WorldWideScience

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. Network Structure and Dynamics, and Emergence of Robustness by Stabilizing Selection in an Artificial Genome

    CERN Document Server

    Rohlf, Thimo

    2008-01-01

    Genetic regulation is a key component in development, but a clear understanding of the structure and dynamics of genetic networks is not yet at hand. In this work we investigate these properties within an artificial genome model originally introduced by Reil. We analyze statistical properties of randomly generated genomes both on the sequence- and network level, and show that this model correctly predicts the frequency of genes in genomes as found in experimental data. Using an evolutionary algorithm based on stabilizing selection for a phenotype, we show that robustness against single base mutations, as well as against random changes in initial network states that mimic stochastic fluctuations in environmental conditions, can emerge in parallel. Evolved genomes exhibit characteristic patterns on both sequence and network level.

  8. 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.

  9. 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...

  10. 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.

  11. 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...... regions and gene ontologies) that provide better model fit and increase predictive ability of the statistical model for this trait....

  12. Sliding Wear Modeling of Artificial Rough Surfaces

    OpenAIRE

    Imam Syafa’at; Budi Setiyana; Muchammad; Jamari

    2012-01-01

    Surface roughness plays an important role in machine design. In the micro-scale when two engineering surfaces are brought into contact, the real contact area occurs at isolated point of asperity. Wear is one of some effects of contacting surfaces. This paper presents a modeling of sliding wear at asperity level on the artificial rough surfaces. The surface roughness is represented by spherical asperities at the hemispherical pin that is developed from the existing model. The wear model is bas...

  13. 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...

  14. 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

  15. 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.

  16. 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)

  17. 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

  18. 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.

  19. 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.

  20. Artificial Neuron Modelling Based on Wave Shape

    Directory of Open Access Journals (Sweden)

    Kieran Greer

    2013-10-01

    Full Text Available This paper describes a new model for an artificial neural network processing unit or neuron. It is slightly different to a traditional feedforward network by the fact that it favours a mechanism of trying to match the wave-like ‘shape’ of the input with the shape of the output against specific value error corrections. The expectation is then that a best fit shape can be transposed into the desired output values more easily. This allows for notions of reinforcement through resonance and also the construction of synapses.

  1. An entropy model for artificial grammar learning

    Directory of Open Access Journals (Sweden)

    Emmanuel Pothos

    2010-06-01

    Full Text Available A model is proposed to characterize the type of knowledge acquired in Artificial Grammar Learning (AGL. In particular, Shannon entropy is employed to compute the complexity of different test items in an AGL task, relative to the training items. According to this model, the more predictable a test item is from the training items, the more likely it is that this item should be selected as compatible with the training items. The predictions of the entropy model are explored in relation to the results from several previous AGL datasets and compared to other AGL measures. This particular approach in AGL resonates well with similar models in categorization and reasoning which also postulate that cognitive processing is geared towards the reduction of entropy.

  2. 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.

  3. Complete Genome Sequence of a Human Cytomegalovirus Strain AD169 Bacterial Artificial Chromosome Clone

    Science.gov (United States)

    Ostermann, Eleonore; Spohn, Michael; Indenbirken, Daniela

    2016-01-01

    The complete sequence of the human cytomegalovirus strain AD169 (variant ATCC) cloned as a bacterial artificial chromosome (AD169-BAC, also known as HB15 or pHB15) was determined. The viral genome has a length of 230,290 bp and shows 52 nucleotide differences compared to a previously sequenced AD169varATCC clone. PMID:27034483

  4. Twenty years of artificial directional selection have shaped the genome of the Italian Large White pig breed.

    Science.gov (United States)

    Schiavo, G; Galimberti, G; Calò, D G; Samorè, A B; Bertolini, F; Russo, V; Gallo, M; Buttazzoni, L; Fontanesi, L

    2016-04-01

    In this study, we investigated at the genome-wide level if 20 years of artificial directional selection based on boar genetic evaluation obtained with a classical BLUP animal model shaped the genome of the Italian Large White pig breed. The most influential boars of this breed (n = 192), born from 1992 (the beginning of the selection program of this breed) to 2012, with an estimated breeding value reliability of >0.85, were genotyped with the Illumina Porcine SNP60 BeadChip. After grouping the boars in eight classes according to their year of birth, filtered single nucleotide polymorphisms (SNPs) were used to evaluate the effects of time on genotype frequency changes using multinomial logistic regression models. Of these markers, 493 had a PBonferroni  genome. The largest proportion of the 493 SNPs was on porcine chromosome (SSC) 7, SSC2, SSC8 and SSC18 for a total of 204 haploblocks. Functional annotations of genomic regions, including the 493 shifted SNPs, reported a few Gene Ontology terms that might underly the biological processes that contributed to increase performances of the pigs over the 20 years of the selection program. The obtained results indicated that the genome of the Italian Large White pigs was shaped by a directional selection program derived by the application of methodologies assuming the infinitesimal model that captured a continuous trend of allele frequency changes in the boar population.

  5. 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.

  6. Construction and characterization of a 10-genome equivalent yeast artificial chromosome library for the laboratory rat, Rattus norvegicus

    Energy Technology Data Exchange (ETDEWEB)

    Cai, L.; Zee, R.Y.L. [Harvard Medical School, Boston, MA (United States); Schalkwyk, L.C. [Max Planck Institute for Molecular Genetics, Berlin (Germany)] [and others

    1997-02-01

    Increasing attention has been focused in recent years on the rat as a model organism for genetic studies, in particular for the investigation of complex traits, but progress has been limited by the lack of availability of large-insert genomic libraries. Here, we report the construction and characterization of an arrayed yeast artificial chromosome (YAC) library for the rat genome containing approximately 40,000 clones in the AB1380 host using the pCGS966 vector. An average size of 736 kb was estimated from 166 randomly chosen clones; thus the library provides 10-fold coverage of the genome, with a 99.99% probability of containing a unique sequence. Eight of 39 YACs analyzed by fluorescence in situ hybridization were found to be chimeric, indicating a proportion of about 20-30% of chimeric clones. The library was spotted on high-density filters to allow the identification of YAC clones by hybridization and was pooled using a 3-dimensional scheme for screening by PCR. Among 48 probes used to screen the library, an average of 9.3 positive clones were found, consistent with the calculated 10-fold genomic coverage of the library. This YAC library represents the first large-insert genomic library for the rat. It will be made available to the research community at large as an important new resource for complex genome analysis in this species. 35 refs., 4 figs.

  7. 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.

  8. 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...

  9. 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.

  10. 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.

  11. 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.

  12. 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...

  13. 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...

  14. 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...

  15. Bacterial DNA Sequence Compression Models Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Armando J. Pinho

    2013-08-01

    Full Text Available It is widely accepted that the advances in DNA sequencing techniques have contributed to an unprecedented growth of genomic data. This fact has increased the interest in DNA compression, not only from the information theory and biology points of view, but also from a practical perspective, since such sequences require storage resources. Several compression methods exist, and particularly, those using finite-context models (FCMs have received increasing attention, as they have been proven to effectively compress DNA sequences with low bits-per-base, as well as low encoding/decoding time-per-base. However, the amount of run-time memory required to store high-order finite-context models may become impractical, since a context-order as low as 16 requires a maximum of 17.2 x 109 memory entries. This paper presents a method to reduce such a memory requirement by using a novel application of artificial neural networks (ANN to build such probabilistic models in a compact way and shows how to use them to estimate the probabilities. Such a system was implemented, and its performance compared against state-of-the art compressors, such as XM-DNA (expert model and FCM-Mx (mixture of finite-context models , as well as with general-purpose compressors. Using a combination of order-10 FCM and ANN, similar encoding results to those of FCM, up to order-16, are obtained using only 17 megabytes of memory, whereas the latter, even employing hash-tables, uses several hundreds of megabytes.

  16. 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

  17. 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.

  18. 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...

  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. 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...

  1. 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.

  2. 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)

  3. 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

  4. Genome Modeling System: A Knowledge Management Platform for Genomics.

    Directory of Open Access Journals (Sweden)

    Malachi Griffith

    2015-07-01

    Full Text Available In this work, we present the Genome Modeling System (GMS, an analysis information management system capable of executing automated genome analysis pipelines at a massive scale. The GMS framework provides detailed tracking of samples and data coupled with reliable and repeatable analysis pipelines. The GMS also serves as a platform for bioinformatics development, allowing a large team to collaborate on data analysis, or an individual researcher to leverage the work of others effectively within its data management system. Rather than separating ad-hoc analysis from rigorous, reproducible pipelines, the GMS promotes systematic integration between the two. As a demonstration of the GMS, we performed an integrated analysis of whole genome, exome and transcriptome sequencing data from a breast cancer cell line (HCC1395 and matched lymphoblastoid line (HCC1395BL. These data are available for users to test the software, complete tutorials and develop novel GMS pipeline configurations. The GMS is available at https://github.com/genome/gms.

  5. 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.

  6. 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''.

  7. Pegylated polystyrene particles as a model system for artificial cells

    NARCIS (Netherlands)

    Meng, Fenghua; Engbers, Gerard H.M.; Gessner, Andrea; Müller, Reiner H.; Feijen, Jan

    2004-01-01

    Pegylated polystyrene particles (PS-PEG) were prepared as a model system for artificial cells, by modification of carboxyl polystyrene particles (PS-COOH) with homo- and hetero-bifunctional polyethylene glycols (PEG, MW 1500, 3400, and 5000) containing an amino end group for immobilization and an am

  8. 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…

  9. 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...

  10. 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

  11. 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

  12. Genome Sequencing Reveals Loci under Artificial Selection that Underlie Disease Phenotypes in the Laboratory Rat

    NARCIS (Netherlands)

    Atanur, Santosh S.; Diaz, Ana Garcia; Maratou, Klio; Sarkis, Allison; Rotival, Maxime; Game, Laurence; Tschannen, Michael R.; Kaisaki, Pamela J.; Otto, Georg W.; Ma, Man Chun John; Keane, Thomas M.; Hummel, Oliver; Saar, Kathrin; Chen, Wei; Guryev, Victor; Gopalakrishnan, Kathirvel; Garrett, Michael R.; Joe, Bina; Citterio, Lorena; Bianchi, Giuseppe; McBride, Martin; Dominiczak, Anna; Adams, David J.; Serikawa, Tadao; Flicek, Paul; Cuppen, Edwin; Hubner, Norbert; Petretto, Enrico; Gauguier, Dominique; Kwitek, Anne; Jacob, Howard; Aitman, Timothy J.

    2013-01-01

    Large numbers of inbred laboratory rat strains have been developed for a range of complex disease phenotypes. To gain insights into the evolutionary pressures underlying selection for these phenotypes, we sequenced the genomes of 27 rat strains, including 11 models of hypertension, diabetes, and ins

  13. Stochastic Differential Equations in Artificial Pancreas Modelling

    DEFF Research Database (Denmark)

    Duun-Henriksen, Anne Katrine

    to assess the timing, intensity and duration of physical exercise in advance, to adjust the insulin dose accordingly. Additionally, several uncontrollable and unpredictable factors such as stress, hormonal cycles and sickness changing the metabolic state make this task even more difficult. The development....... However, uncertainty in the model occurs due to the nature of physiological systems and due to the presence of unknown disturbances. An attractive approach to deal with this uncertainty is to use stochastic differential equations (SDEs). In a model based on SDEs, the noise is separated into two terms: 1...

  14. 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...

  15. 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.

  16. 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.

  17. 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.

  18. 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

  19. 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.

  20. 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

  1. 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.

  2. 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

  3. 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.

  4. Evaluation of articulation simulation system using artificial maxillectomy models.

    Science.gov (United States)

    Elbashti, M E; Hattori, M; Sumita, Y I; Taniguchi, H

    2015-09-01

    Acoustic evaluation is valuable for guiding the treatment of maxillofacial defects and determining the effectiveness of rehabilitation with an obturator prosthesis. Model simulations are important in terms of pre-surgical planning and pre- and post-operative speech function. This study aimed to evaluate the acoustic characteristics of voice generated by an articulation simulation system using a vocal tract model with or without artificial maxillectomy defects. More specifically, we aimed to establish a speech simulation system for maxillectomy defect models that both surgeons and maxillofacial prosthodontists can use in guiding treatment planning. Artificially simulated maxillectomy defects were prepared according to Aramany's classification (Classes I-VI) in a three-dimensional vocal tract plaster model of a subject uttering the vowel /a/. Formant and nasalance acoustic data were analysed using Computerized Speech Lab and the Nasometer, respectively. Formants and nasalance of simulated /a/ sounds were successfully detected and analysed. Values of Formants 1 and 2 for the non-defect model were 675.43 and 976.64 Hz, respectively. Median values of Formants 1 and 2 for the defect models were 634.36 and 1026.84 Hz, respectively. Nasalance was 11% in the non-defect model, whereas median nasalance was 28% in the defect models. The results suggest that an articulation simulation system can be used to help surgeons and maxillofacial prosthodontists to plan post-surgical defects that will be facilitate maxillofacial rehabilitation. PMID:25975670

  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. Construction and Analysis of Siberian Tiger Bacterial Artificial Chromosome Library with Approximately 6.5-Fold Genome Equivalent Coverage

    Directory of Open Access Journals (Sweden)

    Changqing Liu

    2014-03-01

    Full Text Available Bacterial artificial chromosome (BAC libraries are extremely valuable for the genome-wide genetic dissection of complex organisms. The Siberian tiger, one of the most well-known wild primitive carnivores in China, is an endangered animal. In order to promote research on its genome, a high-redundancy BAC library of the Siberian tiger was constructed and characterized. The library is divided into two sub-libraries prepared from blood cells and two sub-libraries prepared from fibroblasts. This BAC library contains 153,600 individually archived clones; for PCR-based screening of the library, BACs were placed into 40 superpools of 10 × 384-deep well microplates. The average insert size of BAC clones was estimated to be 116.5 kb, representing approximately 6.46 genome equivalents of the haploid genome and affording a 98.86% statistical probability of obtaining at least one clone containing a unique DNA sequence. Screening the library with 19 microsatellite markers and a SRY sequence revealed that each of these markers were present in the library; the average number of positive clones per marker was 6.74 (range 2 to 12, consistent with 6.46 coverage of the tiger genome. Additionally, we identified 72 microsatellite markers that could potentially be used as genetic markers. This BAC library will serve as a valuable resource for physical mapping, comparative genomic study and large-scale genome sequencing in the tiger.

  7. 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

  8. Surrogate Modeling of Deformable Joint Contact using Artificial Neural Networks

    Science.gov (United States)

    Eskinazi, Ilan; Fregly, Benjamin J.

    2016-01-01

    Deformable joint contact models can be used to estimate loading conditions for cartilage-cartilage, implant-implant, human-orthotic, and foot-ground interactions. However, contact evaluations are often so expensive computationally that they can be prohibitive for simulations or optimizations requiring thousands or even millions of contact evaluations. To overcome this limitation, we developed a novel surrogate contact modeling method based on artificial neural networks (ANNs). The method uses special sampling techniques to gather input-output data points from an original (slow) contact model in multiple domains of input space, where each domain represents a different physical situation likely to be encountered. For each contact force and torque output by the original contact model, a multi-layer feed-forward ANN is defined, trained, and incorporated into a surrogate contact model. As an evaluation problem, we created an ANN-based surrogate contact model of an artificial tibiofemoral joint using over 75,000 evaluations of a fine-grid elastic foundation (EF) contact model. The surrogate contact model computed contact forces and torques about 1000 times faster than a less accurate coarse grid EF contact model. Furthermore, the surrogate contact model was seven times more accurate than the coarse grid EF contact model within the input domain of a walking motion. For larger input domains, the surrogate contact model showed the expected trend of increasing error with increasing domain size. In addition, the surrogate contact model was able to identify out-of-contact situations with high accuracy. Computational contact models created using our proposed ANN approach may remove an important computational bottleneck from musculoskeletal simulations or optimizations incorporating deformable joint contact models. PMID:26220591

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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

  14. 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.

  15. 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.

  16. 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...

  17. Landslide susceptibility analysis using an artificial neural network model

    Science.gov (United States)

    Mansor, Shattri; Pradhan, Biswajeet; Daud, Mohamed; Jamaludin, Normalina; Khuzaimah, Zailani

    2007-10-01

    This paper deals with landslide susceptibility analysis using an artificial neural network model for Cameron Highland, Malaysia. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys. Topographical/geological data and satellite images were collected and processed using GIS and image processing tools. There are ten landslide inducing parameters which are considered for the landslide hazards. These parameters are topographic slope, aspect, curvature and distance from drainage, all derived from the topographic database; geology and distance from lineament, derived from the geologic database; landuse from Landsat satellite images; soil from the soil database; precipitation amount, derived from the rainfall database; and the vegetation index value from SPOT satellite images. Landslide hazard was analyzed using landslide occurrence factors employing the logistic regression model. The results of the analysis were verified using the landslide location data and compared with logistic regression model. The accuracy of hazard map observed was 85.73%. The qualitative landslide susceptibility analysis was carried out using an artificial neural network model by doing map overlay analysis in GIS environment. This information could be used to estimate the risk to population, property and existing infrastructure like transportation network.

  18. 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.

  19. Application of Artificial Intelligence for Bridge Deterioration Model.

    Science.gov (United States)

    Chen, Zhang; Wu, Yangyang; Li, Li; Sun, Lijun

    2015-01-01

    The deterministic bridge deterioration model updating problem is well established in bridge management, while the traditional methods and approaches for this problem require manual intervention. An artificial-intelligence-based approach was presented to self-updated parameters of the bridge deterioration model in this paper. When new information and data are collected, a posterior distribution was constructed to describe the integrated result of historical information and the new gained information according to Bayesian theorem, which was used to update model parameters. This AI-based approach is applied to the case of updating parameters of bridge deterioration model, which is the data collected from bridges of 12 districts in Shanghai from 2004 to 2013, and the results showed that it is an accurate, effective, and satisfactory approach to deal with the problem of the parameter updating without manual intervention.

  20. Application of Artificial Intelligence for Bridge Deterioration Model

    Directory of Open Access Journals (Sweden)

    Zhang Chen

    2015-01-01

    Full Text Available The deterministic bridge deterioration model updating problem is well established in bridge management, while the traditional methods and approaches for this problem require manual intervention. An artificial-intelligence-based approach was presented to self-updated parameters of the bridge deterioration model in this paper. When new information and data are collected, a posterior distribution was constructed to describe the integrated result of historical information and the new gained information according to Bayesian theorem, which was used to update model parameters. This AI-based approach is applied to the case of updating parameters of bridge deterioration model, which is the data collected from bridges of 12 districts in Shanghai from 2004 to 2013, and the results showed that it is an accurate, effective, and satisfactory approach to deal with the problem of the parameter updating without manual intervention.

  1. 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.

  2. Genome-wide copy number profiling on high-density bacterial artificial chromosomes, single-nucleotide polymorphisms, and oligonucleotide microarrays: a platform comparison based on statistical power analysis.

    NARCIS (Netherlands)

    Hehir-Kwa, J.Y.; Egmont-Peterson, M.; Janssen, I.M.; Smeets, D.F.C.M.; Geurts van Kessel, A.H.M.; Veltman, J.A.

    2007-01-01

    Recently, comparative genomic hybridization onto bacterial artificial chromosome (BAC) arrays (array-based comparative genomic hybridization) has proved to be successful for the detection of submicroscopic DNA copy-number variations in health and disease. Technological improvements to achieve a high

  3. 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

  4. 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.

  5. 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.

  6. 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

  7. 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...

  8. 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...

  9. 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

  10. 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.

  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. 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.

  13. An Application of Finite Element Modelling to Pneumatic Artificial Muscle

    Directory of Open Access Journals (Sweden)

    R. Ramasamy

    2005-01-01

    Full Text Available The purpose of this article was to introduce and to give an overview of the Pneumatic Artificial Muscles (PAMs as a whole and to discuss its numerical modelling, using the Finite Element (FE Method. Thus, more information to understand on its behaviour in generating force for actuation was obtained. The construction of PAMs was mainly consists of flexible, inflatable membranes which having orthotropic material behaviour. The main properties influencing the PAMs will be explained in terms of their load-carrying capacity and low weight in assembly. Discussion on their designs and capacity to function as locomotion device in robotics applications will be laid out, followed by FE modelling to represent PAMs overall structural behaviour under any potential operational conditions.

  14. Variable recruitment fluidic artificial muscles: modeling and experiments

    Science.gov (United States)

    Bryant, Matthew; Meller, Michael A.; Garcia, Ephrahim

    2014-07-01

    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.

  15. Modelling the SOFC behaviours by artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Milewski, Jaroslaw; Swirski, Konrad [Institute of Heat Engineering, Warsaw University of Technology, 25 Nowowiejska Street, 00-665 Warsaw (Poland)

    2009-07-15

    The Artificial Neural Network (ANN) can be applied to simulate an object's behaviour without an algorithmic solution merely by utilizing available experimental data. The ANN is used for modelling singular cell behaviour. The optimal network architecture is shown and commented. The error backpropagation algorithm was used for an ANN training procedure. The ANN based SOFC model has the following input parameters: current density, temperature, fuel volume flow density (ml min{sup -1} cm{sup -2}), and oxidant volume flow density. Based on these input parameters, cell voltage is predicted by the model. Obtained results show that the ANN can be successfully used for modelling the singular solid oxide fuel cell. The self-learning process of the ANN provides an opportunity to adapt the model to new situations (e.g. certain types of impurities at inlet streams etc.). Based on the results from this study it can be concluded that, by using the ANN, an SOFC can be modelled with relatively high accuracy. In contrast to traditional models, the ANN is able to predict cell voltage without knowledge of numerous physical, chemical, and electrochemical factors. (author)

  16. 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......-BA) and Bayesian LASSO (MT-BL) are described that model heterogeneous variance and covariance over the genome, and a model that directly models multiple genomic breeding values (MT-MG), representing different genomic covariance structures. The models are demonstrated on a mouse data set to model the genomic...

  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. HIV lipodystrophy case definition using artificial neural network modelling

    DEFF Research Database (Denmark)

    Ioannidis, John P A; Trikalinos, Thomas A; Law, Matthew;

    2003-01-01

    OBJECTIVE: A case definition of HIV lipodystrophy has recently been developed from a combination of clinical, metabolic and imaging/body composition variables using logistic regression methods. We aimed to evaluate whether artificial neural networks could improve the diagnostic accuracy. METHODS......: The database of the case-control Lipodystrophy Case Definition Study was split into 504 subjects (265 with and 239 without lipodystrophy) used for training and 284 independent subjects (152 with and 132 without lipodystrophy) used for validation. Back-propagation neural networks with one or two middle layers...... were trained and validated. Results were compared against logistic regression models using the same information. RESULTS: Neural networks using clinical variables only (41 items) achieved consistently superior performance than logistic regression in terms of specificity, overall accuracy and area under...

  20. 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 ...

  1. Constitutive models of artificial muscles:a review

    Institute of Scientific and Technical Information of China (English)

    Hui-ming WANG; Shao-xing QU

    2016-01-01

    Artificial muscles are materials which possess muscle-like characteristics; they have many promising applications and many materials have been exploited as artificial muscles. In this review, the artificial muscles discussed are confined to die-lectric elastomers and responsive gels. We focus on their constitutive models based on free energy function theory. For dielectric elastomers, both hyperelastic and visco-hyperelastic models are involved. For responsive gels, we consider different kinds of gels, such as hydrogel, pH-sensitive gel, temperature-sensitive gel, polyelectrolyte gel, reactive gel, etc. With an accurate, relia-ble, and powerful constitutive model, exact theoretical analysis can be achieved and the important intrinsic characteristics of artificial muscle based systems can be revealed.%中文概要题目:人工肌肉本构模型的综述人工肌肉是指具有类似肌肉特性的材料,这些材料在外界激励下,可以实现大变形,且响应速度快。本文总结两类人工肌肉本构模型的研究成果:一类是介电高弹体,另一类是响应性凝胶。本文中提到的本构模型仅限于用自由能函数导出的情形。对于介电高弹体材料,分别综述超弹性模型和粘性超弹性模型。在超弹性模型中,列出目前研究中使用较多的一些本构模型的自由能函数具体表达式;比较 neo-Hookean、Gent、Arruda-Boyce和 Ogden四种模型在单轴拉伸和等双轴拉伸两种情形下的名义应力-伸长曲线;给出了考虑一些重要因素的研究模型,这些因素包括材料可压缩性、取向极化、变介电常数、热耦合、受纤维约束、流体耦合以及空气耦合等。对于响应性凝胶,分别综述水凝胶、pH 敏感性凝胶、温度敏感性凝胶、聚电解质凝胶以及反应性凝胶等的本构模型。这些精确、可靠和有效的本构模型,将有助于开展人工肌肉系统的性能分析和预测,甚至揭示

  2. 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.)

  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. Artificial Neural Network L* from different magnetospheric field models

    Science.gov (United States)

    Yu, Y.; Koller, J.; Zaharia, S. G.; Jordanova, V. K.

    2011-12-01

    The third adiabatic invariant L* plays an important role in modeling and understanding the radiation belt dynamics. The popular way to numerically obtain the L* value follows the recipe described by Roederer [1970], which is, however, slow and computational expensive. This work focuses on a new technique, which can compute the L* value in microseconds without losing much accuracy: artificial neural networks. Since L* is related to the magnetic flux enclosed by a particle drift shell, global magnetic field information needed to trace the drift shell is required. A series of currently popular empirical magnetic field models are applied to create the L* data pool using 1 million data samples which are randomly selected within a solar cycle and within the global magnetosphere. The networks, trained from the above L* data pool, can thereby be used for fairly efficient L* calculation given input parameters valid within the trained temporal and spatial range. Besides the empirical magnetospheric models, a physics-based self-consistent inner magnetosphere model (RAM-SCB) developed at LANL is also utilized to calculate L* values and then to train the L* neural network. This model better predicts the magnetospheric configuration and therefore can significantly improve the L*. The above neural network L* technique will enable, for the first time, comprehensive solar-cycle long studies of radiation belt processes. However, neural networks trained from different magnetic field models can result in different L* values, which could cause mis-interpretation of radiation belt dynamics, such as where the source of the radiation belt charged particle is and which mechanism is dominant in accelerating the particles. Such a fact calls for attention to cautiously choose a magnetospheric field model for the L* calculation.

  5. Network modeling of membrane-based artificial cellular systems

    Science.gov (United States)

    Freeman, Eric C.; Philen, Michael K.; Leo, Donald J.

    2013-04-01

    Computational models are derived for predicting the behavior of artificial cellular networks for engineering applications. The systems simulated involve the use of a biomolecular unit cell, a multiphase material that incorporates a lipid bilayer between two hydrophilic compartments. These unit cells may be considered building blocks that enable the fabrication of complex electrochemical networks. These networks can incorporate a variety of stimuli-responsive biomolecules to enable a diverse range of multifunctional behavior. Through the collective properties of these biomolecules, the system demonstrates abilities that recreate natural cellular phenomena such as mechanotransduction, optoelectronic response, and response to chemical gradients. A crucial step to increase the utility of these biomolecular networks is to develop mathematical models of their stimuli-responsive behavior. While models have been constructed deriving from the classical Hodgkin-Huxley model focusing on describing the system as a combination of traditional electrical components (capacitors and resistors), these electrical elements do not sufficiently describe the phenomena seen in experiment as they are not linked to the molecular scale processes. From this realization an advanced model is proposed that links the traditional unit cell parameters such as conductance and capacitance to the molecular structure of the system. Rather than approaching the membrane as an isolated parallel plate capacitor, the model seeks to link the electrical properties to the underlying chemical characteristics. This model is then applied towards experimental cases in order that a more complete picture of the underlying phenomena responsible for the desired sensing mechanisms may be constructed. In this way the stimuli-responsive characteristics may be understood and optimized.

  6. Applying artificial vision models to human scene understanding

    Directory of Open Access Journals (Sweden)

    Elissa Michele Aminoff

    2015-02-01

    Full Text Available How do we understand the complex patterns of neural responses that underlie scene understanding? Studies of the network of brain regions held to be scene-selective – the parahippocampal/lingual region (PPA, the retrosplenial complex (RSC, and the occipital place area (TOS – have typically focused on single visual dimensions (e.g., size, rather than the high-dimensional feature space in which scenes are likely to be neurally represented. Here we leverage well-specified artificial vision systems to explicate a more complex understanding of how scenes are encoded in this functional network. We correlated similarity matrices within three different scene-spaces arising from: 1 BOLD activity in scene-selective brain regions; 2 behavioral measured judgments of visually-perceived scene similarity; and 3 several different computer vision models. These correlations revealed: 1 models that relied on mid- and high-level scene attributes showed the highest correlations with the patterns of neural activity within the scene-selective network; 2 NEIL and SUN – the models that best accounted for the patterns obtained from PPA and TOS – were different from the GIST model that best accounted for the pattern obtained from RSC; 3 The best performing models outperformed behaviorally-measured judgments of scene similarity in accounting for neural data. One computer vision method – NEIL (Never-Ending-Image-Learner, which incorporates visual features learned as statistical regularities across web-scale numbers of scenes – showed significant correlations with neural activity in all three scene-selective regions and was one of the two models best able to account for variance in the PPA and TOS. We suggest that these results are a promising first step in explicating more fine-grained models of neural scene understanding, including developing a clearer picture of the division of labor among the components of the functional scene-selective brain network.

  7. Modelling artificial sea salt emission in large eddy simulations

    Science.gov (United States)

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

    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 buoyancy caused by water evaporation from aerosols. Additionally, we use a higher model resolution to capture aerosol loss through coagulation near the source point. We find that, with a seawater flux of 15 kg s−1, the cooling due to evaporation can be as much as 1.4 K, causing a delay in particle dispersion of 10–20 min. This delay enhances particle scavenging by a factor of 1.14 compared with simulations without aerosol water. We further show that both cooling and particle dispersion depend on the model resolution, with a maximum particle scavenging efficiency of 20% within 5 h after emission at maximum resolution of 50 m. Based on these results, we suggest further regional high-resolution studies which model several injection periods over several weeks. PMID:25404679

  8. 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.

  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. Promising synergies of simulation model management, software engineering, artificial intelligence, and general system theories

    Energy Technology Data Exchange (ETDEWEB)

    Oren, T.I.

    1982-01-01

    Simulation is viewed within the model management paradigm. Major components of simulation systems as well as elements of model management are outlined. Possible synergies of simulation model management, software engineering, artificial intelligence, and general system theories are systematized. 21 references.

  11. 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.

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

    Directory of Open Access Journals (Sweden)

    L. V. Belyaev

    2011-01-01

    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. 

  13. Modeling the Human Genome Maintenance network

    Science.gov (United States)

    Simão, Éder M.; Cabral, Heleno B.; Castro, Mauro A. A.; Sinigaglia, Marialva; Mombach, José C. M.; Librelotto, Giovani R.

    2010-10-01

    We present the Ontocancro Database ( www.ontocancro.org) illustrated with applications to network modeling and pathway functional analysis. The database compiles information on gene pathways involved in Human Genome Maintenance Mechanisms (GMM) whose dysfunction accounts for cancer and several genetic syndromes. Ontocancro is the most complete, manually curated information resource available providing genomics and interatomics data on 120 GMM pathways (comprising a total of 1435 genes) obtained from curated databases and the literature. It was developed to facilitate the GMM network and functional modeling for the integration of genomic, transcriptomic and interatomic data. The database’s main contribution is the Ontocancro pathways that are expanded versions of standard GMM pathways for including additional genes with evidences of functional involvement in GMM. Using these pathways we find the largest cluster of interacting proteins involving GMM and on it we project a microarray study of adenoma to identify the regions of the network that are highly altered. In the last application we present the dynamical alterations of the pathways in a study of the effect of Cadmium, a known carcinogenic substance, on prostate cells to find that it produces a strong decrease of the pathway activity.

  14. 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.

  15. 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.

  16. 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/.

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

    Science.gov (United States)

    2010-01-01

    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/. PMID:20459813

  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. Estimation of long-terminal repeat element content in the Helicoverpa zea genome from next generation sequencing of reduced representation bacterial artificial chromosome (BAC) pools

    Science.gov (United States)

    The lepidopteran pest insect, Helicoverpa zea, feeds on cultivated corn and cotton crops in North America where control remains challenging due to evolution of resistance to chemical and transgenic insecticidal toxins, yet few genomic resources are available for this species. A bacterial artificial...

  2. Application of Artificial Bee Colony in Model Parameter Identification of Solar Cells

    Directory of Open Access Journals (Sweden)

    Rongjie Wang

    2015-07-01

    Full Text Available The identification of values of solar cell parameters is of great interest for evaluating solar cell performances. The algorithm of an artificial bee colony was used to extract model parameters of solar cells from current-voltage characteristics. Firstly, the best-so-for mechanism was introduced to the original artificial bee colony. Then, a method was proposed to identify parameters for a single diode model and double diode model using this improved artificial bee colony. Experimental results clearly demonstrate the effectiveness of the proposed method and its superior performance compared to other competing methods.

  3. Construction of a full bacterial artificial chromosome (BAC) library of Oryza sativa genome

    Institute of Scientific and Technical Information of China (English)

    TAOQUANZHOU; HAIYINGZHAO; 等

    1994-01-01

    We have constructed a full BAC library for the superior early indica variety of Oryza sativa,Guang Lu Ai 4.The MAX Efficiency DH10B with increased stability of inserts was used as BAC host cells.The potent pBelo BACII with double selection markers was used as cloning vector.The cloning efficiency we have reached was as high as 98%,and the transformation efficiency was raised up to 106 transformants/μg of large fragment DNA.The BAC recombinant transformants were picked at random and analyzed for the size of inserts,which turned out to be of 120 kb in length on average.We have obtained more than 20,000 such BAC clones.According to conventional probability equation,they covered the entire rice genome of 420,000 kb in length.The entire length of inserts of the library obtained has the 5-to 6-fold coverage of the genome.To our knowledge,this is the first reported full BAC library for a complex genome.

  4. NONLINEAR MODELING AND CONTROLLING OF ARTIFICIAL MUSCLE SYSTEM USING NEURAL NETWORKS

    Institute of Scientific and Technical Information of China (English)

    Tian Sheping; Ding Guoqing; Yan Detian; Lin Liangming

    2004-01-01

    The pneumatic artificial muscles are widely used in the fields of medical robots,etc.Neural networks are applied to modeling and controlling of artificial muscle system.A single-joint artificial muscle test system is designed.The recursive prediction error (RPE) algorithm which yields faster convergence than back propagation (BP) algorithm is applied to train the neural networks.The realization of RPE algorithm is given.The difference of modeling of artificial muscles using neural networks with different input nodes and different hidden layer nodes is discussed.On this basis the nonlinear control scheme using neural networks for artificial muscle system has been introduced.The experimental results show that the nonlinear control scheme yields faster response and higher control accuracy than the traditional linear control scheme.

  5. 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.

  6. Modeling of the height control system using artificial neural networks

    Directory of Open Access Journals (Sweden)

    A. R Tahavvor

    2016-09-01

    Full Text Available Introduction Automation of agricultural and machinery construction has generally been enhanced by intelligent control systems due to utility and efficiency rising, ease of use, profitability and upgrading according to market demand. A broad variety of industrial merchandise are now supplied with computerized control systems of earth moving processes to be performed by construction and agriculture field vehicle such as grader, backhoe, tractor and scraper machines. A height control machine which is used in measuring base thickness is consisted of two mechanical and electronic parts. The mechanical part is consisted of conveyor belt, main body, electrical engine and invertors while the electronic part is consisted of ultrasonic, wave transmitter and receiver sensor, electronic board, control set, and microcontroller. The main job of these controlling devices consists of the topographic surveying, cutting and filling of elevated and spotted low area, and these actions fundamentally dependent onthe machine's ability in elevation and thickness measurement and control. In this study, machine was first tested and then some experiments were conducted for data collection. Study of system modeling in artificial neural networks (ANN was done for measuring, controlling the height for bases by input variable input vectors such as sampling time, probe speed, conveyer speed, sound wave speed and speed sensor are finally the maximum and minimum probe output vector on various conditions. The result reveals the capability of this procedure for experimental recognition of sensors' behavior and improvement of field machine control systems. Inspection, calibration and response, diagnosis of the elevation control system in combination with machine function can also be evaluated by some extra development of this system. Materials and Methods Designing and manufacture of the planned apparatus classified in three dissimilar, mechanical and electronic module, courses of

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

    DEFF Research Database (Denmark)

    2016-01-01

    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...... 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....

  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. 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...

  14. 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 ...

  15. STUDY ON THERMODYNAMIC MODEL OF A COMPRESSOR WITH ARTIFICIAL NEURAL NETWORKS

    Institute of Scientific and Technical Information of China (English)

    1999-01-01

    A new compressor thermodynamic model is set up. Artificial neural networks(ANN) which have self-adjusting functions are adopted to calculate volumetric efficiency and electrical efficiency of a compressor. The new compressor model composed of the theoretical model and ANN reaches more precise results than traditional ones. Furthermore, the new compressor model is of better flexibility in a large scale.

  16. An Integrative Breakage Model of genome architecture, reshuffling and evolution: The Integrative Breakage Model of genome evolution, a novel multidisciplinary hypothesis for the study of genome plasticity.

    Science.gov (United States)

    Farré, Marta; Robinson, Terence J; Ruiz-Herrera, Aurora

    2015-05-01

    Our understanding of genomic reorganization, the mechanics of genomic transmission to offspring during germ line formation, and how these structural changes contribute to the speciation process, and genetic disease is far from complete. Earlier attempts to understand the mechanism(s) and constraints that govern genome remodeling suffered from being too narrowly focused, and failed to provide a unified and encompassing view of how genomes are organized and regulated inside cells. Here, we propose a new multidisciplinary Integrative Breakage Model for the study of genome evolution. The analysis of the high-level structural organization of genomes (nucleome), together with the functional constrains that accompany genome reshuffling, provide insights into the origin and plasticity of genome organization that may assist with the detection and isolation of therapeutic targets for the treatment of complex human disorders.

  17. 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

  18. 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

  19. 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...

  20. Modelling human regulatory variation in mouse: finding the function in genome-wide association studies and whole-genome sequencing.

    Directory of Open Access Journals (Sweden)

    Jean-François Schmouth

    Full Text Available An increasing body of literature from genome-wide association studies and human whole-genome sequencing highlights the identification of large numbers of candidate regulatory variants of potential therapeutic interest in numerous diseases. Our relatively poor understanding of the functions of non-coding genomic sequence, and the slow and laborious process of experimental validation of the functional significance of human regulatory variants, limits our ability to fully benefit from this information in our efforts to comprehend human disease. Humanized mouse models (HuMMs, in which human genes are introduced into the mouse, suggest an approach to this problem. In the past, HuMMs have been used successfully to study human disease variants; e.g., the complex genetic condition arising from Down syndrome, common monogenic disorders such as Huntington disease and β-thalassemia, and cancer susceptibility genes such as BRCA1. In this commentary, we highlight a novel method for high-throughput single-copy site-specific generation of HuMMs entitled High-throughput Human Genes on the X Chromosome (HuGX. This method can be applied to most human genes for which a bacterial artificial chromosome (BAC construct can be derived and a mouse-null allele exists. This strategy comprises (1 the use of recombineering technology to create a human variant-harbouring BAC, (2 knock-in of this BAC into the mouse genome using Hprt docking technology, and (3 allele comparison by interspecies complementation. We demonstrate the throughput of the HuGX method by generating a series of seven different alleles for the human NR2E1 gene at Hprt. In future challenges, we consider the current limitations of experimental approaches and call for a concerted effort by the genetics community, for both human and mouse, to solve the challenge of the functional analysis of human regulatory variation.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  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. A Probabilistic Genome-Wide Gene Reading Frame Sequence Model

    DEFF Research Database (Denmark)

    Have, Christian Theil; Mørk, Søren

    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 are evaluated by the effect on prediction performance. Since bacterial gene finding to a large extent is a solved problem it forms an ideal proving ground for evaluating the explicit modeling of larger scale gene sequence composition of genomes. We conclude that the sequential composition of gene reading frames......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...

  8. 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...

  9. 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

  10. 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

  11. 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;

    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...

  12. 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.

  13. 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.

  14. 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)

  15. 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.

  16. Fractional snow cover mapping from MODIS data using wavelet-artificial intelligence hybrid models

    Science.gov (United States)

    Moosavi, Vahid; Malekinezhad, Hossein; Shirmohammadi, Bagher

    2014-04-01

    This study was carried out to evaluate the wavelet-artificial intelligence hybrid models to produce fractional snow cover maps. At first, cloud cover was removed from MODIS data and cloud free images were produced. SVM-based binary classified ETM+ imagery were then used as reference maps in order to obtain train and test data for sub-pixel classification models. ANN and ANFIS-based modeling were performed using raw data (without wavelet-based preprocessing). In the next step, several mother wavelets and levels were used in order to decompose the original data to obtain wavelet coefficients. Then, the decomposed data were used for further modeling processes. ANN, ANFIS, wavelet-ANN and wavelet-ANFIS models were compared to evaluate the effect of wavelet transformation on the ability of artificial intelligence models. It was demonstrated that wavelet transformation as a preprocessing approach can significantly enhance the performance of ANN and ANFIS models. This study indicated an overall accuracy of 92.45% for wavelet-ANFIS model, 86.13% for wavelet-ANN, 72.23% for ANFIS model and 66.78% for ANN model. In fact, hybrid wavelet-artificial intelligence models can extract the characteristics of the original signals (i.e. model inputs) accurately through decomposing the non-stationary and complex signals into several stationary and simpler signals. The positive effect of fuzzification as well as wavelet transformation in the wavelet-ANFIS model was also confirmed.

  17. Vadose zone-attenuated artificial recharge for input to a ground water model.

    Science.gov (United States)

    Nichols, William E; Wurstner, Signe K; Eslinger, Paul W

    2007-01-01

    Accurate representation of artificial recharge is requisite to calibration of a ground water model of an unconfined aquifer for a semiarid or arid site with a vadose zone that imparts significant attenuation of liquid transmission and substantial anthropogenic liquid discharges. Under such circumstances, artificial recharge occurs in response to liquid disposal to the vadose zone in areas that are small relative to the ground water model domain. Natural recharge, in contrast, is spatially variable and occurs over the entire upper boundary of a typical unconfined ground water model. An improved technique for partitioning artificial recharge from simulated total recharge for inclusion in a ground water model is presented. The improved technique is applied using data from the semiarid Hanford Site. From 1944 until the late 1980s, when Hanford's mission was the production of nuclear materials, the quantities of liquid discharged from production facilities to the ground vastly exceeded natural recharge. Nearly all hydraulic head data available for use in calibrating a ground water model at this site were collected during this period or later, when the aquifer was under the diminishing influence of the massive water disposals. The vadose zone is typically 80 to 90 m thick at the Central Plateau where most production facilities were located at this semiarid site, and its attenuation of liquid transmission to the aquifer can be significant. The new technique is shown to improve the representation of artificial recharge and thereby contribute to improvement in the calibration of a site-wide ground water model.

  18. 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.

  19. Artificial Neural Networks Based Modeling and Control of Continuous Stirred Tank Reactor

    Directory of Open Access Journals (Sweden)

    R. S.M.N. Malar

    2009-01-01

    Full Text Available Continuous Stirred Tank Reactor (CSTR is one of the common reactors in chemical plant. Problem statement: Developing a model incorporating the nonlinear dynamics of the system warrants lot of computation. An efficient control of the product concentration can be achieved only through accurate model. Approach: In this study, attempts were made to alleviate the above mentioned problem using “Artificial Intelligence” (AI techniques. One of the AI techniques namely Artificial Neural Networks (ANN was used to model the CSTR incorporating its non-linear characteristics. Two nonlinear models based control strategies namely internal model control and direct inverse control were designed using the neural networks and applied to the control of isothermal CSTR. Results: The simulation results for the above control schemes with set point tracking were presented. Conclusion: Results indicated that neural networks can learn accurate models and give good non-linear control when model equations are not known.

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

    Energy Technology Data Exchange (ETDEWEB)

    Kavaklioglu, Kadir; Ozturk, Harun Kemal; Canyurt, Olcay Ersel [Pamukkale University, Mechanical Engineering Department, Denizli (Turkey); Ceylan, Halim [Pamukkale University, Civil Engineering Department, Denizli (Turkey)

    2009-11-15

    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)

  1. 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.

  2. 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.

  3. Constraints of artificial neural networks for rainfall-runoff modelling: trade-offs in hydrological state representation and model evaluation

    NARCIS (Netherlands)

    Vos, de N.J.; Rientjes, T.H.M.

    2005-01-01

    The application of Artificial Neural Networks (ANNs) in rainfall-runoff modelling needs to be researched more extensively in order to appreciate and fulfil the potential of this modelling approach. This paper reports on the application of multi-layer feedforward ANNs for rainfall-runoff modelling of

  4. 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...

  5. Reservoir computing as an alternative to traditional artificial neural networks in rainfall-runoff modelling

    Directory of Open Access Journals (Sweden)

    N. J. de Vos

    2012-05-01

    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 recently introduced, conceptually simple reservoir computing models for one-day-ahead forecasts on twelve river basins in the Eastern United States, and compares them to a variety of traditional feedforward and recurrent models. Two modifications on the reservoir computing models are made to increase the hydrologically relevant information content of their internal state. The results show that the reservoir computing 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 reservoir computing models can be considered promising alternatives to traditional artificial neural networks in rainfall-runoff modelling.

  6. 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.

  7. 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.

  8. 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).

  9. 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...

  10. Prediction Model of Soil Nutrients Loss Based on Artificial Neural Network

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    On the basis of Artificial Neural Network theory, a back propagation neural network with one middle layer is building in this paper, and its algorithms is also given, Using this BP network model, study the case of Malian - River basin. The results by calculating show that the solution based on BP algorithms are consis tent with those based multiple-variables linear regression model. They also indicate that BP model in this paper is reasonable and BP algorithms are feasible.

  11. Artificial intelligence and finite element modelling for monitoring flood defence structures

    NARCIS (Netherlands)

    Pyayt, A.L.; Mokhov, I.I.; Kozionov, A.; Kusherbaeva, V.; Melnikova, N.B.; Krzhizhanovskaya, V.V.; Meijer, R.J.

    2011-01-01

    We present a hybrid approach to monitoring the stability of flood defence structures equipped with sensors. This approach combines the finite element modelling with the artificial intelligence for real-time signal processing and anomaly detection. This combined method has been developed for the Urba

  12. 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...

  13. 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.

  14. Modeling the thermotaxis behavior of C.elegans based on the artificial neural network.

    Science.gov (United States)

    Li, Mingxu; Deng, Xin; Wang, Jin; Chen, Qiaosong; Tang, Yun

    2016-07-01

    ASBTRACT This research aims at modeling the thermotaxis behavior of C.elegans which is a kind of nematode with full clarified neuronal connections. Firstly, this work establishes the motion model which can perform the undulatory locomotion with turning behavior. Secondly, the thermotaxis behavior is modeled by nonlinear functions and the nonlinear functions are learned by artificial neural network. Once the artificial neural networks have been well trained, they can perform the desired thermotaxis behavior. Last, several testing simulations are carried out to verify the effectiveness of the model for thermotaxis behavior. This work also analyzes the different performances of the model under different environments. The testing results reveal the essence of the thermotaxis of C.elegans to some extent, and theoretically support the research on the navigation of the crawling robots.

  15. 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.

  16. 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...

  17. 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...

  18. 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...

  19. 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.

  20. Methods of artificial enlargement of the training set for statistical shape models.

    Science.gov (United States)

    Koikkalainen, Juha; Tölli, Tuomas; Lauerma, Kirsi; Antila, Kari; Mattila, Elina; Lilja, Mikko; Lötjönen, Jyrki

    2008-11-01

    Due to the small size of training sets, statistical shape models often over-constrain the deformation in medical image segmentation. Hence, artificial enlargement of the training set has been proposed as a solution for the problem to increase the flexibility of the models. In this paper, different methods were evaluated to artificially enlarge a training set. Furthermore, the objectives were to study the effects of the size of the training set, to estimate the optimal number of deformation modes, to study the effects of different error sources, and to compare different deformation methods. The study was performed for a cardiac shape model consisting of ventricles, atria, and epicardium, and built from magnetic resonance (MR) volume images of 25 subjects. Both shape modeling and image segmentation accuracies were studied. The objectives were reached by utilizing different training sets and datasets, and two deformation methods. The evaluation proved that artificial enlargement of the training set improves both the modeling and segmentation accuracy. All but one enlargement techniques gave statistically significantly (p < 0.05) better segmentation results than the standard method without enlargement. The two best enlargement techniques were the nonrigid movement technique and the technique that combines principal component analysis (PCA) and finite element model (FEM). The optimal number of deformation modes was found to be near 100 modes in our application. The active shape model segmentation gave better segmentation accuracy than the one based on the simulated annealing optimization of the model weights.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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 ...

  7. Modeling and Simulation of Road Traffic Noise Using Artificial Neural Network and Regression.

    Science.gov (United States)

    Honarmand, M; Mousavi, S M

    2014-04-01

    Modeling and simulation of noise pollution has been done in a large city, where the population is over 2 millions. Two models of artificial neural network and regression were developed to predict in-city road traffic noise pollution with using the data of noise measurements and vehicle counts at three points of the city for a period of 12 hours. The MATLAB and DATAFIT softwares were used for simulation. The predicted results of noise level were compared with the measured noise levels in three stations. The values of normalized bias, sum of squared errors, mean of squared errors, root mean of squared errors, and squared correlation coefficient calculated for each model show the results of two models are suitable, and the predictions of artificial neural network are closer to the experimental data.

  8. Modeling, Optimization and simulation of Rotary Furnace using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Dr. R, K. Jain,

    2011-04-01

    Full Text Available This paper deals with modeling and simulation of LDO fired rotary furnace using feed forward modeling method of artificial neural network (ANN.The authors conducted experimental investigations onfuel consumption in a rotary furnace in an industry. It was observed that 6% oxygen enrichment of the air preheated up to 4600C simultaneously with reduction of air volume to 75% of its theoretical requirement lowered the specific fuel consumption to 0.260 lit/kg..The compact heat exchanger with 533 fins was used for preheating the air. Accordingly the emission level was also considerably reduced. The feed forward modeling method of artificial neural network contained in MAT LAB software was used for modeling andoptimization of specific fuel consumption. The percentage variation, between actual experimental data and same data when simulated is +1.730%, and other feasible simulated datas is +6.192%,-3.038%,-5.692%,and+0.115%which is fairly acceptable.

  9. 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...

  10. 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...

  11. Mathematical modeling of the growth and development of the mussel Mytilus galloprovincialis on artificial substrates

    Science.gov (United States)

    Vasechkina, E. F.; Kazankova, I. I.

    2014-11-01

    A mathematical model simulating the growth and development of the mussel Mytilus galloprovincialis Lam. on artificial substrates has been constructed. The model is based on experimental data and contains mathematical descriptions of the filtration, respiration, excretion, spawning, and growth of an individual during its ontogenesis from the moment it attaches to a solid substrate to the attainment of a marketable size. The test computations have been compared to the available observation data for mussel farms.

  12. 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...

  13. 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.

  14. 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.

  15. 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

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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

  2. 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.

  3. Evolutionary and ecological genomics of non-model plants

    Institute of Scientific and Technical Information of China (English)

    Bao-Hua SONG; Thomas MITCHELL-OLDS

    2011-01-01

    Dissecting evolutionary dynamics of ecologically important traits is a long-term challenge for biologists.Attempts to understand natural variation and molecular mechanisms have motivated a move from laboratory model systems to non-model systems in diverse natural environments.Next generation sequencing methods,along with an expansion of genomic resources and tools,have fostered new links between diverse disciplines,including molecular biology,evolution,ecology,and genomics.Great progress has been made in a few non-model wild plants,such as Arabidopsis relatives,monkey flowers,and wild sunflowers.Until recently,the lack of comprehensive genomic information has limited evolutionary and ecological studies to larger QTL (quantitative trait locus) regions rather than single gene resolution,and has hindered recognition of general patterns of natural variation and local adaptation.Further efforts in accumulating genomic data and developing bioinformatic and biostatistical tools are now poised to move this field forward.Integrative national and international collaborations and research communities are needed to facilitate development in the field of evolutionary and ecological genomics.

  4. 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...

  5. 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.

  6. 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)

  7. 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.

  8. 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.

  9. 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 determine...... bed gasifier can be successfully predicted by applying neural networks. ANNs models use in the input layer the biomass composition and few operating parameters, two neurons in the hidden layer and the backpropagation algorithm. The results obtained by these ANNs show high agreement with published...

  10. 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.

  11. 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

  12. Evaluating the Risk of Metabolic Syndrome Based on an Artificial Intelligence Model

    Directory of Open Access Journals (Sweden)

    Hui Chen

    2014-01-01

    Full Text Available Metabolic syndrome is worldwide public health problem and is a serious threat to people's health and lives. Understanding the relationship between metabolic syndrome and the physical symptoms is a difficult and challenging task, and few studies have been performed in this field. It is important to classify adults who are at high risk of metabolic syndrome without having to use a biochemical index and, likewise, it is important to develop technology that has a high economic rate of return to simplify the complexity of this detection. In this paper, an artificial intelligence model was developed to identify adults at risk of metabolic syndrome based on physical signs; this artificial intelligence model achieved more powerful capacity for classification compared to the PCLR (principal component logistic regression model. A case study was performed based on the physical signs data, without using a biochemical index, that was collected from the staff of Lanzhou Grid Company in Gansu province of China. The results show that the developed artificial intelligence model is an effective classification system for identifying individuals at high risk of metabolic syndrome.

  13. Decade Review (1999-2009): Artificial Intelligence Techniques in Student Modeling

    Science.gov (United States)

    Drigas, Athanasios S.; Argyri, Katerina; Vrettaros, John

    Artificial Intelligence applications in educational field are getting more and more popular during the last decade (1999-2009) and that is why much relevant research has been conducted. In this paper, we present the most interesting attempts to apply artificial intelligence methods such as fuzzy logic, neural networks, genetic programming and hybrid approaches such as neuro - fuzzy systems and genetic programming neural networks (GPNN) in student modeling. This latest research trend is a part of every Intelligent Tutoring System and aims at generating and updating a student model in order to modify learning content to fit individual needs or to provide reliable assessment and feedback to student's answers. In this paper, we make a brief presentation of methods used to point out their qualities and then we attempt a navigation to the most representative studies sought in the decade of our interest after classifying them according to the principal aim they attempted to serve.

  14. 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.

  15. Double Glow Plasma Surface Alloying Process Modeling Using Artificial Neural Networks

    Institute of Scientific and Technical Information of China (English)

    Jiang XU; Xishan XIE; Zhong XU

    2003-01-01

    A model is developed for predicting the correlation between processing parameters and the technical target of double glowby applying artificial neural network (ANN). The input parameters of the neural network (NN) are source voltage, workpiecevoltage, working pressure and distance between source electrode and workpiece. The output of the NN model is three importanttechnical targets, namely the gross element content, the thickness of surface alloying layer and the absorption rate (the ratioof the mass loss of source materials to the increasing mass of workpiece) in the processing of double glow plasma surfacealloying. The processing parameters and technical target are then used as a training set for an artificial neural network. Themodel is based on multiplayer feedforward neural network. A very good performance of the neural network is achieved and thecalculated results are in good agreement with the experimental ones.

  16. 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.

  17. 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.

  18. 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.

  19. 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...

  20. 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 ...

  1. 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...

  2. 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...

  3. Disease spread models in wild and feral animal populations: application of artificial life models.

    Science.gov (United States)

    Ward, M P; Laffan, S W; Highfield, L D

    2011-08-01

    The role that wild and feral animal populations might play in the incursion and spread of important transboundary animal diseases, such as foot and mouth disease (FMD), has received less attention than is warranted by the potential impacts. An artificial life model (Sirca) has been used to investigate this issue in studies based on spatially referenced data sets from southern Texas. An incursion of FMD in which either feral pig or deer populations were infected could result in between 698 and 1557 infected cattle and affect an area of between 166 km2 and 455 km2 after a 100-day period. Although outbreak size in deer populations can be predicted bythe size of the local deer population initially infected, the resulting outbreaks in feral pig populations are less predictable. Also, in the case of deer, the size of potential outbreaks might depend on the season when the incursion occurs. The impact of various mitigation strategies on disease spread has also been investigated. The approach used in the studies reviewed here explicitly incorporates the spatial distribution and relationships between animal populations, providing a new framework to explore potential impacts, costs, and control strategies.

  4. A Cost Estimation Model for Repair Bridges Based on Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Mohamed Bouabaz

    2008-01-01

    Full Text Available Estimating the total cost of bridges repair and maintenance with high accuracy is an important components, and points to a need for a cost estimation model. This paper focuses on the development of a more accurate estimation model for repair and maintenance of bridges in developing countries using artificial neural networks. Cost and design data for two categories of repair bridges were used for training and testing our neural network model, with only three main parameters used in estimating the total cost of repairing bridges. An accuracy of 96% was achieved.

  5. An artificial pancreas provided a novel model of blood glucose level variability in beagles.

    Science.gov (United States)

    Munekage, Masaya; Yatabe, Tomoaki; Kitagawa, Hiroyuki; Takezaki, Yuka; Tamura, Takahiko; Namikawa, Tsutomu; Hanazaki, Kazuhiro

    2015-12-01

    Although the effects on prognosis of blood glucose level variability have gained increasing attention, it is unclear whether blood glucose level variability itself or the manifestation of pathological conditions that worsen prognosis. Then, previous reports have not been published on variability models of perioperative blood glucose levels. The aim of this study is to establish a novel variability model of blood glucose concentration using an artificial pancreas. We maintained six healthy, male beagles. After anesthesia induction, a 20-G venous catheter was inserted in the right femoral vein and an artificial pancreas (STG-22, Nikkiso Co. Ltd., Tokyo, Japan) was connected for continuous blood glucose monitoring and glucose management. After achieving muscle relaxation, total pancreatectomy was performed. After 1 h of stabilization, automatic blood glucose control was initiated using the artificial pancreas. Blood glucose level varied for 8 h, alternating between the target blood glucose values of 170 and 70 mg/dL. Eight hours later, the experiment was concluded. Total pancreatectomy was performed for 62 ± 13 min. Blood glucose swings were achieved 9.8 ± 2.3 times. The average blood glucose level was 128.1 ± 5.1 mg/dL with an SD of 44.6 ± 3.9 mg/dL. The potassium levels after stabilization and at the end of the experiment were 3.5 ± 0.3 and 3.1 ± 0.5 mmol/L, respectively. In conclusion, the results of the present study demonstrated that an artificial pancreas contributed to the establishment of a novel variability model of blood glucose levels in beagles.

  6. Time-dependent ARMA modeling of genomic sequences

    Directory of Open Access Journals (Sweden)

    Schonfeld Dan

    2008-08-01

    Full Text Available Abstract Background Over the past decade, many investigators have used sophisticated time series tools for the analysis of genomic sequences. Specifically, the correlation of the nucleotide chain has been studied by examining the properties of the power spectrum. The main limitation of the power spectrum is that it is restricted to stationary time series. However, it has been observed over the past decade that genomic sequences exhibit non-stationary statistical behavior. Standard statistical tests have been used to verify that the genomic sequences are indeed not stationary. More recent analysis of genomic data has relied on time-varying power spectral methods to capture the statistical characteristics of genomic sequences. Techniques such as the evolutionary spectrum and evolutionary periodogram have been successful in extracting the time-varying correlation structure. The main difficulty in using time-varying spectral methods is that they are extremely unstable. Large deviations in the correlation structure results from very minor perturbations in the genomic data and experimental procedure. A fundamental new approach is needed in order to provide a stable platform for the non-stationary statistical analysis of genomic sequences. Results In this paper, we propose to model non-stationary genomic sequences by a time-dependent autoregressive moving average (TD-ARMA process. The model is based on a classical ARMA process whose coefficients are allowed to vary with time. A series expansion of the time-varying coefficients is used to form a generalized Yule-Walker-type system of equations. A recursive least-squares algorithm is subsequently used to estimate the time-dependent coefficients of the model. The non-stationary parameters estimated are used as a basis for statistical inference and biophysical interpretation of genomic data. In particular, we rely on the TD-ARMA model of genomic sequences to investigate the statistical properties and

  7. 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.

  8. 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.

  9. 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

  10. A Three-Dimensional Model of the Yeast Genome

    Science.gov (United States)

    Noble, William; Duan, Zhi-Jun; Andronescu, Mirela; Schutz, Kevin; McIlwain, Sean; Kim, Yoo Jung; Lee, Choli; Shendure, Jay; Fields, Stanley; Blau, C. Anthony

    Layered on top of information conveyed by DNA sequence and chromatin are higher order structures that encompass portions of chromosomes, entire chromosomes, and even whole genomes. Interphase chromosomes are not positioned randomly within the nucleus, but instead adopt preferred conformations. Disparate DNA elements co-localize into functionally defined aggregates or factories for transcription and DNA replication. In budding yeast, Drosophila and many other eukaryotes, chromosomes adopt a Rabl configuration, with arms extending from centromeres adjacent to the spindle pole body to telomeres that abut the nuclear envelope. Nonetheless, the topologies and spatial relationships of chromosomes remain poorly understood. Here we developed a method to globally capture intra- and inter-chromosomal interactions, and applied it to generate a map at kilobase resolution of the haploid genome of Saccharomyces cerevisiae. The map recapitulates known features of genome organization, thereby validating the method, and identifies new features. Extensive regional and higher order folding of individual chromosomes is observed. Chromosome XII exhibits a striking conformation that implicates the nucleolus as a formidable barrier to interaction between DNA sequences at either end. Inter-chromosomal contacts are anchored by centromeres and include interactions among transfer RNA genes, among origins of early DNA replication and among sites where chromosomal breakpoints occur. Finally, we constructed a three-dimensional model of the yeast genome. Our findings provide a glimpse of the interface between the form and function of a eukaryotic genome.

  11. 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.

  12. 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.

  13. 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...

  14. Two unisexual artificial polyploid clones constructed by genome addition of common carp (Cyprinus carp) and crucian carp (Carassius auratus)

    Institute of Scientific and Technical Information of China (English)

    吴清江; 叶玉珍; 董新红

    2003-01-01

    A polyploid hybrid fish with natural gynogenesis can prevent segregation and maintain their hybrid vigor in their progenies. Supposing the reproduction mode of induced polyploid fish being natural gynogenesis, allopolyploid hybrid between common carp and crucian carp into allopolyploid was performed. The purpose of this paper is to describe a lineage from sexual diploid carp transforming into allotriploid and allotetraploid unisexual clones by genome addition. The diploid hybrid between common carp and crucian carp reproduces an unreduced nucleus consisting of two parental genomes. This unreduced female pronucleus will fuse with male pronucleus and form allotriploid zygote after penetration of related species sperms. Allotriploid embryos grow normally, and part of female allotriploid can produce unreduced mature ova with three genomes. Mature ova of most allotriploid females are provided with natural gynogenetic trait and their nuclei do not fuse with any entrance sperm. All female offspring are produced by gynogenesis of allotriploid egg under activation of penetrating sperms. These offspring maintain morphological traits of their allotriploid maternal and form an allotetraploid unisexual clone by gynogenetic reproduction mode. However, female nuclei of rare allotriploid female can fuse with penetrating male pronuclei and result in the appearance of allotetraploid individuals by means of genome addition. All allotetraploid females can reproduce unreduced mature eggs containing four genomes. Therefore, mature eggs of allotetraploid maintain gynogenetic trait and allotetraploid unisexual clone is produced under activation of related species sperms.

  15. From amplification to gene in thyroid cancer: A high-resolution mapped bacterial-artificial-chromosome resource for cancer chromosome aberrations guides gene discovery after comparative genome hybridization

    Energy Technology Data Exchange (ETDEWEB)

    Chen, X.N.; Gonsky, R.; Korenberg, J.R. [UCLA School of Medicine, Los Angeles, CA (United States). Cedars-Sinai Research Inst.; Knauf, J.A.; Fagin, J.A. [Univ. of Cincinnati, OH (United States). Div. of Endocrinology/Metabolism; Wang, M.; Lai, E.H. [Univ. of North Carolina, Chapel Hill, NC (United States). Dept. of Pharmacology; Chissoe, S. [Washington Univ. School of Medicine, St. Louis, MO (United States). Genome Sequencing

    1998-08-01

    Chromosome rearrangements associated with neoplasms provide a rich resource for definition of the pathways of tumorigenesis. The power of comparative genome hybridization (CGH) to identify novel genes depends on the existence of suitable markers, which are lacking throughout most of the genome. The authors now report a general approach that translates CGH data into higher-resolution genomic-clone data that are then used to define the genes located in aneuploid regions. They used CGH to study 33 thyroid-tumor DNAs and two tumor-cell-line DNAs. The results revealed amplifications of chromosome band 2p21, with less-intense amplification on 2p13, 19q13.1, and 1p36 and with least-intense amplification on 1p34, 1q42, 5q31, 5q33-34, 9q32-34, and 14q32. To define the 2p21 region amplified, a dense array of 373 FISH-mapped chromosome 2 bacterial artificial chromosomes (BACs) was constructed, and 87 of these were hybridized to a tumor-cell line. Four BACs carried genomic DNA that was amplified in these cells. The maximum amplified region was narrowed to 3--6 Mb by multicolor FISH with the flanking BACs, and the minimum amplicon size was defined by a contig of 420 kb. Sequence analysis of the amplified BAC 1D9 revealed a fragment of the gene, encoding protein kinase C epsilon (PKC{epsilon}), that was then shown to be amplified and rearranged in tumor cells. In summary, CGH combined with a dense mapped resource of BACs and large-scale sequencing has led directly to the definition of PKC{epsilon} as a previously unmapped candidate gene involved in thyroid tumorigenesis.

  16. 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.

  17. Modeling of Steam Distillation Mechanism during Steam Injection Process Using Artificial Intelligence

    Science.gov (United States)

    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

  18. 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

  19. Modeling genomic regulatory networks with big data.

    Science.gov (United States)

    Bolouri, Hamid

    2014-05-01

    High-throughput sequencing, large-scale data generation projects, and web-based cloud computing are changing how computational biology is performed, who performs it, and what biological insights it can deliver. I review here the latest developments in available data, methods, and software, focusing on the modeling and analysis of the gene regulatory interactions in cells. Three key findings are: (i) although sophisticated computational resources are increasingly available to bench biologists, tailored ongoing education is necessary to avoid the erroneous use of these resources. (ii) Current models of the regulation of gene expression are far too simplistic and need updating. (iii) Integrative computational analysis of large-scale datasets is becoming a fundamental component of molecular biology. I discuss current and near-term opportunities and challenges related to these three points.

  20. Artificial Neural Network Modeling of Microstructure During C-Mn and HSLA Plate Rolling

    Institute of Scientific and Technical Information of China (English)

    TAN Wen; LIU Zhen-yu; WU Di; WANG Guo-dong

    2009-01-01

    An artificial neural network (ANN) model for predicting transformed mierostrueture in conventional roll-ing process and thermomechanieal controlled process (TMCP) is proposed. The model uses austenite grain size and retained strain, which can be calculated by using microstrueture evolution models, together with a measured cooling rate and chemical compositions as inputs and the ferrite grain size and ferrite fraction as outputs. The predicted re-sults show that the model can predict the transformed microstructure which is in good agreement with the measured one, and it is better than the empirical equations. Also, the effect of the alloying elements on transformed products has been analyzed by using the model. The tendency is the same as that in the reported articles. The model can be used further for the optimization of processing parameters, mierostructure and properties in TMCP.

  1. Hybrid Hot Strip Rolling Force Prediction using a Bayesian Trained Artificial Neural Network and Analytical Models

    Directory of Open Access Journals (Sweden)

    Abdelkrim Moussaoui

    2006-01-01

    Full Text Available The authors discuss the combination of an Artificial Neural Network (ANN with analytical models to improve the performance of the prediction model of finishing rolling force in hot strip rolling mill process. The suggested model was implemented using Bayesian Evidence based training algorithm. It was found that the Bayesian Evidence based approach provided a superior and smoother fit to the real rolling mill data. Completely independent set of real rolling data were used to evaluate the capacity of the fitted ANN model to predict the unseen regions of data. As a result, test rolls obtained by the suggested hybrid model have shown high prediction quality comparatively to the usual empirical prediction models.

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

    DEFF Research Database (Denmark)

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

    1998-01-01

    kernel estimation method based on Laguerre expansions. The results for the two types of artificial neural networks 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......In this paper, feedforward 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...... via the Laguerre expansion technique achieve this prediction NMSE with approximately half the number of free parameters relative to either neural-network model. However, both approaches are deemed effective in modeling nonlinear dynamic systems and their cooperative use is recommended in general....

  3. 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.

  4. Genome Editing and Its Applications in Model Organisms

    Directory of Open Access Journals (Sweden)

    Dongyuan Ma

    2015-12-01

    Full Text Available Technological advances are important for innovative biological research. Development of molecular tools for DNA manipulation, such as zinc finger nucleases (ZFNs, transcription activator-like effector nucleases (TALENs, and the clustered regularly-interspaced short palindromic repeat (CRISPR/CRISPR-associated (Cas, has revolutionized genome editing. These approaches can be used to develop potential therapeutic strategies to effectively treat heritable diseases. In the last few years, substantial progress has been made in CRISPR/Cas technology, including technical improvements and wide application in many model systems. This review describes recent advancements in genome editing with a particular focus on CRISPR/Cas, covering the underlying principles, technological optimization, and its application in zebrafish and other model organisms, disease modeling, and gene therapy used for personalized medicine.

  5. 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

  6. Genomic breeding value estimation using nonparametric additive regression models

    Directory of Open Access Journals (Sweden)

    Solberg Trygve

    2009-01-01

    Full Text Available Abstract Genomic selection refers to the use of genomewide dense markers for breeding value estimation and subsequently for selection. The main challenge of genomic breeding value estimation is the estimation of many effects from a limited number of observations. Bayesian methods have been proposed to successfully cope with these challenges. As an alternative class of models, non- and semiparametric models were recently introduced. The present study investigated the ability of nonparametric additive regression models to predict genomic breeding values. The genotypes were modelled for each marker or pair of flanking markers (i.e. the predictors separately. The nonparametric functions for the predictors were estimated simultaneously using additive model theory, applying a binomial kernel. The optimal degree of smoothing was determined by bootstrapping. A mutation-drift-balance simulation was carried out. The breeding values of the last generation (genotyped was predicted using data from the next last generation (genotyped and phenotyped. The results show moderate to high accuracies of the predicted breeding values. A determination of predictor specific degree of smoothing increased the accuracy.

  7. 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.

  8. 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

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

    Directory of Open Access Journals (Sweden)

    Parvaneh Shabanzadeh

    2013-01-01

    Full Text Available 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 properties. The nanocrystal silver was synthesized into an interlamellar space of montmorillonite by using the chemical reduction technique. The method has an advantage of size control which is essential in nanometals synthesis. Silver nanoparticles with nanosize and devoid of aggregation are favorable for several properties. In this investigation, the accuracy of artificial neural network training algorithm was applied in studying the effects of different parameters on the particles, including the AgNO3 concentration, reaction temperature, UV-visible wavelength, and montmorillonite (MMT d-spacing on the prediction of size of silver nanoparticles. Analysis of the variance showed that the AgNO3 concentration and temperature were the most significant factors affecting the size of silver nanoparticles. Using the best performing artificial neural network, the optimum conditions predicted were a concentration of AgNO3 of 1.0 (M, MMT d-spacing of 1.27 nm, reaction temperature of 27°C, and wavelength of 397.50 nm.

  10. Software Development Cost and Time Forecasting Using a High Performance Artificial Neural Network Model

    Science.gov (United States)

    Attarzadeh, Iman; Ow, Siew Hock

    Nowadays, mature software companies are more interested to have a precise estimation of software metrics such as project time, cost, quality, and risk at the early stages of software development process. The ability to precisely estimate project time and costs by project managers is one of the essential tasks in software development activities, and it named software effort estimation. The estimated effort at the early stage of project development process is uncertain, vague, and often the least accurate. It is because that very little information is available at the beginning stage of project. Therefore, a reliable and precise effort estimation model is an ongoing challenge for project managers and software engineers. This research work proposes a novel soft computing model incorporating Constructive Cost Model (COCOMO) to improve the precision of software time and cost estimation. The proposed artificial neural network model has good generalisation, adaption capability, and it can be interpreted and validated by software engineers. The experimental results show that applying the desirable features of artificial neural networks on the algorithmic estimation model improves the accuracy of time and cost estimation and estimated effort can be very close to the actual effort.

  11. Genome-scale modeling for metabolic engineering

    Energy Technology Data Exchange (ETDEWEB)

    Simeonidis, E; Price, ND

    2015-01-13

    We focus on the application of constraint-based methodologies and, more specifically, flux balance analysis in the field of metabolic engineering, and enumerate recent developments and successes of the field. We also review computational frameworks that have been developed with the express purpose of automatically selecting optimal gene deletions for achieving improved production of a chemical of interest. The application of flux balance analysis methods in rational metabolic engineering requires a metabolic network reconstruction and a corresponding in silico metabolic model for the microorganism in question. For this reason, we additionally present a brief overview of automated reconstruction techniques. Finally, we emphasize the importance of integrating metabolic networks with regulatory information-an area which we expect will become increasingly important for metabolic engineering-and present recent developments in the field of metabolic and regulatory integration.

  12. 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

  13. Artificial Intelligence versus Statistical Modeling and Optimization of Cholesterol Oxidase Production by using Streptomyces Sp.

    Science.gov (United States)

    Pathak, Lakshmi; Singh, Vineeta; Niwas, Ram; Osama, Khwaja; Khan, Saif; Haque, Shafiul; Tripathi, C K M; Mishra, B N

    2015-01-01

    Cholesterol oxidase (COD) is a bi-functional FAD-containing oxidoreductase which catalyzes the oxidation of cholesterol into 4-cholesten-3-one. The wider biological functions and clinical applications of COD have urged the screening, isolation and characterization of newer microbes from diverse habitats as a source of COD and optimization and over-production of COD for various uses. The practicability of statistical/ artificial intelligence techniques, such as response surface methodology (RSM), artificial neural network (ANN) and genetic algorithm (GA) have been tested to optimize the medium composition for the production of COD from novel strain Streptomyces sp. NCIM 5500. All experiments were performed according to the five factor central composite design (CCD) and the generated data was analysed using RSM and ANN. GA was employed to optimize the models generated by RSM and ANN. Based upon the predicted COD concentration, the model developed with ANN was found to be superior to the model developed with RSM. The RSM-GA approach predicted maximum of 6.283 U/mL COD production, whereas the ANN-GA approach predicted a maximum of 9.93 U/mL COD concentration. The optimum concentrations of the medium variables predicted through ANN-GA approach were: 1.431 g/50 mL soybean, 1.389 g/50 mL maltose, 0.029 g/50 mL MgSO4, 0.45 g/50 mL NaCl and 2.235 ml/50 mL glycerol. The experimental COD concentration was concurrent with the GA predicted yield and led to 9.75 U/mL COD production, which was nearly two times higher than the yield (4.2 U/mL) obtained with the un-optimized medium. This is the very first time we are reporting the statistical versus artificial intelligence based modeling and optimization of COD production by Streptomyces sp. NCIM 5500.

  14. Artificial Intelligence versus Statistical Modeling and Optimization of Cholesterol Oxidase Production by using Streptomyces Sp.

    Science.gov (United States)

    Niwas, Ram; Osama, Khwaja; Khan, Saif; Haque, Shafiul; Tripathi, C. K. M.; Mishra, B. N.

    2015-01-01

    Cholesterol oxidase (COD) is a bi-functional FAD-containing oxidoreductase which catalyzes the oxidation of cholesterol into 4-cholesten-3-one. The wider biological functions and clinical applications of COD have urged the screening, isolation and characterization of newer microbes from diverse habitats as a source of COD and optimization and over-production of COD for various uses. The practicability of statistical/ artificial intelligence techniques, such as response surface methodology (RSM), artificial neural network (ANN) and genetic algorithm (GA) have been tested to optimize the medium composition for the production of COD from novel strain Streptomyces sp. NCIM 5500. All experiments were performed according to the five factor central composite design (CCD) and the generated data was analysed using RSM and ANN. GA was employed to optimize the models generated by RSM and ANN. Based upon the predicted COD concentration, the model developed with ANN was found to be superior to the model developed with RSM. The RSM-GA approach predicted maximum of 6.283 U/mL COD production, whereas the ANN-GA approach predicted a maximum of 9.93 U/mL COD concentration. The optimum concentrations of the medium variables predicted through ANN-GA approach were: 1.431 g/50 mL soybean, 1.389 g/50 mL maltose, 0.029 g/50 mL MgSO4, 0.45 g/50 mL NaCl and 2.235 ml/50 mL glycerol. The experimental COD concentration was concurrent with the GA predicted yield and led to 9.75 U/mL COD production, which was nearly two times higher than the yield (4.2 U/mL) obtained with the un-optimized medium. This is the very first time we are reporting the statistical versus artificial intelligence based modeling and optimization of COD production by Streptomyces sp. NCIM 5500. PMID:26368924

  15. 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.

  16. 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

  17. 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

  18. Multiple Regression (MR) and Artificial Neural Network (ANN) models for prediction of soil suction

    Science.gov (United States)

    Erzin, Yusuf; Yilmaz, Isik

    2010-05-01

    This article presents a comparison of multiple regression (MR) and artificial neural network (ANN) model for prediction of soil suction of clayey soils. The results of the soil suction tests utilizing thermocouple psychrometers on statically compacted specimens of Bentonite-Kaolinite clay mixtures with varying soil properties were used to develope the models. The results obtained from both models were then compared with the experimental results. The performance indices such as coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and variance account for (VAF) were used to control the performance of the prediction capacity of the models developed in this study. ANN model has shown higher prediction performance than regression model according to the performance indices. It is shown that ANN models provide significant improvements in prediction accuracy over statistical models. The potential benefits of soft computing models extend beyond the high computation rates. Higher performances of the soft computing models were sourced from greater degree of robustness and fault tolerance than traditional statistical models because there are many more processing neurons, each with primarily local connections. It appears that there is a possibility of estimating soil suction by using the proposed empirical relationships and soft computing models. The population of the analyzed data is relatively limited in this study. Therefore, the practical outcome of the proposed equations and models could be used, with acceptable accuracy.

  19. 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.

  20. 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

  1. 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...

  2. 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

  3. 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%.

  4. Rolling force prediction for strip casting using theoretical model and artificial intelligence

    Institute of Scientific and Technical Information of China (English)

    CAO Guang-ming; LI Cheng-gang; ZHOU Guo-ping; LIU Zhen-yu; WU Di; WANG Guo-dong; LIU Xiang-hua

    2010-01-01

    Rolling force for strip casting of 1Cr17 ferritic stainless steel was predicted using theoretical model and artificial intelligence.Solution zone was classified into two parts by kiss point position during casting strip.Navier-Stokes equation in fluid mechanics and stream function were introduced to analyze the rheological property of liquid zone and mushy zone,and deduce the analytic equation of unit compression stress distribution.The traditional hot rolling model was still used in the solid zone.Neural networks based on feedforward training algorithm in Bayesian regularization were introduced to build model for kiss point position.The results show that calculation accuracy for verification data of 94.67% is in the range of+7.0%,which indicates that the predicting accuracy of this model is very high.

  5. 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.

  6. 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.

  7. Application of artificial neural engineering and regression models for forecasting shelf life of instant coffee drink

    Directory of Open Access Journals (Sweden)

    Sumit Goyal

    2011-07-01

    Full Text Available Coffee as beverage is prepared from the roasted seeds (beans of the coffee plant. Coffee is the second most important product in the international market in terms of volume trade and the most important in terms of value. Artificial neural engineering and regression models were developed to predict shelf life of instant coffee drink. Colour and appearance, flavour, viscosity and sediment were used as input parameters. Overall acceptability was used as output parameter. The dataset consisted of experimentally developed 50 observations. The dataset was divided into two disjoint subsets, namely, training set containing 40 observations (80% of total observations and test set comprising of 10 observations (20% of total observations. The network was trained with 500 epochs. Neural network toolbox under Matlab 7.0 software was used for training the models. From the investigation it was revealed that multiple linear regression model was superior over radial basis model for forecasting shelf life of instant coffee drink.

  8. 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.

  9. 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.

  10. 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 ( 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

  11. Modeling light-driven proton pumps in artificial photosynthetic reaction centers.

    Science.gov (United States)

    Ghosh, Pulak Kumar; Smirnov, Anatoly Yu; Nori, Franco

    2009-07-21

    We study a model of a light-induced proton pump in artificial reaction centers. The model contains a molecular triad with four electron states (i.e., one donor state, two photosensitive group states, and one acceptor state) as well as a molecular shuttle having one electron and one proton-binding sites. The shuttle diffuses between the sides of the membrane and translocates protons energetically uphill: from the negative side to the positive side of the membrane, harnessing for this purpose the energy of the electron-charge separation produced by light. Using the methods of quantum transport theory we calculate the range of light intensity and transmembrane potentials that maximize both the light-induced proton current and the energy transduction efficiency. We also study the effect of temperature on proton pumping. The light-induced proton pump in our model gives a quantum yield of proton translocation of about 55%. Thus, our results explain previous experiments on these artificial photosynthetic reaction centers.

  12. 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

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

    Energy Technology Data Exchange (ETDEWEB)

    Garg, A. [Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati 781039, Assam (India); Sastry, P.S. [Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati 781039, Assam (India); Pandey, M. [Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati 781039, Assam (India)]. E-mail: manmohan@iitg.ac.in; Dixit, U.S. [Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati 781039, Assam (India); Gupta, S.K. [Atomic Energy Regulatory Board, Mumbai 400085 (India)

    2007-02-15

    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.

  14. 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.

  15. 白眉长臂猿基因组BAC文库的构建%Construction of Genome Bacterial Artificial Chromosome Library of Hylobates Hoolock

    Institute of Scientific and Technical Information of China (English)

    王起明; 孙烨超; 厉申捷; 叶建平

    2015-01-01

    High quality genomic DNA of Hylobates hoolock was obtained by gentle physical homogenization. The DNA was partially digested with EcoRⅠand EcoRⅠmethylase, and cloned to pCC1BAC vector. The positive clones were stored in 384-well plates. The constructed BAC library consists of 85800 clones. DNA from randomly selected 250 BAC clones was restricted with Not I restriction enzyme and fragments were separated by pulsed field gel electrophoresis. The result shows that the average insert size is estimated as approximately 110 kb, and the ratio of non-recombinant clones is 10. 0%. If the genome size of Hylobates hoolock is 3 ×106 kilo-base, the library could cover 3 times the number of genome.%通过温和的物理方法获得白眉长臂猿高质量的基因组DNA,EcoRⅠ和EcoRⅠ甲基化酶部分酶切后经回收、连接、转化、阳性克隆的保存,构建了含有85800个克隆的全基因组BAC( Bacterial artificial chromosome)文库.随机选取250个BAC克隆进行Not I酶切及脉冲场电泳分析,结果表明该文库的平均插入片段大小为110 kb,非重组克隆(无插入片段)的比率为10.0%.假定白眉长臂猿的基因组大小为3×106 kb,根据文库的平均插入片段大小,则该文库具有3倍的基因组覆盖率.

  16. 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.

  17. Development of Artificial Neural-Network-Based Models for the Simulation of Spring Discharge

    Directory of Open Access Journals (Sweden)

    M. Mohan Raju

    2011-01-01

    Full Text Available The present study demonstrates the application of artificial neural networks (ANNs in predicting the weekly spring discharge. The study was based on the weekly spring discharge from a spring located near Ranichauri in Tehri Garhwal district of Uttarakhand, India. Five models were developed for predicting the spring discharge based on a weekly interval using rainfall, evaporation, temperature with a specified lag time. All models were developed both with one and two hidden layers. Each model was developed with many trials by selecting different network architectures and different number of hidden neurons; finally a best predicting model presented against each developed model. The models were trained with three different algorithms, that is, quick-propagation algorithm, batch backpropagation algorithm, and Levenberg-Marquardt algorithm using weekly data from 1999 to 2005. A best model for the simulation was selected from the three presented algorithms using the statistical criteria such as correlation coefficient (, determination coefficient, or Nash Sutcliff's efficiency (DC. Finally, optimized number of neurons were considered for the best model. Training and testing results revealed that the models were predicting the weekly spring discharge satisfactorily. Based on these criteria, ANN-based model results in better agreement for the computation of spring discharge. LMR models were also developed in the study, and they also gave good results, but, when compared with the ANN methodology, ANN resulted in better optimized values.

  18. 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)

  19. 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.

  20. Self-organizing Ising model of artificial financial markets with small-world network topology

    Science.gov (United States)

    Zhao, Haijie; Zhou, Jie; Zhang, Anghui; Su, Guifeng; Zhang, Yi

    2013-01-01

    We study a self-organizing Ising-like model of artificial financial markets with underlying small-world (SW) network topology. The asset price dynamics results from the collective decisions of interacting agents which are located on a small-world complex network (the nodes symbolize the agents of a financial market). The model incorporates the effects of imitation, the impact of external news and private information. We also investigate the influence of different network topologies, from regular lattice to random graph, on the asset price dynamics by adjusting the probability of the rewiring procedure. We find that a specific combination of model parameters reproduce main stylized facts of real-world financial markets.

  1. 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.

  2. PREDICTION OF SITE RESPONSE SPECTRUM UNDER EARTHQUAKE VIBRATION USING AN OPTIMIZED DEVELOPED ARTIFICIAL NEURAL NETWORK MODEL

    Directory of Open Access Journals (Sweden)

    Reza Esmaeilabadi

    2016-06-01

    Full Text Available Site response spectrum is one of the key factors to determine the maximum acceleration and displacement, as well as structure behavior analysis during earthquake vibrations. The main objective of this paper is to develop an optimized model based on artificial neural network (ANN using five different training algorithms to predict nonlinear site response spectrum subjected to Silakhor earthquake vibrations is. The model output was tested for a specified area in west of Iran. The performance and quality of optimized model under all training algorithms have been examined by various statistical, analytical and graph analyses criteria as well as a comparison with numerical methods. The observed adaptabilities in results indicate a feasible and satisfactory engineering alternative method for predicting the analysis of nonlinear site response.

  3. 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.

  4. 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.

  5. 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.

  6. 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

  7. Artificial neural network for modeling the extraction of aromatic hydrocarbons from lube oil cuts

    Energy Technology Data Exchange (ETDEWEB)

    Mehrkesh, A.H.; Hajimirzaee, S. [Islamic Azad University, Majlesi Branch, Isfahan (Iran, Islamic Republic of); Hatamipour, M.S.; Tavakoli, T. [Department of Chemical Engineering, University of Isfahan, Isfahan (Iran, Islamic Republic of)

    2011-03-15

    An artificial neural network (ANN) approach was used to obtain a simulation model to predict the rotating disc contactor (RDC) performance during the extraction of aromatic hydrocarbons from lube oil cuts, to produce a lubricating base oil using furfural as solvent. The field data used for training the ANN model was obtained from a lubricating oil production company. The input parameters of the ANN model were the volumetric flow rates of feed and solvent, the temperatures of feed and solvent, and the disc rotation rate. The output parameters were the volumetric flow rate of the raffinate phase and the extraction yield. In this study, a feed-forward multi-layer perceptron neural network was successfully used to demonstrate the complex relationship between the mentioned input and output parameters. (Copyright copyright 2011 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)

  8. 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

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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)

  16. Artificial snowmaking possibilities and climate change based on regional climate modeling in the Southern Black Forest

    Energy Technology Data Exchange (ETDEWEB)

    Schmidt, Philipp; Matzarakis, Andreas [Freiburg Univ. (Germany). Meteorological Inst.; Steiger, Robert [alpS - Centre for Climate Change Adaptation Technologies, Innsbruck (Austria)

    2012-04-15

    Winter sport, especially ski tourism - is one of those sectors of tourism that will be affected by climate change. Ski resorts across the Alps and in the adjacent low mountain ranges react to warm winter seasons by investing in artificial snowmaking. But snowmaking in warm winter seasons is fraught with risk, because sufficiently low air temperature will become less frequent in the future. The present study deals with the ski resort Feldberg, which has 14 ski lifts and 16 ski slopes which is the biggest ski resort in the German Federal state Baden-Wuerttemberg. The impact of climate change in this region is extraordinary important because winter tourism is the main source of revenue for the whole area around the ski resort. The study area is in altitudinal range of 850 to 1450 meters above sea level. At the moment, it is possible to supply one third of the whole area with artificial snow, but there is plan for artificial snowmaking of the whole Feldberg area by the year 2020. Based on this, more detailed investigations of season length and the needed volume of produced snow are necessary. A ski season simulation model (SkiSim 2.0) was applied in order to assess potential impacts of climate change on the Feldberg ski area for the A1B and B1 emission scenarios based on the ECHAM5 GCM downscaled by the REMO RCM. SkiSim 2.0 calculates daily snow depth (natural and technically produced snow) and the required amount of artificial snow for 100 m altitudinal bands. Analysing the development of the number of potential skiing days, it can be assessed whether ski operation is cost covering or not. Model results of the study show a more pronounced and rapid shortening of the ski season in the lower ranges until the year 2100 in each climate scenario. In both the A1B and B1 scenario runs of REMO, a cost-covering ski season of 100 days cannot be guaranteed in every altitudinal range even if snowmaking is considered. In this context, the obtained high-resolution snow data can

  17. 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

  18. Cancer models, genomic instability and somatic cellular Darwinian evolution

    Directory of Open Access Journals (Sweden)

    Little Mark P

    2010-04-01

    Full Text Available Abstract The biology of cancer is critically reviewed and evidence adduced that its development can be modelled as a somatic cellular Darwinian evolutionary process. The evidence for involvement of genomic instability (GI is also reviewed. A variety of quasi-mechanistic models of carcinogenesis are reviewed, all based on this somatic Darwinian evolutionary hypothesis; in particular, the multi-stage model of Armitage and Doll (Br. J. Cancer 1954:8;1-12, the two-mutation model of Moolgavkar, Venzon, and Knudson (MVK (Math. Biosci. 1979:47;55-77, the generalized MVK model of Little (Biometrics 1995:51;1278-1291 and various generalizations of these incorporating effects of GI (Little and Wright Math. Biosci. 2003:183;111-134; Little et al. J. Theoret. Biol. 2008:254;229-238. Reviewers This article was reviewed by RA Gatenby and M Kimmel.

  19. Lumping, testing, tuning: The invention of an artificial chemistry in atmospheric transport modeling

    Science.gov (United States)

    Heymann, Matthias

    Since the late 1950s computer simulation has been used to investigate the transport of pollutants in the atmosphere. About 20 years later also the chemical transformation of atmospheric pollutants was included in computer models of photochemical smog formation. Due to limited knowledge of atmospheric chemistry and due to limited computer capacity, chemical processes in the atmosphere were modeled with the help of simplified chemical models. In these models chemical substances are lumped together forming artificial virtual compounds with virtual characteristics. The paper aims at studying the practices developed in chemical model building and the creation of confidence in these models. Core of the paper will be the analysis of the Urban Airshed Model (UAM) for the Los Angeles region, a pioneering development in the early 1970s. The construction of the UAM involved the "lumping" of chemical processes and extensive testing and tuning. These practices led to a consistent model representation, in which diverse pieces of information fitted and were mutually stabilized. The pragmatic achievement of consistency created confidence, even though empirical tests of the models remained ambiguous and problematic.

  20. 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.

  1. 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

  2. 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.

  3. 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

  4. 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)

  5. 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.

  6. Modelling the spectral energy distribution of galaxies: introducing the artificial neural network

    CERN Document Server

    Silva, L; Granato, G L; Almeida, C; Baugh, C M; Frenk, C S; Lacey, C G; Paoletti, L; Petrella, A; Selvestrel, D

    2010-01-01

    The spectral energy distribution of galaxies is a complex function of the star formation history and geometrical arrangement of stars and gas in galaxies. The computation of the radiative transfer of stellar radiation through the dust distribution is time-consuming. This aspect becomes unacceptable in particular when dealing with the predictions by semi-analytical galaxy formation models populating cosmological volumes, to be then compared with multi-wavelength surveys. Mainly for this aim, we have implemented an artificial neural network algorithm into the spectro-photometric and radiative transfer code GRASIL in order to compute the spectral energy distribution of galaxies in a short computing time. This allows to avoid the adoption of empirical templates that may have nothing to do with the mock galaxies output by models. The ANN has been implemented to compute the dust emission spectrum (the bottleneck of the computation), and separately for the star-forming molecular clouds and the diffuse dust (due to t...

  7. 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.

  8. 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...

  9. 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.

  10. Artificial Intelligence versus Statistical Modeling and Optimization of Cholesterol Oxidase Production by using Streptomyces Sp.

    Directory of Open Access Journals (Sweden)

    Lakshmi Pathak

    Full Text Available Cholesterol oxidase (COD is a bi-functional FAD-containing oxidoreductase which catalyzes the oxidation of cholesterol into 4-cholesten-3-one. The wider biological functions and clinical applications of COD have urged the screening, isolation and characterization of newer microbes from diverse habitats as a source of COD and optimization and over-production of COD for various uses. The practicability of statistical/ artificial intelligence techniques, such as response surface methodology (RSM, artificial neural network (ANN and genetic algorithm (GA have been tested to optimize the medium composition for the production of COD from novel strain Streptomyces sp. NCIM 5500. All experiments were performed according to the five factor central composite design (CCD and the generated data was analysed using RSM and ANN. GA was employed to optimize the models generated by RSM and ANN. Based upon the predicted COD concentration, the model developed with ANN was found to be superior to the model developed with RSM. The RSM-GA approach predicted maximum of 6.283 U/mL COD production, whereas the ANN-GA approach predicted a maximum of 9.93 U/mL COD concentration. The optimum concentrations of the medium variables predicted through ANN-GA approach were: 1.431 g/50 mL soybean, 1.389 g/50 mL maltose, 0.029 g/50 mL MgSO4, 0.45 g/50 mL NaCl and 2.235 ml/50 mL glycerol. The experimental COD concentration was concurrent with the GA predicted yield and led to 9.75 U/mL COD production, which was nearly two times higher than the yield (4.2 U/mL obtained with the un-optimized medium. This is the very first time we are reporting the statistical versus artificial intelligence based modeling and optimization of COD production by Streptomyces sp. NCIM 5500.

  11. 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

  12. Current state of genome-scale modeling in filamentous fungi

    DEFF Research Database (Denmark)

    Brandl, Julian; Andersen, Mikael Rørdam

    2015-01-01

    The group of filamentous fungi contains important species used in industrial biotechnology for acid, antibiotics and enzyme production. Their unique lifestyle turns these organisms into a valuable genetic reservoir of new natural products and biomass degrading enzymes that has not been used to full...... testing them in vivo. The increasing availability of high quality models and molecular biological tools for manipulating filamentous fungi renders the model-guided engineering of these fungal factories possible with comprehensive metabolic networks. A typical fungal model contains on average 1138 unique...... metabolic reactions and 1050 ORFs, making them a vast knowledge-base of fungal metabolism. In the present review we focus on the current state as well as potential future applications of genome-scale models in filamentous fungi....

  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. A game theoretic framework for incentive-based models of intrinsic motivation in artificial systems.

    Science.gov (United States)

    Merrick, Kathryn E; Shafi, Kamran

    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 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.

  15. A game theoretic framework for incentive-based models of intrinsic motivation in artificial systems.

    Science.gov (United States)

    Merrick, Kathryn E; Shafi, Kamran

    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 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. PMID:24198797

  16. 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)

  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. 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.

  19. An artificial neural network model for rainfall forecasting in Bangkok, Thailand

    Science.gov (United States)

    Hung, N. Q.; Babel, M. S.; Weesakul, S.; Tripathi, N. K.

    2009-08-01

    This paper presents a new approach using an Artificial Neural Network technique to improve rainfall forecast performance. A real world case study was set up in Bangkok; 4 years of hourly data from 75 rain gauge stations in the area were used to develop the ANN model. The developed ANN model is being applied for real time rainfall forecasting and flood management in Bangkok, Thailand. Aimed at providing forecasts in a near real time schedule, different network types were tested with different kinds of input information. Preliminary tests showed that a generalized feedforward ANN model using hyperbolic tangent transfer function achieved the best generalization of rainfall. Especially, the use of a combination of meteorological parameters (relative humidity, air pressure, wet bulb temperature and cloudiness), the rainfall at the point of forecasting and rainfall at the surrounding stations, as an input data, advanced ANN model to apply with continuous data containing rainy and non-rainy period, allowed model to issue forecast at any moment. Additionally, forecasts by ANN model were compared to the convenient approach namely simple persistent method. Results show that ANN forecasts have superiority over the ones obtained by the persistent model. Rainfall forecasts for Bangkok from 1 to 3 h ahead were highly satisfactory. Sensitivity analysis indicated that the most important input parameter besides rainfall itself is the wet bulb temperature in forecasting rainfall.

  20. 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.

  1. Chemometric Amylose Modeling and Sample Selection for Global Calibration Using Artificial Neural Networks

    Institute of Scientific and Technical Information of China (English)

    SHIMIZU N; OKADOME H; WADA D; KIMURA T; OHTSUBO K

    2008-01-01

    Chemometric arnylose modeling for global calibration, using whole grain near infrared transmittance spectra andsample selection, was used in an artificial neural network (ANN), to assess the global and local models generated, based onsamples of newly bred Indica, Japonica and rice. Global sample sets had a wide range of sample variation for amylose content(0 to 25.9%). The local sample set, Japonica sample, had relatively low amylose content and a narrow sample variation(amylose; 12.3% to 21.0%). For sample selection the CENTER algorithm was applied to generate calibration, validation andstop sample sets. Spectral preprocessing was found to reduce the optimum number of partial least squares (PLS) componentsfor amylose content and thus enhance the robustness of the local calibration. The best model was found to be an ANN globalcalibration with spectral preprocessing; the next was a PLS global calibration using standard spectra. These results pose thequestion whether an ANN algorithm with spectral preprocessing could be developed for global and local calibration models orwhether PLS without spectral preprocessing should be developed for global calibration models. We suggest that global calibra-tion models incorporating an ANN may be used as a universal calibration model.

  2. An artificial neural network model for rainfall forecasting in Bangkok, Thailand

    Directory of Open Access Journals (Sweden)

    N. Q. Hung

    2008-01-01

    Full Text Available The present study developed an artificial neural network (ANN model to overcome the difficulties in training the ANN models with continuous data consisting of rainy and non-rainy days. Among the six models analyzed the ANN model which used generalized feedforward type network and a hyperbolic tangent function and a combination of meteorological parameters (relative humidity, air pressure, wet bulb temperature and cloudiness, and the rainfall at the point of forecasting and rainfall at the surrounding stations, as an input for training of the model was found most satisfactory in forecasting rainfall in Bangkok, Thailand. The developed ANN model was applied to derive rainfall forecast from 1 to 6 h ahead at 75 rain gauge stations in the study area as forecast point from the data of 3 consecutive years (1997–1999. Results were highly satisfactory for rainfall forecast 1 to 3 h ahead. Sensitivity analysis indicated that the most important input parameter beside rainfall itself is the wet bulb temperature in forecasting rainfall. Based on these results, it is recommended that the developed ANN model can be used for real-time rainfall forecasting and flood management in Bangkok, Thailand.

  3. An artificial neural network model for rainfall forecasting in Bangkok, Thailand

    Directory of Open Access Journals (Sweden)

    N. Q. Hung

    2009-08-01

    Full Text Available This paper presents a new approach using an Artificial Neural Network technique to improve rainfall forecast performance. A real world case study was set up in Bangkok; 4 years of hourly data from 75 rain gauge stations in the area were used to develop the ANN model. The developed ANN model is being applied for real time rainfall forecasting and flood management in Bangkok, Thailand. Aimed at providing forecasts in a near real time schedule, different network types were tested with different kinds of input information. Preliminary tests showed that a generalized feedforward ANN model using hyperbolic tangent transfer function achieved the best generalization of rainfall. Especially, the use of a combination of meteorological parameters (relative humidity, air pressure, wet bulb temperature and cloudiness, the rainfall at the point of forecasting and rainfall at the surrounding stations, as an input data, advanced ANN model to apply with continuous data containing rainy and non-rainy period, allowed model to issue forecast at any moment. Additionally, forecasts by ANN model were compared to the convenient approach namely simple persistent method. Results show that ANN forecasts have superiority over the ones obtained by the persistent model. Rainfall forecasts for Bangkok from 1 to 3 h ahead were highly satisfactory. Sensitivity analysis indicated that the most important input parameter besides rainfall itself is the wet bulb temperature in forecasting rainfall.

  4. Design and Modeling of RF Power Amplifiers with Radial Basis Function Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Ali Reza Zirak

    2016-06-01

    Full Text Available A radial basis function (RBF artificial neural network model for a designed high efficiency radio frequency class-F power amplifier (PA is presented in this paper. The presented amplifier is designed at 1.8 GHz operating frequency with 12 dB of gain and 36 dBm of 1dB output compression point. The obtained power added efficiency (PAE for the presented PA is 76% under 26 dBm input power. The proposed RBF model uses input and DC power of the PA as inputs variables and considers output power as the output variable. The presented RBF network models the designed class-F PA as a block, which could be applied in circuit design. The presented model could be used to model any RF power amplifier. The obtained results show a good agreement between real data and predicted values by RBF model. The results clearly show that the presented RBF network is more precise than multilayer perceptron (MLP model. According to the results, better than 84% and 92% improvement is achieved in MAE and RMSE, respectively.

  5. Artificial Neural Network Model for Discrimination of Stability of Ancient Landslide in Impounding Area of Three Gorges Project, China

    Institute of Scientific and Technical Information of China (English)

    Zhou Pinggen

    2003-01-01

    The factors of geomorphology, geological setting, effect of ground water and environment dynamic factors (e. g. rainfall and artificial water recharge) should be integrated in the discrimination of the stability of the ancient landslide. As the criterion of landslide stability has been studied, the artificial neural network model was then applied to discriminate the stability of the ancient landslide in the impounding area of the Three Gorges project on the Yangtze River, China. The model has the property of self-adaptive identifying and integrating complex qualitative factors and quantitative factors. The results of the artificial neural network model are coincided well with what were gained by classical limit equilibrinm analysis (the Bishop method and Janbu method) and by other comprehensive discrimination methods.

  6. An Integrative Pathway-based Clinical-genomic Model for Cancer Survival Prediction.

    Science.gov (United States)

    Chen, Xi; Wang, Lily; Ishwaran, Hemant

    2010-09-01

    Prediction models that use gene expression levels are now being proposed for personalized treatment of cancer, but building accurate models that are easy to interpret remains a challenge. In this paper, we describe an integrative clinical-genomic approach that combines both genomic pathway and clinical information. First, we summarize information from genes in each pathway using Supervised Principal Components (SPCA) to obtain pathway-based genomic predictors. Next, we build a prediction model based on clinical variables and pathway-based genomic predictors using Random Survival Forests (RSF). Our rationale for this two-stage procedure is that the underlying disease process may be influenced by environmental exposure (measured by clinical variables) and perturbations in different pathways (measured by pathway-based genomic variables), as well as their interactions. Using two cancer microarray datasets, we show that the pathway-based clinical-genomic model outperforms gene-based clinical-genomic models, with improved prediction accuracy and interpretability.

  7. 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…

  8. Contextual anomaly detection for cyber-physical security in Smart Grids based on an artificial neural network model

    DEFF Research Database (Denmark)

    Kosek, Anna Magdalena

    2016-01-01

    This paper presents a contextual anomaly detection method and its use in the discovery of malicious voltage control actions in the low voltage distribution grid. The model-based anomaly detection uses an artificial neural network model to identify a distributed energy resource’s behaviour under...

  9. 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.

  10. 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%.

  11. Next-generation genome-scale models for metabolic engineering

    DEFF Research Database (Denmark)

    King, Zachary A.; Lloyd, Colton J.; Feist, Adam M.;

    2015-01-01

    Constraint-based reconstruction and analysis (COBRA) methods have become widely used tools for metabolic engineering in both academic and industrial laboratories. By employing a genome-scale in silico representation of the metabolic network of a host organism, COBRA methods can be used to predict...... optimal genetic modifications that improve the rate and yield of chemical production. A new generation of COBRA models and methods is now being developed. -. encompassing many biological processes and simulation strategies. -. and next-generation models enable new types of predictions. Here, three key...... examples of applying COBRA methods to strain optimization are presented and discussed. Then, an outlook is provided on the next generation of COBRA models and the new types of predictions they will enable for systems metabolic engineering....

  12. Artificial Neural Network Integrated Artificial Endocrine System Model%一种与人工神经网络结合的人工内分泌系统模型

    Institute of Scientific and Technical Information of China (English)

    林广栋; 王煦法; 尤海峰

    2012-01-01

    Endocrine system of human body is highly self-adaptive and self-regulated. In endocrine system, thyroid hormone plays an important role. It enables humans to react violently in emergence, thus increases the chance of survival. In this work, a new artificial endocrine model called TAES (Thyroid hormone inspired Artificial Endocrine System) is proposed. TAES is inspired by the secretion, storage, and release mechanism of thyroid hormone. It can increase system's adaptability towards dynamic environment. In this work, TAES is applied to robot control system. Simulated experiments are carried out and the experimental results prove the TAES's validity.%人体的内分泌系统具有强大的自适应、自调节能力.在人体的内分泌系统中,甲状腺激素能使人类在紧急情况下产生异常迅速的反应,从而增加人类的生存概率.本文借鉴甲状腺激素的分泌,储存和释放机制,提出一种新的与人工神经网络结合的人工内分泌模型,称为TAES(Thyroid hormone inspired Artificial Endocrine System)模型.TAES模型可以增加控制系统对动态环境的适应性.本文将TAES模型应用于机器人避障实验中.实验结果表明,该系统可以增加机器人在紧急情况下的避障能力.

  13. 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

  14. An Early Underwater Artificial Vision Model in Ocean Investigations via Independent Component Analysis

    Science.gov (United States)

    Nian, Rui; Liu, Fang; He, Bo

    2013-01-01

    Underwater vision is one of the dominant senses and has shown great prospects in ocean investigations. In this paper, a hierarchical Independent Component Analysis (ICA) framework has been established to explore and understand the functional roles of the higher order statistical structures towards the visual stimulus in the underwater artificial vision system. The model is inspired by characteristics such as the modality, the redundancy reduction, the sparseness and the independence in the early human vision system, which seems to respectively capture the Gabor-like basis functions, the shape contours or the complicated textures in the multiple layer implementations. The simulation results have shown good performance in the effectiveness and the consistence of the approach proposed for the underwater images collected by autonomous underwater vehicles (AUVs). PMID:23863855

  15. An Early Underwater Artificial Vision Model in Ocean Investigations via Independent Component Analysis

    Directory of Open Access Journals (Sweden)

    Bo He

    2013-07-01

    Full Text Available Underwater vision is one of the dominant senses and has shown great prospects in ocean investigations. In this paper, a hierarchical Independent Component Analysis (ICA framework has been established to explore and understand the functional roles of the higher order statistical structures towards the visual stimulus in the underwater artificial vision system. The model is inspired by characteristics such as the modality, the redundancy reduction, the sparseness and the independence in the early human vision system, which seems to respectively capture the Gabor-like basis functions, the shape contours or the complicated textures in the multiple layer implementations. The simulation results have shown good performance in the effectiveness and the consistence of the approach proposed for the underwater images collected by autonomous underwater vehicles (AUVs.

  16. 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.

  17. 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.

  18. 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

  19. 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.

  20. 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

  1. 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.

  2. An artificial muscle model unit based on inorganic nanosheet sliding by photochemical reaction.

    Science.gov (United States)

    Nabetani, Yu; Takamura, Hazuki; Hayasaka, Yuika; Sasamoto, Shin; Tanamura, Yoshihiko; Shimada, Tetsuya; Masui, Dai; Takagi, Shinsuke; Tachibana, Hiroshi; Tong, Zhiwei; Inoue, Haruo

    2013-04-21

    From the viewpoint of developing photoresponsive supramolecular systems in microenvironments to exhibit more sophisticated photo-functions even at the macroscopic level, inorganic/organic hybrid compounds based on clay or niobate nanosheets as the microenvironments were prepared, characterized, and examined for their photoreactions. We show here a novel type of artificial muscle model unit having much similarity with that in natural muscle fibrils. Upon photoirradiation, the organic/inorganic hybrid nanosheets reversibly slide horizontally on a giant scale, and the interlayer spaces in the layered hybrid structure shrink and expand vertically. In particular, our layered hybrid molecular system exhibits a macroscopic morphological change on a giant scale (~1500 nm) compared with the molecular size of ~1 nm, based on a reversible sliding mechanism. PMID:23471173

  3. Solar Radiation Measurement Using Raspberry Pi and Its Modelling Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Priya Selvanathan Shanmuga

    2016-01-01

    Full Text Available The advent of solar energy as the best alternative to traditional energy sources has led to an extensive study on the measurement and prediction of solar radiation. Devices such as pyranometer, pyrrheliometer, global UV radiometer are used for the measurement of solar radiation. The solar radiation measuring instruments available at Innovation Center, MIT Manipal were integrated with a Raspberry Pi to allow remote access to the data through the university Local Area Network. The connections of the data loggers and the Raspberry Pi were enclosed in a plastic box to prevent damage from the rainfall and humidity in Manipal. The solar radiation data was used to validate an Artificial Neural Network model which was developed using various meterological data from 2011-2015.

  4. The Causal Meaning of Genomic Predictors and How It Affects Construction and Comparison of Genome-Enabled Selection Models

    Science.gov (United States)

    Valente, Bruno D.; Morota, Gota; Peñagaricano, Francisco; Gianola, Daniel; Weigel, Kent; Rosa, Guilherme J. M.

    2015-01-01

    The term “effect” in additive genetic effect suggests a causal meaning. However, inferences of such quantities for selection purposes are typically viewed and conducted as a prediction task. Predictive ability as tested by cross-validation is currently the most acceptable criterion for comparing models and evaluating new methodologies. Nevertheless, it does not directly indicate if predictors reflect causal effects. Such evaluations would require causal inference methods that are not typical in genomic prediction for selection. This suggests that the usual approach to infer genetic effects contradicts the label of the quantity inferred. Here we investigate if genomic predictors for selection should be treated as standard predictors or if they must reflect a causal effect to be useful, requiring causal inference methods. Conducting the analysis as a prediction or as a causal inference task affects, for example, how covariates of the regression model are chosen, which may heavily affect the magnitude of genomic predictors and therefore selection decisions. We demonstrate that selection requires learning causal genetic effects. However, genomic predictors from some models might capture noncausal signal, providing good predictive ability but poorly representing true genetic effects. Simulated examples are used to show that aiming for predictive ability may lead to poor modeling decisions, while causal inference approaches may guide the construction of regression models that better infer the target genetic effect even when they underperform in cross-validation tests. In conclusion, genomic selection models should be constructed to aim primarily for identifiability of causal genetic effects, not for predictive ability. PMID:25908318

  5. Application of serum protein fingerprinting coupled with artificial neural network model in diagnosis of hepatocellular carcinoma

    Institute of Scientific and Technical Information of China (English)

    WANG Jia-xiang; ZHANG Bo; YU Jie-kai; LIU Jian; YANG Mei-qin; ZHENG Shu

    2005-01-01

    Background Hepatocellular carcinoma tends to present at a late clinical stage with poor prognosis.Therefore, it is urgent to explore and develop a simple, rapid diagnostic method, which has high sensitivity and specificity for hepatocellular carcinoma at an early stage.In this study, the serum proteins in patients with hepatocellular carcinoma or liver cirrhosis and in normal controls were analysed.Surface enhanced laser desorption/ionization time-of-flight mass (SELDI-TOF-MS) spectrometry was used to fingerprint serum protein using the protein chip technique and explore the value of the fingerprint, coupled with artificial neural network, to diagnose hepatocellular carcinoma.Methods Of the 106 serum samples obtained, 52 were from patients with hepatocellular carcinoma, 22 from patients with liver cirrhosis and 32 from healthy volunteers.The samples were randomly assigned into a training group (n=70, 35 patients with hepatocellular carcinoma, 14 with liver cirrhosis, and 21 normal controls) and a testing group (n=36, 17 patients with hepatocellular carcinoma, 8 with liver cirrhosis, and 11 normal controls).An artificial neural network was trained on data from 70 individuals in the training group to develop an artificial neural network diagnostic model and this model was tested.The 36 sera in the testing group were analysed with blind prediction by using the same flowchart and procedure of data collection.The 36 serum protein spectra were clustered with the preset clustering method and the same mass/charge (M/Z) peak values as those in the training group.Matrix transfer was performed after data were output.Then the data were input into the previously built artificial neural network model to get the prediction value.The M/Z peaks of the samples with more than 2000 M/Z were normalized with biomarker wizard of ProteinChip Software version 3.1 for noise filtering.The first threshold for noise filtering was set at 5, and the second was set at 2.The 10% was the minimum

  6. 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.

  7. Fast radiative transfer of dust reprocessing in semi-analytic models with artificial neural networks

    CERN Document Server

    Silva, Laura; Granato, Gian Luigi

    2012-01-01

    A serious concern for semi-analytical galaxy formation models, aiming to simulate multi-wavelength surveys and to thoroughly explore the model parameter space, is the extremely time consuming numerical solution of the radiative transfer of stellar radiation through dusty media. To overcome this problem, we have implemented an artificial neural network algorithm in the radiative transfer code GRASIL, in order to significantly speed up the computation of the infrared SED. The ANN we have implemented is of general use, in that its input neurons are defined as those quantities effectively determining the shape of the IR SED. Therefore, the training of the ANN can be performed with any model and then applied to other models. We made a blind test to check the algorithm, by applying a net trained with a standard chemical evolution model (i.e. CHE_EVO) to a mock catalogue extracted from the SAM MORGANA, and compared galaxy counts and evolution of the luminosity functions in several near-IR to sub-mm bands, and also t...

  8. 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.

  9. 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

  10. Modeling of methane oxidation in landfill cover soil using an artificial neural network.

    Science.gov (United States)

    Abushammala, Mohammed F M; Basri, Noor Ezlin Ahmad; Elfithri, Rahmah; Younes, Mohammad K; Irwan, Dani

    2014-02-01

    Knowing the fraction of methane (CH4) oxidized in landfill cover soils is an important step in estimating the total CH4 emissions from any landfill. Predicting CH4 oxidation in landfill cover soils is a difficult task because it is controlled by a number of biological and environmental factors. This study proposes an artificial neural network (ANN) approach using feedforward backpropagation to predict CH4 oxidation in landfill cover soil in relation to air temperature, soil moisture content, oxygen (O2) concentration at a depth of 10 cm in cover soil, and CH4 concentration at the bottom of cover soil. The optimum ANN model giving the lowest mean square error (MSE) was configured from three layers, with 12 and 9 neurons at the first and the second hidden layers, respectively, log-sigmoid (logsig) transfer function at the hidden and output layers, and the Levenberg-Marquardt training algorithm. This study revealed that the ANN oxidation model can predict CH4 oxidation with a MSE of 0.0082, a coefficient of determination (R2) between the measured and predicted outputs of up to 0.937, and a model efficiency (E) of 0.8978. To conclude, further developments of the proposed ANN model are required to generalize and apply the model to other landfills with different cover soil properties.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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...

  16. 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.

  17. 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...

  18. 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...

  19. The influence of an antitumor lipid - erucylphosphocholine - on artificial lipid raft system modeled as Langmuir monolayer.

    Science.gov (United States)

    Wnętrzak, Anita; Łątka, Kazimierz; Makyła-Juzak, Katarzyna; Zemla, Joanna; Dynarowicz-Łątka, Patrycja

    2015-01-01

    Outer layer of cellular membrane contains ordered domains enriched in cholesterol and sphingolipids, called 'lipid rafts', which play various biological roles, i.e., are involved in the induction of cell death by apoptosis. Recent studies have shown that these domains may constitute binding sites for selected drugs. For example alkylphosphocholines (APCs), which are new-generation antitumor agents characterized by high selectivity and broad spectrum of activity, are known to have their molecular targets located at cellular membrane and their selective accumulation in tumor cells has been hypothesized to be linked with the alternation of biophysical properties of lipid rafts. To get a deeper insight into this issue, interactions between representative APC: erucylphosphocholine, and artificial lipid raft system, modeled as Langmuir monolayer (composed of cholesterol and sphingomyelin mixed in 1:2 proportion) were investigated. The Langmuir monolayer experiments, based on recording surface pressure-area isotherms, were complemented with Brewster angle microscopy results, which enabled direct visualization of the monolayers structure. In addition, the investigated monolayers were transferred onto solid supports and studied with AFM. The interactions between model raft system and erucylphosphocholine were analyzed qualitatively (with mean molecular area values) as well as quantitatively (with ΔG(exc) function). The obtained results indicate that erucylphosphocholine introduced to raft-mimicking model membrane causes fluidizing effect and weakens the interactions between cholesterol and sphingomyelin, which results in phase separation at high surface pressures. This leads to the redistribution of cholesterol molecules in model raft, which confirms the results observed in biological studies.

  20. 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.

  1. 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.

  2. Artificial neural network modeling for fission gas release in LWR UO 2 fuel under RIA conditions

    Science.gov (United States)

    Koo, Yang-Hyun; Oh, Jae-Yong; Lee, Byung-Ho; Tahk, Young-Wook; Song, Kun-Woo

    2010-10-01

    A fission gas release (FGR) model was developed by using an artificial neural network method to predict fission gas release in UO 2 fuel under reactivity initiated accident (RIA) conditions. Based on the test data obtained in the CABRI test reactor and nuclear safety research reactor, the model takes into account the effect of the five parameters: pellet average burnup, peak fuel enthalpy, the ratio of peak fuel enthalpy to pulse width, fission gas release during base-irradiation, and grain size of a fuel pellet. The parametric study of the model, producing a physically reasonable trend of FGR for each parameter, shows that the pellet average burnup and the ratio of peak fuel enthalpy to pulse width are two of the most important parameters. Depending on the combination of input values for the five parameters, the application of the model to a fuel rod under typical RIA conditions of light water reactor produces 1.7-14.0% of FGR for the pellet average burnup ranging from 20 to 70 MW d/kg U.

  3. 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.

  4. 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.

  5. 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

  6. 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.

  7. Modelling of an ICS solar water heater using artificial neural networks and TRNSYS

    Energy Technology Data Exchange (ETDEWEB)

    Souliotis, M.; Tripanagnostopoulos, Y. [Physics Department, University of Patras, Patras 26504, ACHAIA (Greece); Kalogirou, S. [Department of Mechanical Engineering and Materials Science and Engineering, Cyprus University of Technology, P.O. Box 50329, Limassol 3603 (Cyprus)

    2009-05-15

    A study, in which a suitable artificial neural network (ANN) and TRNSYS are combined in order to predict the performance of an Integrated Collector Storage (ICS) prototype, is presented. Experimental data that have been collected from outdoor tests of an ICS solar water heater with cylindrical water storage tank inside a CPC reflector trough were used to train the ANN. The ANN is then used through the Excel interface (Type 62) in TRNSYS to model the annual performance of the system by running the model with the values of a typical meteorological year for Athens, Greece. In this way the specific capabilities of both approaches are combined, i.e., use of the radiation processing and modelling power of TRNSYS together with the 'black box' modelling approach of ANNs. The details of the calculation steps of both methods that aim to perform an accurate prediction of the system performance are presented and it is shown that this new method can be used effectively for such predictions. (author)

  8. 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

  9. Modeling the binding affinity of structurally diverse industrial chemicals to carbon using the artificial intelligence approaches.

    Science.gov (United States)

    Gupta, Shikha; Basant, Nikita; Rai, Premanjali; Singh, Kunwar P

    2015-11-01

    Binding affinity of chemical to carbon is an important characteristic as it finds vast industrial applications. Experimental determination of the adsorption capacity of diverse chemicals onto carbon is both time and resource intensive, and development of computational approaches has widely been advocated. In this study, artificial intelligence (AI)-based ten different qualitative and quantitative structure-property relationship (QSPR) models (MLPN, RBFN, PNN/GRNN, CCN, SVM, GEP, GMDH, SDT, DTF, DTB) were established for the prediction of the adsorption capacity of structurally diverse chemicals to activated carbon following the OECD guidelines. Structural diversity of the chemicals and nonlinear dependence in the data were evaluated using the Tanimoto similarity index and Brock-Dechert-Scheinkman statistics. The generalization and prediction abilities of the constructed models were established through rigorous internal and external validation procedures performed employing a wide series of statistical checks. In complete dataset, the qualitative models rendered classification accuracies between 97.04 and 99.93%, while the quantitative models yielded correlation (R(2)) values of 0.877-0.977 between the measured and the predicted endpoint values. The quantitative prediction accuracies for the higher molecular weight (MW) compounds (class 4) were relatively better than those for the low MW compounds. Both in the qualitative and quantitative models, the Polarizability was the most influential descriptor. Structural alerts responsible for the extreme adsorption behavior of the compounds were identified. Higher number of carbon and presence of higher halogens in a molecule rendered higher binding affinity. Proposed QSPR models performed well and outperformed the previous reports. A relatively better performance of the ensemble learning models (DTF, DTB) may be attributed to the strengths of the bagging and boosting algorithms which enhance the predictive accuracies. The

  10. 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.

  11. Biological and artificial cognition: what can we learn about mechanisms by modelling physical cognition problems using artificial intelligence planning techniques?

    Science.gov (United States)

    Chappell, Jackie; Hawes, Nick

    2012-01-01

    Do we fully understand the structure of the problems we present to our subjects in experiments on animal cognition, and the information required to solve them? While we currently have a good understanding of the behavioural and neurobiological mechanisms underlying associative learning processes, we understand much less about the mechanisms underlying more complex forms of cognition in animals. In this study, we present a proposal for a new way of thinking about animal cognition experiments. We describe a process in which a physical cognition task domain can be decomposed into its component parts, and models constructed to represent both the causal events of the domain and the information available to the agent. We then implement a simple set of models, using the planning language MAPL within the MAPSIM simulation environment, and applying it to a puzzle tube task previously presented to orangutans. We discuss the results of the models and compare them with the results from the experiments with orangutans, describing the advantages of this approach, and the ways in which it could be extended. PMID:22927571

  12. 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

  13. Soil Heat Flux Modeling Using Artificial Neural Networks and Multispectral Airborne Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    Dario J. Canelón

    2011-08-01

    Full Text Available The estimation of spatially distributed crop water use or evapotranspiration (ET can be achieved using the energy balance for land surface algorithm and multispectral imagery obtained from remote sensing sensors mounted on air- or space-borne platforms. In the energy balance model, net radiation (Rn is well estimated using remote sensing; however, the estimation of soil heat flux (G has had mixed results. Therefore, there is the need to improve the model to estimate soil heat flux and thus improve the efficiency of the energy balance method based on remote sensing inputs. In this study, modeling of airborne remote sensing-based soil heat flux was performed using Artificial Neural Networks (ANN. Soil heat flux was modeled using selected measured data from soybean and corn crop covers in Central Iowa, U.S.A. where measured values were obtained with soil heat flux plate sensors. Results in the modeling of G indicated that the combination Rn with air temperature (Tair and crop height (hc better reproduced measured values when three independent variables were considered. The combination of Rn with leaf area index (LAI from remote sensing, and Rn with surface aerodynamic resistance (rah yielded relative larger overall correlation coefficient values when two independent variables were included using ANN. In addition, air temperature (Tair may be a key variable in the modeling of G as suggested by the ANN application (r of 0.83. Therefore, it is suggested that Rn, LAI, rah and hc and potentially Tair be considered in future modeling studies of G.

  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. 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.

  16. 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.

  17. 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.

  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. 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...

  20. 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…

  1. 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…

  2. 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,…

  3. 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.

  4. 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.

  5. 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.

  6. 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...

  7. 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

  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.

  9. Comparison of Kinetic-based and Artificial Neural Network Modeling Methods for a Pilot Scale Vacuum Gas Oil Hydrocracking Reactor

    Directory of Open Access Journals (Sweden)

    Sepehr Sadighi

    2013-12-01

    Full Text Available An artificial neural network (ANN and kinetic-based models for a pilot scale vacuum gas oil (VGO hydrocracking plant are presented in this paper. Reported experimental data in the literature were used to develop, train, and check these models. The proposed models are capable of predicting the yield of all main hydrocracking products including dry gas, light naphtha, heavy naphtha, kerosene, diesel, and unconverted VGO (residue. Results showed that kinetic-based and artificial neural models have specific capabilities to predict yield of hydrocracking products. The former is able to accurately predict the yield of lighter products, i.e. light naphtha, heavy naphtha and kerosene. However, ANN model is capable of predicting yields of diesel and residue with higher precision. The comparison shows that the ANN model is superior to the kinetic-base models.  © 2013 BCREC UNDIP. All rights reservedReceived: 9th April 2013; Revised: 13rd August 2013; Accepted: 18th August 2013[How to Cite: Sadighi, S., Zahedi, G.R. (2013. Comparison of Kinetic-based and Artificial Neural Network Modeling Methods for a Pilot Scale Vacuum Gas Oil Hydrocracking Reactor. Bulletin of Chemical Reaction Engineering & Catalysis, 8 (2: 125-136. (doi:10.9767/bcrec.8.2.4722.125-136][Permalink/DOI: http://dx.doi.org/10.9767/bcrec.8.2.4722.125-136

  10. Leak Detection Modeling and Simulation for Oil Pipeline with Artificial Intelligence Method

    Directory of Open Access Journals (Sweden)

    Pudjo Sukarno

    2007-05-01

    Full Text Available Leak detection is always interesting research topic, where leak location and leak rate are two pipeline leaking parameters that should be determined accurately to overcome pipe leaking problems. In this research those two parameters are investigated by developing transmission pipeline model and the leak detection model which is developed using Artificial Neural Network. The mathematical approach needs actual leak data to train the leak detection model, however such data could not be obtained from oil fields. Therefore, for training purposes hypothetical data are developed using the transmission pipeline model, by applying various physical configuration of pipeline and applying oil properties correlations to estimate the value of oil density and viscosity. The various leak locations and leak rates are also represented in this model. The prediction of those two leak parameters will be completed until the total error is less than certain value of tolerance, or until iterations level is reached. To recognize the pattern, forward procedure is conducted. The application of this approach produces conclusion that for certain pipeline network configuration, the higher number of iterations will produce accurate result. The number of iterations depend on the leakage rate, the smaller leakage rate, the higher number of iterations are required. The accuracy of this approach is clearly determined by the quality of training data. Therefore, in the preparation of training data the results of pressure drop calculations should be validated by the real measurement of pressure drop along the pipeline. For the accuracy purposes, there are possibility to change the pressure drop and fluid properties correlations, to get the better results. The results of this research are expected to give real contribution for giving an early detection of oil-spill in oil fields.

  11. 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.

  12. 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)

  13. 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.

  14. 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.

  15. 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.

  16. 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)

  17. 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.

  18. Modeling of CVI process in fabrication of carbon/carbon composites by an artificial neural network

    Institute of Scientific and Technical Information of China (English)

    李爱军; 李贺军; 李克智; 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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

  3. 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

  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. 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.

  6. 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...

  7. 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

  8. Genomic comparison of closely related Giant Viruses supports an accordion-like model of evolution.

    Directory of Open Access Journals (Sweden)

    Jonathan eFilée

    2015-06-01

    Full Text Available Genome gigantism occurs so far in Phycodnaviridae and Mimiviridae (order Megavirales. Origin and evolution of these Giant Viruses (GVs remain open questions. Interestingly, availability of a collection of closely related GV genomes enabling genomic comparisons offer the opportunity to better understand the different evolutionary forces acting on these genomes. Whole genome alignment for 5 groups of viruses belonging to the Mimiviridae and Phycodnaviridae families show that there is no trend of genome expansion or general tendency of genome contraction. Instead, GV genomes accumulated genomic mutations over the time with gene gains compensating the different losses. In addition, each lineage displays specific patterns of genome evolution. Mimiviridae (megaviruses and mimiviruses and Chlorella Phycodnaviruses evolved mainly by duplications and losses of genes belonging to large paralogous families (including movements of diverse mobiles genetic elements, whereas Micromonas and Ostreococcus Phycodnaviruses derive most of their genetic novelties thought lateral gene transfers. Taken together, these data support an accordion-like model of evolution in which GV genomes have undergone successive steps of gene gain and gene loss, accrediting the hypothesis that genome gigantism appears early, before the diversification of the different GV lineages.

  9. 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.

  10. Experimental reconstruction of cervical esophageal defect with artificial esophagus made of polyurethane in a dog model.

    Science.gov (United States)

    Jiang, H; Cui, Y; Ma, K; Gong, M; Chang, D; Wang, T

    2016-01-01

    The defect of esophagus after surgical excision in patients is usually replaced by autologous stomach, jejunum, or colon. The operation brings severe trauma and complications. Using artificial esophagus to replace the defect in situ can reduce the operative trauma, simplify the operative procedures, and decrease the influence to digestive function. A variety of experiments have been designed for developing a practical artificial esophagus. Nevertheless, a safe and reliable artificial esophagus is not yet available. The objective is to evaluate the possibility of the artificial esophagus made of non-degradable polyurethane materials being used in reconstruction of the segmental defect of cervical esophagus in beagles, observe the regeneration of esophageal tissue, and gather experience for future study. The cervical esophageal defects in 13 beagles were designed to 2-cm long and were constructed by the artificial esophagus made of non-degradable polyurethane materials. Nutrition supports were given after the operation. The operative mortality, anastomotic leakage, migration of artificial esophagus, and dysphagia were followed up. The regeneration of the esophageal tissues was evaluated by histopathology and immunohistochemical labeled streptavidin-biotin method. The surgical procedures were successfully completed in all beagles, and 12-month follow-ups were done. Only one beagle died of severe infection, and all others survived until being killed. The anastomotic leakage occurred in nine beagles, most of them (8/9) were cured after supportive therapy. The migration of artificial esophagus occurred in all 12 surviving beagles, and one artificial esophagus stayed in situ after migration. All 12 surviving beagles showed dysphagia with taking only fluid or soft food. No beagle died of malnutrition. The neo-esophagus was composed of granulation tissue, and the inner surface was covered by epithelium in 2-3 months completely. But the inner surface of neo-esophagus with

  11. 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...

  12. Effect of Head Position on Cerebrospinal Fluid Pressure in Cats: Comparison with Artificial Model

    Science.gov (United States)

    Klarica, Marijan; Radoš, Milan; Draganić, Pero; Erceg, Gorislav; Orešković, Darko; Maraković, Jurica; Bulat, Marin

    2006-01-01

    Aim To demonstrate that changes in the cerebrospinal fluid (CSF) pressure in the cranial cavity and spinal canal after head elevation from the horizontal level occur primarily due to the biophysical characteristics of the CSF system, ie, distensibility of the spinal dura. Methods Experiments in vivo were performed on cats and a new artificial model of the CSF system with dimensions similar to the CSF system in cats, consisting of non-distensible cranial and distensible spinal part. Measurements of the CSF pressure in the cranial and spinal spaces were performed in chloralose-anesthetized cats (n = 10) in the horizontal position on the base of a stereotaxic apparatus (reference zero point) and in the position in which the head was elevated to 5 cm and 10 cm above that horizontal position. Changes in the CSF pressure in the cranial and spinal part of the model were measured in the cranial part positioned in the same way as the head in cats (n = 5). Results When the cat was in the horizontal position, the values of the CSF pressure in the cranial (11.9 ± 1.1 cm H2O) and spinal (11.8 ± 0.6 cm H2O) space were not significantly different. When the head was elevated 5 cm or 10 cm above the reference zero point, the CSF pressure in the cranium significantly decreased to 7.7 ± 0.6 cm H2O and 4.7 ± 0.7 cm H2O, respectively, while the CSF pressure in the spinal space significantly increased to 13.8 ± 0.7 cm H2O and 18.5 ± 1.6 cm H2O, respectively (P<0.001 for both). When the artificial CSF model was positioned in the horizontal level and its cranial part elevated by 5 cm and 10 cm, the changes in the pressure were the same as those in the cats when in the same hydrostatic position. Conclusions The new model of the CSF system used in our study faithfully mimicked the changes in the CSF pressure in cats during head elevation in relation to the body. Changes in the pressure in the model were not accompanied by the changes in fluid volume in

  13. 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…

  14. 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.

  15. 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

  16. 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

  17. Modeling of a corrugated dielectric elastomer actuator for artificial muscle applications

    Science.gov (United States)

    Kadooka, Kevin; Taya, Minoru; Naito, Keishi; Saito, Makoto

    2015-04-01

    Dielectric elastomer actuators have many advantages, including light weight, simplicity, high energy density, and silent operation. These features make them suitable to replace conventional actuators and transducers, especially in artificial muscle applications where large contractile strains are necessary for lifelike motions. This paper will introduce the concept of a corrugated dielectric elastomer actuator (DEA), which consists of dielectric elastomer (DE) laminated to a thin elastic layer to induce bending motion at each of the corrugations, resulting in large axial deformation. The location of the DE and elastic layers can be configured to provide tensile or compressive axial strain. Such corrugated DE actuators are also highly scalable: linking multiple actuators in series results in greater deformation, whereas multiple actuators in parallel results in larger force output. Analytical closed-form solutions based on linear elasticity were derived for the displacement and force output of curved unimorph and corrugated DEA, both consisting of an arbitrary number of lamina. A total strain energy analysis and Castigiliano's theorem were used to predict the nonlinear force-displacement behavior of the corrugated actuator. Curved unimorph and corrugated DEA were fabricated using VHB F9469PC as the DE material. Displacement of the actuators observed during testing agreed well with the modeling results. Large contractile strain (25.5%) was achieved by the corrugated DEA. Future work includes investigating higher performance DE materials such as plasticized PVDF terpolymers, processed by thin film deposition methods.

  18. 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.

  19. 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.

  20. 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

  1. 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

  2. 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

  3. 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...

  4. 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.

  5. 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

  6. 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...

  7. Rapid genome reshaping by multiple-gene loss after whole-genome duplication in teleost fish suggested by mathematical modeling.

    Science.gov (United States)

    Inoue, Jun; Sato, Yukuto; Sinclair, Robert; Tsukamoto, Katsumi; Nishida, Mutsumi

    2015-12-01

    Whole-genome duplication (WGD) is believed to be a significant source of major evolutionary innovation. Redundant genes resulting from WGD are thought to be lost or acquire new functions. However, the rates of gene loss and thus temporal process of genome reshaping after WGD remain unclear. The WGD shared by all teleost fish, one-half of all jawed vertebrates, was more recent than the two ancient WGDs that occurred before the origin of jawed vertebrates, and thus lends itself to analysis of gene loss and genome reshaping. Using a newly developed orthology identification pipeline, we inferred the post-teleost-specific WGD evolutionary histories of 6,892 protein-coding genes from nine phylogenetically representative teleost genomes on a time-calibrated tree. We found that rapid gene loss did occur in the first 60 My, with a loss of more than 70-80% of duplicated genes, and produced similar genomic gene arrangements within teleosts in that relatively short time. Mathematical modeling suggests that rapid gene loss occurred mainly by events involving simultaneous loss of multiple genes. We found that the subsequent 250 My were characterized by slow and steady loss of individual genes. Our pipeline also identified about 1,100 shared single-copy genes that are inferred to have become singletons before the divergence of clupeocephalan teleosts. Therefore, our comparative genome analysis suggests that rapid gene loss just after the WGD reshaped teleost genomes before the major divergence, and provides a useful set of marker genes for future phylogenetic analysis.

  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. 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 ...

  10. Reference genome sequence of the model plant Setaria

    Energy Technology Data Exchange (ETDEWEB)

    Bennetzen, Jeffrey L [ORNL; Yang, Xiaohan [ORNL; Ye, Chuyu [ORNL; Tuskan, Gerald A [ORNL

    2012-01-01

    We generated a high-quality reference genome sequence for foxtail millet (Setaria italica). The {approx}400-Mb assembly covers {approx}80% of the genome and >95% of the gene space. The assembly was anchored to a 992-locus genetic map and was annotated by comparison with >1.3 million expressed sequence tag reads. We produced more than 580 million RNA-Seq reads to facilitate expression analyses. We also sequenced Setaria viridis, the ancestral wild relative of S. italica, and identified regions of differential single-nucleotide polymorphism density, distribution of transposable elements, small RNA content, chromosomal rearrangement and segregation distortion. The genus Setaria includes natural and cultivated species that demonstrate a wide capacity for adaptation. The genetic basis of this adaptation was investigated by comparing five sequenced grass genomes. We also used the diploid Setaria genome to evaluate the ongoing genome assembly of a related polyploid, switchgrass (Panicum virgatum).

  11. 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.

  12. 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.

  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 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)

  15. 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.

  16. Artificial Neural Network Models for Long Lead Streamflow Forecasts using Climate Information

    Science.gov (United States)

    Kumar, J.; Devineni, N.

    2007-12-01

    Information on season ahead stream flow forecasts is very beneficial for the operation and management of water supply systems. Daily streamflow conditions at any particular reservoir primarily depend on atmospheric and land surface conditions including the soil moisture and snow pack. On the other hand recent studies suggest that developing long lead streamflow forecasts (3 months ahead) typically depends on exogenous climatic conditions particularly Sea Surface Temperature conditions (SST) in the tropical oceans. Examples of some oceanic variables are El Nino Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). Identification of such conditions that influence the moisture transport into a given basin poses many challenges given the nonlinear dependency between the predictors (SST) and predictand (stream flows). In this study, we apply both linear and nonlinear dependency measures to identify the predictors that influence the winter flows into the Neuse basin. The predictor identification approach here adopted uses simple correlation coefficients to spearman rank correlation measures for detecting nonlinear dependency. All these dependency measures are employed with a lag 3 time series of the high flow season (January - February - March) using 75 years (1928-2002) of stream flows recorded in to the Falls Lake, Neuse River Basin. Developing streamflow forecasts contingent on these exogenous predictors will play an important role towards improved water supply planning and management. Recently, the soft computing techniques, such as artificial neural networks (ANNs) have provided an alternative method to solve complex problems efficiently. ANNs are data driven models which trains on the examples given to it. The ANNs functions as universal approximators and are non linear in nature. This paper presents a study aiming towards using climatic predictors for 3 month lead time streamflow forecast. ANN models representing the physical process of the system are

  17. 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...

  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. 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.

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

    Directory of Open Access Journals (Sweden)

    Tamer Khatib

    2015-01-01

    Full Text Available 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 the proposed model has better prediction accuracy compared to some empirical and statistical models. Two error statistics are used in this research to evaluate the proposed model, namely, mean absolute percentage error and root mean square error. These values for the proposed model are 11.8% and −3.1%, respectively. Finally, the proposed model shows better ability in overcoming the sophistic nature of the solar radiation data.

  2. 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.

  3. Using Genome-scale Models to Predict Biological Capabilities

    DEFF Research Database (Denmark)

    O’Brien, Edward J.; Monk, Jonathan M.; Palsson, Bernhard O.

    2015-01-01

    Constraint-based reconstruction and analysis (COBRA) methods at the genome scale have been under development since the first whole-genome sequences appeared in the mid-1990s. A few years ago, this approach began to demonstrate the ability to predict a range of cellular functions, including cellular...... growth capabilities on various substrates and the effect of gene knockouts at the genome scale. Thus, much interest has developed in understanding and applying these methods to areas such as metabolic engineering, antibiotic design, and organismal and enzyme evolution. This Primer will get you started....

  4. Triplet excitons in natural photosynthetic and artificial light harvesting systems: Measurement and modeling

    Science.gov (United States)

    Hartzler, Daniel Allen

    artificial (B)Chl and porphyrin dimers by experimental and computational methods. This data set obtained allowed for the development of an empirical model for prediction of the triplet state site energy from a given singlet site energy and for development and calibration of a T-T coupling model. Use of these models shows that triplet state lowering by pigment-protein interaction provides photoprotection to the FMO complex, while triplet state lowering by triplet exciton formation is insufficient to provide protection to the chlorosome antenna. Additionally, the T-T coupling model shows that in dimers and other aggregates, the coupling is highly sensitive to relative monomer orientation and position, contrary to what was previously assumed. The simple exponential models used to estimate T-T couplings miss this orientation sensitivity, thus in systems with significant contact between adjacent monomers a more accurate approach is required.

  5. Development and Evolution of Neural Networks in an Artificial Chemistry

    CERN Document Server

    Astor, J C; Astor, Jens C.; Adami, Christoph

    1998-01-01

    We present a model of decentralized growth for Artificial Neural Networks (ANNs) inspired by the development and the physiology of real nervous systems. In this model, each individual artificial neuron is an autonomous unit whose behavior is determined only by the genetic information it harbors and local concentrations of substrates modeled by a simple artificial chemistry. Gene expression is manifested as axon and dendrite growth, cell division and differentiation, substrate production and cell stimulation. We demonstrate the model's power with a hand-written genome that leads to the growth of a simple network which performs classical conditioning. To evolve more complex structures, we implemented a platform-independent, asynchronous, distributed Genetic Algorithm (GA) that allows users to participate in evolutionary experiments via the World Wide Web.

  6. Modeling root length density of field grown potatoes under different irrigation strategies and soil textures using artificial neural networks

    DEFF Research Database (Denmark)

    Ahmadi, Seyed Hamid; Sepaskhah, Ali Reza; Andersen, Mathias Neumann;

    2014-01-01

    Root length density (RLD) is a highly wanted parameter for use in crop growth modeling but difficult to measure under field conditions. Therefore, artificial neural networks (ANNs) were implemented to predict the RLD of field grown potatoes that were subject to three irrigation strategies and three......% of the data) and testing (20% of the data). A feed forward three-layer perceptron network and the sigmoid, hyperbolic tangent, and linear transfer functions were used for the ANN modeling. The RLDs (target variable) in different soil layers were predicted by nine ANNs representing combinations (models...

  7. 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.

  8. Adaptive Artificial Intelligence Based Model Base Controller: Applied to Surgical Endoscopy Telemanipulator

    Directory of Open Access Journals (Sweden)

    Farzin Piltan

    2013-08-01

    Full Text Available This research involved developing a surgical robot assistant using an articulated PUMA robot running on a linear or nonlinear axis. The research concentrated on studying the artificial intelligence based switching computed torque controller to localization of an endoscopic tool. Results show that the switching artificial nonlinear control algorithm is capable to design a stable controller. For this system, error was used as the performance metric. Positioning of the endoscopic manipulator relative to the world coordinate frame was possible to within 0.05 inch. Error in maintaining a constant point in space is evident during repositioning however this was caused by limitations in the robot arm.

  9. A new look on the two-dimensional Ising model: thermal artificial spins

    Science.gov (United States)

    Arnalds, Unnar B.; Chico, Jonathan; Stopfel, Henry; Kapaklis, Vassilios; Bärenbold, Oliver; Verschuuren, Marc A.; Wolff, Ulrike; Neu, Volker; Bergman, Anders; Hjörvarsson, Björgvin

    2016-02-01

    We present a direct experimental investigation of the thermal ordering in an artificial analogue of an asymmetric two-dimensional Ising system composed of a rectangular array of nano-fabricated magnetostatically interacting islands. During fabrication and below a critical thickness of the magnetic material the islands are thermally fluctuating and thus the system is able to explore its phase space. Above the critical thickness the islands freeze-in resulting in an arrested thermalized state for the array. Determining the magnetic state we demonstrate a genuine artificial two-dimensional Ising system which can be analyzed in the context of nearest neighbor interactions.

  10. Mapping condition-dependent regulation of metabolism in yeast through genome-scale modeling

    DEFF Research Database (Denmark)

    Österlund, Tobias; Nookaew, Intawat; Bordel, Sergio;

    2013-01-01

    ABSTRACT: BACKGROUND: The genome-scale metabolic model of Saccharomyces cerevisiae, first presented in 2003, was the first genome-scale network reconstruction for a eukaryotic organism. Since then continuous efforts have been made in order to improve and expand the yeast metabolic network. RESULT...

  11. Novel model of a AlGaN/GaN high electron mobility transistor based on an artificial neural network

    Institute of Scientific and Technical Information of China (English)

    Cheng Zhi-Qun; Hu Sha; Liu Jun; Zhang Qi-Jun

    2011-01-01

    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.

  12. 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...

  13. 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 ...

  14. DMU - A Package for Analyzing Multivariate Mixed Models in quantitative Genetics and Genomics

    DEFF Research Database (Denmark)

    Madsen, Per; Jensen, Just; Labouriau, Rodrigo;

    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...... studies, Survival models and double hierarchical generalized linear mixed models...

  15. 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.

  16. 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.

  17. 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.

  18. From DNA to FBA: How to Build Your Own Genome-Scale Metabolic Model.

    Science.gov (United States)

    Cuevas, Daniel A; Edirisinghe, Janaka; Henry, Chris S; Overbeek, Ross; O'Connell, Taylor G; Edwards, Robert A

    2016-01-01

    Microbiological studies are increasingly relying on in silico methods to perform exploration and rapid analysis of genomic data, and functional genomics studies are supplemented by the new perspectives that genome-scale metabolic models offer. A mathematical model consisting of a microbe's entire metabolic map can be rapidly determined from whole-genome sequencing and annotating the genomic material encoded in its DNA. Flux-balance analysis (FBA), a linear programming technique that uses metabolic models to predict the phenotypic responses imposed by environmental elements and factors, is the leading method to simulate and manipulate cellular growth in silico. However, the process of creating an accurate model to use in FBA consists of a series of steps involving a multitude of connections between bioinformatics databases, enzyme resources, and metabolic pathways. We present the methodology and procedure to obtain a metabolic model using PyFBA, an extensible Python-based open-source software package aimed to provide a platform where functional annotations are used to build metabolic models (http://linsalrob.github.io/PyFBA). Backed by the Model SEED biochemistry database, PyFBA contains methods to reconstruct a microbe's metabolic map, run FBA upon different media conditions, and gap-fill its metabolism. The extensibility of PyFBA facilitates novel techniques in creating accurate genome-scale metabolic models. PMID:27379044

  19. 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.

  20. 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.

  1. 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

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. Genomic Predictions of Obesity Related Phenotypes in a Pig model using GBLUP and Bayesian Approaches

    DEFF Research Database (Denmark)

    Pant, Sameer Dinkar; Do, Duy Ngoc; Janss, Luc;

    to partition genomic variances attributable to different SNP groups based on their biological and functional role via systems genetics / biology approaches. We apply different methods to group SNPs: (i) functional relevance of SNPs for obesity based on data mining, (ii) groups based on positions in the genome......, and significance based on previous genome-wide association study in the same data set. Preliminary results from our studies in production pigs indicate that BPL models have higher accuracy but more bias than GBLUP method, although using different power parameters has no effect on predictive ability of the models...

  7. Gene finding with a hidden Markov model of genome structure and evolution

    DEFF Research Database (Denmark)

    Pedersen, Jakob Skou; Hein, Jotun

    2003-01-01

    annotation. The modelling of evolution by the existing comparative gene finders leaves room for improvement. Results: A probabilistic model of both genome structure and evolution is designed. This type of model is called an Evolutionary Hidden Markov Model (EHMM), being composed of an HMM and a set of region...

  8. 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...

  9. Integration of Volterra model with artificial neural networks for rainfall-runoff simulation in forested catchment of northern Iran

    Science.gov (United States)

    Kashani, Mahsa H.; Ghorbani, Mohammad Ali; Dinpashoh, Yagob; Shahmorad, Sedaghat

    2016-09-01

    Rainfall-runoff simulation is an important task in water resources management. In this study, an integrated Volterra model with artificial neural networks (IVANN) was presented to simulate the rainfall-runoff process. The proposed integrated model includes the semi-distributed forms of the Volterra and ANN models which can explore spatial variation in rainfall-runoff process without requiring physical characteristic parameters of the catchments, while taking advantage of the potential of Volterra and ANNs models in nonlinear mapping. The IVANN model was developed using hourly rainfall and runoff data pertaining to thirteen storms to study short-term responses of a forest catchment in northern Iran; and its performance was compared with that of semi-distributed integrated ANN (IANN) model and lumped Volterra model. The Volterra model was applied as a nonlinear model (second-order Volterra (SOV) model) and solved using the ordinary least square (OLS) method. The models performance were evaluated and compared using five performance criteria namely coefficient of efficiency, root mean square error, error of total volume, relative error of peak discharge and error of time for peak to arrive. Results showed that the IVANN model performs well than the other semi-distributed and lumped models to simulate the rainfall-runoff process. Comparing to the integrated models, the lumped SOV model has lower precision to simulate the rainfall-runoff process.

  10. 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.

  11. 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...

  12. 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 ...

  13. 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...

  14. 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,...

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

    OpenAIRE

    Kathryn Elizabeth Merrick; Kamran eShafi

    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...

  16. 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)

  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. 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.

  19. Genome editing revolutionize the creation of genetically modified pigs for modeling human diseases.

    Science.gov (United States)

    Yao, Jing; Huang, Jiaojiao; Zhao, Jianguo

    2016-09-01

    Pigs have anatomical, physiological and genomic characteristics that make them highly suitable for modeling human diseases. Genetically modified (GM) pig models of human diseases are critical for studying pathogenesis, treatment, and prevention. The emergence of nuclease-mediated genome editing technology has been successfully employed for engineering of the pig genome, which has revolutionize the creation of GM pig models with highly complex pathophysiologies and comorbidities. In this review, we summarize the progress of recently developed genome editing technologies, including zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), and the clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein 9 (Cas9), which enable highly efficient and precise introduction of genome modifications into pigs, and tailored disease models that have been generated in various disciplines via genome editing technology. We also summarize the GM pig models that have been generated by conventional transgenic strategies. Additionally, perspectives regarding the application of GM pigs in biomedical research are discussed.

  20. Characterizing the In-Phase Reflection Bandwidth Theoretical Limit of Artificial Magnetic Conductors With a Transmission Line Model

    Science.gov (United States)

    Xie, Yunsong; Fan, Xin; Chen, Yunpeng; Wilson, Jeefrey D.; Simons, Rainee N.; Xiao, John Q.

    2013-01-01

    We validate through simulation and experiment that artificial magnetic conductors (AMC s) can be well characterized by a transmission line model. The theoretical bandwidth limit of the in-phase reflection can be expressed in terms of the effective RLC parameters from the surface patch and the properties of the substrate. It is found that the existence of effective inductive components will reduce the in-phase reflection bandwidth of the AMC. Furthermore, we propose design strategies to optimize AMC structures with an in-phase reflection bandwidth closer to the theoretical limit.

  1. Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America

    Science.gov (United States)

    Soares dos Santos, T.; Mendes, D.; Rodrigues Torres, R.

    2016-01-01

    Several studies have been devoted to dynamic and statistical downscaling for analysis of both climate variability and climate change. This paper introduces an application of artificial neural networks (ANNs) and multiple linear regression (MLR) by principal components to estimate rainfall in South America. This method is proposed for downscaling monthly precipitation time series over South America for three regions: the Amazon; northeastern Brazil; and the La Plata Basin, which is one of the regions of the planet that will be most affected by the climate change projected for the end of the 21st century. The downscaling models were developed and validated using CMIP5 model output and observed monthly precipitation. We used general circulation model (GCM) experiments for the 20th century (RCP historical; 1970-1999) and two scenarios (RCP 2.6 and 8.5; 2070-2100). The model test results indicate that the ANNs significantly outperform the MLR downscaling of monthly precipitation variability.

  2. Hourly photosynthetically active radiation estimation in Midwestern United States from artificial neural networks and conventional regressions models

    Science.gov (United States)

    Yu, Xiaolei; Guo, Xulin

    2016-08-01

    The relationship between hourly photosynthetically active radiation (PAR) and the global solar radiation ( R s ) was analyzed from data gathered over 3 years at Bondville, IL, and Sioux Falls, SD, Midwestern USA. These data were used to determine temporal variability of the PAR fraction and its dependence on different sky conditions, which were defined by the clearness index. Meanwhile, models based on artificial neural networks (ANNs) were established for predicting hourly PAR. The performance of the proposed models was compared with four existing conventional regression models in terms of the normalized root mean square error (NRMSE), the coefficient of determination ( r 2), the mean percentage error (MPE), and the relative standard error (RSE). From the overall analysis, it shows that the ANN model can predict PAR accurately, especially for overcast sky and clear sky conditions. Meanwhile, the parameters related to water vapor do not improve the prediction result significantly.

  3. Does Artificial Ascites Induce the Heat-Sink Phenomenon during Percutaneous Radiofrequency Ablation of the Hepatic Subcapsular Area?: an in vivo Experimental Study Using a Rabbit Model

    OpenAIRE

    Kim, Young-sun; Rhim, Hyunchul; Choi, Dongil; Lim, Hyo K.

    2009-01-01

    Objective To evaluate the effect of the heat-sink phenomenon induced by artificial ascites on the size of the ablation zone during percutaneous radiofrequency (RF) ablation of the hepatic subcapsular area in an in vivo rabbit model. Materials and Methods A total of 21 percutaneous rabbit liver RF ablations were performed with and without artificial ascites (5% dextrose aqueous solution). The rabbits were divided into three groups: a) control group (C, n = 7); b) room temperature ascites group...

  4. A hidden Markov model approach for determining expression from genomic tiling micro arrays

    DEFF Research Database (Denmark)

    Terkelsen, Kasper Munch; Gardner, P. P.; Arctander, Peter;

    2006-01-01

    HMM, that adaptively models tiling data prior to predicting expression on genomic sequence. A hidden Markov model (HMM) is used to model the distributions of tiling array probe scores in expressed and non-expressed regions. The HMM is trained on sets of probes mapped to regions of annotated expression and non...

  5. MultiMetEval : Comparative and Multi-Objective Analysis of Genome-Scale Metabolic Models

    NARCIS (Netherlands)

    Zakrzewski, Piotr; Medema, Marnix H.; Gevorgyan, Albert; Kierzek, Andrzej M.; Breitling, Rainer; Takano, Eriko; Fong, Stephen S.

    2012-01-01

    Comparative metabolic modelling is emerging as a novel field, supported by the development of reliable and standardized approaches for constructing genome-scale metabolic models in high throughput. New software solutions are needed to allow efficient comparative analysis of multiple models in the co

  6. Lukasiewicz-Topos Models of Neural Networks, Cell Genome and Interactome Nonlinear Dynamic Models

    CERN Document Server

    Baianu, I C

    2004-01-01

    A categorical and Lukasiewicz-Topos framework for Lukasiewicz Algebraic Logic models of nonlinear dynamics in complex functional systems such as neural networks, genomes and cell interactomes is proposed. Lukasiewicz Algebraic Logic models of genetic networks and signaling pathways in cells are formulated in terms of nonlinear dynamic systems with n-state components that allow for the generalization of previous logical models of both genetic activities and neural networks. An algebraic formulation of variable 'next-state functions' is extended to a Lukasiewicz Topos with an n-valued Lukasiewicz Algebraic Logic subobject classifier description that represents non-random and nonlinear network activities as well as their transformations in developmental processes and carcinogenesis.

  7. QTL Analysis and Functional Genomics of Animal Model

    DEFF Research Database (Denmark)

    Farajzadeh, Leila

    In recent years, the use of functional genomics and next-generation sequencing technologies has increased the probability of success in studies of complex properties. The integration of large data sets from association studies, DNA resequencing, gene expression profiles and phenotypic data......, for example, has enabled scientists to examine more complex interactions in connection with studies of properties and diseases. In her PhD project, Leila Farajzadeh integrated different organisational levels in biology, including genotype, phenotype, association studies, transcription profiles and genetic...

  8. A Hybrid Model For Phrase Chunking Employing Artificial Immunity System And Rule Based Methods

    Directory of Open Access Journals (Sweden)

    Bindu.M.S

    2011-11-01

    Full Text Available 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. Question Answering (QA systems, another important application of Artificial Intelligence (AImostly requires retrieval of nouns or noun phrases as answers to the questions raised by the users. Also Chunking is an important preprocessing step in full parsing. Due to high ambiguity of natural language, exact parsing of text may become very complex. This ambiguity may be partially resolved by using chunking as an intermediate step. To the best of our knowledge no known work or tag set is available for phrase chunking in Malayalam. To separate the chunks in a document it must be labeled with parts-ofspeech (POS tags. POS Tagging is a difficult task in Malayalam as it is a complex and compounding language. In this paper we describe the application of artificial immunity system (AIS for chunking which is implemented and obtained an accurate output with 96% precision and 93% recall. This system istested on corpuses collected from reputed news papers and magazines. These corpuses contained documents from five different domains such as sports, health, agriculture, science and politics and each document contained sentences –simple, compound, complex-of various levels of complexity. POS tag set with 52 tags is developed for preparing the tagged corpus for Malayalam. The phrase tag set contains 20 phrase tags.

  9. Artificial intelligence and visco-elastic analyses for modelling of pavement structures

    OpenAIRE

    Chabot, Armelle

    2009-01-01

    Since January 1, 2009, Maryam Miradi is working as a software engineer at Logica, Rotterdam. Before that she was working as a PhD student at the department of Road and Railway Engineering of the faculty of Civil Engineering and Geosciences, Delft University of Technology. In 1998 she graduated in software engineering at the Mashad University in Iran. In 2003 she became PhD student at the Road and Railway Engineering Department. The objective of her study was to use Artificial Intelligen...

  10. 公司战略的人工生命建模%Artificial Life Modeling in Corporate Strategy

    Institute of Scientific and Technical Information of China (English)

    阎静; 吕胜富; 钟宁

    2007-01-01

    针对商业公司的竞争建立人工生命的模型,通过遗传算法,有效地洞察到公司政策制定的关键营销因素.将软计算应用在商业竞争中,即在可控的人工环境中,创造出被观察的个体行为.同时,开发了软件AL-GAE,用于模拟个体公司竞争演化中优选基因的特征.实验结果表明,ALGAE可以挖掘出事先未知的一些突发基因和行为特征.经过ALGAE分析,市场优势的预测可以从基因数据中获得,表明该模型的有效性.%Applying soft computing methods to a business problem is an innovative application in artificial intelligence. In this paper, Artificial Life (AL) modeling is used to optimize corporate strategies. This model adapted an AL concept where artificial environments could be created to inspect behaviors of subjects under controlled conditions. This Genetic Algorithm application extracts key marketing factors used in corporate decision-making, and market dominance can be predicted by the ALGAE program developed specially for this research. Evolution of hypothetical business entities demonstrates significant results in optimizing genetic characteristics in competitive environments. Analysis shows emergent genotypes and behaviors which cannot be discovered in advance.

  11. TIGER: Toolbox for integrating genome-scale metabolic models, expression data, and transcriptional regulatory networks

    Directory of Open Access Journals (Sweden)

    Jensen Paul A

    2011-09-01

    Full Text Available Abstract Background Several methods have been developed for analyzing genome-scale models of metabolism and transcriptional regulation. Many of these methods, such as Flux Balance Analysis, use constrained optimization to predict relationships between metabolic flux and the genes that encode and regulate enzyme activity. Recently, mixed integer programming has been used to encode these gene-protein-reaction (GPR relationships into a single optimization problem, but these techniques are often of limited generality and lack a tool for automating the conversion of rules to a coupled regulatory/metabolic model. Results We present TIGER, a Toolbox for Integrating Genome-scale Metabolism, Expression, and Regulation. TIGER converts a series of generalized, Boolean or multilevel rules into a set of mixed integer inequalities. The package also includes implementations of existing algorithms to integrate high-throughput expression data with genome-scale models of metabolism and transcriptional regulation. We demonstrate how TIGER automates the coupling of a genome-scale metabolic model with GPR logic and models of transcriptional regulation, thereby serving as a platform for algorithm development and large-scale metabolic analysis. Additionally, we demonstrate how TIGER's algorithms can be used to identify inconsistencies and improve existing models of transcriptional regulation with examples from the reconstructed transcriptional regulatory network of Saccharomyces cerevisiae. Conclusion The TIGER package provides a consistent platform for algorithm development and extending existing genome-scale metabolic models with regulatory networks and high-throughput data.

  12. A Gene Gravity Model for the Evolution of Cancer Genomes: A Study of 3,000 Cancer Genomes across 9 Cancer Types.

    Directory of Open Access Journals (Sweden)

    Feixiong Cheng

    2015-09-01

    Full Text Available Cancer development and progression result from somatic evolution by an accumulation of genomic alterations. The effects of those alterations on the fitness of somatic cells lead to evolutionary adaptations such as increased cell proliferation, angiogenesis, and altered anticancer drug responses. However, there are few general mathematical models to quantitatively examine how perturbations of a single gene shape subsequent evolution of the cancer genome. In this study, we proposed the gene gravity model to study the evolution of cancer genomes by incorporating the genome-wide transcription and somatic mutation profiles of ~3,000 tumors across 9 cancer types from The Cancer Genome Atlas into a broad gene network. We found that somatic mutations of a cancer driver gene may drive cancer genome evolution by inducing mutations in other genes. This functional consequence is often generated by the combined effect of genetic and epigenetic (e.g., chromatin regulation alterations. By quantifying cancer genome evolution using the gene gravity model, we identified six putative cancer genes (AHNAK, COL11A1, DDX3X, FAT4, STAG2, and SYNE1. The tumor genomes harboring the nonsynonymous somatic mutations in these genes had a higher mutation density at the genome level compared to the wild-type groups. Furthermore, we provided statistical evidence that hypermutation of cancer driver genes on inactive X chromosomes is a general feature in female cancer genomes. In summary, this study sheds light on the functional consequences and evolutionary characteristics of somatic mutations during tumorigenesis by propelling adaptive cancer genome evolution, which would provide new perspectives for cancer research and therapeutics.

  13. Genome-wide prediction, display and refinement of binding sites with information theory-based models

    Directory of Open Access Journals (Sweden)

    Leeder J Steven

    2003-09-01

    . Conclusions Delila-Genome was used to scan the human genome sequence with information weight matrices of transcription factor binding sites, including PXR/RXRα, AHR and NF-κB p50/p65, and matrices for RNA binding sites including splice donor, acceptor, and SC35 recognition sites. Comparisons of genome scans with the original and refined PXR/RXRα information weight matrices indicate that the refined model more accurately predicts the strengths of known binding sites and is more sensitive for detection of novel binding sites.

  14. Generation and Evaluation of a Genome-Scale Metabolic Network Model of Synechococcus elongatus PCC7942

    Directory of Open Access Journals (Sweden)

    Julián Triana

    2014-08-01

    Full Text Available The reconstruction of genome-scale metabolic models and their applications represent a great advantage of systems biology. Through their use as metabolic flux simulation models, production of industrially-interesting metabolites can be predicted. Due to the growing number of studies of metabolic models driven by the increasing genomic sequencing projects, it is important to conceptualize steps of reconstruction and analysis. We have focused our work in the cyanobacterium Synechococcus elongatus PCC7942, for which several analyses and insights are unveiled. A comprehensive approach has been used, which can be of interest to lead the process of manual curation and genome-scale metabolic analysis. The final model, iSyf715 includes 851 reactions and 838 metabolites. A biomass equation, which encompasses elementary building blocks to allow cell growth, is also included. The applicability of the model is finally demonstrated by simulating autotrophic growth conditions of Synechococcus elongatus PCC7942.

  15. Artificial urushi.

    Science.gov (United States)

    Kobayashi, S; Uyama, H; Ikeda, R

    2001-11-19

    A new concept for the design and laccase-catalyzed preparation of "artificial urushi" from new urushiol analogues is described. The curing proceeded under mild reaction conditions to produce the very hard cross-linked film (artificial urushi) with a high gloss surface. A new cross-linkable polyphenol was synthesized by oxidative polymerization of cardanol, a phenol derivative from cashew-nut-shell liquid, by enzyme-related catalysts. The polyphenol was readily cured to produce the film (also artificial urushi) showing excellent dynamic viscoelasticity. PMID:11763444

  16. Artificial intelligence

    CERN Document Server

    Ennals, J R

    1987-01-01

    Artificial Intelligence: State of the Art Report is a two-part report consisting of the invited papers and the analysis. The editor first gives an introduction to the invited papers before presenting each paper and the analysis, and then concludes with the list of references related to the study. The invited papers explore the various aspects of artificial intelligence. The analysis part assesses the major advances in artificial intelligence and provides a balanced analysis of the state of the art in this field. The Bibliography compiles the most important published material on the subject of

  17. Hidden Markov Models for Gene Sequence Classification: Classifying the VSG genes in the Trypanosoma brucei Genome

    OpenAIRE

    Mesa, Andrea; Basterrech, Sebastián; Guerberoff, Gustavo; Alvarez-Valin, Fernando

    2015-01-01

    The article presents an application of Hidden Markov Models (HMMs) for pattern recognition on genome sequences. We apply HMM for identifying genes encoding the Variant Surface Glycoprotein (VSG) in the genomes of Trypanosoma brucei (T. brucei) and other African trypanosomes. These are parasitic protozoa causative agents of sleeping sickness and several diseases in domestic and wild animals. These parasites have a peculiar strategy to evade the host's immune system that consists in periodicall...

  18. Multivariate genomic model improves analysis of oil palm (Elaeis guineensis Jacq.) progeny tests

    OpenAIRE

    Marchal, Alexandre; Legarra Albizu, Andres; Tisne, Sebastien; Carasco-Lacombe, Catherine; Manez, Aurore; Suryana, Edyana; Omoré, Alphonse; Nouy, Bruno; Durand-Gasselin, Tristan; Sanchez, Leopoldo; Bouvet, Jean-Marc; Cros, David

    2016-01-01

    Genomic selection is promising for plant breeding, particularly for perennial crops. Multivariate analysis, which considers several traits jointly, takes advantage of the genetic correlations to increase accuracy. The aim of this study was to empirically evaluate the potential of a univariate and multivariate genomic mixed model (G-BLUP) compared to the traditional univariate pedigree-based BLUP (T-BLUP) when analyzing progeny tests of oil palm, the world’s major oil crop. The dataset compris...

  19. USE OF POM AND ARTIFICIAL NEURAL NETWORKS IN THE THREEDIMENSIONAL MODELING OF LAKES : GOKPINAR DAM RESERVOIR AS A CASE STUDY

    Directory of Open Access Journals (Sweden)

    Mahmut FIRAT

    2006-01-01

    Full Text Available The circulation pattern in lakes and reservoirs varies according to many external factors. In situ measurement of the occuring flow pattern in every point of the lake is a very costly and hard task. For this reason, models determining the velocities and surface fluctuations are developed by using computers. The use of these models enables the generation of the foundation for the prediction of possible environmental problems and water pollution concentrations. Today, three dimensional models are widely used in the modelling of lakes and reservoirs. In this study, the velocity profiles and surface fluctuation values generated under various wind speed and directions at some sections in Gokpinar Lake in Denizli are obtained by applying artificial neural networks (ANN on the results of three dimensional hydrodynamic model of the lake made with Princeton Ocean Model (POM. The developed ANN model is applied to the same sections for different wind conditions and it is found that the results are in accordance with the results of POM. As a result of the comparisons of the models, the superiorities of the models on each other at the model generation and solution phases are determined and mentioned.

  20. Comparative Study of Artificial Neural Network and ARIMA Models in Predicting Exchange Rate

    Directory of Open Access Journals (Sweden)

    karamollah Bagherifard

    2012-11-01

    Full Text Available Capital market as an organized market has an effective role in mobilizing financial resources due to have growth and economic development of countries and many countries now in the finance firms is responsible for the required credits. In the stock market, shareholders are always seeking the highest efficiency, so the stock price prediction is important for them. Since the stock market is a nonlinear system under conditions of political, economic and psychological, it is difficult to predict the correct stock price. Thus, in the present study artificial intelligence and ARIMA method has been used to predict stock prices. Multilayer Perceptron neural network and radial basis functions are two methods used in this research. Evaluation methods, selection methods and exponential smoothing methods are compared to random walk. The results showed that AI-based methods used in predicting stock performance are more accurate. Between two methods used in artificial intelligence, a method based on radial basis functions is capable to estimate stock prices in the future with higher accuracy.