Bit selection using field drilling data and mathematical investigation

NASA Astrophysics Data System (ADS)

Momeni, M. S.; Ridha, S.; Hosseini, S. J.; Meyghani, B.; Emamian, S. S.

2018-03-01

A drilling process will not be complete without the usage of a drill bit. Therefore, bit selection is considered to be an important task in drilling optimization process. To select a bit is considered as an important issue in planning and designing a well. This is simply because the cost of drilling bit in total cost is quite high. Thus, to perform this task, aback propagation ANN Model is developed. This is done by training the model using several wells and it is done by the usage of drilling bit records from offset wells. In this project, two models are developed by the usage of the ANN. One is to find predicted IADC bit code and one is to find Predicted ROP. Stage 1 was to find the IADC bit code by using all the given filed data. The output is the Targeted IADC bit code. Stage 2 was to find the Predicted ROP values using the gained IADC bit code in Stage 1. Next is Stage 3 where the Predicted ROP value is used back again in the data set to gain Predicted IADC bit code value. The output is the Predicted IADC bit code. Thus, at the end, there are two models that give the Predicted ROP values and Predicted IADC bit code values.

Integrated Optics Anisotropic Waveguides and Devices

DTIC Science & Technology

1989-04-30

INTEGRATED OPTICS ANISOTROPIC WAVEGUIDES AND DEVICESto N FINAL REPORT Thomas K. Gaylord April 30, 1989 U. S. ARMY RESEARCH OFFICE Grant Number...DAAL03-86-K-0157 Georgia Institute of Technology ELECTE S JAN2 2 1990 APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED. 90 01 22 13j4 THE VIEW, OPINIONS...Electrical Engr. (if appicable) Georgia Institute of Technolog] U. S. Army Research Office k. ADDRESS (City, State, a"d ZIP Code) 7b. ADDRESS (City, State

Verification of the Hydrodynamic and Sediment Transport Hybrid Modeling System for Cumberland Sound and Kings Bay Navigation Channel, Georgia

DTIC Science & Technology

1989-07-01

TECHNICAL REPORT HL-89-14 VERIFICATION OF THE HYDRODYNAMIC AND Si SEDIMENT TRANSPORT HYBRID MODELING SYSTEM FOR CUMBERLAND SOUND AND I’) KINGS BAY...Hydrodynamic and Sediment Transport Hybrid Modeling System for Cumberland Sound and Kings Bay Navigation Channel, Georgia 12 PERSONAL AUTHOR(S) Granat...Hydrodynamic results from RMA-2V were used in the numerical sediment transport code STUDH in modeling the interaction of the flow transport and

Installation Restoration Program. Phase II. Confirmation/Quantification. Stage 1 for Air Force Plant 6, Cobb County, Georgia. Volume 2.

DTIC Science & Technology

1985-08-09

FL UNLSSIFIED C R NEFF ET AL 99 AUG 95 F33615-84-D-4491 F/O 13/2 NLEmhANNE.mmmmhhhhl smhmhhhhmmhhls mhmmhmmhhhhhl...Occupational and Environmental Gainesville, FL 32602-3052 Health Laboratory I Brookq -kir ForrA R"Pn ’’y 1:;~ Sa. NAME OF FUNOINGiSPONSORING 8b. OFFICE...z ul N z~ I. * 4 11 z z 41 ( z a FL - pJ 0 :, w I.. 0 I z. z w \\, Cl) - u zzw JJ 4,..- 4.0 .J.. a A7 0 z I .4-i~ I _ _ __ _ _ C4.. % 0- .1 z IH~ID3M A

Teachers' Perceptions of the Effect of Their Attire on Middle-School Students' Behavior and Learning

ERIC Educational Resources Information Center

Sampson, Elizabeth Clemons

2016-01-01

Teachers were once held to a professional dress code. This code has become lax, resulting in teachers dressing in more casual attire. A local middle school in rural Georgia was experiencing complaints about teachers' unprofessional attire from other teachers, administrators, and parents. Teachers play an integral role in modeling cultural and…

Comparison of asthma prevalence among African American teenage youth attending public high schools in rural Georgia and urban Detroit.

PubMed

Ownby, Dennis R; Tingen, Martha S; Havstad, Suzanne; Waller, Jennifer L; Johnson, Christine C; Joseph, Christine L M

2015-09-01

The high prevalence of asthma among urban African American (AA) populations has attracted research attention, whereas the prevalence among rural AA populations is poorly documented. We sought to compare the prevalence of asthma among AA youth in rural Georgia and urban Detroit, Michigan. The prevalence of asthma was compared in population-based samples of 7297 youth attending Detroit public high schools and in 2523 youth attending public high schools in rural Georgia. Current asthma was defined as a physician diagnosis and symptoms in the previous 12 months. Undiagnosed asthma was defined as multiple respiratory symptoms in the previous 12 months without a physician diagnosis. In Detroit, 6994 (95.8%) youth were AA compared with 1514 (60.0%) in Georgia. Average population density in high school postal codes was 5628 people/mile(2) in Detroit and 45.1 people/mile(2) in Georgia. The percentages of poverty and of students qualifying for free or reduced lunches were similar in both areas. The prevalence of current diagnosed asthma among AA youth in Detroit and Georgia was similar: 15.0% (95% CI, 14.1-15.8) and 13.7% (95% CI, 12.0-17.1) (P = .22), respectively. The prevalence of undiagnosed asthma in AA youth was 8.0% in Detroit and 7.5% in Georgia (P = .56). Asthma symptoms were reported more frequently among those with diagnosed asthma in Detroit, whereas those with undiagnosed asthma in Georgia reported more symptoms. Among AA youth living in similar socioeconomic circumstances, asthma prevalence is as high in rural Georgia as it is in urban Detroit, suggesting that urban residence is not an asthma risk factor. Copyright © 2015 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

Improved system identification using artificial neural networks and analysis of individual differences in responses of an identified neuron.

PubMed

Costalago Meruelo, Alicia; Simpson, David M; Veres, Sandor M; Newland, Philip L

2016-03-01

Mathematical modelling is used routinely to understand the coding properties and dynamics of responses of neurons and neural networks. Here we analyse the effectiveness of Artificial Neural Networks (ANNs) as a modelling tool for motor neuron responses. We used ANNs to model the synaptic responses of an identified motor neuron, the fast extensor motor neuron, of the desert locust in response to displacement of a sensory organ, the femoral chordotonal organ, which monitors movements of the tibia relative to the femur of the leg. The aim of the study was threefold: first to determine the potential value of ANNs as tools to model and investigate neural networks, second to understand the generalisation properties of ANNs across individuals and to different input signals and third, to understand individual differences in responses of an identified neuron. A metaheuristic algorithm was developed to design the ANN architectures. The performance of the models generated by the ANNs was compared with those generated through previous mathematical models of the same neuron. The results suggest that ANNs are significantly better than LNL and Wiener models in predicting specific neural responses to Gaussian White Noise, but not significantly different when tested with sinusoidal inputs. They are also able to predict responses of the same neuron in different individuals irrespective of which animal was used to develop the model, although notable differences between some individuals were evident. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Validation of the analytical methods in the LWR code BOXER for gadolinium-loaded fuel pins

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

Paratte, J.M.; Arkuszewski, J.J.; Kamboj, B.K.

1990-01-01

Due to the very high absorption occurring in gadolinium-loaded fuel pins, calculations of lattices with such pins present are a demanding test of the analysis methods in light water reactor (LWR) cell and assembly codes. Considerable effort has, therefore, been devoted to the validation of code methods for gadolinia fuel. The goal of the work reported in this paper is to check the analysis methods in the LWR cell/assembly code BOXER and its associated cross-section processing code ETOBOX, by comparison of BOXER results with those from a very accurate Monte Carlo calculation for a gadolinium benchmark problem. Initial results ofmore » such a comparison have been previously reported. However, the Monte Carlo calculations, done with the MCNP code, were performed at Los Alamos National Laboratory using ENDF/B-V data, while the BOXER calculations were performed at the Paul Scherrer Institute using JEF-1 nuclear data. This difference in the basic nuclear data used for the two calculations, caused by the restricted nature of these evaluated data files, led to associated uncertainties in a comparison of the results for methods validation. In the joint investigations at the Georgia Institute of Technology and PSI, such uncertainty in this comparison was eliminated by using ENDF/B-V data for BOXER calculations at Georgia Tech.« less

Visual Form Detection in 3-Dimensional Space.

DTIC Science & Technology

1982-10-01

RR04209 Ann Arbor, Michigan 48109 RR0429002; NR 197-070 - II CONTROLLING OFFICE NAME AND ADDRESS 12. REPORT DATE Engineering Psychology Group ( Code...93940 Pasadena, CA 91106 Dean of Research Administration Office of Naval Research Naval Postgraduate School Scientific Liaison Group Monterey, CA...Eisenhower Avenue Dr. Gloria Chisum Alexandria, VA 22333 Sciences Research Group Code 6003 Naval Air Development Center Warminste.’, PA 18974 -4- Department

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

22 CFR 228.03 - Identification of principal geographic code numbers.

Code of Federal Regulations, 2011 CFR

2011-04-01

..., Belarus, Canada, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Gabon, Georgia...*, Netherlands, New Zealand, Norway, People's Republic of China, Poland, Portugal, Qatar, Romania, Russia, San Marino, Saudi Arabia, Serbia*, Singapore, Slovak Republic, Slovenia, South Africa, Spain, Sweden...

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.

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.

New distributional records of the stygobitic crayfish Cambarus cryptodytes (Decapoda: Cambaridae) in the Floridan Aquifer System of southwestern Georgia

USGS Publications Warehouse

Fenolio, Dante B.; Niemiller, Matthew L.; Gluesenkamp, Andrew G.; Mckee, Anna; Taylor, Steven J.

2017-01-01

Cambarus cryptodytes (Dougherty Plain Cave Crayfish) is an obligate inhabitant of groundwater habitats (i.e., a stygobiont) with troglomorphic adaptations in the Floridan aquifer system of southwestern Georgia and adjacent Florida panhandle, particularly in the Dougherty Plain and Marianna Lowlands. Documented occurrences of Dougherty Plain Cave Crayfish are spatially distributed as 2 primary clusters separated by a region where few caves and springs have been documented; however, the paucity of humanly accessible karst features in this intermediate region has inhibited investigation of the species' distribution. To work around this constraint, we employed bottle traps to sample for Dougherty Plain Cave Crayfish and other groundwater fauna in 18 groundwater-monitoring wells that access the Floridan aquifer system in 10 counties in southwestern Georgia. We captured 32 Dougherty Plain Cave Crayfish in 9 wells in 8 counties between September 2014 and August 2015. We detected crayfish at depths ranging from 17.9 m to 40.6 m, and established new county records for Early, Miller, Mitchell, and Seminole counties in Georgia, increasing the number of occurrences in Georgia from 8 to 17 sites. In addition, a new US Geological Survey (USGS) Hydrologic Unit Code 8 (HUC8) watershed record was established for the Spring Creek watershed. These new records fill in the distribution gap between the 2 previously known clusters in Georgia and Jackson County, FL. Furthermore, this study demonstrates that deployment of bottle traps in groundwater-monitoring wells can be an effective approach to presence—absence surveys of stygobionts, especially in areas where surface access to groundwater is limited.

40 CFR 147.550 - State-administered program.

Code of Federal Regulations, 2012 CFR

2012-07-01

.... (1) Oil and Gas and Deep Drilling Act of 1975, Official Code of Georgia Annotated (O.C.G.A.) §§ 12-4... 40 Protection of Environment 24 2012-07-01 2012-07-01 false State-administered program. 147.550 Section 147.550 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) WATER PROGRAMS...

40 CFR 147.550 - State-administered program.

Code of Federal Regulations, 2014 CFR

2014-07-01

.... (1) Oil and Gas and Deep Drilling Act of 1975, Official Code of Georgia Annotated (O.C.G.A.) §§ 12-4... 40 Protection of Environment 23 2014-07-01 2014-07-01 false State-administered program. 147.550 Section 147.550 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) WATER PROGRAMS...

40 CFR 147.550 - State-administered program.

Code of Federal Regulations, 2011 CFR

2011-07-01

.... (1) Oil and Gas and Deep Drilling Act of 1975, Official Code of Georgia Annotated (O.C.G.A.) §§ 12-4... 40 Protection of Environment 23 2011-07-01 2011-07-01 false State-administered program. 147.550 Section 147.550 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) WATER PROGRAMS...

Bibliography on Mathematical Abilities.

ERIC Educational Resources Information Center

Kilpatrick, Jeremy; Wagner, Sigrid

The items in this bibliography were collected as part of a project, "An Analysis of Research on Mathematical Abilities," conducted at the University of Georgia. The 1,491 entries in the bibliography are listed alphabetically by author. Each entry is preceded by a line containing a name and date code (used in computerized alphabetizing of…

Research and Diagnostic Applications of Monoclonal Antibodies to Coccidioides immitis.

DTIC Science & Technology

1985-01-01

for Human and Animal Mycology , Georgia, May 1985. 17. COSATI CODES 18. SUBJECT TERMS (Co tinue on reverse if necessary and identify by block number...IX Congress of the International Society for Human and Animal Mycology , Atlanta GA, May 1985. ISHAM START ’IResearch and Diagnostic Applications of

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

76 FR 72980 - Request for Certification of Compliance-Rural Industrialization Loan and Grant Program

Federal Register 2010, 2011, 2012, 2013, 2014

2011-11-28

... 4279-2) for the following: Applicant/Location: Jekyll Island Ocean Front Hotel Principal Product/Purpose: The loan, guarantee, or grant application is to construct a new full service hotel, which will be located in Jekyll Island, Georgia. The NAICS industry code for this enterprise is: 721110 (hotels and...

A Specification Technique for the Common APSE (Ada Programming Support Environments) Interface Set.

DTIC Science & Technology

1984-04-01

NOSC So fTech Code 8322 460 Totten Pond Road San Diego, CA 92152 Waltham, MA 02154 Philip Myers Chuck Waltrip Dave Pasterchik Johns Hopkins University...06856 Georgia Tech Atlanta, GA 30332 Reed Kotler Lockheed Missiles & Space Dick Drake 1111 Lockheed Way IBM Sunnyvale, CA 94086 Federal Systems

Presence of animal feeding operations and community socioeconomic factors impact salmonellosis incidence rates: An ecological analysis using data from the Foodborne Diseases Active Surveillance Network (FoodNet), 2004-2010.

PubMed

Shaw, Kristi S; Cruz-Cano, Raul; Jiang, Chengsheng; Malayil, Leena; Blythe, David; Ryan, Patricia; Sapkota, Amy R

2016-10-01

Nontyphoidal Salmonella spp. are a leading cause of foodborne illness. Risk factors for salmonellosis include the consumption of contaminated chicken, eggs, pork and beef. Agricultural, environmental and socioeconomic factors also have been associated with rates of Salmonella infection. However, to our knowledge, these factors have not been modeled together at the community-level to improve our understanding of whether rates of salmonellosis are variable across communities defined by differing factors. To address this knowledge gap, we obtained data on culture-confirmed Salmonella Typhimurium, S. Enteritidis, S. Newport and S. Javiana cases (2004-2010; n=14,297) from the Foodborne Diseases Active Surveillance Network (FoodNet), and socioeconomic, environmental and agricultural data from the 2010 Census of Population and Housing, the 2011 American Community Survey, and the 2007 U.S. Census of Agriculture. We linked data by zip code and derived incidence rate ratios using negative binomial regressions. Multiple community-level factors were associated with salmonellosis rates; however, our findings varied by state. For example, in Georgia (Incidence Rate Ratio (IRR)=1.01; 95% Confidence Interval (CI)=1.005-1.015) Maryland (IRR=1.01; 95% CI=1.003-1.015) and Tennessee (IRR=1.01; 95% CI=1.002-1.012), zip codes characterized by greater rurality had higher rates of S. Newport infections. The presence of broiler chicken operations, dairy operations and cattle operations in a zip code also was associated with significantly higher rates of infection with at least one serotype in states that are leading producers of these animal products. For instance, in Georgia and Tennessee, rates of S. Enteritidis infection were 48% (IRR=1.48; 95% CI=1.12-1.95) and 46% (IRR=1.46; 95% CI=1.17-1.81) higher in zip codes with broiler chicken operations compared to those without these operations. In Maryland, New Mexico and Tennessee, higher poverty levels in zip codes were associated with higher rates of infection with one or more Salmonella serotypes. In Georgia and Tennessee, zip codes with higher percentages of the population composed of African Americans had significantly higher rates of infection with one or more Salmonella serotypes. In summary, our findings show that community-level agricultural, environmental and socioeconomic factors may be important with regard to rates of infection with Salmonella Typhimurium, Enteritidis, Newport and Javiana. Copyright © 2016 Elsevier Inc. All rights reserved.

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

Contraceptive information on pregnancy resource center websites: a statewide content analysis.

PubMed

Swartzendruber, Andrea; Steiner, Riley J; Newton-Levinson, Anna

2018-04-24

Most pregnancy resource centers (PRCs) in the US are affiliated with national organizations that have policies against promoting or providing contraceptives, yet many provide information about contraception on their websites. In 2016, the state of Georgia passed a new law to publicly fund PRCs. This study sought to describe the contraceptive information on Georgia PRC websites. We systematically identified all accessible Georgia PRC websites April-June 2016. We downloaded entire websites and used defined protocols to code and thematically analyze content about contraceptives. Of the 64 websites reviewed, 20 (31%) presented information about contraceptives. Most of the content was dedicated to emergency contraception. Emphasis on risks and side effects was the most prominent theme. However, no site presented information about the frequency or prevalence of risks and side effects. Sites also emphasized contraceptive failure and minimized effectiveness. We found a high degree of inaccurate and misleading information about contraceptives. Georgia PRC websites presented skewed information that may undermine confidence in the safety and efficacy of contraceptive methods and discourage use. Public funding for PRCs, an increasing national trend, should be rigorously examined. Increased regulation is urgently needed to ensure that online information about contraceptives presented by publicly funded centers is unbiased, complete and accurate. We examined contraceptive information on Georgia PRC websites and found sites minimize benefits and emphasize barriers to use. They contain high levels of medically inaccurate and misleading information that may undermine public health goals. Public funding for PRCs should be rigorously examined; increased regulation is urgently needed. Copyright © 2018 Elsevier Inc. All rights reserved.

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.

MKHITAR GOSH'S MEDIEVAL LAW CODE AND ITS IMPLICATIONS FOR ARMENIAN COMMUNITIES ABROAD.

PubMed

Davtyan, Susanna; Khachatryan, Mikayel; Johrian, Ara; Ghazaryan, Karen

2014-07-01

The Law Book of the medieval Armenian legal and economic thought is an exceptional work that encompasses valuable information of the Armenian nation's domestic life. Mkhitar Gosh was considered to be one of the most outstanding figures and lawyers (lawmakers) of all times. Armenian Law Code after Mkhitar Gosh is writhed at 12 century. One of the primary sources for the law code was Armenian customary law. This Code became moral code for guiding for hall Armenians over the world because of high moral spirit reflecting Armenian mentality. This article presents the brief history of extension of legal rules setting out in the Law Code. The Law Code was established and widely used not only in Armenia but also in a number of Armenian communities abroad (Russian, Poland, Georgia, Latvia, India etc.). Law Code was accepted by all Armenians. Moreover, it served for the development of legislation for a number of civilized European and Asian countries.

New ShakeMaps for Georgia Resulting from Collaboration with EMME

NASA Astrophysics Data System (ADS)

Kvavadze, N.; Tsereteli, N. S.; Varazanashvili, O.; Alania, V.

2015-12-01

Correct assessment of probabilistic seismic hazard and risks maps are first step for advance planning and action to reduce seismic risk. Seismic hazard maps for Georgia were calculated based on modern approach that was developed in the frame of EMME (Earthquake Modl for Middle east region) project. EMME was one of GEM's successful endeavors at regional level. With EMME and GEM assistance, regional models were analyzed to identify the information and additional work needed for the preparation national hazard models. Probabilistic seismic hazard map (PSH) provides the critical bases for improved building code and construction. The most serious deficiency in PSH assessment for the territory of Georgia is the lack of high-quality ground motion data. Due to this an initial hybrid empirical ground motion model is developed for PGA and SA at selected periods. An application of these coefficients for ground motion models have been used in probabilistic seismic hazard assessment. Obtained results of seismic hazard maps show evidence that there were gaps in seismic hazard assessment and the present normative seismic hazard map needed a careful recalculation.

Student Assessment System. Student Performance Record. Task Detailing. Cosmetology. Georgia Vocational Education Program Articulation.

ERIC Educational Resources Information Center

Georgia Univ., Athens. Div. of Vocational Education.

This booklet lists tasks and functions the cosmetology student should be able to do upon entering an employment situation or a postsecondary school. (Listings are also available for the areas of allied health occupations/practical nursing and transportation/automotive mechanics.) Tasks are coded to correspond to those on the Student Performance…

The National Shipbuilding Research Program. Proceedings of the REAPS Technical Symposium held June 15-16, 1976 Atlanta, Georgia

DTIC Science & Technology

1976-06-01

the imformation needed to perform the design funct ions, but will also provide the user with the details required to interact with other individuals...several calls of basic drafting (DRA) macros. The deck heights are constant andincluded in the coding. Al - identifies the specific frame to be retrived

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...

76 FR 67209 - Notice of Lodging of Consent Decree Under the Comprehensive Environmental Response, Compensation...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-31

... Response, Compensation, and Liability Act and the Texas Solid Waste Disposal Act Notice is hereby given... Texas Solid Waste Disposal Act, Texas Health & Safety Code Ann. Sec. Sec. 361.001 to 361.966 (hereafter... responding to the releases and threatened releases of solid wastes and hazardous substances at and from the...

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.

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.

Student Assessment System. Student Performance Record. Task Detailing. Transportation/Automotive Mechanics. Georgia Vocational Education Program Articulation.

ERIC Educational Resources Information Center

Georgia Univ., Athens. Div. of Vocational Education.

This booklet lists tasks and functions the student in the transportation cluster should be able to do upon entering an employment situation or a postsecondary school. (Listings are also available for the areas of allied health occupations/practical nursing and cosmetology.) Tasks are coded to correspond to those on the Student Performance Record,…

Environmental Assessment of Proposed Wing Headquarters Facility at Pittsburgh International Airport Air Reserve Station, Pennsylvania

DTIC Science & Technology

2005-03-01

CERCLA Comprehensive Environmental Response, Compensation, and Liability Act CFR Code of Federal Regulations CO carbon monoxide CWA Clean Water...255 Richard Ray Boulevard Robins Air Force Base, Georgia 31098-1637 Project Number: JLSS 97- 9001 MARCH 2005 EA of Proposed Wing...Environmental Statutes and Regulations ...........................1- 5 1.5 Interagency Coordination and Community Involvement

75 FR 22816 - Disease, Disability, and Injury Prevention and Control Special Emphasis Panel: Revitalizing Core...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-04-30

...(c)(4) and (6), Title 5, U.S.C., and the Determination of the Director, Management Analysis and..., PhD, M.P.H., NCIPC/ERPO, CDC, 4770 Buford Highway, NE., M/S F62, Atlanta, Georgia 30341-3724... and Prevention. [FR Doc. 2010-10164 Filed 4-29-10; 8:45 am] BILLING CODE 4163-18-P ...

Implementation and validation of a wake model for vortex-surface interactions in low speed forward flight

NASA Technical Reports Server (NTRS)

Komerath, Narayanan M.; Schreiber, Olivier A.

1987-01-01

The wake model was implemented using a VAX 750 and a Microvax II workstation. Online graphics capability using a DISSPLA graphics package. The rotor model used by Beddoes was significantly extended to include azimuthal variations due to forward flight and a simplified scheme for locating critical points where vortex elements are placed. A test case was obtained for validation of the predictions of induced velocity. Comparison of the results indicates that the code requires some more features before satisfactory predictions can be made over the whole rotor disk. Specifically, shed vorticity due to the azimuthal variation of blade loading must be incorporated into the model. Interactions between vortices shed from the four blades of the model rotor must be included. The Scully code for calculating the velocity field is being modified in parallel with these efforts to enable comparison with experimental data. To date, some comparisons with flow visualization data obtained at Georgia Tech were performed and show good agreement for the isolated rotor case. Comparison of time-resolved velocity data obtained at Georgia Tech also shows good agreement. Modifications are being implemented to enable generation of time-averaged results for comparison with NASA data.

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.

Computational strategies for three-dimensional flow simulations on distributed computer systems

NASA Technical Reports Server (NTRS)

Sankar, Lakshmi N.; Weed, Richard A.

1995-01-01

This research effort is directed towards an examination of issues involved in porting large computational fluid dynamics codes in use within the industry to a distributed computing environment. This effort addresses strategies for implementing the distributed computing in a device independent fashion and load balancing. A flow solver called TEAM presently in use at Lockheed Aeronautical Systems Company was acquired to start this effort. The following tasks were completed: (1) The TEAM code was ported to a number of distributed computing platforms including a cluster of HP workstations located in the School of Aerospace Engineering at Georgia Tech; a cluster of DEC Alpha Workstations in the Graphics visualization lab located at Georgia Tech; a cluster of SGI workstations located at NASA Ames Research Center; and an IBM SP-2 system located at NASA ARC. (2) A number of communication strategies were implemented. Specifically, the manager-worker strategy and the worker-worker strategy were tested. (3) A variety of load balancing strategies were investigated. Specifically, the static load balancing, task queue balancing and the Crutchfield algorithm were coded and evaluated. (4) The classical explicit Runge-Kutta scheme in the TEAM solver was replaced with an LU implicit scheme. And (5) the implicit TEAM-PVM solver was extensively validated through studies of unsteady transonic flow over an F-5 wing, undergoing combined bending and torsional motion. These investigations are documented in extensive detail in the dissertation, 'Computational Strategies for Three-Dimensional Flow Simulations on Distributed Computing Systems', enclosed as an appendix.

Computational strategies for three-dimensional flow simulations on distributed computer systems

NASA Astrophysics Data System (ADS)

Sankar, Lakshmi N.; Weed, Richard A.

1995-08-01

This research effort is directed towards an examination of issues involved in porting large computational fluid dynamics codes in use within the industry to a distributed computing environment. This effort addresses strategies for implementing the distributed computing in a device independent fashion and load balancing. A flow solver called TEAM presently in use at Lockheed Aeronautical Systems Company was acquired to start this effort. The following tasks were completed: (1) The TEAM code was ported to a number of distributed computing platforms including a cluster of HP workstations located in the School of Aerospace Engineering at Georgia Tech; a cluster of DEC Alpha Workstations in the Graphics visualization lab located at Georgia Tech; a cluster of SGI workstations located at NASA Ames Research Center; and an IBM SP-2 system located at NASA ARC. (2) A number of communication strategies were implemented. Specifically, the manager-worker strategy and the worker-worker strategy were tested. (3) A variety of load balancing strategies were investigated. Specifically, the static load balancing, task queue balancing and the Crutchfield algorithm were coded and evaluated. (4) The classical explicit Runge-Kutta scheme in the TEAM solver was replaced with an LU implicit scheme. And (5) the implicit TEAM-PVM solver was extensively validated through studies of unsteady transonic flow over an F-5 wing, undergoing combined bending and torsional motion. These investigations are documented in extensive detail in the dissertation, 'Computational Strategies for Three-Dimensional Flow Simulations on Distributed Computing Systems', enclosed as an appendix.

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

Final Report for Geometric Observers and Particle Filtering for Controlled Active Vision

DTIC Science & Technology

2016-12-15

code) 15-12-2016 Final Report 01Sep06 - 09May11 Final Report for Geometric Observers & Particle Filtering for Controlled Active Vision 49414-NS.1Allen...Observers and Particle Filtering for Controlled Active Vision by Allen R. Tannenbaum School of Electrical and Computer Engineering Georgia Institute of...7 2.2.4 Conformal Area Minimizing Flows . . . . . . . . . . . . . . . . . . . . . . . 8 2.3 Particle Filters

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

Final Environmental Assessment (EA) for Modification of Airspace Units R-3008A/B/C from Visual Flight Rules (VFR) to VFR-Instrument Flight Rules (IFR) at Moody Air Force Base, Georgia

DTIC Science & Technology

2015-09-30

winds. In addition, overcast conditions typically reduce or eliminate the presence of thermals that are used by soaring raptors such as hawks...Title 40, 1508.27. Protection of Environment Council on Environmental Quality. January 1979 . Code of Federal Regulations (C.F.R.), Title 40, Part 50

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.

Subgrid Combustion Modeling for the Next Generation National Combustion Code

NASA Technical Reports Server (NTRS)

Menon, Suresh; Sankaran, Vaidyanathan; Stone, Christopher

2003-01-01

In the first year of this research, a subgrid turbulent mixing and combustion methodology developed earlier at Georgia Tech has been provided to researchers at NASA/GRC for incorporation into the next generation National Combustion Code (called NCCLES hereafter). A key feature of this approach is that scalar mixing and combustion processes are simulated within the LES grid using a stochastic 1D model. The subgrid simulation approach recovers locally molecular diffusion and reaction kinetics exactly without requiring closure and thus, provides an attractive feature to simulate complex, highly turbulent reacting flows of interest. Data acquisition algorithms and statistical analysis strategies and routines to analyze NCCLES results have also been provided to NASA/GRC. The overall goal of this research is to systematically develop and implement LES capability into the current NCC. For this purpose, issues regarding initialization and running LES are also addressed in the collaborative effort. In parallel to this technology transfer effort (that is continuously on going), research has also been underway at Georgia Tech to enhance the LES capability to tackle more complex flows. In particular, subgrid scalar mixing and combustion method has been evaluated in three distinctly different flow field in order to demonstrate its generality: (a) Flame-Turbulence Interactions using premixed combustion, (b) Spatially evolving supersonic mixing layers, and (c) Temporal single and two-phase mixing layers. The configurations chosen are such that they can be implemented in NCCLES and used to evaluate the ability of the new code. Future development and validation will be in spray combustion in gas turbine engine and supersonic scalar mixing.

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.

Environmental Assessment Addressing the Expansion of Sortie-Operations at Moody Air Force Base, Georgia

DTIC Science & Technology

2012-08-01

include the tactical delivery of air-to-ground munitions, laser designation of targets from ground and airborne platforms, and threat evasion. These...world events, which include the tactical delivery of air-to-ground munitions, laser designation of targets from ground and airborne platforms, and...Closure CAA Clean Air Act CAU Classic Associate Unit CEQ Council on Environmental Quality CFR Code of Federal Regulations CO carbon monoxide

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...

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.

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

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

Our flesh is here but our soul stayed there: A qualitative study on resource loss due to war and displacement among internally-displaced women in the Republic of Georgia.

PubMed

Seguin, Maureen; Lewis, Ruth; Amirejibi, Tinatin; Razmadze, Mariam; Makhashvili, Nino; Roberts, Bayard

2016-02-01

Losses experienced by conflict-affected civilians in low and middle income countries is a relatively unexplored area. The aim of our paper is to explore the concept of resource loss in the accounts of internally displaced women in Georgia. We use Hobfoll's Conservation of Resources (COR) theory to guide our approach by examining the loss of objects, personal characteristics, conditions, and energies. Semi-structured interviews were conducted on 42 purposively-selected Georgian women residing in internally displaced persons settlements during fieldwork in Georgia from December 2012 to February 2013. Line-by-line open-coding was conducted on translated and transcribed interviews using Nvivo. The conservation of resources theory was utilised to guide the 'mapping' of the relationships between losses which occurred in the post-conflict period. War-related trauma led to the loss of property, which caused the loss of livelihood and subsequent loss of social networks and mental and physical health. The mental and physical health losses, along with the loss of livelihood, constituted a loss spiral in which losses in one area perpetuated on-going losses in the other areas. Interventions at supporting livelihoods are needed in order to address the cascade of losses resulting from war. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

Solving the "Hidden Line" Problem

NASA Technical Reports Server (NTRS)

1984-01-01

David Hedgley Jr., a mathematician at Dryden Flight Research Center, has developed an accurate computer program that considers whether a line in a graphic model of a three dimensional object should or should not be visible. The Hidden Line Computer Code, program automatically removes superfluous lines and permits the computer to display an object from specific viewpoints, just as the human eye would see it. Users include Rowland Institute for Science in Cambridge, MA, several departments of Lockheed Georgia Co., and Nebraska Public Power District (NPPD).

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

Integrated design and manufacturing for the high speed civil transport

NASA Technical Reports Server (NTRS)

1993-01-01

In June 1992, Georgia Tech's School of Aerospace Engineering was awarded a NASA University Space Research Association (USRA) Advanced Design Program (ADP) to address 'Integrated Design and Manufacturing for the High Speed Civil Transport (HSCT)' in its graduate aerospace systems design courses. This report summarizes the results of the five courses incorporated into the Georgia Tech's USRA ADP program. It covers AE8113: Introduction to Concurrent Engineering, AE4360: Introduction to CAE/CAD, AE4353: Design for Life Cycle Cost, AE6351: Aerospace Systems Design One, and AE6352: Aerospace Systems Design Two. AE8113: Introduction to Concurrent Engineering was an introductory course addressing the basic principles of concurrent engineering (CE) or integrated product development (IPD). The design of a total system was not the objective of this course. The goal was to understand and define the 'up-front' customer requirements, their decomposition, and determine the value objectives for a complex product, such as the high speed civil transport (HSCT). A generic CE methodology developed at Georgia Tech was used for this purpose. AE4353: Design for Life Cycle Cost addressed the basic economic issues for an HSCT using a robust design technique, Taguchi's parameter design optimization method (PDOM). An HSCT economic sensitivity assessment was conducted using a Taguchi PDOM approach to address the robustness of the basic HSCT design. AE4360: Introduction to CAE/CAD permitted students to develop and utilize CAE/CAD/CAM knowledge and skills using CATIA and CADAM as the basic geometric tools. AE6351: Aerospace Systems Design One focused on the conceptual design refinement of a baseline HSCT configuration as defined by Boeing, Douglas, and NASA in their system studies. It required the use of NASA's synthesis codes FLOPS and ACSYNT. A criterion called the productivity index (P.I.) was used to evaluate disciplinary sensitivities and provide refinements of the baseline HSCT configuration. AE6352: Aerospace Systems Design Two was a continuation of Aerospace Systems Design One in which wing concepts were researched and analyzed in more detail. FLOPS and ACSYNT were again used at the system level while other off-the-shelf computer codes were used for more detailed wing disciplinary analysis and optimization. The culmination of all efforts and submission of this report conclude the first year's efforts of Georgia Tech's NASA USRA ADP. It will hopefully provide the foundation for next year's efforts concerning continuous improvement of integrated design and manufacturing for the HSCT.

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.

GT | Government & Community Relations | Georgia Institute of Technology |

Science.gov Websites

Atlanta, GA Skip to content Georgia Tech Georgia Institute of Technology Government & Georgia Tech's Impact State Relations Capitol Jackets Advocacy Network Legislative Priorities & ; Resources Georgia Legislative Internship Program Georgia Tech's Impact 2018 PHELAP Conference Community &

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

ClinicalTrials.gov

2018-04-30

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

The effects of Georgia's Choice curricular reform model on third grade science scores on the Georgia Criterion Referenced Competency Test

NASA Astrophysics Data System (ADS)

Phemister, Art W.

The purpose of this study was to evaluate the effectiveness of the Georgia's Choice reading curriculum on third grade science scores on the Georgia Criterion Referenced Competency Test from 2002 to 2008. In assessing the effectiveness of the Georgia's Choice curriculum model this causal comparative study examined the 105 elementary schools that implemented Georgia's Choice and 105 randomly selected elementary schools that did not elect to use Georgia's Choice. The Georgia's Choice reading program used intensified instruction in an effort to increase reading levels for all students. The study used a non-equivalent control group with a pretest and posttest design to determine the effectiveness of the Georgia's Choice curriculum model. Findings indicated that third grade students in Non-Georgia's Choice schools outscored third grade students in Georgia's Choice schools across the span of the study.

Georgia's Workforce Development Pipeline: One District's Journey

ERIC Educational Resources Information Center

Williams, Melissa H.; Hufstetler, Tammy L.

2011-01-01

Launched in 2006, the Georgia Work Ready initiative seeks to improve the job training and marketability of Georgia's workforce and drive the state's economic growth. Georgia Work Ready is a partnership between the state and the Georgia Chamber of Commerce. Comprised of three components, Georgia's initiative focuses on job profiling, skills…

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.

Build-up Approach to Updating the Mock Quiet Spike(TradeMark) Beam Model

NASA Technical Reports Server (NTRS)

Herrera, Claudia Y.; Pak, Chan-gi

2007-01-01

A crucial part of aircraft design is ensuring that the required margin for flutter is satisfied. A trustworthy flutter analysis, which begins by possessing an accurate dynamics model, is necessary for this task. Traditionally, a model was updated manually by fine tuning specific stiffness parameters until the analytical results matched test data. This is a time consuming iterative process. NASA Dryden Flight Research Center has developed a mode matching code to execute this process in a more efficient manner. Recently, this code was implemented in the F-15B/Quiet Spike(TradeMark) (Gulfstream Aerospace Corporation, Savannah, Georgia) model update. A build-up approach requiring several ground vibration test configurations and a series of model updates was implemented in order to determine the connection stiffness between aircraft and test article. The mode matching code successfully updated various models for the F-15B/Quiet Spike(TradeMark) project to within 1 percent error in frequency and the modal assurance criteria values ranged from 88.51-99.42 percent.

Build-up Approach to Updating the Mock Quiet Spike(TM)Beam Model

NASA Technical Reports Server (NTRS)

Herrera, Claudia Y.; Pak, Chan-gi

2007-01-01

A crucial part of aircraft design is ensuring that the required margin for flutter is satisfied. A trustworthy flutter analysis, which begins by possessing an accurate dynamics model, is necessary for this task. Traditionally, a model was updated manually by fine tuning specific stiffness parameters until the analytical results matched test data. This is a time consuming iterative process. The NASA Dryden Flight Research Center has developed a mode matching code to execute this process in a more efficient manner. Recently, this code was implemented in the F-15B/Quiet Spike (Gulfstream Aerospace Corporation, Savannah, Georgia) model update. A build-up approach requiring several ground vibration test configurations and a series of model updates was implemented to determine the connection stiffness between aircraft and test article. The mode matching code successfully updated various models for the F-15B/Quiet Spike project to within 1 percent error in frequency and the modal assurance criteria values ranged from 88.51-99.42 percent.

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.

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

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.

Georgia | Midmarket Solar Policies in the United States | Solar Research |

Science.gov Websites

Distributed Generation Act Community solar Georgia Public Service Commission: Approval of Georgia Power's . Carve-out: None Tracking system: No formally adopted tracking system The Georgia Public Service . Midmarket customers in the Georgia Power and Tennessee Valley Authority (TVA) service territories may be

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

Simulation of salinity intrusion along the Georgia and South Carolina coasts using climate-change scenarios

USGS Publications Warehouse

Conrads, Paul; Roehl, Edwin A.; Daamen, Ruby C.; Cook, John B.

2013-01-01

Potential changes in climate could alter interactions between environmental and societal systems and adversely affect the availability of water resources in many coastal communities. Changes in streamflow patterns in conjunction with sea-level rise may change the salinity-intrusion dynamics of coastal rivers. Several municipal water-supply intakes are located along the Georgia and South Carolina coast that are proximal to the present day saltwater-freshwater interface of tidal rivers. Increases in the extent of salinity intrusion resulting from climate change could threaten the availability of freshwater supplies in the vicinity of these intakes. To effectively manage these supplies, water-resource managers need estimates of potential changes in the frequency, duration, and magnitude of salinity intrusion near their water-supply intakes that may occur as a result of climate change. This study examines potential effects of climate change, including altered streamflow and sea-level rise, on the dynamics of saltwater intrusion near municipal water-supply intakes in two coastal areas. One area consists of the Atlantic Intracoastal Waterway (AIW) and the Waccamaw River near Myrtle Beach along the Grand Strand of the South Carolina Coast, and the second area is on or near the lower Savannah River near Savannah, Georgia. The study evaluated how future sea-level rise and a reduction in streamflows can potentially affect salinity intrusion and threaten municipal water supplies and the biodiversity of freshwater tidal marshes in these two areas. Salinity intrusion occurs as a result of the interaction between three principal forces—streamflow, mean coastal water levels, and tidal range. To analyze and simulate salinity dynamics at critical coastal gaging stations near four municipal water-supply intakes, various data-mining techniques, including artificial neural network (ANN) models, were used to evaluate hourly streamflow, salinity, and coastal water-level data collected over a period exceeding 10 years. The ANN models were trained (calibrated) to learn the specific interactions that cause salinity intrusions, and resulting models were able to accurately simulate historical salinity dynamics in both study areas. Changes in sea level and streamflow quantity and timing can be simulated by the salinity intrusion models to evaluate various climate-change scenarios. The salinity intrusion models for the study areas are deployed in a decision support system to facilitate the use of the models for management decisions by coastal water-resource managers. The report describes the use of the salinity-intrusion models decision support system to evaluate salinity-intrusion dynamics for various climate-change scenarios, including incremental increases in sea level in combination with incremental decreases in streamflow. Operation of municipal water-treatment plants is problematic when the specific-conductance values for source water are greater than 1,000 to 2,000 microsiemens per centimeter (µS/cm). High specific-conductance values contribute to taste problems that require treatment. Data from a gage downstream from a municipal water intake indicate specific conductance exceeded 1,000 µS/cm about 5.4 percent of the time over the 14-year period from August 1995 to August 2008. Simulations of specific conductance at this gaging station that incorporates sea-level rises resulted in a doubling of the exceedances to 11.0 percent for a 1-foot increase and 17.6 percent for a 2-foot increase. The frequency of intrusion of water with specific conductance values of 1,000 µS/cm was less sensitive to incremental reductions in streamflow than to incremental increases in sea level. Simulations of conditions associated with a 10-percent reduction in streamflow, in combination with a 1-foot rise in sea level, increased the percentage of time specific conductance exceeded 1,000 µS/cm at this site from 11.0 to 13.3 percent, and a 20-percent reduction in streamflow increased the percentage of time to 16.6 percent. Precipitation and temperature data from a global circulation model were used, after scale adjustments, as input to a watershed model of the Yadkin-Pee Dee River basin, which flows into the Waccamaw River and Atlantic Intracoastal Waterway study area in South Carolina. The simulated streamflow for historical conditions and projected climate change in the future was used as input for the ANN model in decision support system. Results of simulations incorporating climate-change projections for alterations in streamflow indicate an increase in the frequency of salinity-intrusion events and a shift in the seasonal occurrence of the intrusion events from the summer to the fall.

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.

On initial Brain Activity Mapping of episodic and semantic memory code in the hippocampus.

PubMed

Tsien, Joe Z; Li, Meng; Osan, Remus; Chen, Guifen; Lin, Longian; Wang, Phillip Lei; Frey, Sabine; Frey, Julietta; Zhu, Dajiang; Liu, Tianming; Zhao, Fang; Kuang, Hui

2013-10-01

It has been widely recognized that the understanding of the brain code would require large-scale recording and decoding of brain activity patterns. In 2007 with support from Georgia Research Alliance, we have launched the Brain Decoding Project Initiative with the basic idea which is now similarly advocated by BRAIN project or Brain Activity Map proposal. As the planning of the BRAIN project is currently underway, we share our insights and lessons from our efforts in mapping real-time episodic memory traces in the hippocampus of freely behaving mice. We show that appropriate large-scale statistical methods are essential to decipher and measure real-time memory traces and neural dynamics. We also provide an example of how the carefully designed, sometime thinking-outside-the-box, behavioral paradigms can be highly instrumental to the unraveling of memory-coding cell assembly organizing principle in the hippocampus. Our observations to date have led us to conclude that the specific-to-general categorical and combinatorial feature-coding cell assembly mechanism represents an emergent property for enabling the neural networks to generate and organize not only episodic memory, but also semantic knowledge and imagination. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

On Initial Brain Activity Mapping of Associative Memory Code in the Hippocampus

PubMed Central

Tsien, Joe Z.; Li, Meng; Osan, Remus; Chen, Guifen; Lin, Longian; Lei Wang, Phillip; Frey, Sabine; Frey, Julietta; Zhu, Dajiang; Liu, Tianming; Zhao, Fang; Kuang, Hui

2013-01-01

It has been widely recognized that the understanding of the brain code would require large-scale recording and decoding of brain activity patterns. In 2007 with support from Georgia Research Alliance, we have launched the Brain Decoding Project Initiative with the basic idea which is now similarly advocated by BRAIN project or Brain Activity Map proposal. As the planning of the BRAIN project is currently underway, we share our insights and lessons from our efforts in mapping real-time episodic memory traces in the hippocampus of freely behaving mice. We show that appropriate large-scale statistical methods are essential to decipher and measure real-time memory traces and neural dynamics. We also provide an example of how the carefully designed, sometime thinking-outside-the-box, behavioral paradigms can be highly instrumental to the unraveling of memory-coding cell assembly organizing principle in the hippocampus. Our observations to date have led us to conclude that the specific-to-general categorical and combinatorial feature-coding cell assembly mechanism represents an emergent property for enabling the neural networks to generate and organize not only episodic memory, but also semantic knowledge and imagination. PMID:23838072

The Effects of Georgia's Choice Curricular Reform Model on Third Grade Science Scores on the Georgia Criterion Referenced Competency Test

ERIC Educational Resources Information Center

Phemister, Art W.

2010-01-01

The purpose of this study was to evaluate the effectiveness of the Georgia's Choice reading curriculum on third grade science scores on the Georgia Criterion Referenced Competency Test from 2002 to 2008. In assessing the effectiveness of the Georgia's Choice curriculum model this causal comparative study examined the 105 elementary schools that…

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.

Georgia Basin-Puget Sound Airshed Characterization Report 2014

EPA Science Inventory

The Georgia Basin - Puget Sound Airshed Characterization Report, 2012 was undertaken to characterize the air quality within the Georgia Basin/Puget Sound region,a vibrant, rapidly growing, urbanized area of the Pacific Northwest. The Georgia Basin - Puget Sound Airshed Characteri...

78 FR 38978 - Change in Bank Control Notices; Acquisitions of Shares of a Bank or Bank Holding Company

Federal Register 2010, 2011, 2012, 2013, 2014

2013-06-28

..., Georgia, and Stevan Reynolds Tuck, Dawson, Georgia, to retain control of Georgia Community Bancorp, Inc..., Reynolds, Georgia. B. Federal Reserve Bank of San Francisco (Gerald C. Tsai, Director, Applications and...

Environmental Assessment for Beddown of Air Force Reserve Command Classic Associate Unit on A/OA-10 Operations and Maintenance Moody Air Force Base, Georgia

DTIC Science & Technology

2008-09-01

Code of Federal Regulations CO carbon monoxide CSAF Chief of Staff of the Air Force dB decibels DNL day-night average sound level DNLmr onset rate...the health and welfare of the general public. These seven pollutants (the “criteria pollutants”) include ozone, carbon monoxide (CO), nitrogen... Monoxide 9 ppm (10 mg/m3) 8-hour (1) None 35 ppm (40 mg/m3) 1-hour (1) None Lead 1.5 µg/m3 Quarterly Average Same as Primary Nitrogen Dioxide 0.053

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

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.

30 CFR 910.700 - Georgia Federal program.

Code of Federal Regulations, 2010 CFR

2010-07-01

....700 Mineral Resources OFFICE OF SURFACE MINING RECLAMATION AND ENFORCEMENT, DEPARTMENT OF THE INTERIOR PROGRAMS FOR THE CONDUCT OF SURFACE MINING OPERATIONS WITHIN EACH STATE GEORGIA § 910.700 Georgia Federal program. (a) This part contains all rules that are applicable to surface coal mining operations in Georgia...

2 CFR Appendix C to Part 230 - Non-Profit Organizations Not Subject to This Part

Code of Federal Regulations, 2010 CFR

2010-01-01

..., Michigan 11. Georgia Institute of Technology/Georgia Tech Applied Research Corporation/Georgia Tech Research Institute, Atlanta, Georgia 12. Hanford Environmental Health Foundation, Richland, Washington 13... Institutes of Research (AIR), Washington DC 4. Argonne National Laboratory, Chicago, Illinois 5. Atomic...

On-Demand Lectures Create an Effective Distributed Education Experience

ERIC Educational Resources Information Center

Lindsey, Stanley D.

2003-01-01

In this article, the author shares his experience teaching senior-level structural engineering courses at the Georgia Institute of Technology's Georgia Tech Regional Engineering Program. The program is a unique partnership of four universities--Georgia Tech, Savannah State University, Armstrong Atlantic State University, and Georgia Southern…

Water Use in Georgia by County for 2005; and Water-Use Trends, 1980-2005

USGS Publications Warehouse

Fanning, Julia L.; Trent, Victoria P.

2009-01-01

Water use for 2005 for each county in Georgia was estimated using data obtained from various Federal and State agencies and local sources. Total consumptive water use also was estimated for each county in Georgia for 2005. Water use is subdivided according to offstream and instream use. Offstream use is defined as water withdrawn or diverted from a ground- or surface-water source and transported to the place of use. Estimates for offstream water use include the categories of public supply, domestic, commercial, industrial, mining, irrigation, livestock, aquaculture, and thermoelectric power. Instream use is that which occurs within a stream channel for such purposes as hydroelectric-power generation, navigation, water-quality improvement, fish propagation, and recreation. The only category of instream use estimated was hydroelectric-power generation. Georgia law (the Georgia Ground-Water Use Act of 1972 and the Georgia Water Supply Act of 1978 [Georgia Department of Natural Resources, 2008a,b]) requires any water user who withdraws more than 100,000 gallons per day on a monthly average to obtain a withdrawal permit from the Georgia Environmental Protection Division. Permit holders generally must report their withdrawals by month. The Georgia Water-Use Program collects the reported information under the withdrawal permit system and the drinking-water permit system and stores the data in the Georgia Water-Use Data System.

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.

Developing Competency-Based Preparation and Performance-Based Certification in Georgia.

ERIC Educational Resources Information Center

Solomon, Lester M.

The state of Georgia has been moving toward competency/performance-based education since the late 1960's. All of the groups concerned with education and the preparation of teachers (higher education institutions, the Georgia Teacher Education Council, professional organizations, and the Georgia Department of Education) have been involved. In…

Measures of Student Success with Textbook Transformations: The Affordable Learning Georgia Initiative

ERIC Educational Resources Information Center

Croteau, Emily

2017-01-01

In 2014, the state of Georgia's budget supported a University System of Georgia (USG) initiative: Affordable Learning Georgia (ALG). The initiative was implemented via Textbook Transformation Grants, which provided grants to USG faculty, libraries and librarians, and institutions to "transform their use of textbooks and other learning…

40 CFR 81.237 - Northeast Georgia Intrastate Air Quality Control Region.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 17 2010-07-01 2010-07-01 false Northeast Georgia Intrastate Air... Air Quality Control Regions § 81.237 Northeast Georgia Intrastate Air Quality Control Region. The Northeast Georgia Intrastate Air Quality Control Region consists of the territorial area encompassed by the...

40 CFR 81.58 - Columbus (Georgia)-Phenix City (Alabama) Interstate Air Quality Control Region.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 17 2010-07-01 2010-07-01 false Columbus (Georgia)-Phenix City... PLANNING PURPOSES Designation of Air Quality Control Regions § 81.58 Columbus (Georgia)-Phenix City (Alabama) Interstate Air Quality Control Region. The Columbus (Georgia)-Phenix City (Alabama) Interstate...

40 CFR 81.58 - Columbus (Georgia)-Phenix City (Alabama) Interstate Air Quality Control Region.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 40 Protection of Environment 17 2011-07-01 2011-07-01 false Columbus (Georgia)-Phenix City... PLANNING PURPOSES Designation of Air Quality Control Regions § 81.58 Columbus (Georgia)-Phenix City (Alabama) Interstate Air Quality Control Region. The Columbus (Georgia)-Phenix City (Alabama) Interstate...

Peanut peg strength and associated pod yield and loss by cultivar

USDA-ARS?s Scientific Manuscript database

Peanut (Arachis hypogaea L.) peg strength and associated pod yield and digging loss were documented for nine cultivars and two breeding genotypes across three harvest dates at two Southwest Georgia locations during 2010 and 2011. Cultivars selected were Georgia Green, Georgia Greener, Georgia-02C, G...

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.

Health research capacity building in Georgia: a case-based needs assessment.

PubMed

Squires, A; Chitashvili, T; Djibuti, M; Ridge, L; Chyun, D

2017-06-01

Research capacity building in the health sciences in low- and middle-income countries (LMICs) has typically focused on bench-science capacity, but research examining health service delivery and health workforce is equally necessary to determine the best ways to deliver care. The Republic of Georgia, formerly a part of the Soviet Union, has multiple issues within its healthcare system that would benefit from expended research capacity, but the current research environment needs to be explored prior to examining research-focused activities. The purpose of this project was to conduct a needs assessment focused on developing research capacity in the Republic of Georgia with an emphasis on workforce and network development. A case study approach guided by a needs assessment format. We conducted in-country, informal, semi-structured interviews in English with key informants and focus groups with faculty, students, and representatives of local non-governmental organizations. Purposive and snowball sampling approaches were used to recruit participants, with key informant interviews scheduled prior to arrival in country. Documents relevant to research capacity building were also included. Interview results were coded via content analysis. Final results were organized into a SWOT (strengths, weaknesses, opportunities, threat) analysis format, with the report shared with participants. There is widespread interest among students and faculty in Georgia around building research capacity. Lack of funding was identified by many informants as a barrier to research. Many critical research skills, such as proposal development, qualitative research skills, and statistical analysis, were reported as very limited. Participants expressed concerns about the ethics of research, with some suggesting that research is undertaken to punish or 'expose' subjects. However, students and faculty are highly motivated to improve their skills, are open to a variety of learning modalities, and have research priorities aligned with Georgian health needs. This study's findings indicate that while the Georgian research infrastructure needs further development, Georgian students and faculty are eager to supplement its gaps by improving their own skills. These findings are consistent with those seen in other developing country contexts. Copyright © 2017 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Integrated mined-area reclamation and land-use planning. Volume 3C. A case study of surface mining and reclamation planning: Georgia Kaolin Company Clay Mines, Washington County, Georgia

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

Guernsey, J L; Brown, L A; Perry, A O

1978-02-01

This case study examines the reclamation practices of the Georgia Kaolin's American Industrial Clay Company Division, a kaolin producer centered in Twiggs, Washington, and Wilkinson Counties, Georgia. The State of Georgia accounts for more than one-fourth of the world's kaolin production and about three-fourths of U.S. kaolin output. The mining of kaolin in Georgia illustrates the effects of mining and reclaiming lands disturbed by area surface mining. The disturbed areas are reclaimed under the rules and regulations of the Georgia Surface Mining Act of 1968. The natural conditions influencing the reclamation methodologies and techniques are markedly unique from those ofmore » other mining operations. The environmental disturbances and procedures used in reclaiming the kaolin mined lands are reviewed and implications for planners are noted.« less

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.

Efficacy of road underpasses for minimizing bear-vehicle collisions on the 4-lane section of Georgia highway 96 - phase I : final report.

DOT National Transportation Integrated Search

2016-10-01

The Central Georgia Bear Population, the smallest of Georgias three populations of American black bear (Ursus americanus), is of special concern due to its size and potential isolation from other bear populations. Plans to widen Georgia State Rout...

78 FR 28118 - Vidalia Onions Grown in Georgia; Change in Reporting and Assessment Requirements

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-14

...; FV13-955-1 IR] Vidalia Onions Grown in Georgia; Change in Reporting and Assessment Requirements AGENCY... Vidalia onions grown in Georgia (order). The order regulates the handling of Vidalia onions grown in Georgia and is administered locally by the Vidalia Onion Committee (Committee). This rule changes the date...

75 FR 41884 - Notice of Inventory Completion: Georgia Department of Transportation, Atlanta, GA; University of...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-07-19

... Department of Transportation, Atlanta, GA; University of West Georgia, Carrollton, GA; and University of... Department of Transportation, Atlanta, GA, and in the possession of the University of West Georgia, Carrollton, GA, and the University of Georgia, Athens, GA. The human remains were removed from Richmond...

[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.

Sexual and Reproductive Health Services and Related Health Information on Pregnancy Resource Center Websites: A Statewide Content Analysis.

PubMed

Swartzendruber, Andrea; Newton-Levinson, Anna; Feuchs, Ashley E; Phillips, Ashley L; Hickey, Jennifer; Steiner, Riley J

Pregnancy resource centers (PRCs) are nonprofit organizations with a primary mission of promoting childbirth among pregnant women. Given a new state grant program to publicly fund PRCs, we analyzed Georgia PRC websites to describe advertised services and related health information. We systematically identified all accessible Georgia PRC websites available from April to June 2016. Entire websites were obtained and coded using defined protocols. Of 64 reviewed websites, pregnancy tests and testing (98%) and options counseling (84%) were most frequently advertised. However, 58% of sites did not provide notice that PRCs do not provide or refer for abortion, and 53% included false or misleading statements regarding the need to make a decision about abortion or links between abortion and mental health problems or breast cancer. Advertised contraceptive services were limited to counseling about natural family planning (3%) and emergency contraception (14%). Most sites (89%) did not provide notice that PRCs do not provide or refer for contraceptives. Two sites (3%) advertised unproven "abortion reversal" services. Approximately 63% advertised ultrasound examinations, 22% sexually transmitted infection testing, and 5% sexually transmitted infection treatment. None promoted consistent and correct condom use; 78% with content about condoms included statements that seemed to be designed to undermine confidence in condom effectiveness. Approximately 84% advertised educational programs, and 61% material resources. Georgia PRC websites contain high levels of false and misleading health information; the advertised services do not seem to align with prevailing medical guidelines. Public funding for PRCs, an increasing national trend, should be rigorously examined. Increased regulation may be warranted to ensure quality health information and services. Copyright © 2017 Jacobs Institute of Women's Health. Published by Elsevier Inc. All rights reserved.

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.

Higher Education in Georgia: Assessment, Evaluation, and Accreditation. Proceedings of the Conference (Athens, Georgia, January 15-16, 1986).

ERIC Educational Resources Information Center

Fincher, Cameron, Ed.; And Others

Higher education assessment, evaluation, and accreditation in Georgia are addressed in these proceedings of a 1986 conference sponsored by the University of Georgia and the Southern Association of Colleges and Schools (SACS). Panel papers cover: assessing student performance and outcomes, academic standards and needs assessment for specific…

78 FR 20091 - Foreign-Trade Zone 26-Atlanta, Georgia, Authorization of Production Activity, Perkins Shibaura...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-04-03

... DEPARTMENT OF COMMERCE Foreign-Trade Zones Board [B-90-2012] Foreign-Trade Zone 26--Atlanta, Georgia, Authorization of Production Activity, Perkins Shibaura Engines, LLC (Diesel Engines), Griffin, Georgia On November 29, 2012, Georgia Foreign-Trade Zone, Inc., grantee of FTZ 26, submitted a notification of proposed production activity to the...

Georgia's Pre-K Professional Development Evaluation: Final Report. Publication #2015-02

ERIC Educational Resources Information Center

Early, Diane M.; Maxwell, Kelly L.; Skinner, Debra; Kraus, Syndee; Hume, Katie; Pan, Yi

2014-01-01

Georgia has been at the forefront of the pre-kindergarten movement since implementing its pre-k program in 1992 and creating the nation's first state-funded universal pre-k program in 1995. Georgia's Pre-K, administered by "Bright from the Start: Georgia Department of Early Care and Learning" (DECAL), aims to provide high-quality…

Guidelines and Standards for Proprietary Schools.

ERIC Educational Resources Information Center

Georgia State Dept. of Education, Atlanta. Office of Planning and Development.

This guide contains information pertaining to the law, rules, regulations, and standards of practice that apply to proprietary schools operating in Georgia, as provided in the Georgia Proprietary School Act (O.C.G.A. Section 20-4-60 and following, Georgia School Laws). The guide has been adopted by the Georgia Board of Education and is used in the…

Various aspects of sustainability analysis in Georgia

Treesearch

C. J. Cieszewski; M. Zasada; B. E. Borders; R. Lowe; M. L. Clutter; R. F. Daniels; R. Izlar

2002-01-01

In 2001 the Georgia Traditional Industries Program (TIP) sponsored a cooperative study at the D.B. Warnell School of Forest Resources, University of Georgia, to analyze the long-term sustainability of the fiber supply in Georgia. The subject of this study is relevant to a diverse array of disciplines, and it offers the opportunity to explore various aspects of...

A Failed Experiment: Georgia's Tax Credit Scholarships for Private Schools. Special Summary

ERIC Educational Resources Information Center

Southern Education Foundation, 2011

2011-01-01

Georgia is one of seven states that currently allow tax credits for scholarships to private schools. The law permits individual taxpayers in Georgia to reduce annual state taxes up to $2,500 for joint returns when they divert funds to a student scholarship organization (SSO). Georgia's law providing tax credits for private school tuition grants or…

A Failed Experiment: Georgia's Tax Credit Scholarships for Private Schools

ERIC Educational Resources Information Center

Southern Education Foundation, 2011

2011-01-01

Georgia is one of seven states that currently allow tax credits for scholarships to private schools. Georgia's law was enacted in May 2008 in order to assist low income students to transfer out of low performing public schools. Operations under the new act began in late 2008. The law permits taxpayers in Georgia to reduce their annual state taxes…

Report of a Planning Conference for Solar Technology Information Transfer in Georgia (Atlanta, Georgia, July 24-25, 1978).

ERIC Educational Resources Information Center

Aldridge, Mark C., Ed.

A summary of the deliberations of the Georgia planning conference of the Solar Technology Transfer Program is presented in this report. Topic areas include background information on the Georgia conference and a summary of the discussions and recommendations dealing with solar information transfer within state systems and the need for greater…

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.

StreamStats in Georgia: a water-resources web application

USGS Publications Warehouse

Gotvald, Anthony J.; Musser, Jonathan W.

2015-07-31

StreamStats is being implemented on a State-by-State basis to allow for customization of the data development and underlying datasets to address their specific needs, issues, and objectives. The USGS, in cooperation with the Georgia Environmental Protection Division and Georgia Department of Transportation, has implemented StreamStats for Georgia. The Georgia StreamStats Web site is available through the national StreamStats Web-page portal at http://streamstats.usgs.gov. Links are provided on this Web page for individual State applications, instructions for using StreamStats, definitions of basin characteristics and streamflow statistics, and other supporting information.

The Norse discovery of America.

PubMed

Langmoen, Iver A

2005-12-01

In the late 8th century, the stage for Viking expansion was set by commercial expansion in northwest Europe, the pressure of an increasing population in limited territorial reserves, and the development of the Viking ships. The Norsemen traveled extensively over the oceans, south to the Holy Land, and north to the White Sea and settled over a wide area from Sicily to Greenland. Historical sources, including the reports by Adam of Bremen and the Icelandic Sagas, describe several expeditions from Greenland to Vinland (somewhere along the east coast of North America) in approximately AD 1000 and later. Historians have arrived at highly different conclusions with respect to the location of Vinland (from Labrador to Georgia), but, in 1960, the Norwegian explorer Helge Ingstad localized ancient house sites on L'Ans aux Meadows, a small fishing village on the Northern beaches of Newfoundland. From 1961 to 1969, Ingstad and his wife, Anne Stine (an archaeologist), led several archaeological expeditions that revealed Viking turf houses with room for approximately 100 people. They also excavated a smithy, outdoor cooking pits, boathouses, a bathhouse, and enclosures for cattle, in addition to several Viking artifacts. The finds were C dated to AD 990 +/- 30. The present report reviews historical and archaeological evidence indicating the sites to which the Vikings traveled and attempted to settle in the new world.

A Study of an Intensive Educational Program Conducted in Six Georgia Counties by the Georgia Cooperative Extension Service.

ERIC Educational Resources Information Center

Sell, William Horace

Based on surveys in 1957 and 1960 in six Georgia counties, this study evaluated an intensive educational program by the University of Georgia, and investigated attitudes and other factors related to farmers' use of fertilizers. Respondents were ranked by amounts of plant nutrients applied per acre in 1957 and by fertility per farm. Findings…

The 1989 Georgia Survey of Adolescent Drug and Alcohol Use. Volume I: The Narrative Report for Survey Findings.

ERIC Educational Resources Information Center

Adams, Ronald D.; And Others

The 1989 Georgia Survey of Adolescent Drug and Alcohol Use was conducted in 373 schools throughout Georgia. The stratified random sample was obtained from schools that participated in the 1987 survey (in which 93% of the school systems in Georgia participated) and were selected randomly from strata based on size of community and geographic…

Handbook for Georgia Legislators, 6th Edition [And] Classroom Activities to Use with Handbook for Georgia Legislators, 6th Edition.

ERIC Educational Resources Information Center

Jackson, Edwin L.

This document contains a handbook and a booklet of classroom activities to use with the handbook. The handbook is a compilation of the law, procedures, and practices which govern the legislative process in Georgia. It addresses the practical problems faced by members of the Georgia legislature. Chapter one discusses the General Assembly, its…

Engaging the Demons. Report on a Collaboration between English Faculty of Baldwin High School and Georgia College & State University, Milledgeville, Georgia: 2001-02.

ERIC Educational Resources Information Center

Carriere, Peter M.; Smith, Melissa

A collaborative project between Georgia College and State University (GC&SU) and Baldwin High School (BHS) in Milledgeville, Georgia, had as its initial goals: to provide an opportunity for two-way mentoring between the GC&SU's Arts and Sciences faculty and BHS's English faculty; to improve curriculum alignment; to establish realistic…

76 FR 27919 - Vidalia Onions Grown in Georgia; Change in Late Payment and Interest Requirements on Past Due...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-05-13

... CFR Part 955 [Doc. No. AMS-FV-11-0016; FV11-955-1 PR] Vidalia Onions Grown in Georgia; Change in Late... assessment requirements in effect under the marketing order for Vidalia onions grown in Georgia (order). The order regulates the handling of Vidalia onions grown in Georgia and is administered locally by the...

The Georgia Feasibility Study: The Development of Alternative Community Services for the Current Residents of Georgia Retardation Center and the Southwest Developmental Center at Bainbridge.

ERIC Educational Resources Information Center

Bradley, Valerie J.; And Others

The report explores the feasibility of placing 565 severely mentally retarded residents of the Georgia Retardation Center and Southwestern Developmental Center at Bainbridge, Georgia, in alternative community living and daytime arrangements. The seven mental retardation service areas which had placed most of these residents were the focus of…

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.

Historic streetcar systems in Georgia.

DOT National Transportation Integrated Search

2012-01-31

The Georgia Department of Transportation (GDOT) and the Federal Highway Administration : (FHWA) have funded the development of a context for resources associated with Georgias : historic streetcar systems, with a focus on the metro Atlanta area, t...

78 FR 21703 - Environmental Impact Statement: Cherokee and Forsyth Counties, Georgia

Federal Register 2010, 2011, 2012, 2013, 2014

2013-04-11

... transportation project (State Route 20) located in Cherokee and Forsyth Counties, Georgia. FOR FURTHER... this program.) Issued on: April 5, 2013. Rodney N. Barry, Division Administrator, Atlanta, Georgia. [FR...

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.

Mansonia titillans: New Resident Species or Infrequent Visitor in Chatham County, Georgia, and Beaufort County, South Carolina, USA.

PubMed

Moulis, Robert A; Peaty, Laura F A W; Heusel, Jeffrey L; Lewandowski, Henry B; Harrison, Bruce A; Kelly, Rosmarie; Hager, Elizabeth J

2015-06-01

In September, October, and November 2014, adult Mansonia titillans were collected at 4 separate sites near Savannah in Chatham County, Georgia, and 1 site in Muscogee County, GA, during routine mosquito surveillance. Although previously recorded from Beaufort County, SC, and several inland southern Georgia counties, recent reports of this species from coastal Georgia or South Carolina are lacking. These newly captured Ma. titillans specimens represent the first documented records for Muscogee County and Chatham County, GA, and may indicate a recent northern expansion or reintroduction of this species along the Georgia and South Carolina coast.

[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.

Project Georgia High School/High Tech

NASA Technical Reports Server (NTRS)

2000-01-01

The High School/High Tech initiative of the President's Committee on Employment of Disabilities, Georgia's application of the collaborative "Georgia Model" and NASA's commitment of funding have shown that opportunities for High School/High Tech students are unlimited. In Georgia, the partnership approach to meeting the needs of this program has opened doors previously closed. As the program grows and develops, reflecting the needs of our students and the marketplace, more opportunities will be available. Our collaboratives are there to provide these opportunities and meet the challenge of matching our students with appropriate education and career goals. Summing up the activities and outcomes of Project Georgia High School/High Tech is not difficult. Significant outcomes have already occurred in the Savannah area as a result of NASA's grant. The support of NASA has enabled Georgia Committee to "grow" High School/High Tech throughout the region-and, by example, the state. The success of the Columbus pilot project has fostered the proliferation of projects, resulting in more than 30 Georgia High School High Tech programs-with eight in the Savannah area.

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.

Culex coronator in coastal Georgia and South Carolina.

PubMed

Moulis, Robert A; Russell, Jennifer D; Lewandowski, Henry B; Thompson, Pamela S; Heusel, Jeffrey L

2008-12-01

In 2007, adult Culex coronator were collected in Chatham County, Georgia, and Jasper County, South Carolina, during nuisance and disease vector surveillance efforts. A total of 75 specimens of this species were collected at 8 widely separated locations in Chatham County, Georgia, and 4 closely situated sites in Jasper County, South Carolina. These represent the first Atlantic coastal records of this species in Georgia and the first confirmed records of Cx. coronator in South Carolina.

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.

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

PubMed

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

2007-01-01

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

Multimodal needs, constraints, and opportunities : observations and lessons learned for Georgia and GDOT.

DOT National Transportation Integrated Search

2013-10-01

This research project assessed the multimodal transportation needs, constraints, and opportunities facing : the state of Georgia and the Georgia Department of Transportation (GDOT). The project report : includes: 1) a literature review focusing on th...

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.

Shared vision, collective impact, and persistent challenges: the first decade of Georgia's oncology research network.

PubMed

Paris, Nancy M; Burke, James J; Schnell, Frederick M

2013-11-01

Ten years ago, Georgia was lauded for dedicating a portion of tobacco settlement funds to the Georgia Cancer Coalition (GCC). The plan championed by then-Governor Roy E. Barnes was designed to make Georgia a leader in prevention, treatment, and research. This plan called for the expansion of clinical trials to ensure Georgians had access to the highest quality care based on the most current treatments and discoveries. As a result, oncologists in the state were engaged in a planning process that resulted in a shared vision to improve the quality of cancer care through research and the formation of a new organization: the Georgia Center for Oncology Research and Education.

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.

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.

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.

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.

Study of Georgia's pavement deterioration/life and potential risks of delayed pavement resurfacing and rehabilitation.

DOT National Transportation Integrated Search

2016-08-01

Georgia has continuously been rated as one of the states with the smoothest pavements in the United States because the Georgia Department of Transportation (GDOT) has established a standardized pavement condition evaluation system (PACES) for consist...

78 FR 11724 - Georgia Disaster #GA-00051

Federal Register 2010, 2011, 2012, 2013, 2014

2013-02-19

... SMALL BUSINESS ADMINISTRATION [Disaster Declaration 13481 and 13482] Georgia Disaster GA-00051 AGENCY: Small Business Administration. ACTION: Notice. SUMMARY: This is a notice of an Administrative declaration of a disaster for the State of Georgia dated 02/08/2013. Incident: Severe Storms and Tornadoes...

77 FR 34037 - Georgia-Alabama-South Carolina System of Projects

Federal Register 2010, 2011, 2012, 2013, 2014

2012-06-08

... Marketing Division, Southeastern Power Administration, Department of Energy, 1166 Athens Tech Road, Elberton... a public information and comment forum for the Georgia-Alabama-South Carolina customers and... before June 5, 2012. The Georgia-Alabama-South Carolina customers, through their representatives, have...

77 FR 1546 - Georgia Disaster #GA-00038

Federal Register 2010, 2011, 2012, 2013, 2014

2012-01-10

... SMALL BUSINESS ADMINISTRATION [Disaster Declaration 12978 and 12979] Georgia Disaster GA-00038 AGENCY: U.S. Small Business Administration. ACTION: Notice. SUMMARY: This is a notice of an Administrative declaration of a disaster for the State of Georgia dated 12/29/2011. Incident: Severe Storms...

Simulation and Particle-Tracking Analysis of Selected Ground-Water Pumping Scenarios at Vogtle Electric Generation Plant, Burke County, Georgia

USGS Publications Warehouse

Cherry, Gregory S.; Clarke, John S.

2007-01-01

The source of ground water to production wells at Vogtle Electric Generation Plant (VEGP), a nuclear power plant in Burke County, Georgia, was simulated under existing (2002) and potential future pumping conditions using an existing U.S. Geological Survey (USGS) MODFLOW ground-water flow model of a 4,455-square-mile area in the Coastal Plain of Georgia and South Carolina. Simulation results for three steady-state pumping scenarios were compared to each other and to a 2002 Base Case condition. The pumping scenarios focused on pumping increases at VEGP resulting from projected future demands and the addition of two electrical-generating reactor units. Scenarios simulated pumping increases at VEGP ranging from 1.09 to 3.42 million gallons per day (Mgal/d), with one of the scenarios simulating the elimination of 5.3 Mgal/d of pumping at the Savannah River Site (SRS), a U.S. Department of Energy facility located across the Savannah River from VEGP. The largest simulated water-level changes at VEGP were for the scenario whereby pumping at the facility was more than tripled, resulting in drawdown exceeding 4-8 feet (ft) in the aquifers screened in the production wells. For the scenario that eliminated pumping at SRS, water-level rises of as much as 4-8 ft were simulated in the same aquifers at SRS. Results of MODFLOW simulations were analyzed using the USGS particle-tracking code MODPATH to determine the source of water and associated time of travel to VEGP production wells. For each of the scenarios, most of the recharge to VEGP wells originated in an upland area near the county line between Burke and Jefferson Counties, Georgia, with none of the recharge originating on SRS or elsewhere in South Carolina. An exception occurs for the scenario whereby pumping at VEGP was more than tripled. For this scenario, some of the recharge originates in an upland area in eastern Barnwell County, South Carolina. Simulated mean time of travel from recharge areas to VEGP wells for the Base Case and the three other pumping scenarios was between about 2,700 and 3,800 years, with some variation related to changes in head gradients because of pumping changes.

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.

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.

A projection of motor fuel tax revenue and analysis of alternative revenue sources in Georgia.

DOT National Transportation Integrated Search

2012-05-01

Motor fuel tax revenue currently supplies the majority of funding for : transportation agencies at the state and federal level. Georgia uses excise and sales taxes : to generate revenue for the Georgia Department of Transportation (GDOT). Inflation a...

Student Assessment Handbook, 2000-2001. Georgia Statewide Student Assessment Program.

ERIC Educational Resources Information Center

Georgia State Dept. of Education, Atlanta. Office of Research, Evaluation, and Assessment.

This handbook contains information about the statewide testing programs in Georgia. These programs provide a comprehensive perspective on students' educational achievement from kindergarten through high school. This guide contains information on these statewide assessments: (1) the Georgia Kindergarten Assessment Program-Revised; (2) the…

77 FR 51099 - Georgia Disaster #GA-00046

Federal Register 2010, 2011, 2012, 2013, 2014

2012-08-23

... SMALL BUSINESS ADMINISTRATION [Disaster Declaration 13213 and 13214] Georgia Disaster GA-00046 AGENCY: U.S. Small Business Administration. ACTION: Notice. SUMMARY: This is a notice of an Administrative declaration of a disaster for the State of Georgia dated 08/14/2012 Incident: Severe storms and...

Georgia Mediagraphy. Second Supplement.

ERIC Educational Resources Information Center

Georgia State Dept. of Education, Atlanta. Office of Instructional Services.

This document is a guide to print and nonprint materials about Georgia and Georgians. Entries are arranged under the subject headings used in "Essential Skills for Georgia Schools." Criteria for inclusion were appropriateness for K-12 students and commercial availability of the item. Six books containing pictorial and photographic…

Study of Georgia's pavement deterioration/life and potential risks of delayed pavement resurfacing and rehabilitation : final report.

DOT National Transportation Integrated Search

2016-08-01

Georgia has continuously been rated as one of the states with the smoothest pavements in the United States because the Georgia Department of Transportation (GDOT) has established a standardized pavement condition evaluation system (PACES) for consist...

Study of Georgia's pavement deterioration/life and potential risks of delayed pavement resurfacing and rehabilitation : final report.

DOT National Transportation Integrated Search

2016-08-01

Georgia has continuously been rated as one of the states with the smoothest pavements in the United States : because the Georgia Department of Transportation (GDOT) has established a standardized pavement condition : evaluation system (PACES) for con...

Georgia's Ground-Water Resources and Monitoring Network, 2006

USGS Publications Warehouse

Nobles, Patricia L.

2006-01-01

The U.S. Geological Survey (USGS) ground-water network for Georgia currently consists of 170 wells in which ground-water levels are continuously monitored. Most of the wells are locatedin the Coastal Plain in the southern part of the State where ground-water pumping stress is high. In particular, there are large concentrations of wells in coastal and southwestern Georgia areas, where there are issues related to ground-water pumping, saltwater intrusion along the coast, and diminished streamflow in southwestern Georgia due to irrigation pumping. The map at right shows the USGS ground-water monitoring network for Georgia. Ground-water levels are monitored in 170 wells statewide, of which 19 transmit data in real time via satellite and posted on the World Wide Web at http://waterdata.usgs.gov/ga/nwis/current/?type=gw . A greater concentration of wells occurs in the Coastal Plain where there are several layers of aquifers and in coastal and southwestern Georgia areas, which are areas with specific ground-water issues.

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.

BEYOND THE INDICES: RELATIONS OF HABITAT AND FISH CHARACTERISTICS IN THE GEORGIA PIEDMONT

EPA Science Inventory

The Georgia Department of Natural Resources has conducted biological sampling at 180 stream sites in the Georgia Piedmont (1998-99) and recorded several trophic and abundance characteristics of the fish assemblages and habitat at each site. These characteristics were combined to ...

Integrating Engineering Design into Technology Education: Georgia's Perspective

ERIC Educational Resources Information Center

Denson, Cameron D.; Kelley, Todd R.; Wicklein, Robert C.

2009-01-01

This descriptive research study reported on Georgia's secondary level (grades 6-12) technology education programs capability to incorporate engineering concepts and/or engineering design into their curriculum. Participants were middle school and high school teachers in the state of Georgia who currently teach technology education. Participants…

78 FR 28776 - Approval and Promulgation of Implementation Plans; Georgia; State Implementation Plan...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-16

... Section of this Federal Register, EPA is approving the State's implementation plan revision as a direct... Promulgation of Implementation Plans; Georgia; State Implementation Plan Miscellaneous Revisions AGENCY... State Implementation Plan (SIP) submitted by the Georgia Environmental Protection Division to EPA in...

75 FR 67950 - The University of Georgia (UGA), et al.;

Federal Register 2010, 2011, 2012, 2013, 2014

2010-11-04

... DEPARTMENT OF COMMERCE International Trade Administration The University of Georgia (UGA), et al.; Notice of Decision on Applications for Duty-Free Entry of Scientific Instruments This is a decision...., NW., Washington, DC. Docket Number: 10-054. Applicant: The University of Georgia (UGA), Athens, GA...

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...

77 FR 24399 - Approval and Promulgation of Implementation Plans; Georgia; Atlanta; Ozone 2002 Base Year...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-04-24

... Promulgation of Implementation Plans; Georgia; Atlanta; Ozone 2002 Base Year Emissions Inventory AGENCY... approve the ozone 2002 base year emissions inventory, portion of the state implementation plan (SIP... Atlanta, Georgia (hereafter referred to as ``the Atlanta Area'' or ``Area''), ozone attainment...

Ten Genome Sequences of Human and Livestock Isolates of Bacillus anthracis from the Country of Georgia

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

Khmaladze, Ekaterine; Dzavashvili, Giorgi; Chanturia, Gvantsa

Bacillus anthracis causes the acute fatal disease anthrax, is a proven biological weapon, and is endemic in Georgia, where human and animal cases are reported annually. Furthermore, we present whole-genome sequences of 10 historical B. anthracis strains from Georgia.

Armenia, Azerbaijan, and Georgia: Political Developments and Implications for U.S. Interests

DTIC Science & Technology

2009-04-09

Defense Minister Irakli Okruashvili in late September 2007, in the wake of his sensational allegations that Saakashvili had once ordered him to...Burjanadze, head of the Democratic Movement-United Georgia Party, and former U.N. ambassador Irakly Alasania, head of the Alliance for Georgia bloc

Armenia, Azerbaijan, and Georgia: Political Developments and Implications for U.S. Interests

DTIC Science & Technology

2009-07-13

detention on corruption charges of former Defense Minister Irakli Okruashvili in late September 2007, in the wake of his sensational allegations that...United Georgia Party, and former U.N. ambassador Irakly Alasania, head of the Alliance for Georgia bloc. The April 9 demonstration was the beginning

Establishment of perennial grass species for cellulosic biofuel production in Georgia

USDA-ARS?s Scientific Manuscript database

In order for biofuels to become a viable alternative energy source in the state of Georgia, appropriate feed stocks must be developed to supply this burgeoning industry. Georgia is optimum for biomass production because of its warm subtropical climate, large number of growing degree days, and an es...

33 CFR 110.72b - St. Simons Island, Georgia.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 33 Navigation and Navigable Waters 1 2011-07-01 2011-07-01 false St. Simons Island, Georgia. 110.72b Section 110.72b Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY ANCHORAGES ANCHORAGE REGULATIONS Special Anchorage Areas § 110.72b St. Simons Island, Georgia. The area...

33 CFR 110.72b - St. Simons Island, Georgia.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 33 Navigation and Navigable Waters 1 2012-07-01 2012-07-01 false St. Simons Island, Georgia. 110.72b Section 110.72b Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY ANCHORAGES ANCHORAGE REGULATIONS Special Anchorage Areas § 110.72b St. Simons Island, Georgia. The area...

33 CFR 110.72b - St. Simons Island, Georgia.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 33 Navigation and Navigable Waters 1 2013-07-01 2013-07-01 false St. Simons Island, Georgia. 110.72b Section 110.72b Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY ANCHORAGES ANCHORAGE REGULATIONS Special Anchorage Areas § 110.72b St. Simons Island, Georgia. The area...

33 CFR 110.72b - St. Simons Island, Georgia.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 33 Navigation and Navigable Waters 1 2014-07-01 2014-07-01 false St. Simons Island, Georgia. 110.72b Section 110.72b Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY ANCHORAGES ANCHORAGE REGULATIONS Special Anchorage Areas § 110.72b St. Simons Island, Georgia. The area...

An Interdisciplinary, Non-Credit Community Course in Adult Development and Aging.

ERIC Educational Resources Information Center

Wray, Robert P.

Aided by the Georgia Community Continuing Education Service (State Agency, Title 1, Higher Educational Act of 1965), the University of Georgia Council on Gerontology induced Georgia colleges and universities to cooperate to help practitioners and community leaders learn about the sociological, physiological, psychological, economic, and community…

The Georgia Health Education Study: A Summary Report.

ERIC Educational Resources Information Center

Georgia Univ., Athens. Dept. of Health and Safety.

This summary review of the Georgia Health Education Study is a statistical presentation of scores achieved by over four thousand freshman college students in the university system of Georgia to questions on health knowledge. Data compiled from the administration of the Fast-Tyson Health Knowledge Test (1975 revision) indicates that subject…

Georgia's Health Professions: A Decade of Change, 1985-1995.

ERIC Educational Resources Information Center

Morris, Libby V.; Little, Catherine J.

This report examines the supply of and demand for health care professionals in the state of Georgia, including information on education, demographics, and workforce changes. Supply data analyzed included licensure and certification records; a survey of Georgia's major health care institutions provided demand data. Additionally, institutions of…

The Case for High-Performance, Healthy Green Schools

ERIC Educational Resources Information Center

Carter, Leesa

2011-01-01

When trying to reach their sustainability goals, schools and school districts often run into obstacles, including financing, training, and implementation tools. Last fall, the U.S. Green Building Council-Georgia (USGBC-Georgia) launched its High Performance, Healthy Schools (HPHS) Program to help Georgia schools overcome those obstacles. By…

78 FR 148 - Additional Designations of Individuals Pursuant to Executive Order 13581

Federal Register 2010, 2011, 2012, 2013, 2014

2013-01-02

..., Russia; Varketili Masivi, 4th Block, 1st Building, Flat 30, Tbilisi, Georgia; DOB 20 Mar 1953; POB Tbilisi, Georgia; citizen Georgia; alt. citizen Russia; Passport 60- 4145924 (Russia); alt. Passport 60-4145934 (Russia) (individual) [TCO] 2. ANAPIYAEV, Almanbet Mamadaminovich (a.k.a. ANAPIYAEV, Almanbaet; a...

Ten Genome Sequences of Human and Livestock Isolates of Bacillus anthracis from the Country of Georgia

DOE PAGES

Khmaladze, Ekaterine; Dzavashvili, Giorgi; Chanturia, Gvantsa; ...

2017-05-11

Bacillus anthracis causes the acute fatal disease anthrax, is a proven biological weapon, and is endemic in Georgia, where human and animal cases are reported annually. Furthermore, we present whole-genome sequences of 10 historical B. anthracis strains from Georgia.

30 CFR 910.817 - Performance standards-underground mining activities.

Code of Federal Regulations, 2013 CFR

2013-07-01

... with the Georgia Safe Dams Act and Rules for Safety of the Natural Resources, Environmental Protection Division; the Solid Waste Management Rules of the Georgia Department of Natural Resources, Environmental Protection Division, Chapter 391-3-4; and the Georgia Seed Laws and Regulation 4. [47 FR 36399, Aug. 19, 1982...

30 CFR 910.816 - Performance standards-surface mining activities.

Code of Federal Regulations, 2013 CFR

2013-07-01

... except in compliance with the Georgia Safe Dams Act and Rules for Safety of the Natural Resources, Environmental Protection Division; the Solid Waste Management Rules of the Georgia Department of Natural Resources, Environmental Protection Division, Chapter 391-3-4; and the Georgia Seed Laws and Regulation 4...

30 CFR 910.817 - Performance standards-underground mining activities.

Code of Federal Regulations, 2014 CFR

2014-07-01

... with the Georgia Safe Dams Act and Rules for Safety of the Natural Resources, Environmental Protection Division; the Solid Waste Management Rules of the Georgia Department of Natural Resources, Environmental Protection Division, Chapter 391-3-4; and the Georgia Seed Laws and Regulation 4. [47 FR 36399, Aug. 19, 1982...

30 CFR 910.817 - Performance standards-underground mining activities.

Code of Federal Regulations, 2012 CFR

2012-07-01

... with the Georgia Safe Dams Act and Rules for Safety of the Natural Resources, Environmental Protection Division; the Solid Waste Management Rules of the Georgia Department of Natural Resources, Environmental Protection Division, Chapter 391-3-4; and the Georgia Seed Laws and Regulation 4. [47 FR 36399, Aug. 19, 1982...

30 CFR 910.816 - Performance standards-surface mining activities.

Code of Federal Regulations, 2012 CFR

2012-07-01

... except in compliance with the Georgia Safe Dams Act and Rules for Safety of the Natural Resources, Environmental Protection Division; the Solid Waste Management Rules of the Georgia Department of Natural Resources, Environmental Protection Division, Chapter 391-3-4; and the Georgia Seed Laws and Regulation 4...

30 CFR 910.816 - Performance standards-surface mining activities.

Code of Federal Regulations, 2014 CFR

2014-07-01

... except in compliance with the Georgia Safe Dams Act and Rules for Safety of the Natural Resources, Environmental Protection Division; the Solid Waste Management Rules of the Georgia Department of Natural Resources, Environmental Protection Division, Chapter 391-3-4; and the Georgia Seed Laws and Regulation 4...

77 FR 67639 - Liberty Energy (Georgia) Corp.; Notice of Application

Federal Register 2010, 2011, 2012, 2013, 2014

2012-11-13

... (Georgia) Corp.; Notice of Application Take notice that on October 25, Liberty Energy (Georgia) Corp..., an application pursuant to section 7(f) of the Natural Gas Act (NGA) requesting the determination of...) such further relief the Commission may deem appropriate, all as more fully set forth in the application...

The Evolution of the Georgia Tech Library Circulation Department

ERIC Educational Resources Information Center

Glover, Karen

2006-01-01

The author reviews the evolution of the Circulation Department at the Georgia Institute of Technology (Georgia Tech) Library and Information Center from 2001 to the present. It is shown how a traditional circulation department with poor customer relations transformed itself by adopting innovative policies and services leading to improved customer…

Economic Yearbook from Georgia Trend Magazine, 1996.

ERIC Educational Resources Information Center

Hamilton, John

Based on information from "Georgia Trend" magazine examining economic conditions across Georgia, Gainesville College (GC) is expected to experience an expanding base of students over the next 5 years. With respect to Hall County and the nine contiguous counties that make up GC's service area, data indicate a population growth in the…

Running around in Circles: Quality Assurance Reforms in Georgia

ERIC Educational Resources Information Center

Jibladze, Elene

2013-01-01

This article investigates the implementation of a quality assurance system in Georgia as a particular case of "Bologna transplant" in a transitioning country. In particular, the article discusses to what extent new concepts, institutions and models framed as "European" have been institutionalised in Georgia. Based on an outcome…

The Feasibility of Establishing Satellite Campuses for Georgia State University.

ERIC Educational Resources Information Center

Strickland, Wayne G.

Georgia State University, one of the southeast's major urban universities, is considering new methods of delivering its educational services. This report addresses the concept of satellite campuses for Georgia State University by examining those factors affecting their development. Topics included are: the market potential for educational services…

Georgia Institute of Technology chilled water system evaluation and master plan

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

NONE

1996-05-15

As the host of the Olympic Village for the 1996 Atlanta Olympics, Georgia Tech has experienced a surge in construction activities over the last three years. Over 1.3 million square feet of new buildings have been constructed on the Georgia Tech campus. This growth has placed a strain on the Georgia Tech community and challenged the facilities support staff charged with planning and organizing utility services. In concert with Olympic construction, utility planners have worked to ensure long term benefits for Georgia Tech facilities while meeting the short term requirements of the Olympic Games. The concentration of building construction inmore » the northwest quadrant of the campus allowed planners to construct a satellite chilled water plant to serve the needs of this area and provide the opportunity to integrate this section of the campus with the main campus chilled water system. This assessment and master plan, funded in part by the US Department of Energy, has evaluated the chilled water infrastructure at Georgia Tech, identified ongoing problems and made recommendations for long term chilled water infrastructure development and efficiency improvements. The Georgia Tech office of Facilities and RDA Engineering, Inc. have worked together to assemble relevant information and prepare the recommendations contained in this document.« 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

Glaciers change over the last century, Caucasus Mountains, Georgia, observed by the old topographical maps, Landsat and ASTER satellite imagery

NASA Astrophysics Data System (ADS)

Tielidze, L. G.

2015-07-01

The study of glaciers in the Caucasus began in the first quarter of the 18th century. The first data on glaciers can be found in the works of great Georgian scientist Vakhushti Bagrationi. After almost hundred years the foreign scientists began to describe the glaciers of Georgia. Information about the glaciers of Georgia can be found in the works of W. Abich (1865), D. Freshfield (1869), G. Radde (1873), N. Dinik (1884), I. Rashevskiy (1904), A. Reinhardt (1916, 1917) etc. The first statistical information about the glaciers of Georgia are found in the catalog of the Caucasus glaciers compiled by K. Podozerskiy in 1911 (Podozerkiy, 1911). Then, in 1960s the large-scale (1:25 000, 1:50 000) topographic maps were published, which were compiled in 1955-1960 on the basis of the space images. On the basis of the mentioned maps R. Gobejishvili gave quite detailed statistical information about the glaciers of Georgia (Gobejishvili, 1989). Then in 1975 the glaciological catalog of the former USSR was published (The Catalog of Glaciers of the USSR, Vol. 8-9, 1975), where the statistical information about the glaciers of Georgia was obtained on the basis of the space images of 1970-1975. Thus, complete statistical information on the glaciers of Georgia has not been published for about last 40 years. Data obtained by us by processing of the space images of Landsat and ASTER is the latest material, which is the best tool for identification of the change in the number and area of the glaciers of Georgia during the last one century. The article presents the percentage and quantitative changes in the number and area of the glaciers of Georgia in the years of 1911-1960-1975-2014, according to the individual river basins. The air temperature course of the Georgia's high mountain weather stations has been studied. The river basins have been revealed, where there are the highest indices of the reduction in area and number of the glaciers and the reasons have been explained.

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.

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.

2011 Atlanta, Georgia, Regional Travel Survey | Transportation Secure Data

Science.gov Websites

Center | NREL Atlanta, Georgia, Regional Travel Survey 2011 Atlanta, Georgia, Regional Travel Survey To improve regional travel demand forecasts, the 2011 Regional Travel Survey collected trip data the Atlanta Regional Commission (ARC), the survey was conducted by PTV NuStats, GeoStats, and PG

"Making the Difficult Choice": Understanding Georgia's Test-Based Grade Retention Policy in Reading

ERIC Educational Resources Information Center

Huddleston, Andrew P.

2015-01-01

The author uses Bourdieu's concepts of field, capital, and habitus to analyze how students, parents, teachers, and administrators are responding to Georgia's test-based grade retention policy in reading at one Georgia elementary school. In this multiple case study, the author interviewed, observed, and collected documents regarding ten fifth…

Forest statistics for Georgia, 1997

Treesearch

Michael T. Thompson

1998-01-01

This report summarizes a 1997 inventory of the forest resources for the State of Georgia. Major findings are highlighted in text and graphs; detailed data are presented in 51 tables. This report highlights the principal findings of the seventh forest survey of Georgia. Field work began in November 1995 and was completed in April 1998. Six previous surveys,...

Georgia Mediagraphy and First Supplement, 1985.

ERIC Educational Resources Information Center

Georgia State Dept. of Education, Atlanta. Office of Instructional Services.

This guide is designed to assist media specialists in building resource collections and teachers in meeting state requirements for the study of Georgia; it will provide support to other curricular areas, as well. It is limited to print and non-print materials about Georgia, and is divided into sections on art, music, language arts, science,…

Focus on the Future of Georgia 1970-1985.

ERIC Educational Resources Information Center

Schabacker, William H., Ed.; And Others

As part of the Georgia Assessment Project (GAP), initiated in January 1969 to provide statewide measurement of the impact of educational programs, services, and resources on children and youth, 19 position papers were prepared by specialists to assist the Advisory Commission on Education Goals. The papers, some with critiques, concern Georgia's…

Race to the Top. Georgia Report. Year 2: School Year 2011-2012. [State-Specific Summary Report

ERIC Educational Resources Information Center

US Department of Education, 2013

2013-01-01

This State-specific summary report serves as an assessment of Georgia's Year 2 Race to the Top implementation, highlighting successes and accomplishments, identifying challenges, and providing lessons learned from implementation from approximately September 2011 through September 2012. During Year 2, Georgia had a range of accomplishments across…

78 FR 2878 - Approval and Promulgation of Implementation Plans; Georgia: New Source Review-Prevention of...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-01-14

... Deterioration'' to approve changes to Georgia's SIP-approved regulations entitled ``Air Quality Control Rule 391... a separate action, the correct version of EPA's proposed rulemaking related to Georgia's Air Quality Control Rule 391-3-.1 is being provided for public comment. This course of action will promote efficiency...

Forests of Georgia, 2013

Treesearch

T.J. Brandeis

2015-01-01

This resource update provides an overview of forest resources in Georgia based on an inventory conducted by the U.S. Forest Service, Forest Inventory and Analysis (FIA) program at the Southern Research Station in cooperation with the Georgia Forestry Commission. Estimates are based on field data collected using the FIA annualized sample design and are updated yearly....

An Experiment in Autonomy: Secondary School Television Use in Two Coastal Georgia Schools.

ERIC Educational Resources Information Center

Haugh, Rita; Neubert, Nancy Malecek

Two high schools in the coastal Georgia area, serviced by a local television broadcasting station, participated in a pilot study for the Regional Instructional Television Project initiated by the Georgia State Board of Education. The Project's overall objective was to overcome two barriers to effective instructional television use in…

78 FR 52219 - State of Georgia Relinquishment of Sealed Source and Device Evaluation and Approval Authority

Federal Register 2010, 2011, 2012, 2013, 2014

2013-08-22

... NUCLEAR REGULATORY COMMISSION [NRC-2013-0196] State of Georgia Relinquishment of Sealed Source and... evaluate and approve sealed source and device (SS&D) applications in the State of Georgia and approved the... regulatory authority for evaluating and approving sealed source and device applications on August 20, 2013...

78 FR 11733 - Georgia Southwestern Railroad, Inc.-Discontinuance of Service Exemption-in Chattahoochee, Marion...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-02-19

... discontinue service, not to abandon the line, trail use/rail banking and public use conditions are not... No. AB 290 (Sub-No. 344X)] Georgia Southwestern Railroad, Inc.--Discontinuance of Service Exemption...--Discontinuance of Service Exemption--in Chattahoochee, Marion, and Schley Counties, GA Central of Georgia...

Physical activity in Georgia state parks: A pilot study

Treesearch

Lincoln R. Larson; Jason W. Whiting; Gary T. Green

2012-01-01

This pilot study assessed the role of Georgia State Parks in the promotion of physical activity among different racial/ethnic and age groups. Data were collected at three state parks in north Georgia during the summer of 2009 using two research methods: behavior observations (N=2281) and intercept surveys (N=473).

75 FR 74673 - Approval and Promulgation of Implementation Plans; Georgia: Stage II Vapor Recovery

Federal Register 2010, 2011, 2012, 2013, 2014

2010-12-01

... includes multiple modifications to Georgia's Air Quality Rules found at Chapter 391-3-1. Previously, EPA...., Atlanta, Georgia 30303-8960. 5. Hand Delivery or Courier: Ms. Lynorae Benjamin, Regulatory Development... (404) 562-9029. Ms. Spann can also be reached via electronic mail at [email protected]epa.gov . SUPPLEMENTARY...

78 FR 45898 - Vidalia Onions Grown in Georgia; Continuance Referendum

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-30

... Service 7 CFR Part 955 [Docket No. AMS-FV-13-0037; FV13-955-2 CR] Vidalia Onions Grown in Georgia... document directs that a referendum be conducted among eligible producers of Vidalia onions grown in Georgia... Vidalia onions produced in the production area. DATES: The referendum will be conducted from September 9...

Forests of Georgia, 2014

Treesearch

Thomas Brandeis; Andy Hartsell

2015-01-01

This resource update provides an overview of forest resources in Georgia based on an inventory conducted by the U.S. Forest Service, Forest Inventory and Analysis (FIA) program at the Southern Research Station in cooperation with the Georgia Forestry Commission. Estimates are based on field data collected using the FIA annualized sample design and are updated yearly....

75 FR 71018 - Approval and Promulgation of Implementation Plans; Georgia; Prevention of Significant...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-11-22

... and Promulgation of Implementation Plans; Georgia; Prevention of Significant Deterioration and... Plan (SIP) submitted by the State of Georgia in three submittals dated October 31, 2006, March 5, 2007... Nonattainment New Source Review (NNSR) permitting rules in the SIP to address changes to the federal New Source...

School Accountability: Mathematics Teachers Struggling with Change

ERIC Educational Resources Information Center

Obara, Samuel

2011-01-01

In this period of accountability advocated by the No Child Left Behind Act of 2001, testing has been selected as a primary means of measuring the performance of schools. The State of Georgia is in the process of replacing its old curriculum--Georgia's Quality Core Curriculum (QCC) with a new curriculum--Georgia Performance Standards (GPS) to…

Battle of the Wills

ERIC Educational Resources Information Center

Powell, Tracie

2009-01-01

Georgia, like many other states, is facing a budget shortfall of about $2.5 billion, according to the Georgia Budget & Policy Institute. To help cope with its money woes, the state's university system alone has to make at least $200 million in cuts, if not more. As the Georgia Senate chairman of the Higher Education Committee, Seth Harp…

Projected climate change for the coastal plain region of Georgia, USA

USDA-ARS?s Scientific Manuscript database

Climatic patterns for the Coastal Plain region of Georgia, USA, centered on Tifton, Georgia (31 28 30N, 83 31 54W) were examined for long term patterns in precipitation and air temperature. Climate projections based upon output from seven Global Circulation Models (GCMs) and three future Green Hous...

Deposits of Claiborne and Jackson age in Georgia

USGS Publications Warehouse

Cooke, Charles Wythe; Shearer, Harold Kurtz

1919-01-01

In 1911 the Geological Survey of Georgia published as Bulletin 26 a "Preliminary report on the geology of the Coastal Plain of Georgia," by Otto Veatch and Lloyd William Stephenson, prepared in cooperation with the United States Geological Survey under the supervision of T. Wayland Vaughan, a geologist in charge of Coastal Plain investigations, who contributed the determinations of the invertebrate fossils of the Tertiary and Quaternary formations. Although this report constituted a decided advance in our knowledge of the geology of the Coastal Plain of Georgia, it was admittedly of reconnaissance character, and corrections and additions to it were to be expected. During the last few years field work has been prosecuted vigorously in the Coastal Plain of Georgia, and the additional information thus accumulated throws light upon certain problems of stratigraphy left unsolved by Veatch and Stephenson and alters considerably some of their correlations. The object of the present paper is to present the new evidence regarding the age and correlation of the Eocene formations of Georgia and to revise in accordance with present knowledge the descriptions of the deposits of Claiborne and Jackson age.

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.

The results of the pilot project in Georgia to install a network of electromagnetic radiation before the earthquake

NASA Astrophysics Data System (ADS)

Machavariani, Kakhaber; Khazaradze, Giorgi; Turazashvili, Ioseb; Kachakhidze, Nino; Kachakhidze, Manana; Gogoberidze, Vitali

2016-04-01

The world's scientific literature recently published many very important and interesting works of VLF / LF electromagnetic emissions, which is observed in the process of earthquake preparation. This works reliable earthquake prediction in terms of trends. Because, Georgia is located in Trans Asian earthquake zone, VLF / LF electromagnetic emissions network are essential. In this regard, it was possible to take first steps. It is true that our university has Shota Rustaveli National Science Foundation № DI / 21 / 9-140 / 13 grant, which included the installation of a receiver in Georgia, but failed due to lack of funds to buy this device. However, European friends helped us (Prof. Dr. PF Biagi and Prof. Dr. Aydın BÜYÜKSARAÇ) and made possible the installation of a receiver. Turkish scientists expedition in Georgia was organized in August 2015. They brought with them VLF / LF electromagnetic emissions receiver and together with Georgian scientists install near Tbilisi. The station was named GEO-TUR. It should be noted that Georgia was involved in the work of the European network. It is possible to completely control the earthquake in Georgia in terms of electromagnetic radiation. This enables scientists to obtain the relevant information not only on the territory of our country, but also on seismically active European countries as well. In order to maintain and develop our country in this new direction, it is necessary to keep independent group of scientists who will learn electromagnetic radiation ahead of an earthquake in Georgia. At this stage, we need to remedy this shortcoming, it is necessary and appropriate specialists to Georgia to engage in a joint international research. The work is carried out in the frame of grant (DI/21/9-140/13 „Pilot project of before earthquake detected Very Low Frequency/Low Frequency electromagnetic emission network installation in Georgia") by financial support of Shota Rustaveli National Science Foundation.

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.

Drinking Water Residence Time in Distribution Networks and Emergency Department Visits for Gastrointestinal Illness in Metro Atlanta, Georgia

PubMed Central

Moe, Christine L.; Klein, Mitchel; Flanders, W. Dana; Uber, Jim; Amirtharajah, Appiah; Singer, Philip; Tolbert, Paige E.

2013-01-01

We examined whether the average water residence time, the time it takes water to travel from the treatment plant to the user, for a zip code was related to the proportion of emergency department (ED) visits for gastrointestinal (GI) illness among residents of that zip code. Individual-level ED data were collected from all hospitals located in the five-county metro Atlanta area from 1993 to 2004. Two of the largest water utilities in the area, together serving 1.7 million people, were considered. People served by these utilities had almost three million total ED visits, 164,937 of them for GI illness. The relationship between water residence time and risk for GI illness was assessed using logistic regression, controlling for potential confounding factors, including patient age and markers of socioeconomic status (SES). We observed a modestly increased risk for GI illness for residents of zip codes with the longest water residence times compared to intermediate residence times (odds ratio (OR) for Utility 1 = 1.07, 95% confidence interval (CI) = 1.03, 1.10; OR for Utility 2 = 1.05, 95% CI = 1.02, 1.08). The results suggest that drinking water contamination in the distribution system may contribute to the burden of endemic GI illness. PMID:19240359

Drinking water residence time in distribution networks and emergency department visits for gastrointestinal illness in Metro Atlanta, Georgia.

PubMed

Tinker, Sarah C; Moe, Christine L; Klein, Mitchel; Flanders, W Dana; Uber, Jim; Amirtharajah, Appiah; Singer, Philip; Tolbert, Paige E

2009-06-01

We examined whether the average water residence time, the time it takes water to travel from the treatment plant to the user, for a zip code was related to the proportion of emergency department (ED) visits for gastrointestinal (GI) illness among residents of that zip code. Individual-level ED data were collected from all hospitals located in the five-county metro Atlanta area from 1993 to 2004. Two of the largest water utilities in the area, together serving 1.7 million people, were considered. People served by these utilities had almost 3 million total ED visits, 164,937 of them for GI illness. The relationship between water residence time and risk for GI illness was assessed using logistic regression, controlling for potential confounding factors, including patient age and markers of socioeconomic status (SES). We observed a modestly increased risk for GI illness for residents of zip codes with the longest water residence times compared with intermediate residence times (odds ratio (OR) for Utility 1 = 1.07, 95% confidence interval (CI) = 1.03, 1.10; OR for Utility 2 = 1.05, 95% CI = 1.02, 1.08). The results suggest that drinking water contamination in the distribution system may contribute to the burden of endemic GI illness.

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.

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.

A More Literate Georgia: An Agenda for Action. A Report by the Dean's Literacy Task Force.

ERIC Educational Resources Information Center

Georgia Univ., Athens. Coll. of Education.

The essays contained in this document, which launches the University of Georgia Education Initiative, attempt to address Georgia's need for increased literacy in realistic and constructive terms. Taken together, these essays constitute an agenda for action--a challenge to all those who wish to provide Georgians with the quality education they…

Implementing a Model for Evaluation of Teacher Preparation Programs: Results from Two Georgia Institutions.

ERIC Educational Resources Information Center

Adams, John R.; Elmore, Randy F.

Comparisons were made of entering teacher education students' characteristics and attitudes at Georgia Southern College (GSC) and at the University of Georgia (UGA). Major findings were that more students at GSC were female and more were transfers at UGA. Students at UGA possessed higher achievement scores and were more intelligent, assertive, and…

Forest statistics for North Georgia, 1998

Treesearch

Michael T. Thompson

1998-01-01

This report summarizes a 1998 inventory of the forest resources of a 21-county area of Georgia. Major findings are highlighted in text and graphs; detailed data are presented in 51 tables. This report highlights the principal findings of the seventh forest survey of North Georgia. Field work began in October 1997 and was completed in April 1998. Six previous...

75 FR 17742 - Filing Dates for the Georgia Special Election in the 9th Congressional District

Federal Register 2010, 2011, 2012, 2013, 2014

2010-04-07

... 9th Congressional District AGENCY: Federal Election Commission. ACTION: Notice of filing dates for special election. SUMMARY: Georgia has scheduled a special general election on May 11, 2010, to fill the U... participate in the Georgia Special General and Special Runoff Elections shall file a 12-day Pre-General Report...

Race to the Top. Georgia Report. Year 1: School Year 2010-2011. [State-Specific Summary Report

ERIC Educational Resources Information Center

US Department of Education, 2012

2012-01-01

This State-specific summary report serves as an assessment of Georgia's Year 1 Race to the Top implementation, highlighting successes and accomplishments, identifying challenges, and providing lessons learned from implementation to date. Georgia's first-year accomplishments include awarding the State's first five Race to the Top Innovation Fund…

Community College Campus Carry Policy Analysis

ERIC Educational Resources Information Center

Alvarado, Joel; Toppin, Sheila

2017-01-01

This study provides a policy network analysis on the implications of HB 792 and HB 280 at urban two-year open campuses, with specific attention to Georgia Piedmont Technical College (GPTC), a unit of the Technical College System of Georgia (TCSG). Georgia state legislators passed House Bills 792 and 280, which authorized any person 18 years of age…

The Legacy of the Soviet Education System and Attempts To Introduce New Methodologies of Teaching in Georgia.

ERIC Educational Resources Information Center

Dundua, Shalva

2003-01-01

Highlights the challenges faced by a teacher educator from the country of Georgia during implementation of the Step by Step and the Reading and Writing for Critical Thinking initiatives. Addresses both the difficulty and the promise of changing traditional institutional culture in Georgia that dates from the Soviet era. (SD)

Teachers' Experiences of Georgia's Early Math Intervention Program: A Phenomenological Study

ERIC Educational Resources Information Center

Scott, Rachel Amanda Garner

2016-01-01

The purpose of this phenomenological study was to investigate the perceptions that K-5 teachers have toward Georgia's mandated Early Intervention Math Program (EIP) on at risk learners in an elementary school in a rural, North Georgia community. The following questions guided the study: 1. How do K-5 teachers describe their experience with…

Georgia Long-Term Pavement Performance (GALTPP) Program – Maintaining Georgia’s Calibration Sites and Identifying the Potential for Using MEPDG for Characterization of Non-Standard Materials and Methods (Phase 1)

DOT National Transportation Integrated Search

2016-10-01

The Georgia Department of Transportation (GDOT) has initiated a Georgia Long-Term Pavement Performance (GALTPP) monitoring program 1) to provide data for calibrating the prediction models in the AASHTO Mechanistic-Empirical Pavement Design Guide (MEP...

Assessment of the Georgia P Index on-farm at the field scale for grassland management

USDA-ARS?s Scientific Manuscript database

In order to better manage agricultural phosphorus (P), most states in the USA have adopted a “P indexing” approach which ranks fields according to potential losses of P. In Georgia, the Georgia P Index was developed to estimate the risk of bioavailable P loss from agricultural land to surface water...

County Government in Georgia [And] Teacher's Manual for County Government in Georgia.

ERIC Educational Resources Information Center

Hepburn, Mary A.

The student textbook and the teacher's manual focus on the services, organization, and funding of county government in Georgia. Designed to be used over a three to six week period, the textbook is arranged into six chapters. Chapter one discusses county government, its services, and its structure. Chapter two focuses on county officials and their…

33 CFR 110.72b - St. Simons Island, Georgia.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 33 Navigation and Navigable Waters 1 2010-07-01 2010-07-01 false St. Simons Island, Georgia. 110... ANCHORAGES ANCHORAGE REGULATIONS Special Anchorage Areas § 110.72b St. Simons Island, Georgia. The area beginning at a point southwest of Frederica River Bridge, St. Simons Island Causeway at latitude 31°09′58″ N...

33 CFR 165.731 - Safety/Security Zone: Cumberland Sound, Georgia and St. Marys River Entrance Channel.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Sound, Georgia and St. Marys River Entrance Channel. 165.731 Section 165.731 Navigation and Navigable... Seventh Coast Guard District § 165.731 Safety/Security Zone: Cumberland Sound, Georgia and St. Marys River... waters and land from bank to bank within Cumberland Sound and the St. Marys Entrance Channel: the...

Applying the APA/AERA/NCME "Standards": Evidence for the Validity and Reliability of Three Statewide Teaching Assessment Instruments.

ERIC Educational Resources Information Center

Rothenberg, Lori; Hessling, Peter A.

The statewide teaching performance assessment instruments being used in Georgia, North Carolina, and Florida were examined. Forty-one reliability and validity studies regarding the instruments in use in each state were collected from state departments and universities. Georgia uses the Georgia Teacher Performance Assessment Instrument. North…

Deer browse resources of north Georgia

Treesearch

Thomas H. Ripley; Joe P. McClure

1963-01-01

Following tests in the coastal plain and Piedmont of Georgia (Moore et al. 1960), a procedure was developed and used to assess browse resources in 21 counties of north Georgia involving a total land area of approximately 4 million acres (fig. 1). Although the Forest Survey is designed primarily to yield information on timber, it also provides an excellent sampling...

Georgia's forests, 2004

Treesearch

Richard A. Harper; Nathan D. McClure; Tony G. Johnson; J. Frank Green; James K. Johnson; David B. Dickinson; James L. Chamerlain; KaDonna C. Randolph; Sonja N. Oswalt

2009-01-01

Between 1997 and 2004, the Forest Service, Forest Inventory and Analysis Program conducted the eighth inventory of Georgia forests. Forest land area remained stable at 24.8 million acres, and covered about two-thirds of the land area in Georgia. About 24.2 million acres of forest land was considered timberland and 92 percent of that was privately owned. Family forest...

Georgia's timber, 1972

Treesearch

Herbert A. Knight; Joe P. McClure

1974-01-01

The fourth survey of Georgia's timber resource, completed in November 1972, shows improved timber supplies across most of the State since 1961. Inventory volume increased from 19.6 to 25.3 billion cubic feet, or by 29 percent. A group of counties south of the Altamaha river in Southeast Georgia was the only extensive area which experienced a reduction in timber...

A brief review of some pathology research supported by the Georgia Commodity Commission for Pecans at the USDA-ARS, Byron

USDA-ARS?s Scientific Manuscript database

With the changes currently taking place nationally in the pecan industry, and the production issues faced specifically by growers in Georgia and elsewhere in the southeastern region, the pathology research projects funded by the Georgia Commodity Commission for Pecans (CC) are reviewed. The results ...

Corruption Risks of Private Tutoring: Case of Georgia

ERIC Educational Resources Information Center

Kobakhidze, Magda Nutsa

2014-01-01

The paper focuses on teacher-supplied private tutoring in the context of post-Soviet Georgia, and elucidates the ways in which teacher-supplied private tutoring can be related to educational corruption. The paper draws on data from in-depth interviews of 18 school teachers in different parts of Georgia in 2013. The findings of the qualitative…

Perceived Effectiveness of Clinical E-Learning for Georgia Midwives

ERIC Educational Resources Information Center

Hunter, Adrienne

2014-01-01

In the state of Georgia, approximately nine out of every 1,000 babies die during birth and approximately 18.6 out of every 1,000 women die from a pregnancy-related cause (Georgia Department of Public Health, 2011). Continuing to build capacities for the continuing education of midwives--specifically Certified Nurse Midwives (CNMs)--can ensure they…

Preparing Science Specific Mentors: A Look at One Successful Georgia Program.

ERIC Educational Resources Information Center

Upson, Leslie; Koballa, Thomas; Gerber, Brian

The state of Georgia has developed the Teacher Support Specialist Program to assist prospective mentors as they begin the process of preparing to provide support and guidance to those new to the profession. Successful completion of this program for either staff development units or college credit enables Georgia teachers to add the teacher support…

The Political History of Developmental Studies in the University System of Georgia

ERIC Educational Resources Information Center

Presley, John W.; Dodd, William M.

2008-01-01

The political history of developmental education in post-secondary education is as revealing as its intellectual history. With a University system-wide Developmental Studies program initiated in 1974, the State of Georgia was a pioneer in remedial education and open access. Unfortunately, the program became linked in Georgia media, and in Georgia…

Regional Child Care Trends: Comparing Georgia to Its Neighbors.

ERIC Educational Resources Information Center

Waits, Lauren; Monaco, Malina; Beck, Lisa; Edwards, Jennifer

As child care becomes an increasingly important public policy issue on the national level, there is emerging concern about Georgia's readiness to meet the needs of its children in care. This study documented the state of child care in Georgia in comparison to other states, to national averages, and to national standards. A group of 12 comparison…

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

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

ERIC Educational Resources Information Center

Fox, Ann L.; Gingras, Jacqui

2012-01-01

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

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…

Seismic Hazard Assessment of Middle East Region: Based on the Example to Georgia (Preliminary results)

NASA Astrophysics Data System (ADS)

Tsereteli, N. S.; Akkar, S.; Askan, A.; Varazanashvili, O.; Adamia, S.; Chkhitunidze, M.

2012-12-01

The country of Georgia is located between Russia and Turkey. The main morphological units of Georgia are the mountain ranges of the Greater and Lesser Caucasus separated by the Black Sea-Rioni and Kura (Mtkvari)-South Caspian intermountain troughs. Recent geodynamics of Georgia and adjacent territories of the Black Sea-Caspian Sea region, as a whole, are determined by its position between the still-converging Eurasian and Africa-Arabian plates. That caused moderate seismicity in the region. However, the risk resulting from these earthquakes is considerably high, as recent events during the last two decades have shown. Seismic hazard and risk assessment is a major research topic in various recent international and national projects. Despite the current efforts, estimation of regional seismic hazard assessment remains as a major problem. Georgia is one of the partners of ongoing regional project EMME (Earthquake Model for Middle East region). The main objective of EMME is calculation of Earthquake hazard uniformly with heights standards. One approach used in the project is the probabilistic seismic hazard assessment PSHA. In this study, we present the preliminary results of PSHA for Georgia in this project attempting to improve gaps especially in such steps as: determination of seismic sources; selection or derivation of ground motion prediction equations models; estimation of maximum magnitude Mmax. Seismic sources (SS) were obtained on the bases of structural geology, parameters of seismicity and seismotectonics. Finely new SS have been developed for Georgia and adjacent region. Each zone was defined with the following parameters: the magnitude-frequency parameters, maximum magnitude, and depth distribution as well as modern dynamical characteristics widely used for complex processes. As the ground motion dataset is absolutely insufficient by itself to derive a ground motion prediction model for Georgia, two approaches were taken in defining ground motions. First the modern procedure for selecting and ranking candidate ground-motion prediction equations (GMPEs) were done (Scherbaum et al. 2004, 2009; Cotton et al. 2006, Kale and Akkar, 2012) under a given ground motion dataset. Second the hybrid-empirical method proposed by Campbell (2003) was used. In the host-to-target simulations, Turkey and Iran was used as the host regions and Georgia as the target region. GMPEs for the Racha and Javakhety regions in Georgia are derived by scaling the pre-determined GMPEs based on the computed scaling coefficients. Finally PSH maps were calculated showing peak ground acceleration and spectral accelerations at 0, 0.2, 1, 2, 4 sec for Georgia.

Migrating Huns and modified heads: Eigenshape analysis comparing intentionally modified crania from Hungary and Georgia in the Migration Period of Europe

PubMed Central

Mayall, Peter; Bitadze, Liana

2017-01-01

An intentionally modified head is a visually distinctive sign of group identity. In the Migration Period of Europe (4th– 7th century AD) the practice of intentional cranial modification was common among several nomadic groups, but was strongly associated with the Huns from the Carpathian Basin in Hungary, where modified crania are abundant in archaeological sites. The frequency of modified crania increased substantially in the Mtskheta region of Georgia in this time period, but there are no records that Huns settled here. We compare the Migration Period modified skulls from Georgia with those from Hungary to test the hypothesis that the Huns were responsible for cranial modification in Georgia. We use extended eigenshape analysis to quantify cranial outlines, enabling a discriminant analysis to assess group separation and identify morphological differences. Twenty-one intentionally modified skulls from Georgia are compared with sixteen from Hungary, using nineteen unmodified crania from a modern population as a comparative baseline. Results indicate that modified crania can be differentiated from modern unmodified crania with 100% accuracy. The Hungarian and Georgian crania show some overlap in shape, but can be classified with 81% accuracy. Shape gradations along the main eigenvectors indicate that the Hungarian crania show little variation in cranial shape, in accordance with a two-bandage binding technique, whereas the Georgian crania had a wider range of variation, fitting with a diversity of binding styles. As modification style is a strong signifier of social identity, our results indicate weak Hunnic influence on cranial modification in Georgia and are equivocal about the presence of Huns in Georgia. We suggest instead that other nomadic groups such as Alans and Sarmatians living in this region were responsible for modified crania in Georgia. PMID:28152046

STEM Career Cluster Engineering and Technology Education pathway in Georgia: Perceptions of Georgia engineering and technology education high school teachers and CTAE administrators as measured by the Characteristics of Engineering and Technology Education survey

NASA Astrophysics Data System (ADS)

Crenshaw, Mark VanBuren

This study examined the perceptions held by Georgia Science, Technology, Engineering, and Mathematics (STEM) Career Cluster Engineering and Technology Education (ETE) high school pathway teachers and Georgia's Career, Technical and Agriculture Education (CTAE) administrators regarding the ETE pathway and its effect on implementation within their district and schools. It provides strategies for ETE teaching methods, curriculum content, STEM integration, and how to improve the ETE pathway program of study. Current teaching and curricular trends were examined in ETE as well as the role ETE should play as related to STEM education. The study, using the Characteristics of Engineering and Technology Education Survey, was conducted to answer the following research questions: (a) Is there a significant difference in the perception of ETE teaching methodology between Georgia ETE high school teachers and CTAE administrators as measured by the Characteristics of Engineering and Technology Education Survey? (b) Is there a significant difference in the perception of ETE curriculum content between Georgia ETE high school teachers and CTAE administrators as measured by the Characteristics of Engineering and Technology Education Survey? (c) Is there a significant difference in the perception of STEM integration in the ETE high school pathway between Georgia ETE high school teachers and CTAE administrators as measured by the Characteristics of Engineering and Technology Education Survey? and (d) Is there a significant difference in the perception of how to improve the ETE high school pathway between Georgia ETE high school teachers and CTAE administrators as measured by the Characteristics of Engineering and Technology Education Survey? Suggestions for further research also were offered.

Comparison of the Medical College of Georgia Complex Figures and the Rey-Osterrieth Complex Figure tests in a normal sample of Japanese university students.

PubMed

Yamashita, Hikari; Yasugi, Mina

2008-08-01

Comparability of copy and recall performance on the four figures of the Medical College of Georgia Complex Figures and the Rey-Osterrieth Complex Figure were examined using an incidental learning paradigm with 60 men and 60 women, healthy volunteers between the ages of 18 and 24 years (M = 21.5 yr., SD = 1.5) at a Japanese university. A between-subjects design was used in which each group of participants (n = 24) responded to five figures. The interrater reliability of each Georgia figure was excellent. While the five figures yielded equivalent copy scores, the Rey-Osterrieth figure had significantly lower scores than the Georgia figures at recall after 3 min. There were no significant differences between the four Georgia figures. These results are consistent with the findings of the original studies in the USA.

Neurosurgery at Medical College of Georgia, Georgia Regents University in Augusta (1956-2013).

PubMed

Viers, Angela; Smith, Joseph; Alleyne, Cargill H; Allen, Marshall B

2014-09-01

: The neurosurgery service at the Medical College of Georgia, Georgia Regents University at Augusta has a rich history spanning almost 6 decades. Here, we review the development of neurological surgery as a specialty in Augusta and the history of the Department of Neurosurgery at Georgia Regents University. This article describes some of the early neurosurgeons in the city and those who have contributed to the field and helped to shape the department. Our functional and stereotactic program is emphasized. Our surgical epilepsy program dates back more than a half-century and remains a highly experienced program. We also describe our affiliation with the medical illustration graduate program, which was the first to be accredited and remains 1 of 4 such programs in the world. Finally, we list our alumni, former faculty, and current faculty, as well as the major accomplishments in our first decade as a full department.

Loss of traditional knowledge aggravates wolf-human conflict in Georgia (Caucasus) in the wake of socio-economic change.

PubMed

Kikvidze, Zaal; Tevzadze, Gigi

2015-09-01

Reports of the damage from wolf attacks have increased considerably over the last decade in Georgia (in the Caucasus). We interviewed locals about this problem in two focal regions: the Lanchkhuti area (in western Georgia) and Kazbegi District (in eastern Georgia) where livestock numbers had increased by an order of magnitude owing to dramatic shifts in the local economies over the last decade. This coincided with expanding habitats for wolves (abandoned plantations, for example). We found that the perceived damage from wolves was positively correlated with a poor knowledge of wolf habits and inappropriate livestock husbandry practices. Our results suggest a loss of traditional knowledge contributes strongly to the wolf-human conflicts in Georgia. Restoring traditional, simple but good practices--such as protecting herds using shepherd dogs and introducing bulls into the herds-can help one solve this problem.

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.

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.

78 FR 32530 - Notice of Final Federal Agency Action on Proposed Highway in Georgia the Northwest I-75/I-575...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-30

... Grove Road on Interstate 75 (I-75) and from I-75 to Sixes Road on I-575) located in Cobb and Cherokee... CONTACT: Mr. Rodney Barry, Division Administrator, Georgia Division, Federal Highway Administration, 61... Friday, 404-562-3630; email: Rodney[email protected] . For Georgia Department of Transportation (GDOT): Mr...

On Common Constitutional Ground: How Georgia's Scholarship Tax Credits Mirror Other State Programs and Expand Educational Opportunity

ERIC Educational Resources Information Center

Carpenter, Dick M., II.; Erickson, Angela C.

2016-01-01

In 2008, Georgia launched a tax-credit scholarship program to expand educational opportunities for the state's pre-K through 12th-grade students by providing them scholarships to attend private schools. Georgia's scholarship tax credit program will help over 13,000 children get the best education for their needs at secular and religious private…

The Impact of the Revolution upon Georgia's Economy, 1775-1789.

ERIC Educational Resources Information Center

Ready, Milton

One of a series of pamphlets about effects of the American Revolution in Georgia, this document reviews Georgia's economy during the years 1775-1789. It can be used as supplementary reading or a two-week unit for junior or senior high school students. A brief teacher's guide is included. The main part of the pamphlet relates the political and…

The Role of Media Specialists with Respect to Instructional Technology in an Urban School District in Georgia

ERIC Educational Resources Information Center

Goetzel, Warren Reid

2011-01-01

Due to the absence of a Georgia Educator Certificate in instructional technology, and the lack of state-wide staffing guidelines or requirements for instructional technology specialists, there is a lack of consistency in the qualifications and staffing of P-12 instructional technology specialists in Georgia public schools. The result is a lack of…

Microclimate environmental parameters indexed for Sudden Oak death in Georgia and South Carolina

Treesearch

Pauline Spaine; William J. Otrosina; Stanley J. Zarnoch; Sharon V. Lumpkin

2008-01-01

We monitored Ericaceous habitat in Georgia and South Carolina for temperature, dew point and humidity ranges throughout a two year period. Temperature and humidity data were used to characterize their range in Georgia and South Carolina where potential SOD susceptible hosts occur. This data suggests risk for SOD development may be more widespread in southeastern forest...

Georgia's Balancing Act: Using, Protecting, and Legislating Student Data

ERIC Educational Resources Information Center

Rickman, Dana

2016-01-01

By combining an overall vision for the use of data, a commitment to protecting student privacy and data integrity, and supportive legislation, Georgia emerged as a leader in the effective use of student data. But it easily could have gone another way. None of the three elements could be taken for granted when Georgia set out to develop its state…

The Georgia Perimeter College MESA Program: Propelling STEM Students to Success

ERIC Educational Resources Information Center

Law, Kouok K.

2011-01-01

From 2006 to 2008, while taking courses at Georgia Perimeter College (GPC), Joel Toussaint worked two jobs, one was at night. Now, he has graduated from Georgia Institute of Technology majoring in mechanical engineering, and he has been admitted to graduate school in mechanical engineer there. His plan for the future is to get his Ph. D. in…

Physician Manpower in Georgia: Report of the Task Force for Physician Manpower to the Georgia Comprehensive Health Planning Council.

ERIC Educational Resources Information Center

Georgia State Dept. of Public Health, Atlanta. Office of Comprehensive Health Planning.

This report is a result of a study of the state's physician manpower by representatives of the fields of medical education and professional practice in Georgia. Contents include introduction and principal findings, recommendations, and analysis of present supply of physicians and other data. Recommendations suggest improvement of the utilization…

Teachers' Attitudes toward Assessment of Student Learning and Teacher Assessment Practices in General Educational Institutions: The Case of Georgia

ERIC Educational Resources Information Center

Kitiashvili, Anastasia

2014-01-01

The aim of this article is to study teachers' attitudes toward assessment of students' learning and their assessment practices in Georgia's general educational institutions. Georgia is a country in the South Caucasus with a population of 4.5 million people, with 2300 general educational institutions and about 559,400 students. The research…

Georgia Computes! An Intervention in a US State, with Formal and Informal Education in a Policy Context

ERIC Educational Resources Information Center

Guzdial, Mark; Ericson, Barbara; Mcklin, Tom; Engelman, Shelly

2014-01-01

Georgia Computes! ("GaComputes") was a six-year (2006-2012) project to improve computing education across the state of Georgia in the United States, funded by the National Science Foundation. The goal of GaComputes was to broaden participation in computing and especially to engage more members of underrepresented groups which includes…

Rapid assessment of wildfire damage using Forest Inventory data: A case in Georgia

Treesearch

Richard A. Harper; John W. Coulsten; Jeffery A. Turner

2009-01-01

The rapid assessment of damage caused by natural disasters is essential for planning the appropriate amount of disaster relief funds and public communication. Annual Forest Inventory and Analysis (FIA) data provided initial estimates of damage to timberland in a timely manner to State leaders during the 2007 Georgia Bay Complex Wildfire in southeast Georgia. FIA plots...

The Use of Instructional Television in Georgia. Final Report to Georgia State Board of Education.

ERIC Educational Resources Information Center

Tressel, G. W.; And Others

For the benefit of the Georgia State Board of Education, the day-to-day impact and actual problems of instructional television (ITV) as encountered in the state's classrooms has been explored and analyzed. Basic considerations were accomplishments to date and methods of improving the services. An overview of the ITV network is provided, and its…

"A Reversal of Fortune": Georgia Legislative Update 1992-2012

ERIC Educational Resources Information Center

Sielke, Catherine C.

2011-01-01

In the 1990s, teachers' and other educators' salaries increased enough to make Georgia number one in salaries in the South and solidly in the Midwest across the nation. Since 2004, school districts have been trying to make do with much less as this recession continues to force more cuts. Georgia has a very high unemployment rate of 10.25%, a high…

Skill Formation and Utilisation in the Post-Soviet Transition: Higher Education Planning in Post-Soviet Georgia

ERIC Educational Resources Information Center

Gvaramadze, Irakli

2010-01-01

Changes in the former Soviet system had a dramatic influence on higher education in Georgia. The main objective of the current article is to analyse implications of the post-Soviet transition for the skill formation and skill utilisation system in Georgia. In particular, the study analyses recent trends in Georgian higher education including…

50 CFR Figure 4 to Part 223 - Georgia TED

Code of Federal Regulations, 2010 CFR

2010-10-01

... 50 Wildlife and Fisheries 7 2010-10-01 2010-10-01 false Georgia TED 4 Figure 4 to Part 223 Wildlife and Fisheries NATIONAL MARINE FISHERIES SERVICE, NATIONAL OCEANIC AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE MARINE MAMMALS THREATENED MARINE AND ANADROMOUS SPECIES Pt. 223, Fig. 4 Figure 4 to Part 223—Georgia TED EC01JY91.048 [52 FR...

The Impact of America's Choice on Writing Performance in Georgia: First-Year Results

ERIC Educational Resources Information Center

May, Henry; Supovitz, Jonathan A.; Lesnick, Joy

2004-01-01

This study investigates the impact of America's Choice on student writing performance in Georgia. The analyses in this study focus on the change that occurred during the first year of implementation, the 2001-2002 school year. Two research questions guided these analyses: (1) What effect did Georgia's Choice have on the writing scores from the…

Genecology of Longleaf Pine in Georgia and Florida

Treesearch

John F. Kraus; Earl R. Sluder

1990-01-01

Fourteen seed sources of Pinus palustrisfrom Georgia, 5 from Florida, and 1 from Alabama were grown at five locations in Georgia and at two in Florida. Results through the 15th year show: (1) survival and early height growth were best for the northern sources; (2) individual stand or seed source contributed strongly to the components of variation; (3) the seed source x...

The Georgia Water-Use Program

USGS Publications Warehouse

Fanning, Julia L.

1985-01-01

WHY COLLECT WATER-USE INFORMATION? Water used in Georgia increased from 5,560 to 6,765 million gallons per day (22 percent) between 1970 and 1980. In 1970 the population of Georgia was about 4,600,000. By 1980 it had rcached an estimated 5,500,000, a 20-percent increase. The amount of irrigated land in the State incrcased from 79,600 acres to nearly one million acres during the decade, which resulted in a 12-fold increase in irrigation water use. The value of goods produced by Georgia's industries increased from $21,000,000 in 1970 to $32,000,000 in 1980 (figures adjusted for inflation). These were the major factors contributing to the significant increase in water use. For years, ground water and surface water in Georgia were thought of as unlimited natural resources. However, with the impact of recent droughts and the increasing demand for water it has become apparent that proper management of Georgia's water resources is necessary to assure continuing supplies of good-quality water. To make decisions on wa ter resources, a manager needs comprehensive, up-to-date information on the quantity of water being used in the State, and the total resources available for use.

Water Resources Data, Georgia, 2002--Volume 1: Continuous water-level, streamflow, water-quality data, and periodic water-quality data, Water Year 2002

USGS Publications Warehouse

Hickey, Andrew C.; Kerestes, John F.; McCallum, Brian E.

2002-01-01

Water resources data for the 2002 water year for Georgia consists of records of stage, discharge, and water quality of streams; and the stage and contents of lakes and reservoirs published in two volumes in a digital format on a CD-ROM. Volume one of this report contains water resources data for Georgia collected during water year 2002, including: discharge records of 154 gaging stations; stage for 165 gaging stations; precipitation for 105 gaging stations; information for 20 lakes and reservoirs; continuous water-quality records for 27 stations; the annual peak stage and annual peak discharge for 72 crest-stage partial-record stations; and miscellaneous streamflow measurements at 50 stations, and miscellaneous water-quality data recorded by the NAWQA program in Georgia. Volume two of this report contains water resources data for Georgia collected during calendar year 2002, including continuous water-level records of 155 ground-water wells and periodic records at 132 water-quality stations. These data represent that part of the National Water Data System collected by the U.S. Geological Survey and cooperating State and Federal agencies in Georgia.

Water Resources Data, Georgia, 2003, Volume 1: Continuous water-level, streamflow, water-quality data, and periodic water-quality data, Water Year 2003

USGS Publications Warehouse

Hickey, Andrew C.; Kerestes, John F.; McCallum, Brian E.

2004-01-01

Water resources data for the 2003 water year for Georgia consists of records of stage, discharge, and water quality of streams; and the stage and contents of lakes and reservoirs published in two volumes in a digital format on a CD-ROM. Volume one of this report contains water resources data for Georgia collected during water year 2003, including: discharge records of 163 gaging stations; stage for 187 gaging stations; precipitation for 140 gaging stations; information for 19 lakes and reservoirs; continuous water-quality records for 40 stations; the annual peak stage and annual peak discharge for 65 crest-stage partial-record stations; and miscellaneous streamflow measurements at 36 stations, and miscellaneous water-quality data at 162 stations in Georgia. Volume two of this report contains water resources data for Georgia collected during calendar year 2003, including continuous water-level records of 156 ground-water wells and periodic records at 130 water-quality stations. These data represent that part of the National Water Data System collected by the U.S. Geological Survey and cooperating State and Federal agencies in Georgia.

Lagtime relations for urban streams in Georgia

USGS Publications Warehouse

Inman, Ernest J.

2000-01-01

Urban flood hydrographs are needed for the design of many highway drainage structures, embankments, and entrances to detention ponds. The three components that are needed to simulate urban flood hydrographs at ungaged sites are the design flood, the dimensionless hydrograph, and lagtime. The design flood and the dimensionless hydrograph have been presented in earlier studies for urban streams in Georgia. The objective of this study was to develop equations for estimating lagtime for urban streams in Georgia. Lagtimes were computed for 329 floods at 69 urban gaging stations in 11 cities in Georgia. These data were used to compute an average lagtime for each gaging station. Multiple regression analysis was then used to define relations between lagtime and certain physical basin characteristics, of which drainage area, slope, and impervious area were found to be significant. A qualitative variable was used to account for a geographical bias in flood-frequency region 4, a small area of southwestern Georgia. Information from this report can be used to simulate a flood hydrograph using a dimensionless hydrograph, the design flood, and the lagtime obtained from regression equations for any urban site with less than a 25-square-mile drainage area in Georgia.

Effects of cavity dimensions, boundary layer, and temperature on cavity noise with emphasis on benchmark data to validate computational aeroacoustic codes

NASA Technical Reports Server (NTRS)

Ahuja, K. K.; Mendoza, J.

1995-01-01

This report documents the results of an experimental investigation on the response of a cavity to external flowfields. The primary objective of this research was to acquire benchmark of data on the effects of cavity length, width, depth, upstream boundary layer, and flow temperature on cavity noise. These data were to be used for validation of computational aeroacoustic (CAA) codes on cavity noise. To achieve this objective, a systematic set of acoustic and flow measurements were made for subsonic turbulent flows approaching a cavity. These measurements were conducted in the research facilities of the Georgia Tech research institute. Two cavity models were designed, one for heated flow and another for unheated flow studies. Both models were designed such that the cavity length (L) could easily be varied while holding fixed the depth (D) and width (W) dimensions of the cavity. Depth and width blocks were manufactured so that these dimensions could be varied as well. A wall jet issuing from a rectangular nozzle was used to simulate flows over the cavity.

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.

The association between sugar-sweetened beverages and dental caries among third-grade students in Georgia.

PubMed

Wilder, Jocelyn R; Kaste, Linda M; Handler, Arden; Chapple-McGruder, Theresa; Rankin, Kristin M

2016-01-01

The purpose of this study is to examine the association between sugar-sweetened beverage (SSB) consumption and caries experience among Georgia third graders. The 2010-2011 Georgia Third Grade Oral Health Study provided a school-based sample for analysis. Data were weighted to be representative of the state of Georgia's third graders. Log-binomial regression was used to assess the association between SSB consumption and caries experience after adjusting for socio-demographic and maternal and child oral health characteristics. Georgia third graders consumed approximately two servings of SSB per day on average (1.7, 95% CI 1.6-1.8). Fifty-two percent of Georgia third graders had caries experience. Daily consumption of SSB and prevalence of caries experience differed significantly by demographic characteristics. After adjustment for socio-demographic and maternal oral health characteristics, caries experience increased 22 percent (adjusted PR = 1.2, 95% CI 1.1, 1.3) for every additional reported serving of SSB consumed per day. Higher consumption of SSBs is associated with higher caries prevalence among Georgia third graders after adjustment for important covariates. Consequently, health messages about SSBs from dentists, physicians, and other healthcare providers as well as policy approaches at the school, state, and national levels to limit consumption of SSBs may collectively impact both the development of dental caries and obesity, leading to overall better health for children. © 2015 American Association of Public Health Dentistry.

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.

The Economic and Fiscal Costs of Failing to Reform K-12 Education in Georgia. School Choice Issues in the State

ERIC Educational Resources Information Center

Gottlob, Brian J.

2009-01-01

This study documents the public costs of high school dropouts in Georgia, and examines how policies that increase school choice, such as the recently-enacted tuition tax credit scholarship program will provide large public benefits by increasing public school graduation rates. The study calculates the annual cost of Georgia dropouts caused by…

50 CFR 622.208 - Minimum mesh size applicable to rock shrimp off Georgia and Florida.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 50 Wildlife and Fisheries 12 2013-10-01 2013-10-01 false Minimum mesh size applicable to rock... mesh size applicable to rock shrimp off Georgia and Florida. (a) The minimum mesh size for the cod end of a rock shrimp trawl net in the South Atlantic EEZ off Georgia and Florida is 17/8 inches (4.8 cm...

50 CFR 622.208 - Minimum mesh size applicable to rock shrimp off Georgia and Florida.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 50 Wildlife and Fisheries 12 2014-10-01 2014-10-01 false Minimum mesh size applicable to rock... mesh size applicable to rock shrimp off Georgia and Florida. (a) The minimum mesh size for the cod end of a rock shrimp trawl net in the South Atlantic EEZ off Georgia and Florida is 17/8 inches (4.8 cm...

Automation and Its Funding in the Library Media Centers in Secondary Schools in Georgia: A Survey.

ERIC Educational Resources Information Center

Baggett, Ann Utsey

This report presents the results of a study whose purpose was to determine what automation is present in the library media centers in Georgia secondary schools and how it has been funded. A three-part questionnaire was sent to the media specialists in 50% of the secondary schools in Georgia, which were randomly selected. The analysis of the…

78 FR 35930 - Change in Bank Control Notices; Acquisitions of Shares of a Bank or Bank Holding Company

Federal Register 2010, 2011, 2012, 2013, 2014

2013-06-14

... Peachtree Street NE., Atlanta, Georgia 30309: 1. Charles P. Stephens, Atlanta, Georgia, individually and as trustee of MAD Trust for S.D. Stephens, SDT U/A 12-23-92 trust, MAD GST for CA Stephens trust, and MAD GST for SR Stephens trust; Sandra D. Stephens, Atlanta, Georgia, individually and as trustee of MAD Trust...

The Rise of Childhood Poverty in Georgia: Implications for Public School Planning and Pedagogy

ERIC Educational Resources Information Center

Boggs, Olivia M.

2011-01-01

The rapid and steady growth of poverty in Georgia's public schools is a clarion call to re-examine the extent to which educators are reaching and teaching all students, regardless of their economic standing. The traditional view of poverty as a marginal condition affecting a minority of students no longer holds as 56% of Georgia's 1.6-million…

Trusted to the Rulers of the People Alone: A Legal Analysis of the Constitutionality of Removing Locally Elected School Board Members in Georgia

ERIC Educational Resources Information Center

Collier Good, Cayanna

2013-01-01

This dissertation explored the constitutionality of a Georgia law, under both the Georgia Constitution and the United States Constitution, which allows for the removal of elected school board members based on threatened loss of district accreditation. The problem is that elected board members are being removed from office based on district…

75 FR 5281 - Approval of Manufacturing Authority, Foreign-Trade Zone 26, Kia Motors Manufacturing Georgia, Inc...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-02-02

... DEPARTMENT OF COMMERCE Foreign-Trade Zones Board Order No. 1648 Approval of Manufacturing Authority, Foreign-Trade Zone 26, Kia Motors Manufacturing Georgia, Inc. (Motor Vehicles), West Point, Georgia Pursuant to its authority under the Foreign-Trade Zones Act of June 18, 1934, as amended (19 U.S.C. 81a-81u) (the Act), the Foreign-Trade Zones...

Rural School Districts and the Fight for Funding Adequacy: The Legal Challenge of "CASFG v. State of Georgia"

ERIC Educational Resources Information Center

Cornelius, Luke M.; Robinson, Charlotte Bunn

2006-01-01

On June 23, 2005, oral arguments were heard in the Fulton County Superior Court in the first round of Georgia's current school finance litigation, "CASFG v. State of Georgia." The hearing was on the state's motion to dismiss the action by a coalition of rural school districts, parents, and students. Four months later Senior Judge…

Effects of five silvicultural treatments on Loblolly pine in the Georgia Piedmont at age 20

Treesearch

M. Boyd Edwards; Barry D. Shiver; Stephen R. Logan

2003-01-01

Age 20data from a designed experimental study installed on 24 plots at one location in the LowerPiediizont in Jones County, Georgia, were used to evaluate the effect of six silviculrural treatments on survival, growth, and yield of cutover site-prepared loblolly pine plantations in the Georgia Piedmont. The following silvicultural treatments were included in the study...

77 FR 29753 - CaterParrott Railnet, L.L.C.-Sublease and Operation Exemption-Georgia & Florida Railway, L.L.C.

Federal Register 2010, 2011, 2012, 2013, 2014

2012-05-18

... Railnet, L.L.C.--Sublease and Operation Exemption-- Georgia & Florida Railway, L.L.C. CaterParrott Railnet, L.L.C. (CPR), a noncarrier, has filed a verified notice of exemption under 49 CFR 1150.31 to sublease from Georgia & Florida Railway, L.L.C. (GRF) and operate approximately 43.2 miles of rail line...

Moving beyond Fuzy Altruism in Business-Education Relationships: The Potential of the Georgia Alliance for Public Education. Supporting Leaders for Tomorrow, Occasional Paper #8.

ERIC Educational Resources Information Center

Trimble, Grace

Georgia's business leadership is concerned about that state's public education system which has consistently ranked near the bottom of the educational ladder. In 1986, the Quality Basic Education (QBE) Act became law, and its provisions are described in this document. The Georgia Alliance for Public Education (the Alliance) was mobilized to…

Panel Discussion: Cover Crops Used at Georgia Forestry Commission Flint River and Walker Nurseries

Treesearch

Jeff Fields

2005-01-01

Flint River Nursery, located near Montezuma, Georgia, has used rye, wheat, brown top millet, and sorghum sudan grass for cover crops. Flint River has just begun to return to a summer cover crop situation. At Walker Nursery, located near Reidsville, Georgia, certified rye has been sown by the State Department of Corrections (DOC) for their harvesting, with a benefit to...

A comparative study of selected Georgia elementary principals' perceptions of environmental knowledge

NASA Astrophysics Data System (ADS)

Campbell, Joyce League

This study sought to establish baseline data on environmental knowledge, opinions, and perceptions of elementary principals and to make comparisons based on academic success rankings of schools and to national results. The self-reported study looked at 200 elementary principals in the state of Georgia. The population selected for the study included principals from the 100 top and 100 bottom academically ranked elementary schools as reported in the Georgia Public Policy Foundation Report Card for Parents. Their scores on the NEETF/Roper Environmental Knowledge Survey were compared between these two Georgia groups and to a national sample. Georgia elementary principals' scores were compared to environmental programs evident in their schools. The two Georgia groups were also compared on environmental opinion and perception responses on mandates, programs in schools and time devoted to these, environmental education as a priority, and the impact of various factors on the strength of environmental studies in schools. Georgia elementary principals leading schools at the bottom of the academic performance scale achieved environmental knowledge scores comparable to the national sample. However, principals of academically successful schools scored significantly higher on environmental knowledge than their colleagues from low performing schools (p < .05) and higher than the national sample (p < .001). Both Georgia principal groups strongly support a mandated environmental education curriculum for Georgia. The two groups were comparable on distributions of time devoted to environmental education across grade levels; however, principals from the more successful schools reported significantly (p < .01) greater amounts of time allotted to environmental studies. Both groups reported the same variety of environmental programs and practices evident in their schools and similar participation in these activities at various grade levels. Most significant (p < .01) was the comparison of ratings each group gave to environmental education as an instructional priority in their schools; principals supervising successful school programs viewed environmental education as a higher priority. These successful principals also recognized the importance of both administrator and staff interest as influencing factors and ranked these two variables as strongly impacting the success or failure of environmental initiatives in schools. Comparison of principals' environmental knowledge scores to numbers of programs shown no significant relationship. (Abstract shortened by UMI.)

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

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.

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.

Perspectives of Nurses Pursuing Doctoral Degrees in Georgia: Implications for Recruitment.

PubMed

Wheeler, Rebecca McCombs; Eichelberger, Lisa Wright

2017-08-01

Increasing the number of nurses with doctorates is a goal of the nursing profession. The Georgia Nursing Leadership Coalition developed a survey to understand the perspectives of nurses pursuing doctoral degrees in Georgia to improve recruitment and retention strategies. A 26-item online survey was distributed to all students enrolled in Georgia-based doctoral programs in nursing in spring 2014. One hundred fifty responses were received (54% response rate). Most students first seriously considered doctoral education during their master's programs or more than 5 years into practice. For most, obtaining a doctoral degree was a personal life goal. Work-life balance was the most significant barrier. Recruitment of nurses to doctoral programs should focus on messaging, timing, and highlighting the unique aspects of programs. Schools should work to reduce barriers. Understanding students' perspectives of doctoral education in nursing can improve recruitment strategies and increase the number of nurses graduating with doctorates in Georgia. [J Nurs Educ. 2017;56(8):466-470.]. Copyright 2017, SLACK Incorporated.

A statewide Crisis Intervention Team (CIT) initiative: evolution of the Georgia CIT program.

PubMed

Oliva, Janet R; Compton, Michael T

2008-01-01

In late 2004, Georgia began implementation of a statewide Crisis Intervention Team (CIT) program to train a portion of its law enforcement officers to respond safely and effectively to individuals with mental illnesses who are in crisis. This overview provides a description of the evolution of the Georgia CIT, including discussions of the historical context in which the program developed; the program's vision, mission, and objectives; the importance of the multidisciplinary Georgia CIT Advisory Board; the training curriculum; the role played by state and local coordinators; the value of stakeholders' meetings; practical operations of the program; the importance of considering the adequacy of community-based and hospital-based psychiatric services; costs and funding; the program's expansion plan; and evaluation, research, and academic collaborations. These detailed descriptions of the Georgia CIT program may be useful for professionals involved in local, regional, or state CIT program planning and may provide a practical synopsis of one example of this collaborative model that is being rapidly disseminated across the U.S.

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.

An examination of the potential applications of automatic classification techniques to Georgia management problems

NASA Technical Reports Server (NTRS)

Rado, B. Q.

1975-01-01

Automatic classification techniques are described in relation to future information and natural resource planning systems with emphasis on application to Georgia resource management problems. The concept, design, and purpose of Georgia's statewide Resource AS Assessment Program is reviewed along with participation in a workshop at the Earth Resources Laboratory. Potential areas of application discussed include: agriculture, forestry, water resources, environmental planning, and geology.

Mapping the spatio-temporal evolution of irrigation in the Coastal Plain of Georgia, USA

Treesearch

Marcus D. Williams; Christie M.S. Hawley; Marguerite Madden; J. Marshall Shepherd

2017-01-01

This study maps the spatial and temporal evolution of acres irrigated in the Coastal Plain of Georgia over a 38 year period. The goal of this analysis is to create a time-series of irrigated areas in the Coastal Plain of Georgia at a sub-county level. From 1976 through 2013, Landsat images were obtained and sampled at four year intervals to manually...

Black Sea and Caspian Sea, Symposium II, Constanta, Romania

DTIC Science & Technology

2007-05-01

Keller, USA Mr. Serghey Konoplyov, Ukraine CPT (A) Irakli Kurasbediani, Georgia BG Simeon Lalidis, Greece Mr. James MacDougall, USA GEN (Ret) Sergiu Medar...official gift to the representatives from the Republic of Georgia, CPT Irakli Kurasbediani and COL Gochia Ratiani, and thanks them for offering to...June 2008, Major Irakli Kurasbediani, Georgia, Head of the Military Intelligence Department (M.I.D.) and Lieutenant General Michael D. Maples, USA

Children's Growth and Classroom Experiences in Georgia's Pre-K Program: Findings from the 2011-2012 Evaluation Study

ERIC Educational Resources Information Center

Peisner-Feinberg, Ellen; Schaaf, Jennifer; LaForett, Dore

2013-01-01

Georgia has one of the few state-funded universal pre-kindergarten programs in the United States, with the aim of providing pre-k services to all 4-year-olds whose families want their children to participate in the program, regardless of family income level. In the 2011-2012 school year, Georgia's Pre-K Program served a total of over 94,000…

A value orientation approach to assess and compare climate change risk perception among trout anglers in Georgia, USA

Treesearch

Ramesh Paudyal; Neelam C. Poudyal; J.M. Bowker; Adrienne M. Dorison; Stanley J. Zarnoch; Gary T. Green

2015-01-01

Trout in Georgia could experience early impacts from climate change as the streams in the region are located at the southern most edge of their North American home range. This study surveyed trout anglers in Georgia to understand how anglers perceive the potential impact of climate change on trout, and whether and how their perception and response to declines in trout...

A Contextual Analysis of the Quality Core Curriculum and the Georgia Performance Standards in Seventh Grade Social Studies: A Critical Race Perspective

ERIC Educational Resources Information Center

Candis, Matthew Reese

2013-01-01

In 1985 the state of Georgia introduced the Quality Core Curriculum (QCC) in accordance with the Quality Basic Education (QBE) Act. These learning standards identified the content knowledge that students were required to learn in each subject area at all grade levels. The QCC was replaced by the Georgia Performance Standards (GPS) to identify the…

Rapid Identification of Bacterial Pathogens of Military Interest Using Surface-Enhanced Raman Spectroscopy

DTIC Science & Technology

2014-06-11

Veterinary Medicine, University of Georgia, Athens, GA NAMRU-SA REPORT #2014-58 DISTRIBUTION A- Approved for Government release; unlimited...Klebsiella pneumoniae in infant formula . Food Control, 21(4), 487-491. doi: http://dx.doi.org/1 0.1 016/j.foodcont.2009.07 .014. Tam, V. H...Physics and Astronomy University of Georgia, Athens, GA Department of Infectious Diseases College of Veterinary Medicine University of Georgia

Seismicity map of the state of Georgia

USGS Publications Warehouse

Reagor, B. Glen; Stover, C.W.; Algermissen, S.T.; Long, L.T.

1987-01-01

This map is one of a series of seimicity maps produced by the U.S. Geological Survey that show earthquake data of individual states or groups of states at the scale of 1:1,000,000.  This map shows only those earthquakes with epicenters located within the boundaries of Georgia, even though earthquakes in nearby states or countries may have been felt or may have caused damage in Georgia.

‘GA 03564-12E6’: A high-yielding soft red winter wheat cultivar adapted to Georgia and the southeastern regions of the United States

USDA-ARS?s Scientific Manuscript database

Soft red winter wheat (SRWW) (Triticum aestivum L.) is a major crop in the southeastern region of the United States and in Georgia. Although wheat acreages have been decreasing in Georgia and the SE region in recent years, more than 100,000 ha were grown to SRWW in 2015. Newly released cultivars mus...

Using Eighth Grade Georgia Criterion-Referenced Competency Tests to Predict Student Achievement on the Georgia End of Course Tests

ERIC Educational Resources Information Center

Darnell, Janice Marie

2012-01-01

The purpose of this correlational study was to examine Georgia's Criterion-Referenced Competency Test (CRCT) scores of 8th grade students and End of Course Test (EOCT) scores of the same students as 9th graders in the areas of language arts and mathematics to test the theory that a relationship exists between the two tests. The study also examined…

Oral Histories of Nurse-Midwives in Georgia, 1970-1989: Blazing Trails, Building Fences, Raising Towers.

PubMed

Thrower, Eileen J B

2018-05-26

This article provides an account of the establishment and development of the contemporary nurse-midwifery profession in Georgia, which was previously undocumented. Oral history interviews with nurse-midwives who were in clinical and educational practice in Georgia during the 1970s and 1980s were collected and analyzed to identify factors that affected the establishment of nurse-midwifery in this state. This study relied on historical methodology. Oral history interviews provided primary sources for analysis. Secondary sources included archives belonging to the narrators' nurse-midwifery services as well as scholarly and professional publications from 1923 to the present. Data were analyzed using Miller-Rosser and colleagues' method. In-depth interviews were conducted with 14 nurse-midwives who worked in clinical practice or education in Georgia in the 1970s and 1980s. The narrators' testimonies revealed facilitators for the establishment of nurse-midwifery in Georgia, including increasing access to care, providing woman-centered care, interprofessional relationships, and the support of peers. Resistance from the medical profession, financial constraints, and public misconceptions were identified as barriers for the profession. Oral histories in this study provided insight into the experiences of nurse-midwives in Georgia as they practiced and taught in the 1970s and 1980s. Interprofessional connections and cooperation supported the nurse-midwifery profession, and relationships with peers anchored the nurse-midwives. Mentoring relationships and interprofessional collaboration supported the nurse-midwives as they adapted and evolved to meet the needs of women in Georgia. © 2018 by the American College of Nurse-Midwives.

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.

Incidence and Pathogenicity of Plant-Parasitic Nematodes Associated with Blueberry (Vaccinium spp.) Replant Disease in Georgia and North Carolina

PubMed Central

Jagdale, Ganpati B.; Holladay, Ted; Brannen, P. M.; Cline, W. O.; Agudelo, P.; Nyczepir, A. P.; Noe, J. P.

2013-01-01

Blueberry replant disease (BRD) is an emerging threat to continued blueberry (Vaccinium spp.) production in Georgia and North Carolina. Since high populations of ring nematode Mesocriconema ornatum were found to be associated with commercially grown blueberries in Georgia, we hypothesized that M. ornatum may be responsible for predisposing blueberry to BRD. We therefore tested the pathogenicity of M. ornatum on 10-wk-old Rabbiteye blueberries (Vaccinium virgatum) by inoculating with initial populations (Pi) of 0 (water control), 10, 100, 1,000. and 10,000 mixed stages of M. ornatum/pot under both greenhouse (25 ± 2°C) and field microplot conditions. Nematode soil population densities and reproduction rates were assessed 75, 150, 225, and 255, and 75, 150, 225, and 375 d after inoculation (DAI) in both the greenhouse and field experiments, respectively. Plant growth parameters were recorded in the greenhouse and field microplot experiments at 255 and 375 DAI, respectively. The highest M. ornatum population density occurred with the highest Pi level, at 75 and 150 DAI under both greenhouse (P < 0.01) and field (P < 0.01) conditions. However, M. ornatum rate of reproduction increased significantly in pots receiving the lowest Pi level of 10 nematodes/plant compared with the pots receiving Pi levels of 100, 1,000, and 10,000 nematodes 75 DAI. Plant-parasitic nematode populations were determined in commercial blueberry replant sites in Georgia and North Carolina during the 2010 growing season. Mesocriconema ornatum and Dolichodorus spp. were the predominant plant-parasitic nematodes in Georgia and North Carolina, respectively, with M. ornatum occurring in nearly half the blueberry fields sampled in Georgia. Other nematode genera detected in both states included Tylenchorhynchus spp., Hoplolaimus spp., Hemicycliophora spp., and Xiphinema spp. Paratrichodorus spp. was also found only in Georgia. In Georgia, our results indicate that blueberry is a host for M. ornatum and its relationship to BRD warrants further investigation. PMID:23833323

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

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.

Is High School Graduation an Illusion? A Study to Determine the Academic and Graduation Progression between Students with Disabilities and Their Non-Disabled Peers in Georgia Public High Schools

ERIC Educational Resources Information Center

Thinguri, Ruth W.

2010-01-01

The study examined the academic and graduation progression of students with disabilities compared to their non-disabled students in Georgia public high schools. Specifically, the Georgia High School Graduation Tests (GHSGT) in math and English and graduation rates were analyzed for their progression since the enactment of the No Child Left Behind…

Children's Growth and Classroom Experiences in Georgia's Pre-K Program: Findings from the 2011-2012 Evaluation Study. Executive Summary

ERIC Educational Resources Information Center

Peisner-Feinberg, Ellen; Schaaf, Jennifer; LaForett, Dore

2013-01-01

Georgia has one of the few state-funded universal pre-kindergarten programs in the United States, with the aim of providing pre-k services to all 4-year-olds whose families want their children to participate in the program, regardless of family income level. In the 2011-2012 school year, Georgia's Pre-K Program served a total of over 94,000…

Armenia, Azerbaijan, and Georgia: Political Developments and Implications for U.S. Interests

DTIC Science & Technology

2013-10-25

with Georgia have evolved from U.S. contacts with its pro -Western leadership. Successive Administrations have supported U.S. private investment in...Georgia after Eduard Shevardnadze (formerly a pro -Western Soviet foreign minister) assumed power there in early 1992. Faced with calls in Congress and...The Roles of Turkey, Iran, and Others The United States has generally viewed Turkey as able to foster pro -Western policies and discourage Iranian

Armenia, Azerbaijan, and Georgia: Political Developments and Implications for U.S. Interests

DTIC Science & Technology

2012-09-27

over its fate. Close ties with Georgia have evolved from U.S. contacts with its pro -Western leadership. Successive Administrations have supported U.S...ties with Georgia after Eduard Shevardnadze (formerly a pro -Western Soviet foreign minister) assumed power there in early 1992. Faced with calls in...generally viewed Turkey as able to foster pro -Western policies and discourage Iranian interference in the South Caucasus states, even though Turkey favors

Dry creek long-term watershed study: the effects of harvesting in streamside management zones and adjacent uplands of riparian corridors on avian communities in the Coastal Plain of Georgia

Treesearch

Merideth P. Grooms; J. Drew Lanham; T. Bently Wigley

2006-01-01

We evaluated the effects of Best Management Practices (BMPs) harvesting on avian communities associated with headwater streams in the Georgia Coastal Plain. Two watersheds served as references, with no timber harvesting, and two treatment watersheds were clearcut with retention of Streamside Management Zones (SMZs) according to Georgia BMPs for forestry. Bird...

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

Aeromechanics and Aeroacoustics Predictions of the Boeing-SMART Rotor Using Coupled-CFD/CSD Analyses

NASA Technical Reports Server (NTRS)

Bain, Jeremy; Sim, Ben W.; Sankar, Lakshmi; Brentner, Ken

2010-01-01

This paper will highlight helicopter aeromechanics and aeroacoustics prediction capabilities developed by Georgia Institute of Technology, the Pennsylvania State University, and Northern Arizona University under the Helicopter Quieting Program (HQP) sponsored by the Tactical Technology Office of the Defense Advanced Research Projects Agency (DARPA). First initiated in 2004, the goal of the HQP was to develop high fidelity, state-of-the-art computational tools for designing advanced helicopter rotors with reduced acoustic perceptibility and enhanced performance. A critical step towards achieving this objective is the development of rotorcraft prediction codes capable of assessing a wide range of helicopter configurations and operations for future rotorcraft designs. This includes novel next-generation rotor systems that incorporate innovative passive and/or active elements to meet future challenging military performance and survivability goals.

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

Sweet, Marshall L.; Francisco, Abby; Roberts, Sydney G.

Rea Ventures Group, LLC (Rea Ventures) partnered with Southface Energy Institute (Southface)—a member of the U.S. Department of Energy’s Partnership for Home Innovation Building America research team—to rehabilitate 418 low-income multifamily rental apartments located at 14 properties in Georgia (International Energy Conservation Code Climate Zones 2–4). These 22-year-old units with individual utility meters were arranged in row house or townhouse style. Rehabilitation plans were developed using a process prescribed by the U.S. Department of Agriculture (USDA) Rural Development program, which partially funded the building upgrades. The USDA is responsible for building, upgrading, and subsidizing housing in rural areas nationwide; thismore » housing includes more than 14,000 existing multifamily housing developments. In 2012, more than $100 million in grants and loans were allocated for that purpose.« less

Advanced information processing system: Hosting of advanced guidance, navigation and control algorithms on AIPS using ASTER

NASA Technical Reports Server (NTRS)

Brenner, Richard; Lala, Jaynarayan H.; Nagle, Gail A.; Schor, Andrei; Turkovich, John

1994-01-01

This program demonstrated the integration of a number of technologies that can increase the availability and reliability of launch vehicles while lowering costs. Availability is increased with an advanced guidance algorithm that adapts trajectories in real-time. Reliability is increased with fault-tolerant computers and communication protocols. Costs are reduced by automatically generating code and documentation. This program was realized through the cooperative efforts of academia, industry, and government. The NASA-LaRC coordinated the effort, while Draper performed the integration. Georgia Institute of Technology supplied a weak Hamiltonian finite element method for optimal control problems. Martin Marietta used MATLAB to apply this method to a launch vehicle (FENOC). Draper supplied the fault-tolerant computing and software automation technology. The fault-tolerant technology includes sequential and parallel fault-tolerant processors (FTP & FTPP) and authentication protocols (AP) for communication. Fault-tolerant technology was incrementally incorporated. Development culminated with a heterogeneous network of workstations and fault-tolerant computers using AP. Draper's software automation system, ASTER, was used to specify a static guidance system based on FENOC, navigation, flight control (GN&C), models, and the interface to a user interface for mission control. ASTER generated Ada code for GN&C and C code for models. An algebraic transform engine (ATE) was developed to automatically translate MATLAB scripts into ASTER.

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.