Adding Scents to Symbols: Using Food Fragrances with Deafblind Young People Making Choices at Mealtimes

ERIC Educational Resources Information Center

Murdoch, Heather; Gough, Anne; Boothroyd, Eileen; Williams, Kate

2014-01-01

This article is written by Heather Murdoch, research consultant for the Seashell Trust, Anne Gough, deputy headteacher at Royal School Manchester/Seashell Trust, Eileen Boothroyd, consultant for the Seashell Trust, and Kate Williams, a creative perfumer for Seven (PZ Cussons). It describes the use of food fragrances with deafblind students who are…

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.

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

Uplift rates of marine terraces as a constraint on fault-propagation fold kinematics: Examples from the Hawkswood and Kate anticlines, North Canterbury, New Zealand

NASA Astrophysics Data System (ADS)

Oakley, David O. S.; Fisher, Donald M.; Gardner, Thomas W.; Stewart, Mary Kate

2018-01-01

Marine terraces on growing fault-propagation folds provide valuable insight into the relationship between fold kinematics and uplift rates, providing a means to distinguish among otherwise non-unique kinematic model solutions. Here, we investigate this relationship at two locations in North Canterbury, New Zealand: the Kate anticline and Haumuri Bluff, at the northern end of the Hawkswood anticline. At both locations, we calculate uplift rates of previously dated marine terraces, using DGPS surveys to estimate terrace inner edge elevations. We then use Markov chain Monte Carlo methods to fit fault-propagation fold kinematic models to structural geologic data, and we incorporate marine terrace uplift into the models as an additional constraint. At Haumuri Bluff, we find that marine terraces, when restored to originally horizontal surfaces, can help to eliminate certain trishear models that would fit the geologic data alone. At Kate anticline, we compare uplift rates at different structural positions and find that the spatial pattern of uplift rates is more consistent with trishear than with a parallel-fault propagation fold kink-band model. Finally, we use our model results to compute new estimates for fault slip rates ( 1-2 m/ka at Kate anticline and 1-4 m/ka at Haumuri Bluff) and ages of the folds ( 1 Ma), which are consistent with previous estimates for the onset of folding in this region. These results are consistent with previous work on the age of onset of folding in this region, provide revised estimates of fault slip rates necessary to understand the seismic hazard posed by these faults, and demonstrate the value of incorporating marine terraces in inverse fold kinematic models as a means to distinguish among non-unique solutions.

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.

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.

Final Technical Report, Wind Generator Project (Ann Arbor)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Geisler, Nathan

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

iAnn: an event sharing platform for the life sciences.

PubMed

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

2013-08-01

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

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…

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

Federal Register 2010, 2011, 2012, 2013, 2014

2012-12-21

... DEPARTMENT OF ENERGY Federal Energy Regulatory Commission [Docket No. ID-6059-001] 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 positions, pursuant to section 305(b) of the...

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

PubMed

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

2017-12-21

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

ANN modeling of DNA sequences: new strategies using DNA shape code.

PubMed

Parbhane, R V; Tambe, S S; Kulkarni, B D

2000-09-01

Two new encoding strategies, namely, wedge and twist codes, which are based on the DNA helical parameters, are introduced to represent DNA sequences in artificial neural network (ANN)-based modeling of biological systems. The performance of the new coding strategies has been evaluated by conducting three case studies involving mapping (modeling) and classification applications of ANNs. The proposed coding schemes have been compared rigorously and shown to outperform the existing coding strategies especially in situations wherein limited data are available for building the ANN models.

Using artificial neural networks (ANN) for open-loop tomography

NASA Astrophysics Data System (ADS)

Osborn, James; De Cos Juez, Francisco Javier; Guzman, Dani; Butterley, Timothy; Myers, Richard; Guesalaga, Andres; Laine, Jesus

2011-09-01

The next generation of adaptive optics (AO) systems require tomographic techniques in order to correct for atmospheric turbulence along lines of sight separated from the guide stars. Multi-object adaptive optics (MOAO) is one such technique. Here, we present a method which uses an artificial neural network (ANN) to reconstruct the target phase given off-axis references sources. This method does not require any input of the turbulence profile and is therefore less susceptible to changing conditions than some existing methods. We compare our ANN method with a standard least squares type matrix multiplication method (MVM) in simulation and find that the tomographic error is similar to the MVM method. In changing conditions the tomographic error increases for MVM but remains constant with the ANN model and no large matrix inversions are required.

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…

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

Code of Federal Regulations, 2013 CFR

2013-10-01

... 46 Shipping 1 2013-10-01 2013-10-01 false Eastport, ME to Cape Ann, MA. 7.10 Section 7.10 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY PROCEDURES APPLICABLE TO THE PUBLIC BOUNDARY LINES Atlantic Coast § 7.10 Eastport, ME to Cape Ann, MA. (a) A line drawn from the easternmost extremity of Kendall...

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

Code of Federal Regulations, 2010 CFR

2010-10-01

... 46 Shipping 1 2010-10-01 2010-10-01 false Eastport, ME to Cape Ann, MA. 7.10 Section 7.10 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY PROCEDURES APPLICABLE TO THE PUBLIC BOUNDARY LINES Atlantic Coast § 7.10 Eastport, ME to Cape Ann, MA. (a) A line drawn from the easternmost extremity of Kendall...

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

Code of Federal Regulations, 2012 CFR

2012-10-01

... 46 Shipping 1 2012-10-01 2012-10-01 false Eastport, ME to Cape Ann, MA. 7.10 Section 7.10 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY PROCEDURES APPLICABLE TO THE PUBLIC BOUNDARY LINES Atlantic Coast § 7.10 Eastport, ME to Cape Ann, MA. (a) A line drawn from the easternmost extremity of Kendall...

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

Code of Federal Regulations, 2014 CFR

2014-10-01

... 46 Shipping 1 2014-10-01 2014-10-01 false Eastport, ME to Cape Ann, MA. 7.10 Section 7.10 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY PROCEDURES APPLICABLE TO THE PUBLIC BOUNDARY LINES Atlantic Coast § 7.10 Eastport, ME to Cape Ann, MA. (a) A line drawn from the easternmost extremity of Kendall...

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

Code of Federal Regulations, 2011 CFR

2011-10-01

... 46 Shipping 1 2011-10-01 2011-10-01 false Eastport, ME to Cape Ann, MA. 7.10 Section 7.10 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY PROCEDURES APPLICABLE TO THE PUBLIC BOUNDARY LINES Atlantic Coast § 7.10 Eastport, ME to Cape Ann, MA. (a) A line drawn from the easternmost extremity of Kendall...

A novel modular ANN architecture for efficient monitoring of gases/odours in real-time

NASA Astrophysics Data System (ADS)

Mishra, A.; Rajput, N. S.

2018-04-01

Data pre-processing is tremendously used for enhanced classification of gases. However, it suppresses the concentration variances of different gas samples. A classical solution of using single artificial neural network (ANN) architecture is also inefficient and renders degraded quantification. In this paper, a novel modular ANN design has been proposed to provide an efficient and scalable solution in real–time. Here, two separate ANN blocks viz. classifier block and quantifier block have been used to provide efficient and scalable gas monitoring in real—time. The classifier ANN consists of two stages. In the first stage, the Net 1-NDSRT has been trained to transform raw sensor responses into corresponding virtual multi-sensor responses using normalized difference sensor response transformation (NDSRT). These responses have been fed to the second stage (i.e., Net 2-classifier ). The Net 2-classifier has been trained to classify various gas samples to their respective class. Further, the quantifier block has parallel ANN modules, multiplexed to quantify each gas. Therefore, the classifier ANN decides class and quantifier ANN decides the exact quantity of the gas/odor present in the respective sample of that class.

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 to...

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 to...

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

Code of Federal Regulations, 2014 CFR

2014-07-01

... 33 Navigation and Navigable Waters 1 2014-07-01 2014-07-01 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 to...

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

Code of Federal Regulations, 2012 CFR

2012-07-01

... 33 Navigation and Navigable Waters 1 2012-07-01 2012-07-01 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 to...

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

Code of Federal Regulations, 2013 CFR

2013-07-01

... 33 Navigation and Navigable Waters 1 2013-07-01 2013-07-01 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 to...

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

NASA Astrophysics Data System (ADS)

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

2017-03-01

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

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.

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.

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

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

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…

Saint Anne: A Multicultural Education Dilemma.

ERIC Educational Resources Information Center

Bruce, Bill; And Others

This 5-hour simulation is designed to give secondary- and college-level students and community persons the opportunity to deal with multicultural issues in a typical organizational and community setting. St. Anne is a fictitious town of 75,000 residents with two major ethnic neighborhoods--one German and the other Swedish. The local paper industry…

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

NASA Astrophysics Data System (ADS)

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

2011-01-01

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.

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

Prediction of Film Cooling Effectiveness on a Gas Turbine Blade Leading Edge Using ANN and CFD

NASA Astrophysics Data System (ADS)

Dávalos, J. O.; García, J. C.; Urquiza, G.; Huicochea, A.; De Santiago, O.

2018-05-01

In this work, the area-averaged film cooling effectiveness (AAFCE) on a gas turbine blade leading edge was predicted by employing an artificial neural network (ANN) using as input variables: hole diameter, injection angle, blowing ratio, hole and columns pitch. The database used to train the network was built using computational fluid dynamics (CFD) based on a two level full factorial design of experiments. The CFD numerical model was validated with an experimental rig, where a first stage blade of a gas turbine was represented by a cylindrical specimen. The ANN architecture was composed of three layers with four neurons in hidden layer and Levenberg-Marquardt was selected as ANN optimization algorithm. The AAFCE was successfully predicted by the ANN with a regression coefficient R2<0.99 and a root mean square error RMSE=0.0038. The ANN weight coefficients were used to estimate the relative importance of the input parameters. Blowing ratio was the most influential parameter with relative importance of 40.36 % followed by hole diameter. Additionally, by using the ANN model, the relationship between input parameters was analyzed.

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.

STS-127 Crew Visit to Anne Beers Elementary

NASA Image and Video Library

2009-09-23

Thomas Tate, a third grade student at Anne Beers Elementary school, asks a question following a presentation by the crew of STS-127, Thursday, Sept. 24, 2009, in Washington. Photo Credit: (NASA/Paul E. Alers)

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

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

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 actualmore » experimental observations.« less

Comparison of ANN and RKS approaches to model SCC strength

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

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)

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.

Day and Night Dust Retrievals from MODIS IR Band Measurements using Artificial Neural Network (ANN) model

NASA Astrophysics Data System (ADS)

Lee, S.; Sohn, B.

2008-12-01

Artificial Neural Network (ANN) on the East Asia domain (20°N-55°N, 90°E-145°E) during the springs of 2006 and 2007 was investigated for retrieving aerosol optical thickness (AOT) of dust aerosol at both daytime and nighttime. The input data for ANN include brightness temperature, BTD (11 μm - 12 μm), spectral emissivity, surface temperature (Land: Price [1984] Equation, Ocean: The IMAPP MODIS Algorithm), relative airmass of satellite, and topography (SRTM30). The D*-parameter is adopted as dust detection algorithm which was developed by Hansell et al [2007]. The target data of the ANN is corresponding AOT at 550nm obtained from MODIS aerosol product (MYD04). After optimization and training, ANN AOT is retrieved. Among the many dust episodes during the spring of 2006, only the 8 April 2006 case was selected for the detailed analysis. Because it is one of the strongest episodes and shows a well-developed root penetrating the Korean peninsula and reaching the Japanese area. It is shown that ANN AOT coincide well with MODIS AOT having correlation coefficient of 0.8502 when the training and applying periods are the same (spring of 2006). Even a different period with training ANN AOT has a good relationship with MODIS AOT with the correlation coefficient of 0.7766 (spring 2007). This yearly difference is resulted from vegetation change and fixed IGBP land cover map. Also notable is that ANN AOT is underestimated in most IGBP types having low slope and negative mean bias. This study showed that ANN model has a good potential to retrieve AOT. More examinations and trials are needed, however, to improve this ANN algorithm using IR bands. Also this model should be extended to specify the dust aerosol property from other aerosols and clouds to assure that it has a capability during both daytime and nighttime.

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

PubMed

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

2017-10-02

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

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.

STS-127 Crew Visit to Anne Beers Elementary

NASA Image and Video Library

2009-09-23

Students, including Marcus Pratt, left, and Ajani Young, second from left, pay close attention as the crew from STS-127 makes their presentation during a visit to Anne Beers Elementary school, Thursday, Sept. 24, 2009, in Washington. Photo Credit: (NASA/Paul E. Alers)

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

PubMed

Kamesh, Reddi; Rani, Kalipatnapu Yamuna

2017-12-01

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

PHOTO DATE: 04-23-15.LOCATION: Bldg. 9NW - ISS Airlock Mockup .SUBJECT: Expedition 48/49 (Soyuz 47) crew members Kate Rubins and Takuya Onishi with Soyuz 49 crew member Peggy Whitson during ISS EVA P/P 1 training with instructor Grant Slusser.PHOTOGRAPHER: BILL STAFFORD

NASA Image and Video Library

2015-04-23

PHOTO DATE: 04-23-15 LOCATION: Bldg. 9NW - ISS Airlock Mockup SUBJECT: Expedition 48/49 (Soyuz 47) crew members Kate Rubins and Takuya Onishi with Soyuz 49 crew member Peggy Whitson during ISS EVA P/P 1 training with instructor Grant Slusser PHOTOGRAPHER: BILL STAFFORD

Writing women into medical history in the 1930s: Kate Campbell Hurd-Mead and "medical women" of the past and present.

PubMed

Appel, Toby A

2014-01-01

Kate Campbell Hurd-Mead (1867–1941), a leader among second-generation women physicians in America, became a pioneer historian of women in medicine in the 1930s. The coalescence of events in her personal life, the declining status of women in medicine, and the growing significance of the new and relatively open field of history of medicine all contributed to this transformation in her career. While she endeavored to become part of the community of male physicians who wrote medical history, her primary identity remained that of a “medical woman.” For Hurd-Mead, the history of women in the past not only filled a vital gap in scholarship but served practical ends that she had earlier pursued by other means—those of inspiring and advancing the careers of women physicians of the present day, promoting organizations of women physicians, and advocating for equality of opportunity in the medical profession.

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

NASA Astrophysics Data System (ADS)

Chandrasekaran, Muthumari; Tamang, Santosh

2017-08-01

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

[Nondestructive discrimination of strawberry varieties by NIR and BP-ANN].

PubMed

Niu, Xiao-ying; Shao, Li-min; Zhao, Zhi-lei; Zhang, Xiao-yu

2012-08-01

Strawberry variety is a main factor that can influence strawberry fruit quality. The use of near-infrared reflectance spectroscopy was explored discriminate among samples of strawberry of different varieties. And the significance of difference among different varieties was analyzed by comparison of the chemical composition of the different varieties samples. The performance of models established using back propagation-artificial neural networks (BP-ANN), least squares-support vector machine and discriminant analysis were evaluated on spectra range of 4545-9090 cm(-1). The optimal model was obtained by BP-ANN with a topology of 12-18-3, which correctly classified 96.68% of calibration set and 97.14% of prediction set. And the 94.95%, 97% and 98.29% classifications were given respectively for "Tianbao" (n=99), "Fengxiang" (n=100) and "Mingxing" (n=117). One-way analysis of variance was made for comparison of the mean values for soluble solids content (SSC), titratable acid (TA), pH value and SSC-TA ratio, and the statistically significant differences were found. Principal component analysis was performed on the four chemical compositions, and obvious clustering tendencies for different varieties were found. These results showed that NIR combined with BP-ANN can discriminate strawberry of different varieties effectively, and the difference in chemical compositions of different varieties strawberry might be a chemical validation for NIR results.

Intelligent MRTD testing for thermal imaging system using ANN

NASA Astrophysics Data System (ADS)

Sun, Junyue; Ma, Dongmei

2006-01-01

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

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

How can we deal with ANN in flood forecasting? As a simulation model or updating kernel!

NASA Astrophysics Data System (ADS)

Hassan Saddagh, Mohammad; Javad Abedini, Mohammad

2010-05-01

Flood forecasting and early warning, as a non-structural measure for flood control, is often considered to be the most effective and suitable alternative to mitigate the damage and human loss caused by flood. Forecast results which are output of hydrologic, hydraulic and/or black box models should secure accuracy of flood values and timing, especially for long lead time. The application of the artificial neural network (ANN) in flood forecasting has received extensive attentions in recent years due to its capability to capture the dynamics inherent in complex processes including flood. However, results obtained from executing plain ANN as simulation model demonstrate dramatic reduction in performance indices as lead time increases. This paper is intended to monitor the performance indices as it relates to flood forecasting and early warning using two different methodologies. While the first method employs a multilayer neural network trained using back-propagation scheme to forecast output hydrograph of a hypothetical river for various forecast lead time up to 6.0 hr, the second method uses 1D hydrodynamic MIKE11 model as forecasting model and multilayer neural network as updating kernel to monitor and assess the performance indices compared to ANN alone in light of increase in lead time. Results presented in both graphical and tabular format indicate superiority of MIKE11 coupled with ANN as updating kernel compared to ANN as simulation model alone. While plain ANN produces more accurate results for short lead time, the errors increase expeditiously for longer lead time. The second methodology provides more accurate and reliable results for longer forecast lead time.

STS-127 Crew Visit to Anne Beers Elementary

NASA Image and Video Library

2009-09-23

Students and teachers look on as STS-127 Commander Mark Polansky, seated left on stage talks about the mission to the International Space Station as other crew members Chris Cassidy, Doug Hurley, David Wolf, Tom Marshburn looks on during a visit to Anne Beers Elementary school, Thursday, Sept. 24, 2009, in Washington. Photo Credit: (NASA/Paul E. Alers)

STS-127 Crew Visit to Anne Beers Elementary

NASA Image and Video Library

2009-09-23

Canadian Space Agency astronaut Julie Payette, right, a mission specialist on STS-127, talks with two unidentified students during a visit to Anne Beers Elementary school, Thursday, Sept. 24, 2009, in Washington. Payette, along with the rest of the crew from STS-127, visited with students at the school Thursday. Photo Credit: (NASA/Paul E. Alers)

STS-127 Crew Visit to Anne Beers Elementary

NASA Image and Video Library

2009-09-23

Ajani Young, a fourth grade student at Anne Beers Elementary school, at podium, introduces the crew of STS-127 during their visit, Thursday, Sept. 24, 2009, in Washington. Seated from left are crew members, Chris Cassidy, Doug Hurley, Commander Mark Polansky, David Wolf, Tom Marshburn and Canadian Space Agency astronaut Julie Payette. Photo Credit: (NASA/Paul E. Alers)

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

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

PubMed

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

2013-11-01

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

Professor Ann De Vaney and a Good Conversation.

ERIC Educational Resources Information Center

Nichols, Randall G.

The author begins this paper by explaining that in examining 10 works by Ann De Vaney his goal is to see what her writing generally reveals about her playing close attention to people-to research subjects, to subjects otherwise addressed in the works, to readers of her works. The author attempts to address each by asking basic questions: (1) What…

The Anne Frank Haven in an Israeli Kibbutz.

PubMed

Dror, Y

1995-01-01

The Anne Frank Haven, founded in 1956, in the Israeli Kibbutz Sasa provides a unique educational program for coping with muticultural and integration problems. It is a holistic, regional junior and senior high school system within the holistic community of three kibbutzim. The Haven has been the subject of much research into "Moral Development," carried out by Wolins (1969, 1971), and mainly by Kohlberg (1971), his doctoral students Reimer and Snarey and other colleagues. In the seventies and eighties they used the Kibbutz example as a model for the "Just Community" approach. In the early nineties, an Israeli group evaluated the success of the program and its rationale, taking into consideration all the "educational factors" of the community, in the Haven, and in the kibbutzim around it. This article offers a comprehensive picture of the Kohlbergian moral-developmental research at the Anne Frank Haven, including all the relevant references and evaluations of the Haven as a part of the "Just Community" approach. It concludes with a suggestion for another approach--"Community Education" research in the same Haven--as an example of present and future studies in the area of "Moral" and "Values" education.

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

DOT National Transportation Integrated Search

1984-10-01

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

Mothers, daughters and midlife (self)-discoveries: gender and aging in the Amanda Cross' Kate Fansler series.

PubMed

Domínguez-Rué, Emma

2012-12-01

In the same way that many aspects of gender cannot be understood aside from their relationship to race, class, culture, nationality and/or sexuality, the interactions between gender and aging constitute an interesting field for academic research, without which we cannot gain full insight into the complex and multi-faceted nature of gender studies. Although the American writer and Columbia professor Carolyn Gold Heilbrun (1926-2003) is more widely known for her best-selling mystery novels, published under the pseudonym of Amanda Cross, she also authored remarkable pieces of non-fiction in which she asserted her long-standing commitment to feminism, while she also challenged established notions on women and aging and advocated for a reassessment of those negative views. To my mind, the Kate Fansler novels became an instrument to reach a massive audience of female readers who might not have read her non-fiction, but who were perhaps finding it difficult to reach fulfillment as women under patriarchy, especially upon reaching middle age. Taking her essays in feminism and literary criticism as a basis and her later fiction as substantiation to my argument, this paper will try to reveal the ways in which Heilbrun's seemingly more superficial and much more commercial mystery novels as Amanda Cross were used a catalyst that informed her feminist principles while vindicating the need to rethink about issues concerning literary representations of mature women and cultural stereotypes about motherhood. Copyright © 2012 Elsevier Inc. All rights reserved.

Damage level prediction of non-reshaped berm breakwater using ANN, SVM and ANFIS models

NASA Astrophysics Data System (ADS)

Mandal, Sukomal; Rao, Subba; N., Harish; Lokesha

2012-06-01

The damage analysis of coastal structure is very important as it involves many design parameters to be considered for the better and safe design of structure. In the present study experimental data for non-reshaped berm breakwater are collected from Marine Structures Laboratory, Department of Applied Mechanics and Hydraulics, NITK, Surathkal, India. Soft computing techniques like Artificial Neural Network (ANN), Support Vector Machine (SVM) and Adaptive Neuro Fuzzy Inference system (ANFIS) models are constructed using experimental data sets to predict the damage level of non-reshaped berm breakwater. The experimental data are used to train ANN, SVM and ANFIS models and results are determined in terms of statistical measures like mean square error, root mean square error, correla-tion coefficient and scatter index. The result shows that soft computing techniques i.e., ANN, SVM and ANFIS can be efficient tools in predicting damage levels of non reshaped berm breakwater.

STS-127 Crew Visit to Anne Beers Elementary

NASA Image and Video Library

2009-09-23

STS-127 Commander Mark Polansky, center, speaks to students during a visit to Anne Beers Elementary school, Thursday, Sept. 24, 2009, in Washington. Polansky, along with the rest of the crew from STS-127, visited with students at the school Thursday. Seated from left are Canadian Space Agency astronaut Julie Payette, Chris Cassidy, Doug Hurley, David Wolf and Tom Marshburn. Photo Credit: (NASA/Paul E. Alers)

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

NASA Astrophysics Data System (ADS)

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

2010-10-01

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

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…

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

1. JoAnn SieburgBaker, Photographer, September 1977. OVERALL VIEW OF ROUNDHOUSE. ...

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

1. JoAnn Sieburg-Baker, Photographer, September 1977. OVERALL VIEW OF ROUNDHOUSE. - Southern Railway Company, Spencer Shops, Salisbury Avenue between Third and Eight Streets, Spencer, Rowan County, NC

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

PubMed

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

2016-11-01

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

10. JoAnn SieburgBaker, Photographer, September 1977. INTERIOR VIEW OF BACK ...

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

10. JoAnn Sieburg-Baker, Photographer, September 1977. INTERIOR VIEW OF BACK SHOP. - Southern Railway Company, Spencer Shops, Salisbury Avenue between Third and Eight Streets, Spencer, Rowan County, NC

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

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.

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

3. JoAnn SieburgBaker, Photographer, September 1977. VIEW OF BACK SHOP ...

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

3. JoAnn Sieburg-Baker, Photographer, September 1977. VIEW OF BACK SHOP FROM SOUTHEAST. - Southern Railway Company, Spencer Shops, Salisbury Avenue between Third and Eight Streets, Spencer, Rowan County, NC

7. JoAnn SieburgBaker, Photographer, September 1977. VIEW OF OFFICES IN ...

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

7. JoAnn Sieburg-Baker, Photographer, September 1977. VIEW OF OFFICES IN BACK SHOP. - Southern Railway Company, Spencer Shops, Salisbury Avenue between Third and Eight Streets, Spencer, Rowan County, NC

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cho, Daniel D; Wernicke, A Gabriella; Nori, Dattatreyudu

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,more » 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.« less

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

PubMed

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

2017-05-01

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

5. JoAnn SieburgBaker, Photographer, September 1977. VIEW OF ICE HOUSE ...

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

5. JoAnn Sieburg-Baker, Photographer, September 1977. VIEW OF ICE HOUSE AND SURROUNDING BUILDINGS. - Southern Railway Company, Spencer Shops, Salisbury Avenue between Third and Eight Streets, Spencer, Rowan County, NC

Lucy Maude Montgomery and Anne of Green Gables: An Early Description of Attention-Deficit/Hyperactivity Disorder.

PubMed

Edison, Jessica Katz; Clardy, Christopher

2017-07-01

Attention-deficit/hyperactivity disorder (ADHD) was added to the Diagnostic and Statistical Manual of Mental Disorders, third edition, revised in 1987. Similar disorders had appeared earlier, and many consider the first description of ADHD to be a lecture in 1902 about children with an "abnormal defect in moral control" but normal intelligence. This definition of ADHD is more alarming than the current one. Anne Shirley, the protagonist of the novel Anne of Green Gables (written by Lucy Maude Montgomery and published in 1908), shares the hyperactive and inattentive qualities that fit the current definition of ADHD. She also lacks the menacing characteristics of the 1902 description. This indicates that ADHD, by its modern definition, was probably present in the early 1900s. Furthermore, the character of Anne Shirley shares many biographical similarities with her author, suggesting that Montgomery herself may have had ADHD. Thus, looking at literature from the past not only provides insight into the timeline of ADHD, but also into the thought process of an individual with ADHD. By viewing literary classics through a medical lens, we may gain insight into other diseases as well. [Pediatr Ann. 2017; 46(7):e270-e272.]. Copyright 2017, SLACK Incorporated.

4. JoAnn SieburgBaker, Photographer, September 1977. OVERALL VIEW OF BACK ...

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

4. JoAnn Sieburg-Baker, Photographer, September 1977. OVERALL VIEW OF BACK SHOP FROM ROOF OF ROUNDHOUSE. - Southern Railway Company, Spencer Shops, Salisbury Avenue between Third and Eight Streets, Spencer, Rowan County, NC

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

DOT National Transportation Integrated Search

1999-10-01

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

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

NASA Astrophysics Data System (ADS)

Patel, Raj Kumar; Giri, V. K.

2017-06-01

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

Archaeological Data Recovery at the Mary Ann Cole Site

DTIC Science & Technology

1981-06-01

documents the methods and results of archaeological excavations conducted at the Mary Ann Cole Site (12Crl) near Leavenworth, Indiana. The purpose of the...the area now range from 363 feet to 953 feet above sea level (Wingard 1975). The pre-Pleistocene drainage systems differed substantially from the...defined for this report, the Wyandotte chert zone consists of different types of "chert which are often stratigraphically distinct, but also Intergrade

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

PubMed

Albaradeyia, Issa; Hani, Azzedine; Shahrour, Isam

2011-09-01

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

Modelling soil erosion in a Mediterranean watershed: Comparison between SWAT and AnnAGNPS models.

PubMed

Abdelwahab, O M M; Ricci, G F; De Girolamo, A M; Gentile, F

2018-06-20

In this study, the simulations generated by two of the most widely used hydrological basin-scale models, the Annualized Agricultural Non-Point Source (AnnAGNPS) and the Soil and Water Assessment Tool (SWAT), were compared in a Mediterranean watershed, the Carapelle (Apulia, Southern Italy). Input data requirements, time and efforts needed for input preparation, strength and weakness points of each model, ease of use and limitations were evaluated in order to give information to users. Models were calibrated and validated at monthly time scale for hydrology and sediment load using a four year period of observations (streamflow and suspended sediment concentrations). In the driest year, the specific sediment load measured at the outlet was 0.89 t ha -1 yr -1 , while the simulated values were 0.83 t ha -1 yr -1 and 1.99 t ha -1 yr -1 for SWAT and AnnAGNPS, respectively. In the wettest year, the specific measured sediment load was 7.45 t ha -1 yr -1 , and the simulated values were 8.27 t ha -1 yr -1 and 6.23 t ha -1 yr -1 for SWAT and AnnAGNPS, respectively. Both models showed from fair to a very good correlation between observed and simulated streamflow and satisfactory for sediment load. Results showed that most of the basin is under moderate (1.4-10 t ha -1 yr -1 ) and high-risk erosion (> 10 t ha -1 yr -1 ). The sediment yield predicted by the SWAT and AnnAGNPS models were compared with estimates of soil erosion simulated by models for Europe (PESERA and RUSLE2015). The average gross erosion estimated by the RUSLE2015 model (12.5 t ha -1 yr -1 ) resulted comparable with the average specific sediment yield estimated by SWAT (8.8 t ha -1 yr -1 ) and AnnAGNPS (5.6 t ha -1 yr -1 ), while it was found that the average soil erosion estimated by PESERA is lower than the other estimates (1.2 t ha -1 yr -1 ). Copyright © 2018 Elsevier Inc. All rights reserved.

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.

2. JoAnn SieburgBaker, Photographer, September 1977. SECTION SHOWING BACK OF ...

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

2. JoAnn Sieburg-Baker, Photographer, September 1977. SECTION SHOWING BACK OF ROUNDHOUSE AND END OF BACK SHOP WHERE CRANE WAS LOCATED. - Southern Railway Company, Spencer Shops, Salisbury Avenue between Third and Eight Streets, Spencer, Rowan County, NC

A Curve Fitting Approach Using ANN for Converting CT Number to Linear Attenuation Coefficient for CT-based PET Attenuation Correction

NASA Astrophysics Data System (ADS)

Lai, Chia-Lin; Lee, Jhih-Shian; Chen, Jyh-Cheng

2015-02-01

Energy-mapping, the conversion of linear attenuation coefficients (μ) calculated at the effective computed tomography (CT) energy to those corresponding to 511 keV, is an important step in CT-based attenuation correction (CTAC) for positron emission tomography (PET) quantification. The aim of this study was to implement energy-mapping step by using curve fitting ability of artificial neural network (ANN). Eleven digital phantoms simulated by Geant4 application for tomographic emission (GATE) and 12 physical phantoms composed of various volume concentrations of iodine contrast were used in this study to generate energy-mapping curves by acquiring average CT values and linear attenuation coefficients at 511 keV of these phantoms. The curves were built with ANN toolbox in MATLAB. To evaluate the effectiveness of the proposed method, another two digital phantoms (liver and spine-bone) and three physical phantoms (volume concentrations of 3%, 10% and 20%) were used to compare the energy-mapping curves built by ANN and bilinear transformation, and a semi-quantitative analysis was proceeded by injecting 0.5 mCi FDG into a SD rat for micro-PET scanning. The results showed that the percentage relative difference (PRD) values of digital liver and spine-bone phantom are 5.46% and 1.28% based on ANN, and 19.21% and 1.87% based on bilinear transformation. For 3%, 10% and 20% physical phantoms, the PRD values of ANN curve are 0.91%, 0.70% and 3.70%, and the PRD values of bilinear transformation are 3.80%, 1.44% and 4.30%, respectively. Both digital and physical phantoms indicated that the ANN curve can achieve better performance than bilinear transformation. The semi-quantitative analysis of rat PET images showed that the ANN curve can reduce the inaccuracy caused by attenuation effect from 13.75% to 4.43% in brain tissue, and 23.26% to 9.41% in heart tissue. On the other hand, the inaccuracy remained 6.47% and 11.51% in brain and heart tissue when the bilinear transformation

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…

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Autonomous Cryogenics Loading Operations Simulation Software: Knowledgebase Autonomous Test Engineer

NASA Technical Reports Server (NTRS)

Wehner, Walter S.

2012-01-01

The Simulation Software, KATE (Knowledgebase Autonomous Test Engineer), is used to demonstrate the automatic identification of faults in a system. The ACLO (Autonomous Cryogenics Loading Operation) project uses KATE to monitor and find faults in the loading of the cryogenics int o a vehicle fuel tank. The KATE software interfaces with the IHM (Integrated Health Management) systems bus to communicate with other systems that are part of ACLO. One system that KATE uses the IHM bus to communicate with is AIS (Advanced Inspection System). KATE will send messages to AIS when there is a detected anomaly. These messages include visual inspection of specific valves, pressure gauges and control messages to have AIS open or close manual valves. My goals include implementing the connection to the IHM bus within KATE and for the AIS project. I will also be working on implementing changes to KATE's Ul and implementing the physics objects in KATE that will model portions of the cryogenics loading operation.

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

PubMed

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

2014-10-01

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

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

NASA Astrophysics Data System (ADS)

Zhao, Yudi; Wei, Ruyi; Liu, Xuebin

2017-10-01

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

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.

The Sally-Anne Test: An Interactional Analysis of a Dyadic Assessment

ERIC Educational Resources Information Center

Korkiakangas, Terhi; Dindar, Katja; Laitila, Aarno; Kärnä, Eija

2016-01-01

Background: The Sally-Anne test has been extensively used to examine children's theory of mind understanding. Many task-related factors have been suggested to impact children's performance on this test. Yet little is known about the interactional aspects of such dyadic assessment situations that might contribute to the ways in which children…

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

Code of Federal Regulations, 2014 CFR

2014-07-01

... 33 Navigation and Navigable Waters 1 2014-07-01 2014-07-01 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 the...

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 the...

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 the...

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

Code of Federal Regulations, 2012 CFR

2012-07-01

... 33 Navigation and Navigable Waters 1 2012-07-01 2012-07-01 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 the...

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. 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 the...

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

PubMed

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

2017-01-01

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

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

PubMed

Ghanizadeh, Ali Reza; Fakhri, Mansour

2014-01-01

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

Predicting PM10 concentration in Seoul metropolitan subway stations using artificial neural network (ANN).

PubMed

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

2018-01-05

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

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

Federal Register 2010, 2011, 2012, 2013, 2014

2013-10-31

... the University of Michigan, Ann Arbor, MI. The human remains were removed from Alpena, Isabella, Grand... removed from the Devil River Mound site (20AL1) in Alpena County, MI. A resident of Ossineke, MI...

Prediction of temperature and HAZ in thermal-based processes with Gaussian heat source by a hybrid GA-ANN model

NASA Astrophysics Data System (ADS)

Fazli Shahri, Hamid Reza; Mahdavinejad, Ramezanali

2018-02-01

Thermal-based processes with Gaussian heat source often produce excessive temperature which can impose thermally-affected layers in specimens. Therefore, the temperature distribution and Heat Affected Zone (HAZ) of materials are two critical factors which are influenced by different process parameters. Measurement of the HAZ thickness and temperature distribution within the processes are not only difficult but also expensive. This research aims at finding a valuable knowledge on these factors by prediction of the process through a novel combinatory model. In this study, an integrated Artificial Neural Network (ANN) and genetic algorithm (GA) was used to predict the HAZ and temperature distribution of the specimens. To end this, a series of full factorial design of experiments were conducted by applying a Gaussian heat flux on Ti-6Al-4 V at first, then the temperature of the specimen was measured by Infrared thermography. The HAZ width of each sample was investigated through measuring the microhardness. Secondly, the experimental data was used to create a GA-ANN model. The efficiency of GA in design and optimization of the architecture of ANN was investigated. The GA was used to determine the optimal number of neurons in hidden layer, learning rate and momentum coefficient of both output and hidden layers of ANN. Finally, the reliability of models was assessed according to the experimental results and statistical indicators. The results demonstrated that the combinatory model predicted the HAZ and temperature more effective than a trial-and-error ANN model.

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…

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

PubMed

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

2013-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

Comparing SVM and ANN based Machine Learning Methods for Species Identification of Food Contaminating Beetles.

PubMed

Bisgin, Halil; Bera, Tanmay; Ding, Hongjian; Semey, Howard G; Wu, Leihong; Liu, Zhichao; Barnes, Amy E; Langley, Darryl A; Pava-Ripoll, Monica; Vyas, Himansu J; Tong, Weida; Xu, Joshua

2018-04-25

Insect pests, such as pantry beetles, are often associated with food contaminations and public health risks. Machine learning has the potential to provide a more accurate and efficient solution in detecting their presence in food products, which is currently done manually. In our previous research, we demonstrated such feasibility where Artificial Neural Network (ANN) based pattern recognition techniques could be implemented for species identification in the context of food safety. In this study, we present a Support Vector Machine (SVM) model which improved the average accuracy up to 85%. Contrary to this, the ANN method yielded ~80% accuracy after extensive parameter optimization. Both methods showed excellent genus level identification, but SVM showed slightly better accuracy  for most species. Highly accurate species level identification remains a challenge, especially in distinguishing between species from the same genus which may require improvements in both imaging and machine learning techniques. In summary, our work does illustrate a new SVM based technique and provides a good comparison with the ANN model in our context. We believe such insights will pave better way forward for the application of machine learning towards species identification and food safety.

ANN Surface Roughness Optimization of AZ61 Magnesium Alloy Finish Turning: Minimum Machining Times at Prime Machining Costs

PubMed Central

Erdakov, Ivan Nikolaevich; Taha, Mohamed~Adel; Soliman, Mahmoud Sayed; El Rayes, Magdy Mostafa

2018-01-01

Magnesium alloys are widely used in aerospace vehicles and modern cars, due to their rapid machinability at high cutting speeds. A novel Edgeworth–Pareto optimization of an artificial neural network (ANN) is presented in this paper for surface roughness (Ra) prediction of one component in computer numerical control (CNC) turning over minimal machining time (Tm) and at prime machining costs (C). An ANN is built in the Matlab programming environment, based on a 4-12-3 multi-layer perceptron (MLP), to predict Ra, Tm, and C, in relation to cutting speed, vc, depth of cut, ap, and feed per revolution, fr. For the first time, a profile of an AZ61 alloy workpiece after finish turning is constructed using an ANN for the range of experimental values vc, ap, and fr. The global minimum length of a three-dimensional estimation vector was defined with the following coordinates: Ra = 0.087 μm, Tm = 0.358 min/cm3, C = $8.2973. Likewise, the corresponding finish-turning parameters were also estimated: cutting speed vc = 250 m/min, cutting depth ap = 1.0 mm, and feed per revolution fr = 0.08 mm/rev. The ANN model achieved a reliable prediction accuracy of ±1.35% for surface roughness. PMID:29772670

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.…

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

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

Input selection and performance optimization of ANN-based streamflow forecasts in the drought-prone Murray Darling Basin region using IIS and MODWT algorithm

NASA Astrophysics Data System (ADS)

Prasad, Ramendra; Deo, Ravinesh C.; Li, Yan; Maraseni, Tek

2017-11-01

Forecasting streamflow is vital for strategically planning, utilizing and redistributing water resources. In this paper, a wavelet-hybrid artificial neural network (ANN) model integrated with iterative input selection (IIS) algorithm (IIS-W-ANN) is evaluated for its statistical preciseness in forecasting monthly streamflow, and it is then benchmarked against M5 Tree model. To develop hybrid IIS-W-ANN model, a global predictor matrix is constructed for three local hydrological sites (Richmond, Gwydir, and Darling River) in Australia's agricultural (Murray-Darling) Basin. Model inputs comprised of statistically significant lagged combination of streamflow water level, are supplemented by meteorological data (i.e., precipitation, maximum and minimum temperature, mean solar radiation, vapor pressure and evaporation) as the potential model inputs. To establish robust forecasting models, iterative input selection (IIS) algorithm is applied to screen the best data from the predictor matrix and is integrated with the non-decimated maximum overlap discrete wavelet transform (MODWT) applied on the IIS-selected variables. This resolved the frequencies contained in predictor data while constructing a wavelet-hybrid (i.e., IIS-W-ANN and IIS-W-M5 Tree) model. Forecasting ability of IIS-W-ANN is evaluated via correlation coefficient (r), Willmott's Index (WI), Nash-Sutcliffe Efficiency (ENS), root-mean-square-error (RMSE), and mean absolute error (MAE), including the percentage RMSE and MAE. While ANN models are seen to outperform M5 Tree executed for all hydrological sites, the IIS variable selector was efficient in determining the appropriate predictors, as stipulated by the better performance of the IIS coupled (ANN and M5 Tree) models relative to the models without IIS. When IIS-coupled models are integrated with MODWT, the wavelet-hybrid IIS-W-ANN and IIS-W-M5 Tree are seen to attain significantly accurate performance relative to their standalone counterparts. Importantly

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

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

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

Optimization of thermal conductivity lightweight brick type AAC (Autoclaved Aerated Concrete) effect of Si & Ca composition by using Artificial Neural Network (ANN)

NASA Astrophysics Data System (ADS)

Zulkifli; Wiryawan, G. P.

2018-03-01

Lightweight brick is the most important component of building construction, therefore it is necessary to have lightweight thermal, mechanical and aqustic thermal properties that meet the standard, in this paper which is discussed is the domain of light brick thermal conductivity properties. The advantage of lightweight brick has a low density (500-650 kg/m3), more economical, can reduce the load 30-40% compared to conventional brick (clay brick). In this research, Artificial Neural Network (ANN) is used to predict the thermal conductivity of lightweight brick type Autoclaved Aerated Concrete (AAC). Based on the training and evaluation that have been done on 10 model of ANN with number of hidden node 1 to 10, obtained that ANN with 3 hidden node have the best performance. It is known from the mean value of MSE (Mean Square Error) validation for three training times of 0.003269. This ANN was further used to predict the thermal conductivity of four light brick samples. The predicted results for each of the AAC1, AAC2, AAC3 and AAC4 light brick samples were 0.243 W/m.K, respectively; 0.29 W/m.K; 0.32 W/m.K; and 0.32 W/m.K. Furthermore, ANN is used to determine the effect of silicon composition (Si), Calcium (Ca), to light brick thermal conductivity. ANN simulation results show that the thermal conductivity increases with increasing Si composition. Si content is allowed maximum of 26.57%, while the Ca content in the range 20.32% - 30.35%.

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.

ANN Surface Roughness Optimization of AZ61 Magnesium Alloy Finish Turning: Minimum Machining Times at Prime Machining Costs.

PubMed

Abbas, Adel Taha; Pimenov, Danil Yurievich; Erdakov, Ivan Nikolaevich; Taha, Mohamed Adel; Soliman, Mahmoud Sayed; El Rayes, Magdy Mostafa

2018-05-16

Magnesium alloys are widely used in aerospace vehicles and modern cars, due to their rapid machinability at high cutting speeds. A novel Edgeworth⁻Pareto optimization of an artificial neural network (ANN) is presented in this paper for surface roughness ( Ra ) prediction of one component in computer numerical control (CNC) turning over minimal machining time ( T m ) and at prime machining costs ( C ). An ANN is built in the Matlab programming environment, based on a 4-12-3 multi-layer perceptron (MLP), to predict Ra , T m , and C , in relation to cutting speed, v c , depth of cut, a p , and feed per revolution, f r . For the first time, a profile of an AZ61 alloy workpiece after finish turning is constructed using an ANN for the range of experimental values v c , a p , and f r . The global minimum length of a three-dimensional estimation vector was defined with the following coordinates: Ra = 0.087 μm, T m = 0.358 min/cm³, C = $8.2973. Likewise, the corresponding finish-turning parameters were also estimated: cutting speed v c = 250 m/min, cutting depth a p = 1.0 mm, and feed per revolution f r = 0.08 mm/rev. The ANN model achieved a reliable prediction accuracy of ±1.35% for surface roughness.

ANN-based calibration model of FTIR used in transformer online monitoring

NASA Astrophysics Data System (ADS)

Li, Honglei; Liu, Xian-yong; Zhou, Fangjie; Tan, Kexiong

2005-02-01

Recently, chromatography column and gas sensor have been used in online monitoring device of dissolved gases in transformer oil. But some disadvantages still exist in these devices: consumption of carrier gas, requirement of calibration, etc. Since FTIR has high accuracy, consume no carrier gas and require no calibration, the researcher studied the application of FTIR in such monitoring device. Experiments of "Flow gas method" were designed, and spectrum of mixture composed of different gases was collected with A BOMEM MB104 FTIR Spectrometer. A key question in the application of FTIR is that: the absorbance spectrum of 3 fault key gases, including C2H4, CH4 and C2H6, are overlapped seriously at 2700~3400cm-1. Because Absorbance Law is no longer appropriate, a nonlinear calibration model based on BP ANN was setup to in the quantitative analysis. The height absorbance of C2H4, CH4 and C2H6 were adopted as quantitative feature, and all the data were normalized before training the ANN. Computing results show that the calibration model can effectively eliminate the cross disturbance to measurement.

Evaluation of effectiveness of wavelet based denoising schemes using ANN and SVM for bearing condition classification.

PubMed

Vijay, G S; Kumar, H S; Srinivasa Pai, P; Sriram, N S; Rao, Raj B K N

2012-01-01

The wavelet based denoising has proven its ability to denoise the bearing vibration signals by improving the signal-to-noise ratio (SNR) and reducing the root-mean-square error (RMSE). In this paper seven wavelet based denoising schemes have been evaluated based on the performance of the Artificial Neural Network (ANN) and the Support Vector Machine (SVM), for the bearing condition classification. The work consists of two parts, the first part in which a synthetic signal simulating the defective bearing vibration signal with Gaussian noise was subjected to these denoising schemes. The best scheme based on the SNR and the RMSE was identified. In the second part, the vibration signals collected from a customized Rolling Element Bearing (REB) test rig for four bearing conditions were subjected to these denoising schemes. Several time and frequency domain features were extracted from the denoised signals, out of which a few sensitive features were selected using the Fisher's Criterion (FC). Extracted features were used to train and test the ANN and the SVM. The best denoising scheme identified, based on the classification performances of the ANN and the SVM, was found to be the same as the one obtained using the synthetic signal.

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

PubMed

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

2014-05-15

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

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.

Simulation of CO2 Solubility in Polystyrene-b-Polybutadieneb-Polystyrene (SEBS) by artificial intelligence network (ANN) method

NASA Astrophysics Data System (ADS)

Sharudin, R. W.; AbdulBari Ali, S.; Zulkarnain, M.; Shukri, M. A.

2018-05-01

This study reports on the integration of Artificial Neural Network (ANNs) with experimental data in predicting the solubility of carbon dioxide (CO2) blowing agent in SEBS by generating highest possible value for Regression coefficient (R2). Basically, foaming of thermoplastic elastomer with CO2 is highly affected by the CO2 solubility. The ability of ANN in predicting interpolated data of CO2 solubility was investigated by comparing training results via different method of network training. Regards to the final prediction result for CO2 solubility by ANN, the prediction trend (output generate) was corroborated with the experimental results. The obtained result of different method of training showed the trend of output generated by Gradient Descent with Momentum & Adaptive LR (traingdx) required longer training time and required more accurate input to produce better output with final Regression Value of 0.88. However, it goes vice versa with Levenberg-Marquardt (trainlm) technique as it produced better output in quick detention time with final Regression Value of 0.91.

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

PubMed

Thillaud, Pierre L; Postel, Jacques

2014-01-01

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

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…

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. Copyright © 2013 Elsevier Ltd. All rights reserved.

Statistical optimization of the phytoremediation of arsenic by Ludwigia octovalvis- in a pilot reed bed using response surface methodology (RSM) versus an artificial neural network (ANN).

PubMed

Titah, Harmin Sulistiyaning; Halmi, Mohd Izuan Effendi Bin; Abdullah, Siti Rozaimah Sheikh; Hasan, Hassimi Abu; Idris, Mushrifah; Anuar, Nurina

2018-06-07

In this study, the removal of arsenic (As) by plant, Ludwigia octovalvis, in a pilot reed bed was optimized. A Box-Behnken design was employed including a comparative analysis of both Response Surface Methodology (RSM) and an Artificial Neural Network (ANN) for the prediction of maximum arsenic removal. The predicted optimum condition using the desirability function of both models was 39 mg kg -1 for the arsenic concentration in soil, an elapsed time of 42 days (the sampling day) and an aeration rate of 0.22 L/min, with the predicted values of arsenic removal by RSM and ANN being 72.6% and 71.4%, respectively. The validation of the predicted optimum point showed an actual arsenic removal of 70.6%. This was achieved with the deviation between the validation value and the predicted values being within 3.49% (RSM) and 1.87% (ANN). The performance evaluation of the RSM and ANN models showed that ANN performs better than RSM with a higher R 2 (0.97) close to 1.0 and very small Average Absolute Deviation (AAD) (0.02) and Root Mean Square Error (RMSE) (0.004) values close to zero. Both models were appropriate for the optimization of arsenic removal with ANN demonstrating significantly higher predictive and fitting ability than RSM.

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)

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.

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

DOT National Transportation Integrated Search

1999-01-01

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

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

ScienceCinema

Mary Ann Piette

2017-12-09

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/

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mary Ann Piette

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/

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

DOT National Transportation Integrated Search

1999-01-01

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

Photoswitchable red fluorescent protein with a large Stokes shift

PubMed Central

Piatkevich, Kiryl D.; English, Brian P.; Malashkevich, Vladimir N.; Xiao, Hui; Almo, Steven C.; Singer, Robert H.; Verkhusha, Vladislav V.

2014-01-01

SUMMARY Subclass of fluorescent proteins, large Stokes shift fluorescent proteins, is characterized by their increased spread between the excitation and emission maxima. Here we report a photoswitchable variant of a red fluorescent protein with a large Stokes shift, PSLSSmKate, which initially exhibits excitation/emission at 445/622 nm, but irradiation with violet light photoswitches PSLSSmKate into a common red form with excitation/emission at 573/621 nm. We characterize spectral, photophysical and biochemical properties of PSLSSmKate in vitro and in mammalian cells, and determine its crystal structure in the large Stokes shift form. Mass-spectrometry, mutagenesis and spectroscopic analysis of PSLSSmKate allow us to propose molecular mechanisms for the large Stokes shift, pH dependence and light-induced chromophore transformation. We demonstrate applicability of PSLSSmKate to superresolution PALM microscopy and protein dynamics in live cells. Given its promising properties, we expect that PSLSSmKate-like phenotype will be further used for photoactivatable imaging and tracking multiple populations of intracellular objects. PMID:25242289

Photoswitchable red fluorescent protein with a large Stokes shift.

PubMed

Piatkevich, Kiryl D; English, Brian P; Malashkevich, Vladimir N; Xiao, Hui; Almo, Steven C; Singer, Robert H; Verkhusha, Vladislav V

2014-10-23

A subclass of fluorescent proteins (FPs), large Stokes shift (LSS) FP, are characterized by increased spread between excitation and emission maxima. We report a photoswitchable variant of a red FP with an LSS, PSLSSmKate, which initially exhibits excitation and emission at 445 and 622 nm, but violet irradiation photoswitches PSLSSmKate into a common red form with excitation and emission at 573 and 621 nm. We characterize spectral, photophysical, and biochemical properties of PSLSSmKate in vitro and in mammalian cells and determine its crystal structure in the LSS form. Mass spectrometry, mutagenesis, and spectroscopy of PSLSSmKate allow us to propose molecular mechanisms for the LSS, pH dependence, and light-induced chromophore transformation. We demonstrate the applicability of PSLSSmKate to superresolution photoactivated localization microscopy and protein dynamics in live cells. Given its promising properties, we expect that PSLSSmKate-like phenotype will be further used for photoactivatable imaging and tracking multiple populations of intracellular objects.

Using AnnAGNPS to Predict the Effects of Tile Drainage Control on Nutrient and Sediment Loads for a River Basin.

PubMed

Que, Z; Seidou, O; Droste, R L; Wilkes, G; Sunohara, M; Topp, E; Lapen, D R

2015-03-01

Controlled tile drainage (CTD) can reduce pollutant loading. The Annualized Agricultural Nonpoint Source model (AnnAGNPS version 5.2) was used to examine changes in growing season discharge, sediment, nitrogen, and phosphorus loads due to CTD for a ∼3900-km agriculturally dominated river basin in Ontario, Canada. Two tile drain depth scenarios were examined in detail to mimic tile drainage control for flat cropland: 600 mm depth (CTD) and 200 mm (CTD) depth below surface. Summed for five growing seasons (CTD), direct runoff, total N, and dissolved N were reduced by 6.6, 3.5, and 13.7%, respectively. However, five seasons of summed total P, dissolved P, and total suspended solid loads increased as a result of CTD by 0.96, 1.6, and 0.23%. The AnnAGNPS results were compared with mass fluxes observed from paired experimental watersheds (250, 470 ha) in the river basin. The "test" experimental watershed was dominated by CTD and the "reference" watershed by free drainage. Notwithstanding environmental/land use differences between the watersheds and basin, comparisons of seasonal observed and predicted discharge reductions were comparable in 100% of respective cases. Nutrient load comparisons were more consistent for dissolved, relative to particulate water quality endpoints. For one season under corn crop production, AnnAGNPS predicted a 55% decrease (CTD) in dissolved N from the basin. AnnAGNPS v. 5.2 treats P transport from a surface pool perspective, which is appropriate for many systems. However, for assessment of tile drainage management practices for relatively flat tile-dominated systems, AnnAGNPS may benefit from consideration of P and particulate transport in the subsurface. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.

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%.

Computer Based Language Training: A Conversation with Duane M. Rumbaugh and Mary Ann Romski.

ERIC Educational Resources Information Center

Thomas, M. Angele

1981-01-01

An interview with Duane Rumbaugh and Mary Ann Romski, researchers on the use of alternative communication systems for severely and profoundly retarded persons, focuses on applications from their primate research and the use of a computerized keyboard system to investigate language acquisition in severely retarded persons. (CL)

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.

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

PubMed Central

AlSharabi, Khalil; Ibrahim, Sutrisno; Alsuwailem, Abdullah

2017-01-01

Autism spectrum disorder (ASD) is a type of neurodevelopmental disorder with core impairments in the social relationships, communication, imagination, or flexibility of thought and restricted repertoire of activity and interest. In this work, a new computer aided diagnosis (CAD) of autism ‎based on electroencephalography (EEG) signal analysis is investigated. The proposed method is based on discrete wavelet transform (DWT), entropy (En), and artificial neural network (ANN). DWT is used to decompose EEG signals into approximation and details coefficients to obtain EEG subbands. The feature vector is constructed by computing Shannon entropy values from each EEG subband. ANN classifies the corresponding EEG signal into normal or autistic based on the extracted features. The experimental results show the effectiveness of the proposed method for assisting autism diagnosis. A receiver operating characteristic (ROC) curve metric is used to quantify the performance of the proposed method. The proposed method obtained promising results tested using real dataset provided by King Abdulaziz Hospital, Jeddah, Saudi Arabia. PMID:28484720

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.

Comparison of geology of Jurassic Norphlet Mary Ann field, Mobile Bay, Alabama, to onshore regional Norphlet trends

DOE Office of Scientific and Technical Information (OSTI.GOV)

Marzono, M.; Pense, G.; Andronaco, P.

The geology of the Mary Ann field is better understood in light of regional studies, which help to establish a depositional model in terms of both facies and thickness variations. These studies also illustrate major differences between onshore and offshore Norphlet deposits concerning topics such as diagenesis, hydrocarbon trapping, and migration. The Jurassic Norphlet sandstone was deposited in an arid basin extending from east Texas to Florida by a fluvial-eolian depositional system, prior to the transgression of the Smackover Formation. Until discovery of the Mary Ann field in 1979, Norphlet production was restricted to onshore areas, mostly along the Pickens-Pollardmore » fault system in Mississippi, Alabama, and Florida. The Mary Ann field is a Norphlet dry-gas accumulation, and was the first offshore field in the Gulf of Mexico to establish economic reserves in the Jurassic. The field is located in Mobile Bay, approximately 25 mi (40 km) south of Mobile, Alabama. Formed by a deep-seated (more than 20,000 ft or 6096 m) faulted salt pillow, Mary Ann field produces from a series of stacked eolian dune sands situated near the Norphlet paleocoastline. Five lithofacies have been recognized in cores from the Mobil 76 No. 2 well. Each lithofacies has a distinct reservoir quality. Optimum reservoir faces are the dune and sheet sands. Nonreservoir facies are interdune (wet and dry), marine reworked, and evaporitic sands. Following deposition, these sediments have undergone varying amounts of diagenesis. Early cementation of well-sorted sands supported the pore system during compaction. However, late cementation by chlorite, silica, and alteration of liquid hydrocarbons to an asphaltic residue have completely occluded the pore system in parts of the reservoir.« less

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

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…

Development Opportunities: The Effect of UMES on the Town of Princess Anne, Maryland.

ERIC Educational Resources Information Center

Sann, David; Tervala, Victor K.

A study was done of the potential economic effect of the University of Maryland Eastern Shore (UMES) campus on the nearby town of Princess Anne, a small rural community. The study used estimates made by a UMES faculty member which projected that UMES students in 1990 spent over $7 million on goods and services unrelated to educational expenses.…

Neuropathological findings processed by artificial neural networks (ANNs) can perfectly distinguish Alzheimer's patients from controls in the Nun Study.

PubMed

Grossi, Enzo; Buscema, Massimo P; Snowdon, David; Antuono, Piero

2007-06-21

Many reports have described that there are fewer differences in AD brain neuropathologic lesions between AD patients and control subjects aged 80 years and older, as compared with the considerable differences between younger persons with AD and controls. In fact some investigators have suggested that since neurofibrillary tangles (NFT) can be identified in the brains of non-demented elderly subjects they should be considered as a consequence of the aging process. At present, there are no universally accepted neuropathological criteria which can mathematically differentiate AD from healthy brain in the oldest old. The aim of this study is to discover the hidden and non-linear associations among AD pathognomonic brain lesions and the clinical diagnosis of AD in participants in the Nun Study through Artificial Neural Networks (ANNs) analysis The analyses were based on 26 clinically- and pathologically-confirmed AD cases and 36 controls who had normal cognitive function. The inputs used for the analyses were just NFT and neuritic plaques counts in neocortex and hippocampus, for which, despite substantial differences in mean lesions counts between AD cases and controls, there was a substantial overlap in the range of lesion counts. By taking into account the above four neuropathological features, the overall predictive capability of ANNs in sorting out AD cases from normal controls reached 100%. The corresponding accuracy obtained with Linear Discriminant Analysis was 92.30%. These results were consistently obtained in ten independent experiments. The same experiments were carried out with ANNs on a subgroup of 13 non severe AD patients and on the same 36 controls. The results obtained in terms of prediction accuracy with ANNs were exactly the same. Input relevance analysis confirmed the relative dominance of NFT in neocortex in discriminating between AD patients and controls and indicated the lesser importance played by NP in the hippocampus. The results of this study

History of psychosurgery at Sainte-Anne Hospital, Paris, France, through translational interactions between psychiatrists and neurosurgeons.

PubMed

Zanello, Marc; Pallud, Johan; Baup, Nicolas; Peeters, Sophie; Turak, Baris; Krebs, Marie Odile; Oppenheim, Catherine; Gaillard, Raphael; Devaux, Bertrand

2017-09-01

Sainte-Anne Hospital is the largest psychiatric hospital in Paris. Its long and fascinating history began in the 18th century. In 1952, it was at Sainte-Anne Hospital that Jean Delay and Pierre Deniker used the first neuroleptic, chlorpromazine, to cure psychiatric patients, putting an end to the expansion of psychosurgery. The Department of Neuro-psychosurgery was created in 1941. The works of successive heads of the Neurosurgery Department at Sainte-Anne Hospital summarized the history of psychosurgery in France. Pierre Puech defined psychosurgery as the necessary cooperation between neurosurgeons and psychiatrists to treat the conditions causing psychiatric symptoms, from brain tumors to mental health disorders. He reported the results of his series of 369 cases and underlined the necessity for proper follow-up and postoperative re-education, illustrating the relative caution of French neurosurgeons concerning psychosurgery. Marcel David and his assistants tried to follow their patients closely postoperatively; this resulted in numerous publications with significant follow-up and conclusions. As early as 1955, David reported intellectual degradation 2 years after prefrontal leucotomies. Jean Talairach, a psychiatrist who eventually trained as a neurosurgeon, was the first to describe anterior capsulotomy in 1949. He operated in several hospitals outside of Paris, including the Sarthe Psychiatric Hospital and the Public Institution of Mental Health in the Lille region. He developed stereotactic surgery, notably stereo-electroencephalography, for epilepsy surgery but also to treat psychiatric patients using stereotactic lesioning with radiofrequency ablation or radioactive seeds of yttrium-90. The evolution of functional neurosurgery has been marked by the development of deep brain stimulation, in particular for obsessive-compulsive disorder, replacing the former lesional stereotactic procedures. The history of Sainte-Anne Hospital's Neurosurgery Department sheds

A study of using smartphone to detect and identify construction workers' near-miss falls based on ANN

NASA Astrophysics Data System (ADS)

Zhang, Mingyuan; Cao, Tianzhuo; Zhao, Xuefeng

2018-03-01

As an effective fall accident preventive method, insight into near-miss falls provides an efficient solution to find out the causes of fall accidents, classify the type of near-miss falls and control the potential hazards. In this context, the paper proposes a method to detect and identify near-miss falls that occur when a worker walks in a workplace based on artificial neural network (ANN). The energy variation generated by workers who meet with near-miss falls is measured by sensors embedded in smart phone. Two experiments were designed to train the algorithm to identify various types of near-miss falls and test the recognition accuracy, respectively. At last, a test was conducted by workers wearing smart phones as they walked around a simulated construction workplace. The motion data was collected, processed and inputted to the trained ANN to detect and identify near-miss falls. Thresholds were obtained to measure the relationship between near-miss falls and fall accidents in a quantitate way. This approach, which integrates smart phone and ANN, will help detect near-miss fall events, identify hazardous elements and vulnerable workers, providing opportunities to eliminate dangerous conditions in a construction site or to alert possible victims that need to change their behavior before the occurrence of a fall accident.

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

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

Analysis of the effects of geological and geomorphological factors on earthquake triggered landslides using artificial neural networks (ANN)

NASA Astrophysics Data System (ADS)

Kawabata, D.; Bandibas, J.

2007-12-01

The occurrence of landslide is the result of the interaction of complex and diverse environmental factors. The geomorphic and geologic features, rock types and vegetative cover are important base factors of landslide occurrence. However, determining the relationship between these factors and landslide occurrence is very difficult using conventional mathematical analysis. The use of an advanced computing technique for this kind of analysis is very important. Artificial neural network (ANN) has recently been included in the list of analytical tools for a wide range of applications in the natural sciences research fields. One of the advantages of using ANN for pattern recognition is that it can handle data at any measurement scale ranging from nominal, ordinal to linear and ratio, and any form of data distribution (Wang et al., 1995). In addition, it can easily handle qualitative variables making it widely used in integrated analysis of spatial data from multiple sources for predicting and classification. This study focuses on the definition of the relationship between geological factors and landslide occurrence using artificial neural networks. The study also focuses on the effect of the DTMs (e.g. ASTER DTM, ALSM, digitized from paper map and digital photogrammetric measurement data). The main aim of the study is to generate landslide susceptibility index map using the defined relationship using ANN. Landslide data in the Chuetsu region were used in this research. The 2004 earthquake triggered many landslides in the region. The initial results of the study showed that ANN is more accurate in defining the relationship between geological and geomorphological factors and landslide occurrence. It also determined the best combination of geological and geomorphological factors that is directly related to landslide occurrence.

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

NASA Astrophysics Data System (ADS)

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

2013-12-01

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

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…

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

PubMed

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

2013-01-01

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

Forecasting currency circulation data of Bank Indonesia by using hybrid ARIMAX-ANN model

NASA Astrophysics Data System (ADS)

Prayoga, I. Gede Surya Adi; Suhartono, Rahayu, Santi Puteri

2017-05-01

The purpose of this study is to forecast currency inflow and outflow data of Bank Indonesia. Currency circulation in Indonesia is highly influenced by the presence of Eid al-Fitr. One way to forecast the data with Eid al-Fitr effect is using autoregressive integrated moving average with exogenous input (ARIMAX) model. However, ARIMAX is a linear model, which cannot handle nonlinear correlation structures of the data. In the field of forecasting, inaccurate predictions can be considered caused by the existence of nonlinear components that are uncaptured by the model. In this paper, we propose a hybrid model of ARIMAX and artificial neural networks (ANN) that can handle both linear and nonlinear correlation. This method was applied for 46 series of currency inflow and 46 series of currency outflow. The results showed that based on out-of-sample root mean squared error (RMSE), the hybrid models are up to10.26 and 10.65 percent better than ARIMAX for inflow and outflow series, respectively. It means that ANN performs well in modeling nonlinear correlation of the data and can increase the accuracy of linear model.

Design of a MATLAB(registered trademark) Image Comparison and Analysis Tool for Augmentation of the Results of the Ann Arbor Distortion Test

DTIC Science & Technology

2016-06-25

The equipment used in this procedure includes: Ann Arbor distortion tester with 50-line grating reticule, IQeye 720 digital video camera with 12...and import them into MATLAB. In order to digitally capture images of the distortion in an optical sample, an IQeye 720 video camera with a 12... video camera and Ann Arbor distortion tester. Figure 8. Computer interface for capturing images seen by IQeye 720 camera. Once an image was

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

... 2005 Base Year Emissions Inventory AGENCY: Environmental Protection Agency (EPA). ACTION: Final rule. SUMMARY: EPA is approving the fine particulate matter (PM 2.5 ) 2005 base year emissions inventory, a... 2005 base year emissions inventory for the Detroit-Ann Arbor area. EPA did not receive any comments...

The Sally-Anne test: an interactional analysis of a dyadic assessment.

PubMed

Korkiakangas, Terhi; Dindar, Katja; Laitila, Aarno; Kärnä, Eija

2016-11-01

The Sally-Anne test has been extensively used to examine children's theory of mind understanding. Many task-related factors have been suggested to impact children's performance on this test. Yet little is known about the interactional aspects of such dyadic assessment situations that might contribute to the ways in which children respond to the test questions. To examine the interactional factors contributing to the performance of two children in the Sally-Anne test. To identify the interactional practices used by the tester administering the task and to describe how interactional features can pose challenges in the critical belief and reality questions for both the tester and the testee. The Sally-Anne test was carried out as part of a project examining children's interactions in a technology-enhanced environment. The present study uses video recordings of two children with communication disorders (one with a current diagnosis of autism spectrum disorder [ASD]) and an adult tester. We draw on a multimodal approach to conversation analysis (CA) to examine the sequential organization of the test questions and answers. The children drew on diverse resources when producing responses to the test questions: responding verbally, pointing or manually handling objects. The tester treated these responses differently depending on how they were produced. When the child pointed at an object and verbally indicated their response, the tester moved on to the next question apparently accepting the child's answer. When the child manually handled an object or produced a quiet verbal response, the tester repeated the question indicating that the child's actions did not constitute an adequate response to a test question. In response to this, both children modified or changed their previous responses. Through monitoring each other, the tester and the child produced actions highly responsive to the features of each other's conduct, which underpinned the conduct of the test itself

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. Copyright © 2015 Elsevier Ltd. All rights reserved.

Geology-based method of assessing sensitivity of streams to acidic deposition in Charles and Anne Arundel Counties, Maryland

USGS Publications Warehouse

Rice, Karen C.; Bricker, Owen P.

1991-01-01

The report describes the results of a study to assess the sensitivity of streams to acidic deposition in Charles and Anne Arundel Counties, Maryland using a geology-based method. Water samples were collected from streams in July and August 1988 when streams were at base-flow conditions. Eighteen water samples collected from streams in Charles County, and 17 water samples from streams in Anne Arundel County were analyzed in the field for pH, specific conductance, and acid-neutralizing capacity (ANC); 8 water samples from streams in Charles County were analyzed in the laboratory for chloride and sulfate concentrations. The assessment revealed that streams in these counties are sensitive to acidification by acidic deposition.

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.

Neuropathological findings processed by artificial neural networks (ANNs) can perfectly distinguish Alzheimer's patients from controls in the Nun Study

PubMed Central

Grossi, Enzo; Buscema, Massimo P; Snowdon, David; Antuono, Piero

2007-01-01

Background Many reports have described that there are fewer differences in AD brain neuropathologic lesions between AD patients and control subjects aged 80 years and older, as compared with the considerable differences between younger persons with AD and controls. In fact some investigators have suggested that since neurofibrillary tangles (NFT) can be identified in the brains of non-demented elderly subjects they should be considered as a consequence of the aging process. At present, there are no universally accepted neuropathological criteria which can mathematically differentiate AD from healthy brain in the oldest old. The aim of this study is to discover the hidden and non-linear associations among AD pathognomonic brain lesions and the clinical diagnosis of AD in participants in the Nun Study through Artificial Neural Networks (ANNs) analysis Methods The analyses were based on 26 clinically- and pathologically-confirmed AD cases and 36 controls who had normal cognitive function. The inputs used for the analyses were just NFT and neuritic plaques counts in neocortex and hippocampus, for which, despite substantial differences in mean lesions counts between AD cases and controls, there was a substantial overlap in the range of lesion counts. Results By taking into account the above four neuropathological features, the overall predictive capability of ANNs in sorting out AD cases from normal controls reached 100%. The corresponding accuracy obtained with Linear Discriminant Analysis was 92.30%. These results were consistently obtained in ten independent experiments. The same experiments were carried out with ANNs on a subgroup of 13 non severe AD patients and on the same 36 controls. The results obtained in terms of prediction accuracy with ANNs were exactly the same. Input relevance analysis confirmed the relative dominance of NFT in neocortex in discriminating between AD patients and controls and indicated the lesser importance played by NP in the hippocampus

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

... of Anthropology, University of Michigan, Ann Arbor, MI AGENCY: National Park Service, Interior. ACTION: Notice. SUMMARY: The Museum of Anthropology, University of Michigan, has completed an inventory... the Museum of Anthropology, University of Michigan. Repatriation of the human remains to the tribe...

Research on Mechanical Fault Prediction Algorithm for Circuit Breaker Based on Sliding Time Window and ANN

NASA Astrophysics Data System (ADS)

Wang, Xiaohua; Rong, Mingzhe; Qiu, Juan; Liu, Dingxin; Su, Biao; Wu, Yi

A new type of algorithm for predicting the mechanical faults of a vacuum circuit breaker (VCB) based on an artificial neural network (ANN) is proposed in this paper. There are two types of mechanical faults in a VCB: operation mechanism faults and tripping circuit faults. An angle displacement sensor is used to measure the main axle angle displacement which reflects the displacement of the moving contact, to obtain the state of the operation mechanism in the VCB, while a Hall current sensor is used to measure the trip coil current, which reflects the operation state of the tripping circuit. Then an ANN prediction algorithm based on a sliding time window is proposed in this paper and successfully used to predict mechanical faults in a VCB. The research results in this paper provide a theoretical basis for the realization of online monitoring and fault diagnosis of a VCB.

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…

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

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

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

... DEPARTMENT OF THE INTERIOR National Park Service [NPS-WASO-NAGPRA-10375; 2200-1100-665] Notice of Inventory Completion: Museum of Anthropology, University of Michigan, Ann Arbor, MI; Correction AGENCY: National Park Service, Interior. ACTION: Notice; correction. Notice is here given in accordance with the...

Modeling of biosorption of Cu(II) by alkali-modified spent tea leaves using response surface methodology (RSM) and artificial neural network (ANN)

NASA Astrophysics Data System (ADS)

Ghosh, Arpita; Das, Papita; Sinha, Keka

2015-06-01

In the present work, spent tea leaves were modified with Ca(OH)2 and used as a new, non-conventional and low-cost biosorbent for the removal of Cu(II) from aqueous solution. Response surface methodology (RSM) and artificial neural network (ANN) were used to develop predictive models for simulation and optimization of the biosorption process. The influence of process parameters (pH, biosorbent dose and reaction time) on the biosorption efficiency was investigated through a two-level three-factor (23) full factorial central composite design with the help of Design Expert. The same design was also used to obtain a training set for ANN. Finally, both modeling methodologies were statistically compared by the root mean square error and absolute average deviation based on the validation data set. Results suggest that RSM has better prediction performance as compared to ANN. The biosorption followed Langmuir adsorption isotherm and it followed pseudo-second-order kinetic. The optimum removal efficiency of the adsorbent was found as 96.12 %.

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.

ANN based Real-Time Estimation of Power Generation of Different PV Module Types

NASA Astrophysics Data System (ADS)

Syafaruddin; Karatepe, Engin; Hiyama, Takashi

Distributed generation is expected to become more important in the future generation system. Utilities need to find solutions that help manage resources more efficiently. Effective smart grid solutions have been experienced by using real-time data to help refine and pinpoint inefficiencies for maintaining secure and reliable operating conditions. This paper proposes the application of Artificial Neural Network (ANN) for the real-time estimation of the maximum power generation of PV modules of different technologies. An intelligent technique is necessary required in this case due to the relationship between the maximum power of PV modules and the open circuit voltage and temperature is nonlinear and can't be easily expressed by an analytical expression for each technology. The proposed ANN method is using input signals of open circuit voltage and cell temperature instead of irradiance and ambient temperature to determine the estimated maximum power generation of PV modules. It is important for the utility to have the capability to perform this estimation for optimal operating points and diagnostic purposes that may be an early indicator of a need for maintenance and optimal energy management. The proposed method is accurately verified through a developed real-time simulator on the daily basis of irradiance and cell temperature changes.

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

... 1625-AA00 Safety Zone, Submarine Cable Replacement Operations, Kent Island Narrows; Queen Anne's County... Guard proposes to establish a temporary safety zone encompassing certain waters of Kent Island Narrows... potential safety hazards associated with the bridge project. Entry into this zone would be prohibited unless...

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

NASA Astrophysics Data System (ADS)

Kavimani, V.; Prakash, K. Soorya

2017-11-01

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

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

NASA Astrophysics Data System (ADS)

Aytaç Korkmaz, Sevcan; Binol, Hamidullah

2018-03-01

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

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…

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

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

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

...We, the U.S. Fish and Wildlife Service (Service), announce the availability of the final comprehensive conservation plan (CCP) and finding of no significant impact (FONSI) for Patuxent Research Refuge (Patuxent RR, refuge), located in Prince George's and Anne Arundel Counties, Maryland. In this final CCP, we describe how we will manage the refuge for the next 15 years.

Mathematical Modeling and Optimizing of in Vitro Hormonal Combination for G × N15 Vegetative Rootstock Proliferation Using Artificial Neural Network-Genetic Algorithm (ANN-GA)

PubMed Central

Arab, Mohammad M.; Yadollahi, Abbas; Ahmadi, Hamed; Eftekhari, Maliheh; Maleki, Masoud

2017-01-01

The efficiency of a hybrid systems method which combined artificial neural networks (ANNs) as a modeling tool and genetic algorithms (GAs) as an optimizing method for input variables used in ANN modeling was assessed. Hence, as a new technique, it was applied for the prediction and optimization of the plant hormones concentrations and combinations for in vitro proliferation of Garnem (G × N15) rootstock as a case study. Optimizing hormones combination was surveyed by modeling the effects of various concentrations of cytokinin–auxin, i.e., BAP, KIN, TDZ, IBA, and NAA combinations (inputs) on four growth parameters (outputs), i.e., micro-shoots number per explant, length of micro-shoots, developed callus weight (CW) and the quality index (QI) of plantlets. Calculation of statistical values such as R2 (coefficient of determination) related to the accuracy of ANN-GA models showed a considerably higher prediction accuracy for ANN models, i.e., micro-shoots number: R2 = 0.81, length of micro-shoots: R2 = 0.87, CW: R2 = 0.88, QI: R2 = 0.87. According to the results, among the input variables, BAP (19.3), KIN (9.64), and IBA (2.63) showed the highest values of variable sensitivity ratio for proliferation rate. The GA showed that media containing 1.02 mg/l BAP in combination with 0.098 mg/l IBA could lead to the optimal proliferation rate (10.53) for G × N15 rootstock. Another objective of the present study was to compare the performance of predicted and optimized cytokinin–auxin combination with the best optimized obtained concentrations of our other experiments. Considering three growth parameters (length of micro-shoots, micro-shoots number, and proliferation rate), the last treatment was found to be superior to the rest of treatments for G × N15 rootstock in vitro multiplication. Very little difference between the ANN predicted and experimental data confirmed high capability of ANN-GA method in predicting new optimized protocols for plant in vitro propagation

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

DOT National Transportation Integrated Search

1999-01-01

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

Clearance Rate and BP-ANN Model in Paraquat Poisoned Patients Treated with Hemoperfusion

PubMed Central

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

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

PubMed

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

2017-11-01

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

At the Cosmonaut Hotel in Baikonur, Kazakhstan, Expedition 48-49 crewmembers Takuya Onishi of the Japan Aerospace Exploration Agency (left) and Anatoly Ivanishin of Roscosmos share a game of ping-pong June 30 during pre-launch activities. They and Kate Rubins of NASA will launch July 7, Baikonur time, on the Soyuz MS-01 spacecraft for a planned four-month mission on the International Space Station...NASA/Alexander Vysotsky.

NASA Image and Video Library

2016-06-30

At the Cosmonaut Hotel in Baikonur, Kazakhstan, Expedition 48-49 crewmembers Takuya Onishi of the Japan Aerospace Exploration Agency (left) and Anatoly Ivanishin of Roscosmos share a game of ping-pong June 30 during pre-launch activities. They and Kate Rubins of NASA will launch July 7, Baikonur time, on the Soyuz MS-01 spacecraft for a planned four-month mission on the International Space Station. NASA/Alexander Vysotsky

A comparison of two adaptive multivariate analysis methods (PLSR and ANN) for winter wheat yield forecasting using Landsat-8 OLI images

NASA Astrophysics Data System (ADS)

Chen, Pengfei; Jing, Qi

2017-02-01

An assumption that the non-linear method is more reasonable than the linear method when canopy reflectance is used to establish the yield prediction model was proposed and tested in this study. For this purpose, partial least squares regression (PLSR) and artificial neural networks (ANN), represented linear and non-linear analysis method, were applied and compared for wheat yield prediction. Multi-period Landsat-8 OLI images were collected at two different wheat growth stages, and a field campaign was conducted to obtain grain yields at selected sampling sites in 2014. The field data were divided into a calibration database and a testing database. Using calibration data, a cross-validation concept was introduced for the PLSR and ANN model construction to prevent over-fitting. All models were tested using the test data. The ANN yield-prediction model produced R2, RMSE and RMSE% values of 0.61, 979 kg ha-1, and 10.38%, respectively, in the testing phase, performing better than the PLSR yield-prediction model, which produced R2, RMSE, and RMSE% values of 0.39, 1211 kg ha-1, and 12.84%, respectively. Non-linear method was suggested as a better method for yield prediction.

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

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…

The application of ANN for zone identification in a complex reservoir

DOE Office of Scientific and Technical Information (OSTI.GOV)

White, A.C.; Molnar, D.; Aminian, K.

1995-12-31

Reservoir characterization plays a critical role in appraising the economic success of reservoir management and development methods. Nearly all reservoirs show some degree of heterogeneity, which invariably impacts production. As a result, the production performance of a complex reservoir cannot be realistically predicted without accurate reservoir description. Characterization of a heterogeneous reservoir is a complex problem. The difficulty stems from the fact that sufficient data to accurately predict the distribution of the formation attributes are not usually available. Generally the geophysical logs are available from a considerable number of wells in the reservoir. Therefore, a methodology for reservoir description andmore » characterization utilizing only well logs data represents a significant technical as well as economic advantage. One of the key issues in the description and characterization of heterogeneous formations is the distribution of various zones and their properties. In this study, several artificial neural networks (ANN) were successfully designed and developed for zone identification in a heterogeneous formation from geophysical well logs. Granny Creek Field in West Virginia has been selected as the study area in this paper. This field has produced oil from Big Injun Formation since the early 1900`s. The water flooding operations were initiated in the 1970`s and are currently still in progress. Well log data on a substantial number of wells in this reservoir were available and were collected. Core analysis results were also available from a few wells. The log data from 3 wells along with the various zone definitions were utilized to train the networks for zone recognition. The data from 2 other wells with previously determined zones, based on the core and log data, were then utilized to verify the developed networks predictions. The results indicated that ANN can be a useful tool for accurately identifying the zones in complex reservoirs.« less

Nightmare book

NASA Astrophysics Data System (ADS)

judithku; jajustin; tyler, d.

2017-02-01

In response to Kate Brown's review of Kate Moore's book The Radium Girls, which tells the depressing but important tale of female radium-dial painters in the early 1900s who contracted radiation poisoning.

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

DOT National Transportation Integrated Search

2017-06-01

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

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

... the appropriate Indian tribe, has determined that the items meet the definition of sacred objects and.... Representatives of any Indian tribe that believes itself to be culturally affiliated with the sacred objects may... of Anthropology, Ann Arbor, MI, that meet the definition of sacred objects under 25 U.S.C. 3001. This...

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

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. 2010 Elsevier B.V. All rights reserved.

Prediction of coagulation and flocculation processes using ANN models and fuzzy regression.

PubMed

Zangooei, Hossein; Delnavaz, Mohammad; Asadollahfardi, Gholamreza

2016-09-01

Coagulation and flocculation are two main processes used to integrate colloidal particles into larger particles and are two main stages of primary water treatment. Coagulation and flocculation processes are only needed when colloidal particles are a significant part of the total suspended solid fraction. Our objective was to predict turbidity of water after the coagulation and flocculation process while other parameters such as types and concentrations of coagulants, pH, and influent turbidity of raw water were known. We used a multilayer perceptron (MLP), a radial basis function (RBF) of artificial neural networks (ANNs) and various kinds of fuzzy regression analysis to predict turbidity after the coagulation and flocculation processes. The coagulant used in the pilot plant, which was located in water treatment plant, was poly aluminum chloride. We used existing data, including the type and concentrations of coagulant, pH and influent turbidity, of the raw water because these types of data were available from the pilot plant for simulation and data was collected by the Tehran water authority. The results indicated that ANNs had more ability in simulating the coagulation and flocculation process and predicting turbidity removal with different experimental data than did the fuzzy regression analysis, and may have the ability to reduce the number of jar tests, which are time-consuming and expensive. The MLP neural network proved to be the best network compared to the RBF neural network and fuzzy regression analysis in this study. The MLP neural network can predict the effluent turbidity of the coagulation and the flocculation process with a coefficient of determination (R 2 ) of 0.96 and root mean square error of 0.0106.

Biosurfactant-biopolymer driven microbial enhanced oil recovery (MEOR) and its optimization by an ANN-GA hybrid technique.

PubMed

Dhanarajan, Gunaseelan; Rangarajan, Vivek; Bandi, Chandrakanth; Dixit, Abhivyakti; Das, Susmita; Ale, Kranthikiran; Sen, Ramkrishna

2017-08-20

A lipopeptide biosurfactant produced by marine Bacillus megaterium and a biopolymer produced by thermophilic Bacillus licheniformis were tested for their application potential in the enhanced oil recovery. The crude biosurfactant obtained after acid precipitation effectively reduced the surface tension of deionized water from 70.5 to 28.25mN/m and the interfacial tension between lube oil and water from 18.6 to 1.5mN/m at a concentration of 250mgL -1 . The biosurfactant exhibited a maximum emulsification activity (E 24 ) of 81.66% against lube oil. The lipopeptide micelles were stabilized by addition of Ca 2+ ions to the biosurfactant solution. The oil recovery efficiency of Ca 2+ conditioned lipopeptide solution from a sand-packed column was optimized by using artificial neural network (ANN) modelling coupled with genetic algorithm (GA) optimization. Three important parameters namely lipopeptide concentration, Ca 2+ concentration and solution pH were considered for optimization studies. In order to further improve the recovery efficiency, a water soluble biopolymer produced by Bacillus licheniformis was used as a flooding agent after biosurfactant incubation. Upon ANN-GA optimization, 45% tertiary oil recovery was achieved, when biopolymer at a concentration of 3gL -1 was used as a flooding agent. Oil recovery was only 29% at optimal conditions predicted by ANN-GA, when only water was used as flooding solution. The important characteristics of biopolymers such as its viscosity, pore plugging capabilities and bio-cementing ability have also been tested. Thus, as a result of biosurfactant incubation and biopolymer flooding under the optimal process conditions, a maximum oil recovery of 45% was achieved. Therefore, this study is novel, timely and interesting for it showed the combined influence of biosurfactant and biopolymer on solubilisation and mobilization of oil from the soil. Copyright © 2017 Elsevier B.V. All rights reserved.

Valorization of aquaculture waste in removal of cadmium from aqueous solution: optimization by kinetics and ANN analysis

NASA Astrophysics Data System (ADS)

Aditya, Gautam; Hossain, Asif

2018-05-01

Cadmium is one of the most hazardous heavy metal concerning human health and aquatic pollution. The removal of cadmium through biosorption is a feasible option for restoration of the ecosystem health of the contaminated freshwater ecosystems. In compliance with this proposition and considering the efficiency of calcium carbonate as biosorbent, the shell dust of the economically important snail Bellamya bengalensis was tested for the removal of cadmium from aqueous medium. Following use of the flesh as a cheap source of protein, the shells of B. bengalensis made up of CaCO3 are discarded as aquaculture waste. The biosorption was assessed through batch sorption studies along with studies to characterize the morphology and surface structures of waste shell dust. The data on the biosorption were subjected to the artificial neural network (ANN) model for optimization of the process. The biosorption process changed as functions of pH of the solution, concentration of heavy metal, biomass of the adsorbent and time of exposure. The kinetic process was well represented by pseudo second order ( R 2 = 0.998), and Langmuir equilibrium ( R 2 = 0.995) had better fits in the equilibrium process with 30.33 mg g-1 of maximum sorption capacity. The regression equation ( R 2 = 0.948) in the ANN model supports predicted values of Cd removal satisfactorily. The normalized importance analysis in ANN predicts Cd2+ concentration, and pH has the most influence in removal than biomass dose and time. The SEM and EDX studies show clear peaks for Cd confirming the biosorption process while the FTIR study depicts the main functional groups (-OH, C-H, C=O, C=C) responsible for the biosorption process. The study indicated that the waste shell dust can be used as an efficient, low cost, environment friendly, sustainable adsorbent for the removal of cadmium from aqueous solution.

Prediction of Chl-a concentrations in an eutrophic lake using ANN models with hybrid inputs

NASA Astrophysics Data System (ADS)

Aksoy, A.; Yuzugullu, O.

2017-12-01

Chlorophyll-a (Chl-a) concentrations in water bodies exhibit both spatial and temporal variations. As a result, frequent sampling is required with higher number of samples. This motivates the use of remote sensing as a monitoring tool. Yet, prediction performances of models that convert radiance values into Chl-a concentrations can be poor in shallow lakes. In this study, Chl-a concentrations in Lake Eymir, a shallow eutrophic lake in Ankara (Turkey), are determined using artificial neural network (ANN) models that use hybrid inputs composed of water quality and meteorological data as well as remotely sensed radiance values to improve prediction performance. Following a screening based on multi-collinearity and principal component analysis (PCA), dissolved-oxygen concentration (DO), pH, turbidity, and humidity were selected among several parameters as the constituents of the hybrid input dataset. Radiance values were obtained from QuickBird-2 satellite. Conversion of the hybrid input into Chl-a concentrations were studied for two different periods in the lake. ANN models were successful in predicting Chl-a concentrations. Yet, prediction performance declined for low Chl-a concentrations in the lake. In general, models with hybrid inputs were superior over the ones that solely used remotely sensed data.

Intelligent estimation of noise and blur variances using ANN for the restoration of ultrasound images.

PubMed

Uddin, Muhammad Shahin; Halder, Kalyan Kumar; Tahtali, Murat; Lambert, Andrew J; Pickering, Mark R; Marchese, Margaret; Stuart, Iain

2016-11-01

Ultrasound (US) imaging is a widely used clinical diagnostic tool in medical imaging techniques. It is a comparatively safe, economical, painless, portable, and noninvasive real-time tool compared to the other imaging modalities. However, the image quality of US imaging is severely affected by the presence of speckle noise and blur during the acquisition process. In order to ensure a high-quality clinical diagnosis, US images must be restored by reducing their speckle noise and blur. In general, speckle noise is modeled as a multiplicative noise following a Rayleigh distribution and blur as a Gaussian function. Hereto, we propose an intelligent estimator based on artificial neural networks (ANNs) to estimate the variances of noise and blur, which, in turn, are used to obtain an image without discernible distortions. A set of statistical features computed from the image and its complex wavelet sub-bands are used as input to the ANN. In the proposed method, we solve the inverse Rayleigh function numerically for speckle reduction and use the Richardson-Lucy algorithm for de-blurring. The performance of this method is compared with that of the traditional methods by applying them to a synthetic, physical phantom and clinical data, which confirms better restoration results by the proposed method.

An ANN-based HRV classifier for cardiac health prognosis.

PubMed

Sunkaria, Ramesh Kumar; Kumar, Vinod; Saxena, Suresh Chandra; Singhal, Achala M

2014-01-01

A multi-layer artificial neural network (ANN)-based heart rate variability (HRV) classifier has been proposed, which gives the cardiac health status as the output based on HRV of the patients independently of the cardiologists' view. The electrocardiogram (ECG) data of 46 patients were recorded in the out-patient department (OPD) of a hospital and HRV was evaluated using self-designed autoregressive-model-based technique. These patients suspected to be suffering from cardiac abnormalities were thoroughly examined by experienced cardiologists. On the basis of symptoms and other investigations, the attending cardiologists advised them to be classified into four categories as per the severity of cardiac health. Out of 46, the HRV data of 28 patients were used for training and data of 18 patients were used for testing of the proposed classifier. The cardiac health classification of each tested patient with the proposed classifier matches with the medical opinion of the cardiologists.

The Picture of the Century with Floyd Thompsona and Ann Hitch Kilgore, Former Mayor of Hampton VA.

NASA Image and Video Library

1966-12-14

Langley Center Director Floyd Thompson shows Ann Kilgore the "picture of the century." This was the first picture of the earth taken from space. From Spaceflight Revolution: "On 23 August 1966 just as Lunar Orbiter I was about to pass behind the moon, mission controllers executed the necessary maneuvers to point the camera away from the lunar surface and toward the earth. The result was the world's first view of the earth from space. It was called "the picture of the century' and "the greatest shot taken since the invention of photography." Not even the color photos of the earth taken during the Apollo missions superseded the impact of this first image of our planet as a little island of life floating in the black and infinite sea of space." -- Published in James R. Hansen, Spaceflight Revolution: NASA Langley Research Center From Sputnik to Apollo, (Washington: NASA, 1995), pp. 345-346. Mayor Ann Kilgore was married to NASA researcher Edwin Carroll Kilgore. Mrs, Kilgore was Mayor from 1963-1971 and again from 1974-1978.

Modeling of policies for reduction of GHG emissions in energy sector using ANN: case study-Croatia (EU).

PubMed

Bolanča, Tomislav; Strahovnik, Tomislav; Ukić, Šime; Stankov, Mirjana Novak; Rogošić, Marko

2017-07-01

This study describes the development of tool for testing different policies for reduction of greenhouse gas (GHG) emissions in energy sector using artificial neural networks (ANNs). The case study of Croatia was elaborated. Two different energy consumption scenarios were used as a base for calculations and predictions of GHG emissions: the business as usual (BAU) scenario and sustainable scenario. Both of them are based on predicted energy consumption using different growth rates; the growth rates within the second scenario resulted from the implementation of corresponding energy efficiency measures in final energy consumption and increasing share of renewable energy sources. Both ANN architecture and training methodology were optimized to produce network that was able to successfully describe the existing data and to achieve reliable prediction of emissions in a forward time sense. The BAU scenario was found to produce continuously increasing emissions of all GHGs. The sustainable scenario was found to decrease the GHG emission levels of all gases with respect to BAU. The observed decrease was attributed to the group of measures termed the reduction of final energy consumption through energy efficiency measures.

Establishment and characterization of in vivo orthotopic bioluminescent xenograft models from human osteosarcoma cell lines in Swiss nude and NSG mice.

PubMed

Marques da Costa, Maria Eugenia; Daudigeos-Dubus, Estelle; Gomez-Brouchet, Anne; Bawa, Olivia; Rouffiac, Valerie; Serra, Massimo; Scotlandi, Katia; Santos, Conceição; Geoerger, Birgit; Gaspar, Nathalie

2018-03-01

Osteosarcoma is one of the most common primary bone tumors in childhood and adolescence. Metastases occurrence at diagnosis or during disease evolution is the main therapeutic challenge. New drug evaluation to improve patient survival requires the development of various preclinical models mimicking at best the complexity of the disease and its metastatic potential. We describe here the development and characteristics of two orthotopic bioluminescent (Luc/mKate2) cell-derived xenograft (CDX) models, Saos-2-B-Luc/mKate2-CDX and HOS-Luc/mKate2-CDX, in different immune (nude and NSG mouse strains) and bone (intratibial and paratibial with periosteum activation) contexts. IVIS SpectrumCT system allowed both longitudinal computed tomography (CT) and bioluminescence real-time follow-up of primary tumor growth and metastatic spread, which was confirmed by histology. The murine immune context influenced tumor engraftment, primary tumor growth, and metastatic spread to lungs, bone, and spleen (an unusual localization in humans). Engraftment in NSG mice was found superior to that found in nude mice and intratibial bone environment more favorable to engraftment compared to paratibial injection. The genetic background of the two CDX models also led to distinct primary tumor behavior observed on CT scan. Saos-2-B-Luc/mKate2-CDX showed osteocondensed, HOS-Luc/mKate2-CDX osteolytic morphology. Bioluminescence defined a faster growth of the primary tumor and metastases in Saos-2-B-Luc/mKate2-CDX than in HOS-Luc/mKate2-CDX. The early detection of primary tumor growth and metastatic spread by bioluminescence allows an improved exploration of osteosarcoma disease at tumor progression, and metastatic spread, as well as the evaluations of anticancer treatments. Our orthotopic models with metastatic spread bring complementary information to other types of existing osteosarcoma models. © 2018 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

Mutation of Phe413 to Tyr in catalase KatE from Escherichia coli leads to side chain damage and main chain cleavage.

PubMed

Jha, Vikash; Donald, Lynda J; Loewen, Peter C

2012-09-15

The monofunctional catalase KatE of Esherichia coli exhibits exceptional resistance to heat denaturation and proteolytic degradation. During an investigation of subtle conformation changes in Arg111 and Phe413 on the proximal side of the heme induced by H(2)O(2), variants at position R111, T115 and F413 were constructed. Because the residues are not situated in the distal side heme cavity where catalysis occurs, significant changes in reactivity were not expected and indeed, only small changes in the kinetic characteristics were observed in all of the variants. However, the F413Y variant was found to have undergone main chain cleavage whereas the R111A, T115A, F413E and F413K variants had not. Two sites of cleavage were identified in the crystal structure and by mass spectrometry at residues 111 and 115. In addition to main chain cleavage, modifications to the side chains of Tyr413, Thr115 and Arg111 were suggested by differences in the electron density maps compared to maps of the native and inactive variant H128N/F413Y. The inactive variant H128N/F413Y and the active variant T115A/F413Y both did not exhibit main chain cleavage and the R11A/F413Y variant exhibited less cleavage. In addition, the apparent modification of three side chains was largely absent in these variants. It is also significant that all three F413 single variants contained heme b suggesting that the fidelity of the phenyl group was important for mediating heme b oxidation to heme d. The reactions are attributed to the introduction of a new reactive center possibly involving a transient radical on Tyr413 formed during catalytic turn over. Copyright © 2011 Elsevier Inc. All rights reserved.

Autonomous Cryogenics Loading Operations Simulation Software: Knowledgebase Autonomous Test Engineer

NASA Technical Reports Server (NTRS)

Wehner, Walter S., Jr.

2013-01-01

Working on the ACLO (Autonomous Cryogenics Loading Operations) project I have had the opportunity to add functionality to the physics simulation software known as KATE (Knowledgebase Autonomous Test Engineer), create a new application allowing WYSIWYG (what-you-see-is-what-you-get) creation of KATE schematic files and begin a preliminary design and implementation of a new subsystem that will provide vision services on the IHM (Integrated Health Management) bus. The functionality I added to KATE over the past few months includes a dynamic visual representation of the fluid height in a pipe based on number of gallons of fluid in the pipe and implementing the IHM bus connection within KATE. I also fixed a broken feature in the system called the Browser Display, implemented many bug fixes and made changes to the GUI (Graphical User Interface).

A Report to the Board of Education of Anne Arundel County on the Status of the Schools.

ERIC Educational Resources Information Center

Anne Arundel County Board of Education, Annapolis, MD.

This annual report for the 1979-1980 school year summarizes and gives the status of the main objectives of the major programs within the public school system of Anne Arundel County. Project objectives for the 1981-82 school year as well as the one immediately following are also presented and explained. In his statement, the Superintendent of…

At the Cosmonaut Hotel in Baikonur, Kazakhstan, Expedition 48-49 crewmember Kate Rubins of NASA takes a spin in a rotating chair to test her vestibular system June 30 as part of pre-launch activities. Rubins, Anatoly Ivanishin of Roscosmos and Takuya Onishi of the Japan Aerospace Exploration Agency will launch July 7, Baikonur time, on the Soyuz MS-01 spacecraft for a planned four-month mission on the International Space Station...NASA/Alexander Vysotsky.

NASA Image and Video Library

2016-06-30

At the Cosmonaut Hotel in Baikonur, Kazakhstan, Expedition 48-49 crewmember Kate Rubins of NASA takes a spin in a rotating chair to test her vestibular system June 30 as part of pre-launch activities. Rubins, Anatoly Ivanishin of Roscosmos and Takuya Onishi of the Japan Aerospace Exploration Agency will launch July 7, Baikonur time, on the Soyuz MS-01 spacecraft for a planned four-month mission on the International Space Station. NASA/Alexander Vysotsky

At the Cosmonaut Hotel in Baikonur, Kazakhstan, Expedition 48-49 crewmembers Anatoly Ivanishin of Roscosmos (foreground) and Takuya Onishi of the Japan Aerospace Exploration Agency conduct tests of their vestibular system on tilt tables June 30 as part of pre-launch activities. They and Kate Rubins of NASA will launch July 7, Baikonur time, on the Soyuz MS-01 spacecraft for a planned four-month mission on the International Space Station...NASA/Alexander Vysotsky.

NASA Image and Video Library

2016-06-30

At the Cosmonaut Hotel in Baikonur, Kazakhstan, Expedition 48-49 crewmembers Anatoly Ivanishin of Roscosmos (foreground) and Takuya Onishi of the Japan Aerospace Exploration Agency conduct tests of their vestibular system on tilt tables June 30 as part of pre-launch activities. They and Kate Rubins of NASA will launch July 7, Baikonur time, on the Soyuz MS-01 spacecraft for a planned four-month mission on the International Space Station. NASA/Alexander Vysotsky

At the Cosmonaut Hotel in Baikonur, Kazakhstan, Expedition 48-49 crewmember Takuya Onishi of the Japan Aerospace Exploration Agency takes a spin in a rotating chair to test his vestibular system June 30 as part of pre-launch activities. Onishi, Kate Rubins of NASA and Anatoly Ivanishin of Roscosmos will launch July 7, Baikonur time, on the Soyuz MS-01 spacecraft for a planned four-month mission on the International Space Station...NASA/Alexander Vysotsky.

NASA Image and Video Library

2016-06-30

At the Cosmonaut Hotel in Baikonur, Kazakhstan, Expedition 48-49 crewmember Takuya Onishi of the Japan Aerospace Exploration Agency takes a spin in a rotating chair to test his vestibular system June 30 as part of pre-launch activities. Onishi, Kate Rubins of NASA and Anatoly Ivanishin of Roscosmos will launch July 7, Baikonur time, on the Soyuz MS-01 spacecraft for a planned four-month mission on the International Space Station. NASA/Alexander Vysotsky

A soft computing scheme incorporating ANN and MOV energy in fault detection, classification and distance estimation of EHV transmission line with FSC.

PubMed

Khadke, Piyush; Patne, Nita; Singh, Arvind; Shinde, Gulab

2016-01-01

In this article, a novel and accurate scheme for fault detection, classification and fault distance estimation for a fixed series compensated transmission line is proposed. The proposed scheme is based on artificial neural network (ANN) and metal oxide varistor (MOV) energy, employing Levenberg-Marquardt training algorithm. The novelty of this scheme is the use of MOV energy signals of fixed series capacitors (FSC) as input to train the ANN. Such approach has never been used in any earlier fault analysis algorithms in the last few decades. Proposed scheme uses only single end measurement energy signals of MOV in all the 3 phases over one cycle duration from the occurrence of a fault. Thereafter, these MOV energy signals are fed as input to ANN for fault distance estimation. Feasibility and reliability of the proposed scheme have been evaluated for all ten types of fault in test power system model at different fault inception angles over numerous fault locations. Real transmission system parameters of 3-phase 400 kV Wardha-Aurangabad transmission line (400 km) with 40 % FSC at Power Grid Wardha Substation, India is considered for this research. Extensive simulation experiments show that the proposed scheme provides quite accurate results which demonstrate complete protection scheme with high accuracy, simplicity and robustness.

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

DOT National Transportation Integrated Search

1999-01-01

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

Autonomous Cryogenic Load Operations: KSC Autonomous Test Engineer

NASA Technical Reports Server (NTRS)

Shrading, Nicholas J.

2012-01-01

The KSC Autonomous Test Engineer (KATE) program has a long history at KSC. Now a part of the Autonomous Cryogenic Load Operations (ACLO) mission, this software system has been sporadically developed over the past 20+ years. Originally designed to provide health and status monitoring for a simple water-based fluid system, it was proven to be a capable autonomous test engineer for determining sources of failure in. the system, As part.of a new goal to provide this same anomaly-detection capability for a complicated cryogenic fluid system, software engineers, physicists, interns and KATE experts are working to upgrade the software capabilities and graphical user interface. Much progress was made during this effort to improve KATE. A display ofthe entire cryogenic system's graph, with nodes for components and edges for their connections, was added to the KATE software. A searching functionality was added to the new graph display, so that users could easily center their screen on specific components. The GUI was also modified so that it displayed information relevant to the new project goals. In addition, work began on adding new pneumatic and electronic subsystems into the KATE knowledgebase, so that it could provide health and status monitoring for those systems. Finally, many fixes for bugs, memory leaks, and memory errors were implemented and the system was moved into a state in which it could be presented to stakeholders. Overall, the KATE system was improved and necessary additional features were added so that a presentation of the program and its functionality in the next few months would be a success.

Applications of AnnAGNPS model for soil loss estimation and nutrient loading for Malaysian conditions

NASA Astrophysics Data System (ADS)

Shamshad, A.; Leow, C. S.; Ramlah, A.; Wan Hussin, W. M. A.; Sanusi, S. A. Mohd.

2008-09-01

The study evaluated the performance and suitability of AnnAGNPS model in assessing runoff, sediment loading and nutrient loading under Malaysian conditions. The watershed of River Kuala Tasik in Malaysia, a combination of two sub-watersheds, was selected as the area of study. The data for the year 2004 was used to calibrate the model and the data for the year 2005 was used for validation purposes. Several input parameters were computed using methods suggested by other researchers and studies carried out in Malaysia. The study shows that runoff was predicted well with an overall R2 value of 0.90 and E value of 0.70. Sediment loading was able to produce a moderate result of R2 = 0.66 and E = 0.49, nitrogen loading predictions were slightly better with R2 = 0.68 and E = 0.53, and phosphorus loading performance was slightly poor with an R2 = 0.63 and E = 0.33. The erosion map developed was in agreement with the erosion risk map produced by the Department of Agriculture, Malaysia. Rubber estates and urban areas were found to be the main contributors to soil erosion. The simulation results showed that AnnAGNPS has the potential to be used as a valuable tool for planning and management of watersheds under Malaysian conditions.

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

NASA Technical Reports Server (NTRS)

Stiffler, A. Kent

1989-01-01

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

Effect of hydrogen fluoride on pollen germination and pollen tube growth in Prunus avium L. cv. Royal Ann

DOE Office of Scientific and Technical Information (OSTI.GOV)

Facteau, T.J.; Wang, S.Y.; Rowe, K.E.

1973-05-01

Increased fluoride (F) fumigation levels resulted in decrease in percent Royal Ann pollen germination and pollen tube growth. As dose (hour x concentration in ..mu.. gF/m/sup 3/) increased, Van pollen tube growth in vivo decreased. A linear relationship between increased dose and fluoride residue in the flowers was shown. 14 references, 5 figures.

"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,…

Meeting report for Odd Pols 2016: Ann Arbor 2.0.

PubMed

Roy-Engel, Astrid M

2017-05-15

The Tenth International Conference on Transcription by RNA Polymerases I, III, IV and V (the 'Odd Pols') was held June 24-28, 2016 at the University of Michigan, Ann Arbor, USA and organized by David Engelke, Deborah Johnson, Richard Maraia, Lawrence Rothblum, David Schneider, Andrzej Wierzbicki and Astrid Engel. The organizers are grateful for the support from the Rackham Graduate School of the University of Michigan for providing the meeting venue. The environment provided a great background with unexpected encounters with fireflies, free live music and a festive fireworks display. The meeting was composed of eleven oral sessions and a poster session. The keynote speaker, Dave Engelke, opened the meeting with his presentation entitled "A personal history of pol III transcription - how we got here from the 'good old days'." The meeting drew attendees from sixteen countries that shared their research discoveries. Here we present some of the highlights from the meeting using summaries provided by the participants. Copyright © 2016 Elsevier B.V. All rights reserved.

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

NASA Technical Reports Server (NTRS)

1993-01-01

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

Two Different Approaches to Teaching Final-Year Projects for Mechanical Engineers and Biotechnologists at Ngee Ann Polytechnic--Case Studies Approach.

ERIC Educational Resources Information Center

Walsh, Kath; Rebaczonok-Padulo, Michael

1993-01-01

Ngee Ann Polytechnic, a leading postsecondary technical institution in Singapore, offers English for academic and occupational purposes to prepare students for writing their final year projects. This article discusses the approaches used in Mechanical Engineering and Biotechnology projects. A sample exercise is appended. (Contains two references.)…

Autonomous Cryogenic Load Operations: Knowledge-Based Autonomous Test Engineer

NASA Technical Reports Server (NTRS)

Schrading, J. Nicolas

2013-01-01

The Knowledge-Based Autonomous Test Engineer (KATE) program has a long history at KSC. Now a part of the Autonomous Cryogenic Load Operations (ACLO) mission, this software system has been sporadically developed over the past 20 years. Originally designed to provide health and status monitoring for a simple water-based fluid system, it was proven to be a capable autonomous test engineer for determining sources of failure in the system. As part of a new goal to provide this same anomaly-detection capability for a complicated cryogenic fluid system, software engineers, physicists, interns and KATE experts are working to upgrade the software capabilities and graphical user interface. Much progress was made during this effort to improve KATE. A display of the entire cryogenic system's graph, with nodes for components and edges for their connections, was added to the KATE software. A searching functionality was added to the new graph display, so that users could easily center their screen on specific components. The GUI was also modified so that it displayed information relevant to the new project goals. In addition, work began on adding new pneumatic and electronic subsystems into the KATE knowledge base, so that it could provide health and status monitoring for those systems. Finally, many fixes for bugs, memory leaks, and memory errors were implemented and the system was moved into a state in which it could be presented to stakeholders. Overall, the KATE system was improved and necessary additional features were added so that a presentation of the program and its functionality in the next few months would be a success.

At the Cosmonaut Hotel in Baikonur, Kazakhstan, Expedition 48-49 prime crewmember Kate Rubins of NASA (left) and her backup, NASA���s Peggy Whitson (right) share a game of chess June 30 during pre-launch activities. Rubins, Anatoly Ivanishin of Roscosmos and Takuya Onishi of the Japan Aerospace Exploration Agency, will launch July 7, Baikonur time, on the Soyuz MS-01 spacecraft for a planned four-month mission on the International Space Station...NASA/Alexander Vysotsky.

NASA Image and Video Library

2016-06-30

At the Cosmonaut Hotel in Baikonur, Kazakhstan, Expedition 48-49 prime crewmember Kate Rubins of NASA (left) and her backup, NASA’s Peggy Whitson (right) share a game of chess June 30 during pre-launch activities. Rubins, Anatoly Ivanishin of Roscosmos and Takuya Onishi of the Japan Aerospace Exploration Agency, will launch July 7, Baikonur time, on the Soyuz MS-01 spacecraft for a planned four-month mission on the International Space Station. NASA/Alexander Vysotsky

Kate Anderson | NREL

Science.gov Websites

private entities with techno-economic modeling and analysis, field assessments, design, and implementation Force. Research Interests Energy optimization Techno-economic modeling Value of resiliency Solar+storage -Resilient Solar Project: Economic and Resiliency Impact of PV and Storage on New York Critical

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

USDA-ARS?s Scientific Manuscript database

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...

Master-Leader-Slave Cuckoo Search with Parameter Control for ANN Optimization and Its Real-World Application to Water Quality Prediction

PubMed Central

Jaddi, Najmeh Sadat; Abdullah, Salwani; Abdul Malek, Marlinda

2017-01-01

Artificial neural networks (ANNs) have been employed to solve a broad variety of tasks. The selection of an ANN model with appropriate weights is important in achieving accurate results. This paper presents an optimization strategy for ANN model selection based on the cuckoo search (CS) algorithm, which is rooted in the obligate brood parasitic actions of some cuckoo species. In order to enhance the convergence ability of basic CS, some modifications are proposed. The fraction Pa of the n nests replaced by new nests is a fixed parameter in basic CS. As the selection of Pa is a challenging issue and has a direct effect on exploration and therefore on convergence ability, in this work the Pa is set to a maximum value at initialization to achieve more exploration in early iterations and it is decreased during the search to achieve more exploitation in later iterations until it reaches the minimum value in the final iteration. In addition, a novel master-leader-slave multi-population strategy is used where the slaves employ the best fitness function among all slaves, which is selected by the leader under a certain condition. This fitness function is used for subsequent Lévy flights. In each iteration a copy of the best solution of each slave is migrated to the master and then the best solution is found by the master. The method is tested on benchmark classification and time series prediction problems and the statistical analysis proves the ability of the method. This method is also applied to a real-world water quality prediction problem with promising results. PMID:28125609

Determining degree of roasting in cocoa beans by artificial neural network (ANN)-based electronic nose system and gas chromatography/mass spectrometry (GC/MS).

PubMed

Tan, Juzhong; Kerr, William L

2018-08-01

Roasting is a critical step in chocolate processing, where moisture content is decreased and unique flavors and texture are developed. The determination of the degree of roasting in cocoa beans is important to ensure the quality of chocolate. Determining the degree of roasting relies on human specialists or sophisticated chemical analyses that are inaccessible to small manufacturers and farmers. In this study, an electronic nose system was constructed consisting of an array of gas sensors and used to detect volatiles emanating from cocoa beans roasted for 0, 20, 30 and 40 min. The several signals were used to train a three-layer artificial neural network (ANN). Headspace samples were also analyzed by gas chromatography/mass spectrometry (GC/MS), with 23 select volatiles used to train a separate ANN. Both ANNs were used to predict the degree of roasting of cocoa beans. The electronic nose had a prediction accuracy of 94.4% using signals from sensors TGS 813, 826, 822, 830, 830, 2620, 2602 and 2610. In comparison, the GC/MS predicted the degree of roasting with an accuracy of 95.8%. The electronic nose system is able to predict the extent of roasting, as well as a more sophisticated approach using GC/MS. © 2018 Society of Chemical Industry. © 2018 Society of Chemical Industry.

Master-Leader-Slave Cuckoo Search with Parameter Control for ANN Optimization and Its Real-World Application to Water Quality Prediction.

PubMed

Jaddi, Najmeh Sadat; Abdullah, Salwani; Abdul Malek, Marlinda

2017-01-01

Artificial neural networks (ANNs) have been employed to solve a broad variety of tasks. The selection of an ANN model with appropriate weights is important in achieving accurate results. This paper presents an optimization strategy for ANN model selection based on the cuckoo search (CS) algorithm, which is rooted in the obligate brood parasitic actions of some cuckoo species. In order to enhance the convergence ability of basic CS, some modifications are proposed. The fraction Pa of the n nests replaced by new nests is a fixed parameter in basic CS. As the selection of Pa is a challenging issue and has a direct effect on exploration and therefore on convergence ability, in this work the Pa is set to a maximum value at initialization to achieve more exploration in early iterations and it is decreased during the search to achieve more exploitation in later iterations until it reaches the minimum value in the final iteration. In addition, a novel master-leader-slave multi-population strategy is used where the slaves employ the best fitness function among all slaves, which is selected by the leader under a certain condition. This fitness function is used for subsequent Lévy flights. In each iteration a copy of the best solution of each slave is migrated to the master and then the best solution is found by the master. The method is tested on benchmark classification and time series prediction problems and the statistical analysis proves the ability of the method. This method is also applied to a real-world water quality prediction problem with promising results.

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.

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

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.

Optimization of microwave-assisted extraction of total extract, stevioside and rebaudioside-A from Stevia rebaudiana (Bertoni) leaves, using response surface methodology (RSM) and artificial neural network (ANN) modelling.

PubMed

Ameer, Kashif; Bae, Seong-Woo; Jo, Yunhee; Lee, Hyun-Gyu; Ameer, Asif; Kwon, Joong-Ho

2017-08-15

Stevia rebaudiana (Bertoni) consists of stevioside and rebaudioside-A (Reb-A). We compared response surface methodology (RSM) and artificial neural network (ANN) modelling for their estimation and predictive capabilities in building effective models with maximum responses. A 5-level 3-factor central composite design was used to optimize microwave-assisted extraction (MAE) to obtain maximum yield of target responses as a function of extraction time (X 1 : 1-5min), ethanol concentration, (X 2 : 0-100%) and microwave power (X 3 : 40-200W). Maximum values of the three output parameters: 7.67% total extract yield, 19.58mg/g stevioside yield, and 15.3mg/g Reb-A yield, were obtained under optimum extraction conditions of 4min X 1 , 75% X 2 , and 160W X 3 . The ANN model demonstrated higher efficiency than did the RSM model. Hence, RSM can demonstrate interaction effects of inherent MAE parameters on target responses, whereas ANN can reliably model the MAE process with better predictive and estimation capabilities. Copyright © 2017. Published by Elsevier Ltd.

Two Different Approaches to Teaching Final-Year Projects for Mechanical Engineers and Biotechnologists at Ngee Ann Polytechnic: Case Studies Approach.

ERIC Educational Resources Information Center

Walsh, Kath; Rebaczonok-Padulo, Michael

The Oral and Written Communication (OWC) course at Ngee Ann Polytechnic was originally intended to equip students with occupational skills (e.g., report- and letter-writing, public speaking) but has expanded to be a course aimed at helping third-year mechanical engineering students to develop third-year project reports. This has been done through…

Modeling and Investigation of the Wear Resistance of Salt Bath Nitrided Aisi 4140 via ANN

NASA Astrophysics Data System (ADS)

Ekinci, Şerafettin; Akdemir, Ahmet; Kahramanli, Humar

2013-05-01

Nitriding is usually used to improve the surface properties of steel materials. In this way, the wear resistance of steels is improved. We conducted a series of studies in order to investigate the microstructural, mechanical and tribological properties of salt bath nitrided AISI 4140 steel. The present study has two parts. For the first phase, the tribological behavior of the AISI 4140 steel which was nitrided in sulfinuz salt bath (SBN) was compared to the behavior of the same steel which was untreated. After surface characterization using metallography, microhardness and sliding wear tests were performed on a block-on-cylinder machine in which carbonized AISI 52100 steel discs were used as the counter face. For the examined AISI 4140 steel samples with and without surface treatment, the evolution of both the friction coefficient and of the wear behavior were determined under various loads, at different sliding velocities and a total sliding distance of 1000 m. The test results showed that wear resistance increased with the nitriding process, friction coefficient decreased due to the sulfur in salt bath and friction coefficient depended systematically on surface hardness. For the second part of this study, four artificial neural network (ANN) models were designed to predict the weight loss and friction coefficient of the nitrided and unnitrided AISI 4140 steel. Load, velocity and sliding distance were used as input. Back-propagation algorithm was chosen for training the ANN. Statistical measurements of R2, MAE and RMSE were employed to evaluate the success of the systems. The results showed that all the systems produced successful results.

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. Copyright © 2015 Elsevier Ltd. All rights reserved.

Understanding the role of the catalase/peroxide genes in H2O2 resistance of E. coli serotype O157:H7 biofilms

USDA-ARS?s Scientific Manuscript database

Introduction: Escherichia coli serotype O157:H7 defenses against H2O2 include the peroxiredoxin AhpC and three catalases: KatG (catalase-peroxidase), KatE (catalase), and the plasmid-encoded KatP (catalase/peroxidase). AhpC, KatG, and KatP are induced by OxyR in exponential phase, while KatE is indu...

Artificial Cochlea Design Using Micro-Electro-Mechanical Systems

DTIC Science & Technology

1996-12-17

FIGURE 2-9 - BLOCK DIAGRAM OF THE KATE’S MODEL ................................................ 2-13 FIGURE 2-10 -- COCHLEAR TUNING CURVES FOR KATES MODEL...2-14 FIGURE 2-11 - TUNING CURVE OF A CAT’S COCHLEA .................................................... 2-15...FIGURE 2-12 - FREQUENCY RESPONSE CURVES OF THE VLSI IMPLEMENTATIONS OF THE AN A LO G CO CH LEA

Prediction of GWL with the help of GRACE TWS for unevenly spaced time series data in India : Analysis of comparative performances of SVR, ANN and LRM

NASA Astrophysics Data System (ADS)

Mukherjee, Amritendu; Ramachandran, Parthasarathy

2018-03-01

Prediction of Ground Water Level (GWL) is extremely important for sustainable use and management of ground water resource. The motivations for this work is to understand the relationship between Gravity Recovery and Climate Experiment (GRACE) derived terrestrial water change (ΔTWS) data and GWL, so that ΔTWS could be used as a proxy measurement for GWL. In our study, we have selected five observation wells from different geographic regions in India. The datasets are unevenly spaced time series data which restricts us from applying standard time series methodologies and therefore in order to model and predict GWL with the help of ΔTWS, we have built Linear Regression Model (LRM), Support Vector Regression (SVR) and Artificial Neural Network (ANN). Comparative performances of LRM, SVR and ANN have been evaluated with the help of correlation coefficient (ρ) and Root Mean Square Error (RMSE) between the actual and fitted (for training dataset) or predicted (for test dataset) values of GWL. It has been observed in our study that ΔTWS is highly significant variable to model GWL and the amount of total variations in GWL that could be explained with the help of ΔTWS varies from 36.48% to 74.28% (0.3648 ⩽R2 ⩽ 0.7428) . We have found that for the model GWL ∼ Δ TWS, for both training and test dataset, performances of SVR and ANN are better than that of LRM in terms of ρ and RMSE. It also has been found in our study that with the inclusion of meteorological variables along with ΔTWS as input parameters to model GWL, the performance of SVR improves and it performs better than ANN. These results imply that for modelling irregular time series GWL data, ΔTWS could be very useful.

Reliable and accurate point-based prediction of cumulative infiltration using soil readily available characteristics: A comparison between GMDH, ANN, and MLR

NASA Astrophysics Data System (ADS)

Rahmati, Mehdi

2017-08-01

Developing accurate and reliable pedo-transfer functions (PTFs) to predict soil non-readily available characteristics is one of the most concerned topic in soil science and selecting more appropriate predictors is a crucial factor in PTFs' development. Group method of data handling (GMDH), which finds an approximate relationship between a set of input and output variables, not only provide an explicit procedure to select the most essential PTF input variables, but also results in more accurate and reliable estimates than other mostly applied methodologies. Therefore, the current research was aimed to apply GMDH in comparison with multivariate linear regression (MLR) and artificial neural network (ANN) to develop several PTFs to predict soil cumulative infiltration point-basely at specific time intervals (0.5-45 min) using soil readily available characteristics (RACs). In this regard, soil infiltration curves as well as several soil RACs including soil primary particles (clay (CC), silt (Si), and sand (Sa)), saturated hydraulic conductivity (Ks), bulk (Db) and particle (Dp) densities, organic carbon (OC), wet-aggregate stability (WAS), electrical conductivity (EC), and soil antecedent (θi) and field saturated (θfs) water contents were measured at 134 different points in Lighvan watershed, northwest of Iran. Then, applying GMDH, MLR, and ANN methodologies, several PTFs have been developed to predict cumulative infiltrations using two sets of selected soil RACs including and excluding Ks. According to the test data, results showed that developed PTFs by GMDH and MLR procedures using all soil RACs including Ks resulted in more accurate (with E values of 0.673-0.963) and reliable (with CV values lower than 11 percent) predictions of cumulative infiltrations at different specific time steps. In contrast, ANN procedure had lower accuracy (with E values of 0.356-0.890) and reliability (with CV values up to 50 percent) compared to GMDH and MLR. The results also revealed

Applying of the Artificial Neural Networks (ANN) to Identify and Characterize Sweet Spots in Shale Gas Formations

NASA Astrophysics Data System (ADS)

Puskarczyk, Edyta

2018-03-01

The main goal of the study was to enhance and improve information about the Ordovician and Silurian gas-saturated shale formations. Author focused on: firstly, identification of the shale gas formations, especially the sweet spots horizons, secondly, classification and thirdly, the accurate characterization of divisional intervals. Data set comprised of standard well logs from the selected well. Shale formations are represented mainly by claystones, siltstones, and mudstones. The formations are also partially rich in organic matter. During the calculations, information about lithology of stratigraphy weren't taken into account. In the analysis, selforganizing neural network - Kohonen Algorithm (ANN) was used for sweet spots identification. Different networks and different software were tested and the best network was used for application and interpretation. As a results of Kohonen networks, groups corresponding to the gas-bearing intervals were found. The analysis showed diversification between gas-bearing formations and surrounding beds. It is also shown that internal diversification in sweet spots is present. Kohonen algorithm was also used for geological interpretation of well log data and electrofacies prediction. Reliable characteristic into groups shows that Ja Mb and Sa Fm which are usually treated as potential sweet spots only partially have good reservoir conditions. It is concluded that ANN appears to be useful and quick tool for preliminary classification of members and sweet spots identification.

Workshop on Surface Science and Technology Held in Ann Arbor, Michigan on 7-9 November 1990

DTIC Science & Technology

1992-03-01

assess the state of the art of surface science and technology as well as to identify new research opportunities essential for the understanding and control...The objective of this workshop was to review and assess the state of the art of surface science and technology as well as to identify new research...AD-A253 566 ’ # 4 - m~~i n~nl lInIir ~~ na Ri1 ?epoi’rt: EN 1Workshop on Surface Science and Technology DTIC ft , L-CTE I OUG0 3192 Ann Arbor

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.

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

DOT National Transportation Integrated Search

1999-01-01

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

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…

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

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

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.more » (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)« less

ISS Expedition 48-49 prime crewmembers Kate Rubins of NASA (left), Anatoly Ivanishin of Roscosmos (center) and Takuya Onishi of the Japan Aerospace Exploration Agency (right) pose for pictures with schoolchildren after arriving in Baikonur, Kazakhstan June 24 for final pre-launch training following a flight from Star City, Russia. The trio will launch July 7 from the Baikonur Cosmodrome in Kazakhstan on the Soyuz MS-01 spacecraft for a planned four-month mission on the International Space Station...NASA/Alexander Vysotsky.

NASA Image and Video Library

2016-06-24

ISS Expedition 48-49 prime crewmembers Kate Rubins of NASA (left), Anatoly Ivanishin of Roscosmos (center) and Takuya Onishi of the Japan Aerospace Exploration Agency (right) pose for pictures with schoolchildren after arriving in Baikonur, Kazakhstan June 24 for final pre-launch training following a flight from Star City, Russia. The trio will launch July 7 from the Baikonur Cosmodrome in Kazakhstan on the Soyuz MS-01 spacecraft for a planned four-month mission on the International Space Station. NASA/Alexander Vysotsky

ISS Expedition 48-49 prime crewmembers Kate Rubins of NASA (left), Takuya Onishi of the Japan Aerospace Exploration Agency (center) and Anatoly Ivanishin of Roscosmos (right) wave to schoolchildren after arriving in Baikonur, Kazakhstan June 24 for final pre-launch training following a flight from Star City, Russia. The trio will launch July 7 from the Baikonur Cosmodrome in Kazakhstan on the Soyuz MS-01 spacecraft for a planned four-month mission on the International Space Station...NASA/Alexander Vysotsky.

NASA Image and Video Library

2016-06-24

ISS Expedition 48-49 prime crewmembers Kate Rubins of NASA (left), Takuya Onishi of the Japan Aerospace Exploration Agency (center) and Anatoly Ivanishin of Roscosmos (right) wave to schoolchildren after arriving in Baikonur, Kazakhstan June 24 for final pre-launch training following a flight from Star City, Russia. The trio will launch July 7 from the Baikonur Cosmodrome in Kazakhstan on the Soyuz MS-01 spacecraft for a planned four-month mission on the International Space Station. NASA/Alexander Vysotsky

An Experimental Investigation into the Optimal Processing Conditions for the CO2 Laser Cladding of 20 MnCr5 Steel Using Taguchi Method and ANN

NASA Astrophysics Data System (ADS)

Mondal, Subrata; Bandyopadhyay, Asish.; Pal, Pradip Kumar

2010-10-01

This paper presents the prediction and evaluation of laser clad profile formed by means of CO2 laser applying Taguchi method and the artificial neural network (ANN). Laser cladding is one of the surface modifying technologies in which the desired surface characteristics of any component can be achieved such as good corrosion resistance, wear resistance and hardness etc. Laser is used as a heat source to melt the anti-corrosive powder of Inconel-625 (Super Alloy) to give a coating on 20 MnCr5 substrate. The parametric study of this technique is also attempted here. The data obtained from experiments have been used to develop the linear regression equation and then to develop the neural network model. Moreover, the data obtained from regression equations have also been used as supporting data to train the neural network. The artificial neural network (ANN) is used to establish the relationship between the input/output parameters of the process. The established ANN model is then indirectly integrated with the optimization technique. It has been seen that the developed neural network model shows a good degree of approximation with experimental data. In order to obtain the combination of process parameters such as laser power, scan speed and powder feed rate for which the output parameters become optimum, the experimental data have been used to develop the response surfaces.

Prediction of size-fractionated airborne particle-bound metals using MLR, BP-ANN and SVM analyses.

PubMed

Leng, Xiang'zi; Wang, Jinhua; Ji, Haibo; Wang, Qin'geng; Li, Huiming; Qian, Xin; Li, Fengying; Yang, Meng

2017-08-01

Size-fractionated heavy metal concentrations were observed in airborne particulate matter (PM) samples collected from 2014 to 2015 (spanning all four seasons) from suburban (Xianlin) and industrial (Pukou) areas in Nanjing, a megacity of southeast China. Rapid prediction models of size-fractionated metals were established based on multiple linear regression (MLR), back propagation artificial neural network (BP-ANN) and support vector machine (SVM) by using meteorological factors and PM concentrations as input parameters. About 38% and 77% of PM 2.5 concentrations in Xianlin and Pukou, respectively, were beyond the Chinese National Ambient Air Quality Standard limit of 75 μg/m 3 . Nearly all elements had higher concentrations in industrial areas, and in winter among the four seasons. Anthropogenic elements such as Pb, Zn, Cd and Cu showed larger percentages in the fine fraction (ø≤2.5 μm), whereas the crustal elements including Al, Ba, Fe, Ni, Sr and Ti showed larger percentages in the coarse fraction (ø > 2.5 μm). SVM showed a higher training correlation coefficient (R), and lower mean absolute error (MAE) as well as lower root mean square error (RMSE), than MLR and BP-ANN for most metals. All the three methods showed better prediction results for Ni, Al, V, Cd and As, whereas relatively poor for Cr and Fe. The daily airborne metal concentrations in 2015 were then predicted by the fully trained SVM models and the results showed the heaviest pollution of airborne heavy metals occurred in December and January, whereas the lightest pollution occurred in June and July. Copyright © 2017 Elsevier Ltd. All rights reserved.

Comparison of ANN and SVM for classification of eye movements in EOG signals

NASA Astrophysics Data System (ADS)

Qi, Lim Jia; Alias, Norma

2018-03-01

Nowadays, electrooculogram is regarded as one of the most important biomedical signal in measuring and analyzing eye movement patterns. Thus, it is helpful in designing EOG-based Human Computer Interface (HCI). In this research, electrooculography (EOG) data was obtained from five volunteers. The (EOG) data was then preprocessed before feature extraction methods were employed to further reduce the dimensionality of data. Three feature extraction approaches were put forward, namely statistical parameters, autoregressive (AR) coefficients using Burg method, and power spectral density (PSD) using Yule-Walker method. These features would then become input to both artificial neural network (ANN) and support vector machine (SVM). The performance of the combination of different feature extraction methods and classifiers was presented and analyzed. It was found that statistical parameters + SVM achieved the highest classification accuracy of 69.75%.

An Emotional ANN (EANN) approach to modeling rainfall-runoff process

NASA Astrophysics Data System (ADS)

Nourani, Vahid

2017-01-01

This paper presents the first hydrological implementation of Emotional Artificial Neural Network (EANN), as a new generation of Artificial Intelligence-based models for daily rainfall-runoff (r-r) modeling of the watersheds. Inspired by neurophysiological form of brain, in addition to conventional weights and bias, an EANN includes simulated emotional parameters aimed at improving the network learning process. EANN trained by a modified version of back-propagation (BP) algorithm was applied to single and multi-step-ahead runoff forecasting of two watersheds with two distinct climatic conditions. Also to evaluate the ability of EANN trained by smaller training data set, three data division strategies with different number of training samples were considered for the training purpose. The overall comparison of the obtained results of the r-r modeling indicates that the EANN could outperform the conventional feed forward neural network (FFNN) model up to 13% and 34% in terms of training and verification efficiency criteria, respectively. The superiority of EANN over classic ANN is due to its ability to recognize and distinguish dry (rainless days) and wet (rainy days) situations using hormonal parameters of the artificial emotional system.

An experimental study of interstitial lung tissue classification in HRCT images using ANN and role of cost functions

NASA Astrophysics Data System (ADS)

Dash, Jatindra K.; Kale, Mandar; Mukhopadhyay, Sudipta; Khandelwal, Niranjan; Prabhakar, Nidhi; Garg, Mandeep; Kalra, Naveen

2017-03-01

In this paper, we investigate the effect of the error criteria used during a training phase of the artificial neural network (ANN) on the accuracy of the classifier for classification of lung tissues affected with Interstitial Lung Diseases (ILD). Mean square error (MSE) and the cross-entropy (CE) criteria are chosen being most popular choice in state-of-the-art implementations. The classification experiment performed on the six interstitial lung disease (ILD) patterns viz. Consolidation, Emphysema, Ground Glass Opacity, Micronodules, Fibrosis and Healthy from MedGIFT database. The texture features from an arbitrary region of interest (AROI) are extracted using Gabor filter. Two different neural networks are trained with the scaled conjugate gradient back propagation algorithm with MSE and CE error criteria function respectively for weight updation. Performance is evaluated in terms of average accuracy of these classifiers using 4 fold cross-validation. Each network is trained for five times for each fold with randomly initialized weight vectors and accuracies are computed. Significant improvement in classification accuracy is observed when ANN is trained by using CE (67.27%) as error function compared to MSE (63.60%). Moreover, standard deviation of the classification accuracy for the network trained with CE (6.69) error criteria is found less as compared to network trained with MSE (10.32) criteria.

[Study of building quantitative analysis model for chlorophyll in winter wheat with reflective spectrum using MSC-ANN algorithm].

PubMed

Liang, Xue; Ji, Hai-yan; Wang, Peng-xin; Rao, Zhen-hong; Shen, Bing-hui

2010-01-01

Preprocess method of multiplicative scatter correction (MSC) was used to reject noises in the original spectra produced by the environmental physical factor effectively, then the principal components of near-infrared spectroscopy were calculated by nonlinear iterative partial least squares (NIPALS) before building the back propagation artificial neural networks method (BP-ANN), and the numbers of principal components were calculated by the method of cross validation. The calculated principal components were used as the inputs of the artificial neural networks model, and the artificial neural networks model was used to find the relation between chlorophyll in winter wheat and reflective spectrum, which can predict the content of chlorophyll in winter wheat. The correlation coefficient (r) of calibration set was 0.9604, while the standard deviation (SD) and relative standard deviation (RSD) was 0.187 and 5.18% respectively. The correlation coefficient (r) of predicted set was 0.9600, and the standard deviation (SD) and relative standard deviation (RSD) was 0.145 and 4.21% respectively. It means that the MSC-ANN algorithm can reject noises in the original spectra produced by the environmental physical factor effectively and set up an exact model to predict the contents of chlorophyll in living leaves veraciously to replace the classical method and meet the needs of fast analysis of agricultural products.

Putting ''place'' a multiscale context: perspectices from the sustainability sciences

DOE PAGES

Wilbanks, Thomas J.

2015-05-04

This paper summarizes a number of perspectives that have emerged from the sustainability sciences in recent decades (NRC, 1999; Kates et al., 2001; NRC, 2006; Kates, 2010) that shed light on the role of place in multi-scale sustainability science and vice-versa, ranging from the importance of the co-production of knowledge for sustainable development to threats to a ''sense of place'' from global environmental and economic changes.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wilbanks, Thomas J.

This paper summarizes a number of perspectives that have emerged from the sustainability sciences in recent decades (NRC, 1999; Kates et al., 2001; NRC, 2006; Kates, 2010) that shed light on the role of place in multi-scale sustainability science and vice-versa, ranging from the importance of the co-production of knowledge for sustainable development to threats to a ''sense of place'' from global environmental and economic changes.

At the Cosmonaut Hotel in Baikonur, Kazakhstan, Expedition 48-49 backup crewmember Peggy Whitson of NASA waters a tree in her name first planted in 2007 during traditional pre-launch activities June 30. Whitson is one of three backups to the prime crewmembers, Kate Rubins of NASA, Anatoly Ivanishin of Roscosmos and Takuya Onishi of the Japan Aerospace Exploration Agency, who will launch July 7, Baikonur time, on the Soyuz MS-01 spacecraft for a planned four-month mission on the International Space Station...NASA/Alexander Vysotsky.

NASA Image and Video Library

2016-06-30

At the Cosmonaut Hotel in Baikonur, Kazakhstan, Expedition 48-49 backup crewmember Peggy Whitson of NASA waters a tree in her name first planted in 2007 during traditional pre-launch activities June 30. Whitson is one of three backups to the prime crewmembers, Kate Rubins of NASA, Anatoly Ivanishin of Roscosmos and Takuya Onishi of the Japan Aerospace Exploration Agency, who will launch July 7, Baikonur time, on the Soyuz MS-01 spacecraft for a planned four-month mission on the International Space Station. NASA/Alexander Vysotsky

At the Cosmonaut Hotel in Baikonur, Kazakhstan, Expedition 48-49 crewmembers Kate Rubins of NASA (left), Anatoly Ivanishin of Roscosmos (center) and Takuya Onishi of the Japan Aerospace Exploration Agency (right) pose for pictures June 30 after Rubins and Onishi, both first-time fliers, planted trees in their names in traditional pre-launch activities. Rubins, Ivanishin and Onishi will launch July 7, Baikonur time, on the Soyuz MS-01 spacecraft for a planned four-month mission on the International Space Station...NASA/Alexander Vysotsky.

NASA Image and Video Library

2016-06-30

At the Cosmonaut Hotel in Baikonur, Kazakhstan, Expedition 48-49 crewmembers Kate Rubins of NASA (left), Anatoly Ivanishin of Roscosmos (center) and Takuya Onishi of the Japan Aerospace Exploration Agency (right) pose for pictures June 30 after Rubins and Onishi, both first-time fliers, planted trees in their names in traditional pre-launch activities. Rubins, Ivanishin and Onishi will launch July 7, Baikonur time, on the Soyuz MS-01 spacecraft for a planned four-month mission on the International Space Station. NASA/Alexander Vysotsky

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

2016-11-01

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.

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. Copyright © 2014 Elsevier Inc. All rights reserved.

[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.

Optimization of delignification of two Pennisetum grass species by NaOH pretreatment using Taguchi and ANN statistical approach.

PubMed

Mohaptra, Sonali; Dash, Preeti Krishna; Behera, Sudhanshu Shekar; Thatoi, Hrudayanath

2016-01-01

In the bioconversion of lignocelluloses for bioethanol, pretreatment seems to be the most important step which improves the elimination of the lignin and hemicelluloses content, exposing cellulose to further hydrolysis. The present study discusses the application of dynamic statistical techniques like the Taguchi method and artificial neural network (ANN) in the optimization of pretreatment of lignocellulosic biomasses such as Hybrid Napier grass (HNG) (Pennisetum purpureum) and Denanath grass (DG) (Pennisetum pedicellatum), using alkali sodium hydroxide. This study analysed and determined a parameter combination with a low number of experiments by using the Taguchi method in which both the substrates can be efficiently pretreated. The optimized parameters obtained from the L16 orthogonal array are soaking time (18 and 26 h), temperature (60°C and 55°C), and alkali concentration (1%) for HNG and DG, respectively. High performance liquid chromatography analysis of the optimized pretreated grass varieties confirmed the presence of glucan (47.94% and 46.50%), xylan (9.35% and 7.95%), arabinan (2.15% and 2.2%), and galactan/mannan (1.44% and 1.52%) for HNG and DG, respectively. Physicochemical characterization studies of native and alkali-pretreated grasses were carried out by scanning electron microscopy and Fourier transformation Infrared spectroscopy which revealed some morphological differences between the native and optimized pretreated samples. Model validation by ANN showed a good agreement between experimental results and the predicted responses.

Monomeric red fluorescent proteins with a large Stokes shift.

PubMed

Piatkevich, Kiryl D; Hulit, James; Subach, Oksana M; Wu, Bin; Abdulla, Arian; Segall, Jeffrey E; Verkhusha, Vladislav V

2010-03-23

Two-photon microscopy has advanced fluorescence imaging of cellular processes in living animals. Fluorescent proteins in the blue-green wavelength range are widely used in two-photon microscopy; however, the use of red fluorescent proteins is limited by the low power output of Ti-Sapphire lasers above 1,000 nm. To overcome this limitation we have developed two red fluorescent proteins, LSS-mKate1 and LSS-mKate2, which possess large Stokes shifts with excitation/emission maxima at 463/624 and 460/605 nm, respectively. These LSS-mKates are characterized by high pH stability, photostability, rapid chromophore maturation, and monomeric behavior. They lack absorbance in the green region, providing an additional red color to the commonly used red fluorescent proteins. Substantial overlap between the two-photon excitation spectra of the LSS-mKates and blue-green fluorophores enables multicolor imaging using a single laser. We applied this approach to a mouse xenograft model of breast cancer to intravitally study the motility and Golgi-nucleus alignment of tumor cells as a function of their distance from blood vessels. Our data indicate that within 40 mum the breast cancer cells show significant polarization towards vessels in living mice.

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

DOT National Transportation Integrated Search

1999-01-01

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

Tell it to someone who cares. The health care industry wants more information--and Anne Arundel Health Care Systems is giving it to them.

PubMed

Appleby, C

1995-10-20

The way to succeed in the health care industry these days is to stay ahead of the information curve. With a lot of ingenuity and a stern eye on the bottom line, Anne Arundel Health Care Systems in Maryland is doing just that. Here's how and why they did it, and how much they paid.

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%.

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.

[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.

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.

Determination of zinc oxide content of mineral medicine calamine using near-infrared spectroscopy based on MIV and BP-ANN algorithm

NASA Astrophysics Data System (ADS)

Zhang, Xiaodong; Chen, Long; Sun, Yangbo; Bai, Yu; Huang, Bisheng; Chen, Keli

2018-03-01

Near-infrared (NIR) spectroscopy has been widely used in the analysis fields of traditional Chinese medicine. It has the advantages of fast analysis, no damage to samples and no pollution. In this research, a fast quantitative model for zinc oxide (ZnO) content in mineral medicine calamine was explored based on NIR spectroscopy. NIR spectra of 57 batches of calamine samples were collected and the first derivative (FD) method was adopted for conducting spectral pretreatment. The content of ZnO in calamine sample was determined using ethylenediaminetetraacetic acid (EDTA) titration and taken as reference value of NIR spectroscopy. 57 batches of calamine samples were categorized into calibration and prediction set using the Kennard-Stone (K-S) algorithm. Firstly, in the calibration set, to calculate the correlation coefficient (r) between the absorbance value and the ZnO content of corresponding samples at each wave number. Next, according to the square correlation coefficient (r2) value to obtain the top 50 wave numbers to compose the characteristic spectral bands (4081.8-4096.3, 4188.9-4274.7, 4335.4, 4763.6,4794.4-4802.1, 4809.9, 4817.6-4875.4 cm- 1), which were used to establish the quantitative model of ZnO content using back propagation artificial neural network (BP-ANN) algorithm. Then, the 50 wave numbers were operated by the mean impact value (MIV) algorithm to choose wave numbers whose absolute value of MIV greater than or equal to 25, to obtain the optimal characteristic spectral bands (4875.4-4836.9, 4223.6-4080.9 cm- 1). And then, both internal cross and external validation were used to screen the number of hidden layer nodes of BP-ANN. Finally, the number 4 of hidden layer nodes was chosen as the best. At last, the BP-ANN model was found to enjoy a high accuracy and strong forecasting capacity for analyzing ZnO content in calamine samples ranging within 42.05-69.98%, with relative mean square error of cross validation (RMSECV) of 1.66% and coefficient of

A frequency-domain approach to improve ANNs generalization quality via proper initialization.

PubMed

Chaari, Majdi; Fekih, Afef; Seibi, Abdennour C; Hmida, Jalel Ben

2018-08-01

The ability to train a network without memorizing the input/output data, thereby allowing a good predictive performance when applied to unseen data, is paramount in ANN applications. In this paper, we propose a frequency-domain approach to evaluate the network initialization in terms of quality of training, i.e., generalization capabilities. As an alternative to the conventional time-domain methods, the proposed approach eliminates the approximate nature of network validation using an excess of unseen data. The benefits of the proposed approach are demonstrated using two numerical examples, where two trained networks performed similarly on the training and the validation data sets, yet they revealed a significant difference in prediction accuracy when tested using a different data set. This observation is of utmost importance in modeling applications requiring a high degree of accuracy. The efficiency of the proposed approach is further demonstrated on a real-world problem, where unlike other initialization methods, a more conclusive assessment of generalization is achieved. On the practical front, subtle methodological and implementational facets are addressed to ensure reproducibility and pinpoint the limitations of the proposed approach. Copyright © 2018 Elsevier Ltd. All rights reserved.

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

NASA Astrophysics Data System (ADS)

Kuçak, R. A.; Özdemir, E.; Erol, S.

2017-05-01

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

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.

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

2015-09-02

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.

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

Symbolic inversion of control relationships in model-based expert systems

NASA Technical Reports Server (NTRS)

Thomas, Stan

1988-01-01

Symbolic inversion is examined from several perspectives. First, a number of symbolic algebra and mathematical tool packages were studied in order to evaluate their capabilities and methods, specifically with respect to symbolic inversion. Second, the KATE system (without hardware interface) was ported to a Zenith Z-248 microcomputer running Golden Common Lisp. The interesting thing about the port is that it allows the user to have measurements vary and components fail in a non-deterministic manner based upon random value from probability distributions. Third, INVERT was studied as currently implemented in KATE, its operation documented, some of its weaknesses identified, and corrections made to it. The corrections and enhancements are primarily in the way that logical conditions involving AND's and OR's and inequalities are processed. In addition, the capability to handle equalities was also added. Suggestions were also made regarding the handling of ranges in INVERT. Last, other approaches to the inversion process were studied and recommendations were made as to how future versions of KATE should perform symbolic inversion.

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

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

Optimizing the Removal of Rhodamine B in Aqueous Solutions by Reduced Graphene Oxide-Supported Nanoscale Zerovalent Iron (nZVI/rGO) Using an Artificial Neural Network-Genetic Algorithm (ANN-GA)

PubMed Central

Shi, Xuedan; Ruan, Wenqian; Hu, Jiwei; Fan, Mingyi; Cao, Rensheng; Wei, Xionghui

2017-01-01

Rhodamine B (Rh B) is a toxic dye that is harmful to the environment, humans, and animals, and thus the discharge of Rh B wastewater has become a critical concern. In the present study, reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) was used to treat Rh B aqueous solutions. The nZVI/rGO composites were synthesized with the chemical deposition method and were characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), Raman spectroscopy, N2-sorption, and X-ray photoelectron spectroscopy (XPS) analysis. The effects of several important parameters (initial pH, initial concentration, temperature, and contact time) on the removal of Rh B by nZVI/rGO were optimized by response surface methodology (RSM) and artificial neural network hybridized with genetic algorithm (ANN-GA). The results suggest that the ANN-GA model was more accurate than the RSM model. The predicted optimum value of Rh B removal efficiency (90.0%) was determined using the ANN-GA model, which was compatible with the experimental value (86.4%). Moreover, the Langmuir, Freundlich, and Temkin isotherm equations were applied to fit the adsorption equilibrium data, and the Freundlich isotherm was the most suitable model for describing the process for sorption of Rh B onto the nZVI/rGO composites. The maximum adsorption capacity based on the Langmuir isotherm was 87.72 mg/g. The removal process of Rh B could be completed within 20 min, which was well described by the pseudo-second order kinetic model. PMID:28587196

Optimizing the Removal of Rhodamine B in Aqueous Solutions by Reduced Graphene Oxide-Supported Nanoscale Zerovalent Iron (nZVI/rGO) Using an Artificial Neural Network-Genetic Algorithm (ANN-GA).

PubMed

Shi, Xuedan; Ruan, Wenqian; Hu, Jiwei; Fan, Mingyi; Cao, Rensheng; Wei, Xionghui

2017-06-03

Rhodamine B (Rh B) is a toxic dye that is harmful to the environment, humans, and animals, and thus the discharge of Rh B wastewater has become a critical concern. In the present study, reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) was used to treat Rh B aqueous solutions. The nZVI/rGO composites were synthesized with the chemical deposition method and were characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), Raman spectroscopy, N₂-sorption, and X-ray photoelectron spectroscopy (XPS) analysis. The effects of several important parameters (initial pH, initial concentration, temperature, and contact time) on the removal of Rh B by nZVI/rGO were optimized by response surface methodology (RSM) and artificial neural network hybridized with genetic algorithm (ANN-GA). The results suggest that the ANN-GA model was more accurate than the RSM model. The predicted optimum value of Rh B removal efficiency (90.0%) was determined using the ANN-GA model, which was compatible with the experimental value (86.4%). Moreover, the Langmuir, Freundlich, and Temkin isotherm equations were applied to fit the adsorption equilibrium data, and the Freundlich isotherm was the most suitable model for describing the process for sorption of Rh B onto the nZVI/rGO composites. The maximum adsorption capacity based on the Langmuir isotherm was 87.72 mg/g. The removal process of Rh B could be completed within 20 min, which was well described by the pseudo-second order kinetic model.

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

USGS Publications Warehouse

Tsou, Ming‐shu; 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.

Bioluminescence Imaging of Transplanted Mesenchymal Stem Cells by Overexpression of Hepatocyte Nuclear Factor4α: Tracking Biodistribution and Survival.

PubMed

Xie, Peiyi; Hu, Xiaojun; Li, Dan; Xie, Sidong; Zhou, Zhiyang; Meng, Xiaochun; Shan, Hong

2018-05-14

The purposes of this study were to construct immortalized human bone marrow mesenchymal stem cells (UE7T-13) with overexpression of the hepatocyte nuclear factor4α (hHNF4α) and luciferase2-mKate2 dual-fusion reporter gene, further investigate their impact on treating acute liver injury (ALI) in rats, and track their biodistribution and survival by bioluminescence imaging (BLI). The hHNF4α and luciferase2-mKate2 genes were transduced by a lentiviral vector into UE7T-13 cells (named E7-hHNF4α-R cells), and expression was verified by immunofluorescence, RT-PCR, and flow cytometry. E7-hGFP-R cells expressing the luciferase2-mKate2/hGFP gene served as a negative group. A correlation between the bioluminescence signal and cell number was detected by BLI. The ALI rats were established and divided into three groups: PBS, E7-hGFP-R, and E7-hHNF4α-R. After transplantation of 2.0 × 10 6 cells, BLI was used to dynamically track their biodistribution and survival. The restoration of biological functions was assessed by serum biochemical and histological analyses. Stable high-level expression of hHNF4α and mKate2 protein was established in the E7-hHNF4α-R cells in vitro. The E7-hHNF4α-R cells strongly expressed hGFP, hHNF4α, and mKate2 proteins, and the hHNF4α gene. hGFP-mKate2 dual-positive cell expression reached approximately 93 %. BLI verified that a linear relationship existed between the cell number and bioluminescence signal (R 2  = 0.9991). The cells improved liver function in vivo after transplantation into the ALI rat liver, as evidenced by the fact that AST and ALT temporarily returned to normal levels in the recipient ALI rats. The presence of the transplanted E7-hGFP-R and E7-hHNF4α-R cells in recipient rat livers was confirmed by BLI and immunohistochemistry. However, the cells were cleared by the immune system a short time after transplantation into ALI rats with a normal immune system. Our data revealed that the E7-hHNF4α-R cells can

A comparison RSM and ANN surface roughness models in thin-wall machining of Ti6Al4V using vegetable oils under MQL-condition

NASA Astrophysics Data System (ADS)

Mohruni, Amrifan Saladin; Yanis, Muhammad; Sharif, Safian; Yani, Irsyadi; Yuliwati, Erna; Ismail, Ahmad Fauzi; Shayfull, Zamree

2017-09-01

Thin-wall components as usually applied in the structural parts of aeronautical industry require significant challenges in machining. Unacceptable surface roughness can occur during machining of thin-wall. Titanium product such Ti6Al4V is mostly applied to get the appropriate surface texture in thin wall designed requirements. In this study, the comparison of the accuracy between Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) in the prediction of surface roughness was conducted. Furthermore, the machining tests were carried out under Minimum Quantity Lubrication (MQL) using AlCrN-coated carbide tools. The use of Coconut oil as cutting fluids was also chosen in order to evaluate its performance when involved in end milling. This selection of cutting fluids is based on the better performance of oxidative stability than that of other vegetable based cutting fluids. The cutting speed, feed rate, radial and axial depth of cut were used as independent variables, while surface roughness is evaluated as the dependent variable or output. The results showed that the feed rate is the most significant factors in increasing the surface roughness value followed by the radial depth of cut and lastly the axial depth of cut. In contrary, the surface becomes smoother with increasing the cutting speed. From a comparison of both methods, the ANN model delivered a better accuracy than the RSM model.

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

PubMed

Mammas, Ioannis N; Spandidos, Demetrios A

2017-10-01

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

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

PubMed Central

Mammas, Ioannis N.; Spandidos, Demetrios A.

2017-01-01

According to Professor Anne Greenough, Professor of Neonatology and Clinical Respiratory Physiology at the King's College London (London, UK), Paediatric Virology is indeed a rapidly increasing educational challenge. Professor Greenough, who in 1992 wrote her book on congenital, perinatal and neonatal infections, believes that during the past 3 decades, paediatric health professionals are becoming increasingly involved in specialised care and follow-up of paediatric patients with viral diseases, who require advanced medical care and innovative technological services. Moreover, she highlights the expected role of new vaccines and antiviral agents that are currently under investigation, as well as the impact of emerging viral diseases that require novel prevention strategies and therapeutic protocols. However, she notes that the number of Paediatric Virologists in any one country is likely to be small; hence, a separate paediatric subspecialty needs to be considered carefully. In the context of the 3rd Workshop on Paediatric Virology, which will be held in Athens, Greece, on October 7th, 2017, Professor Greenough will give her plenary lecture on the impact of viral infections on the long term outcomes of prematurely born infants. PMID:29042914

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

PubMed

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

2017-08-01

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

Development of a partial least squares-artificial neural network (PLS-ANN) hybrid model for the prediction of consumer liking scores of ready-to-drink green tea beverages.

PubMed

Yu, Peigen; Low, Mei Yin; Zhou, Weibiao

2018-01-01

In order to develop products that would be preferred by consumers, the effects of the chemical compositions of ready-to-drink green tea beverages on consumer liking were studied through regression analyses. Green tea model systems were prepared by dosing solutions of 0.1% green tea extract with differing concentrations of eight flavour keys deemed to be important for green tea aroma and taste, based on a D-optimal experimental design, before undergoing commercial sterilisation. Sensory evaluation of the green tea model system was carried out using an untrained consumer panel to obtain hedonic liking scores of the samples. Regression models were subsequently trained to objectively predict the consumer liking scores of the green tea model systems. A linear partial least squares (PLS) regression model was developed to describe the effects of the eight flavour keys on consumer liking, with a coefficient of determination (R 2 ) of 0.733, and a root-mean-square error (RMSE) of 3.53%. The PLS model was further augmented with an artificial neural network (ANN) to establish a PLS-ANN hybrid model. The established hybrid model was found to give a better prediction of consumer liking scores, based on its R 2 (0.875) and RMSE (2.41%). Copyright © 2017 Elsevier Ltd. All rights reserved.

Application of FrontPage 98 to the Development of Web Sites for the Science Division and the Center for the Advancement of Learning and Teaching (CALT) at Anne Arundel Community College.

ERIC Educational Resources Information Center

Bird, Bruce

This paper discusses the development of two World Wide Web sites at Anne Arundel Community College (Maryland). The criteria for the selection of hardware and software for Web site development that led to the decision to use Microsoft FrontPage 98 are described along with its major components and features. The discussion of the Science Division Web…

Optimum coagulant forecasting by modeling jar test experiments using ANNs

NASA Astrophysics Data System (ADS)

Haghiri, Sadaf; Daghighi, Amin; Moharramzadeh, Sina

2018-01-01

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

KATE VII: Kansans' Attitudes toward Education.

ERIC Educational Resources Information Center

Emporia State Univ., KS. Jones Inst. for Educational Excellence.

The Teachers College at Emporia State University sponsors a biennial survey measuring the attitudes of Kansans toward their public schools. The results of the 1991 survey are reported here (the survey was patterned after the national Gallop Poll on public education). The report opens with details of the research procedures, which includes…

At the Cosmonaut Hotel in Baikonur, Kazakhstan, Expedition 48-49 crewmember Takuya Onishi of the Japan Aerospace Exploration Agency (left) lends a hand to NASA���s Kate Rubins (crouching) as she plants a tree in her name June 30 in traditional pre-launch activities. Standing from left to right are backup crewmembers Peggy Whitson of NASA, Thomas Pesquet of the European Space Agency and Oleg Novitskiy of Roscosmos and prime crewmember Anatoly Ivanishin of Roscosmos. Rubins, Ivanishin and Onishi will launch July 7, Baikonur time, on the Soyuz MS-01 spacecraft for a planned four-month mission on the International Space Station...NASA/Alexander Vysotsky.

NASA Image and Video Library

2016-06-30

At the Cosmonaut Hotel in Baikonur, Kazakhstan, Expedition 48-49 crewmember Takuya Onishi of the Japan Aerospace Exploration Agency (left) lends a hand to NASA’s Kate Rubins (crouching) as she plants a tree in her name June 30 in traditional pre-launch activities. Standing from left to right are backup crewmembers Peggy Whitson of NASA, Thomas Pesquet of the European Space Agency and Oleg Novitskiy of Roscosmos and prime crewmember Anatoly Ivanishin of Roscosmos. Rubins, Ivanishin and Onishi will launch July 7, Baikonur time, on the Soyuz MS-01 spacecraft for a planned four-month mission on the International Space Station. NASA/Alexander Vysotsky

77 FR 29648 - Medicare and Medicaid Programs; Quarterly Listing of Program Issuances-January Through March 2012

Federal Register 2010, 2011, 2012, 2013, 2014

2012-05-18

... (Destination Therapy) Facilities. XIII Medicare-Approved Lung Volume Reduction Surgery JoAnna Baldwin, MS (410) 786-7205 Facilities. XIV Medicare-Approved Bariatric Surgery Facilities........ Kate Tillman, RN, MAS...

77 FR 49799 - Medicare and Medicaid Programs; Quarterly Listing of Program Issuances-April Through June 2012

Federal Register 2010, 2011, 2012, 2013, 2014

2012-08-17

...-Approved Lung JoAnna Baldwin, MS.. (410) 786-7205 Volume Reduction Surgery Facilities. XIV Medicare-Approved Kate Tillman, RN, (410) 786-9252 Bariatric Surgery MAS. Facilities. XV Fluorodeoxyglucose Stuart...

76 FR 68467 - Medicare and Medicaid Programs; Quarterly Listing of Program Issuances-April Through June 2011

Federal Register 2010, 2011, 2012, 2013, 2014

2011-11-04

...-Approved Lung JoAnna Baldwin, (410) 786-7205 Volume Reduction Surgery MS. Facilities. XIV Medicare-Approved Kate Tillman, RN, (410) 786-9252 Bariatric Surgery Facilities. MAS. XV Fluorodeoxyglucose Positron...

77 FR 67368 - Medicare and Medicaid Programs; Quarterly Listing of Program Issuances-July through September 2012

Federal Register 2010, 2011, 2012, 2013, 2014

2012-11-09

...) Facilities. XIII Medicare-Approved Lung JoAnna Baldwin, MS. (410) 786-7205 Volume Reduction Surgery Facilities. XIV Medicare-Approved Bariatric Kate Tillman, RN, (410) 786-9252 Surgery Facilities. MAS. XV...

Gaydar, Marriage, and Rip-Roaring Homosexuals: Discourses About Homosexuality in Dear Abby and Ann Landers Advice Columns, 1967-1982.

PubMed

Johnson, Patrick M; Holmes, Kwame A

2017-12-04

Over the past 70 years, the history of acceptance of the lesbian, gay and bisexual (LGB) community within the United States has seen much change and fluctuation. One of the places that this dialogue has been preserved is through the syndicated advice columns of Dear Abby and Ann Landers, in which individuals in the United States were writing in for advice to deal with their anxiety over a newly emerging and highly visible new community of individuals once considered to be mentally ill and dangerous. Using discourse analysis, this article traces the evolution of public and scientific opinions about the LGBT community during the years leading up to the Stonewall riots all the way to right before the AIDs epidemic. This analysis sheds light on several moral panics that emerged regarding this newly visible population, especially in regard to disturbances within the domestic sphere and a stigmatization of bisexuality.

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

PubMed

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

2015-01-05

In this research, ZnO nanoparticle loaded on activated carbon (ZnO-NPs-AC) was synthesized simply by a low cost and nontoxic procedure. The characterization and identification have been completed by different techniques such as SEM and XRD analysis. A three layer artificial neural network (ANN) model is applicable for accurate prediction of dye removal percentage from aqueous solution by ZnO-NRs-AC following conduction of 270 experimental data. The network was trained using the obtained experimental data at optimum pH with different ZnO-NRs-AC amount (0.005-0.015 g) and 5-40 mg/L of sunset yellow dye over contact time of 0.5-30 min. The ANN model was applied for prediction of the removal percentage of present systems with Levenberg-Marquardt algorithm (LMA), a linear transfer function (purelin) at output layer and a tangent sigmoid transfer function (tansig) in the hidden layer with 6 neurons. The minimum mean squared error (MSE) of 0.0008 and coefficient of determination (R(2)) of 0.998 were found for prediction and modeling of SY removal. The influence of parameters including adsorbent amount, initial dye concentration, pH and contact time on sunset yellow (SY) removal percentage were investigated and optimal experimental conditions were ascertained. Optimal conditions were set as follows: pH, 2.0; 10 min contact time; an adsorbent dose of 0.015 g. Equilibrium data fitted truly with the Langmuir model with maximum adsorption capacity of 142.85 mg/g for 0.005 g adsorbent. The adsorption of sunset yellow followed the pseudo-second-order rate equation. Copyright © 2014 Elsevier B.V. All rights reserved.

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.

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.

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.

Hourly predictive Levenberg-Marquardt ANN and multi linear regression models for predicting of dew point temperature

NASA Astrophysics Data System (ADS)

Zounemat-Kermani, Mohammad

2012-08-01

In this study, the ability of two models of multi linear regression (MLR) and Levenberg-Marquardt (LM) feed-forward neural network was examined to estimate the hourly dew point temperature. Dew point temperature is the temperature at which water vapor in the air condenses into liquid. This temperature can be useful in estimating meteorological variables such as fog, rain, snow, dew, and evapotranspiration and in investigating agronomical issues as stomatal closure in plants. The availability of hourly records of climatic data (air temperature, relative humidity and pressure) which could be used to predict dew point temperature initiated the practice of modeling. Additionally, the wind vector (wind speed magnitude and direction) and conceptual input of weather condition were employed as other input variables. The three quantitative standard statistical performance evaluation measures, i.e. the root mean squared error, mean absolute error, and absolute logarithmic Nash-Sutcliffe efficiency coefficient ( {| {{{Log}}({{NS}})} |} ) were employed to evaluate the performances of the developed models. The results showed that applying wind vector and weather condition as input vectors along with meteorological variables could slightly increase the ANN and MLR predictive accuracy. The results also revealed that LM-NN was superior to MLR model and the best performance was obtained by considering all potential input variables in terms of different evaluation criteria.

75 FR 22595 - Sunshine Act Notices

Federal Register 2010, 2011, 2012, 2013, 2014

2010-04-29

... (NDRT), by Marc E. Elias and Kate S. Keane of Perkins Coie LLP, counsel. Report of the Audit Division on the Tennessee Democratic Party (TDP). Report of the Audit Division on Friends for Menor Committee...

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. Copyright © 2014 Elsevier Inc. All rights reserved.

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.

Pseudomonas syringae Catalases Are Collectively Required for Plant Pathogenesis

PubMed Central

Guo, Ming; Block, Anna; Bryan, Crystal D.; Becker, Donald F.

2012-01-01

The bacterial pathogen Pseudomonas syringae pv. tomato DC3000 must detoxify plant-produced hydrogen peroxide (H2O2) in order to survive in its host plant. Candidate enzymes for this detoxification include the monofunctional catalases KatB and KatE and the bifunctional catalase-peroxidase KatG of DC3000. This study shows that KatG is the major housekeeping catalase of DC3000 and provides protection against menadione-generated endogenous H2O2. In contrast, KatB rapidly and substantially accumulates in response to exogenous H2O2. Furthermore, KatB and KatG have nonredundant roles in detoxifying exogenous H2O2 and are required for full virulence of DC3000 in Arabidopsis thaliana. Therefore, the nonredundant ability of KatB and KatG to detoxify plant-produced H2O2 is essential for the bacteria to survive in plants. Indeed, a DC3000 catalase triple mutant is severely compromised in its ability to grow in planta, and its growth can be partially rescued by the expression of katB, katE, or katG. Interestingly, our data demonstrate that although KatB and KatG are the major catalases involved in the virulence of DC3000, KatE can also provide some protection in planta. Thus, our results indicate that these catalases are virulence factors for DC3000 and are collectively required for pathogenesis. PMID:22797762

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

I Never Told Anybody: Four Poetry Writing Ideas.

ERIC Educational Resources Information Center

Koch, Kenneth

1997-01-01

Offers excerpts from Kenneth Koch's classic book in which he tells how he and Kate Farrell taught poetry writing to elderly people in a nursing home. Describes four poetry writing classes, first giving students' poems, then Koch's commentary. (PA)

Lamprey Tagging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Colotelo, Alison; Deters, Kate

2017-05-26

Pacific Northwest National Laboratory has developed a super-small acoustic tracking tag designed just for juvenile lamprey. In this video, PNNL researcher Alison Colotelo describes how she and her colleague Kate Deters inject young lamprey with the PNNL tag.

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).

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. Copyright © 2014 Elsevier Inc. All rights reserved.

ARC-2009-ACD09-0218-009

NASA Image and Video Library

2009-10-06

NASA Conducts Airborne Science Aboard Zeppelin Airship: equipped with two imaging instruments enabling remote sensing and atmospheric science measurements not previously practical. Shown here is Steve Dunagan, NASA Ames scientist. Cabin viewof instrument operaor Steve Dunagan, Pilot Katharing 'Kate' Board.

Temporal dynamics of land use/land cover change and its prediction using CA-ANN model for southwestern coastal Bangladesh.

PubMed

Rahman, M Tauhid Ur; Tabassum, Faheemah; Rasheduzzaman, Md; Saba, Humayra; Sarkar, Lina; Ferdous, Jannatul; Uddin, Syed Zia; Zahedul Islam, A Z M

2017-10-17

Change analysis of land use and land cover (LULC) is a technique to study the environmental degradation and to control the unplanned development. Analysis of the past changing trend of LULC along with modeling future LULC provides a combined opportunity to evaluate and guide the present and future land use policy. The southwest coastal region of Bangladesh, especially Assasuni Upazila of Satkhira District, is the most vulnerable to natural disasters and has faced notable changes in its LULC due to the combined effects of natural and anthropogenic causes. The objectives of this study are to illustrate the temporal dynamics of LULC change in Assasuni Upazila over the last 27 years (i.e., between 1989 and 2015) and also to predict future land use change using CA-ANN (cellular automata and artificial neural network) model for the year 2028. Temporal dynamics of LULC change was analyzed, employing supervised classification of multi-temporal Landsat images. Then, prediction of future LULC was carried out by CA-ANN model using MOLUSCE plugin of QGIS. The analysis of LULC change revealed that the LULC of Assasuni had changed notably during 1989 to 2015. "Bare lands" decreased by 21% being occupied by other land uses, especially by "shrimp farms." Shrimp farm area increased by 25.9% during this period, indicating a major occupational transformation from agriculture to shrimp aquaculture in the study area during the period under study. Reduction in "settlement" area revealed the trend of migration from the Upazila. The predicted LULC for the year 2028 showed that reduction in bare land area would continue and 1595.97 ha bare land would transform into shrimp farm during 2015 to 2028. Also, the impacts of the changing LULC on the livelihood of local people and migration status of the Upazila were analyzed from the data collected through focus group discussions and questionnaire surveys. The analysis revealed that the changing LULC and the occupational shift from paddy

ALT-C 95: Changing Education, Changing Technology. Conference Abstracts of the Association for Learning Technology Conference (2nd, Milton Keynes, England, United Kingdom, September 11-13, 1995).

ERIC Educational Resources Information Center

Hawkridge, David, Ed.

This program for the 1995 Association for Learning Technology Conference summarizes the presentations of the discussions, demonstrations, workshops, and poster sessions. Abstracts of the following papers presented at the conference are included: "New Structures for Learning" (Patrick Allen & Kate Sankey); "Multiple System…

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

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

To Boldly Go: America's Next Era in Space. Sustaining Life on the Earth

NASA Technical Reports Server (NTRS)

2004-01-01

Dr. France Cordova, NASA's Chief Scientist, opened this, the sixth seminar in the Administrator's Seminar Series, by introducing NASA Administrator Daniel S. Goldin. Mr Goldin welcomed the attendees and set the stage for Dr. Cordova's introduction of the first speaker, Dr. Robert Kates of Brown University. Dr. Kates primary concerns are global environmental changes, world hunger, and the size of the population. Human changes, he said, rival the changes of nature. Changes in the size of world population affect the need for more agricultural products, therefore more land for growing food, which leads to deforestation, which affects rainfall, and therefore the water supply which is in increased demand. Human ingenuity can reduce some shortages but generally doesn't keep up with increased demand for life-sustaining essentials. These problems require the concern of intergovernmental organizations, treaties and activities, as well as transnational corporations, and non-governmental and private, volunteer organizations. Next Dr. Diana Liverman of Pennsylvania State University spoke on human interactions regarding climate and society. She considered the effect of changes in land use on climate, using Mexico as an example. Mexicans changed from raising much wheat to raising more fruits and vegetables. This was in response to the demands of the market. The results were more industry, population growth, greater income, drought (because the new crops required more water), and conflicts over water supplies. Dr. Charles Kennel of the Office of Mission to Planet Earth joined Dr.s Cordova, Kates, and Liverman for the question and answer session that followed.

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.

Stories to Be Read Aloud (Booksearch).

ERIC Educational Resources Information Center

English Journal, 1989

1989-01-01

Presents junior and senior high school teachers' suggestions for short stories to read aloud in a single class period, including "The Laughing Man" (J. D. Salinger), "A & P" (John Updike), "Epicac" (Kurt Vonnegut), "The Story of an Hour" (Kate Chopin), and "The Yellow Wallpaper" (Charlotte…

Edna, Epicurus, and Education.

ERIC Educational Resources Information Center

Morse, Jane Fowler

1998-01-01

Portrays the character of Edna from Kate Chopin's novel "The Awakening" as a reflection of current society's motivation in seeking fleeting (kinetic) pleasure instead of lasting (katastematic) pleasure. Presents Epicurean philosophy as a means of teaching katastematic pleasure in modern education. (10 citations) (EMH)

Cause and effect

NASA Astrophysics Data System (ADS)

Clement, Charles; Dawson, Peter

2017-06-01

In response to Kate Brown’s article “Chernobyl’s hidden legacy (Physics World Focus on Nuclear Energy 2017 pp9-11) in which she argues that researchers today should be looking at Soviet-era information on the medical effects of the Chernobyl disaster.

English Leadership Quarterly, 1992.

ERIC Educational Resources Information Center

Strickland, James, Ed.

1992-01-01

These four issues of the English Leadership Quarterly represent those published during 1992. Articles in number 1 deal with testing assessing, and measuring student performance, and include: "Real Evaluation: Portfolios as an Effective Alternative to Standardized Testing" (Kate Kiefer); "No More Objective Tests, Ever" (Carol…

iss049e012018

NASA Image and Video Library

2016-09-27

ISS049e012018 (09/27/2016) --- Expedition 49 crewmember Kate Rubins of NASA works with the airlock inside of Kibo, the Japanese Experiment Module. Rubins was installing the Robotics External Leak Locator (RELL), a technology demonstration designed to locate external ISS ammonia (NH3) leaks.

Books for Summer Reading.

ERIC Educational Resources Information Center

Phi Delta Kappan, 2000

2000-01-01

Recommends leisurely reading for teachers: biographies on St. Augustine and Charles Lindbergh; novels by Edwidge Danticat, Kate Chopin, and Velma Allis; Edward Tufte's three volumes on the visual presentation of information; Jean Vanier's "Becoming Human;" the Harry Potter series, and Michael Tolkin's novel "The Player." (MLH)

78 FR 69048 - Caribbean Fishery Management Council; Public Meetings

Federal Register 2010, 2011, 2012, 2013, 2014

2013-11-18

.... Kate Quigley Comprehensive Island Based FMP Update --Discussion of Option Paper Comprehensive Amendment... completion of discussion relevant to the agenda items. To further accommodate discussion and completion of... in this notice. The meetings are open to the public, and will be conducted in English. Fishers and...

1300243

NASA Image and Video Library

2013-04-18

Alabama Gov. Robert Bentley signs a proclamation declaring April 18, 2013, "NASA Day in Alabama." Looking on, from left, are Marshall Space Flight Center Director Patrick Scheuermann, astronauts Kathleen "Kate" Rubins and Jack Fischer, and State Sen. Bill Holtzclaw of Madison, who represents Madison and Limestone counties.

78 FR 29325 - Prestressed Concrete Steel Rail Tie Wire From Mexico, the People's Republic of China, and...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-20

...] Prestressed Concrete Steel Rail Tie Wire From Mexico, the People's Republic of China, and Thailand: Initiation... (Mexico), Brian Smith (the People's Republic of China (the ``PRC'')), or Kate Johnson (Thailand) at (202... Prestressed Concrete Steel Rail Tie Wire from the People's Republic of China, Mexico, and Thailand...

Whitson Receives Call from President Trump on This Week @NASA - April 28, 2017

NASA Image and Video Library

2017-04-28

On April 24 aboard the International Space Station, NASA astronaut Peggy Whitson set a new record for cumulative time spent in space by a U.S. astronaut. President Donald Trump marked the milestone with a call from the Oval Office, with First Daughter Ivanka Trump, and NASA astronaut Kate Rubins – to the station, where Whitson was joined by NASA’s Jack Fischer. Whitson, who in 2008 became the first woman to command the space station, also holds the record for most spacewalks by a female astronaut. NASA worked with the Department of Education, on behalf of the White House, to make the president’s call to the station available to schools across America. Whitson encouraged students to think about how the steps they take in the classroom today could someday help NASA make the next giant leap in space exploration. Also, First Live 4K Broadcast from Space, Kate Rubins Visits National Institutes of Health, Cassini Begins its Grand Finale, and 2017 Astrobiology Science Conference!

Social Policy Report, 1995.

ERIC Educational Resources Information Center

Thomas, Nancy G., Ed.

1995-01-01

These three newsletter issues present scholarly developmental research results pertaining to social and public policies that affect children. The first 1995 issue, "Escaping Poverty: The Promise of Higher Education" (Erika Kates), discusses results of a study that explored the ways in which institutions of higher education provide a…

From Article to Action: Fostering Literacy Skills for Diverse Learners across Learning Environments

ERIC Educational Resources Information Center

Meyer, Lori Erbrederis

2017-01-01

Kate Zimmer's article ("Enhancing Interactions With Children With Autism Through Storybook Reading: A Caregiver's Guide," v20 n3 p133-144 Sep 2017) encourages practitioners to provide information on effective instructional strategies that could increase positive interactions during shared book reading between children who have an autism…

ARC-2009-ACD09-0218-012

NASA Image and Video Library

2009-10-06

NASA Conducts Airborne Science Aboard Zeppelin Airship: equipped with two imaging instruments enabling remote sensing and atmospheric science measurements not previously practical. Cabin view of Instrument Operator Steve Dunagan, NASA Ames, Pilot Katharine 'Kate' Board, (left) and Crew Chief Matthew Kilkerr (in flight suit) preforming pre-flight checkouts.

Harvard Education Letter. Volume 23, Number 5, September-October 2007

ERIC Educational Resources Information Center

Chauncey, Caroline, Ed.

2007-01-01

"Harvard Education Letter" is published bimonthly by the Harvard Graduate School of Education. This issue of "Harvard Education Letter" contains the following articles: (1) Confronting the Autism Epidemic: New Expectations for Children with Autism Means a New Role for Public Schools (Kate McKenna); (2) Internet Research 101:…

Investigation of an HMM/ANN hybrid structure in pattern recognition application using cepstral analysis of dysarthric (distorted) speech signals.

PubMed

Polur, Prasad D; Miller, Gerald E

2006-10-01

Computer speech recognition of individuals with dysarthria, such as cerebral palsy patients requires a robust technique that can handle conditions of very high variability and limited training data. In this study, application of a 10 state ergodic hidden Markov model (HMM)/artificial neural network (ANN) hybrid structure for a dysarthric speech (isolated word) recognition system, intended to act as an assistive tool, was investigated. A small size vocabulary spoken by three cerebral palsy subjects was chosen. The effect of such a structure on the recognition rate of the system was investigated by comparing it with an ergodic hidden Markov model as a control tool. This was done in order to determine if this modified technique contributed to enhanced recognition of dysarthric speech. The speech was sampled at 11 kHz. Mel frequency cepstral coefficients were extracted from them using 15 ms frames and served as training input to the hybrid model setup. The subsequent results demonstrated that the hybrid model structure was quite robust in its ability to handle the large variability and non-conformity of dysarthric speech. The level of variability in input dysarthric speech patterns sometimes limits the reliability of the system. However, its application as a rehabilitation/control tool to assist dysarthric motor impaired individuals holds sufficient promise.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pletnev, Sergei; Shcherbo, Dmitry; Chudakov, Dmitry M.

The far-red fluorescent protein mKate {lambda}{sup ex}, 588 nm; {lambda}{sub em}, 635 nm; chromophore-forming triad Met{sup 63}-Tyr{sup 64}-Gly{sup 65}, originating from wild-type red fluorescent progenitor eqFP578 (sea anemone Entacmaea quadricolor), is monomeric and characterized by the pronounced pH dependence of fluorescence, relatively high brightness, and high photostability. The protein has been crystallized at a pH ranging from 2 to 9 in three space groups, and four structures have been determined by x-ray crystallography at the resolution of 1.75--2.6 {angstrom}. The pH-dependent fluorescence of mKate has been shown to be due to reversible cis-trans isomerization of the chromophore phenolic ring. Inmore » the non-fluorescent state at pH 2.0, the chromophore of mKate is in the trans-isomeric form. The weakly fluorescent state of the protein at pH 4.2 is characterized by a mixture of trans and cis isomers. The chromophore in a highly fluorescent state at pH 7.0/9.0 adopts the cis form. Three key residues, Ser{sup 143}, Leu{sup 174}, and Arg{sup 197} residing in the vicinity of the chromophore, have been identified as being primarily responsible for the far-red shift in the spectra. A group of residues consisting of Val{sup 93}, Arg{sup 122}, Glu{sup 155}, Arg{sup 157}, Asp{sup 159}, His{sup 169}, Ile{sup 171}, Asn{sup 173}, Val{sup 192}, Tyr{sup 194}, and Val{sup 216}, are most likely responsible for the observed monomeric state of the protein in solution.« less

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Grace L., E-mail: chengra@niaid.nih.go; Lamirande, Elaine W., E-mail: elamirande@niaid.nih.go; Jin Hong, E-mail: jinh@medimmune.co

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-typemore » 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.« less

Teaching Rhetorica: Theory, Pedagogy, Practice

ERIC Educational Resources Information Center

Ronald, Kate, Ed.; Ritchie, Joy, Ed.

2006-01-01

In their breakthrough anthology of women's rhetoric, "Available Means," Kate Ronald and Joy Ritchie presented the first comprehensive collection of women's rhetorical theory and practice from the third century B.C. to 2001. With that expansive gathering of women's rhetoric, they raised questions about gender, difference, and the rhetorical canon,…

Mediating Third-Wave Feminism: Appropriation as Postmodern Media Practice.

ERIC Educational Resources Information Center

Shugart, Helene A.; Waggoner, Catherine Egley; Hallstein, D. Lynn O'Brien

2001-01-01

Analyzes gendered representations of Alanis Morissette, Kate Moss, and Ally McBeal. Argue that, in each case, the appropriation of third-wave feminist tenets is accomplished via a postmodern aesthetic code of juxtaposition that serves to recontextualize and reinscribe those sensibilities in a way that ultimately functions to reify dominant…

Reading Beyond: Children's Lived Spiritual Experiences of Fantasy Literature

ERIC Educational Resources Information Center

Posey, Catherine Ruth

2011-01-01

The purpose of this phenomenological study was to describe four children's lived spiritual experiences of literary texts as generated through their responses to two toy fantasy novels for children, "The Miraculous Journey of Edward Tulane" by Kate DiCamillo (2006) and "The Mouse and his Child" by Russell Hoban (1967). The…

Books for Summer Reading.

ERIC Educational Resources Information Center

Phi Delta Kappan, 1991

1991-01-01

To help replenish educators' supply of ideas, "Kappan" editors suggest several books for summer reading, including many noncurrent titles not specifically on education such as Peter Novick's "That Noble Dream," Joy Kogawa's "Obasan," Zora Neale Hurston's "Their Eyes Were Watching God," Kate Chopin's "The Awakening," Willa Cather's "My Antonia,"…

KatP contributes to OxyR-regulated hydrogen peroxide resistance in Escherichia coli serotype O157:H7

USDA-ARS?s Scientific Manuscript database

Escherichia coli K12 defends against peroxide mediated oxidative damage using two catalases, hydroperoxidase I (katG) and hydroperoxidase II (katE) and the peroxiredoxin, alkyl hydroperoxide reductase (ahpC). In E. coli O157:H7 strain ATCC 43895 (EDL933), plasmid pO157 encodes for an additional cata...

Bodies, Boxes, and Belonging: A Review of "Queer Online"

ERIC Educational Resources Information Center

Paradis, Elise

2009-01-01

This article reviews "Queer Online: Media, Technology and Sexuality," edited by Kate O'Riordan and David J. Phillips (2007). Although essays in "Queer Online" are welcome contributions to cyberqueer studies inasmuch as they underscore critical themes in cyberqueer lives, they sometimes lack the much-needed empirical basis for youth, parents, and…

Writing for Real Purpose

ERIC Educational Resources Information Center

Ikpeze, Chinwe H.

2009-01-01

Working to transform his classroom, fifth grade teacher John Blain of Buffalo (New York) public schools infused technology into his literature lessons by adding an online literature discussion to his more traditional classroom discussion. Students were assigned to read Kate DiCamillo's books "Because of Winn-Dixie" and "The Tiger…

The Year in Review: Reports of Research Conducted by Adult Education Practitioners-Researchers in Virginia. Volume 5: 1995-1996.

ERIC Educational Resources Information Center

Virginia Adult Educators Research Network, Dayton.

This report contains four separate articles of interest to adult English-as-a-second-language (ESL) educators. "Learning Disabilities in Adult ESL: Case Studies and Directions" (Dorothy Almanza, Kate Singleton, Lynda Terrill) looks at three case studies of adult ESL students whom teachers have identified as possibly learning disabled.…

Research Reports into Professional Development.

ERIC Educational Resources Information Center

Australian National Training Authority, Brisbane.

Five reports present findings of research into professional development (PD) issues in vocational education and training (VET) in Australia. "Lessons Learnt: An Analysis of Findings of Recent Evaluation Reports on PD in VET" (Kate Perkins) provides an overview of issues, insights, and ideas emerging from past PD experience that may be…

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chan, Winnie; Zhou, Helen; Kemble, George

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 amore » 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.« less

Trusting families: Responding to Mary Ann Meeker, "Responsive care management: family decision makers in advanced cancer".

PubMed

Nelson, James Lindemann

2011-01-01

Mary Ann Meeker's article admirably reminds readers that family members are involved in--or "responsively manage"--the care of relatives with severe illness in ways that run considerably beyond the stereotypes at play in many bioethical discussions of advance directives. Her observations thus make thinking about the role of families in healthcare provision more adequate to the facts, and this is an important contribution. There's reason to be worried, however, that one explicit aim of the article--to ease the standing anxieties that many clinicians and ethicists have about the reliability of family members as proxy decision makers--will be frustrated by its very success. Those already inclined to suspicion may tend to think that the more intricate and pervasive the ways in which families influence the healthcare decision making of their sick, the more chances they have for altering the connection between patients' interests and the actions of professional providers. To determine whether and when such alterations are something to be concerned about, we'll need to supplement a better grasp of the pertinent facts with a deeper sense of how human agency works and why we value it. We may also need some reminders about the defensibility of diverse moral understandings. Although both professionals and family members may profess an ethic that sets patients' interests above those of non-patients--as Meeker's own results suggest--any strict allegiance to such a framework may be more notional than normative--as her findings also hint. The actual working norms (among professionals, as well as within families) will likely be more complex, but not necessarily any the less defensible for that.

Rubins in U.S. lab

NASA Image and Video Library

2016-10-14

ISS049e038794 (10/14/2016 --- NASA astronaut Kate Rubins holds a communication microphone while floating in the U.S. Destiny Laboratory aboard the International Space Station. Rubins, a first time flier with a degree in molecular biology, is scheduled to return to Earth on Oct. 29, 2016, U.S. time

Back to the Future or towards a Sensory History of Schooling

ERIC Educational Resources Information Center

Grosvenor, Ian

2012-01-01

This conjectural essay was originally written for a symposium "Historiography of the future: Looking back to the future" held at the International Standing Conference for History of Education (ISCHE) 33, July 2011, San Luis Potosi, Mexico organised by Kate Rousmaniere and Frank Simon. Participants were asked to envision future challenges for the…

Kickoffs and Metaphors: Selecting a First Story for the Modern Fiction Course.

ERIC Educational Resources Information Center

Cioe, Paul

2001-01-01

Discusses the importance of the selection of a first short story in a modern fiction class. Proposes that compact stories about memorable protagonists in familiar settings can engage students' interests in first class meetings. Outlines two examples of such stories: Jim Shepard's "Ida" and Kate Chopin's "The Story of an Hour." (PM)

"Computers: Cure-All or Snake Oil?" Proceedings from the Spring Meeting of the Nebraska Library Association, College and University Section (Bellevue, Nebraska, April 20, 1984).

ERIC Educational Resources Information Center

Krzywkowski, Valerie I., Ed.

The 15 papers in this collection discuss various aspects of computer use in libraries and several other aspects of library service not directly related to computers. Following an introduction and a list of officers, the papers are: (1) "Criminal Justice and Related Databases" (Kate E. Adams); (2) "Software and Hard Thought:…

77 FR 58141 - Change in Bank Control Notices; Acquisitions of Shares of a Bank or Bank Holding Company

Federal Register 2010, 2011, 2012, 2013, 2014

2012-09-19

..., Iowa, Kate Garst Trustee; Sarah Garst, West Des Moines, Iowa), as a group acting in concert and... FEDERAL RESERVE SYSTEM Change in Bank Control Notices; Acquisitions of Shares of a Bank or Bank Holding Company The notificants listed below have applied under the Change in Bank Control Act (12 U.S.C...

What a Character! Character Study as a Guide to Literary Meaning Making in Grades K?8

ERIC Educational Resources Information Center

Roser, Nancy L., Ed.; Martinez, Miriam G., Ed.; Yokota, Junko; O'Neal, Sharon

2005-01-01

Bring text and its meaning alive for students! This collection offers the perspectives of classroom teachers, researchers, and children's book authors, including award-winners Kate DiCamillo and Katherine Paterson. Together, they share their thoughts on the power of character study and how to use it to guide elementary-and middle-grade students…

Home on the Wide-Open Range of Gender

ERIC Educational Resources Information Center

Beemyn, Genny

2011-01-01

Kate Bornstein and S. Bear Bergman's "Gender Outlaws: The Next Generation" is a collection of narratives written by individuals with a wide range of gender identities and expressions from around the world. The text shows how gender-nonconforming people are changing how society looks at gender. As many of the individuals who are challenging gender…

Interview with Audrey Jung, President of the International Society for Mental Health Online (ISMHO), Presented at the Online Counselling and Therapy in Action Conference, 25 April 2009

ERIC Educational Resources Information Center

Anthony, Kate; Jung, Audrey; Rosenauer, Dominik; Nagel, Deeanna Merz; Goss, Stephen

2010-01-01

Counselling services provided online have become an increasingly important topic over the last decade. It is now incumbent on all practitioners at least to be aware of the impact of online living for their clients and of the options available for online counselling provision. In this article, Kate Anthony of the Online Therapy Institute interviews…

Nineteenth Century Origins of the Modern Picture Book.

ERIC Educational Resources Information Center

Flowers, Ann A.

1998-01-01

Discusses the origins of the modern children's picture book, describing in particular the contributions of three great children's book illustrators of the late 19th-century (Randolph Caldecott, Walter Crane, and Kate Greenaway), as well as the genius of Beatrix Potter, whose work shows the form recognized today as the children's book. Offers six…

Research Staff | Geothermal Technologies | NREL

Science.gov Websites

Position Email Phone Akar, Sertac Energy Analyst - Geothermal Sertac.Akar@nrel.gov 303-275-3725 Augustine -Geoscience Kate Young joined NREL in 2008. She has worked on analysis of geothermal exploration, drilling ) Toolkit, the Geothermal Resource Portfolio Optimization and Reporting Technique (GeoRePORT), and the

Peroxide resistance in Escherichia coli serotype O157:H7 biofilms is regulated by both RpoS dependent and independent mechanisms

USDA-ARS?s Scientific Manuscript database

In many Escherichia coli serotype O157:H7 strains, defenses against peroxide damage include the peroxiredoxin AhpCF and three catalases: KatG (catalase-peroxidase), KatE (catalase), and the plasmid-encoded KatP (catalase/peroxidase). AhpC, KatG, and KatP are induced by OxyR /s70 in exponential phase...

Flip Turns with Students

ERIC Educational Resources Information Center

Queeney, Kate

2014-01-01

Kate Queeney, a professor of chemistry at Smith College, turned to a former student to receive one-on-one instruction in swimming. The student, who had been unsure and scared in chemistry class, seemed like an entirely different person when teaching the teacher. This article describes how the author learned that there is something undeniably…

Outbreak of Escherichia coli 0157:H7 Associated with Attencdance at a Large Livestock Exhibition - Denver, Colorado, January-February 2009

USDA-ARS?s Scientific Manuscript database

Outbreak of Escherichia coli O157:H7 associated with attendance at a large livestock exhibition – Denver, Colorado, January-February 2009 Nicole Comstock1, Hugh Maguire1, Abby Bronken2, Carol McDonald3, Donna Hite-Bynum4, Mary Kate Cichon1, Lisa Durso5 1 Colorado Department of Public Health and En...

Fluorescence from Multiple Chromophore Hydrogen-Bonding States in the Far-Red Protein TagRFP675.

PubMed

Konold, Patrick E; Yoon, Eunjin; Lee, Junghwa; Allen, Samantha L; Chapagain, Prem P; Gerstman, Bernard S; Regmi, Chola K; Piatkevich, Kiryl D; Verkhusha, Vladislav V; Joo, Taiha; Jimenez, Ralph

2016-08-04

Far-red fluorescent proteins are critical for in vivo imaging applications, but the relative importance of structure versus dynamics in generating large Stokes-shifted emission is unclear. The unusually red-shifted emission of TagRFP675, a derivative of mKate, has been attributed to the multiple hydrogen bonds with the chromophore N-acylimine carbonyl. We characterized TagRFP675 and point mutants designed to perturb these hydrogen bonds with spectrally resolved transient grating and time-resolved fluorescence (TRF) spectroscopies supported by molecular dynamics simulations. TRF results for TagRFP675 and the mKate/M41Q variant show picosecond time scale red-shifts followed by nanosecond time blue-shifts. Global analysis of the TRF spectra reveals spectrally distinct emitting states that do not interconvert during the S1 lifetime. These dynamics originate from photoexcitation of a mixed ground-state population of acylimine hydrogen bond conformers. Strategically tuning the chromophore environment in TagRFP675 might stabilize the most red-shifted conformation and result in a variant with a larger Stokes shift.

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

Evidence that one is more likely to see the aurora near Moscow than near Ann Arbor

NASA Astrophysics Data System (ADS)

Liemohn, Michael; Immel, Thomas; Katus, Roxanne

We present a superposed epoch analysis of solar wind drivers and geomagnetic index responses during magnetic storms, categorized as a function of universal time (UT) of the storm peak, to investigate the dependency of storm intensity on UT. Storms with Dst minimum less than - 100 nT were identified in the 1970 - 2012 era (totaling 310 events), covering four solar cycles. The storms were classified into 6 groups based on the UT of the minimum Dst (36 to 82 events per bin), then each grouping was superposed on a timeline that aligns the time of the minimum Dst. Fifteen different quantities were considered, seven solar wind parameters and eight activity indices derived from ground-based magnetometers. Statistical analyses of the superposed means against each other (between the different UT groupings) were conducted to determine the mathematical significance of similarities and differences in the time series plots. It was found that most of the solar wind parameters have essentially no significant difference between the UT groupings, as expected. The exception is solar wind velocity, which appears to be bifurcated into two levels with three of the UT groupings systematically faster than the other three (although, interestingly, not three consecutive UT bins). The geomagnetic activity indices, however, all show statistically significant differences with UT during the main phase and/or early recovery phase. Specifically, the 16, 20, and 00 UT groupings are stronger storms than those in the other UT bins. That is, storms are stronger when the Asian sector is on the nightside (American sector on the dayside) during the main phase. An inference from these findings, therefore, is that one is more likely to see the aurora near Moscow in Russia than near Ann Arbor, Michigan in the United States, even though these two cities have very similar magnetic latitudes (52 degrees).

Greening the Curriculum? History Joins "The Usual Suspects" in Teaching Climate Change

ERIC Educational Resources Information Center

Hawkey, Kate; James, Jon; Tidmarsh, Celia

2016-01-01

Inspired by the news that Bristol had become the UK's first Green Capital, Kate Hawkey, Jon James and Celia Tidmarsh set out to explore what a "Green Capital" School Curriculum might look like. Hawkey, James and Tidmarsh explain how they created a cross-curricular project to deliver in-school workshops focused on the teaching of climate…

Working-Class Women in the Academy: Laborers in the Knowledge Factory.

ERIC Educational Resources Information Center

Tokarczyk, Michelle M., Ed.; Fay, Elizabeth A., Ed.

This volume contains a collection of essays on the issues and concerns that face women from working-class backgrounds who enter academic careers. Following an introduction and transcript of a dialogue between Kate Ellis and Lillain S. Robinson, the essays are as follows: "What's a Nice Working-Class Girl Like You Doing in a Place Like…

piBox: A Platform for Privacy-Preserving Apps

DTIC Science & Technology

2012-10-03

media Arcade/Action! Books! Brain/Puzzles! Business! Cards/Casino! Casual! Comics! Communication! Education ! Entertainment! Finance! Health/Fitness... Lifestyle ! Live Wallpaper! Media/Video! Medical! Music/Audio! News/Magazines! Personalization! Photography! Productivity! Racing! Shopping! Social! Sports...Cells: A virtual mobile smartphone architecture. In SOSP, 2011. [4] Google App Engine. https://developers. google.com/appengine. [5] M. Backes, A. Kate

Real Revision: Authors' Strategies to Share with Student Writers

ERIC Educational Resources Information Center

Messner, Kate

2011-01-01

How do you show students that revision is more than a classroom exercise to please the teacher? Take them into the real world of writing for publication. In Real Revision, award-winning author and teacher Kate Messner demystifies the revision process for teachers and students alike and provides tried-and-true revision strategies, field tested by…

Working Together during Noncombatant Evacuation Operations

DTIC Science & Technology

2008-04-23

5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR( S ) 5d. PROJECT NUMBER LCDR Kate M. Standifer, USN 5e...TASK NUMBER Paper Advisor (if Any): N/A 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME( S ) AND ADDRESS(ES) 8. PERFORMING...ORGANIZATION REPORT NUMBER Joint Military Operations Department Naval War College 686 Cushing Road

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

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.

Brentuximab Vedotin and Combination Chemotherapy in Treating Patients With Stage II-IV HIV-Associated Hodgkin Lymphoma

ClinicalTrials.gov

2018-06-11

AIDS-Related Hodgkin Lymphoma; Ann Arbor Stage II Hodgkin Lymphoma; Ann Arbor Stage IIA Hodgkin Lymphoma; Ann Arbor Stage IIB Hodgkin Lymphoma; Ann Arbor Stage III Hodgkin Lymphoma; Ann Arbor Stage IIIA Hodgkin Lymphoma; Ann Arbor Stage IIIB Hodgkin Lymphoma; Ann Arbor Stage IV Hodgkin Lymphoma; Ann Arbor Stage IVA Hodgkin Lymphoma; Ann Arbor Stage IVB Hodgkin Lymphoma; Classic Hodgkin Lymphoma; HIV Infection

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.

Kate Puzey Peace Corps Volunteer Protection Act of 2011

THOMAS, 112th Congress

Rep. Poe, Ted [R-TX-2

2011-06-23

House - 09/21/2011 Ordered to be Reported by Unanimous Consent. (All Actions) Notes: For further action, see S.1280, which became Public Law 112-57 on 11/21/2011. Tracker: This bill has the status IntroducedHere are the steps for Status of Legislation:

The Genetics and Evolution of Human Skin Color: The Case of Desiree's Baby

ERIC Educational Resources Information Center

Schneider, Patricia

2004-01-01

This case explores the genetics and evolution of skin color, using a short story by Kate Chopin called "Desiree's Baby" as a starting point. Students read the story and discuss a series of questions probing the genetics of the family in the tale. Students then read an article about the evolution of skin color and write an essay analyzing the…

Military Suicide Research Consortium: Extension to New Opportunities and Challenges

DTIC Science & Technology

2017-04-01

implications for understanding suicidal behavior. Journal of Abnormal Psychology , 123(4), 835-840. doi: 10.1037/a0037480 Johnson, L. L...Consulting Psychology ; Journal of Abnormal Psychology ; International Journal of Psychology ; Archives of Suicide Research; American Journal of...Kate Nassauer, and CAPT Mike Colston, Director of the Defense Centers of Excellence for Psychological Health & Traumatic Brain Injury (DCoE) to

2013 Inaugural Parade

NASA Image and Video Library

2013-01-21

The Orion space capsule along with NASA Astronauts Lee Morin, Alvin Drew, Kjell Lindgren, Serena Aunon, Kate Rubins, and Mike Massimino pass the Presidential viewing stand and President Barack Obama during the inaugural parade honoring Obama, Monday Jan. 21, 2013, in Washington. Obama was sworn-in as the nation's 44th President earlier in the day. Photo Credit: (NASA/Bill Ingalls)

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. Copyright © 2015 Elsevier B.V. All rights reserved.

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.

Combination Chemotherapy in Treating Young Patients With Newly Diagnosed T-Cell Acute Lymphoblastic Leukemia or T-cell Lymphoblastic Lymphoma

ClinicalTrials.gov

2018-01-24

Acute Lymphoblastic Leukemia; Adult T Acute Lymphoblastic Leukemia; Ann Arbor Stage II Adult T-Cell Leukemia/Lymphoma; Ann Arbor Stage II Childhood Lymphoblastic Lymphoma; Ann Arbor Stage II Contiguous Adult Lymphoblastic Lymphoma; Ann Arbor Stage II Non-Contiguous Adult Lymphoblastic Lymphoma; Ann Arbor Stage III Adult Lymphoblastic Lymphoma; Ann Arbor Stage III Adult T-Cell Leukemia/Lymphoma; Ann Arbor Stage III Childhood Lymphoblastic Lymphoma; Ann Arbor Stage IV Adult Lymphoblastic Lymphoma; Ann Arbor Stage IV Adult T-Cell Leukemia/Lymphoma; Ann Arbor Stage IV Childhood Lymphoblastic Lymphoma; Childhood T Acute Lymphoblastic Leukemia; Untreated Adult Acute Lymphoblastic Leukemia; Untreated Childhood Acute Lymphoblastic Leukemia

Doxorubicin Hydrochloride, Vinblastine, Dacarbazine, Brentuximab Vedotin, and Nivolumab in Treating Patients With Stage I-II Hodgkin Lymphoma

ClinicalTrials.gov

2018-04-30

Ann Arbor Stage I Hodgkin Lymphoma; Ann Arbor Stage IA Hodgkin Lymphoma; Ann Arbor Stage IB Hodgkin Lymphoma; Ann Arbor Stage II Hodgkin Lymphoma; Ann Arbor Stage IIA Hodgkin Lymphoma; Ann Arbor Stage IIB Hodgkin Lymphoma

The last straw!: a tool for participatory education about the social determinants of health.

PubMed

Rossiter, Kate; Reeve, Kate

2008-01-01

In response to a scarcity of teaching tools regarding the social determinants of health (SDOH), Kate Reeve and Kate Rossiter created The Last Straw! board game, an innovative participatory education tool to facilitate and engage critical thinking about the SDOH. The Last Straw! is designed to encourage discussion about the SDOH, promote critical thinking, and build empathy with marginalized people. The game begins as each player rolls the dice to create a character profile, including socioeconomic status (SES), race, and gender. Based on this profile, players then receive a certain number of "vitality chips." Moving across the board, players encounter scenarios that cause them to gain and lose chips based on their profile. The player who finishes the game with the most chips wins the game. The game can be facilitated for a variety of audiences, including both players with no prior knowledge of the SDOH and those experienced in the field. The game has been played with students, policymakers, and community workers, among others, and has been met with immense enthusiasm. Here, we detail the game's reception within the community, including benefits, limitations, and next steps.

Comparison of two different artificial neural networks for prostate biopsy indication in two different patient populations.

PubMed

Stephan, Carsten; Xu, Chuanliang; Finne, Patrik; Cammann, Henning; Meyer, Hellmuth-Alexander; Lein, Michael; Jung, Klaus; Stenman, Ulf-Hakan

2007-09-01

Different artificial neural networks (ANNs) using total prostate-specific antigen (PSA) and percentage of free PSA (%fPSA) have been introduced to enhance the specificity of prostate cancer detection. The applicability of independently trained ANN and logistic regression (LR) models to different populations regarding the composition (screening versus referred) and different PSA assays has not yet been tested. Two ANN and LR models using PSA (range 4 to 10 ng/mL), %fPSA, prostate volume, digital rectal examination findings, and patient age were tested. A multilayer perceptron network (MLP) was trained on 656 screening participants (Prostatus PSA assay) and another ANN (Immulite-based ANN [iANN]) was constructed on 606 multicentric urologically referred men. These and other assay-adapted ANN models, including one new iANN-based ANN, were used. The areas under the curve for the iANN (0.736) and MLP (0.745) were equal but showed no differences to %fPSA (0.725) in the Finnish group. Only the new iANN-based ANN reached a significant larger area under the curve (0.77). At 95% sensitivity, the specificities of MLP (33%) and the new iANN-based ANN (34%) were significantly better than the iANN (23%) and %fPSA (19%). Reverse methodology using the MLP model on the referred patients revealed, in contrast, a significant improvement in the areas under the curve for iANN and MLP (each 0.83) compared with %fPSA (0.70). At 90% and 95% sensitivity, the specificities of all LR and ANN models were significantly greater than those for %fPSA. The ANNs based on different PSA assays and populations were mostly comparable, but the clearly different patient composition also allowed with assay adaptation no unbiased ANN application to the other cohort. Thus, the use of ANNs in other populations than originally built is possible, but has limitations.

Sequence to Sequence - Video to Text

DTIC Science & Technology

2015-12-11

Saenko, and S. Guadarrama. Generating natural-language video descriptions using text - mined knowledge. In AAAI, July 2013. 2 [20] P. Kuznetsova, V...Sequence to Sequence – Video to Text Subhashini Venugopalan1 Marcus Rohrbach2,4 Jeff Donahue2 Raymond Mooney1 Trevor Darrell2 Kate Saenko3...1. Introduction Describing visual content with natural language text has recently received increased interest, especially describing images with a

Selectable Optical Diagnostics Instrument Experiment Diffusion Coefficient Mixture-3 (SODI) DCMix-3 Installation

NASA Image and Video Library

2016-09-13

NASA astronaut Kate Rubins works on Selectable Optical Diagnostics Instrument Experiment Diffusion Coefficient Mixture-3 (SODI) DCMix-3 Installation inside the station’s Microgravity Science Glovebox. The glovebox is one of the major dedicated science facilities inside the Destiny laboratory and provides a sealed environment for conducting science and technology experiments. The glovebox is particularly suited for handling hazardous materials when the crew is present.

Ofatumumab and Bendamustine Hydrochloride With or Without Bortezomib in Treating Patients With Untreated Follicular Non-Hodgkin Lymphoma

ClinicalTrials.gov

2018-04-17

Ann Arbor Stage III Grade 1 Follicular Lymphoma; Ann Arbor Stage III Grade 2 Follicular Lymphoma; Ann Arbor Stage III Grade 3 Follicular Lymphoma; Ann Arbor Stage IV Grade 1 Follicular Lymphoma; Ann Arbor Stage IV Grade 2 Follicular Lymphoma; Ann Arbor Stage IV Grade 3 Follicular Lymphoma; Grade 3a Follicular Lymphoma

Comparison of two data mining techniques in labeling diagnosis to Iranian pharmacy claim dataset: artificial neural network (ANN) versus decision tree model.

PubMed

Rezaei-Darzi, Ehsan; Farzadfar, Farshad; Hashemi-Meshkini, Amir; Navidi, Iman; Mahmoudi, Mahmoud; Varmaghani, Mehdi; Mehdipour, Parinaz; Soudi Alamdari, Mahsa; Tayefi, Batool; Naderimagham, Shohreh; Soleymani, Fatemeh; Mesdaghinia, Alireza; Delavari, Alireza; Mohammad, Kazem

2014-12-01

This study aimed to evaluate and compare the prediction accuracy of two data mining techniques, including decision tree and neural network models in labeling diagnosis to gastrointestinal prescriptions in Iran. This study was conducted in three phases: data preparation, training phase, and testing phase. A sample from a database consisting of 23 million pharmacy insurance claim records, from 2004 to 2011 was used, in which a total of 330 prescriptions were assessed and used to train and test the models simultaneously. In the training phase, the selected prescriptions were assessed by both a physician and a pharmacist separately and assigned a diagnosis. To test the performance of each model, a k-fold stratified cross validation was conducted in addition to measuring their sensitivity and specificity. Generally, two methods had very similar accuracies. Considering the weighted average of true positive rate (sensitivity) and true negative rate (specificity), the decision tree had slightly higher accuracy in its ability for correct classification (83.3% and 96% versus 80.3% and 95.1%, respectively). However, when the weighted average of ROC area (AUC between each class and all other classes) was measured, the ANN displayed higher accuracies in predicting the diagnosis (93.8% compared with 90.6%). According to the result of this study, artificial neural network and decision tree model represent similar accuracy in labeling diagnosis to GI prescription.

Brentuximab Vedotin and Combination Chemotherapy in Treating Children and Young Adults With Stage IIB or Stage IIIB-IVB Hodgkin Lymphoma

ClinicalTrials.gov

2018-06-25

Ann Arbor Stage IIB Hodgkin Lymphoma; Ann Arbor Stage IIIB Hodgkin Lymphoma; Ann Arbor Stage IV Hodgkin Lymphoma; Ann Arbor Stage IVA Hodgkin Lymphoma; Ann Arbor Stage IVB Hodgkin Lymphoma; Childhood Hodgkin Lymphoma; Classic Hodgkin Lymphoma

Combination Chemotherapy With or Without Bortezomib in Treating Younger Patients With Newly Diagnosed T-Cell Acute Lymphoblastic Leukemia or Stage II-IV T-Cell Lymphoblastic Lymphoma

ClinicalTrials.gov

2018-06-27

Adult T Acute Lymphoblastic Leukemia; Ann Arbor Stage II Adult Lymphoblastic Lymphoma; Ann Arbor Stage II Childhood Lymphoblastic Lymphoma; Ann Arbor Stage III Adult Lymphoblastic Lymphoma; Ann Arbor Stage III Childhood Lymphoblastic Lymphoma; Ann Arbor Stage IV Adult Lymphoblastic Lymphoma; Ann Arbor Stage IV Childhood Lymphoblastic Lymphoma; Childhood T Acute Lymphoblastic Leukemia; Untreated Adult Acute Lymphoblastic Leukemia; Untreated Childhood Acute Lymphoblastic Leukemia

iss049e040733

NASA Image and Video Library

2016-10-19

ISS049e040733 (10/19/2016) --- NASA astronaut Kate Rubins is pictured inside of the Soyuz MS-01 spacecraft while conducting routine spacesuit checks. Rubins, suited up in a Russian Sokol Launch and Entry suit, was conducting leak checks in advance of her upcoming landing along with Japanese astronaut Takuya Onishi and Russian cosmonaut Anatoly Ivanishin. The trio are scheduled to land Oct. 29, U.S. time.

Homeland Security Organizations: Design Contingencies in Complex Environments

DTIC Science & Technology

2011-09-01

Lawrence & Lorsch, 1967, pp. 159–184), as well as Leavitt’s diamond (Leavitt, 1965) and Galbraith’s STAR (Galbraith, 2002; Kate & Galbraith, 2007...Angeles County Operational area came together in December 2004 to participate in a training exercise called, “Operation Talavera” ( Gardner , 2005). The...information from public health officials to suspect a biological attack, which was done rapidly through their “ syndromic surveillance capabilities.” Once

SSC San Diego Command History Calendar Year 2005

DTIC Science & Technology

2006-03-01

Lichtenstein, Robert Clark, Celia Metz, Rod Anderson, Michael Dwyer , Dr. Randall Moore, Kate Schemensky, Wanda Parise, Jorge Mora, Ken Kaufman, John Laccone...Dynamically Tunable Wavelength Filters" Distinguished Rachel Goshorn, Code 2373 Dr. Visarath In, Code 2373 David Fogliatti, Code 2373 Dr. Joseph Neff, Code...Information Center Fort Belvoir, VA 22060-6218 (4) SSC San Diego Liaison Office C/ O PEO-SCS Arlington, VA 22202-4804 (1) Center for Naval Analyses

SSC San Diego Command History Calendar Year 2005

DTIC Science & Technology

2006-03-01

Celia Metz, Rod Anderson, Michael Dwyer , Dr. Randall Moore, Kate Schemensky, Wanda Parise, Jorge Mora, Ken Kaufman, John Laccone and Mike Phillips...Tunable Wavelength Filters” Distinguished Rachel Goshorn, Code 2373 Dr. Visarath In, Code 2373 David Fogliatti, Code 2373 Dr. Joseph Neff...22060–6218 (4) SSC San Diego Liaison Office C/ O PEO-SCS Arlington, VA 22202–4804 (1) Center for Naval Analyses Alexandria, VA 22302–0268 (1

iss049e039316

NASA Image and Video Library

2016-10-17

ISS049e039316 (10/17/2016) --- NASA astronaut Kate Rubins watches a live video feed of the Orbital ATK CRS-5 launch on Oct. 17, 2016. The commercial company’s Cygnus cargo craft launched atop the Antares rocket for the first time since a previous failure in Oct. 2014. Packed with more than 5,100 pounds of cargo, the spacecraft arrived at the station on October 23.

Agricultural, Nutritional, and Physical Fitness Policies That Support National Security

DTIC Science & Technology

2011-03-24

high fructose corn syrup (HFCS) – could be made inexpensively from corn ...www.nytimes.com/2003/10/12/magazine/12WWLN.html?pagewanted=all (accessed October 9, 2010). 78 Kate McLaughlin, ―The Facts about High Fructose Corn Syrup ...August 24, 2010, linked from the Suite101.com Home Page at ―Health and Wellness,‖ http://www.suite101.com/content/the-facts-about- high - fructose - corn

PDSparc: A Drop-In Replacement for LEON3 Written Using Synopsys Processor Designer

DTIC Science & Technology

2015-09-24

Kate   Thurmer  MIT  Lincoln  Laboratory,  Lexington,   MA,  USA Distribution A: Public Release   ABSTRACT   Microprocessors are the...enabled appliances has opened a significant new niche: the Application Specific Standard Product (ASSP) microprocessor . These processors usually start...out as soft-cores that are parameterized at design time to realize exclusively the specific needs of the application. The microprocessor is a small

Concept Development: An Operational Framework for Resilience

DTIC Science & Technology

2009-08-27

85% of the nation’s critical infrastructure, spanning both hard and soft features.16 Without support from the business community , resilience objectives...E., Robert W. Kates, and Shirley B. Laska. “Three Years After Katrina: Lessons for Community Resilience ” Environment, vol. 50, no. 5 (September 2008...Presentation at Building Community Resilience and a Culture of Preparedness.” NORAD and USNORTHCOM Surgeon’s Conference, March 10-12, 2009. Wang, Fan Xiao

jsc2017e049161

NASA Image and Video Library

2017-04-24

jsc2017e049161 (April 24, 2017) --- Flight Director Brian Smith, Capcom Astronaut Jessica Meir along with Astronaut Jeff Williams monitor activities in Mission Control as President Donald Trump, First Daughter Ivanka Trump and NASA astronaut Kate Rubins make a special Earth-to-space call from the Oval Office to personally congratulate NASA astronaut Peggy Whitson for her record-breaking stay aboard the International Space Station. (Photo Credit: NASA/Robert Markowitz)

jsc2017e049163

NASA Image and Video Library

2017-04-24

jsc2017e049163 (April 24, 2017) --- Flight Director Brian Smith, Capcom Astronaut Jessica Meir along with Astronaut Jeff Williams monitor activities in Mission Control as President Donald Trump, First Daughter Ivanka Trump and NASA astronaut Kate Rubins make a special Earth-to-space call from the Oval Office to personally congratulate NASA astronaut Peggy Whitson for her record-breaking stay aboard the International Space Station. (Photo Credit: NASA/Robert Markowitz)

jsc2017e049160

NASA Image and Video Library

2017-04-24

jsc2017e049160 (April 24, 2017) --- Flight Director Brian Smith, Capcom Astronaut Jessica Meir along with Astronaut Jeff Williams monitor activities in Mission Control as President Donald Trump, First Daughter Ivanka Trump and NASA astronaut Kate Rubins make a special Earth-to-space call from the Oval Office to personally congratulate NASA astronaut Peggy Whitson for her record-breaking stay aboard the International Space Station. (Photo Credit: NASA/Robert Markowitz)

jsc2017e049155

NASA Image and Video Library

2017-04-24

jsc2017e049155 (April 24, 2017) --- Flight Director Brian Smith, Capcom Astronaut Jessica Meir along with Astronaut Jeff Williams monitor activities in Mission Control as President Donald Trump, First Daughter Ivanka Trump and NASA astronaut Kate Rubins make a special Earth-to-space call from the Oval Office to personally congratulate NASA astronaut Peggy Whitson for her record-breaking stay aboard the International Space Station. (Photo Credit: NASA/Robert Markowitz)

jsc2017e049158

NASA Image and Video Library

2017-04-24

jsc2017e049158 (April 24, 2017) --- Flight Director Brian Smith, Capcom Astronaut Jessica Meir along with Astronaut Jeff Williams monitor activities in Mission Control as President Donald Trump, First Daughter Ivanka Trump and NASA astronaut Kate Rubins make a special Earth-to-space call from the Oval Office to personally congratulate NASA astronaut Peggy Whitson for her record-breaking stay aboard the International Space Station. (Photo Credit: NASA/Robert Markowitz)

jsc2017e049157

NASA Image and Video Library

2017-04-24

jsc2017e049157 (April 24, 2017) --- Flight Director Brian Smith, Capcom Astronaut Jessica Meir along with Astronaut Jeff Williams monitor activities in Mission Control as President Donald Trump, First Daughter Ivanka Trump and NASA astronaut Kate Rubins make a special Earth-to-space call from the Oval Office to personally congratulate NASA astronaut Peggy Whitson for her record-breaking stay aboard the International Space Station. (Photo Credit: NASA/Robert Markowitz)

jsc2017e049162

NASA Image and Video Library

2017-04-24

jsc2017e049162 (April 24, 2017) --- Flight Director Brian Smith, Capcom Astronaut Jessica Meir along with Astronaut Jeff Williams monitor activities in Mission Control as President Donald Trump, First Daughter Ivanka Trump and NASA astronaut Kate Rubins make a special Earth-to-space call from the Oval Office to personally congratulate NASA astronaut Peggy Whitson for her record-breaking stay aboard the International Space Station. (Photo Credit: NASA/Robert Markowitz)

jsc2017e049156

NASA Image and Video Library

2017-04-24

jsc2017e049156 (April 24, 2017) --- Flight Director Brian Smith, Capcom Astronaut Jessica Meir along with Astronaut Jeff Williams monitor activities in Mission Control as President Donald Trump, First Daughter Ivanka Trump and NASA astronaut Kate Rubins make a special Earth-to-space call from the Oval Office to personally congratulate NASA astronaut Peggy Whitson for her record-breaking stay aboard the International Space Station. (Photo Credit: NASA/Robert Markowitz)

jsc2017e049159

NASA Image and Video Library

2017-04-24

jsc2017e049159 (April 24, 2017) --- Flight Director Brian Smith, Capcom Astronaut Jessica Meir along with Astronaut Jeff Williams monitor activities in Mission Control as President Donald Trump, First Daughter Ivanka Trump and NASA astronaut Kate Rubins make a special Earth-to-space call from the Oval Office to personally congratulate NASA astronaut Peggy Whitson for her record-breaking stay aboard the International Space Station. (Photo Credit: NASA/Robert Markowitz)

Hierarchical Bayesian Model Averaging for Non-Uniqueness and Uncertainty Analysis of Artificial Neural Networks

NASA Astrophysics Data System (ADS)

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

2012-12-01

Artificial Neural Networks (ANNs) have been widely used to estimate concentration of chemicals in groundwater systems. However, estimation uncertainty is rarely discussed in the literature. Uncertainty in ANN output stems from three sources: ANN inputs, ANN parameters (weights and biases), and ANN structures. Uncertainty in ANN inputs may come from input data selection and/or input data error. ANN parameters are naturally uncertain because they are maximum-likelihood estimated. ANN structure is also uncertain because there is no unique ANN model given a specific case. Therefore, multiple plausible AI models are generally resulted for a study. One might ask why good models have to be ignored in favor of the best model in traditional estimation. What is the ANN estimation variance? How do the variances from different ANN models accumulate to the total estimation variance? To answer these questions we propose a Hierarchical Bayesian Model Averaging (HBMA) framework. Instead of choosing one ANN model (the best ANN model) for estimation, HBMA averages outputs of all plausible ANN models. The model weights are based on the evidence of data. Therefore, the HBMA avoids overconfidence on the single best ANN model. In addition, HBMA is able to analyze uncertainty propagation through aggregation of ANN models in a hierarchy framework. This method is applied for estimation of fluoride concentration in the Poldasht plain and the Bazargan plain in Iran. Unusually high fluoride concentration in the Poldasht and Bazargan plains has caused negative effects on the public health. Management of this anomaly requires estimation of fluoride concentration distribution in the area. The results show that the HBMA provides a knowledge-decision-based framework that facilitates analyzing and quantifying ANN estimation uncertainties from different sources. In addition HBMA allows comparative evaluation of the realizations for each source of uncertainty by segregating the uncertainty sources in

Transplantation of Enhalus acoroides on a sedimentary beach in Ambon Bay

NASA Astrophysics Data System (ADS)

Irawan, Andi

2018-02-01

Coastal development in Ambon Bay has been contributing to coastal ecosystem degradations in recent years. One of the negative effects was the over sedimentation that changes the landscape of coastal ecosystem such as seagrass beds. These changes have made this ecosystem lost some of its functions especially as the habitat for other biotas, because the vegetation has been buried and reduced in density. So, in December 2015, a rehabilitation effort has been done at Kate-kate Beach with transplantation techniques of Enhalus acoroides. After 3-11 months of observation, it was noticed that only the transplants in the deeper area survived; on the contrary, the transplants in exposed and dry area during low tide did not survive. Overall, the survival rate of the transplantation project was 49.73% because the transplants need enough submerged condition to support their lives. The study recommends that to rehabilitate damaged seagrass beds due to the over sedimentation, we have to remove the sediment until certain depth during low tide to ensure the transplants are submerged in seawater. On top of that, the local government has to reduce the sedimentation rate from land because over sedimentation will make the beach profile become too shallow and too exposed during the low tide.

Contribution of the activated catalase to oxidative stress resistance and γ-aminobutyric acid production in Lactobacillus brevis.

PubMed

Lyu, Changjiang; Hu, Sheng; Huang, Jun; Luo, Maiqi; Lu, Tao; Mei, Lehe; Yao, Shanjing

2016-12-05

Lactic acid bacteria (LAB) are generally sensitive to H 2 O 2 , a compound which can paradoxically produce themselves and lead to the growth arrest and cell death. To counteract the potentially toxic effects of this compound, the gene katE encoding a heme-dependent catalase (CAT) belonging to the family of monofunctional CATs was cloned from Lactobacillus brevis CGMCC1306. The enhanced homologous CAT expression was achieved using the NICE system. L. brevis cells with overexpressed CAT showed 685-fold and 823-fold higher survival when exposed to 30mmol/L of H 2 O 2 and long-term aerated stress (after 72h), respectively, than that of the wild type cells. Furtherly, the effects of activated CAT on GABA production in L. brevis were investigated. A GABA production level of 66.4g/L was achieved using two-step biotransformation that successively employed the growing and resting cells derived from engineering L. brevis CAT. These results demonstrated clearly that overexpression of the KatE gene in L. brevis led to a marked increased survival in oxidizing environment, and shed light on a novel feasible approach to enhance the GABA production level by improving the antioxidative properties. Copyright © 2016 Elsevier B.V. All rights reserved.

Julius Caesar and the Gallic Campaign: A Roadmap to the Use of the Instruments of Power

DTIC Science & Technology

2010-03-30

David R Godine Publishing Inc, 1980), 10-13, 17. 13 Kate Gilliver, Caesar’s Gallic Wars, (Oxford: Osprey Publishing, 2002), 74. 14 Plutarch , Fall of...information, see Goldsworthy, Caesar: A Life of a Colossus, 316. 88 Plutarch , 269. 89 Lord Kitchener was the British general credited with winning the...Serverin and Siedler; London: HarperCollins, 1995. Penrose, Jane, ed., Rome and Her Enemies. Oxford, UK: Osprey Publishing Ltd, 2008. Plutarch , Fall of

Installation Restoration Program (IRP) for IRP Sites Numbers 4, 5, 7 and 14. 152 Tactical Reconnaissance Group, Nevada Air National Guard, Reno Tahoe International Airport, Reno, Nevada

DTIC Science & Technology

1996-01-01

For: HQ/ANG/CEVR Andrews AFB, Maryland Prepared By: ERM-West, Inc. 5111. N. Scottsdale Road, Suite 108 ERM Scottsdale, Arizona 85250 FINAL Document...predominantly andesite and andesite porphyry flow rock, hypabyssal intrusives, and minor siliceous welded tuff, which are commonly represented by the Kate...TERTIARY ROCKSI tuslýýj porphyry and olcomic brii I SOURCE ORNL/ETS. 1994 GEOLOGIC MAP OF THE FIGURE 3-1 RENO. NEVADA AREA 152nd TACTICAL RECONNAISSANCE

Legislation would establish commission to assess marine and coastal resources and develop national ocean policy

NASA Astrophysics Data System (ADS)

Showstack, Randy

During 1998, internationally designated as the year of the ocean, perhaps more people are paying heed to the deep seas now than ever before.Transfixed to the big screen by this year's movie blockbuster, they anticipate when the Titanic will scrape into the iceberg and break apart, shiver when household-name heartthrobs Leonardo DiCaprio and Kate Winslet float on the freezing waters, and hum along to the theme sung by Celine Dion.

Nuclear Weapons Security Crisis: What Does History Teach?

DTIC Science & Technology

2013-07-01

Department of Defense. Much of the work to prepare the book for publi- cation was done by NPEC’s research associate, Kate Harrison, and the staff...of the Strategic Studies Insti- tute, especially Dr. James Pierce and Rita Rummel. This book would not have been possible without their help...nuclear security crises detailed in this book gone differently—had the rebel faction of the French military seized the nuclear device that was to

Assessment of runoff and sediment yields using the AnnAGNPS model in a Three-Gorge watershed of China.

PubMed

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

2012-05-01

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 km(2) 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 R(2) of 0.94) and validated using the observed monthly runoff from 2003 to 2005 (Nash-Sutcliffe coefficient of efficiency of 0.93 and R(2) 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

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. Copyright © 2015 Elsevier Ltd. All rights reserved.

Application of artificial neural networks to establish a predictive mortality risk model in children admitted to a paediatric intensive care unit.

PubMed

Chan, C H; Chan, E Y; Ng, D K; Chow, P Y; Kwok, K L

2006-11-01

Paediatric risk of mortality and paediatric index of mortality (PIM) are the commonly-used mortality prediction models (MPM) in children admitted to paediatric intensive care unit (PICU). The current study was undertaken to develop a better MPM using artificial neural network, a domain of artificial intelligence. The purpose of this retrospective case series was to compare an artificial neural network (ANN) model and PIM with the observed mortality in a cohort of patients admitted to a five-bed PICU in a Hong Kong non-teaching general hospital. The patients were under the age of 17 years and admitted to our PICU from April 2001 to December 2004. Data were collected from each patient admitted to our PICU. All data were randomly allocated to either the training or validation set. The data from the training set were used to construct a series of ANN models. The data from the validation set were used to validate the ANN and PIM models. The accuracy of ANN models and PIM was assessed by area under the receiver operator characteristics (ROC) curve and calibration. All data were randomly allocated to either the training (n=274) or validation set (n=273). Three ANN models were developed using the data from the training set, namely ANN8 (trained with variables required for PIM), ANN9 (trained with variables required for PIM and pre-ICU intubation) and ANN23 (trained with variables required for ANN9 and 14 principal ICU diagnoses). Three ANN models and PIM were used to predict mortality in the validation set. We found that PIM and ANN9 had a high ROC curve (PIM: 0.808, 95 percent confidence interval 0.552 to 1.000, ANN9: 0.957, 95 percent confidence interval 0.915 to 1.000), whereas ANN8 and ANN23 gave a suboptimal area under the ROC curve. ANN8 required only five variables for the calculation of risk, compared with eight for PIM. The current study demonstrated the process of predictive mortality risk model development using ANN. Further multicentre studies are required to

Enzalutamide in Treating Patients With Relapsed or Refractory Mantle Cell Lymphoma

ClinicalTrials.gov

2018-03-27

Ann Arbor Stage I Mantle Cell Lymphoma; Ann Arbor Stage II Mantle Cell Lymphoma; Ann Arbor Stage III Mantle Cell Lymphoma; Ann Arbor Stage IV Mantle Cell Lymphoma; Recurrent Mantle Cell Lymphoma; Refractory Mantle Cell Lymphoma

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

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.

Optimization of metformin HCl 500 mg sustained release matrix tablets using Artificial Neural Network (ANN) based on Multilayer Perceptrons (MLP) model.

PubMed

Mandal, Uttam; Gowda, Veeran; Ghosh, Animesh; Bose, Anirbandeep; Bhaumik, Uttam; Chatterjee, Bappaditya; Pal, Tapan Kumar

2008-02-01

The aim of the present study was to apply the simultaneous optimization method incorporating Artificial Neural Network (ANN) using Multi-layer Perceptron (MLP) model to the development of a metformin HCl 500 mg sustained release matrix tablets with an optimized in vitro release profile. The amounts of HPMC K15M and PVP K30 at three levels (-1, 0, +1) for each were selected as casual factors. In vitro dissolution time profiles at four different sampling times (1 h, 2 h, 4 h and 8 h) were chosen as output variables. 13 kinds of metformin matrix tablets were prepared according to a 2(3) factorial design (central composite) with five extra center points, and their dissolution tests were performed. Commercially available STATISTICA Neural Network software (Stat Soft, Inc., Tulsa, OK, U.S.A.) was used throughout the study. The training process of MLP was completed until a satisfactory value of root square mean (RSM) for the test data was obtained using feed forward back propagation method. The root mean square value for the trained network was 0.000097, which indicated that the optimal MLP model was reached. The optimal tablet formulation based on some predetermined release criteria predicted by MLP was 336 mg of HPMC K15M and 130 mg of PVP K30. Calculated difference (f(1) 2.19) and similarity (f(2) 89.79) factors indicated that there was no difference between predicted and experimentally observed drug release profiles for the optimal formulation. This work illustrates the potential for an artificial neural network with MLP, to assist in development of sustained release dosage forms.

The chronic pain initiative and community care of North Carolina.

PubMed

Lancaster, Michael; McKee, Jerry; Mahan, Amelia

2013-01-01

The rate of unintentional deaths from opioid poisoning has reached epidemic proportions. One model of successful intervention is Project Lazarus, an integrated-care pilot program in Wilkes County, North Carolina. Community Care of North Carolina, supported by a grant of $1.3 million from the Kate B. Reynolds Charitable Trust and matching funds of $1.3 million from the North Carolina Office of Rural Health and Community Care, is now expanding the Project Lazarus approach statewide.

An RNAi-enhanced Logic Circuit for Cancer Specific Detection and Destruction

DTIC Science & Technology

2010-07-01

Bcl-2 family: mBax (Mus musculus), hBax ( Homo sapiens ), and its mutant hBax-S184A [4]. A plasmid containing the tested gene was transfected into HEK...the far-red fluorescent protein mKate to express the Gata3 mStaple. Intron- feature sequences – donor site, branch point, poly- pyrimidine tract, and...intron-exon junction. Among the donor and acceptor sequences found in literature our intron features were chosen according SplicePort [5], an

A New Artificial Neural Network Enhanced by the Shuffled Complex Evolution Optimization with Principal Component Analysis (SP-UCI) for Water Resources Management

NASA Astrophysics Data System (ADS)

Hayatbini, N.; Faridzad, M.; Yang, T.; Akbari Asanjan, A.; Gao, X.; Sorooshian, S.

2016-12-01

The Artificial Neural Networks (ANNs) are useful in many fields, including water resources engineering and management. However, due to the non-linear and chaotic characteristics associated with natural processes and human decision making, the use of ANNs in real-world applications is still limited, and its performance needs to be further improved for a broader practical use. The commonly used Back-Propagation (BP) scheme and gradient-based optimization in training the ANNs have already found to be problematic in some cases. The BP scheme and gradient-based optimization methods are associated with the risk of premature convergence, stuck in local optimums, and the searching is highly dependent on initial conditions. Therefore, as an alternative to BP and gradient-based searching scheme, we propose an effective and efficient global searching method, termed the Shuffled Complex Evolutionary Global optimization algorithm with Principal Component Analysis (SP-UCI), to train the ANN connectivity weights. Large number of real-world datasets are tested with the SP-UCI-based ANN, as well as various popular Evolutionary Algorithms (EAs)-enhanced ANNs, i.e., Particle Swarm Optimization (PSO)-, Genetic Algorithm (GA)-, Simulated Annealing (SA)-, and Differential Evolution (DE)-enhanced ANNs. Results show that SP-UCI-enhanced ANN is generally superior over other EA-enhanced ANNs with regard to the convergence and computational performance. In addition, we carried out a case study for hydropower scheduling in the Trinity Lake in the western U.S. In this case study, multiple climate indices are used as predictors for the SP-UCI-enhanced ANN. The reservoir inflows and hydropower releases are predicted up to sub-seasonal to seasonal scale. Results show that SP-UCI-enhanced ANN is able to achieve better statistics than other EAs-based ANN, which implies the usefulness and powerfulness of proposed SP-UCI-enhanced ANN for reservoir operation, water resources engineering and management

Red Fluorescent Proteins for Gene Expression and Protein Localization Studies in Streptococcus pneumoniae and Efficient Transformation with DNA Assembled via the Gibson Assembly Method.

PubMed

Beilharz, Katrin; van Raaphorst, Renske; Kjos, Morten; Veening, Jan-Willem

2015-10-01

During the last decades, a wide range of fluorescent proteins (FPs) have been developed and improved. This has had a great impact on the possibilities in biological imaging and the investigation of cellular processes at the single-cell level. Recently, we have benchmarked a set of green fluorescent proteins (GFPs) and generated a codon-optimized superfolder GFP for efficient use in the important human pathogen Streptococcus pneumoniae and other low-GC Gram-positive bacteria. In the present work, we constructed and compared four red fluorescent proteins (RFPs) in S. pneumoniae. Two orange-red variants, mOrange2 and TagRFP, and two far-red FPs, mKate2 and mCherry, were codon optimized and examined by fluorescence microscopy and plate reader assays. Notably, protein fusions of the RFPs to FtsZ were constructed by direct transformation of linear Gibson assembly (isothermal assembly) products, a method that speeds up the strain construction process significantly. Our data show that mCherry is the fastest-maturing RFP in S. pneumoniae and is best suited for studying gene expression, while mKate2 and TagRFP are more stable and are the preferred choices for protein localization studies. The RFPs described here will be useful for cell biology studies that require multicolor labeling in S. pneumoniae and related organisms. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

Simple and effective method to lower body core temperatures of hyperthermic patients.

PubMed

O'Connor, John P

2017-06-01

Hyperthermia is a potentially life threatening scenario that may occur in patients due to accompanying morbidities, exertion, or exposure to dry and arid environmental conditions. In particular, heat stroke may result from environmental exposure combined with a lack of thermoregulation. Key clinical findings in the diagnosis of heatstroke are (1) a history of heat stress or exposure, (2) a rectal temperature greater than 40 °C, and (3) central nervous system dysfunction (altered mental state, disorientation, stupor, seizures, or coma) (Prendergast and Erickson, 2014 [1]). In these patients, it is important to bring the body's core temperature down to acceptable levels in a short period of time to avoid tissue/organ injury or death (Yoder, 2001; Casa et al., 2007 [2,3]). A number of potential approaches, both non-invasive and invasive, may be used to lower the temperature of these individuals. Non-invasive techniques generally include: evaporative cooling, ice water immersion, whole-body ice packing, strategic ice packing, and convective cooling. Invasive approaches may include gastric lavage or peritoneal lavage (Schraga and Kates [4]). The efficacy of these methods vary and select treatment approaches may be unsuitable for specific individuals (Schraga and Kates [4]). In this work, the effectiveness of radiation cooling of individuals as a stand-alone treatment and comparisons with existing noninvasive techniques are presented. Copyright © 2017 Elsevier Inc. All rights reserved.

[Application of an artificial neural network in the design of sustained-release dosage forms].

PubMed

Wei, X H; Wu, J J; Liang, W Q

2001-09-01

To use the artificial neural network (ANN) in Matlab 5.1 tool-boxes to predict the formulations of sustained-release tablets. The solubilities of nine drugs and various ratios of HPMC: Dextrin for 63 tablet formulations were used as the ANN model input, and in vitro accumulation released at 6 sampling times were used as output. The ANN model was constructed by selecting the optimal number of iterations (25) and model structure in which there are one hidden layer and five hidden layer nodes. The optimized ANN model was used for prediction of formulation based on desired target in vitro dissolution-time profiles. ANN predicted profiles based on ANN predicted formulations were closely similar to the target profiles. The ANN could be used for predicting the dissolution profiles of sustained release dosage form and for the design of optimal formulation.

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

Text to Speech (TTS) Capabilities for the Common Driver Trainer (CDT)

DTIC Science & Technology

2010-10-01

harnessing in’leigle jalClpeno jocelyn linu ~ los angeles lottery margarine mathematlze mathematized mathematized meme memes memol...including Julie, Kate, and Paul . Based upon the names of the voices, it may be that the VoiceText capability is the technology being used currently on...DFTTSExportToFileEx(O, " Paul ", 1, 1033, "Testing the Digital Future Text-to-Speech SDK.", -1, -1, -1, -1, -1, DFTTS_ TEXT_ TYPE_ XML, "test.wav", 0, "", -1

PDSparc: A Drop-in Replacement for LEON3 Written Using Synopsys Processor Designer

DTIC Science & Technology

2015-08-18

Written Using  Synopsys Processor Designer1  David Whelihan, Ph.D. and Kate Thurmer  MIT Lincoln Laboratory, Lexington, MA, USA    ABSTRACT  Microprocessors ...internet-enabled appliances has opened a significant new niche: the Application Specific Standard Product (ASSP) microprocessor . These processors... microprocessor is a small part of a working system and requires peripherals such as DRAM controllers and communication sub-systems to properly carry out its

Interrogation of Detainees: Overview of the McCain Amendment

DTIC Science & Technology

2006-09-25

in another); Miller v. City of Philadelphia, 174 F. 3d 368, 375 (3rd Cir.1999) (“The exact degree of wrongfulness necessary to reach the ‘conscience...v. Crosby, 379 F. 3d 1278 (11th Cir. 2004). 17 Haynes v. Washington, 373 U.S. 503 (1963). See also Greenwald v. Wisconsin, 390 U.S. 519 (1968...Kate Zernike & Sheryl Gay Stolberg, Differences Settled in Deal Over Detainee Treatment, NY TIMES, Sept. 23, 2006, at A9. 28 For purposes of

A Star on the Run

NASA Astrophysics Data System (ADS)

Kohler, Susanna

2015-10-01

via a photometric survey of RSGs in M31, followed up by spectroscopy with the Multiple Mirror Telescope. They determined that the star is also separated from other massive stars in the disk of the galaxy by about 4.6 kpc which is roughly the distance it would be expected to travel, given its discrepant motion, in an assumed age of about 10 Myr.The authors suggest that this star may be a high-mass analog of hypervelocity stars stars within the Milky Way that are moving fast enough to escape the galaxy. The authors demonstrate that the total discrepant speed of RSG J004330.06+405258.4 is probably comparable to the escape velocity of M31s disk.But whether or not this star is moving fast enough to escape turns out to be moot: it will only live another million years, which means it wont have enough time to leave the galaxy before ending its life in a spectacular supernova. Citation: Kate Anne Evans and Philip Massey 2015 AJ 150 149. doi:10.1088/0004-6256/150/5/149

Identification of input variables for feature based artificial neural networks-saccade detection in EOG recordings.

PubMed

Tigges, P; Kathmann, N; Engel, R R

1997-07-01

Though artificial neural networks (ANN) are excellent tools for pattern recognition problems when signal to noise ratio is low, the identification of decision relevant features for ANN input data is still a crucial issue. The experience of the ANN designer and the existing knowledge and understanding of the problem seem to be the only links for a specific construction. In the present study a backpropagation ANN based on modified raw data inputs showed encouraging results. Investigating the specific influences of prototypical input patterns on a specially designed ANN led to a new sparse and efficient input data presentation. This data coding obtained by a semiautomatic procedure combining existing expert knowledge and the internal representation structures of the raw data based ANN yielded a list of feature vectors, each representing the relevant information for saccade identification. The feature based ANN produced a reduction of the error rate of nearly 40% compared with the raw data ANN. An overall correct classification of 92% of so far unknown data was realized. The proposed method of extracting internal ANN knowledge for the production of a better input data representation is not restricted to EOG recordings, and could be used in various fields of signal analysis.

Finger language recognition based on ensemble artificial neural network learning using armband EMG sensors.

PubMed

Kim, Seongjung; Kim, Jongman; Ahn, Soonjae; Kim, Youngho

2018-04-18

Deaf people use sign or finger languages for communication, but these methods of communication are very specialized. For this reason, the deaf can suffer from social inequalities and financial losses due to their communication restrictions. In this study, we developed a finger language recognition algorithm based on an ensemble artificial neural network (E-ANN) using an armband system with 8-channel electromyography (EMG) sensors. The developed algorithm was composed of signal acquisition, filtering, segmentation, feature extraction and an E-ANN based classifier that was evaluated with the Korean finger language (14 consonants, 17 vowels and 7 numbers) in 17 subjects. E-ANN was categorized according to the number of classifiers (1 to 10) and size of training data (50 to 1500). The accuracy of the E-ANN-based classifier was obtained by 5-fold cross validation and compared with an artificial neural network (ANN)-based classifier. As the number of classifiers (1 to 8) and size of training data (50 to 300) increased, the average accuracy of the E-ANN-based classifier increased and the standard deviation decreased. The optimal E-ANN was composed with eight classifiers and 300 size of training data, and the accuracy of the E-ANN was significantly higher than that of the general ANN.

Artificial intelligence against breast cancer (A.N.N.E.S-B.C.-Project).

PubMed

Parmeggiani, Domenico; Avenia, Nicola; Sanguinetti, Alessandro; Ruggiero, Roberto; Docimo, Giovanni; Siciliano, Mattia; Ambrosino, Pasquale; Madonna, Imma; Peltrini, Roberto; Parmeggiani, Umberto

2012-01-01

Our preliminary study examined the development of an advanced innovative technology with the objectives of--developing methodologies and algorithms for a Artificial Neural Network (ANN) system, improving mammography and ultra-sonography images interpretation;--creating autonomous software as a diagnostic tool for the physicians, allowing the possibility for the advanced application of databases using Artificial Intelligence (Expert System). Since 2004 550 F patients over 40 yrs old were divided in two groups: 1) 310 pts underwent echo every 6 months and mammography every year by expert radiologists. 2) 240 pts had the same screening program and were also examined by our diagnosis software, developed with ANN-ES technology by the Engineering Aircraft Research Project team. The information was continually updated and returned to the Expert System, defining the principal rules of automatic diagnosis. In the second group we selected: Expert radiologist decision; ANN-ES decision; Expert radiologists with ANN-ES decision. The second group had significantly better diagnosis for cancer and better specificity for breast lesions risk as well as the highest percentage account when the radiologist's decision was helped by the ANN software. The ANN-ES group was able to select, by anamnestic, diagnostic and genetic means, 8 patients for prophylactic surgery, finding 4 cancers in a very early stage. Although it is only a preliminary study, this innovative diagnostic tool seems to provide better positive and negative predictive value in cancer diagnosis as well as in breast risk lesion identification.

Neural networks in chemistry

NASA Astrophysics Data System (ADS)

Zupan, Jure

1995-04-01

All problems that in some way are linked to handling of multi-variate experiments versus multi-variate responses can be approached by the group of methods that has recently became known as the artificial neural network (ANN) techniques. In this lecture, the types of the problems that can be solved by ANN techniques rather than the ANN techniques themselves will be addressed first. This issue is rather important due to the fact that the ANN techniques can be used for a very broad range of problems and choosing the wrong method can often result in either a failure to produce an effective solution or in a very time consuming and ineffective handling. Among the types of problems that can be solved by different ANN techniques the classification, mapping, look-up table, and modelling will be emphasized and discussed. Because all mentioned methods can be solved by different standard techniques, special emphasis will be paid to stress the advantages and drawbacks when employing different ANN techniques. Due to the fact that the range of possible use of ANN is so broad, even a very specific problem can be solved by many different ANN architectures or even using different learning strategies within ANN. In the second part the main learning strategies and corresponding choices of ANN architectures will be discussed. In this part the parameters and some guidelines how to select the method and the design of the ANNs will be shown on the examples of reported ANN applications in chemistry. The ANN learning strategies discussed will be back-propagation of errors, the Kohonen, and the counter propagation learning. The potential user of ANN should first, consider the problem, second, he must inspect the availability of data and the data themselves to decide for which ANN method they are best suited. In this respect, the amount of data, the dimensionality of the measurement space, the form of data (alphanumeric entries, binary, real, or even mixed forms of data) are crucial. After

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

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.

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

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.

Physics and chemistry-driven artificial neural network for predicting bioactivity of peptides and proteins and their design.

PubMed

Huang, Ri-Bo; Du, Qi-Shi; Wei, Yu-Tuo; Pang, Zong-Wen; Wei, Hang; Chou, Kuo-Chen

2009-02-07

Predicting the bioactivity of peptides and proteins is an important challenge in drug development and protein engineering. In this study we introduce a novel approach, the so-called "physics and chemistry-driven artificial neural network (Phys-Chem ANN)", to deal with such a problem. Unlike the existing ANN approaches, which were designed under the inspiration of biological neural system, the Phys-Chem ANN approach is based on the physical and chemical principles, as well as the structural features of proteins. In the Phys-Chem ANN model the "hidden layers" are no longer virtual "neurons", but real structural units of proteins and peptides. It is a hybridization approach, which combines the linear free energy concept of quantitative structure-activity relationship (QSAR) with the advanced mathematical technique of ANN. The Phys-Chem ANN approach has adopted an iterative and feedback procedure, incorporating both machine-learning and artificial intelligence capabilities. In addition to making more accurate predictions for the bioactivities of proteins and peptides than is possible with the traditional QSAR approach, the Phys-Chem ANN approach can also provide more insights about the relationship between bioactivities and the structures involved than the ANN approach does. As an example of the application of the Phys-Chem ANN approach, a predictive model for the conformational stability of human lysozyme is presented.

Artificial neural networks: fundamentals, computing, design, and application.

PubMed

Basheer, I A; Hajmeer, M

2000-12-01

Artificial neural networks (ANNs) are relatively new computational tools that have found extensive utilization in solving many complex real-world problems. The attractiveness of ANNs comes from their remarkable information processing characteristics pertinent mainly to nonlinearity, high parallelism, fault and noise tolerance, and learning and generalization capabilities. This paper aims to familiarize the reader with ANN-based computing (neurocomputing) and to serve as a useful companion practical guide and toolkit for the ANNs modeler along the course of ANN project development. The history of the evolution of neurocomputing and its relation to the field of neurobiology is briefly discussed. ANNs are compared to both expert systems and statistical regression and their advantages and limitations are outlined. A bird's eye review of the various types of ANNs and the related learning rules is presented, with special emphasis on backpropagation (BP) ANNs theory and design. A generalized methodology for developing successful ANNs projects from conceptualization, to design, to implementation, is described. The most common problems that BPANNs developers face during training are summarized in conjunction with possible causes and remedies. Finally, as a practical application, BPANNs were used to model the microbial growth curves of S. flexneri. The developed model was reasonably accurate in simulating both training and test time-dependent growth curves as affected by temperature and pH.

Diffusion parameter mapping with the combined intravoxel incoherent motion and kurtosis model using artificial neural networks at 3 T.

PubMed

Bertleff, Marco; Domsch, Sebastian; Weingärtner, Sebastian; Zapp, Jascha; O'Brien, Kieran; Barth, Markus; Schad, Lothar R

2017-12-01

Artificial neural networks (ANNs) were used for voxel-wise parameter estimation with the combined intravoxel incoherent motion (IVIM) and kurtosis model facilitating robust diffusion parameter mapping in the human brain. The proposed ANN approach was compared with conventional least-squares regression (LSR) and state-of-the-art multi-step fitting (LSR-MS) in Monte-Carlo simulations and in vivo in terms of estimation accuracy and precision, number of outliers and sensitivity in the distinction between grey (GM) and white (WM) matter. Both the proposed ANN approach and LSR-MS yielded visually increased parameter map quality. Estimations of all parameters (perfusion fraction f, diffusion coefficient D, pseudo-diffusion coefficient D*, kurtosis K) were in good agreement with the literature using ANN, whereas LSR-MS resulted in D* overestimation and LSR yielded increased values for f and D*, as well as decreased values for K. Using ANN, outliers were reduced for the parameters f (ANN, 1%; LSR-MS, 19%; LSR, 8%), D* (ANN, 21%; LSR-MS, 25%; LSR, 23%) and K (ANN, 0%; LSR-MS, 0%; LSR, 15%). Moreover, ANN enabled significant distinction between GM and WM based on all parameters, whereas LSR facilitated this distinction only based on D and LSR-MS on f, D and K. Overall, the proposed ANN approach was found to be superior to conventional LSR, posing a powerful alternative to the state-of-the-art method LSR-MS with several advantages in the estimation of IVIM-kurtosis parameters, which might facilitate increased applicability of enhanced diffusion models at clinical scan times. Copyright © 2017 John Wiley & Sons, Ltd.

Forum: What Has Actually Changed in Physics Departments in the Situation for Women, Graduate Students and Other People?

NASA Astrophysics Data System (ADS)

Mulvey, Patrick; Ivie, Rachel; Campbell, David; Murnane, Margaret; Kirby, Kate; Catlla, Anne

2006-03-01

The decade of the 90's was a period of intense scrutiny of climate issues in physics departments, e.g. the status of women, the job situation for new Ph.D.'s and postdocs, and the preparation of physicists for careers inside and outside of physics. There were many conference sessions on these topics, and both APS members and leadership instigated important efforts to focus on specific areas. These efforts included the program of visiting committees to departments to examine the situation for women by the Committee on the Status of Women in Physics, the AIP's various studies of a statistical nature, and the creation by the APS of a Committee on Careers and the Forum on Graduate Student Affairs, as well as the recent APS-AAPT task force on graduate education. This forum patterned after similar sessions 10 years ago - will examine how physics departments have changed as a result of such efforts. It will begin with short (12-minute) talks by a panel of experts to describe what has happened in key areas. The greater part of the session will be a period of observations, questions, and discussion from the audience and the panel together. The purpose is to have an interchange on these interrelated topics from which we can all learn. THE TOPICS TO BE INTRODUCED IN THE SHORT TALKS AT THE BEGINNING OF THE SESSION ARE: 1) changes in graduate enrollment, composition, and subsequent jobs (Patrick Mulvey); 2) women in physics and astronomy departments 2005 (Rachel Ivie); 3) changes in graduate curricula and environment (David Campbell); 4) CSWP site visits to physics departments what’s been accomplished and learned (Margaret Murnane); 5) survey of ethical issues in physics departments and the physics profession: results and reactions (Kate Kirby); and (6) physics departments from the point of view of younger physicists (Anne Catlla). The bulk of the session will be a public forum, on these and related issues, among the audience and the panel.

iss048e061332

NASA Image and Video Library

2016-08-19

ss048e061332 (08/19/2016) --- Checking the space gloves before and after a spacewalk is part of the detailed check list astronauts go through to provide absolute safety. Both NASA astronaut Jeff Williams and Kate Rubens took part in the important inspections before and after their 19 Aug 2016 spacewalk to install a new docking adapter . A cut in the glove could subject the astronaut to the extreme temperatures of outer space and the escape of oxygen, both of which could be fatal.

Interrogation of Detainees: Overview of the McCain Amendment

DTIC Science & Technology

2006-10-23

shocks in one circumstance might not be considered so egregious in another); Miller v. City of Philadelphia, 174 F. 3d 368, 375 (3rd Cir.1999) (“The...concurring). 15 Hope v. Pelzer, 536 U.S. 730 (2002). 16 Chandler v. Crosby, 379 F. 3d 1278 (11th Cir. 2004). 17 Haynes v. Washington, 373 U.S. 503 (1963...and that taken by S. 3861, S. 3886, and H.R. 6054. Kate Zernike & Sheryl Gay Stolberg, Differences Settled in Deal Over Detainee Treatment, NY

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.

Knowledge and intelligent computing system in medicine.

PubMed

Pandey, Babita; Mishra, R B

2009-03-01

Knowledge-based systems (KBS) and intelligent computing systems have been used in the medical planning, diagnosis and treatment. The KBS consists of rule-based reasoning (RBR), case-based reasoning (CBR) and model-based reasoning (MBR) whereas intelligent computing method (ICM) encompasses genetic algorithm (GA), artificial neural network (ANN), fuzzy logic (FL) and others. The combination of methods in KBS such as CBR-RBR, CBR-MBR and RBR-CBR-MBR and the combination of methods in ICM is ANN-GA, fuzzy-ANN, fuzzy-GA and fuzzy-ANN-GA. The combination of methods from KBS to ICM is RBR-ANN, CBR-ANN, RBR-CBR-ANN, fuzzy-RBR, fuzzy-CBR and fuzzy-CBR-ANN. In this paper, we have made a study of different singular and combined methods (185 in number) applicable to medical domain from mid 1970s to 2008. The study is presented in tabular form, showing the methods and its salient features, processes and application areas in medical domain (diagnosis, treatment and planning). It is observed that most of the methods are used in medical diagnosis very few are used for planning and moderate number in treatment. The study and its presentation in this context would be helpful for novice researchers in the area of medical expert system.

A new evolutionary system for evolving artificial neural networks.

PubMed

Yao, X; Liu, Y

1997-01-01

This paper presents a new evolutionary system, i.e., EPNet, for evolving artificial neural networks (ANNs). The evolutionary algorithm used in EPNet is based on Fogel's evolutionary programming (EP). Unlike most previous studies on evolving ANN's, this paper puts its emphasis on evolving ANN's behaviors. Five mutation operators proposed in EPNet reflect such an emphasis on evolving behaviors. Close behavioral links between parents and their offspring are maintained by various mutations, such as partial training and node splitting. EPNet evolves ANN's architectures and connection weights (including biases) simultaneously in order to reduce the noise in fitness evaluation. The parsimony of evolved ANN's is encouraged by preferring node/connection deletion to addition. EPNet has been tested on a number of benchmark problems in machine learning and ANNs, such as the parity problem, the medical diagnosis problems, the Australian credit card assessment problem, and the Mackey-Glass time series prediction problem. The experimental results show that EPNet can produce very compact ANNs with good generalization ability in comparison with other algorithms.

Application of principal component regression and artificial neural network in FT-NIR soluble solids content determination of intact pear fruit

NASA Astrophysics Data System (ADS)

Ying, Yibin; Liu, Yande; Fu, Xiaping; Lu, Huishan

2005-11-01

The artificial neural networks (ANNs) have been used successfully in applications such as pattern recognition, image processing, automation and control. However, majority of today's applications of ANNs is back-propagate feed-forward ANN (BP-ANN). In this paper, back-propagation artificial neural networks (BP-ANN) were applied for modeling soluble solid content (SSC) of intact pear from their Fourier transform near infrared (FT-NIR) spectra. One hundred and sixty-four pear samples were used to build the calibration models and evaluate the models predictive ability. The results are compared to the classical calibration approaches, i.e. principal component regression (PCR), partial least squares (PLS) and non-linear PLS (NPLS). The effects of the optimal methods of training parameters on the prediction model were also investigated. BP-ANN combine with principle component regression (PCR) resulted always better than the classical PCR, PLS and Weight-PLS methods, from the point of view of the predictive ability. Based on the results, it can be concluded that FT-NIR spectroscopy and BP-ANN models can be properly employed for rapid and nondestructive determination of fruit internal quality.

Verification and Validation of KBS with Neural Network Components

NASA Technical Reports Server (NTRS)

Wen, Wu; Callahan, John

1996-01-01

Artificial Neural Network (ANN) play an important role in developing robust Knowledge Based Systems (KBS). The ANN based components used in these systems learn to give appropriate predictions through training with correct input-output data patterns. Unlike traditional KBS that depends on a rule database and a production engine, the ANN based system mimics the decisions of an expert without specifically formulating the if-than type of rules. In fact, the ANNs demonstrate their superiority when such if-then type of rules are hard to generate by human expert. Verification of traditional knowledge based system is based on the proof of consistency and completeness of the rule knowledge base and correctness of the production engine.These techniques, however, can not be directly applied to ANN based components.In this position paper, we propose a verification and validation procedure for KBS with ANN based components. The essence of the procedure is to obtain an accurate system specification through incremental modification of the specifications using an ANN rule extraction algorithm.

Have artificial neural networks met expectations in drug discovery as implemented in QSAR framework?

PubMed

Dobchev, Dimitar; Karelson, Mati

2016-07-01

Artificial neural networks (ANNs) are highly adaptive nonlinear optimization algorithms that have been applied in many diverse scientific endeavors, ranging from economics, engineering, physics, and chemistry to medical science. Notably, in the past two decades, ANNs have been used widely in the process of drug discovery. In this review, the authors discuss advantages and disadvantages of ANNs in drug discovery as incorporated into the quantitative structure-activity relationships (QSAR) framework. Furthermore, the authors examine the recent studies, which span over a broad area with various diseases in drug discovery. In addition, the authors attempt to answer the question about the expectations of the ANNs in drug discovery and discuss the trends in this field. The old pitfalls of overtraining and interpretability are still present with ANNs. However, despite these pitfalls, the authors believe that ANNs have likely met many of the expectations of researchers and are still considered as excellent tools for nonlinear data modeling in QSAR. It is likely that ANNs will continue to be used in drug development in the future.

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)

Reply to Ann Bradshaw.

PubMed

Allmark, Peter

1996-02-01

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.

Anne K. Starace | NREL

Science.gov Websites

, Thermal Systems Group, 2010-2013 Featured Publications "Effects of Torrefaction Temperature on ;Biomass Catalytic Pyrolysis on Ni/ZSM-5: Effects of Nickel Pretreatment and Loading," Energy and inorganic dispersions be high-temperature heat-transfer and thermal energy storage fluids?" Journal of

Anne Elizabeth Ware | NREL

Science.gov Websites

Accumulation in Leaves of Sorghum bicolor (L.) Moench: A Source of Natural Food Pigment," J. Agricultural and Food Chemistry (2014) "Catalytic deoxygenation of triglycerides and fatty acids to

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

Overview of artificial neural networks.

PubMed

Zou, Jinming; Han, Yi; So, Sung-Sau

2008-01-01

The artificial neural network (ANN), or simply neural network, is a machine learning method evolved from the idea of simulating the human brain. The data explosion in modem drug discovery research requires sophisticated analysis methods to uncover the hidden causal relationships between single or multiple responses and a large set of properties. The ANN is one of many versatile tools to meet the demand in drug discovery modeling. Compared to a traditional regression approach, the ANN is capable of modeling complex nonlinear relationships. The ANN also has excellent fault tolerance and is fast and highly scalable with parallel processing. This chapter introduces the background of ANN development and outlines the basic concepts crucially important for understanding more sophisticated ANN. Several commonly used learning methods and network setups are discussed briefly at the end of the chapter.

[Algorithms of artificial neural networks--practical application in medical science].

PubMed

Stefaniak, Bogusław; Cholewiński, Witold; Tarkowska, Anna

2005-12-01

Artificial Neural Networks (ANN) may be a tool alternative and complementary to typical statistical analysis. However, in spite of many computer applications of various ANN algorithms ready for use, artificial intelligence is relatively rarely applied to data processing. This paper presents practical aspects of scientific application of ANN in medicine using widely available algorithms. Several main steps of analysis with ANN were discussed starting from material selection and dividing it into groups, to the quality assessment of obtained results at the end. The most frequent, typical reasons for errors as well as the comparison of ANN method to the modeling by regression analysis were also described.

Artificial neural networks applied to quantitative elemental analysis of organic material using PIXE

NASA Astrophysics Data System (ADS)

Correa, R.; Chesta, M. A.; Morales, J. R.; Dinator, M. I.; Requena, I.; Vila, I.

2006-08-01

An artificial neural network (ANN) has been trained with real-sample PIXE (particle X-ray induced emission) spectra of organic substances. Following the training stage ANN was applied to a subset of similar samples thus obtaining the elemental concentrations in muscle, liver and gills of Cyprinus carpio. Concentrations obtained with the ANN method are in full agreement with results from one standard analytical procedure, showing the high potentiality of ANN in PIXE quantitative analyses.

Ibrutinib, Rituximab, Etoposide, Prednisone, Vincristine Sulfate, Cyclophosphamide, and Doxorubicin Hydrochloride in Treating Patients With HIV-Positive Stage II-IV Diffuse Large B-Cell Lymphomas

ClinicalTrials.gov

2018-06-11

AIDS-Related Lymphoma; Ann Arbor Stage II Diffuse Large B-Cell Lymphoma; Ann Arbor Stage III Diffuse Large B-Cell Lymphoma; Ann Arbor Stage IV Diffuse Large B-Cell Lymphoma; CD20 Negative; CD20 Positive; Human Immunodeficiency Virus Positive

Wide Area Recovery and Resiliency Program (WARRP) Knowledge Enhancement Events: CBR Workshop After Action Report

DTIC Science & Technology

2012-01-01

Laboratories Walker Ray Walker Engineering Solutions, LLC Williams Patricia Denver Office of Emergency Management Wood- Zika Annmarie Lawrence Livermore...llnl.gov AnnMarie Wood- Zika woodzika1@llnl.gov Pacific Northwest National Laboratory Ann Lesperance ann.lesperance@pnnl.gov Jessica Sandusky

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. Chap...

Brentuximab Vedotin or Crizotinib and Combination Chemotherapy in Treating Patients With Newly Diagnosed Stage II-IV Anaplastic Large Cell Lymphoma

ClinicalTrials.gov

2018-06-25

Anaplastic Large Cell Lymphoma, ALK-Positive; Ann Arbor Stage II Noncutaneous Childhood Anaplastic Large Cell Lymphoma; Ann Arbor Stage III Noncutaneous Childhood Anaplastic Large Cell Lymphoma; Ann Arbor Stage IV Noncutaneous Childhood Anaplastic Large Cell Lymphoma; CD30-Positive Neoplastic Cells Present

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.

Automated knowledge generation

NASA Technical Reports Server (NTRS)

Myler, Harley R.; Gonzalez, Avelino J.

1988-01-01

The general objectives of the NASA/UCF Automated Knowledge Generation Project were the development of an intelligent software system that could access CAD design data bases, interpret them, and generate a diagnostic knowledge base in the form of a system model. The initial area of concentration is in the diagnosis of the process control system using the Knowledge-based Autonomous Test Engineer (KATE) diagnostic system. A secondary objective was the study of general problems of automated knowledge generation. A prototype was developed, based on object-oriented language (Flavors).

Diagnostic accuracy of an artificial neural network compared with statistical quantitation of myocardial perfusion images: a Japanese multicenter study.

PubMed

Nakajima, Kenichi; Kudo, Takashi; Nakata, Tomoaki; Kiso, Keisuke; Kasai, Tokuo; Taniguchi, Yasuyo; Matsuo, Shinro; Momose, Mitsuru; Nakagawa, Masayasu; Sarai, Masayoshi; Hida, Satoshi; Tanaka, Hirokazu; Yokoyama, Kunihiko; Okuda, Koichi; Edenbrandt, Lars

2017-12-01

Artificial neural networks (ANN) might help to diagnose coronary artery disease. This study aimed to determine whether the diagnostic accuracy of an ANN-based diagnostic system and conventional quantitation are comparable. The ANN was trained to classify potentially abnormal areas as true or false based on the nuclear cardiology expert interpretation of 1001 gated stress/rest 99m Tc-MIBI images at 12 hospitals. The diagnostic accuracy of the ANN was compared with 364 expert interpretations that served as the gold standard of abnormality for the validation study. Conventional summed stress/rest/difference scores (SSS/SRS/SDS) were calculated and compared with receiver operating characteristics (ROC) analysis. The ANN generated a better area under the ROC curves (AUC) than SSS (0.92 vs. 0.82, p < 0.0001), indicating better identification of stress defects. The ANN also generated a better AUC than SDS (0.90 vs. 0.75, p < 0.0001) for stress-induced ischemia. The AUC for patients with old myocardial infarction based on rest defects was 0.97 (0.91 for SRS, p = 0.0061), and that for patients with and without a history of revascularization based on stress defects was 0.94 and 0.90 (p = 0.0055 and p < 0.0001 vs. SSS, respectively). The SSS/SRS/SDS steeply increased when ANN values (probability of abnormality) were >0.80. The ANN was diagnostically accurate in various clinical settings, including that of patients with previous myocardial infarction and coronary revascularization. The ANN could help to diagnose coronary artery disease.

[The research of near-infrared blood glucose measurement using particle swarm optimization and artificial neural network].

PubMed

Dai, Juan; Ji, Zhong; Du, Yubao

2017-08-01

Existing near-infrared non-invasive blood glucose detection modelings mostly detect multi-spectral signals with different wavelength, which is not conducive to the popularization of non-invasive glucose meter at home and does not consider the physiological glucose dynamics of individuals. In order to solve these problems, this study presented a non-invasive blood glucose detection model combining particle swarm optimization (PSO) and artificial neural network (ANN) by using the 1 550 nm near-infrared absorbance as the independent variable and the concentration of blood glucose as the dependent variable, named as PSO-2ANN. The PSO-2ANN model was based on two sub-modules of neural networks with certain structures and arguments, and was built up after optimizing the weight coefficients of the two networks by particle swarm optimization. The results of 10 volunteers were predicted by PSO-2ANN. It was indicated that the relative error of 9 volunteers was less than 20%; 98.28% of the predictions of blood glucose by PSO-2ANN were distributed in the regions A and B of Clarke error grid, which confirmed that PSO-2ANN could offer higher prediction accuracy and better robustness by comparison with ANN. Additionally, even the physiological glucose dynamics of individuals may be different due to the influence of environment, temper, mental state and so on, PSO-2ANN can correct this difference only by adjusting one argument. The PSO-2ANN model provided us a new prospect to overcome individual differences in blood glucose prediction.

Boosting Learning Algorithm for Stock Price Forecasting

NASA Astrophysics Data System (ADS)

Wang, Chengzhang; Bai, Xiaoming

2018-03-01

To tackle complexity and uncertainty of stock market behavior, more studies have introduced machine learning algorithms to forecast stock price. ANN (artificial neural network) is one of the most successful and promising applications. We propose a boosting-ANN model in this paper to predict the stock close price. On the basis of boosting theory, multiple weak predicting machines, i.e. ANNs, are assembled to build a stronger predictor, i.e. boosting-ANN model. New error criteria of the weak studying machine and rules of weights updating are adopted in this study. We select technical factors from financial markets as forecasting input variables. Final results demonstrate the boosting-ANN model works better than other ones for stock price forecasting.

76 FR 28068 - Notice of Intent To Repatriate Cultural Items: Museum of Anthropology, University of Michigan...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-05-13

... Cultural Items: Museum of Anthropology, University of Michigan, Ann Arbor, MI AGENCY: National Park Service... Museum of Anthropology, University of Michigan, Ann Arbor, MI, that meet the definition of unassociated... funerary objects should contact Carla Sinopoli, Museum of Anthropology, University of Michigan, Ann Arbor...

Artificial neural network aided non-invasive grading evaluation of hepatic fibrosis by duplex ultrasonography

PubMed Central

2012-01-01

Background Artificial neural networks (ANNs) are widely studied for evaluating diseases. This paper discusses the intelligence mode of an ANN in grading the diagnosis of liver fibrosis by duplex ultrasonogaphy. Methods 239 patients who were confirmed as having liver fibrosis or cirrhosis by ultrasound guided liver biopsy were investigated in this study. We quantified ultrasonographic parameters as significant parameters using a data optimization procedure applied to an ANN. 179 patients were typed at random as the training group; 60 additional patients were consequently enrolled as the validating group. Performance of the ANN was evaluated according to accuracy, sensitivity, specificity, Youden’s index and receiver operating characteristic (ROC) analysis. Results 5 ultrasonographic parameters; i.e., the liver parenchyma, thickness of spleen, hepatic vein (HV) waveform, hepatic artery pulsatile index (HAPI) and HV damping index (HVDI), were enrolled as the input neurons in the ANN model. The sensitivity, specificity and accuracy of the ANN model for quantitative diagnosis of liver fibrosis were 95.0%, 85.0% and 88.3%, respectively. The Youden’s index (YI) was 0.80. Conclusions The established ANN model had good sensitivity and specificity in quantitative diagnosis of hepatic fibrosis or liver cirrhosis. Our study suggests that the ANN model based on duplex ultrasound may help non-invasive grading diagnosis of liver fibrosis in clinical practice. PMID:22716936

An Effective and Novel Neural Network Ensemble for Shift Pattern Detection in Control Charts.

PubMed

Barghash, Mahmoud

2015-01-01

Pattern recognition in control charts is critical to make a balance between discovering faults as early as possible and reducing the number of false alarms. This work is devoted to designing a multistage neural network ensemble that achieves this balance which reduces rework and scrape without reducing productivity. The ensemble under focus is composed of a series of neural network stages and a series of decision points. Initially, this work compared using multidecision points and single-decision point on the performance of the ANN which showed that multidecision points are highly preferable to single-decision points. This work also tested the effect of population percentages on the ANN and used this to optimize the ANN's performance. Also this work used optimized and nonoptimized ANNs in an ensemble and proved that using nonoptimized ANN may reduce the performance of the ensemble. The ensemble that used only optimized ANNs has improved performance over individual ANNs and three-sigma level rule. In that respect using the designed ensemble can help in reducing the number of false stops and increasing productivity. It also can be used to discover even small shifts in the mean as early as possible.

Artificial neural network detects human uncertainty

NASA Astrophysics Data System (ADS)

Hramov, Alexander E.; Frolov, Nikita S.; Maksimenko, Vladimir A.; Makarov, Vladimir V.; Koronovskii, Alexey A.; Garcia-Prieto, Juan; Antón-Toro, Luis Fernando; Maestú, Fernando; Pisarchik, Alexander N.

2018-03-01

Artificial neural networks (ANNs) are known to be a powerful tool for data analysis. They are used in social science, robotics, and neurophysiology for solving tasks of classification, forecasting, pattern recognition, etc. In neuroscience, ANNs allow the recognition of specific forms of brain activity from multichannel EEG or MEG data. This makes the ANN an efficient computational core for brain-machine systems. However, despite significant achievements of artificial intelligence in recognition and classification of well-reproducible patterns of neural activity, the use of ANNs for recognition and classification of patterns in neural networks still requires additional attention, especially in ambiguous situations. According to this, in this research, we demonstrate the efficiency of application of the ANN for classification of human MEG trials corresponding to the perception of bistable visual stimuli with different degrees of ambiguity. We show that along with classification of brain states associated with multistable image interpretations, in the case of significant ambiguity, the ANN can detect an uncertain state when the observer doubts about the image interpretation. With the obtained results, we describe the possible application of ANNs for detection of bistable brain activity associated with difficulties in the decision-making process.

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.

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…

Suspended sediment flux modeling with artificial neural network: An example of the Longchuanjiang River in the Upper Yangtze Catchment, China

NASA Astrophysics Data System (ADS)

Zhu, Yun-Mei; Lu, X. X.; Zhou, Yue

2007-02-01

Artificial neural network (ANN) was used to model the monthly suspended sediment flux in the Longchuanjiang River, the Upper Yangtze Catchment, China. The suspended sediment flux was related to the average rainfall, temperature, rainfall intensity and water discharge. It is demonstrated that ANN is capable of modeling the monthly suspended sediment flux with fairly good accuracy when proper variables and their lag effect on the suspended sediment flux are used as inputs. Compared with multiple linear regression and power relation models, ANN can generate a better fit under the same data requirement. In addition, ANN can provide more reasonable predictions for extremely high or low values, because of the distributed information processing system and the nonlinear transformation involved. Compared with the ANNs that use the values of the dependent variable at previous time steps as inputs, the ANNs established in this research with only climate variables have an advantage because it can be used to assess hydrological responses to climate change.

How Children with Autism Reason about Other's Intentions: False-Belief and Counterfactual Inferences.

PubMed

Rasga, Célia; Quelhas, Ana Cristina; Byrne, Ruth M J

2017-06-01

We examine false belief and counterfactual reasoning in children with autism with a new change-of-intentions task. Children listened to stories, for example, Anne is picking up toys and John hears her say she wants to find her ball. John goes away and the reason for Anne's action changes-Anne's mother tells her to tidy her bedroom. We asked, 'What will John believe is the reason that Anne is picking up toys?' which requires a false-belief inference, and 'If Anne's mother hadn't asked Anne to tidy her room, what would have been the reason she was picking up toys?' which requires a counterfactual inference. We tested children aged 6, 8 and 10 years. Children with autism made fewer correct inferences than typically developing children at 8 years, but by 10 years there was no difference. Children with autism made fewer correct false-belief than counterfactual inferences, just like typically developing children.

Forecasting the discomfort levels within the greater Athens area, Greece using artificial neural networks and multiple criteria analysis

NASA Astrophysics Data System (ADS)

Vouterakos, P. A.; Moustris, K. P.; Bartzokas, A.; Ziomas, I. C.; Nastos, P. T.; Paliatsos, A. G.

2012-12-01

In this work, artificial neural networks (ANNs) were developed and applied in order to forecast the discomfort levels due to the combination of high temperature and air humidity, during the hot season of the year, in eight different regions within the Greater Athens area (GAA), Greece. For the selection of the best type and architecture of ANNs-forecasting models, the multiple criteria analysis (MCA) technique was applied. Three different types of ANNs were developed and tested with the MCA method. Concretely, the multilayer perceptron, the generalized feed forward networks (GFFN), and the time-lag recurrent networks were developed and tested. Results showed that the best ANNs type performance was achieved by using the GFFN model for the prediction of discomfort levels due to high temperature and air humidity within GAA. For the evaluation of the constructed ANNs, appropriate statistical indices were used. The analysis proved that the forecasting ability of the developed ANNs models is very satisfactory at a significant statistical level of p < 0.01.

Improving quantitative structure-activity relationship models using Artificial Neural Networks trained with dropout.

PubMed

Mendenhall, Jeffrey; Meiler, Jens

2016-02-01

Dropout is an Artificial Neural Network (ANN) training technique that has been shown to improve ANN performance across canonical machine learning (ML) datasets. Quantitative Structure Activity Relationship (QSAR) datasets used to relate chemical structure to biological activity in Ligand-Based Computer-Aided Drug Discovery pose unique challenges for ML techniques, such as heavily biased dataset composition, and relatively large number of descriptors relative to the number of actives. To test the hypothesis that dropout also improves QSAR ANNs, we conduct a benchmark on nine large QSAR datasets. Use of dropout improved both enrichment false positive rate and log-scaled area under the receiver-operating characteristic curve (logAUC) by 22-46 % over conventional ANN implementations. Optimal dropout rates are found to be a function of the signal-to-noise ratio of the descriptor set, and relatively independent of the dataset. Dropout ANNs with 2D and 3D autocorrelation descriptors outperform conventional ANNs as well as optimized fingerprint similarity search methods.

Improving Quantitative Structure-Activity Relationship Models using Artificial Neural Networks Trained with Dropout

PubMed Central

Mendenhall, Jeffrey; Meiler, Jens

2016-01-01

Dropout is an Artificial Neural Network (ANN) training technique that has been shown to improve ANN performance across canonical machine learning (ML) datasets. Quantitative Structure Activity Relationship (QSAR) datasets used to relate chemical structure to biological activity in Ligand-Based Computer-Aided Drug Discovery (LB-CADD) pose unique challenges for ML techniques, such as heavily biased dataset composition, and relatively large number of descriptors relative to the number of actives. To test the hypothesis that dropout also improves QSAR ANNs, we conduct a benchmark on nine large QSAR datasets. Use of dropout improved both Enrichment false positive rate (FPR) and log-scaled area under the receiver-operating characteristic curve (logAUC) by 22–46% over conventional ANN implementations. Optimal dropout rates are found to be a function of the signal-to-noise ratio of the descriptor set, and relatively independent of the dataset. Dropout ANNs with 2D and 3D autocorrelation descriptors outperform conventional ANNs as well as optimized fingerprint similarity search methods. PMID:26830599

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…

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);…

Chiral topological phases from artificial neural networks

NASA Astrophysics Data System (ADS)

Kaubruegger, Raphael; Pastori, Lorenzo; Budich, Jan Carl

2018-05-01

Motivated by recent progress in applying techniques from the field of artificial neural networks (ANNs) to quantum many-body physics, we investigate to what extent the flexibility of ANNs can be used to efficiently study systems that host chiral topological phases such as fractional quantum Hall (FQH) phases. With benchmark examples, we demonstrate that training ANNs of restricted Boltzmann machine type in the framework of variational Monte Carlo can numerically solve FQH problems to good approximation. Furthermore, we show by explicit construction how n -body correlations can be kept at an exact level with ANN wave functions exhibiting polynomial scaling with power n in system size. Using this construction, we analytically represent the paradigmatic Laughlin wave function as an ANN state.

The KATE shell: An implementation of model-based control, monitor and diagnosis

NASA Technical Reports Server (NTRS)

Cornell, Matthew

1987-01-01

The conventional control and monitor software currently used by the Space Center for Space Shuttle processing has many limitations such as high maintenance costs, limited diagnostic capabilities and simulation support. These limitations have caused the development of a knowledge based (or model based) shell to generically control and monitor electro-mechanical systems. The knowledge base describes the system's structure and function and is used by a software shell to do real time constraints checking, low level control of components, diagnosis of detected faults, sensor validation, automatic generation of schematic diagrams and automatic recovery from failures. This approach is more versatile and more powerful than the conventional hard coded approach and offers many advantages over it, although, for systems which require high speed reaction times or aren't well understood, knowledge based control and monitor systems may not be appropriate.

Prediction of heat transfer coefficients for forced convective boiling of N2-hydrocarbon mixtures at cryogenic conditions using artificial neural networks

NASA Astrophysics Data System (ADS)

Barroso-Maldonado, J. M.; Belman-Flores, J. M.; Ledesma, S.; Aceves, S. M.

2018-06-01

A key problem faced in the design of heat exchangers, especially for cryogenic applications, is the determination of convective heat transfer coefficients in two-phase flow such as condensation and boiling of non-azeotropic refrigerant mixtures. This paper proposes and evaluates three models for estimating the convective coefficient during boiling. These models are developed using computational intelligence techniques. The performance of the proposed models is evaluated using the mean relative error (mre), and compared to two existing models: the modified Granryd's correlation and the Silver-Bell-Ghaly method. The three proposed models are distinguished by their architecture. The first is based on directly measured parameters (DMP-ANN), the second is based on equivalent Reynolds and Prandtl numbers (eq-ANN), and the third on effective Reynolds and Prandtl numbers (eff-ANN). The results demonstrate that the proposed artificial neural network (ANN)-based approaches greatly outperform available methodologies. While Granryd's correlation predicts experimental data within a mean relative error mre = 44% and the S-B-G method produces mre = 42%, DMP-ANN has mre = 7.4% and eff-ANN has mre = 3.9%. Considering that eff-ANN has the lowest mean relative error (one tenth of previously available methodologies) and the broadest range of applicability, it is recommended for future calculations. Implementation is straightforward within a variety of platforms and the matrices with the ANN weights are given in the appendix for efficient programming.

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

A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting

NASA Astrophysics Data System (ADS)

Luk, K. C.; Ball, J. E.; Sharma, A.

2000-01-01

Artificial neural networks (ANNs), which emulate the parallel distributed processing of the human nervous system, have proven to be very successful in dealing with complicated problems, such as function approximation and pattern recognition. Due to their powerful capability and functionality, ANNs provide an alternative approach for many engineering problems that are difficult to solve by conventional approaches. Rainfall forecasting has been a difficult subject in hydrology due to the complexity of the physical processes involved and the variability of rainfall in space and time. In this study, ANNs were adopted to forecast short-term rainfall for an urban catchment. The ANNs were trained to recognise historical rainfall patterns as recorded from a number of gauges in the study catchment for reproduction of relevant patterns for new rainstorm events. The primary objective of this paper is to investigate the effect of temporal and spatial information on short-term rainfall forecasting. To achieve this aim, a comparison test on the forecast accuracy was made among the ANNs configured with different orders of lag and different numbers of spatial inputs. In developing the ANNs with alternative configurations, the ANNs were trained to an optimal level to achieve good generalisation of data. It was found in this study that the ANNs provided the most accurate predictions when an optimum number of spatial inputs was included into the network, and that the network with lower lag consistently produced better performance.

Computer vision-based method for classification of wheat grains using artificial neural network.

PubMed

Sabanci, Kadir; Kayabasi, Ahmet; Toktas, Abdurrahim

2017-06-01

A simplified computer vision-based application using artificial neural network (ANN) depending on multilayer perceptron (MLP) for accurately classifying wheat grains into bread or durum is presented. The images of 100 bread and 100 durum wheat grains are taken via a high-resolution camera and subjected to pre-processing. The main visual features of four dimensions, three colors and five textures are acquired using image-processing techniques (IPTs). A total of 21 visual features are reproduced from the 12 main features to diversify the input population for training and testing the ANN model. The data sets of visual features are considered as input parameters of the ANN model. The ANN with four different input data subsets is modelled to classify the wheat grains into bread or durum. The ANN model is trained with 180 grains and its accuracy tested with 20 grains from a total of 200 wheat grains. Seven input parameters that are most effective on the classifying results are determined using the correlation-based CfsSubsetEval algorithm to simplify the ANN model. The results of the ANN model are compared in terms of accuracy rate. The best result is achieved with a mean absolute error (MAE) of 9.8 × 10 -6 by the simplified ANN model. This shows that the proposed classifier based on computer vision can be successfully exploited to automatically classify a variety of grains. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

A multifactorial analysis of obesity as CVD risk factor: use of neural network based methods in a nutrigenetics context.

PubMed

Valavanis, Ioannis K; Mougiakakou, Stavroula G; Grimaldi, Keith A; Nikita, Konstantina S

2010-09-08

Obesity is a multifactorial trait, which comprises an independent risk factor for cardiovascular disease (CVD). The aim of the current work is to study the complex etiology beneath obesity and identify genetic variations and/or factors related to nutrition that contribute to its variability. To this end, a set of more than 2300 white subjects who participated in a nutrigenetics study was used. For each subject a total of 63 factors describing genetic variants related to CVD (24 in total), gender, and nutrition (38 in total), e.g. average daily intake in calories and cholesterol, were measured. Each subject was categorized according to body mass index (BMI) as normal (BMI ≤ 25) or overweight (BMI > 25). Two artificial neural network (ANN) based methods were designed and used towards the analysis of the available data. These corresponded to i) a multi-layer feed-forward ANN combined with a parameter decreasing method (PDM-ANN), and ii) a multi-layer feed-forward ANN trained by a hybrid method (GA-ANN) which combines genetic algorithms and the popular back-propagation training algorithm. PDM-ANN and GA-ANN were comparatively assessed in terms of their ability to identify the most important factors among the initial 63 variables describing genetic variations, nutrition and gender, able to classify a subject into one of the BMI related classes: normal and overweight. The methods were designed and evaluated using appropriate training and testing sets provided by 3-fold Cross Validation (3-CV) resampling. Classification accuracy, sensitivity, specificity and area under receiver operating characteristics curve were utilized to evaluate the resulted predictive ANN models. The most parsimonious set of factors was obtained by the GA-ANN method and included gender, six genetic variations and 18 nutrition-related variables. The corresponding predictive model was characterized by a mean accuracy equal of 61.46% in the 3-CV testing sets. The ANN based methods revealed factors

A multifactorial analysis of obesity as CVD risk factor: Use of neural network based methods in a nutrigenetics context

PubMed Central

2010-01-01

Background Obesity is a multifactorial trait, which comprises an independent risk factor for cardiovascular disease (CVD). The aim of the current work is to study the complex etiology beneath obesity and identify genetic variations and/or factors related to nutrition that contribute to its variability. To this end, a set of more than 2300 white subjects who participated in a nutrigenetics study was used. For each subject a total of 63 factors describing genetic variants related to CVD (24 in total), gender, and nutrition (38 in total), e.g. average daily intake in calories and cholesterol, were measured. Each subject was categorized according to body mass index (BMI) as normal (BMI ≤ 25) or overweight (BMI > 25). Two artificial neural network (ANN) based methods were designed and used towards the analysis of the available data. These corresponded to i) a multi-layer feed-forward ANN combined with a parameter decreasing method (PDM-ANN), and ii) a multi-layer feed-forward ANN trained by a hybrid method (GA-ANN) which combines genetic algorithms and the popular back-propagation training algorithm. Results PDM-ANN and GA-ANN were comparatively assessed in terms of their ability to identify the most important factors among the initial 63 variables describing genetic variations, nutrition and gender, able to classify a subject into one of the BMI related classes: normal and overweight. The methods were designed and evaluated using appropriate training and testing sets provided by 3-fold Cross Validation (3-CV) resampling. Classification accuracy, sensitivity, specificity and area under receiver operating characteristics curve were utilized to evaluate the resulted predictive ANN models. The most parsimonious set of factors was obtained by the GA-ANN method and included gender, six genetic variations and 18 nutrition-related variables. The corresponding predictive model was characterized by a mean accuracy equal of 61.46% in the 3-CV testing sets. Conclusions The ANN

Artificial Neural Networks: an overview and their use in the analysis of the AMPHORA-3 dataset.

PubMed

Buscema, Paolo Massimo; Massini, Giulia; Maurelli, Guido

2014-10-01

The Artificial Adaptive Systems (AAS) are theories with which generative algebras are able to create artificial models simulating natural phenomenon. Artificial Neural Networks (ANNs) are the more diffused and best-known learning system models in the AAS. This article describes an overview of ANNs, noting its advantages and limitations for analyzing dynamic, complex, non-linear, multidimensional processes. An example of a specific ANN application to alcohol consumption in Spain, as part of the EU AMPHORA-3 project, during 1961-2006 is presented. Study's limitations are noted and future needed research using ANN methodologies are suggested.

[Methods of artificial intelligence: a new trend in pharmacy].

PubMed

Dohnal, V; Kuca, K; Jun, D

2005-07-01

Artificial neural networks (ANN) and genetic algorithms are one group of methods called artificial intelligence. The application of ANN on pharmaceutical data can lead to an understanding of the inner structure of data and a possibility to build a model (adaptation). In addition, for certain cases it is possible to extract rules from data. The adapted ANN is prepared for the prediction of properties of compounds which were not used in the adaptation phase. The applications of ANN have great potential in pharmaceutical industry and in the interpretation of analytical, pharmacokinetic or toxicological data.

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.

Inside the Actors' Studio: Exploring Dietetics Education Practices through Dialogical Inquiry

ERIC Educational Resources Information Center

Fox, Ann L.; Gingras, Jacqui

2012-01-01

Two colleagues, Ann and Jacqui, came together, within the safety of an imagined actors' studio, to explore the challenges that Ann faced in planning a new graduate program in public health nutrition. They met before, during, and after program implementation to discuss Ann's experiences, and audio-taped and transcribed the discussions. When all…