DoD STINFO Manager Training Course STINFO Documentation

DTIC Science & Technology

2002-05-01

nawcws2.wsmr.army.mil PENNSYLVANIA ONR Manufacturing Technology Det Carderock Div NAVSURFWARCEN Attn: Philip M. Broudy (Code 20) John Bozewicz (Code 911...Available NTIS; 91N33013.) AD-A252 069 11 Pinelli, Thomas E.; Madeline Henderson; Ann P. Bishop; and Philip Doty. Chronology of Selected Literature...Mindy L Kotler . "Japanese Tech- nological Innovation: Implications for Large Commercial Aircraft and Knowledge Diffusion." Paper presented at the

Estimation of Reynolds number for flows around cylinders with lattice Boltzmann methods and artificial neural networks.

PubMed

Carrillo, Mauricio; Que, Ulices; González, José A

2016-12-01

The present work investigates the application of artificial neural networks (ANNs) to estimate the Reynolds (Re) number for flows around a cylinder. The data required to train the ANN was generated with our own implementation of a lattice Boltzmann method (LBM) code performing simulations of a two-dimensional flow around a cylinder. As results of the simulations, we obtain the velocity field (v[over ⃗]) and the vorticity (∇[over ⃗]×v[over ⃗]) of the fluid for 120 different values of Re measured at different distances from the obstacle and use them to teach the ANN to predict the Re. The results predicted by the networks show good accuracy with errors of less than 4% in all the studied cases. One of the possible applications of this method is the development of an efficient tool to characterize a blocked flowing pipe.

Enabling large-scale viscoelastic calculations via neural network acceleration

NASA Astrophysics Data System (ADS)

Robinson DeVries, P.; Thompson, T. B.; Meade, B. J.

2017-12-01

One of the most significant challenges involved in efforts to understand the effects of repeated earthquake cycle activity are the computational costs of large-scale viscoelastic earthquake cycle models. Deep artificial neural networks (ANNs) can be used to discover new, compact, and accurate computational representations of viscoelastic physics. Once found, these efficient ANN representations may replace computationally intensive viscoelastic codes and accelerate large-scale viscoelastic calculations by more than 50,000%. This magnitude of acceleration enables the modeling of geometrically complex faults over thousands of earthquake cycles across wider ranges of model parameters and at larger spatial and temporal scales than have been previously possible. Perhaps most interestingly from a scientific perspective, ANN representations of viscoelastic physics may lead to basic advances in the understanding of the underlying model phenomenology. We demonstrate the potential of artificial neural networks to illuminate fundamental physical insights with specific examples.

Engineering and technology measures to improve large truck safety : state of the practice in Virginia.

DOT National Transportation Integrated Search

2003-01-01

In response to a request by Frank S. Wolf and Jo Ann Davis of the U.S. House of Representatives, Governor Mark Warner formed a Special Task Force on Truck Safety in the fall of 2002. The objective of the task force was to examine ways to reduce the n...

Carbon Nanotube Growth Rate Regression using Support Vector Machines and Artificial Neural Networks

DTIC Science & Technology

2014-03-27

intensity D peak. Reprinted with permission from [38]. The SVM classifier is trained using custom written Java code leveraging the Sequential Minimal...Society Encog is a machine learning framework for Java , C++ and .Net applications that supports Bayesian Networks, Hidden Markov Models, SVMs and ANNs [13...SVM classifiers are trained using Weka libraries and leveraging custom written Java code. The data set is created as an Attribute Relationship File

An Innovative Model to Predict Pediatric Emergency Department Return Visits.

PubMed

Bergese, Ilaria; Frigerio, Simona; Clari, Marco; Castagno, Emanuele; De Clemente, Antonietta; Ponticelli, Elena; Scavino, Enrica; Berchialla, Paola

2016-10-06

Return visit (RV) to the emergency department (ED) is considered a benchmarking clinical indicator for health care quality. The purpose of this study was to develop a predictive model for early readmission risk in pediatric EDs comparing the performances of 2 learning machine algorithms. A retrospective study based on all children younger than 15 years spontaneously returning within 120 hours after discharge was conducted in an Italian university children's hospital between October 2012 and April 2013. Two predictive models, artificial neural network (ANN) and classification tree (CT), were used. Accuracy, specificity, and sensitivity were assessed. A total of 28,341 patient records were evaluated. Among them, 626 patients returned to the ED within 120 hours after their initial visit. Comparing ANN and CT, our analysis has shown that CT is the best model to predict RVs. The CT model showed an overall accuracy of 81%, slightly lower than the one achieved by the ANN (91.3%), but CT outperformed ANN with regard to sensitivity (79.8% vs 6.9%, respectively). The specificity was similar for the 2 models (CT, 97% vs ANN, 98.3%). In addition, the time of arrival and discharge along with the priority code assigned in triage, age, and diagnosis play a pivotal role to identify patients at high risk of RVs. These models provide a promising predictive tool for supporting the ED staff in preventing unnecessary RVs.

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.

Development of performance measures for non-motorized dynamics.

DOT National Transportation Integrated Search

2013-12-27

This report recommends performance measures for non-motorized (pedestrian and bicyclists) : traffic safety for Michigan cities. Based on the data collected from four Michigan cities, Ann : Arbor, East Lansing, Flint, and Grand Rapids, the research te...

Prediction of Driver’s Intention of Lane Change by Augmenting Sensor Information Using Machine Learning Techniques

PubMed Central

Kim, Il-Hwan; Bong, Jae-Hwan; Park, Jooyoung; Park, Shinsuk

2017-01-01

Driver assistance systems have become a major safety feature of modern passenger vehicles. The advanced driver assistance system (ADAS) is one of the active safety systems to improve the vehicle control performance and, thus, the safety of the driver and the passengers. To use the ADAS for lane change control, rapid and correct detection of the driver’s intention is essential. This study proposes a novel preprocessing algorithm for the ADAS to improve the accuracy in classifying the driver’s intention for lane change by augmenting basic measurements from conventional on-board sensors. The information on the vehicle states and the road surface condition is augmented by using an artificial neural network (ANN) models, and the augmented information is fed to a support vector machine (SVM) to detect the driver’s intention with high accuracy. The feasibility of the developed algorithm was tested through driving simulator experiments. The results show that the classification accuracy for the driver’s intention can be improved by providing an SVM model with sufficient driving information augmented by using ANN models of vehicle dynamics. PMID:28604582

Improvement in the Capacity and Safety of Lithium/Inorganic Electrolyte Sulfur Dioxide Rechargeable Cells. Phase 2.

DTIC Science & Technology

1990-11-01

function) 55-21-1 20%/2C 25/75/10 (refused to function) 55-21-2 20%/2C [(Ni mat)] 93.1 61 2,removed.I55-21-3 20%/2C 97ab /3T(ann.bob.)(refused to function...55-25-1 20%/2C 25/75/10(pocket) (refused to function)I55-33-1 20%/3C 97ab /3T(ann.bob.)108 z50 mAh 2,removed. 52-93-6 40%/3C/2%I 25/75/10 121 z50 mAh

An Employee Questionnaire for Assessing Patient Safety in Outpatient Surgery

DTIC Science & Technology

2005-01-01

461 An Employee Questionnaire for Assessing Patient Safety in Outpatient Surgery Pascale Carayon, Carla J. Alvarado, Ann Schoofs Hundt, Scott...Springman, Amanda Borgsdorf, Peter L.T. Hoonakker Abstract This paper provides information on the reliability and validity of an employee ...intervention on both employees and patients. In this paper, we describe the SEIPS employee questionnaire, which surveys various elements of the work system

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.

IEEE International Symposium on Information Theory (ISIT): Abstracts of Papers, Held in Ann Arbor, Michigan on 6-9 October 1986.

DTIC Science & Technology

1986-10-01

BUZO, and FEDERICO KUHLMANN, Universidad Nacional Autdnoma de Mixico, Facultad de Ingenieria , Divisidn Estudios de Posgrado, P.O. Box 70-256, 04510...unsuccess- ful in this area for a long time. It was felt, e.g., in the voiceband modem industry , that the coding gains achievable by error-correction coding...without bandwidth expansion or data rate reduction, when compared to uncoded modulation. The concept was quickly adopted by industry , and is now becoming

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.

HR Public meeting

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

Heuer, Rolf; Catherin, Anne-Sylvie; Vuillemin, Vin

2010-06-25

Cher(e)s collègues, En collaboration avec le Département HR, le Directeur général a le plaisir de vous convier à une réunion publique qui se tiendra le vendredi 25 juin 2010 à 9h30 dans l’Amphithéâtre principal (Bâtiment 500)*. Un café d’accueil y sera servi à partir de 9h. Cette réunion abordera les thèmes suivants : • Valeurs de l’Organisation (Directeur général) • Code de Conduite (Directeur général / Anne-Sylvie Catherin) • Création du nouveau rôle d’Ombudsperson (Vincent Vuillemin); Ces présentations seront suivies d’une séance de questions-réponses. Nous espérons vous retrouver nombreux le 25 juin ! Meilleures salutations, Anne-Sylvie Catherin Chef du Départementmore » des Ressources humaines *Cette réunion sera retransmise simultanément dans l’Amphithéâtre BE de Prévessin (Bâtiment 864) et également disponible à l’adresse suivante : http://webcast.cern.ch. Dear colleagues, In collaboration with HR Department, the Director-General would like to invite you to an information meeting which will be held on Friday 25 June 2010 at 9:30 am in the Main Auditorium (Building 500)*. A welcome coffee will be available from 9:00 am. During this meeting, information will be given about: • Organization’s values (Director-General) • Code of Conduct (Director-General / Anne-Sylvie Catherin) • New Ombudsperson role (Vincent Vuillemin) These presentations will be followed by a questions & answers session. We look forward to seeing you all on 25 June! Best regards, Anne-Sylvie Catherin Head, Human Resources Department. This meeting will be simultaneously retransmitted in BE Auditorium (Building 864) and available at the following address: http://webcast.cern.ch.« less

HR Public meeting

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

None

2010-10-12

Cher(e)s collègues, En collaboration avec le Département HR, le Directeur général a le plaisir de vous convier à une réunion publique qui se tiendra le vendredi 25 juin 2010 à 9h30 dans l’Amphithéâtre principal (Bâtiment 500)*. Un café d’accueil y sera servi à partir de 9h. Cette réunion abordera les thèmes suivants : Valeurs de l’Organisation (Directeur général); Code de Conduite (Directeur général / Anne-Sylvie Catherin); Création du nouveau rôle d’Ombudsperson (Vincent Vuillemin) Ces présentations seront suivies d’une séance de questions-réponses. Nous espérons vous retrouver nombreux le 25 juin ! Meilleures salutations, Anne-Sylvie Catherin Chef du Département des Ressources humainesmore » *Cette réunion sera retransmise simultanément dans l’Amphithéâtre BE de Prévessin (Bâtiment 864) et également disponible à l’adresse suivante : http://webcast.cern.ch [Dear colleagues, In collaboration with HR Department, the Director-General would like to invite you to an information meeting which will be held on Friday 25 June 2010 at 9:30 am in the Main Auditorium (Building 500)*. A welcome coffee will be available from 9:00 am. During this meeting, information will be given about: Organization’s values (Director-General); Code of Conduct (Director-General / Anne-Sylvie Catherin); New Ombudsperson role (Vincent Vuillemin); These presentations will be followed by a questions & answers session. We look forward to seeing you all on 25 June! Best regards, Anne-Sylvie Catherin Head, Human Resources Department *This meeting will be simultaneously retransmitted in BE Auditorium (Building 864) and available at the following address: http://webcast.cern.ch.« less

Release of Iron from Hemoglobin

DTIC Science & Technology

1993-02-17

Medical Research and Development Division of Blood Research SGRD-ULY-BRP Command 6C. ADDRESS KCay. State, And ZIP Code) 7b. ADDRESS (Cjry. Stitt, and...17]. 28. D. P. Derman, A. Green, T. H. Bothwell, B. Graham, L. McNamara, A. P. MacPhail and R. D. Baynes . Ann. Clin. Biochem. 26, 144; 1989. 29. W. W

Recent Advances in the Development of an Improved, Human Anthrax Vaccine

DTIC Science & Technology

1988-03-01

ology of toxin and capsule production and mode component of gram-negative endotoxin, trehalose of action, the improved methods developed for...im- for their safety and efficacy in potentiating immu- munoprophylaxis ot inhalation anthrax. - Abstr. nity to anthrax. Ann. Meeting. Am. Soc

Implementing Signature Neural Networks with Spiking Neurons

PubMed Central

Carrillo-Medina, José Luis; Latorre, Roberto

2016-01-01

Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question for the research community and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed Signature Neural Network paradigm—i.e., neural signatures to identify each unit in the network, local information contextualization during the processing, and multicoding strategies for information propagation regarding the origin and the content of the data—to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the absence of inhibitory connections. These parameters also modulate the memory capabilities of the network. The dynamical modes observed in the different informational dimensions in a given moment are independent and they only depend on the parameters shaping the information processing in this dimension. In view of these results, we argue that plasticity mechanisms inside individual cells and multicoding strategies can provide additional computational properties to spiking neural networks, which could enhance their capacity and performance in a wide variety of real-world tasks. PMID:28066221

Implementing Signature Neural Networks with Spiking Neurons.

PubMed

Carrillo-Medina, José Luis; Latorre, Roberto

2016-01-01

Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question for the research community and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed Signature Neural Network paradigm-i.e., neural signatures to identify each unit in the network, local information contextualization during the processing, and multicoding strategies for information propagation regarding the origin and the content of the data-to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the absence of inhibitory connections. These parameters also modulate the memory capabilities of the network. The dynamical modes observed in the different informational dimensions in a given moment are independent and they only depend on the parameters shaping the information processing in this dimension. In view of these results, we argue that plasticity mechanisms inside individual cells and multicoding strategies can provide additional computational properties to spiking neural networks, which could enhance their capacity and performance in a wide variety of real-world tasks.

Estimating atmospheric visibility using synergy of MODIS data and ground-based observations

NASA Astrophysics Data System (ADS)

Komeilian, H.; Mohyeddin Bateni, S.; Xu, T.; Nielson, J.

2015-05-01

Dust events are intricate climatic processes, which can have adverse effects on human health, safety, and the environment. In this study, two data mining approaches, namely, back-propagation artificial neural network (BP ANN) and supporting vector regression (SVR), were used to estimate atmospheric visibility through the synergistic use of Moderate Resolution Imaging Spectroradiometer (MODIS) Level 1B (L1B) data and ground-based observations at fourteen stations in the province of Khuzestan (southwestern Iran), during 2009-2010. Reflectance and brightness temperature in different bands (from MODIS) along with in situ meteorological data were input to the models to estimate atmospheric visibility. The results show that both models can accurately estimate atmospheric visibility. The visibility estimates from the BP ANN network had a root-mean-square error (RMSE) and Pearson's correlation coefficient (R) of 0.67 and 0.69, respectively. The corresponding RMSE and R from the SVR model were 0.59 and 0.71, implying that the SVR approach outperforms the BP ANN.

Alternative Fuels Data Center: Biodiesel Codes, Standards, and Safety

Science.gov Websites

Codes, Standards, and Safety to someone by E-mail Share Alternative Fuels Data Center: Biodiesel Codes, Standards, and Safety on Facebook Tweet about Alternative Fuels Data Center: Biodiesel Codes , Standards, and Safety on Twitter Bookmark Alternative Fuels Data Center: Biodiesel Codes, Standards, and

Entropy based file type identification and partitioning

DTIC Science & Technology

2017-06-01

energy spectrum,” Proceedings of the Twenty-Ninth International Florida Artificial Intelligence Research Society Conference, pp. 288–293, 2016...ABBREVIATIONS AES Advanced Encryption Standard ANN Artificial Neural Network ASCII American Standard Code for Information Interchange CWT...the identification of file types and file partitioning. This approach has applications in cybersecurity as it allows for a quick determination of

75 FR 37300 - Correction of Code of Federal Regulations: Removal of Temporary Listing of Benzylfentanyl and...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-06-29

... scheduled fentanyl compounds at the University of Michigan Medical School in Ann Arbor and at the Medical College of Virginia in Richmond. The studies indicated that while most of the fentanyl compounds had abuse... samples with other fentanyl analogues and were most likely unreacted intermediates in the synthesis of the...

An assessment of Virginia's law requiring the forfeiture of any vehicle driven by a person under license suspension or revocation.

DOT National Transportation Integrated Search

1975-01-01

In Virginia an individual arrested for the first time for driving while his driver's license is suspended or revoked is subject to the following penalties: He will be jailed for not less than ten days and not more than six months (Va. Code Ann. secti...

40 CFR 147.2050 - State-administered program.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 24, 1984. This program consists of the following elements, as submitted to EPA in the State's program... the Director of the Federal Register effective July 24, 1984. (1) Pollution Control Act, S.C. Code Ann. Sections 48-1-10, 48-1-90, 48-1-100, 48-1-110 (Law. Co-op. 1976 and Supp. 1983). (2) South Carolina...

Using an artificial neural network to classify multicomponent emission lines with integral field spectroscopy from SAMI and S7

NASA Astrophysics Data System (ADS)

Hampton, E. J.; Medling, A. M.; Groves, B.; Kewley, L.; Dopita, M.; Davies, R.; Ho, I.-T.; Kaasinen, M.; Leslie, S.; Sharp, R.; Sweet, S. M.; Thomas, A. D.; Allen, J.; Bland-Hawthorn, J.; Brough, S.; Bryant, J. J.; Croom, S.; Goodwin, M.; Green, A.; Konstantantopoulos, I. S.; Lawrence, J.; López-Sánchez, Á. R.; Lorente, N. P. F.; McElroy, R.; Owers, M. S.; Richards, S. N.; Shastri, P.

2017-09-01

Integral field spectroscopy (IFS) surveys are changing how we study galaxies and are creating vastly more spectroscopic data available than before. The large number of resulting spectra makes visual inspection of emission line fits an infeasible option. Here, we present a demonstration of an artificial neural network (ANN) that determines the number of Gaussian components needed to describe the complex emission line velocity structures observed in galaxies after being fit with lzifu. We apply our ANN to IFS data for the S7 survey, conducted using the Wide Field Spectrograph on the ANU 2.3 m Telescope, and the SAMI Galaxy Survey, conducted using the SAMI instrument on the 4 m Anglo-Australian Telescope. We use the spectral fitting code lzifu (Ho et al. 2016a) to fit the emission line spectra of individual spaxels from S7 and SAMI data cubes with 1-, 2- and 3-Gaussian components. We demonstrate that using an ANN is comparable to astronomers performing the same visual inspection task of determining the best number of Gaussian components to describe the physical processes in galaxies. The advantage of our ANN is that it is capable of processing the spectra for thousands of galaxies in minutes, as compared to the years this task would take individual astronomers to complete by visual inspection.

Waterlogging risk assessment based on self-organizing map (SOM) artificial neural networks: A case study of an urban storm in Beijing

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

Lai, Wen-li; Wang, Hong-rui; Wang, Cheng

Due to rapid urbanization, waterlogging induced by torrential rainfall has become a global concern and a potential risk affecting urban habitant's safety. Widespread waterlogging disasters have occurred almost annually in the urban area of Beijing, the capital of China. Based on a self-organizing map (SOM) artificial neural network (ANN), a graded waterlogging risk assessment was conducted on 56 low-lying points in Beijing, China. Social risk factors, such as Gross domestic product (GDP), population density, and traffic congestion, were utilized as input datasets in this study. The results indicate that SOM-ANN is suitable for automatically and quantitatively assessing risks associated withmore » waterlogging. The greatest advantage of SOM-ANN in the assessment of waterlogging risk is that a priori knowledge about classification categories and assessment indicator weights is not needed. As a result, SOM-ANN can effectively overcome interference from subjective factors, producing classification results that are more objective and accurate. In this paper, the risk level of waterlogging in Beijing was divided into five grades. As a result, the points that were assigned risk grades of IV or V were located mainly in the districts of Chaoyang, Haidian, Xicheng, and Dongcheng.« less

Waterlogging risk assessment based on self-organizing map (SOM) artificial neural networks: A case study of an urban storm in Beijing

DOE PAGES

Lai, Wen-li; Wang, Hong-rui; Wang, Cheng; ...

2017-05-05

Due to rapid urbanization, waterlogging induced by torrential rainfall has become a global concern and a potential risk affecting urban habitant's safety. Widespread waterlogging disasters have occurred almost annually in the urban area of Beijing, the capital of China. Based on a self-organizing map (SOM) artificial neural network (ANN), a graded waterlogging risk assessment was conducted on 56 low-lying points in Beijing, China. Social risk factors, such as Gross domestic product (GDP), population density, and traffic congestion, were utilized as input datasets in this study. The results indicate that SOM-ANN is suitable for automatically and quantitatively assessing risks associated withmore » waterlogging. The greatest advantage of SOM-ANN in the assessment of waterlogging risk is that a priori knowledge about classification categories and assessment indicator weights is not needed. As a result, SOM-ANN can effectively overcome interference from subjective factors, producing classification results that are more objective and accurate. In this paper, the risk level of waterlogging in Beijing was divided into five grades. As a result, the points that were assigned risk grades of IV or V were located mainly in the districts of Chaoyang, Haidian, Xicheng, and Dongcheng.« less

A neural network gravitational arc finder based on the Mediatrix filamentation method

NASA Astrophysics Data System (ADS)

Bom, C. R.; Makler, M.; Albuquerque, M. P.; Brandt, C. H.

2017-01-01

Context. Automated arc detection methods are needed to scan the ongoing and next-generation wide-field imaging surveys, which are expected to contain thousands of strong lensing systems. Arc finders are also required for a quantitative comparison between predictions and observations of arc abundance. Several algorithms have been proposed to this end, but machine learning methods have remained as a relatively unexplored step in the arc finding process. Aims: In this work we introduce a new arc finder based on pattern recognition, which uses a set of morphological measurements that are derived from the Mediatrix filamentation method as entries to an artificial neural network (ANN). We show a full example of the application of the arc finder, first training and validating the ANN on simulated arcs and then applying the code on four Hubble Space Telescope (HST) images of strong lensing systems. Methods: The simulated arcs use simple prescriptions for the lens and the source, while mimicking HST observational conditions. We also consider a sample of objects from HST images with no arcs in the training of the ANN classification. We use the training and validation process to determine a suitable set of ANN configurations, including the combination of inputs from the Mediatrix method, so as to maximize the completeness while keeping the false positives low. Results: In the simulations the method was able to achieve a completeness of about 90% with respect to the arcs that are input into the ANN after a preselection. However, this completeness drops to 70% on the HST images. The false detections are on the order of 3% of the objects detected in these images. Conclusions: The combination of Mediatrix measurements with an ANN is a promising tool for the pattern-recognition phase of arc finding. More realistic simulations and a larger set of real systems are needed for a better training and assessment of the efficiency of the method.

Improving Professional Judgments of Risk and Amenability in Juvenile Justice

ERIC Educational Resources Information Center

Mulvey, Edward P.; Iselin, Anne-Marie R.

2008-01-01

The dual requirement to ensure community safety and promote a youthful offender's positive development permeates policy and frames daily practice in juvenile justice. Balancing those two demands, explain Edward Mulvey and Anne-Marie Iselin, requires justice system professionals at all levels to make extremely difficult decisions about the likely…

Searching for Mercy Street: Protecting Records after the Client's Death.

ERIC Educational Resources Information Center

Schoener, Gary R.

The duties of a therapist to a deceased client are not directly dealt with in codes of ethics. The issues came into focus following the publication of a biography of Anne Sexton, as it contained information from more than 80 hours of therapy that Ms. Sexton's psychologist released to the biographer. This paper considers the question of whether the…

What, Who, or Where? Rejoinder to "Identifying Research Topic Development in Business and Management Education Research Using Legitimation Code Theory"

ERIC Educational Resources Information Center

Harzing, Anne-Wil

2016-01-01

This brief commentary investigates whether article topic, author profile, or journal rank significantly influence an article's citation levels. Anne-Wil Harzing's regression analysis shows that, when all factors are taken into account at the same time, it is "what" is published (topic) and "who" has published it (author) that…

77 FR 71633 - Notice of Lodging of Proposed Consent Decree Under the Clean Water Act

Federal Register 2010, 2011, 2012, 2013, 2014

2012-12-03

... would resolve certain claims under Sections 301, 309, and 402 of the Clean Water Act, 33 U.S.C. 1251, et seq. and under the Mississippi Air and Water Pollution Control Law (``MAWPCL'') (Miss. Code Ann. Sec... DEPARTMENT OF JUSTICE Notice of Lodging of Proposed Consent Decree Under the Clean Water Act On...

HR Public meeting

ScienceCinema

None

2018-05-24

Cher(e)s collègues, En collaboration avec le Département HR, le Directeur général a le plaisir de vous convier à une réunion publique qui se tiendra le vendredi 25 juin 2010 à 9h30 dans l’Amphithéâtre principal (Bâtiment 500)*. Un café d’accueil y sera servi à partir de 9h. Cette réunion abordera les thèmes suivants : Valeurs de l’Organisation (Directeur général); Code de Conduite (Directeur général / Anne-Sylvie Catherin); Création du nouveau rôle d’Ombudsperson (Vincent Vuillemin) Ces présentations seront suivies d’une séance de questions-réponses. Nous espérons vous retrouver nombreux le 25 juin ! Meilleures salutations, Anne-Sylvie Catherin Chef du Département des Ressources humaines *Cette réunion sera retransmise simultanément dans l’Amphithéâtre BE de Prévessin (Bâtiment 864) et également disponible à l’adresse suivante : http://webcast.cern.ch [Dear colleagues, In collaboration with HR Department, the Director-General would like to invite you to an information meeting which will be held on Friday 25 June 2010 at 9:30 am in the Main Auditorium (Building 500)*. A welcome coffee will be available from 9:00 am. During this meeting, information will be given about: Organization’s values (Director-General); Code of Conduct (Director-General / Anne-Sylvie Catherin); New Ombudsperson role (Vincent Vuillemin); These presentations will be followed by a questions & answers session. We look forward to seeing you all on 25 June! Best regards, Anne-Sylvie Catherin Head, Human Resources Department *This meeting will be simultaneously retransmitted in BE Auditorium (Building 864) and available at the following address: http://webcast.cern.ch.

Neural network river forecasting through baseflow separation and binary-coded swarm optimization

NASA Astrophysics Data System (ADS)

Taormina, Riccardo; Chau, Kwok-Wing; Sivakumar, Bellie

2015-10-01

The inclusion of expert knowledge in data-driven streamflow modeling is expected to yield more accurate estimates of river quantities. Modular models (MMs) designed to work on different parts of the hydrograph are preferred ways to implement such approach. Previous studies have suggested that better predictions of total streamflow could be obtained via modular Artificial Neural Networks (ANNs) trained to perform an implicit baseflow separation. These MMs fit separately the baseflow and excess flow components as produced by a digital filter, and reconstruct the total flow by adding these two signals at the output. The optimization of the filter parameters and ANN architectures is carried out through global search techniques. Despite the favorable premises, the real effectiveness of such MMs has been tested only on a few case studies, and the quality of the baseflow separation they perform has never been thoroughly assessed. In this work, we compare the performance of MM against global models (GMs) for nine different gaging stations in the northern United States. Binary-coded swarm optimization is employed for the identification of filter parameters and model structure, while Extreme Learning Machines, instead of ANN, are used to drastically reduce the large computational times required to perform the experiments. The results show that there is no evidence that MM outperform global GM for predicting the total flow. In addition, the baseflow produced by the MM largely underestimates the actual baseflow component expected for most of the considered gages. This occurs because the values of the filter parameters maximizing overall accuracy do not reflect the geological characteristics of the river basins. The results indeed show that setting the filter parameters according to expert knowledge results in accurate baseflow separation but lower accuracy of total flow predictions, suggesting that these two objectives are intrinsically conflicting rather than compatible.

38 CFR 61.20 - Life Safety Code capital grants.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 38 Pensions, Bonuses, and Veterans' Relief 2 2011-07-01 2011-07-01 false Life Safety Code capital... (CONTINUED) VA HOMELESS PROVIDERS GRANT AND PER DIEM PROGRAM § 61.20 Life Safety Code capital grants. (a) This section sets forth provisions for obtaining a Life Safety Code capital grant under 38 U.S.C. 2012...

38 CFR 61.20 - Life Safety Code capital grants.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 38 Pensions, Bonuses, and Veterans' Relief 2 2010-07-01 2010-07-01 false Life Safety Code capital... (CONTINUED) VA HOMELESS PROVIDERS GRANT AND PER DIEM PROGRAM § 61.20 Life Safety Code capital grants. (a) This section sets forth provisions for obtaining a Life Safety Code capital grant under 38 U.S.C. 2012...

38 CFR 61.20 - Life Safety Code capital grants.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 38 Pensions, Bonuses, and Veterans' Relief 2 2012-07-01 2012-07-01 false Life Safety Code capital... (CONTINUED) VA HOMELESS PROVIDERS GRANT AND PER DIEM PROGRAM § 61.20 Life Safety Code capital grants. (a) This section sets forth provisions for obtaining a Life Safety Code capital grant under 38 U.S.C. 2012...

Prediction of U-Mo dispersion nuclear fuels with Al-Si alloy using artificial neural network

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

Susmikanti, Mike, E-mail: mike@batan.go.id; Sulistyo, Jos, E-mail: soj@batan.go.id

2014-09-30

Dispersion nuclear fuels, consisting of U-Mo particles dispersed in an Al-Si matrix, are being developed as fuel for research reactors. The equilibrium relationship for a mixture component can be expressed in the phase diagram. It is important to analyze whether a mixture component is in equilibrium phase or another phase. The purpose of this research it is needed to built the model of the phase diagram, so the mixture component is in the stable or melting condition. Artificial neural network (ANN) is a modeling tool for processes involving multivariable non-linear relationships. The objective of the present work is to developmore » code based on artificial neural network models of system equilibrium relationship of U-Mo in Al-Si matrix. This model can be used for prediction of type of resulting mixture, and whether the point is on the equilibrium phase or in another phase region. The equilibrium model data for prediction and modeling generated from experimentally data. The artificial neural network with resilient backpropagation method was chosen to predict the dispersion of nuclear fuels U-Mo in Al-Si matrix. This developed code was built with some function in MATLAB. For simulations using ANN, the Levenberg-Marquardt method was also used for optimization. The artificial neural network is able to predict the equilibrium phase or in the phase region. The develop code based on artificial neural network models was built, for analyze equilibrium relationship of U-Mo in Al-Si matrix.« less

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

NASA Technical Reports Server (NTRS)

Holloway, C. Michael

2002-01-01

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

Synthesizing Safety Conditions for Code Certification Using Meta-Level Programming

NASA Technical Reports Server (NTRS)

Eusterbrock, Jutta

2004-01-01

In code certification the code consumer publishes a safety policy and the code producer generates a proof that the produced code is in compliance with the published safety policy. In this paper, a novel viewpoint approach towards an implementational re-use oriented framework for code certification is taken. It adopts ingredients from Necula's approach for proof-carrying code, but in this work safety properties can be analyzed on a higher code level than assembly language instructions. It consists of three parts: (1) The specification language is extended to include generic pre-conditions that shall ensure safety at all states that can be reached during program execution. Actual safety requirements can be expressed by providing domain-specific definitions for the generic predicates which act as interface to the environment. (2) The Floyd-Hoare inductive assertion method is refined to obtain proof rules that allow the derivation of the proof obligations in terms of the generic safety predicates. (3) A meta-interpreter is designed and experimentally implemented that enables automatic synthesis of proof obligations for submitted programs by applying the modified Floyd-Hoare rules. The proof obligations have two separate conjuncts, one for functional correctness and another for the generic safety obligations. Proof of the generic obligations, having provided the actual safety definitions as context, ensures domain-specific safety of program execution in a particular environment and is simpler than full program verification.

76 FR 11339 - Update to NFPA 101, Life Safety Code, for State Home Facilities

Federal Register 2010, 2011, 2012, 2013, 2014

2011-03-02

... DEPARTMENT OF VETERANS AFFAIRS 38 CFR Part 51 RIN 2900-AN59 Update to NFPA 101, Life Safety Code..., Life Safety Code. The change is designed to assure that State Home facilities meet current industry- wide standards regarding life safety and fire safety. DATES: Effective Date: This final rule is...

Pre-Service Teachers Institute

NASA Image and Video Library

2008-07-18

The Pre-Service Teachers Institute sponsored by Jackson (Miss.) State University participated in an agencywide Hubble Space Telescope workshop at Stennis Space Center on July 18. Twenty-five JSU junior education majors participated in the workshop, a site tour and educational presentations by Karma Snyder of the NASA SSC Engineering & Safety Center and Anne Peek of the NASA SSC Deputy Science & Technology Division.

Pre-Service Teachers Institute

NASA Technical Reports Server (NTRS)

2008-01-01

The Pre-Service Teachers Institute sponsored by Jackson (Miss.) State University participated in an agencywide Hubble Space Telescope workshop at Stennis Space Center on July 18. Twenty-five JSU junior education majors participated in the workshop, a site tour and educational presentations by Karma Snyder of the NASA SSC Engineering & Safety Center and Anne Peek of the NASA SSC Deputy Science & Technology Division.

Adapting the ADAM Manikin Technology for Injury Probability Assessment

DTIC Science & Technology

1992-02-19

Benson, J.B., Holstein , G.L., Melvin, J.W. Whole Body Response Research Program. Ann Arbor, MI, University of Michigan, Highway Safety Research...Liver Laceration I Fatal 25 227 9. UNKNOWN - COW ’ Aassiou" Ntoobor of 14my~ PAft Irni Type [ rid Ca ry Rados Neck tam 2nd D•s"* I Minimwl 4 Neck

Design and optimization of Artificial Neural Networks for the modelling of superconducting magnets operation in tokamak fusion reactors

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

Froio, A.; Bonifetto, R.; Carli, S.

In superconducting tokamaks, the cryoplant provides the helium needed to cool different clients, among which by far the most important one is the superconducting magnet system. The evaluation of the transient heat load from the magnets to the cryoplant is fundamental for the design of the latter and the assessment of suitable strategies to smooth the heat load pulses, induced by the intrinsically pulsed plasma scenarios characteristic of today's tokamaks, is crucial for both suitable sizing and stable operation of the cryoplant. For that evaluation, accurate but expensive system-level models, as implemented in e.g. the validated state-of-the-art 4C code, weremore » developed in the past, including both the magnets and the respective external cryogenic cooling circuits. Here we show how these models can be successfully substituted with cheaper ones, where the magnets are described by suitably trained Artificial Neural Networks (ANNs) for the evaluation of the heat load to the cryoplant. First, two simplified thermal-hydraulic models for an ITER Toroidal Field (TF) magnet and for the ITER Central Solenoid (CS) are developed, based on ANNs, and a detailed analysis of the chosen networks' topology and parameters is presented and discussed. The ANNs are then inserted into the 4C model of the ITER TF and CS cooling circuits, which also includes active controls to achieve a smoothing of the variation of the heat load to the cryoplant. The training of the ANNs is achieved using the results of full 4C simulations (including detailed models of the magnets) for conventional sigmoid-like waveforms of the drivers and the predictive capabilities of the ANN-based models in the case of actual ITER operating scenarios are demonstrated by comparison with the results of full 4C runs, both with and without active smoothing, in terms of both accuracy and computational time. Exploiting the low computational effort requested by the ANN-based models, a demonstrative optimization study has been finally carried out, with the aim of choosing among different smoothing strategies for the standard ITER plasma operation.« less

Natural Language Interface for Safety Certification of Safety-Critical Software

NASA Technical Reports Server (NTRS)

Denney, Ewen; Fischer, Bernd

2011-01-01

Model-based design and automated code generation are being used increasingly at NASA. The trend is to move beyond simulation and prototyping to actual flight code, particularly in the guidance, navigation, and control domain. However, there are substantial obstacles to more widespread adoption of code generators in such safety-critical domains. Since code generators are typically not qualified, there is no guarantee that their output is correct, and consequently the generated code still needs to be fully tested and certified. The AutoCert generator plug-in supports the certification of automatically generated code by formally verifying that the generated code is free of different safety violations, by constructing an independently verifiable certificate, and by explaining its analysis in a textual form suitable for code reviews.

Failure Diagnosis and Prognosis of Rolling - Element Bearings using Artificial Neural Networks: A Critical Overview

NASA Astrophysics Data System (ADS)

Rao, B. K. N.; Srinivasa Pai, P.; Nagabhushana, T. N.

2012-05-01

Rolling - Element Bearings are extensively used in almost all global industries. Any critical failures in these vitally important components would not only affect the overall systems performance but also its reliability, safety, availability and cost-effectiveness. Proactive strategies do exist to minimise impending failures in real time and at a minimum cost. Continuous innovative developments are taking place in the field of Artificial Neural Networks (ANNs) technology. Significant research and development are taking place in many universities, private and public organizations and a wealth of published literature is available highlighting the potential benefits of employing ANNs in intelligently monitoring, diagnosing, prognosing and managing rolling-element bearing failures. This paper attempts to critically review the recent trends in this topical area of interest.

29 CFR 1915.90 - Safety color code for marking physical hazards.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 29 Labor 7 2013-07-01 2013-07-01 false Safety color code for marking physical hazards. 1915.90 Section 1915.90 Labor Regulations Relating to Labor (Continued) OCCUPATIONAL SAFETY AND HEALTH... General Working Conditions § 1915.90 Safety color code for marking physical hazards. The requirements...

29 CFR 1915.90 - Safety color code for marking physical hazards.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 29 Labor 7 2014-07-01 2014-07-01 false Safety color code for marking physical hazards. 1915.90 Section 1915.90 Labor Regulations Relating to Labor (Continued) OCCUPATIONAL SAFETY AND HEALTH... General Working Conditions § 1915.90 Safety color code for marking physical hazards. The requirements...

29 CFR 1915.90 - Safety color code for marking physical hazards.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 29 Labor 7 2012-07-01 2012-07-01 false Safety color code for marking physical hazards. 1915.90 Section 1915.90 Labor Regulations Relating to Labor (Continued) OCCUPATIONAL SAFETY AND HEALTH... General Working Conditions § 1915.90 Safety color code for marking physical hazards. The requirements...

76 FR 77549 - Colorado River Indian Tribes-Amendment to Health & Safety Code, Article 2. Liquor

Federal Register 2010, 2011, 2012, 2013, 2014

2011-12-13

... Health & Safety Code, Article 2. Liquor AGENCY: Bureau of Indian Affairs, Interior. ACTION: Notice. SUMMARY: This notice publishes the amendment to the Colorado River Tribal Health and Safety Code, Article... Code, Article 2, Liquor by Ordinance No. 10-03 on December 13, 2010. This notice is published in...

Fall Detection Using Smartphone Audio Features.

PubMed

Cheffena, Michael

2016-07-01

An automated fall detection system based on smartphone audio features is developed. The spectrogram, mel frequency cepstral coefficents (MFCCs), linear predictive coding (LPC), and matching pursuit (MP) features of different fall and no-fall sound events are extracted from experimental data. Based on the extracted audio features, four different machine learning classifiers: k-nearest neighbor classifier (k-NN), support vector machine (SVM), least squares method (LSM), and artificial neural network (ANN) are investigated for distinguishing between fall and no-fall events. For each audio feature, the performance of each classifier in terms of sensitivity, specificity, accuracy, and computational complexity is evaluated. The best performance is achieved using spectrogram features with ANN classifier with sensitivity, specificity, and accuracy all above 98%. The classifier also has acceptable computational requirement for training and testing. The system is applicable in home environments where the phone is placed in the vicinity of the user.

Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data.

PubMed

Ching, Travers; Zhu, Xun; Garmire, Lana X

2018-04-01

Artificial neural networks (ANN) are computing architectures with many interconnections of simple neural-inspired computing elements, and have been applied to biomedical fields such as imaging analysis and diagnosis. We have developed a new ANN framework called Cox-nnet to predict patient prognosis from high throughput transcriptomics data. In 10 TCGA RNA-Seq data sets, Cox-nnet achieves the same or better predictive accuracy compared to other methods, including Cox-proportional hazards regression (with LASSO, ridge, and mimimax concave penalty), Random Forests Survival and CoxBoost. Cox-nnet also reveals richer biological information, at both the pathway and gene levels. The outputs from the hidden layer node provide an alternative approach for survival-sensitive dimension reduction. In summary, we have developed a new method for accurate and efficient prognosis prediction on high throughput data, with functional biological insights. The source code is freely available at https://github.com/lanagarmire/cox-nnet.

SAFETY IN THE DESIGN OF SCIENCE LABORATORIES AND BUILDING CODES.

ERIC Educational Resources Information Center

HOROWITZ, HAROLD

THE DESIGN OF COLLEGE AND UNIVERSITY BUILDINGS USED FOR SCIENTIFIC RESEARCH AND EDUCATION IS DISCUSSED IN TERMS OF LABORATORY SAFETY AND BUILDING CODES AND REGULATIONS. MAJOR TOPIC AREAS ARE--(1) SAFETY RELATED DESIGN FEATURES OF SCIENCE LABORATORIES, (2) LABORATORY SAFETY AND BUILDING CODES, AND (3) EVIDENCE OF UNSAFE DESIGN. EXAMPLES EMPHASIZE…

Highway Safety Program Manual: Volume 6: Codes and Laws.

ERIC Educational Resources Information Center

National Highway Traffic Safety Administration (DOT), Washington, DC.

Volume 6 of the 19-volume Highway Safety Program Manual (which provides guidance to State and local governments on preferred safety practices) concentrates on codes and laws. The purpose and specific objectives of the Codes and Laws Program, Federal authority in the area of highway safety, and policies regarding traffic regulation are described.…

Prediction of surface distress using neural networks

NASA Astrophysics Data System (ADS)

Hamdi, Hadiwardoyo, Sigit P.; Correia, A. Gomes; Pereira, Paulo; Cortez, Paulo

2017-06-01

Road infrastructures contribute to a healthy economy throughout a sustainable distribution of goods and services. A road network requires appropriately programmed maintenance treatments in order to keep roads assets in good condition, providing maximum safety for road users under a cost-effective approach. Surface Distress is the key element to identify road condition and may be generated by many different factors. In this paper, a new approach is aimed to predict Surface Distress Index (SDI) values following a data-driven approach. Later this model will be accordingly applied by using data obtained from the Integrated Road Management System (IRMS) database. Artificial Neural Networks (ANNs) are used to predict SDI index using input variables related to the surface of distress, i.e., crack area and width, pothole, rutting, patching and depression. The achieved results show that ANN is able to predict SDI with high correlation factor (R2 = 0.996%). Moreover, a sensitivity analysis was applied to the ANN model, revealing the influence of the most relevant input parameters for SDI prediction, namely rutting (59.8%), crack width (29.9%) and crack area (5.0%), patching (3.0%), pothole (1.7%) and depression (0.3%).

HR Public meeting

ScienceCinema

Heuer, Rolf; Catherin, Anne-Sylvie; Vuillemin, Vin

2018-05-25

Cher(e)s collègues, En collaboration avec le Département HR, le Directeur général a le plaisir de vous convier à une réunion publique qui se tiendra le vendredi 25 juin 2010 à 9h30 dans l’Amphithéâtre principal (Bâtiment 500)*. Un café d’accueil y sera servi à partir de 9h. Cette réunion abordera les thèmes suivants : • Valeurs de l’Organisation (Directeur général) • Code de Conduite (Directeur général / Anne-Sylvie Catherin) • Création du nouveau rôle d’Ombudsperson (Vincent Vuillemin); Ces présentations seront suivies d’une séance de questions-réponses. Nous espérons vous retrouver nombreux le 25 juin ! Meilleures salutations, Anne-Sylvie Catherin Chef du Département des Ressources humaines *Cette réunion sera retransmise simultanément dans l’Amphithéâtre BE de Prévessin (Bâtiment 864) et également disponible à l’adresse suivante : http://webcast.cern.ch. Dear colleagues, In collaboration with HR Department, the Director-General would like to invite you to an information meeting which will be held on Friday 25 June 2010 at 9:30 am in the Main Auditorium (Building 500)*. A welcome coffee will be available from 9:00 am. During this meeting, information will be given about: • Organization’s values (Director-General) • Code of Conduct (Director-General / Anne-Sylvie Catherin) • New Ombudsperson role (Vincent Vuillemin) These presentations will be followed by a questions & answers session. We look forward to seeing you all on 25 June! Best regards, Anne-Sylvie Catherin Head, Human Resources Department. This meeting will be simultaneously retransmitted in BE Auditorium (Building 864) and available at the following address: http://webcast.cern.ch.

78 FR 57175 - Notice of Lodging of Consent Decree Pursuant to the Clean Air Act

Federal Register 2010, 2011, 2012, 2013, 2014

2013-09-17

...'') for violations of Section 504 of the Clean Water Act, 33 U.S.C. 1364(a), and Section 44- 55-90(C)(2002 & Supp. 2011) of the South Carolina Safe Drinking Water Act (``SC SDWA''), S.C. Code Ann. Sec. 44-55-90 (C) (2002 & Supp. 2011), Section 309(b) and (d) of the Clean Water Act, 33 U.S.C. 1319(b) and (d...

41 CFR 128-1.8005 - Seismic safety standards.

Code of Federal Regulations, 2013 CFR

2013-07-01

... the model building codes that the Interagency Committee on Seismic Safety in Construction (ICSSC...) Uniform Building Code (UBC); (2) The 1992 Supplement to the Building Officials and Code Administrators International (BOCA) National Building Code (NBC); and (3) The 1992 Amendments to the Southern Building Code...

38 CFR 39.63 - Architectural design standards.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 5000, Building Construction and Safety Code, and the 2002 edition of the National Electrical Code, NFPA... 5000, Building Construction and Safety Code. Where the adopted codes state conflicting requirements... the standards set forth in this section, all applicable local and State building codes and regulations...

41 CFR 128-1.8005 - Seismic safety standards.

Code of Federal Regulations, 2012 CFR

2012-01-01

... the model building codes that the Interagency Committee on Seismic Safety in Construction (ICSSC...) Uniform Building Code (UBC); (2) The 1992 Supplement to the Building Officials and Code Administrators International (BOCA) National Building Code (NBC); and (3) The 1992 Amendments to the Southern Building Code...

38 CFR 39.63 - Architectural design standards.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 5000, Building Construction and Safety Code, and the 2002 edition of the National Electrical Code, NFPA... 5000, Building Construction and Safety Code. Where the adopted codes state conflicting requirements... the standards set forth in this section, all applicable local and State building codes and regulations...

38 CFR 39.63 - Architectural design standards.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 5000, Building Construction and Safety Code, and the 2002 edition of the National Electrical Code, NFPA... 5000, Building Construction and Safety Code. Where the adopted codes state conflicting requirements... the standards set forth in this section, all applicable local and State building codes and regulations...

41 CFR 128-1.8005 - Seismic safety standards.

Code of Federal Regulations, 2014 CFR

2014-01-01

... the model building codes that the Interagency Committee on Seismic Safety in Construction (ICSSC...) Uniform Building Code (UBC); (2) The 1992 Supplement to the Building Officials and Code Administrators International (BOCA) National Building Code (NBC); and (3) The 1992 Amendments to the Southern Building Code...

41 CFR 128-1.8005 - Seismic safety standards.

Code of Federal Regulations, 2011 CFR

2011-01-01

... the model building codes that the Interagency Committee on Seismic Safety in Construction (ICSSC...) Uniform Building Code (UBC); (2) The 1992 Supplement to the Building Officials and Code Administrators International (BOCA) National Building Code (NBC); and (3) The 1992 Amendments to the Southern Building Code...

Alternative Fuels Data Center: E85 Codes and Standards

Science.gov Websites

Development Equipment Options Equipment Installation Codes, Standards, & Safety Vehicles Laws & ; Incentives Ethanol Codes, Standards, and Safety The U.S. Environmental Protection Agency's (EPA) Office of -Gasoline Blends. The Occupational Safety and Health Administration (OSHA) regulates some fuel-dispensing

7 CFR 1724.50 - Compliance with National Electrical Safety Code (NESC).

Code of Federal Regulations, 2013 CFR

2013-01-01

... 7 Agriculture 11 2013-01-01 2013-01-01 false Compliance with National Electrical Safety Code (NESC... UTILITIES SERVICE, DEPARTMENT OF AGRICULTURE ELECTRIC ENGINEERING, ARCHITECTURAL SERVICES AND DESIGN POLICIES AND PROCEDURES Electric System Design § 1724.50 Compliance with National Electrical Safety Code...

7 CFR 1724.50 - Compliance with National Electrical Safety Code (NESC).

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 11 2010-01-01 2010-01-01 false Compliance with National Electrical Safety Code (NESC... UTILITIES SERVICE, DEPARTMENT OF AGRICULTURE ELECTRIC ENGINEERING, ARCHITECTURAL SERVICES AND DESIGN POLICIES AND PROCEDURES Electric System Design § 1724.50 Compliance with National Electrical Safety Code...

7 CFR 1724.50 - Compliance with National Electrical Safety Code (NESC).

Code of Federal Regulations, 2011 CFR

2011-01-01

... 7 Agriculture 11 2011-01-01 2011-01-01 false Compliance with National Electrical Safety Code (NESC... UTILITIES SERVICE, DEPARTMENT OF AGRICULTURE ELECTRIC ENGINEERING, ARCHITECTURAL SERVICES AND DESIGN POLICIES AND PROCEDURES Electric System Design § 1724.50 Compliance with National Electrical Safety Code...

7 CFR 1724.50 - Compliance with National Electrical Safety Code (NESC).

Code of Federal Regulations, 2012 CFR

2012-01-01

... 7 Agriculture 11 2012-01-01 2012-01-01 false Compliance with National Electrical Safety Code (NESC... UTILITIES SERVICE, DEPARTMENT OF AGRICULTURE ELECTRIC ENGINEERING, ARCHITECTURAL SERVICES AND DESIGN POLICIES AND PROCEDURES Electric System Design § 1724.50 Compliance with National Electrical Safety Code...

7 CFR 1724.50 - Compliance with National Electrical Safety Code (NESC).

Code of Federal Regulations, 2014 CFR

2014-01-01

... 7 Agriculture 11 2014-01-01 2014-01-01 false Compliance with National Electrical Safety Code (NESC... UTILITIES SERVICE, DEPARTMENT OF AGRICULTURE ELECTRIC ENGINEERING, ARCHITECTURAL SERVICES AND DESIGN POLICIES AND PROCEDURES Electric System Design § 1724.50 Compliance with National Electrical Safety Code...

Police accident report forms: safety device coding and enacted laws.

PubMed

Brock, K; Lapidus, G

2008-12-01

Safety device coding on state police accident report (PAR) forms was compared with provisions in state traffic safety laws. PAR forms were obtained from all 50 states and the District of Columbia (states/DC). For seat belts, 22 states/DC had a primary seat belt enforcement law vs 50 with a PAR code. For car seats, all 51 states/DC had a law and a PAR code. For booster seats, 39 states/DC had a law vs nine with a PAR code. For motorcycle helmets, 21 states/DC had an all-age rider helmet law and another 26 a partial-age law vs 50 with a PAR code. For bicycle helmets, 21 states/DC had a partial-age rider helmet law vs 48 with a PAR code. Therefore gaps in the ability of states to fully record accident data reflective of existing state traffic safety laws are revealed. Revising the PAR forms in all states to include complete variables for safety devices should be an important priority, independent of the laws.

Certifying Auto-Generated Flight Code

NASA Technical Reports Server (NTRS)

Denney, Ewen

2008-01-01

Model-based design and automated code generation are being used increasingly at NASA. Many NASA projects now use MathWorks Simulink and Real-Time Workshop for at least some of their modeling and code development. However, there are substantial obstacles to more widespread adoption of code generators in safety-critical domains. Since code generators are typically not qualified, there is no guarantee that their output is correct, and consequently the generated code still needs to be fully tested and certified. Moreover, the regeneration of code can require complete recertification, which offsets many of the advantages of using a generator. Indeed, manual review of autocode can be more challenging than for hand-written code. Since the direct V&V of code generators is too laborious and complicated due to their complex (and often proprietary) nature, we have developed a generator plug-in to support the certification of the auto-generated code. Specifically, the AutoCert tool supports certification by formally verifying that the generated code is free of different safety violations, by constructing an independently verifiable certificate, and by explaining its analysis in a textual form suitable for code reviews. The generated documentation also contains substantial tracing information, allowing users to trace between model, code, documentation, and V&V artifacts. This enables missions to obtain assurance about the safety and reliability of the code without excessive manual V&V effort and, as a consequence, eases the acceptance of code generators in safety-critical contexts. The generation of explicit certificates and textual reports is particularly well-suited to supporting independent V&V. The primary contribution of this approach is the combination of human-friendly documentation with formal analysis. The key technical idea is to exploit the idiomatic nature of auto-generated code in order to automatically infer logical annotations. The annotation inference algorithm itself is generic, and parametrized with respect to a library of coding patterns that depend on the safety policies and the code generator. The patterns characterize the notions of definitions and uses that are specific to the given safety property. For example, for initialization safety, definitions correspond to variable initializations while uses are statements which read a variable, whereas for array bounds safety, definitions are the array declarations, while uses are statements which access an array variable. The inferred annotations are thus highly dependent on the actual program and the properties being proven. The annotations, themselves, need not be trusted, but are crucial to obtain the automatic formal verification of the safety properties without requiring access to the internals of the code generator. The approach has been applied to both in-house and commercial code generators, but is independent of the particular generator used. It is currently being adapted to flight code generated using MathWorks Real-Time Workshop, an automatic code generator that translates from Simulink/Stateflow models into embedded C code.

Nearshore Hydroacoustic Seafloor Mapping In The German Bight (North Sea): Hydroacoustic Interpretation With And Without Classification

NASA Astrophysics Data System (ADS)

Hass, H. C.; Mielck, F.; Papenmeier, S.

2016-12-01

Nearshore habitats are in constant dynamic change. They need regular assessment and appropriate monitoring of areas of special interest. To accomplish this, hydroacoustic seabed characterization tools are applied to allow for cost-effective and efficient mapping of the seafloor. In this context single beam echosounders (SBES) systems provide a comprehensive view by analyzing the hardness and roughness characteristics of the seafloor. Interpolation between transect lines becomes necessary when gapless maps are needed. This study presents a simple method to process and visualize data recorded with RoxAnn (Sonavision, Edinburgh, UK) and similar SBES. Both, hardness and roughness data are merged to one combined parameter that receives a color code (RGB) according to the acoustic properties of the seafloor. This color information is then interpolated to obtain an area-wide map that provides unclassified and thus unbiased seabed information. The RGB color data can subsequently be used for classification and modeling purposes. Four surveys are shown from a morphologically complex nearshore area west of the island of Helgoland (SE North Sea). The area has complex textural and dynamic characteristics reaching from outcropping bedrock via sandy to muddy areas with mostly gradual transitions. RoxAnn data allow to discriminate all seafloor types that were suggested by ground-truth information (seafloor samples, video). The area appears to be fluctuating within certain limits. Sediment import (sand and fluid mud) paths can be reconstructed. Manually, six RoxAnn zones (RZ) were identified and left without hard boundaries to better match the seafloor types of the study site. The k-means fuzzy cluster analysis employed yields best results with 3 classes. We show that interpretations on the basis of largely non-classified, color-coded and interpolated data provide the best gain of information in the highest possible resolution. Classification with hard boundaries is necessary for stakeholders but may cause reduction of information important to science. It becomes apparent that the type of classification addressing stakeholder issues is not always compatible with scientific objectives.

Patient safety principles in family medicine residency accreditation standards and curriculum objectives

PubMed Central

Kassam, Aliya; Sharma, Nishan; Harvie, Margot; O’Beirne, Maeve; Topps, Maureen

2016-01-01

Abstract Objective To conduct a thematic analysis of the College of Family Physicians of Canada’s (CFPC’s) Red Book accreditation standards and the Triple C Competency-based Curriculum objectives with respect to patient safety principles. Design Thematic content analysis of the CFPC’s Red Book accreditation standards and the Triple C curriculum. Setting Canada. Main outcome measures Coding frequency of the patient safety principles (ie, patient engagement; respectful, transparent relationships; complex systems; a just and trusting culture; responsibility and accountability for actions; and continuous learning and improvement) found in the analyzed CFPC documents. Results Within the analyzed CFPC documents, the most commonly found patient safety principle was patient engagement (n = 51 coding references); the least commonly found patient safety principles were a just and trusting culture (n = 5 coding references) and complex systems (n = 5 coding references). Other patient safety principles that were uncommon included responsibility and accountability for actions (n = 7 coding references) and continuous learning and improvement (n = 12 coding references). Conclusion Explicit inclusion of patient safety content such as the use of patient safety principles is needed for residency training programs across Canada to ensure the full spectrum of care is addressed, from community-based care to acute hospital-based care. This will ensure a patient safety culture can be cultivated from residency and sustained into primary care practice. PMID:27965349

29 CFR 1910.144 - Safety color code for marking physical hazards.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 29 Labor 5 2013-07-01 2013-07-01 false Safety color code for marking physical hazards. 1910.144... § 1910.144 Safety color code for marking physical hazards. (a) Color identification—(1) Red. Red shall be the basic color for the identification of: (i) Fire protection equipment and apparatus. [Reserved] (ii...

29 CFR 1910.144 - Safety color code for marking physical hazards.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 29 Labor 5 2014-07-01 2014-07-01 false Safety color code for marking physical hazards. 1910.144... § 1910.144 Safety color code for marking physical hazards. (a) Color identification—(1) Red. Red shall be the basic color for the identification of: (i) Fire protection equipment and apparatus. [Reserved] (ii...

29 CFR 1910.144 - Safety color code for marking physical hazards.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 29 Labor 5 2012-07-01 2012-07-01 false Safety color code for marking physical hazards. 1910.144... § 1910.144 Safety color code for marking physical hazards. (a) Color identification—(1) Red. Red shall be the basic color for the identification of: (i) Fire protection equipment and apparatus. [Reserved] (ii...

29 CFR 1910.144 - Safety color code for marking physical hazards.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 29 Labor 5 2010-07-01 2010-07-01 false Safety color code for marking physical hazards. 1910.144... § 1910.144 Safety color code for marking physical hazards. (a) Color identification—(1) Red. Red shall be... basic color for designating caution and for marking physical hazards such as: Striking against...

30 CFR 905.773 - Requirements for permits and permit processing.

Code of Federal Regulations, 2010 CFR

2010-07-01

..., 42 U.S.C. 7401 et seq California Air Pollution Control Laws, Cal. Health & Safety Code section 39000... (11) Noise Control Act, 42 U.S.C. 4903 California Noise Control Act of 1973, Cal. Health & Safety Code... Pollution Control Laws, Cal. Health & Safety Code section 39000 et seq.; the Hazardous Waste Control Law...

26 CFR 1.42-5 - Monitoring compliance with low-income housing credit requirements.

Code of Federal Regulations, 2012 CFR

2012-04-01

... be required to retain the original local health, safety, or building code violation reports or... account local health, safety, and building codes (or other habitability standards), and the State or local government unit responsible for making local health, safety, or building code inspections did not issue a...

26 CFR 1.42-5 - Monitoring compliance with low-income housing credit requirements.

Code of Federal Regulations, 2010 CFR

2010-04-01

... be required to retain the original local health, safety, or building code violation reports or... account local health, safety, and building codes (or other habitability standards), and the State or local government unit responsible for making local health, safety, or building code inspections did not issue a...

Neuron Learning to Network Organization.

DTIC Science & Technology

1983-12-20

02912 N 0-8 1t COTOLIGOF 1HV AflRS 12. REPORT OATE Pesne an ann Research Program December 20, 1983 Office of Naval Research , Code 442PT 13. NUMBER...visual cortc\\ from R. Cajal, Histologie du Systete Nerveux. mostly hard-wired and perform a great variety of control functions took hundreds of millions of...certain sense there is much that is known. A set of coupled non -linear differential equations. including time delays, can be written down that in

Investigation of Error Patterns in Geographical Databases

NASA Technical Reports Server (NTRS)

Dryer, David; Jacobs, Derya A.; Karayaz, Gamze; Gronbech, Chris; Jones, Denise R. (Technical Monitor)

2002-01-01

The objective of the research conducted in this project is to develop a methodology to investigate the accuracy of Airport Safety Modeling Data (ASMD) using statistical, visualization, and Artificial Neural Network (ANN) techniques. Such a methodology can contribute to answering the following research questions: Over a representative sampling of ASMD databases, can statistical error analysis techniques be accurately learned and replicated by ANN modeling techniques? This representative ASMD sample should include numerous airports and a variety of terrain characterizations. Is it possible to identify and automate the recognition of patterns of error related to geographical features? Do such patterns of error relate to specific geographical features, such as elevation or terrain slope? Is it possible to combine the errors in small regions into an error prediction for a larger region? What are the data density reduction implications of this work? ASMD may be used as the source of terrain data for a synthetic visual system to be used in the cockpit of aircraft when visual reference to ground features is not possible during conditions of marginal weather or reduced visibility. In this research, United States Geologic Survey (USGS) digital elevation model (DEM) data has been selected as the benchmark. Artificial Neural Networks (ANNS) have been used and tested as alternate methods in place of the statistical methods in similar problems. They often perform better in pattern recognition, prediction and classification and categorization problems. Many studies show that when the data is complex and noisy, the accuracy of ANN models is generally higher than those of comparable traditional methods.

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

Triangulating case-finding tools for patient safety surveillance: a cross-sectional case study of puncture/laceration.

PubMed

Taylor, Jennifer A; Gerwin, Daniel; Morlock, Laura; Miller, Marlene R

2011-12-01

To evaluate the need for triangulating case-finding tools in patient safety surveillance. This study applied four case-finding tools to error-associated patient safety events to identify and characterise the spectrum of events captured by these tools, using puncture or laceration as an example for in-depth analysis. Retrospective hospital discharge data were collected for calendar year 2005 (n=48,418) from a large, urban medical centre in the USA. The study design was cross-sectional and used data linkage to identify the cases captured by each of four case-finding tools. Three case-finding tools (International Classification of Diseases external (E) and nature (N) of injury codes, Patient Safety Indicators (PSI)) were applied to the administrative discharge data to identify potential patient safety events. The fourth tool was Patient Safety Net, a web-based voluntary patient safety event reporting system. The degree of mutual exclusion among detection methods was substantial. For example, when linking puncture or laceration on unique identifiers, out of 447 potential events, 118 were identical between PSI and E-codes, 152 were identical between N-codes and E-codes and 188 were identical between PSI and N-codes. Only 100 events that were identified by PSI, E-codes and N-codes were identical. Triangulation of multiple tools through data linkage captures potential patient safety events most comprehensively. Existing detection tools target patient safety domains differently, and consequently capture different occurrences, necessitating the integration of data from a combination of tools to fully estimate the total burden.

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

PubMed

Li, Qiongge; Chan, Maria F

2017-01-01

Over half of cancer patients receive radiotherapy (RT) as partial or full cancer treatment. Daily quality assurance (QA) of RT in cancer treatment closely monitors the performance of the medical linear accelerator (Linac) and is critical for continuous improvement of patient safety and quality of care. Cumulative longitudinal QA measurements are valuable for understanding the behavior of the Linac and allow physicists to identify trends in the output and take preventive actions. In this study, artificial neural networks (ANNs) and autoregressive moving average (ARMA) time-series prediction modeling techniques were both applied to 5-year daily Linac QA data. Verification tests and other evaluations were then performed for all models. Preliminary results showed that ANN time-series predictive modeling has more advantages over ARMA techniques for accurate and effective applicability in the dosimetry and QA field. © 2016 New York Academy of Sciences.

Proceedings of the Annual Meeting of the Association for Education in Journalism and Mass Communication (83rd, Phoenix, Arizona, August 9-12, 2000). Science Communication Interest Group.

ERIC Educational Resources Information Center

Association for Education in Journalism and Mass Communication.

The Science Communication Interest Group section of the proceedings contains the following five papers: "Accounting for the Complexity of Causal Explanations in the Wake of an Environmental Risk" (LeeAnn Kahlor, Sharon Dunwoody and Robert J. Griffin); "Construction of Technology Crisis and Safety: News Media's Framing the Y2K…

Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data

PubMed Central

Ching, Travers; Zhu, Xun

2018-01-01

Artificial neural networks (ANN) are computing architectures with many interconnections of simple neural-inspired computing elements, and have been applied to biomedical fields such as imaging analysis and diagnosis. We have developed a new ANN framework called Cox-nnet to predict patient prognosis from high throughput transcriptomics data. In 10 TCGA RNA-Seq data sets, Cox-nnet achieves the same or better predictive accuracy compared to other methods, including Cox-proportional hazards regression (with LASSO, ridge, and mimimax concave penalty), Random Forests Survival and CoxBoost. Cox-nnet also reveals richer biological information, at both the pathway and gene levels. The outputs from the hidden layer node provide an alternative approach for survival-sensitive dimension reduction. In summary, we have developed a new method for accurate and efficient prognosis prediction on high throughput data, with functional biological insights. The source code is freely available at https://github.com/lanagarmire/cox-nnet. PMID:29634719

75 FR 17644 - Update to NFPA 101, Life Safety Code, for State Home Facilities

Federal Register 2010, 2011, 2012, 2013, 2014

2010-04-07

... DEPARTMENT OF VETERANS AFFAIRS 38 CFR Part 51 RIN 2900-AN59 Update to NFPA 101, Life Safety Code... certain provisions of the 2009 edition of the National Fire Protection Association's NFPA 101, Life Safety... standards regarding life safety and fire safety. DATES: Written comments must be received by VA on or before...

Kayenta Township Building & Safety Department, Tribal Green Building Code Summit Presentation

EPA Pesticide Factsheets

Tribal Green Building Code Summit Presentation by Kayenta Township Building & Safety Department showing how they established the building department, developed a code adoption and enforcement process, and hired staff to carry out the work.

Posttest analysis of the FFTF inherent safety tests

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

Padilla, A. Jr.; Claybrook, S.W.

Inherent safety tests were performed during 1986 in the 400-MW (thermal) Fast Flux Test Facility (FFTF) reactor to demonstrate the effectiveness of an inherent shutdown device called the gas expansion module (GEM). The GEM device provided a strong negative reactivity feedback during loss-of-flow conditions by increasing the neutron leakage as a result of an expanding gas bubble. The best-estimate pretest calculations for these tests were performed using the IANUS plant analysis code (Westinghouse Electric Corporation proprietary code) and the MELT/SIEX3 core analysis code. These two codes were also used to perform the required operational safety analyses for the FFTF reactormore » and plant. Although it was intended to also use the SASSYS systems (core and plant) analysis code, the calibration of the SASSYS code for FFTF core and plant analysis was not completed in time to perform pretest analyses. The purpose of this paper is to present the results of the posttest analysis of the 1986 FFTF inherent safety tests using the SASSYS code.« less

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

Simulation of specific conductance and chloride concentration in Abercorn Creek, Georgia, 2000-2009

USGS Publications Warehouse

Conrads, Paul; Roehl, Edwin A.; Davie, Steven R.

2011-01-01

The City of Savannah operates an industrial and domestic water-supply intake on Abercorn Creek approximately 2 miles from the confluence with the Savannah River upstream from the Interstate 95 bridge. Chloride concentrations are a major concern for the city because industrial customers require water with low chloride concentrations, and elevated chloride concentrations require additional water treatment in order to meet those needs. The proposed deepening of Savannah Harbor could increase chloride concentrations (the major ion in seawater) in the upper reaches of the lower Savannah River estuary, including Abercorn Creek. To address this concern, mechanistic and empirical modeling approaches were used to simulate chloride concentrations at the city's intake to evaluate potential effects from deepening the Savannah Harbor. The first approach modified the mechanistic Environmental Fluid Dynamics Code (EFDC) model developed by Tetra Tech and used for evaluating proposed harbor deepening effects for the Environmental Impact Statement. Chloride concentrations were modeled directly with the EFDC model as a conservative tracer. This effort was done by Tetra Tech under a separate funding agreement with the U.S. Army Corps of Engineers and documented in a separate report. The second approach, described in this report, was to simulate chloride concentrations by developing empirical models from the available data using artificial neural network (ANN) and linear regression models. The empirical models used daily streamflow, specific conductance (field measurement for salinity), water temperature, and water color time series for inputs. Because there are only a few data points that describe the relation between high specific conductance values at the Savannah River at Interstate 95 and the water plant intake, there was a concern that these few data points would determine the extrapolation of the empirical model and potentially underestimate the effect of deepening the harbor on chloride concentrations at the intake. To accommodate these concerns, two ANN chloride models were developed for the intake. The first model (ANN M1e) used all the data. The second model (ANN M2e) only used data when specific conductance at Interstate 95 was less than 175 microsiemens per centimeter at 25 degrees Celsius. Deleting the conductivity data greater than 175 microsiemens per centimeter removed the "plateau" effect observed in the data. The chloride simulations with the ANN M1 model have a low sensitivity to specific conductance (salinity) at Interstate 95, whereas the chloride simulations with the ANN M2 model have a high sensitivity to salinity at Interstate 95. The two modeling approaches (Tetra Tech's EFDC model and the one described in this report) were integrated into a decision support system (DSS) that combines the historical database, output from EFDC, ANN models, ANN model simulation controls, streaming graphics, and model output. The DSS was developed as a Microsoft ExcelTM/Visual Basic for Applications program, which allowed the DSS to be prototyped, easily modified, and distributed in a familiar spreadsheet format. The EFDC and ANN models were used to simulate various harbor deepening scenarios. To accommodate the geometry changes in the harbor, the ANN models used the EFDC model-simulated salinity changes for a historical condition as input. The DSS uses a graphical user interface and allows the user to interrogate the ANN models and EFDC output. Two scenarios were simulated using the Savannah Chloride Model DSS to demonstrate different input options. One scenario decreased winter streamflows to a constant streamflow for 45 days. Streamflows during the period January 1 to February 15 were set to a constant 3,600 cubic feet per second for the simulation period of October 1, 2006, to October 1, 2009. The decreased winter streamflow resulted in predictions of increased specific conductance by as much as 50 microsiemens per centimeter and chlorid

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.

Seismic Safety Of Simple Masonry Buildings

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

Guadagnuolo, Mariateresa; Faella, Giuseppe

2008-07-08

Several masonry buildings comply with the rules for simple buildings provided by seismic codes. For these buildings explicit safety verifications are not compulsory if specific code rules are fulfilled. In fact it is assumed that their fulfilment ensures a suitable seismic behaviour of buildings and thus adequate safety under earthquakes. Italian and European seismic codes differ in the requirements for simple masonry buildings, mostly concerning the building typology, the building geometry and the acceleration at site. Obviously, a wide percentage of buildings assumed simple by codes should satisfy the numerical safety verification, so that no confusion and uncertainty have tomore » be given rise to designers who must use the codes. This paper aims at evaluating the seismic response of some simple unreinforced masonry buildings that comply with the provisions of the new Italian seismic code. Two-story buildings, having different geometry, are analysed and results from nonlinear static analyses performed by varying the acceleration at site are presented and discussed. Indications on the congruence between code rules and results of numerical analyses performed according to the code itself are supplied and, in this context, the obtained result can provide a contribution for improving the seismic code requirements.« less

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

Fire safety

Treesearch

Robert H. White; Mark A. Dietenberger

1999-01-01

Fire safety is an important concern in all types of construction. The high level of national concern for fire safety is reflected in limitations and design requirements in building codes. These code requirements are discussed in the context of fire safety design and evaluation in the initial section of this chapter. Since basic data on fire behavior of wood products...

29 CFR 1910.35 - Compliance with NFPA 101-2000, Life Safety Code.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 29 Labor 5 2011-07-01 2011-07-01 false Compliance with NFPA 101-2000, Life Safety Code. 1910.35 Section 1910.35 Labor Regulations Relating to Labor (Continued) OCCUPATIONAL SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR OCCUPATIONAL SAFETY AND HEALTH STANDARDS Means of Egress § 1910.35 Compliance...

29 CFR Appendix A to Subpart S of... - References for Further Information

Code of Federal Regulations, 2014 CFR

2014-07-01

... Safety, Health, and Environmental Training. ANSI/IEEE C2-2002 National Electrical Safety Code. ANSI K61.1.... NFPA 59-2004 Utility LP-Gas Plant Code. NFPA 70-2002 National Electrical Code. (See also NFPA 70-2005.... NMAB 353-3-1980 Classification of Combustible Dust in Accordance with the National Electrical Code. [72...

29 CFR Appendix A to Subpart S of... - References for Further Information

Code of Federal Regulations, 2011 CFR

2011-07-01

... Safety, Health, and Environmental Training. ANSI/IEEE C2-2002 National Electrical Safety Code. ANSI K61.1.... NFPA 59-2004 Utility LP-Gas Plant Code. NFPA 70-2002 National Electrical Code. (See also NFPA 70-2005.... NMAB 353-3-1980 Classification of Combustible Dust in Accordance with the National Electrical Code. [72...

29 CFR Appendix A to Subpart S of... - References for Further Information

Code of Federal Regulations, 2012 CFR

2012-07-01

... Safety, Health, and Environmental Training. ANSI/IEEE C2-2002 National Electrical Safety Code. ANSI K61.1.... NFPA 59-2004 Utility LP-Gas Plant Code. NFPA 70-2002 National Electrical Code. (See also NFPA 70-2005.... NMAB 353-3-1980 Classification of Combustible Dust in Accordance with the National Electrical Code. [72...

29 CFR Appendix A to Subpart S of... - References for Further Information

Code of Federal Regulations, 2013 CFR

2013-07-01

... Safety, Health, and Environmental Training. ANSI/IEEE C2-2002 National Electrical Safety Code. ANSI K61.1.... NFPA 59-2004 Utility LP-Gas Plant Code. NFPA 70-2002 National Electrical Code. (See also NFPA 70-2005.... NMAB 353-3-1980 Classification of Combustible Dust in Accordance with the National Electrical Code. [72...

77 FR 42654 - Trifloxystrobin; Pesticide Tolerance

Federal Register 2010, 2011, 2012, 2013, 2014

2012-07-20

... code 112). Food manufacturing (NAICS code 311). Pesticide manufacturing (NAICS code 32532). This... filing. III. Aggregate Risk Assessment and Determination of Safety Section 408(b)(2)(A)(i) of FFDCA... dose at which adverse effects of concern are identified (the LOAEL). Uncertainty/safety factors are...

ESAS Deliverable PS 1.1.2.3: Customer Survey on Code Generations in Safety-Critical Applications

NASA Technical Reports Server (NTRS)

Schumann, Johann; Denney, Ewen

2006-01-01

Automated code generators (ACG) are tools that convert a (higher-level) model of a software (sub-)system into executable code without the necessity for a developer to actually implement the code. Although both commercially supported and in-house tools have been used in many industrial applications, little data exists on how these tools are used in safety-critical domains (e.g., spacecraft, aircraft, automotive, nuclear). The aims of the survey, therefore, were threefold: 1) to determine if code generation is primarily used as a tool for prototyping, including design exploration and simulation, or for fiight/production code; 2) to determine the verification issues with code generators relating, in particular, to qualification and certification in safety-critical domains; and 3) to determine perceived gaps in functionality of existing tools.

Knowledge of and attitude towards road traffic codes among commercial motorcycle riders in Anambra State.

PubMed

Adogu, O U; Ilika, A L

2006-12-01

Road traffic accidents (rtas) represent a major epidemic of non communicable disease in the country and has since escalated with the introduction of the new phenomenon of commercial motorcycle transportation such as is found in the two urban towns of nnewi and Awka of Anambra state, Nigeria. making use of a pre-tested, semi structured, interviewer administered questionnaire, relevant data on socio demographic and motorcycle characteristics were collected from a sample of commercial motorcyclists selected by systematic sampling technique. their knowledge of and attitude towards road traffic and safety codes were elicited. The result showed that the all-male commercial motorcyclists had a mean age of 30+8.9 years. one hundred and seventy six (32.6%) possessed good knowledge of road traffic codes and safety, while 35 (6.5%) exhibited good attitude towards them. both knowledge of and attitude towards traffic codes and safety improved with increase in educational level (p<0.005, p<0.001 respectively). the younger motorcyclists also possessed statistically significant better knowledge of traffic codes than their older counterparts (p<0.025). attitude to traffic codes and safety had no association with age of the motorcyclists (p>0.25). the study has provided useful information on the knowledge of and attitude towards road traffic and safety codes among commercial motorcyclists in nigeria. pursuit of knowledge through formal and informal education should run pari pasu with efforts to improve the nigerian economy in order to ensure a sustainable positive attitudinal change towards road traffic codes and safety among commercial motorcyclists.

Fire safety of wood construction

Treesearch

Robert H. White; Mark A. Dietenberger

2010-01-01

Fire safety is an important concern in all types of construction. The high level of national concern for fire safety is reflected in limitations and design requirements in building codes. These code requirements and related fire performance data are discussed in the context of fire safety design and evaluation in the initial section of this chapter. Because basic data...

Formal Safety Certification of Aerospace Software

NASA Technical Reports Server (NTRS)

Denney, Ewen; Fischer, Bernd

2005-01-01

In principle, formal methods offer many advantages for aerospace software development: they can help to achieve ultra-high reliability, and they can be used to provide evidence of the reliability claims which can then be subjected to external scrutiny. However, despite years of research and many advances in the underlying formalisms of specification, semantics, and logic, formal methods are not much used in practice. In our opinion this is related to three major shortcomings. First, the application of formal methods is still expensive because they are labor- and knowledge-intensive. Second, they are difficult to scale up to complex systems because they are based on deep mathematical insights about the behavior of the systems (t.e., they rely on the "heroic proof"). Third, the proofs can be difficult to interpret, and typically stand in isolation from the original code. In this paper, we describe a tool for formally demonstrating safety-relevant aspects of aerospace software, which largely circumvents these problems. We focus on safely properties because it has been observed that safety violations such as out-of-bounds memory accesses or use of uninitialized variables constitute the majority of the errors found in the aerospace domain. In our approach, safety means that the program will not violate a set of rules that can range for the simple memory access rules to high-level flight rules. These different safety properties are formalized as different safety policies in Hoare logic, which are then used by a verification condition generator along with the code and logical annotations in order to derive formal safety conditions; these are then proven using an automated theorem prover. Our certification system is currently integrated into a model-based code generation toolset that generates the annotations together with the code. However, this automated formal certification technology is not exclusively constrained to our code generator and could, in principle, also be integrated with other code generators such as RealTime Workshop or even applied to legacy code. Our approach circumvents the historical problems with formal methods by increasing the degree of automation on all levels. The restriction to safety policies (as opposed to arbitrary functional behavior) results in simpler proof problems that can generally be solved by fully automatic theorem proves. An automated linking mechanism between the safety conditions and the code provides some of the traceability mandated by process standards such as DO-178B. An automated explanation mechanism uses semantic markup added by the verification condition generator to produce natural-language explanations of the safety conditions and thus supports their interpretation in relation to the code. It shows an automatically generated certification browser that lets users inspect the (generated) code along with the safety conditions (including textual explanations), and uses hyperlinks to automate tracing between the two levels. Here, the explanations reflect the logical structure of the safety obligation but the mechanism can in principle be customized using different sets of domain concepts. The interface also provides some limited control over the certification process itself. Our long-term goal is a seamless integration of certification, code generation, and manual coding that results in a "certified pipeline" in which specifications are automatically transformed into executable code, together with the supporting artifacts necessary for achieving and demonstrating the high level of assurance needed in the aerospace domain.

Pressure Safety Program Implementation at ORNL

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

Lower, Mark; Etheridge, Tom; Oland, C. Barry

2013-01-01

The Oak Ridge National Laboratory (ORNL) is a US Department of Energy (DOE) facility that is managed by UT-Battelle, LLC. In February 2006, DOE promulgated worker safety and health regulations to govern contractor activities at DOE sites. These regulations, which are provided in 10 CFR 851, Worker Safety and Health Program, establish requirements for worker safety and health program that reduce or prevent occupational injuries, illnesses, and accidental losses by providing DOE contractors and their workers with safe and healthful workplaces at DOE sites. The regulations state that contractors must achieve compliance no later than May 25, 2007. According tomore » 10 CFR 851, Subpart C, Specific Program Requirements, contractors must have a structured approach to their worker safety and health programs that at a minimum includes provisions for pressure safety. In implementing the structured approach for pressure safety, contractors must establish safety policies and procedures to ensure that pressure systems are designed, fabricated, tested, inspected, maintained, repaired, and operated by trained, qualified personnel in accordance with applicable sound engineering principles. In addition, contractors must ensure that all pressure vessels, boilers, air receivers, and supporting piping systems conform to (1) applicable American Society of Mechanical Engineers (ASME) Boiler and Pressure Vessel Code (2004) Sections I through XII, including applicable code cases; (2) applicable ASME B31 piping codes; and (3) the strictest applicable state and local codes. When national consensus codes are not applicable because of pressure range, vessel geometry, use of special materials, etc., contractors must implement measures to provide equivalent protection and ensure a level of safety greater than or equal to the level of protection afforded by the ASME or applicable state or local codes. This report documents the work performed to address legacy pressure vessel deficiencies and comply with pressure safety requirements in 10 CFR 851. It also describes actions taken to develop and implement ORNL’s Pressure Safety Program.« less

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

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 career. She tried conscientiously to learn Dutch with only moderate success. She started her lectures in Dutch but switched to English when she was excited. For a semester, she talked of black body radiation; the Dutch came out as ``black corpse radiation." The students enjoyed this so much that they never corrected her. While in Utrecht, she served briefly on the editorial board of the Astrophysical Journal. After Utrecht, Anne returned to North America to work with NASA's Goddard Space Flight Center in Greenbelt Maryland. The senior scientists at Goddard were looking for a competent astronomer who could help raise the scientific standards of the laboratory. Anne was successful in this aim, particularly in guiding and encouraging the younger staff. As project scientist for the International Ultraviolet Explorer, she contributed greatly to the success of that project. In 1969, Anne received an honorary degree from York University. The period as Goddard Lab Chief was trying for Anne and she was happy to accept a Senior Scientist position. She spent two years in Paris collaborating with Richard Thomas editing a series of books on astronomy. Of these, she wrote "O-Stars and Wolf Rayet Stars" in collaboration with Peter Conti, and "B Stars With and Without Emission Lines" in collaboration with Vera Doazan. Both books were well received. On return from Paris she continued scientific research until she retired in 1985. Upon retirement, Anne returned to Vancouver and became an honorary professor at the University of British Columbia. She had an office, library facilities and the stimulation of colleagues. She enjoyed helping and mentoring the women students and she was happy to get back to observing at the Dominion Astrophysical Observatory in Victoria. In 1985 she received the D.S. Beals award, given to a Canadian astronomer for outstanding achievement in research. She was also elected a Fellow of the Royal Society of Canada in 1985. She received a D.Sc. from the University of British Columbia in 1992. Anne was one of the world experts on hot stars who influenced many students as well as the entire field. Between 1945 and 1996 she published more than 200 papers in refereed journals or symposium proceedings in addition to books. Her legacy will be long lasting. The following quote from Giusa-Cayrel de Strobel, an acquaintance of 50 years, summarizes the impression she left. ``In writing this brief note, many meetings we attended together are coming in my memory. They evolved almost always in the same way: first, our joy of the encounter, then the appearing of a scientific disagreement between us, and afterwards, before parting, the reconciliation. Anne never held an argument against her opponent; some of the people she admired and liked most were those with whom she argued vehemently." Anne cared passionately about astronomy and defended her views vigorously both individually and at meetings. She had difficulty making friends but those who got beyond the surface found that she was a kind, generous, and caring person as well as good company. Anne was deeply committed to her religious faith and sang in choirs as long as she could. She loved hiking, traveling the world, and music. In 2002, her health began deteriorating and was further weakened by several small strokes. Anne died on 3 July 2003 at the age of 83. She is remembered fondly by her family, friends, and former colleagues.

Application of Gaussian Process Modeling to Analysis of Functional Unreliability

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

R. Youngblood

2014-06-01

This paper applies Gaussian Process (GP) modeling to analysis of the functional unreliability of a “passive system.” GPs have been used widely in many ways [1]. The present application uses a GP for emulation of a system simulation code. Such an emulator can be applied in several distinct ways, discussed below. All applications illustrated in this paper have precedents in the literature; the present paper is an application of GP technology to a problem that was originally analyzed [2] using neural networks (NN), and later [3, 4] by a method called “Alternating Conditional Expectations” (ACE). This exercise enables a multifacetedmore » comparison of both the processes and the results. Given knowledge of the range of possible values of key system variables, one could, in principle, quantify functional unreliability by sampling from their joint probability distribution, and performing a system simulation for each sample to determine whether the function succeeded for that particular setting of the variables. Using previously available system simulation codes, such an approach is generally impractical for a plant-scale problem. It has long been recognized, however, that a well-trained code emulator or surrogate could be used in a sampling process to quantify certain performance metrics, even for plant-scale problems. “Response surfaces” were used for this many years ago. But response surfaces are at their best for smoothly varying functions; in regions of parameter space where key system performance metrics may behave in complex ways, or even exhibit discontinuities, response surfaces are not the best available tool. This consideration was one of several that drove the work in [2]. In the present paper, (1) the original quantification of functional unreliability using NN [2], and later ACE [3], is reprised using GP; (2) additional information provided by the GP about uncertainty in the limit surface, generally unavailable in other representations, is discussed; (3) a simple forensic exercise is performed, analogous to the inverse problem of code calibration, but with an accident management spin: given an observation about containment pressure, what can we say about the system variables? References 1. For an introduction to GPs, see (for example) Gaussian Processes for Machine Learning, C. E. Rasmussen and C. K. I. Williams (MIT, 2006). 2. Reliability Quantification of Advanced Reactor Passive Safety Systems, J. J. Vandenkieboom, PhD Thesis (University of Michigan, 1996). 3. Z. Cui, J. C. Lee, J. J. Vandenkieboom, and R. W. Youngblood, “Unreliability Quantification of a Containment Cooling System through ACE and ANN Algorithms,” Trans. Am. Nucl. Soc. 85, 178 (2001). 4. Risk and Safety Analysis of Nuclear Systems, J. C. Lee and N. J. McCormick (Wiley, 2011). See especially §11.2.4.« less

FBC: a flat binary code scheme for fast Manhattan hash retrieval

NASA Astrophysics Data System (ADS)

Kong, Yan; Wu, Fuzhang; Gao, Lifa; Wu, Yanjun

2018-04-01

Hash coding is a widely used technique in approximate nearest neighbor (ANN) search, especially in document search and multimedia (such as image and video) retrieval. Based on the difference of distance measurement, hash methods are generally classified into two categories: Hamming hashing and Manhattan hashing. Benefitting from better neighborhood structure preservation, Manhattan hashing methods outperform earlier methods in search effectiveness. However, due to using decimal arithmetic operations instead of bit operations, Manhattan hashing becomes a more time-consuming process, which significantly decreases the whole search efficiency. To solve this problem, we present an intuitive hash scheme which uses Flat Binary Code (FBC) to encode the data points. As a result, the decimal arithmetic used in previous Manhattan hashing can be replaced by more efficient XOR operator. The final experiments show that with a reasonable memory space growth, our FBC speeds up more than 80% averagely without any search accuracy loss when comparing to the state-of-art Manhattan hashing methods.

Integration of QSAR and SAR methods for the mechanistic interpretation of predictive models for carcinogenicity

PubMed Central

Fjodorova, Natalja; Novič, Marjana

2012-01-01

The knowledge-based Toxtree expert system (SAR approach) was integrated with the statistically based counter propagation artificial neural network (CP ANN) model (QSAR approach) to contribute to a better mechanistic understanding of a carcinogenicity model for non-congeneric chemicals using Dragon descriptors and carcinogenic potency for rats as a response. The transparency of the CP ANN algorithm was demonstrated using intrinsic mapping technique specifically Kohonen maps. Chemical structures were represented by Dragon descriptors that express the structural and electronic features of molecules such as their shape and electronic surrounding related to reactivity of molecules. It was illustrated how the descriptors are correlated with particular structural alerts (SAs) for carcinogenicity with recognized mechanistic link to carcinogenic activity. Moreover, the Kohonen mapping technique enables one to examine the separation of carcinogens and non-carcinogens (for rats) within a family of chemicals with a particular SA for carcinogenicity. The mechanistic interpretation of models is important for the evaluation of safety of chemicals. PMID:24688639

Evaluation of liquefaction potential of soil based on standard penetration test using multi-gene genetic programming model

NASA Astrophysics Data System (ADS)

Muduli, Pradyut; Das, Sarat

2014-06-01

This paper discusses the evaluation of liquefaction potential of soil based on standard penetration test (SPT) dataset using evolutionary artificial intelligence technique, multi-gene genetic programming (MGGP). The liquefaction classification accuracy (94.19%) of the developed liquefaction index (LI) model is found to be better than that of available artificial neural network (ANN) model (88.37%) and at par with the available support vector machine (SVM) model (94.19%) on the basis of the testing data. Further, an empirical equation is presented using MGGP to approximate the unknown limit state function representing the cyclic resistance ratio (CRR) of soil based on developed LI model. Using an independent database of 227 cases, the overall rates of successful prediction of occurrence of liquefaction and non-liquefaction are found to be 87, 86, and 84% by the developed MGGP based model, available ANN and the statistical models, respectively, on the basis of calculated factor of safety (F s) against the liquefaction occurrence.

The Economy of Romania: How it Compares to Other Centrally-Planned Economies in Eastern Europe.

DTIC Science & Technology

1984-06-01

moonlighting ," with all the positive connotations of supplementing one’s in- come through industry and initiative. It is a broader, more pervasive...Western Stereotypes ." Christian Science Monitor. March 24, 1983, p. 13. Keefe, Eugene K., Violeta 0. Baluyut, William Giloane, Anne K. Long, James M. Moore...Postgraduate School Monterey, CA 93943 8. Marine Corps Representative, Code 0309 Naval Postgraduate School Monterey, CA 93940 9. Captain Grace M. Charney P.O. Box 7267 APO NY 09012 182 . . . . - FILMED 4-85 * DTIC

29 CFR 1910.35 - Compliance with alternate exit-route codes.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 1910.35 Labor Regulations Relating to Labor (Continued) OCCUPATIONAL SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR OCCUPATIONAL SAFETY AND HEALTH STANDARDS Exit Routes and Emergency Planning § 1910.35...-route provisions of NFPA 101, Life Safety Code, 2009 edition, or the exit-route provisions of the...

29 CFR 1910.144 - Safety color code for marking physical hazards.

Code of Federal Regulations, 2011 CFR

2011-07-01

... the basic color for the identification of: (i) Fire protection equipment and apparatus. [Reserved] (ii... 29 Labor 5 2011-07-01 2011-07-01 false Safety color code for marking physical hazards. 1910.144 Section 1910.144 Labor Regulations Relating to Labor (Continued) OCCUPATIONAL SAFETY AND HEALTH...

Mary Ann Franden | NREL

Science.gov Websites

Ann Franden Photo of Mary Ann Franden Mary Franden Researcher IV-Molecular Biology Mary.Ann.Franden @nrel.gov | 303-384-7767 Research Interests Mary Ann Franden is a senior scientist in the Applied Biology University Professional Experience Senior Scientist, NREL, NBC, Applied Biology Group Professional Research

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

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 a hierarchical framework. Fluoride concentration estimation using the HBMA method shows better agreement to the observation data in the test step because they are not based on a single model with a non-dominate weights.

Safety and health in the construction of fixed offshore installations in the petroleum industry

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

Not Available

1981-01-01

A meeting convened by the ILO (International Labor Office) on safety problems in the offshore petroleum industry recommended the preparation of a code of practice setting out standards for safety and health during the construction of fixed offshore installations. Such a code, to be prepared by the ILO in co-operation with other bodies, including the Inter-Governmental Maritime Consultative Organisation (IMCO), was to take into consideration existing standards applicable to offshore construction activities and to supplement the ILO codes of practice on safety and health in building and civil engineering work, shipbuilding and ship repairing. (Copyright (c) International Labour Organisation 1981.)

42 CFR 482.41 - Condition of participation: Physical environment.

Code of Federal Regulations, 2013 CFR

2013-10-01

...: Life safety from fire. (1) Except as otherwise provided in this section— (i) The hospital must meet the applicable provisions of the 2000 edition of the Life Safety Code of the National Fire Protection Association... Life Safety Code, issued January 14, 2000, for incorporation by reference in accordance with 5 U.S.C...

42 CFR 482.41 - Condition of participation: Physical environment.

Code of Federal Regulations, 2014 CFR

2014-10-01

...: Life safety from fire. (1) Except as otherwise provided in this section— (i) The hospital must meet the applicable provisions of the 2000 edition of the Life Safety Code of the National Fire Protection Association... Life Safety Code, issued January 14, 2000, for incorporation by reference in accordance with 5 U.S.C...

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

Energy Storage System Safety: Plan Review and Inspection Checklist

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

Cole, Pam C.; Conover, David R.

Codes, standards, and regulations (CSR) governing the design, construction, installation, commissioning, and operation of the built environment are intended to protect the public health, safety, and welfare. While these documents change over time to address new technology and new safety challenges, there is generally some lag time between the introduction of a technology into the market and the time it is specifically covered in model codes and standards developed in the voluntary sector. After their development, there is also a timeframe of at least a year or two until the codes and standards are adopted. Until existing model codes andmore » standards are updated or new ones are developed and then adopted, one seeking to deploy energy storage technologies or needing to verify the safety of an installation may be challenged in trying to apply currently implemented CSRs to an energy storage system (ESS). The Energy Storage System Guide for Compliance with Safety Codes and Standards1 (CG), developed in June 2016, is intended to help address the acceptability of the design and construction of stationary ESSs, their component parts, and the siting, installation, commissioning, operations, maintenance, and repair/renovation of ESS within the built environment.« less

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 produce a representative ANN-based mortality prediction model for use in different PICUs.

Medicare and Medicaid Programs; Fire Safety Requirements for Certain Health Care Facilities. Final rule.

PubMed

2016-05-04

This final rule will amend the fire safety standards for Medicare and Medicaid participating hospitals, critical access hospitals (CAHs), long-term care facilities, intermediate care facilities for individuals with intellectual disabilities (ICF-IID), ambulatory surgery centers (ASCs), hospices which provide inpatient services, religious non-medical health care institutions (RNHCIs), and programs of all-inclusive care for the elderly (PACE) facilities. Further, this final rule will adopt the 2012 edition of the Life Safety Code (LSC) and eliminate references in our regulations to all earlier editions of the Life Safety Code. It will also adopt the 2012 edition of the Health Care Facilities Code, with some exceptions.

Evaluating the Influence of Road Lighting on Traffic Safety at Accesses Using An Artificial Neural Network.

PubMed

Xu, Yueru; Ye, Zhirui; Wang, Yuan; Wang, Chao; Sun, Cuicui

2018-05-18

This paper focuses on the effect of road lighting on road safety at accesses and tries to quantitatively analyze the relationship between road lighting and road safety. An Artificial Neural Network (ANN) was applied in this study. This method is one of the most popular machine-learning methods in recent years and does not require any pre-defined assumptions. This method was applied using field data collected from ten road segments in Nanjing, Jiangsu Province, China. The results show that the impact of road lighting on road safety at accesses is significant. In addition, road lighting has greater influence when vehicle speeds are higher or the number of lanes is larger. A threshold illuminance was also found in this paper, and the results show that the safety level at accesses will become stable when reaching this value. The improvement of illuminance can decrease the speed variation among vehicles and improve the safety level. In addition, high-grade roads need better illuminance at accesses. A threshold value can also be obtained based on related variables and used to develop scientific guidelines for traffic management organizations.

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.

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

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.

An Internet of Things System for Underground Mine Air Quality Pollutant Prediction Based on Azure Machine Learning

PubMed Central

Jo, ByungWan

2018-01-01

The implementation of wireless sensor networks (WSNs) for monitoring the complex, dynamic, and harsh environment of underground coal mines (UCMs) is sought around the world to enhance safety. However, previously developed smart systems are limited to monitoring or, in a few cases, can report events. Therefore, this study introduces a reliable, efficient, and cost-effective internet of things (IoT) system for air quality monitoring with newly added features of assessment and pollutant prediction. This system is comprised of sensor modules, communication protocols, and a base station, running Azure Machine Learning (AML) Studio over it. Arduino-based sensor modules with eight different parameters were installed at separate locations of an operational UCM. Based on the sensed data, the proposed system assesses mine air quality in terms of the mine environment index (MEI). Principal component analysis (PCA) identified CH4, CO, SO2, and H2S as the most influencing gases significantly affecting mine air quality. The results of PCA were fed into the ANN model in AML studio, which enabled the prediction of MEI. An optimum number of neurons were determined for both actual input and PCA-based input parameters. The results showed a better performance of the PCA-based ANN for MEI prediction, with R2 and RMSE values of 0.6654 and 0.2104, respectively. Therefore, the proposed Arduino and AML-based system enhances mine environmental safety by quickly assessing and predicting mine air quality. PMID:29561777

An Internet of Things System for Underground Mine Air Quality Pollutant Prediction Based on Azure Machine Learning.

PubMed

Jo, ByungWan; Khan, Rana Muhammad Asad

2018-03-21

The implementation of wireless sensor networks (WSNs) for monitoring the complex, dynamic, and harsh environment of underground coal mines (UCMs) is sought around the world to enhance safety. However, previously developed smart systems are limited to monitoring or, in a few cases, can report events. Therefore, this study introduces a reliable, efficient, and cost-effective internet of things (IoT) system for air quality monitoring with newly added features of assessment and pollutant prediction. This system is comprised of sensor modules, communication protocols, and a base station, running Azure Machine Learning (AML) Studio over it. Arduino-based sensor modules with eight different parameters were installed at separate locations of an operational UCM. Based on the sensed data, the proposed system assesses mine air quality in terms of the mine environment index (MEI). Principal component analysis (PCA) identified CH₄, CO, SO₂, and H₂S as the most influencing gases significantly affecting mine air quality. The results of PCA were fed into the ANN model in AML studio, which enabled the prediction of MEI. An optimum number of neurons were determined for both actual input and PCA-based input parameters. The results showed a better performance of the PCA-based ANN for MEI prediction, with R ² and RMSE values of 0.6654 and 0.2104, respectively. Therefore, the proposed Arduino and AML-based system enhances mine environmental safety by quickly assessing and predicting mine air quality.

Common Day Care Safety Renovations: Descriptions, Explanations and Cost Estimates.

ERIC Educational Resources Information Center

Spack, Stan

This booklet explains some of the day care safety features specified by the new Massachusetts State Building Code (January 1, 1975) which must be met before a new day care center can be licensed. The safety features described are those which most often require renovation to meet the building code standards. Best estimates of the costs involved in…

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

O'Malley, Kathleen; Lopez, Hugo; Cairns, Julie

An overview of the main North American codes and standards associated with hydrogen safety sensors is provided. The distinction between a code and a standard is defined, and the relationship between standards and codes is clarified, especially for those circumstances where a standard or a certification requirement is explicitly referenced within a code. The report identifies three main types of standards commonly applied to hydrogen sensors (interface and controls standards, shock and hazard standards, and performance-based standards). The certification process and a list and description of the main standards and model codes associated with the use of hydrogen safety sensorsmore » in hydrogen infrastructure are presented.« less

AFOSR/ONR (Air Force Office of Scientific Research/Office of Naval Research) Contractors’ Meeting - Combustion Rocket Propulsion Diagnostics of Reacting Flow Held in Ann Arbor, Michigan on June 19-23, 1989

DTIC Science & Technology

1989-06-19

ORGANIZATION NAME(S) AND AOORESS(ES) L PERJORMING ORGANIZATION S Air Force Office of Scientific Research REPORT NUMBER Building 410 AF06 IR 1 7 1 j Bolling...AFB DC 20332-6448 Office of Naval Research , Arlington VA 22217-5000 9. SFONSOtrU/MONITOPING AGENCY NAME(S) AND ADORESS(ES) 10. SPONSORINGIMONITORING...CODE Approved for public release; distribution is unlimited 13. ABSTRACT (Muxmmum 200 words*) Abstracts are given for research on airbreathing

Using additional external inputs to forecast water quality with an artificial neural network for contamination event detection in source water

NASA Astrophysics Data System (ADS)

Schmidt, F.; Liu, S.

2016-12-01

Source water quality plays an important role for the safety of drinking water and early detection of its contamination is vital to taking appropriate countermeasures. However, compared to drinking water, it is more difficult to detect contamination events because its environment is less controlled and numerous natural causes contribute to a high variability of the background values. In this project, Artificial Neural Networks (ANNs) and a Contamination Event Detection Process (CED Process) were used to identify events in river water. The ANN models the response of basic water quality sensors obtained in laboratory experiments in an off-line learning stage and continuously forecasts future values of the time line in an on-line forecasting step. During this second stage, the CED Process compares the forecast to the measured value and classifies it as regular background or event value, which modifies the ANN's continuous learning and influences its forecasts. In addition to this basic setup, external information is fed to the CED Process: A so-called Operator Input (OI) is provided to inform about unusual water quality levels that are unrelated to the presence of contamination, for example due to cooling water discharge from a nearby power plant. This study's primary goal is to evaluate how well the OI fits into the design of the combined forecasting ANN and CED Process and to understand its effects on the online forecasting stage. To test this, data from laboratory experiments conducted previously at the School of Environment, Tsinghua University, have been used to perform simulations highlighting features and drawbacks of this method. Applying the OI has been shown to have a positive influence on the ANN's ability to handle a sudden change in background values, which is unrelated to contamination. However, it might also mask the presence of an event, an issue that underlines the necessity to have several instances of the algorithm run in parallel. Other difficulties addressed in this study include the source and the format of the OI. This project tries to add to the ongoing research into algorithms for CED. It provides ideas for how results from the binary classification of time series could be evaluated in a more realistic fashion and shows what the advantages and limitations of such a method would be.

Qualification of Simulation Software for Safety Assessment of Sodium Cooled Fast Reactors. Requirements and Recommendations

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

Brown, Nicholas R.; Pointer, William David; Sieger, Matt

2016-04-01

The goal of this review is to enable application of codes or software packages for safety assessment of advanced sodium-cooled fast reactor (SFR) designs. To address near-term programmatic needs, the authors have focused on two objectives. First, the authors have focused on identification of requirements for software QA that must be satisfied to enable the application of software to future safety analyses. Second, the authors have collected best practices applied by other code development teams to minimize cost and time of initial code qualification activities and to recommend a path to the stated goal.

Code development for ships -- A demonstration

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

Ayyub, B.; Mansour, A.E.; White, G.

1996-12-31

A demonstration summary of a reliability-based structural design code for ships is presented for two ship types, a cruiser and a tanker. For both ship types, code requirements cover four failure modes: hull girder bulking, unstiffened plate yielding and buckling, stiffened plate buckling, and fatigue of critical detail. Both serviceability and ultimate limit states are considered. Because of limitation on the length, only hull girder modes are presented in this paper. Code requirements for other modes will be presented in future publication. A specific provision of the code will be a safety check expression. The design variables are to bemore » taken at their nominal values, typically values in the safe side of the respective distributions. Other safety check expressions for hull girder failure that include load combination factors, as well as consequence of failure factors, are considered. This paper provides a summary of safety check expressions for the hull girder modes.« less

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. The SP-UCI-enhanced ANN is universally applicable to many other regression and prediction problems, and it has a good potential to be an alternative to the classical BP scheme and gradient-based optimization methods.

Using Modified-ISS Model to Evaluate Medication Administration Safety During Bar Code Medication Administration Implementation in Taiwan Regional Teaching Hospital.

PubMed

Ma, Pei-Luen; Jheng, Yan-Wun; Jheng, Bi-Wei; Hou, I-Ching

2017-01-01

Bar code medication administration (BCMA) could reduce medical errors and promote patient safety. This research uses modified information systems success model (M-ISS model) to evaluate nurses' acceptance to BCMA. The result showed moderate correlation between medication administration safety (MAS) to system quality, information quality, service quality, user satisfaction, and limited satisfaction.

An empirical analysis of thermal protective performance of fabrics used in protective clothing.

PubMed

Mandal, Sumit; Song, Guowen

2014-10-01

Fabric-based protective clothing is widely used for occupational safety of firefighters/industrial workers. The aim of this paper is to study thermal protective performance provided by fabric systems and to propose an effective model for predicting the thermal protective performance under various thermal exposures. Different fabric systems that are commonly used to manufacture thermal protective clothing were selected. Laboratory simulations of the various thermal exposures were created to evaluate the protective performance of the selected fabric systems in terms of time required to generate second-degree burns. Through the characterization of selected fabric systems in a particular thermal exposure, various factors affecting the performances were statistically analyzed. The key factors for a particular thermal exposure were recognized based on the t-test analysis. Using these key factors, the performance predictive multiple linear regression and artificial neural network (ANN) models were developed and compared. The identified best-fit ANN models provide a basic tool to study thermal protective performance of a fabric. © The Author 2014. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.

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

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 considering all this factors, the determination of the appropriate neural network architecture can be made. Additionally, the selection the optimal ANN involves the determination of specific internal parameters like the learning rate, the momentum term, the neighbourhood function, the time dependent decrease of corrections, etc. Even after all these decisions have been made the learning procedure itself is not a straightforward task. Here, the division of the entire ensemble of data into three data sets: training, controlling and the test set are crucial. This problem is addressed as well.

Further Analysis of Motorcycle Helmet Effectiveness Using CODES Linked Data

DOT National Transportation Integrated Search

1998-01-01

Linked data from the Crash Outcome Data Evaluation System (CODES) in seven : states was used by the National Highway Traffic Safety Administration as the : basis of a 1996 Report to Congress on the Benefits of Safety Belts and : Motorcycle Helmets (D...

Particle Swarm Optimization approach to defect detection in armour ceramics.

PubMed

Kesharaju, Manasa; Nagarajah, Romesh

2017-03-01

In this research, various extracted features were used in the development of an automated ultrasonic sensor based inspection system that enables defect classification in each ceramic component prior to despatch to the field. Classification is an important task and large number of irrelevant, redundant features commonly introduced to a dataset reduces the classifiers performance. Feature selection aims to reduce the dimensionality of the dataset while improving the performance of a classification system. In the context of a multi-criteria optimization problem (i.e. to minimize classification error rate and reduce number of features) such as one discussed in this research, the literature suggests that evolutionary algorithms offer good results. Besides, it is noted that Particle Swarm Optimization (PSO) has not been explored especially in the field of classification of high frequency ultrasonic signals. Hence, a binary coded Particle Swarm Optimization (BPSO) technique is investigated in the implementation of feature subset selection and to optimize the classification error rate. In the proposed method, the population data is used as input to an Artificial Neural Network (ANN) based classification system to obtain the error rate, as ANN serves as an evaluator of PSO fitness function. Copyright © 2016. Published by Elsevier B.V.

Diagnosis of periodontal diseases using different classification algorithms: a preliminary study.

PubMed

Ozden, F O; Özgönenel, O; Özden, B; Aydogdu, A

2015-01-01

The purpose of the proposed study was to develop an identification unit for classifying periodontal diseases using support vector machine (SVM), decision tree (DT), and artificial neural networks (ANNs). A total of 150 patients was divided into two groups such as training (100) and testing (50). The codes created for risk factors, periodontal data, and radiographically bone loss were formed as a matrix structure and regarded as inputs for the classification unit. A total of six periodontal conditions was the outputs of the classification unit. The accuracy of the suggested methods was compared according to their resolution and working time. DT and SVM were best to classify the periodontal diseases with a high accuracy according to the clinical research based on 150 patients. The performances of SVM and DT were found 98% with total computational time of 19.91 and 7.00 s, respectively. ANN had the worst correlation between input and output variable, and its performance was calculated as 46%. SVM and DT appeared to be sufficiently complex to reflect all the factors associated with the periodontal status, simple enough to be understandable and practical as a decision-making aid for prediction of periodontal disease.

Practical guide to bar coding for patient medication safety.

PubMed

Neuenschwander, Mark; Cohen, Michael R; Vaida, Allen J; Patchett, Jeffrey A; Kelly, Jamie; Trohimovich, Barbara

2003-04-15

Bar coding for the medication administration step of the drug-use process is discussed. FDA will propose a rule in 2003 that would require bar-code labels on all human drugs and biologicals. Even with an FDA mandate, manufacturer procrastination and possible shifts in product availability are likely to slow progress. Such delays should not preclude health systems from adopting bar-code-enabled point-of-care (BPOC) systems to achieve gains in patient safety. Bar-code technology is a replacement for traditional keyboard data entry. The elements of bar coding are content, which determines the meaning; data format, which refers to the embedded data and symbology, which describes the "font" in which the machine-readable code is written. For a BPOC system to deliver an acceptable level of patient protection, the hospital must first establish reliable processes for a patient identification band, caregiver badge, and medication bar coding. Medications can have either drug-specific or patient-specific bar codes. Both varieties result in the desired code that supports patient's five rights of drug administration. When medications are not available from the manufacturer in immediate-container bar-coded packaging, other means of applying the bar code must be devised, including the use of repackaging equipment, overwrapping, manual bar coding, and outsourcing. Virtually all medications should be bar coded, the bar code on the label should be easily readable, and appropriate policies, procedures, and checks should be in place. Bar coding has the potential to be not only cost-effective but to produce a return on investment. By bar coding patient identification tags, caregiver badges, and immediate-container medications, health systems can substantially increase patient safety during medication administration.

National Dam Safety Program. Oldham Pond Dam (NJ00238), Passaic River Basin, Molly Ann Brook, Passaic County, New Jersey. Phase 1 Inspection Report

DTIC Science & Technology

1980-01-01

spookin all ;ruof os portain11g to t& r in exod to on tao o me - ovors tho an dm two svhoh rlean ,-l=]l be ~~~ttlsendt a do ealt ootuni •tkty toes Weo 48f...1 I . f4~~ 4 𔃾 . . . ’?9z - --- -, -𔃾. £ 4, *-.’.-. ~ .’*~ 94.44 9 , - / I LA~ BorrrA-i 7. - ’- N I.f A: .... ,CS HARMO [. tfl. wq N NT~V.- LANL W

78 FR 23497 - Propiconazole; Pesticide Tolerances

Federal Register 2010, 2011, 2012, 2013, 2014

2013-04-19

...). Animal production (NAICS code 112). Food manufacturing (NAICS code 311). Pesticide manufacturing (NAICS.... Aggregate Risk Assessment and Determination of Safety Section 408(b)(2)(A)(i) of FFDCA allows EPA to... dose at which adverse effects of concern are identified (the LOAEL). Uncertainty/safety factors are...

Code of Sustainable Practice in Occupational and Environmental Health and Safety for Corporations.

PubMed

Castleman, Barry; Allen, Barbara; Barca, Stefania; Bohme, Susanna Rankin; Henry, Emmanuel; Kaur, Amarjit; Massard-Guilbaud, Genvieve; Melling, Joseph; Menendez-Navarro, Alfredo; Renfrew, Daniel; Santiago, Myrna; Sellers, Christopher; Tweedale, Geoffrey; Zalik, Anna; Zavestoski, Stephen

2008-01-01

At a conference held at Stony Brook University in December 2007, "Dangerous Trade: Histories of Industrial Hazard across a Globalizing World," participants endorsed a Code of Sustainable Practice in Occupational and Environmental Health and Safety for Corporations. The Code outlines practices that would ensure corporations enact the highest health and environmentally protective measures in all the locations in which they operate. Corporations should observe international guidelines on occupational exposure to air contaminants, plant safety, air and water pollutant releases, hazardous waste disposal practices, remediation of polluted sites, public disclosure of toxic releases, product hazard labeling, sale of products for specific uses, storage and transport of toxic intermediates and products, corporate safety and health auditing, and corporate environmental auditing. Protective measures in all locations should be consonant with the most protective measures applied anywhere in the world, and should apply to the corporations' subsidiaries, contractors, suppliers, distributors, and licensees of technology. Key words: corporations, sustainability, environmental protection, occupational health, code of practice.

Thermal-hydraulic interfacing code modules for CANDU reactors

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

Liu, W.S.; Gold, M.; Sills, H.

1997-07-01

The approach for CANDU reactor safety analysis in Ontario Hydro Nuclear (OHN) and Atomic Energy of Canada Limited (AECL) is presented. Reflecting the unique characteristics of CANDU reactors, the procedure of coupling the thermal-hydraulics, reactor physics and fuel channel/element codes in the safety analysis is described. The experience generated in the Canadian nuclear industry may be useful to other types of reactors in the areas of reactor safety analysis.

Study protocol: Münster tinnitus randomized controlled clinical trial-2013 based on tailor-made notched music training (TMNMT).

PubMed

Pantev, Christo; Rudack, Claudia; Stein, Alwina; Wunderlich, Robert; Engell, Alva; Lau, Pia; Wollbrink, Andreas; Shaykevich, Alex

2014-03-02

Tinnitus is a result of hyper-activity/hyper-synchrony of auditory neurons coding the tinnitus frequency, which has developed to synchronous mass activity owing the lack of inhibition. We assume that removal of exactly these frequency components from an auditory stimulus will cause the brain to reorganize around tonotopic regions coding the tinnitus frequency. Based on this assumption a novel treatment for tonal tinnitus - tailor-made notched music training (TMNMT) (Proc Natl Acad Sci USA 107:1207-1210, 2010; Ann N Y Acad Sci 1252:253-258, 2012; Frontiers Syst Neurosci 6:50, 2012) has been introduced and will be tested in this clinical trial on a large number of tinnitus patients. A randomized controlled trial (RCT) in parallel group design will be performed in a double-blinded manner. The choice of the intervention we are going to apply is based on two "proof of concept" studies in humans (Proc Natl Acad Sci USA 107:1207-1210, 2010; Ann N Y Acad Sci 1252:253-258, 2012; Frontiers Syst Neurosci 6:50, 2012; PloS One 6(9):e24685, 2011) and on a recent animal study (Front Syst Neurosci 7:21, 2013).The RCT includes 100 participants with chronic, tonal tinnitus who listened to tailor-made notched music (TMNM) for two hours a day for three months. The effect of TMNMT is assessed by the tinnitus handicap questionnaire and visual analogue scales (VAS) measuring perceived tinnitus loudness, distress and handicap. This is the first randomized controlled trial applying TMNMT on a larger number of patients with tonal tinnitus. Our data will verify more securely and reliably the effectiveness of this kind of completely non-invasive and low-cost treatment approach on tonal tinnitus. Current Controlled Trials ISRCTN04840953.

Neutronic calculation of fast reactors by the EUCLID/V1 integrated code

NASA Astrophysics Data System (ADS)

Koltashev, D. A.; Stakhanova, A. A.

2017-01-01

This article considers neutronic calculation of a fast-neutron lead-cooled reactor BREST-OD-300 by the EUCLID/V1 integrated code. The main goal of development and application of integrated codes is a nuclear power plant safety justification. EUCLID/V1 is integrated code designed for coupled neutronics, thermomechanical and thermohydraulic fast reactor calculations under normal and abnormal operating conditions. EUCLID/V1 code is being developed in the Nuclear Safety Institute of the Russian Academy of Sciences. The integrated code has a modular structure and consists of three main modules: thermohydraulic module HYDRA-IBRAE/LM/V1, thermomechanical module BERKUT and neutronic module DN3D. In addition, the integrated code includes databases with fuel, coolant and structural materials properties. Neutronic module DN3D provides full-scale simulation of neutronic processes in fast reactors. Heat sources distribution, control rods movement, reactivity level changes and other processes can be simulated. Neutron transport equation in multigroup diffusion approximation is solved. This paper contains some calculations implemented as a part of EUCLID/V1 code validation. A fast-neutron lead-cooled reactor BREST-OD-300 transient simulation (fuel assembly floating, decompression of passive feedback system channel) and cross-validation with MCU-FR code results are presented in this paper. The calculations demonstrate EUCLID/V1 code application for BREST-OD-300 simulating and safety justification.

[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.

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

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.

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.

Safety and Liability.

ERIC Educational Resources Information Center

Berthelot, Ronald J.; And Others

1982-01-01

This series of five articles highlights Pensacola Junior College's occupational safety course, involving simulated emergencies, Florida's standards for teacher liability, electrical safety in the classroom and laboratory, color coding for machine safety, and Florida industrial arts safety instructional materials. (SK)

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.

Minisparker profiles from Jeffreys Ledge and adjacent areas in the western Gulf of Maine

USGS Publications Warehouse

Eskenasy, Diane M.; Bailey, Norman G.

1980-01-01

A total of 250 kilometers of single-channel seismic-reflection data (28 minisparker profiles) were collected in the coastal waters of Massachusetts, north of and immediately south of Cape Ann, and on the western flank of Jeffreys Ledge, western Gulf of Maine, during the September 1978 cruise of the R/V ASTERIAS. The survey was conducted by the U.S. Geological Survey as part of the Massachusetts Cooperative Marine Geologic Program.The seismic systems used included a 1Del Norte minisparker and streamer, an Energy International Streamer, and EPC 3200 and 4100 recorders. Navigational control was established by Radar and Loran-C. The Loran-C navigation data were recorded on a Northstar 6000 system.The purpose of the cruise was to discover the significance and extent of the folded and faulted internal reflections that were first noticed on the esternmost tip of Jeffreys Ledge in line 14 of esternmost tip of Jeffreys Ledge in line 14 of rninisparker data from the 1976 R/V FAY 023 cruise.Sixteen northwest-trending lines were run off Cape Ann to investigate the deformed reflectors, now thought to represent a moraine formed by readvance of continental ice over the last glacial marine Presurnpscot Formation. Lines north and south of Cape Ann were run to locate the offshore extension of the Clinton-Newbury and Bloody Bluff fault systems.The original records can be studied at the U.S. Geological Survey offices at Woods Hole, Mass. Microfilm copies of the records can be purchased only from the National Geophysical and Solar-Terrestrial Data Center, NOAA/EDIS/NGSDC, Code D621, 325 Broadway; Boulder, CO 80303 (303-497-6338)

Identification of an amino acid residue on influenza C virus M1 protein responsible for formation of the cord-like structures of the virus.

PubMed

Muraki, Yasushi; Washioka, Hiroshi; Sugawara, Kanetsu; Matsuzaki, Yoko; Takashita, Emi; Hongo, Seiji

2004-07-01

Influenza C virus-like particles (VLPs) have been generated from cloned cDNAs. A cDNA of the green fluorescent protein (GFP) gene in antisense orientation was flanked by the 5' and 3' non-coding regions of RNA segment 5 of the influenza C virus. The cDNA cassette was inserted between an RNA polymerase I promoter and terminator of the Pol I vector. This plasmid DNA was transfected into 293T cells together with plasmids encoding virus proteins of C/Ann Arbor/1/50 or C/Yamagata/1/88. Transfer of the supernatants of the transfected 293T cells to HMV-II cells resulted in GFP expression in the HMV-II cells. The quantification of the GFP-positive HMV-II cells indicated the presence of approximately 10(6) VLPs (ml supernatant)(-1). Cords 50-300 microm in length were observed on transfected 293T cells, although the cords were not observed when the plasmid for M1 protein of C/Ann Arbor/1/50 was replaced with that of C/Taylor/1233/47. A series of transfection experiments with plasmids encoding M1 mutants of C/Ann Arbor/1/50 or C/Taylor/1233/47 showed that an amino acid at residue 24 of the M1 protein is responsible for cord formation. This finding provides direct evidence for a previous hypothesis that M1 protein is involved in the formation of cord-like structures protruding from the C/Yamagata/1/88-infected cells. Evidence was obtained by electron microscopy that transfected cells bearing cords produced filamentous VLPs, suggesting the potential role of the M1 protein in determining the filamentous/spherical morphology of influenza C virus.

Analysing the 21 cm signal from the epoch of reionization with artificial neural networks

NASA Astrophysics Data System (ADS)

Shimabukuro, Hayato; Semelin, Benoit

2017-07-01

The 21 cm signal from the epoch of reionization should be observed within the next decade. While a simple statistical detection is expected with Square Kilometre Array (SKA) pathfinders, the SKA will hopefully produce a full 3D mapping of the signal. To extract from the observed data constraints on the parameters describing the underlying astrophysical processes, inversion methods must be developed. For example, the Markov Chain Monte Carlo method has been successfully applied. Here, we test another possible inversion method: artificial neural networks (ANNs). We produce a training set that consists of 70 individual samples. Each sample is made of the 21 cm power spectrum at different redshifts produced with the 21cmFast code plus the value of three parameters used in the seminumerical simulations that describe astrophysical processes. Using this set, we train the network to minimize the error between the parameter values it produces as an output and the true values. We explore the impact of the architecture of the network on the quality of the training. Then we test the trained network on the new set of 54 test samples with different values of the parameters. We find that the quality of the parameter reconstruction depends on the sensitivity of the power spectrum to the different parameters at a given redshift, that including thermal noise and sample variance decreases the quality of the reconstruction and that using the power spectrum at several redshifts as an input to the ANN improves the quality of the reconstruction. We conclude that ANNs are a viable inversion method whose main strength is that they require a sparse exploration of the parameter space and thus should be usable with full numerical simulations.

Current and anticipated uses of thermalhydraulic and neutronic codes at PSI

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

Aksan, S.N.; Zimmermann, M.A.; Yadigaroglu, G.

1997-07-01

The thermalhydraulic and/or neutronic codes in use at PSI mainly provide the capability to perform deterministic safety analysis for Swiss NPPs and also serve as analysis tools for experimental facilities for LWR and ALWR simulations. In relation to these applications, physical model development and improvements, and assessment of the codes are also essential components of the activities. In this paper, a brief overview is provided on the thermalhydraulic and/or neutronic codes used for safety analysis of LWRs, at PSI, and also of some experiences and applications with these codes. Based on these experiences, additional assessment needs are indicated, together withmore » some model improvement needs. The future needs that could be used to specify both the development of a new code and also improvement of available codes are summarized.« less

Road safety practices among commercial motorcyclists in a rural town in Nigeria: implications for health education.

PubMed

Amoran, O E; Eme, Owoaje; Giwa, O A; Gbolahan, O B

This cross-sectional, community-based study was carried out among commercial motorcyclists in Igboora. All the commercial motor parks in Igboora were visited and all the commercial motorcyclists who consented to participate in the study were interviewed. Information on the respondents' socio-demographic characteristics, and the practice of road safety measures was collected using an interviewer administered questionnaire. A total of 299 motorcyclists were interviewed. All (100%) of them were males. The mean age of the respondents was 27.4 +/- 7.4 years. One hundred eighty-two (60.7%) of the motorcyclists had the correct knowledge of the purpose of Highway Code. Only 70 (23.3%) could recognize more than half of the currently used road safety codes and 47 (15.7%) obey these road safety codes more than half of the time they see it. Only 183 (61.2%) of them had a driving license and 72 (24.1%) were able to produce these licenses on demand. All (100%) of the respondents did not use any protective helmet. Those who have longer years of working experience, higher level of education and higher knowledge of the safety codes practice it more regularly (r = 0.198, p = 0.001, chi2= 9.31, p = 0.025, and r = 0.28, p = 0.001 respectively). One hundred thirty-six (45.5%) have been involved in at least one accident in the preceding year. The overall incidence of road traffic accident was 2.16 per 1,000. There was however on statistically significant association between the practice of road safety codes and the occurrence of road traffic accidents (chi2= 0.176, p = 0.916). The study shows that the practice of road safety measures was low in this rural Nigerian community and was not associated with the incidence of road traffic accidents. Introducing road safety education particularly targeted at educating the motorcyclists on the importance and practice of road safety measures would lead to an increase in the practice of the safety measures and hopefully a reduction in the incidence of road traffic accidents.

Measuring patient safety culture in maternal and child health institutions in China: a qualitative study.

PubMed

Wang, Yuanyuan; Liu, Weiwei; Shi, Huifeng; Liu, Chaojie; Wang, Yan

2017-07-12

Patient safety culture (PSC) plays a critical role in ensuring safe and quality care. Extensive PSC studies have been undertaken in hospitals. However, little is known about PSC in maternal and child health (MCH) institutions in China, which provide both population-based preventive services as well as individual care for patients. This study aimed to develop a theoretical framework for conceptualising PSC in MCH institutions in China. The study was undertaken in six MCH institutions (three in Hebei and three in Beijing). Participants (n=118) were recruited through stratified purposive sampling: 20 managers/administrators, 59 care providers and 39 patients. In-depth interviews were conducted with the participants. The interview data were coded using both inductive (based on the existing PSC theory developed by the Agency for Healthcare Research and Quality) and deductive (open coding arising from data) approaches. A PSC framework was formulated through axial coding that connected initial codes and selective coding that extracted a small number of themes. The interviewees considered patient safety in relation to six aspects: safety and security in public spaces, safety of medical services, privacy and information security, financial security, psychological safety and gap in services. A 12-dimensional PSC framework was developed, containing 69 items. While the existing PSC theory was confirmed by this study, some new themes emerged from the data. Patients expressed particular concerns about psychological safety and financial security. Defensive medical practices emerged as a PSC dimension that is associated with not only medical safety but also financial security and psychological safety. Patient engagement was also valued by the interviewees, especially the patients, as part of PSC. Although there are some common features in PSC across different healthcare delivery systems, PSC can also be context specific. In MCH settings in China, the meaning of 'patient safety' goes beyond the traditional definition of patients. General well-being, health and disease prevention are important anchor points for defining PSC in such settings. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Application of the Life Safety Code to a Historic Test Stand

NASA Technical Reports Server (NTRS)

Askins, Bruce; Lemke, Paul R.; Lewis, William L.; Covell, Carol C.

2011-01-01

NASA has conducted a study to assess alternatives to refurbishing existing launch vehicle modal test facilities as opposed to developing new test facilities to meet the demands of a very fiscally constrained test and evaluation environment. The results of this study showed that Marshall Space Flight Center (MSFC) Test Stand (TS) 4550 could be made compliant, within reasonable cost and schedule impacts, if safety processes and operational limitations were put in place to meet the safety codes and concerns of the Fire Marshall. Trades were performed with key selection criteria to ensure that appropriate levels of occupant safety are incorporated into test facility design modifications. In preparation for the ground vibration tests that were to be performed on the Ares I launch vehicle, the Ares Flight and Integrated Test Office (FITO) organization evaluated the available test facility options, which included the existing mothballed structural dynamic TS4550 used by Apollo and Shuttle, alternative ground vibration test facilities at other locations, and construction of a new dynamic test stand. After an exhaustive assessment of the alternatives, the results favored modifying the TS4550 because it was the lowest cost option and presented the least schedule risk to the NASA Constellation Program for Ares Integrated Vehicle Ground Vibration Test (IVGVT). As the renovation design plans and drawings were being developed for TS4550, a safety concern was discovered the original design for the construction of the test stand, originally built for the Apollo Program and renovated for the Shuttle Program, was completed before NASA s adoption of the currently imposed safety and building codes per National Fire Protection Association Life Safety Code [NFPA 101] and International Building Codes. The initial FITO assessment of the design changes, required to make TS4550 compliant with current safety and building standards, identified a significant cost increase and schedule impact. An effort was launched to thoroughly evaluate the applicable life safety requirements, examine the context in which they were derived, and determine a means by which the TS4550 modifications could be made within budget and on schedule, while still providing the occupants with appropriate levels of safety.

Use the Bar Code System to Improve Accuracy of the Patient and Sample Identification.

PubMed

Chuang, Shu-Hsia; Yeh, Huy-Pzu; Chi, Kun-Hung; Ku, Hsueh-Chen

2018-01-01

In time and correct sample collection were highly related to patient's safety. The sample error rate was 11.1%, because misbranded patient information and wrong sample containers during January to April, 2016. We developed a barcode system of "Specimens Identify System" through process of reengineering of TRM, used bar code scanners, add sample container instructions, and mobile APP. Conclusion, the bar code systems improved the patient safety and created green environment.

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.

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...

Maniac Talk - Dr. Anne Thompson

NASA Image and Video Library

2014-04-30

Anne Thompson Maniac Lecture, 30 April 2014 NASA climate scientist Dr. Anne Thompson presented a Maniac Talk entitled "A Career in Many Ozone Layers." Anne shared some of her long scientific career both as a researcher at Goddard and Meteorology professor at Penn State. She also described some of the problems she has worked on and tried to convey an enthusiasm for Earth Observations

Maniac Talk - Dr. Anne Douglass

NASA Image and Video Library

2013-03-27

Anne Douglass Maniac Lecture, 27 March, 2013 NASA climate scientist Dr. Anne Douglass presented a Maniac Talk entitled "Satellite Observations - the Touchstone of Atmospheric Modeling." Anne shared some of her scientific career that is filled with unexpected twists and turns and even a few blind alleys, but most important her passion in satellite measurements of ozone and other trace gases, which have been her touchstone.

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.

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

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 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.

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.

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.

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.

Feature extraction and identification in distributed optical-fiber vibration sensing system for oil pipeline safety monitoring

NASA Astrophysics Data System (ADS)

Wu, Huijuan; Qian, Ya; Zhang, Wei; Tang, Chenghao

2017-12-01

High sensitivity of a distributed optical-fiber vibration sensing (DOVS) system based on the phase-sensitivity optical time domain reflectometry (Φ-OTDR) technology also brings in high nuisance alarm rates (NARs) in real applications. In this paper, feature extraction methods of wavelet decomposition (WD) and wavelet packet decomposition (WPD) are comparatively studied for three typical field testing signals, and an artificial neural network (ANN) is built for the event identification. The comparison results prove that the WPD performs a little better than the WD for the DOVS signal analysis and identification in oil pipeline safety monitoring. The identification rate can be improved up to 94.4%, and the nuisance alarm rate can be effectively controlled as low as 5.6% for the identification network with the wavelet packet energy distribution features.

Post-test analysis of dryout test 7B' of the W-1 Sodium Loop Safety Facility Experiment with the SABRE-2P code. [LMFBR

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

Rose, S.D.; Dearing, J.F.

An understanding of conditions that may cause sodium boiling and boiling propagation that may lead to dryout and fuel failure is crucial in liquid-metal fast-breeder reactor safety. In this study, the SABRE-2P subchannel analysis code has been used to analyze the ultimate transient of the in-core W-1 Sodium Loop Safety Facility experiment. This code has a 3-D simple nondynamic boiling model which is able to predict the flow instability which caused dryout. In other analyses dryout has been predicted for out-of-core test bundles and so this study provides additional confirmation of the model.

42 CFR 137.368 - Is the Secretary responsible for oversight and compliance of health and safety codes during...

Code of Federal Regulations, 2010 CFR

2010-10-01

... compliance of health and safety codes during construction projects being performed by a Self-Governance Tribe... SERVICES TRIBAL SELF-GOVERNANCE Construction Roles of the Secretary in Establishing and Implementing Construction Project Agreements § 137.368 Is the Secretary responsible for oversight and compliance of health...

Department of Energy Construction Safety Reference Guide

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

Not Available

1993-09-01

DOE has adopted the Occupational Safety and Health Administration (OSHA) regulations Title 29 Code of Federal Regulations (CFR) 1926 ``Safety and Health Regulations for Construction,`` and related parts of 29 CFR 1910, ``Occupational Safety and Health Standards.`` This nonmandatory reference guide is based on these OSHA regulations and, where appropriate, incorporates additional standards, codes, directives, and work practices that are recognized and accepted by DOE and the construction industry. It covers excavation, scaffolding, electricity, fire, signs/barricades, cranes/hoists/conveyors, hand and power tools, concrete/masonry, stairways/ladders, welding/cutting, motor vehicles/mechanical equipment, demolition, materials, blasting, steel erection, etc.

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.

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.

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.

Generating Customized Verifiers for Automatically Generated Code

NASA Technical Reports Server (NTRS)

Denney, Ewen; Fischer, Bernd

2008-01-01

Program verification using Hoare-style techniques requires many logical annotations. We have previously developed a generic annotation inference algorithm that weaves in all annotations required to certify safety properties for automatically generated code. It uses patterns to capture generator- and property-specific code idioms and property-specific meta-program fragments to construct the annotations. The algorithm is customized by specifying the code patterns and integrating them with the meta-program fragments for annotation construction. However, this is difficult since it involves tedious and error-prone low-level term manipulations. Here, we describe an annotation schema compiler that largely automates this customization task using generative techniques. It takes a collection of high-level declarative annotation schemas tailored towards a specific code generator and safety property, and generates all customized analysis functions and glue code required for interfacing with the generic algorithm core, thus effectively creating a customized annotation inference algorithm. The compiler raises the level of abstraction and simplifies schema development and maintenance. It also takes care of some more routine aspects of formulating patterns and schemas, in particular handling of irrelevant program fragments and irrelevant variance in the program structure, which reduces the size, complexity, and number of different patterns and annotation schemas that are required. The improvements described here make it easier and faster to customize the system to a new safety property or a new generator, and we demonstrate this by customizing it to certify frame safety of space flight navigation code that was automatically generated from Simulink models by MathWorks' Real-Time Workshop.

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.

Safety Education and Science.

ERIC Educational Resources Information Center

Ralph, Richard

1980-01-01

Safety education in the science classroom is discussed, including the beginning of safe management, attitudes toward safety education, laboratory assistants, chemical and health regulation, safety aids, and a case study of a high school science laboratory. Suggestions for safety codes for science teachers, student behavior, and laboratory…

Demonstration of emulator-based Bayesian calibration of safety analysis codes: Theory and formulation

DOE PAGES

Yurko, Joseph P.; Buongiorno, Jacopo; Youngblood, Robert

2015-05-28

System codes for simulation of safety performance of nuclear plants may contain parameters whose values are not known very accurately. New information from tests or operating experience is incorporated into safety codes by a process known as calibration, which reduces uncertainty in the output of the code and thereby improves its support for decision-making. The work reported here implements several improvements on classic calibration techniques afforded by modern analysis techniques. The key innovation has come from development of code surrogate model (or code emulator) construction and prediction algorithms. Use of a fast emulator makes the calibration processes used here withmore » Markov Chain Monte Carlo (MCMC) sampling feasible. This study uses Gaussian Process (GP) based emulators, which have been used previously to emulate computer codes in the nuclear field. The present work describes the formulation of an emulator that incorporates GPs into a factor analysis-type or pattern recognition-type model. This “function factorization” Gaussian Process (FFGP) model allows overcoming limitations present in standard GP emulators, thereby improving both accuracy and speed of the emulator-based calibration process. Calibration of a friction-factor example using a Method of Manufactured Solution is performed to illustrate key properties of the FFGP based process.« less

Current and anticipated uses of thermal hydraulic codes at the Japan Atomic Energy Research Institute

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

Akimoto, Hajime; Kukita; Ohnuki, Akira

1997-07-01

The Japan Atomic Energy Research Institute (JAERI) is conducting several research programs related to thermal-hydraulic and neutronic behavior of light water reactors (LWRs). These include LWR safety research projects, which are conducted in accordance with the Nuclear Safety Commission`s research plan, and reactor engineering projects for the development of innovative reactor designs or core/fuel designs. Thermal-hydraulic and neutronic codes are used for various purposes including experimental analysis, nuclear power plant (NPP) safety analysis, and design assessment.

[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.

Assessment of tissue allograft safety monitoring with administrative healthcare databases: a pilot project using Medicare data.

PubMed

Dhakal, Sanjaya; Burwen, Dale R; Polakowski, Laura L; Zinderman, Craig E; Wise, Robert P

2014-03-01

Assess whether Medicare data are useful for monitoring tissue allograft safety and utilization. We used health care claims (billing) data from 2007 for 35 million fee-for-service Medicare beneficiaries, a predominantly elderly population. Using search terms for transplant-related procedures, we generated lists of ICD-9-CM and CPT(®) codes and assessed the frequency of selected allograft procedures. Step 1 used inpatient data and ICD-9-CM procedure codes. Step 2 added non-institutional provider (e.g., physician) claims, outpatient institutional claims, and CPT codes. We assembled preliminary lists of diagnosis codes for infections after selected allograft procedures. Many ICD-9-CM codes were ambiguous as to whether the procedure involved an allograft. Among 1.3 million persons with a procedure ascertained using the list of ICD-9-CM codes, only 1,886 claims clearly involved an allograft. CPT codes enabled better ascertainment of some allograft procedures (over 17,000 persons had corneal transplants and over 2,700 had allograft skin transplants). For spinal fusion procedures, CPT codes improved specificity for allografts; of nearly 100,000 patients with ICD-9-CM codes for spinal fusions, more than 34,000 had CPT codes indicating allograft use. Monitoring infrequent events (infections) after infrequent exposures (tissue allografts) requires large study populations. A strength of the large Medicare databases is the substantial number of certain allograft procedures. Limitations include lack of clinical detail and donor information. Medicare data can potentially augment passive reporting systems and may be useful for monitoring tissue allograft safety and utilization where codes clearly identify allograft use and coding algorithms can effectively screen for infections.

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.

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.

The Italian experience on T/H best estimate codes: Achievements and perspectives

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

Alemberti, A.; D`Auria, F.; Fiorino, E.

1997-07-01

Themalhydraulic system codes are complex tools developed to simulate the power plants behavior during off-normal conditions. Among the objectives of the code calculations the evaluation of safety margins, the operator training, the optimization of the plant design and of the emergency operating procedures, are mostly considered in the field of the nuclear safety. The first generation of codes was developed in the United States at the end of `60s. Since that time, different research groups all over the world started the development of their own codes. At the beginning of the `80s, the second generation codes were proposed; these differmore » from the first generation codes owing to the number of balance equations solved (six instead of three), the sophistication of the constitutive models and of the adopted numerics. The capabilities of available computers have been fully exploited during the years. The authors then summarize some of the major steps in the process of developing, modifying, and advancing the capabilities of the codes. They touch on the fact that Italian, and for that matter non-American, researchers have not been intimately involved in much of this work. They then describe the application of these codes in Italy, even though there are no operating or under construction nuclear power plants at this time. Much of this effort is directed at the general question of plant safety in the face of transient type events.« less

A multicenter collaborative approach to reducing pediatric codes outside the ICU.

PubMed

Hayes, Leslie W; Dobyns, Emily L; DiGiovine, Bruno; Brown, Ann-Marie; Jacobson, Sharon; Randall, Kelly H; Wathen, Beth; Richard, Heather; Schwab, Carolyn; Duncan, Kathy D; Thrasher, Jodi; Logsdon, Tina R; Hall, Matthew; Markovitz, Barry

2012-03-01

The Child Health Corporation of America formed a multicenter collaborative to decrease the rate of pediatric codes outside the ICU by 50%, double the days between these events, and improve the patient safety culture scores by 5 percentage points. A multidisciplinary pediatric advisory panel developed a comprehensive change package of process improvement strategies and measures for tracking progress. Learning sessions, conference calls, and data submission facilitated collaborative group learning and implementation. Twenty Child Health Corporation of America hospitals participated in this 12-month improvement project. Each hospital identified at least 1 noncritical care target unit in which to implement selected elements of the change package. Strategies to improve prevention, detection, and correction of the deteriorating patient ranged from relatively simple, foundational changes to more complex, advanced changes. Each hospital selected a broad range of change package elements for implementation using rapid-cycle methodologies. The primary outcome measure was reduction in codes per 1000 patient days. Secondary outcomes were days between codes and change in patient safety culture scores. Code rate for the collaborative did not decrease significantly (3% decrease). Twelve hospitals reported additional data after the collaborative and saw significant improvement in code rates (24% decrease). Patient safety culture scores improved by 4.5% to 8.5%. A complex process, such as patient deterioration, requires sufficient time and effort to achieve improved outcomes and create a deeply embedded culture of patient safety. The collaborative model can accelerate improvements achieved by individual institutions.

46 CFR 12.15-3 - General requirements.

Code of Federal Regulations, 2011 CFR

2011-10-01

... years the minimum standards of competence for the following 4 areas of basic safety: (1) Personal... first aid as set out in table A-VI/1-3 of the STCW Code. (4) Personal safety and social responsibilities... of competence set out in STCW Regulation III/4 and Section A-III/4 of the STCW Code (incorporated by...

78 FR 28949 - Fund Availability Under VA's Homeless Providers Grant and Per Diem Program (Rehabilitation)

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-16

... Per Diem Program as a part of the effort to increase the useful life of the facilities of grantees... grantees are required to ensure that facilities rehabilitated under this NOFA meet the Life Safety Code of the National Fire and Protection Association. Please note, typically the Life Safety Code is more...

Quantitative and Qualitative Analysis of Nutrition and Food Safety Information in School Science Textbooks of India

ERIC Educational Resources Information Center

Subba Rao, G. M.; Vijayapushapm, T.; Venkaiah, K.; Pavarala, V.

2012-01-01

Objective: To assess quantity and quality of nutrition and food safety information in science textbooks prescribed by the Central Board of Secondary Education (CBSE), India for grades I through X. Design: Content analysis. Methods: A coding scheme was developed for quantitative and qualitative analyses. Two investigators independently coded the…

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.

A valiant little terminal: A VLT user's manual

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

Weinstein, A.

1992-08-01

VLT came to be used at SLAC (Stanford Linear Accelerator Center), because SLAC wanted to assess the Amiga's usefulness as a color graphics terminal and T{sub E}X workstation. Before the project could really begin, the people at SLAC needed a terminal emulator which could successfully talk to the IBM 3081 (now the IBM ES9000-580) and all the VAXes on the site. Moreover, it had to compete in quality with the Ann Arbor Ambassador GXL terminals which were already in use at the laboratory. Unfortunately, at the time there was no commercial program which fit the bill. Luckily, Willy Langeveld hadmore » been independently hacking up a public domain VT100 emulator written by Dave Wecker et al. and the result, VLT, suited SLAC's purpose. Over the years, as the program was debugged and rewritten, the original code disappeared, so that now, in the present version of VLT, none of the original VT100 code remains.« less

A valiant little terminal: A VLT user`s manual. Revision 4

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

Weinstein, A.

1992-08-01

VLT came to be used at SLAC (Stanford Linear Accelerator Center), because SLAC wanted to assess the Amiga`s usefulness as a color graphics terminal and T{sub E}X workstation. Before the project could really begin, the people at SLAC needed a terminal emulator which could successfully talk to the IBM 3081 (now the IBM ES9000-580) and all the VAXes on the site. Moreover, it had to compete in quality with the Ann Arbor Ambassador GXL terminals which were already in use at the laboratory. Unfortunately, at the time there was no commercial program which fit the bill. Luckily, Willy Langeveld hadmore » been independently hacking up a public domain VT100 emulator written by Dave Wecker et al. and the result, VLT, suited SLAC`s purpose. Over the years, as the program was debugged and rewritten, the original code disappeared, so that now, in the present version of VLT, none of the original VT100 code remains.« less

[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.

78 FR 67048 - Prothioconazole; Pesticide Tolerances

Federal Register 2010, 2011, 2012, 2013, 2014

2013-11-08

... code 111). Animal production (NAICS code 112). Food manufacturing (NAICS code 311). Pesticide manufacturing (NAICS code 32532). B. How can I get electronic access to other related information? You may... Assessment and Determination of Safety Section 408(b)(2)(A)(i) of FFDCA allows EPA to establish a tolerance...

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.

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

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 that interactively contribute to obesity trait and provided predictive models with a promising generalization ability. In general, results showed that ANNs and their hybrids can provide useful tools for the study of complex traits in the context of nutrigenetics.

Improvements and applications of COBRA-TF for stand-alone and coupled LWR safety analyses

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

Avramova, M.; Cuervo, D.; Ivanov, K.

2006-07-01

The advanced thermal-hydraulic subchannel code COBRA-TF has been recently improved and applied for stand-alone and coupled LWR core calculations at the Pennsylvania State Univ. in cooperation with AREVA NP GmbH (Germany)) and the Technical Univ. of Madrid. To enable COBRA-TF for academic and industrial applications including safety margins evaluations and LWR core design analyses, the code programming, numerics, and basic models were revised and substantially improved. The code has undergone through an extensive validation, verification, and qualification program. (authors)

Inventory of Safety-related Codes and Standards for Energy Storage Systems with some Experiences related to Approval and Acceptance

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

Conover, David R.

The purpose of this document is to identify laws, rules, model codes, codes, standards, regulations, specifications (CSR) related to safety that could apply to stationary energy storage systems (ESS) and experiences to date securing approval of ESS in relation to CSR. This information is intended to assist in securing approval of ESS under current CSR and to identification of new CRS or revisions to existing CRS and necessary supporting research and documentation that can foster the deployment of safe ESS.

The science behind codes and standards for safe walkways: changes in level, stairways, stair handrails and slip resistance.

PubMed

Nemire, Kenneth; Johnson, Daniel A; Vidal, Keith

2016-01-01

Walkway codes and standards are often created through consensus by committees based on a number of factors, including historical precedence, common practice, cost, and empirical data. The authors maintain that in the formulation of codes and standards that impact pedestrian safety, the results of pertinent scientific research should be given significant weight. This article examines many elements of common walkway codes and standards related to changes in level, stairways, stair handrails, and slip resistance. It identifies which portions are based on or supported by empirical data; and which could benefit from additional scientific research. This article identifies areas in which additional research, codes, and standards may be beneficial to enhance pedestrian safety. Copyright © 2015 Elsevier Ltd and The Ergonomics Society. All rights reserved.

Poster - 28: Shielding of X-ray Rooms in Ontario in the Absence of Best Practice

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

Frimeth, Jeff; Richer, Jeff; Nesbitt, James

This poster will be strictly based on the Healing Arts Radiation Protection (HARP) Act, Regulation 543 under this Act (X-ray Safety Code), and personal communication the presenting author has had. In Ontario, the process of approval of an X-ray machine installation by the Director of the X-ray Inspection Service (XRIS) follows a certain protocol. Initially, the applicant submits a series of forms, including recommended shielding amounts, in order to satisfy the law. This documentation is then transferred to a third-party vendor (i.e. a professional engineer – P.Eng.) outsourced by the Ministry of Health and Long-term Care (MOHLTC). The P.Eng. thenmore » evaluates the submitted documentation for appropriate fulfillment of the HARP Act and Reg. 543 requirements. If the P.Eng.’s evaluation of the documentation is to their satisfaction, the XRIS is then notified. Finally, the Director will then issue a letter of approval to install the equipment at the facility. The methodology required to be used by the P.Eng. in order to determine the required amounts of protective barriers, and recommended to be used by the applicant, is contained within Safety Code 20A. However, Safety Code 35 has replaced the obsolete Safety Code 20A document and employs best practices in shielding design. This talk will focus further on specific intentions and limitations of Safety Code 20A. Furthermore, this talk will discuss the definition of the “practice of professional engineering” in Ontario. COMP members who are involved in shielding design are strongly encouraged to attend.« less

Hydrogen and Fuel Cell Technology | Transportation Research | NREL

Science.gov Websites

Outlines Safety Considerations for Hydrogen Technologies While safety requirements for industrial uses of vehicles have created the need for additional safety requirements. The new Hydrogen Technologies Safety hydrogen safety in context. For example, code officials reviewing permit applications for hydrogen projects

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.

Delay Analysis of Car-to-Car Reliable Data Delivery Strategies Based on Data Mulling with Network Coding

NASA Astrophysics Data System (ADS)

Park, Joon-Sang; Lee, Uichin; Oh, Soon Young; Gerla, Mario; Lun, Desmond Siumen; Ro, Won Woo; Park, Joonseok

Vehicular ad hoc networks (VANET) aims to enhance vehicle navigation safety by providing an early warning system: any chance of accidents is informed through the wireless communication between vehicles. For the warning system to work, it is crucial that safety messages be reliably delivered to the target vehicles in a timely manner and thus reliable and timely data dissemination service is the key building block of VANET. Data mulling technique combined with three strategies, network codeing, erasure coding and repetition coding, is proposed for the reliable and timely data dissemination service. Particularly, vehicles in the opposite direction on a highway are exploited as data mules, mobile nodes physically delivering data to destinations, to overcome intermittent network connectivity cause by sparse vehicle traffic. Using analytic models, we show that in such a highway data mulling scenario the network coding based strategy outperforms erasure coding and repetition based strategies.

Scanning for safety: an integrated approach to improved bar-code medication administration.

PubMed

Early, Cynde; Riha, Chris; Martin, Jennifer; Lowdon, Karen W; Harvey, Ellen M

2011-03-01

This is a review of lessons learned in the postimplementation evaluation of a bar-code medication administration technology implemented at a major tertiary-care hospital in 2001. In 2006, with a bar-code medication administration scan compliance rate of 82%, a near-miss sentinel event prompted review of this technology as part of an institutional recommitment to a "culture of safety." Multifaceted problems with bar-code medication administration created an environment of circumventing safeguards as demonstrated by an increase in manual overrides to ensure timely medication administration. A multiprofessional team composed of nursing, pharmacy, human resources, quality, and technical services formalized. Each step in the bar-code medication administration process was reviewed. Technology, process, and educational solutions were identified and implemented systematically. Overall compliance with bar-code medication administration rose from 82% to 97%, which resulted in a calculated cost avoidance of more than $2.8 million during this time frame of the project.

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 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.

Development of QSAR models using artificial neural network analysis for risk assessment of repeated-dose, reproductive, and developmental toxicities of cosmetic ingredients.

PubMed

Hisaki, Tomoka; Aiba Née Kaneko, Maki; Yamaguchi, Masahiko; Sasa, Hitoshi; Kouzuki, Hirokazu

2015-04-01

Use of laboratory animals for systemic toxicity testing is subject to strong ethical and regulatory constraints, but few alternatives are yet available. One possible approach to predict systemic toxicity of chemicals in the absence of experimental data is quantitative structure-activity relationship (QSAR) analysis. Here, we present QSAR models for prediction of maximum "no observed effect level" (NOEL) for repeated-dose, developmental and reproductive toxicities. NOEL values of 421 chemicals for repeated-dose toxicity, 315 for reproductive toxicity, and 156 for developmental toxicity were collected from Japan Existing Chemical Data Base (JECDB). Descriptors to predict toxicity were selected based on molecular orbital (MO) calculations, and QSAR models employing multiple independent descriptors as the input layer of an artificial neural network (ANN) were constructed to predict NOEL values. Robustness of the models was indicated by the root-mean-square (RMS) errors after 10-fold cross-validation (0.529 for repeated-dose, 0.508 for reproductive, and 0.558 for developmental toxicity). Evaluation of the models in terms of the percentages of predicted NOELs falling within factors of 2, 5 and 10 of the in-vivo-determined NOELs suggested that the model is applicable to both general chemicals and the subset of chemicals listed in International Nomenclature of Cosmetic Ingredients (INCI). Our results indicate that ANN models using in silico parameters have useful predictive performance, and should contribute to integrated risk assessment of systemic toxicity using a weight-of-evidence approach. Availability of predicted NOELs will allow calculation of the margin of safety, as recommended by the Scientific Committee on Consumer Safety (SCCS).

Safety with surgical lasers.

PubMed

McKenzie, A L

1984-01-01

As the sales of surgical lasers continue to grow, the problem of laser safety in hospitals becomes increasingly more urgent. This article considers both the principles and the practice of laser safety, and indicates how safety codes should be organized within a hospital. Eye safety is of paramount importance, and the effects of different wavelengths of laser radiation on the eye are described, both for intrabeam and extended-source exposure. An account is given of the concept of Maximum Permissible Exposure (MPE) and how it depends upon wavelength and exposure duration. The standard laser classification is developed in relation to MPE. The use of laser protective eyewear is discussed for the surgeon, other theatre staff and the patient. Finally, the role of the Laser Protection Supervisor and of the Laser Protection Adviser are explained in the context of establishing a local laser safety code.

Analysis of the technology acceptance model in examining hospital nurses' behavioral intentions toward the use of bar code medication administration.

PubMed

Song, Lunar; Park, Byeonghwa; Oh, Kyeung Mi

2015-04-01

Serious medication errors continue to exist in hospitals, even though there is technology that could potentially eliminate them such as bar code medication administration. Little is known about the degree to which the culture of patient safety is associated with behavioral intention to use bar code medication administration. Based on the Technology Acceptance Model, this study evaluated the relationships among patient safety culture and perceived usefulness and perceived ease of use, and behavioral intention to use bar code medication administration technology among nurses in hospitals. Cross-sectional surveys with a convenience sample of 163 nurses using bar code medication administration were conducted. Feedback and communication about errors had a positive impact in predicting perceived usefulness (β=.26, P<.01) and perceived ease of use (β=.22, P<.05). In a multiple regression model predicting for behavioral intention, age had a negative impact (β=-.17, P<.05); however, teamwork within hospital units (β=.20, P<.05) and perceived usefulness (β=.35, P<.01) both had a positive impact on behavioral intention. The overall bar code medication administration behavioral intention model explained 24% (P<.001) of the variance. Identified factors influencing bar code medication administration behavioral intention can help inform hospitals to develop tailored interventions for RNs to reduce medication administration errors and increase patient safety by using this technology.

Residential building codes, affordability, and health protection: a risk-tradeoff approach.

PubMed

Hammitt, J K; Belsky, E S; Levy, J I; Graham, J D

1999-12-01

Residential building codes intended to promote health and safety may produce unintended countervailing risks by adding to the cost of construction. Higher construction costs increase the price of new homes and may increase health and safety risks through "income" and "stock" effects. The income effect arises because households that purchase a new home have less income remaining for spending on other goods that contribute to health and safety. The stock effect arises because suppression of new-home construction leads to slower replacement of less safe housing units. These countervailing risks are not presently considered in code debates. We demonstrate the feasibility of estimating the approximate magnitude of countervailing risks by combining the income effect with three relatively well understood and significant home-health risks. We estimate that a code change that increases the nationwide cost of constructing and maintaining homes by $150 (0.1% of the average cost to build a single-family home) would induce offsetting risks yielding between 2 and 60 premature fatalities or, including morbidity effects, between 20 and 800 lost quality-adjusted life years (both discounted at 3%) each year the code provision remains in effect. To provide a net health benefit, the code change would need to reduce risk by at least this amount. Future research should refine these estimates, incorporate quantitative uncertainty analysis, and apply a full risk-tradeoff approach to real-world case studies of proposed code changes.

The new interactive CESAR

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

Fox, P.B.; Yatabe, M.

1987-01-01

In this report the Nuclear Criticality Safety Analytical Methods Resource Center describes a new interactive version of CESAR, a critical experiments storage and retrieval program available on the Nuclear Criticality Information System (NCIS) database at Lawrence Livermore National Laboratory. The original version of CESAR did not include interactive search capabilities. The CESAR database was developed to provide a convenient, readily accessible means of storing and retrieving code input data for the SCALE Criticality Safety Analytical Sequences and the codes comprising those sequences. The database includes data for both cross section preparation and criticality safety calculations. 3 refs., 1 tab.

New interactive CESAR

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

Fox, P.B.; Yatabe, M.

1987-01-01

The Nuclear Criticality Safety Analytical Methods Resource Center announces the availability of a new interactive version of CESAR, a critical experiments storage and retrieval program available on the Nuclear Criticality Information System (NCIS) data base at Lawrence Livermore National Laboratory. The original version of CESAR did not include interactive search capabilities. The CESAR data base was developed to provide a convenient, readily accessible means of storing and retrieving code input data for the SCALE criticality safety analytical sequences and the codes comprising those sequences. The data base includes data for both cross-section preparation and criticality safety calculations.

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.

Progress of IRSN R&D on ITER Safety Assessment

NASA Astrophysics Data System (ADS)

Van Dorsselaere, J. P.; Perrault, D.; Barrachin, M.; Bentaib, A.; Gensdarmes, F.; Haeck, W.; Pouvreau, S.; Salat, E.; Seropian, C.; Vendel, J.

2012-08-01

The French "Institut de Radioprotection et de Sûreté Nucléaire" (IRSN), in support to the French "Autorité de Sûreté Nucléaire", is analysing the safety of ITER fusion installation on the basis of the ITER operator's safety file. IRSN set up a multi-year R&D program in 2007 to support this safety assessment process. Priority has been given to four technical issues and the main outcomes of the work done in 2010 and 2011 are summarized in this paper: for simulation of accident scenarios in the vacuum vessel, adaptation of the ASTEC system code; for risk of explosion of gas-dust mixtures in the vacuum vessel, adaptation of the TONUS-CFD code for gas distribution, development of DUST code for dust transport, and preparation of IRSN experiments on gas inerting, dust mobilization, and hydrogen-dust mixtures explosion; for evaluation of the efficiency of the detritiation systems, thermo-chemical calculations of tritium speciation during transport in the gas phase and preparation of future experiments to evaluate the most influent factors on detritiation; for material neutron activation, adaptation of the VESTA Monte Carlo depletion code. The first results of these tasks have been used in 2011 for the analysis of the ITER safety file. In the near future, this R&D global programme may be reoriented to account for the feedback of the latter analysis or for new knowledge.

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.

Verification and Validation in a Rapid Software Development Process

NASA Technical Reports Server (NTRS)

Callahan, John R.; Easterbrook, Steve M.

1997-01-01

The high cost of software production is driving development organizations to adopt more automated design and analysis methods such as rapid prototyping, computer-aided software engineering (CASE) tools, and high-level code generators. Even developers of safety-critical software system have adopted many of these new methods while striving to achieve high levels Of quality and reliability. While these new methods may enhance productivity and quality in many cases, we examine some of the risks involved in the use of new methods in safety-critical contexts. We examine a case study involving the use of a CASE tool that automatically generates code from high-level system designs. We show that while high-level testing on the system structure is highly desirable, significant risks exist in the automatically generated code and in re-validating releases of the generated code after subsequent design changes. We identify these risks and suggest process improvements that retain the advantages of rapid, automated development methods within the quality and reliability contexts of safety-critical projects.

Post-licensure rapid immunization safety monitoring program (PRISM) data characterization.

PubMed

Baker, Meghan A; Nguyen, Michael; Cole, David V; Lee, Grace M; Lieu, Tracy A

2013-12-30

The Post-Licensure Rapid Immunization Safety Monitoring (PRISM) program is the immunization safety monitoring component of FDA's Mini-Sentinel project, a program to actively monitor the safety of medical products using electronic health information. FDA sought to assess the surveillance capabilities of this large claims-based distributed database for vaccine safety surveillance by characterizing the underlying data. We characterized data available on vaccine exposures in PRISM, estimated how much additional data was gained by matching with select state and local immunization registries, and compared vaccination coverage estimates based on PRISM data with other available data sources. We generated rates of computerized codes representing potential health outcomes relevant to vaccine safety monitoring. Standardized algorithms including ICD-9 codes, number of codes required, exclusion criteria and location of the encounter were used to obtain the background rates. The majority of the vaccines routinely administered to infants, children, adolescents and adults were well captured by claims data. Immunization registry data in up to seven states comprised between 5% and 9% of data for all vaccine categories with the exception of 10% for hepatitis B and 3% and 4% for rotavirus and zoster respectively. Vaccination coverage estimates based on PRISM's computerized data were similar to but lower than coverage estimates from the National Immunization Survey and Healthcare Effectiveness Data and Information Set. For the 25 health outcomes of interest studied, the rates of potential outcomes based on ICD-9 codes were generally higher than rates described in the literature, which are typically clinically confirmed cases. PRISM program's data on vaccine exposures and health outcomes appear complete enough to support robust safety monitoring. Copyright © 2013 Elsevier Ltd. All rights reserved.

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);…

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.

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

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.

Do code of conduct audits improve chemical safety in garment factories? Lessons on corporate social responsibility in the supply chain from Fair Wear Foundation.

PubMed

Lindholm, Henrik; Egels-Zandén, Niklas; Rudén, Christina

2016-10-01

In managing chemical risks to the environment and human health in supply chains, voluntary corporate social responsibility (CSR) measures, such as auditing code of conduct compliance, play an important role. To examine how well suppliers' chemical health and safety performance complies with buyers' CSR policies and whether audited factories improve their performance. CSR audits (n = 288) of garment factories conducted by Fair Wear Foundation (FWF), an independent non-profit organization, were analyzed using descriptive statistics and statistical modeling. Forty-three per cent of factories did not comply with the FWF code of conduct, i.e. received remarks on chemical safety. Only among factories audited 10 or more times was there a significant increase in the number of factories receiving no remarks. Compliance with chemical safety requirements in garment supply chains is low and auditing is statistically correlated with improvements only at factories that have undergone numerous audits.

Do code of conduct audits improve chemical safety in garment factories? Lessons on corporate social responsibility in the supply chain from Fair Wear Foundation

PubMed Central

2016-01-01

Background In managing chemical risks to the environment and human health in supply chains, voluntary corporate social responsibility (CSR) measures, such as auditing code of conduct compliance, play an important role. Objectives To examine how well suppliers’ chemical health and safety performance complies with buyers’ CSR policies and whether audited factories improve their performance. Methods CSR audits (n = 288) of garment factories conducted by Fair Wear Foundation (FWF), an independent non-profit organization, were analyzed using descriptive statistics and statistical modeling. Results Forty-three per cent of factories did not comply with the FWF code of conduct, i.e. received remarks on chemical safety. Only among factories audited 10 or more times was there a significant increase in the number of factories receiving no remarks. Conclusions Compliance with chemical safety requirements in garment supply chains is low and auditing is statistically correlated with improvements only at factories that have undergone numerous audits. PMID:27611103

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...

Development of photovoltaic array and module safety requirements

NASA Technical Reports Server (NTRS)

1982-01-01

Safety requirements for photovoltaic module and panel designs and configurations likely to be used in residential, intermediate, and large-scale applications were identified and developed. The National Electrical Code and Building Codes were reviewed with respect to present provisions which may be considered to affect the design of photovoltaic modules. Limited testing, primarily in the roof fire resistance field was conducted. Additional studies and further investigations led to the development of a proposed standard for safety for flat-plate photovoltaic modules and panels. Additional work covered the initial investigation of conceptual approaches and temporary deployment, for concept verification purposes, of a differential dc ground-fault detection circuit suitable as a part of a photovoltaic array safety system.

76 FR 22383 - National Fire Codes: Request for Proposals for Revision of Codes and Standards

Federal Register 2010, 2011, 2012, 2013, 2014

2011-04-21

... DEPARTMENT OF COMMERCE National Institute of Standards and Technology National Fire Codes: Request... publishing this notice on behalf of the National Fire Protection Association (NFPA) to announce the NFPA's proposal to revise some of its fire safety codes and standards and requests proposals from the public to...

Current and anticipated uses of thermal hydraulic codes in Korea

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

Kim, Kyung-Doo; Chang, Won-Pyo

1997-07-01

In Korea, the current uses of thermal hydraulic codes are categorized into 3 areas. The first application is in designing both nuclear fuel and NSSS. The codes have usually been introduced based on the technology transfer programs agreed between KAERI and the foreign vendors. Another area is in the supporting of the plant operations and licensing by the utility. The third category is research purposes. In this area assessments and some applications to the safety issue resolutions are major activities using the best estimate thermal hydraulic codes such as RELAP5/MOD3 and CATHARE2. Recently KEPCO plans to couple thermal hydraulic codesmore » with a neutronics code for the design of the evolutionary type reactor by 2004. KAERI also plans to develop its own best estimate thermal hydraulic code, however, application range is different from KEPCO developing code. Considering these activities, it is anticipated that use of the best estimate hydraulic analysis code developed in Korea may be possible in the area of safety evaluation within 10 years.« less

44 CFR 206.400 - General.

Code of Federal Regulations, 2014 CFR

2014-10-01

... standards of safety, decency, and sanitation and in conformity with applicable codes, specifications and standards. (b) Applicable codes, specifications, and standards shall include any disaster resistant building code that meets the minimum requirements of the National Flood Insurance Program (NFIP) as well as...

44 CFR 206.400 - General.

Code of Federal Regulations, 2011 CFR

2011-10-01

... standards of safety, decency, and sanitation and in conformity with applicable codes, specifications and standards. (b) Applicable codes, specifications, and standards shall include any disaster resistant building code that meets the minimum requirements of the National Flood Insurance Program (NFIP) as well as...

44 CFR 206.400 - General.

Code of Federal Regulations, 2012 CFR

2012-10-01

... standards of safety, decency, and sanitation and in conformity with applicable codes, specifications and standards. (b) Applicable codes, specifications, and standards shall include any disaster resistant building code that meets the minimum requirements of the National Flood Insurance Program (NFIP) as well as...

44 CFR 206.400 - General.

Code of Federal Regulations, 2013 CFR

2013-10-01

... standards of safety, decency, and sanitation and in conformity with applicable codes, specifications and standards. (b) Applicable codes, specifications, and standards shall include any disaster resistant building code that meets the minimum requirements of the National Flood Insurance Program (NFIP) as well as...

44 CFR 206.400 - General.

Code of Federal Regulations, 2010 CFR

2010-10-01

... standards of safety, decency, and sanitation and in conformity with applicable codes, specifications and standards. (b) Applicable codes, specifications, and standards shall include any disaster resistant building code that meets the minimum requirements of the National Flood Insurance Program (NFIP) as well as...

Patient safety incidents in hospice care: observations from interdisciplinary case conferences.

PubMed

Oliver, Debra Parker; Demiris, George; Wittenberg-Lyles, Elaine; Gage, Ashley; Dewsnap-Dreisinger, Mariah L; Luetkemeyer, Jamie

2013-12-01

In the home hospice environment, issues arise every day presenting challenges to the safety, care, and quality of the dying experience. The literature pertaining to the safety challenges in this environment is limited. The study explored two research questions; 1) What types of patient safety incidents occur in the home hospice setting? 2) How many of these incidents are recognized by the hospice staff and/or the patient or caregiver as a patient safety incident? Video-recordings of hospice interdisciplinary team case conferences were reviewed and coded for patient safety incidents. Patient safety incidents were defined as any event or circumstance that could have resulted or did result in unnecessary harm to the patient or caregiver, or that could have resulted or did result in a negative impact on the quality of the dying experience for the patient. Codes for categories of patient safety incidents were based on the International Classification for Patient Safety. The setting for the study included two rural hospice programs in one Midwestern state in the United States. One hospice team had two separately functioning teams, the second hospice had three teams. 54 video-recordings were reviewed and coded. Patient safety incidents were identified that involved issues in clinical process, medications, falls, family or caregiving, procedural problems, documentation, psychosocial issues, administrative challenges and accidents. This study distinguishes categories of patient safety events that occur in home hospice care. Although the scope and definition of potential patient safety incidents in hospice is unique, the events observed in this study are similar to those observed with in other settings. This study identifies an operating definition and a potential classification for further research on patient safety incidents in hospice. Further research and consensus building of the definition of patient safety incidents and patient safety incidents in this setting is recommended.

Creation of the Naturalistic Engagement in Secondary Tasks (NEST) distracted driving dataset.

PubMed

Owens, Justin M; Angell, Linda; Hankey, Jonathan M; Foley, James; Ebe, Kazutoshi

2015-09-01

Distracted driving has become a topic of critical importance to driving safety research over the past several decades. Naturalistic driving data offer a unique opportunity to study how drivers engage with secondary tasks in real-world driving; however, the complexities involved with identifying and coding relevant epochs of naturalistic data have limited its accessibility to the general research community. This project was developed to help address this problem by creating an accessible dataset of driver behavior and situational factors observed during distraction-related safety-critical events and baseline driving epochs, using the Strategic Highway Research Program 2 (SHRP2) naturalistic dataset. The new NEST (Naturalistic Engagement in Secondary Tasks) dataset was created using crashes and near-crashes from the SHRP2 dataset that were identified as including secondary task engagement as a potential contributing factor. Data coding included frame-by-frame video analysis of secondary task and hands-on-wheel activity, as well as summary event information. In addition, information about each secondary task engagement within the trip prior to the crash/near-crash was coded at a higher level. Data were also coded for four baseline epochs and trips per safety-critical event. 1,180 events and baseline epochs were coded, and a dataset was constructed. The project team is currently working to determine the most useful way to allow broad public access to the dataset. We anticipate that the NEST dataset will be extraordinarily useful in allowing qualified researchers access to timely, real-world data concerning how drivers interact with secondary tasks during safety-critical events and baseline driving. The coded dataset developed for this project will allow future researchers to have access to detailed data on driver secondary task engagement in the real world. It will be useful for standalone research, as well as for integration with additional SHRP2 data to enable the conduct of more complex research. Copyright © 2015 Elsevier Ltd and National Safety Council. All rights reserved.

Construction safety program for the National Ignition Facility, July 30, 1999 (NIF-0001374-OC)

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

Benjamin, D W

1999-07-30

These rules apply to all LLNL employees, non-LLNL employees (including contract labor, supplemental labor, vendors, personnel matrixed/assigned from other National Laboratories, participating guests, visitors and students) and contractors/subcontractors. The General Rules-Code of Safe Practices shall be used by management to promote accident prevention through indoctrination, safety and health training and on-the-job application. As a condition for contracts award, all contractors and subcontractors and their employees must certify on Form S and H A-l that they have read and understand, or have been briefed and understand, the National Ignition Facility OCIP Project General Rules-Code of Safe Practices. (An interpreter must briefmore » those employees who do not speak or read English fluently.) In addition, all contractors and subcontractors shall adopt a written General Rules-Code of Safe Practices that relates to their operations. The General Rules-Code of Safe Practices must be posted at a conspicuous location at the job site office or be provided to each supervisory employee who shall have it readily available. Copies of the General Rules-Code of Safe Practices can also be included in employee safety pamphlets.« less

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...

[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.

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.

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.

Implementation of neural network for color properties of polycarbonates

NASA Astrophysics Data System (ADS)

Saeed, U.; Ahmad, S.; Alsadi, J.; Ross, D.; Rizvi, G.

2014-05-01

In present paper, the applicability of artificial neural networks (ANN) is investigated for color properties of plastics. The neural networks toolbox of Matlab 6.5 is used to develop and test the ANN model on a personal computer. An optimal design is completed for 10, 12, 14,16,18 & 20 hidden neurons on single hidden layer with five different algorithms: batch gradient descent (GD), batch variable learning rate (GDX), resilient back-propagation (RP), scaled conjugate gradient (SCG), levenberg-marquardt (LM) in the feed forward back-propagation neural network model. The training data for ANN is obtained from experimental measurements. There were twenty two inputs including resins, additives & pigments while three tristimulus color values L*, a* and b* were used as output layer. Statistical analysis in terms of Root-Mean-Squared (RMS), absolute fraction of variance (R squared), as well as mean square error is used to investigate the performance of ANN. LM algorithm with fourteen neurons on hidden layer in Feed Forward Back-Propagation of ANN model has shown best result in the present study. The degree of accuracy of the ANN model in reduction of errors is proven acceptable in all statistical analysis and shown in results. However, it was concluded that ANN provides a feasible method in error reduction in specific color tristimulus values.

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 based methods revealed factors that interactively contribute to obesity trait and provided predictive models with a promising generalization ability. In general, results showed that ANNs and their hybrids can provide useful tools for the study of complex traits in the context of nutrigenetics. PMID:20825661

75 FR 59658 - Airworthiness Directives; SOCATA Model TBM 700 Airplanes

Federal Register 2010, 2011, 2012, 2013, 2014

2010-09-28

... drive assemblies. The European Aviation Safety Agency (EASA), which is the Technical Agent for the... United States Code specifies the FAA's authority to issue rules on aviation safety. Subtitle I, section... Part 39 Air transportation, Aircraft, Aviation safety, Incorporation by reference, Safety. The Proposed...

Efficient Type Representation in TAL

NASA Technical Reports Server (NTRS)

Chen, Juan

2009-01-01

Certifying compilers generate proofs for low-level code that guarantee safety properties of the code. Type information is an essential part of safety proofs. But the size of type information remains a concern for certifying compilers in practice. This paper demonstrates type representation techniques in a large-scale compiler that achieves both concise type information and efficient type checking. In our 200,000-line certifying compiler, the size of type information is about 36% of the size of pure code and data for our benchmarks, the best result to the best of our knowledge. The type checking time is about 2% of the compilation time.

Development of the FHR advanced natural circulation analysis code and application to FHR safety analysis

DOE PAGES

Guo, Z.; Zweibaum, N.; Shao, M.; ...

2016-04-19

The University of California, Berkeley (UCB) is performing thermal hydraulics safety analysis to develop the technical basis for design and licensing of fluoride-salt-cooled, high-temperature reactors (FHRs). FHR designs investigated by UCB use natural circulation for emergency, passive decay heat removal when normal decay heat removal systems fail. The FHR advanced natural circulation analysis (FANCY) code has been developed for assessment of passive decay heat removal capability and safety analysis of these innovative system designs. The FANCY code uses a one-dimensional, semi-implicit scheme to solve for pressure-linked mass, momentum and energy conservation equations. Graph theory is used to automatically generate amore » staggered mesh for complicated pipe network systems. Heat structure models have been implemented for three types of boundary conditions (Dirichlet, Neumann and Robin boundary conditions). Heat structures can be composed of several layers of different materials, and are used for simulation of heat structure temperature distribution and heat transfer rate. Control models are used to simulate sequences of events or trips of safety systems. A proportional-integral controller is also used to automatically make thermal hydraulic systems reach desired steady state conditions. A point kinetics model is used to model reactor kinetics behavior with temperature reactivity feedback. The underlying large sparse linear systems in these models are efficiently solved by using direct and iterative solvers provided by the SuperLU code on high performance machines. Input interfaces are designed to increase the flexibility of simulation for complicated thermal hydraulic systems. In conclusion, this paper mainly focuses on the methodology used to develop the FANCY code, and safety analysis of the Mark 1 pebble-bed FHR under development at UCB is performed.« less

Workplace Safety and Health Topics: Safety & Prevention

MedlinePlus

... Health Records (EHRs) and Patient Work Information Engineering Controls Equipment Design in Mining Falls in the Workplace Green, Safe, and Healthy Jobs – Prevention through Design Hierarchy of Controls Industry and Occupation Coding and Support Logging Safety ...

Overview of the U.S. DOE Hydrogen Safety, Codes and Standards Program. Part 4: Hydrogen Sensors; Preprint

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

Buttner, William J.; Rivkin, Carl; Burgess, Robert

Hydrogen sensors are recognized as a critical element in the safety design for any hydrogen system. In this role, sensors can perform several important functions including indication of unintended hydrogen releases, activation of mitigation strategies to preclude the development of dangerous situations, activation of alarm systems and communication to first responders, and to initiate system shutdown. The functionality of hydrogen sensors in this capacity is decoupled from the system being monitored, thereby providing an independent safety component that is not affected by the system itself. The importance of hydrogen sensors has been recognized by DOE and by the Fuel Cellmore » Technologies Office's Safety and Codes Standards (SCS) program in particular, which has for several years supported hydrogen safety sensor research and development. The SCS hydrogen sensor programs are currently led by the National Renewable Energy Laboratory, Los Alamos National Laboratory, and Lawrence Livermore National Laboratory. The current SCS sensor program encompasses the full range of issues related to safety sensors, including development of advance sensor platforms with exemplary performance, development of sensor-related code and standards, outreach to stakeholders on the role sensors play in facilitating deployment, technology evaluation, and support on the proper selection and use of sensors.« less

An exploratory study of the relationship between socioeconomic status and motor vehicle safety features.

PubMed

Girasek, Deborah C; Taylor, Brett

2010-04-01

The purpose of this study was to assess the association between motor vehicle owners' socioeconomic status (SES) and the safety of their motor vehicles. Truncated vehicle identification numbers (VINs) were obtained from the Maryland Motor Vehicle Administration office. ZIP code-level income and educational data were assigned to each VIN. Software was used to identify safety-related vehicle characteristics including crash test rating, availability of electronic stability control and side impact air bags, age, and weight. Correlations and analyses of variance were performed to assess whether a ZIP code's median household income and educational level were associated with its proportion of registered vehicles with safety features. For 13 of the 16 correlations performed, SES was significantly associated with the availability of vehicle safety features in a direction that favored upper-income individuals. Vehicle weight was not associated with income or education. When ZIP codes were divided into median household income quintiles, their mean proportions of safety features also differed significantly, in the same direction, for availability of electronic stability control, side impact air bags, vehicle age, and crash test ratings. Safer motor vehicles appear to be distributed along socioeconomic lines, with lower income groups experiencing more risk. This previously unidentified mechanism of disparity merits further study and the attention of policy makers.

On the thresholds in modeling of high flows via artificial neural networks - A bootstrapping analysis

NASA Astrophysics Data System (ADS)

Panagoulia, D.; Trichakis, I.

2012-04-01

Considering the growing interest in simulating hydrological phenomena with artificial neural networks (ANNs), it is useful to figure out the potential and limits of these models. In this study, the main objective is to examine how to improve the ability of an ANN model to simulate extreme values of flow utilizing a priori knowledge of threshold values. A three-layer feedforward ANN was trained by using the back propagation algorithm and the logistic function as activation function. By using the thresholds, the flow was partitioned in low (x < μ), medium (μ ≤ x ≤ μ + 2σ) and high (x > μ + 2σ) values. The employed ANN model was trained for high flow partition and all flow data too. The developed methodology was implemented over a mountainous river catchment (the Mesochora catchment in northwestern Greece). The ANN model received as inputs pseudo-precipitation (rain plus melt) and previous observed flow data. After the training was completed the bootstrapping methodology was applied to calculate the ANN confidence intervals (CIs) for a 95% nominal coverage. The calculated CIs included only the uncertainty, which comes from the calibration procedure. The results showed that an ANN model trained specifically for high flows, with a priori knowledge of the thresholds, can simulate these extreme values much better (RMSE is 31.4% less) than an ANN model trained with all data of the available time series and using a posteriori threshold values. On the other hand the width of CIs increases by 54.9% with a simultaneous increase by 64.4% of the actual coverage for the high flows (a priori partition). The narrower CIs of the high flows trained with all data may be attributed to the smoothing effect produced from the use of the full data sets. Overall, the results suggest that an ANN model trained with a priori knowledge of the threshold values has an increased ability in simulating extreme values compared with an ANN model trained with all the data and a posteriori knowledge of the thresholds.

Locating and classifying defects using an hybrid data base

NASA Astrophysics Data System (ADS)

Luna-Avilés, A.; Hernández-Gómez, L. H.; Durodola, J. F.; Urriolagoitia-Calderón, G.; Urriolagoitia-Sosa, G.; Beltrán Fernández, J. A.; Díaz Pineda, A.

2011-07-01

A computational inverse technique was used in the localization and classification of defects. Postulated voids of two different sizes (2 mm and 4 mm diameter) were introduced in PMMA bars with and without a notch. The bar dimensions are 200×20×5 mm. One half of them were plain and the other half has a notch (3 mm × 4 mm) which is close to the defect area (19 mm × 16 mm).This analysis was done with an Artificial Neural Network (ANN) and its optimization was done with an Adaptive Neuro Fuzzy Procedure (ANFIS). A hybrid data base was developed with numerical and experimental results. Synthetic data was generated with the finite element method using SOLID95 element of ANSYS code. A parametric analysis was carried out. Only one defect in such bars was taken into account and the first five natural frequencies were calculated. 460 cases were evaluated. Half of them were plain and the other half has a notch. All the input data was classified in two groups. Each one has 230 cases and corresponds to one of the two sort of voids mentioned above. On the other hand, experimental analysis was carried on with PMMA specimens of the same size. The first two natural frequencies of 40 cases were obtained with one void. The other three frequencies were obtained numerically. 20 of these bars were plain and the others have a notch. These experimental results were introduced in the synthetic data base. 400 cases were taken randomly and, with this information, the ANN was trained with the backpropagation algorithm. The accuracy of the results was tested with the 100 cases that were left. In the next stage of this work, the ANN output was optimized with ANFIS. Previous papers showed that localization and classification of defects was reduced as notches were introduced in such bars. In the case of this paper, improved results were obtained when a hybrid data base was used.

Comparing the coding of complications in Queensland and Victorian admitted patient data.

PubMed

Michel, Jude L; Cheng, Diana; Jackson, Terri J

2011-08-01

To examine differences between Queensland and Victorian coding of hospital-acquired conditions and suggest ways to improve the usefulness of these data in the monitoring of patient safety events. Secondary analysis of admitted patient episode data collected in Queensland and Victoria. Comparison of depth of coding, and patterns in the coding of ten commonly coded complications of five elective procedures. Comparison of the mean complication codes assigned per episode revealed Victoria assigns more valid codes than Queensland for all procedures, with the difference between the states being significantly different in all cases. The proportion of the codes flagged as complications was consistently lower for Queensland when comparing 10 common complications for each of the five selected elective procedures. The estimated complication rates for the five procedures showed Victoria to have an apparently higher complication rate than Queensland for 35 of the 50 complications examined. Our findings demonstrate that the coding of complications is more comprehensive in Victoria than in Queensland. It is known that inconsistencies exist between states in routine hospital data quality. Comparative use of patient safety indicators should be viewed with caution until standards are improved across Australia. More exploration of data quality issues is needed to identify areas for improvement.

The EUCLID/V1 Integrated Code for Safety Assessment of Liquid Metal Cooled Fast Reactors. Part 1: Basic Models

NASA Astrophysics Data System (ADS)

Mosunova, N. A.

2018-05-01

The article describes the basic models included in the EUCLID/V1 integrated code intended for safety analysis of liquid metal (sodium, lead, and lead-bismuth) cooled fast reactors using fuel rods with a gas gap and pellet dioxide, mixed oxide or nitride uranium-plutonium fuel under normal operation, under anticipated operational occurrences and accident conditions by carrying out interconnected thermal-hydraulic, neutronics, and thermal-mechanical calculations. Information about the Russian and foreign analogs of the EUCLID/V1 integrated code is given. Modeled objects, equation systems in differential form solved in each module of the EUCLID/V1 integrated code (the thermal-hydraulic, neutronics, fuel rod analysis module, and the burnup and decay heat calculation modules), the main calculated quantities, and also the limitations on application of the code are presented. The article also gives data on the scope of functions performed by the integrated code's thermal-hydraulic module, using which it is possible to describe both one- and twophase processes occurring in the coolant. It is shown that, owing to the availability of the fuel rod analysis module in the integrated code, it becomes possible to estimate the performance of fuel rods in different regimes of the reactor operation. It is also shown that the models implemented in the code for calculating neutron-physical processes make it possible to take into account the neutron field distribution over the fuel assembly cross section as well as other features important for the safety assessment of fast reactors.

Bayesian model selection applied to artificial neural networks used for water resources modeling

NASA Astrophysics Data System (ADS)

Kingston, Greer B.; Maier, Holger R.; Lambert, Martin F.

2008-04-01

Artificial neural networks (ANNs) have proven to be extremely valuable tools in the field of water resources engineering. However, one of the most difficult tasks in developing an ANN is determining the optimum level of complexity required to model a given problem, as there is no formal systematic model selection method. This paper presents a Bayesian model selection (BMS) method for ANNs that provides an objective approach for comparing models of varying complexity in order to select the most appropriate ANN structure. The approach uses Markov Chain Monte Carlo posterior simulations to estimate the evidence in favor of competing models and, in this study, three known methods for doing this are compared in terms of their suitability for being incorporated into the proposed BMS framework for ANNs. However, it is acknowledged that it can be particularly difficult to accurately estimate the evidence of ANN models. Therefore, the proposed BMS approach for ANNs incorporates a further check of the evidence results by inspecting the marginal posterior distributions of the hidden-to-output layer weights, which unambiguously indicate any redundancies in the hidden layer nodes. The fact that this check is available is one of the greatest advantages of the proposed approach over conventional model selection methods, which do not provide such a test and instead rely on the modeler's subjective choice of selection criterion. The advantages of a total Bayesian approach to ANN development, including training and model selection, are demonstrated on two synthetic and one real world water resources case study.

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.

Diagnostic Performance of Artificial Neural Network for Detecting Ischemia in Myocardial Perfusion Imaging.

PubMed

Nakajima, Kenichi; Matsuo, Shinro; Wakabayashi, Hiroshi; Yokoyama, Kunihiko; Bunko, Hisashi; Okuda, Koichi; Kinuya, Seigo; Nyström, Karin; Edenbrandt, Lars

2015-01-01

The purpose of this study was to apply an artificial neural network (ANN) in patients with coronary artery disease (CAD) and to characterize its diagnostic ability compared with conventional visual and quantitative methods in myocardial perfusion imaging (MPI). A total of 106 patients with CAD were studied with MPI, including multiple vessel disease (49%), history of myocardial infarction (27%) and coronary intervention (30%). The ANN detected abnormal areas with a probability of stress defect and ischemia. The consensus diagnosis based on expert interpretation and coronary stenosis was used as the gold standard. The left ventricular ANN value was higher in the stress-defect group than in the no-defect group (0.92±0.11 vs. 0.25±0.32, P<0.0001) and higher in the ischemia group than in the no-ischemia group (0.70±0.40 vs. 0.004±0.032, P<0.0001). Receiver-operating characteristics curve analysis showed comparable diagnostic accuracy between ANN and the scoring methods (0.971 vs. 0.980 for stress defect, and 0.882 vs. 0.937 for ischemia, both P=NS). The relationship between the ANN and defect scores was non-linear, with the ANN rapidly increased in ranges of summed stress score of 2-7 and summed defect score of 2-4. Although the diagnostic ability of ANN was similar to that of conventional scoring methods, the ANN could provide a different viewpoint for judging abnormality, and thus is a promising method for evaluating abnormality in MPI.

An examination of some safety issues among commercial motorcyclists in Nigeria: a case study.

PubMed

Arosanyin, Godwin Tunde; Olowosulu, Adekunle Taiwo; Oyeyemi, Gafar Matanmi

2013-01-01

The reduction of road crashes and injuries among motorcyclists in Nigeria requires a system inquiry into some safety issues at pre-crash, crash and post-crash stages to guide action plans. This paper examines safety issues such as age restriction, motorcycle engine capacity, highway code awareness, licence holding, helmet usage, crash involvement, rescue and payment for treatment among commercial motorcyclists. The primary data derived from a structured questionnaire administered to 334 commercial motorcyclists in Samaru, Zaria were analysed using descriptive statistics and logistic regression technique. There was total compliance with age restriction and motorcycle engine capacity. About 41.8% of the operators were not aware of the existence of the highway code. The odds of licence holding increased with highway code awareness, education with above senior secondary as the reference category and earnings. The odds of crash involvement decreased with highway code awareness, earnings and mode of operation. About 84% of the motorcyclists did not use crash helmet, in spite of being aware of the benefit, and 65.4% of motorcycle crashes was found to be with other road users. The promotion of safety among motorcyclists therefore requires strict traffic law enforcement and modification of road design to segregate traffic and protect pedestrians.

Quick Response codes for surgical safety: a prospective pilot study.

PubMed

Dixon, Jennifer L; Smythe, William Roy; Momsen, Lara S; Jupiter, Daniel; Papaconstantinou, Harry T

2013-09-01

Surgical safety programs have been shown to reduce patient harm; however, there is variable compliance. The purpose of this study is to determine if innovative technology such as Quick Response (QR) codes can facilitate surgical safety initiatives. We prospectively evaluated the use of QR codes during the surgical time-out for 40 operations. Feasibility and accuracy were assessed. Perceptions of the current time-out process and the QR code application were evaluated through surveys using a 5-point Likert scale and binomial yes or no questions. At baseline (n = 53), survey results from the surgical team agreed or strongly agreed that the current time-out process was efficient (64%), easy to use (77%), and provided clear information (89%). However, 65% of surgeons felt that process improvements were needed. Thirty-seven of 40 (92.5%) QR codes scanned successfully, of which 100% were accurate. Three scan failures resulted from excessive curvature or wrinkling of the QR code label on the body. Follow-up survey results (n = 33) showed that the surgical team agreed or strongly agreed that the QR program was clearer (70%), easier to use (57%), and more accurate (84%). Seventy-four percent preferred the QR system to the current time-out process. QR codes accurately transmit patient information during the time-out procedure and are preferred to the current process by surgical team members. The novel application of this technology may improve compliance, accuracy, and outcomes. Copyright © 2013 Elsevier Inc. All rights reserved.

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...

Real-time support for high performance aircraft operation

NASA Technical Reports Server (NTRS)

Vidal, Jacques J.

1989-01-01

The feasibility of real-time processing schemes using artificial neural networks (ANNs) is investigated. A rationale for digital neural nets is presented and a general processor architecture for control applications is illustrated. Research results on ANN structures for real-time applications are given. Research results on ANN algorithms for real-time control are also shown.

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...

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…

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…

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…

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

Application of artificial neural network to fMRI regression analysis.

PubMed

Misaki, Masaya; Miyauchi, Satoru

2006-01-15

We used an artificial neural network (ANN) to detect correlations between event sequences and fMRI (functional magnetic resonance imaging) signals. The layered feed-forward neural network, given a series of events as inputs and the fMRI signal as a supervised signal, performed a non-linear regression analysis. This type of ANN is capable of approximating any continuous function, and thus this analysis method can detect any fMRI signals that correlated with corresponding events. Because of the flexible nature of ANNs, fitting to autocorrelation noise is a problem in fMRI analyses. We avoided this problem by using cross-validation and an early stopping procedure. The results showed that the ANN could detect various responses with different time courses. The simulation analysis also indicated an additional advantage of ANN over non-parametric methods in detecting parametrically modulated responses, i.e., it can detect various types of parametric modulations without a priori assumptions. The ANN regression analysis is therefore beneficial for exploratory fMRI analyses in detecting continuous changes in responses modulated by changes in input values.

Intelligent Color Vision System for Ripeness Classification of Oil Palm Fresh Fruit Bunch

PubMed Central

Fadilah, Norasyikin; Mohamad-Saleh, Junita; Halim, Zaini Abdul; Ibrahim, Haidi; Ali, Syed Salim Syed

2012-01-01

Ripeness classification of oil palm fresh fruit bunches (FFBs) during harvesting is important to ensure that they are harvested during optimum stage for maximum oil production. This paper presents the application of color vision for automated ripeness classification of oil palm FFB. Images of oil palm FFBs of type DxP Yangambi were collected and analyzed using digital image processing techniques. Then the color features were extracted from those images and used as the inputs for Artificial Neural Network (ANN) learning. The performance of the ANN for ripeness classification of oil palm FFB was investigated using two methods: training ANN with full features and training ANN with reduced features based on the Principal Component Analysis (PCA) data reduction technique. Results showed that compared with using full features in ANN, using the ANN trained with reduced features can improve the classification accuracy by 1.66% and is more effective in developing an automated ripeness classifier for oil palm FFB. The developed ripeness classifier can act as a sensor in determining the correct oil palm FFB ripeness category. PMID:23202043

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.

Application of soft computing based hybrid models in hydrological variables modeling: a comprehensive review

NASA Astrophysics Data System (ADS)

Fahimi, Farzad; Yaseen, Zaher Mundher; El-shafie, Ahmed

2017-05-01

Since the middle of the twentieth century, artificial intelligence (AI) models have been used widely in engineering and science problems. Water resource variable modeling and prediction are the most challenging issues in water engineering. Artificial neural network (ANN) is a common approach used to tackle this problem by using viable and efficient models. Numerous ANN models have been successfully developed to achieve more accurate results. In the current review, different ANN models in water resource applications and hydrological variable predictions are reviewed and outlined. In addition, recent hybrid models and their structures, input preprocessing, and optimization techniques are discussed and the results are compared with similar previous studies. Moreover, to achieve a comprehensive view of the literature, many articles that applied ANN models together with other techniques are included. Consequently, coupling procedure, model evaluation, and performance comparison of hybrid models with conventional ANN models are assessed, as well as, taxonomy and hybrid ANN models structures. Finally, current challenges and recommendations for future researches are indicated and new hybrid approaches are proposed.

Intelligent color vision system for ripeness classification of oil palm fresh fruit bunch.

PubMed

Fadilah, Norasyikin; Mohamad-Saleh, Junita; Abdul Halim, Zaini; Ibrahim, Haidi; Syed Ali, Syed Salim

2012-10-22

Ripeness classification of oil palm fresh fruit bunches (FFBs) during harvesting is important to ensure that they are harvested during optimum stage for maximum oil production. This paper presents the application of color vision for automated ripeness classification of oil palm FFB. Images of oil palm FFBs of type DxP Yangambi were collected and analyzed using digital image processing techniques. Then the color features were extracted from those images and used as the inputs for Artificial Neural Network (ANN) learning. The performance of the ANN for ripeness classification of oil palm FFB was investigated using two methods: training ANN with full features and training ANN with reduced features based on the Principal Component Analysis (PCA) data reduction technique. Results showed that compared with using full features in ANN, using the ANN trained with reduced features can improve the classification accuracy by 1.66% and is more effective in developing an automated ripeness classifier for oil palm FFB. The developed ripeness classifier can act as a sensor in determining the correct oil palm FFB ripeness category.

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.

[Methodological approach to the use of artificial neural networks for predicting results in medicine].

PubMed

Trujillano, Javier; March, Jaume; Sorribas, Albert

2004-01-01

In clinical practice, there is an increasing interest in obtaining adequate models of prediction. Within the possible available alternatives, the artificial neural networks (ANN) are progressively more used. In this review we first introduce the ANN methodology, describing the most common type of ANN, the Multilayer Perceptron trained with backpropagation algorithm (MLP). Then we compare the MLP with the Logistic Regression (LR). Finally, we show a practical scheme to make an application based on ANN by means of an example with actual data. The main advantage of the RN is its capacity to incorporate nonlinear effects and interactions between the variables of the model without need to include them a priori. As greater disadvantages, they show a difficult interpretation of their parameters and large empiricism in their process of construction and training. ANN are useful for the computation of probabilities of a given outcome based on a set of predicting variables. Furthermore, in some cases, they obtain better results than LR. Both methodologies, ANN and LR, are complementary and they help us to obtain more valid models.

49 CFR Appendix C to Part 215 - FRA Freight Car Standards Defect Code

Code of Federal Regulations, 2013 CFR

2013-10-01

... 49 Transportation 4 2013-10-01 2013-10-01 false FRA Freight Car Standards Defect Code C Appendix C... ADMINISTRATION, DEPARTMENT OF TRANSPORTATION RAILROAD FREIGHT CAR SAFETY STANDARDS Pt. 215, App. C Appendix C to Part 215—FRA Freight Car Standards Defect Code The following defect code has been established for use...

49 CFR Appendix C to Part 215 - FRA Freight Car Standards Defect Code

Code of Federal Regulations, 2014 CFR

2014-10-01

... 49 Transportation 4 2014-10-01 2014-10-01 false FRA Freight Car Standards Defect Code C Appendix C... ADMINISTRATION, DEPARTMENT OF TRANSPORTATION RAILROAD FREIGHT CAR SAFETY STANDARDS Pt. 215, App. C Appendix C to Part 215—FRA Freight Car Standards Defect Code The following defect code has been established for use...

49 CFR Appendix C to Part 215 - FRA Freight Car Standards Defect Code

Code of Federal Regulations, 2012 CFR

2012-10-01

... 49 Transportation 4 2012-10-01 2012-10-01 false FRA Freight Car Standards Defect Code C Appendix C... ADMINISTRATION, DEPARTMENT OF TRANSPORTATION RAILROAD FREIGHT CAR SAFETY STANDARDS Pt. 215, App. C Appendix C to Part 215—FRA Freight Car Standards Defect Code The following defect code has been established for use...

49 CFR Appendix C to Part 215 - FRA Freight Car Standards Defect Code

Code of Federal Regulations, 2011 CFR

2011-10-01

... 49 Transportation 4 2011-10-01 2011-10-01 false FRA Freight Car Standards Defect Code C Appendix C... ADMINISTRATION, DEPARTMENT OF TRANSPORTATION RAILROAD FREIGHT CAR SAFETY STANDARDS Pt. 215, App. C Appendix C to Part 215—FRA Freight Car Standards Defect Code The following defect code has been established for use...

7 CFR Exhibit E to Subpart A of... - Voluntary National Model Building Codes

Code of Federal Regulations, 2013 CFR

2013-01-01

... 7 Agriculture 12 2013-01-01 2013-01-01 false Voluntary National Model Building Codes E Exhibit E... National Model Building Codes The following documents address the health and safety aspects of buildings and related structures and are voluntary national model building codes as defined in § 1924.4(h)(2) of...

7 CFR Exhibit E to Subpart A of... - Voluntary National Model Building Codes

Code of Federal Regulations, 2014 CFR

2014-01-01

... 7 Agriculture 12 2014-01-01 2013-01-01 true Voluntary National Model Building Codes E Exhibit E to... Model Building Codes The following documents address the health and safety aspects of buildings and related structures and are voluntary national model building codes as defined in § 1924.4(h)(2) of this...

7 CFR Exhibit E to Subpart A of... - Voluntary National Model Building Codes

Code of Federal Regulations, 2012 CFR

2012-01-01

... 7 Agriculture 12 2012-01-01 2012-01-01 false Voluntary National Model Building Codes E Exhibit E... National Model Building Codes The following documents address the health and safety aspects of buildings and related structures and are voluntary national model building codes as defined in § 1924.4(h)(2) of...

Neurocontrol and fuzzy logic: Connections and designs

NASA Technical Reports Server (NTRS)

Werbos, Paul J.

1991-01-01

Artificial neural networks (ANNs) and fuzzy logic are complementary technologies. ANNs extract information from systems to be learned or controlled, while fuzzy techniques mainly use verbal information from experts. Ideally, both sources of information should be combined. For example, one can learn rules in a hybrid fashion, and then calibrate them for better whole-system performance. ANNs offer universal approximation theorems, pedagogical advantages, very high-throughput hardware, and links to neurophysiology. Neurocontrol - the use of ANNs to directly control motors or actuators, etc. - uses five generalized designs, related to control theory, which can work on fuzzy logic systems as well as ANNs. These designs can copy what experts do instead of what they say, learn to track trajectories, generalize adaptive control, and maximize performance or minimize cost over time, even in noisy environments. Design tradeoffs and future directions are discussed throughout.

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.

Simple Scoring System and Artificial Neural Network for Knee Osteoarthritis Risk Prediction: A Cross-Sectional Study

PubMed Central

Yoo, Tae Keun; Kim, Deok Won; Choi, Soo Beom; Oh, Ein; Park, Jee Soo

2016-01-01

Background Knee osteoarthritis (OA) is the most common joint disease of adults worldwide. Since the treatments for advanced radiographic knee OA are limited, clinicians face a significant challenge of identifying patients who are at high risk of OA in a timely and appropriate way. Therefore, we developed a simple self-assessment scoring system and an improved artificial neural network (ANN) model for knee OA. Methods The Fifth Korea National Health and Nutrition Examination Surveys (KNHANES V-1) data were used to develop a scoring system and ANN for radiographic knee OA. A logistic regression analysis was used to determine the predictors of the scoring system. The ANN was constructed using 1777 participants and validated internally on 888 participants in the KNHANES V-1. The predictors of the scoring system were selected as the inputs of the ANN. External validation was performed using 4731 participants in the Osteoarthritis Initiative (OAI). Area under the curve (AUC) of the receiver operating characteristic was calculated to compare the prediction models. Results The scoring system and ANN were built using the independent predictors including sex, age, body mass index, educational status, hypertension, moderate physical activity, and knee pain. In the internal validation, both scoring system and ANN predicted radiographic knee OA (AUC 0.73 versus 0.81, p<0.001) and symptomatic knee OA (AUC 0.88 versus 0.94, p<0.001) with good discriminative ability. In the external validation, both scoring system and ANN showed lower discriminative ability in predicting radiographic knee OA (AUC 0.62 versus 0.67, p<0.001) and symptomatic knee OA (AUC 0.70 versus 0.76, p<0.001). Conclusions The self-assessment scoring system may be useful for identifying the adults at high risk for knee OA. The performance of the scoring system is improved significantly by the ANN. We provided an ANN calculator to simply predict the knee OA risk. PMID:26859664

Robust artificial neural network for reliability and sensitivity analyses of complex non-linear systems.

PubMed

Oparaji, Uchenna; Sheu, Rong-Jiun; Bankhead, Mark; Austin, Jonathan; Patelli, Edoardo

2017-12-01

Artificial Neural Networks (ANNs) are commonly used in place of expensive models to reduce the computational burden required for uncertainty quantification, reliability and sensitivity analyses. ANN with selected architecture is trained with the back-propagation algorithm from few data representatives of the input/output relationship of the underlying model of interest. However, different performing ANNs might be obtained with the same training data as a result of the random initialization of the weight parameters in each of the network, leading to an uncertainty in selecting the best performing ANN. On the other hand, using cross-validation to select the best performing ANN based on the ANN with the highest R 2 value can lead to biassing in the prediction. This is as a result of the fact that the use of R 2 cannot determine if the prediction made by ANN is biased. Additionally, R 2 does not indicate if a model is adequate, as it is possible to have a low R 2 for a good model and a high R 2 for a bad model. Hence, in this paper, we propose an approach to improve the robustness of a prediction made by ANN. The approach is based on a systematic combination of identical trained ANNs, by coupling the Bayesian framework and model averaging. Additionally, the uncertainties of the robust prediction derived from the approach are quantified in terms of confidence intervals. To demonstrate the applicability of the proposed approach, two synthetic numerical examples are presented. Finally, the proposed approach is used to perform a reliability and sensitivity analyses on a process simulation model of a UK nuclear effluent treatment plant developed by National Nuclear Laboratory (NNL) and treated in this study as a black-box employing a set of training data as a test case. This model has been extensively validated against plant and experimental data and used to support the UK effluent discharge strategy. Copyright © 2017 Elsevier Ltd. All rights reserved.

Lidar performance analysis

NASA Technical Reports Server (NTRS)

Spiers, Gary D.

1994-01-01

Section 1 details the theory used to build the lidar model, provides results of using the model to evaluate AEOLUS design instrument designs, and provides snapshots of the visual appearance of the coded model. Appendix A contains a Fortran program to calculate various forms of the refractive index structure function. This program was used to determine the refractive index structure function used in the main lidar simulation code. Appendix B contains a memo on the optimization of the lidar telescope geometry for a line-scan geometry. Appendix C contains the code for the main lidar simulation and brief instruction on running the code. Appendix D contains a Fortran code to calculate the maximum permissible exposure for the eye from the ANSI Z136.1-1992 eye safety standards. Appendix E contains a paper on the eye safety analysis of a space-based coherent lidar presented at the 7th Coherent Laser Radar Applications and Technology Conference, Paris, France, 19-23 July 1993.

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.

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.

PharmARTS: terminology web services for drug safety data coding and retrieval.

PubMed

Alecu, Iulian; Bousquet, Cédric; Degoulet, Patrice; Jaulent, Marie-Christine

2007-01-01

MedDRA and WHO-ART are the terminologies used to encode drug safety reports. The standardisation achieved with these terminologies facilitates: 1) The sharing of safety databases; 2) Data mining for the continuous reassessment of benefit-risk ratio at national or international level or in the pharmaceutical industry. There is some debate about the capacity of these terminologies for retrieving case reports related to similar medical conditions. We have developed a resource that allows grouping similar medical conditions more effectively than WHO-ART and MedDRA. We describe here a software tool facilitating the use of this terminological resource thanks to an RDF framework with support for RDF Schema inferencing and querying. This tool eases coding and data retrieval in drug safety.

Artificial neural network model for ozone concentration estimation and Monte Carlo analysis

NASA Astrophysics Data System (ADS)

Gao, Meng; Yin, Liting; Ning, Jicai

2018-07-01

Air pollution in urban atmosphere directly affects public-health; therefore, it is very essential to predict air pollutant concentrations. Air quality is a complex function of emissions, meteorology and topography, and artificial neural networks (ANNs) provide a sound framework for relating these variables. In this study, we investigated the feasibility of using ANN model with meteorological parameters as input variables to predict ozone concentration in the urban area of Jinan, a metropolis in Northern China. We firstly found that the architecture of network of neurons had little effect on the predicting capability of ANN model. A parsimonious ANN model with 6 routinely monitored meteorological parameters and one temporal covariate (the category of day, i.e. working day, legal holiday and regular weekend) as input variables was identified, where the 7 input variables were selected following the forward selection procedure. Compared with the benchmarking ANN model with 9 meteorological and photochemical parameters as input variables, the predicting capability of the parsimonious ANN model was acceptable. Its predicting capability was also verified in term of warming success ratio during the pollution episodes. Finally, uncertainty and sensitivity analysis were also performed based on Monte Carlo simulations (MCS). It was concluded that the ANN could properly predict the ambient ozone level. Maximum temperature, atmospheric pressure, sunshine duration and maximum wind speed were identified as the predominate input variables significantly influencing the prediction of ambient ozone concentrations.

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.

A novel artificial neural network method for biomedical prediction based on matrix pseudo-inversion.

PubMed

Cai, Binghuang; Jiang, Xia

2014-04-01

Biomedical prediction based on clinical and genome-wide data has become increasingly important in disease diagnosis and classification. To solve the prediction problem in an effective manner for the improvement of clinical care, we develop a novel Artificial Neural Network (ANN) method based on Matrix Pseudo-Inversion (MPI) for use in biomedical applications. The MPI-ANN is constructed as a three-layer (i.e., input, hidden, and output layers) feed-forward neural network, and the weights connecting the hidden and output layers are directly determined based on MPI without a lengthy learning iteration. The LASSO (Least Absolute Shrinkage and Selection Operator) method is also presented for comparative purposes. Single Nucleotide Polymorphism (SNP) simulated data and real breast cancer data are employed to validate the performance of the MPI-ANN method via 5-fold cross validation. Experimental results demonstrate the efficacy of the developed MPI-ANN for disease classification and prediction, in view of the significantly superior accuracy (i.e., the rate of correct predictions), as compared with LASSO. The results based on the real breast cancer data also show that the MPI-ANN has better performance than other machine learning methods (including support vector machine (SVM), logistic regression (LR), and an iterative ANN). In addition, experiments demonstrate that our MPI-ANN could be used for bio-marker selection as well. Copyright © 2013 Elsevier Inc. All rights reserved.

Displacement back analysis for a high slope of the Dagangshan Hydroelectric Power Station based on BP neural network and particle swarm optimization.

PubMed

Liang, Zhengzhao; Gong, Bin; Tang, Chunan; Zhang, Yongbin; Ma, Tianhui

2014-01-01

The right bank high slope of the Dagangshan Hydroelectric Power Station is located in complicated geological conditions with deep fractures and unloading cracks. How to obtain the mechanical parameters and then evaluate the safety of the slope are the key problems. This paper presented a displacement back analysis for the slope using an artificial neural network model (ANN) and particle swarm optimization model (PSO). A numerical model was established to simulate the displacement increment results, acquiring training data for the artificial neural network model. The backpropagation ANN model was used to establish a mapping function between the mechanical parameters and the monitoring displacements. The PSO model was applied to initialize the weights and thresholds of the backpropagation (BP) network model and determine suitable values of the mechanical parameters. Then the elastic moduli of the rock masses were obtained according to the monitoring displacement data at different excavation stages, and the BP neural network model was proved to be valid by comparing the measured displacements, the displacements predicted by the BP neural network model, and the numerical simulation using the back-analyzed parameters. The proposed model is useful for rock mechanical parameters determination and instability investigation of rock slopes.

Displacement Back Analysis for a High Slope of the Dagangshan Hydroelectric Power Station Based on BP Neural Network and Particle Swarm Optimization

PubMed Central

Liang, Zhengzhao; Gong, Bin; Tang, Chunan; Zhang, Yongbin; Ma, Tianhui

2014-01-01

The right bank high slope of the Dagangshan Hydroelectric Power Station is located in complicated geological conditions with deep fractures and unloading cracks. How to obtain the mechanical parameters and then evaluate the safety of the slope are the key problems. This paper presented a displacement back analysis for the slope using an artificial neural network model (ANN) and particle swarm optimization model (PSO). A numerical model was established to simulate the displacement increment results, acquiring training data for the artificial neural network model. The backpropagation ANN model was used to establish a mapping function between the mechanical parameters and the monitoring displacements. The PSO model was applied to initialize the weights and thresholds of the backpropagation (BP) network model and determine suitable values of the mechanical parameters. Then the elastic moduli of the rock masses were obtained according to the monitoring displacement data at different excavation stages, and the BP neural network model was proved to be valid by comparing the measured displacements, the displacements predicted by the BP neural network model, and the numerical simulation using the back-analyzed parameters. The proposed model is useful for rock mechanical parameters determination and instability investigation of rock slopes. PMID:25140345

Artificial Neural Network for Probabilistic Feature Recognition in Liquid Chromatography Coupled to High-Resolution Mass Spectrometry.

PubMed

Woldegebriel, Michael; Derks, Eduard

2017-01-17

In this work, a novel probabilistic untargeted feature detection algorithm for liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) using artificial neural network (ANN) is presented. The feature detection process is approached as a pattern recognition problem, and thus, ANN was utilized as an efficient feature recognition tool. Unlike most existing feature detection algorithms, with this approach, any suspected chromatographic profile (i.e., shape of a peak) can easily be incorporated by training the network, avoiding the need to perform computationally expensive regression methods with specific mathematical models. In addition, with this method, we have shown that the high-resolution raw data can be fully utilized without applying any arbitrary thresholds or data reduction, therefore improving the sensitivity of the method for compound identification purposes. Furthermore, opposed to existing deterministic (binary) approaches, this method rather estimates the probability of a feature being present/absent at a given point of interest, thus giving chance for all data points to be propagated down the data analysis pipeline, weighed with their probability. The algorithm was tested with data sets generated from spiked samples in forensic and food safety context and has shown promising results by detecting features for all compounds in a computationally reasonable time.

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

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...

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…

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…

Current and anticipated uses of the CATHARE code at EDF and FRAMATOME

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

Gandrille, J.L.; Vacher, J.L.; Poizat, F.

1997-07-01

This paper presents current industrial applications of the CATHARE code in the fields of Safety Studies and Simulators where the code is intensively used by FRAMATOME, EDF and CEA, the development partners of CATHARE. Future needs in these fields are also recapitulated.

7 CFR Exhibit E to Subpart A of... - Voluntary National Model Building Codes

Code of Federal Regulations, 2011 CFR

2011-01-01

... National Model Building Codes The following documents address the health and safety aspects of buildings... International, Inc., 4051 West Flossmoor Road, Country Club Hills, Illinois 60477. 2 Southern Building Code Congress International, Inc., 900 Montclair Road, Birmingham, Alabama 35213-1206. 3 International...

42 CFR 403.744 - Condition of participation: Life safety from fire.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 42 Public Health 2 2014-10-01 2014-10-01 false Condition of participation: Life safety from fire... safety from fire. (a) General. An RNHCI must meet the following conditions: (1) Except as otherwise... Safety Code of the National Fire Protection Association. The Director of the Office of the Federal...

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.

Artificial Neural Networks as Decision Support Tools in Cytopathology: Past, Present, and Future.

PubMed

Pouliakis, Abraham; Karakitsou, Efrossyni; Margari, Niki; Bountris, Panagiotis; Haritou, Maria; Panayiotides, John; Koutsouris, Dimitrios; Karakitsos, Petros

2016-01-01

This study aims to analyze the role of artificial neural networks (ANNs) in cytopathology. More specifically, it aims to highlight the importance of employing ANNs in existing and future applications and in identifying unexplored or poorly explored research topics. A systematic search was conducted in scientific databases for articles related to cytopathology and ANNs with respect to anatomical places of the human body where cytopathology is performed. For each anatomic system/organ, the major outcomes described in the scientific literature are presented and the most important aspects are highlighted. The vast majority of ANN applications are related to cervical cytopathology, specifically for the ANN-based, semiautomated commercial diagnostic system PAPNET. For cervical cytopathology, there is a plethora of studies relevant to the diagnostic accuracy; in addition, there are also efforts evaluating cost-effectiveness and applications on primary, secondary, or hybrid screening. For the rest of the anatomical sites, such as the gastrointestinal system, thyroid gland, urinary tract, and breast, there are significantly less efforts relevant to the application of ANNs. Additionally, there are still anatomical systems for which ANNs have never been applied on their cytological material. Cytopathology is an ideal discipline to apply ANNs. In general, diagnosis is performed by experts via the light microscope. However, this approach introduces subjectivity, because this is not a universal and objective measurement process. This has resulted in the existence of a gray zone between normal and pathological cases. From the analysis of related articles, it is obvious that there is a need to perform more thorough analyses, using extensive number of cases and particularly for the nonexplored organs. Efforts to apply such systems within the laboratory test environment are required for their future uptake.

Artificial Neural Networks as Decision Support Tools in Cytopathology: Past, Present, and Future

PubMed Central

Pouliakis, Abraham; Karakitsou, Efrossyni; Margari, Niki; Bountris, Panagiotis; Haritou, Maria; Panayiotides, John; Koutsouris, Dimitrios; Karakitsos, Petros

2016-01-01

OBJECTIVE This study aims to analyze the role of artificial neural networks (ANNs) in cytopathology. More specifically, it aims to highlight the importance of employing ANNs in existing and future applications and in identifying unexplored or poorly explored research topics. STUDY DESIGN A systematic search was conducted in scientific databases for articles related to cytopathology and ANNs with respect to anatomical places of the human body where cytopathology is performed. For each anatomic system/organ, the major outcomes described in the scientific literature are presented and the most important aspects are highlighted. RESULTS The vast majority of ANN applications are related to cervical cytopathology, specifically for the ANN-based, semiautomated commercial diagnostic system PAPNET. For cervical cytopathology, there is a plethora of studies relevant to the diagnostic accuracy; in addition, there are also efforts evaluating cost-effectiveness and applications on primary, secondary, or hybrid screening. For the rest of the anatomical sites, such as the gastrointestinal system, thyroid gland, urinary tract, and breast, there are significantly less efforts relevant to the application of ANNs. Additionally, there are still anatomical systems for which ANNs have never been applied on their cytological material. CONCLUSIONS Cytopathology is an ideal discipline to apply ANNs. In general, diagnosis is performed by experts via the light microscope. However, this approach introduces subjectivity, because this is not a universal and objective measurement process. This has resulted in the existence of a gray zone between normal and pathological cases. From the analysis of related articles, it is obvious that there is a need to perform more thorough analyses, using extensive number of cases and particularly for the nonexplored organs. Efforts to apply such systems within the laboratory test environment are required for their future uptake. PMID:26917984

Neural Networks for Nodal Staging of Non–Small Cell Lung Cancer with FDG PET and CT: Importance of Combining Uptake Values and Sizes of Nodes and Primary Tumor

PubMed Central

Vesselle, Hubert J.

2014-01-01

Purpose To evaluate the effect of adding lymph node size to three previously explored artificial neural network (ANN) input parameters (primary tumor maximum standardized uptake value or tumor uptake, tumor size, and nodal uptake at N1, N2, and N3 stations) in the structure of the ANN. The goal was to allow the resulting ANN structure to relate lymph node uptake for size to primary tumor uptake for size in the determination of the status of nodes as human readers do. Materials and Methods This prospective study was approved by the institutional review board, and informed consent was obtained from all participants. The authors developed a back-propagation ANN with one hidden layer and eight processing units. The data set used to train the network included node and tumor size and uptake from 133 patients with non–small cell lung cancer with surgically proved N status. Statistical analysis was performed with the paired t test. Results The ANN correctly predicted the N stage in 99.2% of cases, compared with 72.4% for the expert reader (P < .001). In categorization of N0 and N1 versus N2 and N3 disease, the ANN performed with 99.2% accuracy versus 92.2% for the expert reader (P < .001). Conclusion The ANN is 99.2% accurate in predicting surgical-pathologic nodal status with use of four fluorine 18 fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT)–derived parameters. Malignant and benign inflammatory lymph nodes have overlapping appearances at FDG PET/CT but can be differentiated by ANNs when the crucial input of node size is used. © RSNA, 2013 Online supplemental material is available for this article. PMID:24056403

An investigation on generalization ability of artificial neural networks and M5 model tree in modeling reference evapotranspiration

NASA Astrophysics Data System (ADS)

Kisi, Ozgur; Kilic, Yasin

2016-11-01

The generalization ability of artificial neural networks (ANNs) and M5 model tree (M5Tree) in modeling reference evapotranspiration ( ET 0 ) is investigated in this study. Daily climatic data, average temperature, solar radiation, wind speed, and relative humidity from six different stations operated by California Irrigation Management Information System (CIMIS) located in two different regions of the USA were used in the applications. King-City Oasis Rd., Arroyo Seco, and Salinas North stations are located in San Joaquin region, and San Luis Obispo, Santa Monica, and Santa Barbara stations are located in the Southern region. In the first part of the study, the ANN and M5Tree models were used for estimating ET 0 of six stations and results were compared with the empirical methods. The ANN and M5Tree models were found to be better than the empirical models. In the second part of the study, the ANN and M5Tree models obtained from one station were tested using the data from the other two stations for each region. ANN models performed better than the CIMIS Penman, Hargreaves, Ritchie, and Turc models in two stations while the M5Tree models generally showed better accuracy than the corresponding empirical models in all stations. In the third part of the study, the ANN and M5Tree models were calibrated using three stations located in San Joaquin region and tested using the data from the other three stations located in the Southern region. Four-input ANN and M5Tree models performed better than the CIMIS Penman in only one station while the two-input ANN models were found to be better than the Hargreaves, Ritchie, and Turc models in two stations.

Neural network modeling and prediction of resistivity structures using VES Schlumberger data over a geothermal area

NASA Astrophysics Data System (ADS)

Singh, Upendra K.; Tiwari, R. K.; Singh, S. B.

2013-03-01

This paper presents the effects of several parameters on the artificial neural networks (ANN) inversion of vertical electrical sounding (VES) data. Sensitivity of ANN parameters was examined on the performance of adaptive backpropagation (ABP) and Levenberg-Marquardt algorithms (LMA) to test the robustness to noisy synthetic as well as field geophysical data and resolving capability of these methods for predicting the subsurface resistivity layers. We trained, tested and validated ANN using the synthetic VES data as input to the networks and layer parameters of the models as network output. ANN learning parameters are varied and corresponding observations are recorded. The sensitivity analysis of synthetic data and real model demonstrate that ANN algorithms applied in VES data inversion should be considered well not only in terms of accuracy but also in terms of high computational efforts. Also the analysis suggests that ANN model with its various controlling parameters are largely data dependent and hence no unique architecture can be designed for VES data analysis. ANN based methods are also applied to the actual VES field data obtained from the tectonically vital geothermal areas of Jammu and Kashmir, India. Analysis suggests that both the ABP and LMA are suitable methods for 1-D VES modeling. But the LMA method provides greater degree of robustness than the ABP in case of 2-D VES modeling. Comparison of the inversion results with known lithology correlates well and also reveals the additional significant feature of reconsolidated breccia of about 7.0 m thickness beneath the overburden in some cases like at sounding point RDC-5. We may therefore conclude that ANN based methods are significantly faster and efficient for detection of complex layered resistivity structures with a relatively greater degree of precision and resolution.

Implementation of non-condensable gases condensation suppression model into the WCOBRA/TRAC-TF2 LOCA safety evaluation code

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

Liao, J.; Cao, L.; Ohkawa, K.

2012-07-01

The non-condensable gases condensation suppression model is important for a realistic LOCA safety analysis code. A condensation suppression model for direct contact condensation was previously developed by Westinghouse using first principles. The model is believed to be an accurate description of the direct contact condensation process in the presence of non-condensable gases. The Westinghouse condensation suppression model is further revised by applying a more physical model. The revised condensation suppression model is thus implemented into the WCOBRA/TRAC-TF2 LOCA safety evaluation code for both 3-D module (COBRA-TF) and 1-D module (TRAC-PF1). Parametric study using the revised Westinghouse condensation suppression model ismore » conducted. Additionally, the performance of non-condensable gases condensation suppression model is examined in the ACHILLES (ISP-25) separate effects test and LOFT L2-5 (ISP-13) integral effects test. (authors)« less

The identification of helicopter noise using a neural network

NASA Technical Reports Server (NTRS)

Cabell, Randolph H.; Fuller, Chris R.; O'Brien, Walter F.

1990-01-01

Experiments were carried out to demonstrate the ability of an artificial neural network (ANN) system to distinguish between the noise of two helicopters. The ANN is taught to identify helicopters by using two types of features: one that is associated with the ratio of the main-rotor to tail-rotor blade passage frequency (BPF), and the ohter that describes the distribution of peaks in the main-rotor spectrum, which is independent of the tail-rotor. It is shown that the ability of the ANN to identify helicopters is comparable to that of a conventional recognition system using the ratio of the main-rotor BPF to the tail-rotor BPF (when both the main- and the tail-rotor noise are present), but the performoance of ANN exceeds the conventional-method performance when the tail-rotor noise is absent. In addition, the results of ANN can be obtained as a function of propagation distance.

A Novel User Classification Method for Femtocell Network by Using Affinity Propagation Algorithm and Artificial Neural Network

PubMed Central

Ahmed, Afaz Uddin; Tariqul Islam, Mohammad; Ismail, Mahamod; Kibria, Salehin; Arshad, Haslina

2014-01-01

An artificial neural network (ANN) and affinity propagation (AP) algorithm based user categorization technique is presented. The proposed algorithm is designed for closed access femtocell network. ANN is used for user classification process and AP algorithm is used to optimize the ANN training process. AP selects the best possible training samples for faster ANN training cycle. The users are distinguished by using the difference of received signal strength in a multielement femtocell device. A previously developed directive microstrip antenna is used to configure the femtocell device. Simulation results show that, for a particular house pattern, the categorization technique without AP algorithm takes 5 indoor users and 10 outdoor users to attain an error-free operation. While integrating AP algorithm with ANN, the system takes 60% less training samples reducing the training time up to 50%. This procedure makes the femtocell more effective for closed access operation. PMID:25133214

A novel user classification method for femtocell network by using affinity propagation algorithm and artificial neural network.

PubMed

Ahmed, Afaz Uddin; Islam, Mohammad Tariqul; Ismail, Mahamod; Kibria, Salehin; Arshad, Haslina

2014-01-01

An artificial neural network (ANN) and affinity propagation (AP) algorithm based user categorization technique is presented. The proposed algorithm is designed for closed access femtocell network. ANN is used for user classification process and AP algorithm is used to optimize the ANN training process. AP selects the best possible training samples for faster ANN training cycle. The users are distinguished by using the difference of received signal strength in a multielement femtocell device. A previously developed directive microstrip antenna is used to configure the femtocell device. Simulation results show that, for a particular house pattern, the categorization technique without AP algorithm takes 5 indoor users and 10 outdoor users to attain an error-free operation. While integrating AP algorithm with ANN, the system takes 60% less training samples reducing the training time up to 50%. This procedure makes the femtocell more effective for closed access operation.

Short-term acoustic forecasting via artificial neural networks for neonatal intensive care units.

PubMed

Young, Jason; Macke, Christopher J; Tsoukalas, Lefteri H

2012-11-01

Noise levels in hospitals, especially neonatal intensive care units (NICUs), have become of great concern for hospital designers. This paper details an artificial neural network (ANN) approach to forecasting the sound loads in NICUs. The ANN is used to learn the relationship between past, present, and future noise levels. By training the ANN with data specific to the location and device used to measure the sound, the ANN is able to produce reasonable predictions of noise levels in the NICU. Best case results show average absolute errors of 5.06 ± 4.04% when used to predict the noise levels one hour ahead, which correspond to 2.53 dBA ± 2.02 dBA. The ANN has the tendency to overpredict during periods of stability and underpredict during large transients. This forecasting algorithm could be of use in any application where prediction and prevention of harmful noise levels are of the utmost concern.

Computer vision system for egg volume prediction using backpropagation neural network

NASA Astrophysics Data System (ADS)

Siswantoro, J.; Hilman, M. Y.; Widiasri, M.

2017-11-01

Volume is one of considered aspects in egg sorting process. A rapid and accurate volume measurement method is needed to develop an egg sorting system. Computer vision system (CVS) provides a promising solution for volume measurement problem. Artificial neural network (ANN) has been used to predict the volume of egg in several CVSs. However, volume prediction from ANN could have less accuracy due to inappropriate input features or inappropriate ANN structure. This paper proposes a CVS for predicting the volume of egg using ANN. The CVS acquired an image of egg from top view and then processed the image to extract its 1D and 2 D size features. The features were used as input for ANN in predicting the volume of egg. The experiment results show that the proposed CSV can predict the volume of egg with a good accuracy and less computation time.

Study of Aided Diagnosis of Hepatic Carcinoma Based on Artificial Neural Network Combined with Tumor Marker Group

NASA Astrophysics Data System (ADS)

Tan, Shanjuan; Feng, Feifei; Wu, Yongjun; Wu, Yiming

To develop a computer-aided diagnostic scheme by using an artificial neural network (ANN) combined with tumor markers for diagnosis of hepatic carcinoma (HCC) as a clinical assistant method. 140 serum samples (50 malignant, 40 benign and 50 normal) were analyzed for α-fetoprotein (AFP), carbohydrate antigen 125 (CA125), carcinoembryonic antigen (CEA), sialic acid (SA) and calcium (Ca). The five tumor marker values were then used as ANN inputs data. The result of ANN was compared with that of discriminant analysis by receiver operating characteristic (ROC) curve (AUC) analysis. The diagnostic accuracy of ANN and discriminant analysis among all samples of the test group was 95.5% and 79.3%, respectively. Analysis of multiple tumor markers based on ANN may be a better choice than the traditional statistical methods for differentiating HCC from benign or normal.

Numerical solution of the nonlinear Schrodinger equation by feedforward neural networks

NASA Astrophysics Data System (ADS)

Shirvany, Yazdan; Hayati, Mohsen; Moradian, Rostam

2008-12-01

We present a method to solve boundary value problems using artificial neural networks (ANN). A trial solution of the differential equation is written as a feed-forward neural network containing adjustable parameters (the weights and biases). From the differential equation and its boundary conditions we prepare the energy function which is used in the back-propagation method with momentum term to update the network parameters. We improved energy function of ANN which is derived from Schrodinger equation and the boundary conditions. With this improvement of energy function we can use unsupervised training method in the ANN for solving the equation. Unsupervised training aims to minimize a non-negative energy function. We used the ANN method to solve Schrodinger equation for few quantum systems. Eigenfunctions and energy eigenvalues are calculated. Our numerical results are in agreement with their corresponding analytical solution and show the efficiency of ANN method for solving eigenvalue problems.

Prediction of pelvic organ prolapse using an artificial neural network.

PubMed

Robinson, Christopher J; Swift, Steven; Johnson, Donna D; Almeida, Jonas S

2008-08-01

The objective of this investigation was to test the ability of a feedforward artificial neural network (ANN) to differentiate patients who have pelvic organ prolapse (POP) from those who retain good pelvic organ support. Following institutional review board approval, patients with POP (n = 87) and controls with good pelvic organ support (n = 368) were identified from the urogynecology research database. Historical and clinical information was extracted from the database. Data analysis included the training of a feedforward ANN, variable selection, and external validation of the model with an independent data set. Twenty variables were used. The median-performing ANN model used a median of 3 (quartile 1:3 to quartile 3:5) variables and achieved an area under the receiver operator curve of 0.90 (external, independent validation set). Ninety percent sensitivity and 83% specificity were obtained in the external validation by ANN classification. Feedforward ANN modeling is applicable to the identification and prediction of POP.

FY2017 Updates to the SAS4A/SASSYS-1 Safety Analysis Code

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

Fanning, T. H.

The SAS4A/SASSYS-1 safety analysis software is used to perform deterministic analysis of anticipated events as well as design-basis and beyond-design-basis accidents for advanced fast reactors. It plays a central role in the analysis of U.S. DOE conceptual designs, proposed test and demonstration reactors, and in domestic and international collaborations. This report summarizes the code development activities that have taken place during FY2017. Extensions to the void and cladding reactivity feedback models have been implemented, and Control System capabilities have been improved through a new virtual data acquisition system for plant state variables and an additional Block Signal for a variablemore » lag compensator to represent reactivity feedback for novel shutdown devices. Current code development and maintenance needs are also summarized in three key areas: software quality assurance, modeling improvements, and maintenance of related tools. With ongoing support, SAS4A/SASSYS-1 can continue to fulfill its growing role in fast reactor safety analysis and help solidify DOE’s leadership role in fast reactor safety both domestically and in international collaborations.« less

Interface requirements to couple thermal hydraulics codes to severe accident codes: ICARE/CATHARE

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

Camous, F.; Jacq, F.; Chatelard, P.

1997-07-01

In order to describe with the same code the whole sequence of severe LWR accidents, up to the vessel failure, the Institute of Protection and Nuclear Safety has performed a coupling of the severe accident code ICARE2 to the thermalhydraulics code CATHARE2. The resulting code, ICARE/CATHARE, is designed to be as pertinent as possible in all the phases of the accident. This paper is mainly devoted to the description of the ICARE2-CATHARE2 coupling.

10 CFR 851.27 - Reference sources.

Code of Federal Regulations, 2013 CFR

2013-01-01

...) American Society of Mechanical Engineers (ASME), P.O. Box 2300 Fairfield, NJ 07007. Telephone: 800-843-2763... Electrical Code,” (2005). (5) NFPA 70E, “Standard for Electrical Safety in the Workplace,” (2004). (6... Engineers (ASME) Boilers and Pressure Vessel Code, sections I through XII including applicable Code Cases...

10 CFR 851.27 - Reference sources.

Code of Federal Regulations, 2014 CFR

2014-01-01

...) American Society of Mechanical Engineers (ASME), P.O. Box 2300 Fairfield, NJ 07007. Telephone: 800-843-2763... Electrical Code,” (2005). (5) NFPA 70E, “Standard for Electrical Safety in the Workplace,” (2004). (6... Engineers (ASME) Boilers and Pressure Vessel Code, sections I through XII including applicable Code Cases...

46 CFR 197.204 - Definitions.

Code of Federal Regulations, 2012 CFR

2012-10-01

... Institute “Code for Pressure Piping, Power Piping.” ASME Code means the American Society of Mechanical Engineers “Boiler and Pressure Vessel Code.” ASME PVHO-1 means the ANSI/ASME standard “Safety Standard for Pressure Vessels for Human Occupancy.” ATA means a measure of pressure expressed in terms of atmosphere...

46 CFR 197.204 - Definitions.

Code of Federal Regulations, 2011 CFR

2011-10-01

... Institute “Code for Pressure Piping, Power Piping.” ASME Code means the American Society of Mechanical Engineers “Boiler and Pressure Vessel Code.” ASME PVHO-1 means the ANSI/ASME standard “Safety Standard for Pressure Vessels for Human Occupancy.” ATA means a measure of pressure expressed in terms of atmosphere...

46 CFR 197.204 - Definitions.

Code of Federal Regulations, 2013 CFR

2013-10-01

... Institute “Code for Pressure Piping, Power Piping.” ASME Code means the American Society of Mechanical Engineers “Boiler and Pressure Vessel Code.” ASME PVHO-1 means the ANSI/ASME standard “Safety Standard for Pressure Vessels for Human Occupancy.” ATA means a measure of pressure expressed in terms of atmosphere...

46 CFR 197.204 - Definitions.

Code of Federal Regulations, 2014 CFR

2014-10-01

... Institute “Code for Pressure Piping, Power Piping.” ASME Code means the American Society of Mechanical Engineers “Boiler and Pressure Vessel Code.” ASME PVHO-1 means the ANSI/ASME standard “Safety Standard for Pressure Vessels for Human Occupancy.” ATA means a measure of pressure expressed in terms of atmosphere...

30 CFR 905.816 - Performance standards-Surface mining activities.

Code of Federal Regulations, 2013 CFR

2013-07-01

... Quality Control Act, Cal. Pub. Res. Code section 13000 et seq.; the California Water Code section 1200 et seq.; the California Air Pollution Control Laws, Cal. Health & Safety Code section 39000 et seq.; the..., DEPARTMENT OF THE INTERIOR PROGRAMS FOR THE CONDUCT OF SURFACE MINING OPERATIONS WITHIN EACH STATE CALIFORNIA...

30 CFR 905.817 - Performance standards-Underground mining activities.

Code of Federal Regulations, 2014 CFR

2014-07-01

... Quality Control Act, Cal. Pub. Res. Code section 13000 et seq.; the California Water Code section 1200 et seq.; the California Air Pollution Control Laws, Cal. Health & Safety Code section 39000 et seq.; the..., DEPARTMENT OF THE INTERIOR PROGRAMS FOR THE CONDUCT OF SURFACE MINING OPERATIONS WITHIN EACH STATE CALIFORNIA...

30 CFR 905.817 - Performance standards-Underground mining activities.

Code of Federal Regulations, 2012 CFR

2012-07-01

... Quality Control Act, Cal. Pub. Res. Code section 13000 et seq.; the California Water Code section 1200 et seq.; the California Air Pollution Control Laws, Cal. Health & Safety Code section 39000 et seq.; the..., DEPARTMENT OF THE INTERIOR PROGRAMS FOR THE CONDUCT OF SURFACE MINING OPERATIONS WITHIN EACH STATE CALIFORNIA...

30 CFR 905.816 - Performance standards-Surface mining activities.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Quality Control Act, Cal. Pub. Res. Code section 13000 et seq.; the California Water Code section 1200 et seq.; the California Air Pollution Control Laws, Cal. Health & Safety Code section 39000 et seq.; the..., DEPARTMENT OF THE INTERIOR PROGRAMS FOR THE CONDUCT OF SURFACE MINING OPERATIONS WITHIN EACH STATE CALIFORNIA...

30 CFR 905.816 - Performance standards-Surface mining activities.

Code of Federal Regulations, 2012 CFR

2012-07-01

... Quality Control Act, Cal. Pub. Res. Code section 13000 et seq.; the California Water Code section 1200 et seq.; the California Air Pollution Control Laws, Cal. Health & Safety Code section 39000 et seq.; the..., DEPARTMENT OF THE INTERIOR PROGRAMS FOR THE CONDUCT OF SURFACE MINING OPERATIONS WITHIN EACH STATE CALIFORNIA...

30 CFR 905.817 - Performance standards-Underground mining activities.

Code of Federal Regulations, 2013 CFR

2013-07-01

... Quality Control Act, Cal. Pub. Res. Code section 13000 et seq.; the California Water Code section 1200 et seq.; the California Air Pollution Control Laws, Cal. Health & Safety Code section 39000 et seq.; the..., DEPARTMENT OF THE INTERIOR PROGRAMS FOR THE CONDUCT OF SURFACE MINING OPERATIONS WITHIN EACH STATE CALIFORNIA...

30 CFR 905.816 - Performance standards-Surface mining activities.

Code of Federal Regulations, 2014 CFR

2014-07-01

... Quality Control Act, Cal. Pub. Res. Code section 13000 et seq.; the California Water Code section 1200 et seq.; the California Air Pollution Control Laws, Cal. Health & Safety Code section 39000 et seq.; the..., DEPARTMENT OF THE INTERIOR PROGRAMS FOR THE CONDUCT OF SURFACE MINING OPERATIONS WITHIN EACH STATE CALIFORNIA...

30 CFR 905.817 - Peformance standards-Underground mining activities.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Quality Control Act, Cal. Pub. Res. Code section 13000 et seq.; the California Water Code section 1200 et seq.; the California Air Pollution Control Laws, Cal. Health & Safety Code section 39000 et seq.; the..., DEPARTMENT OF THE INTERIOR PROGRAMS FOR THE CONDUCT OF SURFACE MINING OPERATIONS WITHIN EACH STATE CALIFORNIA...

7 CFR Exhibit E to Subpart A of... - Voluntary National Model Building Codes

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 12 2010-01-01 2010-01-01 false Voluntary National Model Building Codes E Exhibit E... HOUSING SERVICE, RURAL BUSINESS-COOPERATIVE SERVICE, RURAL UTILITIES SERVICE, AND FARM SERVICE AGENCY... National Model Building Codes The following documents address the health and safety aspects of buildings...

38 CFR 39.63 - Architectural design standards.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Association Life Safety Code and Errata (NFPA 101), the 2003 edition of the NFPA 5000, Building Construction... section, all applicable local and State building codes and regulations must be observed. In areas not subject to local or State building codes, the recommendations contained in the 2003 edition of the NFPA...

Predicting pressure drop in venturi scrubbers with artificial neural networks.

PubMed

Nasseh, S; Mohebbi, A; Jeirani, Z; Sarrafi, A

2007-05-08

In this study a new approach based on artificial neural networks (ANNs) has been used to predict pressure drop in venturi scrubbers. The main parameters affecting the pressure drop are mainly the gas velocity in the throat of venturi scrubber (V(g)(th)), liquid to gas flow rate ratio (L/G), and axial distance of the venturi scrubber (z). Three sets of experimental data from five different venturi scrubbers have been applied to design three independent ANNs. Comparing the results of these ANNs and the calculated results from available models shows that the results of ANNs have a better agreement with experimental data.

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.

Risk factors for Apgar score using artificial neural networks.

PubMed

Ibrahim, Doaa; Frize, Monique; Walker, Robin C

2006-01-01

Artificial Neural Networks (ANNs) have been used in identifying the risk factors for many medical outcomes. In this paper, the risk factors for low Apgar score are introduced. This is the first time, to our knowledge, that the ANNs are used for Apgar score prediction. The medical domain of interest used is the perinatal database provided by the Perinatal Partnership Program of Eastern and Southeastern Ontario (PPPESO). The ability of the feed forward back propagation ANNs to generate strong predictive model with the most influential variables is tested. Finally, minimal sets of variables (risk factors) that are important in predicting Apgar score outcome without degrading the ANN performance are identified.

Robust Bioinformatics Recognition with VLSI Biochip Microsystem

NASA Technical Reports Server (NTRS)

Lue, Jaw-Chyng L.; Fang, Wai-Chi

2006-01-01

A microsystem architecture for real-time, on-site, robust bioinformatic patterns recognition and analysis has been proposed. This system is compatible with on-chip DNA analysis means such as polymerase chain reaction (PCR)amplification. A corresponding novel artificial neural network (ANN) learning algorithm using new sigmoid-logarithmic transfer function based on error backpropagation (EBP) algorithm is invented. Our results show the trained new ANN can recognize low fluorescence patterns better than the conventional sigmoidal ANN does. A differential logarithmic imaging chip is designed for calculating logarithm of relative intensities of fluorescence signals. The single-rail logarithmic circuit and a prototype ANN chip are designed, fabricated and characterized.

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.

Optimization of Melatonin Dissolution from Extended Release Matrices Using Artificial Neural Networking.

PubMed

Martarelli, D; Casettari, L; Shalaby, K S; Soliman, M E; Cespi, M; Bonacucina, G; Fagioli, L; Perinelli, D R; Lam, J K W; Palmieri, G F

2016-01-01

Efficacy of melatonin in treating sleep disorders has been demonstrated in numerous studies. Being with short half-life, melatonin needs to be formulated in extended-release tablets to prevent the fast drop of its plasma concentration. However, an attempt to mimic melatonin natural plasma levels during night time is challenging. In this work, Artificial Neural Networks (ANNs) were used to optimize melatonin release from hydrophilic polymer matrices. Twenty-seven different tablet formulations with different amounts of hydroxypropyl methylcellulose, xanthan gum and Carbopol®974P NF were prepared and subjected to drug release studies. Using dissolution test data as inputs for ANN designed by Visual Basic programming language, the ideal number of neurons in the hidden layer was determined trial and error methodology to guarantee the best performance of constructed ANN. Results showed that the ANN with nine neurons in the hidden layer had the best results. ANN was examined to check its predictability and then used to determine the best formula that can mimic the release of melatonin from a marketed brand using similarity fit factor. This work shows the possibility of using ANN to optimize the composition of prolonged-release melatonin tablets having dissolution profile desired.

A sound quality model for objective synthesis evaluation of vehicle interior noise based on artificial neural network

NASA Astrophysics Data System (ADS)

Wang, Y. S.; Shen, G. Q.; Xing, Y. F.

2014-03-01

Based on the artificial neural network (ANN) technique, an objective sound quality evaluation (SQE) model for synthesis annoyance of vehicle interior noises is presented in this paper. According to the standard named GB/T18697, firstly, the interior noises under different working conditions of a sample vehicle are measured and saved in a noise database. Some mathematical models for loudness, sharpness and roughness of the measured vehicle noises are established and performed by Matlab programming. Sound qualities of the vehicle interior noises are also estimated by jury tests following the anchored semantic differential (ASD) procedure. Using the objective and subjective evaluation results, furthermore, an ANN-based model for synthetical annoyance evaluation of vehicle noises, so-called ANN-SAE, is developed. Finally, the ANN-SAE model is proved by some verification tests with the leave-one-out algorithm. The results suggest that the proposed ANN-SAE model is accurate and effective and can be directly used to estimate sound quality of the vehicle interior noises, which is very helpful for vehicle acoustical designs and improvements. The ANN-SAE approach may be extended to deal with other sound-related fields for product quality evaluations in SQE engineering.

Groundwater-level prediction using multiple linear regression and artificial neural network techniques: a comparative assessment

NASA Astrophysics Data System (ADS)

Sahoo, Sasmita; Jha, Madan K.

2013-12-01

The potential of multiple linear regression (MLR) and artificial neural network (ANN) techniques in predicting transient water levels over a groundwater basin were compared. MLR and ANN modeling was carried out at 17 sites in Japan, considering all significant inputs: rainfall, ambient temperature, river stage, 11 seasonal dummy variables, and influential lags of rainfall, ambient temperature, river stage and groundwater level. Seventeen site-specific ANN models were developed, using multi-layer feed-forward neural networks trained with Levenberg-Marquardt backpropagation algorithms. The performance of the models was evaluated using statistical and graphical indicators. Comparison of the goodness-of-fit statistics of the MLR models with those of the ANN models indicated that there is better agreement between the ANN-predicted groundwater levels and the observed groundwater levels at all the sites, compared to the MLR. This finding was supported by the graphical indicators and the residual analysis. Thus, it is concluded that the ANN technique is superior to the MLR technique in predicting spatio-temporal distribution of groundwater levels in a basin. However, considering the practical advantages of the MLR technique, it is recommended as an alternative and cost-effective groundwater modeling tool.

Improving Gastric Cancer Outcome Prediction Using Single Time-Point Artificial Neural Network Models

PubMed Central

Nilsaz-Dezfouli, Hamid; Abu-Bakar, Mohd Rizam; Arasan, Jayanthi; Adam, Mohd Bakri; Pourhoseingholi, Mohamad Amin

2017-01-01

In cancer studies, the prediction of cancer outcome based on a set of prognostic variables has been a long-standing topic of interest. Current statistical methods for survival analysis offer the possibility of modelling cancer survivability but require unrealistic assumptions about the survival time distribution or proportionality of hazard. Therefore, attention must be paid in developing nonlinear models with less restrictive assumptions. Artificial neural network (ANN) models are primarily useful in prediction when nonlinear approaches are required to sift through the plethora of available information. The applications of ANN models for prognostic and diagnostic classification in medicine have attracted a lot of interest. The applications of ANN models in modelling the survival of patients with gastric cancer have been discussed in some studies without completely considering the censored data. This study proposes an ANN model for predicting gastric cancer survivability, considering the censored data. Five separate single time-point ANN models were developed to predict the outcome of patients after 1, 2, 3, 4, and 5 years. The performance of ANN model in predicting the probabilities of death is consistently high for all time points according to the accuracy and the area under the receiver operating characteristic curve. PMID:28469384

Estimation of umbilical cord blood leptin and insulin based on anthropometric data by means of artificial neural network approach: identifying key maternal and neonatal factors.

PubMed

Guzmán-Bárcenas, José; Hernández, José Alfredo; Arias-Martínez, Joel; Baptista-González, Héctor; Ceballos-Reyes, Guillermo; Irles, Claudine

2016-07-21

Leptin and insulin levels are key factors regulating fetal and neonatal energy homeostasis, development and growth. Both biomarkers are used as predictors of weight gain and obesity during infancy. There are currently no prediction algorithms for cord blood (UCB) hormone levels using Artificial Neural Networks (ANN) that have been directly trained with anthropometric maternal and neonatal data, from neonates exposed to distinct metabolic environments during pregnancy (obese with or without gestational diabetes mellitus or lean women). The aims were: 1) to develop ANN models that simulate leptin and insulin concentrations in UCB based on maternal and neonatal data (ANN perinatal model) or from only maternal data during early gestation (ANN prenatal model); 2) To evaluate the biological relevance of each parameter (maternal and neonatal anthropometric variables). We collected maternal and neonatal anthropometric data (n = 49) in normoglycemic healthy lean, obese or obese with gestational diabetes mellitus women, as well as determined UCB leptin and insulin concentrations by ELISA. The ANN perinatal model consisted of an input layer of 12 variables (maternal and neonatal anthropometric and biochemical data from early gestation and at term) while the ANN prenatal model used only 6 variables (maternal anthropometric from early gestation) in the input layer. For both networks, the output layer contained 1 variable to UCB leptin or to UCB insulin concentration. The best architectures for the ANN perinatal models estimating leptin and insulin were 12-5-1 while for the ANN prenatal models, 6-5-1 and 6-4-1 were found for leptin and insulin, respectively. ANN models presented an excellent agreement between experimental and simulated values. Interestingly, the use of only prenatal maternal anthropometric data was sufficient to estimate UCB leptin and insulin values. Maternal BMI, weight and age as well as neonatal birth were the most influential parameters for leptin while maternal morbidity was the most significant factor for insulin prediction. Low error percentage and short computing time makes these ANN models interesting in a translational research setting, to be applied for the prediction of neonatal leptin and insulin values from maternal anthropometric data, and possibly the on-line estimation during pregnancy.

Alternative Fuels Data Center

Science.gov Websites

Texas Commission on Environmental Quality (TCEQ). Exemptions apply for the following: vehicles with a idling. (Reference Texas Statutes, Health and Safety Code 382.0191; and Texas Administrative Code

The development of fire evaluation system for detention and correctional occupancies

NASA Astrophysics Data System (ADS)

Nelson, H. E.; Shibe, A. J.

1984-12-01

A fire safety evaluation system for detention and correctional occupancies was developed. It can be used for determining if a facility has fire safety equivalent to that obtained by meeting the requirement of a given code. The system was calibrated for use with proposed chapters for detention and correctional occupancies of the Life Safety Code (1985). There are separate sets of requirements for each of four use conditions: one for zoned egress, one for zoned impeded egress, one for impeded egress, and one for contained. Within each set, there are two levels of evaluation: one for partially sprinklered and nonsprinklered buildings, and one for totally sprinklered buildings.

Using narrative text and coded data to develop hazard scenarios for occupational injury interventions

PubMed Central

Lincoln, A; Sorock, G; Courtney, T; Wellman, H; Smith, G; Amoroso, P

2004-01-01

Objective: To determine whether narrative text in safety reports contains sufficient information regarding contributing factors and precipitating mechanisms to prioritize occupational back injury prevention strategies. Design, setting, subjects, and main outcome measures: Nine essential data elements were identified in narratives and coded sections of safety reports for each of 94 cases of back injuries to United States Army truck drivers reported to the United States Army Safety Center between 1987 and 1997. The essential elements of each case were used to reconstruct standardized event sequences. A taxonomy of the event sequences was then developed to identify common hazard scenarios and opportunities for primary interventions. Results: Coded data typically only identified five data elements (broad activity, task, event/exposure, nature of injury, and outcomes) while narratives provided additional elements (contributing factor, precipitating mechanism, primary source) essential for developing our taxonomy. Three hazard scenarios were associated with back injuries among Army truck drivers accounting for 83% of cases: struck by/against events during motor vehicle crashes; falls resulting from slips/trips or loss of balance; and overexertion from lifting activities. Conclusions: Coded data from safety investigations lacked sufficient information to thoroughly characterize the injury event. However, the combination of existing narrative text (similar to that collected by many injury surveillance systems) and coded data enabled us to develop a more complete taxonomy of injury event characteristics and identify common hazard scenarios. This study demonstrates that narrative text can provide the additional information on contributing factors and precipitating mechanisms needed to target prevention strategies. PMID:15314055

Assessment of Literature Related to Combustion Appliance Venting Systems

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

Rapp, V. H.; Less, B. D.; Singer, B. C.

In many residential building retrofit programs, air tightening to increase energy efficiency is often constrained by safety concerns with naturally vented combustion appliances. Tighter residential buildings more readily depressurize when exhaust equipment is operated, making combustion appliances more prone to backdraft or spill combustion exhaust into the living space. Several measures, such as installation guidelines, vent sizing codes, and combustion safety diagnostics, are in place with the intent to prevent backdrafting and combustion spillage, but the diagnostics conflict and the risk mitigation objective is inconsistent. This literature review summarizes the metrics and diagnostics used to assess combustion safety, documents theirmore » technical basis, and investigates their risk mitigations. It compiles information from the following: codes for combustion appliance venting and installation; standards and guidelines for combustion safety diagnostics; research evaluating combustion safety diagnostics; research investigating wind effects on building depressurization and venting; and software for simulating vent system performance.« less

30 CFR 57.19096 - Familiarity with signal code.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 57.19096 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL MINE SAFETY AND HEALTH SAFETY AND HEALTH STANDARDS-UNDERGROUND METAL AND NONMETAL MINES Personnel... signals for cages, skips, and mantrips when persons or materials are being transported shall be familiar...

30 CFR 56.19096 - Familiarity with signal code.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 56.19096 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL MINE SAFETY AND HEALTH SAFETY AND HEALTH STANDARDS-SURFACE METAL AND NONMETAL MINES Personnel... signals for cages, skips, and mantrips when persons or materials are being transported shall be familiar...

29 CFR 1952.101 - Developmental schedule.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Regulations Relating to Labor (Continued) OCCUPATIONAL SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR... effective July 1, 1973. (b) Complete revision of all occupational safety and health codes as proposed within... budget. (e) Establishment of specific occupational safety and health goals by July 1, 1974. These goals...

29 CFR 1952.101 - Developmental schedule.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Regulations Relating to Labor (Continued) OCCUPATIONAL SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR... effective July 1, 1973. (b) Complete revision of all occupational safety and health codes as proposed within... budget. (e) Establishment of specific occupational safety and health goals by July 1, 1974. These goals...

Automated Wildfire Detection Through Artificial Neural Networks

NASA Technical Reports Server (NTRS)

Miller, Jerry; Borne, Kirk; Thomas, Brian; Huang, Zhenping; Chi, Yuechen

2005-01-01

We have tested and deployed Artificial Neural Network (ANN) data mining techniques to analyze remotely sensed multi-channel imaging data from MODIS, GOES, and AVHRR. The goal is to train the ANN to learn the signatures of wildfires in remotely sensed data in order to automate the detection process. We train the ANN using the set of human-detected wildfires in the U.S., which are provided by the Hazard Mapping System (HMS) wildfire detection group at NOAA/NESDIS. The ANN is trained to mimic the behavior of fire detection algorithms and the subjective decision- making by N O M HMS Fire Analysts. We use a local extremum search in order to isolate fire pixels, and then we extract a 7x7 pixel array around that location in 3 spectral channels. The corresponding 147 pixel values are used to populate a 147-dimensional input vector that is fed into the ANN. The ANN accuracy is tested and overfitting is avoided by using a subset of the training data that is set aside as a test data set. We have achieved an automated fire detection accuracy of 80-92%, depending on a variety of ANN parameters and for different instrument channels among the 3 satellites. We believe that this system can be deployed worldwide or for any region to detect wildfires automatically in satellite imagery of those regions. These detections can ultimately be used to provide thermal inputs to climate models.

Artificial neural network model to distinguish follicular adenoma from follicular carcinoma on fine needle aspiration of thyroid.

PubMed

Savala, Rajiv; Dey, Pranab; Gupta, Nalini

2018-03-01

To distinguish follicular adenoma (FA) and follicular carcinoma (FC) of thyroid in fine needle aspiration cytology (FNAC) is a challenging problem. In this article, we attempted to build an artificial neural network (ANN) model from the cytological and morphometric features of the FNAC smears of thyroid to distinguish FA from FC. The cytological features and morphometric analysis were done on the FNAC smears of histology proven cases of FA (26) and FC (31). The cytological features were analysed semi-quantitatively by two independent observers (RS and PD). These data were used to make an ANN model to differentiate FA versus FC on FNAC material. The performance of this ANN model was assessed by analysing the confusion matrix and receiving operator curve. There were 39 cases in training set, 9 cases each in validation and test sets. In the test group, ANN model successfully distinguished all cases (9/9) of FA and FC. The area under receiver operating curve was 1. The present ANN model is efficient to diagnose follicular adenoma and carcinoma cases on cytology smears without any error. In future, this ANN model will be able to diagnose follicular adenoma and carcinoma cases on thyroid aspirate. This study has immense potential in future. This is an open ended ANN model and more parameters and more cases can be included to make the model much stronger. © 2017 Wiley Periodicals, Inc.

Clinical application of modified bag-of-features coupled with hybrid neural-based classifier in dengue fever classification using gene expression data.

PubMed

Chatterjee, Sankhadeep; Dey, Nilanjan; Shi, Fuqian; Ashour, Amira S; Fong, Simon James; Sen, Soumya

2018-04-01

Dengue fever detection and classification have a vital role due to the recent outbreaks of different kinds of dengue fever. Recently, the advancement in the microarray technology can be employed for such classification process. Several studies have established that the gene selection phase takes a significant role in the classifier performance. Subsequently, the current study focused on detecting two different variations, namely, dengue fever (DF) and dengue hemorrhagic fever (DHF). A modified bag-of-features method has been proposed to select the most promising genes in the classification process. Afterward, a modified cuckoo search optimization algorithm has been engaged to support the artificial neural (ANN-MCS) to classify the unknown subjects into three different classes namely, DF, DHF, and another class containing convalescent and normal cases. The proposed method has been compared with other three well-known classifiers, namely, multilayer perceptron feed-forward network (MLP-FFN), artificial neural network (ANN) trained with cuckoo search (ANN-CS), and ANN trained with PSO (ANN-PSO). Experiments have been carried out with different number of clusters for the initial bag-of-features-based feature selection phase. After obtaining the reduced dataset, the hybrid ANN-MCS model has been employed for the classification process. The results have been compared in terms of the confusion matrix-based performance measuring metrics. The experimental results indicated a highly statistically significant improvement with the proposed classifier over the traditional ANN-CS model.

Computerized Classification of Pneumoconiosis on Digital Chest Radiography Artificial Neural Network with Three Stages.

PubMed

Okumura, Eiichiro; Kawashita, Ikuo; Ishida, Takayuki

2017-08-01

It is difficult for radiologists to classify pneumoconiosis from category 0 to category 3 on chest radiographs. Therefore, we have developed a computer-aided diagnosis (CAD) system based on a three-stage artificial neural network (ANN) method for classification based on four texture features. The image database consists of 36 chest radiographs classified as category 0 to category 3. Regions of interest (ROIs) with a matrix size of 32 × 32 were selected from chest radiographs. We obtained a gray-level histogram, histogram of gray-level difference, gray-level run-length matrix (GLRLM) feature image, and gray-level co-occurrence matrix (GLCOM) feature image in each ROI. For ROI-based classification, the first ANN was trained with each texture feature. Next, the second ANN was trained with output patterns obtained from the first ANN. Finally, we obtained a case-based classification for distinguishing among four categories with the third ANN method. We determined the performance of the third ANN by receiver operating characteristic (ROC) analysis. The areas under the ROC curve (AUC) of the highest category (severe pneumoconiosis) case and the lowest category (early pneumoconiosis) case were 0.89 ± 0.09 and 0.84 ± 0.12, respectively. The three-stage ANN with four texture features showed the highest performance for classification among the four categories. Our CAD system would be useful for assisting radiologists in classification of pneumoconiosis from category 0 to category 3.

Effect of yearling steer sequence grazing of perennial and annual forages in an integrated crop and livestock system on grazing performance, delayed feedlot entry, finishing performance, carcass measurements, and systems economics.

PubMed

Sentürklü, Songul; Landblom, Douglas G; Maddock, Robert; Petry, Tim; Wachenheim, Cheryl J; Paisley, Steve I

2018-06-04

In a 2-yr study, spring-born yearling steers (n = 144), previously grown to gain <0.454 kg·steer-1·d-1, following weaning in the fall, were stratified by BW and randomly assigned to three retained ownership rearing systems (three replications) in early May. Systems were 1) feedlot (FLT), 2) steers that grazed perennial crested wheatgrass (CWG) and native range (NR) before FLT entry (PST), and 3) steers that grazed perennial CWG and NR, and then field pea-barley (PBLY) mix and unharvested corn (UC) before FLT entry (ANN). The PST and ANN steers grazed 181 d before FLT entry. During grazing, ADG of ANN steers (1.01 ± SE kg/d) and PST steers (0.77 ± SE kg/d) did not differ (P = 0.31). But even though grazing cost per steer was greater (P = 0.002) for ANN vs. PST, grazing cost per kg of gain did not differ (P = 0.82). The ANN forage treatment improved LM area (P = 0.03) and percent i.m. fat (P = 0.001). The length of the finishing period was greatest (P < 0.001) for FLT (142 d), intermediate for PST (91 d), and least for ANN (66 d). Steer starting (P = 0.015) and ending finishing BW (P = 0.022) of ANN and PST were greater than FLT steers. Total FLT BW gain was greater for FLT steers (P = 0.017), but there were no treatment differences for ADG, (P = 0.16), DMI (P = 0.21), G: F (P = 0.82), and feed cost per kg of gain (P = 0.61). However, feed cost per steer was greatest for FLT ($578.30), least for ANN ($276.12), and intermediate for PST ($381.18) (P = 0.043). There was a tendency for FLT steer HCW to be less than ANN and PST, which did not differ (P = 0.076). There was no difference between treatments for LM area (P = 0.094), backfat depth (P = 0.28), marbling score (P = 0.18), USDA yield grade (P = 0.44), and quality grade (P = 0.47). Grazing steer net return ranged from an ANN system high of $9.09/steer to a FLT control system net loss of -$298 and a PST system that was slightly less than the ANN system (-$30.10). Ten-year (2003 to 2012) hedging and net return sensitivity analysis revealed that the FLT treatment underperformed 7 of 10 yr and futures hedging protection against catastrophic losses were profitable 40, 30, and 20% of the time period for ANN, PST, and FLT, respectively. Retained ownership from birth through slaughter coupled with delayed FLT entry grazing perennial and annual forages has the greatest profitability potential.

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, IIS-W-ANN model accuracy outweighs IIS-ANN, as evidenced by a larger r and WI (by 7.5% and 3.8%, respectively) and a lower RMSE (by 21.3%). In comparison to the IIS-W-M5 Tree model, IIS-W-ANN model yielded larger values of WI = 0.936-0.979 and ENS = 0.770-0.920. Correspondingly, the errors (RMSE and MAE) ranged from 0.162-0.487 m and 0.139-0.390 m, respectively, with relative errors, RRMSE = (15.65-21.00) % and MAPE = (14.79-20.78) %. Distinct geographic signature is evident where the most and least accurately forecasted streamflow data is attained for the Gwydir and Darling River, respectively. Conclusively, this study advocates the efficacy of iterative input selection, allowing the proper screening of model predictors, and subsequently, its integration with MODWT resulting in enhanced performance of the models applied in streamflow forecasting.

Locating Groundwater Pollution Source using Breakthrough Curve Characteristics and Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Kumar, J.; Jain, A.; Srivastava, R.

2005-12-01

The identification of pollution sources in aquifers is an important area of research not only for the hydrologists but also for the local and Federal agencies and defense organizations. Once the data in terms of pollutant concentration measurements at observation wells become known, it is important to identify the polluting industry in order to implement punitive or remedial measures. Traditionally, hydrologists have relied on the conceptual methods for the identification of groundwater pollution sources. The problem of identification of groundwater pollution sources using the conceptual methods requires a thorough understanding of the groundwater flow and contaminant transport processes and inverse modeling procedures that are highly complex and difficult to implement. Recently, the soft computing techniques, such as artificial neural networks (ANNs) and genetic algorithms, have provided an attractive and easy to implement alternative to solve complex problems efficiently. Some researchers have used ANNs for the identification of pollution sources in aquifers. A major problem with most previous studies using ANNs has been the large size of the neural networks that are needed to model the inverse problem. The breakthrough curves at an observation well may consist of hundreds of concentration measurements, and presenting all of them to the input layer of an ANN not only results in humongous networks but also requires large amount of training and testing data sets to develop the ANN models. This paper presents the results of a study aimed at using certain characteristics of the breakthrough curves and ANNs for determining the distance of the pollution source from a given observation well. Two different neural network models are developed that differ in the manner of characterizing the breakthrough curves. The first ANN model uses five parameters, similar to the synthetic unit hydrograph parameters, to characterize the breakthrough curves. The five parameters employed are peak concentration, time to peak concentration, the widths of the breakthrough curves at 50% and 75% of the peak concentration, and the time base of the breakthrough curve. The second ANN model employs only the first four parameters leaving out the time base. The measurement of breakthrough curve at an observation well involves very high costs in sample collection at suitable time intervals and analysis for various contaminants. The receding portions of the breakthrough curves are normally very long and excluding the time base from modeling would result in considerable cost savings. The feed-forward multi-layer perceptron (MLP) type neural networks trained using the back-propagation algorithm, are employed in this study. The ANN models for the two approaches were developed using simulated data generated for conservative pollutant transport through a homogeneous aquifer. A new approach for ANN training using back-propagation is employed that considers two different error statistics to prevent over-training and under-training of the ANNs. The preliminary results indicate that the ANNs are able to identify the location of the pollution source very efficiently from both the methods of the breakthrough curves characterization.

36 CFR 910.37 - Fire and life safety.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 36 Parks, Forests, and Public Property 3 2011-07-01 2011-07-01 false Fire and life safety. 910.37... DEVELOPMENT AREA Standards Uniformly Applicable to the Development Area § 910.37 Fire and life safety. As a... recommended that all new development be guided by standards of the NFPA Codes for fire and life safety and...

36 CFR 910.37 - Fire and life safety.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 36 Parks, Forests, and Public Property 3 2010-07-01 2010-07-01 false Fire and life safety. 910.37... DEVELOPMENT AREA Standards Uniformly Applicable to the Development Area § 910.37 Fire and life safety. As a... recommended that all new development be guided by standards of the NFPA Codes for fire and life safety and...

Data Analysis Approaches for the Risk-Informed Safety Margins Characterization Toolkit

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

Mandelli, Diego; Alfonsi, Andrea; Maljovec, Daniel P.

2016-09-01

In the past decades, several numerical simulation codes have been employed to simulate accident dynamics (e.g., RELAP5-3D, RELAP-7, MELCOR, MAAP). In order to evaluate the impact of uncertainties into accident dynamics, several stochastic methodologies have been coupled with these codes. These stochastic methods range from classical Monte-Carlo and Latin Hypercube sampling to stochastic polynomial methods. Similar approaches have been introduced into the risk and safety community where stochastic methods (such as RAVEN, ADAPT, MCDET, ADS) have been coupled with safety analysis codes in order to evaluate the safety impact of timing and sequencing of events. These approaches are usually calledmore » Dynamic PRA or simulation-based PRA methods. These uncertainties and safety methods usually generate a large number of simulation runs (database storage may be on the order of gigabytes or higher). The scope of this paper is to present a broad overview of methods and algorithms that can be used to analyze and extract information from large data sets containing time dependent data. In this context, “extracting information” means constructing input-output correlations, finding commonalities, and identifying outliers. Some of the algorithms presented here have been developed or are under development within the RAVEN statistical framework.« less

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

ERIC Educational Resources Information Center

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

2017-01-01

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,…

Supervised Learning in CINets

DTIC Science & Technology

2011-07-01

supervised learning process is compared to that of Artificial Neural Network ( ANNs ), fuzzy logic rule set, and Bayesian network approaches...of both fuzzy logic systems and Artificial Neural Networks ( ANNs ). Like fuzzy logic systems, the CINet technique allows the use of human- intuitive...fuzzy rule systems [3] CINets also maintain features common to both fuzzy systems and ANNs . The technique can be be shown to possess the property

Command and Control of Teams of Autonomous Units

DTIC Science & Technology

2012-06-01

done by a hybrid genetic algorithm (GA) particle swarm optimization ( PSO ) algorithm called PIDGION-alternate. This training algorithm is an ANN ...human controller will recognize the behaviors as being safe and correct. As the HyperNEAT approach produces Artificial Neural Nets ( ANN ), we can...optimization technique that generates efficient ANN controls from simple environmental feedback. FALCONET has been tested showing that it can produce

76 FR 23642 - The “100,000 Strong” Initiative Federal Advisory Committee: Notice of the Inaugural Meeting of...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-04-27

... wishing to attend should contact Lee Anne Shaffer of the Department of State's Bureau of East Asian and... are welcome to do so by e-mail to Lee Anne Shaffer at [email protected] . A member of the public... participate by teleconferencing can contact Lee Anne Shaffer at 202-647-7059 to receive the conference call-in...

The application of artificial neural networks in astronomy

NASA Astrophysics Data System (ADS)

Li, Li-Li; Zhang, Yan-Xia; Zhao, Yong-Heng; Yang, Da-Wei

2006-12-01

Artificial Neural Networks (ANNs) are computer algorithms inspired from simple models of human central nervous system activity. They can be roughly divided into two main kinds: supervised and unsupervised. The supervised approach lays the stress on "teaching" a machine to do the work of a mention human expert, usually by showing examples for which the true answer is supplied by the expert. The unsupervised one is aimed at learning new things from the data, and most useful when the data cannot easily be plotted in a two or three dimensional space. ANNs have been used widely and successfully in various fields, for instance, pattern recognition, financial analysis, biology, engineering and so on, because they have many merits such as self-learning, self-adapting, good robustness and dynamically rapid response as well as strong capability of dealing with non-linear problems. In the last few years there has been an increasing interest toward the astronomical applications of ANNs. In this paper, the authors firstly introduce the fundamental principle of ANNs together with the architecture of the network and outline various kinds of learning algorithms and network toplogies. The specific aspects of the applications of ANNs in astronomical problems are also listed, which contain the strong capabilities of approximating to arbitrary accuracy, any nonlinear functional mapping, parallel and distributed storage, tolerance of faulty and generalization of results. They summarize the advantages and disadvantages of main ANN models available to the astronomical community. Furthermore, the application cases of ANNs in astronomy are mainly described in detail. Here, the focus is on some of the most interesting fields of its application, for example: object detection, star/galaxy classification, spectral classification, galaxy morphology classification, the estimation of photometric redshifts of galaxies and time series analysis. In addition, other kinds of applications have been only touched upon. Finally, the development and application prospects of ANNs is discussed. With the increase of quantity and the distributing complexity of astronomical data, its scientific exploitation requires a variety of automated tools, which are capable to perform huge amount of work, such as data preprocessing, feature selection, data reduction, data mining amd data analysis. ANNs, one of intelligent tools, will show more and more superiorities.

Artificial neural networks predict the incidence of portosplenomesenteric venous thrombosis in patients with acute pancreatitis.

PubMed

Fei, Y; Hu, J; Li, W-Q; Wang, W; Zong, G-Q

2017-03-01

Essentials Predicting the occurrence of portosplenomesenteric vein thrombosis (PSMVT) is difficult. We studied 72 patients with acute pancreatitis. Artificial neural networks modeling was more accurate than logistic regression in predicting PSMVT. Additional predictive factors may be incorporated into artificial neural networks. Objective To construct and validate artificial neural networks (ANNs) for predicting the occurrence of portosplenomesenteric venous thrombosis (PSMVT) and compare the predictive ability of the ANNs with that of logistic regression. Methods The ANNs and logistic regression modeling were constructed using simple clinical and laboratory data of 72 acute pancreatitis (AP) patients. The ANNs and logistic modeling were first trained on 48 randomly chosen patients and validated on the remaining 24 patients. The accuracy and the performance characteristics were compared between these two approaches by SPSS17.0 software. Results The training set and validation set did not differ on any of the 11 variables. After training, the back propagation network training error converged to 1 × 10 -20 , and it retained excellent pattern recognition ability. When the ANNs model was applied to the validation set, it revealed a sensitivity of 80%, specificity of 85.7%, a positive predictive value of 77.6% and negative predictive value of 90.7%. The accuracy was 83.3%. Differences could be found between ANNs modeling and logistic regression modeling in these parameters (10.0% [95% CI, -14.3 to 34.3%], 14.3% [95% CI, -8.6 to 37.2%], 15.7% [95% CI, -9.9 to 41.3%], 11.8% [95% CI, -8.2 to 31.8%], 22.6% [95% CI, -1.9 to 47.1%], respectively). When ANNs modeling was used to identify PSMVT, the area under receiver operating characteristic curve was 0.849 (95% CI, 0.807-0.901), which demonstrated better overall properties than logistic regression modeling (AUC = 0.716) (95% CI, 0.679-0.761). Conclusions ANNs modeling was a more accurate tool than logistic regression in predicting the occurrence of PSMVT following AP. More clinical factors or biomarkers may be incorporated into ANNs modeling to improve its predictive ability. © 2016 International Society on Thrombosis and Haemostasis.

SU-E-T-131: Artificial Neural Networks Applied to Overall Survival Prediction for Patients with Periampullary Carcinoma

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

Gong, Y; Yu, J; Yeung, V

Purpose: Artificial neural networks (ANN) can be used to discover complex relations within datasets to help with medical decision making. This study aimed to develop an ANN method to predict two-year overall survival of patients with peri-ampullary cancer (PAC) following resection. Methods: Data were collected from 334 patients with PAC following resection treated in our institutional pancreatic tumor registry between 2006 and 2012. The dataset contains 14 variables including age, gender, T-stage, tumor differentiation, positive-lymph-node ratio, positive resection margins, chemotherapy, radiation therapy, and tumor histology.After censoring for two-year survival analysis, 309 patients were left, of which 44 patients (∼15%) weremore » randomly selected to form testing set. The remaining 265 cases were randomly divided into training set (211 cases, ∼80% of 265) and validation set (54 cases, ∼20% of 265) for 20 times to build 20 ANN models. Each ANN has one hidden layer with 5 units. The 20 ANN models were ranked according to their concordance index (c-index) of prediction on validation sets. To further improve prediction, the top 10% of ANN models were selected, and their outputs averaged for prediction on testing set. Results: By random division, 44 cases in testing set and the remaining 265 cases have approximately equal two-year survival rates, 36.4% and 35.5% respectively. The 20 ANN models, which were trained and validated on the 265 cases, yielded mean c-indexes as 0.59 and 0.63 on validation sets and the testing set, respectively. C-index was 0.72 when the two best ANN models (top 10%) were used in prediction on testing set. The c-index of Cox regression analysis was 0.63. Conclusion: ANN improved survival prediction for patients with PAC. More patient data and further analysis of additional factors may be needed for a more robust model, which will help guide physicians in providing optimal post-operative care. This project was supported by PA CURE Grant.« less

Healthcare associated infection: novel strategies and antimicrobial implants to prevent surgical site infection

PubMed Central

Leaper, David; McBain, Andrew J; Kramer, Axel; Assadian, Ojan; Sanchez, Jose Luis Alfonso; Lumio, Jukka; Kiernan, Martin

2010-01-01

This report is based on a Hygienist Panel Meeting held at St Anne's Manor, Wokingham on 24–25 June 2009. The panel agreed that greater use should be made of antiseptics to reduce reliance on antibiotics with their associated risk of antibiotic resistance. When choosing an antiseptic for clinical use, the Biocompatibility Index, which considers both the microbiocidal activity and any cytotoxic effects of an antiseptic agent, was considered to be a useful tool. The need for longer and more proactive post-discharge surveillance of surgical patients was also agreed to be a priority, especially given the current growth of day-case surgery. The introduction of surgical safety checklists, such as the World Health Organization's Safe Surgery Saves Lives initiative, is a useful contribution to improving safety and prevention of SSIs and should be used universally. Considering sutures as ‘implants’, with a hard or non-shedding surface to which micro-organisms can form biofilm and cause surgical site infections, was felt to be a useful concept. PMID:20819330

Comparison of the LLNL ALE3D and AKTS Thermal Safety Computer Codes for Calculating Times to Explosion in ODTX and STEX Thermal Cookoff Experiments

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

Wemhoff, A P; Burnham, A K

2006-04-05

Cross-comparison of the results of two computer codes for the same problem provides a mutual validation of their computational methods. This cross-validation exercise was performed for LLNL's ALE3D code and AKTS's Thermal Safety code, using the thermal ignition of HMX in two standard LLNL cookoff experiments: the One-Dimensional Time to Explosion (ODTX) test and the Scaled Thermal Explosion (STEX) test. The chemical kinetics model used in both codes was the extended Prout-Tompkins model, a relatively new addition to ALE3D. This model was applied using ALE3D's new pseudospecies feature. In addition, an advanced isoconversional kinetic approach was used in the AKTSmore » code. The mathematical constants in the Prout-Tompkins code were calibrated using DSC data from hermetically sealed vessels and the LLNL optimization code Kinetics05. The isoconversional kinetic parameters were optimized using the AKTS Thermokinetics code. We found that the Prout-Tompkins model calculations agree fairly well between the two codes, and the isoconversional kinetic model gives very similar results as the Prout-Tompkins model. We also found that an autocatalytic approach in the beta-delta phase transition model does affect the times to explosion for some conditions, especially STEX-like simulations at ramp rates above 100 C/hr, and further exploration of that effect is warranted.« less

Results of comparative RBMK neutron computation using VNIIEF codes (cell computation, 3D statics, 3D kinetics). Final report

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

Grebennikov, A.N.; Zhitnik, A.K.; Zvenigorodskaya, O.A.

1995-12-31

In conformity with the protocol of the Workshop under Contract {open_quotes}Assessment of RBMK reactor safety using modern Western Codes{close_quotes} VNIIEF performed a neutronics computation series to compare western and VNIIEF codes and assess whether VNIIEF codes are suitable for RBMK type reactor safety assessment computation. The work was carried out in close collaboration with M.I. Rozhdestvensky and L.M. Podlazov, NIKIET employees. The effort involved: (1) cell computations with the WIMS, EKRAN codes (improved modification of the LOMA code) and the S-90 code (VNIIEF Monte Carlo). Cell, polycell, burnup computation; (2) 3D computation of static states with the KORAT-3D and NEUmore » codes and comparison with results of computation with the NESTLE code (USA). The computations were performed in the geometry and using the neutron constants presented by the American party; (3) 3D computation of neutron kinetics with the KORAT-3D and NEU codes. These computations were performed in two formulations, both being developed in collaboration with NIKIET. Formulation of the first problem maximally possibly agrees with one of NESTLE problems and imitates gas bubble travel through a core. The second problem is a model of the RBMK as a whole with imitation of control and protection system controls (CPS) movement in a core.« less

Revenue sources for financing transportation safety activities in Virginia : phase two, state sources.

DOT National Transportation Integrated Search

1980-01-01

Senate Bill 85, an action of the 1978 General Assembly, amended the Code of Virginia to provide, in part, that the Division of Highway Safety be succeeded by the newly created Department of Transportation Safety effective July 1, 1978. In its Declara...

29 CFR 1910.36 - Design and construction requirements for exit routes.

Code of Federal Regulations, 2012 CFR

2012-07-01

....36 Section 1910.36 Labor Regulations Relating to Labor (Continued) OCCUPATIONAL SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR OCCUPATIONAL SAFETY AND HEALTH STANDARDS Exit Routes and Emergency Planning... of exit routes necessary for your workplace, consult NFPA 101-2000, Life Safety Code. (c) Exit...

A hybrid deep neural network and physically based distributed model for river stage prediction

NASA Astrophysics Data System (ADS)

hitokoto, Masayuki; sakuraba, Masaaki

2016-04-01

We developed the real-time river stage prediction model, using the hybrid deep neural network and physically based distributed model. As the basic model, 4 layer feed-forward artificial neural network (ANN) was used. As a network training method, the deep learning technique was applied. To optimize the network weight, the stochastic gradient descent method based on the back propagation method was used. As a pre-training method, the denoising autoencoder was used. Input of the ANN model is hourly change of water level and hourly rainfall, output data is water level of downstream station. In general, the desirable input of the ANN has strong correlation with the output. In conceptual hydrological model such as tank model and storage-function model, river discharge is governed by the catchment storage. Therefore, the change of the catchment storage, downstream discharge subtracted from rainfall, can be the potent input candidate of the ANN model instead of rainfall. From this point of view, the hybrid deep neural network and physically based distributed model was developed. The prediction procedure of the hybrid model is as follows; first, downstream discharge was calculated by the distributed model, and then estimates the hourly change of catchment storage form rainfall and calculated discharge as the input of the ANN model, and finally the ANN model was calculated. In the training phase, hourly change of catchment storage can be calculated by the observed rainfall and discharge data. The developed model was applied to the one catchment of the OOYODO River, one of the first-grade river in Japan. The modeled catchment is 695 square km. For the training data, 5 water level gauging station and 14 rain-gauge station in the catchment was used. The training floods, superior 24 events, were selected during the period of 2005-2014. Prediction was made up to 6 hours, and 6 models were developed for each prediction time. To set the proper learning parameters and network architecture of the ANN model, sensitivity analysis was done by the case study approach. The prediction result was evaluated by the superior 4 flood events by the leave-one-out cross validation. The prediction result of the basic 4 layer ANN was better than the conventional 3 layer ANN model. However, the result did not reproduce well the biggest flood event, supposedly because the lack of the sufficient high-water level flood event in the training data. The result of the hybrid model outperforms the basic ANN model and distributed model, especially improved the performance of the basic ANN model in the biggest flood event.

Water quality key to protecting patients.

PubMed

Pearson, Susan

2012-11-01

According to David Graham of the Scottish National Blood Transfusion Service (SNBTS), "the importance of the safe diagnosis and treatment of patients cannot be overstated - yet the role played by water quality in patient safety has sometimes been under-stated". David Graham was speaking at a one day Pall Medical-sponsored meeting on the prevention and control of healthcare-associated waterborne infections in healthcare facilities held in Edinburgh earlier this year. David Graham, other speakers, and the chair, Consultant Microbiologist and Infection Prevention and Control Doctor for NHS Grampian, Dr Anne Marie Karcher, stressed that good quality water is essential in healthcare premises to prevent the potentially catastrophic consequences of contaminated water for some patients. Susan Pearson BSc reports.

Next Day Price Forecasting in Deregulated Market by Combination of Artificial Neural Network and ARIMA Time Series Models

NASA Astrophysics Data System (ADS)

Areekul, Phatchakorn; Senjyu, Tomonobu; Urasaki, Naomitsu; Yona, Atsushi

Electricity price forecasting is becoming increasingly relevant to power producers and consumers in the new competitive electric power markets, when planning bidding strategies in order to maximize their benefits and utilities, respectively. This paper proposed a method to predict hourly electricity prices for next-day electricity markets by combination methodology of ARIMA and ANN models. The proposed method is examined on the Australian National Electricity Market (NEM), New South Wales regional in year 2006. Comparison of forecasting performance with the proposed ARIMA, ANN and combination (ARIMA-ANN) models are presented. Empirical results indicate that an ARIMA-ANN model can improve the price forecasting accuracy.

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

Use of artificial neural networks on optical track width measurements.

PubMed

Smith, Richard J; See, Chung W; Somekh, Mike G; Yacoot, Andrew

2007-08-01

We have demonstrated recently that, by using an ultrastable optical interferometer together with artificial neural networks (ANNs), track widths down to 60 nm can be measured with a 0.3 NA objective lens. We investigate the effective conditions for training ANNs. Experimental results will be used to show the characteristics of the training samples and the data format of the ANN inputs required to produce suitably trained ANNs. Results obtained with networks measuring double tracks, and classifying different structures, will be presented to illustrate the capability of the technique. We include a discussion on expansion of the application areas of the system, allowing it to be used as a general purpose instrument.

Use of artificial neural networks on optical track width measurements

NASA Astrophysics Data System (ADS)

Smith, Richard J.; See, Chung W.; Somekh, Mike G.; Yacoot, Andrew

2007-08-01

We have demonstrated recently that, by using an ultrastable optical interferometer together with artificial neural networks (ANNs), track widths down to 60 nm can be measured with a 0.3 NA objective lens. We investigate the effective conditions for training ANNs. Experimental results will be used to show the characteristics of the training samples and the data format of the ANN inputs required to produce suitably trained ANNs. Results obtained with networks measuring double tracks, and classifying different structures, will be presented to illustrate the capability of the technique. We include a discussion on expansion of the application areas of the system, allowing it to be used as a general purpose instrument.

A Novel Higher Order Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Xu, Shuxiang

2010-05-01

In this paper a new Higher Order Neural Network (HONN) model is introduced and applied in several data mining tasks. Data Mining extracts hidden patterns and valuable information from large databases. A hyperbolic tangent function is used as the neuron activation function for the new HONN model. Experiments are conducted to demonstrate the advantages and disadvantages of the new HONN model, when compared with several conventional Artificial Neural Network (ANN) models: Feedforward ANN with the sigmoid activation function; Feedforward ANN with the hyperbolic tangent activation function; and Radial Basis Function (RBF) ANN with the Gaussian activation function. The experimental results seem to suggest that the new HONN holds higher generalization capability as well as abilities in handling missing data.

RELAP-7 Code Assessment Plan and Requirement Traceability Matrix

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

Yoo, Junsoo; Choi, Yong-joon; Smith, Curtis L.

2016-10-01

The RELAP-7, a safety analysis code for nuclear reactor system, is under development at Idaho National Laboratory (INL). Overall, the code development is directed towards leveraging the advancements in computer science technology, numerical solution methods and physical models over the last decades. Recently, INL has also been putting an effort to establish the code assessment plan, which aims to ensure an improved final product quality through the RELAP-7 development process. The ultimate goal of this plan is to propose a suitable way to systematically assess the wide range of software requirements for RELAP-7, including the software design, user interface, andmore » technical requirements, etc. To this end, we first survey the literature (i.e., international/domestic reports, research articles) addressing the desirable features generally required for advanced nuclear system safety analysis codes. In addition, the V&V (verification and validation) efforts as well as the legacy issues of several recently-developed codes (e.g., RELAP5-3D, TRACE V5.0) are investigated. Lastly, this paper outlines the Requirement Traceability Matrix (RTM) for RELAP-7 which can be used to systematically evaluate and identify the code development process and its present capability.« less

Bio-Inspired Microsystem for Robust Genetic Assay Recognition

PubMed Central

Lue, Jaw-Chyng; Fang, Wai-Chi

2008-01-01

A compact integrated system-on-chip (SoC) architecture solution for robust, real-time, and on-site genetic analysis has been proposed. This microsystem solution is noise-tolerable and suitable for analyzing the weak fluorescence patterns from a PCR prepared dual-labeled DNA microchip assay. In the architecture, a preceding VLSI differential logarithm microchip is designed for effectively computing the logarithm of the normalized input fluorescence signals. A posterior VLSI artificial neural network (ANN) processor chip is used for analyzing the processed signals from the differential logarithm stage. A single-channel logarithmic circuit was fabricated and characterized. A prototype ANN chip with unsupervised winner-take-all (WTA) function was designed, fabricated, and tested. An ANN learning algorithm using a novel sigmoid-logarithmic transfer function based on the supervised backpropagation (BP) algorithm is proposed for robustly recognizing low-intensity patterns. Our results show that the trained new ANN can recognize low-fluorescence patterns better than an ANN using the conventional sigmoid function. PMID:18566679

Sound quality recognition using optimal wavelet-packet transform and artificial neural network methods

NASA Astrophysics Data System (ADS)

Xing, Y. F.; Wang, Y. S.; Shi, L.; Guo, H.; Chen, H.

2016-01-01

According to the human perceptional characteristics, a method combined by the optimal wavelet-packet transform and artificial neural network, so-called OWPT-ANN model, for psychoacoustical recognition is presented. Comparisons of time-frequency analysis methods are performed, and an OWPT with 21 critical bands is designed for feature extraction of a sound, as is a three-layer back-propagation ANN for sound quality (SQ) recognition. Focusing on the loudness and sharpness, the OWPT-ANN model is applied on vehicle noises under different working conditions. Experimental verifications show that the OWPT can effectively transfer a sound into a time-varying energy pattern as that in the human auditory system. The errors of loudness and sharpness of vehicle noise from the OWPT-ANN are all less than 5%, which suggest a good accuracy of the OWPT-ANN model in SQ recognition. The proposed methodology might be regarded as a promising technique for signal processing in the human-hearing related fields in engineering.

Different approaches in Partial Least Squares and Artificial Neural Network models applied for the analysis of a ternary mixture of Amlodipine, Valsartan and Hydrochlorothiazide

NASA Astrophysics Data System (ADS)

Darwish, Hany W.; Hassan, Said A.; Salem, Maissa Y.; El-Zeany, Badr A.

2014-03-01

Different chemometric models were applied for the quantitative analysis of Amlodipine (AML), Valsartan (VAL) and Hydrochlorothiazide (HCT) in ternary mixture, namely, Partial Least Squares (PLS) as traditional chemometric model and Artificial Neural Networks (ANN) as advanced model. PLS and ANN were applied with and without variable selection procedure (Genetic Algorithm GA) and data compression procedure (Principal Component Analysis PCA). The chemometric methods applied are PLS-1, GA-PLS, ANN, GA-ANN and PCA-ANN. The methods were used for the quantitative analysis of the drugs in raw materials and pharmaceutical dosage form via handling the UV spectral data. A 3-factor 5-level experimental design was established resulting in 25 mixtures containing different ratios of the drugs. Fifteen mixtures were used as a calibration set and the other ten mixtures were used as validation set to validate the prediction ability of the suggested methods. The validity of the proposed methods was assessed using the standard addition technique.

Artificial neural network techniques to improve the ability of optical coherence tomography to detect optic neuritis.

PubMed

Garcia-Martin, Elena; Herrero, Raquel; Bambo, Maria P; Ara, Jose R; Martin, Jesus; Polo, Vicente; Larrosa, Jose M; Garcia-Feijoo, Julian; Pablo, Luis E

2015-01-01

To analyze the ability of Spectralis optical coherence tomography (OCT) to detect multiple sclerosis (MS) and to distinguish MS eyes with antecedent optic neuritis (ON). To analyze the capability of artificial neural network (ANN) techniques to improve the diagnostic precision. MS patients and controls were enrolled (n = 217). OCT was used to determine the 768 retinal nerve fiber layer thicknesses. Sensitivity and specificity were evaluated to test the ability of OCT to discriminate between MS and healthy eyes, and between MS with and without antecedent ON using ANN. Using ANN technique multilayer perceptrons, OCT could detect MS with a sensitivity of 89.3%, a specificity of 87.6%, and a diagnostic precision of 88.5%. Compared with the OCT-provided parameters, the ANN had a better sensitivity-specificity balance. ANN technique improves the capability of Spectralis OCT to detect MS disease and to distinguish MS eyes with or without antecedent ON.

A New Data Mining Scheme Using Artificial Neural Networks

PubMed Central

Kamruzzaman, S. M.; Jehad Sarkar, A. M.

2011-01-01

Classification is one of the data mining problems receiving enormous attention in the database community. Although artificial neural networks (ANNs) have been successfully applied in a wide range of machine learning applications, they are however often regarded as black boxes, i.e., their predictions cannot be explained. To enhance the explanation of ANNs, a novel algorithm to extract symbolic rules from ANNs has been proposed in this paper. ANN methods have not been effectively utilized for data mining tasks because how the classifications were made is not explicitly stated as symbolic rules that are suitable for verification or interpretation by human experts. With the proposed approach, concise symbolic rules with high accuracy, that are easily explainable, can be extracted from the trained ANNs. Extracted rules are comparable with other methods in terms of number of rules, average number of conditions for a rule, and the accuracy. The effectiveness of the proposed approach is clearly demonstrated by the experimental results on a set of benchmark data mining classification problems. PMID:22163866

Optimal design approach for heating irregular-shaped objects in three-dimensional radiant furnaces using a hybrid genetic algorithm-artificial neural network method

NASA Astrophysics Data System (ADS)

Darvishvand, Leila; Kamkari, Babak; Kowsary, Farshad

2018-03-01

In this article, a new hybrid method based on the combination of the genetic algorithm (GA) and artificial neural network (ANN) is developed to optimize the design of three-dimensional (3-D) radiant furnaces. A 3-D irregular shape design body (DB) heated inside a 3-D radiant furnace is considered as a case study. The uniform thermal conditions on the DB surfaces are obtained by minimizing an objective function. An ANN is developed to predict the objective function value which is trained through the data produced by applying the Monte Carlo method. The trained ANN is used in conjunction with the GA to find the optimal design variables. The results show that the computational time using the GA-ANN approach is significantly less than that of the conventional method. It is concluded that the integration of the ANN with GA is an efficient technique for optimization of the radiant furnaces.

Total Electron Content forecast model over Australia

NASA Astrophysics Data System (ADS)

Bouya, Zahra; Terkildsen, Michael; Francis, Matthew

Ionospheric perturbations can cause serious propagation errors in modern radio systems such as Global Navigation Satellite Systems (GNSS). Forecasting ionospheric parameters is helpful to estimate potential degradation of the performance of these systems. Our purpose is to establish an Australian Regional Total Electron Content (TEC) forecast model at IPS. In this work we present an approach based on the combined use of the Principal Component Analysis (PCA) and Artificial Neural Network (ANN) to predict future TEC values. PCA is used to reduce the dimensionality of the original TEC data by mapping it into its eigen-space. In this process the top- 5 eigenvectors are chosen to reflect the directions of the maximum variability. An ANN approach was then used for the multicomponent prediction. We outline the design of the ANN model with its parameters. A number of activation functions along with different spectral ranges and different numbers of Principal Components (PCs) were tested to find the PCA-ANN models reaching the best results. Keywords: GNSS, Space Weather, Regional, Forecast, PCA, ANN.

Time-dependent fermentation control strategies for enhancing synthesis of marine bacteriocin 1701 using artificial neural network and genetic algorithm.

PubMed

Peng, Jiansheng; Meng, Fanmei; Ai, Yuncan

2013-06-01

The artificial neural network (ANN) and genetic algorithm (GA) were combined to optimize the fermentation process for enhancing production of marine bacteriocin 1701 in a 5-L-stirred-tank. Fermentation time, pH value, dissolved oxygen level, temperature and turbidity were used to construct a "5-10-1" ANN topology to identify the nonlinear relationship between fermentation parameters and the antibiotic effects (shown as in inhibition diameters) of bacteriocin 1701. The predicted values by the trained ANN model were coincided with the observed ones (the coefficient of R(2) was greater than 0.95). As the fermentation time was brought in as one of the ANN input nodes, fermentation parameters could be optimized by stages through GA, and an optimal fermentation process control trajectory was created. The production of marine bacteriocin 1701 was significantly improved by 26% under the guidance of fermentation control trajectory that was optimized by using of combined ANN-GA method. Copyright © 2013 Elsevier Ltd. All rights reserved.

Artificial neural network modelling of a large-scale wastewater treatment plant operation.

PubMed

Güçlü, Dünyamin; Dursun, Sükrü

2010-11-01

Artificial Neural Networks (ANNs), a method of artificial intelligence method, provide effective predictive models for complex processes. Three independent ANN models trained with back-propagation algorithm were developed to predict effluent chemical oxygen demand (COD), suspended solids (SS) and aeration tank mixed liquor suspended solids (MLSS) concentrations of the Ankara central wastewater treatment plant. The appropriate architecture of ANN models was determined through several steps of training and testing of the models. ANN models yielded satisfactory predictions. Results of the root mean square error, mean absolute error and mean absolute percentage error were 3.23, 2.41 mg/L and 5.03% for COD; 1.59, 1.21 mg/L and 17.10% for SS; 52.51, 44.91 mg/L and 3.77% for MLSS, respectively, indicating that the developed model could be efficiently used. The results overall also confirm that ANN modelling approach may have a great implementation potential for simulation, precise performance prediction and process control of wastewater treatment plants.

[Optimization of calcium alginate floating microspheres loading aspirin by artificial neural networks and response surface methodology].

PubMed

Zhang, An-yang; Fan, Tian-yuan

2010-04-18

To investigate the preparation and optimization of calcium alginate floating microspheres loading aspirin. A model was used to predict the in vitro release of aspirin and optimize the formulation by artificial neural networks (ANNs) and response surface methodology (RSM). The amounts of the material in the formulation were used as inputs, while the release and floating rate of the microspheres were used as outputs. The performances of ANNs and RSM were compared. ANNs were more accurate in prediction. There was no significant difference between ANNs and RSM in optimization. Approximately 90% of the optimized microspheres could float on the artificial gastric juice over 4 hours. 42.12% of aspirin was released in 60 min, 60.97% in 120 min and 78.56% in 240 min. The release of the drug from the microspheres complied with Higuchi equation. The aspirin floating microspheres with satisfying in vitro release were prepared successfully by the methods of ANNs and RSM.

Markov chain-incorporated and synthetic data-supported conditional artificial neural network models for forecasting monthly precipitation in arid regions

NASA Astrophysics Data System (ADS)

Aksoy, Hafzullah; Dahamsheh, Ahmad

2018-07-01

For forecasting monthly precipitation in an arid region, the feed forward back-propagation, radial basis function and generalized regression artificial neural networks (ANNs) are used in this study. The ANN models are improved after incorporation of a Markov chain-based algorithm (MC-ANNs) with which the percentage of dry months is forecasted perfectly, thus generation of any non-physical negative precipitation is eliminated. Due to the fact that recorded precipitation time series are usually shorter than the length needed for a proper calibration of ANN models, synthetic monthly precipitation data are generated by Thomas-Fiering model to further improve the performance of forecasting. For case studies from Jordan, it is seen that only a slightly better performance is achieved with the use of MC and synthetic data. A conditional statement is, therefore, established and imbedded into the ANN models after the incorporation of MC and support of synthetic data, to substantially improve the ability of the models for forecasting monthly precipitation in arid regions.

The Crash Outcome Data Evaluation System (CODES)

DOT National Transportation Integrated Search

1996-01-01

The CODES Technical Report presents state-specific results from the Crash : Outcome Data Evaluation System project. These results confirm previous NHTSA : studies and show that safety belts and motorcycle helmets are effective in : reducing fatalitie...

Project : semi-autonomous parking for enhanced safety and efficiency.

DOT National Transportation Integrated Search

2016-04-01

Index coding, a coding formulation traditionally analyzed in the theoretical computer science and : information theory communities, has received considerable attention in recent years due to its value in : wireless communications and networking probl...

Alternative Fuels Data Center

Science.gov Websites

Propane and Natural Gas Safety The Railroad Commission of Texas regulates the safety of the natural gas and propane industries. (Reference Texas Statutes, Natural Resources Code 113.011 and 116.011

FUEL-FLEXIBLE GASIFICATION-COMBUSTION TECHNOLOGY FOR PRODUCTION OF H2 AND SEQUESTRATION-READY CO2

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

George Rizeq; Janice West; Arnaldo Frydman

Further development of a combustion Large Eddy Simulation (LES) code for the design of advanced gaseous combustion systems is described in this sixth quarterly report. CFD Research Corporation (CFDRC) is developing the LES module within the parallel, unstructured solver included in the commercial CFD-ACE+ software. In this quarter, in-situ adaptive tabulation (ISAT) for efficient chemical rate storage and retrieval was implemented and tested within the Linear Eddy Model (LEM). ISAT type 3 is being tested so that extrapolation can be performed and further improve the retrieval rate. Further testing of the LEM for subgrid chemistry was performed for parallel applicationsmore » and for multi-step chemistry. Validation of the software on backstep and bluff-body reacting cases were performed. Initial calculations of the SimVal experiment at Georgia Tech using their LES code were performed. Georgia Tech continues the effort to parameterize the LEM over composition space so that a neural net can be used efficiently in the combustion LES code. A new and improved Artificial Neural Network (ANN), with log-transformed output, for the 1-step chemistry was implemented in CFDRC's LES code and gave reasonable results. This quarter, the 2nd consortium meeting was held at CFDRC. Next quarter, LES software development and testing will continue. Alpha testing of the code will continue to be performed on cases of interest to the industrial consortium. Optimization of subgrid models will be pursued, particularly with the ISAT approach. Also next quarter, the demonstration of the neural net approach, for multi-step chemical kinetics speed-up in CFD-ACE+, will be accomplished.« less

From Heuristic to Mathematical Modeling of Drugs Dissolution Profiles: Application of Artificial Neural Networks and Genetic Programming

PubMed Central

Mendyk, Aleksander; Güres, Sinan; Szlęk, Jakub; Wiśniowska, Barbara; Kleinebudde, Peter

2015-01-01

The purpose of this work was to develop a mathematical model of the drug dissolution (Q) from the solid lipid extrudates based on the empirical approach. Artificial neural networks (ANNs) and genetic programming (GP) tools were used. Sensitivity analysis of ANNs provided reduction of the original input vector. GP allowed creation of the mathematical equation in two major approaches: (1) direct modeling of Q versus extrudate diameter (d) and the time variable (t) and (2) indirect modeling through Weibull equation. ANNs provided also information about minimum achievable generalization error and the way to enhance the original dataset used for adjustment of the equations' parameters. Two inputs were found important for the drug dissolution: d and t. The extrudates length (L) was found not important. Both GP modeling approaches allowed creation of relatively simple equations with their predictive performance comparable to the ANNs (root mean squared error (RMSE) from 2.19 to 2.33). The direct mode of GP modeling of Q versus d and t resulted in the most robust model. The idea of how to combine ANNs and GP in order to escape ANNs' black-box drawback without losing their superior predictive performance was demonstrated. Open Source software was used to deliver the state-of-the-art models and modeling strategies. PMID:26101544

Toward automatic time-series forecasting using neural networks.

PubMed

Yan, Weizhong

2012-07-01

Over the past few decades, application of artificial neural networks (ANN) to time-series forecasting (TSF) has been growing rapidly due to several unique features of ANN models. However, to date, a consistent ANN performance over different studies has not been achieved. Many factors contribute to the inconsistency in the performance of neural network models. One such factor is that ANN modeling involves determining a large number of design parameters, and the current design practice is essentially heuristic and ad hoc, this does not exploit the full potential of neural networks. Systematic ANN modeling processes and strategies for TSF are, therefore, greatly needed. Motivated by this need, this paper attempts to develop an automatic ANN modeling scheme. It is based on the generalized regression neural network (GRNN), a special type of neural network. By taking advantage of several GRNN properties (i.e., a single design parameter and fast learning) and by incorporating several design strategies (e.g., fusing multiple GRNNs), we have been able to make the proposed modeling scheme to be effective for modeling large-scale business time series. The initial model was entered into the NN3 time-series competition. It was awarded the best prediction on the reduced dataset among approximately 60 different models submitted by scholars worldwide.

Modeling the Malaysian motor insurance claim using artificial neural network and adaptive NeuroFuzzy inference system

NASA Astrophysics Data System (ADS)

Mohd Yunos, Zuriahati; Shamsuddin, Siti Mariyam; Ismail, Noriszura; Sallehuddin, Roselina

2013-04-01

Artificial neural network (ANN) with back propagation algorithm (BP) and ANFIS was chosen as an alternative technique in modeling motor insurance claims. In particular, an ANN and ANFIS technique is applied to model and forecast the Malaysian motor insurance data which is categorized into four claim types; third party property damage (TPPD), third party bodily injury (TPBI), own damage (OD) and theft. This study is to determine whether an ANN and ANFIS model is capable of accurately predicting motor insurance claim. There were changes made to the network structure as the number of input nodes, number of hidden nodes and pre-processing techniques are also examined and a cross-validation technique is used to improve the generalization ability of ANN and ANFIS models. Based on the empirical studies, the prediction performance of the ANN and ANFIS model is improved by using different number of input nodes and hidden nodes; and also various sizes of data. The experimental results reveal that the ANFIS model has outperformed the ANN model. Both models are capable of producing a reliable prediction for the Malaysian motor insurance claims and hence, the proposed method can be applied as an alternative to predict claim frequency and claim severity.

Classification of time-of-flight secondary ion mass spectrometry spectra from complex Cu-Fe sulphides by principal component analysis and artificial neural networks.

PubMed

Kalegowda, Yogesh; Harmer, Sarah L

2013-01-08

Artificial neural network (ANN) and a hybrid principal component analysis-artificial neural network (PCA-ANN) classifiers have been successfully implemented for classification of static time-of-flight secondary ion mass spectrometry (ToF-SIMS) mass spectra collected from complex Cu-Fe sulphides (chalcopyrite, bornite, chalcocite and pyrite) at different flotation conditions. ANNs are very good pattern classifiers because of: their ability to learn and generalise patterns that are not linearly separable; their fault and noise tolerance capability; and high parallelism. In the first approach, fragments from the whole ToF-SIMS spectrum were used as input to the ANN, the model yielded high overall correct classification rates of 100% for feed samples, 88% for conditioned feed samples and 91% for Eh modified samples. In the second approach, the hybrid pattern classifier PCA-ANN was integrated. PCA is a very effective multivariate data analysis tool applied to enhance species features and reduce data dimensionality. Principal component (PC) scores which accounted for 95% of the raw spectral data variance, were used as input to the ANN, the model yielded high overall correct classification rates of 88% for conditioned feed samples and 95% for Eh modified samples. Copyright © 2012 Elsevier B.V. All rights reserved.

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.

Comparison of Prediction Model for Cardiovascular Autonomic Dysfunction Using Artificial Neural Network and Logistic Regression Analysis

PubMed Central

Zeng, Fangfang; Li, Zhongtao; Yu, Xiaoling; Zhou, Linuo

2013-01-01

Background This study aimed to develop the artificial neural network (ANN) and multivariable logistic regression (LR) analyses for prediction modeling of cardiovascular autonomic (CA) dysfunction in the general population, and compare the prediction models using the two approaches. Methods and Materials We analyzed a previous dataset based on a Chinese population sample consisting of 2,092 individuals aged 30–80 years. The prediction models were derived from an exploratory set using ANN and LR analysis, and were tested in the validation set. Performances of these prediction models were then compared. Results Univariate analysis indicated that 14 risk factors showed statistically significant association with the prevalence of CA dysfunction (P<0.05). The mean area under the receiver-operating curve was 0.758 (95% CI 0.724–0.793) for LR and 0.762 (95% CI 0.732–0.793) for ANN analysis, but noninferiority result was found (P<0.001). The similar results were found in comparisons of sensitivity, specificity, and predictive values in the prediction models between the LR and ANN analyses. Conclusion The prediction models for CA dysfunction were developed using ANN and LR. ANN and LR are two effective tools for developing prediction models based on our dataset. PMID:23940593

Digital image classification with the help of artificial neural network by simple histogram.

PubMed

Dey, Pranab; Banerjee, Nirmalya; Kaur, Rajwant

2016-01-01

Visual image classification is a great challenge to the cytopathologist in routine day-to-day work. Artificial neural network (ANN) may be helpful in this matter. In this study, we have tried to classify digital images of malignant and benign cells in effusion cytology smear with the help of simple histogram data and ANN. A total of 404 digital images consisting of 168 benign cells and 236 malignant cells were selected for this study. The simple histogram data was extracted from these digital images and an ANN was constructed with the help of Neurointelligence software [Alyuda Neurointelligence 2.2 (577), Cupertino, California, USA]. The network architecture was 6-3-1. The images were classified as training set (281), validation set (63), and test set (60). The on-line backpropagation training algorithm was used for this study. A total of 10,000 iterations were done to train the ANN system with the speed of 609.81/s. After the adequate training of this ANN model, the system was able to identify all 34 malignant cell images and 24 out of 26 benign cells. The ANN model can be used for the identification of the individual malignant cells with the help of simple histogram data. This study will be helpful in the future to identify malignant cells in unknown situations.

From Heuristic to Mathematical Modeling of Drugs Dissolution Profiles: Application of Artificial Neural Networks and Genetic Programming.

PubMed

Mendyk, Aleksander; Güres, Sinan; Jachowicz, Renata; Szlęk, Jakub; Polak, Sebastian; Wiśniowska, Barbara; Kleinebudde, Peter

2015-01-01

The purpose of this work was to develop a mathematical model of the drug dissolution (Q) from the solid lipid extrudates based on the empirical approach. Artificial neural networks (ANNs) and genetic programming (GP) tools were used. Sensitivity analysis of ANNs provided reduction of the original input vector. GP allowed creation of the mathematical equation in two major approaches: (1) direct modeling of Q versus extrudate diameter (d) and the time variable (t) and (2) indirect modeling through Weibull equation. ANNs provided also information about minimum achievable generalization error and the way to enhance the original dataset used for adjustment of the equations' parameters. Two inputs were found important for the drug dissolution: d and t. The extrudates length (L) was found not important. Both GP modeling approaches allowed creation of relatively simple equations with their predictive performance comparable to the ANNs (root mean squared error (RMSE) from 2.19 to 2.33). The direct mode of GP modeling of Q versus d and t resulted in the most robust model. The idea of how to combine ANNs and GP in order to escape ANNs' black-box drawback without losing their superior predictive performance was demonstrated. Open Source software was used to deliver the state-of-the-art models and modeling strategies.

Two Different Points of View through Artificial Intelligence and Vector Autoregressive Models for Ex Post and Ex Ante Forecasting

PubMed Central

Aydin, Alev Dilek; Caliskan Cavdar, Seyma

2015-01-01

The ANN method has been applied by means of multilayered feedforward neural networks (MLFNs) by using different macroeconomic variables such as the exchange rate of USD/TRY, gold prices, and the Borsa Istanbul (BIST) 100 index based on monthly data over the period of January 2000 and September 2014 for Turkey. Vector autoregressive (VAR) method has also been applied with the same variables for the same period of time. In this study, different from other studies conducted up to the present, ENCOG machine learning framework has been used along with JAVA programming language in order to constitute the ANN. The training of network has been done by resilient propagation method. The ex post and ex ante estimates obtained by the ANN method have been compared with the results obtained by the econometric forecasting method of VAR. Strikingly, our findings based on the ANN method reveal that there is a possibility of financial distress or a financial crisis in Turkey starting from October 2017. The results which were obtained with the method of VAR also support the results of ANN method. Additionally, our results indicate that the ANN approach has more superior prediction performance than the VAR method. PMID:26550010

Improving gridded snow water equivalent products in British Columbia, Canada: multi-source data fusion by neural network models

NASA Astrophysics Data System (ADS)

Snauffer, Andrew M.; Hsieh, William W.; Cannon, Alex J.; Schnorbus, Markus A.

2018-03-01

Estimates of surface snow water equivalent (SWE) in mixed alpine environments with seasonal melts are particularly difficult in areas of high vegetation density, topographic relief, and snow accumulations. These three confounding factors dominate much of the province of British Columbia (BC), Canada. An artificial neural network (ANN) was created using as predictors six gridded SWE products previously evaluated for BC. Relevant spatiotemporal covariates were also included as predictors, and observations from manual snow surveys at stations located throughout BC were used as target data. Mean absolute errors (MAEs) and interannual correlations for April surveys were found using cross-validation. The ANN using the three best-performing SWE products (ANN3) had the lowest mean station MAE across the province. ANN3 outperformed each product as well as product means and multiple linear regression (MLR) models in all of BC's five physiographic regions except for the BC Plains. Subsequent comparisons with predictions generated by the Variable Infiltration Capacity (VIC) hydrologic model found ANN3 to better estimate SWE over the VIC domain and within most regions. The superior performance of ANN3 over the individual products, product means, MLR, and VIC was found to be statistically significant across the province.

Neural network models - a novel tool for predicting the efficacy of growth hormone (GH) therapy in children with short stature.

PubMed

Smyczynska, Joanna; Hilczer, Maciej; Smyczynska, Urszula; Stawerska, Renata; Tadeusiewicz, Ryszard; Lewinski, Andrzej

2015-01-01

The leading method for prediction of growth hormone (GH) therapy effectiveness are multiple linear regression (MLR) models. Best of our knowledge, we are the first to apply artificial neural networks (ANN) to solve this problem. For ANN there is no necessity to assume the functions linking independent and dependent variables. The aim of study is to compare ANN and MLR models of GH therapy effectiveness. Analysis comprised the data of 245 GH-deficient children (170 boys) treated with GH up to final height (FH). Independent variables included: patients' height, pre-treatment height velocity, chronological age, bone age, gender, pubertal status, parental heights, GH peak in 2 stimulation tests, IGF-I concentration. The output variable was FH. For testing dataset, MLR model predicted FH SDS with average error (RMSE) 0.64 SD, explaining 34.3% of its variability; ANN model derived on the same pre-processed data predicted FH SDS with RMSE 0.60 SD, explaining 42.0% of its variability; ANN model derived on raw data predicted FH with RMSE 3.9 cm (0.63 SD), explaining 78.7% of its variability. ANN seem to be valuable tool in prediction of GH treatment effectiveness, especially since they can be applied to raw clinical data.

Two Different Points of View through Artificial Intelligence and Vector Autoregressive Models for Ex Post and Ex Ante Forecasting.

PubMed

Aydin, Alev Dilek; Caliskan Cavdar, Seyma

2015-01-01

The ANN method has been applied by means of multilayered feedforward neural networks (MLFNs) by using different macroeconomic variables such as the exchange rate of USD/TRY, gold prices, and the Borsa Istanbul (BIST) 100 index based on monthly data over the period of January 2000 and September 2014 for Turkey. Vector autoregressive (VAR) method has also been applied with the same variables for the same period of time. In this study, different from other studies conducted up to the present, ENCOG machine learning framework has been used along with JAVA programming language in order to constitute the ANN. The training of network has been done by resilient propagation method. The ex post and ex ante estimates obtained by the ANN method have been compared with the results obtained by the econometric forecasting method of VAR. Strikingly, our findings based on the ANN method reveal that there is a possibility of financial distress or a financial crisis in Turkey starting from October 2017. The results which were obtained with the method of VAR also support the results of ANN method. Additionally, our results indicate that the ANN approach has more superior prediction performance than the VAR method.

Fundamental approaches for analysis thermal hydraulic parameter for Puspati Research Reactor

NASA Astrophysics Data System (ADS)

Hashim, Zaredah; Lanyau, Tonny Anak; Farid, Mohamad Fairus Abdul; Kassim, Mohammad Suhaimi; Azhar, Noraishah Syahirah

2016-01-01

The 1-MW PUSPATI Research Reactor (RTP) is the one and only nuclear pool type research reactor developed by General Atomic (GA) in Malaysia. It was installed at Malaysian Nuclear Agency and has reached the first criticality on 8 June 1982. Based on the initial core which comprised of 80 standard TRIGA fuel elements, the very fundamental thermal hydraulic model was investigated during steady state operation using the PARET-code. The main objective of this paper is to determine the variation of temperature profiles and Departure of Nucleate Boiling Ratio (DNBR) of RTP at full power operation. The second objective is to confirm that the values obtained from PARET-code are in agreement with Safety Analysis Report (SAR) for RTP. The code was employed for the hot and average channels in the core in order to calculate of fuel's center and surface, cladding, coolant temperatures as well as DNBR's values. In this study, it was found that the results obtained from the PARET-code showed that the thermal hydraulic parameters related to safety for initial core which was cooled by natural convection was in agreement with the designed values and safety limit in SAR.

25 CFR 559.5 - What must a tribe submit to the Chairman with the copy of each facility license that has been...

Code of Federal Regulations, 2011 CFR

2011-04-01

... on the environment and the public health and safety, as well as mitigating measures the tribe may... environment and public health and safety. ... public health and safety laws, resolutions, codes, policies, standards or procedures applicable to its...

25 CFR 559.5 - What must a tribe submit to the Chairman with the copy of each facility license that has been...

Code of Federal Regulations, 2012 CFR

2012-04-01

... on the environment and the public health and safety, as well as mitigating measures the tribe may... environment and public health and safety. ... public health and safety laws, resolutions, codes, policies, standards or procedures applicable to its...

25 CFR 559.5 - What must a tribe submit to the Chairman with the copy of each facility license that has been...

Code of Federal Regulations, 2010 CFR

2010-04-01

... on the environment and the public health and safety, as well as mitigating measures the tribe may... environment and public health and safety. ... public health and safety laws, resolutions, codes, policies, standards or procedures applicable to its...

3 CFR 8672 - Proclamation 8672 of May 9, 2011. National Building Safety Month, 2011

Code of Federal Regulations, 2012 CFR

2012-01-01

... public and private sectors—to implement effective standards and codes that sustain safe and resilient structures. We need innovation and partnerships at all levels of society to develop transformative... Proclamation Building safety is a critical component of our homeland security, our personal and public safety...

Bleacher Safety: What Do We Look for? What Can We Do?

ERIC Educational Resources Information Center

IEA Environmental Consultant, 1999

1999-01-01

Discusses safety issues surrounding aging bleacher systems, highlighting the following three primary safety considerations: space between seats and footboards; guardrails; and the structural provisions of the 1997 Uniform Building Code. Tips for bleacher accident-prevention assessment and excerpts from federal and Minnesota legislation on bleacher…

30 CFR 75.1107-16 - Inspection of fire suppression devices.

Code of Federal Regulations, 2011 CFR

2011-07-01

...-16 Section 75.1107-16 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR COAL MINE SAFETY AND HEALTH MANDATORY SAFETY STANDARDS-UNDERGROUND COAL MINES Fire Protection Fire... Systems” (NFPA No. 11A—1970). National Fire Code No. 13A “Care and Maintenance of Sprinkler Systems” (NFPA...

30 CFR 75.1107-16 - Inspection of fire suppression devices.

Code of Federal Regulations, 2010 CFR

2010-07-01

...-16 Section 75.1107-16 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR COAL MINE SAFETY AND HEALTH MANDATORY SAFETY STANDARDS-UNDERGROUND COAL MINES Fire Protection Fire... Systems” (NFPA No. 11A—1970). National Fire Code No. 13A “Care and Maintenance of Sprinkler Systems” (NFPA...

77 FR 54836 - Federal Motor Vehicle Safety Standards

Federal Register 2010, 2011, 2012, 2013, 2014

2012-09-06

... DEPARTMENT OF TRANSPORTATION National Highway Traffic Safety Administration 49 CFR Part 571 Federal Motor Vehicle Safety Standards CFR Correction 0 In Title 49 of the Code of Federal Regulations... read as follows: Sec. 571.119 Standard No. 119; New pneumatic tires for motor vehicles with a GVWR of...

Uniform emergency codes: will they improve safety?

PubMed

2005-01-01

There are pros and cons to uniform code systems, according to emergency medicine experts. Uniformity can be a benefit when ED nurses and other staff work at several facilities. It's critical that your staff understand not only what the codes stand for, but what they must do when codes are called. If your state institutes a new system, be sure to hold regular drills to familiarize your ED staff.

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.

Autonomous self-configuration of artificial neural networks for data classification or system control

NASA Astrophysics Data System (ADS)

Fink, Wolfgang

2009-05-01

Artificial neural networks (ANNs) are powerful methods for the classification of multi-dimensional data as well as for the control of dynamic systems. In general terms, ANNs consist of neurons that are, e.g., arranged in layers and interconnected by real-valued or binary neural couplings or weights. ANNs try mimicking the processing taking place in biological brains. The classification and generalization capabilities of ANNs are given by the interconnection architecture and the coupling strengths. To perform a certain classification or control task with a particular ANN architecture (i.e., number of neurons, number of layers, etc.), the inter-neuron couplings and their accordant coupling strengths must be determined (1) either by a priori design (i.e., manually) or (2) using training algorithms such as error back-propagation. The more complex the classification or control task, the less obvious it is how to determine an a priori design of an ANN, and, as a consequence, the architecture choice becomes somewhat arbitrary. Furthermore, rather than being able to determine for a given architecture directly the corresponding coupling strengths necessary to perform the classification or control task, these have to be obtained/learned through training of the ANN on test data. We report on the use of a Stochastic Optimization Framework (SOF; Fink, SPIE 2008) for the autonomous self-configuration of Artificial Neural Networks (i.e., the determination of number of hidden layers, number of neurons per hidden layer, interconnections between neurons, and respective coupling strengths) for performing classification or control tasks. This may provide an approach towards cognizant and self-adapting computing architectures and systems.

Artificial neural network approach to predict surgical site infection after free-flap reconstruction in patients receiving surgery for head and neck cancer.

PubMed

Kuo, Pao-Jen; Wu, Shao-Chun; Chien, Peng-Chen; Chang, Shu-Shya; Rau, Cheng-Shyuan; Tai, Hsueh-Ling; Peng, Shu-Hui; Lin, Yi-Chun; Chen, Yi-Chun; Hsieh, Hsiao-Yun; Hsieh, Ching-Hua

2018-03-02

The aim of this study was to develop an effective surgical site infection (SSI) prediction model in patients receiving free-flap reconstruction after surgery for head and neck cancer using artificial neural network (ANN), and to compare its predictive power with that of conventional logistic regression (LR). There were 1,836 patients with 1,854 free-flap reconstructions and 438 postoperative SSIs in the dataset for analysis. They were randomly assigned tin ratio of 7:3 into a training set and a test set. Based on comprehensive characteristics of patients and diseases in the absence or presence of operative data, prediction of SSI was performed at two time points (pre-operatively and post-operatively) with a feed-forward ANN and the LR models. In addition to the calculated accuracy, sensitivity, and specificity, the predictive performance of ANN and LR were assessed based on area under the curve (AUC) measures of receiver operator characteristic curves and Brier score. ANN had a significantly higher AUC (0.892) of post-operative prediction and AUC (0.808) of pre-operative prediction than LR (both P <0.0001). In addition, there was significant higher AUC of post-operative prediction than pre-operative prediction by ANN (p<0.0001). With the highest AUC and the lowest Brier score (0.090), the post-operative prediction by ANN had the highest overall predictive performance. The post-operative prediction by ANN had the highest overall performance in predicting SSI after free-flap reconstruction in patients receiving surgery for head and neck cancer.

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%.

Forecasting the prognosis of choroidal melanoma with an artificial neural network.

PubMed

Kaiserman, Igor; Rosner, Mordechai; Pe'er, Jacob

2005-09-01

To develop an artificial neural network (ANN) that will forecast the 5-year mortality from choroidal melanoma. Retrospective, comparative, observational cohort study. One hundred fifty-three eyes of 153 consecutive patients with choroidal melanoma (age, 58.4+/-14.6 years) who were treated with ruthenium 106 brachytherapy between 1988 and 1998 at the Department of Ophthalmology, Hadassah University Hospital, Jerusalem, Israel. Patients were observed clinically and ultrasonographically (A- and B-mode standardized ultrasonography). Metastatic screening included liver function tests and liver imaging. Backpropagation ANNs composed of 3 or 4 layers of neurons with various types of transfer functions and training protocols were assessed for their ability to predict the 5-year mortality. The ANNs were trained on 77 randomly selected patients and tested on a different set of 76 patients. Artificial neural networks were compared based on their sensitivity, specificity, forecasting accuracy, area under the receiver operating curves, and likelihood ratios (LRs). The best ANN was compared with the results of logistic regression and the performance of an ocular oncologist. The ability of the ANNs to forecast the 5-year mortality from choroidal melanoma. Thirty-one patients died during the follow-up period of metastatic choroidal melanoma. The best ANN (one hidden layer of 16 neurons) had 84% forecasting accuracy and an LR of 31.5. The number of hidden neurons significantly influenced the ANNs' performance (P<0.001). The performance of the ANNs was not significantly influenced by the training protocol, the number of hidden layers, or the type of transfer function. In comparison, logistic regression reached 86% forecasting accuracy, with a very low LR (0.8), whereas the human expert forecasting ability was <70% (LR, 1.85). Artificial neural networks can be used for forecasting the prognosis of choroidal melanoma and may support decision-making in treating this malignancy.

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

A Squeezed Artificial Neural Network for the Symbolic Network Reliability Functions of Binary-State Networks.

PubMed

Yeh, Wei-Chang

Network reliability is an important index to the provision of useful information for decision support in the modern world. There is always a need to calculate symbolic network reliability functions (SNRFs) due to dynamic and rapid changes in network parameters. In this brief, the proposed squeezed artificial neural network (SqANN) approach uses the Monte Carlo simulation to estimate the corresponding reliability of a given designed matrix from the Box-Behnken design, and then the Taguchi method is implemented to find the appropriate number of neurons and activation functions of the hidden layer and the output layer in ANN to evaluate SNRFs. According to the experimental results of the benchmark networks, the comparison appears to support the superiority of the proposed SqANN method over the traditional ANN-based approach with at least 16.6% improvement in the median absolute deviation in the cost of extra 2 s on average for all experiments.Network reliability is an important index to the provision of useful information for decision support in the modern world. There is always a need to calculate symbolic network reliability functions (SNRFs) due to dynamic and rapid changes in network parameters. In this brief, the proposed squeezed artificial neural network (SqANN) approach uses the Monte Carlo simulation to estimate the corresponding reliability of a given designed matrix from the Box-Behnken design, and then the Taguchi method is implemented to find the appropriate number of neurons and activation functions of the hidden layer and the output layer in ANN to evaluate SNRFs. According to the experimental results of the benchmark networks, the comparison appears to support the superiority of the proposed SqANN method over the traditional ANN-based approach with at least 16.6% improvement in the median absolute deviation in the cost of extra 2 s on average for all experiments.

MO-G-18C-05: Real-Time Prediction in Free-Breathing Perfusion MRI

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

Song, H; Liu, W; Ruan, D

Purpose: The aim is to minimize frame-wise difference errors caused by respiratory motion and eliminate the need for breath-holds in magnetic resonance imaging (MRI) sequences with long acquisitions and repeat times (TRs). The technique is being applied to perfusion MRI using arterial spin labeling (ASL). Methods: Respiratory motion prediction (RMP) using navigator echoes was implemented in ASL. A least-square method was used to extract the respiratory motion information from the 1D navigator. A generalized artificial neutral network (ANN) with three layers was developed to simultaneously predict 10 time points forward in time and correct for respiratory motion during MRI acquisition.more » During the training phase, the parameters of the ANN were optimized to minimize the aggregated prediction error based on acquired navigator data. During realtime prediction, the trained ANN was applied to the most recent estimated displacement trajectory to determine in real-time the amount of spatial Results: The respiratory motion information extracted from the least-square method can accurately represent the navigator profiles, with a normalized chi-square value of 0.037±0.015 across the training phase. During the 60-second training phase, the ANN successfully learned the respiratory motion pattern from the navigator training data. During real-time prediction, the ANN received displacement estimates and predicted the motion in the continuum of a 1.0 s prediction window. The ANN prediction was able to provide corrections for different respiratory states (i.e., inhalation/exhalation) during real-time scanning with a mean absolute error of < 1.8 mm. Conclusion: A new technique enabling free-breathing acquisition during MRI is being developed. A generalized ANN development has demonstrated its efficacy in predicting a continuum of motion profile for volumetric imaging based on navigator inputs. Future work will enhance the robustness of ANN and verify its effectiveness with human subjects. Research supported by National Institutes of Health National Cancer Institute Grant R01 CA159471-01.« less

Application of artificial neural network to predict clay sensitivity in a high landslide prone area using CPTu data- A case study in Southwest of Sweden

NASA Astrophysics Data System (ADS)

Shahri, Abbas; Mousavinaseri, Mahsasadat; Naderi, Shima; Espersson, Maria

2015-04-01

Application of Artificial Neural Networks (ANNs) in many areas of engineering, in particular to geotechnical engineering problems such as site characterization has demonstrated some degree of success. The present paper aims to evaluate the feasibility of several various types of ANN models to predict the clay sensitivity of soft clays form piezocone penetration test data (CPTu). To get the aim, a research database of CPTu data of 70 test points around the Göta River near the Lilli Edet in the southwest of Sweden which is a high prone land slide area were collected and considered as input for ANNs. For training algorithms the quick propagation, conjugate gradient descent, quasi-Newton, limited memory quasi-Newton and Levenberg-Marquardt were developed tested and trained using the CPTu data to provide a comparison between the results of field investigation and ANN models to estimate the clay sensitivity. The reason of using the clay sensitivity parameter in this study is due to its relation to landslides in Sweden.A special high sensitive clay namely quick clay is considered as the main responsible for experienced landslides in Sweden which has high sensitivity and prone to slide. The training and testing program was started with 3-2-1 ANN architecture structure. By testing and trying several various architecture structures and changing the hidden layer in order to have a higher output resolution the 3-4-4-3-1 architecture structure for ANN in this study was confirmed. The tested algorithm showed that increasing the hidden layers up to 4 layers in ANN can improve the results and the 3-4-4-3-1 architecture structure ANNs for prediction of clay sensitivity represent reliable and reasonable response. The obtained results showed that the conjugate gradient descent algorithm with R2=0.897 has the best performance among the tested algorithms. Keywords: clay sensitivity, landslide, Artificial Neural Network

A modified artificial neural network based prediction technique for tropospheric radio refractivity

PubMed Central

Javeed, Shumaila; Javed, Wajahat; Atif, M.; Uddin, Mueen

2018-01-01

Radio refractivity plays a significant role in the development and design of radio systems for attaining the best level of performance. Refractivity in the troposphere is one of the features affecting electromagnetic waves, and hence the communication system interrupts. In this work, a modified artificial neural network (ANN) based model is applied to predict the refractivity. The suggested ANN model comprises three modules: the data preparation module, the feature selection module, and the forecast module. The first module applies pre-processing to make the data compatible for the feature selection module. The second module discards irrelevant and redundant data from the input set. The third module uses ANN for prediction. The ANN model applies a sigmoid activation function and a multi-variate auto regressive model to update the weights during the training process. In this work, the refractivity is predicted and estimated based on ten years (2002–2011) of meteorological data, such as the temperature, pressure, and humidity, obtained from the Pakistan Meteorological Department (PMD), Islamabad. The refractivity is estimated using the method suggested by the International Telecommunication Union (ITU). The refractivity is predicted for the year 2012 using the database of the previous ten years, with the help of ANN. The ANN model is implemented in MATLAB. Next, the estimated and predicted refractivity levels are validated against each other. The predicted and actual values (PMD data) of the atmospheric parameters agree with each other well, and demonstrate the accuracy of the proposed ANN method. It was further found that all parameters have a strong relationship with refractivity, in particular the temperature and humidity. The refractivity values are higher during the rainy season owing to a strong association with the relative humidity. Therefore, it is important to properly cater the signal communication system during hot and humid weather. Based on the results, the proposed ANN method can be used to develop a refractivity database, which is highly important in a radio communication system. PMID:29494609

Genuine worker participation-an indispensable key to effective global OHS.

PubMed

Brown, Garrett

2009-01-01

Working conditions, including workplace safety, in global supply chains of products sold by transnational corporations have only marginally improved over the last 15 years despite the development of hundreds of corporate "codes of conduct," code monitoring systems, and an elaborate new "corporate social responsibility" industry. The two underlying reasons for the lack of significant change are: 1) a schizophrenic business model which fatally undermines "socially responsible" sourcing programs with unyielding dictates for the lowest possible production costs; and 2) the lack of any meaningful participation by shop-floor workers in plant safety programs. Only when trained, empowered, and active workers are an integral part of workplace safety programs will conditions improve over the long term.

MODFLOW 2.0: A program for predicting moderator flow patterns

NASA Astrophysics Data System (ADS)

Peterson, P. F.; Paik, I. K.

1991-07-01

Sudden changes in the temperature of flowing liquids can result in transient buoyancy forces which strongly impact the flow hydrodynamics via flow stratification. These effects have been studied for the case of potential flow of stratified liquids to line sinks, but not for moderator flow in SRS reactors. Standard codes, such as TRAC and COMMIX, do not have the capability to capture the stratification effect, due to strong numerical diffusion which smears away the hot/cold fluid interface. A related problem with standard codes is the inability to track plumes injected into the liquid flow, again due to numerical diffusion. The combined effects of buoyant stratification and plume dispersion have been identified as being important in the operation of the Supplementary Safety System which injects neutron-poison ink into SRS reactors to provide safe shutdown in the event of safety rod failure. The MODFLOW code discussed here provides transient moderator flow pattern information with stratification effects, and tracks the location of ink plumes in the reactor. The code, written in Fortran, is compiled for Macintosh II computers, and includes subroutines for interactive control and graphical output. Removing the graphics capabilities, the code can also be compiled on other computers. With graphics, in addition to the capability to perform safety related computations, MODFLOW also provides an easy tool for becoming familiar with flow distributions in SRS reactors.

Improving short-term forecasting during ramp events by means of Regime-Switching Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Gallego, C.; Costa, A.; Cuerva, A.

2010-09-01

Since nowadays wind energy can't be neither scheduled nor large-scale storaged, wind power forecasting has been useful to minimize the impact of wind fluctuations. In particular, short-term forecasting (characterised by prediction horizons from minutes to a few days) is currently required by energy producers (in a daily electricity market context) and the TSO's (in order to keep the stability/balance of an electrical system). Within the short-term background, time-series based models (i.e., statistical models) have shown a better performance than NWP models for horizons up to few hours. These models try to learn and replicate the dynamic shown by the time series of a certain variable. When considering the power output of wind farms, ramp events are usually observed, being characterized by a large positive gradient in the time series (ramp-up) or negative (ramp-down) during relatively short time periods (few hours). Ramp events may be motivated by many different causes, involving generally several spatial scales, since the large scale (fronts, low pressure systems) up to the local scale (wind turbine shut-down due to high wind speed, yaw misalignment due to fast changes of wind direction). Hence, the output power may show unexpected dynamics during ramp events depending on the underlying processes; consequently, traditional statistical models considering only one dynamic for the hole power time series may be inappropriate. This work proposes a Regime Switching (RS) model based on Artificial Neural Nets (ANN). The RS-ANN model gathers as many ANN's as different dynamics considered (called regimes); a certain ANN is selected so as to predict the output power, depending on the current regime. The current regime is on-line updated based on a gradient criteria, regarding the past two values of the output power. 3 Regimes are established, concerning ramp events: ramp-up, ramp-down and no-ramp regime. In order to assess the skillness of the proposed RS-ANN model, a single-ANN model (without regime classification) is adopted as a reference model. Both models are evaluated in terms of Improvement over Persistence on the Mean Square Error basis (IoP%) when predicting horizons form 1 time-step to 5. The case of a wind farm located in the complex terrain of Alaiz (north of Spain) has been considered. Three years of available power output data with a hourly resolution have been employed: two years for training and validation of the model and the last year for assessing the accuracy. Results showed that the RS-ANN overcame the single-ANN model for one step-ahead forecasts: the overall IoP% was up to 8.66% for the RS-ANN model (depending on the gradient criterion selected to consider the ramp regime triggered) and 6.16% for the single-ANN. However, both models showed similar accuracy for larger horizons. A locally-weighted evaluation during ramp events for one-step ahead was also performed. It was found that the IoP% during ramps-up increased from 17.60% (case of single-ANN) to 22.25% (case of RS-ANN); however, during the ramps-down events this improvement increased from 18.55% to 19.55%. Three main conclusions are derived from this case study: It highlights the importance of considering statistical models capable of differentiate several regimes showed by the output power time series in order to improve the forecasting during extreme events like ramps. On-line regime classification based on available power output data didn't seem to contribute to improve forecasts for horizons beyond one-step ahead. Tacking into account other explanatory variables (local wind measurements, NWP outputs) could lead to a better understanding of ramp events, improving the regime assessment also for further horizons. The RS-ANN model slightly overcame the single-ANN during ramp-down events. If further research reinforce this effect, special attention should be addressed to understand the underlying processes during ramp-down events.

[Occupational medicine: practice and ethical requirements of the new law on health and safety in the workplace (legislative decree 81/2008)].

PubMed

Franco, Giuliano; Mora, Erika

2009-01-01

Decisions in occupational health may involve ethical conflicts arising from conflicts between stakeholders' interests. Codes of ethics can provide a practical guide to solve dilemmas. The new law on health and safety in the workplace in Italy (decree 81/2008) states that occupational health practice must comply with the code of ethics of the International Commission on Occupational Health. The universally acknowledged ethical principles of beneficience/nonmaleficience, autonomy and justice, which are the basis of the Charter of fundamental rights of the European Union, inspired this code. Although the code is not a systematic textbook of occupational health ethics and does not cover all possible aspects arising from the practice, making decisions based on it will assure their effectiveness and compliance with ethical principles, besides the formal respect of the law.

TOOKUIL: A case study in user interface development for safety code application

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

Gray, D.L.; Harkins, C.K.; Hoole, J.G.

1997-07-01

Traditionally, there has been a very high learning curve associated with using nuclear power plant (NPP) analysis codes. Even for seasoned plant analysts and engineers, the process of building or modifying an input model for present day NPP analysis codes is tedious, error prone, and time consuming. Current cost constraints and performance demands place an additional burden on today`s safety analysis community. Advances in graphical user interface (GUI) technology have been applied to obtain significant productivity and quality assurance improvements for the Transient Reactor Analysis Code (TRAC) input model development. KAPL Inc. has developed an X Windows-based graphical user interfacemore » named TOOKUIL which supports the design and analysis process, acting as a preprocessor, runtime editor, help system, and post processor for TRAC. This paper summarizes the objectives of the project, the GUI development process and experiences, and the resulting end product, TOOKUIL.« less

School Dress Codes and Uniform Policies.

ERIC Educational Resources Information Center

Anderson, Wendell

2002-01-01

Opinions abound on what students should wear to class. Some see student dress as a safety issue; others see it as a student-rights issue. The issue of dress codes and uniform policies has been tackled in the classroom, the boardroom, and the courtroom. This Policy Report examines the whole fabric of the debate on dress codes and uniform policies…

Rural School District Dress Code Implementation: Perceptions of Stakeholders after First Year

ERIC Educational Resources Information Center

Wright, Krystal M.

2012-01-01

Schools are continuously searching for solutions to solve truancy, academic, behavioral, safety, and climate issues. One of the latest trends in education is requiring students to adhere to dress codes as a solution to these issues. Dress codes can range from slightly restrictive clothing to the requiring of a uniform. Many school district…

The role of the PIRT process in identifying code improvements and executing code development

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

Wilson, G.E.; Boyack, B.E.

1997-07-01

In September 1988, the USNRC issued a revised ECCS rule for light water reactors that allows, as an option, the use of best estimate (BE) plus uncertainty methods in safety analysis. The key feature of this licensing option relates to quantification of the uncertainty in the determination that an NPP has a {open_quotes}low{close_quotes} probability of violating the safety criteria specified in 10 CFR 50. To support the 1988 licensing revision, the USNRC and its contractors developed the CSAU evaluation methodology to demonstrate the feasibility of the BE plus uncertainty approach. The PIRT process, Step 3 in the CSAU methodology, wasmore » originally formulated to support the BE plus uncertainty licensing option as executed in the CSAU approach to safety analysis. Subsequent work has shown the PIRT process to be a much more powerful tool than conceived in its original form. Through further development and application, the PIRT process has shown itself to be a robust means to establish safety analysis computer code phenomenological requirements in their order of importance to such analyses. Used early in research directed toward these objectives, PIRT results also provide the technical basis and cost effective organization for new experimental programs needed to improve the safety analysis codes for new applications. The primary purpose of this paper is to describe the generic PIRT process, including typical and common illustrations from prior applications. The secondary objective is to provide guidance to future applications of the process to help them focus, in a graded approach, on systems, components, processes and phenomena that have been common in several prior applications.« less

Examination of adult and child bicyclist safety-relevant events using naturalistic bicycling methodology.

PubMed

Hamann, Cara J; Peek-Asa, Corinne

2017-05-01

Among roadway users, bicyclists are considered vulnerable due to their high risk for injury when involved in a crash. Little is known about the circumstances leading to near crashes, crashes, and related injuries or how these vary by age and gender. The purpose of this study was to examine the rates and characteristics of safety-relevant events (crashes, near crashes, errors, and traffic violations) among adult and child bicyclists. Bicyclist trips were captured using Pedal Portal, a data acquisition and coding system which includes a GPS-enabled video camera and graphical user interface. A total of 179 safety-relevant events were manually coded from trip videos. Overall, child errors and traffic violations occurred at a rate of 1.9 per 100min of riding, compared to 6.3 for adults. However, children rode on the sidewalk 56.4% of the time, compared with 12.7% for adults. For both adults and children, the highest safety-relevant event rates occurred on paved roadways with no bicycle facilities present (Adults=8.6 and Children=7.2, per 100min of riding). Our study, the first naturalistic study to compare safety-relevant events among adults and children, indicates large variation in riding behavior and exposure between child and adult bicyclists. The majority of identified events were traffic violations and we were not able to code all risk-relevant data (e.g., subtle avoidance behaviors, failure to check for traffic, probability of collision). Future naturalistic cycling studies would benefit from enhanced instrumentation (e.g., additional camera views) and coding protocols able to fill these gaps. Copyright © 2017 Elsevier Ltd. All rights reserved.

Matrix Concentration Inequalities via the Method of Exchangeable Pairs

DTIC Science & Technology

2012-01-27

viewed as an exchangeable pairs version of the Burkholder –Davis–Gundy (BDG) inequality from classical martingale theory [Bur73]. Matrix extensions of...non-commutative probability. Math. Ann., 319:1–16, 2001. [Bur73] D. L. Burkholder . Distribution function inequalities for martingales. Ann. Probab., 1...Statist. Assoc., 58(301):13–30, 1963. [JX03] M. Junge and Q. Xu. Noncommutative Burkholder /Rosenthal inequalities. Ann. Probab., 31(2):948–995, 2003

RELAP-7 Development Updates

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

Zhang, Hongbin; Zhao, Haihua; Gleicher, Frederick Nathan

RELAP-7 is a nuclear systems safety analysis code being developed at the Idaho National Laboratory, and is the next generation tool in the RELAP reactor safety/systems analysis application series. RELAP-7 development began in 2011 to support the Risk Informed Safety Margins Characterization (RISMC) Pathway of the Light Water Reactor Sustainability (LWRS) program. The overall design goal of RELAP-7 is to take advantage of the previous thirty years of advancements in computer architecture, software design, numerical methods, and physical models in order to provide capabilities needed for the RISMC methodology and to support nuclear power safety analysis. The code is beingmore » developed based on Idaho National Laboratory’s modern scientific software development framework – MOOSE (the Multi-Physics Object-Oriented Simulation Environment). The initial development goal of the RELAP-7 approach focused primarily on the development of an implicit algorithm capable of strong (nonlinear) coupling of the dependent hydrodynamic variables contained in the 1-D/2-D flow models with the various 0-D system reactor components that compose various boiling water reactor (BWR) and pressurized water reactor nuclear power plants (NPPs). During Fiscal Year (FY) 2015, the RELAP-7 code has been further improved with expanded capability to support boiling water reactor (BWR) and pressurized water reactor NPPs analysis. The accumulator model has been developed. The code has also been coupled with other MOOSE-based applications such as neutronics code RattleSnake and fuel performance code BISON to perform multiphysics analysis. A major design requirement for the implicit algorithm in RELAP-7 is that it is capable of second-order discretization accuracy in both space and time, which eliminates the traditional first-order approximation errors. The second-order temporal is achieved by a second-order backward temporal difference, and the one-dimensional second-order accurate spatial discretization is achieved with the Galerkin approximation of Lagrange finite elements. During FY-2015, we have done numerical verification work to verify that the RELAP-7 code indeed achieves 2nd-order accuracy in both time and space for single phase models at the system level.« less

Improving the safety of street-vended food.

PubMed

Moy, G; Hazzard, A; Käferstein, F

1997-01-01

An integrated plan of action for improving street food involving health and other regulatory authorities, vendors and consumers should address not only food safety, but also environmental health management, including consideration of inadequate sanitation and waste management, possible environmental pollution, congestion and disturbances to traffic. However, WHO cautions that, in view of their importance in the diets of urban populations, particularly the socially disadvantaged, every effort should be made to preserve the benefits provided by varied, inexpensive and often nutritious street food. Therefore, authorities concerned with street food management must balance efforts aimed at reducing the negative aspects on the environment with the benefits of street food and its important role in the community. Health authorities charged with responsibility for food safety control should match risk management action to the level of assessed risk. The rigorous application of codes and enforcement of regulations more suited to larger and permanent food service establishments is unlikely to be justifiable. Such rigorous application of codes and regulations may result in disappearance of the trade with consequent aggravation of hunger and malnutrition. Moreover, most codes and regulations have not been based on any systematic identification and assessment of health hazards associated with different types of foods and operations as embodied in the HACCP approach which has been recognized by Codex as the most cost-effective means for promoting food safety. WHO encourages the development of regulations that empower vendors to take greater responsibility for the preparation of safe food, and of codes of practice based on the HACCP system.

Biological and Physical Space Research Laboratory 2002 Science Review

NASA Technical Reports Server (NTRS)

Curreri, P. A. (Editor); Robinson, M. B. (Editor); Murphy, K. L. (Editor)

2003-01-01

With the International Space Station Program approaching core complete, our NASA Headquarters sponsor, the new Code U Enterprise, Biological and Physical Research, is shifting its research emphasis from purely fundamental microgravity and biological sciences to strategic research aimed at enabling human missions beyond Earth orbit. Although we anticipate supporting microgravity research on the ISS for some time to come, our laboratory has been vigorously engaged in developing these new strategic research areas.This Technical Memorandum documents the internal science research at our laboratory as presented in a review to Dr. Ann Whitaker, MSFC Science Director, in July 2002. These presentations have been revised and updated as appropriate for this report. It provides a snapshot of the internal science capability of our laboratory as an aid to other NASA organizations and the external scientific community.

Neural classification of the selected family of butterflies

NASA Astrophysics Data System (ADS)

Zaborowicz, M.; Boniecki, P.; Piekarska-Boniecka, H.; Koszela, K.; Mueller, W.; Górna, K.; Okoń, P.

2017-07-01

There have been noticed growing explorers' interest in drawing conclusions based on information of data coded in a graphic form. The neuronal identification of pictorial data, with special emphasis on both quantitative and qualitative analysis, is more frequently utilized to gain and deepen the empirical data knowledge. Extraction and then classification of selected picture features, such as color or surface structure, enables one to create computer tools in order to identify these objects presented as, for example, digital pictures. The work presents original computer system "Processing the image v.1.0" designed to digitalize pictures on the basis of color criterion. The system has been applied to generate a reference learning file for generating the Artificial Neural Network (ANN) to identify selected kinds of butterflies from the Papilionidae family.

Effect of synapse dilution on the memory retrieval in structured attractor neural networks

NASA Astrophysics Data System (ADS)

Brunel, N.

1993-08-01

We investigate a simple model of structured attractor neural network (ANN). In this network a module codes for the category of the stored information, while another group of neurons codes for the remaining information. The probability distribution of stabilities of the patterns and the prototypes of the categories are calculated, for two different synaptic structures. The stability of the prototypes is shown to increase when the fraction of neurons coding for the category goes down. Then the effect of synapse destruction on the retrieval is studied in two opposite situations : first analytically in sparsely connected networks, then numerically in completely connected ones. In both cases the behaviour of the structured network and that of the usual homogeneous networks are compared. When lesions increase, two transitions are shown to appear in the behaviour of the structured network when one of the patterns is presented to the network. After the first transition the network recognizes the category of the pattern but not the individual pattern. After the second transition the network recognizes nothing. These effects are similar to syndromes caused by lesions in the central visual system, namely prosopagnosia and agnosia. In both types of networks (structured or homogeneous) the stability of the prototype is greater than the stability of individual patterns, however the first transition, for completely connected networks, occurs only when the network is structured.

75 FR 33696 - Safety Zone: July Firework Display in Captain of the Port, Puget Sound AOR

Federal Register 2010, 2011, 2012, 2013, 2014

2010-06-15

...-AA00 Safety Zone: July Firework Display in Captain of the Port, Puget Sound AOR AGENCY: Coast Guard... Captain of the Port, Puget Sound AOR. (a) Safety Zone. The following area is a designated safety zone: all..., Captain of the Port, Puget Sound. [FR Doc. 2010-14294 Filed 6-14-10; 8:45 am] BILLING CODE 9110-04-P ...

EXPERIENCES FROM THE SOURCE-TERM ANALYSIS OF A LOW AND INTERMEDIATE LEVEL RADWASTE DISPOSAL FACILITY

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

Park,Jin Beak; Park, Joo-Wan; Lee, Eun-Young

2003-02-27

Enhancement of a computer code SAGE for evaluation of the Korean concept for a LILW waste disposal facility is discussed. Several features of source term analysis are embedded into SAGE to analyze: (1) effects of degradation mode of an engineered barrier, (2) effects of dispersion phenomena in the unsaturated zone and (3) effects of time dependent sorption coefficient in the unsaturated zone. IAEA's Vault Safety Case (VSC) approach is used to demonstrate the ability of this assessment code. Results of MASCOT are used for comparison purposes. These enhancements of the safety assessment code, SAGE, can contribute to realistic evaluation ofmore » the Korean concept of the LILW disposal project in the near future.« less

Revenue sources for financing transportation safety activities in Virginia : phase three, feasibility of a surcharge on traffic fines.

DOT National Transportation Integrated Search

1981-01-01

Senate Bill 85, an action of the 1978 General Assembly, amended the Code of Virginia to provide, in part, that the Division of Highway Safety be succeeded by the newly created Department of Transportation Safety effective July 1, 1978. In its Declara...

78 FR 11092 - Safety and Health Regulations for Construction

Federal Register 2010, 2011, 2012, 2013, 2014

2013-02-15

... LABOR DEPARTMENT Occupational Safety and Health Administration 29 CFR Part 1926 Safety and Health Regulations for Construction CFR Correction In Title 29 of the Code of Federal Regulations, Part 1926, revised as of July 1, 2012, on page 225, in Sec. 1926.152, paragraph (c)(16) is added to read as follows: Sec...

75 FR 33162 - Airworthiness Directives; Microturbo Saphir 20 Model 095 Auxiliary Power Units (APUs)

Federal Register 2010, 2011, 2012, 2013, 2014

2010-06-11

... information (MCAI) issued by the European Aviation Safety Agency (EASA) to identify and correct an unsafe... States Code specifies the FAA's authority to issue rules on aviation safety. Subtitle I, section 106... the AD docket. List of Subjects in 14 CFR Part 39 Air transportation, Aircraft, Aviation safety...

Predictive modelling for startup and investor relationship based on crowdfunding platform data

NASA Astrophysics Data System (ADS)

Alamsyah, Andry; Buono Asto Nugroho, Tri

2018-03-01

Crowdfunding platform is a place where startup shows off publicly their idea for the purpose to get their project funded. Crowdfunding platform such as Kickstarter are becoming popular today, it provides the efficient way for startup to get funded without liabilities, it also provides variety project category that can be participated. There is an available safety procedure to ensure achievable low-risk environment. The startup promoted project must accomplish their funded goal target. If they fail to reach the target, then there is no investment activity take place. It motivates startup to be more active to promote or disseminate their project idea and it also protect investor from losing money. The study objective is to predict the successfulness of proposed project and mapping investor trend using data mining framework. To achieve the objective, we proposed 3 models. First model is to predict whether a project is going to be successful or failed using K-Nearest Neighbour (KNN). Second model is to predict the number of successful project using Artificial Neural Network (ANN). Third model is to map the trend of investor in investing the project using K-Means clustering algorithm. KNN gives 99.04% model accuracy, while ANN best configuration gives 16-14-1 neuron layers and 0.2 learning rate, and K-Means gives 6 best separation clusters. The results of those models can help startup or investor to make decision regarding startup investment.