prediction project modificado: Topics by Science.gov

Sample records for prediction project modificado

Reflexión bioética sobre el uso de organismos genéticamente modificados

PubMed Central

Yunta, Eduardo Rodríguez

2011-01-01

El presente artículo reflexiona desde los 4 principios de la bioética el uso comercial de organismos genéticamente modificados. Se cuestiona fundamentalmente la falta de transferencia de tecnología entre el mundo desarrollado y en desarrollo y el que el presente sistema de patentamiento de organismos vivos modificados fomenta intereses comerciales y no da debida importancia al desarrollo sostenible de la agricultura y ganadería en los países en desarrollo, donde más se necesita. Se reflexiona sobre la importancia que tiene evaluar los riesgos antes de introducirse en el mercado organismos genéticamente modificados y la necesidad de regulación en los países. PMID:21927675
The NIEHS Predictive-Toxicology Evaluation Project.

PubMed Central

Bristol, D W; Wachsman, J T; Greenwell, A

1996-01-01

The Predictive-Toxicology Evaluation (PTE) project conducts collaborative experiments that subject the performance of predictive-toxicology (PT) methods to rigorous, objective evaluation in a uniquely informative manner. Sponsored by the National Institute of Environmental Health Sciences, it takes advantage of the ongoing testing conducted by the U.S. National Toxicology Program (NTP) to estimate the true error of models that have been applied to make prospective predictions on previously untested, noncongeneric-chemical substances. The PTE project first identifies a group of standardized NTP chemical bioassays either scheduled to be conducted or are ongoing, but not yet complete. The project then announces and advertises the evaluation experiment, disseminates information about the chemical bioassays, and encourages researchers from a wide variety of disciplines to publish their predictions in peer-reviewed journals, using whatever approaches and methods they feel are best. A collection of such papers is published in this Environmental Health Perspectives Supplement, providing readers the opportunity to compare and contrast PT approaches and models, within the context of their prospective application to an actual-use situation. This introduction to this collection of papers on predictive toxicology summarizes the predictions made and the final results obtained for the 44 chemical carcinogenesis bioassays of the first PTE experiment (PTE-1) and presents information that identifies the 30 chemical carcinogenesis bioassays of PTE-2, along with a table of prediction sets that have been published to date. It also provides background about the origin and goals of the PTE project, outlines the special challenge associated with estimating the true error of models that aspire to predict open-system behavior, and summarizes what has been learned to date. PMID:8933048
The Predictive Validity of Projective Measures.

ERIC Educational Resources Information Center

Suinn, Richard M.; Oskamp, Stuart

Written for use by clinical practitioners as well as psychological researchers, this book surveys recent literature (1950-1965) on projective test validity by reviewing and critically evaluating studies which shed light on what may reliably be predicted from projective test results. Two major instruments are covered: the Rorschach and the Thematic…
Project for Solar-Terrestrial Environment Prediction (PSTEP): Towards Predicting Next Solar Cycle

NASA Astrophysics Data System (ADS)

Imada, S.; Iijima, H.; Hotta, H.; Shiota, D.; Kanou, O.; Fujiyama, M.; Kusano, K.

2016-10-01

It is believed that the longer-term variations of the solar activity can affect the Earth's climate. Therefore, predicting the next solar cycle is crucial for the forecast of the "solar-terrestrial environment". To build prediction schemes for the activity level of the next solar cycle is a key for the long-term space weather study. Although three-years prediction can be almost achieved, the prediction of next solar cycle is very limited, so far. We are developing a five-years prediction scheme by combining the Surface Flux Transport (SFT) model and the most accurate measurements of solar magnetic fields as a part of the PSTEP (Project for Solar-Terrestrial Environment Prediction),. We estimate the meridional flow, differential rotation, and turbulent diffusivity from recent modern observations (Hinode and Solar Dynamics Observatory). These parameters are used in the SFT models to predict the polar magnetic fields strength at the solar minimum. In this presentation, we will explain the outline of our strategy to predict the next solar cycle. We also report the present status and the future perspective of our project.
The Arctic Predictability and Prediction on Seasonal-to-Interannual TimEscales (APPOSITE) project: a summary

NASA Astrophysics Data System (ADS)

Hawkins, Ed; Day, Jonny; Tietsche, Steffen

2016-04-01

Recent years have seen significant developments in seasonal-to-interannual timescale climate prediction capabilities. However, until recently the potential of such systems to predict Arctic climate had not been assessed. We describe a multi-model predictability experiment which was run as part of the Arctic Predictability and Prediction On Seasonal to Inter-annual TimEscales (APPOSITE) project. The main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable. In order to achieve this, a coordinated set of idealised initial-value predictability experiments, with seven general circulation models, was conducted. This was the first model intercomparison project designed to quantify the predictability of Arctic climate on seasonal to inter-annual timescales. Here we provide a summary and update of the project's results which include: (1) quantifying the predictability of Arctic climate, especially sea ice; (2) the state-dependence of this predictability, finding that extreme years are potentially more predictable than neutral years; (3) analysing a spring 'predictability barrier' to skillful forecasts; (4) initial sea ice thickness information provides much of the skill for summer forecasts; (5) quantifying the sources of error growth and uncertainty in Arctic predictions. The dataset is now publicly available.
CERAPP: Collaborative Estrogen Receptor Activity Prediction Project

EPA Pesticide Factsheets

Data from a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project) demonstrating using predictive computational models on high-throughput screening data to screen thousands of chemicals against the estrogen receptor.This dataset is associated with the following publication:Mansouri , K., A. Abdelaziz, A. Rybacka, A. Roncaglioni, A. Tropsha, A. Varnek, A. Zakharov, A. Worth, A. Richard , C. Grulke , D. Trisciuzzi, D. Fourches, D. Horvath, E. Benfenati , E. Muratov, E.B. Wedebye, F. Grisoni, G.F. Mangiatordi, G.M. Incisivo, H. Hong, H.W. Ng, I.V. Tetko, I. Balabin, J. Kancherla , J. Shen, J. Burton, M. Nicklaus, M. Cassotti, N.G. Nikolov, O. Nicolotti, P.L. Andersson, Q. Zang, R. Politi, R.D. Beger , R. Todeschini, R. Huang, S. Farag, S.A. Rosenberg, S. Slavov, X. Hu, and R. Judson. (Environmental Health Perspectives) CERAPP: Collaborative Estrogen Receptor Activity Prediction Project. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 1-49, (2016).
Research on cross - Project software defect prediction based on transfer learning

NASA Astrophysics Data System (ADS)

Chen, Ya; Ding, Xiaoming

2018-04-01

According to the two challenges in the prediction of cross-project software defects, the distribution differences between the source project and the target project dataset and the class imbalance in the dataset, proposing a cross-project software defect prediction method based on transfer learning, named NTrA. Firstly, solving the source project data's class imbalance based on the Augmented Neighborhood Cleaning Algorithm. Secondly, the data gravity method is used to give different weights on the basis of the attribute similarity of source project and target project data. Finally, a defect prediction model is constructed by using Trad boost algorithm. Experiments were conducted using data, come from NASA and SOFTLAB respectively, from a published PROMISE dataset. The results show that the method has achieved good values of recall and F-measure, and achieved good prediction results.
CERAPP: Collaborative Estrogen Receptor Activity Prediction Project

PubMed Central

Mansouri, Kamel; Abdelaziz, Ahmed; Rybacka, Aleksandra; Roncaglioni, Alessandra; Tropsha, Alexander; Varnek, Alexandre; Zakharov, Alexey; Worth, Andrew; Richard, Ann M.; Grulke, Christopher M.; Trisciuzzi, Daniela; Fourches, Denis; Horvath, Dragos; Benfenati, Emilio; Muratov, Eugene; Wedebye, Eva Bay; Grisoni, Francesca; Mangiatordi, Giuseppe F.; Incisivo, Giuseppina M.; Hong, Huixiao; Ng, Hui W.; Tetko, Igor V.; Balabin, Ilya; Kancherla, Jayaram; Shen, Jie; Burton, Julien; Nicklaus, Marc; Cassotti, Matteo; Nikolov, Nikolai G.; Nicolotti, Orazio; Andersson, Patrik L.; Zang, Qingda; Politi, Regina; Beger, Richard D.; Todeschini, Roberto; Huang, Ruili; Farag, Sherif; Rosenberg, Sine A.; Slavov, Svetoslav; Hu, Xin; Judson, Richard S.

2016-01-01

Background: Humans are exposed to thousands of man-made chemicals in the environment. Some chemicals mimic natural endocrine hormones and, thus, have the potential to be endocrine disruptors. Most of these chemicals have never been tested for their ability to interact with the estrogen receptor (ER). Risk assessors need tools to prioritize chemicals for evaluation in costly in vivo tests, for instance, within the U.S. EPA Endocrine Disruptor Screening Program. Objectives: We describe a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project) and demonstrate the efficacy of using predictive computational models trained on high-throughput screening data to evaluate thousands of chemicals for ER-related activity and prioritize them for further testing. Methods: CERAPP combined multiple models developed in collaboration with 17 groups in the United States and Europe to predict ER activity of a common set of 32,464 chemical structures. Quantitative structure–activity relationship models and docking approaches were employed, mostly using a common training set of 1,677 chemical structures provided by the U.S. EPA, to build a total of 40 categorical and 8 continuous models for binding, agonist, and antagonist ER activity. All predictions were evaluated on a set of 7,522 chemicals curated from the literature. To overcome the limitations of single models, a consensus was built by weighting models on scores based on their evaluated accuracies. Results: Individual model scores ranged from 0.69 to 0.85, showing high prediction reliabilities. Out of the 32,464 chemicals, the consensus model predicted 4,001 chemicals (12.3%) as high priority actives and 6,742 potential actives (20.8%) to be considered for further testing. Conclusion: This project demonstrated the possibility to screen large libraries of chemicals using a consensus of different in silico approaches. This concept will be applied in future projects related to other
The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project

Treesearch

Lawrence N. Hudson; Joseph Wunderle M.; And Others

2016-01-01

The PREDICTS projectâProjecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)âhas collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity of human impacts relating to land use. We have used this evidence base to...
Increasing Prediction the Original Final Year Project of Student Using Genetic Algorithm

NASA Astrophysics Data System (ADS)

Saragih, Rijois Iboy Erwin; Turnip, Mardi; Sitanggang, Delima; Aritonang, Mendarissan; Harianja, Eva

2018-04-01

Final year project is very important forgraduation study of a student. Unfortunately, many students are not seriouslydidtheir final projects. Many of studentsask for someone to do it for them. In this paper, an application of genetic algorithms to predict the original final year project of a studentis proposed. In the simulation, the data of the final project for the last 5 years is collected. The genetic algorithm has several operators namely population, selection, crossover, and mutation. The result suggest that genetic algorithm can do better prediction than other comparable model. Experimental results of predicting showed that 70% was more accurate than the previous researched.
Studies in Bilingual Evaluation, Work Unit I: Bilingual Prediction Project. Final Report.

ERIC Educational Resources Information Center

de Porcel, Antonio; And Others

The final report of the Bilingual Prediction Project presents a review of the project from its inception in 1975 through completion in 1979. The main goal was to predict a student's academic ability in English. A prediction index was constructed in two stages. The first stage was a description of the target population and their school setting, as…
Genetic Programming as Alternative for Predicting Development Effort of Individual Software Projects

PubMed Central

Chavoya, Arturo; Lopez-Martin, Cuauhtemoc; Andalon-Garcia, Irma R.; Meda-Campaña, M. E.

2012-01-01

Statistical and genetic programming techniques have been used to predict the software development effort of large software projects. In this paper, a genetic programming model was used for predicting the effort required in individually developed projects. Accuracy obtained from a genetic programming model was compared against one generated from the application of a statistical regression model. A sample of 219 projects developed by 71 practitioners was used for generating the two models, whereas another sample of 130 projects developed by 38 practitioners was used for validating them. The models used two kinds of lines of code as well as programming language experience as independent variables. Accuracy results from the model obtained with genetic programming suggest that it could be used to predict the software development effort of individual projects when these projects have been developed in a disciplined manner within a development-controlled environment. PMID:23226305
Developing a stochastic traffic volume prediction model for public-private partnership projects

NASA Astrophysics Data System (ADS)

Phong, Nguyen Thanh; Likhitruangsilp, Veerasak; Onishi, Masamitsu

2017-11-01

Transportation projects require an enormous amount of capital investment resulting from their tremendous size, complexity, and risk. Due to the limitation of public finances, the private sector is invited to participate in transportation project development. The private sector can entirely or partially invest in transportation projects in the form of Public-Private Partnership (PPP) scheme, which has been an attractive option for several developing countries, including Vietnam. There are many factors affecting the success of PPP projects. The accurate prediction of traffic volume is considered one of the key success factors of PPP transportation projects. However, only few research works investigated how to predict traffic volume over a long period of time. Moreover, conventional traffic volume forecasting methods are usually based on deterministic models which predict a single value of traffic volume but do not consider risk and uncertainty. This knowledge gap makes it difficult for concessionaires to estimate PPP transportation project revenues accurately. The objective of this paper is to develop a probabilistic traffic volume prediction model. First, traffic volumes were estimated following the Geometric Brownian Motion (GBM) process. Monte Carlo technique is then applied to simulate different scenarios. The results show that this stochastic approach can systematically analyze variations in the traffic volume and yield more reliable estimates for PPP projects.
Predicting Defects Using Information Intelligence Process Models in the Software Technology Project

PubMed Central

Selvaraj, Manjula Gandhi; Jayabal, Devi Shree; Srinivasan, Thenmozhi; Balasubramanie, Palanisamy

2015-01-01

A key differentiator in a competitive market place is customer satisfaction. As per Gartner 2012 report, only 75%–80% of IT projects are successful. Customer satisfaction should be considered as a part of business strategy. The associated project parameters should be proactively managed and the project outcome needs to be predicted by a technical manager. There is lot of focus on the end state and on minimizing defect leakage as much as possible. Focus should be on proactively managing and shifting left in the software life cycle engineering model. Identify the problem upfront in the project cycle and do not wait for lessons to be learnt and take reactive steps. This paper gives the practical applicability of using predictive models and illustrates use of these models in a project to predict system testing defects thus helping to reduce residual defects. PMID:26495427
Predicting Defects Using Information Intelligence Process Models in the Software Technology Project.

PubMed

Selvaraj, Manjula Gandhi; Jayabal, Devi Shree; Srinivasan, Thenmozhi; Balasubramanie, Palanisamy

2015-01-01

A key differentiator in a competitive market place is customer satisfaction. As per Gartner 2012 report, only 75%-80% of IT projects are successful. Customer satisfaction should be considered as a part of business strategy. The associated project parameters should be proactively managed and the project outcome needs to be predicted by a technical manager. There is lot of focus on the end state and on minimizing defect leakage as much as possible. Focus should be on proactively managing and shifting left in the software life cycle engineering model. Identify the problem upfront in the project cycle and do not wait for lessons to be learnt and take reactive steps. This paper gives the practical applicability of using predictive models and illustrates use of these models in a project to predict system testing defects thus helping to reduce residual defects.
US Climate Variability and Predictability Project

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

Patterson, Mike

The US CLIVAR Project Office administers the US CLIVAR Program with its mission to advance understanding and prediction of climate variability and change across timescales with an emphasis on the role of the ocean and its interaction with other elements of the Earth system. The Project Office promotes and facilitates scientific collaboration within the US and international climate and Earth science communities, addressing priority topics from subseasonal to centennial climate variability and change; the global energy imbalance; the ocean’s role in climate, water, and carbon cycles; climate and weather extremes; and polar climate changes. This project provides essential one-year supportmore » of the Project Office, enabling the participation of US scientists in the meetings of the US CLIVAR bodies that guide scientific planning and implementation, including the scientific steering committee that establishes program goals and evaluates progress of activities to address them, the science team of funded investigators studying the ocean overturning circulation in the Atlantic, and two working groups tackling the priority research topics of Arctic change influence on midlatitude climate and weather extremes and the decadal-scale widening of the tropical belt.« less
The Decadal Climate Prediction Project (DCPP) contribution to CMIP6

DOE PAGES

Boer, George J.; Smith, Douglas M.; Cassou, Christophe; ...

2016-01-01

The Decadal Climate Prediction Project (DCPP) is a coordinated multi-model investigation into decadal climate prediction, predictability, and variability. The DCPP makes use of past experience in simulating and predicting decadal variability and forced climate change gained from the fifth Coupled Model Intercomparison Project (CMIP5) and elsewhere. It builds on recent improvements in models, in the reanalysis of climate data, in methods of initialization and ensemble generation, and in data treatment and analysis to propose an extended comprehensive decadal prediction investigation as a contribution to CMIP6 (Eyring et al., 2016) and to the WCRP Grand Challenge on Near Term Climate Predictionmore » (Kushnir et al., 2016). The DCPP consists of three components. Component A comprises the production and analysis of an extensive archive of retrospective forecasts to be used to assess and understand historical decadal prediction skill, as a basis for improvements in all aspects of end-to-end decadal prediction, and as a basis for forecasting on annual to decadal timescales. Component B undertakes ongoing production, analysis and dissemination of experimental quasi-real-time multi-model forecasts as a basis for potential operational forecast production. Component C involves the organization and coordination of case studies of particular climate shifts and variations, both natural and naturally forced (e.g. the “hiatus”, volcanoes), including the study of the mechanisms that determine these behaviours. Furthermore, groups are invited to participate in as many or as few of the components of the DCPP, each of which are separately prioritized, as are of interest to them.The Decadal Climate Prediction Project addresses a range of scientific issues involving the ability of the climate system to be predicted on annual to decadal timescales, the skill that is currently and potentially available, the mechanisms involved in long timescale variability, and the production
Munition Penetration Depth Prediction: SERDP SEED Project MR 2629

DTIC Science & Technology

2017-08-01

ER D C/ CR RE L TR -1 7- 12 Strategic Environmental Research and Development Program (SERDP) Munition Penetration-Depth Prediction...release; distribution is unlimited. The U.S. Army Engineer Research and Development Center (ERDC) solves the nation’s toughest engineering and... Research and Development Program (SERDP) ERDC/CRREL TR-17-12 August 2017 Munition Penetration-Depth Prediction SERDP SEED Project MR-2629 Arnold J
Geospatial application of the Water Erosion Prediction Project (WEPP) Model

Treesearch

D. C. Flanagan; J. R. Frankenberger; T. A. Cochrane; C. S. Renschler; W. J. Elliot

2011-01-01

The Water Erosion Prediction Project (WEPP) model is a process-based technology for prediction of soil erosion by water at hillslope profile, field, and small watershed scales. In particular, WEPP utilizes observed or generated daily climate inputs to drive the surface hydrology processes (infiltration, runoff, ET) component, which subsequently impacts the rest of the...
Project Evaluation: Validation of a Scale and Analysis of Its Predictive Capacity

ERIC Educational Resources Information Center

Fernandes Malaquias, Rodrigo; de Oliveira Malaquias, Fernanda Francielle

2014-01-01

The objective of this study was to validate a scale for assessment of academic projects. As a complement, we examined its predictive ability by comparing the scores of advised/corrected projects based on the model and the final scores awarded to the work by an examining panel (approximately 10 months after the project design). Results of…

Water Erosion Prediction Project (WEPP) model status and updates

USDA-ARS?s Scientific Manuscript database

This presentation will provide current information on the USDA-ARS Water Erosion Prediction Project (WEPP) model, and its implementation by the USDA-Forest Service (FS), USDA-Natural Resources Conservation Service (NRCS), and other agencies and universities. Most recently, the USDA-NRCS has begun ef...
Rainfall and Extratropical Transition of Tropical Cyclones: Simulation, Prediction, and Projection

NASA Astrophysics Data System (ADS)

Liu, Maofeng

Rainfall and associated flood hazards are one of the major threats of tropical cyclones (TCs) to coastal and inland regions. The interaction of TCs with extratropical systems can lead to enhanced precipitation over enlarged areas through extratropical transition (ET). To achieve a comprehensive understanding of rainfall and ET associated with TCs, this thesis conducts weather-scale analyses by focusing on individual storms and climate-scale analyses by focusing on seasonal predictability and changing properties of climatology under global warming. The temporal and spatial rainfall evolution of individual storms, including Hurricane Irene (2011), Hurricane Hanna (2008), and Hurricane Sandy (2012), is explored using the Weather Research and Forecast (WRF) model and a variety of hydrometeorological datasets. ET and Orographic mechanism are two key players in the rainfall distribution of Irene over regions experiencing most severe flooding. The change of TC rainfall under global warming is explored with the Forecast-oriented Low Ocean Resolution (FLOR) climate model under representative concentration pathway (RCP) 4.5 scenario. Despite decreased TC frequency, FLOR projects increased landfalling TC rainfall over most regions of eastern United States, highlighting the risk of increased flood hazards. Increased storm rain rate is an important player of increased landfalling TC rainfall. A higher atmospheric resolution version of FLOR (HiFLOR) model projects increased TC rainfall at global scales. The increase of TC intensity and environmental water vapor content scaled by the Clausius-Clapeyron relation are two key factors that explain the projected increase of TC rainfall. Analyses on the simulation, prediction, and projection of the ET activity with FLOR are conducted in the North Atlantic. FLOR model exhibits good skills in simulating many aspects of present-day ET climatology. The 21st-century-projection under RCP4.5 scenario demonstrates the dominant role of ET
Inroads to predict in vivo toxicology-an introduction to the eTOX Project.

PubMed

Briggs, Katharine; Cases, Montserrat; Heard, David J; Pastor, Manuel; Pognan, François; Sanz, Ferran; Schwab, Christof H; Steger-Hartmann, Thomas; Sutter, Andreas; Watson, David K; Wichard, Jörg D

2012-01-01

There is a widespread awareness that the wealth of preclinical toxicity data that the pharmaceutical industry has generated in recent decades is not exploited as efficiently as it could be. Enhanced data availability for compound comparison ("read-across"), or for data mining to build predictive tools, should lead to a more efficient drug development process and contribute to the reduction of animal use (3Rs principle). In order to achieve these goals, a consortium approach, grouping numbers of relevant partners, is required. The eTOX ("electronic toxicity") consortium represents such a project and is a public-private partnership within the framework of the European Innovative Medicines Initiative (IMI). The project aims at the development of in silico prediction systems for organ and in vivo toxicity. The backbone of the project will be a database consisting of preclinical toxicity data for drug compounds or candidates extracted from previously unpublished, legacy reports from thirteen European and European operation-based pharmaceutical companies. The database will be enhanced by incorporation of publically available, high quality toxicology data. Seven academic institutes and five small-to-medium size enterprises (SMEs) contribute with their expertise in data gathering, database curation, data mining, chemoinformatics and predictive systems development. The outcome of the project will be a predictive system contributing to early potential hazard identification and risk assessment during the drug development process. The concept and strategy of the eTOX project is described here, together with current achievements and future deliverables.
The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project.

PubMed

Hudson, Lawrence N; Newbold, Tim; Contu, Sara; Hill, Samantha L L; Lysenko, Igor; De Palma, Adriana; Phillips, Helen R P; Alhusseini, Tamera I; Bedford, Felicity E; Bennett, Dominic J; Booth, Hollie; Burton, Victoria J; Chng, Charlotte W T; Choimes, Argyrios; Correia, David L P; Day, Julie; Echeverría-Londoño, Susy; Emerson, Susan R; Gao, Di; Garon, Morgan; Harrison, Michelle L K; Ingram, Daniel J; Jung, Martin; Kemp, Victoria; Kirkpatrick, Lucinda; Martin, Callum D; Pan, Yuan; Pask-Hale, Gwilym D; Pynegar, Edwin L; Robinson, Alexandra N; Sanchez-Ortiz, Katia; Senior, Rebecca A; Simmons, Benno I; White, Hannah J; Zhang, Hanbin; Aben, Job; Abrahamczyk, Stefan; Adum, Gilbert B; Aguilar-Barquero, Virginia; Aizen, Marcelo A; Albertos, Belén; Alcala, E L; Del Mar Alguacil, Maria; Alignier, Audrey; Ancrenaz, Marc; Andersen, Alan N; Arbeláez-Cortés, Enrique; Armbrecht, Inge; Arroyo-Rodríguez, Víctor; Aumann, Tom; Axmacher, Jan C; Azhar, Badrul; Azpiroz, Adrián B; Baeten, Lander; Bakayoko, Adama; Báldi, András; Banks, John E; Baral, Sharad K; Barlow, Jos; Barratt, Barbara I P; Barrico, Lurdes; Bartolommei, Paola; Barton, Diane M; Basset, Yves; Batáry, Péter; Bates, Adam J; Baur, Bruno; Bayne, Erin M; Beja, Pedro; Benedick, Suzan; Berg, Åke; Bernard, Henry; Berry, Nicholas J; Bhatt, Dinesh; Bicknell, Jake E; Bihn, Jochen H; Blake, Robin J; Bobo, Kadiri S; Bóçon, Roberto; Boekhout, Teun; Böhning-Gaese, Katrin; Bonham, Kevin J; Borges, Paulo A V; Borges, Sérgio H; Boutin, Céline; Bouyer, Jérémy; Bragagnolo, Cibele; Brandt, Jodi S; Brearley, Francis Q; Brito, Isabel; Bros, Vicenç; Brunet, Jörg; Buczkowski, Grzegorz; Buddle, Christopher M; Bugter, Rob; Buscardo, Erika; Buse, Jörn; Cabra-García, Jimmy; Cáceres, Nilton C; Cagle, Nicolette L; Calviño-Cancela, María; Cameron, Sydney A; Cancello, Eliana M; Caparrós, Rut; Cardoso, Pedro; Carpenter, Dan; Carrijo, Tiago F; Carvalho, Anelena L; Cassano, Camila R; Castro, Helena; Castro-Luna, Alejandro A; Rolando, Cerda B; Cerezo, Alexis; Chapman, Kim Alan; Chauvat, Matthieu; Christensen, Morten; Clarke, Francis M; Cleary, Daniel F R; Colombo, Giorgio; Connop, Stuart P; Craig, Michael D; Cruz-López, Leopoldo; Cunningham, Saul A; D'Aniello, Biagio; D'Cruze, Neil; da Silva, Pedro Giovâni; Dallimer, Martin; Danquah, Emmanuel; Darvill, Ben; Dauber, Jens; Davis, Adrian L V; Dawson, Jeff; de Sassi, Claudio; de Thoisy, Benoit; Deheuvels, Olivier; Dejean, Alain; Devineau, Jean-Louis; Diekötter, Tim; Dolia, Jignasu V; Domínguez, Erwin; Dominguez-Haydar, Yamileth; Dorn, Silvia; Draper, Isabel; Dreber, Niels; Dumont, Bertrand; Dures, Simon G; Dynesius, Mats; Edenius, Lars; Eggleton, Paul; Eigenbrod, Felix; Elek, Zoltán; Entling, Martin H; Esler, Karen J; de Lima, Ricardo F; Faruk, Aisyah; Farwig, Nina; Fayle, Tom M; Felicioli, Antonio; Felton, Annika M; Fensham, Roderick J; Fernandez, Ignacio C; Ferreira, Catarina C; Ficetola, Gentile F; Fiera, Cristina; Filgueiras, Bruno K C; Fırıncıoğlu, Hüseyin K; Flaspohler, David; Floren, Andreas; Fonte, Steven J; Fournier, Anne; Fowler, Robert E; Franzén, Markus; Fraser, Lauchlan H; Fredriksson, Gabriella M; Freire, Geraldo B; Frizzo, Tiago L M; Fukuda, Daisuke; Furlani, Dario; Gaigher, René; Ganzhorn, Jörg U; García, Karla P; Garcia-R, Juan C; Garden, Jenni G; Garilleti, Ricardo; Ge, Bao-Ming; Gendreau-Berthiaume, Benoit; Gerard, Philippa J; Gheler-Costa, Carla; Gilbert, Benjamin; Giordani, Paolo; Giordano, Simonetta; Golodets, Carly; Gomes, Laurens G L; Gould, Rachelle K; Goulson, Dave; Gove, Aaron D; Granjon, Laurent; Grass, Ingo; Gray, Claudia L; Grogan, James; Gu, Weibin; Guardiola, Moisès; Gunawardene, Nihara R; Gutierrez, Alvaro G; Gutiérrez-Lamus, Doris L; Haarmeyer, Daniela H; Hanley, Mick E; Hanson, Thor; Hashim, Nor R; Hassan, Shombe N; Hatfield, Richard G; Hawes, Joseph E; Hayward, Matt W; Hébert, Christian; Helden, Alvin J; Henden, John-André; Henschel, Philipp; Hernández, Lionel; Herrera, James P; Herrmann, Farina; Herzog, Felix; Higuera-Diaz, Diego; Hilje, Branko; Höfer, Hubert; Hoffmann, Anke; Horgan, Finbarr G; Hornung, Elisabeth; Horváth, Roland; Hylander, Kristoffer; Isaacs-Cubides, Paola; Ishida, Hiroaki; Ishitani, Masahiro; Jacobs, Carmen T; Jaramillo, Víctor J; Jauker, Birgit; Hernández, F Jiménez; Johnson, McKenzie F; Jolli, Virat; Jonsell, Mats; Juliani, S Nur; Jung, Thomas S; Kapoor, Vena; Kappes, Heike; Kati, Vassiliki; Katovai, Eric; Kellner, Klaus; Kessler, Michael; Kirby, Kathryn R; Kittle, Andrew M; Knight, Mairi E; Knop, Eva; Kohler, Florian; Koivula, Matti; Kolb, Annette; Kone, Mouhamadou; Kőrösi, Ádám; Krauss, Jochen; Kumar, Ajith; Kumar, Raman; Kurz, David J; Kutt, Alex S; Lachat, Thibault; Lantschner, Victoria; Lara, Francisco; Lasky, Jesse R; Latta, Steven C; Laurance, William F; Lavelle, Patrick; Le Féon, Violette; LeBuhn, Gretchen; Légaré, Jean-Philippe; Lehouck, Valérie; Lencinas, María V; Lentini, Pia E; Letcher, Susan G; Li, Qi; Litchwark, Simon A; Littlewood, Nick A; Liu, Yunhui; Lo-Man-Hung, Nancy; López-Quintero, Carlos A; Louhaichi, Mounir; Lövei, Gabor L; Lucas-Borja, Manuel Esteban; Luja, Victor H; Luskin, Matthew S; MacSwiney G, M Cristina; Maeto, Kaoru; Magura, Tibor; Mallari, Neil Aldrin; Malone, Louise A; Malonza, Patrick K; Malumbres-Olarte, Jagoba; Mandujano, Salvador; Måren, Inger E; Marin-Spiotta, Erika; Marsh, Charles J; Marshall, E J P; Martínez, Eliana; Martínez Pastur, Guillermo; Moreno Mateos, David; Mayfield, Margaret M; Mazimpaka, Vicente; McCarthy, Jennifer L; McCarthy, Kyle P; McFrederick, Quinn S; McNamara, Sean; Medina, Nagore G; Medina, Rafael; Mena, Jose L; Mico, Estefania; Mikusinski, Grzegorz; Milder, Jeffrey C; Miller, James R; Miranda-Esquivel, Daniel R; Moir, Melinda L; Morales, Carolina L; Muchane, Mary N; Muchane, Muchai; Mudri-Stojnic, Sonja; Munira, A Nur; Muoñz-Alonso, Antonio; Munyekenye, B F; Naidoo, Robin; Naithani, A; Nakagawa, Michiko; Nakamura, Akihiro; Nakashima, Yoshihiro; Naoe, Shoji; Nates-Parra, Guiomar; Navarrete Gutierrez, Dario A; Navarro-Iriarte, Luis; Ndang'ang'a, Paul K; Neuschulz, Eike L; Ngai, Jacqueline T; Nicolas, Violaine; Nilsson, Sven G; Noreika, Norbertas; Norfolk, Olivia; Noriega, Jorge Ari; Norton, David A; Nöske, Nicole M; Nowakowski, A Justin; Numa, Catherine; O'Dea, Niall; O'Farrell, Patrick J; Oduro, William; Oertli, Sabine; Ofori-Boateng, Caleb; Oke, Christopher Omamoke; Oostra, Vicencio; Osgathorpe, Lynne M; Otavo, Samuel Eduardo; Page, Navendu V; Paritsis, Juan; Parra-H, Alejandro; Parry, Luke; Pe'er, Guy; Pearman, Peter B; Pelegrin, Nicolás; Pélissier, Raphaël; Peres, Carlos A; Peri, Pablo L; Persson, Anna S; Petanidou, Theodora; Peters, Marcell K; Pethiyagoda, Rohan S; Phalan, Ben; Philips, T Keith; Pillsbury, Finn C; Pincheira-Ulbrich, Jimmy; Pineda, Eduardo; Pino, Joan; Pizarro-Araya, Jaime; Plumptre, A J; Poggio, Santiago L; Politi, Natalia; Pons, Pere; Poveda, Katja; Power, Eileen F; Presley, Steven J; Proença, Vânia; Quaranta, Marino; Quintero, Carolina; Rader, Romina; Ramesh, B R; Ramirez-Pinilla, Martha P; Ranganathan, Jai; Rasmussen, Claus; Redpath-Downing, Nicola A; Reid, J Leighton; Reis, Yana T; Rey Benayas, José M; Rey-Velasco, Juan Carlos; Reynolds, Chevonne; Ribeiro, Danilo Bandini; Richards, Miriam H; Richardson, Barbara A; Richardson, Michael J; Ríos, Rodrigo Macip; Robinson, Richard; Robles, Carolina A; Römbke, Jörg; Romero-Duque, Luz Piedad; Rös, Matthias; Rosselli, Loreta; Rossiter, Stephen J; Roth, Dana S; Roulston, T'ai H; Rousseau, Laurent; Rubio, André V; Ruel, Jean-Claude; Sadler, Jonathan P; Sáfián, Szabolcs; Saldaña-Vázquez, Romeo A; Sam, Katerina; Samnegård, Ulrika; Santana, Joana; Santos, Xavier; Savage, Jade; Schellhorn, Nancy A; Schilthuizen, Menno; Schmiedel, Ute; Schmitt, Christine B; Schon, Nicole L; Schüepp, Christof; Schumann, Katharina; Schweiger, Oliver; Scott, Dawn M; Scott, Kenneth A; Sedlock, Jodi L; Seefeldt, Steven S; Shahabuddin, Ghazala; Shannon, Graeme; Sheil, Douglas; Sheldon, Frederick H; Shochat, Eyal; Siebert, Stefan J; Silva, Fernando A B; Simonetti, Javier A; Slade, Eleanor M; Smith, Jo; Smith-Pardo, Allan H; Sodhi, Navjot S; Somarriba, Eduardo J; Sosa, Ramón A; Soto Quiroga, Grimaldo; St-Laurent, Martin-Hugues; Starzomski, Brian M; Stefanescu, Constanti; Steffan-Dewenter, Ingolf; Stouffer, Philip C; Stout, Jane C; Strauch, Ayron M; Struebig, Matthew J; Su, Zhimin; Suarez-Rubio, Marcela; Sugiura, Shinji; Summerville, Keith S; Sung, Yik-Hei; Sutrisno, Hari; Svenning, Jens-Christian; Teder, Tiit; Threlfall, Caragh G; Tiitsaar, Anu; Todd, Jacqui H; Tonietto, Rebecca K; Torre, Ignasi; Tóthmérész, Béla; Tscharntke, Teja; Turner, Edgar C; Tylianakis, Jason M; Uehara-Prado, Marcio; Urbina-Cardona, Nicolas; Vallan, Denis; Vanbergen, Adam J; Vasconcelos, Heraldo L; Vassilev, Kiril; Verboven, Hans A F; Verdasca, Maria João; Verdú, José R; Vergara, Carlos H; Vergara, Pablo M; Verhulst, Jort; Virgilio, Massimiliano; Vu, Lien Van; Waite, Edward M; Walker, Tony R; Wang, Hua-Feng; Wang, Yanping; Watling, James I; Weller, Britta; Wells, Konstans; Westphal, Catrin; Wiafe, Edward D; Williams, Christopher D; Willig, Michael R; Woinarski, John C Z; Wolf, Jan H D; Wolters, Volkmar; Woodcock, Ben A; Wu, Jihua; Wunderle, Joseph M; Yamaura, Yuichi; Yoshikura, Satoko; Yu, Douglas W; Zaitsev, Andrey S; Zeidler, Juliane; Zou, Fasheng; Collen, Ben; Ewers, Rob M; Mace, Georgina M; Purves, Drew W; Scharlemann, Jörn P W; Purvis, Andy

2017-01-01

The PREDICTS project-Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)-has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity of human impacts relating to land use. We have used this evidence base to develop global and regional statistical models of how local biodiversity responds to these measures. We describe and make freely available this 2016 release of the database, containing more than 3.2 million records sampled at over 26,000 locations and representing over 47,000 species. We outline how the database can help in answering a range of questions in ecology and conservation biology. To our knowledge, this is the largest and most geographically and taxonomically representative database of spatial comparisons of biodiversity that has been collated to date; it will be useful to researchers and international efforts wishing to model and understand the global status of biodiversity.
Demonstration of the Water Erosion Prediction Project (WEPP) internet interface and services

USDA-ARS?s Scientific Manuscript database

The Water Erosion Prediction Project (WEPP) model is a process-based FORTRAN computer simulation program for prediction of runoff and soil erosion by water at hillslope profile, field, and small watershed scales. To effectively run the WEPP model and interpret results additional software has been de...
GEWEX America Prediction Project (GAPP) Science and Implementation Plan

NASA Technical Reports Server (NTRS)

2004-01-01

The purpose of this Science and Implementation Plan is to describe GAPP science objectives and the activities required to meet these objectives, both specifically for the near-term and more generally for the longer-term. The GEWEX Americas Prediction Project (GAPP) is part of the Global Energy and Water Cycle Experiment (GEWEX) initiative that is aimed at observing, understanding and modeling the hydrological cycle and energy fluxes at various time and spatial scales. The mission of GAPP is to demonstrate skill in predicting changes in water resources over intraseasonal-to-interannual time scales, as an integral part of the climate system.
A Global Perspective: NASA's Prediction of Worldwide Energy Resources (POWER) Project

NASA Technical Reports Server (NTRS)

Zhang, Taiping; Stackhouse, Paul W., Jr.; Chandler, William S.; Hoell, James M.; Westberg, David; Whitlock, Charles H.

2007-01-01

The Prediction of the Worldwide Energy Resources (POWER) Project, initiated under the NASA Science Mission Directorate Applied Science Energy Management Program, synthesizes and analyzes data on a global scale that are invaluable to the renewable energy industries, especially to the solar and wind energy sectors. The POWER project derives its data primarily from NASA's World Climate Research Programme (WCRP)/Global Energy and Water cycle Experiment (GEWEX) Surface Radiation Budget (SRB) project (Version 2.9) and the Global Modeling and Assimilation Office (GMAO) Goddard Earth Observing System (GEOS) assimilation model (Version 4). The latest development of the NASA POWER Project and its plans for the future are presented in this paper.
Validation of Water Erosion Prediction Project (WEPP) model for low-volume forest roads

Treesearch

William Elliot; R. B. Foltz; Charlie Luce

1995-01-01

Erosion rates of recently graded nongravel forest roads were measured under rainfall simulation on five different soils. The erosion rates observed on 24 forest road erosion plots were compared with values predicted by the Water Erosion Prediction Project (WEPP) Model, Version 93.1. Hydraulic conductivity and soil erodibility values were predicted from methods...
Predicting future uncertainty constraints on global warming projections

DOE PAGES

Shiogama, H.; Stone, D.; Emori, S.; ...

2016-01-11

Projections of global mean temperature changes (ΔT) in the future are associated with intrinsic uncertainties. Much climate policy discourse has been guided by "current knowledge" of the ΔTs uncertainty, ignoring the likely future reductions of the uncertainty, because a mechanism for predicting these reductions is lacking. By using simulations of Global Climate Models from the Coupled Model Intercomparison Project Phase 5 ensemble as pseudo past and future observations, we estimate how fast and in what way the uncertainties of ΔT can decline when the current observation network of surface air temperature is maintained. At least in the world of pseudomore » observations under the Representative Concentration Pathways (RCPs), we can drastically reduce more than 50% of the ΔTs uncertainty in the 2040 s by 2029, and more than 60% of the ΔTs uncertainty in the 2090 s by 2049. Under the highest forcing scenario of RCPs, we can predict the true timing of passing the 2°C (3°C) warming threshold 20 (30) years in advance with errors less than 10 years. These results demonstrate potential for sequential decision-making strategies to take advantage of future progress in understanding of anthropogenic climate change.« less
Predicting future uncertainty constraints on global warming projections

PubMed Central

Shiogama, H.; Stone, D.; Emori, S.; Takahashi, K.; Mori, S.; Maeda, A.; Ishizaki, Y.; Allen, M. R.

2016-01-01

Projections of global mean temperature changes (ΔT) in the future are associated with intrinsic uncertainties. Much climate policy discourse has been guided by “current knowledge” of the ΔTs uncertainty, ignoring the likely future reductions of the uncertainty, because a mechanism for predicting these reductions is lacking. By using simulations of Global Climate Models from the Coupled Model Intercomparison Project Phase 5 ensemble as pseudo past and future observations, we estimate how fast and in what way the uncertainties of ΔT can decline when the current observation network of surface air temperature is maintained. At least in the world of pseudo observations under the Representative Concentration Pathways (RCPs), we can drastically reduce more than 50% of the ΔTs uncertainty in the 2040 s by 2029, and more than 60% of the ΔTs uncertainty in the 2090 s by 2049. Under the highest forcing scenario of RCPs, we can predict the true timing of passing the 2 °C (3 °C) warming threshold 20 (30) years in advance with errors less than 10 years. These results demonstrate potential for sequential decision-making strategies to take advantage of future progress in understanding of anthropogenic climate change. PMID:26750491
Predicting future uncertainty constraints on global warming projections

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

Shiogama, H.; Stone, D.; Emori, S.

Projections of global mean temperature changes (ΔT) in the future are associated with intrinsic uncertainties. Much climate policy discourse has been guided by "current knowledge" of the ΔTs uncertainty, ignoring the likely future reductions of the uncertainty, because a mechanism for predicting these reductions is lacking. By using simulations of Global Climate Models from the Coupled Model Intercomparison Project Phase 5 ensemble as pseudo past and future observations, we estimate how fast and in what way the uncertainties of ΔT can decline when the current observation network of surface air temperature is maintained. At least in the world of pseudomore » observations under the Representative Concentration Pathways (RCPs), we can drastically reduce more than 50% of the ΔTs uncertainty in the 2040 s by 2029, and more than 60% of the ΔTs uncertainty in the 2090 s by 2049. Under the highest forcing scenario of RCPs, we can predict the true timing of passing the 2°C (3°C) warming threshold 20 (30) years in advance with errors less than 10 years. These results demonstrate potential for sequential decision-making strategies to take advantage of future progress in understanding of anthropogenic climate change.« less
Geospatial application of the Water Erosion Prediction Project (WEPP) model

USDA-ARS?s Scientific Manuscript database

At the hillslope profile and/or field scale, a simple Windows graphical user interface (GUI) is available to easily specify the slope, soil, and management inputs for application of the USDA Water Erosion Prediction Project (WEPP) model. Likewise, basic small watershed configurations of a few hillsl...
US Climate Variability and Predictability (CLIVAR) Project- Final Report

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

Patterson, Mike

The US CLIVAR Project Office administers the US CLIVAR Program with its mission to advance understanding and prediction of climate variability and change across timescales with an emphasis on the role of the ocean and its interaction with other elements of the Earth system. The Project Office promotes and facilitates scientific collaboration within the US and international climate and Earth science communities, addressing priority topics from subseasonal to centennial climate variability and change; the global energy imbalance; the ocean’s role in climate, water, and carbon cycles; climate and weather extremes; and polar climate changes. This project provides essential one-year supportmore » of the Project Office, enabling the participation of US scientists in the meetings of the US CLIVAR bodies that guide scientific planning and implementation, including the scientific steering committee that establishes program goals and evaluates progress of activities to address them, the science team of funded investigators studying the ocean overturning circulation in the Atlantic, and two working groups tackling the priority research topics of Arctic change influence on midlatitude climate and weather extremes and the decadal-scale widening of the tropical belt.« less
The NASA Seasonal-to-Interannual Prediction Project (NSIPP). [Annual Report for 2000

NASA Technical Reports Server (NTRS)

Rienecker, Michele; Suarez, Max; Adamec, David; Koster, Randal; Schubert, Siegfried; Hansen, James; Koblinsky, Chester (Technical Monitor)

2001-01-01

The goal of the project is to develop an assimilation and forecast system based on a coupled atmosphere-ocean-land-surface-sea-ice model capable of using a combination of satellite and in situ data sources to improve the prediction of ENSO and other major S-I signals and their global teleconnections. The objectives of this annual report are to: (1) demonstrate the utility of satellite data, especially surface height surface winds, air-sea fluxes and soil moisture, in a coupled model prediction system; and (2) aid in the design of the observing system for short-term climate prediction by conducting OSSE's and predictability studies.
Geospatial application of the Water Erosion Prediction Project (WEPP) model

Treesearch

D. C. Flanagan; J. R. Frankenberger; T. A. Cochrane; C. S. Renschler; W. J. Elliot

2013-01-01

At the hillslope profile and/or field scale, a simple Windows graphical user interface (GUI) is available to easily specify the slope, soil, and management inputs for application of the USDA Water Erosion Prediction Project (WEPP) model. Likewise, basic small watershed configurations of a few hillslopes and channels can be created and simulated with this GUI. However,...
Macroweather Predictions and Climate Projections using Scaling and Historical Observations

NASA Astrophysics Data System (ADS)

Hébert, R.; Lovejoy, S.; Del Rio Amador, L.

2017-12-01

There are two fundamental time scales that are pertinent to decadal forecasts and multidecadal projections. The first is the lifetime of planetary scale structures, about 10 days (equal to the deterministic predictability limit), and the second is - in the anthropocene - the scale at which the forced anthropogenic variability exceeds the internal variability (around 16 - 18 years). These two time scales define three regimes of variability: weather, macroweather and climate that are respectively characterized by increasing, decreasing and then increasing varibility with scale.We discuss how macroweather temperature variability can be skilfully predicted to its theoretical stochastic predictability limits by exploiting its long-range memory with the Stochastic Seasonal and Interannual Prediction System (StocSIPS). At multi-decadal timescales, the temperature response to forcing is approximately linear and this can be exploited to make projections with a Green's function, or Climate Response Function (CRF). To make the problem tractable, we exploit the temporal scaling symmetry and restrict our attention to global mean forcing and temperature response using a scaling CRF characterized by the scaling exponent H and an inner scale of linearity τ. An aerosol linear scaling factor α and a non-linear volcanic damping exponent ν were introduced to account for the large uncertainty in these forcings. We estimate the model and forcing parameters by Bayesian inference using historical data and these allow us to analytically calculate a median (and likely 66% range) for the transient climate response, and for the equilibrium climate sensitivity: 1.6K ([1.5,1.8]K) and 2.4K ([1.9,3.4]K) respectively. Aerosol forcing typically has large uncertainty and we find a modern (2005) forcing very likely range (90%) of [-1.0, -0.3] Wm-2 with median at -0.7 Wm-2. Projecting to 2100, we find that to keep the warming below 1.5 K, future emissions must undergo cuts similar to Representative
Predicting the past: a simple reverse stand table projection method

Treesearch

Quang V. Cao; Shanna M. McCarty

2006-01-01

A stand table gives number of trees in each diameter class. Future stand tables can be predicted from current stand tables using a stand table projection method. In the simplest form of this method, a future stand table can be expressed as the product of a matrix of transitional proportions (based on diameter growth rates) and a vector of the current stand table. There...
Physics-based Space Weather Forecasting in the Project for Solar-Terrestrial Environment Prediction (PSTEP) in Japan

NASA Astrophysics Data System (ADS)

Kusano, K.

2016-12-01

Project for Solar-Terrestrial Environment Prediction (PSTEP) is a Japanese nation-wide research collaboration, which was recently launched. PSTEP aims to develop a synergistic interaction between predictive and scientific studies of the solar-terrestrial environment and to establish the basis for next-generation space weather forecasting using the state-of-the-art observation systems and the physics-based models. For this project, we coordinate the four research groups, which develop (1) the integration of space weather forecast system, (2) the physics-based solar storm prediction, (3) the predictive models of magnetosphere and ionosphere dynamics, and (4) the model of solar cycle activity and its impact on climate, respectively. In this project, we will build the coordinated physics-based model to answer the fundamental questions concerning the onset of solar eruptions and the mechanism for radiation belt dynamics in the Earth's magnetosphere. In this paper, we will show the strategy of PSTEP, and discuss about the role and prospect of the physics-based space weather forecasting system being developed by PSTEP.
Adapting the Water Erosion Prediction Project (WEPP) model for forest applications

Treesearch

Shuhui Dun; Joan Q. Wu; William J. Elliot; Peter R. Robichaud; Dennis C. Flanagan; James R. Frankenberger; Robert E. Brown; Arthur C. Xu

2009-01-01

There has been an increasing public concern over forest stream pollution by excessive sedimentation due to natural or human disturbances. Adequate erosion simulation tools are needed for sound management of forest resources. The Water Erosion Prediction Project (WEPP) watershed model has proved useful in forest applications where Hortonian flow is the major form of...
NASA's Prediction Of Worldwide Energy Resource (POWER) Project Unveils a New Geospatial Data Portal

Atmospheric Science Data Center

2018-03-01

The Prediction Of Worldwide Energy Resource (POWER) Project facilitates access to NASA's satellite and modeling analysis for Renewable Energy, Sustainable Buildings and Agroclimatology applications. A new ...

GI-POP: a combinational annotation and genomic island prediction pipeline for ongoing microbial genome projects.

PubMed

Lee, Chi-Ching; Chen, Yi-Ping Phoebe; Yao, Tzu-Jung; Ma, Cheng-Yu; Lo, Wei-Cheng; Lyu, Ping-Chiang; Tang, Chuan Yi

2013-04-10

Sequencing of microbial genomes is important because of microbial-carrying antibiotic and pathogenetic activities. However, even with the help of new assembling software, finishing a whole genome is a time-consuming task. In most bacteria, pathogenetic or antibiotic genes are carried in genomic islands. Therefore, a quick genomic island (GI) prediction method is useful for ongoing sequencing genomes. In this work, we built a Web server called GI-POP (http://gipop.life.nthu.edu.tw) which integrates a sequence assembling tool, a functional annotation pipeline, and a high-performance GI predicting module, in a support vector machine (SVM)-based method called genomic island genomic profile scanning (GI-GPS). The draft genomes of the ongoing genome projects in contigs or scaffolds can be submitted to our Web server, and it provides the functional annotation and highly probable GI-predicting results. GI-POP is a comprehensive annotation Web server designed for ongoing genome project analysis. Researchers can perform annotation and obtain pre-analytic information include possible GIs, coding/non-coding sequences and functional analysis from their draft genomes. This pre-analytic system can provide useful information for finishing a genome sequencing project. Copyright © 2012 Elsevier B.V. All rights reserved.
Decadal climate prediction (project GCEP).

PubMed

Haines, Keith; Hermanson, Leon; Liu, Chunlei; Putt, Debbie; Sutton, Rowan; Iwi, Alan; Smith, Doug

2009-03-13

Decadal prediction uses climate models forced by changing greenhouse gases, as in the International Panel for Climate Change, but unlike longer range predictions they also require initialization with observations of the current climate. In particular, the upper-ocean heat content and circulation have a critical influence. Decadal prediction is still in its infancy and there is an urgent need to understand the important processes that determine predictability on these timescales. We have taken the first Hadley Centre Decadal Prediction System (DePreSys) and implemented it on several NERC institute compute clusters in order to study a wider range of initial condition impacts on decadal forecasting, eventually including the state of the land and cryosphere. The eScience methods are used to manage submission and output from the many ensemble model runs required to assess predictive skill. Early results suggest initial condition skill may extend for several years, even over land areas, but this depends sensitively on the definition used to measure skill, and alternatives are presented. The Grid for Coupled Ensemble Prediction (GCEP) system will allow the UK academic community to contribute to international experiments being planned to explore decadal climate predictability.
A geographic information system to predict soil erosion potential in rural transportation construction project

DOT National Transportation Integrated Search

1995-06-30

Topographic surface modeling using a Geographic Information System (GIS) can be useful for the prediction of soil erosion resulting from highway construction projects. The assumption is that terrain, along with other parameters, will influence the po...
NASA's Prediction Of Worldwide Energy Resource (POWER) Project Unveils a New Geospatial Data Portal

Atmospheric Science Data Center

2018-03-16

NASA's Prediction Of Worldwide Energy Resource (POWER) Project Unveils a New Geospatial Data Portal ... current POWER home page. The new POWER will include improved solar and meteorological data with all parameters available on a 0.5-degree ...
Eastgate Geothermal Borehole Project: Predicting Fracture Geometry at Depth

NASA Astrophysics Data System (ADS)

Beattie, Stewart; Shipton, Zoe K.; Johnson, Gareth; Younger, Paul L.

2013-04-01

In 2004 an exploratory borehole at the Eastgate Geothermal Project encountered part of a vein system within the Weardale granite. At 995m depth brine was at a temperature of around 46°C. The geothermal source is likely related to the Slitt vein system that cuts through c.270m of carboniferous sedimentary strata overlying the Weardale granite pluton. The economic success of the Eastgate geothermal project is dependent on exploiting this vein system in an otherwise low permeability and low geothermal gradient setting. The Slitt vein system has been extensively mined. Mining records show the attitude of the vein through the sedimentary strata, however, the trajectory and magnitude of the vein within the pluton itself is unknown. Using mine records, geological maps and published literature, models of the vein system up to the depth of the pluton were created. To extend this model into the pluton itself requires some knowledge regarding the geometry and evolution of the pluton and subsequently properties of vein systems and other fracture populations at depth. The properties of fracture and vein populations within the granite will depend on forming processes including; cooling and contraction of the pluton, deformation of host rocks during pluton emplacement, and post emplacement deformation. Using published literature and gravity data a 3D model of the geometry of the pluton was constructed. Shape analysis of the pluton allows an estimation of the orientation of fractures within the pluton. Further modelling of the structural evolution of the pluton will enable kinematic or geomechanical strain associated with the structural evolution to be captured and subsequently used as a proxy for modelling both intensity and orientation of fracturing within the pluton. The successful prediction of areas of high fracture intensity and thus increased permeability is critical to the development of potential geothermal resources in low geothermal gradient and low permeability
Predicting outcome following psychological therapy in IAPT (PROMPT): a naturalistic project protocol.

PubMed

Grant, Nina; Hotopf, Matthew; Breen, Gerome; Cleare, Anthony; Grey, Nick; Hepgul, Nilay; King, Sinead; Moran, Paul; Pariante, Carmine M; Wingrove, Janet; Young, Allan H; Tylee, André

2014-06-09

Depression and anxiety are highly prevalent and represent a significant and well described public health burden. Whilst first line psychological treatments are effective for nearly half of attenders, there remain a substantial number of patients who do not benefit. The main objective of the present project is to establish an infrastructure platform for the identification of factors that predict lack of response to psychological treatment for depression and anxiety, in order to better target treatments as well as to support translational and experimental medicine research in mood and anxiety disorders. Predicting outcome following psychological therapy in IAPT (PROMPT) is a naturalistic observational project that began patient recruitment in January 2014. The project is currently taking place in Southwark Psychological Therapies Service, an Improving Access to Psychological Therapies (IAPT) service currently provided by the South London and Maudsley NHS Foundation Trust (SLaM). However, the aim is to roll-out the project across other IAPT services. Participants are approached before beginning treatment and offered a baseline interview whilst they are waiting for therapy to begin. This allows us to test for relationships between predictor variables and patient outcome measures. At the baseline interview, participants complete a diagnostic interview; are asked to give blood and hair samples for relevant biomarkers, and complete psychological and social questionnaire measures. Participants then complete their psychological therapy as offered by Southwark Psychological Therapies Service. Response to psychological therapy will be measured using standard IAPT outcome data, which are routinely collected at each appointment. This project addresses a need to understand treatment response rates in primary care psychological therapy services for those with depression and/or anxiety. Measurement of a range of predictor variables allows for the detection of bio
Implementation of channel-routing routines in the Water Erosion Prediction Project (WEPP) model

Treesearch

Li Wang; Joan Q. Wu; William J. Elliott; Shuhui Dun; Sergey Lapin; Fritz R. Fiedler; Dennis C. Flanagan

2010-01-01

The Water Erosion Prediction Project (WEPP) model is a process-based, continuous-simulation, watershed hydrology and erosion model. It is an important tool for water erosion simulation owing to its unique functionality in representing diverse landuse and management conditions. Its applicability is limited to relatively small watersheds since its current version does...
Predicting environmental mitigation requirements for hydropower projects through the integration of biophysical and socio-political geographies

DOE PAGES

Bevelhimer, Mark S.; DeRolph, Christopher R.; Schramm, Michael P.

2016-06-06

Uncertainty about environmental mitigation needs at existing and proposed hydropower projects makes it difficult for stakeholders to minimize environmental impacts. Hydropower developers and operators desire tools to better anticipate mitigation requirements, while natural resource managers and regulators need tools to evaluate different mitigation scenarios and order effective mitigation. Here we sought to examine the feasibility of using a suite of multidisciplinary explanatory variables within a spatially explicit modeling framework to fit predictive models for future environmental mitigation requirements at hydropower projects across the conterminous U.S. Using a database comprised of mitigation requirements from more than 300 hydropower project licenses, wemore » were able to successfully fit models for nearly 50 types of environmental mitigation and to apply the predictive models to a set of more than 500 non-powered dams identified as having hydropower potential. The results demonstrate that mitigation requirements have been a result of a range of factors, from biological and hydrological to political and cultural. Furthermore, project developers can use these models to inform cost projections and design considerations, while regulators can use the models to more quickly identify likely environmental issues and potential solutions, hopefully resulting in more timely and more effective decisions on environmental mitigation.« less
Predicting environmental mitigation requirements for hydropower projects through the integration of biophysical and socio-political geographies.

PubMed

DeRolph, Christopher R; Schramm, Michael P; Bevelhimer, Mark S

2016-10-01

Uncertainty about environmental mitigation needs at existing and proposed hydropower projects makes it difficult for stakeholders to minimize environmental impacts. Hydropower developers and operators desire tools to better anticipate mitigation requirements, while natural resource managers and regulators need tools to evaluate different mitigation scenarios and order effective mitigation. Here we sought to examine the feasibility of using a suite of multi-faceted explanatory variables within a spatially explicit modeling framework to fit predictive models for future environmental mitigation requirements at hydropower projects across the conterminous U.S. Using a database comprised of mitigation requirements from more than 300 hydropower project licenses, we were able to successfully fit models for nearly 50 types of environmental mitigation and to apply the predictive models to a set of more than 500 non-powered dams identified as having hydropower potential. The results demonstrate that mitigation requirements are functions of a range of factors, from biophysical to socio-political. Project developers can use these models to inform cost projections and design considerations, while regulators can use the models to more quickly identify likely environmental issues and potential solutions, hopefully resulting in more timely and more effective decisions on environmental mitigation. Copyright © 2016 Elsevier B.V. All rights reserved.
Predicting environmental mitigation requirements for hydropower projects through the integration of biophysical and socio-political geographies

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

Bevelhimer, Mark S.; DeRolph, Christopher R.; Schramm, Michael P.

Uncertainty about environmental mitigation needs at existing and proposed hydropower projects makes it difficult for stakeholders to minimize environmental impacts. Hydropower developers and operators desire tools to better anticipate mitigation requirements, while natural resource managers and regulators need tools to evaluate different mitigation scenarios and order effective mitigation. Here we sought to examine the feasibility of using a suite of multidisciplinary explanatory variables within a spatially explicit modeling framework to fit predictive models for future environmental mitigation requirements at hydropower projects across the conterminous U.S. Using a database comprised of mitigation requirements from more than 300 hydropower project licenses, wemore » were able to successfully fit models for nearly 50 types of environmental mitigation and to apply the predictive models to a set of more than 500 non-powered dams identified as having hydropower potential. The results demonstrate that mitigation requirements have been a result of a range of factors, from biological and hydrological to political and cultural. Furthermore, project developers can use these models to inform cost projections and design considerations, while regulators can use the models to more quickly identify likely environmental issues and potential solutions, hopefully resulting in more timely and more effective decisions on environmental mitigation.« less
Predicting contraceptive vigilance in adolescent females: a projective method for assessing ego development.

PubMed

Speier, P L; Mélèse-D'Hospital, I A; Tschann, J M; Moore, P J; Adler, N E

1997-01-01

To test the hypothesis that ego development would predict contraceptive use. Problems in ego development were defined in terms of three factors: (1) realism, (2) complexity, and (3) discontinuity. Forty-one respondents aged 14-17 years were selected from a group of 233 adolescents who were administered a projective pregnancy scenario and participated in a 12-month follow-up. Twenty of these adolescents were randomly selected from the group determined to be effective contraceptive users, while 21 were randomly selected from the group of poor contraceptors. Chi-square test revealed a significant association (p < .0005) between the composite ego maturity (EM) measure and contraceptive outcome (chi 2 = 13.82, with df-1). Low scores on the ego maturity measure predicted poor contraceptive use. EM was unrelated to age but was associated with race (chi 2 = 7.535, .025 < p < .05). However, EM predicted contraceptive use when controlling for the effects of race. A simple, time-efficient projective pregnancy scenario is an effective way of determining adolescent females at risk for poor contraceptive effectiveness and, therefore, untimely pregnancy. These stories are analyzed using factors related to the ego development of the adolescent. Subjects who scored lower on this measure have poor contraceptive effectiveness while subjects with higher levels demonstrated effective contraception use, at 1-year follow-up.
Molecular activity prediction by means of supervised subspace projection based ensembles of classifiers.

PubMed

Cerruela García, G; García-Pedrajas, N; Luque Ruiz, I; Gómez-Nieto, M Á

2018-03-01

This paper proposes a method for molecular activity prediction in QSAR studies using ensembles of classifiers constructed by means of two supervised subspace projection methods, namely nonparametric discriminant analysis (NDA) and hybrid discriminant analysis (HDA). We studied the performance of the proposed ensembles compared to classical ensemble methods using four molecular datasets and eight different models for the representation of the molecular structure. Using several measures and statistical tests for classifier comparison, we observe that our proposal improves the classification results with respect to classical ensemble methods. Therefore, we show that ensembles constructed using supervised subspace projections offer an effective way of creating classifiers in cheminformatics.
Assessing the capability of numerical methods to predict earthquake ground motion: the Euroseistest verification and validation project

NASA Astrophysics Data System (ADS)

Chaljub, E. O.; Bard, P.; Tsuno, S.; Kristek, J.; Moczo, P.; Franek, P.; Hollender, F.; Manakou, M.; Raptakis, D.; Pitilakis, K.

2009-12-01

During the last decades, an important effort has been dedicated to develop accurate and computationally efficient numerical methods to predict earthquake ground motion in heterogeneous 3D media. The progress in methods and increasing capability of computers have made it technically feasible to calculate realistic seismograms for frequencies of interest in seismic design applications. In order to foster the use of numerical simulation in practical prediction, it is important to (1) evaluate the accuracy of current numerical methods when applied to realistic 3D applications where no reference solution exists (verification) and (2) quantify the agreement between recorded and numerically simulated earthquake ground motion (validation). Here we report the results of the Euroseistest verification and validation project - an ongoing international collaborative work organized jointly by the Aristotle University of Thessaloniki, Greece, the Cashima research project (supported by the French nuclear agency, CEA, and the Laue-Langevin institute, ILL, Grenoble), and the Joseph Fourier University, Grenoble, France. The project involves more than 10 international teams from Europe, Japan and USA. The teams employ the Finite Difference Method (FDM), the Finite Element Method (FEM), the Global Pseudospectral Method (GPSM), the Spectral Element Method (SEM) and the Discrete Element Method (DEM). The project makes use of a new detailed 3D model of the Mygdonian basin (about 5 km wide, 15 km long, sediments reach about 400 m depth, surface S-wave velocity is 200 m/s). The prime target is to simulate 8 local earthquakes with magnitude from 3 to 5. In the verification, numerical predictions for frequencies up to 4 Hz for a series of models with increasing structural and rheological complexity are analyzed and compared using quantitative time-frequency goodness-of-fit criteria. Predictions obtained by one FDM team and the SEM team are close and different from other predictions
Personalized risk prediction of postoperative cognitive impairment - rationale for the EU-funded BioCog project.

PubMed

Winterer, G; Androsova, G; Bender, O; Boraschi, D; Borchers, F; Dschietzig, T B; Feinkohl, I; Fletcher, P; Gallinat, J; Hadzidiakos, D; Haynes, J D; Heppner, F; Hetzer, S; Hendrikse, J; Ittermann, B; Kant, I M J; Kraft, A; Krannich, A; Krause, R; Kühn, S; Lachmann, G; van Montfort, S J T; Müller, A; Nürnberg, P; Ofosu, K; Pietsch, M; Pischon, T; Preller, J; Renzulli, E; Scheurer, K; Schneider, R; Slooter, A J C; Spies, C; Stamatakis, E; Volk, H D; Weber, S; Wolf, A; Yürek, F; Zacharias, N

2018-04-01

Postoperative cognitive impairment is among the most common medical complications associated with surgical interventions - particularly in elderly patients. In our aging society, it is an urgent medical need to determine preoperative individual risk prediction to allow more accurate cost-benefit decisions prior to elective surgeries. So far, risk prediction is mainly based on clinical parameters. However, these parameters only give a rough estimate of the individual risk. At present, there are no molecular or neuroimaging biomarkers available to improve risk prediction and little is known about the etiology and pathophysiology of this clinical condition. In this short review, we summarize the current state of knowledge and briefly present the recently started BioCog project (Biomarker Development for Postoperative Cognitive Impairment in the Elderly), which is funded by the European Union. It is the goal of this research and development (R&D) project, which involves academic and industry partners throughout Europe, to deliver a multivariate algorithm based on clinical assessments as well as molecular and neuroimaging biomarkers to overcome the currently unsatisfying situation. Copyright © 2017. Published by Elsevier Masson SAS.
Predicting the 10-Year Risks of Atherosclerotic Cardiovascular Disease in Chinese Population: The China-PAR Project (Prediction for ASCVD Risk in China).

PubMed

Yang, Xueli; Li, Jianxin; Hu, Dongsheng; Chen, Jichun; Li, Ying; Huang, Jianfeng; Liu, Xiaoqing; Liu, Fangchao; Cao, Jie; Shen, Chong; Yu, Ling; Lu, Fanghong; Wu, Xianping; Zhao, Liancheng; Wu, Xigui; Gu, Dongfeng

2016-11-08

The accurate assessment of individual risk can be of great value to guiding and facilitating the prevention of atherosclerotic cardiovascular disease (ASCVD). However, prediction models in common use were formulated primarily in white populations. The China-PAR project (Prediction for ASCVD Risk in China) is aimed at developing and validating 10-year risk prediction equations for ASCVD from 4 contemporary Chinese cohorts. Two prospective studies followed up together with a unified protocol were used as the derivation cohort to develop 10-year ASCVD risk equations in 21 320 Chinese participants. The external validation was evaluated in 2 independent Chinese cohorts with 14 123 and 70 838 participants. Furthermore, model performance was compared with the Pooled Cohort Equations reported in the American College of Cardiology/American Heart Association guideline. Over 12 years of follow-up in the derivation cohort with 21 320 Chinese participants, 1048 subjects developed a first ASCVD event. Sex-specific equations had C statistics of 0.794 (95% confidence interval, 0.775-0.814) for men and 0.811 (95% confidence interval, 0.787-0.835) for women. The predicted rates were similar to the observed rates, as indicated by a calibration χ 2 of 13.1 for men (P=0.16) and 12.8 for women (P=0.17). Good internal and external validations of our equations were achieved in subsequent analyses. Compared with the Chinese equations, the Pooled Cohort Equations had lower C statistics and much higher calibration χ 2 values in men. Our project developed effective tools with good performance for 10-year ASCVD risk prediction among a Chinese population that will help to improve the primary prevention and management of cardiovascular disease. © 2016 American Heart Association, Inc.
Agreement and Predictive Validity Using Less Conservative FNIH Sarcopenia Project Weakness Cutpoints

PubMed Central

Shaffer, Nancy Chiles; Ferrucci, Luigi; Shardell, Michelle; Simonsick, Eleanor M.; Studenski, Stephanie

2016-01-01

OBJECTIVES The FNIH Sarcopenia Project derived conservative definitions for weakness and low lean mass, resulting in low prevalence and low agreement with prior definitions. The FNIH Project also estimated a less conservative cutpoint for low grip strength, potentially yielding a cutpoint for low lean mass more consistent with the European Working Group on Sarcopenia in Older People (EWGSOP). We derived lean mass cutpoints based on the less conservative cutpoint for grip strength (WeakI), and assessed agreement with EWGSOP and prediction of incident slow walking and mortality. DESIGN, SETTING, PARTICIPANTS, MEASUREMENTS Longitudinal analysis of 287 men and 258 women from the Baltimore Longitudinal Study of Aging aged >65 years, with 2–10 years followup. Weakness was determined via hand dynamometer, appendicular lean mass (ALM) via DEXA, and slow walking by 6m usual pace walk <0.8m/s. Analyses used classification and regression tree analysis, Cohen’s Kappa, and Cox models. RESULTS Cutpoints derived from WeakI for ALM (ALMI) and ALM adjusted for body mass index (ALM/BMII) were (ALMI) <21.4kg (men) and <14.1kg (women); and (ALM/BMII) <0.725 (men) and <0.591 (women). Kappas with EWGSOP were (ALMI); 0.65 (men) and 0.75 (women) and ALM/BMII; 0.34 (men) and 0.47 (women). In men, the hazard ratio for incident slow walking by WeakI + ALMI was 2.44 (95% CI:1.02–5.82) versus 2.91 (95% CI:1.11–7.62) by EWGSOP. Neither approach predicted incident slow walking in women. CONCLUSION The ALMI cutpoints agree with EWGSOP and predict slow walking in men. Future studies should explore sex differences in the relationship between body composition and physical function and the impact of change in muscle mass on muscle strength and physical function. PMID:28024092
Modeling streamflow in a snow-dominated forest watershed using the Water Erosion Prediction Project (WEPP) model

USDA-ARS?s Scientific Manuscript database

The Water Erosion Prediction Project (WEPP) model was originally developed for hillslope and small watershed applications. The model simulates complex interactive processes influencing erosion, such as surface runoff, soil-water changes, vegetation growth and senescence, and snow accumulation and me...
Impacts of Process and Prediction Uncertainties on Projected Hanford Waste Glass Amount

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

Gervasio, Vivianaluxa; Vienna, John D.; Kim, Dong-Sang

Analyses were performed to evaluate the impacts of using the advanced glass models, constraints (Vienna et al. 2016), and uncertainty descriptions on projected Hanford glass mass. The maximum allowable WOL was estimated for waste compositions while simultaneously satisfying all applicable glass property and composition constraints with sufficient confidence. Different components of prediction and composition/process uncertainties were systematically included in the calculations to evaluate their impacts on glass mass. The analyses estimated the production of 23,360 MT of IHLW glass when no uncertainties were taken into accound. Accounting for prediction and composition/process uncertainties resulted in 5.01 relative percent increase in estimatedmore » glass mass 24,531 MT. Roughly equal impacts were found for prediction uncertainties (2.58 RPD) and composition/process uncertainties (2.43 RPD). ILAW mass was predicted to be 282,350 MT without uncertainty and with weaste loading “line” rules in place. Accounting for prediction and composition/process uncertainties resulted in only 0.08 relative percent increase in estimated glass mass of 282,562 MTG. Without application of line rules the glass mass decreases by 10.6 relative percent (252,490 MT) for the case with no uncertainties. Addition of prediction uncertainties increases glass mass by 1.32 relative percent and the addition of composition/process uncertainties increase glass mass by an additional 7.73 relative percent (9.06 relative percent increase combined). The glass mass estimate without line rules (275,359 MT) was 2.55 relative percent lower than that with the line rules (282,562 MT), after accounting for all applicable uncertainties.« less
Impacts of Process and Prediction Uncertainties on Projected Hanford Waste Glass Amount

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

Gervasio, V.; Kim, D. S.; Vienna, J. D.

Analyses were performed to evaluate the impacts of using the advanced glass models, constraints, and uncertainty descriptions on projected Hanford glass mass. The maximum allowable waste oxide loading (WOL) was estimated for waste compositions while simultaneously satisfying all applicable glass property and composition constraints with sufficient confidence. Different components of prediction and composition/process uncertainties were systematically included in the calculations to evaluate their impacts on glass mass. The analyses estimated the production of 23,360 MT of immobilized high-level waste (IHLW) glass when no uncertainties were taken into account. Accounting for prediction and composition/process uncertainties resulted in 5.01 relative percent increasemore » in estimated glass mass of 24,531 MT. Roughly equal impacts were found for prediction uncertainties (2.58 RPD) and composition/process uncertainties (2.43 RPD). The immobilized low-activity waste (ILAW) mass was predicted to be 282,350 MT without uncertainty and with waste loading “line” rules in place. Accounting for prediction and composition/process uncertainties resulted in only 0.08 relative percent increase in estimated glass mass of 282,562 MT. Without application of line rules the glass mass decreases by 10.6 relative percent (252,490 MT) for the case with no uncertainties. Addition of prediction uncertainties increases glass mass by 1.32 relative percent and the addition of composition/process uncertainties increase glass mass by an additional 7.73 relative percent (9.06 relative percent increase combined). The glass mass estimate without line rules (275,359 MT) was 2.55 relative percent lower than that with the line rules (282,562 MT), after accounting for all applicable uncertainties.« less
The Climate Variability & Predictability (CVP) Program at NOAA - DYNAMO Recent Project Advancements

NASA Astrophysics Data System (ADS)

Lucas, S. E.; Todd, J. F.; Higgins, W.

2013-12-01

The Climate Variability & Predictability (CVP) Program supports research aimed at providing process-level understanding of the climate system through observation, modeling, analysis, and field studies. This vital knowledge is needed to improve climate models and predictions so that scientists can better anticipate the impacts of future climate variability and change. To achieve its mission, the CVP Program supports research carried out at NOAA and other federal laboratories, NOAA Cooperative Institutes, and academic institutions. The Program also coordinates its sponsored projects with major national and international scientific bodies including the World Climate Research Programme (WCRP), the International Geosphere-Biosphere Programme (IGBP), and the U.S. Global Change Research Program (USGCRP). The CVP program sits within the Earth System Science (ESS) Division at NOAA's Climate Program Office. Dynamics of the Madden-Julian Oscillation (DYNAMO): The Indian Ocean is one of Earth's most sensitive regions because the interactions between ocean and atmosphere there have a discernable effect on global climate patterns. The tropical weather that brews in that region can move eastward along the equator and reverberate around the globe, shaping weather and climate in far-off places. The vehicle for this variability is a phenomenon called the Madden-Julian Oscillation, or MJO. The MJO, which originates over the Indian Ocean roughly every 30 to 90 days, is known to influence the Asian and Australian monsoons. It can also enhance hurricane activity in the northeast Pacific and Gulf of Mexico, trigger torrential rainfall along the west coast of North America, and affect the onset of El Niño. CVP-funded scientists participated in the DYNAMO field campaign in 2011-12. Results from this international campaign are expected to improve researcher's insights into this influential phenomenon. A better understanding of the processes governing MJO is an essential step toward

Toward Evaluating the Predictability of Arctic-related Climate Variations: Initial Results from ArCS Project Theme 5

NASA Astrophysics Data System (ADS)

Hasumi, H.

2016-12-01

We present initial results from the theme 5 of the project ArCS, which is a national flagship project for Arctic research in Japan. The goal of theme 5 is to evaluate the predictability of Arctic-related climate variations, wherein we aim to: (1) establish the scientific basis of climate predictability; and (2) develop a method for predicting/projecting medium- and long-term climate variations. Variability in the Arctic environment remotely influences middle and low latitudes. Since some of the processes specific to the Arctic environment function as a long memory of the state of the climate, understanding of the process of remote connections would lead to higher-precision and longer-term prediction of global climate variations. Conventional climate models have large uncertainty in the Arctic region. By making Arctic processes in climate models more sophisticated, we aim to clarify the role of multi-sphere interaction in the Arctic environment. In this regard, our newly developed high resolution ice-ocean model has revealed the relationship between the oceanic heat transport into the Arctic Ocean and the synoptic scale atmospheric variability. We also aim to reveal the mechanism of remote connections by conducting climate simulations and analyzing various types of climate datasets. Our atmospheric model experiments under possible future situations of Arctic sea ice cover indicate that reduction of sea ice qualitatively alters the basic mechanism of remote connection. Also, our analyses of climate data have identified the cause of recent more frequent heat waves at Eurasian mid-to-high latitudes and clarified the dynamical process which forms the West Pacific pattern, a dominant mode of the atmospheric anomalous circulation in the West Pacific region which also exhibits a significant signal in the Arctic stratosphere.
Enhancements to the Water Erosion Prediction Project (WEPP) for modeling large snow-dominated mountainous forest watersheds

Treesearch

Anurag Srivastava; Joan Q. Wu; William J. Elliot; Erin S. Brooks

2015-01-01

The Water Erosion Prediction Project (WEPP) model, originally developed for hillslope and small watershed applications, simulates complex interactive processes influencing erosion. Recent incorporations to the model have improved the subsurface hydrology components for forest applications. Incorporation of channel routing has made the WEPP model well suited for large...
On Earthquake Prediction in Japan

PubMed Central

UYEDA, Seiya

2013-01-01

Japan’s National Project for Earthquake Prediction has been conducted since 1965 without success. An earthquake prediction should be a short-term prediction based on observable physical phenomena or precursors. The main reason of no success is the failure to capture precursors. Most of the financial resources and manpower of the National Project have been devoted to strengthening the seismographs networks, which are not generally effective for detecting precursors since many of precursors are non-seismic. The precursor research has never been supported appropriately because the project has always been run by a group of seismologists who, in the present author’s view, are mainly interested in securing funds for seismology — on pretense of prediction. After the 1995 Kobe disaster, the project decided to give up short-term prediction and this decision has been further fortified by the 2011 M9 Tohoku Mega-quake. On top of the National Project, there are other government projects, not formally but vaguely related to earthquake prediction, that consume many orders of magnitude more funds. They are also un-interested in short-term prediction. Financially, they are giants and the National Project is a dwarf. Thus, in Japan now, there is practically no support for short-term prediction research. Recently, however, substantial progress has been made in real short-term prediction by scientists of diverse disciplines. Some promising signs are also arising even from cooperation with private sectors. PMID:24213204
Modeling streamflow in a snow-dominated forest watershed using the Water Erosion Prediction Project (WEPP) model

Treesearch

A. Srivastava; J. Q. Wu; W. J. Elliot; E. S. Brooks; D. C. Flanagan

2017-01-01

The Water Erosion Prediction Project (WEPP) model was originally developed for hillslope and small watershed applications. Recent improvements to WEPP have led to enhanced computations for deep percolation, subsurface lateral flow, and frozen soil. In addition, the incorporation of channel routing has made the WEPP model well suited for large watersheds with perennial...
Constructing optimal ensemble projections for predictive environmental modelling in Northern Eurasia

NASA Astrophysics Data System (ADS)

Anisimov, Oleg; Kokorev, Vasily

2013-04-01

Large uncertainties in climate impact modelling are associated with the forcing climate data. This study is targeted at the evaluation of the quality of GCM-based climatic projections in the specific context of predictive environmental modelling in Northern Eurasia. To accomplish this task, we used the output from 36 CMIP5 GCMs from the IPCC AR-5 data base for the control period 1975-2005 and calculated several climatic characteristics and indexes that are most often used in the impact models, i.e. the summer warmth index, duration of the vegetation growth period, precipitation sums, dryness index, thawing degree-day sums, and the annual temperature amplitude. We used data from 744 weather stations in Russia and neighbouring countries to analyze the spatial patterns of modern climatic change and to delineate 17 large regions with coherent temperature changes in the past few decades. GSM results and observational data were averaged over the coherent regions and compared with each other. Ultimately, we evaluated the skills of individual models, ranked them in the context of regional impact modelling and identified top-end GCMs that "better than average" reproduce modern regional changes of the selected meteorological parameters and climatic indexes. Selected top-end GCMs were used to compose several ensembles, each combining results from the different number of models. Ensembles were ranked using the same algorithm and outliers eliminated. We then used data from top-end ensembles for the 2000-2100 period to construct the climatic projections that are likely to be "better than average" in predicting climatic parameters that govern the state of environment in Northern Eurasia. The ultimate conclusions of our study are the following. • High-end GCMs that demonstrate excellent skills in conventional atmospheric model intercomparison experiments are not necessarily the best in replicating climatic characteristics that govern the state of environment in Northern Eurasia
A New Distance Metric in Ground Motion Prediction Equations Based On Array Back-Projections

NASA Astrophysics Data System (ADS)

Feng, T.; Meng, L.

2017-12-01

Traditional Ground Motion Prediction Equations (GMPEs) measure the distances either relative to the epicenter (Repi) or the hypocenter (Rhyp) assuming point-sources, or relative to the closest point of the fault rupture (Rrup) or its surface projection (Rjb) to account for finite earthquake dimensions. However, it has long been proven that for large megathrust earthquakes (M>8), the over-simplification of the earthquake source characteristics in these distance metrics result in significant bias and uncertainty of the ground motion predictions. Recent advances in earthquake source imaging of major subduction earthquakes highlight the frequency-dependent and depth-varying seismic radiations at the plate interfaces. Low-frequency energy mainly emanated from the shallower portion of the megathrusts while dominant high-frequency energy often radiates from the deeper portion of the megathrust. In the 2011 Tohoku-Oki earthquake, all these distance metrics produce severe biases, underestimating the ground accelerations at short distances (<100km) and overestimating them at long distances (>100km). This phenomenon motivates an alternative distance metric based on the array back-projection (BP) technique that effectively captures regions releasing high-frequency energy. Herein, we define Rbp as the distance between nearest BP radiators and the station sites. We studied five large earthquakes in Japan, and found that Rbp outperforms conventional distance metrics in predicting the Psa (Pseudo Spectral Acceleration) in the high-frequency band (0.5-4 Hz). And at low frequencies (0.1-0.5Hz), we find that Rhyp produces better fits to the Psa spectrum. Thus, we propose to combine Rhyp and Rbp in low-frequency (0.1-0.5Hz) and high-frequency (0.5-4 Hz) range to improve the GMPEs. We consider that Rbp reflects the high-frequency characters of the rupture that are complementary to conventional GMPE distance metrics and a more suitable ground motion predictors in many cases. Based on
Watershed-scale evaluation of the Water Erosion Prediction Project (WEPP) model in the Lake Tahoe basin

Treesearch

Erin S. Brooks; Mariana Dobre; William J. Elliot; Joan Q. Wu; Jan Boll

2016-01-01

Forest managers need methods to evaluate the impacts of management at the watershed scale. The Water Erosion Prediction Project (WEPP) has the ability to model disturbed forested hillslopes, but has difficulty addressing some of the critical processes that are important at a watershed scale, including baseflow and water yield. In order to apply WEPP to...
EPA Project Updates: DSSTox and ToxCast Generating New Data and Data Linkages for Use in Predictive Modeling

EPA Science Inventory

EPAs National Center for Computational Toxicology is building capabilities to support a new paradigm for toxicity screening and prediction. The DSSTox project is improving public access to quality structure-annotated chemical toxicity information in less summarized forms than tr...
Simulation and Prediction of Groundwater Pollution from Planned Feed Additive Project in Nanning City Based on GMS Model

NASA Astrophysics Data System (ADS)

Liang, Yimin; Lan, Junkang; Wen, Zhixiong

2018-01-01

In order to predict the pollution of underground aquifers and rivers by the proposed project, Specialized hydrogeological investigation was carried out. After hydrogeological surveying and mapping, drilling, and groundwater level monitoring, the scope of the hydrogeological unit and the regional hydrogeological condition were found out. The permeability coefficients of the aquifers were also obtained by borehole water injection tests. In order to predict the impact on groundwater environment by the project, a GMS software was used in numerical simulation. The simulation results show that when unexpected sewage leakage accident happened, the pollutants will be gradually diluted by groundwater, and the diluted contaminants will slowly spread to southeast with groundwater flow, eventually they are discharged into Gantang River. However, the process of the pollutants discharging into the river is very long, the long-term dilution of the river water will keep Gantang River from being polluted.
Interactive vs. Non-Interactive Ensembles for Weather Prediction and Climate Projection

NASA Astrophysics Data System (ADS)

Duane, Gregory

2013-04-01

If the members of an ensemble of different models are allowed to interact with one another in run time, predictive skill can be improved as compared to that of any individual model or any average of indvidual model outputs. Inter-model connections in such an interactive ensemble can be trained, using historical data, so that the resulting ``supermodel" synchronizes with reality when used in weather-prediction mode, where the individual models perform data assimilation from each other (with trainable inter-model "observation error") as well as from real observations. In climate-projection mode, parameters of the individual models are changed, as might occur from an increase in GHG levels, and one obtains relevant statistical properties of the new supermodel attractor. In simple cases, it has been shown that training of the inter-model connections with the old parameter values gives a supermodel that is still predictive when the parameter values are changed. Here we inquire as to the circumstances under which supermodel performance can be expected to exceed that of the customary weighted average of model outputs. We consider a supermodel formed from quasigeostrophic channel models with different forcing coefficients, and introduce an effective training scheme for the inter-model connections. We show that the blocked-zonal index cycle is reproduced better by the supermodel than by any non-interactive ensemble in the extreme case where the forcing coefficients of the different models are very large or very small. With realistic differences in forcing coefficients, as would be representative of actual differences among IPCC-class models, the usual linearity assumption is justified and a weighted average of model outputs is adequate. It is therefore hypothesized that supermodeling is likely to be useful in situations where there are qualitative model differences, as arising from sub-gridscale parameterizations, that affect overall model behavior. Otherwise the usual ex
A GLOBAL ASSESSMENT OF SOLAR ENERGY RESOURCES: NASA's Prediction of Worldwide Energy Resources (POWER) Project

NASA Astrophysics Data System (ADS)

Zhang, T.; Stackhouse, P. W., Jr.; Chandler, W.; Hoell, J. M.; Westberg, D.; Whitlock, C. H.

2010-12-01

NASA's POWER project, or the Prediction of the Worldwide Energy Resources project, synthesizes and analyzes data on a global scale. The products of the project find valuable applications in the solar and wind energy sectors of the renewable energy industries. The primary source data for the POWER project are NASA's World Climate Research Project (WCRP)/Global Energy and Water cycle Experiment (GEWEX) Surface Radiation Budget (SRB) project (Release 3.0) and the Global Modeling and Assimilation Office (GMAO) Goddard Earth Observing System (GEOS) assimilation model (V 4.0.3). Users of the POWER products access the data through NASA's Surface meteorology and Solar Energy (SSE, Version 6.0) website (http://power.larc.nasa.gov). Over 200 parameters are available to the users. The spatial resolution is 1 degree by 1 degree now and will be finer later. The data covers from July 1983 to December 2007, a time-span of 24.5 years, and are provided as 3-hourly, daily and monthly means. As of now, there have been over 18 million web hits and over 4 million data file downloads. The POWER products have been systematically validated against ground-based measurements, and in particular, data from the Baseline Surface Radiation Network (BSRN) archive, and also against the National Solar Radiation Data Base (NSRDB). Parameters such as minimum, maximum, daily mean temperature and dew points, relative humidity and surface pressure are validated against the National Climate Data Center (NCDC) data. SSE feeds data directly into Decision Support Systems including RETScreen International clean energy project analysis software that is written in 36 languages and has greater than 260,000 users worldwide.
Archaeological predictive model set.

DOT National Transportation Integrated Search

2015-03-01

This report is the documentation for Task 7 of the Statewide Archaeological Predictive Model Set. The goal of this project is to : develop a set of statewide predictive models to assist the planning of transportation projects. PennDOT is developing t...
Replacement of SSE (Release 6) with NASA's Prediction of Worldwide Energy Resource (POWER) Project GIS-enabled Web Data Portal:

Atmospheric Science Data Center

2018-03-15

... effort has been developed under the Prediction Of Worldwide Energy Resource (POWER) Project funded largely by NASA Earth Applied Sciences ... to NASA's satellite and modeling analysis for Renewable Energy, Sustainable Buildings and Agroclimatology applications. A new POWER ...
The Arctic Predictability and Prediction on Seasonal-to-Interannual TimEscales (APPOSITE) data set

NASA Astrophysics Data System (ADS)

Day, J. J.; Tietsche, S.; Collins, M.; Goessling, H. F.; Guemas, V.; Guillory, A.; Hurlin, W. J.; Ishii, M.; Keeley, S. P. E.; Matei, D.; Msadek, R.; Sigmond, M.; Tatebe, H.; Hawkins, E.

2015-10-01

Recent decades have seen significant developments in seasonal-to-interannual timescale climate prediction capabilities. However, until recently the potential of such systems to predict Arctic climate had not been assessed. This paper describes a multi-model predictability experiment which was run as part of the Arctic Predictability and Prediction On Seasonal to Inter-annual Timescales (APPOSITE) project. The main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable. In order to achieve this, a coordinated set of idealised initial-value predictability experiments, with seven general circulation models, was conducted. This was the first model intercomparison project designed to quantify the predictability of Arctic climate on seasonal to inter-annual timescales. Here we present a description of the archived data set (which is available at the British Atmospheric Data Centre) and an update of the project's results. Although designed to address Arctic predictability, this data set could also be used to assess the predictability of other regions and modes of climate variability on these timescales, such as the El Niño Southern Oscillation.
Genome3D: a UK collaborative project to annotate genomic sequences with predicted 3D structures based on SCOP and CATH domains.

PubMed

Lewis, Tony E; Sillitoe, Ian; Andreeva, Antonina; Blundell, Tom L; Buchan, Daniel W A; Chothia, Cyrus; Cuff, Alison; Dana, Jose M; Filippis, Ioannis; Gough, Julian; Hunter, Sarah; Jones, David T; Kelley, Lawrence A; Kleywegt, Gerard J; Minneci, Federico; Mitchell, Alex; Murzin, Alexey G; Ochoa-Montaño, Bernardo; Rackham, Owen J L; Smith, James; Sternberg, Michael J E; Velankar, Sameer; Yeats, Corin; Orengo, Christine

2013-01-01

Genome3D, available at http://www.genome3d.eu, is a new collaborative project that integrates UK-based structural resources to provide a unique perspective on sequence-structure-function relationships. Leading structure prediction resources (DomSerf, FUGUE, Gene3D, pDomTHREADER, Phyre and SUPERFAMILY) provide annotations for UniProt sequences to indicate the locations of structural domains (structural annotations) and their 3D structures (structural models). Structural annotations and 3D model predictions are currently available for three model genomes (Homo sapiens, E. coli and baker's yeast), and the project will extend to other genomes in the near future. As these resources exploit different strategies for predicting structures, the main aim of Genome3D is to enable comparisons between all the resources so that biologists can see where predictions agree and are therefore more trusted. Furthermore, as these methods differ in whether they build their predictions using CATH or SCOP, Genome3D also contains the first official mapping between these two databases. This has identified pairs of similar superfamilies from the two resources at various degrees of consensus (532 bronze pairs, 527 silver pairs and 370 gold pairs).
1986-87 atomic mass predictions

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

Haustein, P.E.

A project to perform a comprehensive update of the atomic mass predictions has recently been concluded and will be published shortly in Atomic Data and Nuclear Data Tables. The project evolved from an ongoing comparison between available mass predictions and reports of newly measured masses of isotopes throughout the mass surface. These comparisons have highlighted a variety of features in current mass models which are responsible for predictions that diverge from masses determined experimentally. The need for a comprehensive update of the atomic mass predictions was therefore apparent and the project was organized and began at the last mass conferencemore » (AMCO-VII). Project participants included: Pape and Anthony; Dussel, Caurier and Zuker; Moeller and Nix; Moeller, Myers, Swiatecki and Treiner; Comay, Kelson, and Zidon; Satpathy and Nayak; Tachibana, Uno, Yamada and Yamada; Spanier and Johansson; Jaenecke and Masson; and Wapstra, Audi and Hoekstra. An overview of the new atomic mass predictions may be obtained by written request.« less
Derivation and Evaluation of a Risk-Scoring Tool to Predict Participant Attrition in a Lifestyle Intervention Project.

PubMed

Jiang, Luohua; Yang, Jing; Huang, Haixiao; Johnson, Ann; Dill, Edward J; Beals, Janette; Manson, Spero M; Roubideaux, Yvette

2016-05-01

Participant attrition in clinical trials and community-based interventions is a serious, common, and costly problem. In order to develop a simple predictive scoring system that can quantify the risk of participant attrition in a lifestyle intervention project, we analyzed data from the Special Diabetes Program for Indians Diabetes Prevention Program (SDPI-DP), an evidence-based lifestyle intervention to prevent diabetes in 36 American Indian and Alaska Native communities. SDPI-DP participants were randomly divided into a derivation cohort (n = 1600) and a validation cohort (n = 801). Logistic regressions were used to develop a scoring system from the derivation cohort. The discriminatory power and calibration properties of the system were assessed using the validation cohort. Seven independent factors predicted program attrition: gender, age, household income, comorbidity, chronic pain, site's user population size, and average age of site staff. Six factors predicted long-term attrition: gender, age, marital status, chronic pain, site's user population size, and average age of site staff. Each model exhibited moderate to fair discriminatory power (C statistic in the validation set: 0.70 for program attrition, and 0.66 for long-term attrition) and excellent calibration. The resulting scoring system offers a low-technology approach to identify participants at elevated risk for attrition in future similar behavioral modification intervention projects, which may inform appropriate allocation of retention resources. This approach also serves as a model for other efforts to prevent participant attrition.
Predicting Plant Invasions Following China's Water Diversion Project.

PubMed

Liu, Dasheng; Wang, Rui; Gordon, Doria R; Sun, Xihua; Chen, Lu; Wang, Yanwen

2017-02-07

China's South to North Water Diversion (SNWD) project connects portions of the Yangtze River in the south to the Yellow River system in the north, overcoming biogeographic barriers to water movement. The diversion will supply potable water to over 110 million people and provide multiple other socioeconomic benefits. However, an inadvertent negative impact of this connection includes creation of conduits for species invasions. Alligator weed (Alternanthera philoxeroides), water hyacinth (Eichhornia crassipes), and water lettuce (Pistia stratiotes) are the only aquatic plant species on China's shortlists for special control. These species are mainly invasive in the Yangtze River basin. If these species are able to invade the SNWD and further spread via the SNWD, they have the potential to alter water supply, including water quantity and quality, as well as local ecology and agriculture, threatening the goals of the diversion. Understanding the full potential for these species to invade northern China is critical to early management decisions to avoid costly negative impacts. We used Maxent modeling to evaluate the probability that each of these species might become invasive in the receiving water regions. The models predict that all three species will be able to expand their ranges northward, with alligator weed and water hyacinth having the greatest potential for range expansion. These results suggest the need for prevention, monitoring, and management strategies for these species to reduce the risk and costs of impacts.
Comparison of models for predicting the changes in phytoplankton community composition in the receiving water system of an inter-basin water transfer project.

PubMed

Zeng, Qinghui; Liu, Yi; Zhao, Hongtao; Sun, Mingdong; Li, Xuyong

2017-04-01

Inter-basin water transfer projects might cause complex hydro-chemical and biological variation in the receiving aquatic ecosystems. Whether machine learning models can be used to predict changes in phytoplankton community composition caused by water transfer projects have rarely been studied. In the present study, we used machine learning models to predict the total algal cell densities and changes in phytoplankton community composition in Miyun reservoir caused by the middle route of the South-to-North Water Transfer Project (SNWTP). The model performances of four machine learning models, including regression trees (RT), random forest (RF), support vector machine (SVM), and artificial neural network (ANN) were evaluated and the best model was selected for further prediction. The results showed that the predictive accuracies (Pearson's correlation coefficient) of the models were RF (0.974), ANN (0.951), SVM (0.860), and RT (0.817) in the training step and RF (0.806), ANN (0.734), SVM (0.730), and RT (0.692) in the testing step. Therefore, the RF model was the best method for estimating total algal cell densities. Furthermore, the predicted accuracies of the RF model for dominant phytoplankton phyla (Cyanophyta, Chlorophyta, and Bacillariophyta) in Miyun reservoir ranged from 0.824 to 0.869 in the testing step. The predicted proportions with water transfer of the different phytoplankton phyla ranged from -8.88% to 9.93%, and the predicted dominant phyla with water transfer in each season remained unchanged compared to the phytoplankton succession without water transfer. The results of the present study provide a useful tool for predicting the changes in phytoplankton community caused by water transfer. The method is transferrable to other locations via establishment of models with relevant data to a particular area. Our findings help better understanding the possible changes in aquatic ecosystems influenced by inter-basin water transfer. Copyright © 2017 Elsevier
Probabilistic population projections with migration uncertainty

PubMed Central

Azose, Jonathan J.; Ševčíková, Hana; Raftery, Adrian E.

2016-01-01

We produce probabilistic projections of population for all countries based on probabilistic projections of fertility, mortality, and migration. We compare our projections to those from the United Nations’ Probabilistic Population Projections, which uses similar methods for fertility and mortality but deterministic migration projections. We find that uncertainty in migration projection is a substantial contributor to uncertainty in population projections for many countries. Prediction intervals for the populations of Northern America and Europe are over 70% wider, whereas prediction intervals for the populations of Africa, Asia, and the world as a whole are nearly unchanged. Out-of-sample validation shows that the model is reasonably well calibrated. PMID:27217571

EU Framework 6 Project: Predictive Toxicology (PredTox)-overview and outcome

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

Suter, Laura, E-mail: Laura.suter-dick@roche.com; Schroeder, Susanne; Meyer, Kirstin

2011-04-15

In this publication, we report the outcome of the integrated EU Framework 6 Project: Predictive Toxicology (PredTox), including methodological aspects and overall conclusions. Specific details including data analysis and interpretation are reported in separate articles in this issue. The project, partly funded by the EU, was carried out by a consortium of 15 pharmaceutical companies, 2 SMEs, and 3 universities. The effects of 16 test compounds were characterized using conventional toxicological parameters and 'omics' technologies. The three major observed toxicities, liver hypertrophy, bile duct necrosis and/or cholestasis, and kidney proximal tubular damage were analyzed in detail. The combined approach ofmore » 'omics' and conventional toxicology proved a useful tool for mechanistic investigations and the identification of putative biomarkers. In our hands and in combination with histopathological assessment, target organ transcriptomics was the most prolific approach for the generation of mechanistic hypotheses. Proteomics approaches were relatively time-consuming and required careful standardization. NMR-based metabolomics detected metabolite changes accompanying histopathological findings, providing limited additional mechanistic information. Conversely, targeted metabolite profiling with LC/GC-MS was very useful for the investigation of bile duct necrosis/cholestasis. In general, both proteomics and metabolomics were supportive of other findings. Thus, the outcome of this program indicates that 'omics' technologies can help toxicologists to make better informed decisions during exploratory toxicological studies. The data support that hypothesis on mode of action and discovery of putative biomarkers are tangible outcomes of integrated 'omics' analysis. Qualification of biomarkers remains challenging, in particular in terms of identification, mechanistic anchoring, appropriate specificity, and sensitivity.« less
CAWSES Related Projects in Japan : Grant-in-Aid for Creative Scientific Research ügBasic Study of Space Weather Predictionüh and CHAIN (Continuous H Alpha Imaging Network)

NASA Astrophysics Data System (ADS)

Shibata, K.; Kurokawa, H.

The Grant-in-Aid for Creative Scientific Research of the Ministry of Education Science Sports Technology and Culture of Japan The Basic Study of Space Weather Prediction PI K Shibata Kyoto Univ has started in 2005 as 5 years projects with total budget 446Myen The purpose of this project is to develop a physical model of solar-terrestrial phenomena and space storms as a basis of space weather prediction by resolving fundamental physics of key phenomena from solar flares and coronal mass ejections to magnetospheric storms under international cooperation program CAWSES Climate and Weather of the Sun-Earth System Continuous H Alpha Imaging Network CHAIN Project led by H Kurokawa is a key project in this space weather study enabling continuous H alpha full Sun observations by connecting many solar telescopes in many countries through internet which provides the basis of the study of space weather prediction
On the monitoring and prediction of flash floods in small and medium-sized catchments - the EXTRUSO project

NASA Astrophysics Data System (ADS)

Wiemann, Stefan; Eltner, Anette; Sardemann, Hannes; Spieler, Diana; Singer, Thomas; Thanh Luong, Thi; Janabi, Firas Al; Schütze, Niels; Bernard, Lars; Bernhofer, Christian; Maas, Hans-Gerd

2017-04-01

Flash floods regularly cause severe socio-economic damage worldwide. In parallel, climate change is very likely to increase the number of such events, due to an increasing frequency of extreme precipitation events (EASAC 2013). Whereas recent work primarily addresses the resilience of large catchment areas, the major impact of hydro-meteorological extremes caused by heavy precipitation is on small areas. Those are very difficult to observe and predict, due to sparse monitoring networks and only few means for hydro-meteorological modelling, especially in small catchment areas. The objective of the EXTRUSO project is to identify and implement appropriate means to close this gap by an interdisciplinary approach, combining comprehensive research expertise from meteorology, hydrology, photogrammetry and geoinformatics. The project targets innovative techniques for achieving spatio-temporal densified monitoring and simulations for the analysis, prediction and warning of local hydro-meteorological extreme events. The following four aspects are of particular interest: 1. The monitoring, analysis and combination of relevant hydro-meteorological parameters from various sources, including existing monitoring networks, ground radar, specific low-cost sensors and crowdsourcing. 2. The determination of relevant hydro-morphological parameters from different photogrammetric sensors (e.g. camera, laser scanner) and sensor platforms (e.g. UAV (unmanned aerial vehicle) and UWV (unmanned water vehicle)). 3. The continuous hydro-meteorological modelling of precipitation, soil moisture and water flows by means of conceptual and data-driven modelling. 4. The development of a collaborative, web-based service infrastructure as an information and communication point, especially in the case of an extreme event. There are three major applications for the planned information system: First, the warning of local extreme events for the population in potentially affected areas, second, the support
The 1986-87 atomic mass predictions

NASA Astrophysics Data System (ADS)

Haustein, P. E.

1987-12-01

A project to perform a comprehensive update of the atomic mass predictions has recently been concluded and will be published shortly in Atomic Data and Nuclear Data Tables. The project evolved from an ongoing comparison between available mass predictions and reports of newly measured masses of isotopes throughout the mass surface. These comparisons have highlighted a variety of features in current mass models which are responsible for predictions that diverge from masses determined experimentally. The need for a comprehensive update of the atomic mass predictions was therefore apparent and the project was organized and began at the last mass conference (AMCO-VII). Project participants included: Pape and Anthony; Dussel, Caurier and Zuker; Möller and Nix; Möller, Myers, Swiatecki and Treiner; Comay, Kelson, and Zidon; Satpathy and Nayak; Tachibana, Uno, Yamada and Yamada; Spanier and Johansson; Jänecke and Masson; and Wapstra, Audi and Hoekstra. An overview of the new atomic mass predictions may be obtained by written request.
Development of new geoinformation methods for modelling and prediction of sea level change over different timescales - overview of the project

NASA Astrophysics Data System (ADS)

Niedzielski, T.; Włosińska, M.; Miziński, B.; Hewelt, M.; Migoń, P.; Kosek, W.; Priede, I. G.

2012-04-01

The poster aims to provide a broad scientific audience with a general overview of a project on sea level change modelling and prediction that has just commenced at the University of Wrocław, Poland. The initiative that the project fits, called the Homing Plus programme, is organised by the Foundation for Polish Science and financially supported by the European Union through the European Regional Development Fund and the Innovative Economy Programme. There are two key research objectives of the project that complement each other. First, emphasis is put on modern satellite altimetric gridded time series from the Archiving, Validation and Interpretation of Satellite Oceanographic data (AVISO) repository. Daily sea level anomaly maps, access to which in near-real time is courtesy of AVISO, are being steadily downloaded every day to our local server in Wroclaw, Poland. These data will be processed within a general framework of modelling and prediction of sea level change in short, medium and long term. Secondly, sea level change over geological time is scrutinised in order to cover very long time scales that go far beyond a history of altimetric and tide-gauge measurements. The aforementioned approaches comprise a few tasks that aim to solve the following detailed problems. Within the first one, our objective is to seek spatio-temporal dependencies in the gridded sea level anomaly time series. Subsequently, predictions that make use of such cross-correlations shall be derived, and near-real time service for automatic update with validation will be implemented. Concurrently, (i.e. apart from spatio-temporal dependencies and their use in the process of forecasting variable sea level topography), threshold models shall be utilised for predicting the El Niño/Southern Oscillation (ENSO) signal that is normally present in sea level anomaly time series of the equatorial Pacific. Within the second approach, however, the entirely different methods are proposed. Links between
Drought Prediction for Socio-Cultural Stability Project

NASA Technical Reports Server (NTRS)

Peters-Lidard, Christa; Eylander, John B.; Koster, Randall; Narapusetty, Balachandrudu; Kumar, Sujay; Rodell, Matt; Bolten, John; Mocko, David; Walker, Gregory; Arsenault, Kristi;

2014-01-01

The primary objective of this project is to answer the question: "Can existing, linked infrastructures be used to predict the onset of drought months in advance?" Based on our work, the answer to this question is "yes" with the qualifiers that skill depends on both lead-time and location, and especially with the associated teleconnections (e.g., ENSO, Indian Ocean Dipole) active in a given region season. As part of this work, we successfully developed a prototype drought early warning system based on existing/mature NASA Earth science components including the Goddard Earth Observing System Data Assimilation System Version 5 (GEOS-5) forecasting model, the Land Information System (LIS) land data assimilation software framework, the Catchment Land Surface Model (CLSM), remotely sensed terrestrial water storage from the Gravity Recovery and Climate Experiment (GRACE) and remotely sensed soil moisture products from the Aqua/Advanced Microwave Scanning Radiometer - EOS (AMSR-E). We focused on a single drought year - 2011 - during which major agricultural droughts occurred with devastating impacts in the Texas-Mexico region of North America (TEXMEX) and the Horn of Africa (HOA). Our results demonstrate that GEOS-5 precipitation forecasts show skill globally at 1-month lead, and can show up to 3 months skill regionally in the TEXMEX and HOA areas. Our results also demonstrate that the CLSM soil moisture percentiles are a goof indicator of drought, as compared to the North American Drought Monitor of TEXMEX and a combination of Famine Early Warning Systems Network (FEWS NET) data and Moderate Resolution Imaging Spectrometer (MODIS)'s Normalizing Difference Vegetation Index (NDVI) anomalies over HOA. The data assimilation experiments produced mixed results. GRACE terrestrial water storage (TWS) assimilation was found to significantly improve soil moisture and evapotransportation, as well as drought monitoring via soil moisture percentiles, while AMSR-E soil moisture

Can performance on summative evaluation of wax-added dental anatomy projects be better predicted from the combination of supervised and unsupervised practice than from supervised practice alone?

PubMed

Radjaeipour, G; Chambers, D W; Geissberger, M

2016-11-01

The study explored the effects of adding student-directed projects in pre-clinical dental anatomy laboratory on improving the predictability of students' eventual performance on summative evaluation exercises, given the presence of intervening faculty-controlled, in-class practice. All students from four consecutive classes (n = 555) completed wax-added home projects (HP), spending as much or as little time as desired and receiving no faculty feedback; followed by similar laboratory projects (LP) with time limits and feedback; and then summative practical projects (PP) in a timed format but without faculty feedback. Path analysis was used to assess if the student-directed HP had any effect over and above the laboratory projects. Average scores were HP = 0.785 (SD = 0.089); LP = 0.736 (SD = 0.092); and PP = 0.743 (SD = 0.108). Path analysis was applied to show the effects of including a student-controlled home practice exercise on summative exercise performance. HP contributed 57% direct effect and 37% mediated effect through the LP condition. Student-directed home practice provided a measureable improvement in ability to predict eventual performance in summative test cases over and above the predictive contribution of intervening faculty-controlled practice conditions. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Aeroheating Testing and Predictions for Project Orion CEV at Turbulent Conditions

NASA Technical Reports Server (NTRS)

Hollis, Brian R.; Berger, Karen T.; Horvath, Thomas J.; Coblish, Joseph J.; Norris, Joseph D.; Lillard, Randolph P.; Kirk, Benjamin S.

2009-01-01

An investigation of the aeroheating environment of the Project Orion Crew Exploration Vehicle was performed in the Arnold Engineering Development Center Hypervelocity Wind Tunnel No. 9 Mach 8 and Mach 10 nozzles and in the NASA Langley Research Center 20 - Inch Mach 6 Air Tunnel. Heating data were obtained using a thermocouple-instrumented approx.0.035-scale model (0.1778-m/7-inch diameter) of the flight vehicle. Runs were performed in the Tunnel 9 Mach 10 nozzle at free stream unit Reynolds numbers of 1x10(exp 6)/ft to 20x10(exp 6)/ft, in the Tunnel 9 Mach 8 nozzle at free stream unit Reynolds numbers of 8 x 10(exp 6)/ft to 48x10(exp 6)/ft, and in the 20-Inch Mach 6 Air Tunnel at free stream unit Reynolds numbers of 1x10(exp 6)/ft to 7x10(exp 6)/ft. In both facilities, enthalpy levels were low and the test gas (N2 in Tunnel 9 and air in the 20-Inch Mach 6) behaved as a perfect-gas. These test conditions produced laminar, transitional and turbulent data in the Tunnel 9 Mach 10 nozzle, transitional and turbulent data in the Tunnel 9 Mach 8 nozzle, and laminar and transitional data in the 20- Inch Mach 6 Air Tunnel. Laminar and turbulent predictions were generated for all wind tunnel test conditions and comparisons were performed with the experimental data to help define the accuracy of computational method. In general, it was found that both laminar data and predictions, and turbulent data and predictions, agreed to within less than the estimated 12% experimental uncertainty estimate. Laminar heating distributions from all three data sets were shown to correlate well and demonstrated Reynolds numbers independence when expressed in terms of the Stanton number based on adiabatic wall-recovery enthalpy. Transition onset locations on the leeside centerline were determined from the data and correlated in terms of boundary-layer parameters. Finally turbulent heating augmentation ratios were determined for several body-point locations and correlated in terms of the
Predictive accuracy of particle filtering in dynamic models supporting outbreak projections.

PubMed

Safarishahrbijari, Anahita; Teyhouee, Aydin; Waldner, Cheryl; Liu, Juxin; Osgood, Nathaniel D

2017-09-26

While a new generation of computational statistics algorithms and availability of data streams raises the potential for recurrently regrounding dynamic models with incoming observations, the effectiveness of such arrangements can be highly subject to specifics of the configuration (e.g., frequency of sampling and representation of behaviour change), and there has been little attempt to identify effective configurations. Combining dynamic models with particle filtering, we explored a solution focusing on creating quickly formulated models regrounded automatically and recurrently as new data becomes available. Given a latent underlying case count, we assumed that observed incident case counts followed a negative binomial distribution. In accordance with the condensation algorithm, each such observation led to updating of particle weights. We evaluated the effectiveness of various particle filtering configurations against each other and against an approach without particle filtering according to the accuracy of the model in predicting future prevalence, given data to a certain point and a norm-based discrepancy metric. We examined the effectiveness of particle filtering under varying times between observations, negative binomial dispersion parameters, and rates with which the contact rate could evolve. We observed that more frequent observations of empirical data yielded super-linearly improved accuracy in model predictions. We further found that for the data studied here, the most favourable assumptions to make regarding the parameters associated with the negative binomial distribution and changes in contact rate were robust across observation frequency and the observation point in the outbreak. Combining dynamic models with particle filtering can perform well in projecting future evolution of an outbreak. Most importantly, the remarkable improvements in predictive accuracy resulting from more frequent sampling suggest that investments to achieve efficient reporting
Project summaries

NASA Technical Reports Server (NTRS)

1990-01-01

Lunar base projects, including a reconfigurable lunar cargo launcher, a thermal and micrometeorite protection system, a versatile lifting machine with robotic capabilities, a cargo transport system, the design of a road construction system for a lunar base, and the design of a device for removing lunar dust from material surfaces, are discussed. The emphasis on the Gulf of Mexico project was on the development of a computer simulation model for predicting vessel station keeping requirements. An existing code, used in predicting station keeping requirements for oil drilling platforms operating in North Shore (Alaska) waters was used as a basis for the computer simulation. Modifications were made to the existing code. The input into the model consists of satellite altimeter readings and water velocity readings from buoys stationed in the Gulf of Mexico. The satellite data consists of altimeter readings (wave height) taken during the spring of 1989. The simulation model predicts water velocity and direction, and wind velocity.
Predicting and mapping potential Whooping Crane stopover habitat to guide site selection for wind energy projects.

PubMed

Belaire, J Amy; Kreakie, Betty J; Keitt, Timothy; Minor, Emily

2014-04-01

Migratory stopover habitats are often not part of planning for conservation or new development projects. We identified potential stopover habitats within an avian migratory flyway and demonstrated how this information can guide the site-selection process for new development. We used the random forests modeling approach to map the distribution of predicted stopover habitat for the Whooping Crane (Grus americana), an endangered species whose migratory flyway overlaps with an area where wind energy development is expected to become increasingly important. We then used this information to identify areas for potential wind power development in a U.S. state within the flyway (Nebraska) that minimize conflicts between Whooping Crane stopover habitat and the development of clean, renewable energy sources. Up to 54% of our study area was predicted to be unsuitable as Whooping Crane stopover habitat and could be considered relatively low risk for conflicts between Whooping Cranes and wind energy development. We suggest that this type of analysis be incorporated into the habitat conservation planning process in areas where incidental take permits are being considered for Whooping Cranes or other species of concern. Field surveys should always be conducted prior to construction to verify model predictions and understand baseline conditions. © 2013 Society for Conservation Biology.
Final Technical Report: Increasing Prediction Accuracy.

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

King, Bruce Hardison; Hansen, Clifford; Stein, Joshua

2015-12-01

PV performance models are used to quantify the value of PV plants in a given location. They combine the performance characteristics of the system, the measured or predicted irradiance and weather at a site, and the system configuration and design into a prediction of the amount of energy that will be produced by a PV system. These predictions must be as accurate as possible in order for finance charges to be minimized. Higher accuracy equals lower project risk. The Increasing Prediction Accuracy project at Sandia focuses on quantifying and reducing uncertainties in PV system performance models.
Recording A Sunrise: A Citizen Science Project to Enhance Sunrise/set Prediction Times

NASA Astrophysics Data System (ADS)

Wilson, Teresa; Chizek Frouard, Malynda; Bartlett, Jennifer L.

2017-01-01

Smartphones, with their ever increasing capabilities, are becoming quite instrumental for data acquisition in a number of fields. Understanding refraction and how it affects what we see on the horizon is no exception. Current algorithms that predict sunrise and sunset times have an error of one to four minutes at mid-latitudes (0° - 55° N/S) due to limitations in the atmospheric models they incorporate. At higher latitudes, slight changes in refraction can cause significant discrepancies, even including difficulties determining when the Sun appears to rise or set. A thorough investigation of the problem requires a substantial data set of observed rise/set times and corresponding meteorological data from around the world, which is currently lacking. We have developed a mobile application so that this data can be taken using smartphones as part of a citizen science project. The app allows the viewer to submit a video of sunrise/set and attaches geographic location along with meteorological data taken from a local weather station. The project will help increase scientific awareness in the public by allowing members of the community to participate in the data-taking process, and give them a greater awareness of the scientific significance of phenomenon they witness every day. The data from the observations will lead to more complete rise/set models that will provide more accurate times to the benefit of astronomers, navigators, and outdoorsmen. The app will be available on the Google Play Store.
The eTOX data-sharing project to advance in silico drug-induced toxicity prediction.

PubMed

Cases, Montserrat; Briggs, Katharine; Steger-Hartmann, Thomas; Pognan, François; Marc, Philippe; Kleinöder, Thomas; Schwab, Christof H; Pastor, Manuel; Wichard, Jörg; Sanz, Ferran

2014-11-14

The high-quality in vivo preclinical safety data produced by the pharmaceutical industry during drug development, which follows numerous strict guidelines, are mostly not available in the public domain. These safety data are sometimes published as a condensed summary for the few compounds that reach the market, but the majority of studies are never made public and are often difficult to access in an automated way, even sometimes within the owning company itself. It is evident from many academic and industrial examples, that useful data mining and model development requires large and representative data sets and careful curation of the collected data. In 2010, under the auspices of the Innovative Medicines Initiative, the eTOX project started with the objective of extracting and sharing preclinical study data from paper or pdf archives of toxicology departments of the 13 participating pharmaceutical companies and using such data for establishing a detailed, well-curated database, which could then serve as source for read-across approaches (early assessment of the potential toxicity of a drug candidate by comparison of similar structure and/or effects) and training of predictive models. The paper describes the efforts undertaken to allow effective data sharing intellectual property (IP) protection and set up of adequate controlled vocabularies) and to establish the database (currently with over 4000 studies contributed by the pharma companies corresponding to more than 1400 compounds). In addition, the status of predictive models building and some specific features of the eTOX predictive system (eTOXsys) are presented as decision support knowledge-based tools for drug development process at an early stage.
Fuels planning: science synthesis and integration; environmental consequences fact sheet 12: Water Erosion Prediction Project (WEPP) Fuel Management (FuMe) tool

Treesearch

William Elliot; David Hall

2005-01-01

The Water Erosion Prediction Project (WEPP) Fuel Management (FuMe) tool was developed to estimate sediment generated by fuel management activities. WEPP FuMe estimates sediment generated for 12 fuel-related conditions from a single input. This fact sheet identifies the intended users and uses, required inputs, what the model does, and tells the user how to obtain the...
Collaborative Physical Chemistry Projects Involving Computational Chemistry

NASA Astrophysics Data System (ADS)

Whisnant, David M.; Howe, Jerry J.; Lever, Lisa S.

2000-02-01

The physical chemistry classes from three colleges have collaborated on two computational chemistry projects using Quantum CAChe 3.0 and Gaussian 94W running on Pentium II PCs. Online communication by email and the World Wide Web was an important part of the collaboration. In the first project, students used molecular modeling to predict benzene derivatives that might be possible hair dyes. They used PM3 and ZINDO calculations to predict the electronic spectra of the molecules and tested the predicted spectra by comparing some with experimental measurements. They also did literature searches for real hair dyes and possible health effects. In the final phase of the project they proposed a synthetic pathway for one compound. In the second project the students were asked to predict which isomer of a small carbon cluster (C3, C4, or C5) was responsible for a series of IR lines observed in the spectrum of a carbon star. After preliminary PM3 calculations, they used ab initio calculations at the HF/6-31G(d) and MP2/6-31G(d) level to model the molecules and predict their vibrational frequencies and rotational constants. A comparison of the predictions with the experimental spectra suggested that the linear isomer of the C5 molecule was responsible for the lines.
Next generation paradigm for urban pluvial flood modelling, prediction, management and vulnerability reduction - Interaction between RainGain and Blue Green Dream projects

NASA Astrophysics Data System (ADS)

Maksimovic, C.

2012-04-01

The effects of climate change and increasing urbanisation call for a new paradigm for efficient planning, management and retrofitting of urban developments to increase resilience to climate change and to maximize ecosystem services. Improved management of urban floods from all sources in required. Time scale for well documented fluvial and coastal floods allows for timely response but surface (pluvial) flooding caused by intense local storms had not been given appropriate attention, Pitt Review (UK). Urban surface floods predictions require fine scale data and model resolutions. They have to be tackled locally by combining central inputs (meteorological services) with the efforts of the local entities. Although significant breakthrough in modelling of pluvial flooding was made there is a need to further enhance short term prediction of both rainfall and surface flooding. These issues are dealt with in the EU Iterreg project Rain Gain (RG). Breakthrough in urban flood mitigation can only be achieved by combined effects of advanced planning design, construction and management of urban water (blue) assets in interaction with urban vegetated areas' (green) assets. Changes in design and operation of blue and green assets, currently operating as two separate systems, is urgently required. Gaps in knowledge and technology will be introduced by EIT's Climate-KIC Blue Green Dream (BGD) project. The RG and BGD projects provide synergy of the "decoupled" blue and green systems to enhance multiple benefits to: urban amenity, flood management, heat island, biodiversity, resilience to drought thus energy requirements, thus increased quality of urban life at lower costs. Urban pluvial flood management will address two priority areas: Short Term rainfall Forecast and Short term flood surface forecast. Spatial resolution of short term rainfall forecast below 0.5 km2 and lead time of a few hours are needed. Improvements are achievable by combining data sources of raingauge networks
An Object-Based Approach to Evaluation of Climate Variability Projections and Predictions

NASA Astrophysics Data System (ADS)

Ammann, C. M.; Brown, B.; Kalb, C. P.; Bullock, R.

2017-12-01

Evaluations of the performance of earth system model predictions and projections are of critical importance to enhance usefulness of these products. Such evaluations need to address specific concerns depending on the system and decisions of interest; hence, evaluation tools must be tailored to inform about specific issues. Traditional approaches that summarize grid-based comparisons of analyses and models, or between current and future climate, often do not reveal important information about the models' performance (e.g., spatial or temporal displacements; the reason behind a poor score) and are unable to accommodate these specific information needs. For example, summary statistics such as the correlation coefficient or the mean-squared error provide minimal information to developers, users, and decision makers regarding what is "right" and "wrong" with a model. New spatial and temporal-spatial object-based tools from the field of weather forecast verification (where comparisons typically focus on much finer temporal and spatial scales) have been adapted to more completely answer some of the important earth system model evaluation questions. In particular, the Method for Object-based Diagnostic Evaluation (MODE) tool and its temporal (three-dimensional) extension (MODE-TD) have been adapted for these evaluations. More specifically, these tools can be used to address spatial and temporal displacements in projections of El Nino-related precipitation and/or temperature anomalies, ITCZ-associated precipitation areas, atmospheric rivers, seasonal sea-ice extent, and other features of interest. Examples of several applications of these tools in a climate context will be presented, using output of the CESM large ensemble. In general, these tools provide diagnostic information about model performance - accounting for spatial, temporal, and intensity differences - that cannot be achieved using traditional (scalar) model comparison approaches. Thus, they can provide more
The Accumulating Data to Optimally Predict Obesity Treatment (ADOPT) Core Measures Project: Rationale and Approach.

PubMed

MacLean, Paul S; Rothman, Alexander J; Nicastro, Holly L; Czajkowski, Susan M; Agurs-Collins, Tanya; Rice, Elise L; Courcoulas, Anita P; Ryan, Donna H; Bessesen, Daniel H; Loria, Catherine M

2018-04-01

Individual variability in response to multiple modalities of obesity treatment is well documented, yet our understanding of why some individuals respond while others do not is limited. The etiology of this variability is multifactorial; however, at present, we lack a comprehensive evidence base to identify which factors or combination of factors influence treatment response. This paper provides an overview and rationale of the Accumulating Data to Optimally Predict obesity Treatment (ADOPT) Core Measures Project, which aims to advance the understanding of individual variability in response to adult obesity treatment. This project provides an integrated model for how factors in the behavioral, biological, environmental, and psychosocial domains may influence obesity treatment responses and identify a core set of measures to be used consistently across adult weight-loss trials. This paper provides the foundation for four companion papers that describe the core measures in detail. The accumulation of data on factors across the four ADOPT domains can inform the design and delivery of effective, tailored obesity treatments. ADOPT provides a framework for how obesity researchers can collectively generate this evidence base and is a first step in an ongoing process that can be refined as the science advances. © 2018 The Obesity Society.
Prediction of Gas Lubricated Foil Journal Bearing Performance

NASA Technical Reports Server (NTRS)

Carpino, Marc; Talmage, Gita

2003-01-01

This report summarizes the progress in the first eight months of the project. The objectives of this research project are to theoretically predict the steady operating conditions and the rotor dynamic coefficients of gas foil journal bearings. The project is currently on or ahead of schedule with the development of a finite element code that predicts steady bearing performance characteristics such as film thickness, pressure, load, and drag. Graphical results for a typical bearing are presented in the report. Project plans for the next year are discussed.

Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project.

PubMed

Alghamdi, Manal; Al-Mallah, Mouaz; Keteyian, Steven; Brawner, Clinton; Ehrman, Jonathan; Sakr, Sherif

2017-01-01

Machine learning is becoming a popular and important approach in the field of medical research. In this study, we investigate the relative performance of various machine learning methods such as Decision Tree, Naïve Bayes, Logistic Regression, Logistic Model Tree and Random Forests for predicting incident diabetes using medical records of cardiorespiratory fitness. In addition, we apply different techniques to uncover potential predictors of diabetes. This FIT project study used data of 32,555 patients who are free of any known coronary artery disease or heart failure who underwent clinician-referred exercise treadmill stress testing at Henry Ford Health Systems between 1991 and 2009 and had a complete 5-year follow-up. At the completion of the fifth year, 5,099 of those patients have developed diabetes. The dataset contained 62 attributes classified into four categories: demographic characteristics, disease history, medication use history, and stress test vital signs. We developed an Ensembling-based predictive model using 13 attributes that were selected based on their clinical importance, Multiple Linear Regression, and Information Gain Ranking methods. The negative effect of the imbalance class of the constructed model was handled by Synthetic Minority Oversampling Technique (SMOTE). The overall performance of the predictive model classifier was improved by the Ensemble machine learning approach using the Vote method with three Decision Trees (Naïve Bayes Tree, Random Forest, and Logistic Model Tree) and achieved high accuracy of prediction (AUC = 0.92). The study shows the potential of ensembling and SMOTE approaches for predicting incident diabetes using cardiorespiratory fitness data.
Evaluating the Predictive Value of Growth Prediction Models

ERIC Educational Resources Information Center

Murphy, Daniel L.; Gaertner, Matthew N.

2014-01-01

This study evaluates four growth prediction models--projection, student growth percentile, trajectory, and transition table--commonly used to forecast (and give schools credit for) middle school students' future proficiency. Analyses focused on vertically scaled summative mathematics assessments, and two performance standards conditions (high…
Predicting fire activity in the US over the next 50 years using new IPCC climate projections

NASA Astrophysics Data System (ADS)

Wang, D.; Morton, D. C.; Collatz, G. J.

2012-12-01

Fire is an integral part of the Earth system with both direct and indirect effects on terrestrial ecosystems, the atmosphere, and human societies (Bowman et al. 2009). Climate conditions regulate fire activities through a variety of ways, e.g., influencing the conditions for ignition and fire spread, changing vegetation growth and decay and thus the accumulation of fuels for combustion (Arora and Boer 2005). Our recent study disclosed the burned area (BA) in US is strongly correlated with potential evaporation (PE), a measurement of climatic dryness derived from National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) climate data (Morton et al. 2012). The correlation varies spatially and temporally. With regard to fire of peak fire seasons, Northwestern US, Great Plains and Alaska have the strongest BA/PE relationship. Using the recently released the Global Fire Emissions Database (GFED) Version 3 (van der Werf et al. 2010), we showed increasing BA in the last decade in most of NCA regions. Longer time series of Monitoring Trends in Burn Severity (MTBS) (Eidenshink et al. 2007) data showed the increasing trends occurred in all NCA regions from 1984 to 2010. This relationship between BA and PE provides us the basis to predict the future fire activities in the projected climate conditions. In this study, we build spatially explicit predictors using the historic PE/BA relationship. PE from 2011 to 2060 is calculated from the Coupled Model Intercomparison Project Phase 5 (CMIP5) data and the historic PE/BA relationship is then used to estimate BA. This study examines the spatial pattern and temporal dynamics of the future US fires driven by new climate predictions for the next 50 years. Reference: Arora, V.K., & Boer, G.J. (2005). Fire as an interactive component of dynamic vegetation models. Journal of Geophysical Research-Biogeosciences, 110 Bowman, D.M.J.S., Balch, J.K., Artaxo, P., Bond, W.J., Carlson, J.M., Cochrane, M.A., D
bayesPop: Probabilistic Population Projections

PubMed Central

Ševčíková, Hana; Raftery, Adrian E.

2016-01-01

We describe bayesPop, an R package for producing probabilistic population projections for all countries. This uses probabilistic projections of total fertility and life expectancy generated by Bayesian hierarchical models. It produces a sample from the joint posterior predictive distribution of future age- and sex-specific population counts, fertility rates and mortality rates, as well as future numbers of births and deaths. It provides graphical ways of summarizing this information, including trajectory plots and various kinds of probabilistic population pyramids. An expression language is introduced which allows the user to produce the predictive distribution of a wide variety of derived population quantities, such as the median age or the old age dependency ratio. The package produces aggregated projections for sets of countries, such as UN regions or trading blocs. The methodology has been used by the United Nations to produce their most recent official population projections for all countries, published in the World Population Prospects. PMID:28077933
Predicting long-term graft survival in adult kidney transplant recipients.

PubMed

Pinsky, Brett W; Lentine, Krista L; Ercole, Patrick R; Salvalaggio, Paolo R; Burroughs, Thomas E; Schnitzler, Mark A

2012-07-01

The ability to accurately predict a population's long-term survival has important implications for quantifying the benefits of transplantation. To identify a model that can accurately predict a kidney transplant population's long-term graft survival, we retrospectively studied the United Network of Organ Sharing data from 13,111 kidney-only transplants completed in 1988- 1989. Nineteen-year death-censored graft survival (DCGS) projections were calculated and compared with the population's actual graft survival. The projection curves were created using a two-part estimation model that (1) fits a Kaplan-Meier survival curve immediately after transplant (Part A) and (2) uses truncated observational data to model a survival function for long-term projection (Part B). Projection curves were examined using varying amounts of time to fit both parts of the model. The accuracy of the projection curve was determined by examining whether predicted survival fell within the 95% confidence interval for the 19-year Kaplan-Meier survival, and the sample size needed to detect the difference in projected versus observed survival in a clinical trial. The 19-year DCGS was 40.7% (39.8-41.6%). Excellent predictability (41.3%) can be achieved when Part A is fit for three years and Part B is projected using two additional years of data. Using less than five total years of data tended to overestimate the population's long-term survival, accurate prediction of long-term DCGS is possible, but requires attention to the quantity data used in the projection method.
The Proteome Folding Project: Proteome-scale prediction of structure and function

PubMed Central

Drew, Kevin; Winters, Patrick; Butterfoss, Glenn L.; Berstis, Viktors; Uplinger, Keith; Armstrong, Jonathan; Riffle, Michael; Schweighofer, Erik; Bovermann, Bill; Goodlett, David R.; Davis, Trisha N.; Shasha, Dennis; Malmström, Lars; Bonneau, Richard

2011-01-01

The incompleteness of proteome structure and function annotation is a critical problem for biologists and, in particular, severely limits interpretation of high-throughput and next-generation experiments. We have developed a proteome annotation pipeline based on structure prediction, where function and structure annotations are generated using an integration of sequence comparison, fold recognition, and grid-computing-enabled de novo structure prediction. We predict protein domain boundaries and three-dimensional (3D) structures for protein domains from 94 genomes (including human, Arabidopsis, rice, mouse, fly, yeast, Escherichia coli, and worm). De novo structure predictions were distributed on a grid of more than 1.5 million CPUs worldwide (World Community Grid). We generated significant numbers of new confident fold annotations (9% of domains that are otherwise unannotated in these genomes). We demonstrate that predicted structures can be combined with annotations from the Gene Ontology database to predict new and more specific molecular functions. PMID:21824995
Prediction of breast cancer risk using a machine learning approach embedded with a locality preserving projection algorithm.

PubMed

Heidari, Morteza; Khuzani, Abolfazl Zargari; Hollingsworth, Alan B; Danala, Gopichandh; Mirniaharikandehei, Seyedehnafiseh; Qiu, Yuchen; Liu, Hong; Zheng, Bin

2018-01-30

In order to automatically identify a set of effective mammographic image features and build an optimal breast cancer risk stratification model, this study aims to investigate advantages of applying a machine learning approach embedded with a locally preserving projection (LPP) based feature combination and regeneration algorithm to predict short-term breast cancer risk. A dataset involving negative mammograms acquired from 500 women was assembled. This dataset was divided into two age-matched classes of 250 high risk cases in which cancer was detected in the next subsequent mammography screening and 250 low risk cases, which remained negative. First, a computer-aided image processing scheme was applied to segment fibro-glandular tissue depicted on mammograms and initially compute 44 features related to the bilateral asymmetry of mammographic tissue density distribution between left and right breasts. Next, a multi-feature fusion based machine learning classifier was built to predict the risk of cancer detection in the next mammography screening. A leave-one-case-out (LOCO) cross-validation method was applied to train and test the machine learning classifier embedded with a LLP algorithm, which generated a new operational vector with 4 features using a maximal variance approach in each LOCO process. Results showed a 9.7% increase in risk prediction accuracy when using this LPP-embedded machine learning approach. An increased trend of adjusted odds ratios was also detected in which odds ratios increased from 1.0 to 11.2. This study demonstrated that applying the LPP algorithm effectively reduced feature dimensionality, and yielded higher and potentially more robust performance in predicting short-term breast cancer risk.
Prediction of breast cancer risk using a machine learning approach embedded with a locality preserving projection algorithm

NASA Astrophysics Data System (ADS)

Heidari, Morteza; Zargari Khuzani, Abolfazl; Hollingsworth, Alan B.; Danala, Gopichandh; Mirniaharikandehei, Seyedehnafiseh; Qiu, Yuchen; Liu, Hong; Zheng, Bin

2018-02-01

In order to automatically identify a set of effective mammographic image features and build an optimal breast cancer risk stratification model, this study aims to investigate advantages of applying a machine learning approach embedded with a locally preserving projection (LPP) based feature combination and regeneration algorithm to predict short-term breast cancer risk. A dataset involving negative mammograms acquired from 500 women was assembled. This dataset was divided into two age-matched classes of 250 high risk cases in which cancer was detected in the next subsequent mammography screening and 250 low risk cases, which remained negative. First, a computer-aided image processing scheme was applied to segment fibro-glandular tissue depicted on mammograms and initially compute 44 features related to the bilateral asymmetry of mammographic tissue density distribution between left and right breasts. Next, a multi-feature fusion based machine learning classifier was built to predict the risk of cancer detection in the next mammography screening. A leave-one-case-out (LOCO) cross-validation method was applied to train and test the machine learning classifier embedded with a LLP algorithm, which generated a new operational vector with 4 features using a maximal variance approach in each LOCO process. Results showed a 9.7% increase in risk prediction accuracy when using this LPP-embedded machine learning approach. An increased trend of adjusted odds ratios was also detected in which odds ratios increased from 1.0 to 11.2. This study demonstrated that applying the LPP algorithm effectively reduced feature dimensionality, and yielded higher and potentially more robust performance in predicting short-term breast cancer risk.
Predicting Flu Season Requirements: An Undergraduate Modeling Project

ERIC Educational Resources Information Center

Kramlich, Gary R., II; Braunstein Fierson, Janet L.; Wright, J. Adam

2010-01-01

This project was designed to be used in a freshman calculus class whose students had already been introduced to logistic functions and basic data modeling techniques. It need not be limited to such an audience, however; it has also been implemented in a topics in mathematics class for college upperclassmen. Originally intended to be presented in…
Grandparenting predicts late-life cognition: Results from the Women's Healthy Ageing Project.

PubMed

Burn, Katherine; Szoeke, Cassandra

2015-06-01

Social engagement provides dynamic stimulation for older individuals that influences cognition. Grandparenting is an increasingly popular form of childcare that provides social activity for older adults. To investigate the relationship between grandparenting, as a form of social engagement, and cognitive function in adults aged over 65, a decade after our previous work. Participants were 224 Australian women (mean age=70) from the longitudinal prospective Women's Healthy Ageing Project (WHAP). Cognitive function was assessed using a neuropsychological battery consisting of previously validated measures including the California Verbal Learning Test, Digit Span task, and Controlled Oral Word Association Test. Individual test scores were combined using factor analysis into executive function and episodic memory scores. Grandmothers minding grandchildren had higher executive function than those who were not minding grandchildren or who did not have grandchildren (p<0.05). Minding grandchildren for one day per week predicted better executive function performance than more frequent grandparenting (p<0.05), consistent with previous findings. While grandparenting is associated with better executive function overall, highly frequent grandparenting is associated with lower executive function, which may be due to demands. Social engagement comprises various aspects which need to be taken into consideration when studying cognition. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
Graduate Student Project: Operations Management Product Plan

ERIC Educational Resources Information Center

Fish, Lynn

2007-01-01

An operations management product project is an effective instructional technique that fills a void in current operations management literature in product planning. More than 94.1% of 286 graduates favored the project as a learning tool, and results demonstrate the significant impact the project had in predicting student performance. The author…
A statistical rain attenuation prediction model with application to the advanced communication technology satellite project. 1: Theoretical development and application to yearly predictions for selected cities in the United States

NASA Technical Reports Server (NTRS)

Manning, Robert M.

1986-01-01

A rain attenuation prediction model is described for use in calculating satellite communication link availability for any specific location in the world that is characterized by an extended record of rainfall. Such a formalism is necessary for the accurate assessment of such availability predictions in the case of the small user-terminal concept of the Advanced Communication Technology Satellite (ACTS) Project. The model employs the theory of extreme value statistics to generate the necessary statistical rainrate parameters from rain data in the form compiled by the National Weather Service. These location dependent rain statistics are then applied to a rain attenuation model to obtain a yearly prediction of the occurrence of attenuation on any satellite link at that location. The predictions of this model are compared to those of the Crane Two-Component Rain Model and some empirical data and found to be very good. The model is then used to calculate rain attenuation statistics at 59 locations in the United States (including Alaska and Hawaii) for the 20 GHz downlinks and 30 GHz uplinks of the proposed ACTS system. The flexibility of this modeling formalism is such that it allows a complete and unified treatment of the temporal aspects of rain attenuation that leads to the design of an optimum stochastic power control algorithm, the purpose of which is to efficiently counter such rain fades on a satellite link.
National Centers for Environmental Prediction

Science.gov Websites

Statistics Observational Data Processing Data Assimilation Monsoon Desk Model Transition Seminars Seminar projects. Starting a Monsoon Mission experiment or research project? Let us know so we can add it to our Modeling Center NOAA Center for Weather and Climate Prediction (NCWCP) 5830 University Research Court
DOE-EPSCOR SPONSORED PROJECT FINAL REPORT

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

Zhu, Jianting

Concern over the quality of environmental management and restoration has motivated the model development for predicting water and solute transport in the vadose zone. Soil hydraulic properties are required inputs to subsurface models of water flow and contaminant transport in the vadose zone. Computer models are now routinely used in research and management to predict the movement of water and solutes into and through the vadose zone of soils. Such models can be used successfully only if reliable estimates of the soil hydraulic parameters are available. The hydraulic parameters considered in this project consist of the saturated hydraulic conductivity andmore » four parameters of the water retention curves. To quantify hydraulic parameters for heterogeneous soils is both difficult and time consuming. The overall objective of this project was to better quantify soil hydraulic parameters which are critical in predicting water flows and contaminant transport in the vadose zone through a comprehensive and quantitative study to predict heterogeneous soil hydraulic properties and the associated uncertainties. Systematic and quantitative consideration of the parametric heterogeneity and uncertainty can properly address and further reduce predictive uncertainty for contamination characterization and environmental restoration at DOE-managed sites. We conducted a comprehensive study to assess soil hydraulic parameter heterogeneity and uncertainty. We have addressed a number of important issues related to the soil hydraulic property characterizations. The main focus centered on new methods to characterize anisotropy of unsaturated hydraulic property typical of layered soil formations, uncertainty updating method, and artificial neural network base pedo-transfer functions to predict hydraulic parameters from easily available data. The work also involved upscaling of hydraulic properties applicable to large scale flow and contaminant transport modeling in the vadose zone and
Methods of Predicting Solid Waste Characteristics.

ERIC Educational Resources Information Center

Boyd, Gail B.; Hawkins, Myron B.

The project summarized by this report involved a preliminary design of a model for estimating and predicting the quantity and composition of solid waste and a determination of its feasibility. The novelty of the prediction model is that it estimates and predicts on the basis of knowledge of materials and quantities before they become a part of the…
Asphalt surface aging prediction (ASAP) system : final report.

DOT National Transportation Integrated Search

2010-09-01

The Asphalt Surface Aging Prediction (ASAP) project has been a 2.5 year effort to predict agerelated : embrittlement in asphalt pavement surfaces and to develop ground-based and airborne : systems to measure key spectral indicators needed for predict...
The Arctic Predictability and Prediction on Seasonal-to-Interannual TimEscales (APPOSITE) data set version 1

NASA Astrophysics Data System (ADS)

Day, Jonathan J.; Tietsche, Steffen; Collins, Mat; Goessling, Helge F.; Guemas, Virginie; Guillory, Anabelle; Hurlin, William J.; Ishii, Masayoshi; Keeley, Sarah P. E.; Matei, Daniela; Msadek, Rym; Sigmond, Michael; Tatebe, Hiroaki; Hawkins, Ed

2016-06-01

Recent decades have seen significant developments in climate prediction capabilities at seasonal-to-interannual timescales. However, until recently the potential of such systems to predict Arctic climate had rarely been assessed. This paper describes a multi-model predictability experiment which was run as part of the Arctic Predictability and Prediction On Seasonal to Interannual Timescales (APPOSITE) project. The main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable. In order to achieve this, a coordinated set of idealised initial-value predictability experiments, with seven general circulation models, was conducted. This was the first model intercomparison project designed to quantify the predictability of Arctic climate on seasonal to interannual timescales. Here we present a description of the archived data set (which is available at the British Atmospheric Data Centre), an assessment of Arctic sea ice extent and volume predictability estimates in these models, and an investigation into to what extent predictability is dependent on the initial state. The inclusion of additional models expands the range of sea ice volume and extent predictability estimates, demonstrating that there is model diversity in the potential to make seasonal-to-interannual timescale predictions. We also investigate whether sea ice forecasts started from extreme high and low sea ice initial states exhibit higher levels of potential predictability than forecasts started from close to the models' mean state, and find that the result depends on the metric. Although designed to address Arctic predictability, we describe the archived data here so that others can use this data set to assess the predictability of other regions and modes of climate variability on these timescales, such as the El Niño-Southern Oscillation.
Prediction of Nursing Workload in Hospital.

PubMed

Fiebig, Madlen; Hunstein, Dirk; Bartholomeyczik, Sabine

2018-01-01

A dissertation project at the Witten/Herdecke University [1] is investigating which (nursing sensitive) patient characteristics are suitable for predicting a higher or lower degree of nursing workload. For this research project four predictive modelling methods were selected. In a first step, SUPPORT VECTOR MACHINE, RANDOM FOREST, and GRADIENT BOOSTING were used to identify potential predictors from the nursing sensitive patient characteristics. The results were compared via FEATURE IMPORTANCE. To predict nursing workload the predictors identified in step 1 were modelled using MULTINOMIAL LOGISTIC REGRESSION. First results from the data mining process will be presented. A prognostic determination of nursing workload can be used not only as a basis for human resource planning in hospital, but also to respond to health policy issues.
NASA's Evolutionary Xenon Thruster (NEXT) Project Qualification Propellant Throughput Milestone: Performance, Erosion, and Thruster Service Life Prediction After 450 kg

NASA Technical Reports Server (NTRS)

Herman, Daniel A.

2010-01-01

The NASA s Evolutionary Xenon Thruster (NEXT) program is tasked with significantly improving and extending the capabilities of current state-of-the-art NSTAR thruster. The service life capability of the NEXT ion thruster is being assessed by thruster wear test and life-modeling of critical thruster components, such as the ion optics and cathodes. The NEXT Long-Duration Test (LDT) was initiated to validate and qualify the NEXT thruster propellant throughput capability. The NEXT thruster completed the primary goal of the LDT; namely to demonstrate the project qualification throughput of 450 kg by the end of calendar year 2009. The NEXT LDT has demonstrated 28,500 hr of operation and processed 466 kg of xenon throughput--more than double the throughput demonstrated by the NSTAR flight-spare. Thruster performance changes have been consistent with a priori predictions. Thruster erosion has been minimal and consistent with the thruster service life assessment, which predicts the first failure mode at greater than 750 kg throughput. The life-limiting failure mode for NEXT is predicted to be loss of structural integrity of the accelerator grid due to erosion by charge-exchange ions.
Subjective analysis of energy-management projects

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

Morris, R.

The most successful energy conservation projects always reflect human effort to fine-tune engineering and technological improvements. Subjective analysis is a technique for predicting and measuring human interaction before a project begins. The examples of a subjective analysis for office buildings incorporate evaluative questions that are structured to produce numeric values for computer scoring. Each project would need to develop its own pertinent questions and determine appropriate values for the answers.

Wind Plant Performance Prediction (WP3) Project

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

Craig, Anna

The methods for analysis of operational wind plant data are highly variable across the wind industry, leading to high uncertainties in the validation and bias-correction of preconstruction energy estimation methods. Lack of credibility in the preconstruction energy estimates leads to significant impacts on project financing and therefore the final levelized cost of energy for the plant. In this work, the variation in the evaluation of a wind plant's operational energy production as a result of variations in the processing methods applied to the operational data is examined. Preliminary results indicate that selection of the filters applied to the data andmore » the filter parameters can have significant impacts in the final computed assessment metrics.« less
Verification and intercomparison of mesoscale ensemble prediction systems in the Beijing 2008 Olympics Research and Development Project

NASA Astrophysics Data System (ADS)

Kunii, Masaru; Saito, Kazuo; Seko, Hiromu; Hara, Masahiro; Hara, Tabito; Yamaguchi, Munehiko; Gong, Jiandong; Charron, Martin; Du, Jun; Wang, Yong; Chen, Dehui

2011-05-01

During the period around the Beijing 2008 Olympic Games, the Beijing 2008 Olympics Research and Development Project (B08RDP) was conducted as part of the World Weather Research Program short-range weather forecasting research project. Mesoscale ensemble prediction (MEP) experiments were carried out by six organizations in near-real time, in order to share their experiences in the development of MEP systems. The purpose of this study is to objectively verify these experiments and to clarify the problems associated with the current MEP systems through the same experiences. Verification was performed using the MEP outputs interpolated into a common verification domain with a horizontal resolution of 15 km. For all systems, the ensemble spreads grew as the forecast time increased, and the ensemble mean improved the forecast errors compared with individual control forecasts in the verification against the analysis fields. However, each system exhibited individual characteristics according to the MEP method. Some participants used physical perturbation methods. The significance of these methods was confirmed by the verification. However, the mean error (ME) of the ensemble forecast in some systems was worse than that of the individual control forecast. This result suggests that it is necessary to pay careful attention to physical perturbations.
Choosing the appropriate forecasting model for predictive parameter control.

PubMed

Aleti, Aldeida; Moser, Irene; Meedeniya, Indika; Grunske, Lars

2014-01-01

All commonly used stochastic optimisation algorithms have to be parameterised to perform effectively. Adaptive parameter control (APC) is an effective method used for this purpose. APC repeatedly adjusts parameter values during the optimisation process for optimal algorithm performance. The assignment of parameter values for a given iteration is based on previously measured performance. In recent research, time series prediction has been proposed as a method of projecting the probabilities to use for parameter value selection. In this work, we examine the suitability of a variety of prediction methods for the projection of future parameter performance based on previous data. All considered prediction methods have assumptions the time series data has to conform to for the prediction method to provide accurate projections. Looking specifically at parameters of evolutionary algorithms (EAs), we find that all standard EA parameters with the exception of population size conform largely to the assumptions made by the considered prediction methods. Evaluating the performance of these prediction methods, we find that linear regression provides the best results by a very small and statistically insignificant margin. Regardless of the prediction method, predictive parameter control outperforms state of the art parameter control methods when the performance data adheres to the assumptions made by the prediction method. When a parameter's performance data does not adhere to the assumptions made by the forecasting method, the use of prediction does not have a notable adverse impact on the algorithm's performance.
WEPPCAT: An Online tool for assessing and managing the potential impacts of climate change on sediment loading to streams using the Water Erosion Prediction Project (WEPP) Model

EPA Science Inventory

WEPPCAT is an on-line tool that provides a flexible capability for creating user-determined climate change scenarios for assessing the potential impacts of climate change on sediment loading to streams using the USDA’s Water Erosion Prediction Project (WEPP) Model. In combination...
Fine-Tuning Dropout Prediction through Discriminant Analysis: The Ethnic Factor.

ERIC Educational Resources Information Center

Wilkinson, L. David; Frazer, Linda H.

In the 1988-89 school year, the Austin (Texas) Independent School District's Office of Research and Evaluation undertook a new dropout research project. Part of this initiative, termed Project GRAD, attempted to develop a statistical equation by which one could predict which students were likely to drop out. If reliable predictive information…
Base-age invariance and inventory projections

Treesearch

C. J. Cieszewski; R. L. Bailey; B. E. Borders; G. H. Brister; B. D. Shiver

2000-01-01

One of the most important functions of forest inventory is to facilitate management decisions towards forest sustainability based on inventory projections into the future. Therefore, most forest inventories are used for predicting future states of the forests, in modern forestry the most common methods used in inventory projections are based on implicit functions...
Projection methods

Treesearch

Michael E. Goerndt; W. Keith Moser; Patrick D. Miles; Dave Wear; Ryan D. DeSantis; Robert J. Huggett; Stephen R. Shifley; Francisco X. Aguilar; Kenneth E. Skog

2016-01-01

One purpose of the Northern Forest Futures Project is to predict change in future forest attributes across the 20 States in the U.S. North for the period that extends from 2010 to 2060. The forest attributes of primary interest are the 54 indicators of forest sustainability identified in the Montreal Process Criteria and Indicators (Montreal Process Working Group, n.d...
The ARAC-RODOS-WSPEEDI Information Exchange Project

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

Sullivan, T J

1999-09-01

Under the auspices of a US DOE-JAPAN Memorandum of Understanding JAERI and LLNL agreed to develop and evaluate a prototype information exchange protocol for nuclear accident emergency situations. This project received some interest from the US DOS and FEMA as it fits nicely under the umbrella of the G-7's GEMINI (Global Emergency Management Information Network Initiative) project. Because of LLNL/ARAC and JAERV WSPEEDI interest in nuclear accident consequence assessment and hazard prediction on all scales, to include global, we were happy to participate. Subsequent to the Spring 1997 RODOS-ARAC Workshop a Memorandum of Agreement was developed to enhance mutual collaborationmore » on matters of emergency systems development. In the summer of 1998 the project leaders of RODOS, WSPEEDI and ARAC met at FZK and agreed to join in a triangular collaboration on the development and demonstration of an emergency information exchange protocol. JAERI and FZK are engaged in developing a formal cooperation agreement. The purpose of this project is to evaluate the prototype information protocol application for technical feasibility and mutual benefit through simulated (real) event; quick exchange of atmospheric modeling products and environmental data during emergencies, distribution of predicted results to other countries having no prediction capabilities, and utilization of the link for collaborative studies.« less
Enhancing seasonal climate prediction capacity for the Pacific countries

NASA Astrophysics Data System (ADS)

Kuleshov, Y.; Jones, D.; Hendon, H.; Charles, A.; Cottrill, A.; Lim, E.-P.; Langford, S.; de Wit, R.; Shelton, K.

2012-04-01

Seasonal and inter-annual climate variability is a major factor in determining the vulnerability of many Pacific Island Countries to climate change and there is need to improve weekly to seasonal range climate prediction capabilities beyond what is currently available from statistical models. In the seasonal climate prediction project under the Australian Government's Pacific Adaptation Strategy Assistance Program (PASAP), we describe a comprehensive project to strengthen the climate prediction capacities in National Meteorological Services in 14 Pacific Island Countries and East Timor. The intent is particularly to reduce the vulnerability of current services to a changing climate, and improve the overall level of information available assist with managing climate variability. Statistical models cannot account for aspects of climate variability and change that are not represented in the historical record. In contrast, dynamical physics-based models implicitly include the effects of a changing climate whatever its character or cause and can predict outcomes not seen previously. The transition from a statistical to a dynamical prediction system provides more valuable and applicable climate information to a wide range of climate sensitive sectors throughout the countries of the Pacific region. In this project, we have developed seasonal climate outlooks which are based upon the current dynamical model POAMA (Predictive Ocean-Atmosphere Model for Australia) seasonal forecast system. At present, meteorological services of the Pacific Island Countries largely employ statistical models for seasonal outlooks. Outcomes of the PASAP project enhanced capabilities of the Pacific Island Countries in seasonal prediction providing National Meteorological Services with an additional tool to analyse meteorological variables such as sea surface temperatures, air temperature, pressure and rainfall using POAMA outputs and prepare more accurate seasonal climate outlooks.
Key Questions in Building Defect Prediction Models in Practice

NASA Astrophysics Data System (ADS)

Ramler, Rudolf; Wolfmaier, Klaus; Stauder, Erwin; Kossak, Felix; Natschläger, Thomas

The information about which modules of a future version of a software system are defect-prone is a valuable planning aid for quality managers and testers. Defect prediction promises to indicate these defect-prone modules. However, constructing effective defect prediction models in an industrial setting involves a number of key questions. In this paper we discuss ten key questions identified in context of establishing defect prediction in a large software development project. Seven consecutive versions of the software system have been used to construct and validate defect prediction models for system test planning. Furthermore, the paper presents initial empirical results from the studied project and, by this means, contributes answers to the identified questions.
Project description and crowdfunding success: an exploratory study.

PubMed

Zhou, Mi Jamie; Lu, Baozhou; Fan, Weiguo Patrick; Wang, G Alan

2018-01-01

Existing research on antecedent of funding success mainly focuses on basic project properties such as funding goal, duration, and project category. In this study, we view the process by which project owners raise funds from backers as a persuasion process through project descriptions. Guided by the unimodel theory of persuasion, this study identifies three exemplary antecedents (length, readability, and tone) from the content of project descriptions and two antecedents (past experience and past expertise) from the trustworthy cue of project descriptions. We then investigate their impacts on funding success. Using data collected from Kickstarter, a popular crowdfunding platform, we find that these antecedents are significantly associated with funding success. Empirical results show that the proposed model that incorporated these antecedents can achieve an accuracy of 73 % (70 % in F-measure). The result represents an improvement of roughly 14 percentage points over the baseline model based on informed guessing and 4 percentage points improvement over the mainstream model based on basic project properties (or 44 % improvement of mainstream's performance over informed guessing). The proposed model also has superior true positive and true negative rates. We also investigate the timeliness of project data and find that old project data is gradually becoming less relevant and losing predictive power to newly created projects. Overall, this study provides evidence that antecedents identified from project descriptions have incremental predictive power and can help project owners evaluate and improve the likelihood of funding success.
Machine learning in updating predictive models of planning and scheduling transportation projects

DOT National Transportation Integrated Search

1997-01-01

A method combining machine learning and regression analysis to automatically and intelligently update predictive models used in the Kansas Department of Transportations (KDOTs) internal management system is presented. The predictive models used...
Statistical Prediction of Sea Ice Concentration over Arctic

NASA Astrophysics Data System (ADS)

Kim, Jongho; Jeong, Jee-Hoon; Kim, Baek-Min

2017-04-01

In this study, a statistical method that predict sea ice concentration (SIC) over the Arctic is developed. We first calculate the Season-reliant Empirical Orthogonal Functions (S-EOFs) of monthly Arctic SIC from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data, which contain the seasonal cycles (12 months long) of dominant SIC anomaly patterns. Then, the current SIC state index is determined by projecting observed SIC anomalies for latest 12 months to the S-EOFs. Assuming the current SIC anomalies follow the spatio-temporal evolution in the S-EOFs, we project the future (upto 12 months) SIC anomalies by multiplying the SI and the corresponding S-EOF and then taking summation. The predictive skill is assessed by hindcast experiments initialized at all the months for 1980-2010. When comparing predictive skill of SIC predicted by statistical model and NCEP CFS v2, the statistical model shows a higher skill in predicting sea ice concentration and extent.
SEIZURE PREDICTION: THE FOURTH INTERNATIONAL WORKSHOP

PubMed Central

Zaveri, Hitten P.; Frei, Mark G.; Arthurs, Susan; Osorio, Ivan

2010-01-01

The recently convened Fourth International Workshop on Seizure Prediction (IWSP4) brought together a diverse international group of investigators, from academia and industry, including epileptologists, neurosurgeons, neuroscientists, computer scientists, engineers, physicists, and mathematicians who are conducting interdisciplinary research on the prediction and control of seizures. IWSP4 allowed the presentation and discussion of results, an exchange of ideas, an assessment of the status of seizure prediction, control and related fields and the fostering of collaborative projects. PMID:20674508
Projecting a Stand Table Through Time

Treesearch

Quang V. Cao; V. Clark Baldwin

1999-01-01

Stand tables provide number of trees per acre for each diameter class. This paper presents a general technique to predict a future stand table, based on the current stand table and future stand summary statistics such as trees and basal area per acre, and average diameter. The stand projection technique involves (a) predicting surviving trees for each class, and (b)...
Predictive Accuracy of the Liverpool Lung Project Risk Model for Stratifying Patients for Computed Tomography Screening for Lung Cancer

PubMed Central

Raji, Olaide Y.; Duffy, Stephen W.; Agbaje, Olorunshola F.; Baker, Stuart G.; Christiani, David C.; Cassidy, Adrian; Field, John K.

2013-01-01

Background External validation of existing lung cancer risk prediction models is limited. Using such models in clinical practice to guide the referral of patients for computed tomography (CT) screening for lung cancer depends on external validation and evidence of predicted clinical benefit. Objective To evaluate the discrimination of the Liverpool Lung Project (LLP) risk model and demonstrate its predicted benefit for stratifying patients for CT screening by using data from 3 independent studies from Europe and North America. Design Case–control and prospective cohort study. Setting Europe and North America. Patients Participants in the European Early Lung Cancer (EUELC) and Harvard case–control studies and the LLP population-based prospective cohort (LLPC) study. Measurements 5-year absolute risks for lung cancer predicted by the LLP model. Results The LLP risk model had good discrimination in both the Harvard (area under the receiver-operating characteristic curve [AUC], 0.76 [95% CI, 0.75 to 0.78]) and the LLPC (AUC, 0.82 [CI, 0.80 to 0.85]) studies and modest discrimination in the EUELC (AUC, 0.67 [CI, 0.64 to 0.69]) study. The decision utility analysis, which incorporates the harms and benefit of using a risk model to make clinical decisions, indicates that the LLP risk model performed better than smoking duration or family history alone in stratifying high-risk patients for lung cancer CT screening. Limitations The model cannot assess whether including other risk factors, such as lung function or genetic markers, would improve accuracy. Lack of information on asbestos exposure in the LLPC limited the ability to validate the complete LLP risk model. Conclusion Validation of the LLP risk model in 3 independent external data sets demonstrated good discrimination and evidence of predicted benefits for stratifying patients for lung cancer CT screening. Further studies are needed to prospectively evaluate model performance and evaluate the optimal population
Aircraft engine hot section technology: An overview of the HOST Project

NASA Technical Reports Server (NTRS)

Sokolowski, Daniel E.; Hirschberg, Marvin H.

1990-01-01

NASA sponsored the Turbine Engine Hot Section (HOST) project to address the need for improved durability in advanced aircraft engine combustors and turbines. Analytical and experimental activities aimed at more accurate prediction of the aerothermal environment, the thermomechanical loads, the material behavior and structural responses to loads, and life predictions for cyclic high temperature operation were conducted from 1980 to 1987. The project involved representatives from six engineering disciplines who are spread across three work disciplines - industry, academia, and NASA. The HOST project not only initiated and sponsored 70 major activities, but also was the keystone in joining the multiple disciplines and work sectors to focus on critical research needs. A broad overview of the project is given along with initial indications of the project's impact.
Modelling obesity trends in Australia: unravelling the past and predicting the future.

PubMed

Hayes, A J; Lung, T W C; Bauman, A; Howard, K

2017-01-01

Modelling is increasingly being used to predict the epidemiology of obesity progression and its consequences. The aims of this study were: (a) to present and validate a model for prediction of obesity among Australian adults and (b) to use the model to project the prevalence of obesity and severe obesity by 2025. Individual level simulation combined with survey estimation techniques to model changing population body mass index (BMI) distribution over time. The model input population was derived from a nationally representative survey in 1995, representing over 12 million adults. Simulations were run for 30 years. The model was validated retrospectively and then used to predict obesity and severe obesity by 2025 among different aged cohorts and at a whole population level. The changing BMI distribution over time was well predicted by the model and projected prevalence of weight status groups agreed with population level data in 2008, 2012 and 2014.The model predicts more growth in obesity among younger than older adult cohorts. Projections at a whole population level, were that healthy weight will decline, overweight will remain steady, but obesity and severe obesity prevalence will continue to increase beyond 2016. Adult obesity prevalence was projected to increase from 19% in 1995 to 35% by 2025. Severe obesity (BMI>35), which was only around 5% in 1995, was projected to be 13% by 2025, two to three times the 1995 levels. The projected rise in obesity severe obesity will have more substantial cost and healthcare system implications than in previous decades. Having a robust epidemiological model is key to predicting these long-term costs and health outcomes into the future.
The accuracy of matrix population model projections for coniferous trees in the Sierra Nevada, California

USGS Publications Warehouse

van Mantgem, P.J.; Stephenson, N.L.

2005-01-01

1 We assess the use of simple, size-based matrix population models for projecting population trends for six coniferous tree species in the Sierra Nevada, California. We used demographic data from 16 673 trees in 15 permanent plots to create 17 separate time-invariant, density-independent population projection models, and determined differences between trends projected from initial surveys with a 5-year interval and observed data during two subsequent 5-year time steps. 2 We detected departures from the assumptions of the matrix modelling approach in terms of strong growth autocorrelations. We also found evidence of observation errors for measurements of tree growth and, to a more limited degree, recruitment. Loglinear analysis provided evidence of significant temporal variation in demographic rates for only two of the 17 populations. 3 Total population sizes were strongly predicted by model projections, although population dynamics were dominated by carryover from the previous 5-year time step (i.e. there were few cases of recruitment or death). Fractional changes to overall population sizes were less well predicted. Compared with a null model and a simple demographic model lacking size structure, matrix model projections were better able to predict total population sizes, although the differences were not statistically significant. Matrix model projections were also able to predict short-term rates of survival, growth and recruitment. Mortality frequencies were not well predicted. 4 Our results suggest that simple size-structured models can accurately project future short-term changes for some tree populations. However, not all populations were well predicted and these simple models would probably become more inaccurate over longer projection intervals. The predictive ability of these models would also be limited by disturbance or other events that destabilize demographic rates. ?? 2005 British Ecological Society.
PubMed

Yunta, Eduardo Rodríguez

2010-01-01

El presente artículo reflexiona desde los 4 principios de la bioética el uso comercial de organismos genéticamente modificados. Se cuestiona fundamentalmente la falta de transferencia de tecnología entre el mundo desarrollado y en desarrollo y el que el presente sistema de patentamiento de organismos vivos modificados fomenta intereses comerciales y no da debida importancia al desarrollo sostenible de la agricultura y ganadería en los países en desarrollo, donde más se necesita. Se reflexiona sobre la importancia que tiene evaluar los riesgos antes de introducirse en el mercado organismos genéticamente modificados y la necesidad de regulación en los países.

Joint Applications Pilot of the National Climate Predictions and Projections Platform and the North Central Climate Science Center: Delivering climate projections on regional scales to support adaptation planning

NASA Astrophysics Data System (ADS)

Ray, A. J.; Ojima, D. S.; Morisette, J. T.

2012-12-01

The DOI North Central Climate Science Center (NC CSC) and the NOAA/NCAR National Climate Predictions and Projections (NCPP) Platform and have initiated a joint pilot study to collaboratively explore the "best available climate information" to support key land management questions and how to provide this information. NCPP's mission is to support state of the art approaches to develop and deliver comprehensive regional climate information and facilitate its use in decision making and adaptation planning. This presentation will describe the evolving joint pilot as a tangible, real-world demonstration of linkages between climate science, ecosystem science and resource management. Our joint pilot is developing a deliberate, ongoing interaction to prototype how NCPP will work with CSCs to develop and deliver needed climate information products, including translational information to support climate data understanding and use. This pilot also will build capacity in the North Central CSC by working with NCPP to use climate information used as input to ecological modeling. We will discuss lessons to date on developing and delivering needed climate information products based on this strategic partnership. Four projects have been funded to collaborate to incorporate climate information as part of an ecological modeling project, which in turn will address key DOI stakeholder priorities in the region: Riparian Corridors: Projecting climate change effects on cottonwood and willow seed dispersal phenology, flood timing, and seedling recruitment in western riparian forests. Sage Grouse & Habitats: Integrating climate and biological data into land management decision models to assess species and habitat vulnerability Grasslands & Forests: Projecting future effects of land management, natural disturbance, and CO2 on woody encroachment in the Northern Great Plains The value of climate information: Supporting management decisions in the Plains and Prairie Potholes LCC. NCCSC's role in
Collaborative Research: Separating Forced and Unforced Decadal Predictability in Models and Observations

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

Tippett, Michael K.

2014-04-09

This report is a progress report of the accomplishments of the research grant “Collaborative Research: Separating Forced and Unforced Decadal Predictability in Models and Observa- tions” during the period 1 May 2011- 31 August 2013. This project is a collaborative one between Columbia University and George Mason University. George Mason University will submit a final technical report at the conclusion of their no-cost extension. The purpose of the proposed research is to identify unforced predictable components on decadal time scales, distinguish these components from forced predictable components, and to assess the reliability of model predictions of these components. Components ofmore » unforced decadal predictability will be isolated by maximizing the Average Predictability Time (APT) in long, multimodel control runs from state-of-the-art climate models. Components with decadal predictability have large APT, so maximizing APT ensures that components with decadal predictability will be detected. Optimal fingerprinting techniques, as used in detection and attribution analysis, will be used to separate variations due to natural and anthropogenic forcing from those due to unforced decadal predictability. This methodology will be applied to the decadal hindcasts generated by the CMIP5 project to assess the reliability of model projections. The question of whether anthropogenic forcing changes decadal predictability, or gives rise to new forms of decadal predictability, also will be investigated.« less
GRAPPA 2015 Research and Education Project Reports.

PubMed

Mease, Philip J; Helliwell, Philip S; Boehncke, Wolf-Henning; Coates, Laura C; FitzGerald, Oliver; Gladman, Dafna D; Deodhar, Atul A; Callis Duffin, Kristina

2016-05-01

At the 2015 annual meeting of the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA), attendees were presented with brief updates on several ongoing initiatives, including educational projects. Updates were presented on the treatment recommendations project, the development of simple criteria to identify inflammatory musculoskeletal disease, new patient/physician Delphi exercises, and BIODAM (identifying biomarkers that predict progressive structural joint damage). The publication committee also gave a report. Herein we summarize those project updates.
Questioning the Faith - Models and Prediction in Stream Restoration (Invited)

NASA Astrophysics Data System (ADS)

Wilcock, P.

2013-12-01

River management and restoration demand prediction at and beyond our present ability. Management questions, framed appropriately, can motivate fundamental advances in science, although the connection between research and application is not always easy, useful, or robust. Why is that? This presentation considers the connection between models and management, a connection that requires critical and creative thought on both sides. Essential challenges for managers include clearly defining project objectives and accommodating uncertainty in any model prediction. Essential challenges for the research community include matching the appropriate model to project duration, space, funding, information, and social constraints and clearly presenting answers that are actually useful to managers. Better models do not lead to better management decisions or better designs if the predictions are not relevant to and accepted by managers. In fact, any prediction may be irrelevant if the need for prediction is not recognized. The predictive target must be developed in an active dialog between managers and modelers. This relationship, like any other, can take time to develop. For example, large segments of stream restoration practice have remained resistant to models and prediction because the foundational tenet - that channels built to a certain template will be able to transport the supplied sediment with the available flow - has no essential physical connection between cause and effect. Stream restoration practice can be steered in a predictive direction in which project objectives are defined as predictable attributes and testable hypotheses. If stream restoration design is defined in terms of the desired performance of the channel (static or dynamic, sediment surplus or deficit), then channel properties that provide these attributes can be predicted and a basis exists for testing approximations, models, and predictions.
Operational prediction of air quality for the United States: applications of satellite observations

NASA Astrophysics Data System (ADS)

Stajner, Ivanka; Lee, Pius; Tong, Daniel; Pan, Li; McQueen, Jeff; Huang, Jianping; Huang, Ho-Chun; Draxler, Roland; Kondragunta, Shobha; Upadhayay, Sikchya

2015-04-01

Operational predictions of ozone and wildfire smoke over United States (U.S.) and predictions of airborne dust over the contiguous 48 states are provided by NOAA at http://airquality.weather.gov/. North American Mesoscale (NAM) weather predictions with inventory based emissions estimates from the U.S. Environmental Protection Agency (EPA) and chemical processes within the Community Multiscale Air Quality (CMAQ) model are combined together to produce ozone predictions. Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model is used to predict wildfire smoke and dust storm predictions. Routine verification of ozone predictions relies on AIRNow compilation of observations from surface monitors. Retrievals of smoke column integrals from GOES satellites and dust column integrals from MODIS satellite instruments are used for verification of smoke and dust predictions. Recent updates of NOAA's operational air quality predictions have focused on mobile emissions using the projections of mobile sources for 2012. Since emission inventories are complex and take years to assemble and evaluate causing a lag of information, we recently began combing inventory information with projections of mobile sources. In order to evaluate this emission update, these changes in projected NOx emissions from 2005-2012 were compared with observed changes in Ozone Monitoring Instrument (OMI) NO2 observations and NOx measured by surface monitors over large U.S. cities over the same period. Comparisons indicate that projected decreases in NOx emissions from 2005 to 2012 are similar, but not as strong as the decreases in the observed NOx concentrations and in OMI NO2 retrievals. Nevertheless, the use of projected mobile NOx emissions in the predictions reduced biases in predicted NOx concentrations, with the largest improvement in the urban areas. Ozone biases are reduced as well, with the largest improvement seen in rural areas. Recent testing of PM2.5 predictions is relying on
Current affairs in earthquake prediction in Japan

NASA Astrophysics Data System (ADS)

Uyeda, Seiya

2015-12-01

As of mid-2014, the main organizations of the earthquake (EQ hereafter) prediction program, including the Seismological Society of Japan (SSJ), the MEXT Headquarters for EQ Research Promotion, hold the official position that they neither can nor want to make any short-term prediction. It is an extraordinary stance of responsible authorities when the nation, after the devastating 2011 M9 Tohoku EQ, most urgently needs whatever information that may exist on forthcoming EQs. Japan's national project for EQ prediction started in 1965, but it has made no success. The main reason for no success is the failure to capture precursors. After the 1995 Kobe disaster, the project decided to give up short-term prediction and this stance has been further fortified by the 2011 M9 Tohoku Mega-quake. This paper tries to explain how this situation came about and suggest that it may in fact be a legitimate one which should have come a long time ago. Actually, substantial positive changes are taking place now. Some promising signs are arising even from cooperation of researchers with private sectors and there is a move to establish an "EQ Prediction Society of Japan". From now on, maintaining the high scientific standards in EQ prediction will be of crucial importance.
Geometric modeling of Plateau borders using the orthographic projection method for closed cell rigid polyurethane foam thermal conductivity prediction

NASA Astrophysics Data System (ADS)

Xu, Jie; Wu, Tao; Peng, Chuang; Adegbite, Stephen

2017-09-01

The geometric Plateau border model for closed cell polyurethane foam was developed based on volume integrations of approximated 3D four-cusp hypocycloid structure. The tetrahedral structure of convex struts was orthogonally projected into 2D three-cusp deltoid with three central cylinders. The idealized single unit strut was modeled by superposition. The volume of each component was calculated by geometric analyses. The strut solid fraction f s and foam porosity coefficient δ were calculated based on representative elementary volume of Kelvin and Weaire-Phelan structures. The specific surface area Sv derived respectively from packing structures and deltoid approximation model were put into contrast against strut dimensional ratio ɛ. The characteristic foam parameters obtained from this semi-empirical model were further employed to predict foam thermal conductivity.
PredictABEL: an R package for the assessment of risk prediction models.

PubMed

Kundu, Suman; Aulchenko, Yurii S; van Duijn, Cornelia M; Janssens, A Cecile J W

2011-04-01

The rapid identification of genetic markers for multifactorial diseases from genome-wide association studies is fuelling interest in investigating the predictive ability and health care utility of genetic risk models. Various measures are available for the assessment of risk prediction models, each addressing a different aspect of performance and utility. We developed PredictABEL, a package in R that covers descriptive tables, measures and figures that are used in the analysis of risk prediction studies such as measures of model fit, predictive ability and clinical utility, and risk distributions, calibration plot and the receiver operating characteristic plot. Tables and figures are saved as separate files in a user-specified format, which include publication-quality EPS and TIFF formats. All figures are available in a ready-made layout, but they can be customized to the preferences of the user. The package has been developed for the analysis of genetic risk prediction studies, but can also be used for studies that only include non-genetic risk factors. PredictABEL is freely available at the websites of GenABEL ( http://www.genabel.org ) and CRAN ( http://cran.r-project.org/).
Stock Market Project.

ERIC Educational Resources Information Center

Distel, Brenda D.

This project is designed to teach students the process of buying stocks and to tracking their investments over the course of a semester. The goals of the course are to teach students about the relationships between conditions in the economy and the stock market; to predict the effect of an economic event on a specific stock or industry; to relate…
Predictive Models and Computational Embryology

EPA Science Inventory

EPA’s ‘virtual embryo’ project is building an integrative systems biology framework for predictive models of developmental toxicity. One schema involves a knowledge-driven adverse outcome pathway (AOP) framework utilizing information from public databases, standardized ontologies...
The Potential for Predicting Precipitation on Seasonal-to-Interannual Timescales

NASA Technical Reports Server (NTRS)

Koster, R. D.

1999-01-01

The ability to predict precipitation several months in advance would have a significant impact on water resource management. This talk provides an overview of a project aimed at developing this prediction capability. NASA's Seasonal-to-Interannual Prediction Project (NSIPP) will generate seasonal-to-interannual sea surface temperature predictions through detailed ocean circulation modeling and will then translate these SST forecasts into forecasts of continental precipitation through the application of an atmospheric general circulation model and a "SVAT"-type land surface model. As part of the process, ocean variables (e.g., height) and land variables (e.g., soil moisture) will be updated regularly via data assimilation. The overview will include a discussion of the variability inherent in such a modeling system and will provide some quantitative estimates of the absolute upper limits of seasonal-to-interannual precipitation predictability.
The Climate Variability & Predictability (CVP) Program at NOAA - Recent Program Advancements

NASA Astrophysics Data System (ADS)

Lucas, S. E.; Todd, J. F.

2015-12-01

The Climate Variability & Predictability (CVP) Program supports research aimed at providing process-level understanding of the climate system through observation, modeling, analysis, and field studies. This vital knowledge is needed to improve climate models and predictions so that scientists can better anticipate the impacts of future climate variability and change. To achieve its mission, the CVP Program supports research carried out at NOAA and other federal laboratories, NOAA Cooperative Institutes, and academic institutions. The Program also coordinates its sponsored projects with major national and international scientific bodies including the World Climate Research Programme (WCRP), the International and U.S. Climate Variability and Predictability (CLIVAR/US CLIVAR) Program, and the U.S. Global Change Research Program (USGCRP). The CVP program sits within NOAA's Climate Program Office (http://cpo.noaa.gov/CVP). The CVP Program currently supports multiple projects in areas that are aimed at improved representation of physical processes in global models. Some of the topics that are currently funded include: i) Improved Understanding of Intraseasonal Tropical Variability - DYNAMO field campaign and post -field projects, and the new climate model improvement teams focused on MJO processes; ii) Climate Process Teams (CPTs, co-funded with NSF) with projects focused on Cloud macrophysical parameterization and its application to aerosol indirect effects, and Internal-Wave Driven Mixing in Global Ocean Models; iii) Improved Understanding of Tropical Pacific Processes, Biases, and Climatology; iv) Understanding Arctic Sea Ice Mechanism and Predictability;v) AMOC Mechanisms and Decadal Predictability Recent results from CVP-funded projects will be summarized. Additional information can be found at http://cpo.noaa.gov/CVP.
Accurate Binding Free Energy Predictions in Fragment Optimization.

PubMed

Steinbrecher, Thomas B; Dahlgren, Markus; Cappel, Daniel; Lin, Teng; Wang, Lingle; Krilov, Goran; Abel, Robert; Friesner, Richard; Sherman, Woody

2015-11-23

Predicting protein-ligand binding free energies is a central aim of computational structure-based drug design (SBDD)--improved accuracy in binding free energy predictions could significantly reduce costs and accelerate project timelines in lead discovery and optimization. The recent development and validation of advanced free energy calculation methods represents a major step toward this goal. Accurately predicting the relative binding free energy changes of modifications to ligands is especially valuable in the field of fragment-based drug design, since fragment screens tend to deliver initial hits of low binding affinity that require multiple rounds of synthesis to gain the requisite potency for a project. In this study, we show that a free energy perturbation protocol, FEP+, which was previously validated on drug-like lead compounds, is suitable for the calculation of relative binding strengths of fragment-sized compounds as well. We study several pharmaceutically relevant targets with a total of more than 90 fragments and find that the FEP+ methodology, which uses explicit solvent molecular dynamics and physics-based scoring with no parameters adjusted, can accurately predict relative fragment binding affinities. The calculations afford R(2)-values on average greater than 0.5 compared to experimental data and RMS errors of ca. 1.1 kcal/mol overall, demonstrating significant improvements over the docking and MM-GBSA methods tested in this work and indicating that FEP+ has the requisite predictive power to impact fragment-based affinity optimization projects.
Predictive vs. Empiric Assessment of Schistosomiasis: Implications for Treatment Projections in Ghana

PubMed Central

Kabore, Achille; Biritwum, Nana-Kwadwo; Downs, Philip W.; Soares Magalhaes, Ricardo J.; Zhang, Yaobi; Ottesen, Eric A.

2013-01-01

Background Mapping the distribution of schistosomiasis is essential to determine where control programs should operate, but because it is impractical to assess infection prevalence in every potentially endemic community, model-based geostatistics (MBG) is increasingly being used to predict prevalence and determine intervention strategies. Methodology/Principal Findings To assess the accuracy of MBG predictions for Schistosoma haematobium infection in Ghana, school surveys were evaluated at 79 sites to yield empiric prevalence values that could be compared with values derived from recently published MBG predictions. Based on these findings schools were categorized according to WHO guidelines so that practical implications of any differences could be determined. Using the mean predicted values alone, 21 of the 25 empirically determined ‘high-risk’ schools requiring yearly praziquantel would have been undertreated and almost 20% of the remaining schools would have been treated despite empirically-determined absence of infection – translating into 28% of the children in the 79 schools being undertreated and 12% receiving treatment in the absence of any demonstrated need. Conclusions/Significance Using the current predictive map for Ghana as a spatial decision support tool by aggregating prevalence estimates to the district level was clearly not adequate for guiding the national program, but the alternative of assessing each school in potentially endemic areas of Ghana or elsewhere is not at all feasible; modelling must be a tool complementary to empiric assessments. Thus for practical usefulness, predictive risk mapping should not be thought of as a one-time exercise but must, as in the current study, be an iterative process that incorporates empiric testing and model refining to create updated versions that meet the needs of disease control operational managers. PMID:23505584
Change in avian abundance predicted from regional forest inventory data

USGS Publications Warehouse

Twedt, Daniel J.; Tirpak, John M.; Jones-Farrand, D. Todd; Thompson, Frank R.; Uihlein, William B.; Fitzgerald, Jane A.

2010-01-01

An inability to predict population response to future habitat projections is a shortcoming in bird conservation planning. We sought to predict avian response to projections of future forest conditions that were developed from nationwide forest surveys within the Forest Inventory and Analysis (FIA) program. To accomplish this, we evaluated the historical relationship between silvicolous bird populations and FIA-derived forest conditions within 25 ecoregions that comprise the southeastern United States. We aggregated forest area by forest ownership, forest type, and tree size-class categories in county-based ecoregions for 5 time periods spanning 1963-2008. We assessed the relationship of forest data with contemporaneous indices of abundance for 24 silvicolous bird species that were obtained from Breeding Bird Surveys. Relationships between bird abundance and forest inventory data for 18 species were deemed sufficient as predictive models. We used these empirically derived relationships between regional forest conditions and bird populations to predict relative changes in abundance of these species within ecoregions that are anticipated to coincide with projected changes in forest variables through 2040. Predicted abundances of these 18 species are expected to remain relatively stable in over a quarter (27%) of the ecoregions. However, change in forest area and redistribution of forest types will likely result in changed abundance of some species within many ecosystems. For example, abundances of 11 species, including pine warbler (Dendroica pinus), brown-headed nuthatch (Sitta pusilla), and chuckwills- widow (Caprimulgus carolinensis), are projected to increase within more ecoregions than ecoregions where they will decrease. For 6 other species, such as blue-winged warbler (Vermivora pinus), Carolina wren (Thryothorus ludovicianus), and indigo bunting (Passerina cyanea), we projected abundances will decrease within more ecoregions than ecoregions where they will
EVA Robotic Assistant Project: Platform Attitude Prediction

NASA Technical Reports Server (NTRS)

Nickels, Kevin M.

2003-01-01

The Robotic Systems Technology Branch is currently working on the development of an EVA Robotic Assistant under the sponsorship of the Surface Systems Thrust of the NASA Cross Enterprise Technology Development Program (CETDP). This will be a mobile robot that can follow a field geologist during planetary surface exploration, carry his tools and the samples that he collects, and provide video coverage of his activity. Prior experiments have shown that for such a robot to be useful it must be able to follow the geologist at walking speed over any terrain of interest. Geologically interesting terrain tends to be rough rather than smooth. The commercial mobile robot that was recently purchased as an initial testbed for the EVA Robotic Assistant Project, an ATRV Jr., is capable of faster than walking speed outside but it has no suspension. Its wheels with inflated rubber tires are attached to axles that are connected directly to the robot body. Any angular motion of the robot produced by driving over rough terrain will directly affect the pointing of the on-board stereo cameras. The resulting image motion is expected to make tracking of the geologist more difficult. This will either require the tracker to search a larger part of the image to find the target from frame to frame or to search mechanically in pan and tilt whenever the image motion is large enough to put the target outside the image in the next frame. This project consists of the design and implementation of a Kalman filter that combines the output of the angular rate sensors and linear accelerometers on the robot to estimate the motion of the robot base. The motion of the stereo camera pair mounted on the robot that results from this motion as the robot drives over rough terrain is then straightforward to compute. The estimates may then be used, for example, to command the robot s on-board pan-tilt unit to compensate for the camera motion induced by the base movement. This has been accomplished in two ways
Scientific Prediction and Prophetic Patenting in Drug Discovery.

PubMed

Curry, Stephen H; Schneiderman, Anne M

2015-01-01

Pharmaceutical patenting involves writing claims based on both discoveries already made, and on prophesy of future developments in an ongoing project. This is necessitated by the very different timelines involved in the drug discovery and product development process on the one hand, and successful patenting on the other. If patents are sought too early there is a risk that patent examiners will disallow claims because of lack of enablement. If patenting is delayed, claims are at risk of being denied on the basis of existence of prior art, because the body of relevant known science will have developed significantly while the project was being pursued. This review examines the role of prophetic patenting in relation to the essential predictability of many aspects of drug discovery science, promoting the concepts of discipline-related and project-related prediction. This is especially directed towards patenting activities supporting commercialization of academia-based discoveries, where long project timelines occur, and where experience, and resources to pay for patenting, are limited. The need for improved collaborative understanding among project scientists, technology transfer professionals in, for example, universities, patent attorneys, and patent examiners is emphasized.
Applying a machine learning model using a locally preserving projection based feature regeneration algorithm to predict breast cancer risk

NASA Astrophysics Data System (ADS)

Heidari, Morteza; Zargari Khuzani, Abolfazl; Danala, Gopichandh; Mirniaharikandehei, Seyedehnafiseh; Qian, Wei; Zheng, Bin

2018-03-01

Both conventional and deep machine learning has been used to develop decision-support tools applied in medical imaging informatics. In order to take advantages of both conventional and deep learning approach, this study aims to investigate feasibility of applying a locally preserving projection (LPP) based feature regeneration algorithm to build a new machine learning classifier model to predict short-term breast cancer risk. First, a computer-aided image processing scheme was used to segment and quantify breast fibro-glandular tissue volume. Next, initially computed 44 image features related to the bilateral mammographic tissue density asymmetry were extracted. Then, an LLP-based feature combination method was applied to regenerate a new operational feature vector using a maximal variance approach. Last, a k-nearest neighborhood (KNN) algorithm based machine learning classifier using the LPP-generated new feature vectors was developed to predict breast cancer risk. A testing dataset involving negative mammograms acquired from 500 women was used. Among them, 250 were positive and 250 remained negative in the next subsequent mammography screening. Applying to this dataset, LLP-generated feature vector reduced the number of features from 44 to 4. Using a leave-onecase-out validation method, area under ROC curve produced by the KNN classifier significantly increased from 0.62 to 0.68 (p < 0.05) and odds ratio was 4.60 with a 95% confidence interval of [3.16, 6.70]. Study demonstrated that this new LPP-based feature regeneration approach enabled to produce an optimal feature vector and yield improved performance in assisting to predict risk of women having breast cancer detected in the next subsequent mammography screening.
Pavement Performance : Approaches Using Predictive Analytics

DOT National Transportation Integrated Search

2018-03-23

Acceptable pavement condition is paramount to road safety. Using predictive analytics techniques, this project attempted to develop models that provide an assessment of pavement condition based on an array of indictors that include pavement distress,...
Drought Predictability and Prediction in a Changing Climate: Assessing Current Predictive Knowledge and Capabilities, User Requirements and Research Priorities

NASA Technical Reports Server (NTRS)

Schubert, Siegfried

2011-01-01

Drought is fundamentally the result of an extended period of reduced precipitation lasting anywhere from a few weeks to decades and even longer. As such, addressing drought predictability and prediction in a changing climate requires foremost that we make progress on the ability to predict precipitation anomalies on subseasonal and longer time scales. From the perspective of the users of drought forecasts and information, drought is however most directly viewed through its impacts (e.g., on soil moisture, streamflow, crop yields). As such, the question of the predictability of drought must extend to those quantities as well. In order to make progress on these issues, the WCRP drought information group (DIG), with the support of WCRP, the Catalan Institute of Climate Sciences, the La Caixa Foundation, the National Aeronautics and Space Administration, the National Oceanic and Atmospheric Administration, and the National Science Foundation, has organized a workshop to focus on: 1. User requirements for drought prediction information on sub-seasonal to centennial time scales 2. Current understanding of the mechanisms and predictability of drought on sub-seasonal to centennial time scales 3. Current drought prediction/projection capabilities on sub-seasonal to centennial time scales 4. Advancing regional drought prediction capabilities for variables and scales most relevant to user needs on sub-seasonal to centennial time scales. This introductory talk provides an overview of these goals, and outlines the occurrence and mechanisms of drought world-wide.

Reduce Manual Curation by Combining Gene Predictions from Multiple Annotation Engines, a Case Study of Start Codon Prediction

PubMed Central

Ederveen, Thomas H. A.; Overmars, Lex; van Hijum, Sacha A. F. T.

2013-01-01

Nowadays, prokaryotic genomes are sequenced faster than the capacity to manually curate gene annotations. Automated genome annotation engines provide users a straight-forward and complete solution for predicting ORF coordinates and function. For many labs, the use of AGEs is therefore essential to decrease the time necessary for annotating a given prokaryotic genome. However, it is not uncommon for AGEs to provide different and sometimes conflicting predictions. Combining multiple AGEs might allow for more accurate predictions. Here we analyzed the ab initio open reading frame (ORF) calling performance of different AGEs based on curated genome annotations of eight strains from different bacterial species with GC% ranging from 35–52%. We present a case study which demonstrates a novel way of comparative genome annotation, using combinations of AGEs in a pre-defined order (or path) to predict ORF start codons. The order of AGE combinations is from high to low specificity, where the specificity is based on the eight genome annotations. For each AGE combination we are able to derive a so-called projected confidence value, which is the average specificity of ORF start codon prediction based on the eight genomes. The projected confidence enables estimating likeliness of a correct prediction for a particular ORF start codon by a particular AGE combination, pinpointing ORFs notoriously difficult to predict start codons. We correctly predict start codons for 90.5±4.8% of the genes in a genome (based on the eight genomes) with an accuracy of 81.1±7.6%. Our consensus-path methodology allows a marked improvement over majority voting (9.7±4.4%) and with an optimal path ORF start prediction sensitivity is gained while maintaining a high specificity. PMID:23675487
A new algorithm for stand table projection models.

Treesearch

Quang V. Cao; V. Clark Baldwin

1999-01-01

The constrained least squares method is proposed as an algorithm for projecting stand tables through time. This method consists of three steps: (1) predict survival in each diameter class, (2) predict diameter growth, and (3) use the least squares approach to adjust the stand table to satisfy the constraints of future survival, average diameter, and stand basal area....
Climate Projections and Uncertainty Communication.

PubMed

Joslyn, Susan L; LeClerc, Jared E

2016-01-01

Lingering skepticism about climate change might be due in part to the way climate projections are perceived by members of the public. Variability between scientists' estimates might give the impression that scientists disagree about the fact of climate change rather than about details concerning the extent or timing. Providing uncertainty estimates might clarify that the variability is due in part to quantifiable uncertainty inherent in the prediction process, thereby increasing people's trust in climate projections. This hypothesis was tested in two experiments. Results suggest that including uncertainty estimates along with climate projections leads to an increase in participants' trust in the information. Analyses explored the roles of time, place, demographic differences (e.g., age, gender, education level, political party affiliation), and initial belief in climate change. Implications are discussed in terms of the potential benefit of adding uncertainty estimates to public climate projections. Copyright © 2015 Cognitive Science Society, Inc.
Predicting discovery rates of genomic features.

PubMed

Gravel, Simon

2014-06-01

Successful sequencing experiments require judicious sample selection. However, this selection must often be performed on the basis of limited preliminary data. Predicting the statistical properties of the final sample based on preliminary data can be challenging, because numerous uncertain model assumptions may be involved. Here, we ask whether we can predict "omics" variation across many samples by sequencing only a fraction of them. In the infinite-genome limit, we find that a pilot study sequencing 5% of a population is sufficient to predict the number of genetic variants in the entire population within 6% of the correct value, using an estimator agnostic to demography, selection, or population structure. To reach similar accuracy in a finite genome with millions of polymorphisms, the pilot study would require ∼15% of the population. We present computationally efficient jackknife and linear programming methods that exhibit substantially less bias than the state of the art when applied to simulated data and subsampled 1000 Genomes Project data. Extrapolating based on the National Heart, Lung, and Blood Institute Exome Sequencing Project data, we predict that 7.2% of sites in the capture region would be variable in a sample of 50,000 African Americans and 8.8% in a European sample of equal size. Finally, we show how the linear programming method can also predict discovery rates of various genomic features, such as the number of transcription factor binding sites across different cell types. Copyright © 2014 by the Genetics Society of America.
Predictive modelling for startup and investor relationship based on crowdfunding platform data

NASA Astrophysics Data System (ADS)

Alamsyah, Andry; Buono Asto Nugroho, Tri

2018-03-01

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

PubMed Central

Jenouvrier, Stéphanie; Caswell, Hal; Barbraud, Christophe; Holland, Marika; Strœve, Julienne; Weimerskirch, Henri

2009-01-01

Studies have reported important effects of recent climate change on Antarctic species, but there has been to our knowledge no attempt to explicitly link those results to forecasted population responses to climate change. Antarctic sea ice extent (SIE) is projected to shrink as concentrations of atmospheric greenhouse gases (GHGs) increase, and emperor penguins (Aptenodytes forsteri) are extremely sensitive to these changes because they use sea ice as a breeding, foraging and molting habitat. We project emperor penguin population responses to future sea ice changes, using a stochastic population model that combines a unique long-term demographic dataset (1962–2005) from a colony in Terre Adélie, Antarctica and projections of SIE from General Circulation Models (GCM) of Earth's climate included in the most recent Intergovernmental Panel on Climate Change (IPCC) assessment report. We show that the increased frequency of warm events associated with projected decreases in SIE will reduce the population viability. The probability of quasi-extinction (a decline of 95% or more) is at least 36% by 2100. The median population size is projected to decline from ≈6,000 to ≈400 breeding pairs over this period. To avoid extinction, emperor penguins will have to adapt, migrate or change the timing of their growth stages. However, given the future projected increases in GHGs and its effect on Antarctic climate, evolution or migration seem unlikely for such long lived species at the remote southern end of the Earth. PMID:19171908
Demographic models and IPCC climate projections predict the decline of an emperor penguin population.

PubMed

Jenouvrier, Stéphanie; Caswell, Hal; Barbraud, Christophe; Holland, Marika; Stroeve, Julienne; Weimerskirch, Henri

2009-02-10

Studies have reported important effects of recent climate change on Antarctic species, but there has been to our knowledge no attempt to explicitly link those results to forecasted population responses to climate change. Antarctic sea ice extent (SIE) is projected to shrink as concentrations of atmospheric greenhouse gases (GHGs) increase, and emperor penguins (Aptenodytes forsteri) are extremely sensitive to these changes because they use sea ice as a breeding, foraging and molting habitat. We project emperor penguin population responses to future sea ice changes, using a stochastic population model that combines a unique long-term demographic dataset (1962-2005) from a colony in Terre Adélie, Antarctica and projections of SIE from General Circulation Models (GCM) of Earth's climate included in the most recent Intergovernmental Panel on Climate Change (IPCC) assessment report. We show that the increased frequency of warm events associated with projected decreases in SIE will reduce the population viability. The probability of quasi-extinction (a decline of 95% or more) is at least 36% by 2100. The median population size is projected to decline from approximately 6,000 to approximately 400 breeding pairs over this period. To avoid extinction, emperor penguins will have to adapt, migrate or change the timing of their growth stages. However, given the future projected increases in GHGs and its effect on Antarctic climate, evolution or migration seem unlikely for such long lived species at the remote southern end of the Earth.
Supersonics Project - Airport Noise Tech Challenge

NASA Technical Reports Server (NTRS)

Bridges, James

2010-01-01

The Airport Noise Tech Challenge research effort under the Supersonics Project is reviewed. While the goal of "Improved supersonic jet noise models validated on innovative nozzle concepts" remains the same, the success of the research effort has caused the thrust of the research to be modified going forward in time. The main activities from FY06-10 focused on development and validation of jet noise prediction codes. This required innovative diagnostic techniques to be developed and deployed, extensive jet noise and flow databases to be created, and computational tools to be developed and validated. Furthermore, in FY09-10 systems studies commissioned by the Supersonics Project showed that viable supersonic aircraft were within reach using variable cycle engine architectures if exhaust nozzle technology could provide 3-5dB of suppression. The Project then began to focus on integrating the technologies being developed in its Tech Challenge areas to bring about successful system designs. Consequently, the Airport Noise Tech Challenge area has shifted efforts from developing jet noise prediction codes to using them to develop low-noise nozzle concepts for integration into supersonic aircraft. The new plan of research is briefly presented by technology and timelines.
Predicting Precipitation in Darwin: An Experiment with Markov Chains

ERIC Educational Resources Information Center

Boncek, John; Harden, Sig

2009-01-01

As teachers of first-year college mathematics and science students, the authors are constantly on the lookout for simple classroom exercises that improve their students' analytical and computational skills. In this article, the authors outline a project entitled "Predicting Precipitation in Darwin." In this project, students: (1) analyze…
Neural Elements for Predictive Coding.

PubMed

Shipp, Stewart

2016-01-01

Predictive coding theories of sensory brain function interpret the hierarchical construction of the cerebral cortex as a Bayesian, generative model capable of predicting the sensory data consistent with any given percept. Predictions are fed backward in the hierarchy and reciprocated by prediction error in the forward direction, acting to modify the representation of the outside world at increasing levels of abstraction, and so to optimize the nature of perception over a series of iterations. This accounts for many 'illusory' instances of perception where what is seen (heard, etc.) is unduly influenced by what is expected, based on past experience. This simple conception, the hierarchical exchange of prediction and prediction error, confronts a rich cortical microcircuitry that is yet to be fully documented. This article presents the view that, in the current state of theory and practice, it is profitable to begin a two-way exchange: that predictive coding theory can support an understanding of cortical microcircuit function, and prompt particular aspects of future investigation, whilst existing knowledge of microcircuitry can, in return, influence theoretical development. As an example, a neural inference arising from the earliest formulations of predictive coding is that the source populations of forward and backward pathways should be completely separate, given their functional distinction; this aspect of circuitry - that neurons with extrinsically bifurcating axons do not project in both directions - has only recently been confirmed. Here, the computational architecture prescribed by a generalized (free-energy) formulation of predictive coding is combined with the classic 'canonical microcircuit' and the laminar architecture of hierarchical extrinsic connectivity to produce a template schematic, that is further examined in the light of (a) updates in the microcircuitry of primate visual cortex, and (b) rapid technical advances made possible by transgenic neural
Neural Elements for Predictive Coding

PubMed Central

Shipp, Stewart

2016-01-01

Predictive coding theories of sensory brain function interpret the hierarchical construction of the cerebral cortex as a Bayesian, generative model capable of predicting the sensory data consistent with any given percept. Predictions are fed backward in the hierarchy and reciprocated by prediction error in the forward direction, acting to modify the representation of the outside world at increasing levels of abstraction, and so to optimize the nature of perception over a series of iterations. This accounts for many ‘illusory’ instances of perception where what is seen (heard, etc.) is unduly influenced by what is expected, based on past experience. This simple conception, the hierarchical exchange of prediction and prediction error, confronts a rich cortical microcircuitry that is yet to be fully documented. This article presents the view that, in the current state of theory and practice, it is profitable to begin a two-way exchange: that predictive coding theory can support an understanding of cortical microcircuit function, and prompt particular aspects of future investigation, whilst existing knowledge of microcircuitry can, in return, influence theoretical development. As an example, a neural inference arising from the earliest formulations of predictive coding is that the source populations of forward and backward pathways should be completely separate, given their functional distinction; this aspect of circuitry – that neurons with extrinsically bifurcating axons do not project in both directions – has only recently been confirmed. Here, the computational architecture prescribed by a generalized (free-energy) formulation of predictive coding is combined with the classic ‘canonical microcircuit’ and the laminar architecture of hierarchical extrinsic connectivity to produce a template schematic, that is further examined in the light of (a) updates in the microcircuitry of primate visual cortex, and (b) rapid technical advances made possible by
A Bayesian Ensemble Approach for Epidemiological Projections

PubMed Central

Lindström, Tom; Tildesley, Michael; Webb, Colleen

2015-01-01

Mathematical models are powerful tools for epidemiology and can be used to compare control actions. However, different models and model parameterizations may provide different prediction of outcomes. In other fields of research, ensemble modeling has been used to combine multiple projections. We explore the possibility of applying such methods to epidemiology by adapting Bayesian techniques developed for climate forecasting. We exemplify the implementation with single model ensembles based on different parameterizations of the Warwick model run for the 2001 United Kingdom foot and mouth disease outbreak and compare the efficacy of different control actions. This allows us to investigate the effect that discrepancy among projections based on different modeling assumptions has on the ensemble prediction. A sensitivity analysis showed that the choice of prior can have a pronounced effect on the posterior estimates of quantities of interest, in particular for ensembles with large discrepancy among projections. However, by using a hierarchical extension of the method we show that prior sensitivity can be circumvented. We further extend the method to include a priori beliefs about different modeling assumptions and demonstrate that the effect of this can have different consequences depending on the discrepancy among projections. We propose that the method is a promising analytical tool for ensemble modeling of disease outbreaks. PMID:25927892
MJO prediction skill of the subseasonal-to-seasonal (S2S) prediction models

NASA Astrophysics Data System (ADS)

Son, S. W.; Lim, Y.; Kim, D.

2017-12-01

The Madden-Julian Oscillation (MJO), the dominant mode of tropical intraseasonal variability, provides the primary source of tropical and extratropical predictability on subseasonal to seasonal timescales. To better understand its predictability, this study conducts quantitative evaluation of MJO prediction skill in the state-of-the-art operational models participating in the subseasonal-to-seasonal (S2S) prediction project. Based on bivariate correlation coefficient of 0.5, the S2S models exhibit MJO prediction skill ranging from 12 to 36 days. These prediction skills are affected by both the MJO amplitude and phase errors, the latter becoming more important with forecast lead times. Consistent with previous studies, the MJO events with stronger initial amplitude are typically better predicted. However, essentially no sensitivity to the initial MJO phase is observed. Overall MJO prediction skill and its inter-model spread are further related with the model mean biases in moisture fields and longwave cloud-radiation feedbacks. In most models, a dry bias quickly builds up in the deep tropics, especially across the Maritime Continent, weakening horizontal moisture gradient. This likely dampens the organization and propagation of MJO. Most S2S models also underestimate the longwave cloud-radiation feedbacks in the tropics, which may affect the maintenance of the MJO convective envelop. In general, the models with a smaller bias in horizontal moisture gradient and longwave cloud-radiation feedbacks show a higher MJO prediction skill, suggesting that improving those processes would enhance MJO prediction skill.
Advanced Ground Systems Maintenance Prognostics Project

NASA Technical Reports Server (NTRS)

Harp, Janicce Leshay

2014-01-01

The project implements prognostics capabilities to predict when a component, system or subsystem will no longer meet desired functional or performance criteria, called the "end of life." The capability also provides an assessment of the "remaining useful life" of a hardware component.
Developing and Testing a Model to Predict Outcomes of Organizational Change

PubMed Central

Gustafson, David H; Sainfort, François; Eichler, Mary; Adams, Laura; Bisognano, Maureen; Steudel, Harold

2003-01-01

Objective To test the effectiveness of a Bayesian model employing subjective probability estimates for predicting success and failure of health care improvement projects. Data Sources Experts' subjective assessment data for model development and independent retrospective data on 221 healthcare improvement projects in the United States, Canada, and the Netherlands collected between 1996 and 2000 for validation. Methods A panel of theoretical and practical experts and literature in organizational change were used to identify factors predicting the outcome of improvement efforts. A Bayesian model was developed to estimate probability of successful change using subjective estimates of likelihood ratios and prior odds elicited from the panel of experts. A subsequent retrospective empirical analysis of change efforts in 198 health care organizations was performed to validate the model. Logistic regression and ROC analysis were used to evaluate the model's performance using three alternative definitions of success. Data Collection For the model development, experts' subjective assessments were elicited using an integrative group process. For the validation study, a staff person intimately involved in each improvement project responded to a written survey asking questions about model factors and project outcomes. Results Logistic regression chi-square statistics and areas under the ROC curve demonstrated a high level of model performance in predicting success. Chi-square statistics were significant at the 0.001 level and areas under the ROC curve were greater than 0.84. Conclusions A subjective Bayesian model was effective in predicting the outcome of actual improvement projects. Additional prospective evaluations as well as testing the impact of this model as an intervention are warranted. PMID:12785571
GEWEX Continental-scale International Project (GCIP)

NASA Technical Reports Server (NTRS)

Try, Paul

1993-01-01

The Global Energy and Water Cycle Experiment (GEWEX) represents the World Climate Research Program activities on clouds, radiation, and land-surface processes. The goal of the program is to reproduce and predict, by means of suitable models, the variations of the global hydrological regime and its impact on atmospheric and oceanic dynamics. However, GEWEX is also concerned with variations in regional hydrological processes and water resources and their response to changes in the environment such as increasing greenhouse gases. In fact, GEWEX contains a major new international project called the GEWEX Continental-scale International Project (GCIP), which is designed to bridge the gap between the small scales represented by hydrological models and those scales that are practical for predicting the regional impacts of climate change. The development and use of coupled mesoscale-hydrological models for this purpose is a high priority in GCIP. The objectives of GCIP are presented.
IMS/Satellite Situation Center report. Predicted orbit plots for Vela 5B, 1976

NASA Technical Reports Server (NTRS)

1975-01-01

Predicted orbit plots for the Vela 5B satellite are presented for the time period January-December 1976. This satellite has been identified as an important possible contributor to the International Magnetospheric Study (IMS) project. The predicted orbit plots are shown in three projections. The time period covered by each set of projections is 4 days 16 hours, corresponding approximately to the period of Vela 5B. The three coordinate systems used are the Geocentric Solar Ecliptic system (GSE), the Geocentric Solar Magnetospheric system (GSM), and the Solar Magnetic system (SM).
Microcirculation and the physiome projects.

PubMed

Bassingthwaighte, James B

2008-11-01

The Physiome projects comprise a loosely knit worldwide effort to define the Physiome through databases and theoretical models, with the goal of better understanding the integrative functions of cells, organs, and organisms. The projects involve developing and archiving models, providing centralized databases, and linking experimental information and models from many laboratories into self-consistent frameworks. Increasingly accurate and complete models that embody quantitative biological hypotheses, adhere to high standards, and are publicly available and reproducible, together with refined and curated data, will enable biological scientists to advance integrative, analytical, and predictive approaches to the study of medicine and physiology. This review discusses the rationale and history of the Physiome projects, the role of theoretical models in the development of the Physiome, and the current status of efforts in this area addressing the microcirculation.
CERAPP: Collaborative Estrogen Receptor Activity Prediction ...

EPA Pesticide Factsheets

Humans potentially are exposed to thousands of man-made chemicals in the environment. Some chemicals mimic natural endocrine hormones and, thus, have the potential to be endocrine disruptors. Many of these chemicals never have been tested for their ability to interact with the estrogen receptor (ER). Risk assessors need tools to prioritize chemicals for assessment in costly in vivo tests, for instance, within the EPA Endocrine Disruptor Screening Program. Here, we describe a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project) demonstrating the efficacy of using predictive computational models on high-throughput screening data to screen thousands of chemicals against the ER. CERAPP combined multiple models developed in collaboration among 17 groups in the United States and Europe to predict ER activity of a common set of 32,464 chemical structures. Quantitative structure-activity relationship models and docking approaches were employed, mostly using a common training set of 1677 compounds provided by EPA, to build a total of 40 categorical and 8 continuous models for binding, agonist, and antagonist ER activity. All predictions were tested using an evaluation set of 7522 chemicals collected from the literature. To overcome the limitations of single models, a consensus was built weighting models using a scoring function (0 to 1) based on their accuracies. Individual model scores ranged from 0.69 to 0.85, showing
Replacement of SSE with NASA's POWER Project GIS-enabled Web Data Portal

Atmospheric Science Data Center

2018-04-30

Replacement of SSE with NASA's POWER Project GIS-enabled Web Data Portal Friday, March ... 2018 Replacement of SSE (Release 6) with NASA's Prediction of Worldwide Energy Resource (POWER) Project GIS-enabled Web ... Worldwide Energy Resource (POWER) Project funded largely by NASA Earth Applied Sciences program. The new POWER web portal ...

The Earth System (ES-DOC) Project

NASA Astrophysics Data System (ADS)

Greenslade, Mark; Murphy, Sylvia; Treshansky, Allyn; DeLuca, Cecilia; Guilyardi, Eric; Denvil, Sebastien

2014-05-01

ESSI1.3 New Paradigms, Modelling, and International Collaboration Strategies for Earth System Sciences Earth System Documentation (ES-DOC) is an international project supplying tools & services in support of earth system documentation creation, analysis and dissemination. It is nurturing a sustainable standards based documentation eco-system that aims to become an integral part of the next generation of exa-scale dataset archives. ES-DOC leverages open source software and places end-user narratives at the heart of all it does. ES-DOC has initially focused upon nurturing the Earth System Model (ESM) documentation eco-system. Within this context ES-DOC leverages emerging documentation standards and supports the following projects: Coupled Model Inter-comparison Project Phase 5 (CMIP5); Dynamical Core Model Inter-comparison Project (DCMIP); National Climate Predictions and Projections Platforms Quantitative Evaluation of Downscaling Workshop. This presentation will introduce the project to a wider audience and demonstrate the range of tools and services currently available for use. It will also demonstrate how international collaborative efforts are essential to the success of ES-DOC.
Predicting Hurricanes with Supercomputers

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

None

2010-01-01

Hurricane Emily, formed in the Atlantic Ocean on July 10, 2005, was the strongest hurricane ever to form before August. By checking computer models against the actual path of the storm, researchers can improve hurricane prediction. In 2010, NOAA researchers were awarded 25 million processor-hours on Argonne's BlueGene/P supercomputer for the project. Read more at http://go.usa.gov/OLh
Traffic Predictive Control: Case Study and Evaluation

DOT National Transportation Integrated Search

2017-06-26

This project developed a quantile regression method for predicting future traffic flow at a signalized intersection by combining both historical and real-time data. The algorithm exploits nonlinear correlations in historical measurements and efficien...
A statistical rain attenuation prediction model with application to the advanced communication technology satellite project. 3: A stochastic rain fade control algorithm for satellite link power via non linear Markow filtering theory

NASA Technical Reports Server (NTRS)

Manning, Robert M.

1991-01-01

The dynamic and composite nature of propagation impairments that are incurred on Earth-space communications links at frequencies in and above 30/20 GHz Ka band, i.e., rain attenuation, cloud and/or clear air scintillation, etc., combined with the need to counter such degradations after the small link margins have been exceeded, necessitate the use of dynamic statistical identification and prediction processing of the fading signal in order to optimally estimate and predict the levels of each of the deleterious attenuation components. Such requirements are being met in NASA's Advanced Communications Technology Satellite (ACTS) Project by the implementation of optimal processing schemes derived through the use of the Rain Attenuation Prediction Model and nonlinear Markov filtering theory.
Forecasting outdoor recreation participation: a cohort-component projection model for Illinois residents

Treesearch

John F. Dwyer

1995-01-01

Population projections for Illinois predicts lower growth, an older population, and increased racial diversity. If percent of the population participating in outdoor recreation activities by age and race remains at present levels, cohort-component projection models suggest that with projected changes in the population between 1990 and 2025, the number of Illinois...
Limits on determining the skill of North Atlantic Ocean decadal predictions.

PubMed

Menary, Matthew B; Hermanson, Leon

2018-04-27

The northern North Atlantic is important globally both through its impact on the Atlantic Meridional Overturning Circulation (AMOC) and through widespread atmospheric teleconnections. The region has been shown to be potentially predictable a decade ahead with the skill of decadal predictions assessed against reanalyses of the ocean state. Here, we show that the prediction skill in this region is strongly dependent on the choice of reanalysis used for validation, and describe the causes. Multiannual skill in key metrics such as Labrador Sea density and the AMOC depends on more than simply the choice of the prediction model. Instead, this skill is related to the similarity between the nature of interannual density variability in the underlying climate model and the chosen reanalysis. The climate models used in these decadal predictions are also used in climate projections, which raises questions about the sensitivity of these projections to the models' innate North Atlantic density variability.
Improving Software Engineering on NASA Projects

NASA Technical Reports Server (NTRS)

Crumbley, Tim; Kelly, John C.

2010-01-01

Software Engineering Initiative: Reduces risk of software failure -Increases mission safety. More predictable software cost estimates and delivery schedules. Smarter buyer of contracted out software. More defects found and removed earlier. Reduces duplication of efforts between projects. Increases ability to meet the challenges of evolving software technology.
Projected changes in Malawi's growing season

NASA Astrophysics Data System (ADS)

Vizy, Edward K.; Cook, Kerry H.; Chimphamba, James; McCusker, Brent

2015-09-01

Regional climate model projections at 30-km resolution are used to predict future mid-century and late-century growing season changes over Malawi due to global warming under the Representative Concentration Pathway 8.5 business-as-usual emissions forcing scenario. Three different methods for estimating growing season characteristics are applied and evaluated. All three methods yield reasonable growing season length, onset, and demise date estimates over Malawi given the wide range of uncertainty of the observations. The projections indicate the likelihood for a shorter growing season in the future over Malawi south of 13.5°S. At mid-century the growing season length is predicted to be 20-40 % (20-55 days) shorter over the southernmost districts and 5-20 % (5-30 days) shorter over the central districts. By late-century the length is predicted to be 25-55 % (20-70 days) shorter with significant differences extending into northern Malawi. The shorter growing season is primarily associated with an earlier demise date, as no significant change in the onset date is predicted. Analysis of the regional circulation and horizontal moisture flux transport indicates that the earlier demise is associated with an intensification of the thermal low over the Kalahari Desert to the south and west of Malawi and an expansion of the mid-tropospheric Kalahari anticyclone over southern Africa. The stronger thermal low/anticyclone enhances the moisture flux divergence over Malawi suppressing the convective activity at the end of the wet season.
Projected climate-induced faunal change in the Western Hemisphere

USGS Publications Warehouse

Lawler, J.J.; Shafer, S.L.; White, D.; Kareiva, P.; Maurer, E.P.; Blaustein, A.R.; Bartlein, P.J.

2009-01-01

Climate change is predicted to be one of the greatest drivers of ecological change in the coming century. Increases in temperature over the last century have clearly been linked to shifts in species distributions. Given the magnitude of projected future climatic changes, we can expect even larger range shifts in the coming century. These changes will, in turn, alter ecological communities and the functioning of ecosystems. Despite the seriousness of predicted climate change, the uncertainty in climate-change projections makes it difficult for conservation managers and planners to proactively respond to climate stresses. To address one aspect of this uncertainty, we identified predictions of faunal change for which a high level of consensus was exhibited by different climate models. Specifically, we assessed the potential effects of 30 coupled atmosphere-ocean general circulation model (AOGCM) future-climate simulations on the geographic ranges of 2954 species of birds, mammals, and amphibians in the Western Hemisphere. Eighty percent of the climate projections based on a relatively low greenhouse-gas emissions scenario result in the local loss of at least 10% of the vertebrate fauna over much of North and South America. The largest changes in fauna are predicted for the tundra, Central America, and the Andes Mountains where, assuming no dispersal constraints, specific areas are likely to experience over 90% turnover, so that faunal distributions in the future will bear little resemblance to those of today. ?? 2009 by the Ecological Society of America.
Pathogenesis and Prediction of Future Rheumatoid Arthritis

DTIC Science & Technology

2016-10-01

AWARD NUMBER: W81XWH-13-1-0408 TITLE: Pathogenesis and Prediction of Future Rheumatoid Arthritis PRINCIPAL INVESTIGATOR: Kevin D. Deane, MD/PhD...SUBTITLE 5a. CONTRACT NUMBER Pathogenesis and Prediction of Rheumatoid Arthritis 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT...preclinical period of rheumatoid arthritis (RA) development that is characterized by abnormalities of the immune system prior to the onset of the
Predicting Mountainous Watershed Biogeochemical Dynamics, Including Response to Droughts and Early Snowmelt

NASA Astrophysics Data System (ADS)

Hubbard, S. S.; Williams, K. H.; Long, P.; Agarwal, D.; Banfield, J. F.; Beller, H. R.; Bouskill, N.; Brodie, E.; Maxwell, R. M.; Nico, P. S.; Steefel, C. I.; Steltzer, H.; Tokunaga, T. K.; Wainwright, H. M.

2016-12-01

Climate change, extreme weather, land-use change, and other perturbations are significantly reshaping interactions with in watersheds throughout the world. While mountainous watersheds are recognized as the water towers for the world, hydrological processes in watersheds also mediate biogeochemical processes that support all terrestrial life. Developing predictive understanding of watershed hydrological and biogeochemical functioning is challenging, as complex interactions occurring within a heterogeneous watershed can lead to a cascade of effects on downstream water availability and quality. Although these interactions can have significant implications for energy production, agriculture, water quality, and other benefits valued by society, uncertainty associated with predicting watershed function is high. The Watershed Function project aims to substantially reduce this uncertainty through developing a predictive understanding of how mountainous watersheds retain and release downgradient water, nutrients, carbon, and metals. In particular, the project is exploring how early snowmelt, drought, and other disturbances will influence mountainous watershed dynamics at seasonal to decadal timescales. The Watershed Function project is being carried out in a headwater mountainous catchment of the Upper Colorado River Basin, within a watershed characterized by significant gradients in elevation, vegetation and hydrogeology. A system-within system project perspective posits that the integrated watershed response to disturbances can be adequately predicted through consideration of interactions and feedbacks occurring within a limited number of subsystems, each having distinct vegetation-subsurface biogeochemical-hydrological characteristics. A key technological goal is the development of scale-adaptive simulation capabilities that can incorporate genomic information where and when it is useful for predicting the overall watershed response to disturbance. Through developing
Predictive Models and Computational Toxicology (II IBAMTOX)

EPA Science Inventory

EPA’s ‘virtual embryo’ project is building an integrative systems biology framework for predictive models of developmental toxicity. One schema involves a knowledge-driven adverse outcome pathway (AOP) framework utilizing information from public databases, standardized ontologies...
TRIM timber projections: an evaluation based on forest inventory measurements.

Treesearch

John R. Mills

1989-01-01

Two consecutive timberland inventories collected from permanent plots in the natural pine type in North Carolina were used to evaluate the timber resource inventory model (TRIM). This study compares model predictions with field measurements and examines the effect of inventory data aggregation on the accuracy of projections. Projections were repeated for two geographic...
Potential Seasonal Predictability of Water Cycle in Observations and Reanalysis

NASA Astrophysics Data System (ADS)

Feng, X.; Houser, P.

2012-12-01

Identification of predictability of water cycle variability is crucial for climate prediction, water resources availability, ecosystem management and hazard mitigation. An analysis that can assess the potential skill in seasonal prediction was proposed by the authors, named as analysis of covariance (ANOCOVA). This method tests whether interannual variability of seasonal means exceeds that due to weather noise under the null hypothesis that seasonal means are identical every year. It has the advantage of taking into account autocorrelation structure in the daily time series but also accounting for the uncertainty of the estimated parameters in the significance test. During the past several years, multiple reanalysis datasets have become available for studying climate variability and understanding climate system. We are motivated to compare the potential predictability of water cycle variation from different reanalysis datasets against observations using the newly proposed ANOCOVA method. The selected eight reanalyses include the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP/NCAR) 40-year Reanalysis Project (NNRP), the National Centers for Environmental Prediction-Department of Energy (NCEP/DOE) Reanalysis Project (NDRP), the European Centre for Medium-Range Weather Forecasts (ECMWF) 40-year Reanalysis, The Japan Meteorological Agency 25-year Reanalysis Project (JRA25), the ECMWF) Interim Reanalysis (ERAINT), the NCEP Climate Forecast System Reanalysis (CFSR), the National Aeronautics and Space Administration (NASA) Modern-Era Retrospective Analysis for Research and Applications (MERRA), and the National Oceanic and Atmospheric Administration-Cooperative Institute for Research in Environmental Sciences (NOAA/CIRES) 20th Century Reanalysis Version 2 (20CR). For key water cycle components, precipitation and evaporation, all reanalyses consistently show high fraction of predictable variance in the tropics, low
Inhibition, Updating Working Memory, and Shifting Predict Reading Disability Symptoms in a Hybrid Model: Project KIDS.

PubMed

Daucourt, Mia C; Schatschneider, Christopher; Connor, Carol M; Al Otaiba, Stephanie; Hart, Sara A

2018-01-01

Recent achievement research suggests that executive function (EF), a set of regulatory processes that control both thought and action necessary for goal-directed behavior, is related to typical and atypical reading performance. This project examines the relation of EF, as measured by its components, Inhibition, Updating Working Memory, and Shifting, with a hybrid model of reading disability (RD). Our sample included 420 children who participated in a broader intervention project when they were in KG-third grade (age M = 6.63 years, SD = 1.04 years, range = 4.79-10.40 years). At the time their EF was assessed, using a parent-report Behavior Rating Inventory of Executive Function (BRIEF), they had a mean age of 13.21 years ( SD = 1.54 years; range = 10.47-16.63 years). The hybrid model of RD was operationalized as a composite consisting of four symptoms, and set so that any child could have any one, any two, any three, any four, or none of the symptoms included in the hybrid model. The four symptoms include low word reading achievement, unexpected low word reading achievement, poorer reading comprehension compared to listening comprehension, and dual-discrepancy response-to-intervention, requiring both low achievement and low growth in word reading. The results of our multilevel ordinal logistic regression analyses showed a significant relation between all three components of EF (Inhibition, Updating Working Memory, and Shifting) and the hybrid model of RD, and that the strength of EF's predictive power for RD classification was the highest when RD was modeled as having at least one or more symptoms. Importantly, the chances of being classified as having RD increased as EF performance worsened and decreased as EF performance improved. The question of whether any one EF component would emerge as a superior predictor was also examined and results showed that Inhibition, Updating Working Memory, and Shifting were equally valuable as predictors of the hybrid model of RD
Comparisons of modeled height predictions to ocular height estimates

Treesearch

W.A. Bechtold; S.J. Zarnoch; W.G. Burkman

1998-01-01

Equations used by USDA Forest Service Forest Inventory and Analysis projects to predict individual tree heights on the basis of species and d.b.h. were improved by the addition of mean overstory height. However, ocular estimates of total height by field crews were more accurate than the statistically improved models, especially for hardwood species. Height predictions...
Human genetics: international projects and personalized medicine.

PubMed

Apellaniz-Ruiz, Maria; Gallego, Cristina; Ruiz-Pinto, Sara; Carracedo, Angel; Rodríguez-Antona, Cristina

2016-03-01

In this article, we present the progress driven by the recent technological advances and new revolutionary massive sequencing technologies in the field of human genetics. We discuss this knowledge in relation with drug response prediction, from the germline genetic variation compiled in the 1000 Genomes Project or in the Genotype-Tissue Expression project, to the phenome-genome archives, the international cancer projects, such as The Cancer Genome Atlas or the International Cancer Genome Consortium, and the epigenetic variation and its influence in gene expression, including the regulation of drug metabolism. This review is based on the lectures presented by the speakers of the Symposium "Human Genetics: International Projects & New Technologies" from the VII Conference of the Spanish Pharmacogenetics and Pharmacogenomics Society, held on the 20th and 21st of April 2015.
The Organization and Objectives of the Project. The Yugoslav Contrastive Analysis Project, Serbo-Croatian and English.

ERIC Educational Resources Information Center

Filipovic, Rudolf

This pamphlet describes the organization and objectives of the Yugoslav Serbo-Croatian-English contrastive analysis project. Interference is common in language contact of any sort, and it is assumed that interference can be predicted on the basis of contrastive analysis, and that the results thus obtained can be used in planning better teaching…
Predicting stroke through genetic risk functions: The CHARGE risk score project

PubMed Central

Ibrahim-Verbaas, Carla A; Fornage, Myriam; Bis, Joshua C; Choi, Seung Hoan; Psaty, Bruce M; Meigs, James B; Rao, Madhu; Nalls, Mike; Fontes, Joao D; O’Donnell, Christopher J.; Kathiresan, Sekar; Ehret, Georg B.; Fox, Caroline S; Malik, Rainer; Dichgans, Martin; Schmidt, Helena; Lahti, Jari; Heckbert, Susan R; Lumley, Thomas; Rice, Kenneth; Rotter, Jerome I; Taylor, Kent D; Folsom, Aaron R; Boerwinkle, Eric; Rosamond, Wayne D; Shahar, Eyal; Gottesman, Rebecca F.; Koudstaal, Peter J; Amin, Najaf; Wieberdink, Renske G.; Dehghan, Abbas; Hofman, Albert; Uitterlinden, André G; DeStefano, Anita L.; Debette, Stephanie; Xue, Luting; Beiser, Alexa; Wolf, Philip A.; DeCarli, Charles; Ikram, M. Arfan; Seshadri, Sudha; Mosley, Thomas H; Longstreth, WT; van Duijn, Cornelia M; Launer, Lenore J

2014-01-01

Background and Purpose Beyond the Framingham Stroke Risk Score (FSRS), prediction of future stroke may improve with a genetic risk score (GRS) based on Single nucleotide polymorphisms (SNPs) associated with stroke and its risk factors. Methods The study includes four population-based cohorts with 2,047 first incident strokes from 22,720 initially stroke-free European origin participants aged 55 years and older, who were followed for up to 20 years. GRS were constructed with 324 SNPs implicated in stroke and 9 risk factors. The association of the GRS to first incident stroke was tested using Cox regression; the GRS predictive properties were assessed with Area under the curve (AUC) statistics comparing the GRS to age sex, and FSRS models, and with reclassification statistics. These analyses were performed per cohort and in a meta-analysis of pooled data. Replication was sought in a case-control study of ischemic stroke (IS). Results In the meta-analysis, adding the GRS to the FSRS, age and sex model resulted in a significant improvement in discrimination (All stroke: Δjoint AUC =0.016, p-value=2.3*10-6; IS: Δ joint AUC =0.021, p-value=3.7*10−7), although the overall AUC remained low. In all studies there was a highly significantly improved net reclassification index (p-values <10−4). Conclusions The SNPs associated with stroke and its risk factors result only in a small improvement in prediction of future stroke compared to the classical epidemiological risk factors for stroke. PMID:24436238
Predicting stroke through genetic risk functions: the CHARGE Risk Score Project.

PubMed

Ibrahim-Verbaas, Carla A; Fornage, Myriam; Bis, Joshua C; Choi, Seung Hoan; Psaty, Bruce M; Meigs, James B; Rao, Madhu; Nalls, Mike; Fontes, Joao D; O'Donnell, Christopher J; Kathiresan, Sekar; Ehret, Georg B; Fox, Caroline S; Malik, Rainer; Dichgans, Martin; Schmidt, Helena; Lahti, Jari; Heckbert, Susan R; Lumley, Thomas; Rice, Kenneth; Rotter, Jerome I; Taylor, Kent D; Folsom, Aaron R; Boerwinkle, Eric; Rosamond, Wayne D; Shahar, Eyal; Gottesman, Rebecca F; Koudstaal, Peter J; Amin, Najaf; Wieberdink, Renske G; Dehghan, Abbas; Hofman, Albert; Uitterlinden, André G; Destefano, Anita L; Debette, Stephanie; Xue, Luting; Beiser, Alexa; Wolf, Philip A; Decarli, Charles; Ikram, M Arfan; Seshadri, Sudha; Mosley, Thomas H; Longstreth, W T; van Duijn, Cornelia M; Launer, Lenore J

2014-02-01

Beyond the Framingham Stroke Risk Score, prediction of future stroke may improve with a genetic risk score (GRS) based on single-nucleotide polymorphisms associated with stroke and its risk factors. The study includes 4 population-based cohorts with 2047 first incident strokes from 22,720 initially stroke-free European origin participants aged ≥55 years, who were followed for up to 20 years. GRSs were constructed with 324 single-nucleotide polymorphisms implicated in stroke and 9 risk factors. The association of the GRS to first incident stroke was tested using Cox regression; the GRS predictive properties were assessed with area under the curve statistics comparing the GRS with age and sex, Framingham Stroke Risk Score models, and reclassification statistics. These analyses were performed per cohort and in a meta-analysis of pooled data. Replication was sought in a case-control study of ischemic stroke. In the meta-analysis, adding the GRS to the Framingham Stroke Risk Score, age and sex model resulted in a significant improvement in discrimination (all stroke: Δjoint area under the curve=0.016, P=2.3×10(-6); ischemic stroke: Δjoint area under the curve=0.021, P=3.7×10(-7)), although the overall area under the curve remained low. In all the studies, there was a highly significantly improved net reclassification index (P<10(-4)). The single-nucleotide polymorphisms associated with stroke and its risk factors result only in a small improvement in prediction of future stroke compared with the classical epidemiological risk factors for stroke.

Projecting technology change to improve space technology planning and systems management

NASA Astrophysics Data System (ADS)

Walk, Steven Robert

2011-04-01

Projecting technology performance evolution has been improving over the years. Reliable quantitative forecasting methods have been developed that project the growth, diffusion, and performance of technology in time, including projecting technology substitutions, saturation levels, and performance improvements. These forecasts can be applied at the early stages of space technology planning to better predict available future technology performance, assure the successful selection of technology, and improve technology systems management strategy. Often what is published as a technology forecast is simply scenario planning, usually made by extrapolating current trends into the future, with perhaps some subjective insight added. Typically, the accuracy of such predictions falls rapidly with distance in time. Quantitative technology forecasting (QTF), on the other hand, includes the study of historic data to identify one of or a combination of several recognized universal technology diffusion or substitution patterns. In the same manner that quantitative models of physical phenomena provide excellent predictions of system behavior, so do QTF models provide reliable technological performance trajectories. In practice, a quantitative technology forecast is completed to ascertain with confidence when the projected performance of a technology or system of technologies will occur. Such projections provide reliable time-referenced information when considering cost and performance trade-offs in maintaining, replacing, or migrating a technology, component, or system. This paper introduces various quantitative technology forecasting techniques and illustrates their practical application in space technology and technology systems management.
The Earth System Documentation (ES-DOC) project

NASA Astrophysics Data System (ADS)

Murphy, S.; Greenslade, M. A.; Treshansky, A.; DeLuca, C.; Guilyardi, E.; Denvil, S.

2013-12-01

Earth System Documentation (ES-DOC) is an international project supplying high quality tools and services in support of Earth system documentation creation, analysis and dissemination. It is nurturing a sustainable standards based documentation ecosystem that aims to become an integral part of the next generation of exa-scale dataset archives. ES-DOC leverages open source software, and applies a software development methodology that places end-user narratives at the heart of all it does. ES-DOC has initially focused upon nurturing the Earth System Model (ESM) documentation eco-system. Within this context ES-DOC leverages the emerging Common Information Model (CIM) metadata standard, which has supported the following projects: ** Coupled Model Inter-comparison Project Phase 5 (CMIP5); ** Dynamical Core Model Inter-comparison Project (DCMIP-2012); ** National Climate Predictions and Projections Platforms (NCPP) Quantitative Evaluation of Downscaling Workshop (QED-2013). This presentation will introduce the project to a wider audience and will demonstrate the current production level capabilities of the eco-system: ** An ESM documentation Viewer embeddable into any website; ** An ESM Questionnaire configurable on a project by project basis; ** An ESM comparison tool reusable across projects; ** An ESM visualization tool reusable across projects; ** A search engine for speedily accessing published documentation; ** Libraries for streamlining document creation, validation and publishing pipelines.
Feasibility of developing LSI microcircuit reliability prediction models

NASA Technical Reports Server (NTRS)

Ryerson, C. M.

1972-01-01

In the proposed modeling approach, when any of the essential key factors are not known initially, they can be approximated in various ways with a known impact on the accuracy of the final predictions. For example, on any program where reliability predictions are started at interim states of project completion, a-priori approximate estimates of the key factors are established for making preliminary predictions. Later these are refined for greater accuracy as subsequent program information of a more definitive nature becomes available. Specific steps to develop, validate and verify these new models are described.
Prediction of embankment settlement over soft soils.

DOT National Transportation Integrated Search

2009-06-01

The objective of this project was to review and verify the current design procedures used by TxDOT : to estimate the total and rate of consolidation settlement in embankments constructed on soft soils. Methods : to improve the settlement predictions ...
A process dissociation approach to objective-projective test score interrelationships.

PubMed

Bornstein, Robert F

2002-02-01

Even when self-report and projective measures of a given trait or motive both predict theoretically related features of behavior, scores on the 2 tests correlate modestly with each other. This article describes a process dissociation framework for personality assessment, derived from research on implicit memory and learning, which can resolve these ostensibly conflicting results. Research on interpersonal dependency is used to illustrate 3 key steps in the process dissociation approach: (a) converging behavioral predictions, (b) modest test score intercorrelations, and (c) delineation of variables that differentially affect self-report and projective test scores. Implications of the process dissociation framework for personality assessment and test development are discussed.
Evaluation of numerical weather predictions performed in the context of the project DAPHNE

NASA Astrophysics Data System (ADS)

Tegoulias, Ioannis; Pytharoulis, Ioannis; Bampzelis, Dimitris; Karacostas, Theodore

2014-05-01

The region of Thessaly in central Greece is one of the main areas of agricultural production in Greece. Severe weather phenomena affect the agricultural production in this region with adverse effects for farmers and the national economy. For this reason the project DAPHNE aims at tackling the problem of drought by means of weather modification through the development of the necessary tools to support the application of a rainfall enhancement program. In the present study the numerical weather prediction system WRF-ARW is used, in order to assess its ability to represent extreme weather phenomena in the region of Thessaly. WRF is integrated in three domains covering Europe, Eastern Mediterranean and Central-Northern Greece (Thessaly and a large part of Macedonia) using telescoping nesting with grid spacing of 15km, 5km and 1.667km, respectively. The cases examined span throughout the transitional and warm period (April to September) of the years 2008 to 2013, including days with thunderstorm activity. Model results are evaluated against all available surface observations and radar products, taking into account the spatial characteristics and intensity of the storms. Preliminary results indicate a good level of agreement between the simulated and observed fields as far as the standard parameters (such as temperature, humidity and precipitation) are concerned. Moreover, the model generally exhibits a potential to represent the occurrence of the convective activity, but not its exact spatiotemporal characteristics. Acknowledgements This research work has been co-financed by the European Union (European Regional Development Fund) and Greek national funds, through the action "COOPERATION 2011: Partnerships of Production and Research Institutions in Focused Research and Technology Sectors" (contract number 11SYN_8_1088 - DAPHNE) in the framework of the operational programme "Competitiveness and Entrepreneurship" and Regions in Transition (OPC II, NSRF 2007-2013)
Variability and Predictability of Land-Atmosphere Interactions: Observational and Modeling Studies

NASA Technical Reports Server (NTRS)

Roads, John; Oglesby, Robert; Marshall, Susan; Robertson, Franklin R.

2002-01-01

The overall goal of this project is to increase our understanding of seasonal to interannual variability and predictability of atmosphere-land interactions. The project objectives are to: 1. Document the low frequency variability in land surface features and associated water and energy cycles from general circulation models (GCMs), observations and reanalysis products. 2. Determine what relatively wet and dry years have in common on a region-by-region basis and then examine the physical mechanisms that may account for a significant portion of the variability. 3. Develop GCM experiments to examine the hypothesis that better knowledge of the land surface enhances long range predictability. This investigation is aimed at evaluating and predicting seasonal to interannual variability for selected regions emphasizing the role of land-atmosphere interactions. Of particular interest are the relationships between large, regional and local scales and how they interact to account for seasonal and interannual variability, including extreme events such as droughts and floods. North and South America, including the Global Energy and Water Cycle Experiment Continental International Project (GEWEX GCIP), MacKenzie, and LBA basins, are currently being emphasized. We plan to ultimately generalize and synthesize to other land regions across the globe, especially those pertinent to other GEWEX projects.
Repurposing High-Throughput Image Assays Enables Biological Activity Prediction for Drug Discovery.

PubMed

Simm, Jaak; Klambauer, Günter; Arany, Adam; Steijaert, Marvin; Wegner, Jörg Kurt; Gustin, Emmanuel; Chupakhin, Vladimir; Chong, Yolanda T; Vialard, Jorge; Buijnsters, Peter; Velter, Ingrid; Vapirev, Alexander; Singh, Shantanu; Carpenter, Anne E; Wuyts, Roel; Hochreiter, Sepp; Moreau, Yves; Ceulemans, Hugo

2018-05-17

In both academia and the pharmaceutical industry, large-scale assays for drug discovery are expensive and often impractical, particularly for the increasingly important physiologically relevant model systems that require primary cells, organoids, whole organisms, or expensive or rare reagents. We hypothesized that data from a single high-throughput imaging assay can be repurposed to predict the biological activity of compounds in other assays, even those targeting alternate pathways or biological processes. Indeed, quantitative information extracted from a three-channel microscopy-based screen for glucocorticoid receptor translocation was able to predict assay-specific biological activity in two ongoing drug discovery projects. In these projects, repurposing increased hit rates by 50- to 250-fold over that of the initial project assays while increasing the chemical structure diversity of the hits. Our results suggest that data from high-content screens are a rich source of information that can be used to predict and replace customized biological assays. Copyright © 2018 Elsevier Ltd. All rights reserved.
Navy MANTECH 2008 Project Book

DTIC Science & Technology

2008-01-01

work center. The project team kept statistics on the actual operations in that work center and assessed the fidelity of those operations with...base, a power- ful analytic and predictive capability, and a database of validated industry best practices. The BMPCOE- developed Collaborative Work ...Today, the EMPF operates as a national electronics manufacturing COE focused on the development , application and
ESIF Call for High-Impact Integrated Projects | Energy Systems Integration

Science.gov Websites

Integrated Projects As a U.S. Department of Energy user facility, the Energy Systems Integration Facility concepts, tools, and technologies needed to measure, analyze, predict, protect, and control the grid of the Facility | NREL ESIF Call for High-Impact Integrated Projects ESIF Call for High-Impact
Predicting subcontractor performance using web-based Evolutionary Fuzzy Neural Networks.

PubMed

Ko, Chien-Ho

2013-01-01

Subcontractor performance directly affects project success. The use of inappropriate subcontractors may result in individual work delays, cost overruns, and quality defects throughout the project. This study develops web-based Evolutionary Fuzzy Neural Networks (EFNNs) to predict subcontractor performance. EFNNs are a fusion of Genetic Algorithms (GAs), Fuzzy Logic (FL), and Neural Networks (NNs). FL is primarily used to mimic high level of decision-making processes and deal with uncertainty in the construction industry. NNs are used to identify the association between previous performance and future status when predicting subcontractor performance. GAs are optimizing parameters required in FL and NNs. EFNNs encode FL and NNs using floating numbers to shorten the length of a string. A multi-cut-point crossover operator is used to explore the parameter and retain solution legality. Finally, the applicability of the proposed EFNNs is validated using real subcontractors. The EFNNs are evolved using 22 historical patterns and tested using 12 unseen cases. Application results show that the proposed EFNNs surpass FL and NNs in predicting subcontractor performance. The proposed approach improves prediction accuracy and reduces the effort required to predict subcontractor performance, providing field operators with web-based remote access to a reliable, scientific prediction mechanism.
Predicting Subcontractor Performance Using Web-Based Evolutionary Fuzzy Neural Networks

PubMed Central

2013-01-01

Subcontractor performance directly affects project success. The use of inappropriate subcontractors may result in individual work delays, cost overruns, and quality defects throughout the project. This study develops web-based Evolutionary Fuzzy Neural Networks (EFNNs) to predict subcontractor performance. EFNNs are a fusion of Genetic Algorithms (GAs), Fuzzy Logic (FL), and Neural Networks (NNs). FL is primarily used to mimic high level of decision-making processes and deal with uncertainty in the construction industry. NNs are used to identify the association between previous performance and future status when predicting subcontractor performance. GAs are optimizing parameters required in FL and NNs. EFNNs encode FL and NNs using floating numbers to shorten the length of a string. A multi-cut-point crossover operator is used to explore the parameter and retain solution legality. Finally, the applicability of the proposed EFNNs is validated using real subcontractors. The EFNNs are evolved using 22 historical patterns and tested using 12 unseen cases. Application results show that the proposed EFNNs surpass FL and NNs in predicting subcontractor performance. The proposed approach improves prediction accuracy and reduces the effort required to predict subcontractor performance, providing field operators with web-based remote access to a reliable, scientific prediction mechanism. PMID:23864830
[Earth Science Technology Office's Computational Technologies Project

NASA Technical Reports Server (NTRS)

Fischer, James (Technical Monitor); Merkey, Phillip

2005-01-01

This grant supported the effort to characterize the problem domain of the Earth Science Technology Office's Computational Technologies Project, to engage the Beowulf Cluster Computing Community as well as the High Performance Computing Research Community so that we can predict the applicability of said technologies to the scientific community represented by the CT project and formulate long term strategies to provide the computational resources necessary to attain the anticipated scientific objectives of the CT project. Specifically, the goal of the evaluation effort is to use the information gathered over the course of the Round-3 investigations to quantify the trends in scientific expectations, the algorithmic requirements and capabilities of high-performance computers to satisfy this anticipated need.
The EMCC / DARPA Massively Parallel Electromagnetic Scattering Project

NASA Technical Reports Server (NTRS)

Woo, Alex C.; Hill, Kueichien C.

1996-01-01

The Electromagnetic Code Consortium (EMCC) was sponsored by the Advanced Research Program Agency (ARPA) to demonstrate the effectiveness of massively parallel computing in large scale radar signature predictions. The EMCC/ARPA project consisted of three parts.
Projecting long term medical spending growth.

PubMed

Borger, Christine; Rutherford, Thomas F; Won, Gregory Y

2008-01-01

We present a dynamic general equilibrium model of the U.S. economy and the medical sector in which the adoption of new medical treatments is endogenous and the demand for medical services is conditional on the state of technology. We use this model to prepare 75-year medical spending forecasts and a projection of the Medicare actuarial balance, and we compare our results to those obtained from a method that has been used by government actuaries. Our baseline forecast predicts slower health spending growth in the long run and a lower Medicare actuarial deficit relative to the previous projection methodology.
The Ensembl genome database project.

PubMed

Hubbard, T; Barker, D; Birney, E; Cameron, G; Chen, Y; Clark, L; Cox, T; Cuff, J; Curwen, V; Down, T; Durbin, R; Eyras, E; Gilbert, J; Hammond, M; Huminiecki, L; Kasprzyk, A; Lehvaslaiho, H; Lijnzaad, P; Melsopp, C; Mongin, E; Pettett, R; Pocock, M; Potter, S; Rust, A; Schmidt, E; Searle, S; Slater, G; Smith, J; Spooner, W; Stabenau, A; Stalker, J; Stupka, E; Ureta-Vidal, A; Vastrik, I; Clamp, M

2002-01-01

The Ensembl (http://www.ensembl.org/) database project provides a bioinformatics framework to organise biology around the sequences of large genomes. It is a comprehensive source of stable automatic annotation of the human genome sequence, with confirmed gene predictions that have been integrated with external data sources, and is available as either an interactive web site or as flat files. It is also an open source software engineering project to develop a portable system able to handle very large genomes and associated requirements from sequence analysis to data storage and visualisation. The Ensembl site is one of the leading sources of human genome sequence annotation and provided much of the analysis for publication by the international human genome project of the draft genome. The Ensembl system is being installed around the world in both companies and academic sites on machines ranging from supercomputers to laptops.
GRECOS project. The use of genetics to predict the vascular recurrence after stroke

PubMed Central

Fernández-Cadenas, Israel; Mendióroz, Maite; Giralt, Dolors; Nafria, Cristina; Garcia, Elena; Carrera, Caty; Gallego-Fabrega, Cristina; Domingues-Montanari, Sophie; Delgado, Pilar; Ribó, Marc; Castellanos, Mar; Martínez, Sergi; Freijo, Mari Mar; Jiménez-Conde, Jordi; Rubiera, Marta; Alvarez-Sabín, José; Molina, Carlos A.; Font, Maria Angels; Olivares, Marta Grau; Palomeras, Ernest; de la Ossa, Natalia Perez; Martinez-Zabaleta, Maite; Masjuan, Jaime; Moniche, Francisco; Canovas, David; Piñana, Carlos; Purroy, Francisco; Cocho, Dolores; Navas, Inma; Tejero, Carlos; Aymerich, Nuria; Cullell, Natalia; Muiño, Elena; Serena, Joaquín; Rubio, Francisco; Davalos, Antoni; Roquer, Jaume; Arenillas, Juan Francisco; Martí-Fábregas, Joan; Keene, Keith; Chen, Wei-Min; Worrall, Bradford; Sale, Michele; Arboix, Adrià; Krupinski, Jerzy; Montaner, Joan

2017-01-01

Background and Purpose Vascular recurrence occurs in 11% of patients during the first year after ischemic stroke (IS) or transient ischemic attack (TIA). Clinical scores do not predict the whole vascular recurrence risk, therefore we aimed to find genetic variants associated with recurrence that might improve the clinical predictive models in IS. Methods We analyzed 256 polymorphisms from 115 candidate genes in three patient cohorts comprising 4,482 IS or TIA patients. The discovery cohort was prospectively recruited and included 1,494 patients, 6.2% of them developed a new IS during the first year of follow-up. Replication analysis was performed in 2,988 patients using SNPlex or HumanOmni1-Quad technology. We generated a predictive model using Cox regression (GRECOS score), and generated risk groups using a classification tree method. Results The analyses revealed that rs1800801 in the MGP gene (HR: 1.33, p= 9×10−03), a gene related to artery calcification, was associated with new IS during the first year of follow-up. This polymorphism was replicated in a Spanish cohort (n=1.305), however it was not significantly associated in a North American cohort (n=1.683). The GRECOS score predicted new IS (p= 3.2×10−09) and could classify patients, from low risk of stroke recurrence (1.9%) to high risk (12.6%). Moreover, the addition of genetic risk factors to the GRECOS score improves the prediction compared to previous SPI-II score (p=0.03). Conclusions The use of genetics could be useful to estimate vascular recurrence risk after IS. Genetic variability in the MGP gene was associated with vascular recurrence in the Spanish population. PMID:28411264
Aqua/Aura Updated Inclination Adjust Maneuver Performance Prediction Model

NASA Technical Reports Server (NTRS)

Boone, Spencer

2017-01-01

This presentation will discuss the updated Inclination Adjust Maneuver (IAM) performance prediction model that was developed for Aqua and Aura following the 2017 IAM series. This updated model uses statistical regression methods to identify potential long-term trends in maneuver parameters, yielding improved predictions when re-planning past maneuvers. The presentation has been reviewed and approved by Eric Moyer, ESMO Deputy Project Manager.
Advanced Ground Systems Maintenance Prognostics Project

NASA Technical Reports Server (NTRS)

Perotti, Jose M.

2015-01-01

The project implements prognostics capabilities to predict when a component system or subsystem will no longer meet desired functional or performance criteria, called the end of life. The capability also provides an assessment of the remaining useful life of a hardware component. The project enables the delivery of system health advisories to ground system operators. This project will use modeling techniques and algorithms to assess components' health andpredict remaining life for such components. The prognostics capability being developed will beused:during the design phase and during pre/post operations to conduct planning and analysis ofsystem design, maintenance & logistics plans, and system/mission operations plansduring real-time operations to monitor changes to components' health and assess their impacton operations.This capability will be interfaced to Ground Operations' command and control system as a part ofthe AGSM project to help assure system availability and mission success. The initial modelingeffort for this capability will be developed for Liquid Oxygen ground loading applications.
Agent-Based Simulations for Project Management

NASA Technical Reports Server (NTRS)

White, J. Chris; Sholtes, Robert M.

2011-01-01

Currently, the most common approach used in project planning tools is the Critical Path Method (CPM). While this method was a great improvement over the basic Gantt chart technique being used at the time, it now suffers from three primary flaws: (1) task duration is an input, (2) productivity impacts are not considered , and (3) management corrective actions are not included. Today, computers have exceptional computational power to handle complex simulations of task e)(eculion and project management activities (e.g ., dynamically changing the number of resources assigned to a task when it is behind schedule). Through research under a Department of Defense contract, the author and the ViaSim team have developed a project simulation tool that enables more realistic cost and schedule estimates by using a resource-based model that literally turns the current duration-based CPM approach "on its head." The approach represents a fundamental paradigm shift in estimating projects, managing schedules, and reducing risk through innovative predictive techniques.

Solar prediction and intelligent machines

NASA Technical Reports Server (NTRS)

Johnson, Gordon G.

1987-01-01

The solar prediction program is aimed at reducing or eliminating the need to throughly understand the process previously developed and to still be able to produce a prediction. Substantial progress was made in identifying the procedures to be coded as well as testing some of the presently coded work. Another project involves work on developing ideas and software that should result in a machine capable of learning as well as carrying on an intelligent conversation over a wide range of topics. The underlying idea is to use primitive ideas and construct higher order ideas from these, which can then be easily related one to another.
Future Projections of ENSO and Drought (Invited)

NASA Astrophysics Data System (ADS)

Cane, M. A.

2009-12-01

Jule Charney, who was my advisor, worked very broadly - and profoundly - on climate dynamics. In this discussion of the present state of knowledge I will focus on two aspects of climate that I view as legacies of his work: our ability to project climate variability in the tropics and to project drought. (I have in mind his work with Shukla on predictability of monsoons, and Charney 1975, Dynamics of deserts and drought in the Sahel., Q. J. Roy. Meteor. Soc., 101, 193-202). First, I will consider the projections of ENSO (El Niño and Southern Oscillation) in a warming world. (My own interest in ENSO was piqued in discussions with Charney and others during the ENSO-influenced blocking events in the late 1970s; in good measure, the approach I took to understanding and modeling ENSO was based in my thesis work.) Current IPCC models differ markedly in their projections of the mean state of the equatorial Pacific, some favoring a more “El Niño- like”, some the opposite. Possible reasons for these disagreements will be considered in the light of our understanding of ENSO and tropical climate more generally. Observational data for the past century and a half will figure prominently. Droughts in the US Southwest have a strong ENSO signal, but IPCC models are fairly consistent in projecting enhanced drought there. The reasons for this will be discussed. Models are less consistent in their predictions of the future Sahel. I will discuss what is understood about causes of drought in the Sahel, which appear to point toward sea surface temperature as the controlling influence, in contrast to Charney’s albedo hypothesis.
Improved analysis tool for concrete pavement : [project summary].

DOT National Transportation Integrated Search

2017-10-01

University of Florida researchers developed 3D-FE models to more accurately predict the behavior of concrete slabs. They also followed up on a project to characterize strain gauge performance for a Florida Department of Transportation (FDOT) concrete...
A Turn-Projected State-Based Conflict Resolution Algorithm

NASA Technical Reports Server (NTRS)

Butler, Ricky W.; Lewis, Timothy A.

2013-01-01

State-based conflict detection and resolution (CD&R) algorithms detect conflicts and resolve them on the basis on current state information without the use of additional intent information from aircraft flight plans. Therefore, the prediction of the trajectory of aircraft is based solely upon the position and velocity vectors of the traffic aircraft. Most CD&R algorithms project the traffic state using only the current state vectors. However, the past state vectors can be used to make a better prediction of the future trajectory of the traffic aircraft. This paper explores the idea of using past state vectors to detect traffic turns and resolve conflicts caused by these turns using a non-linear projection of the traffic state. A new algorithm based on this idea is presented and validated using a fast-time simulator developed for this study.
Prediction of Chemical Function: Model Development and Application

EPA Science Inventory

The United States Environmental Protection Agency’s Exposure Forecaster (ExpoCast) project is developing both statistical and mechanism-based computational models for predicting exposures to thousands of chemicals, including those in consumer products. The high-throughput (...
Challenges associated with projecting urbanization-induced heat-related mortality.

PubMed

Hondula, David M; Georgescu, Matei; Balling, Robert C

2014-08-15

Maricopa County, Arizona, anchor to the fastest growing megapolitan area in the United States, is located in a hot desert climate where extreme temperatures are associated with elevated risk of mortality. Continued urbanization in the region will impact atmospheric temperatures and, as a result, potentially affect human health. We aimed to quantify the number of excess deaths attributable to heat in Maricopa County based on three future urbanization and adaptation scenarios and multiple exposure variables. Two scenarios (low and high growth projections) represent the maximum possible uncertainty range associated with urbanization in central Arizona, and a third represents the adaptation of high-albedo cool roof technology. Using a Poisson regression model, we related temperature to mortality using data spanning 1983-2007. Regional climate model simulations based on 2050-projected urbanization scenarios for Maricopa County generated distributions of temperature change, and from these predicted changes future excess heat-related mortality was estimated. Subject to urbanization scenario and exposure variable utilized, projections of heat-related mortality ranged from a decrease of 46 deaths per year (-95%) to an increase of 339 deaths per year (+359%). Projections based on minimum temperature showed the greatest increase for all expansion and adaptation scenarios and were substantially higher than those for daily mean temperature. Projections based on maximum temperature were largely associated with declining mortality. Low-growth and adaptation scenarios led to the smallest increase in predicted heat-related mortality based on mean temperature projections. Use of only one exposure variable to project future heat-related deaths may therefore be misrepresentative in terms of direction of change and magnitude of effects. Because urbanization-induced impacts can vary across the diurnal cycle, projections of heat-related health outcomes that do not consider place
Uncertainty Analysis for DAM Projects.

DTIC Science & Technology

1987-09-01

overwhelming majority of articles published on the use of statistical methodology for geotechnical engineering focus on performance predictions and design ...Results of the present study do not support the adoption of more esoteric statistical procedures except on a special case basis or in research ...influence that recommended statistical procedures might have had on the Carters Project, had they been applied during planning and design phases
A preliminary approach to quantifying the overall environmental risks posed by development projects during environmental impact assessment.

PubMed

Nicol, Sam; Chadès, Iadine

2017-01-01

Environmental impact assessment (EIA) is used globally to manage the impacts of development projects on the environment, so there is an imperative to demonstrate that it can effectively identify risky projects. However, despite the widespread use of quantitative predictive risk models in areas such as toxicology, ecosystem modelling and water quality, the use of predictive risk tools to assess the overall expected environmental impacts of major construction and development proposals is comparatively rare. A risk-based approach has many potential advantages, including improved prediction and attribution of cause and effect; sensitivity analysis; continual learning; and optimal resource allocation. In this paper we investigate the feasibility of using a Bayesian belief network (BBN) to quantify the likelihood and consequence of non-compliance of new projects based on the occurrence probabilities of a set of expert-defined features. The BBN incorporates expert knowledge and continually improves its predictions based on new data as it is collected. We use simulation to explore the trade-off between the number of data points and the prediction accuracy of the BBN, and find that the BBN could predict risk with 90% accuracy using approximately 1000 data points. Although a further pilot test with real project data is required, our results suggest that a BBN is a promising method to monitor overall risks posed by development within an existing EIA process given a modest investment in data collection.
State of Jet Noise Prediction-NASA Perspective

NASA Technical Reports Server (NTRS)

Bridges, James E.

2008-01-01

This presentation covers work primarily done under the Airport Noise Technical Challenge portion of the Supersonics Project in the Fundamental Aeronautics Program. To provide motivation and context, the presentation starts with a brief overview of the Airport Noise Technical Challenge. It then covers the state of NASA s jet noise prediction tools in empirical, RANS-based, and time-resolved categories. The empirical tools, requires seconds to provide a prediction of noise spectral directivity with an accuracy of a few dB, but only for axisymmetric configurations. The RANS-based tools are able to discern the impact of three-dimensional features, but are currently deficient in predicting noise from heated jets and jets with high speed and require hours to produce their prediction. The time-resolved codes are capable of predicting resonances and other time-dependent phenomena, but are very immature, requiring months to deliver predictions without unknown accuracies and dependabilities. In toto, however, when one considers the progress being made it appears that aeroacoustic prediction tools are soon to approach the level of sophistication and accuracy of aerodynamic engineering tools.
Prediction of earthquake ground motion at rock sites in Japan: evaluation of empirical and stochastic approaches for the PEGASOS Refinement Project

NASA Astrophysics Data System (ADS)

Edwards, Benjamin; Fäh, Donat

2017-11-01

Strong ground-motion databases used to develop ground-motion prediction equations (GMPEs) and calibrate stochastic simulation models generally include relatively few recordings on what can be considered as engineering rock or hard rock. Ground-motion predictions for such sites are therefore susceptible to uncertainty and bias, which can then propagate into site-specific hazard and risk estimates. In order to explore this issue we present a study investigating the prediction of ground motion at rock sites in Japan, where a wide range of recording-site types (from soil to very hard rock) are available for analysis. We employ two approaches: empirical GMPEs and stochastic simulations. The study is undertaken in the context of the PEGASOS Refinement Project (PRP), a Senior Seismic Hazard Analysis Committee (SSHAC) Level 4 probabilistic seismic hazard analysis of Swiss nuclear power plants, commissioned by swissnuclear and running from 2008 to 2013. In order to reduce the impact of site-to-site variability and expand the available data set for rock and hard-rock sites we adjusted Japanese ground-motion data (recorded at sites with 110 m s-1 < Vs30 < 2100 m s-1) to a common hard-rock reference. This was done through deconvolution of: (i) empirically derived amplification functions and (ii) the theoretical 1-D SH amplification between the bedrock and surface. Initial comparison of a Japanese GMPE's predictions with data recorded at rock and hard-rock sites showed systematic overestimation of ground motion. A further investigation of five global GMPEs' prediction residuals as a function of quarter-wavelength velocity showed that they all presented systematic misfit trends, leading to overestimation of median ground motions at rock and hard-rock sites in Japan. In an alternative approach, a stochastic simulation method was tested, allowing the direct incorporation of site-specific Fourier amplification information in forward simulations. We use an adjusted version of the
Species distribution models predict temporal but not spatial variation in forest growth.

PubMed

van der Maaten, Ernst; Hamann, Andreas; van der Maaten-Theunissen, Marieke; Bergsma, Aldo; Hengeveld, Geerten; van Lammeren, Ron; Mohren, Frits; Nabuurs, Gert-Jan; Terhürne, Renske; Sterck, Frank

2017-04-01

Bioclimate envelope models have been widely used to illustrate the discrepancy between current species distributions and their potential habitat under climate change. However, the realism and correct interpretation of such projections has been the subject of considerable discussion. Here, we investigate whether climate suitability predictions correlate to tree growth, measured in permanent inventory plots and inferred from tree-ring records. We use the ensemble classifier RandomForest and species occurrence data from ~200,000 inventory plots to build species distribution models for four important European forestry species: Norway spruce, Scots pine, European beech, and pedunculate oak. We then correlate climate-based habitat suitability with volume measurements from ~50-year-old stands, available from ~11,000 inventory plots. Secondly, habitat projections based on annual historical climate are compared with ring width from ~300 tree-ring chronologies. Our working hypothesis is that habitat suitability projections from species distribution models should to some degree be associated with temporal or spatial variation in these growth records. We find that the habitat projections are uncorrelated with spatial growth records (inventory plot data), but they do predict interannual variation in tree-ring width, with an average correlation of .22. Correlation coefficients for individual chronologies range from values as high as .82 or as low as -.31. We conclude that tree responses to projected climate change are highly site-specific and that local suitability of a species for reforestation is difficult to predict. That said, projected increase or decrease in climatic suitability may be interpreted as an average expectation of increased or reduced growth over larger geographic scales.
Study Predicts Dramatic Shifts in Enrollments.

ERIC Educational Resources Information Center

Evangelauf, Jean

1991-01-01

A new study detailing demographic shifts in the college-age population predicts growth in minority high school graduates and shrinkage or maintenance of White graduation rates. The report is the first to provide state-by-state figures on actual and projected graduates from 1986 through 1995 by racial and ethnic group. (MSE)
EGASP: the human ENCODE Genome Annotation Assessment Project

PubMed Central

Guigó, Roderic; Flicek, Paul; Abril, Josep F; Reymond, Alexandre; Lagarde, Julien; Denoeud, France; Antonarakis, Stylianos; Ashburner, Michael; Bajic, Vladimir B; Birney, Ewan; Castelo, Robert; Eyras, Eduardo; Ucla, Catherine; Gingeras, Thomas R; Harrow, Jennifer; Hubbard, Tim; Lewis, Suzanna E; Reese, Martin G

2006-01-01

Background We present the results of EGASP, a community experiment to assess the state-of-the-art in genome annotation within the ENCODE regions, which span 1% of the human genome sequence. The experiment had two major goals: the assessment of the accuracy of computational methods to predict protein coding genes; and the overall assessment of the completeness of the current human genome annotations as represented in the ENCODE regions. For the computational prediction assessment, eighteen groups contributed gene predictions. We evaluated these submissions against each other based on a 'reference set' of annotations generated as part of the GENCODE project. These annotations were not available to the prediction groups prior to the submission deadline, so that their predictions were blind and an external advisory committee could perform a fair assessment. Results The best methods had at least one gene transcript correctly predicted for close to 70% of the annotated genes. Nevertheless, the multiple transcript accuracy, taking into account alternative splicing, reached only approximately 40% to 50% accuracy. At the coding nucleotide level, the best programs reached an accuracy of 90% in both sensitivity and specificity. Programs relying on mRNA and protein sequences were the most accurate in reproducing the manually curated annotations. Experimental validation shows that only a very small percentage (3.2%) of the selected 221 computationally predicted exons outside of the existing annotation could be verified. Conclusion This is the first such experiment in human DNA, and we have followed the standards established in a similar experiment, GASP1, in Drosophila melanogaster. We believe the results presented here contribute to the value of ongoing large-scale annotation projects and should guide further experimental methods when being scaled up to the entire human genome sequence. PMID:16925836
Transformational leadership, initiating structure, and substitutes for leadership: a longitudinal study of research and development project team performance.

PubMed

Keller, Robert T

2006-01-01

Transformational leadership, initiating structure, and selected substitutes for leadership were studied as longitudinal predictors of performance in 118 research and development (R&D) project teams from 5 firms. As hypothesized, transformational leadership predicted 1-year-later technical quality, schedule performance, and cost performance and 5-year-later profitability and speed to market. Initiating structure predicted all the performance measures. The substitutes of subordinate ability and an intrinsically satisfying task each predicted technical quality and profitability, and ability predicted speed to market. Moderator effects for type of R&D work were hypothesized and found whereby transformational leadership was a stronger predictor of technical quality in research projects, whereas initiating structure was a stronger predictor of technical quality in development projects. Implications for leadership theory and research are discussed. (c) 2006 APA, all rights reserved.
Improving Space Project Cost Estimating with Engineering Management Variables

NASA Technical Reports Server (NTRS)

Hamaker, Joseph W.; Roth, Axel (Technical Monitor)

2001-01-01

Current space project cost models attempt to predict space flight project cost via regression equations, which relate the cost of projects to technical performance metrics (e.g. weight, thrust, power, pointing accuracy, etc.). This paper examines the introduction of engineering management parameters to the set of explanatory variables. A number of specific engineering management variables are considered and exploratory regression analysis is performed to determine if there is statistical evidence for cost effects apart from technical aspects of the projects. It is concluded that there are other non-technical effects at work and that further research is warranted to determine if it can be shown that these cost effects are definitely related to engineering management.
Low-delay predictive audio coding for the HIVITS HDTV codec

NASA Astrophysics Data System (ADS)

McParland, A. K.; Gilchrist, N. H. C.

1995-01-01

The status of work relating to predictive audio coding, as part of the European project on High Quality Video Telephone and HD(TV) Systems (HIVITS), is reported. The predictive coding algorithm is developed, along with six-channel audio coding and decoding hardware. Demonstrations of the audio codec operating in conjunction with the video codec, are given.
Multi-Model Combination techniques for Hydrological Forecasting: Application to Distributed Model Intercomparison Project Results

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

Ajami, N K; Duan, Q; Gao, X

2005-04-11

This paper examines several multi-model combination techniques: the Simple Multi-model Average (SMA), the Multi-Model Super Ensemble (MMSE), Modified Multi-Model Super Ensemble (M3SE) and the Weighted Average Method (WAM). These model combination techniques were evaluated using the results from the Distributed Model Intercomparison Project (DMIP), an international project sponsored by the National Weather Service (NWS) Office of Hydrologic Development (OHD). All of the multi-model combination results were obtained using uncalibrated DMIP model outputs and were compared against the best uncalibrated as well as the best calibrated individual model results. The purpose of this study is to understand how different combination techniquesmore » affect the skill levels of the multi-model predictions. This study revealed that the multi-model predictions obtained from uncalibrated single model predictions are generally better than any single member model predictions, even the best calibrated single model predictions. Furthermore, more sophisticated multi-model combination techniques that incorporated bias correction steps work better than simple multi-model average predictions or multi-model predictions without bias correction.« less
Crop status evaluations and yield predictions

NASA Technical Reports Server (NTRS)

Haun, J. R.

1976-01-01

One phase of the large area crop inventory project is presented. Wheat yield models based on the input of environmental variables potentially obtainable through the use of space remote sensing were developed and demonstrated. By the use of a unique method for visually qualifying daily plant development and subsequent multifactor computer analyses, it was possible to develop practical models for predicting crop development and yield. Development of wheat yield prediction models was based on the discovery that morphological changes in plants are detected and quantified on a daily basis, and that this change during a portion of the season was proportional to yield.
Predicting High Occupancy Vehicle Lane Demand, Final Report

DOT National Transportation Integrated Search

1996-08-01

">THIS REPORT PRESENTS THE INTERIM RESULTS OF THIS PROJECT, SPECIFICALLY: 1. A REVIEW OF THE AVAILABLE LITERATURE AND THE EXPERIENCES OF PUBLIC AGENCIES WITH CURRENT METHODS FOR PREDICTING THE DEMAND FOR HOV LANES, 2. THE PROPOSED NEW METHOD...
Subseasonal-to-Seasonal Science and Prediction Initiatives of the NOAA MAPP Program

NASA Astrophysics Data System (ADS)

Archambault, H. M.; Barrie, D.; Mariotti, A.

2016-12-01

There is great practical interest in developing predictions beyond the 2-week weather timescale. Scientific communities have historically organized themselves around the weather and climate problems, but the subseasonal-to-seasonal (S2S) timescale range overall is recognized as new territory for which a concerted shared effort is needed. For instance, the climate community, as part of programs like CLIVAR, has historically tackled coupled phenomena and modeling, keys to harnessing predictability on longer timescales. In contrast, the weather community has focused on synoptic dynamics, higher-resolution modeling, and enhanced model initialization, of importance at the shorter timescales and especially for the prediction of extremes. The processes and phenomena specific to timescales between weather and climate require a unified approach to science, modeling, and predictions. Internationally, the WWRP/WCRP S2S Prediction Project is a promising catalyzer for these types of activities. Among the various contributing U.S. research programs, the Modeling, Analysis, Predictions and Projections (MAPP) program, as part of the NOAA Climate Program Office, has launched coordinated research and transition activities that help to meet the agency's goals to fill the weather-to-climate prediction gap and will contribute to advance international goals. This presentation will describe ongoing MAPP program S2S science and prediction initiatives, specifically the MAPP S2S Task Force and the SubX prediction experiment.

REDUCING UNCERTAINTIES IN MODEL PREDICTIONS VIA HISTORY MATCHING OF CO2 MIGRATION AND REACTIVE TRANSPORT MODELING OF CO2 FATE AT THE SLEIPNER PROJECT

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

Zhu, Chen

2015-03-31

An important question for the Carbon Capture, Storage, and Utility program is “can we adequately predict the CO2 plume migration?” For tracking CO2 plume development, the Sleipner project in the Norwegian North Sea provides more time-lapse seismic monitoring data than any other sites, but significant uncertainties still exist for some of the reservoir parameters. In Part I, we assessed model uncertainties by applying two multi-phase compositional simulators to the Sleipner Benchmark model for the uppermost layer (Layer 9) of the Utsira Sand and calibrated our model against the time-lapsed seismic monitoring data for the site from 1999 to 2010. Approximatemore » match with the observed plume was achieved by introducing lateral permeability anisotropy, adding CH4 into the CO2 stream, and adjusting the reservoir temperatures. Model-predicted gas saturation, CO2 accumulation thickness, and CO2 solubility in brine—none were used as calibration metrics—were all comparable with the interpretations of the seismic data in the literature. In Part II & III, we evaluated the uncertainties of predicted long-term CO2 fate up to 10,000 years, due to uncertain reaction kinetics. Under four scenarios of the kinetic rate laws, the temporal and spatial evolution of CO2 partitioning into the four trapping mechanisms (hydrodynamic/structural, solubility, residual/capillary, and mineral) was simulated with ToughReact, taking into account the CO2-brine-rock reactions and the multi-phase reactive flow and mass transport. Modeling results show that different rate laws for mineral dissolution and precipitation reactions resulted in different predicted amounts of trapped CO2 by carbonate minerals, with scenarios of the conventional linear rate law for feldspar dissolution having twice as much mineral trapping (21% of the injected CO2) as scenarios with a Burch-type or Alekseyev et al.–type rate law for feldspar dissolution (11%). So far, most reactive transport modeling (RTM
Requirements management: keeping your technology acquisition project under control.

PubMed

Carr, J J

2000-03-01

Whether you are acquiring clinical or business information systems, patient monitoring systems, or therapeutic and diagnostic systems, the odds are good that the project will be delivered late, will cost far more than predicted, and will not provide all the features promised. The principal reason for project failure is improper management of the requirements of the system. Requirements engineering and management is a skill from the systems engineering profession that can be learned by nearly any professional who is managing a technology acquisition project. The author discusses what requirements engineering and management is and how it is done.
Climate predictability and prediction skill on seasonal time scales over South America from CHFP models

NASA Astrophysics Data System (ADS)

Osman, Marisol; Vera, C. S.

2017-10-01

This work presents an assessment of the predictability and skill of climate anomalies over South America. The study was made considering a multi-model ensemble of seasonal forecasts for surface air temperature, precipitation and regional circulation, from coupled global circulation models included in the Climate Historical Forecast Project. Predictability was evaluated through the estimation of the signal-to-total variance ratio while prediction skill was assessed computing anomaly correlation coefficients. Both indicators present over the continent higher values at the tropics than at the extratropics for both, surface air temperature and precipitation. Moreover, predictability and prediction skill for temperature are slightly higher in DJF than in JJA while for precipitation they exhibit similar levels in both seasons. The largest values of predictability and skill for both variables and seasons are found over northwestern South America while modest but still significant values for extratropical precipitation at southeastern South America and the extratropical Andes. The predictability levels in ENSO years of both variables are slightly higher, although with the same spatial distribution, than that obtained considering all years. Nevertheless, predictability at the tropics for both variables and seasons diminishes in both warm and cold ENSO years respect to that in all years. The latter can be attributed to changes in signal rather than in the noise. Predictability and prediction skill for low-level winds and upper-level zonal winds over South America was also assessed. Maximum levels of predictability for low-level winds were found were maximum mean values are observed, i.e. the regions associated with the equatorial trade winds, the midlatitudes westerlies and the South American Low-Level Jet. Predictability maxima for upper-level zonal winds locate where the subtropical jet peaks. Seasonal changes in wind predictability are observed that seem to be related to
Overview of the 1986--1987 atomic mass predictions

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

Haustein, P.E.

1988-07-01

The need for a comprehensive update of earlier sets of atomic mass predictions is documented. A project that grew from this need and which resulted in the preparation of the 1986--1987 Atomic Mass Predictions is summarized. Ten sets of new mass predictions and expository text from a variety of types of mass models are combined with the latest evaluation of experimentally determined atomic masses. The methodology employed in constructing these mass predictions is outlined. The models are compared with regard to their reproduction of the experimental mass surface and their use of varying numbers of adjustable parameters. Plots are presented,more » for each set of predictions, of differences between model calculations and the measured masses. These plots may be used to estimate the reliability of the new mass predictions in unmeasured regions that border the experimetally known mass surface. copyright 1988 Academic Press, Inc.« less
Accelerated Test Method for Corrosion Protective Coatings Project

NASA Technical Reports Server (NTRS)

Falker, John; Zeitlin, Nancy; Calle, Luz

2015-01-01

This project seeks to develop a new accelerated corrosion test method that predicts the long-term corrosion protection performance of spaceport structure coatings as accurately and reliably as current long-term atmospheric exposure tests. This new accelerated test method will shorten the time needed to evaluate the corrosion protection performance of coatings for NASA's critical ground support structures. Lifetime prediction for spaceport structure coatings has a 5-year qualification cycle using atmospheric exposure. Current accelerated corrosion tests often provide false positives and negatives for coating performance, do not correlate to atmospheric corrosion exposure results, and do not correlate with atmospheric exposure timescales for lifetime prediction.
The QWeCI Project: seamlessly linking climate science to society

NASA Astrophysics Data System (ADS)

Morse, A. P.; Caminade, C.; Jones, A. E.; MacLeod, D.; Heath, A. E.

2012-04-01

The EU FP7 QWeCI project Quantifying Weather and Climate Impacts on health in developing countries (www.liv.ac.uk/qweci) has 13 partners with 7 of these in Africa. The geographical focus of the project is in Senegal, Ghana and Malawi. In all three countries the project has a strong scientific dissemination outlook as well as having field based surveillance programmes in Ghana and Senegal to understand more about the local parameters controlling the transmission of malaria and in Senegal of Rift Valley fever. The project has a strong and active climate science activity in using hindcasts of the new System 4 seasonal forecasting system at ECMWF; to further develop the use of monthly to seasonal forecasts from ensemble prediction systems; within project downscaling development; the assessment of decadal ensemble prediction systems; and the development and testing of vector borne disease models for malaria and Rift Valley fever. In parallel with the science programme the project has a large outreach activity involving regular communication and bi-lateral exchanges, science and decision maker focused workshops. In Malawi a long range WiFi network has been established for the dissemination of data. In Senegal where they is a concentration of partners and stakeholders the project is gaining a role as a catalyst for wider health and climate related activity within government departments and national research bodies along with the support and involvement of local communities. Within these wider community discussions we have interactive inputs from African and European scientists who are partners in the project. This paper will show highlights of the work completed so far and give an outline to future development and to encourage a wider user interaction from outside of the current project team and their direct collaborators.
Science in 60 – Tabletop Fire Prediction

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

Cary, Lyle

At the Interagency Fire Center at Los Alamos National Laboratory, experts rely on the state-of-the-art Simtable to help them predict the unpredictable. With algorithms that include camera-based object-tracking and projection developed by the Laboratory, the emergency operations team can simulate a wildland fire spreading across any terrain. The table system, developed and marketed by Simtable, a Santa Fe, N.M., company, takes into account weather, vegetation and fuel conditions. The project also received funding from the LANS Venture Acceleration Fund to improve the user interface.
Fusion Simulation Project Workshop Report

NASA Astrophysics Data System (ADS)

Kritz, Arnold; Keyes, David

2009-03-01

The mission of the Fusion Simulation Project is to develop a predictive capability for the integrated modeling of magnetically confined plasmas. This FSP report adds to the previous activities that defined an approach to integrated modeling in magnetic fusion. These previous activities included a Fusion Energy Sciences Advisory Committee panel that was charged to study integrated simulation in 2002. The report of that panel [Journal of Fusion Energy 20, 135 (2001)] recommended the prompt initiation of a Fusion Simulation Project. In 2003, the Office of Fusion Energy Sciences formed a steering committee that developed a project vision, roadmap, and governance concepts [Journal of Fusion Energy 23, 1 (2004)]. The current FSP planning effort involved 46 physicists, applied mathematicians and computer scientists, from 21 institutions, formed into four panels and a coordinating committee. These panels were constituted to consider: Status of Physics Components, Required Computational and Applied Mathematics Tools, Integration and Management of Code Components, and Project Structure and Management. The ideas, reported here, are the products of these panels, working together over several months and culminating in a 3-day workshop in May 2007.
The Effect of Data Quality on Short-term Growth Model Projections

Treesearch

David Gartner

2005-01-01

This study was designed to determine the effect of FIA's data quality on short-term growth model projections. The data from Georgia's 1996 statewide survey were used for the Southern variant of the Forest Vegetation Simulator to predict Georgia's first annual panel. The effect of several data error sources on growth modeling prediction errors...
Predictive In Vitro Screening of Environmental Chemicals – The ToxCast Project

EPA Science Inventory

ToxCast, the United States Environmental Protection Agency’s chemical prioritization research program, is developing methods for utilizing computational chemistry and bioactivity profiling to predict potential for toxicity and prioritize limited testing resources (www.epa.gov/toc...
Inhibition, Updating Working Memory, and Shifting Predict Reading Disability Symptoms in a Hybrid Model: Project KIDS

PubMed Central

Daucourt, Mia C.; Schatschneider, Christopher; Connor, Carol M.; Al Otaiba, Stephanie; Hart, Sara A.

2018-01-01

Recent achievement research suggests that executive function (EF), a set of regulatory processes that control both thought and action necessary for goal-directed behavior, is related to typical and atypical reading performance. This project examines the relation of EF, as measured by its components, Inhibition, Updating Working Memory, and Shifting, with a hybrid model of reading disability (RD). Our sample included 420 children who participated in a broader intervention project when they were in KG-third grade (age M = 6.63 years, SD = 1.04 years, range = 4.79–10.40 years). At the time their EF was assessed, using a parent-report Behavior Rating Inventory of Executive Function (BRIEF), they had a mean age of 13.21 years (SD = 1.54 years; range = 10.47–16.63 years). The hybrid model of RD was operationalized as a composite consisting of four symptoms, and set so that any child could have any one, any two, any three, any four, or none of the symptoms included in the hybrid model. The four symptoms include low word reading achievement, unexpected low word reading achievement, poorer reading comprehension compared to listening comprehension, and dual-discrepancy response-to-intervention, requiring both low achievement and low growth in word reading. The results of our multilevel ordinal logistic regression analyses showed a significant relation between all three components of EF (Inhibition, Updating Working Memory, and Shifting) and the hybrid model of RD, and that the strength of EF’s predictive power for RD classification was the highest when RD was modeled as having at least one or more symptoms. Importantly, the chances of being classified as having RD increased as EF performance worsened and decreased as EF performance improved. The question of whether any one EF component would emerge as a superior predictor was also examined and results showed that Inhibition, Updating Working Memory, and Shifting were equally valuable as predictors of the hybrid model
Radar-aeolian roughness project

NASA Technical Reports Server (NTRS)

Greeley, Ronald; Dobrovolskis, A.; Gaddis, L.; Iversen, J. D.; Lancaster, N.; Leach, Rodman N.; Rasnussen, K.; Saunders, S.; Vanzyl, J.; Wall, S.

1991-01-01

The objective is to establish an empirical relationship between measurements of radar, aeolian, and surface roughness on a variety of natural surfaces and to understand the underlying physical causes. This relationship will form the basis for developing a predictive equation to derive aeolian roughness from radar backscatter. Results are given from investigations carried out in 1989 on the principal elements of the project, with separate sections on field studies, radar data analysis, laboratory simulations, and development of theory for planetary applications.
Predictability of Brayton electric power system performance

NASA Technical Reports Server (NTRS)

Klann, J. L.; Hettel, H. J.

1972-01-01

Data from the first tests of the 2- to 15-kilowatt space power system in a vacuum chamber were compared with predictions of both a pretest analysis and a modified version of that analysis. The pretest analysis predicted test results with differences of no more than 9 percent of the largest measured value for each quantity. The modified analysis correlated measurements. Differences in conversion efficiency and power output were no greater than plus or minus 2.5 percent. This modified analysis was used to project space performance maps for the current test system.
Using a RIVPACS model to predict expected macrofaunal species richness in Puget Sound

EPA Science Inventory

As part of a project to develop regional indicators for Pacific coastal environments using soft-bottom benthic species, we are evaluating a RIVPACS predictive model (River InVertebrate Prediction and Classification System). This approach was originally developed for rivers and s...
Prospective evaluation of a Bayesian model to predict organizational change.

PubMed

Molfenter, Todd; Gustafson, Dave; Kilo, Chuck; Bhattacharya, Abhik; Olsson, Jesper

2005-01-01

This research examines a subjective Bayesian model's ability to predict organizational change outcomes and sustainability of those outcomes for project teams participating in a multi-organizational improvement collaborative.
A preliminary approach to quantifying the overall environmental risks posed by development projects during environmental impact assessment

PubMed Central

Chadès, Iadine

2017-01-01

Environmental impact assessment (EIA) is used globally to manage the impacts of development projects on the environment, so there is an imperative to demonstrate that it can effectively identify risky projects. However, despite the widespread use of quantitative predictive risk models in areas such as toxicology, ecosystem modelling and water quality, the use of predictive risk tools to assess the overall expected environmental impacts of major construction and development proposals is comparatively rare. A risk-based approach has many potential advantages, including improved prediction and attribution of cause and effect; sensitivity analysis; continual learning; and optimal resource allocation. In this paper we investigate the feasibility of using a Bayesian belief network (BBN) to quantify the likelihood and consequence of non-compliance of new projects based on the occurrence probabilities of a set of expert-defined features. The BBN incorporates expert knowledge and continually improves its predictions based on new data as it is collected. We use simulation to explore the trade-off between the number of data points and the prediction accuracy of the BBN, and find that the BBN could predict risk with 90% accuracy using approximately 1000 data points. Although a further pilot test with real project data is required, our results suggest that a BBN is a promising method to monitor overall risks posed by development within an existing EIA process given a modest investment in data collection. PMID:28686651
Advanced Turbine Technology Applications Project (ATTAP)

NASA Technical Reports Server (NTRS)

1994-01-01

Reports technical effort by AlliedSignal Engines in sixth year of DOE/NASA funded project. Topics include: gas turbine engine design modifications of production APU to incorporate ceramic components; fabrication and processing of silicon nitride blades and nozzles; component and engine testing; and refinement and development of critical ceramics technologies, including: hot corrosion testing and environmental life predictive model; advanced NDE methods for internal flaws in ceramic components; and improved carbon pulverization modeling during impact. ATTAP project is oriented toward developing high-risk technology of ceramic structural component design and fabrication to carry forward to commercial production by 'bridging the gap' between structural ceramics in the laboratory and near-term commercial heat engine application. Current ATTAP project goal is to support accelerated commercialization of advanced, high-temperature engines for hybrid vehicles and other applications. Project objectives are to provide essential and substantial early field experience demonstrating ceramic component reliability and durability in modified, available, gas turbine engine applications; and to scale-up and improve manufacturing processes of ceramic turbine engine components and demonstrate application of these processes in the production environment.
Overview of the Cranked-Arrow Wing Aerodynamics Project International

NASA Technical Reports Server (NTRS)

Obara, Clifford J.; Lamar, John E.

2008-01-01

This paper provides a brief history of the F-16XL-1 aircraft, its role in the High Speed Research program and how it was morphed into the Cranked Arrow Wing Aerodynamics Project. Various flight, wind-tunnel and Computational Fluid Dynamics data sets were generated as part of the project. These unique and open flight datasets for surface pressures, boundary-layer profiles and skin-friction distributions, along with surface flow data, are described and sample data comparisons given. This is followed by a description of how the project became internationalized to be known as Cranked Arrow Wing Aerodynamics Project International and is concluded by an introduction to the results of a four year computational predictive study of data collected at flight conditions by participating researchers.
Divergent surface and total soil moisture projections under global warming

USGS Publications Warehouse

Berg, Alexis; Sheffield, Justin; Milly, Paul C.D.

2017-01-01

Land aridity has been projected to increase with global warming. Such projections are mostly based on off-line aridity and drought metrics applied to climate model outputs but also are supported by climate-model projections of decreased surface soil moisture. Here we comprehensively analyze soil moisture projections from the Coupled Model Intercomparison Project phase 5, including surface, total, and layer-by-layer soil moisture. We identify a robust vertical gradient of projected mean soil moisture changes, with more negative changes near the surface. Some regions of the northern middle to high latitudes exhibit negative annual surface changes but positive total changes. We interpret this behavior in the context of seasonal changes in the surface water budget. This vertical pattern implies that the extensive drying predicted by off-line drought metrics, while consistent with the projected decline in surface soil moisture, will tend to overestimate (negatively) changes in total soil water availability.
Chesapeake Bay Forecast System: Oxygen Prediction for the Sustainable Ecosystem Management

NASA Astrophysics Data System (ADS)

Mathukumalli, B.; Long, W.; Zhang, X.; Wood, R.; Murtugudde, R. G.

2010-12-01

The Chesapeake Bay Forecast System (CBFS) is a flexible, end-to-end expert prediction tool for decision makers that will provide customizable, user-specified predictions and projections of the region’s climate, air and water quality, local chemistry, and ecosystems at days to decades. As a part of CBFS, the long-term water quality data were collected and assembled to develop ecological models for the sustainable management of the Chesapeake Bay. Cultural eutrophication depletes oxygen levels in this ecosystem particularly in summer which has several negative implications on the structure and function of ecosystem. In order to understand dynamics and prediction of spatially-explicit oxygen levels in the Bay, an empirical process based ecological model is developed with long-term control variables (water temperature, salinity, nitrogen and phosphorus). Statistical validation methods were employed to demonstrate usability of predictions for management purposes and the predicted oxygen levels are quite faithful to observations. The predicted oxygen values and other physical outputs from downscaling of regional weather and climate predictions, or forecasts from hydrodynamic models can be used to forecast various ecological components. Such forecasts would be useful for both recreational and commercial users of the bay (for example, bass fishing). Furthermore, this work can also be used to predict extent of hypoxia/anoxia not only from anthropogenic nutrient pollution, but also from global warming. Some hindcasts and forecasts are discussed along with the ongoing efforts at a mechanistic ecosystem model to provide prognostic oxygen predictions and projections and upper trophic modeling using an energetics approach.

Natural neural projection dynamics underlying social behavior

PubMed Central

Gunaydin, Lisa A.; Grosenick, Logan; Finkelstein, Joel C.; Kauvar, Isaac V.; Fenno, Lief E.; Adhikari, Avishek; Lammel, Stephan; Mirzabekov, Julie J.; Airan, Raag D.; Zalocusky, Kelly A.; Tye, Kay M.; Anikeeva, Polina; Malenka, Robert C.; Deisseroth, Karl

2014-01-01

Social interaction is a complex behavior essential for many species, and is impaired in major neuropsychiatric disorders. Pharmacological studies have implicated certain neurotransmitter systems in social behavior, but circuit-level understanding of endogenous neural activity during social interaction is lacking. We therefore developed and applied a new methodology, termed fiber photometry, to optically record natural neural activity in genetically- and connectivity-defined projections to elucidate the real-time role of specified pathways in mammalian behavior. Fiber photometry revealed that activity dynamics of a ventral tegmental area (VTA)-to-nucleus accumbens (NAc) projection could encode and predict key features of social but not novel-object interaction. Consistent with this observation, optogenetic control of cells specifically contributing to this projection was sufficient to modulate social behavior, which was mediated by type-1 dopamine receptor signaling downstream in the NAc. Direct observation of projection-specific activity in this way captures a fundamental and previously inaccessible dimension of circuit dynamics. PMID:24949967
General risks for tunnelling projects: An overview

NASA Astrophysics Data System (ADS)

Siang, Lee Yong; Ghazali, Farid E. Mohamed; Zainun, Noor Yasmin; Ali, Roslinda

2017-10-01

Tunnels are indispensable when installing new infrastructure as well as when enhancing the quality of existing urban living due to their unique characteristics and potential applications. Over the past few decades, there has been a significant increase in the building of tunnels, world-wide. Tunnelling projects are complex endeavors, and risk assessment for tunnelling projects is likewise a complex process. Risk events are often interrelated. Occurrence of a technical risk usually carries cost and schedule consequences. Schedule risks typically impact cost escalation and project overhead. One must carefully consider the likelihood of a risk's occurrence and its impact in the context of a specific set of project conditions and circumstances. A project's goals, organization, and environment impacts in the context of a specific set of project conditions and circumstances. Some projects are primarily schedule driven; other projects are primarily cost or quality driven. Whether a specific risk event is perceived fundamentally as a cost risk or a schedule risk is governed by the project-specific context. Many researchers have pointed out the significance of recognition and control of the complexity, and risks of tunnelling projects. Although all general information on a project such as estimated duration, estimated cost, and stakeholders can be obtained, it is still quite difficult to accurately understand, predict and control the overall situation and development trends of the project, leading to the risks of tunnelling projects. This paper reviews all the key risks for tunnelling projects from several case studies that have been carried out by other researchers. These risks have been identified and reviewed in this paper. As a result, the current risk management plan in tunnelling projects can be enhanced by including all these reviewed risks as key information.
Case Studies in Application of System Engineering Practices to Capstone Projects

NASA Technical Reports Server (NTRS)

Murphy, Gloria; vanSusante, Paul; Carmen, Christina; Morris, Tommy; Schmidt, Peter; Zalewski, Janusz

2011-01-01

The Exploration Systems Mission Directorate (ESMD) of the National Aeronautics and Space Administration (NASA) sponsors a faculty fellowship program that engages researchers with interests aligned with current ESMD development programs. The faculty-members are committed to run a capstone senior design project based- on the materials and experience gained during the fellowship. For the 2010 - 2011 academic year, 5 projects were approved. These projects are in the areas of mechanical and electrical hardware design and optimization, fault prediction and extra planetary civil site preparation. This work summarizes the projects, describes the student teams performing the work, and comments on the integration of Systems Engineering principles into the projects, as well as the affected course curriculums.
Potential impact of initialization on decadal predictions as assessed for CMIP5 models

NASA Astrophysics Data System (ADS)

Branstator, Grant; Teng, Haiyan

2012-06-01

To investigate the potential for initialization to improve decadal range predictions, we quantify the initial value predictability of upper 300 m temperature in the two northern ocean basins for 12 models from Coupled Model Intercomparison Project phase 5 (CMIP5), and we contrast it with the forced predictability in Representative Concentration Pathways (RCP) 4.5 climate change projections. We use a recently introduced method that produces predictability estimates from long control runs. Many initial states are considered, and we find on average 1) initialization has the potential to improve skill in the first 5 years in the North Pacific and the first 9 years in the North Atlantic, and 2) the impact from initialization becomes secondary compared to the impact of RCP4.5 forcing after 6 1/2 and 8 years in the two basins, respectively. Model-to-model and spatial variations in these limits are, however, substantial.
Uncertainty prediction for PUB

NASA Astrophysics Data System (ADS)

Mendiondo, E. M.; Tucci, C. M.; Clarke, R. T.; Castro, N. M.; Goldenfum, J. A.; Chevallier, P.

2003-04-01

IAHS’ initiative of Prediction in Ungaged Basins (PUB) attempts to integrate monitoring needs and uncertainty prediction for river basins. This paper outlines alternative ways of uncertainty prediction which could be linked with new blueprints for PUB, thereby showing how equifinality-based models should be grasped using practical strategies of gauging like the Nested Catchment Experiment (NCE). Uncertainty prediction is discussed from observations of Potiribu Project, which is a NCE layout at representative basins of a suptropical biome of 300,000 km2 in South America. Uncertainty prediction is assessed at the microscale (1 m2 plots), at the hillslope (0,125 km2) and at the mesoscale (0,125 - 560 km2). At the microscale, uncertainty-based models are constrained by temporal variations of state variables with changing likelihood surfaces of experiments using Green-Ampt model. Two new blueprints emerged from this NCE for PUB: (1) the Scale Transferability Scheme (STS) at the hillslope scale and the Integrating Process Hypothesis (IPH) at the mesoscale. The STS integrates a multi-dimensional scaling with similarity thresholds, as a generalization of the Representative Elementary Area (REA), using spatial correlation from point (distributed) to area (lumped) process. In this way, STS addresses uncertainty-bounds of model parameters, into an upscaling process at the hillslope. In the other hand, the IPH approach regionalizes synthetic hydrographs, thereby interpreting the uncertainty bounds of streamflow variables. Multiscale evidences from Potiribu NCE layout show novel pathways of uncertainty prediction under a PUB perspective in representative basins of world biomes.
Earthquake prediction research at the Seismological Laboratory, California Institute of Technology

USGS Publications Warehouse

Spall, H.

1979-01-01

Nevertheless, basic earthquake-related information has always been of consuming interest to the public and the media in this part of California (fig. 2.). So it is not surprising that earthquake prediction continues to be a significant reserach program at the laboratory. Several of the current spectrum of projects related to prediction are discussed below.
Predictability of tick-borne encephalitis fluctuations.

PubMed

Zeman, P

2017-10-01

Tick-borne encephalitis is a serious arboviral infection with unstable dynamics and profound inter-annual fluctuations in case numbers. A dependable predictive model has been sought since the discovery of the disease. The present study demonstrates that four superimposed cycles, approximately 2·4, 3, 5·4, and 10·4 years long, can account for three-fifths of the variation in the disease fluctuations over central Europe. Using harmonic regression, these cycles can be projected into the future, yielding forecasts of sufficient accuracy for up to 4 years ahead. For the years 2016-2018, this model predicts elevated incidence levels in most parts of the region.
Predicting and Mapping Potential Whooping Crane Stopover Habitat to Guide Site Selection for Wind Energy Projects

EPA Science Inventory

Migration is one of the most poorly understood components of a bird’s life cycle. For that reason, migratory stopover habitats are often not part of conservation planning and may be overlooked when planning new development projects. This project highlights and addresses an overl...
Student nurse selection and predictability of academic success: The Multiple Mini Interview project.

PubMed

Gale, Julia; Ooms, Ann; Grant, Robert; Paget, Kris; Marks-Maran, Di

2016-05-01

With recent reports of public enquiries into failure to care, universities are under pressure to ensure that candidates selected for undergraduate nursing programmes demonstrate academic potential as well as characteristics and values such as compassion, empathy and integrity. The Multiple Mini Interview (MMI) was used in one university as a way of ensuring that candidates had the appropriate numeracy and literacy skills as well as a range of communication, empathy, decision-making and problem-solving skills as well as ethical insights and integrity, initiative and team-work. To ascertain whether there is evidence of bias in MMIs (gender, age, nationality and location of secondary education) and to determine the extent to which the MMI is predictive of academic success in nursing. A longitudinal retrospective analysis of student demographics, MMI data and the assessment marks for years 1, 2 and 3. One university in southwest London. One cohort of students who commenced their programme in September 2011, including students in all four fields of nursing (adult, child, mental health and learning disability). Inferential statistics and a Bayesian Multilevel Model. MMI in conjunction with MMI numeracy test and MMI literacy test shows little or no bias in terms of ages, gender, nationality or location of secondary school education. Although MMI in conjunction with numeracy and literacy testing is predictive of academic success, it is only weakly predictive. The MMI used in conjunction with literacy and numeracy testing appears to be a successful technique for selecting candidates for nursing. However, other selection methods such as psychological profiling or testing of emotional intelligence may add to the extent to which selection methods are predictive of academic success on nursing. Copyright © 2016 Elsevier Ltd. All rights reserved.
Potential for western US seasonal snowpack prediction

USGS Publications Warehouse

Kapnick, Sarah B.; Yang, Xiaosong; Vecchi, Gabriel A.; Delworth, Thomas L.; Gudgel, Rich; Malyshev, Sergey; Milly, Paul C. D.; Shevliakova, Elena; Underwood, Seth; Margulis, Steven A.

2018-01-01

Western US snowpack—snow that accumulates on the ground in the mountains—plays a critical role in regional hydroclimate and water supply, with 80% of snowmelt runoff being used for agriculture. While climate projections provide estimates of snowpack loss by the end of th ecentury and weather forecasts provide predictions of weather conditions out to 2 weeks, less progress has been made for snow predictions at seasonal timescales (months to 2 years), crucial for regional agricultural decisions (e.g., plant choice and quantity). Seasonal predictions with climate models first took the form of El Niño predictions 3 decades ago, with hydroclimate predictions emerging more recently. While the field has been focused on single-season predictions (3 months or less), we are now poised to advance our predictions beyond this timeframe. Utilizing observations, climate indices, and a suite of global climate models, we demonstrate the feasibility of seasonal snowpack predictions and quantify the limits of predictive skill 8 month sin advance. This physically based dynamic system outperforms observation-based statistical predictions made on July 1 for March snowpack everywhere except the southern Sierra Nevada, a region where prediction skill is nonexistent for every predictor presently tested. Additionally, in the absence of externally forced negative trends in snowpack, narrow maritime mountain ranges with high hydroclimate variability pose a challenge for seasonal prediction in our present system; natural snowpack variability may inherently be unpredictable at this timescale. This work highlights present prediction system successes and gives cause for optimism for developing seasonal predictions for societal needs.
Enhanced Predictability Through Lagrangian Observations and Analysis

DTIC Science & Technology

2006-09-30

Newark, DE 19716 phone: (302) 831-6836 fax: (302) 831-6521 email: brucel @udel.edu Award Number: N0014-05-1-0092 http://newark.cms.udel.edu... brucel /slmaps LONG-TERM GOALS The long-term goal of this project is develop the capability to predict the origin and fate of ocean water
Advancing Drought Understanding, Monitoring and Prediction

NASA Technical Reports Server (NTRS)

Mariotti, Annarita; Schubert, Siegfried D.; Mo, Kingtse; Peters-Lidard, Christa; Wood, Andy; Pulwarty, Roger; Huang, Jin; Barrie, Dan

2013-01-01

, focused and coordinated research efforts are needed, drawing from excellence across the broad drought research community. To meet this challenge, National Oceanic and Atmospheric Administration (NOAA)'s Drought Task Force was established in October 2011 with the ambitious goal of achieving significant new advances in the ability to understand, monitor, and predict drought over North America. The Task Force (duration of October 2011-September 2014) is an initiative of NOAA's Climate Program Office Modeling, Analysis, Predictions, and Projections (MAPP) program in partnership with NIDIS. It brings together over 30 leading MAPP-funded drought scientists from multiple academic and federal institutions [involves scientists from NOAA's research laboratories and centers, the National Aeronautics and Space Administration (NASA), U.S. Department of Agriculture, National Center for Atmospheric Research (NCAR), and many universities] in a concerted research effort that builds on individual MAPP research projects. These projects span the wide spectrum of drought research needed to make fundamental advances, from those aimed at the basic understanding of drought mechanisms to those aimed at testing new drought monitoring and prediction tools for operational and service purposes (as part of NCEP's Climate Test Bed). The Drought Task Force provides focus and coordination to MAPP drought research activities and also facilitates synergies with other national and international drought research efforts, including those by the GDIS.
A thermal sensation prediction tool for use by the profession

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

Fountain, M.E.; Huizenga, C.

1997-12-31

As part of a recent ASHRAE research project (781-RP), a thermal sensation prediction tool has been developed. This paper introduces the tool, describes the component thermal sensation models, and presents examples of how the tool can be used in practice. Since the main end product of the HVAC industry is the comfort of occupants indoors, tools for predicting occupant thermal response can be an important asset to designers of indoor climate control systems. The software tool presented in this paper incorporates several existing models for predicting occupant comfort.
Projected climate change impacts and short term predictions on staple crops in Sub-Saharan Africa

NASA Astrophysics Data System (ADS)

Mereu, V.; Spano, D.; Gallo, A.; Carboni, G.

2013-12-01

. Multiple combinations of soils and climate conditions, crop management and varieties were considered for the different Agro-Ecological Zones. The climate impact was assessed using future climate prediction, statistically and/or dynamically downscaled, for specific areas. Direct and indirect effects of different CO2 concentrations projected for the future periods were separately explored to estimate their effects on crops. Several adaptation strategies (e.g., introduction of full irrigation, shift of the ordinary sowing/planting date, changes in the ordinary fertilization management) were also evaluated with the aim to reduce the negative impact of climate change on crop production. The results of the study, analyzed at local, AEZ and country level, will be discussed.
Toward Improved Predictions of Slender Airframe Aerodynamics Using the F-16XL Aircraft

NASA Technical Reports Server (NTRS)

Luckring, James M.; Rizzi, Arthur; Davis, M. Bruce

2016-01-01

A coordinated project has been underway to improve computational fluid dynamics predictions of slender airframe aerodynamics. The work is focused on two flow conditions and leverages a unique flight data set obtained with an F-16XL aircraft. These conditions, a low-speed high angle-of-attack case and a transonic low angle-of-attack case, were selected from a prior prediction campaign wherein the computational fluid dynamics failed to provide acceptable results. In this paper, the background, objectives, and approach to the current project are presented. The work embodies predictions from multiple numerical formulations that are contributed from multiple organizations, and the context of this campaign to other multicode, multi-organizational efforts is included. The relevance of this body of work toward future supersonic commercial transport concepts is also briefly addressed.
Predicting DNAPL Source Zone and Plume Response Using Site-Measured Characteristics

DTIC Science & Technology

2017-05-19

FINAL REPORT Predicting DNAPL Source Zone and Plume Response Using Site- Measured Characteristics SERDP Project ER-1613 MAY 2017...Final Report 3. DATES COVERED (From - To) 2007 - 2017 4. TITLE AND SUBTITLE PREDICTING DNAPL SOURCE ZONE AND PLUME RESPONSE USING SITE- MEASURED ...historical record of concentration and head measurements , particularly in the near-source region. For each site considered, currently available data
Projected changes in daily fire spread across Canada over the next century

NASA Astrophysics Data System (ADS)

Wang, Xianli; Parisien, Marc-André; Taylor, Steve W.; Candau, Jean-Noël; Stralberg, Diana; Marshall, Ginny A.; Little, John M.; Flannigan, Mike D.

2017-02-01

In the face of climate change, predicting and understanding future fire regimes across Canada is a high priority for wildland fire research and management. Due in large part to the difficulties in obtaining future daily fire weather projections, one of the major challenges in predicting future fire activity is to estimate how much of the change in weather potential could translate into on-the-ground fire spread. As a result, past studies have used monthly, annual, or multi-decadal weather projections to predict future fires, thereby sacrificing information relevant to day-to-day fire spread. Using climate projections from the fifth phase of the Coupled Model Intercomparison Project (CMIP5), historical weather observations, MODIS fire detection data, and the national fire database of Canada, this study investigated potential changes in the number of active burning days of wildfires by relating ‘spread days’ to patterns of daily fire-conducive weather. Results suggest that climate change over the next century may have significant impacts on fire spread days in almost all parts of Canada’s forested landmass; the number of fire spread days could experience a 2-to-3-fold increase under a high CO2 forcing scenario in eastern Canada, and a greater than 50% increase in western Canada, where the fire potential is already high. The change in future fire spread is critical in understanding fire regime changes, but is also imminently relevant to fire management operations and in fire risk mitigation.
Project SAFE: A Blueprint for Flight Standards. Part 1.

DTIC Science & Technology

1985-01-01

nwanage quota utilization and scheduling was designed to meet a more stable and predictable training environmtent than that which now exists in the Flight...accurate and timely reporting of field office activities, and f. Provide improved capability to conduct national level analyses to predict and prevent...and analysis of the task prfocmd tV filw "lht B tanderds Lstms aIOUMaMM (Brief descriotion ci 7aj project is wIg ;,ude0-01n ’Te objective of the Ml
Analyst-to-Analyst Variability in Simulation-Based Prediction

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

Glickman, Matthew R.; Romero, Vicente J.

This report describes findings from the culminating experiment of the LDRD project entitled, "Analyst-to-Analyst Variability in Simulation-Based Prediction". For this experiment, volunteer participants solving a given test problem in engineering and statistics were interviewed at different points in their solution process. These interviews are used to trace differing solutions to differing solution processes, and differing processes to differences in reasoning, assumptions, and judgments. The issue that the experiment was designed to illuminate -- our paucity of understanding of the ways in which humans themselves have an impact on predictions derived from complex computational simulations -- is a challenging and openmore » one. Although solution of the test problem by analyst participants in this experiment has taken much more time than originally anticipated, and is continuing past the end of this LDRD, this project has provided a rare opportunity to explore analyst-to-analyst variability in significant depth, from which we derive evidence-based insights to guide further explorations in this important area.« less
Summaries of BFRL fire research in-house projects and grants, 1993

NASA Astrophysics Data System (ADS)

Jason, Nora H.

1993-09-01

The report describes the fire research projects performed in the Building and Fire Research Laboratory (BFRL) and under its extramural grants program during fiscal year 1993. The BFRL Fire Research Program has directed its efforts under three program thrusts. The in-house priority projects, grants, and externally-funded efforts thus form an integrated, focussed ensemble. The publication is organized along those lines: fire risk and hazard prediction - carbon monoxide prediction, turbulent combustion, soot, engineering analysis, fire hazard assessment, and large fires; fire safety of products and materials - materials combustion, furniture flammability, and wall and ceiling fires; and advanced technologies for fire sensing and control - fire detection and fire suppression. For the convenience of the reader, an alphabetical listing of all grants is contained in Part 2.0.

An Operational Computational Terminal Area PBL Prediction System

NASA Technical Reports Server (NTRS)

Lin, Yuh-Lang; Kaplan, Michael L.; Weglarz, Ronald P.; Hamilton, David W.

1997-01-01

There are two fundamental goals of this research project. The first and primary goal is to develop a prognostic system which could satisfy the operational weather prediction requirements of the meteorological subsystem within the Aircraft Vortex Spacing System (AVOSS). The secondary goal is to perform indepth diagnostic analyses of the meteorological conditions affecting the Memphis field experiment held during August 1995. These two goals are interdependent because a thorough understanding of the atmospheric dynamical processes which produced the unique meteorology during the Memphis deployment will help us design a prognostic system for the planetary boundary layer (PBL) which could be utilized to support the meteorological subsystem within AVOSS. The secondary goal occupied much of the first year of the research project. This involved extensive data acquisition and indepth analyses of a spectrum of atmospheric observational data sets. Concerning the primary goal, the first part of the four-stage prognostic system in support of AVOSS entitled: Terminal Area PBL Prediction System (TAPPS) was also formulated and tested in a research environment during 1996. We describe this system, and the three stages which are planned to follow. This first part of a software system designed to meet the primary goal of this research project is relatively inexpensive to implement and run operationally.
Charting the Projections: 1996-2006. A Special Issue.

ERIC Educational Resources Information Center

Occupational Outlook Quarterly, 1998

1998-01-01

Depicts labor force projections between 1996 and 2006 in the following areas: occupational employment, demographic composition of the labor force, economic demand, and employment by industry. Indicates that employment is expected to grow in occupations at all levels of education and training. Predicts that labor force growth will be slower than…
[Earth and Space Sciences Project Services for NASA HPCC

NASA Technical Reports Server (NTRS)

Merkey, Phillip

2002-01-01

This grant supported the effort to characterize the problem domain of the Earth Science Technology Office's Computational Technologies Project, to engage the Beowulf Cluster Computing Community as well as the High Performance Computing Research Community so that we can predict the applicability of said technologies to the scientific community represented by the CT project and formulate long term strategies to provide the computational resources necessary to attain the anticipated scientific objectives of the CT project. Specifically, the goal of the evaluation effort is to use the information gathered over the course of the Round-3 investigations to quantify the trends in scientific expectations, the algorithmic requirements and capabilities of high-performance computers to satisfy this anticipated need.
The Predicted Cross Value for Genetic Introgression of Multiple Alleles

PubMed Central

Han, Ye; Cameron, John N.; Wang, Lizhi; Beavis, William D.

2017-01-01

We consider the plant genetic improvement challenge of introgressing multiple alleles from a homozygous donor to a recipient. First, we frame the project as an algorithmic process that can be mathematically formulated. We then introduce a novel metric for selecting breeding parents that we refer to as the predicted cross value (PCV). Unlike estimated breeding values, which represent predictions of general combining ability, the PCV predicts specific combining ability. The PCV takes estimates of recombination frequencies as an input vector and calculates the probability that a pair of parents will produce a gamete with desirable alleles at all specified loci. We compared the PCV approach with existing estimated-breeding-value approaches in two simulation experiments, in which 7 and 20 desirable alleles were to be introgressed from a donor line into a recipient line. Results suggest that the PCV is more efficient and effective for multi-allelic trait introgression. We also discuss how operations research can be used for other crop genetic improvement projects and suggest several future research directions. PMID:28122824
The Water Erosion Prediction Project (WEPP) model for saturation excess conditions: application to an agricultural and a forested watershed.

NASA Astrophysics Data System (ADS)

Crabtree, B.; Brooks, E.; Ostrowski, K.; Elliot, W. J.; Boll, J.

2006-12-01

We incorporated saturation excess overland flow processes in the Water Erosion Prediction Project (WEPP) model for the evaluation of human disturbances in watersheds. In this presentation, we present results of the modified WEPP model to two watersheds: an agricultural watershed with mixed land use, and a forested watershed. The agricultural watershed is Paradise Creek, an intensively monitored watershed with continuous climate, flow and sediment data collection at multiple locations. Restoration efforts in Paradise Creek watershed include changing to minimal tillage or no-tillage sytems, and implementation of structural practices. The forested watershed is the 28 km2 Mica Creek Experimental Watershed (MCEW) where disturbances include clear and partial cutting, and road building. The MCEW has a nested study design, which allows for the analysis of cumulative effects as well as the traditional comparison of treatment versus control. Mica Creek watershed is a high elevation watershed where streamflow is generated mostly by snowmelt. Treatments include road building in 1997, and clearcut and partial-cut logging in 2001. Our results include the simulation of streamflow and sediment delivery at multiple locations within each watershed, and evaluation of the human disturbances.
A recent Cleanroom success story: The Redwing project

NASA Technical Reports Server (NTRS)

Hausler, Philip A.

1992-01-01

Redwing is the largest completed Cleanroom software engineering project in IBM, both in terms of lines of code and project staffing. The product provides a decision-support facility that utilizes artificial intelligence (AI) technology for predicting and preventing complex operating problems in an MVS environment. The project used the Cleanroom process for development and realized a defect rate of 2.6 errors/KLOC, measured from first execution. This represents the total amount of errors that were found in testing and installation at three field test sites. Development productivity was 486 LOC/PM, which included all development labor expended in design specification through completion of incremental testing. In short, the Redwing team produced a complex systems software product with an extraordinarily low error rate, while maintaining high productivity. All of this was accomplished by a project team using Cleanroom for the first time. An 'introductory implementation' of Cleanroom was defined and used on Redwing. This paper describes the quality and productivity results, the Redwing project, and how Cleanroom was implemented.
Formulation Predictive Dissolution (fPD) Testing to Advance Oral Drug Product Development: an Introduction to the US FDA Funded '21st Century BA/BE' Project.

PubMed

Hens, Bart; Sinko, Patrick; Job, Nicholas; Dean, Meagan; Al-Gousous, Jozef; Salehi, Niloufar; Ziff, Robert M; Tsume, Yasuhiro; Bermejo, Marival; Paixão, Paulo; Brasseur, James G; Yu, Alex; Talattof, Arjang; Benninghoff, Gail; Langguth, Peter; Lennernäs, Hans; Hasler, William L; Marciani, Luca; Dickens, Joseph; Shedden, Kerby; Sun, Duxin; Amidon, Gregory E; Amidon, Gordon L

2018-06-23

Over the past decade, formulation predictive dissolution (fPD) testing has gained increasing attention. Another mindset is pushed forward where scientists in our field are more confident to explore the in vivo behavior of an oral drug product by performing predictive in vitro dissolution studies. Similarly, there is an increasing interest in the application of modern computational fluid dynamics (CFD) frameworks and high-performance computing platforms to study the local processes underlying absorption within the gastrointestinal (GI) tract. In that way, CFD and computing platforms both can inform future PBPK-based in silico frameworks and determine the GI-motility-driven hydrodynamic impacts that should be incorporated into in vitro dissolution methods for in vivo relevance. Current compendial dissolution methods are not always reliable to predict the in vivo behavior, especially not for biopharmaceutics classification system (BCS) class 2/4 compounds suffering from a low aqueous solubility. Developing a predictive dissolution test will be more reliable, cost-effective and less time-consuming as long as the predictive power of the test is sufficiently strong. There is a need to develop a biorelevant, predictive dissolution method that can be applied by pharmaceutical drug companies to facilitate marketing access for generic and novel drug products. In 2014, Prof. Gordon L. Amidon and his team initiated a far-ranging research program designed to integrate (1) in vivo studies in humans in order to further improve the understanding of the intraluminal processing of oral dosage forms and dissolved drug along the gastrointestinal (GI) tract, (2) advancement of in vitro methodologies that incorporates higher levels of in vivo relevance and (3) computational experiments to study the local processes underlying dissolution, transport and absorption within the intestines performed with a new unique CFD based framework. Of particular importance is revealing the physiological
Seasonal Prediction with the GEOS GCM

NASA Technical Reports Server (NTRS)

Suarez, Max; Schubert, S.; Chang, Y.

1999-01-01

A number of ensembles of seasonal forecasts have recently been completed as part of NASA's Seasonal to Interannual Prediction Project (NSIPP). The focus is on the extratropical response of the atmosphere to observed SST anomalies during boreal winter. Each prediction consists of nine forecasts starting from slightly different initial conditions. Forecasts are done for every winter from 1981 to 1995 using Version 2 of the GEOS GCM. Comparisons with six long-term integrations (1978-1995) using the same model are used to separate the contributions of initial and boundary conditions to forecast skill. The forecasts also allow us to isolate the SSt forced response (the signal) from the atmosphere's natural variability (the noise).
Uncertainty in weather and climate prediction

PubMed Central

Slingo, Julia; Palmer, Tim

2011-01-01

Following Lorenz's seminal work on chaos theory in the 1960s, probabilistic approaches to prediction have come to dominate the science of weather and climate forecasting. This paper gives a perspective on Lorenz's work and how it has influenced the ways in which we seek to represent uncertainty in forecasts on all lead times from hours to decades. It looks at how model uncertainty has been represented in probabilistic prediction systems and considers the challenges posed by a changing climate. Finally, the paper considers how the uncertainty in projections of climate change can be addressed to deliver more reliable and confident assessments that support decision-making on adaptation and mitigation. PMID:22042896
Development of a multi-ensemble Prediction Model for China

NASA Astrophysics Data System (ADS)

Brasseur, G. P.; Bouarar, I.; Petersen, A. K.

2016-12-01

As part of the EU-sponsored Panda and MarcoPolo Projects, a multi-model prediction system including 7 models has been developed. Most regional models use global air quality predictions provided by the Copernicus Atmospheric Monitoring Service and downscale the forecast at relatively high spatial resolution in eastern China. The paper will describe the forecast system and show examples of forecasts produced for several Chinese urban areas and displayed on a web site developed by the Dutch Meteorological service. A discussion on the accuracy of the predictions based on a detailed validation process using surface measurements from the Chinese monitoring network will be presented.
The NASA Energy and Water Cycle Extreme (NEWSE) Integration Project

NASA Technical Reports Server (NTRS)

House, P. R.; Lapenta, W.; Schiffer, R.

2008-01-01

Skillful predictions of water and energy cycle extremes (flood and drought) are elusive. To better understand the mechanisms responsible for water and energy extremes, and to make decisive progress in predicting these extremes, the collaborative NASA Energy and Water cycle Extremes (NEWSE) Integration Project, is studying these extremes in the U.S. Southern Great Plains (SGP) during 2006-2007, including their relationships with continental and global scale processes, and assessment of their predictability on multiple space and time scales. It is our hypothesis that an integrative analysis of observed extremes which reflects the current understanding of the role of SST and soil moisture variability influences on atmospheric heating and forcing of planetary waves, incorporating recently available global and regional hydro- meteorological datasets (i.e., precipitation, water vapor, clouds, etc.) in conjunction with advances in data assimilation, can lead to new insights into the factors that lead to persistent drought and flooding. We will show initial results of this project, whose goals are to provide an improved definition, attribution and prediction on sub-seasonal to interannual time scales, improved understanding of the mechanisms of decadal drought and its predictability, including the impacts of SST variability and deep soil moisture variability, and improved monitoring/attributions, with transition to applications; a bridging of the gap between hydrological forecasts and stakeholders (utilization of probabilistic forecasts, education, forecast interpretation for different sectors, assessment of uncertainties for different sectors, etc.).
Progress on the DPASS project

NASA Astrophysics Data System (ADS)

Galkin, Sergei A.; Bogatu, I. N.; Svidzinski, V. A.

2015-11-01

A novel project to develop Disruption Prediction And Simulation Suite (DPASS) of comprehensive computational tools to predict, model, and analyze disruption events in tokamaks has been recently started at FAR-TECH Inc. DPASS will eventually address the following aspects of the disruption problem: MHD, plasma edge dynamics, plasma-wall interaction, generation and losses of runaway electrons. DPASS uses the 3-D Disruption Simulation Code (DSC-3D) as a core tool and will have a modular structure. DSC is a one fluid non-linear, time-dependent 3D MHD code to simulate dynamics of tokamak plasma surrounded by pure vacuum B-field in the real geometry of a conducting tokamak vessel. DSC utilizes the adaptive meshless technique with adaptation to the moving plasma boundary, with accurate magnetic flux conservation and resolution of the plasma surface current. DSC has also an option to neglect the plasma inertia to eliminate fast magnetosonic scale. This option can be turned on/off as needed. During Phase I of the project, two modules will be developed: the computational module for modeling the massive gas injection and main plasma respond; and the module for nanoparticle plasma jet injection as an innovative disruption mitigation scheme. We will report on this development progress. Work is supported by the US DOE SBIR grant # DE-SC0013727.
PRESCHOOL PREDICTION AND PREVENTION OF LEARNING DISABILITIES.

ERIC Educational Resources Information Center

BEERY, KEITH E.

THE OBJECTIVES OF THIS INITIAL REPORT OF A FOUR-YEAR PROJECT WERE (1) TO DEMONSTRATE A METHOD FOR THE PREDICTION AND PREVENTION OF LEARNING DISABILITIES, (2) TO FOSTER UNDERSTANDING OF CHILD DEVELOPMENT AMONG TEACHERS, PARENTS, AND PHYSICIANS. SUBJECTS WERE THE 3 1/2 TO 5 1/2 YEAR OLD CHILDREN OF AN ENTIRE SCHOOL DISTRICT. RESEARCHERS WERE…
PROJECTED CLIMATE-INDUCED FAUNAL CHANGE IN THE WESTERN HEMISPHERE

EPA Science Inventory

Climate change is predicted to be one of the greatest drivers of ecological change in the coming century. Increases in temperature over the last century have clearly been linked to shifts in species distributions. Given the magnitude of projected future climatic changes, we can e...
The Teacher's Role When Pupils Work on Task Using ICT in Project Work

ERIC Educational Resources Information Center

Postholm, May Britt

2006-01-01

Background: ICT (information and communication technology) and project work are challenging issues for many teachers to deal with in the classroom. For years there were predictions that teachers, textbooks and even schools would be replaced by new teaching and learning technology. This prediction does not appear to have come about. Cuban has…
Quantitative risk management in gas injection project: a case study from Oman oil and gas industry

NASA Astrophysics Data System (ADS)

Khadem, Mohammad Miftaur Rahman Khan; Piya, Sujan; Shamsuzzoha, Ahm

2017-09-01

The purpose of this research was to study the recognition, application and quantification of the risks associated in managing projects. In this research, the management of risks in an oil and gas project is studied and implemented within a case company in Oman. In this study, at first, the qualitative data related to risks in the project were identified through field visits and extensive interviews. These data were then translated into numerical values based on the expert's opinion. Further, the numerical data were used as an input to Monte Carlo simulation. RiskyProject Professional™ software was used to simulate the system based on the identified risks. The simulation result predicted a delay of about 2 years as a worse case with no chance of meeting the project's on stream date. Also, it has predicted 8% chance of exceeding the total estimated budget. The result of numerical analysis from the proposed model is validated by comparing it with the result of qualitative analysis, which was obtained through discussion with various project managers of company.
Procedures and models for estimating preconstruction costs of highway projects.

DOT National Transportation Integrated Search

2012-07-01

This study presents data driven and component based PE cost prediction models by utilizing critical factors retrieved from ten years of historical project data obtained from ODOT roadway division. The study used factor analysis of covariance and corr...
RNA Secondary Structure Prediction by Using Discrete Mathematics: An Interdisciplinary Research Experience for Undergraduate Students

PubMed Central

Ellington, Roni; Wachira, James

2010-01-01

The focus of this Research Experience for Undergraduates (REU) project was on RNA secondary structure prediction by using a lattice walk approach. The lattice walk approach is a combinatorial and computational biology method used to enumerate possible secondary structures and predict RNA secondary structure from RNA sequences. The method uses discrete mathematical techniques and identifies specified base pairs as parameters. The goal of the REU was to introduce upper-level undergraduate students to the principles and challenges of interdisciplinary research in molecular biology and discrete mathematics. At the beginning of the project, students from the biology and mathematics departments of a mid-sized university received instruction on the role of secondary structure in the function of eukaryotic RNAs and RNA viruses, RNA related to combinatorics, and the National Center for Biotechnology Information resources. The student research projects focused on RNA secondary structure prediction on a regulatory region of the yellow fever virus RNA genome and on an untranslated region of an mRNA of a gene associated with the neurological disorder epilepsy. At the end of the project, the REU students gave poster and oral presentations, and they submitted written final project reports to the program director. The outcome of the REU was that the students gained transferable knowledge and skills in bioinformatics and an awareness of the applications of discrete mathematics to biological research problems. PMID:20810968
RNA secondary structure prediction by using discrete mathematics: an interdisciplinary research experience for undergraduate students.

PubMed

Ellington, Roni; Wachira, James; Nkwanta, Asamoah

2010-01-01

The focus of this Research Experience for Undergraduates (REU) project was on RNA secondary structure prediction by using a lattice walk approach. The lattice walk approach is a combinatorial and computational biology method used to enumerate possible secondary structures and predict RNA secondary structure from RNA sequences. The method uses discrete mathematical techniques and identifies specified base pairs as parameters. The goal of the REU was to introduce upper-level undergraduate students to the principles and challenges of interdisciplinary research in molecular biology and discrete mathematics. At the beginning of the project, students from the biology and mathematics departments of a mid-sized university received instruction on the role of secondary structure in the function of eukaryotic RNAs and RNA viruses, RNA related to combinatorics, and the National Center for Biotechnology Information resources. The student research projects focused on RNA secondary structure prediction on a regulatory region of the yellow fever virus RNA genome and on an untranslated region of an mRNA of a gene associated with the neurological disorder epilepsy. At the end of the project, the REU students gave poster and oral presentations, and they submitted written final project reports to the program director. The outcome of the REU was that the students gained transferable knowledge and skills in bioinformatics and an awareness of the applications of discrete mathematics to biological research problems.
Analysis of Installed Measures and Energy Savings for Single-Family Residential Better Buildings Projects

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

Heaney, M.; Polly, B.

2015-04-30

This report presents an analysis of data for residential single-family projects reported by 37 organizations that were awarded federal financial assistance (cooperative agreements or grants) by the U.S. Department of Energy’s Better Buildings Neighborhood Program.1 The report characterizes the energy-efficiency measures installed for single-family residential projects and analyzes energy savings and savings prediction accuracy for measures installed in a subset of those projects.

Translating Uncertain Sea Level Projections Into Infrastructure Impacts Using a Bayesian Framework

NASA Astrophysics Data System (ADS)

Moftakhari, Hamed; AghaKouchak, Amir; Sanders, Brett F.; Matthew, Richard A.; Mazdiyasni, Omid

2017-12-01

Climate change may affect ocean-driven coastal flooding regimes by both raising the mean sea level (msl) and altering ocean-atmosphere interactions. For reliable projections of coastal flood risk, information provided by different climate models must be considered in addition to associated uncertainties. In this paper, we propose a framework to project future coastal water levels and quantify the resulting flooding hazard to infrastructure. We use Bayesian Model Averaging to generate a weighted ensemble of storm surge predictions from eight climate models for two coastal counties in California. The resulting ensembles combined with msl projections, and predicted astronomical tides are then used to quantify changes in the likelihood of road flooding under representative concentration pathways 4.5 and 8.5 in the near-future (1998-2063) and mid-future (2018-2083). The results show that road flooding rates will be significantly higher in the near-future and mid-future compared to the recent past (1950-2015) if adaptation measures are not implemented.
Establishing the validity and reliability of the Project Talent Personality Inventory

PubMed Central

Pozzebon, Julie; Damian, Rodica I.; Hill, Patrick L.; Lin, Yuchen; Lapham, Susan; Roberts, Brent W.

2013-01-01

Project Talent is a national longitudinal study that started in 1960. The original sample included over 440,000 students, which amounted to a 5% representative sample of high school students across the United States. Previous research has not yet established the validity and reliability of the personality measure used in this study, that is, the Project Talent Personality Inventory (PTPI). Given the potential interest and use of the PTPI in forthcoming research, the goals of the present paper were to establish (a) the construct and predictive validity and (b) the internal consistency and test-retest reliability of the PTPI. This information will be valuable to researchers who might be interested in using the PTPI to predict life course outcomes, such as mortality, occupational success, relationship success, and health. Study 1 found that the 10 sub-scales of the PTPI showed good internal consistency reliability, as well as good construct and predictive validity. With the use of several modern personality measures, we showed how the 10 PTPI scales can be mapped onto the Big Five personality traits, and we examined their relations with health, well-being, and life satisfaction outcomes. Study 2 found that the 10 PTPI scales showed good test-retest reliability. Together, these findings allow researchers to better understand and use the PTPI scales, as they are available in Project Talent. PMID:24399984
Preventing patient absenteeism: validation of a predictive overbooking model.

PubMed

Reid, Mark W; Cohen, Samuel; Wang, Hank; Kaung, Aung; Patel, Anish; Tashjian, Vartan; Williams, Demetrius L; Martinez, Bibiana; Spiegel, Brennan M R

2015-12-01

To develop a model that identifies patients at high risk for missing scheduled appointments ("no-shows" and cancellations) and to project the impact of predictive overbooking in a gastrointestinal endoscopy clinic-an exemplar resource-intensive environment with a high no-show rate. We retrospectively developed an algorithm that uses electronic health record (EHR) data to identify patients who do not show up to their appointments. Next, we prospectively validated the algorithm at a Veterans Administration healthcare network clinic. We constructed a multivariable logistic regression model that assigned a no-show risk score optimized by receiver operating characteristic curve analysis. Based on these scores, we created a calendar of projected open slots to offer to patients and compared the daily performance of predictive overbooking with fixed overbooking and typical "1 patient, 1 slot" scheduling. Data from 1392 patients identified several predictors of no-show, including previous absenteeism, comorbid disease burden, and current diagnoses of mood and substance use disorders. The model correctly classified most patients during the development (area under the curve [AUC] = 0.80) and validation phases (AUC = 0.75). Prospective testing in 1197 patients found that predictive overbooking averaged 0.51 unused appointments per day versus 6.18 for typical booking (difference = -5.67; 95% CI, -6.48 to -4.87; P < .0001). Predictive overbooking could have increased service utilization from 62% to 97% of capacity, with only rare clinic overflows. Information from EHRs can accurately predict whether patients will no-show. This method can be used to overbook appointments, thereby maximizing service utilization while staying within clinic capacity.
Can air temperature be used to project influences of climate change on stream temperature?

USGS Publications Warehouse

Arismendi, Ivan; Safeeq, Mohammad; Dunham, Jason B.; Johnson, Sherri L.

2014-01-01

Worldwide, lack of data on stream temperature has motivated the use of regression-based statistical models to predict stream temperatures based on more widely available data on air temperatures. Such models have been widely applied to project responses of stream temperatures under climate change, but the performance of these models has not been fully evaluated. To address this knowledge gap, we examined the performance of two widely used linear and nonlinear regression models that predict stream temperatures based on air temperatures. We evaluated model performance and temporal stability of model parameters in a suite of regulated and unregulated streams with 11–44 years of stream temperature data. Although such models may have validity when predicting stream temperatures within the span of time that corresponds to the data used to develop them, model predictions did not transfer well to other time periods. Validation of model predictions of most recent stream temperatures, based on air temperature–stream temperature relationships from previous time periods often showed poor performance when compared with observed stream temperatures. Overall, model predictions were less robust in regulated streams and they frequently failed in detecting the coldest and warmest temperatures within all sites. In many cases, the magnitude of errors in these predictions falls within a range that equals or exceeds the magnitude of future projections of climate-related changes in stream temperatures reported for the region we studied (between 0.5 and 3.0 °C by 2080). The limited ability of regression-based statistical models to accurately project stream temperatures over time likely stems from the fact that underlying processes at play, namely the heat budgets of air and water, are distinctive in each medium and vary among localities and through time.
An information model for use in software management estimation and prediction

NASA Technical Reports Server (NTRS)

Li, Ningda R.; Zelkowitz, Marvin V.

1993-01-01

This paper describes the use of cluster analysis for determining the information model within collected software engineering development data at the NASA/GSFC Software Engineering Laboratory. We describe the Software Management Environment tool that allows managers to predict development attributes during early phases of a software project and the modifications we propose to allow it to develop dynamic models for better predictions of these attributes.
Frequency, probability, and prediction: easy solutions to cognitive illusions?

PubMed

Griffin, D; Buehler, R

1999-02-01

Many errors in probabilistic judgment have been attributed to people's inability to think in statistical terms when faced with information about a single case. Prior theoretical analyses and empirical results imply that the errors associated with case-specific reasoning may be reduced when people make frequentistic predictions about a set of cases. In studies of three previously identified cognitive biases, we find that frequency-based predictions are different from-but no better than-case-specific judgments of probability. First, in studies of the "planning fallacy, " we compare the accuracy of aggregate frequency and case-specific probability judgments in predictions of students' real-life projects. When aggregate and single-case predictions are collected from different respondents, there is little difference between the two: Both are overly optimistic and show little predictive validity. However, in within-subject comparisons, the aggregate judgments are significantly more conservative than the single-case predictions, though still optimistically biased. Results from studies of overconfidence in general knowledge and base rate neglect in categorical prediction underline a general conclusion. Frequentistic predictions made for sets of events are no more statistically sophisticated, nor more accurate, than predictions made for individual events using subjective probability. Copyright 1999 Academic Press.
Can decadal climate predictions be improved by ocean ensemble dispersion filtering?

NASA Astrophysics Data System (ADS)

Kadow, C.; Illing, S.; Kröner, I.; Ulbrich, U.; Cubasch, U.

2017-12-01

Decadal predictions by Earth system models aim to capture the state and phase of the climate several years inadvance. Atmosphere-ocean interaction plays an important role for such climate forecasts. While short-termweather forecasts represent an initial value problem and long-term climate projections represent a boundarycondition problem, the decadal climate prediction falls in-between these two time scales. The ocean memorydue to its heat capacity holds big potential skill on the decadal scale. In recent years, more precise initializationtechniques of coupled Earth system models (incl. atmosphere and ocean) have improved decadal predictions.Ensembles are another important aspect. Applying slightly perturbed predictions results in an ensemble. Insteadof using and evaluating one prediction, but the whole ensemble or its ensemble average, improves a predictionsystem. However, climate models in general start losing the initialized signal and its predictive skill from oneforecast year to the next. Here we show that the climate prediction skill of an Earth system model can be improvedby a shift of the ocean state toward the ensemble mean of its individual members at seasonal intervals. Wefound that this procedure, called ensemble dispersion filter, results in more accurate results than the standarddecadal prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions showan increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with largerensembles and higher resolution. Our results demonstrate how decadal climate predictions benefit from oceanensemble dispersion filtering toward the ensemble mean. This study is part of MiKlip (fona-miklip.de) - a major project on decadal climate prediction in Germany.We focus on the Max-Planck-Institute Earth System Model using the low-resolution version (MPI-ESM-LR) andMiKlip's basic initialization strategy as in 2017 published decadal climate forecast: http
Space Mission Human Reliability Analysis (HRA) Project

NASA Technical Reports Server (NTRS)

Boyer, Roger

2014-01-01

The purpose of the Space Mission Human Reliability Analysis (HRA) Project is to extend current ground-based HRA risk prediction techniques to a long-duration, space-based tool. Ground-based HRA methodology has been shown to be a reasonable tool for short-duration space missions, such as Space Shuttle and lunar fly-bys. However, longer-duration deep-space missions, such as asteroid and Mars missions, will require the crew to be in space for as long as 400 to 900 day missions with periods of extended autonomy and self-sufficiency. Current indications show higher risk due to fatigue, physiological effects due to extended low gravity environments, and others, may impact HRA predictions. For this project, Safety & Mission Assurance (S&MA) will work with Human Health & Performance (HH&P) to establish what is currently used to assess human reliabiilty for human space programs, identify human performance factors that may be sensitive to long duration space flight, collect available historical data, and update current tools to account for performance shaping factors believed to be important to such missions. This effort will also contribute data to the Human Performance Data Repository and influence the Space Human Factors Engineering research risks and gaps (part of the HRP Program). An accurate risk predictor mitigates Loss of Crew (LOC) and Loss of Mission (LOM).The end result will be an updated HRA model that can effectively predict risk on long-duration missions.
DOE Contribution to the 2015 US CLIVAR Project Office Budget

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

DeWeaver, Eric; Patterson, Michael

The primary goal of the US Climate Variability and Predictability (CLIVAR) Project Office is to enable science community planning and implementation of research to understand and predict climate variability and change on intraseasonal-to-centennial timescales, through observations and modeling with emphasis on the role of the ocean and its interaction with other elements of the Earth system, and to serve the climate community and society through the coordination and facilitation of research on outstanding climate questions.
A pilot study of NMR-based sensory prediction of roasted coffee bean extracts.

PubMed

Wei, Feifei; Furihata, Kazuo; Miyakawa, Takuya; Tanokura, Masaru

2014-01-01

Nuclear magnetic resonance (NMR) spectroscopy can be considered a kind of "magnetic tongue" for the characterisation and prediction of the tastes of foods, since it provides a wealth of information in a nondestructive and nontargeted manner. In the present study, the chemical substances in roasted coffee bean extracts that could distinguish and predict the different sensations of coffee taste were identified by the combination of NMR-based metabolomics and human sensory test and the application of the multivariate projection method of orthogonal projection to latent structures (OPLS). In addition, the tastes of commercial coffee beans were successfully predicted based on their NMR metabolite profiles using our OPLS model, suggesting that NMR-based metabolomics accompanied with multiple statistical models is convenient, fast and accurate for the sensory evaluation of coffee. Copyright © 2013 Elsevier Ltd. All rights reserved.
Evaluating Forest Vegetation Simulator predictions for southern Appalachian upland hardwoods with a modified mortality model

Treesearch

Philip J. Radtke; Nathan D. Herring; David L. Loftis; Chad E. Keyser

2012-01-01

Prediction accuracy for projected basal area and trees per acre was assessed for the growth and yield model of the Forest Vegetation Simulator Southern Variant (FVS-Sn). Data for comparison with FVS-Sn predictions were compiled from a collection of n
What Time is Your Sunset? Accounting for Refraction in Sunrise/set Prediction Models

NASA Astrophysics Data System (ADS)

Wilson, Teresa; Bartlett, Jennifer Lynn; Chizek Frouard, Malynda; Hilton, James; Phlips, Alan; Edgar, Roman

2018-01-01

Algorithms that predict sunrise and sunset times currently have an uncertainty of one to four minutes at mid-latitudes (0° - 55° N/S) due to limitations in the atmospheric models they incorporate. At higher latitudes, slight changes in refraction can cause significant discrepancies, including difficulties determining whether the Sun appears to rise or set. While different components of refraction are known, how they affect predictions of sunrise/set has not yet been quantified. A better understanding of the contributions from temperature profile, pressure, humidity, and aerosols could significantly improve the standard prediction.We present a sunrise/set calculator that interchanges the refraction component by varying the refraction model. We, then, compared these predictions with data sets of observed rise/set times taken from Mount Wilson Observatory in California, University of Alberta in Edmonton, Alberta, and onboard the SS James Franco in the Atlantic. A thorough investigation of the problem requires a more substantial data set of observed rise/set times and corresponding meteorological data from around the world.We have developed a mobile application, Sunrise & Sunset Observer, so that anyone can capture this astronomical and meteorological data using their smartphone video recorder as part of a citizen science project. The Android app for this project is available in the Google Play store. Videos can also be submitted through the project website (riseset.phy.mtu.edu). Data analysis will lead to more complete models that will provide higher accuracy rise/set predictions to benefit astronomers, navigators, and outdoorsmen everywhere.
Comparison of forward- and back-projection in vivo EPID dosimetry for VMAT treatment of the prostate

NASA Astrophysics Data System (ADS)

Bedford, James L.; Hanson, Ian M.; Hansen, Vibeke N.

2018-01-01

In the forward-projection method of portal dosimetry for volumetric modulated arc therapy (VMAT), the integrated signal at the electronic portal imaging device (EPID) is predicted at the time of treatment planning, against which the measured integrated image is compared. In the back-projection method, the measured signal at each gantry angle is back-projected through the patient CT scan to give a measure of total dose to the patient. This study aims to investigate the practical agreement between the two types of EPID dosimetry for prostate radiotherapy. The AutoBeam treatment planning system produced VMAT plans together with corresponding predicted portal images, and a total of 46 sets of gantry-resolved portal images were acquired in 13 patients using an iViewGT portal imager. For the forward-projection method, each acquisition of gantry-resolved images was combined into a single integrated image and compared with the predicted image. For the back-projection method, iViewDose was used to calculate the dose distribution in the patient for comparison with the planned dose. A gamma index for 3% and 3 mm was used for both methods. The results were investigated by delivering the same plans to a phantom and repeating some of the deliveries with deliberately introduced errors. The strongest agreement between forward- and back-projection methods is seen in the isocentric intensity/dose difference, with moderate agreement in the mean gamma. The strongest correlation is observed within a given patient, with less correlation between patients, the latter representing the accuracy of prediction of the two methods. The error study shows that each of the two methods has its own distinct sensitivity to errors, but that overall the response is similar. The forward- and back-projection EPID dosimetry methods show moderate agreement in this series of prostate VMAT patients, indicating that both methods can contribute to the verification of dose delivered to the patient.
Spatial regression methods capture prediction uncertainty in species distribution model projections through time

Treesearch

Alan K. Swanson; Solomon Z. Dobrowski; Andrew O. Finley; James H. Thorne; Michael K. Schwartz

2013-01-01

The uncertainty associated with species distribution model (SDM) projections is poorly characterized, despite its potential value to decision makers. Error estimates from most modelling techniques have been shown to be biased due to their failure to account for spatial autocorrelation (SAC) of residual error. Generalized linear mixed models (GLMM) have the ability to...
NASA atmospheric effects of aviation projects: Status and plans

NASA Technical Reports Server (NTRS)

Wesoky, Howard L.; Thompson, Anne M.; Stolarski, Richard S.

1994-01-01

NASA's Atmospheric Effects of Aviation Project is developing a scientific basis for assessment of the atmospheric impact of subsonic and supersonic aviation. Issues addressed include predicted ozone changes and climatic impact, and related uncertainties. A primary goal is to assist assessments of United Nations scientific organizations and, hence, consideration of emission standards by the International Civil Aviation Organization. Project focus is on simulation of atmospheric processes by computer models, but studies of aircraft operations, laboratory studies, and remote and in situ observations of chemical, dynamic, and radiative processes are also included.
Project Ukko - Design of a climate service visualisation interface for seasonal wind forecasts

NASA Astrophysics Data System (ADS)

Hemment, Drew; Stefaner, Moritz; Makri, Stephann; Buontempo, Carlo; Christel, Isadora; Torralba-Fernandez, Veronica; Gonzalez-Reviriego, Nube; Doblas-Reyes, Francisco; de Matos, Paula; Dykes, Jason

2016-04-01

Project Ukko is a prototype climate service to visually communicate probabilistic seasonal wind forecasts for the energy sector. In Project Ukko, an interactive visualisation enhances the accessibility and readability to the latests advances in seasonal wind speed predictions developed as part of the RESILIENCE prototype of the EUPORIAS (EC FP7) project. Climate services provide made-to-measure climate information, tailored to the specific requirements of different users and industries. In the wind energy sector, understanding of wind conditions in the next few months has high economic value, for instance, for the energy traders. Current energy practices use retrospective climatology, but access to reliable seasonal predictions based in the recent advances in global climate models has potential to improve their resilience to climate variability and change. Despite their potential benefits, a barrier to the development of commercially viable services is the complexity of the probabilistic forecast information, and the challenge of communicating complex and uncertain information to decision makers in industry. Project Ukko consists of an interactive climate service interface for wind energy users to explore probabilistic wind speed predictions for the coming season. This interface enables fast visual detection and exploration of interesting features and regions likely to experience unusual changes in wind speed in the coming months.The aim is not only to support users to better understand the future variability in wind power resources, but also to bridge the gap between practitioners' traditional approach and the advanced prediction systems developed by the climate science community. Project Ukko is presented as a case study of cross-disciplinary collaboration between climate science and design, for the development of climate services that are useful, usable and effective for industry users. The presentation will reflect on the challenge of developing a climate
Advancing decadal-scale climate prediction in the North Atlantic sector.

PubMed

Keenlyside, N S; Latif, M; Jungclaus, J; Kornblueh, L; Roeckner, E

2008-05-01

The climate of the North Atlantic region exhibits fluctuations on decadal timescales that have large societal consequences. Prominent examples include hurricane activity in the Atlantic, and surface-temperature and rainfall variations over North America, Europe and northern Africa. Although these multidecadal variations are potentially predictable if the current state of the ocean is known, the lack of subsurface ocean observations that constrain this state has been a limiting factor for realizing the full skill potential of such predictions. Here we apply a simple approach-that uses only sea surface temperature (SST) observations-to partly overcome this difficulty and perform retrospective decadal predictions with a climate model. Skill is improved significantly relative to predictions made with incomplete knowledge of the ocean state, particularly in the North Atlantic and tropical Pacific oceans. Thus these results point towards the possibility of routine decadal climate predictions. Using this method, and by considering both internal natural climate variations and projected future anthropogenic forcing, we make the following forecast: over the next decade, the current Atlantic meridional overturning circulation will weaken to its long-term mean; moreover, North Atlantic SST and European and North American surface temperatures will cool slightly, whereas tropical Pacific SST will remain almost unchanged. Our results suggest that global surface temperature may not increase over the next decade, as natural climate variations in the North Atlantic and tropical Pacific temporarily offset the projected anthropogenic warming.
Gray correlation analysis and prediction models of living refuse generation in Shanghai city.

PubMed

Liu, Gousheng; Yu, Jianguo

2007-01-01

A better understanding of the factors that affect the generation of municipal living refuse (MLF) and the accurate prediction of its generation are crucial for municipal planning projects and city management. Up to now, most of the design efforts have been based on a rough prediction of MLF without any actual support. In this paper, based on published data of socioeconomic variables and MLF generation from 1990 to 2003 in the city of Shanghai, the main factors that affect MLF generation have been quantitatively studied using the method of gray correlation coefficient. Several gray models, such as GM(1,1), GIM(1), GPPM(1) and GLPM(1), have been studied, and predicted results are verified with subsequent residual test. Results show that, among the selected seven factors, consumption of gas, water and electricity are the largest three factors affecting MLF generation, and GLPM(1) is the optimized model to predict MLF generation. Through this model, the predicted MLF generation in 2010 in Shanghai will be 7.65 million tons. The methods and results developed in this paper can provide valuable information for MLF management and related municipal planning projects.
Prediction Interval Development for Wind-Tunnel Balance Check-Loading

NASA Technical Reports Server (NTRS)

Landman, Drew; Toro, Kenneth G.; Commo, Sean A.; Lynn, Keith C.

2014-01-01

Results from the Facility Analysis Verification and Operational Reliability project revealed a critical gap in capability in ground-based aeronautics research applications. Without a standardized process for check-loading the wind-tunnel balance or the model system, the quality of the aerodynamic force data collected varied significantly between facilities. A prediction interval is required in order to confirm a check-loading. The prediction interval provides an expected upper and lower bound on balance load prediction at a given confidence level. A method has been developed which accounts for sources of variability due to calibration and check-load application. The prediction interval method of calculation and a case study demonstrating its use is provided. Validation of the methods is demonstrated for the case study based on the probability of capture of confirmation points.
Comparison of WEPP and APEX runoff and erosion prediction at field scale in Goodwater Creek Experimental Watershed

USDA-ARS?s Scientific Manuscript database

The Water Erosion Prediction Project (WEPP) and the Agricultural Policy/Environmental eXtender (APEX) are process-based models that can predict spatial and temporal distributions of erosion for hillslopes and watersheds. This study applies the WEPP model to predict runoff and erosion for a 35-ha fie...

The Climate Variability & Predictability (CVP) Program at NOAA - Recent Program Advancements in Understanding AMOC

NASA Astrophysics Data System (ADS)

Lucas, S. E.

2016-12-01

The Climate Variability & Predictability (CVP) Program supports research aimed at providing process-level understanding of the climate system through observation, modeling, analysis, and field studies. This vital knowledge is needed to improve climate models and predictions so that scientists can better anticipate the impacts of future climate variability and change. To achieve its mission, the CVP Program supports research carried out at NOAA and other federal laboratories, NOAA Cooperative Institutes, and academic institutions. The Program also coordinates its sponsored projects with major national and international scientific bodies including the World Climate Research Programme (WCRP), the International and U.S. Climate Variability and Predictability (CLIVAR/US CLIVAR) Program, and the U.S. Global Change Research Program (USGCRP). The CVP program sits within NOAA's Climate Program Office (http://cpo.noaa.gov/CVP). This poster will present the recently funded CVP projects on improving the understanding Atlantic Meridional Overturning Circulation (AMOC), its impact on decadal predictability, and its relationship with the overall climate system.
The Predictability of Extratropical Transition and of its Impact on the Downstream Flow

DTIC Science & Technology

2008-03-28

is predicted to reach a continent as an extratropical storm . Arguably the larger impact on predictability, however, occurs due to the above mentioned...Office of Naval Research Project The Predictability of Extratropical Transition and of its Impact on the Downstream Flow Award Number: N00014-06-1...12 0 76128 Karlsruhe 0 March 28, 2008 1 * 4 -d 60-( CONTENTS 3 Contents 1 Objectives 5 2 Scientific Importance 6 3 Extratropical Transition in
Does Specification Matter? Experiments with Simple Multiregional Probabilistic Population Projections

PubMed Central

Raymer, James; Abel, Guy J.; Rogers, Andrei

2012-01-01

Population projection models that introduce uncertainty are a growing subset of projection models in general. In this paper, we focus on the importance of decisions made with regard to the model specifications adopted. We compare the forecasts and prediction intervals associated with four simple regional population projection models: an overall growth rate model, a component model with net migration, a component model with in-migration and out-migration rates, and a multiregional model with destination-specific out-migration rates. Vector autoregressive models are used to forecast future rates of growth, birth, death, net migration, in-migration and out-migration, and destination-specific out-migration for the North, Midlands and South regions in England. They are also used to forecast different international migration measures. The base data represent a time series of annual data provided by the Office for National Statistics from 1976 to 2008. The results illustrate how both the forecasted subpopulation totals and the corresponding prediction intervals differ for the multiregional model in comparison to other simpler models, as well as for different assumptions about international migration. The paper ends end with a discussion of our results and possible directions for future research. PMID:23236221
Predicting the Orbits of Satellites with a TI-85 Calculator.

ERIC Educational Resources Information Center

Papay, Kate; And Others

1996-01-01

Describes a project that predicts the orbits of satellites using a TI-85 calculator. Enables students to achieve a richer understanding of longitude, latitude, time zones, orbital mechanics of satellites, and the terms associated with satellite tracking. (JRH)
ISMIP6: Ice Sheet Model Intercomparison Project for CMIP6

NASA Technical Reports Server (NTRS)

Nowicki, S.

2015-01-01

ISMIP6 (Ice Sheet Model Intercomparison Project for CMIP6) targets the Cryosphere in a Changing Climate and the Future Sea Level Grand Challenges of the WCRP (World Climate Research Program). Primary goal is to provide future sea level contribution from the Greenland and Antarctic ice sheets, along with associated uncertainty. Secondary goal is to investigate feedback due to dynamic ice sheet models. Experiment design uses and augment the existing CMIP6 (Coupled Model Intercomparison Project Phase 6) DECK (Diagnosis, Evaluation, and Characterization of Klima) experiments. Additonal MIP (Model Intercomparison Project)- specific experiments will be designed for ISM (Ice Sheet Model). Effort builds on the Ice2sea, SeaRISE (Sea-level Response to Ice Sheet Evolution) and COMBINE (Comprehensive Modelling of the Earth System for Better Climate Prediction and Projection) efforts.
Data driven propulsion system weight prediction model

NASA Astrophysics Data System (ADS)

Gerth, Richard J.

1994-10-01

The objective of the research was to develop a method to predict the weight of paper engines, i.e., engines that are in the early stages of development. The impetus for the project was the Single Stage To Orbit (SSTO) project, where engineers need to evaluate alternative engine designs. Since the SSTO is a performance driven project the performance models for alternative designs were well understood. The next tradeoff is weight. Since it is known that engine weight varies with thrust levels, a model is required that would allow discrimination between engines that produce the same thrust. Above all, the model had to be rooted in data with assumptions that could be justified based on the data. The general approach was to collect data on as many existing engines as possible and build a statistical model of the engines weight as a function of various component performance parameters. This was considered a reasonable level to begin the project because the data would be readily available, and it would be at the level of most paper engines, prior to detailed component design.
Integrative sensing and prediction of urban water for sustainable cities (iSPUW)

NASA Astrophysics Data System (ADS)

Seo, D. J.; Fang, N. Z.; Yu, X.; Zink, M.; Gao, J.; Kerkez, B.

2014-12-01

We describe a newly launched project in the Dallas-Fort Worth Metroplex (DFW) area to develop a cyber-physical prototype system that integrates advanced sensing, modeling and prediction of urban water, to support its early adoption by a spectrum of users and stakeholders, and to educate a new generation of future sustainability scientists and engineers. The project utilizes the very high-resolution precipitation and other sensing capabilities uniquely available in DFW as well as crowdsourcing and cloud computing to advance understanding of the urban water cycle and to improve urban sustainability from transient shocks of heavy-to-extreme precipitation under climate change and urbanization. All available water information from observations and models will be fused objectively via advanced data assimilation to produce the best estimate of the state of the uncertain system. Modeling, prediction and decision support tools will be developed in the ensemble framework to increase the information content of the analysis and prediction and to support risk-based decision making.
The Cosmetics Europe strategy for animal-free genotoxicity testing: project status up-date.

PubMed

Pfuhler, S; Fautz, R; Ouedraogo, G; Latil, A; Kenny, J; Moore, C; Diembeck, W; Hewitt, N J; Reisinger, K; Barroso, J

2014-02-01

The Cosmetics Europe (formerly COLIPA) Genotoxicity Task Force has driven and funded three projects to help address the high rate of misleading positives in in vitro genotoxicity tests: The completed "False Positives" project optimized current mammalian cell assays and showed that the predictive capacity of the in vitro micronucleus assay was improved dramatically by selecting more relevant cells and more sensitive toxicity measures. The on-going "3D skin model" project has been developed and is now validating the use of human reconstructed skin (RS) models in combination with the micronucleus (MN) and Comet assays. These models better reflect the in use conditions of dermally applied products, such as cosmetics. Both assays have demonstrated good inter- and intra-laboratory reproducibility and are entering validation stages. The completed "Metabolism" project investigated enzyme capacities of human skin and RS models. The RS models were shown to have comparable metabolic capacity to native human skin, confirming their usefulness for testing of compounds with dermal exposure. The program has already helped to improve the initial test battery predictivity and the RS projects have provided sound support for their use as a follow-up test in the assessment of the genotoxic hazard of cosmetic ingredients in the absence of in vivo data. Copyright © 2013 Elsevier Ltd. All rights reserved.
Predictive Values of the New Sarcopenia Index by the Foundation for the National Institutes of Health Sarcopenia Project for Mortality among Older Korean Adults.

PubMed

Moon, Joon Ho; Kim, Kyoung Min; Kim, Jung Hee; Moon, Jae Hoon; Choi, Sung Hee; Lim, Soo; Lim, Jae-Young; Kim, Ki Woong; Park, Kyong Soo; Jang, Hak Chul

2016-01-01

We evaluated the Foundation for the National Institutes of Health (FNIH) Sarcopenia Project's recommended criteria for sarcopenia's association with mortality among older Korean adults. We conducted a community-based prospective cohort study which included 560 (285 men and 275 women) older Korean adults aged ≥65 years. Muscle mass (appendicular skeletal muscle mass-to-body mass index ratio (ASM/BMI)), handgrip strength, and walking velocity were evaluated in association with all-cause mortality during 6-year follow-up. Both the lowest quintile for each parameter (ethnic-specific cutoff) and FNIH-recommended values were used as cutoffs. Forty men (14.0%) and 21 women (7.6%) died during 6-year follow-up. The deceased subjects were older and had lower ASM, handgrip strength, and walking velocity. Sarcopenia defined by both low lean mass and weakness had a 4.13 (95% CI, 1.69-10.11) times higher risk of death, and sarcopenia defined by a combination of low lean mass, weakness, and slowness had a 9.56 (3.16-28.90) times higher risk of death after adjusting for covariates in men. However, these significant associations were not observed in women. In terms of cutoffs of each parameter, using the lowest quintile showed better predictive values in mortality than using the FNIH-recommended values. Moreover, new muscle mass index, ASM/BMI, provided better prognostic values than ASM/height2 in all associations. New sarcopenia definition by FNIH was better able to predict 6-year mortality among Korean men. Moreover, ethnic-specific cutoffs, the lowest quintile for each parameter, predicted the higher risk of mortality than the FNIH-recommended values.
Project Echo: Antenna Steering System

NASA Technical Reports Server (NTRS)

Klahn, R.; Norton, J. A.; Githens, J. A.

1961-01-01

The Project Echo communications experiment employed large, steerable,transmitting and receiving antennas at the ground terminals. It was necessary that these highly directional antennas be continuously and accurately pointed at the passing satellite. This paper describes a new type of special purpose data converter for directing narrow-beam communication antennas on the basis of predicted information. The system is capable of converting digital input data into real-time analog voltage commands with a dynamic accuracy of +/- 0.05 degree, which meets the requirements of the present antennas.
Development of a funding, cost, and spending model for satellite projects

NASA Technical Reports Server (NTRS)

Johnson, Jesse P.

1989-01-01

The need for a predictive budget/funging model is obvious. The current models used by the Resource Analysis Office (RAO) are used to predict the total costs of satellite projects. An effort to extend the modeling capabilities from total budget analysis to total budget and budget outlays over time analysis was conducted. A statistical based and data driven methodology was used to derive and develop the model. Th budget data for the last 18 GSFC-sponsored satellite projects were analyzed and used to build a funding model which would describe the historical spending patterns. This raw data consisted of dollars spent in that specific year and their 1989 dollar equivalent. This data was converted to the standard format used by the RAO group and placed in a database. A simple statistical analysis was performed to calculate the gross statistics associated with project length and project cost ant the conditional statistics on project length and project cost. The modeling approach used is derived form the theory of embedded statistics which states that properly analyzed data will produce the underlying generating function. The process of funding large scale projects over extended periods of time is described by Life Cycle Cost Models (LCCM). The data was analyzed to find a model in the generic form of a LCCM. The model developed is based on a Weibull function whose parameters are found by both nonlinear optimization and nonlinear regression. In order to use this model it is necessary to transform the problem from a dollar/time space to a percentage of total budget/time space. This transformation is equivalent to moving to a probability space. By using the basic rules of probability, the validity of both the optimization and the regression steps are insured. This statistically significant model is then integrated and inverted. The resulting output represents a project schedule which relates the amount of money spent to the percentage of project completion.
Science in 60 â Tabletop Fire Prediction

ScienceCinema

Cary, Lyle

2018-01-16

At the Interagency Fire Center at Los Alamos National Laboratory, experts rely on the state-of-the-art Simtable to help them predict the unpredictable. With algorithms that include camera-based object-tracking and projection developed by the Laboratory, the emergency operations team can simulate a wildland fire spreading across any terrain. The table system, developed and marketed by Simtable, a Santa Fe, N.M., company, takes into account weather, vegetation and fuel conditions. The project also received funding from the LANS Venture Acceleration Fund to improve the user interface.
A supply model for nurse workforce projection in Malaysia.

PubMed

Abas, Zuraida Abal; Ramli, Mohamad Raziff; Desa, Mohamad Ishak; Saleh, Nordin; Hanafiah, Ainul Nadziha; Aziz, Nuraini; Abidin, Zaheera Zainal; Shibghatullah, Abdul Samad; Rahman, Ahmad Fadzli Nizam Abdul; Musa, Haslinda

2017-08-18

The paper aims to provide an insight into the significance of having a simulation model to forecast the supply of registered nurses for health workforce planning policy using System Dynamics. A model is highly in demand to predict the workforce demand for nurses in the future, which it supports for complete development of a needs-based nurse workforce projection using Malaysia as a case study. The supply model consists of three sub-models to forecast the number of registered nurses for the next 15 years: training model, population model and Full Time Equivalent (FTE) model. In fact, the training model is for predicting the number of newly registered nurses after training is completed. Furthermore, the population model is for indicating the number of registered nurses in the nation and the FTE model is useful for counting the number of registered nurses with direct patient care. Each model is described in detail with the logical connection and mathematical governing equation for accurate forecasting. The supply model is validated using error analysis approach in terms of the root mean square percent error and the Theil inequality statistics, which is mportant for evaluating the simulation results. Moreover, the output of simulation results provides a useful insight for policy makers as a what-if analysis is conducted. Some recommendations are proposed in order to deal with the nursing deficit. It must be noted that the results from the simulation model will be used for the next stage of the Needs-Based Nurse Workforce projection project. The impact of this study is that it provides the ability for greater planning and policy making with better predictions.
DNA Fingerprinting of Trout Lilies: A High School Student Project.

ERIC Educational Resources Information Center

Vann, Carolyn N.; Saxon, Herbert L.; Brblic, Tom; Elliades, Stacie; Lambert, Scott; Shaw, Jake; Smith, Ryan; Inman, Megan

1998-01-01

Reports on a student's research project on the degree of genetic diversity within the trout lily species. Enables a rough prediction of the continuance of the species and provides insight into how to manage plants that might be endangered. Contains 16 references. (DDR)
StePS: Stereographically Projected Cosmological Simulations

NASA Astrophysics Data System (ADS)

Rácz, Gábor; Szapudi, István; Csabai, István; Dobos, László

2018-05-01

StePS (Stereographically Projected Cosmological Simulations) compactifies the infinite spatial extent of the Universe into a finite sphere with isotropic boundary conditions to simulate the evolution of the large-scale structure. This eliminates the need for periodic boundary conditions, which are a numerical convenience unsupported by observation and which modifies the law of force on large scales in an unrealistic fashion. StePS uses stereographic projection for space compactification and naive O(N2) force calculation; this arrives at a correlation function of the same quality more quickly than standard (tree or P3M) algorithms with similar spatial and mass resolution. The N2 force calculation is easy to adapt to modern graphics cards, hence StePS can function as a high-speed prediction tool for modern large-scale surveys.
Predicted phototoxicities of carbon nano-material by quantum mechanical calculations.

EPA Science Inventory

The basis of this research is obtaining the best quantum mechanical structure of carbon nanomaterials and is fundamental in determining their other properties. Therefore, their predictive phototoxicity is directly related to the materials’ structure. The results of this project w...
Estimation and prediction of origin-destination matrices for I-66.

DOT National Transportation Integrated Search

2011-09-01

This project uses the Box-Jenkins time-series technique to model and forecast the traffic flows and then : uses the flow forecasts to predict the origin-destination matrices. First, a detailed analysis was conducted : to investigate the best data cor...
Human genomics projects and precision medicine.

PubMed

Carrasco-Ramiro, F; Peiró-Pastor, R; Aguado, B

2017-09-01

The completion of the Human Genome Project (HGP) in 2001 opened the floodgates to a deeper understanding of medicine. There are dozens of HGP-like projects which involve from a few tens to several million genomes currently in progress, which vary from having specialized goals or a more general approach. However, data generation, storage, management and analysis in public and private cloud computing platforms have raised concerns about privacy and security. The knowledge gained from further research has changed the field of genomics and is now slowly permeating into clinical medicine. The new precision (personalized) medicine, where genome sequencing and data analysis are essential components, allows tailored diagnosis and treatment according to the information from the patient's own genome and specific environmental factors. P4 (predictive, preventive, personalized and participatory) medicine is introducing new concepts, challenges and opportunities. This review summarizes current sequencing technologies, concentrates on ongoing human genomics projects, and provides some examples in which precision medicine has already demonstrated clinical impact in diagnosis and/or treatment.
Bangladesh Agro-Climatic Environmental Monitoring Project

NASA Technical Reports Server (NTRS)

Vermillion, C.; Maurer, H.; Williams, M.; Kamowski, J.; Moore, T.; Maksimovich, W.; Obler, H.; Gilbert, E.

1988-01-01

The Agro-Climatic Environmental Monitoring Project (ACEMP) is based on a Participating Agency Service Agreement (PASA) between the Agency for International Development (AID) and the National Oceanic and Atmospheric Administration (NOAA). In FY80, the Asia Bureau and Office of Federal Disaster Assistance (OFDA), worked closely to develop a funding mechanism which would meet Bangladesh's needs both for flood and cyclone warning capability and for application of remote sensing data to development problems. In FY90, OFDA provided for a High Resolution Picture Transmission (HRPT) receiving capability to improve their forecasting accuracy for cyclones, flooding and storm surges. That equipment is primarily intended as a disaster prediction and preparedness measure. The ACEM Project was designed to focus on the development applications of remote sensing technology. Through this Project, AID provided to the Bangladesh Government (BDG) the equipment, technical assistance, and training necessary to collect and employ remote sensing data made available by satellites as well as hydrological data obtained from data collection platforms placed in major rivers. The data collected will enable the BDG to improve the management of its natural resources.
Projection x-space magnetic particle imaging.

PubMed

Goodwill, Patrick W; Konkle, Justin J; Zheng, Bo; Saritas, Emine U; Conolly, Steven M

2012-05-01

Projection magnetic particle imaging (MPI) can improve imaging speed by over 100-fold over traditional 3-D MPI. In this work, we derive the 2-D x-space signal equation, 2-D image equation, and introduce the concept of signal fading and resolution loss for a projection MPI imager. We then describe the design and construction of an x-space projection MPI scanner with a field gradient of 2.35 T/m across a 10 cm magnet free bore. The system has an expected resolution of 3.5 × 8.0 mm using Resovist tracer, and an experimental resolution of 3.8 × 8.4 mm resolution. The system images 2.5 cm × 5.0 cm partial field-of views (FOVs) at 10 frames/s, and acquires a full field-of-view of 10 cm × 5.0 cm in 4 s. We conclude by imaging a resolution phantom, a complex "Cal" phantom, mice injected with Resovist tracer, and experimentally confirm the theoretically predicted x-space spatial resolution.

The Predictive Validity of the Minnesota Reading Assessment for Students in Postsecondary Vocational Education Programs.

ERIC Educational Resources Information Center

Brown, James M.; Chang, Gerald

1982-01-01

The predictive validity of the Minnesota Reading Assessment (MRA) when used to project potential performance of postsecondary vocational-technical education students was examined. Findings confirmed the MRA to be a valid predictor, although the error in prediction varied between the criterion variables. (Author/GK)
Predicting past and future diameter growth for trees in the northeastern United States

Treesearch

James A. Westfall

2006-01-01

Tree diameter growth models are widely used in forestry applications, often to predict tree size at a future point in time. Also, there are instances where projections of past diameters are needed. A relative diameter growth model was developed to allow prediction of both future and past growth rates. Coefficients were estimated for 15 species groups that cover most...
Predictive Temperature Equations for Three Sites at the Grand Canyon

NASA Astrophysics Data System (ADS)

McLaughlin, Katrina Marie Neitzel

Climate data collected at a number of automated weather stations were used to create a series of predictive equations spanning from December 2009 to May 2010 in order to better predict the temperatures along hiking trails within the Grand Canyon. The central focus of this project is how atmospheric variables interact and can be combined to predict the weather in the Grand Canyon at the Indian Gardens, Phantom Ranch, and Bright Angel sites. Through the use of statistical analysis software and data regression, predictive equations were determined. The predictive equations are simple or multivariable best fits that reflect the curvilinear nature of the data. With data analysis software curves resulting from the predictive equations were plotted along with the observed data. Each equation's reduced chi2 was determined to aid the visual examination of the predictive equations' ability to reproduce the observed data. From this information an equation or pair of equations was determined to be the best of the predictive equations. Although a best predictive equation for each month and season was determined for each site, future work may refine equations to result in a more accurate predictive equation.
Improving longleaf pine mortality predictions in the Southern Variant of the Forest Vegetation Simulator

Treesearch

R. Justin DeRose; John D. Shaw; Giorgio Vacchiano; James N. Long

2008-01-01

The Southern Variant of the Forest Vegetation Simulator (FVS-SN) is made up of individual submodels that predict tree growth, recruitment and mortality. Forest managers on Ft. Bragg, North Carolina, discovered biologically unrealistic longleaf pine (Pinus palustris) size-density predictions at large diameters when using FVS-SN to project red-cockaded...
The projective field of a retinal amacrine cell

PubMed Central

de Vries, Saskia E. J.; Baccus, Stephen A.; Meister, Markus

2011-01-01

In sensory systems, neurons are generally characterized by their receptive field, namely the sensitivity to activity patterns at the circuit's input. To assess the neuron's role in the system, one must also know its projective field, namely the spatio-temporal effects the neuron exerts on all the circuit's outputs. We studied both the receptive and projective fields of an amacrine interneuron in the salamander retina. This amacrine type has a sustained OFF response with a small receptive field, but its output projects over a much larger region. Unlike other amacrines, this type is remarkably promiscuous and affects nearly every ganglion cell within reach of its dendrites. Its activity modulates the sensitivity of visual responses in ganglion cells, while leaving their kinetics unchanged. The projective field displays a center-surround structure: Depolarizing a single amacrine suppresses the visual sensitivity of ganglion cells nearby, and enhances it at greater distances. This change in sign is seen even within the receptive field of one ganglion cell; thus the modulation occurs presynaptically on bipolar cell terminals, most likely via GABAB receptors. Such an antagonistic projective field could contribute to the retina's mechanisms for predictive coding. PMID:21653863
The use of weather data to predict non-recurring traffic congestion

DOT National Transportation Integrated Search

2006-08-01

This project will demonstrate the quantitative relationship between weather patterns and surface traffic conditions. The aviation and maritime industries use weather measurements and predictions as a normal part of operations, and this can be extende...
Challenges in predicting climate change impacts on pome fruit phenology

NASA Astrophysics Data System (ADS)

Darbyshire, Rebecca; Webb, Leanne; Goodwin, Ian; Barlow, E. W. R.

2014-08-01

Climate projection data were applied to two commonly used pome fruit flowering models to investigate potential differences in predicted full bloom timing. The two methods, fixed thermal time and sequential chill-growth, produced different results for seven apple and pear varieties at two Australian locations. The fixed thermal time model predicted incremental advancement of full bloom, while results were mixed from the sequential chill-growth model. To further investigate how the sequential chill-growth model reacts under climate perturbed conditions, four simulations were created to represent a wider range of species physiological requirements. These were applied to five Australian locations covering varied climates. Lengthening of the chill period and contraction of the growth period was common to most results. The relative dominance of the chill or growth component tended to predict whether full bloom advanced, remained similar or was delayed with climate warming. The simplistic structure of the fixed thermal time model and the exclusion of winter chill conditions in this method indicate it is unlikely to be suitable for projection analyses. The sequential chill-growth model includes greater complexity; however, reservations in using this model for impact analyses remain. The results demonstrate that appropriate representation of physiological processes is essential to adequately predict changes to full bloom under climate perturbed conditions with greater model development needed.
Towards Bridging the Gaps in Holistic Transition Prediction via Numerical Simulations

NASA Technical Reports Server (NTRS)

Choudhari, Meelan M.; Li, Fei; Duan, Lian; Chang, Chau-Lyan; Carpenter, Mark H.; Streett, Craig L.; Malik, Mujeeb R.

2013-01-01

The economic and environmental benefits of laminar flow technology via reduced fuel burn of subsonic and supersonic aircraft cannot be realized without minimizing the uncertainty in drag prediction in general and transition prediction in particular. Transition research under NASA's Aeronautical Sciences Project seeks to develop a validated set of variable fidelity prediction tools with known strengths and limitations, so as to enable "sufficiently" accurate transition prediction and practical transition control for future vehicle concepts. This paper provides a summary of selected research activities targeting the current gaps in high-fidelity transition prediction, specifically those related to the receptivity and laminar breakdown phases of crossflow induced transition in a subsonic swept-wing boundary layer. The results of direct numerical simulations are used to obtain an enhanced understanding of the laminar breakdown region as well as to validate reduced order prediction methods.
Predictive models for suicidal thoughts and behaviors among Spanish University students: rationale and methods of the UNIVERSAL (University & mental health) project.

PubMed

Blasco, Maria Jesús; Castellví, Pere; Almenara, José; Lagares, Carolina; Roca, Miquel; Sesé, Albert; Piqueras, José Antonio; Soto-Sanz, Victoria; Rodríguez-Marín, Jesús; Echeburúa, Enrique; Gabilondo, Andrea; Cebrià, Ana Isabel; Miranda-Mendizábal, Andrea; Vilagut, Gemma; Bruffaerts, Ronny; Auerbach, Randy P; Kessler, Ronald C; Alonso, Jordi

2016-05-04

Suicide is a leading cause of death among young people. While suicide prevention is considered a research and intervention priority, longitudinal data is needed to identify risk and protective factors associate with suicidal thoughts and behaviors. Here we describe the UNIVERSAL (University and Mental Health) project which aims are to: (1) test prevalence and 36-month incidence of suicidal thoughts and behaviors; and (2) identify relevant risk and protective factors associated with the incidence of suicidal thoughts and behaviors among university students in Spain. An ongoing multicenter, observational, prospective cohort study of first year university students in 5 Spanish universities. Students will be assessed annually during a 36 month follow-up. The surveys will be administered through an online, secure web-based platform. A clinical reappraisal will be completed among a subsample of respondents. Suicidal thoughts and behaviors will be assess with the Self-Injurious Thoughts and Behaviors Interview (SITBI) and the Columbia-Suicide Severity Rating Scale (C-SSRS). Risk and protective factors will include: mental disorders, measured with the Composite International Diagnostic Interview version 3.0 (CIDI 3.0) and Screening Scales (CIDI-SC), and the Epi-Q Screening Survey (EPI-Q-SS), socio-demographic variables, self-perceived health status, health behaviors, well-being, substance use disorders, service use and treatment. The UNIVERSAL project is part of the International College Surveys initiative, which is a core project within the World Mental Health consortium. Lifetime and the 12-month prevalence will be calculated for suicide ideation, plans and attempts. Cumulative incidence of suicidal thoughts and behaviors, and mental disorders will be measured using the actuarial method. Risk and protective factors of suicidal thoughts and behaviors will be analyzed by Cox proportional hazard models. The study will provide valid, innovative and useful data for developing
Predicting and Supplying Human Resource Requirements for the Future.

ERIC Educational Resources Information Center

Blake, Larry J.

After asserting that public institutions should not provide training for nonexistent jobs, this paper reviews problems associated with the accurate prediction of future manpower needs. The paper reviews the processes currently used to project labor force needs and notes the difficulty of accurately forecasting labor market "surprises,"…
Collaborative Project. Understanding the effects of tides and eddies on the ocean dynamics, sea ice cover and decadal/centennial climate prediction using the Regional Arctic Climate Model (RACM)

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

Hutchings, Jennifer; Joseph, Renu

2013-09-14

The goal of this project is to develop an eddy resolving ocean model (POP) with tides coupled to a sea ice model (CICE) within the Regional Arctic System Model (RASM) to investigate the importance of ocean tides and mesoscale eddies in arctic climate simulations and quantify biases associated with these processes and how their relative contribution may improve decadal to centennial arctic climate predictions. Ocean, sea ice and coupled arctic climate response to these small scale processes will be evaluated with regard to their influence on mass, momentum and property exchange between oceans, shelf-basin, ice-ocean, and ocean-atmosphere. The project willmore » facilitate the future routine inclusion of polar tides and eddies in Earth System Models when computing power allows. As such, the proposed research addresses the science in support of the BER’s Climate and Environmental Sciences Division Long Term Measure as it will improve the ocean and sea ice model components as well as the fully coupled RASM and Community Earth System Model (CESM) and it will make them more accurate and computationally efficient.« less
PREDICT-CP: study protocol of implementation of comprehensive surveillance to predict outcomes for school-aged children with cerebral palsy.

PubMed

Boyd, Roslyn N; Davies, Peter Sw; Ziviani, Jenny; Trost, Stewart; Barber, Lee; Ware, Robert; Rose, Stephen; Whittingham, Koa; Sakzewski, Leanne; Bell, Kristie; Carty, Christopher; Obst, Steven; Benfer, Katherine; Reedman, Sarah; Edwards, Priya; Kentish, Megan; Copeland, Lisa; Weir, Kelly; Davenport, Camilla; Brooks, Denise; Coulthard, Alan; Pelekanos, Rebecca; Guzzetta, Andrea; Fiori, Simona; Wynter, Meredith; Finn, Christine; Burgess, Andrea; Morris, Kym; Walsh, John; Lloyd, Owen; Whitty, Jennifer A; Scuffham, Paul A

2017-07-12

Cerebral palsy (CP) remains the world's most common childhood physical disability with total annual costs of care and lost well-being of $A3.87b. The PREDICT-CP (NHMRC 1077257 Partnership Project: Comprehensive surveillance to PREDICT outcomes for school age children with CP) study will investigate the influence of brain structure, body composition, dietary intake, oropharyngeal function, habitual physical activity, musculoskeletal development (hip status, bone health) and muscle performance on motor attainment, cognition, executive function, communication, participation, quality of life and related health resource use costs. The PREDICT-CP cohort provides further follow-up at 8-12 years of two overlapping preschool-age cohorts examined from 1.5 to 5 years (NHMRC 465128 motor and brain development; NHMRC 569605 growth, nutrition and physical activity). This population-based cohort study undertakes state-wide surveillance of 245 children with CP born in Queensland (birth years 2006-2009). Children will be classified for Gross Motor Function Classification System; Manual Ability Classification System, Communication Function Classification System and Eating and Drinking Ability Classification System. Outcomes include gross motor function, musculoskeletal development (hip displacement, spasticity, muscle contracture), upper limb function, communication difficulties, oropharyngeal dysphagia, dietary intake and body composition, participation, parent-reported and child-reported quality of life and medical and allied health resource use. These detailed phenotypical data will be compared with brain macrostructure and microstructure using 3 Tesla MRI (3T MRI). Relationships between brain lesion severity and outcomes will be analysed using multilevel mixed-effects models. The PREDICT-CP protocol is a prospectively registered and ethically accepted study protocol. The study combines data at 1.5-5 then 8-12 years of direct clinical assessment to enable prediction of outcomes
PREDICT-CP: study protocol of implementation of comprehensive surveillance to predict outcomes for school-aged children with cerebral palsy

PubMed Central

Boyd, Roslyn N; Davies, Peter SW; Ziviani, Jenny; Trost, Stewart; Barber, Lee; Ware, Robert; Rose, Stephen; Whittingham, Koa; Bell, Kristie; Carty, Christopher; Obst, Steven; Benfer, Katherine; Reedman, Sarah; Edwards, Priya; Kentish, Megan; Copeland, Lisa; Weir, Kelly; Davenport, Camilla; Brooks, Denise; Coulthard, Alan; Pelekanos, Rebecca; Guzzetta, Andrea; Fiori, Simona; Wynter, Meredith; Finn, Christine; Burgess, Andrea; Morris, Kym; Walsh, John; Lloyd, Owen; Whitty, Jennifer A; Scuffham, Paul A

2017-01-01

Objectives Cerebral palsy (CP) remains the world’s most common childhood physical disability with total annual costs of care and lost well-being of $A3.87b. The PREDICT-CP (NHMRC 1077257 Partnership Project: Comprehensive surveillance to PREDICT outcomes for school age children with CP) study will investigate the influence of brain structure, body composition, dietary intake, oropharyngeal function, habitual physical activity, musculoskeletal development (hip status, bone health) and muscle performance on motor attainment, cognition, executive function, communication, participation, quality of life and related health resource use costs. The PREDICT-CP cohort provides further follow-up at 8–12 years of two overlapping preschool-age cohorts examined from 1.5 to 5 years (NHMRC 465128 motor and brain development; NHMRC 569605 growth, nutrition and physical activity). Methods and analyses This population-based cohort study undertakes state-wide surveillance of 245 children with CP born in Queensland (birth years 2006–2009). Children will be classified for Gross Motor Function Classification System; Manual Ability Classification System, Communication Function Classification System and Eating and Drinking Ability Classification System. Outcomes include gross motor function, musculoskeletal development (hip displacement, spasticity, muscle contracture), upper limb function, communication difficulties, oropharyngeal dysphagia, dietary intake and body composition, participation, parent-reported and child-reported quality of life and medical and allied health resource use. These detailed phenotypical data will be compared with brain macrostructure and microstructure using 3 Tesla MRI (3T MRI). Relationships between brain lesion severity and outcomes will be analysed using multilevel mixed-effects models. Ethics and dissemination The PREDICT-CP protocol is a prospectively registered and ethically accepted study protocol. The study combines data at 1.5–5 then 8–12
Implementation and Evaluation of Weather Responsive Traffic Estimation and Prediction System

DOT National Transportation Integrated Search

2012-06-01

The objective of the project is to develop a framework and procedures for implementing and evaluating weather-responsive traffic management (WRTM) strategies using Traffic Estimation and Prediction System (TrEPS) methodologies. In a previous FHWA-fun...
Mental models accurately predict emotion transitions.

PubMed

Thornton, Mark A; Tamir, Diana I

2017-06-06

Successful social interactions depend on people's ability to predict others' future actions and emotions. People possess many mechanisms for perceiving others' current emotional states, but how might they use this information to predict others' future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others' emotional dynamics. People could then use these mental models of emotion transitions to predict others' future emotions from currently observable emotions. To test this hypothesis, studies 1-3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants' ratings of emotion transitions predicted others' experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation-valence, social impact, rationality, and human mind-inform participants' mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants' accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone.
Mental models accurately predict emotion transitions

PubMed Central

Thornton, Mark A.; Tamir, Diana I.

2017-01-01

Successful social interactions depend on people’s ability to predict others’ future actions and emotions. People possess many mechanisms for perceiving others’ current emotional states, but how might they use this information to predict others’ future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others’ emotional dynamics. People could then use these mental models of emotion transitions to predict others’ future emotions from currently observable emotions. To test this hypothesis, studies 1–3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants’ ratings of emotion transitions predicted others’ experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation—valence, social impact, rationality, and human mind—inform participants’ mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants’ accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone. PMID:28533373
The NASA Severe Thunderstorm Observations and Regional Modeling (NASA STORM) Project

NASA Technical Reports Server (NTRS)

Schultz, Christopher J.; Gatlin, Patrick N.; Lang, Timothy J.; Srikishen, Jayanthi; Case, Jonathan L.; Molthan, Andrew L.; Zavodsky, Bradley T.; Bailey, Jeffrey; Blakeslee, Richard J.; Jedlovec, Gary J.

2016-01-01

The NASA Severe Storm Thunderstorm Observations and Regional Modeling(NASA STORM) project enhanced NASA’s severe weather research capabilities, building upon existing Earth Science expertise at NASA Marshall Space Flight Center (MSFC). During this project, MSFC extended NASA’s ground-based lightning detection capacity to include a readily deployable lightning mapping array (LMA). NASA STORM also enabled NASA’s Short-term Prediction and Research Transition (SPoRT) to add convection allowing ensemble modeling to its portfolio of regional numerical weather prediction (NWP) capabilities. As a part of NASA STORM, MSFC developed new open-source capabilities for analyzing and displaying weather radar observations integrated from both research and operational networks. These accomplishments enabled by NASA STORM are a step towards enhancing NASA’s capabilities for studying severe weather and positions them for any future NASA related severe storm field campaigns.
Criteria for predicting scour of erodible rock in West Virginia.

DOT National Transportation Integrated Search

2013-09-01

The research project Criteria for Predicting Scour of Erodible Rock in West Virginia (RP-273) was conducted to characterize the hydraulic scour of rock at : 15 selected bridge sites in West Virginia (at least one site in each of WVDOHs ten d...
Selecting sequence variants to improve genomic predictions for dairy cattle

USDA-ARS?s Scientific Manuscript database

Millions of genetic variants have been identified by population-scale sequencing projects, but subsets are needed for routine genomic predictions or to include on genotyping arrays. Methods of selecting sequence variants were compared using both simulated sequence genotypes and actual data from run ...
Development of predictive weather scenarios for early prediction of rice yield in South Korea

NASA Astrophysics Data System (ADS)

Shin, Y.; Cho, J.; Jung, I.

2017-12-01

International grain prices are becoming unstable due to frequent occurrence of abnormal weather phenomena caused by climate change. Early prediction of grain yield using weather forecast data is important for stabilization of international grain prices. The APEC Climate Center (APCC) is providing seasonal forecast data based on monthly climate prediction models for global seasonal forecasting services. The 3-month and 6-month seasonal forecast data using the multi-model ensemble (MME) technique are provided in their own website, ADSS (APCC Data Service System, http://adss.apcc21.org/). The spatial resolution of seasonal forecast data for each individual model is 2.5°×2.5°(about 250km) and the time scale is created as monthly. In this study, we developed customized weather forecast scenarios that are combined seasonal forecast data and observational data apply to early rice yield prediction model. Statistical downscale method was applied to produce meteorological input data of crop model because field scale crop model (ORYZA2000) requires daily weather data. In order to determine whether the forecasting data is suitable for the crop model, we produced spatio-temporal downscaled weather scenarios and evaluated the predictability by comparison with observed weather data at 57 ASOS stations in South Korea. The customized weather forecast scenarios can be applied to various application fields not only early rice yield prediction. Acknowledgement This work was carried out with the support of "Cooperative Research Program for Agriculture Science and Technology Development (Project No: PJ012855022017)" Rural Development Administration, Republic of Korea.

Testing protocol for predicting driven pile behavior within pre-bored soil : research project capsule.

DOT National Transportation Integrated Search

2014-02-01

The objective of this project is to compile the state-of-the-art and best practice : results available on the subject of pre-bored piles and develop a research and : instrumentation testing plan for fi eld data collection and select multiple pile : d...
Toward Seamless Weather-Climate Prediction with a Global Cloud Resolving Model

DTIC Science & Technology

2016-01-14

distribution is unlimited. TOWARD SEAMLESS WEATHER- CLIMATE PREDICTION WITH A GLOBAL CLOUD RESOLVING MODEL PI: Tim Li IPRC/SOEST, University of Hawaii at...Project Final Report 3. DATES COVERED (From - To) 1 May 2012 - 30 September 2015 4. TITLE AND SUBTITLE TOWARD SEAMLESS WEATHER- CLIMATE PREDICTION WITH...A GLOBAL CLOUD RESOLVING MODEL 5a. CONTRACT NUMBER 5b. GRANT NUMBER N000141210450 5c. PROGRAM ELEMENT NUMBER ONR Marine Meteorology Program 6
Has the prediction of the South China Sea summer monsoon improved since the late 1970s?

NASA Astrophysics Data System (ADS)

Fan, Yi; Fan, Ke; Tian, Baoqiang

2016-12-01

Based on the evaluation of state-of-the-art coupled ocean-atmosphere general circulation models (CGCMs) from the ENSEMBLES (Ensemble-based Predictions of Climate Changes and Their Impacts) and DEMETER (Development of a European Multimodel Ensemble System for Seasonal to Interannual Prediction) projects, it is found that the prediction of the South China Sea summer monsoon (SCSSM) has improved since the late 1970s. These CGCMs show better skills in prediction of the atmospheric circulation and precipitation within the SCSSM domain during 1979-2005 than that during 1960-1978. Possible reasons for this improvement are investigated. First, the relationship between the SSTs over the tropical Pacific, North Pacific and tropical Indian Ocean, and SCSSM has intensified since the late 1970s. Meanwhile, the SCSSM-related SSTs, with their larger amplitude of interannual variability, have been better predicted. Moreover, the larger amplitude of the interannual variability of the SCSSM and improved initializations for CGCMs after the late 1970s contribute to the better prediction of the SCSSM. In addition, considering that the CGCMs have certain limitations in SCSSM rainfall prediction, we applied the year-to-year increment approach to these CGCMs from the DEMETER and ENSEMBLES projects to improve the prediction of SCSSM rainfall before and after the late 1970s.
Early Prediction of Student Dropout and Performance in MOOCSs Using Higher Granularity Temporal Information

ERIC Educational Resources Information Center

Ye, Cheng; Biswas, Gautam

2014-01-01

Our project is motivated by the early dropout and low completion rate problem in MOOCs. We have extended traditional features for MOOC analysis with richer and higher granularity information to make more accurate predictions of dropout and performance. The results show that finer-grained temporal information increases the predictive power in the…
Projection of the future diabetes burden in the United States through 2060.

PubMed

Lin, Ji; Thompson, Theodore J; Cheng, Yiling J; Zhuo, Xiaohui; Zhang, Ping; Gregg, Edward; Rolka, Deborah B

2018-06-15

In the United States, diabetes has increased rapidly, exceeding prior predictions. Projections of the future diabetes burden need to reflect changes in incidence, mortality, and demographics. We applied the most recent data available to develop an updated projection through 2060. A dynamic Markov model was used to project prevalence of diagnosed diabetes among US adults by age, sex, and race (white, black, other). Incidence and current prevalence were from the National Health Interview Survey (NHIS) 1985-2014. Relative mortality was from NHIS 2000-2011 follow-up data linked to the National Death Index. Future population estimates including birth, death, and migration were from the 2014 Census projection. The projected number and percent of adults with diagnosed diabetes would increase from 22.3 million (9.1%) in 2014 to 39.7 million (13.9%) in 2030, and to 60.6 million (17.9%) in 2060. The number of people with diabetes aged 65 years or older would increase from 9.2 million in 2014 to 21.0 million in 2030, and to 35.2 million in 2060. The percent prevalence would increase in all race-sex groups, with black women and men continuing to have the highest diabetes percent prevalence, and black women and women of other race having the largest relative increases. By 2060, the number of US adults with diagnosed diabetes is projected to nearly triple, and the percent prevalence double. Our estimates are essential to predict health services needs and plan public health programs aimed to reduce the future burden of diabetes.
San Mateo County Geographic Information Systems (GIS) project

USGS Publications Warehouse

Brabb, E.E.

1986-01-01

Earthquakes and ground failures in the United States cause billions of dollars of damages each year, but techniques for predicting and reducing these hazardous geologic processes remain elusive. geologists, geophysicists, hydrologists, engineers, cartographers, and computer specialists from the U.S geological Survey in Menlo Park, California, are working together on a project involving GIS techniques to determine how to predict the consequences of earthquakes and landslides, using San Mateo County as a subject area. Together with members of the Planning and Emergency Serivces Departments of San Mateo County and the Association of Bay Area Governments, They are also determining how to reduce the losses caused by hazards.
UK Environmental Prediction - integration and evaluation at the convective scale

NASA Astrophysics Data System (ADS)

Lewis, Huw; Brunet, Gilbert; Harris, Chris; Best, Martin; Saulter, Andrew; Holt, Jason; Bricheno, Lucy; Brerton, Ashley; Reynard, Nick; Blyth, Eleanor; Martinez de la Torre, Alberto

2015-04-01

It has long been understood that accurate prediction and warning of the impacts of severe weather requires an integrated approach to forecasting. This was well demonstrated in the UK throughout winter 2013/14 when an exceptional run of severe winter storms, often with damaging high winds and intense rainfall led to significant damage from the large waves and storm surge along coastlines, and from saturated soils, high river flows and significant flooding inland. The substantial impacts on individuals, businesses and infrastructure indicate a pressing need to understand better the value that might be delivered through more integrated environmental prediction. To address this need, the Met Office, Centre for Ecology & Hydrology and National Oceanography Centre have begun to develop the foundations of a coupled high resolution probabilistic forecast system for the UK at km-scale. This links together existing model components of the atmosphere, coastal ocean, land surface and hydrology. Our initial focus on a 2-year Prototype project will demonstrate the UK coupled prediction concept in research mode, including an analysis of the winter 2013/14 storms and its impacts. By linking science development to operational collaborations such as the UK Natural Hazards Partnership, we can ensure that science priorities are rooted in user requirements. This presentation will provide an overview of UK environmental prediction activities and an update on progress during the first year of the Prototype project. We will present initial results from the coupled model development and discuss the challenges to realise the potential of integrated regional coupled forecasting for improving predictions and applications.
Incorporating climate change projections into riparian restoration planning and design

USGS Publications Warehouse

Perry, Laura G.; Reynolds, Lindsay V.; Beechie, Timothy J.; Collins, Mathias J.; Shafroth, Patrick B.

2015-01-01

Climate change and associated changes in streamflow may alter riparian habitats substantially in coming decades. Riparian restoration provides opportunities to respond proactively to projected climate change effects, increase riparian ecosystem resilience to climate change, and simultaneously address effects of both climate change and other human disturbances. However, climate change may alter which restoration methods are most effective and which restoration goals can be achieved. Incorporating climate change into riparian restoration planning and design is critical to long-term restoration of desired community composition and ecosystem services. In this review, we discuss and provide examples of how climate change might be incorporated into restoration planning at the key stages of assessing the project context, establishing restoration goals and design criteria, evaluating design alternatives, and monitoring restoration outcomes. Restoration planners have access to numerous tools to predict future climate, streamflow, and riparian ecology at restoration sites. Planners can use those predictions to assess which species or ecosystem services will be most vulnerable under future conditions, and which sites will be most suitable for restoration. To accommodate future climate and streamflow change, planners may need to adjust methods for planting, invasive species control, channel and floodplain reconstruction, and water management. Given the considerable uncertainty in future climate and streamflow projections, riparian ecological responses, and effects on restoration outcomes, planners will need to consider multiple potential future scenarios, implement a variety of restoration methods, design projects with flexibility to adjust to future conditions, and plan to respond adaptively to unexpected change.
Comprehensive summary--Predict-IV: A systems toxicology approach to improve pharmaceutical drug safety testing.

PubMed

Mueller, Stefan O; Dekant, Wolfgang; Jennings, Paul; Testai, Emanuela; Bois, Frederic

2015-12-25

This special issue of Toxicology in Vitro is dedicated to disseminating the results of the EU-funded collaborative project "Profiling the toxicity of new drugs: a non animal-based approach integrating toxicodynamics and biokinetics" (Predict-IV; Grant 202222). The project's overall aim was to develop strategies to improve the assessment of drug safety in the early stage of development and late discovery phase, by an intelligent combination of non animal-based test systems, cell biology, mechanistic toxicology and in silico modeling, in a rapid and cost effective manner. This overview introduces the scope and overall achievements of Predict-IV. Copyright © 2014 Elsevier Ltd. All rights reserved.
Life Prediction of Spent Fuel Storage Canister Material

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

Ballinger, Ronald

The original purpose of this project was to develop a probabilistic model for SCC-induced failure of spent fuel storage canisters, exposed to a salt-air environment in the temperature range 30-70°C for periods up to and exceeding 100 years. The nature of this degradation process, which involves multiple degradation mechanisms, combined with variable and uncertain environmental conditions dictates a probabilistic approach to life prediction. A final report for the original portion of the project was submitted earlier. However, residual stress measurements for as-welded and repair welds could not be performed within the original time of the project. As a result ofmore » this, a no-cost extension was granted in order to complete these tests. In this report, we report on the results of residual stress measurements.« less
Predictive models of glucose control: roles for glucose-sensing neurones.

PubMed

Kosse, C; Gonzalez, A; Burdakov, D

2015-01-01

The brain can be viewed as a sophisticated control module for stabilizing blood glucose. A review of classical behavioural evidence indicates that central circuits add predictive (feedforward/anticipatory) control to the reactive (feedback/compensatory) control by peripheral organs. The brain/cephalic control is constructed and engaged, via associative learning, by sensory cues predicting energy intake or expenditure (e.g. sight, smell, taste, sound). This allows rapidly measurable sensory information (rather than slowly generated internal feedback signals, e.g. digested nutrients) to control food selection, glucose supply for fight-or-flight responses or preparedness for digestion/absorption. Predictive control is therefore useful for preventing large glucose fluctuations. We review emerging roles in predictive control of two classes of widely projecting hypothalamic neurones, orexin/hypocretin (ORX) and melanin-concentrating hormone (MCH) cells. Evidence is cited that ORX neurones (i) are activated by sensory cues (e.g. taste, sound), (ii) drive hepatic production, and muscle uptake, of glucose, via sympathetic nerves, (iii) stimulate wakefulness and exploration via global brain projections and (iv) are glucose-inhibited. MCH neurones are (i) glucose-excited, (ii) innervate learning and reward centres to promote synaptic plasticity, learning and memory and (iii) are critical for learning associations useful for predictive control (e.g. using taste to predict nutrient value of food). This evidence is unified into a model for predictive glucose control. During associative learning, inputs from some glucose-excited neurones may promote connections between the 'fast' senses and reward circuits, constructing neural shortcuts for efficient action selection. In turn, glucose-inhibited neurones may engage locomotion/exploration and coordinate the required fuel supply. Feedback inhibition of the latter neurones by glucose would ensure that glucose fluxes they stimulate
Performance of the operational high-resolution numerical weather predictions of the Daphne project

NASA Astrophysics Data System (ADS)

Tegoulias, Ioannis; Pytharoulis, Ioannis; Karacostas, Theodore; Kartsios, Stergios; Kotsopoulos, Stelios; Bampzelis, Dimitrios

2015-04-01

In the framework of the DAPHNE project, the Department of Meteorology and Climatology (http://meteo.geo.auth.gr) of the Aristotle University of Thessaloniki, Greece, utilizes the nonhydrostatic Weather Research and Forecasting model with the Advanced Research dynamic solver (WRF-ARW) in order to produce high-resolution weather forecasts over Thessaly in central Greece. The aim of the DAPHNE project is to tackle the problem of drought in this area by means of Weather Modification. Cloud seeding assists the convective clouds to produce rain more efficiently or reduce hailstone size in favour of raindrops. The most favourable conditions for such a weather modification program in Thessaly occur in the period from March to October when convective clouds are triggered more frequently. Three model domains, using 2-way telescoping nesting, cover: i) Europe, the Mediterranean sea and northern Africa (D01), ii) Greece (D02) and iii) the wider region of Thessaly (D03; at selected periods) at horizontal grid-spacings of 15km, 5km and 1km, respectively. This research work intents to describe the atmospheric model setup and analyse its performance during a selected period of the operational phase of the project. The statistical evaluation of the high-resolution operational forecasts is performed using surface observations, gridded fields and radar data. Well established point verification methods combined with novel object based upon these methods, provide in depth analysis of the model skill. Spatial characteristics are adequately captured but a variable time lag between forecast and observation is noted. Acknowledgments: This research work has been co-financed by the European Union (European Regional Development Fund) and Greek national funds, through the action "COOPERATION 2011: Partnerships of Production and Research Institutions in Focused Research and Technology Sectors" (contract number 11SYN_8_1088 - DAPHNE) in the framework of the operational programme "Competitiveness
Metric analysis and data validation across FORTRAN projects

NASA Technical Reports Server (NTRS)

Basili, Victor R.; Selby, Richard W., Jr.; Phillips, Tsai-Yun

1983-01-01

The desire to predict the effort in developing or explaining the quality of software has led to the proposal of several metrics. As a step toward validating these metrics, the Software Engineering Laboratory (SEL) has analyzed the software science metrics, cyclomatic complexity, and various standard program measures for their relation to effort (including design through acceptance testing), development errors (both discrete and weighted according to the amount of time to locate and fix), and one another. The data investigated are collected from a project FORTRAN environment and examined across several projects at once, within individual projects and by reporting accuracy checks demonstrating the need to validate a database. When the data comes from individual programmers or certain validated projects, the metrics' correlations with actual effort seem to be strongest. For modules developed entirely by individual programmers, the validity ratios induce a statistically significant ordering of several of the metrics' correlations. When comparing the strongest correlations, neither software science's E metric cyclomatic complexity not source lines of code appears to relate convincingly better with effort than the others.
Toward Improved CFD Predictions of Slender Airframe Aerodynamics Using the F-16XL Aircraft (CAWAPI-2)

NASA Technical Reports Server (NTRS)

Luckring, James M.; Rizzi, Arthur; Davis, M. Bruce

2014-01-01

A coordinated project has been underway to improve CFD predictions of slender airframe aerodynamics. The work is focused on two flow conditions and leverages a unique flight data set obtained with an F-16XL aircraft. These conditions, a low-speed high angleof- attack case and a transonic low angle-of-attack case, were selected from a prior prediction campaign wherein the CFD failed to provide acceptable results. In this paper the background, objectives and approach to the current project are presented. The work embodies predictions from multiple numerical formulations that are contributed from multiple organizations, and the context of this campaign to other multi-code, multiorganizational efforts is included. The relevance of this body of work toward future supersonic commercial transport concepts is also briefly addressed.
Bayesian probabilistic population projections for all countries.

PubMed

Raftery, Adrian E; Li, Nan; Ševčíková, Hana; Gerland, Patrick; Heilig, Gerhard K

2012-08-28

Projections of countries' future populations, broken down by age and sex, are widely used for planning and research. They are mostly done deterministically, but there is a widespread need for probabilistic projections. We propose a bayesian method for probabilistic population projections for all countries. The total fertility rate and female and male life expectancies at birth are projected probabilistically using bayesian hierarchical models estimated via Markov chain Monte Carlo using United Nations population data for all countries. These are then converted to age-specific rates and combined with a cohort component projection model. This yields probabilistic projections of any population quantity of interest. The method is illustrated for five countries of different demographic stages, continents and sizes. The method is validated by an out of sample experiment in which data from 1950-1990 are used for estimation, and applied to predict 1990-2010. The method appears reasonably accurate and well calibrated for this period. The results suggest that the current United Nations high and low variants greatly underestimate uncertainty about the number of oldest old from about 2050 and that they underestimate uncertainty for high fertility countries and overstate uncertainty for countries that have completed the demographic transition and whose fertility has started to recover towards replacement level, mostly in Europe. The results also indicate that the potential support ratio (persons aged 20-64 per person aged 65+) will almost certainly decline dramatically in most countries over the coming decades.
Bayesian probabilistic population projections for all countries

PubMed Central

Raftery, Adrian E.; Li, Nan; Ševčíková, Hana; Gerland, Patrick; Heilig, Gerhard K.

2012-01-01

Projections of countries’ future populations, broken down by age and sex, are widely used for planning and research. They are mostly done deterministically, but there is a widespread need for probabilistic projections. We propose a Bayesian method for probabilistic population projections for all countries. The total fertility rate and female and male life expectancies at birth are projected probabilistically using Bayesian hierarchical models estimated via Markov chain Monte Carlo using United Nations population data for all countries. These are then converted to age-specific rates and combined with a cohort component projection model. This yields probabilistic projections of any population quantity of interest. The method is illustrated for five countries of different demographic stages, continents and sizes. The method is validated by an out of sample experiment in which data from 1950–1990 are used for estimation, and applied to predict 1990–2010. The method appears reasonably accurate and well calibrated for this period. The results suggest that the current United Nations high and low variants greatly underestimate uncertainty about the number of oldest old from about 2050 and that they underestimate uncertainty for high fertility countries and overstate uncertainty for countries that have completed the demographic transition and whose fertility has started to recover towards replacement level, mostly in Europe. The results also indicate that the potential support ratio (persons aged 20–64 per person aged 65+) will almost certainly decline dramatically in most countries over the coming decades. PMID:22908249
The Galics Project: Virtual Galaxy: from Cosmological N-body Simulations

NASA Astrophysics Data System (ADS)

Guiderdoni, B.

The GalICS project develops extensive semi-analytic post-processing of large cosmological simulations to describe hierarchical galaxy formation. The multiwavelength statistical properties of high-redshift and local galaxies are predicted within the large-scale structures. The fake catalogs and mock images that are generated from the outputs are used for the analysis and preparation of deep surveys. The whole set of results is now available in an on-line database that can be easily queried. The GalICS project represents a first step towards a 'Virtual Observatory of virtual galaxies'.
A Framework of Covariance Projection on Constraint Manifold for Data Fusion.

PubMed

Bakr, Muhammad Abu; Lee, Sukhan

2018-05-17

A general framework of data fusion is presented based on projecting the probability distribution of true states and measurements around the predicted states and actual measurements onto the constraint manifold. The constraint manifold represents the constraints to be satisfied among true states and measurements, which is defined in the extended space with all the redundant sources of data such as state predictions and measurements considered as independent variables. By the general framework, we mean that it is able to fuse any correlated data sources while directly incorporating constraints and identifying inconsistent data without any prior information. The proposed method, referred to here as the Covariance Projection (CP) method, provides an unbiased and optimal solution in the sense of minimum mean square error (MMSE), if the projection is based on the minimum weighted distance on the constraint manifold. The proposed method not only offers a generalization of the conventional formula for handling constraints and data inconsistency, but also provides a new insight into data fusion in terms of a geometric-algebraic point of view. Simulation results are provided to show the effectiveness of the proposed method in handling constraints and data inconsistency.
NGA-West2 Research Project

USGS Publications Warehouse

Bozorgnia, Yousef; Abrahamson, Norman A.; Al Atik, Linda; Ancheta, Timothy D.; Atkinson, Gail M.; Baker, Jack W.; Baltay, Annemarie S.; Boore, David M.; Campbell, Kenneth W.; Chiou, Brian S.J.; Darragh, Robert B.; Day, Steve; Donahue, Jennifer; Graves, Robert W.; Gregor, Nick; Hanks, Thomas C.; Idriss, I. M.; Kamai, Ronnie; Kishida, Tadahiro; Kottke, Albert; Mahin, Stephen A.; Rezaeian, Sanaz; Rowshandel, Badie; Seyhan, Emel; Shahi, Shrey; Shantz, Tom; Silva, Walter; Spudich, Paul A.; Stewart, Jonathan P.; Watson-Lamprey, Jennie; Wooddell, Kathryn; Youngs, Robert

2014-01-01

The NGA-West2 project is a large multidisciplinary, multi-year research program on the Next Generation Attenuation (NGA) models for shallow crustal earthquakes in active tectonic regions. The research project has been coordinated by the Pacific Earthquake Engineering Research Center (PEER), with extensive technical interactions among many individuals and organizations. NGA-West2 addresses several key issues in ground-motion seismic hazard, including updating the NGA database for a magnitude range of 3.0–7.9; updating NGA ground-motion prediction equations (GMPEs) for the “average” horizontal component; scaling response spectra for damping values other than 5%; quantifying the effects of directivity and directionality for horizontal ground motion; resolving discrepancies between the NGA and the National Earthquake Hazards Reduction Program (NEHRP) site amplification factors; analysis of epistemic uncertainty for NGA GMPEs; and developing GMPEs for vertical ground motion. This paper presents an overview of the NGA-West2 research program and its subprojects.
Signal Prediction With Input Identification

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan; Chen, Ya-Chin

1999-01-01

A novel coding technique is presented for signal prediction with applications including speech coding, system identification, and estimation of input excitation. The approach is based on the blind equalization method for speech signal processing in conjunction with the geometric subspace projection theory to formulate the basic prediction equation. The speech-coding problem is often divided into two parts, a linear prediction model and excitation input. The parameter coefficients of the linear predictor and the input excitation are solved simultaneously and recursively by a conventional recursive least-squares algorithm. The excitation input is computed by coding all possible outcomes into a binary codebook. The coefficients of the linear predictor and excitation, and the index of the codebook can then be used to represent the signal. In addition, a variable-frame concept is proposed to block the same excitation signal in sequence in order to reduce the storage size and increase the transmission rate. The results of this work can be easily extended to the problem of disturbance identification. The basic principles are outlined in this report and differences from other existing methods are discussed. Simulations are included to demonstrate the proposed method.

Development of the NASA Digital Astronaut Project Muscle Model

NASA Technical Reports Server (NTRS)

Lewandowski, Beth E.; Pennline, James A.; Thompson, W. K.; Humphreys, B. T.; Ryder, J. W.; Ploutz-Snyder, L. L.; Mulugeta, L.

2015-01-01

This abstract describes development work performed on the NASA Digital Astronaut Project Muscle Model. Muscle atrophy is a known physiological response to exposure to a low gravity environment. The DAP muscle model computationally predicts the change in muscle structure and function vs. time in a reduced gravity environment. The spaceflight muscle model can then be used in biomechanical models of exercise countermeasures and spaceflight tasks to: 1) develop site specific bone loading input to the DAP bone adaptation model over the course of a mission; 2) predict astronaut performance of spaceflight tasks; 3) inform effectiveness of new exercise countermeasures concepts.
NASA Subsonic Rotary Wing Project - Structures and Materials Discipline

NASA Technical Reports Server (NTRS)

Halbig, Michael C.; Johnson, Susan M.

2008-01-01

The Structures & Materials Discipline within the NASA Subsonic Rotary Wing Project is focused on developing rotorcraft technologies. The technologies being developed are within the task areas of: 5.1.1 Life Prediction Methods for Engine Structures & Components 5.1.2 Erosion Resistant Coatings for Improved Turbine Blade Life 5.2.1 Crashworthiness 5.2.2 Methods for Prediction of Fatigue Damage & Self Healing 5.3.1 Propulsion High Temperature Materials 5.3.2 Lightweight Structures and Noise Integration The presentation will discuss rotorcraft specific technical challenges and needs as well as details of the work being conducted in the six task areas.
Landscape genomic prediction for restoration of a Eucalyptus foundation species under climate change.

PubMed

Supple, Megan Ann; Bragg, Jason G; Broadhurst, Linda M; Nicotra, Adrienne B; Byrne, Margaret; Andrew, Rose L; Widdup, Abigail; Aitken, Nicola C; Borevitz, Justin O

2018-04-24

As species face rapid environmental change, we can build resilient populations through restoration projects that incorporate predicted future climates into seed sourcing decisions. Eucalyptus melliodora is a foundation species of a critically endangered community in Australia that is a target for restoration. We examined genomic and phenotypic variation to make empirical based recommendations for seed sourcing. We examined isolation by distance and isolation by environment, determining high levels of gene flow extending for 500 km and correlations with climate and soil variables. Growth experiments revealed extensive phenotypic variation both within and among sampling sites, but no site-specific differentiation in phenotypic plasticity. Model predictions suggest that seed can be sourced broadly across the landscape, providing ample diversity for adaptation to environmental change. Application of our landscape genomic model to E. melliodora restoration projects can identify genomic variation suitable for predicted future climates, thereby increasing the long term probability of successful restoration. © 2018, Supple et al.
The cortisol awakening response predicts major depression: predictive stability over a 4-year follow-up and effect of depression history.

PubMed

Vrshek-Schallhorn, S; Doane, L D; Mineka, S; Zinbarg, R E; Craske, M G; Adam, E K

2013-03-01

The cortisol awakening response (CAR) has been shown to predict major depressive episodes (MDEs) over a 1-year period. It is unknown whether this effect: (a) is stable over longer periods of time; (b) is independent of prospective stressful life events; and (c) differentially predicts first onsets or recurrences of MDEs. A total of 270 older adolescents (mean age 17.06 years at cortisol measurement) from the larger prospective Northwestern-UCLA Youth Emotion Project completed baseline diagnostic and life stress interviews, questionnaires, and a 3-day cortisol sampling protocol measuring the CAR and diurnal rhythm, as well as up to four annual follow-up interviews of diagnoses and life stress. Non-proportional person-month survival analyses revealed that higher levels of the baseline CAR significantly predict MDEs for 2.5 years following cortisol measurement. However, the strength of prediction of depressive episodes significantly decays over time, with the CAR no longer significantly predicting MDEs after 2.5 years. Elevations in the CAR did not significantly increase vulnerability to prospective major stressful life events. They did, however, predict MDE recurrences more strongly than first onsets. These results suggest that a high CAR represents a time-limited risk factor for onsets of MDEs, which increases risk for depression independently of future major stressful life events. Possible explanations for the stronger effect of the CAR for predicting MDE recurrences than first onsets are discussed.
Incorporating adaptive responses into future projections of coral bleaching.

PubMed

Logan, Cheryl A; Dunne, John P; Eakin, C Mark; Donner, Simon D

2014-01-01

Climate warming threatens to increase mass coral bleaching events, and several studies have projected the demise of tropical coral reefs this century. However, recent evidence indicates corals may be able to respond to thermal stress though adaptive processes (e.g., genetic adaptation, acclimatization, and symbiont shuffling). How these mechanisms might influence warming-induced bleaching remains largely unknown. This study compared how different adaptive processes could affect coral bleaching projections. We used the latest bias-corrected global sea surface temperature (SST) output from the NOAA/GFDL Earth System Model 2 (ESM2M) for the preindustrial period through 2100 to project coral bleaching trajectories. Initial results showed that, in the absence of adaptive processes, application of a preindustrial climatology to the NOAA Coral Reef Watch bleaching prediction method overpredicts the present-day bleaching frequency. This suggests that corals may have already responded adaptively to some warming over the industrial period. We then modified the prediction method so that the bleaching threshold either permanently increased in response to thermal history (e.g., simulating directional genetic selection) or temporarily increased for 2-10 years in response to a bleaching event (e.g., simulating symbiont shuffling). A bleaching threshold that changes relative to the preceding 60 years of thermal history reduced the frequency of mass bleaching events by 20-80% compared with the 'no adaptive response' prediction model by 2100, depending on the emissions scenario. When both types of adaptive responses were applied, up to 14% more reef cells avoided high-frequency bleaching by 2100. However, temporary increases in bleaching thresholds alone only delayed the occurrence of high-frequency bleaching by ca. 10 years in all but the lowest emissions scenario. Future research should test the rate and limit of different adaptive responses for coral species across latitudes and
Electric Power Engineering Cost Predicting Model Based on the PCA-GA-BP

NASA Astrophysics Data System (ADS)

Wen, Lei; Yu, Jiake; Zhao, Xin

2017-10-01

In this paper a hybrid prediction algorithm: PCA-GA-BP model is proposed. PCA algorithm is established to reduce the correlation between indicators of original data and decrease difficulty of BP neural network in complex dimensional calculation. The BP neural network is established to estimate the cost of power transmission project. The results show that PCA-GA-BP algorithm can improve result of prediction of electric power engineering cost.
RNA Secondary Structure Prediction by Using Discrete Mathematics: An Interdisciplinary Research Experience for Undergraduate Students

ERIC Educational Resources Information Center

Ellington, Roni; Wachira, James; Nkwanta, Asamoah

2010-01-01

The focus of this Research Experience for Undergraduates (REU) project was on RNA secondary structure prediction by using a lattice walk approach. The lattice walk approach is a combinatorial and computational biology method used to enumerate possible secondary structures and predict RNA secondary structure from RNA sequences. The method uses…
Contact with Counter-Stereotypical Women Predicts Less Sexism, Less Rape Myth Acceptance, Less Intention to Rape (in Men) and Less Projected Enjoyment of Rape (in Women).

PubMed

Taschler, Miriam; West, Keon

2017-01-01

Intergroup contact-(positive) interactions with people from different social groups-is a widely researched and strongly supported prejudice-reducing mechanism shown to reduce prejudice against a wide variety of outgroups. However, no known previous research has investigated whether intergroup contact can also reduce sexism against women. Sexism has an array of negative outcomes. One of the most detrimental and violent ones is rape, which is both justified and downplayed by rape myth acceptance. We hypothesised that more frequent, higher quality contact with counter-stereotypical women would predict lower levels of sexism and thus less rape myth acceptance (in men) and less sexualised projected responses to rape (in women). Two studies using online surveys with community samples supported these hypotheses. In Study 1, 170 male participants who experienced more positive contact with counter-stereotypical women reported less intention to rape. Similarly, in Study 2, 280 female participants who experienced more positive contact with counter-stereotypical women reported less projected sexual arousal at the thought of being raped. Thus, the present research is the first known to show that contact could be a potential tool to combat sexism, rape myth acceptance, intentions to rape in men, and sexualisation of rape by women.
Prediction Markets: Another Tool in the Intelligence Kitbag

DTIC Science & Technology

2007-02-20

the eminent British anthropologist and statistician, Francis Galton . He was passing by an English county fair when he noticed an advertisement for a...available from http://libnt4.lib.tcu.edu/staff/bellinger/essays/ untruth.htm; Internet; accessed 14 December 2006. 18 Francis Galton , Memories of My Life...USAWC STRATEGY RESEARCH PROJECT PREDICTION MARKETS: ANOTHER TOOL IN THE INTELLIGENCE KITBAG by Colonel
How to acquire new biological activities in old compounds by computer prediction

NASA Astrophysics Data System (ADS)

Poroikov, V. V.; Filimonov, D. A.

2002-11-01

Due to the directed way of testing chemical compounds' in drug research and development many projects fail because serious adverse effects and toxicity are discovered too late, and many existing prospective activities remain unstudied. Evaluation of the general biological potential of molecules is possible using a computer program PASS that predicts more than 780 pharmacological effects, mechanisms of action, mutagenicity, carcinogenicity, etc. on the basis of structural formulae of compounds, with average accuracy ˜85%. PASS applications to both databases of available samples included hundreds of thousands compounds, and small collections of compounds synthesized by separate medicinal chemists are described. It is shown that 880 compounds from Prestwick chemical library represent a very diverse pharmacological space. New activities can be found in existing compounds by prediction. Therefore, on this basis, the selection of compounds with required and without unwanted properties is possible. Even when PASS cannot predict very new activities, it may recognize some unwanted actions at the early stage of R&D, providing the medicinal chemist with the means to increase the efficiency of projects.
EPG therapy for children with long-standing speech disorders: predictions and outcomes.

PubMed

Carter, Penny; Edwards, Susan

2004-01-01

This paper reports on a project using a series of single subjects to investigate the effectiveness of using electropalatography (EPG) in treating ten children with persisting speech difficulties of no known organic aetiology. The aims of the project were two-fold, firstly to assess whether the subjects selected benefited from this treatment, and secondly to investigate whether it was possible to predict which children would make maximum improvement. A number of factors were identified as possible predictors for successful EPG therapy and subjects were then ranked according to these predictions. Baseline measures of each subject's speech were taken using word lists. Scores reflected the correct number of realizations of consonants produced by each subject. Subjects received the same number of therapy sessions and were then re-tested. Scores before and after therapy were compared and found to be significantly different although the initial predictions as to the magnitude of improvement for each subject were not verified. The selection of appropriate candidates for therapy and the need for objective means of establishing effectiveness are discussed.
Project Management Using Modern Guidance, Navigation and Control Theory

NASA Technical Reports Server (NTRS)

Hill, Terry R.

2011-01-01

of the system. The behavior of a system, as captured in the system modeling, when properly done will aid in accurately predicting future system performance. The Feedback Control system understands the state and behavior of the system and uses feedback to adjust control inputs into the system. The feedback, which is the right arm of the Control system, allows change to be affected in the overall system; it therefore is important to not only correctly identify the system feedback inputs, but also the system response to the feedback inputs. The Navigation system takes multiple data inputs and based on a priori knowledge of the inputs, develops a statistically based weighting of the inputs and measurements to determine the system's state. Guidance and Planning Logic of the system, complete with an understanding of where the system is (provided by the Navigation system), will in turn determine where the system needs to be and how to get it there. With any system/project, it is critical that the objective of the system/project be clearly defined -- not only to plan but to measure performance and to aid in guiding the system or the project. The system principles discussed above, which can be and have been applied to the current CxP space suit development project, can also be mapped to real-world constituents, thus allowing project managers to apply systems theories that are well defined in engineering and mathematics to a discipline (i.e., Project Management) that historically has been based in personal experience and intuition. This mapping of GN&C theory to Project Management will, in turn, permit a direct, methodical approach to Project Management, planning and control providing a tool to help predict (and guide) performance and an understanding of the project constraints, how the project can be controlled, and the impacts to external influences and inputs. This approach, to a project manager, flows down to the three bottom-line variables of cost, schedule, and scope
The Materials Genome Project

NASA Astrophysics Data System (ADS)

Aourag, H.

2008-09-01

In the past, the search for new and improved materials was characterized mostly by the use of empirical, trial- and-error methods. This picture of materials science has been changing as the knowledge and understanding of fundamental processes governing a material's properties and performance (namely, composition, structure, history, and environment) have increased. In a number of cases, it is now possible to predict a material's properties before it has even been manufactured thus greatly reducing the time spent on testing and development. The objective of modern materials science is to tailor a material (starting with its chemical composition, constituent phases, and microstructure) in order to obtain a desired set of properties suitable for a given application. In the short term, the traditional "empirical" methods for developing new materials will be complemented to a greater degree by theoretical predictions. In some areas, computer simulation is already used by industry to weed out costly or improbable synthesis routes. Can novel materials with optimized properties be designed by computers? Advances in modelling methods at the atomic level coupled with rapid increases in computer capabilities over the last decade have led scientists to answer this question with a resounding "yes'. The ability to design new materials from quantum mechanical principles with computers is currently one of the fastest growing and most exciting areas of theoretical research in the world. The methods allow scientists to evaluate and prescreen new materials "in silico" (in vitro), rather than through time consuming experimentation. The Materials Genome Project is to pursue the theory of large scale modeling as well as powerful methods to construct new materials, with optimized properties. Indeed, it is the intimate synergy between our ability to predict accurately from quantum theory how atoms can be assembled to form new materials and our capacity to synthesize novel materials atom
Early Student Support to Investigate the Role of Sea Ice-Albedo Feedback in Sea Ice Predictions

DTIC Science & Technology

2014-09-30

Ice - Albedo Feedback in Sea Ice Predictions Cecilia M. Bitz Atmospheric Sciences MS351640 University of Washington Seattle, WA 98196-1640 phone...TERM GOALS The overarching goals of this project are to understand the role of sea ice - albedo feedback on sea ice predictability, to improve how... sea - ice albedo is modeled and how sea ice predictions are initialized, and then to evaluate how these improvements
Assessing agreement among alternative climate change projections to inform conservation recommendations in the contiguous United States.

PubMed

Belote, R Travis; Carroll, Carlos; Martinuzzi, Sebastián; Michalak, Julia; Williams, John W; Williamson, Matthew A; Aplet, Gregory H

2018-06-21

Addressing uncertainties in climate vulnerability remains a challenge for conservation planning. We evaluate how confidence in conservation recommendations may change with agreement among alternative climate projections and metrics of climate exposure. We assessed agreement among three multivariate estimates of climate exposure (forward velocity, backward velocity, and climate dissimilarity) using 18 alternative climate projections for the contiguous United States. For each metric, we classified maps into quartiles for each alternative climate projections, and calculated the frequency of quartiles assigned for each gridded location (high quartile frequency = more agreement among climate projections). We evaluated recommendations using a recent climate adaptation heuristic framework that recommends emphasizing various conservation strategies to land based on current conservation value and expected climate exposure. We found that areas where conservation strategies would be confidently assigned based on high agreement among climate projections varied substantially across regions. In general, there was more agreement in forward and backward velocity estimates among alternative projections than agreement in estimates of local dissimilarity. Consensus of climate predictions resulted in the same conservation recommendation assignments in a few areas, but patterns varied by climate exposure metric. This work demonstrates an approach for explicitly evaluating alternative predictions in geographic patterns of climate change.
Polling the face: prediction and consensus across cultures.

PubMed

Rule, Nicholas O; Ambady, Nalini; Adams, Reginald B; Ozono, Hiroki; Nakashima, Satoshi; Yoshikawa, Sakiko; Watabe, Motoki

2010-01-01

Previous work has shown that individuals agree across cultures on the traits that they infer from faces. Previous work has also shown that inferences from faces can be predictive of important outcomes within cultures. The current research merges these two lines of work. In a series of cross-cultural studies, the authors asked American and Japanese participants to provide naïve inferences of traits from the faces of U.S. political candidates (Studies 1 and 3) and Japanese political candidates (Studies 2 and 4). Perceivers showed high agreement in their ratings of the faces, regardless of culture, and both sets of judgments were predictive of an important ecological outcome (the percentage of votes that each candidate received in the actual election). The traits predicting electoral success differed, however, depending on the targets' culture. Thus, when American and Japanese participants were asked to provide explicit inferences of how likely each candidate would be to win an election (Studies 3-4), judgments were predictive only for same-culture candidates. Attempts to infer the electoral success for the foreign culture showed evidence of self-projection. Therefore, perceivers can reliably infer predictive information from faces but require knowledge about the target's culture to make these predictions accurately.
Climate downscaling effects on predictive ecological models: a case study for threatened and endangered vertebrates in the southeastern United States

USGS Publications Warehouse

Bucklin, David N.; Watling, James I.; Speroterra, Carolina; Brandt, Laura A.; Mazzotti, Frank J.; Romañach, Stephanie S.

2013-01-01

High-resolution (downscaled) projections of future climate conditions are critical inputs to a wide variety of ecological and socioeconomic models and are created using numerous different approaches. Here, we conduct a sensitivity analysis of spatial predictions from climate envelope models for threatened and endangered vertebrates in the southeastern United States to determine whether two different downscaling approaches (with and without the use of a regional climate model) affect climate envelope model predictions when all other sources of variation are held constant. We found that prediction maps differed spatially between downscaling approaches and that the variation attributable to downscaling technique was comparable to variation between maps generated using different general circulation models (GCMs). Precipitation variables tended to show greater discrepancies between downscaling techniques than temperature variables, and for one GCM, there was evidence that more poorly resolved precipitation variables contributed relatively more to model uncertainty than more well-resolved variables. Our work suggests that ecological modelers requiring high-resolution climate projections should carefully consider the type of downscaling applied to the climate projections prior to their use in predictive ecological modeling. The uncertainty associated with alternative downscaling methods may rival that of other, more widely appreciated sources of variation, such as the general circulation model or emissions scenario with which future climate projections are created.
PREDICTION OF TOTAL DISSOLVED GAS EXCHANGE AT HYDROPOWER DAMS

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

Hadjerioua, Boualem; Pasha, MD Fayzul K; Stewart, Kevin M

2012-07-01

Total dissolved gas (TDG) supersaturation in waters released at hydropower dams can cause gas bubble trauma in fisheries resulting in physical injuries and eyeball protrusion that can lead to mortality. Elevated TDG pressures in hydropower releases are generally caused by the entrainment of air in spillway releases and the subsequent exchange of atmospheric gasses into solution during passage through the stilling basin. The network of dams throughout the Columbia River Basin (CRB) are managed for irrigation, hydropower production, flood control, navigation, and fish passage that frequently result in both voluntary and involuntary spillway releases. These dam operations are constrained bymore » state and federal water quality standards for TDG saturation which balance the benefits of spillway operations designed for Endangered Species Act (ESA)-listed fisheries versus the degradation to water quality as defined by TDG saturation. In the 1970s, the United States Environmental Protection Agency (USEPA), under the federal Clean Water Act (Section 303(d)), established a criterion not to exceed the TDG saturation level of 110% in order to protect freshwater and marine aquatic life. The states of Washington and Oregon have adopted special water quality standards for TDG saturation in the tailrace and forebays of hydropower facilities on the Columbia and Snake Rivers where spillway operations support fish passage objectives. The physical processes that affect TDG exchange at hydropower facilities have been studied throughout the CRB in site-specific studies and routine water quality monitoring programs. These data have been used to quantify the relationship between project operations, structural properties, and TDG exchange. These data have also been used to develop predictive models of TDG exchange to support real-time TDG management decisions. These empirically based predictive models have been developed for specific projects and account for both the fate of spillway
A New NOAA Research Initiative on the Seasonal Prediction of U.S. Coastal High Water Levels

NASA Astrophysics Data System (ADS)

Mariotti, A.; Archambault, H. M.; Barrie, D.; Huang, J.

2017-12-01

A crucial part of NOAA's service mission is to make U.S. communities more resilient to rises in coastal sea level, which on a seasonal timescale may increase the threat for nuisance ("sunny day") flooding, as well as enhance the severity of storm surge events. Over a season, variability in climate or ocean dynamics, in combination with longer-term trends, can influence coastal sea level in a way that is potentially predictable. To leverage these emerging scientific findings, the Climate Program Office's Modeling, Analysis, Predictions, and Projections Program, in partnership with the National Marine Fisheries Service, has funded a set of three-year projects starting in FY 2017 to help develop NOAA's capability to produce skillful seasonal (i.e, 2-9 month) predictions of coastal high water levels as well as changing living marine resources. This presentation will describe the goals, scope and intended activities of this research initiative and its coordination via a new MAPP Ocean Prediction Task Force.
Operational Precipitation prediction in Support of Real-Time Flash Flood Prediction and Reservoir Management

NASA Astrophysics Data System (ADS)

Georgakakos, K. P.

2006-05-01

The presentation will outline the implementation and performance evaluation of a number of national and international projects pertaining to operational precipitation estimation and prediction in the context of hydrologic warning systems and reservoir management support. In all cases, uncertainty measures of the estimates and predictions are an integral part of the precipitation models. Outstanding research issues whose resolution is likely to lead to improvements in the operational environment are presented. The presentation draws from the experience of the Hydrologic Research Center (http://www.hrc-lab.org) prototype implementation projects at the Panama Canal, Central America, Northern California, and South-Central US. References: Carpenter, T.M, and K.P. Georgakakos, "Discretization Scale Dependencies of the Ensemble Flow Range versus Catchment Area Relationship in Distributed Hydrologic Modeling," Journal of Hydrology, 2006, in press. Carpenter, T.M., and K.P. Georgakakos, "Impacts of Parametric and Radar Rainfall Uncertainty on the Ensemble Streamflow Simulations of a Distributed Hydrologic Model," Journal of Hydrology, 298, 202-221, 2004. Georgakakos, K.P., Graham, N.E., Carpenter, T.M., Georgakakos, A.P., and H. Yao, "Integrating Climate- Hydrology Forecasts and Multi-Objective Reservoir Management in Northern California," EOS, 86(12), 122,127, 2005. Georgakakos, K.P., and J.A. Sperfslage, "Operational Rainfall and Flow Forecasting for the Panama Canal Watershed," in The Rio Chagres: A Multidisciplinary Profile of a Tropical Watershed, R.S. Harmon, ed., Kluwer Academic Publishers, The Netherlands, Chapter 16, 323-334, 2005. Georgakakos, K. P., "Analytical results for operational flash flood guidance," Journal of Hydrology, doi:10.1016/j.jhydrol.2005.05.009, 2005.

The International Prognostic Index predicts outcome in aggressive adult T-cell leukemia/lymphoma: analysis of 126 patients from the International Peripheral T-Cell Lymphoma Project.

PubMed

Suzumiya, J; Ohshima, K; Tamura, K; Karube, K; Uike, N; Tobinai, K; Gascoyne, R D; Vose, J M; Armitage, J O; Weisenburger, D D

2009-04-01

The International Peripheral T-cell Lymphoma Project was organized to better understand the T-cell and natural killer (NK) cell lymphomas, and our task is to present the clinicopathologic correlations and therapeutic results for adult T-cell leukemia/lymphoma (ATL). Among 1153 patients with T-cell or NK cell lymphomas, 126 patients (9.6%) with ATL were represented in this project. All were categorized as aggressive ATL, i.e. acute or lymphoma type, and 87% fell into the lymphoma type. The median age was 62 years and the male to female ratio was 1.2 : 1. Significant prognostic factors for overall survival (OS) by univariate analysis were the presence of B symptoms (P = 0.018), platelet count <150 x 10(9)/l (P = 0.065), and the International Prognostic Index (IPI; P = 0.019). However, multivariate analysis indicated that only the IPI was an independent predictor of OS. Combination chemotherapy including anthracyclines was given as the initial therapy in 109 of the 116 patients (94%) who received treatment, and the overall and complete response rates were 70% and 34%, respectively. However, there was no survival benefit for those receiving an anthracycline-containing regimen. Patients with aggressive ATL have a poor clinical outcome and the IPI is a useful model for predicting outcome in ATL of the lymphoma type.
NASA Space Radiation Risk Project: Overview and Recent Results

NASA Technical Reports Server (NTRS)

Blattnig, Steve R.; Chappell, Lori J.; George, Kerry A.; Hada, Megumi; Hu, Shaowen; Kidane, Yared H.; Kim, Myung-Hee Y.; Kovyrshina, Tatiana; Norman, Ryan B.; Nounu, Hatem N.;

2015-01-01

The NASA Space Radiation Risk project is responsible for integrating new experimental and computational results into models to predict risk of cancer and acute radiation syndrome (ARS) for use in mission planning and systems design, as well as current space operations. The project has several parallel efforts focused on proving NASA's radiation risk projection capability in both the near and long term. This presentation will give an overview, with select results from these efforts including the following topics: verification, validation, and streamlining the transition of models to use in decision making; relative biological effectiveness and dose rate effect estimation using a combination of stochastic track structure simulations, DNA damage model calculations and experimental data; ARS model improvements; pathway analysis from gene expression data sets; solar particle event probabilistic exposure calculation including correlated uncertainties for use in design optimization.

Future projections of the climate and surface mass balance of Svalbard with the regional climate model MAR

NASA Astrophysics Data System (ADS)

Lang, C.; Fettweis, X.; Erpicum, M.

2015-01-01

We have performed future projections of the climate and surface mass balance (SMB) of Svalbard with the MAR regional climate model forced by the MIROC5 global model, following the RCP8.5 scenario at a spatial resolution of 10 km. MAR predicts a similar evolution of increasing surface melt everywhere in Svalbard followed by a sudden acceleration of the melt around 2050, with a larger melt increase in the south compared to the north of the archipelago and the ice caps. This melt acceleration around 2050 is mainly driven by the albedo-melt feedback associated with the expansion of the ablation/bare ice zone. This effect is dampened in part as the solar radiation itself is projected to decrease due to cloudiness increase. The near-surface temperature is projected to increase more in winter than in summer as the temperature is already close to 0 °C in summer. The model also projects a strong winter west-to-east temperature gradient, related to the large decrease of sea ice cover around Svalbard. At the end of the century (2070-2099 mean), SMB is projected to be negative over the entire Svalbard and, by 2085, all glaciated regions of Svalbard are predicted to undergo net ablation, meaning that, under the RCP8.5 scenario, all the glaciers and ice caps are predicted to start their irreversible retreat before the end of the 21st century.
Predicting fatigue service life extension of RC bridges with externally bonded CFRP repairs : [project brief].

DOT National Transportation Integrated Search

2015-12-01

Externally bonded carbon fiber reinforced polymer composites (CFRPs) are increasingly used to : repair concrete bridges. CFRP design techniques are a proven approach for enhancing the strength : of existing structures. This project investigated the d...
Cardiopulmonary Circuit Models for Predicting Injury to the Heart

NASA Astrophysics Data System (ADS)

Ward, Richard; Wing, Sarah; Bassingthwaighte, James; Neal, Maxwell

2004-11-01

Circuit models have been used extensively in physiology to describe cardiopulmonary function. Such models are being used in the DARPA Virtual Soldier (VS) Project* to predict the response to injury or physiological stress. The most complex model consists of systemic circulation, pulmonary circulation, and a four-chamber heart sub-model. This model also includes baroreceptor feedback, airway mechanics, gas exchange, and pleural pressure influence on the circulation. As part of the VS Project, Oak Ridge National Laboratory has been evaluating various cardiopulmonary circuit models for predicting the effects of injury to the heart. We describe, from a physicist's perspective, the concept of building circuit models, discuss both unstressed and stressed models, and show how the stressed models are used to predict effects of specific wounds. *This work was supported by a grant from the DARPA, executed by the U.S. Army Medical Research and Materiel Command/TATRC Cooperative Agreement, Contract # W81XWH-04-2-0012. The submitted manuscript has been authored by the U.S. Department of Energy, Office of Science of the Oak Ridge National Laboratory, managed for the U.S. DOE by UT-Battelle, LLC, under contract No. DE-AC05-00OR22725. Accordingly, the U.S. Government retains a non-exclusive, royalty-free license to publish or reproduce the published form of this contribution, or allow others to do so, for U.S. Government purpose.
Synthesis of User Needs for Arctic Sea Ice Predictions

NASA Astrophysics Data System (ADS)

Wiggins, H. V.; Turner-Bogren, E. J.; Sheffield Guy, L.

2017-12-01

Forecasting Arctic sea ice on sub-seasonal to seasonal scales in a changing Arctic is of interest to a diverse range of stakeholders. However, sea ice forecasting is still challenging due to high variability in weather and ocean conditions and limits to prediction capabilities; the science needs for observations and modeling are extensive. At a time of challenged science funding, one way to prioritize sea ice prediction efforts is to examine the information needs of various stakeholder groups. This poster will present a summary and synthesis of existing surveys, reports, and other literature that examines user needs for sea ice predictions. The synthesis will include lessons learned from the Sea Ice Prediction Network (a collaborative, multi-agency-funded project focused on seasonal Arctic sea ice predictions), the Sea Ice for Walrus Outlook (a resource for Alaska Native subsistence hunters and coastal communities, that provides reports on weather and sea ice conditions), and other efforts. The poster will specifically compare the scales and variables of sea ice forecasts currently available, as compared to what information is requested by various user groups.
Second derivatives for approximate spin projection methods

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

Thompson, Lee M.; Hratchian, Hrant P., E-mail: hhratchian@ucmerced.edu

2015-02-07

The use of broken-symmetry electronic structure methods is required in order to obtain correct behavior of electronically strained open-shell systems, such as transition states, biradicals, and transition metals. This approach often has issues with spin contamination, which can lead to significant errors in predicted energies, geometries, and properties. Approximate projection schemes are able to correct for spin contamination and can often yield improved results. To fully make use of these methods and to carry out exploration of the potential energy surface, it is desirable to develop an efficient second energy derivative theory. In this paper, we formulate the analytical secondmore » derivatives for the Yamaguchi approximate projection scheme, building on recent work that has yielded an efficient implementation of the analytical first derivatives.« less
One project`s waste is another project`s resource

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

Short, J.

1997-02-01

The author describes the efforts being made toward pollution prevention within the DOE complex, as a way to reduce overall project costs, in addition to decreasing the amount of waste to be handled. Pollution prevention is a concept which is trying to be ingrained into project planning. Part of the program involves the concept that ultimately the responsibility for waste comes back to the generator. Parts of the program involve efforts to reuse materials and equipment on new projects, to recycle wastes to generate offsetting revenue, and to increase awareness, accountability and incentives so as to stimulate action on thismore » plan. Summaries of examples are presented in tables.« less
Projecting effects of improvements in passive safety of the New Zealand light vehicle fleet.

PubMed

Keall, Michael; Newstead, Stuart; Jones, Wayne

2007-09-01

In the year 2000, as part of the process for setting New Zealand road safety targets, a projection was made for a reduction in social cost of 15.5 percent associated with improvements in crashworthiness, which is a measure of the occupant protection of the light passenger vehicle fleet. Since that document was produced, new estimates of crashworthiness have become available, allowing for a more accurate projection. The objective of this paper is to describe a methodology for projecting changes in casualty rates associated with passive safety features and to apply this methodology to produce a new prediction. The shape of the age distribution of the New Zealand light passenger vehicle fleet was projected to 2010. Projected improvements in crashworthiness and associated reductions in social cost were also modeled based on historical trends. These projections of changes in the vehicle fleet age distribution and of improvements in crashworthiness together provided a basis for estimating the future performance of the fleet in terms of secondary safety. A large social cost reduction of about 22 percent for 2010 compared to the year 2000 was predicted due to the expected huge impact of improvements in passive vehicle features on road trauma in New Zealand. Countries experiencing improvements in their vehicle fleets can also expect significant reductions in road injury compared to a less crashworthy passenger fleet. Such road safety gains can be analyzed using some of the methodology described here.
Overview of the SHIELDS Project at LANL

NASA Astrophysics Data System (ADS)

Jordanova, V.; Delzanno, G. L.; Henderson, M. G.; Godinez, H. C.; Jeffery, C. A.; Lawrence, E. C.; Meierbachtol, C.; Moulton, D.; Vernon, L.; Woodroffe, J. R.; Toth, G.; Welling, D. T.; Yu, Y.; Birn, J.; Thomsen, M. F.; Borovsky, J.; Denton, M.; Albert, J.; Horne, R. B.; Lemon, C. L.; Markidis, S.; Young, S. L.

2015-12-01

The near-Earth space environment is a highly dynamic and coupled system through a complex set of physical processes over a large range of scales, which responds nonlinearly to driving by the time-varying solar wind. Predicting variations in this environment that can affect technologies in space and on Earth, i.e. "space weather", remains a big space physics challenge. We present a recently funded project through the Los Alamos National Laboratory (LANL) Directed Research and Development (LDRD) program that is developing a new capability to understand, model, and predict Space Hazards Induced near Earth by Large Dynamic Storms, the SHIELDS framework. The project goals are to specify the dynamics of the hot (keV) particles (the seed population for the radiation belts) on both macro- and micro-scale, including important physics of rapid particle injection and acceleration associated with magnetospheric storms/substorms and plasma waves. This challenging problem is addressed using a team of world-class experts in the fields of space science and computational plasma physics and state-of-the-art models and computational facilities. New data assimilation techniques employing data from LANL instruments on the Van Allen Probes and geosynchronous satellites are developed in addition to physics-based models. This research will provide a framework for understanding of key radiation belt drivers that may accelerate particles to relativistic energies and lead to spacecraft damage and failure. The ability to reliably distinguish between various modes of failure is critically important in anomaly resolution and forensics. SHIELDS will enhance our capability to accurately specify and predict the near-Earth space environment where operational satellites reside.
Predicting moisture-induced damage to asphaltic concrete : field evaluation : final report.

DOT National Transportation Integrated Search

1981-01-01

Virginia was one of seven agencies that participated in the evaluation of a stripping test developed under National Cooperative Highway Research Program Project 4-8(3). The test was used to predict stripping of a field test section and the test resul...
Plate Boundaries and Earthquake Prediction. Crustal Evolution Education Project. Teacher's Guide [and] Student Investigation.

ERIC Educational Resources Information Center

Stoever, Edward C., Jr.

Crustal Evolution Education Project (CEEP) modules were designed to: (1) provide students with the methods and results of continuing investigations into the composition, history, and processes of the earth's crust and the application of this knowledge to man's activities and (2) to be used by teachers with little or no previous background in the…
European cancer mortality predictions for the year 2014.

PubMed

Malvezzi, M; Bertuccio, P; Levi, F; La Vecchia, C; Negri, E

2014-08-01

From most recent available data, we projected cancer mortality statistics for 2014, for the European Union (EU) and its six more populous countries. Specific attention was given to pancreatic cancer, the only major neoplasm showing unfavorable trends in both sexes. Population and death certification data from stomach, colorectum, pancreas, lung, breast, uterus, prostate, leukemias and total cancers were obtained from the World Health Organisation database and Eurostat. Figures were derived for the EU, France, Germany, Italy, Poland, Spain and the UK. Projected 2014 numbers of deaths by age group were obtained by linear regression on estimated numbers of deaths over the most recent time period identified by a joinpoint regression model. In the EU in 2014, 1,323,600 deaths from cancer are predicted (742,500 men and 581,100 women), corresponding to standardized death rates of 138.1/100,000 men and 84.7/100,000 women, falling by 7% and 5%, respectively, since 2009. In men, predicted rates for the three major cancers (lung, colorectum and prostate cancer) are lower than in 2009, falling by 8%, 4% and 10%, respectively. In women, breast and colorectal cancers had favorable trends (-9% and -7%), but female lung cancer rates are predicted to rise 8%. Pancreatic cancer is the only neoplasm with a negative outlook in both sexes. Only in the young (25-49 years), EU trends become more favorable in men, while women keep registering slight predicted rises. Cancer mortality predictions for 2014 confirm the overall favorable cancer mortality trend in the EU, translating to an overall 26% fall in men since its peak in 1988, and 20% in women, and the avoidance of over 250,000 deaths in 2014 compared with the peak rate. Notable exceptions are female lung cancer and pancreatic cancer in both sexes. © The Author 2014. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: journals.permissions@oup.com.
Deterministic and Probabilistic Metrics of Surface Air Temperature and Precipitation in the MiKlip Decadal Prediction System

NASA Astrophysics Data System (ADS)

Kadow, Christopher; Illing, Sebastian; Kunst, Oliver; Pohlmann, Holger; Müller, Wolfgang; Cubasch, Ulrich

2014-05-01

Decadal forecasting of climate variability is a growing need for different parts of society, industry and economy. The German initiative MiKlip (www.fona-miklip.de) focuses on the ongoing processes of medium-term climate prediction. The scientific major project funded by the Federal Ministry of Education and Research in Germany (BMBF) develops a forecast system, that aims for reliable predictions on decadal timescales. Using a single earth system model from the Max-Planck institute (MPI-ESM) and moving from the uninitialized runs on to the first initialized 'Coupled Model Intercomparison Project Phase 5' (CMIP5) hindcast experiments identified possibilities and open scientific tasks. The MiKlip decadal prediction system was improved on different aspects through new initialization techniques and datasets of the ocean and atmosphere. To accompany and emphasize such an improvement of a forecast system, a standardized evaluation system designed by the MiKlip sub-project 'Integrated data and evaluation system for decadal scale prediction' (INTEGRATION) analyzes every step of its evolution. This study aims at combining deterministic and probabilistic skill scores of this prediction system from its unitialized state to anomaly and then full-field oceanic initialization. The improved forecast skill in these different decadal hindcast experiments of surface air temperature and precipitation in the Pacific region and the complex area of the North Atlantic illustrate potential sources of skill. A standardized evaluation leads prediction systems depending on development to find its way to produce reliable forecasts. Different aspects of these research dependencies, e.g. ensemble size, resolution, initializations, etc. will be discussed.
Differentiating self-projection from simulation during mentalizing: evidence from fMRI.

PubMed

Schurz, Matthias; Kogler, Christoph; Scherndl, Thomas; Kronbichler, Martin; Kühberger, Anton

2015-01-01

We asked participants to predict which of two colors a similar other (student) and a dissimilar other (retiree) likes better. We manipulated if color-pairs were two hues from the same color-category (e.g. green) or two conceptually different colors (e.g. green versus blue). In the former case, the mental state that has to be represented (i.e., the percept of two different hues of green) is predominantly non-conceptual or phenomenal in nature, which should promote mental simulation as a strategy for mentalizing. In the latter case, the mental state (i.e. the percept of green versus blue) can be captured in thought by concepts, which facilitates the use of theories for mentalizing. In line with the self-projection hypothesis, we found that cortical midline areas including vmPFC / orbitofrontal cortex and precuneus were preferentially activated for mentalizing about a similar other. However, activation was not affected by the nature of the color-difference, suggesting that self-projection subsumes simulation-like processes but is not limited to them. This indicates that self-projection is a universal strategy applied in different contexts--irrespective of the availability of theories for mentalizing. Along with midline activations linked to self-projection, we also observed activation in right lateral frontal and dorsal parietal areas showing a theory-like pattern. Taken together, this shows that mentalizing does not operate based on simulation or theory, but that both strategies are used concurrently to predict the choices of others.
Development and Field Test of the Trial Battery for Project A. Improving the Selection, Classification and Utilization of Army Enlisted Personnel. Project A: Improving the Selection, Classification and Utilization of Army Enlisted Personnel. ARI Technical Report 739.

ERIC Educational Resources Information Center

Peterson, Norman G., Ed.

As part of the United States Army's Project A, research has been conducted to develop and field test a battery of experimental tests to complement the Armed Services Vocational Aptitude Battery in predicting soldiers' job performance. Project A is the United States Army's large-scale manpower effort to improve selection, classification, and…
The Role of Education in Agricultural Projects for Food Security and Poverty Reduction in Kenya

NASA Astrophysics Data System (ADS)

Walingo, Mary Khakoni

2006-05-01

Agricultural development projects have been promoted in many places as a feature of poverty-reduction strategies. Such projects have often been implemented without a strong in-built education component, and hence have had little success. Agricultural projects seek to improve food security by diversifying a household's resource base and facilitating the social and economic empowerment of women. The present study presents a survey designed to assess the relationship between education level and ability to benefit from dairy-development projects in Kenya. Results reveal higher occupation and employment levels among beneficiary than non-beneficiary households. On the other hand, beneficiaries of poverty-reduction schemes require specialized training. Apart from project-specific training, the level of general education alone cannot predict the attainment of project objectives.
Projections of leaf area index in earth system models

NASA Astrophysics Data System (ADS)

Mahowald, Natalie; Lo, Fiona; Zheng, Yun; Harrison, Laura; Funk, Chris; Lombardozzi, Danica; Goodale, Christine

2016-03-01

The area of leaves in the plant canopy, measured as leaf area index (LAI), modulates key land-atmosphere interactions, including the exchange of energy, moisture, carbon dioxide (CO2), and other trace gases and aerosols, and is therefore an essential variable in predicting terrestrial carbon, water, and energy fluxes. Here our goal is to characterize the LAI projections from the latest generation of earth system models (ESMs) for the Representative Concentration Pathway (RCP) 8.5 and RCP4.5 scenarios. On average, the models project increases in LAI in both RCP8.5 and RCP4.5 over most of the globe, but also show decreases in some parts of the tropics. Because of projected increases in variability, there are also more frequent periods of low LAI across broad regions of the tropics. Projections of LAI changes varied greatly among models: some models project very modest changes, while others project large changes, usually increases. Modeled LAI typically increases with modeled warming in the high latitudes, but often decreases with increasing local warming in the tropics. The models with the most skill in simulating current LAI in the tropics relative to satellite observations tend to project smaller increases in LAI in the tropics in the future compared to the average of all the models. Using LAI projections to identify regions that may be vulnerable to climate change presents a slightly different picture than using precipitation projections, suggesting LAI may be an additional useful tool for understanding climate change impacts. Going forward, users of LAI projections from the CMIP5 ESMs evaluated here should be aware that model outputs do not exhibit clear-cut relationships to vegetation carbon and precipitation. Our findings underscore the need for more attention to LAI projections, in terms of understanding the drivers of projected changes and improvements to model skill.
Projections of leaf area index in earth system models

DOE PAGES

Mahowald, Natalie; Lo, Fiona; Zheng, Yun; ...

2016-03-09

The area of leaves in the plant canopy, measured as leaf area index (LAI), modulates key land–atmosphere interactions, including the exchange of energy, moisture, carbon dioxide (CO 2), and other trace gases and aerosols, and is therefore an essential variable in predicting terrestrial carbon, water, and energy fluxes. Here our goal is to characterize the LAI projections from the latest generation of earth system models (ESMs) for the Representative Concentration Pathway (RCP) 8.5 and RCP4.5 scenarios. On average, the models project increases in LAI in both RCP8.5 and RCP4.5 over most of the globe, but also show decreases in somemore » parts of the tropics. Because of projected increases in variability, there are also more frequent periods of low LAI across broad regions of the tropics. Projections of LAI changes varied greatly among models: some models project very modest changes, while others project large changes, usually increases. Modeled LAI typically increases with modeled warming in the high latitudes, but often decreases with increasing local warming in the tropics. The models with the most skill in simulating current LAI in the tropics relative to satellite observations tend to project smaller increases in LAI in the tropics in the future compared to the average of all the models. Using LAI projections to identify regions that may be vulnerable to climate change presents a slightly different picture than using precipitation projections, suggesting LAI may be an additional useful tool for understanding climate change impacts. Going forward, users of LAI projections from the CMIP5 ESMs evaluated here should be aware that model outputs do not exhibit clear-cut relationships to vegetation carbon and precipitation. Lastly, our findings underscore the need for more attention to LAI projections, in terms of understanding the drivers of projected changes and improvements to model skill.« less
North Atlantic climate model bias influence on multiyear predictability

NASA Astrophysics Data System (ADS)

Wu, Y.; Park, T.; Park, W.; Latif, M.

2018-01-01

The influences of North Atlantic biases on multiyear predictability of unforced surface air temperature (SAT) variability are examined in the Kiel Climate Model (KCM). By employing a freshwater flux correction over the North Atlantic to the model, which strongly alleviates both North Atlantic sea surface salinity (SSS) and sea surface temperature (SST) biases, the freshwater flux-corrected integration depicts significantly enhanced multiyear SAT predictability in the North Atlantic sector in comparison to the uncorrected one. The enhanced SAT predictability in the corrected integration is due to a stronger and more variable Atlantic Meridional Overturning Circulation (AMOC) and its enhanced influence on North Atlantic SST. Results obtained from preindustrial control integrations of models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) support the findings obtained from the KCM: models with large North Atlantic biases tend to have a weak AMOC influence on SAT and exhibit a smaller SAT predictability over the North Atlantic sector.

Time Dependent Predictive Modeling of DIII-D ITER Baseline Scenario using Predictive TRANSP

NASA Astrophysics Data System (ADS)

Grierson, B. A.; Andre, R. G.; Budny, R. V.; Solomon, W. M.; Yuan, X.; Candy, J.; Pinsker, R. I.; Staebler, G. M.; Holland, C.; Rafiq, T.

2015-11-01

ITER baseline scenario discharges on DIII-D are modeled with TGLF and MMM transitioning from combined ECH (3.3MW) +NBI(2.8MW) heating to NBI only (3.0 MW) heating maintaining βN = 2.0 on DIII-D predicting temperature, density and rotation for comparison to experimental measurements. These models capture the reduction of confinement associated with direct electron heating H98y2 = 0.89 vs. 1.0) consistent with stiff electron transport. Reasonable agreement between experimental and modeled temperature profiles is achieved for both heating methods, whereas density and momentum predictions differ significantly. Transport fluxes from TGLF indicate that on DIII-D the electron energy flux has reached a transition from low-k to high-k turbulence with more stiff high-k transport that inhibits an increase in core electron stored energy with additional electron heating. Projections to ITER also indicate high electron stiffness. Supported by US DOE DE-AC02-09CH11466, DE-FC02-04ER54698, DE-FG02-07ER54917, DE-FG02-92-ER54141.
Effects of DTM resolution on slope steepness and soil loss prediction on hillslope profiles

Treesearch

Eder Paulo Moreira; William J. Elliot; Andrew T. Hudak

2011-01-01

Topographic attributes play a critical role in predicting erosion in models such as the Water Erosion Prediction Project (WEPP). The effects of four different high resolution hillslope profiles were studied using four different DTM resolutions: 1-m, 3-m, 5-m and 10-m. The WEPP model used a common scenario encountered in the forest environment and the selected hillslope...
Ceramic Technology Project semiannual progress report, October 1992--March 1993

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

Johnson, D.R.

1993-09-01

This project was developed to meet the ceramic technology requirements of the OTS`s automotive technology programs. Although progress has been made in developing reliable structural ceramics, further work is needed to reduce cost. The work described in this report is organized according to the following work breakdown structure project elements: Materials and processing (monolithics [Si nitride, carbide], ceramic composites, thermal and wear coatings, joining, cost effective ceramic machining), materials design methodology (contact interfaces, new concepts), data base and life prediction (structural qualification, time-dependent behavior, environmental effects, fracture mechanics, nondestructive evaluation development), and technology transfer.
Genome-to-Watershed Predictive Understanding of Terrestrial Environments

NASA Astrophysics Data System (ADS)

Hubbard, S. S.; Agarwal, D.; Banfield, J. F.; Beller, H. R.; Brodie, E.; Long, P.; Nico, P. S.; Steefel, C. I.; Tokunaga, T. K.; Williams, K. H.

2014-12-01

Although terrestrial environments play a critical role in cycling water, greenhouse gasses, and other life-critical elements, the complexity of interactions among component microbes, plants, minerals, migrating fluids and dissolved constituents hinders predictive understanding of system behavior. The 'Sustainable Systems 2.0' project is developing genome-to-watershed scale predictive capabilities to quantify how the microbiome affects biogeochemical watershed functioning, how watershed-scale hydro-biogeochemical processes affect microbial functioning, and how these interactions co-evolve with climate and land-use changes. Development of such predictive capabilities is critical for guiding the optimal management of water resources, contaminant remediation, carbon stabilization, and agricultural sustainability - now and with global change. Initial investigations are focused on floodplains in the Colorado River Basin, and include iterative model development, experiments and observations with an early emphasis on subsurface aspects. Field experiments include local-scale experiments at Rifle CO to quantify spatiotemporal metabolic and geochemical responses to O2and nitrate amendments as well as floodplain-scale monitoring to quantify genomic and biogeochemical response to natural hydrological perturbations. Information obtained from such experiments are represented within GEWaSC, a Genome-Enabled Watershed Simulation Capability, which is being developed to allow mechanistic interrogation of how genomic information stored in a subsurface microbiome affects biogeochemical cycling. This presentation will describe the genome-to-watershed scale approach as well as early highlights associated with the project. Highlights include: first insights into the diversity of the subsurface microbiome and metabolic roles of organisms involved in subsurface nitrogen, sulfur and hydrogen and carbon cycling; the extreme variability of subsurface DOC and hydrological controls on carbon and
Predictive Analytics for Coordinated Optimization in Distribution Systems

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

Yang, Rui

This talk will present NREL's work on developing predictive analytics that enables the optimal coordination of all the available resources in distribution systems to achieve the control objectives of system operators. Two projects will be presented. One focuses on developing short-term state forecasting-based optimal voltage regulation in distribution systems; and the other one focuses on actively engaging electricity consumers to benefit distribution system operations.
Development and application of new quality model for software projects.

PubMed

Karnavel, K; Dillibabu, R

2014-01-01

The IT industry tries to employ a number of models to identify the defects in the construction of software projects. In this paper, we present COQUALMO and its limitations and aim to increase the quality without increasing the cost and time. The computation time, cost, and effort to predict the residual defects are very high; this was overcome by developing an appropriate new quality model named the software testing defect corrective model (STDCM). The STDCM was used to estimate the number of remaining residual defects in the software product; a few assumptions and the detailed steps of the STDCM are highlighted. The application of the STDCM is explored in software projects. The implementation of the model is validated using statistical inference, which shows there is a significant improvement in the quality of the software projects.
Collaborative Research: Robust Climate Projections and Stochastic Stability of Dynamical Systems

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

Ilya Zaliapin

This project focused on conceptual exploration of El Nino/Southern Oscillation (ENSO) variability and sensitivity using a Delay Differential Equation developed in the project. We have (i) established the existence and continuous dependence of solutions of the model (ii) explored multiple models solutions, and the distribution of solutions extrema, and (iii) established and explored the phase locking phenomenon and the existence of multiple solutions for the same values of model parameters. In addition, we have applied to our model the concept of pullback attractor, which greatly facilitated predictive understanding of the nonlinear model's behavior.
Effect of accuracy of wind power prediction on power system operator

NASA Technical Reports Server (NTRS)

Schlueter, R. A.; Sigari, G.; Costi, T.

1985-01-01

This research project proposed a modified unit commitment that schedules connection and disconnection of generating units in response to load. A modified generation control is also proposed that controls steam units under automatic generation control, fast responding diesels, gas turbines and hydro units under a feedforward control, and wind turbine array output under a closed loop array control. This modified generation control and unit commitment require prediction of trend wind power variation one hour ahead and the prediction of error in this trend wind power prediction one half hour ahead. An improved meter for predicting trend wind speed variation is developed. Methods for accurately simulating the wind array power from a limited number of wind speed prediction records was developed. Finally, two methods for predicting the error in the trend wind power prediction were developed. This research provides a foundation for testing and evaluating the modified unit commitment and generation control that was developed to maintain operating reliability at a greatly reduced overall production cost for utilities with wind generation capacity.
Seasonal Prediction of Hydro-Climatic Extremes in the Greater Horn of Africa Under Evolving Climate Conditions to Support Adaptation Strategies

NASA Astrophysics Data System (ADS)

Tadesse, T.; Zaitchik, B. F.; Habib, S.; Funk, C. C.; Senay, G. B.; Dinku, T.; Policelli, F. S.; Block, P.; Baigorria, G. A.; Beyene, S.; Wardlow, B.; Hayes, M. J.

2014-12-01

The development of effective strategies to adapt to changes in the character of droughts and floods in Africa will rely on improved seasonal prediction systems that are robust to an evolving climate baseline and can be integrated into disaster preparedness and response. Many efforts have been made to build models to improve seasonal forecasts in the Greater Horn of Africa region (GHA) using satellite and climate data, but these efforts and models must be improved and translated into future conditions under evolving climate conditions. This has considerable social significance, but is challenged by the nature of climate predictability and the adaptability of coupled natural and human systems facing exposure to climate extremes. To address these issues, work is in progress under a project funded by NASA. The objectives of the project include: 1) Characterize and explain large-scale drivers in the ocean-atmosphere-land system associated with years of extreme flood or drought in the GHA. 2) Evaluate the performance of state-of-the-art seasonal forecast methods for prediction of decision-relevant metrics of hydrologic extremes. 3) Apply seasonal forecast systems to prediction of socially relevant impacts on crops, flood risk, and economic outcomes, and assess the value of these predictions to decision makers. 4) Evaluate the robustness of seasonal prediction systems to evolving climate conditions. The National Drought Mitigation Center (University of Nebraska-Lincoln, USA) is leading this project in collaboration with the USGS, Johns Hopkins University, University of Wisconsin-Madison, the International Research Institute for Climate and Society, NASA, and GHA local experts. The project is also designed to have active engagement of end users in various sectors, university researchers, and extension agents in GHA through workshops and/or webinars. This project is expected improve and implement new and existing climate- and remote sensing-based agricultural
A Novel Modelling Approach for Predicting Forest Growth and Yield under Climate Change.

PubMed

Ashraf, M Irfan; Meng, Fan-Rui; Bourque, Charles P-A; MacLean, David A

2015-01-01

Global climate is changing due to increasing anthropogenic emissions of greenhouse gases. Forest managers need growth and yield models that can be used to predict future forest dynamics during the transition period of present-day forests under a changing climatic regime. In this study, we developed a forest growth and yield model that can be used to predict individual-tree growth under current and projected future climatic conditions. The model was constructed by integrating historical tree growth records with predictions from an ecological process-based model using neural networks. The new model predicts basal area (BA) and volume growth for individual trees in pure or mixed species forests. For model development, tree-growth data under current climatic conditions were obtained using over 3000 permanent sample plots from the Province of Nova Scotia, Canada. Data to reflect tree growth under a changing climatic regime were projected with JABOWA-3 (an ecological process-based model). Model validation with designated data produced model efficiencies of 0.82 and 0.89 in predicting individual-tree BA and volume growth. Model efficiency is a relative index of model performance, where 1 indicates an ideal fit, while values lower than zero means the predictions are no better than the average of the observations. Overall mean prediction error (BIAS) of basal area and volume growth predictions was nominal (i.e., for BA: -0.0177 cm(2) 5-year(-1) and volume: 0.0008 m(3) 5-year(-1)). Model variability described by root mean squared error (RMSE) in basal area prediction was 40.53 cm(2) 5-year(-1) and 0.0393 m(3) 5-year(-1) in volume prediction. The new modelling approach has potential to reduce uncertainties in growth and yield predictions under different climate change scenarios. This novel approach provides an avenue for forest managers to generate required information for the management of forests in transitional periods of climate change. Artificial intelligence technology
A Novel Modelling Approach for Predicting Forest Growth and Yield under Climate Change

PubMed Central

Ashraf, M. Irfan; Meng, Fan-Rui; Bourque, Charles P.-A.; MacLean, David A.

2015-01-01

Global climate is changing due to increasing anthropogenic emissions of greenhouse gases. Forest managers need growth and yield models that can be used to predict future forest dynamics during the transition period of present-day forests under a changing climatic regime. In this study, we developed a forest growth and yield model that can be used to predict individual-tree growth under current and projected future climatic conditions. The model was constructed by integrating historical tree growth records with predictions from an ecological process-based model using neural networks. The new model predicts basal area (BA) and volume growth for individual trees in pure or mixed species forests. For model development, tree-growth data under current climatic conditions were obtained using over 3000 permanent sample plots from the Province of Nova Scotia, Canada. Data to reflect tree growth under a changing climatic regime were projected with JABOWA-3 (an ecological process-based model). Model validation with designated data produced model efficiencies of 0.82 and 0.89 in predicting individual-tree BA and volume growth. Model efficiency is a relative index of model performance, where 1 indicates an ideal fit, while values lower than zero means the predictions are no better than the average of the observations. Overall mean prediction error (BIAS) of basal area and volume growth predictions was nominal (i.e., for BA: -0.0177 cm2 5-year-1 and volume: 0.0008 m3 5-year-1). Model variability described by root mean squared error (RMSE) in basal area prediction was 40.53 cm2 5-year-1 and 0.0393 m3 5-year-1 in volume prediction. The new modelling approach has potential to reduce uncertainties in growth and yield predictions under different climate change scenarios. This novel approach provides an avenue for forest managers to generate required information for the management of forests in transitional periods of climate change. Artificial intelligence technology has substantial
Biodiversity in environmental assessment-current practice and tools for prediction

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

Gontier, Mikael; Balfors, Berit; Moertberg, Ulla

Habitat loss and fragmentation are major threats to biodiversity. Environmental impact assessment and strategic environmental assessment are essential instruments used in physical planning to address such problems. Yet there are no well-developed methods for quantifying and predicting impacts of fragmentation on biodiversity. In this study, a literature review was conducted on GIS-based ecological models that have potential as prediction tools for biodiversity assessment. Further, a review of environmental impact statements for road and railway projects from four European countries was performed, to study how impact prediction concerning biodiversity issues was addressed. The results of the study showed the existing gapmore » between research in GIS-based ecological modelling and current practice in biodiversity assessment within environmental assessment.« less
Evaluation of runoff prediction from WEPP-based erosion models for harvested and burned forest watersheds

Treesearch

S. A. Covert; P. R. Robichaud; W. J. Elliot; T. E. Link

2005-01-01

This study evaluates runoff predictions generated by GeoWEPP (Geo-spatial interface to the Water Erosion Prediction Project) and a modified version of WEPP v98.4 for forest soils. Three small (2 to 9 ha) watersheds in the mountains of the interior Northwest were monitored for several years following timber harvest and prescribed fires. Observed climate variables,...
Low-Level Convergence and the Prediction of Convective Precipitation in South Florida.

DTIC Science & Technology

1981-02-01

Af" -Ao97 55 ILLINOIS STATE WATER SURVEY URBANA F/6 4/2 LOWLEVEL CONVERGENCE AND THE PREDICTION OF CONVECTIVE PRECIPIT-ETC(U) FEB 81 A I WATSON. R L...varying meteorological cond’tions and ti: == UNCLASSIFIED of day. SECURITY CLASSIFICATIOR OF TNIS PAGE (Whven Data Etered) State Water Survey...1981 1: 4. i0 The project "Low-level Convergence and the Prediction of Convective Precipitation" is a coordinated research effort by the State Water
Examining the Predictive Validity of NIH Peer Review Scores

PubMed Central

Lindner, Mark D.; Nakamura, Richard K.

2015-01-01

The predictive validity of peer review at the National Institutes of Health (NIH) has not yet been demonstrated empirically. It might be assumed that the most efficient and expedient test of the predictive validity of NIH peer review would be an examination of the correlation between percentile scores from peer review and bibliometric indices of the publications produced from funded projects. The present study used a large dataset to examine the rationale for such a study, to determine if it would satisfy the requirements for a test of predictive validity. The results show significant restriction of range in the applications selected for funding. Furthermore, those few applications that are funded with slightly worse peer review scores are not selected at random or representative of other applications in the same range. The funding institutes also negotiate with applicants to address issues identified during peer review. Therefore, the peer review scores assigned to the submitted applications, especially for those few funded applications with slightly worse peer review scores, do not reflect the changed and improved projects that are eventually funded. In addition, citation metrics by themselves are not valid or appropriate measures of scientific impact. The use of bibliometric indices on their own to measure scientific impact would likely increase the inefficiencies and problems with replicability already largely attributed to the current over-emphasis on bibliometric indices. Therefore, retrospective analyses of the correlation between percentile scores from peer review and bibliometric indices of the publications resulting from funded grant applications are not valid tests of the predictive validity of peer review at the NIH. PMID:26039440
Developing a Framework for Seamless Prediction of Sub-Seasonal to Seasonal Extreme Precipitation Events in the United States.

NASA Astrophysics Data System (ADS)

Rosendahl, D. H.; Ćwik, P.; Martin, E. R.; Basara, J. B.; Brooks, H. E.; Furtado, J. C.; Homeyer, C. R.; Lazrus, H.; Mcpherson, R. A.; Mullens, E.; Richman, M. B.; Robinson-Cook, A.

2017-12-01

Extreme precipitation events cause significant damage to homes, businesses, infrastructure, and agriculture, as well as many injures and fatalities as a result of fast-moving water or waterborne diseases. In the USA, these natural hazard events claimed the lives of more than 300 people during 2015 - 2016 alone, with total damage reaching $24.4 billion. Prior studies of extreme precipitation events have focused on the sub-daily to sub-weekly timeframes. However, many decisions for planning, preparing and resilience-building require sub-seasonal to seasonal timeframes (S2S; 14 to 90 days), but adequate forecasting tools for prediction do not exist. Therefore, the goal of this newly funded project is an enhancement in understanding of the large-scale forcing and dynamics of S2S extreme precipitation events in the United States, and improved capability for modeling and predicting such events. Here, we describe the project goals, objectives, and research activities that will take place over the next 5 years. In this project, a unique team of scientists and stakeholders will identify and understand weather and climate processes connected with the prediction of S2S extreme precipitation events by answering these research questions: 1) What are the synoptic patterns associated with, and characteristic of, S2S extreme precipitation evens in the contiguous U.S.? 2) What role, if any, do large-scale modes of climate variability play in modulating these events? 3) How predictable are S2S extreme precipitation events across temporal scales? 4) How do we create an informative prediction of S2S extreme precipitation events for policymaking and planing? This project will use observational data, high-resolution radar composites, dynamical climate models and workshops that engage stakeholders (water resource managers, emergency managers and tribal environmental professionals) in co-production of knowledge. The overarching result of this project will be predictive models to reduce of
Predictability of Precipitation Over the Conterminous U.S. Based on the CMIP5 Multi-Model Ensemble

PubMed Central

Jiang, Mingkai; Felzer, Benjamin S.; Sahagian, Dork

2016-01-01

Characterizing precipitation seasonality and variability in the face of future uncertainty is important for a well-informed climate change adaptation strategy. Using the Colwell index of predictability and monthly normalized precipitation data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model ensembles, this study identifies spatial hotspots of changes in precipitation predictability in the United States under various climate scenarios. Over the historic period (1950–2005), the recurrent pattern of precipitation is highly predictable in the East and along the coastal Northwest, and is less so in the arid Southwest. Comparing the future (2040–2095) to the historic period, larger changes in precipitation predictability are observed under Representative Concentration Pathways (RCP) 8.5 than those under RCP 4.5. Finally, there are region-specific hotspots of future changes in precipitation predictability, and these hotspots often coincide with regions of little projected change in total precipitation, with exceptions along the wetter East and parts of the drier central West. Therefore, decision-makers are advised to not rely on future total precipitation as an indicator of water resources. Changes in precipitation predictability and the subsequent changes on seasonality and variability are equally, if not more, important factors to be included in future regional environmental assessment. PMID:27425819
Predictability of Precipitation Over the Conterminous U.S. Based on the CMIP5 Multi-Model Ensemble.

PubMed

Jiang, Mingkai; Felzer, Benjamin S; Sahagian, Dork

2016-07-18

Characterizing precipitation seasonality and variability in the face of future uncertainty is important for a well-informed climate change adaptation strategy. Using the Colwell index of predictability and monthly normalized precipitation data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model ensembles, this study identifies spatial hotspots of changes in precipitation predictability in the United States under various climate scenarios. Over the historic period (1950-2005), the recurrent pattern of precipitation is highly predictable in the East and along the coastal Northwest, and is less so in the arid Southwest. Comparing the future (2040-2095) to the historic period, larger changes in precipitation predictability are observed under Representative Concentration Pathways (RCP) 8.5 than those under RCP 4.5. Finally, there are region-specific hotspots of future changes in precipitation predictability, and these hotspots often coincide with regions of little projected change in total precipitation, with exceptions along the wetter East and parts of the drier central West. Therefore, decision-makers are advised to not rely on future total precipitation as an indicator of water resources. Changes in precipitation predictability and the subsequent changes on seasonality and variability are equally, if not more, important factors to be included in future regional environmental assessment.
Supersonics Project: Airport Noise Technical Challenge

NASA Technical Reports Server (NTRS)

Bridges, James E.

2008-01-01

This presentation gives an overview of the work being done under the Airport Noise Technical Challenge portion of the Supersonics Project in the Fundamental Aeronautics Program. The objective of the Challenge is to provide technology (e.g. low noise nozzle concepts) and engineering tools required for a viable supersonic aircraft. To accomplish this we have activities divided into Prediction, Diagnostics, and Engineering elements. Each of the tasks reviewed here have potential applications to work being done at other flight regimes and other aircraft and are of interest to the Acoustics Technical Working Group.
Improving Prediction of Large-scale Regime Transitions

NASA Astrophysics Data System (ADS)

Gyakum, J. R.; Roebber, P.; Bosart, L. F.; Honor, A.; Bunker, E.; Low, Y.; Hart, J.; Bliankinshtein, N.; Kolly, A.; Atallah, E.; Huang, Y.

2017-12-01

Cool season atmospheric predictability over the CONUS on subseasonal times scales (1-4 weeks) is critically dependent upon the structure, configuration, and evolution of the North Pacific jet stream (NPJ). The NPJ can be perturbed on its tropical side on synoptic time scales by recurving and transitioning tropical cyclones (TCs) and on subseasonal time scales by longitudinally varying convection associated with the Madden-Julian Oscillation (MJO). Likewise, the NPJ can be perturbed on its poleward side on synoptic time scales by midlatitude and polar disturbances that originate over the Asian continent. These midlatitude and polar disturbances can often trigger downstream Rossby wave propagation across the North Pacific, North America, and the North Atlantic. The project team is investigating the following multiscale processes and features: the spatiotemporal distribution of cyclone clustering over the Northern Hemisphere; cyclone clustering as influenced by atmospheric blocking and the phases and amplitudes of the major teleconnection indices, ENSO and the MJO; composite and case study analyses of representative cyclone clustering events to establish the governing dynamics; regime change predictability horizons associated with cyclone clustering events; Arctic air mass generation and modification; life cycles of the MJO; and poleward heat and moisture transports of subtropical air masses. A critical component of the study is weather regime classification. These classifications are defined through: the spatiotemporal clustering of surface cyclogenesis; a general circulation metric combining data at 500-hPa and the dynamic tropopause; Self Organizing Maps (SOM), constructed from dynamic tropopause and 850 hPa equivalent potential temperature data. The resultant lattice of nodes is used to categorize synoptic classes and their predictability, as well as to determine the robustness of the CFSv2 model climate relative to observations. Transition pathways between these

Collaborative Project: Improving the Representation of Coastal and Estuarine Processes in Earth System Models

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

Bryan, Frank; Dennis, John; MacCready, Parker

This project aimed to improve long term global climate simulations by resolving and enhancing the representation of the processes involved in the cycling of freshwater through estuaries and coastal regions. This was a collaborative multi-institution project consisting of physical oceanographers, climate model developers, and computational scientists. It specifically targeted the DOE objectives of advancing simulation and predictive capability of climate models through improvements in resolution and physical process representation.
Development of a model for predicting NASA/MSFC program success

NASA Technical Reports Server (NTRS)

Riggs, Jeffrey; Miller, Tracy; Finley, Rosemary

1990-01-01

Research conducted during the execution of a previous contract (NAS8-36955/0039) firmly established the feasibility of developing a tool to aid decision makers in predicting the potential success of proposed projects. The final report from that investigation contains an outline of the method to be applied in developing this Project Success Predictor Model. As a follow-on to the previous study, this report describes in detail the development of this model and includes full explanation of the data-gathering techniques used to poll expert opinion. The report includes the presentation of the model code itself.
Predictive Compensator Optimization for Head Tracking Lag in Virtual Environments

NASA Technical Reports Server (NTRS)

Adelstein, Barnard D.; Jung, Jae Y.; Ellis, Stephen R.

2001-01-01

We examined the perceptual impact of plant noise parameterization for Kalman Filter predictive compensation of time delays intrinsic to head tracked virtual environments (VEs). Subjects were tested in their ability to discriminate between the VE system's minimum latency and conditions in which artificially added latency was then predictively compensated back to the system minimum. Two head tracking predictors were parameterized off-line according to cost functions that minimized prediction errors in (1) rotation, and (2) rotation projected into translational displacement with emphasis on higher frequency human operator noise. These predictors were compared with a parameterization obtained from the VE literature for cost function (1). Results from 12 subjects showed that both parameterization type and amount of compensated latency affected discrimination. Analysis of the head motion used in the parameterizations and the subsequent discriminability results suggest that higher frequency predictor artifacts are contributory cues for discriminating the presence of predictive compensation.
Predicting moisture-induced damage to asphaltic concrete : field evaluation phase, interim report.

DOT National Transportation Integrated Search

1977-01-01

Virginia is one of seven state and federal agencies participating in a field evaluation of a stripping test method developed under NCHRP Project 4-8 (3), "Predicting Moisture- Induced Damage to Asphaltic Concrete." The test method is being used to ev...
WEPP model implementation project with the USDA-Natural Resources Conservation Service

USDA-ARS?s Scientific Manuscript database

The Water Erosion Prediction Project (WEPP) is a physical process-based soil erosion model that can be used to estimate runoff, soil loss, and sediment yield from hillslope profiles, fields, and small watersheds. Initially developed from 1985-1995, WEPP has been applied and validated across a wide r...
Predicting landscape vegetation dynamics using state-and-transition simulation models

Treesearch

Colin J. Daniel; Leonardo Frid

2012-01-01

This paper outlines how state-and-transition simulation models (STSMs) can be used to project changes in vegetation over time across a landscape. STSMs are stochastic, empirical simulation models that use an adapted Markov chain approach to predict how vegetation will transition between states over time, typically in response to interactions between succession,...
Prediction of human population responses to toxic compounds by a collaborative competition.

PubMed

Eduati, Federica; Mangravite, Lara M; Wang, Tao; Tang, Hao; Bare, J Christopher; Huang, Ruili; Norman, Thea; Kellen, Mike; Menden, Michael P; Yang, Jichen; Zhan, Xiaowei; Zhong, Rui; Xiao, Guanghua; Xia, Menghang; Abdo, Nour; Kosyk, Oksana; Friend, Stephen; Dearry, Allen; Simeonov, Anton; Tice, Raymond R; Rusyn, Ivan; Wright, Fred A; Stolovitzky, Gustavo; Xie, Yang; Saez-Rodriguez, Julio

2015-09-01

The ability to computationally predict the effects of toxic compounds on humans could help address the deficiencies of current chemical safety testing. Here, we report the results from a community-based DREAM challenge to predict toxicities of environmental compounds with potential adverse health effects for human populations. We measured the cytotoxicity of 156 compounds in 884 lymphoblastoid cell lines for which genotype and transcriptional data are available as part of the Tox21 1000 Genomes Project. The challenge participants developed algorithms to predict interindividual variability of toxic response from genomic profiles and population-level cytotoxicity data from structural attributes of the compounds. 179 submitted predictions were evaluated against an experimental data set to which participants were blinded. Individual cytotoxicity predictions were better than random, with modest correlations (Pearson's r < 0.28), consistent with complex trait genomic prediction. In contrast, predictions of population-level response to different compounds were higher (r < 0.66). The results highlight the possibility of predicting health risks associated with unknown compounds, although risk estimation accuracy remains suboptimal.
Consensus models to predict endocrine disruption for all ...

EPA Pesticide Factsheets

Humans are potentially exposed to tens of thousands of man-made chemicals in the environment. It is well known that some environmental chemicals mimic natural hormones and thus have the potential to be endocrine disruptors. Most of these environmental chemicals have never been tested for their ability to disrupt the endocrine system, in particular, their ability to interact with the estrogen receptor. EPA needs tools to prioritize thousands of chemicals, for instance in the Endocrine Disruptor Screening Program (EDSP). Collaborative Estrogen Receptor Activity Prediction Project (CERAPP) was intended to be a demonstration of the use of predictive computational models on HTS data including ToxCast and Tox21 assays to prioritize a large chemical universe of 32464 unique structures for one specific molecular target – the estrogen receptor. CERAPP combined multiple computational models for prediction of estrogen receptor activity, and used the predicted results to build a unique consensus model. Models were developed in collaboration between 17 groups in the U.S. and Europe and applied to predict the common set of chemicals. Structure-based techniques such as docking and several QSAR modeling approaches were employed, mostly using a common training set of 1677 compounds provided by U.S. EPA, to build a total of 42 classification models and 8 regression models for binding, agonist and antagonist activity. All predictions were evaluated on ToxCast data and on an exte
Using Machine Learning to Predict MCNP Bias

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

Grechanuk, Pavel Aleksandrovi

For many real-world applications in radiation transport where simulations are compared to experimental measurements, like in nuclear criticality safety, the bias (simulated - experimental k eff) in the calculation is an extremely important quantity used for code validation. The objective of this project is to accurately predict the bias of MCNP6 [1] criticality calculations using machine learning (ML) algorithms, with the intention of creating a tool that can complement the current nuclear criticality safety methods. In the latest release of MCNP6, the Whisper tool is available for criticality safety analysts and includes a large catalogue of experimental benchmarks, sensitivity profiles,more » and nuclear data covariance matrices. This data, coming from 1100+ benchmark cases, is used in this study of ML algorithms for criticality safety bias predictions.« less
Ceramic Technology for Advanced Heat Engines Project

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

Not Available

1989-08-01

The Ceramic Technology for Advanced Heat Engines Project was developed by the Department of Energy's Office of Transportation Systems (OTS) in Conservation and Renewable Energy. This project, part of the OTS's Advanced Materials Development Program, was developed to meet the ceramic technology requirements of the OTS's automotive technology programs. Significant accomplishments in fabricating ceramic components for the Department of Energy (DOE), National Aeronautics and Space Administration (NASA), and Department of Defense (DoD) advanced heat engine programs have provided evidence that the operation of ceramic parts in high-temperature engine environments is feasible. However, these programs have also demonstrated that additional researchmore » is needed in materials and processing development, design methodology, and data base and life prediction before industry will have a sufficient technology base from which to produce reliable cost-effective ceramic engine components commercially.« less
The performance of projective standardization for digital subtraction radiography.

PubMed

Mol, André; Dunn, Stanley M

2003-09-01

We sought to test the performance and robustness of projective standardization in preserving invariant properties of subtraction images in the presence of irreversible projection errors. Study design Twenty bone chips (1-10 mg each) were placed on dentate dry mandibles. Follow-up images were obtained without the bone chips, and irreversible projection errors of up to 6 degrees were introduced. Digitized image intensities were normalized, and follow-up images were geometrically reconstructed by 2 operators using anatomical and fiduciary landmarks. Subtraction images were analyzed by 3 observers. Regression analysis revealed a linear relationship between radiographic estimates of mineral loss and actual mineral loss (R(2) = 0.99; P <.05). The effect of projection error was not significant (general linear model [GLM]: P >.05). There was no difference between the radiographic estimates from images standardized with anatomical landmarks and those standardized with fiduciary landmarks (Wilcoxon signed rank test: P >.05). Operator variability was low for image analysis alone (R(2) = 0.99; P <.05), as well as for the entire procedure (R(2) = 0.98; P <.05). The predicted detection limit was smaller than 1 mg. Subtraction images registered by projective standardization yield estimates of osseous change that are invariant to irreversible projection errors of up to 6 degrees. Within these limits, operator precision is high and anatomical landmarks can be used to establish correspondence.
Project management of life-science research projects: project characteristics, challenges and training needs.

PubMed

Beukers, Margot W

2011-02-01

Thirty-four project managers of life-science research projects were interviewed to investigate the characteristics of their projects, the challenges they faced and their training requirements. A set of ten discriminating parameters were identified based on four project categories: contract research, development, discovery and call-based projects--projects set up to address research questions defined in a call for proposals. The major challenges these project managers are faced with relate to project members, leadership without authority and a lack of commitment from the respective organization. Two-thirds of the project managers indicated that they would be interested in receiving additional training, mostly on people-oriented, soft skills. The training programs that are currently on offer, however, do not meet their needs. Copyright © 2010 Elsevier Ltd. All rights reserved.
In vitro models for the prediction of in vivo performance of oral dosage forms.

PubMed

Kostewicz, Edmund S; Abrahamsson, Bertil; Brewster, Marcus; Brouwers, Joachim; Butler, James; Carlert, Sara; Dickinson, Paul A; Dressman, Jennifer; Holm, René; Klein, Sandra; Mann, James; McAllister, Mark; Minekus, Mans; Muenster, Uwe; Müllertz, Anette; Verwei, Miriam; Vertzoni, Maria; Weitschies, Werner; Augustijns, Patrick

2014-06-16

Accurate prediction of the in vivo biopharmaceutical performance of oral drug formulations is critical to efficient drug development. Traditionally, in vitro evaluation of oral drug formulations has focused on disintegration and dissolution testing for quality control (QC) purposes. The connection with in vivo biopharmaceutical performance has often been ignored. More recently, the switch to assessing drug products in a more biorelevant and mechanistic manner has advanced the understanding of drug formulation behavior. Notwithstanding this evolution, predicting the in vivo biopharmaceutical performance of formulations that rely on complex intraluminal processes (e.g. solubilization, supersaturation, precipitation…) remains extremely challenging. Concomitantly, the increasing demand for complex formulations to overcome low drug solubility or to control drug release rates urges the development of new in vitro tools. Development and optimizing innovative, predictive Oral Biopharmaceutical Tools is the main target of the OrBiTo project within the Innovative Medicines Initiative (IMI) framework. A combination of physico-chemical measurements, in vitro tests, in vivo methods, and physiology-based pharmacokinetic modeling is expected to create a unique knowledge platform, enabling the bottlenecks in drug development to be removed and the whole process of drug development to become more efficient. As part of the basis for the OrBiTo project, this review summarizes the current status of predictive in vitro assessment tools for formulation behavior. Both pharmacopoeia-listed apparatus and more advanced tools are discussed. Special attention is paid to major issues limiting the predictive power of traditional tools, including the simulation of dynamic changes in gastrointestinal conditions, the adequate reproduction of gastrointestinal motility, the simulation of supersaturation and precipitation, and the implementation of the solubility-permeability interplay. It is
Consistent prediction of GO protein localization.

PubMed

Spetale, Flavio E; Arce, Debora; Krsticevic, Flavia; Bulacio, Pilar; Tapia, Elizabeth

2018-05-17

The GO-Cellular Component (GO-CC) ontology provides a controlled vocabulary for the consistent description of the subcellular compartments or macromolecular complexes where proteins may act. Current machine learning-based methods used for the automated GO-CC annotation of proteins suffer from the inconsistency of individual GO-CC term predictions. Here, we present FGGA-CC + , a class of hierarchical graph-based classifiers for the consistent GO-CC annotation of protein coding genes at the subcellular compartment or macromolecular complex levels. Aiming to boost the accuracy of GO-CC predictions, we make use of the protein localization knowledge in the GO-Biological Process (GO-BP) annotations to boost the accuracy of GO-CC prediction. As a result, FGGA-CC + classifiers are built from annotation data in both the GO-CC and GO-BP ontologies. Due to their graph-based design, FGGA-CC + classifiers are fully interpretable and their predictions amenable to expert analysis. Promising results on protein annotation data from five model organisms were obtained. Additionally, successful validation results in the annotation of a challenging subset of tandem duplicated genes in the tomato non-model organism were accomplished. Overall, these results suggest that FGGA-CC + classifiers can indeed be useful for satisfying the huge demand of GO-CC annotation arising from ubiquitous high throughout sequencing and proteomic projects.
Unravelling the structure of species extinction risk for predictive conservation science.

PubMed

Lee, Tien Ming; Jetz, Walter

2011-05-07

Extinction risk varies across species and space owing to the combined and interactive effects of ecology/life history and geography. For predictive conservation science to be effective, large datasets and integrative models that quantify the relative importance of potential factors and separate rapidly changing from relatively static threat drivers are urgently required. Here, we integrate and map in space the relative and joint effects of key correlates of The International Union for Conservation of Nature-assessed extinction risk for 8700 living birds. Extinction risk varies significantly with species' broad-scale environmental niche, geographical range size, and life-history and ecological traits such as body size, developmental mode, primary diet and foraging height. Even at this broad scale, simple quantifications of past human encroachment across species' ranges emerge as key in predicting extinction risk, supporting the use of land-cover change projections for estimating future threat in an integrative setting. A final joint model explains much of the interspecific variation in extinction risk and provides a remarkably strong prediction of its observed global geography. Our approach unravels the species-level structure underlying geographical gradients in extinction risk and offers a means of disentangling static from changing components of current and future threat. This reconciliation of intrinsic and extrinsic, and of past and future extinction risk factors may offer a critical step towards a more continuous, forward-looking assessment of species' threat status based on geographically explicit environmental change projections, potentially advancing global predictive conservation science.
Reduction of initial shock in decadal predictions using a new initialization strategy

NASA Astrophysics Data System (ADS)

He, Yujun; Wang, Bin; Liu, Mimi; Liu, Li; Yu, Yongqiang; Liu, Juanjuan; Li, Ruizhe; Zhang, Cheng; Xu, Shiming; Huang, Wenyu; Liu, Qun; Wang, Yong; Li, Feifei

2017-08-01

A novel full-field initialization strategy based on the dimension-reduced projection four-dimensional variational data assimilation (DRP-4DVar) is proposed to alleviate the well-known initial shock occurring in the early years of decadal predictions. It generates consistent initial conditions, which best fit the monthly mean oceanic analysis data along the coupled model trajectory in 1 month windows. Three indices to measure the initial shock intensity are also proposed. Results indicate that this method does reduce the initial shock in decadal predictions by Flexible Global Ocean-Atmosphere-Land System model, Grid-point version 2 (FGOALS-g2) compared with the three-dimensional variational data assimilation-based nudging full-field initialization for the same model and is comparable to or even better than the different initialization strategies for other fifth phase of the Coupled Model Intercomparison Project (CMIP5) models. Better hindcasts of global mean surface air temperature anomalies can be obtained than in other FGOALS-g2 experiments. Due to the good model response to external forcing and the reduction of initial shock, higher decadal prediction skill is achieved than in other CMIP5 models.
Novel inter and intra prediction tools under consideration for the emerging AV1 video codec

NASA Astrophysics Data System (ADS)

Joshi, Urvang; Mukherjee, Debargha; Han, Jingning; Chen, Yue; Parker, Sarah; Su, Hui; Chiang, Angie; Xu, Yaowu; Liu, Zoe; Wang, Yunqing; Bankoski, Jim; Wang, Chen; Keyder, Emil

2017-09-01

Google started the WebM Project in 2010 to develop open source, royalty- free video codecs designed specifically for media on the Web. The second generation codec released by the WebM project, VP9, is currently served by YouTube, and enjoys billions of views per day. Realizing the need for even greater compression efficiency to cope with the growing demand for video on the web, the WebM team embarked on an ambitious project to develop a next edition codec AV1, in a consortium of major tech companies called the Alliance for Open Media, that achieves at least a generational improvement in coding efficiency over VP9. In this paper, we focus primarily on new tools in AV1 that improve the prediction of pixel blocks before transforms, quantization and entropy coding are invoked. Specifically, we describe tools and coding modes that improve intra, inter and combined inter-intra prediction. Results are presented on standard test sets.
Proactive Supply Chain Performance Management with Predictive Analytics

PubMed Central

Stefanovic, Nenad

2014-01-01

Today's business climate requires supply chains to be proactive rather than reactive, which demands a new approach that incorporates data mining predictive analytics. This paper introduces a predictive supply chain performance management model which combines process modelling, performance measurement, data mining models, and web portal technologies into a unique model. It presents the supply chain modelling approach based on the specialized metamodel which allows modelling of any supply chain configuration and at different level of details. The paper also presents the supply chain semantic business intelligence (BI) model which encapsulates data sources and business rules and includes the data warehouse model with specific supply chain dimensions, measures, and KPIs (key performance indicators). Next, the paper describes two generic approaches for designing the KPI predictive data mining models based on the BI semantic model. KPI predictive models were trained and tested with a real-world data set. Finally, a specialized analytical web portal which offers collaborative performance monitoring and decision making is presented. The results show that these models give very accurate KPI projections and provide valuable insights into newly emerging trends, opportunities, and problems. This should lead to more intelligent, predictive, and responsive supply chains capable of adapting to future business environment. PMID:25386605
Proactive supply chain performance management with predictive analytics.

PubMed

Stefanovic, Nenad

2014-01-01

Today's business climate requires supply chains to be proactive rather than reactive, which demands a new approach that incorporates data mining predictive analytics. This paper introduces a predictive supply chain performance management model which combines process modelling, performance measurement, data mining models, and web portal technologies into a unique model. It presents the supply chain modelling approach based on the specialized metamodel which allows modelling of any supply chain configuration and at different level of details. The paper also presents the supply chain semantic business intelligence (BI) model which encapsulates data sources and business rules and includes the data warehouse model with specific supply chain dimensions, measures, and KPIs (key performance indicators). Next, the paper describes two generic approaches for designing the KPI predictive data mining models based on the BI semantic model. KPI predictive models were trained and tested with a real-world data set. Finally, a specialized analytical web portal which offers collaborative performance monitoring and decision making is presented. The results show that these models give very accurate KPI projections and provide valuable insights into newly emerging trends, opportunities, and problems. This should lead to more intelligent, predictive, and responsive supply chains capable of adapting to future business environment.
Early experiences building a software quality prediction model

NASA Technical Reports Server (NTRS)

Agresti, W. W.; Evanco, W. M.; Smith, M. C.

1990-01-01

Early experiences building a software quality prediction model are discussed. The overall research objective is to establish a capability to project a software system's quality from an analysis of its design. The technical approach is to build multivariate models for estimating reliability and maintainability. Data from 21 Ada subsystems were analyzed to test hypotheses about various design structures leading to failure-prone or unmaintainable systems. Current design variables highlight the interconnectivity and visibility of compilation units. Other model variables provide for the effects of reusability and software changes. Reported results are preliminary because additional project data is being obtained and new hypotheses are being developed and tested. Current multivariate regression models are encouraging, explaining 60 to 80 percent of the variation in error density of the subsystems.

A community effort to assess and improve drug sensitivity prediction algorithms

PubMed Central

Costello, James C; Heiser, Laura M; Georgii, Elisabeth; Gönen, Mehmet; Menden, Michael P; Wang, Nicholas J; Bansal, Mukesh; Ammad-ud-din, Muhammad; Hintsanen, Petteri; Khan, Suleiman A; Mpindi, John-Patrick; Kallioniemi, Olli; Honkela, Antti; Aittokallio, Tero; Wennerberg, Krister; Collins, James J; Gallahan, Dan; Singer, Dinah; Saez-Rodriguez, Julio; Kaski, Samuel; Gray, Joe W; Stolovitzky, Gustavo

2015-01-01

Predicting the best treatment strategy from genomic information is a core goal of precision medicine. Here we focus on predicting drug response based on a cohort of genomic, epigenomic and proteomic profiling data sets measured in human breast cancer cell lines. Through a collaborative effort between the National Cancer Institute (NCI) and the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we analyzed a total of 44 drug sensitivity prediction algorithms. The top-performing approaches modeled nonlinear relationships and incorporated biological pathway information. We found that gene expression microarrays consistently provided the best predictive power of the individual profiling data sets; however, performance was increased by including multiple, independent data sets. We discuss the innovations underlying the top-performing methodology, Bayesian multitask MKL, and we provide detailed descriptions of all methods. This study establishes benchmarks for drug sensitivity prediction and identifies approaches that can be leveraged for the development of new methods. PMID:24880487
A community effort to assess and improve drug sensitivity prediction algorithms.

PubMed

Costello, James C; Heiser, Laura M; Georgii, Elisabeth; Gönen, Mehmet; Menden, Michael P; Wang, Nicholas J; Bansal, Mukesh; Ammad-ud-din, Muhammad; Hintsanen, Petteri; Khan, Suleiman A; Mpindi, John-Patrick; Kallioniemi, Olli; Honkela, Antti; Aittokallio, Tero; Wennerberg, Krister; Collins, James J; Gallahan, Dan; Singer, Dinah; Saez-Rodriguez, Julio; Kaski, Samuel; Gray, Joe W; Stolovitzky, Gustavo

2014-12-01

Predicting the best treatment strategy from genomic information is a core goal of precision medicine. Here we focus on predicting drug response based on a cohort of genomic, epigenomic and proteomic profiling data sets measured in human breast cancer cell lines. Through a collaborative effort between the National Cancer Institute (NCI) and the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we analyzed a total of 44 drug sensitivity prediction algorithms. The top-performing approaches modeled nonlinear relationships and incorporated biological pathway information. We found that gene expression microarrays consistently provided the best predictive power of the individual profiling data sets; however, performance was increased by including multiple, independent data sets. We discuss the innovations underlying the top-performing methodology, Bayesian multitask MKL, and we provide detailed descriptions of all methods. This study establishes benchmarks for drug sensitivity prediction and identifies approaches that can be leveraged for the development of new methods.
Effects of people-centred factors on enterprise resource planning implementation project success: empirical evidence from Sri Lanka

NASA Astrophysics Data System (ADS)

Wickramasinghe, Vathsala; Gunawardena, Vathsala

2010-08-01

Extant literature suggests people-centred factors as one of the major areas influencing enterprise resource planning (ERP) implementation project success. Yet, to date, few empirical studies attempted to validate the link between people-centred factors and ERP implementation project success. The purpose of this study is to empirically identify people-centred factors that are critical to ERP implementation projects in Sri Lanka. The study develops and empirically validates a framework for people-centred factors that influence the success of ERP implementation projects. Survey research methodology was used and collected data from 74 ERP implementation projects in Sri Lanka. The people-centred factors of 'project team competence', 'rewards' and 'communication and change' were found to predict significantly the ERP implementation project success.
Projected changes in distributions of Australian tropical savanna birds under climate change using three dispersal scenarios

PubMed Central

Reside, April E; VanDerWal, Jeremy; Kutt, Alex S

2012-01-01

Identifying the species most vulnerable to extinction as a result of climate change is a necessary first step in mitigating biodiversity decline. Species distribution modeling (SDM) is a commonly used tool to assess potential climate change impacts on distributions of species. We use SDMs to predict geographic ranges for 243 birds of Australian tropical savannas, and to project changes in species richness and ranges under a future climate scenario between 1990 and 2080. Realistic predictions require recognition of the variability in species capacity to track climatically suitable environments. Here we assess the effect of dispersal on model results by using three approaches: full dispersal, no dispersal and a partial-dispersal scenario permitting species to track climate change at a rate of 30 km per decade. As expected, the projected distributions and richness patterns are highly sensitive to the dispersal scenario. Projected future range sizes decreased for 66% of species if full dispersal was assumed, but for 89% of species when no dispersal was assumed. However, realistic future predictions should not assume a single dispersal scenario for all species and as such, we assigned each species to the most appropriate dispersal category based on individual mobility and habitat specificity; this permitted the best estimates of where species will be in the future. Under this “realistic” dispersal scenario, projected ranges sizes decreased for 67% of species but showed that migratory and tropical-endemic birds are predicted to benefit from climate change with increasing distributional area. Richness hotspots of tropical savanna birds are expected to move, increasing in southern savannas and southward along the east coast of Australia, but decreasing in the arid zone. Understanding the complexity of effects of climate change on species’ range sizes by incorporating dispersal capacities is a crucial step toward developing adaptation policies for the conservation of
Acoustic Prediction State of the Art Assessment

NASA Technical Reports Server (NTRS)

Dahl, Milo D.

2007-01-01

The acoustic assessment task for both the Subsonic Fixed Wing and the Supersonic projects under NASA s Fundamental Aeronautics Program was designed to assess the current state-of-the-art in noise prediction capability and to establish baselines for gauging future progress. The documentation of our current capabilities included quantifying the differences between predictions of noise from computer codes and measurements of noise from experimental tests. Quantifying the accuracy of both the computed and experimental results further enhanced the credibility of the assessment. This presentation gives sample results from codes representative of NASA s capabilities in aircraft noise prediction both for systems and components. These include semi-empirical, statistical, analytical, and numerical codes. System level results are shown for both aircraft and engines. Component level results are shown for a landing gear prototype, for fan broadband noise, for jet noise from a subsonic round nozzle, and for propulsion airframe aeroacoustic interactions. Additional results are shown for modeling of the acoustic behavior of duct acoustic lining and the attenuation of sound in lined ducts with flow.
Climate Modeling and Causal Identification for Sea Ice Predictability

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

Hunke, Elizabeth Clare; Urrego Blanco, Jorge Rolando; Urban, Nathan Mark

This project aims to better understand causes of ongoing changes in the Arctic climate system, particularly as decreasing sea ice trends have been observed in recent decades and are expected to continue in the future. As part of the Sea Ice Prediction Network, a multi-agency effort to improve sea ice prediction products on seasonal-to-interannual time scales, our team is studying sensitivity of sea ice to a collection of physical process and feedback mechanism in the coupled climate system. During 2017 we completed a set of climate model simulations using the fully coupled ACME-HiLAT model. The simulations consisted of experiments inmore » which cloud, sea ice, and air-ocean turbulent exchange parameters previously identified as important for driving output uncertainty in climate models were perturbed to account for parameter uncertainty in simulated climate variables. We conducted a sensitivity study to these parameters, which built upon a previous study we made for standalone simulations (Urrego-Blanco et al., 2016, 2017). Using the results from the ensemble of coupled simulations, we are examining robust relationships between climate variables that emerge across the experiments. We are also using causal discovery techniques to identify interaction pathways among climate variables which can help identify physical mechanisms and provide guidance in predictability studies. This work further builds on and leverages the large ensemble of standalone sea ice simulations produced in our previous w14_seaice project.« less
An ensemble predictive modeling framework for breast cancer classification.

PubMed

Nagarajan, Radhakrishnan; Upreti, Meenakshi

2017-12-01

Molecular changes often precede clinical presentation of diseases and can be useful surrogates with potential to assist in informed clinical decision making. Recent studies have demonstrated the usefulness of modeling approaches such as classification that can predict the clinical outcomes from molecular expression profiles. While useful, a majority of these approaches implicitly use all molecular markers as features in the classification process often resulting in sparse high-dimensional projection of the samples often comparable to that of the sample size. In this study, a variant of the recently proposed ensemble classification approach is used for predicting good and poor-prognosis breast cancer samples from their molecular expression profiles. In contrast to traditional single and ensemble classifiers, the proposed approach uses multiple base classifiers with varying feature sets obtained from two-dimensional projection of the samples in conjunction with a majority voting strategy for predicting the class labels. In contrast to our earlier implementation, base classifiers in the ensembles are chosen based on maximal sensitivity and minimal redundancy by choosing only those with low average cosine distance. The resulting ensemble sets are subsequently modeled as undirected graphs. Performance of four different classification algorithms is shown to be better within the proposed ensemble framework in contrast to using them as traditional single classifier systems. Significance of a subset of genes with high-degree centrality in the network abstractions across the poor-prognosis samples is also discussed. Copyright © 2017 Elsevier Inc. All rights reserved.
Homogenisation in project management for large German research projects in the Earth system sciences: overcoming the institutional coordination bias

NASA Astrophysics Data System (ADS)

Rauser, Florian; Vamborg, Freja

2016-04-01

The interdisciplinary project on High Definition Clouds and Precipitation for advancing climate prediction HD(CP)2 (hdcp2.eu) is an example for the trend in fundamental research in Europe to increasingly focus on large national and international research programs that require strong scientific coordination. The current system has traditionally been host-based: project coordination activities and funding is placed at the host institute of the central lead PI of the project. This approach is simple and has the advantage of strong collaboration between project coordinator and lead PI, while exhibiting a list of strong, inherent disadvantages that are also mentioned in this session's description: no community best practice development, lack of integration between similar projects, inefficient methodology development and usage, and finally poor career development opportunities for the coordinators. Project coordinators often leave the project before it is finalized, leaving some of the fundamentally important closing processes to the PIs. This systematically prevents the creation of professional science management expertise within academia, which leads to an automatic imbalance that hinders the outcome of large research programs to help future funding decisions. Project coordinators in academia often do not work in a professional project office environment that could distribute activities and use professional tools and methods between different projects. Instead, every new project manager has to focus on methodological work anew (communication infrastructure, meetings, reporting), even though the technological needs of large research projects are similar. This decreases the efficiency of the coordination and leads to funding that is effectively misallocated. We propose to challenge this system by creating a permanent, virtual "Centre for Earth System Science Management CESSMA" (cessma.com), and changing the approach from host- based to centre-based. This should
Geomechanical/Geochemical Modeling Studies Conducted within theInternational DECOVALEX Project

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

Birkholzer, J.T.; Rutqvist, J.; Sonnenthal, E.L.

2005-10-19

The DECOVALEX project is an international cooperative project initiated by SKI, the Swedish Nuclear Power Inspectorate, with participation of about 10 international organizations. The general goal of this project is to encourage multidisciplinary interactive and cooperative research on modeling coupled thermo-hydro-mechanical-chemical (THMC) processes in geologic formations in support of the performance assessment for underground storage of radioactive waste. One of the research tasks, initiated in 2004 by the U.S. Department of Energy (DOE), addresses the long-term impact of geomechanical and geochemical processes on the flow conditions near waste emplacement tunnels. Within this task, four international research teams conduct predictive analysismore » of the coupled processes in two generic repositories, using multiple approaches and different computer codes. Below, we give an overview of the research task and report its current status.« less
Geomechanical/ Geochemical Modeling Studies onducted Within the International DECOVALEX Project

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

J.T. Birkholzer; J. Rutqvist; E.L. Sonnenthal

2006-02-01

The DECOVALEX project is an international cooperative project initiated by SKI, the Swedish Nuclear Power Inspectorate, with participation of about 10 international organizations. The general goal of this project is to encourage multidisciplinary interactive and cooperative research on modeling coupled thermo-hydro-mechanical-chemical (THMC) processes in geologic formations in support of the performance assessment for underground storage of radioactive waste. One of the research tasks, initiated in 2004 by the U.S. Department of Energy (DOE), addresses the long-term impact of geomechanical and geochemical processes on the flow conditions near waste emplacement tunnels. Within this task, four international research teams conduct predictive analysismore » of the coupled processes in two generic repositories, using multiple approaches and different computer codes. Below, we give an overview of the research task and report its current status.« less
Development and Application of New Quality Model for Software Projects

PubMed Central

Karnavel, K.; Dillibabu, R.

2014-01-01

The IT industry tries to employ a number of models to identify the defects in the construction of software projects. In this paper, we present COQUALMO and its limitations and aim to increase the quality without increasing the cost and time. The computation time, cost, and effort to predict the residual defects are very high; this was overcome by developing an appropriate new quality model named the software testing defect corrective model (STDCM). The STDCM was used to estimate the number of remaining residual defects in the software product; a few assumptions and the detailed steps of the STDCM are highlighted. The application of the STDCM is explored in software projects. The implementation of the model is validated using statistical inference, which shows there is a significant improvement in the quality of the software projects. PMID:25478594
Variances in the projections, resulting from CLIMEX, Boosted Regression Trees and Random Forests techniques

NASA Astrophysics Data System (ADS)

Shabani, Farzin; Kumar, Lalit; Solhjouy-fard, Samaneh

2017-08-01

The aim of this study was to have a comparative investigation and evaluation of the capabilities of correlative and mechanistic modeling processes, applied to the projection of future distributions of date palm in novel environments and to establish a method of minimizing uncertainty in the projections of differing techniques. The location of this study on a global scale is in Middle Eastern Countries. We compared the mechanistic model CLIMEX (CL) with the correlative models MaxEnt (MX), Boosted Regression Trees (BRT), and Random Forests (RF) to project current and future distributions of date palm ( Phoenix dactylifera L.). The Global Climate Model (GCM), the CSIRO-Mk3.0 (CS) using the A2 emissions scenario, was selected for making projections. Both indigenous and alien distribution data of the species were utilized in the modeling process. The common areas predicted by MX, BRT, RF, and CL from the CS GCM were extracted and compared to ascertain projection uncertainty levels of each individual technique. The common areas identified by all four modeling techniques were used to produce a map indicating suitable and unsuitable areas for date palm cultivation for Middle Eastern countries, for the present and the year 2100. The four different modeling approaches predict fairly different distributions. Projections from CL were more conservative than from MX. The BRT and RF were the most conservative methods in terms of projections for the current time. The combination of the final CL and MX projections for the present and 2100 provide higher certainty concerning those areas that will become highly suitable for future date palm cultivation. According to the four models, cold, hot, and wet stress, with differences on a regional basis, appears to be the major restrictions on future date palm distribution. The results demonstrate variances in the projections, resulting from different techniques. The assessment and interpretation of model projections requires reservations
Human Splicing Finder: an online bioinformatics tool to predict splicing signals.

PubMed

Desmet, François-Olivier; Hamroun, Dalil; Lalande, Marine; Collod-Béroud, Gwenaëlle; Claustres, Mireille; Béroud, Christophe

2009-05-01

Thousands of mutations are identified yearly. Although many directly affect protein expression, an increasing proportion of mutations is now believed to influence mRNA splicing. They mostly affect existing splice sites, but synonymous, non-synonymous or nonsense mutations can also create or disrupt splice sites or auxiliary cis-splicing sequences. To facilitate the analysis of the different mutations, we designed Human Splicing Finder (HSF), a tool to predict the effects of mutations on splicing signals or to identify splicing motifs in any human sequence. It contains all available matrices for auxiliary sequence prediction as well as new ones for binding sites of the 9G8 and Tra2-beta Serine-Arginine proteins and the hnRNP A1 ribonucleoprotein. We also developed new Position Weight Matrices to assess the strength of 5' and 3' splice sites and branch points. We evaluated HSF efficiency using a set of 83 intronic and 35 exonic mutations known to result in splicing defects. We showed that the mutation effect was correctly predicted in almost all cases. HSF could thus represent a valuable resource for research, diagnostic and therapeutic (e.g. therapeutic exon skipping) purposes as well as for global studies, such as the GEN2PHEN European Project or the Human Variome Project.
Human Splicing Finder: an online bioinformatics tool to predict splicing signals

PubMed Central

Desmet, François-Olivier; Hamroun, Dalil; Lalande, Marine; Collod-Béroud, Gwenaëlle; Claustres, Mireille; Béroud, Christophe

2009-01-01

Thousands of mutations are identified yearly. Although many directly affect protein expression, an increasing proportion of mutations is now believed to influence mRNA splicing. They mostly affect existing splice sites, but synonymous, non-synonymous or nonsense mutations can also create or disrupt splice sites or auxiliary cis-splicing sequences. To facilitate the analysis of the different mutations, we designed Human Splicing Finder (HSF), a tool to predict the effects of mutations on splicing signals or to identify splicing motifs in any human sequence. It contains all available matrices for auxiliary sequence prediction as well as new ones for binding sites of the 9G8 and Tra2-β Serine-Arginine proteins and the hnRNP A1 ribonucleoprotein. We also developed new Position Weight Matrices to assess the strength of 5′ and 3′ splice sites and branch points. We evaluated HSF efficiency using a set of 83 intronic and 35 exonic mutations known to result in splicing defects. We showed that the mutation effect was correctly predicted in almost all cases. HSF could thus represent a valuable resource for research, diagnostic and therapeutic (e.g. therapeutic exon skipping) purposes as well as for global studies, such as the GEN2PHEN European Project or the Human Variome Project. PMID:19339519
A linear regression model for predicting PNW estuarine temperatures in a changing climate

EPA Science Inventory

Pacific Northwest coastal regions, estuaries, and associated ecosystems are vulnerable to the potential effects of climate change, especially to changes in nearshore water temperature. While predictive climate models simulate future air temperatures, no such projections exist for...
The PreViBOSS project: study the short term predictability of the visibility change during the Fog life cycle, from surface and satellite observation

NASA Astrophysics Data System (ADS)

Elias, T.; Haeffelin, M.; Ramon, D.; Gomes, L.; Brunet, F.; Vrac, M.; Yiou, P.; Hello, G.; Petithomme, H.

2010-07-01

Fog prejudices major activities as transport and Earth observation, by critically reducing atmospheric visibility with no continuity in time and space. Fog is also an essential factor of air quality and climate as it modifies particle properties of the surface atmospheric layer. Complexity, diversity and the fine scale of processes make uncertain by current numerical weather prediction models, not only visibility diagnosis but also fog event prediction. Extensive measurements of atmospheric parameters are made on the SIRTA since 1997 to document physical processes over the atmospheric column, in the Paris suburb area, typical of an environment intermittently under oceanic influence and affected by urban and industrial pollution. The ParisFog field campaign hosted in SIRTA during 6-month in winter 2006-2007 resulted in the deployment of instrumentation specifically dedicated to study physical processes in the fog life cycle: thermodynamical, radiative, dynamical, microphysical processes. Analysis of the measurements provided a preliminary climatology of the episodes of reduced visibility, chronology of processes was delivered by examining time series of measured parameters and a closure study was performed on optical and microphysical properties of particles (aerosols to droplets) during the life cycle of a radiative fog, providing the relative contribution of several particle groups to extinction in clear-sky conditions, in haze and in fog. PreViBOSS is a 3-year project scheduled to start this year. The aim is to improve the short term prediction of changes in atmospheric visibility, at a local scale. It proposes an innovative approach: applying the Generalised Additive Model statistical method to the detailed and extended dataset acquired at SIRTA. This method offers the opportunity to explore non linear relationships between parameters, which are not yet integrated in current numerical models. Emphasis will be put on aerosols and their impact on the fog life
MISST: The Multi-Sensor Improved Sea Surface Temperature Project

DTIC Science & Technology

2009-06-01

climate change studies, fisheries management, and a wide range of other applications. Measurements are taken by several satellites carrying infrared and...TEMPERATURE PROJECT ABSTRACT. Sea surface temperature (SST) measurements are vital to global weather prediction, climate change studies, fisheries management...important variables related to the global ocean-atmosphere system. It is a key indicator of climate change , is widely applied to studies of upper
The Chlamydomonas genome project: a decade on

PubMed Central

Blaby, Ian K.; Blaby-Haas, Crysten; Tourasse, Nicolas; Hom, Erik F. Y.; Lopez, David; Aksoy, Munevver; Grossman, Arthur; Umen, James; Dutcher, Susan; Porter, Mary; King, Stephen; Witman, George; Stanke, Mario; Harris, Elizabeth H.; Goodstein, David; Grimwood, Jane; Schmutz, Jeremy; Vallon, Olivier; Merchant, Sabeeha S.; Prochnik, Simon

2014-01-01

The green alga Chlamydomonas reinhardtii is a popular unicellular organism for studying photosynthesis, cilia biogenesis and micronutrient homeostasis. Ten years since its genome project was initiated, an iterative process of improvements to the genome and gene predictions has propelled this organism to the forefront of the “omics” era. Housed at Phytozome, the Joint Genome Institute’s (JGI) plant genomics portal, the most up-to-date genomic data include a genome arranged on chromosomes and high-quality gene models with alternative splice forms supported by an abundance of RNA-Seq data. Here, we present the past, present and future of Chlamydomonas genomics. Specifically, we detail progress on genome assembly and gene model refinement, discuss resources for gene annotations, functional predictions and locus ID mapping between versions and, importantly, outline a standardized framework for naming genes. PMID:24950814
Geologic input to enhanced oil recovery project planning in south Oman

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

Watts, N.L.; Ellis, D.; Heward, A.P.

1986-05-01

South Oman clastic reservoirs contain a combined stock-tank oil in place of more than 1.9 billion m/sup 3/ of predominantly heavy oil distributed in almost 40 fields of varying size. Successful early application of such enhanced oil recovery (EOR) methods as steam flood, polymer drive, and steam soak could realize undiscounted incremental recoveries of 244 million m/sup 3/ of oil. Target oil is contained in three reservoir intervals with distinct characteristics relevant to EOR. (1) The Cambrian-Ordovician Haima Group is a thick monotonous sequence of continental and coastal sands; major problems are steam-rock reactions, recovery factors, effective kv/kh (ratio ofmore » vertical to horizontal permeability), and aquifer strength. (2) The Permian-Carboniferous Al Khlata Formation is a glacial package showing severe heterogeneity, strong permeability anisotropy, and poor predictability. (3) The Permian Gharif Formation is a coastal to fluvial sequence with isolated and multilayer channel sands, smectitic clays, and anomalous primary production performance. Several EOR pilot projects are either ongoing or in preparation as part of a longer term EOR strategy. Geologic input is important at four essential stages of pilot planning: initial project ranking, optimization of pilot location, definition of pilot size, and predictive/history match simulations. Each stage is illustrated using a specific project example from south Oman to show the diverse geologic and logistic problems of the area. Although geologic aspects are highlighted, EOR project planning in south Oman is multidisciplinary, with integration being aided by a dedicated EOR coordination department.« less
Using Single-trial EEG to Predict and Analyze Subsequent Memory

PubMed Central

Noh, Eunho; Herzmann, Grit; Curran, Tim; de Sa, Virginia R.

2013-01-01

We show that it is possible to successfully predict subsequent memory performance based on single-trial EEG activity before and during item presentation in the study phase. Two-class classification was conducted to predict subsequently remembered vs. forgotten trials based on subjects’ responses in the recognition phase. The overall accuracy across 18 subjects was 59.6 % by combining pre- and during-stimulus information. The single-trial classification analysis provides a dimensionality reduction method to project the high-dimensional EEG data onto a discriminative space. These projections revealed novel findings in the pre- and during-stimulus period related to levels of encoding. It was observed that the pre-stimulus information (specifically oscillatory activity between 25–35Hz) −300 to 0 ms before stimulus presentation and during-stimulus alpha (7–12 Hz) information between 1000–1400 ms after stimulus onset distinguished between recollection and familiarity while the during-stimulus alpha information and temporal information between 400–800 ms after stimulus onset mapped these two states to similar values. PMID:24064073

Predictive Modeling of Cardiac Ischemia

NASA Technical Reports Server (NTRS)

Anderson, Gary T.

1996-01-01

The goal of the Contextual Alarms Management System (CALMS) project is to develop sophisticated models to predict the onset of clinical cardiac ischemia before it occurs. The system will continuously monitor cardiac patients and set off an alarm when they appear about to suffer an ischemic episode. The models take as inputs information from patient history and combine it with continuously updated information extracted from blood pressure, oxygen saturation and ECG lines. Expert system, statistical, neural network and rough set methodologies are then used to forecast the onset of clinical ischemia before it transpires, thus allowing early intervention aimed at preventing morbid complications from occurring. The models will differ from previous attempts by including combinations of continuous and discrete inputs. A commercial medical instrumentation and software company has invested funds in the project with a goal of commercialization of the technology. The end product will be a system that analyzes physiologic parameters and produces an alarm when myocardial ischemia is present. If proven feasible, a CALMS-based system will be added to existing heart monitoring hardware.
CUBES Project Support

NASA Technical Reports Server (NTRS)

Jenkins, Kenneth T., Jr.

2012-01-01

CUBES stands for Creating Understanding and Broadening Education through Satellites. The goal of the project is to allow high school students to build a small satellite, or CubeSat. Merritt Island High School (MIHS) was selected to partner with NASA, and California Polytechnic State University (Cal-Poly}, to build a CubeSat. The objective of the mission is to collect flight data to better characterize maximum predicted environments inside the CubeSat launcher, Poly-Picosatellite Orbital Deplorer (P-POD), while attached to the launch vehicle. The MIHS CubeSat team will apply to the NASA CubeSat Launch Initiative, which provides opportunities for small satellite development teams to secure launch slots on upcoming expendable launch vehicle missions. The MIHS team is working to achieve a test launch, or proof of concept flight aboard a suborbital launch vehicle in early 2013.
Tree mortality predicted from drought-induced vascular damage

USGS Publications Warehouse

Anderegg, William R.L.; Flint, Alan L.; Huang, Cho-ying; Flint, Lorraine E.; Berry, Joseph A.; Davis, Frank W.; Sperry, John S.; Field, Christopher B.

2015-01-01

The projected responses of forest ecosystems to warming and drying associated with twenty-first-century climate change vary widely from resiliency to widespread tree mortality1, 2, 3. Current vegetation models lack the ability to account for mortality of overstorey trees during extreme drought owing to uncertainties in mechanisms and thresholds causing mortality4, 5. Here we assess the causes of tree mortality, using field measurements of branch hydraulic conductivity during ongoing mortality in Populus tremuloides in the southwestern United States and a detailed plant hydraulics model. We identify a lethal plant water stress threshold that corresponds with a loss of vascular transport capacity from air entry into the xylem. We then use this hydraulic-based threshold to simulate forest dieback during historical drought, and compare predictions against three independent mortality data sets. The hydraulic threshold predicted with 75% accuracy regional patterns of tree mortality as found in field plots and mortality maps derived from Landsat imagery. In a high-emissions scenario, climate models project that drought stress will exceed the observed mortality threshold in the southwestern United States by the 2050s. Our approach provides a powerful and tractable way of incorporating tree mortality into vegetation models to resolve uncertainty over the fate of forest ecosystems in a changing climate.
An Overview of LANL's New Hurricane Lightning Project (Invited)

NASA Astrophysics Data System (ADS)

Jeffery, C. A.; Shao, X.; Reisner, J.; Kao, C. J.; Brockwell, M.; Chylek, P.; Fierro, A.; Galassi, M.; Godinez, H. C.; Guimond, S.; Hamlin, T.; Henderson, B. G.; Ho, C.; Holden, D.; Light, T. E.; O'Connor, N.; Suszcynsky, D. M.

2009-12-01

For the last two years, Los Alamos National Laboratory has sponsored an internal hurricane lightning project with four main goals: (1) To develop and deploy a new dual VLF/VHF lightning mapping array in the Mississippi River Delta south of New Orleans. (2) To develop a new hurricane forecast capability with fully prognostic cloud electrification and lightning discharge physics, based on a model framework developed at Oklahoma University. (3) To develop a new data assimilation approach for ingesting LANL lightning data into our forecast model that exploits the phenomenological relationship between lightning occurrence and intense convection. (4) To demonstrate that the assimilation of lightning data from the new LANL Gulf array into the hurricane forecast model improves the prediction of rapid intensification (RI), when RI is driven by eyewall adjustment (axisymmetrization) triggered by intense convective events (hot towers). In this talk, I present an overview of LANL's new hurricane lighting project, and the progress we have made to-date in achieving the project's four main goals.
A system for predicting the amount of Phellinus (Fomes) igniarius rot in trembling aspen stands

Treesearch

Robert L. Anderson; Arthur L. Jr. Schipper

1978-01-01

The occurrence of Phellinus (Fomes) igniarius white trunk rot in 45- to 50-year-old trembling aspen stands can be predicted by applying a constant to the stand basal area with P. igniarius conks to estimate the total basal area with P. igniarius rot. Future decay projections can be made by reapplying the basal area of hidden decay for each 6 years projected. This paper...
XBeach-G: a tool for predicting gravel barrier response to extreme storm conditions

NASA Astrophysics Data System (ADS)

Masselink, Gerd; Poate, Tim; McCall, Robert; Roelvink, Dano; Russell, Paul; Davidson, Mark

2014-05-01

Gravel beaches protect low-lying back-barrier regions from flooding during storm events and their importance to society is widely acknowledged. Unfortunately, breaching and extensive storm damage has occurred at many gravel sites and this is likely to increase as a result of sea-level rise and enhanced storminess due to climate change. Limited scientific guidance is currently available to provide beach managers with operational management tools to predict the response of gravel beaches to storms. The New Understanding and Prediction of Storm Impacts on Gravel beaches (NUPSIG) project aims to improve our understanding of storm impacts on gravel coastal environments and to develop a predictive capability by modelling these impacts. The NUPSIG project uses a 5-pronged approach to address its aim: (1) analyse hydrodynamic data collected during a proto-type laboratory experiment on a gravel beach; (2) collect hydrodynamic field data on a gravel beach under a range of conditions, including storm waves with wave heights up to 3 m; (3) measure swash dynamics and beach response on 10 gravel beaches during extreme wave conditions with wave heights in excess of 3 m; (4) use the data collected under 1-3 to develop and validate a numerical model to model hydrodynamics and morphological response of gravel beaches under storm conditions; and (5) develop a tool for end-users, based on the model formulated under (4), for predicting storm response of gravel beaches and barriers. The aim of this presentation is to present the key results of the NUPSIG project and introduce the end-user tool for predicting storm response on gravel beaches. The model is based on the numerical model XBeach, and different forcing scenarios (wave and tides), barrier configurations (dimensions) and sediment characteristics are easily uploaded for model simulations using a Graphics User Interface (GUI). The model can be used to determine the vulnerability of gravel barriers to storm events, but can also be
IMS/Satellite Situation Center report. Predicted orbit plots for Hawkeye 1, 1976

NASA Technical Reports Server (NTRS)

1975-01-01

The predicted orbit plots are shown in three projections. The time period covered by each set of projections is 2 days 1 hour, corresponding approximately to the period of Hawkeye 1. The three coordinate systems used are the Geocentric Solar Ecliptic system (GSE), the Geocentric Solar Magnetospheric system (GSM), and the Solar Magnetic system (SM). For each of the three projections, time ticks and codes are given on the satellite trajectories. The codes are interpreted in the table at the base of each plot. Time is given in the table as year/day/decimal hour. The total time covered by each plot is shown at the bottom of each table, and an additional variable is given in the table for each time tick. For the GSM and SM projection this variable is the geocentric distance to the satellite in earth radii, and for the GSE projection the variable is satellite ecliptic latitude in degrees.
nGASP - the nematode genome annotation assessment project

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

Coghlan, A; Fiedler, T J; McKay, S J

2008-12-19

While the C. elegans genome is extensively annotated, relatively little information is available for other Caenorhabditis species. The nematode genome annotation assessment project (nGASP) was launched to objectively assess the accuracy of protein-coding gene prediction software in C. elegans, and to apply this knowledge to the annotation of the genomes of four additional Caenorhabditis species and other nematodes. Seventeen groups worldwide participated in nGASP, and submitted 47 prediction sets for 10 Mb of the C. elegans genome. Predictions were compared to reference gene sets consisting of confirmed or manually curated gene models from WormBase. The most accurate gene-finders were 'combiner'more » algorithms, which made use of transcript- and protein-alignments and multi-genome alignments, as well as gene predictions from other gene-finders. Gene-finders that used alignments of ESTs, mRNAs and proteins came in second place. There was a tie for third place between gene-finders that used multi-genome alignments and ab initio gene-finders. The median gene level sensitivity of combiners was 78% and their specificity was 42%, which is nearly the same accuracy as reported for combiners in the human genome. C. elegans genes with exons of unusual hexamer content, as well as those with many exons, short exons, long introns, a weak translation start signal, weak splice sites, or poorly conserved orthologs were the most challenging for gene-finders. While the C. elegans genome is extensively annotated, relatively little information is available for other Caenorhabditis species. The nematode genome annotation assessment project (nGASP) was launched to objectively assess the accuracy of protein-coding gene prediction software in C. elegans, and to apply this knowledge to the annotation of the genomes of four additional Caenorhabditis species and other nematodes. Seventeen groups worldwide participated in nGASP, and submitted 47 prediction sets for 10 Mb of the C. elegans
Predicting appointment misses in hospitals using data analytics

PubMed Central

Karpagam, Sylvia; Ma, Nang Laik

2017-01-01

Background There is growing attention over the last few years about non-attendance in hospitals and its clinical and economic consequences. There have been several studies documenting the various aspects of non-attendance in hospitals. Project Predicting Appoint Misses (PAM) was started with the intention of being able to predict the type of patients that would not come for appointments after making bookings. Methods Historic hospital appointment data merged with “distance from hospital” variable was used to run Logistic Regression, Support Vector Machine and Recursive Partitioning to decide the contributing variables to missed appointments. Results Variables that are “class”, “time”, “demographics” related have an effect on the target variable, however, prediction models may not perform effectively due to very subtle influence on the target variable. Previously assumed major contributors like “age”, “distance” did not have a major effect on the target variable. Conclusions With the given data it will be very difficult to make any moderate/strong prediction of the Appointment misses. That being said with the help of the cut off we are able to capture all of the “appointment misses” in addition to also capturing the actualized appointments. PMID:28567409
A-Level Results in Comprehensive Schools: The COMBSE Project, Year 1.

ERIC Educational Resources Information Center

Fitz-Gibbon, Carol T.

1985-01-01

Results of the first year of the ongoing Confidential, Measurement-Based, Self-Evaluation Project that examines variables related to A-level English and Mathematics achievement in 10 comprehensive schools in the United Kingdom are presented. A-level results were found to be strongly predicted by teacher assessments and by prior O-level achievement…
Tree cover and aridity projections to 2060: a technical document supporting the Forest Service 2010 RPA assessment

Treesearch

Eric J. Greenfield; David J. Nowak

2013-01-01

Future projections of tree cover and climate change are useful to natural resource managers as they illustrate potential changes to our natural resources and the ecosystem services they provide. This report a) details three projections of tree cover change across the conterminous United States based on predicted land-use changes from 2000 to 2060; b) evaluates nine...
NREL's Battery Life Predictive Model Helps Companies Take Charge | News |

Science.gov Websites

lithium-ion (Li-ion) batteries, are complex electrochemical systems. There are typically several different NREL NREL's Battery Life Predictive Model Helps Companies Take Charge NREL's Battery Life monitor. An example of a stationary, grid-connected battery is the NREL project from Erigo/EaglePicher
Projecting the Demand for Dental Care in 2040.

PubMed

Manski, Richard J; Meyerhoefer, Chad D

2017-08-01

The purpose of this study was to provide a forward-thinking assessment of the underlying factors likely to impact trends in dental care demand and the need for dental providers in 2020, 2025, and beyond. Dental workforce trends and their likely impact on the need for dentists are a function of predicted dental care demand, which will in turn be determined by the size and characteristics of our population size, economic outlook, the state of public and private dental care insurance, trends in dental care delivery, professionally determined dental care need, and population health beliefs. Projecting rates of dental care utilization far into the future is difficult because projections must be made using historical data, and established trends may not persist if there is structural change in the future. Nonetheless, when structural change occurs, it does not typically affect all aspects of the economy, so there is value in describing the likely future impact of current trends. This article was written as part of the project "Advancing Dental Education in the 21 st Century."
Leveraging Publically Available Chemical Functional Use Data in Support of Exposure Prediction

EPA Science Inventory

The U.S. EPA Exposure Forecasting (ExpoCast) project aims to provide rapid screening-level exposure predictions for thousands of chemicals, most of which lack detailed exposure data. Chemical functional use - the role a chemical plays in processes or products (e.g. solvent, ant...
Prediction and assessment of flammability hazards associated with metered-dose inhalers containing flammable propellants.

PubMed

Dalby, R N

1992-05-01

Several potential replacements for chlorofluorocarbons (CFCs) in metered-dose inhalers (MDIs) are flammable. The flammability hazard associated with their use was assessed using a range of MDIs containing 0-100% (w/w) n-butane (flammable) in HFC-134a (non-flammable) fitted with either 25-, 63-, or 100-microliters metering valves or continuous valves. In flame projection tests each MDI was fired horizontally into a flame, and the ignited flume length emitted from the MDI was measured. Flame projections of greater than or equal to 60 cm were produced by all formulations fitted with continuous valves which contained greater than or equal to 40% (w/w) n-butane in HFC-134a. Using metering valves the maximum flame projection obtained was 30 cm. This was observed with a formulation containing 90% (w/w) n-butane in HFC-134a and a 100-microliters valve. For a particular formulation, smaller metering valves produced shorter flame projections. Because many MDIs are used in conjunction with extension devices, the likelihood of accidental propellant vapor ignition was determined in Nebuhaler and Inspirease reservoirs and a Breathancer spacer. Ignition was predictable based on propellant composition, metered volume, number of actuations, and spacer capacity. Calculated n-butane concentrations in excess of the lower flammability limit [LFL; 1.9% (v/v)] but below the upper flammability limit [UFL; 8.5% (v/v)] were usually predictive of flammability following ignition by a glowing nichrome wire mounted inside the extension device. No ignition was predicted or observed following one or two 25-microliters actuations of 100% n-butane into large volume Nebuhaler (750 ml) or Inspirease (660 ml) devices.(ABSTRACT TRUNCATED AT 250 WORDS)
Understanding Proximal-Distal Economic Projections of the Benefits of Childhood Preventive Interventions

PubMed Central

Slade, Eric P.; Becker, Kimberly D.

2014-01-01

This paper discusses the steps and decisions involved in proximal-distal economic modeling, in which social, behavioral, and academic outcomes data for children may be used to inform projections of the economic consequences of interventions. Economic projections based on proximal-distal modeling techniques may be used in cost-benefit analyses when information is unavailable for certain long term outcomes data in adulthood or to build entire cost-benefit analyses. Although examples of proximal-distal economic analyses of preventive interventions exist in policy reports prepared for governmental agencies, such analyses have rarely been completed in conjunction with research trials. The modeling decisions on which these prediction models are based are often opaque to policymakers and other end-users. This paper aims to illuminate some of the key steps and considerations involved in constructing proximal-distal prediction models and to provide examples and suggestions that may help guide future proximal-distal analyses. PMID:24337979
Prediction of Job Performance: Review of Military Studies

DTIC Science & Technology

1982-03-01

NPRDC TN 52-37 MARCH 1982 0 PREDICTION OF JOB PERFORMANCE: 0 REVIEW OF MILITARY STUDIES NAVY PERSONNEL RESEARCH AND D~EVELOPMENT CENTER Sen Digoo...Personnel Research and Dlevelopment Center k San Diego, California 92152 UNCLASS IFIED SECURITY CLASSIFICATION Of THIC PAGE (35.., Date EunteaE REPORT...A00111141 t0. PROGRAM ELEMENT, PROJECT. TASK ARE A A WJRK UN IT NUMBERS Human Resources Research Organization Carmel, California 93923 ZF63-520-001-030
Prediction of response of aircraft panels subjected to acoustic and thermal loads

NASA Technical Reports Server (NTRS)

Mei, Chuh

1992-01-01

The primary effort of this research project has been focused on the development of analytical methods for the prediction of random response of structural panels subjected to combined and intense acoustic and thermal loads. The accomplishments on various acoustic fatigue research activities are described first, then followed by publications and theses. Topics covered include: transverse shear deformation; finite element models of vibrating composite laminates; large deflection vibration modeling; finite element analysis of thermal buckling; and prediction of three dimensional duct using boundary element method.
Using demography and movement behavior to predict range expansion of the southern sea otter.

USGS Publications Warehouse

Tinker, M.T.; Doak, D.F.; Estes, J.A.

2008-01-01

In addition to forecasting population growth, basic demographic data combined with movement data provide a means for predicting rates of range expansion. Quantitative models of range expansion have rarely been applied to large vertebrates, although such tools could be useful for restoration and management of many threatened but recovering populations. Using the southern sea otter (Enhydra lutris nereis) as a case study, we utilized integro-difference equations in combination with a stage-structured projection matrix that incorporated spatial variation in dispersal and demography to make forecasts of population recovery and range recolonization. In addition to these basic predictions, we emphasize how to make these modeling predictions useful in a management context through the inclusion of parameter uncertainty and sensitivity analysis. Our models resulted in hind-cast (1989–2003) predictions of net population growth and range expansion that closely matched observed patterns. We next made projections of future range expansion and population growth, incorporating uncertainty in all model parameters, and explored the sensitivity of model predictions to variation in spatially explicit survival and dispersal rates. The predicted rate of southward range expansion (median = 5.2 km/yr) was sensitive to both dispersal and survival rates; elasticity analysis indicated that changes in adult survival would have the greatest potential effect on the rate of range expansion, while perturbation analysis showed that variation in subadult dispersal contributed most to variance in model predictions. Variation in survival and dispersal of females at the south end of the range contributed most of the variance in predicted southward range expansion. Our approach provides guidance for the acquisition of further data and a means of forecasting the consequence of specific management actions. Similar methods could aid in the management of other recovering populations.
The statistical properties and possible causes of polar motion prediction errors

NASA Astrophysics Data System (ADS)

Kosek, Wieslaw; Kalarus, Maciej; Wnek, Agnieszka; Zbylut-Gorska, Maria

2015-08-01

The pole coordinate data predictions from different prediction contributors of the Earth Orientation Parameters Combination of Prediction Pilot Project (EOPCPPP) were studied to determine the statistical properties of polar motion forecasts by looking at the time series of differences between them and the future IERS pole coordinates data. The mean absolute errors, standard deviations as well as the skewness and kurtosis of these differences were computed together with their error bars as a function of prediction length. The ensemble predictions show a little smaller mean absolute errors or standard deviations however their skewness and kurtosis values are similar as the for predictions from different contributors. The skewness and kurtosis enable to check whether these prediction differences satisfy normal distribution. The kurtosis values diminish with the prediction length which means that the probability distribution of these prediction differences is becoming more platykurtic than letptokurtic. Non zero skewness values result from oscillating character of these differences for particular prediction lengths which can be due to the irregular change of the annual oscillation phase in the joint fluid (atmospheric + ocean + land hydrology) excitation functions. The variations of the annual oscillation phase computed by the combination of the Fourier transform band pass filter and the Hilbert transform from pole coordinates data as well as from pole coordinates model data obtained from fluid excitations are in a good agreement.

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