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. 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. .

  3. [Application and potential of genome engineering by artificial enzymes].

    Science.gov (United States)

    Nomura, Wataru

    2015-01-01

    Artificial zinc finger proteins (ZFPs) consist of Cys2-His2-type modules composed of approximately 30 amino acids that adopt a ββα structure and coordinate a zinc ion. ZFPs recognizing specific DNA target sequences can substitute for the binding domains of various DNA-modifying enzymes to create designer nucleases, recombinases, and methylases with programmable sequence specificity. Enzymatic genome editing and modification can be applied to many fields of basic research and medicine. The recent development of new platforms using transcription activator-like effector (TALE) proteins or the clustered regularly interspaced short palindromic repeats/CRISPR-associated protein (CRISPR/Cas) system has expanded the range of possibilities for genome-editing technologies. These technologies empower investigators with the ability to efficiently knockout or regulate the functions of genes of interest. In this review, we discuss historical advancements in artificial ZFP applications and important issues that may influence the future of genome editing and engineering technologies. The development of artificial ZFPs has greatly increased the feasibility of manipulating endogenous gene functions through transcriptional control and gene modification. Advances in the ZFP, TALE, and CRISPR/Cas platforms have paved the way for the next generation of genome engineering approaches. Perspectives for the future of genome engineering are also discussed, including applications of targeting specific genomic alleles and studies in synthetic biology.

  4. 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.

  5. 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.

  6. Genome editing using artificial site-specific nucleases in zebrafish.

    Science.gov (United States)

    Hisano, Yu; Ota, Satoshi; Kawahara, Atsuo

    2014-01-01

    Zebrafish is a model vertebrate suitable for genetic analysis. Forward genetic analysis via chemical mutagenesis screening has established a variety of zebrafish mutants that are defective in various types of organogenesis, and the genes responsible for the individual mutants have been identified from genome mapping. On the other hand, reverse genetic analysis via targeted gene disruption using embryonic stem (ES) cells (e.g., knockout mouse) can uncover gene functions by investigating the phenotypic effects. However, this approach is mostly limited to mice among the vertebrate models because of the difficulty in establishing ES cells. Recently, new gene targeting technologies, such as the transcription activator-like effector nucleases (TALEN) and clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 systems, have been developed: that can directly introduce genome modifications at the targeted genomic locus. Here, we summarize these new and powerful genome editing techniques for the study of zebrafish.

  7. Plant Growth Models Using Artificial Neural Networks

    Science.gov (United States)

    Bubenheim, David

    1997-01-01

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

  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. Genomic Feature Models

    DEFF Research Database (Denmark)

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

    Whole-genome sequences and multiple trait phenotypes from large numbers of individuals will soon be available in many populations. Well established statistical modeling approaches enable the genetic analyses of complex trait phenotypes while accounting for a variety of additive and non-additive g......Whole-genome sequences and multiple trait phenotypes from large numbers of individuals will soon be available in many populations. Well established statistical modeling approaches enable the genetic analyses of complex trait phenotypes while accounting for a variety of additive and non......-additive genetic mechanisms. These modeling approaches have proven to be highly useful to determine population genetic parameters as well as prediction of genetic risk or value. We present a series of statistical modelling approaches that use prior biological information for evaluating the collective action...... of sets of genetic variants. We have applied these approaches to whole genome sequences and a complex trait phenotype resistance to starvation collected on inbred lines from the Drosophila Genome Reference Panel population. We identified a number of genomic features classification schemes (e.g. prior QTL...

  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. Modelling Microwave Devices Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Andrius Katkevičius

    2012-04-01

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

  12. Induction of genomic instability and activation of autophagy in artificial human aneuploid cells

    Energy Technology Data Exchange (ETDEWEB)

    Ariyoshi, Kentaro [Hirosaki University, Institute of Radiation Emergency Medicine, 66-1 Hon-cho, Hirosaki 036-8564 (Japan); Miura, Tomisato; Kasai, Kosuke; Fujishima, Yohei [Department of Biomedical Sciences, Hirosaki University Graduate School of Health Sciences, 66-1 Hon-cho, Hirosaki 036-8564 (Japan); Oshimura, Mitsuo [Chromosome Engineering Research Center (CERC), Tottori University, Nishicho 86, Yonago, Tottori 683-8503 (Japan); Yoshida, Mitsuaki A., E-mail: ariyoshi@hirosaki-u.ac.jp [Hirosaki University, Institute of Radiation Emergency Medicine, 66-1 Hon-cho, Hirosaki 036-8564 (Japan)

    2016-08-15

    Highlights: • Clones with artificial aneuploidy of chromosome 8 or chromosome 22 both show inhibited proliferation and genomic instability. • Increased autophagy was observed in the artificially aneuploid clones. • Inhibition of autophagy resulted in increased genomic instability and DNA damage. • Intracellular levels of reactive oxygen species were up-regulated in the artificially aneuploid clones. - Abstract: Chromosome missegregation can lead to a change in chromosome number known as aneuploidy. Although aneuploidy is a known hallmark of cancer cells, the various mechanisms by which altered gene and/or DNA copy number facilitate tumorigenesis remain unclear. To understand the effect of aneuploidy occurring in non-tumorigenic human breast epithelial cells, we generated clones harboring artificial aneuploidy using microcell-mediated chromosome transfer. Our results demonstrate that clones with artificial aneuploidy of chromosome 8 or chromosome 22 both show inhibited proliferation and genomic instability. Also, the increased autophagy was observed in the artificially aneuploidy clones, and inhibition of autophagy resulted in increased genomic instability and DNA damage. In addition, the intracellular levels of reactive oxygen species were up-regulated in the artificially aneuploid clones, and inhibition of autophagy further increased the production of reactive oxygen species. Together, these results suggest that even a single extraneous chromosome can induce genomic instability, and that autophagy triggered by aneuploidy-induced stress is a mechanism to protect cells bearing abnormal chromosome number.

  13. Psychometric Measurement Models and Artificial Neural Networks

    Science.gov (United States)

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

    2004-01-01

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

  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.

  15. Power analysis of artificial selection experiments using efficient whole genome simulation of quantitative traits.

    Science.gov (United States)

    Kessner, Darren; Novembre, John

    2015-04-01

    Evolve and resequence studies combine artificial selection experiments with massively parallel sequencing technology to study the genetic basis for complex traits. In these experiments, individuals are selected for extreme values of a trait, causing alleles at quantitative trait loci (QTL) to increase or decrease in frequency in the experimental population. We present a new analysis of the power of artificial selection experiments to detect and localize quantitative trait loci. This analysis uses a simulation framework that explicitly models whole genomes of individuals, quantitative traits, and selection based on individual trait values. We find that explicitly modeling QTL provides qualitatively different insights than considering independent loci with constant selection coefficients. Specifically, we observe how interference between QTL under selection affects the trajectories and lengthens the fixation times of selected alleles. We also show that a substantial portion of the genetic variance of the trait (50-100%) can be explained by detected QTL in as little as 20 generations of selection, depending on the trait architecture and experimental design. Furthermore, we show that power depends crucially on the opportunity for recombination during the experiment. Finally, we show that an increase in power is obtained by leveraging founder haplotype information to obtain allele frequency estimates.

  16. 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

  17. Genome-wide identification of specific oligonucleotides using artificial neural network and computational genomic analysis

    Directory of Open Access Journals (Sweden)

    Chen Jiun-Ching

    2007-05-01

    Full Text Available Abstract Background Genome-wide identification of specific oligonucleotides (oligos is a computationally-intensive task and is a requirement for designing microarray probes, primers, and siRNAs. An artificial neural network (ANN is a machine learning technique that can effectively process complex and high noise data. Here, ANNs are applied to process the unique subsequence distribution for prediction of specific oligos. Results We present a novel and efficient algorithm, named the integration of ANN and BLAST (IAB algorithm, to identify specific oligos. We establish the unique marker database for human and rat gene index databases using the hash table algorithm. We then create the input vectors, via the unique marker database, to train and test the ANN. The trained ANN predicted the specific oligos with high efficiency, and these oligos were subsequently verified by BLAST. To improve the prediction performance, the ANN over-fitting issue was avoided by early stopping with the best observed error and a k-fold validation was also applied. The performance of the IAB algorithm was about 5.2, 7.1, and 6.7 times faster than the BLAST search without ANN for experimental results of 70-mer, 50-mer, and 25-mer specific oligos, respectively. In addition, the results of polymerase chain reactions showed that the primers predicted by the IAB algorithm could specifically amplify the corresponding genes. The IAB algorithm has been integrated into a previously published comprehensive web server to support microarray analysis and genome-wide iterative enrichment analysis, through which users can identify a group of desired genes and then discover the specific oligos of these genes. Conclusion The IAB algorithm has been developed to construct SpecificDB, a web server that provides a specific and valid oligo database of the probe, siRNA, and primer design for the human genome. We also demonstrate the ability of the IAB algorithm to predict specific oligos through

  18. Modelling natural and artificial hands with synergies.

    Science.gov (United States)

    Bicchi, Antonio; Gabiccini, Marco; Santello, Marco

    2011-11-12

    We report on recent work in modelling the process of grasping and active touch by natural and artificial hands. Starting from observations made in human hands about the correlation of degrees of freedom in patterns of more frequent use (postural synergies), we consider the implications of a geometrical model accounting for such data, which is applicable to the pre-grasping phase occurring when shaping the hand before actual contact with the grasped object. To extend applicability of the synergy model to study force distribution in the actual grasp, we introduce a modified model including the mechanical compliance of the hand's musculotendinous system. Numerical results obtained by this model indicate that the same principal synergies observed from pre-grasp postural data are also fundamental in achieving proper grasp force distribution. To illustrate the concept of synergies in the dual domain of haptic sensing, we provide a review of models of how the complexity and heterogeneity of sensory information from touch can be harnessed in simplified, tractable abstractions. These abstractions are amenable to fast processing to enable quick reflexes as well as elaboration of high-level percepts. Applications of the synergy model to the design and control of artificial hands and tactile sensors are illustrated.

  19. Artificial Intelligence Software Engineering (AISE) model

    Science.gov (United States)

    Kiss, Peter A.

    1990-01-01

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

  20. 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

  1. 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.

  2. 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 stabl...... 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 infectious BAC DNAs from a single full-length PCR product....

  3. 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.

  4. 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.

  5. Construction of a llama bacterial artificial chromosome library with approximately 9-fold genome equivalent coverage.

    Science.gov (United States)

    Airmet, K W; Hinckley, J D; Tree, L T; Moss, M; Blumell, S; Ulicny, K; Gustafson, A K; Weed, M; Theodosis, R; Lehnardt, M; Genho, J; Stevens, M R; Kooyman, D L

    2012-01-01

    The Ilama is an important agricultural livestock in much of South America. The llama is increasing in popularity in the United States as a companion animal. Little work has been done to improve llama production using modern technology. A paucity of information is available regarding the llama genome. We report the construction of a llama bacterial artificial chromosome (BAC) library of about 196,224 clones in the vector pECBAC1. Using flow cytometry and bovine, human, mouse, and chicken as controls, we determined the llama genome size to be 2.4 × 10⁹ bp. The average insert size of the library is 137.8 kb corresponding to approximately 9-fold genome coverage. Further studies are needed to further characterize the library and llama genome. We anticipate that this new library will help facilitate future genomic studies in the llama.

  6. 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.

  7. 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.

  8. Microbial growth modelling with artificial neural networks.

    Science.gov (United States)

    Jeyamkonda, S; Jaya, D S; Holle, R A

    2001-03-20

    There is a growing interest in modelling microbial growth as an alternative to time-consuming, traditional, microbiological enumeration techniques. Several statistical models have been reported to describe the growth of different microorganisms, but there are accuracy problems. An alternate technique 'artificial neural networks' (ANN) for modelling microbial growth is explained and evaluated. Published data were used to build separate general regression neural network (GRNN) structures for modelling growth of Aeromonas hydrophila, Shigella flexneri, and Brochothrix thermosphacta. Both GRNN and published statistical model predictions were compared against the experimental data using six statistical indices. For training data sets, the GRNN predictions were far superior than the statistical model predictions, whereas the GRNN predictions were similar or slightly worse than statistical model predictions for test data sets for all the three data sets. GRNN predictions can be considered good, considering its performance for unseen data. Graphical plots, mean relative percentage residual, mean absolute relative residual, and root mean squared residual were identified as suitable indices for comparing competing models. ANN can now become a vehicle whereby predictive microbiology can be applied in food product development and food safety risk assessment.

  9. Multiple models for Rosaceae genomics.

    Science.gov (United States)

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

    2008-07-01

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

  10. Artificial Spin-Ice and Vertex Models

    Science.gov (United States)

    Cugliandolo, Leticia F.

    2017-01-01

    In classical and quantum frustrated magnets the interactions in combination with the lattice structure impede the spins to order in optimal configurations at zero temperature. The theoretical interest in their classical realisations has been boosted by the artificial manufacture of materials with these properties, that are of flexible design. This note summarises work on the use of vertex models to study bidimensional spin-ices samples, done in collaboration with R. A. Borzi, M. V. Ferreyra, L. Foini, G. Gonnella, S. A. Grigera, P. Guruciaga, D. Levis, A. Pelizzola and M. Tarzia, in recent years. It is an invited contribution to a J. Stat. Mech. special issue dedicated to the memory of Leo P. Kadanoff.

  11. 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.

  12. Natural - synthetic - artificial!

    DEFF Research Database (Denmark)

    Nielsen, Peter E

    2010-01-01

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

  13. 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.

  14. Credit Scoring Model Hybridizing Artificial Intelligence with Logistic Regression

    Directory of Open Access Journals (Sweden)

    Han Lu

    2013-01-01

    Full Text Available Today the most commonly used techniques for credit scoring are artificial intelligence and statistics. In this paper, we started a new way to use these two kinds of models. Through logistic regression filters the variables with a high degree of correlation, artificial intelligence models reduce complexity and accelerate convergence, while these models hybridizing logistic regression have better explanations in statistically significance, thus improve the effect of artificial intelligence models. With experiments on German data set, we find an interesting phenomenon defined as ‘Dimensional interference’ with support vector machine and from cross validation it can be seen that the new method gives a lot of help with credit scoring.

  15. 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.

  16. 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.

  17. 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...... of the insulin pump and the CGM has paved the way for a fully automatic treatment regime, the artificial pancreas. The idea is to connect the CGM with the insulin pump via a control algorithm running on e.g. the patients smart phone. The CGM observations are sent to the smart phone and based on this information...... of the system directly. The purpose of this PhD-project was to investigate the potential of SDEs in the artificial pancreas development. Especially, the emerging continuous monitoring of glucose levels makes SDEs highly applicable to this field. The current thesis aims at demonstrating and discussing...

  18. A Suitable Artificial Intelligence Model for Inventory Level Optimization

    Directory of Open Access Journals (Sweden)

    Tereza Sustrova

    2016-05-01

    Full Text Available Purpose of the article: To examine suitable methods of artificial neural networks and their application in business operations, specifically to the supply chain management. The article discusses construction of an artificial neural networks model that can be used to facilitate optimization of inventory level and thus improve the ordering system and inventory management. For the data analysis from the area of wholesale trade with connecting material is used. Methodology/methods: Methods used in the paper consists especially of artificial neural networks and ANN-based modelling. For data analysis and preprocessing, MS Office Excel software is used. As an instrument for neural network forecasting MathWorks MATLAB Neural Network Tool was used. Deductive quantitative methods for research are also used. Scientific aim: The effort is directed at finding whether the method of prediction using artificial neural networks is suitable as a tool for enhancing the ordering system of an enterprise. The research also focuses on finding what architecture of the artificial neural networks model is the most suitable for subsequent prediction. Findings of the research show that artificial neural networks models can be used for inventory management and lot-sizing problem successfully. A network with the TRAINGDX training function and TANSIG transfer function and 6-8-1 architecture can be considered the most suitable for artificial neural network, as it shows the best results for subsequent prediction.. Conclusions resulting from the paper are beneficial for further research. It can be concluded that the created model of artificial neural network can be successfully used for predicting order size and therefore for improving the order cycle of an enterprise.

  19. 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

  20. 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.

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

    Science.gov (United States)

    Roth, Wolff-Michael

    2000-01-01

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

  3. 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.

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

    Directory of Open Access Journals (Sweden)

    H. Hashemi

    2013-02-01

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

  5. 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

  6. Genome sequencing reveals loci under artificial selection that underlie disease phenotypes in the laboratory rat

    NARCIS (Netherlands)

    Atanur, S.S.; Diaz, A.G.; Maratou, K.; Sarkis, A.; Rotival, M.; Game, L.; Tschannen, M.R.; Kaisaki, P.J.; Otto, G.W.; Ma, M.C.; Keane, T.M.; Hummel, O.; Saar, K.; Chen, W.; Guryev, V.; Gopalakrishnan, K.; Garrett, M.R.; Joe, B.; Citterio, L.; Bianchi, G.; McBride, M.; Dominiczak, A.; Adams, D.J.; Serikawa, T.; Flicek, P.; Cuppen, E.; Hubner, N.; Petretto, E.; Gauguier, D.; Kwitek, A.; Jacob, H.; Aitman, T.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

  7. 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…

  8. 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

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. Construction and analysis of Siberian tiger bacterial artificial chromosome library with approximately 6.5-fold genome equivalent coverage.

    Science.gov (United States)

    Liu, Changqing; Bai, Chunyu; Guo, Yu; Liu, Dan; Lu, Taofeng; Li, Xiangchen; Ma, Jianzhang; Ma, Yuehui; Guan, Weijun

    2014-03-07

    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.

  14. 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.

  15. Runoff forecasting by artificial neural network and conventional model

    Directory of Open Access Journals (Sweden)

    A.R. Ghumman

    2011-12-01

    Full Text Available Rainfall runoff models are highly useful for water resources planning and development. In the present study rainfall–runoff model based on Artificial Neural Networks (ANNs was developed and applied on a watershed in Pakistan. The model was developed to suite the conditions in which the collected dataset is short and the quality of dataset is questionable. The results of ANN models were compared with a mathematical conceptual model. The cross validation approach was adopted for the generalization of ANN models. The precipitation used data was collected from Meteorological Department Karachi Pakistan. The results confirmed that ANN model is an important alternative to conceptual models and it can be used when the range of collected dataset is short and data is of low standard.

  16. 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.

  17. Cancer genomics object model: an object model for multiple functional genomics data for cancer research.

    Science.gov (United States)

    Park, Yu Rang; Lee, Hye Won; Cho, Sung Bum; Kim, Ju Han

    2007-01-01

    The development of functional genomics including transcriptomics, proteomics and metabolomics allow us to monitor a large number of key cellular pathways simultaneously. Several technology-specific data models have been introduced for the representation of functional genomics experimental data, including the MicroArray Gene Expression-Object Model (MAGE-OM), the Proteomics Experiment Data Repository (PEDRo), and the Tissue MicroArray-Object Model (TMA-OM). Despite the increasing number of cancer studies using multiple functional genomics technologies, there is still no integrated data model for multiple functional genomics experimental and clinical data. We propose an object-oriented data model for cancer genomics research, Cancer Genomics Object Model (CaGe-OM). We reference four data models: Functional Genomic-Object Model, MAGE-OM, TMAOM and PEDRo. The clinical and histopathological information models are created by analyzing cancer management workflow and referencing the College of American Pathology Cancer Protocols and National Cancer Institute Common Data Elements. The CaGe-OM provides a comprehensive data model for integrated storage and analysis of clinical and multiple functional genomics data.

  18. 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

  19. 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.

  20. 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 (P 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.

  1. 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.

  2. 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.

  3. Construction and Characterization of a Bacterial Artificial Chromosome Library for the A-Genome of Cotton (G. arboreum L.

    Directory of Open Access Journals (Sweden)

    Yan Hu

    2011-01-01

    Full Text Available A bacterial artificial chromosome (BAC library for the A-genome of cotton has been constructed from the leaves of G. arboreum L cv. Jianglinzhongmian. It is used as elite A-genome germplasm resources in the present cotton breeding program and has been used to build a genetic reference map of cotton. The BAC library consists of 123,648 clones stored in 322 384-well plates. Statistical analysis of a set of 103 randomly selected BAC clones indicated that each clone has an average insert length of 100.2 kb per plasmid, with a range of 30 to 190 kb. Theoretically, this represents 7.2 haploid genome equivalents based on an A-genome size of 1697 Mb. The BAC library has been arranged in column pools and superpools allowing screening with various PCR-based markers. In the future, the A-genome cotton BAC library will serve as both a giant gene resource and a valuable tool for map-based gene isolation, physical mapping and comparative genome analysis.

  4. 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...

  5. 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.

  6. 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.

  7. 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.

  8. Human artificial chromosome assembly by transposon-based retrofitting of genomic BACs with synthetic alpha-satellite arrays.

    Science.gov (United States)

    Basu, Joydeep; Willard, Huntington F; Stromberg, Gregory

    2007-01-01

    The development of methodologies for the rapid assembly of synthetic alpha-satellite arrays recapitulating the higher-order periodic organization of native human centromeres permits the systematic investigation of the significance of primary sequence and sequence organization in centromere function. Synthetic arrays with defined mutations affecting sequence and/or organization may be evaluated in a de novo human artificial chromosome assay. This unit describes strategies for the assembly of custom built alpha-satellite arrays containing any desired mutation as well as strategies for the construction and manipulation of alpha satellite-based transposons. Transposons permit the rapid and reliable retrofitting of any genomic bacterial artificial chromosome (BAC) with synthetic alpha-satellite arrays and other functional components, thereby facilitating conversion into BAC-based human artificial chromosome vectors. These techniques permit identification and optimization of the critical parameters underlying the unique ability of alpha-satellite DNA to facilitate de novo centromere assembly, and they will establish the foundation for the next generation of human artificial chromosome vectors.

  9. 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.

  10. Modeling biodegradation and kinetics of glyphosate by artificial neural network.

    Science.gov (United States)

    Nourouzi, Mohsen M; Chuah, Teong G; Choong, Thomas S Y; Rabiei, F

    2012-01-01

    An artificial neural network (ANN) model was developed to simulate the biodegradation of herbicide glyphosate [2-(Phosphonomethylamino) acetic acid] in a solution with varying parameters pH, inoculum size and initial glyphosate concentration. The predictive ability of ANN model was also compared with Monod model. The result showed that ANN model was able to accurately predict the experimental results. A low ratio of self-inhibition and half saturation constants of Haldane equations (glyphosate on bacteria growth. The value of K(i)/K(s) increased when the mixed inoculum size was increased from 10(4) to 10(6) bacteria/mL. It was found that the percentage of glyphosate degradation reached a maximum value of 99% at an optimum pH 6-7 while for pH values higher than 9 or lower than 4, no degradation was observed.

  11. 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.

  12. 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.

  13. 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.

  14. Inferences from Genomic Models in Stratified Populations

    DEFF Research Database (Denmark)

    Janss, Luc; de los Campos, Gustavo; Sheehan, Nuala

    2012-01-01

    Unaccounted population stratification can lead to spurious associations in genome-wide association studies (GWAS) and in this context several methods have been proposed to deal with this problem. An alternative line of research uses whole-genome random regression (WGRR) models that fit all markers...... are unsatisfactory. Here we address this problem and describe a reparameterization of a WGRR model, based on an eigenvalue decomposition, for simultaneous inference of parameters and unobserved population structure. This allows estimation of genomic parameters with and without inclusion of marker......-derived eigenvectors that account for stratification. The method is illustrated with grain yield in wheat typed for 1279 genetic markers, and with height, HDL cholesterol and systolic blood pressure from the British 1958 cohort study typed for 1 million SNP genotypes. Both sets of data show signs of population...

  15. 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.

  16. 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

  17. 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.

  18. 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.

  19. 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)

  20. 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...

  1. Development of new transformation-competent artificial chromosome vectors and rice genomic libraries for efficient gene cloning.

    Science.gov (United States)

    Liu, Yao-Guang; Liu, Hongmei; Chen, Letian; Qiu, Weihua; Zhang, Qunyu; Wu, Hao; Yang, Chunyi; Su, Jing; Wang, Zhonghua; Tian, Dongsheng; Mei, Mantong

    2002-01-09

    The transformation-competent artificial chromosome vector (TAC) system has been shown to be very useful for efficient gene isolation in Arabidopsis thaliana (Proc. Natl. Acad. Sci. USA 96 (1998) 6535). To adapt the vector system for gene isolation in crops, two new TAC vectors and rice genomic libraries were developed. The new vectors pYLTAC17 and pYLTAC27 use the Bar gene and Hpt gene driven by the rice Act1 promoter as the plant selectable markers, respectively, and are suitable for transformation of rice and other grasses. Two representative genomic libraries (I and II) of an Indica rice variety Minghui63, a fertility restorer line for hybrid rice, were constructed with pYLTAC17 using different size classes of partially digested DNA fragments. Library I and library II consisted of 34,560 and 1.2 x 10(5) clones, with average insert sizes of approximately 77 and 39 kb, respectively. The genome coverage of the libraries I and II was estimated to be about 5 and 11 haploid genome equivalents, respectively. Clones of the library I were stored individually in ninety 384-well plates, and those of the library II were collected as bulked pools each containing 30-50 clones and stored in eight 384-well plates. A number of probes were used to hybridize high-density colony filters of the library I prepared by an improved replicating method and each detected 2-9 positive clones. A method for rapid screening of the library II by pooled colony hybridization was developed. A TAC clone having an 80 kb rice DNA insert was successfully transferred into rice genome via Agrobacterium-mediated transformation. The new vectors and the genomic libraries should be useful for gene cloning and genetic engineering in rice and other crops.

  2. 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.

  3. 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.

  4. Genome scale metabolic modeling of cancer

    DEFF Research Database (Denmark)

    Nilsson, Avlant; Nielsen, Jens

    2016-01-01

    been used as scaffolds for analysis of high throughput data to allow mechanistic interpretation of changes in expression. Finally, GEMs allow quantitative flux predictions using flux balance analysis (FBA). Here we critically review the requirements for successful FBA simulations of cancer cells......Cancer cells reprogram metabolism to support rapid proliferation and survival. Energy metabolism is particularly important for growth and genes encoding enzymes involved in energy metabolism are frequently altered in cancer cells. A genome scale metabolic model (GEM) is a mathematical formalization...... of metabolism which allows simulation and hypotheses testing of metabolic strategies. It has successfully been applied to many microorganisms and is now used to study cancer metabolism. Generic models of human metabolism have been reconstructed based on the existence of metabolic genes in the human genome...

  5. 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

  6. Phosphorus fate, management, and modeling in artificially drained systems.

    Science.gov (United States)

    Kleinman, Peter J A; Smith, Douglas R; Bolster, Carl H; Easton, Zachary M

    2015-03-01

    Phosphorus (P) losses in agricultural drainage waters, both surface and subsurface, are among the most difficult form of nonpoint source pollution to mitigate. This special collection of papers on P in drainage waters documents the range of field conditions leading to P loss in drainage water, the potential for drainage and nutrient management practices to control drainage losses of P, and the ability of models to represent P loss to drainage systems. A review of P in tile drainage and case studies from North America, Europe, and New Zealand highlight the potential for artificial drainage to exacerbate watershed loads of dissolved and particulate P via rapid, bypass flow and shorter flow path distances. Trade-offs are identified in association with drainage intensification, tillage, cover crops, and manure management. While P in drainage waters tends to be tied to surface sources of P (soil, amendments or vegetation) that are in highest concentration, legacy sources of P may occur at deeper depths or other points along drainage flow paths. Most startling, none of the major fate-and-transport models used to predict management impacts on watershed P losses simulate the dominant processes of P loss to drainage waters. Because P losses to drainage waters can be so difficult to manage and to model, major investment are needed (i) in systems that can provide necessary drainage for agronomic production while detaining peak flows and promoting P retention and (ii) in models that can adequately describe P loss to drainage waters.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. Construction of a Bacterial Artificial Chromosome Library of TM-1, a Standard Line for Genetics and Genomics in Upland Cotton

    Institute of Scientific and Technical Information of China (English)

    Yan Hu; Wang-Zhen Guo; Tian-Zhen Zhang

    2009-01-01

    A bacterial artificial chromosome (BAC) library was constructed for Gossyplum hirsutum acc. TM-1, a genetic and genomic standard line for Upland cotton. The library consists of 147 456 clones with an average insert size of 122.8 kb ranging from 97 to 240 kb. About 96.0% of the clones have inserts over 100 kb. Therefore, this library represents theoretically 7.4 haploid genome equivalents based on an AD genome size of 2 425 Mb. Clones were stored in 384 384- well plates and arrayed into multiplex pools for rapid and reliable library screening. BAC screening was carded out by four-round polymerase chain reactions using 23 simple sequence repeats (SSR) markers, three sequence-related amplified polymorphism markers and one pair of pdmere for a gene associated with fiber development to test the quality of the library. Correspondingly, in total 92 positive BAC clones were Identified with an average four positive clones per SSR marker, ranging from one to eight hits. Additionally, since these SSR markers have been localized to chromosome 12 (A12) and 26 (D12) according to the genetic map, these BAC clonee are expected to serve as seeds for the physical mapping of these two homologous chromosomes, sequentially map-based cloning of quantitative trait loci or genes associated with Important agronomic traits.

  13. 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

  14. Modelling artificial sea salt emission in large eddy simulations.

    Science.gov (United States)

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

    2014-12-28

    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.

  15. 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. 

  16. 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.

  17. 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/.

  18. 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

  19. 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.

  20. 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

  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. 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...

  3. Correction for Measurement Error from Genotyping-by-Sequencing in Genomic Variance and Genomic Prediction Models

    DEFF Research Database (Denmark)

    Ashraf, Bilal; Janss, Luc; Jensen, Just

    . In the current work we show how the correction for measurement error in GBSeq can also be applied in whole genome genomic variance and genomic prediction models. Bayesian whole-genome random regression models are proposed to allow implementation of large-scale SNP-based models with a per-SNP correction......Genotyping-by-sequencing (GBSeq) is becoming a cost-effective genotyping platform for species without available SNP arrays. GBSeq considers to sequence short reads from restriction sites covering a limited part of the genome (e.g., 5-10%) with low sequencing depth per individual (e.g., 5-10X per...... sample). The GBSeq data can be used directly in genomic models in the form of individual SNP allele-frequency estimates (e.g., reference reads/total reads per polymorphic site per individual), but is subject to measurement error due to the low sequencing depth per individual. Due to technical reasons...

  4. An artificial blood vessel implanted three-dimensional microsystem for modeling transvascular migration of tumor cells.

    Science.gov (United States)

    Wang, Xue-Ying; Pei, Ying; Xie, Min; Jin, Zi-He; Xiao, Ya-Shi; Wang, Yang; Zhang, Li-Na; Li, Yan; Huang, Wei-Hua

    2015-02-21

    Reproducing a tumor microenvironment consisting of blood vessels and tumor cells for modeling tumor invasion in vitro is particularly challenging. Here, we report an artificial blood vessel implanted 3D microfluidic system for reproducing transvascular migration of tumor cells. The transparent, porous and elastic artificial blood vessels are obtained by constructing polysaccharide cellulose-based microtubes using a chitosan sacrificial template, and possess excellent cytocompatibility, permeability, and mechanical characteristics. The artificial blood vessels are then fully implanted into the collagen matrix to reconstruct the 3D microsystem for modeling transvascular migration of tumor cells. Well-defined simulated vascular lumens were obtained by proliferation of the human umbilical vein endothelial cells (HUVECs) lining the artificial blood vessels, which enables us to reproduce structures and functions of blood vessels and replicate various hemodynamic parameters. Based on this model, the adhesion and transvascular migration of tumor cells across the artificial blood vessel have been well reproduced.

  5. 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

    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...

  6. Genome-wide survey of artificial mutations induced by ethyl methanesulfonate and gamma rays in tomato.

    Science.gov (United States)

    Shirasawa, Kenta; Hirakawa, Hideki; Nunome, Tsukasa; Tabata, Satoshi; Isobe, Sachiko

    2016-01-01

    Genome-wide mutations induced by ethyl methanesulfonate (EMS) and gamma irradiation in the tomato Micro-Tom genome were identified by a whole-genome shotgun sequencing analysis to estimate the spectrum and distribution of whole-genome DNA mutations and the frequency of deleterious mutations. A total of ~370 Gb of paired-end reads for four EMS-induced mutants and three gamma-ray-irradiated lines as well as a wild-type line were obtained by next-generation sequencing technology. Using bioinformatics analyses, we identified 5920 induced single nucleotide variations and insertion/deletion (indel) mutations. The predominant mutations in the EMS mutants were C/G to T/A transitions, while in the gamma-ray mutants, C/G to T/A transitions, A/T to T/A transversions, A/T to G/C transitions and deletion mutations were equally common. Biases in the base composition flanking mutations differed between the mutagenesis types. Regarding the effects of the mutations on gene function, >90% of the mutations were located in intergenic regions, and only 0.2% were deleterious. In addition, we detected 1,140,687 spontaneous single nucleotide polymorphisms and indel polymorphisms in wild-type Micro-Tom lines. We also found copy number variation, deletions and insertions of chromosomal segments in both the mutant and wild-type lines. The results provide helpful information not only for mutation research, but also for mutant screening methodology with reverse-genetic approaches.

  7. 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.

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

    DEFF Research Database (Denmark)

    Have, Christian Theil; Mørk, Søren

    We introduce a new type of probabilistic sequence model, that model the sequential composition of reading frames of genes in a genome. Our approach extends gene finders with a model of the sequential composition of genes at the genome-level -- effectively producing a sequential genome annotation...... 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...... as output. The model can be used to obtain the most probable genome annotation based on a combination of i: a gene finder score of each gene candidate and ii: the sequence of the reading frames of gene candidates through a genome. The model --- as well as a higher order variant --- is developed and tested...

  9. On the Effects of Artificial Feeding on Bee Colony Dynamics: A Mathematical Model

    Science.gov (United States)

    Paiva, Juliana Pereira Lisboa Mohallem; Paiva, Henrique Mohallem; Esposito, Elisa; Morais, Michelle Manfrini

    2016-01-01

    This paper proposes a new mathematical model to evaluate the effects of artificial feeding on bee colony population dynamics. The proposed model is based on a classical framework and contains differential equations that describe the changes in the number of hive bees, forager bees, and brood cells, as a function of amounts of natural and artificial food. The model includes the following elements to characterize the artificial feeding scenario: a function to model the preference of the bees for natural food over artificial food; parameters to quantify the quality and palatability of artificial diets; a function to account for the efficiency of the foragers in gathering food under different environmental conditions; and a function to represent different approaches used by the beekeeper to feed the hive with artificial food. Simulated results are presented to illustrate the main characteristics of the model and its behavior under different scenarios. The model results are validated with experimental data from the literature involving four different artificial diets. A good match between simulated and experimental results was achieved. PMID:27875589

  10. 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

  11. 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.

  12. Metabolic model reconstruction and analysis of an artificial microbial ecosystem for vitamin C production.

    Science.gov (United States)

    Ye, Chao; Zou, Wei; Xu, Nan; Liu, Liming

    2014-07-20

    An artificial microbial ecosystem (AME) consisting of Ketogulonicigenium vulgare and Bacillus megaterium is currently used in a two-step fermentation process for vitamin C production. In order to obtain a comprehensive understanding of the metabolic interactions between the two bacteria, a two-species stoichiometric metabolic model (iWZ-KV-663-BM-1055) consisting of 1718 genes, 1573 metabolites, and 1891 reactions (excluding exchange reactions) was constructed based on separate genome-scale metabolic models (GSMMs) of K. vulgare and B. megaterium. These two compartments (k and b) of iWZ-KV-663-BM-1055 shared 453 reactions and 548 metabolites. Compartment b was richer in subsystems than compartment k. In minimal media with glucose (MG), metabolite exchange between compartments was assessed by constraint-based analysis. Compartment b secreted essential amino acids, nucleic acids, vitamins and cofactors important for K. vulgare growth and biosynthesis of 2-keto-l-gulonic acid (2-KLG). Further research showed that when co-cultured with B. megaterium in l-sorbose-CSLP medium, the growth rate of K. vulgare and 2-KLG production were increased by 111.9% and 29.42%, respectively, under the constraints employed. Our study demonstrated that GSMMs and constraint-based methods can be used to decode the physiological features and inter-species interactions of AMEs used in industrial biotechnology, which will be of benefit for improving regulation and refinement in future industrial processes.

  13. 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.

  14. 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...

  15. A simple, closed-form, mathematical model for gas exchange in microchannel artificial lungs.

    Science.gov (United States)

    Potkay, Joseph A

    2013-06-01

    Microfabrication techniques are attractive for constructing artificial lungs due to the ability to create features similar in size to those in the natural lung. However, a simple and intuitive mathematical model capable of accurately predicting the gas exchange performance of microchannel artificial lungs does not currently exist. Such a model is critical to understanding and optimizing these devices. Here, we describe a simple, closed-form mathematical model for gas exchange in microchannel artificial lungs and qualify it through application to experimental data from several research groups. We utilize lumped parameters and several assumptions to obtain a closed-form set of equations that describe gas exchange. This work is intended to augment computational models by providing a more intuitive, albeit potentially less accurate, understanding of the operation and trade-offs inherent in microchannel artificial lung devices.

  16. 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...

  17. 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.

  18. 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.

  19. 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.

  20. 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.

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

    Science.gov (United States)

    Jamieson, Randall K; Hauri, Brian R

    2012-06-01

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

  2. MODELLING OF THIN LAYER DRYING KINETICS OF COCOA BEANS DURING ARTIFICIAL AND NATURAL DRYING

    Directory of Open Access Journals (Sweden)

    C.L. HII

    2008-04-01

    Full Text Available Drying experiments were conducted using air-ventilated oven and sun dryer to simulate the artificial and natural drying processes of cocoa beans. The drying data were fitted with several published thin layer drying models. A new model was introduced which is a combination of the Page and two-term drying model. Selection of the best model was investigated by comparing the determination of coefficient (R2, reduced chi-square (2 and root mean square error (RMSE between the experimental and predicted values. The results showed that the new model was found best described the artificial and natural drying kinetics of cocoa under the conditions tested.

  3. 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.

  4. 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.

  5. 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.

  6. 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

  7. 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.

  8. A second generation integrated map of the rainbow trout (Oncorhynchus mykiss) genome: analysis of synteny with model fish genomes

    Science.gov (United States)

    In this paper we generated DNA fingerprints and end sequences from bacterial artificial chromosomes (BACs) from two new libraries to improve the first generation integrated physical and genetic map of the rainbow trout (Oncorhynchus mykiss) genome. The current version of the physical map is compose...

  9. Building a data sharing model for global genomic research.

    Science.gov (United States)

    Kosseim, Patricia; Dove, Edward S; Baggaley, Carman; Meslin, Eric M; Cate, Fred H; Kaye, Jane; Harris, Jennifer R; Knoppers, Bartha M

    2014-08-11

    Data sharing models designed to facilitate global business provide insights for improving transborder genomic data sharing. We argue that a flexible, externally endorsed, multilateral arrangement, combined with an objective third-party assurance mechanism, can effectively balance privacy with the need to share genomic data globally.

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

    Directory of Open Access Journals (Sweden)

    Weitemier Kevin

    2011-05-01

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

  11. Forecasting Stock Exchange Movements Using Artificial Neural Network Models and Hybrid Models

    Science.gov (United States)

    Güreşen, Erkam; Kayakutlu, Gülgün

    Forecasting stock exchange rates is an important financial problem that is receiving increasing attention. During the last few years, a number of neural network models and hybrid models have been proposed for obtaining accurate prediction results, in an attempt to outperform the traditional linear and nonlinear approaches. This paper evaluates the effectiveness of neural network models; recurrent neural network (RNN), dynamic artificial neural network (DAN2) and the hybrid neural networks which use generalized autoregressive conditional heteroscedasticity (GARCH) and exponential generalized autoregressive conditional heteroscedasticity (EGARCH) to extract new input variables. The comparison for each model is done in two view points: MSE and MAD using real exchange daily rate values of Istanbul Stock Exchange (ISE) index XU10).

  12. Using chaotic artificial neural networks to model memory in the brain

    Science.gov (United States)

    Aram, Zainab; Jafari, Sajad; Ma, Jun; Sprott, Julien C.; Zendehrouh, Sareh; Pham, Viet-Thanh

    2017-03-01

    In the current study, a novel model for human memory is proposed based on the chaotic dynamics of artificial neural networks. This new model explains a biological fact about memory which is not yet explained by any other model: There are theories that the brain normally works in a chaotic mode, while during attention it shows ordered behavior. This model uses the periodic windows observed in a previously proposed model for the brain to store and then recollect the information.

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

    Science.gov (United States)

    Stiffler, A. Kent

    1989-01-01

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

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

    Science.gov (United States)

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

    2016-01-01

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

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

    DEFF Research Database (Denmark)

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

    2004-01-01

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

  16. 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)

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. Macrobenthos habitat potential mapping using GIS-based artificial neural network models.

    Science.gov (United States)

    Lee, Saro; Park, Inhye; Koo, Bon Joo; Ryu, Joo-Hyung; Choi, Jong-Kuk; Woo, Han Jun

    2013-02-15

    This paper proposes and tests a method of producing macrobenthos habitat potential maps in Hwangdo tidal flat, Korea based on an artificial neural network. Samples of macrobenthos were collected during field work, and eight control factors were compiled as a spatial database from remotely sensed data and GIS analysis. The macrobenthos habitat potential maps were produced using an artificial neural network model. Macrobenthos habitat potential maps were made for Macrophthalmus dilatatus, Cerithideopsilla cingulata, and Armandia lanceolata. The maps were validated by compared with the surveyed habitat locations. A strong correlation between the potential maps and species locations was revealed. The validation result showed average accuracies of 74.9%, 78.32%, and 73.27% for M. dilatatus, C. cingulata, and A. lanceolata, respectively. A GIS-based artificial neural network model combined with remote sensing techniques is an effective tool for mapping the areas of macrobenthos habitat potential in tidal flats.

  2. Forecasting TRY/USD Exchange Rate with Various Artificial Neural Network Models

    Directory of Open Access Journals (Sweden)

    Cagatay Bal

    2017-02-01

    Full Text Available Exchange rate forecasting is one of the most common subjects among the forecasting problem field. Researchers and academicians from many different disciplines proposed various approaches for better exchange rate forecasting. In recent years, for solving the stated forecasting problem artificial neural networks have become successful tool to obtain solutions. Many different artificial neural networks have been used, developed and still developing for even better and trustable forecasts. In this study, TRY/USD exchange rate forecasting is modeled with different learning algorithms, activations functions and performance measures. Various Artificial Neural Network (ANN models for better forecasting were investigated, compared and the obtained forecasting results interpreted respectively. The results of the application show that Variable Learning Rate Backpropagation learning algorithm with tan-sigmoid activation function has the best performance for TRY/USD exchange rate forecasting.

  3. Exploiting linkage disequilibrium in statistical modelling in quantitative genomics

    DEFF Research Database (Denmark)

    Wang, Lei

    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...... 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 improves...

  4. 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)

  5. 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.

  6. 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.

  7. 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

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

    Directory of Open Access Journals (Sweden)

    Sumit Tulshidas Patil

    2015-03-01

    Full Text Available 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 of viscera and asked 150 students of 1st year MBBS to answer it. Results: All the 150 students agreed that bones are necessary for study but only 36 students have bone set (14 original & 22 artificial. Maximum students get the bones only when made available from department. While 107 (71% students said they would prefer original specimen of viscera, over POP models, for studying; but still 126 (84% wanted to keep the POP models of viscera while studying its relations from text book. Conclusions: Good quality artificial bones should be promoted for students, if original bones are not available. It will be better than having nothing. POP models of viscera cannot replace original viscera but due to its handy quality will be helpful for understanding.

  9. A multiscale approach for modeling actuation response of polymeric artificial muscles.

    Science.gov (United States)

    Sharafi, Soodabeh; Li, Guoqiang

    2015-05-21

    Artificial muscles are emerging materials in the field of smart materials with applications in aerospace, robotic, and biomedical industries. Despite extensive experimental investigations in this field, there is a need for numerical modeling techniques that facilitate cutting edge research and development. This work aims at studying an artificial muscle made of twisted Nylon 6.6 fibers that are highly cold-drawn. A computationally efficient phenomenological thermo-mechanical constitutive model is developed in which several physical properties of the artificial muscles are incorporated to minimize the trial-and-error numerical curve fitting processes. Two types of molecular chains are considered at the micro-scale level that control training and actuation processes viz. (a) helically oriented chains which are structural switches that store a twisted shape in their low temperature phase and restore their random configuration during the thermal actuation process, and (b) entropic chains which are highly drawn chains that could actuate as soon as the muscle heats up, and saturates when coil contact temperature is reached. The thermal actuation response of the muscle over working temperatures has been elaborated in the Modeling section. The performance of the model is validated by available experiments in the literature. The model may provide a design platform for future artificial muscle developments.

  10. A self-taught artificial agent for multi-physics computational model personalization.

    Science.gov (United States)

    Neumann, Dominik; Mansi, Tommaso; Itu, Lucian; Georgescu, Bogdan; Kayvanpour, Elham; Sedaghat-Hamedani, Farbod; Amr, Ali; Haas, Jan; Katus, Hugo; Meder, Benjamin; Steidl, Stefan; Hornegger, Joachim; Comaniciu, Dorin

    2016-12-01

    Personalization is the process of fitting a model to patient data, a critical step towards application of multi-physics computational models in clinical practice. Designing robust personalization algorithms is often a tedious, time-consuming, model- and data-specific process. We propose to use artificial intelligence concepts to learn this task, inspired by how human experts manually perform it. The problem is reformulated in terms of reinforcement learning. In an off-line phase, Vito, our self-taught artificial agent, learns a representative decision process model through exploration of the computational model: it learns how the model behaves under change of parameters. The agent then automatically learns an optimal strategy for on-line personalization. The algorithm is model-independent; applying it to a new model requires only adjusting few hyper-parameters of the agent and defining the observations to match. The full knowledge of the model itself is not required. Vito was tested in a synthetic scenario, showing that it could learn how to optimize cost functions generically. Then Vito was applied to the inverse problem of cardiac electrophysiology and the personalization of a whole-body circulation model. The obtained results suggested that Vito could achieve equivalent, if not better goodness of fit than standard methods, while being more robust (up to 11% higher success rates) and with faster (up to seven times) convergence rate. Our artificial intelligence approach could thus make personalization algorithms generalizable and self-adaptable to any patient and any model.

  11. Artificial Intelligence for Constructing Accurate, Low-Cost Models and

    Science.gov (United States)

    2005-01-01

    0.5 1 1.5 2 2.5 -12 -7 -2 3 8 13 AOA C l Equation Model Human Predicition Machine Learning Test Data Figure C-14 NACA 4421 Model Comparison...Natrajan, Anand, and Srinivasan, Sudhir, (1997) “Consistency Maintenance in Multiresolution Simulation”, ACM Transactions on Modeling and

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

    Directory of Open Access Journals (Sweden)

    Marijana Zekić-Sušac

    2013-02-01

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

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

    OpenAIRE

    Abdelkrim Moussaoui; Yacine Selaimia; Hadj A. Abbassi

    2006-01-01

    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 capa...

  14. 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.

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

    OpenAIRE

    Khalil, Khaled M.; Abdel-Aziz, 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, a...

  16. 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

  17. Multiobjective training of artificial neural networks for rainfall-runoff modeling

    NARCIS (Netherlands)

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

    2008-01-01

    This paper presents results on the application of various optimization algorithms for the training of artificial neural network rainfall-runoff models. Multilayered feed-forward networks for forecasting discharge from two mesoscale catchments in different climatic regions have been developed for thi

  18. 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.

  19. The influence of artificial-thunderstorm cell polarity on discharge initiation by model hydrometeor arrays

    Science.gov (United States)

    Temnikov, A. G.; Chernenskii, L. L.; Orlov, A. V.; Lysov, N. Yu.; Belova, O. S.; Kalugina, I. E.; Gerastenok, T. K.; Zhuravkova, D. S.

    2017-02-01

    The initiation of discharge by model hydrometeors between an artificial-thunderstorm cell (aerosol cloud) of negative or positive polarity and ground has been experimentally studied. It is established for the first time that the conditions of cloud-ground spark discharge initiation by hydrometeors, as well as the characteristics of discharge significantly depend on the polarity of charged cloud. The effect of hydrometeor arrays can be manifested by the cloud-ground lightning initiated in a thundercloud and used for developing scientific principles of artificial lightning discharge.

  20. 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.

  1. Network Based Prediction Model for Genomics Data Analysis*

    OpenAIRE

    Huang, Ying; Wang, Pei

    2012-01-01

    Biological networks, such as genetic regulatory networks and protein interaction networks, provide important information for studying gene/protein activities. In this paper, we propose a new method, NetBoosting, for incorporating a priori biological network information in analyzing high dimensional genomics data. Specially, we are interested in constructing prediction models for disease phenotypes of interest based on genomics data, and at the same time identifying disease susceptible genes. ...

  2. 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

    2016-07-03

    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.

  3. Gremlin: an interactive visualization model for analyzing genomic rearrangements.

    Science.gov (United States)

    O'Brien, Trevor M; Ritz, Anna M; Raphael, Benjamin J; Laidlaw, David H

    2010-01-01

    In this work we present, apply, and evaluate a novel, interactive visualization model for comparative analysis of structural variants and rearrangements in human and cancer genomes, with emphasis on data integration and uncertainty visualization. To support both global trend analysis and local feature detection, this model enables explorations continuously scaled from the high-level, complete genome perspective, down to the low-level, structural rearrangement view, while preserving global context at all times. We have implemented these techniques in Gremlin, a genomic rearrangement explorer with multi-scale, linked interactions, which we apply to four human cancer genome data sets for evaluation. Using an insight-based evaluation methodology, we compare Gremlin to Circos, the state-of-the-art in genomic rearrangement visualization, through a small user study with computational biologists working in rearrangement analysis. Results from user study evaluations demonstrate that this visualization model enables more total insights, more insights per minute, and more complex insights than the current state-of-the-art for visual analysis and exploration of genome rearrangements.

  4. Application of artificial intelligence models in water quality forecasting.

    Science.gov (United States)

    Yeon, I S; Kim, J H; Jun, K W

    2008-06-01

    The real-time data of the continuous water quality monitoring station at the Pyeongchang river was analyzed separately during the rainy period and non-rainy period. Total organic carbon data observed during the rainy period showed a greater mean value, maximum value and standard deviation than the data observed during the non-rainy period. Dissolved oxygen values during the rainy period were lower than those observed during the non-rainy period. It was analyzed that the discharge due to rain fall from the basin affects the change of the water quality. A model for the forecasting of water quality was constructed and applied using the neural network model and the adaptive neuro-fuzzy inference system. Regarding the models of levenberg-marquardt neural network, modular neural network and adaptive neuro-fuzzy inference system, all three models showed good results for the simulation of total organic carbon. The levenberg-marquardt neural network and modular neural network models showed better results than the adaptive neuro-fuzzy inference system model in the forecasting of dissolved oxygen. The modular neural network model, which was applied with the qualitative data of time in addition to quantitative data, showed the least error.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. Comparative analysis of regression and artificial neural network models for wind speed prediction

    Science.gov (United States)

    Bilgili, Mehmet; Sahin, Besir

    2010-11-01

    In this study, wind speed was modeled by linear regression (LR), nonlinear regression (NLR) and artificial neural network (ANN) methods. A three-layer feedforward artificial neural network structure was constructed and a backpropagation algorithm was used for the training of ANNs. To get a successful simulation, firstly, the correlation coefficients between all of the meteorological variables (wind speed, ambient temperature, atmospheric pressure, relative humidity and rainfall) were calculated taking two variables in turn for each calculation. All independent variables were added to the simple regression model. Then, the method of stepwise multiple regression was applied for the selection of the “best” regression equation (model). Thus, the best independent variables were selected for the LR and NLR models and also used in the input layer of the ANN. The results obtained by all methods were compared to each other. Finally, the ANN method was found to provide better performance than the LR and NLR methods.

  11. 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.

  12. 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.

  13. 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...... 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...

  14. Modeling Consideration Sets and Brand Choice Using Artificial Neural Networks

    NARCIS (Netherlands)

    B.L.K. Vroomen (Björn); Ph.H.B.F. Franses (Philip Hans); J.E.M. van Nierop

    2001-01-01

    textabstractThe concept of consideration sets makes brand choice a two-step process. House-holds first construct a consideration set which not necessarily includes all available brands and conditional on this set they make a final choice. In this paper we put forward a parametric econometric model f

  15. Modelling of Surface Ships using Artificial Neural Networks

    DEFF Research Database (Denmark)

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

    are costly and time consuming - at least in the context of sampling sufficient data for statistical analyses purposes. Furthermore, a modelling of the process of navigating could be used in a vessel autopilot to guide a vessel on a fixed heading (course - keeping) or a new heading (course -changing)....

  16. An integrated 3-Dimensional Genome Modeling Engine for data-driven simulation of spatial genome organization.

    Science.gov (United States)

    Szałaj, Przemysław; Tang, Zhonghui; Michalski, Paul; Pietal, Michal J; Luo, Oscar J; Sadowski, Michał; Li, Xingwang; Radew, Kamen; Ruan, Yijun; Plewczynski, Dariusz

    2016-12-01

    ChIA-PET is a high-throughput mapping technology that reveals long-range chromatin interactions and provides insights into the basic principles of spatial genome organization and gene regulation mediated by specific protein factors. Recently, we showed that a single ChIA-PET experiment provides information at all genomic scales of interest, from the high-resolution locations of binding sites and enriched chromatin interactions mediated by specific protein factors, to the low resolution of nonenriched interactions that reflect topological neighborhoods of higher-order chromosome folding. This multilevel nature of ChIA-PET data offers an opportunity to use multiscale 3D models to study structural-functional relationships at multiple length scales, but doing so requires a structural modeling platform. Here, we report the development of 3D-GNOME (3-Dimensional Genome Modeling Engine), a complete computational pipeline for 3D simulation using ChIA-PET data. 3D-GNOME consists of three integrated components: a graph-distance-based heat map normalization tool, a 3D modeling platform, and an interactive 3D visualization tool. Using ChIA-PET and Hi-C data derived from human B-lymphocytes, we demonstrate the effectiveness of 3D-GNOME in building 3D genome models at multiple levels, including the entire genome, individual chromosomes, and specific segments at megabase (Mb) and kilobase (kb) resolutions of single average and ensemble structures. Further incorporation of CTCF-motif orientation and high-resolution looping patterns in 3D simulation provided additional reliability of potential biologically plausible topological structures.

  17. 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.

  18. Log-Linear Model Based Behavior Selection Method for Artificial Fish Swarm Algorithm

    Directory of Open Access Journals (Sweden)

    Zhehuang Huang

    2015-01-01

    Full Text Available Artificial fish swarm algorithm (AFSA is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm.

  19. 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.

  20. Log-linear model based behavior selection method for artificial fish swarm algorithm.

    Science.gov (United States)

    Huang, Zhehuang; Chen, Yidong

    2015-01-01

    Artificial fish swarm algorithm (AFSA) is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm.

  1. Modeling and optoelectronic realization of an artificial cortex

    Science.gov (United States)

    Pashaie, Ramin

    Cortex, the outermost layer of the cerebrum, is recognized as the most developed part of the brain. It is believed that the higher-level functionality of the brain, the operations such as perception, cognition, and learning of both static and dynamic sensory information, originates from the dynamics of the massively interconnected gray cells of cortex. Because of the compact three-dimensional architecture of this biological computational paradigm, realization of bio-inspired machines that imitate such functionalities, including all the cellular details, is prohibitively difficult even if we consider the available nano-fabrication technologies. Based on this logical deduction, instead of considering each single neuron, an intriguing conjecture is to build aggregate level models that mimic the behavior of a population of neurons with collective emergent properties. In our approach, which is presented in this dissertation, cortex is assumed to be a composition of a sequence of discernable interconnected cortical patches. Each concerned patch is a network of asymmetrically coupled complex processing elements whose dynamics contain not only fixed-point and periodic attractors but also bifurcation and chaos. Dynamics of the complex processing elements, in this dissertation, is mathematically modeled by a slight modification of the time evolution of netlets adapted from computational neuroscience. Regarding this modification, the dynamics of a netlet is approximated by that of a quadratic return map. Studying the previous experimental observations demonstrates that a smart way of coupling such processing units is to couple them through their bifurcation parameters. Putting all pieces of this puzzle together, we model each cortical patch by a network of parametrically coupled quadratic return maps. Our simulations prove the ability of this network to emulate many salient features of cortical information processing, such as clustering, classification, generation of sparse

  2. The prediction of brick wall strengths with artificial neural networks model

    Science.gov (United States)

    Demir, Ali; Kumanlioglu, Ahmet Ali

    2017-01-01

    The aim of this study is to predict with Artificial Neural Networks (ANN) shear strength of brick masonry walls. Shear strength of the walls is determined with diagonal shear tests. It is very difficult to determine strengths of brick masonry walls with experimental procedures. Therefore, an Artificial Neural Networks model is developed with data obtained by investigating many papers from literature and experiments carried out by the authors. Finally, a good degree of coherency is obtained between the experimental and predicted data. The model that is developed makes it possible to easily predict shear strength of the masonry walls. Additionally, this model can be continuously trained with new data and its applicability range can easily be expanded.

  3. 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.

  4. 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

  5. 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...

  6. Artificial intelligence and exponential technologies business models evolution and new investment opportunities

    CERN Document Server

    Corea, Francesco

    2017-01-01

    Artificial Intelligence is a huge breakthrough technology that is changing our world. It requires some degrees of technical skills to be developed and understood, so in this book we are going to first of all define AI and categorize it with a non-technical language. We will explain how we reached this phase and what historically happened to artificial intelligence in the last century. Recent advancements in machine learning, neuroscience, and artificial intelligence technology will be addressed, and new business models introduced for and by artificial intelligence research will be analyzed. Finally, we will describe the investment landscape, through the quite comprehensive study of almost 14,000 AI companies and we will discuss important features and characteristics of both AI investors as well as investments. This is the “Internet of Thinks” era. AI is revolutionizing the world we live in. It is augmenting the human experiences, and it targets to amplify human intelligence in a future not so distant from...

  7. Score-based prediction of genomic islands in prokaryotic genomes using hidden Markov models

    Directory of Open Access Journals (Sweden)

    Surovcik Katharina

    2006-03-01

    Full Text Available Abstract Background Horizontal gene transfer (HGT is considered a strong evolutionary force shaping the content of microbial genomes in a substantial manner. It is the difference in speed enabling the rapid adaptation to changing environmental demands that distinguishes HGT from gene genesis, duplications or mutations. For a precise characterization, algorithms are needed that identify transfer events with high reliability. Frequently, the transferred pieces of DNA have a considerable length, comprise several genes and are called genomic islands (GIs or more specifically pathogenicity or symbiotic islands. Results We have implemented the program SIGI-HMM that predicts GIs and the putative donor of each individual alien gene. It is based on the analysis of codon usage (CU of each individual gene of a genome under study. CU of each gene is compared against a carefully selected set of CU tables representing microbial donors or highly expressed genes. Multiple tests are used to identify putatively alien genes, to predict putative donors and to mask putatively highly expressed genes. Thus, we determine the states and emission probabilities of an inhomogeneous hidden Markov model working on gene level. For the transition probabilities, we draw upon classical test theory with the intention of integrating a sensitivity controller in a consistent manner. SIGI-HMM was written in JAVA and is publicly available. It accepts as input any file created according to the EMBL-format. It generates output in the common GFF format readable for genome browsers. Benchmark tests showed that the output of SIGI-HMM is in agreement with known findings. Its predictions were both consistent with annotated GIs and with predictions generated by different methods. Conclusion SIGI-HMM is a sensitive tool for the identification of GIs in microbial genomes. It allows to interactively analyze genomes in detail and to generate or to test hypotheses about the origin of acquired

  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 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.

  10. 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

    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......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...

  11. Human Cancer Models Initiative | Office of Cancer Genomics

    Science.gov (United States)

    The Human Cancer Models Initiative (HCMI) is an international consortium that is generating novel human tumor-derived culture models, which are annotated with genomic and clinical data. In an effort to advance cancer research and more fully understand how in vitro findings are related to clinical biology, HCMI-developed models and related data will be available as a community resource for cancer research.

  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. Artificial neural network modeling of jatropha oil fueled diesel engine for emission predictions

    Directory of Open Access Journals (Sweden)

    Ganapathy Thirunavukkarasu

    2009-01-01

    Full Text Available This paper deals with artificial neural network modeling of diesel engine fueled with jatropha oil to predict the unburned hydrocarbons, smoke, and NOx emissions. The experimental data from the literature have been used as the data base for the proposed neural network model development. For training the networks, the injection timing, injector opening pressure, plunger diameter, and engine load are used as the input layer. The outputs are hydrocarbons, smoke, and NOx emissions. The feed forward back propagation learning algorithms with two hidden layers are used in the networks. For each output a different network is developed with required topology. The artificial neural network models for hydrocarbons, smoke, and NOx emissions gave R2 values of 0.9976, 0.9976, and 0.9984 and mean percent errors of smaller than 2.7603, 4.9524, and 3.1136, respectively, for training data sets, while the R2 values of 0.9904, 0.9904, and 0.9942, and mean percent errors of smaller than 6.5557, 6.1072, and 4.4682, respectively, for testing data sets. The best linear fit of regression to the artificial neural network models of hydrocarbons, smoke, and NOx emissions gave the correlation coefficient values of 0.98, 0.995, and 0.997, respectively.

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

    Science.gov (United States)

    Mykles, Donald L; Hui, Jerome H L

    2015-11-01

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

  15. 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.

  16. 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.

  17. Modeling of submerged membrane bioreactor treating cheese whey wastewater by artificial neural network.

    Science.gov (United States)

    Cinar, Ozer; Hasar, Halil; Kinaci, Cumali

    2006-05-17

    A submerged membrane bioreactor receiving cheese whey was modeled by artificial neural network and its performance over a period of 100 days at different solids retention times was evaluated with this robust tool. A cascade-forward network was used to model the membrane bioreactor and normalization was used as a preprocessing method. The network was fed with two subsets of operational data, with two-thirds being used for training and one-third for testing the performance of the artificial neural network. The training procedure for effluent chemical oxygen demand (COD), ammonia, nitrate and total phosphate concentrations was very successful and a perfect match was obtained between the measured and the calculated concentrations. The results of the confirmation (or testing) procedure for effluent ammonia and nitrate concentrations were very successful; however, the results of the confirmation procedure for effluent COD and total phosphate concentrations were only satisfactory.

  18. 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.

  19. Prediction Model of Antibacterial Activities for Inorganic Antibacterial Agents Based on Artificial Neural Networks

    Institute of Scientific and Technical Information of China (English)

    刘雪峰; 张利; 涂铭旌

    2004-01-01

    Quantitatively evaluation of antibacterial activities of inorganic antibacterial agents is an urgent problem to be solved. Using experimental data by an orthogonal design, a prediction model of the relation between conditions of preparing inorganic antibacterial agents and their antibacterial activities has been developed. This is accomplished by introducing BP artificial neural networks in the study of inorganic antibacterial agents..It provides a theoretical support for the development and research on inorganic antibacterial agents.

  20. Analysis of multicriteria models application for selection of an optimal artificial lift method in oil production

    OpenAIRE

    Crnogorac, Miroslav P.; Danilović, Dušan Š.; Karović-Maričić, Vesna D.; Leković, Branko A.

    2016-01-01

    In the world today for the exploitation of oil reservoirs by artificial lift methods are applied different types of deep pumps (piston, centrifugal, screw, hydraulic), water jet pumps and gas lift (continuous, intermittent and plunger). Maximum values of oil production achieved by these exploitation methods are significantly different. In order to select the optimal exploitation method of oil well, the multicriteria analysis models are used. In this paper is presented an analysis of the multi...

  1. Application of Artificial Neural Networks in the Heart Electrical Axis Position Conclusion Modeling

    Science.gov (United States)

    Bakanovskaya, L. N.

    2016-08-01

    The article touches upon building of a heart electrical axis position conclusion model using an artificial neural network. The input signals of the neural network are the values of deflections Q, R and S; and the output signal is the value of the heart electrical axis position. Training of the network is carried out by the error propagation method. The test results allow concluding that the created neural network makes a conclusion with a high degree of accuracy.

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

    DEFF Research Database (Denmark)

    Denell, Robin; Gibbs, Richard; Muzny, Donna

    2008-01-01

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

  3. The genome sequence of the model ascomycete fungus Podospora anserina

    NARCIS (Netherlands)

    Espagne, Eric; Lespinet, Olivier; Malagnac, Fabienne; Da Silva, Corinne; Jaillon, Olivier; Porcel, Betina M; Couloux, Arnaud; Aury, Jean-Marc; Ségurens, Béatrice; Poulain, Julie; Anthouard, Véronique; Grossetete, Sandrine; Khalili, Hamid; Coppin, Evelyne; Déquard-Chablat, Michelle; Picard, Marguerite; Contamine, Véronique; Arnaise, Sylvie; Bourdais, Anne; Berteaux-Lecellier, Véronique; Gautheret, Daniel; de Vries, Ronald P; Battaglia, Evy; Coutinho, Pedro M; Danchin, Etienne Gj; Henrissat, Bernard; Khoury, Riyad El; Sainsard-Chanet, Annie; Boivin, Antoine; Pinan-Lucarré, Bérangère; Sellem, Carole H; Debuchy, Robert; Wincker, Patrick; Weissenbach, Jean; Silar, Philippe

    2008-01-01

    BACKGROUND: The dung-inhabiting ascomycete fungus Podospora anserina is a model used to study various aspects of eukaryotic and fungal biology, such as ageing, prions and sexual development. RESULTS: We present a 10X draft sequence of P. anserina genome, linked to the sequences of a large expressed

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

    Directory of Open Access Journals (Sweden)

    Sumit Goyal

    2012-04-01

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

  5. 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.

  6. 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.

  7. 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.

  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. Optimization of biopharmaceutical downstream processes supported by mechanistic models and artificial neural networks.

    Science.gov (United States)

    Pirrung, Silvia M; van der Wielen, Luuk A M; van Beckhoven, Ruud F W C; van de Sandt, Emile J A X; Eppink, Michel H M; Ottens, Marcel

    2017-01-05

    Downstream process development is a major area of importance within the field of bioengineering. During the design of such a downstream process, important decisions have to be made regarding the type of unit operations as well as their sequence and their operating conditions. Current computational approaches addressing these issues either show a high level of simplification or struggle with computational speed. Therefore, this article presents a new approach that combines detailed mechanistic models and speed-enhancing artificial neural networks. This approach was able to simultaneously optimize a process with three different chromatographic columns toward yield with a minimum purity of 99.9%. The addition of artificial neural networks greatly accelerated this optimization. Due to high computational speed, the approach is easily extendable to include more unit operations. Therefore, it can be of great help in the acceleration of downstream process development. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 2017.

  10. 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)

    WU; Qingjiang; (吴清江); YE; Yuzhen; (叶玉珍); DONG; Xinhong; (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.

  11. 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.

  12. Past, present and prospect of an Artificial Intelligence (AI) based model for sediment transport prediction

    Science.gov (United States)

    Afan, Haitham Abdulmohsin; El-shafie, Ahmed; Mohtar, Wan Hanna Melini Wan; Yaseen, Zaher Mundher

    2016-10-01

    An accurate model for sediment prediction is a priority for all hydrological researchers. Many conventional methods have shown an inability to achieve an accurate prediction of suspended sediment. These methods are unable to understand the behaviour of sediment transport in rivers due to the complexity, noise, non-stationarity, and dynamism of the sediment pattern. In the past two decades, Artificial Intelligence (AI) and computational approaches have become a remarkable tool for developing an accurate model. These approaches are considered a powerful tool for solving any non-linear model, as they can deal easily with a large number of data and sophisticated models. This paper is a review of all AI approaches that have been applied in sediment modelling. The current research focuses on the development of AI application in sediment transport. In addition, the review identifies major challenges and opportunities for prospective research. Throughout the literature, complementary models superior to classical modelling.

  13. 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.

  14. 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.

  15. 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

    is by active truncated models. In these models only the very top part of the system is represented by a physical model whereas the behavior of the part below the truncation is calculated by numerical models and accounted for in the physical model by active actuators applying relevant forces to the physical...... model. Hence, in principal it is possible to achieve reliable experimental data for much larger water depths than what the actual depth of the test basin would suggest. However, since the computations must be faster than real time, as the numerical simulations and the physical experiment run...... 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...

  16. 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.

  17. Genome Editing and Its Applications in Model Organisms.

    Science.gov (United States)

    Ma, Dongyuan; Liu, Feng

    2015-12-01

    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.

  18. 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.

  19. 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.

  20. Artificial neural network model of constitutive relations for shock-prestrained copper

    Institute of Scientific and Technical Information of China (English)

    杨扬; 朱远志; 李正华; 张新明; 杨立斌; 陈志永

    2001-01-01

    Data from the deformation on Split-Hopkinson Bar were used for constructing an artificial neural network model. When putting the thermodynamic parameters of the metals into the trained network model, the corresponding yielding stress can be predicted. The results show that the systematic error is small when the objective function is 0.5, the number of the nodes in the hidden layer is 6 and the learning rate is about 0.1, and the accuracy of the rate-error is less than 3%.

  1. 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.

  2. 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.

  3. 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

  4. Modeling protein network evolution under genome duplication and domain shuffling

    Directory of Open Access Journals (Sweden)

    Isambert Hervé

    2007-11-01

    Full Text Available Abstract Background Successive whole genome duplications have recently been firmly established in all major eukaryote kingdoms. Such exponential evolutionary processes must have largely contributed to shape the topology of protein-protein interaction (PPI networks by outweighing, in particular, all time-linear network growths modeled so far. Results We propose and solve a mathematical model of PPI network evolution under successive genome duplications. This demonstrates, from first principles, that evolutionary conservation and scale-free topology are intrinsically linked properties of PPI networks and emerge from i prevailing exponential network dynamics under duplication and ii asymmetric divergence of gene duplicates. While required, we argue that this asymmetric divergence arises, in fact, spontaneously at the level of protein-binding sites. This supports a refined model of PPI network evolution in terms of protein domains under exponential and asymmetric duplication/divergence dynamics, with multidomain proteins underlying the combinatorial formation of protein complexes. Genome duplication then provides a powerful source of PPI network innovation by promoting local rearrangements of multidomain proteins on a genome wide scale. Yet, we show that the overall conservation and topology of PPI networks are robust to extensive domain shuffling of multidomain proteins as well as to finer details of protein interaction and evolution. Finally, large scale features of direct and indirect PPI networks of S. cerevisiae are well reproduced numerically with only two adjusted parameters of clear biological significance (i.e. network effective growth rate and average number of protein-binding domains per protein. Conclusion This study demonstrates the statistical consequences of genome duplication and domain shuffling on the conservation and topology of PPI networks over a broad evolutionary scale across eukaryote kingdoms. In particular, scale

  5. 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.

  6. 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.

  7. Improving peak flow estimates in artificial neural network river flow models

    Science.gov (United States)

    Sudheer, K. P.; Nayak, P. C.; Ramasastri, K. S.

    2003-02-01

    In this paper, the concern of accuracy in peak estimation by the artificial neural network (ANN) river flow models is discussed and a suitable statistical procedure to get better estimates from these models is presented. The possible cause for underestimation of peak flow values has been attributed to the local variations in the function being mapped due to varying skewness in the data series, and theoretical considerations of the network functioning confirm this. It is envisaged that an appropriate data transformation will reduce the local variations in the function being mapped, and thus any ANN model built on the transformed series should perform better. This heuristic is illustrated and confirmed by many case studies and the results suggest that the model performance is significantly improved by data transformation. The model built on transformed data outperforms the model built on raw data in terms of various statistical performance indices. The peak estimates are improved significantly by data transformation.

  8. 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.

  9. 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.

  10. Genome-scale modeling for metabolic engineering.

    Science.gov (United States)

    Simeonidis, Evangelos; Price, Nathan D

    2015-03-01

    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.

  11. 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.

  12. 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.

  13. Implementing an Agent Based Artificial Stock Market Model in JADE – An Illustration

    Directory of Open Access Journals (Sweden)

    PN Kumar

    2013-06-01

    Full Text Available Agent-based approach to economic and financial analysis is a suitable research methodolgy for developing and understanding the complex patterns and phenomena that are observed in economic systems. In agent-based financial market models, prices can be endogenously formed by the system itself as the result of interaction of market participants. By using agents for the study, heterogeneous, boundedly rational, and adaptive behaviour of market participants can be analysed and its impact assessed. The collective behaviour of such groups is determined by the interaction of individual behaviours distributed across the group. This being the scenario prevailing in stock markets, agent based models are suitable for the study. Through this paper, we have attempted to illustrate a detailed implementation of multi agents in an artificial stock market invoking the agent-based methodology on Java Agent Development (JADE environment, a platform to develop multi-agent systems. The Extended Glosten and Milgrom Model, an agent based artificial stock market model, has been chosen to depict the multi-agent environment model in JADE.

  14. 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

  15. 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

  16. 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.

  17. 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

  18. Connectionist models of artificial grammar learning: what type of knowledge is acquired?

    Science.gov (United States)

    Kinder, Annette; Lotz, Anja

    2009-09-01

    Two experiments are presented that test the predictions of two associative learning models of Artificial Grammar Learning. The two models are the simple recurrent network (SRN) and the competitive chunking (CC) model. The two experiments investigate acquisition of different types of knowledge in this task: knowledge of frequency and novelty of stimulus fragments (Experiment 1) and knowledge of letter positions, of small fragments, and of large fragments up to entire strings (Experiment 2). The results show that participants acquired all types of knowledge. Simulation studies demonstrate that the CC model explains the acquisition of all types of fragment knowledge but fails to account for the acquisition of positional knowledge. The SRN model, by contrast, accounts for the entire pattern of results found in the two experiments.

  19. 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.

  20. Artificial Neural Network Turbulent Modeling for Predicting the Pressure Drop of Nanofluid

    Directory of Open Access Journals (Sweden)

    M. S. Youssef

    2013-10-01

    Full Text Available An Artificial Neural Network (ANN model was developed to predict the pressure drop of titanium dioxide-water (TiO2-water. The model was developed based on experimentally measured data. Experimental measurements of fully developed turbulent flow in pipe at different particle volumetric concentrations, nanoparticle diameters, nanofluid temperature and Reynolds number were used to construct the proposed model. The ANN model was validated by comparing the predicted results with the experimental measured data at different experimental conditions. It was shown that, the present ANN model performed well in predicting the pressure drop of TiO2-water nanofluid under different flow conditions with a high degree of accuracy.

  1. Explaining the internal behaviour of artificial neural network river flow models

    Science.gov (United States)

    Sudheer, K. P.; Jain, Ashu

    2004-03-01

    A novel method of visualizing and understanding the internal functional behaviour of an artificial neural network (ANN) river flow model is presented. The method hypothesizes that an ANN is able to map a function similar to the flow duration curve while modelling the river flow. A mathematical analysis of the hypothesis is presented, and a case study of an ANN river flow model confirms its significance. The proposed approach is also useful within other models that improve the performance of an ANN. The reasons why these models improve a raw ANN can be clearly understood using this approach. While the field of ANN knowledge-extraction is one that continues to attract considerable interest, it is anticipated that the current approach will initiate further research and make ANNs more useful to the hydrologic community.

  2. Implementation of recurrent artificial neural networks for nonlinear dynamic modeling in biomedical applications.

    Science.gov (United States)

    Stošovic, Miona V Andrejevic; Litovski, Vanco B

    2013-11-01

    Simulation is indispensable during the design of many biomedical prostheses that are based on fundamental electrical and electronic actions. However, simulation necessitates the use of adequate models. The main difficulties related to the modeling of such devices are their nonlinearity and dynamic behavior. Here we report the application of recurrent artificial neural networks for modeling of a nonlinear, two-terminal circuit equivalent to a specific implantable hearing device. The method is general in the sense that any nonlinear dynamic two-terminal device or circuit may be modeled in the same way. The model generated was successfully used for simulation and optimization of a driver (operational amplifier)-transducer ensemble. This confirms our claim that in addition to the proper design and optimization of the hearing actuator, optimization in the electronic domain, at the electronic driver circuit-to-actuator interface, should take place in order to achieve best performance of the complete hearing aid.

  3. Modeling regulatory cascades using Artificial Neural Networks: the case of transcriptional regulatory networks shaped during the yeast stress response.

    Science.gov (United States)

    Manioudaki, Maria E; Poirazi, Panayiota

    2013-01-01

    Over the last decade, numerous computational methods have been developed in order to infer and model biological networks. Transcriptional networks in particular have attracted significant attention due to their critical role in cell survival. The majority of network inference methods use genome-wide experimental data to search for modules of genes with coherent expression profiles and common regulators, often ignoring the multi-layer structure of transcriptional cascades. Modeling methodologies on the other hand assume a given network structure and vary significantly in their algorithmic approach, ranging from over-simplified representations (e.g., Boolean networks) to detailed -but computationally expensive-network simulations (e.g., with differential equations). In this work we use Artificial Neural Networks (ANNs) to model transcriptional regulatory cascades that emerge during the stress response in Saccharomyces cerevisiae and extend in three layers. We confine the structure of the ANNs to match the structure of the biological networks as determined by gene expression, DNA-protein interaction and experimental evidence provided in publicly available databases. Trained ANNs are able to predict the expression profile of 11 target genes across multiple experimental conditions with a correlation coefficient >0.7. When time-dependent interactions between upstream transcription factors (TFs) and their indirect targets are also included in the ANNs, accurate predictions are achieved for 30/34 target genes. Moreover, heterodimer formation is taken into account. We show that ANNs can be used to (1) accurately predict the expression of downstream genes in a 3-layer transcriptional cascade based on the expression of their indirect regulators and (2) infer the condition- and time-dependent activity of various TFs as well as during heterodimer formation. We show that a three-layer regulatory cascade whose structure is determined by co-expressed gene modules and their

  4. 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...... 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....

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

    Directory of Open Access Journals (Sweden)

    Pouraria Hassan

    2016-01-01

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

  6. 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

  7. Molecular recognition of genomic DNA in a condensate with a model surfactant for potential gene-delivery applications.

    Science.gov (United States)

    Singh, Priya; Choudhury, Susobhan; Chandra, Goutam Kumar; Lemmens, Peter; Pal, Samir Kumar

    2016-04-01

    The functionality of a gene carrying nucleic acid in an artificial gene-delivery system is important for the overall efficiency of the vehicle in vivo. Here, we have studied a well-known artificial gene-delivery system, which is a condensate of calf thymus DNA (CT-DNA) with a model cationic surfactant cetyltrimethylammonium bromide (CTAB) to investigate the molecular recognition of the genomic DNA in the condensate. While dynamic light scattering (DLS) and circular dichroism (CD) reveal structural aspects of the condensate and the constituting DNA respectively, picosecond resolved polarization gated spectroscopy and Förster resonance energy transfer (FRET) reveal molecular recognition of the genomic DNA in the condensate. We have considered ethidium bromide (EB) and crystal violet (CV), which are well known DNA-binding agents through intercalative (specific) and electrostatic (non-specific) interactions, respectively, as model ligands for the molecular recognition studies. A fluorescent cationic surfactant, Nonyl Acridine Orange (NAO) is considered to be a mimic of CTAB in the condensate. The polarization gated fluorescence of NAO at various temperatures has been used to investigate the local microviscosity of the condensate. The excellent spectral overlap of NAO emission and the absorption spectra of both EB and CV allow us to investigate FRET-distances of the ligands with respect to NAO in the condensate at various temperatures and thermal stability of ligand-binding of the genomic DNA. The thermodynamic properties of the molecular recognition have also been explored using Van't Hoff equation. We have also extended our studies to molecular recognition of the genomic DNA in the condensate as dried thin films. This has important implications for its application in bioelectronics.

  8. Artificial neural network modeling and optimization of ultrahigh pressure extraction of green tea polyphenols.

    Science.gov (United States)

    Xi, Jun; Xue, Yujing; Xu, Yinxiang; Shen, Yuhong

    2013-11-01

    In this study, the ultrahigh pressure extraction of green tea polyphenols was modeled and optimized by a three-layer artificial neural network. A feed-forward neural network trained with an error back-propagation algorithm was used to evaluate the effects of pressure, liquid/solid ratio and ethanol concentration on the total phenolic content of green tea extracts. The neural network coupled with genetic algorithms was also used to optimize the conditions needed to obtain the highest yield of tea polyphenols. The obtained optimal architecture of artificial neural network model involved a feed-forward neural network with three input neurons, one hidden layer with eight neurons and one output layer including single neuron. The trained network gave the minimum value in the MSE of 0.03 and the maximum value in the R(2) of 0.9571, which implied a good agreement between the predicted value and the actual value, and confirmed a good generalization of the network. Based on the combination of neural network and genetic algorithms, the optimum extraction conditions for the highest yield of green tea polyphenols were determined as follows: 498.8 MPa for pressure, 20.8 mL/g for liquid/solid ratio and 53.6% for ethanol concentration. The total phenolic content of the actual measurement under the optimum predicated extraction conditions was 582.4 ± 0.63 mg/g DW, which was well matched with the predicted value (597.2mg/g DW). This suggests that the artificial neural network model described in this work is an efficient quantitative tool to predict the extraction efficiency of green tea polyphenols.

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

    Science.gov (United States)

    Nourani, Vahid; Mousavi, Shahram

    2016-05-01

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

  10. 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.

  11. Complete chloroplast genome sequence of an orchid model plant candidate: Erycina pusilla apply in tropical Oncidium breeding.

    Directory of Open Access Journals (Sweden)

    I-Chun Pan

    Full Text Available Oncidium is an important ornamental plant but the study of its functional genomics is difficult. Erycina pusilla is a fast-growing Oncidiinae species. Several characteristics including low chromosome number, small genome size, short growth period, and its ability to complete its life cycle in vitro make E. pusilla a good model candidate and parent for hybridization for orchids. Although genetic information remains limited, systematic molecular analysis of its chloroplast genome might provide useful genetic information. By combining bacterial artificial chromosome (BAC clones and next-generation sequencing (NGS, the chloroplast (cp genome of E. pusilla was sequenced accurately, efficiently and economically. The cp genome of E. pusilla shares 89 and 84% similarity with Oncidium Gower Ramsey and Phalanopsis aphrodite, respectively. Comparing these 3 cp genomes, 5 regions have been identified as showing diversity. Using PCR analysis of 19 species belonging to the Epidendroideae subfamily, a conserved deletion was found in the rps15-trnN region of the Cymbidieae tribe. Because commercial Oncidium varieties in Taiwan are limited, identification of potential parents using molecular breeding method has become very important. To demonstrate the relationship between taxonomic position and hybrid compatibility of E. pusilla, 4 DNA regions of 36 tropically adapted Oncidiinae varieties have been analyzed. The results indicated that trnF-ndhJ and trnH-psbA were suitable for phylogenetic analysis. E. pusilla proved to be phylogenetically closer to Rodriguezia and Tolumnia than Oncidium, despite its similar floral appearance to Oncidium. These results indicate the hybrid compatibility of E. pusilla, its cp genome providing important information for Oncidium breeding.

  12. Radiation induced genome instability: multiscale modelling and data analysis

    Science.gov (United States)

    Andreev, Sergey; Eidelman, Yuri

    2012-07-01

    Genome instability (GI) is thought to be an important step in cancer induction and progression. Radiation induced GI is usually defined as genome alterations in the progeny of irradiated cells. The aim of this report is to demonstrate an opportunity for integrative analysis of radiation induced GI on the basis of multiscale modelling. Integrative, systems level modelling is necessary to assess different pathways resulting in GI in which a variety of genetic and epigenetic processes are involved. The multilevel modelling includes the Monte Carlo based simulation of several key processes involved in GI: DNA double strand breaks (DSBs) generation in cells initially irradiated as well as in descendants of irradiated cells, damage transmission through mitosis. Taking the cell-cycle-dependent generation of DNA/chromosome breakage into account ensures an advantage in estimating the contribution of different DNA damage response pathways to GI, as to nonhomologous vs homologous recombination repair mechanisms, the role of DSBs at telomeres or interstitial chromosomal sites, etc. The preliminary estimates show that both telomeric and non-telomeric DSB interactions are involved in delayed effects of radiation although differentially for different cell types. The computational experiments provide the data on the wide spectrum of GI endpoints (dicentrics, micronuclei, nonclonal translocations, chromatid exchanges, chromosome fragments) similar to those obtained experimentally for various cell lines under various experimental conditions. The modelling based analysis of experimental data demonstrates that radiation induced GI may be viewed as processes of delayed DSB induction/interaction/transmission being a key for quantification of GI. On the other hand, this conclusion is not sufficient to understand GI as a whole because factors of DNA non-damaging origin can also induce GI. Additionally, new data on induced pluripotent stem cells reveal that GI is acquired in normal mature

  13. 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

  14. A 3D model describing the initial structure of an artificial hydrological catchment

    Science.gov (United States)

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

    2009-04-01

    The initial development stages of artificially constructed hydrologic catchments are characterized by the absence of vegetation, soil organic matter and soil horizons. This results in increased surface runoff and favors erosion processes that dominate the initial phase. Hydraulic conditions on artificial catchments thus are governed by rapidly changing surface structures as well as by the primary internal structural framework. Contemporary hydrological modeling does not consider any dynamic change of relevant structural features but rather assumes a stable, invariant landscape. The objective of this study was the digital visualization and quantitative description of the initial state and its early structural dynamics, exemplified for the small artificial hydrological catchment "Huehnerwasser" near Cottbus, Germany. Photogrammetric surveys of surface and internal structural units (clay basis liner) during the construction phase provided spatially and temporally resolved data for digital elevation models (DEM). Interpolated physical and chemical soil properties obtained at a borehole grid (e.g., texture) are used for the visualization of spatial distribution of relevant (hydraulic) parameters. The data are merged in a database and visualized in the 3D-GIS application GoCAD. The specific technological construction processes determines the internal structure of the artificial catchment. Resulting differences in bulk density and texture are supposed to have considerable impact on hydraulic properties. A structure generator program was implemented to reproduce the initial structure of the sediment layer as closely as possible. Results of the digital structure generation are checked with non-invasive geophysical measurements, on-site bore holes data and off-site 2D vertical spoil exploration. The accuracy of structure generator results will be compared with predictions of different interpolation methods. Thus, the structure model will serve as a basis for deriving the 3D

  15. An Artificial Neural Network Model for the Wholesale Company Order's Cycle Management

    Directory of Open Access Journals (Sweden)

    Tereza Sustrova

    2016-06-01

    Full Text Available The purpose of this article is to verify the possibility of using artificial neural networks (ANN in business management processes, primarily in the area of supply chain management. The author has designed several neural network models featuring different architectures to optimize the level of the company’s inventory. The results of the research show that ANN can be used for managing a company’s order cycle and lead to reduced levels of goods purchased and storage costs. Optimal neural networks show suitable results for subsequent prediction of the amount of items to be ordered and for achieving reduced inventory purchase and keeping costs down.

  16. 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.

  17. 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.

  18. 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.

  19. DECISION WITH ARTIFICIAL NEURAL NETWORKS IN DISCRETE EVENT SIMULATION MODELS ON A TRAFFIC SYSTEM

    Directory of Open Access Journals (Sweden)

    Marília Gonçalves Dutra da Silva

    2016-04-01

    Full Text Available ABSTRACT This work aims to demonstrate the use of a mechanism to be applied in the development of the discrete-event simulation models that perform decision operations through the implementation of an artificial neural network. Actions that involve complex operations performed by a human agent in a process, for example, are often modeled in simplified form with the usual mechanisms of simulation software. Therefore, it was chosen a traffic system controlled by a traffic officer with a flow of vehicles and pedestrians to demonstrate the proposed solution. From a module built in simulation software itself, it was possible to connect the algorithm for intelligent decision to the simulation model. The results showed that the model elaborated responded as expected when it was submitted to actions, which required different decisions to maintain the operation of the system with changes in the flow of people and vehicles.

  20. Behavioral Modeling of a C-Band Ring Hybrid Coupler Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    E. Demircioglu

    2010-12-01

    Full Text Available Artificial Neural Networks (ANNs gained importance on the RF microwave (MW design area and behavioral modeling of MW components in the past few decades. This paper presents a cost effective neural network (NN approach to overcome design, modeling and optimization problems of an 180deg ring hybrid coupler operating in C-Band. The proposed NN model is trained by data sets obtained from electromagnetic (EM simulators and neural test results are compared with simulator findings to determine the network accuracy. Moreover, necessary trade-offs are applied to improve the networks’ performance. Finally correlation factors, which are defined as comparison criteria between EM-simulator and proposed neural models, are calculated for each trade-off case.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

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

    Science.gov (United States)

    Tahavvor, Ali Reza

    2016-06-01

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

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

    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.

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

    Science.gov (United States)

    Tahavvor, Ali Reza

    2017-03-01

    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.

  9. 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.

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

    Science.gov (United States)

    Fitzpatrick, Matthew C; Keller, Stephen R

    2015-01-01

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

  11. An object model for genome information at all levels of resolution

    Energy Technology Data Exchange (ETDEWEB)

    Honda, S.; Parrott, N.W.; Smith, R.; Lawrence, C.

    1993-12-31

    An object model for genome data at all levels of resolution is described. The model was derived by considering the requirements for representing genome related objects in three application domains: genome maps, large-scale DNA sequencing, and exploring functional information in gene and protein sequences. The methodology used for the object-oriented analysis is also described.

  12. Feature selection and survival modeling in The Cancer Genome Atlas

    Directory of Open Access Journals (Sweden)

    Kim H

    2013-09-01

    Full Text Available Hyunsoo Kim,1 Markus Bredel2 1Department of Pathology, The University of Alabama at Birmingham, Birmingham, AL, USA; 2Department of Radiation Oncology, and Comprehensive Cancer Center, The University of Alabama at Birmingham, Birmingham, AL, USA Purpose: Personalized medicine is predicated on the concept of identifying subgroups of a common disease for better treatment. Identifying biomarkers that predict disease subtypes has been a major focus of biomedical science. In the era of genome-wide profiling, there is controversy as to the optimal number of genes as an input of a feature selection algorithm for survival modeling. Patients and methods: The expression profiles and outcomes of 544 patients were retrieved from The Cancer Genome Atlas. We compared four different survival prediction methods: (1 1-nearest neighbor (1-NN survival prediction method; (2 random patient selection method and a Cox-based regression method with nested cross-validation; (3 least absolute shrinkage and selection operator (LASSO optimization using whole-genome gene expression profiles; or (4 gene expression profiles of cancer pathway genes. Results: The 1-NN method performed better than the random patient selection method in terms of survival predictions, although it does not include a feature selection step. The Cox-based regression method with LASSO optimization using whole-genome gene expression data demonstrated higher survival prediction power than the 1-NN method, but was outperformed by the same method when using gene expression profiles of cancer pathway genes alone. Conclusion: The 1-NN survival prediction method may require more patients for better performance, even when omitting censored data. Using preexisting biological knowledge for survival prediction is reasonable as a means to understand the biological system of a cancer, unless the analysis goal is to identify completely unknown genes relevant to cancer biology. Keywords: brain, feature selection

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

    Directory of Open Access Journals (Sweden)

    Aminmohammad Saberian

    2014-01-01

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

  14. Enhancing Wireless Sensor Network Security using Artificial Neural Network based Trust Model

    Directory of Open Access Journals (Sweden)

    Adwan Yasin

    2016-09-01

    Full Text Available Wireless sensor network (WSN is widely used in environmental conditions where the systems depend on sensing and monitoring approach. Water pollution monitoring system depends on a network of wireless sensing nodes which communicate together depending on a specific topological order. The nodes distributed in a harsh environment to detect the polluted zones within the WSN range based on the sensed data. WSN exposes several malicious attacks as a consequence of its presence in such open environment, so additional techniques are needed alongside with the existing cryptography approach. In this paper an enhanced trust model based on the use of radial base artificial neural network (RBANN is presented to predict the future behavior of each node based on its weighted direct and indirect behaviors, in order to provide a comprehensive trust model that helps to detect and eliminate malicious nodes within the WSN. The proposed model considered the limited power, storage and processing capabilities of the system.

  15. 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.

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

    Science.gov (United States)

    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

  17. 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.

  18. 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.

  19. Artificial neural network approach to modelling of metal contents in different types of chocolates.

    Science.gov (United States)

    Podunavac-Kuzmanović, Sanja; Jevrić, Lidija; Švarc-Gajić, Jaroslava; Kovačević, Strahinja; Vasiljević, Ivana; Kecojević, Isidora; Ivanović, Evica

    2015-01-01

    The relationships between the contents of various metals in different types of chocolates were studied using chemometric approach. Chemometric analysis was based on the application of artificial neural networks (ANN). ANN was performed in order to select the significant models for predicting the metal contents. ANN equations, that represent the content of one metal as a function of the contents of other metals were established. The statistical quality of the generated mathematical models was determined by standard statistical measures and cross-validation parameters. High agreement between experimental and predicted values, obtained in the validation procedure, indicated the good quality of the models. The obtained results indicate the possibility of predicting the metal contents in different types of chocolate.

  20. Predictive time-series modeling using artificial neural networks for Linac beam symmetry: an empirical study.

    Science.gov (United States)

    Li, Qiongge; Chan, Maria F

    2017-01-01

    Over half of cancer patients receive radiotherapy (RT) as partial or full cancer treatment. Daily quality assurance (QA) of RT in cancer treatment closely monitors the performance of the medical linear accelerator (Linac) and is critical for continuous improvement of patient safety and quality of care. Cumulative longitudinal QA measurements are valuable for understanding the behavior of the Linac and allow physicists to identify trends in the output and take preventive actions. In this study, artificial neural networks (ANNs) and autoregressive moving average (ARMA) time-series prediction modeling techniques were both applied to 5-year daily Linac QA data. Verification tests and other evaluations were then performed for all models. Preliminary results showed that ANN time-series predictive modeling has more advantages over ARMA techniques for accurate and effective applicability in the dosimetry and QA field.

  1. 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

  2. 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.

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

    Institute of Scientific and Technical Information of China (English)

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

    2007-01-01

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

  4. Residential Saudi load forecasting using analytical model and Artificial Neural Networks

    Science.gov (United States)

    Al-Harbi, Ahmad Abdulaziz

    In recent years, load forecasting has become one of the main fields of study and research. Short Term Load Forecasting (STLF) is an important part of electrical power system operation and planning. This work investigates the applicability of different approaches; Artificial Neural Networks (ANNs) and hybrid analytical models to forecast residential load in Kingdom of Saudi Arabia (KSA). These two techniques are based on model human modes behavior formulation. These human modes represent social, religious, official occasions and environmental parameters impact. The analysis is carried out on residential areas for three regions in two countries exposed to distinct people activities and weather conditions. The collected data are for Al-Khubar and Yanbu industrial city in KSA, in addition to Seattle, USA to show the validity of the proposed models applied on residential load. For each region, two models are proposed. First model is next hour load forecasting while second model is next day load forecasting. Both models are analyzed using the two techniques. The obtained results for ANN next hour models yield very accurate results for all areas while relatively reasonable results are achieved when using hybrid analytical model. For next day load forecasting, the two approaches yield satisfactory results. Comparative studies were conducted to prove the effectiveness of the models proposed.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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)

  10. 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

  11. 3D artificial bones for bone repair prepared by computed tomography-guided fused deposition modeling for bone repair.

    Science.gov (United States)

    Xu, Ning; Ye, Xiaojian; Wei, Daixu; Zhong, Jian; Chen, Yuyun; Xu, Guohua; He, Dannong

    2014-09-10

    The medical community has expressed significant interest in the development of new types of artificial bones that mimic natural bones. In this study, computed tomography (CT)-guided fused deposition modeling (FDM) was employed to fabricate polycaprolactone (PCL)/hydroxyapatite (HA) and PCL 3D artificial bones to mimic natural goat femurs. The in vitro mechanical properties, in vitro cell biocompatibility, and in vivo performance of the artificial bones in a long load-bearing goat femur bone segmental defect model were studied. All of the results indicate that CT-guided FDM is a simple, convenient, relatively low-cost method that is suitable for fabricating natural bonelike artificial bones. Moreover, PCL/HA 3D artificial bones prepared by CT-guided FDM have more close mechanics to natural bone, good in vitro cell biocompatibility, biodegradation ability, and appropriate in vivo new bone formation ability. Therefore, PCL/HA 3D artificial bones could be potentially be of use in the treatment of patients with clinical bone defects.

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

    Directory of Open Access Journals (Sweden)

    Sungwon Kim

    2015-06-01

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

  13. 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.

  14. Urban Growth Modelling with Artificial Neural Network and Logistic Regression. Case Study: Sanandaj City, Iran

    Directory of Open Access Journals (Sweden)

    SASSAN MOHAMMADY

    2013-01-01

    Full Text Available Cities have shown remarkable growth due to attraction, economic, social and facilities centralization in the past few decades. Population and urban expansion especially in developing countries, led to lack of resources, land use change from appropriate agricultural land to urban land use and marginalization. Under these circumstances, land use activity is a major issue and challenge for town and country planners. Different approaches have been attempted in urban expansion modelling. Artificial Neural network (ANN models are among knowledge-based models which have been used for urban growth modelling. ANNs are powerful tools that use a machine learning approach to quantify and model complex behaviour and patterns. In this research, ANN and logistic regression have been employed for interpreting urban growth modelling. Our case study is Sanandaj city and we used Landsat TM and ETM+ imageries acquired at 2000 and 2006. The dataset used includes distance to main roads, distance to the residence region, elevation, slope, and distance to green space. Percent Area Match (PAM obtained from modelling of these changes with ANN is equal to 90.47% and the accuracy achieved for urban growth modelling with Logistic Regression (LR is equal to 88.91%. Percent Correct Match (PCM and Figure of Merit for ANN method were 91.33% and 59.07% and then for LR were 90.84% and 57.07%, respectively.

  15. 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.

  16. Analytical modeling of gas production rate in tight channel sand formation and optimization of artificial fracture.

    Science.gov (United States)

    Wang, Ruifei; Song, Hongqing; Tang, Hewei; Wang, Yuhe; Killough, John; Huang, Gang

    2016-01-01

    Permeability variation in tight channel sand formation makes an important role in gas production. Based on the features of channel sand formation, a mathematical model has been established considering anisotropy of permeability. The analytical solutions were derived for productivity of both vertical wells and vertically fractured wells. Simulation results show that, gas production rate of anisotropic channel sand formation is less than that of isotropic formation. For vertically fractured well, artificial fracture direction, drainage radius, permeability ratio and fracture half-length have considerable influence on production rate. The optimum fracture direction should be deviated less than π/8 from the maximum permeability direction (or the channel direction). In addition, the analytical model was verified by in situ measured data. The research provides theoretical basis for the development of tight channel sand gas reservoirs.

  17. 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.

  18. 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.

  19. Discussion of using artificial neural nets to identify the well-test interpretation model

    Energy Technology Data Exchange (ETDEWEB)

    Yeung, K. (Univ. of Alberta, Edmonton, Alberta (Canada)); Chakrabarty, C. (Golder Associates, Nottingham (United Kingdom)); Wu, S. (Univ. of Melbourne (Australia))

    1994-09-01

    Use of artificial neural nets (ANN's) to identify noisy and apparently unrecognizable patterns is common for many real-world problems, ranging from applications such as speech recognition to stock market prediction. ANN approaches are often good candidates for recognizing patterns when rigid mathematical models do not exist or are insufficient to meet a full-scale identification requirement. Al-Kaabi and Lee's proposal of using ANN's to identify the well-test interpretation model is appropriate because well-test data is often highly nonlinear and noisy. The purpose of this discussion is to present some of the authors results in a similar study and to suggest a simple technique that would enhance the use of ANN's in Al-Kaabi and Lee's approach.

  20. 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...

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

    Directory of Open Access Journals (Sweden)

    Filiz Ozkan

    2012-01-01

    Full Text Available 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 US Dollar and Euro are predicted. In models, inflation levels for domestic and foreign countries, money supply, interest rates and economic indicators are used. The time period between 1986 and 2010 is covered for the USD and the time period between 1999 and 2010 is covered for the EU. Results of this study show that ANN, which is recently being used for the prediction problems, reached a high level prediction performance.

  2. 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.

  3. Artificial neural Network-Based modeling and monitoring of photovoltaic generator

    Directory of Open Access Journals (Sweden)

    H. MEKKI

    2015-03-01

    Full Text Available In this paper, an artificial neural network based-model (ANNBM is introduced for partial shading detection losses in photovoltaic (PV panel. A Multilayer Perceptron (MLP is used to estimate the electrical outputs (current and voltage of the photovoltaic module using the external meteorological data: solar irradiation G (W/m2 and the module temperature T (°C. Firstly, a database of the BP150SX photovoltaic module operating without any defect has been used to train the considered MLP. Subsequently, in the first case of this study, the developed model is used to estimate the output current and voltage of the PV module considering the partial shading effect. Results confirm the good ability of the ANNBM to detect the partial shading effect in the photovoltaic module with logical accuracy. The proposed strategy could also be used for the online monitoring and supervision of PV modules.

  4. Pollutant intrusion modeling in water distribution networks using artificial neural networks.

    Science.gov (United States)

    Singh, Raj Mohan; Rahul, Akhouri Ishan

    2011-07-01

    The development and implementation of water quality models for water distribution systems have been growing interest for both environment and hydraulic researchers. It is imperative that the system is able to distribute disinfectants and/or chemicals efficiently for specified quality standards and recover the actual quality of water in case of intrusion of a pollutant into the distribution network. The present work presents hydraulic and quality analysis in a typical water distribution system to obtain the concentration at the sources (pumping station or tanks) affected by typical pollutants utilizing water quality at monitoring points as inputs to artificial neural network (ANN) model. The universal function approximation property of the ANN architecture is being employed for inverse mapping to predict the water quality at the source using the water quality at arbitrary monitoring locations in the distribution system. The optimal monitoring points are identified by water age analysis. The performance evaluation results are encouraging and demonstrate the potential applicability of the methodology.

  5. 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...

  6. 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.

  7. Artificial intelligence for the modeling and control of combustion processes: a review

    Energy Technology Data Exchange (ETDEWEB)

    Soteris A. Kalogirou, [Higher Technical Institute, Nicosia (Cyprus). Department of Mechanical Engineering

    2003-07-01

    Artificial intelligence (AI) systems are widely accepted as a technology offering an alternative way to tackle complex and ill-defined problems. They can learn from examples, are fault tolerant in the sense that they are able to handle noisy and incomplete data, are able to deal with non-linear problems, and once trained can perform prediction and generalization at high speed. They have been used in diverse applications in control, robotics, pattern recognition, forecasting, medicine, power systems, manufacturing, optimization, signal processing, and social/psychological sciences. They are particularly useful in system modeling such as in implementing complex mappings and system identification. AI systems comprise areas like, expert systems, artificial neural networks, genetic algorithms, fuzzy logic and various hybrid systems, which combine two or more techniques. The major objective of this paper is to illustrate how AI techniques might play an important role in modeling and prediction of the performance and control of combustion process. The paper outlines an understanding of how AI systems operate by way of presenting a number of problems in the different disciplines of combustion engineering. The various applications of AI are presented in a thematic rather than a chronological or any other order. Problems presented include two main areas: combustion systems and internal combustion (IC) engines. Combustion systems include boilers, furnaces and incinerators modeling and emissions prediction, whereas, IC engines include diesel and spark ignition engines and gas engines modeling and control. Results presented in this paper, are testimony to the potential of AI as a design tool in many areas of combustion engineering. 109 refs., 31 figs., 11 tabs.

  8. Artificial intelligence for the modeling and control of combustion processes: a review

    Energy Technology Data Exchange (ETDEWEB)

    Kalogirou, S.A. [Higher Technical Inst., Nicosia, Cyprus (Greece). Dept. of Mechanical Engineering

    2003-07-01

    Artificial intelligence (AI) systems are widely accepted as a technology offering an alternative way to tackle complex and ill-defined problems. They can learn from examples, are fault tolerant in the sense that they are able to handle noisy and incomplete data, are able to deal with non-linear problems, and once trained can perform prediction and generalization at high speed. They have been used in diverse applications in control, robotics, pattern recognition, forecasting, medicine, power systems, manufacturing, optimization, signal processing, and social/psychological sciences. They are particularly useful in system modeling such as in implementing complex mappings and system identification. Al systems comprise areas like, expert systems, artificial neural networks, genetic algorithms, fuzzy logic and various hybrid systems, which combine two or more techniques. The major objective of this paper is to illustrate how Al techniques might play an important role in modeling and prediction of the performance and control of combustion process. The paper outlines an understanding of how AI systems operate by way of presenting a number of problems in the different disciplines of combustion engineering. The various applications of AI are presented in a thematic rather than a chronological or any other order. Problems presented include two main areas: combustion systems and internal combustion (IC) engines. Combustion systems include boilers, furnaces and incinerators modeling and emissions prediction, whereas, IC engines include diesel and spark ignition engines and gas engines modeling and control. Results presented in this paper, are testimony to the potential of Al as a design tool in many areas of combustion engineering. (author)

  9. 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.

  10. 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)

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  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. An Artificial Neural Network Model for Highway Accident Prediction: A Case Study of Erzurum, Turkey

    Directory of Open Access Journals (Sweden)

    Muhammed Yasin Çodur

    2015-06-01

    Full Text Available This study presents an accident prediction model of Erzurum’s Highways in Turkey using artificial neural network (ANN approaches. There are many ANN models for predicting the number of accidents on highways that were developed using 8 years with 7,780 complete accident reports of historical data (2005-2012. The best ANN model was chosen for this task and the model parameters included years, highway sections, section length (km, annual average daily traffic (AADT, the degree of horizontal curvature, the degree of vertical curvature, traffic accidents with heavy vehicles (percentage, and traffic accidents that occurred in summer (percentage. In the ANN model development, the sigmoid activation function was employed with Levenberg-Marquardt algorithm. The performance of the developed ANN model was evaluated by mean square error (MSE, the root mean square error (RMSE, and the coefficient of determination (R2. The model results indicate that the degree of vertical curvature is the most important parameter that affects the number of accidents on highways.

  19. 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.

  20. 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…

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

    Science.gov (United States)

    Culbertson, Jennifer; Smolensky, Paul

    2012-01-01

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

  2. 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...

  3. 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 c...

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

    Science.gov (United States)

    King, Zachary A; Lloyd, Colton J; Feist, Adam M; Palsson, Bernhard O

    2015-12-01

    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.

  5. 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.

  6. 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%.

  7. Comparison of Different Artificial Neural Network (ANN) Architectures in Modeling of Chlorella sp. Flocculation.

    Science.gov (United States)

    Zenooz, Alireza Moosavi; Ashtiani, Farzin Zokaee; Ranjbar, Reza; Nikbakht, Fatemeh; Bolouri, Oberon

    2017-01-03

    Biodiesel production from microalgae feedstock should be performed after growth and harvesting of the cells and the most feasible method for harvesting and dewatering of microalgae is flocculation. Flocculation modeling can be used for evaluation and prediction of its performance under different affective parameters. However, the modeling of flocculation in microalgae is not simple and has not performed yet, under all experimental conditions, mostly due to different behaviors of microalgae cells during the process under different flocculation conditions. In the current study, the modeling of microalgae flocculation is studied with different neural network architectures. Microalgae specie, Chlorella sp., was flocculated with ferric chloride under different conditions and then the experimental data modeled using artificial neural network (ANN). Neural network architectures of Multilayer Perceptron (MLP) and Radial Basis Function (RBF) architectures, failed to predict the targets successfully, though, modeling was effective with ensemble architecture of MLP networks. Comparison between the performances of the ensemble and each individual network explains the ability of the ensemble architecture in microalgae flocculation modeling.

  8. Validation of a Novel Traditional Chinese Medicine Pulse Diagnostic Model Using an Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Anson Chui Yan Tang

    2012-01-01

    Full Text Available In view of lacking a quantifiable traditional Chinese medicine (TCM pulse diagnostic model, a novel TCM pulse diagnostic model was introduced to quantify the pulse diagnosis. Content validation was performed with a panel of TCM doctors. Criterion validation was tested with essential hypertension. The gold standard was brachial blood pressure measured by a sphygmomanometer. Two hundred and sixty subjects were recruited (139 in the normotensive group and 121 in the hypertensive group. A TCM doctor palpated pulses at left and right cun, guan, and chi points, and quantified pulse qualities according to eight elements (depth, rate, regularity, width, length, smoothness, stiffness, and strength on a visual analog scale. An artificial neural network was used to develop a pulse diagnostic model differentiating essential hypertension from normotension. Accuracy, specificity, and sensitivity were compared among various diagnostic models. About 80% accuracy was attained among all models. Their specificity and sensitivity varied, ranging from 70% to nearly 90%. It suggested that the novel TCM pulse diagnostic model was valid in terms of its content and diagnostic ability.

  9. Microwave-vacuum drying of sour cherry: comparison of mathematical models and artificial neural networks.

    Science.gov (United States)

    Motavali, Ali; Najafi, Gholam Hassan; Abbasi, Solayman; Minaei, Saeid; Ghaderi, Abdurrahman

    2013-08-01

    Drying characteristics of sour cherries were determined using microwave vacuum drier at various microwave powers (360, 600, 840, 1200 W) and absolute pressures (200, 400, 600, 800 mbars). In addition, using the artificial neural networks (ANN), trained by standard Back-Propagation algorithm, the effects of microwave power, pressure and drying time on moisture ratio (MR) and drying rate (DR) were investigated Based on the evaluation of experimental data fitting with semi-theoretical and empirical models, the Midilli et al. model was selected as the most appropriate one. Furthermore, the ANN model was able to predict the moisture ratio and drying rate quite well with determination coefficients (R(2)) of 0.9996, 0.9961 and 0.9958 for training, validation and testing, respectively. The prediction Mean Square Error of ANN was about 0.0003, 0.0071 and 0.0053 for training, validation and testing, respectively. This parameter signifies the difference between the desired outputs (as measured values) and the simulated values by the model. The good agreement between the experimental data and ANN model leads to the conclusion that the model adequately describes the drying behavior of sour cherries, in the range of operating conditions tested.

  10. Development of artificial neural network models for supercritical fluid solvency in presence of co-solvents

    Energy Technology Data Exchange (ETDEWEB)

    Shokir, Eissa Mohamed El-Moghawry; El-Midany, Ayman Abdel-Hamid [Cairo University, Giza (Egypt); Al-Homadhi, Emad Souliman; Al-Mahdy, Osama [King Saud University, Riyadh (Saudi Arabia)

    2014-08-15

    This paper presents the application of artificial neural networks (ANN) to develop new models of liquid solvent dissolution of supercritical fluids with solutes in the presence of cosolvents. The neural network model of the liquid solvent dissolution of CO{sub 2} was built as a function of pressure, temperature, and concentrations of the solutes and cosolvents. Different experimental measurements of liquid solvent dissolution of supercritical fluids (CO{sub 2}) with solutes in the presence of cosolvents were collected. The collected data are divided into two parts. The first part was used in building the models, and the second part was used to test and validate the developed models against the Peng- Robinson equation of state. The developed ANN models showed high accuracy, within the studied variables range, in predicting the solubility of the 2-naphthol, anthracene, and aspirin in the supercritical fluid in the presence and absence of co-solvents compared to (EoS). Therefore, the developed ANN models could be considered as a good tool in predicting the solubility of tested solutes in supercritical fluid.

  11. 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

  12. GENOME-BASED MODELING AND DESIGN OF METABOLIC INTERACTIONS IN MICROBIAL COMMUNITIES

    Directory of Open Access Journals (Sweden)

    Radhakrishnan Mahadevan

    2012-10-01

    With the advent of genome sequencing, omics technologies, bioinformatics and genome-scale modeling, researchers now have unprecedented capabilities to analyze and engineer the metabolism of microbial communities. The goal of this review is to summarize recent applications of genome-scale metabolic modeling to microbial communities. A brief introduction to lumped community models is used to motivate the need for genome-level descriptions of individual species and their metabolic interactions. The review of genome-scale models begins with static modeling approaches, which are appropriate for communities where the extracellular environment can be assumed to be time invariant or slowly varying. Dynamic extensions of the static modeling approach are described, and then applications of genome-scale models for design of synthetic microbial communities are reviewed. The review concludes with a summary of metagenomic tools for analyzing community metabolism and an outlook for future research.

  13. Grope in the Dark - Hydrological modelling of the artificial Chicken Creek catchment without validation possibilities

    Science.gov (United States)

    Hölzel, H.; Rössler, O.; Diekkrüger, B.

    Environmental modelling is often reduced to a calibration-validation exercise in which a thorough understanding of the processes involved is less important than sophisticated parameter estimation tools. A problem occurs when no data are available for calibration and validation. To compare the ability of different models and modellers to describe the hydrological behaviour of ungauged catchments, a model comparison was initiated by Transregional Collaboration Research Centre 38 (SFB/TRR 38). The comparison was conducted in several consecutive stages, in which knowledge of the investigated artificial catchment of approximately 6 ha was enhanced stepwise. For modelling purposes, all basic input data were given, but neither discharge nor soil moisture measurements were provided. This hard information was supplemented stepwise by soft (qualitative) information mostly obtained by a field inspection after the first modelling stage. In this study, the process-based distributed Water balance Simulation Model (WaSiM-ETH) was applied. An initial perceptual model was developed without site-specific knowledge. Significant modifications to the perceptual model were conducted using the available soft information. These modifications resulted in more reliable water fluxes. The main changes, such as the importance of a sealed topsoil layer, vegetation development, initial conditions, and the effects of a small pond on discharge behaviour, were related to process understanding. Although hard information on the water balance was still missing, the qualitative behaviour of the model seemed to be realistic. This underlines the importance of soft information for successful modelling, which is especially important if ungauged catchments are to be investigated.

  14. 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

  15. 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.

  16. The Extent of the Crack on Artificial Simulation Models with CBCT and Periapical Radiography

    Science.gov (United States)

    Wang, Shuang; Xu, Yiran; Shen, Zhengyan; Li, Minghua; Wu, Ligeng

    2017-01-01

    Background The aim of this study was to investigate the extent of the crack of a cracked tooth on an artificial simulation model with Periapical Radiography (PR) and cone beam computed tomography (CBCT) in vitro, providing the basis for early diagnosis and an appropriate treatment plan. Methods Forty-four teeth with different extents of artificial cracks, created by exposure to liquid nitrogen after hot water at 100°C, were collected. They were subjected to PR and CBCT. Micro-computed tomography (micro-CT) examination, regarded as a relatively more accurate measurement than others, was used to measure and record the crack depth. Three observers, an endodontic graduate student, an experienced endodontist, and an experienced radiologist, examined the PR and CBCT results independently, and the presence or absence of cracks with PR and CBCT were respectively recorded. The external consistency ICC with 95% confidence interval (95% CI) was used to analyze the consistency among the graduate student, endodontist, and radiologist; ROC curves were used for the analysis of diagnostic performance of both radiographic modalities for tooth cracks with crack depth. Results For the interpretation of the PR results, there were statistically significant differences among the three different observers (P < 0.001), and the interpretation of the CBCT results (P < 0.001). In the group of results read by the graduate student, the sensitivity of diagnosis with CBCT and PR was 77.27% and 22.73%, respectively (P < 0.001). In the group of results read by the endodontist, the sensitivity of diagnosis with CBCT and PR was 81.81% and 8.19%, respectively (P < 0.001). In the group of results read by the radiologist, the sensitivity of diagnosis with CBCT and PR was 88.64% and 11.36%, respectively (P < 0.001). As for CBCT diagnosis, the critical value for the graduate, endodontist, and radiologist was 3.20 mm, 2.06 mm, and 1.24 mm, respectively. For the PR diagnosis, the critical value for the

  17. 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.

  18. 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.

  19. Accelerometer signal-based human activity recognition using augmented autoregressive model coefficients and artificial neural nets.

    Science.gov (United States)

    Khan, A M; Lee, Y K; Kim, T S

    2008-01-01

    Automatic recognition of human activities is one of the important and challenging research areas in proactive and ubiquitous computing. In this work, we present some preliminary results of recognizing human activities using augmented features extracted from the activity signals measured using a single triaxial accelerometer sensor and artificial neural nets. The features include autoregressive (AR) modeling coefficients of activity signals, signal magnitude areas (SMA), and title angles (TA). We have recognized four human activities using AR coefficients (ARC) only, ARC with SMA, and ARC with SMA and TA. With the last augmented features, we have achieved the recognition rate above 99% for all four activities including lying, standing, walking, and running. With our proposed technique, real time recognition of some human activities is possible.

  20. 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

  1. 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.

  2. Interaction prediction between groundwater and quarry extension using discrete choice models and artificial neural networks

    CERN Document Server

    Barthélemy, Johan; Collier, Louise; Hallet, Vincent; Moriamé, Marie; Sartenaer, Annick

    2016-01-01

    Groundwater and rock are intensively exploited in the world. When a quarry is deepened the water table of the exploited geological formation might be reached. A dewatering system is therefore installed so that the quarry activities can continue, possibly impacting the nearby water catchments. In order to recommend an adequate feasibility study before deepening a quarry, we propose two interaction indices between extractive activity and groundwater resources based on hazard and vulnerability parameters used in the assessment of natural hazards. The levels of each index (low, medium, high, very high) correspond to the potential impact of the quarry on the regional hydrogeology. The first index is based on a discrete choice modelling methodology while the second is relying on an artificial neural network. It is shown that these two complementary approaches (the former being probabilistic while the latter fully deterministic) are able to predict accurately the level of interaction. Their use is finally illustrate...

  3. 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

  4. 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.

  5. 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.

  6. Modeling and simulation of xylitol production in bioreactor by Debaryomyces nepalensis NCYC 3413 using unstructured and artificial neural network models.

    Science.gov (United States)

    Pappu, J Sharon Mano; Gummadi, Sathyanarayana N

    2016-11-01

    This study examines the use of unstructured kinetic model and artificial neural networks as predictive tools for xylitol production by Debaryomyces nepalensis NCYC 3413 in bioreactor. An unstructured kinetic model was proposed in order to assess the influence of pH (4, 5 and 6), temperature (25°C, 30°C and 35°C) and volumetric oxygen transfer coefficient kLa (0.14h(-1), 0.28h(-1) and 0.56h(-1)) on growth and xylitol production. A feed-forward back-propagation artificial neural network (ANN) has been developed to investigate the effect of process condition on xylitol production. ANN configuration of 6-10-3 layers was selected and trained with 339 experimental data points from bioreactor studies. Results showed that simulation and prediction accuracy of ANN was apparently higher when compared to unstructured mechanistic model under varying operational conditions. ANN was found to be an efficient data-driven tool to predict the optimal harvest time in xylitol production.

  7. Estimation of storage time of yogurt with artificial neural network modeling.

    Science.gov (United States)

    Sofu, A; Ekinci, F Y

    2007-07-01

    Changes in the physical, chemical, and microbiological structure of yogurt determine the storage and shelf life of the product. In this study, microbial counts and pH values of yogurt during storage were determined at d 1, 7, and 14. Simultaneously, image processing of yogurt was digitized by using a machine vision system (MVS) to determine color changes during storage, and the obtained data were modeled with an artificial neural network (ANN) for prediction of shelf life of set-type whole-fat and low-fat yogurts. The ANN models were developed using back-propagation networks with a single hidden layer and sigmoid activation functions. The input variables of the network were pH; total aerobic, yeast, mold, and coliform counts; and color analysis values measured by the machine vision system. The output variable was the storage time of the yogurt. The modeling results showed that there was excellent agreement between the experimental data and predicted values, with a high determination coefficient (R2 = 0.9996) showing that the developed model was able to analyze nonlinear multivariant data with very good performance, fewer parameters, and shorter calculation time. The model might be an alternative method to control the expiration date of yogurt shown in labeling and provide consumers with a safer food supply.

  8. 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...

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

    CERN Document Server

    Dmitriev, A V

    2013-01-01

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

  10. Artificial neural network modelling of continuous wet granulation using a twin-screw extruder.

    Science.gov (United States)

    Shirazian, Saeed; Kuhs, Manuel; Darwish, Shaza; Croker, Denise; Walker, Gavin M

    2017-04-15

    Computational modelling of twin-screw granulation was conducted by using an artificial neural network (ANN) approach. Various ANN configurations were considered with changing hidden layers, nodes and activation functions to determine the optimum model for the prediction of the process. The neural networks were trained using experimental data obtained for granulation of pure microcrystalline cellulose using a 12mm twin-screw extruder. The experimental data were obtained for various liquid binder (water) to solid ratios, screw speeds, material throughputs, and screw configurations. The granulate particle size distribution, represented by d-values (d10, d50, d90) were considered the response in the experiments and the ANN model. Linear and non-linear activation functions were taken into account in the simulations and more accurate results were obtained for non-linear function in terms of prediction. Moreover, 2 hidden layers with 2 nodes per layer and 3-Fold cross-validation method gave the most accurate simulation. The results revealed that the developed ANN model is capable of predicting granule size distribution in high-shear twin-screw granulation with a high accuracy in different conditions, and can be used for implementation of model predictive control in continuous pharmaceutical manufacturing.

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

    Science.gov (United States)

    Omenzetter, Piotr; de Lautour, Oliver R.

    2007-04-01

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

  12. Use of artificial neural networks for electrical conductivity modeling in Asi River

    Science.gov (United States)

    Ghorbani, Mohammad Ali; Aalami, Mohammad Taghi; Naghipour, Leila

    2015-10-01

    This study aims to model monthly electrical conductivity (EC) values in the Asi River using artificial neural networks (ANNs) to evaluate water quality conditions using pH, temperature, water discharge, sodium, sum of calcium and magnesium concentrations. The results are compared using multiple linear regression (MLR). Recorded data are available at a gauging site in Antakya, Turkey, for the period from 1984 to 2008. Comparing the modeled values by ANNs with the experimental data indicates that neural network model with seven neurons in hidden layer provides accurate results (R 2 = 0.968, RMSE = 46.927 µS/cm, MAE = 32.462 µS/cm and MRSE = 0.0029 for the training data and R 2 = 0.965, RMSE = 50.810 µS/cm, MAE = 37.495 µS/cm and MRSE = 0.0024 for the testing data). The Garson method of the connection weights of the network was used to study the relative % contribution of each of the input variables. It was found that the sum of calcium and magnesium concentration and temperature had the most effect on the predicted EC. The results indicate that two proposed models were able to approximate the EC parameter reasonably well; however, the ANN was found to perform better than the MLR model.

  13. 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.

  14. Towards a Humanized Mouse Model of Liver Stage Malaria Using Ectopic Artificial Livers

    Science.gov (United States)

    Ng, Shengyong; March, Sandra; Galstian, Ani; Gural, Nil; Stevens, Kelly R.; Mota, Maria M.; Bhatia, Sangeeta N.

    2017-01-01

    The malaria liver stage is an attractive target for antimalarial development, and preclinical malaria models are essential for testing such candidates. Given ethical concerns and costs associated with non‐human primate models, humanized mouse models containing chimeric human livers offer a valuable alternative as small animal models of liver stage human malaria. The best available human liver chimeric mice rely on cellular transplantation into mice with genetically engineered liver injury, but these systems involve a long and variable humanization process, are expensive, and require the use of breeding-challenged mouse strains which are not widely accessible. We previously incorporated primary human hepatocytes into engineered polyethylene glycol (PEG)-based nanoporous human ectopic artificial livers (HEALs), implanted them in mice without liver injury, and rapidly generated human liver chimeric mice in a reproducible and scalable fashion. By re-designing the PEG scaffold to be macroporous, we demonstrate the facile fabrication of implantable porous HEALs that support liver stage human malaria (P. falciparum) infection in vitro, and also after implantation in mice with normal liver function, 60% of the time. This proof-of-concept study demonstrates the feasibility of applying a tissue engineering strategy towards the development of scalable preclinical models of liver stage malaria infection for future applications. PMID:28361899

  15. Chemogenomics: a discipline at the crossroad of high throughput technologies, biomarker research, combinatorial chemistry, genomics, cheminformatics, bioinformatics and artificial intelligence.

    Science.gov (United States)

    Maréchal, Eric

    2008-09-01

    Chemogenomics is the study of the interaction of functional biological systems with exogenous small molecules, or in broader sense the study of the intersection of biological and chemical spaces. Chemogenomics requires expertises in biology, chemistry and computational sciences (bioinformatics, cheminformatics, large scale statistics and machine learning methods) but it is more than the simple apposition of each of these disciplines. Biological entities interacting with small molecules can be isolated proteins or more elaborate systems, from single cells to complete organisms. The biological space is therefore analyzed at various postgenomic levels (genomic, transcriptomic, proteomic or any phenotypic level). The space of small molecules is partially real, corresponding to commercial and academic collections of compounds, and partially virtual, corresponding to the chemical space possibly synthesizable. Synthetic chemistry has developed novel strategies allowing a physical exploration of this universe of possibilities. A major challenge of cheminformatics is to charter the virtual space of small molecules using realistic biological constraints (bioavailability, druggability, structural biological information). Chemogenomics is a descendent of conventional pharmaceutical approaches, since it involves the screening of chemolibraries for their effect on biological targets, and benefits from the advances in the corresponding enabling technologies and the introduction of new biological markers. Screening was originally motivated by the rigorous discovery of new drugs, neglecting and throwing away any molecule that would fail to meet the standards required for a therapeutic treatment. It is now the basis for the discovery of small molecules that might or might not be directly used as drugs, but which have an immense potential for basic research, as probes to explore an increasing number of biological phenomena. Concerns about the environmental impact of chemical industry

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

    DEFF Research Database (Denmark)

    2016-01-01

    Genome-scale metabolic models are mathematically-structured knowledge bases that can be used to predict metabolic pathway usage and growth phenotypes. Furthermore, they can generate and test hypotheses when integrated with experimental data. To maximize the value of these models, centralized...

  18. Diagnostics for stochastic genome-scale modeling via model slicing and debugging.

    Directory of Open Access Journals (Sweden)

    Kevin J Tsai

    Full Text Available Modeling of biological behavior has evolved from simple gene expression plots represented by mathematical equations to genome-scale systems biology networks. However, due to obstacles in complexity and scalability of creating genome-scale models, several biological modelers have turned to programming or scripting languages and away from modeling fundamentals. In doing so, they have traded the ability to have exchangeable, standardized model representation formats, while those that remain true to standardized model representation are faced with challenges in model complexity and analysis. We have developed a model diagnostic methodology inspired by program slicing and debugging and demonstrate the effectiveness of the methodology on a genome-scale metabolic network model published in the BioModels database. The computer-aided identification revealed specific points of interest such as reversibility of reactions, initialization of species amounts, and parameter estimation that improved a candidate cell's adenosine triphosphate production. We then compared the advantages of our methodology over other modeling techniques such as model checking and model reduction. A software application that implements the methodology is available at http://gel.ym.edu.tw/gcs/.

  19. Diagnostics for stochastic genome-scale modeling via model slicing and debugging.

    Science.gov (United States)

    Tsai, Kevin J; Chang, Chuan-Hsiung

    2014-01-01

    Modeling of biological behavior has evolved from simple gene expression plots represented by mathematical equations to genome-scale systems biology networks. However, due to obstacles in complexity and scalability of creating genome-scale models, several biological modelers have turned to programming or scripting languages and away from modeling fundamentals. In doing so, they have traded the ability to have exchangeable, standardized model representation formats, while those that remain true to standardized model representation are faced with challenges in model complexity and analysis. We have developed a model diagnostic methodology inspired by program slicing and debugging and demonstrate the effectiveness of the methodology on a genome-scale metabolic network model published in the BioModels database. The computer-aided identification revealed specific points of interest such as reversibility of reactions, initialization of species amounts, and parameter estimation that improved a candidate cell's adenosine triphosphate production. We then compared the advantages of our methodology over other modeling techniques such as model checking and model reduction. A software application that implements the methodology is available at http://gel.ym.edu.tw/gcs/.

  20. 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.

  1. Prediction of soil temperature using regression and artificial neural network models

    Science.gov (United States)

    Bilgili, Mehmet

    2010-12-01

    In this study, monthly soil temperature was modeled by linear regression (LR), nonlinear regression (NLR) and artificial neural network (ANN) methods. The soil temperature and other meteorological parameters, which have been taken from Adana meteorological station, were observed between the years of 2000 and 2007 by the Turkish State Meteorological Service (TSMS). The soil temperatures were measured at depths of 5, 10, 20, 50 and 100 cm below the ground level. A three-layer feed-forward ANN structure was constructed and a back-propagation algorithm was used for the training of ANNs. In order to get a successful simulation, the correlation coefficients between all of the meteorological variables (soil temperature, atmospheric temperature, atmospheric pressure, relative humidity, wind speed, rainfall, global solar radiation and sunshine duration) were calculated taking them two by two. First, all independent variables were split into two time periods such as cold and warm seasons. They were added to the enter regression model. Then, the method of stepwise multiple regression was applied for the selection of the "best" regression equation (model). Thus, the best independent variables were selected for the LR and NLR models and they were also used in the input layer of the ANN method. Results of these methods were compared to each other. Finally, the ANN method was found to provide better performance than the LR and NLR methods.

  2. 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.

  3. 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.

  4. 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.

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

    Science.gov (United States)

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

    1997-01-01

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

  6. 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.

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

    Directory of Open Access Journals (Sweden)

    AbdoulAhad Validi

    2012-01-01

    Full Text Available A Dynamic model of Homogeneous Charge Compression Ignition (HCCI, based on chemical kinetics principles and artificial intelligence, is developed. The model can rapidly predict the combustion probability, thermochemistry properties, and exact timing of the Start of Combustion (SOC. A realization function is developed on the basis of the Sandia National Laboratory chemical kinetics model, and GRI3.0 methane chemical mechanism. The inlet conditions are optimized by Genetic Algorithm (GA, so that combustion initiates and SOC timing posits in the desired crank angle. The best SOC timing to achieve higher performance and efficiency in HCCI engines is between 5 and 15 degrees crank angle (CAD after top dead center (TDC. To achieve this SOC timing, in the first case, the inlet temperature and equivalence ratio are optimized simultaneously and in the second case, compression ratio is optimized by GA. The model’s results are validated with previous works. The SOC timing can be predicted in less than 0.01 second and the CPU time savings are encouraging. This model can successfully be used for real engine control applications.

  8. Modeling Self-Healing of Concrete Using Hybrid Genetic Algorithm–Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Ahmed Ramadan Suleiman

    2017-02-01

    Full Text Available This paper presents an approach to predicting the intrinsic self-healing in concrete using a hybrid genetic algorithm–artificial neural network (GA–ANN. A genetic algorithm was implemented in the network as a stochastic optimizing tool for the initial optimal weights and biases. This approach can assist the network in achieving a global optimum and avoid the possibility of the network getting trapped at local optima. The proposed model was trained and validated using an especially built database using various experimental studies retrieved from the open literature. The model inputs include the cement content, water-to-cement ratio (w/c, type and dosage of supplementary cementitious materials, bio-healing materials, and both expansive and crystalline additives. Self-healing indicated by means of crack width is the model output. The results showed that the proposed GA–ANN model is capable of capturing the complex effects of various self-healing agents (e.g., biochemical material, silica-based additive, expansive and crystalline components on the self-healing performance in cement-based materials.

  9. Artificial Neural Network Model to Estimate the Viscosity of Polymer Solutions for Enhanced Oil Recovery

    Directory of Open Access Journals (Sweden)

    Pan-Sang Kang

    2016-06-01

    Full Text Available Polymer flooding is now considered a technically- and commercially-proven method for enhanced oil recovery (EOR. The viscosity of the injected polymer solution is the key property for successful polymer flooding. Given that the viscosity of a polymer solution has a non-linear relationship with various influential parameters (molecular weight, degree of hydrolysis, polymer concentration, cation concentration of polymer solution, shear rate, temperature and that measurement of viscosity based on these parameters is a time-consuming process, the range of solution samples and the measurement conditions need to be limited and precise. Viscosity estimation of the polymer solution is effective for these purposes. An artificial neural network (ANN was applied to the viscosity estimation of FlopaamTM 3330S, FlopaamTM 3630S and AN-125 solutions, three commonly-used EOR polymers. The viscosities measured and estimated by ANN and the Carreau model using Lee’s correlation, the only method for estimating the viscosity of an EOR polymer solution in unmeasured conditions, were compared. Estimation accuracy was evaluated by the average absolute relative deviation, which has been widely used for accuracy evaluation of the results of ANN models. In all conditions, the accuracy of the ANN model is higher than that of the Carreau model using Lee’s correlation.

  10. 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.

  11. Numerical model of total artificial heart hemodynamics and the effect of its size on stress accumulation.

    Science.gov (United States)

    Marom, Gil; Chiu, Wei-Che; Slepian, Marvin J; Bluestein, Danny

    2014-01-01

    The total artificial heart (TAH) is a bi-ventricular mechanical circulatory support device that replaces the heart in patients with end-stage congestive heart failure. The device acts as blood pump via pneumatic activation of diaphragms altering the volume of the ventricular chambers. Flow in and out of the ventricles is controlled by mechanical heart valves. The aim of this study is to evaluate the flow regime in the TAH and to estimate the thrombogenic potential during systole. Toward that goal, three numerical models of TAHs of differing sizes, that include the deforming diaphragm and the blood flow from the left chamber to the aorta, are introduced. A multiphase model with injection of platelet particles is employed to calculate their trajectories. The shear stress accumulation in the three models are calculated along the platelets trajectories and their probability density functions, which represent the `thrombogenic footprint' of the device are compared. The calculated flow regime successfully captures the mitral regurgitation and the flows that open and close the aortic valve during systole. Physiological velocity magnitudes are found in all three models, with higher velocities and increased stress accumulation predicted for smaller devices.

  12. 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)

  13. 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

  14. 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.

  15. Feed Forward Artificial Neural Network Model to Estimate the TPH Removal Efficiency in Soil Washing Process

    Directory of Open Access Journals (Sweden)

    Hossein Jafari Mansoorian

    2017-01-01

    Full Text Available Background & Aims of the Study: A feed forward artificial neural network (FFANN was developed to predict the efficiency of total petroleum hydrocarbon (TPH removal from a contaminated soil, using soil washing process with Tween 80. The main objective of this study was to assess the performance of developed FFANN model for the estimation of   TPH removal. Materials and Methods: Several independent repressors including pH, shaking speed, surfactant concentration and contact time were used to describe the removal of TPH as a dependent variable in a FFANN model. 85% of data set observations were used for training the model and remaining 15% were used for model testing, approximately. The performance of the model was compared with linear regression and assessed, using Root of Mean Square Error (RMSE as goodness-of-fit measure Results: For the prediction of TPH removal efficiency, a FANN model with a three-hidden-layer structure of 4-3-1 and a learning rate of 0.01 showed the best predictive results. The RMSE and R2 for the training and testing steps of the model were obtained to be 2.596, 0.966, 10.70 and 0.78, respectively. Conclusion: For about 80% of the TPH removal efficiency can be described by the assessed regressors the developed model. Thus, focusing on the optimization of soil washing process regarding to shaking speed, contact time, surfactant concentration and pH can improve the TPH removal performance from polluted soils. The results of this study could be the basis for the application of FANN for the assessment of soil washing process and the control of petroleum hydrocarbon emission into the environments.

  16. Artificial intelligence modeling to evaluate field performance of photocatalytic asphalt pavement for ambient air purification.

    Science.gov (United States)

    Asadi, Somayeh; Hassan, Marwa; Nadiri, Ataallah; Dylla, Heather

    2014-01-01

    In recent years, the application of titanium dioxide (TiO₂) as a photocatalyst in asphalt pavement has received considerable attention for purifying ambient air from traffic-emitted pollutants via photocatalytic processes. In order to control the increasing deterioration of ambient air quality, urgent and proper risk assessment tools are deemed necessary. However, in practice, monitoring all process parameters for various operating conditions is difficult due to the complex and non-linear nature of air pollution-based problems. Therefore, the development of models to predict air pollutant concentrations is very useful because it can provide early warnings to the population and also reduce the number of measuring sites. This study used artificial neural network (ANN) and neuro-fuzzy (NF) models to predict NOx concentration in the air as a function of traffic count (Tr) and climatic conditions including humidity (H), temperature (T), solar radiation (S), and wind speed (W) before and after the application of TiO₂ on the pavement surface. These models are useful for modeling because of their ability to be trained using historical data and because of their capability for modeling highly non-linear relationships. To build these models, data were collected from a field study where an aqueous nano TiO₂ solution was sprayed on a 0.2-mile of asphalt pavement in Baton Rouge, LA. Results of this study showed that the NF model provided a better fitting to NOx measurements than the ANN model in the training, validation, and test steps. Results of a parametric study indicated that traffic level, relative humidity, and solar radiation had the most influence on photocatalytic efficiency.

  17. Artificial intelligence in hematology.

    Science.gov (United States)

    Zini, Gina

    2005-10-01

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

  18. Computer models of bacterial cells: from generalized coarsegrained to genome-specific modular models

    Science.gov (United States)

    Nikolaev, Evgeni V.; Atlas, Jordan C.; Shuler, Michael L.

    2006-09-01

    We discuss a modular modelling framework to rapidly develop mathematical models of bacterial cells that would explicitly link genomic details to cell physiology and population response. An initial step in this approach is the development of a coarse-grained model, describing pseudo-chemical interactions between lumped species. A hybrid model of interest can then be constructed by embedding genome-specific detail for a particular cellular subsystem (e.g. central metabolism), called here a module, into the coarse-grained model. Specifically, a new strategy for sensitivity analysis of the cell division limit cycle is introduced to identify which pseudo-molecular processes should be delumped to implement a particular biological function in a growing cell (e.g. ethanol overproduction or pathogen viability). To illustrate the modeling principles and highlight computational challenges, the Cornell coarsegrained model of Escherichia coli B/r-A is used to benchmark the proposed framework.

  19. A single-step genomic model with direct estimation of marker effects.

    Science.gov (United States)

    Liu, Z; Goddard, M E; Reinhardt, F; Reents, R

    2014-09-01

    Compared with the currently widely used multi-step genomic models for genomic evaluation, single-step genomic models can provide more accurate genomic evaluation by jointly analyzing phenotypes and genotypes of all animals and can properly correct for the effect of genomic preselection on genetic evaluations. The objectives of this study were to introduce a single-step genomic model, allowing a direct estimation of single nucleotide polymorphism (SNP) effects, and to develop efficient computing algorithms for solving equations of the single-step SNP model. We proposed an alternative to the current single-step genomic model based on the genomic relationship matrix by including an additional step for estimating the effects of SNP markers. Our single-step SNP model allowed flexible modeling of SNP effects in terms of the number and variance of SNP markers. Moreover, our single-step SNP model included a residual polygenic effect with trait-specific variance for reducing inflation in genomic prediction. A kernel calculation of the SNP model involved repeated multiplications of the inverse of the pedigree relationship matrix of genotyped animals with a vector, for which numerical methods such as preconditioned conjugate gradients can be used. For estimating SNP effects, a special updating algorithm was proposed to separate residual polygenic effects from the SNP effects. We extended our single-step SNP model to general multiple-trait cases. By taking advantage of a block-diagonal (co)variance matrix of SNP effects, we showed how to estimate multivariate SNP effects in an efficient way. A general prediction formula was derived for candidates without phenotypes, which can be used for frequent, interim genomic evaluations without running the whole genomic evaluation process. We discussed various issues related to implementation of the single-step SNP model in Holstein populations with an across-country genomic reference population.

  20. THE ROLE OF POROUS MEDIA IN MODELING FLUID FLOW WITHIN HOLLOW FIBER MEMBRANES OF THE TOTAL ARTIFICIAL LUNG.

    Science.gov (United States)

    Khanafer, Khalil; Cook, Keith; Marafie, Alia

    2012-01-01

    A numerical study was conducted to analyze fluid flow within hollow fiber membranes of the artificial lungs. The hollow fiber bundle was approximated using a porous media model. In addition, the transport equations were solved using the finite-element formulation based on the Galerkin method of weighted residuals. Comparisons with previously published work on the basis of special cases were performed and found to be in excellent agreement. A Newtonian viscous fluid model for the blood was used. Different flow models for porous media, such as the Brinkman-extended Darcy model, Darcy's law model, and the generalized flow model, were considered. Results were obtained in terms of streamlines, velocity vectors, and pressure distribution for various Reynolds numbers and Darcy numbers. The results from this investigation showed that the pressure drop across the artificial lung device increased with an increase in the Reynolds number. In addition, the pressure drop was found to increase significantly for small Darcy numbers.

  1. 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

    2012-10-05

    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.

  2. 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

  3. Analysis of genetic variation and potential applications in genome-scale metabolic modeling

    Directory of Open Access Journals (Sweden)

    João Gonçalo Rocha Cardoso

    2015-02-01

    Full Text Available Genetic variation is the motor of evolution and allows organisms to overcome the environmental challenges they encounter. It can be both beneficial and harmful in the process of engineering cell factories for the production of proteins and chemicals. Throughout the history of biotechnology, there have been efforts to exploit genetic variation in our favor to create strains with favorable phenotypes. Genetic variation can either be present in natural populations or it can be artificially created by mutagenesis and selection or adaptive laboratory evolution. On the other hand, unintended genetic variation during a long term production process may lead to significant economic losses and it is important to understand how to control this type of variation. With the emergence of next-generation sequencing technologies, genetic variation in microbial strains can now be determined on an unprecedented scale and resolution by re-sequencing thousands of strains systematically. In this article, we review challenges in the integration and analysis of large-scale re-sequencing data, present an extensive overview of bioinformatics methods for predicting the effects of genetic variants on protein function, and discuss approaches for interfacing existing bioinformatics approaches with genome-scale models of cellular processes in order to predict effects of sequence variation on cellular phenotypes.

  4. 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,…

  5. 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...

  6. 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…

  7. Curriculum Assessment Using Artificial Neural Network and Support Vector Machine Modeling Approaches: A Case Study. IR Applications. Volume 29

    Science.gov (United States)

    Chen, Chau-Kuang

    2010-01-01

    Artificial Neural Network (ANN) and Support Vector Machine (SVM) approaches have been on the cutting edge of science and technology for pattern recognition and data classification. In the ANN model, classification accuracy can be achieved by using the feed-forward of inputs, back-propagation of errors, and the adjustment of connection weights. In…

  8. 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.

  9. Artificial Neural Networks and risk stratification models in Emergency Departments: The policy maker's perspective.

    Science.gov (United States)

    Casagranda, Ivo; Costantino, Giorgio; Falavigna, Greta; Furlan, Raffaello; Ippoliti, Roberto

    2016-01-01

    The primary goal of Emergency Department (ED) physicians is to discriminate between individuals at low risk, who can be safely discharged, and patients at high risk, who require prompt hospitalization. The problem of correctly classifying patients is an issue involving not only clinical but also managerial aspects, since reducing the rate of admission of patients to EDs could dramatically cut costs. Nevertheless, a trade-off might arise due to the need to find a balance between economic interests and the health conditions of patients. This work considers patients in EDs after a syncope event and presents a comparative analysis between two models: a multivariate logistic regression model, as proposed by the scientific community to stratify the expected risk of severe outcomes in the short and long run, and Artificial Neural Networks (ANNs), an innovative model. The analysis highlights differences in correct classification of severe outcomes at 10 days (98.30% vs. 94.07%) and 1 year (97.67% vs. 96.40%), pointing to the superiority of Neural Networks. According to the results, there is also a significant superiority of ANNs in terms of false negatives both at 10 days (3.70% vs. 5.93%) and at 1 year (2.33% vs. 10.07%). However, considering the false positives, the adoption of ANNs would cause an increase in hospital costs, highlighting the potential trade-off which policy makers might face.

  10. 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.

  11. Artificial intelligence

    CERN Document Server

    Hunt, Earl B

    1975-01-01

    Artificial Intelligence provides information pertinent to the fundamental aspects of artificial intelligence. This book presents the basic mathematical and computational approaches to problems in the artificial intelligence field.Organized into four parts encompassing 16 chapters, this book begins with an overview of the various fields of artificial intelligence. This text then attempts to connect artificial intelligence problems to some of the notions of computability and abstract computing devices. Other chapters consider the general notion of computability, with focus on the interaction bet

  12. 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.

  13. 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.

  14. Simulation of river stage using artificial neural network and MIKE 11 hydrodynamic model

    Science.gov (United States)

    Panda, Rabindra K.; Pramanik, Niranjan; Bala, Biplab

    2010-06-01

    Simulation of water levels at different sections of a river using physically based flood routing models is quite cumbersome, because it requires many types of data such as hydrologic time series, river geometry, hydraulics of existing control structures and channel roughness coefficients. Normally in developing countries like India it is not easy to collect these data because of poor monitoring and record keeping. Therefore, an artificial neural network (ANN) technique is used as an effective alternative in hydrologic simulation studies. The present study aims at comparing the performance of the ANN technique with a widely used physically based hydrodynamic model in the MIKE 11 environment. The MIKE 11 hydrodynamic model was calibrated and validated for the monsoon periods (June-September) of the years 2006 and 2001, respectively. Feed forward neural network architecture with Levenberg-Marquardt (LM) back propagation training algorithm was used to train the neural network model using hourly water level data of the period June-September 2006. The trained ANN model was tested using data for the same period of the year 2001. Simulated water levels by the MIKE 11HD were compared with the corresponding water levels predicted by the ANN model. The results obtained from the ANN model were found to be much better than that of the MIKE 11HD results as indicated by the values of the goodness of fit indices used in the study. The Nash-Sutcliffe index ( E) and root mean square error (RMSE) obtained in case of the ANN model were found to be 0.8419 and 0.8939 m, respectively, during model testing, whereas in case of MIKE 11HD, the values of E and RMSE were found to be 0.7836 and 1.00 m, respectively, during model validation. The difference between the observed and simulated peak water levels obtained from the ANN model was found to be much lower than that of MIKE 11HD. The study reveals that the use of Levenberg-Marquardt algorithm with eight hidden neurons in the hidden layer

  15. Modelling and analysis of ozone concentration by artificial intelligent techniques for estimating air quality

    Science.gov (United States)

    Taylan, Osman

    2017-02-01

    High ozone concentration is an important cause of air pollution mainly due to its role in the greenhouse gas emission. Ozone is produced by photochemical processes which contain nitrogen oxides and volatile organic compounds in the lower atmospheric level. Therefore, monitoring and controlling the quality of air in the urban environment is very important due to the public health care. However, air quality prediction is a highly complex and non-linear process; usually several attributes have to be considered. Artificial intelligent (AI) techniques can be employed to monitor and evaluate the ozone concentration level. The aim of this study is to develop an Adaptive Neuro-Fuzzy inference approach (ANFIS) to determine the influence of peripheral factors on air quality and pollution which is an arising problem due to ozone level in Jeddah city. The concentration of ozone level was considered as a factor to predict the Air Quality (AQ) under the atmospheric conditions. Using Air Quality Standards of Saudi Arabia, ozone concentration level was modelled by employing certain factors such as; nitrogen oxide (NOx), atmospheric pressure, temperature, and relative humidity. Hence, an ANFIS model was developed to observe the ozone concentration level and the model performance was assessed by testing data obtained from the monitoring stations established by the General Authority of Meteorology and Environment Protection of Kingdom of Saudi Arabia. The outcomes of ANFIS model were re-assessed by fuzzy quality charts using quality specification and control limits based on US-EPA air quality standards. The results of present study show that the ANFIS model is a comprehensive approach for the estimation and assessment of ozone level and is a reliable approach to produce more genuine outcomes.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. Numerical model of nonhydrostatic ocean dynamics based on methods of artificial compressibility and multicomponent splitting

    Science.gov (United States)

    Zalesny, V. B.; Gusev, A. V.; Fomin, V. V.

    2016-11-01

    An algorithm is proposed for solving three-dimensional ocean hydrodynamics equations without hydrostatic approximation and traditional simplification of Coriolis acceleration. It is based on multicomponent splitting of the modified model with artificial compressibility. The original system of equations is split into two subsystems describing the transport of three velocity components and adjustment of the density and velocity fields. At the adjustment stage, the horizontal velocity components are represented as a sum of the depth means and deviations; the two corresponding subsystems are derived. For barotropic dynamics, the compressibility effect is represented as the boundary condition at the free surface, while for the baroclinic subsystem, it is introduced as ɛ-regularization of the continuity equation. Then, the baroclinic equations are split into two subsystems describing the hydrostatic and nonhydrostatic dynamics. The nonhydrostatic dynamics is computed at a separate splitting stage. The algorithm is included into the Institute of Numerical Mathematics of the Russian Academy of Sciences model based on "primitive" equations and verified by solving the hydrodynamics problem for the Sea of Marmara.

  2. 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.

  3. Biological model of vision for an artificial system that learns to perceive its environment

    Energy Technology Data Exchange (ETDEWEB)

    Blackburn, M.R.; Nguyen, H.G.

    1989-06-01

    The objective is to design an artificial vision system for use in robotics applications. Because the desired performance is equivalent to that achieved by nature, the authors anticipate that the objective will be accomplished most efficiently through modeling aspects of the neuroanatomy and neurophysiology of the biological visual system. Information enters the biological visual system through the retina and is passed to the lateral geniculate and optic tectum. The lateral geniculate nucleus (LGN) also receives information from the cerebral cortex and the result of these two inflows is returned to the cortex. The optic tectum likewise receives the retinal information in a context of other converging signals and organizes motor responses. A computer algorithm is described which implements models of the biological visual mechanisms of the retina, thalamic lateral geniculate and perigeniculate nuclei, and primary visual cortex. Motion and pattern analyses are performed in parallel and interact in the cortex to construct perceptions. We hypothesize that motion reflexes serve as unconditioned pathways for the learning and recall of pattern information. The algorithm demonstrates this conditioning through a learning function approximating heterosynaptic facilitation.

  4. Potential Habitat Modelling of Ferula ovina Using Artificial Neural Network in Fereydunshahr Region, Isfahan Province

    Directory of Open Access Journals (Sweden)

    Z. Rahmati

    2015-06-01

    Full Text Available Species distribution maps have been widely developed based on ecological niche theory together with statistical and geographical information system in plant ecology. The current study aimed to evaluate Artificial Neural Network (ANN in mapping potential habitat of Ferula ovina Boiss in Ferydunshar rangelands, Isfahan. This is known as valuable forage and medicinal species. Environmental data (independent variables and species occurrence data (dependent variable were required to determine potential habitat of a given species. Some physical and chemical soil properties, climate and physiographic variables were mapped for the entire studied area using krigging and inverse distance weighting methods. F. ovina occurrence data were collected from 278 sites including 137 presence and 141 absence sites. The relationships between the studied environmental variables and F. ovina occurrence data were explored using ANN method. According to the sensitivity analysis, occurrence of F. ovina mostly correlated with silt and sand percentage, elevation slope, and organic matter. Model evaluation based on Kappa coefficient (0.66 and Receiver operating characteristic (ROC=0.9 showed good model fitness in relation to reality on local scales. The ANN technique enables managers to identify appropriate areas for rehabilitation practices such as direct seeding and planting.                       

  5. 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)

  6. 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.

  7. The impact of antimicrobial photodynamic therapy on Streptococcus mutans in an artificial biofilm model

    Science.gov (United States)

    Schneider, Martin; Kirfel, Gregor; Krause, Felix; Berthold, Michael; Brede, Olivier; Frentzen, Matthias; Braun, Andreas

    2010-02-01

    The aim of the study was to assess the impact of laser induced antimicrobial photodynamic therapy on the viability of Streptococcus mutans cells employing an aritificial biofilm model. Employing sterile chambered coverglasses, a salivary pellicle layer formation was induced in 19 chambers. Streptococcus mutans cells were inoculated in a sterile culture medium. Using a live/dead bacterial viability kit, bacteria with intact cell membranes stain fluorescent green. Test chambers containing each the pellicle layer and 0.5 ml of the bacterial culture were analyzed using a confocal laser scan microscope within a layer of 10 μm at intervals of 1 μm from the pellicle layer. A photosensitizer was added to the test chambers and irradiated with a diode laser (wavelength: 660 nm, output power: 100 mW, Helbo) for 2 min each. Comparing the baseline fluorescence (median: 13.8 [U], min: 3.7, max: 26.2) with the values after adding the photosensitizer (median: 3.7, min: 1.1, max: 9), a dilution caused decrease of fluorescence could be observed (p0.05). The present study indicates that antimicrobial photodynamic therapy can reduce living bacteria within a layer of 10 μm in an artificial biofilm model. Further studies have to evaluate the maximum biofilm thickness that still allows a toxic effect on microorganisms.

  8. Cardiopulmonary values in dogs with artificial model of caval syndrome in heartworm disease.

    Science.gov (United States)

    Kuwahara, Y; Kitagawa, H; Sasaki, Y; Ishihara, K

    1991-02-01

    Cardiopulmonary values were determined in dogs with an artificial model of heartworm caval syndrome, which was produced by insertion of heartworm-like silicone tubes into the tricuspid valve orifice and right atrium. Fifteen to 25 tubes with some knots were inserted in 6 dogs (knot group), and 7 to 11 tubes (small-number group) or 29 to 37 tubes (large-number group) without a knot in 3 dogs, respectively. After tube insertion, angiographic contrast medium infused into the right ventricle regurgitated to the right atrium in all cases, and the regurgitation was the most severe in the large-number group. On electrocardiographic findings, the atrial and/or ventricular premature beat developed. The height of a- and v-wave of right atrial pressure curves elevated in all groups. The elevation in v-wave was obvious in the large-number group. The pulmonary arterial pressure tended to fall or to elevate slightly, and total pulmonary resistance increased in all groups. The right cardiac output decreased significantly in all cases. The right heart hemodynamics of the model might resemble those in spontaneous cases without disturbed pulmonary circulation.

  9. 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.

  10. 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.

  11. 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.

  12. Application of artificial neural network model combined with four biomarkers in auxiliary diagnosis of lung cancer.

    Science.gov (United States)

    Duan, Xiaoran; Yang, Yongli; Tan, Shanjuan; Wang, Sihua; Feng, Xiaolei; Cui, Liuxin; Feng, Feifei; Yu, Songcheng; Wang, Wei; Wu, Yongjun

    2016-10-20

    The purpose of the study was to explore the application of artificial neural network model in the auxiliary diagnosis of lung cancer and compare the effects of back-propagation (BP) neural network with Fisher discrimination model for lung cancer screening by the combined detections of four biomarkers of p16, RASSF1A and FHIT gene promoter methylation levels and the relative telomere length. Real-time quantitative methylation-specific PCR was used to detect the levels of three-gene promoter methylation, and real-time PCR method was applied to determine the relative telomere length. BP neural network and Fisher discrimination analysis were used to establish the discrimination diagnosis model. The levels of three-gene promoter methylation in patients with lung cancer were significantly higher than those of the normal controls. The values of Z(P) in two groups were 2.641 (0.008), 2.075 (0.038) and 3.044 (0.002), respectively. The relative telomere lengths of patients with lung cancer (0.93 ± 0.32) were significantly lower than those of the normal controls (1.16 ± 0.57), t = 4.072, P neural network were 0.670 (0.569-0.761) and 0.760 (0.664-0.840). The AUC of BP neural network was higher than that of Fisher discrimination analysis, and Z(P) was 0.76. Four biomarkers are associated with lung cancer. BP neural network model for the prediction of lung cancer is better than Fisher discrimination analysis, and it can provide an excellent and intelligent diagnosis tool for lung cancer.

  13. Comparative evaluation of the use of artificial neural networks for modelling the epidemiology of schistosomiasis mansoni.

    Science.gov (United States)

    Hammad, T A; Abdel-Wahab, M F; DeClaris, N; El-Sahly, A; El-Kady, N; Strickland, G T

    1996-01-01

    There has been a marked increase in the application of approaches based on artificial intelligence (AI) in the field of computer science and medical diagnosis, but AI is still relatively unused in epidemiological settings. In this study we report results of the application of neural networks (NN; a special category of AI) to schistosomiasis. It was possible to design an NN structure which can process and fit epidemiological data collected from 251 schoolchildren in Egypt using the first year's data to predict second and third years' infection rates. Data collected over 3 years included age, gender, exposure to canal water and agricultural activities, medical history and examination, and stool and urine parasitology. Schistosoma mansoni infection rates were 50%, 78% and 66% at the baseline and the 2 follow-up periods, respectively. NN modelling was based on the standard back-propagation algorithm, in which we built a suitable configuration of the network, using the first year's data, that optimized performance. It was implemented on an IBM compatible computer using commercially available software. The performance of the NN model in the first year compared favourably with logistic regression (NN sensitivity = 83% (95% confidence interval [CI] 78-88%) and positive predictive value (PPV) = 63% (95% CI 57-69%); logistic regression sensitivity = 66% (95% CI 60%-72%) and PPV = 59% (95% CI 53%-65%). The NN model generalized the criteria for predicting infection over time better than logistic regression and showed more stability over time, as it retained its sensitivity and specificity and had better false positive and negative profiles than logistic regression. The potential of NN-based models to analyse and predict wide-scale control programme data, which are inevitably based on unstable egg excretion rates and insensitive laboratory techniques, is promising but still untapped.

  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. An artificial neural network prediction model of congenital heart disease based on risk factors

    Science.gov (United States)

    Li, Huixia; Luo, Miyang; Zheng, Jianfei; Luo, Jiayou; Zeng, Rong; Feng, Na; Du, Qiyun; Fang, Junqun

    2017-01-01

    Abstract An artificial neural network (ANN) model was developed to predict the risks of congenital heart disease (CHD) in pregnant women. This hospital-based case-control study involved 119 CHD cases and 239 controls all recruited from birth defect surveillance hospitals in Hunan Province between July 2013 and June 2014. All subjects were interviewed face-to-face to fill in a questionnaire that covered 36 CHD-related variables. The 358 subjects were randomly divided into a training set and a testing set at the ratio of 85:15. The training set was used to identify the significant predictors of CHD by univariate logistic regression analyses and develop a standard feed-forward back-propagation neural network (BPNN) model for the prediction of CHD. The testing set was used to test and evaluate the performance of the ANN model. Univariate logistic regression analyses were performed on SPSS 18.0. The ANN models were developed on Matlab 7.1. The univariate logistic regression identified 15 predictors that were significantly associated with CHD, including education level (odds ratio  = 0.55), gravidity (1.95), parity (2.01), history of abnormal reproduction (2.49), family history of CHD (5.23), maternal chronic disease (4.19), maternal upper respiratory tract infection (2.08), environmental pollution around maternal dwelling place (3.63), maternal exposure to occupational hazards (3.53), maternal mental stress (2.48), paternal chronic disease (4.87), paternal exposure to occupational hazards (2.51), intake of vegetable/fruit (0.45), intake of fish/shrimp/meat/egg (0.59), and intake of milk/soymilk (0.55). After many trials, we selected a 3-layer BPNN model with 15, 12, and 1 neuron in the input, hidden, and output layers, respectively, as the best prediction model. The prediction model has accuracies of 0.91 and 0.86 on the training and testing sets, respectively. The sensitivity, specificity, and Yuden Index on the testing set (training set) are 0.78 (0.83), 0.90 (0

  16. Data Assimilation using Artificial Neural Networks for the global FSU atmospheric model

    Science.gov (United States)

    Cintra, Rosangela; Cocke, Steven; Campos Velho, Haroldo

    2015-04-01

    Data assimilation is the process by which measurements and model predictions are combined to obtain an accurate representation of the state of the modeled system. Uncertainty is the characteristic of the atmosphere, coupled with inevitable inadequacies in observations and computer models and increase errors in weather forecasts. Data assimilation is a technique to generate an initial condition to a weather or climate forecasts. This paper shows the results of a data assimilation technique using artificial neural networks (ANN) to obtain the initial condition to the atmospheric general circulation model (AGCM) for the Florida State University in USA. The Local Ensemble Transform Kalman filter (LETKF) is implemented with Florida State University Global Spectral Model (FSUGSM). The ANN data assimilation is made to emulate the initial condition from LETKF to run the FSUGSM. LETKF is a version of Kalman filter with Monte-Carlo ensembles of short-term forecasts to solve the data assimilation problem. The model FSUGSM is a multilevel (27 vertical levels) spectral primitive equation model with a vertical sigma coordinate. All variables are expanded horizontally in a truncated series of spherical harmonic functions (at resolution T63) and a transform technique is applied to calculate the physical processes in real space. The LETKF data assimilation experiments are based in synthetic observations data (surface pressure, absolute temperature, zonal component wind, meridional component wind and humidity). For the ANN data assimilation scheme, we use Multilayer Perceptron (MLP-DA) with supervised training algorithm where ANN receives input vectors with their corresponding response or target output from LETKF scheme. An automatic tool that finds the optimal representation to these ANNs configures the MLP-DA in this experiment. After the training process, the scheme MLP-DA is seen as a function of data assimilation where the inputs are observations and a short-range forecast to

  17. 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

  18. THE ROLE OF POROUS MEDIA IN MODELING FLUID FLOW WITHIN HOLLOW FIBER MEMBRANES OF THE TOTAL ARTIFICIAL LUNG

    OpenAIRE

    2010-01-01

    A numerical study was conducted to analyze fluid flow within hollow fiber membranes of the artificial lungs. The hollow fiber bundle was approximated using a porous media model. In addition, the transport equations were solved using the finite-element formulation based on the Galerkin method of weighted residuals. Comparisons with previously published work on the basis of special cases were performed and found to be in excellent agreement. A Newtonian viscous fluid model for the blood was use...

  19. 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, A R; 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...... under a range of soil physical conditions with a high degree of accuracy and may be used in crop growth modeling....

  20. 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…

  1. Genomic Prediction of Genotype × Environment Interaction Kernel Regression Models.

    Science.gov (United States)

    Cuevas, Jaime; Crossa, José; Soberanis, Víctor; Pérez-Elizalde, Sergio; Pérez-Rodríguez, Paulino; Campos, Gustavo de Los; Montesinos-López, O A; Burgueño, Juan

    2016-11-01

    In genomic selection (GS), genotype × environment interaction (G × E) can be modeled by a marker × environment interaction (M × E). The G × E may be modeled through a linear kernel or a nonlinear (Gaussian) kernel. In this study, we propose using two nonlinear Gaussian kernels: the reproducing kernel Hilbert space with kernel averaging (RKHS KA) and the Gaussian kernel with the bandwidth estimated through an empirical Bayesian method (RKHS EB). We performed single-environment analyses and extended to account for G × E interaction (GBLUP-G × E, RKHS KA-G × E and RKHS EB-G × E) in wheat ( L.) and maize ( L.) data sets. For single-environment analyses of wheat and maize data sets, RKHS EB and RKHS KA had higher prediction accuracy than GBLUP for all environments. For the wheat data, the RKHS KA-G × E and RKHS EB-G × E models did show up to 60 to 68% superiority over the corresponding single environment for pairs of environments with positive correlations. For the wheat data set, the models with Gaussian kernels had accuracies up to 17% higher than that of GBLUP-G × E. For the maize data set, the prediction accuracy of RKHS EB-G × E and RKHS KA-G × E was, on average, 5 to 6% higher than that of GBLUP-G × E. The superiority of the Gaussian kernel models over the linear kernel is due to more flexible kernels that accounts for small, more complex marker main effects and marker-specific interaction effects.

  2. 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.

  3. 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

  4. 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.

  5. 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.

  6. 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.

  7. Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem

    Directory of Open Access Journals (Sweden)

    Onur Satir

    2016-09-01

    Full Text Available Forest fires are one of the most important factors in environmental risk assessment and it is the main cause of forest destruction in the Mediterranean region. Forestlands have a number of known benefits such as decreasing soil erosion, containing wild life habitats, etc. Additionally, forests are also important player in carbon cycle and decreasing the climate change impacts. This paper discusses forest fire probability mapping of a Mediterranean forestland using a multiple data assessment technique. An artificial neural network (ANN method was used to map forest fire probability in Upper Seyhan Basin (USB in Turkey. Multi-layer perceptron (MLP approach based on back propagation algorithm was applied in respect to physical, anthropogenic, climate and fire occurrence datasets. Result was validated using relative operating characteristic (ROC analysis. Coefficient of accuracy of the MLP was 0.83. Landscape features input to the model were assessed statistically to identify the most descriptive factors on forest fire probability mapping using the Pearson correlation coefficient. Landscape features like elevation (R = −0.43, tree cover (R = 0.93 and temperature (R = 0.42 were strongly correlated with forest fire probability in the USB region.

  8. Development of artificial model of caval syndrome in canine heartworm disease.

    Science.gov (United States)

    Kitagawa, H; Sasaki, Y; Ishihara, K; Kuwahara, Y

    1990-10-01

    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.

  9. 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 < 0.01), and circuit resistance and minimal blood lactate levels were significantly lower in the parallel circuit group, compared with our previous single circuit group. 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.

  10. 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

  11. 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.

  12. 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.

  13. 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

  14. Genome downsizing and karyotype constancy in diploid and polyploid congeners: a model of genome size variation.

    Science.gov (United States)

    Poggio, Lidia; Realini, María Florencia; Fourastié, María Florencia; García, Ana María; González, Graciela Esther

    2014-06-26

    Evolutionary chromosome change involves significant variation in DNA amount in diploids and genome downsizing in polyploids. Genome size and karyotype parameters of Hippeastrum species with different ploidy level were analysed. In Hippeastrum, polyploid species show less DNA content per basic genome than diploid species. The rate of variation is lower at higher ploidy levels. All the species have a basic number x = 11 and bimodal karyotypes. The basic karyotypes consist of four short metacentric chromosomes and seven large chromosomes (submetacentric and subtelocentric). The bimodal karyotype is preserved maintaining the relative proportions of members of the haploid chromosome set, even in the presence of genome downsizing. The constancy of the karyotype is maintained because changes in DNA amount are proportional to the length of the whole-chromosome complement and vary independently in the long and short sets of chromosomes. This karyotype constancy in taxa of Hippeastrum with different genome size and ploidy level indicates that the distribution of extra DNA within the complement is not at random and suggests the presence of mechanisms selecting for constancy, or against changes, in karyotype morphology.

  15. Artificial Neural Networks

    OpenAIRE

    Chung-Ming Kuan

    2006-01-01

    Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods.

  16. Artificial Intelligence.

    Science.gov (United States)

    Waltz, David L.

    1982-01-01

    Describes kinds of results achieved by computer programs in artificial intelligence. Topics discussed include heuristic searches, artificial intelligence/psychology, planning program, backward chaining, learning (focusing on Winograd's blocks to explore learning strategies), concept learning, constraint propagation, language understanding…

  17. Emergence of gamma motor activity in an artificial neural network model of the corticospinal system.

    Science.gov (United States)

    Grandjean, Bernard; Maier, Marc A

    2017-02-01

    Muscle spindle discharge during active movement is a function of mechanical and neural parameters. Muscle length changes (and their derivatives) represent its primary mechanical, fusimotor drive its neural component. However, neither the action nor the function of fusimotor and in particular of γ-drive, have been clearly established, since γ-motor activity during voluntary, non-locomotor movements remains largely unknown. Here, using a computational approach, we explored whether γ-drive emerges in an artificial neural network model of the corticospinal system linked to a biomechanical antagonist wrist simulator. The wrist simulator included length-sensitive and γ-drive-dependent type Ia and type II muscle spindle activity. Network activity and connectivity were derived by a gradient descent algorithm to generate reciprocal, known target α-motor unit activity during wrist flexion-extension (F/E) movements. Two tasks were simulated: an alternating F/E task and a slow F/E tracking task. Emergence of γ-motor activity in the alternating F/E network was a function of α-motor unit drive: if muscle afferent (together with supraspinal) input was required for driving α-motor units, then γ-drive emerged in the form of α-γ coactivation, as predicted by empirical studies. In the slow F/E tracking network, γ-drive emerged in the form of α-γ dissociation and provided critical, bidirectional muscle afferent activity to the cortical network, containing known bidirectional target units. The model thus demonstrates the complementary aspects of spindle output and hence γ-drive: i) muscle spindle activity as a driving force of α-motor unit activity, and ii) afferent activity providing continuous sensory information, both of which crucially depend on γ-drive.

  18. A model to discriminate malignant from benign thyroid nodules using artificial neural network.

    Directory of Open Access Journals (Sweden)

    Lu-Cheng Zhu

    Full Text Available OBJECTIVE: This study aimed to construct a model for using in differentiating benign and malignant nodules with the artificial neural network and to increase the objective diagnostic accuracy of US. MATERIALS AND METHODS: 618 consecutive patients (528 women, 161 men with 689 thyroid nodules (425 malignant and 264 benign nodules were enrolled in the present study. The presence and absence of each sonographic feature was assessed for each nodule - shape, margin, echogenicity, internal composition, presence of calcifications, peripheral halo and vascularity on color Doppler. The variables meet the following criteria: important sonographic features and statistically significant difference were selected as the input layer to build the ANN for predicting the malignancy of nodules. RESULTS: Six sonographic features including shape (Taller than wide, p<0.001, margin (Not Well-circumscribed, p<0.001, echogenicity (Hypoechogenicity, p<0.001, internal composition (Solid, p<0.001, presence of calcifications (Microcalcification, p<0.001 and peripheral halo (Absent, p<0.001 were significantly associated with malignant nodules. A three-layer 6-8-1 feed-forward ANN model was built. In the training cohort, the accuracy of the ANN in predicting malignancy of thyroid nodules was 82.3% (AURO  = 0.818, the sensitivity and specificity was 84.5% and 79.1%, respectively. In the validation cohort, the accuracy, sensitivity and specificity was 83.1%, 83.8% and 81.8%, respectively. The AUROC was 0.828. CONCLUSION: ANN constructed by sonographic features can discriminate benign and malignant thyroid nodules with high diagnostic accuracy.

  19. Predicting the fibre diameter of melt blown nonwovens: comparison of physical, statistical and artificial neural network models

    Science.gov (United States)

    Chen, Ting; Li, Liqing; Huang, Xiubao

    2005-06-01

    Physical, statistical and artificial neural network (ANN) models are established for predicting the fibre diameter of melt blown nonwovens from the processing parameters. The results show that the ANN model yields a very accurate prediction (average error of 0.013%), and a reasonably good ANN model can be achieved with relatively few data points. Because the physical model is based on the inherent physical principles of the phenomena of interest, it can yield reasonably good prediction results when experimental data are not available and the entire physical procedure is of interest. This area of research has great potential in the field of computer assisted design in melt blowing technology.

  20. Application of response surface methodology and artificial neural network methods in modelling and optimization of biosorption process.

    Science.gov (United States)

    Witek-Krowiak, Anna; Chojnacka, Katarzyna; Podstawczyk, Daria; Dawiec, Anna; Pokomeda, Karol

    2014-05-01

    A review on the application of response surface methodology (RSM) and artificial neural networks (ANN) in biosorption modelling and optimization is presented. The theoretical background of the discussed methods with the application procedure is explained. The paper describes most frequently used experimental designs, concerning their limitations and typical applications. The paper also presents ways to determine the accuracy and the significance of model fitting for both methodologies described herein. Furthermore, recent references on biosorption modelling and optimization with the use of RSM and the ANN approach are shown. Special attention was paid to the selection of factors and responses, as well as to statistical analysis of the modelling results.

  1. 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.

  2. Genomic sequence and experimental tractability of a new decapod shrimp model, Neocaridina denticulata.

    Science.gov (United States)

    Kenny, Nathan J; Sin, Yung Wa; Shen, Xin; Zhe, Qu; Wang, Wei; Chan, Ting Fung; Tobe, Stephen S; Shimeld, Sebastian M; Chu, Ka Hou; Hui, Jerome H L

    2014-03-11

    The speciose Crustacea is the largest subphylum of arthropods on the planet after the Insecta. To date, however, the only publically available sequenced crustacean genome is that of the water flea, Daphnia pulex, a member of the Branchiopoda. While Daphnia is a well-established ecotoxicological model, previous study showed that one-third of genes contained in its genome are lineage-specific and could not be identified in any other metazoan genomes. To better understand the genomic evolution of crustaceans and arthropods, we have sequenced the genome of a novel shrimp model, Neocaridina denticulata, and tested its experimental malleability. A library of 170-bp nominal fragment size was constructed from DNA of a starved single adult and sequenced using the Illumina HiSeq2000 platform. Core eukaryotic genes, the mitochondrial genome, developmental patterning genes (such as Hox) and microRNA processing pathway genes are all present in this animal, suggesting it has not undergone massive genomic loss. Comparison with the published genome of Daphnia pulex has allowed us to reveal 3750 genes that are indeed specific to the lineage containing malacostracans and branchiopods, rather than Daphnia-specific (E-value: 10⁻⁶). We also show the experimental tractability of N. denticulata, which, together with the genomic resources presented here, make it an ideal model for a wide range of further aquacultural, developmental, ecotoxicological, food safety, genetic, hormonal, physiological and reproductive research, allowing better understanding of the evolution of crustaceans and other arthropods.

  3. Genomic Sequence and Experimental Tractability of a New Decapod Shrimp Model, Neocaridina denticulata

    Directory of Open Access Journals (Sweden)

    Nathan J. Kenny

    2014-03-01

    Full Text Available The speciose Crustacea is the largest subphylum of arthropods on the planet after the Insecta. To date, however, the only publically available sequenced crustacean genome is that of the water flea, Daphnia pulex, a member of the Branchiopoda. While Daphnia is a well-established ecotoxicological model, previous study showed that one-third of genes contained in its genome are lineage-specific and could not be identified in any other metazoan genomes. To better understand the genomic evolution of crustaceans and arthropods, we have sequenced the genome of a novel shrimp model, Neocaridina denticulata, and tested its experimental malleability. A library of 170-bp nominal fragment size was constructed from DNA of a starved single adult and sequenced using the Illumina HiSeq2000 platform. Core eukaryotic genes, the mitochondrial genome, developmental patterning genes (such as Hox and microRNA processing pathway genes are all present in this animal, suggesting it has not undergone massive genomic loss. Comparison with the published genome of Daphnia pulex has allowed us to reveal 3750 genes that are indeed specific to the lineage containing malacostracans and branchiopods, rather than Daphnia-specific (E-value: 10−6. We also show the experimental tractability of N. denticulata, which, together with the genomic resources presented here, make it an ideal model for a wide range of further aquacultural, developmental, ecotoxicological, food safety, genetic, hormonal, physiological and reproductive research, allowing better understanding of the evolution of crustaceans and other arthropods.

  4. Development of a multi-layer perceptron artificial neural network model to determine haul trucks energy consumption

    Institute of Scientific and Technical Information of China (English)

    Soofastaei Ali; Aminossadati Saiied M.; Arefi Mohammad M.; Kizil Mehmet S.

    2016-01-01

    The mining industry annually consumes trillions of British thermal units of energy, a large part of which is saveable. Diesel fuel is a significant source of energy in surface mining operations and haul trucks are the major users of this energy source. Gross vehicle weight, truck velocity and total resistance have been recognised as the key parameters affecting the fuel consumption. In this paper, an artificial neural net-work model was developed to predict the fuel consumption of haul trucks in surface mines based on the gross vehicle weight, truck velocity and total resistance. The network was trained and tested using real data collected from a surface mining operation. The results indicate that the artificial neural network modelling can accurately predict haul truck fuel consumption based on the values of the haulage param-eters considered in this study.

  5. 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.

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

    Science.gov (United States)

    A limitation of many genome-wide association studies (GWA) in animal breeding is that there are many loci with small effect sizes; thus, larger sample sizes (N) are required to guarantee suitable power of detection. To increase sample size, results from different GWA can be combined in a meta-analys...

  7. A foundation for provitamin A biofortification of maize: genome-wide association and genomic prediction models of carotenoid levels.

    Science.gov (United States)

    Owens, Brenda F; Lipka, Alexander E; Magallanes-Lundback, Maria; Tiede, Tyler; Diepenbrock, Christine H; Kandianis, Catherine B; Kim, Eunha; Cepela, Jason; Mateos-Hernandez, Maria; Buell, C Robin; Buckler, Edward S; DellaPenna, Dean; Gore, Michael A; Rocheford, Torbert

    2014-12-01

    Efforts are underway for development of crops with improved levels of provitamin A carotenoids to help combat dietary vitamin A deficiency. As a global staple crop with considerable variation in kernel carotenoid composition, maize (Zea mays L.) could have a widespread impact. We performed a genome-wide association study (GWAS) of quantified seed carotenoids across a panel of maize inbreds ranging from light yellow to dark orange in grain color to identify some of the key genes controlling maize grain carotenoid composition. Significant associations at the genome-wide level were detected within the coding regions of zep1 and lut1, carotenoid biosynthetic genes not previously shown to impact grain carotenoid composition in association studies, as well as within previously associated lcyE and crtRB1 genes. We leveraged existing biochemical and genomic information to identify 58 a priori candidate genes relevant to the biosynthesis and retention of carotenoids in maize to test in a pathway-level analysis. This revealed dxs2 and lut5, genes not previously associated with kernel carotenoids. In genomic prediction models, use of markers that targeted a small set of quantitative trait loci associated with carotenoid levels in prior linkage studies were as effective as genome-wide markers for predicting carotenoid traits. Based on GWAS, pathway-level analysis, and genomic prediction studies, we outline a flexible strategy involving use of a small number of genes that can be selected for rapid conversion of elite white grain germplasm, with minimal amounts of carotenoids, to orange grain versions containing high levels of provitamin A.

  8. 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....

  9. Determination of Pole and Rotation Period of not Stabilized Artificial Satellite by Use of Model "diffuse Cylinder"

    Science.gov (United States)

    Kolesnik, S. Ya.; Dobrovolsky, A. V.; Paltsev, N. G.

    The algorithm of determination of orientation of rotation axis (pole) and rotation period of satellite, simulated by a cylinder, which is precessing around of vector of angular moment of pulse with constant nutation angle is offered. The Lambert's law of light reflection is accepted. Simultaneously, dependence of light reflection coefficient versus phase angle is determined. The model's simulation confirm applicability of this method. Results of the calculations for artificial satellite No 28506 are carried out.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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...

  15. 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...

  16. Modeling and optimization of ethanol fermentation using Saccharomyces cerevisiae: Response surface methodology and artificial neural network

    Directory of Open Access Journals (Sweden)

    Esfahanian Mehri

    2013-01-01

    Full Text Available In this study, the capabilities of response surface methodology (RSM and artificial neural networks (ANN for modeling and optimization of ethanol production from glucoseusing Saccharomyces cerevisiae in batch fermentation process were investigated. Effect of three independent variables in a defined range of pH (4.2-5.8, temperature (20-40ºC and glucose concentration (20-60 g/l on the cell growth and ethanol production was evaluated. Results showed that prediction accuracy of ANN was apparently similar to RSM. At optimum condition of temperature (32°C, pH (5.2 and glucose concentration (50 g/l suggested by the statistical methods, the maximum cell dry weight and ethanol concentration obtained from RSM were 12.06 and 16.2 g/l whereas experimental values were 12.09 and 16.53 g/l, respectively. The present study showed that using ANN as fitness function, the maximum cell dry weight and ethanol concentration were 12.05 and 16.16 g/l, respectively. Also, the coefficients of determination for biomass and ethanol concentration obtained from RSM were 0.9965 and 0.9853 and from ANN were 0.9975 and 0.9936, respectively. The process parameters optimization was successfully conducted using RSM and ANN; however prediction by ANN was slightly more precise than RSM. Based on experimental data maximum yield of ethanol production of 0.5 g ethanol/g substrate (97 % of theoretical yield was obtained.

  17. 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.

  18. 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.

  19. Spatiotemporal realization of an artificial retina model and performance evaluation through ISI- and spike count-based image reconstruction methods

    OpenAIRE

    2014-01-01

    Development of an artificial retina model that can mimic the biologic retina is a highly challenging task and this task is an important step in the development of a visual prosthesis. The receptive field structure of the retina layer is usually modeled as a 2D difference of Gaussian (DOG) filter profile. In the present study, as a different approach, a retina model including a 3D 2-stage DOG filter (3D-ADOG) that has an adaptively changing bandwidth with respect to the local image stati...

  20. Developing an artificial neural network model for predicting concrete’s compression strength and electrical resistivity

    Directory of Open Access Journals (Sweden)

    Juan Manuel Lizarazo Marriaga

    2010-04-01

    Full Text Available The present study was conducted for predicting the compressive strength of concrete based on unit weight ultrasonic and pulse velocity (UPV for 41 different concrete mixtures. This research emerged from the need for a rapid test for predicting concrete’s compressive strength. The research was also conducted for predicting concrete’s electrical resistivity based on unit weight ultrasonic, pulse velocity (UPV and compressive strength with the same mixes. The prediction was made using simple regression analysis and artificial neural networks. The results revealed that artificial neural networks can be used for effectively predicting compressive strength and electrical resistivity.

  1. 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.

  2. Incorporating Protein Biosynthesis into the Saccharomyces cerevisiae Genome-scale Metabolic Model

    DEFF Research Database (Denmark)

    Olivares Hernandez, Roberto

    Based on stoichiometric biochemical equations that occur into the cell, the genome-scale metabolic models can quantify the metabolic fluxes, which are regarded as the final representation of the physiological state of the cell. For Saccharomyces Cerevisiae the genome scale model has been......, translation initiation, translation elongation, translation termination, translation elongation, and mRNA decay. Considering these information from the mechanisms of transcription and translation, we will include this stoichiometric reactions into the genome scale model for S. Cerevisiae to obtain the first...

  3. 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.

  4. 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.

  5. 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.

  6. Using Artificial Neural Networks in Educational Research: Some Comparisons with Linear Statistical Models.

    Science.gov (United States)

    Everson, Howard T.; And Others

    This paper explores the feasibility of neural computing methods such as artificial neural networks (ANNs) and abductory induction mechanisms (AIM) for use in educational measurement. ANNs and AIMS methods are contrasted with more traditional statistical techniques, such as multiple regression and discriminant function analyses, for making…

  7. Testing the infinitely many genes model for the evolution of the bacterial core genome and pangenome.

    Science.gov (United States)

    Collins, R Eric; Higgs, Paul G

    2012-11-01

    When groups of related bacterial genomes are compared, the number of core genes found in all genomes is usually much less than the mean genome size, whereas the size of the pangenome (the set of genes found on at least one of the genomes) is much larger than the mean size of one genome. We analyze 172 complete genomes of Bacilli and compare the properties of the pangenomes and core genomes of monophyletic subsets taken from this group. We then assess the capabilities of several evolutionary models to predict these properties. The infinitely many genes (IMG) model is based on the assumption that each new gene can arise only once. The predictions of the model depend on the shape of the evolutionary tree that underlies the divergence of the genomes. We calculate results for coalescent trees, star trees, and arbitrary phylogenetic trees of predefined fixed branch length. On a star tree, the pangenome size increases linearly with the number of genomes, as has been suggested in some previous studies, whereas on a coalescent tree, it increases logarithmically. The coalescent tree gives a better fit to the data, for all the examples we consider. In some cases, a fixed phylogenetic tree proved better than the coalescent tree at reproducing structure in the gene frequency spectrum, but little improvement was gained in predictions of the core and pangenome sizes. Most of the data are well explained by a model with three classes of gene: an essential class that is found in all genomes, a slow class whose rate of origination and deletion is slow compared with the time of divergence of the genomes, and a fast class showing rapid origination and deletion. Although the majority of genes originating in a genome are in the fast class, these genes are not retained for long periods, and the majority of genes present in a genome are in the slow or essential classes. In general, we show that the IMG model is useful for comparison with experimental genome data both for species level and

  8. 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.

  9. 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.

  10. 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.

  11. Phenolics extraction from sweet potato peels: modelling and optimization by response surface modelling and artificial neural network.

    Science.gov (United States)

    Anastácio, Ana; Silva, Rúben; Carvalho, Isabel S

    2016-12-01

    Sweet potato peels (SPP) are a major waste generated during root processing and currently have little commercial value. Phenolics with free radical scavenging activity from SPP may represent a possible added-value product for the food industry. The aqueous extraction of phenolics from SPP was studied using a Central Composite Design with solvent to solid ratio (30-60 mL g(-1)), time (30-90 min) and temperature (25-75 °C) as independent variables. The comparison of response surface methodology (RSM) and artificial neural network (ANN) analysis on extraction modelling and optimising was performed. Temperature and solvent to solid ratio, alone and in interaction, presented a positive effect in TPC, ABTS and DPPH assays. Time was only significant for ABTS assay with a negative influence both as main effect and in interaction with other independent variables. RSM and ANN models predicted the same optimal extraction conditions as 60 mL g(-1) for solvent to solid ratio, 30 min for time and 75 °C for temperature. The obtained responses in the optimized conditions were as follow: 11.87 ± 0.69 mg GAE g(-1) DM for TPC, 12.91 ± 0.42 mg TE g(-1) DM for ABTS assay and 46.35 ± 3.08 mg TE g(-1) DM for DPPH assay. SPP presented similar optimum extraction conditions and phenolic content than peels of potato, tea fruit and bambangan. Predictive models and the optimized extraction conditions offers an opportunity for food processors to generate products with high potential health benefits.

  12. Comparison between artificial neural network and multilinear regression models in an evaluation of cognitive workload in a flight simulator.

    Science.gov (United States)

    Hannula, Manne; Huttunen, Kerttu; Koskelo, Jukka; Laitinen, Tomi; Leino, Tuomo

    2008-01-01

    In this study, the performances of artificial neural network (ANN) analysis and multilinear regression (MLR) model-based estimation of heart rate were compared in an evaluation of individual cognitive workload. The data comprised electrocardiography (ECG) measurements and an evaluation of cognitive load that induces psychophysiological stress (PPS), collected from 14 interceptor fighter pilots during complex simulated F/A-18 Hornet air battles. In our data, the mean absolute error of the ANN estimate was 11.4 as a visual analog scale score, being 13-23% better than the mean absolute error of the MLR model in the estimation of cognitive workload.

  13. 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.

  14. Comparison between artificial neural network and Cox regression model in predicting the survival rate of gastric cancer patients.

    Science.gov (United States)

    Zhu, Lucheng; Luo, Wenhua; Su, Meng; Wei, Hangping; Wei, Juan; Zhang, Xuebang; Zou, Changlin

    2013-09-01

    The aim of this study was to determine the prognostic factors and their significance in gastric cancer (GC) patients, using the artificial neural network (ANN) and Cox regression hazard (CPH) models. A retrospective analysis was undertaken, including 289 patients with GC who had undergone gastrectomy between 2006 and 2007. According to the CPH analysis, disease stage, peritoneal dissemination, radical surgery and body mass index (BMI) were selected as the significant variables. According to the ANN model, disease stage, radical surgery, serum CA19-9 levels, peritoneal dissemination and BMI were selected as the significant variables. The true prediction of the ANN was 85.3% and of the CPH model 81.9%. In conclusion, the present study demonstrated that the ANN model is a more powerful tool in determining the significant prognostic variables for GC patients, compared to the CPH model. Therefore, this model is recommended for determining the risk factors of such patients.

  15. Development and performance evaluation of a novel knowledge guided artificial neural network (KGANN model for exchange rate prediction

    Directory of Open Access Journals (Sweden)

    Pradyot Ranjan Jena

    2015-10-01

    Full Text Available This paper presents a new adaptive forecasting model using a knowledge guided artificial neural network (KGANN structure for efficient prediction of exchange rate. The new structure has two parallel systems. The first system is a least mean square (LMS trained adaptive linear combiner, whereas the second system employs an adaptive FLANN model to supplement the knowledge base with an objective to improve its performance value. The output of a trained LMS model is added to an adaptive FLANN model to provide a more accurate exchange rate compared to that predicted by either a simple LMS or a FLANN model. This finding has been demonstrated through an exhausting computer simulation study and using real life data. Thus the proposed KGANN is an efficient forecasting model for exchange rate prediction.

  16. Precision cancer mouse models through genome editing with CRISPR-Cas9

    OpenAIRE

    Mou, Haiwei; Kennedy, Zachary; Anderson, Daniel G.; Yin, Hao; Xue, Wen

    2015-01-01

    The cancer genome is highly complex, with hundreds of point mutations, translocations, and chromosome gains and losses per tumor. To understand the effects of these alterations, precise models are needed. Traditional approaches to the construction of mouse models are time-consuming and laborious, requiring manipulation of embryonic stem cells and multiple steps. The recent development of the clustered regularly interspersed short palindromic repeats (CRISPR)-Cas9 system, a powerful genome-edi...

  17. 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.

  18. Molecular biology of mycoplasmas: from the minimum cell concept to the artificial cell

    Directory of Open Access Journals (Sweden)

    CAIO M.M. CORDOVA

    2016-01-01

    Full Text Available ABSTRACT Mycoplasmas are a large group of bacteria, sorted into different genera in the Mollicutes class, whose main characteristic in common, besides the small genome, is the absence of cell wall. They are considered cellular and molecular biology study models. We present an updated review of the molecular biology of these model microorganisms and the development of replicative vectors for the transformation of mycoplasmas. Synthetic biology studies inspired by these pioneering works became possible and won the attention of the mainstream media. For the first time, an artificial genome was synthesized (a minimal genome produced from consensus sequences obtained from mycoplasmas. For the first time, a functional artificial cell has been constructed by introducing a genome completely synthesized within a cell envelope of a mycoplasma obtained by transformation techniques. Therefore, this article offers an updated insight to the state of the art of these peculiar organisms' molecular biology.

  19. Molecular biology of mycoplasmas: from the minimum cell concept to the artificial cell.

    Science.gov (United States)

    Cordova, Caio M M; Hoeltgebaum, Daniela L; Machado, Laís D P N; Santos, Larissa Dos

    2016-01-01

    Mycoplasmas are a large group of bacteria, sorted into different genera in the Mollicutes class, whose main characteristic in common, besides the small genome, is the absence of cell wall. They are considered cellular and molecular biology study models. We present an updated review of the molecular biology of these model microorganisms and the development of replicative vectors for the transformation of mycoplasmas. Synthetic biology studies inspired by these pioneering works became possible and won the attention of the mainstream media. For the first time, an artificial genome was synthesized (a minimal genome produced from consensus sequences obtained from mycoplasmas). For the first time, a functional artificial cell has been constructed by introducing a genome completely synthesized within a cell envelope of a mycoplasma obtained by transformation techniques. Therefore, this article offers an updated insight to the state of the art of these peculiar organisms' molecular biology.

  20. 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.

  1. 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...

  2. 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.

  3. 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)

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

    DEFF Research Database (Denmark)

    Pedersen, Jakob Skou; Hein, Jotun

    2003-01-01

    the model are linear in alignment length and genome number. The model is applied to the problem of gene finding. The benefit of modelling sequence evolution is demonstrated both in a range of simulations and on a set of orthologous human/mouse gene pairs. AVAILABILITY: Free availability over the Internet......-specific evolutionary models based on a phylogenetic tree. All parameters can be estimated by maximum likelihood, including the phylogenetic tree. It can handle any number of aligned genomes, using their phylogenetic tree to model the evolutionary correlations. The time complexity of all algorithms used for handling...

  5. The Volterra series as special case of artificial neural network model

    Science.gov (United States)

    Napiorkowski, J.; O Kane, J. P.

    2003-04-01

    !1! (2) In the above equation x is the input to the system, y is the output from the model, is the first order kernel which reflects the linear properties of the system, is the second order kernel which reflects the quadratic properties , and so on. Modelling of hydrological processes by means of a Volterra series has been developed independently of other methods of describing dynamic systems, in particular by state equation formulation. The problem of series identification has been solved by numerical methods applied to input record and its corresponding output by means of kernel expansion in orthonormal polynomials. It can be shown (Napiórkowski and Strupczewski, 1979; Napiórkowski and O'Kane, 1984) that if the function f in eq.(1) is differentiable as many times as required the state-space equation (1) can be approximated by the Volterra series (2) by means of Taylor series expansion for operators. The structure of the first two kernels was shown to be h_1 (τ ) = aH_n (τ ) % MathType!End!2!1! (3) h_2 (τ _1 ,τ _2 ) = bleft\\{ {H_n (τ _1 )sumlimitsk = 1^n {H_k (τ _2 ) + } H_n (τ _2 )sumlimitsk = 1^n {H_k (τ _1 ) - H_n [max (τ _1 ,τ _1 )]} } right\\} % MathType!End!2!1! (4) where Y(i) = sumlimitsk = 1N_s {H_1 (k)X(i - k) + sumlimitsk = 1N_s {sumlimitsl = 1N_s {H_2 (k,l)X(i - k)X(i - l)} } } + ... % MathType!End!2!1! (6) where Ns is the number representing the memory of t he system i.e. k, l =1,...Ns, NT is the number of observations, i.e. i=1,2,...NT. It is easy to see that discrete Volterra series described by eq.(6) is a special case of artificial network model that is called in resent publications as Volterra net. In the paper we focus our attention on of the existence and uniqueness of the solution of the described above identification problem. Literature Napiórkowski J.J., O'Kane P., 1984. A new nonlinear conceptual model of flood waves. Journal of Hydrology, 69, 43 58. Napiórkowski J.J., Strupczewski W.G., 1979. The analytical determination of the

  6. The mouse genome database: genotypes, phenotypes, and models of human disease.

    Science.gov (United States)

    Bult, Carol J; Eppig, Janan T; Blake, Judith A; Kadin, James A; Richardson, Joel E

    2013-01-01

    The laboratory mouse is the premier animal model for studying human biology because all life stages can be accessed experimentally, a completely sequenced reference genome is publicly available and there exists a myriad of genomic tools for comparative and experimental research. In the current era of genome scale, data-driven biomedical research, the integration of genetic, genomic and biological data are essential for realizing the full potential of the mouse as an experimental model. The Mouse Genome Database (MGD; http://www.informatics.jax.org), the community model organism database for the laboratory mouse, is designed to facilitate the use of the laboratory mouse as a model system for understanding human biology and disease. To achieve this goal, MGD integrates genetic and genomic data related to the functional and phenotypic characterization of mouse genes and alleles and serves as a comprehensive catalog for mouse models of human disease. Recent enhancements to MGD include the addition of human ortholog details to mouse Gene Detail pages, the inclusion of microRNA knockouts to MGD's catalog of alleles and phenotypes, the addition of video clips to phenotype images, providing access to genotype and phenotype data associated with quantitative trait loci (QTL) and improvements to the layout and display of Gene Ontology annotations.

  7. 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.

  8. A Bayesian model of biases in artificial language learning: the case of a word-order universal.

    Science.gov (United States)

    Culbertson, Jennifer; Smolensky, Paul

    2012-01-01

    In this article, we develop a hierarchical Bayesian model of learning in a general type of artificial language-learning experiment in which learners are exposed to a mixture of grammars representing the variation present in real learners' input, particularly at times of language change. The modeling goal is to formalize and quantify hypothesized learning biases. The test case is an experiment (Culbertson, Smolensky, & Legendre, 2012) targeting the learning of word-order patterns in the nominal domain. The model identifies internal biases of the experimental participants, providing evidence that learners impose (possibly arbitrary) properties on the grammars they learn, potentially resulting in the cross-linguistic regularities known as typological universals. Learners exposed to mixtures of artificial grammars tended to shift those mixtures in certain ways rather than others; the model reveals how learners' inferences are systematically affected by specific prior biases. These biases are in line with a typological generalization-Greenberg's Universal 18-which bans a particular word-order pattern relating nouns, adjectives, and numerals.

  9. 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.

  10. Artificial blood.

    Science.gov (United States)

    Sarkar, Suman

    2008-07-01

    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.

  11. 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. RESULTS......: Here we present iTO977, a comprehensive genome-scale metabolic model that contains more reactions, metabolites and genes than previous models. The model was constructed based on two earlier reconstructions, namely iIN800 and the consensus network, and then improved and expanded using gap......-filling methods and by introducing new reactions and pathways based on studies of the literature and databases. The model was shown to perform well both for growth simulations in different media and gene essentiality analysis for single and double knock-outs. Further, the model was used as a scaffold...

  12. Comparison of Artificial Neural Network And M5 Model Tree Technique In Water Level Forecasting of Solo River

    Science.gov (United States)

    Lasminto, Umboro; Hery Mularta, Listya

    2010-05-01

    Flood events along the Solo River flow at the end of December 2007 has caused lose of properties and lives. Floods occurred in the city of Ngawi, Madiun, Bojonegoro, Babat and surrounding areas. To reduce future losses, one of the important efforts that will occur during a flood is to get information about the magnitude and time will be floods, so that people can make an effort to reduce its impact. Flood forecasting model can provide information of water level in the river some time before the incident. This paper will compare the flood forecasting model at Bojonegoro City was built using the technique of Artificial Neural Network (ANN) and M5 Model Tree (M5MT). The model will forecast the water level of 1, 3 and 6 hours ahead at the point of water level recorders in the City of Bojonegoro using input from the water level at some point water level recorders in the upstream such as Karangnongko, Sekayu, Jurug and Wonogiri. The same data set of hourly water level records are used to build the model of ANN and M5MT technique. The selection of parameters and setup of ANN and M5MT technique is done to obtain the best result. The results of the model are evaluated by calculating the Root Mean Square Error (RMSE) between the predictions and observations. RMSE produced by the water level forecasting model 1, 3 and 6 hours ahead with M5MT technique are 0.2723, 0.6279 and 0.7176 meters. While the ANN technique are 0.1829, 0.3192 and 0517 meters. ANN technique has a better ability in predicting low flow, whereas M5 Model Tree technique has a better ability in predicting high flow. Keywords : Water level forecasting, Solo River, M5 Model Tree, Artificial Neural Network

  13. Application of a hybrid method combining grey model and back propagation artificial neural networks to forecast hepatitis B in china.

    Science.gov (United States)

    Gan, Ruijing; Chen, Xiaojun; Yan, Yu; Huang, Daizheng

    2015-01-01

    Accurate incidence forecasting of infectious disease provides potentially valuable insights in its own right. It is critical for early prevention and may contribute to health services management and syndrome surveillance. This study investigates the use of a hybrid algorithm combining grey model (GM) and back propagation artificial neural networks (BP-ANN) to forecast hepatitis B in China based on the yearly numbers of hepatitis B and to evaluate the method's feasibility. The results showed that the proposal method has advantages over GM (1, 1) and GM (2, 1) in all the evaluation indexes.

  14. Author's reply to discussion of using artificial neural nets to identify the well-test interpretation model

    Energy Technology Data Exchange (ETDEWEB)

    Al-Kaabi, A.U.; Lee, W.J. (Texas A and M Univ., College Station, TX (United States))

    1994-09-01

    The authors thank Yeung et al. for their discussion about their original paper. They agree with Yeung et al. that their proposed scaling method, when applied to patterns with distinct subparts such as the one shown, represents an improvement on the method they proposed. This is particularly true because Yeung et al.'s method eliminates the need to train the artificial neural networks (ANN's) on different sizes (scales) of the same pattern of a specific interpretation model. This paper presents the following comments for discussion and suggestions for further improvement.

  15. 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

  16. 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.

  17. 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.

  18. 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.

  19. Genome Editing and Its Applications in Model Organisms

    OpenAIRE

    Dongyuan Ma; Feng Liu

    2015-01-01

    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, subs...

  20. 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...