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Sample records for anne luik luule

  1. Load forecasting by ANN

    SciTech Connect

    Highley, D.D.; Hilmes, T.J. )

    1993-07-01

    This article discusses the use and training of artificial neural networks (ANNs) for load forecasting. The topics of the article include a brief overview of neural networks, interest in ANNs, training of neural networks, a case study in load forecasting, and the potential for using an artificial neural network to perform short-term load forecasting.

  2. Ann Wagner, Mechanical Engineer.

    ERIC Educational Resources Information Center

    Bennett, Betsy K.

    1996-01-01

    Presents a profile of Ann Wagner, a mechanical engineer at the Goddard Space Flight Center in Maryland, and her job responsibilities there. Also includes a brief history of mechanical engineering as well as a sample graph and data activity sheet with answers. (AIM)

  3. ANN - based distribution system reconfiguration

    SciTech Connect

    Momoh, J.A.; Wang, Yanchun; Rizy, D.T.

    1997-08-01

    This paper describes an Artificial Neural Network (ANN) - based distribution system reconfiguration scheme to reduce system loss. The ANN is trained for different load levels and different network topologies. The proposed scheme has been tested using a 38 - bus distribution system. The results are very promising.

  4. Carole Anne Klonarides: An Interview.

    ERIC Educational Resources Information Center

    Rapaport, Herman

    1995-01-01

    Presents an interview with videographer Carole Anne Klonarides that explores her manipulation of space, time, and visual texture, which often results in an altered sense of history. Notes how her goal to create believable sequences together with her desire to move beyond the stereotypical uses and formats of contemporary television show the…

  5. DOLLY ANN ROADLESS AREA, VIRGINIA.

    USGS Publications Warehouse

    Lesure, Frank G.; Jones, Jay G.

    1984-01-01

    Mineral-resource surveys indicate that much of the Dolly Ann Roadless Area, in the George Washington National Forest, Alleghany County, Virginia, has substantiated iron resource potential. Inferred low-grade iron resources occur in folded sedimentary rocks of Paleozoic age. The area has an estimated 540 million long tons of contained iron in hematitic sandstone and 700,000 long tons contained iron in deposits of sandy Limonite. Other mineral resources include various rocks suitable for crushed rock, quartzite suitable for high-silica uses, limestone suitable for agricultural uses, and clay and shale suitable for structural clay products, all of which can be readily obtained outside the wilderness. A potential for natural gas and geothermal energy may exist but cannot be quantified from present knowledge.

  6. Phosphorus component in AnnAGNPS

    USGS Publications Warehouse

    Yuan, Y.; Bingner, R.L.; Theurer, F.D.; Rebich, R.A.; Moore, P.A.

    2005-01-01

    The USDA Annualized Agricultural Non-Point Source Pollution model (AnnAGNPS) has been developed to aid in evaluation of watershed response to agricultural management practices. Previous studies have demonstrated the capability of the model to simulate runoff and sediment, but not phosphorus (P). The main purpose of this article is to evaluate the performance of AnnAGNPS on P simulation using comparisons with measurements from the Deep Hollow watershed of the Mississippi Delta Management Systems Evaluation Area (MDMSEA) project. A sensitivity analysis was performed to identify input parameters whose impact is the greatest on P yields. Sensitivity analysis results indicate that the most sensitive variables of those selected are initial soil P contents, P application rate, and plant P uptake. AnnAGNPS simulations of dissolved P yield do not agree well with observed dissolved P yield (Nash-Sutcliffe coefficient of efficiency of 0.34, R2 of 0.51, and slope of 0.24); however, AnnAGNPS simulations of total P yield agree well with observed total P yield (Nash-Sutcliffe coefficient of efficiency of 0.85, R2 of 0.88, and slope of 0.83). The difference in dissolved P yield may be attributed to limitations in model simulation of P processes. Uncertainties in input parameter selections also affect the model's performance.

  7. Katherine Anne Porter on Her Contemporaries.

    ERIC Educational Resources Information Center

    Bridges, Phyllis

    Personal experiences with and critical judgments of leading artists and intellectuals of the twentieth century are recorded in Katherine Anne Porter's essays, letters and conversations which provide snapshots of her attitudes and encounters. Porter's commentaries about such contemporaries as Ernest Hemingway, William Faulkner, Saul Bellow,…

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

    SciTech Connect

    Not Available

    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.

  9. Obituary: Anne Barbara Underhill, 1920-2003

    NASA Astrophysics Data System (ADS)

    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

  10. Obituary: Anne Barbara Underhill, 1920-2003

    NASA Astrophysics Data System (ADS)

    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

  11. Wind Power Forecasting techniques in complex terrain: ANN vs. ANN-CFD hybrid approach

    NASA Astrophysics Data System (ADS)

    Castellani, Francesco; Astolfi, Davide; Mana, Matteo; Burlando, Massimiliano; Meißner, Cathérine; Piccioni, Emanuele

    2016-09-01

    Due to technology developments, renewable energies are becoming competitive against fossil sources and the number of wind farms is growing, which have to be integrated into power grids. Therefore, accurate power forecast is needed and often operators are charged with penalties in case of imbalance. Yet, wind is a stochastic and very local phenomenon, and therefore hard to predict. It has a high variability in space and time and wind power forecast is challenging. Statistical methods, as Artificial Neural Networks (ANN), are often employed for power forecasting, but they have some shortcomings: they require data sets over several years and are not able to capture tails of wind power distributions. In this work a pure ANN power forecast is compared against a hybrid method, based on the combination of ANN and a physical method using computational fluid dynamics (CFD). The validation case is a wind farm sited in southern Italy in a very complex terrain, with a wide spread turbine layout.

  12. An Overview of ANN Application in the Power Industry

    NASA Technical Reports Server (NTRS)

    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.

  13. Ann Eliza Young: A Nineteenth Century Champion of Women's Rights.

    ERIC Educational Resources Information Center

    Cullen, Jack B.

    Concentrating on the efforts of such nineteenth century women's rights advocates as Susan B. Anthony and Elizabeth Cady Stanton, communication researchers have largely overlooked the contributions made to the cause by Ann Eliza Young. The nineteenth wife of Mormon leader Brigham Young, Ann Eliza Young left her husband and took to the speaker's…

  14. 77 FR 75629 - Pramaggiore, Anne R.; Notice of Filing

    Federal Register 2010, 2011, 2012, 2013, 2014

    2012-12-21

    ... From the Federal Register Online via the Government Publishing Office DEPARTMENT OF ENERGY Federal Energy Regulatory Commission Pramaggiore, Anne R.; Notice of Filing Take notice that on December 14, 2012, Anne R. Pramaggiore submitted for filing, an application for authority to hold interlocking...

  15. An ANN That Applies Pragmatic Decision on Texts.

    ERIC Educational Resources Information Center

    Aretoulaki, Maria; Tsujii, Jun-ichi

    A computer-based artificial neural network (ANN) that learns to classify sentences in a text as important or unimportant is described. The program is designed to select the sentences that are important enough to be included in composition of an abstract of the text. The ANN is embedded in a conventional symbolic environment consisting of…

  16. Anne Frank: One of Hundreds of Thousands. [Lesson Plan].

    ERIC Educational Resources Information Center

    2002

    Anne Frank was one of the hundreds of thousands of Jewish children who died in the Holocaust. In that sense, she is not unique; however, through the very ordinary act of writing a diary, through her youthful wisdom and budding literary talent, Anne remains today an extraordinary "symbol of the lost promise of the children who died in the…

  17. Bedrock geology of the Cape Ann Area, Massachusetts. Technical report

    SciTech Connect

    Not Available

    1981-09-01

    Cape Ann on the Massachusetts eastern shore is dominated by igneous rocks, intruded into an igneous and metamorphic complex all cut by numerous faults. Geophysical investigations include total intensity aeromagnetic and gravity and magnetic studies. This report addresses structural features, stratigraphy, economic and environmental geology at the bedrock geology of the Cape Ann area.

  18. Applications of artificial neural networks (ANNs) in food science.

    PubMed

    Huang, Yiqun; Kangas, Lars J; Rasco, Barbara A

    2007-01-01

    Artificial neural networks (ANNs) have been applied in almost every aspect of food science over the past two decades, although most applications are in the development stage. ANNs are useful tools for food safety and quality analyses, which include modeling of microbial growth and from this predicting food safety, interpreting spectroscopic data, and predicting physical, chemical, functional and sensory properties of various food products during processing and distribution. ANNs hold a great deal of promise for modeling complex tasks in process control and simulation and in applications of machine perception including machine vision and electronic nose for food safety and quality control. This review discusses the basic theory of the ANN technology and its applications in food science, providing food scientists and the research community an overview of the current research and future trend of the applications of ANN technology in the field.

  19. Applications of Artificial Neural Networks (ANNs) in Food Science

    SciTech Connect

    HUang, Yiqun; Kangas, Lars J.; Rasco, Barbara A.

    2007-02-01

    Abstract Artificial neural networks (ANNs) have been applied in almost every aspect of food science over the past two decade, although most applications are in the development stage. ANNs are useful tools for food safety and quality analyses, which include modeling of microbial growth and from this predicting food safety, interpreting spectroscopic data, and predicting physical, chemical, functional and sensory properties of various food products during processing and distribution. ANNs have a great deal of promise for modeling complex tasks in process control and simulation, and in applications of machine perception including machine vision and the electronic nose for food safety and quality control. This review discusses the basic theory of the ANN technology and its applications in food science, providing food scientists and the research community an overview of the current research and future trend of the applications of ANN technology in this field.

  20. Application of artificial neural networks (ANNs) in wine technology.

    PubMed

    Baykal, Halil; Yildirim, Hatice Kalkan

    2013-01-01

    In recent years, neural networks have turned out as a powerful method for numerous practical applications in a wide variety of disciplines. In more practical terms neural networks are one of nonlinear statistical data modeling tools. They can be used to model complex relationships between inputs and outputs or to find patterns in data. In food technology artificial neural networks (ANNs) are useful for food safety and quality analyses, predicting chemical, functional and sensory properties of various food products during processing and distribution. In wine technology, ANNs have been used for classification and for predicting wine process conditions. This review discusses the basic ANNs technology and its possible applications in wine technology.

  1. Short-term load forecasting with local ANN predictors

    SciTech Connect

    Drezga, I.; Rahman, S.

    1999-08-01

    A new technique for artificial neural network (ANN) based short-term load forecasting (STLF) is present in this paper. The technique implemented active selection of training data, employing the k-nearest neighbors concept. A novel concept of pilot simulation was used to determine the number of hidden units for the ANNs. The ensemble of local ANN predictors was used to produce the final forecast, whereby the iterative forecasting procedure used a simple average of ensemble ANNs. Results obtained using data from two US utilities showed forecasting accuracy comparable to those using similar techniques. Excellent forecasts for one-hour-ahead and five-days-ahead forecasting, robust behavior for sudden and large weather changes, low maximum errors and accurate peak-load predictions are some of the findings discussed in the paper.

  2. BIG PATUXENT RIVER BRIDGE. ARUNDEL, ANNE ARUNDEL CO., MD. Sec. ...

    Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

    BIG PATUXENT RIVER BRIDGE. ARUNDEL, ANNE ARUNDEL CO., MD. Sec. 1201, MP 115.61. - Northeast Railroad Corridor, Amtrak route between District of Columbia/Maryland state line & Maryland/Delaware state line, Baltimore, Independent City, MD

  3. SEVERN RUN CULVERT. MAYFIELD, ANNE ARUNDEL CO., MD Sec. 1201, ...

    Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

    SEVERN RUN CULVERT. MAYFIELD, ANNE ARUNDEL CO., MD Sec. 1201, MP 112.17. - Northeast Railroad Corridor, Amtrak route between District of Columbia/Maryland state line & Maryland/Delaware state line, Baltimore, Independent City, MD

  4. Risk Quantification for ANN Based Short-Term Load Forecasting

    NASA Astrophysics Data System (ADS)

    Iwashita, Daisuke; Mori, Hiroyuki

    A new risk assessment method for short-term load forecasting is proposed. The proposed method makes use of an Artificial Neural Network (ANN) to forecast one-step ahead daily maximum loads and evaluate uncertainty of in load forecasting. As ANN the model, the Radial Basis Function (RBF) network is employed to forecast loads due to the good performance. Sufficient realistic pseudo-scenarios are required to carry out quantitative risk analysis. The multivariate normal distribution with the correlation between input variables is used to give more realistic results to ANN. In addition, the method of Moment Matching is used to improve the accuracy of the multivariate normal distribution. The Peak Over Threshold (POT) approach is used to evaluate risk that exceeds the upper bounds of generation capacity. The proposed method is successfully applied to real data of daily maximum load forecasting.

  5. Artificial neural networks (ANNs) and modeling of powder flow.

    PubMed

    Kachrimanis, K; Karamyan, V; Malamataris, S

    2003-01-01

    Effects of micromeritic properties (bulk, tapped and particle density, particle size and shape) on the flow rate through circular orifices are investigated, for three pharmaceutical excipients (Lactose, Emcompress and Starch) separated in four sieve fractions, and are modeled with the help of artificial neural networks (ANNs). Eight variables were selected as inputs and correlated by applying the Spearman product-moment correlation matrix and the visual component planes of trained Self-Organizing Maps (SOMs). Back-propagation feed-forward ANN with six hidden units in a single hidden layer was selected for modeling experimental data and its predictions were compared with those of the flow equation proposed by. It was found that SOMs are efficient for the identification of co-linearity in the input variables and the ANN is superior to the flow equation since it does not require separate regression for each excipient and its predictive ability is higher. Besides the orifice diameter, most influential and important variable was the difference between tapped and bulk density. From the pruned ANN an approximate non-linear model was extracted, which describes powder flow rate in terms of the four network's input variables of the greatest predictive importance or saliency (difference between tapped and bulk density (x(2)), orifice diameter (x(3)), circle equivalent particle diameter (x(4)) and particle density [equation in text].

  6. Prediction aluminum corrosion inhibitor efficiency using artificial neural network (ANN)

    NASA Astrophysics Data System (ADS)

    Ebrahimi, Sh; Kalhor, E. G.; Nabavi, S. R.; Alamiparvin, L.; Pogaku, R.

    2016-06-01

    In this study, activity of some Schiff bases as aluminum corrosion inhibitor was investigated using artificial neural network (ANN). Hence, corrosion inhibition efficiency of Schiff bases (in any type) were gathered from different references. Then these molecules were drawn and optimized in Hyperchem software. Molecular descriptors generating and descriptors selection were fulfilled by Dragon software and principal component analysis (PCA) method, respectively. These structural descriptors along with environmental descriptors (ambient temperature, time of exposure, pH and the concentration of inhibitor) were used as input variables. Furthermore, aluminum corrosion inhibition efficiency was used as output variable. Experimental data were split into three sets: training set (for model building) and test set (for model validation) and simulation (for general model). Modeling was performed by Multiple linear regression (MLR) methods and artificial neural network (ANN). The results obtained in linear models showed poor correlation between experimental and theoretical data. However nonlinear model presented satisfactory results. Higher correlation coefficient of ANN (R > 0.9) revealed that ANN can be successfully applied for prediction of aluminum corrosion inhibitor efficiency of Schiff bases in different environmental conditions.

  7. Reactions to Ann Arbor: Vernacular Black English and Education.

    ERIC Educational Resources Information Center

    Whiteman, Marcia Farr, Ed.

    The papers in this collection provide a brief state-of-the-art statement on the role of non-standard dialects of English in education and on some implications of the Ann Arbor decision. The following papers are included: (1) "Vernacular Black English: Setting the Issues in Time," by Roger W. Shuy; (2) "Beyond Black English: Implications of the Ann…

  8. Decolonizing the Choctaws: Teaching LeAnne Howe's "Shell Shaker"

    ERIC Educational Resources Information Center

    Hollrah, Patrice

    2004-01-01

    "Shell Shaker" (2001) by LeAnne Howe (Choctaw) is a novel that gives students an opportunity to learn that the history and culture of the Choctaw Nation of Oklahoma are alive today. Winner of the Before Columbus Foundation American Book Award in 2002, the novel deals with two parallel stories that converge in the present, one about the eighteenth…

  9. Anne S. Young: Professor and Variable Star Observer Extraordinaire

    NASA Astrophysics Data System (ADS)

    Bracher, Katherine

    2011-05-01

    Anne Sewell Young (1871-1961) was one of the eight original members of the AAVSO, to which she contributed more than 6500 observations over 33 years. She also taught astronomy for 37 years at Mount Holyoke College; among her students was Helen Sawyer Hogg. This paper will look at her life and career both at Mount Holyoke and with the AAVSO.

  10. Research Review: Ann Markusen's Concept of Artistic Dividend

    ERIC Educational Resources Information Center

    Hornbacher, Judy, Ed.

    2008-01-01

    This issue of the "Research Review" features the work of Ann Markusen, Director of the Humphrey Institute's Project on Regional and Industrial Economics at the University of Minnesota. Through the six articles reviewed here, Markusen develops the concept of "The Artistic Dividend," examines the impact of Artists' Centers, explores how artists earn…

  11. An Interview with Dr. Anne LaBastille.

    ERIC Educational Resources Information Center

    Griffin, Elizabeth

    1982-01-01

    Anne LaBastille, a role model for women interested in exploring the wilderness, gives hints on lessening the effects of acid rain, tells outdoor educators to encourage women to explore the wilderness and to take children outdoors to experience nature, and predicts a future economic slump for outdoor education. (LC)

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

    NASA Astrophysics Data System (ADS)

    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.

  13. 75 FR 418 - Certificate of Alternative Compliance for the Offshore Supply Vessel KELLY ANN CANDIES

    Federal Register 2010, 2011, 2012, 2013, 2014

    2010-01-05

    ... SECURITY Coast Guard Certificate of Alternative Compliance for the Offshore Supply Vessel KELLY ANN CANDIES... Alternative Compliance was issued for the offshore supply vessel KELLY ANN CANDIES as required by 33 U.S.C.... SUPPLEMENTARY INFORMATION: Background and Purpose The offshore supply vessel KELLY ANN CANDIES will be used...

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

    Cancer.gov

    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. |

  15. Anne S. Young: Professor and Variable Star Observer Extraordinaire

    NASA Astrophysics Data System (ADS)

    Bracher, K.

    2012-06-01

    One of the original eight members of the AAVSO, but not well known today, was Professor Anne Sewell Young of Mount Holyoke College. Miss Young taught there for thirty-seven years, and trained many women astronomers during the first third of the 20th century. This paper will attempt to present her life as an inspiring teacher, as well as a contributor of more than 6,500 variable star observations to the AAVSO.

  16. JoAnn Morgan looks at Newscapade exhibit.

    NASA Technical Reports Server (NTRS)

    2000-01-01

    JoAnn Morgan, associate director for Advanced Development and Shuttle Upgrades at KSC, studies posters of space-related news stories in the mobile exhibition called 'NewsCapade with Al Neuharth.' The exhibit started its cross-country tour in San Francisco in April. It is a traveling version of the Newseum in Arlington, Va. Morgan was among four speakers discussing 'Space, the Media and the Millennium' at a reception Jan. 24 kicking off the display at KSC.

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

    NASA Astrophysics Data System (ADS)

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

    2016-08-01

    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.

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

    NASA Astrophysics Data System (ADS)

    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.

  19. Coupling SWAT and ANN models for enhanced daily streamflow prediction

    NASA Astrophysics Data System (ADS)

    Noori, Navideh; Kalin, Latif

    2016-02-01

    To improve daily flow prediction in unmonitored watersheds a hybrid model was developed by combining a quasi-distributed watershed model and artificial neural network (ANN). Daily streamflow data from 29 nearby watersheds in and around the city of Atlanta, Southeastern United States, with leave-one-site-out jackknifing technique were used to build the flow predictive models during warm and cool seasons. Daily streamflow was first simulated with the Soil and Water Assessment Tool (SWAT) and then the SWAT simulated baseflow and stormflow were used as inputs to ANN. Out of the total 29 test watersheds, 62% and 83% of them had Nash-Sutcliffe values above 0.50 during the cool and warm seasons, respectively (considered good or better). As the percent forest cover or the size of test watershed increased, the performances of the models gradually decreased during both warm and cool seasons. This indicates that the developed models work better in urbanized watersheds. In addition, SWAT and SWAT Calibration Uncertainty Procedure (SWAT-CUP) program were run separately for each station to compare the flow prediction accuracy of the hybrid approach to SWAT. Only 31% of the sites during the calibration and 34% of validation runs had ENASH values ⩾0.50. This study showed that coupling ANN with semi-distributed models can lead to improved daily streamflow predictions in ungauged watersheds.

  20. Improved Artificial Neural Network-Pedotransfer Functions (ANN-PTFs) for Estimating Soil Hydraulic Parameters

    NASA Astrophysics Data System (ADS)

    Gautam, M. R.; Zhu, J.; Ye, M.; Meyer, P. D.; Hassan, A. E.

    2008-12-01

    ANN-PTFs have become popular means of mapping easily available soil data into hard-to-measure soil hydraulic parameters in the recent years. These parameters and their distributions are the indispensable inputs to subsurface flow and transport models which provide basis for environmental planning, management and decision making. While improved ANN prediction together with the preservation of probability distributions of hydraulic parameters in ANN training is important, ANN-PTFs have been typically found using conventional ANN training approach with the mean square error as an error function, which may not preserve the probability distribution of the parameters. Moreover, the conventional ANN training can itself introduce correlation among predicted parameters and could not preserve the actual correlation among the measured parameters. The present study describes approaches to deal with such shortcomings of conventional ANN- PTF training algorithms by using new types of error functions and presents a group of improved ANN-PTF models developed on the basis of the new approaches with different levels of data availability. In the study, the bootstrap method is used as part of ANN-PTF development for generating independent training and validation sets, and calculating uncertainty estimates of the ANN predictions. The results demonstrate the merit of the new approaches of the ANN training and the physical significance of various types of less costly soil data in the prediction of soil hydraulic parameters.

  1. Groundwater Pollution Source Identification using Linked ANN-Optimization Model

    NASA Astrophysics Data System (ADS)

    Ayaz, Md; Srivastava, Rajesh; Jain, Ashu

    2014-05-01

    Groundwater is the principal source of drinking water in several parts of the world. Contamination of groundwater has become a serious health and environmental problem today. Human activities including industrial and agricultural activities are generally responsible for this contamination. Identification of groundwater pollution source is a major step in groundwater pollution remediation. Complete knowledge of pollution source in terms of its source characteristics is essential to adopt an effective remediation strategy. Groundwater pollution source is said to be identified completely when the source characteristics - location, strength and release period - are known. Identification of unknown groundwater pollution source is an ill-posed inverse problem. It becomes more difficult for real field conditions, when the lag time between the first reading at observation well and the time at which the source becomes active is not known. We developed a linked ANN-Optimization model for complete identification of an unknown groundwater pollution source. The model comprises two parts- an optimization model and an ANN model. Decision variables of linked ANN-Optimization model contain source location and release period of pollution source. An objective function is formulated using the spatial and temporal data of observed and simulated concentrations, and then minimized to identify the pollution source parameters. In the formulation of the objective function, we require the lag time which is not known. An ANN model with one hidden layer is trained using Levenberg-Marquardt algorithm to find the lag time. Different combinations of source locations and release periods are used as inputs and lag time is obtained as the output. Performance of the proposed model is evaluated for two and three dimensional case with error-free and erroneous data. Erroneous data was generated by adding uniformly distributed random error (error level 0-10%) to the analytically computed concentration

  2. ANN expert system screening for illicit amphetamines using molecular descriptors

    NASA Astrophysics Data System (ADS)

    Gosav, S.; Praisler, M.; Dorohoi, D. O.

    2007-05-01

    The goal of this study was to develop and an artificial neural network (ANN) based on computed descriptors, which would be able to classify the molecular structures of potential illicit amphetamines and to derive their biological activity according to the similarity of their molecular structure with amphetamines of known toxicity. The system is necessary for testing new molecular structures for epidemiological, clinical, and forensic purposes. It was built using a database formed by 146 compounds representing drugs of abuse (mainly central stimulants, hallucinogens, sympathomimetic amines, narcotics and other potent analgesics), precursors, or derivatized counterparts. Their molecular structures were characterized by computing three types of descriptors: 38 constitutional descriptors (CDs), 69 topological descriptors (TDs) and 160 3D-MoRSE descriptors (3DDs). An ANN system was built for each category of variables. All three networks (CD-NN, TD-NN and 3DD-NN) were trained to distinguish between stimulant amphetamines, hallucinogenic amphetamines, and nonamphetamines. A selection of variables was performed when necessary. The efficiency with which each network identifies the class identity of an unknown sample was evaluated by calculating several figures of merit. The results of the comparative analysis are presented.

  3. Upper Wisconsinan submarine end moraines off Cape Ann, Massachusetts

    USGS Publications Warehouse

    Oldale, R.N.

    1985-01-01

    Seismic profiles across the southwest end of Jeffreys Ledge, a bathymetric high north of Cape Ann, Massachusetts, reveal two end moraines. The moraines overlie upper Wisconsinan glacialmarine silty clay and are composed mostly of subaqueous ice-contact deposits and outwash. They were formed below sea level in water depths of as much as 120 m during fluctuations of a calving ice front. The moraines are late Wisconsinan in age and were formed after the Cambridge readvance, about 14,000 yr B.P., and before the Kennebunk readvance, about 13,000 yr B.P. They represent fluctuations of the ice front during overall retreat of Laurentide ice from the Gulf of Maine and New England. ?? 1985.

  4. Predicting Dynamic Behavior of a Biological System Using ANNs

    NASA Astrophysics Data System (ADS)

    Osman, Mohd Haniff; Ibrahim, Ratnawati; Hashim, Ishak; Yeun, Liong Choong; Bakar, Azuraliza Abu; Hussein, Zeti Azura Mohamed

    2008-01-01

    In this paper, artificial neural networks (ANNs) are applied to predict protein concentrations of a biological system. The input data are generated from a nonlinear mathematical model of the protein concentration. The protein concentrations from CDC6 data with actual kinetic parameter are taken as the target output. The data are then trained using multilayer perceptron (MLP) neural network with a 6-6-6 configuration. The allocation of the data will be distributed into 3 categories that are 80% as training data, 10% as validation data, and 10% as test data. The learning rules used in this work to determine the best model are gradient descent, conjugate gradient, scaled conjugate gradient. It is found that the MLP with scaled conjugate gradient learning rule gives the best prediction rate.

  5. A drowned Holocene barrier spit off Cape Ann, Massachusetts

    USGS Publications Warehouse

    Oldale, Robert N.

    1985-01-01

    Seismic profiles and bathymetric contours reveal a drowned barrier spit on Jeffreys Ledge off Cape Ann, Massachusetts. Seaward-dipping internal reflectors indicate that a regressive barrier formed during the early Holocene low sea-level stillstand. Preservation of the barrier spit may have been favored by its large size (as much as 20 m thick), by an ample sediment supply from unconsolidated glacial drift, and by the subsequent rapid sea-level rise. The barrier spit is present in water depths of 50 to 70 m and indicates a low relative sea-level stand of −50 m. This value confirms the low relative sea-level stand of −47 m postulated by Oldale et al. (1983) for northeast Massachusetts and New Hampshire on the basis of the submerged delta of the Merrimack River, and it indicates that the barrier and delta were contemporaneous (Oldale et al., 1983).

  6. 33 CFR 80.120 - Cape Ann, MA to Marblehead Neck, MA.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 33 Navigation and Navigable Waters 1 2010-07-01 2010-07-01 false Cape Ann, MA to Marblehead Neck, MA. 80.120 Section 80.120 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY INTERNATIONAL NAVIGATION RULES COLREGS DEMARCATION LINES Atlantic Coast § 80.120 Cape Ann, MA...

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

    ERIC Educational Resources Information Center

    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…

  8. Anne Frank in Historical Perspective: A Teaching Guide for Secondary Schools.

    ERIC Educational Resources Information Center

    Grobman, Alex; Fishman, Joel

    This guide helps secondary students to understand "The Diary of Anne Frank" through a series of short essays, maps, and photographs. In view of new scholarship, the historical context in which Anne Frank wrote may be studied to improve the student's perspective of recent history and of the present. A drawing shows the hiding place in the home…

  9. 33 CFR 80.120 - Cape Ann, MA to Marblehead Neck, MA.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 33 Navigation and Navigable Waters 1 2011-07-01 2011-07-01 false Cape Ann, MA to Marblehead Neck, MA. 80.120 Section 80.120 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY INTERNATIONAL NAVIGATION RULES COLREGS DEMARCATION LINES Atlantic Coast § 80.120 Cape Ann, MA...

  10. 33 CFR 80.115 - Portland Head, ME to Cape Ann, MA.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... 33 Navigation and Navigable Waters 1 2013-07-01 2013-07-01 false Portland Head, ME to Cape Ann, MA... INTERNATIONAL NAVIGATION RULES COLREGS DEMARCATION LINES Atlantic Coast § 80.115 Portland Head, ME to Cape Ann... harbors, bays, and inlets on the east coast of Maine, New Hampshire, and Massachusetts from Portland...

  11. 78 FR 41993 - Ann Arbor Railroad, Inc.-Lease Exemption-Norfolk Southern Railway Company

    Federal Register 2010, 2011, 2012, 2013, 2014

    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...

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

    PubMed Central

    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

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

    PubMed Central

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

    2015-01-01

    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. PMID:25985163

  14. Predicting stream water quality using artificial neural networks (ANN)

    SciTech Connect

    Bowers, J.A.

    2000-05-17

    Predicting point and nonpoint source runoff of dissolved and suspended materials into their receiving streams is important to protecting water quality and traditionally has been modeled using deterministic or statistical methods. The purpose of this study was to predict water quality in small streams using an Artificial Neural Network (ANN). The selected input variables were local precipitation, stream flow rates and turbidity for the initial prediction of suspended solids in the stream. A single hidden-layer feedforward neural network using backpropagation learning algorithms was developed with a detailed analysis of model design of those factors affecting successful implementation of the model. All features of a feedforward neural model were investigated including training set creation, number and layers of neurons, neural activation functions, and backpropagation algorithms. Least-squares regression was used to compare model predictions with test data sets. Most of the model configurations offered excellent predictive capabilities. Using either the logistic or the hyperbolic tangent neural activation function did not significantly affect predicted results. This was also true for the two learning algorithms tested, the Levenberg-Marquardt and Polak-Ribiere conjugate-gradient descent methods. The most important step during model development and training was the representative selection of data records for training of the model.

  15. Daily reservoir inflow forecasting combining QPF into ANNs model

    NASA Astrophysics Data System (ADS)

    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.

  16. Optimization of Nd: YAG Laser Marking of Alumina Ceramic Using RSM And ANN

    SciTech Connect

    Peter, Josephine; Doloi, B.; Bhattacharyya, B.

    2011-01-17

    The present research papers deals with the artificial neural network (ANN) and the response surface methodology (RSM) based mathematical modeling and also an optimization analysis on marking characteristics on alumina ceramic. The experiments have been planned and carried out based on Design of Experiment (DOE). It also analyses the influence of the major laser marking process parameters and the optimal combination of laser marking process parametric setting has been obtained. The output of the RSM optimal data is validated through experimentation and ANN predictive model. A good agreement is observed between the results based on ANN predictive model and actual experimental observations.

  17. 4. JoAnn SieburgBaker, Photographer, September 1977. VIEW OF POWER BUILDING ...

    Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

    4. JoAnn Sieburg-Baker, Photographer, September 1977. VIEW OF POWER BUILDING (ELECTRICAL TRANSFORMER). - Salem Manufacturing Company, Arista Cotton Mill, Brookstown & Marshall Streets, Winston-Salem, Forsyth County, NC

  18. Flowering a Feminist Garden: The Writings and Poetry of Anne Spencer.

    ERIC Educational Resources Information Center

    Ford, Charita M.

    1988-01-01

    Reviews the life and poetry of Anne Spencer, one of the "lost" poets of the Harlem Renaissance. Discusses her varied and effective use of imagery and examines the Black and feminist perspectives of her work. (FMW)

  19. In Praise of Dissidence: Anne Lang-Etienne (1932-1991). Muriel Driver Memorial Lecture.

    ERIC Educational Resources Information Center

    Thibeault, Rachel

    2002-01-01

    Discusses the life and work of Anne Lang-Etienne, a dissident occupational therapist who moved and shaped a generation of Canadian therapists and spurred the profession to action for social change. Includes 18 references. (JOW)

  20. ANN hybrid model versus ARIMA and ARIMAX models of runoff coefficient

    NASA Astrophysics Data System (ADS)

    Pektaş, Ali Osman; Kerem Cigizoglu, H.

    2013-09-01

    In this study, monthly runoff coefficients of seven southern large basins are calculated and modeled to forecast a holdout dataset by using univariate autoregressive integrated moving average (ARIMA), multivariate ARIMA (ARIMAX), and Artificial neural network (ANN) models. The applied traditional model performances are found insufficient, since the characteristic behaviors of the time series of direct runoff coefficients are very complicated. Therefore, a new Hybrid approach is adopted by using time series decomposition procedure and ANN. ARIMA, ARIMAX, ANN, and Hybrid models are compared with each other. The results indicate that the new generated Hybrid approach can be generalized to boost the prediction capability of ANNs in complicated time series data. It is seen that the new model captures the physical behavior of the direct runoff coefficient time series. The semi-random spikes of the direct runoff coefficient series are approximated sufficiently.

  1. Prediction of transmembrane regions of beta-barrel proteins using ANN- and SVM-based methods.

    PubMed

    Natt, Navjyot K; Kaur, Harpreet; Raghava, G P S

    2004-07-01

    This article describes a method developed for predicting transmembrane beta-barrel regions in membrane proteins using machine learning techniques: artificial neural network (ANN) and support vector machine (SVM). The ANN used in this study is a feed-forward neural network with a standard back-propagation training algorithm. The accuracy of the ANN-based method improved significantly, from 70.4% to 80.5%, when evolutionary information was added to a single sequence as a multiple sequence alignment obtained from PSI-BLAST. We have also developed an SVM-based method using a primary sequence as input and achieved an accuracy of 77.4%. The SVM model was modified by adding 36 physicochemical parameters to the amino acid sequence information. Finally, ANN- and SVM-based methods were combined to utilize the full potential of both techniques. The accuracy and Matthews correlation coefficient (MCC) value of SVM, ANN, and combined method are 78.5%, 80.5%, and 81.8%, and 0.55, 0.63, and 0.64, respectively. These methods were trained and tested on a nonredundant data set of 16 proteins, and performance was evaluated using "leave one out cross-validation" (LOOCV). Based on this study, we have developed a Web server, TBBPred, for predicting transmembrane beta-barrel regions in proteins (available at http://www.imtech.res.in/raghava/tbbpred).

  2. Estimating Parameters of Delaminations in GRP Pipes Using Thermal NDE and ANN

    SciTech Connect

    Vijayaraghavan, G. K.; Ramachandran, K. P.; Muruganandam, A.; Govindarajan, L.; Majumder, M. C.

    2010-10-26

    Thermographic Non-Destructive Evaluation (TNDE) is one of the techniques that have been widely used over the decades to evaluate the integrity of structures. To meet the increased demand for robust and effective inspection in complex TNDE tasks, Artificial Neural Networks (ANNs) have been recently deployed in many problems. The aim of the paper is to adopt an inverse technique using ANNs in the field of TNDE to estimate various parameters of delamination in Glass Reinforced Polymer (GRP) pipes by supplying thermal contrast evolution data as input. A Radial Basis Network (RBN) is employed with 80 input and 3 output neurons. The estimation capability of the network was evaluated with the data obtained from numerical simulations. The overall absolute errors show that the estimation capability of ANN is good.

  3. ANN-Assisted Planning of Conjunctive Use of Canal and Ground Water in Canal Commands

    NASA Astrophysics Data System (ADS)

    Kashyap, Deepak; Ghosh, Susmita

    2010-05-01

    Presented herein is an algorithm for ANN-assisted planning of the optimal cropping pattern and the associated groundwater development in a canal command area, followed by an illustration. The planning in the illustration ensures the maximization of the cropped area subject to the constraint of limiting the maximum water table depth to an acceptable limit. A multilayer feed forward ANN model, relating the maximum water table depth to the crop areas, is trained using back-propagation learning algorithm. The data invoked for the training comprise an array of the cropping patterns, and the corresponding maximum water table depths. These data are generated through a pre-calibrated numerical model of groundwater flow. Subsequently, the trained ANN model is linked to a GA based optimizer for arriving at the optimal cropping pattern and the associated pumping pattern.

  4. Wavelet and ANN combination model for prediction of daily suspended sediment load in rivers.

    PubMed

    Rajaee, Taher

    2011-07-01

    In this research, a new wavelet artificial neural network (WANN) model was proposed for daily suspended sediment load (SSL) prediction in rivers. In the developed model, wavelet analysis was linked to an artificial neural network (ANN). For this purpose, daily observed time series of river discharge (Q) and SSL in Yadkin River at Yadkin College, NC station in the USA were decomposed to some sub-time series at different levels by wavelet analysis. Then, these sub-time series were imposed to the ANN technique for SSL time series modeling. To evaluate the model accuracy, the proposed model was compared with ANN, multi linear regression (MLR), and conventional sediment rating curve (SRC) models. The comparison of prediction accuracy of the models illustrated that the WANN was the most accurate model in SSL prediction. Results presented that the WANN model could satisfactorily simulate hysteresis phenomenon, acceptably estimate cumulative SSL, and reasonably predict high SSL values.

  5. Modeling and forecasting of KLCI weekly return using WT-ANN integrated model

    NASA Astrophysics Data System (ADS)

    Liew, Wei-Thong; Liong, Choong-Yeun; Hussain, Saiful Izzuan; Isa, Zaidi

    2013-04-01

    The forecasting of weekly return is one of the most challenging tasks in investment since the time series are volatile and non-stationary. In this study, an integrated model of wavelet transform and artificial neural network, WT-ANN is studied for modeling and forecasting of KLCI weekly return. First, the WT is applied to decompose the weekly return time series in order to eliminate noise. Then, a mathematical model of the time series is constructed using the ANN. The performance of the suggested model will be evaluated by root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE). The result shows that the WT-ANN model can be considered as a feasible and powerful model for time series modeling and prediction.

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

    PubMed Central

    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

  7. Multi-output ANN Model for Prediction of Seven Meteorological Parameters in a Weather Station

    NASA Astrophysics Data System (ADS)

    Raza, Khalid; Jothiprakash, V.

    2014-12-01

    The meteorological parameters plays a vital role for determining various water demand in the water resource systems, planning, management and operation. Thus, accurate prediction of meteorological variables at different spatial and temporal intervals is the key requirement. Artificial Neural Network (ANN) is one of the most widely used data driven modelling techniques with lots of good features like, easy applications, high accuracy in prediction and to predict the multi-output complex non-linear relationships. In this paper, a Multi-input Multi-output (MIMO) ANN model has been developed and applied to predict seven important meteorological parameters, such as maximum temperature, minimum temperature, relative humidity, wind speed, sunshine hours, dew point temperature and evaporation concurrently. Several types of ANN, such as multilayer perceptron, generalized feedforward neural network, radial basis function and recurrent neural network with multi hidden layer and varying number of neurons at the hidden layer, has been developed, trained, validated and tested. From the results, it is found that the recurrent MIMO-ANN having 28 neurons in a single hidden layer, trained using hyperbolic tangent transfer function with a learning rate of 0.3 and momentum factor of 0.7 performed well over the other types of MIMO-ANN models. The MIMO ANN model performed well for all parameters with higher correlation and other performance indicators except for sunshine hours. Due to erratic nature, the importance of each of the input over the output through sensitivity analysis indicated that relative humidity has highest influence while others have equal influence over the output.

  8. Application of ANN and fuzzy logic algorithms for streamflow modelling of Savitri catchment

    NASA Astrophysics Data System (ADS)

    Kothari, Mahesh; Gharde, K. D.

    2015-07-01

    The streamflow prediction is an essentially important aspect of any watershed modelling. The black box models (soft computing techniques) have proven to be an efficient alternative to physical (traditional) methods for simulating streamflow and sediment yield of the catchments. The present study focusses on development of models using ANN and fuzzy logic (FL) algorithm for predicting the streamflow for catchment of Savitri River Basin. The input vector to these models were daily rainfall, mean daily evaporation, mean daily temperature and lag streamflow used. In the present study, 20 years (1992-2011) rainfall and other hydrological data were considered, of which 13 years (1992-2004) was for training and rest 7 years (2005-2011) for validation of the models. The mode performance was evaluated by R, RMSE, EV, CE, and MAD statistical parameters. It was found that, ANN model performance improved with increasing input vectors. The results with fuzzy logic models predict the streamflow with single input as rainfall better in comparison to multiple input vectors. While comparing both ANN and FL algorithms for prediction of streamflow, ANN model performance is quite superior.

  9. The influence of emerging administrative scientists: an interview with Anne Miller.

    PubMed

    Miller, Anne; Adams, Jeffrey M

    2015-04-01

    This department highlights emerging nursing leaders and scientists demonstrating promise in advancing innovation and patient care leadership in practice, policy, research, education, and theory. This interview profiles Anne Miller, PhD, BA, assistant professor jointly appointed to the Center of Interdisciplinary Health Workforce Studies and the School of Nursing at Vanderbilt University Medical Center. PMID:25803799

  10. The Search for Family: Comedy and Pathos in Anne Tyler's Later Novels.

    ERIC Educational Resources Information Center

    Zoghby, Mary D.

    Anne Tyler's rare talent for combining comedy and pathos enables her to create characters whose pain is felt by the reader or student even as that same reader is led into laughter by the ludicrous situations in which Tyler places these characters. In her last three novels, "Dinner at the Homesick Restaurant,""The Accidental Tourist," and…

  11. Master Builder of Bridges between Research and Practice: An Interview with Ann Robinson

    ERIC Educational Resources Information Center

    Henshon, Suzanna E.

    2010-01-01

    This article presents an interview with Ann Robinson, a professor of educational psychology and founding director of the Center for Gifted Education at the University of Arkansas at Little Rock. She is the president of the National Association for Gifted Children (NAGC), a former editor of the Gifted Child Quarterly (GCQ), and was the first Editor…

  12. 76 FR 80392 - Notice of Inventory Completion: University of Michigan Museum of Anthropology, Ann Arbor, MI

    Federal Register 2010, 2011, 2012, 2013, 2014

    2011-12-23

    ... National Park Service Notice of Inventory Completion: University of Michigan Museum of Anthropology, Ann... the human remains was made by University of Michigan officials and its Museum of Anthropology... residence to the University of Michigan, where they were accessioned into the Museum of...

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

    ERIC Educational Resources Information Center

    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 writer. Over…

  14. Should You Teach "Anne Frank: The Diary of a Young Girl"?

    ERIC Educational Resources Information Center

    Rochman, Hazel

    1998-01-01

    Considers the value of teaching "Anne Frank" and suggests that the book be just a starting point for exposing readers to other views of the Holocaust. Annotated bibliographies are included for other personal accounts of the Holocaust, with appropriate grade levels indicated. (LRW)

  15. 33 CFR 80.115 - Portland Head, ME to Cape Ann, MA.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 33 Navigation and Navigable Waters 1 2010-07-01 2010-07-01 false Portland Head, ME to Cape Ann, MA. 80.115 Section 80.115 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY..., MA. (a) Except inside lines specifically described in this section, the 72 COLREGS shall apply on...

  16. Evaluation of alternative management practices with the AnnAGNPS model in the Carapelle Watershed

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The Annualized Agricultural Non-point Source (AnnAGNPS) model can be used to analyze the effectiveness of management and conservation practices that can control the impact of erosion and subsequent sediment loads in agricultural watersheds. A Mediterranean medium-size watershed (Carapelle) in Apulia...

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

    ERIC Educational Resources Information Center

    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 provides…

  18. Childhood and Modernity: Dark Themes in Carol Ann Duffy's Poetry for Children

    ERIC Educational Resources Information Center

    Whitley, David

    2007-01-01

    Carol Ann Duffy's three volumes of children's poetry are important and interesting because they emerge from the work of a writer whose adult poetry has persistently associated childhood with dark and difficult areas of experience. This article explores what happens to such challenging material when a poet of major significance changes the focus of…

  19. 33 CFR 80.115 - Portland Head, ME to Cape Ann, MA.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 33 Navigation and Navigable Waters 1 2011-07-01 2011-07-01 false Portland Head, ME to Cape Ann, MA. 80.115 Section 80.115 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY..., MA. (a) Except inside lines specifically described in this section, the 72 COLREGS shall apply on...

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

    NASA Astrophysics Data System (ADS)

    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.

  1. Audits of Meaning: A Festschrift in Honor of Ann E. Berthoff.

    ERIC Educational Resources Information Center

    Smith, Louise Z., Ed.

    A tribute to the work of Ann Berthoff, this book--a collection of 6 poems and 21 essays--explores issues in the philosophy and teaching of composition. Each of the five sections is introduced with a poem by Marie Ponsot. Essays and their authors consist of (1) "Genre Theory, Academic Discourse, and Writing within Disciplines" (James F. Slevin);…

  2. The influence of emerging administrative scientists: an interview with Anne Miller.

    PubMed

    Miller, Anne; Adams, Jeffrey M

    2015-04-01

    This department highlights emerging nursing leaders and scientists demonstrating promise in advancing innovation and patient care leadership in practice, policy, research, education, and theory. This interview profiles Anne Miller, PhD, BA, assistant professor jointly appointed to the Center of Interdisciplinary Health Workforce Studies and the School of Nursing at Vanderbilt University Medical Center.

  3. 78 FR 65366 - Notice of Inventory Completion: University of Michigan, Ann Arbor, MI

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-10-31

    ... National Park Service 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... remains should submit a written request to the University of Michigan. If no additional requestors...

  4. 78 FR 65367 - Notice of Inventory Completion: University of Michigan, Ann Arbor, MI

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-10-31

    ... National Park Service 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... submit a written request to the University of Michigan. If no additional requestors come...

  5. 78 FR 65369 - Notice of Inventory Completion: University of Michigan, Ann Arbor, MI

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-10-31

    ... National Park Service 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... remains should submit a written request to the University of Michigan. If no additional requestors...

  6. 78 FR 65380 - Notice of Inventory Completion: University of Michigan, Ann Arbor, MI

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-10-31

    ... National Park Service 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... remains should submit a written request to the University of Michigan. If no additional requestors...

  7. 78 FR 65382 - Notice of Inventory Completion: University of Michigan, Ann Arbor, MI

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-10-31

    ... National Park Service 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... remains should submit a written request to the University of Michigan. If no additional requestors...

  8. 78 FR 65375 - Notice of Inventory Completion: University of Michigan, Ann Arbor, MI

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-10-31

    ... National Park Service 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... submit a written request to the University of Michigan. If no additional requestors come...

  9. 78 FR 65364 - Notice of Inventory Completion: University of Michigan, Ann Arbor, MI

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-10-31

    ... National Park Service 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... submit a written request to the University of Michigan. If no additional requestors come...

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

    ERIC Educational Resources Information Center

    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…

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

    ERIC Educational Resources Information Center

    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…

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

    ERIC Educational Resources Information Center

    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 Jung was…

  13. Friendly Letters on the Correspondence of Helen Keller, Anne Sullivan, and Alexander Graham Bell.

    ERIC Educational Resources Information Center

    Blatt, Burton

    1985-01-01

    Excerpts from the letters between Alexander Graham Bell and Anne Sullivan and Helen Keller are given to illustrate the educational and personal growth of Helen Keller as well as the educational philosophy of Bell regarding the education of the deaf blind. (DB)

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

    PubMed

    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. PMID:25062991

  15. Beneficial application of landfill mining Millersville Landfill, Anne Arundel County, MD

    SciTech Connect

    Vanetti, D.J.

    1995-09-01

    Several studies and investigations have been completed for the Millersville Sanitary Landfill in Anne Arundel County, Maryland. The studies and reports range from detailed hydrogeologic investigations through review of closure alternatives for the individual refuse disposal cells located at the landfill. As a result of the evaluations and studies, one recommendation that was put before Anne Arundel County is the excavation and relocation of refuse from Cell 3 to: (1) create an infiltration basin; and (2) reduce the overall refuse footprint at the site, resulting in reduce long term environmental impacts and closure costs. Subsequent to this recommendation, several preliminary reviews have been held between Anne Arundel County, regulatory agencies and their consultants, Stearns & Wheler. These discussions indicated that it would be feasible, and the concept acceptable, to relocate the refuse in Cell 3 to ultimately create an infiltration basin. Subsequent to the preliminary meetings, a project plan and construction Contract Documents and Drawings were developed by Stearns & Wheler. The Project Plan was submitted to the State regulatory agencies (Maryland and Department of Environment (MDE) and Maryland Department of Natural Resources (DNR)), Millersville Landfill Citizen`s Advisory Committee, and Anne Arundel County (Department of public Works (DPW), Permit Acquisition and Code Enforcement (PACE) and Soil Conservation District (SCD)) for review and comment prior to undertaking the relocation of refuse in Cell 3.

  16. 78 FR 65360 - Notice of Inventory Completion: University of Michigan, Ann Arbor, MI

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-10-31

    ... National Park Service 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... notice is published as part of the National Park Service's administrative responsibilities under...

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

    ERIC Educational Resources Information Center

    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…

  18. Constructing Anne Frank: Critical Literacy and the Holocaust in Eighth-Grade English

    ERIC Educational Resources Information Center

    Spector, Karen; Jones, Stephanie

    2007-01-01

    Using the assumption that texts actively work to position readers and readers actively work to position texts, the authors argue that moral lessons emerge from the interactions between texts, readers, and the ideological narratives that inspire both. After differentiating versions of Anne Frank's diary and explicating motives behind their…

  19. A calcium-binding protein, rice annexin OsANN1, enhances heat stress tolerance by modulating the production of H2O2.

    PubMed

    Qiao, Bei; Zhang, Qian; Liu, Dongliang; Wang, Haiqi; Yin, Jingya; Wang, Rui; He, Mengli; Cui, Meng; Shang, Zhonglin; Wang, Dekai; Zhu, Zhengge

    2015-09-01

    OsANN1 is a member of the annexin protein family in rice. The function of this protein and the mechanisms of its involvement in stress responses and stress tolerance are largely unknown. Here it is reported that OsANN1 confers abiotic stress tolerance by modulating antioxidant accumulation under abiotic stress. OsANN1-knockdown [RNA interference (RNAi)] plants were more sensitive to heat and drought stresses, whereas OsANN1-overexpression (OE) lines showed improved growth with higher expression of OsANN1 under abiotic stress. Overexpression of OsANN1 promoted SOD (superoxide dismutase) and CAT (catalase) activities, which regulate H2O2 content and redox homeostasis, suggesting the existence of a feedback mechanism between OsANN1 and H2O2 production under abiotic stress. Higher expression of OsANN1 can provide overall cellular protection against abiotic stress-induced damage, and a significant accumulation of OsANN1-green fluorescent protein (GFP) signals was found in the cytosol after heat shock treatment. OsANN1 also has calcium-binding and ATPase activities in vitro, indicating that OsANN1 has multiple functions in rice growth. Furthermore, yeast two-hybrid and bimolecular fluorescence complementation (BiFC) assays demonstrated that OsANN1 interacts with OsCDPK24. This cross-talk may provide additional layers of regulation in the abiotic stress response. PMID:26085678

  20. Overexpression of Arabidopsis AnnAt8 Alleviates Abiotic Stress in Transgenic Arabidopsis and Tobacco

    PubMed Central

    Yadav, Deepanker; Ahmed, Israr; Shukla, Pawan; Boyidi, Prasanna; Kirti, Pulugurtha Bharadwaja

    2016-01-01

    Abiotic stress results in massive loss of crop productivity throughout the world. Because of our limited knowledge of the plant defense mechanisms, it is very difficult to exploit the plant genetic resources for manipulation of traits that could benefit multiple stress tolerance in plants. To achieve this, we need a deeper understanding of the plant gene regulatory mechanisms involved in stress responses. Understanding the roles of different members of plant gene families involved in different stress responses, would be a step in this direction. Arabidopsis, which served as a model system for the plant research, is also the most suitable system for the functional characterization of plant gene families. Annexin family in Arabidopsis also is one gene family which has not been fully explored. Eight annexin genes have been reported in the genome of Arabidopsis thaliana. Expression studies of different Arabidopsis annexins revealed their differential regulation under various abiotic stress conditions. AnnAt8 (At5g12380), a member of this family has been shown to exhibit ~433 and ~175 fold increase in transcript levels under NaCl and dehydration stress respectively. To characterize Annexin8 (AnnAt8) further, we have generated transgenic Arabidopsis and tobacco plants constitutively expressing AnnAt8, which were evaluated under different abiotic stress conditions. AnnAt8 overexpressing transgenic plants exhibited higher seed germination rates, better plant growth, and higher chlorophyll retention when compared to wild type plants under abiotic stress treatments. Under stress conditions transgenic plants showed comparatively higher levels of proline and lower levels of malondialdehyde compared to the wild-type plants. Real-Time PCR analyses revealed that the expression of several stress-regulated genes was altered in AnnAt8 over-expressing transgenic tobacco plants, and the enhanced tolerance exhibited by the transgenic plants can be correlated with altered expressions of

  1. ANN based Estimation of Ultra High Energy (UHE) Shower Size using Radio Data

    NASA Astrophysics Data System (ADS)

    Sinha, Kalpana Roy; Datta, Pranayee; Sarma, Kandarpa Kumar

    2013-02-01

    Size estimation is a challenging area in the field of Ultra High Energy (UHE) showers where actual measurements are always associated with uncertainty of events and imperfections in detection mechanisms. The subtle variations resulting out of such factors incorporate certain random behaviour in the readings provided by shower detectors for subsequent processing. Field strength recorded by radio detectors may also be affected by this statistical nature. Hence there is a necessity of development of a system which can remain immune to such random behaviour and provide resilient readings to subsequent stages. Here, we propose a system based on Artificial Neural Network (ANN) which accepts radio field strength recorded by radio detectors and provides estimates of shower sizes in the UHE region. The ANN in feed-forward form is trained with a range of shower events with which it can effectively handle the randomness observed in the detector reading due to imperfections in the experimental apparatus and related set-up.

  2. Artificial neural networks (ANN) approach for modeling of removal of Lanaset Red G on Chara contraria.

    PubMed

    Celekli, Abuzer; Geyik, Faruk

    2011-05-01

    A three-layer artificial neural network (ANN) was constructed to predict the removal efficiency of Lanaset Red (LR) G on Chara contraria based on 2304 experimental sets. The effects of operating variables (particle size, adsorbent dosage, pH regimes, dye concentration, and contact time) were studied to optimize the sorption conditions of this dye. The operating variables were used as the input to the constructed neural network to predict the dye uptake at any time as the output. This adsorbent was characterized by FTIR. Pseudo second-order model was also fitted to the experimental data. According to values of error analyses and determinations coefficient, the ANN was more appropriate to describe this adsorption process. Result of this model indicated that pH regimes had the highest importance effect (49%) on the dye uptake.

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

    PubMed

    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. PMID:27279067

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

    PubMed

    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.

  5. A Practically Validated Intelligent Calibration Circuit Using Optimized ANN for Flow Measurement by Venturi

    NASA Astrophysics Data System (ADS)

    Venkata, Santhosh Krishnan; Roy, Binoy Krishna

    2016-03-01

    Design of an intelligent flow measurement technique using venturi flow meter is reported in this paper. The objectives of the present work are: (1) to extend the linearity range of measurement to 100 % of full scale input range, (2) to make the measurement technique adaptive to variations in discharge coefficient, diameter ratio of venturi nozzle and pipe (β), liquid density, and liquid temperature, and (3) to achieve the objectives (1) and (2) using an optimized neural network. The output of venturi flow meter is differential pressure. It is converted to voltage by using a suitable data conversion unit. A suitable optimized artificial neural network (ANN) is added, in place of conventional calibration circuit. ANN is trained, tested with simulated data considering variations in discharge coefficient, diameter ratio between venturi nozzle and pipe, liquid density, and liquid temperature. The proposed technique is then subjected to practical data for validation. Results show that the proposed technique has fulfilled the objectives.

  6. Discrete wavelet transform coupled with ANN for daily discharge forecasting into Três Marias reservoir

    NASA Astrophysics Data System (ADS)

    Santos, C. A. G.; Freire, P. K. M. M.; Silva, G. B. L.; Silva, R. M.

    2014-09-01

    This paper proposes the use of discrete wavelet transform (DWT) to remove the high-frequency components (details) of an original signal, because the noises generally present in time series (e.g. streamflow records) may influence the prediction quality. Cleaner signals could then be used as inputs to an artificial neural network (ANN) in order to improve the model performance of daily discharge forecasting. Wavelet analysis provides useful decompositions of original time series in high and low frequency components. The present application uses the Coiflet wavelets to decompose hydrological data, as there have been few reports in the literature. Finally, the proposed technique is tested using the inflow records to the Três Marias reservoir in São Francisco River basin, Brazil. This transformed signal is used as input for an ANN model to forecast inflows seven days ahead, and the error RMSE decreased by more than 50% (i.e. from 454.2828 to 200.0483).

  7. Processing RoxAnn sonar data to improve its categorization of lake bed surficial sediments

    USGS Publications Warehouse

    Cholwek, Gary; Bonde, John; Li, Xing; Richards, Carl; Yin, Karen

    2000-01-01

    To categorize spawning and nursery habitat for lake trout in Minnesota's near shore waters of Lake Superior, data was collected with a single beam echo sounder coupled with a RoxAnn bottom classification sensor. Test areas representative of different bottom surficial substrates were sampled. The collected data consisted of acoustic signals which showed both depth and substrate type. The location of the signals was tagged in real-time with a DGPS. All data was imported into a GIS database. To better interpret the output signal from the RoxAnn, several pattern classifiers were developed by multivariate statistical method. From the data a detailed and accurate map of lake bed bathymetry and surficial substrate types was produced. This map will be of great value to fishery and other natural resource managers.

  8. An ANN-Based Smart Tomographic Reconstructor in a Dynamic Environment

    PubMed Central

    de Cos Juez, Francisco J.; Lasheras, Fernando Sánchez; Roqueñí, Nieves; Osborn, James

    2012-01-01

    In astronomy, the light emitted by an object travels through the vacuum of space and then the turbulent atmosphere before arriving at a ground based telescope. By passing through the atmosphere a series of turbulent layers modify the light's wave-front in such a way that Adaptive Optics reconstruction techniques are needed to improve the image quality. A novel reconstruction technique based in Artificial Neural Networks (ANN) is proposed. The network is designed to use the local tilts of the wave-front measured by a Shack Hartmann Wave-front Sensor (SHWFS) as inputs and estimate the turbulence in terms of Zernike coefficients. The ANN used is a Multi-Layer Perceptron (MLP) trained with simulated data with one turbulent layer changing in altitude. The reconstructor was tested using three different atmospheric profiles and compared with two existing reconstruction techniques: Least Squares type Matrix Vector Multiplication (LS) and Learn and Apply (L + A). PMID:23012524

  9. Intensive care 1650: the revival of Anne Greene (c. 1628-59).

    PubMed

    Breathnach, Caoimhghin S; Moynihan, John B

    2009-02-01

    On a cold December day in 1650, 22-year-old Anne Greene was hanged in Oxford. When taken down after half an hour, she was found to show signs of life and over the next few days William Petty (1623-87), Thomas Willis (1621-75), Ralph Bathurst (1620-74) and Henry Clerke (1622-87) ministered to her full recovery. She was later pardoned of the charge of infanticide and, with the coffin wherein she had lain as a trophy, went into the country, became the subject not only of a prose and poetic narrative but also of a woodcut. Anne married happily, bore three children and lived until 1659. A combination of low-body temperature and external (pedal) cardiac massage after her failed execution, it is suggested, helped to keep her alive until the arrival of the physicians who had come to make an anatomical dissection but serendipitously won golden opinions.

  10. Long-term solar UV radiation reconstructed by Artificial Neural Networks (ANN)

    NASA Astrophysics Data System (ADS)

    Feister, U.; Junk, J.; Woldt, M.

    2008-01-01

    Artificial Neural Networks (ANN) are efficient tools to derive solar UV radiation from measured meteorological parameters such as global radiation, aerosol optical depths and atmospheric column ozone. The ANN model has been tested with different combinations of data from the two sites Potsdam and Lindenberg, and used to reconstruct solar UV radiation at eight European sites by more than 100 years into the past. Annual totals of UV radiation derived from reconstructed daily UV values reflect interannual variations and long-term patterns that are compatible with variabilities and changes of measured input data, in particular global dimming by about 1980-1990, subsequent global brightening, volcanic eruption effects such as that of Mt. Pinatubo, and the long-term ozone decline since the 1970s. Patterns of annual erythemal UV radiation are very similar at sites located at latitudes close to each other, but different patterns occur between UV radiation at sites in different latitude regions.

  11. Prophecy, patriarchy, and violence in the early modern household: the revelations of Anne Wentworth.

    PubMed

    Johnston, Warren

    2009-10-01

    In 1676 the apostate Baptist prophet Anne Wentworth (1629/30-1693?) published "A True Account of Anne Wentworths Being Cruelly, Unjustly, and Unchristianly Dealt with by Some of Those People called Anabaptists," the first in a series of pamphlets that would continue to the end of the decade. Orignially a member of a London Baptist church, Wentworth left the congregation and eventually her own home after her husband used physical force to stop her writing and prophesying. Yet Wentworth persisted in her "revelations." These prophecies increasingly focused on her response to those who were trying to stop her efforts, especially within her own household. This article examines Wentworth's writings as an effort by an early modern woman, using arguments of spiritual agency, to assert ideas about proper gender roles and household responsibilities to denounce her husband and rebut those who criticized and attempted to suppress her. PMID:19999636

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

    PubMed

    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.

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

    Cancer.gov

    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. |

  14. The Anne Boleyn Illusion is a Six-Fingered Salute to Sensory Remapping

    PubMed Central

    Wong, Dominic Y.; Howard, Ellen M.; Silver, Eden

    2016-01-01

    The Anne Boleyn Illusion exploits the somatotopic representation of touch to create the illusion of an extra digit and demonstrates the instantaneous remapping of relative touch location into body-based coordinates through visuo-tactile integration. Performed successfully on thousands, it is also a simple demonstration of the flexibility of body representations for use at public events, in schools or in the home and can be implemented anywhere by anyone with a mirror and some degree of bimanual coordination. PMID:27708755

  15. Carbon Cycle 2.0: Mary Ann Piette: Impact of efficient buildings

    ScienceCinema

    Mary Ann Piette

    2016-07-12

    Mary Ann Piette speaks at the Carbon Cycle 2.0 kick-off symposium Feb. 2, 2010. We emit more carbon into the atmosphere than natural processes are able to remove - an imbalance with negative consequences. Carbon Cycle 2.0 is a Berkeley Lab initiative to provide the science needed to restore this balance by integrating the Labs diverse research activities and delivering creative solutions toward a carbon-neutral energy future. http://carboncycle2.lbl.gov/

  16. Carbon Cycle 2.0: Mary Ann Piette: Impact of efficient buildings

    SciTech Connect

    Mary Ann Piette

    2010-02-09

    Mary Ann Piette speaks at the Carbon Cycle 2.0 kick-off symposium Feb. 2, 2010. We emit more carbon into the atmosphere than natural processes are able to remove - an imbalance with negative consequences. Carbon Cycle 2.0 is a Berkeley Lab initiative to provide the science needed to restore this balance by integrating the Labs diverse research activities and delivering creative solutions toward a carbon-neutral energy future. http://carboncycle2.lbl.gov/

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

    PubMed Central

    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. PMID:24688400

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

    NASA Astrophysics Data System (ADS)

    Patel, Raj Kumar; Giri, V. K.

    2016-07-01

    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.

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

    PubMed

    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.

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

    NASA Astrophysics Data System (ADS)

    Chandrasekaran, Muthumari; Tamang, Santosh

    2016-06-01

    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.

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

    NASA Astrophysics Data System (ADS)

    Erzin, Yusuf; Yilmaz, Isik

    2010-05-01

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

  2. Clustering of soybean genotypes via Ward-MLM and ANNs associated with mixed models.

    PubMed

    Teodoro, P E; Torres, F E; Corrêa, A M

    2016-01-01

    The objectives of this study were to use mixed models to confirm the presence of genetic variability in 16 soybean genotypes, to compare clusters generated by artificial neural networks (ANNs) with those created by the Ward modified location model (MLM) technique, and to indicate parental combinations that hold promise for obtaining superior segregating populations of soybean. A field trial was conducted between November 2014 and February 2015 at Universidade Estadual de Mato Grosso do Sul, Aquidauana, MS. The experimental design consisted of four replications of randomized blocks, each containing 16 treatments. We assessed the following agronomic traits: plant height, first pod height, number of branches per plant, number of pods per plant, number of grains per pod, hundred-grain weight, and grain yield. Mixed models were used to estimate variance components and genetic parameters, and obtain genotypic values for each trait. After verifying the presence of genetic variability for all traits, genotypic values were submitted to both a Ward-MLM procedure and ANNs to estimate genetic divergence among genotypes. The number of groups formed by both methods was the same, but there were differences in group constitutions. ANN analysis improved soybean genotypes clustering patterns compared to Ward-MLM procedure. Based on these methods, divergent crosses may be made between genotype 97R73 with genotypes AS3797 and SYN9070, whereas convergent crosses may be made between genotypes AS3797 and SYN9070. PMID:27525912

  3. [The family relationships of Antoine Brulon, apothecary to the king, and his wife, Anne de Furnes, in Auvergne and Paris in the 17th century. Anne de Furnes and Molière in Paris and the village of Auteuil].

    PubMed

    Warolin, Christian

    2011-07-01

    This article presents the biography of Anne de Fumes, wife of Antoine Brulon, the king's apothecary. Due to the successive deaths of her husband and her only daughter, Geneviève, her sisters-in-law, Géraude and Anne Brulon, living in Auvergne, inherited the property of their niece. Anne de Fumes, who inherited the movable assets, carried out a series of transactions to acquire the totality of the property rights, which she obtained, but at the considerable cost of 100,000 pounds. After her death, her five nephews and nieces, her sole legatees, inherited her estate. From 1666, Molière was the tenant of an apartment in a building in the Rue Saint-Thomas-du-Louvre, Place du Palais-Royal, in Paris, which belonged to Anne de Fumes. She lived in the neighbouring house in the Rue Saint-Honoré of which she was also the owner. Three apothecaries, Philibert Boudin, Jean Morel and Pierre Frapin, successively rented the shop and entresol of the house in the Rue Saint-Thomas-du-Louvre. Pierre Frapin, tenant of the shop from 1668, supplied Molière with medicine. Like Molière, Anne de Fumes rented accommodation in a house in the village of Auteuil belonging to Jacques de Grout de Beaufort and his wife Marie Filz. Reports show that the famous actor and Anne de Fumes cohabited in Auteuil during the period 1667 to 1672. PMID:21998974

  4. [The family relationships of Antoine Brulon, apothecary to the king, and his wife, Anne de Furnes, in Auvergne and Paris in the 17th century. Anne de Furnes and Molière in Paris and the village of Auteuil].

    PubMed

    Warolin, Christian

    2011-07-01

    This article presents the biography of Anne de Fumes, wife of Antoine Brulon, the king's apothecary. Due to the successive deaths of her husband and her only daughter, Geneviève, her sisters-in-law, Géraude and Anne Brulon, living in Auvergne, inherited the property of their niece. Anne de Fumes, who inherited the movable assets, carried out a series of transactions to acquire the totality of the property rights, which she obtained, but at the considerable cost of 100,000 pounds. After her death, her five nephews and nieces, her sole legatees, inherited her estate. From 1666, Molière was the tenant of an apartment in a building in the Rue Saint-Thomas-du-Louvre, Place du Palais-Royal, in Paris, which belonged to Anne de Fumes. She lived in the neighbouring house in the Rue Saint-Honoré of which she was also the owner. Three apothecaries, Philibert Boudin, Jean Morel and Pierre Frapin, successively rented the shop and entresol of the house in the Rue Saint-Thomas-du-Louvre. Pierre Frapin, tenant of the shop from 1668, supplied Molière with medicine. Like Molière, Anne de Fumes rented accommodation in a house in the village of Auteuil belonging to Jacques de Grout de Beaufort and his wife Marie Filz. Reports show that the famous actor and Anne de Fumes cohabited in Auteuil during the period 1667 to 1672.

  5. Application of EOS/MODIS remote sensing dataset to ANN/GA modeling of distributed precipitation estimation

    NASA Astrophysics Data System (ADS)

    Hu, Guangyi; Zhang, Qiuwen; Li, Wenbo

    2007-11-01

    The main meteorological parameters which influencing the rainfall can be distilled from the MODIS satellite cloud imagery and the artificial neural network (ANN) model constructed by these meteorological parameters and can be applied on distributed rainfall estimation. Because it is difficult to decide the structure of back propagation neural network (BPNN) and to solve the problem of local convergence, an appropriate training and modeling method of ANN such as the real code genetic algorithm (RGA) is vital to the accuracy of rainfall estimation. The data of the simulation tests show that the Mean Relative Error (MRE) of BPA model is 23.6%, while the MRE of RGA model is 20.7%, Compared with the ANN trained by BPA, the estimation error of the ANN trained by RGA is cut down by 2.9%, and the Root Mean Squared Error (RMSE) is cut down by 2.5% at the same time, hence, the results prove that the ANN model trained using RGA will significantly outperform the back propagation algorithm (BPA) trained ANN model and improve the precision of rainfall estimation.

  6. Flooded area cartography and damage assessment from the combined use of Landsat TM and ANNs

    NASA Astrophysics Data System (ADS)

    Alouene, Yosra; Petropoulos, George P.

    2013-04-01

    Use of Earth Observation (EO) data has generally shown a very promising potential in performing rapidly and cost-effectively mapping as well as damage assessment in different types of natural hazards, including floods. The recent technological progress in remote sensing has resulted to the development of a vast number of image processing techniques applied to different types of EO data in performing flooded area mapping and damage assessment. When optical EO data is used for this purpose supervised image classification is regarded as one of the most widely exploited approaches employed for this purpose. In the present study we evaluated the use of different classifiers based on Artificial Neural Network (ANNs) in obtaining flooded area cartography and performing a damage assessment when those combined with optical multispectral data from Landsat TM. In this context, the inclusion of different spectral layers derived from the processing of the original TM bands for improving the estimation of the flooded area was explored. A flooding event occurred in 2010 in Evros river - located north of Greece - was used as a case study. Accuracy of ANN-derived flooded area estimates was based on the error matrix statistics but also statistical comparisons performed against corresponding estimates obtained from the Greek local authorities. Damage assessment was performed on the basis of land use/cover information derived from CORINE2000. Results generally evidenced the capability of the ANNs in obtaining cartography of the flooded area and in performing a flooding damage assessment when combined with the TM imagery. The inclusion of the additional spectral information showed variable results in terms of improving the accuracy of the flooded area extraction. From all scenarios examined, most accurate results in terms of flooded area mapping were obtained when the original TM spectral bands were combined with the Tasseled Cap additional bands. Keywords: flooded area mapping

  7. Precipitation Estimation from Remotely Sensed Information using ANN-Cloud Classification System

    NASA Astrophysics Data System (ADS)

    Hong, Y.; Hsu, K.; Sorooshian, S.

    2003-12-01

    Artificial Neural Network (ANN) models, which contain flexible architectures and are capable of discerning the underlying functional relationships from data, are recognized as very useful tools in geophysical applications. In this study, we demonstrate a hybrid ANN modeling system to estimate surface rainfall from satellite infrared imagery. The proposed network, Precipitation Estimation from Remotely Sensed Information using ANN-Cloud Classification System (PERSIANN-CCS), includes several components: (1) cloud image segmentation, (2) cloud patch feature selection, (3) patch feature classification using a self-organizing feature map network, and (4) patch-based rainfall estimates from a group of multiple nonlinear cloud top temperature and rainfall functions. The PERSIANN-CCS model was first calibrated using observations from Geostationary Operational Environmental Satellite (GOES) infrared imagery and the Next Generation Radar (NEXRAD) rainfall network. To further extend PERSIANN-CCS rainfall estimates over the remote regions, Tropical Rainfall Measurement Mission (TRMM) microwave rainfall estimates (TMI product 2A12) were used to adjust PERSIANN-CCS model parameters. The calibrated nonlinear cloud top temperature and rainfall (Tb-R) functions of classified cloud patches show highly variability, reflecting the complexity of dominant cloud-precipitation processes over various regions. Case studies show that PERSIANN-CCS captures the variability in rain rate at 12kmx12km grid and 3-hour resolutions, with a standard error of 3.0mm/hr and a correlation coefficient around 0.65. Additional insights into the cloud evolution and precipitation process from the classified PERSIANN-CCS cloud patch features and rainfall distributions are discussed.

  8. A new method based on WMRA and ANN for GPS/SINS integration for aerocraft navigation

    NASA Astrophysics Data System (ADS)

    Zhu, Xuefen; Chen, Xiyuan; Li, Zigang

    2006-11-01

    Global Positioning System (GPS) can provide precise positioning information to an unlimited number of users anywhere on the earth. However, the defect cannot be neglected, because there exists one blind district when the aerocraft flying through some altitude space. During the short time in the blind district, all radio signals can't be attained including the GPS signals. An integrated GPS/SINS (Strapdown Inertial Navigation System) Navigation system is presented in this paper. The SINS based on numerical computing platform has many advantages such as high reliability, small bulk and low cost ect. The integration of GPS and SINS, therefore, provides a navigation system that has superior performance in comparison with either a GPS or a SINS stand-alone system. This paper presents a new model-less algorithm that can perform the self-following of the aerocraft under all conditions. For improving the precision of the hybrid GPS/SINS navigation system, fusing data from a SINS and GPS hardware utilizes wavelet multi-resolution analysis (WMRA) and Radial Basis Function (RBF) Artificial Neural Networks (ANN). The WMRA is used to compare the SINS and GPS position outputs at different resolution levels. These differences represent, in general, the SINS errors, which are used to correct for the SINS outputs during GPS outages. The RBF-ANN model is then trained to predict the SINS position errors in real time and provide accurate positioning of the moving aerocraft. The simulations show that good results in SINS/GPS positioning accuracy can be obtained by applying the WMRA and RBF-ANN methods.

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

    SciTech Connect

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

    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.

  10. Effect of DEM mesh size on AnnAGNPS simulation and slope correction.

    PubMed

    Wang, Xiaoyan; Lin, Q

    2011-08-01

    The objective of this paper is to study the impact of the mesh size of the digital elevation model (DEM) on terrain attributes within an Annualized AGricultural NonPoint Source pollution (AnnAGNPS) Model simulation at watershed scale and provide a correction of slope gradient for low resolution DEMs. The effect of different grid sizes of DEMs on terrain attributes was examined by comparing eight DEMs (30, 40, 50, 60, 70, 80, 90, and 100 m). The accuracy of the AnnAGNPS stimulation on runoff, sediments, and nutrient loads is evaluated. The results are as follows: (1) Rnoff does not vary much with decrease of DEM resolution whereas soil erosion and total nitrogen (TN) load change prominently. There is little effect on runoff simulation of AnnAGNPS modeling by the amended slope using an adjusted 50 m DEM. (2) A decrease of sediment yield and TN load is observed with an increase of DEM mesh size from 30 to 60 m; a slight decrease of sediment and TN load with the DEM mesh size bigger than 60 m. There is similar trend for total phosphorus (TP) variation, but with less range of variation, the simulation of sediment, TN, and TP increase, in which sediment increase up to 1.75 times compared to the model using unadjusted 50 m DEM. In all, the amended simulation still has a large difference relative to the results using 30 m DEM. AnnAGNPS is less reliable for sediment loading prediction in a small hilly watershed. (3) Resolution of DEM has significant impact on slope gradient. The average, minimum, maximum of slope from the various DEMs reduced obviously with the decrease of DEM precision. For the grade of 0∼15°, the slopes at lower resolution DEM are generally bigger than those at higher resolution DEM. But for the grade bigger than 15°, the slopes at lower resolution DEM are generally smaller than those at higher resolution DEM. So it is necessary to adjust the slope with a fitting equation. A cubic model is used for correction of slope gradient from lower resolution to

  11. Internal Model Controller of an ANN Speed Sensorless Controlled Induction Motor Drives

    NASA Astrophysics Data System (ADS)

    Hamed Mouna, Ben; Lassaad, Sbita

    This study deals with the performance analysis and implementation of a robust sensorless speed controller. The robustness is guaranteed by the use of the Internal Model Controller (IMC). An intelligent algorithm is evolved to eliminate the mechanical speed. It is based on the Artificial Neural Network (ANN) principle. Verification of the proposed robust sensorless controller is provided by experimental realistic tests on a scalar controlled induction motor drive. Sensorless robust speed control at low speeds and in field weakening region (high speeds) is studied in order to show the robustness of the speed controller under a wide range of load.

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

    USGS Publications Warehouse

    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.

  13. ANN modeling for flood prediction in the upstream Eure's catchment (France)

    NASA Astrophysics Data System (ADS)

    Kharroubi, Ouissem; masson, Eric; Blanpain, Olivier; Lallahem, Sami

    2013-04-01

    Rainfall-Runoff relationship at basin scale is strongly depending on the catchment complexity including multi-scale interactions. In extreme events cases (i.e. floods and droughts) this relationship is even more complex and differs from average hydrological conditions making extreme runoff prediction very difficult to achieve. However, flood warning, flood prevention and flood mitigation rely on the possibility to predict both flood peak runoff and lag time. This point is crucial for decision making and flood warning to prevent populations and economical stakes to be damaged by extreme hydrological events. Since 2003 in France, a dedicated state service is in charge of producing flood warning from national level (i.e. SCHAPI) to regional level (i.e. SPC). This flood warning service is combining national weather forecast agency (i.e. Meteo France) together with a fully automated realtime hydrological network (i.e. Rainfall-Runoff) in order to produce a flood warning national map online and provide a set of hydro-meteorological data to the SPC in charge of flood prediction from regional to local scale. The SPC is in fact the flood service delivering hydrological prediction at operational level for decision making about flood alert for municipalities and first help services. Our research in collaboration with the SPC SACN (i.e. "Seine Aval et fleuves Côtiers Normands") is focused on the implementation of an Artificial Neural Network model (ANN) for flood prediction in deferent key points of the Eure's catchment and main subcatchment. Our contribution will focus on the ANN model developed for Saint-Luperce gauging station in the upstream part of the Eure's catchment. Prediction of extreme runoff at Saint-Luperce station is of high importance for flood warning in the Eure's catchment because it gives a good indicator on the extreme status and the downstream propagation of a potential flood event. Despite a good runoff monitoring since 27 years Saint Luperce flood

  14. A general ANN-based multitasking model for the discovery of potent and safer antibacterial agents.

    PubMed

    Speck-Planche, A; Cordeiro, M N D S

    2015-01-01

    Bacteria have been one of the world's most dangerous and deadliest pathogens for mankind, nowadays giving rise to significant public health concerns. Given the prevalence of these microbial pathogens and their increasing resistance to existing antibiotics, there is a pressing need for new antibacterial drugs. However, development of a successful drug is a complex, costly, and time-consuming process. Quantitative Structure-Activity Relationships (QSAR)-based approaches are valuable tools for shortening the time of lead compound identification but also for focusing and limiting time-costly synthetic activities and in vitro/vivo evaluations. QSAR-based approaches, supported by powerful statistical techniques such as artificial neural networks (ANNs), have evolved to the point of integrating dissimilar types of chemical and biological data. This chapter reports an overview of the current research and potential applications of QSAR modeling tools toward the rational design of more efficient antibacterial agents. Particular emphasis is given to the setup of multitasking models along with ANNs aimed at jointly predicting different antibacterial activities and safety profiles of drugs/chemicals under diverse experimental conditions.

  15. A general ANN-based multitasking model for the discovery of potent and safer antibacterial agents.

    PubMed

    Speck-Planche, A; Cordeiro, M N D S

    2015-01-01

    Bacteria have been one of the world's most dangerous and deadliest pathogens for mankind, nowadays giving rise to significant public health concerns. Given the prevalence of these microbial pathogens and their increasing resistance to existing antibiotics, there is a pressing need for new antibacterial drugs. However, development of a successful drug is a complex, costly, and time-consuming process. Quantitative Structure-Activity Relationships (QSAR)-based approaches are valuable tools for shortening the time of lead compound identification but also for focusing and limiting time-costly synthetic activities and in vitro/vivo evaluations. QSAR-based approaches, supported by powerful statistical techniques such as artificial neural networks (ANNs), have evolved to the point of integrating dissimilar types of chemical and biological data. This chapter reports an overview of the current research and potential applications of QSAR modeling tools toward the rational design of more efficient antibacterial agents. Particular emphasis is given to the setup of multitasking models along with ANNs aimed at jointly predicting different antibacterial activities and safety profiles of drugs/chemicals under diverse experimental conditions. PMID:25502375

  16. Clearance rate and BP-ANN model in paraquat poisoned patients treated with hemoperfusion.

    PubMed

    Hu, Lufeng; Hong, Guangliang; Ma, Jianshe; Wang, Xianqin; Lin, Guanyang; Zhang, Xiuhua; Lu, Zhongqiu

    2015-01-01

    In order to investigate the effect of hemoperfusion (HP) on the clearance rate of paraquat (PQ) and develop a clearance model, 41 PQ-poisoned patients who acquired acute PQ intoxication received HP treatment. PQ concentrations were determined by high performance liquid chromatography (HPLC). According to initial PQ concentration, study subjects were divided into two groups: Low-PQ group (0.05-1.0 μg/mL) and High-PQ group (1.0-10 μg/mL). After initial HP treatment, PQ concentrations decreased in both groups. However, in the High-PQ group, PQ levels remained in excess of 0.05 μg/mL and increased when the second HP treatment was initiated. Based on the PQ concentrations before and after HP treatment, the mean clearance rate of PQ calculated was 73 ± 15%. We also established a backpropagation artificial neural network (BP-ANN) model, which set PQ concentrations before HP treatment as input data and after HP treatment as output data. When it is used to predict PQ concentration after HP treatment, high prediction accuracy (R = 0.9977) can be obtained in this model. In conclusion, HP is an effective way to clear PQ from the blood, and the PQ concentration after HP treatment can be predicted by BP-ANN model. PMID:25695058

  17. Identification of pain from infant cry vocalizations using artificial neural networks (ANNs)

    NASA Astrophysics Data System (ADS)

    Petroni, Marco; Malowany, Alfred S.; Johnston, C. Celeste; Stevens, Bonnie J.

    1995-04-01

    The analysis of infant cry vocalizations has been the focus of a number of efforts over the past thirty years. Since the infant cry is one of the only means that an infant has for communicating with its care-giving environment, it is thought that information regarding the state of an infant, such as hunger or pain, can be determined from an infant's cry. To date, research groups have determined that adult listeners can differentiate between different types of cries auditorialy, and at least one group has attempted to automate this classification process. This paper presents the results of another attempt at automating the discrimination process, this time using artificial neural networks (ANNs). The input data consists of successive frames of one or two parametric representations generated from the first second of a cry following the application of either an anger, fear, or pain stimulus. From tests conducted to date, it is determined that ANNs are a useful tool for cry classification and merit further study in this domain.

  18. Elemental Study on Auscultaiting Diagnosis Support System of Hemodialysis Shunt Stenosis by ANN

    NASA Astrophysics Data System (ADS)

    Suzuki, Yutaka; Fukasawa, Mizuya; Mori, Takahiro; Sakata, Osamu; Hattori, Asobu; Kato, Takaya

    It is desired to detect stenosis at an early stage to use hemodailysis shunt for longer time. Stethoscope auscultation of vascular murmurs is useful noninvasive diagnostic approach, but an experienced expert operator is necessary. Some experts often say that the high-pitch murmurs exist if the shunt becomes stenosed, and some studies report that there are some features detected at high frequency by time-frequency analysis. However, some of the murmurs are difficult to detect, and the final judgment is difficult. This study proposes a new diagnosis support system to screen stenosis by using vascular murmurs. The system is performed using artificial neural networks (ANN) with the analyzed frequency data by maximum entropy method (MEM). The author recorded vascular murmurs both before percutaneous transluminal angioplasty (PTA) and after. Examining the MEM spectral characteristics of the high-pitch stenosis murmurs, three features could be classified, which covered 85 percent of stenosis vascular murmurs. The features were learnt by the ANN, and judged. As a result, a percentage of judging the classified stenosis murmurs was 100%, and that of normal was 86%.

  19. Prediction of Cutting Forces Using ANNs Approach in Hard Turning of AISI 52100 steel

    SciTech Connect

    Makhfi, Souad; Habak, Malek; Velasco, Raphael; Haddouche, Kamel; Vantomme, Pascal

    2011-05-04

    In this study, artificial neural networks (ANNs) was used to predict cutting forces in the case of machining the hard turning of AISI 52100 bearing steel using cBN cutting tool. Cutting forces evolution is considered as the key factors which affect machining. Predicting cutting forces evolution allows optimizing machining by an adaptation of cutting conditions. In this context, it seems interesting to study the contribution that could have artificial neural networks (ANNs) on the machining forces prediction in both numerical and experiment studies. Feed-forward multi-layer neural networks trained by the error back-propagation (BP) algorithm are used. Levenberg-Marquardt (LM) optimization algorithm was used for finding out weights. The training of the network is carried out with experimental machining data.The input dataset used are cutting speed, feed rate, cutting depth and hardness of the material. The output dataset used are cutting forces (Ft-cutting force, Fa- feed force and Fr- radial force).Results of the neural networks approach, in comparison with experimental data are discussed in last part of this paper.

  20. Mineral resource potential map of the Dolly Ann Roadless Area, Alleghany County, Virginia

    USGS Publications Warehouse

    Lesure, Frank G.; Jones, Jay G.

    1983-01-01

    The Dolly Ann Roadless Area comprises 7,900 acres (3,200 ha) in the George Washington National Forest in the Valley and Ridge physiographic province of west-central Virginia. The area is at the southern ·end of Warm Springs Mountain in Alleghany County just northeast of Covington, the county seat (index map). U.S. Highway 220 forms part of the western boundary, and U.S. Forest Service Road 125, which parallels Pounding Mill Creek, forms the eastern boundary. The principal streams draining the area are Pounding Mill Creek, Dry Run, and Roaring Run, all tributaries of the Jackson River. The highest point in the area is Big Knob at the north end, 4,072 ft (1241 m) above sea level; the lowest points, about 1,400 ft (427 m) above sea level, are at the south side, along Dry Run and Pounding Mill Creek. In general, the hill slopes are steep and heavily wooded with second- or third-growth hardwoods and scattered pine and hemlock. Dolly Ann Hollow near the east end of the area is a steep, boulder-strewn gorge, quite picturesque, but containing no good trails. A good trail up Dry Run connects a trail crossing the ridge between Bald Knob and Big Knob. No other trails cross the area.

  1. Dynamically stable associative learning: a neurobiologically based ANN and its applications

    NASA Astrophysics Data System (ADS)

    Vogl, Thomas P.; Blackwell, Kim L.; Barbour, Garth; Alkon, Daniel L.

    1992-07-01

    Most currently popular artificial neural networks (ANN) are based on conceptions of neuronal properties that date back to the 1940s and 50s, i.e., to the ideas of McCullough, Pitts, and Hebb. Dystal is an ANN based on current knowledge of neurobiology at the cellular and subcellular level. Networks based on these neurobiological insights exhibit the following advantageous properties: (1) A theoretical storage capacity of bN non-orthogonal memories, where N is the number of output neurons sharing common inputs and b is the number of distinguishable (gray shade) levels. (2) The ability to learn, store, and recall associations among noisy, arbitrary patterns. (3) A local synaptic learning rule (learning depends neither on the output of the post-synaptic neuron nor on a global error term), some of whose consequences are: (4) Feed-forward, lateral, and feed-back connections (as well as time-sensitive connections) are possible without alteration of the learning algorithm; (5) Storage allocation (patch creation) proceeds dynamically as associations are learned (self- organizing); (6) The number of training set presentations required for learning is small (< 10) and does not change with pattern size or content; and (7) The network exhibits monotonic convergence, reaching equilibrium (fully trained) values without oscillating. The performance of Dystal on pattern completion tasks such as faces with different expressions and/or corrupted by noise, and on reading hand-written digits (98% accuracy) and hand-printed Japanese Kanji (90% accuracy) is demonstrated.

  2. Application of PC-ANN and PC-LS-SVM in QSAR of CCR1 antagonist compounds: a comparative study.

    PubMed

    Shahlaei, Mohsen; Fassihi, Afshin; Saghaie, Lotfollah

    2010-04-01

    Principal component regression (PCR), principal component-artificial neural network (PC-ANN), and principal component-least squares-support vector machine (PC-LS-SVM) as regression methods were investigated for building quantitative structure-activity relationships for the prediction of inhibitory activity of some CCR1 antagonists. Nonlinear methods (PC-ANN and PC-LS-SVM) were better than the PCR method considerably in the goodness of fit and predictivity parameters and other criteria for evaluation of the proposed model. These results reflect a nonlinear relationship between the principal components obtained from molecular descriptors and the inhibitory activity of this set of molecules. The maximum variance in activity of the molecules, in PCR method was 45.5%, whereas nonlinear methods, PC-ANN and PC-LS-SVM, could explain more than 93.7% and 95.6% variance in activity data respectively.

  3. Solving Capelin Time Series Ecosystem Problem Using Hybrid ANN-GAs Model and Multiple Linear Regression Model

    NASA Astrophysics Data System (ADS)

    Eghnam, Karam M.; Sheta, Alaa F.

    2008-06-01

    Development of accurate models is necessary in critical applications such as prediction. In this paper, a solution to the stock prediction problem of the Barents Sea capelin is introduced using Artificial Neural Network (ANN) and Multiple Linear model Regression (MLR) models. The Capelin stock in the Barents Sea is one of the largest in the world. It normally maintained a fishery with annual catches of up to 3 million tons. The Capelin stock problem has an impact in the fish stock development. The proposed prediction model was developed using an ANNs with their weights adapted using Genetic Algorithm (GA). The proposed model was compared to traditional linear model the MLR. The results showed that the ANN-GA model produced an overall accuracy of 21% better than the MLR model.

  4. Queer paradox/paradoxical queer: Anne Garréta's Pas un jour (2002).

    PubMed

    Cairns, Lucille

    2007-01-01

    This paper shows how Anne Garréta's Pas un jour (2002) is a decidedly queer text, in both the new and the old sense of that contested epithet. I examine three interrelated concerns central to Pas un jour. First, I analyze Garréta's mediation of desire in general: her own experiences of it; modalities thereof which subvert more 'normative' models of lesbianism; and her convergences with other gay, but male writers and theorists of desire such as Guy Hocquenghem, Gilles Deleuze and Michel Foucault. Second, I interrogate Garréta's dichotomy between desire and friendship, and adumbrate contrasts with Foucauldian theory. Finally, I scrutinize the meaning and value attributed to the particular body of desire with which Garréta is most commonly associated-homosexuality- and their links with those of a contemporary gay male writer, Dominique Fernandez. PMID:17804371

  5. MLR and ANN models of significant wave height on the west coast of India

    NASA Astrophysics Data System (ADS)

    Asma, Senay; Sezer, Ahmet; Ozdemir, Ozer

    2012-12-01

    Multiple linear regression (MLR) and artificial neural network (ANN) models are used in the present work to describe the significant wave height off Goa, located in the west Indian coast. A comparison study was carried out with the purpose of verifying when the artificial neural network and multiple linear regression models are appropriate for prediction of the significant wave height. Discussions of advantages and disadvantages are given in different point of view for both the methods. Several meteorological factors are used during the analysis and the ones affecting more to the model are kept. We concluded that non-linear models with wind speed and wind gust at a previous time step and air pressure, water temperature and air temperature at the same time step yield to better significant wave height models.

  6. [A heroine for the Resistance: Anne-Mary, Jeanne Menut at Riom (1914-1944)].

    PubMed

    Guyotjeannin, C

    1997-01-01

    Anne-Mary Lafaye was born on may 16th 1914 just before world war I. Her parents were teachers and helped her to go up one step of the social scale; in 1939 she became a pharmacist. Quite an ordinary life, but another world war came. She married Max Menut, early resistant worker, and she was more and more involved, providing the maquis with bandages and medicines. She had made the choice of her values, and time of abnegation came. She had to hide her identity under the name of Marinette. She sold her pharmacy in Riom, she entrusted her daughter to her mother's care, and she had to take care of wounded fighters. Rapidly she was denounced, arrested, tortured and savagely executed.

  7. Ali, Cunich: Halley's Churches: Halley and the London Queen Anne Churches

    NASA Astrophysics Data System (ADS)

    Ali, Jason R.; Cunich, Peter

    2005-04-01

    Edmond Halley's enormous contribution to science has received much attention. New research adds an intriguing chapter to his story and concerns his hitherto unexplored association with the baroque architectural visionary Nicholas Hawksmoor, and some important Temple-inspired churches that were built in London in the early 1700s. We argue that Christchurch Spitalfields and St Anne's Limehouse, which were both started in the summer of 1714, were aligned exactly eastwards using ``corrected'' magnetic-compass bearings and that Halley influenced or aided Hawksmoor. By this time the men had probably known each other for 30 years and had recently worked together on the Clarendon Building in Oxford. Despite there being more than 1500 years of Chinese and about 500 years of Western compass technology at the time, these probably represent the first constructions planned using a modern-day ``scientific'' technique. The research also throws light on Halley's contended religious position.

  8. [The utility boiler low NOx combustion optimization based on ANN and simulated annealing algorithm].

    PubMed

    Zhou, Hao; Qian, Xinping; Zheng, Ligang; Weng, Anxin; Cen, Kefa

    2003-11-01

    With the developing restrict environmental protection demand, more attention was paid on the low NOx combustion optimizing technology for its cheap and easy property. In this work, field experiments on the NOx emissions characteristics of a 600 MW coal-fired boiler were carried out, on the base of the artificial neural network (ANN) modeling, the simulated annealing (SA) algorithm was employed to optimize the boiler combustion to achieve a low NOx emissions concentration, and the combustion scheme was obtained. Two sets of SA parameters were adopted to find a better SA scheme, the result show that the parameters of T0 = 50 K, alpha = 0.6 can lead to a better optimizing process. This work can give the foundation of the boiler low NOx combustion on-line control technology.

  9. The heart of Blessed Anne-Madeleine Remuzat: a biomedical approach of "miraculous" heart conservation.

    PubMed

    Charlier, Philippe; Huynh-Charlier, Isabelle; Poupon, Joël; Fox, Carles Lalueza; Keyser, Christine; Mougniot, Christian; Popescu, Spéranta-Maria; Brun, Luc; Pietri, Sylvia; Thévenard, Frédéric; Laquay, Laëtitia; Hurel, Agathe; Ellul, Jean-Pierre; Hervé, Christian

    2014-01-01

    We present here the results of our inter-disciplinary examination of the mummified heart of Blessed Anne-Madeleine Remuzat (1696-1730). This organ has been examined in the context of a canonization process. This analysis is related to important aspects of the early history of anatomy in Europe, that of "Holy autopsies", and to the relationship between anatomical investigations, Catholic theology, and religious/medical customs. According to anatomical, genetic, toxicological, and palynological analyses, it has been shown that this organ has not been naturally ("miraculously") conserved but embalmed using myrtle, honey, and lime. Moreover, a right ventricle dilatation has been diagnosed, that may represent a post-tuberculosis condition and may have played a role in the cause of death of this religious figure.

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

    PubMed Central

    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 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. PMID:21776225

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

    USGS Publications Warehouse

    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.

  12. 78 FR 10172 - Lisa Anne Cornell and G. Ware Cornell, Jr. v. Princess Cruise Lines, Ltd. (Corp), Carnival PLC...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-02-13

    ... Lisa Anne Cornell and G. Ware Cornell, Jr. v. Princess Cruise Lines, Ltd. (Corp), Carnival PLC, and..., Jr., hereinafter ``Complainants,'' against Princess Cruise Lines, Ltd (Corp), Carnival plc, and... common carrier for hire of passengers from ports in the United States;'' Respondent Carnival plc ``is...

  13. 76 FR 44947 - Notice of Intent To Repatriate Cultural Items: University of Michigan Museum of Anthropology, Ann...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2011-07-27

    ... Anthropology, Ann Arbor, MI AGENCY: National Park Service, Interior. ACTION: Notice. SUMMARY: The University of Michigan Museum of Anthropology, in consultation with the appropriate Indian tribe, has determined that the... Anthropology. DATES: Representatives of any Indian tribe that believes it has a cultural affiliation with...

  14. Boundary Crosser: Anne Whitelaw and Her Leadership Role in Girls' Secondary Schooling in England, New Zealand and East Africa

    ERIC Educational Resources Information Center

    Matthews, Kay Morris

    2005-01-01

    This paper highlights the intersections of history, gender and educational administration through a case study of an influential woman educator, Anne Whitelaw. It draws upon manuscripts, school archives, school histories, official files and periodicals from "both sides" of the world. Although known in New Zealand as the first Headmistress of…

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

    PubMed Central

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

    2016-01-01

    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/. PMID:27179030

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

    PubMed

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

    2016-07-01

    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/. PMID:27179030

  17. "Anne of Green Gables": A One-Act Musical Based on Lucy Maud Montgomery's Novel. Cue Sheet for Teachers.

    ERIC Educational Resources Information Center

    Flynn, Rosalind

    This performance guide is designed for teachers to use with students before and after a performance of the one-act musical based on Lucy Maud Montgomery's novel, "Anne of Green Gables," with music by Richard DeRosa and book and lyrics by Greg Gunning. The guide is designed to help teachers foster students' appreciation of theatre, dance, and…

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

    PubMed

    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. PMID:24271727

  19. A computation ANN model for quantifying the global solar radiation: A case study of Al-Aqabah-Jordan

    NASA Astrophysics Data System (ADS)

    Abolgasem, I. M.; Alghoul, M. A.; Ruslan, M. H.; Chan, H. Y.; Khrit, N. G.; Sopian, K.

    2015-09-01

    In this paper, a computation model is developed to predict the global solar radiation (GSR) in Aqaba city based on the data recorded with association of Artificial Neural Networks (ANN). The data used in this work are global solar radiation (GSR), sunshine duration, maximum & minimum air temperature and relative humidity. These data are available from Jordanian meteorological station over a period of two years. The quality of GSR forecasting is compared by using different Learning Algorithms. The decision of changing the ANN architecture is essentially based on the predicted results to obtain the best ANN model for monthly and seasonal GSR. Different configurations patterns were tested using available observed data. It was found that the model using mainly sunshine duration and air temperature as inputs gives accurate results. The ANN model efficiency and the mean square error values show that the prediction model is accurate. It is found that the effect of the three learning algorithms on the accuracy of the prediction model at the training and testing stages for each time scale is mostly within the same accuracy range.

  20. Prediction of the Rock Mass Diggability Index by Using Fuzzy Clustering-Based, ANN and Multiple Regression Methods

    NASA Astrophysics Data System (ADS)

    Saeidi, Omid; Torabi, Seyed Rahman; Ataei, Mohammad

    2014-03-01

    Rock mass classification systems are one of the most common ways of determining rock mass excavatability and related equipment assessment. However, the strength and weak points of such rating-based classifications have always been questionable. Such classification systems assign quantifiable values to predefined classified geotechnical parameters of rock mass. This causes particular ambiguities, leading to the misuse of such classifications in practical applications. Recently, intelligence system approaches such as artificial neural networks (ANNs) and neuro-fuzzy methods, along with multiple regression models, have been used successfully to overcome such uncertainties. The purpose of the present study is the construction of several models by using an adaptive neuro-fuzzy inference system (ANFIS) method with two data clustering approaches, including fuzzy c-means (FCM) clustering and subtractive clustering, an ANN and non-linear multiple regression to estimate the basic rock mass diggability index. A set of data from several case studies was used to obtain the real rock mass diggability index and compared to the predicted values by the constructed models. In conclusion, it was observed that ANFIS based on the FCM model shows higher accuracy and correlation with actual data compared to that of the ANN and multiple regression. As a result, one can use the assimilation of ANNs with fuzzy clustering-based models to construct such rigorous predictor tools.

  1. 78 FR 72745 - Bureau of Political-Military Affairs; Administrative Debarment of LeAnne Lesmeister Under the...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-12-03

    ... of Political-Military Affairs; Administrative Debarment of LeAnne Lesmeister Under the Arms Export... CONTACT: Sue Gainor, Director, Office of Defense Trade Controls Compliance, Bureau of Political-Military... services. Section 127.7(a) of the ITAR authorizes the Assistant Secretary of State for...

  2. Bridge-Builders and Storytellers: Anne M. Dunn, Ojibwe Storyteller, Shares in the Revival of Teaching and Learning Stories.

    ERIC Educational Resources Information Center

    Merritt, Judy

    1995-01-01

    Based on her belief that all of our lives are stories that are pieces to a puzzle forming the truth behind the sacredness of life, Anne Dunn--Ojibwe storyteller and author--seeks to build bridges between cultures, between generations, and between oral and written storytelling. Includes a review of her book "When Beaver Was Very Great." (SV)

  3. Bodies in Space/Bodies in Motion/Bodies in Character: Adolescents Bear Witness to Anne Frank

    ERIC Educational Resources Information Center

    Chisholm, James S.; Whitmore, Kathryn F.

    2016-01-01

    Situated at the intersection of research on Holocaust education and embodied literacies this study examines how an arts-based instructional approach engaged middle school learners in developing empathetic perspectives on the Anne Frank narrative. We addressed the research question: What can adolescents who are using their bodies to gain empathy…

  4. 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.

    PubMed

    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. PMID:24080294

  5. [Simulation of nitrogen and phosphorus loss in Siling Reservoir watershed with AnnAGNPS].

    PubMed

    Bian, Jin-yun; Wang, Fei-er; Yang, Jia; Yu, Jie; Lou, Li-ping; Yu, Dan-ping

    2012-08-01

    By using annual agricultural non-point source model (AnnAGNPS), this study simulated the export loading of nitrogen and phosphorus in Siling Reservoir watershed in Tiaoxi Basin, and integrated with the simulation results, the spatial distribution characteristics of non-point source pollution in the watershed was analyzed. The result showed that the export loading of nitrogen and phosphorus had similar characteristics: in the study area, the export loading of nutrients were higher in southern and western regions and lower in northern and eastern regions. Forest land mainly made up of bamboo was the main export source of nitrogen and phosphorus loading with the contribution above 90% of nutrient load of whole watershed. Three fertilization practices such as no fertilizer (CK), site-specific nutrient management (SSNM) and farmers' fertilizaction practice (FFP) were used in the scenario analysis. The scenario analysis showed that to a certain degree, SSNM could reduce the nitrogen and phosphorus loss. Comparing with FFP, the reduction of SSNM in dissolved nitrogen (DN), particle nitrogen (PN), dissolved phosphorus (DP) and particle phosphorus (PP) was 8.17%, 4.33%, 9.08% and 1.02%, respectively.

  6. The Anne Frank Haven: A case of an alternative educational program in an integrative Kibbutz setting

    NASA Astrophysics Data System (ADS)

    Ben-Peretz, Miriam; Giladi, Moshe; Dror, Yuval

    1992-01-01

    The essential features of the programme of the Anne Frank Haven are the complete integration of children from low SES and different cultural backgrounds with Kibbutz children; a holistic approach to education; and the involvement of the whole community in an "open" residential school. After 33 years, it is argued that the experiment has proved successful in absorbing city-born youth in the Kibbutz, enabling at-risk populations to reach significant academic achievements, and ensuring their continued participation in the dominant culture. The basic integration model consists of "layers" of concentric circles, in dynamic interaction. The innermost circle is the class, the learning community. The Kibbutz community and the foster parents form a supportive, enveloping circle, which enables students to become part of the outer community and to intervene in it. A kind of meta-environment, the inter-Kibbutz partnership and the Israeli educational system, influence the program through decision making and guidance. Some of the principles of the Haven — integration, community involvement, a year's induction for all new students, and open residential settings — could be useful for cultures and societies outside the Kibbutz. The real "secret" of success of an alternative educational program is the dedicated, motivated and highly trained staff.

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

    PubMed

    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.

  8. Chlorophenols identification in water using an electronic nose and ANNs (artificial neural networks) classification.

    PubMed

    Vázquez, M J; Lorenzo, R A; Cela, R

    2004-01-01

    Electronic artificial noses are being developed as systems for the automated detection and classification of odours, vapors and gases. In the food industry, such devices are used as aids for quality control or process-monitoring tools. An electronic nose (EN) is generally composed of a chemical sensing system and a pattern recognition system (e.g. artificial neural network). An EN based on a non-specific conducting polymer array was used to monitor chlorophenols in water samples. Operational parameters for the EN were optimized by a Plackett-Burman factorial design. The experimental parameters studied were: sample volume, platen temperature, sample equilibration time, loop fill time, sample pressurization time and injection time. Optimal experimental conditions were applied to chlorophenols determination and differentiation in ultrapure water samples spiked with the EPA listed chlorophenols. Data analysis was carried out using principal component analysis (PCA) and artificial neural networks (ANNs) to predict the chlorophenols presence in water samples. The obtained results showed that it was possible to differentiate the five chlorophenol groups: monochlorophenol, dichlorophenol, trichlorophenol, tetrachlorophenol and pentachlorophenol. Differentiation of chlorophenol groups was based on Mahalanobis distance between the formed clusters. This Mahalanobis distance is designated by the Quality Factor, a value >2 for this quality factor means a good differentiation between the clusters.

  9. Amygdalostriatal projections in the neurocircuitry for motivation: A neuroanatomical thread through the career of Ann Kelley

    PubMed Central

    Zorrilla, Eric P.; Koob, George F.

    2013-01-01

    In MacLean’s triune brain, the amygdala putatively subserves motivated behavior by modulating the “reptilian” basal ganglia. Accordingly, Ann Kelley, with Domesick and Nauta, influentially showed that amygdalostriatal projections are much more extensive than were appreciated. Caudal of the anterior commissure, the entire striatum receives afferents from deep basal nuclei of the amygdala. They highlighted that amygdalar projections to the rostral ventromedial striatum converged with projections from the ventral tegmental area and cingulate cortex, forming a “limbic striatum”. Orthologous topographic projections subsequently were observed in fish, amphibians, and reptiles. Subsequent functional studies linked acquired value to action via this neuroanatomical substrate. From Dr. Kelley’s work evolved insights into components of the distributed, interconnected network that subserves motivated behavior, including the nucleus accumbens shell and core and the striatal-like extended amygdala macrostructure. These heuristic frameworks provide a neuroanatomical basis for adaptively translating motivation into behavior. The ancient amygdala-to-striatum pathways remain a current functional thread not only for stimulus–response valuation, but also for the psychopathological plasticity that underlies addictionrelated memory, craving and relapse. PMID:23220696

  10. Amygdalostriatal projections in the neurocircuitry for motivation: a neuroanatomical thread through the career of Ann Kelley.

    PubMed

    Zorrilla, Eric P; Koob, George F

    2013-11-01

    In MacLean's triune brain, the amygdala putatively subserves motivated behavior by modulating the "reptilian" basal ganglia. Accordingly, Ann Kelley, with Domesick and Nauta, influentially showed that amygdalostriatal projections are much more extensive than were appreciated. They highlighted that amygdalar projections to the rostral ventromedial striatum converged with projections from the ventral tegmental area and cingulate cortex, forming a "limbic striatum". Caudal of the anterior commissure, the entire striatum receives afferents from deep basal nuclei of the amygdala. Orthologous topographic projections subsequently were observed in fish, amphibians, and reptiles. Subsequent functional studies linked acquired value to action via this neuroanatomical substrate. From Dr. Kelley's work evolved insights into components of the distributed, interconnected network that subserves motivated behavior, including the nucleus accumbens shell and core and the striatal-like extended amygdala macrostructure. These heuristic frameworks provide a neuroanatomical basis for adaptively translating motivation into behavior. The ancient amygdala-to-striatum pathways remain a current functional thread not only for stimulus-response valuation, but also for the psychopathological plasticity that underlies addiction-related memory, craving and relapse.

  11. Prediction of moving bed biofilm reactor (MBBR) performance for the treatment of aniline using artificial neural networks (ANN).

    PubMed

    Delnavaz, M; Ayati, B; Ganjidoust, H

    2010-07-15

    In this study, the results of 1-year efficiency forecasting using artificial neural networks (ANN) models of a moving bed biofilm reactor (MBBR) for a toxic and hard biodegradable aniline removal were investigated. The reactor was operated in an aerobic batch and continuous condition with 50% by volume which was filled with light expanded clay aggregate (LECA) as carrier. Efficiency evaluation of the reactors was obtained at different retention time (RT) of 8, 24, 48 and 72 h with an influent COD from 100 to 4000 mg/L. Exploratory data analysis was used to detect relationships between the data and dependent evaluated one. The appropriate architecture of the neural network models was determined using several steps of training and testing of the models. The ANN-based models were found to provide an efficient and a robust tool in predicting MBBR performance for treating aromatic amine compounds. PMID:20399558

  12. Application of artificial neural networks (ANNs) and genetic programming (GP) for prediction of drug release from solid lipid matrices.

    PubMed

    Güres, Sinan; Mendyk, Aleksander; Jachowicz, Renata; Dorożyński, Przemysław; Kleinebudde, Peter

    2012-10-15

    The aim of the present study was to develop a semi-empirical mathematical model, which is able to predict the release profiles of solid lipid extrudates of different dimensions. The development of the model was based on the application of ANNs and GP. ANNs' abilities to deal with multidimensional data were exploited. GP programming was used to determine the constants of the model function, a modified Weibull equation. Differently dimensioned extrudates consisting of diprophylline, tristearin and polyethylene glycol were produced by the use of a twin-screw extruder and their dissolution behaviour was studied. Experimentally obtained dissolution curves were compared to the calculated release profiles, derived from the semi-empirical mathematical model.

  13. William Henry, Duke of Gloucester (1689-1700), son of Queen Anne (1665-1714), could have ruled Great Britain.

    PubMed

    Holmes, G E F; Holmes, F F

    2008-02-01

    Hope for continuation of the Stuart dynasty in Britain ended with the death, from pneumonia in 1700, of the 11-year-old son of Princess Anne and Prince George, William Henry Duke of Gloucester. Considered by some to have been physically and mentally unfit to reign, careful examination of primary source materials shows him to have been a bright and interesting boy with mild hydrocephalus. Had he lived, he could have ruled. PMID:18463064

  14. AE index forecast at different time scales through an ANN algorithm based on L1 IMF and plasma measurements

    NASA Astrophysics Data System (ADS)

    Pallocchia, G.; Amata, E.; Consolini, G.; Marcucci, M. F.; Bertello, I.

    2008-02-01

    The AE index is known to have two main components, one directly driven by the solar wind and the other related to the magnetotail unloading process. As regards the role played by the IMF and solar wind parameters, recently several authors used artificial neural networks (ANN) to forecast AE from solar wind data. Following this track, in this paper we present a study of the AE forecast at different time scales, from 5 min to 1 h, in order to check whether the performance of the ANN prediction varies significantly as a function of the AE time resolution.The study is based on a new ANN Elman network with Bz (in GSM) and Vx as inputs, one hidden layer containing four neurons, four context units and one output neuron. We find that the forecast AE values, during disturbed AE periods, result to be always smaller than the experimental values; on the other hand, the algorithm performance improves as the time scale increases, i.e. the total standard deviation (calculated over a test data set) between the forecast and the Kyoto AE decreases as the averaging time increases. Under the hypothesis that this decrease follows an exponential law, we find that the 1 h scale normalised standard deviation is 0.975, very close to the asymptotic value of 0.95 for an infinite averaging time. We interpret our results in the sense that the unloading component of the AE variations cannot be predicted from IMF and solar wind parameters only.

  15. Long-term solar UV radiation reconstructed by ANN modelling with emphasis on spatial characteristics of input data

    NASA Astrophysics Data System (ADS)

    Feister, U.; Junk, J.; Woldt, M.; Bais, A.; Helbig, A.; Janouch, M.; Josefsson, W.; Kazantzidis, A.; Lindfors, A.; den Outer, P. N.; Slaper, H.

    2008-06-01

    Artificial Neural Networks (ANN) are efficient tools to derive solar UV radiation from measured meteorological parameters such as global radiation, aerosol optical depths and atmospheric column ozone. The ANN model has been tested with different combinations of data from the two sites Potsdam and Lindenberg, and used to reconstruct solar UV radiation at eight European sites by more than 100 years into the past. Special emphasis will be given to the discussion of small-scale characteristics of input data to the ANN model. Annual totals of UV radiation derived from reconstructed daily UV values reflect interannual variations and long-term patterns that are compatible with variabilities and changes of measured input data, in particular global dimming by about 1980/1990, subsequent global brightening, volcanic eruption effects such as that of Mt. Pinatubo, and the long-term ozone decline since the 1970s. Patterns of annual erythemal UV radiation are very similar at sites located at latitudes close to each other, but different patterns occur between UV radiation at sites in different latitude regions.

  16. Assessment of strawberry aroma through SPME/GC and ANN methods. Classification and discrimination of varieties.

    PubMed

    Urruty, Louise; Giraudel, Jean-Luc; Lek, Sovan; Roudeillac, Philippe; Montury, Michel

    2002-05-22

    To provide an efficient and running analytical tool to strawberry plant breeders who have to characterize and compare the aromatic properties of new cultivars to those already known, a HS-SPME/GC-MS analysis method has been coupled with a statistical treatment method issued from the current development of artificial neuron networks (ANN), and more specifically, the unsupervised learning systems called Kohonen self-organizing maps (SOMs). So, 70 strawberry samples harvested at CIREF from 17 known varieties have been extracted by using a DVB/Carboxen/PDMS SPME fiber according to the headspace procedure, and then chromatographed. A panel of 23 characteristic aromatic constituents has been selected according to published results relative to strawberry aroma. The complex resulting matrix, collecting the relative abundance of the 23 selected constituents for each sample, has been input into the SOM software adapted and optimized from the Kohonen approach described by one of the authors. After a period of training, the self-organized system affords a map of virtual strawberries to which real samples are compared and plotted in the best matching unit (BMU) of the map. The efficiency for discriminating the real samples according to their variety is dependent on the number of units selected to define the map. In this case, a 24-unit map allowed the complete discrimination of the 17 selected varieties. Moreover, to test the validity of this approach, two additional samples were blind-analyzed and the results were computed according to the same procedure. At the end of this treatment, both samples were plotted into the same unit as those of the same variety used for training the map.

  17. Progress in developing an ANN model for air pollution index forecast

    NASA Astrophysics Data System (ADS)

    Jiang, Dahe; Zhang, Yang; Hu, Xiang; Zeng, Yun; Tan, Jianguo; Shao, Demin

    An air pollution index (API) reporting system is introduced to selected cities of China for public communication on air quality data. Shanghai is the first city in China providing daily average API reports and forecasts. This paper describes the development of an artificial neural network (ANN) model for the API forecasting in Shanghai. It is a multiple layer perceptron (MLP) network, with meteorological forecasting data as the main input, to output the next day average API values. However, the initial version of the MLP model did not work well. To improve the model, a series of tests were conducted with respect to the training method and structure optimization. Based on the test results, the training algorithm was modified and a new model was built. The new model is now being used in Shanghai for API forecasting. Its performance is shown reasonably well in comparison with observation. The application of the old model was only weakly correlated with observation. In 1-year application, the correlation coefficients were 0.2314, 0.1022 and 0.1710 for TSP, SO2 and NOx, respectively. But for the new model, for over 8 months application, the correlation coefficients are raised to 0.6056, 0.6993 and 0.6300 for PM10, SO2, and NO2. Further, the new algorithm does not rely on manpower intervention so that it is now being applied in several other Chinese cities with quite different meteorological conditions. The structure of the model and the application results are presented in this paper and also the problems to be further studied.

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

    NASA Technical Reports Server (NTRS)

    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

  19. ANN-based mapping of febrile subjects in mass thermogram screening: facts and myths.

    PubMed

    Ng, E Y K; Chong, C

    2006-01-01

    Severe Acute Respiratory Syndrome (SARS) is a highly infectious disease caused by a coronavirus. Screening to detect potential SARS-infected subjects with elevated body temperature plays an important role in preventing the spread of SARS. Thermography is being used with ANN/AI to analyse the data collected from the designated SARS hospital in Singapore, and conclusive results are drawn. The current work evaluates the correlations (and classifications) between facial skin temperatures, including eye range and forehead, to aural temperature using a neural network (NN) approach, namely training backpropagation (BP) and Kohonen self-organizing map (SOM), to confirm the suitability of thermal imagers for human temperature screening. Both BP and SOM can form an opinion about the type of network that is better able to complement thermogram technology in fever diagnosis. This can produce better parameters for reducing the size of the NN classifier, while maintaining good classification accuracy. We observe that BP performs better than SOM NN. Confusion matrix (CM), an alternative display instrument, is able to process a high volume of input data and show the clustered output rapidly and accurately. The current research application will remain an interesting and useful reference for both local and overseas manufacturers of thermal scanners, users and various government and private establishments. As the elevation of body temperature is a common presenting symptom for many illnesses, including infectious diseases such as SARS, thermal imagers are useful and essential tools for mass screening of body temperature. This is true not only for SARS but also during other public health crises where widespread transmission of infection such as the danger of avian flu pandemic is a concern, in particular at places like hospitals and cross-border checkpoints.

  20. Thiamine Deficiency Complex Workshop final report: November 6-7, 2008, Ann Arbor, MI

    USGS Publications Warehouse

    Honeyfield, Dale C.; Tillitt, Donald E.; Riley, Stephen C.

    2008-01-01

    Fry mortality which was first observed in the late 1960s in Great Lakes salmonines and in Baltic Sea salmon in 1974 has now been linked to thiamine deficiency (historically referred to as Early Mortality Syndrome, or EMS and M74, respectively). Over the past 14 years significant strides have been made in our understanding of this perplexing problem. It is now known that thiamine deficiency causes embryonic mortality in these salmonids. Both overt mortality and secondary effects of thiamine deficiency are observed in juvenile and adult animals. Collectively the morbidity and mortality (fry and adult mortality, secondary metabolic and behavior affects in juveniles and adult fish) are referred to as Thiamine Deficiency Complex (TDC). A workshop was held in Ann Arbor, MI on 6-7 November 2008 that brought together 38 federal, state, provincial, tribal and university scientists to share information, present data and discuss the latest observations on thiamine status of aquatic animals with thiamine deficiency and the causative agent, thiaminase. Twenty presentations (13 oral and 7 posters) detailed current knowledge. In Lake Huron, low alewife Alosa pseudoharengus abundance has persisted and egg thiamine concentrations in salmonines continue to increase, along with evidence of natural reproduction in lake trout Salvelinus namaycush. Lake Michigan Chinook salmon Oncorhynchus tshawytscha appear to have a lower thiamine requirement than other salmonids in the lake. Lake Ontario American eel Anguilla rostrata foraging on alewife have approximately one third the muscle thiamine compared to eels not feeding on alewife, suggesting that eels may be suffering from thiamine deficiency. Secondary effects of low thiamine exist in Great Lakes salmonines and should not be ignored. Thiaminase activity in dreissenid mussels is extremely high but a connection to TDC has not been made. Thiaminase in net plankton was found more consistently in lakes Michigan and Ontario than other lakes

  1. The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data

    SciTech Connect

    Behrang, M.A.; Assareh, E.; Ghanbarzadeh, A.; Noghrehabadi, A.R.

    2010-08-15

    The main objective of present study is to predict daily global solar radiation (GSR) on a horizontal surface, based on meteorological variables, using different artificial neural network (ANN) techniques. Daily mean air temperature, relative humidity, sunshine hours, evaporation, and wind speed values between 2002 and 2006 for Dezful city in Iran (32 16'N, 48 25'E), are used in this study. In order to consider the effect of each meteorological variable on daily GSR prediction, six following combinations of input variables are considered: (I)Day of the year, daily mean air temperature and relative humidity as inputs and daily GSR as output. (II)Day of the year, daily mean air temperature and sunshine hours as inputs and daily GSR as output. (III)Day of the year, daily mean air temperature, relative humidity and sunshine hours as inputs and daily GSR as output. (IV)Day of the year, daily mean air temperature, relative humidity, sunshine hours and evaporation as inputs and daily GSR as output. (V)Day of the year, daily mean air temperature, relative humidity, sunshine hours and wind speed as inputs and daily GSR as output. (VI)Day of the year, daily mean air temperature, relative humidity, sunshine hours, evaporation and wind speed as inputs and daily GSR as output. Multi-layer perceptron (MLP) and radial basis function (RBF) neural networks are applied for daily GSR modeling based on six proposed combinations. The measured data between 2002 and 2005 are used to train the neural networks while the data for 214 days from 2006 are used as testing data. The comparison of obtained results from ANNs and different conventional GSR prediction (CGSRP) models shows very good improvements (i.e. the predicted values of best ANN model (MLP-V) has a mean absolute percentage error (MAPE) about 5.21% versus 10.02% for best CGSRP model (CGSRP 5)). (author)

  2. Using ANN to predict E. coli accumulation in coves based on interaction amongst various physical, chemical and biological factors

    NASA Astrophysics Data System (ADS)

    Dwivedi, D.; Mohanty, B. P.; Lesikar, B. J.

    2008-12-01

    The accumulation of Escherichia Coli (E. coli) in canals, coves and streams is the result of a number of interacting processes operating at multiple spatial and temporal scales. Fate and transport of E. coli in surface water systems is governed by different physical, chemical, and biological processes. Various models developed to quantify each of these processes occurring at different scales are not so far pooled into a single predictive model. At present, very little is known about the fate and transport of E. coli in the environment. We hypothesize that E. coli population heterogeneity in canals and coves is affected by physical factors (average stream width and/ depth, secchi depth, flow and flow severity, day since precipitation, aquatic vegetation, solar radiation, dissolved and total suspended solids etc.); chemical factors (basic water quality, nutrients, organic compounds, pH, and toxicity etc.); and biological factors (type of bacterial strain, predation, and antagonism etc.). The specific objectives of this study are to: (1) examine the interactions between E. coli and various coupled physical, chemical and biological factors; (2) examine the interactions between E. coli and toxic organic pollutants and other pathogens (viruses); and (3) evaluate qualitatively the removal efficiency of E. coli. We suggest that artificial neural networks (ANN) may be used to provide a possible solution to this problem. To demonstrate the application of the approach, we develop an ANN representing E. coli accumulation in two polluted sites at Lake Granbury in the upper part of the Brazos River in North Central Texas. The graphical structure of ANN explicitly represents cause- and-effect relationship between system variables. Each of these relationships can then be quantified independently using an approach suitable for the type and scale of information available. Preliminary results revealed that E. coli concentrations in canals show seasonal variations regardless of change

  3. AnnAGNPS model as a potential tool for seeking adequate agriculture land management in Navarre (Spain)

    NASA Astrophysics Data System (ADS)

    Chahor, Y.; Giménez, R.; Casalí, J.

    2012-04-01

    Nowadays agricultural activities face two important challenges. They must be efficient from an economic point of view but with low environment impacts (soil erosion risk, nutrient/pesticide contamination, greenhouse gases emissions, etc.). In this context, hydrological and erosion models appear as remarkable tools when looking for the best management practices. AnnAGNPS (Annualized Agricultural Non Point Source Pollution) is a continuous simulation watershed-scale model that estimates yield and transit of surface water, sediment, nutrients, and pesticides through a watershed. This model has been successfully evaluated -in terms of annual runoff and sediment yield- in a small (around 200 ha) agricultural watershed located in central eastern part of Navarre (Spain), named Latxaga. The watershed is under a humid Sub-Mediterranean climate. It is cultivated almost entirely with winter cereals (wheat and barley) following conventional soil and tillage management practices. The remaining 15% of the watershed is covered by urban and shrub areas. The aim of this work is to evaluate in Latxga watershed the effect of potential and realistic changes in land use and management on surface runoff and sediment yield by using AnnAGNPS. Six years (2003 - 2008) of daily climate data were considered in the simulation. This dataset is the same used in the model evaluation previously made. Six different scenarios regarding soil use and management were considered: i) 60% cereals25% sunflower; ii) 60% cereals, 25% rapeseed; iii) 60% cereals, 25% legumes; iv) 60% cereals, 25% sunflower + rapeseed+ legumes, in equal parts; v) cereals, and alternatively different amount of shrubs (from 20% to 100% ); vi) only cereal but under different combinations of conventional tillage and no-tillage management. Overall, no significant differences in runoff generation were observed with the exception of scenario iii (in which legume is the main alternative crops), whit a slight increase in predicted

  4. Probing for leptophilic gauge boson Zl at ILC with $\\sqrt{s} = 1~{\\rm TeV}$ by using ANN

    NASA Astrophysics Data System (ADS)

    Kara, S. Okan; Akkoyun, Serkan; Bayram, Tuncay

    2014-11-01

    We search for leptophilic gauge boson Zl via the process e+e- → μ+μ- at ILC with √ {s} = 1 TeV. In the leptonic extension of SM (SUC(3) × SUW(2) × UY(1) × Ul'(1)) we have predicted that ILC with √ {s} = 1 TeV will enable searching Zl with masses up to the center-of-mass energy if the related coupling constant gl exceeds 10-3 for 3σ observations and 5σ discovery. Furthermore similar results have been obtained by using artificial neural network (ANN) method.

  5. Modeling daily reference ET in the karst area of northwest Guangxi (China) using gene expression programming (GEP) and artificial neural network (ANN)

    NASA Astrophysics Data System (ADS)

    Wang, Sheng; Fu, Zhi-yong; Chen, Hong-song; Nie, Yun-peng; Wang, Ke-lin

    2015-08-01

    Nonlinear complexity is a characteristic of hydrologic processes. Using fewer model parameters is recommended to reduce error. This study investigates, and compares, the ability of gene expression programming (GEP) and artificial neural network (ANN) techniques in modeling ET0 by using fewer meteorological parameters in the karst area of northwest Guangxi province, China. Over a 5-year period (2008-2012), meteorological data consisting of maximum and minimum air temperature, relative humidity, wind speed, and sunshine duration were collected from four weather stations: BaiSe, DuAn, HeChi, and RongAn. The ET0 calculated by the FAO-56 PM equation was used as a reference to evaluate results for GEP, ANN, and Hargreaves models. The coefficient of determination (R 2) and the root mean square error (RMSE) were used as statistical indicators. Evaluations revealed that GEP, and ANN, can be used to successfully model ET0. In most cases, when using the same input variables, ANN models were superior to GEP. We then established ET0 equations with fewer parameters under various conditions. GEP can produce simple explicit mathematical formulations which are easier to use than the ANN models.

  6. A Characterization of Hot Flow Behaviors Involving Different Softening Mechanisms by ANN for As-Forged Ti-10V-2Fe-3Al Alloy

    NASA Astrophysics Data System (ADS)

    Quan, Guo-zheng; Zou, Zhen-yu; Wen, Hai-rong; Pu, Shi-ao; Lv, Wen-quan

    2015-11-01

    The isothermal compressions of as-forged Ti-10V-2Fe-3Al alloy at the deformation temperature range of 948-1,123 K and the strain rates in the range of 0.001-10 s-1 with a height reduction of 60% were conducted on a Gleeble-3500 thermo-mechanical simulator. The flow behaviors show nonlinear sensitivity to strain, strain rate and temperature. Based on the experimental data, an artificial neural network (ANN) with back-propagation algorithm was developed to deal with the complex deformation behavior characteristics. In the present ANN model, strain, strain rate and temperature were taken as inputs, and flow stress as output. A comparative study on the constitutive relationships based on regression and ANN methods was conducted. According to the predicted and experimental results, the predictabilities of the two models have been evaluated in terms of correlation coefficient (R) and average absolute relative error (AARE). The R-value and the AARE-value at strain of 0.5 from the ANN model is 0.9998 and 0.572%, respectively, better than 0.9902 and 6.583% from the regression model. The predicted strain-stress curves outside of experimental conditions indicate similar characteristics with experimental curves. The results have sufficiently articulated that the well-trained ANN model with back-propagation algorithm has excellent capability to deal with the complex flow behaviors of as-forged Ti-10V-2Fe-3Al alloy.

  7. Use of computer-assisted methods for the modeling of the retention time of a variety of volatile organic compounds: a PCA-MLR-ANN approach.

    PubMed

    Jalali-Heravi, M; Kyani, A

    2004-01-01

    A hybrid method consisting of principal component analysis (PCA), multiple linear regressions (MLR), and artificial neural network (ANN) was developed to predict the retention time of 149 C(3)-C(12) volatile organic compounds for a DB-1 stationary phase. PCA and MLR methods were used as feature-selection tools, and a neural network was employed for predicting the retention times. The regression method was also used as a calibration model for calculating the retention time of VOCs and investigating their linear characteristics. The descriptors of the total information index of atomic composition, IAC, Wiener number, W, solvation connectivity index, X1sol, and number of substituted aromatic C(sp(2)), nCaR, appeared in the MLR model and were used as inputs for the ANN generation. Appearance of these parameters shows the importance of the dispersion interactions in the mechanism of retention. Comparison of the MLR and 5-2-1 ANN models indicates the superiority of the ANN over that of the MLR model. The values of 0.913 and 0.738 were obtained for the standard error of prediction set of MLR and ANN models, respectively. PMID:15272841

  8. Optoelectronic performance and artificial neural networks (ANNs) modeling of n-InSe/p-Si solar cell

    NASA Astrophysics Data System (ADS)

    Darwish, A. A. A.; Hanafy, T. A.; Attia, A. A.; Habashy, D. M.; El-Bakry, M. Y.; El-Nahass, M. M.

    2015-07-01

    Nanostructure thin film of InSe deposited on p-Si single crystal to fabricate n-InSe/p-Si heterojunction. Electrical and photoelectrical have been studied by the current density-voltage (J-V). The fabricated cell exhibited rectifying characteristics. Analyzing the results of dark forward J-V shows that there are differrent conduction mechanisms. At low voltages, the current density is controlled by a Schottky emission mechanism. While at a relatively high voltage, a space charge-limited-conduction mechanism is observed with a single trap level. The cell also exhibited a photovoltaic characteristic with a power conversion efficiency of 3.42%. Moreover, artificial neural networks (ANNs) are adopted to model the J-V through the obtained functions. Different network configurations and many runs were trying to achieve good performance and finally obtained the current density, J, as a function of the junction temperature, T, and applied voltage, V. In all cases studied, we compared our obtained functions produced by the ANN technique with the corresponding experimental data and the excellent matching was so clear.

  9. Design of an Experiment to Measure ann Using 3H(γ, pn)n at HIγS★

    NASA Astrophysics Data System (ADS)

    Friesen, F. Q. L.; Ahmed, M. W.; Crowe, B. J.; Crowell, A. S.; Cumberbatch, L. C.; Fallin, B.; Han, Z.; Howell, C. R.; Malone, R. M.; Markoff, D.; Tornow, W.; Witała, H.

    2016-03-01

    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. This research supported in part by the DOE Office of Nuclear Physics Grant Number DE-FG02-97ER41033

  10. A method to improve the stability and accuracy of ANN- and SVM-based time series models for long-term groundwater level predictions

    NASA Astrophysics Data System (ADS)

    Yoon, Heesung; Hyun, Yunjung; Ha, Kyoochul; Lee, Kang-Kun; Kim, Gyoo-Bum

    2016-05-01

    The prediction of long-term groundwater level fluctuations is necessary to effectively manage groundwater resources and to assess the effects of changes in rainfall patterns on groundwater resources. In the present study, a weighted error function approach was utilised to improve the performance of artificial neural network (ANN)- and support vector machine (SVM)-based recursive prediction models for the long-term prediction of groundwater levels in response to rainfall. The developed time series models were applied to groundwater level data from 5 groundwater-monitoring stations in South Korea. The results demonstrated that the weighted error function approach can improve the stability and accuracy of recursive prediction models, especially for ANN models. The comparison of the model performance showed that the recursive prediction performance of the SVM was superior to the performance of the ANN in this case study.

  11. Comparison of estimation capabilities of response surface methodology (RSM) with artificial neural network (ANN) in lipase-catalyzed synthesis of palm-based wax ester

    PubMed Central

    Basri, Mahiran; Rahman, Raja Noor Zaliha Raja Abd; Ebrahimpour, Afshin; Salleh, Abu Bakar; Gunawan, Erin Ryantin; Rahman, Mohd Basyaruddin Abd

    2007-01-01

    Background Wax esters are important ingredients in cosmetics, pharmaceuticals, lubricants and other chemical industries due to their excellent wetting property. Since the naturally occurring wax esters are expensive and scarce, these esters can be produced by enzymatic alcoholysis of vegetable oils. In an enzymatic reaction, study on modeling and optimization of the reaction system to increase the efficiency of the process is very important. The classical method of optimization involves varying one parameter at a time that ignores the combined interactions between physicochemical parameters. RSM is one of the most popular techniques used for optimization of chemical and biochemical processes and ANNs are powerful and flexible tools that are well suited to modeling biochemical processes. Results The coefficient of determination (R2) and absolute average deviation (AAD) values between the actual and estimated responses were determined as 1 and 0.002844 for ANN training set, 0.994122 and 1.289405 for ANN test set, and 0.999619 and 0.0256 for RSM training set respectively. The predicted optimum condition was: reaction time 7.38 h, temperature 53.9°C, amount of enzyme 0.149 g, and substrate molar ratio 1:3.41. The actual experimental percentage yield was 84.6% at optimum condition, which compared well to the maximum predicted value by ANN (83.9%) and RSM (85.4%). The order of effective parameters on wax ester percentage yield were; respectively, time with 33.69%, temperature with 30.68%, amount of enzyme with 18.78% and substrate molar ratio with 16.85%, whereas R2 and AAD were determined as 0.99998696 and 1.377 for ANN, and 0.99991515 and 3.131 for RSM respectively. Conclusion Though both models provided good quality predictions in this study, yet the ANN showed a clear superiority over RSM for both data fitting and estimation capabilities. PMID:17760990

  12. Dating of pollen samples from the sediment core of Lake St Anne in the East Carpathian Mountains, Romania

    NASA Astrophysics Data System (ADS)

    Hubay, Katalin; Katalin Magyari, Enikö; Braun, Mihály; Schabitz, Frank; Molnár, Mihály

    2016-04-01

    Lake St Anne (950 m a.s.l.) is situated in the Ciomadul volcano crater, the youngest volcano in the Carpathians. Aims driving forward the studies there are twofold, one is dating the latest eruption of the Ciomadul volcano and the other is the multi-proxy palaeoenvironmental reconstruction of this region. The sediment of Lake St Anne was sampled several times already, but never reached the bottom of the lake before. During the winter of 2013 at a new core location drilling started at 600 cm water depth and finally reached the bottom of the lake sediment at approximately 2300 cm including water depth. As for all multi-proxy studies essential requirement was to build a reliable chronology. Sediments were dated by radiocarbon method. Previous radiocarbon dates were measured on plant macrofossils, charcoal, Cladocera eggs, chironomid head capsules and bulk lake sediments. Lake St Anne has volcanic origin and there is intensive upwelling of CO2it is important to study and take into consideration, whether there is any local reservoir effect at the case of samples where it could be problematic. Furthermore the late part of the sediment section (between 15,000 and 30,000 cal. yr BP) has low organic matter content (less than 2-4%) with scarcity of datable plant macrofossil material. In this review a different fraction of pollen samples with terrestrial origin was tested and studied as a novel sample type for the radiocarbon dating. Pollen samples were extracted from the lake sediment cores. This type of organic material could be an ideal candidate for radiocarbon based chronological studies as it has terrestrial source and is present in the whole core in contrast with the terrestrial macrofossils. Although the pollen remains were present in the whole core, in many cases their amount give a challenge even for the AMS technic. Samples were measured with EnvironMICADAS AMS and its gas ion source in the HEKAL laboratory (Debrecen, Hungary). We examine the reliability the

  13. Mining multi-channel EEG for its information content: an ANN-based method for a brain-computer interface.

    PubMed

    Peters, Bjorn O.; Pfurtscheller, Gert; Flyvbjerg, Henrik

    1998-10-01

    We have studied 56-channel electroencephalograms (EEG) from three subjects who planned and performed three kinds of movements, left and right index finger, and right foot movement. Using autoregressive modeling of EEG time series and artificial neural nets (ANN), we have developed a classifier that can tell which movement is performed from a segment of the EEG signal from a single trial. The classifier's rate of recognition of EEGs not seen before was 92-99% on the basis of a 1s segment per trial. The recognition rate provides a pragmatic measure of the information content of the EEG signal. This high recognition rate makes the classifier suitable for a so-called 'Brain-Computer Interface', a system that allows one to control a computer, or another device, with ones brain waves. Our classifier Laplace filters the EEG spatially, but makes use of its entire frequency range, and automatically locates regions of relevant activity on the skull.

  14. General neural computer architecture and its ANN-based task assignment method for parallel-distributed processing

    NASA Astrophysics Data System (ADS)

    Chao, Hu; Ray, Sylvian R.; Zheng, Nanning

    1995-06-01

    A new DSP-based neural simulating computer architecture and its ANN-based assignment method for parallel distributed processing are proposed. The hardware of the proposed neural simulating computer can be reconfigured in terms of a variety of research interests and requirements of pattern recognition. The software programming environment utilizes an intelligent compiler to perform static task assignment in both the cases of single-task muliprocessor and multitask processor. An improved Hopfield neural network which can converge to global optical solution is employed by the compiler to map different tasks or neurons to their corresponding real processors. An approach of introducing hidden layer to increase the computation ability of the neural simulating computer is also developed. Finally, a proof is given which shows that the use of improved Hopfield algorithm and the modification to network structure doesn't change the intrinsic properties of the original network.

  15. Estimation of runoff and sediment yield in the Redrock Creek watershed using AnnAGNPS and GIS

    USGS Publications Warehouse

    Tsou, M.-S.; Zhan, X.-Y.

    2004-01-01

    Sediment has been identified as a significant threat to water quality and channel clogging that in turn may lead to river flooding. With the increasing awareness of the impairment from sediment to water bodies in a watershed, identifying the locations of the major sediment sources and reducing the sediment through management practices will be important for an effective watershed management. The annualized agricultural non-point source pollution (AnnAGNPS) model and newly developed GIS interface for it were applied in a small agricultural watershed, Redrock Creek watershed, Kansas, in this pilot study for exploring the effectiveness of using this model as a management tool. The calibrated model appropriately simulated monthly runoff and sediment yield through the practices in this study and potentially suggested the ways of sediment reduction through evaluating the changes of land use and field operation in the model for the purpose of watershed management.

  16. Women's translations of scientific texts in the 18th century: a case study of Marie-Anne Lavoisier.

    PubMed

    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. PMID:22606747

  17. Predicting spatial kelp abundance in shallow coastal waters using the acoustic ground discrimination system RoxAnn

    NASA Astrophysics Data System (ADS)

    Mielck, F.; Bartsch, I.; Hass, H. C.; Wölfl, A.-C.; Bürk, D.; Betzler, C.

    2014-04-01

    Kelp forests represent a major habitat type in coastal waters worldwide and their structure and distribution is predicted to change due to global warming. Despite their ecological and economical importance, there is still a lack of reliable spatial information on their abundance and distribution. In recent years, various hydroacoustic mapping techniques for sublittoral environments evolved. However, in turbid coastal waters, such as off the island of Helgoland (Germany, North Sea), the kelp vegetation is present in shallow water depths normally excluded from hydroacoustic surveys. In this study, single beam survey data consisting of the two seafloor parameters roughness and hardness were obtained with RoxAnn from water depth between 2 and 18 m. Our primary aim was to reliably detect the kelp forest habitat with different densities and distinguish it from other vegetated zones. Five habitat classes were identified using underwater-video and were applied for classification of acoustic signatures. Subsequently, spatial prediction maps were produced via two classification approaches: Linear discriminant analysis (LDA) and manual classification routine (MC). LDA was able to distinguish dense kelp forest from other habitats (i.e. mixed seaweed vegetation, sand, and barren bedrock), but no variances in kelp density. In contrast, MC also provided information on medium dense kelp distribution which is characterized by intermediate roughness and hardness values evoked by reduced kelp abundances. The prediction maps reach accordance levels of 62% (LDA) and 68% (MC). The presence of vegetation (kelp and mixed seaweed vegetation) was determined with higher prediction abilities of 75% (LDA) and 76% (MC). Since the different habitat classes reveal acoustic signatures that strongly overlap, the manual classification method was more appropriate for separating different kelp forest densities and low-lying vegetation. It became evident that the occurrence of kelp in this area is not

  18. [Sensitivity analysis of AnnAGNPS model's hydrology and water quality parameters based on the perturbation analysis method].

    PubMed

    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.

  19. Women's translations of scientific texts in the 18th century: a case study of Marie-Anne Lavoisier.

    PubMed

    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.

  20. [Discriminating and quantifying potential adulteration in virgin olive oil by near infrared spectroscopy with BP-ANN and PLS].

    PubMed

    Weng, Xin-Xin; Lu, Feng; Wang, Chuan-Xian; Qi, Yun-Peng

    2009-12-01

    In the present paper, the use of near infrared spectroscopy (NIR) as a rapid and cost-effective classification and quantification techniques for the authentication of virgin olive oil were preliminarily investigated. NIR spectra in the range of 12 000 - 3 700 cm(-1) were recorded for pure virgin olive oil and virgin olive oil samples adulterated with varying concentrations of sesame oil, soybean oil and sunflower oil (5%-50% adulterations in the weight of virgin olive oil). The spectral range from 12 000 to 5 390 cm(-1) was adopted to set up an analysis model. In order to handle these data efficiently, after pretreatment, firstly, principal component analysis (PCA) was used to compress thousands of spectral data into several variables and to describe the body of the spectra, and the analysis suggested that the cumulate reliabilities of the first six components was more than 99.999%. Then ANN-BP was chosen as further research method. The six components were secondly applied as ANN-BP inputs. The experiment took a total of 100 samples as original model examples and left 52 samples as unknown samples to predict. Finally, the results showed that the 52 test samples were discriminated accurately. And the calibration models of quantitative analysis were built using partial-least-square (PLS). The R values for PLS model are 98.77, 99.37 and 99.44 for sesame oil, soybean oil and sunflower oil respectively, the root mean standard errors of cross validation (RMSECV) are 1.3, 1.1 and 1.04 respectively. Overall, the near infrared spectroscopic method in the present paper played a good role in the discrimination and quantification, and offered a new approach to the rapid discrimination of pure and adulterated virgin olive oil. PMID:20210151

  1. The use of artificial neural network (ANN) for the prediction and simulation of oil degradation in wastewater by AOP.

    PubMed

    Mustafa, Yasmen A; Jaid, Ghydaa M; Alwared, Abeer I; Ebrahim, Mothana

    2014-06-01

    The application of advanced oxidation process (AOP) in the treatment of wastewater contaminated with oil was investigated in this study. The AOP investigated is the homogeneous photo-Fenton (UV/H2O2/Fe(+2)) process. The reaction is influenced by the input concentration of hydrogen peroxide H2O2, amount of the iron catalyst Fe(+2), pH, temperature, irradiation time, and concentration of oil in the wastewater. The removal efficiency for the used system at the optimal operational parameters (H2O2 = 400 mg/L, Fe(+2) = 40 mg/L, pH = 3, irradiation time = 150 min, and temperature = 30 °C) for 1,000 mg/L oil load was found to be 72%. The study examined the implementation of artificial neural network (ANN) for the prediction and simulation of oil degradation in aqueous solution by photo-Fenton process. The multilayered feed-forward networks were trained by using a backpropagation algorithm; a three-layer network with 22 neurons in the hidden layer gave optimal results. The results show that the ANN model can predict the experimental results with high correlation coefficient (R (2) = 0.9949). The sensitivity analysis showed that all studied variables (H2O2, Fe(+2), pH, irradiation time, temperature, and oil concentration) have strong effect on the oil degradation. The pH was found to be the most influential parameter with relative importance of 20.6%. PMID:24595749

  2. [Sensitivity analysis of AnnAGNPS model's hydrology and water quality parameters based on the perturbation analysis method].

    PubMed

    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. PMID:25055665

  3. 1977-78 Evaluation of the Title VII Bilingual Program Project Exito at the Ann Street Bilingual School of Hartford, Connecticut.

    ERIC Educational Resources Information Center

    Hartford Public Schools, CT.

    Evaluation results for Hartford, Connecticut's 1977-78 Title VII Bilingual Education Program at Ann Street School are presented. Student and staff accomplishments, a section on selected curricular activities, a restatement of the 1977-78 results in terms of the Title VII proposal objectives, and conclusions and recommendations for consideration of…

  4. Evaluation of the AnnAGNPS model for predicting runoff and sediment yield in a small Mediterranean agricultural watershed in Navarre (Spain)

    Technology Transfer Automated Retrieval System (TEKTRAN)

    AnnAGNPS (Annualized Agricultural Non-Point Source Pollution Model) is a system of computer models developed to predict non-point source pollutant loadings within agricultural watersheds. It contains a daily time step distributed parameter continuous simulation surface runoff model designed to assis...

  5. Artificial neural network (ANN) modeling of adsorption of methylene blue by NaOH-modified rice husk in a fixed-bed column system.

    PubMed

    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.

  6. Let Them Aspire! A Plea and Proposal for Equality of Opportunity for Males and Females in the Ann Arbor Public Schools. Fourth Edition.

    ERIC Educational Resources Information Center

    Federbush, Marcia

    The report compiled by the Committee to Eliminate Sex Discrimination in the Public Schools, Ann Arbor, Michigan, for their Board of Education, points out the areas of school life in which females are not given the chance or the encouragement to aspire to competence. Four areas of particular disadvantage are stereotyping in books, athletic…

  7. Proceedings of Workshop on Priority Great Lakes Environmental Research Initiatives (Great Lakes Environmental Research Laboratory, Ann Arbor, Michigan, October 10-11, 1974).

    ERIC Educational Resources Information Center

    Pinsak, Arthur P., Ed.

    This publication contains the proceedings of a workshop held in Ann Arbor, Michigan to identify the priority Great Lakes environmental research initiatives. The five major objectives of the workshop include the determination of research initiatives, opportunities for university research communities to discuss and recommend future research…

  8. Evaluation of the AnnAGNPS model for predicting runoff and sediment yield in a small Mediterranean agricultural watershed

    NASA Astrophysics Data System (ADS)

    Chahor, Youssef; Casalí, Javier; Goñi, Mikel; Giménez, Rafael; Campo, Miguel A.; Del Valle de Lersundi, Jokin

    2010-05-01

    Four experimental watersheds with contrasting land uses located in Navarre (Spain), and maintained by the local government have been monitored and studied since 1996 (La Tejería and Latxaga) and 2001 (Oskotz principal and Oskotz woodland). As a result, a detailed description and a general characterization of the hydrological and erosion behaviour of these watersheds were published recently by the same authors of this current research. This information is of great utility for evaluation of modelling tools; however, we have been done few efforts until now in this research line. The Annualized Agricultural Non Point Source Pollution Model (AnnAGNPS) is a well known and widely used model developed by the USDA-ARS and the USDA- NRCS, to assess the hydrologic and water quality responses of watersheds. More precisely, it is a distributed parameter, physically based, continuous simulation, and daily time step model. The purpose of this study is then to evaluate the AnnAGNPS model capability to simulate runoff and sediment yield with data sets from one of our agricultural watershed: Latxaga. Latxaga watershed covers an area of 207 ha and is located in the central eastern part of Navarre. Its climate is humid Sub-Mediterranean, with an average annual precipitation of 835 mm, and an average annual temperature of 12.8 °C. Geologically, the area is underlined by clay marls and sandstones. Prevailing soils are alkaline with a fine texture top-layer. Regarding land use, 80-90% of the total area is cultivated with winter grain crops. The model was calibrated using two years (2003 and 2004) of continuous 10-min/daily data, whereas another whole year (2005) was used for model validation. The calibration process was carried out by modifying Curve Number (CN) values obtained by standard procedure. CN represents a key factor in obtaining accurate prediction of runoff and sediment yield; besides it is the most important input parameter to which the runoff is sensitive. The target

  9. BP-ANN for Fitting the Temperature-Germination Model and Its Application in Predicting Sowing Time and Region for Bermudagrass

    PubMed Central

    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 R2 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

  10. Carol Anne Bond v the United States of America: how a woman scorned threatened the Chemical Weapons Convention.

    PubMed

    Muldoon, Anna; Kornblet, Sarah; Katz, Rebecca

    2011-09-01

    The case of Carol Anne Bond v the United States of America stemmed from a domestic dispute when Ms. Bond attempted to retaliate against her best friend by attacking her with chemical agents. What has emerged is a much greater issue--a test of standing on whether a private citizen can challenge the Tenth Amendment. Instead of being prosecuted in state court for assault, Ms. Bond was charged and tried in district court under a federal criminal statute passed as part of implementation of the Chemical Weapons Convention (CWC). Ms. Bond's argument rests on the claim that the statute exceeded the federal government's enumerated powers in criminalizing her behavior and violated the Constitution, while the government contends legislation implementing treaty obligations is well within its purview. This question remains unanswered because there is dispute among the lower courts as to whether Ms. Bond, as a citizen, even has the right to challenge an amendment guaranteeing states rights when a state is not a party to the action. The Supreme Court heard the case on February 22, 2011, and, if it decides to grant Ms. Bond standing to challenge her conviction, the case will be returned to the lower courts. Should the court decide Ms. Bond has the standing to challenge her conviction and further questions the constitutionality of the law, it would be a significant blow to implementation of the CWC in the U.S. and the effort of the federal government to ensure we are meeting our international obligations. PMID:21819227

  11. A novel and generalized approach in the inversion of geoelectrical resistivity data using Artificial Neural Networks (ANN)

    NASA Astrophysics Data System (ADS)

    Raj, A. Stanley; Srinivas, Y.; Oliver, D. Hudson; Muthuraj, D.

    2014-03-01

    The non-linear apparent resistivity problem in the subsurface study of the earth takes into account the model parameters in terms of resistivity and thickness of individual subsurface layers using the trained synthetic data by means of Artificial Neural Networks (ANN). Here we used a single layer feed-forward neural network with fast back propagation learning algorithm. So on proper training of back propagation networks it tends to give the resistivity and thickness of the subsurface layer model of the field resistivity data with reference to the synthetic data trained in the appropriate network. During training, the weights and biases of the network are iteratively adjusted to make network performance function level more efficient. On adequate training, errors are minimized and the best result is obtained using the artificial neural networks. The network is trained with more number of VES data and this trained network is demonstrated by the field data. The accuracy of inversion depends upon the number of data trained. In this novel and specially designed algorithm, the interpretation of the vertical electrical sounding has been done successfully with the more accurate layer model.

  12. Biotreatment of zinc-containing wastewater in a sulfidogenic CSTR: Performance and artificial neural network (ANN) modelling studies.

    PubMed

    Sahinkaya, Erkan

    2009-05-15

    Sulfidogenic treatment of sulfate (2-10g/L) and zinc (65-677mg/L) containing simulated wastewater was studied in a mesophilic (35 degrees C) CSTR. Ethanol was supplemented (COD/sulfate=0.67) as carbon and energy source for sulfate-reducing bacteria (SRB). The robustness of the system was studied by increasing Zn, COD and sulfate loadings. Sulfate removal efficiency, which was 70% at 2g/L feed sulfate concentration, steadily decreased with increasing feed sulfate concentration and reached 40% at 10g/L. Over 99% Zn removal was attained due to the formation of zinc-sulfide precipitate. COD removal efficiency at 2g/L feed sulfate concentration was over 94%, whereas, it steadily decreased due to the accumulation of acetate at higher loadings. Alkalinity produced from acetate oxidation increased wastewater pH remarkably when feed sulfate concentration was 5g/L or lower. Electron flow from carbon oxidation to sulfate reduction averaged 83+/-13%. The rest of the electrons were most likely coupled with fermentative reactions as the amount of methane production was insignificant. The developed ANN model was very successful as an excellent to reasonable match was obtained between the measured and the predicted concentrations of sulfate (R=0.998), COD (R=0.993), acetate (R=0.976) and zinc (R=0.827) in the CSTR effluent.

  13. Experimental and numerical study of nanofluid in heat exchanger fitted by modified twisted tape: exergy analysis and ANN prediction model

    NASA Astrophysics Data System (ADS)

    Maddah, Heydar; Ghasemi, Nahid; Keyvani, Bahram; Cheraghali, Ramin

    2016-09-01

    Present study provides an experimental investigation of the exergetic efficiency due to the flow and heat transfer of nanofluids in different geometries and flow regimes of the double pipe heat exchangers. The experiments with different Geometrical Progression Ratio (GPR) of twists as the new modified twisted tapes and different nanofluid concentration were performed under similar operation condition. Pitch length of the proposed twisted tapes and consequently the twist ratios changed along the twists with respect to the Geometrical Progression Ratio (GPR) whether reducer (RGPR < 1) or increaser (IGPR > 1). Regarding the experimental data, utilization of RGPR twists together with nanofluids tends to increase exergetic efficiency. Since the Prediction of exergetic efficiency from experimental process is complex and time consuming, artificial neural networks for identification of the relationship, which may exist between the thermal and flow parameters and exergetic efficiency, have been utilized. The network input consists of five parameters (Re,Pr ,φ, Tr, GPR) that crucially dominate the heat transfer process. The results proved that the introduced ANN model is reliable and capable in proposing a proper development plan for a heat exchanger and/or to determine the optimal plan of operation for heat transfer process.

  14. Renal coccidiosis and other parasitologic conditions in lesser snow goose goslings at Tha-anne River, west coast Hudson Bay.

    PubMed

    Gomis, S; Didiuk, A B; Neufeld, J; Wobeser, G

    1996-07-01

    Lesser snow goose (Chen caerulescens caerulescens) goslings, approximately 5 weeks of age, were collected near the mouth of Tha-anne River, Northwest Territories, Canada, during mid-August 1991. Many dead goslings had been observed in the area from 1988 to 1990. Goslings from near the coast, where habitat degradation by grazing geese was severe, were smaller, weighed less, and had a greater prevalence of renal coccidiosis (Eimeria truncata) and cecal nematode (Trichostrongylus spp.) infection than did goslings from inland areas, where habitat destruction was not evident. Prevalence of infection with intestinal cestodes was greater at inland than at coastal sites. Prevalences of gizzard nematodes (Epomidiostomum spp.) and Leucocytozoon spp. were not significantly different at the two sites. Histological examination of kidneys and examination of kidney homogenates for oocysts were more sensitive methods than gross examination of the kidneys for detecting renal coccidial infection. The number of oocysts present in droppings was not a good indicator of the severity of renal coccidial infection in individual birds; however, the average number of oocysts in droppings was indicative of the average severity of infection among groups of goslings. PMID:8827676

  15. Maps and seismic profiles showing geology of the inner continental shelf, Cape Ann, Massachusetts to New Hampshire

    USGS Publications Warehouse

    Oldale, R.N.; Wommack, L.E.

    1987-01-01

    Early studies in the western Gulf of Maine have outlined the general geology and geologic history of the region. Seismic-reflection data have defined the major stratigraphic units and unconformities (Oldale and Uchupi, 1970; Ballard and Uchupi, 1972; Oldale and others, 1973). Two long cores provided information on the glacial and postglacial sediments in the deep offshore basins (Tucholke and Hollister, 1973). Generalized bottom-sediment type and distribution were determined by Schlee and others (1973) and by Folger and others (1975). Investigations on land, which have provided information on the late Quaternary history of the offshore area, include descriptions of ice retreat and marine submergence (Bloom, 1963; Smith, 1982; Stone and Peper, 1982; Thompson, 1982). Radiocarbon dates from coastal marsh peats have established the middle to late Holocene sea-level-rise history (McIntire and Morgan, 1964; Keene, 1971). Submarine moraincs that recently were recognized off Cape Ann provide additional information on the nature and chronology of ice retreat (Oldale, 1985a). A submerged delta of the Merrimack River and a submerged barrier spit have been used to establish an early IIolocene lowstand of 588 level of about 50 meters (m) below present sea level (Oldale and others, 1983; Oldale, 1985b).

  16. The collaboration of Antoine and Marie-Anne Lavoisier and the first measurements of human oxygen consumption.

    PubMed

    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. PMID:24097559

  17. Early diagnosis of systemic lupus erythmatosus using ANN models of dsDNA binding antibody sequence data.

    PubMed

    Bahari, Mohamad Hasan; Mahmoudi, Mahmoud; Azemi, Asad; Mirsalehi, Mir Mojtaba; Khademi, Morteza

    2010-01-01

    In this paper a new method based on artificial neural networks (ANN), is introduced for identifying pathogenic antibodies in Systemic Lupus Erythmatosus (SLE). dsDNA binding antibodies have been implicated in the pathogenesis of this autoimmune disease. In order to identify these dsDNA binding antibodies, the protein sequences of 42 dsDNA binding and 608 non-dsDNA binding antibodies were extracted from Kabat database and encoded using a physicochemical property of their amino acids namely Hydrophilicity. Encoded antibodies were used as the training patterns of a general regression neural network (GRNN). Simulation results show that the accuracy of proposed method in recognizing dsDNA binding antibodies is 83.2%. We have also investigated the roles of the light and heavy chains of anti-dsDNA antibodies in binding to DNA. Simulation results concur with the published experimental findings that in binding to DNA, the heavy chain of anti-dsDNA is more important than their light chain. PMID:21346864

  18. The collaboration of Antoine and Marie-Anne Lavoisier and the first measurements of human oxygen consumption.

    PubMed

    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.

  19. Controller development of photo bioreactor for closed-loop regulation of O2 production based on ANN model reference control and computer simulation

    NASA Astrophysics Data System (ADS)

    Hu, Dawei; Zhang, Houkai; Zhou, Rui; Li, Ming; Sun, Yi

    2013-02-01

    When Bioregenerative Life Support System (BLSS) is used for long-term deep space exploration in the future, it is possible to perform closed-loop control on growth of microalgae to effectively regulate O2 production process in emergencies. However, designing controller of microalgae cultivating device (MCD) by means of traditional methods is very difficult or even impossible due to its highly nonlinearity and large operation scope. In our research, the Artificial Neural Network Model Reference Control (ANN-MRC) method was therefore utilized for model identification and controller design for O2 production process of a specific MCD prototype—photo bioreactor (PBR), based on actual experiment and computer simulation. The results demonstrated that the ANN-MRC servo controller could robustly and self-adaptively control and regulate the light intensity of PBR to make O2 concentrations in vent pipe be in line with step reference concentrations with prescribed dynamic response performance.

  20. Use of Artificial Neural Networks (ANNs) for the Analysis and Modeling of Factors That Affect Occupational Injuries in Large Construction Industries

    PubMed Central

    Mohammadfam, Iraj; Soltanzadeh, Ahmad; Moghimbeigi, Abbas; Savareh, Behrouz Alizadeh

    2015-01-01

    Introduction Occupational injuries as a workforce’s health problem are very important in large-scale workplaces. Analysis and modeling the health-threatening factors are good ways to promote the workforce’s health and a fundamental step in developing health programs. The purpose of this study was ANN modeling of the severity of occupational injuries to determine the health-threatening factors and to introduce a model to predict the severity of occupational injuries. Methods This analytical chain study was conducted in 10 large construction industries during a 10-year period (2005–2014). Nine hundred sixty occupational injuries were analyzed and modeled based on feature weighting by the rough set theory and artificial neural networks (ANNs). Two analytical software programs, i.e., RSES and MATLAB 2014 were used in the study. Results The severity of occupational injuries was calculated as 557.47 ± 397.87 days. The findings of both models showed that the injuries’ severity as a health problem resulted in various factors, including individual, organizational, health and safety (H&S) training, and risk management factors, which could be considered as causal and predictive factors of accident severity rate (ASR). Conclusion The results indicated that ANNs were a reliable tool that can be used to analyze and model the severity of occupational injuries as one of the important health problems in large-scale workplaces. Additionally, the combination of rough set and ANNs is a good and proper chain approach to modeling the factors that threaten the health of workforces and other H&S problems. PMID:26767107

  1. Estimation of daily global solar radiation using wavelet regression, ANN, GEP and empirical models: A comparative study of selected temperature-based approaches

    NASA Astrophysics Data System (ADS)

    Sharifi, Sayed Saber; Rezaverdinejad, Vahid; Nourani, Vahid

    2016-11-01

    Although the sunshine-based models generally have a better performance than temperature-based models for estimating solar radiation, the limited availability of sunshine duration records makes the development of temperature-based methods inevitable. This paper presents a comparative study between Artificial Neural Networks (ANNs), Gene Expression Programming (GEP), Wavelet Regression (WR) and 5 selected temperature-based empirical models for estimating the daily global solar radiation. A new combination of inputs including four readily accessible parameters have been employed: daily mean clearness index (KT), temperature range (ΔT), theoretical sunshine duration (N) and extraterrestrial radiation (Ra). Ten statistical indicators in a form of GPI (Global Performance Indicator) is used to ascertain the suitability of the models. The performance of selected models across the range of solar radiation values, was depicted by the quantile-quantile (Q-Q) plots. Comparing these plots makes it evident that ANNs can cover a broader range of solar radiation values. The results shown indicate that the performance of ANN model was clearly superior to the other models. The findings also demonstrated that WR model performed well and presented high accuracy in estimations of daily global solar radiation.

  2. Prediction of the likely impact of climate change on monthly mean maximum and minimum temperature in the Chaliyar river basin, India, using ANN-based models

    NASA Astrophysics Data System (ADS)

    Chithra, N. R.; Thampi, Santosh G.; Surapaneni, Sujith; Nannapaneni, Revanth; Reddy, A. Ashok Kumar; Kumar, J. Dinesh

    2015-08-01

    In this work, an approach based on Artificial Neural Networks (ANN) has been employed to assess the likely impact of climate change on mean monthly maximum and minimum temperature ( T max and T min) in the Chaliyar river basin, Kerala, India. ANN is trained to downscale temperature from the General Circulation Model (GCM) from a coarser resolution to the required resolution of the river basin. The work aims to estimate the GCMs' output to the scales compatible with that employed in a hydrologic model of the river basin. In order to satiate this purpose, predictor variables were obtained from the National Centre for Environmental Prediction and National Centre for Atmospheric Research (NCEP/NCAR) reanalysis data; this was utilized for training the ANN using a feed-forward network with a back-propagation algorithm. These models were validated further and used to downscale CGCM3 GCM simulations for the scenarios outlined in the IPCC Special Report on Emission Scenarios (SRES). Results showed that both T max and T min are increasing consistently in all the scenarios. T max exhibited an average increase of maximum 3 °C during the dry season (December-May) and 1 °C during the wet season (June-November) by the year 2100, while T min showed an average increase of 2.5 °C in the dry season and 0.5 °C in the wet season.

  3. The adventures of Ann: A case study of a kindergarten teacher and her beliefs as she explored integrating science into her literacy curriculum

    NASA Astrophysics Data System (ADS)

    Potter, Gregory Ralph

    Science education is an often neglected portion of the curriculum in elementary school, particularly in the primary grades. While early childhood educators have many choices in their curricula, two constants remain, literacy and math education. Ideally, young children need science along with literacy and mathematics. This study investigated how one kindergarten teacher used science to enhance her literacy program and how this use of science in her classroom affected her teaching beliefs. The case study took place in a publicly funded early childhood education center devoted to teaching kindergarten children in the small town of Summers in rural northern California. "Ann" was a master kindergarten teacher who historically used developmentally appropriate activities to support her literacy instruction. She was posed with the suggestion of infusing science into her literacy program and over the course of one school year, she was observed planning, implementing, and reflecting on six integrated science and literacy units. Ann's general teaching beliefs as well as her beliefs about teaching literacy and science were explored in order to investigate whether her experience with the integrated science and literacy units had altered her teaching beliefs. It was discovered that not only had Ann significantly changed the way she taught science, her beliefs about teaching science had changed and had moved towards mimicking her pro-active and positive beliefs about teaching literacy.

  4. Span-to-depth ratio effect on shear strength of steel fiber-reinforced high-strength concrete deep beams using ANN model

    NASA Astrophysics Data System (ADS)

    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.

  5. Reply to Ann Bradshaw.

    PubMed Central

    Allmark, Peter

    1996-01-01

    My original paper suggested that an ethics of care which failed to specify how, and about what, to care would be devoid of normative and descriptive content. Bradshaw's approach provides such a specification and is, therefore, not devoid of such content. However, as all ethical approaches suggest something about the 'what' and 'how' of care, they are all 'ethics of care' in this broader sense. This reinforces rather than undermines my original conclusion. Furthermore, Bradshaw's 'ethics of care' has philosophical and historical problems which I outline. PMID:11644877

  6. Ann Richards Middle School.

    ERIC Educational Resources Information Center

    Kell, John H.

    2001-01-01

    Presents photos and basic information about a Texas middle school whose architecture reflects the hybrid culture of the borderlands and "regionalism" in which it is located. A line drawing of the site plan is included. (GR)

  7. Prediction of subcooled vapor pressures (log PL) of 399 polychlorinated trans-azoxybenzenes by using the QSPR and ANN approach.

    PubMed

    Piliszek, Sławomir; Wilczyńska-Piliszek, Agata J; Falandysz, Jerzy

    2012-01-01

    Environmentally relevant partitioning properties such as the sub-cooled vapor pressures (log PL) have been predicted for 399 congeners of chloro-trans-azoxybenzene (C-t-AOBs) by two computational methods. The quantitative structure-property relationship (QSPR), an approach which is based on geometry optimalization and quantum-chemical structural descriptors in RM1 and DFT methods and artificial neural networks (ANNs), an approach that predicts abilities that give similar results of estimated log P(L) and the accuracy of the methods was also similar. The RM1 method was less time consuming and less costly compared to calculations by the DFT method. Estimated from the RM1 and DFT methods of log P(L) values of 399 Ct-AOBs varied between -1.98 to -0.93 and -1.83 to -0.79 for Mono-, 3.12 to -1.46 and -3.00 to -1.46 for Di-, -4.03 to -1.39 and -3.53 to -1.67 for Tri-, -4.75 to -2.33 and -4.59 to -1.91 for Tetra-, -5.37 to -2.59 and -5.42 to -2.09 for Penta-, -5.82 to -2.88 and -5.66 to -2.58 for Hexa-, -5.88 to -3.24 and -5.60 to -2.93 for Hepta-, -6.28 to -4.33 and -5.60 to -4.29 for Octa-, -6.54 to -5.28 and -5.66 to -4.93 for NonaCt-AOBs, and -6.59 and -5.61 for DecaCt-AOB. According to a common classification of environmental contaminants and by sub-cooled vapor pressure values, MonoCt-AOBs and a few of the Di- and TriCt-AOBs (log P(L)from -2 to 0) fall into the group of compounds that are relatively well mobile in the ambient environment, while most of the Di- to HeptaCt-AOBs (log P(L) < -4 to -2) mobility is relatively weak. Octa- and NonaCt-AOBs and DecaCt-AOB (log P(L) < -4) are also weak mobile contaminants.

  8. [Nonpoint source pollution model, AnnAGNPS, assessment for a mixed forested watershed in Three Gorges Reservoir area].

    PubMed

    Huang, Zhi-lin; Tian, Yao-wu; Xiao, Wen-fa; Zeng, Li-xiong; Ma, De-ju

    2009-10-15

    Watershed models provide a cost-effective and efficient means of estimating the pollutant loadings entering surface waters, especially when combined with traditional water quality sampling and analyses. But there have often been questions about the accuracy or certainty of models and their predictions. The main goal of this study was to evaluate the performance of AnnAGNPS (Annualized AGricultural NonPoint Source)Pollution Model, in simulating runoff, sediment loading and nutrient loadings under Three Gorges Reservoir area. Most of model input parameters were sourced from Zigui Forest Ecology Station in Three Gorges Reservoir area, State Forestry Administration. Data year 2003 was used for calibration while data year 2004 was used for validation of the model. The whole evaluation consisted of determining the coefficient of determination (R2), Nash-Sutcliffe coefficient of efficiency (E), and the percentage volume error (VE). Results showed that the model predicted the daily runoff volume within the range of acceptable accuracy. The runoff on a daily basis was underpredicted by 5.0% with R2 of 0.93 (p < 0.05) during calibration and underpredicted by 6.7% with R2 of 0.90 (p < 0.05) during validation. But sediment loading was able to produce a moderate result. The model underpredicted the event-based sediment loading by 15.1% with R2 of 0.63 (p < 0.05) during calibration and 26.7% with R2 of 0.59 (p < 0.05) during validation. For the events of small magnitude, the model generally overpredicted sediment loading, while the opposite was true for larger events. Nitrogen loading prediction was slightly better with R2 = 0.68 (p < 0.05), and phosphorus loading performance was slightly poor with R2 = 0.65 (p < 0.05). In general, the model performs well in simulating runoff compare to sediment loading and nutrient loadings, and as a watershed management tools it can be used for Three Gorges Reservoir area conditions that with mixed types of land uses and steep slopes.

  9. Assessment of Runoff and Sediment Yields Using the AnnAGNPS Model in a Three-Gorge Watershed of China

    PubMed Central

    Hua, Lizhong; He, Xiubin; Yuan, Yongping; Nan, Hongwei

    2012-01-01

    Soil erosion has been recognized as one of the major threats to our environment and water quality worldwide, especially in China. To mitigate nonpoint source water quality problems caused by soil erosion, best management practices (BMPs) and/or conservation programs have been adopted. Watershed models, such as the Annualized Agricultural Non-Point Source Pollutant Loading model (AnnAGNPS), have been developed to aid in the evaluation of watershed response to watershed management practices. The model has been applied worldwide and proven to be a very effective tool in identifying the critical areas which had serious erosion, and in aiding in decision-making processes for adopting BMPs and/or conservation programs so that cost/benefit can be maximized and non-point source pollution control can be achieved in the most efficient way. The main goal of this study was to assess the characteristics of soil erosion, sediment and sediment delivery of a watershed so that effective conservation measures can be implemented. To achieve the overall objective of this study, all necessary data for the 4,184 km2 Daning River watershed in the Three-Gorge region of the Yangtze River of China were assembled. The model was calibrated using observed monthly runoff from 1998 to 1999 (Nash-Sutcliffe coefficient of efficiency of 0.94 and R2 of 0.94) and validated using the observed monthly runoff from 2003 to 2005 (Nash-Sutcliffe coefficient of efficiency of 0.93 and R2 of 0.93). Additionally, the model was validated using annual average sediment of 2000–2002 (relative error of −0.34) and 2003–2004 (relative error of 0.18) at Wuxi station. Post validation simulation showed that approximately 48% of the watershed was under the soil loss tolerance released by the Ministry of Water Resources of China (500 t·km−2·y−1). However, 8% of the watershed had soil erosion of exceeding 5,000 t·km−2·y−1. Sloping areas and low coverage areas are the main source of soil loss in the

  10. Le groupe de recherches transfusionnelles d’Afrique francophone: bilan des cinq premières années

    PubMed Central

    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

  11. Prediction of high spatio-temporal resolution land surface temperature under cloudy conditions using microwave vegetation index and ANN

    NASA Astrophysics Data System (ADS)

    Shwetha, H. R.; Kumar, D. Nagesh

    2016-07-01

    Land Surface Temperature (LST) with high spatio-temporal resolution is in demand for hydrology, climate change, ecology, urban climate and environmental studies, etc. Moderate Resolution Imaging Spectroradiometer (MODIS) is one of the most commonly used sensors owing to its high spatial and temporal availability over the globe, but is incapable of providing LST data under cloudy conditions, resulting in gaps in the data. In contrast, microwave measurements have a capability to penetrate under clouds. The current study proposes a methodology by exploring this property to predict high spatio-temporal resolution LST under cloudy conditions during daytime and nighttime without employing in-situ LST measurements. To achieve this, Artificial Neural Networks (ANNs) based models are employed for different land cover classes, utilizing Microwave Polarization Difference Index (MPDI) at finer resolution with ancillary data. MPDI was derived using resampled (from 0.25° to 1 km) brightness temperatures (Tb) at 36.5 GHz channel of dual polarization from Advance Microwave Scanning Radiometer (AMSR)-Earth Observing System and AMSR2 sensors. The proposed methodology is tested over Cauvery basin in India and the performance of the model is quantitatively evaluated through performance measures such as correlation coefficient (r), Nash Sutcliffe Efficiency (NSE) and Root Mean Square Error (RMSE). Results revealed that during daytime, AMSR-E(AMSR2) derived LST under clear sky conditions corresponds well with MODIS LST resulting in values of r ranging from 0.76(0.78) to 0.90(0.96), RMSE from 1.76(1.86) K to 4.34(4.00) K and NSE from 0.58(0.61) to 0.81(0.90) for different land cover classes. During nighttime, r values ranged from 0.76(0.56) to 0.87(0.90), RMSE from 1.71(1.70) K to 2.43(2.12) K and NSE from 0.43(0.28) to 0.80(0.81) for different land cover classes. RMSE values found between predicted LST and MODIS LST during daytime under clear sky conditions were within acceptable

  12. 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.

    PubMed

    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.

  13. Statistical analysis and ANN modeling for predicting hydrological extremes under climate change scenarios: the example of a small Mediterranean agro-watershed.

    PubMed

    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. PMID:25710600

  14. Development of CAD based on ANN analysis of power spectra for pneumoconiosis in chest radiographs: effect of three new enhancement methods.

    PubMed

    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.

  15. 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.

    PubMed

    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. PMID:21350755

  16. Statistical analysis and ANN modeling for predicting hydrological extremes under climate change scenarios: the example of a small Mediterranean agro-watershed.

    PubMed

    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.

  17. Mapping Seabed Habitats and Dynamic Bedforms using Hydroacoustic Discrimination (Sidescan Sonar and RoxAnn) in the Sylt-Rømø Basin (German Wadden Sea)

    NASA Astrophysics Data System (ADS)

    Mielck, F.; Hass, H. C.

    2012-04-01

    Seabed habitat mapping is important to understand hydrodynamic processes as well as marine ecosystems. The study site (4 km2) is located in the Sylt-Rømø Basin (German Wadden Sea). It includes deep channels that harbor strong tidal currents and shallower sublittoral areas. The investigations were performed with two different hydroacoustic devices: (1) IMAGENEX YellowFin (Model 872) sidescan sonar to take imagery of the seafloor and (2) SONAVISION RoxAnn (Model GD-X) acoustic ground-discrimination system to classify the seafloor according to its hardness and roughness properties. The devices worked with frequencies of 200 kHz (RoxAnn) and 770 kHz (sidescan sonar). For ground truthing purposes 124 sediment-surface samples were collected using a HELCOM grab sampler. The results reveal varying environments and bedforms within this small survey site. The seafloor surface sediments range from fine to coarse sand. Bedforms include subaquatic dunes of different size, partly superimposed by smaller ripple structures. Coarser sediments characterize the deeper tidal channels (water depth ~ 20 m) where also the measured roughness and hardness values strongly increase. The shallower areas reveal finer sediments and lower backscatter. The hydrodynamic processes that govern the distribution of surficial sediments are also responsible for the formation of the characteristic bedforms. Sidescan sonography makes it possible to investigate alignment and the migration direction of the bedforms which allows to reconstruct the general hydrodynamic properties of the study area and beyond. In combination with RoxAnn data, it is possible to assign seabed habitat classes especially with regard to their hardness and roughness properties. Apparently, areas showing similar grain-size spectra can show totally different roughness and hardness values. Hence, they cannot be classified as the same habitat. Further comparison between sidescan sonography and RoxAnn results reveals that roughness and

  18. Artificial neural network (ANN) approach for modeling of Pb(II) adsorption from aqueous solution by Antep pistachio (Pistacia Vera L.) shells.

    PubMed

    Yetilmezsoy, Kaan; Demirel, Sevgi

    2008-05-30

    A three-layer artificial neural network (ANN) model was developed to predict the efficiency of Pb(II) ions removal from aqueous solution by Antep pistachio (Pistacia Vera L.) shells based on 66 experimental sets obtained in a laboratory batch study. The effect of operational parameters such as adsorbent dosage, initial concentration of Pb(II) ions, initial pH, operating temperature, and contact time were studied to optimise the conditions for maximum removal of Pb(II) ions. On the basis of batch test results, optimal operating conditions were determined to be an initial pH of 5.5, an adsorbent dosage of 1.0 g, an initial Pb(II) concentration of 30 ppm, and a temperature of 30 degrees C. Experimental results showed that a contact time of 45 min was generally sufficient to achieve equilibrium. After backpropagation (BP) training combined with principal component analysis (PCA), the ANN model was able to predict adsorption efficiency with a tangent sigmoid transfer function (tansig) at hidden layer with 11 neurons and a linear transfer function (purelin) at output layer. The Levenberg-Marquardt algorithm (LMA) was found as the best of 11 BP algorithms with a minimum mean squared error (MSE) of 0.000227875. The linear regression between the network outputs and the corresponding targets were proven to be satisfactory with a correlation coefficient of about 0.936 for five model variables used in this study.

  19. Constitutive Equations and ANN Approach to Predict the Flow Stress of Ti-6Al-4V Alloy Based on ABI Tests

    NASA Astrophysics Data System (ADS)

    Wang, Fuzeng; Zhao, Jun; Zhu, Ningbo

    2016-09-01

    The flow behavior of Ti-6Al-4V alloy was studied by automated ball indentation (ABI) tests in a wide range of temperatures (293, 493, 693, and 873 K) and strain rates (10-6, 10-5, and 10-4 s-1). Based on the experimental true stress-plastic strain data derived from the ABI tests, the Johnson-Cook (JC), Khan-Huang-Liang (KHL) and modified Zerilli-Armstrong (ZA) constitutive models, as well as artificial neural network (ANN) methods, were employed to predict the flow behavior of Ti-6Al-4V. A comparative study was made on the reliability of the four models, and their predictability was evaluated in terms of correlation coefficient (R) and mean absolute percentage error. It is found that the flow stresses of Ti-6Al-4V alloy are more sensitive to temperature than strain rate under current experimental conditions. The predicted flow stresses obtained from JC model and KHL model show much better agreement with the experimental results than modified ZA model. Moreover, the ANN model is much more efficient and shows a higher accuracy in predicting the flow behavior of Ti-6Al-4V alloy than the constitutive equations.

  20. Evaluation of the AnnAGNPS Model for Predicting Runoff and Nutrient Export in a Typical Small Watershed in the Hilly Region of Taihu Lake.

    PubMed

    Luo, Chuan; Li, Zhaofu; Li, Hengpeng; Chen, Xiaomin

    2015-09-01

    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. PMID:26364642

  1. Evaluation of the AnnAGNPS Model for Predicting Runoff and Nutrient Export in a Typical Small Watershed in the Hilly Region of Taihu Lake

    PubMed Central

    Luo, Chuan; Li, Zhaofu; Li, Hengpeng; Chen, Xiaomin

    2015-01-01

    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. PMID:26364642

  2. Modeling multiple land use changes using ANN, CART and MARS: Comparing tradeoffs in goodness of fit and explanatory power of data mining tools

    NASA Astrophysics Data System (ADS)

    Tayyebi, Amin; Pijanowski, Bryan C.

    2014-05-01

    Over half of the earth's terrestrial surface has been modified by humans. This modification is called land use change and its pattern is known to occur in a non-linear way. The land use change modeling community can advance its models using data mining tools. Here, we present three data mining land use change models, one based on Artificial Neural Network (ANN), another on Classification And Regression Trees (CART) and another Multivariate Adaptive Regression Splines (MARS). We reconfigured the three data mining models to concurrently simulate multiple land use classes (e.g. agriculture, forest and urban) in South-Eastern Wisconsin (SEWI), USA (time interval 1990-2000) and in Muskegon River Watershed (MRW), Michigan, USA (time interval 1978-1998). We compared the results of the three data mining tools using relative operating characteristic (ROC) and percent correct match (PCM). We found that ANN provided the best accuracy in both areas for three land use classes (e.g. urban, agriculture and forest). In addition, in both regions, CART and MARS both showed that forest gain occurred in areas close to current forests, agriculture patches and away from roads. Urban increased in areas of high urban density, close to roads and in areas with few forests and wetlands. We also found that agriculture gain is more likely for the areas closer to the agriculture and forest patches. Elevation strongly influenced urbanization and forest gain in MRW while it has no effect in SEWI.

  3. Support vector machine regression (LS-SVM)--an alternative to artificial neural networks (ANNs) for the analysis of quantum chemistry data?

    PubMed

    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

  4. Support vector machine regression (LS-SVM)--an alternative to artificial neural networks (ANNs) for the analysis of quantum chemistry data?

    PubMed

    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

  5. 3D fluid-structure modelling and vibration analysis for fault diagnosis of Francis turbine using multiple ANN and multiple ANFIS

    NASA Astrophysics Data System (ADS)

    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.

  6. Artificial neural network (ANN) method for modeling of sunset yellow dye adsorption using zinc oxide nanorods loaded on activated carbon: Kinetic and isotherm study

    NASA Astrophysics Data System (ADS)

    Maghsoudi, M.; Ghaedi, M.; Zinali, A.; Ghaedi, A. M.; Habibi, M. H.

    2015-01-01

    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 (R2) 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.

  7. Preliminary Analysis of the efficacy of Artificial neural Network (ANN) and Cellular Automaton (CA) based Land Use Models in Urban Land-Use Planning

    NASA Astrophysics Data System (ADS)

    Harun, R.

    2013-05-01

    This research provides an opportunity of collaboration between urban planners and modellers by providing a clear theoretical foundations on the two most widely used urban land use models, and assessing the effectiveness of applying the models in urban planning context. Understanding urban land cover change is an essential element for sustainable urban development as it affects ecological functioning in urban ecosystem. Rapid urbanization due to growing inclination of people to settle in urban areas has increased the complexities in predicting that at what shape and size cities will grow. The dynamic changes in the spatial pattern of urban landscapes has exposed the policy makers and environmental scientists to great challenge. But geographic science has grown in symmetry to the advancements in computer science. Models and tools are developed to support urban planning by analyzing the causes and consequences of land use changes and project the future. Of all the different types of land use models available in recent days, it has been found by researchers that the most frequently used models are Cellular Automaton (CA) and Artificial Neural Networks (ANN) models. But studies have demonstrated that the existing land use models have not been able to meet the needs of planners and policy makers. There are two primary causes identified behind this prologue. First, there is inadequate understanding of the fundamental theories and application of the models in urban planning context i.e., there is a gap in communication between modellers and urban planners. Second, the existing models exclude many key drivers in the process of simplification of the complex urban system that guide urban spatial pattern. Thus the models end up being effective in assessing the impacts of certain land use policies, but cannot contribute in new policy formulation. This paper is an attempt to increase the knowledge base of planners on the most frequently used land use model and also assess the

  8. Facteurs prédictifs de succès des étudiants en première année de médecine à l'université de Parakou

    PubMed Central

    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<0,05 a été considéré comme statistiquement significatif. Résultats Parmi les 414 étudiants régulièrement inscrits les données de 407 ont pu être exploitées. Ils étaient âgés de 15 à 31 ans; 262 (64,4%) étaient de sexe masculin. 98 étaient admis avec un taux de succès de 23,7%. Le sexe masculin, la note obtenue en mathématiques, en sciences physiques, la moyenne au BAC et la mention étaient associés au succès en fin d'année mais en analyse multivariée seule une note en sciences physiques > 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

  9. Mixture of expert 3D massive-training ANNs for reduction of multiple types of false positives in CAD for detection of polyps in CT colonography.

    PubMed

    Suzuki, Kenji; Yoshida, Hiroyuki; Näppi, Janne; Armato, Samuel G; Dachman, Abraham H

    2008-02-01

    One of the major challenges in computer-aided detection (CAD) of polyps in CT colonography (CTC) is the reduction of false-positive detections (FPs) without a concomitant reduction in sensitivity. A large number of FPs is likely to confound the radiologist's task of image interpretation, lower the radiologist's efficiency, and cause radiologists to lose their confidence in CAD as a useful tool. Major sources of FPs generated by CAD schemes include haustral folds, residual stool, rectal tubes, the ileocecal valve, and extra-colonic structures such as the small bowel and stomach. Our purpose in this study was to develop a method for the removal of various types of FPs in CAD of polyps while maintaining a high sensitivity. To achieve this, we developed a "mixture of expert" three-dimensional (3D) massive-training artificial neural networks (MTANNs) consisting of four 3D MTANNs that were designed to differentiate between polyps and four categories of FPs: (1) rectal tubes, (2) stool with bubbles, (3) colonic walls with haustral folds, and (4) solid stool. Each expert 3D MTANN was trained with examples from a specific non-polyp category along with typical polyps. The four expert 3D MTANNs were combined with a mixing artificial neural network (ANN) such that different types of FPs could be removed. Our database consisted of 146 CTC datasets obtained from 73 patients whose colons were prepared by standard pre-colonoscopy cleansing. Each patient was scanned in both supine and prone positions. Radiologists established the locations of polyps through the use of optical-colonoscopy reports. Fifteen patients had 28 polyps, 15 of which were 5-9 mm and 13 were 10-25 mm in size. The CTC cases were subjected to our previously reported CAD method consisting of centerline-based extraction of the colon, shape-based detection of polyp candidates, and a Bayesian-ANN-based classification of polyps. The original CAD method yielded 96.4% (27/28) by-polyp sensitivity with an average of 3

  10. Spectral feature extraction of EEG signals and pattern recognition during mental tasks of 2-D cursor movements for BCI using SVM and ANN.

    PubMed

    Bascil, M Serdar; Tesneli, Ahmet Y; Temurtas, Feyzullah

    2016-09-01

    Brain computer interface (BCI) is a new communication way between man and machine. It identifies mental task patterns stored in electroencephalogram (EEG). So, it extracts brain electrical activities recorded by EEG and transforms them machine control commands. The main goal of BCI is to make available assistive environmental devices for paralyzed people such as computers and makes their life easier. This study deals with feature extraction and mental task pattern recognition on 2-D cursor control from EEG as offline analysis approach. The hemispherical power density changes are computed and compared on alpha-beta frequency bands with only mental imagination of cursor movements. First of all, power spectral density (PSD) features of EEG signals are extracted and high dimensional data reduced by principle component analysis (PCA) and independent component analysis (ICA) which are statistical algorithms. In the last stage, all features are classified with two types of support vector machine (SVM) which are linear and least squares (LS-SVM) and three different artificial neural network (ANN) structures which are learning vector quantization (LVQ), multilayer neural network (MLNN) and probabilistic neural network (PNN) and mental task patterns are successfully identified via k-fold cross validation technique. PMID:27376723

  11. Hydrology and sediment budget of Los Laureles Canyon, Tijuana, MX: Modelling channel, gully, and rill erosion with 3D photo-reconstruction, CONCEPTS, and AnnAGNPS

    NASA Astrophysics Data System (ADS)

    Taniguchi, Kristine; Gudiño, Napoleon; Biggs, Trent; Castillo, Carlos; Langendoen, Eddy; Bingner, Ron; Taguas, Encarnación; Liden, Douglas; Yuan, Yongping

    2015-04-01

    Several watersheds cross the US-Mexico boundary, resulting in trans-boundary environmental problems. Erosion in Tijuana, Mexico, increases the rate of sediment deposition in the Tijuana Estuary in the United States, altering the structure and function of the ecosystem. The well-being of residents in Tijuana is compromised by damage to infrastructure and homes built adjacent to stream channels, gully formation in dirt roads, and deposition of trash. We aim to understand the dominant source of sediment contributing to the sediment budget of the watershed (channel, gully, or rill erosion), where the hotspots of erosion are located, and what the impact of future planned and unplanned land use changes and Best Management Practices (BMPs) will be on sediment and storm flow. We will be using a mix of field methods, including 3D photo-reconstruction of stream channels, with two models, CONCEPTS and AnnAGNPS to constrain estimates of the sediment budget and impacts of land use change. Our research provides an example of how 3D photo-reconstruction and Structure from Motion (SfM) can be used to model channel evolution.

  12. Spectral feature extraction of EEG signals and pattern recognition during mental tasks of 2-D cursor movements for BCI using SVM and ANN.

    PubMed

    Bascil, M Serdar; Tesneli, Ahmet Y; Temurtas, Feyzullah

    2016-09-01

    Brain computer interface (BCI) is a new communication way between man and machine. It identifies mental task patterns stored in electroencephalogram (EEG). So, it extracts brain electrical activities recorded by EEG and transforms them machine control commands. The main goal of BCI is to make available assistive environmental devices for paralyzed people such as computers and makes their life easier. This study deals with feature extraction and mental task pattern recognition on 2-D cursor control from EEG as offline analysis approach. The hemispherical power density changes are computed and compared on alpha-beta frequency bands with only mental imagination of cursor movements. First of all, power spectral density (PSD) features of EEG signals are extracted and high dimensional data reduced by principle component analysis (PCA) and independent component analysis (ICA) which are statistical algorithms. In the last stage, all features are classified with two types of support vector machine (SVM) which are linear and least squares (LS-SVM) and three different artificial neural network (ANN) structures which are learning vector quantization (LVQ), multilayer neural network (MLNN) and probabilistic neural network (PNN) and mental task patterns are successfully identified via k-fold cross validation technique.

  13. PC-ANN assisted to the determination of Vanadium (IV) ion using an optical sensor based on immobilization of Eriochorome Cyanine R on a triacetylcellulose film.

    PubMed

    Bordbar, Mohammad Mahdi; Khajehsharifi, Habibollah; Solhjoo, Aida

    2015-01-01

    More detailed analytical studies of an optical sensor based on immobilization of Eriochorome Cyanine R (ECR) on a triacetylcellulose film have been described to determine Vanadium (IV) ions in some real samples. The sensor based on complex formation between Vanadium (IV) ions and ECR in acidic media caused the color of the film to change from violet to blue along with the appearance of a strong peak appears at 595 nm. At the optimal conditions, the calibration curve showed a linear range of 9.90×10(-7)-8.25×10(-5)mol L(-1). Vanadium (IV) ions can be detected with a detection limit of 1.03×10(-7)mol L(-1) within 15 min depending on its concentration. Also, the working range was improved by using PC-ANN algorithm. The sensor could regenerate with dilute acetic acid solution and could be completely reversible. The proposed sensor was successfully applied for determining V (IV) ions in environmental water and tea leaves.

  14. Safety, immunogencity, and efficacy of a cold-adapted A/Ann Arbor/6/60 (H2N2) vaccine in mice and ferrets

    SciTech Connect

    Chen, Grace L.; Lamirande, Elaine W.; Jin Hong; Kemble, George; Subbarao, Kanta

    2010-03-01

    We studied the attenuation, immunogenicity and efficacy of the cold-adapted A/Ann Arbor/6/60 (AA ca) (H2N2) virus in mice and ferrets to evaluate its use in the event of an H2 influenza pandemic. The AA ca virus was restricted in replication in the respiratory tract of mice and ferrets. In mice, 2 doses of vaccine elicited a > 4-fold rise in hemagglutination-inhibition (HAI) titer and resulted in complete inhibition of viral replication following lethal homologous wild-type virus challenge. In ferrets, a single dose of the vaccine elicited a > 4-fold rise in HAI titer and conferred complete protection against homologous wild-type virus challenge in the upper respiratory tract. In both mice and ferrets, the AA ca virus provided significant protection from challenge with heterologous H2 virus challenge in the respiratory tract. The AA ca vaccine is safe, immunogenic, and efficacious against homologous and heterologous challenge in mice and ferrets, supporting the evaluation of this vaccine in clinical trials.

  15. First principles study of structural, electronic, mechanical and magnetic properties of actinide nitrides AnN (An = U, Np and Pu)

    NASA Astrophysics Data System (ADS)

    Murugan, A.; Priyanga, G. Sudha; Rajeswarapalanichamy, R.; Santhosh, M.; Iyakutti, K.

    2016-09-01

    The electronic, structural, mechanical and magnetic properties of Actinide nitrides AnN (An = U, Np and Pu) are investigated in three cubic phases, namely, NaCl (B1), CsCl (B2) and zinc blende (B3). At normal pressure, UN is stable in antiferromagnetic state while the other two nitrides are stable in the ferromagnetic state with NaCl (B1) structure. A pressure induced structural phase transition from B1 to B3 phase is predicted in these nitrides. The electronic structure reveals that these nitrides are metallic in nature. The magnetic phase transition from antiferromagnetic to non-magnetic state is observed in UN at a pressure of 127 GPa while ferromagnetic to non-magnetic state is observed in NpN and PuN at the pressures of 67 GPa and 102.3 GPa respectively. The computed structural parameters, bulk modulus density of states and charge density distributions are compared with experimental and other theoretical calculations.

  16. PC-ANN assisted to the determination of Vanadium (IV) ion using an optical sensor based on immobilization of Eriochorome Cyanine R on a triacetylcellulose film.

    PubMed

    Bordbar, Mohammad Mahdi; Khajehsharifi, Habibollah; Solhjoo, Aida

    2015-12-01

    More detailed analytical studies of an optical sensor based on immobilization of Eriochorome Cyanine R (ECR) on a triacetylcellulose film have been described to determine Vanadium (IV) ions in some real samples. The sensor based on complex formation between Vanadium (IV) ions and ECR in acidic media caused the color of the film to change from violet to blue along with the appearance of a strong peak appears at 595 nm. At the optimal conditions, the calibration curve showed a linear range of 9.90×10(-7)-8.25×10(-5)mol L(-1). Vanadium (IV) ions can be detected with a detection limit of 1.03×10(-7)mol L(-1) within 15 min depending on its concentration. Also, the working range was improved by using PC-ANN algorithm. The sensor could regenerate with dilute acetic acid solution and could be completely reversible. The proposed sensor was successfully applied for determining V (IV) ions in environmental water and tea leaves. PMID:26142655

  17. Erratum to “Axial and transverse acoustic radiation forces on a fluid sphere placed arbitrarily in Bessel beam standing wave tweezers” [Ann. Phys. 342 (3) (2014) 158-170

    NASA Astrophysics Data System (ADS)

    Mitri, F. G.

    2014-09-01

    A typographical error is corrected in three equations in the article [Ann. Phys. 342 (3) (2014) 158-170, http://dx.doi.org/10.1016/j.aop.2013.12.009]. They are Eqs. (12)-(14), where the factor (1+Sp,q) should have been printed as (1+sp(ka)). The numerical computations and plots used the correct factor (1+sp(ka)) in the related equations.

  18. Relative immunogenicity of the cold-adapted influenza virus A/Ann Arbor/6/60 (A/AA/6/60-ca), recombinants of A/AA/6/60-ca, and parental strains with similar surface antigens.

    PubMed Central

    Tannock, G A; Paul, J A; Barry, R D

    1984-01-01

    The immunogenicity of several cold-adapted (ca) viruses was compared in CSL mice with that of wild-type parental viruses with similar surface antigens, according to the vaccinating dose required to clear a challenge consisting of 10(4.5) 50% tissue culture infective doses of the wild-type virus. All ca viruses were less immunogenic than their wild-type parental strains by a factor of 10(1.3) to 10(3.4), probably due to the restricted capacity of ca viruses to replicate in the respiratory tracts of mice. However, their immunogenicity was considerably enhanced when two quite small doses were administered 3 weeks apart. The immunogenicity of ca viruses when administered in two doses and wild-type viruses when administered as a single dose varied according to their surface antigens. It was highest for viruses with the H2N2 A/Ann Arbor/6/60 and H3N2 A/Queensland/6/72 surface antigens and lowest for those with H1N1 A/HK/123/77 surface antigens. When two doses consisting of 10(5.0) 50% tissue culture infective doses of A/Ann Arbor/6/60-ca were administered at an interval of 3 weeks, solid immunity was induced against the wild-type A/Ann Arbor/6/60 parental virus, two heterologous H3N2 strains, and an H1N1 strain. PMID:6693167

  19. Geology of the Round Bay quadrangle, Anne Arundel County, Maryland, with a section on Dinoflagellate-acritarch palynology, and a section on Cretaceous pollen

    USGS Publications Warehouse

    Minard, James Pierson; May, Fred E.; Christopher, Raymond A.

    1980-01-01

    Six Coastal Plain formations and one group crop out in the Round Bay quadrangle near the inner edge of the Atlantic Coastal Plain physiographic province. The quadrangle lies astride the Severn River, in Anne Arundel County, near Annapolis, Md. The seven stratigraphic units aggregate as much as 128 m in outcrop. In ascending order, the units are: the upper part of the Potomac Group and the Magothy, Matawan, and Severn Formations, all of Cretaceous age; the Brightseat and Aquia Formations of Paleocene age and the Calvert Formation of Miocene age. Quaternary deposits are thin and cover only small areas; they are all mapped under one unit. Several small, thin deposits of Tertiary alluvium are mapped separately. The largely unconsolidated Cretaceous and Tertiary formations consist chiefly of quartz, glauconite, clays, muscovite, chlorite, lignite, feldspar, and pyrite. Quaternary sediments are mostly locally derived sands, silts, and clays with some gravel and, in the finer sediments, considerable amounts of organic matter. The Cretaceous and Tertiary units strike generally northeast; the younger the formation, the more easterly it strikes. Dips are gentle, 3.6 to 15 m per kilometer toward the southeast, and decrease upward through the section. The Round Bay quadrangle is near the southern limit of several formations that thin progressively toward the southwest from New Jersey. Some pinch out between Betterton, on the eastern shore of Chesapeake Bay, and Round Bay, on the western shore, whereas others are present only as thin remnants 1-2 m thick. Resources of the quadrangle include abundant ground water, sand, and high land values near water.

  20. Biological Laboratory, Ann Arbor, Michigan

    USGS Publications Warehouse

    Moffett, James W.

    1963-01-01

    This laboratory located about 40 miles west of Detroit, near the intersection of highways I-94 and US-23, can be reached by bus, railroad, or via commercial airlines to Detroit Willow Run or Metropolitan airports. Field biological stations are located in Wisconsin at Ashland; in Ohio at Sandusky; and in Michigan at Ludington, Marquette, Millersburg, and Northville.

  1. Identification of Relevant Phytochemical Constituents for Characterization and Authentication of Tomatoes by General Linear Model Linked to Automatic Interaction Detection (GLM-AID) and Artificial Neural Network Models (ANNs)

    PubMed Central

    Hernández Suárez, Marcos; Astray Dopazo, Gonzalo; Larios López, Dina; Espinosa, Francisco

    2015-01-01

    There are a large number of tomato cultivars with a wide range of morphological, chemical, nutritional and sensorial characteristics. Many factors are known to affect the nutrient content of tomato cultivars. A complete understanding of the effect of these factors would require an exhaustive experimental design, multidisciplinary scientific approach and a suitable statistical method. Some multivariate analytical techniques such as Principal Component Analysis (PCA) or Factor Analysis (FA) have been widely applied in order to search for patterns in the behaviour and reduce the dimensionality of a data set by a new set of uncorrelated latent variables. However, in some cases it is not useful to replace the original variables with these latent variables. In this study, Automatic Interaction Detection (AID) algorithm and Artificial Neural Network (ANN) models were applied as alternative to the PCA, AF and other multivariate analytical techniques in order to identify the relevant phytochemical constituents for characterization and authentication of tomatoes. To prove the feasibility of AID algorithm and ANN models to achieve the purpose of this study, both methods were applied on a data set with twenty five chemical parameters analysed on 167 tomato samples from Tenerife (Spain). Each tomato sample was defined by three factors: cultivar, agricultural practice and harvest date. General Linear Model linked to AID (GLM-AID) tree-structured was organized into 3 levels according to the number of factors. p-Coumaric acid was the compound the allowed to distinguish the tomato samples according to the day of harvest. More than one chemical parameter was necessary to distinguish among different agricultural practices and among the tomato cultivars. Several ANN models, with 25 and 10 input variables, for the prediction of cultivar, agricultural practice and harvest date, were developed. Finally, the models with 10 input variables were chosen with fit’s goodness between 44 and

  2. Ultrasound assisted biodiesel production from sesame (Sesamum indicum L.) oil using barium hydroxide as a heterogeneous catalyst: Comparative assessment of prediction abilities between response surface methodology (RSM) and artificial neural network (ANN).

    PubMed

    Sarve, Antaram; Sonawane, Shriram S; Varma, Mahesh N

    2015-09-01

    The present study estimates the prediction capability of response surface methodology (RSM) and artificial neural network (ANN) models for biodiesel synthesis from sesame (Sesamum indicum L.) oil under ultrasonication (20 kHz and 1.2 kW) using barium hydroxide as a basic heterogeneous catalyst. RSM based on a five level, four factor central composite design, was employed to obtain the best possible combination of catalyst concentration, methanol to oil molar ratio, temperature and reaction time for maximum FAME content. Experimental data were evaluated by applying RSM integrating with desirability function approach. The importance of each independent variable on the response was investigated by using sensitivity analysis. The optimum conditions were found to be catalyst concentration (1.79 wt%), methanol to oil molar ratio (6.69:1), temperature (31.92°C), and reaction time (40.30 min). For these conditions, experimental FAME content of 98.6% was obtained, which was in reasonable agreement with predicted one. The sensitivity analysis confirmed that catalyst concentration was the main factors affecting the FAME content with the relative importance of 36.93%. The lower values of correlation coefficient (R(2)=0.781), root mean square error (RMSE=4.81), standard error of prediction (SEP=6.03) and relative percent deviation (RPD=4.92) for ANN compared to those R(2) (0.596), RMSE (6.79), SEP (8.54) and RPD (6.48) for RSM proved better prediction capability of ANN in predicting the FAME content. PMID:25630700

  3. Estimation of the chemical-induced eye injury using a Weight-of-Evidence (WoE) battery of 21 artificial neural network (ANN) c-QSAR models (QSAR-21): part II: corrosion potential.

    PubMed

    Verma, Rajeshwar P; Matthews, Edwin J

    2015-03-01

    This is part II of an in silico investigation of chemical-induced eye injury that was conducted at FDA's CFSAN. Serious eye damage caused by chemical (eye corrosion) is assessed using the rabbit Draize test, and this endpoint is an essential part of hazard identification and labeling of industrial and consumer products to ensure occupational and consumer safety. There is an urgent need to develop an alternative to the Draize test because EU's 7th amendment to the Cosmetic Directive (EC, 2003; 76/768/EEC) and recast Regulation now bans animal testing on all cosmetic product ingredients and EU's REACH Program limits animal testing for chemicals in commerce. Although in silico methods have been reported for eye irritation (reversible damage), QSARs specific for eye corrosion (irreversible damage) have not been published. This report describes the development of 21 ANN c-QSAR models (QSAR-21) for assessing eye corrosion potential of chemicals using a large and diverse CFSAN data set of 504 chemicals, ADMET Predictor's three sensitivity analyses and ANNE classification functionalities with 20% test set selection from seven different methods. QSAR-21 models were internally and externally validated and exhibited high predictive performance: average statistics for the training, verification, and external test sets of these models were 96/96/94% sensitivity and 91/91/90% specificity. PMID:25510831

  4. Estimation of the chemical-induced eye injury using a weight-of-evidence (WoE) battery of 21 artificial neural network (ANN) c-QSAR models (QSAR-21): part I: irritation potential.

    PubMed

    Verma, Rajeshwar P; Matthews, Edwin J

    2015-03-01

    Evaluation of potential chemical-induced eye injury through irritation and corrosion is required to ensure occupational and consumer safety for industrial, household and cosmetic ingredient chemicals. The historical method for evaluating eye irritant and corrosion potential of chemicals is the rabbit Draize test. However, the Draize test is controversial and its use is diminishing - the EU 7th Amendment to the Cosmetic Directive (76/768/EEC) and recast Regulation now bans marketing of new cosmetics having animal testing of their ingredients and requires non-animal alternative tests for safety assessments. Thus, in silico and/or in vitro tests are advocated. QSAR models for eye irritation have been reported for several small (congeneric) data sets; however, large global models have not been described. This report describes FDA/CFSAN's development of 21 ANN c-QSAR models (QSAR-21) to predict eye irritation using the ADMET Predictor program and a diverse training data set of 2928 chemicals. The 21 models had external (20% test set) and internal validation and average training/verification/test set statistics were: 88/88/85(%) sensitivity and 82/82/82(%) specificity, respectively. The new method utilized multiple artificial neural network (ANN) molecular descriptor selection functionalities to maximize the applicability domain of the battery. The eye irritation models will be used to provide information to fill the critical data gaps for the safety assessment of cosmetic ingredient chemicals. PMID:25497990

  5. Estimation of the chemical-induced eye injury using a Weight-of-Evidence (WoE) battery of 21 artificial neural network (ANN) c-QSAR models (QSAR-21): part II: corrosion potential.

    PubMed

    Verma, Rajeshwar P; Matthews, Edwin J

    2015-03-01

    This is part II of an in silico investigation of chemical-induced eye injury that was conducted at FDA's CFSAN. Serious eye damage caused by chemical (eye corrosion) is assessed using the rabbit Draize test, and this endpoint is an essential part of hazard identification and labeling of industrial and consumer products to ensure occupational and consumer safety. There is an urgent need to develop an alternative to the Draize test because EU's 7th amendment to the Cosmetic Directive (EC, 2003; 76/768/EEC) and recast Regulation now bans animal testing on all cosmetic product ingredients and EU's REACH Program limits animal testing for chemicals in commerce. Although in silico methods have been reported for eye irritation (reversible damage), QSARs specific for eye corrosion (irreversible damage) have not been published. This report describes the development of 21 ANN c-QSAR models (QSAR-21) for assessing eye corrosion potential of chemicals using a large and diverse CFSAN data set of 504 chemicals, ADMET Predictor's three sensitivity analyses and ANNE classification functionalities with 20% test set selection from seven different methods. QSAR-21 models were internally and externally validated and exhibited high predictive performance: average statistics for the training, verification, and external test sets of these models were 96/96/94% sensitivity and 91/91/90% specificity.

  6. Estimation of the chemical-induced eye injury using a weight-of-evidence (WoE) battery of 21 artificial neural network (ANN) c-QSAR models (QSAR-21): part I: irritation potential.

    PubMed

    Verma, Rajeshwar P; Matthews, Edwin J

    2015-03-01

    Evaluation of potential chemical-induced eye injury through irritation and corrosion is required to ensure occupational and consumer safety for industrial, household and cosmetic ingredient chemicals. The historical method for evaluating eye irritant and corrosion potential of chemicals is the rabbit Draize test. However, the Draize test is controversial and its use is diminishing - the EU 7th Amendment to the Cosmetic Directive (76/768/EEC) and recast Regulation now bans marketing of new cosmetics having animal testing of their ingredients and requires non-animal alternative tests for safety assessments. Thus, in silico and/or in vitro tests are advocated. QSAR models for eye irritation have been reported for several small (congeneric) data sets; however, large global models have not been described. This report describes FDA/CFSAN's development of 21 ANN c-QSAR models (QSAR-21) to predict eye irritation using the ADMET Predictor program and a diverse training data set of 2928 chemicals. The 21 models had external (20% test set) and internal validation and average training/verification/test set statistics were: 88/88/85(%) sensitivity and 82/82/82(%) specificity, respectively. The new method utilized multiple artificial neural network (ANN) molecular descriptor selection functionalities to maximize the applicability domain of the battery. The eye irritation models will be used to provide information to fill the critical data gaps for the safety assessment of cosmetic ingredient chemicals.

  7. [Antoine Brulon, a wealthy privileged apothecary in Paris in the seventeenth century, and Anne de Furnes, his wife. Their illustrious tenant in the Place du Palais Royal - Molière].

    PubMed

    Warolin, Christian

    2009-04-01

    In the seventeenth century, Antoine Brulon, originally from the Auvergne, had a particularly prosperous career. He acquired a privilege as apothecary and was syndic for the Society of royal and princely apothecaries. He spent his whole life in the Rue Saint-Honoré where he practised pharmacy in his status of privileged apothecary. In 1651 he bought a house close to the Quinze-Vingts hospital, and the following year he married Anne de Furnes, the daughter of a lawyer. In 1658 he bought three old houses on the Rue Saint-Thomas-du-Louvre, soon selling two of them to Louis-Henry Daquin, doctor in ordinary to the king. They both built new houses. Daquin let two apartments in succession to Molière between the years 1661 and 1665. In January 1666, Molière became the tenant of the house built by Antoine Brulon and owned by his widow, Anne de Furnes. This building was occupied successively by three royal apothecaries who rented the shop and its outbuildings: Philbert Boudin, apothecary in ordinary to the Queen, Jean Morel, apothecary to the King's camp and armies, and finally Pierre Frapin, apothecary to the Grande Ecurie, and supplier of medicine to Molière, as we have previously shown. The two latter apothecaries thus lived in turn in the same building as Molière between January 1666 and July 1672. Antoine Brulon died 5th March 1665. The inventory of his goods indicates not only the rich décor of his apartment, but also a sum of 75,780 livres in cash, representing 96% of his total fortune. This was remarkable wealth for a Paris apothecary of the time. PMID:19824347

  8. [Antoine Brulon, a wealthy privileged apothecary in Paris in the seventeenth century, and Anne de Furnes, his wife. Their illustrious tenant in the Place du Palais Royal - Molière].

    PubMed

    Warolin, Christian

    2009-04-01

    In the seventeenth century, Antoine Brulon, originally from the Auvergne, had a particularly prosperous career. He acquired a privilege as apothecary and was syndic for the Society of royal and princely apothecaries. He spent his whole life in the Rue Saint-Honoré where he practised pharmacy in his status of privileged apothecary. In 1651 he bought a house close to the Quinze-Vingts hospital, and the following year he married Anne de Furnes, the daughter of a lawyer. In 1658 he bought three old houses on the Rue Saint-Thomas-du-Louvre, soon selling two of them to Louis-Henry Daquin, doctor in ordinary to the king. They both built new houses. Daquin let two apartments in succession to Molière between the years 1661 and 1665. In January 1666, Molière became the tenant of the house built by Antoine Brulon and owned by his widow, Anne de Furnes. This building was occupied successively by three royal apothecaries who rented the shop and its outbuildings: Philbert Boudin, apothecary in ordinary to the Queen, Jean Morel, apothecary to the King's camp and armies, and finally Pierre Frapin, apothecary to the Grande Ecurie, and supplier of medicine to Molière, as we have previously shown. The two latter apothecaries thus lived in turn in the same building as Molière between January 1666 and July 1672. Antoine Brulon died 5th March 1665. The inventory of his goods indicates not only the rich décor of his apartment, but also a sum of 75,780 livres in cash, representing 96% of his total fortune. This was remarkable wealth for a Paris apothecary of the time.

  9. The cold adapted and temperature sensitive influenza A/Ann Arbor/6/60 virus, the master donor virus for live attenuated influenza vaccines, has multiple defects in replication at the restrictive temperature

    SciTech Connect

    Chan, Winnie; Zhou, Helen; Kemble, George; Jin Hong

    2008-10-25

    We have previously determined that the temperature sensitive (ts) and attenuated (att) phenotypes of the cold adapted influenza A/Ann Arbor/6/60 strain (MDV-A), the master donor virus for the live attenuated influenza A vaccines (FluMist), are specified by the five amino acids in the PB1, PB2 and NP gene segments. To understand how these loci control the ts phenotype of MDV-A, replication of MDV-A at the non-permissive temperature (39 deg. C) was compared with recombinant wild-type A/Ann Arbor/6/60 (rWt). The mRNA and protein synthesis of MDV-A in the infected MDCK cells were not significantly reduced at 39 deg. C during a single-step replication, however, vRNA synthesis was reduced and the nuclear-cytoplasmic export of viral RNP (vRNP) was blocked. In addition, the virions released from MDV-A infected cells at 39 deg. C exhibited irregular morphology and had a greatly reduced amount of the M1 protein incorporated. The reduced M1 protein incorporation and vRNP export blockage correlated well with the virus ts phenotype because these defects could be partially alleviated by removing the three ts loci from the PB1 gene. The virions and vRNPs isolated from the MDV-A infected cells contained a higher level of heat shock protein 70 (Hsp70) than those of rWt, however, whether Hsp70 is involved in thermal inhibition of MDV-A replication remains to be determined. Our studies demonstrate that restrictive replication of MDV-A at the non-permissive temperature occurs in multiple steps of the virus replication cycle.

  10. Épidémiologie descriptive de la brûlure dans un territoire de santé exemple du « territoire nord franche-comté » durant l’année 2014

    PubMed Central

    Fortin, J.L.; Bitar, M.P.; Marx, T.; Macher, J.M.; Desmettre, T.; Ravat, F.; Labourey, J.M.; Capellier, G.

    2015-01-01

    Summary Cette étude est une analyse épidémiologique rétrospective du recours aux services de santé du nord de la Franche-Comté en raison d’une brûlure durant l’année 2014 (114 patients). L’âge moyen était de 26 ans (8 mois-81 ans), 1/3 des brûlures ont touché des enfants de moins de 15 ans. Les brûlures, plus fréquentes l’été, surviennent principalement autour de l’heure des repas, les jours « sans école », à domicile, avec un liquide chaud. Elles sont peu étendues (4,81% de la SCT) et souvent superficielles. Elles nécessitent un passage dans un Service d’Accueil des Urgences dans 88,59% des cas, suivi d’un transfert en CTB (Lyon plus que Nancy ou Metz) dans 12,28% des cas. PMID:27252605

  11. Alice Cogswell and Anne Sullivan Macy Act

    THOMAS, 113th Congress

    Rep. Cartwright, Matt [D-PA-17

    2014-02-11

    06/13/2014 Referred to the Subcommittee on Early Childhood, Elementary, and Secondary Education. (All Actions) Tracker: This bill has the status IntroducedHere are the steps for Status of Legislation:

  12. Reply to Tone Kvernbekk and Ann Lewis

    ERIC Educational Resources Information Center

    Tangen, Reidun

    2008-01-01

    The purpose of this reply to Kvernbekk and Lewis's comments regarding the discussion on epistemological and ethical problems of listening to children's voices, is not to propose a coherent foundation free from any epistemological tensions. Rather, Tangen's intent is primarily to explore different perspectives in order to disclose some of their…

  13. Morbidité et mortalité des nouveau-nés hospitalisés sur 10 années à la Clinique El Fateh-Suka (Ouagadougou, Burkina Faso)

    PubMed Central

    Nagalo, Kisito; Dao, Fousséni; Tall, François Housséini; Yé, Diarra

    2013-01-01

    Introduction La mortalité néonatale demeure un problème majeur de santé publique dans les pays en développement. Notre étude avait pour but de déterminer la morbidité et la mortalité des nouveau-nés à Ouagadougou, Burkina Faso. Méthodes Une étude rétrospective sur 10 années a permis d'inclure tous les nouveau-nés admis dans l'Unité de Néonatologie de la Clinique El Fateh-Suka. Résultats Au total, 697 nouveau-nés étaient hospitalisés sur la période d'étude. Les principaux diagnostics étaient les infections néonatales (23.5%), les anomalies liées à la durée de la gestation et à la croissance du fætus (17.9%) et le paludisme congénital (15.1%). Les 91 (13.1%) décès étaient dus aux anomalies liées à la durée de la grossesse et à la croissance du fætus (46.1%), à l'hypoxie intra-utérine et à l'asphyxie obstétricale (20,9%) et aux infections néonatales (17.6%). Ces décès survenaient dans 81.3% dans les 72 heures, dans 93.4% des cas dans la première semaine d'hospitalisation. Le facteur de risque associé à ces décès était la voie basse d'accouchement (p = 0.02). Conclusion Cette étude a identifié des pathologies évitables déjà décrites comme les principales causes d'hospitalisations et de décès néonatals. La voie basse d'accouchement était le facteur de risque associé à ces décès, ce qui n'avait pas encore été rapporté. Les efforts pour améliorer la qualité des services de soins périnatals doivent être intensifiés afin de réduire la mortalité néonatale dans les pays en développement. PMID:23785558

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

    Cancer.gov

    The Division of Cancer Prevention (DCP) conducts and supports research to determine a person's risk of cancer and to find ways to reduce the risk. This knowledge is critical to making progress against cancer because risk varies over the lifespan as genetic and epigenetic changes can transform healthy tissue into invasive cancer.

  15. "Dear Ann ..." An Activity for Synthesizing and Applying Interpersonal Concepts

    ERIC Educational Resources Information Center

    Brule, Nancy J.

    2007-01-01

    All students struggle with interpersonal based problems, be it a troublesome roommate, problems with a partner, conflict with a significant other, or relational issues with parents or children. The interpersonal communication classroom can be enhanced by discussing these problems and experiences. This article presents an activity that aims to (1)…

  16. Modelling of Reservoir Operations using Fuzzy Logic and ANNs

    NASA Astrophysics Data System (ADS)

    Van De Giesen, N.; Coerver, B.; Rutten, M.

    2015-12-01

    Today, almost 40.000 large reservoirs, containing approximately 6.000 km3 of water and inundating an area of almost 400.000 km2, can be found on earth. Since these reservoirs have a storage capacity of almost one-sixth of the global annual river discharge they have a large impact on the timing, volume and peaks of river discharges. Global Hydrological Models (GHM) are thus significantly influenced by these anthropogenic changes in river flows. We developed a parametrically parsimonious method to extract operational rules based on historical reservoir storage and inflow time-series. Managing a reservoir is an imprecise and vague undertaking. Operators always face uncertainties about inflows, evaporation, seepage losses and various water demands to be met. They often base their decisions on experience and on available information, like reservoir storage and the previous periods inflow. We modeled this decision-making process through a combination of fuzzy logic and artificial neural networks in an Adaptive-Network-based Fuzzy Inference System (ANFIS). In a sensitivity analysis, we compared results for reservoirs in Vietnam, Central Asia and the USA. ANFIS can indeed capture reservoirs operations adequately when fed with a historical monthly time-series of inflows and storage. It was shown that using ANFIS, operational rules of existing reservoirs can be derived without much prior knowledge about the reservoirs. Their validity was tested by comparing actual and simulated releases with each other. For the eleven reservoirs modelled, the normalised outflow, <0,1>, was predicted with a MSE of 0.002 to 0.044. The rules can be incorporated into GHMs. After a network for a specific reservoir has been trained, the inflow calculated by the hydrological model can be combined with the release and initial storage to calculate the storage for the next time-step using a mass balance. Subsequently, the release can be predicted one time-step ahead using the inflow and storage.

  17. AnnAGNPS Ephemeral Gully Erosion Simulation Technology

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Sheet and rill erosion conservation management technologies, such as the Revised Universal Soil Loss Equation (RUSLE), have provided valuable tools in reducing cropland erosion, but have not considered the impact of ephemeral gully erosion. Tillage-induced ephemeral gully erosion has been shown to ...

  18. Quadrivalent Ann Arbor strain live-attenuated influenza vaccine.

    PubMed

    Toback, Seth L; Levin, Myron J; Block, Stan L; Belshe, Robert B; Ambrose, Christopher S; Falloon, Judith

    2012-11-01

    Influenza B is responsible for significant morbidity in children and adults worldwide. For more than 25 years, two antigenically distinct lineages of influenza B viruses, B/Yamagata and B/Victoria, have cocirculated globally. Current influenza vaccine formulations are trivalent and contain two influenza subtype A strains (A/H1N1 and A/H3N2) but only one B strain. In a half of recent influenza seasons, the predominant circulating influenza B lineage was different from that contained in trivalent influenza vaccines. A quadrivalent live-attenuated influenza vaccine (Q/LAIV) that contains two B strains, one from each lineage, has been developed to help provide broad protection against influenza B. Q/LAIV was recently approved for use in the USA in eligible individuals 2-49 years of age. This review summarizes clinical trial data in support of Q/LAIV.

  19. The Community Arbitration Project, Anne Arundel County, Maryland.

    ERIC Educational Resources Information Center

    Blew, Carol Holliday; Rosenblum, Robert

    This examines an exemplary project of community arbitration, a juvenile justice alternative. Essential elements of this project are described and include: (1) prompt case processing, (2) court-like setting, (3) involvement of victims, (4) assurance of due process, (5) use of community resources, and (6) constructive dispositions. Facets of CAP…

  20. Documentary Report of the Ann Arbor Symposium on the Applications of Psychology to the Teaching and Learning of Music: Session III. Motivation and Creativity. Ann Arbor Symposium (3rd, Ann Arbor, Michigan).

    ERIC Educational Resources Information Center

    Music Educators National Conference, Reston, VA.

    Eight papers from a 1983 symposium of music educators and psychologists summarize current knowledge and theory in motivation and creativity and apply this research to the teaching and learning of music at all levels. Emphasis is on practical implications for music teachers in day to day instruction. Topics addressed in the papers are: task…

  1. Report of the Ann Arbor Workshop on an International Social Science Research Program on Global Change (Ann Arbor, Michigan, September 9-10, 1987).

    ERIC Educational Resources Information Center

    Jacobson, Harold K.; Shanks, Cheryl

    The International Geosphere-Biosphere Programme: A Study of Global Change (IGBP), a natural science research program, has been inaugurated by the International Council of Scientific Unions (ICSU). Recently, initial steps have been taken to develop an international social science research program on global change that would either be part of the…

  2. Modelling of chemical oxygen demand by using ANNs, ANFIS and k-means clustering techniques

    NASA Astrophysics Data System (ADS)

    Ay, Murat; Kisi, Ozgur

    2014-04-01

    This paper proposes integration of k-means clustering and multi-layer perceptron (k-means-MLP) methods in modelling chemical oxygen demand (COD) concentration. This proposed method was tested by using daily measured water suspended solids, pH, temperature, discharge and COD concentration data of upstream of the municipal wastewater treatment plant system in Adapazari province of Turkey. Performance of the k-means-MLP method was compared with multi-linear regression, multi-layer perceptron, radial-based neural network, generalized regression neural network, and two different adaptive neuro-fuzzy inference system techniques (subtractive clustering and grid partition). Root mean square error, mean absolute error, mean absolute relative error and determination coefficient statistics were employed for the evaluation accuracy of each model. It was found that the k-means-MLP performed better than the other techniques in estimating COD. Moreover, the k-means clustering combined with the MLP could be used as a tool in modelling daily COD concentration.

  3. Simulation of Wetland Nitrogen Removal at the Watershed Scale Using AnnAGNPS

    EPA Science Inventory

    The Future Midwest Landscape (FML) project is part of the U.S. Environmental Protection Agency’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...

  4. Assessment of Subsurface Drainage and Fertilizer Management Practices to Reduce Nutrient Loadings using AnnAGNPS

    EPA Science Inventory

    The Future Midwest Landscape (FML) project is part of the US Environmental Protection Agency (EPA)’s new Ecological Service Research Program (ESRP), undertaken to examine the variety of ways in which landscapes that include crop lands, conservation areas, wetlands, lakes, and str...

  5. Real-time video compression using entropy-biased ANN codebooks

    NASA Astrophysics Data System (ADS)

    Ahalt, Stanley C.; Fowler, James E.

    1994-03-01

    We describe hardware that has been built to compress video in real time using full-search vector quantization (VQ). This architecture implements a differential-vector-quantization (DVQ) algorithm which features entropy-biased codebooks designed using an artificial neural network. A special-purpose digital associative memory, the VAMPIRE chip, performs the VQ processing. We describe the DVQ algorithm, its adaptations for sampled NTSC composite- color video, and details of its hardware implementation. We conclude by presenting results drawn from real-time operation of the DVQ hardware.

  6. Prediction Model for Predicting Powdery Mildew using ANN for Medicinal Plant—Picrorhiza kurrooa

    NASA Astrophysics Data System (ADS)

    Shivling, V. D.; Ghanshyam, C.; Kumar, Rakesh; Kumar, Sanjay; Sharma, Radhika; Kumar, Dinesh; Sharma, Atul; Sharma, Sudhir Kumar

    2016-06-01

    Plant disease fore casting system is an important system as it can be used for prediction of disease, further it can be used as an alert system to warn the farmers in advance so as to protect their crop from being getting infected. Fore casting system will predict the risk of infection for crop by using the environmental factors that favor in germination of disease. In this study an artificial neural network based system for predicting the risk of powdery mildew in Picrorhiza kurrooa was developed. For development, Levenberg-Marquardt backpropagation algorithm was used having a single hidden layer of ten nodes. Temperature and duration of wetness are the major environmental factors that favor infection. Experimental data was used as a training set and some percentage of data was used for testing and validation. The performance of the system was measured in the form of the coefficient of correlation (R), coefficient of determination (R2), mean square error and root mean square error. For simulating the network an inter face was developed. Using this interface the network was simulated by putting temperature and wetness duration so as to predict the level of risk at that particular value of the input data.

  7. User Activity Recognition in Smart Homes Using Pattern Clustering Applied to Temporal ANN Algorithm.

    PubMed

    Bourobou, Serge Thomas Mickala; Yoo, Younghwan

    2015-01-01

    This paper discusses the possibility of recognizing and predicting user activities in the IoT (Internet of Things) based smart environment. The activity recognition is usually done through two steps: activity pattern clustering and activity type decision. Although many related works have been suggested, they had some limited performance because they focused only on one part between the two steps. This paper tries to find the best combination of a pattern clustering method and an activity decision algorithm among various existing works. For the first step, in order to classify so varied and complex user activities, we use a relevant and efficient unsupervised learning method called the K-pattern clustering algorithm. In the second step, the training of smart environment for recognizing and predicting user activities inside his/her personal space is done by utilizing the artificial neural network based on the Allen's temporal relations. The experimental results show that our combined method provides the higher recognition accuracy for various activities, as compared with other data mining classification algorithms. Furthermore, it is more appropriate for a dynamic environment like an IoT based smart home. PMID:26007738

  8. Zimbabweite, a new alkali-lead arsenic tantalate from St Anns mine, Karoi district, Zimbabwe.

    USGS Publications Warehouse

    Foord, E.E.; Taggart, J.E.; Gaines, R.V.; Grubb, P.L.C.; Kristiansen, R.

    1986-01-01

    Zimbabweite occurs as honey yellow-brown crystals (= or 2.10, 2Vgamma approx 80o, dispersion very strong v > r; alpha (pale yellow brown) = c, beta (light reddish brown = b, gamma (reddish brown) = a. Mean reflectance in air for an (010) = alpha -gamma cleavage plate is 16.6% at 589 nm. Chemical analysis gave Ta2O5 46.5, As2O3 26.5, PbO 15.0, Nb2O5 4.8, Na2O 3.1 K2O 1.5, TiO2 1.4, BaO 0.4, UO2 0.3, Bi2O3 0.2, H2O 0.19, SnO2 0.1, F 0.04, SrO 0.02, less O = F 0.02, = 100.05, leading to the idealized formula (Na,K)2PbAs4(Ta,Nb,Ti)4O18. Indexed XRD powder data are tabulated; strongest lines 3.195(100), 2.990(70), 2.882(70), 3.033(60), 3.823(55), 2.548(50), 1.913(50) A; a 12.233, b 15.292, c 8.665; A; space group Ccma or Cc2a, Z = 4. The TG curve shows a rapid weight loss of approx 22.5% at 840-900oC, due to the loss of As2O5.-R.A.H.

  9. 77 FR 61624 - Patuxent Research Refuge, Prince George's and Anne Arundel Counties, MD; Draft Comprehensive...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2012-10-10

    .... We will announce upcoming public meetings in local news media, via our project mailing list, and on... Federal Register (75 FR 12563; March 16, 2010). Patuxent RR was established in 1936 by Executive Order by... available to the public, including opportunities for hunting, fishing, wildlife observation and...

  10. 78 FR 67086 - Safety Zone, Submarine Cable Replacement Operations, Kent Island Narrows; Queen Anne's County, MD

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-11-08

    .... SUPPLEMENTARY INFORMATION: Table of Acronyms DHS Department of Homeland Security FR Federal Register NPRM Notice... public dockets in the January 17, 2008, issue of the Federal Register (73 FR 3316). 4. Public Meeting We... position latitude 38 58'12.8'' N, longitude 076 14'50.8'' W; thence northerly to point of origin...

  11. 78 FR 70099 - Requested Administrative Waiver of the Coastwise Trade Laws: Vessel LADY ANN; Invitation for...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-11-22

    ...As authorized by 46 U.S.C. 12121, the Secretary of Transportation, as represented by the Maritime Administration (MARAD), is authorized to grant waivers of the U.S.-build requirement of the coastwise laws under certain circumstances. A request for such a waiver has been received by MARAD. The vessel, and a brief description of the proposed service, is listed...

  12. 78 FR 76169 - Patuxent Research Refuge, Prince George's and Anne Arundel Counties, MD; Final Comprehensive...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-12-16

    ... opportunities for environmental education and interpretation in an urban setting. It is home to the U.S..., fishing, wildlife observation and photography, environmental education and interpretation. We will review... technology to update refuge buildings and grounds, constructing additional space for environmental...

  13. 77 FR 45532 - Approval and Promulgation of Implementation Plans; Michigan; Detroit-Ann Arbor Nonattainment Area...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2012-08-01

    ... Nonattainment Area; Fine Particulate Matter 2005 Base Year Emissions Inventory AGENCY: Environmental Protection Agency (EPA). ACTION: Proposed rule. SUMMARY: EPA is proposing to approve the fine particulate matter (PM... other information whose disclosure is restricted by statute. Certain other material, such as...

  14. An ANN-GA model based promoter prediction in Arabidopsis thaliana using tilling microarray data

    PubMed Central

    Mishra, Hrishikesh; Singh, Nitya; Misra, Krishna; Lahiri, Tapobrata

    2011-01-01

    Identification of promoter region is an important part of gene annotation. Identification of promoters in eukaryotes is important as promoters modulate various metabolic functions and cellular stress responses. In this work, a novel approach utilizing intensity values of tilling microarray data for a model eukaryotic plant Arabidopsis thaliana, was used to specify promoter region from non-promoter region. A feed-forward back propagation neural network model supported by genetic algorithm was employed to predict the class of data with a window size of 41. A dataset comprising of 2992 data vectors representing both promoter and non-promoter regions, chosen randomly from probe intensity vectors for whole genome of Arabidopsis thaliana generated through tilling microarray technique was used. The classifier model shows prediction accuracy of 69.73% and 65.36% on training and validation sets, respectively. Further, a concept of distance based class membership was used to validate reliability of classifier, which showed promising results. The study shows the usability of micro-array probe intensities to predict the promoter regions in eukaryotic genomes. PMID:21887014

  15. 77 FR 66547 - Approval and Promulgation of Implementation Plans; Michigan; Detroit-Ann Arbor Nonattainment Area...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2012-11-06

    ... Nonattainment Area; Fine Particulate Matter 2005 Base Year Emissions Inventory AGENCY: Environmental Protection... base year emissions inventory, a portion of the State Implementation Plan (SIP) revision submitted by... Order Reviews I. What action is EPA taking? On August 1, 2012 (77 FR 45532), EPA published a...

  16. User Activity Recognition in Smart Homes Using Pattern Clustering Applied to Temporal ANN Algorithm.

    PubMed

    Bourobou, Serge Thomas Mickala; Yoo, Younghwan

    2015-01-01

    This paper discusses the possibility of recognizing and predicting user activities in the IoT (Internet of Things) based smart environment. The activity recognition is usually done through two steps: activity pattern clustering and activity type decision. Although many related works have been suggested, they had some limited performance because they focused only on one part between the two steps. This paper tries to find the best combination of a pattern clustering method and an activity decision algorithm among various existing works. For the first step, in order to classify so varied and complex user activities, we use a relevant and efficient unsupervised learning method called the K-pattern clustering algorithm. In the second step, the training of smart environment for recognizing and predicting user activities inside his/her personal space is done by utilizing the artificial neural network based on the Allen's temporal relations. The experimental results show that our combined method provides the higher recognition accuracy for various activities, as compared with other data mining classification algorithms. Furthermore, it is more appropriate for a dynamic environment like an IoT based smart home.

  17. 78 FR 65357 - Notice of Inventory Completion: University of Michigan, Ann Arbor, MI

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-10-31

    ...'' pipe fragment and 1 animal bone. In 1973, human remains representing, at minimum, 1 individual were... funerary objects. Ancient modifications were noted on one set of long bones with the ends cut, shaved, and... mandible fragment, 2 animal teeth, 1 unworked snail shell, 35 ceramic sherds, and 1 worked flint. In...

  18. 76 FR 73670 - Notice of Inventory Completion: University of Michigan Museum of Anthropology, Ann Arbor, MI

    Federal Register 2010, 2011, 2012, 2013, 2014

    2011-11-29

    ... bone lot with beaver incisors, black bear maxilla, bird and mammal bones; 8 stone fragments including...; 1 red ground stone or palette; 2 ground stones; 3 flint cores; 13 stone flakes; 3 bone chisels; 3 harpoon heads; 2 small bone awls; 2 large bone awls; 1 otter skull with soil; 1 lot consisting of a...

  19. 78 FR 65376 - Notice of Inventory Completion: University of Michigan, Ann Arbor, MI

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-10-31

    ... deer bone fragments, 13 ceramic sherds, 2 lithic shatter flakes, and 1 retouched lithic shatter flake... present are 1 modified animal bone, 1 lot of small animal bones, 3 animal bone fragments, 1 hoe (made from... objects present are 1 unworked deer scapula, 3 worked animal bones, 1 unworked turkey bone, 5 slate...

  20. 78 FR 65371 - Notice of Inventory Completion: University of Michigan, Ann Arbor, MI

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-10-31

    ... bird bones. On an unknown date prior to 1956, human remains representing, at minimum, 9 individuals... perforations. Hinsdale also noted that fish bones were found in the fill dirt, but they are not present in the... flakes and points, 1 lithic scraper, 2 lithic fragments, 23 clay fragments, 1 ceramic elbow pipe, and...

  1. [50 years after Anne Frank: reactive of a transgenerational trauma in countertransference].

    PubMed

    Halberstadt-Freud, H C

    1995-01-01

    Traumatizations suffered in the course of the atrocities perpetrated by the National Socialists defy symbolization and, if unresolved, are passed on to subsequent generations. With reference to the psychoanalytic treatment of a forty-year-old homosexual of the "second generation", the author traces the problematic of the transgenerational handing-down of unresolved traumatizations. The son of a Jewess whose brother was murdered in a concentration camp, the patient had to stand in for his lost uncle and remained caught up in a "symbiotic illusion" with this omni-present/absent object and with the mother, thus being unable to attain to a personality of his own and achieve the Oedipal triad. The article concentrates entirely on this case and provides an impressive record of the mechanisms of transference and counter-transference involved and the various stages of the psychoanalytic process. PMID:7871187

  2. Adaptive Selective Harmonic Minimization Based on ANNs for Cascade Multilevel Inverters With Varying DC Sources

    SciTech Connect

    Filho, Faete; Maia, Helder Z; Mateus, Tiago Henrique D; Ozpineci, Burak; Tolbert, Leon M; Pinto, Joao Onofre P

    2013-01-01

    A new approach for modulation of an 11-level cascade multilevel inverter using selective harmonic elimination is presented in this paper. The dc sources feeding the multilevel inverter are considered to be varying in time, and the switching angles are adapted to the dc source variation. This method uses genetic algorithms to obtain switching angles offline for different dc source values. Then, artificial neural networks are used to determine the switching angles that correspond to the real-time values of the dc sources for each phase. This implies that each one of the dc sources of this topology can have different values at any time, but the output fundamental voltage will stay constant and the harmonic content will still meet the specifications. The modulating switching angles are updated at each cycle of the output fundamental voltage. This paper gives details on the method in addition to simulation and experimental results.

  3. 75 FR 23745 - Jo-Ann Stores, Inc., Provisional Acceptance of a Settlement Agreement and Order

    Federal Register 2010, 2011, 2012, 2013, 2014

    2010-05-04

    ... ensure that they did not bear or contain lead-containing paint, thereby creating a risk of lead poisoning... Lead-Containing Paint and Certain Consumer Products Bearing Lead-Containing Paint, 16 CFR part 1303 (the ``Lead-Paint Ban''). Under the Lead-Paint Ban, toys and other children's articles must not bear...

  4. 46 CFR 7.10 - Eastport, ME to Cape Ann, MA.

    Code of Federal Regulations, 2013 CFR

    2013-10-01

    ... 44°48.5′ N. longitude 66°56.4′ W. (Sail Rock Lighted Whistle Buoy “1”); thence to latitude 44°37.5′ N. longitude 67°09.8′ W. (Little River Lighted Whistle Buoy “2LR”); thence to latitude 44°14.5′ N. longitude 67°57.2′ W. (Frenchman Bay Approach Lighted Whistle Buoy “FB”); thence to Mount Desert Light; thence...

  5. 46 CFR 7.10 - Eastport, ME to Cape Ann, MA.

    Code of Federal Regulations, 2014 CFR

    2014-10-01

    ... 44°48.5′ N. longitude 66°56.4′ W. (Sail Rock Lighted Whistle Buoy “1”); thence to latitude 44°37.5′ N. longitude 67°09.8′ W. (Little River Lighted Whistle Buoy “2LR”); thence to latitude 44°14.5′ N. longitude 67°57.2′ W. (Frenchman Bay Approach Lighted Whistle Buoy “FB”); thence to Mount Desert Light; thence...

  6. 77 FR 34991 - Notice of Inventory Completion: Museum of Anthropology, University of Michigan, Ann Arbor, MI...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2012-06-12

    ... FR 42094-42095, August 20, 2009). In the Federal Register (74 FR 42094, August 20, 2009), paragraph... FR 42094, August 20, 2009), paragraph four is corrected by inserting the following sentences at the... Native American ancestry. In the Federal Register (74 FR 42094, August 20, 2009), paragraph seven...

  7. Pancreatic Reference Set Application- Ann Killary-MD Anderson (2012) — EDRN Public Portal

    Cancer.gov

    ELISA assays. We propose to screen a two gene panel (TNC/TFPI) from functional genomic pathways approaches, by ELISA assays, in a collection of blinded plasma samples with the PCCG. In subsequent years, we will add to this panel additional candidates identified in the parent EDRN grant that are in the migration signature network or the 20q amplicon. For ELISA assays, we will utilize commercially available kits. Plasma levels of the predominant isoform of TNC, TNC-large variant (TNC-L) will be determined using a Human Tenascin-C Large (HMV) (FNIII-B) ELISA kit (IBL-America, Minneapolis, MN), which detects human TNC high molecular weight variant by sandwich ELISA. Plasma TFPI levels will be determined using a commercially available Quantikine Human TFPI ELISA kit (DTFP10) (R&D; Systems, Inc. Minneapolis, MN), which detects predominantly free TFPI and a very small percentage of LDL and HDL-bound TFPI by sandwich ELISA. Additional ELISA assays will be added in years 2-3 using ingenuity network analysis to identify secreted proteins that interact with migration signature proteins identified using functional genomic approaches. In addition, Dr. Sen’s lab has identified candidate biomarkers mapping with the 20q amplicon and which are both amplified and overexpressed as well as are secreted proteins. In years 2-3, we will examine all candidate markers by ELISA to determine 20q pathway markers increase the sensitivity and specificity of the 3p panel. Validation of a 4 miR Panel. For assaying the levels of miRs in plasma, qRT-PCR assays will be performed, as described (9). For the selected panel of miRs, we propose to develop a high throughput qRT-PCR assay by adapting our published method to a 96 well format where the critical steps of purification, such as LiCl precipitation and DNAse treatments will be done in successive steps in the same plates. In years 2-3, additional miRs that target both the 20q amplicon genes as well as miRs that target migration genes in Dr. Killary’

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

    PubMed Central

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

    2015-01-01

    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. PMID:26007724

  9. 46 CFR 7.10 - Eastport, ME to Cape Ann, MA.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... Head to latitude 44°54′45″ N. longitude 66°58′30″ W.; thence to the range marker located in approximate position latitude 44°51′45″ N. longitude 66°59″ W. (b) A line drawn from West Quoddy Head Light to latitude 44°48.5′ N. longitude 66°56.4′ W. (Sail Rock Lighted Whistle Buoy “1”); thence to latitude 44°37.5′...

  10. 46 CFR 7.10 - Eastport, ME to Cape Ann, MA.

    Code of Federal Regulations, 2012 CFR

    2012-10-01

    ... Head to latitude 44°54′45″ N. longitude 66°58′30″ W.; thence to the range marker located in approximate position latitude 44°51′45″ N. longitude 66°59″ W. (b) A line drawn from West Quoddy Head Light to latitude 44°48.5′ N. longitude 66°56.4′ W. (Sail Rock Lighted Whistle Buoy “1”); thence to latitude 44°37.5′...

  11. 46 CFR 7.10 - Eastport, ME to Cape Ann, MA.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... Head to latitude 44°54′45″ N. longitude 66°58′30″ W.; thence to the range marker located in approximate position latitude 44°51′45″ N. longitude 66°59″ W. (b) A line drawn from West Quoddy Head Light to latitude 44°48.5′ N. longitude 66°56.4′ W. (Sail Rock Lighted Whistle Buoy “1”); thence to latitude 44°37.5′...

  12. ERIC/RCS: Implications of the Ann Arbor Decision: Black English and the Reading Teacher.

    ERIC Educational Resources Information Center

    Monteith, Mary K.

    1980-01-01

    Presents 13 points about the relationship between Black English and reading; reports on books about Black English and teaching reading to Blacks; and suggests inservice materials for increasing teachers' knowledge about dialects. (JT)

  13. Increasing spatial resolution of CHIRPS rainfall datasets for Cyprus with Artificial Neural Networks (ANN)

    NASA Astrophysics Data System (ADS)

    Tymvios, Filippos; Michaelides, Silas; Retalis, Adrianos; Katsanos, Dimitrios; Lelieveld, Jos

    2016-04-01

    The use of high resolution rainfall datasets is an alternative way of studying climatological patterns in regions where conventional rain measurements are sparse or not available. Starting in 1981 to near-present, CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) dataset incorporates a 5x5km2 resolution satellite imagery with in-situ station data to create gridded rainfall time series for trend analysis, severe events and seasonal drought monitoring. The aim of this work is to further increase the resolution of this rainfall dataset for Cyprus to 1x1km2 by correlating the CHIRPS dataset with altitute information, NDVI vegetation index from satellite images at 1x1km2and precipitation measurements from the official raingauge network of the Cyprus Department of Meteorology, utilizing Artificial Neural Network models.

  14. "[This] Book of Odd Tales/Which Transform the Brothers Grimm": Teaching Anne Sexton's "Transformations"

    ERIC Educational Resources Information Center

    Keely, Karen A.

    2008-01-01

    Folklorist Andrew Lang, in his Preface to his 1910 edited collection "The Lilac Fairy Book", celebrates the ongoing repetition and retelling of fairy tales, including both the cozy retelling by the family fireplace. In the century since Lang wrote these words, many authors have joined the ranks of Shakespeare and Homer in putting fairy tale…

  15. AnnAGNPS Model Application for the Future Midwest Landscape Study

    EPA Science Inventory

    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 af...

  16. Annual Adult Education Research Conference. Proceedings (20th, Ann Arbor, Michigan, April 4-6, 1979).

    ERIC Educational Resources Information Center

    1979

    Papers from numerous research areas in the adult education field are presented. The proceedings contain thirty-five papers, five symposia, one alternate symposium, and eighteen alternate papers. Among the papers included are "A Comparison of Approaches to Measuring Outcomes in Adult Basic Education,""A Critical Analysis of Hill's 'Cognitive Style…

  17. Issues in Software System Safety: Polly Ann Smith Co. versus Ned I. Ludd

    NASA Technical Reports Server (NTRS)

    Holloway, C. Michael

    2002-01-01

    This paper is a work of fiction, but it is fiction with a very real purpose: to stimulate careful thought and friendly discussion about some questions for which thought is often careless and discussion is often unfriendly. To accomplish this purpose, the paper creates a fictional legal case. The most important issue in this fictional case is whether certain proffered expert testimony about software engineering for safety critical systems should be admitted. Resolving this issue requires deciding the extent to which current practices and research in software engineering, especially for safety-critical systems, can rightly be considered based on knowledge, rather than opinion.

  18. 75 FR 12563 - Patuxent Research Refuge, Anne Arundel and Prince George's Counties, MD

    Federal Register 2010, 2011, 2012, 2013, 2014

    2010-03-16

    ... information by any of the following methods. Electronic mail: northeastplanning@fws.gov . Include ``Patuxent... process by mail, electronic mail, or facsimile (see ADDRESSES). There will be additional opportunities to... your address, phone number, electronic mail address, or other personal identifying information in...

  19. Nineteenth-Century African American Women's Autobiography as Social Discourse: The Example of Harriet Ann Jacobs

    ERIC Educational Resources Information Center

    Stover, Johnnie M.

    2003-01-01

    The power of black women's personal narratives emerging out of nineteenth-century America rested in their sociopolitical as well as in their literary contributions. These slave narratives and autobiographical texts, however, were either ignored, sentimentalized, or reduced in scope as addenda to the abolitionist-inspired texts produced by black…

  20. Innovation and application of ANN in Europe demonstrated by Kohonen maps

    NASA Technical Reports Server (NTRS)

    Goser, Karl

    1994-01-01

    One of the most important contributions to neural networks comes from Kohonen, Helsinki/Espoo, Finland, who had the idea of self-organizating maps in 1981. He verified his idea by an algorithm of which many applications make use of. The impetus for this idea came from biology, a field where the Europeans have always been very active at several research laboratories. The challenge was to model the self-organization found in the brain. Today one goal is the development of more sophisticated neurons which model the biological neurons more exactly. They should come to a better performance of neural nets with only a few complex neurons instead of many simple ones. A lot of application concepts arise from this idea: Kohonen himself applied it to speech recognition, but the project did not overcome much more than the recognition of the numerals one to ten at that time. A more promising application for self-organizing maps is process control and process monitoring. Several proposals were made which concern parameter classification of semiconductor technologies, design of integrated circuits, and control of chemical processes. Self-organizing maps were applied to robotics. The neural concept was introduced into electric power systems. At Dortmund we are working on a system which has to monitor the quality and the reliability of gears and electrical motors in equipment installed in coal mines. The results are promising and the probability to apply the system in the field is very high. A special feature of the system is that linguistic rules which are embedded in a fuzzy controller analyze the data of the self-organizing map in regard to life expectation of the gears. It seems that the fuzzy technique will introduce the technology of neural networks in a tandem mode. These technologies together with the genetic algorithms start to form the attractive field of computational intelligence.

  1. Assessment of Subsurface Drainage Management Practices to Reduce Nitrogen Loadings Using AnnAGNPS

    EPA Science Inventory

    The goal of the Future Midwest Landscape project is to quantify current and future landscape services across the region and examine changes expected to occur as a result of two alternative drivers of future change: the growing demand for biofuels; and hypothetical increases in in...

  2. On the use of ANN interconnection weights in optimal structural design

    NASA Technical Reports Server (NTRS)

    Hajela, P.; Szewczyk, Z.

    1992-01-01

    The present paper describes the use of interconnection weights of a multilayer, feedforward network, to extract information pertinent to the mapping space that the network is assumed to represent. In particular, these weights can be used to determine an appropriate network architecture, and an adequate number of training patterns (input-output pairs) have been used for network training. The weight analysis also provides an approach to assess the influence of each input parameter on a selected output component. The paper shows the significance of this information in decomposition driven optimal design.

  3. Nurse clowns in the OR. An interview with Barbara Ann D'Anna.

    PubMed

    D'Anna, B A

    1993-01-01

    1. There is always some degree of stress in the OR. The trick is to play up the eustress (positive, motivating energy) and alleviate the distress (negative/draining energy). Eustress is characterized by smiles, chatter, purposeful movement, and an OR that hums with efficiency. Distress can be assessed by unhappy faces, loud grumbling, dragging feet, and an OR that grinds to a halt. 2. Humor relaxes people and situations. It allows creative juices to flow and enhances what people take away from meetings by facilitating the creative process. The staff members feel good about themselves and what they are doing, and they become more productive. 3. The OR can be both a wonderful (eustress) and a horrible (distress) place in which to work. We need to savor the wonderful--and season the horrible with humor to make it palatable. As directors use humor to reduce staff stress levels, they should remember to bring laughter and humor into their own lives as well.

  4. 76 FR 36151 - Notice of Inventory Completion: Museum of Anthropology, University of Michigan, Ann Arbor, MI

    Federal Register 2010, 2011, 2012, 2013, 2014

    2011-06-21

    ... Corydon La Ford, MD (tenure 1854-1894), University of Michigan, Medical School, Department of Anatomy. The... individuals until the early 20th century. The collection was used for anatomy teaching in the Medical...

  5. Simulating land use change by integrating landscape metrics into ANN-CA in a new way

    NASA Astrophysics Data System (ADS)

    Yang, Xin; Zhao, Yu; Chen, Rui; Zheng, Xinqi

    2016-06-01

    Landscape metrics are measurements of landuse patterns and land-use change, but even so, have rarely been integrated into land-use change simulation models. This paper proposes a new artificial neural network-cellular automaton by integrating landscape metrics into the model. In this model, each cell acquires unique landscape metric values. The landscape metric values of each cell are actually the landscape metric values of land use type in its neighborhood, which takes the cell as center. The calculation of landscape metrics ensures that those of each cell can represent cellular spatial environmental characteristics. The model is used to simulate land use change in the Changping district of Beijing, China. Comparisons of the simulated land use map with the actual map show that the proposed model is effective for land use change simulation. The validation is further carried out by comparing the simulated land use map with that simulated by an artificial neural network-cellular automaton model, which has not been integrated with landscape metrics. Results indicate that the proposed model is more appropriate for simulating both quantity and spatial distribution of land use change in the study area.

  6. Distributed Genetic Algorithm for Feature Selection in Gaia RVS Spectra: Application to ANN Parameterization

    NASA Astrophysics Data System (ADS)

    Fustes, Diego; Ordóñez, Diego; Dafonte, Carlos; Manteiga, Minia; Arcay, Bernardino

    This work presents an algorithm that was developed to select the most relevant areas of a stellar spectrum to extract its basic atmospheric parameters. We consider synthetic spectra obtained from models of stellar atmospheres in the spectral region of the radial velocity spectrograph instrument of the European Space Agency's Gaia space mission. The algorithm that demarcates the areas of the spectra sensitive to each atmospheric parameter (effective temperature and gravity, metallicity, and abundance of alpha elements) is a genetic algorithm, and the parameterization takes place through the learning of artificial neural networks. Due to the high computational cost of processing, we present a distributed implementation in both multiprocessor and multicomputer environments.

  7. Listening to the Voice of Living Life with Aphasia: Anne's Story

    ERIC Educational Resources Information Center

    Barrow, Rozanne

    2008-01-01

    Background: Listening to how people talk about the consequences of acquired aphasia helps one gain insight into how people construe disability and communication disability in particular. It has been found that some of these construals can be more of a disabling barrier in re-engaging with life than the communication impairment itself. Aims: To…

  8. Establishing the Balance: Re-Examining Students' Androcentric Readings of Katherine Anne Porter's "Rope."

    ERIC Educational Resources Information Center

    Nudd, Donna Marie

    1991-01-01

    Analyzes undergraduates' interpretations of a short story that presents a relatively balanced argument between a man and a woman. Finds both male and female students arrived at androcentric readings by solving text ambiguities in favor of the male character. Presents methods that encourage students to recognize and reevaluate their reading…

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

    EPA Science Inventory

    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 different phosphorus fertilization rates on phosphorus losses, the US...

  10. "INDEPENDENT CONFIRMATORY SURVEY SUMMARY AND RESULTS FOR THE FORD NUCLEAR REACTOR, REVISION 1, ANN ARBOR, MICHIGAN

    SciTech Connect

    ALTIC, NICK A

    2013-08-01

    At the NRC's request, ORAU conducted confirmatory surveys of the FNR during the period of December 4 through 6, 2012. The survey activities included visual inspections and measurement and sampling activities. Confirmatory activities also included the review and assessment of UM's project documentation and methodologies. Surface scans identified elevated activity in two areas. The first area was on a wall outside of Room 3103 and the second area was in the southwest section on the first floor. The first area was remediated to background levels. However, the second area was due to gamma shine from a neighboring source storage area. A retrospective analysis of UM's FSS data shows that for the SUs investigated by the ORAU survey team, UM met the survey requirements set forth in the FSSP. The total mean surface activity values were directly compared with the mean total surface activity reported by UM. Mean surface activity values determined by UM were within two standard deviations of the mean determined by ORAU. Additionally, all surface activity values were less than the corresponding gross beta DCGL{sub W}. Laboratory analysis of the soil showed that COC concentrations were less than the respective DCGL{sub W} values. For the inter-lab comparison, the DER was above 3 for only one sample. However, since the sum of fractions for each of the soil samples was below 1, thus none of the samples would fail to meet release guidelines. Based on the findings of the side-by-side direct measurements, and after discussion with the NRC and ORAU, UM decided to use a more appropriate source efficiency in their direct measurement calculations and changed their source efficiency from 0.37 to 0.25.

  11. INDEPENDENT CONFIRMATORY SURVEY SUMMARY AND RESULTS FOR THE FORD NUCLEAR REACTOR, ANN ARBOR, MICHIGAN

    SciTech Connect

    ALTIC, NICK A

    2013-07-25

    At the NRC's request, ORAU conducted confirmatory surveys of the FNR during the period of December 4 through 6, 2012. The survey activities included visual inspections and measurement and sampling activities. Confirmatory activities also included the review and assessment of UM's project documentation and methodologies. Surface scans identified elevated activity in two areas. The first area was on a wall outside of Room 3103 and the second area was in the southwest section on the first floor. The first area was remediated to background levels. However, the second area was due to gamma shine from a neighboring source storage area. A retrospective analysis of UM's FSS data shows that for the SUs investigated by the ORAU survey team, UM met the survey requirements set forth in the FSSP. The total mean surface activity values were directly compared with the mean total surface activity reported by UM. Mean surface activity values determined by UM were within two standard deviations of the mean determined by ORAU. Additionally, all surface activity values were less than the corresponding gross beta DCGLW. Laboratory analysis of the soil showed that COC concentrations were less than the respective DCGLW values. For the inter-lab comparison, the DER was above 3 for only one sample. However, since the sum of fractions for each of the soil samples was below 1, thus none of the samples would fail to meet release guidelines. Based on the findings of the side-by-side direct measurements, and after discussion with the NRC and ORAU, UM decided to use a more appropriate source efficiency in their direct measurement calculations and changed their source efficiency from 0.37 to 0.25.

  12. Study on ann-based multi-step prediction model of short-term climatic variation

    NASA Astrophysics Data System (ADS)

    Jin, Long; Ju, Weimin; Miao, Qilong

    2000-03-01

    In the context of 1905 1995 series from Nanjing and Hangzhou, study is undertaken of estab-lishing a predictive model of annual mean temperature in 1996 2005 to come over the Changjiang (Yangtze River) delta region through mean generating function and artificial neural network in combination. Results show that the established model yields mean error of 0.45°C for their abso-lute values of annual mean temperature from 10 yearly independent samples (1986 1995) and the difference between the mean predictions and related measurements is 0.156°C. The developed model is found superior to a mean generating function regression model both in historical data fit-ting and independent sample prediction.

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

    EPA Science Inventory

    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 af...

  14. Evaluating ephemeral gully erosion impact on Zea mays L. yield and economics using AnnAGNPS

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Ephemeral gully erosion causes serious water quality and economic problems in the Midwest United States. A critical barrier to soil conservation practice adoption is often the implementation cost, although it is recognized that erosion reduces farm income. Yet few, if any, understand the relationshi...

  15. Living Well with COPD, Q&A: Grace Anne Koppel | NIH MedlinePlus the Magazine

    MedlinePlus

    ... What needs to be done to address this gender disparity? Through recent research, we have come to ... also that women have a more toxic reaction. Gender is not the only disparity in COPD. Those ...

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

    Cancer.gov

    The Division of Cancer Prevention (DCP) conducts and supports research to determine a person's risk of cancer and to find ways to reduce the risk. This knowledge is critical to making progress against cancer because risk varies over the lifespan as genetic and epigenetic changes can transform healthy tissue into invasive cancer.

  17. Tolerance Education in Morocco. "Anne Frank: A History for Today"--Learning about Our Past--Contributing to Our Future

    ERIC Educational Resources Information Center

    Polak, Karen

    2010-01-01

    This paper describes recent developments in the field of history education and human rights education in Morocco. Educational reform in Morocco is ongoing and includes measures such as mandating that all schools create after-school Human Rights Clubs. These developments are then related to the possibility of teaching about the history of the…

  18. Teachers of Literacy, Love of Reading, and the Literate Self: A Response to Ann Powell-Brown

    ERIC Educational Resources Information Center

    Gomez, Kimberley

    2005-01-01

    The author asserts that literacy teacher training programs should design opportunities for teachers to become more reflective about the literate self. Graduate students were queried about the relationship between their personal, historical, and professional literate selves. They documented their memories of reading and considered what it means to…

  19. AnnAGNPS GIS-based tool for watershed-scale identification and mapping of cropland potential ephemeral gullies

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The formation of ephemeral gullies in agricultural fields has been recognized as an important source of sediment contributing to environmental degradation and compromising crop productivity. Methodologies are being developed for assessing gully formation and gully sediment yield. The Annualized Agri...

  20. Making the Past Relevant to Future Generations. The Work of the Anne Frank House in Latin America

    ERIC Educational Resources Information Center

    Chyrikins, Mariela; Vieyra, Magdalena

    2010-01-01

    This paper provides the context and outlines the barriers and opportunities for developing promising Holocaust education programmes in Latin America, especially working with diverse communities and societies. In particular, the conflictual history of Latin American and recent democratization processes present opportunities for educational work. It…

  1. Offline signature verification and skilled forgery detection using HMM and sum graph features with ANN and knowledge based classifier

    NASA Astrophysics Data System (ADS)

    Mehta, Mohit; Choudhary, Vijay; Das, Rupam; Khan, Ilyas

    2010-02-01

    Signature verification is one of the most widely researched areas in document analysis and signature biometric. Various methodologies have been proposed in this area for accurate signature verification and forgery detection. In this paper we propose a unique two stage model of detecting skilled forgery in the signature by combining two feature types namely Sum graph and HMM model for signature generation and classify them with knowledge based classifier and probability neural network. We proposed a unique technique of using HMM as feature rather than a classifier as being widely proposed by most of the authors in signature recognition. Results show a higher false rejection than false acceptance rate. The system detects forgeries with an accuracy of 80% and can detect the signatures with 91% accuracy. The two stage model can be used in realistic signature biometric applications like the banking applications where there is a need to detect the authenticity of the signature before processing documents like checks.

  2. A Library Service Center for Suburban Maryland County Library Systems, Anne Arundel, Baltimore, Montgomery, Prince George's; An Establishment Proposal.

    ERIC Educational Resources Information Center

    Duchac, Kenneth F.

    Based on a year of inquiry and consultation, this report of the Suburban Maryland Project confirms the feasibility of cooperative technical service functions for the four public library systems of suburban Maryland. It is recommended that the proposed Library Service Center be assigned the ordering, acquisition, cataloging, preparation for book…

  3. OPPORTUNITIES FOR WOMEN THROUGH EDUCATION, CONFERENCE-WORKSHOP PROCEEDINGS (UNIVERSITY OF MICHIGAN, ANN ARBOR, MARCH 16, 1965).

    ERIC Educational Resources Information Center

    Michigan Univ., Ann Arbor. Center for Continuing Education for Women.

    A CONFERENCE ON OPPORTUNITIES FOR WOMEN EMPHASIZED THE EDUCATIONAL AND TRAINING REQUIREMENTS FOR EMPLOYMENT IN THE FIELDS OF TEACHING, SOCIAL WORK, HEALTH SCIENCES, MATHEMATICS, PHYSICAL SCIENCES, ENGINEERING, AND LIBRARY SCIENCE, AND OPPORTUNITIES FOR CONTINUING EDUCATION IN UNDERGRADUATE LIBERAL ARTS. THE MORNING ADDRESS DESCRIBED THE SARAH…

  4. Assessment of the Effect of Blast Hole Diameter on the Number of Oversize Boulders Using ANN Model

    NASA Astrophysics Data System (ADS)

    Dhekne, Prakash; Pradhan, Manoj; Jade, Ravi Krishnarao

    2016-04-01

    Now-a-days, blasts are planned using large diameter blast holes. The loading density (kg/m) and subsequently the energy available for the breakage of the rockmass increase with the diameter. The in-hole velocity of detonation (VoD) of non-ideal explosive also boosts up with the increase in diameter till the optimum diameter is reached. The increase in the energy content and in-hole VoD cause a sizable effect on the rock fragmentation. The effect can be assessed by counting the number of oversize boulders. This paper explains as to how the technique of artificial neural network modeling was used to predict the number of oversize boulders resulting from ANFO and SME blasts with blast holes of different diameters. The results from ANFO blasts indicated that there was no significant variation in the number of oversize boulders with the diameter whereas a perceptible variation was noticed in case of SME blasts with the change in the diameter. The change in the number of oversize boulders in ANFO blasts was negligible because mean energy factor remained almost same even when the diameter of the blast holes was altered. The decrease in the number of oversize boulders in SME blasts was on account of increase in mean energy factor when the blast hole diameter was increased. The increase in the in-hole VoD due to increase in the diameter of the hole was not found to have an effect on the generation of oversize boulders as this increase was not substantial both in SME and ANFO blasts.

  5. Outcomes of long bone fractures treated by open intramedullary nailing at the St. Ann's Bay Hospital, Jamaica.

    PubMed

    Barnes, D; McDowell, D

    2010-10-01

    Between May 2001 to August 2004, 35 patients had open nailing of long bones. There were 40 fractures fixed. Of these 40 fractures, there were 25 femoral fractures, 11 were tibial fractures and 4 were humeral fractures. There were 33 (82.5%) closed fractures and 7 (17.5%) open fractures. In the group of patients with open fractures, there were two Grade I, two Grade II and three Grade IIIB. Seven (20%) patients were lost to follow-up; all of whom had closed fractures. The final analysis as it relates to complications was done using 28 patients with 32 fractures. The majority of fractures healed without significant complication. All the patients with closed fractures went on to bony union. There was one non-union and three delayed unions. There were two infections (osteomyelitis) and this was from the open fracture cohort. This represents an infection rate of 28.6% in this cohort. Two (7.0%) patients had persistent pain and one (3.6%) patient had early removal of the nail because of failure of fixation. The mean time from injury to surgery for the fractured femur was 15.5 (range 0-49) days; fractured tibia 24.4 (range 0-40), days and fractured humerus 41.5 (20-81) days. The mean hospital stay was 18.9 (range 9-37) days for patients with fractured femur; for fractured tibia, it was 20.5 (range 3-82) days and for fractured humerus, it was 22.7 (range 3-82) days. The mean postoperative stay was 4.1 (range 1-14) days for fractured femur, 4.5 (range 1-14) days for fractured tibia and 4.0 (range 1-10) days for fractured humerus. The mean time to healing (consolidation) as defined by X-rays was 5.0 (range 3-11) months for fractured femur 5.2 (range 3-11) months for tibia and 7.0 (range 6-8) months for fractured humerus.

  6. Proceedings of the Conference on Nutrition and Mental Retardation (Ann Arbor, Michigan, February 10-11, 1971).

    ERIC Educational Resources Information Center

    Springer, Ninfa Saturnino, Ed.

    The conference, planned primarily for nutritionists and dieticians, dealt with the role of nutrition in the prevention and management of mental retardation. Proceedings include an overview of mental retardation, an examination of nutrition manpower needs in the fields of mental health and mental retardation on both the national and state levels,…

  7. GIS-based channel flow and sediment transport simulation using CCHE1D coupled with AnnAGNPS

    Technology Transfer Automated Retrieval System (TEKTRAN)

    CCHE1D (Center for Computational Hydroscience and Engineering 1-Dimensional model) simulates unsteady free-surface flows with nonequilibrium, nonuniform sediment transport in dendritic channel networks. Since early 1990’s, the model and its software packages have been developed and continuously main...

  8. Tissue distribution model and pharmacokinetics of nuciferine based on UPLC-MS/MS and BP-ANN.

    PubMed

    Xu, Yanyan; Bao, Shihui; Tian, Weiqiang; Wen, Congcong; Hu, Lufeng; Lin, Chongliang

    2015-01-01

    Nuciferine has shown remarkable biological activities and been considered as a promising drug. In this study, a sensitive and selective ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method was developed and validated for determination of nuciferine in tissue and plasma. An electrospray ionization source was applied and operated in positive ion mode; multiple reactions monitoring (MRM) mode was used for quantification using target fragment ions m/z 296.0→265.1 for nuciferine, and m/z 322.0→307.0 for berberrubine internal standard (IS). Based on the UPLC-MS/MS method, the tissue distribution profile of nuciferine in mice and plasma pharmacokinetics in rat were studied. The results showed nuciferine was absorbed through intestinal tract and distributed into tissues rapidly. The bioavailability of nuciferine was identified at 17.9%. It can across through blood brain barrier, the concentrations in liver and kidney are highest, then followed by spleen, lung heart and brain. Nuciferine is eliminated quickly in the tissues and plasma, the t1/2 within 5 hour. The concentrations in these tissues are correlated to each other, and can be predicted by a back-propagation artificial neural network model. PMID:26770351

  9. From "Lawrence Kohlberg's Approach to Moral Education" by F. Clark Power, Ann Higgins, and Lawrence Kohlberg, with Judy Codding (1989)

    ERIC Educational Resources Information Center

    Schools: Studies in Education, 2011

    2011-01-01

    This article is an excerpt from "Lawrence Kohlberg's Approach to Moral Education." It refers several times to Kohlberg's "six stages of moral development." Stages 3 and 4 belong to the second level of moral development, which Kohlberg calls "conventional." At stage 3, one becomes aware of conventions as one sees what is right in terms of living up…

  10. Development of Water Quality Forecasting Models Based on the SOM-ANN on TMDL Unit Watershed in Nakdong River

    NASA Astrophysics Data System (ADS)

    KIM, M.; Kim, J.; Baek, J.; Kim, C.; Shin, H.

    2013-12-01

    It has being happened as flush flood or red/green tide in various natural phenomena due to climate change and indiscreet development of river or land. Especially, water being very important to man should be protected and managed from water quality pollution, and in water resources management, real-time watershed monitoring system is being operated with the purpose of keeping watch and managing on rivers. It is especially important to monitor and forecast water quality in watershed. A study area selected Nak_K as one site among TMDL unit watershed in Nakdong River. This study is to develop a water quality forecasting model connected with making full use of observed data of 8 day interval from Nakdong River Environment Research Center. When forecasting models for each of the BOD, DO, COD, and chlorophyll-a are established considering correlation of various water quality factors, it is needed to select water quality factors showing highly considerable correlation with each water quality factor which is BOD, DO, COD, and chlorophyll-a. For analyzing the correlation of the factors (reservoir discharge, precipitation, air temperature, DO, BOD, COD, Tw, TN, TP, chlorophyll-a), in this study, self-organizing map was used and cross correlation analysis method was also used for comparing results drawn. Based on the results, each forecasting model for BOD, DO, COD, and chlorophyll-a was developed during the short period as 8, 16, 24, 32 days at 8 day interval. The each forecasting model is based on neural network with back propagation algorithm. That is, the study is connected with self-organizing map for analyzing correlation among various factors and neural network model for forecasting of water quality. It is considerably effective to manage the water quality in plenty of rivers, then, it specially is possible to monitor a variety of accidents in water quality. It will work well to protect water quality and to prevent destruction of the environment becoming more and more serious before occurring.

  11. Tissue distribution model and pharmacokinetics of nuciferine based on UPLC-MS/MS and BP-ANN

    PubMed Central

    Xu, Yanyan; Bao, Shihui; Tian, Weiqiang; Wen, Congcong; Hu, Lufeng; Lin, Chongliang

    2015-01-01

    Nuciferine has shown remarkable biological activities and been considered as a promising drug. In this study, a sensitive and selective ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method was developed and validated for determination of nuciferine in tissue and plasma. An electrospray ionization source was applied and operated in positive ion mode; multiple reactions monitoring (MRM) mode was used for quantification using target fragment ions m/z 296.0→265.1 for nuciferine, and m/z 322.0→307.0 for berberrubine internal standard (IS). Based on the UPLC-MS/MS method, the tissue distribution profile of nuciferine in mice and plasma pharmacokinetics in rat were studied. The results showed nuciferine was absorbed through intestinal tract and distributed into tissues rapidly. The bioavailability of nuciferine was identified at 17.9%. It can across through blood brain barrier, the concentrations in liver and kidney are highest, then followed by spleen, lung heart and brain. Nuciferine is eliminated quickly in the tissues and plasma, the t1/2 within 5 hour. The concentrations in these tissues are correlated to each other, and can be predicted by a back-propagation artificial neural network model. PMID:26770351

  12. Improvement of Adaptive GAs and Back Propagation ANNs Performance in Condition Diagnosis of Multiple Bearing System Using Grey Relational Analysis

    PubMed Central

    Wulandhari, Lili A.; Wibowo, Antoni; Desa, Mohammad I.

    2014-01-01

    Condition diagnosis of multiple bearings system is one of the requirements in industry field, because bearings are used in many equipment and their failure can result in total breakdown. Conditions of bearings commonly are reflected by vibration signals data. In multiple bearing condition diagnosis, it will involve many types of vibration signals data; thus, consequently, it will involve many features extraction to obtain precise condition diagnosis. However, large number of features extraction will increase the complexity of the diagnosis system. Therefore, in this paper, we presented a diagnosis method which is hybridization of adaptive genetic algorithms (AGAs), back propagation neural networks (BPNNs), and grey relational analysis (GRA) to diagnose the condition of multiple bearings system. AGAs are used in the diagnosis algorithm to determine the best initial weights of BPNNs in order to improve the diagnosis accuracy. In addition, GRA is applied to determine and select the dominant features from the vibration signal data which will provide good diagnosis of multiple bearings system in less features extraction. The experiments results show that AGAs-BPNNs with GRA approaches can increase the accuracy of diagnosis in shorter processing time, compared with the AGAs-BPNNs without the GRA. PMID:25587265

  13. Watershed runoff and sediment transport impacts from management decisions using integrated AnnAGNPS and CCHE1D models

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Conservation planning tools that consider all sources of erosion, sheet and rill, gully, and channels, is critical to developing an effective watershed management plan that considers the integrated effect of all practices on the watershed system. The Annualized Agricultural Non-Point Source polluta...

  14. Molecular Docking Guided Comparative GFA, G/PLS, SVM and ANN Models of Structurally Diverse Dual Binding Site Acetylcholinesterase Inhibitors.

    PubMed

    Gupta, Shikhar; Fallarero, Adyary; Vainio, Mikko J; Saravanan, P; Santeri Puranen, J; Järvinen, Päivi; Johnson, Mark S; Vuorela, Pia M; Mohan, C Gopi

    2011-08-01

    Recently discovered 42 AChE inhibitors binding at the catalytic and peripheral anionic site were identified on the basis of molecular docking approach, and its comparative quantitative structure-activity relationship (QSAR) models were developed. These structurally diverse inhibitors were obtained by our previously reported high-throughput in vitro screening technique using 384-well plate's assay based on colorimetric method of Ellman. QSAR models were developed using (i) genetic function algorithm, (ii) genetic partial least squares, (iii) support vector machine and (iv) artificial neural network techniques. The QSAR model robustness and significance was critically assessed using different cross-validation techniques on test data set. The generated QSAR models using thermodynamic, electrotopological and electronic descriptors showed that nonlinear methods are more robust than linear methods, and provide insight into the structural features of compounds that are important for AChE inhibition.

  15. Symposium (International) on Combustion, 20th, University of Michigan, Ann Arbor, MI, August 12-17, 1984, Proceedings

    NASA Technical Reports Server (NTRS)

    1985-01-01

    The present conference on combustion phenomena considers topics in automotive engine combustion, turbulent reacting flows, the modeling of practical combustion systems, reaction kinetics, combustion-generated particulates, combustion diagnostics, coal combustion process characteristics, fire-related phenomena, explosion/detonation phenomena, spray combustion, ignition/extinction, laminar flames, pollutant formation processes, practical combustor devices, and rocket propellant combustion. Attention is given to the contributions of combustion science to piston engine design, modeling and measurement techniques for turbulent combustion, the specific effects of energy, collisions, and transport processes in combustion chemistry kinetics, the formation of large molecules, particulates and ions in premixed hydrocarbon flames, the application of laser diagnostics to combustion systems, spark ignition energies for dust-air mixtures, the controlling mechanisms of flow-assisted flame spread, the ignition and combustion of coal-water slurries, spontaneous ignition of methane, turbulent and accelerating dust flames, and the temperature sensitivity of double base propellants.

  16. ANN based inversion of DC resistivity data for groundwater exploration in hard rock terrain of western Maharashtra (India)

    NASA Astrophysics Data System (ADS)

    Maiti, Saumen; Erram, Vinit C.; Gupta, Gautam; Tiwari, Ram Krishna

    2012-09-01

    SummaryInversion of vertical electrical sounding (VES) data, especially from the crystalline hard rock area, assumes a special significance for groundwater exploration. Here we used a newly developed algorithm based on the Bayesian neural network (BNN) theory combined with Hybrid Monte Carlo (HMC)/Markov Chain Monte Carlo (MCMC) simulation scheme to invert the Direct Current (DC) VES measurements obtained from 30-locations around Tenduli-Vengurla, Sindhudurg district, Maharashtra, India. The inversion results suggest that the top layer is mostly comprised of laterites followed by mixture of clay/clayey sand and garnulites/granite as basement rocks. The source of groundwater appears to be accessible in weathered/semi-weathered layer of laterite/clayey sand that exists within the depth of 10-15 m from the surface. The NW-SE trending major lineaments and its criss-crosses are also identified from the apparent and true resistivity surface map. The pseudo-section at different depths in the western part of the area, near Nivti, shows extensive influence of saltwater intrusion and its impact reaching up to the depth of 30 m from the surface along the coastal area. Our results also show that intrusion of saline water decreases from the western part to the eastern part of the region. Two dimensional modeling of four resistivity profiles from the study region identified two potential groundwater reservoirs; one lying between Path-Tenduli and another in between Mat and Zaraph. The deduced true electrical resistivity section against depth correlates well with available borehole lithology in the area. The results presented here would be useful for interpreting the geological signatures like fractures, major joints and lineaments, which in turn will be helpful for identifying groundwater reservoirs and drainage pattern in the crystalline hard rock area. The newly developed HMC-based BNN method is robust and would provide insights for constraining the geophysical models and criteria for modeling resistivity data.

  17. Performance Parameters Analysis of an XD3P Peugeot Engine Using Artificial Neural Networks (ANN) Concept in MATLAB

    NASA Astrophysics Data System (ADS)

    Rangaswamy, T.; Vidhyashankar, S.; Madhusudan, M.; Bharath Shekar, H. R.

    2015-04-01

    The current trends of engineering follow the basic rule of innovation in mechanical engineering aspects. For the engineers to be efficient, problem solving aspects need to be viewed in a multidimensional perspective. One such methodology implemented is the fusion of technologies from other disciplines in order to solve the problems. This paper mainly deals with the application of Neural Networks in order to analyze the performance parameters of an XD3P Peugeot engine (used in Ministry of Defence). The basic propaganda of the work is divided into two main working stages. In the former stage, experimentation of an IC engine is carried out in order to obtain the primary data. In the latter stage the primary database formed is used to design and implement a predictive neural network in order to analyze the output parameters variation with respect to each other. A mathematical governing equation for the neural network is obtained. The obtained polynomial equation describes the characteristic behavior of the built neural network system. Finally, a comparative study of the results is carried out.

  18. Application of PCA-based data compression in the ANN-supported conceptual cost estimation of residential buildings

    NASA Astrophysics Data System (ADS)

    Juszczyk, Michał

    2016-06-01

    The paper presents concisely some research results on the application of principal component analysis for the data compression and the use of compressed data as the variables describing the model in the issue of conceptual cost estimation of residential buildings. The goal of the research was to investigate the possibility of use of compressed input data of the model in neural modelling - the basic information about residential buildings available in the early stage of design and construction cost. The results for chosen neural networks that were trained with use of the compressed input data are presented in the paper. In the summary the results obtained for the neural networks with PCA-based data compression are compared with the results obtained in the previous stage of the research for the network committees.

  19. Thermospheric wind effects on the global distribution of helium in the earth's upper atmosphere. Ph.D. Thesis - Michigan Univ., Ann Arbor

    NASA Technical Reports Server (NTRS)

    Reber, C. A.

    1973-01-01

    The momentum and continuity equations for a minor gas are combined with the momentum equation for the major constituents to obtain the time dependent continuity equation for the minor species reflecting a wind field in the background gas. This equation is used to study the distributions of helium and argon at times of low, medium, and high solar activity for a variety of latitudinal-seasonal wind cells. For helium, the exospheric return flow at the higher thermospheric temperatures dominates the distribution to the extent that much larger latitudinal gradients can be maintained during periods of low solar activity than during periods of high activity. By comparison to the exospheric flow, the smoothing effect of horizontal diffusion is almost negligible. The latitudinal variation of helium observed by satellite mass spectrometers can be reproduced by the effect of a wind system of air rising in the summer hemisphere, flowing across the equator with speeds on the order of 100 to 200 m/sec, and descending in the winter hemisphere. Argon, being heavier than the mean mass in the lower thermosphere, reacts oppositely to helium in that it is enhanced in the summer hemisphere and depleted in the winter.

  20. International Symposium on Remote Sensing of Environment, 9th, University of Michigan, Ann Arbor, Mich., April 15-19, 1974, Proceedings. Volumes 1, 2 & 3

    NASA Technical Reports Server (NTRS)

    1974-01-01

    The present work gathers together numerous papers describing the use of remote sensing technology for mapping, monitoring, and management of earth resources and man's environment. Studies using various types of sensing equipment are described, including multispectral scanners, radar imagery, spectrometers, lidar, and aerial photography, and both manual and computer-aided data processing techniques are described. Some of the topics covered include: estimation of population density in Tokyo districts from ERTS-1 data, a clustering algorithm for unsupervised crop classification, passive microwave sensing of moist soils, interactive computer processing for land use planning, the use of remote sensing to delineate floodplains, moisture detection from Skylab, scanning thermal plumes, electrically scanning microwave radiometers, oil slick detection by X-band synthetic aperture radar, and the use of space photos for search of oil and gas fields. Individual items are announced in this issue.

  1. Proceedings of the Annual Midwest Research-to-Practice Conference in Adult and Continuing Education (4th, Ann Arbor, Michigan, October 10-11, 1985).

    ERIC Educational Resources Information Center

    Berlin, L. S., Ed.

    This document contains the following papers on practical applications of research on adult and continuing education: "Elderly Criminal Behavior: Linking Research to Practice," by Donald J. Bachand and Carl I. Brahce; "Father? Teacher? Friend? Instructor-Student Relationships in a Refugee Class," by Gary J. Bekker; "The Small Group: Understanding…

  2. Proceedings of the Annual Eastern Michigan University Conference on Languages for Business and the Professions (5th, Ann Arbor, Michigan, April 10-12, 1986).

    ERIC Educational Resources Information Center

    Voght, Geoffrey M., Comp.

    Forty-five conference papers are presented in six sections: getting started in languages for special purposes (concerning teaching, curriculum development, finding, and resources); Spanish for business and the professions; French for business and the professions; other languages (English as a second language, German, Arabic, Mandarin Chinese, and…

  3. The Growth Edge: Creative Use of Computers for Facilitating Learning and Enhancing Personal Development. Papers from the Workshop (Ann Arbor, Michigan, June 27-30, 1986).

    ERIC Educational Resources Information Center

    Walz, Garry R., Ed.; Bleuer, Jeanne C., Ed.

    This document is the fourth publication in a series devoted to the use of computers in counseling. The outgrowth of the 1986 ERIC/CAPS workshop, it contains four of the major presentations made at the conference. "The Impact of Computers on the Future of Counseling: Boom or Boomerang" (Edwin L. Herr) examines the effect of technology upon society…

  4. International Symposium on Remote Sensing of Environment, 21st, University of Michigan, Ann Arbor, Oct. 26-30, 1987, Proceedings. Volumes 1 and 2

    SciTech Connect

    Not Available

    1987-01-01

    Papers on remote sensors, data systems, and scientific investigations related to land, open ocean, ice, astmosphere, and climate research are presented. Topics include the High-Resolution Imaging Spectrometer for NASA's Earth Observing System; the future of remote sensing techniques; airborne electrooptical imaging; airborne stereo line imager data; a helicopter-borne scatterometer; SAR image data compression; international remote-sensing satellites; Radarsat; the integration of remote sensing and geographic information systems; the Argos system; TM data screening; surveillance radar; the use of microwave radiometry in hydrology; and the use of Landsat, AVHRR, and SPOT data in environmental studies. Research using remote-sensing techniques is presented, covering topics such as the measurement of currents, intense and tornadic thunderstorms, suspended sediments in estuaries, calculating land and forest cover, flash flood potential, sea-level variations, agricultural monitoring, fire detection, anaylsis of marine shallow water-bottom features, detection of human-induced environmental change, crop yield estimation, the composition of volcanic rocks, ice surveillance, snow-cover mapping, road detection, surface wind-speed measurements, and mineral exploration.

  5. A Classroom Exercise for Testing Urban Myth: Does Wedding Rice Cause Birds to Explode or Were Ann Landers, Martha Stewart & Bart Simpson Wrong?

    ERIC Educational Resources Information Center

    Krupa, James J.

    2005-01-01

    In this article, the author first provides the history of the wedding rice myth. He then details an interactive class activity that involved students in his large, non-majors biology classes. These students developed a series of experiments that scientifically determined if rice could be harmful to birds. Finally, he provides suggestions on how…

  6. International Symposium on Remote Sensing of Environment, 10th, University of Michigan, Ann Arbor, Mich., October 6-10, 1975, Proceedings. Volumes 1 & 2

    NASA Technical Reports Server (NTRS)

    1975-01-01

    Topics treated include the application of a Fourier transform spectrometer to infrared remote sensing, the performance optimization of a satellite-borne thematic mapper, a data handling system to be integrated with a digital airborne multispectral scanner, infrared thermography for micro- and mesometeorological measurements, satellite interrogated data collection platforms for river and flood forecasting and the automatic measurement of sea surface temperature from a GOES satellite. Solar and atmospheric effects on satellite imagery derived from aircraft reflectance measurements, methods for determining haze levels from multispectral scanner data, restoration of Landsat images by discrete two-dimensional deconvolution and the automatic classification of aircraft and satellite multispectral images using mixed integer programming are also discussed. Individual items are announced in this issue.

  7. ARSENIC REMOVAL FROM DRINKING WATER BY ADSORPTIVE MEDIA. EPA DEMONSTRATION PROJECT AT QUEEN ANNES COUNTY, MARYLAND SIX-MONTH EVALUATION REPORT

    EPA Science Inventory

    This report documents the activities performed and the results obtained from the first six months of the arsenic removal treatment technology demonstration project at the community of Prospect Bay at Grasonville in Queen Anne’s County, MD. The objectives of the project were to ev...

  8. International Symposium on Remote Sensing of Environment, 13th, Ann Arbor, Mich., April 23-27, 1979, Proceedings. Volumes 1, 2 & 3

    NASA Technical Reports Server (NTRS)

    1979-01-01

    The presentations document current activities in the field of remote sensing. Papers include those concerned with data collection, processing, and analysis hardware and methodology, as well as the application of this technology to monitoring and managing the earth's resources and man's global environment. Ground-based, airborne, and spaceborne sensor systems and both manual and machine-assisted data analysis and interpretation are considered.

  9. International Symposium on Remote Sensing of Environment, 15th, University of Michigan, Ann Arbor, MI, May 11-15, 1981, Proceedings. Volumes 1, 2 & 3

    NASA Technical Reports Server (NTRS)

    1981-01-01

    Developments related to advanced sensors and sensor systems are being examined, taking into account advanced aerospace remote sensing systems for global resource applications, spaceborne radar observation of the earth surface, a concept for an advanced earth resources satellite system, technologies for the multispectral mapping of earth resources, and the use of Landsat images and morphologic analogs in space exploration. Other topics discussed are related to modeling for terrain analysis, digital processing and analysis of remotely sensed data, microwave remote sensing, new discoveries from planetary remote sensing, and data base utilization. Advances in the area of luminescence are also considered along with future plans and prospects concerning the remote sensing of the earth from space.

  10. Strates de croissance et cinématique à court-terme de la déformation (dizaines à centaines de milliers d'années)

    NASA Astrophysics Data System (ADS)

    Castelltort, Sébastien; Pochat, Stéphane; Van den Driessche, Jean

    2004-02-01

    High-frequency stratigraphic cycles (10 s to 100 s ka) often show, at a specific location, an alternation of 'dynamic' (proximal-energetic), and 'non-dynamic' (distal-pelagic) processes with time. When sedimentation is syn-deformation, these processes tend respectively to fill-up tectonically-induced topography or to drape it. As a consequence, growth strata are alternatively thickened and isopach across the growth structure. High-resolution kinematic studies of growth structures (folds and faults), which assume that sedimentation always fills up topographies ('fill-to-the-top' model), may therefore mistake sedimentary cyclicity for tectonic cyclicity. We address this problem with one example of growth anticline in the Spanish Pyrenees, and we discuss the fill-to-the-top model. To cite this article: S. Castelltort et al., C. R. Geoscience 336 (2004).

  11. Predicting Student Grade Based on Free-Style Comments Using Word2Vec and ANN by Considering Prediction Results Obtained in Consecutive Lessons

    ERIC Educational Resources Information Center

    Luo, Jingyi; Sorour, Shaymaa E.; Goda, Kazumasa; Mine, Tsunenori

    2015-01-01

    Continuously tracking students during a whole semester plays a vital role to enable a teacher to grasp their learning situation, attitude and motivation. It also helps to give correct assessment and useful feedback to them. To this end, we ask students to write their comments just after each lesson, because student comments reflect their learning…

  12. Assessment of Runoff and Sediment Yields Using the AnnAGNPS Model from the Daning River Watershed in Three-Gorge Area of China

    EPA Science Inventory

    Soil erosion has been recognized as one of the major threats to our environment and water quality worldwide, especially in China. To mitigate nonpoint source water quality problems caused by soil erosion, best management practices (BMPs) and/or conservation programs have been ado...

  13. International Symposium on Remote Sensing of Environment, 17th, University of Michigan, Ann Arbor, MI, May 9-13, 1983, Proceedings. Volumes 1, 2 & 3

    NASA Technical Reports Server (NTRS)

    1984-01-01

    The collection, processing, and analysis of remote-sensing data from ground-based, airborne, and spaceborne instruments for application to the monitoring and management of the earth and environment and resources are examined in reviews and reports, some in summary form. Subject areas covered include US policy and directions on remote sensing (RS); the future of terrestrial RS from space; RS of land, oceans, and atmosphere from a global perspective; RS in hydrological modeling; microprocessing technology; array processors; geobased information systems; artificial intelligence; the Shuttle imaging radar; and current results from Landsat-4. Among the specific topics discussed are RS application to hydrocarbon exploration, airborne gamma-radiation assessment of snow water equivalent, surface-vegetation-biomass modeling from AVHRR and Landsat data, Landsat imagery of Mediterranean pollution, fast two-dimensional filtering of thermal-scanner data, RS of severe convective storms, registration of rotated images by invariant moments, and the geometric accuracy of Landsat-4 Thematic-Mapper P-tapes.

  14. Girls and Science and Technology. Proceedings and Contributions of the GASAT Conference (4th, Ann Arbor, Michigan, July 24-29, 1987).

    ERIC Educational Resources Information Center

    Daniels, Jane Zimmer, Ed.; Kahle, Jane Butler, Ed.

    In 1979, North European researchers met informally to discuss issues regarding women in science. In addition to discovering that the issues raised crossed national boundaries and cultural differences, they found that numerous efforts were underway to address their concerns. What started as an informal meeting has evolved today into an…

  15. Providing for the reappointment of Shirley Ann Jackson as a citizen regent of the Board of Regents of the Smithsonian Institution.

    THOMAS, 112th Congress

    Rep. Johnson, Sam [R-TX-3

    2011-02-16

    02/16/2011 Referred to the House Committee on House Administration. (All Actions) Notes: For further action, see S.J.RES.7, which became Public Law 112-19 on 6/24/2011. Tracker: This bill has the status IntroducedHere are the steps for Status of Legislation:

  16. Modeling the Contribution of Ephemeral Gully Erosion Under Different Soil Management in An Olive Orchard Microcatchment Using AnnAGNPS Model

    EPA Science Inventory

    In Spain, few studies have been carried out to explore the erosion caused by processes other than interrill and rill erosion, such as gully and ephemeral gully erosion, especially because most of the available studies have evaluated the erosion at plot scale. A study about the en...

  17. "H TEXNH: Research Methods and Topics for the History of Rhetoric"; Proceedings of the Speech Communications Association Doctoral Honors Seminar (Ann Arbor, Michigan, March 3-5, 1978).

    ERIC Educational Resources Information Center

    Enos, Richard Leo, Ed.; Wiethoff, William E., Ed.

    The interpretation of the history of rhetoric was the subject of the seminar reported in this document. After a preface and a discussion of the "promise" of rhetoric, abstracts of seminar presentations are grouped under the headings of rhetoric and culture, rhetoric and philosophy, methodological considerations, and discourse analysis. Abstracts…

  18. Implementation of artificial neural networks (ANNs) to analysis of inter-taxa communities of benthic microorganisms and macroinvertebrates in a polluted stream.

    PubMed

    Kim, Byunghyuk; Lee, Se-Eun; Song, Mi-Young; Choi, Jung-Hye; Ahn, Soon-Mo; Lee, Kun-Seop; Cho, Eungchun; Chon, Tae-Soo; Koh, Sung-Cheol

    2008-02-01

    This study was performed to gain an understanding of the structural and functional relationships between inter-taxa communities (macroinvertebrates as consumers, and microbes as decomposers or preys for the invertebrates) in a polluted stream using artificial neural networks techniques. Sediment samples, carrying microorganisms (eubacteria) and macroinvertebrates, were seasonally collected from similar habitats in streams with different levels of pollution. Microbial community taxa and densities were determined using polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) and 16S rDNA sequence analysis techniques. The identity and density of macroinvertebrates were concurrently determined. In general, differences were observed on grouping by self-organizing map (SOM) in polluted, clean and recovering sites based on the microbial densities, while the community patterns were partly dependent on the sampling period. A Spearman rank order correlation analysis revealed correlations of several eubacterial species with those of macroinvertebrates: a negative correlation was observed between Acidovorax sp. (from polluted sites) and Gammaridae (mostly from the clean site), while Herbaspirillum sp. and Janthinobacterium sp. appeared to have positive correlations with some macroinvertebrate species. The population dynamics of the tolerant texa, Tubificidae and Chironomidae, appeared to be related with changes in the densities of Acidovorax sp. This study revealed community relationships between macroinvertebrates and microorganisms, reflecting the connectivity between the two communities via the food chain. A further physio-ecological and symbiological study on the invertebrate-microorganism relationships will be required to understand the degradation and utilization of detritus in aquatic ecosystems as well as to elucidate the roles of the inter-taxa in the recovery of polluted aquatic environments.

  19. 30 CFR 941.780 - Surface mining permit applications-minimum requirements for reclamation and operation plan.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... demonstrate compliance with the South Dakota laws on air pollution, S. D. Comp. Laws Ann. Chap. 34A-1, water pollution control, S. D. Comp. Laws Ann. Chap. 34A-2, and solid waste disposal, S. D. Comp. Laws Ann....

  20. 30 CFR 941.780 - Surface mining permit applications-minimum requirements for reclamation and operation plan.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... demonstrate compliance with the South Dakota laws on air pollution, S. D. Comp. Laws Ann. Chap. 34A-1, water pollution control, S. D. Comp. Laws Ann. Chap. 34A-2, and solid waste disposal, S. D. Comp. Laws Ann....

  1. 30 CFR 941.780 - Surface mining permit applications-minimum requirements for reclamation and operation plan.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... demonstrate compliance with the South Dakota laws on air pollution, S. D. Comp. Laws Ann. Chap. 34A-1, water pollution control, S. D. Comp. Laws Ann. Chap. 34A-2, and solid waste disposal, S. D. Comp. Laws Ann....

  2. Optimization of the Production of Extracellular Polysaccharide from the Shiitake Medicinal Mushroom Lentinus edodes (Agaricomycetes) Using Mutation and a Genetic Algorithm-Coupled Artificial Neural Network (GA-ANN).

    PubMed

    Adeeyo, Adeyemi Ojutalayo; Lateef, Agbaje; Gueguim-Kana, Evariste Bosco

    2016-01-01

    Exopolysaccharide (EPS) production by a strain of Lentinus edodes was studied via the effects of treatments with ultraviolet (UV) irradiation and acridine orange. Furthermore, optimization of EPS production was studied using a genetic algorithm coupled with an artificial neural network in submerged fermentation. Exposure to irradiation and acridine orange resulted in improved EPS production (2.783 and 5.548 g/L, respectively) when compared with the wild strain (1.044 g/L), whereas optimization led to improved productivity (23.21 g/L). The EPS produced by various strains also demonstrated good DPPH scavenging activities of 45.40-88.90%, and also inhibited the growth of Escherichia coli and Klebsiella pneumoniae. This study shows that multistep optimization schemes involving physical-chemical mutation and media optimization can be an attractive strategy for improving the yield of bioactives from medicinal mushrooms. To the best of our knowledge, this report presents the first reference of a multistep approach to optimizing EPS production in L. edodes. PMID:27649726

  3. Erratum to "Effect of the time-dependent coupling on a superconducting qubit-field system under decoherence: Entanglement and Wehrl entropy" [Ann. Physics 361 (2015) 247-258

    NASA Astrophysics Data System (ADS)

    Abdel-Khalek, S.; Berrada, K.; Eleuch, H.

    2016-09-01

    The dynamics of a superconducting (SC) qubit interacting with a field under decoherence with and without time-dependent coupling effect is analyzed. Quantum features like the collapse-revivals for the dynamics of population inversion, sudden birth and sudden death of entanglement, and statistical properties are investigated under the phase damping effect. Analytic results for certain parametric conditions are obtained. We analyze the influence of decoherence on the negativity and Wehrl entropy for different values of the physical parameters. We also explore an interesting relation between the SC-field entanglement and Wehrl entropy behavior during the time evolution. We show that the amount of SC-field entanglement can be enhanced as the field tends to be more classical. The studied model of SC-field system with the time-dependent coupling has high practical importance due to their experimental accessibility which may open new perspectives in different tasks of quantum formation processing.

  4. Remarks of Jo Anne B. Barnhart, Assistant Secretary, Administration for Children and Families, U.S. Department of Health and Human Services before the National Association of Child Care Resource and Referral Agencies (Washington, D.C., February 20, 1992).

    ERIC Educational Resources Information Center

    Barnhart, Jo Anne B.

    One of the goals of the America 2000 initiative is that by the year 2000, all children in the United States will start school ready to learn. Child care will play a major role in the achievement of this goal due to the fact that nearly half of all preschool children spend a significant portion of time in child care settings outside of the home.…

  5. USAID's HIV / AIDS strategy for Asia: promoting early intervention. An inverview with Kerri-Ann Jones of the U.S. Agency for International Development's Asia Bureau about the USAID HIV / AIDS Strategic Plan for Asia.

    PubMed

    1994-08-01

    Although the World Health Organization estimates that $1.5-2.9 billion is needed per year to adequately support global HIV/AIDS prevention efforts, less than $200 million/year is being spent to prevent the transmission of HIV in the developing world. Worse still, US Agency for International Development (USAID) resources are shrinking. Two factors make HIV/AIDS prevention action in Asia a particularly urgent priority: more than half of the world's population lives in the region and the AIDS epidemic is still in its formative stages there. Rapid urbanization, high levels of migration, and large commercial sex and transportation industries are also typical in most countries of the region. In this context, the Asia Bureau of USAID adopted a strategic plan against HIV/AIDS in June 1993. In so doing, the bureau plans to work with the AIDSCAP program to coordinate prevention efforts and maximize the use of all available resources against the epidemic. Strategy focuses upon policy dialogue, communication to promote behavior change, improving the management of sexually transmitted diseases, improving access to condoms, monitoring and evaluation, and behavioral research. Interventions recommended by the plan, USAID coordination with other donors in the region, coordination with other AIDS prevention efforts already underway in a country, mission response to the plan, the interest of Asian governments in AIDS prevention, and the mechanism by which the plan is carried out are discussed.

  6. Parental Acceptance/Involvement, Self-Esteem and Academic Achievement: The Role of Hope as a Mediator (Anne-Babadan Algilanan Kabul/Ilgi, Benlik Saygisi Ve Akademik Basari: Umudun Araci Rolü)

    ERIC Educational Resources Information Center

    Aydin, Betül; Sari, Serkan Volkan; Sahin, Mustafa

    2014-01-01

    In this study, examining the relationship of parental acceptance/involvement to self-esteem, hope and academic achievement besides, mediating role of hope on the relationship between perception of parental acceptance/involvement, self esteem and academic achievement were aimed. The study was carried out with 297 students from different…

  7. Des analogues naturels de sites de stockage de déchets nucléaires vieux de 2 milliards d'années : les réacteurs de fission nucléaire naturels du Gabon (Afrique)

    NASA Astrophysics Data System (ADS)

    Gauthier-Lafaye, François

    2002-10-01

    Two billion years ago, the increase of oxygen in atmosphere and the high 235U/ 238U uranium ratio (>3%) made possible the occurrence of natural nuclear reactors on Earth. These reactors are considered to be a good natural analogue for nuclear waste disposal. Their preservation during such a long period of time is mainly due to the geological stability of the site, the occurrence of clays surrounding the reactors and acting as an impermeable shield, and the occurrence of organic matter that maintained the environment in reducing conditions, favourable for the stability of uraninite. Hydrogeochemical studies and modelling have shown the complexity of the geochemical system at Oklo and Bangombé (Gabon) and the lack of precise data about uranium and fission products retention and migration mechanisms in geological environments. To cite this article: F. Gauthier-Lafaye, C. R. Physique 3 (2002) 839-849.

  8. Artificial neural networks in medicine

    SciTech Connect

    Keller, P.E.

    1994-07-01

    This Technology Brief provides an overview of artificial neural networks (ANN). A definition and explanation of an ANN is given and situations in which an ANN is used are described. ANN applications to medicine specifically are then explored and the areas in which it is currently being used are discussed. Included are medical diagnostic aides, biochemical analysis, medical image analysis and drug development.

  9. Technical and analytical support to the ARPA Artificial Neural Network Technology Program

    SciTech Connect

    1995-09-16

    Strategic Analysis (SA) has provided ongoing work for the Advanced Research Projects Agency (ARPA) Artificial Neural Network (ANN) technology program. This effort provides technical and analytical support to the ARPA ANN technology program in support of the following information areas of interest: (1) Alternative approaches for application of ANN technology, hardware approaches that utilize the inherent massive parallelism of ANN technology, and novel ANN theory and modeling analyses. (2) Promising military applications for ANN technology. (3) Measures to use in judging success of ANN technology research and development. (4) Alternative strategies for ARPA involvement in ANN technology R&D. These objectives were accomplished through the development of novel information management tools, strong SA knowledge base, and effective communication with contractors, agents, and other program participants. These goals have been realized. Through enhanced tracking and coordination of research, the ANN program is healthy and recharged for future technological breakthroughs.

  10. 75 FR 68000 - Notice of Inventory Completion: U.S. Department of Agriculture, Forest Service, Hiawatha National...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2010-11-04

    ..., Hiawatha National Forest, Escanaba, MI and University of Michigan, Museum of Anthropology, Ann Arbor, MI... Anthropology, Ann Arbor, MI. The human remains were removed from Naomikong Point Site, Chippewa County,...

  11. Hepatitis B

    MedlinePlus

    ... U.S. Preventive Services Task Force recommendation statement. Ann Intern Med . 2014;161(1):58-66. PMID 24863637 ... Development Conference Statement: Management of hepatitis B. Ann Intern Med . 2009;150:104-10. PMID: 19124811 www. ...

  12. 78 FR 26867 - Quarterly Publication of Individuals, Who Have Chosen To Expatriate

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-05-08

    ... BEATRICE ] HAGMANN WALTER HAKOZAKI SEIJI HALEY-UHLMANN ALICE HALL KATRINA FAVELL HAMILTON WILLIAM NEILSON... ANN SILVERSTEIN A JAY SIMON DOUGLAS NORMAN SIMONI ANNE WINKLER SIMONI CARLO ALBERTO SLEE...

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

    NASA Technical Reports Server (NTRS)

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

    1994-01-01

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

  14. Electricity price short-term forecasting using artificial neural networks

    SciTech Connect

    Szkuta, B.R.; Sanabria, L.A.; Dillon, T.S.

    1999-08-01

    This paper presents the System Marginal Price (SMP) short-term forecasting implementation using the Artificial Neural Networks (ANN) computing technique. The described approach uses the three-layered ANN paradigm with back-propagation. The retrospective SMP real-world data, acquired from the deregulated Victorian power system, was used for training and testing the ANN. The results presented in this paper confirm considerable value of the ANN based approach in forecasting the SMP.

  15. The impact of single and dual hydrothermal modifications on the molecular structure and physicochemical properties of normal corn starch.

    PubMed

    Chung, Hyun-Jung; Hoover, Ratnajothi; Liu, Qiang

    2009-03-01

    Effect of single and dual hydrothermal modifications with annealing (ANN) and heat-moisture treatment (HMT) on molecular structure and physicochemical properties of corn starch was investigated. Normal corn starch was modified by ANN at 70% moisture at 50 degrees C for 24h and HMT at 30% moisture at 120 degrees C for 24h as well as by the combination of ANN and HMT. The apparent amylose content and swelling factor (SF) decreased on ANN and HMT, but amylose leaching (AML) increased. These changes were more pronounced on dual modification. The crystallinity (determined by X-ray diffraction), the gelatinization enthalpy (determined by differential scanning calorimetry) and ratio of 1047 cm(-1)/1022 cm(-1) (determined by Fourier transform infrared spectroscopy) slightly increased on ANN and decreased on HMT. The ANN and subsequent HMT (ANN-HMT) resulted in the lowest crystallinity, gelatinization enthalpy and ratio of 1047 cm(-1)/1022 cm(-1). The gelatinization temperature range decreased on ANN but increased on HMT. However, the gelatinization range of dually modified starches (ANN-HMT and HMT-ANN) was between ANN starch and HMT starch. Birefringence remained unchanged on ANN but slightly decreased on HMT as well as dual modification. Average chain length and amount of longer branch chains (DP> or =37) remained almost unchanged on ANN but decreased on HMT and dual modifications (ANN-HMT and HMT-ANN). HMT and dual modifications resulted in highly reduced pasting viscosity. ANN and HMT as well as dual modifications increased RDS content and decreased SDS and RS content.

  16. Reflective Learning in Practice.

    ERIC Educational Resources Information Center

    Brockbank, Anne, Ed.; McGill, Ian, Ed.; Beech, Nic, Ed.

    This book contains 22 papers on reflective learning in practice. The following papers are included: "Our Purpose" (Ann Brockbank, Ian McGill, Nic Beech); "The Nature and Context of Learning" (Ann Brockbank, Ian McGill, Nic Beech); "Reflective Learning and Organizations" (Ann Brockbank, Ian McGill, Nic Beech); "Reflective Learning in Practice" (Ann…

  17. Modelling for Prediction vs. Modelling for Understanding: Commentary on Musso et al. (2013)

    ERIC Educational Resources Information Center

    Edelsbrunner, Peter; Schneider, Michael

    2013-01-01

    Musso et al. (2013) predict students' academic achievement with high accuracy one year in advance from cognitive and demographic variables, using artificial neural networks (ANNs). They conclude that ANNs have high potential for theoretical and practical improvements in learning sciences. ANNs are powerful statistical modelling tools but they can…

  18. 75 FR 30457 - Self-Regulatory Organizations; Financial Industry Regulatory Authority, Inc.; Notice of Filing of...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2010-06-01

    ... meeting of such members.\\4\\ \\3\\ Del. Code Ann. tit. 8 Sec. 215(c) (2010). \\4\\ Del. Code Ann. tit. 8 Sec... a meeting of such members.\\5\\ \\5\\ Del. Code Ann. tit. 8 Sec. 215(c)(1) (2010). On August 1,...

  19. 75 FR 82033 - National Institute of Environmental Health Sciences; Notice of Closed Meetings

    Federal Register 2010, 2011, 2012, 2013, 2014

    2010-12-29

    ... Call). Contact Person: RoseAnne M McGee, Associate Scientific Review Administrator, Scientific Review...). Contact Person: RoseAnne M McGee, Associate Scientific Review Administrator, Scientific Review Branch..., Research Triangle Park, NC 27709. (Telephone Conference Call). Contact Person: RoseAnne M McGee,...

  20. Achieving Consistent Near-Optimal Pattern Recognition Accuracy Using Particle Swarm Optimization to Pre-Train Artificial Neural Networks

    ERIC Educational Resources Information Center

    Nikelshpur, Dmitry O.

    2014-01-01

    Similar to mammalian brains, Artificial Neural Networks (ANN) are universal approximators, capable of yielding near-optimal solutions to a wide assortment of problems. ANNs are used in many fields including medicine, internet security, engineering, retail, robotics, warfare, intelligence control, and finance. "ANNs have a tendency to get…

  1. Real-time support for high performance aircraft operation

    NASA Technical Reports Server (NTRS)

    Vidal, Jacques J.

    1989-01-01

    The feasibility of real-time processing schemes using artificial neural networks (ANNs) is investigated. A rationale for digital neural nets is presented and a general processor architecture for control applications is illustrated. Research results on ANN structures for real-time applications are given. Research results on ANN algorithms for real-time control are also shown.

  2. Short-term electric load forecasting using neural networks

    SciTech Connect

    Daugherty, E.; Bartlett, E.

    1993-12-31

    Short-term electric load forecasting (STELF) plays an important role in electric utilities, and several techniques are used to perform these predictions and system modelings. Recently, artificial neural networks (ANN`s) have been implemented for STELF with some success. This paper will examine improved STELF by optimization of ANN techniques. The strategy for the research involves careful selection of input variables and utilization of effective generalization. Some results have been obtained which show that, with the selection of another input variable, the ANN`s use for STELF can be improved.

  3. Error bounds on the output of artificial neural networks

    SciTech Connect

    Bartlett, E.B.; Kim, H. )

    1993-01-01

    Resolving the uncertainties associated with solutions obtained from artificial neural networks (ANNs) is a major concern for ANN researchers. Error bounds on the solutions are important because they are an integral part of verification and validation. In this research, stacked generalization (SG) is applied to provide error bounds for novel solutions obtained from ANNS. An outline of SG and its use is given. The data used in this demonstration of SG are given. This work shows that SG can provide error bounds on ANN results. We have applied SG to nuclear power plant fault detection for verification of diagnoses provided by ANNs.

  4. Adaptive conventional power system stabilizer based on artificial neural network

    SciTech Connect

    Kothari, M.L.; Segal, R.; Ghodki, B.K.

    1995-12-31

    This paper deals with an artificial neural network (ANN) based adaptive conventional power system stabilizer (PSS). The ANN comprises an input layer, a hidden layer and an output layer. The input vector to the ANN comprises real power (P) and reactive power (Q), while the output vector comprises optimum PSS parameters. A systematic approach for generating training set covering wide range of operating conditions, is presented. The ANN has been trained using back-propagation training algorithm. Investigations reveal that the dynamic performance of ANN based adaptive conventional PSS is quite insensitive to wide variations in loading conditions.

  5. Monthly river flow forecasting using artificial neural network and support vector regression models coupled with wavelet transform

    NASA Astrophysics Data System (ADS)

    Kalteh, Aman Mohammad

    2013-04-01

    Reliable and accurate forecasts of river flow is needed in many water resources planning, design development, operation and maintenance activities. In this study, the relative accuracy of artificial neural network (ANN) and support vector regression (SVR) models coupled with wavelet transform in monthly river flow forecasting is investigated, and compared to regular ANN and SVR models, respectively. The relative performance of regular ANN and SVR models is also compared to each other. For this, monthly river flow data of Kharjegil and Ponel stations in Northern Iran are used. The comparison of the results reveals that both ANN and SVR models coupled with wavelet transform, are able to provide more accurate forecasting results than the regular ANN and SVR models. However, it is found that SVR models coupled with wavelet transform provide better forecasting results than ANN models coupled with wavelet transform. The results also indicate that regular SVR models perform slightly better than regular ANN models.

  6. FIRST Quantum-(1980)-Computing DISCOVERY in Siegel-Rosen-Feynman-...A.-I. Neural-Networks: Artificial(ANN)/Biological(BNN) and Siegel FIRST Semantic-Web and Siegel FIRST ``Page''-``Brin'' ``PageRank'' PRE-Google Search-Engines!!!

    NASA Astrophysics Data System (ADS)

    Rosen, Charles; Siegel, Edward Carl-Ludwig; Feynman, Richard; Wunderman, Irwin; Smith, Adolph; Marinov, Vesco; Goldman, Jacob; Brine, Sergey; Poge, Larry; Schmidt, Erich; Young, Frederic; Goates-Bulmer, William-Steven; Lewis-Tsurakov-Altshuler, Thomas-Valerie-Genot; Ibm/Exxon Collaboration; Google/Uw Collaboration; Microsoft/Amazon Collaboration; Oracle/Sun Collaboration; Ostp/Dod/Dia/Nsa/W.-F./Boa/Ubs/Ub Collaboration

    2013-03-01

    Belew[Finding Out About, Cambridge(2000)] and separately full-decade pre-Page/Brin/Google FIRST Siegel-Rosen(Machine-Intelligence/Atherton)-Feynman-Smith-Marinov(Guzik Enterprises/Exxon-Enterprises/A.-I./Santa Clara)-Wunderman(H.-P.) [IBM Conf. on Computers and Mathematics, Stanford(1986); APS Mtgs.(1980s): Palo Alto/Santa Clara/San Francisco/...(1980s) MRS Spring-Mtgs.(1980s): Palo Alto/San Jose/San Francisco/...(1980-1992) FIRST quantum-computing via Bose-Einstein quantum-statistics(BEQS) Bose-Einstein CONDENSATION (BEC) in artificial-intelligence(A-I) artificial neural-networks(A-N-N) and biological neural-networks(B-N-N) and Siegel[J. Noncrystalline-Solids 40, 453(1980); Symp. on Fractals..., MRS Fall-Mtg., Boston(1989)-5-papers; Symp. on Scaling..., (1990); Symp. on Transport in Geometric-Constraint (1990)

  7. Evapotranspiration and Dual Crop Coefficients Sonisa Sharma1, Ayse Irmak12, Anne Parkhurst3, Elizabeth walter-Shea1 and Kenneth G. Hubbard1 1School of Natural Resources, 2Civil Engineering, 3Departments of Statistics, University of Nebraska-Lincoln

    NASA Astrophysics Data System (ADS)

    Sharma, S.

    2012-12-01

    Accurate estimation of water content in the crop root zone is most important for water conservation and management practices like irrigation. The objective of this study is to use the FA0-56 dual crop cefficients: basal crop coefficient Kcb and the soil evaporation coefficient Ke for a large corn/soybean field in the year 2005 at the Mead Turf Farm in the state of Nebraska, USA..Dual crop coefficients can be used to estimate both transpiration from crops and evaporation from soil. The Kcb has a low value of 0.15(K cb, in) during the initial period, increases rapidly to a maximum of 1.14 (K cb, mid) for the entire midseason and decreases rapidly to 0.5 at the end of the corn growing season (K cb,end). When examined together with precipitation, the dual crop coefficient was higher following rainfall or irrigation, as expected. The data suggests that the dual crop coefficient approach is a good estimation of water loss from well-watered crops. Irrigation can be scheduled to replace the loss of water from the crop/soil system. Similarly, when we compared the measured daily ET and the ET calculated from dual crop coefficients, it gives 98 % R2.; Comparision of calculated ET from dual crop coefficient appraoch with Weather Station ET

  8. Modeling a scientific career: an essential component of the mentorship process. An interview with John A. Williams, Professor of Molecular and Integrative Physiology, University Of Michigan, Ann Arbor, Mich., USA by Martín E. Fernández-Zapico.

    PubMed

    Williams, John A

    2010-01-01

    In the current interview article, Dr. John A. Williams shares his experiences, and provides career advice to junior investigators. Dr. Williams is one of the world's leading physiologists working on signal transduction mechanisms in pancreatic acinar cells. He is worldwide recognized for his contribution to many areas of pancreatology, especially the understanding of GI hormone regulation of pancreatic exocrine function. and IAP.

  9. Ann Sevi Ak Tout Entelijans Elev Ayisyen Yo: Yon Seri leson matematik ak syans pou elev edikasyon jeneral ak elev edikasyon espesyal (4em-8em ane) = Tapping into Haitian Students' Multiple Intelligences: A Collection of Mathematics and Science Lessons for General and Special Education Students (Grades 4-8).

    ERIC Educational Resources Information Center

    New York City Board of Education, Brooklyn, NY. Office of Bilingual Education.

    The materials consist of five mathematics and five science lessons for Haitian bilingual students in general and special education in grades 4-8. A thematic/interdisciplinary approach was used in designing the lesson, incorporating theory of multiple intelligences, Bloom's taxonomy of educational objectives, and other learning theories. The…

  10. Artificial neural networks in chest radiographs: detection and characterization of interstitial lung disease

    NASA Astrophysics Data System (ADS)

    Ishida, Takayuki; Katsuragawa, Shigehiko; Ashizawa, Kazuto; MacMahon, Heber; Doi, Kunio

    1997-04-01

    We have developed a computerized scheme for detection of interstitial lung disease by using artificial neural networks (ANNs) on quantitative analysis of digital image data. Three separate ANNs wee applied for the ANN scheme. The first ANN was trained with horizontal profiles in the ROIs selected from digital chest radiographs. The second ANN was trained with vertical output pattern obtained from the 1st ANN in each ROI. The output from the 2nd ANN was used to distinguish between normal and abnormal ROIs. In order to improve the performance, we attempted a density correction and rib edge removal. The Az value was improved from 0.906 to 0.934 by incorporating density correction. For the classification of each chest image, we employed a rule-based method and a rule-based plus the third ANN method. A high Az value was obtained with the rule-based plus ANN method. The ANNs can learn certain statistical properties associate with patterns of interstitial infiltrates in chest radiographs.

  11. Simulation of structural response using a recurrent radial basis function network

    SciTech Connect

    Paez, T.L.

    1994-08-01

    System behaviors can be accurately simulated using artificial neural networks (ANNs), and one that performs well in simulation of structural response is the radial basis function network. A specific implementation of this is the connectionist normalized linear spline (CNLS) network, investigated in this study. A useful framework for ANN simulation of structural response is the recurrent network. This framework simulates the response of a structure one step at a time. It requires as inputs some measures of the excitation, and the response at previous times. On output, the recurrent ANN yields the response at some time in the future. This framework is practical to implement because every ANN requires training, and this is executed by showing the ANN examples of correct input/output behavior (exemplars), and requiring the ANN to simulate this behavior. In practical applications, hundreds or, perhaps, thousands, of exemplars are required for ANN training. The usual laboratory and non-neural numerical applications to be simulated by ANNs produce these amounts of information. Once the recurrent ANN is trained, it can be provided with excitation information, and used to propagate structural response, simulating the response it was trained to approximate. The structural characteristics, parameters in the CNLS network, and degree of training influence the accuracy of approximation. This investigation studies the accuracy of structural response simulation for a single-degree-of-freedom (SDF), nonlinear system excited by random vibration loading. The ANN used to simulate structural response is a recurrent CNLS network. We investigate the error in structural system simulation.

  12. Forecasting S&P 500 index using artificial neural networks and design of experiments

    NASA Astrophysics Data System (ADS)

    Niaki, Seyed Taghi Akhavan; Hoseinzade, Saeid

    2013-02-01

    The main objective of this research is to forecast the daily direction of Standard & Poor's 500 (S&P 500) index using an artificial neural network (ANN). In order to select the most influential features (factors) of the proposed ANN that affect the daily direction of S&P 500 (the response), design of experiments are conducted to determine the statistically significant factors among 27 potential financial and economical variables along with a feature defined as the number of nodes of the ANN. The results of employing the proposed methodology show that the ANN that uses the most influential features is able to forecast the daily direction of S&P 500 significantly better than the traditional logit model. Furthermore, experimental results of employing the proposed ANN on the trades in a test period indicate that ANN could significantly improve the trading profit as compared with the buy-and-hold strategy.

  13. On-line biomass estimation in biosurfactant production process by Candida lipolytica UCP 988.

    PubMed

    da Costa Albuquerque, Clarissa Daisy; de Campos-Takaki, Galba Maria; Fileti, Ana Maria Frattini

    2008-11-01

    Biomass is an important variable in biosurfactant production process. However, such bioprocess variable, usually, is collected by sampling and determined by off-line analysis, with significant time delay. Therefore, simple and reliable on-line biomass estimation procedures are highly desirable. An artificial neural network model (ANN) is presented for the on-line estimation of biomass concentration, in biosurfactant production by Candida lipolytica UCP 988, as a nonlinear function of pH and dissolved oxygen. Several configurations were evaluated while developing the optimal ANN model. The optimal ANN model consists of one hidden layer with four neurons. The performance of the ANN was checked using experimental data. The results obtained indicate a very good predictive capacity for the ANN-based software sensor with values of R2 of 0.969 and RMSE of 0.021 for biomass concentration. Estimated biomass using the ANN was proved to be a simple, robust and accurate method.

  14. Artificial neural networks and prostate cancer--tools for diagnosis and management.

    PubMed

    Hu, Xinhai; Cammann, Henning; Meyer, Hellmuth-A; Miller, Kurt; Jung, Klaus; Stephan, Carsten

    2013-03-01

    Artificial neural networks (ANNs) are mathematical models that are based on biological neural networks and are composed of interconnected groups of artificial neurons. ANNs are used to map and predict outcomes in complex relationships between given 'inputs' and sought-after 'outputs' and can also be used find patterns in datasets. In medicine, ANN applications have been used in cancer diagnosis, staging and recurrence prediction since the mid-1990s, when an enormous effort was initiated, especially in prostate cancer detection. Modern ANNs can incorporate new biomarkers and imaging data to improve their predictive power and can offer a number of advantages as clinical decision making tools, such as easy handling of distribution-free input parameters. Most importantly, ANNs consider nonlinear relationships among input data that cannot always be recognized by conventional analyses. In the future, complex medical diagnostic and treatment decisions will be increasingly based on ANNs and other multivariate models.

  15. Spatial Treatment of the Slab-geometry Discrete Ordinates Equations Using Artificial Neural Networks

    SciTech Connect

    Brantley, P S

    2001-03-23

    An artificial neural network (ANN) method is developed for treating the spatial variable of the one-group slab-geometry discrete ordinates (S{sub N}) equations in a homogeneous medium with linearly anisotropic scattering. This ANN method takes advantage of the function approximation capability of multilayer ANNs. The discrete ordinates angular flux is approximated by a multilayer ANN with a single input representing the spatial variable x and N outputs representing the angular flux in each of the discrete ordinates angular directions. A global objective function is formulated which measures how accurately the output of the ANN approximates the solution of the discrete ordinates equations and boundary conditions at specified spatial points. Minimization of this objective function determines the appropriate values for the parameters of the ANN. Numerical results are presented demonstrating the accuracy of the method for both fixed source and incident angular flux problems.

  16. Neural network based short term load forecasting

    SciTech Connect

    Lu, C.N.; Wu, H.T. . Dept. of Electrical Engineering); Vemuri, S. . Controls and Composition Div.)

    1993-02-01

    The artificial neural network (ANN) technique for short term load forecasting (STLF) has been proposed by several authors, and gained a lot of attention recently. In order to evaluate ANN as a viable technique for STLF, one has to evaluate the performance of ANN methodology for practical considerations of STLF problems. This paper makes an attempt to address these issues. The paper presents the results of a study to investigate whether the ANN model is system dependent, and/or case dependent. Data from two utilities were used in modeling and forecasting. In addition, the effectiveness of a next 24 hour ANN model is predicting 24 hour load profile at one time was compared with the traditional next one hour ANN model.

  17. Neural network modeling of the light profile in a novel photobioreactor.

    PubMed

    Salazar-Peña, R; Alcaraz-González, V; González-Álvarez, V; Snell-Castro, R; Méndez-Acosta, H O

    2014-06-01

    An artificial neural network (ANN) was implemented to model the light profile pattern inside a photobioreactor (PBR) that uses a toroidal light arrangement. The PBR uses Tequila vinasses as culture medium and purple non-sulfur bacteria Rhodopseudomonas palustris as biocatalyzer. The performance of the ANN was tested for a number of conditions and compared to those obtained by using deterministic models. Both ANN and deterministic models were validated experimentally. In all cases, at low biomass concentration, model predictions yielded determination coefficients greater than 0.9. Nevertheless, ANN yielded the more accurate predictions of the light pattern, at both low and high biomass concentration, when the bioreactor radius, the depth, the rotational speed of the stirrer and the biomass concentration were incorporated in the ANN structure. In comparison, most of the deterministic models failed to correlate the empirical data at high biomass concentration. These results show the usefulness of ANNs in the modeling of the light profile pattern in photobioreactors.

  18. Physicochemical properties and digestibility of hydrothermally treated waxy rice starch.

    PubMed

    Zeng, Feng; Ma, Fei; Kong, Fansheng; Gao, Qunyu; Yu, Shujuan

    2015-04-01

    Waxy rice starch was subjected to annealing (ANN) and heat-moisture treatment (HMT). These starches were also treated by a combination of ANN and HMT. The impact of single and dual modifications (ANN-HMT and HMT-ANN) on the molecular weight (M(w)), crystalline structure, thermal properties, and the digestibility were investigated. The relative crystallinity and short-range order on the granule surface increased on ANN, whereas decreased on HMT. All treated starches showed lower M(w) than that of the native starch. Gelatinization onset temperature, peak temperature and conclusion temperature increased for both single and dual treatments. Increased slowly digestible starch content was found on HMT and ANN-HMT. However, resistant starch levels decreased in all treated starches as compared with native starch. The results would imply that hydrothermal treatment induced structural changes in waxy rice starch significantly affected its digestibility.

  19. A CO2 Laser Weld Shape-Predicting Neural Network

    SciTech Connect

    Fuerschbach, P.W.; Knorovsky, G.A.

    1998-10-05

    We describe two artificial neural networks (ANN) which predict CO2 partial penetration laser welds on grade 304 stainless steel. Given the laser irradiance and travel speed, one ANN (direct) predicts the resulting weld's depth, width, overall shape, energy transfer efficiency, melting efficiency and porosity likelihood in the weld fusion zone. Given the weld size and shape, the second ANN (inverse) predicts the irradiance and travel speed necessary to provide such a weld. The ANNs used 3 nodal layers and perception-type neurons. For the first ANN, with 2 inputs and 17 outputs (12 for shape, and 5 for size, efficiencies and porosity predictions), 12 to 17 intermediate layer neurons were necessary, while for the second, with 14 inputs and 2 outputs, 25 were necessary. Besides their description, data interpretation and weld schedule development via the ANNs will be shown.

  20. Annexin5 Plays a Vital Role in Arabidopsis Pollen Development via Ca2+-Dependent Membrane Trafficking

    PubMed Central

    Zhu, Jingen; Wu, Xiaorong; Yuan, Shunjie; Qian, Dong; Nan, Qiong

    2014-01-01

    The regulation of pollen development and pollen tube growth is a complicated biological process that is crucial for sexual reproduction in flowering plants. Annexins are widely distributed from protists to higher eukaryotes and play multiple roles in numerous cellular events by acting as a putative “linker” between Ca2+ signaling, the actin cytoskeleton and the membrane, which are required for pollen development and pollen tube growth. Our recent report suggested that downregulation of the function of Arabidopsis annexin 5 (Ann5) in transgenic Ann5-RNAi lines caused severely sterile pollen grains. However, little is known about the underlying mechanisms of the function of Ann5 in pollen. This study demonstrated that Ann5 associates with phospholipid membrane and this association is stimulated by Ca2+ in vitro. Brefeldin A (BFA) interferes with endomembrane trafficking and inhibits pollen germination and pollen tube growth. Both pollen germination and pollen tube growth of Ann5-overexpressing plants showed increased resistance to BFA treatment, and this effect was regulated by calcium. Overexpression of Ann5 promoted Ca2+-dependent cytoplasmic streaming in pollen tubes in vivo in response to BFA. Lactrunculin (LatB) significantly prohibited pollen germination and tube growth by binding with high affinity to monomeric actin and preferentially targeting dynamic actin filament arrays and preventing actin polymerization. Overexpression of Ann5 did not affect pollen germination or pollen tube growth in response to LatB compared with wild-type, although Ann5 interacts with actin filaments in a manner similar to some animal annexins. In addition, the sterile pollen phenotype could be only partially rescued by Ann5 mutants at Ca2+-binding sites when compared to the complete recovery by wild-type Ann5. These data demonstrated that Ann5 is involved in pollen development, germination and pollen tube growth through the promotion of endomembrane trafficking modulated by

  1. Applying backpropagation neural network in the control of medullary reflex pattern

    NASA Astrophysics Data System (ADS)

    Dalcin, Bruno Luiz Galluzzi; Cruz, Frederico Alan de Oliveira; Cortez, Célia Martins; Passos, Emmanuel Lopes

    2015-12-01

    We introduced in an artificial neural network (ANN) values of the data matrix that was built with results from simulations performed with the model for the control circuit of spinal reflex presented by Dalcin et al. (2005). Standard multi-layered feed-forward backpropagation network was used to train the ANNs. Results showed that the backpropagation ANN architecture supported the specific classificatory requirements of the study.

  2. Automated development of artificial neural networks for clinical purposes: Application for predicting the outcome of choledocholithiasis surgery.

    PubMed

    Vukicevic, Arso M; Stojadinovic, Miroslav; Radovic, Milos; Djordjevic, Milena; Cirkovic, Bojana Andjelkovic; Pejovic, Tomislav; Jovicic, Gordana; Filipovic, Nenad

    2016-08-01

    Among various expert systems (ES), Artificial Neural Network (ANN) has shown to be suitable for the diagnosis of concurrent common bile duct stones (CBDS) in patients undergoing elective cholecystectomy. However, their application in practice remains limited since the development of ANNs represents a slow process that requires additional expertize from potential users. The aim of this study was to propose an ES for automated development of ANNs and validate its performances on the problem of prediction of CBDS. Automated development of the ANN was achieved by applying the evolutionary assembling approach, which assumes optimal configuring of the ANN parameters by using Genetic algorithm. Automated selection of optimal features for the ANN training was performed using a Backward sequential feature selection algorithm. The assessment of the developed ANN included the evaluation of predictive ability and clinical utility. For these purposes, we collected data from 303 patients who underwent surgery in the period from 2008 to 2014. The results showed that the total bilirubin, alanine aminotransferase, common bile duct diameter, number of stones, size of the smallest calculus, biliary colic, acute cholecystitis and pancreatitis had the best prognostic value of CBDS. Compared to the alternative approaches, the ANN obtained by the proposed ES had better sensitivity and clinical utility, which are considered to be the most important for the particular problem. Besides the fact that it enabled the development of ANNs with better performances, the proposed ES significantly reduced the complexity of ANNs' development compared to previous studies that required manual selection of optimal features and/or ANN configuration. Therefore, it is concluded that the proposed ES represents a robust and user-friendly framework that, apart from the prediction of CBDS, could advance and simplify the application of ANNs for solving a wider range of problems.

  3. Artificial Neural Networks in Spectrometry and Neutron Dosimetry

    SciTech Connect

    Vega-Carrillo, H. R.; Martinez-Blanco, M. R.; Ortiz-Rodriguez, J. M.; Hernandez-Davila, V. M.

    2010-12-07

    The ANN technology has been applied to unfold the neutron spectra of three neutron sources and to estimate their dosimetric features. To compare these results, neutron spectra were also unfolded with the BUNKIUT code. Both unfolding procedures were carried out using the count rates of a Bonner sphere spectrometer. The spectra unfolded with ANN result similar to those unfolded with the BUNKIUT code. The H*(10) values obtained with ANN agrees well with H*(10) values calculated with the BUNKIUT code.

  4. Annexin5 plays a vital role in Arabidopsis pollen development via Ca2+-dependent membrane trafficking.

    PubMed

    Zhu, Jingen; Wu, Xiaorong; Yuan, Shunjie; Qian, Dong; Nan, Qiong; An, Lizhe; Xiang, Yun

    2014-01-01

    The regulation of pollen development and pollen tube growth is a complicated biological process that is crucial for sexual reproduction in flowering plants. Annexins are widely distributed from protists to higher eukaryotes and play multiple roles in numerous cellular events by acting as a putative "linker" between Ca2+ signaling, the actin cytoskeleton and the membrane, which are required for pollen development and pollen tube growth. Our recent report suggested that downregulation of the function of Arabidopsis annexin 5 (Ann5) in transgenic Ann5-RNAi lines caused severely sterile pollen grains. However, little is known about the underlying mechanisms of the function of Ann5 in pollen. This study demonstrated that Ann5 associates with phospholipid membrane and this association is stimulated by Ca2+ in vitro. Brefeldin A (BFA) interferes with endomembrane trafficking and inhibits pollen germination and pollen tube growth. Both pollen germination and pollen tube growth of Ann5-overexpressing plants showed increased resistance to BFA treatment, and this effect was regulated by calcium. Overexpression of Ann5 promoted Ca2+-dependent cytoplasmic streaming in pollen tubes in vivo in response to BFA. Lactrunculin (LatB) significantly prohibited pollen germination and tube growth by binding with high affinity to monomeric actin and preferentially targeting dynamic actin filament arrays and preventing actin polymerization. Overexpression of Ann5 did not affect pollen germination or pollen tube growth in response to LatB compared with wild-type, although Ann5 interacts with actin filaments in a manner similar to some animal annexins. In addition, the sterile pollen phenotype could be only partially rescued by Ann5 mutants at Ca2+-binding sites when compared to the complete recovery by wild-type Ann5. These data demonstrated that Ann5 is involved in pollen development, germination and pollen tube growth through the promotion of endomembrane trafficking modulated by calcium

  5. Using Artificial Neural Networks to Assess Changes in Microbial Communities

    SciTech Connect

    Brandt, C.C.; Macnaughton, S.; Palumbo, A.V.; Pfiffner, S.M.; Schryver, J.C.

    1999-04-19

    We evaluated artificial neural networks (ANNs) as a technique for assessing changes in soil microbial communities following exposure to metals. We analyzed signature lipid biomarker (SLB) data collected from two soil microcosm experiments using traditional statistical techniques and ANN. Two phases of data analysis were done; pattern recognition and prediction. In general, the ANNs were better able to detect patterns and relationships in the SLB data than were the traditional statistical techniques.

  6. Applicability of artificial neural networks for obtaining velocity models from synthetic seismic data

    NASA Astrophysics Data System (ADS)

    Baronian, C.; Riahi, M. A.; Lucas, C.

    2009-07-01

    Seismic velocity analysis is a crucial part of seismic data processing and interpretation which has been practiced using different methods. In contrast to time consuming and complicated numerical methods, artificial neural networks (ANNs) are found to be of potential applicability. ANN ability to establish a relationship between an input and output space is considered to be appropriate for mapping seismic velocity corresponding to travel times picked from seismograms. Accordingly a preliminary attempt is made to evaluate the applicability of ANNs to determine velocity and dips of dipping layered earth models corresponding to travel time data. The study is based on synthetic data generated using inverse modeling approach for three earth models. The models include a three-layer structure with same dips and same directions, a three-layer model with different dips and same directions, as well as a two-layer model with different dips and directions. An ANN structure is designed in three layers, namely, input, output, and hidden ones. The training and testing process of the ANN is successfully accomplished using the synthetic data. The evaluation of the applicability of the trained ANN to unknown data sets indicates that the ANN can satisfactorily compute velocity and dips corresponding to travel times. The error intervals between the desired and calculated velocity and dips are shown to be acceptably small in all cases. The applicability of the trained ANN in extrapolating is also evaluated using a number of data outside of the range already known to ANN. The results indicate that the trained ANN acceptably approximates the velocity and dips. Furthermore, the trained ANN is also evaluated in terms of capability of handling deficiency in input data where acceptable results were also achieved in velocity and dip calculations. Generally, this study shows that velocity analysis using ANNs can promisingly tackle the challenge of retrieving an initial velocity model from the

  7. Pattern recognition in lithology classification: modeling using neural networks, self-organizing maps and genetic algorithms

    NASA Astrophysics Data System (ADS)

    Sahoo, Sasmita; Jha, Madan K.

    2016-10-01

    Effective characterization of lithology is vital for the conceptualization of complex aquifer systems, which is a prerequisite for the development of reliable groundwater-flow and contaminant-transport models. However, such information is often limited for most groundwater basins. This study explores the usefulness and potential of a hybrid soft-computing framework; a traditional artificial neural network with gradient descent-momentum training (ANN-GDM) and a traditional genetic algorithm (GA) based ANN (ANN-GA) approach were developed and compared with a novel hybrid self-organizing map (SOM) based ANN (SOM-ANN-GA) method for the prediction of lithology at a basin scale. This framework is demonstrated through a case study involving a complex multi-layered aquifer system in India, where well-log sites were clustered on the basis of sand-layer frequencies; within each cluster, subsurface layers were reclassified into four depth classes based on the maximum drilling depth. ANN models for each depth class were developed using each of the three approaches. Of the three, the hybrid SOM-ANN-GA models were able to recognize incomplete geologic pattern more reasonably, followed by ANN-GA and ANN-GDM models. It is concluded that the hybrid soft-computing framework can serve as a promising tool for characterizing lithology in groundwater basins with missing lithologic patterns.

  8. Squeezing the turnip with artificial neural nets.

    PubMed

    Francl, Leonard J

    2004-09-01

    ABSTRACT Modeling in epidemiology has followed many different strategies and philosophies. Artificial neural networks (ANNs) comprise a family of highly flexible and adaptive models that have shown promise for application to modeling disease phenomena in general and plant disease forecasting in particular. ANN modeling requires the availability of representative, robust input data and exhaustive testing of model aptness and optimization; meanwhile, ANNs sacrifice much of the biological insight often derived through other model forms. On the other hand, ANNs may extract previously undetected and possibly complex relationships, which can increase prediction accuracy over mainstream statistical methods, usually in an incremental manner.

  9. Students in Austin, Texas Learn About Space Exploration and Science

    NASA Video Gallery

    From NASA's International Space Station Mission Control Center, Christie Sauers, Orion Cockpit Working Group Deputy, participates in a Digital Learning Network (DLN) event with students at the Ann ...

  10. Applications of artificial neural networks in medical science.

    PubMed

    Patel, Jigneshkumar L; Goyal, Ramesh K

    2007-09-01

    Computer technology has been advanced tremendously and the interest has been increased for the potential use of 'Artificial Intelligence (AI)' in medicine and biological research. One of the most interesting and extensively studied branches of AI is the 'Artificial Neural Networks (ANNs)'. Basically, ANNs are the mathematical algorithms, generated by computers. ANNs learn from standard data and capture the knowledge contained in the data. Trained ANNs approach the functionality of small biological neural cluster in a very fundamental manner. They are the digitized model of biological brain and can detect complex nonlinear relationships between dependent as well as independent variables in a data where human brain may fail to detect. Nowadays, ANNs are widely used for medical applications in various disciplines of medicine especially in cardiology. ANNs have been extensively applied in diagnosis, electronic signal analysis, medical image analysis and radiology. ANNs have been used by many authors for modeling in medicine and clinical research. Applications of ANNs are increasing in pharmacoepidemiology and medical data mining. In this paper, authors have summarized various applications of ANNs in medical science.

  11. 78 FR 23959 - Manufacturer of Controlled Substances; Notice of Registration; Cayman Chemical Company

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-04-23

    ... FR 70188, Cayman Chemical Company, 1180 East Ellsworth Road, Ann Arbor, Michigan 48108, made... substances for distribution to their research and forensic customers conducting drug testing and analysis....

  12. High-sensitivity and specificity of laser-induced autofluorescence spectra for detection of colorectal cancer with an artificial neural network

    NASA Astrophysics Data System (ADS)

    Kwek, L. C.; Fu, Sheng; Chia, T. C.; Diong, C. H.; Tang, C. L.; Krishnan, S. M.

    2005-07-01

    An artificial neural network (ANN) has been used in various clinical research for the prediction and classification of data in cancer disease. Previous research in this direction focused on the correlation between various input parameters such as age, antigen, and size of tumor growth. Recently, laser-induced autofluorescence (LIAF) techniques have been shown to be a useful noninvasive early diagnostic tool for various cancer diseases. We report on a successful application of ANN to in vitro LIAF spectra. We show that classification of tumor samples with ANN can be done with high sensitivity, specificity, and accuracy. Thus a combination of LIAF techniques and ANN can provide a robust method for clinical diagnosis.

  13. Next Day Price Forecasting in Deregulated Market by Combination of Artificial Neural Network and ARIMA Time Series Models

    NASA Astrophysics Data System (ADS)

    Areekul, Phatchakorn; Senjyu, Tomonobu; Urasaki, Naomitsu; Yona, Atsushi

    Electricity price forecasting is becoming increasingly relevant to power producers and consumers in the new competitive electric power markets, when planning bidding strategies in order to maximize their benefits and utilities, respectively. This paper proposed a method to predict hourly electricity prices for next-day electricity markets by combination methodology of ARIMA and ANN models. The proposed method is examined on the Australian National Electricity Market (NEM), New South Wales regional in year 2006. Comparison of forecasting performance with the proposed ARIMA, ANN and combination (ARIMA-ANN) models are presented. Empirical results indicate that an ARIMA-ANN model can improve the price forecasting accuracy.

  14. Modeling of Abrasion Resistance Performance of Persian Handmade Wool Carpets Using Artificial Neural Network

    NASA Astrophysics Data System (ADS)

    Gupta, Shravan Kumar; Goswami, Kamal Kanti

    2015-10-01

    This paper presents the application of Artificial Neural Network (ANN) modeling for the prediction of abrasion resistance of Persian handmade wool carpets. Four carpet constructional parameters, namely knot density, pile height, number of ply in pile yarn and pile yarn twist have been used as input parameters for ANN model. The prediction performance was judged in terms of statistical parameters like correlation coefficient ( R) and Mean Absolute Percentage Error ( MAPE). Though the training performance of ANN was very good, the generalization ability was not up to the mark. This implies that large number of training data should be used for the adequate training of ANN models.

  15. Robust Bioinformatics Recognition with VLSI Biochip Microsystem

    NASA Technical Reports Server (NTRS)

    Lue, Jaw-Chyng L.; Fang, Wai-Chi

    2006-01-01

    A microsystem architecture for real-time, on-site, robust bioinformatic patterns recognition and analysis has been proposed. This system is compatible with on-chip DNA analysis means such as polymerase chain reaction (PCR)amplification. A corresponding novel artificial neural network (ANN) learning algorithm using new sigmoid-logarithmic transfer function based on error backpropagation (EBP) algorithm is invented. Our results show the trained new ANN can recognize low fluorescence patterns better than the conventional sigmoidal ANN does. A differential logarithmic imaging chip is designed for calculating logarithm of relative intensities of fluorescence signals. The single-rail logarithmic circuit and a prototype ANN chip are designed, fabricated and characterized.

  16. Optimisation of artificial neural network structure using Direct Encoding Graph Syntax (DEGS)

    SciTech Connect

    Kothari, B.; Esat, I.

    1996-12-31

    An artificial neural network (ANN) is intended to represent usually a complex non-linear mapping between the two data sets that can then be able to generalize on unseen data for the solution of a particular task. The evaluation of the correct ANN structure (and hence the mapping) is very often, solely a ANN and error procedure which may not lead to the required solution. The Genetic algorithm (GA) has been perceived by researchers as a effective systematic technique for the design of ANNs. However the GA can be hampered by the difficulty of generating a variety of ANN structures. In addition there is the problem of a significant increase of the search space for network architectures as the network size increases (scalability problem). Even if these problems are addressed, the ANN structures produced by the GA must be viable and then efficiently trainable by a competent training algorithm. A network is not viable if it is incomplete with isolated processing units. Also the possibility of encountering the permutation problem which refers to the creation of ANNs that are different in structure but are equivalent geometrically also has to be reduced as this significantly reduces the efficiency of the GA. The above characteristics are indicative of other encoding schemes that poorly encode the ANN. This paper describes a direct encoding scheme, Direct Encoding Graph Syntax (DEGS), that endeavors to overcome these flaws. Its successful implementation in conjunction with the GA, for the design of ANNs to evaluate the 9-bit parity problem is also discussed.

  17. Effects of single and dual physical modifications on pinhão starch.

    PubMed

    Pinto, Vânia Zanella; Vanier, Nathan Levien; Deon, Vinicius Gonçalves; Moomand, Khalid; El Halal, Shanise Lisie Mello; Zavareze, Elessandra da Rosa; Lim, Loong-Tak; Dias, Alvaro Renato Guerra

    2015-11-15

    Pinhão starch was modified by annealing (ANN), heat-moisture (HMT) or sonication (SNT) treatments. The starch was also modified by a combination of these treatments (ANN-HMT, ANN-SNT, HMT-ANN, HMT-SNT, SNT-ANN, SNT-HMT). Whole starch and debranched starch fractions were analyzed by gel-permeation chromatography. Moreover, crystallinity, morphology, swelling power, solubility, pasting and gelatinization characteristics were evaluated. Native and single ANN and SNT-treated starches exhibited a CA-type crystalline structure while other modified starches showed an A-type structure. The relative crystallinity increased in ANN-treated starches and decreased in single HMT- and SNT-treated starches. The ANN, HMT and SNT did not provide visible cracks, notches or grooves to pinhão starch granule. SNT applied as second treatment was able to increase the peak viscosity of single ANN- and HMT-treated starches. HMT used alone or in dual modifications promoted the strongest effect on gelatinization temperatures and enthalpy.

  18. 77 FR 66463 - Change in Bank Control Notices; Acquisitions of Shares of a Bank or Bank Holding Company

    Federal Register 2010, 2011, 2012, 2013, 2014

    2012-11-05

    ... with their immediate family members (Sue Ann Bond, Louisville, Kentucky; Patty Jo Murphy, Alvaton, Kentucky; Callie Jo Cromer, New Orleans, Louisiana; Amanda Kay Johnson, Spring Hill, Tennessee; Emily...

  19. Dover Schools' Unintelligent Design

    ERIC Educational Resources Information Center

    Barlow, Dudley

    2006-01-01

    The author of this article was surprised to read in the December 21, 2005, Ann Arbor News that "The Ann Arbor-based Thomas More Law Center, which represented the Dover [Pennsylvania] School District in its federal case for the teaching of intelligent design, has threatened to sue Gull Lake [Michigan] Community Schools over its policy that…

  20. 75 FR 60089 - Final Environmental Impact Statement (EIS) Addressing Campus Development at Fort Meade

    Federal Register 2010, 2011, 2012, 2013, 2014

    2010-09-29

    ... . Additional copies of the Final EIS are available at the Fort Meade Main Post Library, 4418 Llewellyn Avenue, Fort Meade, MD 20755; the Anne Arundel County Public Library North County Area Branch, 1010 Eastway, Glen Burnie, MD 21060; and the Anne Arundel County Public Library West County Area Branch,...

  1. Prediction of Soil Deformation in Tunnelling Using Artificial Neural Networks

    PubMed Central

    Lai, Jinxing

    2016-01-01

    In the past few decades, as a new tool for analysis of the tough geotechnical problems, artificial neural networks (ANNs) have been successfully applied to address a number of engineering problems, including deformation due to tunnelling in various types of rock mass. Unlike the classical regression methods in which a certain form for the approximation function must be presumed, ANNs do not require the complex constitutive models. Additionally, it is traced that the ANN prediction system is one of the most effective ways to predict the rock mass deformation. Furthermore, it could be envisaged that ANNs would be more feasible for the dynamic prediction of displacements in tunnelling in the future, especially if ANN models are combined with other research methods. In this paper, we summarized the state-of-the-art and future research challenges of ANNs on the tunnel deformation prediction. And the application cases as well as the improvement of ANN models were also presented. The presented ANN models can serve as a benchmark for effective prediction of the tunnel deformation with characters of nonlinearity, high parallelism, fault tolerance, learning, and generalization capability. PMID:26819587

  2. Teachers' Memories and Their Relevance Today

    ERIC Educational Resources Information Center

    McCaw, Liz

    2006-01-01

    The author suggests that the connection between teachers' early memories and the way they conceptualize their work is likely to be enormously important in the long run. Anne Lindbergh is one of the author's literary heroes. For years the author has been enchanted by Anne's ability to describe commonplace events in the most profound ways. In a way,…

  3. A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242).

    PubMed

    Almusawi, Ahmed R J; Dülger, L Canan; Kapucu, Sadettin

    2016-01-01

    This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the applicability and the efficiency of the proposed approach in robotic motion control. The inclusion of current configuration of joint angles in ANN significantly increased the accuracy of ANN estimation of the joint angles output. The new controller design has advantages over the existing techniques for minimizing the position error in unconventional tasks and increasing the accuracy of ANN in estimation of robot's joint angles. PMID:27610129

  4. 78 FR 53272 - Approval and Promulgation of Air Quality Implementation Plans; Michigan; Redesignation of the...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-08-29

    ... attaining the 1997 annual and 2006 24-hour PM 2.5 standards (78 FR 39654) and that the Detroit-Ann Arbor... rulemaking of July 2, 2013 (78 FR 39654). V. Final Action EPA is determining that the Detroit-Ann Arbor area... state's SIP revision containing a maintenance plan for the area. On July 2, 2013, (78 FR 39654),...

  5. A Comparison of Logistic Regression Model and Artificial Neural Networks in Predicting of Student’s Academic Failure

    PubMed Central

    Teshnizi, Saeed Hosseini; Ayatollahi, Sayyed Mohhamad Taghi

    2015-01-01

    Background and objective: Artificial Neural Networks (ANNs) have recently been applied in situations where an analysis based on the logistic regression (LR) is a standard statistical approach; direct comparisons of the results, however, are seldom attempted. In this study, we compared both logistic regression models and feed-forward neural networks on the academic failure data set. Methods: The data for this study included 18 questions about study situation of 275 undergraduate students selected randomly from among nursing and midwifery and paramedic schools of Hormozgan University of Medical Sciences in 2013. Logistic regression with forward method and feed forward Artificial Neural Network with 15 neurons in hidden layer were fitted to the dataset. The accuracy of the models in predicting academic failure was compared by using ROC (Receiver Operating Characteristic) and classification accuracy. Results: Among nine ANNs, the ANN with 15 neurons in hidden layer was a better ANN compared with LR. The Area Under Receiver Operating Characteristics (AUROC) of the LR model and ANN with 15 neurons in hidden layers, were estimated as 0.55 and 0.89, respectively and ANN was significantly greater than the LR. The LR and ANN models respectively classified 77.5% and 84.3% of the students correctly. Conclusion: Based on this dataset, it seems the classification of the students in two groups with and without academic failure by using ANN with 15 neurons in the hidden layer is better than the LR model. PMID:26635438

  6. Connectionist Modelling and Education.

    ERIC Educational Resources Information Center

    Evers, Colin W.

    2000-01-01

    Provides a detailed, technical introduction to the state of cognitive science research, in particular the rise of the "new cognitive science," especially artificial neural net (ANN) models. Explains one influential ANN model and describes diverse applications and their implications for education. (EV)

  7. Robustness against S.E.U. of an artificial neural network space application

    SciTech Connect

    Assoum, A.; Radi, N.E.; Velazco, R.; Elie, F.; Ecoffet, R.

    1996-06-01

    The authors study the sensitivity of Artificial Neural Networks (ANN) to Single Event Upsets (SEU). A neural network designed to detect electronic and protonic whistlers has been implemented using a dedicated VLSI circuit: the LNeuro neural processor. Results of both SEU software simulations and heavy ion tests point out the fault tolerance properties of ANN hardware implementations.

  8. Artificial Neural Networks and Instructional Technology.

    ERIC Educational Resources Information Center

    Carlson, Patricia A.

    1991-01-01

    Artificial neural networks (ANN), part of artificial intelligence, are discussed. Such networks are fed sample cases (training sets), learn how to recognize patterns in the sample data, and use this experience in handling new cases. Two cognitive roles for ANNs (intelligent filters and spreading, associative memories) are examined. Prototypes…

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

    ERIC Educational Resources Information Center

    Chen, Chau-Kuang

    2010-01-01

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

  10. Introducing Artificial Neural Networks through a Spreadsheet Model

    ERIC Educational Resources Information Center

    Rienzo, Thomas F.; Athappilly, Kuriakose K.

    2012-01-01

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

  11. Artificial Neural Networks in Policy Research: A Current Assessment.

    ERIC Educational Resources Information Center

    Woelfel, Joseph

    1993-01-01

    Suggests that artificial neural networks (ANNs) exhibit properties that promise usefulness for policy researchers. Notes that ANNs have found extensive use in areas once reserved for multivariate statistical programs such as regression and multiple classification analysis and are developing an extensive community of advocates for processing text…

  12. Automatic Keyword Identification by Artificial Neural Networks Compared to Manual Identification by Users of Filtering Systems.

    ERIC Educational Resources Information Center

    Boger, Zvi; Kuflik, Tsvi; Shoval, Peretz; Shapira, Bracha

    2001-01-01

    Discussion of information filtering (IF) and information retrieval focuses on the use of an artificial neural network (ANN) as an alternative method for both IF and term selection and compares its effectiveness to that of traditional methods. Results show that the ANN relevance prediction out-performs the prediction of an IF system. (Author/LRW)

  13. Statistical Classification for Cognitive Diagnostic Assessment: An Artificial Neural Network Approach

    ERIC Educational Resources Information Center

    Cui, Ying; Gierl, Mark; Guo, Qi

    2016-01-01

    The purpose of the current investigation was to describe how the artificial neural networks (ANNs) can be used to interpret student performance on cognitive diagnostic assessments (CDAs) and evaluate the performances of ANNs using simulation results. CDAs are designed to measure student performance on problem-solving tasks and provide useful…

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

    NASA Astrophysics Data System (ADS)

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

    2008-04-01

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

  15. COMPUTATIONAL TOXICOLOGY ADVANCES: EMERGING CAPABILITIES FOR DATA EXPLORATION AND SAR MODEL DEVELOPMENT

    EPA Science Inventory

    Computational Toxicology Advances: Emerging capabilities for data exploration and SAR model development
    Ann M. Richard and ClarLynda R. Williams, National Health & Environmental Effects Research Laboratory, US EPA, Research Triangle Park, NC, USA; email: richard.ann@epa.gov

  16. The Redesign of Teacher Education for the Twenty-First Century. International Perspectives on the Preparation of Educational Personnel. Selected Papers from the Thirtieth Anniversary World Assembly of the International Council of Education for Teaching (Washington, DC, July 11-15, 1983).

    ERIC Educational Resources Information Center

    Yff, Joost, Ed.

    This volume is organized according to themes chosen for the 30th Annual World Assembly of the International Council on Education for Teaching (ICET). A keynote speech by Anne Flowers discussed "Teacher Education for the Twenty-First Century." The first theme, "The Redesign of Teacher Education," was discussed through presentations by Anne Flowers,…

  17. Computerized classification of liver disease in MRI using an artificial neural network

    NASA Astrophysics Data System (ADS)

    Zhang, Xuejun; Kanematsu, Masayuki; Fujita, Hiroshi; Hara, Takeshi; Hoshi, Hiroaki

    2001-07-01

    We developed a software named LiverANN based on artificial neural network (ANN) technique for distinguishing the pathologies of focal liver lesions in magnetic resonance (MR) imaging, which helps radiologists integrate the imaging findings with different pulse sequences and raise the diagnostic accuracy even with radiologists inexperienced in liver MR imaging. In each patient, regions of focal liver lesion on T1-weighted, T2-weighted, and gadolinium-enhanced dynamic MR images obtained in the hepatic arterial and equilibrium phases were placed by a radiologist (M.K.), then the program automatically calculated the brightness and homogeneity into numerical data within the selected areas as the input signals to the ANN. The outputs from the ANN were the 5 categories of focal hepatic diseases: liver cyst, cavernous hemangioma, dysplasia, hepatocellular carcinoma, and metastasis. Fifty cases were used for training the ANN, while 30 cases for testing the performance. The result showed that the LiverANN classified 5 types of focal liver lesions with sensitivity of 93%, which demonstrated the ability of ANN to fuse the complex relationships among the image findings with different sequences, and the ANN-based software may provide radiologists with referential opinion during the radiologic diagnostic procedure.

  18. Earthquake prediction in seismogenic areas of the Iberian Peninsula based on computational intelligence

    NASA Astrophysics Data System (ADS)

    Morales-Esteban, A.; Martínez-Álvarez, F.; Reyes, J.

    2013-05-01

    A method to predict earthquakes in two of the seismogenic areas of the Iberian Peninsula, based on Artificial Neural Networks (ANNs), is presented in this paper. ANNs have been widely used in many fields but only very few and very recent studies have been conducted on earthquake prediction. Two kinds of predictions are provided in this study: a) the probability of an earthquake, of magnitude equal or larger than a preset threshold magnitude, within the next 7 days, to happen; b) the probability of an earthquake of a limited magnitude interval to happen, during the next 7 days. First, the physical fundamentals related to earthquake occurrence are explained. Second, the mathematical model underlying ANNs is explained and the configuration chosen is justified. Then, the ANNs have been trained in both areas: The Alborán Sea and the Western Azores-Gibraltar fault. Later, the ANNs have been tested in both areas for a period of time immediately subsequent to the training period. Statistical tests are provided showing meaningful results. Finally, ANNs were compared to other well known classifiers showing quantitatively and qualitatively better results. The authors expect that the results obtained will encourage researchers to conduct further research on this topic. Development of a system capable of predicting earthquakes for the next seven days Application of ANN is particularly reliable to earthquake prediction. Use of geophysical information modeling the soil behavior as ANN's input data Successful analysis of one region with large seismic activity

  19. 40 CFR 147.2300 - State-administered program.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... the Underground Water Source Protection Program Pursuant to the Safe Drinking Water Act and 40 CFR 145... was approved by the Director of the Federal Register July 6, 1984. (1) Vt. Stat. Ann. tit. 10... are part of the approved State-administered program: (1) Vt. Stat. Ann. tit. 10, sections 1251...

  20. 40 CFR 147.2300 - State-administered program.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... the Underground Water Source Protection Program Pursuant to the Safe Drinking Water Act and 40 CFR 145... was approved by the Director of the Federal Register July 6, 1984. (1) Vt. Stat. Ann. tit. 10... are part of the approved State-administered program: (1) Vt. Stat. Ann. tit. 10, sections 1251...

  1. Inventing Music Education Games

    ERIC Educational Resources Information Center

    Ghere, David; Amram, Fred M. B.

    2007-01-01

    The first British patent describing an educational game designed for musical "amusement and instruction" was granted in 1801 to Ann Young of Edinburgh, Scotland. The authors' discovery of Young's game box has prompted an examination of the nature and purpose of the six games she designed. Ann Young's patent is discussed in the context of…

  2. Comparison of Advection–Diffusion Models and Neural Networks for Prediction of Advanced Water Treatment Effluent

    PubMed Central

    Mortula, Mohammed Maruf; Abdalla, Jamal; Ghadban, Ahmad A.

    2012-01-01

    Abstract An artificial neural network (ANN) can help in the prediction of advanced water treatment effluent and thus facilitate design practices. In this study, sets of 225 experimental data were obtained from a wastewater treatment process for the removal of phosphorus using oven-dried alum residuals in fixed-bed adsorbers. Five input variables (pH, initial phosphorus concentration, wastewater flow rate, porosity, and time) were used to test the efficiency of phosphorus removal at different times, and ANNs were then used to predict the effluent phosphorus concentration. Results of experiments that were conducted for different values of the input parameters made up the data used to train and test a multilayer perceptron using the back-propagation algorithm of the ANN. Values predicted by the ANN and the experimentally measured values were compared, and the accuracy of the ANN was evaluated. When ANN results were compared to the experimental results, it was concluded that the ANN results were accurate, especially during conditions of high phosphorus concentration. While the ANN model was able to predict the breakthrough point with good accuracy, the conventional advection–diffusion equation was not as accurate. A parametric study conducted to examine the effect of the initial pH and initial phosphorus concentration on the effluent phosphorus concentration at different times showed that lower influent pH values are the most suitable for this advanced treatment system. PMID:22783063

  3. Using support vector machines to develop pedotransfer functions for water retention of soils in Poland

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Pedotransfer functions (PTF), which estimate soil hydraulic parameters from better known soil properties, are the important data source for hydrologic modeling. Recently artificial neural networks (ANNs) became the tool of choice in PTF development. Training of ANN can be viewed as finding the minim...

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

    ERIC Educational Resources Information Center

    Everson, Howard T.; And Others

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

  5. Using support vector machines to develop pedotransfer functions for water retention of soils in Poland

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Pedotransfer functions (PTF), which estimate soil hydraulic parameters from better known soil properties, are the important data source for hydrologic modeling. Recently artificial neural networks (ANNs) became the tool of choice in PTF development. Training of ANN consists of finding of minimum of ...

  6. The GIST Model for Selection and Modification of Scientific Research for the College Teaching Laboratory Based on Root Competition Investigations

    ERIC Educational Resources Information Center

    Elliott, Shannon Snyder

    2007-01-01

    The purpose of this study is to first develop an 8-week college teaching module based on root competition literature. The split-root technique is adapted for the teaching laboratory, and the Sugar Ann English pea (Pisum sativum var. Sugar Ann English) is selected as the species of interest prior to designing experiments, either original or…

  7. A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242)

    PubMed Central

    Dülger, L. Canan; Kapucu, Sadettin

    2016-01-01

    This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the applicability and the efficiency of the proposed approach in robotic motion control. The inclusion of current configuration of joint angles in ANN significantly increased the accuracy of ANN estimation of the joint angles output. The new controller design has advantages over the existing techniques for minimizing the position error in unconventional tasks and increasing the accuracy of ANN in estimation of robot's joint angles.

  8. An artificial neural network approach to transformer fault diagnosis

    SciTech Connect

    Zhang, Y.; Ding, X.; Liu, Y.; Griffin, P.J.

    1996-10-01

    This paper presents an artificial neural network (ANN) approach to diagnose and detect faults in oil-filled power transformers based on dissolved gas-in-oil analysis. A two-step ANN method is used to detect faults with or without cellulose involved. Good diagnosis accuracy is obtained with the proposed approach.

  9. Online Learning. Special Report.

    ERIC Educational Resources Information Center

    Training, 1998

    1998-01-01

    Special section includes "World Wide Weeds" (Ann M. Bauer), about trainers as webmasters; "Get the Picture?" (Frank Jossi)--the role of digital video in computer-based training; and "The Reluctant Executive" (Anne K. Fredrickson), how to get administrators into the information age. (JOW)

  10. a New Framework for Geospatial Site Selection Using Artificial Neural Networks as Decision Rules: a Case Study on Landfill Sites

    NASA Astrophysics Data System (ADS)

    Abujayyab, S. K. M.; Ahamad, M. A. S.; Yahya, A. S.; Saad, A.-M. H. Y.

    2015-10-01

    This paper briefly introduced the theory and framework of geospatial site selection (GSS) and discussed the application and framework of artificial neural networks (ANNs). The related literature on the use of ANNs as decision rules in GSS is scarce from 2000 till 2015. As this study found, ANNs are not only adaptable to dynamic changes but also capable of improving the objectivity of acquisition in GSS, reducing time consumption, and providing high validation. ANNs make for a powerful tool for solving geospatial decision-making problems by enabling geospatial decision makers to implement their constraints and imprecise concepts. This tool offers a way to represent and handle uncertainty. Specifically, ANNs are decision rules implemented to enhance conventional GSS frameworks. The main assumption in implementing ANNs in GSS is that the current characteristics of existing sites are indicative of the degree of suitability of new locations with similar characteristics. GSS requires several input criteria that embody specific requirements and the desired site characteristics, which could contribute to geospatial sites. In this study, the proposed framework consists of four stages for implementing ANNs in GSS. A multilayer feed-forward network with a backpropagation algorithm was used to train the networks from prior sites to assess, generalize, and evaluate the outputs on the basis of the inputs for the new sites. Two metrics, namely, confusion matrix and receiver operating characteristic tests, were utilized to achieve high accuracy and validation. Results proved that ANNs provide reasonable and efficient results as an accurate and inexpensive quantitative technique for GSS.

  11. School Reform through PBIS. National Dropout Prevention Center/Network Newsletter. Volume 21, Number 1

    ERIC Educational Resources Information Center

    Duckenfield, Marty, Ed.

    2009-01-01

    The "National Dropout Prevention Newsletter" is published quarterly by the National Dropout Prevention Center/Network. This issue contains the following articles: (1) Intercepting the Dropout Trajectory (JoAnne Malloy); (2) The NH APEX Dropout Prevention Model; (3) How PBIS Can Lead to School Improvement (Julie King and JoAnne Malloy); and (4)…

  12. Prediction of Soil Deformation in Tunnelling Using Artificial Neural Networks.

    PubMed

    Lai, Jinxing; Qiu, Junling; Feng, Zhihua; Chen, Jianxun; Fan, Haobo

    2016-01-01

    In the past few decades, as a new tool for analysis of the tough geotechnical problems, artificial neural networks (ANNs) have been successfully applied to address a number of engineering problems, including deformation due to tunnelling in various types of rock mass. Unlike the classical regression methods in which a certain form for the approximation function must be presumed, ANNs do not require the complex constitutive models. Additionally, it is traced that the ANN prediction system is one of the most effective ways to predict the rock mass deformation. Furthermore, it could be envisaged that ANNs would be more feasible for the dynamic prediction of displacements in tunnelling in the future, especially if ANN models are combined with other research methods. In this paper, we summarized the state-of-the-art and future research challenges of ANNs on the tunnel deformation prediction. And the application cases as well as the improvement of ANN models were also presented. The presented ANN models can serve as a benchmark for effective prediction of the tunnel deformation with characters of nonlinearity, high parallelism, fault tolerance, learning, and generalization capability. PMID:26819587

  13. A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242)

    PubMed Central

    Dülger, L. Canan; Kapucu, Sadettin

    2016-01-01

    This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the applicability and the efficiency of the proposed approach in robotic motion control. The inclusion of current configuration of joint angles in ANN significantly increased the accuracy of ANN estimation of the joint angles output. The new controller design has advantages over the existing techniques for minimizing the position error in unconventional tasks and increasing the accuracy of ANN in estimation of robot's joint angles. PMID:27610129

  14. 78 FR 77664 - Defense Policy Board (DPB); Notice of Federal Advisory Committee Meeting

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-12-24

    ... Wednesday, January 15, 2014, from 7:00 a.m. to 9:30 a.m. ADDRESSES: The Pentagon, 2000 Defense Pentagon, Washington, DC 20301- 2000. FOR FURTHER INFORMATION CONTACT: Ms. Ann Hansen, 2000 Defense Pentagon... classified material. Committee's Designated Federal Officer or Point of Contact: Ann Hansen,...

  15. 40 CFR Appendix to Part 243 - Recommended Bibliography

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... refuse collection equipment. New York. The American National Standards Institute. 2. Decision-Makers..., as PB 213 511). 5. National Sanitation Foundation standard no. 31 for polyethylene refuse bags. Ann... no. 32 for paper refuse sacks. Ann Arbor, The National Sanitation Foundation, Nov. 13, 1970. 6 p....

  16. 40 CFR Appendix to Part 243 - Recommended Bibliography

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... refuse collection equipment. New York. The American National Standards Institute. 2. Decision-Makers..., as PB 213 511). 5. National Sanitation Foundation standard no. 31 for polyethylene refuse bags. Ann... no. 32 for paper refuse sacks. Ann Arbor, The National Sanitation Foundation, Nov. 13, 1970. 6 p....

  17. Summary of the Spring 1978 Conference of the National Consortium on Testing; June 5, 1978.

    ERIC Educational Resources Information Center

    Haney, Walt

    Remarks made at several panel discussions are summarized in this narrative report. The discussion topics and speakers include: (1) public education and testing--Tom Tomlinson, Ann Kahn, Herb Mack, and Jean Nazzaro, with remarks by Patricia Albjerg Graham; (2) standards regarding testing--Walt Haney, Barbara Lerner, Ann Cook, Willo White, and Bob…

  18. Lost Youth in the Global City: Class, Culture and the Urban Imaginary

    ERIC Educational Resources Information Center

    Nayak, Anoop; Williamson, Howard; Bjork, Mikela; Restler, Victoria; Anyon, Jean

    2012-01-01

    This article presents a review of "Lost youth in the global city: class, culture and the urban imaginary," by Jo-Anne Dillabough and Jacqueline Kennelly. In many ways the "juke-box boys" would today form a stratum of the "lost youth" that Jo-Anne Dillabough and Jacqueline Kennelly discuss in their thoughtful account of young people on the urban…

  19. "Instruments of Seduction": A Tale of Two Women.

    ERIC Educational Resources Information Center

    Van Burkleo, Sandra F.

    1995-01-01

    Relates the similar stories of Anne Hutchinson and Ann Hibbens, two religious dissenters in U.S. colonial history. Describes the background, role of sex discrimination, and the religious-judicial proceedings that caused both women to be convicted and jailed. Includes primary source readings from both trials. (CFR)

  20. 40 CFR 81.321 - Maryland.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... Unclassifiable/Attainment Calvert County Charles County St. Mary's County 1 This date is November 15, 1990... County and Queen Anne's County Area: Kent County 10/21/04 Attainment Queen Anne's County 10/21/04.... Washington, DC Area: Calvert County Nonattainment 3/25/03 Severe Charles County Nonattainment 3/25/03...

  1. 40 CFR 81.321 - Maryland.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... Unclassifiable/Attainment Calvert County Charles County St. Mary's County 1 This date is November 15, 1990... County and Queen Anne's County Area: Kent County 10/21/04 Attainment Queen Anne's County 10/21/04.... Washington, DC Area: Calvert County Nonattainment 3/25/03 Severe Charles County Nonattainment 3/25/03...

  2. 40 CFR 81.321 - Maryland.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... Unclassifiable/Attainment Calvert County Charles County St. Mary's County 1 This date is November 15, 1990... County and Queen Anne's County Area: Kent County 10/21/04 Attainment Queen Anne's County 10/21/04.... Washington, DC Area: Calvert County Nonattainment 3/25/03 Severe Charles County Nonattainment 3/25/03...

  3. "Fear of Success" Revisited: A Replication of Matina Horner's Study 30 Years Later.

    ERIC Educational Resources Information Center

    Engle, Jennifer

    This study updated and extended the classic "fear of success" study conducted by Matina Horner more than 30 years ago. Horner (1970) asked college students to respond to a scenario in which "Anne" or "John" is at the top of her/his medical school class. Based on the negative responses of students to "Anne," Horner concluded that women have a…

  4. A new approach to training back-propagation artificial neural networks: empirical evaluation on ten data sets from clinical studies.

    PubMed

    Ciampi, Antonio; Zhang, Fulin

    2002-05-15

    We present a new approach to training back-propagation artificial neural nets (BP-ANN) based on regularization and cross-validation and on initialization by a logistic regression (LR) model. The new approach is expected to produce a BP-ANN predictor at least as good as the LR-based one. We have applied the approach to ten data sets of biomedical interest and systematically compared BP-ANN and LR. In all data sets, taking deviance as criterion, the BP-ANN predictor outperforms the LR predictor used in the initialization, and in six cases the improvement is statistically significant. The other evaluation criteria used (C-index, MSE and error rate) yield variable results, but, on the whole, confirm that, in practical situations of clinical interest, proper training may significantly improve the predictive performance of a BP-ANN.

  5. Quantification of Human Brain Metabolites from in Vivo1H NMR Magnitude Spectra Using Automated Artificial Neural Network Analysis

    NASA Astrophysics Data System (ADS)

    Hiltunen, Yrjö; Kaartinen, Jouni; Pulkkinen, Juhani; Häkkinen, Anna-Maija; Lundbom, Nina; Kauppinen, Risto A.

    2002-01-01

    Long echo time (TE=270 ms) in vivo proton NMR spectra resembling human brain metabolite patterns were simulated for lineshape fitting (LF) and quantitative artificial neural network (ANN) analyses. A set of experimental in vivo1H NMR spectra were first analyzed by the LF method to match the signal-to-noise ratios and linewidths of simulated spectra to those in the experimental data. The performance of constructed ANNs was compared for the peak area determinations of choline-containing compounds (Cho), total creatine (Cr), and N-acetyl aspartate (NAA) signals using both manually phase-corrected and magnitude spectra as inputs. The peak area data from ANN and LF analyses for simulated spectra yielded high correlation coefficients demonstrating that the peak areas quantified with ANN gave similar results as LF analysis. Thus, a fully automated ANN method based on magnitude spectra has demonstrated potential for quantification of in vivo metabolites from long echo time spectroscopic imaging.

  6. Different approaches in Partial Least Squares and Artificial Neural Network models applied for the analysis of a ternary mixture of Amlodipine, Valsartan and Hydrochlorothiazide

    NASA Astrophysics Data System (ADS)

    Darwish, Hany W.; Hassan, Said A.; Salem, Maissa Y.; El-Zeany, Badr A.

    2014-03-01

    Different chemometric models were applied for the quantitative analysis of Amlodipine (AML), Valsartan (VAL) and Hydrochlorothiazide (HCT) in ternary mixture, namely, Partial Least Squares (PLS) as traditional chemometric model and Artificial Neural Networks (ANN) as advanced model. PLS and ANN were applied with and without variable selection procedure (Genetic Algorithm GA) and data compression procedure (Principal Component Analysis PCA). The chemometric methods applied are PLS-1, GA-PLS, ANN, GA-ANN and PCA-ANN. The methods were used for the quantitative analysis of the drugs in raw materials and pharmaceutical dosage form via handling the UV spectral data. A 3-factor 5-level experimental design was established resulting in 25 mixtures containing different ratios of the drugs. Fifteen mixtures were used as a calibration set and the other ten mixtures were used as validation set to validate the prediction ability of the suggested methods. The validity of the proposed methods was assessed using the standard addition technique.

  7. Study of Aided Diagnosis of Hepatic Carcinoma Based on Artificial Neural Network Combined with Tumor Marker Group

    NASA Astrophysics Data System (ADS)

    Tan, Shanjuan; Feng, Feifei; Wu, Yongjun; Wu, Yiming

    To develop a computer-aided diagnostic scheme by using an artificial neural network (ANN) combined with tumor markers for diagnosis of hepatic carcinoma (HCC) as a clinical assistant method. 140 serum samples (50 malignant, 40 benign and 50 normal) were analyzed for α-fetoprotein (AFP), carbohydrate antigen 125 (CA125), carcinoembryonic antigen (CEA), sialic acid (SA) and calcium (Ca). The five tumor marker values were then used as ANN inputs data. The result of ANN was compared with that of discriminant analysis by receiver operating characteristic (ROC) curve (AUC) analysis. The diagnostic accuracy of ANN and discriminant analysis among all samples of the test group was 95.5% and 79.3%, respectively. Analysis of multiple tumor markers based on ANN may be a better choice than the traditional statistical methods for differentiating HCC from benign or normal.

  8. Neurocontrol and fuzzy logic: Connections and designs

    NASA Technical Reports Server (NTRS)

    Werbos, Paul J.

    1991-01-01

    Artificial neural networks (ANNs) and fuzzy logic are complementary technologies. ANNs extract information from systems to be learned or controlled, while fuzzy techniques mainly use verbal information from experts. Ideally, both sources of information should be combined. For example, one can learn rules in a hybrid fashion, and then calibrate them for better whole-system performance. ANNs offer universal approximation theorems, pedagogical advantages, very high-throughput hardware, and links to neurophysiology. Neurocontrol - the use of ANNs to directly control motors or actuators, etc. - uses five generalized designs, related to control theory, which can work on fuzzy logic systems as well as ANNs. These designs can copy what experts do instead of what they say, learn to track trajectories, generalize adaptive control, and maximize performance or minimize cost over time, even in noisy environments. Design tradeoffs and future directions are discussed throughout.

  9. Neural networks in support of manned space

    NASA Technical Reports Server (NTRS)

    Werbos, Paul J.

    1989-01-01

    Many lobbyists in Washington have argued that artificial intelligence (AI) is an alternative to manned space activity. In actuality, this is the opposite of the truth, especially as regards artificial neural networks (ANNs), that form of AI which has the greatest hope of mimicking human abilities in learning, ability to interface with sensors and actuators, flexibility and balanced judgement. ANNs and their relation to expert systems (the more traditional form of AI), and the limitations of both technologies are briefly reviewed. A Few highlights of recent work on ANNs, including an NSF-sponsored workshop on ANNs for control applications are given. Current thinking on ANNs for use in certain key areas (the National Aerospace Plane, teleoperation, the control of large structures, fault diagnostics, and docking) which may be crucial to the long term future of man in space is discussed.

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

    NASA Astrophysics Data System (ADS)

    Lin, Jing-Fung; Sheu, Jer-Jia

    2016-06-01

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

  11. Artificial neural network based permanent magnet DC motor drives

    SciTech Connect

    Hoque, M.A. Zaman, M.R.; Rahman, M.A.

    1995-12-31

    A novel scheme for the speed control of a permanent magnet (PM) dc motor drive incorporating artificial neural network (ANN) is proposed. The drive system includes an ANN speed controller, micro-processor based dc-dc converter and a laboratory PM dc motor. A multi-layer artificial neural network structure with a feedback loop is designed in order to precisely operate the control circuit for the dc-dc converter. The complete drive system is simulated and implemented in real time. Both the simulation and experimental results prove the inherent capability of the ANN which makes it possible to maintain desired speed control in the presence of parameter variations and load disturbances. The performances of the ANN based PM dc drive system are compared with the simulated results of the conventionally controlled drive system. This clearly indicates the better performance of the ANN based PM dc motor drive system, particularly in case of parameter and load variations.

  12. The truth will come to light: directions and challenges in extracting the knowledge embedded within trained artificial neural networks.

    PubMed

    Tickle, A B; Andrews, R; Golea, M; Diederich, J

    1998-01-01

    To date, the preponderance of techniques for eliciting the knowledge embedded in trained artificial neural networks (ANN's) has focused primarily on extracting rule-based explanations from feedforward ANN's. The ADT taxonomy for categorizing such techniques was proposed in 1995 to provide a basis for the systematic comparison of the different approaches. This paper shows that not only is this taxonomy applicable to a cross section of current techniques for extracting rules from trained feedforward ANN's but also how the taxonomy can be adapted and extended to embrace a broader range of ANN types (e.g., recurrent neural networks) and explanation structures. In addition the paper identifies some of the key research questions in extracting the knowledge embedded within ANN's including the need for the formulation of a consistent theoretical basis for what has been, until recently, a disparate collection of empirical results.

  13. Computer-Aided Prediction of Long-Term Prognosis of Patients with Ulcerative Colitis after Cytoapheresis Therapy

    PubMed Central

    Takayama, Tetsuro; Okamoto, Susumu; Hisamatsu, Tadakazu; Naganuma, Makoto; Matsuoka, Katsuyoshi; Mizuno, Shinta; Bessho, Rieko; Hibi, Toshifumi; Kanai, Takanori

    2015-01-01

    Cytoapheresis (CAP) therapy is widely used in ulcerative colitis (UC) patients with moderate to severe activity in Japan. The aim of this study is to predict the need of operation after CAP therapy of UC patients on an individual level using an artificial neural network system (ANN). Ninety UC patients with moderate to severe activity were treated with CAP. Data on the patients’ demographics, medication, clinical activity index (CAI) and efficacy of CAP were collected. Clinical data were divided into training data group and validation data group and analyzed using ANN to predict individual outcomes. The sensitivity and specificity of predictive expression by ANN were 0.96 and 0.97, respectively. Events of admission, operation, and use of immunomodulator, and efficacy of CAP were significantly correlated to the outcome. Requirement of operation after CAP therapy was successfully predicted by using ANN. This newly established ANN strategy would be used as powerful support of physicians in the clinical practice. PMID:26111148

  14. A novel user classification method for femtocell network by using affinity propagation algorithm and artificial neural network.

    PubMed

    Ahmed, Afaz Uddin; Islam, Mohammad Tariqul; Ismail, Mahamod; Kibria, Salehin; Arshad, Haslina

    2014-01-01

    An artificial neural network (ANN) and affinity propagation (AP) algorithm based user categorization technique is presented. The proposed algorithm is designed for closed access femtocell network. ANN is used for user classification process and AP algorithm is used to optimize the ANN training process. AP selects the best possible training samples for faster ANN training cycle. The users are distinguished by using the difference of received signal strength in a multielement femtocell device. A previously developed directive microstrip antenna is used to configure the femtocell device. Simulation results show that, for a particular house pattern, the categorization technique without AP algorithm takes 5 indoor users and 10 outdoor users to attain an error-free operation. While integrating AP algorithm with ANN, the system takes 60% less training samples reducing the training time up to 50%. This procedure makes the femtocell more effective for closed access operation. PMID:25133214

  15. Differential expression of members of the annexin multigene family in Arabidopsis

    NASA Technical Reports Server (NTRS)

    Clark, G. B.; Sessions, A.; Eastburn, D. J.; Roux, S. J.

    2001-01-01

    Although in most plant species no more than two annexin genes have been reported to date, seven annexin homologs have been identified in Arabidopsis, Annexin Arabidopsis 1-7 (AnnAt1--AnnAt7). This establishes that annexins can be a diverse, multigene protein family in a single plant species. Here we compare and analyze these seven annexin gene sequences and present the in situ RNA localization patterns of two of these genes, AnnAt1 and AnnAt2, during different stages of Arabidopsis development. Sequence analysis of AnnAt1--AnnAt7 reveals that they contain the characteristic four structural repeats including the more highly conserved 17-amino acid endonexin fold region found in vertebrate annexins. Alignment comparisons show that there are differences within the repeat regions that may have functional importance. To assess the relative level of expression in various tissues, reverse transcription-PCR was carried out using gene-specific primers for each of the Arabidopsis annexin genes. In addition, northern blot analysis using gene-specific probes indicates differences in AnnAt1 and AnnAt2 expression levels in different tissues. AnnAt1 is expressed in all tissues examined and is most abundant in stems, whereas AnnAt2 is expressed mainly in root tissue and to a lesser extent in stems and flowers. In situ RNA localization demonstrates that these two annexin genes display developmentally regulated tissue-specific and cell-specific expression patterns. These patterns are both distinct and overlapping. The developmental expression patterns for both annexins provide further support for the hypothesis that annexins are involved in the Golgi-mediated secretion of polysaccharides.

  16. Estimation of static formation temperatures in geothermal wells by using an artificial neural network approach

    NASA Astrophysics Data System (ADS)

    Bassam, A.; Santoyo, E.; Andaverde, J.; Hernández, J. A.; Espinoza-Ojeda, O. M.

    2010-09-01

    An artificial neural network (ANN) approach was used to develop a new predictive model for the calculation of static formation temperature (SFT) in geothermal wells. A three-layer ANN architecture was successfully trained using a geothermal borehole database, which contains "statistically normalised" SFT estimates. These estimates were inferred from seven analytical methods commonly used in geothermal industry. Bottom-hole temperature (BHT) measurements and shut-in times were used as main input variables for the ANN training. Transient temperature gradients were used as secondary variables. The Levenberg-Marquardt (LM) learning algorithm, the hyperbolic tangent sigmoid transfer function and the linear transfer function were used for the ANN optimisation. The best training data set was obtained with an ANN architecture composed by five neurons in the hidden layer, which made possible to predict the SFT with a satisfactory efficiency ( R2>0.95). A suitable accuracy of the ANN model was achieved with a percentage error less than ±5%. The SFTs predicted by the ANN model were statistically analyzed and compared with "true" SFTs measured in synthetic experiments and actual BHT logs collected in geothermal boreholes during long shut-in times. These data sets were processed both to validate the new ANN model and to avoid bias. The SFT estimates inferred from the ANN validation process were in good agreement ( R2>0.95) with the "true" SFT data reported for synthetic and field experiments. The results suggest that the new ANN model could be used as a practical tool for the reliable prediction of SFT in geothermal wells using BHT and shut-in time as input data only.

  17. Multiple regression and Artificial Neural Network for long-term rainfall forecasting using large scale climate modes

    NASA Astrophysics Data System (ADS)

    Mekanik, F.; Imteaz, M. A.; Gato-Trinidad, S.; Elmahdi, A.

    2013-10-01

    In this study, the application of Artificial Neural Networks (ANN) and Multiple regression analysis (MR) to forecast long-term seasonal spring rainfall in Victoria, Australia was investigated using lagged El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) as potential predictors. The use of dual (combined lagged ENSO-IOD) input sets for calibrating and validating ANN and MR Models is proposed to investigate the simultaneous effect of past values of these two major climate modes on long-term spring rainfall prediction. The MR models that did not violate the limits of statistical significance and multicollinearity were selected for future spring rainfall forecast. The ANN was developed in the form of multilayer perceptron using Levenberg-Marquardt algorithm. Both MR and ANN modelling were assessed statistically using mean square error (MSE), mean absolute error (MAE), Pearson correlation (r) and Willmott index of agreement (d). The developed MR and ANN models were tested on out-of-sample test sets; the MR models showed very poor generalisation ability for east Victoria with correlation coefficients of -0.99 to -0.90 compared to ANN with correlation coefficients of 0.42-0.93; ANN models also showed better generalisation ability for central and west Victoria with correlation coefficients of 0.68-0.85 and 0.58-0.97 respectively. The ability of multiple regression models to forecast out-of-sample sets is compatible with ANN for Daylesford in central Victoria and Kaniva in west Victoria (r = 0.92 and 0.67 respectively). The errors of the testing sets for ANN models are generally lower compared to multiple regression models. The statistical analysis suggest the potential of ANN over MR models for rainfall forecasting using large scale climate modes.

  18. Efficiency of neural network-based combinatorial model predicting optimal culture conditions for maximum biomass yields in hairy root cultures.

    PubMed

    Mehrotra, Shakti; Prakash, O; Khan, Feroz; Kukreja, A K

    2013-02-01

    KEY MESSAGE : ANN-based combinatorial model is proposed and its efficiency is assessed for the prediction of optimal culture conditions to achieve maximum productivity in a bioprocess in terms of high biomass. A neural network approach is utilized in combination with Hidden Markov concept to assess the optimal values of different environmental factors that result in maximum biomass productivity of cultured tissues after definite culture duration. Five hidden Markov models (HMMs) were derived for five test culture conditions, i.e. pH of liquid growth medium, volume of medium per culture vessel, sucrose concentration (%w/v) in growth medium, nitrate concentration (g/l) in the medium and finally the density of initial inoculum (g fresh weight) per culture vessel and their corresponding fresh weight biomass. The artificial neural network (ANN) model was represented as the function of these five Markov models, and the overall simulation of fresh weight biomass was done with this combinatorial ANN-HMM. The empirical results of Rauwolfia serpentina hairy roots were taken as model and compared with simulated results obtained from pure ANN and ANN-HMMs. The stochastic testing and Cronbach's α-value of pure and combinatorial model revealed more internal consistency and skewed character (0.4635) in histogram of ANN-HMM compared to pure ANN (0.3804). The simulated results for optimal conditions of maximum fresh weight production obtained from ANN-HMM and ANN model closely resemble the experimentally optimized culture conditions based on which highest fresh weight was obtained. However, only 2.99 % deviation from the experimental values could be observed in the values obtained from combinatorial model when compared to the pure ANN model (5.44 %). This comparison showed 45 % better potential of combinatorial model for the prediction of optimal culture conditions for the best growth of hairy root cultures.

  19. Modeling flow and sediment transport in a river system using an artificial neural network.

    PubMed

    Yitian, Li; Gu, Roy R

    2003-01-01

    A river system is a network of intertwining channels and tributaries, where interacting flow and sediment transport processes are complex and floods may frequently occur. In water resources management of a complex system of rivers, it is important that instream discharges and sediments being carried by streamflow are correctly predicted. In this study, a model for predicting flow and sediment transport in a river system is developed by incorporating flow and sediment mass conservation equations into an artificial neural network (ANN), using actual river network to design the ANN architecture, and expanding hydrological applications of the ANN modeling technique to sediment yield predictions. The ANN river system model is applied to modeling daily discharges and annual sediment discharges in the Jingjiang reach of the Yangtze River and Dongting Lake, China. By the comparison of calculated and observed data, it is demonstrated that the ANN technique is a powerful tool for real-time prediction of flow and sediment transport in a complex network of rivers. A significant advantage of applying the ANN technique to model flow and sediment phenomena is the minimum data requirements for topographical and morphometric information without significant loss of model accuracy. The methodology and results presented show that it is possible to integrate fundamental physical principles into a data-driven modeling technique and to use a natural system for ANN construction. This approach may increase model performance and interpretability while at the same time making the model more understandable to the engineering community.

  20. Inversion of 2-D DC resistivity data using rapid optimization and minimal complexity neural network

    NASA Astrophysics Data System (ADS)

    Singh, U. K.; Tiwari, R. K.; Singh, S. B.

    2010-02-01

    The backpropagation (BP) artificial neural network (ANN) technique of optimization based on steepest descent algorithm is known to be inept for its poor performance and does not ensure global convergence. Nonlinear and complex DC resistivity data require efficient ANN model and more intensive optimization procedures for better results and interpretations. Improvements in the computational ANN modeling process are described with the goals of enhancing the optimization process and reducing ANN model complexity. Well-established optimization methods, such as Radial basis algorithm (RBA) and Levenberg-Marquardt algorithms (LMA) have frequently been used to deal with complexity and nonlinearity in such complex geophysical records. We examined here the efficiency of trained LMA and RB networks by using 2-D synthetic resistivity data and then finally applied to the actual field vertical electrical resistivity sounding (VES) data collected from the Puga Valley, Jammu and Kashmir, India. The resulting ANN reconstruction resistivity results are compared with the result of existing inversion approaches, which are in good agreement. The depths and resistivity structures obtained by the ANN methods also correlate well with the known drilling results and geologic boundaries. The application of the above ANN algorithms proves to be robust and could be used for fast estimation of resistive structures for other complex earth model also.