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Sample records for austad ann norderhaug

  1. Anne Fine

    Directory of Open Access Journals (Sweden)

    Philip Gaydon

    2015-04-01

    Full Text Available An interview with Anne Fine with an introduction and aside on the role of children’s literature in our lives and development, and our adult perceptions of the suitability of childhood reading material. Since graduating from Warwick in 1968 with a BA in Politics and History, Anne Fine has written over fifty books for children and eight for adults, won the Carnegie Medal twice (for Goggle-Eyes in 1989 and Flour Babies in 1992, been a highly commended runner-up three times (for Bill’s New Frock in 1989, The Tulip Touch in 1996, and Up on Cloud Nine in 2002, been shortlisted for the Hans Christian Andersen Award (the highest recognition available to a writer or illustrator of children’s books, 1998, undertaken the positon of Children’s Laureate (2001-2003, and been awarded an OBE for her services to literature (2003. Warwick presented Fine with an Honorary Doctorate in 2005. Philip Gaydon’s interview with Anne Fine was recorded as part of the ‘Voices of the University’ oral history project, co-ordinated by Warwick’s Institute of Advanced Study.

  2. Introduction to Ann-categories

    OpenAIRE

    Nguyen, Tien Quang

    2007-01-01

    In this paper, we present new concepts of Ann-categories, Ann-functors, and a transmission of the structure of categories based on Ann-equivalences. We build Ann-category of Pic-funtors and prove that each Ann-category can be faithfully embedded into an almost strictly Ann-category.

  3. Ann tuleb Rakverest Võrru

    Index Scriptorium Estoniae

    2009-01-01

    Võru kultuurimajas Kannel etendub 17. aprillil Rakvere teatri noortelavastus "Kuidas elad? ...Ann?!" Aidi Valliku jutustuse põhjal. Lavastaja Sven Heiberg. Mängivad ka Viljandi Kultuuriakadeemia teatritudengid

  4. Ann C Neville

    Indian Academy of Sciences (India)

    Home; Journals; Resonance – Journal of Science Education. Ann C Neville. Articles written in Resonance – Journal of Science Education. Volume 23 Issue 2 February 2018 pp 235-239 Classics. Observations of Radio Galaxies with the One-mile Telescope at Cambridge · Martin Ryle B Elsmore Ann C Neville · More Details ...

  5. iAnn

    DEFF Research Database (Denmark)

    Jimenez, Rafael C; Albar, Juan P; Bhak, Jong

    2013-01-01

    We present iAnn, an open source community-driven platform for dissemination of life science events, such as courses, conferences and workshops. iAnn allows automatic visualisation and integration of customised event reports. A central repository lies at the core of the platform: curators add...... submitted events, and these are subsequently accessed via web services. Thus, once an iAnn widget is incorporated into a website, it permanently shows timely relevant information as if it were native to the remote site. At the same time, announcements submitted to the repository are automatically...... disseminated to all portals that query the system. To facilitate the visualization of announcements, iAnn provides powerful filtering options and views, integrated in Google Maps and Google Calendar. All iAnn widgets are freely available....

  6. erica ann ronchetto

    Indian Academy of Sciences (India)

    Home; Journals; Bulletin of Materials Science. ERICA ANN RONCHETTO. Articles written in Bulletin of Materials Science. Volume 40 Issue 4 August 2017 pp 831-839. Effects of iron concentration and redox states on failure of boron-free E-glass fibres under applied stress in different conditions · QINGWEI WANG RICHARD ...

  7. Ann Tenno salapaigad / Margit Tõnson

    Index Scriptorium Estoniae

    Tõnson, Margit, 1978-

    2011-01-01

    Fotograaf Ann Tenno aiandushuvist, pildistamisest maailma erinevates paikades. Uutest suundadest (fototöötlus, fractal art, soojuskaameraga pildistamine) tema loomingus. Katkendeid Ann Tenno 2010. aastal ilmunud proosaraamatust "Üle unepiiri"

  8. Kõnelused Tartus / Anne Untera

    Index Scriptorium Estoniae

    Untera, Anne, 1951-

    2007-01-01

    8.-10. V Tartus toimunud eesti, läti ja saksa kunstiteadlaste ühisseminarist. Alexander Knorre rääkis Karl August Senffi, Ilona Audere Friedrich Ludwig von Maydelli, Mai Levin Karl Alexander von Winkleri, Kristiana Abele Johann Walter-Kurau (1869-1932), Anne Untera Konstantin ja Sally von Kügelgeni, Epp Preem Julie Hagen-Schwartzi, Friedrich Gross Eduard von Gebhardti ja Katharina Hadding Ida Kerkoviuse (1879-1970) loomingust

  9. "Kuidas elad ? ... Ann ?!" sai auhinna

    Index Scriptorium Estoniae

    2007-01-01

    20. märtsil rahvusvahelisel laste ja noorte teatripäeval anti kätte auhind aasta tähelepanuväärseimale noortetükile. Preemia sai Rakvere Teatris lavale tulnud noortelavastus "Kuidas elad ? ...Ann ?!" trupp eesotsas selle lavastaja Sven Heibergiga

  10. "Kuidas elad? ...Ann?!" sai auhinna

    Index Scriptorium Estoniae

    2007-01-01

    20. märtsil, rahvusvahelisel laste ja noorte teatripäeval, anti kätte auhind aasta tähelepanuväärseimale noortetükile. Preemia sai Rakvere Teatris lavale tulnud noortelavastuse "Kuidas elad? ...Ann?!" trupp eesotsas selle lavastaja Sven Heibergiga

  11. Ado Vabbe preemia Anne Parmastole

    Index Scriptorium Estoniae

    2003-01-01

    Tartu Kunstimajas Tartu kunsti aastalõpunäitus. Kujundaja Mari Nõmmela. Anne Parmastole A. Vabbe, Silja Salmistule E-Kunstisalongi, Lii Jürgensonile EDA, Jüri Marranile Wilde kohviku, Sami Makkonenile AS Vunder ja Tartu Õlletehase A. Le Coq ning Eda Lõhmusele AS Merko Tartu preemia

  12. Anne - et sympoetisk kulturopprør

    DEFF Research Database (Denmark)

    Ørjasæter, Kristin

    2002-01-01

    Artikkelen diskuterer modernismen i Paal-Helge Haugens punktroman Anne (1968) i lys av romantisk estetikk.......Artikkelen diskuterer modernismen i Paal-Helge Haugens punktroman Anne (1968) i lys av romantisk estetikk....

  13. Annäherung Approaching

    Directory of Open Access Journals (Sweden)

    Carola Hilmes

    2007-03-01

    Full Text Available Das von Stefan Moses zusammengestellte „Bilderbuch“ zeigt Fotos von Ilse Aichinger. Sie selbst kommt durch eine Reihe von Geschichten und Gedichten zu Wort. In diesen intimen Dialog werden auch die Leser/-innen einbezogen. Das ermöglicht Annäherung.This “Picture Book”, compiled by Stefan Moses, displays photographs of Ilse Aichinger. She is also given voice through a series of stories and poems. The reader is also drawn into this intimate dialogue, thus making it possible for image, text, and reader to converge.

  14. [Anne Arold. Kontrastive Analyse...] / Paul Alvre

    Index Scriptorium Estoniae

    Alvre, Paul, 1921-2008

    2001-01-01

    Arvustus: Arold, Anne. Kontrastive analyse der Wortbildungsmuster im Deutschen und im Estnischen (am Beispiel der Aussehensadjektive). Tartu, 2000. (Dissertationes philologiae germanicae Universitatis Tartuensis)

  15. Mary Anne Chambers | IDRC - International Development Research ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    A former Member of Provincial Parliament, Mary Anne served as Minister of Training, Colleges and Universities, and Minister of Children and Youth Services in the Government of Ontario. She is also a former senior vice-president of Scotiabank. A graduate of the University of Toronto, Mary Anne has received honorary ...

  16. THE FEMINISM AND FEMININITY OF ANN VERONICA IN H. G. WELLS' ANN VERONICA

    Directory of Open Access Journals (Sweden)

    Liem Satya Limanta

    2002-01-01

    Full Text Available H.G. Well's Ann Veronica structurally seems to be divided into two parts; the first deals with Ann Veronica's struggle to get equality with men and freedom in most aspects of life, such as in politics, economics, education, and sexuality; the second describes much the other side of her individuality which she cannot deny, namely her femininity, such as her crave for love, marriage, maternity, and beauty. H.G. Wells describes vividly the two elements in Ann Veronica, feminism and femininity. As a feminist, Ann Veronica rebelled against her authoritative Victorian father, who regarded women only as men's property to be protected from the harsh world outside. On the other side, Ann could not deny her being a woman after she fell in love with Capes. Her femininity from the second half of the novel then is explored. Although the novel ends with the depiction of the domestic life of Ann Veronica, it does not mean that the feminism is gone altogether. The key point is that the family life Ann chooses as a `submissive' wife and good mother is her choice. It is very different if it is forced on her to do. Thus, this novel depicts both sides of Ann Veronica, her feminism and her femininity.

  17. An ANN application for water quality forecasting.

    Science.gov (United States)

    Palani, Sundarambal; Liong, Shie-Yui; Tkalich, Pavel

    2008-09-01

    Rapid urban and coastal developments often witness deterioration of regional seawater quality. As part of the management process, it is important to assess the baseline characteristics of the marine environment so that sustainable development can be pursued. In this study, artificial neural networks (ANNs) were used to predict and forecast quantitative characteristics of water bodies. The true power and advantage of this method lie in its ability to (1) represent both linear and non-linear relationships and (2) learn these relationships directly from the data being modeled. The study focuses on Singapore coastal waters. The ANN model is built for quick assessment and forecasting of selected water quality variables at any location in the domain of interest. Respective variables measured at other locations serve as the input parameters. The variables of interest are salinity, temperature, dissolved oxygen, and chlorophyll-alpha. A time lag up to 2Delta(t) appeared to suffice to yield good simulation results. To validate the performance of the trained ANN, it was applied to an unseen data set from a station in the region. The results show the ANN's great potential to simulate water quality variables. Simulation accuracy, measured in the Nash-Sutcliffe coefficient of efficiency (R(2)), ranged from 0.8 to 0.9 for the training and overfitting test data. Thus, a trained ANN model may potentially provide simulated values for desired locations at which measured data are unavailable yet required for water quality models.

  18. Biogas engine performance estimation using ANN

    Directory of Open Access Journals (Sweden)

    Yusuf Kurtgoz

    2017-12-01

    Full Text Available Artificial neural network (ANN method was used to estimate the thermal efficiency (TE, brake specific fuel consumption (BSFC and volumetric efficiency (VE values of a biogas engine with spark ignition at different methane (CH4 ratios and engine load values. For this purpose, the biogas used in the biogas engine was produced by the anaerobic fermentation method from bovine manure and different CH4 contents (51%, 57%, 87% were obtained by purification of CO2 and H2S. The data used in the ANN models were obtained experimentally from a 4-stroke four-cylinder, spark ignition engine, at constant speed for different load and CH4 ratios. Using some of the obtained experimental data, ANN models were developed, and the rest was used to test the developed models. In the ANN models, the CH4 ratio of the fuel, engine load, inlet air temperature (Tin, air fuel ratio and the maximum cylinder pressure are chosen as the input parameters. TE, BSFC and VE are used as the output parameters. Root mean square error (RMSE, mean absolute percentage error (MAPE and correlation coefficient (R performance indicators are used to compare measured and predicted values. It has been shown that ANN models give good results in spark ignition biogas engines with high correlation and low error rates for TE, BSFC and VE values.

  19. Ann Arbor Session I: Breaking Ground.

    Science.gov (United States)

    Music Educators Journal, 1979

    1979-01-01

    Summarizes the first session of the National Symposium on the Applications of Psychology to the Teaching and Learning of Music held at Ann Arbor from October 30 to November 2, 1978. Sessions concerned auditory perception, motor learning, child development, memory and information processing, and affect and motivation. (SJL)

  20. Obituary: Anne Barbara Underhill, 1920-2003

    Science.gov (United States)

    Roman, Nancy Grace

    2003-12-01

    Anne was born in Vancouver, British Columbia on 12 June 1920. Her parents were Frederic Clare Underhill, a civil engineer and Irene Anna (née Creery) Underhill. She had a twin brother and three younger brothers. As a young girl she was active in Girl Guides and graduated from high school winning the Lieutenant Governor's medal as one of the top students in the Province. She also excelled in high school sports. Her mother died when Anne was 18 and, while undertaking her university studies, Anne assisted in raising her younger brothers. Her twin brother was killed in Italy during World War II (1944), a loss that Anne felt deeply. Possibly because of fighting to get ahead in astronomy, a field overwhelming male when she started, she frequently appeared combative. At the University of British Columbia, Anne obtained a BA (honors) in Chemistry (1942), followed by a MA in 1944. After working for the NRC in Montreal for a year, she studied at the University of Toronto prior to entering the University of Chicago in 1946 to obtain her PhD. Her thesis was the first model computed for a multi-layered stellar atmosphere (1948). During this time she worked with Otto Struve, developing a lifetime interest in hot stars and the analysis of their high dispersion spectra. She received two fellowships from the University Women of Canada. She received a U.S. National Research Fellowship to work at the Copenhagen Observatory, and upon its completion, she returned to British Columbia to work at the Dominion Astrophysical Observatory as a research scientist from 1949--1962. During this period she spent a year at Harvard University as a visiting professor and at Princeton where she used their advanced computer to write the first code for modeling stellar atmospheres. Anne was invited to the University of Utrecht (Netherlands) as a full professor in 1962. She was an excellent teacher, well liked by the students in her classes, and by the many individuals that she guided throughout her

  1. Ilmus artiklikogumik "Eesti teadlased paguluses" / Anne Valmas

    Index Scriptorium Estoniae

    Valmas, Anne, 1941-2017

    2009-01-01

    TLÜ AR väliseesti kirjanduse keskuse ja TTÜ Raamatukogu koostöös 24.03.2009 toimunud konverentsist "Eesti teadlased paguluses", mis tutvustas väliseesti teadlaste osa maailmateaduses. Ettekannete põhjal valminud artiklikogumikust "Eesti teadlased paguluses", koostajad Vahur Mägi ja Anne Valmas. Tallinn : Tallinna Ülikooli Kirjastus, 2009

  2. Ann Arbor, Michigan: Solar in Action (Brochure)

    Energy Technology Data Exchange (ETDEWEB)

    2011-10-01

    This brochure provides an overview of the challenges and successes of Ann Arbor, Michigan, a 2007 Solar America City awardee, on the path toward becoming a solar-powered community. Accomplishments, case studies, key lessons learned, and local resource information are given.

  3. Multiresolution wavelet-ANN model for significant wave height forecasting.

    Digital Repository Service at National Institute of Oceanography (India)

    Deka, P.C.; Mandal, S.; Prahlada, R.

    (ANN) modeling. The transformed output data are used as inputs to ANN models. Various decomposition levels have been tried for a db3 wavelet to obtain optimal results. It is found that the performance of hybrid WLNN is better than that of ANN when lead...

  4. 75 FR 13334 - Notice of Availability of Draft Environmental Assessment; Ann Arbor Municipal Airport, Ann Arbor, MI

    Science.gov (United States)

    2010-03-19

    ... Federal Aviation Administration Notice of Availability of Draft Environmental Assessment; Ann Arbor Municipal Airport, Ann Arbor, MI AGENCY: The Federal Aviation Administration is issuing this notice on... extension of runway 6/24 at the Ann Arbor Municipal Airport. While not required for an EA, the FAA is...

  5. ANN-implemented robust vision model

    Science.gov (United States)

    Teng, Chungte; Ligomenides, Panos A.

    1991-02-01

    A robust vision model has been developed and implemented with a self-organizing/unsupervised artificial neural network (ANN) classifier-KART which is a novel hybrid model of a modified Kohonen''s feature map and the Carpenter/Grossberg''s ART architecture. The six moment invariants have been mapped onto a 7-dimensional unit hypersphere and have been applied to the KART classifier. In this paper the KART model will be presented. The non-adaptive neural implementations on the image processing and the moment invariant feature extraction will be discussed. In addition the simulation results that illustrate the capabilities of this model will also be provided. 1.

  6. Anneli Randla kaitses doktorikraadi Cambridge'is / Anneli Randla ; interv. Reet Varblane

    Index Scriptorium Estoniae

    Randla, Anneli, 1970-

    1999-01-01

    5. mail kaitses Cambridge'is esimese eesti kunstiteadlasena doktorikraadi Anneli Randla. Töö teema: kerjusmungaordukloostrite arhitektuur Põhja-Euroopas. Juhendaja dr. Deborah Howard. Doktorikraadile esitatavatest nõudmistest, doktoritöö kaitsmisest, magistrikraadi kaitsnu õppimisvõimalustest Cambridge's.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2004-07-01

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

  8. An Overview of ANN Application in the Power Industry

    Science.gov (United States)

    Niebur, D.

    1995-01-01

    The paper presents a survey on the development and experience with artificial neural net (ANN) applications for electric power systems, with emphasis on operational systems. The organization and constraints of electric utilities are reviewed, motivations for investigating ANN are identified, and a current assessment is given from the experience of 2400 projects using ANN for load forecasting, alarm processing, fault detection, component fault diagnosis, static and dynamic security analysis, system planning, and operation planning.

  9. Sensitivity based voltage instability alleviation using ANN

    Energy Technology Data Exchange (ETDEWEB)

    Chauhan, S. [National Inst. of Technology, Hamirpur (India). Electrical Engineering Dept.; Dave, M.P. [Indian Inst. of Technology, Delhi (India). Electrical Engineering Dept.

    2003-10-01

    Today an average transmission line is loaded more heavily than ever before and this has given rise to serious problem of voltage instability. A noble method for power system voltage instability estimation and improvement using ANN is presented. The method is based on the fact that reactive power injections at critical buses of the power system help to steer the system away from a developing voltage collapse. The location and quantum of reactive power support has been computed based on sensitivity. The sensitivity matrix is formulated by cascading the output/input sensitivity of multiplayer perceptron model with that of input features versus reactive power injections. The effectiveness of the proposed method is demonstrated on Ward-Hale 6-bus, IEEE 14-bus and IEEE 30-bus test systems. The method can be effectively used to make the system secure against voltage collapse condition in system planning and on-line operation. (author)

  10. In memoriam dr. Anne van Wijngaarden (1925-2004)

    NARCIS (Netherlands)

    Broekhuizen, S.; Laar, van V.

    2005-01-01

    Op 4 oktober 2004 overleed Dr. Anne van Wijngaarden op 78-jarige leeftijd in zijn huis bij Millac- Carlux, Frankrijk. Hij was een van de Nederlandse oprichters van de Vereniging voor Zoogdierkunde en Zoogdierbescherming. Nadat zijn eindexamen op de middelbare school wilde Anne aanvankelijk geologie

  11. Partitioning and interpolation based hybrid ARIMA–ANN model for ...

    Indian Academy of Sciences (India)

    One such hybrid model, namely auto regressive integrated moving average – artificial neural network (ARIMA–ANN) is devised in many different ways in the literature. However, the prediction accuracy of hybrid ARIMA–ANN model can be further improved by devising suitable processing techniques. In this paper, a hybrid ...

  12. Alice-Anne Martin (1926 - 2016)

    CERN Multimedia

    2016-01-01

    Alice-Anne Martin, known as “Schu” from her maiden name Schubert, passed away on 8 January 2016.   (Image: Gérard Bertin) Hired the year CERN was founded, 1954, when the construction of the Laboratory had not even begun, Schu first worked at the Villa de Cointrin (a historic building now within the grounds of Geneva airport) as a secretary. In this role, she typed the convention between CERN and the Swiss Confederation, prepared by Stéphanie Tixier, as well as some of the "Yellow Reports" that have marked key points in the Laboratory’s history. For example, using a special typewriter with two keyboards – Latin and Greek – she typed the Yellow Report on the KAM theorem by Rolf Hagedorn. Schu also worked with Felix Bloch, the first Director-General of CERN, and later became the secretary of Herbert Coblenz, the first CERN librarian. She was head of the team that edited the proceedings of the ...

  13. Final Technical Report, Wind Generator Project (Ann Arbor)

    Energy Technology Data Exchange (ETDEWEB)

    Geisler, Nathan [City of Ann Arbor, MI (United States)

    2017-03-20

    A Final Technical Report (57 pages) describing educational exhibits and devices focused on wind energy, and related outreach activities and programs. Project partnership includes the City of Ann Arbor, MI and the Ann Arbor Hands-on Museum, along with additional sub-recipients, and U.S. Department of Energy/Office of Energy Efficiency and Renewable Energy (EERE). Report relays key milestones and sub-tasks as well as numerous graphics and images of five (5) transportable wind energy demonstration devices and five (5) wind energy exhibits designed and constructed between 2014 and 2016 for transport and use by the Ann Arbor Hands-on Museum.

  14. Anne, ma soeur Anne, ne vois-tu rien venir? Que oui! | CRDI ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    23 déc. 2010 ... Les montagnes peuvent nous donner des signes précurseurs du sort réservé à la planète tout entière. L'Organisation des Nations Unies a désigné 2002 Année internationale de la montagne. Ce choix peut paraître bien inusité. Après tout, 60 pour cent de la population mondiale ne vit-elle pas dans un ...

  15. Professor Anne Khademian named National Academy of Public Administration Fellow

    OpenAIRE

    Chadwick, Heather Riley

    2009-01-01

    Anne Khademian, professor with Virginia Tech's Center for Public Administration and Policy, School of Public and International Affairs, at the Alexandria, Va., campus has been elected a National Academy of Public Administration (NAPA) Fellow.

  16. Application of PSO based ann model for STLF

    International Nuclear Information System (INIS)

    Hassnain, S.R.U.; Asar, A.U.; Khan, A.

    2008-01-01

    This paper presents a new approach for modeling STLF (Short Term Load Forecasting) in which STLF-ANN forecaster is trained using swarm intelligence. ANN (Artificial Neural Network) has been used successfully for STLF. However, ANN-based STLF models use BP (Backward Propagation) algorithm for training which does not ensure convergence and hangs in local optima more often. Moreover, BP requires much longer time for training which makes it difficult for real-time application. In this paper, we propose smaller ANN models of STLF based on hourly load data and train it through the use of PSO (Particle Swarm Optimization) Algorithm. The approach gives better trained models capable of performing well over time varying window and results in fairly accurate forecasts. (author)

  17. The Royal Summer Palace, Ferdinand I and Anne

    Czech Academy of Sciences Publication Activity Database

    Dobalová, Sylva

    2015-01-01

    Roč. 7, č. 2 (2015), s. 162-175 ISSN 1804-1132 Institutional support: RVO:68378033 Keywords : Anne of Jagiello * Prague Castle * Ferdinand I of Habsburg * olive tree * dynasticism Subject RIV: AL - Art, Architecture, Cultural Heritage

  18. Anne-Marie Sargueil: ilu on kasulik / intervjueerinud Emilie Toomela

    Index Scriptorium Estoniae

    Sargueil, Anne-Marie

    2015-01-01

    Prantsuse Disainiinstituudi juht Anne-Marie Sargueil rääkis prantsuse ja skandinaavia disainist, prantslaste disainieelistustest, uutest suundadest disaini valdkonnas, Eesti Tarbekunsti- ja Disainimuuseumis avatud näitusest "20 prantsuse disainiikooni"

  19. Spectrophotometric determination of synthetic colorants using PSO-GA-ANN.

    Science.gov (United States)

    Benvidi, Ali; Abbasi, Saleheh; Gharaghani, Sajjad; Dehghan Tezerjani, Marzieh; Masoum, Saeed

    2017-04-01

    Four common food colorants, containing tartrazine, sunset yellow, ponceau 4R and methyl orange, are simultaneously quantified without prior chemical separation. In this study, an effective artificial neural network (ANN) method is designed for modeling multicomponent absorbance data with the presence of shifts or changes of peak shapes in spectroscopic analysis. Gradient descent methods such as Levenberg-Marquardt function are usually used to determine the parameters of ANN. However, these methods may provide inappropriate parameters. In this paper, we propose combination of genetic algorithms (GA) and partial swarm optimization (PSO) to optimize parameters of ANN, and then the algorithm is used to process the relationship between the absorbance data and the concentration of analytes. The hybrid algorithm has the benefits of both PSO and GA techniques. The performance of this algorithm is compared to the performance of PSO-ANN, PC-ANN and ANN based Levenberg-Marquardt function. The obtained results revealed that the designed model can accurately determine colorant concentrations in real and synthetic samples. According to the observations, it is clear that the proposed hybrid method is a powerful tool to estimate the concentration of food colorants with a high degree of overlap using nonlinear artificial neural network. Copyright © 2016 Elsevier Ltd. All rights reserved.

  20. Playing tag with ANN: boosted top identification with pattern recognition

    Energy Technology Data Exchange (ETDEWEB)

    Almeida, Leandro G. [Institut de Biologie de l’École Normale Supérieure (IBENS), Inserm 1024- CNRS 8197,46 rue d’Ulm, 75005 Paris (France); Backović, Mihailo [Center for Cosmology, Particle Physics and Phenomenology - CP3,Universite Catholique de Louvain,Louvain-la-neuve (Belgium); Cliche, Mathieu [Laboratory for Elementary Particle Physics, Cornell University,Ithaca, NY 14853 (United States); Lee, Seung J. [Department of Physics, Korea Advanced Institute of Science and Technology,335 Gwahak-ro, Yuseong-gu, Daejeon 305-701 (Korea, Republic of); School of Physics, Korea Institute for Advanced Study,Seoul 130-722 (Korea, Republic of); Perelstein, Maxim [Laboratory for Elementary Particle Physics, Cornell University,Ithaca, NY 14853 (United States)

    2015-07-17

    Many searches for physics beyond the Standard Model at the Large Hadron Collider (LHC) rely on top tagging algorithms, which discriminate between boosted hadronic top quarks and the much more common jets initiated by light quarks and gluons. We note that the hadronic calorimeter (HCAL) effectively takes a “digital image' of each jet, with pixel intensities given by energy deposits in individual HCAL cells. Viewed in this way, top tagging becomes a canonical pattern recognition problem. With this motivation, we present a novel top tagging algorithm based on an Artificial Neural Network (ANN), one of the most popular approaches to pattern recognition. The ANN is trained on a large sample of boosted tops and light quark/gluon jets, and is then applied to independent test samples. The ANN tagger demonstrated excellent performance in a Monte Carlo study: for example, for jets with p{sub T} in the 1100–1200 GeV range, 60% top-tag efficiency can be achieved with a 4% mis-tag rate. We discuss the physical features of the jets identified by the ANN tagger as the most important for classification, as well as correlations between the ANN tagger and some of the familiar top-tagging observables and algorithms.

  1. Playing tag with ANN: boosted top identification with pattern recognition

    International Nuclear Information System (INIS)

    Almeida, Leandro G.; Backović, Mihailo; Cliche, Mathieu; Lee, Seung J.; Perelstein, Maxim

    2015-01-01

    Many searches for physics beyond the Standard Model at the Large Hadron Collider (LHC) rely on top tagging algorithms, which discriminate between boosted hadronic top quarks and the much more common jets initiated by light quarks and gluons. We note that the hadronic calorimeter (HCAL) effectively takes a “digital image" of each jet, with pixel intensities given by energy deposits in individual HCAL cells. Viewed in this way, top tagging becomes a canonical pattern recognition problem. With this motivation, we present a novel top tagging algorithm based on an Artificial Neural Network (ANN), one of the most popular approaches to pattern recognition. The ANN is trained on a large sample of boosted tops and light quark/gluon jets, and is then applied to independent test samples. The ANN tagger demonstrated excellent performance in a Monte Carlo study: for example, for jets with p T in the 1100–1200 GeV range, 60% top-tag efficiency can be achieved with a 4% mis-tag rate. We discuss the physical features of the jets identified by the ANN tagger as the most important for classification, as well as correlations between the ANN tagger and some of the familiar top-tagging observables and algorithms.

  2. LFC based adaptive PID controller using ANN and ANFIS techniques

    Directory of Open Access Journals (Sweden)

    Mohamed I. Mosaad

    2014-12-01

    Full Text Available This paper presents an adaptive PID Load Frequency Control (LFC for power systems using Neuro-Fuzzy Inference Systems (ANFIS and Artificial Neural Networks (ANN oriented by Genetic Algorithm (GA. PID controller parameters are tuned off-line by using GA to minimize integral error square over a wide-range of load variations. The values of PID controller parameters obtained from GA are used to train both ANFIS and ANN. Therefore, the two proposed techniques could, online, tune the PID controller parameters for optimal response at any other load point within the operating range. Testing of the developed techniques shows that the adaptive PID-LFC could preserve optimal performance over the whole loading range. Results signify superiority of ANFIS over ANN in terms of performance measures.

  3. The Royal Summer Palace, Ferdinand I and Anne

    Directory of Open Access Journals (Sweden)

    Sylva Dobalová

    2015-12-01

    Full Text Available This essay examines the iconography of the best-known relief from the renaissance Royal Summer Palace at the Prague Castle, depicting Ferdinand I of Habsburg and his wife Anne Jagiello. It highlights its marriage symbolism and the question of the dowry. In the relief Anne, heiress to the Czech Lands, gives her husband an olive branch symbolising peace. In the context of the political significance of the palace’s decoration the relief expresses Ferdinand’s view of his claim to the Bohemian throne, based on his marriage to the heiress. Due to opposition from the Bohemian Estates, this finally became his lawful right in 1545, 24 years after the royal wedding. The Italian sculptor Paolo della Stella expressed a search for a peaceful solution to Ferdinand’s succession. The relief was carved between 1540 and 1550. The interpretations do not rule out the possibility that it was made after Anne had died (1547.

  4. Anne Martin-Fugier. Galeristes : entretiens

    OpenAIRE

    Maldonado, Guitemie

    2012-01-01

    Anne Martin-Fugier est historienne, spécialiste de la vie culturelle et sociale au XIXe siècle. Depuis le milieu des années 1970, elle est également collectionneuse et sacrifie avec bonheur, le samedi, au rituel tour des galeries parisiennes. Dans cette fréquentation assidue, elle a tissé des relations d’estime, voire d’amitié, avec certains de leurs directeurs, relations dont témoignent les onze entretiens réalisés entre décembre 2008 et décembre 2009 et publiés ici. La forme choisie, vivant...

  5. Vene ja prantsuse kunsti imetlemisest Londonis / Ann Alari

    Index Scriptorium Estoniae

    Alari, Ann

    2008-01-01

    Näitus "Venemaalt pärit prantsuse ja vene shedöövrid" kuningliku kunstiakadeemia saalides Londonis. Vaatluse all oli ajavahemik 1870 kuni 1925. Tööd olid pärit Moskvas elanud tekstiilitöösturitest suurärimeeste Sergei Shtshukini ja Ivan Morozovi kogudest, mis 1917. a. natsionaliseeriti. Kuraator Ann Dumas

  6. Leipzigi muinsuskaitse mess ja konverents 2006. aastal / Anneli Randla

    Index Scriptorium Estoniae

    Randla, Anneli, 1970-

    2007-01-01

    25.-28. X Leipzigis toimunud Euroopa restaureerimise, muinsuskaitse ja linnauuenduse messist "Denkmal 2006", kus osales 40 riiki 380 väljapanekuga. Konverentsist " Denkmalpflege in Europa. Aktuelle Tendenzen im Umgang mit dem historischen Erbe". Anneli Randla ettekanne puudutas konserveerimise teooriat ja praktikat Eestis

  7. Carol Ann Duffy: A Preliminary Bibliography | Bala | Gender and ...

    African Journals Online (AJOL)

    Carol Ann Duffy: A Preliminary Bibliography. I Bala. Abstract. No Abstract. Full Text: EMAIL FULL TEXT EMAIL FULL TEXT · DOWNLOAD FULL TEXT DOWNLOAD FULL TEXT · AJOL African Journals Online. HOW TO USE AJOL... for Researchers · for Librarians · for Authors · FAQ's · More about AJOL · AJOL's Partners ...

  8. Comparison of single and modular ANN based fault detector and ...

    African Journals Online (AJOL)

    Effects of variations in pre-fault power flow angle, fault inception angle, source strength, fault resistance, fault type, fault distance and CT saturation have been investigated extensively on the performance of the ANN based protection scheme. Additionally, the effects of network changes: double circuit and single circuit ...

  9. 2011 : Qu'elle année !

    CERN Multimedia

    Staff Association

    2012-01-01

    « Quelle année ! Et quelle fin d’année ! La star de l’année a été le LHC, avec ses expériences, qui une fois de plus ont été sous les feux de la rampe. Mais on doit aussi citer toute une troupe d’acteurs importants, dans des domaines aussi différents que l’antimatière et l’expérience CLOUD. » Voilà ce que le Directeur général nous a écrit le 20 décembre dans son message avec ses vœux de fin d’année. Sans oublier, bien sûr, les fameux neutrinos hyperrapides vers Gran Sasso qui ont mis le CERN sur le devant de la scène mondiale. Ces succès qui font la fierté et la force de l’Organisation ont été rendus possibles «&...

  10. 2016-2017 Travel Expense Reports for Mary Anne Chambers ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    chantal taylor

    Purpose: Board meetings. Date(s):. 2017-03-19 to 2017-03-22. Destination(s):. Ottawa. Airfare: $121.05. Other. Transportation: $51.92. Accommodation: $926.48. Meals and. Incidentals: $190.40. Other: $0.00. Total: $1,289.85. Comments: 2016-2017 Travel Expense Reports for Mary. Anne Chambers, Governor ...

  11. 2017-2018 Travel Expense Reports for Mary Anne Chambers ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Chantal Taylor

    Ottawa. Airfare: $563.72. Other. Transportation: $74.26. Accommodation: $0.00. Meals and. Incidentals: $46.17. Other: $30.00. Total: $714.15. Comments: From residence in Thornhill, Ontario. 2017-2018 Travel Expense Reports for Mary. Anne Chambers, Governor, Chairperson of the. Human Resources Committee.

  12. 2016-2017 Travel Expense Reports for Mary Anne Chambers ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Beata Bialic

    Purpose: Board meetings. Date(s):. 2016-11-20 to 2016-11-23. Destination(s):. Ottawa. Airfare: $445.14. Other. Transportation: $29.05. Accommodation: $786.80. Meals and. Incidentals: $76.79. Other: $0.00. Total: $1,337.78. Comments: 2016-2017 Travel Expense Reports for Mary. Anne Chambers, Governor, Chairperson ...

  13. 2016-2017 Travel Expense Reports for Mary Anne Chambers ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Beata Bialic

    Date(s):. 2016-08-14 to 2016-08-23. Destination(s):. Peru/Colombia. Airfare: $3,484.87. Other. Transportation: $0.00. Accommodation: $1,942.21. Meals and. Incidentals: $395.27. Other: $75.50. Total: $5,897.85. Comments: 2016-2017 Travel Expense Reports for Mary. Anne Chambers, Governor, Chairperson of the.

  14. 2016-2017 Travel Expense Reports for Margaret Ann Biggs ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Beata Bialic

    Purpose: Internal IDRC meetings. Date(s):. 2016-07-04 to 2016-07-06. Destination(s):. Ottawa. Airfare: $0.00. Other. Transportation: $39.00. Accommodation: $0.00. Meals and. Incidentals: $25.43. Other: $0.00. Total: $64.43. Comments: 2016-2017 Travel Expense Reports for. Margaret Ann Biggs, Chairperson.

  15. 2017-2018 Travel Expense Reports for Mary Anne Chambers ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Chantal Taylor

    Ottawa. Airfare: $368.41. Other. Transportation: $69.95. Accommodation: $542.79. Meals and. Incidentals: $164.42. Other: $0.00. Total: $1,145.57. Comments: From residence in Thornhill, Ontario. 2017-2018 Travel Expense Reports for Mary. Anne Chambers, Governor, Chairperson of the. Human Resources Committee.

  16. 2016-2017 Travel Expense Reports for Mary Anne Chambers ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Beata Bialic

    Date(s):. 2016-07-06. Destination(s):. Ottawa. Airfare: $482.11. Other. Transportation: $64.30. Accommodation: $0.00. Meals and. Incidentals: $25.28. Other: $0.00. Total: $571.69. Comments: 2016-2017 Travel Expense Reports for Mary. Anne Chambers, Governor, Chairperson of the. Human Resources Committee.

  17. Why philosophy and history matter : A conversation with Ann Taves

    NARCIS (Netherlands)

    von Stuckrad, C.K.M.

    2010-01-01

    The article picks up some ideas that Ann Taves presents in her book Religious Experience Reconsidered, and looks at possible conversations that are not fleshed out in detail in Taves' book. In particular, it is argued that the disciplinary confrontation with philosophy and with historiography is of

  18. Application of ANN and fuzzy logic algorithms for streamflow ...

    Indian Academy of Sciences (India)

    1Department of Soil and Water Engineering, College of Technology and Engineering, Maharana Pratap ... evaporation, mean daily temperature and lag streamflow used. .... Evaporation, E, mm. 2383. 16.0. 0.2. 3.36. 1.25. 0.106. 0.016. 0.02. Table 2. Input parameters and ANN structure of different model for Savitri Basin.

  19. Optimization of PV array inclination in India using ANN estimator ...

    Indian Academy of Sciences (India)

    Department of Electrical Engineering, National Institute of Technology,. Raipur 0788-2202088 ..... bias of neuron and activation transfer function of each hidden layer is given by (16). Where,. Pj×1 ∈ {HgJan ... Inner layer and number of neurons in each layer of ANN, where i = 4, 2, 1 is the number of neurons in output layer ...

  20. On The Comparison of Artificial Neural Network (ANN) and ...

    African Journals Online (AJOL)

    This work presented the results of an experimental comparison of two models: Multinomial Logistic Regression (MLR) and Artificial Neural Network (ANN) for classifying students based on their academic performance. The predictive accuracy for each model was measured by their average Classification Correct Rate (CCR).

  1. Hiina tervendus / kommenteerivad Anne, Julia, Weihong Song, Fagang Ren

    Index Scriptorium Estoniae

    2013-01-01

    Tallinnas Tulika 19 asuvast Bai Lan Hiina massaažisalongist, kus ravitakse kuputeraapia, gua sha kraapimisplaatide, moksa, nõelravi ja punktmassaaži abil. Tui na massaaži ja hiina loodusteraapia protseduure kommenteerivad spetsialistid ning patsiendid Anne ja Julia

  2. Ants Orasest ja Anne Lange monograafiast / Jüri Talvet

    Index Scriptorium Estoniae

    Talvet, Jüri, 1945-

    2005-01-01

    Arvustus: Oras, Ants. Luulekool. I, Apoloogia / koostajad Hando Runnel ja Jaak Rähesoo. Tartu : Ilmamaa, 2003 ; Oras, Ants. Luulekool II, Meistriklass. Tartu : Ilmamaa, 2004 ; Lange, Anne. Ants Oras : [kirjandusteadlane, -kriitik ja tõlkija (1900-1982)]. Tartu : Ilmamaa, 2004

  3. ANNE TONER. Ellipsis in English Literature: Signs of Omission

    DEFF Research Database (Denmark)

    Lupton, Tina Jane

    2016-01-01

    As a freshman, I once met a PhD Student writing about the use of the comma in Jane Austen. For years the thesis topic kept me entertained as an example of how narrowly focused literary study could become. Reading Anne Toner's Ellipsis in English Literature commits me to a recantation of that joke...

  4. Toimus Endel Annuse bibliograafiapäev / Anne Valmas

    Index Scriptorium Estoniae

    Valmas, Anne, 1941-2017

    2012-01-01

    15. veebruaril 2012 peeti TLÜAR-is bibliograafiapäeva esimest korda Endel Annuse nimelisena, anti üle bibliograafiaauhind. Parimaks tunnistati Anneli Sepa koostatud "Uku Masing 100 : bibliograafia 1923-2009" ning "Eesti tehnikaartiklid 1936-1944. Registrid 1918-1944", mille koostas Riina Prööm ning isikunimede koostaja oli Helje Riives

  5. Book Review Winnicott's children By Ann Horn and Monica Lanyado ...

    African Journals Online (AJOL)

    Remember me, or Register. DOWNLOAD FULL TEXT Open Access DOWNLOAD FULL TEXT Subscription or Fee Access. Book Review Winnicott's children. By Ann Horn and Monica Lanyado (2012). Rod Anderson. Abstract. Routledge, London and New York 206 pages. Paperback, ISBN 978-0-415-67291-7. ZAR 503.00

  6. Klaas ja mõis / Maie-Ann Raun

    Index Scriptorium Estoniae

    Raun, Maie-Ann, 1938-

    2007-01-01

    Klaasikunstinäitus "Ringkäik" Albu mõisas, kuraatorid Virve Kiil, Kati Kerstna, Kairi Orgusaar. Eksponeeritakse Tiina Sarapu, Mare Saare, Eeva Käsperi, Kai Kiudsoo-Värvi, Pilvi Ojamaa, Merle Bukoveci, Kalli Seina, Viivi-Ann Keerdo, Liisi Junolaineni, Kristiina Uslari, Ivo Lille töid

  7. Eesti NATO ukselävel / Mari-Ann Kelam

    Index Scriptorium Estoniae

    Kelam, Mari-Ann, 1946-

    2002-01-01

    Seda, et NATO liitumisläbirääkimistele kutsutavate seas on ka Eesti, saab veel tänagi pidada üheks meie iseseisva riikluse suursaavutuseks, kui mitte imeks, kirjutab Riigikogu liige Mari-Ann Kelam. Autor: Isamaaliit. Parlamendisaadik

  8. Optimization of PV array inclination in India using ANN estimator ...

    Indian Academy of Sciences (India)

    Home; Journals; Sadhana; Volume 40; Issue 5. Optimization of PV array inclination in India using ANN estimator: Method comparison study ... Although different non-linear, empirical models have been proposed by different researchers in India, they have too many constraints and needs complex and rigorous computational ...

  9. Comparison of Conventional and ANN Models for River Flow Forecasting

    Science.gov (United States)

    Jain, A.; Ganti, R.

    2011-12-01

    Hydrological models are useful in many water resources applications such as flood control, irrigation and drainage, hydro power generation, water supply, erosion and sediment control, etc. Estimates of runoff are needed in many water resources planning, design development, operation and maintenance activities. River flow is generally estimated using time series or rainfall-runoff models. Recently, soft artificial intelligence tools such as Artificial Neural Networks (ANNs) have become popular for research purposes but have not been extensively adopted in operational hydrological forecasts. There is a strong need to develop ANN models based on real catchment data and compare them with the conventional models. In this paper, a comparative study has been carried out for river flow forecasting using the conventional and ANN models. Among the conventional models, multiple linear, and non linear regression, and time series models of auto regressive (AR) type have been developed. Feed forward neural network model structure trained using the back propagation algorithm, a gradient search method, was adopted. The daily river flow data derived from Godavari Basin @ Polavaram, Andhra Pradesh, India have been employed to develop all the models included here. Two inputs, flows at two past time steps, (Q(t-1) and Q(t-2)) were selected using partial auto correlation analysis for forecasting flow at time t, Q(t). A wide range of error statistics have been used to evaluate the performance of all the models developed in this study. It has been found that the regression and AR models performed comparably, and the ANN model performed the best amongst all the models investigated in this study. It is concluded that ANN model should be adopted in real catchments for hydrological modeling and forecasting.

  10. Space partitioning strategies for indoor WLAN positioning with cascade-connected ANN structures.

    Science.gov (United States)

    Borenović, Miloš; Nešković, Aleksandar; Budimir, Djuradj

    2011-02-01

    Position information in indoor environments can be procured using diverse approaches. Due to the ubiquitous presence of WLAN networks, positioning techniques in these environments are the scope of intense research. This paper explores two strategies for space partitioning when utilizing cascade-connected Artificial Neural Networks (ANNs) structures for indoor WLAN positioning. A set of cascade-connected ANN structures with different space partitioning strategies are compared mutually and to the single ANN structure. The benefits of using cascade-connected ANNs structures are shown and discussed in terms of the size of the environment, number of subspaces and partitioning strategy. The optimal cascade-connected ANN structures with space partitioning show up to 50% decrease in median error and up to 12% decrease in the average error with respect to the single ANN model. Finally, the single ANN and the optimal cascade-connected ANN model are compared against other well-known positioning techniques.

  11. Anne Frank relaunched in the world of comics and graphic novels

    NARCIS (Netherlands)

    Ribbens, Kees

    2017-01-01

    Recently the Basel-based Anne Frank Fonds proudly presented the Graphic Diary of Anne Frank. The impression is created as if this is the first ever comic book version of Anne Frank’s narrative. This article shows that there were various predecessors.

  12. 75 FR 418 - Certificate of Alternative Compliance for the Offshore Supply Vessel KELLY ANN CANDIES

    Science.gov (United States)

    2010-01-05

    ... Compliance for the Offshore Supply Vessel KELLY ANN CANDIES AGENCY: Coast Guard, DHS. ACTION: Notice. SUMMARY... supply vessel KELLY ANN CANDIES as required by 33 U.S.C. 1605(c) and 33 CFR 81.18. DATES: The Certificate... Purpose The offshore supply vessel KELLY ANN CANDIES will be used for offshore supply operations. Full...

  13. 33 CFR 80.120 - Cape Ann, MA to Marblehead Neck, MA.

    Science.gov (United States)

    2010-07-01

    ... 33 Navigation and Navigable Waters 1 2010-07-01 2010-07-01 false Cape Ann, MA to Marblehead Neck... SECURITY INTERNATIONAL NAVIGATION RULES COLREGS DEMARCATION LINES Atlantic Coast § 80.120 Cape Ann, MA to... apply on the harbors, bays and inlets on the east coast of Massachusetts from Halibut Point at Cape Ann...

  14. 78 FR 65364 - Notice of Inventory Completion: University of Michigan, Ann Arbor, MI

    Science.gov (United States)

    2013-10-31

    ... National Park Service Notice of Inventory Completion: University of Michigan, Ann Arbor, MI AGENCY... Research, 4080 Fleming Building, 503 Thompson St., Ann Arbor, MI 48109-1340, telephone (734) 647-9085... of human remains and associated funerary objects under the control of the University of Michigan, Ann...

  15. 78 FR 65371 - Notice of Inventory Completion: University of Michigan, Ann Arbor, MI

    Science.gov (United States)

    2013-10-31

    ... National Park Service Notice of Inventory Completion: University of Michigan, Ann Arbor, MI AGENCY... Research, 4080 Fleming Building, 503 Thompson St., Ann Arbor, MI 48109-1340, telephone (734) 647-9085... of human remains and associated funerary objects under the control of the University of Michigan, Ann...

  16. 77 FR 34991 - Notice of Inventory Completion: Museum of Anthropology, University of Michigan, Ann Arbor, MI...

    Science.gov (United States)

    2012-06-12

    ... National Park Service Notice of Inventory Completion: Museum of Anthropology, University of Michigan, Ann... Michigan, Ann Arbor, MI. The human remains were removed from Emmet County, MI. This notice is published as... Building, 503 Thompson St., Ann Arbor, MI 48109-1340; telephone (734) 647-9085, before July 12, 2012...

  17. 33 CFR 80.115 - Portland Head, ME to Cape Ann, MA.

    Science.gov (United States)

    2010-07-01

    ... 33 Navigation and Navigable Waters 1 2010-07-01 2010-07-01 false Portland Head, ME to Cape Ann, MA... INTERNATIONAL NAVIGATION RULES COLREGS DEMARCATION LINES Atlantic Coast § 80.115 Portland Head, ME to Cape Ann... to Halibut Point at Cape Ann. (b) A line drawn from the southernmost tower on Gerrish Island charted...

  18. 78 FR 65360 - Notice of Inventory Completion: University of Michigan, Ann Arbor, MI

    Science.gov (United States)

    2013-10-31

    ... National Park Service Notice of Inventory Completion: University of Michigan, Ann Arbor, MI AGENCY... Research, 4080 Fleming Building, 503 Thompson St., Ann Arbor, MI 48109-1340, telephone (734) 647-9085... of human remains and associated funerary objects under the control of the University of Michigan, Ann...

  19. 77 FR 66547 - Approval and Promulgation of Implementation Plans; Michigan; Detroit-Ann Arbor Nonattainment Area...

    Science.gov (United States)

    2012-11-06

    ... AGENCY 40 CFR Part 52 Approval and Promulgation of Implementation Plans; Michigan; Detroit-Ann Arbor... the state's Detroit-Ann Arbor (Livingston, Macomb, Monroe, Oakland, St. Clair, Washtenaw, and Wayne... rulemaking to approve Michigan's PM 2.5 2005 base year emissions inventory for the Detroit-Ann Arbor area...

  20. 78 FR 65367 - Notice of Inventory Completion: University of Michigan, Ann Arbor, MI

    Science.gov (United States)

    2013-10-31

    ... National Park Service Notice of Inventory Completion: University of Michigan, Ann Arbor, MI AGENCY... Research, 4080 Fleming Building, 503 Thompson St., Ann Arbor, MI 48109-1340, telephone (734) 647-9085... of human remains and associated funerary objects under the control of the University of Michigan, Ann...

  1. 46 CFR 7.10 - Eastport, ME to Cape Ann, MA.

    Science.gov (United States)

    2010-10-01

    ... 46 Shipping 1 2010-10-01 2010-10-01 false Eastport, ME to Cape Ann, MA. 7.10 Section 7.10 Shipping... Coast § 7.10 Eastport, ME to Cape Ann, MA. (a) A line drawn from the easternmost extremity of Kendall...°31.2′ W. (Cape Ann Lighted Whistle Buoy “2”). ...

  2. Estimation of Paddy Equilibrium Moisture Sorption Using ANNs

    Science.gov (United States)

    Amiri Chayjan, R.; Moazez, Y.

    In this research, Artificial Neural Networks (ANNs) used for prediction of Equilibrium Moisture Content (EMC) of three varieties of paddy (Sadri, Tarom and Khazar) as a new method. Feed forward back propagation and cascade forward back propagation networks with Levenberg-Marquardt and Bayesian regularization training algorithms used for training of input patterns. Optimized trained network has the ability of EMC prediction to test patterns at thermal boundary of 20-40°C and relative humidity boundary of 13.5-87% with R2 = 0.9929 and mean absolute error 0.0229. Comparison between optimized ANN result and empirical model of Henderson showed that artificial neural network not only can simultaneously predict the EMC of samples of all varieties but also has better coefficient of determination and less mean absolute error.

  3. Jo Ann Baumgartner and Sam Earnshaw: Organizers and Farmers

    OpenAIRE

    Rabkin, Sarah

    2010-01-01

    Jo Ann Baumgartner directs the Wild Farm Alliance, based in Watsonville, California. WFA’s mission, as described on the organization’s website, is “to promote agriculture that helps to protect and restore wild Nature.” Through research, publications, presentations, events, policy work, and consulting, the organization works to “connect food systems with ecosystems.” Sam Earnshaw is Central Coast regional coordinator of the Community Alliance with Family Farmers. Working with CAFF’s f...

  4. Comparison of ANN and RKS approaches to model SCC strength

    Science.gov (United States)

    Prakash, Aravind J.; Sathyan, Dhanya; Anand, K. B.; Aravind, N. R.

    2018-02-01

    Self compacting concrete (SCC) is a high performance concrete that has high flowability and can be used in heavily reinforced concrete members with minimal compaction segregation and bleeding. The mix proportioning of SCC is highly complex and large number of trials are required to get the mix with the desired properties resulting in the wastage of materials and time. The research on SCC has been highly empirical and no theoretical relationships have been developed between the mixture proportioning and engineering properties of SCC. In this work effectiveness of artificial neural network (ANN) and random kitchen sink algorithm(RKS) with regularized least square algorithm(RLS) in predicting the split tensile strength of the SCC is analysed. Random kitchen sink algorithm is used for mapping data to higher dimension and classification of this data is done using Regularized least square algorithm. The training and testing data for the algorithm was obtained experimentally using standard test procedures and materials available. Total of 40 trials were done which were used as the training and testing data. Trials were performed by varying the amount of fine aggregate, coarse aggregate, dosage and type of super plasticizer and water. Prediction accuracy of the ANN and RKS model is checked by comparing the RMSE value of both ANN and RKS. Analysis shows that eventhough the RKS model is good for large data set, its prediction accuracy is as good as conventional prediction method like ANN so the split tensile strength model developed by RKS can be used in industries for the proportioning of SCC with tailor made property.

  5. Jo Ann Rinaudo, PhD | Division of Cancer Prevention

    Science.gov (United States)

    Dr. Jo Ann Rinaudo is a Program Director in the Cancer Biomarkers Research Group in the Division of Cancer Prevention at the National Cancer Institute. She received a doctoral degree from the University of Toronto, where she studied chemical carcinogenesis in the liver. She was in the pathology department and has a broad background in human disease. Post-graduate training included further studies on the cell cycle during liver regeneration and cancer. |

  6. Intelligent MRTD testing for thermal imaging system using ANN

    Science.gov (United States)

    Sun, Junyue; Ma, Dongmei

    2006-01-01

    The Minimum Resolvable Temperature Difference (MRTD) is the most widely accepted figure for describing the performance of a thermal imaging system. Many models have been proposed to predict it. The MRTD testing is a psychophysical task, for which biases are unavoidable. It requires laboratory conditions such as normal air condition and a constant temperature. It also needs expensive measuring equipments and takes a considerable period of time. Especially when measuring imagers of the same type, the test is time consuming. So an automated and intelligent measurement method should be discussed. This paper adopts the concept of automated MRTD testing using boundary contour system and fuzzy ARTMAP, but uses different methods. It describes an Automated MRTD Testing procedure basing on Back-Propagation Network. Firstly, we use frame grabber to capture the 4-bar target image data. Then according to image gray scale, we segment the image to get 4-bar place and extract feature vector representing the image characteristic and human detection ability. These feature sets, along with known target visibility, are used to train the ANN (Artificial Neural Networks). Actually it is a nonlinear classification (of input dimensions) of the image series using ANN. Our task is to justify if image is resolvable or uncertainty. Then the trained ANN will emulate observer performance in determining MRTD. This method can reduce the uncertainties between observers and long time dependent factors by standardization. This paper will introduce the feature extraction algorithm, demonstrate the feasibility of the whole process and give the accuracy of MRTD measurement.

  7. Kuidas Soomes, Ann? Aidi Valliku noorsooraamatute retseptsioonist Soomes

    Directory of Open Access Journals (Sweden)

    Ele Süvalep

    2010-05-01

    Full Text Available The Finnish reception of the translations of two youth books by Aidi Vallik is analysed on the basis of published reviews, web comments and judgements, and the opinions expressed by a class on Estonian children’s literature held at the University of Oulu in 2008. The introductory part touches upon the concepts of young adult literature and youth literature and, briefly, upon some specific traits of youth literature which are important from the perspective of translation and reception. The analysis of the material revealed that the readers would mainly argue against the fi nal resolutions of the stories as those seem to lay too much emphasis on the adult viewpoint and the didactic aspect. The language use was also found problematic. Notably, in “How are you, Ann?” the main character’s narrative seemed too literary, for some readers, especially in comparison with the colourful diary of Ann’s mother, which makes Ann less interesting than her mother. The general attitude, especially for the first book, was nevertheless positive enough. Despite the reservations typical of youth literature the books of Ann were found interesting by most readers, providing an idea of Estonian youth problems and youth literature.

  8. A Hybrid FEM-ANN Approach for Slope Instability Prediction

    Science.gov (United States)

    Verma, A. K.; Singh, T. N.; Chauhan, Nikhil Kumar; Sarkar, K.

    2016-09-01

    Assessment of slope stability is one of the most critical aspects for the life of a slope. In any slope vulnerability appraisal, Factor Of Safety (FOS) is the widely accepted index to understand, how close or far a slope from the failure. In this work, an attempt has been made to simulate a road cut slope in a landslide prone area in Rudrapryag, Uttarakhand, India which lies near Himalayan geodynamic mountain belt. A combination of Finite Element Method (FEM) and Artificial Neural Network (ANN) has been adopted to predict FOS of the slope. In ANN, a three layer, feed- forward back-propagation neural network with one input layer and one hidden layer with three neurons and one output layer has been considered and trained using datasets generated from numerical analysis of the slope and validated with new set of field slope data. Mean absolute percentage error estimated as 1.04 with coefficient of correlation between the FOS of FEM and ANN as 0.973, which indicates that the system is very vigorous and fast to predict FOS for any slope.

  9. Using ANNs to predict cooling requirements for residential buildings

    Energy Technology Data Exchange (ETDEWEB)

    Karatasou, S.; Santamouris, M.; Geros, V. [University of Athens (Greece). Physics Dept.

    2004-07-01

    Artificial neural networks (ANNs) have been used for the prediction of cooling loads of residential buildings in Athens, Greece. The investigation was performed for the summer period, where for Southern European countries, short time cooling load forecasting in residential buildings with lead times from 1 hour to 7 days can play a key role in the economic and energy efficient operation of cooling appliances. The objective of this work is to produce a simulation algorithm, using ANNs, capable to forecast the following 24-hour cooling load profiles. Reliable cooling consumption measurements are required but are not usually available for residential buildings. State-ofthe- art building simulation software, TRNSYS, was used to calculate energy demand for cooling for five selected apartments in Athens, Greece, using detailed building data (geometry, wall construction, occupancy etc) and Athens climate conditions. These data are used to train artificial neural networks in order to generate the relationship between selected inputs and the desired output, the next day building energy consumption for cooling. A multiplayer perceptron architecture using the standard back-propagation learning algorithm has been applied yielded to satisfactory results and the conclusion that when ANNs are trained on reliable data they can simulate the behavior of the building, thus they can be effectively used to predict future performance. (orig.)

  10. Ann Back Propagation For Forecasting And Simulation Hydroclimatology Data

    Directory of Open Access Journals (Sweden)

    Syaefudin Suhaedi

    2017-10-01

    Full Text Available Government policies in distributing fertilizers and seeds of food crops such as rice and crops depend on the growing season of the farmers. Therefore before conducting the distribution it is necessary to spread early planting season in each region farmers so that the result of distribution is optimal. One of the alternatives that must be done first is to predict the pattern of hydroclimatological data cycle of the coming year to see the pattern of data of previous years. In this case required a method that can be used to predict the hydroclimatological data. The exact method used to make predictions is Artificial Neural Network ANN Back Propagation. As a follow-up step will be predicted by this ANN will be used to build system planning optimal cropping pattern for agricultural crops to avoid harvest failure puso in order to obtain maximum production results so as to support national food security. Based on the results of the simulation is known that ANN Back Propagation with two hidden layer are able to predict hydroclimatological data with an average accuracy of 95.72 - 96.61. While the prediction validation obtained an average percentage error of 1.12 with the accuracy of 99.76. The data used for training testing validation and prediction are data in Central Lombok NTB Indonesia.

  11. Anne Veesaar: Tartus tuli võõras naine mind kallistama : "Hea, et teie, Anne, elus olete!" / Aigi Viira

    Index Scriptorium Estoniae

    Viira, Aigi

    2006-01-01

    4. nov. Tartu-Tallinna maanteel Mõhkküla risti lähedal kokku põrganud Fordis ja Hyundais hukkus kolm inimest, nende seas Rakvere Teatri näitleja Dajan Ahmet. Õnnetuses said kannatada ka näitlejad Rednar Annus, Marika Korolev, Anne Veesaar ja Ksenia Agarkova. Näitleja Dan Põldroos juhtunust "Küünal on hinges..."

  12. Luksuslik ruum kõigile : vestlus arhitekt Anne Lacatoniga = Luxury space for everyone : interview with Anne Lacaton / intervjueerinud Katrin Paadam

    Index Scriptorium Estoniae

    Lacaton, Anne, 1955-

    2013-01-01

    Prantsuse arhitekt Anne Lacaton oma büroo (Lacaton & Vassal, Frédéric Druot, 2011) linnaehituslikult uuendusliku projekti järgi ümber ehitatud 1960. aastate korterelamust Tour du Bois-le-Prêtre Pariisis, sotsiaalelamute ehitusest Prantsusmaal, 1960.-1970. aastatel ehitatud elamute taaskasutusega seotud probleemidest, rekonstrueerimise kasulikkusest võrreldes elamu lammutamise ja uuesti ehitamisega, linnas elavatele inimestele paremate elamistingimuste loomisest, linnaplaneerimisest

  13. Doktoritööd : [2007, Anne Lange jt.

    Index Scriptorium Estoniae

    2007-01-01

    20. veebr. kaitseb Anne Lange doktoritööd "The poetics of translation of Ants Oras". 19. jaan. kaitses Tuuli Oder doktoritööd "The model of contemporary professional foreign language teacher". 16. jaan. kaitses Tiit Maran doktoritööd "Conservation biology of the European mink, Mustela lutreola (Linnaeus 1761): decline and causes of extinction". 12. jaan. kaitses Maris Saagpakk doktoritööd "Deutschbaltische Autobiographien als Dokumente des Zeit- und Selbstempfindens: vom Ende des 19. Jh. bis zur Umsiedlung 1939"

  14. Mary Anne Chambers | CRDI - Centre de recherches pour le ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Ancienne députée à l'Assemblée législative de l'Ontario, Mary Anne a été ministre de la Formation, des Collèges et des Universités ainsi que ministre des Services à l'enfance et à la jeunesse au gouvernement de l'Ontario. Elle a en outre occupé le poste de vice-présidente principale à la Banque Scotia. Diplômée de ...

  15. EEG-based classification of bilingual unspoken speech using ANN.

    Science.gov (United States)

    Balaji, Advait; Haldar, Aparajita; Patil, Keshav; Ruthvik, T Sai; Ca, Valliappan; Jartarkar, Mayur; Baths, Veeky

    2017-07-01

    The ability to interpret unspoken or imagined speech through electroencephalography (EEG) is of therapeutic interest for people suffering from speech disorders and `lockedin' syndrome. It is also useful for brain-computer interface (BCI) techniques not involving articulatory actions. Previous work has involved using particular words in one chosen language and training classifiers to distinguish between them. Such studies have reported accuracies of 40-60% and are not ideal for practical implementation. Furthermore, in today's multilingual society, classifiers trained in one language alone might not always have the desired effect. To address this, we present a novel approach to improve accuracy of the current model by combining bilingual interpretation and decision making. We collect data from 5 subjects with Hindi and English as primary and secondary languages respectively and ask them 20 `Yes'/`No' questions (`Haan'/`Na' in Hindi) in each language. We choose sensors present in regions important to both language processing and decision making. Data is preprocessed, and Principal Component Analysis (PCA) is carried out to reduce dimensionality. This is input to Support Vector Machine (SVM), Random Forest (RF), AdaBoost (AB), and Artificial Neural Networks (ANN) classifiers for prediction. Experimental results reveal best accuracy of 85.20% and 92.18% for decision and language classification respectively using ANN. Overall accuracy of bilingual speech classification is 75.38%.

  16. The Indecisive Feminist: Study of Anne Sexton's Revisionist Fairy Tales

    Directory of Open Access Journals (Sweden)

    Nadia Fayidh Mohammed

    2015-02-01

    Full Text Available Fairy tales to female writers are major resource for their abundant writings, but for the feminist poets since 1960s, they become essential subject matter to often deal with in their literary production. With the motivation to address the conventional tradition of patriarchal society, and re-address the stereotype females inhabiting these tales, feminist writers set upon revealing the underlying sub-context of these tales, presenting them with more adult-suited themes. Anne Sexton's Transformation is a pioneering revision of Grimm's fairy tales in which the poet does not only satirize the patriarchal society she grew up in, but she also rejects the female stereotype that her upbringing intended her to be. In the following paper, the feminist messages which Sexton's fairy tales intended to deliver are examined to reveal the poet's position concerning feminism and her relationship with female role-models and the male figures she presents in her fairy tales. Keywords: Anne Sexton, feminism, fairy tales, revisionism, postmodernist poetry, Transformations

  17. Evaluation of PCA and Gamma test techniques on ANN operation for weekly solid waste prediction.

    Science.gov (United States)

    Noori, Roohollah; Karbassi, Abdulreza; Salman Sabahi, Mohammad

    2010-01-01

    Artificial neural networks (ANNs) are suitable for modeling solid waste generation. In the present study, four training functions, including resilient backpropagation (RP), scale conjugate gradient (SCG), one step secant (OSS), and Levenberg-Marquardt (LM) algorithms have been used. The main goal of this research is to develop an ANN model with a simple structure and ample accuracy. In the first step, an appropriate ANN model with 13 input variables is developed using the afore-mentioned algorithms to optimize the network parameters for weekly solid waste prediction in Mashhad, Iran. Subsequently, principal component analysis (PCA) and Gamma test (GT) techniques are used to reduce the number of input variables. Finally, comparison amongst the operation of ANN, PCA-ANN, and GT-ANN models is made. Findings indicated that the PCA-ANN and GT-ANN models have more effective results than the ANN model. These two models decrease the number of input variables from 13 to 7 and 5, respectively. 2009 Elsevier Ltd. All rights reserved.

  18. Anne-Sylvie Catherin, Head of the Human Resources Department

    CERN Multimedia

    2009-01-01

    Anne-Sylvie Catherin has been appointed Head of the Human Resources Department with effect from 1 August 2009. Mrs Catherin is a lawyer specialized in International Administration and joined CERN in 1996 as legal advisor within the Office of the HR Department Head. After having been promoted to the position of Group Leader responsible for social and statutory conditions in 2000, Mrs Catherin was appointed Deputy of the Head of the Human Resources Department and Group Leader responsible for Strategy, Management and Development from 2005 to date. Since 2005, she has also served as a member of CCP and TREF. In the execution of her mandate as Deputy HR Department Head, Mrs Catherin closely assisted the HR Department Head in the organization of the Department and in devising new HR policies and strategies. She played an instrumental role in the last five-yearly review and in the revision of the Staff Rules and Regulations.

  19. ANN Modeling of a Chemical Humidity Sensing Mechanism

    Directory of Open Access Journals (Sweden)

    Souhil KOUDA

    2010-10-01

    Full Text Available This work aims to achieve a modeling of a resistive-type humidity sensing mechanism (RHSM. This model takes into account the parameters of non-linearity, hysteresis, temperature, frequency, substrate type. Furthermore, we investigated the TiO2 and PMAPTAC concentrations effects on the humidity sensing properties in our model. Using neuronal networks and Matlab environment, we have done the training to realize an analytical model ANN and create a component, accurately express the above parameters variations, for our sensing mechanism model in the PSPICE simulator library. Simulation has been used to evaluate the effect of variations of non-linearity, hysteresis, temperature, frequency, substrate type and TiO2 and PMAPTAC concentrations effects, where the output of this model is identical to the output of the chemical humidity sensing mechanism used.

  20. DESIGN OF A VISUAL INTERFACE FOR ANN BASED SYSTEMS

    Directory of Open Access Journals (Sweden)

    Ramazan BAYINDIR

    2008-01-01

    Full Text Available Artificial intelligence application methods have been used for control of many systems with parallel of technological development besides conventional control techniques. Increasing of artificial intelligence applications have required to education in this area. In this paper, computer based an artificial neural network (ANN software has been presented to learning and understanding of artificial neural networks. By means of the developed software, the training of the artificial neural network according to the inputs provided and a test action can be performed by changing the components such as iteration number, momentum factor, learning ratio, and efficiency function of the artificial neural networks. As a result of the study a visual education set has been obtained that can easily be adapted to the real time application.

  1. Hybrid LSA-ANN Based Home Energy Management Scheduling Controller for Residential Demand Response Strategy

    Directory of Open Access Journals (Sweden)

    Maytham S. Ahmed

    2016-09-01

    Full Text Available Demand response (DR program can shift peak time load to off-peak time, thereby reducing greenhouse gas emissions and allowing energy conservation. In this study, the home energy management scheduling controller of the residential DR strategy is proposed using the hybrid lightning search algorithm (LSA-based artificial neural network (ANN to predict the optimal ON/OFF status for home appliances. Consequently, the scheduled operation of several appliances is improved in terms of cost savings. In the proposed approach, a set of the most common residential appliances are modeled, and their activation is controlled by the hybrid LSA-ANN based home energy management scheduling controller. Four appliances, namely, air conditioner, water heater, refrigerator, and washing machine (WM, are developed by Matlab/Simulink according to customer preferences and priority of appliances. The ANN controller has to be tuned properly using suitable learning rate value and number of nodes in the hidden layers to schedule the appliances optimally. Given that finding proper ANN tuning parameters is difficult, the LSA optimization is hybridized with ANN to improve the ANN performances by selecting the optimum values of neurons in each hidden layer and learning rate. Therefore, the ON/OFF estimation accuracy by ANN can be improved. Results of the hybrid LSA-ANN are compared with those of hybrid particle swarm optimization (PSO based ANN to validate the developed algorithm. Results show that the hybrid LSA-ANN outperforms the hybrid PSO based ANN. The proposed scheduling algorithm can significantly reduce the peak-hour energy consumption during the DR event by up to 9.7138% considering four appliances per 7-h period.

  2. Anne Tyler møder Shakespeare, og god litteratur opstår

    DEFF Research Database (Denmark)

    Davidsen-Nielsen, Niels

    2016-01-01

    Eftertanken. Amerikanske Anne Tyler er helt sin egen, en original og modig forfatter, der ikke viger tilbage fra at skrive opløftende litteratur.......Eftertanken. Amerikanske Anne Tyler er helt sin egen, en original og modig forfatter, der ikke viger tilbage fra at skrive opløftende litteratur....

  3. 76 FR 81991 - Tecumseh Products Corporation, Ann Arbor, MI; Notice of Termination of Investigation

    Science.gov (United States)

    2011-12-29

    ... From the Federal Register Online via the Government Publishing Office DEPARTMENT OF LABOR Employment and Training Administration Tecumseh Products Corporation, Ann Arbor, MI; Notice of Termination of... Application for Reconsideration applicable to workers and former workers of Tecumseh Products Corporation, Ann...

  4. 78 FR 70099 - Requested Administrative Waiver of the Coastwise Trade Laws: Vessel LADY ANN; Invitation for...

    Science.gov (United States)

    2013-11-22

    ... From the Federal Register Online via the Government Publishing Office DEPARTMENT OF TRANSPORTATION Maritime Administration Requested Administrative Waiver of the Coastwise Trade Laws: Vessel LADY ANN... of the vessel LADY ANN is: Intended Commercial Use Of Vessel: ``Charter cruises.'' Geographic Region...

  5. 78 FR 41993 - Ann Arbor Railroad, Inc.-Lease Exemption-Norfolk Southern Railway Company

    Science.gov (United States)

    2013-07-12

    ... Surface Transportation Board Ann Arbor Railroad, Inc.--Lease Exemption--Norfolk Southern Railway Company... of exemption should be issued, and does so here. Notice Ann Arbor Railroad, Inc. (AARR), a Class III... Southern Railway Company (NSR) two rail lines totaling 3.69 miles: (1) A line of railroad between milepost...

  6. Visual NNet: An Educational ANN's Simulation Environment Reusing Matlab Neural Networks Toolbox

    Science.gov (United States)

    Garcia-Roselló, Emilio; González-Dacosta, Jacinto; Lado, Maria J.; Méndez, Arturo J.; Garcia Pérez-Schofield, Baltasar; Ferrer, Fátima

    2011-01-01

    Artificial Neural Networks (ANN's) are nowadays a common subject in different curricula of graduate and postgraduate studies. Due to the complex algorithms involved and the dynamic nature of ANN's, simulation software has been commonly used to teach this subject. This software has usually been developed specifically for learning purposes, because…

  7. Flow forecast by SWAT model and ANN in Pracana basin, Portugal

    NARCIS (Netherlands)

    Demirel, M.C.; Venancio, Anabela; Kahya, Ercan

    2009-01-01

    This study provides a unique opportunity to analyze the issue of flow forecast based on the soil and water assessment tool (SWAT) and artificial neural network (ANN) models. In last two decades, the ANNs have been extensively applied to various water resources system problems. In this study, the

  8. iAnn: an event sharing platform for the life sciences

    Science.gov (United States)

    Jimenez, Rafael C.; Albar, Juan P.; Bhak, Jong; Blatter, Marie-Claude; Blicher, Thomas; Brazas, Michelle D.; Brooksbank, Cath; Budd, Aidan; De Las Rivas, Javier; Dreyer, Jacqueline; van Driel, Marc A.; Dunn, Michael J.; Fernandes, Pedro L.; van Gelder, Celia W. G.; Hermjakob, Henning; Ioannidis, Vassilios; Judge, David P.; Kahlem, Pascal; Korpelainen, Eija; Kraus, Hans-Joachim; Loveland, Jane; Mayer, Christine; McDowall, Jennifer; Moran, Federico; Mulder, Nicola; Nyronen, Tommi; Rother, Kristian; Salazar, Gustavo A.; Schneider, Reinhard; Via, Allegra; Villaveces, Jose M.; Yu, Ping; Schneider, Maria V.; Attwood, Teresa K.; Corpas, Manuel

    2013-01-01

    Summary: We present iAnn, an open source community-driven platform for dissemination of life science events, such as courses, conferences and workshops. iAnn allows automatic visualisation and integration of customised event reports. A central repository lies at the core of the platform: curators add submitted events, and these are subsequently accessed via web services. Thus, once an iAnn widget is incorporated into a website, it permanently shows timely relevant information as if it were native to the remote site. At the same time, announcements submitted to the repository are automatically disseminated to all portals that query the system. To facilitate the visualization of announcements, iAnn provides powerful filtering options and views, integrated in Google Maps and Google Calendar. All iAnn widgets are freely available. Availability: http://iann.pro/iannviewer Contact: manuel.corpas@tgac.ac.uk PMID:23742982

  9. Modification of an RBF ANN-Based Temperature Compensation Model of Interferometric Fiber Optical Gyroscopes.

    Science.gov (United States)

    Cheng, Jianhua; Qi, Bing; Chen, Daidai; Landry, René

    2015-05-13

    This paper presents modification of Radial Basis Function Artificial Neural Network (RBF ANN)-based temperature compensation models for Interferometric Fiber Optical Gyroscopes (IFOGs). Based on the mathematical expression of IFOG output, three temperature relevant terms are extracted, which include: (1) temperature of fiber loops; (2) temperature variation of fiber loops; (3) temperature product term of fiber loops. Then, the input-modified RBF ANN-based temperature compensation scheme is established, in which temperature relevant terms are transferred to train the RBF ANN. Experimental temperature tests are conducted and sufficient data are collected and post-processed to form the novel RBF ANN. Finally, we apply the modified RBF ANN based on temperature compensation model in two IFOGs with temperature compensation capabilities. The experimental results show the proposed temperature compensation model could efficiently reduce the influence of environment temperature on the output of IFOG, and exhibit a better temperature compensation performance than conventional scheme without proposed improvements.

  10. Daily reservoir inflow forecasting combining QPF into ANNs model

    Science.gov (United States)

    Zhang, Jun; Cheng, Chun-Tian; Liao, Sheng-Li; Wu, Xin-Yu; Shen, Jian-Jian

    2009-01-01

    Daily reservoir inflow predictions with lead-times of several days are essential to the operational planning and scheduling of hydroelectric power system. The demand for quantitative precipitation forecasting (QPF) is increasing in hydropower operation with the dramatic advances in the numerical weather prediction (NWP) models. This paper presents a simple and an effective algorithm for daily reservoir inflow predictions which solicits the observed precipitation, forecasted precipitation from QPF as predictors and discharges in following 1 to 6 days as predicted targets for multilayer perceptron artificial neural networks (MLP-ANNs) modeling. An improved error back-propagation algorithm with self-adaptive learning rate and self-adaptive momentum coefficient is used to make the supervised training procedure more efficient in both time saving and search optimization. Several commonly used error measures are employed to evaluate the performance of the proposed model and the results, compared with that of ARIMA model, show that the proposed model is capable of obtaining satisfactory forecasting not only in goodness of fit but also in generalization. Furthermore, the presented algorithm is integrated into a practical software system which has been severed for daily inflow predictions with lead-times varying from 1 to 6 days of more than twenty reservoirs operated by the Fujian Province Grid Company, China.

  11. News from HR: a word from Anne-Sylvie Catherin

    CERN Multimedia

    2016-01-01

    Anne-Sylvie Catherin, head of HR Department, looks back over her years at CERN before taking up her new position at the European Central Bank.   At the end of July, I will be leaving CERN on a special leave of absence to take up a new position at the European Central Bank. This is a new chapter in my career, in a new context with its own challenges, and as I prepare for it, I would like to take a little time to look back over my years at CERN and share with you the enriching journey it has been, both for myself and for HR. It has always been my strong belief that any organisation’s greatest asset is its people. When an HR professional believes that, it’s only a short step to the conclusion that the best way to nurture those people is by adopting a professional approach to HR. In this respect, I arrived at a very fortuitous time. Enrico Chiaveri was head of HR and, although his background is in physics, we shared that same conviction. Enrico was the icebreaker in driving change, a...

  12. Review of Artificial Neural Networks (ANN) applied to corrosion monitoring

    International Nuclear Information System (INIS)

    Mabbutt, S; Picton, P; Shaw, P; Black, S

    2012-01-01

    The assessment of corrosion within an engineering system often forms an important aspect of condition monitoring but it is a parameter that is inherently difficult to measure and predict. The electrochemical nature of the corrosion process allows precise measurements to be made. Advances in instruments, techniques and software have resulted in devices that can gather data and perform various analysis routines that provide parameters to identify corrosion type and corrosion rate. Although corrosion rates are important they are only useful where general or uniform corrosion dominates. However, pitting, inter-granular corrosion and environmentally assisted cracking (stress corrosion) are examples of corrosion mechanisms that can be dangerous and virtually invisible to the naked eye. Electrochemical noise (EN) monitoring is a very useful technique for detecting these types of corrosion and it is the only non-invasive electrochemical corrosion monitoring technique commonly available. Modern instrumentation is extremely sensitive to changes in the system and new experimental configurations for gathering EN data have been proven. In this paper the identification of localised corrosion by different data analysis routines has been reviewed. In particular the application of Artificial Neural Network (ANN) analysis to corrosion data is of key interest. In most instances data needs to be used with conventional theory to obtain meaningful information and relies on expert interpretation. Recently work has been carried out using artificial neural networks to investigate various types of corrosion data in attempts to predict corrosion behaviour with some success. This work aims to extend this earlier work to identify reliable electrochemical indicators of localised corrosion onset and propagation stages.

  13. Biosorption of chromium (VI) from aqueous solutions and ANN modelling.

    Science.gov (United States)

    Nag, Soma; Mondal, Abhijit; Bar, Nirjhar; Das, Sudip Kumar

    2017-08-01

    The use of sustainable, green and biodegradable natural wastes for Cr(VI) detoxification from the contaminated wastewater is considered as a challenging issue. The present research is aimed to assess the effectiveness of seven different natural biomaterials, such as jackfruit leaf, mango leaf, onion peel, garlic peel, bamboo leaf, acid treated rubber leaf and coconut shell powder, for Cr(VI) eradication from aqueous solution by biosorption process. Characterizations were conducted using SEM, BET and FTIR spectroscopy. The effects of operating parameters, viz., pH, initial Cr(VI) ion concentration, adsorbent dosages, contact time and temperature on metal removal efficiency, were studied. The biosorption mechanism was described by the pseudo-second-order model and Langmuir isotherm model. The biosorption process was exothermic, spontaneous and chemical (except garlic peel) in nature. The sequence of adsorption capacity was mango leaf > jackfruit leaf > acid treated rubber leaf > onion peel > bamboo leaf > garlic peel > coconut shell with maximum Langmuir adsorption capacity of 35.7 mg g -1 for mango leaf. The treated effluent can be reused. Desorption study suggested effective reuse of the adsorbents up to three cycles, and safe disposal method of the used adsorbents suggested biodegradability and sustainability of the process by reapplication of the spent adsorbent and ultimately leading towards zero wastages. The performances of the adsorbents were verified with wastewater from electroplating industry. The scale-up study reported for industrial applications. ANN modelling using multilayer perception with gradient descent (GD) and Levenberg-Marquart (LM) algorithm had been successfully used for prediction of Cr(VI) removal efficiency. The study explores the undiscovered potential of the natural waste materials for sustainable existence of small and medium sector industries, especially in the third world countries by protecting the environment by eco-innovation.

  14. Optimum coagulant forecasting by modeling jar test experiments using ANNs

    Science.gov (United States)

    Haghiri, Sadaf; Daghighi, Amin; Moharramzadeh, Sina

    2018-01-01

    Currently, the proper utilization of water treatment plants and optimizing their use is of particular importance. Coagulation and flocculation in water treatment are the common ways through which the use of coagulants leads to instability of particles and the formation of larger and heavier particles, resulting in improvement of sedimentation and filtration processes. Determination of the optimum dose of such a coagulant is of particular significance. A high dose, in addition to adding costs, can cause the sediment to remain in the filtrate, a dangerous condition according to the standards, while a sub-adequate dose of coagulants can result in the reducing the required quality and acceptable performance of the coagulation process. Although jar tests are used for testing coagulants, such experiments face many constraints with respect to evaluating the results produced by sudden changes in input water because of their significant costs, long time requirements, and complex relationships among the many factors (turbidity, temperature, pH, alkalinity, etc.) that can influence the efficiency of coagulant and test results. Modeling can be used to overcome these limitations; in this research study, an artificial neural network (ANN) multi-layer perceptron (MLP) with one hidden layer has been used for modeling the jar test to determine the dosage level of used coagulant in water treatment processes. The data contained in this research have been obtained from the drinking water treatment plant located in Ardabil province in Iran. To evaluate the performance of the model, the mean squared error (MSE) and correlation coefficient (R2) parameters have been used. The obtained values are within an acceptable range that demonstrates the high accuracy of the models with respect to the estimation of water-quality characteristics and the optimal dosages of coagulants; so using these models will allow operators to not only reduce costs and time taken to perform experimental jar tests

  15. Tallinna Ülikool / Leif Kalev, Anneli Leinpere, Tiit Land, Alessandro Centonze, Hannes Palang, Liis Kelberg

    Index Scriptorium Estoniae

    2008-01-01

    Ülikooli ja seal õpetatavaid erialasid tutvustavad dotsent Leif Kalev, kunstimagistrant Anneli Leinpere, professor Tiit Land, välisüliõpilane Milanost Alessandro Centonze, vanemteadur Hannes Palang ning kirjaliku tõlke magistrant Liis Kelberg

  16. Artificial Neural Networks (ANNs for flood forecasting at Dongola Station in the River Nile, Sudan

    Directory of Open Access Journals (Sweden)

    Sulafa Hag Elsafi

    2014-09-01

    Full Text Available Heavy seasonal rains cause the River Nile in Sudan to overflow and flood the surroundings areas. The floods destroy houses, crops, roads, and basic infrastructure, resulting in the displacement of people. This study aimed to forecast the River Nile flow at Dongola Station in Sudan using an Artificial Neural Network (ANN as a modeling tool and validated the accuracy of the model against actual flow. The ANN model was formulated to simulate flows at a certain location in the river reach, based on flow at upstream locations. Different procedures were applied to predict flooding by the ANN. Readings from stations along the Blue Nile, White Nile, Main Nile, and River Atbara between 1965 and 2003 were used to predict the likelihood of flooding at Dongola Station. The analysis indicated that the ANN provides a reliable means of detecting the flood hazard in the River Nile.

  17. Igal mõisal on oma lugu / Mari-Ann Remmel

    Index Scriptorium Estoniae

    Remmel, Mari-Ann

    2008-01-01

    Kirjastuselt "Tänapäev" ilmus raamat "Mõisalegendid. Harjumaa", koostaja Mari-Ann Remmel, kujundaja Angelika Schneider. Kogumik sisaldab ka ajaloolist ning genealoogilist teavet mõisahoonete ning -omanike kohta

  18. Fiber nonlinearity-induced penalty reduction in CO-OFDM by ANN-based nonlinear equalization.

    Science.gov (United States)

    Giacoumidis, Elias; Le, Son T; Ghanbarisabagh, Mohammad; McCarthy, Mary; Aldaya, Ivan; Mhatli, Sofien; Jarajreh, Mutsam A; Haigh, Paul A; Doran, Nick J; Ellis, Andrew D; Eggleton, Benjamin J

    2015-11-01

    We experimentally demonstrate ∼2  dB quality (Q)-factor enhancement in terms of fiber nonlinearity compensation of 40  Gb/s 16 quadrature amplitude modulation coherent optical orthogonal frequency-division multiplexing at 2000 km, using a nonlinear equalizer (NLE) based on artificial neural networks (ANN). Nonlinearity alleviation depends on escalation of the ANN training overhead and the signal bit rate, reporting ∼4  dBQ-factor enhancement at 70  Gb/s, whereas a reduction of the number of ANN neurons annihilates the NLE performance. An enhanced performance by up to ∼2  dB in Q-factor compared to the inverse Volterra-series transfer function NLE leads to a breakthrough in the efficiency of ANN.

  19. ANN application for prediction of atmospheric nitrogen deposition to aquatic ecosystems.

    Science.gov (United States)

    Palani, Sundarambal; Tkalich, Pavel; Balasubramanian, Rajasekhar; Palanichamy, Jegathambal

    2011-06-01

    The occurrences of increased atmospheric nitrogen deposition (ADN) in Southeast Asia during smoke haze episodes have undesired consequences on receiving aquatic ecosystems. A successful prediction of episodic ADN will allow a quantitative understanding of its possible impacts. In this study, an artificial neural network (ANN) model is used to estimate atmospheric deposition of total nitrogen (TN) and organic nitrogen (ON) concentrations to coastal aquatic ecosystems. The selected model input variables were nitrogen species from atmospheric deposition, Total Suspended Particulates, Pollutant Standards Index and meteorological parameters. ANN models predictions were also compared with multiple linear regression model having the same inputs and output. ANN model performance was found relatively more accurate in its predictions and adequate even for high-concentration events with acceptable minimum error. The developed ANN model can be used as a forecasting tool to complement the current TN and ON analysis within the atmospheric deposition-monitoring program in the region. Copyright © 2011 Elsevier Ltd. All rights reserved.

  20. Benthic Habitat Mapping - Dry Tortugas RoxAnn Acoustic Sensor Data Points

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — During April 2001 RoxAnn single-beam acoustic surveys were conducted at the North Unit of the Tortugas Ecological Reserve, Florida. Thirteen transects associated...

  1. Thesis defence: Anneli Baran Possibilities for studying semantics in phraseology / Outi Lauhakangas

    Index Scriptorium Estoniae

    Lauhakangas, Outi

    2011-01-01

    15. märtsil 2011 kaitses Anneli Baran Tartu Ülikooli kultuuriteaduste ja kunstide instituudis doktoriväitekirja Fraseologismide semantika uurimisvõimalused, juhendaja Arvo Krikmann (Eesti Kirjandusmuuseum), oponent dr Outi Lauhakangas (Soome Kirjanduse Selts)

  2. Enamik Mõtteloo Sihtkapitali rahast kasvab USA aktsiates / Mart Trummal ; interv. Anne Oja

    Index Scriptorium Estoniae

    Trummal, Mart

    2006-01-01

    Intervjuu Eesti Mõtteloo Sihtkapitali juhi Mart Trummaliga. Tabel: Mõtteloo Sihtkapitali vara on USA-s noteeritud indeksifondides; Investeeringud Eesti aktsiatesse. Diagramm: Finantsinvesteeringute maht. Vt. samas: Anne Oja. Dividendisaajate esirinnas. Kommenteerib Annika Matson

  3. Prediction of Splitting Tensile Strength of Concrete Containing Zeolite and Diatomite by ANN

    Directory of Open Access Journals (Sweden)

    E. Gülbandılar

    2017-01-01

    Full Text Available This study was designed to investigate with two different artificial neural network (ANN prediction model for the behavior of concrete containing zeolite and diatomite. For purpose of constructing this model, 7 different mixes with 63 specimens of the 28, 56 and 90 days splitting tensile strength experimental results of concrete containing zeolite, diatomite, both zeolite and diatomite used in training and testing for ANN systems was gathered from the tests. The data used in the ANN models are arranged in a format of seven input parameters that cover the age of samples, Portland cement, zeolite, diatomite, aggregate, water and hyper plasticizer and an output parameter which is splitting tensile strength of concrete. In the model, the training and testing results have shown that two different ANN systems have strong potential as a feasible tool for predicting 28, 56 and 90 days the splitting tensile strength of concrete containing zeolite and diatomite.

  4. Evaluation of the advanced operating system of the Ann Arbor Transit Authority

    Science.gov (United States)

    1999-10-01

    These reports constitute an evaluation of the intelligent transportation system deployment efforts of the Ann Arbor Transportation Authority. These efforts, collectively termed "Advanced Operating System" (AOS), represent a vision of an integrated ad...

  5. Evaluation Of The Advanced Operating System Of The Ann Arbor Transportation Authority : AATA Web Survey

    Science.gov (United States)

    1999-01-01

    During 1997, visitors to the Ann Arbor (Michigan) Transportation Authority's worldwide web site were invited to complete an electronic questionnaire about their experience with the site. Eighty surveys were collected, representing a non-scientific se...

  6. Evaluation Of The Advanced Operating System Of The Ann Arbor Transportation Authority : Archives And Records

    Science.gov (United States)

    1999-01-01

    This study examines data regularly maintained by the AATA (Ann Arbor Transportation Authority) for evidence of AOS (Advanced Operating System) impact. These data include on-time performance, bus trips broken because of maintenance or other incidents,...

  7. Annelies Noordhof-Hoorn, De stem van de student. Nederlandse studentenbladen in de negentiende eeuw.

    Directory of Open Access Journals (Sweden)

    Inge de Wilde

    2017-06-01

    Full Text Available Annelies Noordhof-Hoorn, De stem van de student. Nederlandse studentenbladen in de negentiende eeuw (Studies over de Geschiedenis van de Groningse Universiteit 9; Hilversum: Verloren, 2016, 382 pp., isbn 978 90 8704 589 0.

  8. 2006 Maryland Department of Natural Resources Lidar: Caroline, Kent and Queen Anne Counties

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Maryland Department of Natural Resources requested the collection of LIDAR data over Kent, Queen Anne and Caroline Counties, MD. In response, EarthData acquired the...

  9. ANN-Based Control of a Wheeled Inverted Pendulum System Using an Extended DBD Learning Algorithm

    Directory of Open Access Journals (Sweden)

    David Cruz

    2016-05-01

    Full Text Available This paper presents a dynamic model for a self-balancing vehicle using the Euler-Lagrange approach. The design and deployment of an artificial neuronal network (ANN in a closed-loop control is described. The ANN is characterized by integration of the extended delta-bar-delta algorithm (DBD, which accelerates the adjustment of synaptic weights. The results of the control strategy in the dynamic model of the robot are also presented.

  10. Mapping brain circuits of reward and motivation: In the footsteps of Ann Kelley

    OpenAIRE

    Richard, Jocelyn M.; Castro, Daniel C.; DiFeliceantonio, Alexandra G.; Robinson, Mike J.F.; Berridge, Kent C.

    2012-01-01

    Ann Kelley was a scientific pioneer in reward neuroscience. Her many notable discoveries included demonstrations of accumbens/striatal circuitry roles in eating behavior and in food reward, explorations of limbic interactions with hypothalamic regulatory circuits, and additional interactions of motivation circuits with learning functions. Ann Kelley's accomplishments inspired other researchers to follow in her footsteps, including our own laboratory group. Here we describe results from severa...

  11. ANN Prediction of Metabolic Syndrome: a Complex Puzzle that will be Completed.

    Science.gov (United States)

    Ivanović, Darko; Kupusinac, Aleksandar; Stokić, Edita; Doroslovački, Rade; Ivetić, Dragan

    2016-12-01

    The diagnosis of metabolic syndrome (MetS) has a leading role in the early prevention of chronic disease, such as cardiovascular disease, type 2 diabetes, cancers and chronic kidney disease. It would be very greatful that MetS diagnosis can be predicted in everyday clinical practice. This paper presents artificial neural network (ANN) prediction of the diagnosis of MetS that includes solely non-invasive, low-cost and easily-obtained diagnostic methods. This solution can extract the risky persons and suggests complete tests only on them by saving money and time. ANN input vectors are very simple and contain solely non-invasive, low-cost and easily-obtained parameters: gender, age, body mass index, waist-to-height ratio, systolic and diastolic blood pressures. ANN output is M e t S-coefficient in true/false form, obtained from MetS definition of International Diabetes Federation (IDF). ANN training, validation and testing are conducted on the large dataset that includes 2928 persons. Feed-forward ANNs with 1-100 hidden neurons were considered and an optimal architecture were determinated. Comparison with other authors leads to the conclusion that our solution achieves the highest positive predictive value P P V = 0.8579. Further, obtained negative predictive value N P V = 0.8319 is also high and close to PPV, which means that our ANN solution is suitable both for positive and negative MetS prediction.

  12. ANN-Based Prediction and Optimization of Cooling System in Hotel Rooms

    Directory of Open Access Journals (Sweden)

    Jin Woo Moon

    2015-09-01

    Full Text Available This study aimed at developing an artificial-neural-network (ANN-based model that can calculate the required time for restoring the current indoor temperature during the setback period in accommodation buildings to the normal set-point temperature in the cooling season. By applying the calculated time in the control logic, the operation of the cooling system can be predetermined to condition the indoor temperature comfortably in a more energy-efficient manner. Three major steps employing the numerical computer simulation method were conducted for developing an ANN model and testing its prediction performance. In the development process, the initial ANN model was determined to have input neurons that had a significant statistical relationship with the output neuron. In addition, the structure of the ANN model and learning methods were optimized through the parametrical analysis of the prediction performance. Finally, through the performance tests in terms of prediction accuracy, the optimized ANN model presented a lower mean biased error (MBE rate between the simulation and prediction results under generally accepted levels. Thus, the developed ANN model was proven to have the potential to be applied to thermal control logic.

  13. The Anne Frank hiding place: A phase of the homosexual development

    Directory of Open Access Journals (Sweden)

    Federico Crisalidi

    2013-12-01

    Full Text Available The article describes the peculiar elements of the psychosexual development of homosexual: the few existing studies on the development of the homosexual identity have no developed what the authors consider the most distinctive element in gay adolescence, that is the “hide”.The study put in evidence the parallelism between the homosexual teenager and Anne Frank; in fact the gay teenager is forcing himself to hide in a “secret flat” that could be comparable to the one of Anne Frank during the holocaust. In the article a lot of parallelism between the psychological experience of Anne Frank and the homosexuals are shown. This takes care of the differences between the two experiences, in fact Anne Frank has been forced to hide while for homosexuals is a psychological experience.This auto-segregation in the secret flat foresee an evolutionary phase of the psychosexual development of a gay person. In the article links and connections of psychological experiences between Anne Frank and homosexual are showed. These experiences and connections allows to develop what the authors define “the Anne Frank phase”.

  14. A Design Space Exploration Framework for ANN-Based Fault Detection in Hardware Systems

    Directory of Open Access Journals (Sweden)

    Andreas G. Savva

    2017-01-01

    Full Text Available This work presents a design exploration framework for developing a high level Artificial Neural Network (ANN for fault detection in hardware systems. ANNs can be used for fault detection purposes since they have excellent characteristics such as generalization capability, robustness, and fault tolerance. Designing an ANN in order to be used for fault detection purposes includes different parameters. Through this work, those parameters are presented and analyzed based on simulations. Moreover, after the development of the ANN, in order to evaluate it, a case study scenario based on Networks on Chip is used for detection of interrouter link faults. Simulation results with various synthetic traffic models show that the proposed work can detect up to 96–99% of interrouter link faults with a delay less than 60 cycles. Added to this, the size of the ANN is kept relatively small and they can be implemented in hardware easily. Synthesis results indicate an estimated amount of 0.0523 mW power consumption per neuron for the implemented ANN when computing a complete cycle.

  15. Optimum coagulant forecasting by modeling jar test experiments using ANNs

    Directory of Open Access Journals (Sweden)

    S. Haghiri

    2018-01-01

    Full Text Available Currently, the proper utilization of water treatment plants and optimizing their use is of particular importance. Coagulation and flocculation in water treatment are the common ways through which the use of coagulants leads to instability of particles and the formation of larger and heavier particles, resulting in improvement of sedimentation and filtration processes. Determination of the optimum dose of such a coagulant is of particular significance. A high dose, in addition to adding costs, can cause the sediment to remain in the filtrate, a dangerous condition according to the standards, while a sub-adequate dose of coagulants can result in the reducing the required quality and acceptable performance of the coagulation process. Although jar tests are used for testing coagulants, such experiments face many constraints with respect to evaluating the results produced by sudden changes in input water because of their significant costs, long time requirements, and complex relationships among the many factors (turbidity, temperature, pH, alkalinity, etc. that can influence the efficiency of coagulant and test results. Modeling can be used to overcome these limitations; in this research study, an artificial neural network (ANN multi-layer perceptron (MLP with one hidden layer has been used for modeling the jar test to determine the dosage level of used coagulant in water treatment processes. The data contained in this research have been obtained from the drinking water treatment plant located in Ardabil province in Iran. To evaluate the performance of the model, the mean squared error (MSE and correlation coefficient (R2 parameters have been used. The obtained values are within an acceptable range that demonstrates the high accuracy of the models with respect to the estimation of water-quality characteristics and the optimal dosages of coagulants; so using these models will allow operators to not only reduce costs and time taken to perform

  16. Modeling and prediction of copper removal from aqueous solutions by nZVI/rGO magnetic nanocomposites using ANN-GA and ANN-PSO.

    Science.gov (United States)

    Fan, Mingyi; Hu, Jiwei; Cao, Rensheng; Xiong, Kangning; Wei, Xionghui

    2017-12-21

    Reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) magnetic nanocomposites were prepared and then applied in the Cu(II) removal from aqueous solutions. Scanning electron microscopy, transmission electron microscopy, X-ray photoelectron spectroscopy and superconduction quantum interference device magnetometer were performed to characterize the nZVI/rGO nanocomposites. In order to reduce the number of experiments and the economic cost, response surface methodology (RSM) combined with artificial intelligence (AI) techniques, such as artificial neural network (ANN), genetic algorithm (GA) and particle swarm optimization (PSO), has been utilized as a major tool that can model and optimize the removal processes, because a tremendous advance has recently been made on AI that may result in extensive applications. Based on RSM, ANN-GA and ANN-PSO were employed to model the Cu(II) removal process and optimize the operating parameters, e.g., operating temperature, initial pH, initial concentration and contact time. The ANN-PSO model was proven to be an effective tool for modeling and optimizing the Cu(II) removal with a low absolute error and a high removal efficiency. Furthermore, the isotherm, kinetic, thermodynamic studies and the XPS analysis were performed to explore the mechanisms of Cu(II) removal process.

  17. Outcome prediction for prostate cancer detection rate with artificial neural network (ANN) in daily routine.

    Science.gov (United States)

    Ecke, Thorsten H; Bartel, Peter; Hallmann, Steffen; Koch, Stefan; Ruttloff, Jürgen; Cammann, Henning; Lein, Michael; Schrader, Mark; Miller, Kurt; Stephan, Carsten

    2012-01-01

    We evaluated the use of the artificial neural network (ANN) program "ProstataClass" of the Department of Urology and the Institute of Medical Informatics at the Charité-Universitätsmedizin Berlin in daily routine to increase prostate cancer (CaP) detection rate and to reduce unnecessary biopsies. From May 2005 to April 2007, a total of 204 patients were included in the study. The Beckman Access PSA assay was used, and pretreatment prostate specific antigen (PSA) was measured prior to digital rectal examination (DRE) and 12 core systematic transrectal ultrasound (TRUS) guided biopsies. The individual ANN predictions were generated with the use of the ANN application for the Beckman Access PSA and free PSA assays, which relies on age, PSA, percent free prostate specific antigen (%fPSA), prostate volume, and DRE. Diagnostic validity of total prostate specific antigen (tPSA), %fPSA, and the ANN was evaluated by ROC curve analysis. PSA and %fPSA ranged from 4.01 to 9.91 ng/ml (median: 6.65) and 5% to 48% (median: 15%), respectively. Of all men, 46 (22.5%) demonstrated suspicious DRE findings. Total prostate volume ranged from 7.1 to 119.2 cc (median: 35). Overall, 71 (34.8%) CaP were detected. Of men with suspicious DRE, 28 (60.9%) had CaP on initial biopsy. The ANN was 78% accurate in the original report. The AUC of ROC curve analysis was 0.51 for PSA, 0.66 for %PSA, and 0.72 for the ANN-Output, respectively. Our results in this independent cohort show that ANN is a very helpful parameter in daily routine to increase the CaP detection rate and reduce unnecessary biopsies. Copyright © 2012 Elsevier Inc. All rights reserved.

  18. New consensus multivariate models based on PLS and ANN studies of sigma-1 receptor antagonists.

    Science.gov (United States)

    Oliveira, Aline A; Lipinski, Célio F; Pereira, Estevão B; Honorio, Kathia M; Oliveira, Patrícia R; Weber, Karen C; Romero, Roseli A F; de Sousa, Alexsandro G; da Silva, Albérico B F

    2017-10-02

    The treatment of neuropathic pain is very complex and there are few drugs approved for this purpose. Among the studied compounds in the literature, sigma-1 receptor antagonists have shown to be promising. In order to develop QSAR studies applied to the compounds of 1-arylpyrazole derivatives, multivariate analyses have been performed in this work using partial least square (PLS) and artificial neural network (ANN) methods. A PLS model has been obtained and validated with 45 compounds in the training set and 13 compounds in the test set (r 2 training = 0.761, q 2 = 0.656, r 2 test = 0.746, MSE test = 0.132 and MAE test = 0.258). Additionally, multi-layer perceptron ANNs (MLP-ANNs) were employed in order to propose non-linear models trained by gradient descent with momentum backpropagation function. Based on MSE test values, the best MLP-ANN models were combined in a MLP-ANN consensus model (MLP-ANN-CM; r 2 test = 0.824, MSE test = 0.088 and MAE test = 0.197). In the end, a general consensus model (GCM) has been obtained using PLS and MLP-ANN-CM models (r 2 test = 0.811, MSE test = 0.100 and MAE test = 0.218). Besides, the selected descriptors (GGI6, Mor23m, SRW06, H7m, MLOGP, and μ) revealed important features that should be considered when one is planning new compounds of the 1-arylpyrazole class. The multivariate models proposed in this work are definitely a powerful tool for the rational drug design of new compounds for neuropathic pain treatment. Graphical abstract Main scaffold of the 1-arylpyrazole derivatives and the selected descriptors.

  19. Novel Formulation of Adaptive MPC as EKF Using ANN Model: Multiproduct Semibatch Polymerization Reactor Case Study.

    Science.gov (United States)

    Kamesh, Reddi; Rani, Kalipatnapu Yamuna

    2017-12-01

    In this paper, a novel formulation for nonlinear model predictive control (MPC) has been proposed incorporating the extended Kalman filter (EKF) control concept using a purely data-driven artificial neural network (ANN) model based on measurements for supervisory control. The proposed scheme consists of two modules focusing on online parameter estimation based on past measurements and control estimation over control horizon based on minimizing the deviation of model output predictions from set points along the prediction horizon. An industrial case study for temperature control of a multiproduct semibatch polymerization reactor posed as a challenge problem has been considered as a test bed to apply the proposed ANN-EKFMPC strategy at supervisory level as a cascade control configuration along with proportional integral controller [ANN-EKFMPC with PI (ANN-EKFMPC-PI)]. The proposed approach is formulated incorporating all aspects of MPC including move suppression factor for control effort minimization and constraint-handling capability including terminal constraints. The nominal stability analysis and offset-free tracking capabilities of the proposed controller are proved. Its performance is evaluated by comparison with a standard MPC-based cascade control approach using the same adaptive ANN model. The ANN-EKFMPC-PI control configuration has shown better controller performance in terms of temperature tracking, smoother input profiles, as well as constraint-handling ability compared with the ANN-MPC with PI approach for two products in summer and winter. The proposed scheme is found to be versatile although it is based on a purely data-driven model with online parameter estimation.

  20. Development and Application of ANN Model for Worker Assignment into Virtual Cells of Large Sized Configurations

    International Nuclear Information System (INIS)

    Murali, R. V.; Fathi, Khalid; Puri, A. B.

    2010-01-01

    This paper presents an extended version of study already undertaken on development of an artificial neural networks (ANNs) model for assigning workforce into virtual cells under virtual cellular manufacturing systems (VCMS) environments. Previously, the same authors have introduced this concept and applied it to virtual cells of two-cell configuration and the results demonstrated that ANNs could be a worth applying tool for carrying out workforce assignments. In this attempt, three-cell configurations problems are considered for worker assignment task. Virtual cells are formed under dual resource constraint (DRC) context in which the number of available workers is less than the total number of machines available. Since worker assignment tasks are quite non-linear and highly dynamic in nature under varying inputs and conditions and, in parallel, ANNs have the ability to model complex relationships between inputs and outputs and find similar patterns effectively, an attempt was earlier made to employ ANNs into the above task. In this paper, the multilayered perceptron with feed forward (MLP-FF) neural network model has been reused for worker assignment tasks of three-cell configurations under DRC context and its performance at different time periods has been analyzed. The previously proposed worker assignment model has been reconfigured and cell formation solutions available for three-cell configuration in the literature are used in combination to generate datasets for training ANNs framework. Finally, results of the study have been presented and discussed.

  1. Daily suspended sediment concentration simulation using ANN and neuro-fuzzy models.

    Science.gov (United States)

    Rajaee, Taher; Mirbagheri, Seyed Ahmad; Zounemat-Kermani, Mohammad; Nourani, Vahid

    2009-08-15

    In the present study, artificial neural networks (ANNs), neuro-fuzzy (NF), multi linear regression (MLR) and conventional sediment rating curve (SRC) models are considered for time series modeling of suspended sediment concentration (SSC) in rivers. As for the artificial intelligence systems, feed forward back propagation (FFBP) method and Sugeno inference system are used for ANNs and NF models, respectively. The models are trained using daily river discharge and SSC data belonging to Little Black River and Salt River gauging stations in the USA. Obtained results demonstrate that ANN and NF models are in good agreement with the observed SSC values; while they depict better results than MLR and SRC methods. For example, in Little Black River station, the determination coefficient is 0.697 for NF model, while it is 0.457, 0.257 and 0.225 for ANN, MLR and SRC models, respectively. The values of cumulative suspended sediment load estimated by ANN and NF models are closer to the observed data than the other models. In general, the results illustrate that NF model presents better performance in SSC prediction in compression to other models.

  2. Evaluation of wavelet performance via an ANN-based electrical conductivity prediction model.

    Science.gov (United States)

    Ravansalar, Masoud; Rajaee, Taher

    2015-06-01

    The prediction of water quality parameters plays an important role in water resources and environmental systems. The use of electrical conductivity (EC) as a water quality indicator is one of the important parameters for estimating the amount of mineralization. This study describes the application of artificial neural network (ANN) and wavelet-neural network hybrid (WANN) models to predict the monthly EC of the Asi River at the Demirköprü gauging station, Turkey. In the proposed hybrid WANN model, the discrete wavelet transform (DWT) was linked to the ANN model for EC prediction using a feed-forward back propagation (FFBP) training algorithm. For this purpose, the original time series of monthly EC and discharge (Q) values were decomposed to several sub-time series by DWT, and these sub-time series were then presented to the ANN model as an input dataset to predict the monthly EC. Comparing the values predicted by the models indicated that the performance of the proposed WANN model was better than the conventional ANN model. The correlation of determination (R (2)) were 0.949 and 0.381 for the WANN and ANN models, respectively. The results indicate that the peak EC values predicted by the WANN model are closer to the observed values, and this model simulates the hysteresis phenomena at an acceptable level as well.

  3. RegnANN: Reverse Engineering Gene Networks using Artificial Neural Networks.

    Directory of Open Access Journals (Sweden)

    Marco Grimaldi

    Full Text Available RegnANN is a novel method for reverse engineering gene networks based on an ensemble of multilayer perceptrons. The algorithm builds a regressor for each gene in the network, estimating its neighborhood independently. The overall network is obtained by joining all the neighborhoods. RegnANN makes no assumptions about the nature of the relationships between the variables, potentially capturing high-order and non linear dependencies between expression patterns. The evaluation focuses on synthetic data mimicking plausible submodules of larger networks and on biological data consisting of submodules of Escherichia coli. We consider Barabasi and Erdös-Rényi topologies together with two methods for data generation. We verify the effect of factors such as network size and amount of data to the accuracy of the inference algorithm. The accuracy scores obtained with RegnANN is methodically compared with the performance of three reference algorithms: ARACNE, CLR and KELLER. Our evaluation indicates that RegnANN compares favorably with the inference methods tested. The robustness of RegnANN, its ability to discover second order correlations and the agreement between results obtained with this new methods on both synthetic and biological data are promising and they stimulate its application to a wider range of problems.

  4. Using ANN and EPR models to predict carbon monoxide concentrations in urban area of Tabriz

    Directory of Open Access Journals (Sweden)

    Mohammad Shakerkhatibi

    2015-09-01

    Full Text Available Background: Forecasting of air pollutants has become a popular topic of environmental research today. For this purpose, the artificial neural network (AAN technique is widely used as a reliable method for forecasting air pollutants in urban areas. On the other hand, the evolutionary polynomial regression (EPR model has recently been used as a forecasting tool in some environmental issues. In this research, we compared the ability of these models to forecast carbon monoxide (CO concentrations in the urban area of Tabriz city. Methods: The dataset of CO concentrations measured at the fixed stations operated by the East Azerbaijan Environmental Office along with meteorological data obtained from the East Azerbaijan Meteorological Bureau from March 2007 to March 2013, were used as input for the ANN and EPR models. Results: Based on the results, the performance of ANN is more reliable in comparison with EPR. Using the ANN model, the correlation coefficient values at all monitoring stations were calculated above 0.85. Conversely, the R2 values for these stations were obtained <0.41 using the EPR model. Conclusion: The EPR model could not overcome the nonlinearities of input data. However, the ANN model displayed more accurate results compared to the EPR. Hence, the ANN models are robust tools for predicting air pollutant concentrations.

  5. Identification of drought in Dhalai river watershed using MCDM and ANN models

    Science.gov (United States)

    Aher, Sainath; Shinde, Sambhaji; Guha, Shantamoy; Majumder, Mrinmoy

    2017-03-01

    An innovative approach for drought identification is developed using Multi-Criteria Decision Making (MCDM) and Artificial Neural Network (ANN) models from surveyed drought parameter data around the Dhalai river watershed in Tripura hinterlands, India. Total eight drought parameters, i.e., precipitation, soil moisture, evapotranspiration, vegetation canopy, cropping pattern, temperature, cultivated land, and groundwater level were obtained from expert, literature and cultivator survey. Then, the Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP) were used for weighting of parameters and Drought Index Identification (DII). Field data of weighted parameters in the meso scale Dhalai River watershed were collected and used to train the ANN model. The developed ANN model was used in the same watershed for identification of drought. Results indicate that the Limited-Memory Quasi-Newton algorithm was better than the commonly used training method. Results obtained from the ANN model shows the drought index developed from the study area ranges from 0.32 to 0.72. Overall analysis revealed that, with appropriate training, the ANN model can be used in the areas where the model is calibrated, or other areas where the range of input parameters is similar to the calibrated region for drought identification.

  6. The Application of ANN for Downscaling GCMs Outputs for Prediction of Precipitation in Across Southern Iran

    Directory of Open Access Journals (Sweden)

    N. Ahmadi Baseri

    2015-06-01

    Full Text Available In this study, the artificial neural networks (ANNs and regression models were used to downscale the simulated outputs of the general circulation models (GCMs. The simulated precipitation for 25.18 º N to 34.51 º N and 45 º E to 60 º E, geopotential height at 850 mb and zonal wind at 200 mb for 12.56° N to 43.25° N and 19.68° E to 61.87° E data sets as the predictors were extracted from ECHAM5 GCM for the period 1960-2005. The observed monthly precipitation data of Abadan, Abadeh, Ahwaz, Bandar Abbas, Bushehr, Shiraz and Fasa stations as the predictand were extracted for the period 1960-2005. The principal components (PCs of the simulated data sets were extracted and then six PCs were considered as the input file of the ANN and multiple regression models. Also the combinations of the simulated data sets were used as the input file of these models. The periods 1960-2000 and 2001-2005 were considered as the train and test data in the ANN, respectively. The Pearson correlation coefficient and normalized root mean square error results indicated that ANN predicts precipitation more accurate than multiple regression. For the monthly time scale, the geopotential height is the best predictor and for the seasonal time scale (winter the simulated precipitation is the best predictor in ANN based standardized precipitation principal components.

  7. Mapping brain circuits of reward and motivation: In the footsteps of Ann Kelley

    Science.gov (United States)

    Richard, Jocelyn M.; Castro, Daniel C.; DiFeliceantonio, Alexandra G.; Robinson, Mike J.F.; Berridge, Kent C.

    2013-01-01

    Ann Kelley was a scientific pioneer in reward neuroscience. Her many notable discoveries included demonstrations of accumbens/striatal circuitry roles in eating behavior and in food reward, explorations of limbic interactions with hypothalamic regulatory circuits, and additional interactions of motivation circuits with learning functions. Ann Kelley's accomplishments inspired other researchers to follow in her footsteps, including our own laboratory group. Here we describe results from several lines of our research that sprang in part from earlier findings by Kelley and colleagues. We describe hedonic hotspots for generating intense pleasure `liking', separate identities of `wanting' versus `liking' systems, a novel role for dorsal neostriatum in generating motivation to eat, a limbic keyboard mechanism in nucleus accumbens for generating intense desire versus intense dread, and dynamic limbic transformations of learned memories into motivation. We describe how origins for each of these themes can be traced to fundamental contributions by Ann Kelley. PMID:23261404

  8. Artificial neural network (ANN) approach for modeling Zn(II) adsorption in batch process

    Energy Technology Data Exchange (ETDEWEB)

    Yildiz, Sayiter [Engineering Faculty, Cumhuriyet University, Sivas (Turkmenistan)

    2017-09-15

    Artificial neural networks (ANN) were applied to predict adsorption efficiency of peanut shells for the removal of Zn(II) ions from aqueous solutions. Effects of initial pH, Zn(II) concentrations, temperature, contact duration and adsorbent dosage were determined in batch experiments. The sorption capacities of the sorbents were predicted with the aid of equilibrium and kinetic models. The Zn(II) ions adsorption onto peanut shell was better defined by the pseudo-second-order kinetic model, for both initial pH, and temperature. The highest R{sup 2} value in isotherm studies was obtained from Freundlich isotherm for the inlet concentration and from Temkin isotherm for the sorbent amount. The high R{sup 2} values prove that modeling the adsorption process with ANN is a satisfactory approach. The experimental results and the predicted results by the model with the ANN were found to be highly compatible with each other.

  9. A page is turned with the departure of Anne-Sylvie Catherin

    CERN Multimedia

    Staff Association

    2016-01-01

    The Staff Association wants to thank Anne Sylvie Catherin for her achievements during her career at CERN, and in particular during her mandate of head of the Human Resources Department. Anne-Sylvie Catherin arrived at CERN as a lawyer specialized in labor law of International Organizations, and she brought along her knowledge, as well as an unparalleled energy and professionalism. The Staff Association has particularly appreciated her collaborative approach during discussions in the concertation process. This attitude has clearly contributed to the implementation of significant change in the management of human resources, while preserving social peace. We expect that the person who will succeed Anne-Sylvie Catherin will have the same constructive attitude, in respect of the concertation process, as well as a continuity in the definition and implementation of HR processes. Finally, we hope that the new head of HR will be able to develop a long-term vision for the Organization and its staff, measuring to the vi...

  10. Mapping brain circuits of reward and motivation: in the footsteps of Ann Kelley.

    Science.gov (United States)

    Richard, Jocelyn M; Castro, Daniel C; Difeliceantonio, Alexandra G; Robinson, Mike J F; Berridge, Kent C

    2013-11-01

    Ann Kelley was a scientific pioneer in reward neuroscience. Her many notable discoveries included demonstrations of accumbens/striatal circuitry roles in eating behavior and in food reward, explorations of limbic interactions with hypothalamic regulatory circuits, and additional interactions of motivation circuits with learning functions. Ann Kelley's accomplishments inspired other researchers to follow in her footsteps, including our own laboratory group. Here we describe results from several lines of our research that sprang in part from earlier findings by Kelley and colleagues. We describe hedonic hotspots for generating intense pleasure 'liking', separate identities of 'wanting' versus 'liking' systems, a novel role for dorsal neostriatum in generating motivation to eat, a limbic keyboard mechanism in nucleus accumbens for generating intense desire versus intense dread, and dynamic limbic transformations of learned memories into motivation. We describe how origins for each of these themes can be traced to fundamental contributions by Ann Kelley. Copyright © 2013 Elsevier Ltd. All rights reserved.

  11. Parameters estimation of squirrel-cage induction motors using ANN and ANFIS

    Directory of Open Access Journals (Sweden)

    Mehdi Ahmadi Jirdehi

    2016-03-01

    Full Text Available In the transient behavior analysis of a squirrel-cage induction motor, the parameters of the single-cage and double-cage models are studied. These parameters are usually hard to obtain. This paper presents two new methods to predict the induction motor parameters in the single-cage and double-cage models based on artificial neural network (ANN and adaptive neuro-fuzzy inference system (ANFIS. For this purpose, the experimental data (manufacturer data of 20 induction motors with the different power are used. The experimental data are including of the starting torque and current, maximum torque, full load sleep, efficiency, rated active power and reactive power. The obtained results from the proposed ANN and ANFIS models are compared with each other and with the experimental data, which show a good agreement between the predicted values and the experimental data. But the proposed ANFIS model is more accurate than the proposed ANN model.

  12. Modelling and automatic reactive power control of isolated wind-diesel hybrid power systems using ANN

    International Nuclear Information System (INIS)

    Bansal, R.C.

    2008-01-01

    This paper presents an artificial neural network (ANN) based approach to tune the parameters of the static var compensator (SVC) reactive power controller over a wide range of typical load model parameters. The gains of PI (proportional integral) based SVC are optimised for typical values of the load voltage characteristics (n q ) by conventional techniques. Using the generated data, the method of multi-layer feed forward ANN with error back propagation training is employed to tune the parameters of the SVC. An ANN tuned SVC controller has been applied to control the reactive power of a variable slip/speed isolated wind-diesel hybrid power system. It is observed that the maximum deviations of all parameters are more for larger values of n q . It has been shown that initially synchronous generator supplies the reactive power required by the induction generator and/or load, and the latter reactive power is purely supplied by the SVC

  13. Estimation of Anti-HIV Activity of HEPT Analogues Using MLR, ANN, and SVM Techniques.

    Science.gov (United States)

    Shaik, Basheerulla; Zafar, Tabassum; Agrawal, Vijay K

    2013-01-01

    The present study deals with the estimation of the anti-HIV activity (log1/C) of a large set of 107 HEPT analogues using molecular descriptors which are responsible for the anti-HIV activity. The study has been undertaken by three techniques MLR, ANN, and SVM. The MLR model fits the train set with R (2)=0.856 while in ANN and SVM with higher values of R (2) = 0.850, 0.874, respectively. SVM model shows improvement to estimate the anti-HIV activity of trained data, while in test set ANN have higher R (2) value than those of MLR and SVM techniques. R m (2) = metrics and ridge regression analysis indicated that the proposed four-variable model MATS5e, RDF080u, T(O⋯O), and MATS5m as correlating descriptors is the best for estimating the anti-HIV activity (log 1/C) present set of compounds.

  14. Prediction of scour below submerged pipeline crossing a river using ANN.

    Science.gov (United States)

    Azamathulla, H M; Zakaria, Nor Azazi

    2011-01-01

    The process involved in the local scour below pipelines is so complex that it makes it difficult to establish a general empirical model to provide accurate estimation for scour. This paper describes the use of artificial neural networks (ANN) to estimate the pipeline scour depth. The data sets of laboratory measurements were collected from published works and used to train the network or evolve the program. The developed networks were validated by using the observations that were not involved in training. The performance of ANN was found to be more effective when compared with the results of regression equations in predicting the scour depth around pipelines.

  15. Effectiveness of ANN for seismic behaviour prediction considering geometric configuration effect in concrete gravity dams

    Directory of Open Access Journals (Sweden)

    Mohd. Saqib

    2016-09-01

    Full Text Available In this study, an Artificial Neural Networks (ANN model is built and verified for quick estimation of the structural parameter obtained for a concrete gravity dam section due to seismic excitation. The database of numerous inputs and outputs obtained through Abaqus which are further converted into dimensionless forms in the statistical software (MATLAB to build the ANN model. The developed model can be used for accurate estimation of this parameter. The results showed an excellent capability of the model to predict the outputs with high accuracy and reduced computational time.

  16. Process Control Strategies for Dual-Phase Steel Manufacturing Using ANN and ANFIS

    Science.gov (United States)

    Vafaeenezhad, H.; Ghanei, S.; Seyedein, S. H.; Beygi, H.; Mazinani, M.

    2014-11-01

    In this research, a comprehensive soft computational approach is presented for the analysis of the influencing parameters on manufacturing of dual-phase steels. A set of experimental data have been gathered to obtain the initial database used for the training and testing of both artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS). The parameters used in the strategy were intercritical annealing temperature, carbon content, and holding time which gives off martensite percentage as an output. A fraction of the data set was chosen to train both ANN and ANFIS, and the rest was put into practice to authenticate the act of the trained networks while seeing unseen data. To compare the obtained results, coefficient of determination and root mean squared error indexes were chosen. Using artificial intelligence methods, it is not necessary to consider and establish a preliminary mathematical model and formulate its affecting parameters on its definition. In conclusion, the martensite percentages corresponding to the manufacturing parameters can be determined prior to a production using these controlling algorithms. Although the results acquired from both ANN and ANFIS are very encouraging, the proposed ANFIS has enhanced performance over the ANN and takes better effect on cost-reduction profit.

  17. Joseph Campbell, Jung, Anne Tyler, and "The Cards": The Spiritual Journey in "Searching for Caleb."

    Science.gov (United States)

    Thomson, Karen M.

    Joseph Campbell, Carl Jung, and Anne Tyler have all dealt with spiritual journeys and card reading in their writings. In his book "Tarot Revelations," Joseph Campbell discusses his first association with tarot cards, dating from 1943, when he was introduced to the symoblism of playing cards by his friend and mentor, Heinrich Zimmer. Carl…

  18. Artificial neural network (ANN)-based prediction of depth filter loading capacity for filter sizing.

    Science.gov (United States)

    Agarwal, Harshit; Rathore, Anurag S; Hadpe, Sandeep Ramesh; Alva, Solomon J

    2016-11-01

    This article presents an application of artificial neural network (ANN) modelling towards prediction of depth filter loading capacity for clarification of a monoclonal antibody (mAb) product during commercial manufacturing. The effect of operating parameters on filter loading capacity was evaluated based on the analysis of change in the differential pressure (DP) as a function of time. The proposed ANN model uses inlet stream properties (feed turbidity, feed cell count, feed cell viability), flux, and time to predict the corresponding DP. The ANN contained a single output layer with ten neurons in hidden layer and employed a sigmoidal activation function. This network was trained with 174 training points, 37 validation points, and 37 test points. Further, a pressure cut-off of 1.1 bar was used for sizing the filter area required under each operating condition. The modelling results showed that there was excellent agreement between the predicted and experimental data with a regression coefficient (R 2 ) of 0.98. The developed ANN model was used for performing variable depth filter sizing for different clarification lots. Monte-Carlo simulation was performed to estimate the cost savings by using different filter areas for different clarification lots rather than using the same filter area. A 10% saving in cost of goods was obtained for this operation. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1436-1443, 2016. © 2016 American Institute of Chemical Engineers.

  19. Mässuline Ann võidab iga tunniga sõpru juurde / Bianca Mikovitsh

    Index Scriptorium Estoniae

    Mikovitsh, Bianca

    2006-01-01

    Aidi Valliku-Sven Heibergi "Kuidas elad? ...Ann?!" Rakvere Teatris, lavastaja Sven Heiberg. Muljeid etenduselt, mida käis vaatamas bussitäis Paide gümnaasiumi õpilasi. Väljavõtteid ka V klassi õpilaste tagasisidest etendusele

  20. A Fault Diagnosis Model Based on LCD-SVD-ANN-MIV and VPMCD for Rotating Machinery

    Directory of Open Access Journals (Sweden)

    Songrong Luo

    2016-01-01

    Full Text Available The fault diagnosis process is essentially a class discrimination problem. However, traditional class discrimination methods such as SVM and ANN fail to capitalize the interactions among the feature variables. Variable predictive model-based class discrimination (VPMCD can adequately use the interactions. But the feature extraction and selection will greatly affect the accuracy and stability of VPMCD classifier. Aiming at the nonstationary characteristics of vibration signal from rotating machinery with local fault, singular value decomposition (SVD technique based local characteristic-scale decomposition (LCD was developed to extract the feature variables. Subsequently, combining artificial neural net (ANN and mean impact value (MIV, ANN-MIV as a kind of feature selection approach was proposed to select more suitable feature variables as input vector of VPMCD classifier. In the end of this paper, a novel fault diagnosis model based on LCD-SVD-ANN-MIV and VPMCD is proposed and proved by an experimental application for roller bearing fault diagnosis. The results show that the proposed method is effective and noise tolerant. And the comparative results demonstrate that the proposed method is superior to the other methods in diagnosis speed, diagnosis success rate, and diagnosis stability.

  1. Mapping a Self, Mapping Absence in Sally-Ann Murray's Small ...

    African Journals Online (AJOL)

    The essay analyses the figurative mapping in Sally-Ann Murray's first novel, Small Moving Parts (2009), which is a coming-of-age novel about the young Halley Murphy who grows up in the suburb of Umbilo in Durban in the 1960s. The essay begins by analysing the narrative topography of Durban in the 1960s in terms of J.

  2. 78 FR 67086 - Safety Zone, Submarine Cable Replacement Operations, Kent Island Narrows; Queen Anne's County, MD

    Science.gov (United States)

    2013-11-08

    ... 1625-AA00 Safety Zone, Submarine Cable Replacement Operations, Kent Island Narrows; Queen Anne's County... vessels on navigable waters during submarine cable replacement operations at the Kent Island Narrows (MD... involves the installation of a submarine cable within a federal navigation channel requiring divers, a...

  3. [Reinhold Reith. Torsten Meyer (Hrsg.) Luxus und Konsum : eine historische Annäherung] / Raimo Pullat

    Index Scriptorium Estoniae

    Pullat, Raimo, 1935-

    2009-01-01

    Arvustus: Reith, Reinhold. Torsten Meyer (Hrsg.) Luxus und Konsum : eine historische Annäherung. Cottbuser Studien zur Geschichte von Technik, Arbeit und Umwelt. hrsg. von Günter Bayerl. Bd. 21. 2003. Collegium Johann Beckmann'i kolmanda teaduskonverentsi ettekannete kogumikust

  4. Bethany Ann Teachman: Award for Distinguished Scientific Early Career Contributions to Psychology

    Science.gov (United States)

    American Psychologist, 2012

    2012-01-01

    Presents a short biography of one of the winners of the American Psychological Association's Award for Distinguished Scientific Early Career Contributions to Psychology. The 2012 winner is Bethany Ann Teachman for transformative, translational research integrating social cognition, life-span, and perceptual approaches to investigating clinical…

  5. Late-Night Shared-Ride Taxi Transit in Ann Arbor, MI

    Science.gov (United States)

    1984-10-01

    The Ann Arbor Transportation Authority introduced Night Ride, a late-night shared-ride taxi transit service, in mid-March 1982. The service was provided through a contract with a local taxicab company and funded through a demonstration grant from the...

  6. Optimization of culture medium and modeling of curdlan production from Paenibacillus polymyxa by RSM and ANN.

    Science.gov (United States)

    Rafigh, Sayyid Mahdi; Yazdi, Ali Vaziri; Vossoughi, Manouchehr; Safekordi, Ali Akbar; Ardjmand, Mehdi

    2014-09-01

    Paenibacillus polymyxa ATCC 21830 was used for the production of curdlan gum for first time. A Box-Behnken experimental design was applied to optimize six variables of batch fermentation culture each at three levels. Statistical analyses were employed to investigate the direct and interactive effects of variables on curdlan production. Optimum cultural conditions were temperature (50°C), pH (7), fermentation time (96 h), glucose (100 g/L), yeast extract (3 g/L) and agitation speed (150 rpm). The yield of curdlan production was 6.89 g/L at optimum condition medium. Response surface methodology (RSM) and artificial neural network (ANN) were used to model cultural conditions of curdlan production. The maximum yield of curdlan production were predicted to be 6.68 and 6.85 g/L by RSM and ANN at optimum condition. The prediction capabilities of RSM and ANN were then statistically compared. The results showed that the ANN model is much more accurate in prediction as compared to the RSM. The infrared (IR) and NMR spectra, the thermogram of DSC and pattern of X-ray diffraction for the curdlan of the present study were almost identical to those of the commercial curdlan sample. The average molecular weight of the purified curdlan was determined to be 170 kDa by gel permeation chromatography. Copyright © 2014 Elsevier B.V. All rights reserved.

  7. English for Specific Purposes: The State of the Art (An Online Interview with Ann M. Johns)

    Science.gov (United States)

    Johns, Ann M.; Salmani Nodoushan, M. A.

    2015-01-01

    This forum paper is based on a friendly and informative interview conducted with Professor Ann M. Johns. In providing answers to the interview questions, Professor Johns suggests that all good teaching is ESP, and also distinguishes between EOP and ESP in that the former entails much more "just in time" learning while the latter may be…

  8. Assessment of spatial distribution of soil heavy metals using ANN-GA, MSLR and satellite imagery.

    Science.gov (United States)

    Naderi, Arman; Delavar, Mohammad Amir; Kaboudin, Babak; Askari, Mohammad Sadegh

    2017-05-01

    This study aims to assess and compare heavy metal distribution models developed using stepwise multiple linear regression (MSLR) and neural network-genetic algorithm model (ANN-GA) based on satellite imagery. The source identification of heavy metals was also explored using local Moran index. Soil samples (n = 300) were collected based on a grid and pH, organic matter, clay, iron oxide contents cadmium (Cd), lead (Pb) and zinc (Zn) concentrations were determined for each sample. Visible/near-infrared reflectance (VNIR) within the electromagnetic ranges of satellite imagery was applied to estimate heavy metal concentrations in the soil using MSLR and ANN-GA models. The models were evaluated and ANN-GA model demonstrated higher accuracy, and the autocorrelation results showed higher significant clusters of heavy metals around the industrial zone. The higher concentration of Cd, Pb and Zn was noted under industrial lands and irrigation farming in comparison to barren and dryland farming. Accumulation of industrial wastes in roads and streams was identified as main sources of pollution, and the concentration of soil heavy metals was reduced by increasing the distance from these sources. In comparison to MLSR, ANN-GA provided a more accurate indirect assessment of heavy metal concentrations in highly polluted soils. The clustering analysis provided reliable information about the spatial distribution of soil heavy metals and their sources.

  9. "Boob teab" / Kalju Komissarov ja Rait Avestik ; kommenteerinud ja toimetanud Anneli Saro

    Index Scriptorium Estoniae

    Komissarov, Kalju, 1946-2017

    2010-01-01

    Teatriseminar sarjast "Eesti algupärandid Eesti teatris" toimus Tartu Kirjanduse Maja krüptis 28. märtsil 2005, seminari juhtis Anneli Saro. Urmas Lennuki näidendi kolmest lavastusest: Tallinna Linnateatris (esietendus 28. veebruaril 2004, lavastas Jaanus Rohumaa), Rakvere Teatris (esietendus 10. septembril 2004, lavastas Kalju Komissarov) ja Vikerraadios (esietendus 4. septembril 2004, lavastas Tamur Tohver)

  10. Eesti ja Soome Nordea devalveerimisriskis eri meelt / Hille Tressum, Anne Oja

    Index Scriptorium Estoniae

    Tressum, Hille

    2007-01-01

    Nordea Markets soovitab investoritel kaitsta end Eesti krooni devalveerimise riski vastu. Nordea Eesti kontor peab aga devalveerimisohtu ülepaisutatuks. Diagramm: Krooni ja lati laenuintressi vahe. Vt. samas: Kindlustus kaitset ei paku; Välispangad külvavad devalveerimishirmu; Agnes Ojala, Anne Oja. Eesti Pank: Citibank unustab, et elame ELis; Klientide käitumises muret ei paista; Poliitik: majandus on tsunamiohus

  11. The Nation behind the Diary: Anne Frank and the Holocaust of the Dutch Jews

    Science.gov (United States)

    Foray, Jennifer L.

    2011-01-01

    Since its first appearance in 1947, "The Diary of Anne Frank" has been translated into sixty-five different languages, including Welsh, Esperanto, and Faroese. Millions and perhaps even billions of readers, scattered throughout the globe and now spanning multiple generations, are familiar with the life and work of this young Jewish…

  12. Exploring Social Studies through Multicultural Literature: "Legend of the St Ann's Flood"

    Science.gov (United States)

    Fry, Sara Winstead

    2009-01-01

    The search for literature that is of high quality and interest, is written at age-appropriate levels for adolescent readers, addresses social studies topics, and presents multicultural perspectives can be daunting. "Legend of the St Ann's Flood" is a fiction trade book that meets all of these criteria. Its setting in Trinidad and Tobago…

  13. 75 FR 23745 - Jo-Ann Stores, Inc., Provisional Acceptance of a Settlement Agreement and Order

    Science.gov (United States)

    2010-05-04

    ... Acceptance of a Settlement Agreement and Order AGENCY: Consumer Product Safety Commission. ACTION: Notice... under the laws of the State of Ohio, with its principal offices located in Hudson, Ohio. At all times relevant hereto, Jo-Ann imported, offered for sale and sold various children's products. Staff Allegations...

  14. Uudised : Indi-sahinad. Anne Erm ja Eero Raun on heliloojad. Imelaste lemmik on heavy metal

    Index Scriptorium Estoniae

    2007-01-01

    Ameerika laulja Kimya Dawson 7. juulil Kilingi-Nõmmel minifestivalil "Schilling". Pianist Martti Raide esitab festivali Eesti muusika päevad 2007 suurkontserdil 16. aprillil Eero Rauna klaveripala "Tritonata", üllatusheliloojaks on ka "Jazzkaare" produtsent Anne Erm. Inglismaal Noorte Talentide Rahvuslikus Akadeemias tehtud uuringust

  15. 78 FR 65382 - Notice of Inventory Completion: University of Michigan, Ann Arbor, MI

    Science.gov (United States)

    2013-10-31

    ....S.C. 3003, of the completion of an inventory of human remains under the control of the University of....R50000] Notice of Inventory Completion: University of Michigan, Ann Arbor, MI AGENCY: National Park Service, Interior. ACTION: Notice. SUMMARY: The University of Michigan has completed an inventory of human...

  16. 78 FR 65369 - Notice of Inventory Completion: University of Michigan, Ann Arbor, MI

    Science.gov (United States)

    2013-10-31

    ....S.C. 3003, of the completion of an inventory of human remains under the control of the University of....R50000] Notice of Inventory Completion: University of Michigan, Ann Arbor, MI AGENCY: National Park Service, Interior. ACTION: Notice. SUMMARY: The University of Michigan has completed an inventory of human...

  17. 78 FR 65366 - Notice of Inventory Completion: University of Michigan, Ann Arbor, MI

    Science.gov (United States)

    2013-10-31

    ....S.C. 3003, of the completion of an inventory of human remains under the control of the University of....R50000] Notice of Inventory Completion: University of Michigan, Ann Arbor, MI AGENCY: National Park Service, Interior. ACTION: Notice. SUMMARY: The University of Michigan has completed an inventory of human...

  18. Anne Applebaum: Kas NATO lõpp on lähedal? / Indrek Veiserik

    Index Scriptorium Estoniae

    Veiserik, Indrek

    2009-01-01

    USA ajakirjaniku Anne Applebaumi kriitikast NATO kui organisatsiooni toimimise ja Afganistani missiooni suhtes. Kesknädala arvates võiks president Toomas Hendrik Ilves, kes on annetanud A. Applebaumile Maarjamaa Risti III klassi teenetemärgi, kuulata tema välispoliitilisi seisukohti. Viidatakse ka Kanada kindrali Rick Hillieri kriitikale NATO kohta

  19. A comparative study of ANN and neuro-fuzzy for the prediction of ...

    Indian Academy of Sciences (India)

    Istanbul Technical University, Faculty of Civil Engineering, Hydraulics and Water. Resources Division, Maslak 34469, Istanbul, Turkey. Singh et al (2005) examined the potential of the ANN and neuro-fuzzy systems application for the prediction of dynamic constant of rockmass. However, the model proposed by them has ...

  20. A call for water-efficient technologies (an interview with dr. Anne Elings)

    NARCIS (Netherlands)

    Elings, A.

    2012-01-01

    During the recent International Flower Trade Expo in Nairobi, Wageningen UR Greenhouse Horticulture project leader and greenhouse horticulture specialist Anne Elings pointed out that Kenya is not a water scarce country but management of the same is wanting. HortiNews had a chat with him.

  1. Re-vision as Remediation : Hypermediacy and Translation in Anne Carson’s Nox

    NARCIS (Netherlands)

    Brillenburg Wurth, C.A.W.

    2013-01-01

    This article explores Anne Carson’s Nox (2010) in the light of remediation. Nox is a book about death and the recording of loss: lost time, a lost brother, and lost presence. It conveys this loss through the logic of hypermediacy and a word-for-word translation of Catullus 101. Nox reworks the

  2. A comparative study of ANN and neuro-fuzzy for the prediction of ...

    Indian Academy of Sciences (India)

    Comments on 'A comparative study of ANN and neuro-fuzzy for the prediction of dynamic constant of rockmass' by T N Singh, R Kanchan, A K Verma and K Saigal. (J. Earth Syst. Sci., 114, February 2005, 75–86). Tarkan Erdik and Zekai Sen. Istanbul Technical University, Faculty of Civil Engineering, Hydraulics and Water.

  3. Symbolism--The Main Artistic Style of Katherine Anne Porter's Short Stories

    Science.gov (United States)

    Wang, Ru

    2010-01-01

    The paper takes Katherine Anne Porter's two short stories: "Flowering Judas", "The Grave" as objects of study. It will try to analyze Porter's writing style through her imaginary conception, vivid psychological description and multiple symbolisms so that we can understand her studies and her attitudes to female psychological…

  4. Practitioner Profile: An Interview with Anne Brennan Malec, Ph.D.

    Directory of Open Access Journals (Sweden)

    Anne Brennan Malec

    2015-12-01

    Full Text Available Dr. Anne Brennan Malec is the founder and managing partner of Symmetry Counseling, a counseling, coaching, and psychotherapy group practice located in downtown Chicago. She has been the driving force behind Symmetry Counseling’s success – what started in 2011 with six offices and five counselors now houses over 25 clinicians.

  5. 76 FR 80392 - Notice of Inventory Completion: University of Michigan Museum of Anthropology, Ann Arbor, MI

    Science.gov (United States)

    2011-12-23

    ...: University of Michigan Museum of Anthropology, Ann Arbor, MI AGENCY: National Park Service, Interior. ACTION... Michigan officials and its Museum of Anthropology professional staff in consultation with representatives... accessioned into the Museum of Anthropology. Between 2007 and 2009 the remains were inventoried at the...

  6. 76 FR 73670 - Notice of Inventory Completion: University of Michigan Museum of Anthropology, Ann Arbor, MI

    Science.gov (United States)

    2011-11-29

    ...: University of Michigan Museum of Anthropology, Ann Arbor, MI AGENCY: National Park Service, Interior. ACTION... Museum of Anthropology NAGPRA collections staff in consultation with representatives of the Bay Mills... Anthropology purchased the human remains from Reverend L. P. Rowland in November of 1924 as part of a larger...

  7. Beginning and ending of Anne Hebert's 'Burden of Dreams' : Polysemy of a journey of identity

    NARCIS (Netherlands)

    Lintvelt, Jaap

    2007-01-01

    The protagonist of Anne Hebert's Burden of Dreams (1992) goes on a journey from Quebec to Paris that contributes to the evolution of his personal and cultural identity The novel's title already tells us that "dreams" will play. a key role both on a thematic level and in shaping the reading protocol.

  8. PAPERS OF THE ARABIC TEACHERS' WORKSHOP (ANN ARBOR, JUNE 8-18, 1965).

    Science.gov (United States)

    Center for Applied Linguistics, Washington, DC.

    THIS REPORT IS BASED ON PAPERS GIVEN AT THE ARABIC TEACHERS' WORKSHOP HELD IN ANN ARBOR, MICHIGAN, JUNE 8-18, 1965. THE REPORT IS DIVIDED INTO THREE PARTS--(1) METHODS OF TEACHING MODERN STANDARD ARABIC, (2) CONTENT OF ELEMENTARY ARABIC INSTRUCTION, (3) SELECTIVE LIST OF INSTRUCTIONAL MATERIALS FOR MODERN STANDARD ARABIC. THE SPECIAL PROBLEMS OF…

  9. Prediction of IRI in short and long terms for flexible pavements: ANN and GMDH methods

    NARCIS (Netherlands)

    Ziari, H.; Sobhani, J.; Ayoubinejad, J.; Hartmann, Timo

    2015-01-01

    Prediction of pavement condition is one of the most important issues in pavement management systems. In this paper, capabilities of artificial neural networks (ANNs) and group method of data handling (GMDH) methods in predicting flexible pavement conditions were analysed in three levels: in 1 year,

  10. Comparative Analysis of ANN and SVM Models Combined with Wavelet Preprocess for Groundwater Depth Prediction

    Directory of Open Access Journals (Sweden)

    Ting Zhou

    2017-10-01

    Full Text Available Reliable prediction of groundwater depth fluctuations has been an important component in sustainable water resources management. In this study, a data-driven prediction model combining discrete wavelet transform (DWT preprocess and support vector machine (SVM was proposed for groundwater depth forecasting. Regular artificial neural networks (ANN, regular SVM, and wavelet preprocessed artificial neural networks (WANN models were also developed for comparison. These methods were applied to the monthly groundwater depth records over a period of 37 years from ten wells in the Mengcheng County, China. Relative absolute error (RAE, Pearson correlation coefficient (r, root mean square error (RMSE, and Nash-Sutcliffe efficiency (NSE were adopted for model evaluation. The results indicate that wavelet preprocess extremely improved the training and test performance of ANN and SVM models. The WSVM model provided the most precise and reliable groundwater depth prediction compared with ANN, SVM, and WSVM models. The criterion of RAE, r, RMSE, and NSE values for proposed WSVM model are 0.20, 0.97, 0.18 and 0.94, respectively. Comprehensive comparisons and discussion revealed that wavelet preprocess extremely improves the prediction precision and reliability for both SVM and ANN models. The prediction result of SVM model is superior to ANN model in generalization ability and precision. Nevertheless, the performance of WANN is superior to SVM model, which further validates the power of data preprocess in data-driven prediction models. Finally, the optimal model, WSVM, is discussed by comparing its subseries performances as well as model performance stability, revealing the efficiency and universality of WSVM model in data driven prediction field.

  11. Anmeldelse: Anne Gjelsvik & Rikke Schubart : Women of ice and fire: Gender, Game of Thrones, and multiple media engagements, New York/London: Bloomsbury, 2016

    DEFF Research Database (Denmark)

    Konzack, Lars

    2017-01-01

    Anmeldelse af bogen Women of Ice and F ire: Gender, Game of Thrones redigeret af Anne Gjelsvik & Rikke Schubart.......Anmeldelse af bogen Women of Ice and F ire: Gender, Game of Thrones redigeret af Anne Gjelsvik & Rikke Schubart....

  12. Comparison of artificial neural network (ANN) and partial least squares (PLS) regression models for predicting respiratory ventilation: an exploratory study.

    Science.gov (United States)

    Lin, Ming-I Brandon; Groves, William A; Freivalds, Andris; Lee, Eun Gyung; Harper, Martin

    2012-05-01

    The objective of this study was to assess the potential for using artificial neural networks (ANN) to predict inspired minute ventilation (V(I)) during exercise activities. Six physiological/kinematic measurements obtained from a portable ambulatory monitoring system, along with individual's anthropometric and demographic characteristics, were employed as input variables to develop and optimize the ANN configuration with respect to reference values simultaneously measured using a pneumotachograph (PT). The generalization ability of the resulting two-hidden-layer ANN model was compared with a linear predictive model developed through partial least squares (PLS) regression, as well as other V(I) predictive models proposed in the literature. Using an independent dataset recorded from nine 80-min step tests, the results showed that the ANN-estimated V(I) was highly correlated (R(2) = 0.88) with V(I) measured by the PT, with a mean difference of approximately 0.9%. In contrast, the PLS and other regression-based models resulted in larger average errors ranging from 7 to 34%. In addition, the ANN model yielded estimates of cumulative total volume that were on average within 1% of reference PT measurements. Compared with established statistical methods, the proposed ANN model demonstrates the potential to provide improved prediction of respiratory ventilation in workplace applications for which the use of traditional laboratory-based instruments is not feasible. Further research should be conducted to investigate the performance of ANNs for different types of physical activity in larger and more varied worker populations.

  13. Influence of Fiber Properties on Shear Failure of Steel Fiber Reinforced Beams Without Web Reinforcement: ANN Modeling

    Directory of Open Access Journals (Sweden)

    Yassir M. Abbas

    Full Text Available Abstract In this paper, an artificial neural network (ANN-10 model was developed to predict the ultimate shear strength of steel fiber reinforced concrete (SFRC beams without web reinforcement. ANN-10 is a four-layered feed forward network with a back propagation training algorithm. The experimental data of 70 SFRC beams reported in the technical literature were utilized to train and test the validity of ANN-10. The input layer receives 10 input signals for the fiber properties (type, aspect ratio, length and volume content, section properties (width, overall depth and effective depth and beam properties (longitudinal reinforcement ratio, compressive strength of concrete and shear span to effective depth ratio. ANN-10 has exhibited excellent predictive performance for both training and testing data sets, with an average of 1.002 for the average of predicted to experimental values. This performance of ANN-10 established the promising potential of Artificial Neural Networks (ANNs to simulate the complex shear behavior of SFRC beams. ANN-10 was applied to investigate the influence of the fiber volume content, type, aspect ratio and length on the ultimate shear strength of SFRC.

  14. ANN based controller for three phase four leg shunt active filter for power quality improvement

    Directory of Open Access Journals (Sweden)

    J. Jayachandran

    2016-03-01

    Full Text Available In this paper, an artificial neural network (ANN based one cycle control (OCC strategy is proposed for the DSTATCOM shunted across the load in three phase four wire distribution system. The proposed control strategy mitigates harmonic/reactive currents, ensures balanced and sinusoidal source current from the supply mains that are nearly in phase with the supply voltage and compensates neutral current under varying source and load conditions. The proposed control strategy is superior over conventional methods as it eliminates, the sensors needed for sensing load current and coupling inductor current, in addition to the multipliers and the calculation of reference currents. ANN controllers are implemented to maintain voltage across the capacitor and as a compensator to compensate neutral current. The DSTATCOM performance is validated for all possible conditions of source and load by simulation using MATLAB software and simulation results prove the efficacy of the proposed control over conventional control strategy.

  15. Development of an ANN optimized mucoadhesive buccal tablet containing flurbiprofen and lidocaine for dental pain

    Directory of Open Access Journals (Sweden)

    Hussain Amjad

    2016-06-01

    Full Text Available A novel mucoadhesive buccal tablet containing flurbiprofen (FLB and lidocaine HCl (LID was prepared to relieve dental pain. Tablet formulations (F1-F9 were prepared using variable quantities of mucoadhesive agents, hydroxypropyl methyl cellulose (HPMC and sodium alginate (SA. The formulations were evaluated for their physicochemical properties, mucoadhesive strength and mucoadhesion time, swellability index and in vitro release of active agents. Release of both drugs depended on the relative ratio of HPMC:SA. However, mucoadhesive strength and mucoadhesion time were better in formulations, containing higher proportions of HPMC compared to SA. An artificial neural network (ANN approach was applied to optimise formulations based on known effective parameters (i.e., mucoadhesive strength, mucoadhesion time and drug release, which proved valuable. This study indicates that an effective buccal tablet formulation of flurbiprofen and lidocaine can be prepared via an optimized ANN approach.

  16. Employing 3D virtual reality games to develop ANN for device control: a pilot study.

    Science.gov (United States)

    Patterson, P E

    2001-01-01

    Non-immersive virtual reality (VR) game scenarios were developed to aid in the collection of EMG parameters from the biceps and triceps while subjects performed a sequenced series of tasks in the virtual environment. For each subject the best ANN configuration (combination of hidden layers and transfer functions) was chosen, with the resulting optimized algorithms used to classify the sequence of contractions and the function type of the subjects while playing new game scenarios. The wide variety of individually configured ANN developed show why it is difficult to train new users of myoelectric devices with a single algorithm. The use of VR-based games shows promise as a training technique for individuals needing to develop control for prosthetic limbs.

  17. FPGA implementation of adaptive ANN controller for speed regulation of permanent magnet stepper motor drives

    International Nuclear Information System (INIS)

    Hasanien, Hany M.

    2011-01-01

    This paper presents a novel adaptive artificial neural network (ANN) controller, which applies on permanent magnet stepper motor (PMSM) for regulating its speed. The dynamic response of the PMSM with the proposed controller is studied during the starting process under the full load torque and under load disturbance. The effectiveness of the proposed adaptive ANN controller is then compared with that of the conventional PI controller. The proposed methodology solves the problem of nonlinearities and load changes of PMSM drives. The proposed controller ensures fast and accurate dynamic response with an excellent steady state performance. Matlab/Simulink tool is used for this dynamic simulation study. The main contribution of this work is the implementation of the proposed controller on field programmable gate array (FPGA) hardware to drive the stepper motor. The driver is built on FPGA Spartan-3E Starter from Xilinx. Experimental results are presented to demonstrate the validity and effectiveness of the proposed control scheme.

  18. Les premières années de The Himalayan Journal (1929-1940

    Directory of Open Access Journals (Sweden)

    Michel Raspaud

    2001-05-01

    Full Text Available IntroductionLa création de The Himalayan Club, sur le territoire de l’Empire des Indes à la fin de l’année 1927, s’accompagne presque aussitôt de la publication de The Himalayan Journal, en février 1929, soit mois de dix-huit mois après la création officielle de l’institution. L’objet du présent texte concerne donc la vie de The Himalayan Club lors de ses premières années d’existence, depuis la date de sa création jusqu’au début de la seconde guerre mondiale. Cependant, l’intérêt se focaliser...

  19. Development of wavelet-ANN models to predict water quality parameters in Hilo Bay, Pacific Ocean.

    Science.gov (United States)

    Alizadeh, Mohamad Javad; Kavianpour, Mohamad Reza

    2015-09-15

    The main objective of this study is to apply artificial neural network (ANN) and wavelet-neural network (WNN) models for predicting a variety of ocean water quality parameters. In this regard, several water quality parameters in Hilo Bay, Pacific Ocean, are taken under consideration. Different combinations of water quality parameters are applied as input variables to predict daily values of salinity, temperature and DO as well as hourly values of DO. The results demonstrate that the WNN models are superior to the ANN models. Also, the hourly models developed for DO prediction outperform the daily models of DO. For the daily models, the most accurate model has R equal to 0.96, while for the hourly model it reaches up to 0.98. Overall, the results show the ability of the model to monitor the ocean parameters, in condition with missing data, or when regular measurement and monitoring are impossible. Copyright © 2015 Elsevier Ltd. All rights reserved.

  20. Development of an ANN optimized mucoadhesive buccal tablet containing flurbiprofen and lidocaine for dental pain.

    Science.gov (United States)

    Hussain, Amjad; Syed, Muhammad Ali; Abbas, Nasir; Hanif, Sana; Arshad, Muhammad Sohail; Bukhari, Nadeem Irfan; Hussain, Khalid; Akhlaq, Muhammad; Ahmad, Zeeshan

    2016-06-01

    A novel mucoadhesive buccal tablet containing flurbiprofen (FLB) and lidocaine HCl (LID) was prepared to relieve dental pain. Tablet formulations (F1-F9) were prepared using variable quantities of mucoadhesive agents, hydroxypropyl methyl cellulose (HPMC) and sodium alginate (SA). The formulations were evaluated for their physicochemical properties, mucoadhesive strength and mucoadhesion time, swellability index and in vitro release of active agents. Release of both drugs depended on the relative ratio of HPMC:SA. However, mucoadhesive strength and mucoadhesion time were better in formulations, containing higher proportions of HPMC compared to SA. An artificial neural network (ANN) approach was applied to optimise formulations based on known effective parameters (i.e., mucoadhesive strength, mucoadhesion time and drug release), which proved valuable. This study indicates that an effective buccal tablet formulation of flurbiprofen and lidocaine can be prepared via an optimized ANN approach.

  1. EEG-Based Computer Aided Diagnosis of Autism Spectrum Disorder Using Wavelet, Entropy, and ANN.

    Science.gov (United States)

    Djemal, Ridha; AlSharabi, Khalil; Ibrahim, Sutrisno; Alsuwailem, Abdullah

    2017-01-01

    Autism spectrum disorder (ASD) is a type of neurodevelopmental disorder with core impairments in the social relationships, communication, imagination, or flexibility of thought and restricted repertoire of activity and interest. In this work, a new computer aided diagnosis (CAD) of autism ‎based on electroencephalography (EEG) signal analysis is investigated. The proposed method is based on discrete wavelet transform (DWT), entropy (En), and artificial neural network (ANN). DWT is used to decompose EEG signals into approximation and details coefficients to obtain EEG subbands. The feature vector is constructed by computing Shannon entropy values from each EEG subband. ANN classifies the corresponding EEG signal into normal or autistic based on the extracted features. The experimental results show the effectiveness of the proposed method for assisting autism diagnosis. A receiver operating characteristic (ROC) curve metric is used to quantify the performance of the proposed method. The proposed method obtained promising results tested using real dataset provided by King Abdulaziz Hospital, Jeddah, Saudi Arabia.

  2. A Woman Voice in an Epic: Tracing Gendered Motifs in Anne Vabarna's Peko

    Directory of Open Access Journals (Sweden)

    Andreas Kalkun

    2008-12-01

    Full Text Available In the article the gendered motifs found in Anne Vabarna’s Seto epic Peko are analysed. Besides the narrative telling of the life of the male hero, the motives regarding eating, refusing to eat or offering food, and the aspect of the female body or its control deserve to be noticed. These scenes do not communicate the main plot, they are often related to minor characters of the epic and slow down the narrative, but at the same time they clearly carry artistic purpose and meaning. I consider these motifs, present in the liminal parts of the epic, to be the dominant symbols of the epic where the author’s feminine world is being exposed. Observing these motifs of Peko in the context of Seto religious worldview, the life of Anne Vabarna and the social position of Seto women, the symbols become eloquent and informative.

  3. Identification of Constitutive Parameters Using Inverse Strategy Coupled to an ANN Model

    International Nuclear Information System (INIS)

    Aguir, H.; Chamekh, A.; BelHadjSalah, H.; Hambli, R.

    2007-01-01

    This paper deals with the identification of material parameters using an inverse strategy. In the classical methods, the inverse technique is generally coupled with a finite element code which leads to a long computing time. In this work an inverse strategy coupled with an ANN procedure is proposed. This method has the advantage of being faster than the classical one. To validate this approach an experimental plane tensile and bulge tests are used in order to identify material behavior. The ANN model is trained from finite element simulations of the two tests. In order to reduce the gap between the experimental responses and the numerical ones, the proposed method is coupled with an optimization procedure to identify material parameters for the AISI304. The identified material parameters are the hardening curve and the anisotropic coefficients

  4. Steni muinasjutuvõistluse võitjad selgunud / Ants Roos, Ann Roos

    Index Scriptorium Estoniae

    Roos, Ants

    2008-01-01

    Steni XVI muinasjutuvõistluse žüriisse kuulusid: Ann Roos, Ants Roos, Leelo Tungal, Krista Kumberg, Leida Olszak, Ülle Väljataga. Tulemused: I koht Siim Niinelaid, II koht Julius Air Kull, III koht Mihkel Rammu. Žürii eriauhinnad: Anna Kristin Peterson, Elis Ruus, Rain Hallikas, Mariliis Peterson, Marjaliisa Palu, Karl Kirsimäe, Margaret Pulk. Ergutusauhinnad: Karmel Klaus, Martti Kaljuste, Kristina Korell, Mirjam Võsaste, Mihkel Põder, Iirys Kalde, Miriam Jamul, Mari-Ann Mägi, Ketlin Saar, Liisbeth Kirss. Muud eriauhinnad said: Allan Läll, Berle Mees, Anett Kuuse, Karl Erik Kübarsepp, Grete Tamm, Siim Niinelaid, Kaisa Marie Sipelgas, Ellen Anett Põldmaa, Evelin Laul, Karl Laas, Karl Stamm, Kerli Retter

  5. An artificial neural network (ANN)-based lung-tumor motion predictor for intrafractional MR tumor tracking.

    Science.gov (United States)

    Yun, Jihyun; Mackenzie, Marc; Rathee, Satyapal; Robinson, Don; Fallone, B G

    2012-07-01

    To address practical issues of implementing artificial neural networks (ANN) for lung-tumor motion prediction in MRI-based intrafractional lung-tumor tracking. A feedforward four-layered ANN structure is used to predict future tumor positions. A back-propagation algorithm is used for ANN learning. Adaptive learning is incorporated by continuously updating weights and learning rate during prediction. An ANN training scheme specific for MRI-based tracking is developed. A multiple-ANN structure is developed to reduce tracking failures caused by the lower imaging rates of MRI. We used particle swarm optimization to optimize the ANN structure and initial weights (IW) for each patient and treatment fraction. Prediction accuracy is evaluated using the 1D superior-inferior lung-tumor motions of 29 lung cancer patients for system delays of 120-520 ms, in increments of 80 ms. The result is compared with four different scenarios: (1), (2) ANN structure optimization + with∕without IW optimization, and (3), (4) no ANN structure optimization + with∕without IW optimization, respectively. An additional simulation is performed to assess the value of optimizing the ANN structure for each treatment fraction. For 120-520 ms system delays, mean RMSE values (ranges 0.0-2.8 mm from 29 patients) of 0.5-0.9 mm are observed, respectively. Using patient specific ANN structures, a 30%-60% decrease in mean RMSE values is observed as a result of IW optimization, alone. No significant advantages in prediction performance are observed, however, by optimizing for each fraction. A new ANN-based lung-tumor motion predictor is developed for MRI-based intrafractional tumor tracking. The prediction accuracy of our predictor is evaluated using a realistic simulated MR imaging rate and system delays. For 120-520 ms system delays, mean RMSE values of 0.5-0.9 mm (ranges 0.0-2.8 mm from 29 patients) are achieved. Further, the advantage of patient specific ANN structure and IW in lung-tumor motion

  6. L'européanisation de la politique environnementale dans les années 1970

    DEFF Research Database (Denmark)

    Meyer, Jan-Henrik

    2012-01-01

    the European impact on member state legislation. Elaboré à l'échelle internationale, le concept politique d'environnement offre l'exemple rare du transfert d'un domaine d'action politique vers les Communautés européennes depuis d'autres internnationales. Cet article explore le processus complexe d......' de la politique environnementale, comme un transfert à directions multiples d'un concept forgé dans les années 1970....

  7. Le 'problème anglophone' au Cameroun dans les années 1990

    NARCIS (Netherlands)

    Konings, P.J.J.

    1996-01-01

    À la suite de la libéralisation politique au Cameroun au début des années 1990, une partie de l'élite anglophone a commencé à s'organiser en de nombreuses associations et groupes de pression pour protester contre la prétendue position subordonnée de la minorité anglophone dans un État unitaire

  8. Assessing the Long Term Impact of Phosphorus Fertilization on Phosphorus Loadings Using AnnAGNPS

    OpenAIRE

    Yuan, Yongping; Bingner, Ronald L.; Locke, Martin A.; Stafford, Jim; Theurer, Fred D.

    2011-01-01

    High phosphorus (P) loss from agricultural fields has been an environmental concern because of potential water quality problems in streams and lakes. To better understand the process of P loss and evaluate the effects of different phosphorus fertilization rates on phosphorus losses, the USDA Annualized AGricultural Non-Point Source (AnnAGNPS) pollutant loading model was applied to the Ohio Upper Auglaize watershed, located in the southern portion of the Maumee River Basin. In this study, the ...

  9. [The embroidery work of the lady at Saint-Anne Hospital].

    Science.gov (United States)

    Thillaud, Pierre L; Postel, Jacques

    2014-01-01

    In July 1974, a 72 old woman had been a patient for forty years in Sainte-Anne Hospital, Ward C. As she had again a violent brawl with her neighbour patient, she revealed being a tremendous artist. She had been confined on account of dementia paralytica in the Mecca of malariotherapy, and passionately devoted herself to embroidery. Her fancy work was rather a matter for Jean Dubuffet's art through its perfect expression and deserved being known.

  10. Implementation of ANN on CCHP system to predict trigeneration performance with consideration of various operative factors

    International Nuclear Information System (INIS)

    Anvari, Simin; Taghavifar, Hadi; Saray, Rahim Khoshbakhti; Khalilarya, Shahram; Jafarmadar, Samad

    2015-01-01

    Highlights: • ANN modeling tool was implemented on the CCHP system. • The best ANN topology was detected 10–8–9 with Levenberg–Marquadt algorithm. • The system is more sensitive of CC outlet temperature and turbine isentropic efficiency. • The lowest RMSE = 3.13e−5 and the best R 2 = 0.999 is related to lambda and second law efficiency terms, respectively. - Abstract: A detailed investigation was aimed based on numerical thermodynamic survey and artificial neural network (ANN) modeling of the trigeneration system. The results are presented in two pivotal frameworks namely the sensitivity analysis and ANN prediction capability of proposed modeling. The underlying operative parameters were chosen as input parameters from different cycles and components, while the exergy efficiency, exergy loss, coefficient of performance, heating load exergy, lambda, gas turbine power, exergy destruction, actual outlet air compressor temperature, and heat recovery gas steam generator (HRSG) outlet temperature were taken as objective output parameters for the modeling purpose. Up to now, no significant step was taken to investigate the compound power plant with thermodynamic analyses and network predictability hybrid in such a detailed oriented approach. It follows that multilayer perceptron neural network with back propagation algorithm deployed with 10–8–9 configuration results in the modeling reliability ranged within R 2 = 0.995–0.999. When dataset treated with trainlm learning algorithm and diversified neurons, the mean square error (MSE) is obtained equal to 0.2175. This denotes a powerful modeling achievement in both scientific and industrial scale to save considerable computational cost on combined cooling, heating, and power system in accomplishment of boosting the energy efficiency and system maintenance

  11. Rapports 2016-2017 sur les frais de voyage pour Margaret Ann ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Beata Bialic

    Date(s). 2016-09-09 à 2016-09-29. Destination(s). Ottawa. Billet d'avion. 0.00 $. Frais de transport au sol ou autrement. 66.00 $. Frais de logement. 0.00 $. Repas et frais divers. 0.00 $. Autre frais. 0.00 $. Total. 66.00 $. Commentaires. Rapports 2016-2017 sur les frais de voyage pour. Margaret Ann Biggs, Présidente du ...

  12. Rapports 2016-2017 sur les frais de voyage pour Margaret Ann ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Beata Bialic

    Date(s). 2016-07-14 à 2016-07-21. Destination(s). Ottawa. Billet d'avion. 0.00 $. Frais de transport au sol ou autrement. 36.00 $. Frais de logement. 0.00 $. Repas et frais divers. 0.00 $. Autre frais. 0.00 $. Total. 36.00 $. Commentaires. Rapports 2016-2017 sur les frais de voyage pour. Margaret Ann Biggs, Présidente du ...

  13. Rapports 2016-2017 sur les frais de voyage pour Margaret Ann ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Beata Bialic

    Date(s). 2016-06-20 à 2016-06-22. Destination(s). Ottawa. Billet d'avion. 0.00 $. Frais de transport au sol ou autrement. 28.00 $. Frais de logement. 0.00 $. Repas et frais divers. 50.55 $. Autre frais. 0.00 $. Total. 78.55 $. Commentaires. Rapports 2016-2017 sur les frais de voyage pour. Margaret Ann Biggs, Présidente du ...

  14. Rapports 2017-2018 sur les frais de voyage pour Mary Anne ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Chantal Taylor

    Billet d'avion. 368.41 $. Frais de transport au sol ou autrement. 69.95 $. Frais de logement. 542.79 $. Repas et frais divers. 164.42 $. Autre frais. 0.00 $. Total. 1 145.57 $. Commentaires. À partir de sa résidence à Thornhill, Ontario. Rapports 2017-2018 sur les frais de voyage pour. Mary Anne Chambers, gouverneur, ...

  15. Rapports 2016-2017 sur les frais de voyage pour Margaret Ann ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Beata Bialic

    Date(s). 2016-07-04 à 2016-07-06. Destination(s). Ottawa. Billet d'avion. 0.00 $. Frais de transport au sol ou autrement. 39.00 $. Frais de logement. 0.00 $. Repas et frais divers. 25.43 $. Autre frais. 0.00 $. Total. 64.43 $. Commentaires. Rapports 2016-2017 sur les frais de voyage pour. Margaret Ann Biggs, Présidente du ...

  16. Speaker-Adaptation for Hybrid HMM-ANN Continuous Speech Recognition System

    OpenAIRE

    Neto, Joao; Almeida, Luis; Hochberg, Mike; Martins, Ciro; Nunes, Luis; Renals, Steve; Robinson, Tony

    1995-01-01

    It is well known that recognition performance degrades significantly when moving from a speaker-dependent to a speaker-independent system. Traditional hidden Markov model (HMM) systems have successfully applied speaker-adaptation approaches to reduce this degradation. In this paper we present and evaluate some techniques for speaker-adaptation of a hybrid HMM-artificial neural network (ANN) continuous speech recognition system. These techniques are applied to a well trained, speaker-independe...

  17. Rapport de frais de 2017-2018 pour Mary Anne Chambers | CRDI ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Rapport de frais de 2017-2018 pour Mary Anne Chambers. Total des frais de déplacement : CAD$17,362.55. Réunion du Conseil des gouverneurs. 19 novembre 2017 au 24 novembre 2017. CAD$2,185.41. Réunions au CRDI 1 novembre 2017 CAD$714.15. Constatation de l'impact de la recherche en Afrique de l'Est.

  18. Ann O'Mara, PhD, RN, MPH | Division of Cancer Prevention

    Science.gov (United States)

    Dr. Ann O'Mara is Head of Palliative Research in the NCI Division of Cancer Prevention. She manages a portfolio of symptom management and palliative and end-of-life care research projects. The majority of these projects focus on the more common morbidities associated with cancer and its treatment, e.g., pain, chemotherapy induced neuropathy, fatigue, sleep disturbances, and psychosocial issues, such as distress, anxiety and depression. |

  19. Ocular adnexal lymphoma staging and treatment: American Joint Committee on Cancer versus Ann Arbor.

    Science.gov (United States)

    Graue, Gerardo F; Finger, Paul T; Maher, Elizabeth; Della Rocca, David; Della Rocca, Robert; Lelli, Gary J; Milman, Tatyana

    2013-01-01

    To evaluate the prognostic utility of the American Joint Committee on Cancer (AJCC) staging system for ocular adnexal lymphoma (OAL).
 A multicenter, consecutive case series of patients with biopsy-proven conjunctival, orbit, eyelid, or lacrimal gland/sac lymphoma was performed. The electronic pathology and clinical records were reviewed for new or recurrent cases of ocular adnexal lymphoma. The main outcome measures included pathology and clinical staging (AJCC and Ann Arbor systems), treatment, and recurrence (local and systemic). Statistical analysis included demographic evaluations and the Kaplan-Meier survival probability method.
 Extranodal marginal zone B-cell lymphoma of mucosa-associated lymphoid tissue were the most common (n=60/83, 72%). The most common Ann Arbor clinical stages were IE (76%) followed by IIE (17%) and IIIE (7%). Pathology identified 13 cases (15%) that were upstaged to group IV (p=0.017). Similarly, AJCC clinical stages were cT1NOMO (21.7%), cT2NOMO (44.6%), cT3N0M0 (5%), and cT4NOMO (2.4%). Local control was achieved in 75% of treated patients. There were 19 local recurrences from which 14 (74%) belonged to the non-radiation treatment groups. Lower-risk groups (T1 and T2 without lymph node involvement or metastatic disease of AJCC and IE of Ann Arbor) had longer disease-free survival than the higher-risk groups (AJCC T1, T2 with nodal involvement or metastatic disease, T3, and T4 as well as Ann Arbor II, III, and IV). The overall mean follow-up was 43.3 months (range 6-274).
 Regardless of stage, recurrence and disease-free survival were more closely related to treatment and histopathology rather than tumor size or site-specific location.

  20. Data-Driven Modeling of Complex Systems by means of a Dynamical ANN

    Science.gov (United States)

    Seleznev, A.; Mukhin, D.; Gavrilov, A.; Loskutov, E.; Feigin, A.

    2017-12-01

    The data-driven methods for modeling and prognosis of complex dynamical systems become more and more popular in various fields due to growth of high-resolution data. We distinguish the two basic steps in such an approach: (i) determining the phase subspace of the system, or embedding, from available time series and (ii) constructing an evolution operator acting in this reduced subspace. In this work we suggest a novel approach combining these two steps by means of construction of an artificial neural network (ANN) with special topology. The proposed ANN-based model, on the one hand, projects the data onto a low-dimensional manifold, and, on the other hand, models a dynamical system on this manifold. Actually, this is a recurrent multilayer ANN which has internal dynamics and capable of generating time series. Very important point of the proposed methodology is the optimization of the model allowing us to avoid overfitting: we use Bayesian criterion to optimize the ANN structure and estimate both the degree of evolution operator nonlinearity and the complexity of nonlinear manifold which the data are projected on. The proposed modeling technique will be applied to the analysis of high-dimensional dynamical systems: Lorenz'96 model of atmospheric turbulence, producing high-dimensional space-time chaos, and quasi-geostrophic three-layer model of the Earth's atmosphere with the natural orography, describing the dynamics of synoptical vortexes as well as mesoscale blocking systems. The possibility of application of the proposed methodology to analyze real measured data is also discussed. The study was supported by the Russian Science Foundation (grant #16-12-10198).

  1. 'In the developed world, people talk and shop'- a review by Anne ...

    African Journals Online (AJOL)

    'In the developed world, people talk and shop'- a review by Anne Derges. A. Salleh. http://dx.doi.org/10.4314/safere.v3i1.23966 · AJOL African Journals Online. HOW TO USE AJOL... for Researchers · for Librarians · for Authors · FAQ's · More about AJOL · AJOL's Partners · Terms and Conditions of Use · Contact AJOL ...

  2. Artificial neural networks (ANN): prediction of sensory measurements from instrumental data

    OpenAIRE

    Carvalho,Naiara Barbosa; Minim,Valéria Paula Rodrigues; Silva,Rita de Cássia dos Santos Navarro; Della Lucia,Suzana Maria; Minim,Luis Aantonio

    2013-01-01

    The objective of this study was to predict by means of Artificial Neural Network (ANN), multilayer perceptrons, the texture attributes of light cheesecurds perceived by trained judges based on instrumental texture measurements. Inputs to the network were the instrumental texture measurements of light cheesecurd (imitative and fundamental parameters). Output variables were the sensory attributes consistency and spreadability. Nine light cheesecurd formulations composed of different combination...

  3. Numerical weather prediction (NWP) and hybrid ARMA/ANN model to predict global radiation

    International Nuclear Information System (INIS)

    Voyant, Cyril; Muselli, Marc; Paoli, Christophe; Nivet, Marie-Laure

    2012-01-01

    We propose in this paper an original technique to predict global radiation using a hybrid ARMA/ANN model and data issued from a numerical weather prediction model (NWP). We particularly look at the multi-layer perceptron (MLP). After optimizing our architecture with NWP and endogenous data previously made stationary and using an innovative pre-input layer selection method, we combined it to an ARMA model from a rule based on the analysis of hourly data series. This model has been used to forecast the hourly global radiation for five places in Mediterranean area. Our technique outperforms classical models for all the places. The nRMSE for our hybrid model MLP/ARMA is 14.9% compared to 26.2% for the naïve persistence predictor. Note that in the standalone ANN case the nRMSE is 18.4%. Finally, in order to discuss the reliability of the forecaster outputs, a complementary study concerning the confidence interval of each prediction is proposed. -- Highlights: ► Time series forecasting with hybrid method based on the use of ALADIN numerical weather model, ANN and ARMA. ► Innovative pre-input layer selection method. ► Combination of optimized MLP and ARMA model obtained from a rule based on the analysis of hourly data series. ► Stationarity process (method and control) for the global radiation time series.

  4. Modeling of an Aged Porous Silicon Humidity Sensor Using ANN Technique

    Directory of Open Access Journals (Sweden)

    Tarikul ISLAM

    2006-10-01

    Full Text Available Porous silicon (PS sensor based on capacitive technique used for measuring relative humidity has the advantages of low cost, ease of fabrication with controlled structure and CMOS compatibility. But the response of the sensor is nonlinear function of humidity and suffers from errors due to aging and stability. One adaptive linear (ADALINE ANN model has been developed to model the behavior of the sensor with a view to estimate these errors and compensate them. The response of the sensor is represented by third order polynomial basis function whose coefficients are determined by the ANN technique. The drift in sensor output due to aging of PS layer is also modeled by adapting the weights of the polynomial function. ANN based modeling is found to be more suitable than conventional physical modeling of PS humidity sensor in changing environment and drift due to aging. It helps online estimation of nonlinearity as well as monitoring of the fault of the PS humidity sensor using the coefficients of the model.

  5. Estimating SPT-N Value Based on Soil Resistivity using Hybrid ANN-PSO Algorithm

    Science.gov (United States)

    Nur Asmawisham Alel, Mohd; Ruben Anak Upom, Mark; Asnida Abdullah, Rini; Hazreek Zainal Abidin, Mohd

    2018-04-01

    Standard Penetration Resistance (N value) is used in many empirical geotechnical engineering formulas. Meanwhile, soil resistivity is a measure of soil’s resistance to electrical flow. For a particular site, usually, only a limited N value data are available. In contrast, resistivity data can be obtained extensively. Moreover, previous studies showed evidence of a correlation between N value and resistivity value. Yet, no existing method is able to interpret resistivity data for estimation of N value. Thus, the aim is to develop a method for estimating N-value using resistivity data. This study proposes a hybrid Artificial Neural Network-Particle Swarm Optimization (ANN-PSO) method to estimate N value using resistivity data. Five different ANN-PSO models based on five boreholes were developed and analyzed. The performance metrics used were the coefficient of determination, R2 and mean absolute error, MAE. Analysis of result found that this method can estimate N value (R2 best=0.85 and MAEbest=0.54) given that the constraint, Δ {\\bar{l}}ref, is satisfied. The results suggest that ANN-PSO method can be used to estimate N value with good accuracy.

  6. Predicting PM10concentration in Seoul metropolitan subway stations using artificial neural network (ANN).

    Science.gov (United States)

    Park, Sechan; Kim, Minjeong; Kim, Minhae; Namgung, Hyeong-Gyu; Kim, Ki-Tae; Cho, Kyung Hwa; Kwon, Soon-Bark

    2018-01-05

    The indoor air quality of subway systems can significantly affect the health of passengers since these systems are widely used for short-distance transit in metropolitan urban areas in many countries. The particles generated by abrasion during subway operations and the vehicle-emitted pollutants flowing in from the street in particular affect the air quality in underground subway stations. Thus the continuous monitoring of particulate matter (PM) in underground station is important to evaluate the exposure level of PM to passengers. However, it is difficult to obtain indoor PM data because the measurement systems are expensive and difficult to install and operate for significant periods of time in spaces crowded with people. In this study, we predicted the indoor PM concentration using the information of outdoor PM, the number of subway trains running, and information on ventilation operation by the artificial neural network (ANN) model. As well, we investigated the relationship between ANN's performance and the depth of underground subway station. ANN model showed a high correlation between the predicted and actual measured values and it was able to predict 67∼80% of PM at 6 subway station. In addition, we found that platform shape and depth influenced the model performance. Copyright © 2017 Elsevier B.V. All rights reserved.

  7. ANN based Performance Evaluation of BDI for Condition Monitoring of Induction Motor Bearings

    Science.gov (United States)

    Patel, Raj Kumar; Giri, V. K.

    2017-06-01

    One of the critical parts in rotating machines is bearings and most of the failure arises from the defective bearings. Bearing failure leads to failure of a machine and the unpredicted productivity loss in the performance. Therefore, bearing fault detection and prognosis is an integral part of the preventive maintenance procedures. In this paper vibration signal for four conditions of a deep groove ball bearing; normal (N), inner race defect (IRD), ball defect (BD) and outer race defect (ORD) were acquired from a customized bearing test rig, under four different conditions and three different fault sizes. Two approaches have been opted for statistical feature extraction from the vibration signal. In the first approach, raw signal is used for statistical feature extraction and in the second approach statistical features extracted are based on bearing damage index (BDI). The proposed BDI technique uses wavelet packet node energy coefficients analysis method. Both the features are used as inputs to an ANN classifier to evaluate its performance. A comparison of ANN performance is made based on raw vibration data and data chosen by using BDI. The ANN performance has been found to be fairly higher when BDI based signals were used as inputs to the classifier.

  8. Prediction of Frequency for Simulation of Asphalt Mix Fatigue Tests Using MARS and ANN

    Directory of Open Access Journals (Sweden)

    Ali Reza Ghanizadeh

    2014-01-01

    Full Text Available Fatigue life of asphalt mixes in laboratory tests is commonly determined by applying a sinusoidal or haversine waveform with specific frequency. The pavement structure and loading conditions affect the shape and the frequency of tensile response pulses at the bottom of asphalt layer. This paper introduces two methods for predicting the loading frequency in laboratory asphalt fatigue tests for better simulation of field conditions. Five thousand (5000 four-layered pavement sections were analyzed and stress and strain response pulses in both longitudinal and transverse directions was determined. After fitting the haversine function to the response pulses by the concept of equal-energy pulse, the effective length of the response pulses were determined. Two methods including Multivariate Adaptive Regression Splines (MARS and Artificial Neural Network (ANN methods were then employed to predict the effective length (i.e., frequency of tensile stress and strain pulses in longitudinal and transverse directions based on haversine waveform. It is indicated that, under controlled stress and strain modes, both methods (MARS and ANN are capable of predicting the frequency of loading in HMA fatigue tests with very good accuracy. The accuracy of ANN method is, however, more than MARS method. It is furthermore shown that the results of the present study can be generalized to sinusoidal waveform by a simple equation.

  9. IDI diesel engine performance and exhaust emission analysis using biodiesel with an artificial neural network (ANN

    Directory of Open Access Journals (Sweden)

    K. Prasada Rao

    2017-09-01

    Full Text Available Biodiesel is receiving increasing attention each passing day because of its fuel properties and compatibility. This study investigates the performance and emission characteristics of single cylinder four stroke indirect diesel injection (IDI engine fueled with Rice Bran Methyl Ester (RBME with Isopropanol additive. The investigation is done through a combination of experimental data analysis and artificial neural network (ANN modeling. The study used IDI engine experimental data to evaluate nine engine performance and emission parameters including Exhaust Gas Temperature (E.G.T, Brake Specific Fuel Consumption (BSFC, Brake Thermal Efficiency (B.The and various emissions like Hydrocarbons (HC, Carbon monoxide (CO, Carbon dioxide (CO2, Oxygen (O2, Nitrogen oxides (NOX and smoke. For the ANN modeling standard back propagation algorithm was found to be the optimum choice for training the model. A multi-layer perception (MLP network was used for non-linear mapping between the input and output parameters. It was found that ANN was able to predict the engine performance and exhaust emissions with a correlation coefficient of 0.995, 0.980, 0.999, 0.985, 0.999, 0.999, 0.980, 0.999, and 0.999 for E.G.T, BSFC, B.The, HC, O2, CO2, CO, NOX, smoke respectively.

  10. Interpretation of ANN-based QSAR models for prediction of antioxidant activity of flavonoids.

    Science.gov (United States)

    Žuvela, Petar; David, Jonathan; Wong, Ming Wah

    2018-02-05

    Quantitative structure-activity relationships (QSARs) built using machine learning methods, such as artificial neural networks (ANNs) are powerful in prediction of (antioxidant) activity from quantum mechanical (QM) parameters describing the molecular structure, but are usually not interpretable. This obvious difficulty is one of the most common obstacles in application of ANN-based QSAR models for design of potent antioxidants or elucidating the underlying mechanism. Interpreting the resulting models is often omitted or performed erroneously altogether. In this work, a comprehensive comparative study of six methods (PaD, PaD 2 , weights, stepwise, perturbation and profile) for exploration and interpretation of ANN models built for prediction of Trolox-equivalent antioxidant capacity (TEAC) QM descriptors, is presented. Sum of ranking differences (SRD) was used for ranking of the six methods with respect to the contributions of the calculated QM molecular descriptors toward TEAC. The results show that the PaD, PaD 2 and profile methods are the most stable and give rise to realistic interpretation of the observed correlations. Therefore, they are safely applicable for future interpretations without the opinion of an experienced chemist or bio-analyst. © 2018 Wiley Periodicals, Inc. © 2018 Wiley Periodicals, Inc.

  11. Prediction of frequency for simulation of asphalt mix fatigue tests using MARS and ANN.

    Science.gov (United States)

    Ghanizadeh, Ali Reza; Fakhri, Mansour

    2014-01-01

    Fatigue life of asphalt mixes in laboratory tests is commonly determined by applying a sinusoidal or haversine waveform with specific frequency. The pavement structure and loading conditions affect the shape and the frequency of tensile response pulses at the bottom of asphalt layer. This paper introduces two methods for predicting the loading frequency in laboratory asphalt fatigue tests for better simulation of field conditions. Five thousand (5000) four-layered pavement sections were analyzed and stress and strain response pulses in both longitudinal and transverse directions was determined. After fitting the haversine function to the response pulses by the concept of equal-energy pulse, the effective length of the response pulses were determined. Two methods including Multivariate Adaptive Regression Splines (MARS) and Artificial Neural Network (ANN) methods were then employed to predict the effective length (i.e., frequency) of tensile stress and strain pulses in longitudinal and transverse directions based on haversine waveform. It is indicated that, under controlled stress and strain modes, both methods (MARS and ANN) are capable of predicting the frequency of loading in HMA fatigue tests with very good accuracy. The accuracy of ANN method is, however, more than MARS method. It is furthermore shown that the results of the present study can be generalized to sinusoidal waveform by a simple equation.

  12. Measurement and ANN prediction of pH-dependent solubility of nitrogen-heterocyclic compounds.

    Science.gov (United States)

    Sun, Feifei; Yu, Qingni; Zhu, Jingke; Lei, Lecheng; Li, Zhongjian; Zhang, Xingwang

    2015-09-01

    Based on the solubility of 25 nitrogen-heterocyclic compounds (NHCs) measured by saturation shake-flask method, artificial neural network (ANN) was employed to the study of the quantitative relationship between the structure and pH-dependent solubility of NHCs. With genetic algorithm-multivariate linear regression (GA-MLR) approach, five out of the 1497 molecular descriptors computed by Dragon software were selected to describe the molecular structures of NHCs. Using the five selected molecular descriptors as well as pH and the partial charge on the nitrogen atom of NHCs (QN) as inputs of ANN, a quantitative structure-property relationship (QSPR) model without using Henderson-Hasselbalch (HH) equation was successfully developed to predict the aqueous solubility of NHCs in different pH water solutions. The prediction model performed well on the 25 model NHCs with an absolute average relative deviation (AARD) of 5.9%, while HH approach gave an AARD of 36.9% for the same model NHCs. It was found that QN played a very important role in the description of NHCs and, with QN, ANN became a potential tool for the prediction of pH-dependent solubility of NHCs. Copyright © 2015 Elsevier Ltd. All rights reserved.

  13. ANN-PSO Integrated Optimization Methodology for Intelligent Control of MMC Machining

    Science.gov (United States)

    Chandrasekaran, Muthumari; Tamang, Santosh

    2017-08-01

    Metal Matrix Composites (MMC) show improved properties in comparison with non-reinforced alloys and have found increased application in automotive and aerospace industries. The selection of optimum machining parameters to produce components of desired surface roughness is of great concern considering the quality and economy of manufacturing process. In this study, a surface roughness prediction model for turning Al-SiCp MMC is developed using Artificial Neural Network (ANN). Three turning parameters viz., spindle speed ( N), feed rate ( f) and depth of cut ( d) were considered as input neurons and surface roughness was an output neuron. ANN architecture having 3 -5 -1 is found to be optimum and the model predicts with an average percentage error of 7.72 %. Particle Swarm Optimization (PSO) technique is used for optimizing parameters to minimize machining time. The innovative aspect of this work is the development of an integrated ANN-PSO optimization method for intelligent control of MMC machining process applicable to manufacturing industries. The robustness of the method shows its superiority for obtaining optimum cutting parameters satisfying desired surface roughness. The method has better convergent capability with minimum number of iterations.

  14. Dispersion compensation of fiber optic communication system with direct detection using artificial neural networks (ANNs)

    Science.gov (United States)

    Maghrabi, Mahmoud M. T.; Kumar, Shiva; Bakr, Mohamed H.

    2018-02-01

    This work introduces a powerful digital nonlinear feed-forward equalizer (NFFE), exploiting multilayer artificial neural network (ANN). It mitigates impairments of optical communication systems arising due to the nonlinearity introduced by direct photo-detection. In a direct detection system, the detection process is nonlinear due to the fact that the photo-current is proportional to the absolute square of the electric field intensity. The proposed equalizer provides the most efficient computational cost with high equalization performance. Its performance is comparable to the benchmark compensation performance achieved by maximum-likelihood sequence estimator. The equalizer trains an ANN to act as a nonlinear filter whose impulse response removes the intersymbol interference (ISI) distortions of the optical channel. Owing to the proposed extensive training of the equalizer, it achieves the ultimate performance limit of any feed-forward equalizer (FFE). The performance and efficiency of the equalizer is investigated by applying it to various practical short-reach fiber optic communication system scenarios. These scenarios are extracted from practical metro/media access networks and data center applications. The obtained results show that the ANN-NFFE compensates for the received BER degradation and significantly increases the tolerance to the chromatic dispersion distortion.

  15. On-line dynamic monitoring automotive exhausts: using BP-ANN for distinguishing multi-components

    Science.gov (United States)

    Zhao, Yudi; Wei, Ruyi; Liu, Xuebin

    2017-10-01

    Remote sensing-Fourier Transform infrared spectroscopy (RS-FTIR) is one of the most important technologies in atmospheric pollutant monitoring. It is very appropriate for on-line dynamic remote sensing monitoring of air pollutants, especially for the automotive exhausts. However, their absorption spectra are often seriously overlapped in the atmospheric infrared window bands, i.e. MWIR (3 5μm). Artificial Neural Network (ANN) is an algorithm based on the theory of the biological neural network, which simplifies the partial differential equation with complex construction. For its preferable performance in nonlinear mapping and fitting, in this paper we utilize Back Propagation-Artificial Neural Network (BP-ANN) to quantitatively analyze the concentrations of four typical industrial automotive exhausts, including CO, NO, NO2 and SO2. We extracted the original data of these automotive exhausts from the HITRAN database, most of which virtually overlapped, and established a mixed multi-component simulation environment. Based on Beer-Lambert Law, concentrations can be retrieved from the absorbance of spectra. Parameters including learning rate, momentum factor, the number of hidden nodes and iterations were obtained when the BP network was trained with 80 groups of input data. By improving these parameters, the network can be optimized to produce necessarily higher precision for the retrieved concentrations. This BP-ANN method proves to be an effective and promising algorithm on dealing with multi-components analysis of automotive exhausts.

  16. Development of a new software tool, based on ANN technology, in neutron spectrometry and dosimetry research

    Energy Technology Data Exchange (ETDEWEB)

    Ortiz R, J.M.; Martinez B, M.R.; Vega C, H.R. [Universidad Autonoma de Zacatecas, Av. Ramon Lopez Velarde 801, A.P. 336, 98000 Zacatecas (Mexico)

    2007-07-01

    Artificial Intelligence is a branch of study which enhances the capability of computers by giving them human-like intelligence. The brain architecture has been extensively studied and attempts have been made to emulate it as in the Artificial Neural Network technology. A large variety of neural network architectures have been developed and they have gained wide-spread popularity over the last few decades. Their application is considered as a substitute for many classical techniques that have been used for many years, as in the case of neutron spectrometry and dosimetry research areas. In previous works, a new approach called Robust Design of Artificial Neural network was applied to build an ANN topology capable to solve the neutron spectrometry and dosimetry problems within the Mat lab programming environment. In this work, the knowledge stored at Mat lab ANN's synaptic weights was extracted in order to develop for first time a customized software application based on ANN technology, which is proposed to be used in the neutron spectrometry and simultaneous dosimetry fields. (Author)

  17. Estimation of Costs and Durations of Construction of Urban Roads Using ANN and SVM

    Directory of Open Access Journals (Sweden)

    Igor Peško

    2017-01-01

    Full Text Available Offer preparation has always been a specific part of a building process which has significant impact on company business. Due to the fact that income greatly depends on offer’s precision and the balance between planned costs, both direct and overheads, and wished profit, it is necessary to prepare a precise offer within required time and available resources which are always insufficient. The paper presents a research of precision that can be achieved while using artificial intelligence for estimation of cost and duration in construction projects. Both artificial neural networks (ANNs and support vector machines (SVM are analysed and compared. The best SVM has shown higher precision, when estimating costs, with mean absolute percentage error (MAPE of 7.06% compared to the most precise ANNs which has achieved precision of 25.38%. Estimation of works duration has proved to be more difficult. The best MAPEs were 22.77% and 26.26% for SVM and ANN, respectively.

  18. Development of a new software tool, based on ANN technology, in neutron spectrometry and dosimetry research

    International Nuclear Information System (INIS)

    Ortiz R, J.M.; Martinez B, M.R.; Vega C, H.R.

    2007-01-01

    Artificial Intelligence is a branch of study which enhances the capability of computers by giving them human-like intelligence. The brain architecture has been extensively studied and attempts have been made to emulate it as in the Artificial Neural Network technology. A large variety of neural network architectures have been developed and they have gained wide-spread popularity over the last few decades. Their application is considered as a substitute for many classical techniques that have been used for many years, as in the case of neutron spectrometry and dosimetry research areas. In previous works, a new approach called Robust Design of Artificial Neural network was applied to build an ANN topology capable to solve the neutron spectrometry and dosimetry problems within the Mat lab programming environment. In this work, the knowledge stored at Mat lab ANN's synaptic weights was extracted in order to develop for first time a customized software application based on ANN technology, which is proposed to be used in the neutron spectrometry and simultaneous dosimetry fields. (Author)

  19. FE-ANN based modeling of 3D Simple Reinforced Concrete Girders for Objective Structural Health Evaluation : Tech Transfer Summary

    Science.gov (United States)

    2017-06-01

    The objective of this study was to develop an objective, quantitative method for evaluating damage to bridge girders by using artificial neural networks (ANNs). This evaluation method, which is a supplement to visual inspection, requires only the res...

  20. Review: Miller, Michelle Ann (2009, Rebellion and Reform in Indonesia – Jakarta’s Security and Autonomy Policies in Aceh

    Directory of Open Access Journals (Sweden)

    Antje Missbach

    2009-01-01

    Full Text Available Review of the monograph: Miller, Michelle Ann, Rebellion and Reform in Indonesia – Jakarta’s Security and Autonomy Policies in Aceh, London/ New York: Routledge, 2009, ISBN 13: 978-0-415-45467-4, 240 pages.

  1. Kommunikatsioonijuht saab aidata arsti ja patsienti / Eda Amur, Anneli Bogens, Svea Talving, Krista Valdvee ; intervjueerinud Küllike Heide

    Index Scriptorium Estoniae

    2013-01-01

    Meditsiinivaldkonna kommunikatsiooniga seotud küsimuste ja probleemide üle arutlevad nelja haigla kommunikatsioonijuhid: Eda Amur Pärnu haiglast, Anneli Bogens Ida-Viru keskhaiglast, Svea Talving Ida-Tallinna keskhaiglast ning Krista Valdvee Viljandi haiglast

  2. 'Transatlantic Print Culture, 1880-1940: Emerging Media, Emerging Modernisms', edited by Ann Ardis and Patrick Collier

    Directory of Open Access Journals (Sweden)

    Janet Floyd

    2009-11-01

    Full Text Available A review of 'Transatlantic Print Culture, 1880-1940: Emerging Media, Emerging Modernisms', edited by Ann Ardis and Patrick Collier (London: Palgrave Macmillan, 2008. Hardback, 259 pages, £50, ISBN 9780554269.

  3. Evaluation Of The Advanced Operating System Of The Ann Arbor Transportation Authority : Evaluation Of Automatic Vehicle Location Accuracy

    Science.gov (United States)

    1999-01-01

    In 1997, the Ann Arbor (Michigan) Transportation Authority began deploying advanced public transportation systems (APTS) technologies in its fixed route and paratransit operations. The project's concept is the integration of a range of such technolog...

  4. Tagasipöördumine esteetika juurde = Return to aesthetics / Keith Moxey, Michael Ann Holly ; interv. Anu Allas

    Index Scriptorium Estoniae

    Moxey, Keith

    2007-01-01

    Ameerika kunstiajaloolased Keith Moxey ning Michael Ann Holly tutvustavad uut lähenemist kunstiajaloole (new art history). Nn. naasmine esteetika juurde tähendab püüet integreerida kunstiajalukku ja -teooriasse uuesti ja tugevamalt kunstiteos

  5. Simultaneous measurements of spin observables AN and ANN in elastic pp scattering (extension of the SPASCHARM program at U-70).

    Science.gov (United States)

    Abramov, V. V.; Bogdanov, A. A.; Chetvertkov, M. A.; Chetvertkova, V. A.; Mochalov, V. V.; Moiseev, V. V.; Novikov, K. D.; Nurushev, S. B.; Nurusheva, M. B.; Okorokov, V. A.; Semenov, P. A.; Strikhanov, M. N.; Vasiliev, A. N.

    2017-12-01

    We propose to measure spin observables AN and ANN in elastic pp scattering by using the transversely polarized proton beam and target at momenta p = 12-45 GeV/c. Existence of both polarized target and beam gives us unique possibility to measure AN simultaneously and independently for polarized beam (AB) and target (AT) to carry out and verify experimental measurements of single-spin and double-spin (ANN) measurements in diffractive region.

  6. Comparative study of ANN and RSM for simultaneous optimization of multiple targets in Fenton treatment of landfill leachate.

    Science.gov (United States)

    Sabour, Mohammad Reza; Amiri, Allahyar

    2017-07-01

    In this study, two modeling methods, namely response surface methodology (RSM) and artificial neural networks (ANN), were applied to investigate the Fenton process performance in landfill leachate treatment. For this purpose, three targets were used to cover different aspects of post-treatment products such as supernatant and sludge: mass content ratio (MCR) and mass removal efficiency (MRE). It was observed that coagulation was dominant mechanism in all responses. The proposed models were evaluated based on correlation coefficient (R 2 ), root mean square error (RMSE) and average error (AE) and both models seemed satisfactory. However, the better results of 0.97-0.98 for R 2 , 1.45-1.86 for RMSE and 2-4% for error, indicated relative superiority of ANN compared to RSM. In addition, it was revealed that [H 2 O 2 ]/[Fe 2+ ] mole ratio had the greatest effect in the targets, while Fe dosage and pH had lower ones. Finally, to investigate the predictive performance of both models, some additional experiments were conducted in expected optimum conditions that resulted to 27% sludge MCR, 14% effluent MCR, and 56% MRE. The results showed low deviation from predicted values with maximum errors of 8% and 9% for RSM and ANN, respectively. Though in most cases, ANN error values were lower than RSM values. Also, it was proved that setting RSM prior to ANN (as a feeding tool) improves the predictive capability of ANN significantly. Copyright © 2017 Elsevier Ltd. All rights reserved.

  7. Estimation of Release History of Pollutant Source and Dispersion Coefficient of Aquifer Using Trained ANN Model

    Science.gov (United States)

    Srivastava, R.; Ayaz, M.; Jain, A.

    2013-12-01

    Knowledge of the release history of a groundwater pollutant source is critical in the prediction of the future trend of the pollutant movement and in choosing an effective remediation strategy. Moreover, for source sites which have undergone an ownership change, the estimated release history can be utilized for appropriate allocation of the costs of remediation among different parties who may be responsible for the contamination. Estimation of the release history with the help of concentration data is an inverse problem that becomes ill-posed because of the irreversible nature of the dispersion process. Breakthrough curves represent the temporal variation of pollutant concentration at a particular location, and contain significant information about the source and the release history. Several methodologies have been developed to solve the inverse problem of estimating the source and/or porous medium properties using the breakthrough curves as a known input. A common problem in the use of the breakthrough curves for this purpose is that, in most field situations, we have little or no information about the time of measurement of the breakthrough curve with respect to the time when the pollutant source becomes active. We develop an Artificial Neural Network (ANN) model to estimate the release history of a groundwater pollutant source through the use of breakthrough curves. It is assumed that the source location is known but the time dependent contaminant source strength is unknown. This temporal variation of the strength of the pollutant source is the output of the ANN model that is trained using the Levenberg-Marquardt algorithm utilizing synthetically generated breakthrough curves as inputs. A single hidden layer was used in the neural network and, to utilize just sufficient information and reduce the required sampling duration, only the upper half of the curve is used as the input pattern. The second objective of this work was to identify the aquifer parameters. An

  8. ANN-QSAR model for selection of anticancer leads from structurally heterogeneous series of compounds.

    Science.gov (United States)

    González-Díaz, Humberto; Bonet, Isis; Terán, Carmen; De Clercq, Erik; Bello, Rafael; García, Maria M; Santana, Lourdes; Uriarte, Eugenio

    2007-05-01

    Developing a model for predicting anticancer activity of any classes of organic compounds based on molecular structure is very important goal for medicinal chemist. Different molecular descriptors can be used to solve this problem. Stochastic molecular descriptors so-called the MARCH-INSIDE approach, shown to be very successful in drug design. Nevertheless, the structural diversity of compounds is so vast that we may need non-linear models such as artificial neural networks (ANN) instead of linear ones. SmartMLP-ANN analysis used to model the anticancer activity of organic compounds has shown high average accuracy of 93.79% (train performance) and predictability of 90.88% (validation performance) for the 8:3-MLP topology with different training and predicting series. This ANN model favourably compares with respect to a previous linear discriminant analysis (LDA) model [H. González-Díaz et al., J. Mol. Model 9 (2003) 395] that showed only 80.49% of accuracy and 79.34% of predictability. The present SmartMLP approach employed shorter training times of only 10h while previous models give accuracies of 70-89% only after 25-46 h of training. In order to illustrate the practical use of the model in bioorganic medicinal chemistry, we report the in silico prediction, and in vitro evaluation of six new synthetic tegafur analogues having IC(50) values in a broad range between 37.1 and 138 microgmL(-1) for leukemia (L1210/0) and human T-lymphocyte (Molt4/C8, CEM/0) cells. Theoretical predictions coincide very well with experimental results.

  9. Rimbaud’s influence on Jayne Anne Phillips: from Sweethearts to Shelter

    Directory of Open Access Journals (Sweden)

    Stéphanie Durrans

    2012-06-01

    Full Text Available Arthur Rimbaud emerges from Jayne Anne Phillips���s essays as a continual source of fascination. This paper explores the patterns of convergence that unite these two writers one century apart while aiming to provide a deeper and more meaningful appreciation of Phillips’ accomplishments in Shelter. It focuses on Rimbaud’s and Phillips’ conception of language and their emphasis on "visionary writing", before investigating the significance of such patterns on Shelter and exploring the stylistic affinities linking their respective works. In the end, linguistic deconstruction and regeneration appears as one of the ways in which both writers seek to express the hidden traumas of a society in the grips of violence.A en juger par les quelques essais de Jayne Anne Phillips dans lesquels il est mentionné, Arthur Rimbaud semble avoir exercé une véritable fascination sur la jeune nouvelliste et romancière américaine au moins jusque dans les années 1990. Cet article explore la filiation littéraire entre deux auteurs mus par un même souci de renouveler la langue par l’élaboration d’une « écriture visionnaire », à rebours des cadres normatifs et de toute pensée logique. A ce titre, le roman Shelter (1994 apparaît comme l’hommage le plus appuyé rendu par Phillips au poète français, notamment par le double processus de déconstruction et de régénération linguistique qui permet à son auteur d’exprimer les traumatismes individuels et collectifs d’une société hantée par la violence.

  10. ANN-Benchmarks: A Benchmarking Tool for Approximate Nearest Neighbor Algorithms

    DEFF Research Database (Denmark)

    Aumüller, Martin; Bernhardsson, Erik; Faithfull, Alexander

    2017-01-01

    visualise these as images, Open image in new window plots, and websites with interactive plots. ANN-Benchmarks aims to provide a constantly updated overview of the current state of the art of k-NN algorithms. In the short term, this overview allows users to choose the correct k-NN algorithm and parameters...... for their similarity search task; in the longer term, algorithm designers will be able to use this overview to test and refine automatic parameter tuning. The paper gives an overview of the system, evaluates the results of the benchmark, and points out directions for future work. Interestingly, very different...

  11. The Short Fiction of Bobbie Ann Mason: Silent Voices, Silenced Voices, Voicing Silence.

    OpenAIRE

    Delgado Marín, Candela

    2015-01-01

    La presente tesis doctoral se enmarca dentro del campo de los estudios norteamericanos, centrándose en el relato breve de la escritora sureña Bobbie Ann Mason. El título de este proyecto apunta al foco de la investigación: el silencio. He utilizado tres puntos de vista diferentes para examinar los cuentos de esta escritora: el contexto socio-cultural, estudios comparativos literarios y teóricos y, por último, el análisis textual de su narrativa. Considero la producción literaria de Bobbie ...

  12. A contribuição de Ann Sharp: uma conversa com Matthew Lipman

    OpenAIRE

    Kennedy, David Knowles; Montclair State University

    2010-01-01

    A recente morte de Ann Sharp, co-fundadora e diretora associada do Instituto para o Desenvolvimento da Filosofia para Crianças, aos 68 anos de idade, deixou muitos dos comprometidos com o movimento de filosofia para/com crianças despojados, sem dúvida de diferentes formas. A afetividade e a intensidade de sua dedicação pessoal e profissional, a claridade de seu pensamento e sua ilimitada energia no trabalho pela disseminação da concepção e prática do filosofar com crianças ressoa ainda mais ...

  13. High-Resolution Geologic Mapping of the Inner Continental Shelf: Cape Ann to Salisbury Beach, Massachusetts

    Science.gov (United States)

    Barnhardt, Walter A.; Andrews, Brian D.; Ackerman, Seth D.; Baldwin, Wayne E.; Hein, Christopher J.

    2009-01-01

    The geologic framework of the Massachusetts inner continental shelf between Cape Ann and Salisbury Beach has been shaped by a complicated history of glaciation, deglaciation, and changes in relative sea level. New geophysical data (swath bathymetry, sidescan sonar and seismic-reflection profiling), sediment samples, and seafloor photography provide insight into the geomorphic and stratigraphic record generated by these processes. High-resolution spatial data and geologic maps in this report support coastal research and efforts to understand the type, distribution, and quality of subtidal marine habitats in the Massachusetts coastal ocean.

  14. “Emotions, Media and Political Campaign” by Professor Ann Crigler

    OpenAIRE

    Ricaud, Raphaël; Lechaux, Bleuwenn

    2012-01-01

    Ann Crigler, Professor of Political Science and Chair at the University of Southern California’s Dornsife College of Letters, Arts and Sciences, came to Paris VIII’s LabTop final 2010/2011 seminar on June 27, 2011, for a lecture entitled “Emotions, Media and Political Campaign”. She started out by insisting that the sum of the data and original findings she was presenting us with was a collaborative piece. Professor Crigler, two of her colleagues (Matthew Baum, from Harvard University and Mar...

  15. SU-E-T-206: Improving Radiotherapy Toxicity Based On Artificial Neural Network (ANN) for Head and Neck Cancer Patients

    International Nuclear Information System (INIS)

    Cho, Daniel D; Wernicke, A Gabriella; Nori, Dattatreyudu; Chao, KSC; Parashar, Bhupesh; Chang, Jenghwa

    2014-01-01

    Purpose/Objective(s): The aim of this study is to build the estimator of toxicity using artificial neural network (ANN) for head and neck cancer patients Materials/Methods: An ANN can combine variables into a predictive model during training and considered all possible correlations of variables. We constructed an ANN based on the data from 73 patients with advanced H and N cancer treated with external beam radiotherapy and/or chemotherapy at our institution. For the toxicity estimator we defined input data including age, sex, site, stage, pathology, status of chemo, technique of external beam radiation therapy (EBRT), length of treatment, dose of EBRT, status of post operation, length of follow-up, the status of local recurrences and distant metastasis. These data were digitized based on the significance and fed to the ANN as input nodes. We used 20 hidden nodes (for the 13 input nodes) to take care of the correlations of input nodes. For training ANN, we divided data into three subsets such as training set, validation set and test set. Finally, we built the estimator for the toxicity from ANN output. Results: We used 13 input variables including the status of local recurrences and distant metastasis and 20 hidden nodes for correlations. 59 patients for training set, 7 patients for validation set and 7 patients for test set and fed the inputs to Matlab neural network fitting tool. We trained the data within 15% of errors of outcome. In the end we have the toxicity estimation with 74% of accuracy. Conclusion: We proved in principle that ANN can be a very useful tool for predicting the RT outcomes for high risk H and N patients. Currently we are improving the results using cross validation

  16. SU-E-T-206: Improving Radiotherapy Toxicity Based On Artificial Neural Network (ANN) for Head and Neck Cancer Patients

    Energy Technology Data Exchange (ETDEWEB)

    Cho, Daniel D; Wernicke, A Gabriella; Nori, Dattatreyudu; Chao, KSC; Parashar, Bhupesh; Chang, Jenghwa [Weill Cornell Medical College, NY, NY (United States)

    2014-06-01

    Purpose/Objective(s): The aim of this study is to build the estimator of toxicity using artificial neural network (ANN) for head and neck cancer patients Materials/Methods: An ANN can combine variables into a predictive model during training and considered all possible correlations of variables. We constructed an ANN based on the data from 73 patients with advanced H and N cancer treated with external beam radiotherapy and/or chemotherapy at our institution. For the toxicity estimator we defined input data including age, sex, site, stage, pathology, status of chemo, technique of external beam radiation therapy (EBRT), length of treatment, dose of EBRT, status of post operation, length of follow-up, the status of local recurrences and distant metastasis. These data were digitized based on the significance and fed to the ANN as input nodes. We used 20 hidden nodes (for the 13 input nodes) to take care of the correlations of input nodes. For training ANN, we divided data into three subsets such as training set, validation set and test set. Finally, we built the estimator for the toxicity from ANN output. Results: We used 13 input variables including the status of local recurrences and distant metastasis and 20 hidden nodes for correlations. 59 patients for training set, 7 patients for validation set and 7 patients for test set and fed the inputs to Matlab neural network fitting tool. We trained the data within 15% of errors of outcome. In the end we have the toxicity estimation with 74% of accuracy. Conclusion: We proved in principle that ANN can be a very useful tool for predicting the RT outcomes for high risk H and N patients. Currently we are improving the results using cross validation.

  17. Using ANNS to predict energy consumption of split AC systems in residential buildings and offices

    Energy Technology Data Exchange (ETDEWEB)

    Karatasou, S.; Santamouris, M.; Geros, V. [National and Kapodistrian Univ. of Athens., Athens (Greece). Dept. of Physics

    2007-07-01

    Artificial neural networks (ANNs) were used to predict AC power consumption in residential and small office buildings in Greece. The aim of the study was to produce a simple algorithm capable of predicting AC power consumption for a period of 24 hours. The performance of short-term predictors was evaluated. The predictive abilities of single step and 24-step predictors were then compared. Real data from an apartment building and a small office building in Athens were used. Datasets covered the summer period, and input variables were pre-selected among the available environmental and calendar variables. Feed forward ANNs with a single hidden layer of units were used. A single linear output to predict hourly energy consumptions consisted of 3 parts: the identification of all potential relevant inputs; the selection of hidden units for the preliminary set of inputs; and the removal of irrelevant inputs and useless hidden units through a subtractive phase. A Lavenberg Marquardt (LM) algorithm was used to train the networks. The network architecture was determined for both datasets through the selection procedures. Performance of the predictors was evaluated using the considered training and test sets. Results showed that both the single step and the 24-step predictors were accurate in the case of office buildings. However, the apartment building mean bias error (MBE) was approximately 10 per cent. Attempts to predict the residential building's energy consumption over a 24 hour period yielded an MBE of more than 30 per cent. 10 refs., 2 tabs., 3 figs.

  18. Performance measurement of plate fin heat exchanger by exploration: ANN, ANFIS, GA, and SA

    Directory of Open Access Journals (Sweden)

    A.K. Gupta

    2017-01-01

    Full Text Available An experimental work is conducted on counter flow plate fin compact heat exchanger using offset strip fin under different mass flow rates. The training, testing, and validation set of data has been collected by conducting experiments. Next, artificial neural network merged with Genetic Algorithm (GA utilized to measure the performance of plate-fin compact heat exchanger. The main aim of present research is to measure the performance of plate-fin compact heat exchanger and to provide full explanations. An artificial neural network predicted simulated data, which verified with experimental data under 10–20% error. Then, the authors examined two well-known global search techniques, simulated annealing and the genetic algorithm. The proposed genetic algorithm and Simulated Annealing (SA results have been summarized. The parameters are impartially important for good results. With the emergence of a new data-driven modeling technique, Neuro-fuzzy based systems are established in academic and practical applications. The neuro-fuzzy interference system (ANFIS has also been examined to undertake the problem related to plate-fin heat exchanger performance measurement under various parameters. Moreover, Parallel with ANFIS model and Artificial Neural Network (ANN model has been created with emphasizing the accuracy of the different techniques. A wide range of statistical indicators used to assess the performance of the models. Based on the comparison, it was revealed that technical ANFIS improve the accuracy of estimates in the small pool and tropical ANN.

  19. Wives, love and animals: themes in the Poetry of Adrienne Rich and Carol Ann Duffy

    Directory of Open Access Journals (Sweden)

    Eleonora Rao

    2013-10-01

    Full Text Available This paper discusses firstly, the  poetical space of celebrated American poet Adrienne Rich who died in March 2012, at the age of 82. The analysis focuses on Rich’s complex  figurations of female subjectivity as well as on her nuanced positions in relation to the public role of the poet today. Rich’s attention to the political dimension did not exclude intimate reflections on personal relationships and on their modalities. In this respect her poetry is close to another important lesbian author, the poet laureate Coral Ann Duffy. In The World’s Wife (1999, Carol Ann Duffy presents thirty sketches of famed men from both history and mythology by their wives. Each wife extols or criticizes her own husband in a combination of sarcasm and sentimentalism, with peaks of  extreme bitterness and self-pity. Such singular and irreverent feminine versions show a series of references to the animal world. As a matter of fact, Duffy creates a downright vast and varied bestiary, which focuses on the problematic association between the female and the animal body. The aim of this essay is to explore the multiple possibilities of representation and placement of the human body in the space, through the lenses of ecocriticism and posthumanism.

  20. Experimental Study and ANN Dual-Time Scale Perturbation Model of Electrokinetic Properties of Microbiota.

    Science.gov (United States)

    Liu, Yong; Munteanu, Cristian R; Fernandez-Lozano, Carlos; Pazos, Alejandro; Ran, Tao; Tan, Zhiliang; Yu, Yizun; Zhou, Chuanshe; Tang, Shaoxun; González-Díaz, Humberto

    2017-01-01

    The electrokinetic properties of the rumen microbiota are involved in cell surface adhesion and microbial metabolism. An in vitro study was carried out in batch culture to determine the effects of three levels of special surface area (SSA) of biomaterials and four levels of surface tension (ST) of culture medium on electrokinetic properties (Zeta potential, ξ; electrokinetic mobility, μ e ), fermentation parameters (volatile fatty acids, VFAs), and ST over fermentation processes (ST-a, γ). The obtained results were combined with previously published data (digestibility, D; pH; concentration of ammonia nitrogen, c(NH 3 -N)) to establish a predictive artificial neural network (ANN) model. Concepts of dual-time series analysis, perturbation theory (PT), and Box-Jenkins Operators were applied for the first time to develop an ANN model to predict the variations of the electrokinetic properties of microbiota. The best dual-time series Radial Basis Functions (RBR) model for ξ of rumen microbiota predicted ξ for >30,000 cases with a correlation coefficient >0.8. This model provided insight into the correlations between electrokinetic property (zeta potential) of rumen microbiota and the perturbations of physical factors (specific surface area and surface tension) of media, digestibility of substrate, and their metabolites (NH 3 -N, VFAs) in relation to environmental factors.

  1. Tribological behaviour predictions of r-GO reinforced Mg composite using ANN coupled Taguchi approach

    Science.gov (United States)

    Kavimani, V.; Prakash, K. Soorya

    2017-11-01

    This paper deals with the fabrication of reduced graphene oxide (r-GO) reinforced Magnesium Metal Matrix Composite (MMC) through a novel solvent based powder metallurgy route. Investigations over basic and functional properties of developed MMC reveals that addition of r-GO improvises the microhardness upto 64 HV but however decrement in specific wear rate is also notified. Visualization of worn out surfaces through SEM images clearly explains for the occurrence of plastic deformation and the presence of wear debris because of ploughing out action. Taguchi coupled Artificial Neural Network (ANN) technique is adopted to arrive at optimal values of the input parameters such as load, reinforcement weight percentage, sliding distance and sliding velocity and thereby achieve minimal target output value viz. specific wear rate. Influence of any of the input parameter over specific wear rate studied through ANOVA reveals that load acting on pin has a major influence with 38.85% followed by r-GO wt. % of 25.82%. ANN model developed to predict specific wear rate value based on the variation of input parameter facilitates better predictability with R-value of 98.4% when compared with the outcomes of regression model.

  2. A new near-lossless EEG compression method using ANN-based reconstruction technique.

    Science.gov (United States)

    Hejrati, Behzad; Fathi, Abdolhossein; Abdali-Mohammadi, Fardin

    2017-08-01

    Compression algorithm is an essential part of Telemedicine systems, to store and transmit large amount of medical signals. Most of existing compression methods utilize fixed transforms such as discrete cosine transform (DCT) and wavelet and usually cannot efficiently extract signal redundancy especially for non-stationary signals such as electroencephalogram (EEG). In this paper, we first propose learning-based adaptive transform using combination of DCT and artificial neural network (ANN) reconstruction technique. This adaptive ANN-based transform is applied to the DCT coefficients of EEG data to reduce its dimensionality and also to estimate the original DCT coefficients of EEG in the reconstruction phase. To develop a new near lossless compression method, the difference between the original DCT coefficients and estimated ones are also quantized. The quantized error is coded using Arithmetic coding and sent along with the estimated DCT coefficients as compressed data. The proposed method was applied to various datasets and the results show higher compression rate compared to the state-of-the-art methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

  3. Anne Sütü ve Mikrobiyota Gelişimi

    OpenAIRE

    GÜNEY, Rabiye; ÇINAR, Nursan

    2017-01-01

    Sağlıklı mikrobiyotanın etkisine yönelik yapılan çalışmalarda, çocukların gelecekteki sağlığı için mikrobiyota gelişiminin büyük önem taşıdığı vurgulanmaktadır. Astım, şeker hastalığı, obezite gibi birçok hastalığın zarar görmüş ya da gelişmemiş bağırsak mikrobiyotası ile yakın ilişkisi bulunmaktadır. Anne sütü, sağlıklı bir bağırsak mikrobiyotasının gelişmesi için bebeğe aktarılan çok sayıda non-patojen bakteriyi içinde barındırmaktadır. Bununla birlikte, anne sütündeki mikroorganizmaların n...

  4. Estimation of Optimum Dilution in the GMAW Process Using Integrated ANN-GA

    Directory of Open Access Journals (Sweden)

    P. Sreeraj

    2013-01-01

    Full Text Available To improve the corrosion resistant properties of carbon steel, usually cladding process is used. It is a process of depositing a thick layer of corrosion resistant material over carbon steel plate. Most of the engineering applications require high strength and corrosion resistant materials for long-term reliability and performance. By cladding these properties can be achieved with minimum cost. The main problem faced on cladding is the selection of optimum combinations of process parameters for achieving quality clad and hence good clad bead geometry. This paper highlights an experimental study to optimize various input process parameters (welding current, welding speed, gun angle, and contact tip to work distance and pinch to get optimum dilution in stainless steel cladding of low carbon structural steel plates using gas metal arc welding (GMAW. Experiments were conducted based on central composite rotatable design with full replication technique, and mathematical models were developed using multiple regression method. The developed models have been checked for adequacy and significance. In this study, artificial neural network (ANN and genetic algorithm (GA techniques were integrated and labeled as integrated ANN-GA to estimate optimal process parameters in GMAW to get optimum dilution.

  5. USING ARTIFICIAL NEURAL NETWORKS (ANNs FOR SEDIMENT LOAD FORECASTING OF TALKHEROOD RIVER MOUTH

    Directory of Open Access Journals (Sweden)

    Vahid Nourani

    2009-01-01

    Full Text Available Without a doubt the carried sediment load by a river is the most important factor in creating and formation of the related Delta in the river mouth. Therefore, accurate forecasting of the river sediment load can play a significant role for study on the river Delta. However considering the complexity and non-linearity of the phenomenon, the classic experimental or physical-based approaches usually could not handle the problem so well. In this paper, Artificial Neural Network (ANN as a non-linear black box interpolator tool is used for modeling suspended sediment load which discharges to the Talkherood river mouth, located in northern west Iran. For this purpose, observed time series of water discharge at current and previous time steps are used as the model input neurons and the model output neuron will be the forecasted sediment load at the current time step. In this way, various schemes of the ANN approach are examined in order to achieve the best network as well as the best architecture of the model. The obtained results are also compared with the results of two other classic methods (i.e., linear regression and rating curve methods in order to approve the efficiency and ability of the proposed method.

  6. Artificial Neural Network (ANN) Model to Predict Depression among Geriatric Population at a Slum in Kolkata, India.

    Science.gov (United States)

    Sau, Arkaprabha; Bhakta, Ishita

    2017-05-01

    Depression is one of the most important causes of mortality and morbidity among the geriatric population. Although, the aging brain is more vulnerable to depression, it cannot be considered as physiological and an inevitable part of ageing. Various sociodemographic and morbidity factors are responsible for the depression among them. Using Artificial Neural Network (ANN) model depression can be predicted from various sociodemographic variables and co morbid conditions even at community level by the grass root level health care workers. To predict depression among geriatric population from sociodemographic and morbidity attributes using ANN. An observational descriptive study with cross-sectional design was carried out at a slum under the service area of Bagbazar Urban Health and Training Centre (UHTC) in Kolkata. Among 126 elderlies under Bagbazar UHTC, 105 were interviewed using predesigned and pretested schedule. Depression status was assessed using 30 item Geriatric Depression Scale. WEKA 3.8.0 was used to develop the ANN model and test its performance. Prevalence of depression among the study population was 45.7%. Various sociodemographic variables like age, gender, literacy, living spouse, working status, personal income, family type, substance abuse and co morbid conditions like visual problem, mobility problem, hearing problem and sleeping problem were taken into consideration to develop the model. Prediction accuracy of this ANN model was 97.2%. Depression among geriatric population can be predicted accurately using ANN model from sociodemographic and morbidity attributes.

  7. [Improving the prediction model of protein in milk powder using GA-PLS combined with PC-ANN arithmetic].

    Science.gov (United States)

    Sun, Qian; Wang, Jia-Hua; Han, Dong-Hai

    2009-07-01

    The present paper presents a new NIR analysis method with partial least square regression (PLS) and artificial neural network (ANN) to improve the prediction precision of the protein model for milk powder. First, an efficient method named region selecting by genetic algorithms (RS-GA) was used to select the calibration region, and then the GA-PLS model was made to predict the linear part of the protein content in milk powder. And then in the region selected by RS-GA method, principal component analysis (PCA) was calculated. The principal components were taken as the input of ANN model. The remnant values by subtracting the standard values and the GA-PLS validation values were regarded as the output of ANN. The ANN model was made to predict the nonlinear part of the protein content. The final result of the model was the addition of the two model's validation values, and the root mean squared error of prediction (RMSEP) was used to estimate the mixed model. A full region PLS model (Fr-PLS) was also made, and the RMSEP of the Fr-PLS, GA-PLS and GA-PLS+PC-ANN model was 0.511, 0.440 and 0.235, respectively. The results show that the prediction precision of the protein model for milk powder was largely improved when adding the nonlinear port in the NIR model, and this method can also be used for other complex material to improve the prediction precision.

  8. Performance of the Angstrom-Prescott Model (A-P) and SVM and ANN techniques to estimate daily global solar irradiation in Botucatu/SP/Brazil

    Science.gov (United States)

    da Silva, Maurício Bruno Prado; Francisco Escobedo, João; Juliana Rossi, Taiza; dos Santos, Cícero Manoel; da Silva, Sílvia Helena Modenese Gorla

    2017-07-01

    This study describes the comparative study of different methods for estimating daily global solar irradiation (H): Angstrom-Prescott (A-P) model and two Machine Learning techniques (ML) - Support Vector Machine (SVM) and Artificial Neural Network (ANN). The H database was measured from 1996 to 2011 in Botucatu/SP/Brazil. Different combinations of input variables were adopted. MBE, RMSE, d Willmott, r and r2 statistical indicators obtained in the validation of A-P and SVM and ANN models showed that: SVM technique has better performance in estimating H than A-P and ANN models. A-P model has better performance in estimating H than ANN.

  9. Comparative study of landslides susceptibility mapping methods: Multi-Criteria Decision Making (MCDM) and Artificial Neural Network (ANN)

    Science.gov (United States)

    Salleh, S. A.; Rahman, A. S. A. Abd; Othman, A. N.; Mohd, W. M. N. Wan

    2018-02-01

    As different approach produces different results, it is crucial to determine the methods that are accurate in order to perform analysis towards the event. This research aim is to compare the Rank Reciprocal (MCDM) and Artificial Neural Network (ANN) analysis techniques in determining susceptible zones of landslide hazard. The study is based on data obtained from various sources such as local authority; Dewan Bandaraya Kuala Lumpur (DBKL), Jabatan Kerja Raya (JKR) and other agencies. The data were analysed and processed using Arc GIS. The results were compared by quantifying the risk ranking and area differential. It was also compared with the zonation map classified by DBKL. The results suggested that ANN method gives better accuracy compared to MCDM with 18.18% higher accuracy assessment of the MCDM approach. This indicated that ANN provides more reliable results and it is probably due to its ability to learn from the environment thus portraying realistic and accurate result.

  10. pK(a) modelling and prediction of drug molecules through GA-KPLS and L-M ANN.

    Science.gov (United States)

    Noorizadeh, H; Farmany, A; Noorizadeh, M

    2013-02-01

    Genetic algorithm and partial least square (GA-PLS), kernel PLS (GA-KPLS) and Levenberg- Marquardt artificial neural network (L-M ANN) techniques were used to investigate the correlation between dissociation constant (pK(a) ) and descriptors for 60 drug compounds. The applied internal (leave-group-out cross validation (LGO-CV)) and external (test set) validation methods were used for the predictive power of models. Descriptors of GA-KPLS model were selected as inputs in L-M ANN model. The results indicate that L-M ANN can be used as an alternative modeling tool for quantitative structure-property relationship (QSPR) studies. Copyright © 2011 John Wiley & Sons, Ltd.

  11. Exploring QSARs of the interaction of flavonoids with GABA (A) receptor using MLR, ANN and SVM techniques.

    Science.gov (United States)

    Deeb, Omar; Shaik, Basheerulla; Agrawal, Vijay K

    2014-10-01

    Quantitative Structure-Activity Relationship (QSAR) models for binding affinity constants (log Ki) of 78 flavonoid ligands towards the benzodiazepine site of GABA (A) receptor complex were calculated using the machine learning methods: artificial neural network (ANN) and support vector machine (SVM) techniques. The models obtained were compared with those obtained using multiple linear regression (MLR) analysis. The descriptor selection and model building were performed with 10-fold cross-validation using the training data set. The SVM and MLR coefficient of determination values are 0.944 and 0.879, respectively, for the training set and are higher than those of ANN models. Though the SVM model shows improvement of training set fitting, the ANN model was superior to SVM and MLR in predicting the test set. Randomization test is employed to check the suitability of the models.

  12. ANN QSAR workflow for predicting the inhibition of HIV-1 reverse transcriptase by pyridinone non-nucleoside derivatives.

    Science.gov (United States)

    Barzegar, Abolfazl; Zamani-Gharehchamani, Elham; Kadkhodaie-Ilkhchi, Ali

    2017-07-01

    Pyridinone derivatives have high potency against non-nucleoside reverse transcriptase inhibitor (NNRTI)-resistant human immunodeficiency virus type-1 strains. Quantitative structure-activity relationship (QSAR) studies on a series of pyridinone derivatives acting as NNRTIs are very important in designing the next generation of NNRTIs. Methodology & results: The QSAR models were developed using linear (single and forward stepwise) and combined nonlinear artificial neural network (ANN) approaches. ANN provided QSAR model with highly correlating values of 0.963, 0.964, 0.920 and 0.917, corresponding to the biological activity pIC 50 of the training, validation, testing and all samples, respectively. The nonlinear ANN-QSAR model based on the topological polarizability, geometrical steric, hydrophobicity and substituted benzene functional group indices might be able to help for designing novel pyridinone NNRTIs.

  13. Strategic planning for minimizing CO2 emissions using LP model based on forecasted energy demand by PSO Algorithm and ANN

    Energy Technology Data Exchange (ETDEWEB)

    Yousefi, M.; Omid, M.; Rafiee, Sh. [Department of Agricultural Machinery Engineering, University of Tehran, Karaj (Iran, Islamic Republic of); Ghaderi, S.F. [Department of Industrial Engineering, University of Tehran, Tehran (Iran, Islamic Republic of)

    2013-07-01

    Iran's primary energy consumption (PEC) was modeled as a linear function of five socioeconomic and meteorological explanatory variables using particle swarm optimization (PSO) and artificial neural networks (ANNs) techniques. Results revealed that ANN outperforms PSO model to predict test data. However, PSO technique is simple and provided us with a closed form expression to forecast PEC. Energy demand was forecasted by PSO and ANN using represented scenario. Finally, adapting about 10% renewable energy revealed that based on the developed linear programming (LP) model under minimum CO2 emissions, Iran will emit about 2520 million metric tons CO2 in 2025. The LP model indicated that maximum possible development of hydropower, geothermal and wind energy resources will satisfy the aim of minimization of CO2 emissions. Therefore, the main strategic policy in order to reduce CO2 emissions would be exploitation of these resources.

  14. Passenger Flows Estimation of Light Rail Transit (LRT System in Izmir, Turkey Using Multiple Regression and ANN Methods

    Directory of Open Access Journals (Sweden)

    Mustafa Özuysal

    2012-01-01

    Full Text Available Passenger flow estimation of transit systems is essential for new decisions about additional facilities and feeder lines. For increasing the efficiency of an existing transit line, stations which are insufficient for trip production and attraction should be examined first. Such investigation supports decisions for feeder line projects which may seem necessary or futile according to the findings. In this study, passenger flow of a light rail transit (LRT system in Izmir, Turkey is estimated by using multiple regression and feed-forward back-propagation type of artificial neural networks (ANN. The number of alighting passengers at each station is estimated as a function of boarding passengers from other stations. It is found that ANN approach produced significantly better estimations specifically for the low passenger attractive stations. In addition, ANN is found to be more capable for the determination of trip-attractive parts of LRT lines.   Keywords: light rail transit, multiple regression, artificial neural networks, public transportation

  15. WEPP and ANN models for simulating soil loss and runoff in a semi-arid Mediterranean region.

    Science.gov (United States)

    Albaradeyia, Issa; Hani, Azzedine; Shahrour, Isam

    2011-09-01

    This paper presents the use of both the Water Erosion Prediction Project (WEPP) and the artificial neural network (ANN) for the prediction of runoff and soil loss in the central highland mountainous of the Palestinian territories. Analyses show that the soil erosion is highly dependent on both the rainfall depth and the rainfall event duration rather than on the rainfall intensity as mostly mentioned in the literature. The results obtained from the WEPP model for the soil loss and runoff disagree with the field data. The WEPP underestimates both the runoff and soil loss. Analyses conducted with the ANN agree well with the observation. In addition, the global network models developed using the data of all the land use type show a relatively unbiased estimation for both runoff and soil loss. The study showed that the ANN model could be used as a management tool for predicting runoff and soil loss.

  16. RETRACTED — Simple and efficient ANN model proposed for the temperature dependence of EDFA gain based on experimental results

    Science.gov (United States)

    Yucel, Murat; Celebi, Fatih V.; Haldun Goktas, H.

    2013-02-01

    This study deals with the Artificial Neural Network (ANN) model of erbium-doped fiber amplifier (EDFA) gain in C band based on our experimental results at the temperature range of 0-60 °C. An ANN with three inputs and one output is considered where the inputs are signal power, wavelength, temperature and the output is EDFA gain. The network parameters are optimized by monitoring mean square error (MSE) at the output. The proposed dynamic model tremendously reduces the computational in the order of milliseconds which computes the EDFA gain at different operating conditions and is in very good agreement with our experimental findings.

  17. Retours d'expérience sur deux années de Mooc Inria.

    OpenAIRE

    Mariais, Christelle; Comte, Marie-Hélène; Rey, Isabelle; Bayle, Aurélie; Hasenfratz, Jean-Marc

    2016-01-01

    Entre novembre 2014 et juin 2015, six Mooc (Massive Open Online Courses) Inria ont été diffusés sur la plateforme France UniversitéNumérique représentant deux années d’activité du Mooc Lab Inria. Cette activité découle d’une volonté politique d’Inria de comprendrece nouvel outil numérique de formation et a été soutenue dans le cadre du projet uTop. Ce document se veut être un recueil de données quantitatives, qualitatives et de réflexions. Il est construit sous la forme d’une série de questio...

  18. Application of ANN and PCA to two-phase flow evaluation using radioisotopes

    Directory of Open Access Journals (Sweden)

    Hanus Robert

    2017-01-01

    Full Text Available In the two-phase flow measurements a method involving the absorption of gamma radiation can be applied among others. Analysis of the signals from the scintillation probes can be used to determine the number of flow parameters and to recognize flow structure. Three types of flow regimes as plug, bubble, and transitional plug – bubble flows were considered in this work. The article shows how features of the signals in the time and frequency domain can be used to build the artificial neural network (ANN to recognize the structure of the gas-liquid flow in a horizontal pipeline. In order to reduce the number of signal features the principal component analysis (PCA was used. It was found that the reduction of signals features allows for building a network with better performance.

  19. "You, I, we created the poet": Anne Sexton's recorded therapy, November 1963.

    Science.gov (United States)

    Skorczewski, Dawn

    2010-06-01

    In 1991, when it was revealed that the psychiatrist Martin Orne had released tapes of his therapy sessions with Anne Sexton to her biographer, mental health professionals expressed concern and outrage. Those who actually listen to the controversial tapes would be curious to find a debate between Sexton and Orne about mental illness, creativity, and therapeutic process. To what extent did Sexton's creative accomplishments point to aspects of her psychiatric progress that might otherwise have been overlooked? While Sexton asserted her achievements and sought affirmation from her psychiatrist, Orne persistently responded by stating that the poetry is not as important as the person. Their different ways of understanding the relationships between poetry and therapeutic process speak volumes about the power of the creative imagination to challenge existing structures of thought, even structures designed to define the psyche itself.

  20. Results of the radiological survey at 14 Saint Ann Place, Rochelle Park, New Jersey (MJ032)

    International Nuclear Information System (INIS)

    Foley, R.D.; Floyd, L.M.

    1990-03-01

    Maywood Chemical Works (MCW) of Maywood, New Jersey, generated process wastes and residues associated with the production and refining of thorium and thorium compounds from monazite ores from 1916 to 1956. At the request of the US Department of Energy (DOE), a group from Oak Ridge National Laboratory conducts investigative radiological surveys of properties in the vicinity of MCW to determine whether a property is contaminated with radioactive residues, principally 232 Th, derived from the MCW site. The survey typically includes direct measurement of gamma radiation levels and soil sampling for radionuclide analyses. The survey of this site, 14 Saint Ann Place, Rochelle Park, New Jersey (MJ032), was conducted during 1987. 4 refs., 3 tabs

  1. Artificial neural networks (ANN: prediction of sensory measurements from instrumental data

    Directory of Open Access Journals (Sweden)

    Naiara Barbosa Carvalho

    2013-12-01

    Full Text Available The objective of this study was to predict by means of Artificial Neural Network (ANN, multilayer perceptrons, the texture attributes of light cheesecurds perceived by trained judges based on instrumental texture measurements. Inputs to the network were the instrumental texture measurements of light cheesecurd (imitative and fundamental parameters. Output variables were the sensory attributes consistency and spreadability. Nine light cheesecurd formulations composed of different combinations of fat and water were evaluated. The measurements obtained by the instrumental and sensory analyses of these formulations constituted the data set used for training and validation of the network. Network training was performed using a back-propagation algorithm. The network architecture selected was composed of 8-3-9-2 neurons in its layers, which quickly and accurately predicted the sensory texture attributes studied, showing a high correlation between the predicted and experimental values for the validation data set and excellent generalization ability, with a validation RMSE of 0.0506.

  2. ANN and RSM approach for modelling and multi objective optimization of abrasive water jet machining process

    Directory of Open Access Journals (Sweden)

    Srinath Reddy N.

    2018-09-01

    Full Text Available Abrasive Water Jet Machining is one of the novel nontraditional cutting processes found diverse applications in machining different kinds of difficult-to-machine materials. Process parameters play an important role in finding the economics of machining process at good quality. This research focused on the predictive models for explaining the functional relationship between input and output parameters of AWJ machining process. No single set of parametric combination of machining variables can suggest the better responses concurrently, due to its conflicting nature. Hence, an approach of Multi-objective has been attempted for the best combination of process parameters by modelling AWJM process using of ANN. It served a set of optimal process parameters to AWJ machining process, which shows a development with an enhanced productivity. Wide set of trail experiments have been considered with a broader range of machining parameters for modelling and, then, for validating. The model is capable of predicting optimized responses.

  3. CUDA-accelerated genetic feedforward-ANN training for data mining

    International Nuclear Information System (INIS)

    Patulea, Catalin; Peace, Robert; Green, James

    2010-01-01

    We present an implementation of genetic algorithm (GA) training of feedforward artificial neural networks (ANNs) targeting commodity graphics cards (GPUs). By carefully mapping the problem onto the unique GPU architecture, we achieve order-of-magnitude speedup over a conventional CPU implementation. Furthermore, we show that the speedup is consistent across a wide range of data set sizes, making this implementation ideal for large data sets. This performance boost enables the genetic algorithm to search a larger subset of the solution space, which results in more accurate pattern classification. Finally, we demonstrate this method in the context of the 2009 UC San Diego Data Mining Contest, achieving a world-class lift on a data set of 94682 e-commerce transactions.

  4. CUDA-accelerated genetic feedforward-ANN training for data mining

    Energy Technology Data Exchange (ETDEWEB)

    Patulea, Catalin; Peace, Robert; Green, James, E-mail: cpatulea@sce.carleton.ca, E-mail: rpeace@sce.carleton.ca, E-mail: jrgreen@sce.carleton.ca [School of Systems and Computer Engineering, Carleton University, Ottawa, K1S 5B6 (Canada)

    2010-11-01

    We present an implementation of genetic algorithm (GA) training of feedforward artificial neural networks (ANNs) targeting commodity graphics cards (GPUs). By carefully mapping the problem onto the unique GPU architecture, we achieve order-of-magnitude speedup over a conventional CPU implementation. Furthermore, we show that the speedup is consistent across a wide range of data set sizes, making this implementation ideal for large data sets. This performance boost enables the genetic algorithm to search a larger subset of the solution space, which results in more accurate pattern classification. Finally, we demonstrate this method in the context of the 2009 UC San Diego Data Mining Contest, achieving a world-class lift on a data set of 94682 e-commerce transactions.

  5. Monica Manolescu et Anne-Marie Paquet-Deyris. Lolita, cartographies de l’obsession

    Directory of Open Access Journals (Sweden)

    René ALLADAYE

    2011-03-01

    Full Text Available Près de quinze ans après le travail de Maurice Couturier, c’était un défi que de publier un nouveau cours du CNED consacré à Lolita à l’occasion de la réapparition du roman au programme de l’agrégation. Certes, la donne a un peu changé depuis 1995 puisque ce programme s’enrichit de la présence de l’adaptation cinématographique de Stanley Kubrick, mais l’aventure n’en demeurait pas moins risquée. Ce défi, Monica Manolescu et Anne-Marie Paquet-Deyris le relèvent avec brio dans Lolita, cartograp...

  6. Assessing the long term impact of phosphorus fertilization on phosphorus loadings using AnnAGNPS.

    Science.gov (United States)

    Yuan, Yongping; Bingner, Ronald L; Locke, Martin A; Stafford, Jim; Theurer, Fred D

    2011-06-01

    High phosphorus (P) loss from agricultural fields has been an environmental concern because of potential water quality problems in streams and lakes. To better understand the process of P loss and evaluate the effects of different phosphorus fertilization rates on phosphorus losses, the USDA Annualized AGricultural Non-Point Source (AnnAGNPS) pollutant loading model was applied to the Ohio Upper Auglaize watershed, located in the southern portion of the Maumee River Basin. In this study, the AnnAGNPS model was calibrated using USGS monitored data; and then the effects of different phosphorus fertilization rates on phosphorus loadings were assessed. It was found that P loadings increase as fertilization rate increases, and long term higher P application would lead to much higher P loadings to the watershed outlet. The P loadings to the watershed outlet have a dramatic change after some time with higher P application rate. This dramatic change of P loading to the watershed outlet indicates that a "critical point" may exist in the soil at which soil P loss to water changes dramatically. Simulations with different initial soil P contents showed that the higher the initial soil P content is, the less time it takes to reach the "critical point" where P loadings to the watershed outlet increases dramatically. More research needs to be done to understand the processes involved in the transfer of P between the various stable, active and labile states in the soil to ensure that the model simulations are accurate. This finding may be useful in setting up future P application and management guidelines.

  7. Hybrid intelligence systems and artificial neural network (ANN approach for modeling of surface roughness in drilling

    Directory of Open Access Journals (Sweden)

    Ch. Sanjay

    2014-12-01

    Full Text Available In machining processes, drilling operation is material removal process that has been widely used in manufacturing since industrial revolution. The useful life of cutting tool and its operating conditions largely controls the economics of machining operations. Drilling is most frequently performed material removing process and is used as a preliminary step for many operations, such as reaming, tapping, and boring. Drill wear has a bad effect on the surface finish and dimensional accuracy of the work piece. The surface finish of a machined part is one of the most important quality characteristics in manufacturing industries. The primary objective of this research is the prediction of suitable parameters for surface roughness in drilling. Cutting speed, cutting force, and machining time were given as inputs to the adaptive fuzzy neural network and neuro-fuzzy analysis for estimating the values of surface roughness by using 2, 3, 4, and 5 membership functions. The best structures were selected based on minimum of summation of square with the actual values with the estimated values by artificial neural fuzzy inference system (ANFIS and neuro-fuzzy systems. For artificial neural network (ANN analysis, the number of neurons was selected from 1, 2, 3, … , 20. The learning rate was selected as .5 and .5 smoothing factor was used. The inputs were selected as cutting speed, feed, machining time, and thrust force. The best structures of neural networks were selected based on the criteria as the minimum of summation of square with the actual value of surface roughness. Drilling experiments with 10 mm size were performed at two cutting speeds and feeds. Comparative analysis has been done between the actual values and the estimated values obtained by ANFIS, neuro-fuzzy, and ANN analysis.

  8. Evaluation of a non-point source pollution model, AnnAGNPS, in a tropical watershed

    Science.gov (United States)

    Polyakov, V.; Fares, A.; Kubo, D.; Jacobi, J.; Smith, C.

    2007-01-01

    Impaired water quality caused by human activity and the spread of invasive plant and animal species has been identified as a major factor of degradation of coastal ecosystems in the tropics. The main goal of this study was to evaluate the performance of AnnAGNPS (Annualized Non-Point Source Pollution Model), in simulating runoff and soil erosion in a 48 km2 watershed located on the Island of Kauai, Hawaii. The model was calibrated and validated using 2 years of observed stream flow and sediment load data. Alternative scenarios of spatial rainfall distribution and canopy interception were evaluated. Monthly runoff volumes predicted by AnnAGNPS compared well with the measured data (R2 = 0.90, P < 0.05); however, up to 60% difference between the actual and simulated runoff were observed during the driest months (May and July). Prediction of daily runoff was less accurate (R2 = 0.55, P < 0.05). Predicted and observed sediment yield on a daily basis was poorly correlated (R2 = 0.5, P < 0.05). For the events of small magnitude, the model generally overestimated sediment yield, while the opposite was true for larger events. Total monthly sediment yield varied within 50% of the observed values, except for May 2004. Among the input parameters the model was most sensitive to the values of ground residue cover and canopy cover. It was found that approximately one third of the watershed area had low sediment yield (0-1 t ha-1 y-1), and presented limited erosion threat. However, 5% of the area had sediment yields in excess of 5 t ha-1 y-1. Overall, the model performed reasonably well, and it can be used as a management tool on tropical watersheds to estimate and compare sediment loads, and identify "hot spots" on the landscape. ?? 2007 Elsevier Ltd. All rights reserved.

  9. Visit of Mme Anne-Marie Comparini, President of the Rhône-Alpes regional authority.

    CERN Document Server

    Patrice Loïez

    2001-01-01

    Photo 11: Signature of the Guests Book by Mr Jean Pépin, Président du Conseil général de l'Ain, on the occasion of the visit of Mrs Anne-Marie Comparini, Présidente du Conseil régional de Rhones-Alpes and Mr Ernest Nycollin, Président du Conseil général de la Haute-Savoie, at SM18. Here with Prof. Luciano Maiani, CERN Director General. Photo 18: Technicians and engineers of the Rhone-Alpes/CERN programme(PRAC) at SM18 on the occasion of the visit of Mrs Anne-Marie Comparini, Présidente du Conseil régional de Rhones-Alpes. From l. to r.:Prof. Luciano Maiani, Directeur General, CERN; Mr Ernest Nycollin, Président du Conseil général de la Haute-Savoie; Mrs Anne-Marie Comparini, Présidente du Conseil régional de Rhones-Alpes; Mr Jean Pépin, Président du Conseil général de l'Ain. Photo 20 : Mrs Anne-Marie Comparini, Présidente du Conseil régional de Rhones-Alpes during her visit at SM18 with technicians and engineers of the Rhone-Alpes/CERN programme (PRAC).

  10. Finding the Hole in the Wreck: Shamanic practice in the Poetry of Adrienne Rich and Anne Sexton

    DEFF Research Database (Denmark)

    Elias, Camelia

    2013-01-01

    to argue that both Adrienne Rich and Anne Sexton manipulate with visualization techniques in their symbolic imagery in order to create an atmosphere that is akin to a shamanic journey. The poetic examples that I want to discuss demonstrate how the “language of the suicides” (Sexton) and “the thing itself...

  11. Aivar Riisalu : loomulikult läksin üle piiri ja vabandan / Aivar Riisalu ; interv. Anneli Ammas

    Index Scriptorium Estoniae

    Riisalu, Aivar, 1961-

    2006-01-01

    Aivar Riisalu selgitab oma mõtteavaldusi Kanal 2 telesaates "Top 10" ning ütleb, et temas ei ole rassivaenu. Vt. samas: Anneli Ammas, Sigrid Laev. Kaitsepolitsei võib algatada kriminaalasja; Sigrid Laev. Riisalu pääses kriminaalmenetlusest. Lisa: Riisalu avaldused "Top 10" saates

  12. Quantification of phenylpropanoids in commercial Echinacea products using TLC with video densitometry as detection technique and ANN for data modelling.

    Science.gov (United States)

    Agatonovic-Kustrin, S; Loescher, Christine M; Singh, Ragini

    2013-01-01

    Echinacea preparations are among the most popular herbal remedies worldwide. Although it is generally assigned immune enhancement activities, the effectiveness of Echinacea is highly dependent on the Echinacea species, part of the plant used, the age of the plant, its location and the method of extraction. The aim of this study was to investigate the capacity of an artificial neural network (ANN) to analyse thin-layer chromatography (TLC) chromatograms as fingerprint patterns for quantitative estimation of three phenylpropanoid markers (chicoric acid, chlorogenic acid and echinacoside) in commercial Echinacea products. By applying samples with different weight ratios of marker compounds to the system, a database of chromatograms was constructed. One hundred and one signal intensities in each of the TLC chromatograms were correlated to the amounts of applied echinacoside, chlorogenic acid and chicoric acid using an ANN. The developed ANN correlation was used to quantify the amounts of three marker compounds in Echinacea commercial formulations. The minimum quantifiable level of 63, 154 and 98 ng and the limit of detection of 19, 46 and 29 ng were established for echinacoside, chlorogenic acid and chicoric acid respectively. A novel method for quality control of herbal products, based on TLC separation, high-resolution digital plate imaging and ANN data analysis has been developed. The method proposed can be adopted for routine evaluation of the phytochemical variability in Echinacea formulations available in the market. Copyright © 2012 John Wiley & Sons, Ltd.

  13. Estimation of the phenolic waste attenuation capacity of some fine-grained soils with the help of ANN modeling.

    Science.gov (United States)

    Pal, Supriya; Mukherjee, Somnath; Ghosh, Sudipta

    2014-03-01

    In the present investigation, batch experiments were undertaken in the laboratory for different initial phenol concentration ranging from 10 to 40 mg/L using various types of fine-grained soils namely types A, B, C, D, and E based on physical compositions. The batch kinetic data were statistically analyzed with a three-layered feed-forward artificial neural network (ANN) model for predicting the phenol removal efficiency from the water environment. The input parameters considered were the adsorbent dose, initial phenol concentration, contact time, and percentage of clay and silt content in soils. The response output of the ANN model was considered as the phenol removal efficiency. The predicted results of phenol removal efficiency were compared with the experimental values as obtained from batch tests and also tests for goodness of fitting in ANN model with experimental results. The estimated values of coefficient of correlation (R = 0.99) and mean squared error (MSE = 0.006) reveals a reasonable closeness of experimental and predicted values. Out of five different types of soil, type E exhibited the highest removal efficiency (31.6 %) corresponding to 20 mg/L of initial phenol concentration. A sensitivity analysis was also carried out on the ANN model to ascertain the degree of effectiveness of various input variables.

  14. Ann Hutchinson (as subject), Dr. Joan Vernikos (R), Dee O'Hara (L), J. Evans and E. Lowe pose for

    Science.gov (United States)

    1993-01-01

    Ann Hutchinson (as subject), Dr. Joan Vernikos (R), Dee O'Hara (L), J. Evans and E. Lowe pose for pictures in the NASA Magazine aritcle 'How it Feels to be a Human Test Subject' as they prepare for a bed rest study to simulate the efects of microgravity on the human body.

  15. Small City Transit : Ann Arbor, Michigan : Pilot Dial-A-Ride Project in a Sector of the City

    Science.gov (United States)

    1976-03-01

    Ann Arbor, Michigan, is an illustration of a pilot dial-a-ride project implemented to test the feasibility of a coordinated dial-a-ride/fixed route service. This case study is one of thirteen examples of a transit service in a samll community. The ba...

  16. Modelling and optimization of Mn/activate carbon nanocatalysts for NO reduction: comparison of RSM and ANN techniques.

    Science.gov (United States)

    Mousavi, Seyed Mahdi; Niaei, Aligholi; Salari, Dariush; Panahi, Parvaneh Nakhostin; Samandari, Masoud

    2013-01-01

    A response surface methodology (RSM) involving a central composite design was applied to the modelling and optimization of a preparation of Mn/active carbon nanocatalysts in NH3-SCR of NO at 250 degrees C and the results were compared with the artificial neural network (ANN) predicted values. The catalyst preparation parameters, including metal loading (wt%), calcination temperature and pre-oxidization degree (v/v% HNO3) were selected as influence factors on catalyst efficiency. In the RSM model, the predicted values of NO conversion were found to be in good agreement with the experimental values. Pareto graphic analysis showed that all the chosen parameters and some of the interactions were effective on response. The optimization results showed that maximum NO conversion was achieved at the optimum conditions: 10.2 v/v% HNO3, 6.1 wt% Mn loading and calcination at 480 degrees C. The ANN model was developed by a feed-forward back propagation network with the topology 3, 8 and 1 and a Levenberg-Marquardt training algorithm. The mean square error for the ANN and RSM models were 0.339 and 1.176, respectively, and the R2 values were 0.991 and 0.972, respectively, indicating the superiority of ANN in capturing the nonlinear behaviour of the system and being accurate in estimating the values of the NO conversion.

  17. Ann modeling of kerf transfer in Co2 laser cutting and optimization of cutting parameters using monte carlo method

    Directory of Open Access Journals (Sweden)

    Miloš Madić

    2015-01-01

    Full Text Available In this paper, an attempt has been made to develop a mathematical model in order to study the relationship between laser cutting parameters such as laser power, cutting speed, assist gas pressure and focus position, and kerf taper angle obtained in CO2 laser cutting of AISI 304 stainless steel. To this aim, a single hidden layer artificial neural network (ANN trained with gradient descent with momentum algorithm was used. To obtain an experimental database for the ANN training, laser cutting experiment was planned as per Taguchi’s L27 orthogonal array with three levels for each of the cutting parameters. Statistically assessed as adequate, ANN model was then used to investigate the effect of the laser cutting parameters on the kerf taper angle by generating 2D and 3D plots. It was observed that the kerf taper angle was highly sensitive to the selected laser cutting parameters, as well as their interactions. In addition to modeling, by applying the Monte Carlo method on the developed kerf taper angle ANN model, the near optimal laser cutting parameter settings, which minimize kerf taper angle, were determined.

  18. Anne K. Bang: Islamic Sufi Networks in the Western Indian Ocean (c. 1880-1940. Ripples of Reform.

    Directory of Open Access Journals (Sweden)

    Angelika Brodersen

    2015-03-01

    Full Text Available This contribution offers a review of Anne K. Bang's book: Islamic Sufi Networks in the Western Indian Ocean (c. 1880-1940. Ripples of Reform. Islam in Africa, Volume 16. Leiden: Brill 2014. xiv + 227 pages, € 104.00, ISBN 978-900-425-1342.

  19. DeAnnIso: a tool for online detection and annotation of isomiRs from small RNA sequencing data.

    Science.gov (United States)

    Zhang, Yuanwei; Zang, Qiguang; Zhang, Huan; Ban, Rongjun; Yang, Yifan; Iqbal, Furhan; Li, Ao; Shi, Qinghua

    2016-07-08

    Small RNA (sRNA) Sequencing technology has revealed that microRNAs (miRNAs) are capable of exhibiting frequent variations from their canonical sequences, generating multiple variants: the isoforms of miRNAs (isomiRs). However, integrated tool to precisely detect and systematically annotate isomiRs from sRNA sequencing data is still in great demand. Here, we present an online tool, DeAnnIso (Detection and Annotation of IsomiRs from sRNA sequencing data). DeAnnIso can detect all the isomiRs in an uploaded sample, and can extract the differentially expressing isomiRs from paired or multiple samples. Once the isomiRs detection is accomplished, detailed annotation information, including isomiRs expression, isomiRs classification, SNPs in miRNAs and tissue specific isomiR expression are provided to users. Furthermore, DeAnnIso provides a comprehensive module of target analysis and enrichment analysis for the selected isomiRs. Taken together, DeAnnIso is convenient for users to screen for isomiRs of their interest and useful for further functional studies. The server is implemented in PHP + Perl + R and available to all users for free at: http://mcg.ustc.edu.cn/bsc/deanniso/ and http://mcg2.ustc.edu.cn/bsc/deanniso/. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

  20. Application of Artificial Neural Networks (ANNs for Weight Predictions of Blue Crabs (Callinectes sapidus RATHBUN, 1896 Using Predictor Variables

    Directory of Open Access Journals (Sweden)

    C. TURELI BILEN

    2011-10-01

    Full Text Available An evaluation of the performance of artificial networks (ANNs to estimate the weights of blue crab (Callinectes sapidus catches in Yumurtalık Cove (Iskenderun Bay that uses measured predictor variables is presented, including carapace width (CW, sex (male, female and female with eggs, and sampling month. Blue crabs (n=410 were collected each month between 15 September 1996 and 15 May 1998. Sex, CW, and sampling month were used and specified in the input layer of the network. The weights of the blue crabs were utilized in the output layer of the network. A multi-layer perception architecture model was used and was calibrated with the Levenberg Marguardt (LM algorithm. Finally, the values were determined by the ANN model using the actual data. The mean square error (MSE was measured as 3.3, and the best results had a correlation coefficient (R of 0.93. We compared the predictive capacity of the general linear model (GLM versus the Artificial Neural Network model (ANN for the estimation of the weights of blue crabs from independent field data. The results indicated the higher performance capacity of the ANN to predict weights compared to the GLM (R=0.97 vs. R=0.95, raw variable when evaluated against independent field data.

  1. 76 FR 44947 - Notice of Intent To Repatriate Cultural Items: University of Michigan Museum of Anthropology, Ann...

    Science.gov (United States)

    2011-07-27

    ... Cultural Items: University of Michigan Museum of Anthropology, Ann Arbor, MI AGENCY: National Park Service, Interior. ACTION: Notice. SUMMARY: The University of Michigan Museum of Anthropology, in consultation with... contact the University of Michigan Museum of Anthropology. DATES: Representatives of any Indian tribe that...

  2. Tunnustati parimaid üliõpilasteadlasi / Elsa Suuster, Marit Alas, Annely Aleksejev ; küsitlenud Vilma Rauniste

    Index Scriptorium Estoniae

    Putku, Elsa, 1984-

    2009-01-01

    Vestlus üliõpilaste teadustööde riikliku konkursi kolme laureaadiga, kelle uurimistöö oli tehtud Saare maakonnas. Nendeks on Elsa Suuster ja Annely Aleksejev Eesti Maaülikoolist ning Marit Alas Tallinna Ülikoolist

  3. Tandem Paul ja Anne Daniela Rodgers, Kaunase modernistid ja arenev Meiu Münt / Juta Kivimäe

    Index Scriptorium Estoniae

    Kivimäe, Juta, 1952-

    2006-01-01

    Kuni 23. IV Tartu Kunstimuuseumis avatud Paul ja Anne Daniela Rodgersi näitusest. 15. II-13. III Tartu kaheksas galeriis avatud Kaunase kunstnike näitusest, Arvidas Zhalpyse installatsioonist "Kõik toolist" ja Agne Jonkute maalist "Olematuse faktid" Tartu Kunstimajas. Kuni 25. III G-galeriis avatud Meiu Mündi näitusest "Päev on pime"

  4. Konverentsimuljeid tegijailt ja osalejailt / Monika Läänesaar, Ann Lumiste, Andrus Laansalu...[jt.] ; interv. Madis Kolk

    Index Scriptorium Estoniae

    2000-01-01

    Rahvusvahelisest konverentsist "Lavailm" ja näitusest "Teatrikunstnik...kes ta on?" (kuraator Ann Lumiste) Rotermanni soolalaos, teatrikunstniku positsioonist teatris, uue meedia ja teatri koostöö võimalikkusest, keskkonna ja visuaalsete elementide mõju lavastaja- ja näitlejatööle.

  5. Kuis saada läbi Siirita? / Mari-Ann Kelam, Erika Salumäe, Sirje Endre

    Index Scriptorium Estoniae

    Kelam, Mari-Ann, 1946-

    2001-01-01

    Riigikogu uude juhatusse ei kuulu ühtegi naist : Keskerakond valis Siiri Oviiri asemele Riigikogu aseesimehe kohale Peeter Kreitzbergi. Naiste osalemisvõimalust poliitikas kommenteerivad Riigikogu naisliikmed Mari-Ann Kelam, Erika Salumäe, Sirje Endre, Jaana Padrik, Viive Rosenberg, Mai Treial, Ivi Eenmaa, Liis Klaar, Liia Hänni, Laine Tarvis

  6. Prediction by Artificial Neural Networks (ANN of the diffusivity, mass, moisture, volume and solids on osmotically dehydrated yacon (Smallantus sonchifolius

    Directory of Open Access Journals (Sweden)

    Julio Rojas Naccha

    2012-09-01

    Full Text Available The predictive ability of Artificial Neural Network (ANN on the effect of the concentration (30, 40, 50 y 60 % w/w and temperature (30, 40 y 50°C of fructooligosaccharides solution, in the mass, moisture, volume and solids of osmodehydrated yacon cubes, and in the coefficients of the water means effective diffusivity with and without shrinkage was evaluated. The Feedforward type ANN with the Backpropagation training algorithms and the Levenberg-Marquardt weight adjustment was applied, using the following topology: 10-5 goal error, 0.01 learning rate, 0.5 moment coefficient, 2 input neurons, 6 output neurons, one hidden layer with 18 neurons, 15 training stages and logsig-pureline transfer functions. The overall average error achieved by the ANN was 3.44% and correlation coefficients were bigger than 0.9. No significant differences were found between the experimental values and the predicted values achieved by the ANN and with the predicted values achieved by a statistical model of second-order polynomial regression (p > 0.95.

  7. Inverse problems using ANN in long range atmospheric dispersion with signature analysis picked scattered numerical sensors from CFD

    International Nuclear Information System (INIS)

    Sharma, Pavan K.; Gera, B.; Ghosh, A.K.; Kushwaha, H.S.

    2010-01-01

    Scalar dispersion in the atmosphere is an important area wherein different approaches are followed in development of good analytical model. The analyses based on Computational Fluid Dynamics (CFD) codes offer an opportunity of model development based on first principles of physics and hence such models have an edge over the existing models. Both forward and backward calculation methods are being developed for atmospheric dispersion around NPPs at BARC Forward modeling methods, which describe the atmospheric transport from sources to receptors, use forward-running transport and dispersion models or computational fluid dynamics models which are run many times, and the resulting dispersion field is compared to observations from multiple sensors. Backward or inverse modeling methods use only one model run in the reverse direction from the receptors to estimate the upwind sources. Inverse modeling methods include adjoint and tangent linear models, Kalman filters, and variational data assimilation, and neural network. The present paper is aimed at developing a new approach where the identified specific signatures at receptor points form the basis for source estimation or inversions. This approach is expected to reduce the large transient data sets to reduced and meaningful data sets. In fact this reduces the inherently transient data set into a time independent mean data set. Forward computation were carried out with CFD code for various case to generate a large set of data to train the ANN. Specific signature analysis was carried out to find the parameters of interest for ANN training like peak concentration, time to reach peak concentration and time to fall, the ANN was trained with data and source strength and location were predicted from ANN. Inverse problem was performed using ANN approach in long range atmospheric dispersion. An illustration of application of CFD code for atmospheric dispersion studies for a hypothetical case is also included in the paper. (author)

  8. Comparison of TS and ANN Models with the Results of Emission Scenarios in Rainfall Prediction

    Directory of Open Access Journals (Sweden)

    S. Babaei Hessar

    2016-02-01

    Full Text Available Introduction: Precipitation is one of the most important and sensitive parameters of the tropical climate that influence the catchments hydrological regime. The prediction of rainfall is vital for strategic planning and water resources management. Despite its importance, statistical rainfall forecasting, especially for long-term, has been proven to be a great challenge due to the dynamic nature of climate phenomena and random fluctuations involved in the process. Various methods, such as time series and artificial neural network models, have been proposed to predict the level of rainfall. But there is not enough attention to global warming and climate change issues. The main aim of this study is to investigate the conformity of artificial neural network and time series models with climate scenarios. Materials and Methods: For this study, 50 years of daily rainfall data (1961 to 2010 of the synoptic station of Urmia, Tabriz and Khoy was investigated. Data was obtained from Meteorological Organization of Iran. In the present study, the results of two Artificial Neural Network (ANN and Time Seri (TS methods were compared with the result of the Emission Scenarios (A2 & B1. HadCM3 model in LARS-WG software was used to generate rainfall for the next 18 years (2011-2029. The results of models were compared with climate scenarios over the next 18 years in the three synoptic stations located in the basin of the Lake Urmia. At the first stage, the best model of time series method was selected. The precipitation was estimated for the next 18 years using these models. For the same period, precipitation was forecast using artificial neural networks. Finally, the results of two models were compared with data generated under two scenarios (B1 and A2 in LARS-WG. Results and Discussion: Different order of AR, MA and ARMA was examined to select the best model of TS The results show that AR(1 was suitable for Tabriz and Khoy stations .In the Urmia station MA(1 was

  9. A Hybrid ANN-GA Model to Prediction of Bivariate Binary Responses: Application to Joint Prediction of Occurrence of Heart Block and Death in Patients with Myocardial Infarction.

    Science.gov (United States)

    Mirian, Negin-Sadat; Sedehi, Morteza; Kheiri, Soleiman; Ahmadi, Ali

    2016-01-01

    In medical studies, when the joint prediction about occurrence of two events should be anticipated, a statistical bivariate model is used. Due to the limitations of usual statistical models, other methods such as Artificial Neural Network (ANN) and hybrid models could be used. In this paper, we propose a hybrid Artificial Neural Network-Genetic Algorithm (ANN-GA) model to prediction the occurrence of heart block and death in myocardial infarction (MI) patients simultaneously. For fitting and comparing the models, 263 new patients with definite diagnosis of MI hospitalized in Cardiology Ward of Hajar Hospital, Shahrekord, Iran, from March, 2014 to March, 2016 were enrolled. Occurrence of heart block and death were employed as bivariate binary outcomes. Bivariate Logistic Regression (BLR), ANN and hybrid ANN-GA models were fitted to data. Prediction accuracy was used to compare the models. The codes were written in Matlab 2013a and Zelig package in R3.2.2. The prediction accuracy of BLR, ANN and hybrid ANN-GA models was obtained 77.7%, 83.69% and 93.85% for the training and 78.48%, 84.81% and 96.2% for the test data, respectively. In both training and test data set, hybrid ANN-GA model had better accuracy. ANN model could be a suitable alternative for modeling and predicting bivariate binary responses when the presuppositions of statistical models are not met in actual data. In addition, using optimization methods, such as hybrid ANN-GA model, could improve precision of ANN model.

  10. Using an Artificial Neural Networks (ANNs) Model for Prediction of Intensive Care Unit (ICU) Outcome and Length of Stay at Hospital in Traumatic Patients.

    Science.gov (United States)

    Gholipour, Changiz; Rahim, Fakher; Fakhree, Abolghasem; Ziapour, Behrad

    2015-04-01

    Currently applications of artificial neural network (ANN) models in outcome predicting of patients have made considerable strides in clinical medicine. This project aims to use a neural network for predicting survival and length of stay of patients in the ward and the intensive care unit (ICU) of trauma patients and to obtain predictive power of the current method. We used Neuro-Solution software (NS), a leading-edge neural network software for data mining to create highly accurate and predictive models using advanced preprocessing techniques, intelligent automated neural network topology through cutting-edge distributed computing. This ANN model was used based on back-propagation, feed forward, and fed by Trauma and injury severity score (TRISS) components, biochemical findings, risk factors and outcome of 95 patients. In the next step a trained ANN was used to predict outcome, ICU and ward length of stay for 30 test group patients by processing primary data. The sensitivity and specificity of an ANN for predicting the outcome of traumatic patients in this study calculated 75% and 96.26%, respectively. 93.33% of outcome predictions obtained by ANN were correct. In 3.33% of predictions, results of ANN were optimistic and 3.33% of cases predicted ANN results were worse than the actual outcome of patients. Neither difference in average length of stay in the ward and ICU with predicted ANN results, were statistically significant. Correlation coefficient of two variables of ANN prediction and actual length of stay in hospital was equal to 0.643. Using ANN model based on clinical and biochemical variables in patients with moderate to severe traumatic injury, resulted in satisfactory outcome prediction when applied to a test set.

  11. AnnAGNPS Model Application for Nitrogen Loading Assessment for the Future Midwest Landscape Study

    Directory of Open Access Journals (Sweden)

    Michael A. Jackson

    2011-02-01

    Full Text Available The Future Midwest Landscape (FML project is part of the US Environmental Protection Agency (EPA’s new Ecosystem Services Research Program, undertaken to examine the variety of ways in which landscapes that include crop lands, conservation areas, wetlands, lakes, and streams affect human well-being. The goal of the FML project is to quantify current and future ecosystem services across the region and to examine changes expected to occur as a result of the growing demand for biofuels. This study is one of several pilots taking place under the umbrella of the FML research project. In this study, the USDA Annualized Agricultural Non-Point Source Pollution (AnnAGNPS model was applied to the East Fork Kaskaskia River watershed (289.3 km2 located in the Kaskaskia River Basin within the Upper Mississippi River Basin in Illinois. The effect of different spatial resolutions on model performance was investigated by comparing the observed runoff with the AnnAGNPS simulated results. Alternative future scenarios such as meeting future biofuel target were also simulated and analyzed. All delineations of the study area (coarser to finer produced satisfactory results in simulating monthly and annual runoff. However, the size of the delineation does impact the simulation results. Finer delineations better represented the actual landscape and captured small critical areas that would be homogenized in coarser delineation. Those small critical areas are important to target to achieve maximum environment benefit. Simulations of alternative future scenarios showed that as corn production increases to meet future biofuel needs, total nitrogen loss increases. For this watershed, total N loss would be more than doubled if converting all corn/soybean rotation (15,871.2 ha to continuous corn comparing with the base year total N loss which is 11.2 kg/ha. Conservation practices are needed to reduce total nitrogen loss from the watershed. This study provides an important

  12. ANN modelling of sediment concentration in the dynamic glacial environment of Gangotri in Himalaya.

    Science.gov (United States)

    Singh, Nandita; Chakrapani, G J

    2015-08-01

    The present study explores for the first time the possibility of modelling sediment concentration with artificial neural networks (ANNs) at Gangotri, the source of Bhagirathi River in the Himalaya. Discharge, rainfall and temperature have been considered as the main controlling factors of variations in sediment concentration in the dynamic glacial environment of Gangotri. Fourteen feed forward neural networks with error back propagation algorithm have been created, trained and tested for prediction of sediment concentration. Seven models (T1-T7) have been trained and tested in the non-updating mode whereas remaining seven models (T1a-T7a) have been trained in the updating mode. The non-updating mode refers to the scenario where antecedent time (previous time step) values are not used as input to the model. In case of the updating mode, antecedent time values are used as network inputs. The inputs applied in the models are either the variables mentioned above as individual factors (single input networks) or a combination of them (multi-input networks). The suitability of employing antecedent time-step values as network inputs has hence been checked by comparative analysis of model performance in the two modes. The simple feed forward network has been improvised with a series parallel non-linear autoregressive with exogenous input (NARX) architecture wherein true values of sediment concentration have been fed as input during training. In the glacial scenario of Gangotri, maximum sediment movement takes place during the melt period (May-October). Hence, daily data of discharge, rainfall, temperature and sediment concentration for five consecutive melt periods (May-October, 2000-2004) have been used for modelling. High Coefficient of determination values [0.77-0.88] have been obtained between observed and ANN-predicted values of sediment concentration. The study has brought out relationships between variables that are not reflected in normal statistical analysis. A

  13. Analysis of Closely Related Antioxidant Nutraceuticals Using the Green Analytical Methodology of ANN and Smart Spectrophotometric Methods.

    Science.gov (United States)

    Korany, Mohamed A; Gazy, Azza A; Khamis, Essam F; Ragab, Marwa A A; Kamal, Miranda F

    2017-01-01

    Two new, simple, and specific green analytical methods are proposed: zero-crossing first-derivative and chemometric-based spectrophotometric artificial neural network (ANN). The proposed methods were used for the simultaneous estimation of two closely related antioxidant nutraceuticals, coenzyme Q10 (Q10) and vitamin E, in their mixtures and pharmaceutical preparations. The first method is based on the handling of spectrophotometric data with the first-derivative technique, in which both nutraceuticals were determined in ethanol, each at the zero crossing of the other. The amplitudes of the first-derivative spectra for Q10 and vitamin E were recorded at 285 and 235 nm respectively, and correlated with their concentrations. The linearity ranges of Q10 and vitamin E were 10-60 and 5.6-70 μg⋅mL-1, respectively. The second method, ANN, is a multivariate calibration method and it was developed and applied for the simultaneous determination of both analytes. A training set of 90 different synthetic mixtures containing Q10 and vitamin E in the ranges of 0-100 and 0-556 μg⋅mL-1, respectively, was prepared in ethanol. The absorption spectra of the training set were recorded in the spectral region of 230-300 nm. By relating the concentration sets (x-block) with their corresponding absorption data (y-block), gradient-descent back-propagation ANN calibration could be computed. To validate the proposed network, a set of 45 synthetic mixtures of the two drugs was used. Both proposed methods were successfully applied for the assay of Q10 and vitamin E in their laboratory-prepared mixtures and in their pharmaceutical tablets with excellent recovery. These methods offer advantages over other methods because of low-cost equipment, time-saving measures, and environmentally friendly materials. In addition, no chemical separation prior to analysis was needed. The ANN method was superior to the derivative technique because ANN can determine both drugs under nonlinear experimental

  14. RSM and ANN modeling-based optimization approach for the development of ultrasound-assisted liposome encapsulation of piceid.

    Science.gov (United States)

    Huang, Shang-Ming; Kuo, Chia-Hung; Chen, Chun-An; Liu, Yung-Chuan; Shieh, Chwen-Jen

    2017-05-01

    Piceid, a naturally occurring derivative of resveratrol found in many plants, has recently been considered as a potential nutraceutical. However, its poorly water-soluble property could cause a coupled problem of biological activities concerning drug dispersion and absorption in human body, which is still unsolved now. Liposome, a well-known aqueous carrier for water-insoluble ingredients, is commonly applied in drug delivery systems. In this study, a feasible approach for solving the problem is that the targeted piceid was encapsulated into a liposomal formula as aqueous substrate to overcome its poor water-solubility. The encapsulation process was assisted by ultrasound, with investigation of lipid content, ultrasound power and ultrasound time, for controlling encapsulation efficiency (E.E%), absolute loading (A.L%) and particle size (PS). Moreover, both RSM and ANN methodologies were further applied to optimize the ultrasound-assisted encapsulation process. The data indicated that the most important effects on the encapsulation performance were found to be of lipid content followed by ultrasound time and ultrasound power. The maximum E.E% (75.82%) and A.L% (2.37%) were exhibited by ultrasound assistance with the parameters of 160mg lipid content, ultrasound time for 24min and ultrasound power of 90W. By methodological aspects of processing, the predicted E.E% and A.L% were respectively in good agreement with the experimental results for both RSM and ANN. Moreover, RMSE, R 2 and AAD statistics were further used to compare the prediction abilities of RSM and ANN based on the validation data set. The results indicated that the prediction accuracy of ANN was better than that of RSM. In conclusion, ultrasound-assisted liposome encapsulation can be an efficient strategy for producing well-soluble/dispersed piceid, which could be further applied to promote human health by increased efficiency of biological absorption, and the process of ultrasound-mediated liposome

  15. Artificial neural networks (ANN's) characterisation of soil pollution: the polycyclic hydrocarbons (PAHs) case study; Contribution des reseaux de neurones artificiels (RNA) a la caracterisation des pollutions de sol. Exemples des pollutions en hydrocarbures aromatiques polycycliques (HAP)

    Energy Technology Data Exchange (ETDEWEB)

    Dan, A.; Oosterbaan, J.; Jamet, Ph. [Ecole Nationale Superieure des Mines, 77 - Fontainebleau (France). Centre d' Information Geologique

    2002-10-01

    We develop the ANNs (Artificial Neural Networks) method to explore contaminant concentration profiles observed in soils of polluted sites. ANNs are particularly efficient in simultaneous analysis of numerous parameters and in identification of complex relations involving field data. Applying the ANN models on a PAH (Polycyclic Aromatic Hydrocarbon) database, we extracted the most characteristic components of known contaminations and applied it to identify the source type of similar polluted sites. The performed tests prove the generalisation capability of the selected ANN model. (authors)

  16. The educational challenge of Paediatric Virology: An interview with Professor of Neonatology Anne Greenough.

    Science.gov (United States)

    Mammas, Ioannis N; Spandidos, Demetrios A

    2017-10-01

    According to Professor Anne Greenough, Professor of Neonatology and Clinical Respiratory Physiology at the King's College London (London, UK), Paediatric Virology is indeed a rapidly increasing educational challenge. Professor Greenough, who in 1992 wrote her book on congenital, perinatal and neonatal infections, believes that during the past 3 decades, paediatric health professionals are becoming increasingly involved in specialised care and follow-up of paediatric patients with viral diseases, who require advanced medical care and innovative technological services. Moreover, she highlights the expected role of new vaccines and antiviral agents that are currently under investigation, as well as the impact of emerging viral diseases that require novel prevention strategies and therapeutic protocols. However, she notes that the number of Paediatric Virologists in any one country is likely to be small; hence, a separate paediatric subspecialty needs to be considered carefully. In the context of the 3rd Workshop on Paediatric Virology, which will be held in Athens, Greece, on October 7th, 2017, Professor Greenough will give her plenary lecture on the impact of viral infections on the long term outcomes of prematurely born infants.

  17. ANN Control Based on Patterns Recognition for a Robotic Hand under Different Load Conditions

    Directory of Open Access Journals (Sweden)

    Ihsan Abdulhussein Baqer ihsan.qadi@gmail.com

    2018-03-01

    Full Text Available In this paper, the Artificial Neural Network (ANN is trained on the patterns of the normal component to tangential component ratios at the time of slippage occurrence, so that it can be able to distinguish the slippage occurrence under different type of load (quasi-static and dynamic loads, and then generates a feedback signal used as an input signal to run the actuator. This process is executed without the need for any information about the characteristics of the grasped object, such as weight, surface texture, shape, coefficient of the friction and the type of the load exerted on the grasped object. For fulfillment this approach, a new fingertip design has been proposed in order to detect the slippage in multi-direction between the grasped object and the artificial fingertips. This design is composed of two under-actuated fingers with an actuation system which includes flexible parts (compressive springs. These springs operate as a compensator for the grasping force at the time of slippage occurrence in spite of the actuator is in stopped situation. The contact force component ratios can be calculated via a conventional sensor (Flexiforce sensor after processed the force data using Matlab/Simulink program through a specific mathematical model which is derived according to the mechanism of the artificial finger.

  18. Enhancement of RWSN Lifetime via Firework Clustering Algorithm Validated by ANN

    Directory of Open Access Journals (Sweden)

    Ahmad Ali

    2018-03-01

    Full Text Available Nowadays, wireless power transfer is ubiquitously used in wireless rechargeable sensor networks (WSNs. Currently, the energy limitation is a grave concern issue for WSNs. However, lifetime enhancement of sensor networks is a challenging task need to be resolved. For addressing this issue, a wireless charging vehicle is an emerging technology to expand the overall network efficiency. The present study focuses on the enhancement of overall network lifetime of the rechargeable wireless sensor network. To resolve the issues mentioned above, we propose swarm intelligence based hard clustering approach using fireworks algorithm with the adaptive transfer function (FWA-ATF. In this work, the virtual clustering method has been applied in the routing process which utilizes the firework optimization algorithm. Still now, an FWA-ATF algorithm yet not applied by any researcher for RWSN. Furthermore, the validation study of the proposed method using the artificial neural network (ANN backpropagation algorithm incorporated in the present study. Different algorithms are applied to evaluate the performance of proposed technique that gives the best results in this mechanism. Numerical results indicate that our method outperforms existing methods and yield performance up to 80% regarding energy consumption and vacation time of wireless charging vehicle.

  19. The Segmentation of Point Clouds with K-Means and ANN (artifical Neural Network)

    Science.gov (United States)

    Kuçak, R. A.; Özdemir, E.; Erol, S.

    2017-05-01

    Segmentation of point clouds is recently used in many Geomatics Engineering applications such as the building extraction in urban areas, Digital Terrain Model (DTM) generation and the road or urban furniture extraction. Segmentation is a process of dividing point clouds according to their special characteristic layers. The present paper discusses K-means and self-organizing map (SOM) which is a type of ANN (Artificial Neural Network) segmentation algorithm which treats the segmentation of point cloud. The point clouds which generate with photogrammetric method and Terrestrial Lidar System (TLS) were segmented according to surface normal, intensity and curvature. Thus, the results were evaluated. LIDAR (Light Detection and Ranging) and Photogrammetry are commonly used to obtain point clouds in many remote sensing and geodesy applications. By photogrammetric method or LIDAR method, it is possible to obtain point cloud from terrestrial or airborne systems. In this study, the measurements were made with a Leica C10 laser scanner in LIDAR method. In photogrammetric method, the point cloud was obtained from photographs taken from the ground with a 13 MP non-metric camera.

  20. Prediction and Control of Cutting Tool Vibration in Cnc Lathe with Anova and Ann

    Directory of Open Access Journals (Sweden)

    S. S. Abuthakeer

    2011-06-01

    Full Text Available Machining is a complex process in which many variables can deleterious the desired results. Among them, cutting tool vibration is the most critical phenomenon which influences dimensional precision of the components machined, functional behavior of the machine tools and life of the cutting tool. In a machining operation, the cutting tool vibrations are mainly influenced by cutting parameters like cutting speed, depth of cut and tool feed rate. In this work, the cutting tool vibrations are controlled using a damping pad made of Neoprene. Experiments were conducted in a CNC lathe where the tool holder is supported with and without damping pad. The cutting tool vibration signals were collected through a data acquisition system supported by LabVIEW software. To increase the buoyancy and reliability of the experiments, a full factorial experimental design was used. Experimental data collected were tested with analysis of variance (ANOVA to understand the influences of the cutting parameters. Empirical models have been developed using analysis of variance (ANOVA. Experimental studies and data analysis have been performed to validate the proposed damping system. Multilayer perceptron neural network model has been constructed with feed forward back-propagation algorithm using the acquired data. On the completion of the experimental test ANN is used to validate the results obtained and also to predict the behavior of the system under any cutting condition within the operating range. The onsite tests show that the proposed system reduces the vibration of cutting tool to a greater extend.

  1. Gênero e tradução: a escritora quebequense Anne Hébert em foco

    Directory of Open Access Journals (Sweden)

    Lilian Virginia Porto

    2014-07-01

    Full Text Available http://dx.doi.org/10.5007/2175-7968.2014v1n33p51 Este trabalho tem por objetivo mostrar a importância da escritora quebequense Anne Hébert (1916-2000 no desencadeamento de uma nova prática de tradução no Canadá, a tradução feminista, que tem como maiores representantes Barbara Godard, Susanne de Lotbinière-Harwood, Luise von Flotow e Sherry Simon. Destaca-se a discussão, entre Hébert e Frank Scott, em torno da tradução do poema hebertiano Le tombeau des rois, realizada por Scott. São mencionados os temas recorrentes da obra de Hébert que, a partir dos anos de 1990, passou a ser lida pelo viés feminista: relações familiares conflituosas, revolta e violência das personagens, clausura interior do ser humano e seu desejo de liberação, ressaltando-se o tratamento dado ao universo feminino. São comentados, ainda, problemas de gênero envolvendo duas traduções do romance hebertiano Kamouraska.

  2. Pedestrian Stride Length Estimation from IMU Measurements and ANN Based Algorithm

    Directory of Open Access Journals (Sweden)

    Haifeng Xing

    2017-01-01

    Full Text Available Pedestrian dead reckoning (PDR can be used for continuous position estimation when satellite or other radio signals are not available, and the accuracy of the stride length measurement is important. Current stride length estimation algorithms, including linear and nonlinear models, consider a few variable factors, and some rely on high precision and high cost equipment. This paper puts forward a stride length estimation algorithm based on a back propagation artificial neural network (BP-ANN, using a consumer-grade inertial measurement unit (IMU; it then discusses various factors in the algorithm. The experimental results indicate that the error of the proposed algorithm in estimating the stride length is approximately 2%, which is smaller than that of the frequency and nonlinear models. Compared with the latter two models, the proposed algorithm does not need to determine individual parameters in advance if the trained neural net is effective. It can, thus, be concluded that this algorithm shows superior performance in estimating pedestrian stride length.

  3. Baring Skills, Not Soul: Carol Ann Duffy’s Intertextual Games

    Directory of Open Access Journals (Sweden)

    Catherine LANONE

    2008-10-01

    Full Text Available Maîtrisant à merveille la contre-interpellation ludique dans The World’s Wife, Carol Ann Duffy  est réputée pour ses jeux intertextuels et ses réécritures féministes des grands mythes, donnant la parole aux figures féminines que l’histoire effaçait pour faire triompher le féminin et rire du masculin. Mais son recueil Rapture, publié en 2005, s’écarte radicalement du modèle féministe pour tenter une appropriation plus discrète de la tradition pétrarquisante de la poésie amoureuse. Resémiotisant les clichés, jouant sur une simplicité incantatoire, Duffy pratique ici l’écho plutôt que la réécriture, dans un dépouillement sensuel, explorant amour puis désamour au rythme d’une poésie qui emprunte au jazz ses variations fluides ou syncopées.

  4. A Map of Things Known and Lost in Anne Enright’s The Green Road

    Directory of Open Access Journals (Sweden)

    Margarita Estévez-Saá

    2016-03-01

    Full Text Available The present contribution interprets Anne Enright’s most recent novel, The Green Road (2015, as the story of two decades of an Irish family that is used by the writer to offer an alternative fictional rendering of the history of Ireland and the Irish from the 1980s till the early twenty-first century, as well as, formally speaking, a further contribution to the Irish writers’ penchant for destabilizing the conventions of a literary genre too frequently associated with British settlement and stability (Eagleton 1995 and with nineteenth-century realism (Hand 2011; and, therefore, recurrently considered as unable to apprehend the disruptive and multifaceted condition of Ireland and the Irish. Enright goes from the particular to the universal: the story of the Madigans serves to cover the recent history of Ireland as well as to deal with concerns such as motherhood, religion, sex, aging depression, illness, materialism and migrations, among others. Formally speaking, Enright’s latest novel is undoubtedly the most daring and innovative text in her already vast literary output and can and should be interpreted as the author’s most remarkable contribution to a literary genre with which Irish writers have not ceased to experiment.

  5. Classifying Sources Influencing Indoor Air Quality (IAQ) Using Artificial Neural Network (ANN).

    Science.gov (United States)

    Saad, Shaharil Mad; Andrew, Allan Melvin; Shakaff, Ali Yeon Md; Saad, Abdul Rahman Mohd; Kamarudin, Azman Muhamad Yusof; Zakaria, Ammar

    2015-05-20

    Monitoring indoor air quality (IAQ) is deemed important nowadays. A sophisticated IAQ monitoring system which could classify the source influencing the IAQ is definitely going to be very helpful to the users. Therefore, in this paper, an IAQ monitoring system has been proposed with a newly added feature which enables the system to identify the sources influencing the level of IAQ. In order to achieve this, the data collected has been trained with artificial neural network or ANN--a proven method for pattern recognition. Basically, the proposed system consists of sensor module cloud (SMC), base station and service-oriented client. The SMC contain collections of sensor modules that measure the air quality data and transmit the captured data to base station through wireless network. The IAQ monitoring system is also equipped with IAQ Index and thermal comfort index which could tell the users about the room's conditions. The results showed that the system is able to measure the level of air quality and successfully classify the sources influencing IAQ in various environments like ambient air, chemical presence, fragrance presence, foods and beverages and human activity.

  6. Det er ikke godt å si: Om tilblivelser i Anne Biringvads malerier

    Directory of Open Access Journals (Sweden)

    Sissel Gunnerød

    2014-07-01

    Full Text Available Våren 2012 stilte Anne Biringvad ut ekspressive malerier med dristig bruk av tekstile fragmenter fra loppemarkeder inkorporert i verkene. Denne hendelsen fant sted på Galleri Semmingsen på Galleri Trafo i Asker. Hva er det som skjer i Biringsvads malerier? Tekstilene blir et slags begjær som poder seg på maleriene og knoppskyter. I denne artikkelen vil jeg nærme meg maleriene som en hendelse, noe som skjer, en sanselig tilblivelse, hvor jeg låner Gilles Deleuze’ og Félix Guattaris’ begrep om kunst som sansning gjennom affekter og persepter. Jeg spør også om Biringvads malerier kan betraktes som en mindre estetisk praksis som deterritorialiserer hovedspråket slik Deleuze og Guattari formulerer det i Kafka – for en mindre litteratur (1994. Deleuze og Guattari gjør «mindre» til en foretrukket egenskap ved kunsten. Med Michel Foucaults begrep om heterotopier vil jeg spørre om disse maleriene snur om på de plasseringene som finnes i kulturen.

  7. Modeling the QSAR of ACE-Inhibitory Peptides with ANN and Its Applied Illustration

    Directory of Open Access Journals (Sweden)

    Ronghai He

    2012-01-01

    Full Text Available A quantitative structure-activity relationship (QSAR model of angiotensin-converting enzyme- (ACE- inhibitory peptides was built with an artificial neural network (ANN approach based on structural or activity data of 58 dipeptides (including peptide activity, hydrophilic amino acids content, three-dimensional shape, size, and electrical parameters, the overall correlation coefficient of the predicted versus actual data points is =0.928, and the model was applied in ACE-inhibitory peptides preparation from defatted wheat germ protein (DWGP. According to the QSAR model, the C-terminal of the peptide was found to have principal importance on ACE-inhibitory activity, that is, if the C-terminal is hydrophobic amino acid, the peptide's ACE-inhibitory activity will be high, and proteins which contain abundant hydrophobic amino acids are suitable to produce ACE-inhibitory peptides. According to the model, DWGP is a good protein material to produce ACE-inhibitory peptides because it contains 42.84% of hydrophobic amino acids, and structural information analysis from the QSAR model showed that proteases of Alcalase and Neutrase were suitable candidates for ACE-inhibitory peptides preparation from DWGP. Considering higher DH and similar ACE-inhibitory activity of hydrolysate compared with Neutrase, Alcalase was finally selected through experimental study.

  8. THE SEGMENTATION OF POINT CLOUDS WITH K-MEANS AND ANN (ARTIFICAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    R. A. Kuçak

    2017-05-01

    Full Text Available Segmentation of point clouds is recently used in many Geomatics Engineering applications such as the building extraction in urban areas, Digital Terrain Model (DTM generation and the road or urban furniture extraction. Segmentation is a process of dividing point clouds according to their special characteristic layers. The present paper discusses K-means and self-organizing map (SOM which is a type of ANN (Artificial Neural Network segmentation algorithm which treats the segmentation of point cloud. The point clouds which generate with photogrammetric method and Terrestrial Lidar System (TLS were segmented according to surface normal, intensity and curvature. Thus, the results were evaluated. LIDAR (Light Detection and Ranging and Photogrammetry are commonly used to obtain point clouds in many remote sensing and geodesy applications. By photogrammetric method or LIDAR method, it is possible to obtain point cloud from terrestrial or airborne systems. In this study, the measurements were made with a Leica C10 laser scanner in LIDAR method. In photogrammetric method, the point cloud was obtained from photographs taken from the ground with a 13 MP non-metric camera.

  9. Application of Particle Swarm Optimization Algorithm for Optimizing ANN Model in Recognizing Ripeness of Citrus

    Science.gov (United States)

    Diyana Rosli, Anis; Adenan, Nur Sabrina; Hashim, Hadzli; Ezan Abdullah, Noor; Sulaiman, Suhaimi; Baharudin, Rohaiza

    2018-03-01

    This paper shows findings of the application of Particle Swarm Optimization (PSO) algorithm in optimizing an Artificial Neural Network that could categorize between ripeness and unripeness stage of citrus suhuensis. The algorithm would adjust the network connections weights and adapt its values during training for best results at the output. Initially, citrus suhuensis fruit’s skin is measured using optically non-destructive method via spectrometer. The spectrometer would transmit VIS (visible spectrum) photonic light radiation to the surface (skin of citrus) of the sample. The reflected light from the sample’s surface would be received and measured by the same spectrometer in terms of reflectance percentage based on VIS range. These measured data are used to train and test the best optimized ANN model. The accuracy is based on receiver operating characteristic (ROC) performance. The result outcomes from this investigation have shown that the achieved accuracy for the optimized is 70.5% with a sensitivity and specificity of 60.1% and 80.0% respectively.

  10. On the recovery of an ancient text: Principles of editing, The diaries of Lady Anne Barnard

    Directory of Open Access Journals (Sweden)

    M. Lenta

    1997-04-01

    Full Text Available The unrevised and handwritten Cape diaries of Lady Anne Barnard for the years 1799 and 1800 have recently been transcribed and are now in the process of being edited. Since they are very long, and would be expensive to publish in their entirely, the question has arisen for their editors what principles of selection and emphasis should be followed in the editorial process. The diaries are private documents, intended to be read by no one but the author herself, and they are frequently non-standard in punctuation, spelling and even at times in syntax. The editors therefore face other issues, concerning their right to correct or standardise the text, which as it stands, is an illustration of the practice of a highly intelligent and experienced woman with almost no formal education - a woman who in many respects is representative of her time and class. The different kinds of interest present within the text - Cape and European history, the history of women, of slaves and of colonialism, as well as of the indigenous peoples of the Cape hinterland, may well represent alternative focuses between which the editors, in an abbreviated text, must choose, since the final decision concerning publication is likely to be an economic one. Finally the editors’ recommendations are likely to be based on the degree of interest possessed by the text in its component parts - are all its subjects equally interesting to the envisaged reader, the amateur of history of the present day?

  11. Wavelet transform and ANNs for detection and classification of power signal disturbances

    International Nuclear Information System (INIS)

    Memon, A.P.; Uqaili, M.A.; Memon, Z.A.

    2012-01-01

    This article proposes WT (Wavelet Transform) and an ANN (Artificial Neural Network) based approach for detection and classification of EPQDs (Electrical Power Quality Disturbances). A modified WT known as ST (Stockwell Transform) is suggested for feature extraction and PNN (probabilistic Neural Network) for pattern classification. The ST possesses outstanding time-frequency resolution characteristics and its phase correction techniques determine the phase of the WT to the zero time point The feature vectors for the input of PNN are extracted using ST technique and these obtained features are discrete, logical, and unaffected to noisy data of distorted signals. The data of the models required to develop the distorted EPQ (Electrical Power Quality) signals, is obtained within the ranges specified by IEEE 1159-1995 in its literatures. The features vectors including noisy time varying data during steady state or transient condition and extracted using the ST, are trained through PNN for pattern classification. Their simulation results demonstrate that the proposed methodology is successful and can classify EPQDs even under a noisy environment very efficiently with an average classification accuracy of 96%. (author)

  12. Application of ann for predicting water quality parameters in the mediterranean sea along gaza-palestine

    International Nuclear Information System (INIS)

    Zaqoot, H.A.; Unar, M.A.

    2008-01-01

    Seawater pollution problems are gaining interest world wide because of their health impacts and other environmental issues. Intense human activities in areas surrounding enclosed and semi-enclosed seas such as the Mediterranean Sea always produce in the long term a strong environmental impact in the form of coastal and marine degradation. This paper is concerned with the use of ANNs (Artificial Neural Networks) MLP ( Multilayer Perceptron) model for the prediction of pH and EC (Electrical Conductivity) in water quality parameters along Gaza city coast. MLP neural networks are trained and developed with reference to three major oceanographic parameters (water temperature, wind speed and turbidity) to predict the values of pH and EC; these parameters are considered as inputs of the neural network. The data collected comprised of four years and collected from nine locations along Gaza coastline. Results show that the model has high capability and accuracy in predicting both parameters. The network performance has been validated with different data sets and the results show satisfactory performance. Results of the developed model have been compared with multiple regression statistical models and found that MLP predictions are slightly better than the conventional methods. Prediction results prove that the proposed approach is suitable for modeling the water quality in the Mediterranean Sea along Gaza. (author)

  13. ANN modeling of water consumption in the lead-acid batteries

    Science.gov (United States)

    Karimi, Mohammad Ali; Karami, Hassan; Mahdipour, Maryam

    Due to importance of the quantity of water loss in the life cycle of lead-acid batteries, water consumption tests were performed on 72 lead-acid batteries with low antimony grid alloy at different charge voltages and temperatures. Weight loss of batteries was measured during a period of 10 days. The behavior of batteries in different charge voltages and temperatures were modeled by artificial neural networks (ANNs) using MATLAB 7 media. Four temperatures were used in the training set, out of which three were used in prediction set and one in validation set. The network was trained by training and prediction data sets, and then was used for predicting water consumption in all three temperatures of prediction set. Finally, the network obtained was verified while being used in predicting water loss in defined temperatures of validation set. To achieve a better evaluation of the model ability, three models with different validation temperatures were used (model 1 = 50 °C, model 2 = 60 °C and model 3 = 70 °C). There was a good agreement between predicted and experimental results at prediction and validation sets for all the models. Mean prediction errors in modeling charge voltage-temperature-time behavior in the water consumption quantity for models 1-3 were below 0.99%, 0.03%, and 0.76%, respectively. The model can be simply used by inexpert operators working in lead-acid battery industry.

  14. SVM and ANN Based Classification of Plant Diseases Using Feature Reduction Technique

    Directory of Open Access Journals (Sweden)

    Jagadeesh D.Pujari

    2016-06-01

    Full Text Available Computers have been used for mechanization and automation in different applications of agriculture/horticulture. The critical decision on the agricultural yield and plant protection is done with the development of expert system (decision support system using computer vision techniques. One of the areas considered in the present work is the processing of images of plant diseases affecting agriculture/horticulture crops. The first symptoms of plant disease have to be correctly detected, identified, and quantified in the initial stages. The color and texture features have been used in order to work with the sample images of plant diseases. Algorithms for extraction of color and texture features have been developed, which are in turn used to train support vector machine (SVM and artificial neural network (ANN classifiers. The study has presented a reduced feature set based approach for recognition and classification of images of plant diseases. The results reveal that SVM classifier is more suitable for identification and classification of plant diseases affecting agriculture/horticulture crops.

  15. Prediction of shear and tensile strength of the diffusion bonded AA5083 and AA7075 aluminium alloy using ANN

    International Nuclear Information System (INIS)

    Sagai Francis Britto, A.; Raj, R. Edwin; Mabel, M. Carolin

    2017-01-01

    Diffusion bonding is a pressure welding technique to establish bonds by inter diffusion of atoms. Bonding characteristics were generated by varying the significant process conditions such as the bonding temperature, the pressing load and the duration of pressure while bonding the aluminium alloys AA5083 and AA7075. Deriving analytical correlation with the process variables to weld strength is quite involved due to the non-linear dependency of the process variables with the mechanical strength of the joints. An arbitrary function approximation mechanism, the artificial neural network (ANN) is therefore employed to develop the models for predicting the mechanical properties of the bonded joints. Back propagation technique, which alters the network weights to minimize the mean square error was used to develop the ANN models. The models were tested, validated and found to be satisfactory with good prediction accuracy.

  16. Uhked naised / Kozik, Eerika; Tomingas, Triin; Sepp, Karin; Liik, Kadi; Kliimask, Katrin; Saunpere, Anne ; tekst Kärt Kross

    Index Scriptorium Estoniae

    2007-01-01

    Mille üle oled enda juures tõeliselt uhke, mida võid enda kiituseks öelda? Küsimusele vastavad Starland Salongi omanik ja Loyd Tootmine OÜ osanik Eerika Kozik, psühholoog Triin Tomingas, Kontuur Leo Burnett projektidirektor Karin Sepp, Tallinna Ülikooli õppejõud Kadi Liik, AS Koolibri turundusjuht Katrin Kliimask ja eraettevõtja Anne Saunpere

  17. Connecting membrane fluidity and surface charge to pore-forming antimicrobial peptides resistance by an ANN-based predictive model.

    Science.gov (United States)

    Mehla, Jitender; Sood, S K

    2013-05-01

    Efficiency of antibacterial chemotherapy is gradually more challenged by the emergence of pathogenic strains exhibiting high levels of antibiotic resistance. Pore-forming antimicrobial peptides (PF-AMPs) such as alamethicin (Alm) are therefore in the focus of extensive research efforts. In the present study, an artificial neural network (ANN)-based quantitative structure-activity relationship (SAR) modeling of membrane phospholipids vs. PF-AMPs, in context to membrane fluidity and surface charge, was carried out. We observed that the potency of PF-AMPs depends on the fatty acyl chain and polar head group of phospholipids. Alm showed surface interactions with zwitterionic phospholipids however could penetrate deeper inside the hydrophobic core of anionic membranes. Here, the resistance developed in bacterial cells was coupled to membrane fluidity and surface charge, and simultaneously, these principles could be applied for combating resistance against PF-AMPs. The correlation coefficient between observed CR and predicted CR using ANN was found to be 0.757. Thus, ANN could be used as a reliable modeling method for predicting CR, given the structure of the biomimetic membrane in terms of membrane fluidity and surface charge. Fully explored mechanisms of resistance, a forward modeling step in the design cycle of AMPs, can be cross-linked to the inward modeling using ANN to complete the peptide design cycle. The SAR between membrane phospholipids and PF-AMPs could furnish valuable information regarding their design to provide us efficacious peptides against premier pathogens. So far, this is the only report available to predict and quantify interactions of PF-AMPs with membrane phospholipids.

  18. Predicting quality of life after breast cancer surgery using ANN-based models: performance comparison with MR.

    Science.gov (United States)

    Tsai, Jinn-Tsong; Hou, Ming-Feng; Chen, Yao-Mei; Wan, Thomas T H; Kao, Hao-Yun; Shi, Hon-Yi

    2013-05-01

    The goal was to develop models for predicting long-term quality of life (QOL) after breast cancer surgery. Data were obtained from 203 breast cancer patients who completed the SF-36 health survey before and 2 years after surgery. Two of the models used to predict QOL after surgery were artificial neural networks (ANNs), which included one multilayer perceptron (MLP) network and one radial basis function (RBF) network. The third model was a multiple regression (MR) model. The criteria for evaluating the accuracy of the system models were mean square error (MSE) and mean absolute percentage error (MAPE). Compared to the MR model, the ANN-based models generally had smaller MSE values and smaller MAPE values in the test data set. One exception was the second year MSE for the test value. Most MAPE values for the ANN models ranged from 10 to 20 %. The one exception was the 6-month physical component summary score (PCS), which ranged from 23.19 to 26.86 %. Comparison of criteria for evaluating system performance showed that the ANN-based systems outperformed the MR system in terms of prediction accuracy. In both the MLP and RBF networks, surgical procedure type was the most sensitive parameter affecting PCS, and preoperative functional status was the most sensitive parameter affecting mental component summary score. The three systems can be combined to obtain a conservative prediction, and a combined approach is a potential supplemental tool for predicting long-term QOL after surgical treatment for breast cancer. Patients should also be advised that their postoperative QOL might depend not only on the success of their operations but also on their preoperative functional status.

  19. A Comparison of SWAT and ANN Models for Daily Runoff Simulation in Different Climatic Zones of Peninsular Spain

    OpenAIRE

    Patricia Jimeno-Sáez; Javier Senent-Aparicio; Julio Pérez-Sánchez; David Pulido-Velazquez

    2018-01-01

    Streamflow data are of prime importance to water-resources planning and management, and the accuracy of their estimation is very important for decision making. The Soil and Water Assessment Tool (SWAT) and Artificial Neural Network (ANN) models have been evaluated and compared to find a method to improve streamflow estimation. For a more complete evaluation, the accuracy and ability of these streamflow estimation models was also established separately based on their performance during differe...

  20. Huviharidus ei ole ainult soe bussipeatus / Piret Hartman, Ardo Rohtla, Annely Köster ... [jt.] ; intervjueerinud Maris Hellrand

    Index Scriptorium Estoniae

    2016-01-01

    Noorte huvitegevuse toetussüsteemi kontseptsioon toob järgmisel sügisel valdkonda lisaraha. Vestlusringis huvihariduse probleemide üle osalesid kultuuriministri nõunik Piret Hartman, haridus- ja teadusministeeriumi noorteosakonna asejuhataja Ardo Rohtla, Sally Stuudio juhataja Annely Köster, Eesti Muusikakoolide Liidu juhatuse esimees Kadri Leivategija, Rakvere abilinnapea Kairit Pihlak, Eesti Teadushuvihariduse Liidu juhatuse liige Heilo Altin ja MTÜ Loovkirjutamise Keskus juhatuse liige Katriin Fisch-Uibopuu

  1. Seafloor monitoring west of Helgoland (German Bight, North Sea) using the acoustic ground discrimination system RoxAnn

    Science.gov (United States)

    Hass, H. Christian; Mielck, Finn; Fiorentino, Dario; Papenmeier, Svenja; Holler, Peter; Bartholomä, Alexander

    2017-04-01

    Marine habitats of shelf seas are in constant dynamic change and therefore need regular assessment particularly in areas of special interest. In this study, the single-beam acoustic ground discrimination system RoxAnn served to assess seafloor hardness and roughness, and combine these parameters into one variable expressed as RGB (red green blue) color code followed by k-means fuzzy cluster analysis (FCA). The data were collected at a monitoring site west of the island of Helgoland (German Bight, SE North Sea) in the course of four surveys between September 2011 and November 2014. The study area has complex characteristics varying from outcropping bedrock to sandy and muddy sectors with mostly gradual transitions. RoxAnn data enabled to discriminate all seafloor types that were suggested by ground-truth information (seafloor samples, video). The area appears to be quite stable overall; sediment import (including fluid mud) was detected only from the NW. Although hard substrates (boulders, bedrock) are clearly identified, the signal can be modified by inclination and biocover. Manually, six RoxAnn zones were identified; for the FCA, only three classes are suggested. The latter classification based on `hard' boundaries would suffice for stakeholder issues, but the former classification based on `soft' boundaries is preferred to meet state-of-the-art scientific objectives.

  2. The modelling of lead removal from water by deep eutectic solvents functionalized CNTs: artificial neural network (ANN) approach.

    Science.gov (United States)

    Fiyadh, Seef Saadi; AlSaadi, Mohammed Abdulhakim; AlOmar, Mohamed Khalid; Fayaed, Sabah Saadi; Hama, Ako R; Bee, Sharifah; El-Shafie, Ahmed

    2017-11-01

    The main challenge in the lead removal simulation is the behaviour of non-linearity relationships between the process parameters. The conventional modelling technique usually deals with this problem by a linear method. The substitute modelling technique is an artificial neural network (ANN) system, and it is selected to reflect the non-linearity in the interaction among the variables in the function. Herein, synthesized deep eutectic solvents were used as a functionalized agent with carbon nanotubes as adsorbents of Pb 2+ . Different parameters were used in the adsorption study including pH (2.7 to 7), adsorbent dosage (5 to 20 mg), contact time (3 to 900 min) and Pb 2+ initial concentration (3 to 60 mg/l). The number of experimental trials to feed and train the system was 158 runs conveyed in laboratory scale. Two ANN types were designed in this work, the feed-forward back-propagation and layer recurrent; both methods are compared based on their predictive proficiency in terms of the mean square error (MSE), root mean square error, relative root mean square error, mean absolute percentage error and determination coefficient (R 2 ) based on the testing dataset. The ANN model of lead removal was subjected to accuracy determination and the results showed R 2 of 0.9956 with MSE of 1.66 × 10 -4 . The maximum relative error is 14.93% for the feed-forward back-propagation neural network model.

  3. Development of LC-MS determination method and back-propagation ANN pharmacokinetic model of corynoxeine in rat.

    Science.gov (United States)

    Ma, Jianshe; Cai, Jinzhang; Lin, Guanyang; Chen, Huilin; Wang, Xianqin; Wang, Xianchuan; Hu, Lufeng

    2014-05-15

    Corynoxeine(CX), isolated from the extract of Uncaria rhynchophylla, is a useful and prospective compound in the prevention and treatment for vascular diseases. A simple and selective liquid chromatography mass spectrometry (LC-MS) method was developed to determine the concentration of CX in rat plasma. The chromatographic separation was achieved on a Zorbax SB-C18 (2.1 mm × 150 mm, 5 μm) column with acetonitrile-0.1% formic acid in water as mobile phase. Selective ion monitoring (SIM) mode was used for quantification using target ions m/z 383 for CX and m/z 237 for the carbamazepine (IS). After the LC-MS method was validated, it was applied to a back-propagation artificial neural network (BP-ANN) pharmacokinetic model study of CX in rats. The results showed that after intravenous administration of CX, it was mainly distributed in blood and eliminated quickly, t1/2 was less than 1h. The predicted concentrations generated by BP-ANN model had a high correlation coefficient (R>0.99) with experimental values. The developed BP-ANN pharmacokinetic model can be used to predict the concentration of CX in rats. Copyright © 2014 Elsevier B.V. All rights reserved.

  4. Rapid Identification of Asteraceae Plants with Improved RBF-ANN Classification Models Based on MOS Sensor E-Nose.

    Science.gov (United States)

    Zou, Hui-Qin; Li, Shuo; Huang, Ying-Hua; Liu, Yong; Bauer, Rudolf; Peng, Lian; Tao, Ou; Yan, Su-Rong; Yan, Yong-Hong

    2014-01-01

    Plants from Asteraceae family are widely used as herbal medicines and food ingredients, especially in Asian area. Therefore, authentication and quality control of these different Asteraceae plants are important for ensuring consumers' safety and efficacy. In recent decades, electronic nose (E-nose) has been studied as an alternative approach. In this paper, we aim to develop a novel discriminative model by improving radial basis function artificial neural network (RBF-ANN) classification model. Feature selection algorithms, including principal component analysis (PCA) and BestFirst + CfsSubsetEval (BC), were applied in the improvement of RBF-ANN models. Results illustrate that in the improved RBF-ANN models with lower dimension data classification accuracies (100%) remained the same as in the original model with higher-dimension data. It is the first time to introduce feature selection methods to get valuable information on how to attribute more relevant MOS sensors; namely, in this case, S1, S3, S4, S6, and S7 show better capability to distinguish these Asteraceae plants. This paper also gives insights to further research in this area, for instance, sensor array optimization and performance improvement of classification model.

  5. A Comparison of SWAT and ANN Models for Daily Runoff Simulation in Different Climatic Zones of Peninsular Spain

    Directory of Open Access Journals (Sweden)

    Patricia Jimeno-Sáez

    2018-02-01

    Full Text Available Streamflow data are of prime importance to water-resources planning and management, and the accuracy of their estimation is very important for decision making. The Soil and Water Assessment Tool (SWAT and Artificial Neural Network (ANN models have been evaluated and compared to find a method to improve streamflow estimation. For a more complete evaluation, the accuracy and ability of these streamflow estimation models was also established separately based on their performance during different periods of flows using regional flow duration curves (FDCs. Specifically, the FDCs were divided into five sectors: very low, low, medium, high and very high flow. This segmentation of flow allows analysis of the model performance for every important discharge event precisely. In this study, the models were applied in two catchments in Peninsular Spain with contrasting climatic conditions: Atlantic and Mediterranean climates. The results indicate that SWAT and ANNs were generally good tools in daily streamflow modelling. However, SWAT was found to be more successful in relation to better simulation of lower flows, while ANNs were superior at estimating higher flows in all cases.

  6. Classification of molecular structure images by using ANN, RF, LBP, HOG, and size reduction methods for early stomach cancer detection

    Science.gov (United States)

    Aytaç Korkmaz, Sevcan; Binol, Hamidullah

    2018-03-01

    Patients who die from stomach cancer are still present. Early diagnosis is crucial in reducing the mortality rate of cancer patients. Therefore, computer aided methods have been developed for early detection in this article. Stomach cancer images were obtained from Fırat University Medical Faculty Pathology Department. The Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG) features of these images are calculated. At the same time, Sammon mapping, Stochastic Neighbor Embedding (SNE), Isomap, Classical multidimensional scaling (MDS), Local Linear Embedding (LLE), Linear Discriminant Analysis (LDA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Laplacian Eigenmaps methods are used for dimensional the reduction of the features. The high dimension of these features has been reduced to lower dimensions using dimensional reduction methods. Artificial neural networks (ANN) and Random Forest (RF) classifiers were used to classify stomach cancer images with these new lower feature sizes. New medical systems have developed to measure the effects of these dimensions by obtaining features in different dimensional with dimensional reduction methods. When all the methods developed are compared, it has been found that the best accuracy results are obtained with LBP_MDS_ANN and LBP_LLE_ANN methods.

  7. Application of Artificial Neural Network (ANN for the prediction of EL-AGAMY wastewater treatment plant performance-EGYPT

    Directory of Open Access Journals (Sweden)

    Mahmoud S. Nasr

    2012-03-01

    Full Text Available A reliable model for any Wastewater Treatment Plant WWTP is essential in order to provide a tool for predicting its performance and to form a basis for controlling the operation of the process. This would minimize the operation costs and assess the stability of environmental balance. This paper focuses on applying an Artificial Neural Network (ANN approach with a Feed-Forward Back-Propagation to predict the performance of EL-AGAMY WWTP-Alexandria in terms of Chemical Oxygen Demand (COD, Biochemical Oxygen Demand (BOD and Total Suspended Solids (TSSs data gathered during a research over a 1-year period. The study signifies that the ANN can predict the plant performance with correlation coefficient (R between the observed and predicted output variables reached up to 0.90. Moreover, ANN provides an effective analyzing and diagnosing tool to understand and simulate the non-linear behavior of the plant, and is used as a valuable performance assessment tool for plant operators and decision makers.

  8. Optimal factor evaluation for the dissolution of alumina from Azaraegbelu clay in acid solution using RSM and ANN comparative analysis

    Directory of Open Access Journals (Sweden)

    P.E. Ohale

    2017-12-01

    Full Text Available Artificial neural network (ANN and Response Surface Methodology based on a 25−1 fractional factorial design were used as tools for simulation and optimisation of the dissolution process for Azaraegbelu clay. A feedforward neural network model with Levenberg–Marquard back propagating training algorithm was adapted to predict the response (alumina yield. The studied input variables were temperature, stirring speed, clay to acid dosage, leaching time and leachant concentration. The raw clay was characterized for structure elucidation via FTIR, SEM and X-ray diffraction spectroscopic techniques and the result indicates that the clay is predominantly kaolinite. Leachant concentration and dosage ratio were found to be the most significant process parameter with p-value of 0.0001. The performance of the ANN and RSM model showed adequate prediction of the response, with AAD of 11.6% and 3.6%, and R2 of 0.9733 and 0.9568, respectively. A non-dominated optimal response of 81.45% yield of alumina at 4.6 M sulphuric acid concentration, 214 min leaching time, 0.085 g/ml dosage and 214 rpm stirring speed was established as a viable route for reduced material and operating cost via RSM. Keywords: Alumina dissolution, ANN modelling, Azaraegbelu, Clay, RSM

  9. Data fusion with artificial neural networks (ANN) for classification of earth surface from microwave satellite measurements

    Science.gov (United States)

    Lure, Y. M. Fleming; Grody, Norman C.; Chiou, Y. S. Peter; Yeh, H. Y. Michael

    1993-01-01

    A data fusion system with artificial neural networks (ANN) is used for fast and accurate classification of five earth surface conditions and surface changes, based on seven SSMI multichannel microwave satellite measurements. The measurements include brightness temperatures at 19, 22, 37, and 85 GHz at both H and V polarizations (only V at 22 GHz). The seven channel measurements are processed through a convolution computation such that all measurements are located at same grid. Five surface classes including non-scattering surface, precipitation over land, over ocean, snow, and desert are identified from ground-truth observations. The system processes sensory data in three consecutive phases: (1) pre-processing to extract feature vectors and enhance separability among detected classes; (2) preliminary classification of Earth surface patterns using two separate and parallely acting classifiers: back-propagation neural network and binary decision tree classifiers; and (3) data fusion of results from preliminary classifiers to obtain the optimal performance in overall classification. Both the binary decision tree classifier and the fusion processing centers are implemented by neural network architectures. The fusion system configuration is a hierarchical neural network architecture, in which each functional neural net will handle different processing phases in a pipelined fashion. There is a total of around 13,500 samples for this analysis, of which 4 percent are used as the training set and 96 percent as the testing set. After training, this classification system is able to bring up the detection accuracy to 94 percent compared with 88 percent for back-propagation artificial neural networks and 80 percent for binary decision tree classifiers. The neural network data fusion classification is currently under progress to be integrated in an image processing system at NOAA and to be implemented in a prototype of a massively parallel and dynamically reconfigurable Modular

  10. AI-based (ANN and SVM) statistical downscaling methods for precipitation estimation under climate change scenarios

    Science.gov (United States)

    Mehrvand, Masoud; Baghanam, Aida Hosseini; Razzaghzadeh, Zahra; Nourani, Vahid

    2017-04-01

    Since statistical downscaling methods are the most largely used models to study hydrologic impact studies under climate change scenarios, nonlinear regression models known as Artificial Intelligence (AI)-based models such as Artificial Neural Network (ANN) and Support Vector Machine (SVM) have been used to spatially downscale the precipitation outputs of Global Climate Models (GCMs). The study has been carried out using GCM and station data over GCM grid points located around the Peace-Tampa Bay watershed weather stations. Before downscaling with AI-based model, correlation coefficient values have been computed between a few selected large-scale predictor variables and local scale predictands to select the most effective predictors. The selected predictors are then assessed considering grid location for the site in question. In order to increase AI-based downscaling model accuracy pre-processing has been developed on precipitation time series. In this way, the precipitation data derived from various GCM data analyzed thoroughly to find the highest value of correlation coefficient between GCM-based historical data and station precipitation data. Both GCM and station precipitation time series have been assessed by comparing mean and variances over specific intervals. Results indicated that there is similar trend between GCM and station precipitation data; however station data has non-stationary time series while GCM data does not. Finally AI-based downscaling model have been applied to several GCMs with selected predictors by targeting local precipitation time series as predictand. The consequences of recent step have been used to produce multiple ensembles of downscaled AI-based models.

  11. Evaluation Of The Advanced Operating System Of The Ann Arbor Transportation Authority : Cost Study : Before, During And After AOS Implementation (October 1996-May 1999)

    Science.gov (United States)

    1999-01-01

    In 1997, the Ann Arbor (Michigan) Transportation Authority (AATA) began deploying advanced public transportation systems (APTS) technologies in its fixed route and paratransit operations. The project's concept is the integration of a range of such te...

  12. E-kursus nõuab aega ja pühendumist / Ivo Leito, Irja Helm, Anne Krull ; intervjueerinud Laura Vetik

    Index Scriptorium Estoniae

    Leito, Ivo, 1972-

    2015-01-01

    Intervjuu Tartu Ülikooli keemiakursuse "Estimation of Measurement Uncertainty in Chemical Analysis" autorite Ivo Leito ja Irja Helmiga ning Tartu Kutsehariduskeskuse e-kursuse "Praktiline keemia" autori Anne Krulliga

  13. Robert Delpire, un précurseur dans l’édition pour la jeunesse des années 1950-1970

    Directory of Open Access Journals (Sweden)

    Michèle Piquard

    2010-06-01

    Full Text Available Je vais tenter, au cours de cet exposé, de montrer comment certains facteurs hétérogènes peuvent expliquer la stabilité relative de la production des éditeurs pour la jeunesse du début des années 1950 jusqu’au milieu des années 1960, puis son renouveau au cours des dix années suivantes, et quel rôle précurseur a joué, dans ce secteur, Robert Delpire.L’édition pour la jeunesse, dans les années 1950, est en partie l’héritière d’une industrie capitaliste qui s’est structurée au XIXe siècle autou...

  14. Application of back-propagation artificial neural network (ANN) to predict crystallite size and band gap energy of ZnO quantum dots

    Science.gov (United States)

    Pelicano, Christian Mark; Rapadas, Nick; Cagatan, Gerard; Magdaluyo, Eduardo

    2017-12-01

    Herein, the crystallite size and band gap energy of zinc oxide (ZnO) quantum dots were predicted using artificial neural network (ANN). Three input factors including reagent ratio, growth time, and growth temperature were examined with respect to crystallite size and band gap energy as response factors. The generated results from neural network model were then compared with the experimental results. Experimental crystallite size and band gap energy of ZnO quantum dots were measured from TEM images and absorbance spectra, respectively. The Levenberg-Marquardt (LM) algorithm was used as the learning algorithm for the ANN model. The performance of the ANN model was then assessed through mean square error (MSE) and regression values. Based on the results, the ANN modelling results are in good agreement with the experimental data.

  15. Modeling and optimization of thermal conductivity and viscosity of MnFe2O4nanofluid under magnetic field using an ANN.

    Science.gov (United States)

    Amani, Mohammad; Amani, Pouria; Kasaeian, Alibakhsh; Mahian, Omid; Pop, Ioan; Wongwises, Somchai

    2017-12-12

    This research investigates the applicability of an ANN and genetic algorithms for modeling and multiobjective optimization of the thermal conductivity and viscosity of water-based spinel-type MnFe 2 O 4 nanofluid. Levenberg-Marquardt, quasi-Newton, and resilient backpropagation methods are employed to train the ANN. The support vector machine (SVM) method is also presented for comparative purposes. Experimental results demonstrate the efficacy of the developed ANN with the LM-BR training algorithm and the 3-10-10-2 structure for the prediction of the thermophysical properties of nanofluids in terms of the significantly superior accuracy compared to developing the correlation and employing SVM regression. Moreover, the genetic algorithm is implemented to determine the optimal conditions, i.e., maximum thermal conductivity and minimum nanofluid viscosity, based on the developed ANN.

  16. Evaluation Of The Advanced Operating System Of The Ann Arbor Transportation Authority : Driver And Dispatcher Perceptions Of AATA'S Advanced Operating System

    Science.gov (United States)

    1999-01-01

    In 1997, the Ann Arbor (Michigan) Transportation Authority began deploying advanced public transportation systems (APTS) technologies in its fixed route and paratransit operations. The project's concept is the integration of a range of such technolog...

  17. Evaluation Of The Advanced Operating System Of The Ann Arbor Transportation Authority : Transfer And On-Time Performance Study : Before And After AOS Implementation, October 1996 - May 1999

    Science.gov (United States)

    1999-01-01

    In 1997, the Ann Arbor (Michigan) Transportation Authority began deploying advanced public transportation systems (APTS) technologies in its fixed route and paratransit operations. The project's concept is the integration of a range of such technolog...

  18. L'année internationale des légumineuses n'était qu'un début | CRDI ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    17 janv. 2017 ... Photo : CRDI. Les légumineuses sont une famille de plantes cultivées qui comprend les haricots secs, les petits pois, les lentilles et les pois chiches; elles sont toutes très importantes pour la nutrition, la santé et la subsistance. Bien que l'année 2016 -- célébrée comme l'Année internationale des ...

  19. Ann Modeling for Grey Particles Produced from Interactions of Different Projectiles with Emulsion Nuclei at 4.5 AGEV/C

    International Nuclear Information System (INIS)

    El-Bakry, M.N.Y.; Basha, A.M.; Rashed, N.; Mahmoud, M.A.; Radi, A.

    2008-01-01

    Artificial Neural Network (ANN) is one of the important tools in high energy physics. In this paper, we are using ANN for modeling the multiplicity distributions of grey particles produced from interactions of P, 3 He, 4 He, 6 Li, 12 C, 24 Mg, and 32 S with emulsion nuclei, light nuclei (CNO), and heavy nuclei (Ag Br). The equations of these distributions were obtained

  20. An Application of ANN Model with Bayesian Regularization Learning Algorithm for Computing the Operating Frequency of C-Shaped Patch Antennas

    Directory of Open Access Journals (Sweden)

    Ahmet Kayabasi

    2016-10-01

    Full Text Available In this paper, an application of artificial neural network (ANN using bayesian regularization (BR learning algorithm based on multilayer perceptron (MLP model is presented for computing the operating frequency of C-shaped patch antennas (CPAs in UHF band. Firstly, the operating frequencies of 144 CPAs having varied dimensions and electrical parameters were simulated by the XFDTD software package based on the finite-difference time domain (FDTD method in order to generate the data set for the training and testing processes of the ANN-BR model. Then ANN-BR model was built with data set and while 129 simulated CPAs and remaining 15 simulated CPAs were employed for ANN-BR model training and testing respectively. In order to demonstrate its validity and accuracy, the proposed ANN-BR model was also tested over the simulation data given in the literature. The obtained results show that ANN-BR technique can be successfully used to compute the operating frequency of CPAs without involving any sophisticated methods.

  1. Land Degradation Monitoring in the Ordos Plateau of China Using an Expert Knowledge and BP-ANN-Based Approach

    Directory of Open Access Journals (Sweden)

    Yaojie Yue

    2016-11-01

    Full Text Available Land degradation monitoring is of vital importance to provide scientific information for promoting sustainable land utilization. This paper presents an expert knowledge and BP-ANN-based approach to detect and monitor land degradation in an effort to overcome the deficiencies of image classification and vegetation index-based approaches. The proposed approach consists of three generic steps: (1 extraction of knowledge on the relationship between land degradation degree and predisposing factors, which are NDVI and albedo, from domain experts; (2 establishment of a land degradation detecting model based on the BP-ANN algorithm; and (3 land degradation dynamic analysis. A comprehensive analysis was conducted on the development of land degradation in the Ordos Plateau of China in 1990, 2000 and 2010. The results indicate that the proposed approach is reliable for monitoring land degradation, with an overall accuracy of 91.2%. From 1990–2010, a reverse trend of land degradation is observed in Ordos Plateau. Regions with relatively high land degradation dynamic were mostly located in the northeast of Ordos Plateau. Additionally, most of the regions have transferred from a hot spot of land degradation to a less changed area. It is suggested that land utilization optimization plays a key role for effective land degradation control. However, it should be highlighted that the goals of such strategies should aim at the main negative factors causing land degradation, and the land use type and its quantity must meet the demand of population and be reconciled with natural conditions. Results from this case study suggest that the expert knowledge and BP-ANN-based approach is effective in mapping land degradation.

  2. Valorization of aquaculture waste in removal of cadmium from aqueous solution: optimization by kinetics and ANN analysis

    Science.gov (United States)

    Aditya, Gautam; Hossain, Asif

    2018-05-01

    Cadmium is one of the most hazardous heavy metal concerning human health and aquatic pollution. The removal of cadmium through biosorption is a feasible option for restoration of the ecosystem health of the contaminated freshwater ecosystems. In compliance with this proposition and considering the efficiency of calcium carbonate as biosorbent, the shell dust of the economically important snail Bellamya bengalensis was tested for the removal of cadmium from aqueous medium. Following use of the flesh as a cheap source of protein, the shells of B. bengalensis made up of CaCO3 are discarded as aquaculture waste. The biosorption was assessed through batch sorption studies along with studies to characterize the morphology and surface structures of waste shell dust. The data on the biosorption were subjected to the artificial neural network (ANN) model for optimization of the process. The biosorption process changed as functions of pH of the solution, concentration of heavy metal, biomass of the adsorbent and time of exposure. The kinetic process was well represented by pseudo second order ( R 2 = 0.998), and Langmuir equilibrium ( R 2 = 0.995) had better fits in the equilibrium process with 30.33 mg g-1 of maximum sorption capacity. The regression equation ( R 2 = 0.948) in the ANN model supports predicted values of Cd removal satisfactorily. The normalized importance analysis in ANN predicts Cd2+ concentration, and pH has the most influence in removal than biomass dose and time. The SEM and EDX studies show clear peaks for Cd confirming the biosorption process while the FTIR study depicts the main functional groups (-OH, C-H, C=O, C=C) responsible for the biosorption process. The study indicated that the waste shell dust can be used as an efficient, low cost, environment friendly, sustainable adsorbent for the removal of cadmium from aqueous solution.

  3. Biosurfactant-biopolymer driven microbial enhanced oil recovery (MEOR) and its optimization by an ANN-GA hybrid technique.

    Science.gov (United States)

    Dhanarajan, Gunaseelan; Rangarajan, Vivek; Bandi, Chandrakanth; Dixit, Abhivyakti; Das, Susmita; Ale, Kranthikiran; Sen, Ramkrishna

    2017-08-20

    A lipopeptide biosurfactant produced by marine Bacillus megaterium and a biopolymer produced by thermophilic Bacillus licheniformis were tested for their application potential in the enhanced oil recovery. The crude biosurfactant obtained after acid precipitation effectively reduced the surface tension of deionized water from 70.5 to 28.25mN/m and the interfacial tension between lube oil and water from 18.6 to 1.5mN/m at a concentration of 250mgL -1 . The biosurfactant exhibited a maximum emulsification activity (E 24 ) of 81.66% against lube oil. The lipopeptide micelles were stabilized by addition of Ca 2+ ions to the biosurfactant solution. The oil recovery efficiency of Ca 2+ conditioned lipopeptide solution from a sand-packed column was optimized by using artificial neural network (ANN) modelling coupled with genetic algorithm (GA) optimization. Three important parameters namely lipopeptide concentration, Ca 2+ concentration and solution pH were considered for optimization studies. In order to further improve the recovery efficiency, a water soluble biopolymer produced by Bacillus licheniformis was used as a flooding agent after biosurfactant incubation. Upon ANN-GA optimization, 45% tertiary oil recovery was achieved, when biopolymer at a concentration of 3gL -1 was used as a flooding agent. Oil recovery was only 29% at optimal conditions predicted by ANN-GA, when only water was used as flooding solution. The important characteristics of biopolymers such as its viscosity, pore plugging capabilities and bio-cementing ability have also been tested. Thus, as a result of biosurfactant incubation and biopolymer flooding under the optimal process conditions, a maximum oil recovery of 45% was achieved. Therefore, this study is novel, timely and interesting for it showed the combined influence of biosurfactant and biopolymer on solubilisation and mobilization of oil from the soil. Copyright © 2017 Elsevier B.V. All rights reserved.

  4. Determination of oil well production performance using artificial neural network (ANN linked to the particle swarm optimization (PSO tool

    Directory of Open Access Journals (Sweden)

    Mohammad Ali Ahmadi

    2015-06-01

    In this work, novel and rigorous methods based on two different types of intelligent approaches including the artificial neural network (ANN linked to the particle swarm optimization (PSO tool are developed to precisely forecast the productivity of horizontal wells under pseudo-steady-state conditions. It was found that there is very good match between the modeling output and the real data taken from the literature, so that a very low average absolute error percentage is attained (e.g., <0.82%. The developed techniques can be also incorporated in the numerical reservoir simulation packages for the purpose of accuracy improvement as well as better parametric sensitivity analysis.

  5. Continental visions: Ann Seidman, Reginald H. Green and the economics of African unity in 1960s Ghana

    OpenAIRE

    Serra, Gerardo

    2014-01-01

    The paper presents the history of the contribution of two American economists to a radical cause: the establishment of a socialist and politically united Africa. The setting is 1960s Ghana which under Kwame Nkrumah, the man who led the country to independence from British colonial rule, emerged as the epicentre of this Pan-African vision. Ann Seidman and Reginald H. Green became, as members of the research team on 'The Economics of African Unity' established at the University of Ghana in 1963...

  6. An analysis with Bion: an interview with James Gooch. Interview by JoAnn Culbert-Koehn.

    Science.gov (United States)

    Gooch, James

    2011-02-01

    In 1968 Wilfred Bion moved to Los Angeles, escaping the perils of fame in London. He lived in Los Angeles until a few months before his death in Oxford in 1979. He made a deep impact on psychoanalysis in Los Angeles through those he analysed and what he wrote. James Gooch, psychiatrist and founding president of the Psychoanalytic Center of California describes in detail the transformative experience of his analysis with Bion in an interview with JoAnn Culbert-Koehn, Jungian analyst. Dr. Gooch describes important differences between his analysis with Bion and his classical Freudian analysis during his analytic training. © 2011, The Society of Analytical Psychology.

  7. Ann Crabbé and Pieter Leroy, 2008, The Handbook of Environmental Policy Evaluation, Earthscan, London, 202 p.

    Directory of Open Access Journals (Sweden)

    François Destandau

    2010-09-01

    Full Text Available Ann Crabbé de l’Université d’Anvers (Belgique et Pieter Leroy de l’Université de Nijmegen (Pays-Bas proposent ici un ouvrage pionnier sur l’évaluation appliquée aux politiques environnementales. Ce manuel s’inspire des enseignements théoriques et méthodologiques des sciences politiques et sociales. Les auteurs y présentent un panel de méthodes adaptées aux politiques environnementales, de façon pratique et accessible. « Peut-on identifier une relation de causalité entre l’intervention polit...

  8. Maps and seismic profiles showing geology of the inner continental shelf, Cape Ann, Massachusetts to New Hampshire

    Science.gov (United States)

    Oldale, R.N.; Wommack, L.E.

    1987-01-01

    This interpretation of the geology of the Inner Continental Shelf from Cape Ann, Mass. to New Hampshire (fig. 1) is based on high-resolution seismic-reflection surveys conducted in 1979 and 1980 as part of a cooperative program between the Massachusetts Department of Public Works and the U.S. Geological Survey. Seismic data were collected aboard the RV Gilliss along 104 kilometers (km) of widely spaced trackline (fig. 2). These tracks trend subparallel to the coast. About 290 km cf trackline, spaced approximately 2 km apart and oriented roughly normal to the coast, were taken aboard the RV Asterias (fig. 2).

  9. WE-A-201-00: Anne and Donald Herbert Distinguished Lectureship On Modern Statistical Modeling

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2016-06-15

    Regulatory Commission and may be remembered for his critique of the National Academy of Sciences BEIR III report (stating that their methodology “imposes a Delphic quality to the .. risk estimates”.) This led to his appointment as a member of the BEIR V committee. Don presented refresher courses at the AAPM, ASTRO and RSNA meetings and was active in the AAPM as a member or chair of several committees. He was the principal author of AAPM Report 43, which is essentially a critique of established clinical studies prior to 1992. He was co-editor of the Proceedings of many symposia on Time, Dose and Fractionation held in Madison, Wisconsin. He received the AAPM lifetime Achievement award in 2004. Don’s second wife of 46 years, Ann, predeceased him and he is survived by daughters Hillary and Emily, son John and two grandsons. Don was a true gentleman with a unique and erudite writing style illuminated by pithy quotations. If he had a fault it was, perhaps, that he did not realize how much smarter he was than the rest of us. This presentation draws heavily on a biography and video interview in the History and Heritage section of the AAPM website. The quote is his own. Andrzej Niemierko: Statistical modeling plays an essential role in modern medicine for quantitative evaluation of the effect of treatment. This session will feature an overview of statistical modeling techniques used for analyzing the many types of research data and an exploration of recent advances in new statistical modeling methodologies. Learning Objectives: To learn basics of statistical modeling methodology. To discuss statistical models that are frequently used in radiation oncology To discuss advanced modern statistical modeling methods and applications.

  10. WE-A-201-00: Anne and Donald Herbert Distinguished Lectureship On Modern Statistical Modeling

    International Nuclear Information System (INIS)

    2016-01-01

    Regulatory Commission and may be remembered for his critique of the National Academy of Sciences BEIR III report (stating that their methodology “imposes a Delphic quality to the .. risk estimates”.) This led to his appointment as a member of the BEIR V committee. Don presented refresher courses at the AAPM, ASTRO and RSNA meetings and was active in the AAPM as a member or chair of several committees. He was the principal author of AAPM Report 43, which is essentially a critique of established clinical studies prior to 1992. He was co-editor of the Proceedings of many symposia on Time, Dose and Fractionation held in Madison, Wisconsin. He received the AAPM lifetime Achievement award in 2004. Don’s second wife of 46 years, Ann, predeceased him and he is survived by daughters Hillary and Emily, son John and two grandsons. Don was a true gentleman with a unique and erudite writing style illuminated by pithy quotations. If he had a fault it was, perhaps, that he did not realize how much smarter he was than the rest of us. This presentation draws heavily on a biography and video interview in the History and Heritage section of the AAPM website. The quote is his own. Andrzej Niemierko: Statistical modeling plays an essential role in modern medicine for quantitative evaluation of the effect of treatment. This session will feature an overview of statistical modeling techniques used for analyzing the many types of research data and an exploration of recent advances in new statistical modeling methodologies. Learning Objectives: To learn basics of statistical modeling methodology. To discuss statistical models that are frequently used in radiation oncology To discuss advanced modern statistical modeling methods and applications.

  11. The neurological examination of non-complicated preterm newborns using the Saint-Anne Dargassies Scale from birth to term

    Directory of Open Access Journals (Sweden)

    Carla Ismirna Santos Alves

    2010-12-01

    Full Text Available OBJECTIVE: To describe the maturational development of 20 (aged 32-36 weeks premature newborns (PNBs without clinical or neurological complications from birth until term. METHOD: The Saint-Anne Dargassies Scale was applied every two weeks until the age of 37 weeks. RESULTS: The PNBs showed normal Apgar and the growth in head circumference was adequate for postmentrual age. The Friedman ANOVA test found a significant difference only for the heel-to-ear angles from birth until term. The Saint-Anne Dargassies Scale detected changes in 11 PNBs and the most altered reflexes were: cardinal points, Moro, cross extension reflexe and automatic walking. These changes were found in the first 48 hours of life and in subsequent weeks until term. CONCLUSION: This finding alone justifies the neurological examination of PNBs, even if they show no clinical/neurological complications in the perinatal period and the importance of neuromotor assessment in preterm infants, as it enables detection and appropriate intervention.

  12. Applying of the Artificial Neural Networks (ANN) to Identify and Characterize Sweet Spots in Shale Gas Formations

    Science.gov (United States)

    Puskarczyk, Edyta

    2018-03-01

    The main goal of the study was to enhance and improve information about the Ordovician and Silurian gas-saturated shale formations. Author focused on: firstly, identification of the shale gas formations, especially the sweet spots horizons, secondly, classification and thirdly, the accurate characterization of divisional intervals. Data set comprised of standard well logs from the selected well. Shale formations are represented mainly by claystones, siltstones, and mudstones. The formations are also partially rich in organic matter. During the calculations, information about lithology of stratigraphy weren't taken into account. In the analysis, selforganizing neural network - Kohonen Algorithm (ANN) was used for sweet spots identification. Different networks and different software were tested and the best network was used for application and interpretation. As a results of Kohonen networks, groups corresponding to the gas-bearing intervals were found. The analysis showed diversification between gas-bearing formations and surrounding beds. It is also shown that internal diversification in sweet spots is present. Kohonen algorithm was also used for geological interpretation of well log data and electrofacies prediction. Reliable characteristic into groups shows that Ja Mb and Sa Fm which are usually treated as potential sweet spots only partially have good reservoir conditions. It is concluded that ANN appears to be useful and quick tool for preliminary classification of members and sweet spots identification.

  13. Satellite image analysis and a hybrid ESSS/ANN model to forecast solar irradiance in the tropics

    International Nuclear Information System (INIS)

    Dong, Zibo; Yang, Dazhi; Reindl, Thomas; Walsh, Wilfred M.

    2014-01-01

    Highlights: • Satellite image analysis is performed and cloud cover index is classified using self-organizing maps (SOM). • The ESSS model is used to forecast cloud cover index. • Solar irradiance is estimated using multi-layer perceptron (MLP). • The proposed model shows better accuracy than other investigated models. - Abstract: We forecast hourly solar irradiance time series using satellite image analysis and a hybrid exponential smoothing state space (ESSS) model together with artificial neural networks (ANN). Since cloud cover is the major factor affecting solar irradiance, cloud detection and classification are crucial to forecast solar irradiance. Geostationary satellite images provide cloud information, allowing a cloud cover index to be derived and analysed using self-organizing maps (SOM). Owing to the stochastic nature of cloud generation in tropical regions, the ESSS model is used to forecast cloud cover index. Among different models applied in ANN, we favour the multi-layer perceptron (MLP) to derive solar irradiance based on the cloud cover index. This hybrid model has been used to forecast hourly solar irradiance in Singapore and the technique is found to outperform traditional forecasting models

  14. Usefulness of ANN-based model for copper removal from aqueous solutions using agro industrial waste materials

    Directory of Open Access Journals (Sweden)

    Petrović Marija S.

    2015-01-01

    Full Text Available The purpose of this study was to investigate the adsorption properties of locally available lignocelluloses biomaterials as biosorbents for the removal of copper ions from aqueous solution. Materials are generated from juice production (apricot stones and from the corn milling process (corn cob. Such solid wastes have little or no economic value and very often present a disposal problem. Using batch adsorption techniques the effects of initial Cu(II ions concentration (Ci, amount of biomass (m and volume of metal solution (V, on biosorption efficiency and capacity were studied for both materials, without any pre-treatments. The optimal parameters for both biosorbents were selected depending on a highest sorption capability of biosorbent, in removal of Cu(II. Experimental data were compared with second order polynomial regression models (SOPs and artificial neural networks (ANNs. SOPs showed acceptable coefficients of determination (0.842 - 0.997, while ANNs performed high prediction accuracy (0.980-0.986 in comparison to experimental results. [Projekat Ministarstva nauke Republike Srbije, br. TR 31003, TR 31055

  15. Modeling of policies for reduction of GHG emissions in energy sector using ANN: case study-Croatia (EU).

    Science.gov (United States)

    Bolanča, Tomislav; Strahovnik, Tomislav; Ukić, Šime; Stankov, Mirjana Novak; Rogošić, Marko

    2017-07-01

    This study describes the development of tool for testing different policies for reduction of greenhouse gas (GHG) emissions in energy sector using artificial neural networks (ANNs). The case study of Croatia was elaborated. Two different energy consumption scenarios were used as a base for calculations and predictions of GHG emissions: the business as usual (BAU) scenario and sustainable scenario. Both of them are based on predicted energy consumption using different growth rates; the growth rates within the second scenario resulted from the implementation of corresponding energy efficiency measures in final energy consumption and increasing share of renewable energy sources. Both ANN architecture and training methodology were optimized to produce network that was able to successfully describe the existing data and to achieve reliable prediction of emissions in a forward time sense. The BAU scenario was found to produce continuously increasing emissions of all GHGs. The sustainable scenario was found to decrease the GHG emission levels of all gases with respect to BAU. The observed decrease was attributed to the group of measures termed the reduction of final energy consumption through energy efficiency measures.

  16. Prediction of the Effect of Using Stone Column in Clayey Soil on the Behavior of Circular Footing by ANN Model

    Directory of Open Access Journals (Sweden)

    Omar Khaleel Ismael Al-Kubaisi

    2018-05-01

    Full Text Available Shallow foundations are usually used for structures with light to moderate loads where the soil underneath can carry them. In some cases, soil strength and/or other properties are not adequate and require improvement using one of the ground improvement techniques. Stone column is one of the common improvement techniques in which a column of stone is installed vertically in clayey soils. Stone columns are usually used to increase soil strength and to accelerate soil consolidation by acting as vertical drains. Many researches have been done to estimate the behavior of the improved soil. However, none of them considered the effect of stone column geometry on the behavior of the circular footing. In this research, finite element models have been conducted to evaluate the behavior of a circular footing with different stone column configurations. Moreover, an Artificial Neural Network (ANN model has been generated for predicting these effects. The results showed a reduction in the bending moment, the settlement, and the vertical stresses with the increment of the stone column length, while both the horizontal stress and the shear force were increased. ANN model showed a good relationship between the predicted and the calculated results.

  17. Designing the input vector to ANN-based models for short-term load forecast in electricity distribution systems

    International Nuclear Information System (INIS)

    Santos, P.J.; Martins, A.G.; Pires, A.J.

    2007-01-01

    The present trend to electricity market restructuring increases the need for reliable short-term load forecast (STLF) algorithms, in order to assist electric utilities in activities such as planning, operating and controlling electric energy systems. Methodologies such as artificial neural networks (ANN) have been widely used in the next hour load forecast horizon with satisfactory results. However, this type of approach has had some shortcomings. Usually, the input vector (IV) is defined in a arbitrary way, mainly based on experience, on engineering judgment criteria and on concern about the ANN dimension, always taking into consideration the apparent correlations within the available endogenous and exogenous data. In this paper, a proposal is made of an approach to define the IV composition, with the main focus on reducing the influence of trial-and-error and common sense judgments, which usually are not based on sufficient evidence of comparative advantages over previous alternatives. The proposal includes the assessment of the strictly necessary instances of the endogenous variable, both from the point of view of the contiguous values prior to the forecast to be made, and of the past values representing the trend of consumption at homologous time intervals of the past. It also assesses the influence of exogenous variables, again limiting their presence at the IV to the indispensable minimum. A comparison is made with two alternative IV structures previously proposed in the literature, also applied to the distribution sector. The paper is supported by a real case study at the distribution sector. (author)

  18. Comparison between Possibilistic c-Means (PCM and Artificial Neural Network (ANN Classification Algorithms in Land use/ Land cover Classification

    Directory of Open Access Journals (Sweden)

    Ganchimeg Ganbold

    2017-03-01

    Full Text Available There are several statistical classification algorithms available for landuse/land cover classification. However, each has a certain bias orcompromise. Some methods like the parallel piped approach in supervisedclassification, cannot classify continuous regions within a feature. Onthe other hand, while unsupervised classification method takes maximumadvantage of spectral variability in an image, the maximally separableclusters in spectral space may not do much for our perception of importantclasses in a given study area. In this research, the output of an ANNalgorithm was compared with the Possibilistic c-Means an improvementof the fuzzy c-Means on both moderate resolutions Landsat8 and a highresolution Formosat 2 images. The Formosat 2 image comes with an8m spectral resolution on the multispectral data. This multispectral imagedata was resampled to 10m in order to maintain a uniform ratio of1:3 against Landsat 8 image. Six classes were chosen for analysis including:Dense forest, eucalyptus, water, grassland, wheat and riverine sand. Using a standard false color composite (FCC, the six features reflecteddifferently in the infrared region with wheat producing the brightestpixel values. Signature collection per class was therefore easily obtainedfor all classifications. The output of both ANN and FCM, were analyzedseparately for accuracy and an error matrix generated to assess the qualityand accuracy of the classification algorithms. When you compare theresults of the two methods on a per-class-basis, ANN had a crisperoutput compared to PCM which yielded clusters with pixels especiallyon the moderate resolution Landsat 8 imagery.

  19. Optimization of delignification of two Pennisetum grass species by NaOH pretreatment using Taguchi and ANN statistical approach.

    Science.gov (United States)

    Mohaptra, Sonali; Dash, Preeti Krishna; Behera, Sudhanshu Shekar; Thatoi, Hrudayanath

    2016-01-01

    In the bioconversion of lignocelluloses for bioethanol, pretreatment seems to be the most important step which improves the elimination of the lignin and hemicelluloses content, exposing cellulose to further hydrolysis. The present study discusses the application of dynamic statistical techniques like the Taguchi method and artificial neural network (ANN) in the optimization of pretreatment of lignocellulosic biomasses such as Hybrid Napier grass (HNG) (Pennisetum purpureum) and Denanath grass (DG) (Pennisetum pedicellatum), using alkali sodium hydroxide. This study analysed and determined a parameter combination with a low number of experiments by using the Taguchi method in which both the substrates can be efficiently pretreated. The optimized parameters obtained from the L16 orthogonal array are soaking time (18 and 26 h), temperature (60°C and 55°C), and alkali concentration (1%) for HNG and DG, respectively. High performance liquid chromatography analysis of the optimized pretreated grass varieties confirmed the presence of glucan (47.94% and 46.50%), xylan (9.35% and 7.95%), arabinan (2.15% and 2.2%), and galactan/mannan (1.44% and 1.52%) for HNG and DG, respectively. Physicochemical characterization studies of native and alkali-pretreated grasses were carried out by scanning electron microscopy and Fourier transformation Infrared spectroscopy which revealed some morphological differences between the native and optimized pretreated samples. Model validation by ANN showed a good agreement between experimental results and the predicted responses.

  20. Designing the input vector to ANN-based models for short-term load forecast in electricity distribution systems

    Energy Technology Data Exchange (ETDEWEB)

    Santos, P.J. [LabSEI-ESTSetubal-Department of Electrical Engineering at Escola Superior de Tecnologia, Polytechnic Institute of Setubal Rua Vale de Chaves Estefanilha, 2910-761 Setubal (Portugal); Martins, A.G. [Department of Electrical Engineering, FCTUC/INESC, Polo 2 University of Coimbra, Pinhal de Marrocos, 3030 Coimbra (Portugal); Pires, A.J. [LabSEI-ESTSetubal-Department of Electrical Engineering at Escola Superior de Tecnologia, Polytechnic Institute of Setubal Rua Vale de, Chaves Estefanilha, 2910-761 Setubal (Portugal)

    2007-05-15

    The present trend to electricity market restructuring increases the need for reliable short-term load forecast (STLF) algorithms, in order to assist electric utilities in activities such as planning, operating and controlling electric energy systems. Methodologies such as artificial neural networks (ANN) have been widely used in the next hour load forecast horizon with satisfactory results. However, this type of approach has had some shortcomings. Usually, the input vector (IV) is defined in a arbitrary way, mainly based on experience, on engineering judgment criteria and on concern about the ANN dimension, always taking into consideration the apparent correlations within the available endogenous and exogenous data. In this paper, a proposal is made of an approach to define the IV composition, with the main focus on reducing the influence of trial-and-error and common sense judgments, which usually are not based on sufficient evidence of comparative advantages over previous alternatives. The proposal includes the assessment of the strictly necessary instances of the endogenous variable, both from the point of view of the contiguous values prior to the forecast to be made, and of the past values representing the trend of consumption at homologous time intervals of the past. It also assesses the influence of exogenous variables, again limiting their presence at the IV to the indispensable minimum. A comparison is made with two alternative IV structures previously proposed in the literature, also applied to the distribution sector. The paper is supported by a real case study at the distribution sector. (author)

  1. Design of an Experiment to Measure ann Using 3H(γ, pnn at HIγS★

    Directory of Open Access Journals (Sweden)

    Friesen F.Q.L.

    2016-01-01

    Full Text Available We provide an update on the development of an experiment at TUNL for determining the 1S0 neutron-neutron (nn scattering length (ann from differential cross-section measurements of three-body photodisintegration of the triton. The experiment will be conducted using a linearly polarized gamma-ray beam at the High Intensity Gamma-ray Source (HIγS and tritium gas contained in thin-walled cells. The main components of the planned experiment are a 230 Ci gas target system, a set of wire chambers and silicon strip detectors on each side of the beam axis, and an array of neutron detectors on each side beyond the silicon detectors. The protons emitted in the reaction are tracked in the wire chambers and their energy and position are measured in silicon strip detectors. The first iteration of the experiment will be simplified, making use of a collimator system, and silicon detectors to interrogate the main region of interest near 90° in the polar angle. Monte-Carlo simulations based on rigorous 3N calculations have been conducted to validate the sensitivity of the experimental setup to ann.

  2. Development of experimental design approach and ANN-based models for determination of Cr(VI) ions uptake rate from aqueous solution onto the solid biodiesel waste residue.

    Science.gov (United States)

    Shanmugaprakash, M; Sivakumar, V

    2013-11-01

    In the present work, the evaluation capacities of two optimization methodologies such as RSM and ANN were employed and compared for predication of Cr(VI) uptake rate using defatted pongamia oil cake (DPOC) in both batch and column mode. The influence of operating parameters was investigated through a central composite design (CCD) of RSM using Design Expert 8.0.7.1 software. The same data was fed as input in ANN to obtain a trained the multilayer feed-forward networks back-propagation algorithm using MATLAB. The performance of the developed ANN models were compared with RSM mathematical models for Cr(VI) uptake rate in terms of the coefficient of determination (R(2)), root mean square error (RMSE) and absolute average deviation (AAD). The estimated values confirm that ANN predominates RSM representing the superiority of a trained ANN models over RSM models in order to capture the non-linear behavior of the given system. Copyright © 2013 Elsevier Ltd. All rights reserved.

  3. Development of ANN-based models to predict the static response and dynamic response of a heat exchanger in a real MVAC system

    International Nuclear Information System (INIS)

    Hu Qinhua; So, Albert T P; Tse, W L; Ren, Qingchang

    2005-01-01

    This paper presents a systematic approach to develop artificial neural network (ANN) models to predict the performance of a heat exchanger operating in real mechanical ventilation and air-conditioning (MVAC) system. Two approaches were attempted and presented. Every detailed components of the MVAC system have been considered and we attempt to model each of them by one ANN. This study used the neural network technique to obtain a static and a dynamic model for a heat exchanger mounted in an air handler unit (AHU), which is the key component of the MVAC system. It has been verified that almost all of the predicted values of the ANN model were within 95% - 105% of the measured values, with a consistent mean relative error (MRE) smaller than 2.5%. The paper details our experiences in using ANNs, especially those with back-propagation (BP) structures. Also, the weights and biases of our trained-up ANN models are listed out, which serve as good reference for readers to deal with their own situations

  4. Prediction of temperature and HAZ in thermal-based processes with Gaussian heat source by a hybrid GA-ANN model

    Science.gov (United States)

    Fazli Shahri, Hamid Reza; Mahdavinejad, Ramezanali

    2018-02-01

    Thermal-based processes with Gaussian heat source often produce excessive temperature which can impose thermally-affected layers in specimens. Therefore, the temperature distribution and Heat Affected Zone (HAZ) of materials are two critical factors which are influenced by different process parameters. Measurement of the HAZ thickness and temperature distribution within the processes are not only difficult but also expensive. This research aims at finding a valuable knowledge on these factors by prediction of the process through a novel combinatory model. In this study, an integrated Artificial Neural Network (ANN) and genetic algorithm (GA) was used to predict the HAZ and temperature distribution of the specimens. To end this, a series of full factorial design of experiments were conducted by applying a Gaussian heat flux on Ti-6Al-4 V at first, then the temperature of the specimen was measured by Infrared thermography. The HAZ width of each sample was investigated through measuring the microhardness. Secondly, the experimental data was used to create a GA-ANN model. The efficiency of GA in design and optimization of the architecture of ANN was investigated. The GA was used to determine the optimal number of neurons in hidden layer, learning rate and momentum coefficient of both output and hidden layers of ANN. Finally, the reliability of models was assessed according to the experimental results and statistical indicators. The results demonstrated that the combinatory model predicted the HAZ and temperature more effective than a trial-and-error ANN model.

  5. An ANN-based approach to predict blast-induced ground vibration of Gol-E-Gohar iron ore mine, Iran

    Directory of Open Access Journals (Sweden)

    Mahdi Saadat

    2014-02-01

    Full Text Available Blast-induced ground vibration is one of the inevitable outcomes of blasting in mining projects and may cause substantial damage to rock mass as well as nearby structures and human beings. In this paper, an attempt has been made to present an application of artificial neural network (ANN to predict the blast-induced ground vibration of the Gol-E-Gohar (GEG iron mine, Iran. A four-layer feed-forward back propagation multi-layer perceptron (MLP was used and trained with Levenberg–Marquardt algorithm. To construct ANN models, the maximum charge per delay, distance from blasting face to monitoring point, stemming and hole depth were taken as inputs, whereas peak particle velocity (PPV was considered as an output parameter. A database consisting of 69 data sets recorded at strategic and vulnerable locations of GEG iron mine was used to train and test the generalization capability of ANN models. Coefficient of determination (R2 and mean square error (MSE were chosen as the indicators of the performance of the networks. A network with architecture 4-11-5-1 and R2 of 0.957 and MSE of 0.000722 was found to be optimum. To demonstrate the supremacy of ANN approach, the same 69 data sets were used for the prediction of PPV with four common empirical models as well as multiple linear regression (MLR analysis. The results revealed that the proposed ANN approach performs better than empirical and MLR models.

  6. Review: Jennifer Browdy de Hernandez, Pauline Dongala, Omotayo Jolaosho and Anne Serafin (eds., African Women Writing Resistance: An Anthology of Contemporary Voices (2010 Buchbesprechung: Jennifer Browdy de Hernandez, Pauline Dongala, Omotayo Jolaosho und Anne Serafin (Hrsg., African Women Writing Resistance: An Anthology of Contemporary Voices (2010

    Directory of Open Access Journals (Sweden)

    Anja Oed

    2011-01-01

    Full Text Available Review of the edited volume: Jennifer Browdy de Hernandez, Pauline Dongala, Omotayo Jolaosho and Anne Serafin (eds., African Women Writing Resistance: An Anthology of Contemporary Voices, Oxford: Pambazuka Press, 2010, ISBN 978-0-299-23664-9, 376 pp.Besprechung des Sammelbandes: Jennifer Browdy de Hernandez, Pauline Dongala, Omotayo Jolaosho und Anne Serafin (Hrsg., African Women Writing Resistance: An Anthology of Contemporary Voices, Oxford: Pambazuka Press, 2010, ISBN 978-0-299-23664-9, 376 Seiten

  7. The Inflatable Mini Anne® Manikin May be Used as an Inexpensive Alternative to a Standard Life-size Resuscitation Manikin During Instructor-led BLS/AED Training - A Randomized Controlled Study

    DEFF Research Database (Denmark)

    Bang, Camilla; Cordsen, Anna-Sophie N; Hoe, Masja B

    2017-01-01

    Introduction: The inflatable and inexpensive Mini Anne® resuscitation manikin is widely used with a self-instruction video and allows dissemination of BLS/AED skills to large groups. The learning outcome following instructor-led BLS/AED training using a Mini Anne® compared to a standard life...... using the Mini Anne® manikin was not significantly different compared with training on a standard life-size manikin. The Mini Anne® manikin may be used as an inexpensive alternative to a standard life-size resuscitation manikin.Author Disclosures: C. Bang: None. A.N. Cordsen: None. M.B. Hoe: None. S...

  8. Comparative study on the predictability of statistical models (RSM and ANN) on the behavior of optimized buccoadhesive wafers containing Loratadine and their in vivo assessment.

    Science.gov (United States)

    Chakraborty, Prithviraj; Parcha, Versha; Chakraborty, Debarupa D; Ghosh, Amitava

    2016-01-01

    Buccoadhesive wafer dosage form containing Loratadine is formulated utilizing Formulation by Design (FbD) approach incorporating sodium alginate and lactose monohydrate as independent variable employing solvent casting method. The wafers were statistically optimized using Response Surface Methodology (RSM) and Artificial Neural Network algorithm (ANN) for predicting physicochemical and physico-mechanical properties of the wafers as responses. Morphologically wafers were tested using SEM. Quick disintegration of the samples was examined employing Optical Contact Angle (OCA). The comparison of the predictability of RSM and ANN showed a high prognostic capacity of RSM model over ANN model in forecasting mechanical and physicochemical properties of the wafers. The in vivo assessment of the optimized buccoadhesive wafer exhibits marked increase in bioavailability justifying the administration of Loratadine through buccal route, bypassing hepatic first pass metabolism.

  9. Undervisning i håndskrivning. Interview med dansklærer Anne Mette Fræhr Møller

    DEFF Research Database (Denmark)

    Lund, Henriette

    2016-01-01

    På Thomasskolen i Skovlunde har indskolingslærer i dansk Anne Mette Fræhr Møller de sidste mange år arbejdet fokuseret med at udvikle sine elevers håndskrivning. Relevansen af undervisningen er hun ikke et sekund i tvivl om: det er en sikker vej ind i bogstavindlæringen og giver børnene et godt...... dansklærere om deres undervisning i håndskrivning. Anne Mette Fræhr Møller er en af dem....

  10. Facteurs prédictifs de succès des étudiants en première année de ...

    African Journals Online (AJOL)

    L'objectif de cette étude était d'évaluer la relation entre les résultats des étudiants au BAC et le succès en première année de médecine. Méthodes: Nous avons ... La prise en compte de ces éléments dans le recrutement des étudiants en première année pourrait améliorer les résultats académiques. Pan African Medical ...

  11. Forward Greedy ANN input selection in a stacked framework with Adaboost.RT - A streamflow forecasting case study exploiting radar rainfall estimates

    Science.gov (United States)

    Brochero, D.; Anctil, F.; Gagné, C.

    2012-04-01

    In input selection (or feature selection), modellers are interested in identifying k of the d dimensions that provide the most information. In hydrology, this problem is particularly relevant when dealing with temporally and spatially distributed data such as radar rainfall estimates or meteorological ensemble forecasts. The most common approaches for input determination of artifitial neural networks (ANN) in water resources are cross-correlation, heuristics, embedding window analysis (chaos theory), and sensitivity analyses. We resorted here to Forward Greedy Selection (FGS), a sensitivity analysis, for identifying the inputs that maximize the performance of ANN forecasting. It consists of a pool of ANNs with different structures, initial weights, and training data subsets. The stacked ANN model was setup through the joint use of stop training and a special type of boosting for regression known as AdaBoost.RT. Several ANN are then used in series, each one exploiting, with incremental probability, data with relative estimation error higher than a pre-set threshold value. The global estimate is then obtained from the aggregation of the estimates of the models (here the median value). Two schemes are compared here, which differ in their input type. The first scheme looks at lagged radar rainfall estimates averaged over entire catchment (the average scenario), while the second scheme deals with the spatial variation fields of the radar rainfall estimates (the distributed scenario). Results lead to three major findings. First, stacked ANN response outperforms the best single ANN (in the same way as many others reports). Second, a positive gain in the test subset of around 20%, when compared to the average scenario, is observed in the distributed scenario. However, the most important result from the selecting process is the final structure of the inputs, for the distributed scenario clearly outlines the areas with the greatest impact on forecasting in terms of the

  12. Exact estimation of biodiesel cetane number (CN) from its fatty acid methyl esters (FAMEs) profile using partial least square (PLS) adapted by artificial neural network (ANN)

    International Nuclear Information System (INIS)

    Hosseinpour, Soleiman; Aghbashlo, Mortaza; Tabatabaei, Meisam; Khalife, Esmail

    2016-01-01

    Highlights: • Estimating the biodiesel CN from its FAMEs profile using ANN-based PLS approach. • Comparing the capability of ANN-adapted PLS approach with the standard PLS model. • Exact prediction of biodiesel CN from it FAMEs profile using ANN-based PLS method. • Developing an easy-to-use software using ANN-PLS model for computing the biodiesel CN. - Abstract: Cetane number (CN) is among the most important properties of biodiesel because it quantifies combustion speed or in better words, ignition quality. Experimental measurement of biodiesel CN is rather laborious and expensive. However, the high proportionality of biodiesel fatty acid methyl esters (FAMEs) profile with its CN is very appealing to develop straightforward and inexpensive computerized tools for biodiesel CN estimation. Unfortunately, correlating the chemical structure of biodiesel to its CN using conventional statistical and mathematical approaches is very difficult. To solve this issue, partial least square (PLS) adapted by artificial neural network (ANN) was introduced and examined herein as an innovative approach for the exact estimation of biodiesel CN from its FAMEs profile. In the proposed approach, ANN paradigm was used for modeling the inner relation between the input and the output PLS score vectors. In addition, the capability of the developed method in predicting the biodiesel CN was compared with the basal PLS method. The accuracy of the developed approaches for computing the biodiesel CN was assessed using three statistical criteria, i.e., coefficient of determination (R 2 ), mean-squared error (MSE), and percentage error (PE). The ANN-adapted PLS method predicted the biodiesel CN with an R 2 value higher than 0.99 demonstrating the fidelity of the developed model over the classical PLS method with a markedly lower R 2 value of about 0.85. In order to facilitate the use of the proposed model, an easy-to-use computer program was also developed on the basis of ANN-adapted PLS

  13. A Curve Fitting Approach Using ANN for Converting CT Number to Linear Attenuation Coefficient for CT-based PET Attenuation Correction

    Science.gov (United States)

    Lai, Chia-Lin; Lee, Jhih-Shian; Chen, Jyh-Cheng

    2015-02-01

    Energy-mapping, the conversion of linear attenuation coefficients (μ) calculated at the effective computed tomography (CT) energy to those corresponding to 511 keV, is an important step in CT-based attenuation correction (CTAC) for positron emission tomography (PET) quantification. The aim of this study was to implement energy-mapping step by using curve fitting ability of artificial neural network (ANN). Eleven digital phantoms simulated by Geant4 application for tomographic emission (GATE) and 12 physical phantoms composed of various volume concentrations of iodine contrast were used in this study to generate energy-mapping curves by acquiring average CT values and linear attenuation coefficients at 511 keV of these phantoms. The curves were built with ANN toolbox in MATLAB. To evaluate the effectiveness of the proposed method, another two digital phantoms (liver and spine-bone) and three physical phantoms (volume concentrations of 3%, 10% and 20%) were used to compare the energy-mapping curves built by ANN and bilinear transformation, and a semi-quantitative analysis was proceeded by injecting 0.5 mCi FDG into a SD rat for micro-PET scanning. The results showed that the percentage relative difference (PRD) values of digital liver and spine-bone phantom are 5.46% and 1.28% based on ANN, and 19.21% and 1.87% based on bilinear transformation. For 3%, 10% and 20% physical phantoms, the PRD values of ANN curve are 0.91%, 0.70% and 3.70%, and the PRD values of bilinear transformation are 3.80%, 1.44% and 4.30%, respectively. Both digital and physical phantoms indicated that the ANN curve can achieve better performance than bilinear transformation. The semi-quantitative analysis of rat PET images showed that the ANN curve can reduce the inaccuracy caused by attenuation effect from 13.75% to 4.43% in brain tissue, and 23.26% to 9.41% in heart tissue. On the other hand, the inaccuracy remained 6.47% and 11.51% in brain and heart tissue when the bilinear transformation

  14. Le Yémen dans les années 80 : entre crises et développement

    Directory of Open Access Journals (Sweden)

    Paul Dresch

    2002-09-01

    Full Text Available Au cours des années 1980 se sont dessinées les tendances structurelles qui allaient modeler le régime politique et le développement économique du Yémen du Nord et, par la suite, le pays tout entier. Les « bonnes feuilles » qui suivent sont extraites de l’ouvrage A History of Modern Yemen, de Paul Dresch. Elles ont une double vocation : donner quelques aperçus de cet arrière-plan historique essentiel et encourager le lecteur à aller découvrir, dans cet ouvrage fondamental, des clefs de lecture qui lui permettront de décrypter la situation complexe du  Yémen contemporain.

  15. Rezension von: Catherine M. Orr, Ann Braithwaite, Diane Lichtenstein (Eds.: Rethinking Women’s and Gender Studies. London: Routledge 2012.

    Directory of Open Access Journals (Sweden)

    Jennifer Bühner

    2014-03-01

    Full Text Available Der Sammelband von Catherine M. Orr, Ann Braithwaite und Diane Lichtenstein bietet eine aktuelle Auseinandersetzung mit den zentralen Konzepten der Selbst-/Zuschreibung in den Women’s and Gender Studies (WGS im Kontext der gegenwärtigen Umstrukturierung der Universitäten – wie z. B. Methoden, Pädagogik, Community, Disziplin und Institutionalisierung. Dabei wird zum einen ein genealogischer Zugang gewählt, um die Funktionsweise dieser Konzepte aufzuzeigen, und zum anderen werden durch Selbstreflexion der Autor_innen auf ihre eigene Lehre und Position innerhalb der WGS Änderungsvorschläge eingebracht, um potentiell neue Richtungen für die Frauen- und Geschlechterforschung aufzuzeigen.

  16. Book review. Ann-Hege Lorvik Waterhouse: In the material world: Perspectives and practices in kindergarten art activities

    Directory of Open Access Journals (Sweden)

    Nina Scott Frisch

    2013-12-01

    Full Text Available In the review of Ann-Hege Lorvik Waterhouse’s book I materialenes verden; perspektiver og praksiser i barnehagens kunstneriske virksomhet (In the material world: Perspectives and practices in kindergarten art activities,  Frisch states that despite the fact that the author is in some ways critical of the impact Reggio Emilia has had on Norwegian kindergartens, in her opinion, the book's content rests on the shoulders of the exploratory child-centred educational philosophy. The book offers great, relevant images related to new reflective concrete ideas, and it has a beautiful layout. Waterhouse points out her core argument several places in the text: a good kindergarten teacher is a creative kindergarten teacher – and the book reviewer adds, a good kindergarten teacher is a reading, reflecting kindergarten teacher.

  17. Developing an ANN model to simulate ASTM C1012-95 test considering different cement types and different pozzolanic additives

    Directory of Open Access Journals (Sweden)

    O.A. Hodhod

    2013-04-01

    In this research a study is presented to build a model by ANN equivalent to ASTM C1012-95. The input parameter was obtained from 16 different mortars according to ASTM C1012-95. Plain Portland cement mortars, mortars with cement combined with fly ash (FA, and mortars with cement combined with slag (GGBFS were tested by using ASTM C1012-95. Four cements, two ratio of FA, and one GGBFS were obtained from the literature. ASTM C1012-95 modeling techniques can help us understand the influence of aggressive environments on the concrete performance more readily, faster, and accurately. Such an understanding improves the decision making process in every stage of construction and maintenance and will help in better administration of resources.

  18. Introduction aux finances et à la fiscalité de Philippe le Bon dans les années 1420

    Directory of Open Access Journals (Sweden)

    Takemi Kanao

    2004-03-01

    Full Text Available Mes études sur la cour de Bourgogne, notamment sur les messagers et l’organisation des messageries 1, m’ont conduit à m’intéresser, plus récemment, au fonctionnement des finances et de la fiscalité ducale.  Entre avril 2003 et mars 2004, lors d’un séjour à Dijon, j’ai pu consulter les archives bourguignonnes et donner suite à mes recherches, dont on trouvera ici le compte-rendu, avec les perspectives générales de l'enquête.Pourquoi la Bourgogne et les années 1420 ?Dès les origines, Philippe l...

  19. Denier du reve de M. Yourcenar: lecture pirandellienne d’une critique politique des années trente

    Directory of Open Access Journals (Sweden)

    Maria Rosa Chiapparo

    2008-08-01

    Full Text Available Les essais yourcenariens publiés autour des années 1920-30 constituent un véritable laboratoire d’idées où l’auteur a puisé pour la création de ses œuvres. Le bref renvoi à Pirandello présent dans “Diagnostic de l’Europe” atteste notre hypothèse nous permettant de procéder à une relecture intéressante de Denier du rêve. Partant des propos de Pirandello sur le cinéma, nous avons pu saisir un autre aspect du roman, tout en éclaircissant la critique du fascisme que Yourcenar y élabore, à savoir : le rôle de la culture de l’image dans la mise en place d’un régime totalitaire comme le fut le fascisme italien.

  20. Nouveaux formats, nouvelles images : les expériences des années cinquante

    OpenAIRE

    Berthomé, Jean-Pierre

    2013-01-01

    Le cinéma américain ne se porte pas bien, en ce début des années cinquante. Après l’euphorie d’une courte période qui a vu ses recettes culminer en 1946 avec 90 millions d’entrées par semaine, la désaffection du public a commencé à se manifester, accélérée par l’obligation faite aux grands studios de se séparer de leurs circuits de cinémas. En 1950, les entrées sont tombées à 60 millions par semaine. Il n’y en aura plus que 46 en 1953, 40 en 1960. Parallèlement, les 14 000 postes de télévisio...

  1. Imposition des fortunes et finance offshore durant les années 1920: Aux origines de la concurrence fiscale internationale

    OpenAIRE

    Farquet, Christophe

    2015-01-01

    En se fondant sur un large panel de sources inédites issues de différentes archives européennes, l’article analyse l’impact des fuites de capitaux, de la finance offshore et de l’évasion fiscale internationale sur les systèmes d’imposition dans les années suivant la Première Guerre mondiale. Après avoir fourni des données sur l’ampleur du contournement par les détenteurs de capitaux des nouvelles taxes introduites à la sortie du conflit, le papier montre que la pression structurelle exercée s...

  2. Input selection and performance optimization of ANN-based streamflow forecasts in the drought-prone Murray Darling Basin region using IIS and MODWT algorithm

    Science.gov (United States)

    Prasad, Ramendra; Deo, Ravinesh C.; Li, Yan; Maraseni, Tek

    2017-11-01

    Forecasting streamflow is vital for strategically planning, utilizing and redistributing water resources. In this paper, a wavelet-hybrid artificial neural network (ANN) model integrated with iterative input selection (IIS) algorithm (IIS-W-ANN) is evaluated for its statistical preciseness in forecasting monthly streamflow, and it is then benchmarked against M5 Tree model. To develop hybrid IIS-W-ANN model, a global predictor matrix is constructed for three local hydrological sites (Richmond, Gwydir, and Darling River) in Australia's agricultural (Murray-Darling) Basin. Model inputs comprised of statistically significant lagged combination of streamflow water level, are supplemented by meteorological data (i.e., precipitation, maximum and minimum temperature, mean solar radiation, vapor pressure and evaporation) as the potential model inputs. To establish robust forecasting models, iterative input selection (IIS) algorithm is applied to screen the best data from the predictor matrix and is integrated with the non-decimated maximum overlap discrete wavelet transform (MODWT) applied on the IIS-selected variables. This resolved the frequencies contained in predictor data while constructing a wavelet-hybrid (i.e., IIS-W-ANN and IIS-W-M5 Tree) model. Forecasting ability of IIS-W-ANN is evaluated via correlation coefficient (r), Willmott's Index (WI), Nash-Sutcliffe Efficiency (ENS), root-mean-square-error (RMSE), and mean absolute error (MAE), including the percentage RMSE and MAE. While ANN models are seen to outperform M5 Tree executed for all hydrological sites, the IIS variable selector was efficient in determining the appropriate predictors, as stipulated by the better performance of the IIS coupled (ANN and M5 Tree) models relative to the models without IIS. When IIS-coupled models are integrated with MODWT, the wavelet-hybrid IIS-W-ANN and IIS-W-M5 Tree are seen to attain significantly accurate performance relative to their standalone counterparts. Importantly

  3. Gesa Anne Busche: Über-Leben nach Folter und Flucht. Resilienz kurdischer Frauen in Deutschland. Bielefeld: transcript Verlag 2013.

    Directory of Open Access Journals (Sweden)

    Heinz-Jürgen Voß

    2013-10-01

    Full Text Available Die Asylbedingungen in der Bundesrepublik Deutschland sind für Menschen, die vor Verfolgung und Folter fliehen mussten, problematisch und teilweise lebensbedrohlich. Traumatisierungen werden nicht oder unzureichend behandelt und durch die Asylbedingungen häufig noch verstärkt. Gesa Anne Busche befragte vier kurdische Frauen, die vor Verfolgung und Folter in der Türkei geflohen sind und in der Bundesrepublik Asyl suchten. Sie gewährt mit ihrem Buch einen Zugang, der jedoch fundierter und näher an der Perspektive der interviewten Frauen hätte sein müssen. Ihre eigenen Vorannahmen hätten im Forschungsprozess aktiv und unter anderem auf Grundlage von Arbeiten von Frauen of Color reflektiert werden können – dass das nicht geschehen ist, schmälert den Ertrag des Buches deutlich.The conditions for asylum in the Federal Republic of Germany are problematic and in parts even life-threatening for people, who had to flee from persecution and torture. Traumatizations are not or insufficiently treated and often even intensified through the conditions for asylum. Gesa Anne Busche interviewed four Kurdish women, who fled from persecution and torture in Turkey and sought asylum in Germany. Her book offers an approach, which should, however, have been closer to the perspective of the interviewed women. During the research process, her own presuppositions could have been reflected on, among others, based on works by Women of Color – the fact that this was not done reduces the book’s benefit considerably.

  4. [Sensitivity analysis of AnnAGNPS model's hydrology and water quality parameters based on the perturbation analysis method].

    Science.gov (United States)

    Xi, Qing; Li, Zhao-Fu; Luo, Chuan

    2014-05-01

    Sensitivity analysis of hydrology and water quality parameters has a great significance for integrated model's construction and application. Based on AnnAGNPS model's mechanism, terrain, hydrology and meteorology, field management, soil and other four major categories of 31 parameters were selected for the sensitivity analysis in Zhongtian river watershed which is a typical small watershed of hilly region in the Taihu Lake, and then used the perturbation method to evaluate the sensitivity of the parameters to the model's simulation results. The results showed that: in the 11 terrain parameters, LS was sensitive to all the model results, RMN, RS and RVC were generally sensitive and less sensitive to the output of sediment but insensitive to the remaining results. For hydrometeorological parameters, CN was more sensitive to runoff and sediment and relatively sensitive for the rest results. In field management, fertilizer and vegetation parameters, CCC, CRM and RR were less sensitive to sediment and particulate pollutants, the six fertilizer parameters (FR, FD, FID, FOD, FIP, FOP) were particularly sensitive for nitrogen and phosphorus nutrients. For soil parameters, K is quite sensitive to all the results except the runoff, the four parameters of the soil's nitrogen and phosphorus ratio (SONR, SINR, SOPR, SIPR) were less sensitive to the corresponding results. The simulation and verification results of runoff in Zhongtian watershed show a good accuracy with the deviation less than 10% during 2005- 2010. Research results have a direct reference value on AnnAGNPS model's parameter selection and calibration adjustment. The runoff simulation results of the study area also proved that the sensitivity analysis was practicable to the parameter's adjustment and showed the adaptability to the hydrology simulation in the Taihu Lake basin's hilly region and provide reference for the model's promotion in China.

  5. Women's translations of scientific texts in the 18th century: a case study of Marie-Anne Lavoisier.

    Science.gov (United States)

    Kawashima, Keiko

    2011-01-01

    In the 18th century, many outstanding translations of scientific texts were done by women. These women were important mediators of science. However, I would like to raise the issue that the 'selection,' which is the process by which intellectual women chose to conduct translation works, and those 'selections' made by male translators, would not be made at the same level. For example, Émilie du Châtelet (1706-1749), the only French translator of Newton's "Principia," admitted her role as participating in important work, but, still, she was not perfectly satisfied with the position. For du Châtelet, the role as a translator was only an option under the current conditions that a female was denied the right to be a creator by society. In the case of Marie-Anne Lavoisier (1743-1794), like du Châtelet, we find an acute feeling in her mind that translation was not the work of creators. Because of her respect toward creative geniuses and her knowledge about the practical situation and concrete results of scientific studies, the translation works done by Marie-Anne Lavoisier were excellent. At the same time, the source of this excellence appears paradoxical at a glance: this excellence of translation was related closely with her low self-estimation in the field of science. Hence, we should not forget the gender problem that is behind such translations of scientific works done by women in that era. Such a possibility was a ray of light that was grasped by females, the sign of a gender that was eliminated from the center of scientific study due to social systems and norms and one of the few valuable opportunities to let people know of her own existence in the field of science.

  6. Prediction of size-fractionated airborne particle-bound metals using MLR, BP-ANN and SVM analyses.

    Science.gov (United States)

    Leng, Xiang'zi; Wang, Jinhua; Ji, Haibo; Wang, Qin'geng; Li, Huiming; Qian, Xin; Li, Fengying; Yang, Meng

    2017-08-01

    Size-fractionated heavy metal concentrations were observed in airborne particulate matter (PM) samples collected from 2014 to 2015 (spanning all four seasons) from suburban (Xianlin) and industrial (Pukou) areas in Nanjing, a megacity of southeast China. Rapid prediction models of size-fractionated metals were established based on multiple linear regression (MLR), back propagation artificial neural network (BP-ANN) and support vector machine (SVM) by using meteorological factors and PM concentrations as input parameters. About 38% and 77% of PM 2.5 concentrations in Xianlin and Pukou, respectively, were beyond the Chinese National Ambient Air Quality Standard limit of 75 μg/m 3 . Nearly all elements had higher concentrations in industrial areas, and in winter among the four seasons. Anthropogenic elements such as Pb, Zn, Cd and Cu showed larger percentages in the fine fraction (ø≤2.5 μm), whereas the crustal elements including Al, Ba, Fe, Ni, Sr and Ti showed larger percentages in the coarse fraction (ø > 2.5 μm). SVM showed a higher training correlation coefficient (R), and lower mean absolute error (MAE) as well as lower root mean square error (RMSE), than MLR and BP-ANN for most metals. All the three methods showed better prediction results for Ni, Al, V, Cd and As, whereas relatively poor for Cr and Fe. The daily airborne metal concentrations in 2015 were then predicted by the fully trained SVM models and the results showed the heaviest pollution of airborne heavy metals occurred in December and January, whereas the lightest pollution occurred in June and July. Copyright © 2017 Elsevier Ltd. All rights reserved.

  7. Anneli Remme soovitab : Skazki / Anneli Remme

    Index Scriptorium Estoniae

    Remme, Anneli, 1968-

    2007-01-01

    Kontserdist "Skazki" - "Mõistujutud" 3. märtsil Tartu Ülikooli aulas ja 4. märtsil Tallinnas Teatris NO99 (kavas Igor Stravinski teosed ja Galina Grogorjeva kammerooperi "Sipelgas John J. Plenty ja Rohutirts Dan" esiettekanne)

  8. Anneli Remme soovitab : Napoleon muusikas / Anneli Remme

    Index Scriptorium Estoniae

    Remme, Anneli, 1968-

    2007-01-01

    ERSO ja Eesti Filharmoonia Kammerkoori kontserdist "Napoleon muusikas" 8. nov. Võru kultuurimajas Kannel ja 9. nov. Estonia kontserdisaaalis, esitusel L. van Beethoveni Kolmas sümfooniaja J. Haydni "Nelsoni missa"

  9. Baltoscandal vajab rakverelast! / Anne-Ly Sova

    Index Scriptorium Estoniae

    Sova, Anne-Ly, 1976-

    2008-01-01

    Kaja Kann ja Juha Valkeapää vajavad oma lavastusse "Viiskümmend armast viisi surra" ("50 Lovely Ways to Die") 14 vabatahtlikku ja Austria-Prantsuse trupp Superamas oma lavastusse "Big 3rd Episode. Happy/End" kümmet vabatahtlikku tantsijat

  10. Baltoscandal alustas proovidega / Anne-Ly Sova

    Index Scriptorium Estoniae

    Sova, Anne-Ly, 1976-

    2008-01-01

    Kaja Kann ja Juha Valkeapää ning 15 rakverelast alustasid "50 Lovely Ways to Die" proovidega. Samuti alustas proove lavastaja Erki Kasemets Rakvere teatri ja Polügoonteatri ühisettevõtmisega "Kool". Välisesinejatest tutvustavalt

  11. Prediction ofWater Quality Parameters (NO3, CL in Karaj Riverby Usinga Combinationof Wavelet Neural Network, ANN and MLRModels

    Directory of Open Access Journals (Sweden)

    T. Rajaee

    2016-10-01

    Full Text Available IntroductionThe water quality is an issue of ongoing concern. Evaluation of the quantity and quality of running waters is considerable in hydro-environmental management.The prediction and control of the quality of Karaj river water, as one of the important needed water supply sources of Tehran, possesses great importance. In this study, Performance of Artificial Neural Network (ANN, Wavelet Neural Network combination (WANN and multi linear regression (MLR models, to predict next month the Nitrate (NO3 and Chloride (CL ions of "gate ofBylaqan sluice" station located in Karaj River has been evaluated. Materials and MethodsIn this research two separate ANN models for prediction of NO3 and CL has been expanded. Each one of the parameters for prediction (NO3 / CL has been put related to the past amounts of the same time series (NO3 / CL and its amounts of Q in past months.From astatisticalperiod of10yearswas usedforthe input of the models. Hence 80% of entire data from (96 initial months of data as training set, next 10% of data (12 months and 10% of the end of time series (terminal 12 months were considered as for validation and test of the models, respectively. In WANNcombination model, the real monthly observed time series of river discharge (Q and mentioned qualityparameters(NO3 / CL were decomposed to some sub-time series at different levels by wavelet analysis.Then the decomposed quality parameters to predict and Q time series were used at different levels as inputs to the ANN technique for predicting one-step-ahead Nitrate and Chloride. These time series play various roles in the original time series and the behavior of each is distinct, so the contribution to the original time series varies from each other. In addition, prediction of high NO3 and CL values greater than mean of data that have great importancewere investigated by the models. The capability of the models was evaluated by Coefficient of Efficiency (E and the Root Mean Square

  12. Global Alliance'i president: harige ennast, sest kogemusest tänapäeval ei piisa / Anne Gregory ; intervjueerinud Annela Laaneots

    Index Scriptorium Estoniae

    Gregory, Anne

    2013-01-01

    Intervjuu ajakirja Kaja mõttekojas esinenud maailma suhtekorralduse ja kommunikatsioonijuhtimise organisatsioonide ühenduse Global Alliance presidendi Anne Gregoryga, kes annab nõu, mida kommunikatsioonijuht peaks tegema, et olla organisatsioonis hinnatud ning enda eesmärkidest Global Alliance'i presidendina

  13. Comparison of artificial neural network (ANN) and response surface methodology (RSM) in optimization of the immobilization conditions for lipase from Candida rugosa on Amberjet(®) 4200-Cl.

    Science.gov (United States)

    Fatiha, Benamia; Sameh, Bouchagra; Youcef, Saihi; Zeineddine, Djeghaba; Nacer, Rebbani

    2013-01-01

    Candida rugosa lipase (CRL) is an important industrial enzyme that is successfully utilized in a variety of hydrolysis and esterification reactions. This work describes the optimization of immobilization conditions (enzyme/support ratio, immobilization temperature, and buffer concentration) of CRL on the anionic resin Amberjet® 4200-Cl, using enantioselectivity (E) as the reference parameter. The model reaction used for this purpose is the acylation of (R,S)-1-phenylethanol. Optimal conditions for immobilization have been investigated through a response surface methodology (RSM) and artificial neural network (ANN). The coefficient of determination (R(2)) and the root mean square error (RMSE) values between the calculated and estimated responses were respectively equal to 0.99 and 0.06 for the ANN training set, 0.97 and 0.2 for the ANN testing set, and 0.94 and 0.4 for the RSM training set. Both models provided good quality predictions, yet the ANN showed a clear superiority over RSM for both data fitting and estimation capabilities.

  14. Artificial neural network (ANN) modeling of adsorption of methylene blue by NaOH-modified rice husk in a fixed-bed column system.

    Science.gov (United States)

    Chowdhury, Shamik; Saha, Papita Das

    2013-02-01

    In this study, rice husk was modified with NaOH and used as adsorbent for dynamic adsorption of methylene blue (MB) from aqueous solutions. Continuous removal of MB from aqueous solutions was studied in a laboratory scale fixed-bed column packed with NaOH-modified rice husk (NMRH). Effect of different flow rates and bed heights on the column breakthrough performance was investigated. In order to determine the most suitable model for describing the adsorption kinetics of MB in the fixed-bed column system, the bed depth service time (BDST) model as well as the Thomas model was fitted to the experimental data. An artificial neural network (ANN)-based model was also developed for describing the dynamic dye adsorption process. An extensive error analysis was carried out between experimental data and data predicted by the models by using the following error functions: correlation coefficient (R(2)), average relative error, sum of the absolute error and Chi-square statistic test (χ(2)). Results show that with increasing bed height and decreasing flow rate, the breakthrough time was delayed. All the error functions yielded minimum values for the ANN model than the traditional models (BDST and Thomas), suggesting that the ANN model is the most suitable model to describe the fixed-bed adsorption of MB by NMRH. It is also more rational and reliable to interpret dynamic dye adsorption data through a process of ANN architecture.

  15. Two Different Approaches to Teaching Final-Year Projects for Mechanical Engineers and Biotechnologists at Ngee Ann Polytechnic: Case Studies Approach.

    Science.gov (United States)

    Walsh, Kath; Rebaczonok-Padulo, Michael

    The Oral and Written Communication (OWC) course at Ngee Ann Polytechnic was originally intended to equip students with occupational skills (e.g., report- and letter-writing, public speaking) but has expanded to be a course aimed at helping third-year mechanical engineering students to develop third-year project reports. This has been done through…

  16. Two Different Approaches to Teaching Final-Year Projects for Mechanical Engineers and Biotechnologists at Ngee Ann Polytechnic--Case Studies Approach.

    Science.gov (United States)

    Walsh, Kath; Rebaczonok-Padulo, Michael

    1993-01-01

    Ngee Ann Polytechnic, a leading postsecondary technical institution in Singapore, offers English for academic and occupational purposes to prepare students for writing their final year projects. This article discusses the approaches used in Mechanical Engineering and Biotechnology projects. A sample exercise is appended. (Contains two references.)…

  17. Design of a MATLAB(registered trademark) Image Comparison and Analysis Tool for Augmentation of the Results of the Ann Arbor Distortion Test

    Science.gov (United States)

    2016-06-25

    carried into the new versions. This also led to the use of the Signal Processing Toolbox due to the shape of the luminance curves . 11 Figure 10...21702-5012 USAMRMC Approved for public release; distribution is unlimited. The Ann Arbor distortion test, an important test in assessing the quality of an...11  11. Figure of the luminance profiles across a standard image

  18. A prediction of storm surge using the artificial neural networks (ANNs) based on a JTWC best track and tide-surge model

    Science.gov (United States)

    Park, Junghyun; Yuk, Jin-Hee; An, Jooneun; Joh, Minsu; Kim, Seung-woo

    2017-04-01

    There is huge damage caused by tropical typhoons every year in the South Korea. The storm surge due to landing of typhoon leads to severe flooding and casualty damage in coastal areas. Generally, the storm surge height is defined as the difference between the sea levels observed and predicted considering tide only. This advancing surge combines with the normal tides to create the typhoon storm surge height, which can increase the mean water level from only 1 to more than 2 m by the typhoon characteristics in Korea. To efficiently describe the phenomenon of storm surge in the coastal area, many researchers have used the numerical model of fluid dynamics. However, recently, research activities based on not the numerical model but big data have gotten a lot of attention and the Artificial Neural Networks (ANNs) among these activities have shown powerful pattern classification and pattern recognition capabilities. The ANNs provide an attractive alternative tool for both forecasting researchers and practitioners. In particular, the ANNs have been widely applied to various areas to overcome the nonlinear natural disaster problems. This paper is aimed to propose the application of the ANNs for prediction of the storm surge. Many storm surge data stored for a long time are required to predict storm surge accurately using ANNs. But, because of the lack of storm surge data in the past years, we calculated storm surges due to 53 typhoons which had affected the South Korea from 1978 to 2014 using a finite element tide-surge model (ADvanced CIRCulation Model) and the typhoon information of JTWC (Joint Typhoon Warning Center). Factors such as the six hourly best track data of typhoon, head direction and velocity of typhoons, maximum sustained wind speed, minimum sea level pressure, radius of the last closed isobar, and radius of max winds were used to test the accuracy of the suggested ANNs model. The normalized root mean squared error (RMSE) and correlation coefficient (CC

  19. PREDIKSI MASA KEDALUWARSA WAFER DENGAN ARTIFICIAL NEURAL NETWORK (ANN BERDASARKAN PARAMETER NILAI KAPASITANSI (Prediction of Wafer Shelf Life Using Artificial Neural Network Based on Capacitance Parameter

    Directory of Open Access Journals (Sweden)

    Erna Rusliana Muhamad Saleh

    2014-02-01

    Full Text Available Wafer is type of biscuit frequently found on expired condition in market, therefore prediction method should be implemented to avoid this condition. apart from the prediction of shelf-life of wafer done by laboratory test, which were time-consuming, expensive, required trained panelists, complex equipment and suitable ambience, artificial neural network (ANN based dielectric parameters was proposed in nthis study. The aim of study was to develop model to predict shelf-life employing aNN based capacitance parameter. Back propagation algorithm with trial and error was applied in variations of nodes per hidden layer, number of hidden layers, activation functions, the function of learnings and epochs. The result of study was the model was able to predict wafer shelf-life. The accuracy level was shown by low MSE value (0.01 and high coefficient correlation value (89.25%. Keywords: artificial Neural Network, shelf-life, waffer, dielectric, capacitance   ABSTRAK Wafer adalah jenis makanan kering yang sering ditemukan kedaluwarsa. Penentuan masa kedaluwarsa dengan observasi laboratorium memiliki beberapa kelemahan, diantaranya memakan waktu, panelis terlatih, suasana yang tepat, biaya dan alat uji yang kompleks. alternatif solusinya adalah penggunaan artificial Neural Network (ANN berbasiskan parameter kapasitansi. Tujuan kerja ilmiah ini adalah untuk memprediksi masa kedaluwarsa wafer menggunakan aNN berbasiskan parameter kapasitansi. algoritma pembelajaran yang digunakan adalah Backpropagation dengan trial and error variasi jumlah node per hidden layer, jumlah hidden layer, fungsi aktivasi, fungsi pembelajaran dan epoch. Hasil prediksi menunjukkan bahwa aNN hasil pelatihan yang dikombinasikan dengan parameter kapasitansi mampu memprediksi masa kedaluwarsa wafer dengan MSE terendah 0,01 dan R tertinggi 89,25%. Kata kunci: Jaringan Syaraf Tiruan, masa kedaluwarsa, wafer, dielektrik, kapasitansi

  20. Using AnnAGNPS to Predict the Effects of Tile Drainage Control on Nutrient and Sediment Loads for a River Basin.

    Science.gov (United States)

    Que, Z; Seidou, O; Droste, R L; Wilkes, G; Sunohara, M; Topp, E; Lapen, D R

    2015-03-01

    Controlled tile drainage (CTD) can reduce pollutant loading. The Annualized Agricultural Nonpoint Source model (AnnAGNPS version 5.2) was used to examine changes in growing season discharge, sediment, nitrogen, and phosphorus loads due to CTD for a ∼3900-km agriculturally dominated river basin in Ontario, Canada. Two tile drain depth scenarios were examined in detail to mimic tile drainage control for flat cropland: 600 mm depth (CTD) and 200 mm (CTD) depth below surface. Summed for five growing seasons (CTD), direct runoff, total N, and dissolved N were reduced by 6.6, 3.5, and 13.7%, respectively. However, five seasons of summed total P, dissolved P, and total suspended solid loads increased as a result of CTD by 0.96, 1.6, and 0.23%. The AnnAGNPS results were compared with mass fluxes observed from paired experimental watersheds (250, 470 ha) in the river basin. The "test" experimental watershed was dominated by CTD and the "reference" watershed by free drainage. Notwithstanding environmental/land use differences between the watersheds and basin, comparisons of seasonal observed and predicted discharge reductions were comparable in 100% of respective cases. Nutrient load comparisons were more consistent for dissolved, relative to particulate water quality endpoints. For one season under corn crop production, AnnAGNPS predicted a 55% decrease (CTD) in dissolved N from the basin. AnnAGNPS v. 5.2 treats P transport from a surface pool perspective, which is appropriate for many systems. However, for assessment of tile drainage management practices for relatively flat tile-dominated systems, AnnAGNPS may benefit from consideration of P and particulate transport in the subsurface. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.

  1. TNM staging system may be superior to Lugano and Ann Arbor systems in predicting the overall survival of patients with primary gastrointestinal lymphoma.

    Science.gov (United States)

    Chang, Shujian; Shi, Xin; Xu, Zhenyu; Liu, Quan

    2015-01-01

    To assess the survival predicting value of TNM, Lugano, and Ann Arbor staging systems in patients with primary gastrointestinal lymphoma (PGL). 101 patients with PGL were reviewed. All of them were staged according to TNM, Lugano, or Ann Arbor staging system. Five-year survival overall survival/OS rate was used as major clinical outcome. The prognostic value of different variables like depth of tumor infiltration (T), lymph node status (N), metastasis (M), sex, age, LDH, ECOG performance status (PS), subtypes, and tumor sites were assessed in relation to clinical outcome. The median follow-up time was 46.6 months (range 1.3-158.6). The estimated 5-year OS rate was 74.22%. In gastric lymphoma ,the 5-year OS rate was well correlated with stage in the TNM system (stage I 100.00%, stage II 87.18%, stage III 75.17%, and stage IV 16.67%. pAnn Arbor systems (69.47% in stage IIE, 66.67% in stage IIIE). In aggressive lymphomas, the 5-year OS of TNM stage I, stage II, stage III , and stage IV was 100.00%, 81.34%, 63.52%, and 16.00%, respectively (p=0.0002), but there were overlapped survival curves in Lugano and Ann Arbor systems. The 5-year OS of patients with T1 or T2 was significantly superior compared to patients with T3 or T4 (96.15 vs 67.92%, p=0.0087), and multivariate Cox analysis showed that T (p=0.0181) and M (p=0.0031) were the covariates prognostically significant for OS. TNM staging system may be superior to Lugano and Ann Arbor system in predicting OS of patients with PGL.

  2. BP-ANN for fitting the temperature-germination model and its application in predicting sowing time and region for Bermudagrass.

    Directory of Open Access Journals (Sweden)

    Erxu Pi

    Full Text Available Temperature is one of the most significant environmental factors that affects germination of grass seeds. Reliable prediction of the optimal temperature for seed germination is crucial for determining the suitable regions and favorable sowing timing for turf grass cultivation. In this study, a back-propagation-artificial-neural-network-aided dual quintic equation (BP-ANN-QE model was developed to improve the prediction of the optimal temperature for seed germination. This BP-ANN-QE model was used to determine optimal sowing times and suitable regions for three Cynodon dactylon cultivars (C. dactylon, 'Savannah' and 'Princess VII'. Prediction of the optimal temperature for these seeds was based on comprehensive germination tests using 36 day/night (high/low temperature regimes (both ranging from 5/5 to 40/40°C with 5°C increments. Seed germination data from these temperature regimes were used to construct temperature-germination correlation models for estimating germination percentage with confidence intervals. Our tests revealed that the optimal high/low temperature regimes required for all the three bermudagrass cultivars are 30/5, 30/10, 35/5, 35/10, 35/15, 35/20, 40/15 and 40/20°C; constant temperatures ranging from 5 to 40°C inhibited the germination of all three cultivars. While comparing different simulating methods, including DQEM, Bisquare ANN-QE, and BP-ANN-QE in establishing temperature based germination percentage rules, we found that the R(2 values of germination prediction function could be significantly improved from about 0.6940-0.8177 (DQEM approach to 0.9439-0.9813 (BP-ANN-QE. These results indicated that our BP-ANN-QE model has better performance than the rests of the compared models. Furthermore, data of the national temperature grids generated from monthly-average temperature for 25 years were fit into these functions and we were able to map the germination percentage of these C. dactylon cultivars in the national scale

  3. Mathematical Modeling and Optimizing ofin VitroHormonal Combination for G × N15 Vegetative Rootstock Proliferation Using Artificial Neural Network-Genetic Algorithm (ANN-GA).

    Science.gov (United States)

    Arab, Mohammad M; Yadollahi, Abbas; Ahmadi, Hamed; Eftekhari, Maliheh; Maleki, Masoud

    2017-01-01

    The efficiency of a hybrid systems method which combined artificial neural networks (ANNs) as a modeling tool and genetic algorithms (GAs) as an optimizing method for input variables used in ANN modeling was assessed. Hence, as a new technique, it was applied for the prediction and optimization of the plant hormones concentrations and combinations for in vitro proliferation of Garnem (G × N15) rootstock as a case study. Optimizing hormones combination was surveyed by modeling the effects of various concentrations of cytokinin-auxin, i.e., BAP, KIN, TDZ, IBA, and NAA combinations (inputs) on four growth parameters (outputs), i.e., micro-shoots number per explant, length of micro-shoots, developed callus weight (CW) and the quality index (QI) of plantlets. Calculation of statistical values such as R 2 (coefficient of determination) related to the accuracy of ANN-GA models showed a considerably higher prediction accuracy for ANN models, i.e., micro-shoots number: R 2 = 0.81, length of micro-shoots: R 2 = 0.87, CW: R 2 = 0.88, QI: R 2 = 0.87. According to the results, among the input variables, BAP (19.3), KIN (9.64), and IBA (2.63) showed the highest values of variable sensitivity ratio for proliferation rate. The GA showed that media containing 1.02 mg/l BAP in combination with 0.098 mg/l IBA could lead to the optimal proliferation rate (10.53) for G × N15 rootstock. Another objective of the present study was to compare the performance of predicted and optimized cytokinin-auxin combination with the best optimized obtained concentrations of our other experiments. Considering three growth parameters (length of micro-shoots, micro-shoots number, and proliferation rate), the last treatment was found to be superior to the rest of treatments for G × N15 rootstock in vitro multiplication. Very little difference between the ANN predicted and experimental data confirmed high capability of ANN-GA method in predicting new optimized protocols for plant in vitro propagation.

  4. BP-ANN for fitting the temperature-germination model and its application in predicting sowing time and region for Bermudagrass.

    Science.gov (United States)

    Pi, Erxu; Mantri, Nitin; Ngai, Sai Ming; Lu, Hongfei; Du, Liqun

    2013-01-01

    Temperature is one of the most significant environmental factors that affects germination of grass seeds. Reliable prediction of the optimal temperature for seed germination is crucial for determining the suitable regions and favorable sowing timing for turf grass cultivation. In this study, a back-propagation-artificial-neural-network-aided dual quintic equation (BP-ANN-QE) model was developed to improve the prediction of the optimal temperature for seed germination. This BP-ANN-QE model was used to determine optimal sowing times and suitable regions for three Cynodon dactylon cultivars (C. dactylon, 'Savannah' and 'Princess VII'). Prediction of the optimal temperature for these seeds was based on comprehensive germination tests using 36 day/night (high/low) temperature regimes (both ranging from 5/5 to 40/40°C with 5°C increments). Seed germination data from these temperature regimes were used to construct temperature-germination correlation models for estimating germination percentage with confidence intervals. Our tests revealed that the optimal high/low temperature regimes required for all the three bermudagrass cultivars are 30/5, 30/10, 35/5, 35/10, 35/15, 35/20, 40/15 and 40/20°C; constant temperatures ranging from 5 to 40°C inhibited the germination of all three cultivars. While comparing different simulating methods, including DQEM, Bisquare ANN-QE, and BP-ANN-QE in establishing temperature based germination percentage rules, we found that the R(2) values of germination prediction function could be significantly improved from about 0.6940-0.8177 (DQEM approach) to 0.9439-0.9813 (BP-ANN-QE). These results indicated that our BP-ANN-QE model has better performance than the rests of the compared models. Furthermore, data of the national temperature grids generated from monthly-average temperature for 25 years were fit into these functions and we were able to map the germination percentage of these C. dactylon cultivars in the national scale of China, and

  5. Study of parent-child communication in joint-reading process according the investigation of Beth Ann Beschorner, foreign researcher

    Directory of Open Access Journals (Sweden)

    Maksimova A.A.

    2016-01-01

    Full Text Available The article presents the analysis carried out by Ph. D. Beth Ann Beschorner (University of Iowa, USA which concerns the training program for parents aimed at teaching them how to arrange the Dialogic reading with their childrenand and which makes it possible to conclude that due to the experience and direct contact with the written language in preschool age the idea of literacy was being formed. The article compares the empirical data obtained independently in different areas of scientific knowledge, i.e., philosophy and psychology: the study of B.A. Beschorner has a lot in common with the principles of cultural-historical psychology, formulated by L. Vygotsky, M. Lisina and other national psychologists. Although B. A. Beschorner do not stick directly to cultural-historical and activity theory, her results correspond with the basic provisions of these theories. The analysis of B.A. Beschorner’s works confirms the commonality of her findings to those obtained in terms of the cultural-historical theory. It proves that scientific thoughts even going in independent ways, may lead to similar results, which ultimately demonstrates the validity of the findings and the versatility of approaches to the problem

  6. The collaboration of Antoine and Marie-Anne Lavoisier and the first measurements of human oxygen consumption.

    Science.gov (United States)

    West, John B

    2013-12-01

    Antoine Lavoisier (1743-1794) was one of the most eminent scientists of the late 18th century. He is often referred to as the father of chemistry, in part because of his book Elementary Treatise on Chemistry. In addition he was a major figure in respiratory physiology, being the first person to recognize the true nature of oxygen, elucidating the similarities between respiration and combustion, and making the first measurements of human oxygen consumption under various conditions. Less well known are the contributions made by his wife, Marie-Anne Lavoisier. However, she was responsible for drawings of the experiments on oxygen consumption when the French revolution was imminent. These are of great interest because written descriptions are not available. Possible interpretations of the experiments are given here. In addition, her translations from English to French of papers by Priestley and others were critical in Lavoisier's demolition of the erroneous phlogiston theory. She also provided the engravings for her husband's textbook, thus documenting the extensive new equipment that he developed. In addition she undertook editorial work, for example in preparing his posthumous memoirs. The scientific collaboration of this husband-wife team is perhaps unique among the giants of respiratory physiology.

  7. Prediction of liver injury using the BP-ANN model with metabolic parameters in overweight and obese Chinese subjects.

    Science.gov (United States)

    Hu, Lufeng; Wang, Fan; Xu, Jinzhong; Wang, Xiaofang; Lin, Hong; Zhang, Yi; Yu, Yang; Wang, Youpei; Pang, Lingxia; Zhang, Xi; Liu, Qi; Qiu, Guoshi; Jiang, Yongsheng; Xie, Longteng; Liu, Yanlong

    2015-01-01

    Nonalcoholic fatty liver disease (NAFLD) is often associated with dyslipidemia. Metabolic disequilibrium, resulting from being overweight and obesity, increases risk to cardiovascular system and chronic liver disease. Alanine aminotransferase (ALT), aspartate aminotransferase (AST) and gamma-glutamyl transferase (GGT) are standard clinical markers for liver injury. In this study, we examined association of body mass index (BMI) and metabolic markers with serum ALT, AST and GGT activity in an overweight and obese Chinese population. A total of 421 overweight and obese Chinese adults (211 males and 210 females) from The First Affiliated Hospital of Wenzhou Medical University were recruited in this study in 2014. All participants underwent anthropometric measures and phlebotomy after an overnight fast. Elevated ALT, AST and GGT levels were found in 17%, 5% and 24%, respectively. There were significant correlations between ALT and BMI, plasma triglycerides (TG), cholesterol, HDL and glucose, and between AST and plasma TG and cholesterol. GGT also correlated with plasma TG, cholesterol and glucose. The levels of ALT, AST and GGT could be predicted by BMI, plasma TG, cholesterol, HDL and glucose using the back propagation artificial neural network model (BP-ANN). These data suggest that abnormal metabolic markers could be used to monitor liver function to determine whether liver damage has occurred in overweight and obese individuals. This approach has clinical utility with respect to early scanning of liver injury or NAFLD based on routinely available metabolic data in overweight and obese population.

  8. Artificial Neural Networks (ANN) for the Simultaneous Spectrophotometric Determination of Fluoxetine and Sertraline in Pharmaceutical Formulations and Biological Fluid.

    Science.gov (United States)

    Akbari Hasanjani, Hamid Reza; Sohrabi, Mahmoud Reza

    2017-01-01

    Simultaneous spectrophotometric estimation of Fluoxetine and Sertraline in tablets were performed using UV-Vis spectroscopic and Artificial Neural Networks (ANN). Absorption spectra of two components were recorded in 200-300 nm wavelengths region with an interval of 1 nm. The calibration models were thoroughly evaluated at several concentration levels using the spectra of synthetic binary mixture (prepared using orthogonal design). Three layers feed-forward neural networks using the back-propagation algorithm (B.P) has been employed for building and testing models. Several parameters such as the number of neurons in the hidden layer, learning rate and the number of epochs were optimized. The Relative Standard Deviation (RSD) for each component in real sample was calculated as 1.06 and 1.33 for Fluoxetine and Sertraline, respectively. The results showed a very good agreement between true values and predicted concentration values. The proposed procedure is a simple, precise and convenient method for the determination of Fluoxetine and Sertraline in commercial tablets.

  9. A Historical Analysis of Media Practices and Technologies in Protest Movements: A Review of Crisis and Critique by Anne Kaun

    Directory of Open Access Journals (Sweden)

    Anne Laajalahti

    2017-05-01

    Full Text Available Dr. Anne Kaun’s book, Crisis and Critique: A Brief History of Media Participation in Times of Crisis (London: Zed Books, 2016, 131 pp., ISBN: 978-1-78360-736-5, is a concise but comprehensive analysis of the changing media practices and technologies in protest movements. The book overviews the topic within the context of major economic crises and scrutinises three richly detailed case studies in the United States: (a the unemployed workers’ movement during the Great Depression in the 1930s, (b the tenants’ rent strike movement of the early 1970s, and (c the Occupy Wall Street movement following the Great Recession of 2008. Kaun begins her book with an introduction to economic crises and protest movements and highlights the relationship of crisis and critique to media practices. She goes on to investigate historical forms of media participation in protest movements from three different perspectives: (a protest time, (b protest space, and (c protest speed. The book contributes to the recent discussion on the emerging role of social media in protest by providing a historically nuanced analysis of the media participation in times of crisis. As a whole, the book is valuable to anyone interested in media and social activism.

  10. Análise das principais etiologias de deficiência auditiva em Escola Especial "Anne Sullivan"

    Directory of Open Access Journals (Sweden)

    Cecatto Suzana B.

    2003-01-01

    Full Text Available OBJETIVO: Determinar as principais etiologias de deficiência auditiva em estudantes da Escola de Ensino Especial para surdos "Anne Sullivan" em São Caetano do Sul e comparar com os dados da literatura mundial. FORMA DE ESTUDO: Estudo retrospectivo. MATERIAL E MÉTODO: Cento e trinta e um alunos da escola no ano de 2001 foram avaliados através de análise de seus prontuários, levando-se em conta dados de anamnese com a família, exame físico otorrinolaringológico, avaliação fonoaudiológica e psicológica. RESULTADOS: Dos 131 pacientes, 67 (51% eram do sexo masculino e 64 (49% do sexo feminino. A perda auditiva sensorioneural foi a mais encontrada, representando 99% dos casos. Quanto ao grau de disacusia, 65% foi classificado como profundo. Quanto à etiologia, 24% foi classificada como desconhecida e das causas identificáveis a rubéola congênita foi a mais encontrada (22%. Na maioria dos pacientes a suspeita e o diagnóstico foram feitos com 12 meses de idade. CONCLUSÕES: A etiologia não definida foi a mais representativa, seguida pela rubéola, e a idade de diagnóstico predominou entre 12 e 30 meses.

  11. Cinquante années de recherches sur les débuts de l'Aurignacien en Europe occidentale

    Directory of Open Access Journals (Sweden)

    François Djindjian

    2002-01-01

    Full Text Available Une histoire des cinquante dernières années de rechercties sur les débuts de l'Aurignacien est tentée ici. Cet historique retrace brièvement les débuts entre 1860 et 1950 de la connaissance de l'Aurignacien resituant dans leur contexte les apports respectifs de Lartet, De Mortlllet, Breuil et Peyrony. Puis, les résultats des recherches des années 1950 à 1990 des différents acteurs (D. de Sonnevllle-Bordes, F. Bordes, H. M. Movius, H. Delporte, G. Laplace, J. Hahn, N. Soler, F. Bazile, F. Champagne, etc. concernant les débuts de l'Aurignacien sont analysés à la lueur des nouvelles données de fouilles en Aquitaine (Roc de Combe, La Ferrassie, Le Facteur, Caminade, Le Flageolet I, Le Plage, en Pyrénées (Gatzarria, Cueva h/iorin, en Jura-Souabe (Geissenklosterle et sur la côte méditerranéenne (abri l\\Aochi, La Laouza, l'Arbreda, L'Esquicho-Grapaou. Les apports des recherches sur le paléoenvironnement pour la reconstitution du climat et des méthodes mathématiques et informatiques dans les années 70 à la structuration chronologique de l'Aurignacien sont développés. Les différentes données à l'origine de l'existence d'un Protoaurignacien, d'un Aurignacien 0 en Périgord, d'un Aurignacien initial et la question de l'interstratification entre Castelperronien et Aurignacien sont discutées. Les récentes critiques depuis le début des années 90 concernant la fiabilité du cadre paléoclimatique des remplissages d'abrissous- roctie et la pertinence des approches typologiques sont examinées. Les conclusions amènent l'auteur à proposer suggérer que les débuts de I'Aurignacien, encore mal connus, ne sont pas uniformes suivant les régions et dans le temps. En Europe occidentale, l'existence d'un Aurignacien initial semble prouvé sur la côte méditerranéenne de la Ligurie jusqu'en Catalogne. L'expansion aurignacienne suit alors la bordure septentrionale pyrénéenne de l'Aude jusqu'en Cantabres et en Asturies. Puis, sous

  12. BALANSARD, ANNE, «Technè» dans les «Dialogues » de Platon: l’empreinte de la sophistique. Por Nuria Sánchez Madrid

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    Nuria Sánchez Madrid

    2012-05-01

    Full Text Available Autor: Anne Balansard (2001. Editorial: Sankt Augustin, Academia Verlag. International Plato Studies; vol. 14. 430 pp., ISBN:3-89665-154-4. Quizá lo más significativo de esta obrade Anne Balansard —tesis doctoral de la autora dirigida por la helenista S. Saïd y defendida en diciembre de 1997 en la Universidadde Paris X-Nanterre— sea el habersabido encontrar un lugar de confluencia entre un marcado método e interés filológico y la sincera pretensión de generar debate en el seno de los estudios platónicos actuales [...

  13. Fookuses on lugemine : üks küsimus ajakirja "Raamatukogu" toimetuskolleegiumi liikmeile / Anneli Sepp, Hele Ellermaa, Mihkel Volt ... [jt.

    Index Scriptorium Estoniae

    2010-01-01

    Vastavad ERÜ esimees Anneli Sepp, Kõrveküla raamatukogu direktor Hele Ellermaa, Eesti Rahvusraamatukogu teadus- ja arenduskeskuse juhataja Mihkel Volt, Tartu Ülikooli Raamatukogu käsikirjade ja haruldaste raamatute osakonna juhataja Malle Ermel, Tartu Ülikooli Viljandi Kultuuriakadeemia infohariduse osakonna juhataja Ilmar Vaaro, Tallinna Ülikooli Akadeemlise Raamatukogu baltika ja haruldaste raamatute osakonna juhataja Katrin Kaugver, Tallinna Tehnikaülikooli Raamatukogu asedirektor Gerda Koidla ja Tartu Lutsu-nimelise Linnaraamatukogu direktor Asko Tamme

  14. Rapport sur les deux premières années d'activités de l'Initiative ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    18 mars 2011 ... Au cours de ses deux premières années d'activités, l'Initiative Think tank a soutenu 52 institutions établies dans quatre régions. Lisez le rapport​ pour en savoir plus sur les progrès réalisés, les bénéficiaires des subventions et les plans pour la suite.

  15. Modeling the impact of in-cylinder combustion parameters of DI engines on soot and NOx emissions at rated EGR levels using ANN approach

    International Nuclear Information System (INIS)

    Taghavifar, Hamid; Taghavifar, Hadi; Mardani, Aref; Mohebbi, Arash

    2014-01-01

    Highlights: • Effect of in-cylinder combustion parameters on soot and NOx emissions at rated EGR levels was studied. • ANN model was adopted to predict the emissions under the effect of combustion parameters. • A trainlm ANN with 5-19-17-2 structure denoted MSE equal to 0.0004627 as outperforming model. • Increment of EGR reduced the emissions where the equivalence ratio had contradictory effect. - Abstract: This study examines the effect of in-cylinder combustion parameters on soot and NOx emissions at rated EGR levels by using the data obtained from the CFD implemented code. The obtained data were subsequently used to construct an artificial neural network (ANN) model to predict the soot and NOx productions. To this aim, at three different engine speeds of 2000, 3000 and 4000 rpm, heat release rate, equivalence ratio, turbulence kinetic energy and temperature varied to obtain the relevant soot and NOx data at three EGR levels of 0.2, 0.3 and 0.4. It was discovered that wherein the application of higher EGR rates reduced the NOx as a result of mixture dilution, equivalence ratio increment makes soot production to be increased as well as NOx emission. It was also found that the application of higher EGR from 20% to 40% decreased soot mass fraction in the combustion chamber. Increment of EGR reduced the emissions where the equivalence ratio had contradictory effect on the produced emissions. Various ANN topological configurations and training algorithms were incorporated to yield the optimal solution to the modeling problem applying statistical criteria. Among the four adopted training algorithms of trainlm, trainscg, trainrp, and traingdx, the training function of Levenberg–Marquardt (trainlm) with topological structure of 5-19-17-2 denoted MSE equal to 0.0004627

  16. Kaasaegne kunstiõpetus, uks praegusesse ülevisualiseeritud maailma / Tõnu Talve, Anne Susanna Lindström ; interv. Reet Varblane

    Index Scriptorium Estoniae

    Talve, Tõnu

    2007-01-01

    Soome kunstiõpetaja, Eesti Kunstiakadeemia õpetajate koolituskeskuse dotsent Anne Susanna Lindström ja Keila gümnaasiumi kunstiõpetaja Tõnu Talve Heidelbergi ja Karlsruhe pedagoogikaülikooli organiseeritud kunstiõpetajate maailmakongressist "Horisondid 2007" ning kunstiõpetusest. A. S Lindström tutvustas kongressil Eesti ja Soome kunstiõpetuse ainekavade võrdlev-ajaloolist uuringut ja T. Talve esitles videofilmi "Fragile kunstitund 3"

  17. Applying high-frequency surrogate measurements and a wavelet-ANN model to provide early warnings of rapid surface water quality anomalies.

    Science.gov (United States)

    Shi, Bin; Wang, Peng; Jiang, Jiping; Liu, Rentao

    2018-01-01

    It is critical for surface water management systems to provide early warnings of abrupt, large variations in water quality, which likely indicate the occurrence of spill incidents. In this study, a combined approach integrating a wavelet artificial neural network (wavelet-ANN) model and high-frequency surrogate measurements is proposed as a method of water quality anomaly detection and warning provision. High-frequency time series of major water quality indexes (TN, TP, COD, etc.) were produced via a regression-based surrogate model. After wavelet decomposition and denoising, a low-frequency signal was imported into a back-propagation neural network for one-step prediction to identify the major features of water quality variations. The precisely trained site-specific wavelet-ANN outputs the time series of residual errors. A warning is triggered when the actual residual error exceeds a given threshold, i.e., baseline pattern, estimated based on long-term water quality variations. A case study based on the monitoring program applied to the Potomac River Basin in Virginia, USA, was conducted. The integrated approach successfully identified two anomaly events of TP variations at a 15-minute scale from high-frequency online sensors. A storm event and point source inputs likely accounted for these events. The results show that the wavelet-ANN model is slightly more accurate than the ANN for high-frequency surface water quality prediction, and it meets the requirements of anomaly detection. Analyses of the performance at different stations and over different periods illustrated the stability of the proposed method. By combining monitoring instruments and surrogate measures, the presented approach can support timely anomaly identification and be applied to urban aquatic environments for watershed management. Copyright © 2017 Elsevier B.V. All rights reserved.

  18. Prediction of GWL with the help of GRACE TWS for unevenly spaced time series data in India : Analysis of comparative performances of SVR, ANN and LRM

    Science.gov (United States)

    Mukherjee, Amritendu; Ramachandran, Parthasarathy

    2018-03-01

    Prediction of Ground Water Level (GWL) is extremely important for sustainable use and management of ground water resource. The motivations for this work is to understand the relationship between Gravity Recovery and Climate Experiment (GRACE) derived terrestrial water change (ΔTWS) data and GWL, so that ΔTWS could be used as a proxy measurement for GWL. In our study, we have selected five observation wells from different geographic regions in India. The datasets are unevenly spaced time series data which restricts us from applying standard time series methodologies and therefore in order to model and predict GWL with the help of ΔTWS, we have built Linear Regression Model (LRM), Support Vector Regression (SVR) and Artificial Neural Network (ANN). Comparative performances of LRM, SVR and ANN have been evaluated with the help of correlation coefficient (ρ) and Root Mean Square Error (RMSE) between the actual and fitted (for training dataset) or predicted (for test dataset) values of GWL. It has been observed in our study that ΔTWS is highly significant variable to model GWL and the amount of total variations in GWL that could be explained with the help of ΔTWS varies from 36.48% to 74.28% (0.3648 ⩽R2 ⩽ 0.7428) . We have found that for the model GWL ∼ Δ TWS, for both training and test dataset, performances of SVR and ANN are better than that of LRM in terms of ρ and RMSE. It also has been found in our study that with the inclusion of meteorological variables along with ΔTWS as input parameters to model GWL, the performance of SVR improves and it performs better than ANN. These results imply that for modelling irregular time series GWL data, ΔTWS could be very useful.

  19. Louis Kahni mateeria, valguse ja energia arhitektuur = Louis Kahn's Architecture of Matter, Light and Energy / Anne Griswold Tyng ; tõlk. Tiina Randus

    Index Scriptorium Estoniae

    Tyng, Anne Griswold

    2007-01-01

    Louis Kahni betoonarhitektuurist (Weissi maja, 1947-1949), Yale'i kunstigaleriist (1951-1953), City Toweri projektist (1952-1958), Trentoni supelmajast (1954-1956), Salki instituudist (1959-1965, La Jolla, California), Kimbelli kunstimuuseumist (1968-1974), pealinnakompleksist Dhakas (1965-1974). Anne Griswold Tyng hakkas L. Kahni juures tööle 1945. a., tema algkooli projektist (1949-50), oma Philadelphia maja juurdeehitusest (1965-1968)

  20. Determining degree of roasting in cocoa beans by artificial neural network (ANN)-based electronic nose system and gas chromatography/mass spectrometry (GC/MS).

    Science.gov (United States)

    Tan, Juzhong; Kerr, William L

    2018-01-24

    Roasting is a critical step in chocolate processing, where moisture content is decreased and unique flavors and texture are developed. The determination of the degree of roasting in cocoa beans is important to ensure the quality of chocolate. Determining the degree of roasting relies on human specialists or sophisticated chemical analyses that are inaccessible to small manufacturers and farmers. In this study, an electronic nose system was constructed consisting of an array of gas sensors and used to detect volatiles emanating from cocoa beans roasted for 0, 20, 30 and 40 min. The several signals were used to train a three-layer artificial neural network (ANN). Headspace samples were also analyzed by gas chromatography/mass spectrometry (GC/MS), with 23 select volatiles used to train a separate ANN. Both ANNs were used to predict the degree of roasting of cocoa beans. The electronic nose had a prediction accuracy of 94.4% using signals from sensors TGS 813, 826, 822, 830, 830, 2620, 2602 and 2610. In comparison, the GC/MS predicted the degree of roasting with an accuracy of 95.8%. The electronic nose system is able to predict the extent of roasting, as well as a more sophisticated approach using GC/MS. © 2018 Society of Chemical Industry. © 2018 Society of Chemical Industry.

  1. Correlations of Complete Blood Count with Alanine and Aspartate Transaminase in Chinese Subjects and Prediction Based on Back-Propagation Artificial Neural Network (BP-ANN).

    Science.gov (United States)

    Yu, Jiong; Pan, Qiaoling; Yang, Jinfeng; Zhu, Chengxing; Jin, Linfeng; Hao, Guangshu; Shi, Xiaowei; Cao, Hongcui; Lin, Feiyan

    2017-06-19

    BACKGROUND The complete blood count (CBC) is the most common examination used to monitor overall health in clinical practice. Whether there is a relationship between CBC indexes and alanine transaminase (ALT) and aspartate aminotransferase (AST) has been unclear. MATERIAL AND METHODS In this study, 572 normal-weight and 346 overweight Chinese subjects were recruited. The relationship between CBC indexes with ALT and AST were analyzed by Pearson and Spearman correlations according to their sex, then we conducted colinearity diagnostics and multiple linear regression (MLR) analysis. A prediction model was developed by a back-propagation artificial neural network (BP-ANN). RESULTS ALT was related to 4 CBC indexes in the male normal-weight group and 3 CBC indexes in the female group. In the overweight group, ALT had a similar relationship with the normal group, but there was only 1 index related with AST in the normal-weight group and male overweight groups. The ALT regression models were developed in normal-weight and overweight people, which had better correlation coefficient (R>0.3). After training 1000 epochs, the BP-ANN models of ALT achieved higher correlations than MLR models in normal-weight and overweight people. CONCLUSIONS ALT is a more suitable index than AST for developing a regression model. ALT can be predicted by CBC indexes in normal-weight and overweight individuals based on a BP-ANN model, which was better than MLR analysis.

  2. Use of artificial neural network (ANN) for the development of bioprocess using Pinus roxburghii fallen foliages for the release of polyphenols and reducing sugars.

    Science.gov (United States)

    Vats, Siddharth; Negi, Sangeeta

    2013-07-01

    In present study, different parameters, i.e., percentage of NaOH, loading volume, microwave power (watt) and volume of water during pretreatment were optimized by ANN for release of polyphenols and sugars from pine fallen foliage. ANN used was feed forward back propagation type with 72 input, 72 output and 10 hidden layers coupled with Lvenberg-Marquardt (LM) training algorithms. The predicted optimal values by generated neural network for alkali pretreatment were 6 ml (0.5% NaOH)/g of substrate, soaking time of 10 min followed by 1 min of 100 W microwave. Pretreated sample on enzymatic hydrolysis at 50°C for 20 h with cocktail of cellulase, xylanase and laccase produced by locally isolated consortia released 668.9 mg/g of total sugar and 265.06 mg/g of total polyphenols. Optimization by ANN showed good yield, therefore, indicating its suitability for bioprocess modeling and control for release of reducing sugars and polyphenols from pine foliage. Copyright © 2013 Elsevier Ltd. All rights reserved.

  3. Span-to-depth ratio effect on shear strength of steel fiber-reinforced high-strength concrete deep beams using ANN model

    Science.gov (United States)

    Naik, Uday; Kute, Sunil

    2013-12-01

    The paper predicts the shear strength of high-strength steel fiber-reinforced concrete deep beams. It studies the effect of clear span-to-overall depth ratio on shear capacity of steel fiber high-strength deep beams using artificial neural network (ANN8). The three-layered model has eight input nodes which represent width, effective depth, volume fraction, fiber aspect ratio and shear span-to-depth ratio, longitudinal steel, compressive strength of concrete, and clear span-to-overall depth ratio. The model predicts the shear strength of high-strength steel fiber deep beams to be reasonably good when compared with the results of proposed equations by researchers as well as the results obtained by neural network (ANN7) which is developed for seven inputs excluding span-to-depth ratio. The developed neural network ANN8 proves the versatility of artificial neural networks to establish the relations between various parameters affecting complex behavior of steel fiber-reinforced concrete deep beams and costly experimental processes.

  4. Application of Ann for Prediction of Co2+, Cd2+ and Zn2+ Ions Uptake by R. Squarrosus Biomass in Single and Binary Mixtures

    Directory of Open Access Journals (Sweden)

    Nemeček Peter

    2014-06-01

    Full Text Available Discharge of heavy metals into aquatic ecosystems has become a matter of concern over the last few decades. The search for new technologies involving the removal of toxic metals from wastewaters has directed the attention to biosorption, based on metal binding capacities of various biological materials. Degree of sorbent affinity for the sorbate determines its distribution between the solid and liquid phases and this behavior can be described by adsorption isotherm models (Freundlich and Langmuir isotherm models representing the classical approach. In this study, an artificial neural network (ANN was proposed to predict the sorption efficiency in single and binary component solutions of Cd2+, Zn2+ and Co2+ ions by biosorbent prepared from biomass of moss Rhytidiadelphus squarrosus. Calculated non-linear ANN models presented in this paper are advantageous for its capability of successful prediction, which can be problematic in the case of classical isotherm approach. Quality of prediction was proved by strong agreement between calculated and measured data, expressed by the coefficient of determination in both, single and binary metal systems (R2= 0.996 and R2= 0.987, respectively. Another important benefit of these models is necessity of significantly smaller amount of data (about 50% for the model calculation. Also, it is possible to calculate Qeq for all studied metals by one combined ANN model, which totally overcomes a classical isotherm approach

  5. Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM and Artificial Neural Network (ANN

    Directory of Open Access Journals (Sweden)

    Maria Grazia De Giorgi

    2014-08-01

    Full Text Available A high penetration of wind energy into the electricity market requires a parallel development of efficient wind power forecasting models. Different hybrid forecasting methods were applied to wind power prediction, using historical data and numerical weather predictions (NWP. A comparative study was carried out for the prediction of the power production of a wind farm located in complex terrain. The performances of Least-Squares Support Vector Machine (LS-SVM with Wavelet Decomposition (WD were evaluated at different time horizons and compared to hybrid Artificial Neural Network (ANN-based methods. It is acknowledged that hybrid methods based on LS-SVM with WD mostly outperform other methods. A decomposition of the commonly known root mean square error was beneficial for a better understanding of the origin of the differences between prediction and measurement and to compare the accuracy of the different models. A sensitivity analysis was also carried out in order to underline the impact that each input had in the network training process for ANN. In the case of ANN with the WD technique, the sensitivity analysis was repeated on each component obtained by the decomposition.

  6. A soft computing scheme incorporating ANN and MOV energy in fault detection, classification and distance estimation of EHV transmission line with FSC.

    Science.gov (United States)

    Khadke, Piyush; Patne, Nita; Singh, Arvind; Shinde, Gulab

    2016-01-01

    In this article, a novel and accurate scheme for fault detection, classification and fault distance estimation for a fixed series compensated transmission line is proposed. The proposed scheme is based on artificial neural network (ANN) and metal oxide varistor (MOV) energy, employing Levenberg-Marquardt training algorithm. The novelty of this scheme is the use of MOV energy signals of fixed series capacitors (FSC) as input to train the ANN. Such approach has never been used in any earlier fault analysis algorithms in the last few decades. Proposed scheme uses only single end measurement energy signals of MOV in all the 3 phases over one cycle duration from the occurrence of a fault. Thereafter, these MOV energy signals are fed as input to ANN for fault distance estimation. Feasibility and reliability of the proposed scheme have been evaluated for all ten types of fault in test power system model at different fault inception angles over numerous fault locations. Real transmission system parameters of 3-phase 400 kV Wardha-Aurangabad transmission line (400 km) with 40 % FSC at Power Grid Wardha Substation, India is considered for this research. Extensive simulation experiments show that the proposed scheme provides quite accurate results which demonstrate complete protection scheme with high accuracy, simplicity and robustness.

  7. An Experimental Investigation into the Optimal Processing Conditions for the CO2 Laser Cladding of 20 MnCr5 Steel Using Taguchi Method and ANN

    Science.gov (United States)

    Mondal, Subrata; Bandyopadhyay, Asish.; Pal, Pradip Kumar

    2010-10-01

    This paper presents the prediction and evaluation of laser clad profile formed by means of CO2 laser applying Taguchi method and the artificial neural network (ANN). Laser cladding is one of the surface modifying technologies in which the desired surface characteristics of any component can be achieved such as good corrosion resistance, wear resistance and hardness etc. Laser is used as a heat source to melt the anti-corrosive powder of Inconel-625 (Super Alloy) to give a coating on 20 MnCr5 substrate. The parametric study of this technique is also attempted here. The data obtained from experiments have been used to develop the linear regression equation and then to develop the neural network model. Moreover, the data obtained from regression equations have also been used as supporting data to train the neural network. The artificial neural network (ANN) is used to establish the relationship between the input/output parameters of the process. The established ANN model is then indirectly integrated with the optimization technique. It has been seen that the developed neural network model shows a good degree of approximation with experimental data. In order to obtain the combination of process parameters such as laser power, scan speed and powder feed rate for which the output parameters become optimum, the experimental data have been used to develop the response surfaces.

  8. Le rôle politique des revues conservatrices aux Etats-Unis depuis les années 1980

    Directory of Open Access Journals (Sweden)

    Marie-Cécile Naves

    2004-09-01

    Full Text Available L’article vise à étudier, à travers l’exemple de la théorie de la « Fin de l’Histoire » de Fukuyama, en quoi la revue américaine The National Interest est, à l’instar d’autres revues conservatrices, un instrument médiatique crucial pour la diffusion de certains idéaux du Parti Républicain aux Etats-Unis depuis le milieu des années 1980. Il s’agit par là même de réfléchir aux conditions de production et de diffusion de cette théorie, dans un contexte géopolitique et intellectuel exceptionnel, celui de la fin de la guerre froide. L’évolution des liens entre intellectuels et pouvoir politique américains, avec pour enjeu, depuis le début des années 1980, la politique étrangère, sont au cœur de cette réflexion.El artículo intenta estudiar a través del ejemplo de la teoría del « Fin de la Historia » de Fukuyama, hasta que punto la revista americana The National Interest es, a semejanza de otras revistas conservadoras, un instrumento crucial para la difusión de algunos ideales del partido republicano en los estados Unidos desde mediados de los años ochenta. Se trata de reflexionar sobre las condiciones de producción y de difusión de esta teoría, en un contexto geopolítico e intelectual excepcional, el del final de la guerra fría. El centro de esta reflexión es la evolución de las relaciones entre los intelectuales y el poder político americano, teniendo como telón de fondo, la política exterior.The article aims at studying, through the example of Fukuyama’s theory of the « End of History », how the American journal The National Interest, as other conservative journals, has, since the mid 1980s, been a crucial media instrument for the dissemination of some ideals of the Republican Party. We have thus to think about the conditions of production and dissemination of this theory in an exceptional geopolitical and intellectual context – the end of the cold war. The changing links between

  9. A smartphone-based apple yield estimation application using imaging features and the ANN method in mature period

    Directory of Open Access Journals (Sweden)

    Jianping Qian

    Full Text Available ABSTRACT: Apple yield estimation using a smartphone with image processing technology offers advantages such as low cost, quick access and simple operation. This article proposes distribution framework consisting of the acquisition of fruit tree images, yield prediction in smarphone client, data processing and model calculation in server client for estimating the potential fruit yield. An image processing method was designed including the core steps of image segmentation with R/B value combined with V value and circle-fitting using curvature analysis. This method enabled four parameters to be obtained, namely, total identified pixel area (TP, fitting circle amount (FC, average radius of the fitting circle (RC and small polygon pixel area (SP. A individual tree yield estimation model on an ANN (Artificial Neural Network was developed with three layers, four input parameters, 14 hidden neurons, and one output parameter. The system was used on an experimental Fuji apple (Malus domestica Borkh. cv. Red Fuji orchard. Twenty-six tree samples were selected from a total of 80 trees according to the multiples of the number three for the establishment model, whereby 21 groups of data were trained and 5 groups o data were validated. The R2 value for the training datasets was 0.996 and the relative root mean squared error (RRMSE value 0.063. The RRMSE value for the validation dataset was 0.284 Furthermore, a yield map with 80 apple trees was generated, and the space distribution o the yield was identified. It provided appreciable decision support for site-specific management.

  10. Simulation of Snowmelt Runoff Using SRM Model and Comparison With Neural Networks ANN and ANFIS (Case Study: Kardeh dam basin

    Directory of Open Access Journals (Sweden)

    morteza akbari

    2017-03-01

    of the basin with 2962 meters above sea level. Kardeh dam was primarily constructed on the Kardehriver for providing drinking and agriculture water demand with an annual volume rate of 21.23 million cubic meters. Satellite image: To estimate the level of snow cover, the satellite Landsat ETM+ data at path 35-159, rows 34-159 over the period 2001-2002 were used. Surfaces covered with snow were separated bysnow distinction normalized index (NDSI, But due to the lack of training data for image classification (areas with snow and no snow, the k-means unsupervised classification algorithm was used. Extracting the data from the meteorological and hydrological Since only a gauging station exists at the Kardeh dam site, the daily discharge data recorded at these stations was used. To extract meteorological parameters such as precipitation and temperature data, the records of the three stations Golmakan, Mashhad and Ghouchan, as the stations closest to the dam basin Kardeh were used. The purpose of this study was to simulate snowmelt runoff using SRM hydrological models and to compare the results with the outputs of the neural network models such as the ANN and the ANFIS model. Flow simulation was carried out using SRM, ANN model with the Multilayer Perceptron with back-propagation algorithm, and Sugeno type ANFIS. To evaluate the performance of the models in addition to the standard statistics such as mean square error or mean absolute percentage error, the regression coefficient measures and the difference in volume were used. The results showed that all three models are almost similar in terms of statistical parameters MSE and R and the differences were negligible. SRM model: SRM model is a daily hydrological model. This equation is composed of different components including 14 parameters. The input values were calculated based on the equations of degree-day factor. The evaluation of the model was performed with flow subside factor, coefficient and subtracting volume

  11. Empirical antibiotic therapy (ABT) of lower respiratory tract infections (LRTI) in the elderly: application of artificial neural network (ANN). Preliminary results.

    Science.gov (United States)

    Gueli, Nicolò; Martinez, Andrea; Verrusio, Walter; Linguanti, Adele; Passador, Paola; Martinelli, Valentina; Longo, Giovanni; Marigliano, Benedetta; Cacciafesta, Flaminia; Cacciafesta, Mauro

    2012-01-01

    LRTI are among the most common diseases in developed countries, including chronic obstructive pulmonary disease (COPD), one of the most frequent conditions. Their treatment in general practice is often unsuccessful and this increases hospital admissions. We know, bacterial infections in the elderly show a higher morbidity and mortality, either for more severe symptoms, than in younger adults, or because the causing agent often remains unknown. The need for a quick initiation of ABT often requires to chose on empirical grounds. To date there are no official guidelines for empirical ABT of COPD exacerbations, but only heterogeneous and often conflicting recommendations exist. The aim of our study was to identify a tool to guide the choice of the most effective empirical ABT when symptoms are acute and bacteriological tests cannot be performed. We used an ANN to study 117 patients aged between 55 and 97 years (mean 81.5 ± 8.7 years) (± S.D.), admitted with a diagnosis of pneumonia, COPD exacerbation or pneumonia with respiratory failure. We registered symptoms at onset and some individual variables such as age, sex, risk factors, comorbidity, current drug therapies. Then the ANN was applied to choose ABT in 20 patients versus 20 subjects whose therapy was chosen by the physicians, comparing these groups for therapy's efficacy, mean durations of therapy and hospitalization (H). In the learning phase, the ANN could predict the resolution index 99.05% of the time (i.e., 104 times) with a ± S.D. = 0.23. After the training, during the test phase, the network predicted the resolution index 91.67% of the time (i.e., 11 times) with a ± S.D. = 0.54, thus proving the validity of the relations identified during the learning phase. Preliminary results of the application of our tool, show the ANN allowed us to greatly reduce the duration of the ABT and subsequently of the H. Based on preliminary results, we assume that the use of ANN can make a valuable contribution in the

  12. La decision de Anne (2009: estudio de la creación de embriones genéticamente seleccionados para la curación de pacientes crónicos

    Directory of Open Access Journals (Sweden)

    Ester CASILLAS SAGRADO

    2016-09-01

    Full Text Available La decisión de Anne/ My sister´s keeper (2009 es una película de Nick Cassavetes que cuenta la historia de Anne Fitzgerald, una niña de 11 años que fue concebida para salvar a su hermana mayor, Karen, con Leucemia Promielocítica Aguda. Cuando se le plantea la necesidad de realizar un trasplante de riñón a su hermana enferma, Anne decide demandar a sus padres sobre los derechos de su cuerpo para no tener que someterse a ese procedimiento que puede condicionar para siempre su calidad de vida. Ante esta situación la madre de Anne comienza una lucha judicial contra la menor de sus hijas en un intento de disuadirla para que realice la donación aunque esto implique seguir siendo el soporte del que dependa la supervivencia de su hermana de forma indefinida.

  13. Pop / Anneli Remme

    Index Scriptorium Estoniae

    Remme, Anneli, 1968-

    2004-01-01

    Heliplaatidest: Hortus Musicus "Eesti heliloojad III", Brad Mehldau Trio "Anything Goes", Vitamins For You "I'm sorry for ever and for always", The Coral "Nightfreak and the Sons of Becker", Lionel Richie "Just For You", David Byrne "Grown Backwards"

  14. Helena liinid / Anneli Remme

    Index Scriptorium Estoniae

    Remme, Anneli, 1968-

    2008-01-01

    Helena Tulve autoriplaadist "Lijnen". Esitavad NYYD Ensemble, dirigent Olari Elts, Stockholm Saxophone Quartet, Silesian String Quartet, solistid Arianna Savall, Emmanuelle Ophele-Gaubert ja Mihkel Peäske

  15. Kestvusratsutajate aasta / Anne Rohtla

    Index Scriptorium Estoniae

    Rohtla, Anne

    2012-01-01

    2012. aastal viidi Eestis läbi kaheksa kestvusratsutamise võistlust, Väljaspool Eestit võisteldi Põhjamaade meistrivõistlustel Soomes, FEI noorhobuste MMil Ungaris, Euoopa juunioride meistrivõistlustel Belgias ning erinevatel CEI2* ja 3* võistlustel Soomes, Leedus ning Tšehhis

  16. Uut moodi / Anne Vetik

    Index Scriptorium Estoniae

    Vetik, Anne

    2010-01-01

    Eesti moebrändidest, mis püüavad käivitada korrapärast tootmist ja haarata laiemat turgu. Lähemalt Mari Martini loodud kaubamärkidest Tallinn Dolls ja ReUse Republic, Kristina Viirpalu moeärist, Baltmani peadisaineri Antonio tegevusest

  17. Maakunst Rakveres / Anne Kokkov

    Index Scriptorium Estoniae

    Kokkov, Anne

    2001-01-01

    Rahvusvaheline kunstirühmitus "The Circle" korraldab 5. augustini Rakveres V maakunstisümpoosioni. Osalejad on Eestist, Rootsist, Saksamaalt ja Venemaalt, nende seas rahvusvaheliselt tuntud kunstnikud Susan Walke ja Hans "Limbus" Tjörneryd, kes loob Rakvere kohal õhuskulptuuri.

  18. Graafikakiri Krakowist / Anne Untera

    Index Scriptorium Estoniae

    Untera, Anne, 1951-

    2006-01-01

    Krakowi graafikatriennaalist, mis tähistab tänavu 40. aastapäeva. 1991. aastast muutus graafikabiennaali rütm kolmeaastaseks - triennaaliks. Ühe kümnest võrdsest preemiast pälvis Evi Tihemets töödega "Pühendus (Leib)" ja "Pühendus (Pirnid)". Grand prix' - Ingrid Ledent. Virge Jõekalda, Marje Üksise, Ülle Marksi, Jüri Kassi, Davida Kiddi ja Basil Colin Franki töödest triennaalil. Torunis kutsutud osalejatega näitusel "Värv graafikas" osalevad Eestist Benjamin Vasserman ja Virge Jõekalda

  19. Teaduskohtumine Kanadas / Ann Paal

    Index Scriptorium Estoniae

    Paal, Ann

    2010-01-01

    Kanadas Vancouveris 7.-10. juunini toimunud 54. SRHSB (Society for research into Hydrocephalus and Spina Bifida) seltsi aastakoosolekust, kus kogunesid seljaajusonga- ja vesipeahaigetega tegelevad professionaalid üle maailma

  20. Kopli impressionism / Anne Vetik

    Index Scriptorium Estoniae

    Vetik, Anne

    2006-01-01

    Kunstiteadlase Marioni ja Tõnise 2006. a. valminud kuuetoaline korter Koplis Angerja trammipeatuse juures asuvas Stalini-aegses majas, kus varem asusid Tallinna Tehnikaülikooli auditooriumid ja arhiiv. Korter on sisustatud peamiselt Eestis valmistatud mööbliga. 8 värv. ill

  1. Provintsirokokoost klassitsismini / Anne Lõugas

    Index Scriptorium Estoniae

    Lõugas, Anne, 1951-

    2002-01-01

    Portreekunsti arengust 18. sajandil ja seda ala viljelevatest kunstnikest Eestis, portreedel kujutatud isikute ja kunstnike identifitseerimisest. G. C.Groothi (1716-1749), W. D. Budbergi (1740-1784), G. C. Welté (1748/49-1792), F. H. Barisieni (1724-1796), J. A. Darbes'i (1747-1810), K. F. Vernet' (1774-1825), P. Rotari (1707-1762), A. Graffi (1736-1813), K. A. Senffi (1770-1838), G. F. Kügelgeni (1772-1820) jt. portreeloomingust, ka miniatuurmaalidest ja siluettidest

  2. Rahvusvahelised korporatsioonid / Ann Doherty

    Index Scriptorium Estoniae

    Doherty, Ann

    2001-01-01

    Rahvusvaheliste korporatsioonide tegevuse jälgimine ja neilt vastutuse nõudmine on aasta-aastalt muutunud raskemaks, kuna korporatsioonid on liitunud, muutnud nimesid ja loonud lähedasi sidemeid poliitiliste institutsioonidega

  3. artificial neural network (ann)

    African Journals Online (AJOL)

    2004-08-18

    Aug 18, 2004 ... forecasting models and artificial intelligence techniques and have become one of the major research fields (Kher and Joshin, 2003). (a) Artificial Neural Network and Electrical Load. Prediction. Neural network analysis is an Artificial Intelligence. (AI) approach to mathematical modeling. Neural. Networks ...

  4. Le groupe de recherches transfusionnelles d’Afrique francophone: bilan des cinq premières années

    Science.gov (United States)

    Tagny, Claude Tayou; Murphy, Edward L.; Lefrère, Jean-Jacques

    2016-01-01

    Les travaux de recherches sur la sécurité transfusionnelle en Afrique sub-saharienne sont peu nombreux, souvent limités à des initiatives locales avec des conclusions difficilement représentatives de cette région. Le Groupe de recherches transfusionnelles en Afrique sub-saharienne francophone a été créé en mai 2007 avec pour objectif de développer des stratégies globales d’amélioration de la sécurité transfusionnelle mais adaptables à la situation de chaque pays. Les activités du Groupe à ce jour ont porté essentiellement sur l’obtention de données épidémiologiques et de laboratoire sur la transfusion sanguine et à proposer des stratégies de sécurité transfusionnelle dans le domaine des infections transmissibles par la transfusion. Pour mener à bien ces activités de recherche, le Groupe travaille en étroite collaboration avec les Centres nationaux de transfusion sanguine (CNTS), les Centres régionaux de transfusion sanguine (CRTS), les banques de sang hospitalières (BSH) et les postes de collecte de sang. Pour les 5 premières années, quatre priorités de recherche ont été identifiées: (i) des études descriptives sur les caractéristiques des donneurs de sang et des centres de transfusion; (ii) une estimation du risque résiduel post-transfusionnel des principales infections virales transmissibles par la transfusion; (iii) une analyse des stratégies de sélection médicale des donneurs de sang; et (iv) une description des stratégies de dépistage des ITT et une description du système d’assurance qualité externe existant. Durant cette période, sept projets ont été mis en œuvre au niveau national et publiés et cinq études multicentriques ont été réalisées et publiées. La présente étude rapporte les principales observations et recommandations de ces études. PMID:24360798

  5. Les peuplements de poissons de l'année de quelques types d'annexes fluviales dans la plaine de la Bassée (Seine

    Directory of Open Access Journals (Sweden)

    TALES E.

    1996-07-01

    Full Text Available Dans un secteur de la Seine moyennement aménagé, les peuplements de poissons de l'année sont étudiés simultanément dans le chenal principal ainsi que dans cinq annexes fluviales de différents types. Six campagnes de pêche électrique réalisées durant l'été 1994 ont permis la capture de 3302 poissons de l'année représentant 18 espèces. L'analyse des abondances des espèces montre un gradient spatial, des milieux lotiques aux milieux lentiques. Elle met également en évidence l'évolution saisonnière subie par les peuplements de poissons de l'année. En analysant les préférences des espèces vis-à-vis de chaque milieu, au regard de leur appartenance aux groupes de reproduction, ce gradient spatial des annexes est affiné et, en particulier, singularise les annexes d'origine anthropique. Ainsi, cette étude montre la omplémentarité entre, d'une part, les annexes et le chenal principal et, d'autre part, entre les différents types d'annexés vis-à-vis de la reproduction des espèces de poissons d'un cours d'eau de plaine alluviale.

  6. Development of CAD based on ANN analysis of power spectra for pneumoconiosis in chest radiographs: effect of three new enhancement methods.

    Science.gov (United States)

    Okumura, Eiichiro; Kawashita, Ikuo; Ishida, Takayuki

    2014-07-01

    We have been developing a computer-aided detection (CAD) scheme for pneumoconiosis based on a rule-based plus artificial neural network (ANN) analysis of power spectra. In this study, we have developed three enhancement methods for the abnormal patterns to reduce false-positive and false-negative values. The image database consisted of 2 normal and 15 abnormal chest radiographs. The International Labour Organization standard chest radiographs with pneumoconiosis were categorized as subcategory, size, and shape of pneumoconiosis. Regions of interest (ROIs) with a matrix size of 32 × 32 were selected from normal and abnormal lungs. Three new enhanced methods were obtained by window function, top-hat transformation, and gray-level co-occurrence matrix analysis. We calculated the power spectrum (PS) of all ROIs by Fourier transform. For the classification between normal and abnormal ROIs, we applied a combined analysis using the ruled-based plus the ANN method. To evaluate the overall performance of this CAD scheme, we employed ROC analysis for distinguishing between normal and abnormal ROIs. On the chest radiographs of the highest categories (severe pneumoconiosis) and the lowest categories (early pneumoconiosis), this CAD scheme achieved area under the curve (AUC) values of 0.93 ± 0.02 and 0.72 ± 0.03. The combined rule-based plus ANN method with the three new enhanced methods obtained the highest classification performance for distinguishing between abnormal and normal ROIs. Our CAD system based on the three new enhanced methods would be useful in assisting radiologists in the classification of pneumoconiosis.

  7. Master-Leader-Slave Cuckoo Search with Parameter Control for ANN Optimization and Its Real-World Application to Water Quality Prediction

    Science.gov (United States)

    Jaddi, Najmeh Sadat; Abdullah, Salwani; Abdul Malek, Marlinda

    2017-01-01

    Artificial neural networks (ANNs) have been employed to solve a broad variety of tasks. The selection of an ANN model with appropriate weights is important in achieving accurate results. This paper presents an optimization strategy for ANN model selection based on the cuckoo search (CS) algorithm, which is rooted in the obligate brood parasitic actions of some cuckoo species. In order to enhance the convergence ability of basic CS, some modifications are proposed. The fraction Pa of the n nests replaced by new nests is a fixed parameter in basic CS. As the selection of Pa is a challenging issue and has a direct effect on exploration and therefore on convergence ability, in this work the Pa is set to a maximum value at initialization to achieve more exploration in early iterations and it is decreased during the search to achieve more exploitation in later iterations until it reaches the minimum value in the final iteration. In addition, a novel master-leader-slave multi-population strategy is used where the slaves employ the best fitness function among all slaves, which is selected by the leader under a certain condition. This fitness function is used for subsequent Lévy flights. In each iteration a copy of the best solution of each slave is migrated to the master and then the best solution is found by the master. The method is tested on benchmark classification and time series prediction problems and the statistical analysis proves the ability of the method. This method is also applied to a real-world water quality prediction problem with promising results. PMID:28125609

  8. Statistical analysis and ANN modeling for predicting hydrological extremes under climate change scenarios: the example of a small Mediterranean agro-watershed.

    Science.gov (United States)

    Kourgialas, Nektarios N; Dokou, Zoi; Karatzas, George P

    2015-05-01

    The purpose of this study was to create a modeling management tool for the simulation of extreme flow events under current and future climatic conditions. This tool is a combination of different components and can be applied in complex hydrogeological river basins, where frequent flood and drought phenomena occur. The first component is the statistical analysis of the available hydro-meteorological data. Specifically, principal components analysis was performed in order to quantify the importance of the hydro-meteorological parameters that affect the generation of extreme events. The second component is a prediction-forecasting artificial neural network (ANN) model that simulates, accurately and efficiently, river flow on an hourly basis. This model is based on a methodology that attempts to resolve a very difficult problem related to the accurate estimation of extreme flows. For this purpose, the available measurements (5 years of hourly data) were divided in two subsets: one for the dry and one for the wet periods of the hydrological year. This way, two ANNs were created, trained, tested and validated for a complex Mediterranean river basin in Crete, Greece. As part of the second management component a statistical downscaling tool was used for the creation of meteorological data according to the higher and lower emission climate change scenarios A2 and B1. These data are used as input in the ANN for the forecasting of river flow for the next two decades. The final component is the application of a meteorological index on the measured and forecasted precipitation and flow data, in order to assess the severity and duration of extreme events. Copyright © 2015 Elsevier Ltd. All rights reserved.

  9. Master-Leader-Slave Cuckoo Search with Parameter Control for ANN Optimization and Its Real-World Application to Water Quality Prediction.

    Science.gov (United States)

    Jaddi, Najmeh Sadat; Abdullah, Salwani; Abdul Malek, Marlinda

    2017-01-01

    Artificial neural networks (ANNs) have been employed to solve a broad variety of tasks. The selection of an ANN model with appropriate weights is important in achieving accurate results. This paper presents an optimization strategy for ANN model selection based on the cuckoo search (CS) algorithm, which is rooted in the obligate brood parasitic actions of some cuckoo species. In order to enhance the convergence ability of basic CS, some modifications are proposed. The fraction Pa of the n nests replaced by new nests is a fixed parameter in basic CS. As the selection of Pa is a challenging issue and has a direct effect on exploration and therefore on convergence ability, in this work the Pa is set to a maximum value at initialization to achieve more exploration in early iterations and it is decreased during the search to achieve more exploitation in later iterations until it reaches the minimum value in the final iteration. In addition, a novel master-leader-slave multi-population strategy is used where the slaves employ the best fitness function among all slaves, which is selected by the leader under a certain condition. This fitness function is used for subsequent Lévy flights. In each iteration a copy of the best solution of each slave is migrated to the master and then the best solution is found by the master. The method is tested on benchmark classification and time series prediction problems and the statistical analysis proves the ability of the method. This method is also applied to a real-world water quality prediction problem with promising results.

  10. Support vector machine regression (SVR/LS-SVM)--an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data.

    Science.gov (United States)

    Balabin, Roman M; Lomakina, Ekaterina I

    2011-04-21

    In this study, we make a general comparison of the accuracy and robustness of five multivariate calibration models: partial least squares (PLS) regression or projection to latent structures, polynomial partial least squares (Poly-PLS) regression, artificial neural networks (ANNs), and two novel techniques based on support vector machines (SVMs) for multivariate data analysis: support vector regression (SVR) and least-squares support vector machines (LS-SVMs). The comparison is based on fourteen (14) different datasets: seven sets of gasoline data (density, benzene content, and fractional composition/boiling points), two sets of ethanol gasoline fuel data (density and ethanol content), one set of diesel fuel data (total sulfur content), three sets of petroleum (crude oil) macromolecules data (weight percentages of asphaltenes, resins, and paraffins), and one set of petroleum resins data (resins content). Vibrational (near-infrared, NIR) spectroscopic data are used to predict the properties and quality coefficients of gasoline, biofuel/biodiesel, diesel fuel, and other samples of interest. The four systems presented here range greatly in composition, properties, strength of intermolecular interactions (e.g., van der Waals forces, H-bonds), colloid structure, and phase behavior. Due to the high diversity of chemical systems studied, general conclusions about SVM regression methods can be made. We try to answer the following question: to what extent can SVM-based techniques replace ANN-based approaches in real-world (industrial/scientific) applications? The results show that both SVR and LS-SVM methods are comparable to ANNs in accuracy. Due to the much higher robustness of the former, the SVM-based approaches are recommended for practical (industrial) application. This has been shown to be especially true for complicated, highly nonlinear objects.

  11. ANN Model for Predicting the Impact of Submerged Aquatic Weeds Existence on the Hydraulic Performance of Branched Open Channel System Accompanied by Water Structures

    International Nuclear Information System (INIS)

    Abdeen, Mostafa A. M.; Abdin, Alla E.

    2007-01-01

    The existence of hydraulic structures in a branched open channel system urges the need for considering the gradually varied flow criterion in evaluating the different hydraulic characteristics in this type of open channel system. Computations of hydraulic characteristics such as flow rates and water surface profiles in branched open channel system with hydraulic structures require tremendous numerical effort especially when the flow cannot be assumed uniform. In addition, the existence of submerged aquatic weeds in this branched open channel system adds to the complexity of the evaluation of the different hydraulic characteristics for this system. However, this existence of aquatic weeds can not be neglected since it is very common in Egyptian open channel systems. Artificial Neural Network (ANN) has been widely utilized in the past decade in civil engineering applications for the simulation and prediction of the different physical phenomena and has proven its capabilities in the different fields. The present study aims towards introducing the use of ANN technique to model and predict the impact of submerged aquatic weeds existence on the hydraulic performance of branched open channel system. Specifically the current paper investigates a branched open channel system that consists of main channel supplies water to two branch channels that are infested by submerged aquatic weeds and have water structures such as clear over fall weirs and sluice gates. The results of this study showed that ANN technique was capable, with small computational effort and high accuracy, of predicting the impact of different infestation percentage for submerged aquatic weeds on the hydraulic performance of branched open channel system with two different hydraulic structures

  12. Trente années qui ébranlèrent la physique histoire de la théorie quantique

    CERN Document Server

    Gamow, George

    1968-01-01

    G. Gamow, dans cet ouvrage, déploie une fois encore ses qualités d'historien, de vulgarisateur et d'homme d'esprit. L'"histoire de la théorie quantique" raconte la naissance de la physique moderne au cours des trente premières années du siècle, en nous guidant ainsi à travers cette galerie de portraits où les grands noms de la physique sont présentés, par les textes, les photographies et les croquis, sous leur aspect le moins académique.

  13. Reliable and accurate point-based prediction of cumulative infiltration using soil readily available characteristics: A comparison between GMDH, ANN, and MLR

    Science.gov (United States)

    Rahmati, Mehdi

    2017-08-01

    Developing accurate and reliable pedo-transfer functions (PTFs) to predict soil non-readily available characteristics is one of the most concerned topic in soil science and selecting more appropriate predictors is a crucial factor in PTFs' development. Group method of data handling (GMDH), which finds an approximate relationship between a set of input and output variables, not only provide an explicit procedure to select the most essential PTF input variables, but also results in more accurate and reliable estimates than other mostly applied methodologies. Therefore, the current research was aimed to apply GMDH in comparison with multivariate linear regression (MLR) and artificial neural network (ANN) to develop several PTFs to predict soil cumulative infiltration point-basely at specific time intervals (0.5-45 min) using soil readily available characteristics (RACs). In this regard, soil infiltration curves as well as several soil RACs including soil primary particles (clay (CC), silt (Si), and sand (Sa)), saturated hydraulic conductivity (Ks), bulk (Db) and particle (Dp) densities, organic carbon (OC), wet-aggregate stability (WAS), electrical conductivity (EC), and soil antecedent (θi) and field saturated (θfs) water contents were measured at 134 different points in Lighvan watershed, northwest of Iran. Then, applying GMDH, MLR, and ANN methodologies, several PTFs have been developed to predict cumulative infiltrations using two sets of selected soil RACs including and excluding Ks. According to the test data, results showed that developed PTFs by GMDH and MLR procedures using all soil RACs including Ks resulted in more accurate (with E values of 0.673-0.963) and reliable (with CV values lower than 11 percent) predictions of cumulative infiltrations at different specific time steps. In contrast, ANN procedure had lower accuracy (with E values of 0.356-0.890) and reliability (with CV values up to 50 percent) compared to GMDH and MLR. The results also revealed

  14. Le difficile retour du " collectif " dans la gestion de l’eau : Regards sur 15 années d’efforts en Camargue gardoise

    Directory of Open Access Journals (Sweden)

    Daniel Petit

    2004-11-01

    Full Text Available Suite aux grandes mutations des années 60-70 en Camargue gardoise et à l’obsolescence du Traité des Marais qui réglait la gestion collective des niveaux d’eau, les acteurs économiques liés à l’eau se sont positionnés sur des logiques individualistes. L’eau est devenue l’objet de maints conflits avec de graves conséquences sur les ressources et les milieux. La mise en œuvre d’une Charte de l’environnement et d’un SAGE au début des années 90 a constitué les prémices du retour du " collectif " dans la gestion de l’eau. Mais on est encore loin de l’eau comme vecteur d’une construction territoriale à travers une communauté de projet liée à l’eau.

  15. First and Second-Law Efficiency Analysis and ANN Prediction of a Diesel Cycle with Internal Irreversibility, Variable Specific Heats, Heat Loss, and Friction Considerations

    Directory of Open Access Journals (Sweden)

    M. M. Rashidi

    2014-04-01

    Full Text Available The variability of specific heats, internal irreversibility, heat and frictional losses are neglected in air-standard analysis for different internal combustion engine cycles. In this paper, the performance of an air-standard Diesel cycle with considerations of internal irreversibility described by using the compression and expansion efficiencies, variable specific heats, and losses due to heat transfer and friction is investigated by using finite-time thermodynamics. Artificial neural network (ANN is proposed for predicting the thermal efficiency and power output values versus the minimum and the maximum temperatures of the cycle and also the compression ratio. Results show that the first-law efficiency and the output power reach their maximum at a critical compression ratio for specific fixed parameters. The first-law efficiency increases as the heat leakage decreases; however the heat leakage has no direct effect on the output power. The results also show that irreversibilities have depressing effects on the performance of the cycle. Finally, a comparison between the results of the thermodynamic analysis and the ANN prediction shows a maximum difference of 0.181% and 0.194% in estimating the thermal efficiency and the output power. The obtained results in this paper can be useful for evaluating and improving the performance of practical Diesel engines.

  16. A comparison RSM and ANN surface roughness models in thin-wall machining of Ti6Al4V using vegetable oils under MQL-condition

    Science.gov (United States)

    Mohruni, Amrifan Saladin; Yanis, Muhammad; Sharif, Safian; Yani, Irsyadi; Yuliwati, Erna; Ismail, Ahmad Fauzi; Shayfull, Zamree

    2017-09-01

    Thin-wall components as usually applied in the structural parts of aeronautical industry require significant challenges in machining. Unacceptable surface roughness can occur during machining of thin-wall. Titanium product such Ti6Al4V is mostly applied to get the appropriate surface texture in thin wall designed requirements. In this study, the comparison of the accuracy between Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) in the prediction of surface roughness was conducted. Furthermore, the machining tests were carried out under Minimum Quantity Lubrication (MQL) using AlCrN-coated carbide tools. The use of Coconut oil as cutting fluids was also chosen in order to evaluate its performance when involved in end milling. This selection of cutting fluids is based on the better performance of oxidative stability than that of other vegetable based cutting fluids. The cutting speed, feed rate, radial and axial depth of cut were used as independent variables, while surface roughness is evaluated as the dependent variable or output. The results showed that the feed rate is the most significant factors in increasing the surface roughness value followed by the radial depth of cut and lastly the axial depth of cut. In contrary, the surface becomes smoother with increasing the cutting speed. From a comparison of both methods, the ANN model delivered a better accuracy than the RSM model.

  17. Removal of high concentration of sulfate from pigment industry effluent by chemical precipitation using barium chloride: RSM and ANN modeling approach.

    Science.gov (United States)

    Navamani Kartic, D; Aditya Narayana, B Ch; Arivazhagan, M

    2018-01-15

    Sulfate ions pose a major threat and challenge in the treatment of industrial effluents. The sample of wastewater obtained from a pigment industry contained large quantities of sulfate in the form of sodium sulfate which resulted in high TDS. As the removal of sulfate from pigment industry effluent was not reported previously, this work was focused on removing the sulfate ions from the effluent by chemical precipitation using barium chloride. The efficiency of sulfate removal was nearly 100% at an excess dosage of barium chloride, which precipitates the dissolved sulfate ions in the form of barium sulfate. Optimization of the parameters was done using Response Surface Methodology (RSM). This work is the first attempt for modeling the removal of sulfate from pigment industry effluent using RSM and Artificial Neural Network (ANN). Prediction by both the models was evaluated and both of them exhibited good performance (R 2 value > 0.99). It was observed that the prediction by RSM (R 2 value 0.9986) was closer to the experimental results than ANN prediction (R 2 value 0.9955). The influence on the pH and conductivity of the solution by dosage of precipitant was also studied. The formation of barium sulfate was confirmed by characterization of the precipitate. Therefore, the sulfate removed from the effluent was converted into a commercially valuable precipitate. Copyright © 2017 Elsevier Ltd. All rights reserved.

  18. Anne von Streit: Entgrenzter Alltag – Arbeiten ohne Grenzen? Das Internet und die raum-zeitlichen Organisationsstrategien von Wissensarbeitern. Bielefeld: transcript Verlag 2011.

    Directory of Open Access Journals (Sweden)

    Tanja M. Brinkmann

    2012-05-01

    Full Text Available Anne von Streit geht in dieser Veröffentlichung ihrer Dissertation der Fragestellung nach, wie sich die räumlich und zeitlich flexibilisierten Arbeitsbedingungen auf die Alltagsgestaltung von selbständigen Frauen und Männern in der Internetbranche auswirken. Als Geographin geht es ihr dabei nicht nur um zeitliche, sondern auch um räumliche Entgrenzungsprozesse. Diese teildisziplinäre Grenzüberschreitung liefert einige Erkenntnisse. In der durchaus geschlechtersensiblen Studie mit einem komplexen, vorwiegend qualitativen Design kann die Autorin aus geschlechterbezogener Perspektive sowohl deutliche Unterschiede wie auch Homogenitäten zwischen den Geschlechtern nachweisen. Dieses genauer auszubuchstabieren und zu begründen, bleibt sie jedoch weitgehend schuldig.In this published dissertation, Anne von Streit considers the question of how the flexibilization of spatial and temporal working conditions of self-employed women and men in the internet industry affects their daily routine. As a geographer, she is not only interested in temporal, but also in spatial processes of debordering. This subdisciplinary border crossing leads to several insights. In this, indeed gender-sensitive, study with a complex and mainly qualitative design, the author is able to prove, from a gender-related perspective, both striking differences and homogeneities between the genders. However, she largely fails to explain and spell this out in more detail.

  19. Using a hybrid of traditional 3D FE model, Artificial Neural Networks (ANN), and Genetic Algorithms (GA), to help locating DNAPL sources in a contaminated site

    Science.gov (United States)

    Orr, S.; Boger, Z.

    2008-12-01

    After many years of unsuccessful pump and treat in a contaminated site in Washington, it has become clear that the actual locations of Carbon Tetrachloride (CTC) sources, particularly their interception points at the water table, were unknown. In fact, the actual sources at the surface were partly known, particularly where the history of dumping and leaking from several facilities was known. However, due to a 200-feet thick and complex vadose zone, conducive to lateral migration, this knowledge was practically misleading at the high- resolution required by the pump-treat-inject operation. A quick, data-driven Artificial Neural network (ANN) was used to imitate FEFLOW (a 3D finite-element code), in two ways: (a) by direct inverse; and (b) by indirect inverse modeling (which includes forward simulations). Since the ANN is many folds faster than FEFLOW, it could generate millions of scenarios, and then use genetic algorithms (GA) to efficiently search for the combination of sources that minimizes the errors between simulated and measured concentrations in all wells, over time. While results from the direct inverse were not consistent with simulated concentration profiles in the 31 wells, results from the more robust indirect inverse were much closer to the real concentration trends in all wells, as verified by a FEFLOW simulation using the sources determined by indirect inverse.

  20. Evaluation of the AnnAGNPS Model for Predicting Runoff and Nutrient Export in a Typical Small Watershed in the Hilly Region of Taihu Lake

    Directory of Open Access Journals (Sweden)

    Chuan Luo

    2015-09-01

    Full Text Available The application of hydrological and water quality models is an efficient approach to better understand the processes of environmental deterioration. This study evaluated the ability of the Annualized Agricultural Non-Point Source (AnnAGNPS model to predict runoff, total nitrogen (TN and total phosphorus (TP loading in a typical small watershed of a hilly region near Taihu Lake, China. Runoff was calibrated and validated at both an annual and monthly scale, and parameter sensitivity analysis was performed for TN and TP before the two water quality components were calibrated. The results showed that the model satisfactorily simulated runoff at annual and monthly scales, both during calibration and validation processes. Additionally, results of parameter sensitivity analysis showed that the parameters Fertilizer rate, Fertilizer organic, Canopy cover and Fertilizer inorganic were more sensitive to TN output. In terms of TP, the parameters Residue mass ratio, Fertilizer rate, Fertilizer inorganic and Canopy cover were the most sensitive. Based on these sensitive parameters, calibration was performed. TN loading produced satisfactory results for both the calibration and validation processes, whereas the performance of TP loading was slightly poor. The simulation results showed that AnnAGNPS has the potential to be used as a valuable tool for the planning and management of watersheds.

  1. Narratifs Médiatiques sur la relation entre le corps et les jeux olympiques dans les années 1890 et 1900

    Directory of Open Access Journals (Sweden)

    Fausto Amaro

    2016-09-01

    Full Text Available Les jeux olympiques se présentées devenus l’habitant de Rio de Janeiro des années 1890 et 1900 comme quelque chose au-delà du sport. Cirque, théâtre et cinéma étaient espaces culturels où ces événements pourraient être assistés, comme les journaux annonçaient. Les Jeux Olympiques du Comité International Olympique ont également été publiés, mais rivalisaient à l’attention du lecteur de Rio avec des jeux non officiels, tels que ceux qui sont détenus à Montevideo en l’année 1907. Compris ce contexte, je présente dans cet article quelques questions concernant le corps idéal dans cette période et son dialogue avec la nouveauté représenté par les jeux olympiques.

  2. Predicting fuelwood prices in Greece with the use of ARIMA models, artificial neural networks and a hybrid ARIMA-ANN model

    International Nuclear Information System (INIS)

    Koutroumanidis, Theodoros; Ioannou, Konstantinos; Arabatzis, Garyfallos

    2009-01-01

    Throughout history, energy resources have acquired a strategic significance for the economic growth and social welfare of any country. The large-scale oil crisis of 1973 coupled with various environmental protection issues, have led many countries to look for new, alternative energy sources. Biomass and fuelwood in particular, constitutes a major renewable energy source (RES) that can make a significant contribution, as a substitute for oil. This paper initially provides a description of the contribution of renewable energy sources to the production of electricity, and also examines the role of forests in the production of fuelwood in Greece. Following this, autoregressive integrated moving average (ARIMA) models, artificial neural networks (ANN) and a hybrid model are used to predict the future selling prices of the fuelwood (from broadleaved and coniferous species) produced by Greek state forest farms. The use of the ARIMA-ANN hybrid model provided the optimum prediction results, thus enabling decision-makers to proceed with a more rational planning for the production and fuelwood market. (author)

  3. ANN Synthesis Model of Single-Feed Corner-Truncated Circularly Polarized Microstrip Antenna with an Air Gap for Wideband Applications

    Directory of Open Access Journals (Sweden)

    Zhongbao Wang

    2014-01-01

    Full Text Available A computer-aided design model based on the artificial neural network (ANN is proposed to directly obtain patch physical dimensions of the single-feed corner-truncated circularly polarized microstrip antenna (CPMA with an air gap for wideband applications. To take account of the effect of the air gap, an equivalent relative permittivity is introduced and adopted to calculate the resonant frequency and Q-factor of square microstrip antennas for obtaining the training data sets. ANN architectures using multilayered perceptrons (MLPs and radial basis function networks (RBFNs are compared. Also, six learning algorithms are used to train the MLPs for comparison. It is found that MLPs trained with the Levenberg-Marquardt (LM algorithm are better than RBFNs for the synthesis of the CPMA. An accurate model is achieved by using an MLP with three hidden layers. The model is validated by the electromagnetic simulation and measurements. It is enormously useful to antenna engineers for facilitating the design of the single-feed CPMA with an air gap.

  4. A TLBO based gradient descent learning-functional link higher order ANN: An efficient model for learning from non-linear data

    Directory of Open Access Journals (Sweden)

    Bighnaraj Naik

    2018-01-01

    Full Text Available All the higher order ANNs (HONNs including functional link ANN (FLANN are sensitive to random initialization of weight and rely on the learning algorithms adopted. Although a selection of efficient learning algorithms for HONNs helps to improve the performance, on the other hand, initialization of weights with optimized weights rather than random weights also play important roles on its efficiency. In this paper, the problem solving approach of the teaching learning based optimization (TLBO along with learning ability of the gradient descent learning (GDL is used to obtain the optimal set of weight of FLANN learning model. TLBO does not require any specific parameters rather it requires only some of the common independent parameters like number of populations, number of iterations and stopping criteria, thereby eliminating the intricacy in selection of algorithmic parameters for adjusting the set of weights of FLANN model. The proposed TLBO-FLANN is implemented in MATLAB and compared with GA-FLANN, PSO-FLANN and HS-FLANN. The TLBO-FLANN is tested on various 5-fold cross validated benchmark data sets from UCI machine learning repository and analyzed under the null-hypothesis by using Friedman test, Holm’s procedure and post hoc ANOVA statistical analysis (Tukey test & Dunnett test.

  5. Le nationalisme métis des années 1970 au Canada : un tournant politique majeur pour une plus grande reconnaissance

    Directory of Open Access Journals (Sweden)

    Nathalie Kermoal

    2013-01-01

    Full Text Available En 1969, le gouvernement Trudeau publie son Livre blanc sur la politique indienne. Cet épisode marque le début d’un mouvement de mobilisation autochtone important qui s’organise autour du livre d’Harold Cardinal, The Unjust Society. Cet ouvrage a cependant éclipsé un autre discours nationaliste autochtone montant, celui des Métis, donnant la fausse impression qu’ils partageaient les mêmes préoccupations que les Premières nations. Le but de notre article est avant tout de mettre au jour le discours nationaliste métis de la fin des années 1960 et du début des années 1970 puisqu’il a été supplanté par le discours des Premières nations, mais aussi de démontrer en quoi il se distingue de ce dernier et où il se situe face à la question nationale canadienne.

  6. Support vector machine regression (LS-SVM)--an alternative to artificial neural networks (ANNs) for the analysis of quantum chemistry data?

    Science.gov (United States)

    Balabin, Roman M; Lomakina, Ekaterina I

    2011-06-28

    A multilayer feed-forward artificial neural network (MLP-ANN) with a single, hidden layer that contains a finite number of neurons can be regarded as a universal non-linear approximator. Today, the ANN method and linear regression (MLR) model are widely used for quantum chemistry (QC) data analysis (e.g., thermochemistry) to improve their accuracy (e.g., Gaussian G2-G4, B3LYP/B3-LYP, X1, or W1 theoretical methods). In this study, an alternative approach based on support vector machines (SVMs) is used, the least squares support vector machine (LS-SVM) regression. It has been applied to ab initio (first principle) and density functional theory (DFT) quantum chemistry data. So, QC + SVM methodology is an alternative to QC + ANN one. The task of the study was to estimate the Møller-Plesset (MPn) or DFT (B3LYP, BLYP, BMK) energies calculated with large basis sets (e.g., 6-311G(3df,3pd)) using smaller ones (6-311G, 6-311G*, 6-311G**) plus molecular descriptors. A molecular set (BRM-208) containing a total of 208 organic molecules was constructed and used for the LS-SVM training, cross-validation, and testing. MP2, MP3, MP4(DQ), MP4(SDQ), and MP4/MP4(SDTQ) ab initio methods were tested. Hartree-Fock (HF/SCF) results were also reported for comparison. Furthermore, constitutional (CD: total number of atoms and mole fractions of different atoms) and quantum-chemical (QD: HOMO-LUMO gap, dipole moment, average polarizability, and quadrupole moment) molecular descriptors were used for the building of the LS-SVM calibration model. Prediction accuracies (MADs) of 1.62 ± 0.51 and 0.85 ± 0.24 kcal mol(-1) (1 kcal mol(-1) = 4.184 kJ mol(-1)) were reached for SVM-based approximations of ab initio and DFT energies, respectively. The LS-SVM model was more accurate than the MLR model. A comparison with the artificial neural network approach shows that the accuracy of the LS-SVM method is similar to the accuracy of ANN. The extrapolation and interpolation results show that LS-SVM is

  7. Contribution des réseaux de neurones artificiels (RNA) à la caractérisation des pollutions de sol. Exemples des pollutions en hydrocarbures aromatiques polycycliques (HAP)Artificial Neural Networks (ANNs) characterisation of soil pollution: the Polycyclic Aromatic Hydrocarbons (PAHs) case study

    Science.gov (United States)

    Dan, Adrian; Oosterbaan, Jasha; Jamet, Philippe

    2002-10-01

    We develop the ANNs (Artificial Neural Networks) method to explore contaminant concentration profiles observed in soils of polluted sites. ANNs are particularly efficient in simultaneous analysis of numerous parameters and in identification of complex relations involving field data. Applying the ANN models on a PAH (Polycyclic Aromatic Hydrocarbon) database, we extracted the most characteristic components of known contaminations and applied it to identify the source type of similar polluted sites. The performed tests prove the generalisation capability of the selected ANN model. To cite this article: A. Dan et al., C. R. Geoscience 334 (2002) 957-965.

  8. Determination of zinc oxide content of mineral medicine calamine using near-infrared spectroscopy based on MIV and BP-ANN algorithm

    Science.gov (United States)

    Zhang, Xiaodong; Chen, Long; Sun, Yangbo; Bai, Yu; Huang, Bisheng; Chen, Keli

    2018-03-01

    Near-infrared (NIR) spectroscopy has been widely used in the analysis fields of traditional Chinese medicine. It has the advantages of fast analysis, no damage to samples and no pollution. In this research, a fast quantitative model for zinc oxide (ZnO) content in mineral medicine calamine was explored based on NIR spectroscopy. NIR spectra of 57 batches of calamine samples were collected and the first derivative (FD) method was adopted for conducting spectral pretreatment. The content of ZnO in calamine sample was determined using ethylenediaminetetraacetic acid (EDTA) titration and taken as reference value of NIR spectroscopy. 57 batches of calamine samples were categorized into calibration and prediction set using the Kennard-Stone (K-S) algorithm. Firstly, in the calibration set, to calculate the correlation coefficient (r) between the absorbance value and the ZnO content of corresponding samples at each wave number. Next, according to the square correlation coefficient (r2) value to obtain the top 50 wave numbers to compose the characteristic spectral bands (4081.8-4096.3, 4188.9-4274.7, 4335.4, 4763.6,4794.4-4802.1, 4809.9, 4817.6-4875.4 cm- 1), which were used to establish the quantitative model of ZnO content using back propagation artificial neural network (BP-ANN) algorithm. Then, the 50 wave numbers were operated by the mean impact value (MIV) algorithm to choose wave numbers whose absolute value of MIV greater than or equal to 25, to obtain the optimal characteristic spectral bands (4875.4-4836.9, 4223.6-4080.9 cm- 1). And then, both internal cross and external validation were used to screen the number of hidden layer nodes of BP-ANN. Finally, the number 4 of hidden layer nodes was chosen as the best. At last, the BP-ANN model was found to enjoy a high accuracy and strong forecasting capacity for analyzing ZnO content in calamine samples ranging within 42.05-69.98%, with relative mean square error of cross validation (RMSECV) of 1.66% and coefficient of

  9. Facteurs prédictifs de succès des étudiants en première année de médecine à l'université de Parakou

    Science.gov (United States)

    Adoukonou, Thierry; Tognon-Tchegnonsi, Francis; Mensah, Emile; Allodé, Alexandre; Adovoekpe, Jean-Marie; Gandaho, Prosper; Akpona, Simon

    2016-01-01

    Introduction Plusieurs facteurs dont les notes obtenues au BAC peuvent influencer les performances académiques des étudiants en première année de médecine. L'objectif de cette étude était d’évaluer la relation entre les résultats des étudiants au BAC et le succès en première année de médecine. Méthodes Nous avons réalisé une étude analytique ayant inclus l'ensemble des étudiants régulièrement inscrits en première année à la Faculté de Médecine de l'université de Parakou durant l'année académique 2010-2011. Les données concernant les notes par discipline et mention obtenue au BAC ont été collectées. Une analyse multivariée utilisant la régression logistique et la régression linéaire multiple a permis d’établir les meilleurs prédicteurs du succès et de la moyenne de l’étudiant en fin d'année. Le logiciel SPSS version 17.0 a été utilisé pour l'analyse des données et un p 15/20 était associée au succès (OR: 2,8 [1,32- 6,00]). Pour la moyenne générale obtenue en fin d'année seule une mention bien obtenue au BAC était associée (coefficient de l'erreur standard: 0,130 Bêta =0,370 et p=0,00001). Conclusion Les meilleurs prédicateurs du succès en première année étaient une bonne moyenne en sciences physiques au BAC et une mention bien. La prise en compte de ces éléments dans le recrutement des étudiants en première année pourrait améliorer les résultats académiques. PMID:27313819

  10. 24 années d’épidémie de sida dans l’archipel indonésien

    Directory of Open Access Journals (Sweden)

    Laurence Husson

    2012-10-01

    Full Text Available L’ONUSIDA dans son bilan 2007 estimait le nombre de personnes infectées par le VIH dans le monde à 33 millions, avec 2,7 millions de nouvelles contaminations et 2 millions de décès dans l’année. Si l’Afrique subsaharienne était de loin la région du monde la plus durement touchée avec 22 millions de personnes infectées, l’Asie du Sud et du Sud-Est était aussi très concernée avec 4,2 millions de personnes porteuses du virus. Au sein de cette Asie, l’Indonésie, avec 240 millions d’habitants en 2...

  11. Histoire des forêts du versant nord des Pyrénées au cours des 30000 dernières années

    OpenAIRE

    Jalut, Guy; Galop, Didier; Belet, Jean-Marc; Aubert, Sandrine; Esteban Amat, A.; Bouchette, A.; Dedoubat, Jean-Jacques; Fontugne, Michel

    1998-01-01

    Les recherches playnologiques réalisées dans le versant nord des Pyrénées ainsi que les données anthracologiques disponibles et les études de macrorestes végétaux permettent de décrire les étapes de la mise en place des principales essences forestières au cours des 30000 dernières années. Dans les Pyrénées centrales, avant 30000 BP, l'épicéa était bien développé. Les données anthracologiques permettent de penser qu'il a disparu des Pyrénées vers 18000-17000 BP. Entre 11000 BP et 10000 BP, l'u...

  12. ANFIS, SVM and ANN soft-computing techniques to estimate daily global solar radiation in a warm sub-humid environment

    Science.gov (United States)

    Quej, Victor H.; Almorox, Javier; Arnaldo, Javier A.; Saito, Laurel

    2017-03-01

    Daily solar radiation is an important variable in many models. In this paper, the accuracy and performance of three soft computing techniques (i.e., adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and support vector machine (SVM) were assessed for predicting daily horizontal global solar radiation from measured meteorological variables in the Yucatán Peninsula, México. Model performance was assessed with statistical indicators such as root mean squared error (RMSE), mean absolute error (MAE) and coefficient of determination (R2). The performance assessment indicates that the SVM technique with requirements of daily maximum and minimum air temperature, extraterrestrial solar radiation and rainfall has better performance than the other techniques and may be a promising alternative to the usual approaches for predicting solar radiation.

  13. Gaydar, Marriage, and Rip-Roaring Homosexuals: Discourses About Homosexuality in Dear Abby and Ann Landers Advice Columns, 1967-1982.

    Science.gov (United States)

    Johnson, Patrick M; Holmes, Kwame A

    2017-12-04

    Over the past 70 years, the history of acceptance of the lesbian, gay and bisexual (LGB) community within the United States has seen much change and fluctuation. One of the places that this dialogue has been preserved is through the syndicated advice columns of Dear Abby and Ann Landers, in which individuals in the United States were writing in for advice to deal with their anxiety over a newly emerging and highly visible new community of individuals once considered to be mentally ill and dangerous. Using discourse analysis, this article traces the evolution of public and scientific opinions about the LGBT community during the years leading up to the Stonewall riots all the way to right before the AIDs epidemic. This analysis sheds light on several moral panics that emerged regarding this newly visible population, especially in regard to disturbances within the domestic sphere and a stigmatization of bisexuality.

  14. Investigating the Effect of Sympathetic Skin Response Parameters on the Psychological Test Scores in Patients with Fibromyalgia Syndrome by Using ANNS

    Directory of Open Access Journals (Sweden)

    Murat Yıldız

    2013-01-01

    Full Text Available In this study, psychological tests such as Visual Analogue Pain Scale, Verbal Pain Scale, Beck Depression Inventory, Beck Anxiety Inventory, Hamilton Depression Rating Scale and Hamilton Anxiety Scale were applied to the selected healthy subjects and patients with Fibromyalgia Syndrome (FMS in Suleyman Demirel University, Faculty of Medicine, Department of Physical Medicine and Rehabilitation and the scores were recorded. A measurement system was established in the same department of the university to measure the sympathetic skin response (SSR from the subjects. The SSR was measured and recorded. The parameters such as latency time, maximum amplitude and the elapsed time were calculated by using Matlab software from the recorded SSR data. SSR parameters were added to the scores and diagnosis accuracy percentages of the FMS calculated by using artificial neural networks (ANNs. Obtained results from the simulations showed that the specified parameters of the SSR and FMS were concerned and these parameters can be used as a diagnostic method in FMS.

  15. Les mobilisations sociales dans les territoires périphériques de Casablanca pendant les années 1990

    OpenAIRE

    Belarbi, Wafae

    2015-01-01

    Cette contribution s’interroge sur les formes, les visées et les modes organisationnels des mobilisations des habitants dans la périphérie sud de Casablanca pendant les années 1990. Ensemble de territoires d’habitat non réglementaire fragmentés, de formation urbaine récente, cette périphérie sud concentre une population reléguée socialement, privée d’influence et de relais avec les centres de pouvoirs métropolitains, qui a développé ses propres compétences d’intégration urbaine. Les mobilisat...

  16. Cours interactif et performance académique d’étudiants de première année universitaire en économie

    OpenAIRE

    de Crombrugghe, Alain; Romainville, Marc

    2015-01-01

    L’article analyse la valeur ajoutée d’un enseignement interactif avec suivi régulier, par rapport à un enseignement classique combinant cours magistral en amphithéâtre et travaux dirigés, pour un cours d’introduction à l’économie destiné à des étudiants de première année dans le cadre d’une expérimentation réalisée à l’Université de Namur (Belgique). L’évaluation est menée sur la base, d’une part, d’une mesure des aptitudes initiales des étudiants et, d’autre part, de leurs performances aux e...

  17. L’enseignement-apprentissage de la lecture en classe de FLE le cas d’une classe de 3ème année primaire.

    OpenAIRE

    BENCHOHRA, Khadda

    2016-01-01

    L’enseignement / apprentissage de la lecture en Français est un domaine très important de la didactique de l’écrit. L’acquisition de cette compétence est primordiale voire capitale, elle l’est encore plus lorsqu’elle est engagée avec de jeunes apprenants, en premier contact scolaire avec la langue. Notre recherche porte sur la découverte des méthodes et moyens contribuant à l’installation de cette compétence, chez les apprenants en première année de Français. Il s’agit de re...

  18. 3D fluid-structure modelling and vibration analysis for fault diagnosis of Francis turbine using multiple ANN and multiple ANFIS

    Science.gov (United States)

    Saeed, R. A.; Galybin, A. N.; Popov, V.

    2013-01-01

    This paper discusses condition monitoring and fault diagnosis in Francis turbine based on integration of numerical modelling with several different artificial intelligence (AI) techniques. In this study, a numerical approach for fluid-structure (turbine runner) analysis is presented. The results of numerical analysis provide frequency response functions (FRFs) data sets along x-, y- and z-directions under different operating load and different position and size of faults in the structure. To extract features and reduce the dimensionality of the obtained FRF data, the principal component analysis (PCA) has been applied. Subsequently, the extracted features are formulated and fed into multiple artificial neural networks (ANN) and multiple adaptive neuro-fuzzy inference systems (ANFIS) in order to identify the size and position of the damage in the runner and estimate the turbine operating conditions. The results demonstrated the effectiveness of this approach and provide satisfactory accuracy even when the input data are corrupted with certain level of noise.

  19. Examination and comparative study of the Ascension of The Prophet of Islam In The View Of Michael Sells And Anne-mari e Shamil with Inter- Religious Attitude

    Directory of Open Access Journals (Sweden)

    Mahdi Azadi

    2015-09-01

    Full Text Available Michael Sells, American scholar of Quran,about the Ascension (Mi,raj of the prophet (PBUH focuses on three issues: First, the Mi,raj term is not used in the Quran and the ascension of the explanation is not enough. second, Mohammad is no different from the miracle of the Quran is the miracle of God,he is not anything else, Quran states. Third, Ascension of the prophet (PBUH has been in sleep and dream.According to him, the discussion about the layers of the subject is based mainly on the evidence of Quran.In this case, only limited information can be found in the Asra chapter (sooreh of the Quran.In addition, he has tried to make the Ascension event from Jewish traditions and the effects Bvdaysm.actually the orientalist`s goal is to prove the absence of ascension of the prophet(PBUH.  In contrast, Anne-marie Shamil stayes that prophet`s ascension derived from the first verse of Asra sura and believes that two processes(horizontal and verticalfor prophet happened.and unlike Michael,he knows mi,raj from the God miracles.Anne-Marie because her sufficient the Sunni sources is doubt with belief in the physical and spiritual ascension,in some of her votes,such as; visible or not visible in the ascension of God by the prophet.Despite the fundamental criticism that some elements of the theory of two Orientalists arrived,positive points are observed in their ideas.in this article we have tried to express the views of the Orientalists, then to review their ideas considered

  20. Artificial neural network (ANN) method for modeling of sunset yellow dye adsorption using zinc oxide nanorods loaded on activated carbon: Kinetic and isotherm study.

    Science.gov (United States)

    Maghsoudi, M; Ghaedi, M; Zinali, A; Ghaedi, A M; Habibi, M H

    2015-01-05

    In this research, ZnO nanoparticle loaded on activated carbon (ZnO-NPs-AC) was synthesized simply by a low cost and nontoxic procedure. The characterization and identification have been completed by different techniques such as SEM and XRD analysis. A three layer artificial neural network (ANN) model is applicable for accurate prediction of dye removal percentage from aqueous solution by ZnO-NRs-AC following conduction of 270 experimental data. The network was trained using the obtained experimental data at optimum pH with different ZnO-NRs-AC amount (0.005-0.015 g) and 5-40 mg/L of sunset yellow dye over contact time of 0.5-30 min. The ANN model was applied for prediction of the removal percentage of present systems with Levenberg-Marquardt algorithm (LMA), a linear transfer function (purelin) at output layer and a tangent sigmoid transfer function (tansig) in the hidden layer with 6 neurons. The minimum mean squared error (MSE) of 0.0008 and coefficient of determination (R(2)) of 0.998 were found for prediction and modeling of SY removal. The influence of parameters including adsorbent amount, initial dye concentration, pH and contact time on sunset yellow (SY) removal percentage were investigated and optimal experimental conditions were ascertained. Optimal conditions were set as follows: pH, 2.0; 10 min contact time; an adsorbent dose of 0.015 g. Equilibrium data fitted truly with the Langmuir model with maximum adsorption capacity of 142.85 mg/g for 0.005 g adsorbent. The adsorption of sunset yellow followed the pseudo-second-order rate equation. Copyright © 2014 Elsevier B.V. All rights reserved.