floodwater spreading system is the main contributor to recharge with 80% and the ephemeral river channel with 20% of total recharge in the studied area. Uncertainty analysis revealed that the river channel recharge estimation represents relatively more uncertainty in comparison to the artificial recharge zones. The model is also less sensitive to the river channel. The results show that by expanding the artificial recharge system, the recharge volume can be increased even for small flood events, while the recharge through the river channel increases only for major flood events.

  12. Bacterial Artificial Chromosomes: A Functional Genomics Tool for the Study of Positive-strand RNA Viruses.

    Science.gov (United States)

    Yun, Sang-Im; Song, Byung-Hak; Kim, Jin-Kyoung; Lee, Young-Min

    2015-01-01

    Reverse genetics, an approach to rescue infectious virus entirely from a cloned cDNA, has revolutionized the field of positive-strand RNA viruses, whose genomes have the same polarity as cellular mRNA. The cDNA-based reverse genetics system is a seminal method that enables direct manipulation of the viral genomic RNA, thereby generating recombinant viruses for molecular and genetic studies of both viral RNA elements and gene products in viral replication and pathogenesis. It also provides a valuable platform that allows the development of genetically defined vaccines and viral vectors for the delivery of foreign genes. For many positive-strand RNA viruses such as Japanese encephalitis virus (JEV), however, the cloned cDNAs are unstable, posing a major obstacle to the construction and propagation of the functional cDNA. Here, the present report describes the strategic considerations in creating and amplifying a genetically stable full-length infectious JEV cDNA as a bacterial artificial chromosome (BAC) using the following general experimental procedures: viral RNA isolation, cDNA synthesis, cDNA subcloning and modification, assembly of a full-length cDNA, cDNA linearization, in vitro RNA synthesis, and virus recovery. This protocol provides a general methodology applicable to cloning full-length cDNA for a range of positive-strand RNA viruses, particularly those with a genome of >10 kb in length, into a BAC vector, from which infectious RNAs can be transcribed in vitro with a bacteriophage RNA polymerase. PMID:26780115

  13. Minimal model for genome evolution and growth

    CERN Document Server

    Hsieh, L C; Ji, F M; Lee, H C

    2002-01-01

    Textual analysis of typical microbial genomes reveals that they have the statistical characteristics of a DNA sequence of a much shorter length. This peculiar property supports an evolutionary model in which a genome evolves by random mutation but primarily grows by random segmental self-copying. That genomes grew mostly by self-copying is consistent with the observation that repeat sequences in all genomes are widespread and intragenomic and intergenomic homologous genes are preponderance across all life forms. The model predicates the coexistence of the two competing modes of evolution: the gradual changes of classical Darwinism and the stochastic spurts envisioned in ``punctuated equilibrium''.

  14. An artificial ecosystem model used in the study of social, economic and technological dynamics: An artificial electrical energy market

    International Nuclear Information System (INIS)

    This paper will present the artificial ecosystem as a tool, in the development of multi agent models for the simulation of economic and technological dynamics (as well as other possible applications). This tool is based on the mechanics of an artificial society and consists of autonomous artificial agents that interact with individuals that have different characteristics and behavior and other that have a similar conduct to their own. Initial conditions are assumed not to be controllable, however they can be influenced. The importance of the concept of the ecosystem is in understanding great units in the light of their own components which are relevant for the analysis and become interdependent among themselves and with other essential components that hold the total operation of the system. Ideas for the development of a simulation model based on autonomous intelligent agents are presented. These agents will have a brain that is based on artificial intelligence technologies. The Sand Kings Simulation Model, an artificial ecosystem model developed by the author, is described as well as the application of artificial intelligence to this artificial life model. An application to a real life problem is also offered as an artificial energy market that is currently being developed by the author is described

  15. A psychoanalyst artificial intelligence model in a computer game

    OpenAIRE

    Muñoz Fernández, Enrique

    2012-01-01

    Projecte realitzat en el marc d'un programa de mobilitat amb la Vienna University of Technology. [ANGLÈS] Implementation of an artificial intelligence model based on the psychoanalytic theory of the ID-Ego-SuperEgo of Sigmund Freud into the computer game Unreal Tournament 2004. [CASTELLÀ] Implementación de un modelo de inteligencia artificial basado en la teoría psicoanalítica del ID-Ego-SuperEgo de Sigmund Freud en el videojuego Unreal Tournament 2004. [CATALÀ] Implementació d'un mo...

  16. Introducing Artificial Neural Networks through a Spreadsheet Model

    Science.gov (United States)

    Rienzo, Thomas F.; Athappilly, Kuriakose K.

    2012-01-01

    Business students taking data mining classes are often introduced to artificial neural networks (ANN) through point and click navigation exercises in application software. Even if correct outcomes are obtained, students frequently do not obtain a thorough understanding of ANN processes. This spreadsheet model was created to illuminate the roles of…

  17. Cloning and Mutagenesis of the Murine Gammaherpesvirus 68 Genome as an Infectious Bacterial Artificial Chromosome

    OpenAIRE

    Adler, Heiko; Messerle, Martin; Wagner, Markus; Koszinowski, Ulrich H.

    2000-01-01

    Gammaherpesviruses cause important infections of humans, in particular in immunocompromised patients. Recently, murine gammaherpesvirus 68 (MHV-68) infection of mice has been developed as a small animal model of gammaherpesvirus pathogenesis. Efficient generation of mutants of MHV-68 would significantly contribute to the understanding of viral gene functions in virus-host interaction, thereby further enhancing the potential of this model. To this end, we cloned the MHV-68 genome as a bacteria...

  18. Integrated Model of DNA Sequence Numerical Representation and Artificial Neural Network for Human Donor and Acceptor Sites Prediction

    Directory of Open Access Journals (Sweden)

    Mohammed Abo-Zahhad

    2014-07-01

    Full Text Available Human Genome Project has led to a huge inflow of genomic data. After the completion of human genome sequencing, more and more effort is being put into identification of splicing sites of exons and introns (donor and acceptor sites. These invite bioinformatics to analysis the genome sequences and identify the location of exon and intron boundaries or in other words prediction of splicing sites. Prediction of splice sites in genic regions of DNA sequence is one of the most challenging aspects of gene structure recognition. Over the last two decades, artificial neural networks gradually became one of the essential tools in bioinformatics. In this paper artificial neural networks with different numerical mapping techniques have been employed for building integrated model for splice site prediction in genes. An artificial neural network is trained and then used to find splice sites in human genes. A comparison between different mapping methods using trained neural network in terms of their precision in prediction of donor and acceptor sites will be presented in this paper. Training and measuring performance of neural network are carried out using sequences of the human genome (GRch37/hg19- chr21. Simulation results indicate that using Electron-Ion Interaction Potential numerical mapping method with neural network yields to the best performance in prediction.

  19. Multiscale modeling of three-dimensional genome

    Science.gov (United States)

    Zhang, Bin; Wolynes, Peter

    The genome, the blueprint of life, contains nearly all the information needed to build and maintain an entire organism. A comprehensive understanding of the genome is of paramount interest to human health and will advance progress in many areas, including life sciences, medicine, and biotechnology. The overarching goal of my research is to understand the structure-dynamics-function relationships of the human genome. In this talk, I will be presenting our efforts in moving towards that goal, with a particular emphasis on studying the three-dimensional organization, the structure of the genome with multi-scale approaches. Specifically, I will discuss the reconstruction of genome structures at both interphase and metaphase by making use of data from chromosome conformation capture experiments. Computationally modeling of chromatin fiber at atomistic level from first principles will also be presented as our effort for studying the genome structure from bottom up.

  20. Artificial Neural Network Model for Predicting Compressive

    Directory of Open Access Journals (Sweden)

    Salim T. Yousif

    2013-05-01

    Full Text Available   Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS, and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature.    The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c is the most significant factor  affecting the output of the model.     The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.

  1. Modelling of artificial radioactivity migration in environment: a survey

    International Nuclear Information System (INIS)

    The aim of this report is to present a compilation and description of models to assess the environmental behaviour and effects of accidental and routine releases of artificial radioactivity from nuclear power facilities. About 60 models are described and a card is given for each one, to indicate in summarized form its features and data content. This collection is intended to help in developing specific personal models by assembling different parts chosen among the most suitable ones of different models of various degrees of sophistication

  2. Artificial Life of Soybean Plant Growth Modeling Using Intelligence Approaches

    Directory of Open Access Journals (Sweden)

    Atris Suyantohadi

    2010-03-01

    Full Text Available The natural process on plant growth system has a complex system and it has could be developed on characteristic studied using intelligent approaches conducting with artificial life system. The approaches on examining the natural process on soybean (Glycine Max L.Merr plant growth have been analyzed and synthesized in these research through modeling using Artificial Neural Network (ANN and Lindenmayer System (L-System methods. Research aimed to design and to visualize plant growth modeling on the soybean varieties which these could help for studying botany of plant based on fertilizer compositions on plant growth with Nitrogen (N, Phosphor (P and Potassium (K. The soybean plant growth has been analyzed based on the treatments of plant fertilizer compositions in the experimental research to develop plant growth modeling. By using N, P, K fertilizer compositions, its capable result on the highest production 2.074 tons/hectares. Using these models, the simulation on artificial life for describing identification and visualization on the characteristic of soybean plant growth could be demonstrated and applied.

  3. Complete Genome Sequence of Cell Culture-Attenuated Guinea Pig Cytomegalovirus Cloned as an Infectious Bacterial Artificial Chromosome

    OpenAIRE

    Yang, Dongmei; Alam, Zohaib; Cui, Xiaohong; Chen, Michael; Sherrod, Carly J.; McVoy, Michael A.; Schleiss, Mark R.; Dittmer, Dirk P

    2014-01-01

    The complete genome sequence of attenuated guinea pig cytomegalovirus cloned as bacterial artificial chromosome N13R10 was determined. Comparison to pathogenic salivary gland-derived virus revealed 13 differences, 1 of which disrupted overlapping open reading frames encoding GP129 and GP130. Attenuation of N13R10 may arise from an inability to express GP129 and/or GP130.

  4. The autonomy of biological individuals and artificial models.

    Science.gov (United States)

    Moreno, Alvaro; Etxeberria, Arantza; Umerez, Jon

    2008-02-01

    This paper aims to offer an overview of the meaning of autonomy for biological individuals and artificial models rooted in a specific perspective that pays attention to the historical and structural aspects of its origins and evolution. Taking autopoiesis and the recursivity characteristic of its circular logic as a starting point, we depart from some of its consequences to claim that the theory of autonomy should also take into account historical and structural features. Autonomy should not be considered only in internal or constitutive terms, the largely neglected interactive aspects stemming from it should be equally addressed. Artificial models contribute to get a better understanding of the role of autonomy for life and the varieties of its organization and phenomenological diversity. PMID:17719170

  5. Construction and Preliminary Characterization Analysis of Wuzhishan Miniature Pig Bacterial Artificial Chromosome Library with Approximately 8-Fold Genome Equivalent Coverage

    Directory of Open Access Journals (Sweden)

    Changqing Liu

    2013-01-01

    Full Text Available Bacterial artificial chromosome (BAC libraries have been invaluable tools for the genome-wide genetic dissection of complex organisms. Here, we report the construction and characterization of a high-redundancy BAC library from a very valuable pig breed in China, Wuzhishan miniature pig (Sus scrofa, using its blood cells and fibroblasts, respectively. The library contains approximately 153,600 clones ordered in 40 superpools of 10 × 384-deep well microplates. The average insert size of BAC clones was estimated to be 152.3 kb, representing approximately 7.68 genome equivalents of the porcine haploid genome and a 99.93% statistical probability of obtaining at least one clone containing a unique DNA sequence in the library. 19 pairs of microsatellite marker primers covering porcine chromosomes were used for screening the BAC library, which showed that each of these markers was positive in the library; the positive clone number was 2 to 9, and the average number was 7.89, which was consistent with 7.68-fold coverage of the porcine genome. And there were no significant differences of genomic BAC library from blood cells and fibroblast cells. Therefore, we identified 19 microsatellite markers that could potentially be used as genetic markers. As a result, this BAC library will serve as a valuable resource for gene identification, physical mapping, and comparative genomics and large-scale genome sequencing in the porcine.

  6. Ab Initio Modeling of Ecosystems with Artificial Life

    OpenAIRE

    Adami, C.

    2002-01-01

    Artificial Life provides the opportunity to study the emergence and evolution of simple ecosystems in real time. We give an overview of the advantages and limitations of such an approach, as well as its relation to individual-based modeling techniques. The Digital Life system Avida is introduced and prospects for experiments with ab initio evolution (evolution "from scratch"), maintenance, as well as stability of ecosystems are discussed.

  7. Construction of a nurse shark (Ginglymostoma cirratum bacterial artificial chromosome (BAC library and a preliminary genome survey

    Directory of Open Access Journals (Sweden)

    Inoko Hidetoshi

    2006-05-01

    Full Text Available Abstract Background Sharks are members of the taxonomic class Chondrichthyes, the oldest living jawed vertebrates. Genomic studies of this group, in comparison to representative species in other vertebrate taxa, will allow us to theorize about the fundamental genetic, developmental, and functional characteristics in the common ancestor of all jawed vertebrates. Aims In order to obtain mapping and sequencing data for comparative genomics, we constructed a bacterial artificial chromosome (BAC library for the nurse shark, Ginglymostoma cirratum. Results The BAC library consists of 313,344 clones with an average insert size of 144 kb, covering ~4.5 × 1010 bp and thus providing an 11-fold coverage of the haploid genome. BAC end sequence analyses revealed, in addition to LINEs and SINEs commonly found in other animal and plant genomes, two new groups of nurse shark-specific repetitive elements, NSRE1 and NSRE2 that seem to be major components of the nurse shark genome. Screening the library with single-copy or multi-copy gene probes showed 6–28 primary positive clones per probe of which 50–90% were true positives, demonstrating that the BAC library is representative of the different regions of the nurse shark genome. Furthermore, some BAC clones contained multiple genes, making physical mapping feasible. Conclusion We have constructed a deep-coverage, high-quality, large insert, and publicly available BAC library for a cartilaginous fish. It will be very useful to the scientific community interested in shark genomic structure, comparative genomics, and functional studies. We found two new groups of repetitive elements specific to the nurse shark genome, which may contribute to the architecture and evolution of the nurse shark genome.

  8. Modelling of word usage frequency dynamics using artificial neural network

    International Nuclear Information System (INIS)

    In this paper the method for modelling of word usage frequency time series is proposed. An artificial feedforward neural network was used to predict word usage frequencies. The neural network was trained using the maximum likelihood criterion. The Google Books Ngram corpus was used for the analysis. This database provides a large amount of data on frequency of specific word forms for 7 languages. Statistical modelling of word usage frequency time series allows finding optimal fitting and filtering algorithm for subsequent lexicographic analysis and verification of frequency trend models

  9. Artificial intelligence support for scientific model-building

    Science.gov (United States)

    Keller, Richard M.

    1992-01-01

    Scientific model-building can be a time-intensive and painstaking process, often involving the development of large and complex computer programs. Despite the effort involved, scientific models cannot easily be distributed and shared with other scientists. In general, implemented scientific models are complex, idiosyncratic, and difficult for anyone but the original scientific development team to understand. We believe that artificial intelligence techniques can facilitate both the model-building and model-sharing process. In this paper, we overview our effort to build a scientific modeling software tool that aids the scientist in developing and using models. This tool includes an interactive intelligent graphical interface, a high-level domain specific modeling language, a library of physics equations and experimental datasets, and a suite of data display facilities.

  10. Median Approximations for Genomes Modeled as Matrices.

    Science.gov (United States)

    Zanetti, Joao Paulo Pereira; Biller, Priscila; Meidanis, Joao

    2016-04-01

    The genome median problem is an important problem in phylogenetic reconstruction under rearrangement models. It can be stated as follows: Given three genomes, find a fourth that minimizes the sum of the pairwise rearrangement distances between it and the three input genomes. In this paper, we model genomes as matrices and study the matrix median problem using the rank distance. It is known that, for any metric distance, at least one of the corners is a [Formula: see text]-approximation of the median. Our results allow us to compute up to three additional matrix median candidates, all of them with approximation ratios at least as good as the best corner, when the input matrices come from genomes. We also show a class of instances where our candidates are optimal. From the application point of view, it is usually more interesting to locate medians farther from the corners, and therefore, these new candidates are potentially more useful. In addition to the approximation algorithm, we suggest a heuristic to get a genome from an arbitrary square matrix. This is useful to translate the results of our median approximation algorithm back to genomes, and it has good results in our tests. To assess the relevance of our approach in the biological context, we ran simulated evolution tests and compared our solutions to those of an exact DCJ median solver. The results show that our method is capable of producing very good candidates. PMID:27072561

  11. Artificial crawler model for texture analysis on silk fibroin scaffolds

    International Nuclear Information System (INIS)

    Texture plays an important role in computer vision tasks. Several methods of texture analysis are available. However, these methods are not capable of extracting rich detail in images. This paper presents a novel approach to image texture classification based on the artificial crawler model. Here, we propose a new rule of movement that moves artificial crawler agents not only toward higher intensities but also toward lower ones. This strategy is able of capturing more detail because the agents explore the peaks as well as the valleys. Thus, compared with the state-of-the-art method, this approach shows an increased discriminatory power. Experiments on the most well known benchmark demonstrate the superior performance of our approach. We also tested our approach on silk fibroin scaffold analysis, and results indicate that our method is consistent and can be applied in real-world situations. (paper)

  12. Super capacitor modeling with artificial neural network (ANN)

    Energy Technology Data Exchange (ETDEWEB)

    Marie-Francoise, J.N.; Gualous, H.; Berthon, A. [Universite de Franche-Comte, Lab. en Electronique, Electrotechnique et Systemes (L2ES), UTBM, INRETS (LRE T31) 90 - Belfort (France)

    2004-07-01

    This paper presents super-capacitors modeling using Artificial Neural Network (ANN). The principle consists on a black box nonlinear multiple inputs single output (MISO) model. The system inputs are temperature and current, the output is the super-capacitor voltage. The learning and the validation of the ANN model from experimental charge and discharge of super-capacitor establish the relationship between inputs and output. The learning and the validation of the ANN model use experimental results of 2700 F, 3700 F and a super-capacitor pack. Once the network is trained, the ANN model can predict the super-capacitor behaviour with temperature variations. The update parameters of the ANN model are performed thanks to Levenberg-Marquardt method in order to minimize the error between the output of the system and the predicted output. The obtained results with the ANN model of super-capacitor and experimental ones are in good agreement. (authors)

  13. Evolvable mathematical models: A new artificial Intelligence paradigm

    Science.gov (United States)

    Grouchy, Paul

    We develop a novel Artificial Intelligence paradigm to generate autonomously artificial agents as mathematical models of behaviour. Agent/environment inputs are mapped to agent outputs via equation trees which are evolved in a manner similar to Symbolic Regression in Genetic Programming. Equations are comprised of only the four basic mathematical operators, addition, subtraction, multiplication and division, as well as input and output variables and constants. From these operations, equations can be constructed that approximate any analytic function. These Evolvable Mathematical Models (EMMs) are tested and compared to their Artificial Neural Network (ANN) counterparts on two benchmarking tasks: the double-pole balancing without velocity information benchmark and the challenging discrete Double-T Maze experiments with homing. The results from these experiments show that EMMs are capable of solving tasks typically solved by ANNs, and that they have the ability to produce agents that demonstrate learning behaviours. To further explore the capabilities of EMMs, as well as to investigate the evolutionary origins of communication, we develop NoiseWorld, an Artificial Life simulation in which interagent communication emerges and evolves from initially noncommunicating EMM-based agents. Agents develop the capability to transmit their x and y position information over a one-dimensional channel via a complex, dialogue-based communication scheme. These evolved communication schemes are analyzed and their evolutionary trajectories examined, yielding significant insight into the emergence and subsequent evolution of cooperative communication. Evolved agents from NoiseWorld are successfully transferred onto physical robots, demonstrating the transferability of EMM-based AIs from simulation into physical reality.

  14. Genomic, genetic and functional dissection of bitter taste responses to artificial sweeteners.

    Science.gov (United States)

    Roudnitzky, Natacha; Bufe, Bernd; Thalmann, Sophie; Kuhn, Christina; Gunn, Howard C; Xing, Chao; Crider, Bill P; Behrens, Maik; Meyerhof, Wolfgang; Wooding, Stephen P

    2011-09-01

    Bitter taste perception is initiated by TAS2R receptors, which respond to agonists by triggering depolarization of taste bud cells. Mutations in TAS2Rs are known to affect taste phenotypes by altering receptor function. Evidence that TAS2Rs overlap in ligand specificity suggests that they may also contribute joint effects. To explore this aspect of gustation, we examined bitter perception of saccharin and acesulfame K, widely used artificial sweeteners with aversive aftertastes. Both substances are agonists of TAS2R31 and -43, which belong to a five-member subfamily (TAS2R30-46) responsive to a diverse constellation of compounds. We analyzed sequence variation and linkage structure in the ∼140 kb genomic region encoding TAS2R30-46, taste responses to the two sweeteners in subjects, and functional characteristics of receptor alleles. Whole-gene sequences from TAS2R30-46 in 60 Caucasian subjects revealed extensive diversity including 34 missense mutations, two nonsense mutations and high-frequency copy-number variants. Thirty markers, including non-synonymous variants in all five genes, were associated (Pacesulfame K. However, linkage disequilibrium (LD) in the region was high (D', r(2) > 0.95). Haplotype analyses revealed that most associations were spurious, arising from LD with variants in TAS2R31. In vitro assays confirmed the functional importance of four TAS2R31 mutations, which had independent effects on receptor response. The existence of high LD spanning functionally distinct TAS2R loci predicts that bitter taste responses to many compounds will be strongly correlated even when they are mediated by different genes. Integrative approaches combining phenotypic, genetic and functional analysis will be essential in dissecting these complex relationships. PMID:21672920

  15. Artificial Neural Network Model for Optical Fiber Direction Coupler Design

    Institute of Scientific and Technical Information of China (English)

    李九生; 鲍振武

    2004-01-01

    A new approach to the design of the optical fiber direction coupler by using neural network is proposed. To train the artificial neural network,the coupling length is defined as the input sample, and the coupling ratio is defined as the output sample. Compared with the numerical value calculation of the theoretical formula, the error of the neural network model output is 1% less.Then, through the model, to design a broadband or a single wavelength optical fiber direction coupler becomes easy. The method is proved to be reliable, accurate and time-saving. So it is promising in the field of both investigation and application.

  16. Artificial Neural Network Model of Hydrocarbon Migration and Accumulation

    Institute of Scientific and Technical Information of China (English)

    刘海滨; 吴冲龙

    2002-01-01

    Based on the dynamic simulation of the 3-D structure the sedimentary modeling, the unit entity model has been adopted to transfer the heterogeneous complex pas sage system into limited simple homogeneous entity, and then the traditional dyn amic simulation has been used to calculate the phase and the drive forces of the hyd rocarbon , and the artificial neural network(ANN) technology has been applied to resolve such problems as the direction, velocity and quantity of the hydrocarbo n migration among the unit entities. Through simulating of petroleum migration a nd accumulation in Zhu Ⅲ depression, the complex mechanism of hydrocarbon migra tion and accumulation has been opened out.

  17. Advances in Swine Biomedical Model Genomics

    Directory of Open Access Journals (Sweden)

    Joan K. Lunney

    2007-01-01

    Full Text Available This review is a short update on the diversity of swine biomedical models and the importance of genomics in their continued development. The swine has been used as a major mammalian model for human studies because of the similarity in size and physiology, and in organ development and disease progression. The pig model allows for deliberately timed studies, imaging of internal vessels and organs using standard human technologies, and collection of repeated peripheral samples and, at kill, detailed mucosal tissues. The ability to use pigs from the same litter, or cloned or transgenic pigs, facilitates comparative analyses and genetic mapping. The availability of numerous well defined cell lines, representing a broad range of tissues, further facilitates testing of gene expression, drug susceptibility, etc. Thus the pig is an excellent biomedical model for humans. For genomic applications it is an asset that the pig genome has high sequence and chromosome structure homology with humans. With the swine genome sequence now well advanced there are improving genetic and proteomic tools for these comparative analyses. The review will discuss some of the genomic approaches used to probe these models. The review will highlight genomic studies of melanoma and of infectious disease resistance, discussing issues to consider in designing such studies. It will end with a short discussion of the potential for genomic approaches to develop new alternatives for control of the most economically important disease of pigs, porcine reproductive and respiratory syndrome (PRRS, and the potential for applying knowledge gained with this virus for human viral infectious disease studies.

  18. An analysis of urban collisions using an artificial intelligence model.

    Science.gov (United States)

    Mussone, L; Ferrari, A; Oneta, M

    1999-11-01

    Traditional studies on road accidents estimate the effect of variables (such as vehicular flows, road geometry, vehicular characteristics), and the calculation of the number of accidents. A descriptive statistical analysis of the accidents (those used in the model) over the period 1992-1995 is proposed. The paper describes an alternative method based on the use of artificial neural networks (ANN) in order to work out a model that relates to the analysis of vehicular accidents in Milan. The degree of danger of urban intersections using different scenarios is quantified by the ANN model. Methodology is the first result, which allows us to tackle the modelling of urban vehicular accidents by the innovative use of ANN. Other results deal with model outputs: intersection complexity may determine a higher accident index depending on the regulation of intersection. The highest index for running over of pedestrian occurs at non-signalised intersections at night-time. PMID:10487346

  19. Searching for turbulence models by artificial neural network

    CERN Document Server

    Gamahara, Masataka

    2016-01-01

    Artificial neural network (ANN) is tested as a tool for finding a new subgrid model of the subgrid-scale (SGS) stress in large-eddy simulation. ANN is used to establish a functional relation between the grid-scale (GS) flow field and the SGS stress without any assumption of the form of function. Data required for training and test of ANN are provided by direct numerical simulation (DNS) of a turbulent channel flow. It is shown that ANN can establish a model similar to the gradient model. The correlation coefficients between the real SGS stress and the output of ANN are comparable to or larger than similarity models, but smaller than a two-parameter dynamic mixed model.

  20. Advances in Swine biomedical Model Genomics

    Science.gov (United States)

    This manuscript is a short update on the diversity of swine biomedical models and the importance of genomics in their continued development. The swine has been used as a major mammalian model for human studies because of the similarity in size and physiology, and in organ development and disease pro...

  1. An application of artificial intelligence for rainfall–runoff modeling

    Indian Academy of Sciences (India)

    Ali Aytek; M Asce; Murat Alp

    2008-04-01

    This study proposes an application of two techniques of artificial intelligence (AI) for rainfall–runoff modeling: the artificial neural networks (ANN) and the evolutionary computation (EC). Two different ANN techniques, the feed forward back propagation (FFBP) and generalized regression neural network (GRNN) methods are compared with one EC method, Gene Expression Programming (GEP) which is a new evolutionary algorithm that evolves computer programs. The daily hydrometeorological data of three rainfall stations and one streamflow station for Juniata River Basin in Pennsylvania state of USA are taken into consideration in the model development. Statistical parameters such as average, standard deviation, coefficient of variation, skewness, minimum and maximum values, as well as criteria such as mean square error (MSE) and determination coefficient (2) are used to measure the performance of the models. The results indicate that the proposed genetic programming (GP) formulation performs quite well compared to results obtained by ANNs and is quite practical for use. It is concluded from the results that GEP can be proposed as an alternative to ANN models.

  2. Transport modeling of sorbing tracers in artificial fractures

    Energy Technology Data Exchange (ETDEWEB)

    Keum, Dong Kwon; Baik, Min Hoon; Park, Chung Kyun; Cho, Young Hwan; Hahn, Phil Soo

    1998-02-01

    This study was performed as part of a fifty-man year attachment program between AECL (Atomic Energy Canada Limited) and KAERI. Three kinds of computer code, HDD, POMKAP and VAMKAP, were developed to predict transport of contaminants in fractured rock. MDDM was to calculate the mass transport of contaminants in a single fracture using a simple hydrodynamic dispersion diffusion model. POMKAP was to predict the mass transport of contaminants by a two-dimensional variable aperture model. In parallel with modeling, the validation of models was also performed through the analysis of the migration experimental data obtained in acrylic plastic and granite artificial fracture system at the Whiteshell laboratories, AECL, Canada. (author). 34 refs., 11 tabs., 76 figs.

  3. Construction of a bacterial artificial chromosome library from the spikemoss Selaginella moellendorffii: a new resource for plant comparative genomics

    Directory of Open Access Journals (Sweden)

    Chapple Clint

    2005-06-01

    Full Text Available Abstract Background The lycophytes are an ancient lineage of vascular plants that diverged from the seed plant lineage about 400 Myr ago. Although the lycophytes occupy an important phylogenetic position for understanding the evolution of plants and their genomes, no genomic resources exist for this group of plants. Results Here we describe the construction of a large-insert bacterial artificial chromosome (BAC library from the lycophyte Selaginella moellendorffii. Based on cell flow cytometry, this species has the smallest genome size among the different lycophytes tested, including Huperzia lucidula, Diphaiastrum digita, Isoetes engelmanii and S. kraussiana. The arrayed BAC library consists of 9126 clones; the average insert size is estimated to be 122 kb. Inserts of chloroplast origin account for 2.3% of the clones. The BAC library contains an estimated ten genome-equivalents based on DNA hybridizations using five single-copy and two duplicated S. moellendorffii genes as probes. Conclusion The S. moellenforffii BAC library, the first to be constructed from a lycophyte, will be useful to the scientific community as a resource for comparative plant genomics and evolution.

  4. Complete Genomes of Classical Swine Fever Virus Cloned into Bacterial Artificial Chromosomes

    DEFF Research Database (Denmark)

    Rasmussen, Thomas Bruun; Reimann, I.; Uttenthal, Åse;

    Complete genome amplification of viral RNA provides a new tool for the generation of modified pestiviruses. We have used our full-genome amplification strategy for generation of amplicons representing complete genomes of classical swine fever virus. The amplicons were cloned directly into a stable...

  5. Forecasting municipal solid waste generation using artificial intelligence modelling approaches.

    Science.gov (United States)

    Abbasi, Maryam; El Hanandeh, Ali

    2016-10-01

    Municipal solid waste (MSW) management is a major concern to local governments to protect human health, the environment and to preserve natural resources. The design and operation of an effective MSW management system requires accurate estimation of future waste generation quantities. The main objective of this study was to develop a model for accurate forecasting of MSW generation that helps waste related organizations to better design and operate effective MSW management systems. Four intelligent system algorithms including support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and k-nearest neighbours (kNN) were tested for their ability to predict monthly waste generation in the Logan City Council region in Queensland, Australia. Results showed artificial intelligence models have good prediction performance and could be successfully applied to establish municipal solid waste forecasting models. Using machine learning algorithms can reliably predict monthly MSW generation by training with waste generation time series. In addition, results suggest that ANFIS system produced the most accurate forecasts of the peaks while kNN was successful in predicting the monthly averages of waste quantities. Based on the results, the total annual MSW generated in Logan City will reach 9.4×10(7)kg by 2020 while the peak monthly waste will reach 9.37×10(6)kg. PMID:27297046

  6. Artificial Neural Network Modeling of Forest Tree Growth

    CERN Document Server

    Gordon, C

    1999-01-01

    The problem of modeling forest tree growth curves with an artificial neural network (NN) is examined. The NN parametric form is shown to be a suitable model if each forest tree plot is assumed to consist of several differently growing sub-plots. The predictive Bayesian approach is used in estimating the NN output. Data from the correlated curve trend (CCT) experiments are used. The NN predictions are compared with those of one of the best parametric solutions, the Schnute model. Analysis of variance (ANOVA) methods are used to evaluate whether any observed differences are statistically significant. From a Frequentist perspective the differences between the Schnute and NN approach are found not to be significant. However, a Bayesian ANOVA indicates that there is a 93% probability of the NN approach producing better predictions on average.

  7. Modeling of methane emissions using artificial neural network approach

    Directory of Open Access Journals (Sweden)

    Stamenković Lidija J.

    2015-01-01

    Full Text Available The aim of this study was to develop a model for forecasting CH4 emissions at the national level, using Artificial Neural Networks (ANN with broadly available sustainability, economical and industrial indicators as their inputs. ANN modeling was performed using two different types of architecture; a Backpropagation Neural Network (BPNN and a General Regression Neural Network (GRNN. A conventional multiple linear regression (MLR model was also developed in order to compare model performance and assess which model provides the best results. ANN and MLR models were developed and tested using the same annual data for 20 European countries. The ANN model demonstrated very good performance, significantly better than the MLR model. It was shown that a forecast of CH4 emissions at the national level using the ANN model can be made successfully and accurately for a future period of up to two years, thereby opening the possibility to apply such a modeling technique which can be used to support the implementation of sustainable development strategies and environmental management policies. [Projekat Ministarstva nauke Republike Srbije, br. 172007

  8. Advances in Swine Biomedical Model Genomics

    OpenAIRE

    Lunney, Joan K.

    2007-01-01

    This review is a short update on the diversity of swine biomedical models and the importance of genomics in their continued development. The swine has been used as a major mammalian model for human studies because of the similarity in size and physiology, and in organ development and disease progression. The pig model allows for deliberately timed studies, imaging of internal vessels and organs using standard human technologies, and collection of repeated peripheral samples and, at kill, deta...

  9. Predicting chick body mass by artificial intelligence-based models

    Directory of Open Access Journals (Sweden)

    Patricia Ferreira Ponciano Ferraz

    2014-07-01

    Full Text Available The objective of this work was to develop, validate, and compare 190 artificial intelligence-based models for predicting the body mass of chicks from 2 to 21 days of age subjected to different duration and intensities of thermal challenge. The experiment was conducted inside four climate-controlled wind tunnels using 210 chicks. A database containing 840 datasets (from 2 to 21-day-old chicks - with the variables dry-bulb air temperature, duration of thermal stress (days, chick age (days, and the daily body mass of chicks - was used for network training, validation, and tests of models based on artificial neural networks (ANNs and neuro-fuzzy networks (NFNs. The ANNs were most accurate in predicting the body mass of chicks from 2 to 21 days of age after they were subjected to the input variables, and they showed an R² of 0.9993 and a standard error of 4.62 g. The ANNs enable the simulation of different scenarios, which can assist in managerial decision-making, and they can be embedded in the heating control systems.

  10. Optimization of the artificial urinary sphincter: modelling and experimental validation

    International Nuclear Information System (INIS)

    The artificial urinary sphincter should be long enough to prevent strangulation effects of the urethral tissue and short enough to avoid the improper dissection of the surrounding tissue. To optimize the sphincter length, the empirical three-parameter urethra compression model is proposed based on the mechanical properties of the urethra: wall pressure, tissue response rim force and sphincter periphery length. In vitro studies using explanted animal or human urethras and different artificial sphincters demonstrate its applicability. The pressure of the sphincter to close the urethra is shown to be a linear function of the bladder pressure. The force to close the urethra depends on the sphincter length linearly. Human urethras display the same dependences as the urethras of pig, dog, sheep and calf. Quantitatively, however, sow urethras resemble best the human ones. For the human urethras, the mean wall pressure corresponds to (-12.6 ± 0.9) cmH2O and (-8.7 ± 1.1) cmH2O, the rim length to (3.0 ± 0.3) mm and (5.1 ± 0.3) mm and the rim force to (60 ± 20) mN and (100 ± 20) mN for urethra opening and closing, respectively. Assuming an intravesical pressure of 40 cmH2O, and an external pressure on the urethra of 60 cmH2O, the model leads to the optimized sphincter length of (17.3 ± 3.8) mm

  11. Artificial neural network modeling of dissolved oxygen in reservoir.

    Science.gov (United States)

    Chen, Wei-Bo; Liu, Wen-Cheng

    2014-02-01

    The water quality of reservoirs is one of the key factors in the operation and water quality management of reservoirs. Dissolved oxygen (DO) in water column is essential for microorganisms and a significant indicator of the state of aquatic ecosystems. In this study, two artificial neural network (ANN) models including back propagation neural network (BPNN) and adaptive neural-based fuzzy inference system (ANFIS) approaches and multilinear regression (MLR) model were developed to estimate the DO concentration in the Feitsui Reservoir of northern Taiwan. The input variables of the neural network are determined as water temperature, pH, conductivity, turbidity, suspended solids, total hardness, total alkalinity, and ammonium nitrogen. The performance of the ANN models and MLR model was assessed through the mean absolute error, root mean square error, and correlation coefficient computed from the measured and model-simulated DO values. The results reveal that ANN estimation performances were superior to those of MLR. Comparing to the BPNN and ANFIS models through the performance criteria, the ANFIS model is better than the BPNN model for predicting the DO values. Study results show that the neural network particularly using ANFIS model is able to predict the DO concentrations with reasonable accuracy, suggesting that the neural network is a valuable tool for reservoir management in Taiwan. PMID:24078053

  12. Application of artificial neural networks for modeling localized corrosion

    International Nuclear Information System (INIS)

    Artificial neural networks (ANN) were applied to modeling localized corrosion of Incoloy Alloy 825 in simulated J - 13 well water. ANN as a non linear models can represent accurately localized corrosion phenomena caused by an environment containing chlorides, nitrates, fluorides and sulfates at various temperature ranges. Although the nature of the dependent variable of the ANN models, the visual rating of the localized corrosion is qualitative, a good correspondence between the output of the model and the actual indications is determined. Accurate ANN modeling has been carried out by using the visual inspection of the specimen surface, in contrast to linear modeling where in order to get a sound correlation between the system variables, a complex dependent parameter, having no clear physical meaning has been chosen. It has also been found that one can extrapolate to a certain extent, beyond the ability to interpolate (as with linear models). The ANN model predicted with a low relative error the visual rating of the corrosion rate of records which where part of the testing set of the ANN and belonging to the original full factorial design experiment. Thus, such models can be used for detailed analysis procedures as sensitivity, knowledge acquisition and optimization. (author). 7 refs, 9 figs

  13. Passage relevance models for genomics search

    Directory of Open Access Journals (Sweden)

    Frieder Ophir

    2009-03-01

    Full Text Available Abstract We present a passage relevance model for integrating syntactic and semantic evidence of biomedical concepts and topics using a probabilistic graphical model. Component models of topics, concepts, terms, and document are represented as potential functions within a Markov Random Field. The probability of a passage being relevant to a biologist's information need is represented as the joint distribution across all potential functions. Relevance model feedback of top ranked passages is used to improve distributional estimates of query concepts and topics in context, and a dimensional indexing strategy is used for efficient aggregation of concept and term statistics. By integrating multiple sources of evidence including dependencies between topics, concepts, and terms, we seek to improve genomics literature passage retrieval precision. Using this model, we are able to demonstrate statistically significant improvements in retrieval precision using a large genomics literature corpus.

  14. Variable recruitment fluidic artificial muscles: modeling and experiments

    International Nuclear Information System (INIS)

    We investigate taking advantage of the lightweight, compliant nature of fluidic artificial muscles to create variable recruitment actuators in the form of artificial muscle bundles. Several actuator elements at different diameter scales are packaged to act as a single actuator device. The actuator elements of the bundle can be connected to the fluidic control circuit so that different groups of actuator elements, much like individual muscle fibers, can be activated independently depending on the required force output and motion. This novel actuation concept allows us to save energy by effectively impedance matching the active size of the actuators on the fly based on the instantaneous required load. This design also allows a single bundled actuator to operate in substantially different force regimes, which could be valuable for robots that need to perform a wide variety of tasks and interact safely with humans. This paper proposes, models and analyzes the actuation efficiency of this actuator concept. The analysis shows that variable recruitment operation can create an actuator that reduces throttling valve losses to operate more efficiently over a broader range of its force–strain operating space. We also present preliminary results of the design, fabrication and experimental characterization of three such bioinspired variable recruitment actuator prototypes. (paper)

  15. A MITE-based genotyping method to reveal hundreds of DNA polymorphisms in an animal genome after a few generations of artificial selection

    Directory of Open Access Journals (Sweden)

    Tetreau Guillaume

    2008-10-01

    Full Text Available Abstract Background For most organisms, developing hundreds of genetic markers spanning the whole genome still requires excessive if not unrealistic efforts. In this context, there is an obvious need for methodologies allowing the low-cost, fast and high-throughput genotyping of virtually any species, such as the Diversity Arrays Technology (DArT. One of the crucial steps of the DArT technique is the genome complexity reduction, which allows obtaining a genomic representation characteristic of the studied DNA sample and necessary for subsequent genotyping. In this article, using the mosquito Aedes aegypti as a study model, we describe a new genome complexity reduction method taking advantage of the abundance of miniature inverted repeat transposable elements (MITEs in the genome of this species. Results Ae. aegypti genomic representations were produced following a two-step procedure: (1 restriction digestion of the genomic DNA and simultaneous ligation of a specific adaptor to compatible ends, and (2 amplification of restriction fragments containing a particular MITE element called Pony using two primers, one annealing to the adaptor sequence and one annealing to a conserved sequence motif of the Pony element. Using this protocol, we constructed a library comprising more than 6,000 DArT clones, of which at least 5.70% were highly reliable polymorphic markers for two closely related mosquito strains separated by only a few generations of artificial selection. Within this dataset, linkage disequilibrium was low, and marker redundancy was evaluated at 2.86% only. Most of the detected genetic variability was observed between the two studied mosquito strains, but individuals of the same strain could still be clearly distinguished. Conclusion The new complexity reduction method was particularly efficient to reveal genetic polymorphisms in Ae. egypti. Overall, our results testify of the flexibility of the DArT genotyping technique and open new

  16. An artificial vector model for generating abnormal electrocardiographic rhythms

    International Nuclear Information System (INIS)

    We present generalizations of our previously published artificial models for generating multi-channel ECG to provide simulations of abnormal cardiac rhythms. Using a three-dimensional vectorcardiogram (VCG) formulation, we generate the normal cardiac dipole for a patient using a sum of Gaussian kernels, fitted to real VCG recordings. Abnormal beats are specified either as perturbations to the normal dipole or as new dipole trajectories. Switching between normal and abnormal beat types is achieved using a first-order Markov chain. Probability transitions can be learned from real data or modeled by coupling to heart rate and sympathovagal balance. Natural morphology changes from beat-to-beat are incorporated by varying the angular frequency of the dipole as a function of the inter-beat (RR) interval. The RR interval time series is generated using our previously described model whereby time- and frequency-domain heart rate (HR) and heart rate variability characteristics can be specified. QT-HR hysteresis is simulated by coupling the Gaussian kernels associated with the T-wave in the model with a nonlinear factor related to the local HR (determined from the last n RR intervals). Morphology changes due to respiration are simulated by introducing a rotation matrix couple to the respiratory frequency. We demonstrate an example of the use of this model by simulating HR-dependent T-wave alternans (TWA) with and without phase-switching due to ectopy. Application of our model also reveals previously unreported effects of common TWA estimation methods

  17. Artificial emotional model based on finite state machine

    Institute of Scientific and Technical Information of China (English)

    MENG Qing-mei; WU Wei-guo

    2008-01-01

    According to the basic emotional theory, the artificial emotional model based on the finite state machine(FSM) was presented. In finite state machine model of emotion, the emotional space included the basic emotional space and the multiple emotional spaces. The emotion-switching diagram was defined and transition function was developed using Markov chain and linear interpolation algorithm. The simulation model was built using Stateflow toolbox and Simulink toolbox based on the Matlab platform.And the model included three subsystems: the input one, the emotion one and the behavior one. In the emotional subsystem, the responses of different personalities to the external stimuli were described by defining personal space. This model takes states from an emotional space and updates its state depending on its current state and a state of its input (also a state-emotion). The simulation model realizes the process of switching the emotion from the neutral state to other basic emotions. The simulation result is proved to correspond to emotion-switching law of human beings.

  18. Consumer Choice Prediction: Artificial Neural Networks versus Logistic Models

    Directory of Open Access Journals (Sweden)

    Christopher Gan

    2005-01-01

    Full Text Available Conventional econometric models, such as discriminant analysis and logistic regression have been used to predict consumer choice. However, in recent years, there has been a growing interest in applying artificial neural networks (ANN to analyse consumer behaviour and to model the consumer decision-making process. The purpose of this paper is to empirically compare the predictive power of the probability neural network (PNN, a special class of neural networks and a MLFN with a logistic model on consumers’ choices between electronic banking and non-electronic banking. Data for this analysis was obtained through a mail survey sent to 1,960 New Zealand households. The questionnaire gathered information on the factors consumers’ use to decide between electronic banking versus non-electronic banking. The factors include service quality dimensions, perceived risk factors, user input factors, price factors, service product characteristics and individual factors. In addition, demographic variables including age, gender, marital status, ethnic background, educational qualification, employment, income and area of residence are considered in the analysis. Empirical results showed that both ANN models (MLFN and PNN exhibit a higher overall percentage correct on consumer choice predictions than the logistic model. Furthermore, the PNN demonstrates to be the best predictive model since it has the highest overall percentage correct and a very low percentage error on both Type I and Type II errors.

  19. Functional genomics of the chicken - a model organism

    Science.gov (United States)

    The chicken has reached model organism status after genome sequencing and development of high-throughput tools for the exploration of functional elements of the genome. Functional genomics focuses on understanding the function and regulation of genes and gene products on a global or genome-wide scal...

  20. The Mouse Genome Database (MGD): premier model organism resource for mammalian genomics and genetics

    OpenAIRE

    Blake, J. A.; Bult, C. J.; J.A. Kadin; J.E. Richardson; Eppig, J T

    2010-01-01

    The Mouse Genome Database (MGD) is the community model organism database for the laboratory mouse and the authoritative source for phenotype and functional annotations of mouse genes. MGD includes a complete catalog of mouse genes and genome features with integrated access to genetic, genomic and phenotypic information, all serving to further the use of the mouse as a model system for studying human biology and disease. MGD is a major component of the Mouse Genome Informatics (MGI, http://www...

  1. Artificial neural network model for earthquake prediction with radon monitoring

    International Nuclear Information System (INIS)

    Apart from the linear monitoring studies concerning the relationship between radon and earthquake, an artificial neural networks (ANNs) model approach is presented starting out from non-linear changes of the eight different parameters during the earthquake occurrence. A three-layer Levenberg-Marquardt feedforward learning algorithm is used to model the earthquake prediction process in the East Anatolian Fault System (EAFS). The proposed ANN system employs individual training strategy with fixed-weight and supervised models leading to estimations. The average relative error between the magnitudes of the earthquakes acquired by ANN and measured data is about 2.3%. The relative error between the test and earthquake data varies between 0% and 12%. In addition, the factor analysis was applied on all data and the model output values to see the statistical variation. The total variance of 80.18% was explained with four factors by this analysis. Consequently, it can be concluded that ANN approach is a potential alternative to other models with complex mathematical operations

  2. A Hybrid Artificial Neural Network Model for Forecasting Short Time Series

    OpenAIRE

    Mohan, Anil

    2012-01-01

    Forecasting has long been the domain of traditional statistical models. Recent research has shown that novel and complex forecasting models do not necessarily outperform simpler models. These include in particular Artificial Neural Networks (ANNs). Even though claims of superior forecasting performance were made by Neural Network researchers, these claims were often unsubstantiated. Artificial neural networks are information processing paradigms motivated by the information ...

  3. Natural and artificial mutants as valuable resources for functional genomics and molecular breeding

    Directory of Open Access Journals (Sweden)

    Shu-Ye Jiang, Srinivasan Ramachandran

    2010-01-01

    Full Text Available With the completion of rice genome sequencing, large collection of expression data and the great efforts in annotating rice genomes, the next challenge is to systematically assign functions to all predicted genes in the genome. The generations and collections of mutants at the genome-wide level form technological platform of functional genomics. In this study, we have reviewed currently employed tools to generate such mutant populations. These tools include natural, physical, chemical, tissue culture, T-DNA, transposon or gene silencing based mutagenesis. We also reviewed how these tools were used to generate a large collection of mutants and how these mutants can be screened and detected for functional analysis of a gene. The data suggested that the current population of mutants might be large enough to tag all predicted genes. However, the collection of flanking sequencing tags (FSTs is limited due to the relatively higher cost. Thus, we have proposed a new strategy to generate gene-silencing mutants at the genome-wide level. Due to the large collection of insertion mutants, the next step to rice functional genomics should be focusing on functional characterization of tagged genes by detailed survey of corresponding mutants. Additionally, we also evaluated the utilization of these mutants as valuable resources for molecular breeding.

  4. Hybrid Systems Knowledge Representation Using Modelling Environment System Techniques Artificial Intelligence

    OpenAIRE

    Latif, Kamran

    2014-01-01

    Knowledge-based or Artificial Intelligence techniques are used increasingly as alternatives to more classical techniques to model ENVIRONMENTAL SYSTEMS. Use of Artificial Intelligence (AI) in environmental modelling has increased with recognition of its potential. In this paper we examine the DIFFERENT TECHNIQUES of Artificial intelligence with profound examples of human perception, learning and reasoning to solve complex problems. However with the increase of complexity better methods are re...

  5. Analysis of herpesvirus host specificity determinants using herpesvirus genomes as bacterial artificial chromosomes.

    Science.gov (United States)

    Arii, Jun; Kato, Kentaro; Kawaguchi, Yasushi; Tohya, Yukinobu; Akashi, Hiroomi

    2009-08-01

    Almost all mammalian alphaherpesviruses can grow in cells derived from several types of animals in vitro. However, FHV-1 can only infect feline cell lines. For this reason, FHV-1 should be a good model to investigate species barriers to herpesviruses in vivo. To apply bacterial mutagenesis of FHV-1, we cloned the FHV-1 genome as a BAC. Using lambda and flp recombinations, we introduced a monomeric red fluorescence protein into the C-terminus of glycoprotein D. Although GFP in the constructed recombinant FHV-1, a transfectant of the bacmid of FHV-1 that possessed the GFP, acted in non-feline cell lines, the virus could not enter non-feline cell lines, demonstrating that the host specificity of FHV-1 was restricted in an early step of infection. The host range of canine herpesvirus is limited to dogs in vitro and in vivo; it cannot enter non-canine cell lines as a result of infection but the GFP is active by transfection, revealing the same result that the restriction step is at an early stage of infection. These results suggest the possibility of breaking species barriers of FHV-1 and CHV by modifying the gene(s) that act at the early stage of infection. PMID:19659927

  6. Modelling urban air quality using artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Nagendra, S.M. Shiva; Khare, Mukesh [Indian Institute of Technology Delhi, Department of Civil Engineering, New Delhi (India)

    2005-05-01

    This paper describes the development of artificial neural network-based vehicular exhaust emission models for predicting 8-h average carbon monoxide concentrations at two air quality control regions (AQCRs) in the city of Delhi, India, viz. a typical traffic intersection (AQCR1) and a typical arterial road (AQCR2). Maximum of ten meteorological and six traffic characteristic variables have been used in the models' formulation. Three scenarios were considered - considering both meteorological and traffic characteristics input parameters; only meteorological inputs; and only traffic characteristics input data. The performance of all the developed models was evaluated on the basis of index of agreement (d) and other statistical parameters, viz. the mean and the deviations of the observed and predicted concentrations, mean bias error, mean square error, systematic and unsystematic root mean square error, coefficient of determination and linear best fit constant and gradient (Willmott in B Am Meteorol Soc 63:1309, 1982). The forecast performance of the developed models, with meteorological and traffic characteristics (d=0.78 for AQCR1 and d=0.69 for AQCR2) and with only meteorological inputs (d=0.77 for AQCR1 and d=0.67 for AQCR2), were comparable with the measured data. (orig.)

  7. The "artificial artery" as in vitro perfusion model.

    Directory of Open Access Journals (Sweden)

    Doreen Janke

    . Taken together, the "artificial artery" provides a solid in vitro model to test pharmacological active compounds for their impact on arterio-damaging or arterio-protective properties on vascular response.

  8. A factor analysis model for functional genomics

    OpenAIRE

    Shioda Romy; Kustra Rafal; Zhu Mu

    2006-01-01

    Abstract Background Expression array data are used to predict biological functions of uncharacterized genes by comparing their expression profiles to those of characterized genes. While biologically plausible, this is both statistically and computationally challenging. Typical approaches are computationally expensive and ignore correlations among expression profiles and functional categories. Results We propose a factor analysis model (FAM) for functional genomics and give a two-step algorith...

  9. VOICE RECOGNITION USING ARTIFICIAL NEURAL NETWORKS AND GAUSSIAN MIXTURE MODELS

    Directory of Open Access Journals (Sweden)

    AARON NICHIE

    2013-05-01

    Full Text Available The ability of recognition systems to correctly recognize speakers based on their speech waveform distribution depends largely on how the recognition system can train the model parameters so as to provide the best class of discrimination. This paper presents the results of an effort to recognize the voice of individual speakers based on their continuous speech waveform distribution using the combined frameworks of artificial neural networks (ANN and statistical Gaussian mixture models (GMM. A feed-forward multilayer ANN architecture with 30 hidden neurons was implemented for discriminative classification and training and the statistical GMM model computed scores that were transferred to best match the speech features. The decision system determines the recognized speakers using correlation coefficient analysis to measure the goodness of match of speech feature frames of the detected speaker from the ANN and GMM frameworks. To validate performance of the system, experiments were conducted using speech utterances from 30 different speakers (20 males and 10 females. System performance showed average recognition rates of 77% for 5-word utterances and 43% when the lengths of the utterances were increased to 20-word utterances for cases of trained speech utterances. With unknown utterances, recognition rate of 18% achieved for 20-word utterances.

  10. Spatiotemporal modeling of monthly soil temperature using artificial neural networks

    Science.gov (United States)

    Wu, Wei; Tang, Xiao-Ping; Guo, Nai-Jia; Yang, Chao; Liu, Hong-Bin; Shang, Yue-Feng

    2013-08-01

    Soil temperature data are critical for understanding land-atmosphere interactions. However, in many cases, they are limited at both spatial and temporal scales. In the current study, an attempt was made to predict monthly mean soil temperature at a depth of 10 cm using artificial neural networks (ANNs) over a large region with complex terrain. Gridded independent variables, including latitude, longitude, elevation, topographic wetness index, and normalized difference vegetation index, were derived from a digital elevation model and remote sensing images with a resolution of 1 km. The good performance and robustness of the proposed ANNs were demonstrated by comparisons with multiple linear regressions. On average, the developed ANNs presented a relative improvement of about 44 % in root mean square error, 70 % in mean absolute percentage error, and 18 % in coefficient of determination over classical linear models. The proposed ANN models were then applied to predict soil temperatures at unsampled locations across the study area. Spatiotemporal variability of soil temperature was investigated based on the obtained database. Future work will be needed to test the applicability of ANNs for estimating soil temperature at finer scales.

  11. COMPUTER MODELING IN THE DEVELOPMENT OF ARTIFICIAL VENTRICLES OF HEART

    Directory of Open Access Journals (Sweden)

    L. V. Belyaev

    2011-12-01

    Full Text Available In article modern researches of processes of development of artificial ventricles of heart are described. Advanta- ges of application computer (CAD/CAE technologies are shown by development of artificial ventricles of heart. The systems developed with application of the given technologies are submitted. 

  12. Construction of a bacterial artificial chromosome library from the spikemoss Selaginella moellendorffii: a new resource for plant comparative genomics

    OpenAIRE

    Chapple Clint; Carlson John; Arumuganathan K; Mueller Christopher; Kudrna Dave; Weng Jing-Ke; Kim Hye Ran; Sisneros Nicholas; Luo Meizhong; Tanurdzic Milos; Wang Wenming; de Pamphilis Claude; Mandoli Dina; Tomkins Jeff; Wing Rod A

    2005-01-01

    Abstract Background The lycophytes are an ancient lineage of vascular plants that diverged from the seed plant lineage about 400 Myr ago. Although the lycophytes occupy an important phylogenetic position for understanding the evolution of plants and their genomes, no genomic resources exist for this group of plants. Results Here we describe the construction of a large-insert bacterial artificial chromosome (BAC) library from the lycophyte Selaginella moellendorffii. Based on cell flow cytomet...

  13. Merging Marine Ecosystem Models and Genomics

    Science.gov (United States)

    Coles, V.; Hood, R. R.; Stukel, M. R.; Moran, M. A.; Paul, J. H.; Satinsky, B.; Zielinski, B.; Yager, P. L.

    2015-12-01

    oceanography. One of the grand challenges of oceanography is to develop model techniques to more effectively incorporate genomic information. As one approach, we developed an ecosystem model whose community is determined by randomly assigning functional genes to build each organism's "DNA". Microbes are assigned a size that sets their baseline environmental responses using allometric response cuves. These responses are modified by the costs and benefits conferred by each gene in an organism's genome. The microbes are embedded in a general circulation model where environmental conditions shape the emergent population. This model is used to explore whether organisms constructed from randomized combinations of metabolic capability alone can self-organize to create realistic oceanic biogeochemical gradients. Realistic community size spectra and chlorophyll-a concentrations emerge in the model. The model is run repeatedly with randomly-generated microbial communities and each time realistic gradients in community size spectra, chlorophyll-a, and forms of nitrogen develop. This supports the hypothesis that the metabolic potential of a community rather than the realized species composition is the primary factor setting vertical and horizontal environmental gradients. Vertical distributions of nitrogen and transcripts for genes involved in nitrification are broadly consistent with observations. Modeled gene and transcript abundance for nitrogen cycling and processing of land-derived organic material match observations along the extreme gradients in the Amazon River plume, and they help to explain the factors controlling observed variability.

  14. eHive: An Artificial Intelligence workflow system for genomic analysis

    Directory of Open Access Journals (Sweden)

    Gordon Leo

    2010-05-01

    Full Text Available Abstract Background The Ensembl project produces updates to its comparative genomics resources with each of its several releases per year. During each release cycle approximately two weeks are allocated to generate all the genomic alignments and the protein homology predictions. The number of calculations required for this task grows approximately quadratically with the number of species. We currently support 50 species in Ensembl and we expect the number to continue to grow in the future. Results We present eHive, a new fault tolerant distributed processing system initially designed to support comparative genomic analysis, based on blackboard systems, network distributed autonomous agents, dataflow graphs and block-branch diagrams. In the eHive system a MySQL database serves as the central blackboard and the autonomous agent, a Perl script, queries the system and runs jobs as required. The system allows us to define dataflow and branching rules to suit all our production pipelines. We describe the implementation of three pipelines: (1 pairwise whole genome alignments, (2 multiple whole genome alignments and (3 gene trees with protein homology inference. Finally, we show the efficiency of the system in real case scenarios. Conclusions eHive allows us to produce computationally demanding results in a reliable and efficient way with minimal supervision and high throughput. Further documentation is available at: http://www.ensembl.org/info/docs/eHive/.

  15. Soil NO emissions modelling using artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Delon, Claire. e-mail: delc@aero.obs-mip.fr; Serca, Dominique; Dupont, Richard; Delmas, Robert [Laboratoire d' Aerologie, 14 avenue E. Belin, 31400 Toulouse (France); Boissard, Christophe; Dutot, Alain [Laboratoire Interuniversitaire des Systemes Atmospheriques, 94010 Creteil (France); Laville, Patricia [INRA, EGC, 78830 Thiverval-Grignon (France); Rosnay, Patricia de [CESBIO, 31400 Toulouse (France)

    2007-07-15

    Soils are considered as an important source for NO emissions, but the uncertainty in quantifying these emissions worldwide remains large due to the lack of field experiments and high variability in time and space of environmental parameters influencing NO emissions. In this study, the development of a relationship for NO flux emission from soil with pertinent environmental parameters is proposed. An Artificial Neural Network (ANN) is used to find the best non-linear regression between NO fluxes and seven environmental variables, introduced step by step: soil surface temperature, surface water filled pore space, soil temperature at depth (20-30 cm), fertilisation rate, sand percentage in the soil, pH and wind speed. The network performance is evaluated each time a new variable is introduced in the network, i.e. each variable is justified and evaluated in improving the network performance. A resulting equation linking NO flux from soil and the seven variables is proposed, and shows to perform well with measurements (R2 = 0.71), whereas other regression models give a poor correlation coefficient between calculation and measurements (R2 = 0.12 for known algorithms used at regional or global scales). ANN algorithm is shown to be a good alternative between biogeochemical and large-scale models, for future application at regional scale.

  16. A Multidisciplinary Artificial Intelligence Model of an Affective Robot

    Directory of Open Access Journals (Sweden)

    Hooman Aghaebrahimi Samani

    2012-03-01

    The advanced artificial intelligence of the system includes three modules, namely Probabilistic Love Assembly (PLA, based on the psychology of love, Artificial Endocrine System (AES, based on the physiology of love, and Affective State Transition (AST, based on emotions. The PLA module employs a Bayesian network to incorporate psychological parameters of affection in the robot. The AES module employs artificial emotional and biological hormones via a Dynamic Bayesian Network (DBN. The AST module uses a novel transition method for handling affective states of the robot. These three modules work together to manage emotional behaviours of the robot.

  17. Bayesian Model Averaging of Artificial Intelligence Models for Hydraulic Conductivity Estimation

    Science.gov (United States)

    Nadiri, A.; Chitsazan, N.; Tsai, F. T.; Asghari Moghaddam, A.

    2012-12-01

    This research presents a Bayesian artificial intelligence model averaging (BAIMA) method that incorporates multiple artificial intelligence (AI) models to estimate hydraulic conductivity and evaluate estimation uncertainties. Uncertainty in the AI model outputs stems from error in model input as well as non-uniqueness in selecting different AI methods. Using one single AI model tends to bias the estimation and underestimate uncertainty. BAIMA employs Bayesian model averaging (BMA) technique to address the issue of using one single AI model for estimation. BAIMA estimates hydraulic conductivity by averaging the outputs of AI models according to their model weights. In this study, the model weights were determined using the Bayesian information criterion (BIC) that follows the parsimony principle. BAIMA calculates the within-model variances to account for uncertainty propagation from input data to AI model output. Between-model variances are evaluated to account for uncertainty due to model non-uniqueness. We employed Takagi-Sugeno fuzzy logic (TS-FL), artificial neural network (ANN) and neurofuzzy (NF) to estimate hydraulic conductivity for the Tasuj plain aquifer, Iran. BAIMA combined three AI models and produced better fitting than individual models. While NF was expected to be the best AI model owing to its utilization of both TS-FL and ANN models, the NF model is nearly discarded by the parsimony principle. The TS-FL model and the ANN model showed equal importance although their hydraulic conductivity estimates were quite different. This resulted in significant between-model variances that are normally ignored by using one AI model.

  18. Modeling Common-Sense Decisions in Artificial Intelligence

    Science.gov (United States)

    Zak, Michail

    2010-01-01

    A methodology has been conceived for efficient synthesis of dynamical models that simulate common-sense decision- making processes. This methodology is intended to contribute to the design of artificial-intelligence systems that could imitate human common-sense decision making or assist humans in making correct decisions in unanticipated circumstances. This methodology is a product of continuing research on mathematical models of the behaviors of single- and multi-agent systems known in biology, economics, and sociology, ranging from a single-cell organism at one extreme to the whole of human society at the other extreme. Earlier results of this research were reported in several prior NASA Tech Briefs articles, the three most recent and relevant being Characteristics of Dynamics of Intelligent Systems (NPO -21037), NASA Tech Briefs, Vol. 26, No. 12 (December 2002), page 48; Self-Supervised Dynamical Systems (NPO-30634), NASA Tech Briefs, Vol. 27, No. 3 (March 2003), page 72; and Complexity for Survival of Living Systems (NPO- 43302), NASA Tech Briefs, Vol. 33, No. 7 (July 2009), page 62. The methodology involves the concepts reported previously, albeit viewed from a different perspective. One of the main underlying ideas is to extend the application of physical first principles to the behaviors of living systems. Models of motor dynamics are used to simulate the observable behaviors of systems or objects of interest, and models of mental dynamics are used to represent the evolution of the corresponding knowledge bases. For a given system, the knowledge base is modeled in the form of probability distributions and the mental dynamics is represented by models of the evolution of the probability densities or, equivalently, models of flows of information. Autonomy is imparted to the decisionmaking process by feedback from mental to motor dynamics. This feedback replaces unavailable external information by information stored in the internal knowledge base. Representation

  19. Forecasting Financial Time-Series using Artificial Market Models

    CERN Document Server

    Gupta, N; Johnson, N F; Gupta, Nachi; Hauser, Raphael; Johnson, Neil F.

    2005-01-01

    We discuss the theoretical machinery involved in predicting financial market movements using an artificial market model which has been trained on real financial data. This approach to market prediction - in particular, forecasting financial time-series by training a third-party or 'black box' game on the financial data itself -- was discussed by Johnson et al. in cond-mat/0105303 and cond-mat/0105258 and was based on some encouraging preliminary investigations of the dollar-yen exchange rate, various individual stocks, and stock market indices. However, the initial attempts lacked a clear formal methodology. Here we present a detailed methodology, using optimization techniques to build an estimate of the strategy distribution across the multi-trader population. In contrast to earlier attempts, we are able to present a systematic method for identifying 'pockets of predictability' in real-world markets. We find that as each pocket closes up, the black-box system needs to be 'reset' - which is equivalent to sayi...

  20. A Multidisciplinary Artificial Intelligence Model of an Affective Robot

    OpenAIRE

    Hooman Aghaebrahimi Samani; Elham Saadatian

    2012-01-01

    A multidisciplinary approach to a novel artificial intelligence system for an affective robot is presented in this paper. The general objective of the system is to develop a robotic system which strives to achieve a high level of emotional bond between humans and robot by exploring human love. Such a relationship is a contingent process of attraction, affection and attachment from humans towards robots, and the belief of the vice versa from robots to humans. The advanced artificial intelli...

  1. Metaplasticity Artificial Neural Networks Model Application to Radar Detection

    OpenAIRE

    Diego Andina; Juan Fombellida

    2007-01-01

    Many Artificial Neural Networks design algorithms or learning methods imply the minimization of an error objective function. During learning, weight values are updated following a strategy that tends to minimize the final mean error in the Network performance. Weight values are classically seen as a representation of the synaptic weights in biological neurons and their ability to change its value could be interpreted as artificial plasticity inspired by this biological property of neurons. In...

  2. Genetic Algorithm Optimization of Artificial Neural Networks for Hydrological Modelling

    Science.gov (United States)

    Abrahart, R. J.

    2004-05-01

    This paper will consider the case for genetic algorithm optimization in the development of an artificial neural network model. It will provide a methodological evaluation of reported investigations with respect to hydrological forecasting and prediction. The intention in such operations is to develop a superior modelling solution that will be: \\begin{itemize} more accurate in terms of output precision and model estimation skill; more tractable in terms of personal requirements and end-user control; and/or more robust in terms of conceptual and mechanical power with respect to adverse conditions. The genetic algorithm optimization toolbox could be used to perform a number of specific roles or purposes and it is the harmonious and supportive relationship between neural networks and genetic algorithms that will be highlighted and assessed. There are several neural network mechanisms and procedures that could be enhanced and potential benefits are possible at different stages in the design and construction of an operational hydrological model e.g. division of inputs; identification of structure; initialization of connection weights; calibration of connection weights; breeding operations between successful models; and output fusion associated with the development of ensemble solutions. Each set of opportunities will be discussed and evaluated. Two strategic questions will also be considered: [i] should optimization be conducted as a set of small individual procedures or as one large holistic operation; [ii] what specific function or set of weighted vectors should be optimized in a complex software product e.g. timings, volumes, or quintessential hydrological attributes related to the 'problem situation' - that might require the development flood forecasting, drought estimation, or record infilling applications. The paper will conclude with a consideration of hydrological forecasting solutions developed on the combined methodologies of co-operative co-evolution and

  3. Testing normality in bivariate probit models : a simple artificial regression based LM test

    OpenAIRE

    Anthony Murphy

    1994-01-01

    A simple and convenient LM test of normality in the bivariate probit model is derived. The alternative hypothesis is based on a form of truncated Gram Charlier Type series. The LM test may be calculated as an artificial regression. However, the proposed artificial regression does not use the outer product gradient form. Thus it is likely to perform reasonably well in small samples.

  4. Artificial Exo-Society Modeling: a New Tool for SETI Research

    Science.gov (United States)

    Gardner, James N.

    2002-01-01

    One of the newest fields of complexity research is artificial society modeling. Methodologically related to artificial life research, artificial society modeling utilizes agent-based computer simulation tools like SWARM and SUGARSCAPE developed by the Santa Fe Institute, Los Alamos National Laboratory and the Bookings Institution in an effort to introduce an unprecedented degree of rigor and quantitative sophistication into social science research. The broad aim of artificial society modeling is to begin the development of a more unified social science that embeds cultural evolutionary processes in a computational environment that simulates demographics, the transmission of culture, conflict, economics, disease, the emergence of groups and coadaptation with an environment in a bottom-up fashion. When an artificial society computer model is run, artificial societal patterns emerge from the interaction of autonomous software agents (the "inhabitants" of the artificial society). Artificial society modeling invites the interpretation of society as a distributed computational system and the interpretation of social dynamics as a specialized category of computation. Artificial society modeling techniques offer the potential of computational simulation of hypothetical alien societies in much the same way that artificial life modeling techniques offer the potential to model hypothetical exobiological phenomena. NASA recently announced its intention to begin exploring the possibility of including artificial life research within the broad portfolio of scientific fields comprised by the interdisciplinary astrobiology research endeavor. It may be appropriate for SETI researchers to likewise commence an exploration of the possible inclusion of artificial exo-society modeling within the SETI research endeavor. Artificial exo-society modeling might be particularly useful in a post-detection environment by (1) coherently organizing the set of data points derived from a detected

  5. Water Turbidity Modelling During Water Treatment Processes Using Artificial Neural Networks

    OpenAIRE

    Rak, Adam

    2013-01-01

    Artificial neural networks are increasingly being used in the research and analysis of unit and technical processes related to water treatment. An artificial neural network model was created to predict the turbidity of treated water in a newly operating water treatment system for surface and retention water at the Sosnówka reservoir, Poland. To model water turbidity during the water treatment process for a selected system, a flexible Bayesian model of neural networks, Gaussian processes a...

  6. The application of neural networks with artificial intelligence technique in the modeling of industrial processes

    International Nuclear Information System (INIS)

    Neural networks are a relatively new artificial intelligence technique that emulates the behavior of biological neural systems in digital software or hardware. These networks can 'learn', automatically, complex relationships among data. This feature makes the technique very useful in modeling processes for which mathematical modeling is difficult or impossible. The work described here outlines some examples of the application of neural networks with artificial intelligence technique in the modeling of industrial processes.

  7. Advanced Artificial Science. The development of an artificial science and engineering research infrastructure to facilitate innovative computational modeling, analysis, and application to interdisciplinary areas of scientific investigation.

    Energy Technology Data Exchange (ETDEWEB)

    Saffer, Shelley (Sam) I.

    2014-12-01

    This is a final report of the DOE award DE-SC0001132, Advanced Artificial Science. The development of an artificial science and engineering research infrastructure to facilitate innovative computational modeling, analysis, and application to interdisciplinary areas of scientific investigation. This document describes the achievements of the goals, and resulting research made possible by this award.

  8. A Probabilistic Genome-Wide Gene Reading Frame Sequence Model

    DEFF Research Database (Denmark)

    Have, Christian Theil; Mørk, Søren

    We introduce a new type of probabilistic sequence model, that model the sequential composition of reading frames of genes in a genome. Our approach extends gene finders with a model of the sequential composition of genes at the genome-level -- effectively producing a sequential genome annotation as...... output. The model can be used to obtain the most probable genome annotation based on a combination of i: a gene finder score of each gene candidate and ii: the sequence of the reading frames of gene candidates through a genome. The model --- as well as a higher order variant --- is developed and tested...... using the probabilistic logic programming language and machine learning system PRISM - a fast and efficient model prototyping environment, using bacterial gene finding performance as a benchmark of signal strength. The model is used to prune a set of gene predictions from an underlying gene finder and...

  9. PERCEPTION OF MEDICAL STUDENTS TOWARDS ARTIFICIAL BONES AND POP MODELS OF VISCERA

    OpenAIRE

    Sumit Tulshidas Patil; Nazia Quadir; Rashmi Deopujari; Vivekanand Gajbhiye

    2015-01-01

    Background: In learning of anatomy, bones and viscera are very important. Now days, artificial bones are replacing the original bones for study purpose due to unavailability. Original viscera are available for students only at dissection hours. So we have tried to find out perception of medical students towards artificial bones and POP models of viscera. Materials and Methods: We had prepared a questionnaire consisting of 20 questions, 10 related to bones and 10 related to the POP models o...

  10. Genome-scale models of bacterial metabolism: reconstruction and applications

    OpenAIRE

    Durot, Maxime; Bourguignon, Pierre-Yves; Schachter, Vincent

    2008-01-01

    Genome-scale metabolic models bridge the gap between genome-derived biochemical information and metabolic phenotypes in a principled manner, providing a solid interpretative framework for experimental data related to metabolic states, and enabling simple in silico experiments with whole-cell metabolism. Models have been reconstructed for almost 20 bacterial species, so far mainly through expert curation efforts integrating information from the literature with genome annotation. A wide variety...

  11. Sequence modelling and an extensible data model for genomic database

    Energy Technology Data Exchange (ETDEWEB)

    Li, Peter Wei-Der [California Univ., San Francisco, CA (United States)]|[Lawrence Berkeley Lab., CA (United States)

    1992-01-01

    The Human Genome Project (HGP) plans to sequence the human genome by the beginning of the next century. It will generate DNA sequences of more than 10 billion bases and complex marker sequences (maps) of more than 100 million markers. All of these information will be stored in database management systems (DBMSs). However, existing data models do not have the abstraction mechanism for modelling sequences and existing DBMS`s do not have operations for complex sequences. This work addresses the problem of sequence modelling in the context of the HGP and the more general problem of an extensible object data model that can incorporate the sequence model as well as existing and future data constructs and operators. First, we proposed a general sequence model that is application and implementation independent. This model is used to capture the sequence information found in the HGP at the conceptual level. In addition, abstract and biological sequence operators are defined for manipulating the modelled sequences. Second, we combined many features of semantic and object oriented data models into an extensible framework, which we called the ``Extensible Object Model``, to address the need of a modelling framework for incorporating the sequence data model with other types of data constructs and operators. This framework is based on the conceptual separation between constructors and constraints. We then used this modelling framework to integrate the constructs for the conceptual sequence model. The Extensible Object Model is also defined with a graphical representation, which is useful as a tool for database designers. Finally, we defined a query language to support this model and implement the query processor to demonstrate the feasibility of the extensible framework and the usefulness of the conceptual sequence model.

  12. Sequence modelling and an extensible data model for genomic database

    Energy Technology Data Exchange (ETDEWEB)

    Li, Peter Wei-Der (California Univ., San Francisco, CA (United States) Lawrence Berkeley Lab., CA (United States))

    1992-01-01

    The Human Genome Project (HGP) plans to sequence the human genome by the beginning of the next century. It will generate DNA sequences of more than 10 billion bases and complex marker sequences (maps) of more than 100 million markers. All of these information will be stored in database management systems (DBMSs). However, existing data models do not have the abstraction mechanism for modelling sequences and existing DBMS's do not have operations for complex sequences. This work addresses the problem of sequence modelling in the context of the HGP and the more general problem of an extensible object data model that can incorporate the sequence model as well as existing and future data constructs and operators. First, we proposed a general sequence model that is application and implementation independent. This model is used to capture the sequence information found in the HGP at the conceptual level. In addition, abstract and biological sequence operators are defined for manipulating the modelled sequences. Second, we combined many features of semantic and object oriented data models into an extensible framework, which we called the Extensible Object Model'', to address the need of a modelling framework for incorporating the sequence data model with other types of data constructs and operators. This framework is based on the conceptual separation between constructors and constraints. We then used this modelling framework to integrate the constructs for the conceptual sequence model. The Extensible Object Model is also defined with a graphical representation, which is useful as a tool for database designers. Finally, we defined a query language to support this model and implement the query processor to demonstrate the feasibility of the extensible framework and the usefulness of the conceptual sequence model.

  13. Modeling of Relative Humidity Using Artificial Neural Network

    OpenAIRE

    Samer AlSadi; Tamer Khatib

    2012-01-01

    This paper presents a relative humidity predictions using feedforward artificial neural network (FFNN). Relative humidity values obtained from weather records for Malaysia are used in training the FFNNs. The prediction of the relative humidity is in terms of Sun shine ration and cloud cover. However, three statistical parameters, namely, mean absolute percentage error, MAPE, mean bias error, MBE, and root mean square error, RMSE are used to evaluate the neural networks. Based on results, the ...

  14. Modelling artificial sea salt emission in large eddy simulations

    OpenAIRE

    Maalick, Z.; Korhonen, H.; H. Kokkola; Kühn, T.; S. Romakkaniemi

    2014-01-01

    We study the dispersion of sea salt particles from artificially injected sea spray at a cloud-resolving scale. Understanding of how different aerosol processes affect particle dispersion is crucial when designing emission sources for marine cloud brightening. Compared with previous studies, we include for the first time an explicit treatment of aerosol water, which takes into account condensation, evaporation and their effect on ambient temperature. This enables us to capture the negative buo...

  15. An exemplar model of performance in the artificial grammar task: holographic representation.

    Science.gov (United States)

    Jamieson, Randall K; Hauri, Brian R

    2012-06-01

    We apply a multitrace model of memory to explain performance in the artificial grammar task. The model blends the convolution method for representation from Jones and Mewhort's BEAGLE model (Jones, M. N., & Mewhort, D. J. K. (2007). Representing word meaning and order information in a composite holographic lexicon. Psychological Review, 114, 1-37) of semantic memory with the multitrace storage and retrieval model from Hintzman's MINERVA 2 model (Hintzman, D. L. (1986). "Schema abstraction" in a multiple-trace memory model. Psychological Review, 93, 411-428) of episodic memory. We report an artificial grammar experiment, and we fit the model to those data at the level of individual items. We argue that performance in the artificial grammar task is best understood as a process of retrospective inference from memory. PMID:22686158

  16. Cloning of the Full-Length Rhesus Cytomegalovirus Genome as an Infectious and Self-Excisable Bacterial Artificial Chromosome for Analysis of Viral Pathogenesis

    OpenAIRE

    Chang, W. L. William; Peter A Barry

    2003-01-01

    Rigorous investigation of many functions encoded by cytomegaloviruses (CMVs) requires analysis in the context of virus-host interactions. To facilitate the construction of rhesus CMV (RhCMV) mutants for in vivo studies, a bacterial artificial chromosome (BAC) containing an enhanced green fluorescent protein (EGFP) cassette was engineered into the intergenic region between unique short 1 (US1) and US2 of the full-length viral genome by Cre/lox-mediated recombination. Infectious virions were re...

  17. A high-coverage artificial chromosome library for the genome-wide screening of drug-resistance genes in malaria parasites

    OpenAIRE

    Iwanaga, Shiroh; Kaneko, Izumi; Yuda, Masao

    2012-01-01

    The global spread of drug-resistant parasites is a serious problem for the treatment of malaria. Although identifying drug-resistance genes is crucial for the efforts against resistant parasites, an effective approach has not yet been developed. Here, we report a robust method for identifying resistance genes from parasites by using a Plasmodium artificial chromosome (PAC). Large genomic DNA fragments (10–50 kb) from the drug-resistant rodent malaria parasite Plasmodium berghei were ligated i...

  18. Genome Informed Trait-Based Models

    Science.gov (United States)

    Karaoz, U.; Cheng, Y.; Bouskill, N.; Tang, J.; Beller, H. R.; Brodie, E.; Riley, W. J.

    2013-12-01

    Trait-based approaches are powerful tools for representing microbial communities across both spatial and temporal scales within ecosystem models. Trait-based models (TBMs) represent the diversity of microbial taxa as stochastic assemblages with a distribution of traits constrained by trade-offs between these traits. Such representation with its built-in stochasticity allows the elucidation of the interactions between the microbes and their environment by reducing the complexity of microbial community diversity into a limited number of functional ';guilds' and letting them emerge across spatio-temporal scales. From the biogeochemical/ecosystem modeling perspective, the emergent properties of the microbial community could be directly translated into predictions of biogeochemical reaction rates and microbial biomass. The accuracy of TBMs depends on the identification of key traits of the microbial community members and on the parameterization of these traits. Current approaches to inform TBM parameterization are empirical (i.e., based on literature surveys). Advances in omic technologies (such as genomics, metagenomics, metatranscriptomics, and metaproteomics) pave the way to better-initialize models that can be constrained in a generic or site-specific fashion. Here we describe the coupling of metagenomic data to the development of a TBM representing the dynamics of metabolic guilds from an organic carbon stimulated groundwater microbial community. Illumina paired-end metagenomic data were collected from the community as it transitioned successively through electron-accepting conditions (nitrate-, sulfate-, and Fe(III)-reducing), and used to inform estimates of growth rates and the distribution of metabolic pathways (i.e., aerobic and anaerobic oxidation, fermentation) across a spatially resolved TBM. We use this model to evaluate the emergence of different metabolisms and predict rates of biogeochemical processes over time. We compare our results to observational

  19. Aquatic models, genomics and chemical risk management.

    Science.gov (United States)

    Cheng, Keith C; Hinton, David E; Mattingly, Carolyn J; Planchart, Antonio

    2012-01-01

    The 5th Aquatic Animal Models for Human Disease meeting follows four previous meetings (Nairn et al., 2001; Schmale, 2004; Schmale et al., 2007; Hinton et al., 2009) in which advances in aquatic animal models for human disease research were reported, and community discussion of future direction was pursued. At this meeting, discussion at a workshop entitled Bioinformatics and Computational Biology with Web-based Resources (20 September 2010) led to an important conclusion: Aquatic model research using feral and experimental fish, in combination with web-based access to annotated anatomical atlases and toxicological databases, yields data that advance our understanding of human gene function, and can be used to facilitate environmental management and drug development. We propose here that the effects of genes and environment are best appreciated within an anatomical context - the specifically affected cells and organs in the whole animal. We envision the use of automated, whole-animal imaging at cellular resolution and computational morphometry facilitated by high-performance computing and automated entry into toxicological databases, as anchors for genetic and toxicological data, and as connectors between human and model system data. These principles should be applied to both laboratory and feral fish populations, which have been virtually irreplaceable sentinals for environmental contamination that results in human morbidity and mortality. We conclude that automation, database generation, and web-based accessibility, facilitated by genomic/transcriptomic data and high-performance and cloud computing, will potentiate the unique and potentially key roles that aquatic models play in advancing systems biology, drug development, and environmental risk management. PMID:21763781

  20. Testing normality in bivariate probit models : a simple artificial regression based LM test

    OpenAIRE

    Murphy, Anthony

    1994-01-01

    A simple and convenient LM test of normality in the bivariate probit model is derived. The alternative hypothesis is based on a form of truncated Gram Charlier Type series. The LM test may be calculated as an artificial regression. However, the proposed artificial regression does not use the outer product gradient form. Thus it is likely to perform reasonably well in small samples. non-peer-reviewed

  1. An artificial neural network for modeling reliability, availability and maintainability of a repairable system

    International Nuclear Information System (INIS)

    The paper explores the application of artificial neural networks to model the behaviour of a complex, repairable system. A composite measure of reliability, availability and maintainability parameters has been proposed for measuring the system performance. The artificial neural network has been trained using past data of a helicopter transportation facility. It is used to simulate behaviour of the facility under various constraints. The insights obtained from results of simulation are useful in formulating strategies for optimal operation of the system

  2. NEW ANTIMICROBIAL SENSITIVITY TESTS OF BIOFILM OF STREPTOCOCCUS MUTANS IN ARTIFICIAL MOUTH MODEL

    Institute of Scientific and Technical Information of China (English)

    李鸣宇; 汪俊; 刘正; 朱彩莲

    2004-01-01

    Objective To develop a new antimicrobial sensitivity test model for oral products in vitro.Methods A biofilm artificial mouth model for antimicrobial sensitivity tests was established by modifying the LKI chromatography chamber. Using sodium fluoride and Tea polyphenol as antimicrobial agent and Streptococcus mutans as target, sensitivity tests were studied. Results The modeling biofilm assay resulted in a MIC of 1.28mg/ml for fluoride against S. mutans, which was 32 times the MIC for broth maco-dilution method. The differential resistance of bacteria bioflim to antimicrobial agent relative to planktonic cells was also demonstrated. Conclusion The biofilm artificial mouth model may be useful in oral products test.

  3. RBF-Type Artificial Neural Network Model Applied in Alloy Design of Steels

    Institute of Scientific and Technical Information of China (English)

    YOU Wei; LIU Ya-xiu; BAI Bing-zhe; FANG Hong-sheng

    2008-01-01

    RBF model, a new type of artificial neural network model was developed to design the content of carbon in low-alloy engineering steels. The errors of the ANN model are. MSE 0. 052 1, MSRE 17. 85%, and VOF 1. 932 9. The results obtained are satisfactory. The method is a powerful aid for designing new steels.

  4. A Multilayered Model for Artificial Intelligence of Game Character as Agent Architecture

    OpenAIRE

    Miyake, Youichiro; Miyake, Yoichiro

    2015-01-01

    As all mathematics have a beautiful structure, an inner mind model of Artificial Intelligence has a grand architecture. It consists of information flow and software modules. In this twenty years, an agent's inner intelligence model has been researched and developed by many game AI programmers in game titles. A whole image of an agent's intelligent model is explained.

  5. Genome sequence analysis of the model grass Brachypodium distachyon: insights into grass genome evolution

    Energy Technology Data Exchange (ETDEWEB)

    Schulman, Al

    2009-08-09

    Three subfamilies of grasses, the Erhardtoideae (rice), the Panicoideae (maize, sorghum, sugar cane and millet), and the Pooideae (wheat, barley and cool season forage grasses) provide the basis of human nutrition and are poised to become major sources of renewable energy. Here we describe the complete genome sequence of the wild grass Brachypodium distachyon (Brachypodium), the first member of the Pooideae subfamily to be completely sequenced. Comparison of the Brachypodium, rice and sorghum genomes reveals a precise sequence- based history of genome evolution across a broad diversity of the grass family and identifies nested insertions of whole chromosomes into centromeric regions as a predominant mechanism driving chromosome evolution in the grasses. The relatively compact genome of Brachypodium is maintained by a balance of retroelement replication and loss. The complete genome sequence of Brachypodium, coupled to its exceptional promise as a model system for grass research, will support the development of new energy and food crops

  6. Genome Editing of Structural Variations: Modeling and Gene Correction.

    Science.gov (United States)

    Park, Chul-Yong; Sung, Jin Jea; Kim, Dong-Wook

    2016-07-01

    The analysis of chromosomal structural variations (SVs), such as inversions and translocations, was made possible by the completion of the human genome project and the development of genome-wide sequencing technologies. SVs contribute to genetic diversity and evolution, although some SVs can cause diseases such as hemophilia A in humans. Genome engineering technology using programmable nucleases (e.g., ZFNs, TALENs, and CRISPR/Cas9) has been rapidly developed, enabling precise and efficient genome editing for SV research. Here, we review advances in modeling and gene correction of SVs, focusing on inversion, translocation, and nucleotide repeat expansion. PMID:27016031

  7. Microbial comparative pan-genomics using binomial mixture models

    DEFF Research Database (Denmark)

    Ussery, David; Snipen, L; Almøy, T

    2009-01-01

    The size of the core- and pan-genome of bacterial species is a topic of increasing interest due to the growing number of sequenced prokaryote genomes, many from the same species. Attempts to estimate these quantities have been made, using regression methods or mixture models. We extend the latter...

  8. Time series model based on global structure of complete genome

    CERN Document Server

    Yu, Z G; Anh, Vo

    2001-01-01

    A time series model based on the global structure of the complete genome is proposed. Three kinds of length sequences of the complete genome are considered. The correlation dimensions and Hurst exponents of the length sequences are calculated. Using these two exponents, some interesting results related to the problem of classification and evolution relationship of bacteria are obtained.

  9. Modeling Lactococcus lactis using a genome-scale flux model

    Directory of Open Access Journals (Sweden)

    Nielsen Jens

    2005-06-01

    Full Text Available Abstract Background Genome-scale flux models are useful tools to represent and analyze microbial metabolism. In this work we reconstructed the metabolic network of the lactic acid bacteria Lactococcus lactis and developed a genome-scale flux model able to simulate and analyze network capabilities and whole-cell function under aerobic and anaerobic continuous cultures. Flux balance analysis (FBA and minimization of metabolic adjustment (MOMA were used as modeling frameworks. Results The metabolic network was reconstructed using the annotated genome sequence from L. lactis ssp. lactis IL1403 together with physiological and biochemical information. The established network comprised a total of 621 reactions and 509 metabolites, representing the overall metabolism of L. lactis. Experimental data reported in the literature was used to fit the model to phenotypic observations. Regulatory constraints had to be included to simulate certain metabolic features, such as the shift from homo to heterolactic fermentation. A minimal medium for in silico growth was identified, indicating the requirement of four amino acids in addition to a sugar. Remarkably, de novo biosynthesis of four other amino acids was observed even when all amino acids were supplied, which is in good agreement with experimental observations. Additionally, enhanced metabolic engineering strategies for improved diacetyl producing strains were designed. Conclusion The L. lactis metabolic network can now be used for a better understanding of lactococcal metabolic capabilities and potential, for the design of enhanced metabolic engineering strategies and for integration with other types of 'omic' data, to assist in finding new information on cellular organization and function.

  10. Building a model: developing genomic resources for common milkweed (Asclepias syriaca with low coverage genome sequencing

    Directory of Open Access Journals (Sweden)

    Weitemier Kevin

    2011-05-01

    Full Text Available Abstract Background Milkweeds (Asclepias L. have been extensively investigated in diverse areas of evolutionary biology and ecology; however, there are few genetic resources available to facilitate and compliment these studies. This study explored how low coverage genome sequencing of the common milkweed (Asclepias syriaca L. could be useful in characterizing the genome of a plant without prior genomic information and for development of genomic resources as a step toward further developing A. syriaca as a model in ecology and evolution. Results A 0.5× genome of A. syriaca was produced using Illumina sequencing. A virtually complete chloroplast genome of 158,598 bp was assembled, revealing few repeats and loss of three genes: accD, clpP, and ycf1. A nearly complete rDNA cistron (18S-5.8S-26S; 7,541 bp and 5S rDNA (120 bp sequence were obtained. Assessment of polymorphism revealed that the rDNA cistron and 5S rDNA had 0.3% and 26.7% polymorphic sites, respectively. A partial mitochondrial genome sequence (130,764 bp, with identical gene content to tobacco, was also assembled. An initial characterization of repeat content indicated that Ty1/copia-like retroelements are the most common repeat type in the milkweed genome. At least one A. syriaca microread hit 88% of Catharanthus roseus (Apocynaceae unigenes (median coverage of 0.29× and 66% of single copy orthologs (COSII in asterids (median coverage of 0.14×. From this partial characterization of the A. syriaca genome, markers for population genetics (microsatellites and phylogenetics (low-copy nuclear genes studies were developed. Conclusions The results highlight the promise of next generation sequencing for development of genomic resources for any organism. Low coverage genome sequencing allows characterization of the high copy fraction of the genome and exploration of the low copy fraction of the genome, which facilitate the development of molecular tools for further study of a target species

  11. Characterisation of the plasma density with two artificial neural network models

    International Nuclear Information System (INIS)

    This paper establishes two artificial neural network models by using a multi layer perceptron algorithm and radial based function algorithm in order to predict the plasma density in a plasma system. In this model, the input layer is composed of five neurons: the radial position, the axial position, the gas pressure, the microwave power and the magnet coil current. The output layer is the target output neuron: the plasma density. The accuracy of prediction is tested with the experimental data obtained by the Langmuir probe. The effectiveness of two artificial neural network models are demonstrated, the results show good agreements with corresponding experimental data. The ability of the artificial neural network model to predict the plasma density accurately in an electron cyclotron resonance-plasma enhanced chemical vapour deposition system can be concluded, and the radial based function is more suitable than the multi layer perceptron in this work. (general)

  12. Robust nonlinear autoregressive moving average model parameter estimation using stochastic recurrent artificial neural networks

    DEFF Research Database (Denmark)

    Chon, K H; Hoyer, D; Armoundas, A A; Holstein-Rathlou, N H; Marsh, D J

    1999-01-01

    In this study, we introduce a new approach for estimating linear and nonlinear stochastic autoregressive moving average (ARMA) model parameters, given a corrupt signal, using artificial recurrent neural networks. This new approach is a two-step approach in which the parameters of the deterministic...... part of the stochastic ARMA model are first estimated via a three-layer artificial neural network (deterministic estimation step) and then reestimated using the prediction error as one of the inputs to the artificial neural networks in an iterative algorithm (stochastic estimation step). The prediction...... error is obtained by subtracting the corrupt signal of the estimated ARMA model obtained via the deterministic estimation step from the system output response. We present computer simulation examples to show the efficacy of the proposed stochastic recurrent neural network approach in obtaining accurate...

  13. Artificial intelligence in process control: Knowledge base for the shuttle ECS model

    Science.gov (United States)

    Stiffler, A. Kent

    1989-01-01

    The general operation of KATE, an artificial intelligence controller, is outlined. A shuttle environmental control system (ECS) demonstration system for KATE is explained. The knowledge base model for this system is derived. An experimental test procedure is given to verify parameters in the model.

  14. Multiscale Modeling of Gene-Behavior Associations in an Artificial Neural Network Model of Cognitive Development.

    Science.gov (United States)

    Thomas, Michael S C; Forrester, Neil A; Ronald, Angelica

    2016-01-01

    In the multidisciplinary field of developmental cognitive neuroscience, statistical associations between levels of description play an increasingly important role. One example of such associations is the observation of correlations between relatively common gene variants and individual differences in behavior. It is perhaps surprising that such associations can be detected despite the remoteness of these levels of description, and the fact that behavior is the outcome of an extended developmental process involving interaction of the whole organism with a variable environment. Given that they have been detected, how do such associations inform cognitive-level theories? To investigate this question, we employed a multiscale computational model of development, using a sample domain drawn from the field of language acquisition. The model comprised an artificial neural network model of past-tense acquisition trained using the backpropagation learning algorithm, extended to incorporate population modeling and genetic algorithms. It included five levels of description-four internal: genetic, network, neurocomputation, behavior; and one external: environment. Since the mechanistic assumptions of the model were known and its operation was relatively transparent, we could evaluate whether cross-level associations gave an accurate picture of causal processes. We established that associations could be detected between artificial genes and behavioral variation, even under polygenic assumptions of a many-to-one relationship between genes and neurocomputational parameters, and when an experience-dependent developmental process interceded between the action of genes and the emergence of behavior. We evaluated these associations with respect to their specificity (to different behaviors, to function vs. structure), to their developmental stability, and to their replicability, as well as considering issues of missing heritability and gene-environment interactions. We argue that gene

  15. Modeling the Probability of Surface Artificialization in Zêzere Watershed (Portugal Using Environmental Data

    Directory of Open Access Journals (Sweden)

    Bruno M. Meneses

    2016-07-01

    Full Text Available The land use and land cover (LUC of the Zêzere watershed (Portugal have undergone major changes in recent decades, with the increase of artificial surfaces. This trend is quantified in some studies, but the probability of the increase of this type of LUC, nor the places where the next transitions or land use/cover changes (LUCC for artificial surfaces will have high probability of occurrence has not yet been assessed. This research presents an evaluation of these two aspects, by means of bivariate statistical models (fuzzy logic and information value and environmental data. The artificialization probability by sectors within the same watershed is also evaluated, to further understand which areas will require greater attention, taking into account the environmental conditions favorable to the occurrence of this process and bearing in mind the conditions under which this process took place in the past. The results obtained using these models were assessed independently, through curves of success, noting that the modeling through the fuzzy gamma presents slightly better efficiency in determining the probability of artificialization surfaces in the study area. The area with the highest probability of artificialization is mostly located in the SW of this watershed, but high probabilities are also present in the upstream sector, being those areas that require further preventive measures once they have influence on the water quality and quantity in the main reservoirs of this watershed.

  16. Use of genome-scale microbial models for metabolic engineering

    DEFF Research Database (Denmark)

    Patil, Kiran Raosaheb; Åkesson, M.; Nielsen, Jens

    2004-01-01

    Metabolic engineering serves as an integrated approach to design new cell factories by providing rational design procedures and valuable mathematical and experimental tools. Mathematical models have an important role for phenotypic analysis, but can also be used for the design of optimal metabolic...... network structures. The major challenge for metabolic engineering in the post-genomic era is to broaden its design methodologies to incorporate genome-scale biological data. Genome-scale stoichiometric models of microorganisms represent a first step in this direction....

  17. Applications of artificial neural networks for microbial water quality modeling

    Energy Technology Data Exchange (ETDEWEB)

    Brion, G.M.; Lingireddy, S. [Univ. of Kentucky, Dept. of Civil Engineering, Lexington, Kentucky (United States)]. E-mail: gbrion@engr.uky.edu

    2002-06-15

    There has been a significant shift in the recent past towards protecting chemical and microbial quality of source waters rather than developing advanced methods to treat heavily polluted water. The key to successful best management practices in protecting the source waters is to identify sources of non-point pollution and their collective impact on the quality of water at the intake. This article presents a few successful applications where artificial neural networks (ANN) have proven to be the useful mathematical tools in correlating the nonlinear relationships between routinely measured parameters (such as rainfall, turbidity, fecal coliforms etc.) and quality of source waters and/or nature of fecal sources. These applications include, prediction of peak concentrations of Giardia and Cryptosporidium, sorting of fecal sources (e.g. agricultural animals vs. urban animals), predicting relative ages of the runoff sources, identifying the potential for sewage contamination. The ability of ANNs to work with complex, inter-related multiparameter databases, and provide superior predictive power in non-linear relationships has been the key for their successful application to microbial water quality studies. (author)

  18. Applications of artificial neural networks for microbial water quality modeling

    International Nuclear Information System (INIS)

    There has been a significant shift in the recent past towards protecting chemical and microbial quality of source waters rather than developing advanced methods to treat heavily polluted water. The key to successful best management practices in protecting the source waters is to identify sources of non-point pollution and their collective impact on the quality of water at the intake. This article presents a few successful applications where artificial neural networks (ANN) have proven to be the useful mathematical tools in correlating the nonlinear relationships between routinely measured parameters (such as rainfall, turbidity, fecal coliforms etc.) and quality of source waters and/or nature of fecal sources. These applications include, prediction of peak concentrations of Giardia and Cryptosporidium, sorting of fecal sources (e.g. agricultural animals vs. urban animals), predicting relative ages of the runoff sources, identifying the potential for sewage contamination. The ability of ANNs to work with complex, inter-related multiparameter databases, and provide superior predictive power in non-linear relationships has been the key for their successful application to microbial water quality studies. (author)

  19. Bayesian model selection applied to artificial neural networks used for water resources modeling

    Science.gov (United States)

    Kingston, Greer B.; Maier, Holger R.; Lambert, Martin F.

    2008-04-01

    Artificial neural networks (ANNs) have proven to be extremely valuable tools in the field of water resources engineering. However, one of the most difficult tasks in developing an ANN is determining the optimum level of complexity required to model a given problem, as there is no formal systematic model selection method. This paper presents a Bayesian model selection (BMS) method for ANNs that provides an objective approach for comparing models of varying complexity in order to select the most appropriate ANN structure. The approach uses Markov Chain Monte Carlo posterior simulations to estimate the evidence in favor of competing models and, in this study, three known methods for doing this are compared in terms of their suitability for being incorporated into the proposed BMS framework for ANNs. However, it is acknowledged that it can be particularly difficult to accurately estimate the evidence of ANN models. Therefore, the proposed BMS approach for ANNs incorporates a further check of the evidence results by inspecting the marginal posterior distributions of the hidden-to-output layer weights, which unambiguously indicate any redundancies in the hidden layer nodes. The fact that this check is available is one of the greatest advantages of the proposed approach over conventional model selection methods, which do not provide such a test and instead rely on the modeler's subjective choice of selection criterion. The advantages of a total Bayesian approach to ANN development, including training and model selection, are demonstrated on two synthetic and one real world water resources case study.

  20. Artificial neural network models for biomass gasification in fluidized bed gasifiers

    DEFF Research Database (Denmark)

    Puig Arnavat, Maria; Hernández, J. Alfredo; Bruno, Joan Carles;

    2013-01-01

    Artificial neural networks (ANNs) have been applied for modeling biomass gasification process in fluidized bed reactors. Two architectures of ANNs models are presented; one for circulating fluidized bed gasifiers (CFB) and the other for bubbling fluidized bed gasifiers (BFB). Both models determin...... experimental data used R2 > 0.98. Furthermore a sensitivity analysis has been applied in each ANN model showing that all studied input variables are important....

  1. Genome sequencing and analysis of the model grass Brachypodium distachyon.

    Science.gov (United States)

    2010-02-11

    Three subfamilies of grasses, the Ehrhartoideae, Panicoideae and Pooideae, provide the bulk of human nutrition and are poised to become major sources of renewable energy. Here we describe the genome sequence of the wild grass Brachypodium distachyon (Brachypodium), which is, to our knowledge, the first member of the Pooideae subfamily to be sequenced. Comparison of the Brachypodium, rice and sorghum genomes shows a precise history of genome evolution across a broad diversity of the grasses, and establishes a template for analysis of the large genomes of economically important pooid grasses such as wheat. The high-quality genome sequence, coupled with ease of cultivation and transformation, small size and rapid life cycle, will help Brachypodium reach its potential as an important model system for developing new energy and food crops. PMID:20148030

  2. Azolla—A Model Organism for Plant Genomic Studies

    Institute of Scientific and Technical Information of China (English)

    Yin-LongQiu; JunYu

    2003-01-01

    The aquatic ferns of the genus Azolla are nitrogen-fixing plants that have great potentials in agricultural production and environmental conservation.Azolla in many aspects is qualified to serve as a model organism for genomic studies because of its importance in agriculture,its unique position in plant evolution,its symbiotic relationship with the N2-fixing cyanobacterium,Anabaena azollae,and its moderate-sized genome.The goals of this genome project are not only to understand the biology of the Azolla genome to promote its applications in biological research and agriculture practice but also to gain critical insights about evolution of plant genomes.Together with the strategic and technical improvement as well as cost reduction of DNA sequencing,the deciphering of their genetic code is imminent.

  3. Azolla - A Model Organism for Plant Genomic Studies

    Institute of Scientific and Technical Information of China (English)

    Yin-Long Qiu; Jun Yu

    2003-01-01

    The aquatic ferns of the genus Azolla are nitrogen-fixing plants that have great potentials in agricultural production and environmental conservation. Azolla in many aspects is qualified to serve as a model organism for genomic studies because of its importance in agriculture, its unique position in plant evolution, its symbiotic relationship with the N2-fixing cyanobacterium, Anabaena azollae, and its moderate-sized genome. The goals of this genome project are not only to understand the biology of the Azolla genome to promote its applications in biological research and agriculture practice but also to gain critical insights about evolution of plant genomes. Together with the strategic and technical improvement as well as cost reduction of DNA sequencing, the deciphering of their genetic code is imminent.

  4. Modeling Broadband Microwave Structures by Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    V. Otevrel

    2004-06-01

    Full Text Available The paper describes the exploitation of feed-forward neural networksand recurrent neural networks for replacing full-wave numerical modelsof microwave structures in complex microwave design tools. Building aneural model, attention is turned to the modeling accuracy and to theefficiency of building a model. Dealing with the accuracy, we describea method of increasing it by successive completing a training set.Neural models are mutually compared in order to highlight theiradvantages and disadvantages. As a reference model for comparisons,approximations based on standard cubic splines are used. Neural modelsare used to replace both the time-domain numeric models and thefrequency-domain ones.

  5. Modeling and prediction of Turkey's electricity consumption using Artificial Neural Networks

    International Nuclear Information System (INIS)

    Artificial Neural Networks are proposed to model and predict electricity consumption of Turkey. Multi layer perceptron with backpropagation training algorithm is used as the neural network topology. Tangent-sigmoid and pure-linear transfer functions are selected in the hidden and output layer processing elements, respectively. These input-output network models are a result of relationships that exist among electricity consumption and several other socioeconomic variables. Electricity consumption is modeled as a function of economic indicators such as population, gross national product, imports and exports. It is also modeled using export-import ratio and time input only. Performance comparison among different models is made based on absolute and percentage mean square error. Electricity consumption of Turkey is predicted until 2027 using data from 1975 to 2006 along with other economic indicators. The results show that electricity consumption can be modeled using Artificial Neural Networks, and the models can be used to predict future electricity consumption.

  6. Modeling and prediction of Turkey's electricity consumption using Artificial Neural Networks

    International Nuclear Information System (INIS)

    Artificial Neural Networks are proposed to model and predict electricity consumption of Turkey. Multi layer perceptron with backpropagation training algorithm is used as the neural network topology. Tangent-sigmoid and pure-linear transfer functions are selected in the hidden and output layer processing elements, respectively. These input-output network models are a result of relationships that exist among electricity consumption and several other socioeconomic variables. Electricity consumption is modeled as a function of economic indicators such as population, gross national product, imports and exports. It is also modeled using export-import ratio and time input only. Performance comparison among different models is made based on absolute and percentage mean square error. Electricity consumption of Turkey is predicted until 2027 using data from 1975 to 2006 along with other economic indicators. The results show that electricity consumption can be modeled using Artificial Neural Networks, and the models can be used to predict future electricity consumption. (author)

  7. Exploiting linkage disequilibrium in statistical modelling in quantitative genomics

    DEFF Research Database (Denmark)

    Wang, Lei

    the recently proposed antedependence models, which treat neighbouring marker effects as correlated; another approach involves use of haplotype block information derived using the program Beagle. The overall conclusion is that taking LD information into account in genomic prediction models potentially......Alleles at two loci are said to be in linkage disequilibrium (LD) when they are correlated or statistically dependent. Genomic prediction and gene mapping rely on the existence of LD between gentic markers and causul variants of complex traits. In the first part of the thesis, a novel method to...... quantify and visualize local variation in LD along chromosomes in describet, and applied to characterize LD patters at the local and genome-wide scale in three Danish pig breeds. In the second part, different ways of taking LD into account in genomic prediction models are studied. One approach is to use...

  8. Transport energy demand modeling of South Korea using artificial neural network

    International Nuclear Information System (INIS)

    Artificial neural network models were developed to forecast South Korea's transport energy demand. Various independent variables, such as GDP, population, oil price, number of vehicle registrations, and passenger transport amount, were considered and several good models (Model 1 with GDP, population, and passenger transport amount; Model 2 with GDP, number of vehicle registrations, and passenger transport amount; and Model 3 with oil price, number of vehicle registrations, and passenger transport amount) were selected by comparing with multiple linear regression models. Although certain regression models obtained better R-squared values than neural network models, this does not guarantee the fact that the former is better than the latter because root mean squared errors of the former were much inferior to those of the latter. Also, certain regression model had structural weakness based on P-value. Instead, neural network models produced more robust results. Forecasted results using the neural network models show that South Korea will consume around 37 MTOE of transport energy in 2025. - Highlights: → Transport energy demand of South Korea was forecasted using artificial neural network. → Various variables (GDP, population, oil price, number of registrations, etc.) were considered. → Results of artificial neural network were compared with those of multiple linear regression.

  9. COMBINING PCA ANALYSIS AND ARTIFICIAL NEURAL NETWORKS IN MODELLING ENTREPRENEURIAL INTENTIONS OF STUDENTS

    Directory of Open Access Journals (Sweden)

    Marijana Zekić-Sušac

    2013-02-01

    Full Text Available Despite increased interest in the entrepreneurial intentions and career choices of young adults, reliable prediction models are yet to be developed. Two nonparametric methods were used in this paper to model entrepreneurial intentions: principal component analysis (PCA and artificial neural networks (ANNs. PCA was used to perform feature extraction in the first stage of modelling, while artificial neural networks were used to classify students according to their entrepreneurial intentions in the second stage. Four modelling strategies were tested in order to find the most efficient model. Dataset was collected in an international survey on entrepreneurship self-efficacy and identity. Variables describe students’ demographics, education, attitudes, social and cultural norms, self-efficacy and other characteristics. The research reveals benefits from the combination of the PCA and ANNs in modeling entrepreneurial intentions, and provides some ideas for further research.

  10. A classical Master equation approach to modeling an artificial protein motor

    International Nuclear Information System (INIS)

    Inspired by biomolecular motors, as well as by theoretical concepts for chemically driven nanomotors, there is significant interest in constructing artificial molecular motors. One driving force is the opportunity to create well-controlled model systems that are simple enough to be modeled in detail. A remaining challenge is the fact that such models need to take into account processes on many different time scales. Here we describe use of a classical Master equation approach, integrated with input from Langevin and molecular dynamics modeling, to stochastically model an existing artificial molecular motor concept, the Tumbleweed, across many time scales. This enables us to study how interdependencies between motor processes, such as center-of-mass diffusion and track binding/unbinding, affect motor performance. Results from our model help guide the experimental realization of the proposed motor, and potentially lead to insights that apply to a wider class of molecular motors.

  11. BiGG Models: A platform for integrating, standardizing and sharing genome-scale models

    DEFF Research Database (Denmark)

    2016-01-01

    Genome-scale metabolic models are mathematically-structured knowledge bases that can be used to predict metabolic pathway usage and growth phenotypes. Furthermore, they can generate and test hypotheses when integrated with experimental data. To maximize the value of these models, centralized...... repositories of high-quality models must be established, models must adhere to established standards and model components must be linked to relevant databases. Tools for model visualization further enhance their utility. To meet these needs, we present BiGG Models (http://bigg.ucsd.edu), a completely...... redesigned Biochemical, Genetic and Genomic knowledge base. BiGG Models contains more than 75 high-quality, manually-curated genome-scale metabolic models. On the website, users can browse, search and visualize models. BiGG Models connects genome-scale models to genome annotations and external databases...

  12. Modeling Expectations with GENEFER -- an Artificial Intelligence Approach

    OpenAIRE

    Eric Ringhut; Stefan Kooths

    2003-01-01

    Economic modeling of financial markets attempts to model highly complex systems in which expectations can be among the dominant driving forces. It is necessary, then, to focus on how agents form expectations. We believe that they look for patterns, hypothesize, try, make mistakes, learn and adapt. Agents' bounded rationality leads us to a rule-based approach which we model using Fuzzy Rule Bases. For example if a single agent believes the exchange rate is determined by a set of possible input...

  13. Artificial intelligence techniques for modeling database user behavior

    Science.gov (United States)

    Tanner, Steve; Graves, Sara J.

    1990-01-01

    The design and development of the adaptive modeling system is described. This system models how a user accesses a relational database management system in order to improve its performance by discovering use access patterns. In the current system, these patterns are used to improve the user interface and may be used to speed data retrieval, support query optimization and support a more flexible data representation. The system models both syntactic and semantic information about the user's access and employs both procedural and rule-based logic to manipulate the model.

  14. Modeling of human colonic blood flow for a novel artificial anal sphincter system

    Institute of Scientific and Technical Information of China (English)

    Peng ZAN; Guo-zheng YAN; Hua LIU

    2008-01-01

    A novel artificial anal sphincter system has been developed to simulate the normal physiology of the human anorectum. With the goal of engineering a safe and reliable device, the model of human colonic blood flow has been built and the relationship between the colonic blood flow rate and the operating occlusion pressure of the anorectum is achieved. The tissue ischemia is analyzed based on constitutive relations for human anorectum. The results suggest that at the planned operating occlusion pressure of less than 4 kPa the artificial anal sphincter should not risk the vaseularity of the human colon.

  15. Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese

    OpenAIRE

    Sumit Goyal; Gyanendra Kumar Goyal

    2012-01-01

    This paper presents the capability of Time–delay artificial neural network models for predicting shelf life of processed cheese. Datasets were divided into two subsets (30 for training and 6 for validation). Models with single and multi layers were developed and compared with each other. Mean Square Error, Root Mean Square Error, Coefficient of Determination and Nash -
    Sutcliffo Coefficient were used as performance evaluators, Time- delay model predicted the shelf life of...

  16. HCCI Intelligent Rapid Modeling by Artificial Neural Network and Genetic Algorithm

    OpenAIRE

    AbdoulAhad Validi; Jyh-Yuan Chen; Akbar Ghafourian

    2012-01-01

    A Dynamic model of Homogeneous Charge Compression Ignition (HCCI), based on chemical kinetics principles and artificial intelligence, is developed. The model can rapidly predict the combustion probability, thermochemistry properties, and exact timing of the Start of Combustion (SOC). A realization function is developed on the basis of the Sandia National Laboratory chemical kinetics model, and GRI3.0 methane chemical mechanism. The inlet conditions are optimized by Genetic Algorithm (GA), so ...

  17. Connecting adaptive behaviour and expectations in models of innovation: The Potential Role of Artificial Neural Networks

    OpenAIRE

    Murat Yildizoglu

    2002-01-01

    In this methodological work I explore the possibility of explicitly modelling expectations conditioning the R&D decisions of firms. In order to isolate this problem from the controversies of cognitive science, I propose a black box strategy through the concept of “internal model”. The last part of the article uses artificial neural networks to model the expectations of firms in a model of industry dynamics based on Nelson & Winter (1982).

  18. Disentangling Pleiotropy along the Genome using Sparse Latent Variable Models

    DEFF Research Database (Denmark)

    Janss, Luc

    Bayesian models are described that use atent variables to model covariances. These models are flexible, scale up linearly in the number of traits, and allow separating covariance structures in different components at the trait level and at the genomic level. Multi-trait version of the BayesA (MT...

  19. Validation of computer models of an artificial hip joint

    Directory of Open Access Journals (Sweden)

    S. Ziemba

    2007-05-01

    Full Text Available Purpose: Problems of the modelling of the surgical cement behaviour during implantation have been presented in the paper. The purpose was to validate the FEM model describing the temperature fields in the bone during the surgery treatment.Design/methodology/approach: The physical laboratory modelling has been used to perform validation of the model that makes it possible to predict the temperature influence on the bone tissue during polymerization process.Findings: Due to its non-invasive nature, the computer models’ validation method applied in the study seems to be the right solution for the research on surgical procedures of endoprosthesis implantation. However, a particular emphasis should be placed on a correct selection of thermophysical properties of the designed laboratory models. Relying on the calculations and research results, similar local values of maximum temperatures were obtained. Practical implications: The computer modelling methods presented in the paper together with the analytical approach are of great importance to both forecasting the implants’ behaviour during a surgical procedure and in their operational conditions, as well as in the selection and modification process of surgical cements’ material properties. The analysis carried out makes it possible to determine the location of zones most threatened with an adverse effect of an elevated temperature. They are located in the vicinity of the top of the endoprosthesis stem.Originality/value: The work presents the own method of validation of the FEM model used for heat flow modelling.

  20. Comprehensive copy number profiles of breast cancer cell model genomes

    OpenAIRE

    Shadeo, Ashleen; Lam, Wan L.

    2006-01-01

    Introduction Breast cancer is the most commonly diagnosed cancer in women worldwide and consequently has been extensively investigated in terms of histopathology, immunochemistry and familial history. Advances in genome-wide approaches have contributed to molecular classification with respect to genomic changes and their subsequent effects on gene expression. Cell lines have provided a renewable resource that is readily used as model systems for breast cancer cell biology. A thorough characte...

  1. Specialized Hidden Markov Model Databases for Microbial Genomics

    OpenAIRE

    Martin Gollery

    2003-01-01

    As hidden Markov models (HMMs) become increasingly more important in the analysis of biological sequences, so too have databases of HMMs expanded in size, number and importance. While the standard paradigm a short while ago was the analysis of one or a few sequences at a time, it has now become standard procedure to submit an entire microbial genome. In the future, it will be common to submit large groups of completed genomes to run simultaneously against a dozen public databas...

  2. Artificial neural network modelling in heavy ion collisions

    International Nuclear Information System (INIS)

    The neural network (NN) model and parton two fireball model (PTFM) have been used to study the pseudo-rapidity distribution of the shower particles for C 12, O 16, Si 28 and S 32 on nuclear emulsion. The trained NN shows a better fitting with experimental data than the PTFM calculations. The NN is then used to predict the distributions that are not present in the training set and matched them effectively. The NN simulation results prove a strong presence modeling in heavy ion collisions

  3. MODELLING EXPECTATIONS WITH GENEFER- AN ARTIFICIAL INTELLIGENCE APPROACH

    OpenAIRE

    Stefan Kooths; Eric Ringhut

    2000-01-01

    Economic modelling of financial markets means to model highly complex systems in which expectations can be the dominant driving forces. Therefore it is necessary to focus on how agents form their expectations. We believe that they look for patterns, hypothesize, try, make mistakes, learn and adapt. AgentsÆ bounded rationality leads us to a rule-based approach which we model using Fuzzy Rule-Bases. E. g. if a single agent believes the exchange rate is determined by a set of possible inputs and...

  4. Modeling of artificial stiction in steam turbine control valve

    International Nuclear Information System (INIS)

    The steam turbine control valves play a pivotal role in regulating the output power of the turbine in a commercial nuclear power plant. In this paper the turbine system refers to Ulchin units 3 and 4. The modeling of friction in steam turbine control valve is presented. Instead of a detailed physical model of the control valve friction, the data-driven models are adopted for modeling the friction to obtain an easier friction identification and faster calculation time. Some computational results by using the MARS thermal hydraulic analysis code are presented to show the effect of friction on the total mass flow at the inlet of the high pressure turbine. The computational results demonstrate that the friction will initiate fluctuations on the total mass flow at the turbine inlet

  5. From agent-based models to artificial economies

    OpenAIRE

    Teglio, Andrea

    2011-01-01

    The aim of this thesis is to propose and illustrate an alternative approach to economic modeling and policy design that is grounded in the innovative field of agent-based computational economics (ACE). The recent crisis pointed out the fundamental role played by macroeconomic policy design in order to preserve social welfare, and the consequent necessity of understanding the effects of coordinated policy measures on the economic system. Classic approaches to macroeconomic modeling, mainly rep...

  6. Weather Radar Estimations Feeding an Artificial Neural Network Model

    OpenAIRE

    Gustavo Cerda Villafaña; Sergio Ledesma; Dawei Han

    2008-01-01

    The application of ANNs (Artifi cial Neural Networks) has been studied by many researchers in modelling rainfall runoff processes. However, the work so far has been focused on the rainfall data from traditional raingauges. Weather radar is a modern technology which could provide high resolution rainfall in time and space. In this study, a comparison in rainfall runoff modelling between the raingauge and weather radar has been carried out. The data were collected from Brue catchment in Southwe...

  7. Modelling of solar energy potential in Nigeria using an artificial neural network model

    International Nuclear Information System (INIS)

    In this study, an artificial neural network (ANN) based model for prediction of solar energy potential in Nigeria (lat. 4-14oN, log. 2-15oE) was developed. Standard multilayered, feed-forward, back-propagation neural networks with different architecture were designed using neural toolbox for MATLAB. Geographical and meteorological data of 195 cities in Nigeria for period of 10 years (1983-1993) from the NASA geo-satellite database were used for the training and testing the network. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration, mean temperature, and relative humidity) were used as inputs to the network, while the solar radiation intensity was used as the output of the network. The results show that the correlation coefficients between the ANN predictions and actual mean monthly global solar radiation intensities for training and testing datasets were higher than 90%, thus suggesting a high reliability of the model for evaluation of solar radiation in locations where solar radiation data are not available. The predicted solar radiation values from the model were given in form of monthly maps. The monthly mean solar radiation potential in northern and southern regions ranged from 7.01-5.62 to 5.43-3.54 kW h/m2 day, respectively. A graphical user interface (GUI) was developed for the application of the model. The model can be used easily for estimation of solar radiation for preliminary design of solar applications.

  8. MODELLING STUDIES BY APPLICATION OF ARTIFICIAL NEURAL NETWORK USING MATLAB

    Directory of Open Access Journals (Sweden)

    K. S. ARJUN

    2015-11-01

    Full Text Available Four ANN models to estimate Bubble point pressure (Pb, Oil Formation Volume Factor (Bob, Bubble point solution Gas Oil Ratio (Rsob and Stock Tank Vent GOR (RST in the absence of Pressure, Volume and Temperature (PVT analysis, were proposed as a function of readily available field data. The estimated Rsob and RST values from the proposed models can be used as a basic input variable in many PVT correlations in order to estimate other fluid properties such as the Pb and Bob. Another proposed ANN model has the ability to predict and interpolate average reservoir pressure accurately by employing oil, water and gas production rates and number of producers are used as four inputs for the proposed model without the wells having to be closed. Another ANN model proposed is to predict the performance of oil production within water injection reservoirs, which can be utilized to find the most economical scenario of water injection to maximize ultimate oil recovery. It has reasonable accuracy, requires little data and can forecast quickly. ANN approach to solving the identified pipeline damage problem gives satisfactory results as the error between the ANN output and the target is very tolerable. The results conclusively proved with error 0.0027 that it has the ability to accurately predict the pipeline damage probability by employing the model data obtained in this study.

  9. A top-down multi-scale modeling for actuation response of polymeric artificial muscles

    Science.gov (United States)

    Yang, Qianxi; Li, Guoqiang

    2016-07-01

    A class of innovative artificial muscles made of high-strength polymeric fibers such as fishing lines or sewing threads have been discovered recently. These muscles are fabricated by a simple "twist-insertion" procedure, which have attracted increasing attention due to their low cost and readily availability, giant tensile stroke, record energy density, and easy controllability. In the present paper, we established a multi-scale modeling framework for the thermomechanical actuation responses by a top-down strategy, spanning from macro-scale helical spring analysis down to molecular level chain interaction study. Comparison between modeling results and experimental results exhibited excellent agreement. The effect of the micro-, meso- and macro-scale parameters on the actuation responses of the artificial muscle was further discussed through a parametric study per the validated model. This work helps understand the physical origin behind the remarkable tensile actuation behavior of the twisted-then-coiled polymeric artificial muscles and also provides inspirations for optimal design of advanced artificial muscles made by twist-insertion procedure.

  10. Development of Improved Artificial Neural Network Model for Stock Market Prediction

    Directory of Open Access Journals (Sweden)

    PRATAP KISHORE PADHIARY

    2011-02-01

    Full Text Available In recent year’s prediction of stock market returns is a hottest field of research in finance. Artificial Neural Network (ANN is a technique that is heavily researched and widely used in applications for engineering and scientific fields for various purposes ranging from control systems to artificial intelligence. This paper surveys key issues in financial forecasting and propose an ANN methodologywhich could be better for long term (one month, two month as well as short term (one day prediction of stock price of any leading stock market indices. Survey of existing literature reveals that adaptivelearning rate will give more accurate result than fixed learning rate parameter for ANN models. Many researchers noted that slight parameter changed causes major variations in the behavior of the network. So there is no theory which could be guideline for finding best network topology. The proposed trigonometric functional link artificial neural network (FLANN model employs standard least meansquare (LMS algorithm with search-then-converge scheduling which could effectively calculate learning rate parameter that changes with time and may require less experiments to train the model. The objective of this paper is to introduce a functional link single layer artificial neural network (FLANN for long term as well as short term stock market prediction.

  11. Proton exchange membrane fuel cells modeling based on artificial neural networks

    Institute of Scientific and Technical Information of China (English)

    Yudong Tian; Xinjian Zhu; Guangyi Cao

    2005-01-01

    To understand the complexity of the mathematical models of a proton exchange membrane fuel cell (PEMFC) and their shortage of practical PEMFC control, the PEMFC complex mechanism and the existing PEMFC models are analyzed, and artificial neural networks based PEMFC modeling is advanced. The structure, algorithm, training and simulation of PEMFC modeling based on improved BP networks are given out in detail. The computer simulation and conducted experiment verify that this model is fast and accurate, and can be used as a suitable operational model for PEMFC real-time control.

  12. Artificial Immune Danger Theory Based Model for Network Security Evaluation

    Directory of Open Access Journals (Sweden)

    Feixian Sun

    2011-02-01

    Full Text Available Inspired by the principles of immune danger theory, a danger theory based model for network security risk assessment is presented in this paper. Firstly, the principle of the danger theory is introduced. And then, with the improved concepts and formal definitions of antigen, antibody, danger signal, and detection lymphocyte for network security risk assessment presented, the distributed architecture of the proposed model is described. Following that, the principle of network intrusion detection is expounded. Finally, the method of network security risk assessment is given. Theoretical analysis and simulation results show that the proposed model can evaluate the network attack threats in real time. Thus, it provides an effective risk evaluation solution to network security.

  13. Developing energy forecasting model using hybrid artificial intelligence method

    Institute of Scientific and Technical Information of China (English)

    Shahram Mollaiy-Berneti

    2015-01-01

    An important problem in demand planning for energy consumption is developing an accurate energy forecasting model. In fact, it is not possible to allocate the energy resources in an optimal manner without having accurate demand value. A new energy forecasting model was proposed based on the back-propagation (BP) type neural network and imperialist competitive algorithm. The proposed method offers the advantage of local search ability of BP technique and global search ability of imperialist competitive algorithm. Two types of empirical data regarding the energy demand (gross domestic product (GDP), population, import, export and energy demand) in Turkey from 1979 to 2005 and electricity demand (population, GDP, total revenue from exporting industrial products and electricity consumption) in Thailand from 1986 to 2010 were investigated to demonstrate the applicability and merits of the present method. The performance of the proposed model is found to be better than that of conventional back-propagation neural network with low mean absolute error.

  14. Cognitive model of image interpretation for artificial intelligence applications

    International Nuclear Information System (INIS)

    A cognitive model of imaging diagnosis was devised to aid in the development of expert systems that assist in the interpretation of diagnostic images. In this cognitive model, a small set of observations that are strongly predictive of a particular diagnosis lead to a search for other observations that would support this diagnosis but are not necessarily specific for it. Then a set of alternative diagnoses is considered. This is followed by a search for observations that might allow differentiation of the primary diagnostic consideration from the alternatives. The production rules needed to implement this model can be classified into three major categories, each of which have certain general characteristics. Knowledge of these characteristics simplifies the development of these expert systems

  15. Modelling of Surface Ships using Artificial Neural Networks

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Jensen, F. M.; Thoft-Christensen, Palle

    For various design and planning purposes there is at present an increasing interest and a need for numerical modelling of the process of navigating a vessel (or a floating body in general). The reasons for this is that experiments in "full mission" simulators with human navigators at the handles...

  16. Artificial Neural Network based Diagnostic Model For Causes of Success and Failures

    OpenAIRE

    Kaur, Bikrampal; Aggarwal, Himanshu

    2010-01-01

    In this paper an attempt has been made to identify most important human resource factors and propose a diagnostic model based on the back-propagation and connectionist model approaches of artificial neural network (ANN). The focus of the study is on the mobile -communication industry of India. The ANN based approach is particularly important because conventional approaches (such as algorithmic) to the problem solving have their inherent disadvantages. The algorithmic approach is well-suited t...

  17. Short-Term Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature Parameters

    OpenAIRE

    Hongshan Zhao; Shi Su; Zengqiang Mi; Fei Wang

    2012-01-01

    Short-term solar irradiance forecasting (STSIF) is of great significance for the optimal operation and power predication of grid-connected photovoltaic (PV) plants. However, STSIF is very complex to handle due to the random and nonlinear characteristics of solar irradiance under changeable weather conditions. Artificial Neural Network (ANN) is suitable for STSIF modeling and many research works on this topic are presented, but the conciseness and robustness of the existing models still need t...

  18. Spatial Disaggregation of Areal Rainfall Using Two Different Artificial Neural Networks Models

    OpenAIRE

    Sungwon Kim; Singh, Vijay P.

    2015-01-01

    The objective of this study is to develop artificial neural network (ANN) models, including multilayer perceptron (MLP) and Kohonen self-organizing feature map (KSOFM), for spatial disaggregation of areal rainfall in the Wi-stream catchment, an International Hydrological Program (IHP) representative catchment, in South Korea. A three-layer MLP model, using three training algorithms, was used to estimate areal rainfall. The Levenberg–Marquardt training algorithm was found to be more sensitive...

  19. An Artificial Neural Network Model for Wastewater Treatment Plant of Konya

    OpenAIRE

    Tumer, Abdullah Erdal; Edebali, Serpil

    2015-01-01

    In this study, modelling of Konya wastewater treatment plant was studied by using artificial neural network with different architectures in Matlab software. All data were obtained from wastewater treatment plant of Konya during daily records over four month. Treatment efficiency of the plant was determined by taking into account of input values of pH, temperature, COD, TSS and BOD with output values TSS. Performance of the model was compared via the parameters of Mean Squared Error (MSE), and...

  20. Modeling Slump of Ready Mix Concrete Using Genetically Evolved Artificial Neural Networks

    OpenAIRE

    Vinay Chandwani; Vinay Agrawal; Ravindra Nagar

    2014-01-01

    Artificial neural networks (ANNs) have been the preferred choice for modeling the complex and nonlinear material behavior where conventional mathematical approaches do not yield the desired accuracy and predictability. Despite their popularity as a universal function approximator and wide range of applications, no specific rules for deciding the architecture of neural networks catering to a specific modeling task have been formulated. The research paper presents a methodology for automated de...

  1. A structure generator for modelling the initial sediment distribution of an artificial hydrologic catchment

    OpenAIRE

    Maurer, T.; Schneider, A.; H. H. Gerke

    2011-01-01

    Artificially-created hydrological catchments are characterized by sediment structures from technological construction processes that can potentially be important for modelling of flow and transport and for understanding initial soil and ecosystem development. The subsurface spatial structures of such catchments have not yet been sufficiently explored and described. Our objective was to develop a structure generator programme for modelling the 3-D spatial sediment distribution patterns dependi...

  2. A structure generator for modelling the initial sediment distribution of an artificial hydrologic catchment

    OpenAIRE

    Maurer, T.; Schneider, A.; H. H. Gerke

    2011-01-01

    Artificially-created hydrological catchments are characterised by sediment structures from technological construction processes that can potentially be important for modelling of flow and transport and for understanding initial soil and ecosystem development. The subsurface spatial structures of such catchments have not yet been sufficiently explored and described. Our objective was to develop a structure generator programme for modelling the 3-D spatial distribution patterns of dumped sedime...

  3. Microbial comparative pan-genomics using binomial mixture models

    Directory of Open Access Journals (Sweden)

    Ussery David W

    2009-08-01

    Full Text Available Abstract Background The size of the core- and pan-genome of bacterial species is a topic of increasing interest due to the growing number of sequenced prokaryote genomes, many from the same species. Attempts to estimate these quantities have been made, using regression methods or mixture models. We extend the latter approach by using statistical ideas developed for capture-recapture problems in ecology and epidemiology. Results We estimate core- and pan-genome sizes for 16 different bacterial species. The results reveal a complex dependency structure for most species, manifested as heterogeneous detection probabilities. Estimated pan-genome sizes range from small (around 2600 gene families in Buchnera aphidicola to large (around 43000 gene families in Escherichia coli. Results for Echerichia coli show that as more data become available, a larger diversity is estimated, indicating an extensive pool of rarely occurring genes in the population. Conclusion Analyzing pan-genomics data with binomial mixture models is a way to handle dependencies between genomes, which we find is always present. A bottleneck in the estimation procedure is the annotation of rarely occurring genes.

  4. Practical implications for genetic modeling in the genomics era.

    Science.gov (United States)

    VanRaden, P M

    2016-03-01

    Genetic models convert data into estimated breeding values and other information useful to breeders. The goal is to provide accurate and timely predictions of the future performance for each animal (or embryo). Modeling involves defining traits, editing raw data, removing environmental effects, including genetic by environmental interactions and correlations among traits, and accounting for nonadditive inheritance or nonnormal distributions. Data include phenotypes and pedigrees during the last century and genotypes within the last decade. The genomic data can include single nucleotide polymorphisms, quantitative trait loci, insertions, deletions, and haplotypes. Subsets must be selected to reduce computation because total numbers of variants that can be imputed have increased rapidly from thousands to millions. Current computation using 60,671 markers takes just a few days. Nonlinear models can account for the nonnormal distribution of genomic effects, but reliability is usually better than that of linear models only for traits influenced by major genes. Numbers of genotyped animals have also increased rapidly in the joint North American database from a few thousand in 2009 to over 1 million in 2015. Most are young females and will contribute to estimating allele effects in the future, but only about 150,000 have phenotypes so far. Genomic preselection can bias traditional animal models because Mendelian sampling of phenotyped progeny and mates is no longer expected to average zero; however, estimates of bias are small in current US data. Single-step models that combine pedigree and genomic relationships can account for preselection, but approximations are required for affordable computation. Traditional animal models may include all breeds and crossbreds, but most genomic evaluations are still computed within breed. Models that include inbreeding, heterosis, dominance, and interactions can improve predictions for individual matings. Multitrait genomic models may

  5. Proximal caries detection using digital subtraction radiography in the artificial caries activity model

    International Nuclear Information System (INIS)

    The purpose of the experiment was to evaluating the diagnostic ability of dental caries detection using digital subtraction in the artificial caries activity model. Digital radiographs of five teeth with 8 proximal surfaces were obtained by CCD sensor (Kodak RVG 6100 using a size no.2). The digital radiographic images and subtraction images from artificial proximal caries were examined and interpreted. In this study, we proposed novel caries detection method which could diagnose the dental proximal caries from single digital radiographic image. In artificial caries activity model, the range of lesional depth was 572-1,374 μm and the range of lesional area was 36.95-138.52 mm2. The lesional depth and the area were significantly increased with demineralization time (p<0.001). Furthermore, the proximal caries detection using digital subtraction radiography showed high detection rate compared to the proximal caries examination using simple digital radiograph. The results demonstrated that the digital subtraction radiography from single radiographic image of artificial caries was highly efficient in the detection of dental caries compared to the data from simple digital radiograph.

  6. Proximal caries detection using digital subtraction radiography in the artificial caries activity model

    Energy Technology Data Exchange (ETDEWEB)

    Park, Jong Hoon; Lee, Gi Ja; Choi, Sam Jin; Park, Young Ho; Kim, Kyung Soo; Jin, Hyun Seok; Hong, Kyung Won; Oh, Berm Seok; Park, Hun Kuk [Department of Biomedical Engineering, School of Medicine, Kyung Hee University, Seoul (Korea, Republic of); Choi, Yong Suk; Hwang, Eui Hwan [Department of Oral and Maxillofacial Radiology, Institute of Oral Biology, School of Dentistry, Kyung Hee University, Seoul (Korea, Republic of)

    2009-03-15

    The purpose of the experiment was to evaluating the diagnostic ability of dental caries detection using digital subtraction in the artificial caries activity model. Digital radiographs of five teeth with 8 proximal surfaces were obtained by CCD sensor (Kodak RVG 6100 using a size no.2). The digital radiographic images and subtraction images from artificial proximal caries were examined and interpreted. In this study, we proposed novel caries detection method which could diagnose the dental proximal caries from single digital radiographic image. In artificial caries activity model, the range of lesional depth was 572-1,374 {mu}m and the range of lesional area was 36.95-138.52 mm{sup 2}. The lesional depth and the area were significantly increased with demineralization time (p<0.001). Furthermore, the proximal caries detection using digital subtraction radiography showed high detection rate compared to the proximal caries examination using simple digital radiograph. The results demonstrated that the digital subtraction radiography from single radiographic image of artificial caries was highly efficient in the detection of dental caries compared to the data from simple digital radiograph.

  7. Artificial Life: Visual Model with Simulation of a Plant Pet

    Institute of Scientific and Technical Information of China (English)

    JIChen; QIShen

    2004-01-01

    This paper introduces an L-system model, which simulates a plant pet. The plant, named Wilson, is able to respond to the change of its virtual environment and to user's behaviors. However it does not speak or move. What users can see are only the changes of its appearance. The connection between the plant's appearance and external inputs is implemented with some parameters embedded in the L-system.

  8. MEMOSys: Bioinformatics platform for genome-scale metabolic models

    Directory of Open Access Journals (Sweden)

    Agren Rasmus

    2011-01-01

    Full Text Available Abstract Background Recent advances in genomic sequencing have enabled the use of genome sequencing in standard biological and biotechnological research projects. The challenge is how to integrate the large amount of data in order to gain novel biological insights. One way to leverage sequence data is to use genome-scale metabolic models. We have therefore designed and implemented a bioinformatics platform which supports the development of such metabolic models. Results MEMOSys (MEtabolic MOdel research and development System is a versatile platform for the management, storage, and development of genome-scale metabolic models. It supports the development of new models by providing a built-in version control system which offers access to the complete developmental history. Moreover, the integrated web board, the authorization system, and the definition of user roles allow collaborations across departments and institutions. Research on existing models is facilitated by a search system, references to external databases, and a feature-rich comparison mechanism. MEMOSys provides customizable data exchange mechanisms using the SBML format to enable analysis in external tools. The web application is based on the Java EE framework and offers an intuitive user interface. It currently contains six annotated microbial metabolic models. Conclusions We have developed a web-based system designed to provide researchers a novel application facilitating the management and development of metabolic models. The system is freely available at http://www.icbi.at/MEMOSys.

  9. An ecological model organism flies into the genomics era.

    Science.gov (United States)

    Santure, Anna W

    2016-03-01

    Despite the very rapid 'genomicization' of the field of Molecular Ecology in recent years, there have been relatively few annotated whole-genome assemblies of nonmodel organisms published. Instead, molecular ecologists have more frequently utilized next-generation sequencing technologies to develop genome-wide markers or to generate transcriptome data. Whole-genome assemblies are more expensive and require considerable computational resources and bioinformatic expertise. However, the availability of an annotated genome offers exciting opportunities to address fundamental questions in ecology and evolution that are difficult to address with moderate sets of markers or by transcriptome sequencing. Such questions include elucidating the roles of natural and sexual selection in shaping diversity, determining the roles of regulatory and protein-coding change in the evolution of traits, and determining the genomic architecture of sex-specific trait variation. Arguably, these questions are most tractable - and most interesting - in well-characterized species for which there is already some knowledge of natural and sexual selection, and of the traits that are most likely to link to fitness. In this issue, Mueller et al. () present the assembly and annotation of the genome of the blue tit (Cyanistes caeruleus), a model ecological species. In addition, by sequencing the transcriptome of male and female blue tits, the authors identify and annotate sex-biased gene expression and conclude that noncoding RNA genes are likely to play a significant role in sex-biased expression. By making their assembly and annotation publically available and accessible via a genome browser, Mueller et al. () offer exciting possibilities for further research into the genomic basis of adaptation, and investigation of the roles of natural and sexual selection, in this well-studied ecological model species. PMID:26813493

  10. Cascade Artificial Neural Network Models for Predicting Shelf Life of Processed Cheese

    Directory of Open Access Journals (Sweden)

    Gyanendra Kumar Goyal

    2013-05-01

    Full Text Available The purpose of this study is to develop artificial neural network (ANN models for predicting shelf life of processed cheese stored at 7-8ºC. Body & texture, aroma & flavour, moisture and free fatty acids were taken as input parameters, and sensory score as output parameter for developing the models. The developed Cascade single layer ANN models were compared with each other. Bayesian regularization was used for training ANN models. Network was trained with 100 epochs, and neurons in each hidden layer(s varied from 3 to 20.  Cascade ANN models very well predicted the shelf life of processed cheese.

  11. Echo state networks as an alternative to traditional artificial neural networks in rainfall–runoff modelling

    Directory of Open Access Journals (Sweden)

    N. J. de Vos

    2013-01-01

    Full Text Available Despite theoretical benefits of recurrent artificial neural networks over their feedforward counterparts, it is still unclear whether the former offer practical advantages as rainfall–runoff models. The main drawback of recurrent networks is the increased complexity of the training procedure due to their architecture. This work uses the recently introduced and conceptually simple echo state networks for streamflow forecasts on twelve river basins in the Eastern United States, and compares them to a variety of traditional feedforward and recurrent approaches. Two modifications on the echo state network models are made that increase the hydrologically relevant information content of their internal state. The results show that the echo state networks outperform feedforward networks and are competitive with state-of-the-art recurrent networks, across a range of performance measures. This, along with their simplicity and ease of training, suggests that they can be considered promising alternatives to traditional artificial neural networks in rainfall–runoff modelling.

  12. Echo state networks as an alternative to traditional artificial neural networks in rainfall-runoff modelling

    Science.gov (United States)

    de Vos, N. J.

    2013-01-01

    Despite theoretical benefits of recurrent artificial neural networks over their feedforward counterparts, it is still unclear whether the former offer practical advantages as rainfall-runoff models. The main drawback of recurrent networks is the increased complexity of the training procedure due to their architecture. This work uses the recently introduced and conceptually simple echo state networks for streamflow forecasts on twelve river basins in the Eastern United States, and compares them to a variety of traditional feedforward and recurrent approaches. Two modifications on the echo state network models are made that increase the hydrologically relevant information content of their internal state. The results show that the echo state networks outperform feedforward networks and are competitive with state-of-the-art recurrent networks, across a range of performance measures. This, along with their simplicity and ease of training, suggests that they can be considered promising alternatives to traditional artificial neural networks in rainfall-runoff modelling.

  13. Model of Cholera Forecasting Using Artificial Neural Network in Chabahar City, Iran

    Directory of Open Access Journals (Sweden)

    Pezeshki

    2016-02-01

    Full Text Available Background Cholera as an endemic disease remains a health issue in Iran despite decrease in incidence. Since forecasting epidemic diseases provides appropriate preventive actions in disease spread, different forecasting methods including artificial neural networks have been developed to study parameters involved in incidence and spread of epidemic diseases such as cholera. Objectives In this study, cholera in rural area of Chabahar, Iran was investigated to achieve a proper forecasting model. Materials and Methods Data of cholera was gathered from 465 villages, of which 104 reported cholera during ten years period of study. Logistic regression modeling and correlate bivariate were used to determine risk factors and achieve possible predictive model one-hidden-layer perception neural network with backpropagation training algorithm and the sigmoid activation function was trained and tested between the two groups of infected and non-infected villages after preprocessing. For determining validity of prediction, the ROC diagram was used. The study variables included climate conditions and geographical parameters. Results After determining significant variables of cholera incidence, the described artificial neural network model was capable of forecasting cholera event among villages of test group with accuracy up to 80%. The highest accuracy was achieved when model was trained with variables that were significant in statistical analysis describing that the two methods confirm the result of each other. Conclusions Application of artificial neural networking assists forecasting cholera for adopting protective measures. For a more accurate prediction, comprehensive information is required including data on hygienic, social and demographic parameters.

  14. A new method to estimate parameters of linear compartmental models using artificial neural networks

    International Nuclear Information System (INIS)

    At present, the preferred tool for parameter estimation in compartmental analysis is an iterative procedure; weighted nonlinear regression. For a large number of applications, observed data can be fitted to sums of exponentials whose parameters are directly related to the rate constants/coefficients of the compartmental models. Since weighted nonlinear regression often has to be repeated for many different data sets, the process of fitting data from compartmental systems can be very time consuming. Furthermore the minimization routine often converges to a local (as opposed to global) minimum. In this paper, we examine the possibility of using artificial neural networks instead of weighted nonlinear regression in order to estimate model parameters. We train simple feed-forward neural networks to produce as outputs the parameter values of a given model when kinetic data are fed to the networks' input layer. The artificial neural networks produce unbiased estimates and are orders of magnitude faster than regression algorithms. At noise levels typical of many real applications, the neural networks are found to produce lower variance estimates than weighted nonlinear regression in the estimation of parameters from mono- and biexponential models. These results are primarily due to the inability of weighted nonlinear regression to converge. These results establish that artificial neural networks are powerful tools for estimating parameters for simple compartmental models. (author)

  15. Generalized in vitro-in vivo relationship (IVIVR model based on artificial neural networks

    Directory of Open Access Journals (Sweden)

    Mendyk A

    2013-03-01

    Full Text Available Aleksander Mendyk,1 Pawel Tuszynski,1 Sebastian Polak,2 Renata Jachowicz1 1Department of Pharmaceutical Technology and Biopharmaceutics, 2Department of Social Pharmacy, Faculty of Pharmacy, Jagiellonian University Medical College, Kraków, Poland Background: The aim of this study was to develop a generalized in vitro-in vivo relationship (IVIVR model based on in vitro dissolution profiles together with quantitative and qualitative composition of dosage formulations as covariates. Such a model would be of substantial aid in the early stages of development of a pharmaceutical formulation, when no in vivo results are yet available and it is impossible to create a classical in vitro-in vivo correlation (IVIVC/IVIVR. Methods: Chemoinformatics software was used to compute the molecular descriptors of drug substances (ie, active pharmaceutical ingredients and excipients. The data were collected from the literature. Artificial neural networks were used as the modeling tool. The training process was carried out using the 10-fold cross-validation technique. Results: The database contained 93 formulations with 307 inputs initially, and was later limited to 28 in a course of sensitivity analysis. The four best models were introduced into the artificial neural network ensemble. Complete in vivo profiles were predicted accurately for 37.6% of the formulations. Conclusion: It has been shown that artificial neural networks can be an effective predictive tool for constructing IVIVR in an integrated generalized model for various formulations. Because IVIVC/IVIVR is classically conducted for 2–4 formulations and with a single active pharmaceutical ingredient, the approach described here is unique in that it incorporates various active pharmaceutical ingredients and dosage forms into a single model. Thus, preliminary IVIVC/IVIVR can be available without in vivo data, which is impossible using current IVIVC/IVIVR procedures. Keywords: artificial neural networks

  16. An Inherited Efficiencies Model of Non-Genomic Evolution

    Science.gov (United States)

    New, Michael H.; Pohorille, Andrew

    1999-01-01

    A model for the evolution of biological systems in the absence of a nucleic acid-like genome is proposed and applied to model the earliest living organisms -- protocells composed of membrane encapsulated peptides. Assuming that the peptides can make and break bonds between amino acids, and bonds in non-functional peptides are more likely to be destroyed than in functional peptides, it is demonstrated that the catalytic capabilities of the system as a whole can increase. This increase is defined to be non-genomic evolution. The relationship between the proposed mechanism for evolution and recent experiments on self-replicating peptides is discussed.

  17. An artificial neural network ensemble model for estimating global solar radiation from Meteosat satellite images

    International Nuclear Information System (INIS)

    An optimized artificial neural network ensemble model is built to estimate daily global solar radiation over large areas. The model uses clear-sky estimates and satellite images as input variables. Unlike most studies using satellite imagery based on visible channels, our model also exploits all information within infrared channels of the Meteosat 9 satellite. A genetic algorithm is used to optimize selection of model inputs, for which twelve are selected – eleven 3-km Meteosat 9 channels and one clear-sky term. The model is validated in Andalusia (Spain) from January 2008 through December 2008. Measured data from 83 stations across the region are used, 65 for training and 18 independent ones for testing the model. At the latter stations, the ensemble model yields an overall root mean square error of 6.74% and correlation coefficient of 99%; the generated estimates are relatively accurate and errors spatially uniform. The model yields reliable results even on cloudy days, improving on current models based on satellite imagery. - Highlights: • Daily solar radiation data are generated using an artificial neural network ensemble. • Eleven Meteosat channels observations and a clear sky term are used as model inputs. • Model exploits all information within infrared Meteosat channels. • Measured data for a year from 83 ground stations are used. • The proposed approach has better performance than existing models on daily basis

  18. A bacterial artificial chromosome library for the Australian saltwater crocodile (Crocodylus porosus) and its utilization in gene isolation and genome characterization

    Science.gov (United States)

    2009-01-01

    Background Crocodilians (Order Crocodylia) are an ancient vertebrate group of tremendous ecological, social, and evolutionary importance. They are the only extant reptilian members of Archosauria, a monophyletic group that also includes birds, dinosaurs, and pterosaurs. Consequently, crocodilian genomes represent a gateway through which the molecular evolution of avian lineages can be explored. To facilitate comparative genomics within Crocodylia and between crocodilians and other archosaurs, we have constructed a bacterial artificial chromosome (BAC) library for the Australian saltwater crocodile, Crocodylus porosus. This is the first BAC library for a crocodile and only the second BAC resource for a crocodilian. Results The C. porosus BAC library consists of 101,760 individually archived clones stored in 384-well microtiter plates. NotI digestion of random clones indicates an average insert size of 102 kb. Based on a genome size estimate of 2778 Mb, the library affords 3.7 fold (3.7×) coverage of the C. porosus genome. To investigate the utility of the library in studying sequence distribution, probes derived from CR1a and CR1b, two crocodilian CR1-like retrotransposon subfamilies, were hybridized to C. porosus macroarrays. The results indicate that there are a minimum of 20,000 CR1a/b elements in C. porosus and that their distribution throughout the genome is decidedly non-random. To demonstrate the utility of the library in gene isolation, we probed the C. porosus macroarrays with an overgo designed from a C-mos (oocyte maturation factor) partial cDNA. A BAC containing C-mos was identified and the C-mos locus was sequenced. Nucleotide and amino acid sequence alignment of the C. porosus C-mos coding sequence with avian and reptilian C-mos orthologs reveals greater sequence similarity between C. porosus and birds (specifically chicken and zebra finch) than between C. porosus and squamates (green anole). Conclusion We have demonstrated the utility of the

  19. Modeling of mass transfer of Phospholipids in separation process with supercritical CO2 fluid by RBF artificial neural networks

    Science.gov (United States)

    An artificial Radial Basis Function (RBF) neural network model was developed for the prediction of mass transfer of the phospholipids from canola meal in supercritical CO2 fluid. The RBF kind of artificial neural networks (ANN) with orthogonal least squares (OLS) learning algorithm were used for mod...

  20. Smart Artificial Intelligence Computerized Models for Shelf Life Prediction of Processed Cheese

    Directory of Open Access Journals (Sweden)

    Sumit Goyal

    2012-06-01

    Full Text Available Linear Layer (Design and multiple linear regression artificial intelligence computerized models were developed for predicting shelf life of processed cheese stored at 7-8ºC. Mean Square Error, Root Mean Square Error, Coefficient of Determination and Nash - Sutcliffo Coefficient were applied for comparing the prediction ability of the developed models. The modelling results showed excellent agreement between the experimental data and predicted values with a high determination coefficient, suggesting that the Linear Layer (Design and MLR models are very efficient in predicting the shelf life of processed cheese stored at 7-8oC.

  1. Modeling of biodistribution of 90 Y-DOTA-hR3 by using artificial intelligence techniques

    International Nuclear Information System (INIS)

    In this work the biodistribution of radioimmunoconjugate 90Y-DOTA-hR3 was modeled by using an artificial neural network. In vivo stability of 90Y-DOTA-hR3 was determined in healthy male Wistar rats at 4, 24 and 48 hours, in different organs. A model describing the relationship between, by one hand, the incorporated dose and, by the other hand, organ and time was developed by using a multilayer perceptron neural network. Adjusted model was analyzed by several statistical tests. Outcomes shown that proposed neural model describes the relationship between the studied variables in a proper way. (Author)

  2. Artificial Immune Systems Metaphor for Agent Based Modeling of Crisis Response Operations

    CERN Document Server

    Khalil, Khaled M; Nazmy, Taymour T; Salem, Abdel-Badeeh M

    2010-01-01

    Crisis response requires information intensive efforts utilized for reducing uncertainty, calculating and comparing costs and benefits, and managing resources in a fashion beyond those regularly available to handle routine problems. This paper presents an Artificial Immune Systems (AIS) metaphor for agent based modeling of crisis response operations. The presented model proposes integration of hybrid set of aspects (multi-agent systems, built-in defensive model of AIS, situation management, and intensity-based learning) for crisis response operations. In addition, the proposed response model is applied on the spread of pandemic influenza in Egypt as a case study.

  3. Study on the Overfitting of the Artificial Neural Network Forecasting Model

    Institute of Scientific and Technical Information of China (English)

    JIN Long; KUANG Xueyuan; HUANG Haihong; QIN Zhinian; WANG Yehong

    2005-01-01

    Because of overfitting and the improvement of generalization capability (GC) available in the construction of forecasting models using artificial neural network (ANN), a new method is proposed for model establishment by means of making a low-dimension ANN learning matrix through principal component analysis (PCA). The results show that the PCA is able to construct an ANN model without the need of finding an optimal structure with the appropriate number of hidden-layer nodes, thus avoids overfitting by condensing forecasting information, reducing dimension and removing noise, and GC is greatly raised compared to the traditional ANN and stepwise regression techniques for model establishment.

  4. A BOD-DO coupling model for water quality simulation by artificial neural network

    Institute of Scientific and Technical Information of China (English)

    郭劲松; LONG; Tengrui; 等

    2002-01-01

    A one-dimensional BOD-DO coupling model for water quality simulation is presented,which adopts Streeter-Phelps equations and the theory of back-propagation artificial neural network.The water quality data of Yangtze River in the Chongqing region in the year of 1989 are divided into 5 groups and used in the learning and testing courses of this model.The result shows that such model is feasible for water quality simulation and is more accurate than traditional models.

  5. Numerical modeling of artificial ionospheric layers driven by high-power HF-heating

    Science.gov (United States)

    Milikh, G. M.; Eliasson, B.; Shao, X.; Mishin, E. V.; Papadopoulos, K.

    2012-12-01

    We present a multi-scale dynamic model for the creation and propagation of artificial plasma layers in the ionosphere observed during high-power high frequency heating experiments at HAARP. Ordinary mode electromagnetic waves excite parametric instabilities and strong Langmuir turbulence near the reflection point. The coupling between high frequency electromagnetic and Langmuir waves and low-frequency ion acoustic waves is numerically simulated using a generalized Zakharov equation. The acceleration of plasma electrons is described by a Fokker-Planck model with an effective diffusion coefficient constructed using the simulated Langmuir wave spectrum. The propagation of the accelerated electrons through the non-uniform ionosphere is simulated by a kinetic model accounting for elastic and inelastic collisions with neutrals. The resulting ionization of neutral gas increases the plasma density below the acceleration region, so that the pump wave is reflected at a lower altitude. This leads to a new turbulent layer at the lower altitude, resulting in a descending artificial ionized layer, that moves from near 230 km to about 150 km. At the terminal altitude, ionization, recombination, and ambipolar diffusion reach equilibrium, so the descent stops. The modeling results reproduce artificial ionospheric layers produced for similar sets of parameters during the high-power HF experiments at HAARP.

  6. Estimating tree bole volume using artificial neural network models for four species in Turkey.

    Science.gov (United States)

    Ozçelik, Ramazan; Diamantopoulou, Maria J; Brooks, John R; Wiant, Harry V

    2010-01-01

    Tree bole volumes of 89 Scots pine (Pinus sylvestris L.), 96 Brutian pine (Pinus brutia Ten.), 107 Cilicica fir (Abies cilicica Carr.) and 67 Cedar of Lebanon (Cedrus libani A. Rich.) trees were estimated using Artificial Neural Network (ANN) models. Neural networks offer a number of advantages including the ability to implicitly detect complex nonlinear relationships between input and output variables, which is very helpful in tree volume modeling. Two different neural network architectures were used and produced the Back propagation (BPANN) and the Cascade Correlation (CCANN) Artificial Neural Network models. In addition, tree bole volume estimates were compared to other established tree bole volume estimation techniques including the centroid method, taper equations, and existing standard volume tables. An overview of the features of ANNs and traditional methods is presented and the advantages and limitations of each one of them are discussed. For validation purposes, actual volumes were determined by aggregating the volumes of measured short sections (average 1 meter) of the tree bole using Smalian's formula. The results reported in this research suggest that the selected cascade correlation artificial neural network (CCANN) models are reliable for estimating the tree bole volume of the four examined tree species since they gave unbiased results and were superior to almost all methods in terms of error (%) expressed as the mean of the percentage errors. PMID:19880241

  7. Pilot biomechanical design of biomaterials for artificial nucleus prosthesis using 3D finite-element modeling

    Institute of Scientific and Technical Information of China (English)

    Qijin Huang; Guoquan Liu; Yong Li; Jin Gao; Zhengqiu Gu; Yuanzheng Ma; Haibin Xue

    2004-01-01

    Pilot biomechanical design of biomaterials for artificial nucleus prosthesis was carried out based on the 3D finite-element method. Two 3D models of lumbar intervertebral disc respectively with a real human nucleus and with the nucleus removed were developed and validated using published experimental and clinical data. Then the models with a stainless steel nucleus prosthesis implanted and with polymer nucleus prostheses of various properties implanted were used for the 3D finite-element biomechanical analysis. All the above simulation and analysis were carried out for the L4/L5 disc under a human worst-daily compression load of 2000 N. The results show that the polymer materials with Young's modulus of elasticity E = 0.1-100 MPa and Poisson's ratio v=0.35-0.5 are suitable to produce artificial nucleus prosthesis in view of biomechanical consideration.

  8. Neocaridina denticulata: A Decapod Crustacean Model for Functional Genomics.

    Science.gov (United States)

    Mykles, Donald L; Hui, Jerome H L

    2015-11-01

    A decapod crustacean model is needed for understanding the molecular mechanisms underlying physiological processes, such as reproduction, sex determination, molting and growth, immunity, regeneration, and response to stress. Criteria for selection are: life-history traits, adult size, availability and ease of culture, and genomics and genetic manipulation. Three freshwater species are considered: cherry shrimp, Neocaridina denticulata; red swamp crayfish, Procambarus clarkii; and redclaw crayfish, Cherax quadricarinatus. All three are readily available, reproduce year round, and grow rapidly. The crayfish species require more space for culture than does N. denticulata. The transparent cuticle of cherry shrimp provides for direct assessment of reproductive status, stage of molt, and tissue-specific expression of reporter genes, and facilitates screening of mutations affecting phenotype. Moreover, a preliminary genome of N. denticulata is available and efforts toward complete genome sequencing and transcriptome sequencing have been initiated. Neocaridina denticulata possesses the best combination of traits that make it most suitable as a model for functional genomics. The next step is to obtain the complete genome sequence and to develop molecular technologies for the screening of mutants and for manipulating tissue-specific gene expression. PMID:26002561

  9. Abundance Distributions in Artificial Life and Stochastic Models: "Age and Area" revisited

    OpenAIRE

    Adami, C.; Brown, C. T.; Haggerty, M.

    1995-01-01

    Using an artificial system of self-replicating strings, we show a correlation between the age of a genotype and its abundance that reflects a punctuated rather than gradual picture of evolution, as suggested long ago by Willis. In support of this correlation, we measure genotype abundance distributions and find universal coefficients. Finally, we propose a simple stochastic model which describes the dynamics of equilibrium periods and which correctly predicts most of the observed distributions.

  10. Abundance distributions in artificial life and stochastic models "age and area" revisited

    CERN Document Server

    Adami, C; Haggerty, M; Brown, C T; Haggerty, M

    1995-01-01

    Using an artificial system of self-replicating strings, we show a correlation between the age of a genotype and its abundance that reflects a punctuated rather than gradual picture of evolution, as suggested long ago by Willis. In support of this correlation, we measure genotype abundance distributions and find universal coefficients. Finally, we propose a simple stochastic model which describes the dynamics of equilibrium periods and which correctly predicts most of the observed distributions.

  11. Artificial neural networks in models of specialization, guild evolution and sympatric speciation

    OpenAIRE

    Holmgren, Noél M A; Norrström, Niclas; Getz, Wayne M.

    2007-01-01

    Sympatric speciation can arise as a result of disruptive selection with assortative mating as a pleiotropic by-product. Studies on host choice, employing artificial neural networks as models for the host recognition system in exploiters, illustrate how disruptive selection on host choice coupled with assortative mating can arise as a consequence of selection for specialization. Our studies demonstrate that a generalist exploiter population can evolve into a guild of specialists with an ‘ideal...

  12. Modelling of Biotechnological Processes - An approach based on Artificial Neural Networks

    OpenAIRE

    Valente, Eduardo; Rocha, Miguel; Ferreira, E.C.; Rocha, I

    2009-01-01

    In this chapter we describe a software tool for modelling fermentation processes, the FerMoANN, which allows researchers in biology and biotechnology areas to access the potential of Artificial Neural Networks (ANNs) for this task. The FerMoANN is tested and validated using two fermentation processes, an Escherichia coli recombinant protein production and the production of a secreted protein with Saccharomyces cerevisiae in fed-batch reactors. The application to these two case studies, tested...

  13. A Comparison of the Monetary Model and Artificial Neural Networks in Exchange Rate Forecasting

    OpenAIRE

    Filiz Ozkan

    2012-01-01

    Exchange value is one of the significant tools for investors in decision making. Since exchange values are volatile and they change within short periods, investors need an effective method to minimize the risk. This study compares the prediction performances of artificial neural networks, which is recently being used as an effective tool of prediction, and the monetary model, which is one of the methods to predict structural exchange rates. In this study exchange rates of Turkish Lira against...

  14. Application of artificial intelligence (AI) concepts to the development of space flight parts approval model

    Science.gov (United States)

    Krishnan, G. S.

    1997-01-01

    A cost effective model which uses the artificial intelligence techniques in the selection and approval of parts is presented. The knowledge which is acquired from the specialists for different part types are represented in a knowledge base in the form of rules and objects. The parts information is stored separately in a data base and is isolated from the knowledge base. Validation, verification and performance issues are highlighted.

  15. Generalized Gap Model for Bacterial Artificial Chromosome Clone Fingerprint Mapping and Shotgun Sequencing

    OpenAIRE

    Wendl, Michael C; Robert H Waterston

    2002-01-01

    We develop an extension to the Lander-Waterman theory for characterizing gaps in bacterial artificial chromosome fingerprint mapping and shotgun sequencing projects. It supports a larger set of descriptive statistics and is applicable to a wider range of project parameters. We show that previous assertions regarding inconsistency of the Lander-Waterman theory at higher coverages are incorrect and that another well-known but ostensibly different model is in fact the same. The apparent paradox ...

  16. Investigation of a New Artificial Immune System Model Applied to Pattern Recognition

    OpenAIRE

    Alexandrino, Jos&#; Lima; Zanchettin, Cleber; Filho, Edson C. de B. Carvalho

    2008-01-01

    This chapter describes an application based on Artificial Immune System (AIS) with biologically inspired characteristics, such as the grouping of similar antibodies and memory antibodies were studied to allow the evolution of the AIS. The focus of this chapter was to evaluate the quality of this model to recognize handwritten digits patterns and evolving its performance and comparing it to other technique. This architecture combined different techniques to form a hybrid immune algorithm. It w...

  17. Modelling an artificial stock market: When cognitive institutions influence market dynamics

    OpenAIRE

    Lavigne, Stéphanie

    2004-01-01

    The paper presents an artificial financial market designed to analyse market dynamics from the behaviour of investors. The model especially allows highlighting the role of a particular kind of institution in the orientation of market dynamics. The information – delivered by financial intermediaries as rating agencies and considered as a cognitive institution – directs the decisions of investors who are heterogeneous agents endowed with capabilities of learning in a changing environment. We de...

  18. Artificial Intelligence in Numerical Modeling of Silver Nanoparticles Prepared in Montmorillonite Interlayer Space

    OpenAIRE

    Parvaneh Shabanzadeh; Norazak Senu; Kamyar Shameli; Maryam Mohaghegh Tabar

    2013-01-01

    Artificial neural network (ANN) models have the capacity to eliminate the need for expensive experimental investigation in various areas of manufacturing processes, including the casting methods. An understanding of the interrelationships between input variables is essential for interpreting the sensitivity data and optimizing the design parameters. Silver nanoparticles (Ag-NPs) have attracted considerable attention for chemical, physical, and medical applications due to their exceptional pro...

  19. A nonlinear full model of switched reluctance motor with artificial neural network

    International Nuclear Information System (INIS)

    This paper presents a novel nonlinear full model developed by using artificial neural networks (ANNs) for switched reluctance motors (SRMs). The proposed ANN based nonlinear model consists of two different models, namely forward and inverse model. The purpose of the forward model is to estimate the flux linkage and torque of the SRM as a function of stator current and rotor position. And, the purpose of the inverse model is to estimate stator current and flux linkage of the SRM as a function of torque and rotor position. Also conversions can be achieved between torque, stator current and flux linkage with these models. Computational load of the processor has been considered and minimized to use the developed model in real industrial applications. The experimental tests are realized to verify the accuracy and feasibility of the proposed model.

  20. Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese

    Directory of Open Access Journals (Sweden)

    Sumit Goyal

    2012-04-01

    Full Text Available This paper presents the capability of Time–delay artificial neural network models for predicting shelf life of processed cheese. Datasets were divided into two subsets (30 for training and 6 for validation. Models with single and multi layers were developed and compared with each other. Mean Square Error, Root Mean Square Error, Coefficient of Determination and Nash -
    Sutcliffo Coefficient were used as performance evaluators, Time- delay model predicted the shelf life of processed cheese as 28.25 days, which is very close to experimental shelf life of 30 days.

  1. An artificial economy based on reinforcement learning and agent based modeling

    OpenAIRE

    Fernando Lozano; Jaime Lozano; Mario García

    2007-01-01

    In this paper we employ techniques from artificial intelligence such as reinforcement learning and agent based modeling as building blocks of a computational model for an economy based on convention. First we model the interaction among firms in the private sector. These firms behave in an information environment based on conventions meaning that a firm is likely to behave as it neighbors if it observes that their actions lead to a good pay-off. On the other hand, we propose the use of reinfo...

  2. The genome of the model beetle and pest Tribolium castaneum

    DEFF Research Database (Denmark)

    Denell, Robin; Gibbs, Richard; Muzny, Donna;

    2008-01-01

    Tribolium castaneum is a member of the most species-rich eukaryotic order, a powerful model organism for the study of generalized insect development, and an important pest of stored agricultural products. We describe its genome sequence here. This omnivorous beetle has evolved the ability to inte...

  3. Comparative nonlinear modeling of renal autoregulation in rats: Volterra approach versus artificial neural networks.

    Science.gov (United States)

    Chon, K H; Holstein-Rathlou, N H; Marsh, D J; Marmarelis, V Z

    1998-01-01

    Volterra models have been increasingly popular in modeling studies of nonlinear physiological systems. In this paper, feedforward artificial neural networks with two types of activation functions (sigmoidal and polynomial) are utilized for modeling the nonlinear dynamic relation between renal blood pressure and flow data, and their performance is compared to Volterra models obtained by use of the leading kernel estimation method based on Laguerre expansions. The results for the two types of artificial neural networks (sigmoidal and polynomial) and the Volterra models are comparable in terms of normalized mean-square error (NMSE) of the respective output prediction for independent testing data. However, the Volterra models obtained via the Laguerre expansion technique achieve this prediction NMSE with approximately half the number of free parameters relative to either neural-network model. Nonetheless, both approaches are deemed effective in modeling nonlinear dynamic systems and their cooperative use is recommended in general, since they may exhibit different strengths and weaknesses depending on the specific characteristics of each application. PMID:18252466

  4. Artificial Neural Network Modeling to Evaluate the Dynamic Flow Stress of 7050 Aluminum Alloy

    Science.gov (United States)

    Quan, Guo-zheng; Wang, Tong; Li, Yong-le; Zhan, Zong-yang; Xia, Yu-feng

    2016-02-01

    The flow stress data have been obtained by a set of isothermal hot compression tests, which were carried out in the temperature range of 573-723 K and strain rates of 0.01, 0.1, 1, and 10 s-1 with a reduction of 60% on a Gleeble-1500 thermo-mechanical simulator. On the basis of the experimental data, constitutive equation and an artificial neural network model were developed for the analysis and simulation of the flow behavior of the 7050 aluminum alloy. After training with standard back-propagation learning algorithm, the artificial neural network model has the ability to present the intrinsic relationship between the flow stress and the processing variables. In the present model, the temperature, strain, and strain rate were chosen as inputs, and the flow stress was chosen as output. By comparing the values of correlation coefficient and average absolute relative error, the prediction accuracy of the model and the improved Arrhenius-type model can be evaluated. The results indicated that the well-trained artificial neural network model is more accurate than the improved Arrhenius-type model in predicting the hot compressive behavior of the as-extruded 7050 aluminum alloy. Based on the predicted stress data and experimental stress data, the 3D continuous stress-strain maps at different strains, temperatures, and strain rates were plotted subsequently. Besides, the flow stress values at arbitrary temperature, strain rate, and strain are explicit on the 3D continuous stress-strain maps, which would be beneficial to articulate working processes more validly.

  5. BiGG Models: A platform for integrating, standardizing and sharing genome-scale models.

    Science.gov (United States)

    King, Zachary A; Lu, Justin; Dräger, Andreas; Miller, Philip; Federowicz, Stephen; Lerman, Joshua A; Ebrahim, Ali; Palsson, Bernhard O; Lewis, Nathan E

    2016-01-01

    Genome-scale metabolic models are mathematically-structured knowledge bases that can be used to predict metabolic pathway usage and growth phenotypes. Furthermore, they can generate and test hypotheses when integrated with experimental data. To maximize the value of these models, centralized repositories of high-quality models must be established, models must adhere to established standards and model components must be linked to relevant databases. Tools for model visualization further enhance their utility. To meet these needs, we present BiGG Models (http://bigg.ucsd.edu), a completely redesigned Biochemical, Genetic and Genomic knowledge base. BiGG Models contains more than 75 high-quality, manually-curated genome-scale metabolic models. On the website, users can browse, search and visualize models. BiGG Models connects genome-scale models to genome annotations and external databases. Reaction and metabolite identifiers have been standardized across models to conform to community standards and enable rapid comparison across models. Furthermore, BiGG Models provides a comprehensive application programming interface for accessing BiGG Models with modeling and analysis tools. As a resource for highly curated, standardized and accessible models of metabolism, BiGG Models will facilitate diverse systems biology studies and support knowledge-based analysis of diverse experimental data. PMID:26476456

  6. Modeling river total bed material load discharge using artificial intelligence approaches (based on conceptual inputs)

    Science.gov (United States)

    Roushangar, Kiyoumars; Mehrabani, Fatemeh Vojoudi; Shiri, Jalal

    2014-06-01

    This study presents Artificial Intelligence (AI)-based modeling of total bed material load through developing the accuracy level of the predictions of traditional models. Gene expression programming (GEP) and adaptive neuro-fuzzy inference system (ANFIS)-based models were developed and validated for estimations. Sediment data from Qotur River (Northwestern Iran) were used for developing and validation of the applied techniques. In order to assess the applied techniques in relation to traditional models, stream power-based and shear stress-based physical models were also applied in the studied case. The obtained results reveal that developed AI-based models using minimum number of dominant factors, give more accurate results than the other applied models. Nonetheless, it was revealed that k-fold test is a practical but high-cost technique for complete scanning of applied data and avoiding the over-fitting.

  7. MODEL IMPROVEMENT AND EXPERI-MENT VALIDATION OF PNEUMATIC ARTIFICIAL MUSCLES

    Institute of Scientific and Technical Information of China (English)

    Zhou Aiguo; Shi Guanglin; Zhong Tingxiu

    2004-01-01

    According to the deficiency of the present model of pneumatic artificial muscles (PAM), a serial model is built up based on the PAM's essential working principle with the elastic theory, it is validated by the quasi-static and dynamic experiment results, which are gained from two experiment systems.The experiment results and the simulation results illustrate that the serial model has made a great success compared with Chou's model, which can describe the force characteristics of PAM more precisely.A compensation item considering the braid's elasticity and the coulomb damp is attached to the serial model based on the analysis of the experiment results.The dynamic experiment proves that the viscous damp of the PAM could be ignored in order to simplify the model of PAM.Finally, an improved serial model of PAM is obtained.

  8. Genomic responses in mouse models poorly mimic human inflammatory diseases

    OpenAIRE

    Seok, Junhee; Warren, H. Shaw; Cuenca, Alex G.; Mindrinos, Michael N.; Baker, Henry V.; Xu, Weihong; Richards, Daniel R; McDonald-Smith, Grace P.; Gao, Hong; Hennessy, Laura; Finnerty, Celeste C.; López, Cecilia M.; Honari, Shari; Moore, Ernest E; Minei, Joseph P.

    2013-01-01

    A cornerstone of modern biomedical research is the use of mouse models to explore basic pathophysiological mechanisms, evaluate new therapeutic approaches, and make go or no-go decisions to carry new drug candidates forward into clinical trials. Systematic studies evaluating how well murine models mimic human inflammatory diseases are nonexistent. Here, we show that, although acute inflammatory stresses from different etiologies result in highly similar genomic responses in humans, the respon...

  9. Mitochondrial genome transmission in Chlamydomonas diploids obtained by sexual crosses and artificial fusions: role of the mating type and of a 1 kb intron.

    Science.gov (United States)

    Remacle, C; Bovie, C; Michel-Wolwertz, M R; Loppes, R; Matagne, R F

    1990-09-01

    The linear mitochondrial DNAs of the two infertile algal species Chlamydomonas smithii and C. reinhardtii are co-linear with the exception of a 1 kb intron (alpha intron) located in the cytochrome b gene of C. smithii. C. smithii also possesses an additional HpaI restriction site (H marker) located in the COXI gene, about 5 kb from the intron. In reciprocal crosses, C. smithii (H+ alpha +) x C. reinhardtii (H- alpha -), the alpha intron is transmitted to all diploid progeny, whereas the H marker is frequently transmitted either biparentally or paternally depending on whether the C. smithii parent is maternal (mt+) or paternal (mt-). In diploids resulting from artificial fusion between vegetative cells, the absolute transmission of alpha is accompanied by the frequent transmission of the H+ marker, irrespective of the mating type of the parental strains. Finally, in reciprocal crosses between C. smithii (H+ alpha +) and recombinant H- alpha + clones, the transmission of the H marker is predominantly paternal or biparental. These results allow us to conclude that (1) the alpha intron behaves as a group I intron whose unidirectional conversion influences the transmission of the H marker; and (2) the mt- paternal mitochondrial genome is transmitted more often than the mt+. The mating type has no effect in diploids obtained by artificial fusion. PMID:2250646

  10. Meta-analysis of genome-wide association from genomic prediction models.

    Science.gov (United States)

    Bernal Rubio, Y L; Gualdrón Duarte, J L; Bates, R O; Ernst, C W; Nonneman, D; Rohrer, G A; King, A; Shackelford, S D; Wheeler, T L; Cantet, R J C; Steibel, J P

    2016-02-01

    Genome-wide association (GWA) studies based on GBLUP models are a common practice in animal breeding. However, effect sizes of GWA tests are small, requiring larger sample sizes to enhance power of detection of rare variants. Because of difficulties in increasing sample size in animal populations, one alternative is to implement a meta-analysis (MA), combining information and results from independent GWA studies. Although this methodology has been used widely in human genetics, implementation in animal breeding has been limited. Thus, we present methods to implement a MA of GWA, describing the proper approach to compute weights derived from multiple genomic evaluations based on animal-centric GBLUP models. Application to real datasets shows that MA increases power of detection of associations in comparison with population-level GWA, allowing for population structure and heterogeneity of variance components across populations to be accounted for. Another advantage of MA is that it does not require access to genotype data that is required for a joint analysis. Scripts related to the implementation of this approach, which consider the strength of association as well as the sign, are distributed and thus account for heterogeneity in association phase between QTL and SNPs. Thus, MA of GWA is an attractive alternative to summarizing results from multiple genomic studies, avoiding restrictions with genotype data sharing, definition of fixed effects and different scales of measurement of evaluated traits. PMID:26607299

  11. Hybrid Modeling and Optimization of Manufacturing Combining Artificial Intelligence and Finite Element Method

    CERN Document Server

    Quiza, Ramón; Davim, J Paulo

    2012-01-01

    Artificial intelligence (AI) techniques and the finite element method (FEM) are both powerful computing tools, which are extensively used for modeling and optimizing manufacturing processes. The combination of these tools has resulted in a new flexible and robust approach as several recent studies have shown. This book aims to review the work already done in this field as well as to expose the new possibilities and foreseen trends. The book is expected to be useful for postgraduate students and researchers, working in the area of modeling and optimization of manufacturing processes.

  12. Advances in Swine Biomedical Model Genomics

    Science.gov (United States)

    The swine has been a major biomedical model species, for transplantation, heart disease, allergies and asthma, as well as normal neonatal development and reproductive physiology. Swine have been used extensively for studies of infectious disease processes and analyses of preventative strategies, inc...

  13. Modeling of steam distillation mechanism during steam injection process using artificial intelligence.

    Science.gov (United States)

    Daryasafar, Amin; Ahadi, Arash; Kharrat, Riyaz

    2014-01-01

    Steam distillation as one of the important mechanisms has a great role in oil recovery in thermal methods and so it is important to simulate this process experimentally and theoretically. In this work, the simulation of steam distillation is performed on sixteen sets of crude oil data found in the literature. Artificial intelligence (AI) tools such as artificial neural network (ANN) and also adaptive neurofuzzy interference system (ANFIS) are used in this study as effective methods to simulate the distillate recoveries of these sets of data. Thirteen sets of data were used to train the models and three sets were used to test the models. The developed models are highly compatible with respect to input oil properties and can predict the distillate yield with minimum entry. For showing the performance of the proposed models, simulation of steam distillation is also done using modified Peng-Robinson equation of state. Comparison between the calculated distillates by ANFIS and neural network models and also equation of state-based method indicates that the errors of the ANFIS model for training data and test data sets are lower than those of other methods. PMID:24883365

  14. Daily Crude Oil Price Forecasting Using Hybridizing Wavelet and Artificial Neural Network Model

    Directory of Open Access Journals (Sweden)

    Ani Shabri

    2014-01-01

    Full Text Available A new method based on integrating discrete wavelet transform and artificial neural networks (WANN model for daily crude oil price forecasting is proposed. The discrete Mallat wavelet transform is used to decompose the crude price series into one approximation series and some details series (DS. The new series obtained by adding the effective one approximation series and DS component is then used as input into the ANN model to forecast crude oil price. The relative performance of WANN model was compared to regular ANN model for crude oil forecasting at lead times of 1 day for two main crude oil price series, West Texas Intermediate (WTI and Brent crude oil spot prices. In both cases, WANN model was found to provide more accurate crude oil prices forecasts than individual ANN model.

  15. Fault diagnosis using noise modeling and a new artificial immune system based algorithm

    Science.gov (United States)

    Abbasi, Farshid; Mojtahedi, Alireza; Ettefagh, Mir Mohammad

    2015-12-01

    A new fault classification/diagnosis method based on artificial immune system (AIS) algorithms for the structural systems is proposed. In order to improve the accuracy of the proposed method, i.e., higher success rate, Gaussian and non-Gaussian noise generating models are applied to simulate environmental noise. The identification of noise model, known as training process, is based on the estimation of the noise model parameters by genetic algorithms (GA) utilizing real experimental features. The proposed fault classification/diagnosis algorithm is applied to the noise contaminated features. Then, the results are compared to that obtained without noise modeling. The performance of the proposed method is examined using three laboratory case studies in two healthy and damaged conditions. Finally three different types of noise models are studied and it is shown experimentally that the proposed algorithm with non-Gaussian noise modeling leads to more accurate clustering of memory cells as the major part of the fault classification procedure.

  16. Towards artificial tissue models: past, present, and future of 3D bioprinting.

    Science.gov (United States)

    Arslan-Yildiz, Ahu; El Assal, Rami; Chen, Pu; Guven, Sinan; Inci, Fatih; Demirci, Utkan

    2016-03-01

    Regenerative medicine and tissue engineering have seen unprecedented growth in the past decade, driving the field of artificial tissue models towards a revolution in future medicine. Major progress has been achieved through the development of innovative biomanufacturing strategies to pattern and assemble cells and extracellular matrix (ECM) in three-dimensions (3D) to create functional tissue constructs. Bioprinting has emerged as a promising 3D biomanufacturing technology, enabling precise control over spatial and temporal distribution of cells and ECM. Bioprinting technology can be used to engineer artificial tissues and organs by producing scaffolds with controlled spatial heterogeneity of physical properties, cellular composition, and ECM organization. This innovative approach is increasingly utilized in biomedicine, and has potential to create artificial functional constructs for drug screening and toxicology research, as well as tissue and organ transplantation. Herein, we review the recent advances in bioprinting technologies and discuss current markets, approaches, and biomedical applications. We also present current challenges and provide future directions for bioprinting research. PMID:26930133

  17. Experimental infection of Phlebotomus perniciosus by bioluminescent Leishmania infantum using murine model and artificial feeder

    Science.gov (United States)

    Cannet, Arnaud; Akhoundi, Mohammad; Michel, Gregory; Marty, Pierre; Delaunay, Pascal

    2016-01-01

    Leishmaniasis is a vector-borne disease that is transmitted by sandflies and caused by obligate intracellular protozoa of the genus Leishmania. In the present study, we carried out a screening on the experimental infection of Phlebotomus pernioucus by bioluminescent Leishmania infantum using murine model and artificial feeder. We developed a real-time polymerase chain reaction (RT-PCR)-based method to determine individually the number of Leishmania promastigotes fed by infected flies. Among 1840 new emerged female sand flies, 428 were fed on the infected mice. After their death, they were analysed individually by RT-PCR. Our results demonstrated just a single Leishmania positive female at sixth day post meal. A total of 1070 female sand flies were exposed in contact with artificial feeder containing the human blood with two different quantities of Leishmania parasites: 2.106/mL and 1.107/mL. A blood meal including 1.107/mL LUC-promastigotes was proposed to 270 females and 75 (28%) flies were engorged. Among them, 44 (59%) were positive by RT-PCR analysis, with a relative average of 50551 Leishmania parasites. In case of blood feeding of females with 2.106/mL promastigotes, 57 out of 800 (7%) females succeed to feed from artificial feeder which 22 (39%) were positive with a relative average of 6487 parasites. PMID:27439032

  18. Application of artificial neural networks in modelling of quenched and tempered structural steels mechanical properties

    Directory of Open Access Journals (Sweden)

    L.A. Dobrzański

    2010-05-01

    Full Text Available Purpose: This paper presents the application of artificial neural networks for mechanical properties prediction of structuralal steels after quenching and tempering processes.Design/methodology/approach: On the basis of input parameters, which are chemical composition, parameters of mechanical and heat treatment and dimensions of elements, steels’ mechanical properties : yield stress, tensile strength stress, elongation, area reduction, impact strength and hardness are predicted.Findings: Results obtained in the given ranges of input parameters indicates on very good ability of artificial neural networks to values prediction of described mechanical properties for steels after quenching and tempering processes. The uniform distribution of descriptive vectors in all, training, validation and testing sets, indicates on good ability of the networks to results generalisation.Practical implications: Artificial neural networks, created during modelling, allows easy prediction of steels properties and allows the better selection of both chemical composition and the processing parameters of investigated materials. It’s possible to obtain steels, which are qualitatively better, cheaper and more optimised under customers needs.Originality/value: The prediction possibility of the material mechanical properties is valuable for manufacturers and constructors. It allows the preservation of customers quality requirements and brings also measurable financial advantages

  19. Genome scale metabolic modeling of the riboflavin overproducer Ashbya gossypii.

    Science.gov (United States)

    Ledesma-Amaro, Rodrigo; Kerkhoven, Eduard J; Revuelta, José Luis; Nielsen, Jens

    2014-06-01

    Ashbya gossypii is a filamentous fungus that naturally overproduces riboflavin, or vitamin B2. Advances in genetic and metabolic engineering of A. gossypii have permitted the switch from industrial chemical synthesis to the current biotechnological production of this vitamin. Additionally, A. gossypii is a model organism with one of the smallest eukaryote genomes being phylogenetically close to Saccharomyces cerevisiae. It has therefore been used to study evolutionary aspects of bakers' yeast. We here reconstructed the first genome scale metabolic model of A. gossypii, iRL766. The model was validated by biomass growth, riboflavin production and substrate utilization predictions. Gene essentiality analysis of the A. gossypii model in comparison with the S. cerevisiae model demonstrated how the whole-genome duplication event that separates the two species has led to an even spread of paralogs among all metabolic pathways. Additionally, iRL766 was used to integrate transcriptomics data from two different growth stages of A. gossypii, comparing exponential growth to riboflavin production stages. Both reporter metabolite analysis and in silico identification of transcriptionally regulated enzymes demonstrated the important involvement of beta-oxidation and the glyoxylate cycle in riboflavin production. PMID:24374726

  20. A conceptual and computational model of moral decision making in human and artificial agents.

    Science.gov (United States)

    Wallach, Wendell; Franklin, Stan; Allen, Colin

    2010-07-01

    Recently, there has been a resurgence of interest in general, comprehensive models of human cognition. Such models aim to explain higher-order cognitive faculties, such as deliberation and planning. Given a computational representation, the validity of these models can be tested in computer simulations such as software agents or embodied robots. The push to implement computational models of this kind has created the field of artificial general intelligence (AGI). Moral decision making is arguably one of the most challenging tasks for computational approaches to higher-order cognition. The need for increasingly autonomous artificial agents to factor moral considerations into their choices and actions has given rise to another new field of inquiry variously known as Machine Morality, Machine Ethics, Roboethics, or Friendly AI. In this study, we discuss how LIDA, an AGI model of human cognition, can be adapted to model both affective and rational features of moral decision making. Using the LIDA model, we will demonstrate how moral decisions can be made in many domains using the same mechanisms that enable general decision making. Comprehensive models of human cognition typically aim for compatibility with recent research in the cognitive and neural sciences. Global workspace theory, proposed by the neuropsychologist Bernard Baars (1988), is a highly regarded model of human cognition that is currently being computationally instantiated in several software implementations. LIDA (Franklin, Baars, Ramamurthy, & Ventura, 2005) is one such computational implementation. LIDA is both a set of computational tools and an underlying model of human cognition, which provides mechanisms that are capable of explaining how an agent's selection of its next action arises from bottom-up collection of sensory data and top-down processes for making sense of its current situation. We will describe how the LIDA model helps integrate emotions into the human decision-making process, and we

  1. A Hybrid Fresh Apple Export Volume Forecasting Model Based on Time Series and Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Lihua Yang

    2015-04-01

    Full Text Available Export volume forecasting of fresh fruits is a complex task due to the large number of factors affecting the demand. In order to guide the fruit growers’ sales, decreasing the cultivating cost and increasing their incomes, a hybrid fresh apple export volume forecasting model is proposed. Using the actual data of fresh apple export volume, the Seasonal Decomposition (SD model of time series and Radial Basis Function (RBF model of artificial neural network are built. The predictive results are compared among the three forecasting model based on the criterion of Mean Absolute Percentage Error (MAPE. The result indicates that the proposed combined forecasting model is effective because it can improve the prediction accuracy of fresh apple export volumes.

  2. A Nonlinear Elastic Model for Triaxial Compressive Properties of Artificial Methane-Hydrate-Bearing Sediment Samples

    Directory of Open Access Journals (Sweden)

    Tsutomu Yamaguchi

    2012-10-01

    Full Text Available A constitutive model for marine sediments containing natural gas hydrate is essential for the simulation of the geomechanical response to gas extraction from a gas-hydrate reservoir. In this study, the triaxial compressive properties of artificial methane-hydrate-bearing sediment samples reported in an earlier work were analyzed to examine the applicability of a nonlinear elastic constitutive model based on the Duncan-Chang model. The presented model considered the dependences of the mechanical properties on methane hydrate saturation and effective confining pressure. Some parameters were decided depending on the type of sand forming a specimen. The behaviors of lateral strain versus axial strain were also formulated as a function of effective confining pressure. The constitutive model presented in this study will provide a basis for an elastic analysis of the geomechanical behaviors of the gas-hydrate reservoir in the future study, although it is currently available to a limited extent.

  3. Genome-scale constraint-based modeling of Geobacter metallireducens

    Directory of Open Access Journals (Sweden)

    Famili Iman

    2009-01-01

    Full Text Available Abstract Background Geobacter metallireducens was the first organism that can be grown in pure culture to completely oxidize organic compounds with Fe(III oxide serving as electron acceptor. Geobacter species, including G. sulfurreducens and G. metallireducens, are used for bioremediation and electricity generation from waste organic matter and renewable biomass. The constraint-based modeling approach enables the development of genome-scale in silico models that can predict the behavior of complex biological systems and their responses to the environments. Such a modeling approach was applied to provide physiological and ecological insights on the metabolism of G. metallireducens. Results The genome-scale metabolic model of G. metallireducens was constructed to include 747 genes and 697 reactions. Compared to the G. sulfurreducens model, the G. metallireducens metabolic model contains 118 unique reactions that reflect many of G. metallireducens' specific metabolic capabilities. Detailed examination of the G. metallireducens model suggests that its central metabolism contains several energy-inefficient reactions that are not present in the G. sulfurreducens model. Experimental biomass yield of G. metallireducens growing on pyruvate was lower than the predicted optimal biomass yield. Microarray data of G. metallireducens growing with benzoate and acetate indicated that genes encoding these energy-inefficient reactions were up-regulated by benzoate. These results suggested that the energy-inefficient reactions were likely turned off during G. metallireducens growth with acetate for optimal biomass yield, but were up-regulated during growth with complex electron donors such as benzoate for rapid energy generation. Furthermore, several computational modeling approaches were applied to accelerate G. metallireducens research. For example, growth of G. metallireducens with different electron donors and electron acceptors were studied using the genome

  4. Modeling the cooling performance of vortex tube using a genetic algorithm-based artificial neural network

    Directory of Open Access Journals (Sweden)

    Pouraria Hassan

    2016-01-01

    Full Text Available In this study, artificial neural networks (ANNs have been used to model the effects of four important parameters consist of the ratio of the length to diameter(L/D, the ratio of the cold outlet diameter to the tube diameter(d/D, inlet pressure(P, and cold mass fraction (Y on the cooling performance of counter flow vortex tube. In this approach, experimental data have been used to train and validate the neural network model with MATLAB software. Also, genetic algorithm (GA has been used to find the optimal network architecture. In this model, temperature drop at the cold outlet has been considered as the cooling performance of the vortex tube. Based on experimental data, cooling performance of the vortex tube has been predicted by four inlet parameters (L/D, d/D, P, Y. The results of this study indicate that the genetic algorithm-based artificial neural network model is capable of predicting the cooling performance of vortex tube in a wide operating range and with satisfactory precision.

  5. Reduced-Order Modeling for Flutter/LCO Using Recurrent Artificial Neural Network

    Science.gov (United States)

    Yao, Weigang; Liou, Meng-Sing

    2012-01-01

    The present study demonstrates the efficacy of a recurrent artificial neural network to provide a high fidelity time-dependent nonlinear reduced-order model (ROM) for flutter/limit-cycle oscillation (LCO) modeling. An artificial neural network is a relatively straightforward nonlinear method for modeling an input-output relationship from a set of known data, for which we use the radial basis function (RBF) with its parameters determined through a training process. The resulting RBF neural network, however, is only static and is not yet adequate for an application to problems of dynamic nature. The recurrent neural network method [1] is applied to construct a reduced order model resulting from a series of high-fidelity time-dependent data of aero-elastic simulations. Once the RBF neural network ROM is constructed properly, an accurate approximate solution can be obtained at a fraction of the cost of a full-order computation. The method derived during the study has been validated for predicting nonlinear aerodynamic forces in transonic flow and is capable of accurate flutter/LCO simulations. The obtained results indicate that the present recurrent RBF neural network is accurate and efficient for nonlinear aero-elastic system analysis

  6. Investigation and modeling on protective textiles using artificial neural networks for defense applications

    International Nuclear Information System (INIS)

    Kevlar 29 is a class of Kevlar fiber used for protective applications primarily by the military and law enforcement agencies for bullet resistant vests, hence for these reasons military has found that armors reinforced with Kevlar 29 multilayer fabrics which offer 25-40% better fragmentation resistance and provide better fit with greater comfort. The objective of this study is to investigate and develop an artificial neural network model for analyzing the performance of ballistic fabrics made from Kevlar 29 single layer fabrics using their material properties as inputs. Data from fragment simulation projectile (FSP) ballistic penetration measurements at 244 m/s has been used to demonstrate the modeling aspects of artificial neural networks. The neural network models demonstrated in this paper is based on back propagation (BP) algorithm which is inbuilt in MATLAB 7.1 software and is used for studies in science, technology and engineering. In the present research, comparisons are also made between the measured values of samples selected for building the neural network model and network predicted results. The analysis of the results for network predicted and experimental samples used in this study showed similarity.

  7. Genome-wide association for the outcome of fixed-time artificial insemination of Brahman heifers in northern Australia.

    Science.gov (United States)

    Porto-Neto, L R; Edwards, S; Fortes, M R S; Lehnert, S A; Reverter, A; McGowan, M

    2015-11-01

    Fixed-time AI (FTAI) is a powerful tool for genetic improvement of extensively managed beef cattle. A genomewide association study (GWAS) was conducted to investigate genes and genetic markers associated with the outcome (pregnant or not pregnant) of FTAI in 614 commercial Brahman heifers genotyped for 18,895 SNP and imputed to 51,588 SNP. The likelihood of Brahman heifers becoming pregnant after hormonal treatment to synchronize ovulation followed by FTAI was influenced by the content of their genomes, as determined by a principal component analysis. The principal component analysis involved comparisons between the studied heifers and populations of known and ancestry. The heritability of FTAI outcome was = 0.18, which is higher than for most other reproductive outcome traits. The number of SNP associated with FTAI outcome was 101 ( Brahman heifer puberty and postpartum anestrus interval, 3 genomic regions emerge as important for overall Brahman heifer fertility, which mapped to chromosomes 1, 7, and 9. Further analyses, including improved genome annotation, are required to elucidate the link between these regions and heifer fertility. Additional studies are needed to confirm SNP and gene associations reported herein and further elucidate the genetics of FTAI outcome. Future GWAS should target other Braham populations and additional cattle breeds with FTAI records, including breeds with higher ancestry. PMID:26641032

  8. The genome of the sea urchin Strongylocentrotus purpuratus.

    OpenAIRE

    SEA URCHIN GENOME SEQUENCING CONSORTIUM; SODERGREN E; WEINSTOCK GM; DAVIDSON EH; CAMERON RA; GIBBS RA; ANGERER RC; ANGERER LM; ARNONE MI; BURGESS DR; BURKE RD; COFFMAN JA; Dean, M.; ELPHICK MR; ETTENSOHN CA

    2006-01-01

    We report the sequence and analysis of the 814-megabase genome of the sea urchin Strongylocentrotus purpuratus, a model for developmental and systems biology. The sequencing strategy combined whole-genome shotgun and bacterial artificial chromosome (BAC) sequences. This use of BAC clones, aided by a pooling strategy, overcame difficulties associated with high heterozygosity of the genome. The genome encodes about 23,300 genes, including many previously thought to be vertebrate innovations or ...

  9. Spatiotemporal groundwater level modeling using hybrid artificial intelligence-meshless method

    Science.gov (United States)

    Nourani, Vahid; Mousavi, Shahram

    2016-05-01

    Uncertainties of the field parameters, noise of the observed data and unknown boundary conditions are the main factors involved in the groundwater level (GL) time series which limit the modeling and simulation of GL. This paper presents a hybrid artificial intelligence-meshless model for spatiotemporal GL modeling. In this way firstly time series of GL observed in different piezometers were de-noised using threshold-based wavelet method and the impact of de-noised and noisy data was compared in temporal GL modeling by artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). In the second step, both ANN and ANFIS models were calibrated and verified using GL data of each piezometer, rainfall and runoff considering various input scenarios to predict the GL at one month ahead. In the final step, the simulated GLs in the second step of modeling were considered as interior conditions for the multiquadric radial basis function (RBF) based solve of governing partial differential equation of groundwater flow to estimate GL at any desired point within the plain where there is not any observation. In order to evaluate and compare the GL pattern at different time scales, the cross-wavelet coherence was also applied to GL time series of piezometers. The results showed that the threshold-based wavelet de-noising approach can enhance the performance of the modeling up to 13.4%. Also it was found that the accuracy of ANFIS-RBF model is more reliable than ANN-RBF model in both calibration and validation steps.

  10. Building predictive models for feature selection in genomic mining

    OpenAIRE

    Figini, Silvia; Giudici, Paolo

    2006-01-01

    Building predictive models for genomic mining requires feature selection, as an essential preliminary step to reduce the large number of variable available. Feature selection is a process to select a subset of features which is the most essential for the intended tasks such as classification, clustering or regression analysis. In gene expression microarray data, being able to select a few genes not only makes data analysis efficient but also helps their biological interpretation. Microarray d...

  11. The Evolution of a Malignancy Risk Prediction Model for Thyroid Nodules Using the Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Shahram Paydar

    2016-01-01

    Full Text Available Background: Clinically frank thyroid nodules are common and believed to be present in 4% to 10% of the adult population in the United States. In the current literature, fine needle aspiration biopsies are considered to be the milestone of a model which helps the physician decide whether a certain thyroid nodule needs a surgical approach or not. A considerable fact is that sensitivity and specificity of the fine needle aspiration varies significantly as it remains highly dependent on the operator as well as the cytologist’s skills. Practically, in the above group of patients, thyroid lobectomy/isthmusectomy becomes mandatory for attaining a definitive diagnosis where the majority (70%-80% have a benign surgical pathology. The scattered nature of clinically gathered data and analysis of their relevant variables need a compliant statistical method. The artificial neural network is a branch of artificial intelligence. We have hypothesized that conduction of an artificial neural network applied to certain clinical attributes could develop a malignancy risk assessment tool to help physicians interpret the fine needle aspiration biopsy results of thyroid nodules in a context composed of patient’s clinical variables, known as malignancy related risk factors. Methods: We designed and trained an artificial neural network on a prospectively formed cohort gathered over a four year period (2007-2011. The study population comprised 345 subjects who underwent thyroid resection at Nemazee and Rajaee hospitals, tertiary care centers of Shiraz University of Medical Sciences, and Rajaee Hospital as a level I trauma center in Shiraz, Iran after having undergone thyroid fine needle aspiration. Histopathological results of the fine needle aspirations and surgical specimens were analyzed and compared by experienced, board-certified pathologists who lacked knowledge of the fine needle aspiration results for thyroid malignancy. Results: We compared the preoperative

  12. Improved simultaneous estimation of tracer kinetic models with artificial immune network based optimization method.

    Science.gov (United States)

    Liu, L; Ding, H; Huang, H B

    2016-01-01

    Tracer kinetic modeling (TKM) is a promising quantitative method for physiological and biochemical processes in vivo. In this paper, we investigated the applications of an immune-inspired method to better address the issues of Simultaneous Estimation (SIME) of TKM with multimodal optimization. Experiments of dynamic FDG PET imaging experiments and simulation studies were carried out. The proposed artificial immune network (TKM_AIN) shows more scalable and effective when compared with the gradient-based Marquardt-Levenberg algorithm and the scholastic-based simulated annealing method. PMID:26433131

  13. Research on WNN modeling for gold price forecasting based on improved artificial bee colony algorithm.

    Science.gov (United States)

    Li, Bai

    2014-01-01

    Gold price forecasting has been a hot issue in economics recently. In this work, wavelet neural network (WNN) combined with a novel artificial bee colony (ABC) algorithm is proposed for this gold price forecasting issue. In this improved algorithm, the conventional roulette selection strategy is discarded. Besides, the convergence statuses in a previous cycle of iteration are fully utilized as feedback messages to manipulate the searching intensity in a subsequent cycle. Experimental results confirm that this new algorithm converges faster than the conventional ABC when tested on some classical benchmark functions and is effective to improve modeling capacity of WNN regarding the gold price forecasting scheme. PMID:24744773

  14. Calculation of artificial radionuclides in the ocean by an ocean general circulation model

    International Nuclear Information System (INIS)

    The concentrations of three artificial radionuclides (90Sr, 137Cs and 239+240Pu) introduced into seawaters from global fallout were simulated from 1957 to 1994 by using an ocean general circulation model. The distributions of the calculated 137Cs and 90Sr concentrations were in good agreement with the observed concentrations. The vertical distribution of the calculated 239+240Pu concentration in the South Pacific also agreed with the observed data. However, the calculated 239+240Pu concentrations in the North Pacific water columns were significantly underestimated in comparison with the observed data, which strongly suggests the presence of additional sources of plutonium input to the North Pacific. (author)

  15. A New Method for Cardiovascular Disease Clinical Diagnosis Based on Artificial Neural Network Model

    Directory of Open Access Journals (Sweden)

    Huang Zhao-Ming

    2013-01-01

    Full Text Available Diagnosis. In order to improve the accuracy of Clinical Diagnosis for Cardiovascular Disease, ANN(Artificial Neural Network is introduced in this paper. 200 cases of cardiovascular disease which have similar symptom and different diagnosis are sampled from our database. BP Network model in Matlab environment is created for these cases. Simulation results show that the Diagnosis after training is much better than that with the doctor’ diagnosis opinion. It can be concluded that this new method is of important value for realistic scale in clinical.

  16. Ruthenium (II) polypyridyl complexes as models for artificial photosynthesis: synthesis and characterisation

    OpenAIRE

    Killeen, Jonathan Scott

    2001-01-01

    This thesis presents a study on the synthesis and characterisation of a series of novel ruthenium (II) polypyridyl complexes, which are potentially models for artificial photosynthesis. Chapter 1, the introduction, highlights the literature relevant to the topic. In Chapter 2, the experimental conditions of the various methods of characterisation are described. The synthesis and characterisation of a range of novel 5-phenyI-3-(2- pyridyl)-1,2,4-triazoles, their Ru(bpy)2 and their Ru(dg-bp...

  17. Artificial neural network modeling of fixed bed biosorption using radial basis approach

    Science.gov (United States)

    Saha, Dipendu; Bhowal, Avijit; Datta, Siddhartha

    2010-04-01

    In modern day scenario, biosorption is a cost effective separation technology for the removal of various pollutants from wastewater and waste streams from various process industries. The difficulties associated in rigorous mathematical modeling of a fixed bed bio-adsorbing systems due to the complexities of the process often makes the development of pure black-box artificial neural network (ANN) models particularly useful in this field. In this work, radial basis function network has been employed as ANN to model the breakthrough curves in fixed bed biosorption. The prediction has been compared to the experimental breakthrough curves of Cadmium, Lanthanum and a dye available in the literature. Results show that this network gives fairly accurate representation of the actual breakthrough curves. The results obtained from ANN modeling approach shows the better agreement between experimental and predicted breakthrough curves as the error for all these situations are within 6%.

  18. Modeling and prediction of retardance in citric acid coated ferrofluid using artificial neural network

    Science.gov (United States)

    Lin, Jing-Fung; Sheu, Jer-Jia

    2016-06-01

    Citric acid coated (citrate-stabilized) magnetite (Fe3O4) magnetic nanoparticles have been conducted and applied in the biomedical fields. Using Taguchi-based measured retardances as the training data, an artificial neural network (ANN) model was developed for the prediction of retardance in citric acid (CA) coated ferrofluid (FF). According to the ANN simulation results in the training stage, the correlation coefficient between predicted retardances and measured retardances was found to be as high as 0.9999998. Based on the well-trained ANN model, the predicted retardance at excellent program from Taguchi method showed less error of 2.17% compared with a multiple regression (MR) analysis of statistical significance. Meanwhile, the parameter analysis at excellent program by the ANN model had the guiding significance to find out a possible program for the maximum retardance. It was concluded that the proposed ANN model had high ability for the prediction of retardance in CA coated FF.

  19. Establishment of constitutive relationship model for 2519 aluminum alloy based on BP artificial neural network

    Institute of Scientific and Technical Information of China (English)

    LIN Qi-quan; PENG Da-shu; ZHU Yuan-zhi

    2005-01-01

    An isothermal compressive experiment using Gleeble 1500 thermal simulator was studied to acquire flow stress at different deformation temperatures, strains and strain rates. The artificial neural networks with the error back propagation(BP) algorithm was used to establish constitutive model of 2519 aluminum alloy based on the experiment data. The model results show that the systematical error is small(δ=3.3%) when the value of objective function is 0.2, the number of nodes in the hidden layer is 5 and the learning rate is 0.1. Flow stresses of the material under various thermodynamic conditions are predicted by the neural network model, and the predicted results correspond with the experimental results. A knowledge-based constitutive relation model is developed.

  20. Direct, inverse, and combined problems in complex engineered system modeling by artificial neural networks

    Science.gov (United States)

    Terekhoff, Serge A.

    1997-04-01

    This paper summarizes theoretical findings and applications of artificial neural networks to modeling of complex engineered system response in the abnormal environments. The thermal fire impact on the industrial container for waste and fissile materials was investigated using model and experimental data. Solutions for the direct problem show that the generalization properties of neural network based model are significantly better than those for standard interpolation methods. Minimal amount of data required for good prediction of system response is estimated in computer experiments with MLP network. It was shown that Kohonen's self-organizing map with counterpropagation may also estimate local accuracy of regularized solution for inverse and combined problems. Feature space regions of partial correctness of the inverse model can be automatically extracted using adaptive clustering. Practical findings include time strategy recommendations for fire-safe services when industrial or transport accidents occur.

  1. Modeling mechanical properties of cast aluminum alloy using artificial neural network

    International Nuclear Information System (INIS)

    Modeling is widely used to investigate the mechanical properties of engineering materials due to increasing demand of low cost and high strength to weight ratio for many engineering applications. The aluminum casting alloys are cost competitive material and possess the desired properties. The mechanical properties largely depend upon composition of alloys and their processing method. Alloy design involves controlling mechanical properties via optimization of the composition and processing parameters. For optimization the possible root is empirical modeling and its more refined version is the analysis of the wide range of data using ANN (Artificial Neural Networks) modeling. The modeling of mechanical properties of the aluminum alloys are the main objective of present work. For this purpose, some data were collected and experimentally prepared using conventional casting method. A MLP (Multilayer Perceptron) network was developed, which is trained by using the error back propagation algorithm. (author)

  2. Modeling Spammer Behavior: Na\\"ive Bayes vs. Artificial Neural Networks

    CERN Document Server

    Islam, Md Saiful; Farhan, Khalid; Rahman, Md Abdur; Rahman, Joy

    2010-01-01

    Addressing the problem of spam emails in the Internet, this paper presents a comparative study on Na\\"ive Bayes and Artificial Neural Networks (ANN) based modeling of spammer behavior. Keyword-based spam email filtering techniques fall short to model spammer behavior as the spammer constantly changes tactics to circumvent these filters. The evasive tactics that the spammer uses are themselves patterns that can be modeled to combat spam. It has been observed that both Na\\"ive Bayes and ANN are best suitable for modeling spammer common patterns. Experimental results demonstrate that both of them achieve a promising detection rate of around 92%, which is considerably an improvement of performance compared to the keyword-based contemporary filtering approaches.

  3. Artificial neural network models for prediction of intestinal permeability of oligopeptides

    Directory of Open Access Journals (Sweden)

    Kim Min-Kook

    2007-07-01

    Full Text Available Abstract Background Oral delivery is a highly desirable property for candidate drugs under development. Computational modeling could provide a quick and inexpensive way to assess the intestinal permeability of a molecule. Although there have been several studies aimed at predicting the intestinal absorption of chemical compounds, there have been no attempts to predict intestinal permeability on the basis of peptide sequence information. To develop models for predicting the intestinal permeability of peptides, we adopted an artificial neural network as a machine-learning algorithm. The positive control data consisted of intestinal barrier-permeable peptides obtained by the peroral phage display technique, and the negative control data were prepared from random sequences. Results The capacity of our models to make appropriate predictions was validated by statistical indicators including sensitivity, specificity, enrichment curve, and the area under the receiver operating characteristic (ROC curve (the ROC score. The training and test set statistics indicated that our models were of strikingly good quality and could discriminate between permeable and random sequences with a high level of confidence. Conclusion We developed artificial neural network models to predict the intestinal permeabilities of oligopeptides on the basis of peptide sequence information. Both binary and VHSE (principal components score Vectors of Hydrophobic, Steric and Electronic properties descriptors produced statistically significant training models; the models with simple neural network architectures showed slightly greater predictive power than those with complex ones. We anticipate that our models will be applicable to the selection of intestinal barrier-permeable peptides for generating peptide drugs or peptidomimetics.

  4. Optimization of geometry and modeling parameters of artificial neural networks using genetic algorithms

    Science.gov (United States)

    Sahoo, G. B.

    2007-12-01

    In recent years, artificial neural networks (ANNs) appear to be viable alternative to models that use phenomenological hypotheses (i.e. knowledge based models) for cases (1) the available data are not detailed and sufficient for using a process based model and (2) the detailed complex physics of the system is partially understood. ANNs have been widely used in many fields such as chemical and environmental engineering, hydrology, and water resources applications for optimum prediction of system parameters and variables. However, in most cases, parameters and system variables were forecasted employing suboptimal ANNs. The geometry and modeling parameters of an artificial neural network (ANN) and the training dataset have significant effects on its predictive performance efficiency. The combination of ANN modeling parameter and geometry arranged in the modeling domain (i.e. lower and upper bounds of each modeling parameter and geometry) is large enough (i.e. greater than 100000) that it is difficult to examine all cases using trial and error approach for the selection of an optimum set. Thus, one could easily end up with finding a set of suboptimal values. This study presents the use of genetic algorithms (GAs) to search for the optimal geometry and values of modeling parameters of a multilayer feedforward backpropagation neural network (BPNN) and a radial basis function network (RBFN). The predictive performance efficiency of the GA and ANN combination is examined using two datasets derived from the same population for training. It is illustrated that (1) the GA optimized ANN outperforms to the ANN using a trial and error approach, and (2) ANN predictive performance and geometry depend on the number of samples and the characteristics of samples included in the training dataset.

  5. Analysis on evaluation ability of nonlinear safety assessment model of coal mines based on artificial neural network

    Institute of Scientific and Technical Information of China (English)

    SHI Shi-liang; LIU Hai-bo; LIU Ai-hua

    2004-01-01

    Based on the integration analysis of goods and shortcomings of various methods used in safety assessment of coal mines, combining nonlinear feature of mine safety sub-system, this paper establishes the neural network assessment model of mine safety, analyzes the ability of artificial neural network to evaluate mine safety state, and lays the theoretical foundation of artificial neural network using in the systematic optimization of mine safety assessment and getting reasonable accurate safety assessment result.

  6. Modeling of frost crystal growth over a flat plate using artificial neural networks and fractal geometries

    Science.gov (United States)

    Tahavvor, Ali Reza

    2016-06-01

    In the present study artificial neural network and fractal geometry are used to predict frost thickness and density on a cold flat plate having constant surface temperature under forced convection for different ambient conditions. These methods are very applicable in this area because phase changes such as melting and solidification are simulated by conventional methods but frost formation is a most complicated phase change phenomenon consists of coupled heat and mass transfer. Therefore conventional mathematical techniques cannot capture the effects of all parameters on its growth and development because this process influenced by many factors and it is a time dependent process. Therefore, in this work soft computing method such as artificial neural network and fractal geometry are used to do this manner. The databases for modeling are generated from the experimental measurements. First, multilayer perceptron network is used and it is found that the back-propagation algorithm with Levenberg-Marquardt learning rule is the best choice to estimate frost growth properties due to accurate and faster training procedure. Second, fractal geometry based on the Von-Koch curve is used to model frost growth procedure especially in frost thickness and density. Comparison is performed between experimental measurements and soft computing methods. Results show that soft computing methods can be used more efficiently to determine frost properties over a flat plate. Based on the developed models, wide range of frost formation over flat plates can be determined for various conditions.

  7. Numerical simulation and artificial neural network modeling of natural circulation boiling water reactor

    International Nuclear Information System (INIS)

    Numerical simulation of natural circulation boiling water reactor is important in order to study its performance for different designs and under various off-design conditions. Numerical simulations can be performed by using thermal-hydraulic codes. Very fast numerical simulations, useful for extensive parametric studies and for solving design optimization problems, can be achieved by using an artificial neural network (ANN) model of the system. In the present work, numerical simulations of natural circulation boiling water reactor have been performed with RELAP5 code for different values of design parameters and operational conditions. Parametric trends observed have been discussed. The data obtained from these simulations have been used to train artificial neural networks, which in turn have been used for further parametric studies and design optimization. The ANN models showed error within ±5% for all the simulated data. Two most popular methods, multilayer perceptron (MLP) and radial basis function (RBF) networks, have been used for the training of ANN model. Sequential quadratic programming (SQP) has been used for optimization

  8. Artificial neural network and multiple regression model for nickel(II) adsorption on powdered activated carbons.

    Science.gov (United States)

    Hema, M; Srinivasan, K

    2011-07-01

    Nickel removal efficiency of powered activated carbons of coconut oilcake, neem oilcake and commercial carbon was investigated by using artificial neural network. The effective parameters for the removal of nickel (%R) by adsorption process, which included the pH, contact time (T), distinctiveness of activated carbon (Cn), amount of activated carbon (Cw) and initial concentration of nickel (Co) were investigated. Levenberg-Marquardt (LM) Back-propagation algorithm is used to train the network. The network topology was optimized by varying number of hidden layer and number of neurons in hidden layer. The model was developed in terms of training; validation and testing of experimental data, the test subsets that each of them contains 60%, 20% and 20% of total experimental data, respectively. Multiple regression equation was developed for nickel adsorption system and the output was compared with both simulated and experimental outputs. Standard deviation (SD) with respect to experimental output was quite higher in the case of regression model when compared with ANN model. The obtained experimental data best fitted with the artificial neural network. PMID:23029923

  9. Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation.

    Science.gov (United States)

    Fitzpatrick, Matthew C; Keller, Stephen R

    2015-01-01

    Local adaptation is a central feature of most species occupying spatially heterogeneous environments, and may factor critically in responses to environmental change. However, most efforts to model the response of species to climate change ignore intraspecific variation due to local adaptation. Here, we present a new perspective on spatial modelling of organism-environment relationships that combines genomic data and community-level modelling to develop scenarios regarding the geographic distribution of genomic variation in response to environmental change. Rather than modelling species within communities, we use these techniques to model large numbers of loci across genomes. Using balsam poplar (Populus balsamifera) as a case study, we demonstrate how our framework can accommodate nonlinear responses of loci to environmental gradients. We identify a threshold response to temperature in the circadian clock gene GIGANTEA-5 (GI5), suggesting that this gene has experienced strong local adaptation to temperature. We also demonstrate how these methods can map ecological adaptation from genomic data, including the identification of predicted differences in the genetic composition of populations under current and future climates. Community-level modelling of genomic variation represents an important advance in landscape genomics and spatial modelling of biodiversity that moves beyond species-level assessments of climate change vulnerability. PMID:25270536

  10. ARTIFICIAL NEURAL NETWORK AND FUZZY LOGIC CONTROLLER FOR GTAW MODELING AND CONTROL

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    An artificial neural network(ANN) and a self-adjusting fuzzy logic controller(FLC) for modeling and control of gas tungsten arc welding(GTAW) process are presented. The discussion is mainly focused on the modeling and control of the weld pool depth with ANN and the intelligent control for weld seam tracking with FLC. The proposed neural network can produce highly complex nonlinear multi-variable model of the GTAW process that offers the accurate prediction of welding penetration depth. A self-adjusting fuzzy controller used for seam tracking adjusts the control parameters on-line automatically according to the tracking errors so that the torch position can be controlled accurately.

  11. Application of artificial neural networks to infer subcriticality level through kinetic models - 173

    International Nuclear Information System (INIS)

    The paper presents some recent advances in the study of the inverse kinetics for subcritical systems. A neural-based approach is adopted to predict the reactivity of the multiplying medium through the analysis of the reactor response to a source pulse. An artificial neural network is designed to infer the subcriticality level through the analysis of power evolution. The training set is computed using an approximate model and its performances are then tested directly on experimental measures, here simulated through a detailed space-energy kinetic model. In order to improve the accuracy of the reactivity estimation, various strategies are proposed and compared, including a multi-transient inversion and the use of different kinetic models for the training. The issue of robustness of the inversion scheme to experimental noise is also addressed (authors)

  12. Studying electromagnetic interference spectrum in antenna under aircraft radome using models with artificial charged aerosol clouds

    Science.gov (United States)

    Temnikov, A. G.; Gilyazov, M. Z.; Matveev, D. A.; Voronkova, A. Yu.; Chernenskii, L. L.; Orlov, A. V.

    2011-09-01

    The spectrum of electromagnetic interference that is induced by discharges in an antenna arranged under an aircraft radome in an artificial charged aqueous aerosol cloud has been experimentally studied. It is established that, among different possible variants of lightning-arrest radomes, the minimum level of the spectral density of interference signals in the antenna is provided by vertical stripe electrodes on the radome surface. The maximum characteristic frequencies of signals in a model spherical antenna are several times lower than those in model lightning diverters, while the flat model antennas of disk or rectangular shapes exhibit the opposite trend. It has been suggested that a significant role in determining the characteristics of the electromagnetic interference spectrum in weather radar antennas is played by currents of discharges generated by charges accumulated on the dielectric radome surface.

  13. Modelling and Prediction of Photovoltaic Power Output Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Aminmohammad Saberian

    2014-01-01

    Full Text Available This paper presents a solar power modelling method using artificial neural networks (ANNs. Two neural network structures, namely, general regression neural network (GRNN feedforward back propagation (FFBP, have been used to model a photovoltaic panel output power and approximate the generated power. Both neural networks have four inputs and one output. The inputs are maximum temperature, minimum temperature, mean temperature, and irradiance; the output is the power. The data used in this paper started from January 1, 2006, until December 31, 2010. The five years of data were split into two parts: 2006–2008 and 2009-2010; the first part was used for training and the second part was used for testing the neural networks. A mathematical equation is used to estimate the generated power. At the end, both of these networks have shown good modelling performance; however, FFBP has shown a better performance comparing with GRNN.

  14. Comparison of Groundwater Level Models Based on Artificial Neural Networks and ANFIS

    Directory of Open Access Journals (Sweden)

    Nevenka Djurovic

    2015-01-01

    Full Text Available Water table forecasting plays an important role in the management of groundwater resources in agricultural regions where there are drainage systems in river valleys. The results presented in this paper pertain to an area along the left bank of the Danube River, in the Province of Vojvodina, which is the northern part of Serbia. Two soft computing techniques were used in this research: an adaptive neurofuzzy inference system (ANFIS and an artificial neural network (ANN model for one-month water table forecasts at several wells located at different distances from the river. The results suggest that both these techniques represent useful tools for modeling hydrological processes in agriculture, with similar computing and memory capabilities, such that they constitute an exceptionally good numerical framework for generating high-quality models.

  15. Equivalent Circuit Model for Designing of Jerusalem Cross-Based Artificial Magnetic Conductors

    Directory of Open Access Journals (Sweden)

    M. Hosseinipanah

    2009-12-01

    Full Text Available This paper introduces an equivalent circuit model to estimate the resonant frequency and reflection phase of Jerusalem cross-based artificial magnetic conductor (JC-AMC structures for waves normally incident on the structure. The JC-AMC structure is composed of an array of Jerusalem cross-shaped frequency selective surfaces (JC-FSSs printed on a metal-backed dielectric slab without vias. This approach is based on the coupled microstrip lines and transmission line theories. Analytical expressions for the resonant frequency and reflection phase resulting from our model are thoroughly verified by full-wave simulations for different samples reported in the open literature. A comprehensive parametric study of the design parameters of JC-AMC structure and the influence of these parameters on the reflection phase properties are presented. This model can easily be extended to complex-shaped AMCs.

  16. Application of artificial neural networks for response surface modelling in HPLC method development

    Directory of Open Access Journals (Sweden)

    Mohamed A. Korany

    2012-01-01

    Full Text Available This paper discusses the usefulness of artificial neural networks (ANNs for response surface modelling in HPLC method development. In this study, the combined effect of pH and mobile phase composition on the reversed-phase liquid chromatographic behaviour of a mixture of salbutamol (SAL and guaiphenesin (GUA, combination I, and a mixture of ascorbic acid (ASC, paracetamol (PAR and guaiphenesin (GUA, combination II, was investigated. The results were compared with those produced using multiple regression (REG analysis. To examine the respective predictive power of the regression model and the neural network model, experimental and predicted response factor values, mean of squares error (MSE, average error percentage (Er%, and coefficients of correlation (r were compared. It was clear that the best networks were able to predict the experimental responses more accurately than the multiple regression analysis.

  17. Artificial intelligence-based computer modeling tools for controlling slag foaming in electric arc furnaces

    Science.gov (United States)

    Wilson, Eric Lee

    Due to increased competition in a world economy, steel companies are currently interested in developing techniques that will allow for the improvement of the steelmaking process, either by increasing output efficiency or by improving the quality of their product, or both. Slag foaming is one practice that has been shown to contribute to both these goals. However, slag foaming is highly dynamic and difficult to model or control. This dissertation describes an effort to use artificial intelligence-based tools (genetic algorithms, fuzzy logic, and neural networks) to both model and control the slag foaming process. Specifically, a neural network is trained and tested on slag foaming data provided by a steel plant. This neural network model is then controlled by a fuzzy logic controller, which in turn is optimized by a genetic algorithm. This tuned controller is then installed at a steel plant and given control be a more efficient slag foaming controller than what was previously used by the steel plant.

  18. Comparison of Groundwater Level Models Based on Artificial Neural Networks and ANFIS.

    Science.gov (United States)

    Djurovic, Nevenka; Domazet, Milka; Stricevic, Ruzica; Pocuca, Vesna; Spalevic, Velibor; Pivic, Radmila; Gregoric, Enika; Domazet, Uros

    2015-01-01

    Water table forecasting plays an important role in the management of groundwater resources in agricultural regions where there are drainage systems in river valleys. The results presented in this paper pertain to an area along the left bank of the Danube River, in the Province of Vojvodina, which is the northern part of Serbia. Two soft computing techniques were used in this research: an adaptive neurofuzzy inference system (ANFIS) and an artificial neural network (ANN) model for one-month water table forecasts at several wells located at different distances from the river. The results suggest that both these techniques represent useful tools for modeling hydrological processes in agriculture, with similar computing and memory capabilities, such that they constitute an exceptionally good numerical framework for generating high-quality models. PMID:26759830

  19. Artificial Neural Networks for Reducing Computational Effort in Active Truncated Model Testing of Mooring Lines

    DEFF Research Database (Denmark)

    Christiansen, Niels Hørbye; Voie, Per Erlend Torbergsen; Høgsberg, Jan Becker

    2015-01-01

    simultaneously, this method is very demanding in terms of numerical efficiency and computational power. Therefore, this method has not yet proved to be feasible. It has recently been shown how a hybrid method combining classical numerical models and artificial neural networks (ANN) can provide a dramatic...... model. Hence, in principal it is possible to achieve reliable experimental data for much larger water depths than what the actual depth of the test basin would suggest. However, since the computations must be faster than real time, as the numerical simulations and the physical experiment run...... reduction in computational effort when performing time domain simulation of mooring lines. The hybrid method uses a classical numerical model to generate simulation data, which are then subsequently used to train the ANN. After successful training the ANN is able to take over the simulation at a speed two...

  20. Bayesian predictive modeling for genomic based personalized treatment selection.

    Science.gov (United States)

    Ma, Junsheng; Stingo, Francesco C; Hobbs, Brian P

    2016-06-01

    Efforts to personalize medicine in oncology have been limited by reductive characterizations of the intrinsically complex underlying biological phenomena. Future advances in personalized medicine will rely on molecular signatures that derive from synthesis of multifarious interdependent molecular quantities requiring robust quantitative methods. However, highly parameterized statistical models when applied in these settings often require a prohibitively large database and are sensitive to proper characterizations of the treatment-by-covariate interactions, which in practice are difficult to specify and may be limited by generalized linear models. In this article, we present a Bayesian predictive framework that enables the integration of a high-dimensional set of genomic features with clinical responses and treatment histories of historical patients, providing a probabilistic basis for using the clinical and molecular information to personalize therapy for future patients. Our work represents one of the first attempts to define personalized treatment assignment rules based on large-scale genomic data. We use actual gene expression data acquired from The Cancer Genome Atlas in the settings of leukemia and glioma to explore the statistical properties of our proposed Bayesian approach for personalizing treatment selection. The method is shown to yield considerable improvements in predictive accuracy when compared to penalized regression approaches. PMID:26575856

  1. A physically consistent model for artificial dissipation in transonic potential flow computations

    Science.gov (United States)

    Dulikravich, George S.; Mortara, Karl W.; Marraffa, Lionel

    1988-01-01

    The effect that artificial dissipation has on numerical solutions of the transonic Full Potential Equation (FPE) are investigated by comparing the artificially dissipative FPE to a Physically Dissipative Potential (PDP) equation. Analytic expressions were derived from the variables C and M sub c that are used in the artificial density formulation. It was shown that these new values generate artificial dissipation which is equivalent to the physical dissipation existing in the PDP equation. The new expression for the variables C and M sub c can easily be incorporated into the existing full potential codes which are based either on the artificial density or on the artificial viscosity formulation. A comparison of Physically Dissipative Potential (PDP), Artificial Density or Viscosity (ADV), Artificial Mass Flux (AMF), and ADV with variable C and M sub c formulation (MCC) is also presented.

  2. Modeling of a PEM Fuel Cell Stack using Partial Least Squares and Artificial Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

    Han, In-Su; Shin, Hyun Khil [GS Caltex Corp, Daejeon (Korea, Republic of)

    2015-04-15

    We present two data-driven modeling methods, partial least square (PLS) and artificial neural network (ANN), to predict the major operating and performance variables of a polymer electrolyte membrane (PEM) fuel cell stack. PLS and ANN models were constructed using the experimental data obtained from the testing of a 30 kW-class PEM fuel cell stack, and then were compared with each other in terms of their prediction and computational performances. To reduce the complexity of the models, we combined a variables importance on PLS projection (VIP) as a variable selection method into the modeling procedure in which the predictor variables are selected from a set of input operation variables. The modeling results showed that the ANN models outperformed the PLS models in predicting the average cell voltage and cathode outlet temperature of the fuel cell stack. However, the PLS models also offered satisfactory prediction performances although they can only capture linear correlations between the predictor and output variables. Depending on the degree of modeling accuracy and speed, both ANN and PLS models can be employed for performance predictions, offline and online optimizations, controls, and fault diagnoses in the field of PEM fuel cell designs and operations.

  3. Prediction of human skin permeability using artificial neural network (ANN)modeling

    Institute of Scientific and Technical Information of China (English)

    Long-jian CHEN; Guo-ping LIAN; Lu-jia HAN

    2007-01-01

    Aim: To develop an artificial neural network (ANN) model for predicting skin permeability (log Kp) of new chemical entities. Methods: A large dataset of 215experimental data points was compiled from the literature. The dataset was subdi-vided into 5 subsets and 4 of them were used to train and validate an ANN model.The same 4 datasets were also used to build a multiple linear regression (MLR)model. The remaining dataset was then used to test the 2 models. Abraham descriptors were employed as inputs into the 2 models. Model predictions were compared with the experimental results. In addition, the relationship between log Kp and Abraham descriptors were investigated. Results: The regression re-sults of the MLR model were n=215, determination coefficient (R2)=0.699, mean square error (MSE)=0.243, and F=493.556. The ANN model gave improved results with n=215, R2=0.832, MSE=0.136, and F=1050.653. The ANN model suggests that the relationship between log Kp and Abraham descriptors is non-linear. Conclusion:The study suggests that Abraham descriptors may be used to predict skin permeability, and the ANN model gives improved prediction of skin permeability.

  4. Response surface and artificial neural network prediction model and optimization for surface roughness in machining

    Directory of Open Access Journals (Sweden)

    Ashok Kumar Sahoo

    2015-04-01

    Full Text Available The present paper deals with the development of prediction model using response surface methodology and artificial neural network and optimizes the process parameter using 3D surface plot. The experiment has been conducted using coated carbide insert in machining AISI 1040 steel under dry environment. The coefficient of determination value for RSM model is found to be high (R2 = 0.99 close to unity. It indicates the goodness of fit for the model and high significance of the model. The percentage of error for RSM model is found to be only from -2.63 to 2.47. The maximum error between ANN model and experimental lies between -1.27 and 0.02 %, which is significantly less than the RSM model. Hence, both the proposed RSM and ANN prediction model sufficiently predict the surface roughness, accurately. However, ANN prediction model seems to be better compared with RSM model. From the 3D surface plots, the optimal parametric combination for the lowest surface roughness is d1-f1-v3 i.e. depth of cut of 0.1 mm, feed of 0.04 mm/rev and cutting speed of 260 m/min respectively.

  5. Modeling of a PEM Fuel Cell Stack using Partial Least Squares and Artificial Neural Networks

    International Nuclear Information System (INIS)

    We present two data-driven modeling methods, partial least square (PLS) and artificial neural network (ANN), to predict the major operating and performance variables of a polymer electrolyte membrane (PEM) fuel cell stack. PLS and ANN models were constructed using the experimental data obtained from the testing of a 30 kW-class PEM fuel cell stack, and then were compared with each other in terms of their prediction and computational performances. To reduce the complexity of the models, we combined a variables importance on PLS projection (VIP) as a variable selection method into the modeling procedure in which the predictor variables are selected from a set of input operation variables. The modeling results showed that the ANN models outperformed the PLS models in predicting the average cell voltage and cathode outlet temperature of the fuel cell stack. However, the PLS models also offered satisfactory prediction performances although they can only capture linear correlations between the predictor and output variables. Depending on the degree of modeling accuracy and speed, both ANN and PLS models can be employed for performance predictions, offline and online optimizations, controls, and fault diagnoses in the field of PEM fuel cell designs and operations

  6. Management of multipartite genomes: the Vibrio cholerae model.

    Science.gov (United States)

    Val, Marie-Eve; Soler-Bistué, Alfonso; Bland, Michael J; Mazel, Didier

    2014-12-01

    A minority of bacterial species has been found to carry a genome divided among several chromosomes. Among these, all Vibrio species harbor a genome split into two chromosomes of uneven size, with distinctive replication origins whose replication firing involves common and specific factors. Most of our current knowledge on replication and segregation in multi-chromosome bacteria has come from the study of Vibrio cholerae, which is now the model organism for this field. It has been firmly established that replication of the two V. cholerae chromosomes is temporally regulated and coupled to the cell cycle, but the mediators of these processes are as yet mostly unknown. The two chromosomes are also organized along different patterns within the cell and occupy different subcellular domains. The selective advantages provided by this partitioning into two replicons are still unclear and are a key motivation for these studies. PMID:25460805

  7. Development of Artificial Neural Network Model for Diesel Fuel Properties Prediction using Vibrational Spectroscopy.

    Science.gov (United States)

    Bolanča, Tomislav; Marinović, Slavica; Ukić, Sime; Jukić, Ante; Rukavina, Vinko

    2012-06-01

    This paper describes development of artificial neural network models which can be used to correlate and predict diesel fuel properties from several FTIR-ATR absorbances and Raman intensities as input variables. Multilayer feed forward and radial basis function neural networks have been used to rapid and simultaneous prediction of cetane number, cetane index, density, viscosity, distillation temperatures at 10% (T10), 50% (T50) and 90% (T90) recovery, contents of total aromatics and polycyclic aromatic hydrocarbons of commercial diesel fuels. In this study two-phase training procedures for multilayer feed forward networks were applied. While first phase training algorithm was constantly the back propagation one, two second phase training algorithms were varied and compared, namely: conjugate gradient and quasi Newton. In case of radial basis function network, radial layer was trained using K-means radial assignment algorithm and three different radial spread algorithms: explicit, isotropic and K-nearest neighbour. The number of hidden layer neurons and experimental data points used for the training set have been optimized for both neural networks in order to insure good predictive ability by reducing unnecessary experimental work. This work shows that developed artificial neural network models can determine main properties of diesel fuels simultaneously based on a single and fast IR or Raman measurement. PMID:24061237

  8. Artificial neural network model for prediction of safety performance indicators goals in nuclear plants

    Energy Technology Data Exchange (ETDEWEB)

    Souto, Kelling C.; Nunes, Wallace W. [Instituto Federal de Educacao, Ciencia e Tecnologia do Rio de Janeiro, Nilopolis, RJ (Brazil). Lab. de Aplicacoes Computacionais; Machado, Marcelo D., E-mail: dornemd@eletronuclear.gov.b [ELETROBRAS Termonuclear S.A. (ELETRONUCLEAR), Rio de Janeiro, RJ (Brazil). Gerencia de Combustivel Nuclear - GCN.T

    2011-07-01

    Safety performance indicators have been developed to provide a quantitative indication of the performance and safety in various industry sectors. These indexes can provide assess to aspects ranging from production, design, and human performance up to management issues in accordance with policy, objectives and goals of the company. The use of safety performance indicators in nuclear power plants around the world is a reality. However, it is necessary to periodically set goal values. Such goals are targets relating to each of the indicators to be achieved by the plant over a predetermined period of operation. The current process of defining these goals is carried out by experts in a subjective way, based on actual data from the plant, and comparison with global indices. Artificial neural networks are computational techniques that present a mathematical model inspired by the neural structure of intelligent organisms that acquire knowledge through experience. This paper proposes an artificial neural network model aimed at predicting values of goals to be used in the evaluation of safety performance indicators for nuclear power plants. (author)

  9. Deterministic and Stochastic Modeling of an Artificial Bistable Switch in E. coli

    Science.gov (United States)

    Finkelstein, Daniel; Buchler, Nicolas; Karapetyan, Sargis

    Networks of mutually interacting genes are common in natural regulatory networks. To better understand these interactions, scientists have recently been constructing artificial genetic networks. Much of the effort is focused on creating genetic oscillators and bistable switches. In this project, we analyzed the possibility to create a bistable switch in E. coli. In this realization of the switch, the Repressor (basic leucine zipper CEBP/alpha) represses the transcription of the Inhibitor (artificial dominant negative 3HF). The Inhibitor, in turn, sequesters the Repressor by binding to it. Using deterministic modeling we identified a range of parameters suitable for bistability. We then analyzed the resulting solutions with the full model taking the reaction rates corresponding to E. coli and the including stochastic nature of gene expression. We have shown that the bistability in not destroyed by stochastic fluctuations if several copies of genes are present. Specifically, taking a realistic number of plasmids (10) we show that the number of proteins in the systems undergoes sizable fluctuations; however, the two states with low and high concentrations of inhibitor stay distinct in the relevant range of parameters.

  10. The application of artificial neural network model in the non-invasive diagnosis of liver fibrosis

    Directory of Open Access Journals (Sweden)

    Bo LI

    2012-12-01

    Full Text Available Objective  To construct and evaluate an artificial neural network (ANN model as a new non-invasive diagnostic method for clinical assessment of liver fibrosis at early stage. Methods  The model was set up and tested among 683 chronic hepatitis B (CHB patients, with authentic positive clinical biopsy results, proved to have liver fibrosis or cirrhosis, admitted to 302 Hospital of PLA from May 2008 to March 2011. Among 683 samples, 504 samples were diagnosed as cirrhosis as a result of CHB, and 179 liver fibrosis due to other liver diseases. 134 out of 683 patients were included in training group by stratified sampling, and the others for verification. Six items (age, AST, PTS, PLT, GGT and DBil were selected as input layer indexes to set up the model for evaluation. Results  The ANN model for diagnosis of liver fibrosis was set up. The diagnostic accuracy was 77.4%, sensitivity was 76.8%, and specificity was 77.8%. Its Kappa concordance tests showed the diagnosis result of the model was consistent with biopsy result (Kappa index=0.534. The accuracy, sensitivity and specificity of CHB patients were 80.4%, 79.9% and 80.7% (Kappa index=0.598 respectively, and those for other liver diseases were 67.9%, 64.3% and 69.7% (Kappa index=0.316. Conclusion  The artificial neural network model established by the authors demonstrates its high sensitivity and specificity as a new non-invasive diagnostic method for liver fibrosis induced by HBV infection. However, it shows limited diagnostic reliability to fibrosis as a result of other liver diseases.

  11. Study on Fault Diagnostics of a Turboprop Engine Using Inverse Performance Model and Artificial Intelligent Methods

    Science.gov (United States)

    Kong, Changduk; Lim, Semyeong

    2011-12-01

    Recently, the health monitoring system of major gas path components of gas turbine uses mostly the model based method like the Gas Path Analysis (GPA). This method is to find quantity changes of component performance characteristic parameters such as isentropic efficiency and mass flow parameter by comparing between measured engine performance parameters such as temperatures, pressures, rotational speeds, fuel consumption, etc. and clean engine performance parameters without any engine faults which are calculated by the base engine performance model. Currently, the expert engine diagnostic systems using the artificial intelligent methods such as Neural Networks (NNs), Fuzzy Logic and Genetic Algorithms (GAs) have been studied to improve the model based method. Among them the NNs are mostly used to the engine fault diagnostic system due to its good learning performance, but it has a drawback due to low accuracy and long learning time to build learning data base if there are large amount of learning data. In addition, it has a very complex structure for finding effectively single type faults or multiple type faults of gas path components. This work builds inversely a base performance model of a turboprop engine to be used for a high altitude operation UAV using measured performance data, and proposes a fault diagnostic system using the base engine performance model and the artificial intelligent methods such as Fuzzy logic and Neural Network. The proposed diagnostic system isolates firstly the faulted components using Fuzzy Logic, then quantifies faults of the identified components using the NN leaned by fault learning data base, which are obtained from the developed base performance model. In leaning the NN, the Feed Forward Back Propagation (FFBP) method is used. Finally, it is verified through several test examples that the component faults implanted arbitrarily in the engine are well isolated and quantified by the proposed diagnostic system.

  12. Spatial Disaggregation of Areal Rainfall Using Two Different Artificial Neural Networks Models

    Directory of Open Access Journals (Sweden)

    Sungwon Kim

    2015-06-01

    Full Text Available The objective of this study is to develop artificial neural network (ANN models, including multilayer perceptron (MLP and Kohonen self-organizing feature map (KSOFM, for spatial disaggregation of areal rainfall in the Wi-stream catchment, an International Hydrological Program (IHP representative catchment, in South Korea. A three-layer MLP model, using three training algorithms, was used to estimate areal rainfall. The Levenberg–Marquardt training algorithm was found to be more sensitive to the number of hidden nodes than were the conjugate gradient and quickprop training algorithms using the MLP model. Results showed that the networks structures of 11-5-1 (conjugate gradient and quickprop and 11-3-1 (Levenberg-Marquardt were the best for estimating areal rainfall using the MLP model. The networks structures of 1-5-11 (conjugate gradient and quickprop and 1-3-11 (Levenberg–Marquardt, which are the inverse networks for estimating areal rainfall using the best MLP model, were identified for spatial disaggregation of areal rainfall using the MLP model. The KSOFM model was compared with the MLP model for spatial disaggregation of areal rainfall. The MLP and KSOFM models could disaggregate areal rainfall into individual point rainfall with spatial concepts.

  13. Genomic responses in mouse models poorly mimic human inflammatory diseases

    Science.gov (United States)

    Seok, Junhee; Warren, H. Shaw; Cuenca, Alex G.; Mindrinos, Michael N.; Baker, Henry V.; Xu, Weihong; Richards, Daniel R.; McDonald-Smith, Grace P.; Gao, Hong; Hennessy, Laura; Finnerty, Celeste C.; López, Cecilia M.; Honari, Shari; Moore, Ernest E.; Minei, Joseph P.; Cuschieri, Joseph; Bankey, Paul E.; Johnson, Jeffrey L.; Sperry, Jason; Nathens, Avery B.; Billiar, Timothy R.; West, Michael A.; Jeschke, Marc G.; Klein, Matthew B.; Gamelli, Richard L.; Gibran, Nicole S.; Brownstein, Bernard H.; Miller-Graziano, Carol; Calvano, Steve E.; Mason, Philip H.; Cobb, J. Perren; Rahme, Laurence G.; Lowry, Stephen F.; Maier, Ronald V.; Moldawer, Lyle L.; Herndon, David N.; Davis, Ronald W.; Xiao, Wenzhong; Tompkins, Ronald G.; Abouhamze, Amer; Balis, Ulysses G. J.; Camp, David G.; De, Asit K.; Harbrecht, Brian G.; Hayden, Douglas L.; Kaushal, Amit; O’Keefe, Grant E.; Kotz, Kenneth T.; Qian, Weijun; Schoenfeld, David A.; Shapiro, Michael B.; Silver, Geoffrey M.; Smith, Richard D.; Storey, John D.; Tibshirani, Robert; Toner, Mehmet; Wilhelmy, Julie; Wispelwey, Bram; Wong, Wing H

    2013-01-01

    A cornerstone of modern biomedical research is the use of mouse models to explore basic pathophysiological mechanisms, evaluate new therapeutic approaches, and make go or no-go decisions to carry new drug candidates forward into clinical trials. Systematic studies evaluating how well murine models mimic human inflammatory diseases are nonexistent. Here, we show that, although acute inflammatory stresses from different etiologies result in highly similar genomic responses in humans, the responses in corresponding mouse models correlate poorly with the human conditions and also, one another. Among genes changed significantly in humans, the murine orthologs are close to random in matching their human counterparts (e.g., R2 between 0.0 and 0.1). In addition to improvements in the current animal model systems, our study supports higher priority for translational medical research to focus on the more complex human conditions rather than relying on mouse models to study human inflammatory diseases. PMID:23401516

  14. Application of artificial neural networks in hydrological modeling: A case study of runoff simulation of a Himalayan glacier basin

    Science.gov (United States)

    Buch, A. M.; Narain, A.; Pandey, P. C.

    1994-01-01

    The simulation of runoff from a Himalayan Glacier basin using an Artificial Neural Network (ANN) is presented. The performance of the ANN model is found to be superior to the Energy Balance Model and the Multiple Regression model. The RMS Error is used as the figure of merit for judging the performance of the three models, and the RMS Error for the ANN model is the latest of the three models. The ANN is faster in learning and exhibits excellent system generalization characteristics.

  15. Forecasting model for the incidence of hepatitis A based on artificial neural network

    Institute of Scientific and Technical Information of China (English)

    Peng Guan; De-Sheng Huang; Bao-Sen Zhou

    2004-01-01

    AIM: To study the application of artificial neural network (ANN) in forecasting the incidence of hepatitis A, which had an autoregression phenomenon.METHODS: The data of the incidence of hepatitis A in Liaoning Province from 1981 to 2001 were obtained from Liaoning Disease Control and Prevention Center. We used the autoregressive integrated moving average (ARIMA)model of time series analysis to determine whether there was any autoregression phenomenon in the data. Then the data of the incidence were switched into [0,1] intervals as the network theoretical output. The data from 1981 to 1997 were used as the training and verifying sets and the data from 1998 to 2001 were made up into the test set.STATISTICA neural network (ST NN) was used to construct,train and simulate the artificial neural network.RESULTS: Twenty-four networks were tested and seven were retained. The best network we found had excellent performance, its regression ratio was 0.73, and its correlatior, was 0.69. There were 2 input variables in the network, one was AR(1), and the other was time. The number of units in hidden layer was 3. In ARIMA time series analysis results, the best model was first order autoregression without difference and smoothness. The total sum square error of the ANN model was 9 090.21, the sum square error of the training set and testing set was 8 377.52 and 712.69,respectively, they were all less than that of ARIMA model.The corresponding value of ARIMA was 12 291.79, 8 944.95and 3 346.84, respectively. The correlation coefficient of nonlinear regression (RNL) of ANN was 0.71, while the RNL of ARIMA linear autoregression model was 0.66.CONCLUSION: ANN is superior to conventional methods in forecasting the incidence of hepatitis A which has an autoregression phenomenon.

  16. An experimental artificial-neural-network-based modeling of magneto-rheological fluid dampers

    International Nuclear Information System (INIS)

    A static model for a magneto-rheological (MR) damper based on artificial neural networks (ANNs) is proposed, and an intensive and experimental study is presented for designing the ANN structure. The ANN model does not require time delays in the input vector. Besides the electric current signal, only one additional sensor is used to achieve a reliable MR damper structure. The model is experimentally validated with two commercial MR dampers of different characteristics: MR1 damper with continuous actuation and MR2 damper with two levels of actuation. The error to signal ratio (ESR) index is used to measure the model accuracy; for both MR dampers, an average value of 6.03% of total error is obtained from different experiments, which are designed to explore the nonlinearities of the MR phenomenon at different frequencies by including the impact of the electric current fluctuations. The proposed ANN model is compared with other well known parametric models; the qualitative and quantitative comparison among the models highlights the advantages of the ANN for representing a commercial MR damper. The ESR index was reduced by the ANN-based model by up to 29% with respect to the parametric models for the MR1 damper and up to 40% for the MR2 damper. The force–velocity diagram is used to compare the modeling properties of each approach: (1) the Bingham model cannot describe the hysteresis of both MR dampers and the distribution function of the modeled force varies from the experimental data, (2) the algebraic models have complications in representing the nonlinear behavior of the asymmetric damper (MR2) and, (3) the ANN-based MR damper can model the nonlinearities of both MR dampers and presents good scalability; the accuracy of the results supports the use of this model for the validation of semi-active suspension control systems for a vehicle, by using nonlinear simulations. (paper)

  17. A computer model of the artificially ventilated human respiratory system in adult intensive care.

    Science.gov (United States)

    Wilson, A J; Murphy, C M; Brook, B S; Breen, D; Miles, A W; Tilley, D G

    2009-11-01

    A multi-technique approach to modelling artificially ventilated patients on the adult general intensive care unit (ICU) is proposed. Compartmental modelling techniques were used to describe the mechanical ventilator and the flexible hoses that connect it to the patient. 3D CFD techniques were used to model flow in the major airways and a Windkessel style balloon model was used to model the mechanical properties of the lungs. A multi-compartment model of the lung based on bifurcating tree structures representing the conducting airways and pulmonary circulation allowed lung disease to be modelled in terms of altered V/Q ratios within a lognormal distribution of values and it is from these that gas exchange was determined. A compartmental modelling tool, Bathfp, was used to integrate the different modelling techniques into a single model. The values of key parameters in the model could be obtained from measurements on patients in an ICU whilst a sensitivity analysis showed that the model was insensitive to the value of other parameters within it. Measured and modelled values for arterial blood gases and airflow parameters are compared for 46 ventilator settings obtained from 6 ventilator dependent patients. The results show correlation coefficients of 0.88 and 0.85 for the arterial partial pressures of the O(2) and CO(2), respectively (p<0.01) and of 0.99 and 0.96 for upper airway pressure and tidal volume, respectively (p<0.01). The difference between measured and modelled values was large in physiological terms, suggesting that some optimisation of the model is required. PMID:19699134

  18. Multi-criteria validation of artificial neural network rainfall-runoff modeling

    Directory of Open Access Journals (Sweden)

    R. Modarres

    2008-12-01

    Full Text Available In this study we propose a comprehensive multi-criteria validation test for rainfall-runoff modeling by artificial neural networks. This study applies 17 global statistics and 3 additional non-parametric tests to evaluate the ANNs. The weakness of global statistics for validation of ANN is demonstrated by rainfall-runoff modeling of the Plasjan Basin in the western region of the Zayandehrud watershed, Iran. Although the global statistics showed that the multi layer perceptron with 4 hidden layers (MLP4 is the best ANN for the basin comparing with other MLP networks and empirical regression model, but the non-parametric tests illustrate that neither the ANNs nor the regression model are able to reproduce the probability distribution of observed runoff in validation phase. However, the MLP4 network is the best network to reproduce the mean and variance of the observed runoff based on non-parametric tests. The performance of ANNs and empirical model was also demonstrated for low-medium and high flows. Although the MLP4 network gives the best performance among ANNs for low-medium and high flows based on different statistics but the empirical model shows better results. However, none of the models is able to simulate the frequency distribution of low-medium and high flows according to non-parametric tests. This study illustrates that the modelers should select appropriate and relevant evaluation measures from the set of existing metrics based on the particular requirements of each individual applications.

  19. Modeling subjective evaluation of soundscape quality in urban open spaces: An artificial neural network approach.

    Science.gov (United States)

    Yu, Lei; Kang, Jian

    2009-09-01

    This research aims to explore the feasibility of using computer-based models to predict the soundscape quality evaluation of potential users in urban open spaces at the design stage. With the data from large scale field surveys in 19 urban open spaces across Europe and China, the importance of various physical, behavioral, social, demographical, and psychological factors for the soundscape evaluation has been statistically analyzed. Artificial neural network (ANN) models have then been explored at three levels. It has been shown that for both subjective sound level and acoustic comfort evaluation, a general model for all the case study sites is less feasible due to the complex physical and social environments in urban open spaces; models based on individual case study sites perform well but the application range is limited; and specific models for certain types of location/function would be reliable and practical. The performance of acoustic comfort models is considerably better than that of sound level models. Based on the ANN models, soundscape quality maps can be produced and this has been demonstrated with an example. PMID:19739730

  20. Development of sediment load estimation models by using artificial neural networking techniques.

    Science.gov (United States)

    Hassan, Muhammad; Ali Shamim, M; Sikandar, Ali; Mehmood, Imran; Ahmed, Imtiaz; Ashiq, Syed Zishan; Khitab, Anwar

    2015-11-01

    This study aims at the development of an artificial neural network-based model for the estimation of weekly sediment load at a catchment located in northern part of Pakistan. The adopted methodology has been based upon antecedent sediment conditions, discharge, and temperature information. Model input and data length selection was carried out using a novel mathematical tool, Gamma test. Model training was carried out by using three popular algorithms namely Broyden-Fletcher-Goldfarb-Shanno (BFGS), back propagation (BP), and local linear regression (LLR) using forward selection of input variables. Evaluation of the best model was carried out on the basis of basic statistical parameters namely R-square, root mean squared error (RMSE), and mean biased error (MBE). Results indicated that BFGS-based ANN model outperformed all other models with significantly low values of RMSE and MBE. A strong correlation was also found between the observed and estimated sediment load values for the same model as the value of Nash-Sutcliffe model efficiency coefficient (R-square) was found to be quite high as well. PMID:26463089

  1. MaNN: Multiple Artificial Neural Networks for modelling the Interstellar Medium

    CERN Document Server

    Grassi, T; Piovan, L; Buonomo, U; Chiosi, C

    2011-01-01

    Modelling the complex physics of the Interstellar Medium (ISM) in the context of large-scale numerical simulations is a challenging task. A number of methods have been proposed to embed a description of the ISM into different codes. We propose a new way to achieve this task: Artificial Neural Networks (ANNs). The ANN has been trained on a pre-compiled model database, and its predictions have been compared to the expected theoretical ones, finding good agreement both in static and in dynamical tests run using the Padova Tree-SPH code \\textsc{EvoL}. A neural network can reproduce the details of the interstellar gas evolution, requiring limited computational resources. We suggest that such an algorithm can replace a real-time calculation of mass elements chemical evolution in hydrodynamical codes.

  2. Back-Propagation Artificial Neural Networks for Water Supply Pipeline Model

    Institute of Scientific and Technical Information of China (English)

    朱东海; 张土乔; 毛根海

    2002-01-01

    Water supply pipelines are the lifelines of a city. When pipelines burst, the burst site is difficult to locate by traditional methods such as manual tools or only by watching. In this paper, the burst site was identified using back-propagation (BP) artificial neural networks (ANN). The study is based on an indoor urban water supply model experiment. The key to appling BP ANN is to optimize the ANN's topological structure and learning parameters. This paper presents the optimizing method for a 3-layer BP neural network's topological structure and its learning parameters-learning ratio and the momentum factor. The indoor water supply pipeline model experimental results show that BP ANNs can be used to locate the burst point in urban water supply systems. The topological structure and learning parameters were optimized using the experimental results.

  3. A simple numerical model for membrane oxygenation of an artificial lung machine

    Science.gov (United States)

    Subraveti, Sai Nikhil; Sai, P. S. T.; Viswanathan Pillai, Vinod Kumar; Patnaik, B. S. V.

    2015-11-01

    Optimal design of membrane oxygenators will have far reaching ramification in the development of artificial heart-lung systems. In the present CFD study, we simulate the gas exchange between the venous blood and air that passes through the hollow fiber membranes on a benchmark device. The gas exchange between the tube side fluid and the shell side venous liquid is modeled by solving mass, momentum conservation equations. The fiber bundle was modelled as a porous block with a bundle porosity of 0.6. The resistance offered by the fiber bundle was estimated by the standard Ergun correlation. The present numerical simulations are validated against available benchmark data. The effect of bundle porosity, bundle size, Reynolds number, non-Newtonian constitutive relation, upstream velocity distribution etc. on the pressure drop, oxygen saturation levels etc. are investigated. To emulate the features of gas transfer past the alveoli, the effect of pulsatility on the membrane oxygenation is also investigated.

  4. Artificial Neural Network based Diagnostic Model For Causes of Success and Failures

    CERN Document Server

    Kaur, Bikrampal

    2010-01-01

    In this paper an attempt has been made to identify most important human resource factors and propose a diagnostic model based on the back-propagation and connectionist model approaches of artificial neural network (ANN). The focus of the study is on the mobile -communication industry of India. The ANN based approach is particularly important because conventional approaches (such as algorithmic) to the problem solving have their inherent disadvantages. The algorithmic approach is well-suited to the problems that are well-understood and known solution(s). On the other hand the ANNs have learning by example and processing capabilities similar to that of a human brain. ANN has been followed due to its inherent advantage over conversion algorithmic like approaches and having capabilities, training and human like intuitive decision making capabilities. Therefore, this ANN based approach is likely to help researchers and organizations to reach a better solution to the problem of managing the human resource. The stud...

  5. Determination of porosity and permeability in reservoir intervals by artificial neural network modelling, offshore Eastern Canada

    Energy Technology Data Exchange (ETDEWEB)

    Huang, Z.; Williamson, M.A. [Geological Survey of Canada, Dartmouth, NS (Canada). Atlantic Geoscience Centre

    1997-09-01

    A technique for the quantitative integration of porosity/permeability measurements and well log data from the major reservoir intervals throughout the Jeanne d`Arc Basin was presented. The integration used data from 13 wells for the Avalon Formation, 7 wells for the Hibernia Formation and 12 wells for the Jeanne d`Arc Formation. A model for the relationship between porosity, permeability and well log response was then established using the back-propagation artificial neural network (BP-ANN) technique. The model was used to construct profiles of porosity and permeability in both cored and uncored wells for the three formations. The curves obtained for porosity and permeability provide a solid basis for further reservoir studies, such as inter-well recognition and correlations of permeable units and basin-wide reservoir quality evaluation.

  6. Very short-term wind speed prediction: A new artificial neural network-Markov chain model

    Energy Technology Data Exchange (ETDEWEB)

    Pourmousavi Kani, S.A. [Electrical and Computer Engineering Department, 627 Cobleigh Hall, Montana State University, Bozeman, MT 59717 (United States); Ardehali, M.M. [Energy Research Center, Department of Electrical Engineering, Amirkabir University of Technology (Tehran Polytechnic), 424 Hafez Ave., Tehran 15914 (Iran, Islamic Republic of)

    2011-01-15

    As the objective of this study, artificial neural network (ANN) and Markov chain (MC) are used to develop a new ANN-MC model for forecasting wind speed in very short-term time scale. For prediction of very short-term wind speed in a few seconds in the future, data patterns for short-term (about an hour) and very short-term (about minutes or seconds) recorded prior to current time are considered. In this study, the short-term patterns in wind speed data are captured by ANN and the long-term patterns are considered utilizing MC approach and four neighborhood indices. The results are validated and the effectiveness of the new ANN-MC model is demonstrated. It is found that the prediction errors can be decreased, while the uncertainty of the predictions and calculation time are reduced. (author)

  7. Very short-term wind speed prediction: A new artificial neural network-Markov chain model

    International Nuclear Information System (INIS)

    As the objective of this study, artificial neural network (ANN) and Markov chain (MC) are used to develop a new ANN-MC model for forecasting wind speed in very short-term time scale. For prediction of very short-term wind speed in a few seconds in the future, data patterns for short-term (about an hour) and very short-term (about minutes or seconds) recorded prior to current time are considered. In this study, the short-term patterns in wind speed data are captured by ANN and the long-term patterns are considered utilizing MC approach and four neighborhood indices. The results are validated and the effectiveness of the new ANN-MC model is demonstrated. It is found that the prediction errors can be decreased, while the uncertainty of the predictions and calculation time are reduced.

  8. Modeling of global horizontal irradiance in the United Arab Emirates with artificial neural networks

    International Nuclear Information System (INIS)

    This paper employs ANN (Artificial Neural Network) models to estimate GHI (global horizontal irradiance) for three major cities in the UAE (United Arab Emirates), namely Abu Dhabi, Dubai and Al-Ain. City data are then used to develop a comprehensive global GHI model for other nearby locations in the UAE. The ANN models use MLP (Multi-Layer Perceptron) and RBF (Radial Basis Function) techniques with comprehensive training algorithms, architectures, and different combinations of inputs. The UAE models are tested and validated against individual city models and data available from the UAE Solar Atlas with good agreement as attested by the computed statistical error parameters. The optimal ANN model is MLP-based and requires four mean daily weather parameters; namely, maximum temperature, wind speed, sunshine hours, and relative humidity. The computed statistical error parameters for the optimal MLP-ANN model in relation to the measured three-cities mean data (referred to as UAE data) are MBE (mean bias error) = −0.0003 kWh/m2, RMSE = 0.179 kWh/m2, R2 = 99%, NSE (Nash-Sutcliffe model Efficiency coefficient) = 99%, and t-statistic = 0.005 at 5% significance level. Results prove the suitability of the ANN models for estimating the monthly mean daily GHI in different locations of the UAE. - Highlights: • ANN prediction models for the GHI (global horizontal irradiance) in the UAE. • Models used to estimate the potential of global solar radiation for UAE cities. • Data from the UAE Solar Atlas are used to validate developed ANN models. • ANN models are more efficient than regression models in predicting GHI

  9. Characterization and modeling of ionic polymeric smart materials as artificial muscles and robotic swimming structures

    Science.gov (United States)

    Mojarrad, Mehran

    2001-07-01

    In this dissertation document, a thorough review and investigation of works in connection with the ionic polymeric gels as artificial muscles and electrically controllable polymeric network structures were performed. Where possible, comparisons were made with biological muscles and applications in marine propulsion using such polymeric materials were investigated. Furthermore, methods of fabrication of several chemically active ionic polymeric gel muscles such as PolyAcryloNitrile (PAN), Poly(2-Acrylamido-2-Methyl-1-PropaneSulfonic) acid (PAMPS), and PolyAcrylic-acid-bis-AcrylaMide (PAAM) as well as a new class of electrically active composite muscle such as Ion-Exchange-Metal-Composites (IEMC) or Ionic Polymer Metal Composites (IPMC) materials are introduced and investigated that resulted in two US patents regarding their fabrication and application capabilities as actuators and sensors. In this research, various forms of the IPMC fabrication were explored and reported. In addition, characterization of PAN muscles, bundling and encapsulation were investigated. Conversion of chemical to electrical artificial muscles were also investigated using chemical plating techniques as well as physical vapor deposition methods of the pH-activated muscles like PAN fibers. Experimental methods were devised to characterize contraction, expansion, and bending of various actuators using isometric, isoionic, and isotonic characterization methods. Several apparatuses for modeling and testing of the various artificial muscles were built to show the viability of the application of both chemoactive and electroactive muscles. Furthermore PAN fiber muscles in different configurations such as spring-loaded fiber bundles, biceps, triceps, ribbon type muscles, and segmented fiber bundles were fabricated to make a variety of actuators. Additionally, swimming robotic structures and associated hardware were built to incorporate IPMC as biomimetic propulsion fin actuators. In addition, various

  10. A structure generator for modelling the initial sediment distribution of an artificial hydrologic catchment

    Directory of Open Access Journals (Sweden)

    T. Maurer

    2011-12-01

    Full Text Available Artificially-created hydrological catchments are characterised by sediment structures from technological construction processes that can potentially be important for modelling of flow and transport and for understanding initial soil and ecosystem development. The subsurface spatial structures of such catchments have not yet been sufficiently explored and described. Our objective was to develop a structure generator programme for modelling the 3-D spatial distribution patterns of dumped sediments depending on the technical earth-moving and deposition processes. We are focussing in a first step on integrating sediment dumping, particle size, and bulk density modification processes on the catchment scale. For the model development, the artificially-constructed hydrological catchment "Chicken Creek" located in Lower Lusatia, Germany, served as an example. The structure generator describes 3-D technological sediment distributions at two scales: (i for a 2-D-vertical cross-section, texture and bulk density distributions are generated within individual spoil cones that result from mass dumping, particle segregation, and compaction and (ii for the whole catchment, the spoil cones are horizontally arranged along trajectories of mass dumping controlled by the belt stacker-machine relative to the catchment's clay layer topography. The generated 3-D texture and bulk density distributions are interpolated and visualised as a gridded 3-D-volume body using 3-D computer-aided design software. The generated subsurface sediment distribution for the Chicken Creek catchment was found to correspond to observed patterns already without calibration. Spatial aggregation and interpolation in the gridded volume body modified the generated distributions towards more uniform (unimodal distributions and lower values of the standard deviations. The modelling approach is generally applicable to all situations where large masses of unconsolidated sediment are moved and dumped

  11. A structure generator for modelling the initial sediment distribution of an artificial hydrologic catchment

    Science.gov (United States)

    Maurer, T.; Schneider, A.; Gerke, H. H.

    2011-12-01

    Artificially-created hydrological catchments are characterised by sediment structures from technological construction processes that can potentially be important for modelling of flow and transport and for understanding initial soil and ecosystem development. The subsurface spatial structures of such catchments have not yet been sufficiently explored and described. Our objective was to develop a structure generator programme for modelling the 3-D spatial distribution patterns of dumped sediments depending on the technical earth-moving and deposition processes. We are focussing in a first step on integrating sediment dumping, particle size, and bulk density modification processes on the catchment scale. For the model development, the artificially-constructed hydrological catchment "Chicken Creek" located in Lower Lusatia, Germany, served as an example. The structure generator describes 3-D technological sediment distributions at two scales: (i) for a 2-D-vertical cross-section, texture and bulk density distributions are generated within individual spoil cones that result from mass dumping, particle segregation, and compaction and (ii) for the whole catchment, the spoil cones are horizontally arranged along trajectories of mass dumping controlled by the belt stacker-machine relative to the catchment's clay layer topography. The generated 3-D texture and bulk density distributions are interpolated and visualised as a gridded 3-D-volume body using 3-D computer-aided design software. The generated subsurface sediment distribution for the Chicken Creek catchment was found to correspond to observed patterns already without calibration. Spatial aggregation and interpolation in the gridded volume body modified the generated distributions towards more uniform (unimodal) distributions and lower values of the standard deviations. The modelling approach is generally applicable to all situations where large masses of unconsolidated sediment are moved and dumped thereby allowing

  12. A study of groundwater monitoring data analysis using Artificial Neural Network model

    International Nuclear Information System (INIS)

    The results of groundwater flow modeling are to be justified using groundwater monitoring data in the hydrogeological characterization. On the other hand, hydraulic continuities of the geological structures, all of which are considered to have great effect on groundwater flow and/or groundwater quality, are to be estimated using the groundwater flow monitoring data with hydraulic response to some impacts such as borehole drilling, pumping test and so on. Therefore, the groundwater monitoring is important for characterizing the geological and hydrogeological environments. In order to characterize of hydrogeological environment using the monitoring data, it is important to evaluate the influence of artificial and natural impact on the monitoring data. In this study, the following three research works are carried out based on the groundwater monitoring data collected at the Tono area. Artificial Neural Network (ANN) was adopted as the tool for monitoring data analysis. Runoff analysis for assessment of importance of soil moisture on runoff estimation in a catchment. Analysis of water level fluctuation for determination influence factors in the water level fluctuation and for filtering out the influence factors from the water level data . Analysis of hydraulic pressure fluctuation in deep geological formations for hydrogeological characterization and assessment of human influence on the pore pressure in deep formation. Through this study, applicability of ANN for analysis and interpretation of the groundwater monitoring data could be confirmed and methodology for utilization the monitoring data for understanding and characterization of hydrogeological environment could be developed. (author)

  13. A Game Theoretic Framework for Incentive-Based Models of Intrinsic Motivation in Artificial Systems

    Directory of Open Access Journals (Sweden)

    Kathryn Elizabeth Merrick

    2013-10-01

    Full Text Available An emerging body of research is focusing on understanding and building artificial systems that can achieve open-ended development influenced by intrinsic motivations. In particular, research in robotics and machine learning is yielding systems and algorithms with increasing capacity for self-directed learning and autonomy. Traditional software architectures and algorithms are being augmented with intrinsic motivations to drive cumulative acquisition of knowledge and skills. Intrinsic motivations have recently been considered in reinforcement learning, active learning and supervised learning settings among others. This paper considers game theory as a novel setting for intrinsic motivation. A game theoretic framework for intrinsic motivation is formulated by introducing the concept of optimally motivating incentive as a lens through which players perceive a game. Transformations of four well-known mixed-motive games are presented to demonstrate the perceived games when players’ optimally motivating incentive falls in three cases corresponding to strong power, affiliation and achievement motivation. We use agent-based simulations to demonstrate that players with different optimally motivating incentive act differently as a result of their altered perception of the game. We discuss the implications of these results both for modeling human behavior and for designing artificial agents or robots.

  14. Evaluating portland cement concrete degradation by sulphate exposure through artificial neural networks modeling

    International Nuclear Information System (INIS)

    A concrete is durable if it has accomplished the desired service life in the environment in which it is exposed. The durability of concrete materials can be limited as a result of adverse performance of its cement-paste matrix or aggregate constituents under either chemical or physical attack. Among other aggressive chemical exposures, the sulphate attack is an important concern. Water, soils and gases, which contain sulphate, represent a potential threat to the durability of concrete structures. Sulphate attack in concrete leads to the conversion of the hydration products of cement to ettringite, gypsum, and other phases, and also it leads to the destabilization of the primary strength generating calcium silicate hydrate (C-S-H) gel. The formation of ettringite and gypsum is common in cementitious systems exposed to most types of sulphate solutions. The present work presents the application of the neural networks for estimating deterioration of various concrete mixtures due to exposure to sulphate solutions. A neural networks model was constructed, trained and tested using the available database. In general, artificial neural networks could be successfully used in function approximation problems in order to approach the data generation function. Once data generation function is known, artificial neural network structure is tested using data not presented to the network during training. This paper is intent to provide the technical requirements related to the production of a durable concrete to be used in the structures of the Brazilian near-surface repository of radioactive wastes. (author)

  15. Evaluating portland cement concrete degradation by sulphate exposure through artificial neural networks modeling

    Energy Technology Data Exchange (ETDEWEB)

    Oliveira, Douglas Nunes de; Bourguignon, Lucas Gabriel Garcia; Tolentino, Evandro, E-mail: tolentino@timoteo.cefetmg.br [Centro Federal de Educacao Tecnologica de Minas Gerais (CEFET-MG), Timoteo, MG (Brazil); Costa, Rodrigo Moyses, E-mail: rodrigo@moyses.com.br [Universidade de Itauna, Itauna, MG (Brazil); Tello, Cledola Cassia Oliveira de, E-mail: tellocc@cdtn.br [Centro de Desenvolvimento da Tecnologia Nucelar (CDTN/CNEN-MG), Belo Horizonte, MG (Brazil)

    2015-07-01

    A concrete is durable if it has accomplished the desired service life in the environment in which it is exposed. The durability of concrete materials can be limited as a result of adverse performance of its cement-paste matrix or aggregate constituents under either chemical or physical attack. Among other aggressive chemical exposures, the sulphate attack is an important concern. Water, soils and gases, which contain sulphate, represent a potential threat to the durability of concrete structures. Sulphate attack in concrete leads to the conversion of the hydration products of cement to ettringite, gypsum, and other phases, and also it leads to the destabilization of the primary strength generating calcium silicate hydrate (C-S-H) gel. The formation of ettringite and gypsum is common in cementitious systems exposed to most types of sulphate solutions. The present work presents the application of the neural networks for estimating deterioration of various concrete mixtures due to exposure to sulphate solutions. A neural networks model was constructed, trained and tested using the available database. In general, artificial neural networks could be successfully used in function approximation problems in order to approach the data generation function. Once data generation function is known, artificial neural network structure is tested using data not presented to the network during training. This paper is intent to provide the technical requirements related to the production of a durable concrete to be used in the structures of the Brazilian near-surface repository of radioactive wastes. (author)

  16. Artificial neural network and non-linear models for prediction of transformer oil residual operating time

    Energy Technology Data Exchange (ETDEWEB)

    Wahab, Mohamed A.A.; Hamada, Mohamed. M. [Electrical Engineering Department, Faculty of Engineering, Minia University, Minia, Minia 61111 (Egypt); Mohamed, Ahmed [Electrical and Computer Engineering Department, College of Electrical and Computer Engineering, Energy Systems Research Laboratory, Florida International University, 10555 W Flagler Street, Room EC-3810, Miami, FL 33174 (United States)

    2011-01-15

    This paper presents two modeling techniques for the prediction and monitoring of the characteristics of transformer oil. The first employs artificial neural network (ANN) and the second employs non-linear modeling (nlm). The proposed techniques are implemented for predicting the transformer oil residual operating time (t{sub rot}) which is defined as the service period after which the breakdown voltage (BDV) violates the limits given in the standard specifications. The selection of the most influential characteristics on residual operating time (t{sub rot}) in the proposed techniques is obtained by statistical analysis. The non-linear model depends on linear combination of non-linear functions for each characteristic. The ANN technique for modeling these characteristics preserves the non-linear relationship between these characteristics and (t{sub rot}). The results are compared with previously published modeling techniques namely multiple linear regression and polynomial regression models. Different evaluation indices have been used to justify the superiority of the proposed modeling techniques for predicting (t{sub rot}). (author)

  17. Artificial neural network model for photosynthetic pigments identification using multi wavelength chromatographic data

    Science.gov (United States)

    Prilianti, K. R.; Hariyanto, S.; Natali, F. D. D.; Indriatmoko, Adhiwibawa, M. A. S.; Limantara, L.; Brotosudarmo, T. H. P.

    2016-04-01

    The development of rapid and automatic pigment characterization method become an important issue due to the fact that there are only less than 1% of plant pigments in the earth have been explored. In this research, a mathematical model based on artificial intelligence approach was developed to simplify and accelerate pigment characterization process from HPLC (high-performance liquid chromatography) procedure. HPLC is a widely used technique to separate and identify pigments in a mixture. Input of the model is chromatographic data from HPLC device and output of the model is a list of pigments which is the spectrum pattern is discovered in it. This model provides two dimensional (retention time and wavelength) fingerprints for pigment characterization which is proven to be more accurate than one dimensional fingerprint (fixed wavelength). Moreover, by mimicking interconnection of the neuron in the nervous systems of the human brain, the model have learning ability that could be replacing expert judgement on evaluating spectrum pattern. In the preprocessing step, principal component analysis (PCA) was used to reduce the huge dimension of the chromatographic data. The aim of this step is to simplify the model and accelerate the identification process. Six photosynthetic pigments i.e. zeaxantin, pheophytin a, α-carotene, β-carotene, lycopene and lutein could be well identified by the model with accuracy up to 85.33% and processing time less than 1 second.

  18. Artificial neural networks: Principle and application to model based control of drying systems -- A review

    Energy Technology Data Exchange (ETDEWEB)

    Thyagarajan, T.; Ponnavaikko, M. [Crescent Engineering Coll., Madras (India); Shanmugam, J. [Madras Inst. of Tech. (India); Panda, R.C.; Rao, P.G. [Central Leather Research Inst., Madras (India)

    1998-07-01

    This paper reviews the developments in the model based control of drying systems using Artificial Neural Networks (ANNs). Survey of current research works reveals the growing interest in the application of ANN in modeling and control of non-linear, dynamic and time-variant systems. Over 115 articles published in this area are reviewed. All landmark papers are systematically classified in chronological order, in three distinct categories; namely, conventional feedback controllers, model based controllers using conventional methods and model based controllers using ANN for drying process. The principles of ANN are presented in detail. The problems and issues of the drying system and the features of various ANN models are dealt with up-to-date. ANN based controllers lead to smoother controller outputs, which would increase actuator life. The paper concludes with suggestions for improving the existing modeling techniques as applied to predicting the performance characteristics of dryers. The hybridization techniques, namely, neural with fuzzy logic and genetic algorithms, presented, provide, directions for pursuing further research for the implementation of appropriate control strategies. The authors opine that the information presented here would be highly beneficial for pursuing research in modeling and control of drying process using ANN. 118 refs.

  19. Short-Term Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature Parameters

    Directory of Open Access Journals (Sweden)

    Hongshan Zhao

    2012-05-01

    Full Text Available Short-term solar irradiance forecasting (STSIF is of great significance for the optimal operation and power predication of grid-connected photovoltaic (PV plants. However, STSIF is very complex to handle due to the random and nonlinear characteristics of solar irradiance under changeable weather conditions. Artificial Neural Network (ANN is suitable for STSIF modeling and many research works on this topic are presented, but the conciseness and robustness of the existing models still need to be improved. After discussing the relation between weather variations and irradiance, the characteristics of the statistical feature parameters of irradiance under different weather conditions are figured out. A novel ANN model using statistical feature parameters (ANN-SFP for STSIF is proposed in this paper. The input vector is reconstructed with several statistical feature parameters of irradiance and ambient temperature. Thus sufficient information can be effectively extracted from relatively few inputs and the model complexity is reduced. The model structure is determined by cross-validation (CV, and the Levenberg-Marquardt algorithm (LMA is used for the network training. Simulations are carried out to validate and compare the proposed model with the conventional ANN model using historical data series (ANN-HDS, and the results indicated that the forecast accuracy is obviously improved under variable weather conditions.

  20. A Bayesian Model of Biases in Artificial Language Learning: The Case of a Word-Order Universal

    Science.gov (United States)

    Culbertson, Jennifer; Smolensky, Paul

    2012-01-01

    In this article, we develop a hierarchical Bayesian model of learning in a general type of artificial language-learning experiment in which learners are exposed to a mixture of grammars representing the variation present in real learners' input, particularly at times of language change. The modeling goal is to formalize and quantify hypothesized…

  1. A CFBPN Artificial Neural Network Model for Educational Qualitative Data Analyses: Example of Students' Attitudes Based on Kellerts' Typologies

    Science.gov (United States)

    Yorek, Nurettin; Ugulu, Ilker

    2015-01-01

    In this study, artificial neural networks are suggested as a model that can be "trained" to yield qualitative results out of a huge amount of categorical data. It can be said that this is a new approach applied in educational qualitative data analysis. In this direction, a cascade-forward back-propagation neural network (CFBPN) model was…

  2. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research.

    Science.gov (United States)

    Agatonovic-Kustrin, S; Beresford, R

    2000-06-01

    Artificial neural networks (ANNs) are biologically inspired computer programs designed to simulate the way in which the human brain processes information. ANNs gather their knowledge by detecting the patterns and relationships in data and learn (or are trained) through experience, not from programming. An ANN is formed from hundreds of single units, artificial neurons or processing elements (PE), connected with coefficients (weights), which constitute the neural structure and are organised in layers. The power of neural computations comes from connecting neurons in a network. Each PE has weighted inputs, transfer function and one output. The behavior of a neural network is determined by the transfer functions of its neurons, by the learning rule, and by the architecture itself. The weights are the adjustable parameters and, in that sense, a neural network is a parameterized system. The weighed sum of the inputs constitutes the activation of the neuron. The activation signal is passed through transfer function to produce a single output of the neuron. Transfer function introduces non-linearity to the network. During training, the inter-unit connections are optimized until the error in predictions is minimized and the network reaches the specified level of accuracy. Once the network is trained and tested it can be given new input information to predict the output. Many types of neural networks have been designed already and new ones are invented every week but all can be described by the transfer functions of their neurons, by the learning rule, and by the connection formula. ANN represents a promising modeling technique, especially for data sets having non-linear relationships which are frequently encountered in pharmaceutical processes. In terms of model specification, artificial neural networks require no knowledge of the data source but, since they often contain many weights that must be estimated, they require large training sets. In addition, ANNs can combine

  3. Using artificial neural networks to model extrusion processes for the manufacturing of polymeric micro-tubes

    Science.gov (United States)

    Mekras, N.; Artemakis, I.

    2012-09-01

    In this paper a methodology and an application example are presented aiming to show how Artificial Neural Networks (ANNs) can be used to model manufacturing processes when mathematical models are missing or are not applicable e.g. due to the micro- & nano-scaling, due to non-conventional processes, etc. Besides the ANNs methodology, the results of a Software System developed will be presented, which was used to create ANNs models for micro & nano manufacturing processes. More specifically results of a specific application example will be presented, concerning the modeling of extrusion processes for polymeric micro-tubes. ANNs models are capable for modeling manufacturing processes as far as adequate experimental and/or historical data of processes' inputs & outputs are available for their training. The POLYTUBES ANNs models have been trained and tested with experimental data records of process' inputs and outputs concerning a micro-extrusion process of polymeric micro-tubes for several materials such as: COC, PC, PET, PETG, PP and PVDF. The main ANN model of the extrusion application example has 3 inputs and 9 outputs. The inputs are: tube's inner & outer diameters, and the material density. The model outputs are 9 process parameters, which correspond to the specific inputs e.g. process temperature, die inner & outer diameters, extrusion pressure, draw speed etc. The training of the ANN model was completed, when the errors for the model's outputs, which expressed the difference between the training target values and the ANNs outputs, were minimized to acceptable levels. After the training, the micro-extrusion ANN is capable to simulate the process and can be used to calculate model's outputs, which are the process parameters for any new set of inputs. By this way a satisfactory functional approximation of the whole process is achieved. This research work has been supported by the EU FP7 NMP project POLYTUBES.

  4. Applications of Artificial Neural Networks in Structural Engineering with Emphasis on Continuum Models

    Science.gov (United States)

    Kapania, Rakesh K.; Liu, Youhua

    1998-01-01

    The use of continuum models for the analysis of discrete built-up complex aerospace structures is an attractive idea especially at the conceptual and preliminary design stages. But the diversity of available continuum models and hard-to-use qualities of these models have prevented them from finding wide applications. In this regard, Artificial Neural Networks (ANN or NN) may have a great potential as these networks are universal approximators that can realize any continuous mapping, and can provide general mechanisms for building models from data whose input-output relationship can be highly nonlinear. The ultimate aim of the present work is to be able to build high fidelity continuum models for complex aerospace structures using the ANN. As a first step, the concepts and features of ANN are familiarized through the MATLAB NN Toolbox by simulating some representative mapping examples, including some problems in structural engineering. Then some further aspects and lessons learned about the NN training are discussed, including the performances of Feed-Forward and Radial Basis Function NN when dealing with noise-polluted data and the technique of cross-validation. Finally, as an example of using NN in continuum models, a lattice structure with repeating cells is represented by a continuum beam whose properties are provided by neural networks.

  5. Modeling of ammonia emission in the USA and EU countries using an artificial neural network approach.

    Science.gov (United States)

    Stamenković, Lidija J; Antanasijević, Davor Z; Ristić, Mirjana Đ; Perić-Grujić, Aleksandra A; Pocajt, Viktor V

    2015-12-01

    Ammonia emissions at the national level are frequently estimated by applying the emission inventory approach, which includes the use of emission factors, which are difficult and expensive to determine. Emission factors are therefore the subject of estimation, and as such they contribute to inherent uncertainties in the estimation of ammonia emissions. This paper presents an alternative approach for the prediction of ammonia emissions at the national level based on artificial neural networks and broadly available sustainability and economical/agricultural indicators as model inputs. The Multilayer Perceptron (MLP) architecture was optimized using a trial-and-error procedure, including the number of hidden neurons, activation function, and a back-propagation algorithm. Principal component analysis (PCA) was applied to reduce mutual correlation between the inputs. The obtained results demonstrate that the MLP model created using the PCA transformed inputs (PCA-MLP) provides a more accurate prediction than the MLP model based on the original inputs. In the validation stage, the MLP and PCA-MLP models were tested for ammonia emission predictions for up to 2 years and compared with a principal component regression model. Among the three models, the PCA-MLP demonstrated the best performance, providing predictions for the USA and the majority of EU countries with a relative error of less than 20%. PMID:26201663

  6. A basal deuterostome genome viewed as a natural experiment

    OpenAIRE

    Cameron, R. Andrew; Davidson, Eric H.

    2007-01-01

    With the determination of its genome sequence the utility of the sea urchin model system increases. The phylogenetic position of the sea urchin among the deuterostomes allows for informative comparisons to vertebrate research models. A combined whole genome shotgun and bacterial artificial chromosome based strategy yielded a high quality draft genome sequence of 814 Mb. The predicted gene set estimated to include 23,300 genes was annotated and compared to those of other metazoan animals. Gene...

  7. Modeling a thick unsaturated zone at San Gorgonio Pass, California: lessons learned after five years of artificial recharge

    Science.gov (United States)

    Flint, Alan L.; Ellett, Kevin M.; Christensen, Allen H.; Martin, Peter

    2012-01-01

    The information flow among the tasks of framework assessment, numerical modeling, model forecasting and hind casting, and system-performance monitoring is illustrated. Results provide an understanding of artificial recharge in high-altitude desert settings where large vertical distances may separate application ponds from their target aquifers. Approximately 3.8 million cubic meters of surface water was applied to spreading ponds from 2003–2007 to artificially recharge the underlying aquifer through a 200-meter thick unsaturated zone in the San Gorgonio Pass area in southern California. A study was conducted between 1997 and 2003, and a numerical model was developed to help determine the suitability of the site for artificial recharge. Ongoing monitoring results indicated that the existing model needed to be modified and recalibrated to more accurately predict artificial recharge at the site. The objective of this work was to recalibrate the model by using observation of the application rates, the rise and fall of the water level above a perching layer, and the approximate arrival time to the water table during the 5-yr monitoring period following initiation of long-term artificial recharge. Continuous monitoring of soil-matric potential, temperature, and water levels beneath the site indicated that artificial recharge reached the underlying water table between 3.75 and 4.5 yr after the initial application of the recharge water. The model was modified to allow the simulation to more adequately match the perching layer dynamics and the time of arrival at the water table. The instrumentation also showed that the lag time between changes in application of water at the surface and the response at the perching layer decreased from about 4 mo to less than 1 mo due to the wet-up of the unsaturated zone and the increase in relative permeability. The results of this study demonstrate the importance of iteratively monitoring and modeling the unsaturated zone in layered

  8. Simulation and Prediction of Water Allocation Using Artificial Neural Networks and a Spatially Distributed Hydrological Model

    Directory of Open Access Journals (Sweden)

    A. Papagera

    2014-12-01

    Full Text Available Lake Koronia is located in the North part of Greece and is protected by the Ramsar Convention of wetlands. A deficit in the water balance has been presented at the last twenty years due to the excessive water consumption for agricultural uses. This research is an attempt to simulate water flow with MIKE SHE model in order to observe how the water is allocated in the study area. The results of water flow module used for the estimation of Lake’s water balance for 4 hydrological years (2008-2012. Furthermore the Artificial Neural Networks (ANNs was used for the prediction of water flow in two sub-catchments. The coefficient correlation (R was found for Bogdanas (0.9 and Kolxikos (0.86. The Root Mean Square Error (RMSE and the Mean Absolute Percentages Error (MAPE were also calculated in order to evaluate the quality of the ANNs results.

  9. Back stepping-Based-PID-Controller Designed for an Artificial Pancreas model

    Directory of Open Access Journals (Sweden)

    ShaimaMahmou Mahdi

    2011-01-01

    Full Text Available Artificial pancreas is simulated to handle Type I diabetic patients under intensive care by automatically controlling the insulin infusion rate. A Backstepping technique is used to apply the effect of PID controller to blood glucose level since there is no direct relation between insulin infusion (the manipulated variable and glucose level in Bergman’s system model subjected to an oral glucose tolerance test by applying a meal translated into a disturbance. Backstepping technique is usually recommended to stabilize and control the states of Bergman's class of nonlinear systems. The results showed a very satisfactory behavior of glucose deviation to a sudden rise represented by the meal that increase the blood glucose

  10. Vibroacoustic Modeling of Mechanically Coupled Structures: Artificial Spring Technique Applied to Light and Heavy Mediums

    Directory of Open Access Journals (Sweden)

    L. Cheng

    1996-01-01

    Full Text Available This article deals with the modeling of vibrating structures immersed in both light and heavy fluids, and possible applications to noise control problems and industrial vessels containing fluids. A theoretical approach, using artificial spring systems to characterize the mechanical coupling between substructures, is extended to include fluid loading. A structure consisting of a plate-ended cylindrical shell and its enclosed acoustic cavity is analyzed. After a brief description of the proposed technique, a number of numerical results are presented. The analysis addresses the following specific issues: the coupling between the plate and the shell; the coupling between the structure and the enclosure; the possibilities and difficulties regarding internal soundproofing through modifications of the joint connections; and the effects of fluid loading on the vibration of the structure.

  11. An artificial neural network model for prediction of quality characteristics of apples during convective dehydration

    Directory of Open Access Journals (Sweden)

    Karina Di Scala

    2013-09-01

    Full Text Available In this study, the effects of hot-air drying conditions on color, water holding capacity, and total phenolic content of dried apple were investigated using artificial neural network as an intelligent modeling system. After that, a genetic algorithm was used to optimize the drying conditions. Apples were dried at different temperatures (40, 60, and 80 °C and at three air flow-rates (0.5, 1, and 1.5 m/s. Applying the leave-one-out cross validation methodology, simulated and experimental data were in good agreement presenting an error < 2.4 %. Quality index optimal values were found at 62.9 °C and 1.0 m/s using genetic algorithm.

  12. Computational Approaches to Modeling Artificial Emotion -– An overview of the Proposed Solutions

    Directory of Open Access Journals (Sweden)

    Zdzislaw eKOWALCZUK

    2016-04-01

    Full Text Available Cybernetic approach to modeling artificial emotion through the use of different theories of psychology is considered in this paper, presenting a review of twelve proposed solutions: ActAffAct, FLAME, EMA, ParleE, FearNot!, FAtiMA, WASABI, Cathexis, KARO, MAMID, FCM, and xEmotion. The main motivation for this study is founded on the hypothesis that emotions can play a definite utility role of scheduling variables in the construction of intelligent autonomous systems, agents and mobile robots. In this review we also include an innovative and panoptical, comprehensive system, referred to as the Intelligent System of Decision-making (ISD, which has been employed in practical applications of various autonomous units, and which applies as its part the xEmotion, taking into consideration the personal aspects of emotions, affects (short term emotions and mood (principally, long term emotions.

  13. A Parallelized Pumpless Artificial Placenta System Significantly Prolonged Survival Time in a Preterm Lamb Model.

    Science.gov (United States)

    Miura, Yuichiro; Matsuda, Tadashi; Usuda, Haruo; Watanabe, Shimpei; Kitanishi, Ryuta; Saito, Masatoshi; Hanita, Takushi; Kobayashi, Yoshiyasu

    2016-05-01

    An artificial placenta (AP) is an arterio-venous extracorporeal life support system that is connected to the fetal circulation via the umbilical vasculature. Previously, we published an article describing a pumpless AP system with a small priming volume. We subsequently developed a parallelized system, hypothesizing that the reduced circuit resistance conveyed by this modification would enable healthy fetal survival time to be prolonged. We conducted experiments using a premature lamb model to test this hypothesis. As a result, the fetal survival period was significantly prolonged (60.4 ± 3.8 vs. 18.2 ± 3.2 h, P Fetal physiological parameters remained stable until the conclusion of the experiments. In summary, parallelization of the AP system was associated with reduced circuit resistance and lactate levels and allowed preterm lamb fetuses to survive for a significantly longer period when compared with previous studies. PMID:26644374

  14. Comprehensive Resources for Tomato Functional Genomics Based on the Miniature Model Tomato Micro-Tom

    OpenAIRE

    Matsukura, C; Aoki, K.; Fukuda, N.; Mizoguchi, T.; Asamizu, E; Saito, T.; Shibata, D.; Ezura, H.

    2008-01-01

    Tomato (Solanum lycopersicum L., Solanaceae) is an excellent model plant for genomic research of solanaceous plants, as well as for studying the development, ripening, and metabolism of fruit. In 2003, the International Solanaceae Project (SOL, www.sgn.cornell.edu ) was initiated by members from more than 30 countries, and the tomato genome-sequencing project is currently underway. Genome sequence of tomato obtained by this project will provide a firm foundation for forthcoming genomic studie...

  15. Sensitivity analysis of groundwater level in Jinci Spring Basin (China) based on artificial neural network modeling

    Science.gov (United States)

    Li, Xian; Shu, Longcang; Liu, Lihong; Yin, Dan; Wen, Jinmei

    2012-06-01

    Jinci Spring in Shanxi, north China, is a major local water source. It dried up in April 1994 due to groundwater overexploitation. The groundwater system is complex, involving many nonlinear and uncertain factors. Artificial neural network (ANN) models are statistical techniques to study parameter nonlinear relationships of groundwater systems. However, ANN models offer little explanatory insight into the mechanisms of prediction models. Sensitivity analysis can overcome this shortcoming. In this study, a back-propagation neural network model was built based on the relationship between groundwater level and its sensitivity factors in Jinci Spring Basin; these sensitivity factors included precipitation, river seepage, mining drainage, groundwater withdrawals and lateral discharge to the associated Quaternary aquifer. All the sensitivity factors were analyzed with Garson's algorithm based on the connection weights of the neural network model. The concept of "sensitivity range" was proposed to describe the value range of the input variables to which the output variables are most sensitive. The sensitivity ranges were analyzed by a local sensitivity approach. The results showed that coal mining drainage is the most sensitive anthropogenic factor, having a large effect on groundwater level of the Jinci Spring Basin.

  16. Cost prediction of antipsychotic medication of psychiatric disorder using artificial neural network model

    Directory of Open Access Journals (Sweden)

    Arash Mirabzadeh

    2013-01-01

    Full Text Available Background: Antipsychotic monotherapy or polypharmacy (concurrent use of two or more antipsychotics are used for treating patients with psychiatric disorders (PDs. Usually, antipsychotic monotherapy has a lower cost than polypharmacy. This study aimed to predict the cost of antipsychotic medications (AM of psychiatric patients in Iran. Materials and Methods: For this purpose, 790 patients with PDs who were discharged between June and September 2010 were selected from Razi Psychiatric Hospital, Tehran, Iran. For cost prediction of AM of PD, neural network (NN and multiple linear regression (MLR models were used. Analysis of data was performed with R 2.15.1 software. Results: Mean ± standard deviation (SD of the duration of hospitalization (days in patients who were on monotherapy and polypharmacy was 31.19 ± 15.55 and 36.69 ± 15.93, respectively (P < 0.001. Mean and median costs of medication for monotherapy (n = 507 were $8.25 and $6.23 and for polypharmacy (n =192 were $13.30 and $9.48, respectively (P = 0.001. The important variables for cost prediction of AM were duration of hospitalization, type of treatment, and type of psychiatric ward in the MLR model, and duration of hospitalization, type of diagnosed disorder, type of treatment, age, Chlorpromazine dosage, and duration of disorder in the NN model. Conclusion: Our findings showed that the artificial NN (ANN model can be used as a flexible model for cost prediction of AM.

  17. Three-dimensional artificial neural network model of the dayside magnetopause

    CERN Document Server

    Dmitriev, A V

    2013-01-01

    Artificial Neural Networks (ANN) from package NeuroShell 2 are applied for modeling of dayside magnetopause. The model data set is based on the magnetopause crossings and on the corresponding hour-averaged measurements of solar wind plasma and interplanetary magnetic field. ANN model represents 3D shape and size of the dayside magnetopause in dependence on solar wind dynamic pressure, Bz and By components of interplanetary magnetic field. The model permits firstly to describe dynamics of the magnetic cusp and global asymmetry of the dayside magnetopause under wide range of the external conditions. Slightly change of the magnetopause size with change of By absolute value is presented quantitatively in the form of analytical expression. ANN model shows three regimes of the magnetopause dynamics that controlled by Bz component: pressure balance regime (Bz>0 nT), reconnection regime (0>Bz>-10 nT) and regime of reconnection saturation (Bz<-10 nT). Three different regimes of magnetopause dynamics at the subsolar...

  18. An evaluation of tannery industry wastewater treatment sludge gasification by artificial neural network modeling

    Energy Technology Data Exchange (ETDEWEB)

    Ongen, Atakan, E-mail: aongen@istanbul.edu.tr; Kurtulus Ozcan, H.; Arayıcı, Semiha

    2013-12-15

    Highlights: • We model calorific value of syn-gas from tannery industry treatment sludge. • We monitor variation of gas composition in produced gas. • Heating value of produced gas is around 1500 kcal/m{sup 3}. • Model predictions are in close accordance with real values. -- Abstract: This paper reports on the calorific value of synthetic gas (syngas) produced by gasification of dewatered sludge derived from treatment of tannery wastewater. Proximate and ultimate analyses of samples were performed. Thermochemical conversion alters the chemical structure of the waste. Dried air was used as a gasification agent at varying flow rates, which allowed the feedstock to be quickly converted into gas by means of different heterogeneous reactions. A lab-scale updraft fixed-bed steel reactor was used for thermochemical conversion of sludge samples. Artificial neural network (ANN) modeling techniques were used to observe variations in the syngas related to operational conditions. Modeled outputs showed that temporal changes of model predictions were in close accordance with real values. Correlation coefficients (r) showed that the ANN used in this study gave results with high sensitivity.

  19. Rainfall-runoff modelling using artificial neural networks: comparison of network types

    Science.gov (United States)

    Senthil Kumar, A. R.; Sudheer, K. P.; Jain, S. K.; Agarwal, P. K.

    2005-04-01

    Growing interest in the use of artificial neural networks (ANNs) in rainfall-runoff modelling has suggested certain issues that are still not addressed properly. One such concern is the use of network type, as theoretical studies on a multi-layer perceptron (MLP) with a sigmoid transfer function enlightens certain limitations for its use. Alternatively, there is a strong belief in the general ANN user community that a radial basis function (RBF) network performs better than an MLP, as the former bases its nonlinearities on the training data set. This argument is not yet substantiated by applications in hydrology. This paper presents a comprehensive evaluation of the performance of MLP- and RBF-type neural network models developed for rainfall-runoff modelling of two Indian river basins. The performance of both the MLP and RBF network models were comprehensively evaluated in terms of their generalization properties, predicted hydrograph characteristics, and predictive uncertainty. The results of the study indicate that the choice of the network type certainly has an impact on the model prediction accuracy. The study suggests that both the networks have merits and limitations. For instance, the MLP requires a long trial-and-error procedure to fix the optimal number of hidden nodes, whereas for an RBF the structure of the network can be fixed using an appropriate training algorithm. However, a judgment on which is superior is not clearly possible from this study.

  20. An optimisation methodology of artificial neural network models for predicting solar radiation: a case study

    Science.gov (United States)

    Rezrazi, Ahmed; Hanini, Salah; Laidi, Maamar

    2016-02-01

    The right design and the high efficiency of solar energy systems require accurate information on the availability of solar radiation. Due to the cost of purchase and maintenance of the radiometers, these data are not readily available. Therefore, there is a need to develop alternative ways of generating such data. Artificial neural networks (ANNs) are excellent and effective tools for learning, pinpointing or generalising data regularities, as they have the ability to model nonlinear functions; they can also cope with complex `noisy' data. The main objective of this paper is to show how to reach an optimal model of ANNs for applying in prediction of solar radiation. The measured data of the year 2007 in Ghardaïa city (Algeria) are used to demonstrate the optimisation methodology. The performance evaluation and the comparison of results of ANN models with measured data are made on the basis of mean absolute percentage error (MAPE). It is found that MAPE in the ANN optimal model reaches 1.17 %. Also, this model yields a root mean square error (RMSE) of 14.06 % and an MBE of 0.12. The accuracy of the outputs exceeded 97 % and reached up 99.29 %. Results obtained indicate that the optimisation strategy satisfies practical requirements. It can successfully be generalised for any location in the world and be used in other fields than solar radiation estimation.

  1. Reservoir Modeling by Data Integration via Intermediate Spaces and Artificial Intelligence Tools in MPS Simulation Frameworks

    International Nuclear Information System (INIS)

    Conditioning stochastic simulations are very important in many geostatistical applications that call for the introduction of nonlinear and multiple-point data in reservoir modeling. Here, a new methodology is proposed for the incorporation of different data types into multiple-point statistics (MPS) simulation frameworks. Unlike the previous techniques that call for an approximate forward model (filter) for integration of secondary data into geologically constructed models, the proposed approach develops an intermediate space where all the primary and secondary data are easily mapped onto. Definition of the intermediate space, as may be achieved via application of artificial intelligence tools like neural networks and fuzzy inference systems, eliminates the need for using filters as in previous techniques. The applicability of the proposed approach in conditioning MPS simulations to static and geologic data is verified by modeling a real example of discrete fracture networks using conventional well-log data. The training patterns are well reproduced in the realizations, while the model is also consistent with the map of secondary data

  2. Artificial neural network modeling of geothermal district heating system thought exergy analysis

    International Nuclear Information System (INIS)

    Highlights: ► ANN has been modeled for predicting exergy efficiency a GDHS thought exergy analysis. ► The network yields a maximum correlation coefficient with minimum coefficient of variance and root mean square values. ► The ANN modeling can provide high accuracy and reliability for predicting the exergy efficiency of GDHSs. ► Thus, online monitoring system and the performance of GDHS can be implemented. - Abstract: This paper deals with an artificial neural network (ANN) modeling to predict the exergy efficiency of geothermal district heating system under a broad range of operating conditions. As a case study, the Afyonkarahisar geothermal district heating system (AGDHS) in Turkey is considered. The average daily actual thermal data acquired from the AGDHS in the 2009–2010 heating season are collected and employed for exergy analysis. An ANN modeling is developed based on backpropagation learning algorithm for predicting the exergy efficiency of the system according to parameters of the system, namely the ambient temperature, flow rate and well head temperature. Then, the recorded and calculated data conducted in the AGDHS at different dates are used for training the network. The results showed that the network yields a maximum correlation coefficient with minimum coefficient of variance and root mean square values. The results confirmed that the ANN modeling can be applied successfully and can provide high accuracy and reliability for predicting the exergy performance of geothermal district heating systems.

  3. Offline and online detection of damage using autoregressive models and artificial neural networks

    Science.gov (United States)

    Omenzetter, Piotr; de Lautour, Oliver R.

    2007-04-01

    Developed to study long, regularly sampled streams of data, time series analysis methods are being increasingly investigated for the use of Structural Health Monitoring. In this research, Autoregressive (AR) models are used in conjunction with Artificial Neural Networks (ANNs) for damage detection, localisation and severity assessment. In the first reported experimental exercise, AR models were used offline to fit the acceleration time histories of a 3-storey test structure in undamaged and various damaged states when excited by earthquake motion simulated on a shake table. Damage was introduced into the structure by replacing the columns with those of a thinner thickness. Analytical models of the structure in both damaged and undamaged states were also developed and updated using experimental data in order to determine structural stiffness. The coefficients of AR models were used as damage sensitive features and input into an ANN to build a relationship between them and the remaining structural stiffness. In the second, analytical exercise, a system with gradually progressing damage was numerically simulated and acceleration AR models with exogenous inputs were identified recursively. A trained ANN was then required to trace the structural stiffness online. The results for the offline and online approach showed the efficiency of using AR coefficient as damage sensitive features and good performance of the ANNs for damage detection, localization and quantification.

  4. Comparison of Artificial Neural Networks and ARIMA statistical models in simulations of target wind time series

    Science.gov (United States)

    Kolokythas, Kostantinos; Vasileios, Salamalikis; Athanassios, Argiriou; Kazantzidis, Andreas

    2015-04-01

    The wind is a result of complex interactions of numerous mechanisms taking place in small or large scales, so, the better knowledge of its behavior is essential in a variety of applications, especially in the field of power production coming from wind turbines. In the literature there is a considerable number of models, either physical or statistical ones, dealing with the problem of simulation and prediction of wind speed. Among others, Artificial Neural Networks (ANNs) are widely used for the purpose of wind forecasting and, in the great majority of cases, outperform other conventional statistical models. In this study, a number of ANNs with different architectures, which have been created and applied in a dataset of wind time series, are compared to Auto Regressive Integrated Moving Average (ARIMA) statistical models. The data consist of mean hourly wind speeds coming from a wind farm on a hilly Greek region and cover a period of one year (2013). The main goal is to evaluate the models ability to simulate successfully the wind speed at a significant point (target). Goodness-of-fit statistics are performed for the comparison of the different methods. In general, the ANN showed the best performance in the estimation of wind speed prevailing over the ARIMA models.

  5. A structure generator for modelling the initial sediment distribution of an artificial hydrologic catchment

    Directory of Open Access Journals (Sweden)

    T. Maurer

    2011-05-01

    Full Text Available Artificially-created hydrological catchments are characterized by sediment structures from technological construction processes that can potentially be important for modelling of flow and transport and for understanding initial soil and ecosystem development. The subsurface spatial structures of such catchments have not yet been sufficiently explored and described. Our objective was to develop a structure generator programme for modelling the 3-D spatial sediment distribution patterns depending on the technical earth-moving and deposition processes. For the development, the artificially-constructed hydrological catchment "Chicken Creek" located in Lower Lusatia, Germany, served as an example. The structure generator describes 3-D technological sediment distributions at two scales: (i for a 2-D-vertical cross-section, texture and bulk density distributions are generated within individual spoil cones that result from mass dumping, particle segregation, and compaction and (ii for the whole catchment area, the spoil cones are horizontally arranged along trajectories of mass dumping controlled by the belt stacker-machine relative to the catchment's clay layer topography. The generated 3-D texture and bulk density distributions are interpolated and visualized as a gridded 3-D-volume body using 3-D computer-aided design software. The generated subsurface sediment distribution for the Chicken Creek catchment was found to correspond to observed patterns although still without any calibration. Spatial aggregation and interpolation in the gridded volume body modified the generated distributions towards more uniform (unimodal distributions and lower values of the standard deviations. After incorporating variations and pedotransfer approaches, generated sediment distributions can be used for deriving realizations of the 3-D hydraulic catchment structure.

  6. A Model for Hourly Solar Radiation Data Generation from Daily Solar Radiation Data Using a Generalized Regression Artificial Neural Network

    OpenAIRE

    Tamer Khatib; Wilfried Elmenreich

    2015-01-01

    This paper presents a model for predicting hourly solar radiation data using daily solar radiation averages. The proposed model is a generalized regression artificial neural network. This model has three inputs, namely, mean daily solar radiation, hour angle, and sunset hour angle. The output layer has one node which is mean hourly solar radiation. The training and development of the proposed model are done using MATLAB and 43800 records of hourly global solar radiation. The results show that...

  7. Discrimination-based Artificial Immune System: Modeling the Learning Mechanism of Self and Non-self Discrimination for Classification

    Directory of Open Access Journals (Sweden)

    Kazushi Igawa

    2007-01-01

    Full Text Available This study presents a new artificial immune system for classification. It was named discrimination-based artificial immune system (DAIS and was based on the principle of self and non-self discrimination by T cells in the human immune system. Ability of a natural immune system to distinguish between self and non-self molecules was applicable for classification in a way that one class was distinguished from others. We model this and the mechanism of the education in a thymus for classification. Especially, we introduce the method to decide the recognition distance threshold of the artificial lymphocyte, as the negative selection algorithm. We apply DAIS to real world datasets and show its performance to be comparable to that of other classifier systems. We conclude that this modeling was appropriate and DAIS was a useful classifier.

  8. Zebrafish models for the functional genomics of neurogenetic disorders.

    Science.gov (United States)

    Kabashi, Edor; Brustein, Edna; Champagne, Nathalie; Drapeau, Pierre

    2011-03-01

    In this review, we consider recent work using zebrafish to validate and study the functional consequences of mutations of human genes implicated in a broad range of degenerative and developmental disorders of the brain and spinal cord. Also we present technical considerations for those wishing to study their own genes of interest by taking advantage of this easily manipulated and clinically relevant model organism. Zebrafish permit mutational analyses of genetic function (gain or loss of function) and the rapid validation of human variants as pathological mutations. In particular, neural degeneration can be characterized at genetic, cellular, functional, and behavioral levels. Zebrafish have been used to knock down or express mutations in zebrafish homologs of human genes and to directly express human genes bearing mutations related to neurodegenerative disorders such as spinal muscular atrophy, ataxia, hereditary spastic paraplegia, amyotrophic lateral sclerosis (ALS), epilepsy, Huntington's disease, Parkinson's disease, fronto-temporal dementia, and Alzheimer's disease. More recently, we have been using zebrafish to validate mutations of synaptic genes discovered by large-scale genomic approaches in developmental disorders such as autism, schizophrenia, and non-syndromic mental retardation. Advances in zebrafish genetics such as multigenic analyses and chemical genetics now offer a unique potential for disease research. Thus, zebrafish hold much promise for advancing the functional genomics of human diseases, the understanding of the genetics and cell biology of degenerative and developmental disorders, and the discovery of therapeutics. This article is part of a Special Issue entitled Zebrafish Models of Neurological Diseases. PMID:20887784

  9. Tracer test modeling for local scale residence time distribution characterization in an artificial recharge site

    OpenAIRE

    Valhondo, Cristina; Martínez-Landa, Lurdes; Carrera, Jesús; Hidalgo, Juan J.; Tubau, Isabel; Pourcq, Katrien; Grau-Martínez, Alba; Ayora, Carlos

    2016-01-01

    Artificial recharge of aquifers is a technique for improving water quality and increasing groundwater resources. Understanding the fate of a potential contaminant requires knowledge of the residence times distribution (RTD) of the water beneath the artificial recharge infrastructure. A simple way to obtain the RTDs is to perform a tracer test. We performed a pulse injection tracer test in an artificial recharge system through an infiltration basin to obtain the breakthrough curves, which y...

  10. Artificial intelligence in hematology.

    Science.gov (United States)

    Zini, Gina

    2005-10-01

    Artificial intelligence (AI) is a computer based science which aims to simulate human brain faculties using a computational system. A brief history of this new science goes from the creation of the first artificial neuron in 1943 to the first artificial neural network application to genetic algorithms. The potential for a similar technology in medicine has immediately been identified by scientists and researchers. The possibility to store and process all medical knowledge has made this technology very attractive to assist or even surpass clinicians in reaching a diagnosis. Applications of AI in medicine include devices applied to clinical diagnosis in neurology and cardiopulmonary diseases, as well as the use of expert or knowledge-based systems in routine clinical use for diagnosis, therapeutic management and for prognostic evaluation. Biological applications include genome sequencing or DNA gene expression microarrays, modeling gene networks, analysis and clustering of gene expression data, pattern recognition in DNA and proteins, protein structure prediction. In the field of hematology the first devices based on AI have been applied to the routine laboratory data management. New tools concern the differential diagnosis in specific diseases such as anemias, thalassemias and leukemias, based on neural networks trained with data from peripheral blood analysis. A revolution in cancer diagnosis, including the diagnosis of hematological malignancies, has been the introduction of the first microarray based and bioinformatic approach for molecular diagnosis: a systematic approach based on the monitoring of simultaneous expression of thousands of genes using DNA microarray, independently of previous biological knowledge, analysed using AI devices. Using gene profiling, the traditional diagnostic pathways move from clinical to molecular based diagnostic systems. PMID:16203606

  11. Incorporating Artificial Neural Networks in the dynamic thermal-hydraulic model of a controlled cryogenic circuit

    Science.gov (United States)

    Carli, S.; Bonifetto, R.; Savoldi, L.; Zanino, R.

    2015-09-01

    A model based on Artificial Neural Networks (ANNs) is developed for the heated line portion of a cryogenic circuit, where supercritical helium (SHe) flows and that also includes a cold circulator, valves, pipes/cryolines and heat exchangers between the main loop and a saturated liquid helium (LHe) bath. The heated line mimics the heat load coming from the superconducting magnets to their cryogenic cooling circuits during the operation of a tokamak fusion reactor. An ANN is trained, using the output from simulations of the circuit performed with the 4C thermal-hydraulic (TH) code, to reproduce the dynamic behavior of the heated line, including for the first time also scenarios where different types of controls act on the circuit. The ANN is then implemented in the 4C circuit model as a new component, which substitutes the original 4C heated line model. For different operational scenarios and control strategies, a good agreement is shown between the simplified ANN model results and the original 4C results, as well as with experimental data from the HELIOS facility confirming the suitability of this new approach which, extended to an entire magnet systems, can lead to real-time control of the cooling loops and fast assessment of control strategies for heat load smoothing to the cryoplant.

  12. Linear and nonlinear ARMA model parameter estimation using an artificial neural network

    Science.gov (United States)

    Chon, K. H.; Cohen, R. J.

    1997-01-01

    This paper addresses parametric system identification of linear and nonlinear dynamic systems by analysis of the input and output signals. Specifically, we investigate the relationship between estimation of the system using a feedforward neural network model and estimation of the system by use of linear and nonlinear autoregressive moving-average (ARMA) models. By utilizing a neural network model incorporating a polynomial activation function, we show the equivalence of the artificial neural network to the linear and nonlinear ARMA models. We compare the parameterization of the estimated system using the neural network and ARMA approaches by utilizing data generated by means of computer simulations. Specifically, we show that the parameters of a simulated ARMA system can be obtained from the neural network analysis of the simulated data or by conventional least squares ARMA analysis. The feasibility of applying neural networks with polynomial activation functions to the analysis of experimental data is explored by application to measurements of heart rate (HR) and instantaneous lung volume (ILV) fluctuations.

  13. Artificial intelligence based models for stream-flow forecasting: 2000-2015

    Science.gov (United States)

    Yaseen, Zaher Mundher; El-shafie, Ahmed; Jaafar, Othman; Afan, Haitham Abdulmohsin; Sayl, Khamis Naba

    2015-11-01

    The use of Artificial Intelligence (AI) has increased since the middle of the 20th century as seen in its application in a wide range of engineering and science problems. The last two decades, for example, has seen a dramatic increase in the development and application of various types of AI approaches for stream-flow forecasting. Generally speaking, AI has exhibited significant progress in forecasting and modeling non-linear hydrological applications and in capturing the noise complexity in the dataset. This paper explores the state-of-the-art application of AI in stream-flow forecasting, focusing on defining the data-driven of AI, the advantages of complementary models, as well as the literature and their possible future application in modeling and forecasting stream-flow. The review also identifies the major challenges and opportunities for prospective research, including, a new scheme for modeling the inflow, a novel method for preprocessing time series frequency based on Fast Orthogonal Search (FOS) techniques, and Swarm Intelligence (SI) as an optimization approach.

  14. Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model.

    Science.gov (United States)

    Qiu, Mingyue; Song, Yu

    2016-01-01

    In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index; hence, there is a great deal of research being conducted regarding the prediction of the direction of stock price index movement. Many factors such as political events, general economic conditions, and traders' expectations may have an influence on the stock market index. There are numerous research studies that use similar indicators to forecast the direction of the stock market index. In this study, we compare two basic types of input variables to predict the direction of the daily stock market index. The main contribution of this study is the ability to predict the direction of the next day's price of the Japanese stock market index by using an optimized artificial neural network (ANN) model. To improve the prediction accuracy of the trend of the stock market index in the future, we optimize the ANN model using genetic algorithms (GA). We demonstrate and verify the predictability of stock price direction by using the hybrid GA-ANN model and then compare the performance with prior studies. Empirical results show that the Type 2 input variables can generate a higher forecast accuracy and that it is possible to enhance the performance of the optimized ANN model by selecting input variables appropriately. PMID:27196055

  15. Predicting Free Flow Speed and Crash Risk of Bicycle Traffic Flow Using Artificial Neural Network Models

    Directory of Open Access Journals (Sweden)

    Cheng Xu

    2015-01-01

    Full Text Available Free flow speed is a fundamental measure of traffic performance and has been found to affect the severity of crash risk. However, the previous studies lack analysis and modelling of impact factors on bicycles’ free flow speed. The main focus of this study is to develop multilayer back propagation artificial neural network (BPANN models for the prediction of free flow speed and crash risk on the separated bicycle path. Four different models with considering different combinations of input variables (e.g., path width, traffic condition, bicycle type, and cyclists’ characteristics were developed. 459 field data samples were collected from eleven bicycle paths in Hangzhou, China, and 70% of total samples were used for training, 15% for validation, and 15% for testing. The results show that considering the input variables of bicycle types and characteristics of cyclists will effectively improve the accuracy of the prediction models. Meanwhile, the parameters of bicycle types have more significant effect on predicting free flow speed of bicycle compared to those of cyclists’ characteristics. The findings could contribute for evaluation, planning, and management of bicycle safety.

  16. Artificial neural network modeling of mechanical alloying process for synthesizing of metal matrix nanocomposite powders

    International Nuclear Information System (INIS)

    An artificial neural network model was developed for modeling of the effects of mechanical alloying parameters including milling time, milling speed and ball to powder weight ratio on the characteristics of Al-8 vol%SiC nanocomposite powders. The crystallite size and lattice strain of the aluminum matrix were considered for modeling. This nanostructured nanocomposite powder was synthesized by utilizing planetary high energy ball mill and the required data for training were collected from the experimental results. The characteristics of the particles were determined by X-ray diffraction, scanning and transmission electron microscopy. Two types of neural network architecture, i.e. multi-layer perceptron (MLP) and radial basis function (RBF), were used. The steepest descent along with variable learning rate back-propagation algorithm known as a heuristic technique was utilized for training the MLP network. It was found that MLP network yields better results compared to RBF network, giving an acceptable mapping between the network responses and the target data with a high correlation coefficients. The response surfaces between the response variables, i.e. crystallite size, lattice strain of the aluminum matrix and the processing parameters are presented. The procedure modeling can be used for optimization of the MA process for synthesizing of nanostructured metal matrix nanocomposites

  17. Modeling of Vibration Monitoring of Steam Turbine in Nuclear Power Plant using Modular Artificial Neural Network

    International Nuclear Information System (INIS)

    This paper states a methodology for using a Modular Artificial Neural Network (ANN) in modeling the vibration monitoring of the Steam Turbine (ST) in Nuclear Power Plant (NPP). The input and the output signals of the vibration transducer are used as a source of the training data for the neural network model. The type of the network used in this methodology is the supervised Multilayer Feed-Forward Neural Networks with the Back-Propagation (BP) algorithm. The module architecture is according to the Human Factors (HF) Considerations in designing the Human-System Interface (HSI). The Vibration Severity limits are determined by the International Organization for Standardization (ISO) 10816. The model also contained 2 out of 3 voting and dynamic trip limit value ANNs. The results show that the proposed Modular ANN has good generalization capability to monitor and protect the machine from the Vibration Severity, increasing the reliability of (ST), and good HSI. This modeling methodology can be used for the other non-redundant components in NPP such as Reactor Coolant Pump (RCP)

  18. Predicting Global Solar Radiation Using an Artificial Neural Network Single-Parameter Model

    Directory of Open Access Journals (Sweden)

    Karoro Angela

    2011-01-01

    Full Text Available We used five years of global solar radiation data to estimate the monthly average of daily global solar irradiation on a horizontal surface based on a single parameter, sunshine hours, using the artificial neural network method. The station under the study is located in Kampala, Uganda at a latitude of 0.19°N, a longitude of 32.34°E, and an altitude of 1200 m above sea level. The five-year data was split into two parts in 2003–2006 and 2007-2008; the first part was used for training, and the latter was used for testing the neural network. Amongst the models tested, the feed-forward back-propagation network with one hidden layer (65 neurons and with the tangent sigmoid as the transfer function emerged as the more appropriate model. Results obtained using the proposed model showed good agreement between the estimated and actual values of global solar irradiation. A correlation coefficient of 0.963 was obtained with a mean bias error of 0.055 MJ/m2 and a root mean square error of 0.521 MJ/m2. The single-parameter ANN model shows promise for estimating global solar irradiation at places where monitoring stations are not established and stations where we have one common parameter (sunshine hours.

  19. Modeling of Soft sensor based on Artificial Neural Network for Galactic Cosmic Rays Application

    International Nuclear Information System (INIS)

    For successful designing of space radiation Galactic Cosmic Rays (GCRs) model, we develop a soft sensor based on the Artificial Neural Network (ANN) model. At the first step, the soft sensor based ANN was constructed as an alternative to model space radiation environment. The structure of ANN in this model is using Multilayer Perceptron (MLP) and Levenberg Marquardt algorithms with 3 inputs and 2 outputs. In the input variable, we use 12 years data (Corr, Uncorr and Press) of GCR particles obtained from Neutron Monitor of Bartol University (Fort Smith area) and the target output is (Corr and Press) from the same source but for Inuvik area in the Polar Regions. In the validation step, we obtained the Root Mean Square Error (RMSE) value of Corr 3.8670e-004 and Press 1.3414e-004 and Variance Accounted For (VAF) of Corr 99.9839 % and Press 99.9831% during the training section. After all the results obtained, then we applied into a Matlab GUI simulation (soft sensor simulation). This simulation will display the estimation of output value from input (Corr and Press). Testing results showed an error of 0.133% and 0.014% for Corr and Press, respectively

  20. Prediction of Drug Lipophilicity Using Back Propagation Artificial Neural Network Modeling

    Directory of Open Access Journals (Sweden)

    Saadi Saaidpour

    2014-06-01

    Full Text Available A quantitative structure–property relationship (QSPR study was performed to develop models those relate the structures of 150 drug organic compounds to their n-octanol–water partition coefficients (logPo/w. Molecular descriptors derived solely from 3D structures of the molecular drugs. A genetic algorithm was also applied as a variable selection tools in QSPR analysis. The models were constructed based on 110 training compounds, and predictive ability was tested on 40 compounds reserved for that purpose. Application of the developed models to a testing set of 40 drug organic compounds demonstrates that the new models are reliable with good predictive accuracy and simple formulation. Modeling of logarithm of logPo/w of these compounds as a function of the theoretically derived descriptors was established by artificial neural network (ANN. The neural network employed here is a connected back-propagation model with a 4-4-1 architecture. Four descriptors for these compounds molecular volume (MV (Geometrical, hydrophilic-lipophilic balance (HLB (Constitutional, hydrogen bond forming ability (HB (Electronic and polar surface area (PSA (Electrostatic are taken as inputs for the models. The use of descriptors calculated only from molecular structure eliminates the need for experimental determination of properties for use in the correlation and allows for the estimation of logPo/w for molecules not yet synthesized. The prediction results are in good agreement with the experimental value. The root mean square error of prediction (RMSEP and square correlation coefficient (R2 for ANN model were 0.1838, 0.9876 for the prediction set logPo/w, respectively.

  1. Artificial neural network models of relationships between Alternaria spores and meteorological factors in Szczecin (Poland)

    Science.gov (United States)

    Grinn-Gofroń, Agnieszka; Strzelczak, Agnieszka

    2008-11-01

    Alternaria is an airborne fungal spore type known to trigger respiratory allergy symptoms in sensitive patients. Aiming to reduce the risk for allergic individuals, we constructed predictive models for the fungal spore circulation in Szczecin, Poland. Monthly forecasting models were developed for the airborne spore concentrations of Alternaria, which is one of the most abundant fungal taxa in the area. Aerobiological sampling was conducted over 2004-2007, using a Lanzoni trap. Simultaneously, the following meteorological parameters were recorded: daily level of precipitation; maximum and average wind speed; relative humidity; and maximum, minimum, average, and dew point temperature. The original factors as well as with lags (up to 3 days) were used as the explaining variables. Due to non-linearity and non-normality of the data set, the modelling technique applied was the artificial neural network (ANN) method. The final model was a split model with classification (spore presence or absence) followed by regression for spore seasons and log(x+1) transformed Alternaria spore concentration. All variables except maximum wind speed and precipitation were important factors in the overall classification model. In the regression model for spore seasons, close relationships were noted between Alternaria spore concentration and average and maximum temperature (on the same day and 3 days previously), humidity (with lag 1) and maximum wind speed 2 days previously. The most important variable was humidity recorded on the same day. Our study illustrates a novel approach to modelling of time series with short spore seasons, and indicates that the ANN method provides the possibility of forecasting Alternaria spore concentration with high accuracy.

  2. Quality Model and Artificial Intelligence Base Fuel Ratio Management with Applications to Automotive Engine

    Directory of Open Access Journals (Sweden)

    Mojdeh Piran

    2014-01-01

    Full Text Available In this research, manage the Internal Combustion (IC engine modeling and a multi-input-multi-output artificial intelligence baseline chattering free sliding mode methodology scheme is developed with guaranteed stability to simultaneously control fuel ratios to desired levels under various air flow disturbances by regulating the mass flow rates of engine PFI and DI injection systems. Modeling of an entire IC engine is a very important and complicated process because engines are nonlinear, multi inputs-multi outputs and time variant. One purpose of accurate modeling is to save development costs of real engines and minimizing the risks of damaging an engine when validating controller designs. Nevertheless, developing a small model, for specific controller design purposes, can be done and then validated on a larger, more complicated model. Analytical dynamic nonlinear modeling of internal combustion engine is carried out using elegant Euler-Lagrange method compromising accuracy and complexity. A baseline estimator with varying parameter gain is designed with guaranteed stability to allow implementation of the proposed state feedback sliding mode methodology into a MATLAB simulation environment, where the sliding mode strategy is implemented into a model engine control module (“software”. To estimate the dynamic model of IC engine fuzzy inference engine is applied to baseline sliding mode methodology. The fuzzy inference baseline sliding methodology performance was compared with a well-tuned baseline multi-loop PID controller through MATLAB simulations and showed improvements, where MATLAB simulations were conducted to validate the feasibility of utilizing the developed controller and state estimator for automotive engines. The proposed tracking method is designed to optimally track the desired FR by minimizing the error between the trapped in-cylinder mass and the product of the desired FR and fuel mass over a given time interval.

  3. Development of surrogate models using artificial neural network for building shell energy labelling

    International Nuclear Information System (INIS)

    Surrogate models are an important part of building energy labelling programs, but these models still present low accuracy, particularly in cooling-dominated climates. The objective of this study was to evaluate the feasibility of using an artificial neural network (ANN) to improve the accuracy of surrogate models for labelling purposes. An ANN was applied to model the building stock of a city in Brazil, based on the results of extensive simulations using the high-resolution building energy simulation program EnergyPlus. Sensitivity and uncertainty analyses were carried out to evaluate the behaviour of the ANN model, and the variations in the best and worst performance for several typologies were analysed in relation to variations in the input parameters and building characteristics. The results obtained indicate that an ANN can represent the interaction between input and output data for a vast and diverse building stock. Sensitivity analysis showed that no single input parameter can be identified as the main factor responsible for the building energy performance. The uncertainty associated with several parameters plays a major role in assessing building energy performance, together with the facade area and the shell-to-floor ratio. The results of this study may have a profound impact as ANNs could be applied in the future to define regulations in many countries, with positive effects on optimizing the energy consumption. - Highlights: • We model several typologies which have variation in input parameters. • We evaluate the accuracy of surrogate models for labelling purposes. • ANN is applied to model the building stock. • Uncertainty in building plays a major role in the building energy performance. • Results show that ANN could help to develop building energy labelling systems

  4. Application of Artificial Intelligence (Artificial Neural Network) to Assess Credit Risk : A Predictive Model For Credit Card Scoring

    OpenAIRE

    Islam, Md. Samsul; Zhou, Lin; LI Fei

    2009-01-01

    Credit Decisions are extremely vital for any type of financial institution because it can stimulate huge financial losses generated from defaulters. A number of banks use judgmental decisions, means credit analysts go through every application separately and other banks use credit scoring system or combination of both. Credit scoring system uses many types of statistical models. But recently, professionals started looking for alternative algorithms that can provide better accuracy regarding c...

  5. Modelling survival after treatment of intraocular melanoma using artificial neural networks and Bayes theorem

    International Nuclear Information System (INIS)

    This paper describes the development of an artificial intelligence (AI) system for survival prediction from intraocular melanoma. The system used artificial neural networks (ANNs) with five input parameters: coronal and sagittal tumour location, anterior tumour margin, largest basal tumour diameter and the cell type. After excluding records with missing data, 2331 patients were included in the study. These were split randomly into training and test sets. Date censorship was applied to the records to deal with patients who were lost to follow-up and patients who died from general causes. Bayes theorem was then applied to the ANN output to construct survival probability curves. A validation set with 34 patients unseen to both training and test sets was used to compare the AI system with Cox's regression (CR) and Kaplan-Meier (KM) analyses. Results showed large differences in the mean 5 year survival probability figures when the number of records with matching characteristics was small. However, as the number of matches increased to >100 the system tended to agree with CR and KM. The validation set was also used to compare the system with a clinical expert in predicting time to metastatic death. The rms error was 3.7 years for the system and 4.3 years for the clinical expert for 15 years survival. For <10 years survival, these figures were 2.7 and 4.2, respectively. We concluded that the AI system can match if not better the clinical expert's prediction. There were significant differences with CR and KM analyses when the number of records was small, but it was not known which model is more accurate

  6. Modelling survival after treatment of intraocular melanoma using artificial neural networks and Bayes theorem

    Science.gov (United States)

    Taktak, Azzam F. G.; Fisher, Anthony C.; Damato, Bertil E.

    2004-01-01

    This paper describes the development of an artificial intelligence (AI) system for survival prediction from intraocular melanoma. The system used artificial neural networks (ANNs) with five input parameters: coronal and sagittal tumour location, anterior tumour margin, largest basal tumour diameter and the cell type. After excluding records with missing data, 2331 patients were included in the study. These were split randomly into training and test sets. Date censorship was applied to the records to deal with patients who were lost to follow-up and patients who died from general causes. Bayes theorem was then applied to the ANN output to construct survival probability curves. A validation set with 34 patients unseen to both training and test sets was used to compare the AI system with Cox's regression (CR) and Kaplan-Meier (KM) analyses. Results showed large differences in the mean 5 year survival probability figures when the number of records with matching characteristics was small. However, as the number of matches increased to >100 the system tended to agree with CR and KM. The validation set was also used to compare the system with a clinical expert in predicting time to metastatic death. The rms error was 3.7 years for the system and 4.3 years for the clinical expert for 15 years survival. For survival, these figures were 2.7 and 4.2, respectively. We concluded that the AI system can match if not better the clinical expert's prediction. There were significant differences with CR and KM analyses when the number of records was small, but it was not known which model is more accurate.

  7. Curriculum Assessment Using Artificial Neural Network and Support Vector Machine Modeling Approaches: A Case Study. IR Applications. Volume 29

    Science.gov (United States)

    Chen, Chau-Kuang

    2010-01-01

    Artificial Neural Network (ANN) and Support Vector Machine (SVM) approaches have been on the cutting edge of science and technology for pattern recognition and data classification. In the ANN model, classification accuracy can be achieved by using the feed-forward of inputs, back-propagation of errors, and the adjustment of connection weights. In…

  8. A comparative study between nonlinear regression and artificial neural network approaches for modelling wild oat (Avena fatua) field emergence

    Science.gov (United States)

    Non-linear regression techniques are used widely to fit weed field emergence patterns to soil microclimatic indices using S-type functions. Artificial neural networks present interesting and alternative features for such modeling purposes. In this work, a univariate hydrothermal-time based Weibull m...

  9. Predictions on the Development Dimensions of Provincial Tourism Discipline Based on the Artificial Neural Network BP Model

    Science.gov (United States)

    Yang, Yang; Hu, Jun; Lv, Yingchun; Zhang, Mu

    2013-01-01

    As the tourism industry has gradually become the strategic mainstay industry of the national economy, the scope of the tourism discipline has developed rigorously. This paper makes a predictive study on the development of the scope of Guangdong provincial tourism discipline based on the artificial neural network BP model in order to find out how…

  10. Fuzzy Logic, Neural Networks, Genetic Algorithms: Views of Three Artificial Intelligence Concepts Used in Modeling Scientific Systems

    Science.gov (United States)

    Sunal, Cynthia Szymanski; Karr, Charles L.; Sunal, Dennis W.

    2003-01-01

    Students' conceptions of three major artificial intelligence concepts used in the modeling of systems in science, fuzzy logic, neural networks, and genetic algorithms were investigated before and after a higher education science course. Students initially explored their prior ideas related to the three concepts through active tasks. Then,…

  11. Artificial intelligence

    CERN Document Server

    Hunt, Earl B

    1975-01-01

    Artificial Intelligence provides information pertinent to the fundamental aspects of artificial intelligence. This book presents the basic mathematical and computational approaches to problems in the artificial intelligence field.Organized into four parts encompassing 16 chapters, this book begins with an overview of the various fields of artificial intelligence. This text then attempts to connect artificial intelligence problems to some of the notions of computability and abstract computing devices. Other chapters consider the general notion of computability, with focus on the interaction bet

  12. Artificial neural network based modeling of performance characteristics of deep well pumps with splitter blade

    International Nuclear Information System (INIS)

    Experimental studies were made to investigate the effects of splitter blade length (25%, 35%, 50%, 60% and 80% of the main blade length) on the pump characteristics of deep well pumps for different blade numbers (z=3, 4, 5, 6 and 7). In this study, an artificial neural network (ANN) was used for modeling the performance of deep well pumps with splitter blades. Two hundred and ten experimental results were used to train and test. Forty-two patterns have been randomly selected and used as the test data. The main parameters for the experiments are the blade number (z), non-dimensional splitter blade length (L-bar ), flow rate (Q, l/s), head (Hm, m), efficiency (η, %) and power (Pe, kW). z, L-bar and Q have been used as the input layer, and Hm and η have also been used as the output layer. The best training algorithm and number of neurons were obtained. Training of the network was performed using the Levenberg-Marquardt (LM) algorithm. To determine the effect of the transfer function, different ANN models are trained, and the results of these ANN models are compared. Some statistical methods; fraction of variance (R2) and root mean squared error (RMSE) values, have been used for comparison

  13. A research model for investigating the effects of artificial food colorings on children with ADHD.

    Science.gov (United States)

    Kleinman, Ronald E; Brown, Ronald T; Cutter, Gary R; Dupaul, George J; Clydesdale, Fergus M

    2011-06-01

    The United Kingdom and European Union recently restricted the use of artificial food colorings (AFCs) to improve the health of children. These decisions provide an interesting case study of the role of scientific evidence in the assessment of food additives and risk to children's health and formulation of food policy. Although there continues to be uncertainty concerning the link between AFCs and attention-deficit/hyperactivity disorder (ADHD), policy decisions have been made that have far-reaching implications. In addition, publicity surrounding the policy changes may shape public perceptions concerning effective management of ADHD. We believe that the balance of existing evidence neither refutes nor supports the link between AFCs and ADHD, which highlights the need for carefully designed studies to further investigate the link between AFCs and ADHD. In this article we describe a model for such studies. In developing our model, we drew from current investigative standards in ADHD research, such as those used in the landmark Multimodal Treatment Study of Children With ADHD. These standards encompass methodologic considerations including sample selection, outcome assessment, and data analyses. It is our hope that this model research methodology may prove valuable in addressing design considerations in future studies of AFCs and ADHD with the goal of producing reliable data that will enable policy-makers to better formulate effective, evidence-based food-policy decisions. PMID:21576306

  14. URBAN GROWTH MODELING USING AN ARTIFICIAL NEURAL NETWORK A CASE STUDY OF SANANDAJ CITY, IRAN

    Directory of Open Access Journals (Sweden)

    S. Mohammady

    2014-10-01

    Full Text Available Land use activity is a major issue and challenge for town and country planners. Modelling and managing urban growth is a complex problem. Cities are now recognized as complex, non-linear and dynamic process systems. The design of a system that can handle these complexities is a challenging prospect. Local governments that implement urban growth models need to estimate the amount of urban land required in the future given anticipated growth of housing, business, recreation and other urban uses within the boundary. There are so many negative implications related with the type of inappropriate urban development such as increased traffic and demand for mobility, reduced landscape attractively, land use fragmentation, loss of biodiversity and alterations of the hydrological cycle. The aim of this study is to use the Artificial Neural Network (ANN to make a powerful tool for simulating urban growth patterns. Our study area is Sanandaj city located in the west of Iran. Landsat imageries acquired at 2000 and 2006 are used. Dataset were used include distance to principle roads, distance to residential areas, elevation, slope, distance to green spaces and distance to region centers. In this study an appropriate methodology for urban growth modelling using satellite remotely sensed data is presented and evaluated. Percent Correct Match (PCM and Figure of Merit were used to evaluate ANN results.

  15. Urban Growth Modeling Using AN Artificial Neural Network a Case Study of Sanandaj City, Iran

    Science.gov (United States)

    Mohammady, S.; Delavar, M. R.; Pahlavani, P.

    2014-10-01

    Land use activity is a major issue and challenge for town and country planners. Modelling and managing urban growth is a complex problem. Cities are now recognized as complex, non-linear and dynamic process systems. The design of a system that can handle these complexities is a challenging prospect. Local governments that implement urban growth models need to estimate the amount of urban land required in the future given anticipated growth of housing, business, recreation and other urban uses within the boundary. There are so many negative implications related with the type of inappropriate urban development such as increased traffic and demand for mobility, reduced landscape attractively, land use fragmentation, loss of biodiversity and alterations of the hydrological cycle. The aim of this study is to use the Artificial Neural Network (ANN) to make a powerful tool for simulating urban growth patterns. Our study area is Sanandaj city located in the west of Iran. Landsat imageries acquired at 2000 and 2006 are used. Dataset were used include distance to principle roads, distance to residential areas, elevation, slope, distance to green spaces and distance to region centers. In this study an appropriate methodology for urban growth modelling using satellite remotely sensed data is presented and evaluated. Percent Correct Match (PCM) and Figure of Merit were used to evaluate ANN results.

  16. Constructing prediction interval for artificial neural network rainfall runoff models based on ensemble simulations

    Science.gov (United States)

    Kasiviswanathan, K. S.; Cibin, R.; Sudheer, K. P.; Chaubey, I.

    2013-08-01

    This paper presents a method of constructing prediction interval for artificial neural network (ANN) rainfall runoff models during calibration with a consideration of generating ensemble predictions. A two stage optimization procedure is envisaged in this study for construction of prediction interval for the ANN output. In Stage 1, ANN model is trained with genetic algorithm (GA) to obtain optimal set of weights and biases vector. In Stage 2, possible variability of ANN parameters (obtained in Stage 1) is optimized so as to create an ensemble of models with the consideration of minimum residual variance for the ensemble mean, while ensuring a maximum of the measured data to fall within the estimated prediction interval. The width of the prediction interval is also minimized simultaneously. The method is demonstrated using a real world case study of rainfall runoff data for an Indian basin. The method was able to produce ensembles with a prediction interval (average width) of 26.49 m3/s with 97.17% of the total observed data points lying within the interval in validation. One specific advantage of the method is that when ensemble mean value is considered as a forecast, the peak flows are predicted with improved accuracy by this method compared to traditional single point forecasted ANNs.

  17. Modeling by artificial neural networks. Application to the management of fuel in a nuclear power plant

    International Nuclear Information System (INIS)

    The determination of the family of optimum core loading patterns for Pressurized Water Reactors (PWRs) involves the assessment of the core attributes, such as the power peaking factor for thousands of candidate loading patterns. Despite the rapid advances in computer architecture, the direct calculation of these attributes by a neutronic code needs a lot of of time and memory. With the goal of reducing the calculation time and optimizing the loading pattern, we propose in this thesis a method based on ideas of neural and statistical learning to provide a feed forward neural network capable of calculating the power peaking corresponding to an eighth core PWR. We use statistical methods to deduct judicious inputs (reduction of the input space dimension) and neural methods to train the model (learning capabilities). Indeed, on one hand, a principal component analysis allows us to characterize more efficiently the fuel assemblies (neural model inputs) and the other hand, the introduction of the a priori knowledge allows us to reducing the number of freedom parameters in the neural network. The model was built using a multi layered perceptron trained with the standard back propagation algorithm. We introduced our neural network in the automatic optimization code FORMOSA, and on EDF real problems we showed an important saving in time. Finally, we propose an hybrid method which combining the best characteristics of the linear local approximator GPT (Generalized Perturbation Theory) and the artificial neural network. (author)

  18. Creep Crack Growth Modeling of Low Alloy Steel using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    F. Djavanroodi

    2013-07-01

    Full Text Available Prediction of crack growth under creep condition is prime requirement in order to avoid costly and time-consuming creep crack growth tests. To predict, in a reliable way, the growth of a major crack in a structural components operating at high temperatures, requires a fracture mechanics based approach. In this Study a novel technique, which uses Finite Element Method (FEM together with Artificial Neural Networks (ANN has been developed to predict the fracture mechanics parameter (C* in a 1%Cr1%MoV low alloy rotor steel under wide range of loading and temperatures. After confirming the validity of the FEM model with experimental data, a collection of numerical and experimental data has been used for training the various neural networks models. Three networks have been used to simulate the process, the perceptron multilayer network with tangent transfer function that uses 9 neurons in the hidden layer, gives the best results. Finally, for validation three case studies at 538°C, 550°C and 594°C temperatures are employed. The proposed model has proved that a combinations of ANN and FEM simulation performs well in estimation of C* and it is a powerful designing tool for creep crack growth characterization.

  19. Modeling Slump of Ready Mix Concrete Using Genetically Evolved Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Vinay Chandwani

    2014-01-01

    Full Text Available Artificial neural networks (ANNs have been the preferred choice for modeling the complex and nonlinear material behavior where conventional mathematical approaches do not yield the desired accuracy and predictability. Despite their popularity as a universal function approximator and wide range of applications, no specific rules for deciding the architecture of neural networks catering to a specific modeling task have been formulated. The research paper presents a methodology for automated design of neural network architecture, replacing the conventional trial and error technique of finding the optimal neural network. The genetic algorithms (GA stochastic search has been harnessed for evolving the optimum number of hidden layer neurons, transfer function, learning rate, and momentum coefficient for backpropagation ANN. The methodology has been applied for modeling slump of ready mix concrete based on its design mix constituents, namely, cement, fly ash, sand, coarse aggregates, admixture, and water-binder ratio. Six different statistical performance measures have been used for evaluating the performance of the trained neural networks. The study showed that, in comparison to conventional trial and error technique of deciding the neural network architecture and training parameters, the neural network architecture evolved through GA was of reduced complexity and provided better prediction performance.

  20. The spatial prediction of landslide susceptibility applying artificial neural network and logistic regression models: A case study of Inje, Korea

    Directory of Open Access Journals (Sweden)

    Saro Lee

    2016-02-01

    Full Text Available The aim of this study is to predict landslide susceptibility caused using the spatial analysis by the application of a statistical methodology based on the GIS. Logistic regression models along with artificial neutral network were applied and validated to analyze landslide susceptibility in Inje, Korea. Landslide occurrence area in the study were identified based on interpretations of optical remote sensing data (Aerial photographs followed by field surveys. A spatial database considering forest, geophysical, soil and topographic data, was built on the study area using the Geographical Information System (GIS. These factors were analysed using artificial neural network (ANN and logistic regression models to generate a landslide susceptibility map. The study validates the landslide susceptibility map by comparing them with landslide occurrence areas. The locations of landslide occurrence were divided randomly into a training set (50% and a test set (50%. A training set analyse the landslide susceptibility map using the artificial network along with logistic regression models, and a test set was retained to validate the prediction map. The validation results revealed that the artificial neural network model (with an accuracy of 80.10% was better at predicting landslides than the logistic regression model (with an accuracy of 77.05%. Of the weights used in the artificial neural network model, ‘slope’ yielded the highest weight value (1.330, and ‘aspect’ yielded the lowest value (1.000. This research applied two statistical analysis methods in a GIS and compared their results. Based on the findings, we were able to derive a more effective method for analyzing landslide susceptibility.

  1. The spatial prediction of landslide susceptibility applying artificial neural network and logistic regression models: A case study of Inje, Korea

    Science.gov (United States)

    Saro, Lee; Woo, Jeon Seong; Kwan-Young, Oh; Moung-Jin, Lee

    2016-02-01

    The aim of this study is to predict landslide susceptibility caused using the spatial analysis by the application of a statistical methodology based on the GIS. Logistic regression models along with artificial neutral network were applied and validated to analyze landslide susceptibility in Inje, Korea. Landslide occurrence area in the study were identified based on interpretations of optical remote sensing data (Aerial photographs) followed by field surveys. A spatial database considering forest, geophysical, soil and topographic data, was built on the study area using the Geographical Information System (GIS). These factors were analysed using artificial neural network (ANN) and logistic regression models to generate a landslide susceptibility map. The study validates the landslide susceptibility map by comparing them with landslide occurrence areas. The locations of landslide occurrence were divided randomly into a training set (50%) and a test set (50%). A training set analyse the landslide susceptibility map using the artificial network along with logistic regression models, and a test set was retained to validate the prediction map. The validation results revealed that the artificial neural network model (with an accuracy of 80.10%) was better at predicting landslides than the logistic regression model (with an accuracy of 77.05%). Of the weights used in the artificial neural network model, `slope' yielded the highest weight value (1.330), and `aspect' yielded the lowest value (1.000). This research applied two statistical analysis methods in a GIS and compared their results. Based on the findings, we were able to derive a more effective method for analyzing landslide susceptibility.

  2. Hierarchical Bayesian Model Averaging for Non-Uniqueness and Uncertainty Analysis of Artificial Neural Networks

    Science.gov (United States)

    Fijani, E.; Chitsazan, N.; Nadiri, A.; Tsai, F. T.; Asghari Moghaddam, A.

    2012-12-01

    Artificial Neural Networks (ANNs) have been widely used to estimate concentration of chemicals in groundwater systems. However, estimation uncertainty is rarely discussed in the literature. Uncertainty in ANN output stems from three sources: ANN inputs, ANN parameters (weights and biases), and ANN structures. Uncertainty in ANN inputs may come from input data selection and/or input data error. ANN parameters are naturally uncertain because they are maximum-likelihood estimated. ANN structure is also uncertain because there is no unique ANN model given a specific case. Therefore, multiple plausible AI models are generally resulted for a study. One might ask why good models have to be ignored in favor of the best model in traditional estimation. What is the ANN estimation variance? How do the variances from different ANN models accumulate to the total estimation variance? To answer these questions we propose a Hierarchical Bayesian Model Averaging (HBMA) framework. Instead of choosing one ANN model (the best ANN model) for estimation, HBMA averages outputs of all plausible ANN models. The model weights are based on the evidence of data. Therefore, the HBMA avoids overconfidence on the single best ANN model. In addition, HBMA is able to analyze uncertainty propagation through aggregation of ANN models in a hierarchy framework. This method is applied for estimation of fluoride concentration in the Poldasht plain and the Bazargan plain in Iran. Unusually high fluoride concentration in the Poldasht and Bazargan plains has caused negative effects on the public health. Management of this anomaly requires estimation of fluoride concentration distribution in the area. The results show that the HBMA provides a knowledge-decision-based framework that facilitates analyzing and quantifying ANN estimation uncertainties from different sources. In addition HBMA allows comparative evaluation of the realizations for each source of uncertainty by segregating the uncertainty sources in

  3. Analysis of genetic variation and potential applications in genome-scale metabolic modeling

    Directory of Open Access Journals (Sweden)

    João Gonçalo Rocha Cardoso

    2015-02-01

    Full Text Available Genetic variation is the motor of evolution and allows organisms to overcome the environmental challenges they encounter. It can be both beneficial and harmful in the process of engineering cell factories for the production of proteins and chemicals. Throughout the history of biotechnology, there have been efforts to exploit genetic variation in our favor to create strains with favorable phenotypes. Genetic variation can either be present in natural populations or it can be artificially created by mutagenesis and selection or adaptive laboratory evolution. On the other hand, unintended genetic variation during a long term production process may lead to significant economic losses and it is important to understand how to control this type of variation. With the emergence of next-generation sequencing technologies, genetic variation in microbial strains can now be determined on an unprecedented scale and resolution by re-sequencing thousands of strains systematically. In this article, we review challenges in the integration and analysis of large-scale re-sequencing data, present an extensive overview of bioinformatics methods for predicting the effects of genetic variants on protein function, and discuss approaches for interfacing existing bioinformatics approaches with genome-scale models of cellular processes in order to predict effects of sequence variation on cellular phenotypes.

  4. Genomic prediction and genomic variance partitioning of daily and residual feed intake in pigs using Bayesian Power Lasso models

    DEFF Research Database (Denmark)

    Do, Duy Ngoc; Janss, L. L. G.; Strathe, Anders Bjerring;

    Improvement of feed efficiency is essential in pig breeding and selection for reduced residual feed intake (RFI) is an option. The study applied Bayesian Power LASSO (BPL) models with different power parameter to investigate genetic architecture, to predict genomic breeding values, and to partition...... genomic variance for RFI and daily feed intake (DFI). A total of 1272 Duroc pigs had both genotypic and phenotypic records for these traits. Significant SNPs were detected on chromosome 1 (SSC 1) and SSC 14 for RFI and on SSC 1 for DFI. BPL models had similar accuracy and bias as GBLUP method but use of...

  5. Cloning of the Koi Herpesvirus Genome as an Infectious Bacterial Artificial Chromosome Demonstrates That Disruption of the Thymidine Kinase Locus Induces Partial Attenuation in Cyprinus carpio koi▿

    Science.gov (United States)

    Costes, B.; Fournier, G.; Michel, B.; Delforge, C.; Raj, V. Stalin; Dewals, B.; Gillet, L.; Drion, P.; Body, A.; Schynts, F.; Lieffrig, F.; Vanderplasschen, A.

    2008-01-01

    Koi herpesvirus (KHV) is the causative agent of a lethal disease in koi and common carp. In the present study, we describe the cloning of the KHV genome as a stable and infectious bacterial artificial chromosome (BAC) clone that can be used to produce KHV recombinant strains. This goal was achieved by the insertion of a loxP-flanked BAC cassette into the thymidine kinase (TK) locus. This insertion led to a BAC plasmid that was stably maintained in bacteria and was able to regenerate virions when permissive cells were transfected with the plasmid. Reconstituted virions free of the BAC cassette but carrying a disrupted TK locus (the FL BAC-excised strain) were produced by the transfection of Cre recombinase-expressing cells with the BAC. Similarly, virions with a wild-type revertant TK sequence (the FL BAC revertant strain) were produced by the cotransfection of cells with the BAC and a DNA fragment encoding the wild-type TK sequence. Reconstituted recombinant viruses were compared to the wild-type parental virus in vitro and in vivo. The FL BAC revertant strain and the FL BAC-excised strain replicated comparably to the parental FL strain. The FL BAC revertant strain induced KHV infection in koi carp that was indistinguishable from that induced by the parental strain, while the FL BAC-excised strain exhibited a partially attenuated phenotype. Finally, the usefulness of the KHV BAC for recombination studies was demonstrated by the production of an ORF16-deleted strain by using prokaryotic recombination technology. The availability of the KHV BAC is an important advance that will allow the study of viral genes involved in KHV pathogenesis, as well as the production of attenuated recombinant candidate vaccines. PMID:18337580

  6. Integrating microarray gene expression object model and clinical document architecture for cancer genomics research.

    Science.gov (United States)

    Park, Yu Rang; Lee, Hye Won; Kim, Ju Han

    2005-01-01

    Systematic integration of genomic-scale expression profiles with clinical information may facilitate cancer genomics research. MAGE-OM (Microarray Gene Expression Object Model) defines standard objects for genomic but not for clinical data. HL7 CDA (Clinical Document Architecture) is a document model for clinical information, describing syntax (generic structure) but not semantics. We designed a document template in XML Schema with additional constraints for CDA to define content semantics, enabling data model-level integration of MAGE-OM and CDA for cancer genomics research. PMID:16779360

  7. Whole genome comparative analysis of channel catfish (Ictalurus punctatus) with four model fish species

    OpenAIRE

    Jiang, Yanliang; Gao, Xiaoyu; Liu, Shikai; Zhang, Yu; Liu, Hong; Sun, Fanyue; Bao, Lisui; Waldbieser, Geoff; Liu, Zhanjiang

    2013-01-01

    Background Comparative mapping is a powerful tool to study evolution of genomes. It allows transfer of genome information from the well-studied model species to non-model species. Catfish is an economically important aquaculture species in United States. A large amount of genome resources have been developed from catfish including genetic linkage maps, physical maps, BAC end sequences (BES), integrated linkage and physical maps using BES-derived markers, physical map contig-specific sequences...

  8. Modeling the BOD of Danube River in Serbia using spatial, temporal, and input variables optimized artificial neural network models.

    Science.gov (United States)

    Šiljić Tomić, Aleksandra N; Antanasijević, Davor Z; Ristić, Mirjana Đ; Perić-Grujić, Aleksandra A; Pocajt, Viktor V

    2016-05-01

    This paper describes the application of artificial neural network models for the prediction of biological oxygen demand (BOD) levels in the Danube River. Eighteen regularly monitored water quality parameters at 17 stations on the river stretch passing through Serbia were used as input variables. The optimization of the model was performed in three consecutive steps: firstly, the spatial influence of a monitoring station was examined; secondly, the monitoring period necessary to reach satisfactory performance was determined; and lastly, correlation analysis was applied to evaluate the relationship among water quality parameters. Root-mean-square error (RMSE) was used to evaluate model performance in the first two steps, whereas in the last step, multiple statistical indicators of performance were utilized. As a result, two optimized models were developed, a general regression neural network model (labeled GRNN-1) that covers the monitoring stations from the Danube inflow to the city of Novi Sad and a GRNN model (labeled GRNN-2) that covers the stations from the city of Novi Sad to the border with Romania. Both models demonstrated good agreement between the predicted and actually observed BOD values. PMID:27094057

  9. Modeling of membrane bioreactor treating hypersaline oily wastewater by artificial neural network

    International Nuclear Information System (INIS)

    Highlights: → Hypersaline oily wastewater was treated in a membrane bioreactor. → The effects of salinity and organic loading rate were evaluated. → The system was modeled by neural network and optimized by genetic algorithm. → The model prediction agrees well with experimental values. → The model can be used to obtain effluent characteristics less than discharge limits. - Abstract: A membrane sequencing batch reactor (MSBR) treating hypersaline oily wastewater was modeled by artificial neural network (ANN). The MSBR operated at different total dissolved solids (TDSs) (35,000; 50,000; 100,000; 150,000; 200,000; 250,000 mg/L), various organic loading rates (OLRs) (0.281, 0.563, 1.124, 2.248, and 3.372 kg COD/(m3 day)) and cyclic time (12, 24, and 48 h). A feed-forward neural network trained by batch back propagation algorithm was employed to model the MSBR. A set of 193 operational data from the wastewater treatment with the MSBR was used to train the network. The training, validating and testing procedures for the effluent COD, total organic carbon (TOC) and oil and grease (O and G) concentrations were successful and a good correlation was observed between the measured and predicted values. The results showed that at OLR of 2.44 kg COD/(m3 day), TDS of 78,000 mg/L and reaction time (RT) of 40 h, the average removal rate of COD was 98%. In these conditions, the average effluent COD concentration was less than 100 mg/L and met the discharge limits.

  10. Artificial Neural Network Modeling of Pt/C Cathode Degradation in PEM Fuel Cells

    Science.gov (United States)

    Maleki, Erfan; Maleki, Nasim

    2016-08-01

    Use of computational modeling with a few experiments is considered useful to obtain the best possible result for a final product, without performing expensive and time-consuming experiments. Proton exchange membrane fuel cells (PEMFCs) can produce clean electricity, but still require further study. An oxygen reduction reaction (ORR) takes place at the cathode, and carbon-supported platinum (Pt/C) is commonly used as an electrocatalyst. The harsh conditions during PEMFC operation result in Pt/C degradation. Observation of changes in the Pt/C layer under operating conditions provides a tool to study the lifetime of PEMFCs and overcome durability issues. Recently, artificial neural networks (ANNs) have been used to solve, predict, and optimize a wide range of scientific problems. In this study, several rates of change at the cathode were modeled using ANNs. The backpropagation (BP) algorithm was used to train the network, and experimental data were employed for network training and testing. Two different models are constructed in the present study. First, the potential cycles, temperature, and humidity are used as inputs to predict the resulting Pt dissolution rate of the Pt/C at the cathode as the output parameter of the network. Thereafter, the Pt dissolution rate and Pt ion diffusivity are regarded as inputs to obtain values of the Pt particle radius change rate, Pt mass loss rate, and surface area loss rate as outputs. The networks are finely tuned, and the modeling results agree well with experimental data. The modeled responses of the ANNs are acceptable for this application.

  11. Hierarchical model of the movement of nutrients and artificial radionuclides in the soil-plant system

    International Nuclear Information System (INIS)

    A model is presented of the movement of nutrients and artificial radionuclides in the soil-plant system. The model has a hierarchical structure and is composed of modular elements (submodels) that integrate themselves additively at different levels, with each sub-model containing the submodels of the previous levels. This integration is achieved using the state and flow variables of each submodel as auxiliary variables of the higher level submodels. This hierarchical scheme is an operative solution that simplifies the construction of complex models with different and strongly related dynamic processes. The levels of the model are represented by the following processes: level I: plant biomass dynamics; level II: nutrient movement between the soil and the plant; and level III: deposition of radionuclides and movement between the soil and the plant. Each submodel is structured in different compartments an has its own boundaries. Definition of the compartments of a given submodel is not only dependent on the process that it represents but is also determined by its subsequent integration at higher levels. The plant biomass dynamics submodel is composed of four compartments: root + shoots, young leaves, old leaves and litter. The boundaries of this submodel are defined by the plants themselves. The nutrient dynamics submodel adds two new compartments representing the soil system and uses the differentiation between young and old leaves, established at the previous level according to the changes that take place in their chemical composition. The boundaries of this submodel are defined by the above ground parts of the plants and by the depth of the roots underground. The radionuclide dynamics submodel integrates the two previous submodels and incorporates some additional compartments for the surface of the leaves and the above ground parts of the root + shoots fraction. This submodel has the same boundaries as the previous one. (author). 14 refs, 4 figs

  12. ARTIFICIAL NEURAL NETWORK MODEL OF CONSTITUTIVE RELATIONSHIP FOR 2A70 ALUMINUM ALLOY

    Institute of Scientific and Technical Information of China (English)

    F. Liu; D.B. Shan; Y. Lu; Y.Y. Yang

    2005-01-01

    The hot deformation behavior of 2A70 aluminum alloy was investigated by means of isothermal compression tests performed on a Gleeble-1500 thermal simulator over a wide range of temperatures 360-480℃ with strain rates of 0.01-1s-1 and the largest deformation of 60%, and the true stress of the material was obtained under the above-mentioned conditions. The experimental results shows that 2A70 aluminum alloy is a kind of aluminum alloy with the property of dynamic recovery; its flow stress declines with the increase of temperature, while its flow stress increases with the increase of strain rates. On the basis of experiments, the constitutive relationship of the 2A70 aluminum alloy was constructed using a BP artificial neural network. Comparison of the predicted values with the experimental data shows that the relative error of the trained model is less than ±3% for the sampled data while it is less than ±6% for the nonsampled data. It is evident that the model constructed by BP ANN can accurately predict the flow stress of the 2A70 alloy.

  13. Mathematical Modeling of Pneumatic Artificial Muscle Actuation via Hydrogen Driving Metal Hydride-LaNi5

    Institute of Scientific and Technical Information of China (English)

    Thananchai Leephakpreeda

    2012-01-01

    Quantitative understanding of mechanical actuation of intricate Pneumatic Artificial Muscle (PAM) actuators is technically required in control system design for effective real-time implementation.This paper presents mathematical modeling of the PAM driven by hydrogen-gas pressure due to absorption and desorption of metal hydride.Empirical models of both mechanical actuation of industrial PAM and chemical reaction of the metal hydride-LaNi5 are derived systematically where their interactions comply with the continuity principle and energy balance in describing actual dynamic behaviors of the PAM actuator (PAM and hydriding/dehydriding-reaction bed).Simulation studies of mechanical actuation under various loads are conducted so as to present dynamic responses of the PAM actuators.From the promising results,it is intriguing that the heat input for the PAM actuator can be supplied to,or pumped from the reaction bed,in such a way that absorption and desorption of hydrogen gas take place,respectively,in controlling the pressure of hydrogen gas within the PAM actuator.Accordingly,this manipulation results in desired mechanical actuation of the PAM actuator in practical uses.

  14. MODELING AND ANALYSIS OF ALGAL BLOOMS IN ARAS DAM BY ARTIFICIAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    JAHANGIRI-RAD MAHSA

    2015-03-01

    Full Text Available Man made practices have contributed to large-scale algal blooms that have caused serious ecological, aesthetic, water purification and water distribution problems. Aras Dam, which provides Arasful city with drinking water, has chronic algal blooms since 1990. This study addresses the use of artificial neural network (ANN model to anticipate the chlorophyll-a concentration in water of dam reservoir. Operation tests carried out by collecting water samples from 5 stations and examined for physical quality parameters namely: water temperature, total suspended solids (TSS, biochemical oxygen demands (BOD, ortophosphate, total phosphorous and nitrate concentrations using standard methods. Chlorophyll-a was also checked separately in order to investigate the accuracy of the predicted results by ANN. The results showed that a network was highly accurate in predicting the Chl-a concentration. A good agreement between actual data and the ANN outputs for training was observed, indicating the validation of testing data sets. The initial results of the research indicate that the dam is enriched with nutrients (phosphorus and nitrogen. The Chl-a concentration that were predicted by the model were beyond the standard levels; indicating the possibility of eutrophication especially during fall season.

  15. An Improved Artificial Colony Algorithm Model for Forecasting Chinese Electricity Consumption and Analyzing Effect Mechanism

    Directory of Open Access Journals (Sweden)

    Jingmin Wang

    2016-01-01

    Full Text Available Electricity consumption forecast is perceived to be a growing hot topic in such a situation that China’s economy has entered a period of new normal and the demand of electric power has slowed down. Therefore, exploring Chinese electricity consumption influence mechanism and forecasting electricity consumption are crucial to formulate electrical energy plan scientifically and guarantee the sustainable economic and social development. Research has identified medium and long term electricity consumption forecast as a difficult study influenced by various factors. This paper proposed an improved Artificial Bee Colony (ABC algorithm which combined with multivariate linear regression (MLR for exploring the influencing mechanism of various factors on Chinese electricity consumption and forecasting electricity consumption in the future. The results indicated that the improved ABC algorithm in view of the various factors is superior to traditional models just considering unilateralism in accuracy and persuasion. The overall findings cast light on this model which provides a new scientific and effective way to forecast the medium and long term electricity consumption.

  16. Production of Engineered Fabrics Using Artificial Neural Network-Genetic Algorithm Hybrid Model

    Science.gov (United States)

    Mitra, Ashis; Majumdar, Prabal Kumar; Banerjee, Debamalya

    2015-10-01

    The process of fabric engineering which is generally practised in most of the textile mills is very complicated, repetitive, tedious and time consuming. To eliminate this trial and error approach, a new approach of fabric engineering has been attempted in this work. Data sets of construction parameters [comprising of ends per inch, picks per inch, warp count and weft count] and three fabric properties (namely drape coefficient, air permeability and thermal resistance) of 25 handloom cotton fabrics have been used. The weights and biases of three artificial neural network (ANN) models developed for the prediction of drape coefficient, air permeability and thermal resistance were used to formulate the fitness or objective function and constraints of the optimization problem. The optimization problem was solved using genetic algorithm (GA). In both the fabrics which were attempted for engineering, the target and simulated fabric properties were very close. The GA was able to search the optimum set of fabric construction parameters with reasonably good accuracy except in case of EPI. However, the overall result is encouraging and can be improved further by using larger data sets of handloom fabrics by hybrid ANN-GA model.

  17. Phase-change model for the simulation of artificial soil freezing in tunneling

    International Nuclear Information System (INIS)

    In contrast to seasonal freezing, artificial freezing is controllable and can be used to provide structural support and/or exclude groundwater from a tunnel site until the construction of the final lining provides permanent security. The formation of ice in the saturated pore space is mainly controlled by two factors: the temperature and the binding of water to the surface of particles by means of chemical bonds and electrostatic forces. Even at very low temperatures, water molecule layers close to the pore wall remain unfrozen, commonly referred to as unfrozen water content. In this paper, a phase-change model accounting for the influence of the temperature and the particle-water interaction on the freezing process of porous media is presented. For this purpose, fully-saturated specimens of silty sand were subjected to temperature gradients allowing, as a first step, to quantify macroscopic thermal properties of the unfrozen medium. Inverse homogenization is employed for determination of the respective properties of the different ingredients at the microscale. At this scale, freezing of water is introduced. Analytical bounds for thermal properties of the frozen microstructure (homogenization) are adapted from micro mechanics in order to estimate macroscopic thermal properties of frozen sand. The obtained properties are introduced into a macroscopic model and are assessed by means of re-analysis of a freezing experiment performed at Vienna University of Technology. Finally, results of a numerical simulation of an in-situ freezing test at a construction site in Vienna will be presented. Refs. 3 (author)

  18. Combining artificial neural networks and circulation type classification: does it improve downscaling models?

    Science.gov (United States)

    Philipp, Andreas; Beck, Christoph; Kaspar, Severin; Jacobeit, Jucundus

    2014-05-01

    Circulation type classifications may be used for downscaling in so called reference class forecasting (RCF), i.e. to to assign atmospheric circulation predictors to a certain type of a circulation type classification and use the value for the target variable associated with this type in the past as a model value. Doing so often already leads to useful statistical assessment models. However a generally superior method is that of artificial neural networks (NNW). Using adequate configuration, the latter are able to outperform the RCF method in virtually all cases. However the adequate configuration of NNWs is often not easy to decide and the training of the network weights may be an extensive and slow process while RCF is relatively fast. In the context of a starting project dealing with alpine climate change studies (Virtual Alpine Observatory II, VAO2), this study evaluates if a combination of both statistical approaches (called neural networks of classification types, NNC) may lead to an improvement for statistical downscaling. Preliminary results suggest that the gain in skill and the computational speed for the network training largely depends on the configuration of both: the circulation type classification and the network configuration regarding, topology, learning rate, predictors and so on. In this context it is important to consider the evolution of the learning process, where sometimes the NNW is superior and sometimes the NNC.

  19. Modeling of CVI process in fabrication of carbon/carbon composites by an artificial neural network

    Institute of Scientific and Technical Information of China (English)

    LI; Aijun; (李爱军); LI; Hejun; (李贺军); LI; Kezhi; (李克智); GU; Zhengbing; (顾正彬)

    2003-01-01

    The chemical vapor infiltration(CVI) process in fabrication of carbon-carbon composites is very complex and highly inefficient, which adds considerably to the cost of fabrication and limits the application of the material. This paper tries to use a supervised artificial neural network(ANN) to model the nonlinear relationship between parameters of isothermal CVI(ICVI) processes and physical properties of C/C composites. A model for preprocessing dataset and selecting its topology is developed using the Levenberg-Marquardt training algorithm and trained with comprehensive dataset of tubal C/C components collected from experimental data and abundant simulated data obtained by the finite element method. A basic repository on the domain knowledge of CVI processes is established via sufficient data mining by the network. With the help of the repository stored in the trained network, not only the time-dependent effects of parameters in CVI processes but also their coupling effects can be analyzed and predicted. The results show that the ANN system is effective and successful for optimizing CVI processes in fabrication of C/C composites.

  20. Artificial neural networks for automatic modelling of the pectus excavatum corrective prosthesis

    Science.gov (United States)

    Rodrigues, Pedro L.; Moreira, António H. J.; Rodrigues, Nuno F.; Pinho, ACM; Fonseca, Jaime C.; Correia-Pinto, Jorge; Vilaça, João. L.

    2014-03-01

    Pectus excavatum is the most common deformity of the thorax and usually comprises Computed Tomography (CT) examination for pre-operative diagnosis. Aiming at the elimination of the high amounts of CT radiation exposure, this work presents a new methodology for the replacement of CT by a laser scanner (radiation-free) in the treatment of pectus excavatum using personally modeled prosthesis. The complete elimination of CT involves the determination of ribs external outline, at the maximum sternum depression point for prosthesis placement, based on chest wall skin surface information, acquired by a laser scanner. The developed solution resorts to artificial neural networks trained with data vectors from 165 patients. Scaled Conjugate Gradient, Levenberg-Marquardt, Resilient Back propagation and One Step Secant gradient learning algorithms were used. The training procedure was performed using the soft tissue thicknesses, determined using image processing techniques that automatically segment the skin and rib cage. The developed solution was then used to determine the ribs outline in data from 20 patient scanners. Tests revealed that ribs position can be estimated with an average error of about 6.82+/-5.7 mm for the left and right side of the patient. Such an error range is well below current prosthesis manual modeling (11.7+/-4.01 mm) even without CT imagiology, indicating a considerable step forward towards CT replacement by a 3D scanner for prosthesis personalization.

  1. Development of distributed topographical forecasting model for wind resource assessment using artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Narayana, P.B. [Green Life Energy Solutions LLP, Secunderabad (India); Rao, S.S. [National Institute of Technology. Dept. of Mechanical Engineering, Warangal (India); Reddy, K.H. [JNT Univ.. Dept. of Mechanical Engineering, Anantapur (India)

    2012-07-01

    Economics of wind power projects largely depend on the availability of wind power density. Wind resource assessment is a study estimating wind speeds and wind power densities in the region under consideration. The accuracy and reliability of data sets comprising of wind speeds and wind power densities at different heights per topographic region characterized by elevation or mean sea level, is important for wind power projects. Indian Wind Resource Assessment program conducted in 80's consisted of wind data measured by monitoring stations at different topographies in order to measure wind power density values at 25 and 50 meters above the ground level. In this paper, an attempt has been made to assess wind resource at a given location using artificial neural networks. Existing wind resource data has been used to train the neural networks. Location topography (characterized by longitude, latitude and mean sea level), air density, mean annual wind speed (MAWS) are used as inputs to the neural network. Mean annual wind power density (MAWPD) in watt/m{sup 2} is predicted for a new topographic location. Simple back propagation based neural network has been found to be sufficient for predicting these values with suitable accuracy. This model is closely linked to the problem of wind energy forecasting considering the variations of specific atmospheric variables with time horizons. This model will help the wind farm developers to have an initial estimation of the wind energy potential at a particular topography. (Author)

  2. An artificial neural network model of the landscape pattern in Shanghai metropolitan region,China

    Institute of Scientific and Technical Information of China (English)

    ZHANG Liquan; ZHEN Yu

    2006-01-01

    To characterize the urbanization pattern quantitatively,a study on the mechanisms of the landscape pattern formation could facilitate the understanding on urban landscape patterns and processes,the ecological and socioeconomic consequences of urbanization,as well as the establishment of more effective strategies for landscape management.In this study,we integrated a Geographic Information System (GIS)-based analysis on landscape pattern with an artificial neural network (ANN) to quantitatively characterize the urbanization pattern of the metropolitan area of Shanghai,China,and to establish an ANN model that could preferably simulate the responses of urban landscape pattern to the natural and socioeconomic factors such as residence area,road density,population density,urban development history and the Huangpu River as an element of economic change.Our results showed that the ANN model seems appropriate for studying the nonlinear relationship among the forcing factors of urbanization and the urban landscape patterns,which provided an effective and practical approach for further understanding the mechanisms of the landscape formation pattern and the reciprocal relationship between landscape spatial pattern and ecological process.

  3. Genomic distance entrained clustering and regression modelling highlights interacting genomic regions contributing to proliferation in breast cancer

    Directory of Open Access Journals (Sweden)

    Dexter Tim J

    2010-09-01

    Full Text Available Abstract Background Genomic copy number changes and regional alterations in epigenetic states have been linked to grade in breast cancer. However, the relative contribution of specific alterations to the pathology of different breast cancer subtypes remains unclear. The heterogeneity and interplay of genomic and epigenetic variations means that large datasets and statistical data mining methods are required to uncover recurrent patterns that are likely to be important in cancer progression. Results We employed ridge regression to model the relationship between regional changes in gene expression and proliferation. Regional features were extracted from tumour gene expression data using a novel clustering method, called genomic distance entrained agglomerative (GDEC clustering. Using gene expression data in this way provides a simple means of integrating the phenotypic effects of both copy number aberrations and alterations in chromatin state. We show that regional metagenes derived from GDEC clustering are representative of recurrent regions of epigenetic regulation or copy number aberrations in breast cancer. Furthermore, detected patterns of genomic alterations are conserved across independent oestrogen receptor positive breast cancer datasets. Sequential competitive metagene selection was used to reveal the relative importance of genomic regions in predicting proliferation rate. The predictive model suggested additive interactions between the most informative regions such as 8p22-12 and 8q13-22. Conclusions Data-mining of large-scale microarray gene expression datasets can reveal regional clusters of co-ordinate gene expression, independent of cause. By correlating these clusters with tumour proliferation we have identified a number of genomic regions that act together to promote proliferation in ER+ breast cancer. Identification of such regions should enable prioritisation of genomic regions for combinatorial functional studies to pinpoint

  4. Genetical Genomics of Behavior: A Novel Chicken Genomic Model for Anxiety Behavior.

    Science.gov (United States)

    Johnsson, Martin; Williams, Michael J; Jensen, Per; Wright, Dominic

    2016-01-01

    The identification of genetic variants responsible for behavioral variation is an enduring goal in biology, with wide-scale ramifications, ranging from medical research to evolutionary theory on personality syndromes. Here, we use for the first time a large-scale genetical genomics analysis in the brains of chickens to identify genes affecting anxiety as measured by an open field test. We combine quantitative trait locus (QTL) analysis in 572 individuals and expression QTL (eQTL) analysis in 129 individuals from an advanced intercross between domestic chickens and Red Junglefowl. We identify 10 putative quantitative trait genes affecting anxiety behavior. These genes were tested for an association in the mouse Heterogeneous Stock anxiety (open field) data set and human GWAS data sets for bipolar disorder, major depressive disorder, and schizophrenia. Although comparisons between species are complex, associations were observed for four of the candidate genes in mice and three of the candidate genes in humans. Using a multimodel approach we have therefore identified a number of putative quantitative trait genes affecting anxiety behavior, principally in chickens but also with some potentially translational effects as well. This study demonstrates that chickens are an excellent model organism for the genetic dissection of behavior. PMID:26733665

  5. Modeling Distillation Column Using ARX Model Structure and Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Reza Pirmoradi

    2012-04-01

    Full Text Available Distillation is a complex and highly nonlinear industrial process. In general it is not always possible to obtain accurate first principles models for high-purity distillation columns. On the other hand the development of first principles models is usually time consuming and expensive. To overcome these problems, empirical models such as neural networks can be used. One major drawback of empirical models is that the prediction is valid only inside the data domain that is sufficiently covered by measurement data. Modeling distillation columns by means of neural networks is reported in literature by using recursive networks. The recursive networks are proper for modeling purpose, but such models have the problems of high complexity and high computational cost. The objective of this paper is to propose a simple and reliable model for distillation column. The proposed model uses feed forward neural networks which results in a simple model with less parameters and faster training time. Simulation results demonstrate that predictions of the proposed model in all regions are close to outputs of the dynamic model and the error in negligible. This implies that the model is reliable in all regions.

  6. Modeling the benefits of an artificial gravity countermeasure coupled with exercise and vibration

    Science.gov (United States)

    Goel, Rahul; Kaderka, Justin; Newman, Dava

    2012-01-01

    The current, system-specific countermeasures to space deconditioning have limited success with the musculoskeletal system in long duration missions. Artificial gravity (AG) that is produced by short radius centrifugation has been hypothesized as an effective countermeasure because it reintroduces an acceleration field in space; however, AG alone might not be enough stimuli to preserve the musculoskeletal system. A novel combination of AG coupled with one-legged squats on a vibrating platform may preserve muscle and bone in the lower limbs to a greater extent than the current exercise paradigm. The benefits of the proposed countermeasure have been analyzed through the development of a simulation platform. Ground reaction force data and motion data were collected using a motion capture system while performing one-legged and two-legged squats in 1-G. The motion was modeled in OpenSim, an open-source software, and inverse dynamics were applied in order to determine the muscle and reaction forces of lower limb joints. Vibration stimulus was modeled by adding a 20 Hz sinusoidal force of 0.5 body weight to the force plate data. From the numerical model in a 1-G acceleration field, muscle forces for quadriceps femoris, plantar flexors and glutei increased substantially for one-legged squats with vibration compared to one- or two-legged squats without vibration. Additionally, joint reaction forces for one-legged squats with vibration also increased significantly compared to two-legged squats with or without vibration. Higher muscle forces and joint reaction forces might help to stimulate muscle activation and bone modeling and thus might reduce musculoskeletal deconditioning. These results indicate that the proposed countermeasure might surpass the performance of the current space countermeasures and should be further studied as a method of mitigating musculoskeletal deconditioning.

  7. Ustvarjanje modela rezalnih sil z uporabo umetne inteligence: Generation of a model for cutting forces using artificial intelligence:

    OpenAIRE

    Milfelner, Matjaž; Čuš, Franc; Župerl, Uroš

    2005-01-01

    Being able to predict the cutting forces during milling with a ball-end milling cutter is very important for determining the optimal cutting parameters in the milling process. The already developed models of cutting forces in ball-end milling are based on analytical methods and are determined by means of theoretical and practical knowledge as well as experiments. This paper presents the development of a genetic model of cutting forces for a ball-end milling cutter using artificial intelligenc...

  8. Artificial Neural Network (ANN) of Simultaneous Heat and Mass Transfer Model during Reconstitution of Gari Granules into Thick Paste

    OpenAIRE

    S.S. Sobowale; S. O. Awonorin; T. A. Shittu; E. S. A. Ajisegiri

    2014-01-01

    Artificial neural network (ANN) based model of transient simultaneous heat and mass transfer was used for the prediction of some thermo-physical during reconstitution of gari into thick paste. Temperature changes in the paste and moisture losses were recorded over a period of two hours while the granules are being reconstituted. Data on convective heat and mass transfer coefficients were obtained during reconstitution of gari into paste. In developing the ANN model, several configurations wer...

  9. Artificial neural networks in the modeling and optimization of aspirin extended release tablets with eudragit L 100 as matrix substance

    OpenAIRE

    Ibrić, Svetlana; Jovanović, Milica; Djurić, Zorica; Parojčić, Jelena; Petrović, Slobodan D.; Solomun, Ljiljana; Stupar, Biljana

    2003-01-01

    The purpose of the present study was to model the effects of the concentration of Eudragit L 100 and compression pressure as the most important process and formulation variables on the in vitro release profile of aspirin from matrix tables formulated with Eudragit L 100 as matrix substance and to optimize the formulation by artificial neural network. As model formulations, 10 kinds of aspirin matrix tablets were prepared. The amount of Eudragit L 100 and the compression pressure were selected...

  10. Genomics Analogy Model for Educators (GAME): From Jumping Genes to Alternative Splicing

    Science.gov (United States)

    Corn, Joanie; Pittendrigh, Barry R.; Orvis, Kathryn S.

    2004-01-01

    Studies have shown that there is usually a lack of understanding concerning the fields of genetics and genomics among high school students (Lewis and Wood-Robinson, 2000). A recent article (Kirkpatrick et al, 2002) introduced the GAME (Genomics Analogy Model for Educators) model and two of its components: (1) explaining sequencing technology with…

  11. Human genome education model project. Ethical, legal, and social implications of the human genome project: Education of interdisciplinary professionals

    Energy Technology Data Exchange (ETDEWEB)

    Weiss, J.O. [Alliance of Genetic Support Groups, Chevy Chase, MD (United States); Lapham, E.V. [Georgetown Univ., Washington, DC (United States). Child Development Center

    1996-12-31

    This meeting was held June 10, 1996 at Georgetown University. The purpose of this meeting was to provide a multidisciplinary forum for exchange of state-of-the-art information on the human genome education model. Topics of discussion include the following: psychosocial issues; ethical issues for professionals; legislative issues and update; and education issues.

  12. Genomic prediction and genomic variance partitioning of daily and residual feed intake in pigs using Bayesian Power Lasso models

    DEFF Research Database (Denmark)

    Do, Duy Ngoc; Janss, Luc L G; Strathe, Anders B;

    Improvement of feed efficiency is essential in pig breeding and selection for reduced residual feed intake (RFI) is an option. The study applied Bayesian Power LASSO (BPL) models with different power parameter to investigate genetic architecture, to predict genomic breeding values, and to partition...... genomic variance for RFI and daily feed intake (DFI). A total of 1272 Duroc pigs had both genotypic and phenotypic records for these traits. Significant SNPs were detected on chromosome 1 (SSC 1) and SSC 14 for RFI and on SSC 1 for DFI. BPL had similar accuracy and bias as GBLUP but power parameters had...

  13. Daylighting analysis of rooflights through model measurements in the artificial sky

    Energy Technology Data Exchange (ETDEWEB)

    Filetoht, Levente I. [University of Technnology and Economics, Budapest, Hungary (Hungary)

    2000-07-01

    In investigating the daylighting of rooflights, it can be assumed that they are behaving as luminaires-having a major role in the lighting features of the interior space. Until now there is not enough precise information on these daylighting systems, and because of this, it is difficult to predict precisely the quality and quantity of illuminance and the light distribution in the interior. The exact behavior of the rooflights are complex and sophisticated. Their features are based on their geometry, reflectance and transmittance of their non-transparent, transparent and translucent surfaces. The effect of these features can not be predicted of calculated using the traditional mathematical methods. I am investigating roof-lighting systems taking into account the complex effects of their parts, using a completely new method based on a series of model measurements under artificial sky. Investigating the roof-light as a complex system which consist of relatively large surfaces and the effects of surfaces on other surfaces. Considering the different illuminance fields of the different types of roof-lighting systems. I will be able to determine the light distribution and efficiency of the system as well. The results of this research will lead to the development of a new computer software which is beyond the scope of this paper. [Spanish] Al investigar la iluminacion de luz de dia de luces de techo puede suponerse que se estan comportando como luminarias teniendo un papel principal en las caracteristicas de iluminacion del espacio interior. Hasta ahora no existe ninguna informacion precisa de estos sistemas de iluminacion de dia y a causa de ello es dificil predecir con precision la calidad y cantidad de iluminancia y la distribucion de la luz en el interior. El comportamiento exacto de las luces de techo es complejo y sofisticado. Sus caracteristicas estan basadas en su geometria, reflectancia y trasmitancia de sus superficies no transparentes, transparentes y

  14. Development of artificial model of caval syndrome in canine heartworm disease

    International Nuclear Information System (INIS)

    In order to develop an artificial model of caval syndrome (dirofilarial hemoglobinuria), heartworm-like silicone tubes were inserted into the tricuspid valve orifice and right atrium of dogs. Fifteen to 25 tubes with some knots were inserted through the posterior vena cava in 6 dogs (knot-tube group), 7 to 12 tubes without knot (small-number group) through the jugular vein in another 5 dogs, or 25 to 35 tubes (large-number group) in yet another 5 dogs. The tubes remained in the right atrium, and a part of them protruded into the tricuspid valve orifice. The number of tubes at the tricuspid valve orifice was the greatest in the large-number group. After tube insertion, the signs of so-called ''caval syndrome'', such as systolic cardiac murmur, jugular pulse, anemia, and so on, were observed in almost all cases of the 3 groups, the signs were severest in the large-number group. Urine hemoglobin was detected in almost all cases of the knot-tube and large-number groups, and in 1 case in the small-number group. Ascites was observed in 1 case of the knot-tube group at 6 weeks, in 1 case of the small-number group at 7 days and in 3 cases of the large-number group at 7 days after insertion

  15. Prediction of hydrate formation temperature by both statistical models and artificial neural network approaches

    International Nuclear Information System (INIS)

    In this study, various estimation methods have been reviewed for hydrate formation temperature (HFT) and two procedures have been presented. In the first method, two general correlations have been proposed for HFT. One of the correlations has 11 parameters, and the second one has 18 parameters. In order to obtain constants in proposed equations, 203 experimental data points have been collected from literatures. The Engineering Equation Solver (EES) and Statistical Package for the Social Sciences (SPSS) soft wares have been employed for statistical analysis of the data. Accuracy of the obtained correlations also has been declared by comparison with experimental data and some recent common used correlations. In the second method, HFT is estimated by artificial neural network (ANN) approach. In this case, various architectures have been checked using 70% of experimental data for training of ANN. Among the various architectures multi layer perceptron (MLP) network with trainlm training algorithm was found as the best architecture. Comparing the obtained ANN model results with 30% of unseen data confirms ANN excellent estimation performance. It was found that ANN is more accurate than traditional methods and even our two proposed correlations for HFT estimation.

  16. Algorithm for Modeling Wire Cut Electrical Discharge Machine Parameters using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    G.Sankara Narayanan

    2014-03-01

    Full Text Available Unconventional machining process finds lot of application in aerospace and precision industries. It is preferred over other conventional methods because of the advent of composite and high strength to weight ratio materials, complex parts and also because of its high accuracy and precision. Usually in unconventional machine tools, trial and error method is used to fix the values of process parameters which increase the production time and material wastage. A mathematical model functionally relating process parameters and operating parameters of a wire cut electric discharge machine (WEDM is developed incorporating Artificial neural network (ANN and the work piece material is SKD11 tool steel. This is accomplished by training a feed forward neural network with back propagation learning Levenberg-Marquardt algorithm. The required data used for training and testing the ANN are obtained by conducting trial runs in wire cut electric discharge machine in a small scale industry from South India. The programs for training and testing the neural network are developed, using matlab 7.0.1 package. In this work, we have considered the parameters such as thickness, time and wear as the input values and from that the values of the process parameters are related and a algorithm is arrived. Hence, the proposed algorithm reduces the time taken by trial runs to set the input process parameters of WEDM and thus reduces the production time along with reduction in material wastage. Thus the cost of machining processes is reduced and thereby increases the overall productivity.

  17. Application of backpropagation artificial neural network prediction model for the PAH bioremediation of polluted soil.

    Science.gov (United States)

    Olawoyin, Richard

    2016-10-01

    The backpropagation (BP) artificial neural network (ANN) is a renowned and extensively functional mathematical tool used for time-series predictions and approximations; which also define results for non-linear functions. ANNs are vital tools in the predictions of toxicant levels, such as polycyclic aromatic hydrocarbons (PAH) potentially derived from anthropogenic activities in the microenvironment. In the present work, BP ANN was used as a prediction tool to study the potential toxicity of PAH carcinogens (PAHcarc) in soils. Soil samples (16 × 4 = 64) were collected from locations in South-southern Nigeria. The concentration of PAHcarc in laboratory cultivated white melilot, Melilotus alba roots grown on treated soils was predicted using ANN model training. Results indicated the Levenberg-Marquardt back-propagation training algorithm converged in 2.5E+04 epochs at an average RMSE value of 1.06E-06. The averagedR(2) comparison between the measured and predicted outputs was 0.9994. It may be deduced from this study that, analytical processes involving environmental risk assessment as used in this study can successfully provide prompt prediction and source identification of major soil toxicants. PMID:27424056

  18. Application of artificial neural networks in modelling of normalised structural steels mechanical properties

    Directory of Open Access Journals (Sweden)

    L.A. Dobrzański

    2009-01-01

    Full Text Available Purpose: This paper presents the application of artificial neural networks for mechanical properties prediction of constructional steels after heat treatment.Design/methodology/approach: On the basis of such input parameteres, such as the chemical composition, the ones of mechanical and heat treatment and dimensions of elements, mechanical properties such as strength, impact resistance or hardness are predicted.Findings: Results obtained in the given ranges of input parameters show very good ability of constructed neural networks to predict described mechanical properties for steels after heat treatment. The uniform distribution of descriptive vectors in all, training, validation and testing sets, indicate about the good ability of the networks to results generalisation.Practical implications: Created tool makes possible the easy modelling of described properties and allows the better selection of both chemical composition and the processing parameters of investigated materials. At the same time the obtainment of steels, which are qualitatively better, cheaper and more optimised under customers needs is made possible.Originality/value: The prediction possibility of the material mechanical properties is valuable for manufacturers and constructors. It allows preserving the customers quality requirements and brings also measurable financial advantages.

  19. Predictive modeling of performance of a helium charged Stirling engine using an artificial neural network

    International Nuclear Information System (INIS)

    Highlights: ► Max torque and power values were obtained at 3.5 bar Pch, 1273 K Hst and 1.4:1 r. ► According to ANOVA, the most influential parameter on power was Hst with 48.75%. ► According to ANOVA, the most influential parameter on torque was Hst with 41.78%. ► ANN (R2 = 99.8% for T, P) was superior to regression method (R2 = 92% for T, 81% for P). ► LM was the best learning algorithm in predicting both power and torque. - Abstract: In this study, an artificial neural network (ANN) model was developed to predict the torque and power of a beta-type Stirling engine using helium as the working fluid. The best results were obtained by 5-11-7-1 and 5-13-7-1 network architectures, with double hidden layers for the torque and power respectively. For these network architectures, the Levenberg–Marquardt (LM) learning algorithm was used. Engine performance values predicted with the developed ANN model were compared with the actual performance values measured experimentally, and substantially coinciding results were observed. After ANN training, correlation coefficients (R2) of both engine performance values for testing and training data were very close to 1. Similarly, root-mean-square error (RMSE) and mean error percentage (MEP) values for the testing and training data were less than 0.02% and 3.5% respectively. These results showed that the ANN is an acceptable model for prediction of the torque and power of the beta-type Stirling engine

  20. Sulfur dioxide AQI modeling by artificial neural network in Tehran between 2007 and 2013

    Directory of Open Access Journals (Sweden)

    Saeed Motesaddi

    2015-12-01

    Full Text Available Background: Air pollution and concerns about health impacts have been raised in metropolitan cities like Tehran. Trend and prediction of air pollutants can show the effectiveness of strategies for the management and control of air pollution. Artificial neural network (ANN technique is widely used as a reliable method for modeling of air pollutants in urban areas. Therefore, the aim of current study was to evaluate the trend of sulfur dioxide (SO2 air quality index (AQI in Tehran using ANN. Methods: The dataset of SO2 concentration and AQI in Tehran between 2007 and 2013 for 2550 days were obtained from air quality monitoring fix stations belonging to the Department of Environment (DOE. These data were used as input for the ANN and nonlinear autoregressive (NAR model using Matlab (R2014a software. Results: Daily and annual mean concentration of SO2 except 2008 (0.037 ppm was less than the EPA standard (0.14 and 0.03 ppm, respectively. Trend of SO2 AQI showed the variation of SO2 during different days, but the study declined overtime and the predicted trend is higher than the actual trend. Conclusion: The trend of SO2 AQI in this study, despite daily fluctuations in ambient air of Tehran over the period of the study have decreased and the difference between the predicted and actual trends can be related to various factors, such as change in management and control of SO2 emissions strategy and lack of effective parameters in SO2 emissions in predicting model.

  1. Modeling of Steam Distillation Mechanism during Steam Injection Process Using Artificial Intelligence

    OpenAIRE

    2014-01-01

    Steam distillation as one of the important mechanisms has a great role in oil recovery in thermal methods and so it is important to simulate this process experimentally and theoretically. In this work, the simulation of steam distillation is performed on sixteen sets of crude oil data found in the literature. Artificial intelligence (AI) tools such as artificial neural network (ANN) and also adaptive neurofuzzy interference system (ANFIS) are used in this study as effective methods to simulat...

  2. Engineering Computational Emotion - A Reference Model for Emotion in Artificial Systems

    OpenAIRE

    Sanchez Escribano, Guadalupe

    2016-01-01

    Emotion is generally argued to be an influence on the behavior of life systems, largely concerning flexibility and adaptivity. The way in which life systems acts in response to a particular situations of the environment, has revealed the decisive and crucial importance of this feature in the success of behaviors. And this source of inspiration has influenced the way of thinking artificial systems. During the last decades, artificial systems have undergone such an evolution that each day m...

  3. The genome of the sea urchin Strongylocentrotus purpuratus.

    Science.gov (United States)

    Sodergren, Erica; Weinstock, George M; Davidson, Eric H; Cameron, R Andrew; Gibbs, Richard A; Angerer, Robert C; Angerer, Lynne M; Arnone, Maria Ina; Burgess, David R; Burke, Robert D; Coffman, James A; Dean, Michael; Elphick, Maurice R; Ettensohn, Charles A; Foltz, Kathy R; Hamdoun, Amro; Hynes, Richard O; Klein, William H; Marzluff, William; McClay, David R; Morris, Robert L; Mushegian, Arcady; Rast, Jonathan P; Smith, L Courtney; Thorndyke, Michael C; Vacquier, Victor D; Wessel, Gary M; Wray, Greg; Zhang, Lan; Elsik, Christine G; Ermolaeva, Olga; Hlavina, Wratko; Hofmann, Gretchen; Kitts, Paul; Landrum, Melissa J; Mackey, Aaron J; Maglott, Donna; Panopoulou, Georgia; Poustka, Albert J; Pruitt, Kim; Sapojnikov, Victor; Song, Xingzhi; Souvorov, Alexandre; Solovyev, Victor; Wei, Zheng; Whittaker, Charles A; Worley, Kim; Durbin, K James; Shen, Yufeng; Fedrigo, Olivier; Garfield, David; Haygood, Ralph; Primus, Alexander; Satija, Rahul; Severson, Tonya; Gonzalez-Garay, Manuel L; Jackson, Andrew R; Milosavljevic, Aleksandar; Tong, Mark; Killian, Christopher E; Livingston, Brian T; Wilt, Fred H; Adams, Nikki; Bellé, Robert; Carbonneau, Seth; Cheung, Rocky; Cormier, Patrick; Cosson, Bertrand; Croce, Jenifer; Fernandez-Guerra, Antonio; Genevière, Anne-Marie; Goel, Manisha; Kelkar, Hemant; Morales, Julia; Mulner-Lorillon, Odile; Robertson, Anthony J; Goldstone, Jared V; Cole, Bryan; Epel, David; Gold, Bert; Hahn, Mark E; Howard-Ashby, Meredith; Scally, Mark; Stegeman, John J; Allgood, Erin L; Cool, Jonah; Judkins, Kyle M; McCafferty, Shawn S; Musante, Ashlan M; Obar, Robert A; Rawson, Amanda P; Rossetti, Blair J; Gibbons, Ian R; Hoffman, Matthew P; Leone, Andrew; Istrail, Sorin; Materna, Stefan C; Samanta, Manoj P; Stolc, Viktor; Tongprasit, Waraporn; Tu, Qiang; Bergeron, Karl-Frederik; Brandhorst, Bruce P; Whittle, James; Berney, Kevin; Bottjer, David J; Calestani, Cristina; Peterson, Kevin; Chow, Elly; Yuan, Qiu Autumn; Elhaik, Eran; Graur, Dan; Reese, Justin T; Bosdet, Ian; Heesun, Shin; Marra, Marco A; Schein, Jacqueline; Anderson, Michele K; Brockton, Virginia; Buckley, Katherine M; Cohen, Avis H; Fugmann, Sebastian D; Hibino, Taku; Loza-Coll, Mariano; Majeske, Audrey J; Messier, Cynthia; Nair, Sham V; Pancer, Zeev; Terwilliger, David P; Agca, Cavit; Arboleda, Enrique; Chen, Nansheng; Churcher, Allison M; Hallböök, F; Humphrey, Glen W; Idris, Mohammed M; Kiyama, Takae; Liang, Shuguang; Mellott, Dan; Mu, Xiuqian; Murray, Greg; Olinski, Robert P; Raible, Florian; Rowe, Matthew; Taylor, John S; Tessmar-Raible, Kristin; Wang, D; Wilson, Karen H; Yaguchi, Shunsuke; Gaasterland, Terry; Galindo, Blanca E; Gunaratne, Herath J; Juliano, Celina; Kinukawa, Masashi; Moy, Gary W; Neill, Anna T; Nomura, Mamoru; Raisch, Michael; Reade, Anna; Roux, Michelle M; Song, Jia L; Su, Yi-Hsien; Townley, Ian K; Voronina, Ekaterina; Wong, Julian L; Amore, Gabriele; Branno, Margherita; Brown, Euan R; Cavalieri, Vincenzo; Duboc, Véronique; Duloquin, Louise; Flytzanis, Constantin; Gache, Christian; Lapraz, François; Lepage, Thierry; Locascio, Annamaria; Martinez, Pedro; Matassi, Giorgio; Matranga, Valeria; Range, Ryan; Rizzo, Francesca; Röttinger, Eric; Beane, Wendy; Bradham, Cynthia; Byrum, Christine; Glenn, Tom; Hussain, Sofia; Manning, Gerard; Miranda, Esther; Thomason, Rebecca; Walton, Katherine; Wikramanayke, Athula; Wu, Shu-Yu; Xu, Ronghui; Brown, C Titus; Chen, Lili; Gray, Rachel F; Lee, Pei Yun; Nam, Jongmin; Oliveri, Paola; Smith, Joel; Muzny, Donna; Bell, Stephanie; Chacko, Joseph; Cree, Andrew; Curry, Stacey; Davis, Clay; Dinh, Huyen; Dugan-Rocha, Shannon; Fowler, Jerry; Gill, Rachel; Hamilton, Cerrissa; Hernandez, Judith; Hines, Sandra; Hume, Jennifer; Jackson, Laronda; Jolivet, Angela; Kovar, Christie; Lee, Sandra; Lewis, Lora; Miner, George; Morgan, Margaret; Nazareth, Lynne V; Okwuonu, Geoffrey; Parker, David; Pu, Ling-Ling; Thorn, Rachel; Wright, Rita

    2006-11-10

    We report the sequence and analysis of the 814-megabase genome of the sea urchin Strongylocentrotus purpuratus, a model for developmental and systems biology. The sequencing strategy combined whole-genome shotgun and bacterial artificial chromosome (BAC) sequences. This use of BAC clones, aided by a pooling strategy, overcame difficulties associated with high heterozygosity of the genome. The genome encodes about 23,300 genes, including many previously thought to be vertebrate innovations or known only outside the deuterostomes. This echinoderm genome provides an evolutionary outgroup for the chordates and yields insights into the evolution of deuterostomes. PMID:17095691

  4. Artificial Limbs

    Science.gov (United States)

    ... you are missing an arm or leg, an artificial limb can sometimes replace it. The device, which ... activities such as walking, eating, or dressing. Some artificial limbs let you function nearly as well as ...

  5. Application of artificial neural networks for modelling correlations in age hardenable aluminium alloys

    Directory of Open Access Journals (Sweden)

    F. Musharavati

    2010-07-01

    Full Text Available Purpose: This paper discusses some of the preliminary results of an ongoing research on the applications of artificial neural networks (ANNs in modelling, predicting and simulating correlations between mechanical properties of age hardenable aluminium alloys as a function of alloy composition.Design/methodology/approach: Appropriate combinations of inputs and outputs were selected for neural network modelling. Multilayer feedforward networks were created and trained using datasets from public literature. Influences of alloying elements, alloy composition and processing parameters on mechanical properties of aluminium alloys were predicted and simulated using ANNs models.Two sample t-tests were used to analyze the prediction accuracy of the trained ANNs.Findings: Good performances of the neural network models were achieved. The models were able to predict mechanical properties within acceptable margins of error and were able to provide relevant simulated data for correlating alloy composition and processing parameters with mechanical properties. Therefore, ANNs models are convenient and powerful tools that can provide useful information which can be used to identify desired properties in new aluminium alloys for practical applications in new and/or improved aluminium products.Research limitations/implications: Few public data bases are available for modelling properties. Minor contradictions on the experimental values of properties and alloy compositions were also observed. Future work will include further development of simulated data into property charts.Practical implications: Correlations between mechanical properties and alloy compositions can help in identifying a suitable alloy for a new or improved aluminum product application. In addition, availability of simulated structure-process-property data or charts assists in reducing the time and costs of trial and error experimental approaches by providing near-optimal values that can be used

  6. Synthesis of TiO2 nanoparticles in different thermal conditions and modeling its photocatalytic activity with artificial neural network

    Institute of Scientific and Technical Information of China (English)

    Fatemeh Ghanbary; Nasser Modirshahla; Morteza Khosravi; Mohammad Ali Behnajady

    2012-01-01

    Titanium dioxide (TiO2) nanoparticles were prepared by sol gel route.The preparation parameters were optimized in the removal of 4-nitrophenol (4-NP).All catalysts were analyzed by X-ray diffraction (XRD) and scanning electron microscopy (SEM).An artificial neural network model (ANN) was developed to predict the photocatalytic removal of 4-NP in the presence of TiO2 nanoparticles prepared under desired conditions.The comparison between the predicted results by designed ANN model and the experimental data proved that modeling of the removal process of 4-NP using artificial neural network was a precise method to predict the extent of 4-NP removal under different conditions.

  7. A reduced-order modeling for efficient design study of artificial valve in enlarged ventricular outflow tracts.

    Science.gov (United States)

    Caiazzo, A; Guibert, R; Vignon-Clementel, I E

    2016-09-01

    A computational approach is proposed for efficient design study of a reducer stent to be percutaneously implanted in enlarged right ventricular outflow tracts (RVOT). The need for such a device is driven by the absence of bovine or artificial valves which could be implanted in these RVOT to replace the absent or incompetent native valve, as is often the case over time after Tetralogy of Fallot repair. Hemodynamics are simulated in the stented RVOT via a reduce order model based on proper orthogonal decomposition, while the artificial valve is modeled as a thin resistive surface. The reduced order model is obtained from the numerical solution on a reference device configuration, then varying the geometrical parameters (diameter) for design purposes. To validate the approach, forces exerted on the valve and on the reducer are monitored, varying with geometrical parameters, and compared with the results of full CFD simulations. Such an approach could also be useful for uncertainty quantification. PMID:26738807

  8. New Markov Model Approaches to Deciphering Microbial Genome Function and Evolution: Comparative Genomics of Laterally Transferred Genes

    Energy Technology Data Exchange (ETDEWEB)

    Borodovsky, M.

    2013-04-11

    Algorithmic methods for gene prediction have been developed and successfully applied to many different prokaryotic genome sequences. As the set of genes in a particular genome is not homogeneous with respect to DNA sequence composition features, the GeneMark.hmm program utilizes two Markov models representing distinct classes of protein coding genes denoted "typical" and "atypical". Atypical genes are those whose DNA features deviate significantly from those classified as typical and they represent approximately 10% of any given genome. In addition to the inherent interest of more accurately predicting genes, the atypical status of these genes may also reflect their separate evolutionary ancestry from other genes in that genome. We hypothesize that atypical genes are largely comprised of those genes that have been relatively recently acquired through lateral gene transfer (LGT). If so, what fraction of atypical genes are such bona fide LGTs? We have made atypical gene predictions for all fully completed prokaryotic genomes; we have been able to compare these results to other "surrogate" methods of LGT prediction.

  9. GEM System: automatic prototyping of cell-wide metabolic pathway models from genomes

    Directory of Open Access Journals (Sweden)

    Nakayama Yoichi

    2006-03-01

    Full Text Available Abstract Background Successful realization of a "systems biology" approach to analyzing cells is a grand challenge for our understanding of life. However, current modeling approaches to cell simulation are labor-intensive, manual affairs, and therefore constitute a major bottleneck in the evolution of computational cell biology. Results We developed the Genome-based Modeling (GEM System for the purpose of automatically prototyping simulation models of cell-wide metabolic pathways from genome sequences and other public biological information. Models generated by the GEM System include an entire Escherichia coli metabolism model comprising 968 reactions of 1195 metabolites, achieving 100% coverage when compared with the KEGG database, 92.38% with the EcoCyc database, and 95.06% with iJR904 genome-scale model. Conclusion The GEM System prototypes qualitative models to reduce the labor-intensive tasks required for systems biology research. Models of over 90 bacterial genomes are available at our web site.

  10. Modeling and Prediction of Monthly Total Ozone Concentrations by Use of an Artificial Neural Network Based on Principal Component Analysis

    Science.gov (United States)

    Chattopadhyay, Surajit; Chattopadhyay, Goutami

    2012-10-01

    In the work discussed in this paper we considered total ozone time series over Kolkata (22°34'10.92″N, 88°22'10.92″E), an urban area in eastern India. Using cloud cover, average temperature, and rainfall as the predictors, we developed an artificial neural network, in the form of a multilayer perceptron with sigmoid non-linearity, for prediction of monthly total ozone concentrations from values of the predictors in previous months. We also estimated total ozone from values of the predictors in the same month. Before development of the neural network model we removed multicollinearity by means of principal component analysis. On the basis of the variables extracted by principal component analysis, we developed three artificial neural network models. By rigorous statistical assessment it was found that cloud cover and rainfall can act as good predictors for monthly total ozone when they are considered as the set of input variables for the neural network model constructed in the form of a multilayer perceptron. In general, the artificial neural network has good potential for predicting and estimating monthly total ozone on the basis of the meteorological predictors. It was further observed that during pre-monsoon and winter seasons, the proposed models perform better than during and after the monsoon.

  11. Artificial vesicles as an animal cell model for the study of biological application of non-thermal plasma

    Science.gov (United States)

    Ki, S. H.; Park, J. K.; Sung, C.; Lee, C. B.; Uhm, H.; Choi, E. H.; Baik, K. Y.

    2016-03-01

    Artificial cell-like model systems can provide information which is hard to obtain with real biological cells. Giant unilamellar vesicles (GUV) containing intra-membrane DNA or OH radical-binding molecules are used to visualize the cytolytic activity of OH radicals. Changes in the GUV membrane are observed by microscopy or flow cytometry as performed for animal cells after non-thermal plasma treatment. The experimental data shows that OH radicals can be detected inside the membrane, although the biological effects are not as significant as for H2O2. This artificial model system can provide a systemic means to elucidate the complex interactions between biological materials and non-thermal plasma.

  12. Modeling geological brines in salt-dome high level nuclear waste isolation repositories by artificial neural networks

    International Nuclear Information System (INIS)

    In order to study the influence of various parameters on the acidity of simulated geological brines, an artificial intelligence technique based on neural network modelling has been developed. It has been found that the pH of simulated salt repository brines lies within the range of 3.2-5 as the temperature of the brine decays from 250oC to 125oC. This environment might cause severe corrosion damage to canisters fabricated from carbon steel, particularly under slightly oxidizing conditions because of autocatalytical attack. It has also been demonstrated that artificial neural networks are efficient tools for analysing complex chemical systems, especially when conventional modelling is precluded by a lack of knowledge of the species and equilibria involved in the system. (Author)

  13. Artificial vesicles as an animal cell model for the study of biological application of non-thermal plasma

    International Nuclear Information System (INIS)

    Artificial cell-like model systems can provide information which is hard to obtain with real biological cells. Giant unilamellar vesicles (GUV) containing intra-membrane DNA or OH radical-binding molecules are used to visualize the cytolytic activity of OH radicals. Changes in the GUV membrane are observed by microscopy or flow cytometry as performed for animal cells after non-thermal plasma treatment. The experimental data shows that OH radicals can be detected inside the membrane, although the biological effects are not as significant as for H2O2. This artificial model system can provide a systemic means to elucidate the complex interactions between biological materials and non-thermal plasma. (paper)

  14. Modeling compressive strength of recycled aggregate concrete by Artificial Neural Network, Model Tree and Non-linear Regression

    Directory of Open Access Journals (Sweden)

    Neela Deshpande

    2014-12-01

    Full Text Available In the recent past Artificial Neural Networks (ANN have emerged out as a promising technique for predicting compressive strength of concrete. In the present study back propagation was used to predict the 28 day compressive strength of recycled aggregate concrete (RAC along with two other data driven techniques namely Model Tree (MT and Non-linear Regression (NLR. Recycled aggregate is the current need of the hour owing to its environmental friendly aspect of re-use of the construction waste. The study observed that, prediction of 28 day compressive strength of RAC was done better by ANN than NLR and MT. The input parameters were cubic meter proportions of Cement, Natural fine aggregate, Natural coarse Aggregates, recycled aggregates, Admixture and Water (also called as raw data. The study also concluded that ANN performs better when non-dimensional parameters like Sand–Aggregate ratio, Water–total materials ratio, Aggregate–Cement ratio, Water–Cement ratio and Replacement ratio of natural aggregates by recycled aggregates, were used as additional input parameters. Study of each network developed using raw data and each non dimensional parameter facilitated in studying the impact of each parameter on the performance of the models developed using ANN, MT and NLR as well as performance of the ANN models developed with limited number of inputs. The results indicate that ANN learn from the examples and grasp the fundamental domain rules governing strength of concrete.

  15. Application of Artificial Neural Networks in Modelling the Hydrological Balance of the Ploucnice River valley, Northern Bohemia (Czech Republic)

    Czech Academy of Sciences Publication Activity Database

    Neruda, M.; Neruda, Roman

    Praha : Academia, 2007 - (Hartel, H.; Cílek, V.; Herben, T.; Jackson, A.; Williams, R.), s. 90-92 ISBN 978-80-200-1577-8 R&D Projects: GA ČR GA526/03/Z042 Institutional research plan: CEZ:AV0Z10300504 Keywords : rainfall-runoff models * Ploucnice River valley * artificial neural networks Subject RIV: DA - Hydrology ; Limnology

  16. Artificial Neural Network Approach to the Modeling of Prosody in the Speech Synthesizer of the Czech Language

    Czech Academy of Sciences Publication Activity Database

    Tučková, J.; Šebesta, Václav

    Anaheim : ACTA Press, 2007 - (del Pobil, A.), s. 1-6 ISBN 978-0-88986-693-5. [ASC 2007. IASTED International Conference on Artificial Intelligence and Soft Computing /11./. Palma de Mallorca (ES), 29.08.2007-31.08.2007] R&D Projects: GA ČR GA102/05/0278 Institutional research plan: CEZ:AV0Z10300504 Keywords : neural network s * prosody modelling * pruning method Subject RIV: IN - Informatics, Computer Science

  17. The spatial prediction of landslide susceptibility applying artificial neural network and logistic regression models: A case study of Inje, Korea

    OpenAIRE

    Saro Lee; Woo Jeon Seong; Kwan-Young Oh; Moung-Jin Lee

    2016-01-01

    The aim of this study is to predict landslide susceptibility caused using the spatial analysis by the application of a statistical methodology based on the GIS. Logistic regression models along with artificial neutral network were applied and validated to analyze landslide susceptibility in Inje, Korea. Landslide occurrence area in the study were identified based on interpretations of optical remote sensing data (Aerial photographs) followed by field surveys. A spatial database considering fo...

  18. Embryonic development of goldfish (Carassius auratus): A model for the study of evolutionary change in developmental mechanisms by artificial selection

    OpenAIRE

    Tsai, Hsin-Yuan; Chang, Mariann; Liu, Shih-Chieh; Abe, Gembu; Ota, Kinya G

    2013-01-01

    Background: Highly divergent morphology among the different goldfish strains (Carassius auratus) may make it a suitable model for investigating how artificial selection has altered developmental mechanisms. Here we describe the embryological development of the common goldfish (the single fin Wakin), which retains the ancestral morphology of this species. Results: We divided goldfish embryonic development into seven periods consisting of 34 stages, using previously reported developmental indic...

  19. Weed management through herbicide application in direct-seeded rice and yield modeling by artificial neural network

    OpenAIRE

    Dibakar Ghosh; Singh, Udai P; Krishnendu Ray; Anupam Das

    2016-01-01

    In direct seeded rice (DSR) cultivation, weed is the major constraint mainly due to absence of puddling in field. The yield loss due to weed interference is huge, may be up to 100%. In this perspective, the present experiment was conducted to study the efficacy of selected herbicides, and to predict the rice yield using artificial neural network (ANN) models. The dry weight and density of weeds were recorded at different growth stages and consequently herbicidal efficacy was evaluated. Experi...

  20. Study on Artificial Neural Network Model for Crop EVapotranspiration%作物蒸发蒸腾量的人工神经网络模型研究

    Institute of Scientific and Technical Information of China (English)

    冯雪; 潘英华; 张振华

    2007-01-01

    Based on potted plant experiment,BP-artificial neural network was used to simulate crop evapotranspimtion and 3 kinds of artificial neural network models wero constructed as ET1(meteomlogical factors),ET2(meteorological factors and sowing days)and ET3(meteomlogical factors,sowing days and water content). And the predicted result was compared with actual value ET that Was obtained by weishing method.The results showed that the ET3 model had higher calculation precision and an optimum BP-artificial neural network model for calculating crop evapotranspiration.

  1. Advances in Intelligent Modelling and Simulation Artificial Intelligence-Based Models and Techniques in Scalable Computing

    CERN Document Server

    Khan, Samee; Burczy´nski, Tadeusz

    2012-01-01

    One of the most challenging issues in today’s large-scale computational modeling and design is to effectively manage the complex distributed environments, such as computational clouds, grids, ad hoc, and P2P networks operating under  various  types of users with evolving relationships fraught with  uncertainties. In this context, the IT resources and services usually belong to different owners (institutions, enterprises, or individuals) and are managed by different administrators. Moreover, uncertainties are presented to the system at hand in various forms of information that are incomplete, imprecise, fragmentary, or overloading, which hinders in the full and precise resolve of the evaluation criteria, subsequencing and selection, and the assignment scores. Intelligent scalable systems enable the flexible routing and charging, advanced user interactions and the aggregation and sharing of geographically-distributed resources in modern large-scale systems.   This book presents new ideas, theories, models...

  2. Monthly river flow forecasting using artificial neural network and support vector regression models coupled with wavelet transform

    Science.gov (United States)

    Kalteh, Aman Mohammad

    2013-04-01

    Reliable and accurate forecasts of river flow is needed in many water resources planning, design development, operation and maintenance activities. In this study, the relative accuracy of artificial neural network (ANN) and support vector regression (SVR) models coupled with wavelet transform in monthly river flow forecasting is investigated, and compared to regular ANN and SVR models, respectively. The relative performance of regular ANN and SVR models is also compared to each other. For this, monthly river flow data of Kharjegil and Ponel stations in Northern Iran are used. The comparison of the results reveals that both ANN and SVR models coupled with wavelet transform, are able to provide more accurate forecasting results than the regular ANN and SVR models. However, it is found that SVR models coupled with wavelet transform provide better forecasting results than ANN models coupled with wavelet transform. The results also indicate that regular SVR models perform slightly better than regular ANN models.

  3. DMU - A Package for Analyzing Multivariate Mixed Models in quantitative Genetics and Genomics

    DEFF Research Database (Denmark)

    Madsen, Per; Jensen, Just; Labouriau, Rodrigo; Christensen, Ole Fredslund; Sahana, Goutam

    The DMU-package for Analyzing Multivariate Mixed Models has been developed over a period of more than 25 years. This paper gives an overview of new features and the recent developments around the DMU-package, including: Genomic prediction (SNP-BLUP, G-BLUP and “One-Step”), Genome-wide association...

  4. Applied genomics in ruminants-new discoveries and model for predictive medicine

    Science.gov (United States)

    An overview of the progress for Dr. Sonstegard’s work in applied genomics in dairy cattle will be presented. The overview will include how applied research in livestock offers unique investigative models to discover gene function as a result of genetic load or inbreeding and also how genome selectio...

  5. The available capacity computation model based on artificial neural network for lead-acid batteries in electric vehicles

    Science.gov (United States)

    Chan, C. C.; Lo, E. W. C.; Weixiang, Shen

    The available capacity computation model based on the artificial neural network (ANN) for lead-acid batteries in an electric vehicle (EV) is presented. Comparing with the methods based on the Peukert equation, which is often used for the calculation of the available capacity for lead-acid batteries in EVs, this model is more accurate. The results of the experiment have proven the accuracy of the proposed model; the computation values are in good agreement with experimental data, the associated error has been considered acceptable from an engineering point of view.

  6. Use of Artificial Neural Network for Testing Effectiveness of Intelligent Computing Models for Predicting Shelf Life of Processed Cheese

    Directory of Open Access Journals (Sweden)

    GOYAL Kumar Gyanendra

    2012-10-01

    Full Text Available This paper presents the suitability of artificial neural network (ANN models for predicting the shelf life of processed cheese stored at 7-8ºC. Soluble nitrogen, pH; standard plate count, yeast & mould count, and spore count were input variables, and sensory score was output variable. Mean square error, root mean square error, coefficient of determination and Nash - sutcliffo coefficient were used in order to test the effectiveness of the developed ANN models. Excellent agreement was found between experimental results and these mathematical parameters, thus confirming that ANN models are very effective in predicting the shelf life of processed cheese.

  7. Landsat 7 thermal-IR image sharpening using an artificial neural network and sensor model

    Science.gov (United States)

    Lemeshewsky, G.P.; Schowengerdt, R.A.

    2001-01-01

    The enhanced thematic mapper (plus) (ETM+) instrument on Landsat 7 shares the same basic design as the TM sensors on Landsats 4 and 5, with some significant improvements. In common are six multispectral bands with a 30-m ground-projected instantaneous field of view (GIFOV). However, the thermaL-IR (TIR) band now has a 60-m GIFOV, instead of 120-m. Also, a 15-m panchromatic band has been added. The artificial neural network (NN) image sharpening method described here uses data from the higher spatial resolution ETM+ bands to enhance (sharpen) the spatial resolution of the TIR imagery. It is based on an assumed correlation over multiple scales of resolution, between image edge contrast patterns in the TIR band and several other spectral bands. A multilayer, feedforward NN is trained to approximate TIR data at 60m, given degraded (from 30-m to 60-m) spatial resolution input from spectral bands 7, 5, and 2. After training, the NN output for full-resolution input generates an approximation of a TIR image at 30-m resolution. Two methods are used to degrade the spatial resolution of the imagery used for NN training, and the corresponding sharpening results are compared. One degradation method uses a published sensor transfer function (TF) for Landsat 5 to simulate sensor coarser resolution imagery from higher resolution imagery. For comparison, the second degradation method is simply Gaussian low pass filtering and subsampling, wherein the Gaussian filter approximates the full width at half maximum amplitude characteristics of the TF-based spatial filter. Two fixed-size NNs (that is, number of weights and processing elements) were trained separately with the degraded resolution data, and the sharpening results compared. The comparison evaluates the relative influence of the degradation technique employed and whether or not it is desirable to incorporate a sensor TF model. Preliminary results indicate some improvements for the sensor model-based technique. Further

  8. Developing an artificial neural network model for predicting concrete’s compression strength and electrical resistivity

    Directory of Open Access Journals (Sweden)

    Juan Manuel Lizarazo Marriaga

    2010-04-01

    Full Text Available The present study was conducted for predicting the compressive strength of concrete based on unit weight ultrasonic and pulse velocity (UPV for 41 different concrete mixtures. This research emerged from the need for a rapid test for predicting concrete’s compressive strength. The research was also conducted for predicting concrete’s electrical resistivity based on unit weight ultrasonic, pulse velocity (UPV and compressive strength with the same mixes. The prediction was made using simple regression analysis and artificial neural networks. The results revealed that artificial neural networks can be used for effectively predicting compressive strength and electrical resistivity.

  9. Modeling genome evolution with a diffusion approximation of a birth-and-death process

    OpenAIRE

    Karev, Georgy P.; Berezovskaya, Faina S; Koonin, Eugene V.

    2005-01-01

    In our previous studies, we developed discrete-space Birth, Death and Innovation Models (BDIM) of genome evolution. These models explain the origin of the characteristic Pareto distribution of paralogous gene family sizes in genomes, and model parameters that provide for the evolution of these distributions within a realistic timeframe have been identified. Here we develop the diffusion version of BDIM whose dynamics is described by the Fokker-Plank equation and the stationary solution could ...

  10. Novel model of a AlGaN/GaN high electron mobility transistor based on an artificial neural network

    International Nuclear Information System (INIS)

    In this paper we present a novel approach to modeling AlGaN/GaN high electron mobility transistor (HEMT) with an artificial neural network (ANN). The AlGaN/GaN HEMT device structure and its fabrication process are described. The circuit-based Neuro-space mapping (neuro-SM) technique is studied in detail. The EEHEMT model is implemented according to the measurement results of the designed device, which serves as a coarse model. An ANN is proposed to model AlGaN/GaN HEMT based on the coarse model. Its optimization is performed. The simulation results from the model are compared with the measurement results. It is shown that the simulation results obtained from the ANN model of AlGaN/GaN HEMT are more accurate than those obtained from the EEHEMT model. (condensed matter: structural, mechanical, and thermal properties)

  11. Simplivariate Models: Uncovering the Underlying Biology in Functional Genomics Data

    OpenAIRE

    Edoardo Saccenti; Westerhuis, Johan A.; Smilde, Age K.; van der Werf, Mariët J; Jos A Hageman; Hendriks, Margriet M. W. B.

    2011-01-01

    One of the first steps in analyzing high-dimensional functional genomics data is an exploratory analysis of such data. Cluster Analysis and Principal Component Analysis are then usually the method of choice. Despite their versatility they also have a severe drawback: they do not always generate simple and interpretable solutions. On the basis of the observation that functional genomics data often contain both informative and non-informative variation, we propose a method that finds sets of va...

  12. Application Of Artificial Neural Networks In Modeling Of Manufactured Front Metallization Contact Resistance For Silicon Solar Cells

    Directory of Open Access Journals (Sweden)

    Musztyfaga-Staszuk M.

    2015-09-01

    Full Text Available This paper presents the application of artificial neural networks for prediction contact resistance of front metallization for silicon solar cells. The influence of the obtained front electrode features on electrical properties of solar cells was estimated. The front electrode of photovoltaic cells was deposited using screen printing (SP method and next to manufactured by two methods: convectional (1. co-fired in an infrared belt furnace and unconventional (2. Selective Laser Sintering. Resistance of front electrodes solar cells was investigated using Transmission Line Model (TLM. Artificial neural networks were obtained with the use of Statistica Neural Network by Statsoft. Created artificial neural networks makes possible the easy modelling of contact resistance of manufactured front metallization and allows the better selection of production parameters. The following technological recommendations for the screen printing connected with co-firing and selective laser sintering technology such as optimal paste composition, morphology of the silicon substrate, co-firing temperature and the power and scanning speed of the laser beam to manufacture the front electrode of silicon solar cells were experimentally selected in order to obtain uniformly melted structure well adhered to substrate, of a small front electrode substrate joint resistance value. The prediction possibility of contact resistance of manufactured front metallization is valuable for manufacturers and constructors. It allows preserving the customers’ quality requirements and bringing also measurable financial advantages.

  13. Isolation and characterization of bovine herpesvirus 4 (BoHV-4 from a cow affected by post partum metritis and cloning of the genome as a bacterial artificial chromosome

    Directory of Open Access Journals (Sweden)

    Cavirani Sandro

    2009-08-01

    Full Text Available Abstract Background Bovine herpesvirus 4 (BoHV-4 is a gammaherpesvirus with a Worldwide distribution in cattle and is often isolated from the uterus of animals with postpartum metritis or pelvic inflammatory disease. Virus strain adaptation to an organ, tissue or cell type is an important issue for the pathogenesis of disease. To explore the mechanistic role of viral strain variation for uterine disease, the present study aimed to develop a tool enabling precise genetic discrimination between strains of BoHV-4 and to easily manipulate the viral genome. Methods A strain of BoHV-4 was isolated from the uterus of a persistently infected cow and designated BoHV-4-U. The authenticity of the isolate was confirmed by RFLP-PCR and sequencing using the TK and IE2 loci as genetic marker regions for the BoHV-4 genome. The isolated genome was cloned as a Bacterial Artificial Chromosome (BAC and manipulated through recombineering technology Results The BoHV-4-U genome was successfully cloned as a BAC, and the stability of the pBAC-BoHV-4-U clone was confirmed over twenty passages, with viral growth similar to the wild type virus. The feasibility of using BoHV-4-U for mutagenesis was demonstrated using the BAC recombineering system. Conclusion The analysis of genome strain variation is a key method for investigating genes associated with disease. A resource for dissection of the interactions between BoHV-4 and host endometrial cells was generated by cloning the genome of BoHV-4 as a BAC.

  14. Using Artificial Neural Networks in Educational Research: Some Comparisons with Linear Statistical Models.

    Science.gov (United States)

    Everson, Howard T.; And Others

    This paper explores the feasibility of neural computing methods such as artificial neural networks (ANNs) and abductory induction mechanisms (AIM) for use in educational measurement. ANNs and AIMS methods are contrasted with more traditional statistical techniques, such as multiple regression and discriminant function analyses, for making…

  15. The effects of artificial ageining on model colour layeres containing neutral verdigris

    Czech Academy of Sciences Publication Activity Database

    Bauerová, P.; Bezdička, Petr

    PRAHA : Akademie výtvarných umění v Praze, 2012, s. 349-350. ISBN 978-80-87108-33-8. [Interdisciplinary Conference of ALMA /4./. Praha (CZ), 21.11.2012-23.11.2012] Institutional support: RVO:61388980 Keywords : neutral verdigris * artificial ageing * colour change * degradation Subject RIV: CB - Analytical Chemistry, Separation

  16. Modeling Human Infant Learning in Embodied Artificial Entities to Produce Grounded Concepts

    OpenAIRE

    Berkowitz, Eric

    2003-01-01

    I present a system for concept development in an artificial entity. The concept development is designed around the foundations of human cognition while at the same time remaining grounded in the agent or robot’s own perception of its world.

  17. Bag-of-visual-words model for artificial pornographic images recognition

    Institute of Scientific and Technical Information of China (English)

    李芳芳; 罗四伟; 刘熙尧; 邹北骥

    2016-01-01

    It is illegal to spread and transmit pornographic images over internet, either in real or in artificial format. The traditional methods are designed to identify real pornographic images and they are less efficient in dealing with artificial images. Therefore, criminals turn to release artificial pornographic images in some specific scenes, e.g., in social networks. To efficiently identify artificial pornographic images, a novel bag-of-visual-words based approach is proposed in the work. In the bag-of-words (BoW) framework, speeded-up robust feature (SURF) is adopted for feature extraction at first, then a visual vocabulary is constructed through K-means clustering and images are represented by an improved BoW encoding method, and finally the visual words are fed into a learning machine for training and classification. Different from the traditional BoW method, the proposed method sets a weight on each visual word according to the number of features that each cluster contains. Moreover, a non-binary encoding method and cross-matching strategy are utilized to improve the discriminative power of the visual words. Experimental results indicate that the proposed method outperforms the traditional method.

  18. Exploiting proteomic data for genome annotation and gene model validation in Aspergillus niger

    Directory of Open Access Journals (Sweden)

    Grigoriev Igor V

    2009-02-01

    Full Text Available Abstract Background Proteomic data is a potentially rich, but arguably unexploited, data source for genome annotation. Peptide identifications from tandem mass spectrometry provide prima facie evidence for gene predictions and can discriminate over a set of candidate gene models. Here we apply this to the recently sequenced Aspergillus niger fungal genome from the Joint Genome Institutes (JGI and another predicted protein set from another A.niger sequence. Tandem mass spectra (MS/MS were acquired from 1d gel electrophoresis bands and searched against all available gene models using Average Peptide Scoring (APS and reverse database searching to produce confident identifications at an acceptable false discovery rate (FDR. Results 405 identified peptide sequences were mapped to 214 different A.niger genomic loci to which 4093 predicted gene models clustered, 2872 of which contained the mapped peptides. Interestingly, 13 (6% of these loci either had no preferred predicted gene model or the genome annotators' chosen "best" model for that genomic locus was not found to be the most parsimonious match to the identified peptides. The peptides identified also boosted confidence in predicted gene structures spanning 54 introns from different gene models. Conclusion This work highlights the potential of integrating experimental proteomics data into genomic annotation pipelines much as expressed sequence tag (EST data has been. A comparison of the published genome from another strain of A.niger sequenced by DSM showed that a number of the gene models or proteins with proteomics evidence did not occur in both genomes, further highlighting the utility of the method.

  19. An ecological model of the artificial ecosystem (northern Hangzhou Bay, China): analysis of ecosystem structure and fishing impacts

    Science.gov (United States)

    Chen, Zuozhi; Xu, Shannan; He, Peimin

    2011-06-01

    The artificial ecosystem is a large-scale enclosure in northern Hangzhou Bay, China. Using the Ecopath with Ecosim software, a trophic structure model is constructed for 2006-2007 to characterize the food web structure, functioning, and describing the ecosystem impacts of fishing. Input information for the model were gathered from published and unpublished reports and from our own estimates during the period 2006-2007. Pedigree work and simple sensitivity analysis were carried out to evaluate the quality and the uncertainty of the model. Results show that the food web in the enclosed sea area was dominated by a detritus pathway. The trophic levels of the groups varied from 1.00 for primary producers and detritus to 3.90 for piscivorous fish in the artificial system. Using network analysis, the system network was mapped into a linear food chain, and five discrete trophic levels were found with a mean transfer efficiency of 9.8% from detritus, 9.4% from primary producer within the ecosystem. The geometric mean of the trophic transfer efficiencies was 9.5%. Detritus contributed 57% of the total energy flux, and the other 43% came from primary producers. The ecosystem maturity indices-TPP/TR (total primary production/total respiration), FCI (Finn cycling index), A (ascendancy) and TB/TDET were 2.672, 25%, 31.5%, and 0.013, respectively, showing that the artificial system is at developmental stage according to Odum's theory of ecosystem development. The `Keystoneness' result indicates that herbivorous zooplankton was identified as keystone species in this system. Furthermore, a simple dynamical simulation was preformed for varying fishing mortality over 10 years. The biomass of most fish groups has a small increase when the fishing mortality at current level. Increasing fishing mortality by twofold resulted in a marked decrease in biomass of piscivorous fish accompanied by an increase in that of other fish groups, notable zooplanktivorous fish. Generally, this study

  20. Artificial Neural Network and Response Surface Methodology Modeling in Ionic Conductivity Predictions of Phthaloylchitosan-Based Gel Polymer Electrolyte

    Directory of Open Access Journals (Sweden)

    Ahmad Danial Azzahari

    2016-01-01

    Full Text Available A gel polymer electrolyte system based on phthaloylchitosan was prepared. The effects of process variables, such as lithium iodide, caesium iodide, and 1-butyl-3-methylimidazolium iodide were investigated using a distance-based ternary mixture experimental design. A comparative approach was made between response surface methodology (RSM and artificial neural network (ANN to predict the ionic conductivity. The predictive capabilities of the two methodologies were compared in terms of coefficient of determination R2 based on the validation data set. It was shown that the developed ANN model had better predictive outcome as compared to the RSM model.

  1. Genome-Scale Model Reveals Metabolic Basis of Biomass Partitioning in a Model Diatom.

    Directory of Open Access Journals (Sweden)

    Jennifer Levering

    Full Text Available Diatoms are eukaryotic microalgae that contain genes from various sources, including bacteria and the secondary endosymbiotic host. Due to this unique combination of genes, diatoms are taxonomically and functionally distinct from other algae and vascular plants and confer novel metabolic capabilities. Based on the genome annotation, we performed a genome-scale metabolic network reconstruction for the marine diatom Phaeodactylum tricornutum. Due to their endosymbiotic origin, diatoms possess a complex chloroplast structure which complicates the prediction of subcellular protein localization. Based on previous work we implemented a pipeline that exploits a series of bioinformatics tools to predict protein localization. The manually curated reconstructed metabolic network iLB1027_lipid accounts for 1,027 genes associated with 4,456 reactions and 2,172 metabolites distributed across six compartments. To constrain the genome-scale model, we determined the organism specific biomass composition in terms of lipids, carbohydrates, and proteins using Fourier transform infrared spectrometry. Our simulations indicate the presence of a yet unknown glutamine-ornithine shunt that could be used to transfer reducing equivalents generated by photosynthesis to the mitochondria. The model reflects the known biochemical composition of P. tricornutum in defined culture conditions and enables metabolic engineering strategies to improve the use of P. tricornutum for biotechnological applications.

  2. Genome-Scale Model Reveals Metabolic Basis of Biomass Partitioning in a Model Diatom

    Science.gov (United States)

    Broddrick, Jared; Dupont, Christopher L.; Peers, Graham; Beeri, Karen; Mayers, Joshua; Gallina, Alessandra A.; Allen, Andrew E.; Palsson, Bernhard O.; Zengler, Karsten

    2016-01-01

    Diatoms are eukaryotic microalgae that contain genes from various sources, including bacteria and the secondary endosymbiotic host. Due to this unique combination of genes, diatoms are taxonomically and functionally distinct from other algae and vascular plants and confer novel metabolic capabilities. Based on the genome annotation, we performed a genome-scale metabolic network reconstruction for the marine diatom Phaeodactylum tricornutum. Due to their endosymbiotic origin, diatoms possess a complex chloroplast structure which complicates the prediction of subcellular protein localization. Based on previous work we implemented a pipeline that exploits a series of bioinformatics tools to predict protein localization. The manually curated reconstructed metabolic network iLB1027_lipid accounts for 1,027 genes associated with 4,456 reactions and 2,172 metabolites distributed across six compartments. To constrain the genome-scale model, we determined the organism specific biomass composition in terms of lipids, carbohydrates, and proteins using Fourier transform infrared spectrometry. Our simulations indicate the presence of a yet unknown glutamine-ornithine shunt that could be used to transfer reducing equivalents generated by photosynthesis to the mitochondria. The model reflects the known biochemical composition of P. tricornutum in defined culture conditions and enables metabolic engineering strategies to improve the use of P. tricornutum for biotechnological applications. PMID:27152931

  3. Genome-Scale Model Reveals Metabolic Basis of Biomass Partitioning in a Model Diatom.

    Science.gov (United States)

    Levering, Jennifer; Broddrick, Jared; Dupont, Christopher L; Peers, Graham; Beeri, Karen; Mayers, Joshua; Gallina, Alessandra A; Allen, Andrew E; Palsson, Bernhard O; Zengler, Karsten

    2016-01-01

    Diatoms are eukaryotic microalgae that contain genes from various sources, including bacteria and the secondary endosymbiotic host. Due to this unique combination of genes, diatoms are taxonomically and functionally distinct from other algae and vascular plants and confer novel metabolic capabilities. Based on the genome annotation, we performed a genome-scale metabolic network reconstruction for the marine diatom Phaeodactylum tricornutum. Due to their endosymbiotic origin, diatoms possess a complex chloroplast structure which complicates the prediction of subcellular protein localization. Based on previous work we implemented a pipeline that exploits a series of bioinformatics tools to predict protein localization. The manually curated reconstructed metabolic network iLB1027_lipid accounts for 1,027 genes associated with 4,456 reactions and 2,172 metabolites distributed across six compartments. To constrain the genome-scale model, we determined the organism specific biomass composition in terms of lipids, carbohydrates, and proteins using Fourier transform infrared spectrometry. Our simulations indicate the presence of a yet unknown glutamine-ornithine shunt that could be used to transfer reducing equivalents generated by photosynthesis to the mitochondria. The model reflects the known biochemical composition of P. tricornutum in defined culture conditions and enables metabolic engineering strategies to improve the use of P. tricornutum for biotechnological applications. PMID:27152931

  4. Intelligence: Real or artificial?

    OpenAIRE

    Schlinger, Henry D

    1992-01-01

    Throughout the history of the artificial intelligence movement, researchers have strived to create computers that could simulate general human intelligence. This paper argues that workers in artificial intelligence have failed to achieve this goal because they adopted the wrong model of human behavior and intelligence, namely a cognitive essentialist model with origins in the traditional philosophies of natural intelligence. An analysis of the word “intelligence” suggests that it originally r...

  5. Integrating Microarray Gene Expression Object Model and Clinical Document Architecture for Cancer Genomics Research

    OpenAIRE

    Park, Yu Rang; Lee, Hye Won; Kim, Ju Han

    2005-01-01

    Systematic integration of gene expression profiling with clinical information may facilitate cancer genomics research. MAGE-OM (MicroArray Gene Expression Object Model) defines standard objects for genomic but not for clinical data. HL7 CDA (Clinical Document Architecture) is a document model for clinical information, describing syntax but not semantics. We designed a document template and common data elements in XML Schema with additional constraints for CDA to define conte...

  6. Modeling daily discharge responses of a large karstic aquifer using soft computing methods: Artificial neural network and neuro-fuzzy

    Science.gov (United States)

    Kurtulus, Bedri; Razack, Moumtaz

    2010-02-01

    SummaryThis paper compares two methods for modeling karst aquifers, which are heterogeneous, highly non-linear, and hierarchical systems. There is a clear need to model these systems given the crucial role they play in water supply in many countries. In recent years, the main components of soft computing (fuzzy logic (FL), and Artificial Neural Networks, (ANNs)) have come to prevail in the modeling of complex non-linear systems in different scientific and technologic disciplines. In this study, Artificial Neural Networks and Adaptive Neuro-Fuzzy Interface System (ANFIS) methods were used for the prediction of daily discharge of karstic aquifers and their capability was compared. The approach was applied to 7 years of daily data of La Rochefoucauld karst system in south-western France. In order to predict the karst daily discharges, single-input (rainfall, piezometric level) vs. multiple-input (rainfall and piezometric level) series were used. In addition to these inputs, all models used measured or simulated discharges from the previous days with a specified delay. The models were designed in a Matlab™ environment. An automatic procedure was used to select the best calibrated models. Daily discharge predictions were then performed using the calibrated models. Comparing predicted and observed hydrographs indicates that both models (ANN and ANFIS) provide close predictions of the karst daily discharges. The summary statistics of both series (observed and predicted daily discharges) are comparable. The performance of both models is improved when the number of inputs is increased from one to two. The root mean square error between the observed and predicted series reaches a minimum for two-input models. However, the ANFIS model demonstrates a better performance than the ANN model to predict peak flow. The ANFIS approach demonstrates a better generalization capability and slightly higher performance than the ANN, especially for peak discharges.

  7. Predicting the Water Level Fluctuation in an Alpine Lake Using Physically Based, Artificial Neural Network, and Time Series Forecasting Models

    Directory of Open Access Journals (Sweden)

    Chih-Chieh Young

    2015-01-01

    Full Text Available Accurate prediction of water level fluctuation is important in lake management due to its significant impacts in various aspects. This study utilizes four model approaches to predict water levels in the Yuan-Yang Lake (YYL in Taiwan: a three-dimensional hydrodynamic model, an artificial neural network (ANN model (back propagation neural network, BPNN, a time series forecasting (autoregressive moving average with exogenous inputs, ARMAX model, and a combined hydrodynamic and ANN model. Particularly, the black-box ANN model and physically based hydrodynamic model are coupled to more accurately predict water level fluctuation. Hourly water level data (a total of 7296 observations was collected for model calibration (training and validation. Three statistical indicators (mean absolute error, root mean square error, and coefficient of correlation were adopted to evaluate model performances. Overall, the results demonstrate that the hydrodynamic model can satisfactorily predict hourly water level changes during the calibration stage but not for the validation stage. The ANN and ARMAX models better predict the water level than the hydrodynamic model does. Meanwhile, the results from an ANN model are superior to those by the ARMAX model in both training and validation phases. The novel proposed concept using a three-dimensional hydrodynamic model in conjunction with an ANN model has clearly shown the improved prediction accuracy for the water level fluctuation.

  8. Genomic Bayesian Prediction Model for Count Data with Genotype × Environment Interaction

    Science.gov (United States)

    Montesinos-López, Abelardo; Montesinos-López, Osval A.; Crossa, José; Burgueño, Juan; Eskridge, Kent M.; Falconi-Castillo, Esteban; He, Xinyao; Singh, Pawan; Cichy, Karen

    2016-01-01

    Genomic tools allow the study of the whole genome, and facilitate the study of genotype-environment combinations and their relationship with phenotype. However, most genomic prediction models developed so far are appropriate for Gaussian phenotypes. For this reason, appropriate genomic prediction models are needed for count data, since the conventional regression models used on count data with a large sample size (nT) and a small number of parameters (p) cannot be used for genomic-enabled prediction where the number of parameters (p) is larger than the sample size (nT). Here, we propose a Bayesian mixed-negative binomial (BMNB) genomic regression model for counts that takes into account genotype by environment (G×E) interaction. We also provide all the full conditional distributions to implement a Gibbs sampler. We evaluated the proposed model using a simulated data set, and a real wheat data set from the International Maize and Wheat Improvement Center (CIMMYT) and collaborators. Results indicate that our BMNB model provides a viable option for analyzing count data. PMID:26921298

  9. A game theoretic framework for incentive-based models of intrinsic motivation in artificial systems

    OpenAIRE

    KathrynElizabethMerrick

    2013-01-01

    An emerging body of research is focusing on understanding and building artificial systems that can achieve open-ended development influenced by intrinsic motivations. In particular, research in robotics and machine learning is yielding systems and algorithms with increasing capacity for self-directed learning and autonomy. Traditional software architectures and algorithms are being augmented with intrinsic motivations to drive cumulative acquisition of knowledge and skills. Intrinsic motivati...

  10. Application of artificial neural networks in modelling of normalised structural steels mechanical properties

    OpenAIRE

    L.A. Dobrzański; R. Honysz

    2009-01-01

    Purpose: This paper presents the application of artificial neural networks for mechanical properties prediction of constructional steels after heat treatment.Design/methodology/approach: On the basis of such input parameteres, such as the chemical composition, the ones of mechanical and heat treatment and dimensions of elements, mechanical properties such as strength, impact resistance or hardness are predicted.Findings: Results obtained in the given ranges of input parameters show very good ...

  11. Application of artificial neural networks in modelling of quenched and tempered structural steels mechanical properties

    OpenAIRE

    L.A. Dobrzański; R. Honysz

    2010-01-01

    Purpose: This paper presents the application of artificial neural networks for mechanical properties prediction of structuralal steels after quenching and tempering processes.Design/methodology/approach: On the basis of input parameters, which are chemical composition, parameters of mechanical and heat treatment and dimensions of elements, steels’ mechanical properties : yield stress, tensile strength stress, elongation, area reduction, impact strength and hardness are predicted.Findings: Res...

  12. Modelling fed-batch fermentation processes : an approach based on artificial neural networks

    OpenAIRE

    Valente, Eduardo; Rocha, I; Rocha, Miguel

    2009-01-01

    Publicado em "2nd International Workshop on Practical Applications of Computational Biology and Bioinformatics (IWPACBB 2008)", ISBN 978-3-540-85860-7 Artificial Neural Networks (ANNs) have shown to be powerful tools for solving several problems which, due to their complexity, are extremely difficult to unravel with other methods. Their capabilities of massive parallel processing and learning from the environment make these structures ideal for prediction of nonlinear events. In this work,...

  13. A Hybrid Model For Phrase Chunking Employing Artificial Immunity System And Rule Based Methods

    OpenAIRE

    Bindu.M.S; Sumam Mary Idicula

    2011-01-01

    Natural language Understanding (NLU), an important field of Artificial Intelligence (AI) is concerned with the speech and language understanding between human and computer. Understanding language means knowing what concept a word or phrase stands for and how to link them to form meaningful sentence. Identification of phrases or phrase chunking is an important step in natural language understanding (NLU). Chunker identifies and divides sentences into syntactically correlated word groups. Qu...

  14. How to advance general game playing artificial intelligence by player modelling

    OpenAIRE

    Cowley, Benjamin Ultan

    2016-01-01

    General game playing artificial intelligence has recently seen important advances due to the various techniques known as 'deep learning'. However the advances conceal equally important limitations in their reliance on: massive data sets; fortuitously constructed problems; and absence of any human-level complexity, including other human opponents. On the other hand, deep learning systems which do beat human champions, such as in Go, do not generalise well. The power of deep learning simultaneo...

  15. Enhancement of Polyethylene Terephthalate Artificial Ligament Graft Osseointegration using a Periosteum Patch in a Goat Model.

    Science.gov (United States)

    Dai, Z; Bao, W; Li, S; Li, H; Jiang, J; Chen, S

    2016-06-01

    The purpose of this study is to investigate whether a periosteum patch could enhance polyethylene terephthalate (PET) artificial ligament graft osseointegration in a bone tunnel. 12 female goats underwent ACL reconstruction with a PET artificial ligament graft in the right knees. Right knees in 6 goats were reconstructed with periosteum patch-enveloped PET grafts (Periosteum group) in the tibia bone tunnel, whereas the other 6 goats had no periosteum patch and served as the Control group. All the goats were sacrificed at 12 months after surgery. 3 tibial-graft complex samples in each group were harvested consecutively for microcomputed tomography (micro-CT) scan, magnetic resonance imaging (MRI) scan and histological evaluation. The other 3 tibial-graft complex samples in each group were harvested for biomechanical testing. The mean pull-out load of the Periosteum group (208±25 N) at 12 months was significantly higher than that of the Control group (107±13 N) (p=0.0044). According to the micro-CT scan, more new bone formation was observed at the graft-bone interface in the Periosteum group compared with the Control group. Furthermore, MRI showed that the Periosteum group appeared to have a better graft osseointegration within the bone tunnel compared with the Control group. Histologically, application of a periosteum patch induced more new bone and Sharpey's fiber formation between the graft and bone tunnel compared with the controls. The study has shown that periosteum enveloping of the PET artificial ligament has a positive effect in the induction of artificial ligament osseointegration within the bone tunnel. PMID:26990720

  16. Simulation of a pulsatile total artificial heart: Development of a partitioned Fluid Structure Interaction model

    Science.gov (United States)

    Sonntag, Simon J.; Kaufmann, Tim A. S.; Büsen, Martin R.; Laumen, Marco; Linde, Torsten; Schmitz-Rode, Thomas; Steinseifer, Ulrich

    2013-04-01

    Heart disease is one of the leading causes of death in the world. Due to a shortage in donor organs artificial hearts can be a bridge to transplantation or even serve as a destination therapy for patients with terminal heart insufficiency. A pusher plate driven pulsatile membrane pump, the Total Artificial Heart (TAH) ReinHeart, is currently under development at the Institute of Applied Medical Engineering of RWTH Aachen University.This paper presents the methodology of a fully coupled three-dimensional time-dependent Fluid Structure Interaction (FSI) simulation of the TAH using a commercial partitioned block-Gauss-Seidel coupling package. Partitioned coupling of the incompressible fluid with the slender flexible membrane as well as a high fluid/structure density ratio of about unity led inherently to a deterioration of the stability (‘artificial added mass instability’). The objective was to conduct a stable simulation with high accuracy of the pumping process. In order to achieve stability, a combined resistance and pressure outlet boundary condition as well as the interface artificial compressibility method was applied. An analysis of the contact algorithm and turbulence condition is presented. Independence tests are performed for the structural and the fluid mesh, the time step size and the number of pulse cycles. Because of the large deformation of the fluid domain, a variable mesh stiffness depending on certain mesh properties was specified for the fluid elements. Adaptive remeshing was avoided. Different approaches for the mesh stiffness function are compared with respect to convergence, preservation of mesh topology and mesh quality. The resulting mesh aspect ratios, mesh expansion factors and mesh orthogonalities are evaluated in detail. The membrane motion and flow distribution of the coupled simulations are compared with a top-view recording and stereo Particle Image Velocimetry (PIV) measurements, respectively, of the actual pump.

  17. Single-step genomic evaluation using multitrait random regression model and test-day data.

    Science.gov (United States)

    Koivula, M; Strandén, I; Pösö, J; Aamand, G P; Mäntysaari, E A

    2015-04-01

    The objectives of this study were to evaluate the feasibility of use of the test-day (TD) single-step genomic BLUP (ssGBLUP) using phenotypic records of Nordic Red Dairy cows. The critical point in ssGBLUP is how genomically derived relationships (G) are integrated with population-based pedigree relationships (A) into a combined relationship matrix (H). Therefore, we also tested how different weights for genomic and pedigree relationships affect ssGBLUP, validation reliability, and validation regression coefficients. Deregressed proofs for 305-d milk, protein, and fat yields were used for a posteriori validation. The results showed that the use of phenotypic TD records in ssGBLUP is feasible. Moreover, the TD ssGBLUP model gave considerably higher validation reliabilities and validation regression coefficients than the TD model without genomic information. No significant differences were found in validation reliability between the different TD ssGBLUP models according to bootstrap confidence intervals. However, the degree of inflation in genomic enhanced breeding values is affected by the method used in construction of the H matrix. The results showed that ssGBLUP provides a good alternative to the currently used multi-step approach but there is a great need to find the best option to combine pedigree and genomic information in the genomic matrix. PMID:25660739

  18. Reconstruction of genome-scale human metabolic models using omics data

    DEFF Research Database (Denmark)

    Ryu, Jae Yong; Kim, Hyun Uk; Lee, Sang Yup

    2015-01-01

    The impact of genome-scale human metabolic models on human systems biology and medical sciences is becoming greater, thanks to increasing volumes of model building platforms and publicly available omics data. The genome-scale human metabolic models started with Recon 1 in 2007, and have since been...... used to describe metabolic phenotypes of healthy and diseased human tissues and cells, and to predict therapeutic targets. Here we review recent trends in genome-scale human metabolic modeling, including various generic and tissue/cell type-specific human metabolic models developed to date, and methods......, databases and platforms used to construct them. For generic human metabolic models, we pay attention to Recon 2 and HMR 2.0 with emphasis on data sources used to construct them. Draft and high-quality tissue/cell type-specific human metabolic models have been generated using these generic human metabolic...

  19. Artificial neural network model to predict slag viscosity over a broad range of temperatures and slag compositions

    Energy Technology Data Exchange (ETDEWEB)

    Duchesne, Marc A. [Chemical and Biological Engineering Department, University of Ottawa, 161 Louis Pasteur, Ottawa, Ont. (Canada); CanmetENERGY, 1 Haanel Drive, Ottawa, Ontario (Canada); Macchi, Arturo [Chemical and Biological Engineering Department, University of Ottawa, 161 Louis Pasteur, Ottawa, Ont. (Canada); Lu, Dennis Y.; Hughes, Robin W.; McCalden, David; Anthony, Edward J. [CanmetENERGY, 1 Haanel Drive, Ottawa, Ontario (Canada)

    2010-08-15

    Threshold slag viscosity heuristics are often used for the initial assessment of coal gasification projects. Slag viscosity predictions are also required for advanced combustion and gasification models. Due to unsatisfactory performance of theoretical equations, an artificial neural network model was developed to predict slag viscosity over a broad range of temperatures and slag compositions. This model outperforms other slag viscosity models, resulting in an average error factor of 5.05 which is lower than the best obtained with other available models. Genesee coal ash viscosity predictions were made to investigate the effect of adding Canadian limestone and dolomite. The results indicate that magnesium in the fluxing agent provides a greater viscosity reduction than calcium for the threshold slag tapping temperature range. (author)

  20. Artificial blood

    Directory of Open Access Journals (Sweden)

    Sarkar Suman

    2008-01-01

    Full Text Available Artificial blood is a product made to act as a substitute for red blood cells. While true blood serves many different functions, artificial blood is designed for the sole purpose of transporting oxygen and carbon dioxide throughout the body. Depending on the type of artificial blood, it can be produced in different ways using synthetic production, chemical isolation, or recombinant biochemical technology. Development of the first blood substitutes dates back to the early 1600s, and the search for the ideal blood substitute continues. Various manufacturers have products in clinical trials; however, no truly safe and effective artificial blood product is currently marketed. It is anticipated that when an artificial blood product is available, it will have annual sales of over $7.6 billion in the United States alone.