Modeling critical habitat for Flammulated Owls (Otus flammeolus)

Treesearch

David A. Christie; Astrid M. van Woudenberg

1997-01-01

Multiple logistic regression analysis was used to produce a prediction model for Flammulated Owl (Otus flammeolus) breeding habitat within the Kamloops Forest Region in south-central British Columbia. Using the model equation, a pilot habitat prediction map was created within a Geographic Information System (GIS) environment that had a 75.7 percent...

Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do.

PubMed

Zhao, Linlin; Wang, Wenyi; Sedykh, Alexander; Zhu, Hao

2017-06-30

Numerous chemical data sets have become available for quantitative structure-activity relationship (QSAR) modeling studies. However, the quality of different data sources may be different based on the nature of experimental protocols. Therefore, potential experimental errors in the modeling sets may lead to the development of poor QSAR models and further affect the predictions of new compounds. In this study, we explored the relationship between the ratio of questionable data in the modeling sets, which was obtained by simulating experimental errors, and the QSAR modeling performance. To this end, we used eight data sets (four continuous endpoints and four categorical endpoints) that have been extensively curated both in-house and by our collaborators to create over 1800 various QSAR models. Each data set was duplicated to create several new modeling sets with different ratios of simulated experimental errors (i.e., randomizing the activities of part of the compounds) in the modeling process. A fivefold cross-validation process was used to evaluate the modeling performance, which deteriorates when the ratio of experimental errors increases. All of the resulting models were also used to predict external sets of new compounds, which were excluded at the beginning of the modeling process. The modeling results showed that the compounds with relatively large prediction errors in cross-validation processes are likely to be those with simulated experimental errors. However, after removing a certain number of compounds with large prediction errors in the cross-validation process, the external predictions of new compounds did not show improvement. Our conclusion is that the QSAR predictions, especially consensus predictions, can identify compounds with potential experimental errors. But removing those compounds by the cross-validation procedure is not a reasonable means to improve model predictivity due to overfitting.

Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do

PubMed Central

2017-01-01

Numerous chemical data sets have become available for quantitative structure–activity relationship (QSAR) modeling studies. However, the quality of different data sources may be different based on the nature of experimental protocols. Therefore, potential experimental errors in the modeling sets may lead to the development of poor QSAR models and further affect the predictions of new compounds. In this study, we explored the relationship between the ratio of questionable data in the modeling sets, which was obtained by simulating experimental errors, and the QSAR modeling performance. To this end, we used eight data sets (four continuous endpoints and four categorical endpoints) that have been extensively curated both in-house and by our collaborators to create over 1800 various QSAR models. Each data set was duplicated to create several new modeling sets with different ratios of simulated experimental errors (i.e., randomizing the activities of part of the compounds) in the modeling process. A fivefold cross-validation process was used to evaluate the modeling performance, which deteriorates when the ratio of experimental errors increases. All of the resulting models were also used to predict external sets of new compounds, which were excluded at the beginning of the modeling process. The modeling results showed that the compounds with relatively large prediction errors in cross-validation processes are likely to be those with simulated experimental errors. However, after removing a certain number of compounds with large prediction errors in the cross-validation process, the external predictions of new compounds did not show improvement. Our conclusion is that the QSAR predictions, especially consensus predictions, can identify compounds with potential experimental errors. But removing those compounds by the cross-validation procedure is not a reasonable means to improve model predictivity due to overfitting. PMID:28691113

lazar: a modular predictive toxicology framework

PubMed Central

Maunz, Andreas; Gütlein, Martin; Rautenberg, Micha; Vorgrimmler, David; Gebele, Denis; Helma, Christoph

2013-01-01

lazar (lazy structure–activity relationships) is a modular framework for predictive toxicology. Similar to the read across procedure in toxicological risk assessment, lazar creates local QSAR (quantitative structure–activity relationship) models for each compound to be predicted. Model developers can choose between a large variety of algorithms for descriptor calculation and selection, chemical similarity indices, and model building. This paper presents a high level description of the lazar framework and discusses the performance of example classification and regression models. PMID:23761761

Evaluation of a Computational Model of Situational Awareness

NASA Technical Reports Server (NTRS)

Burdick, Mark D.; Shively, R. Jay; Rutkewski, Michael (Technical Monitor)

2000-01-01

Although the use of the psychological construct of situational awareness (SA) assists researchers in creating a flight environment that is safer and more predictable, its true potential remains untapped until a valid means of predicting SA a priori becomes available. Previous work proposed a computational model of SA (CSA) that sought to Fill that void. The current line of research is aimed at validating that model. The results show that the model accurately predicted SA in a piloted simulation.

Offset-Free Model Predictive Control of Open Water Channel Based on Moving Horizon Estimation

NASA Astrophysics Data System (ADS)

Ekin Aydin, Boran; Rutten, Martine

2016-04-01

Model predictive control (MPC) is a powerful control option which is increasingly used by operational water managers for managing water systems. The explicit consideration of constraints and multi-objective management are important features of MPC. However, due to the water loss in open water systems by seepage, leakage and evaporation a mismatch between the model and the real system will be created. These mismatch affects the performance of MPC and creates an offset from the reference set point of the water level. We present model predictive control based on moving horizon estimation (MHE-MPC) to achieve offset free control of water level for open water canals. MHE-MPC uses the past predictions of the model and the past measurements of the system to estimate unknown disturbances and the offset in the controlled water level is systematically removed. We numerically tested MHE-MPC on an accurate hydro-dynamic model of the laboratory canal UPC-PAC located in Barcelona. In addition, we also used well known disturbance modeling offset free control scheme for the same test case. Simulation experiments on a single canal reach show that MHE-MPC outperforms disturbance modeling offset free control scheme.

A Bayesian Performance Prediction Model for Mathematics Education: A Prototypical Approach for Effective Group Composition

ERIC Educational Resources Information Center

Bekele, Rahel; McPherson, Maggie

2011-01-01

This research work presents a Bayesian Performance Prediction Model that was created in order to determine the strength of personality traits in predicting the level of mathematics performance of high school students in Addis Ababa. It is an automated tool that can be used to collect information from students for the purpose of effective group…

Hybrid experimental/analytical models of structural dynamics - Creation and use for predictions

NASA Technical Reports Server (NTRS)

Balmes, Etienne

1993-01-01

An original complete methodology for the construction of predictive models of damped structural vibrations is introduced. A consistent definition of normal and complex modes is given which leads to an original method to accurately identify non-proportionally damped normal mode models. A new method to create predictive hybrid experimental/analytical models of damped structures is introduced, and the ability of hybrid models to predict the response to system configuration changes is discussed. Finally a critical review of the overall methodology is made by application to the case of the MIT/SERC interferometer testbed.

Brain regions engaged by part- and whole-task performance in a video game: a model-based test of the decomposition hypothesis.

PubMed

Anderson, John R; Bothell, Daniel; Fincham, Jon M; Anderson, Abraham R; Poole, Ben; Qin, Yulin

2011-12-01

Part- and whole-task conditions were created by manipulating the presence of certain components of the Space Fortress video game. A cognitive model was created for two-part games that could be combined into a model that performed the whole game. The model generated predictions both for behavioral patterns and activation patterns in various brain regions. The activation predictions concerned both tonic activation that was constant in these regions during performance of the game and phasic activation that occurred when there was resource competition. The model's predictions were confirmed about how tonic and phasic activation in different regions would vary with condition. These results support the Decomposition Hypothesis that the execution of a complex task can be decomposed into a set of information-processing components and that these components combine unchanged in different task conditions. In addition, individual differences in learning gains were predicted by individual differences in phasic activation in those regions that displayed highest tonic activity. This individual difference pattern suggests that the rate of learning of a complex skill is determined by capacity limits.

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.

Modeling long period swell in Southern California: Practical boundary conditions from buoy observations and global wave model predictions

NASA Astrophysics Data System (ADS)

Crosby, S. C.; O'Reilly, W. C.; Guza, R. T.

2016-02-01

Accurate, unbiased, high-resolution (in space and time) nearshore wave predictions are needed to drive models of beach erosion, coastal flooding, and alongshore transport of sediment, biota and pollutants. On highly sheltered shorelines, wave predictions are sensitive to the directions of onshore propagating waves, and nearshore model prediction error is often dominated by uncertainty in offshore boundary conditions. Offshore islands and shoals, and coastline curvature, create complex sheltering patterns over the 250km span of southern California (SC) shoreline. Here, regional wave model skill in SC was compared for different offshore boundary conditions created using offshore buoy observations and global wave model hindcasts (National Oceanographic and Atmospheric Administration Wave Watch 3, WW3). Spectral ray-tracing methods were used to transform incident offshore swell (0.04-0.09Hz) energy at high directional resolution (1-deg). Model skill is assessed for predictions (wave height, direction, and alongshore radiation stress) at 16 nearshore buoy sites between 2000 and 2009. Model skill using buoy-derived boundary conditions is higher than with WW3-derived boundary conditions. Buoy-driven nearshore model results are similar with various assumptions about the true offshore directional distribution (maximum entropy, Bayesian direct, and 2nd derivative smoothness). Two methods combining offshore buoy observations with WW3 predictions in the offshore boundary condition did not improve nearshore skill above buoy-only methods. A case example at Oceanside harbor shows strong sensitivity of alongshore sediment transport predictions to different offshore boundary conditions. Despite this uncertainty in alongshore transport magnitude, alongshore gradients in transport (e.g. the location of model accretion and erosion zones) are determined by the local bathymetry, and are similar for all predictions.

High-resolution prediction of leaf onset date in Japan in the 21st century under the IPCC A1B scenario.

PubMed

Hadano, Mayumi; Nasahara, Kenlo Nishida; Motohka, Takeshi; Noda, Hibiki Muraoka; Murakami, Kazutaka; Hosaka, Masahiro

2013-06-01

Reports indicate that leaf onset (leaf flush) of deciduous trees in cool-temperate ecosystems is occurring earlier in the spring in response to global warming. In this study, we created two types of phenology models, one driven only by warmth (spring warming [SW] model) and another driven by both warmth and winter chilling (parallel chill [PC] model), to predict such phenomena in the Japanese Islands at high spatial resolution (500 m). We calibrated these models using leaf onset dates derived from satellite data (Terra/MODIS) and in situ temperature data derived from a dense network of ground stations Automated Meteorological Data Acquisition System. We ran the model using future climate predictions created by the Japanese Meteorological Agency's MRI-AGCM3.1S model. In comparison to the first decade of the 2000s, our results predict that the date of leaf onset in the 2030s will advance by an average of 12 days under the SW model and 7 days under the PC model throughout the study area. The date of onset in the 2090s will advance by 26 days under the SW model and by 15 days under the PC model. The greatest impact will occur on Hokkaido (the northernmost island) and in the central mountains.

Modelling the Relationship Between Land Surface Temperature and Landscape Patterns of Land Use Land Cover Classification Using Multi Linear Regression Models

NASA Astrophysics Data System (ADS)

Bernales, A. M.; Antolihao, J. A.; Samonte, C.; Campomanes, F.; Rojas, R. J.; dela Serna, A. M.; Silapan, J.

2016-06-01

The threat of the ailments related to urbanization like heat stress is very prevalent. There are a lot of things that can be done to lessen the effect of urbanization to the surface temperature of the area like using green roofs or planting trees in the area. So land use really matters in both increasing and decreasing surface temperature. It is known that there is a relationship between land use land cover (LULC) and land surface temperature (LST). Quantifying this relationship in terms of a mathematical model is very important so as to provide a way to predict LST based on the LULC alone. This study aims to examine the relationship between LST and LULC as well as to create a model that can predict LST using class-level spatial metrics from LULC. LST was derived from a Landsat 8 image and LULC classification was derived from LiDAR and Orthophoto datasets. Class-level spatial metrics were created in FRAGSTATS with the LULC and LST as inputs and these metrics were analysed using a statistical framework. Multi linear regression was done to create models that would predict LST for each class and it was found that the spatial metric "Effective mesh size" was a top predictor for LST in 6 out of 7 classes. The model created can still be refined by adding a temporal aspect by analysing the LST of another farming period (for rural areas) and looking for common predictors between LSTs of these two different farming periods.

Historical Maps from Modern Images: Using Remote Sensing to Model and Map Century-Long Vegetation Change in a Fire-Prone Region

PubMed Central

Callister, Kate E.; Griffioen, Peter A.; Avitabile, Sarah C.; Haslem, Angie; Kelly, Luke T.; Kenny, Sally A.; Nimmo, Dale G.; Farnsworth, Lisa M.; Taylor, Rick S.; Watson, Simon J.; Bennett, Andrew F.; Clarke, Michael F.

2016-01-01

Understanding the age structure of vegetation is important for effective land management, especially in fire-prone landscapes where the effects of fire can persist for decades and centuries. In many parts of the world, such information is limited due to an inability to map disturbance histories before the availability of satellite images (~1972). Here, we describe a method for creating a spatial model of the age structure of canopy species that established pre-1972. We built predictive neural network models based on remotely sensed data and ecological field survey data. These models determined the relationship between sites of known fire age and remotely sensed data. The predictive model was applied across a 104,000 km2 study region in semi-arid Australia to create a spatial model of vegetation age structure, which is primarily the result of stand-replacing fires which occurred before 1972. An assessment of the predictive capacity of the model using independent validation data showed a significant correlation (rs = 0.64) between predicted and known age at test sites. Application of the model provides valuable insights into the distribution of vegetation age-classes and fire history in the study region. This is a relatively straightforward method which uses widely available data sources that can be applied in other regions to predict age-class distribution beyond the limits imposed by satellite imagery. PMID:27029046

Ensemble method for dengue prediction.

PubMed

Buczak, Anna L; Baugher, Benjamin; Moniz, Linda J; Bagley, Thomas; Babin, Steven M; Guven, Erhan

2018-01-01

In the 2015 NOAA Dengue Challenge, participants made three dengue target predictions for two locations (Iquitos, Peru, and San Juan, Puerto Rico) during four dengue seasons: 1) peak height (i.e., maximum weekly number of cases during a transmission season; 2) peak week (i.e., week in which the maximum weekly number of cases occurred); and 3) total number of cases reported during a transmission season. A dengue transmission season is the 12-month period commencing with the location-specific, historical week with the lowest number of cases. At the beginning of the Dengue Challenge, participants were provided with the same input data for developing the models, with the prediction testing data provided at a later date. Our approach used ensemble models created by combining three disparate types of component models: 1) two-dimensional Method of Analogues models incorporating both dengue and climate data; 2) additive seasonal Holt-Winters models with and without wavelet smoothing; and 3) simple historical models. Of the individual component models created, those with the best performance on the prior four years of data were incorporated into the ensemble models. There were separate ensembles for predicting each of the three targets at each of the two locations. Our ensemble models scored higher for peak height and total dengue case counts reported in a transmission season for Iquitos than all other models submitted to the Dengue Challenge. However, the ensemble models did not do nearly as well when predicting the peak week. The Dengue Challenge organizers scored the dengue predictions of the Challenge participant groups. Our ensemble approach was the best in predicting the total number of dengue cases reported for transmission season and peak height for Iquitos, Peru.

Ensemble method for dengue prediction

PubMed Central

Baugher, Benjamin; Moniz, Linda J.; Bagley, Thomas; Babin, Steven M.; Guven, Erhan

2018-01-01

Background In the 2015 NOAA Dengue Challenge, participants made three dengue target predictions for two locations (Iquitos, Peru, and San Juan, Puerto Rico) during four dengue seasons: 1) peak height (i.e., maximum weekly number of cases during a transmission season; 2) peak week (i.e., week in which the maximum weekly number of cases occurred); and 3) total number of cases reported during a transmission season. A dengue transmission season is the 12-month period commencing with the location-specific, historical week with the lowest number of cases. At the beginning of the Dengue Challenge, participants were provided with the same input data for developing the models, with the prediction testing data provided at a later date. Methods Our approach used ensemble models created by combining three disparate types of component models: 1) two-dimensional Method of Analogues models incorporating both dengue and climate data; 2) additive seasonal Holt-Winters models with and without wavelet smoothing; and 3) simple historical models. Of the individual component models created, those with the best performance on the prior four years of data were incorporated into the ensemble models. There were separate ensembles for predicting each of the three targets at each of the two locations. Principal findings Our ensemble models scored higher for peak height and total dengue case counts reported in a transmission season for Iquitos than all other models submitted to the Dengue Challenge. However, the ensemble models did not do nearly as well when predicting the peak week. Conclusions The Dengue Challenge organizers scored the dengue predictions of the Challenge participant groups. Our ensemble approach was the best in predicting the total number of dengue cases reported for transmission season and peak height for Iquitos, Peru. PMID:29298320

Updraft Fixed Bed Gasification Aspen Plus Model

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

2007-09-27

The updraft fixed bed gasification model provides predictive modeling capabilities for updraft fixed bed gasifiers, when devolatilization data is available. The fixed bed model is constructed using Aspen Plus, process modeling software, coupled with a FORTRAN user kinetic subroutine. Current updraft gasification models created in Aspen Plus have limited predictive capabilities and must be "tuned" to reflect a generalized gas composition as specified in literature or by the gasifier manufacturer. This limits the applicability of the process model.

Incorporation of Predictive Population Modeling into the AOP Famework: A Case Study with White Suckers Exposed to Pulp Effluent

EPA Science Inventory

A need in ecological risk assessment is the ability to create linkages between chemically-induced alterations at molecular and biochemical levels of organization with adverse outcomes in whole organisms and populations. A predictive model was developed to translate changes in th...

Assessment and Mapping of Forest Parcel Sizes

Treesearch

Brett J. Butler; Susan L. King

2005-01-01

A method for analyzing and mapping forest parcel sizes in the Northeastern United States is presented. A decision tree model was created that predicts forest parcel size from spatially explicit predictor variables: population density, State, percentage forest land cover, and road density. The model correctly predicted parcel size for 60 percent of the observations in a...

Microarray-based cancer prediction using soft computing approach.

PubMed

Wang, Xiaosheng; Gotoh, Osamu

2009-05-26

One of the difficulties in using gene expression profiles to predict cancer is how to effectively select a few informative genes to construct accurate prediction models from thousands or ten thousands of genes. We screen highly discriminative genes and gene pairs to create simple prediction models involved in single genes or gene pairs on the basis of soft computing approach and rough set theory. Accurate cancerous prediction is obtained when we apply the simple prediction models for four cancerous gene expression datasets: CNS tumor, colon tumor, lung cancer and DLBCL. Some genes closely correlated with the pathogenesis of specific or general cancers are identified. In contrast with other models, our models are simple, effective and robust. Meanwhile, our models are interpretable for they are based on decision rules. Our results demonstrate that very simple models may perform well on cancerous molecular prediction and important gene markers of cancer can be detected if the gene selection approach is chosen reasonably.

Risk terrain modeling predicts child maltreatment.

PubMed

Daley, Dyann; Bachmann, Michael; Bachmann, Brittany A; Pedigo, Christian; Bui, Minh-Thuy; Coffman, Jamye

2016-12-01

As indicated by research on the long-term effects of adverse childhood experiences (ACEs), maltreatment has far-reaching consequences for affected children. Effective prevention measures have been elusive, partly due to difficulty in identifying vulnerable children before they are harmed. This study employs Risk Terrain Modeling (RTM), an analysis of the cumulative effect of environmental factors thought to be conducive for child maltreatment, to create a highly accurate prediction model for future substantiated child maltreatment cases in the City of Fort Worth, Texas. The model is superior to commonly used hotspot predictions and more beneficial in aiding prevention efforts in a number of ways: 1) it identifies the highest risk areas for future instances of child maltreatment with improved precision and accuracy; 2) it aids the prioritization of risk-mitigating efforts by informing about the relative importance of the most significant contributing risk factors; 3) since predictions are modeled as a function of easily obtainable data, practitioners do not have to undergo the difficult process of obtaining official child maltreatment data to apply it; 4) the inclusion of a multitude of environmental risk factors creates a more robust model with higher predictive validity; and, 5) the model does not rely on a retrospective examination of past instances of child maltreatment, but adapts predictions to changing environmental conditions. The present study introduces and examines the predictive power of this new tool to aid prevention efforts seeking to improve the safety, health, and wellbeing of vulnerable children. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Modelling of dissolved oxygen content using artificial neural networks: Danube River, North Serbia, case study.

PubMed

Antanasijević, Davor; Pocajt, Viktor; Povrenović, Dragan; Perić-Grujić, Aleksandra; Ristić, Mirjana

2013-12-01

The aims of this study are to create an artificial neural network (ANN) model using non-specific water quality parameters and to examine the accuracy of three different ANN architectures: General Regression Neural Network (GRNN), Backpropagation Neural Network (BPNN) and Recurrent Neural Network (RNN), for prediction of dissolved oxygen (DO) concentration in the Danube River. The neural network model has been developed using measured data collected from the Bezdan monitoring station on the Danube River. The input variables used for the ANN model are water flow, temperature, pH and electrical conductivity. The model was trained and validated using available data from 2004 to 2008 and tested using the data from 2009. The order of performance for the created architectures based on their comparison with the test data is RNN > GRNN > BPNN. The ANN results are compared with multiple linear regression (MLR) model using multiple statistical indicators. The comparison of the RNN model with the MLR model indicates that the RNN model performs much better, since all predictions of the RNN model for the test data were within the error of less than ± 10 %. In case of the MLR, only 55 % of predictions were within the error of less than ± 10 %. The developed RNN model can be used as a tool for the prediction of DO in river waters.

Flow control using audio tones in resonant microfluidic networks: towards cell-phone controlled lab-on-a-chip devices.

PubMed

Phillips, Reid H; Jain, Rahil; Browning, Yoni; Shah, Rachana; Kauffman, Peter; Dinh, Doan; Lutz, Barry R

2016-08-16

Fluid control remains a challenge in development of portable lab-on-a-chip devices. Here, we show that microfluidic networks driven by single-frequency audio tones create resonant oscillating flow that is predicted by equivalent electrical circuit models. We fabricated microfluidic devices with fluidic resistors (R), inductors (L), and capacitors (C) to create RLC networks with band-pass resonance in the audible frequency range available on portable audio devices. Microfluidic devices were fabricated from laser-cut adhesive plastic, and a "buzzer" was glued to a diaphragm (capacitor) to integrate the actuator on the device. The AC flowrate magnitude was measured by imaging oscillation of bead tracers to allow direct comparison to the RLC circuit model across the frequency range. We present a systematic build-up from single-channel systems to multi-channel (3-channel) networks, and show that RLC circuit models predict complex frequency-dependent interactions within multi-channel networks. Finally, we show that adding flow rectifying valves to the network creates pumps that can be driven by amplified and non-amplified audio tones from common audio devices (iPod and iPhone). This work shows that RLC circuit models predict resonant flow responses in multi-channel fluidic networks as a step towards microfluidic devices controlled by audio tones.

Using CFD Techniques to Predict Slosh Force Frequency and Damping Rate

NASA Technical Reports Server (NTRS)

Marsell, Brandon; Gangadharan, Sathya; Chatman, Yadira; Sudermann, James

2009-01-01

Resonant effects and energy dissipation due to sloshing fuel inside propellant tanks are problems that arise in the initial design of any spacecraft or launch vehicle. A faster and more reliable method for calculating these effects during the design stages is needed. Using Computational Fluid Dynamics (CFD) techniques, a model of these fuel tanks can be created and used to predict important parameters such as resonant slosh frequency and damping rate. This initial study addresses the case of free surface slosh. Future studies will focus on creating models for tanks fitted with propellant management devices (PMD) such as diaphragms and baffles.

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,...

High-resolution prediction of leaf onset date in Japan in the 21st century under the IPCC A1B scenario

PubMed Central

Hadano, Mayumi; Nasahara, Kenlo Nishida; Motohka, Takeshi; Noda, Hibiki Muraoka; Murakami, Kazutaka; Hosaka, Masahiro

2013-01-01

Reports indicate that leaf onset (leaf flush) of deciduous trees in cool-temperate ecosystems is occurring earlier in the spring in response to global warming. In this study, we created two types of phenology models, one driven only by warmth (spring warming [SW] model) and another driven by both warmth and winter chilling (parallel chill [PC] model), to predict such phenomena in the Japanese Islands at high spatial resolution (500 m). We calibrated these models using leaf onset dates derived from satellite data (Terra/MODIS) and in situ temperature data derived from a dense network of ground stations Automated Meteorological Data Acquisition System. We ran the model using future climate predictions created by the Japanese Meteorological Agency's MRI-AGCM3.1S model. In comparison to the first decade of the 2000s, our results predict that the date of leaf onset in the 2030s will advance by an average of 12 days under the SW model and 7 days under the PC model throughout the study area. The date of onset in the 2090s will advance by 26 days under the SW model and by 15 days under the PC model. The greatest impact will occur on Hokkaido (the northernmost island) and in the central mountains. PMID:23789086

Biokinetic modelling development and analysis of arsenic dissolution into the gastrointestinal tract using SAAM II

NASA Astrophysics Data System (ADS)

Perama, Yasmin Mohd Idris; Siong, Khoo Kok

2018-04-01

A mathematical model comprising 8 compartments were designed to describe the kinetic dissolution of arsenic (As) from water leach purification (WLP) waste samples ingested into the gastrointestinal system. A totally reengineered software system named Simulation, Analysis and Modelling II (SAAM II) was employed to aid in the experimental design and data analysis. As a powerful tool that creates, simulate and analyze data accurately and rapidly, SAAM II computationally creates a system of ordinary differential equations according to the specified compartmental model structure and simulates the solutions based upon the parameter and model inputs provided. The experimental design of in vitro DIN approach was applied to create an artificial gastric and gastrointestinal fluids. These synthetic fluids assay were produced to determine the concentrations of As ingested into the gastrointestinal tract. The model outputs were created based upon the experimental inputs and the recommended fractional transfer rates parameter. As a result, the measured and predicted As concentrations in gastric fluids were much similar against the time of study. In contrast, the concentrations of As in the gastrointestinal fluids were only similar during the first hour and eventually started decreasing until the fifth hours of study between the measured and predicted values. This is due to the loss of As through the fractional transfer rates of q2 compartment to corresponding compartments of q3 and q5 which are involved with excretion and distribution to the whole body, respectively. The model outputs obtained after best fit to the data were influenced significantly by the fractional transfer rates between each compartment. Therefore, a series of compartmental model created with the association of fractional transfer rates parameter with the aid of SAAM II provides better estimation that simulate the kinetic behavior of As ingested into the gastrointestinal system.

Validation of behave fire behavior predictions in oak savannas

USGS Publications Warehouse

Grabner, Keith W.; Dwyer, John; Cutter, Bruce E.

1997-01-01

Prescribed fire is a valuable tool in the restoration and management of oak savannas. BEHAVE, a fire behavior prediction system developed by the United States Forest Service, can be a useful tool when managing oak savannas with prescribed fire. BEHAVE predictions of fire rate-of-spread and flame length were validated using four standardized fuel models: Fuel Model 1 (short grass), Fuel Model 2 (timber and grass), Fuel Model 3 (tall grass), and Fuel Model 9 (hardwood litter). Also, a customized oak savanna fuel model (COSFM) was created and validated. Results indicate that standardized fuel model 2 and the COSFM reliably estimate mean rate-of-spread (MROS). The COSFM did not appreciably reduce MROS variation when compared to fuel model 2. Fuel models 1, 3, and 9 did not reliably predict MROS. Neither the standardized fuel models nor the COSFM adequately predicted flame lengths. We concluded that standardized fuel model 2 should be used with BEHAVE when predicting fire rates-of-spread in established oak savannas.

In silico platform for xenobiotics ADME-T pharmacological properties modeling and prediction. Part II: The body in a Hilbertian space.

PubMed

Jacob, Alexandre; Pratuangdejkul, Jaturong; Buffet, Sébastien; Launay, Jean-Marie; Manivet, Philippe

2009-04-01

We have broken old surviving dogmas and concepts used in computational chemistry and created an efficient in silico ADME-T pharmacological properties modeling and prediction toolbox for any xenobiotic. With the help of an innovative and pragmatic approach combining various in silico techniques, like molecular modeling, quantum chemistry and in-house developed algorithms, the interactions between drugs and those enzymes, transporters and receptors involved in their biotransformation can be studied. ADME-T pharmacological parameters can then be predicted after in vitro and in vivo validations of in silico models.

Individualized prediction of seizure relapse and outcomes following antiepileptic drug withdrawal after pediatric epilepsy surgery.

PubMed

Lamberink, Herm J; Boshuisen, Kim; Otte, Willem M; Geleijns, Karin; Braun, Kees P J

2018-03-01

The objective of this study was to create a clinically useful tool for individualized prediction of seizure outcomes following antiepileptic drug withdrawal after pediatric epilepsy surgery. We used data from the European retrospective TimeToStop study, which included 766 children from 15 centers, to perform a proportional hazard regression analysis. The 2 outcome measures were seizure recurrence and seizure freedom in the last year of follow-up. Prognostic factors were identified through systematic review of the literature. The strongest predictors for each outcome were selected through backward selection, after which nomograms were created. The final models included 3 to 5 factors per model. Discrimination in terms of adjusted concordance statistic was 0.68 (95% confidence interval [CI] 0.67-0.69) for predicting seizure recurrence and 0.73 (95% CI 0.72-0.75) for predicting eventual seizure freedom. An online prediction tool is provided on www.epilepsypredictiontools.info/ttswithdrawal. The presented models can improve counseling of patients and parents regarding postoperative antiepileptic drug policies, by estimating individualized risks of seizure recurrence and eventual outcome. Wiley Periodicals, Inc. © 2018 International League Against Epilepsy.

A Matter of Classes: Stratifying Health Care Populations to Produce Better Estimates of Inpatient Costs

PubMed Central

Rein, David B

2005-01-01

Objective To stratify traditional risk-adjustment models by health severity classes in a way that is empirically based, is accessible to policy makers, and improves predictions of inpatient costs. Data Sources Secondary data created from the administrative claims from all 829,356 children aged 21 years and under enrolled in Georgia Medicaid in 1999. Study Design A finite mixture model was used to assign child Medicaid patients to health severity classes. These class assignments were then used to stratify both portions of a traditional two-part risk-adjustment model predicting inpatient Medicaid expenditures. Traditional model results were compared with the stratified model using actuarial statistics. Principal Findings The finite mixture model identified four classes of children: a majority healthy class and three illness classes with increasing levels of severity. Stratifying the traditional two-part risk-adjustment model by health severity classes improved its R2 from 0.17 to 0.25. The majority of additional predictive power resulted from stratifying the second part of the two-part model. Further, the preference for the stratified model was unaffected by months of patient enrollment time. Conclusions Stratifying health care populations based on measures of health severity is a powerful method to achieve more accurate cost predictions. Insurers who ignore the predictive advances of sample stratification in setting risk-adjusted premiums may create strong financial incentives for adverse selection. Finite mixture models provide an empirically based, replicable methodology for stratification that should be accessible to most health care financial managers. PMID:16033501

Computational modeling to predict mechanical function of joints: application to the lower leg with simulation of two cadaver studies.

PubMed

Liacouras, Peter C; Wayne, Jennifer S

2007-12-01

Computational models of musculoskeletal joints and limbs can provide useful information about joint mechanics. Validated models can be used as predictive devices for understanding joint function and serve as clinical tools for predicting the outcome of surgical procedures. A new computational modeling approach was developed for simulating joint kinematics that are dictated by bone/joint anatomy, ligamentous constraints, and applied loading. Three-dimensional computational models of the lower leg were created to illustrate the application of this new approach. Model development began with generating three-dimensional surfaces of each bone from CT images and then importing into the three-dimensional solid modeling software SOLIDWORKS and motion simulation package COSMOSMOTION. Through SOLIDWORKS and COSMOSMOTION, each bone surface file was filled to create a solid object and positioned necessary components added, and simulations executed. Three-dimensional contacts were added to inhibit intersection of the bones during motion. Ligaments were represented as linear springs. Model predictions were then validated by comparison to two different cadaver studies, syndesmotic injury and repair and ankle inversion following ligament transection. The syndesmotic injury model was able to predict tibial rotation, fibular rotation, and anterior/posterior displacement. In the inversion simulation, calcaneofibular ligament extension and angles of inversion compared well. Some experimental data proved harder to simulate accurately, due to certain software limitations and lack of complete experimental data. Other parameters that could not be easily obtained experimentally can be predicted and analyzed by the computational simulations. In the syndesmotic injury study, the force generated in the tibionavicular and calcaneofibular ligaments reduced with the insertion of the staple, indicating how this repair technique changes joint function. After transection of the calcaneofibular ligament in the inversion stability study, a major increase in force was seen in several of the ligaments on the lateral aspect of the foot and ankle, indicating the recruitment of other structures to permit function after injury. Overall, the computational models were able to predict joint kinematics of the lower leg with particular focus on the ankle complex. This same approach can be taken to create models of other limb segments such as the elbow and wrist. Additional parameters can be calculated in the models that are not easily obtained experimentally such as ligament forces, force transmission across joints, and three-dimensional movement of all bones. Muscle activation can be incorporated in the model through the action of applied forces within the software for future studies.

Separating predictable and unpredictable work to manage interruptions and promote safe and effective work flow.

PubMed

Kowinsky, Amy M; Shovel, Judith; McLaughlin, Maribeth; Vertacnik, Lisa; Greenhouse, Pamela K; Martin, Susan Christie; Minnier, Tamra E

2012-01-01

Predictable and unpredictable patient care tasks compete for caregiver time and attention, making it difficult for patient care staff to reliably and consistently meet patient needs. We have piloted a redesigned care model that separates the work of patient care technicians based on task predictability and creates role specificity. This care model shows promise in improving the ability of staff to reliably complete tasks in a more consistent and timely manner.

Predicting early cognitive decline in newly-diagnosed Parkinson's patients: A practical model.

PubMed

Hogue, Olivia; Fernandez, Hubert H; Floden, Darlene P

2018-06-19

To create a multivariable model to predict early cognitive decline among de novo patients with Parkinson's disease, using brief, inexpensive assessments that are easily incorporated into clinical flow. Data for 351 drug-naïve patients diagnosed with idiopathic Parkinson's disease were obtained from the Parkinson's Progression Markers Initiative. Baseline demographic, disease history, motor, and non-motor features were considered as candidate predictors. Best subsets selection was used to determine the multivariable baseline symptom profile that most accurately predicted individual cognitive decline within three years. Eleven per cent of the sample experienced cognitive decline. The final logistic regression model predicting decline included five baseline variables: verbal memory retention, right-sided bradykinesia, years of education, subjective report of cognitive impairment, and REM behavior disorder. Model discrimination was good (optimism-adjusted concordance index = .749). The associated nomogram provides a tool to determine individual patient risk of meaningful cognitive change in the early stages of the disease. Through the consideration of easily-implemented or routinely-gathered assessments, we have identified a multidimensional baseline profile and created a convenient, inexpensive tool to predict cognitive decline in the earliest stages of Parkinson's disease. The use of this tool would generate prediction at the individual level, allowing clinicians to tailor medical management for each patient and identify at-risk patients for clinical trials aimed at disease modifying therapies. Copyright © 2018. Published by Elsevier Ltd.

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...

An Examination of Pennsylvania's Classroom Diagnostic Testing as a Predictive Model of Pennsylvania System of School Assessment Performance

ERIC Educational Resources Information Center

Matsanka, Christopher

2017-01-01

The purpose of this non-experimental quantitative study was to investigate the relationship between Pennsylvania's Classroom Diagnostic Tools (CDT) interim assessments and the state-mandated Pennsylvania System of School Assessment (PSSA) and to create linear regression equations that could be used as models to predict student performance on the…

Brain Regions Engaged by Part- and Whole-task Performance in a Video Game: A Model-based Test of the Decomposition Hypothesis

PubMed Central

Anderson, John R.; Bothell, Daniel; Fincham, Jon M.; Anderson, Abraham R.; Poole, Ben; Qin, Yulin

2013-01-01

Part- and whole-task conditions were created by manipulating the presence of certain components of the Space Fortress video game. A cognitive model was created for two-part games that could be combined into a model that performed the whole game. The model generated predictions both for behavioral patterns and activation patterns in various brain regions. The activation predictions concerned both tonic activation that was constant in these regions during performance of the game and phasic activation that occurred when there was resource competition. The model’s predictions were confirmed about how tonic and phasic activation in different regions would vary with condition. These results support the Decomposition Hypothesis that the execution of a complex task can be decomposed into a set of information-processing components and that these components combine unchanged in different task conditions. In addition, individual differences in learning gains were predicted by individual differences in phasic activation in those regions that displayed highest tonic activity. This individual difference pattern suggests that the rate of learning of a complex skill is determined by capacity limits. PMID:21557648

Automatic speech recognition using a predictive echo state network classifier.

PubMed

Skowronski, Mark D; Harris, John G

2007-04-01

We have combined an echo state network (ESN) with a competitive state machine framework to create a classification engine called the predictive ESN classifier. We derive the expressions for training the predictive ESN classifier and show that the model was significantly more noise robust compared to a hidden Markov model in noisy speech classification experiments by 8+/-1 dB signal-to-noise ratio. The simple training algorithm and noise robustness of the predictive ESN classifier make it an attractive classification engine for automatic speech recognition.

Investigation into the influence of build parameters on failure of 3D printed parts

NASA Astrophysics Data System (ADS)

Fornasini, Giacomo

Additive manufacturing, including fused deposition modeling (FDM), is transforming the built world and engineering education. Deep understanding of parts created through FDM technology has lagged behind its adoption in home, work, and academic environments. Properties of parts created from bulk materials through traditional manufacturing are understood well enough to accurately predict their behavior through analytical models. Unfortunately, Additive Manufacturing (AM) process parameters create anisotropy on a scale that fundamentally affects the part properties. Understanding AM process parameters (implemented by program algorithms called slicers) is necessary to predict part behavior. Investigating algorithms controlling print parameters (slicers) revealed stark differences between the generation of part layers. In this work, tensile testing experiments, including a full factorial design, determined that three key factors, width, thickness, infill density, and their interactions, significantly affect the tensile properties of 3D printed test samples.

Prediction of XV-15 tilt rotor discrete frequency aeroacoustic noise with WOPWOP

NASA Technical Reports Server (NTRS)

Coffen, Charles D.; George, Albert R.

1990-01-01

The results, methodology, and conclusions of noise prediction calculations carried out to study several possible discrete frequency harmonic noise mechanisms of the XV-15 Tilt Rotor Aircraft in hover and helicopter mode forward flight are presented. The mechanisms studied were thickness and loading noise. In particular, the loading noise caused by flow separation and the fountain/ground plane effect were predicted with calculations made using WOPWOP, a noise prediction program developed by NASA Langley. The methodology was to model the geometry and aerodynamics of the XV-15 rotor blades in hover and steady level flight and then create corresponding FORTRAN subroutines which were used an input for WOPWOP. The models are described and the simplifying assumptions made in creating them are evaluated, and the results of the computations are presented. The computations lead to the following conclusions: The fountain/ground plane effect is an important source of aerodynamic noise for the XV-15 in hover. Unsteady flow separation from the airfoil passing through the fountain at high angles of attack significantly affects the predicted sound spectra and may be an important noise mechanism for the XV-15 in hover mode. The various models developed did not predict the sound spectra in helicopter forward flight. The experimental spectra indicate the presence of blade vortex interactions which were not modeled in these calculations. A need for further study and development of more accurate aerodynamic models, including unsteady stall in hover and blade vortex interactions in forward flight.

High-Throughput Physiologically Based Toxicokinetic Models for ToxCast Chemicals

EPA Science Inventory

Physiologically based toxicokinetic (PBTK) models aid in predicting exposure doses needed to create tissue concentrations equivalent to those identified as bioactive by ToxCast. We have implemented four empirical and physiologically-based toxicokinetic (TK) models within a new R ...

PSO-MISMO modeling strategy for multistep-ahead time series prediction.

PubMed

Bao, Yukun; Xiong, Tao; Hu, Zhongyi

2014-05-01

Multistep-ahead time series prediction is one of the most challenging research topics in the field of time series modeling and prediction, and is continually under research. Recently, the multiple-input several multiple-outputs (MISMO) modeling strategy has been proposed as a promising alternative for multistep-ahead time series prediction, exhibiting advantages compared with the two currently dominating strategies, the iterated and the direct strategies. Built on the established MISMO strategy, this paper proposes a particle swarm optimization (PSO)-based MISMO modeling strategy, which is capable of determining the number of sub-models in a self-adaptive mode, with varying prediction horizons. Rather than deriving crisp divides with equal-size s prediction horizons from the established MISMO, the proposed PSO-MISMO strategy, implemented with neural networks, employs a heuristic to create flexible divides with varying sizes of prediction horizons and to generate corresponding sub-models, providing considerable flexibility in model construction, which has been validated with simulated and real datasets.

Understanding Elementary Astronomy by Making Drawing-Based Models

NASA Astrophysics Data System (ADS)

van Joolingen, W. R.; Aukes, Annika V. A.; Gijlers, H.; Bollen, L.

2015-04-01

Modeling is an important approach in the teaching and learning of science. In this study, we attempt to bring modeling within the reach of young children by creating the SimSketch modeling system, which is based on freehand drawings that can be turned into simulations. This system was used by 247 children (ages ranging from 7 to 15) to create a drawing-based model of the solar system. The results show that children in the target age group are capable of creating a drawing-based model of the solar system and can use it to show the situations in which eclipses occur. Structural equation modeling predicting post-test knowledge scores based on learners' pre-test knowledge scores, the quality of their drawings and motivational aspects yielded some evidence that such drawing contributes to learning. Consequences for using modeling with young children are considered.

Fuzzy association rule mining and classification for the prediction of malaria in South Korea.

PubMed

Buczak, Anna L; Baugher, Benjamin; Guven, Erhan; Ramac-Thomas, Liane C; Elbert, Yevgeniy; Babin, Steven M; Lewis, Sheri H

2015-06-18

Malaria is the world's most prevalent vector-borne disease. Accurate prediction of malaria outbreaks may lead to public health interventions that mitigate disease morbidity and mortality. We describe an application of a method for creating prediction models utilizing Fuzzy Association Rule Mining to extract relationships between epidemiological, meteorological, climatic, and socio-economic data from Korea. These relationships are in the form of rules, from which the best set of rules is automatically chosen and forms a classifier. Two classifiers have been built and their results fused to become a malaria prediction model. Future malaria cases are predicted as Low, Medium or High, where these classes are defined as a total of 0-2, 3-16, and above 17 cases, respectively, for a region in South Korea during a two-week period. Based on user recommendations, HIGH is considered an outbreak. Model accuracy is described by Positive Predictive Value (PPV), Sensitivity, and F-score for each class, computed on test data not previously used to develop the model. For predictions made 7-8 weeks in advance, model PPV and Sensitivity are 0.842 and 0.681, respectively, for the HIGH classes. The F0.5 and F3 scores (which combine PPV and Sensitivity) are 0.804 and 0.694, respectively, for the HIGH classes. The overall FARM results (as measured by F-scores) are significantly better than those obtained by Decision Tree, Random Forest, Support Vector Machine, and Holt-Winters methods for the HIGH class. For the Medium class, Random Forest and FARM obtain comparable results, with FARM being better at F0.5, and Random Forest obtaining a higher F3. A previously described method for creating disease prediction models has been modified and extended to build models for predicting malaria. In addition, some new input variables were used, including indicators of intervention measures. The South Korea malaria prediction models predict Low, Medium or High cases 7-8 weeks in the future. This paper demonstrates that our data driven approach can be used for the prediction of different diseases.

An Expected Value Air Combat Model Simulation Algorithm to Predict Missions Performance in Tactical Air Operations.

DTIC Science & Technology

1983-09-01

Approved by: Me<i W4 1tsZ7 CaifI ,KDpartmento I inistrative Science 3 ( ABSTRACT >This thesis intends to create the basic...a need for a small scale model which allows a student analyst of tactical air operations to create his own battles and to test his own strategies with...iconic model is a large or small-scale repre- sentation of states-objects, or events. For example a scale model airplance resembles the system under the

Methods for evaluating the predictive accuracy of structural dynamic models

NASA Technical Reports Server (NTRS)

Hasselman, Timothy K.; Chrostowski, Jon D.

1991-01-01

Modeling uncertainty is defined in terms of the difference between predicted and measured eigenvalues and eigenvectors. Data compiled from 22 sets of analysis/test results was used to create statistical databases for large truss-type space structures and both pretest and posttest models of conventional satellite-type space structures. Modeling uncertainty is propagated through the model to produce intervals of uncertainty on frequency response functions, both amplitude and phase. This methodology was used successfully to evaluate the predictive accuracy of several structures, including the NASA CSI Evolutionary Structure tested at Langley Research Center. Test measurements for this structure were within + one-sigma intervals of predicted accuracy for the most part, demonstrating the validity of the methodology and computer code.

Challenges in Real-Time Prediction of Infectious Disease: A Case Study of Dengue in Thailand

PubMed Central

Lauer, Stephen A.; Sakrejda, Krzysztof; Iamsirithaworn, Sopon; Hinjoy, Soawapak; Suangtho, Paphanij; Suthachana, Suthanun; Clapham, Hannah E.; Salje, Henrik; Cummings, Derek A. T.; Lessler, Justin

2016-01-01

Epidemics of communicable diseases place a huge burden on public health infrastructures across the world. Producing accurate and actionable forecasts of infectious disease incidence at short and long time scales will improve public health response to outbreaks. However, scientists and public health officials face many obstacles in trying to create such real-time forecasts of infectious disease incidence. Dengue is a mosquito-borne virus that annually infects over 400 million people worldwide. We developed a real-time forecasting model for dengue hemorrhagic fever in the 77 provinces of Thailand. We created a practical computational infrastructure that generated multi-step predictions of dengue incidence in Thai provinces every two weeks throughout 2014. These predictions show mixed performance across provinces, out-performing seasonal baseline models in over half of provinces at a 1.5 month horizon. Additionally, to assess the degree to which delays in case reporting make long-range prediction a challenging task, we compared the performance of our real-time predictions with predictions made with fully reported data. This paper provides valuable lessons for the implementation of real-time predictions in the context of public health decision making. PMID:27304062

Challenges in Real-Time Prediction of Infectious Disease: A Case Study of Dengue in Thailand.

PubMed

Reich, Nicholas G; Lauer, Stephen A; Sakrejda, Krzysztof; Iamsirithaworn, Sopon; Hinjoy, Soawapak; Suangtho, Paphanij; Suthachana, Suthanun; Clapham, Hannah E; Salje, Henrik; Cummings, Derek A T; Lessler, Justin

2016-06-01

Epidemics of communicable diseases place a huge burden on public health infrastructures across the world. Producing accurate and actionable forecasts of infectious disease incidence at short and long time scales will improve public health response to outbreaks. However, scientists and public health officials face many obstacles in trying to create such real-time forecasts of infectious disease incidence. Dengue is a mosquito-borne virus that annually infects over 400 million people worldwide. We developed a real-time forecasting model for dengue hemorrhagic fever in the 77 provinces of Thailand. We created a practical computational infrastructure that generated multi-step predictions of dengue incidence in Thai provinces every two weeks throughout 2014. These predictions show mixed performance across provinces, out-performing seasonal baseline models in over half of provinces at a 1.5 month horizon. Additionally, to assess the degree to which delays in case reporting make long-range prediction a challenging task, we compared the performance of our real-time predictions with predictions made with fully reported data. This paper provides valuable lessons for the implementation of real-time predictions in the context of public health decision making.

Analyses of the most influential factors for vibration monitoring of planetary power transmissions in pellet mills by adaptive neuro-fuzzy technique

NASA Astrophysics Data System (ADS)

Milovančević, Miloš; Nikolić, Vlastimir; Anđelković, Boban

2017-01-01

Vibration-based structural health monitoring is widely recognized as an attractive strategy for early damage detection in civil structures. Vibration monitoring and prediction is important for any system since it can save many unpredictable behaviors of the system. If the vibration monitoring is properly managed, that can ensure economic and safe operations. Potentials for further improvement of vibration monitoring lie in the improvement of current control strategies. One of the options is the introduction of model predictive control. Multistep ahead predictive models of vibration are a starting point for creating a successful model predictive strategy. For the purpose of this article, predictive models of are created for vibration monitoring of planetary power transmissions in pellet mills. The models were developed using the novel method based on ANFIS (adaptive neuro fuzzy inference system). The aim of this study is to investigate the potential of ANFIS for selecting the most relevant variables for predictive models of vibration monitoring of pellet mills power transmission. The vibration data are collected by PIC (Programmable Interface Controller) microcontrollers. The goal of the predictive vibration monitoring of planetary power transmissions in pellet mills is to indicate deterioration in the vibration of the power transmissions before the actual failure occurs. The ANFIS process for variable selection was implemented in order to detect the predominant variables affecting the prediction of vibration monitoring. It was also used to select the minimal input subset of variables from the initial set of input variables - current and lagged variables (up to 11 steps) of vibration. The obtained results could be used for simplification of predictive methods so as to avoid multiple input variables. It was preferable to used models with less inputs because of overfitting between training and testing data. While the obtained results are promising, further work is required in order to get results that could be directly applied in practice.

Improving the prediction of African savanna vegetation variables using time series of MODIS products

NASA Astrophysics Data System (ADS)

Tsalyuk, Miriam; Kelly, Maggi; Getz, Wayne M.

2017-09-01

African savanna vegetation is subject to extensive degradation as a result of rapid climate and land use change. To better understand these changes detailed assessment of vegetation structure is needed across an extensive spatial scale and at a fine temporal resolution. Applying remote sensing techniques to savanna vegetation is challenging due to sparse cover, high background soil signal, and difficulty to differentiate between spectral signals of bare soil and dry vegetation. In this paper, we attempt to resolve these challenges by analyzing time series of four MODIS Vegetation Products (VPs): Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation (FPAR) for Etosha National Park, a semiarid savanna in north-central Namibia. We create models to predict the density, cover, and biomass of the main savanna vegetation forms: grass, shrubs, and trees. To calibrate remote sensing data we developed an extensive and relatively rapid field methodology and measured herbaceous and woody vegetation during both the dry and wet seasons. We compared the efficacy of the four MODIS-derived VPs in predicting vegetation field measured variables. We then compared the optimal time span of VP time series to predict ground-measured vegetation. We found that Multiyear Partial Least Square Regression (PLSR) models were superior to single year or single date models. Our results show that NDVI-based PLSR models yield robust prediction of tree density (R2 = 0.79, relative Root Mean Square Error, rRMSE = 1.9%) and tree cover (R2 = 0.78, rRMSE = 0.3%). EVI provided the best model for shrub density (R2 = 0.82) and shrub cover (R2 = 0.83), but was only marginally superior over models based on other VPs. FPAR was the best predictor of vegetation biomass of trees (R2 = 0.76), shrubs (R2 = 0.83), and grass (R2 = 0.91). Finally, we addressed an enduring challenge in the remote sensing of semiarid vegetation by examining the transferability of predictive models through space and time. Our results show that models created in the wetter part of Etosha could accurately predict trees' and shrubs' variables in the drier part of the reserve and vice versa. Moreover, our results demonstrate that models created for vegetation variables in the dry season of 2011 could be successfully applied to predict vegetation in the wet season of 2012. We conclude that extensive field data combined with multiyear time series of MODIS vegetation products can produce robust predictive models for multiple vegetation forms in the African savanna. These methods advance the monitoring of savanna vegetation dynamics and contribute to improved management and conservation of these valuable ecosystems.

Prostate Cancer Probability Prediction By Machine Learning Technique.

PubMed

Jović, Srđan; Miljković, Milica; Ivanović, Miljan; Šaranović, Milena; Arsić, Milena

2017-11-26

The main goal of the study was to explore possibility of prostate cancer prediction by machine learning techniques. In order to improve the survival probability of the prostate cancer patients it is essential to make suitable prediction models of the prostate cancer. If one make relevant prediction of the prostate cancer it is easy to create suitable treatment based on the prediction results. Machine learning techniques are the most common techniques for the creation of the predictive models. Therefore in this study several machine techniques were applied and compared. The obtained results were analyzed and discussed. It was concluded that the machine learning techniques could be used for the relevant prediction of prostate cancer.

Using integrated modeling for generating watershed-scale dynamic flood maps for Hurricane Harvey

NASA Astrophysics Data System (ADS)

Saksena, S.; Dey, S.; Merwade, V.; Singhofen, P. J.

2017-12-01

Hurricane Harvey, which was categorized as a 1000-year return period event, produced unprecedented rainfall and flooding in Houston. Although the expected rainfall was forecasted much before the event, there was no way to identify which regions were at higher risk of flooding, the magnitude of flooding, and when the impacts of rainfall would be highest. The inability to predict the location, duration, and depth of flooding created uncertainty over evacuation planning and preparation. This catastrophic event highlighted that the conventional approach to managing flood risk using 100-year static flood inundation maps is inadequate because of its inability to predict flood duration and extents for 500-year or 1000-year return period events in real-time. The purpose of this study is to create models that can dynamically predict the impacts of rainfall and subsequent flooding, so that necessary evacuation and rescue efforts can be planned in advance. This study uses a 2D integrated surface water-groundwater model called ICPR (Interconnected Channel and Pond Routing) to simulate both the hydrology and hydrodynamics for Hurricane Harvey. The methodology involves using the NHD stream network to create a 2D model that incorporates rainfall, land use, vadose zone properties and topography to estimate streamflow and generate dynamic flood depths and extents. The results show that dynamic flood mapping captures the flood hydrodynamics more accurately and is able to predict the magnitude, extent and time of occurrence for extreme events such as Hurricane Harvey. Therefore, integrated modeling has the potential to identify regions that are more susceptible to flooding, which is especially useful for large-scale planning and allocation of resources for protection against future flood risk.

Creating and validating cis-regulatory maps of tissue-specific gene expression regulation

PubMed Central

O'Connor, Timothy R.; Bailey, Timothy L.

2014-01-01

Predicting which genomic regions control the transcription of a given gene is a challenge. We present a novel computational approach for creating and validating maps that associate genomic regions (cis-regulatory modules–CRMs) with genes. The method infers regulatory relationships that explain gene expression observed in a test tissue using widely available genomic data for ‘other’ tissues. To predict the regulatory targets of a CRM, we use cross-tissue correlation between histone modifications present at the CRM and expression at genes within 1 Mbp of it. To validate cis-regulatory maps, we show that they yield more accurate models of gene expression than carefully constructed control maps. These gene expression models predict observed gene expression from transcription factor binding in the CRMs linked to that gene. We show that our maps are able to identify long-range regulatory interactions and improve substantially over maps linking genes and CRMs based on either the control maps or a ‘nearest neighbor’ heuristic. Our results also show that it is essential to include CRMs predicted in multiple tissues during map-building, that H3K27ac is the most informative histone modification, and that CAGE is the most informative measure of gene expression for creating cis-regulatory maps. PMID:25200088

Source Term Model for Vortex Generator Vanes in a Navier-Stokes Computer Code

NASA Technical Reports Server (NTRS)

Waithe, Kenrick A.

2004-01-01

A source term model for an array of vortex generators was implemented into a non-proprietary Navier-Stokes computer code, OVERFLOW. The source term models the side force created by a vortex generator vane. The model is obtained by introducing a side force to the momentum and energy equations that can adjust its strength automatically based on the local flow. The model was tested and calibrated by comparing data from numerical simulations and experiments of a single low profile vortex generator vane on a flat plate. In addition, the model was compared to experimental data of an S-duct with 22 co-rotating, low profile vortex generators. The source term model allowed a grid reduction of about seventy percent when compared with the numerical simulations performed on a fully gridded vortex generator on a flat plate without adversely affecting the development and capture of the vortex created. The source term model was able to predict the shape and size of the stream-wise vorticity and velocity contours very well when compared with both numerical simulations and experimental data. The peak vorticity and its location were also predicted very well when compared to numerical simulations and experimental data. The circulation predicted by the source term model matches the prediction of the numerical simulation. The source term model predicted the engine fan face distortion and total pressure recovery of the S-duct with 22 co-rotating vortex generators very well. The source term model allows a researcher to quickly investigate different locations of individual or a row of vortex generators. The researcher is able to conduct a preliminary investigation with minimal grid generation and computational time.

A new predictive model for continuous positive airway pressure in the treatment of obstructive sleep apnea.

PubMed

Ebben, Matthew R; Narizhnaya, Mariya; Krieger, Ana C

2017-05-01

Numerous mathematical formulas have been developed to determine continuous positive airway pressure (CPAP) without an in-laboratory titration study. Recent studies have shown that style of CPAP mask can affect the optimal pressure requirement. However, none of the current models take mask style into account. Therefore, the goal of this study was to develop new predictive models of CPAP that take into account the style of mask interface. Data from 200 subjects with attended CPAP titrations during overnight polysomnograms using nasal masks and 132 subjects using oronasal masks were randomized and split into either a model development or validation group. Predictive models were then created in each model development group and the accuracy of the models was then tested in the model validation groups. The correlation between our new oronasal model and laboratory determined optimal CPAP was significant, r = 0.61, p < 0.001. Our nasal formula was also significantly related to laboratory determined optimal CPAP, r = 0.35, p < 0.001. The oronasal model created in our study significantly outperformed the original CPAP predictive model developed by Miljeteig and Hoffstein, z = 1.99, p < 0.05. The predictive performance of our new nasal model did not differ significantly from Miljeteig and Hoffstein's original model, z = -0.16, p < 0.90. The best predictors for the nasal mask group were AHI, lowest SaO2, and neck size, whereas the top predictors in the oronasal group were AHI and lowest SaO2. Our data show that predictive models of CPAP that take into account mask style can significantly improve the formula's accuracy. Most of the past models likely focused on model development with nasal masks (mask style used for model development was not typically reported in previous investigations) and are not well suited for patients using an oronasal interface. Our new oronasal CPAP prediction equation produced significantly improved performance compared to the well-known Miljeteig and Hoffstein formula in patients titrated on CPAP with an oronasal mask and was also significantly related to laboratory determined optimal CPAP.

Near infra red spectroscopy as a multivariate process analytical tool for predicting pharmaceutical co-crystal concentration.

PubMed

Wood, Clive; Alwati, Abdolati; Halsey, Sheelagh; Gough, Tim; Brown, Elaine; Kelly, Adrian; Paradkar, Anant

2016-09-10

The use of near infra red spectroscopy to predict the concentration of two pharmaceutical co-crystals; 1:1 ibuprofen-nicotinamide (IBU-NIC) and 1:1 carbamazepine-nicotinamide (CBZ-NIC) has been evaluated. A partial least squares (PLS) regression model was developed for both co-crystal pairs using sets of standard samples to create calibration and validation data sets with which to build and validate the models. Parameters such as the root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP) and correlation coefficient were used to assess the accuracy and linearity of the models. Accurate PLS regression models were created for both co-crystal pairs which can be used to predict the co-crystal concentration in a powder mixture of the co-crystal and the active pharmaceutical ingredient (API). The IBU-NIC model had smaller errors than the CBZ-NIC model, possibly due to the complex CBZ-NIC spectra which could reflect the different arrangement of hydrogen bonding associated with the co-crystal compared to the IBU-NIC co-crystal. These results suggest that NIR spectroscopy can be used as a PAT tool during a variety of pharmaceutical co-crystal manufacturing methods and the presented data will facilitate future offline and in-line NIR studies involving pharmaceutical co-crystals. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Patient-Specific Computational Modeling of Upper Extremity Arteriovenous Fistula Creation: Its Feasibility to Support Clinical Decision-Making

PubMed Central

Bosboom, E. Marielle H.; Kroon, Wilco; van der Linden, Wim P. M.; Planken, R. Nils; van de Vosse, Frans N.; Tordoir, Jan H. M.

2012-01-01

Introduction Inadequate flow enhancement on the one hand, and excessive flow enhancement on the other hand, remain frequent complications of arteriovenous fistula (AVF) creation, and hamper hemodialysis therapy in patients with end-stage renal disease. In an effort to reduce these, a patient-specific computational model, capable of predicting postoperative flow, has been developed. The purpose of this study was to determine the accuracy of the patient-specific model and to investigate its feasibility to support decision-making in AVF surgery. Methods Patient-specific pulse wave propagation models were created for 25 patients awaiting AVF creation. Model input parameters were obtained from clinical measurements and literature. For every patient, a radiocephalic AVF, a brachiocephalic AVF, and a brachiobasilic AVF configuration were simulated and analyzed for their postoperative flow. The most distal configuration with a predicted flow between 400 and 1500 ml/min was considered the preferred location for AVF surgery. The suggestion of the model was compared to the choice of an experienced vascular surgeon. Furthermore, predicted flows were compared to measured postoperative flows. Results Taken into account the confidence interval (25th and 75th percentile interval), overlap between predicted and measured postoperative flows was observed in 70% of the patients. Differentiation between upper and lower arm configuration was similar in 76% of the patients, whereas discrimination between two upper arm AVF configurations was more difficult. In 3 patients the surgeon created an upper arm AVF, while model based predictions allowed for lower arm AVF creation, thereby preserving proximal vessels. In one patient early thrombosis in a radiocephalic AVF was observed which might have been indicated by the low predicted postoperative flow. Conclusions Postoperative flow can be predicted relatively accurately for multiple AVF configurations by using computational modeling. This model may therefore be considered a valuable additional tool in the preoperative work-up of patients awaiting AVF creation. PMID:22496816

SGS Dynamics and Modeling near a Rough Wall.

NASA Astrophysics Data System (ADS)

Juneja, Anurag; Brasseur, James G.

1998-11-01

Large-eddy simulation (LES) of the atmospheric boundary layer (ABL) using classical subgrid-scale (SGS) models is known to poorly predict mean shear at the first few grid cells near the rough surface, creating error which can propogate vertically to infect the entire ABL. Our goal was to determine the first-order errors in predicted SGS terms that arise as a consequence of necessary under-resolution of integral scales and anisotropy which exist at the first few grid levels in LES of rough wall turbulence. Analyzing the terms predicted from eddy-viscosity and similarity closures with DNS anisotropic datasets of buoyancy- and shear-driven turbulence, we uncover three important issues which should be addressed in the design of SGS closures for rough walls and we provide a priori tests for the SGS model. Firstly, we identify a strong spurious coupling between the anisotropic structure of the resolved velocity field and predicted SGS dynamics which can create a feedback loop to incorrectly enhance certain components of the predicted resolved velocity. Secondly, we find that eddy viscosity and similarity SGS models do not contain enough degrees of freedom to capture, at a sufficient level of accuracy, both RS-SGS energy flux and SGS-RS dynamics. Thirdly, to correctly capture pressure transport near a wall, closures must be made more flexible to accommodate proper partitioning between SGS stress divergence and SGS pressure gradient.

Development and Validation of an Empiric Tool to Predict Favorable Neurologic Outcomes Among PICU Patients.

PubMed

Gupta, Punkaj; Rettiganti, Mallikarjuna; Gossett, Jeffrey M; Daufeldt, Jennifer; Rice, Tom B; Wetzel, Randall C

2018-01-01

To create a novel tool to predict favorable neurologic outcomes during ICU stay among children with critical illness. Logistic regression models using adaptive lasso methodology were used to identify independent factors associated with favorable neurologic outcomes. A mixed effects logistic regression model was used to create the final prediction model including all predictors selected from the lasso model. Model validation was performed using a 10-fold internal cross-validation approach. Virtual Pediatric Systems (VPS, LLC, Los Angeles, CA) database. Patients less than 18 years old admitted to one of the participating ICUs in the Virtual Pediatric Systems database were included (2009-2015). None. A total of 160,570 patients from 90 hospitals qualified for inclusion. Of these, 1,675 patients (1.04%) were associated with a decline in Pediatric Cerebral Performance Category scale by at least 2 between ICU admission and ICU discharge (unfavorable neurologic outcome). The independent factors associated with unfavorable neurologic outcome included higher weight at ICU admission, higher Pediatric Index of Morality-2 score at ICU admission, cardiac arrest, stroke, seizures, head/nonhead trauma, use of conventional mechanical ventilation and high-frequency oscillatory ventilation, prolonged hospital length of ICU stay, and prolonged use of mechanical ventilation. The presence of chromosomal anomaly, cardiac surgery, and utilization of nitric oxide were associated with favorable neurologic outcome. The final online prediction tool can be accessed at https://soipredictiontool.shinyapps.io/GNOScore/. Our model predicted 139,688 patients with favorable neurologic outcomes in an internal validation sample when the observed number of patients with favorable neurologic outcomes was among 139,591 patients. The area under the receiver operating curve for the validation model was 0.90. This proposed prediction tool encompasses 20 risk factors into one probability to predict favorable neurologic outcome during ICU stay among children with critical illness. Future studies should seek external validation and improved discrimination of this prediction tool.

Planning, creating and documenting a NASTRAN finite element model of a modern helicopter

NASA Technical Reports Server (NTRS)

Gabal, R.; Reed, D.; Ricks, R.; Kesack, W.

1985-01-01

Mathematical models based on the finite element method of structural analysis as embodied in the NASTRAN computer code are widely used by the helicopter industry to calculate static internal loads and vibration of airframe structure. The internal loads are routinely used for sizing structural members. The vibration predictions are not yet relied on during design. NASA's Langley Research Center sponsored a program to conduct an application of the finite element method with emphasis on predicting structural vibration. The Army/Boeing CH-47D helicopter was used as the modeling subject. The objective was to engender the needed trust in vibration predictions using these models and establish a body of modeling guides which would enable confident future prediction of airframe vibration as part of the regular design process.

Use of model calibration to achieve high accuracy in analysis of computer networks

DOEpatents

Frogner, Bjorn; Guarro, Sergio; Scharf, Guy

2004-05-11

A system and method are provided for creating a network performance prediction model, and calibrating the prediction model, through application of network load statistical analyses. The method includes characterizing the measured load on the network, which may include background load data obtained over time, and may further include directed load data representative of a transaction-level event. Probabilistic representations of load data are derived to characterize the statistical persistence of the network performance variability and to determine delays throughout the network. The probabilistic representations are applied to the network performance prediction model to adapt the model for accurate prediction of network performance. Certain embodiments of the method and system may be used for analysis of the performance of a distributed application characterized as data packet streams.

The Problem with Big Data: Operating on Smaller Datasets to Bridge the Implementation Gap.

PubMed

Mann, Richard P; Mushtaq, Faisal; White, Alan D; Mata-Cervantes, Gabriel; Pike, Tom; Coker, Dalton; Murdoch, Stuart; Hiles, Tim; Smith, Clare; Berridge, David; Hinchliffe, Suzanne; Hall, Geoff; Smye, Stephen; Wilkie, Richard M; Lodge, J Peter A; Mon-Williams, Mark

2016-01-01

Big datasets have the potential to revolutionize public health. However, there is a mismatch between the political and scientific optimism surrounding big data and the public's perception of its benefit. We suggest a systematic and concerted emphasis on developing models derived from smaller datasets to illustrate to the public how big data can produce tangible benefits in the long term. In order to highlight the immediate value of a small data approach, we produced a proof-of-concept model predicting hospital length of stay. The results demonstrate that existing small datasets can be used to create models that generate a reasonable prediction, facilitating health-care delivery. We propose that greater attention (and funding) needs to be directed toward the utilization of existing information resources in parallel with current efforts to create and exploit "big data."

Cure modeling in real-time prediction: How much does it help?

PubMed

Ying, Gui-Shuang; Zhang, Qiang; Lan, Yu; Li, Yimei; Heitjan, Daniel F

2017-08-01

Various parametric and nonparametric modeling approaches exist for real-time prediction in time-to-event clinical trials. Recently, Chen (2016 BMC Biomedical Research Methodology 16) proposed a prediction method based on parametric cure-mixture modeling, intending to cover those situations where it appears that a non-negligible fraction of subjects is cured. In this article we apply a Weibull cure-mixture model to create predictions, demonstrating the approach in RTOG 0129, a randomized trial in head-and-neck cancer. We compare the ultimate realized data in RTOG 0129 to interim predictions from a Weibull cure-mixture model, a standard Weibull model without a cure component, and a nonparametric model based on the Bayesian bootstrap. The standard Weibull model predicted that events would occur earlier than the Weibull cure-mixture model, but the difference was unremarkable until late in the trial when evidence for a cure became clear. Nonparametric predictions often gave undefined predictions or infinite prediction intervals, particularly at early stages of the trial. Simulations suggest that cure modeling can yield better-calibrated prediction intervals when there is a cured component, or the appearance of a cured component, but at a substantial cost in the average width of the intervals. Copyright © 2017 Elsevier Inc. All rights reserved.

Bayesian molecular design with a chemical language model

NASA Astrophysics Data System (ADS)

Ikebata, Hisaki; Hongo, Kenta; Isomura, Tetsu; Maezono, Ryo; Yoshida, Ryo

2017-04-01

The aim of computational molecular design is the identification of promising hypothetical molecules with a predefined set of desired properties. We address the issue of accelerating the material discovery with state-of-the-art machine learning techniques. The method involves two different types of prediction; the forward and backward predictions. The objective of the forward prediction is to create a set of machine learning models on various properties of a given molecule. Inverting the trained forward models through Bayes' law, we derive a posterior distribution for the backward prediction, which is conditioned by a desired property requirement. Exploring high-probability regions of the posterior with a sequential Monte Carlo technique, molecules that exhibit the desired properties can computationally be created. One major difficulty in the computational creation of molecules is the exclusion of the occurrence of chemically unfavorable structures. To circumvent this issue, we derive a chemical language model that acquires commonly occurring patterns of chemical fragments through natural language processing of ASCII strings of existing compounds, which follow the SMILES chemical language notation. In the backward prediction, the trained language model is used to refine chemical strings such that the properties of the resulting structures fall within the desired property region while chemically unfavorable structures are successfully removed. The present method is demonstrated through the design of small organic molecules with the property requirements on HOMO-LUMO gap and internal energy. The R package iqspr is available at the CRAN repository.

Bayesian molecular design with a chemical language model.

PubMed

Ikebata, Hisaki; Hongo, Kenta; Isomura, Tetsu; Maezono, Ryo; Yoshida, Ryo

2017-04-01

The aim of computational molecular design is the identification of promising hypothetical molecules with a predefined set of desired properties. We address the issue of accelerating the material discovery with state-of-the-art machine learning techniques. The method involves two different types of prediction; the forward and backward predictions. The objective of the forward prediction is to create a set of machine learning models on various properties of a given molecule. Inverting the trained forward models through Bayes' law, we derive a posterior distribution for the backward prediction, which is conditioned by a desired property requirement. Exploring high-probability regions of the posterior with a sequential Monte Carlo technique, molecules that exhibit the desired properties can computationally be created. One major difficulty in the computational creation of molecules is the exclusion of the occurrence of chemically unfavorable structures. To circumvent this issue, we derive a chemical language model that acquires commonly occurring patterns of chemical fragments through natural language processing of ASCII strings of existing compounds, which follow the SMILES chemical language notation. In the backward prediction, the trained language model is used to refine chemical strings such that the properties of the resulting structures fall within the desired property region while chemically unfavorable structures are successfully removed. The present method is demonstrated through the design of small organic molecules with the property requirements on HOMO-LUMO gap and internal energy. The R package iqspr is available at the CRAN repository.

Habitat models to predict wetland bird occupancy influenced by scale, anthropogenic disturbance, and imperfect detection

USGS Publications Warehouse

Glisson, Wesley J.; Conway, Courtney J.; Nadeau, Christopher P.; Borgmann, Kathi L.

2017-01-01

Understanding species–habitat relationships for endangered species is critical for their conservation. However, many studies have limited value for conservation because they fail to account for habitat associations at multiple spatial scales, anthropogenic variables, and imperfect detection. We addressed these three limitations by developing models for an endangered wetland bird, Yuma Ridgway's rail (Rallus obsoletus yumanensis), that examined how the spatial scale of environmental variables, inclusion of anthropogenic disturbance variables, and accounting for imperfect detection in validation data influenced model performance. These models identified associations between environmental variables and occupancy. We used bird survey and spatial environmental data at 2473 locations throughout the species' U.S. range to create and validate occupancy models and produce predictive maps of occupancy. We compared habitat-based models at three spatial scales (100, 224, and 500 m radii buffers) with and without anthropogenic disturbance variables using validation data adjusted for imperfect detection and an unadjusted validation dataset that ignored imperfect detection. The inclusion of anthropogenic disturbance variables improved the performance of habitat models at all three spatial scales, and the 224-m-scale model performed best. All models exhibited greater predictive ability when imperfect detection was incorporated into validation data. Yuma Ridgway's rail occupancy was negatively associated with ephemeral and slow-moving riverine features and high-intensity anthropogenic development, and positively associated with emergent vegetation, agriculture, and low-intensity development. Our modeling approach accounts for common limitations in modeling species–habitat relationships and creating predictive maps of occupancy probability and, therefore, provides a useful framework for other species.

Applying decision tree for identification of a low risk population for type 2 diabetes. Tehran Lipid and Glucose Study.

PubMed

Ramezankhani, Azra; Pournik, Omid; Shahrabi, Jamal; Khalili, Davood; Azizi, Fereidoun; Hadaegh, Farzad

2014-09-01

The aim of this study was to create a prediction model using data mining approach to identify low risk individuals for incidence of type 2 diabetes, using the Tehran Lipid and Glucose Study (TLGS) database. For a 6647 population without diabetes, aged ≥20 years, followed for 12 years, a prediction model was developed using classification by the decision tree technique. Seven hundred and twenty-nine (11%) diabetes cases occurred during the follow-up. Predictor variables were selected from demographic characteristics, smoking status, medical and drug history and laboratory measures. We developed the predictive models by decision tree using 60 input variables and one output variable. The overall classification accuracy was 90.5%, with 31.1% sensitivity, 97.9% specificity; and for the subjects without diabetes, precision and f-measure were 92% and 0.95, respectively. The identified variables included fasting plasma glucose, body mass index, triglycerides, mean arterial blood pressure, family history of diabetes, educational level and job status. In conclusion, decision tree analysis, using routine demographic, clinical, anthropometric and laboratory measurements, created a simple tool to predict individuals at low risk for type 2 diabetes. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Efficient statistical mapping of avian count data

USGS Publications Warehouse

Royle, J. Andrew; Wikle, C.K.

2005-01-01

We develop a spatial modeling framework for count data that is efficient to implement in high-dimensional prediction problems. We consider spectral parameterizations for the spatially varying mean of a Poisson model. The spectral parameterization of the spatial process is very computationally efficient, enabling effective estimation and prediction in large problems using Markov chain Monte Carlo techniques. We apply this model to creating avian relative abundance maps from North American Breeding Bird Survey (BBS) data. Variation in the ability of observers to count birds is modeled as spatially independent noise, resulting in over-dispersion relative to the Poisson assumption. This approach represents an improvement over existing approaches used for spatial modeling of BBS data which are either inefficient for continental scale modeling and prediction or fail to accommodate important distributional features of count data thus leading to inaccurate accounting of prediction uncertainty.

Neuropsychological tests for predicting cognitive decline in older adults

PubMed Central

Baerresen, Kimberly M; Miller, Karen J; Hanson, Eric R; Miller, Justin S; Dye, Richelin V; Hartman, Richard E; Vermeersch, David; Small, Gary W

2015-01-01

Summary Aim To determine neuropsychological tests likely to predict cognitive decline. Methods A sample of nonconverters (n = 106) was compared with those who declined in cognitive status (n = 24). Significant univariate logistic regression prediction models were used to create multivariate logistic regression models to predict decline based on initial neuropsychological testing. Results Rey–Osterrieth Complex Figure Test (RCFT) Retention predicted conversion to mild cognitive impairment (MCI) while baseline Buschke Delay predicted conversion to Alzheimer’s disease (AD). Due to group sample size differences, additional analyses were conducted using a subsample of demographically matched nonconverters. Analyses indicated RCFT Retention predicted conversion to MCI and AD, and Buschke Delay predicted conversion to AD. Conclusion Results suggest RCFT Retention and Buschke Delay may be useful in predicting cognitive decline. PMID:26107318

Influence of lake surface area and total phosphorus on annual bluegill growth in small impoundments of central Georgia

USGS Publications Warehouse

Jennings, Cecil A.; Sundmark, Aaron P.

2017-01-01

The relationships between environmental variables and the growth rates of fishes are important and rapidly expanding topics in fisheries ecology. We used an informationtheoretic approach to evaluate the influence of lake surface area and total phosphorus on the age-specific growth rates of Lepomis macrochirus (Bluegill) in 6 small impoundments in central Georgia. We used model averaging to create composite models and determine the relative importance of the variables within each model. Results indicated that surface area was the most important factor in the models predicting growth of Bluegills aged 1–4 years; total phosphorus was also an important predictor for the same age-classes. These results suggest that managers can use water quality and lake morphometry variables to create predictive models specific to their waterbody or region to help develop lake-specific management plans that select for and optimize local-level habitat factors for enhancing Bluegill growth.

Sex-Specific Prediction Models for Sleep Apnea From the Hispanic Community Health Study/Study of Latinos.

PubMed

Shah, Neomi; Hanna, David B; Teng, Yanping; Sotres-Alvarez, Daniela; Hall, Martica; Loredo, Jose S; Zee, Phyllis; Kim, Mimi; Yaggi, H Klar; Redline, Susan; Kaplan, Robert C

2016-06-01

We developed and validated the first-ever sleep apnea (SA) risk calculator in a large population-based cohort of Hispanic/Latino subjects. Cross-sectional data on adults from the Hispanic Community Health Study/Study of Latinos (2008-2011) were analyzed. Subjective and objective sleep measurements were obtained. Clinically significant SA was defined as an apnea-hypopnea index ≥ 15 events per hour. Using logistic regression, four prediction models were created: three sex-specific models (female-only, male-only, and a sex × covariate interaction model to allow differential predictor effects), and one overall model with sex included as a main effect only. Models underwent 10-fold cross-validation and were assessed by using the C statistic. SA and its predictive variables; a total of 17 variables were considered. A total of 12,158 participants had complete sleep data available; 7,363 (61%) were women. The population-weighted prevalence of SA (apnea-hypopnea index ≥ 15 events per hour) was 6.1% in female subjects and 13.5% in male subjects. Male-only (C statistic, 0.808) and female-only (C statistic, 0.836) prediction models had the same predictor variables (ie, age, BMI, self-reported snoring). The sex-interaction model (C statistic, 0.836) contained sex, age, age × sex, BMI, BMI × sex, and self-reported snoring. The final overall model (C statistic, 0.832) contained age, BMI, snoring, and sex. We developed two websites for our SA risk calculator: one in English (https://www.montefiore.org/sleepapneariskcalc.html) and another in Spanish (http://www.montefiore.org/sleepapneariskcalc-es.html). We created an internally validated, highly discriminating, well-calibrated, and parsimonious prediction model for SA. Contrary to the study hypothesis, the variables did not have different predictive magnitudes in male and female subjects. Copyright © 2016 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

Reverse engineering of legacy agricultural phenology modeling system

USDA-ARS?s Scientific Manuscript database

A program which implements predictive phenology modeling is a valuable tool for growers and scientists. Such a program was created in the late 1980's by the creators of general phenology modeling as proof of their techniques. However, this first program could not continue to meet the needs of the fi...

Simple Predictive Model of Early Failure among Patients Undergoing First-Time Arteriovenous Fistula Creation.

PubMed

Eslami, Mohammad H; Zhu, Clara K; Rybin, Denis; Doros, Gheorghe; Siracuse, Jeffrey J; Farber, Alik

2016-08-01

Native arteriovenous fistulas (AVFs) have a high 1 year failure rate leading to a need for secondary procedures. We set out to create a predictive model of early failure in patients undergoing first-time AVF creation, to identify failure-associated factors and stratify initial failure risk. The Vascular Study Group of New England (VSGNE) (2010-2014) was queried to identify patients undergoing first-time AVF creation. Patients with early (within 3 months postoperation) AVF failure (EF) or no failure (NF) were compared, failure being defined as any AVF that could not be used for dialysis. A multivariate logistic regression predictive model of EF based on perioperative clinical variables was created. Backward elimination with alpha level of 0.2 was used to create a parsimonious model. We identified 376 first-time AVF patients with follow-up data available in VSGNE. EF rate was 17.5%. Patients in the EF group had lower rates of hypertension (80.3% vs. 93.2%, P = 0.003) and diabetes (47.0% vs. 61.3%, P = 0.039). EF patients were also more likely to have radial artery inflow (57.6% vs. 38.4%, P = 0.011) and have forearm cephalic vein outflow (57.6% vs. 36.5%, P = 0.008). Additionally, the EF group was noted to have significantly smaller mean diameters of target artery (3.1 ± 0.9 vs. 3.6 ± 1.1, P = 0.002) and vein (3.1 ± 0.7 vs. 3.6 ± 0.9, P < 0.001). Multivariate analyses revealed that hypertension, diabetes, and vein larger than 3 mm were protective of EF (P < 0.05). The discriminating ability of this model was good (C-statistic = 0.731) and the model fits the data well (Hosmer-Lemeshow P = 0.149). β-estimates of significant factors were used to create a point system and assign probabilities of EF. We developed a simple model that robustly predicts first-time AVF EF and suggests that anatomical and clinical factors directly affect early AVF outcomes. The risk score has the potential to be used in clinical settings to stratify risk and make informed follow-up plans for AVF patients. Copyright © 2016 Elsevier Inc. All rights reserved.

Scalable nanohelices for predictive studies and enhanced 3D visualization.

PubMed

Meagher, Kwyn A; Doblack, Benjamin N; Ramirez, Mercedes; Davila, Lilian P

2014-11-12

Spring-like materials are ubiquitous in nature and of interest in nanotechnology for energy harvesting, hydrogen storage, and biological sensing applications. For predictive simulations, it has become increasingly important to be able to model the structure of nanohelices accurately. To study the effect of local structure on the properties of these complex geometries one must develop realistic models. To date, software packages are rather limited in creating atomistic helical models. This work focuses on producing atomistic models of silica glass (SiO₂) nanoribbons and nanosprings for molecular dynamics (MD) simulations. Using an MD model of "bulk" silica glass, two computational procedures to precisely create the shape of nanoribbons and nanosprings are presented. The first method employs the AWK programming language and open-source software to effectively carve various shapes of silica nanoribbons from the initial bulk model, using desired dimensions and parametric equations to define a helix. With this method, accurate atomistic silica nanoribbons can be generated for a range of pitch values and dimensions. The second method involves a more robust code which allows flexibility in modeling nanohelical structures. This approach utilizes a C++ code particularly written to implement pre-screening methods as well as the mathematical equations for a helix, resulting in greater precision and efficiency when creating nanospring models. Using these codes, well-defined and scalable nanoribbons and nanosprings suited for atomistic simulations can be effectively created. An added value in both open-source codes is that they can be adapted to reproduce different helical structures, independent of material. In addition, a MATLAB graphical user interface (GUI) is used to enhance learning through visualization and interaction for a general user with the atomistic helical structures. One application of these methods is the recent study of nanohelices via MD simulations for mechanical energy harvesting purposes.

Landscape structure and management alter the outcome of a pesticide ERA: Evaluating impacts of endocrine disruption using the ALMaSS European Brown Hare model.

PubMed

Topping, Chris J; Dalby, Lars; Skov, Flemming

2016-01-15

There is a gradual change towards explicitly considering landscapes in regulatory risk assessment. To realise the objective of developing representative scenarios for risk assessment it is necessary to know how detailed a landscape representation is needed to generate a realistic risk assessment, and indeed how to generate such landscapes. This paper evaluates the contribution of landscape and farming components to a model based risk assessment of a fictitious endocrine disruptor on hares. In addition, we present methods and code examples for generation of landscape structures and farming simulation from data collected primarily for EU agricultural subsidy support and GIS map data. Ten different Danish landscapes were generated and the ERA carried out for each landscape using two different assumed toxicities. The results showed negative impacts in all cases, but the extent and form in terms of impacts on abundance or occupancy differed greatly between landscapes. A meta-model was created, predicting impact from landscape and farming characteristics. Scenarios based on all combinations of farming and landscape for five landscapes representing extreme and middle impacts were created. The meta-models developed from the 10 real landscapes failed to predict impacts for these 25 scenarios. Landscape, farming, and the emergent density of hares all influenced the results of the risk assessment considerably. The study indicates that prediction of a reasonable worst case scenario is difficult from structural, farming or population metrics; rather the emergent properties generated from interactions between landscape, management and ecology are needed. Meta-modelling may also fail to predict impacts, even when restricting inputs to combinations of those used to create the model. Future ERA may therefore need to make use of multiple scenarios representing a wide range of conditions to avoid locally unacceptable risks. This approach could now be feasible Europe wide given the landscape generation methods presented.

Methods for Determining Likelihood of Tweet Deletion

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

Few works exist that attempt to build predictive models for tweet deletion. Zhou et al. (2015) focus on a subset of deleted tweets – regrettable tweets. These are tweets that the authors believe to contain inappropriate content. Inappropriate can range from vulgar language to sharing private content such as a personal email address. The presence of inappropriate content doesn’t guarantee that a tweet will be deleted, however intuition dictates it can be in an important factor in the tweet being deleted. In their work, the authors create a predictive model for identifying regrettable tweets. It is important to note themore » authors focus on predicting regrettable tweets that are distinctly not spam and only written in English. Through manual investigation, the authors identify ten major topics including negative sentiment, cursing, and relationships that are prevalent in regrettable tweets. The authors then exploit WordNet and UrbanDictionary to create keyword lists related to the ten topics. Finally, using a combination of existing lexica and the topic keywords as features, the authors build classifiers to test the accuracy of their model. The authors complement 700 manually labeled regrettable tweets with 700 normal tweets to create their evaluation dataset. The authors’ best performance from 10-fold cross-validation was an f1 score of 0.85 using a J48 classifier on a balanced dataset of deleted and non-deleted tweets.« less

Model Predictive Control Based Motion Drive Algorithm for a Driving Simulator

NASA Astrophysics Data System (ADS)

Rehmatullah, Faizan

In this research, we develop a model predictive control based motion drive algorithm for the driving simulator at Toronto Rehabilitation Institute. Motion drive algorithms exploit the limitations of the human vestibular system to formulate a perception of motion within the constrained workspace of a simulator. In the absence of visual cues, the human perception system is unable to distinguish between acceleration and the force of gravity. The motion drive algorithm determines control inputs to displace the simulator platform, and by using the resulting inertial forces and angular rates, creates the perception of motion. By using model predictive control, we can optimize the use of simulator workspace for every maneuver while simulating the vehicle perception. With the ability to handle nonlinear constraints, the model predictive control allows us to incorporate workspace limitations.

Wavefront shaping with an electrowetting liquid lens using surface harmonics (Conference Presentation)

NASA Astrophysics Data System (ADS)

Strauch, Matthias; Konijnenberg, Sander; Shao, Yifeng; Urbach, H. Paul

2017-02-01

Liquid lenses are used to correct for low order wavefront aberrations. Electrowetting liquid lenses can nowadays control defocus and astigmatism effectively, so they start being used for ophthalmology applications. To increase the performance and applicability, we introduce a new driving mechanism to create, detect and correct higher order aberrations using standing waves on the liquid interface. The speed of a liquid lens is in general limited, because the liquid surface cannot follow fast voltage changes, while providing a spherical surface. Surface waves are created instead and with them undesired aberrations. We try to control those surface waves to turn them into an effective wavefront shaping tool. We introduce a model, which treats the liquid lens as a circular vibrating membrane with adjusted boundary conditions. Similar to tunable acoustic gradient (TAG) lenses, the nature of the surface modes are predicted to be Bessel functions. Since Bessel functions are a full set of orthogonal basis functions any surface can be created as a linear combination of different Bessel functions. The model was investigated experimentally in two setups. First the point spread functions were studied and compared to a simulation of the intensity distribution created by Fresnel propagated Bessel surfaces. Second the wavefronts were measured directly using a spatial light modulator. The surface resonance frequencies confirm the predictions made by the model as well as the wavefront measurements. By superposition of known surface modes, it is possible to create new surface shapes, which can be used to simulate and measure the human eye.

United States‐Mexican border watershed assessment: Modeling nonpoint source pollution in Ambos Nogales

USGS Publications Warehouse

Norman, Laura M.

2007-01-01

Ecological considerations need to be interwoven with economic policy and planning along the United States‐Mexican border. Non‐point source pollution can have significant implications for the availability of potable water and the continued health of borderland ecosystems in arid lands. However, environmental assessments in this region present a host of unique issues and problems. A common obstacle to the solution of these problems is the integration of data with different resolutions, naming conventions, and quality to create a consistent database across the binational study area. This report presents a simple modeling approach to predict nonpoint source pollution that can be used for border watersheds. The modeling approach links a hillslopescale erosion‐prediction model and a spatially derived sediment‐delivery model within a geographic information system to estimate erosion, sediment yield, and sediment deposition across the Ambos Nogales watershed in Sonora, Mexico, and Arizona. This paper discusses the procedures used for creating a watershed database to apply the models and presents an example of the modeling approach applied to a conservation‐planning problem.

A Computational Workflow for the Automated Generation of Models of Genetic Designs.

PubMed

Misirli, Göksel; Nguyen, Tramy; McLaughlin, James Alastair; Vaidyanathan, Prashant; Jones, Timothy S; Densmore, Douglas; Myers, Chris; Wipat, Anil

2018-06-05

Computational models are essential to engineer predictable biological systems and to scale up this process for complex systems. Computational modeling often requires expert knowledge and data to build models. Clearly, manual creation of models is not scalable for large designs. Despite several automated model construction approaches, computational methodologies to bridge knowledge in design repositories and the process of creating computational models have still not been established. This paper describes a workflow for automatic generation of computational models of genetic circuits from data stored in design repositories using existing standards. This workflow leverages the software tool SBOLDesigner to build structural models that are then enriched by the Virtual Parts Repository API using Systems Biology Open Language (SBOL) data fetched from the SynBioHub design repository. The iBioSim software tool is then utilized to convert this SBOL description into a computational model encoded using the Systems Biology Markup Language (SBML). Finally, this SBML model can be simulated using a variety of methods. This workflow provides synthetic biologists with easy to use tools to create predictable biological systems, hiding away the complexity of building computational models. This approach can further be incorporated into other computational workflows for design automation.

Predicting p Ka values from EEM atomic charges

PubMed Central

2013-01-01

The acid dissociation constant p Ka is a very important molecular property, and there is a strong interest in the development of reliable and fast methods for p Ka prediction. We have evaluated the p Ka prediction capabilities of QSPR models based on empirical atomic charges calculated by the Electronegativity Equalization Method (EEM). Specifically, we collected 18 EEM parameter sets created for 8 different quantum mechanical (QM) charge calculation schemes. Afterwards, we prepared a training set of 74 substituted phenols. Additionally, for each molecule we generated its dissociated form by removing the phenolic hydrogen. For all the molecules in the training set, we then calculated EEM charges using the 18 parameter sets, and the QM charges using the 8 above mentioned charge calculation schemes. For each type of QM and EEM charges, we created one QSPR model employing charges from the non-dissociated molecules (three descriptor QSPR models), and one QSPR model based on charges from both dissociated and non-dissociated molecules (QSPR models with five descriptors). Afterwards, we calculated the quality criteria and evaluated all the QSPR models obtained. We found that QSPR models employing the EEM charges proved as a good approach for the prediction of p Ka (63% of these models had R2 > 0.9, while the best had R2 = 0.924). As expected, QM QSPR models provided more accurate p Ka predictions than the EEM QSPR models but the differences were not significant. Furthermore, a big advantage of the EEM QSPR models is that their descriptors (i.e., EEM atomic charges) can be calculated markedly faster than the QM charge descriptors. Moreover, we found that the EEM QSPR models are not so strongly influenced by the selection of the charge calculation approach as the QM QSPR models. The robustness of the EEM QSPR models was subsequently confirmed by cross-validation. The applicability of EEM QSPR models for other chemical classes was illustrated by a case study focused on carboxylic acids. In summary, EEM QSPR models constitute a fast and accurate p Ka prediction approach that can be used in virtual screening. PMID:23574978

A Severe Sepsis Mortality Prediction Model and Score for Use with Administrative Data

PubMed Central

Ford, Dee W.; Goodwin, Andrew J.; Simpson, Annie N.; Johnson, Emily; Nadig, Nandita; Simpson, Kit N.

2016-01-01

Objective Administrative data is used for research, quality improvement, and health policy in severe sepsis. However, there is not a sepsis-specific tool applicable to administrative data with which to adjust for illness severity. Our objective was to develop, internally validate, and externally validate a severe sepsis mortality prediction model and associated mortality prediction score. Design Retrospective cohort study using 2012 administrative data from five US states. Three cohorts of patients with severe sepsis were created: 1) ICD-9-CM codes for severe sepsis/septic shock, 2) ‘Martin’ approach, and 3) ‘Angus’ approach. The model was developed and internally validated in ICD-9-CM cohort and externally validated in other cohorts. Integer point values for each predictor variable were generated to create a sepsis severity score. Setting Acute care, non-federal hospitals in NY, MD, FL, MI, and WA Subjects Patients in one of three severe sepsis cohorts: 1) explicitly coded (n=108,448), 2) Martin cohort (n=139,094), and 3) Angus cohort (n=523,637) Interventions None Measurements and Main Results Maximum likelihood estimation logistic regression to develop a predictive model for in-hospital mortality. Model calibration and discrimination assessed via Hosmer-Lemeshow goodness-of-fit (GOF) and C-statistics respectively. Primary cohort subset into risk deciles and observed versus predicted mortality plotted. GOF demonstrated p>0.05 for each cohort demonstrating sound calibration. C-statistic ranged from low of 0.709 (sepsis severity score) to high of 0.838 (Angus cohort) suggesting good to excellent model discrimination. Comparison of observed versus expected mortality was robust although accuracy decreased in highest risk decile. Conclusions Our sepsis severity model and score is a tool that provides reliable risk adjustment for administrative data. PMID:26496452

Forecasting in the presence of expectations

NASA Astrophysics Data System (ADS)

Allen, R.; Zivin, J. G.; Shrader, J.

2016-05-01

Physical processes routinely influence economic outcomes, and actions by economic agents can, in turn, influence physical processes. This feedback creates challenges for forecasting and inference, creating the potential for complementarity between models from different academic disciplines. Using the example of prediction of water availability during a drought, we illustrate the potential biases in forecasts that only take part of a coupled system into account. In particular, we show that forecasts can alter the feedbacks between supply and demand, leading to inaccurate prediction about future states of the system. Although the example is specific to drought, the problem of feedback between expectations and forecast quality is not isolated to the particular model-it is relevant to areas as diverse as population assessments for conservation, balancing the electrical grid, and setting macroeconomic policy.

Creating Digital Games as Interactive Learning Environments: Factors That Affect Palestinian Teachers' Success in Modifying Video Games for Instruction

ERIC Educational Resources Information Center

Younis, Bilal Khaleel

2012-01-01

The purpose of this study was to investigate the factors that might predict Palestinian teachers' success in modding games for instruction. An instructional game design model named Game Modding for Non-Professionals (GMNP) was created specifically for the training of Palestinian teachers during this study. This study addressed the question: To…

Comparison of machine-learning algorithms to build a predictive model for detecting undiagnosed diabetes - ELSA-Brasil: accuracy study.

PubMed

Olivera, André Rodrigues; Roesler, Valter; Iochpe, Cirano; Schmidt, Maria Inês; Vigo, Álvaro; Barreto, Sandhi Maria; Duncan, Bruce Bartholow

2017-01-01

Type 2 diabetes is a chronic disease associated with a wide range of serious health complications that have a major impact on overall health. The aims here were to develop and validate predictive models for detecting undiagnosed diabetes using data from the Longitudinal Study of Adult Health (ELSA-Brasil) and to compare the performance of different machine-learning algorithms in this task. Comparison of machine-learning algorithms to develop predictive models using data from ELSA-Brasil. After selecting a subset of 27 candidate variables from the literature, models were built and validated in four sequential steps: (i) parameter tuning with tenfold cross-validation, repeated three times; (ii) automatic variable selection using forward selection, a wrapper strategy with four different machine-learning algorithms and tenfold cross-validation (repeated three times), to evaluate each subset of variables; (iii) error estimation of model parameters with tenfold cross-validation, repeated ten times; and (iv) generalization testing on an independent dataset. The models were created with the following machine-learning algorithms: logistic regression, artificial neural network, naïve Bayes, K-nearest neighbor and random forest. The best models were created using artificial neural networks and logistic regression. -These achieved mean areas under the curve of, respectively, 75.24% and 74.98% in the error estimation step and 74.17% and 74.41% in the generalization testing step. Most of the predictive models produced similar results, and demonstrated the feasibility of identifying individuals with highest probability of having undiagnosed diabetes, through easily-obtained clinical data.

A burnout prediction model based around char morphology

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

Tao Wu; Edward Lester; Michael Cloke

Several combustion models have been developed that can make predictions about coal burnout and burnout potential. Most of these kinetic models require standard parameters such as volatile content and particle size to make a burnout prediction. This article presents a new model called the char burnout (ChB) model, which also uses detailed information about char morphology in its prediction. The input data to the model is based on information derived from two different image analysis techniques. One technique generates characterization data from real char samples, and the other predicts char types based on characterization data from image analysis of coalmore » particles. The pyrolyzed chars in this study were created in a drop tube furnace operating at 1300{sup o}C, 200 ms, and 1% oxygen. Modeling results were compared with a different carbon burnout kinetic model as well as the actual burnout data from refiring the same chars in a drop tube furnace operating at 1300{sup o}C, 5% oxygen, and residence times of 200, 400, and 600 ms. A good agreement between ChB model and experimental data indicates that the inclusion of char morphology in combustion models could well improve model predictions. 38 refs., 5 figs., 6 tabs.« less

Modelling proteins' hidden conformations to predict antibiotic resistance

NASA Astrophysics Data System (ADS)

Hart, Kathryn M.; Ho, Chris M. W.; Dutta, Supratik; Gross, Michael L.; Bowman, Gregory R.

2016-10-01

TEM β-lactamase confers bacteria with resistance to many antibiotics and rapidly evolves activity against new drugs. However, functional changes are not easily explained by differences in crystal structures. We employ Markov state models to identify hidden conformations and explore their role in determining TEM's specificity. We integrate these models with existing drug-design tools to create a new technique, called Boltzmann docking, which better predicts TEM specificity by accounting for conformational heterogeneity. Using our MSMs, we identify hidden states whose populations correlate with activity against cefotaxime. To experimentally detect our predicted hidden states, we use rapid mass spectrometric footprinting and confirm our models' prediction that increased cefotaxime activity correlates with reduced Ω-loop flexibility. Finally, we design novel variants to stabilize the hidden cefotaximase states, and find their populations predict activity against cefotaxime in vitro and in vivo. Therefore, we expect this framework to have numerous applications in drug and protein design.

A First Step towards a Clinical Decision Support System for Post-traumatic Stress Disorders.

PubMed

Ma, Sisi; Galatzer-Levy, Isaac R; Wang, Xuya; Fenyö, David; Shalev, Arieh Y

2016-01-01

PTSD is distressful and debilitating, following a non-remitting course in about 10% to 20% of trauma survivors. Numerous risk indicators of PTSD have been identified, but individual level prediction remains elusive. As an effort to bridge the gap between scientific discovery and practical application, we designed and implemented a clinical decision support pipeline to provide clinically relevant recommendation for trauma survivors. To meet the specific challenge of early prediction, this work uses data obtained within ten days of a traumatic event. The pipeline creates personalized predictive model for each individual, and computes quality metrics for each predictive model. Clinical recommendations are made based on both the prediction of the model and its quality, thus avoiding making potentially detrimental recommendations based on insufficient information or suboptimal model. The current pipeline outperforms the acute stress disorder, a commonly used clinical risk factor for PTSD development, both in terms of sensitivity and specificity.

A New Scoring System to Predict the Risk for High-risk Adenoma and Comparison of Existing Risk Calculators.

PubMed

Murchie, Brent; Tandon, Kanwarpreet; Hakim, Seifeldin; Shah, Kinchit; O'Rourke, Colin; Castro, Fernando J

2017-04-01

Colorectal cancer (CRC) screening guidelines likely over-generalizes CRC risk, 35% of Americans are not up to date with screening, and there is growing incidence of CRC in younger patients. We developed a practical prediction model for high-risk colon adenomas in an average-risk population, including an expanded definition of high-risk polyps (≥3 nonadvanced adenomas), exposing higher than average-risk patients. We also compared results with previously created calculators. Patients aged 40 to 59 years, undergoing first-time average-risk screening or diagnostic colonoscopies were evaluated. Risk calculators for advanced adenomas and high-risk adenomas were created based on age, body mass index, sex, race, and smoking history. Previously established calculators with similar risk factors were selected for comparison of concordance statistic (c-statistic) and external validation. A total of 5063 patients were included. Advanced adenomas, and high-risk adenomas were seen in 5.7% and 7.4% of the patient population, respectively. The c-statistic for our calculator was 0.639 for the prediction of advanced adenomas, and 0.650 for high-risk adenomas. When applied to our population, all previous models had lower c-statistic results although one performed similarly. Our model compares favorably to previously established prediction models. Age and body mass index were used as continuous variables, likely improving the c-statistic. It also reports absolute predictive probabilities of advanced and high-risk polyps, allowing for more individualized risk assessment of CRC.

Multi input single output model predictive control of non-linear bio-polymerization process

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

Arumugasamy, Senthil Kumar; Ahmad, Z.

This paper focuses on Multi Input Single Output (MISO) Model Predictive Control of bio-polymerization process in which mechanistic model is developed and linked with the feedforward neural network model to obtain a hybrid model (Mechanistic-FANN) of lipase-catalyzed ring-opening polymerization of ε-caprolactone (ε-CL) for Poly (ε-caprolactone) production. In this research, state space model was used, in which the input to the model were the reactor temperatures and reactor impeller speeds and the output were the molecular weight of polymer (M{sub n}) and polymer polydispersity index. State space model for MISO created using System identification tool box of Matlab™. This state spacemore » model is used in MISO MPC. Model predictive control (MPC) has been applied to predict the molecular weight of the biopolymer and consequently control the molecular weight of biopolymer. The result shows that MPC is able to track reference trajectory and give optimum movement of manipulated variable.« less

NIR techniques create added values for the pellet and biofuel industry.

PubMed

Lestander, Torbjörn A; Johnsson, Bo; Grothage, Morgan

2009-02-01

A 2(3)-factorial experiment was carried out in an industrial plant producing biofuel pellets with sawdust as feedstock. The aim was to use on-line near infrared (NIR) spectra from sawdust for real time predictions of moisture content, blends of sawdust and energy consumption of the pellet press. The factors varied were: drying temperature and wood powder dryness in binary blends of sawdust from Norway spruce and Scots pine. The main results were excellent NIR calibration models for on-line prediction of moisture content and binary blends of sawdust from the two species, but also for the novel finding that the consumption of electrical energy per unit pelletized biomass can be predicted by NIR reflectance spectra from sawdust entering the pellet press. This power consumption model, explaining 91.0% of the variation, indicated that NIR data contained information of the compression and friction properties of the biomass feedstock. The moisture content model was validated using a running NIR calibration model in the pellet plant. It is shown that the adjusted prediction error was 0.41% moisture content for grinded sawdust dried to ca. 6-12% moisture content. Further, although used drying temperatures influenced NIR spectra the models for drying temperature resulted in low prediction accuracy. The results show that on-line NIR can be used as an important tool in the monitoring and control of the pelletizing process and that the use of NIR technique in fuel pellet production has possibilities to better meet customer specifications, and therefore create added production values.

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

Shiraishi, Satomi; Moore, Kevin L., E-mail: kevinmoore@ucsd.edu

Purpose: To demonstrate knowledge-based 3D dose prediction for external beam radiotherapy. Methods: Using previously treated plans as training data, an artificial neural network (ANN) was trained to predict a dose matrix based on patient-specific geometric and planning parameters, such as the closest distance (r) to planning target volume (PTV) and organ-at-risks (OARs). Twenty-three prostate and 43 stereotactic radiosurgery/radiotherapy (SRS/SRT) cases with at least one nearby OAR were studied. All were planned with volumetric-modulated arc therapy to prescription doses of 81 Gy for prostate and 12–30 Gy for SRS. Using these clinically approved plans, ANNs were trained to predict dose matrixmore » and the predictive accuracy was evaluated using the dose difference between the clinical plan and prediction, δD = D{sub clin} − D{sub pred}. The mean (〈δD{sub r}〉), standard deviation (σ{sub δD{sub r}}), and their interquartile range (IQR) for the training plans were evaluated at a 2–3 mm interval from the PTV boundary (r{sub PTV}) to assess prediction bias and precision. Initially, unfiltered models which were trained using all plans in the cohorts were created for each treatment site. The models predict approximately the average quality of OAR sparing. Emphasizing a subset of plans that exhibited superior to the average OAR sparing during training, refined models were created to predict high-quality rectum sparing for prostate and brainstem sparing for SRS. Using the refined model, potentially suboptimal plans were identified where the model predicted further sparing of the OARs was achievable. Replans were performed to test if the OAR sparing could be improved as predicted by the model. Results: The refined models demonstrated highly accurate dose distribution prediction. For prostate cases, the average prediction bias for all voxels irrespective of organ delineation ranged from −1% to 0% with maximum IQR of 3% over r{sub PTV} ∈ [ − 6, 30] mm. The average prediction error was less than 10% for the same r{sub PTV} range. For SRS cases, the average prediction bias ranged from −0.7% to 1.5% with maximum IQR of 5% over r{sub PTV} ∈ [ − 4, 32] mm. The average prediction error was less than 8%. Four potentially suboptimal plans were identified for each site and subsequent replanning demonstrated improved sparing of rectum and brainstem. Conclusions: The study demonstrates highly accurate knowledge-based 3D dose predictions for radiotherapy plans.« less

Modeling Students' Problem Solving Performance in the Computer-Based Mathematics Learning Environment

ERIC Educational Resources Information Center

Lee, Young-Jin

2017-01-01

Purpose: The purpose of this paper is to develop a quantitative model of problem solving performance of students in the computer-based mathematics learning environment. Design/methodology/approach: Regularized logistic regression was used to create a quantitative model of problem solving performance of students that predicts whether students can…

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

Smith, Kandler A; Usseglio Viretta, Francois L; Graf, Peter A

This presentation describes research work led by NREL with team members from Argonne National Laboratory and Texas A&M University in microstructure analysis, modeling and validation under DOE's Computer-Aided Engineering of Batteries (CAEBAT) program. The goal of the project is to close the gaps between CAEBAT models and materials research by creating predictive models that can be used for electrode design.

Directed Nanopatterning with Nonlinear Laser Lithography

NASA Astrophysics Data System (ADS)

Tokel, Onur; Yavuz, Ozgun; Ergecen, Emre; Pavlov, Ihor; Makey, Ghaith; Ilday, Fatih Omer

In spite of the successes of maskless optical nanopatterning methods, it remains extremely challenging to create any isotropic, periodic nanopattern. Further, available optical techniques lack the long-range coverage and high periodicity demanded by photonics and photovoltaics applications. Here, we provide a novel solution with Nonlinear Laser Lithography (NLL) approach. Notably, we demonstrate that self-organized nanopatterns can be produced in all possible Bravais lattice types. Further, we show that carefully chosen defects or structued noise can direct NLL symmetries. Exploitation of directed self-organizatio to select or guide to predetermined symmetries is a new capability. Predictive capabilities for such far-from-equilibrium, dissipative systems is very limited due to a lack of experimental systems with predictive models. Here we also present a completely predictive model, and experimentally confirm that the emergence of motifs can be regulated by engineering defects, while the polarization of the ultrafast laser prescribes lattice symmetry, which in turn reinforces translational invariance. Thus, NLL enables a novel, maskless nanofabrication approach, where laser-induced nanopatterns can be rapidly created in any lattice symmetry

Predicting age groups of Twitter users based on language and metadata features

PubMed Central

Morgan-Lopez, Antonio A.; Chew, Robert F.; Ruddle, Paul

2017-01-01

Health organizations are increasingly using social media, such as Twitter, to disseminate health messages to target audiences. Determining the extent to which the target audience (e.g., age groups) was reached is critical to evaluating the impact of social media education campaigns. The main objective of this study was to examine the separate and joint predictive validity of linguistic and metadata features in predicting the age of Twitter users. We created a labeled dataset of Twitter users across different age groups (youth, young adults, adults) by collecting publicly available birthday announcement tweets using the Twitter Search application programming interface. We manually reviewed results and, for each age-labeled handle, collected the 200 most recent publicly available tweets and user handles’ metadata. The labeled data were split into training and test datasets. We created separate models to examine the predictive validity of language features only, metadata features only, language and metadata features, and words/phrases from another age-validated dataset. We estimated accuracy, precision, recall, and F1 metrics for each model. An L1-regularized logistic regression model was conducted for each age group, and predicted probabilities between the training and test sets were compared for each age group. Cohen’s d effect sizes were calculated to examine the relative importance of significant features. Models containing both Tweet language features and metadata features performed the best (74% precision, 74% recall, 74% F1) while the model containing only Twitter metadata features were least accurate (58% precision, 60% recall, and 57% F1 score). Top predictive features included use of terms such as “school” for youth and “college” for young adults. Overall, it was more challenging to predict older adults accurately. These results suggest that examining linguistic and Twitter metadata features to predict youth and young adult Twitter users may be helpful for informing public health surveillance and evaluation research. PMID:28850620

Predicting age groups of Twitter users based on language and metadata features.

PubMed

Morgan-Lopez, Antonio A; Kim, Annice E; Chew, Robert F; Ruddle, Paul

2017-01-01

Health organizations are increasingly using social media, such as Twitter, to disseminate health messages to target audiences. Determining the extent to which the target audience (e.g., age groups) was reached is critical to evaluating the impact of social media education campaigns. The main objective of this study was to examine the separate and joint predictive validity of linguistic and metadata features in predicting the age of Twitter users. We created a labeled dataset of Twitter users across different age groups (youth, young adults, adults) by collecting publicly available birthday announcement tweets using the Twitter Search application programming interface. We manually reviewed results and, for each age-labeled handle, collected the 200 most recent publicly available tweets and user handles' metadata. The labeled data were split into training and test datasets. We created separate models to examine the predictive validity of language features only, metadata features only, language and metadata features, and words/phrases from another age-validated dataset. We estimated accuracy, precision, recall, and F1 metrics for each model. An L1-regularized logistic regression model was conducted for each age group, and predicted probabilities between the training and test sets were compared for each age group. Cohen's d effect sizes were calculated to examine the relative importance of significant features. Models containing both Tweet language features and metadata features performed the best (74% precision, 74% recall, 74% F1) while the model containing only Twitter metadata features were least accurate (58% precision, 60% recall, and 57% F1 score). Top predictive features included use of terms such as "school" for youth and "college" for young adults. Overall, it was more challenging to predict older adults accurately. These results suggest that examining linguistic and Twitter metadata features to predict youth and young adult Twitter users may be helpful for informing public health surveillance and evaluation research.

Neural networks to predict exosphere temperature corrections

NASA Astrophysics Data System (ADS)

Choury, Anna; Bruinsma, Sean; Schaeffer, Philippe

2013-10-01

Precise orbit prediction requires a forecast of the atmospheric drag force with a high degree of accuracy. Artificial neural networks are universal approximators derived from artificial intelligence and are widely used for prediction. This paper presents a method of artificial neural networking for prediction of the thermosphere density by forecasting exospheric temperature, which will be used by the semiempirical thermosphere Drag Temperature Model (DTM) currently developed. Artificial neural network has shown to be an effective and robust forecasting model for temperature prediction. The proposed model can be used for any mission from which temperature can be deduced accurately, i.e., it does not require specific training. Although the primary goal of the study was to create a model for 1 day ahead forecast, the proposed architecture has been generalized to 2 and 3 days prediction as well. The impact of artificial neural network predictions has been quantified for the low-orbiting satellite Gravity Field and Steady-State Ocean Circulation Explorer in 2011, and an order of magnitude smaller orbit errors were found when compared with orbits propagated using the thermosphere model DTM2009.

Multicomponent ionic liquid CMC prediction.

PubMed

Kłosowska-Chomiczewska, I E; Artichowicz, W; Preiss, U; Jungnickel, C

2017-09-27

We created a model to predict CMC of ILs based on 704 experimental values published in 43 publications since 2000. Our model was able to predict CMC of variety of ILs in binary or ternary system in a presence of salt or alcohol. The molecular volume of IL (V m ), solvent-accessible surface (Ŝ), solvation enthalpy (Δ solv G ∞ ), concentration of salt (C s ) or alcohol (C a ) and their molecular volumes (V ms and V ma , respectively) were chosen as descriptors, and Kernel Support Vector Machine (KSVM) and Evolutionary Algorithm (EA) as regression methodologies to create the models. Data was split into training and validation set (80/20) and subjected to bootstrap aggregation. KSVM provided better fit with average R 2 of 0.843, and MSE of 0.608, whereas EA resulted in R 2 of 0.794 and MSE of 0.973. From the sensitivity analysis it was shown that V m and Ŝ have the highest impact on ILs micellization in both binary and ternary systems, however surprisingly in the presence of alcohol the V m becomes insignificant/irrelevant. Micelle stabilizing or destabilizing influence of the descriptors depends upon the additives. Previous attempts at modelling the CMC of ILs was generally limited to small number of ILs in simplified (binary) systems. We however showed successful prediction of the CMC over a range of different systems (binary and ternary).

MicroRNA based Pan-Cancer Diagnosis and Treatment Recommendation.

PubMed

Cheerla, Nikhil; Gevaert, Olivier

2017-01-13

The current state-of-the-art in cancer diagnosis and treatment is not ideal; diagnostic tests are accurate but invasive, and treatments are "one-size fits-all" instead of being personalized. Recently, miRNA's have garnered significant attention as cancer biomarkers, owing to their ease of access (circulating miRNA in the blood) and stability. There have been many studies showing the effectiveness of miRNA data in diagnosing specific cancer types, but few studies explore the role of miRNA in predicting treatment outcome. Here we go a step further, using tissue miRNA and clinical data across 21 cancers from the 'The Cancer Genome Atlas' (TCGA) database. We use machine learning techniques to create an accurate pan-cancer diagnosis system, and a prediction model for treatment outcomes. Finally, using these models, we create a web-based tool that diagnoses cancer and recommends the best treatment options. We achieved 97.2% accuracy for classification using a support vector machine classifier with radial basis. The accuracies improved to 99.9-100% when climbing up the embryonic tree and classifying cancers at different stages. We define the accuracy as the ratio of the total number of instances correctly classified to the total instances. The classifier also performed well, achieving greater than 80% sensitivity for many cancer types on independent validation datasets. Many miRNAs selected by our feature selection algorithm had strong previous associations to various cancers and tumor progression. Then, using miRNA, clinical and treatment data and encoding it in a machine-learning readable format, we built a prognosis predictor model to predict the outcome of treatment with 85% accuracy. We used this model to create a tool that recommends personalized treatment regimens. Both the diagnosis and prognosis model, incorporating semi-supervised learning techniques to improve their accuracies with repeated use, were uploaded online for easy access. Our research is a step towards the final goal of diagnosing cancer and predicting treatment recommendations using non-invasive blood tests.

Basic Diagnosis and Prediction of Persistent Contrail Occurrence using High-resolution Numerical Weather Analyses/Forecasts and Logistic Regression. Part I: Effects of Random Error

NASA Technical Reports Server (NTRS)

Duda, David P.; Minnis, Patrick

2009-01-01

Straightforward application of the Schmidt-Appleman contrail formation criteria to diagnose persistent contrail occurrence from numerical weather prediction data is hindered by significant bias errors in the upper tropospheric humidity. Logistic models of contrail occurrence have been proposed to overcome this problem, but basic questions remain about how random measurement error may affect their accuracy. A set of 5000 synthetic contrail observations is created to study the effects of random error in these probabilistic models. The simulated observations are based on distributions of temperature, humidity, and vertical velocity derived from Advanced Regional Prediction System (ARPS) weather analyses. The logistic models created from the simulated observations were evaluated using two common statistical measures of model accuracy, the percent correct (PC) and the Hanssen-Kuipers discriminant (HKD). To convert the probabilistic results of the logistic models into a dichotomous yes/no choice suitable for the statistical measures, two critical probability thresholds are considered. The HKD scores are higher when the climatological frequency of contrail occurrence is used as the critical threshold, while the PC scores are higher when the critical probability threshold is 0.5. For both thresholds, typical random errors in temperature, relative humidity, and vertical velocity are found to be small enough to allow for accurate logistic models of contrail occurrence. The accuracy of the models developed from synthetic data is over 85 percent for both the prediction of contrail occurrence and non-occurrence, although in practice, larger errors would be anticipated.

Prediction of ECS and SSC Models for Flux-Limited Samples of Gamma-Ray Blazars

NASA Technical Reports Server (NTRS)

Lister, Matthew L.; Marscher, Alan P.

1999-01-01

The external Compton scattering (ECS) and synchrotron self-Compton (SSC) models make distinct predictions for the amount of Doppler boosting of high-energy gamma-rays emitted by Nazar. We examine how these differences affect the predicted properties of active galactic nucleus (AGN) samples selected on the basis of Murray emission. We create simulated flux-limited samples based on the ECS and SSC models, and compare their properties to those of identified EGRET blazars. We find that for small gamma-ray-selected samples, the two models make very similar predictions, and cannot be reliably distinguished. This is primarily due to the fact that not only the Doppler factor, but also the cosmological distance and intrinsic luminosity play a role in determining whether an AGN is included in a flux-limited gamma-ray sample.

Compaction of North-sea chalk by pore-failure and pressure solution in a producing reservoir

NASA Astrophysics Data System (ADS)

Keszthelyi, Daniel; Dysthe, Dag; Jamtveit, Bjorn

2016-02-01

The Ekofisk field, Norwegian North sea,is an example of compacting chalk reservoir with considerable subsequent seafloor subsidence due to petroleum production. Previously, a number of models were created to predict the compaction using different phenomenological approaches. Here we present a different approach, we use a new creep model based on microscopic mechanisms with no fitting parameters to predict strain rate at core scale and at reservoir scale. The model is able to reproduce creep experiments and the magnitude of the observed subsidence making it the first microstructural model which can explain the Ekofisk compaction.

Innovation in prediction planning for anterior open bite correction.

PubMed

Almuzian, Mohammed; Almukhtar, Anas; O'Neil, Michael; Benington, Philip; Al Anezi, Thamer; Ayoub, Ashraf

2015-05-01

This study applies recent advances in 3D virtual imaging for application in the prediction planning of dentofacial deformities. Stereo-photogrammetry has been used to create virtual and physical models, which are creatively combined in planning the surgical correction of anterior open bite. The application of these novel methods is demonstrated through the surgical correction of a case.

Enrollment Management in Medical School Admissions: A Novel Evidence-Based Approach at One Institution.

PubMed

Burkhardt, John C; DesJardins, Stephen L; Teener, Carol A; Gay, Steven E; Santen, Sally A

2016-11-01

In higher education, enrollment management has been developed to accurately predict the likelihood of enrollment of admitted students. This allows evidence to dictate numbers of interviews scheduled, offers of admission, and financial aid package distribution. The applicability of enrollment management techniques for use in medical education was tested through creation of a predictive enrollment model at the University of Michigan Medical School (U-M). U-M and American Medical College Application Service data (2006-2014) were combined to create a database including applicant demographics, academic application scores, institutional financial aid offer, and choice of school attended. Binomial logistic regression and multinomial logistic regression models were estimated in order to study factors related to enrollment at the local institution versus elsewhere and to groupings of competing peer institutions. A predictive analytic "dashboard" was created for practical use. Both models were significant at P < .001 and had similar predictive performance. In the binomial model female, underrepresented minority students, grade point average, Medical College Admission Test score, admissions committee desirability score, and most individual financial aid offers were significant (P < .05). The significant covariates were similar in the multinomial model (excluding female) and provided separate likelihoods of students enrolling at different institutional types. An enrollment-management-based approach would allow medical schools to better manage the number of students they admit and target recruitment efforts to improve their likelihood of success. It also performs a key institutional research function for understanding failed recruitment of highly desirable candidates.

Weather Research and Forecasting Model Sensitivity Comparisons for Warm Season Convective Initiation

NASA Technical Reports Server (NTRS)

Watson, Leela R.

2007-01-01

This report describes the work done by the Applied Meteorology Unit (AMU) in assessing the success of different model configurations in predicting warm season convection over East-Central Florida. The Weather Research and Forecasting Environmental Modeling System (WRF EMS) software allows users to choose among two dynamical cores - the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM). There are also data assimilation analysis packages available for the initialization of the WRF model - the Local Analysis and Prediction System (LAPS) and the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS). Besides model core and initialization options, the WRF model can be run with one- or two-way nesting. Having a series of initialization options and WRF cores, as well as many options within each core, creates challenges for local forecasters, such as determining which configuration options are best to address specific forecast concerns. This project assessed three different model intializations available to determine which configuration best predicts warm season convective initiation in East-Central Florida. The project also examined the use of one- and two-way nesting in predicting warm season convection.

New predictive model for microsurgical outcome of intracranial arteriovenous malformations: study protocol.

PubMed

Tong, Xianzeng; Wu, Jun; Cao, Yong; Zhao, Yuanli; Wang, Shuo

2017-01-27

Although microsurgical resection is currently the first-line treatment modality for arteriovenous malformations (AVMs), microsurgery of these lesions is complicated due to the fact that they are very heterogeneous vascular anomalies. The Spetzler-Martin grading system and the supplementary grading system have demonstrated excellent performances in predicting the risk of AVM surgery. However, there are currently no predictive models based on multimodal MRI techniques. The purpose of this study is to propose a predictive model based on multimodal MRI techniques to assess the microsurgical risk of intracranial AVMs. The study consists of 2 parts: the first part is to conduct a single-centre retrospective analysis of 201 eligible patients to create a predictive model of AVM surgery based on multimodal functional MRIs (fMRIs); the second part is to validate the efficacy of the predictive model in a prospective multicentre cohort study of 400 eligible patients. Patient characteristics, AVM features and multimodal fMRI data will be collected. The functional status at pretreatment and 6 months after surgery will be analysed using the modified Rankin Scale (mRS) score. The patients in each part of this study will be dichotomised into 2 groups: those with improved or unchanged functional status (a decreased or unchanged mRS 6 months after surgery) and those with worsened functional status (an increased mRS). The first part will determine the risk factors of worsened functional status after surgery and create a predictive model. The second part will validate the predictive model and then a new AVM grading system will be proposed. The study protocol and informed consent form have been reviewed and approved by the Institutional Review Board of Beijing Tiantan Hospital Affiliated to Capital Medical University (KY2016-031-01). The results of this study will be disseminated through printed media. NCT02868008. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Predicting cyanobacterial abundance, microcystin, and geosmin in a eutrophic drinking-water reservoir using a 14-year dataset

USGS Publications Warehouse

Harris, Ted D.; Graham, Jennifer L.

2017-01-01

Cyanobacterial blooms degrade water quality in drinking water supply reservoirs by producing toxic and taste-and-odor causing secondary metabolites, which ultimately cause public health concerns and lead to increased treatment costs for water utilities. There have been numerous attempts to create models that predict cyanobacteria and their secondary metabolites, most using linear models; however, linear models are limited by assumptions about the data and have had limited success as predictive tools. Thus, lake and reservoir managers need improved modeling techniques that can accurately predict large bloom events that have the highest impact on recreational activities and drinking-water treatment processes. In this study, we compared 12 unique linear and nonlinear regression modeling techniques to predict cyanobacterial abundance and the cyanobacterial secondary metabolites microcystin and geosmin using 14 years of physiochemical water quality data collected from Cheney Reservoir, Kansas. Support vector machine (SVM), random forest (RF), boosted tree (BT), and Cubist modeling techniques were the most predictive of the compared modeling approaches. SVM, RF, and BT modeling techniques were able to successfully predict cyanobacterial abundance, microcystin, and geosmin concentrations <60,000 cells/mL, 2.5 µg/L, and 20 ng/L, respectively. Only Cubist modeling predicted maxima concentrations of cyanobacteria and geosmin; no modeling technique was able to predict maxima microcystin concentrations. Because maxima concentrations are a primary concern for lake and reservoir managers, Cubist modeling may help predict the largest and most noxious concentrations of cyanobacteria and their secondary metabolites.

Technology Solutions Case Study: Predicting Envelope Leakage in Attached Dwellings

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

None

2013-11-01

The most common method of measuring air leakage is to perform single (or solo) blower door pressurization and/or depressurization test. In detached housing, the single blower door test measures leakage to the outside. In attached housing, however, this “solo” test method measures both air leakage to the outside and air leakage between adjacent units through common surfaces. In an attempt to create a simplified tool for predicting leakage to the outside, Building America team Consortium for Advanced Residential Buildings (CARB) performed a preliminary statistical analysis on blower door test results from 112 attached dwelling units in four apartment complexes. Althoughmore » the subject data set is limited in size and variety, the preliminary analyses suggest significant predictors are present and support the development of a predictive model. Further data collection is underway to create a more robust prediction tool for use across different construction types, climate zones, and unit configurations.« less

Interpreting forest biome productivity and cover utilizing nested scales of image resolution and biogeographical analysis

NASA Technical Reports Server (NTRS)

Iverson, Louis R.; Cook, Elizabeth A.; Graham, Robin L.; Olson, Jerry S.; Frank, Thomas D.; Ying, KE

1988-01-01

The objective was to relate spectral imagery of varying resolution with ground-based data on forest productivity and cover, and to create models to predict regional estimates of forest productivity and cover with a quantifiable degree of accuracy. A three stage approach was outlined. In the first stage, a model was developed relating forest cover or productivity to TM surface reflectance values (TM/FOREST models). The TM/FOREST models were more accurate when biogeographic information regarding the landscape was either used to stratigy the landscape into more homogeneous units or incorporated directly into the TM/FOREST model. In the second stage, AVHRR/FOREST models that predicted forest cover and productivity on the basis of AVHRR band values were developed. The AVHRR/FOREST models had statistical properties similar to or better than those of the TM/FOREST models. In the third stage, the regional predictions were compared with the independent U.S. Forest Service (USFS) data. To do this regional forest cover and forest productivity maps were created using AVHRR scenes and the AVHRR/FOREST models. From the maps the county values of forest productivity and cover were calculated. It is apparent that the landscape has a strong influence on the success of the approach. An approach of using nested scales of imagery in conjunction with ground-based data can be successful in generating regional estimates of variables that are functionally related to some variable a sensor can detect.

Differentiation and cryovolcanism on Charon: A view before and after New Horizons

NASA Astrophysics Data System (ADS)

Desch, S. J.; Neveu, M.

2017-05-01

Before the arrival of the New Horizons probe at the Pluto-Charon system, we developed a series of models that predicted that Kuiper Belt Objects, even as small and as cold as Charon, have experienced internal ice-rock differentiation and possibly cryovolcanism. Confronting these predictions is a wide array of spectroscopy, imagery, and other data from New Horizons. In this article we compare the predictions against the new observations, and find that they largely support the expected history of the Pluto system and the evolution of Charon. Following the collision of two partially differentiated impactors with radii ≈1000 km, a disk of material formed around Pluto, from which Charon and Pluto's other moons formed. Because the impactors did not completely differentiate, the disk contained rocky material from their crusts, explaining the moons' different densities and compositions. Long-lived radionuclides in Charon, assisted by ammonia antifreeze in the ice, melted ice and created a subsurface ocean that eventually refroze ≈ 1.7 - 2.5 Gyr ago. The freezing of this ocean would have created extensional stresses that possibly created Serenity Chasma, and could have led to widespread resurfacing, explaining the apparently younger age of Vulcan Planum. Buildup of radiogenic heat then created a second, smaller ocean that refroze 0.5-1.7 Gyr ago. As it froze, cryovolcanism would have been enabled, possibly creating Kubrick Mons. Charon's ;moated mountains; such as Kubrick Mons have a natural explanation as cryovolcanoes depressing a thin lithosphere over a cryomagma chamber. We offer further predictions about other aspects of Charon's surface. Our previous predictions that Charon is a world shaped by geological activity have been largely borne out by New Horizons observations.

Modeling of Triangular Lattice Space Structures with Curved Battens

NASA Technical Reports Server (NTRS)

Chen, Tzikang; Wang, John T.

2005-01-01

Techniques for simulating an assembly process of lattice structures with curved battens were developed. The shape of the curved battens, the tension in the diagonals, and the compression in the battens were predicted for the assembled model. To be able to perform the assembly simulation, a cable-pulley element was implemented, and geometrically nonlinear finite element analyses were performed. Three types of finite element models were created from assembled lattice structures for studying the effects of design and modeling variations on the load carrying capability. Discrepancies in the predictions from these models were discussed. The effects of diagonal constraint failure were also studied.

Assessing Incentives for Service-Level Selection In Private Health Insurance Exchanges

PubMed Central

McGuire, Thomas G.; Newhouse, Joseph P.; Normand, Sharon-Lise; Shi, Julie; Zuvekas, Samuel

2014-01-01

Even with open enrollment and mandated purchase, incentives created by adverse selection may undermine the efficiency of service offerings by plans in the new health insurance Exchanges created by the Affordable Care Act. Using data on persons likely to participate in Exchanges drawn from five waves of the Medical Expenditure Panel Survey, we measure plan incentives in two ways. First, we construct predictive ratios, improving on current methods by taking into account the role of premiums in financing plans. Second, relying on an explicit model of plan profit maximization, we measure incentives based on the predictability and predictiveness of various medical diagnoses. Among the chronic diseases studied, plans have the greatest incentive to skimp on care for cancer, and mental health and substance abuse. PMID:24603443

Software reliability studies

NASA Technical Reports Server (NTRS)

Wilson, Larry W.

1989-01-01

The longterm goal of this research is to identify or create a model for use in analyzing the reliability of flight control software. The immediate tasks addressed are the creation of data useful to the study of software reliability and production of results pertinent to software reliability through the analysis of existing reliability models and data. The completed data creation portion of this research consists of a Generic Checkout System (GCS) design document created in cooperation with NASA and Research Triangle Institute (RTI) experimenters. This will lead to design and code reviews with the resulting product being one of the versions used in the Terminal Descent Experiment being conducted by the Systems Validations Methods Branch (SVMB) of NASA/Langley. An appended paper details an investigation of the Jelinski-Moranda and Geometric models for software reliability. The models were given data from a process that they have correctly simulated and asked to make predictions about the reliability of that process. It was found that either model will usually fail to make good predictions. These problems were attributed to randomness in the data and replication of data was recommended.

Design for disassembly and sustainability assessment to support aircraft end-of-life treatment

NASA Astrophysics Data System (ADS)

Savaria, Christian

Gas turbine engine design is a multidisciplinary and iterative process. Many design iterations are necessary to address the challenges among the disciplines. In the creation of a new engine architecture, the design time is crucial in capturing new business opportunities. At the detail design phase, it was proven very difficult to correct an unsatisfactory design. To overcome this difficulty, the concept of Multi-Disciplinary Optimization (MDO) at the preliminary design phase (Preliminary MDO or PMDO) is used allowing more freedom to perform changes in the design. PMDO also reduces the design time at the preliminary design phase. The concept of PMDO was used was used to create parametric models, and new correlations for high pressure gas turbine housing and shroud segments towards a new design process. First, dedicated parametric models were created because of their reusability and versatility. Their ease of use compared to non-parameterized models allows more design iterations thus reduces set up and design time. Second, geometry correlations were created to minimize the number of parameters used in turbine housing and shroud segment design. Since the turbine housing and the shroud segment geometries are required in tip clearance analyses, care was taken as to not oversimplify the parametric formulation. In addition, a user interface was developed to interact with the parametric models and improve the design time. Third, the cooling flow predictions require many engine parameters (i.e. geometric and performance parameters and air properties) and a reference shroud segments. A second correlation study was conducted to minimize the number of engine parameters required in the cooling flow predictions and to facilitate the selection of a reference shroud segment. Finally, the parametric models, the geometry correlations, and the user interface resulted in a time saving of 50% and an increase in accuracy of 56% in the new design system compared to the existing design system. Also, regarding the cooling flow correlations, the number of engine parameters was reduced by a factor of 6 to create a simplified prediction model and hence a faster shroud segment selection process. None

Multiple Pathways from Stress to Suicidality and the Protective Effect of Social Support in Hong Kong Adolescents

ERIC Educational Resources Information Center

Cheng, Sheung-Tak; Chan, Alfred C. M.

2007-01-01

Two theoretical models were constructed to illustrate how stressful events, family and friends support, depression, substance use, and death attitude mutually influence to create cumulative risks for suicide. The models were evaluated using structural equation modeling. Results showed that suicidality was strongly predicted by death attitude,…

Application of a Tenax Model to Assess Bioavailability of Polychlorinated Biphenyls in Field Sediments

EPA Science Inventory

Recent literature has shown that bioavailability-based techniques, such as Tenax extraction, can estimate sediment exposure to benthos. In a previous study by the authors,Tenax extraction was used to create and validate a literature-based Tenax model to predict oligochaete bioac...

An Optimization-Based System Model of Disturbance-Generated Forest Biomass Utilization

ERIC Educational Resources Information Center

Curry, Guy L.; Coulson, Robert N.; Gan, Jianbang; Tchakerian, Maria D.; Smith, C. Tattersall

2008-01-01

Disturbance-generated biomass results from endogenous and exogenous natural and cultural disturbances that affect the health and productivity of forest ecosystems. These disturbances can create large quantities of plant biomass on predictable cycles. A systems analysis model has been developed to quantify aspects of system capacities (harvest,…

A Predictive Logistic Regression Model of World Conflict Using Open Source Data

DTIC Science & Technology

2015-03-26

Added to the United Nations list are Palestine (West Bank and Gaza) and Kosovo. The total number of modeled nations is 182. Not all of these...The 26 variables are listed in Table 4. Also listed in Table 4 are the year the dataset was first collected, the data lag and the number of nation...state of violent conflict in 2015, seventeen of them are new to conflict since the last published list in 2013. A prediction tool is created to allow

Nonlinear modeling of chaotic time series: Theory and applications

NASA Astrophysics Data System (ADS)

Casdagli, M.; Eubank, S.; Farmer, J. D.; Gibson, J.; Desjardins, D.; Hunter, N.; Theiler, J.

We review recent developments in the modeling and prediction of nonlinear time series. In some cases, apparent randomness in time series may be due to chaotic behavior of a nonlinear but deterministic system. In such cases, it is possible to exploit the determinism to make short term forecasts that are much more accurate than one could make from a linear stochastic model. This is done by first reconstructing a state space, and then using nonlinear function approximation methods to create a dynamical model. Nonlinear models are valuable not only as short term forecasters, but also as diagnostic tools for identifying and quantifying low-dimensional chaotic behavior. During the past few years, methods for nonlinear modeling have developed rapidly, and have already led to several applications where nonlinear models motivated by chaotic dynamics provide superior predictions to linear models. These applications include prediction of fluid flows, sunspots, mechanical vibrations, ice ages, measles epidemics, and human speech.

Comparison of Analytical and Numerical Performance Predictions for an International Space Station Node 3 Internal Active Thermal Control System Regenerative Heat Exchanger

NASA Technical Reports Server (NTRS)

Wise, Stephen A.; Holt, James M.

2002-01-01

The complexity of International Space Station (ISS) systems modeling often necessitates the concurrence of various dissimilar, parallel analysis techniques to validate modeling. This was the case with a feasibility and performance study of the ISS Node 3 Regenerative Heat Exchanger (RHX). A thermo-hydraulic network model was created and analyzed in SINDA/FLUINT. A less complex, closed form solution of the systems dynamics was created using an Excel Spreadsheet. The purpose of this paper is to provide a brief description of the modeling processes utilized, the results and benefits of each to the ISS Node 3 RHX study.

Comparison of Analytical and Numerical Performance Predictions for a Regenerative Heat Exchanger in the International Space Station Node 3 Internal Active Thermal Control System

NASA Technical Reports Server (NTRS)

Wise, Stephen A.; Holt, James M.; Turner, Larry D. (Technical Monitor)

2001-01-01

The complexity of International Space Station (ISS) systems modeling often necessitates the concurrence of various dissimilar, parallel analysis techniques to validate modeling. This was the case with a feasibility and performance study of the ISS Node 3 Regenerative Heat Exchanger (RHX). A thermo-hydraulic network model was created and analyzed in SINDA/FLUINT. A less complex, closed form solution of the system dynamics was created using Excel. The purpose of this paper is to provide a brief description of the modeling processes utilized, the results and benefits of each to the ISS Node 3 RHX study.

Predicting severe injury using vehicle telemetry data.

PubMed

Ayoung-Chee, Patricia; Mack, Christopher D; Kaufman, Robert; Bulger, Eileen

2013-01-01

In 2010, the National Highway Traffic Safety Administration standardized collision data collected by event data recorders, which may help determine appropriate emergency medical service (EMS) response. Previous models (e.g., General Motors ) predict severe injury (Injury Severity Score [ISS] > 15) using occupant demographics and collision data. Occupant information is not automatically available, and 12% of calls from advanced automatic collision notification providers are unanswered. To better inform EMS triage, our goal was to create a predictive model only using vehicle collision data. Using the National Automotive Sampling System Crashworthiness Data System data set, we included front-seat occupants in late-model vehicles (2000 and later) in nonrollover and rollover crashes in years 2000 to 2010. Telematic (change in velocity, direction of force, seat belt use, vehicle type and curb weight, as well as multiple impact) and nontelematic variables (maximum intrusion, narrow impact, and passenger ejection) were included. Missing data were multiply imputed. The University of Washington model was tested to predict severe injury before application of guidelines (Step 0) and for occupants who did not meet Steps 1 and 2 criteria (Step 3) of the Centers for Disease Control and Prevention Field Triage Guidelines. A probability threshold of 20% was chosen in accordance with Centers for Disease Control and Prevention recommendations. There were 28,633 crashes, involving 33,956 vehicles and 52,033 occupants, of whom 9.9% had severe injury. At Step 0, the University of Washington model sensitivity was 40.0% and positive predictive value (PPV) was 20.7%. At Step 3, the sensitivity was 32.3 % and PPV was 10.1%. Model analysis excluding nontelematic variables decreased sensitivity and PPV. The sensitivity of the re-created General Motors model was 38.5% at Step 0 and 28.1% at Step 3. We designed a model using only vehicle collision data that was predictive of severe injury at collision notification and in the field and was comparable with an existing model. These models demonstrate the potential use of advanced automatic collision notification in planning EMS response. Prognostic study, level II.

Prediction of car cabin environment by means of 1D and 3D cabin model

NASA Astrophysics Data System (ADS)

Fišer, J.; Pokorný, J.; Jícha, M.

2012-04-01

Thermal comfort and also reduction of energy requirements of air-conditioning system in vehicle cabins are currently very intensively investigated and up-to-date issues. The article deals with two approaches of modelling of car cabin environment; the first model was created in simulation language Modelica (typical 1D approach without cabin geometry) and the second one was created in specialized software Theseus-FE (3D approach with cabin geometry). Performance and capabilities of this tools are demonstrated on the example of the car cabin and the results from simulations are compared with the results from the real car cabin climate chamber measurements.

Testing the effects of topography, geometry, and kinematics on modeled thermochronometer cooling ages in the eastern Bhutan Himalaya

NASA Astrophysics Data System (ADS)

Gilmore, Michelle E.; McQuarrie, Nadine; Eizenhöfer, Paul R.; Ehlers, Todd A.

2018-05-01

In this study, reconstructions of a balanced geologic cross section in the Himalayan fold-thrust belt of eastern Bhutan are used in flexural-kinematic and thermokinematic models to understand the sensitivity of predicted cooling ages to changes in fault kinematics, geometry, topography, and radiogenic heat production. The kinematics for each scenario are created by sequentially deforming the cross section with ˜ 10 km deformation steps while applying flexural loading and erosional unloading at each step to develop a high-resolution evolution of deformation, erosion, and burial over time. By assigning ages to each increment of displacement, we create a suite of modeled scenarios that are input into a 2-D thermokinematic model to predict cooling ages. Comparison of model-predicted cooling ages to published thermochronometer data reveals that cooling ages are most sensitive to (1) the location and size of fault ramps, (2) the variable shortening rates between 68 and 6.4 mm yr-1, and (3) the timing and magnitude of out-of-sequence faulting. The predicted ages are less sensitive to (4) radiogenic heat production and (5) estimates of topographic evolution. We used the observed misfit of predicted to measured cooling ages to revise the cross section geometry and separate one large ramp previously proposed for the modern décollement into two smaller ramps. The revised geometry results in an improved fit to observed ages, particularly young AFT ages (2-6 Ma) located north of the Main Central Thrust. This study presents a successful approach for using thermochronometer data to test the viability of a proposed cross section geometry and kinematics and describes a viable approach to estimating the first-order topographic evolution of a compressional orogen.

PREDICTING INDIVIDUAL WELL-BEING THROUGH THE LANGUAGE OF SOCIAL MEDIA.

PubMed

Schwartz, H Andrew; Sap, Maarten; Kern, Margaret L; Eichstaedt, Johannes C; Kapelner, Adam; Agrawal, Megha; Blanco, Eduardo; Dziurzynski, Lukasz; Park, Gregory; Stillwell, David; Kosinski, Michal; Seligman, Martin E P; Ungar, Lyle H

2016-01-01

We present the task of predicting individual well-being, as measured by a life satisfaction scale, through the language people use on social media. Well-being, which encompasses much more than emotion and mood, is linked with good mental and physical health. The ability to quickly and accurately assess it can supplement multi-million dollar national surveys as well as promote whole body health. Through crowd-sourced ratings of tweets and Facebook status updates, we create message-level predictive models for multiple components of well-being. However, well-being is ultimately attributed to people, so we perform an additional evaluation at the user-level, finding that a multi-level cascaded model, using both message-level predictions and userlevel features, performs best and outperforms popular lexicon-based happiness models. Finally, we suggest that analyses of language go beyond prediction by identifying the language that characterizes well-being.

Modelling proteins’ hidden conformations to predict antibiotic resistance

PubMed Central

Hart, Kathryn M.; Ho, Chris M. W.; Dutta, Supratik; Gross, Michael L.; Bowman, Gregory R.

2016-01-01

TEM β-lactamase confers bacteria with resistance to many antibiotics and rapidly evolves activity against new drugs. However, functional changes are not easily explained by differences in crystal structures. We employ Markov state models to identify hidden conformations and explore their role in determining TEM’s specificity. We integrate these models with existing drug-design tools to create a new technique, called Boltzmann docking, which better predicts TEM specificity by accounting for conformational heterogeneity. Using our MSMs, we identify hidden states whose populations correlate with activity against cefotaxime. To experimentally detect our predicted hidden states, we use rapid mass spectrometric footprinting and confirm our models’ prediction that increased cefotaxime activity correlates with reduced Ω-loop flexibility. Finally, we design novel variants to stabilize the hidden cefotaximase states, and find their populations predict activity against cefotaxime in vitro and in vivo. Therefore, we expect this framework to have numerous applications in drug and protein design. PMID:27708258

Clinical and genetic predictors of weight gain in patients diagnosed with breast cancer

PubMed Central

Reddy, S M; Sadim, M; Li, J; Yi, N; Agarwal, S; Mantzoros, C S; Kaklamani, V G

2013-01-01

Background: Post-diagnosis weight gain in breast cancer patients has been associated with increased cancer recurrence and mortality. Our study was designed to identify risk factors for this weight gain and create a predictive model to identify a high-risk population for targeted interventions. Methods: Chart review was conducted on 459 breast cancer patients from Northwestern Robert H. Lurie Cancer Centre to obtain weights and body mass indices (BMIs) over an 18-month period from diagnosis. We also recorded tumour characteristics, demographics, clinical factors, and treatment regimens. Blood samples were genotyped for 14 single-nucleotide polymorphisms (SNPs) in fat mass and obesity-associated protein (FTO) and adiponectin pathway genes (ADIPOQ and ADIPOR1). Results: In all, 56% of patients had >0.5 kg m–2 increase in BMI from diagnosis to 18 months, with average BMI and weight gain of 1.9 kg m–2 and 5.1 kg, respectively. Our best predictive model was a primarily SNP-based model incorporating all 14 FTO and adiponectin pathway SNPs studied, their epistatic interactions, and age and BMI at diagnosis, with area under receiver operating characteristic curve of 0.85 for 18-month weight gain. Conclusion: We created a powerful risk prediction model that can identify breast cancer patients at high risk for weight gain. PMID:23922112

Cheminformatics Analysis of EPA ToxCast Chemical Libraries to Identify Domains of Applicability for Predictive Toxicity Models and Prioritize Compounds for Toxicity Testing

EPA Science Inventory

An important goal of toxicology research is the development of robust methods that use in vitro and chemical structure information to predict in vivo toxicity endpoints. The US EPA ToxCast program is addressing this goal using ~600 in vitro assays to create bioactivity profiles o...

Crop Yield Predictions - High Resolution Statistical Model for Intra-season Forecasts Applied to Corn in the US

NASA Astrophysics Data System (ADS)

Cai, Y.

2017-12-01

Accurately forecasting crop yields has broad implications for economic trading, food production monitoring, and global food security. However, the variation of environmental variables presents challenges to model yields accurately, especially when the lack of highly accurate measurements creates difficulties in creating models that can succeed across space and time. In 2016, we developed a sequence of machine-learning based models forecasting end-of-season corn yields for the US at both the county and national levels. We combined machine learning algorithms in a hierarchical way, and used an understanding of physiological processes in temporal feature selection, to achieve high precision in our intra-season forecasts, including in very anomalous seasons. During the live run, we predicted the national corn yield within 1.40% of the final USDA number as early as August. In the backtesting of the 2000-2015 period, our model predicts national yield within 2.69% of the actual yield on average already by mid-August. At the county level, our model predicts 77% of the variation in final yield using data through the beginning of August and improves to 80% by the beginning of October, with the percentage of counties predicted within 10% of the average yield increasing from 68% to 73%. Further, the lowest errors are in the most significant producing regions, resulting in very high precision national-level forecasts. In addition, we identify the changes of important variables throughout the season, specifically early-season land surface temperature, and mid-season land surface temperature and vegetation index. For the 2017 season, we feed 2016 data to the training set, together with additional geospatial data sources, aiming to make the current model even more precise. We will show how our 2017 US corn yield forecasts converges in time, which factors affect the yield the most, as well as present our plans for 2018 model adjustments.

A system identification approach for developing model predictive controllers of antibody quality attributes in cell culture processes

PubMed Central

Schmitt, John; Beller, Justin; Russell, Brian; Quach, Anthony; Hermann, Elizabeth; Lyon, David; Breit, Jeffrey

2017-01-01

As the biopharmaceutical industry evolves to include more diverse protein formats and processes, more robust control of Critical Quality Attributes (CQAs) is needed to maintain processing flexibility without compromising quality. Active control of CQAs has been demonstrated using model predictive control techniques, which allow development of processes which are robust against disturbances associated with raw material variability and other potentially flexible operating conditions. Wide adoption of model predictive control in biopharmaceutical cell culture processes has been hampered, however, in part due to the large amount of data and expertise required to make a predictive model of controlled CQAs, a requirement for model predictive control. Here we developed a highly automated, perfusion apparatus to systematically and efficiently generate predictive models using application of system identification approaches. We successfully created a predictive model of %galactosylation using data obtained by manipulating galactose concentration in the perfusion apparatus in serialized step change experiments. We then demonstrated the use of the model in a model predictive controller in a simulated control scenario to successfully achieve a %galactosylation set point in a simulated fed‐batch culture. The automated model identification approach demonstrated here can potentially be generalized to many CQAs, and could be a more efficient, faster, and highly automated alternative to batch experiments for developing predictive models in cell culture processes, and allow the wider adoption of model predictive control in biopharmaceutical processes. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 33:1647–1661, 2017 PMID:28786215

Thermal Vacuum Test Correlation of a Zero Propellant Load Case Thermal Capacitance Propellant Gauging Analytical Model

NASA Technical Reports Server (NTRS)

Mckim, Stephen A.

2016-01-01

This thesis describes the development and correlation of a thermal model that forms the foundation of a thermal capacitance spacecraft propellant load estimator. Specific details of creating the thermal model for the diaphragm propellant tank used on NASA's Magnetospheric Multiscale spacecraft using ANSYS and the correlation process implemented are presented. The thermal model was correlated to within plus or minus 3 degrees Celsius of the thermal vacuum test data, and was determined sufficient to make future propellant predictions on MMS. The model was also found to be relatively sensitive to uncertainties in applied heat flux and mass knowledge of the tank. More work is needed to improve temperature predictions in the upper hemisphere of the propellant tank where predictions were found to be 2 to 2.5 C lower than the test data. A road map for applying the model to predict propellant loads on the actual MMS spacecraft toward its end of life in 2017-2018 is also presented.

Performance Comparison of Systemic Inflammatory Response Syndrome with Logistic Regression Models to Predict Sepsis in Neonates

PubMed Central

Thakur, Jyoti; Pahuja, Sharvan Kumar; Pahuja, Roop

2017-01-01

In 2005, an international pediatric sepsis consensus conference defined systemic inflammatory response syndrome (SIRS) for children <18 years of age, but excluded premature infants. In 2012, Hofer et al. investigated the predictive power of SIRS for term neonates. In this paper, we examined the accuracy of SIRS in predicting sepsis in neonates, irrespective of their gestational age (i.e., pre-term, term, and post-term). We also created two prediction models, named Model A and Model B, using binary logistic regression. Both models performed better than SIRS. We also developed an android application so that physicians can easily use Model A and Model B in real-world scenarios. The sensitivity, specificity, positive likelihood ratio (PLR) and negative likelihood ratio (NLR) in cases of SIRS were 16.15%, 95.53%, 3.61, and 0.88, respectively, whereas they were 29.17%, 97.82%, 13.36, and 0.72, respectively, in the case of Model A, and 31.25%, 97.30%, 11.56, and 0.71, respectively, in the case of Model B. All models were significant with p < 0.001. PMID:29257099

Machine Learning Techniques for Prediction of Early Childhood Obesity.

PubMed

Dugan, T M; Mukhopadhyay, S; Carroll, A; Downs, S

2015-01-01

This paper aims to predict childhood obesity after age two, using only data collected prior to the second birthday by a clinical decision support system called CHICA. Analyses of six different machine learning methods: RandomTree, RandomForest, J48, ID3, Naïve Bayes, and Bayes trained on CHICA data show that an accurate, sensitive model can be created. Of the methods analyzed, the ID3 model trained on the CHICA dataset proved the best overall performance with accuracy of 85% and sensitivity of 89%. Additionally, the ID3 model had a positive predictive value of 84% and a negative predictive value of 88%. The structure of the tree also gives insight into the strongest predictors of future obesity in children. Many of the strongest predictors seen in the ID3 modeling of the CHICA dataset have been independently validated in the literature as correlated with obesity, thereby supporting the validity of the model. This study demonstrated that data from a production clinical decision support system can be used to build an accurate machine learning model to predict obesity in children after age two.

Basophile: Accurate Fragment Charge State Prediction Improves Peptide Identification Rates

DOE PAGES

Wang, Dong; Dasari, Surendra; Chambers, Matthew C.; ...

2013-03-07

In shotgun proteomics, database search algorithms rely on fragmentation models to predict fragment ions that should be observed for a given peptide sequence. The most widely used strategy (Naive model) is oversimplified, cleaving all peptide bonds with equal probability to produce fragments of all charges below that of the precursor ion. More accurate models, based on fragmentation simulation, are too computationally intensive for on-the-fly use in database search algorithms. We have created an ordinal-regression-based model called Basophile that takes fragment size and basic residue distribution into account when determining the charge retention during CID/higher-energy collision induced dissociation (HCD) of chargedmore » peptides. This model improves the accuracy of predictions by reducing the number of unnecessary fragments that are routinely predicted for highly-charged precursors. Basophile increased the identification rates by 26% (on average) over the Naive model, when analyzing triply-charged precursors from ion trap data. Basophile achieves simplicity and speed by solving the prediction problem with an ordinal regression equation, which can be incorporated into any database search software for shotgun proteomic identification.« less

A Case-Series Test of the Interactive Two-step Model of Lexical Access: Predicting Word Repetition from Picture Naming

PubMed Central

Dell, Gary S.; Martin, Nadine; Schwartz, Myrna F.

2010-01-01

Lexical access in language production, and particularly pathologies of lexical access, are often investigated by examining errors in picture naming and word repetition. In this article, we test a computational approach to lexical access, the two-step interactive model, by examining whether the model can quantitatively predict the repetition-error patterns of 65 aphasic subjects from their naming errors. The model’s characterizations of the subjects’ naming errors were taken from the companion paper to this one (Schwartz, Dell, N. Martin, Gahl & Sobel, 2006), and their repetition was predicted from the model on the assumption that naming involves two error prone steps, word and phonological retrieval, whereas repetition only creates errors in the second of these steps. A version of the model in which lexical-semantic and lexical-phonological connections could be independently lesioned was generally successful in predicting repetition for the aphasics. An analysis of the few cases in which model predictions were inaccurate revealed the role of input phonology in the repetition task. PMID:21085621

Combining quantitative trait loci analysis with physiological models to predict genotype-specific transpiration rates.

PubMed

Reuning, Gretchen A; Bauerle, William L; Mullen, Jack L; McKay, John K

2015-04-01

Transpiration is controlled by evaporative demand and stomatal conductance (gs ), and there can be substantial genetic variation in gs . A key parameter in empirical models of transpiration is minimum stomatal conductance (g0 ), a trait that can be measured and has a large effect on gs and transpiration. In Arabidopsis thaliana, g0 exhibits both environmental and genetic variation, and quantitative trait loci (QTL) have been mapped. We used this information to create a genetically parameterized empirical model to predict transpiration of genotypes. For the parental lines, this worked well. However, in a recombinant inbred population, the predictions proved less accurate. When based only upon their genotype at a single g0 QTL, genotypes were less distinct than our model predicted. Follow-up experiments indicated that both genotype by environment interaction and a polygenic inheritance complicate the application of genetic effects into physiological models. The use of ecophysiological or 'crop' models for predicting transpiration of novel genetic lines will benefit from incorporating further knowledge of the genetic control and degree of independence of core traits/parameters underlying gs variation. © 2014 John Wiley & Sons Ltd.

Random forest models to predict aqueous solubility.

PubMed

Palmer, David S; O'Boyle, Noel M; Glen, Robert C; Mitchell, John B O

2007-01-01

Random Forest regression (RF), Partial-Least-Squares (PLS) regression, Support Vector Machines (SVM), and Artificial Neural Networks (ANN) were used to develop QSPR models for the prediction of aqueous solubility, based on experimental data for 988 organic molecules. The Random Forest regression model predicted aqueous solubility more accurately than those created by PLS, SVM, and ANN and offered methods for automatic descriptor selection, an assessment of descriptor importance, and an in-parallel measure of predictive ability, all of which serve to recommend its use. The prediction of log molar solubility for an external test set of 330 molecules that are solid at 25 degrees C gave an r2 = 0.89 and RMSE = 0.69 log S units. For a standard data set selected from the literature, the model performed well with respect to other documented methods. Finally, the diversity of the training and test sets are compared to the chemical space occupied by molecules in the MDL drug data report, on the basis of molecular descriptors selected by the regression analysis.

From points to forecasts: Predicting invasive species habitat suitability in the near term

USGS Publications Warehouse

Holcombe, Tracy R.; Stohlgren, Thomas J.; Jarnevich, Catherine S.

2010-01-01

We used near-term climate scenarios for the continental United States, to model 12 invasive plants species. We created three potential habitat suitability models for each species using maximum entropy modeling: (1) current; (2) 2020; and (3) 2035. Area under the curve values for the models ranged from 0.92 to 0.70, with 10 of the 12 being above 0.83 suggesting strong and predictable species-environment matching. Change in area between the current potential habitat and 2035 ranged from a potential habitat loss of about 217,000 km2, to a potential habitat gain of about 133,000 km2.

Evaluation of Fish Passage at Whitewater Parks Using 2D and 3D Hydraulic Modeling

NASA Astrophysics Data System (ADS)

Hardee, T.; Nelson, P. A.; Kondratieff, M.; Bledsoe, B. P.

2016-12-01

In-stream whitewater parks (WWPs) are increasingly popular recreational amenities that typically create waves by constricting flow through a chute to increase velocities and form a hydraulic jump. However, the hydraulic conditions these structures create can limit longitudinal habitat connectivity and potentially inhibit upstream fish migration, especially of native fishes. An improved understanding of the fundamental hydraulic processes and potential environmental effects of whitewater parks is needed to inform management decisions about Recreational In-Channel Diversions (RICDs). Here, we use hydraulic models to compute a continuous and spatially explicit description of velocity and depth along potential fish swimming paths in the flow field, and the ensemble of potential paths are compared to fish swimming performance data to predict fish passage via logistic regression analysis. While 3d models have been shown to accurately predict trout movement through WWP structures, 2d methods can provide a more cost-effective and manager-friendly approach to assessing the effects of similar hydraulic structures on fish passage when 3d analysis in not feasible. Here, we use 2d models to examine the hydraulics in several WWP structures on the North Fork of the St. Vrain River at Lyons, Colorado, and we compare these model results to fish passage predictions from a 3d model. Our analysis establishes a foundation for a practical, transferable and physically-rigorous 2d modeling approach for mechanistically evaluating the effects of hydraulic structures on fish passage.

Prediction of metabolites of epoxidation reaction in MetaTox.

PubMed

Rudik, A V; Dmitriev, A V; Bezhentsev, V M; Lagunin, A A; Filimonov, D A; Poroikov, V V

2017-10-01

Biotransformation is a process of the chemical modifications which may lead to the reactive metabolites, in particular the epoxides. Epoxide reactive metabolites may cause the toxic effects. The prediction of such metabolites is important for drug development and ecotoxicology studies. Epoxides are formed by some oxidation reactions, usually catalysed by cytochromes P450, and represent a large class of three-membered cyclic ethers. Identification of molecules, which may be epoxidized, and indication of the specific location of epoxide functional group (which is called SOE - site of epoxidation) are important for prediction of epoxide metabolites. Datasets from 355 molecules and 615 reactions were created for training and validation. The prediction of SOE is based on a combination of LMNA (Labelled Multilevel Neighbourhood of Atom) descriptors and Bayesian-like algorithm implemented in PASS software and MetaTox web-service. The average invariant accuracy of prediction (AUC) calculated in leave-one-out and 20-fold cross-validation procedures is 0.9. Prediction of epoxide formation based on the created SAR model is included as the component of MetaTox web-service ( http://www.way2drug.com/mg ).

Using a Guided Machine Learning Ensemble Model to Predict Discharge Disposition following Meningioma Resection.

PubMed

Muhlestein, Whitney E; Akagi, Dallin S; Kallos, Justiss A; Morone, Peter J; Weaver, Kyle D; Thompson, Reid C; Chambless, Lola B

2018-04-01

Objective  Machine learning (ML) algorithms are powerful tools for predicting patient outcomes. This study pilots a novel approach to algorithm selection and model creation using prediction of discharge disposition following meningioma resection as a proof of concept. Materials and Methods  A diversity of ML algorithms were trained on a single-institution database of meningioma patients to predict discharge disposition. Algorithms were ranked by predictive power and top performers were combined to create an ensemble model. The final ensemble was internally validated on never-before-seen data to demonstrate generalizability. The predictive power of the ensemble was compared with a logistic regression. Further analyses were performed to identify how important variables impact the ensemble. Results  Our ensemble model predicted disposition significantly better than a logistic regression (area under the curve of 0.78 and 0.71, respectively, p  = 0.01). Tumor size, presentation at the emergency department, body mass index, convexity location, and preoperative motor deficit most strongly influence the model, though the independent impact of individual variables is nuanced. Conclusion  Using a novel ML technique, we built a guided ML ensemble model that predicts discharge destination following meningioma resection with greater predictive power than a logistic regression, and that provides greater clinical insight than a univariate analysis. These techniques can be extended to predict many other patient outcomes of interest.

Modeling postshock evolution of large electropores

NASA Astrophysics Data System (ADS)

Neu, John C.; Krassowska, Wanda

2003-02-01

The Smoluchowski equation (SE), which describes the evolution of pores created by electric shocks, cannot be applied to modeling large and long-lived pores for two reasons: (1) it does not predict pores of radius above 20 nm without also predicting membrane rupture; (2) it does not predict postshock growth of pores. This study proposes a model in which pores are coupled by membrane tension, resulting in a nonlinear generalization of SE. The predictions of the model are explored using examples of homogeneous (all pore radii r are equal) and heterogeneous (0⩽r⩽rmax) distributions of pores. Pores in a homogeneous population either shrink to zero or assume a stable radius corresponding to the minimum of the bilayer energy. For a heterogeneous population, such a stable radius does not exist. All pores, except rmax, shrink to zero and rmax grows to infinity. However, the unbounded growth of rmax is not physical because the number of pores per cell decreases in time and the continuum model loses validity. When the continuum formulation is replaced by the discrete one, the model predicts the coarsening process: all pores, except rmax, shrink to zero and rmax assumes a stable radius. Thus, the model with tension-coupled pores does not predict membrane rupture and the predicted postshock growth of pores is consistent with experimental evidence.

Are Subject-Specific Musculoskeletal Models Robust to the Uncertainties in Parameter Identification?

PubMed Central

Valente, Giordano; Pitto, Lorenzo; Testi, Debora; Seth, Ajay; Delp, Scott L.; Stagni, Rita; Viceconti, Marco; Taddei, Fulvia

2014-01-01

Subject-specific musculoskeletal modeling can be applied to study musculoskeletal disorders, allowing inclusion of personalized anatomy and properties. Independent of the tools used for model creation, there are unavoidable uncertainties associated with parameter identification, whose effect on model predictions is still not fully understood. The aim of the present study was to analyze the sensitivity of subject-specific model predictions (i.e., joint angles, joint moments, muscle and joint contact forces) during walking to the uncertainties in the identification of body landmark positions, maximum muscle tension and musculotendon geometry. To this aim, we created an MRI-based musculoskeletal model of the lower limbs, defined as a 7-segment, 10-degree-of-freedom articulated linkage, actuated by 84 musculotendon units. We then performed a Monte-Carlo probabilistic analysis perturbing model parameters according to their uncertainty, and solving a typical inverse dynamics and static optimization problem using 500 models that included the different sets of perturbed variable values. Model creation and gait simulations were performed by using freely available software that we developed to standardize the process of model creation, integrate with OpenSim and create probabilistic simulations of movement. The uncertainties in input variables had a moderate effect on model predictions, as muscle and joint contact forces showed maximum standard deviation of 0.3 times body-weight and maximum range of 2.1 times body-weight. In addition, the output variables significantly correlated with few input variables (up to 7 out of 312) across the gait cycle, including the geometry definition of larger muscles and the maximum muscle tension in limited gait portions. Although we found subject-specific models not markedly sensitive to parameter identification, researchers should be aware of the model precision in relation to the intended application. In fact, force predictions could be affected by an uncertainty in the same order of magnitude of its value, although this condition has low probability to occur. PMID:25390896

Effects and detection of raw material variability on the performance of near-infrared calibration models for pharmaceutical products.

PubMed

Igne, Benoit; Shi, Zhenqi; Drennen, James K; Anderson, Carl A

2014-02-01

The impact of raw material variability on the prediction ability of a near-infrared calibration model was studied. Calibrations, developed from a quaternary mixture design comprising theophylline anhydrous, lactose monohydrate, microcrystalline cellulose, and soluble starch, were challenged by intentional variation of raw material properties. A design with two theophylline physical forms, three lactose particle sizes, and two starch manufacturers was created to test model robustness. Further challenges to the models were accomplished through environmental conditions. Along with full-spectrum partial least squares (PLS) modeling, variable selection by dynamic backward PLS and genetic algorithms was utilized in an effort to mitigate the effects of raw material variability. In addition to evaluating models based on their prediction statistics, prediction residuals were analyzed by analyses of variance and model diagnostics (Hotelling's T(2) and Q residuals). Full-spectrum models were significantly affected by lactose particle size. Models developed by selecting variables gave lower prediction errors and proved to be a good approach to limit the effect of changing raw material characteristics. Hotelling's T(2) and Q residuals provided valuable information that was not detectable when studying only prediction trends. Diagnostic statistics were demonstrated to be critical in the appropriate interpretation of the prediction of quality parameters. © 2013 Wiley Periodicals, Inc. and the American Pharmacists Association.

Do bioclimate variables improve performance of climate envelope models?

USGS Publications Warehouse

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

2012-01-01

Climate envelope models are widely used to forecast potential effects of climate change on species distributions. A key issue in climate envelope modeling is the selection of predictor variables that most directly influence species. To determine whether model performance and spatial predictions were related to the selection of predictor variables, we compared models using bioclimate variables with models constructed from monthly climate data for twelve terrestrial vertebrate species in the southeastern USA using two different algorithms (random forests or generalized linear models), and two model selection techniques (using uncorrelated predictors or a subset of user-defined biologically relevant predictor variables). There were no differences in performance between models created with bioclimate or monthly variables, but one metric of model performance was significantly greater using the random forest algorithm compared with generalized linear models. Spatial predictions between maps using bioclimate and monthly variables were very consistent using the random forest algorithm with uncorrelated predictors, whereas we observed greater variability in predictions using generalized linear models.

Impact assessment of extreme storm events using a Bayesian network

USGS Publications Warehouse

den Heijer, C.(Kees); Knipping, Dirk T.J.A.; Plant, Nathaniel G.; van Thiel de Vries, Jaap S. M.; Baart, Fedor; van Gelder, Pieter H. A. J. M.

2012-01-01

This paper describes an investigation on the usefulness of Bayesian Networks in the safety assessment of dune coasts. A network has been created that predicts the erosion volume based on hydraulic boundary conditions and a number of cross-shore profile indicators. Field measurement data along a large part of the Dutch coast has been used to train the network. Corresponding storm impact on the dunes was calculated with an empirical dune erosion model named duros+. Comparison between the Bayesian Network predictions and the original duros+ results, here considered as observations, results in a skill up to 0.88, provided that the training data covers the range of predictions. Hence, the predictions from a deterministic model (duros+) can be captured in a probabilistic model (Bayesian Network) such that both the process knowledge and uncertainties can be included in impact and vulnerability assessments.

The Anatomy of a Likely Donor: Econometric Evidence on Philanthropy to Higher Education

ERIC Educational Resources Information Center

Lara, Christen; Johnson, Daniel

2014-01-01

In 2011, philanthropic giving to higher education institutions totaled $30.3 billion, an 8.2% increase over the previous year. Roughly, 26% of those funds came from alumni donations. This article builds upon existing economic models to create an econometric model to explain and predict the pattern of alumni giving. We test the model using data…

Modeling the spatial and temporal dynamics of isolated emerald ash borer populations

Treesearch

Nathan W. Siegert; Andrew M. Liebhold; Deborah G. McCullough

2008-01-01

The ability to predict the distance and rate of emerald ash borer (EAB) spread in outlier populations is needed to continue development of effective management strategies for improved EAB control. We have developed a coupled map lattice model to estimate the spread and dispersal of isolated emerald ash borer populations. This model creates an artificial environment in...

Assessing Ecosystem Model Performance in Semiarid Systems

NASA Astrophysics Data System (ADS)

Thomas, A.; Dietze, M.; Scott, R. L.; Biederman, J. A.

2017-12-01

In ecosystem process modelling, comparing outputs to benchmark datasets observed in the field is an important way to validate models, allowing the modelling community to track model performance over time and compare models at specific sites. Multi-model comparison projects as well as models themselves have largely been focused on temperate forests and similar biomes. Semiarid regions, on the other hand, are underrepresented in land surface and ecosystem modelling efforts, and yet will be disproportionately impacted by disturbances such as climate change due to their sensitivity to changes in the water balance. Benchmarking models at semiarid sites is an important step in assessing and improving models' suitability for predicting the impact of disturbance on semiarid ecosystems. In this study, several ecosystem models were compared at a semiarid grassland in southwestern Arizona using PEcAn, or the Predictive Ecosystem Analyzer, an open-source eco-informatics toolbox ideal for creating the repeatable model workflows necessary for benchmarking. Models included SIPNET, DALEC, JULES, ED2, GDAY, LPJ-GUESS, MAESPA, CLM, CABLE, and FATES. Comparison between model output and benchmarks such as net ecosystem exchange (NEE) tended to produce high root mean square error and low correlation coefficients, reflecting poor simulation of seasonality and the tendency for models to create much higher carbon sources than observed. These results indicate that ecosystem models do not currently adequately represent semiarid ecosystem processes.

A Computational Model Quantifies the Effect of Anatomical Variability on Velopharyngeal Function

ERIC Educational Resources Information Center

Inouye, Joshua M.; Perry, Jamie L.; Lin, Kant Y.; Blemker, Silvia S.

2015-01-01

Purpose: This study predicted the effects of velopharyngeal (VP) anatomical parameters on VP function to provide a greater understanding of speech mechanics and aid in the treatment of speech disorders. Method: We created a computational model of the VP mechanism using dimensions obtained from magnetic resonance imaging measurements of 10 healthy…

Distribution of submerged aquatic vegetation in the St. Louis River estuary: Maps and models

EPA Science Inventory

In late summer of 2011 and 2012 we used echo-sounding gear to map the distribution of submerged aquatic vegetation (SAV) in the St. Louis River Estuary (SLRE). From these data we produced maps of SAV distribution and we created logistic models to predict the probability of occurr...

Personality heterogeneity in PTSD: distinct temperament and interpersonal typologies.

PubMed

Thomas, Katherine M; Hopwood, Christopher J; Donnellan, M Brent; Wright, Aidan G C; Sanislow, Charles A; McDevitt-Murphy, Meghan E; Ansell, Emily B; Grilo, Carlos M; McGlashan, Thomas H; Shea, M Tracie; Markowitz, John C; Skodol, Andrew E; Zanarini, Mary C; Morey, Leslie C

2014-03-01

Researchers examining personality typologies of posttraumatic stress disorder (PTSD) have consistently identified 3 groups: low pathology, internalizing, and externalizing. These groups have been found to predict functional severity and psychiatric comorbidity. In this study, we employed Latent Profile Analysis to compare this previously established typology, grounded in temperament traits (negative emotionality; positive emotionality; constraint), to a novel typology rooted in interpersonal traits (dominance; warmth) in a sample of individuals with PTSD (n = 155). Using Schedule for Nonadaptive and Adaptive Personality (SNAP) traits to create latent profiles, the 3-group temperament model was replicated. Using Interpersonal Circumplex (IPC) traits to create latent profiles, we identified a 4-group solution with groups varying in interpersonal style. These models were nonredundant, indicating that the depiction of personality variability in PTSD depends on how personality is assessed. Whereas the temperament model was more effective for distinguishing individuals based on distress and comorbid disorders, the interpersonal model was more effective for predicting the chronicity of PTSD over the 10 year course of the study. We discuss the potential for integrating these complementary temperament and interpersonal typologies in the clinical assessment of PTSD. 2014 APA

Oil prices and long-run risk

NASA Astrophysics Data System (ADS)

Ready, Robert Clayton

I show that relative levels of aggregate consumption and personal oil consumption provide an excellent proxy for oil prices, and that high oil prices predict low future aggregate consumption growth. Motivated by these facts, I add an oil consumption good to the long-run risk model of Bansal and Yaron [2004] to study the asset pricing implications of observed changes in the dynamic interaction of consumption and oil prices. Empirically I observe that, compared to the first half of my 1987--2010 sample, oil consumption growth in the last 10 years is unresponsive to levels of oil prices, creating an decrease in the mean-reversion of oil prices, and an increase in the persistence of oil price shocks. The model implies that the change in the dynamics of oil consumption generates increased systematic risk from oil price shocks due to their increased persistence. However, persistent oil prices also act as a counterweight for shocks to expected consumption growth, with high expected growth creating high expectations of future oil prices which in turn slow down growth. The combined effect is to reduce overall consumption risk and lower the equity premium. The model also predicts that these changes affect the riskiness of of oil futures contracts, and combine to create a hump shaped term structure of oil futures, consistent with recent data.

Forecasting model of Corylus, Alnus, and Betula pollen concentration levels using spatiotemporal correlation properties of pollen count.

PubMed

Nowosad, Jakub; Stach, Alfred; Kasprzyk, Idalia; Weryszko-Chmielewska, Elżbieta; Piotrowska-Weryszko, Krystyna; Puc, Małgorzata; Grewling, Łukasz; Pędziszewska, Anna; Uruska, Agnieszka; Myszkowska, Dorota; Chłopek, Kazimiera; Majkowska-Wojciechowska, Barbara

The aim of the study was to create and evaluate models for predicting high levels of daily pollen concentration of Corylus , Alnus , and Betula using a spatiotemporal correlation of pollen count. For each taxon, a high pollen count level was established according to the first allergy symptoms during exposure. The dataset was divided into a training set and a test set, using a stratified random split. For each taxon and city, the model was built using a random forest method. Corylus models performed poorly. However, the study revealed the possibility of predicting with substantial accuracy the occurrence of days with high pollen concentrations of Alnus and Betula using past pollen count data from monitoring sites. These results can be used for building (1) simpler models, which require data only from aerobiological monitoring sites, and (2) combined meteorological and aerobiological models for predicting high levels of pollen concentration.

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

Delaney, Alexander R., E-mail: a.delaney@vumc.nl; Tol, Jim P.; Dahele, Max

Purpose: RapidPlan, a commercial knowledge-based planning solution, uses a model library containing the geometry and associated dosimetry of existing plans. This model predicts achievable dosimetry for prospective patients that can be used to guide plan optimization. However, it is unknown how suboptimal model plans (outliers) influence the predictions or resulting plans. We investigated the effect of, first, removing outliers from the model (cleaning it) and subsequently adding deliberate dosimetric outliers. Methods and Materials: Clinical plans from 70 head and neck cancer patients comprised the uncleaned (UC) Model{sub UC}, from which outliers were cleaned (C) to create Model{sub C}. The lastmore » 5 to 40 patients of Model{sub C} were replanned with no attempt to spare the salivary glands. These substantial dosimetric outliers were reintroduced to the model in increments of 5, creating Model{sub 5} to Model{sub 40} (Model{sub 5-40}). These models were used to create plans for a 10-patient evaluation group. Plans from Model{sub UC} and Model{sub C}, and Model{sub C} and Model{sub 5-40} were compared on the basis of boost (B) and elective (E) target volume homogeneity indexes (HI{sub B}/HI{sub E}) and mean doses to oral cavity, composite salivary glands (comp{sub sal}) and swallowing (comp{sub swal}) structures. Results: On average, outlier removal (Model{sub C} vs Model{sub UC}) had minimal effects on HI{sub B}/HI{sub E} (0%-0.4%) and sparing of organs at risk (mean dose difference to oral cavity and comp{sub sal}/comp{sub swal} were ≤0.4 Gy). Model{sub 5-10} marginally improved comp{sub sal} sparing, whereas adding a larger number of outliers (Model{sub 20-40}) led to deteriorations in comp{sub sal} up to 3.9 Gy, on average. These increases are modest compared to the 14.9 Gy dose increases in the added outlier plans, due to the placement of optimization objectives below the inferior boundary of the dose-volume histogram-predicted range. Conclusions: Overall, dosimetric outlier removal from or addition of 5 to 10 outliers to a 70-patient model had marginal effects on resulting plan quality. Although the addition of >20 outliers deteriorated plan quality, the effect was modest. In this study, RapidPlan demonstrated robustness for moderate proportions of salivary gland dosimetric outliers.« less

Automated confidence ranked classification of randomized controlled trial articles: an aid to evidence-based medicine

PubMed Central

Smalheiser, Neil R; McDonagh, Marian S; Yu, Clement; Adams, Clive E; Davis, John M; Yu, Philip S

2015-01-01

Objective: For many literature review tasks, including systematic review (SR) and other aspects of evidence-based medicine, it is important to know whether an article describes a randomized controlled trial (RCT). Current manual annotation is not complete or flexible enough for the SR process. In this work, highly accurate machine learning predictive models were built that include confidence predictions of whether an article is an RCT. Materials and Methods: The LibSVM classifier was used with forward selection of potential feature sets on a large human-related subset of MEDLINE to create a classification model requiring only the citation, abstract, and MeSH terms for each article. Results: The model achieved an area under the receiver operating characteristic curve of 0.973 and mean squared error of 0.013 on the held out year 2011 data. Accurate confidence estimates were confirmed on a manually reviewed set of test articles. A second model not requiring MeSH terms was also created, and performs almost as well. Discussion: Both models accurately rank and predict article RCT confidence. Using the model and the manually reviewed samples, it is estimated that about 8000 (3%) additional RCTs can be identified in MEDLINE, and that 5% of articles tagged as RCTs in Medline may not be identified. Conclusion: Retagging human-related studies with a continuously valued RCT confidence is potentially more useful for article ranking and review than a simple yes/no prediction. The automated RCT tagging tool should offer significant savings of time and effort during the process of writing SRs, and is a key component of a multistep text mining pipeline that we are building to streamline SR workflow. In addition, the model may be useful for identifying errors in MEDLINE publication types. The RCT confidence predictions described here have been made available to users as a web service with a user query form front end at: http://arrowsmith.psych.uic.edu/cgi-bin/arrowsmith_uic/RCT_Tagger.cgi. PMID:25656516

Pathway Tools version 13.0: integrated software for pathway/genome informatics and systems biology

PubMed Central

Paley, Suzanne M.; Krummenacker, Markus; Latendresse, Mario; Dale, Joseph M.; Lee, Thomas J.; Kaipa, Pallavi; Gilham, Fred; Spaulding, Aaron; Popescu, Liviu; Altman, Tomer; Paulsen, Ian; Keseler, Ingrid M.; Caspi, Ron

2010-01-01

Pathway Tools is a production-quality software environment for creating a type of model-organism database called a Pathway/Genome Database (PGDB). A PGDB such as EcoCyc integrates the evolving understanding of the genes, proteins, metabolic network and regulatory network of an organism. This article provides an overview of Pathway Tools capabilities. The software performs multiple computational inferences including prediction of metabolic pathways, prediction of metabolic pathway hole fillers and prediction of operons. It enables interactive editing of PGDBs by DB curators. It supports web publishing of PGDBs, and provides a large number of query and visualization tools. The software also supports comparative analyses of PGDBs, and provides several systems biology analyses of PGDBs including reachability analysis of metabolic networks, and interactive tracing of metabolites through a metabolic network. More than 800 PGDBs have been created using Pathway Tools by scientists around the world, many of which are curated DBs for important model organisms. Those PGDBs can be exchanged using a peer-to-peer DB sharing system called the PGDB Registry. PMID:19955237

Facultative Stabilization Pond: Measuring Biological Oxygen Demand using Mathematical Approaches

NASA Astrophysics Data System (ADS)

Wira S, Ihsan; Sunarsih, Sunarsih

2018-02-01

Pollution is a man-made phenomenon. Some pollutants which discharged directly to the environment could create serious pollution problems. Untreated wastewater will cause contamination and even pollution on the water body. Biological Oxygen Demand (BOD) is the amount of oxygen required for the oxidation by bacteria. The higher the BOD concentration, the greater the organic matter would be. The purpose of this study was to predict the value of BOD contained in wastewater. Mathematical modeling methods were chosen in this study to depict and predict the BOD values contained in facultative wastewater stabilization ponds. Measurements of sampling data were carried out to validate the model. The results of this study indicated that a mathematical approach can be applied to predict the BOD contained in the facultative wastewater stabilization ponds. The model was validated using Absolute Means Error with 10% tolerance limit, and AME for model was 7.38% (< 10%), so the model is valid. Furthermore, a mathematical approach can also be applied to illustrate and predict the contents of wastewater.

Weather Research and Forecasting Model Wind Sensitivity Study at Edwards Air Force Base, CA

NASA Technical Reports Server (NTRS)

Watson, Leela R.; Bauman, William H., III; Hoeth, Brian

2009-01-01

This abstract describes work that will be done by the Applied Meteorology Unit (AMU) in assessing the success of different model configurations in predicting "wind cycling" cases at Edwards Air Force Base, CA (EAFB), in which the wind speeds and directions oscillate among towers near the EAFB runway. The Weather Research and Forecasting (WRF) model allows users to choose among two dynamical cores - the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM). There are also data assimilation analysis packages available for the initialization of the WRF model - the Local Analysis and Prediction System (LAPS) and the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS). Having a series of initialization options and WRF cores, as well as many options within each core, creates challenges for local forecasters, such as determining which configuration options are best to address specific forecast concerns. The goal of this project is to assess the different configurations available and determine which configuration will best predict surface wind speed and direction at EAFB.

Predictive model for risk of cesarean section in pregnant women after induction of labor.

PubMed

Hernández-Martínez, Antonio; Pascual-Pedreño, Ana I; Baño-Garnés, Ana B; Melero-Jiménez, María R; Tenías-Burillo, José M; Molina-Alarcón, Milagros

2016-03-01

To develop a predictive model for risk of cesarean section in pregnant women after induction of labor. A retrospective cohort study was conducted of 861 induced labors during 2009, 2010, and 2011 at Hospital "La Mancha-Centro" in Alcázar de San Juan, Spain. Multivariate analysis was used with binary logistic regression and areas under the ROC curves to determine predictive ability. Two predictive models were created: model A predicts the outcome at the time the woman is admitted to the hospital (before the decision to of the method of induction); and model B predicts the outcome at the time the woman is definitely admitted to the labor room. The predictive factors in the final model were: maternal height, body mass index, nulliparity, Bishop score, gestational age, macrosomia, gender of fetus, and the gynecologist's overall cesarean section rate. The predictive ability of model A was 0.77 [95% confidence interval (CI) 0.73-0.80] and model B was 0.79 (95% CI 0.76-0.83). The predictive ability for pregnant women with previous cesarean section with model A was 0.79 (95% CI 0.64-0.94) and with model B was 0.80 (95% CI 0.64-0.96). For a probability of estimated cesarean section ≥80%, the models A and B presented a positive likelihood ratio (+LR) for cesarean section of 22 and 20, respectively. Also, for a likelihood of estimated cesarean section ≤10%, the models A and B presented a +LR for vaginal delivery of 13 and 6, respectively. These predictive models have a good discriminative ability, both overall and for all subgroups studied. This tool can be useful in clinical practice, especially for pregnant women with previous cesarean section and diabetes.

Learning Layouts for Single-Page Graphic Designs.

PubMed

O'Donovan, Peter; Agarwala, Aseem; Hertzmann, Aaron

2014-08-01

This paper presents an approach for automatically creating graphic design layouts using a new energy-based model derived from design principles. The model includes several new algorithms for analyzing graphic designs, including the prediction of perceived importance, alignment detection, and hierarchical segmentation. Given the model, we use optimization to synthesize new layouts for a variety of single-page graphic designs. Model parameters are learned with Nonlinear Inverse Optimization (NIO) from a small number of example layouts. To demonstrate our approach, we show results for applications including generating design layouts in various styles, retargeting designs to new sizes, and improving existing designs. We also compare our automatic results with designs created using crowdsourcing and show that our approach performs slightly better than novice designers.

Diffusion-controlled reference material for VOC emissions testing: proof of concept.

PubMed

Cox, S S; Liu, Z; Little, J C; Howard-Reed, C; Nabinger, S J; Persily, A

2010-10-01

Because of concerns about indoor air quality, there is growing awareness of the need to reduce the rate at which indoor materials and products emit volatile organic compounds (VOCs). To meet consumer demand for low emitting products, manufacturers are increasingly submitting materials to independent laboratories for emissions testing. However, the same product tested by different laboratories can result in very different emissions profiles because of a general lack of test validation procedures. There is a need for a reference material that can be used as a known emissions source and that will have the same emission rate when tested by different laboratories under the same conditions. A reference material was created by loading toluene into a polymethyl pentene film. A fundamental emissions model was used to predict the toluene emissions profile. Measured VOC emissions profiles using small-chamber emissions tests compared reasonably well to the emissions profile predicted using the emissions model, demonstrating the feasibility of the proposed approach to create a diffusion-controlled reference material. To calibrate emissions test chambers and improve the reproducibility of VOC emission measurements among different laboratories, a reference material has been created using a polymer film loaded with a representative VOC. Initial results show that the film's VOC emission profile measured in a conventional test chamber compares well to predictions based on independently determined material/chemical properties and a fundamental emissions model. The use of such reference materials has the potential to build consensus and confidence in emissions testing as well as 'level the playing field' for product testing laboratories and manufacturers.

Hybrid Large Eddy Simulation / Reynolds Averaged Navier-Stokes Modeling in Directed Energy Applications

NASA Astrophysics Data System (ADS)

Zilberter, Ilya Alexandrovich

In this work, a hybrid Large Eddy Simulation / Reynolds-Averaged Navier Stokes (LES/RANS) turbulence model is applied to simulate two flows relevant to directed energy applications. The flow solver blends the Menter Baseline turbulence closure near solid boundaries with a Lenormand-type subgrid model in the free-stream with a blending function that employs the ratio of estimated inner and outer turbulent length scales. A Mach 2.2 mixing nozzle/diffuser system representative of a gas laser is simulated under a range of exit pressures to assess the ability of the model to predict the dynamics of the shock train. The simulation captures the location of the shock train responsible for pressure recovery but under-predicts the rate of pressure increase. Predicted turbulence production at the wall is found to be highly sensitive to the behavior of the RANS turbulence model. A Mach 2.3, high-Reynolds number, three-dimensional cavity flow is also simulated in order to compute the wavefront aberrations of an optical beam passing thorough the cavity. The cavity geometry is modeled using an immersed boundary method, and an auxiliary flat plate simulation is performed to replicate the effects of the wind-tunnel boundary layer on the computed optical path difference. Pressure spectra extracted on the cavity walls agree with empirical predictions based on Rossiter's formula. Proper orthogonal modes of the wavefront aberrations in a beam originating from the cavity center agree well with experimental data despite uncertainty about in flow turbulence levels and boundary layer thicknesses over the wind tunnel window. Dynamic mode decomposition of a planar wavefront spanning the cavity reveals that wavefront distortions are driven by shear layer oscillations at the Rossiter frequencies; these disturbances create eddy shocklets that propagate into the free-stream, creating additional optical wavefront distortion.

Simplified realistic human head model for simulating Tumor Treating Fields (TTFields).

PubMed

Wenger, Cornelia; Bomzon, Ze'ev; Salvador, Ricardo; Basser, Peter J; Miranda, Pedro C

2016-08-01

Tumor Treating Fields (TTFields) are alternating electric fields in the intermediate frequency range (100-300 kHz) of low-intensity (1-3 V/cm). TTFields are an anti-mitotic treatment against solid tumors, which are approved for Glioblastoma Multiforme (GBM) patients. These electric fields are induced non-invasively by transducer arrays placed directly on the patient's scalp. Cell culture experiments showed that treatment efficacy is dependent on the induced field intensity. In clinical practice, a software called NovoTalTM uses head measurements to estimate the optimal array placement to maximize the electric field delivery to the tumor. Computational studies predict an increase in the tumor's electric field strength when adapting transducer arrays to its location. Ideally, a personalized head model could be created for each patient, to calculate the electric field distribution for the specific situation. Thus, the optimal transducer layout could be inferred from field calculation rather than distance measurements. Nonetheless, creating realistic head models of patients is time-consuming and often needs user interaction, because automated image segmentation is prone to failure. This study presents a first approach to creating simplified head models consisting of convex hulls of the tissue layers. The model is able to account for anisotropic conductivity in the cortical tissues by using a tensor representation estimated from Diffusion Tensor Imaging. The induced electric field distribution is compared in the simplified and realistic head models. The average field intensities in the brain and tumor are generally slightly higher in the realistic head model, with a maximal ratio of 114% for a simplified model with reasonable layer thicknesses. Thus, the present pipeline is a fast and efficient means towards personalized head models with less complexity involved in characterizing tissue interfaces, while enabling accurate predictions of electric field distribution.

Create full-scale predictive economic models on ROI and innovation with performance computing

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

Joseph, Earl C.; Conway, Steve

The U.S. Department of Energy (DOE), the world's largest buyer and user of supercomputers, awarded IDC Research, Inc. a grant to create two macroeconomic models capable of quantifying, respectively, financial and non-financial (innovation) returns on investments in HPC resources. Following a 2013 pilot study in which we created the models and tested them on about 200 real-world HPC cases, DOE authorized us to conduct a full-out, three-year grant study to collect and measure many more examples, a process that would also subject the methodology to further testing and validation. A secondary, "stretch" goal of the full-out study was to advancemore » the methodology from association toward (but not all the way to) causation, by eliminating the effects of some of the other factors that might be contributing, along with HPC investments, to the returns produced in the investigated projects.« less

Geometrical modeling of complete dental shapes by using panoramic X-ray, digital mouth data and anatomical templates.

PubMed

Barone, Sandro; Paoli, Alessandro; Razionale, Armando Viviano

2015-07-01

In the field of orthodontic planning, the creation of a complete digital dental model to simulate and predict treatments is of utmost importance. Nowadays, orthodontists use panoramic radiographs (PAN) and dental crown representations obtained by optical scanning. However, these data do not contain any 3D information regarding tooth root geometries. A reliable orthodontic treatment should instead take into account entire geometrical models of dental shapes in order to better predict tooth movements. This paper presents a methodology to create complete 3D patient dental anatomies by combining digital mouth models and panoramic radiographs. The modeling process is based on using crown surfaces, reconstructed by optical scanning, and root geometries, obtained by adapting anatomical CAD templates over patient specific information extracted from radiographic data. The radiographic process is virtually replicated on crown digital geometries through the Discrete Radon Transform (DRT). The resulting virtual PAN image is used to integrate the actual radiographic data and the digital mouth model. This procedure provides the root references on the 3D digital crown models, which guide a shape adjustment of the dental CAD templates. The entire geometrical models are finally created by merging dental crowns, captured by optical scanning, and root geometries, obtained from the CAD templates. Copyright © 2015 Elsevier Ltd. All rights reserved.

Southern Ocean bottom water characteristics in CMIP5 models

NASA Astrophysics Data System (ADS)

Heuzé, CéLine; Heywood, Karen J.; Stevens, David P.; Ridley, Jeff K.

2013-04-01

Southern Ocean deep water properties and formation processes in climate models are indicative of their capability to simulate future climate, heat and carbon uptake, and sea level rise. Southern Ocean temperature and density averaged over 1986-2005 from 15 CMIP5 (Coupled Model Intercomparison Project Phase 5) climate models are compared with an observed climatology, focusing on bottom water. Bottom properties are reasonably accurate for half the models. Ten models create dense water on the Antarctic shelf, but it mixes with lighter water and is not exported as bottom water as in reality. Instead, most models create deep water by open ocean deep convection, a process occurring rarely in reality. Models with extensive deep convection are those with strong seasonality in sea ice. Optimum bottom properties occur in models with deep convection in the Weddell and Ross Gyres. Bottom Water formation processes are poorly represented in ocean models and are a key challenge for improving climate predictions.

Fire risk in San Diego County, California: A weighted Bayesian model approach

USGS Publications Warehouse

Kolden, Crystal A.; Weigel, Timothy J.

2007-01-01

Fire risk models are widely utilized to mitigate wildfire hazards, but models are often based on expert opinions of less understood fire-ignition and spread processes. In this study, we used an empirically derived weights-of-evidence model to assess what factors produce fire ignitions east of San Diego, California. We created and validated a dynamic model of fire-ignition risk based on land characteristics and existing fire-ignition history data, and predicted ignition risk for a future urbanization scenario. We then combined our empirical ignition-risk model with a fuzzy fire behavior-risk model developed by wildfire experts to create a hybrid model of overall fire risk. We found that roads influence fire ignitions and that future growth will increase risk in new rural development areas. We conclude that empirically derived risk models and hybrid models offer an alternative method to assess current and future fire risk based on management actions.

The origin of the asymmetry in the Iceland hotspot along the Mid-Atlantic Ridge from continental breakup to present-day

NASA Astrophysics Data System (ADS)

Howell, Samuel M.; Ito, Garrett; Breivik, Asbjørn J.; Rai, Abhishek; Mjelde, Rolf; Hanan, Barry; Sayit, Kaan; Vogt, Peter

2014-04-01

The Iceland hotspot has profoundly influenced the creation of oceanic crust throughout the North Atlantic basin. Enigmatically, the geographic extent of the hotspot influence along the Mid-Atlantic Ridge has been asymmetric for most of the spreading history. This asymmetry is evident in crustal thickness along the present-day ridge system and anomalously shallow seafloor of ages ∼49-25 Ma created at the Reykjanes Ridge (RR), SSW of the hotspot center, compared to deeper seafloor created by the now-extinct Aegir Ridge (AR) the same distance NE of the hotspot center. The cause of this asymmetry is explored with 3-D numerical models that simulate a mantle plume interacting with the ridge system using realistic ridge geometries and spreading rates that evolve from continental breakup to present-day. The models predict plume-influence to be symmetric at continental breakup, then to rapidly contract along the ridges, resulting in widely influenced margins next to uninfluenced oceanic crust. After this initial stage, varying degrees of asymmetry along the mature ridge segments are predicted. Models in which the lithosphere is created by the stiffening of the mantle due to the extraction of water near the base of the melting zone predict a moderate amount of asymmetry; the plume expands NE along the AR ∼70-80% as far as it expands SSW along the RR. Without dehydration stiffening, the lithosphere corresponds to the near-surface, cool, thermal boundary layer; in these cases, the plume is predicted to be even more asymmetric, expanding only 40-50% as far along the AR as it does along the RR. Estimates of asymmetry and seismically measured crustal thicknesses are best explained by model predictions of an Iceland plume volume flux of ∼100-200 m/s, and a lithosphere controlled by a rheology in which dehydration stiffens the mantle, but to a lesser degree than simulated here. The asymmetry of influence along the present-day ridge system is predicted to be a transient configuration in which plume influence along the Reykjanes Ridge is steady, but is still widening along the Kolbeinsey Ridge, as it has been since this ridge formed at ∼25 Ma.

Creation of a Scorecard to Predict In-Hospital Death in Patients Undergoing Operations for Acute Type A Aortic Dissection.

PubMed

Leontyev, Sergey; Légaré, Jean-Francois; Borger, Michael A; Buth, Karen J; Funkat, Anne K; Gerhard, Jochann; Mohr, Friedrich W

2016-05-01

This study evaluated preoperative predictors of in-hospital death for the surgical treatment of patients with acute type A aortic dissection (Type A) and created an easy-to-use scorecard to predict in-hospital death. We reviewed retrospectively all consecutive patients who underwent operations for acute Type A between 1996 and 2011 at 2 tertiary care institutions. A logistic regression model was created to identify independent preoperative predictors of in-hospital death. The results were used to create a scorecard predicting operative risk. Emergency operations were performed in 534 consecutive patients for acute Type A. Mean age was 61 ± 14 years and 36.3% were women. Critical preoperative state was present in 31% of patients and malperfusion of one or more end organs in 36%. Unadjusted in-hospital mortality was 18.7% and not significantly different between institutions. Independent predictors of in-hospital death were age 50 to 70 years (odds ratio [OR], 3.8; p = 0.001), age older than 70 years (OR, 2.8; p = 0.03), critical preoperative state (OR, 3.2; p < 0.001), visceral malperfusion (OR, 3.0; p = 0.003), and coronary artery disease (OR, 2.2; p = 0.006). Age younger than 50 years (OR, 0.3; p = 0.01) was protective for early survival. Using this information, we created an easily usable mortality risk score based on these variables. The patients were stratified into four risk categories predicting in-hospital death: less than 10%, 10% to 25%, 25% to 50%, and more than 50%. This represents one of the largest series of patients with Type A in which a risk model was created. Using our approach, we have shown that age, critical preoperative state, and malperfusion syndrome were strong independent risk factors for early death and could be used for the preoperative risk assessment. Copyright © 2016 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement.

PubMed

Collins, G S; Reitsma, J B; Altman, D G; Moons, K G M

2015-01-20

Prediction models are developed to aid health-care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health-care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement.

PubMed

Collins, Gary S; Reitsma, Johannes B; Altman, Douglas G; Moons, Karel G M

2015-02-01

Prediction models are developed to aid healthcare providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision-making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) initiative developed a set of recommendations for the reporting of studies developing, validating or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, healthcare professionals and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). © 2015 Stichting European Society for Clinical Investigation Journal Foundation.

Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement.

PubMed

Collins, Gary S; Reitsma, Johannes B; Altman, Douglas G; Moons, Karel G M

2015-01-06

Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).

Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD)

PubMed Central

Reitsma, Johannes B.; Altman, Douglas G.; Moons, Karel G.M.

2015-01-01

Background— Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. Methods— The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. Results— The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. Conclusions— To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). PMID:25561516

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement

PubMed Central

Collins, G S; Reitsma, J B; Altman, D G; Moons, K G M

2015-01-01

Prediction models are developed to aid health-care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health-care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). PMID:25562432

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement.

PubMed

Collins, G S; Reitsma, J B; Altman, D G; Moons, K G M

2015-02-01

Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). © 2015 Royal College of Obstetricians and Gynaecologists.

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. The TRIPOD Group.

PubMed

Collins, Gary S; Reitsma, Johannes B; Altman, Douglas G; Moons, Karel G M

2015-01-13

Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). © 2015 The Authors.

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement.

PubMed

Collins, Gary S; Reitsma, Johannes B; Altman, Douglas G; Moons, Karel G M

2015-01-06

Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).

Transparent reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement.

PubMed

Collins, Gary S; Reitsma, Johannes B; Altman, Douglas G; Moons, Karel G M

2015-02-01

Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). Copyright © 2015 Elsevier Inc. All rights reserved.

A system identification approach for developing model predictive controllers of antibody quality attributes in cell culture processes.

PubMed

Downey, Brandon; Schmitt, John; Beller, Justin; Russell, Brian; Quach, Anthony; Hermann, Elizabeth; Lyon, David; Breit, Jeffrey

2017-11-01

As the biopharmaceutical industry evolves to include more diverse protein formats and processes, more robust control of Critical Quality Attributes (CQAs) is needed to maintain processing flexibility without compromising quality. Active control of CQAs has been demonstrated using model predictive control techniques, which allow development of processes which are robust against disturbances associated with raw material variability and other potentially flexible operating conditions. Wide adoption of model predictive control in biopharmaceutical cell culture processes has been hampered, however, in part due to the large amount of data and expertise required to make a predictive model of controlled CQAs, a requirement for model predictive control. Here we developed a highly automated, perfusion apparatus to systematically and efficiently generate predictive models using application of system identification approaches. We successfully created a predictive model of %galactosylation using data obtained by manipulating galactose concentration in the perfusion apparatus in serialized step change experiments. We then demonstrated the use of the model in a model predictive controller in a simulated control scenario to successfully achieve a %galactosylation set point in a simulated fed-batch culture. The automated model identification approach demonstrated here can potentially be generalized to many CQAs, and could be a more efficient, faster, and highly automated alternative to batch experiments for developing predictive models in cell culture processes, and allow the wider adoption of model predictive control in biopharmaceutical processes. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 33:1647-1661, 2017. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers.

Evaluation of Industry Standard Turbulence Models on an Axisymmetric Supersonic Compression Corner

NASA Technical Reports Server (NTRS)

DeBonis, James R.

2015-01-01

Reynolds-averaged Navier-Stokes computations of a shock-wave/boundary-layer interaction (SWBLI) created by a Mach 2.85 flow over an axisymmetric 30-degree compression corner were carried out. The objectives were to evaluate four turbulence models commonly used in industry, for SWBLIs, and to evaluate the suitability of this test case for use in further turbulence model benchmarking. The Spalart-Allmaras model, Menter's Baseline and Shear Stress Transport models, and a low-Reynolds number k- model were evaluated. Results indicate that the models do not accurately predict the separation location; with the SST model predicting the separation onset too early and the other models predicting the onset too late. Overall the Spalart-Allmaras model did the best job in matching the experimental data. However there is significant room for improvement, most notably in the prediction of the turbulent shear stress. Density data showed that the simulations did not accurately predict the thermal boundary layer upstream of the SWBLI. The effect of turbulent Prandtl number and wall temperature were studied in an attempt to improve this prediction and understand their effects on the interaction. The data showed that both parameters can significantly affect the separation size and location, but did not improve the agreement with the experiment. This case proved challenging to compute and should provide a good test for future turbulence modeling work.

Assimilation of Satellite to Improve Cloud Simulation in Wrf Model

NASA Astrophysics Data System (ADS)

Park, Y. H.; Pour Biazar, A.; McNider, R. T.

2012-12-01

A simple approach has been introduced to improve cloud simulation spatially and temporally in a meteorological model. The first step for this approach is to use Geostationary Operational Environmental Satellite (GOES) observations to identify clouds and estimate the clouds structure. Then by comparing GOES observations to model cloud field, we identify areas in which model has under-predicted or over-predicted clouds. Next, by introducing subsidence in areas with over-prediction and lifting in areas with under-prediction, erroneous clouds are removed and new clouds are formed. The technique estimates a vertical velocity needed for the cloud correction and then uses a one dimensional variation schemes (1D_Var) to calculate the horizontal divergence components and the consequent horizontal wind components needed to sustain such vertical velocity. Finally, the new horizontal winds are provided as a nudging field to the model. This nudging provides the dynamical support needed to create/clear clouds in a sustainable manner. The technique was implemented and tested in the Weather Research and Forecast (WRF) Model and resulted in substantial improvement in model simulated clouds. Some of the results are presented here.

Modelling the snowmelt and the snow water equivalent by creating a simplified energy balance conceptual snow model

NASA Astrophysics Data System (ADS)

Riboust, Philippe; Thirel, Guillaume; Le Moine, Nicolas; Ribstein, Pierre

2016-04-01

A better knowledge of the accumulated snow on the watersheds will help flood forecasting centres and hydro-power companies to predict the amount of water released during spring snowmelt. Since precipitations gauges are sparse at high elevations and integrative measurements of the snow accumulated on watershed surface are hard to obtain, using snow models is an adequate way to estimate snow water equivalent (SWE) on watersheds. In addition to short term prediction, simulating accurately SWE with snow models should have many advantages. Validating the snow module on both SWE and snowmelt should give a more reliable model for climate change studies or regionalization for ungauged watersheds. The aim of this study is to create a new snow module, which has a structure that allows the use of measured snow data for calibration or assimilation. Energy balance modelling seems to be the logical choice for designing a model in which internal variables, such as SWE, could be compared to observations. Physical models are complex, needing high computational resources and many different types of inputs that are not widely measured at meteorological stations. At the opposite, simple conceptual degree-day models offer to simulate snowmelt using only temperature and precipitation as inputs with fast computing. Its major drawback is to be empirical, i.e. not taking into account all of the processes of the energy balance, which makes this kind of model more difficult to use when willing to compare SWE to observed measurements. In order to reach our objectives, we created a snow model structured by a simplified energy balance where each of the processes is empirically parameterized in order to be calculated using only temperature, precipitation and cloud cover variables. This model's structure is similar to the one created by M.T. Walter (2005), where parameterizations from the literature were used to compute all of the processes of the energy balance. The conductive fluxes into the snowpack were modelled by using analytical solutions to the heat equation taking phase change into account. This approach has the advantage to use few forcing variables and to take into account all the processes of the energy balance. Indeed, the simulations should be quick enough to allow, for example, ensemble prediction or simulation of numerous basins, more easily than physical snow models. The snow module formulation has been completed and is in its validation phase using data from the experimental station of Col de Porte, Alpes, France. Data from the US SNOTEL product will be used in order to test the model structure on a larger scale and to test diverse calibration procedures, since the aim is to use it on a basin scale for discharge modelling purposes.

Twist Model Development and Results from the Active Aeroelastic Wing F/A-18 Aircraft

NASA Technical Reports Server (NTRS)

Lizotte, Andrew M.; Allen, Michael J.

2007-01-01

Understanding the wing twist of the active aeroelastic wing (AAW) F/A-18 aircraft is a fundamental research objective for the program and offers numerous benefits. In order to clearly understand the wing flexibility characteristics, a model was created to predict real-time wing twist. A reliable twist model allows the prediction of twist for flight simulation, provides insight into aircraft performance uncertainties, and assists with computational fluid dynamic and aeroelastic issues. The left wing of the aircraft was heavily instrumented during the first phase of the active aeroelastic wing program allowing deflection data collection. Traditional data processing steps were taken to reduce flight data, and twist predictions were made using linear regression techniques. The model predictions determined a consistent linear relationship between the measured twist and aircraft parameters, such as surface positions and aircraft state variables. Error in the original model was reduced in some cases by using a dynamic pressure-based assumption. This technique produced excellent predictions for flight between the standard test points and accounted for nonlinearities in the data. This report discusses data processing techniques and twist prediction validation, and provides illustrative and quantitative results.

Twist Model Development and Results From the Active Aeroelastic Wing F/A-18 Aircraft

NASA Technical Reports Server (NTRS)

Lizotte, Andrew; Allen, Michael J.

2005-01-01

Understanding the wing twist of the active aeroelastic wing F/A-18 aircraft is a fundamental research objective for the program and offers numerous benefits. In order to clearly understand the wing flexibility characteristics, a model was created to predict real-time wing twist. A reliable twist model allows the prediction of twist for flight simulation, provides insight into aircraft performance uncertainties, and assists with computational fluid dynamic and aeroelastic issues. The left wing of the aircraft was heavily instrumented during the first phase of the active aeroelastic wing program allowing deflection data collection. Traditional data processing steps were taken to reduce flight data, and twist predictions were made using linear regression techniques. The model predictions determined a consistent linear relationship between the measured twist and aircraft parameters, such as surface positions and aircraft state variables. Error in the original model was reduced in some cases by using a dynamic pressure-based assumption and by using neural networks. These techniques produced excellent predictions for flight between the standard test points and accounted for nonlinearities in the data. This report discusses data processing techniques and twist prediction validation, and provides illustrative and quantitative results.

Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles.

PubMed

Lampa, Samuel; Alvarsson, Jonathan; Spjuth, Ola

2016-01-01

Predictive modelling in drug discovery is challenging to automate as it often contains multiple analysis steps and might involve cross-validation and parameter tuning that create complex dependencies between tasks. With large-scale data or when using computationally demanding modelling methods, e-infrastructures such as high-performance or cloud computing are required, adding to the existing challenges of fault-tolerant automation. Workflow management systems can aid in many of these challenges, but the currently available systems are lacking in the functionality needed to enable agile and flexible predictive modelling. We here present an approach inspired by elements of the flow-based programming paradigm, implemented as an extension of the Luigi system which we name SciLuigi. We also discuss the experiences from using the approach when modelling a large set of biochemical interactions using a shared computer cluster.Graphical abstract.

Comparative Analysis of Soft Computing Models in Prediction of Bending Rigidity of Cotton Woven Fabrics

NASA Astrophysics Data System (ADS)

Guruprasad, R.; Behera, B. K.

2015-10-01

Quantitative prediction of fabric mechanical properties is an essential requirement for design engineering of textile and apparel products. In this work, the possibility of prediction of bending rigidity of cotton woven fabrics has been explored with the application of Artificial Neural Network (ANN) and two hybrid methodologies, namely Neuro-genetic modeling and Adaptive Neuro-Fuzzy Inference System (ANFIS) modeling. For this purpose, a set of cotton woven grey fabrics was desized, scoured and relaxed. The fabrics were then conditioned and tested for bending properties. With the database thus created, a neural network model was first developed using back propagation as the learning algorithm. The second model was developed by applying a hybrid learning strategy, in which genetic algorithm was first used as a learning algorithm to optimize the number of neurons and connection weights of the neural network. The Genetic algorithm optimized network structure was further allowed to learn using back propagation algorithm. In the third model, an ANFIS modeling approach was attempted to map the input-output data. The prediction performances of the models were compared and a sensitivity analysis was reported. The results show that the prediction by neuro-genetic and ANFIS models were better in comparison with that of back propagation neural network model.

Standardization of milk infrared spectra for the retroactive application of calibration models.

PubMed

Bonfatti, V; Fleming, A; Koeck, A; Miglior, F

2017-03-01

The objective of this study was to standardize the infrared spectra obtained over time and across 2 milk laboratories of Canada to create a uniform historical database and allow (1) the retroactive application of calibration models for prediction of fine milk composition; and (2) the direct use of spectral information for the development of indicators of animal health and efficiency. Spectral variation across laboratories and over time was inspected by principal components analysis (PCA). Shifts in the PCA scores were detected over time, leading to the definition of different subsets of spectra having homogeneous infrared signal. To evaluate the possibility of using common equations on spectra collected by the 2 instruments and over time, we developed a standardization (STD) method. For each subset of data having homogeneous infrared signal, a total of 99 spectra corresponding to the percentiles of the distribution of the absorbance at each wavenumber were created and used to build the STD matrices. Equations predicting contents of saturated fatty acids, short-chain fatty acids, and C18:0 were created and applied on different subsets of spectra, before and after STD. After STD, bias and root mean squared error of prediction decreased by 66% and 32%, respectively. When calibration equations were applied to the historical nonstandardized database of spectra, shifts in the predictions could be observed over time for all investigated traits. Shifts in the distribution of the predictions over time corresponded to the shifts identified by the inspection of the PCA scores. After STD, shifts in the predicted fatty acid contents were greatly reduced. Standardization reduced spectral variability between instruments and over time, allowing the merging of milk spectra data from different instruments into a common database, the retroactive use of calibrations equations, or the direct use of the spectral data without restrictions. Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Fine-Scale Mapping by Spatial Risk Distribution Modeling for Regional Malaria Endemicity and Its Implications under the Low-to-Moderate Transmission Setting in Western Cambodia

PubMed Central

Okami, Suguru; Kohtake, Naohiko

2016-01-01

The disease burden of malaria has decreased as malaria elimination efforts progress. The mapping approach that uses spatial risk distribution modeling needs some adjustment and reinvestigation in accordance with situational changes. Here we applied a mathematical modeling approach for standardized morbidity ratio (SMR) calculated by annual parasite incidence using routinely aggregated surveillance reports, environmental data such as remote sensing data, and non-environmental anthropogenic data to create fine-scale spatial risk distribution maps of western Cambodia. Furthermore, we incorporated a combination of containment status indicators into the model to demonstrate spatial heterogeneities of the relationship between containment status and risks. The explanatory model was fitted to estimate the SMR of each area (adjusted Pearson correlation coefficient R2 = 0.774; Akaike information criterion AIC = 149.423). A Bayesian modeling framework was applied to estimate the uncertainty of the model and cross-scale predictions. Fine-scale maps were created by the spatial interpolation of estimated SMRs at each village. Compared with geocoded case data, corresponding predicted values showed conformity [Spearman’s rank correlation r = 0.662 in the inverse distance weighed interpolation and 0.645 in ordinal kriging (95% confidence intervals of 0.414–0.827 and 0.368–0.813, respectively), Welch’s t-test; Not significant]. The proposed approach successfully explained regional malaria risks and fine-scale risk maps were created under low-to-moderate malaria transmission settings where reinvestigations of existing risk modeling approaches were needed. Moreover, different representations of simulated outcomes of containment status indicators for respective areas provided useful insights for tailored interventional planning, considering regional malaria endemicity. PMID:27415623

Toward Enhancing Automated Credibility Assessment: A Model for Question Type Classification and Tools for Linguistic Analysis

ERIC Educational Resources Information Center

Moffitt, Kevin Christopher

2011-01-01

The three objectives of this dissertation were to develop a question type model for predicting linguistic features of responses to interview questions, create a tool for linguistic analysis of documents, and use lexical bundle analysis to identify linguistic differences between fraudulent and non-fraudulent financial reports. First, The Moffitt…

Interactions of changing climate and shifts in forest composition on stand carbon balance

Treesearch

Chiang Jyh-Min; Louis Iverson; Anantha Prasad; Kim Brown

2006-01-01

Given that climate influences forest biogeographic distribution, many researchers have created models predicting shifts in tree species range with future climate change scenarios. The objective of this study is to investigate the forest carbon consequences of shifts in stand species composition with current and future climate scenarios using such a model.

Mapping the current and potential distribution of red spruce in Virginia: implications for the restoration of degraded high elevation habitat

Treesearch

Heather Griscom; Helmut Kraenzle; Zachary. Bortolot

2010-01-01

The objective of our project is to create a habitat suitability model to predict potential and future red spruce forest distributions. This model will be used to better understand the influence of climate change on red spruce distribution and to help guide forest restoration efforts.

A general-purpose machine learning framework for predicting properties of inorganic materials

DOE PAGES

Ward, Logan; Agrawal, Ankit; Choudhary, Alok; ...

2016-08-26

A very active area of materials research is to devise methods that use machine learning to automatically extract predictive models from existing materials data. While prior examples have demonstrated successful models for some applications, many more applications exist where machine learning can make a strong impact. To enable faster development of machine-learning-based models for such applications, we have created a framework capable of being applied to a broad range of materials data. Our method works by using a chemically diverse list of attributes, which we demonstrate are suitable for describing a wide variety of properties, and a novel method formore » partitioning the data set into groups of similar materials to boost the predictive accuracy. In this manuscript, we demonstrate how this new method can be used to predict diverse properties of crystalline and amorphous materials, such as band gap energy and glass-forming ability.« less

Investigation into the propagation of Omega very low frequency signals and techniques for improvement of navigation accuracy including differential and composite omega

NASA Technical Reports Server (NTRS)

1973-01-01

An analysis of Very Low Frequency propagation in the atmosphere in the 10-14 kHz range leads to a discussion of some of the more significant causes of phase perturbation. The method of generating sky-wave corrections to predict the Omega phase is discussed. Composite Omega is considered as a means of lane identification and of reducing Omega navigation error. A simple technique for generating trapezoidal model (T-model) phase prediction is presented and compared with the Navy predictions and actual phase measurements. The T-model prediction analysis illustrates the ability to account for the major phase shift created by the diurnal effects on the lower ionosphere. An analysis of the Navy sky-wave correction table is used to provide information about spatial and temporal correlation of phase correction relative to the differential mode of operation.

A general-purpose machine learning framework for predicting properties of inorganic materials

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

Ward, Logan; Agrawal, Ankit; Choudhary, Alok

A very active area of materials research is to devise methods that use machine learning to automatically extract predictive models from existing materials data. While prior examples have demonstrated successful models for some applications, many more applications exist where machine learning can make a strong impact. To enable faster development of machine-learning-based models for such applications, we have created a framework capable of being applied to a broad range of materials data. Our method works by using a chemically diverse list of attributes, which we demonstrate are suitable for describing a wide variety of properties, and a novel method formore » partitioning the data set into groups of similar materials to boost the predictive accuracy. In this manuscript, we demonstrate how this new method can be used to predict diverse properties of crystalline and amorphous materials, such as band gap energy and glass-forming ability.« less

First-Principles Prediction of Liquid/Liquid Interfacial Tension.

PubMed

Andersson, M P; Bennetzen, M V; Klamt, A; Stipp, S L S

2014-08-12

The interfacial tension between two liquids is the free energy per unit surface area required to create that interface. Interfacial tension is a determining factor for two-phase liquid behavior in a wide variety of systems ranging from water flooding in oil recovery processes and remediation of groundwater aquifers contaminated by chlorinated solvents to drug delivery and a host of industrial processes. Here, we present a model for predicting interfacial tension from first principles using density functional theory calculations. Our model requires no experimental input and is applicable to liquid/liquid systems of arbitrary compositions. The consistency of the predictions with experimental data is significant for binary, ternary, and multicomponent water/organic compound systems, which offers confidence in using the model to predict behavior where no data exists. The method is fast and can be used as a screening technique as well as to extend experimental data into conditions where measurements are technically too difficult, time consuming, or impossible.

Nonlinear modeling of chaotic time series: Theory and applications

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

Casdagli, M.; Eubank, S.; Farmer, J.D.

1990-01-01

We review recent developments in the modeling and prediction of nonlinear time series. In some cases apparent randomness in time series may be due to chaotic behavior of a nonlinear but deterministic system. In such cases it is possible to exploit the determinism to make short term forecasts that are much more accurate than one could make from a linear stochastic model. This is done by first reconstructing a state space, and then using nonlinear function approximation methods to create a dynamical model. Nonlinear models are valuable not only as short term forecasters, but also as diagnostic tools for identifyingmore » and quantifying low-dimensional chaotic behavior. During the past few years methods for nonlinear modeling have developed rapidly, and have already led to several applications where nonlinear models motivated by chaotic dynamics provide superior predictions to linear models. These applications include prediction of fluid flows, sunspots, mechanical vibrations, ice ages, measles epidemics and human speech. 162 refs., 13 figs.« less

Automated docking of ligands to an artificial active site: augmenting crystallographic analysis with computer modeling

NASA Astrophysics Data System (ADS)

Rosenfeld, Robin J.; Goodsell, David S.; Musah, Rabi A.; Morris, Garrett M.; Goodin, David B.; Olson, Arthur J.

2003-08-01

The W191G cavity of cytochrome c peroxidase is useful as a model system for introducing small molecule oxidation in an artificially created cavity. A set of small, cyclic, organic cations was previously shown to bind in the buried, solvent-filled pocket created by the W191G mutation. We docked these ligands and a set of non-binders in the W191G cavity using AutoDock 3.0. For the ligands, we compared docking predictions with experimentally determined binding energies and X-ray crystal structure complexes. For the ligands, predicted binding energies differed from measured values by ± 0.8 kcal/mol. For most ligands, the docking simulation clearly predicted a single binding mode that matched the crystallographic binding mode within 1.0 Å RMSD. For 2 ligands, where the docking procedure yielded an ambiguous result, solutions matching the crystallographic result could be obtained by including an additional crystallographically observed water molecule in the protein model. For the remaining 2 ligands, docking indicated multiple binding modes, consistent with the original electron density, suggesting disordered binding of these ligands. Visual inspection of the atomic affinity grid maps used in docking calculations revealed two patches of high affinity for hydrogen bond donating groups. Multiple solutions are predicted as these two sites compete for polar hydrogens in the ligand during the docking simulation. Ligands could be distinguished, to some extent, from non-binders using a combination of two trends: predicted binding energy and level of clustering. In summary, AutoDock 3.0 appears to be useful in predicting key structural and energetic features of ligand binding in the W191G cavity.

Modeling and measurement of hydrogen radical densities of in situ plasma-based Sn cleaning source

NASA Astrophysics Data System (ADS)

Elg, Daniel T.; Panici, Gianluca A.; Peck, Jason A.; Srivastava, Shailendra N.; Ruzic, David N.

2017-04-01

Extreme ultraviolet (EUV) lithography sources expel Sn debris. This debris deposits on the collector optic used to focus the EUV light, lowering its reflectivity and EUV throughput to the wafer. Consequently, the collector must be cleaned, causing source downtime. To solve this, a hydrogen plasma source was developed to clean the collector in situ by using the collector as an antenna to create a hydrogen plasma and create H radicals, which etch Sn as SnH4. This technique has been shown to remove Sn from a 300-mm-diameter stainless steel dummy collector. The H radical density is of key importance in Sn etching. The effects of power, pressure, and flow on radical density are explored. A catalytic probe has been used to measure radical density, and a zero-dimensional model is used to provide the fundamental science behind radical creation and predict radical densities. Model predictions and experimental measurements are in good agreement. The trends observed in radical density, contrasted with measured Sn removal rates, show that radical density is not the limiting factor in this etching system; other factors, such as SnH4 redeposition and energetic ion bombardment, must be more fully understood in order to predict removal rates.

Subarachnoid hemorrhage admissions retrospectively identified using a prediction model

PubMed Central

McIntyre, Lauralyn; Fergusson, Dean; Turgeon, Alexis; dos Santos, Marlise P.; Lum, Cheemun; Chassé, Michaël; Sinclair, John; Forster, Alan; van Walraven, Carl

2016-01-01

Objective: To create an accurate prediction model using variables collected in widely available health administrative data records to identify hospitalizations for primary subarachnoid hemorrhage (SAH). Methods: A previously established complete cohort of consecutive primary SAH patients was combined with a random sample of control hospitalizations. Chi-square recursive partitioning was used to derive and internally validate a model to predict the probability that a patient had primary SAH (due to aneurysm or arteriovenous malformation) using health administrative data. Results: A total of 10,322 hospitalizations with 631 having primary SAH (6.1%) were included in the study (5,122 derivation, 5,200 validation). In the validation patients, our recursive partitioning algorithm had a sensitivity of 96.5% (95% confidence interval [CI] 93.9–98.0), a specificity of 99.8% (95% CI 99.6–99.9), and a positive likelihood ratio of 483 (95% CI 254–879). In this population, patients meeting criteria for the algorithm had a probability of 45% of truly having primary SAH. Conclusions: Routinely collected health administrative data can be used to accurately identify hospitalized patients with a high probability of having a primary SAH. This algorithm may allow, upon validation, an easy and accurate method to create validated cohorts of primary SAH from either ruptured aneurysm or arteriovenous malformation. PMID:27629096

Countering imbalanced datasets to improve adverse drug event predictive models in labor and delivery.

PubMed

Taft, L M; Evans, R S; Shyu, C R; Egger, M J; Chawla, N; Mitchell, J A; Thornton, S N; Bray, B; Varner, M

2009-04-01

The IOM report, Preventing Medication Errors, emphasizes the overall lack of knowledge of the incidence of adverse drug events (ADE). Operating rooms, emergency departments and intensive care units are known to have a higher incidence of ADE. Labor and delivery (L&D) is an emergency care unit that could have an increased risk of ADE, where reported rates remain low and under-reporting is suspected. Risk factor identification with electronic pattern recognition techniques could improve ADE detection rates. The objective of the present study is to apply Synthetic Minority Over Sampling Technique (SMOTE) as an enhanced sampling method in a sparse dataset to generate prediction models to identify ADE in women admitted for labor and delivery based on patient risk factors and comorbidities. By creating synthetic cases with the SMOTE algorithm and using a 10-fold cross-validation technique, we demonstrated improved performance of the Naïve Bayes and the decision tree algorithms. The true positive rate (TPR) of 0.32 in the raw dataset increased to 0.67 in the 800% over-sampled dataset. Enhanced performance from classification algorithms can be attained with the use of synthetic minority class oversampling techniques in sparse clinical datasets. Predictive models created in this manner can be used to develop evidence based ADE monitoring systems.

Using the landmark method for creating prediction models in large datasets derived from electronic health records.

PubMed

Wells, Brian J; Chagin, Kevin M; Li, Liang; Hu, Bo; Yu, Changhong; Kattan, Michael W

2015-03-01

With the integration of electronic health records (EHRs), health data has become easily accessible and abounded. The EHR has the potential to provide important healthcare information to researchers by creating study cohorts. However, accessing this information comes with three major issues: 1) Predictor variables often change over time, 2) Patients have various lengths of follow up within the EHR, and 3) the size of the EHR data can be computationally challenging. Landmark analyses provide a perfect complement to EHR data and help to alleviate these three issues. We present two examples that utilize patient birthdays as landmark times for creating dynamic datasets for predicting clinical outcomes. The use of landmark times help to solve these three issues by incorporating information that changes over time, by creating unbiased reference points that are not related to a patient's exposure within the EHR, and reducing the size of a dataset compared to true time-varying analysis. These techniques are shown using two example cohort studies from the Cleveland Clinic that utilized 4.5 million and 17,787 unique patients, respectively.

Narrowing the scope of failure prediction using targeted fault load injection

NASA Astrophysics Data System (ADS)

Jordan, Paul L.; Peterson, Gilbert L.; Lin, Alan C.; Mendenhall, Michael J.; Sellers, Andrew J.

2018-05-01

As society becomes more dependent upon computer systems to perform increasingly critical tasks, ensuring that those systems do not fail becomes increasingly important. Many organizations depend heavily on desktop computers for day-to-day operations. Unfortunately, the software that runs on these computers is written by humans and, as such, is still subject to human error and consequent failure. A natural solution is to use statistical machine learning to predict failure. However, since failure is still a relatively rare event, obtaining labelled training data to train these models is not a trivial task. This work presents new simulated fault-inducing loads that extend the focus of traditional fault injection techniques to predict failure in the Microsoft enterprise authentication service and Apache web server. These new fault loads were successful in creating failure conditions that were identifiable using statistical learning methods, with fewer irrelevant faults being created.

Surrogate Analysis and Index Developer (SAID) tool

USGS Publications Warehouse

Domanski, Marian M.; Straub, Timothy D.; Landers, Mark N.

2015-10-01

The regression models created in SAID can be used in utilities that have been developed to work with the USGS National Water Information System (NWIS) and for the USGS National Real-Time Water Quality (NRTWQ) Web site. The real-time dissemination of predicted SSC and prediction intervals for each time step has substantial potential to improve understanding of sediment-related water quality and associated engineering and ecological management decisions.

Comparison of Basic and Ensemble Data Mining Methods in Predicting 5-Year Survival of Colorectal Cancer Patients.

PubMed

Pourhoseingholi, Mohamad Amin; Kheirian, Sedigheh; Zali, Mohammad Reza

2017-12-01

Colorectal cancer (CRC) is one of the most common malignancies and cause of cancer mortality worldwide. Given the importance of predicting the survival of CRC patients and the growing use of data mining methods, this study aims to compare the performance of models for predicting 5-year survival of CRC patients using variety of basic and ensemble data mining methods. The CRC dataset from The Shahid Beheshti University of Medical Sciences Research Center for Gastroenterology and Liver Diseases were used for prediction and comparative study of the base and ensemble data mining techniques. Feature selection methods were used to select predictor attributes for classification. The WEKA toolkit and MedCalc software were respectively utilized for creating and comparing the models. The obtained results showed that the predictive performance of developed models was altogether high (all greater than 90%). Overall, the performance of ensemble models was higher than that of basic classifiers and the best result achieved by ensemble voting model in terms of area under the ROC curve (AUC= 0.96). AUC Comparison of models showed that the ensemble voting method significantly outperformed all models except for two methods of Random Forest (RF) and Bayesian Network (BN) considered the overlapping 95% confidence intervals. This result may indicate high predictive power of these two methods along with ensemble voting for predicting 5-year survival of CRC patients.

Developing a Global Database of Historic Flood Events to Support Machine Learning Flood Prediction in Google Earth Engine

NASA Astrophysics Data System (ADS)

Tellman, B.; Sullivan, J.; Kettner, A.; Brakenridge, G. R.; Slayback, D. A.; Kuhn, C.; Doyle, C.

2016-12-01

There is an increasing need to understand flood vulnerability as the societal and economic effects of flooding increases. Risk models from insurance companies and flood models from hydrologists must be calibrated based on flood observations in order to make future predictions that can improve planning and help societies reduce future disasters. Specifically, to improve these models both traditional methods of flood prediction from physically based models as well as data-driven techniques, such as machine learning, require spatial flood observation to validate model outputs and quantify uncertainty. A key dataset that is missing for flood model validation is a global historical geo-database of flood event extents. Currently, the most advanced database of historical flood extent is hosted and maintained at the Dartmouth Flood Observatory (DFO) that has catalogued 4320 floods (1985-2015) but has only mapped 5% of these floods. We are addressing this data gap by mapping the inventory of floods in the DFO database to create a first-of- its-kind, comprehensive, global and historical geospatial database of flood events. To do so, we combine water detection algorithms on MODIS and Landsat 5,7 and 8 imagery in Google Earth Engine to map discrete flood events. The created database will be available in the Earth Engine Catalogue for download by country, region, or time period. This dataset can be leveraged for new data-driven hydrologic modeling using machine learning algorithms in Earth Engine's highly parallelized computing environment, and we will show examples for New York and Senegal.

WIFIRE Data Model and Catalog for Wildfire Data and Tools

NASA Astrophysics Data System (ADS)

Altintas, I.; Crawl, D.; Cowart, C.; Gupta, A.; Block, J.; de Callafon, R.

2014-12-01

The WIFIRE project (wifire.ucsd.edu) is building an end-to-end cyberinfrastructure for real-time and data-driven simulation, prediction and visualization of wildfire behavior. WIFIRE may be used by wildfire management authorities in the future to predict wildfire rate of spread and direction, and assess the effectiveness of high-density sensor networks in improving fire and weather predictions. WIFIRE has created a data model for wildfire resources including sensed and archived data, sensors, satellites, cameras, modeling tools, workflows and social information including Twitter feeds. This data model and associated wildfire resource catalog includes a detailed description of the HPWREN sensor network, SDG&E's Mesonet, and NASA MODIS. In addition, the WIFIRE data-model describes how to integrate the data from multiple heterogeneous sources to provide detailed fire-related information. The data catalog describes 'Observables' captured by each instrument using multiple ontologies including OGC SensorML and NASA SWEET. Observables include measurements such as wind speed, air temperature, and relative humidity, as well as their accuracy and resolution. We have implemented a REST service for publishing to and querying from the catalog using Web Application Description Language (WADL). We are creating web-based user interfaces and mobile device Apps that use the REST interface for dissemination to wildfire modeling community and project partners covering academic, private, and government laboratories while generating value to emergency officials and the general public. Additionally, the Kepler scientific workflow system is instrumented to interact with this data catalog to access real-time streaming and archived wildfire data and stream it into dynamic data-driven wildfire models at scale.

Individualized Prediction of Heat Stress in Firefighters: A Data-Driven Approach Using Classification and Regression Trees.

PubMed

Mani, Ashutosh; Rao, Marepalli; James, Kelley; Bhattacharya, Amit

2015-01-01

The purpose of this study was to explore data-driven models, based on decision trees, to develop practical and easy to use predictive models for early identification of firefighters who are likely to cross the threshold of hyperthermia during live-fire training. Predictive models were created for three consecutive live-fire training scenarios. The final predicted outcome was a categorical variable: will a firefighter cross the upper threshold of hyperthermia - Yes/No. Two tiers of models were built, one with and one without taking into account the outcome (whether a firefighter crossed hyperthermia or not) from the previous training scenario. First tier of models included age, baseline heart rate and core body temperature, body mass index, and duration of training scenario as predictors. The second tier of models included the outcome of the previous scenario in the prediction space, in addition to all the predictors from the first tier of models. Classification and regression trees were used independently for prediction. The response variable for the regression tree was the quantitative variable: core body temperature at the end of each scenario. The predicted quantitative variable from regression trees was compared to the upper threshold of hyperthermia (38°C) to predict whether a firefighter would enter hyperthermia. The performance of classification and regression tree models was satisfactory for the second (success rate = 79%) and third (success rate = 89%) training scenarios but not for the first (success rate = 43%). Data-driven models based on decision trees can be a useful tool for predicting physiological response without modeling the underlying physiological systems. Early prediction of heat stress coupled with proactive interventions, such as pre-cooling, can help reduce heat stress in firefighters.

Maximizing lipocalin prediction through balanced and diversified training set and decision fusion.

PubMed

Nath, Abhigyan; Subbiah, Karthikeyan

2015-12-01

Lipocalins are short in sequence length and perform several important biological functions. These proteins are having less than 20% sequence similarity among paralogs. Experimentally identifying them is an expensive and time consuming process. The computational methods based on the sequence similarity for allocating putative members to this family are also far elusive due to the low sequence similarity existing among the members of this family. Consequently, the machine learning methods become a viable alternative for their prediction by using the underlying sequence/structurally derived features as the input. Ideally, any machine learning based prediction method must be trained with all possible variations in the input feature vector (all the sub-class input patterns) to achieve perfect learning. A near perfect learning can be achieved by training the model with diverse types of input instances belonging to the different regions of the entire input space. Furthermore, the prediction performance can be improved through balancing the training set as the imbalanced data sets will tend to produce the prediction bias towards majority class and its sub-classes. This paper is aimed to achieve (i) the high generalization ability without any classification bias through the diversified and balanced training sets as well as (ii) enhanced the prediction accuracy by combining the results of individual classifiers with an appropriate fusion scheme. Instead of creating the training set randomly, we have first used the unsupervised Kmeans clustering algorithm to create diversified clusters of input patterns and created the diversified and balanced training set by selecting an equal number of patterns from each of these clusters. Finally, probability based classifier fusion scheme was applied on boosted random forest algorithm (which produced greater sensitivity) and K nearest neighbour algorithm (which produced greater specificity) to achieve the enhanced predictive performance than that of individual base classifiers. The performance of the learned models trained on Kmeans preprocessed training set is far better than the randomly generated training sets. The proposed method achieved a sensitivity of 90.6%, specificity of 91.4% and accuracy of 91.0% on the first test set and sensitivity of 92.9%, specificity of 96.2% and accuracy of 94.7% on the second blind test set. These results have established that diversifying training set improves the performance of predictive models through superior generalization ability and balancing the training set improves prediction accuracy. For smaller data sets, unsupervised Kmeans based sampling can be an effective technique to increase generalization than that of the usual random splitting method. Copyright © 2015 Elsevier Ltd. All rights reserved.

Landscapes of facilitation: how self-organized patchiness of aquatic macrophytes promotes diversity in streams.

PubMed

Cornacchia, Loreta; van de Koppel, Johan; van der Wal, Daphne; Wharton, Geraldene; Puijalon, Sara; Bouma, Tjeerd J

2018-04-01

Spatial heterogeneity plays a crucial role in the coexistence of species. Despite recognition of the importance of self-organization in creating environmental heterogeneity in otherwise uniform landscapes, the effects of such self-organized pattern formation in promoting coexistence through facilitation are still unknown. In this study, we investigated the effects of pattern formation on species interactions and community spatial structure in ecosystems with limited underlying environmental heterogeneity, using self-organized patchiness of the aquatic macrophyte Callitriche platycarpa in streams as a model system. Our theoretical model predicted that pattern formation in aquatic vegetation - due to feedback interactions between plant growth, water flow and sedimentation processes - could promote species coexistence, by creating heterogeneous flow conditions inside and around the plant patches. The spatial plant patterns predicted by our model agreed with field observations at the reach scale in naturally vegetated rivers, where we found a significant spatial aggregation of two macrophyte species around C. platycarpa. Field transplantation experiments showed that C. platycarpa had a positive effect on the growth of both beneficiary species, and the intensity of this facilitative effect was correlated with the heterogeneous hydrodynamic conditions created within and around C. platycarpa patches. Our results emphasize the importance of self-organized patchiness in promoting species coexistence by creating a landscape of facilitation, where new niches and facilitative effects arise in different locations. Understanding the interplay between competition and facilitation is therefore essential for successful management of biodiversity in many ecosystems. © 2018 The Authors Ecology published by Wiley Periodicals, Inc. on behalf of Ecological Society of America.

PREDICTING CME EJECTA AND SHEATH FRONT ARRIVAL AT L1 WITH A DATA-CONSTRAINED PHYSICAL MODEL

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

Hess, Phillip; Zhang, Jie, E-mail: phess4@gmu.edu

2015-10-20

We present a method for predicting the arrival of a coronal mass ejection (CME) flux rope in situ, as well as the sheath of solar wind plasma accumulated ahead of the driver. For faster CMEs, the front of this sheath will be a shock. The method is based upon geometrical separate measurement of the CME ejecta and sheath. These measurements are used to constrain a drag-based model, improved by including both a height dependence and accurate de-projected velocities. We also constrain the geometry of the model to determine the error introduced as a function of the deviation of the CMEmore » nose from the Sun–Earth line. The CME standoff-distance in the heliosphere fit is also calculated, fit, and combined with the ejecta model to determine sheath arrival. Combining these factors allows us to create predictions for both fronts at the L1 point and compare them against observations. We demonstrate an ability to predict the sheath arrival with an average error of under 3.5 hr, with an rms error of about 1.58 hr. For the ejecta the error is less than 1.5 hr, with an rms error within 0.76 hr. We also discuss the physical implications of our model for CME expansion and density evolution. We show the power of our method with ideal data and demonstrate the practical implications of having a permanent L5 observer with space weather forecasting capabilities, while also discussing the limitations of the method that will have to be addressed in order to create a real-time forecasting tool.« less

The Hyper-Envelope Modeling Interface (HEMI): A Novel Approach Illustrated Through Predicting Tamarisk (Tamarix spp.) Habitat in the Western USA

USGS Publications Warehouse

Graham, Jim; Young, Nick; Jarnevich, Catherine S.; Newman, Greg; Evangelista, Paul; Stohlgren, Thomas J.

2013-01-01

Habitat suitability maps are commonly created by modeling a species’ environmental niche from occurrences and environmental characteristics. Here, we introduce the hyper-envelope modeling interface (HEMI), providing a new method for creating habitat suitability models using Bezier surfaces to model a species niche in environmental space. HEMI allows modeled surfaces to be visualized and edited in environmental space based on expert knowledge and does not require absence points for model development. The modeled surfaces require relatively few parameters compared to similar modeling approaches and may produce models that better match ecological niche theory. As a case study, we modeled the invasive species tamarisk (Tamarix spp.) in the western USA. We compare results from HEMI with those from existing similar modeling approaches (including BioClim, BioMapper, and Maxent). We used synthetic surfaces to create visualizations of the various models in environmental space and used modified area under the curve (AUC) statistic and akaike information criterion (AIC) as measures of model performance. We show that HEMI produced slightly better AUC values, except for Maxent and better AIC values overall. HEMI created a model with only ten parameters while Maxent produced a model with over 100 and BioClim used only eight. Additionally, HEMI allowed visualization and editing of the model in environmental space to develop alternative potential habitat scenarios. The use of Bezier surfaces can provide simple models that match our expectations of biological niche models and, at least in some cases, out-perform more complex approaches.

Thermal Vacuum Test Correlation of A Zero Propellant Load Case Thermal Capacitance Propellant Gauging Analytics Model

NASA Technical Reports Server (NTRS)

McKim, Stephen A.

2016-01-01

This thesis describes the development and test data validation of the thermal model that is the foundation of a thermal capacitance spacecraft propellant load estimator. Specific details of creating the thermal model for the diaphragm propellant tank used on NASA's Magnetospheric Multiscale spacecraft using ANSYS and the correlation process implemented to validate the model are presented. The thermal model was correlated to within plus or minus 3 degrees Centigrade of the thermal vacuum test data, and was found to be relatively insensitive to uncertainties in applied heat flux and mass knowledge of the tank. More work is needed, however, to refine the thermal model to further improve temperature predictions in the upper hemisphere of the propellant tank. Temperatures predictions in this portion were found to be 2-2.5 degrees Centigrade lower than the test data. A road map to apply the model to predict propellant loads on the actual MMS spacecraft toward its end of life in 2017-2018 is also presented.

Biotic and abiotic factors predicting the global distribution and population density of an invasive large mammal

PubMed Central

Lewis, Jesse S.; Farnsworth, Matthew L.; Burdett, Chris L.; Theobald, David M.; Gray, Miranda; Miller, Ryan S.

2017-01-01

Biotic and abiotic factors are increasingly acknowledged to synergistically shape broad-scale species distributions. However, the relative importance of biotic and abiotic factors in predicting species distributions is unclear. In particular, biotic factors, such as predation and vegetation, including those resulting from anthropogenic land-use change, are underrepresented in species distribution modeling, but could improve model predictions. Using generalized linear models and model selection techniques, we used 129 estimates of population density of wild pigs (Sus scrofa) from 5 continents to evaluate the relative importance, magnitude, and direction of biotic and abiotic factors in predicting population density of an invasive large mammal with a global distribution. Incorporating diverse biotic factors, including agriculture, vegetation cover, and large carnivore richness, into species distribution modeling substantially improved model fit and predictions. Abiotic factors, including precipitation and potential evapotranspiration, were also important predictors. The predictive map of population density revealed wide-ranging potential for an invasive large mammal to expand its distribution globally. This information can be used to proactively create conservation/management plans to control future invasions. Our study demonstrates that the ongoing paradigm shift, which recognizes that both biotic and abiotic factors shape species distributions across broad scales, can be advanced by incorporating diverse biotic factors. PMID:28276519

Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD Statement.

PubMed

Collins, G S; Reitsma, J B; Altman, D G; Moons, K G M

2015-02-01

Prediction models are developed to aid healthcare providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision-making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a web-based survey and revised during a 3-day meeting in June 2011 with methodologists, healthcare professionals and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. A complete checklist is available at http://www.tripod-statement.org. © 2015 American College of Physicians.

Structure-based CoMFA as a predictive model - CYP2C9 inhibitors as a test case.

PubMed

Yasuo, Kazuya; Yamaotsu, Noriyuki; Gouda, Hiroaki; Tsujishita, Hideki; Hirono, Shuichi

2009-04-01

In this study, we tried to establish a general scheme to create a model that could predict the affinity of small compounds to their target proteins. This scheme consists of a search for ligand-binding sites on a protein, a generation of bound conformations (poses) of ligands in each of the sites by docking, identifications of the correct poses of each ligand by consensus scoring and MM-PBSA analysis, and a construction of a CoMFA model with the obtained poses to predict the affinity of the ligands. By using a crystal structure of CYP 2C9 and the twenty known CYP inhibitors as a test case, we obtained a CoMFA model with a good statistics, which suggested that the classification of the binding sites as well as the predicted bound poses of the ligands should be reasonable enough. The scheme described here would give a method to predict the affinity of small compounds with a reasonable accuracy, which is expected to heighten the value of computational chemistry in the drug design process.

CREATION OF A MODEL TO PREDICT SURVIVAL IN PATIENTS WITH REFRACTORY COELIAC DISEASE USING A MULTINATIONAL REGISTRY

PubMed Central

Rubio-Tapia, Alberto; Malamut, Georgia; Verbeek, Wieke H.M.; van Wanrooij, Roy L.J.; Leffler, Daniel A.; Niveloni, Sonia I.; Arguelles-Grande, Carolina; Lahr, Brian D.; Zinsmeister, Alan R.; Murray, Joseph A.; Kelly, Ciaran P.; Bai, Julio C.; Green, Peter H.; Daum, Severin; Mulder, Chris J.J.; Cellier, Christophe

2016-01-01

Background Refractory coeliac disease is a severe complication of coeliac disease with heterogeneous outcome. Aim To create a prognostic model to estimate survival of patients with refractory coeliac disease. Methods We evaluated predictors of 5-year mortality using Cox proportional hazards regression on subjects from a multinational registry. Bootstrap re-sampling was used to internally validate the individual factors and overall model performance. The mean of the estimated regression coefficients from 400 bootstrap models was used to derive a risk score for 5-year mortality. Results The multinational cohort was composed of 232 patients diagnosed with refractory coeliac disease across 7 centers (range of 11–63 cases per center). The median age was 53 years and 150 (64%) were women. A total of 51 subjects died during 5-year follow-up (cumulative 5-year all-cause mortality = 30%). From a multiple variable Cox proportional hazards model, the following variables were significantly associated with 5-year mortality: age at refractory coeliac disease diagnosis (per 20 year increase, hazard ratio = 2.21; 95% confidence interval: 1.38, 3.55), abnormal intraepithelial lymphocytes (hazard ratio = 2.85; 95% confidence interval: 1.22, 6.62), and albumin (per 0.5 unit increase, hazard ratio = 0.72; 95% confidence interval: 0.61, 0.85). A simple weighted 3-factor risk score was created to estimate 5-year survival. Conclusions Using data from a multinational registry and previously-reported risk factors, we create a prognostic model to predict 5-year mortality among patients with refractory coeliac disease. This new model may help clinicians to guide treatment and follow-up. PMID:27485029

Creation of a model to predict survival in patients with refractory coeliac disease using a multinational registry.

PubMed

Rubio-Tapia, A; Malamut, G; Verbeek, W H M; van Wanrooij, R L J; Leffler, D A; Niveloni, S I; Arguelles-Grande, C; Lahr, B D; Zinsmeister, A R; Murray, J A; Kelly, C P; Bai, J C; Green, P H; Daum, S; Mulder, C J J; Cellier, C

2016-10-01

Refractory coeliac disease is a severe complication of coeliac disease with heterogeneous outcome. To create a prognostic model to estimate survival of patients with refractory coeliac disease. We evaluated predictors of 5-year mortality using Cox proportional hazards regression on subjects from a multinational registry. Bootstrap resampling was used to internally validate the individual factors and overall model performance. The mean of the estimated regression coefficients from 400 bootstrap models was used to derive a risk score for 5-year mortality. The multinational cohort was composed of 232 patients diagnosed with refractory coeliac disease across seven centres (range of 11-63 cases per centre). The median age was 53 years and 150 (64%) were women. A total of 51 subjects died during a 5-year follow-up (cumulative 5-year all-cause mortality = 30%). From a multiple variable Cox proportional hazards model, the following variables were significantly associated with 5-year mortality: age at refractory coeliac disease diagnosis (per 20 year increase, hazard ratio = 2.21; 95% confidence interval, CI: 1.38-3.55), abnormal intraepithelial lymphocytes (hazard ratio = 2.85; 95% CI: 1.22-6.62), and albumin (per 0.5 unit increase, hazard ratio = 0.72; 95% CI: 0.61-0.85). A simple weighted three-factor risk score was created to estimate 5-year survival. Using data from a multinational registry and previously reported risk factors, we create a prognostic model to predict 5-year mortality among patients with refractory coeliac disease. This new model may help clinicians to guide treatment and follow-up. © 2016 John Wiley & Sons Ltd.

Predictive model of outcome of targeted nodal assessment in colorectal cancer.

PubMed

Nissan, Aviram; Protic, Mladjan; Bilchik, Anton; Eberhardt, John; Peoples, George E; Stojadinovic, Alexander

2010-02-01

Improvement in staging accuracy is the principal aim of targeted nodal assessment in colorectal carcinoma. Technical factors independently predictive of false negative (FN) sentinel lymph node (SLN) mapping should be identified to facilitate operative decision making. To define independent predictors of FN SLN mapping and to develop a predictive model that could support surgical decisions. Data was analyzed from 2 completed prospective clinical trials involving 278 patients with colorectal carcinoma undergoing SLN mapping. Clinical outcome of interest was FN SLN(s), defined as one(s) with no apparent tumor cells in the presence of non-SLN metastases. To assess the independent predictive effect of a covariate for a nominal response (FN SLN), a logistic regression model was constructed and parameters estimated using maximum likelihood. A probabilistic Bayesian model was also trained and cross validated using 10-fold train-and-test sets to predict FN SLN mapping. Area under the curve (AUC) from receiver operating characteristics curves of these predictions was calculated to determine the predictive value of the model. Number of SLNs (<3; P = 0.03) and tumor-replaced nodes (P < 0.01) independently predicted FN SLN. Cross validation of the model created with Bayesian Network Analysis effectively predicted FN SLN (area under the curve = 0.84-0.86). The positive and negative predictive values of the model are 83% and 97%, respectively. This study supports a minimum threshold of 3 nodes for targeted nodal assessment in colorectal cancer, and establishes sufficient basis to conclude that SLN mapping and biopsy cannot be justified in the presence of clinically apparent tumor-replaced nodes.

Generating Performance Models for Irregular Applications

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

Friese, Ryan D.; Tallent, Nathan R.; Vishnu, Abhinav

2017-05-30

Many applications have irregular behavior --- non-uniform input data, input-dependent solvers, irregular memory accesses, unbiased branches --- that cannot be captured using today's automated performance modeling techniques. We describe new hierarchical critical path analyses for the \\Palm model generation tool. To create a model's structure, we capture tasks along representative MPI critical paths. We create a histogram of critical tasks with parameterized task arguments and instance counts. To model each task, we identify hot instruction-level sub-paths and model each sub-path based on data flow, instruction scheduling, and data locality. We describe application models that generate accurate predictions for strong scalingmore » when varying CPU speed, cache speed, memory speed, and architecture. We present results for the Sweep3D neutron transport benchmark; Page Rank on multiple graphs; Support Vector Machine with pruning; and PFLOTRAN's reactive flow/transport solver with domain-induced load imbalance.« less

An Introduction to System-Level, Steady-State and Transient Modeling and Optimization of High-Power-Density Thermoelectric Generator Devices Made of Segmented Thermoelectric Elements

NASA Astrophysics Data System (ADS)

Crane, D. T.

2011-05-01

High-power-density, segmented, thermoelectric (TE) elements have been intimately integrated into heat exchangers, eliminating many of the loss mechanisms of conventional TE assemblies, including the ceramic electrical isolation layer. Numerical models comprising simultaneously solved, nonlinear, energy balance equations have been created to simulate these novel architectures. Both steady-state and transient models have been created in a MATLAB/Simulink environment. The models predict data from experiments in various configurations and applications over a broad range of temperature, flow, and current conditions for power produced, efficiency, and a variety of other important outputs. Using the validated models, devices and systems are optimized using advanced multiparameter optimization techniques. Devices optimized for particular steady-state operating conditions can then be dynamically simulated in a transient operating model. The transient model can simulate a variety of operating conditions including automotive and truck drive cycles.

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

Lovelace, III, Henry H.

In accelerator physics, models of a given machine are used to predict the behaviors of the beam, magnets, and radiofrequency cavities. The use of the computational model has become wide spread to ease the development period of the accelerator lattice. There are various programs that are used to create lattices and run simulations of both transverse and longitudinal beam dynamics. The programs include Methodical Accelerator Design(MAD) MAD8, MADX, Zgoubi, Polymorphic Tracking Code (PTC), and many others. In this discussion the BMAD (Baby Methodical Accelerator Design) is presented as an additional tool in creating and simulating accelerator lattices for the studymore » of beam dynamics in the Relativistic Heavy Ion Collider (RHIC).« less

Evaluation and Verification of Decadal Predictions using the MiKlip Central Evaluation System - a Case Study using the MiKlip Prototype Model Data

NASA Astrophysics Data System (ADS)

Illing, Sebastian; Schuster, Mareike; Kadow, Christopher; Kröner, Igor; Richling, Andy; Grieger, Jens; Kruschke, Tim; Lang, Benjamin; Redl, Robert; Schartner, Thomas; Cubasch, Ulrich

2016-04-01

MiKlip is project for medium-term climate prediction funded by the Federal Ministry of Education and Research in Germany (BMBF) and aims to create a model system that is able provide reliable decadal climate forecasts. During the first project phase of MiKlip the sub-project INTEGRATION located at Freie Universität Berlin developed a framework for scientific infrastructures (FREVA). More information about FREVA can be found in EGU2016-13060. An instance of this framework is used as Central Evaluation System (CES) during the MiKlip project. Throughout the first project phase various sub-projects developed over 25 analysis tools - so called plugins - for the CES. The main focus of these plugins is on the evaluation and verification of decadal climate prediction data, but most plugins are not limited to this scope. They target a wide range of scientific questions. Starting from preprocessing tools like the "LeadtimeSelector", which creates lead-time dependent time-series from decadal hindcast sets, over tracking tools like the "Zykpak" plugin, which can objectively locate and track mid-latitude cyclones, to plugins like "MurCSS" or "SPECS", which calculate deterministic and probabilistic skill metrics. We also integrated some analyses from Model Evaluation Tools (MET), which was developed at NCAR. We will show the theoretical background, technical implementation strategies, and some interesting results of the evaluation of the MiKlip Prototype decadal prediction system for a selected set of these tools.

Use of regression‐based models to map sensitivity of aquatic resources to atmospheric deposition in Yosemite National Park, USA

USGS Publications Warehouse

Clow, David W.; Nanus, Leora; Huggett, Brian

2010-01-01

An abundance of exposed bedrock, sparse soil and vegetation, and fast hydrologic flushing rates make aquatic ecosystems in Yosemite National Park susceptible to nutrient enrichment and episodic acidification due to atmospheric deposition of nitrogen (N) and sulfur (S). In this study, multiple linear regression (MLR) models were created to estimate fall‐season nitrate and acid neutralizing capacity (ANC) in surface water in Yosemite wilderness. Input data included estimated winter N deposition, fall‐season surface‐water chemistry measurements at 52 sites, and basin characteristics derived from geographic information system layers of topography, geology, and vegetation. The MLR models accounted for 84% and 70% of the variance in surface‐water nitrate and ANC, respectively. Explanatory variables (and the sign of their coefficients) for nitrate included elevation (positive) and the abundance of neoglacial and talus deposits (positive), unvegetated terrain (positive), alluvium (negative), and riparian (negative) areas in the basins. Explanatory variables for ANC included basin area (positive) and the abundance of metamorphic rocks (positive), unvegetated terrain (negative), water (negative), and winter N deposition (negative) in the basins. The MLR equations were applied to 1407 stream reaches delineated in the National Hydrography Data Set for Yosemite, and maps of predicted surface‐water nitrate and ANC concentrations were created. Predicted surface‐water nitrate concentrations were highest in small, high‐elevation cirques, and concentrations declined downstream. Predicted ANC concentrations showed the opposite pattern, except in high‐elevation areas underlain by metamorphic rocks along the Sierran Crest, which had relatively high predicted ANC (>200 μeq L−1). Maps were created to show where basin characteristics predispose aquatic resources to nutrient enrichment and acidification effects from N and S deposition. The maps can be used to help guide development of water‐quality programs designed to monitor and protect natural resources in national parks.

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.

Modeling Techniques for Shipboard Manning: A Review and Plan for Development

DTIC Science & Technology

1993-02-01

manning levels. Once manning models have been created, experiments can be conducted to show how changes in the manning structure might affect ship safety...these predictions, users of the manning models can evaluate how changes in crew configurations, manning levels, and voyage profiles affect ship safety...mitigate emergency situations would provide crucial information on how changes in manning structure would affect overall ship safety. Like emergency

Validation databases for simulation models: aboveground biomass and net primary productive, (NPP) estimation using eastwide FIA data

Treesearch

Jennifer C. Jenkins; Richard A. Birdsey

2000-01-01

As interest grows in the role of forest growth in the carbon cycle, and as simulation models are applied to predict future forest productivity at large spatial scales, the need for reliable and field-based data for evaluation of model estimates is clear. We created estimates of potential forest biomass and annual aboveground production for the Chesapeake Bay watershed...

Use of artificial intelligence in the design of small peptide antibiotics effective against a broad spectrum of highly antibiotic-resistant superbugs.

PubMed

Cherkasov, Artem; Hilpert, Kai; Jenssen, Håvard; Fjell, Christopher D; Waldbrook, Matt; Mullaly, Sarah C; Volkmer, Rudolf; Hancock, Robert E W

2009-01-16

Increased multiple antibiotic resistance in the face of declining antibiotic discovery is one of society's most pressing health issues. Antimicrobial peptides represent a promising new class of antibiotics. Here we ask whether it is possible to make small broad spectrum peptides employing minimal assumptions, by capitalizing on accumulating chemical biology information. Using peptide array technology, two large random 9-amino-acid peptide libraries were iteratively created using the amino acid composition of the most active peptides. The resultant data was used together with Artificial Neural Networks, a powerful machine learning technique, to create quantitative in silico models of antibiotic activity. On the basis of random testing, these models proved remarkably effective in predicting the activity of 100,000 virtual peptides. The best peptides, representing the top quartile of predicted activities, were effective against a broad array of multidrug-resistant "Superbugs" with activities that were equal to or better than four highly used conventional antibiotics, more effective than the most advanced clinical candidate antimicrobial peptide, and protective against Staphylococcus aureus infections in animal models.

Tradespace Exploration for the Engineering of Resilient Systems

DTIC Science & Technology

2015-05-01

world scenarios. The types of tools within the SAE set include visualization, decision analysis, and M&S, so it is difficult to categorize this toolset... overpopulated , or questionable. ERS Tradespace Workshop Create predictive models using multiple techniques (e.g., regression, Kriging, neural nets

Conformal Regression for Quantitative Structure-Activity Relationship Modeling-Quantifying Prediction Uncertainty.

PubMed

Svensson, Fredrik; Aniceto, Natalia; Norinder, Ulf; Cortes-Ciriano, Isidro; Spjuth, Ola; Carlsson, Lars; Bender, Andreas

2018-05-29

Making predictions with an associated confidence is highly desirable as it facilitates decision making and resource prioritization. Conformal regression is a machine learning framework that allows the user to define the required confidence and delivers predictions that are guaranteed to be correct to the selected extent. In this study, we apply conformal regression to model molecular properties and bioactivity values and investigate different ways to scale the resultant prediction intervals to create as efficient (i.e., narrow) regressors as possible. Different algorithms to estimate the prediction uncertainty were used to normalize the prediction ranges, and the different approaches were evaluated on 29 publicly available data sets. Our results show that the most efficient conformal regressors are obtained when using the natural exponential of the ensemble standard deviation from the underlying random forest to scale the prediction intervals, but other approaches were almost as efficient. This approach afforded an average prediction range of 1.65 pIC50 units at the 80% confidence level when applied to bioactivity modeling. The choice of nonconformity function has a pronounced impact on the average prediction range with a difference of close to one log unit in bioactivity between the tightest and widest prediction range. Overall, conformal regression is a robust approach to generate bioactivity predictions with associated confidence.

Gravitational redshift of galaxies in clusters as predicted by general relativity.

PubMed

Wojtak, Radosław; Hansen, Steen H; Hjorth, Jens

2011-09-28

The theoretical framework of cosmology is mainly defined by gravity, of which general relativity is the current model. Recent tests of general relativity within the Lambda Cold Dark Matter (ΛCDM) model have found a concordance between predictions and the observations of the growth rate and clustering of the cosmic web. General relativity has not hitherto been tested on cosmological scales independently of the assumptions of the ΛCDM model. Here we report an observation of the gravitational redshift of light coming from galaxies in clusters at the 99 per cent confidence level, based on archival data. Our measurement agrees with the predictions of general relativity and its modification created to explain cosmic acceleration without the need for dark energy (the f(R) theory), but is inconsistent with alternative models designed to avoid the presence of dark matter. © 2011 Macmillan Publishers Limited. All rights reserved

A multilateral modelling of Youth Soccer Performance Index (YSPI)

NASA Astrophysics Data System (ADS)

Bisyri Husin Musawi Maliki, Ahmad; Razali Abdullah, Mohamad; Juahir, Hafizan; Abdullah, Farhana; Ain Shahirah Abdullah, Nurul; Muazu Musa, Rabiu; Musliha Mat-Rasid, Siti; Adnan, Aleesha; Azura Kosni, Norlaila; Muhamad, Wan Siti Amalina Wan; Afiqah Mohamad Nasir, Nur

2018-04-01

This study aims to identify the most dominant factors that influencing performance of soccer player and to predict group performance for soccer players. A total of 184 of youth soccer players from Malaysia sport school and six soccer academy encompasses as respondence of the study. Exploratory factor analysis (EFA) and Confirmatory factor analysis (CFA) were computed to identify the most dominant factors whereas reducing the initial 26 parameters with recommended >0.5 of factor loading. Meanwhile, prediction of the soccer performance was predicted by regression model. CFA revealed that sit and reach, vertical jump, VO2max, age, weight, height, sitting height, calf circumference (cc), medial upper arm circumference (muac), maturation, bicep, triceps, subscapular, suprailiac, 5M, 10M, and 20M speed were the most dominant factors. Further index analysis forming Youth Soccer Performance Index (YSPI) resulting by categorizing three groups namely, high, moderate, and low. The regression model for this study was significant set as p < 0.001 and R2 is 0.8222 which explained that the model contributed a total of 82% prediction ability to predict the whole set of the variables. The significant parameters in contributing prediction of YSPI are discussed. As a conclusion, the precision of the prediction models by integrating a multilateral factor reflecting for predicting potential soccer player and hopefully can create a competitive soccer games.

Combining multiple earthquake models in real time for earthquake early warning

USGS Publications Warehouse

Minson, Sarah E.; Wu, Stephen; Beck, James L; Heaton, Thomas H.

2017-01-01

The ultimate goal of earthquake early warning (EEW) is to provide local shaking information to users before the strong shaking from an earthquake reaches their location. This is accomplished by operating one or more real‐time analyses that attempt to predict shaking intensity, often by estimating the earthquake’s location and magnitude and then predicting the ground motion from that point source. Other EEW algorithms use finite rupture models or may directly estimate ground motion without first solving for an earthquake source. EEW performance could be improved if the information from these diverse and independent prediction models could be combined into one unified, ground‐motion prediction. In this article, we set the forecast shaking at each location as the common ground to combine all these predictions and introduce a Bayesian approach to creating better ground‐motion predictions. We also describe how this methodology could be used to build a new generation of EEW systems that provide optimal decisions customized for each user based on the user’s individual false‐alarm tolerance and the time necessary for that user to react.

Antibacterial Activity of Imidazolium-Based Ionic Liquids Investigated by QSAR Modeling and Experimental Studies.

PubMed

Hodyna, Diana; Kovalishyn, Vasyl; Rogalsky, Sergiy; Blagodatnyi, Volodymyr; Petko, Kirill; Metelytsia, Larisa

2016-09-01

Predictive QSAR models for the inhibitors of B. subtilis and Ps. aeruginosa among imidazolium-based ionic liquids were developed using literary data. The regression QSAR models were created through Artificial Neural Network and k-nearest neighbor procedures. The classification QSAR models were constructed using WEKA-RF (random forest) method. The predictive ability of the models was tested by fivefold cross-validation; giving q(2) = 0.77-0.92 for regression models and accuracy 83-88% for classification models. Twenty synthesized samples of 1,3-dialkylimidazolium ionic liquids with predictive value of activity level of antimicrobial potential were evaluated. For all asymmetric 1,3-dialkylimidazolium ionic liquids, only compounds containing at least one radical with alkyl chain length of 12 carbon atoms showed high antibacterial activity. However, the activity of symmetric 1,3-dialkylimidazolium salts was found to have opposite relationship with the length of aliphatic radical being maximum for compounds based on 1,3-dioctylimidazolium cation. The obtained experimental results suggested that the application of classification QSAR models is more accurate for the prediction of activity of new imidazolium-based ILs as potential antibacterials. © 2016 John Wiley & Sons A/S.

Losing function through wetland mitigation in central Pennsylvania, USA.

PubMed

Hoeltje, S M; Cole, C A

2007-03-01

In the United States, the Clean Water Act requires mitigation for wetlands that are negatively impacted by dredging and filling activities. During the mitigation process, there generally is little effort to assess function for mitigation sites and function is usually inferred based on vegetative cover and acreage. In our study, hydrogeomorphic (HGM) functional assessment models were used to compare predicted and potential levels of functional capacity in created and natural reference wetlands. HGM models assess potential function by measurement of a suite of structural variables and these modeled functions can then be compared to those in natural, reference wetlands. The created wetlands were built in a floodplain setting of a valley in central Pennsylvania to replace natural ridge-side slope wetlands. Functional assessment models indicated that the created sites differed significantly from natural wetlands that represented the impacted sites for seven of the ten functions assessed. This was expected because the created wetlands were located in a different geomorphic setting than the impacted sites, which would affect the type and degree of functions that occur. However, functional differences were still observed when the created sites were compared with a second set of reference wetlands that were located in a similar geomorphic setting (floodplain). Most of the differences observed in both comparisons were related to unnatural hydrologic regimes and to the characteristics of the surrounding landscape. As a result, the created wetlands are not fulfilling the criteria for successful wetland mitigation.

Deep nets vs expert designed features in medical physics: An IMRT QA case study.

PubMed

Interian, Yannet; Rideout, Vincent; Kearney, Vasant P; Gennatas, Efstathios; Morin, Olivier; Cheung, Joey; Solberg, Timothy; Valdes, Gilmer

2018-03-30

The purpose of this study was to compare the performance of Deep Neural Networks against a technique designed by domain experts in the prediction of gamma passing rates for Intensity Modulated Radiation Therapy Quality Assurance (IMRT QA). A total of 498 IMRT plans across all treatment sites were planned in Eclipse version 11 and delivered using a dynamic sliding window technique on Clinac iX or TrueBeam Linacs. Measurements were performed using a commercial 2D diode array, and passing rates for 3%/3 mm local dose/distance-to-agreement (DTA) were recorded. Separately, fluence maps calculated for each plan were used as inputs to a convolution neural network (CNN). The CNNs were trained to predict IMRT QA gamma passing rates using TensorFlow and Keras. A set of model architectures, inspired by the convolutional blocks of the VGG-16 ImageNet model, were constructed and implemented. Synthetic data, created by rotating and translating the fluence maps during training, was created to boost the performance of the CNNs. Dropout, batch normalization, and data augmentation were utilized to help train the model. The performance of the CNNs was compared to a generalized Poisson regression model, previously developed for this application, which used 78 expert designed features. Deep Neural Networks without domain knowledge achieved comparable performance to a baseline system designed by domain experts in the prediction of 3%/3 mm Local gamma passing rates. An ensemble of neural nets resulted in a mean absolute error (MAE) of 0.70 ± 0.05 and the domain expert model resulted in a 0.74 ± 0.06. Convolutional neural networks (CNNs) with transfer learning can predict IMRT QA passing rates by automatically designing features from the fluence maps without human expert supervision. Predictions from CNNs are comparable to a system carefully designed by physicist experts. © 2018 American Association of Physicists in Medicine.

Flooding Fragility Experiments and Prediction

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

Smith, Curtis L.; Tahhan, Antonio; Muchmore, Cody

2016-09-01

This report describes the work that has been performed on flooding fragility, both the experimental tests being carried out and the probabilistic fragility predictive models being produced in order to use the text results. Flooding experiments involving full-scale doors have commenced in the Portal Evaluation Tank. The goal of these experiments is to develop a full-scale component flooding experiment protocol and to acquire data that can be used to create Bayesian regression models representing the fragility of these components. This work is in support of the Risk-Informed Safety Margin Characterization (RISMC) Pathway external hazards evaluation research and development.

OAST planning model for space systems technology

NASA Technical Reports Server (NTRS)

Sadin, S. R.

1978-01-01

The NASA Office of Aeronautics and Space Technology (OAST) planning model for space systems technology is described, and some space technology forecasts of a general nature are reported. Technology forecasts are presented as a span of technology levels; uncertainties in level of commitment to project and in required time are taken into account, with emphasis on differences resulting from high or low commitment. Forecasts are created by combining several types of data, including information on past technology trends, the trends of past predictions, the rate of advancement predicted by experts in the field, and technology forecasts already published.

Development of a computer model to predict platform station keeping requirements in the Gulf of Mexico using remote sensing data

NASA Technical Reports Server (NTRS)

Barber, Bryan; Kahn, Laura; Wong, David

1990-01-01

Offshore operations such as oil drilling and radar monitoring require semisubmersible platforms to remain stationary at specific locations in the Gulf of Mexico. Ocean currents, wind, and waves in the Gulf of Mexico tend to move platforms away from their desired locations. A computer model was created to predict the station keeping requirements of a platform. The computer simulation uses remote sensing data from satellites and buoys as input. A background of the project, alternate approaches to the project, and the details of the simulation are presented.

A synopsis of climate change effects on groundwater recharge

NASA Astrophysics Data System (ADS)

Smerdon, Brian D.

2017-12-01

Six review articles published between 2011 and 2016 on groundwater and climate change are briefly summarized. This synopsis focuses on aspects related to predicting changes to groundwater recharge conditions, with several common conclusions between the review articles being noted. The uncertainty of distribution and trend in future precipitation from General Circulation Models (GCMs) results in varying predictions of recharge, so much so that modelling studies are often not able to predict the magnitude and direction (increase or decrease) of future recharge conditions. Evolution of modelling approaches has led to the use of multiple GCMs and hydrologic models to create an envelope of future conditions that reflects the probability distribution. The choice of hydrologic model structure and complexity, and the choice of emissions scenario, has been investigated and somewhat resolved; however, recharge results remain sensitive to downscaling methods. To overcome uncertainty and provide practical use in water management, the research community indicates that modelling at a mesoscale, somewhere between watersheds and continents, is likely ideal. Improvements are also suggested for incorporating groundwater processes within GCMs.

Humanized mouse lines and their application for prediction of human drug metabolism and toxicological risk assessment

PubMed Central

Cheung, Connie; Gonzalez, Frank J

2008-01-01

Cytochrome P450s (P450s) are important enzymes involved in the metabolism of xenobiotics, particularly clinically used drugs, and are also responsible for metabolic activation of chemical carcinogens and toxins. Many xenobiotics can activate nuclear receptors that in turn induce the expression of genes encoding xenobiotic metabolizing enzymes and drug transporters. Marked species differences in the expression and regulation of cytochromes P450 and xenobiotic nuclear receptors exist. Thus obtaining reliable rodent models to accurately reflect human drug and carcinogen metabolism is severely limited. Humanized transgenic mice were developed in an effort to create more reliable in vivo systems to study and predict human responses to xenobiotics. Human P450s or human xenobiotic-activated nuclear receptors were introduced directly or replaced the corresponding mouse gene, thus creating “humanized” transgenic mice. Mice expressing human CYP1A1/CYP1A2, CYP2E1, CYP2D6, CYP3A4, CY3A7, PXR, PPARα were generated and characterized. These humanized mouse models offers a broad utility in the evaluation and prediction of toxicological risk that may aid in the development of safer drugs. PMID:18682571

Improving Simulations of Precipitation Phase and Snowpack at a Site Subject to Cold Air Intrusions: Snoqualmie Pass, WA

NASA Astrophysics Data System (ADS)

Wayand, N. E.; Stimberis, J.; Zagrodnik, J.; Mass, C.; Lundquist, J. D.

2016-12-01

Low-level cold air from eastern Washington state often flows westward through mountain passes in the Washington Cascades, creating localized inversions and locally reducing climatological temperatures. The persistence of this inversion during a frontal passage can result in complex patterns of snow and rain that are difficult to predict. Yet, these predictions are critical to support highway avalanche control, ski resort operations, and modeling of headwater snowpack storage. In this study we used observations of precipitation phase from a disdrometer and snow depth sensors across Snoqualmie Pass, WA, to evaluate surface-air-temperature-based and mesoscale-model-based predictions of precipitation phase during the anomalously warm 2014-2015 winter. The skill of surface-based methods was greatly improved by using air temperature from a nearby higher-elevation station, which was less impacted by low-level inversions. Alternatively, we found a hybrid method that combines surface-based predictions with output from the Weather Research and Forecasting mesoscale model to have improved skill over both parent models. These results suggest that prediction of precipitation phase in mountain passes can be improved by incorporating observations or models from above the surface layer.

Computational Fluid Dynamics Modeling of Nickel Hydrogen Batteries

NASA Technical Reports Server (NTRS)

Cullion, R.; Gu, W. B.; Wang, C. Y.; Timmerman, P.

2000-01-01

An electrochemical Ni-H2 battery model has been expanded to include thermal effects. A thermal energy conservation equation was derived from first principles. An electrochemical and thermal coupled model was created by the addition of this equation to an existing multiphase, electrochemical model. Charging at various rates was investigated and the results validated against experimental data. Reaction currents, pressure changes, temperature profiles, and concentration variations within the cell are predicted numerically and compared with available data and theory.

Numerical modeling of consolidation processes in hydraulically deposited soils

NASA Astrophysics Data System (ADS)

Brink, Nicholas Robert

Hydraulically deposited soils are encountered in many common engineering applications including mine tailing and geotextile tube fills, though the consolidation process for such soils is highly nonlinear and requires the use of advanced numerical techniques to provide accurate predictions. Several commercially available finite element codes poses the ability to model soil consolidation, and it was the goal of this research to assess the ability of two of these codes, ABAQUS and PLAXIS, to model the large-strain, two-dimensional consolidation processes which occur in hydraulically deposited soils. A series of one- and two-dimensionally drained rectangular models were first created to assess the limitations of ABAQUS and PLAXIS when modeling consolidation of highly compressible soils. Then, geotextile tube and TSF models were created to represent actual scenarios which might be encountered in engineering practice. Several limitations were discovered, including the existence of a minimum preconsolidation stress below which numerical solutions become unstable.

AIC and the challenge of complexity: A case study from ecology.

PubMed

Moll, Remington J; Steel, Daniel; Montgomery, Robert A

2016-12-01

Philosophers and scientists alike have suggested Akaike's Information Criterion (AIC), and other similar model selection methods, show predictive accuracy justifies a preference for simplicity in model selection. This epistemic justification of simplicity is limited by an assumption of AIC which requires that the same probability distribution must generate the data used to fit the model and the data about which predictions are made. This limitation has been previously noted but appears to often go unnoticed by philosophers and scientists and has not been analyzed in relation to complexity. If predictions are about future observations, we argue that this assumption is unlikely to hold for models of complex phenomena. That in turn creates a practical limitation for simplicity's AIC-based justification because scientists modeling such phenomena are often interested in predicting the future. We support our argument with an ecological case study concerning the reintroduction of wolves into Yellowstone National Park, U.S.A. We suggest that AIC might still lend epistemic support for simplicity by leading to better explanations of complex phenomena. Copyright © 2016 Elsevier Ltd. All rights reserved.

Optimizing heat shock protein expression induced by prostate cancer laser therapy through predictive computational models

NASA Astrophysics Data System (ADS)

Rylander, Marissa N.; Feng, Yusheng; Zhang, Yongjie; Bass, Jon; Stafford, Roger J.; Hazle, John D.; Diller, Kenneth R.

2006-07-01

Thermal therapy efficacy can be diminished due to heat shock protein (HSP) induction in regions of a tumor where temperatures are insufficient to coagulate proteins. HSP expression enhances tumor cell viability and imparts resistance to chemotherapy and radiation treatments, which are generally employed in conjunction with hyperthermia. Therefore, an understanding of the thermally induced HSP expression within the targeted tumor must be incorporated into the treatment plan to optimize the thermal dose delivery and permit prediction of the overall tissue response. A treatment planning computational model capable of predicting the temperature, HSP27 and HSP70 expression, and damage fraction distributions associated with laser heating in healthy prostate tissue and tumors is presented. Measured thermally induced HSP27 and HSP70 expression kinetics and injury data for normal and cancerous prostate cells and prostate tumors are employed to create the first HSP expression predictive model and formulate an Arrhenius damage model. The correlation coefficients between measured and model predicted temperature, HSP27, and HSP70 were 0.98, 0.99, and 0.99, respectively, confirming the accuracy of the model. Utilization of the treatment planning model in the design of prostate cancer thermal therapies can enable optimization of the treatment outcome by controlling HSP expression and injury.

Using handgrip strength to screen for diabetes in developing countries.

PubMed

Eckman, Molly; Gigliotti, Christopher; Sutermaster, Staci; Butler, Peter J; Mehta, Khanjan

2016-01-01

Lack of access to healthcare in the developing world has created a need for locally-based primary and pre-primary healthcare systems. Many regions of the world have adopted Community Health Worker (CHW) programmes, but volunteers in these programmes lack the tools and resources to screen for disease. Because of its simplicity of operation, handgrip strength (HGS) measurements have the potential to be an affordable and effective screening tool for conditions that cause muscle weakness in this context. In the study described in this report, translators were used to collect data on age, gender, height, weight, blood pressure, HGS and key demographic data. HGS was significantly lower for diabetics than patients without diabetes. A simple binary logistic model was created that used HGS, age, blood pressure and BMI to predict a patient's probability of having diabetes. This study develops a predictive model for diabetes using HGS and other basic health measurements and shows that HGS-based screening is a viable method of early detection of diabetes.

An experimental study on the manufacture and characterization of in-plane fibre-waviness defects in composites.

PubMed

Christian, W J R; DiazDelaO, F A; Atherton, K; Patterson, E A

2018-05-01

A new method has been developed for creating localized in-plane fibre waviness in composite coupons and used to create a large batch of specimens. This method could be used by manufacturers to experimentally explore the effect of fibre waviness on composite structures both directly and indirectly to develop and validate computational models. The specimens were assessed using ultrasound, digital image correlation and a novel inspection technique capable of measuring residual strain fields. To explore how the defect affects the performance of composite structures, the specimens were then loaded to failure. Predictions of remnant strength were made using a simple ultrasound damage metric and a new residual strain-based damage metric. The predictions made using residual strain measurements were found to be substantially more effective at characterizing ultimate strength than ultrasound measurements. This suggests that residual strains have a significant effect on the failure of laminates containing fibre waviness and that these strains could be incorporated into computational models to improve their ability to simulate the defect.

Callus remodelling model

NASA Astrophysics Data System (ADS)

Miodowska, Justyna; Bielski, Jan; Kromka-Szydek, Magdalena

2018-01-01

The objective of this paper is to investigate the healing process of the callus using bone remodelling approach. A new mathematical model of bone remodelling is proposed including both underload and overload resorption, as well as equilibrium and bone growth states. The created model is used to predict the stress-stimulated change in the callus density. The permanent and intermittent loading programs are considered. The analyses indicate that obtaining a sufficiently high values of the callus density (and hence the elasticity) modulus is only possible using time-varying load parameters. The model predictions also show that intermittent loading program causes delayed callus healing. Understanding how mechanical conditions influence callus remodelling process may be relevant in the bone fracture treatment and initial bone loading during rehabilitation.

Geospatial Predictive Modelling for Climate Mapping of Selected Severe Weather Phenomena Over Poland: A Methodological Approach

NASA Astrophysics Data System (ADS)

Walawender, Ewelina; Walawender, Jakub P.; Ustrnul, Zbigniew

2017-02-01

The main purpose of the study is to introduce methods for mapping the spatial distribution of the occurrence of selected atmospheric phenomena (thunderstorms, fog, glaze and rime) over Poland from 1966 to 2010 (45 years). Limited in situ observations as well the discontinuous and location-dependent nature of these phenomena make traditional interpolation inappropriate. Spatially continuous maps were created with the use of geospatial predictive modelling techniques. For each given phenomenon, an algorithm identifying its favourable meteorological and environmental conditions was created on the basis of observations recorded at 61 weather stations in Poland. Annual frequency maps presenting the probability of a day with a thunderstorm, fog, glaze or rime were created with the use of a modelled, gridded dataset by implementing predefined algorithms. Relevant explanatory variables were derived from NCEP/NCAR reanalysis and downscaled with the use of a Regional Climate Model. The resulting maps of favourable meteorological conditions were found to be valuable and representative on the country scale but at different correlation ( r) strength against in situ data (from r = 0.84 for thunderstorms to r = 0.15 for fog). A weak correlation between gridded estimates of fog occurrence and observations data indicated the very local nature of this phenomenon. For this reason, additional environmental predictors of fog occurrence were also examined. Topographic parameters derived from the SRTM elevation model and reclassified CORINE Land Cover data were used as the external, explanatory variables for the multiple linear regression kriging used to obtain the final map. The regression model explained 89 % of annual frequency of fog variability in the study area. Regression residuals were interpolated via simple kriging.

“Teens are from Mars, Adults are from Venus”: Analyzing and Predicting Age Groups with Behavioral Characteristics in Instagram

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

Han, Kyungsik; Lee, Sanghack; Jang, Jin

We present behavioral characteristics of teens and adults in Instagram and prediction of them from their behaviors. Based on two independently created datasets from user profiles and tags, we identify teens and adults, and carry out comparative analyses on their online behaviors. Our study reveals: (1) significant behavioral differences between two age groups; (2) the empirical evidence of classifying teens and adults with up to 82% accuracy, using traditional predictive models, while two baseline methods achieve 68% at best; and (3) the robustness of our models by achieving 76%—81% when tested against an independent dataset obtained without using user profilesmore » or tags.« less

Fostering marginalized youths' political participation: longitudinal roles of parental political socialization and youth sociopolitical development.

PubMed

Diemer, Matthew A

2012-09-01

This study examines the roles of parental political socialization and the moral commitment to change social inequalities in predicting marginalized youths' (defined here as lower-SES youth of color) political participation. These issues are examined by applying structural equation modeling to a longitudinal panel of youth. Because tests of measurement invariance suggested racial/ethnic heterogeneity, the structural model was fit separately for three racial/ethnic groups. For each group, parental political socialization: discussion predicted youths' commitment to produce social change and for two groups, longitudinally predicted political participation. This study contributes to the literature by examining civic/political participation among disparate racial/ethnic groups, addresses an open scholarly question (whether youths' commitment to create social change predicts their "traditional" participation), and emphasizes parents' role in fostering marginalized youths' civic and political participation.

Validation Assessment of a Glass-to-Metal Seal Finite-Element Model

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

Jamison, Ryan Dale; Buchheit, Thomas E.; Emery, John M

Sealing glasses are ubiquitous in high pressure and temperature engineering applications, such as hermetic feed-through electrical connectors. A common connector technology are glass-to-metal seals where a metal shell compresses a sealing glass to create a hermetic seal. Though finite-element analysis has been used to understand and design glass-to-metal seals for many years, there has been little validation of these models. An indentation technique was employed to measure the residual stress on the surface of a simple glass-to-metal seal. Recently developed rate- dependent material models of both Schott 8061 and 304L VAR stainless steel have been applied to a finite-element modelmore » of the simple glass-to-metal seal. Model predictions of residual stress based on the evolution of material models are shown. These model predictions are compared to measured data. Validity of the finite- element predictions is discussed. It will be shown that the finite-element model of the glass-to-metal seal accurately predicts the mean residual stress in the glass near the glass-to-metal interface and is valid for this quantity of interest.« less

Contrail Tracking and ARM Data Product Development

NASA Technical Reports Server (NTRS)

Duda, David P.; Russell, James, III

2005-01-01

A contrail tracking system was developed to help in the assessment of the effect of commercial jet contrails on the Earth's radiative budget. The tracking system was built by combining meteorological data from the Rapid Update Cycle (RUC) numerical weather prediction model with commercial air traffic flight track data and satellite imagery. A statistical contrail-forecasting model was created a combination of surface-based contrail observations and numerical weather analyses and forecasts. This model allows predictions of widespread contrail occurrences for contrail research on either a real-time basis or for long-term time scales. Satellite-derived cirrus cloud properties in polluted and unpolluted regions were compared to determine the impact of air traffic on cirrus.

RRegrs: an R package for computer-aided model selection with multiple regression models.

PubMed

Tsiliki, Georgia; Munteanu, Cristian R; Seoane, Jose A; Fernandez-Lozano, Carlos; Sarimveis, Haralambos; Willighagen, Egon L

2015-01-01

Predictive regression models can be created with many different modelling approaches. Choices need to be made for data set splitting, cross-validation methods, specific regression parameters and best model criteria, as they all affect the accuracy and efficiency of the produced predictive models, and therefore, raising model reproducibility and comparison issues. Cheminformatics and bioinformatics are extensively using predictive modelling and exhibit a need for standardization of these methodologies in order to assist model selection and speed up the process of predictive model development. A tool accessible to all users, irrespectively of their statistical knowledge, would be valuable if it tests several simple and complex regression models and validation schemes, produce unified reports, and offer the option to be integrated into more extensive studies. Additionally, such methodology should be implemented as a free programming package, in order to be continuously adapted and redistributed by others. We propose an integrated framework for creating multiple regression models, called RRegrs. The tool offers the option of ten simple and complex regression methods combined with repeated 10-fold and leave-one-out cross-validation. Methods include Multiple Linear regression, Generalized Linear Model with Stepwise Feature Selection, Partial Least Squares regression, Lasso regression, and Support Vector Machines Recursive Feature Elimination. The new framework is an automated fully validated procedure which produces standardized reports to quickly oversee the impact of choices in modelling algorithms and assess the model and cross-validation results. The methodology was implemented as an open source R package, available at https://www.github.com/enanomapper/RRegrs, by reusing and extending on the caret package. The universality of the new methodology is demonstrated using five standard data sets from different scientific fields. Its efficiency in cheminformatics and QSAR modelling is shown with three use cases: proteomics data for surface-modified gold nanoparticles, nano-metal oxides descriptor data, and molecular descriptors for acute aquatic toxicity data. The results show that for all data sets RRegrs reports models with equal or better performance for both training and test sets than those reported in the original publications. Its good performance as well as its adaptability in terms of parameter optimization could make RRegrs a popular framework to assist the initial exploration of predictive models, and with that, the design of more comprehensive in silico screening applications.Graphical abstractRRegrs is a computer-aided model selection framework for R multiple regression models; this is a fully validated procedure with application to QSAR modelling.

Subtropical high predictability establishes a promising way for monsoon and tropical storm predictions.

PubMed

Wang, Bin; Xiang, Baoqiang; Lee, June-Yi

2013-02-19

Monsoon rainfall and tropical storms (TSs) impose great impacts on society, yet their seasonal predictions are far from successful. The western Pacific Subtropical High (WPSH) is a prime circulation system affecting East Asian summer monsoon (EASM) and western North Pacific TS activities, but the sources of its variability and predictability have not been established. Here we show that the WPSH variation faithfully represents fluctuations of EASM strength (r = -0.92), the total TS days over the subtropical western North Pacific (r = -0.81), and the total number of TSs impacting East Asian coasts (r = -0.76) during 1979-2009. Our numerical experiment results establish that the WPSH variation is primarily controlled by central Pacific cooling/warming and a positive atmosphere-ocean feedback between the WPSH and the Indo-Pacific warm pool oceans. With a physically based empirical model and the state-of-the-art dynamical models, we demonstrate that the WPSH is highly predictable; this predictability creates a promising way for prediction of monsoon and TS. The predictions using the WPSH predictability not only yields substantially improved skills in prediction of the EASM rainfall, but also enables skillful prediction of the TS activities that the current dynamical models fail. Our findings reveal that positive WPSH-ocean interaction can provide a source of climate predictability and highlight the importance of subtropical dynamics in understanding monsoon and TS predictability.

Subtropical High predictability establishes a promising way for monsoon and tropical storm predictions

PubMed Central

Wang, Bin; Xiang, Baoqiang; Lee, June-Yi

2013-01-01

Monsoon rainfall and tropical storms (TSs) impose great impacts on society, yet their seasonal predictions are far from successful. The western Pacific Subtropical High (WPSH) is a prime circulation system affecting East Asian summer monsoon (EASM) and western North Pacific TS activities, but the sources of its variability and predictability have not been established. Here we show that the WPSH variation faithfully represents fluctuations of EASM strength (r = –0.92), the total TS days over the subtropical western North Pacific (r = –0.81), and the total number of TSs impacting East Asian coasts (r = –0.76) during 1979–2009. Our numerical experiment results establish that the WPSH variation is primarily controlled by central Pacific cooling/warming and a positive atmosphere-ocean feedback between the WPSH and the Indo-Pacific warm pool oceans. With a physically based empirical model and the state-of-the-art dynamical models, we demonstrate that the WPSH is highly predictable; this predictability creates a promising way for prediction of monsoon and TS. The predictions using the WPSH predictability not only yields substantially improved skills in prediction of the EASM rainfall, but also enables skillful prediction of the TS activities that the current dynamical models fail. Our findings reveal that positive WPSH–ocean interaction can provide a source of climate predictability and highlight the importance of subtropical dynamics in understanding monsoon and TS predictability. PMID:23341624

Reduced Fragment Diversity for Alpha and Alpha-Beta Protein Structure Prediction using Rosetta.

PubMed

Abbass, Jad; Nebel, Jean-Christophe

2017-01-01

Protein structure prediction is considered a main challenge in computational biology. The biannual international competition, Critical Assessment of protein Structure Prediction (CASP), has shown in its eleventh experiment that free modelling target predictions are still beyond reliable accuracy, therefore, much effort should be made to improve ab initio methods. Arguably, Rosetta is considered as the most competitive method when it comes to targets with no homologues. Relying on fragments of length 9 and 3 from known structures, Rosetta creates putative structures by assembling candidate fragments. Generally, the structure with the lowest energy score, also known as first model, is chosen to be the "predicted one". A thorough study has been conducted on the role and diversity of 3-mers involved in Rosetta's model "refinement" phase. Usage of the standard number of 3-mers - i.e. 200 - has been shown to degrade alpha and alpha-beta protein conformations initially achieved by assembling 9-mers. Therefore, a new prediction pipeline is proposed for Rosetta where the "refinement" phase is customised according to a target's structural class prediction. Over 8% improvement in terms of first model structure accuracy is reported for alpha and alpha-beta classes when decreasing the number of 3- mers. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Prediction of Acoustic Loads Generated by Propulsion Systems

NASA Technical Reports Server (NTRS)

Perez, Linamaria; Allgood, Daniel C.

2011-01-01

NASA Stennis Space Center is one of the nation's premier facilities for conducting large-scale rocket engine testing. As liquid rocket engines vary in size, so do the acoustic loads that they produce. When these acoustic loads reach very high levels they may cause damages both to humans and to actual structures surrounding the testing area. To prevent these damages, prediction tools are used to estimate the spectral content and levels of the acoustics being generated by the rocket engine plumes and model their propagation through the surrounding atmosphere. Prior to the current work, two different acoustic prediction tools were being implemented at Stennis Space Center, each having their own advantages and disadvantages depending on the application. Therefore, a new prediction tool was created, using NASA SP-8072 handbook as a guide, which would replicate the same prediction methods as the previous codes, but eliminate any of the drawbacks the individual codes had. Aside from replicating the previous modeling capability in a single framework, additional modeling functions were added thereby expanding the current modeling capability. To verify that the new code could reproduce the same predictions as the previous codes, two verification test cases were defined. These verification test cases also served as validation cases as the predicted results were compared to actual test data.

Assessing predictability of a hydrological stochastic-dynamical system

NASA Astrophysics Data System (ADS)

Gelfan, Alexander

2014-05-01

The water cycle includes the processes with different memory that creates potential for predictability of hydrological system based on separating its long and short memory components and conditioning long-term prediction on slower evolving components (similar to approaches in climate prediction). In the face of the Panta Rhei IAHS Decade questions, it is important to find a conceptual approach to classify hydrological system components with respect to their predictability, define predictable/unpredictable patterns, extend lead-time and improve reliability of hydrological predictions based on the predictable patterns. Representation of hydrological systems as the dynamical systems subjected to the effect of noise (stochastic-dynamical systems) provides possible tool for such conceptualization. A method has been proposed for assessing predictability of hydrological system caused by its sensitivity to both initial and boundary conditions. The predictability is defined through a procedure of convergence of pre-assigned probabilistic measure (e.g. variance) of the system state to stable value. The time interval of the convergence, that is the time interval during which the system losses memory about its initial state, defines limit of the system predictability. The proposed method was applied to assess predictability of soil moisture dynamics in the Nizhnedevitskaya experimental station (51.516N; 38.383E) located in the agricultural zone of the central European Russia. A stochastic-dynamical model combining a deterministic one-dimensional model of hydrothermal regime of soil with a stochastic model of meteorological inputs was developed. The deterministic model describes processes of coupled heat and moisture transfer through unfrozen/frozen soil and accounts for the influence of phase changes on water flow. The stochastic model produces time series of daily meteorological variables (precipitation, air temperature and humidity), whose statistical properties are similar to those of the corresponding series of the actual data measured at the station. Beginning from the initial conditions and being forced by Monte-Carlo generated synthetic meteorological series, the model simulated diverging trajectories of soil moisture characteristics (water content of soil column, moisture of different soil layers, etc.). Limit of predictability of the specific characteristic was determined through time of stabilization of variance of the characteristic between the trajectories, as they move away from the initial state. Numerical experiments were carried out with the stochastic-dynamical model to analyze sensitivity of the soil moisture predictability assessments to uncertainty in the initial conditions, to determine effects of the soil hydraulic properties and processes of soil freezing on the predictability. It was found, particularly, that soil water content predictability is sensitive to errors in the initial conditions and strongly depends on the hydraulic properties of soil under both unfrozen and frozen conditions. Even if the initial conditions are "well-established", the assessed predictability of water content of unfrozen soil does not exceed 30-40 days, while for frozen conditions it may be as long as 3-4 months. The latter creates opportunity for utilizing the autumn water content of soil as the predictor for spring snowmelt runoff in the region under consideration.

THE EARTH SYSTEM PREDICTION SUITE: Toward a Coordinated U.S. Modeling Capability

PubMed Central

Theurich, Gerhard; DeLuca, C.; Campbell, T.; Liu, F.; Saint, K.; Vertenstein, M.; Chen, J.; Oehmke, R.; Doyle, J.; Whitcomb, T.; Wallcraft, A.; Iredell, M.; Black, T.; da Silva, AM; Clune, T.; Ferraro, R.; Li, P.; Kelley, M.; Aleinov, I.; Balaji, V.; Zadeh, N.; Jacob, R.; Kirtman, B.; Giraldo, F.; McCarren, D.; Sandgathe, S.; Peckham, S.; Dunlap, R.

2017-01-01

The Earth System Prediction Suite (ESPS) is a collection of flagship U.S. weather and climate models and model components that are being instrumented to conform to interoperability conventions, documented to follow metadata standards, and made available either under open source terms or to credentialed users. The ESPS represents a culmination of efforts to create a common Earth system model architecture, and the advent of increasingly coordinated model development activities in the U.S. ESPS component interfaces are based on the Earth System Modeling Framework (ESMF), community-developed software for building and coupling models, and the National Unified Operational Prediction Capability (NUOPC) Layer, a set of ESMF-based component templates and interoperability conventions. This shared infrastructure simplifies the process of model coupling by guaranteeing that components conform to a set of technical and semantic behaviors. The ESPS encourages distributed, multi-agency development of coupled modeling systems, controlled experimentation and testing, and exploration of novel model configurations, such as those motivated by research involving managed and interactive ensembles. ESPS codes include the Navy Global Environmental Model (NavGEM), HYbrid Coordinate Ocean Model (HYCOM), and Coupled Ocean Atmosphere Mesoscale Prediction System (COAMPS®); the NOAA Environmental Modeling System (NEMS) and the Modular Ocean Model (MOM); the Community Earth System Model (CESM); and the NASA ModelE climate model and GEOS-5 atmospheric general circulation model. PMID:29568125

THE EARTH SYSTEM PREDICTION SUITE: Toward a Coordinated U.S. Modeling Capability.

PubMed

Theurich, Gerhard; DeLuca, C; Campbell, T; Liu, F; Saint, K; Vertenstein, M; Chen, J; Oehmke, R; Doyle, J; Whitcomb, T; Wallcraft, A; Iredell, M; Black, T; da Silva, A M; Clune, T; Ferraro, R; Li, P; Kelley, M; Aleinov, I; Balaji, V; Zadeh, N; Jacob, R; Kirtman, B; Giraldo, F; McCarren, D; Sandgathe, S; Peckham, S; Dunlap, R

2016-07-01

The Earth System Prediction Suite (ESPS) is a collection of flagship U.S. weather and climate models and model components that are being instrumented to conform to interoperability conventions, documented to follow metadata standards, and made available either under open source terms or to credentialed users. The ESPS represents a culmination of efforts to create a common Earth system model architecture, and the advent of increasingly coordinated model development activities in the U.S. ESPS component interfaces are based on the Earth System Modeling Framework (ESMF), community-developed software for building and coupling models, and the National Unified Operational Prediction Capability (NUOPC) Layer, a set of ESMF-based component templates and interoperability conventions. This shared infrastructure simplifies the process of model coupling by guaranteeing that components conform to a set of technical and semantic behaviors. The ESPS encourages distributed, multi-agency development of coupled modeling systems, controlled experimentation and testing, and exploration of novel model configurations, such as those motivated by research involving managed and interactive ensembles. ESPS codes include the Navy Global Environmental Model (NavGEM), HYbrid Coordinate Ocean Model (HYCOM), and Coupled Ocean Atmosphere Mesoscale Prediction System (COAMPS ® ); the NOAA Environmental Modeling System (NEMS) and the Modular Ocean Model (MOM); the Community Earth System Model (CESM); and the NASA ModelE climate model and GEOS-5 atmospheric general circulation model.

The Earth System Prediction Suite: Toward a Coordinated U.S. Modeling Capability

NASA Technical Reports Server (NTRS)

Theurich, Gerhard; DeLuca, C.; Campbell, T.; Liu, F.; Saint, K.; Vertenstein, M.; Chen, J.; Oehmke, R.; Doyle, J.; Whitcomb, T.;

Towards personal health care with model-guided medicine: long-term PPPM-related strategies and realisation opportunities within 'Horizon 2020'.

PubMed

Lemke, Heinz U; Golubnitschaja, Olga

2014-01-01

At the international EPMA Summit carried out in the EU Parliament (September 2013), the main challenges in Predictive, Preventive and Personalised Medicine have been discussed and strategies outlined in order to implement scientific and technological innovation in medicine and healthcare utilising new strategic programmes such as 'Horizon 2020'. The joint EPMA (European Association for Predictive, Preventive and Personalised Medicine) / IFCARS (International Foundation for Computer Assisted Radiology and Surgery) paper emphasises the consolidate position of the leading experts who are aware of the great responsibility of being on a forefront of predictive, preventive and personalised medicine. Both societies consider long-term international partnerships and multidisciplinary projects to create PPPM relevant innovation in science, technological tools and practical implementation in healthcare. Personalisation in healthcare urgently needs innovation in design of PPPM-related medical services, new products, research, education, didactic materials, propagation of targeted prevention in the society and treatments tailored to the person. For the paradigm shift from delayed reactive to predictive, preventive and personalised medicine, a new culture should be created in communication between individual professional domains, between doctor and patient, as well as in communication with individual social (sub)groups and patient cohorts. This is a long-term mission in personalised healthcare with the whole spectrum of instruments available and to be created in the field.

Towards personal health care with model-guided medicine: long-term PPPM-related strategies and realisation opportunities within ‘Horizon 2020’

PubMed Central

2014-01-01

At the international EPMA Summit carried out in the EU Parliament (September 2013), the main challenges in Predictive, Preventive and Personalised Medicine have been discussed and strategies outlined in order to implement scientific and technological innovation in medicine and healthcare utilising new strategic programmes such as ‘Horizon 2020’. The joint EPMA (European Association for Predictive, Preventive and Personalised Medicine) / IFCARS (International Foundation for Computer Assisted Radiology and Surgery) paper emphasises the consolidate position of the leading experts who are aware of the great responsibility of being on a forefront of predictive, preventive and personalised medicine. Both societies consider long-term international partnerships and multidisciplinary projects to create PPPM relevant innovation in science, technological tools and practical implementation in healthcare. Personalisation in healthcare urgently needs innovation in design of PPPM-related medical services, new products, research, education, didactic materials, propagation of targeted prevention in the society and treatments tailored to the person. For the paradigm shift from delayed reactive to predictive, preventive and personalised medicine, a new culture should be created in communication between individual professional domains, between doctor and patient, as well as in communication with individual social (sub)groups and patient cohorts. This is a long-term mission in personalised healthcare with the whole spectrum of instruments available and to be created in the field. PMID:24883142

A Comparison of a Machine Learning Model with EuroSCORE II in Predicting Mortality after Elective Cardiac Surgery: A Decision Curve Analysis.

PubMed

Allyn, Jérôme; Allou, Nicolas; Augustin, Pascal; Philip, Ivan; Martinet, Olivier; Belghiti, Myriem; Provenchere, Sophie; Montravers, Philippe; Ferdynus, Cyril

2017-01-01

The benefits of cardiac surgery are sometimes difficult to predict and the decision to operate on a given individual is complex. Machine Learning and Decision Curve Analysis (DCA) are recent methods developed to create and evaluate prediction models. We conducted a retrospective cohort study using a prospective collected database from December 2005 to December 2012, from a cardiac surgical center at University Hospital. The different models of prediction of mortality in-hospital after elective cardiac surgery, including EuroSCORE II, a logistic regression model and a machine learning model, were compared by ROC and DCA. Of the 6,520 patients having elective cardiac surgery with cardiopulmonary bypass, 6.3% died. Mean age was 63.4 years old (standard deviation 14.4), and mean EuroSCORE II was 3.7 (4.8) %. The area under ROC curve (IC95%) for the machine learning model (0.795 (0.755-0.834)) was significantly higher than EuroSCORE II or the logistic regression model (respectively, 0.737 (0.691-0.783) and 0.742 (0.698-0.785), p < 0.0001). Decision Curve Analysis showed that the machine learning model, in this monocentric study, has a greater benefit whatever the probability threshold. According to ROC and DCA, machine learning model is more accurate in predicting mortality after elective cardiac surgery than EuroSCORE II. These results confirm the use of machine learning methods in the field of medical prediction.

Towards a chromatographic similarity index to establish localised quantitative structure-retention relationships for retention prediction. II Use of Tanimoto similarity index in ion chromatography.

PubMed

Park, Soo Hyun; Talebi, Mohammad; Amos, Ruth I J; Tyteca, Eva; Haddad, Paul R; Szucs, Roman; Pohl, Christopher A; Dolan, John W

2017-11-10

Quantitative Structure-Retention Relationships (QSRR) are used to predict retention times of compounds based only on their chemical structures encoded by molecular descriptors. The main concern in QSRR modelling is to build models with high predictive power, allowing reliable retention prediction for the unknown compounds across the chromatographic space. With the aim of enhancing the prediction power of the models, in this work, our previously proposed QSRR modelling approach called "federation of local models" is extended in ion chromatography to predict retention times of unknown ions, where a local model for each target ion (unknown) is created using only structurally similar ions from the dataset. A Tanimoto similarity (TS) score was utilised as a measure of structural similarity and training sets were developed by including ions that were similar to the target ion, as defined by a threshold value. The prediction of retention parameters (a- and b-values) in the linear solvent strength (LSS) model in ion chromatography, log k=a - blog[eluent], allows the prediction of retention times under all eluent concentrations. The QSRR models for a- and b-values were developed by a genetic algorithm-partial least squares method using the retention data of inorganic and small organic anions and larger organic cations (molecular mass up to 507) on four Thermo Fisher Scientific columns (AS20, AS19, AS11HC and CS17). The corresponding predicted retention times were calculated by fitting the predicted a- and b-values of the models into the LSS model equation. The predicted retention times were also plotted against the experimental values to evaluate the goodness of fit and the predictive power of the models. The application of a TS threshold of 0.6 was found to successfully produce predictive and reliable QSRR models (Q ext(F2) 2 >0.8 and Mean Absolute Error<0.1), and hence accurate retention time predictions with an average Mean Absolute Error of 0.2min. Crown Copyright © 2017. Published by Elsevier B.V. All rights reserved.

Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement.

PubMed

Collins, G S; Reitsma, J B; Altman, D G; Moons, K G M

2015-02-01

Prediction models are developed to aid healthcare providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision-making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) initiative developed a set of recommendations for the reporting of studies developing, validating or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a web-based survey and revised during a 3-day meeting in June 2011 with methodologists, healthcare professionals and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study, regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). © 2015 Joint copyright. The Authors and Annals of Internal Medicine. Diabetic Medicine published by John Wiley Ltd. on behalf of Diabetes UK.

Building hierarchical models of avian distributions for the State of Georgia

USGS Publications Warehouse

Howell, J.E.; Peterson, J.T.; Conroy, M.J.

2008-01-01

To predict the distributions of breeding birds in the state of Georgia, USA, we built hierarchical models consisting of 4 levels of nested mapping units of decreasing area: 90,000 ha, 3,600 ha, 144 ha, and 5.76 ha. We used the Partners in Flight database of point counts to generate presence and absence data at locations across the state of Georgia for 9 avian species: Acadian flycatcher (Empidonax virescens), brownheaded nuthatch (Sitta pusilla), Carolina wren (Thryothorus ludovicianus), indigo bunting (Passerina cyanea), northern cardinal (Cardinalis cardinalis), prairie warbler (Dendroica discolor), yellow-billed cuckoo (Coccyxus americanus), white-eyed vireo (Vireo griseus), and wood thrush (Hylocichla mustelina). At each location, we estimated hierarchical-level-specific habitat measurements using the Georgia GAP Analysis18 class land cover and other Geographic Information System sources. We created candidate, species-specific occupancy models based on previously reported relationships, and fit these using Markov chain Monte Carlo procedures implemented in OpenBugs. We then created a confidence model set for each species based on Akaike's Information Criterion. We found hierarchical habitat relationships for all species. Three-fold cross-validation estimates of model accuracy indicated an average overall correct classification rate of 60.5%. Comparisons with existing Georgia GAP Analysis models indicated that our models were more accurate overall. Our results provide guidance to wildlife scientists and managers seeking predict avian occurrence as a function of local and landscape-level habitat attributes.

Finite Element Simulation of Shot Peening: Prediction of Residual Stresses and Surface Roughness

NASA Astrophysics Data System (ADS)

Gariépy, Alexandre; Perron, Claude; Bocher, Philippe; Lévesque, Martin

Shot peening is a surface treatment that consists of bombarding a ductile surface with numerous small and hard particles. Each impact creates localized plastic strains that permanently stretch the surface. Since the underlying material constrains this stretching, compressive residual stresses are generated near the surface. This process is commonly used in the automotive and aerospace industries to improve fatigue life. Finite element analyses can be used to predict residual stress profiles and surface roughness created by shot peening. This study investigates further the parameters and capabilities of a random impact model by evaluating the representative volume element and the calculated stress distribution. Using an isotropic-kinematic hardening constitutive law to describe the behaviour of AA2024-T351 aluminium alloy, promising results were achieved in terms of residual stresses.

Risk factors for the treatment outcome of retreated pulmonary tuberculosis patients in China: an optimized prediction model.

PubMed

Wang, X-M; Yin, S-H; Du, J; Du, M-L; Wang, P-Y; Wu, J; Horbinski, C M; Wu, M-J; Zheng, H-Q; Xu, X-Q; Shu, W; Zhang, Y-J

2017-07-01

Retreatment of tuberculosis (TB) often fails in China, yet the risk factors associated with the failure remain unclear. To identify risk factors for the treatment failure of retreated pulmonary tuberculosis (PTB) patients, we analyzed the data of 395 retreated PTB patients who received retreatment between July 2009 and July 2011 in China. PTB patients were categorized into 'success' and 'failure' groups by their treatment outcome. Univariable and multivariable logistic regression were used to evaluate the association between treatment outcome and socio-demographic as well as clinical factors. We also created an optimized risk score model to evaluate the predictive values of these risk factors on treatment failure. Of 395 patients, 99 (25·1%) were diagnosed as retreatment failure. Our results showed that risk factors associated with treatment failure included drug resistance, low education level, low body mass index (6 months), standard treatment regimen, retreatment type, positive culture result after 2 months of treatment, and the place where the first medicine was taken. An Optimized Framingham risk model was then used to calculate the risk scores of these factors. Place where first medicine was taken (temporary living places) received a score of 6, which was highest among all the factors. The predicted probability of treatment failure increases as risk score increases. Ten out of 359 patients had a risk score >9, which corresponded to an estimated probability of treatment failure >70%. In conclusion, we have identified multiple clinical and socio-demographic factors that are associated with treatment failure of retreated PTB patients. We also created an optimized risk score model that was effective in predicting the retreatment failure. These results provide novel insights for the prognosis and improvement of treatment for retreated PTB patients.

Predicting United States Medical Licensure Examination Step 2 clinical knowledge scores from previous academic indicators.

PubMed

Monteiro, Kristina A; George, Paul; Dollase, Richard; Dumenco, Luba

2017-01-01

The use of multiple academic indicators to identify students at risk of experiencing difficulty completing licensure requirements provides an opportunity to increase support services prior to high-stakes licensure examinations, including the United States Medical Licensure Examination (USMLE) Step 2 clinical knowledge (CK). Step 2 CK is becoming increasingly important in decision-making by residency directors because of increasing undergraduate medical enrollment and limited available residency vacancies. We created and validated a regression equation to predict students' Step 2 CK scores from previous academic indicators to identify students at risk, with sufficient time to intervene with additional support services as necessary. Data from three cohorts of students (N=218) with preclinical mean course exam score, National Board of Medical Examination subject examinations, and USMLE Step 1 and Step 2 CK between 2011 and 2013 were used in analyses. The authors created models capable of predicting Step 2 CK scores from academic indicators to identify at-risk students. In model 1, preclinical mean course exam score and Step 1 score accounted for 56% of the variance in Step 2 CK score. The second series of models included mean preclinical course exam score, Step 1 score, and scores on three NBME subject exams, and accounted for 67%-69% of the variance in Step 2 CK score. The authors validated the findings on the most recent cohort of graduating students (N=89) and predicted Step 2 CK score within a mean of four points (SD=8). The authors suggest using the first model as a needs assessment to gauge the level of future support required after completion of preclinical course requirements, and rescreening after three of six clerkships to identify students who might benefit from additional support before taking USMLE Step 2 CK.

Screening Tool for Early Postnatal Prediction of Retinopathy of Prematurity in Preterm Newborns (STEP-ROP).

PubMed

Ricard, Caroline A; Dammann, Christiane E L; Dammann, Olaf

2017-01-01

Retinopathy of prematurity (ROP) is a disorder of the preterm newborn characterized by neurovascular disruption in the immature retina that may cause visual impairment and blindness. To develop a clinical screening tool for early postnatal prediction of ROP in preterm newborns based on risk information available within the first 48 h of postnatal life. Using data submitted to the Vermont Oxford Network (VON) between 1995 and 2015, we created logistic regression models based on infants born <28 completed weeks gestational age. We developed a model with 60% of the data and identified birth weight, gestational age, respiratory distress syndrome, non-Hispanic ethnicity, and multiple gestation as predictors of ROP. We tested the model in the remaining 40%, performed tenfold cross-validation, and tested the score in ELGAN study data. Of the 1,052 newborns in the VON database, 627 recorded an ROP status. Forty percent had no ROP, 40% had mild ROP (stages 1 and 2), and 20% had severe ROP (stages 3-5). We created a weighted score to predict any ROP based on the multivariable regression model. A cutoff score of 5 had the best sensitivity (95%, 95% CI 93-97), while maintaining a strong positive predictive value (63%, 95% CI 57-68). When applied to the ELGAN data, sensitivity was lower (72%, 95% CI 69-75), but PPV was higher (80%, 95% CI 77-83). STEP-ROP is a promising screening tool. It is easy to calculate, does not rely on extensive postnatal data collection, and can be calculated early after birth. Early ROP screening may help physicians limit patient exposure to additional risk factors, and may be useful for risk stratification in clinical trials aimed at reducing ROP. © 2017 S. Karger AG, Basel.

A motivational model for environmentally responsible behavior.

PubMed

Tabernero, Carmen; Hernández, Bernardo

2012-07-01

This paper presents a study examining whether self-efficacy and intrinsic motivation are related to environmentally responsible behavior (ERB). The study analysed past environmental behavior, self-regulatory mechanisms (self-efficacy, satisfaction, goals), and intrinsic and extrinsic motivation in relation to ERBs in a sample of 156 university students. Results show that all the motivational variables studied are linked to ERB. The effects of self-efficacy on ERB are mediated by the intrinsic motivation responses of the participants. A theoretical model was created by means of path analysis, revealing the power of motivational variables to predict ERB. Structural equation modeling was used to test and fit the research model. The role of motivational variables is discussed with a view to creating adequate learning contexts and experiences to generate interest and new sensations in which self-efficacy and affective reactions play an important role.

Predicting arsenic concentrations in groundwater of San Luis Valley, Colorado: implications for individual-level lifetime exposure assessment.

PubMed

James, Katherine A; Meliker, Jaymie R; Buttenfield, Barbara E; Byers, Tim; Zerbe, Gary O; Hokanson, John E; Marshall, Julie A

2014-08-01

Consumption of inorganic arsenic in drinking water at high levels has been associated with chronic diseases. Risk is less clear at lower levels of arsenic, in part due to difficulties in estimating exposure. Herein we characterize spatial and temporal variability of arsenic concentrations and develop models for predicting aquifer arsenic concentrations in the San Luis Valley, Colorado, an area of moderately elevated arsenic in groundwater. This study included historical water samples with total arsenic concentrations from 595 unique well locations. A longitudinal analysis established temporal stability in arsenic levels in individual wells. The mean arsenic levels for a random sample of 535 wells were incorporated into five kriging models to predict groundwater arsenic concentrations at any point in time. A separate validation dataset (n = 60 wells) was used to identify the model with strongest predictability. Findings indicate that arsenic concentrations are temporally stable (r = 0.88; 95 % CI 0.83-0.92 for samples collected from the same well 15-25 years apart) and the spatial model created using ordinary kriging best predicted arsenic concentrations (ρ = 0.72 between predicted and observed validation data). These findings illustrate the value of geostatistical modeling of arsenic and suggest the San Luis Valley is a good region for conducting epidemiologic studies of groundwater metals because of the ability to accurately predict variation in groundwater arsenic concentrations.

Simulating the Historical Process To Create Laboratory Exercises That Teach Research Methods.

ERIC Educational Resources Information Center

Alcock, James

1994-01-01

Explains how controlling student access to data can be used as a strategy enabling students to take the role of a research geologist. Students develop models based on limited data and conduct field tests by comparing their predictions with the additional data. (DDR)

NCEP Central Operations

Science.gov Websites

Climate Climate Prediction Climate Archives Weather Safety Storm Ready NOAA Central Library Photo Library NCO's MISSION * Execute the NCEP operational model suite - Create climate, weather, ocean, space and ) NCO Organizational Chart NOAA's Weather and Climate Operational Supercomputing System is known as

Patient specific computerized phantoms to estimate dose in pediatric CT

NASA Astrophysics Data System (ADS)

Segars, W. P.; Sturgeon, G.; Li, X.; Cheng, L.; Ceritoglu, C.; Ratnanather, J. T.; Miller, M. I.; Tsui, B. M. W.; Frush, D.; Samei, E.

2009-02-01

We create a series of detailed computerized phantoms to estimate patient organ and effective dose in pediatric CT and investigate techniques for efficiently creating patient-specific phantoms based on imaging data. The initial anatomy of each phantom was previously developed based on manual segmentation of pediatric CT data. Each phantom was extended to include a more detailed anatomy based on morphing an existing adult phantom in our laboratory to match the framework (based on segmentation) defined for the target pediatric model. By morphing a template anatomy to match the patient data in the LDDMM framework, it was possible to create a patient specific phantom with many anatomical structures, some not visible in the CT data. The adult models contain thousands of defined structures that were transformed to define them in each pediatric anatomy. The accuracy of this method, under different conditions, was tested using a known voxelized phantom as the target. Errors were measured in terms of a distance map between the predicted organ surfaces and the known ones. We also compared calculated dose measurements to see the effect of different magnitudes of errors in morphing. Despite some variations in organ geometry, dose measurements from morphing predictions were found to agree with those calculated from the voxelized phantom thus demonstrating the feasibility of our methods.

Improving residential miscellaneous electrical load modeling

NASA Astrophysics Data System (ADS)

Burgett, Joseph M.

Over the past 30 years, the intensity of all major energy use categories has decreased in the residential market with the exception of miscellaneous electrical loads (MELs). MELs include primarily 120V plug-loads and some hard wired loads. MELs stand alone as the only category in which energy intensity has steadily increased over time. While MELs constitute approximately 15% - 25% of a typical home's total energy use, it is projected to increase to 36% by 2020. Despite the significant percentage of the home's total load, MELs are the least researched energy end use category and most poorly modeled. The Home Energy Rating System (HERS) index is the most widely used residential energy rating system and uses a simple square foot multiplier to model MELs. This study improves upon the HERS model by including occupant characteristics as part of the MEL model. This "new model" was created by regressing and explanatory equation from the Energy Information Agency's Residential Energy Consumption Survey (RECS). The RECS has a very large sample size of 12,083 respondents who answered over 90 pages of questions related to home structure, appliances they own and demographical information. The information provided by the respondents was used to calculate a MEL for all the RECS households. A stepwise regression process was used to create a model that included size of the home, household income, number of household members and presence of a home business to predict the MEL. The new model was then tested on 24 actual homes to compare its predictive power with the HERS model. The new model more closely predicted the actual MEL for 17 of the 24 test houses (71%). Additionally, the standard deviation or the "tightness of fit" of the new model was less than half of the HERS model when used on the RECS respondents. What this study found was that using occupant characteristics to supplement a square foot multiplier significantly increased the precision of MEL modeling.

Topography and vegetation as predictors of snow water equivalent across the alpine treeline ecotone at Lee Ridge, Glacier National Park, Montana, U.S.A.

USGS Publications Warehouse

Geddes, C.A.; Brown, D.G.; Fagre, D.B.

2005-01-01

We derived and implemented two spatial models of May snow water equivalent (SWE) at Lee Ridge in Glacier National Park, Montana. We used the models to test the hypothesis that vegetation structure is a control on snow redistribution at the alpine treeline ecotone (ATE). The statistical models were derived using stepwise and "best" subsets regression techniques. The first model was derived from field measurements of SWE, topography, and vegetation taken at 27 sample points. The second model was derived using GIS-based measures of topography and vegetation. Both the field- (R² = 0.93) and GIS-based models (R² = 0.69) of May SWE included the following variables: site type (based on vegetation), elevation, maximum slope, and general slope aspect. Site type was identified as the most important predictor of SWE in both models, accounting for 74.0% and 29.5% of the variation, respectively. The GIS-based model was applied to create a predictive map of SWE across Lee Ridge, predicting little snow accumulation on the top of the ridge where vegetation is scarce. The GIS model failed in large depressions, including ephemeral stream channels. The models supported the hypothesis that upright vegetation has a positive effect on accumulation of SWE above and beyond the effects of topography. Vegetation, therefore, creates a positive feedback in which it modifies its, environment and could affect the ability of additional vegetation to become established.

Advanced earth observation spacecraft computer-aided design software: Technical, user and programmer guide

NASA Technical Reports Server (NTRS)

Farrell, C. E.; Krauze, L. D.

1983-01-01

The IDEAS computer of NASA is a tool for interactive preliminary design and analysis of LSS (Large Space System). Nine analysis modules were either modified or created. These modules include the capabilities of automatic model generation, model mass properties calculation, model area calculation, nonkinematic deployment modeling, rigid-body controls analysis, RF performance prediction, subsystem properties definition, and EOS science sensor selection. For each module, a section is provided that contains technical information, user instructions, and programmer documentation.

Solar Dynamics Observatory (SDO) HGAS Induced Jitter

NASA Technical Reports Server (NTRS)

Liu, Alice; Blaurock, Carl; Liu, Kuo-Chia; Mule, Peter

2008-01-01

This paper presents the results of a comprehensive assessment of High Gain Antenna System induced jitter on the Solar Dynamics Observatory. The jitter prediction is created using a coupled model of the structural dynamics, optical response, control systems, and stepper motor actuator electromechanical dynamics. The paper gives an overview of the model components, presents the verification processes used to evaluate the models, describes validation and calibration tests and model-to-measurement comparison results, and presents the jitter analysis methodology and results.

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.

Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement.

PubMed

Collins, Gary S; Reitsma, Johannes B; Altman, Douglas G; Moons, Karel G M

2015-06-01

Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

Technosocial Predictive Analytics in Support of Naturalistic Decision Making

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

Sanfilippo, Antonio P.; Cowell, Andrew J.; Malone, Elizabeth L.

2009-06-23

A main challenge we face in fostering sustainable growth is to anticipate outcomes through predictive and proactive across domains as diverse as energy, security, the environment, health and finance in order to maximize opportunities, influence outcomes and counter adversities. The goal of this paper is to present new methods for anticipatory analytical thinking which address this challenge through the development of a multi-perspective approach to predictive modeling as a core to a creative decision making process. This approach is uniquely multidisciplinary in that it strives to create decision advantage through the integration of human and physical models, and leverages knowledgemore » management and visual analytics to support creative thinking by facilitating the achievement of interoperable knowledge inputs and enhancing the user’s cognitive access. We describe a prototype system which implements this approach and exemplify its functionality with reference to a use case in which predictive modeling is paired with analytic gaming to support collaborative decision-making in the domain of agricultural land management.« less

Prediction of Peaks of Seasonal Influenza in Military Health-Care Data

PubMed Central

Buczak, Anna L.; Baugher, Benjamin; Guven, Erhan; Moniz, Linda; Babin, Steven M.; Chretien, Jean-Paul

2016-01-01

Influenza is a highly contagious disease that causes seasonal epidemics with significant morbidity and mortality. The ability to predict influenza peak several weeks in advance would allow for timely preventive public health planning and interventions to be used to mitigate these outbreaks. Because influenza may also impact the operational readiness of active duty personnel, the US military places a high priority on surveillance and preparedness for seasonal outbreaks. A method for creating models for predicting peak influenza visits per total health-care visits (ie, activity) weeks in advance has been developed using advanced data mining techniques on disparate epidemiological and environmental data. The model results are presented and compared with those of other popular data mining classifiers. By rigorously testing the model on data not used in its development, it is shown that this technique can predict the week of highest influenza activity for a specific region with overall better accuracy than other methods examined in this article. PMID:27127415

Practical quantum mechanics-based fragment methods for predicting molecular crystal properties.

PubMed

Wen, Shuhao; Nanda, Kaushik; Huang, Yuanhang; Beran, Gregory J O

2012-06-07

Significant advances in fragment-based electronic structure methods have created a real alternative to force-field and density functional techniques in condensed-phase problems such as molecular crystals. This perspective article highlights some of the important challenges in modeling molecular crystals and discusses techniques for addressing them. First, we survey recent developments in fragment-based methods for molecular crystals. Second, we use examples from our own recent research on a fragment-based QM/MM method, the hybrid many-body interaction (HMBI) model, to analyze the physical requirements for a practical and effective molecular crystal model chemistry. We demonstrate that it is possible to predict molecular crystal lattice energies to within a couple kJ mol(-1) and lattice parameters to within a few percent in small-molecule crystals. Fragment methods provide a systematically improvable approach to making predictions in the condensed phase, which is critical to making robust predictions regarding the subtle energy differences found in molecular crystals.

Multi-model analysis in hydrological prediction

NASA Astrophysics Data System (ADS)

Lanthier, M.; Arsenault, R.; Brissette, F.

2017-12-01

Hydrologic modelling, by nature, is a simplification of the real-world hydrologic system. Therefore ensemble hydrological predictions thus obtained do not present the full range of possible streamflow outcomes, thereby producing ensembles which demonstrate errors in variance such as under-dispersion. Past studies show that lumped models used in prediction mode can return satisfactory results, especially when there is not enough information available on the watershed to run a distributed model. But all lumped models greatly simplify the complex processes of the hydrologic cycle. To generate more spread in the hydrologic ensemble predictions, multi-model ensembles have been considered. In this study, the aim is to propose and analyse a method that gives an ensemble streamflow prediction that properly represents the forecast probabilities and reduced ensemble bias. To achieve this, three simple lumped models are used to generate an ensemble. These will also be combined using multi-model averaging techniques, which generally generate a more accurate hydrogram than the best of the individual models in simulation mode. This new predictive combined hydrogram is added to the ensemble, thus creating a large ensemble which may improve the variability while also improving the ensemble mean bias. The quality of the predictions is then assessed on different periods: 2 weeks, 1 month, 3 months and 6 months using a PIT Histogram of the percentiles of the real observation volumes with respect to the volumes of the ensemble members. Initially, the models were run using historical weather data to generate synthetic flows. This worked for individual models, but not for the multi-model and for the large ensemble. Consequently, by performing data assimilation at each prediction period and thus adjusting the initial states of the models, the PIT Histogram could be constructed using the observed flows while allowing the use of the multi-model predictions. The under-dispersion has been largely corrected on short-term predictions. For the longer term, the addition of the multi-model member has been beneficial to the quality of the predictions, although it is too early to determine whether the gain is related to the addition of a member or if multi-model member has plus-value itself.

The cutting mechanism of the electrosurgical scalpel

NASA Astrophysics Data System (ADS)

Gjika, Eda; Pekker, Mikhail; Shashurin, Alexey; Shneider, Mikhail; Zhuang, Taisen; Canady, Jerome; Keidar, Michael

2017-01-01

Electrosurgical cutting is a well-known technique for creating incisions often used for the removal of benign and malignant tumors. The proposed mathematical model suggests that incisions are created due to the localized heating of the tissue. The model estimates a volume of tissue heating in the order of 2 · 10-4 mm3. This relatively small predicted volume explains why the heat generated from the very tip of the scalpel is unable to cause extensive damage to the tissue adjacent to the incision site. The scalpel exposes the target region to an RF field in 60 ms pulses until a temperature of around 100 °C is reached. This process leads to desiccation where the tissue is characterized by a significantly low electrical conductivity, which prevents further heating and charring. Subsequently, the incision is created from the mechanical scraping process that follows.

A model for predicting thermal properties of asphalt mixtures from their constituents

NASA Astrophysics Data System (ADS)

Keller, Merlin; Roche, Alexis; Lavielle, Marc

Numerous theoretical and experimental approaches have been developed to predict the effective thermal conductivity of composite materials such as polymers, foams, epoxies, soils and concrete. None of such models have been applied to asphalt concrete. This study attempts to develop a model to predict the thermal conductivity of asphalt concrete from its constituents that will contribute to the asphalt industry by reducing costs and saving time on laboratory testing. The necessity to do the laboratory testing would be no longer required when a mix for the pavement is created with desired thermal properties at the design stage by selecting correct constituents. This thesis investigated six existing predictive models for applicability to asphalt mixtures, and four standard mathematical techniques were used to develop a regression model to predict the effective thermal conductivity. The effective thermal conductivities of 81 asphalt specimens were used as the response variables, and the thermal conductivities and volume fractions of their constituents were used as the predictors. The conducted statistical analyses showed that the measured values of thermal conductivities of the mixtures are affected by the bitumen and aggregate content, but not by the air content. Contrarily, the predicted data for some investigated models are highly sensitive to air voids, but not to bitumen and/or aggregate content. Additionally, the comparison of the experimental with analytical data showed that none of the existing models gave satisfactory results; on the other hand, two regression models (Exponential 1* and Linear 3*) are promising for asphalt concrete.

Validation of Statistical Predictive Models Meant to Select Melanoma Patients for Sentinel Lymph Node Biopsy

PubMed Central

Sabel, Michael S.; Rice, John D.; Griffith, Kent A.; Lowe, Lori; Wong, Sandra L.; Chang, Alfred E.; Johnson, Timothy M.; Taylor, Jeremy M.G.

2013-01-01

Introduction To identify melanoma patients at sufficiently low risk of nodal metastases who could avoid SLN biopsy (SLNB). Several statistical models have been proposed based upon patient/tumor characteristics, including logistic regression, classification trees, random forests and support vector machines. We sought to validate recently published models meant to predict sentinel node status. Methods We queried our comprehensive, prospectively-collected melanoma database for consecutive melanoma patients undergoing SLNB. Prediction values were estimated based upon 4 published models, calculating the same reported metrics: negative predictive value (NPV), rate of negative predictions (RNP), and false negative rate (FNR). Results Logistic regression performed comparably with our data when considering NPV (89.4% vs. 93.6%); however the model’s specificity was not high enough to significantly reduce the rate of biopsies (SLN reduction rate of 2.9%). When applied to our data, the classification tree produced NPV and reduction in biopsies rates that were lower 87.7% vs. 94.1% and 29.8% vs. 14.3%, respectively. Two published models could not be applied to our data due to model complexity and the use of proprietary software. Conclusions Published models meant to reduce the SLNB rate among patients with melanoma either underperformed when applied to our larger dataset, or could not be validated. Differences in selection criteria and histopathologic interpretation likely resulted in underperformance. Development of statistical predictive models must be created in a clinically applicable manner to allow for both validation and ultimately clinical utility. PMID:21822550

The Value of 3D Printing Models of Left Atrial Appendage Using Real-Time 3D Transesophageal Echocardiographic Data in Left Atrial Appendage Occlusion: Applications toward an Era of Truly Personalized Medicine.

PubMed

Liu, Peng; Liu, Rijing; Zhang, Yan; Liu, Yingfeng; Tang, Xiaoming; Cheng, Yanzhen

The objective of this study was to assess the clinical feasibility of generating 3D printing models of left atrial appendage (LAA) using real-time 3D transesophageal echocardiogram (TEE) data for preoperative reference of LAA occlusion. Percutaneous LAA occlusion can effectively prevent patients with atrial fibrillation from stroke. However, the anatomical structure of LAA is so complicated that adequate information of its structure is essential for successful LAA occlusion. Emerging 3D printing technology has the demonstrated potential to structure more accurately than conventional imaging modalities by creating tangible patient-specific models. Typically, 3D printing data sets are acquired from CT and MRI, which may involve intravenous contrast, sedation, and ionizing radiation. It has been reported that 3D models of LAA were successfully created by the data acquired from CT. However, 3D printing of the LAA using real-time 3D TEE data has not yet been explored. Acquisition of 3D transesophageal echocardiographic data from 8 patients with atrial fibrillation was performed using the Philips EPIQ7 ultrasound system. Raw echocardiographic image data were opened in Philips QLAB and converted to 'Cartesian DICOM' format and imported into Mimics® software to create 3D models of LAA, which were printed using a rubber-like material. The printed 3D models were then used for preoperative reference and procedural simulation in LAA occlusion. We successfully printed LAAs of 8 patients. Each LAA costs approximately CNY 800-1,000 and the total process takes 16-17 h. Seven of the 8 Watchman devices predicted by preprocedural 2D TEE images were of the same sizes as those placed in the real operation. Interestingly, 3D printing models were highly reflective of the shape and size of LAAs, and all device sizes predicted by the 3D printing model were fully consistent with those placed in the real operation. Also, the 3D printed model could predict operating difficulty and the presence of a peridevice leak. 3D printing of the LAA using real-time 3D transesophageal echocardiographic data has a perfect and rapid application in LAA occlusion to assist with physician planning and decision making. © 2016 S. Karger AG, Basel.

Numerical simulation of human orientation perception during lunar landing

NASA Astrophysics Data System (ADS)

Clark, Torin K.; Young, Laurence R.; Stimpson, Alexander J.; Duda, Kevin R.; Oman, Charles M.

2011-09-01

In lunar landing it is necessary to select a suitable landing point and then control a stable descent to the surface. In manned landings, astronauts will play a critical role in monitoring systems and adjusting the descent trajectory through either supervisory control and landing point designations, or by direct manual control. For the astronauts to ensure vehicle performance and safety, they will have to accurately perceive vehicle orientation. A numerical model for human spatial orientation perception was simulated using input motions from lunar landing trajectories to predict the potential for misperceptions. Three representative trajectories were studied: an automated trajectory, a landing point designation trajectory, and a challenging manual control trajectory. These trajectories were studied under three cases with different cues activated in the model to study the importance of vestibular cues, visual cues, and the effect of the descent engine thruster creating dust blowback. The model predicts that spatial misperceptions are likely to occur as a result of the lunar landing motions, particularly with limited or incomplete visual cues. The powered descent acceleration profile creates a somatogravic illusion causing the astronauts to falsely perceive themselves and the vehicle as upright, even when the vehicle has a large pitch or roll angle. When visual pathways were activated within the model these illusions were mostly suppressed. Dust blowback, obscuring the visual scene out the window, was also found to create disorientation. These orientation illusions are likely to interfere with the astronauts' ability to effectively control the vehicle, potentially degrading performance and safety. Therefore suitable countermeasures, including disorientation training and advanced displays, are recommended.

Prostate Health Index improves multivariable risk prediction of aggressive prostate cancer.

PubMed

Loeb, Stacy; Shin, Sanghyuk S; Broyles, Dennis L; Wei, John T; Sanda, Martin; Klee, George; Partin, Alan W; Sokoll, Lori; Chan, Daniel W; Bangma, Chris H; van Schaik, Ron H N; Slawin, Kevin M; Marks, Leonard S; Catalona, William J

2017-07-01

To examine the use of the Prostate Health Index (PHI) as a continuous variable in multivariable risk assessment for aggressive prostate cancer in a large multicentre US study. The study population included 728 men, with prostate-specific antigen (PSA) levels of 2-10 ng/mL and a negative digital rectal examination, enrolled in a prospective, multi-site early detection trial. The primary endpoint was aggressive prostate cancer, defined as biopsy Gleason score ≥7. First, we evaluated whether the addition of PHI improves the performance of currently available risk calculators (the Prostate Cancer Prevention Trial [PCPT] and European Randomised Study of Screening for Prostate Cancer [ERSPC] risk calculators). We also designed and internally validated a new PHI-based multivariable predictive model, and created a nomogram. Of 728 men undergoing biopsy, 118 (16.2%) had aggressive prostate cancer. The PHI predicted the risk of aggressive prostate cancer across the spectrum of values. Adding PHI significantly improved the predictive accuracy of the PCPT and ERSPC risk calculators for aggressive disease. A new model was created using age, previous biopsy, prostate volume, PSA and PHI, with an area under the curve of 0.746. The bootstrap-corrected model showed good calibration with observed risk for aggressive prostate cancer and had net benefit on decision-curve analysis. Using PHI as part of multivariable risk assessment leads to a significant improvement in the detection of aggressive prostate cancer, potentially reducing harms from unnecessary prostate biopsy and overdiagnosis. © 2016 The Authors BJU International © 2016 BJU International Published by John Wiley & Sons Ltd.

TACD: a transportable ant colony discrimination model for corporate bankruptcy prediction

NASA Astrophysics Data System (ADS)

Lalbakhsh, Pooia; Chen, Yi-Ping Phoebe

2017-05-01

This paper presents a transportable ant colony discrimination strategy (TACD) to predict corporate bankruptcy, a topic of vital importance that is attracting increasing interest in the field of economics. The proposed algorithm uses financial ratios to build a binary prediction model for companies with the two statuses of bankrupt and non-bankrupt. The algorithm takes advantage of an improved version of continuous ant colony optimisation (CACO) at the core, which is used to create an accurate, simple and understandable linear model for discrimination. This also enables the algorithm to work with continuous values, leading to more efficient learning and adaption by avoiding data discretisation. We conduct a comprehensive performance evaluation on three real-world data sets under a stratified cross-validation strategy. In three different scenarios, TACD is compared with 11 other bankruptcy prediction strategies. We also discuss the efficiency of the attribute selection methods used in the experiments. In addition to its simplicity and understandability, statistical significance tests prove the efficiency of TACD against the other prediction algorithms in both measures of AUC and accuracy.

Spatiotemporal models for predicting high pollen concentration level of Corylus, Alnus, and Betula.

PubMed

Nowosad, Jakub

2016-06-01

Corylus, Alnus, and Betula trees are among the most important sources of allergic pollen in the temperate zone of the Northern Hemisphere and have a large impact on the quality of life and productivity of allergy sufferers. Therefore, it is important to predict high pollen concentrations, both in time and space. The aim of this study was to create and evaluate spatiotemporal models for predicting high Corylus, Alnus, and Betula pollen concentration levels, based on gridded meteorological data. Aerobiological monitoring was carried out in 11 cities in Poland and gathered, depending on the site, between 2 and 16 years of measurements. According to the first allergy symptoms during exposure, a high pollen count level was established for each taxon. An optimizing probability threshold technique was used for mitigation of the problem of imbalance in the pollen concentration levels. For each taxon, the model was built using a random forest method. The study revealed the possibility of moderately reliable prediction of Corylus and highly reliable prediction of Alnus and Betula high pollen concentration levels, using preprocessed gridded meteorological data. Cumulative growing degree days and potential evaporation proved to be two of the most important predictor variables in the models. The final models predicted not only for single locations but also for continuous areas. Furthermore, the proposed modeling framework could be used to predict high pollen concentrations of Corylus, Alnus, Betula, and other taxa, and in other countries.

Spatiotemporal models for predicting high pollen concentration level of Corylus, Alnus, and Betula

NASA Astrophysics Data System (ADS)

Nowosad, Jakub

2016-06-01

Corylus, Alnus, and Betula trees are among the most important sources of allergic pollen in the temperate zone of the Northern Hemisphere and have a large impact on the quality of life and productivity of allergy sufferers. Therefore, it is important to predict high pollen concentrations, both in time and space. The aim of this study was to create and evaluate spatiotemporal models for predicting high Corylus, Alnus, and Betula pollen concentration levels, based on gridded meteorological data. Aerobiological monitoring was carried out in 11 cities in Poland and gathered, depending on the site, between 2 and 16 years of measurements. According to the first allergy symptoms during exposure, a high pollen count level was established for each taxon. An optimizing probability threshold technique was used for mitigation of the problem of imbalance in the pollen concentration levels. For each taxon, the model was built using a random forest method. The study revealed the possibility of moderately reliable prediction of Corylus and highly reliable prediction of Alnus and Betula high pollen concentration levels, using preprocessed gridded meteorological data. Cumulative growing degree days and potential evaporation proved to be two of the most important predictor variables in the models. The final models predicted not only for single locations but also for continuous areas. Furthermore, the proposed modeling framework could be used to predict high pollen concentrations of Corylus, Alnus, Betula, and other taxa, and in other countries.

From 'solution shop' model to 'focused factory' in hospital surgery: increasing care value and predictability.

PubMed

Cook, David; Thompson, Jeffrey E; Habermann, Elizabeth B; Visscher, Sue L; Dearani, Joseph A; Roger, Veronique L; Borah, Bijan J

2014-05-01

The full-service US hospital has been described organizationally as a "solution shop," in which medical problems are assumed to be unstructured and to require expert physicians to determine each course of care. If universally applied, this model contributes to unwarranted variation in care, which leads to lower quality and higher costs. We purposely disrupted the adult cardiac surgical practice that we led at Mayo Clinic, in Rochester, Minnesota, by creating a "focused factory" model (characterized by a uniform approach to delivering a limited set of high-quality products) within the practice's solution shop. Key elements of implementing the new model were mapping the care process, segmenting the patient population, using information technology to communicate clearly defined expectations, and empowering nonphysician providers at the bedside. Using a set of criteria, we determined that the focused-factory model was appropriate for 67 percent of cardiac surgical patients. We found that implementation of the model reduced resource use, length-of-stay, and cost. Variation was markedly reduced, and outcomes were improved. Assigning patients to different care models increases care value and the predictability of care process, outcomes, and costs while preserving (in a lesser clinical footprint) the strengths of the solution shop. We conclude that creating a focused-factory model within a solution shop, by applying industrial engineering principles and health information technology tools and changing the model of work, is very effective in both improving quality and reducing costs.

The use of GIS and multi-criteria evaluation (MCE) to identify agricultural land management practices which cause surface water pollution in drinking water supply catchments.

PubMed

Grayson, Richard; Kay, Paul; Foulger, Miles

2008-01-01

Diffuse pollution poses a threat to water quality and results in the need for treatment for potable water supplies which can prove costly. Within the Yorkshire region, UK, nitrates, pesticides and water colour present particular treatment problems. Catchment management techniques offer an alternative to 'end of pipe' solutions and allow resources to be targeted to the most polluting areas. This project has attempted to identify such areas using GIS based modelling approaches in catchments where water quality data were available. As no model exists to predict water colour a model was created using an MCE method which is capable of predicting colour concentrations at the catchment scale. CatchIS was used to predict pesticide and nitrate N concentrations and was found to be generally capable of reliably predicting nitrate N loads at the catchment scale. The pesticides results did not match the historic data possibly due to problems with the historic pesticide data and temporal and spatially variability in pesticide usage. The use of these models can be extended to predict water quality problems in catchments where water quality data are unavailable and highlight areas of concern. IWA Publishing 2008.

Efficient search, mapping, and optimization of multi-protein genetic systems in diverse bacteria

PubMed Central

Farasat, Iman; Kushwaha, Manish; Collens, Jason; Easterbrook, Michael; Guido, Matthew; Salis, Howard M

2014-01-01

Developing predictive models of multi-protein genetic systems to understand and optimize their behavior remains a combinatorial challenge, particularly when measurement throughput is limited. We developed a computational approach to build predictive models and identify optimal sequences and expression levels, while circumventing combinatorial explosion. Maximally informative genetic system variants were first designed by the RBS Library Calculator, an algorithm to design sequences for efficiently searching a multi-protein expression space across a > 10,000-fold range with tailored search parameters and well-predicted translation rates. We validated the algorithm's predictions by characterizing 646 genetic system variants, encoded in plasmids and genomes, expressed in six gram-positive and gram-negative bacterial hosts. We then combined the search algorithm with system-level kinetic modeling, requiring the construction and characterization of 73 variants to build a sequence-expression-activity map (SEAMAP) for a biosynthesis pathway. Using model predictions, we designed and characterized 47 additional pathway variants to navigate its activity space, find optimal expression regions with desired activity response curves, and relieve rate-limiting steps in metabolism. Creating sequence-expression-activity maps accelerates the optimization of many protein systems and allows previous measurements to quantitatively inform future designs. PMID:24952589

Are the Performance Based Logistics Prophets Using Science or Alchemy to Create Life-Cycle Affordability? Using Theory to Predict the Efficacy of Performance Based Logistics

DTIC Science & Technology

2013-10-01

Based Logistics Prophets Using Science or Alchemy to Create Life-Cycle Affordability? Using Theory to Predict the Efficacy of Performance Based...Using Science or Alchemy to Create Life-Cycle Affordability? Using Theory to Predict the Efficacy of Performance Based Logistics 5a. CONTRACT NUMBER 5b...Are the PBL Prophets Using Science or Alchemy to Create Life Cycle Affordability? 328Defense ARJ, October 2013, Vol. 20 No. 3 : 325–348 Defense

Groundwater Controls on Vegetation Composition and Patterning in Mountain Meadows

NASA Astrophysics Data System (ADS)

Loheide, S. P.; Lowry, C.; Moore, C. E.; Lundquist, J. D.

2010-12-01

Mountain meadows are groundwater dependent ecosystems that are hotspots of biodiversity and productivity in the Sierra Nevada of California. Meadow vegetation relies on shallow groundwater during the region’s dry summer growing season. Vegetation composition in this environment is influenced both by 1) oxygen stress that occurs when portions of the root zone are saturated and anaerobic conditions are created that limit root respiration and 2) water stress that occurs when the water table drops and water-limited conditions are created in the root zone. A watershed model that explicitly accounts for snowmelt processes was linked to a fine resolution groundwater flow model of Tuolumne Meadows in Yosemite National Park, CA to simulated spatially distributed water table dynamics. This linked hydrologic model was calibrated to observations from a well observation network for 2006-2008, and validated using data from 2009. A vegetation survey was also conducted at the site in which the three dominant species were identified at more than 200 plots distributed across the meadow. Nonparametric multiplicative regression was performed to create and select the best models for predicting vegetation dominance based on simulated hydrologic regime. The hydrologic niche of three vegetation types representing wet, moist, and dry meadow vegetation communities was best described using both 1) a sum exceedance value calculated as the integral of water table position above a threshold of oxygen stress and 2) a sum deceedance value calculated as the integral of water table position below a threshold of water stress. This linked hydrologic and vegetative modeling framework advances our ability to predict the propagation of human-induced climatic and land-use/-cover changes through the hydrologic system to the ecosystem.

Predicting Failure of Glyburide Therapy in Gestational Diabetes

PubMed Central

Harper, Lorie M.; Glover, Angelica V.; Biggio, Joseph R.; Tita, Alan

2016-01-01

Objective We sought to develop a prediction model to identify women with gestational diabetes (GDM) who require insulin to achieve glycemic control. Study Design Retrospective cohort of all singletons with GDM treated with glyburide 2007–2013. Glyburide failure was defined as reaching glyburide 20 mg/day and receiving insulin. Glyburide success was defined as any glyburide dose without insulin and >70% of visits with glycemic control. Multivariable logistic regression analysis was performed to create a prediction model. Results Of 360 women, 63 (17.5%) qualified as glyburide failure and 157 (43.6%) glyburide success. The final prediction model for glyburide failure included prior GDM, GDM diagnosis ≤26 weeks, 1-hour GCT ≥228 mg/dL, 3-hour GTT 1-hour value ≥221 mg/dL, ≥7 post-prandial blood sugars >120 mg/dL in the week glyburide started, and ≥1 blood sugar >200 mg/dL. The model accurately classified 81% of subjects. Conclusions Women with GDM who will require insulin can be identified at initiation of pharmacologic therapy. PMID:26796130

Predicting failure of glyburide therapy in gestational diabetes.

PubMed

Harper, L M; Glover, A V; Biggio, J R; Tita, A

2016-05-01

We sought to develop a prediction model to identify women with gestational diabetes (GDM) who require insulin to achieve glycemic control. Retrospective cohort of all singletons with GDM treated with glyburide from 2007 to 2013. Glyburide failure was defined as reaching glyburide 20 mg day(-1) and receiving insulin. Glyburide success was defined as any glyburide dose without insulin and >70% of visits with glycemic control. Multivariable logistic regression analysis was performed to create a prediction model. Of the 360 women, 63 (17.5%) qualified as glyburide failure and 157 (43.6%) as glyburide success. The final prediction model for glyburide failure included prior GDM, GDM diagnosis ⩽26 weeks, 1-h glucose challenge test ⩾228 mg dl(-1), 3-h glucose tolerance test 1-h value ⩾221 mg dl(-1), ⩾7 postprandial blood sugars >120 mg dl(-1) in the week glyburide started and ⩾1 blood sugar >200 mg dl(-1). The model accurately classified 81% of subjects. Women with GDM who will require insulin can be identified at the initiation of pharmacological therapy.

A Proposed Method to Predict Preterm Birth Using Clinical Data, Standard Maternal Serum Screening, and Cholesterol

PubMed Central

ALLEMAN, Brandon W.; SMITH, Amanda R.; BYERS, Heather M.; BEDELL, Bruce; RYCKMAN, Kelli K.; MURRAY, Jeffrey C.; BOROWSKI, Kristi S.

2013-01-01

Objective To create a predictive model for preterm birth (PTB) from available clinical data and serum analytes. Study Design Serum analytes, routine pregnancy screening plus cholesterol and corresponding health information were linked to birth certificate data for a cohort of 2699 Iowa women with serum sampled in the first and second trimester. Stepwise logistic regression was used to select the best predictive model for PTB. Results Serum screening markers remained significant predictors of PTB even after controlling for maternal characteristics. The best predictive model included maternal characteristics, first trimester total cholesterol (TC), TC change between trimesters and second trimester alpha-fetoprotein and inhibin A. The model showed better discriminatory ability than PTB history alone and performed similarly in subgroups of women without past PTB. Conclusions Using clinical and serum screening data a potentially useful predictor of PTB was constructed. Validation and replication in other populations, and incorporation of other measures that identify PTB risk, like cervical length, can be a step towards identifying additional women who may benefit from new or currently available interventions. PMID:23500456

Computational Approach using Mouse Embryonic Stem Cells to Define a Mechanistic Applicability Domain for Prenatal Developmental Toxicity

EPA Science Inventory

Identification of mechanisms responsible for adverse developmental effects is the first step in creating predictive toxicity models. Identification of putative mechanisms was performed by co-analyzing three datasets for the effects of ToxCast phase Ia and II chemicals: 1.In vitro...

Air Force Laboratory’s 2005 Technology Milestones

DTIC Science & Technology

2006-01-01

Computational materials science methods can benefit the design and property prediction of complex real-world materials. With these models , scientists and...Warfighter Page Air High - Frequency Acoustic System...800) 203-6451 High - Frequency Acoustic System Payoff Scientists created the High - Frequency Acoustic Suppression Technology (HiFAST) airflow control

Predicting thermal regimes of stream networks across the northeast United States: Natural and anthropogenic influences

EPA Science Inventory

We used STARS (Spatial Tools for the Analysis of River Systems), an ArcGIS geoprocessing toolbox, to create spatial stream networks. We then developed and assessed spatial statistical models for each of these metrics, incorporating spatial autocorrelation based on both distance...

Modeling snowmelt infiltration in seasonally frozen ground

NASA Astrophysics Data System (ADS)

Budhathoki, S.; Ireson, A. M.

2017-12-01

In cold regions, freezing and thawing of the soil govern soil hydraulic properties that shape the surface and subsurface hydrological processes. The partitioning of snowmelt into infiltration and runoff has also important implications for integrated water resource management and flood risk. However, there is an inadequate representation of the snowmelt infiltration into frozen soils in most land-surface and hydrological models, creating the need for improved models and methods. Here we apply, the Frozen Soil Infiltration Model, FroSIn, which is a novel algorithm for infiltration in frozen soils that can be implemented in physically based models of coupled flow and heat transport. In this study, we apply the model in a simple configuration to reproduce observations from field sites in the Canadian prairies, specifically St Denis and Brightwater Creek in Saskatchewan, Canada. We demonstrate the limitations of conventional approaches to simulate infiltration, which systematically over-predict runoff and under predict infiltration. The findings show that FroSIn enables models to predict more reasonable infiltration volumes in frozen soils, and also represent how infiltration-runoff partitioning is impacted by antecedent soil moisture.

Creating and parameterizing patient-specific deep brain stimulation pathway-activation models using the hyperdirect pathway as an example.

PubMed

Gunalan, Kabilar; Chaturvedi, Ashutosh; Howell, Bryan; Duchin, Yuval; Lempka, Scott F; Patriat, Remi; Sapiro, Guillermo; Harel, Noam; McIntyre, Cameron C

2017-01-01

Deep brain stimulation (DBS) is an established clinical therapy and computational models have played an important role in advancing the technology. Patient-specific DBS models are now common tools in both academic and industrial research, as well as clinical software systems. However, the exact methodology for creating patient-specific DBS models can vary substantially and important technical details are often missing from published reports. Provide a detailed description of the assembly workflow and parameterization of a patient-specific DBS pathway-activation model (PAM) and predict the response of the hyperdirect pathway to clinical stimulation. Integration of multiple software tools (e.g. COMSOL, MATLAB, FSL, NEURON, Python) enables the creation and visualization of a DBS PAM. An example DBS PAM was developed using 7T magnetic resonance imaging data from a single unilaterally implanted patient with Parkinson's disease (PD). This detailed description implements our best computational practices and most elaborate parameterization steps, as defined from over a decade of technical evolution. Pathway recruitment curves and strength-duration relationships highlight the non-linear response of axons to changes in the DBS parameter settings. Parameterization of patient-specific DBS models can be highly detailed and constrained, thereby providing confidence in the simulation predictions, but at the expense of time demanding technical implementation steps. DBS PAMs represent new tools for investigating possible correlations between brain pathway activation patterns and clinical symptom modulation.

Does a better model yield a better argument? An info-gap analysis

NASA Astrophysics Data System (ADS)

Ben-Haim, Yakov

2017-04-01

Theories, models and computations underlie reasoned argumentation in many areas. The possibility of error in these arguments, though of low probability, may be highly significant when the argument is used in predicting the probability of rare high-consequence events. This implies that the choice of a theory, model or computational method for predicting rare high-consequence events must account for the probability of error in these components. However, error may result from lack of knowledge or surprises of various sorts, and predicting the probability of error is highly uncertain. We show that the putatively best, most innovative and sophisticated argument may not actually have the lowest probability of error. Innovative arguments may entail greater uncertainty than more standard but less sophisticated methods, creating an innovation dilemma in formulating the argument. We employ info-gap decision theory to characterize and support the resolution of this problem and present several examples.

Generalized additive models and Lucilia sericata growth: assessing confidence intervals and error rates in forensic entomology.

PubMed

Tarone, Aaron M; Foran, David R

2008-07-01

Forensic entomologists use blow fly development to estimate a postmortem interval. Although accurate, fly age estimates can be imprecise for older developmental stages and no standard means of assigning confidence intervals exists. Presented here is a method for modeling growth of the forensically important blow fly Lucilia sericata, using generalized additive models (GAMs). Eighteen GAMs were created to predict the extent of juvenile fly development, encompassing developmental stage, length, weight, strain, and temperature data, collected from 2559 individuals. All measures were informative, explaining up to 92.6% of the deviance in the data, though strain and temperature exerted negligible influences. Predictions made with an independent data set allowed for a subsequent examination of error. Estimates using length and developmental stage were within 5% of true development percent during the feeding portion of the larval life cycle, while predictions for postfeeding third instars were less precise, but within expected error.

Using landscape limnology to classify freshwater ecosystems for multi-ecosystem management and conservation

USGS Publications Warehouse

Soranno, Patricia A.; Cheruvelil, Kendra Spence; Webster, Katherine E.; Bremigan, Mary T.; Wagner, Tyler; Stow, Craig A.

2010-01-01

Governmental entities are responsible for managing and conserving large numbers of lake, river, and wetland ecosystems that can be addressed only rarely on a case-by-case basis. We present a system for predictive classification modeling, grounded in the theoretical foundation of landscape limnology, that creates a tractable number of ecosystem classes to which management actions may be tailored. We demonstrate our system by applying two types of predictive classification modeling approaches to develop nutrient criteria for eutrophication management in 1998 north temperate lakes. Our predictive classification system promotes the effective management of multiple ecosystems across broad geographic scales by explicitly connecting management and conservation goals to the classification modeling approach, considering multiple spatial scales as drivers of ecosystem dynamics, and acknowledging the hierarchical structure of freshwater ecosystems. Such a system is critical for adaptive management of complex mosaics of freshwater ecosystems and for balancing competing needs for ecosystem services in a changing world.

Cognitive control over learning: Creating, clustering and generalizing task-set structure

PubMed Central

Collins, Anne G.E.; Frank, Michael J.

2013-01-01

Executive functions and learning share common neural substrates essential for their expression, notably in prefrontal cortex and basal ganglia. Understanding how they interact requires studying how cognitive control facilitates learning, but also how learning provides the (potentially hidden) structure, such as abstract rules or task-sets, needed for cognitive control. We investigate this question from three complementary angles. First, we develop a new computational “C-TS” (context-task-set) model inspired by non-parametric Bayesian methods, specifying how the learner might infer hidden structure and decide whether to re-use that structure in new situations, or to create new structure. Second, we develop a neurobiologically explicit model to assess potential mechanisms of such interactive structured learning in multiple circuits linking frontal cortex and basal ganglia. We systematically explore the link betweens these levels of modeling across multiple task demands. We find that the network provides an approximate implementation of high level C-TS computations, where manipulations of specific neural mechanisms are well captured by variations in distinct C-TS parameters. Third, this synergism across models yields strong predictions about the nature of human optimal and suboptimal choices and response times during learning. In particular, the models suggest that participants spontaneously build task-set structure into a learning problem when not cued to do so, which predicts positive and negative transfer in subsequent generalization tests. We provide evidence for these predictions in two experiments and show that the C-TS model provides a good quantitative fit to human sequences of choices in this task. These findings implicate a strong tendency to interactively engage cognitive control and learning, resulting in structured abstract representations that afford generalization opportunities, and thus potentially long-term rather than short-term optimality. PMID:23356780

The Bi-Directional Prediction of Carbon Fiber Production Using a Combination of Improved Particle Swarm Optimization and Support Vector Machine.

PubMed

Xiao, Chuncai; Hao, Kuangrong; Ding, Yongsheng

2014-12-30

This paper creates a bi-directional prediction model to predict the performance of carbon fiber and the productive parameters based on a support vector machine (SVM) and improved particle swarm optimization (IPSO) algorithm (SVM-IPSO). In the SVM, it is crucial to select the parameters that have an important impact on the performance of prediction. The IPSO is proposed to optimize them, and then the SVM-IPSO model is applied to the bi-directional prediction of carbon fiber production. The predictive accuracy of SVM is mainly dependent on its parameters, and IPSO is thus exploited to seek the optimal parameters for SVM in order to improve its prediction capability. Inspired by a cell communication mechanism, we propose IPSO by incorporating information of the global best solution into the search strategy to improve exploitation, and we employ IPSO to establish the bi-directional prediction model: in the direction of the forward prediction, we consider productive parameters as input and property indexes as output; in the direction of the backward prediction, we consider property indexes as input and productive parameters as output, and in this case, the model becomes a scheme design for novel style carbon fibers. The results from a set of the experimental data show that the proposed model can outperform the radial basis function neural network (RNN), the basic particle swarm optimization (PSO) method and the hybrid approach of genetic algorithm and improved particle swarm optimization (GA-IPSO) method in most of the experiments. In other words, simulation results demonstrate the effectiveness and advantages of the SVM-IPSO model in dealing with the problem of forecasting.

New Integrated Modeling Capabilities: MIDAS' Recent Behavioral Enhancements

NASA Technical Reports Server (NTRS)

Gore, Brian F.; Jarvis, Peter A.

2005-01-01

The Man-machine Integration Design and Analysis System (MIDAS) is an integrated human performance modeling software tool that is based on mechanisms that underlie and cause human behavior. A PC-Windows version of MIDAS has been created that integrates the anthropometric character "Jack (TM)" with MIDAS' validated perceptual and attention mechanisms. MIDAS now models multiple simulated humans engaging in goal-related behaviors. New capabilities include the ability to predict situations in which errors and/or performance decrements are likely due to a variety of factors including concurrent workload and performance influencing factors (PIFs). This paper describes a new model that predicts the effects of microgravity on a mission specialist's performance, and its first application to simulating the task of conducting a Life Sciences experiment in space according to a sequential or parallel schedule of performance.

Modeling and Simulation of Nanoindentation

NASA Astrophysics Data System (ADS)

Huang, Sixie; Zhou, Caizhi

2017-11-01

Nanoindentation is a hardness test method applied to small volumes of material which can provide some unique effects and spark many related research activities. To fully understand the phenomena observed during nanoindentation tests, modeling and simulation methods have been developed to predict the mechanical response of materials during nanoindentation. However, challenges remain with those computational approaches, because of their length scale, predictive capability, and accuracy. This article reviews recent progress and challenges for modeling and simulation of nanoindentation, including an overview of molecular dynamics, the quasicontinuum method, discrete dislocation dynamics, and the crystal plasticity finite element method, and discusses how to integrate multiscale modeling approaches seamlessly with experimental studies to understand the length-scale effects and microstructure evolution during nanoindentation tests, creating a unique opportunity to establish new calibration procedures for the nanoindentation technique.

Wavelet synthetic method for turbulent flow.

PubMed

Zhou, Long; Rauh, Cornelia; Delgado, Antonio

2015-07-01

Based on the idea of random cascades on wavelet dyadic trees and the energy cascade model known as the wavelet p model, a series of velocity increments in two-dimensional space are constructed in different levels of scale. The dynamics is imposed on the generated scales by solving the Euler equation in the Lagrangian framework. A dissipation model is used in order to cover the shortage of the p model, which only predicts in inertial range. Wavelet reconstruction as well as the multiresolution analysis are then performed on each scales. As a result, a type of isotropic velocity field is created. The statistical properties show that the constructed velocity fields share many important features with real turbulence. The pertinence of this approach in the prediction of flow intermittency is also discussed.

Real-time flood forecasting

USGS Publications Warehouse

Lai, C.; Tsay, T.-K.; Chien, C.-H.; Wu, I.-L.

2009-01-01

Researchers at the Hydroinformatic Research and Development Team (HIRDT) of the National Taiwan University undertook a project to create a real time flood forecasting model, with an aim to predict the current in the Tamsui River Basin. The model was designed based on deterministic approach with mathematic modeling of complex phenomenon, and specific parameter values operated to produce a discrete result. The project also devised a rainfall-stage model that relates the rate of rainfall upland directly to the change of the state of river, and is further related to another typhoon-rainfall model. The geographic information system (GIS) data, based on precise contour model of the terrain, estimate the regions that were perilous to flooding. The HIRDT, in response to the project's progress, also devoted their application of a deterministic model to unsteady flow of thermodynamics to help predict river authorities issue timely warnings and take other emergency measures.

Numerical Modeling of Propellant Boil-Off in a Cryogenic Storage Tank

NASA Technical Reports Server (NTRS)

Majumdar, A. K.; Steadman, T. E.; Maroney, J. L.; Sass, J. P.; Fesmire, J. E.

2007-01-01

A numerical model to predict boil-off of stored propellant in large spherical cryogenic tanks has been developed. Accurate prediction of tank boil-off rates for different thermal insulation systems was the goal of this collaboration effort. The Generalized Fluid System Simulation Program, integrating flow analysis and conjugate heat transfer for solving complex fluid system problems, was used to create the model. Calculation of tank boil-off rate requires simultaneous simulation of heat transfer processes among liquid propellant, vapor ullage space, and tank structure. The reference tank for the boil-off model was the 850,000 gallon liquid hydrogen tank at Launch Complex 39B (LC- 39B) at Kennedy Space Center, which is under study for future infrastructure improvements to support the Constellation program. The methodology employed in the numerical model was validated using a sub-scale model and tank. Experimental test data from a 1/15th scale version of the LC-39B tank using both liquid hydrogen and liquid nitrogen were used to anchor the analytical predictions of the sub-scale model. Favorable correlations between sub-scale model and experimental test data have provided confidence in full-scale tank boil-off predictions. These methods are now being used in the preliminary design for other cases including future launch vehicles

Validation of Predictors of Fall Events in Hospitalized Patients With Cancer.

PubMed

Weed-Pfaff, Samantha H; Nutter, Benjamin; Bena, James F; Forney, Jennifer; Field, Rosemary; Szoka, Lynn; Karius, Diana; Akins, Patti; Colvin, Christina M; Albert, Nancy M

2016-10-01

A seven-item cancer-specific fall risk tool (Cleveland Clinic Capone-Albert [CC-CA] Fall Risk Score) was shown to have a strong concordance index for predicting falls; however, validation of the model is needed. The aims of this study were to validate that the CC-CA Fall Risk Score, made up of six factors, predicts falls in patients with cancer and to determine if the CC-CA Fall Risk Score performs better than the Morse Fall Tool. Using a prospective, comparative methodology, data were collected from electronic health records of patients hospitalized for cancer care in four hospitals. Risk factors from each tool were recorded, when applicable. Multivariable models were created to predict the probability of a fall. A concordance index for each fall tool was calculated. The CC-CA Fall Risk Score provided higher discrimination than the Morse Fall Tool in predicting fall events in patients hospitalized for cancer management.

[Application of Kohonen Self-Organizing Feature Maps in QSAR of human ADMET and kinase data sets].

PubMed

Hegymegi-Barakonyi, Bálint; Orfi, László; Kéri, György; Kövesdi, István

2013-01-01

QSAR predictions have been proven very useful in a large number of studies for drug design, such as kinase inhibitor design as targets for cancer therapy, however the overall predictability often remains unsatisfactory. To improve predictability of ADMET features and kinase inhibitory data, we present a new method using Kohonen's Self-Organizing Feature Map (SOFM) to cluster molecules based on explanatory variables (X) and separate dissimilar ones. We calculated SOFM clusters for a large number of molecules with human ADMET and kinase inhibitory data, and we showed that chemically similar molecules were in the same SOFM cluster, and within such clusters the QSAR models had significantly better predictability. We used also target variables (Y, e.g. ADMET) jointly with X variables to create a novel type of clustering. With our method, cells of loosely coupled XY data could be identified and separated into different model building sets.

Pumping Optimization Model for Pump and Treat Systems - 15091

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

Baker, S.; Ivarson, Kristine A.; Karanovic, M.

2015-01-15

Pump and Treat systems are being utilized to remediate contaminated groundwater in the Hanford 100 Areas adjacent to the Columbia River in Eastern Washington. Design of the systems was supported by a three-dimensional (3D) fate and transport model. This model provided sophisticated simulation capabilities but requires many hours to calculate results for each simulation considered. Many simulations are required to optimize system performance, so a two-dimensional (2D) model was created to reduce run time. The 2D model was developed as a equivalent-property version of the 3D model that derives boundary conditions and aquifer properties from the 3D model. It producesmore » predictions that are very close to the 3D model predictions, allowing it to be used for comparative remedy analyses. Any potential system modifications identified by using the 2D version are verified for use by running the 3D model to confirm performance. The 2D model was incorporated into a comprehensive analysis system (the Pumping Optimization Model, POM) to simplify analysis of multiple simulations. It allows rapid turnaround by utilizing a graphical user interface that: 1 allows operators to create hypothetical scenarios for system operation, 2 feeds the input to the 2D fate and transport model, and 3 displays the scenario results to evaluate performance improvement. All of the above is accomplished within the user interface. Complex analyses can be completed within a few hours and multiple simulations can be compared side-by-side. The POM utilizes standard office computing equipment and established groundwater modeling software.« less

Hierarchical singleton-type recurrent neural fuzzy networks for noisy speech recognition.

PubMed

Juang, Chia-Feng; Chiou, Chyi-Tian; Lai, Chun-Lung

2007-05-01

This paper proposes noisy speech recognition using hierarchical singleton-type recurrent neural fuzzy networks (HSRNFNs). The proposed HSRNFN is a hierarchical connection of two singleton-type recurrent neural fuzzy networks (SRNFNs), where one is used for noise filtering and the other for recognition. The SRNFN is constructed by recurrent fuzzy if-then rules with fuzzy singletons in the consequences, and their recurrent properties make them suitable for processing speech patterns with temporal characteristics. In n words recognition, n SRNFNs are created for modeling n words, where each SRNFN receives the current frame feature and predicts the next one of its modeling word. The prediction error of each SRNFN is used as recognition criterion. In filtering, one SRNFN is created, and each SRNFN recognizer is connected to the same SRNFN filter, which filters noisy speech patterns in the feature domain before feeding them to the SRNFN recognizer. Experiments with Mandarin word recognition under different types of noise are performed. Other recognizers, including multilayer perceptron (MLP), time-delay neural networks (TDNNs), and hidden Markov models (HMMs), are also tested and compared. These experiments and comparisons demonstrate good results with HSRNFN for noisy speech recognition tasks.

Personalized Risk Prediction in Clinical Oncology Research: Applications and Practical Issues Using Survival Trees and Random Forests.

PubMed

Hu, Chen; Steingrimsson, Jon Arni

2018-01-01

A crucial component of making individualized treatment decisions is to accurately predict each patient's disease risk. In clinical oncology, disease risks are often measured through time-to-event data, such as overall survival and progression/recurrence-free survival, and are often subject to censoring. Risk prediction models based on recursive partitioning methods are becoming increasingly popular largely due to their ability to handle nonlinear relationships, higher-order interactions, and/or high-dimensional covariates. The most popular recursive partitioning methods are versions of the Classification and Regression Tree (CART) algorithm, which builds a simple interpretable tree structured model. With the aim of increasing prediction accuracy, the random forest algorithm averages multiple CART trees, creating a flexible risk prediction model. Risk prediction models used in clinical oncology commonly use both traditional demographic and tumor pathological factors as well as high-dimensional genetic markers and treatment parameters from multimodality treatments. In this article, we describe the most commonly used extensions of the CART and random forest algorithms to right-censored outcomes. We focus on how they differ from the methods for noncensored outcomes, and how the different splitting rules and methods for cost-complexity pruning impact these algorithms. We demonstrate these algorithms by analyzing a randomized Phase III clinical trial of breast cancer. We also conduct Monte Carlo simulations to compare the prediction accuracy of survival forests with more commonly used regression models under various scenarios. These simulation studies aim to evaluate how sensitive the prediction accuracy is to the underlying model specifications, the choice of tuning parameters, and the degrees of missing covariates.

Predicting length of children's psychiatric hospitalizations: an "ecologic" approach.

PubMed

Mossman, D; Songer, D A; Baker, D G

1991-08-01

This article describes the development and validation of a simple and modestly successful model for predicting inpatient length of stay (LOS) at a state-funded facility providing acute to long term care for children and adolescents in Ohio. Six variables--diagnostic group, legal status at time of admission, attending physician, age, sex, and county of residence--explained 30% of the variation in log10LOS in the subgroup used to create the model, and 26% of log10LOS variation in the cross-validation subgroup. The model also identified LOS outliers with moderate accuracy (ROC area = .68-0.76). The authors attribute the model's success to inclusion of variables that are correlated to idiosyncratic "ecologic" factors as well as variables related to severity of illness. Future attempts to construct LOS models may adopt similar approaches.

Predicting the combinatorial effects of water activity, pH and organic acids on Listeria growth in media and complex food matrices.

PubMed

Nyhan, L; Begley, M; Mutel, A; Qu, Y; Johnson, N; Callanan, M

2018-09-01

The aim of this study was to develop a model to predict growth of Listeria in complex food matrices as a function of pH, water activity and undissociated acetic and propionic acid concentration i.e. common food hurdles. Experimental growth curves of Listeria in food products and broth media were collected from ComBase, the literature and industry sources from which a bespoke secondary gamma model was constructed. Model performance was evaluated by comparing predictions to measured growth rates in growth media (BHI broth) and two adjusted food matrices (zucchini purée and béarnaise sauce). In general, observed growth rates were higher in broth than in the food matrices which resulted in the model over-estimating growth in the adjusted food matrices. In addition, model outputs were more accurate for conditions without acids, indicating that the organic acid component of the model was a source of inaccuracy. In summary, a new predictive growth model for innovating or renovating food products that rely on multi-hurdle technology was created. This study is the first to report on modelling of propionic acid as an inhibitor of Listeria in combination with other hurdles. Our findings provide valuable insights into predictive model design and performance and highlight the importance of experimental validation of models in real food matrices rather than laboratory media alone. Copyright © 2018 Elsevier Ltd. All rights reserved.

Measuring Socioeconomic Inequalities With Predicted Absolute Incomes Rather Than Wealth Quintiles: A Comparative Assessment Using Child Stunting Data From National Surveys.

PubMed

Fink, Günther; Victora, Cesar G; Harttgen, Kenneth; Vollmer, Sebastian; Vidaletti, Luís Paulo; Barros, Aluisio J D

2017-04-01

To compare the predictive power of synthetic absolute income measures with that of asset-based wealth quintiles in low- and middle-income countries (LMICs) using child stunting as an outcome. We pooled data from 239 nationally representative household surveys from LMICs and computed absolute incomes in US dollars based on households' asset rank as well as data on national consumption and inequality levels. We used multivariable regression models to compare the predictive power of the created income measure with the predictive power of existing asset indicator measures. In cross-country analysis, log absolute income predicted 54.5% of stunting variation observed, compared with 20% of variation explained by wealth quintiles. For within-survey analysis, we also found absolute income gaps to be predictive of the gaps between stunting in the wealthiest and poorest households (P < .001). Our results suggest that absolute income levels can greatly improve the prediction of stunting levels across and within countries over time, compared with models that rely solely on relative wealth quintiles.

Predicting Health Care Utilization After Behavioral Health Referral Using Natural Language Processing and Machine Learning.

PubMed

Roysden, Nathaniel; Wright, Adam

2015-01-01

Mental health problems are an independent predictor of increased healthcare utilization. We created random forest classifiers for predicting two outcomes following a patient's first behavioral health encounter: decreased utilization by any amount (AUROC 0.74) and ultra-high absolute utilization (AUROC 0.88). These models may be used for clinical decision support by referring providers, to automatically detect patients who may benefit from referral, for cost management, or for risk/protection factor analysis.

Automated body weight prediction of dairy cows using 3-dimensional vision.

PubMed

Song, X; Bokkers, E A M; van der Tol, P P J; Groot Koerkamp, P W G; van Mourik, S

2018-05-01

The objectives of this study were to quantify the error of body weight prediction using automatically measured morphological traits in a 3-dimensional (3-D) vision system and to assess the influence of various sources of uncertainty on body weight prediction. In this case study, an image acquisition setup was created in a cow selection box equipped with a top-view 3-D camera. Morphological traits of hip height, hip width, and rump length were automatically extracted from the raw 3-D images taken of the rump area of dairy cows (n = 30). These traits combined with days in milk, age, and parity were used in multiple linear regression models to predict body weight. To find the best prediction model, an exhaustive feature selection algorithm was used to build intermediate models (n = 63). Each model was validated by leave-one-out cross-validation, giving the root mean square error and mean absolute percentage error. The model consisting of hip width (measurement variability of 0.006 m), days in milk, and parity was the best model, with the lowest errors of 41.2 kg of root mean square error and 5.2% mean absolute percentage error. Our integrated system, including the image acquisition setup, image analysis, and the best prediction model, predicted the body weights with a performance similar to that achieved using semi-automated or manual methods. Moreover, the variability of our simplified morphological trait measurement showed a negligible contribution to the uncertainty of body weight prediction. We suggest that dairy cow body weight prediction can be improved by incorporating more predictive morphological traits and by improving the prediction model structure. The Authors. Published by FASS Inc. and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

Perceptual tools for quality-aware video networks

NASA Astrophysics Data System (ADS)

Bovik, A. C.

2014-01-01

Monitoring and controlling the quality of the viewing experience of videos transmitted over increasingly congested networks (especially wireless networks) is a pressing problem owing to rapid advances in video-centric mobile communication and display devices that are straining the capacity of the network infrastructure. New developments in automatic perceptual video quality models offer tools that have the potential to be used to perceptually optimize wireless video, leading to more efficient video data delivery and better received quality. In this talk I will review key perceptual principles that are, or could be used to create effective video quality prediction models, and leading quality prediction models that utilize these principles. The goal is to be able to monitor and perceptually optimize video networks by making them "quality-aware."

Creating a Coastal National Elevation Database (CoNED) for science and conservation applications

USGS Publications Warehouse

Thatcher, Cindy A.; Brock, John C.; Danielson, Jeffrey J.; Poppenga, Sandra K.; Gesch, Dean B.; Palaseanu-Lovejoy, Monica; Barras, John; Evans, Gayla A.; Gibbs, Ann

2016-01-01

The U.S. Geological Survey is creating the Coastal National Elevation Database, an expanding set of topobathymetric elevation models that extend seamlessly across coastal regions of high societal or ecological significance in the United States that are undergoing rapid change or are threatened by inundation hazards. Topobathymetric elevation models are raster datasets useful for inundation prediction and other earth science applications, such as the development of sediment-transport and storm surge models. These topobathymetric elevation models are being constructed by the broad regional assimilation of numerous topographic and bathymetric datasets, and are intended to fulfill the pressing needs of decision makers establishing policies for hazard mitigation and emergency preparedness, coastal managers tasked with coastal planning compatible with predictions of inundation due to sea-level rise, and scientists investigating processes of coastal geomorphic change. A key priority of this coastal elevation mapping effort is to foster collaborative lidar acquisitions that meet the standards of the USGS National Geospatial Program's 3D Elevation Program, a nationwide initiative to systematically collect high-quality elevation data. The focus regions are located in highly dynamic environments, for example in areas subject to shoreline change, rapid wetland loss, hurricane impacts such as overwash and wave scouring, and/or human-induced changes to coastal topography.

The Influence of Life History Variability on Population Connectivity: Development and Application of a Trait-Based Biophysical Model of Individuals

NASA Astrophysics Data System (ADS)

Wong-Ala, J.; Neuheimer, A. B.; Hixon, M.; Powell, B.

2016-02-01

Connectivity estimates, which measure the exchange of individuals among populations, are necessary to create effective reserves for marine life. Connectivity can be influenced by a combination of biology (e.g. spawning time) and physics (e.g. currents). In the past a dispersal model was created in an effort to explain connectivity for the highly sought after reef fish Lau`ipala (Yellow Tang, Zebrasoma flavescens) around Hawai`i Island using physics alone, but this was shown to be insufficient. Here we created an individual based model (IBM) to describe Lau`ipala life history and behavior forced with ocean currents and temperature (via coupling to a physical model) to examine biophysical interactions. The IBM allows for tracking of individual fish from spawning to settlement, and individual variability in modeled processes. We first examined the influence of different reproductive (e.g. batch vs. constant spawners), developmental (e.g. pelagic larval duration), and behavioral (e.g. active vs. passive buoyancy control) traits on modeled connectivity estimates for larval reef fish around Hawai`i Island and compared results to genetic observations of parent-offspring pair distribution. Our model is trait-based which allows individuals to vary in life history strategies enabling mechanistic links between predictions and underlying traits and straightforward applications to other species and sites.

Predictors of Outcome in Traumatic Brain Injury: New Insight Using Receiver Operating Curve Indices and Bayesian Network Analysis.

PubMed

Zador, Zsolt; Sperrin, Matthew; King, Andrew T

2016-01-01

Traumatic brain injury remains a global health problem. Understanding the relative importance of outcome predictors helps optimize our treatment strategies by informing assessment protocols, clinical decisions and trial designs. In this study we establish importance ranking for outcome predictors based on receiver operating indices to identify key predictors of outcome and create simple predictive models. We then explore the associations between key outcome predictors using Bayesian networks to gain further insight into predictor importance. We analyzed the corticosteroid randomization after significant head injury (CRASH) trial database of 10008 patients and included patients for whom demographics, injury characteristics, computer tomography (CT) findings and Glasgow Outcome Scale (GCS) were recorded (total of 13 predictors, which would be available to clinicians within a few hours following the injury in 6945 patients). Predictions of clinical outcome (death or severe disability at 6 months) were performed using logistic regression models with 5-fold cross validation. Predictive performance was measured using standardized partial area (pAUC) under the receiver operating curve (ROC) and we used Delong test for comparisons. Variable importance ranking was based on pAUC targeted at specificity (pAUCSP) and sensitivity (pAUCSE) intervals of 90-100%. Probabilistic associations were depicted using Bayesian networks. Complete AUC analysis showed very good predictive power (AUC = 0.8237, 95% CI: 0.8138-0.8336) for the complete model. Specificity focused importance ranking highlighted age, pupillary, motor responses, obliteration of basal cisterns/3rd ventricle and midline shift. Interestingly when targeting model sensitivity, the highest-ranking variables were age, severe extracranial injury, verbal response, hematoma on CT and motor response. Simplified models, which included only these key predictors, had similar performance (pAUCSP = 0.6523, 95% CI: 0.6402-0.6641 and pAUCSE = 0.6332, 95% CI: 0.62-0.6477) compared to the complete models (pAUCSP = 0.6664, 95% CI: 0.6543-0.679, pAUCSE = 0.6436, 95% CI: 0.6289-0.6585, de Long p value 0.1165 and 0.3448 respectively). Bayesian networks showed the predictors that did not feature in the simplified models were associated with those that did. We demonstrate that importance based variable selection allows simplified predictive models to be created while maintaining prediction accuracy. Variable selection targeting specificity confirmed key components of clinical assessment in TBI whereas sensitivity based ranking suggested extracranial injury as one of the important predictors. These results help refine our approach to head injury assessment, decision-making and outcome prediction targeted at model sensitivity and specificity. Bayesian networks proved to be a comprehensive tool for depicting probabilistic associations for key predictors giving insight into why the simplified model has maintained accuracy.

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.

A comprehensive combustion model for biodiesel-fueled engine simulations

NASA Astrophysics Data System (ADS)

Brakora, Jessica L.

Engine models for alternative fuels are available, but few are comprehensive, well-validated models that include accurate physical property data as well as a detailed description of the fuel chemistry. In this work, a comprehensive biodiesel combustion model was created for use in multi-dimensional engine simulations, specifically the KIVA3v R2 code. The model incorporates realistic physical properties in a vaporization model developed for multi-component fuel sprays and applies an improved mechanism for biodiesel combustion chemistry. A reduced mechanism was generated from the methyl decanoate (MD) and methyl-9-decenoate (MD9D) mechanism developed at Lawrence Livermore National Laboratory. It was combined with a multi-component mechanism to include n-heptane in the fuel chemistry. The biodiesel chemistry was represented using a combination of MD, MD9D and n-heptane, which varied for a given fuel source. The reduced mechanism, which contained 63 species, accurately predicted ignition delay times of the detailed mechanism over a range of engine-specific operating conditions. Physical property data for the five methyl ester components of biodiesel were added to the KIVA library. Spray simulations were performed to ensure that the models adequately reproduce liquid penetration observed in biodiesel spray experiments. Fuel composition impacted liquid length as expected, with saturated species vaporizing more and penetrating less. Distillation curves were created to ensure the fuel vaporization process was comparable to available data. Engine validation was performed against a low-speed, high-load, conventional combustion experiments and the model was able to predict the performance and NOx formation seen in the experiment. High-speed, low-load, low-temperature combustion conditions were also modeled, and the emissions (HC, CO, NOx) and fuel consumption were well-predicted for a sweep of injection timings. Finally, comparisons were made between the results of biodiesel composition (palm vs. soy) and fuel blends (neat vs. B20). The model effectively reproduced the trends observed in the experiments.

Creation of a Rapid High-Fidelity Aerodynamics Module for a Multidisciplinary Design Environment

NASA Technical Reports Server (NTRS)

Srinivasan, Muktha; Whittecar, William; Edwards, Stephen; Mavris, Dimitri N.

2012-01-01

In the traditional aerospace vehicle design process, each successive design phase is accompanied by an increment in the modeling fidelity of the disciplinary analyses being performed. This trend follows a corresponding shrinking of the design space as more and more design decisions are locked in. The correlated increase in knowledge about the design and decrease in design freedom occurs partly because increases in modeling fidelity are usually accompanied by significant increases in the computational expense of performing the analyses. When running high fidelity analyses, it is not usually feasible to explore a large number of variations, and so design space exploration is reserved for conceptual design, and higher fidelity analyses are run only once a specific point design has been selected to carry forward. The designs produced by this traditional process have been recognized as being limited by the uncertainty that is present early on due to the use of lower fidelity analyses. For example, uncertainty in aerodynamics predictions produces uncertainty in trajectory optimization, which can impact overall vehicle sizing. This effect can become more significant when trajectories are being shaped by active constraints. For example, if an optimal trajectory is running up against a normal load factor constraint, inaccuracies in the aerodynamic coefficient predictions can cause a feasible trajectory to be considered infeasible, or vice versa. For this reason, a trade must always be performed between the desired fidelity and the resources available. Apart from this trade between fidelity and computational expense, it is very desirable to use higher fidelity analyses earlier in the design process. A large body of work has been performed to this end, led by efforts in the area of surrogate modeling. In surrogate modeling, an up-front investment is made by running a high fidelity code over a Design of Experiments (DOE); once completed, the DOE data is used to create a surrogate model, which captures the relationships between input variables and responses into regression equations. Depending on the dimensionality of the problem and the fidelity of the code for which a surrogate model is being created, the initial DOE can itself be computationally prohibitive to run. Cokriging, a modeling approach from the field of geostatistics, provides a desirable compromise between computational expense and fidelity. To do this, cokriging leverages a large body of data generated by a low fidelity analysis, combines it with a smaller set of data from a higher fidelity analysis, and creates a kriging surrogate model with prediction fidelity approaching that of the higher fidelity analysis. When integrated into a multidisciplinary environment, a disciplinary analysis module employing cokriging can raise the analysis fidelity without drastically impacting the expense of design iterations. This is demonstrated through the creation of an aerodynamics analysis module in NASA s OpenMDAO framework. Aerodynamic analyses including Missile DATCOM, APAS, and USM3D are leveraged to create high fidelity aerodynamics decks for parametric vehicle geometries, which are created in NASA s Vehicle Sketch Pad (VSP). Several trade studies are performed to examine the achieved level of model fidelity, and the overall impact to vehicle design is quantified.

A simplified clinical prediction rule for prognosticating independent walking after spinal cord injury: a prospective study from a Canadian multicenter spinal cord injury registry.

PubMed

Hicks, Katharine E; Zhao, Yichen; Fallah, Nader; Rivers, Carly S; Noonan, Vanessa K; Plashkes, Tova; Wai, Eugene K; Roffey, Darren M; Tsai, Eve C; Paquet, Jerome; Attabib, Najmedden; Marion, Travis; Ahn, Henry; Phan, Philippe

2017-10-01

Traumatic spinal cord injury (SCI) is a debilitating condition with limited treatment options for neurologic or functional recovery. The ability to predict the prognosis of walking post injury with emerging prediction models could aid in rehabilitation strategies and reintegration into the community. To revalidate an existing clinical prediction model for independent ambulation (van Middendorp et al., 2011) using acute and long-term post-injury follow-up data, and to investigatethe accuracy of a simplified model using prospectively collected data from a Canadian multicenter SCI database, the Rick Hansen Spinal Cord Injury Registry (RHSCIR). Prospective cohort study. The analysis cohort consisted of 278 adult individuals with traumatic SCI enrolled in the RHSCIR for whom complete neurologic examination data and Functional Independence Measure (FIM) outcome data were available. The FIM locomotor score was used to assess independent walking ability (defined as modified or complete independence in walk or combined walk and wheelchair modality) at 1-year follow-up for each participant. A logistic regression (LR) model based on age and four neurologic variables was applied to our cohort of 278 RHSCIR participants. Additionally, a simplified LR model was created. The Hosmer-Lemeshow goodness of fit test was used to check if the predictive model is applicable to our data set. The performance of the model was verified by calculating the area under the receiver operating characteristic curve (AUC). The accuracy of the model was tested using a cross-validation technique. This study was supported by a grant from The Ottawa Hospital Academic Medical Organization ($50,000 over 2 years). The RHSCIR is sponsored by the Rick Hansen Institute and is supported by funding from Health Canada, Western Economic Diversification Canada, and the provincial governments of Alberta, British Columbia, Manitoba, and Ontario. ET and JP report receiving grants from the Rick Hansen Institute (approximately $60,000 and $30,000 per year, respectively). DMR reports receiving remuneration for consulting services provided to Palladian Health, LLC and Pacira Pharmaceuticals, Inc ($20,000-$30,000 annually), although neither relationship presents a potential conflict of interest with the submitted work. KEH received a grant for involvement in the present study from the Government of Canada as part of the Canada Summer Jobs Program ($3,000). JP reports receiving an educational grant from Medtronic Canada outside of the submitted work ($75,000 annually). TM reports receiving educational fellowship support from AO Spine, AO Trauma, and Medtronic; however, none of these relationships are financial in nature. All remaining authors have no conflicts of interest to disclose. The fitted prediction model generated 85% overall classification accuracy, 79% sensitivity, and 90% specificity. The prediction model was able to accurately classify independent walking ability (AUC 0.889, 95% confidence interval [CI] 0.846-0.933, p<.001) compared with the existing prediction model, despite the use of a different outcome measure (FIM vs. Spinal Cord Independence Measure) to qualify walking ability. A simplified, three-variable LR model based on age and two neurologic variables had an overall classification accuracy of 84%, with 76% sensitivity and 90% specificity, demonstrating comparable accuracy with its five-variable prediction model counterpart. The AUC was 0.866 (95% CI 0.816-0.916, p<.01), only marginally less than that of the existing prediction model. A simplified predictive model with similar accuracy to a more complex model for predicting independent walking was created, which improves utility in a clinical setting. Such models will allow clinicians to better predict the prognosis of ambulation in individuals who have sustained a traumatic SCI. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

The Earth System Prediction Suite: Toward a Coordinated U.S. Modeling Capability

DOE PAGES

Theurich, Gerhard; DeLuca, C.; Campbell, T.; ...

2016-08-22

The Earth System Prediction Suite (ESPS) is a collection of flagship U.S. weather and climate models and model components that are being instrumented to conform to interoperability conventions, documented to follow metadata standards, and made available either under open-source terms or to credentialed users. Furthermore, the ESPS represents a culmination of efforts to create a common Earth system model architecture, and the advent of increasingly coordinated model development activities in the United States. ESPS component interfaces are based on the Earth System Modeling Framework (ESMF), community-developed software for building and coupling models, and the National Unified Operational Prediction Capability (NUOPC)more » Layer, a set of ESMF-based component templates and interoperability conventions. Our shared infrastructure simplifies the process of model coupling by guaranteeing that components conform to a set of technical and semantic behaviors. The ESPS encourages distributed, multiagency development of coupled modeling systems; controlled experimentation and testing; and exploration of novel model configurations, such as those motivated by research involving managed and interactive ensembles. ESPS codes include the Navy Global Environmental Model (NAVGEM), the Hybrid Coordinate Ocean Model (HYCOM), and the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS); the NOAA Environmental Modeling System (NEMS) and the Modular Ocean Model (MOM); the Community Earth System Model (CESM); and the NASA ModelE climate model and the Goddard Earth Observing System Model, version 5 (GEOS-5), atmospheric general circulation model.« less

The Earth System Prediction Suite: Toward a Coordinated U.S. Modeling Capability

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

Theurich, Gerhard; DeLuca, C.; Campbell, T.

The Earth System Prediction Suite (ESPS) is a collection of flagship U.S. weather and climate models and model components that are being instrumented to conform to interoperability conventions, documented to follow metadata standards, and made available either under open-source terms or to credentialed users. Furthermore, the ESPS represents a culmination of efforts to create a common Earth system model architecture, and the advent of increasingly coordinated model development activities in the United States. ESPS component interfaces are based on the Earth System Modeling Framework (ESMF), community-developed software for building and coupling models, and the National Unified Operational Prediction Capability (NUOPC)more » Layer, a set of ESMF-based component templates and interoperability conventions. Our shared infrastructure simplifies the process of model coupling by guaranteeing that components conform to a set of technical and semantic behaviors. The ESPS encourages distributed, multiagency development of coupled modeling systems; controlled experimentation and testing; and exploration of novel model configurations, such as those motivated by research involving managed and interactive ensembles. ESPS codes include the Navy Global Environmental Model (NAVGEM), the Hybrid Coordinate Ocean Model (HYCOM), and the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS); the NOAA Environmental Modeling System (NEMS) and the Modular Ocean Model (MOM); the Community Earth System Model (CESM); and the NASA ModelE climate model and the Goddard Earth Observing System Model, version 5 (GEOS-5), atmospheric general circulation model.« less

Scientific reporting is suboptimal for aspects that characterize genetic risk prediction studies: a review of published articles based on the Genetic RIsk Prediction Studies statement.

PubMed

Iglesias, Adriana I; Mihaescu, Raluca; Ioannidis, John P A; Khoury, Muin J; Little, Julian; van Duijn, Cornelia M; Janssens, A Cecile J W

2014-05-01

Our main objective was to raise awareness of the areas that need improvements in the reporting of genetic risk prediction articles for future publications, based on the Genetic RIsk Prediction Studies (GRIPS) statement. We evaluated studies that developed or validated a prediction model based on multiple DNA variants, using empirical data, and were published in 2010. A data extraction form based on the 25 items of the GRIPS statement was created and piloted. Forty-two studies met our inclusion criteria. Overall, more than half of the evaluated items (34 of 62) were reported in at least 85% of included articles. Seventy-seven percentage of the articles were identified as genetic risk prediction studies through title assessment, but only 31% used the keywords recommended by GRIPS in the title or abstract. Seventy-four percentage mentioned which allele was the risk variant. Overall, only 10% of the articles reported all essential items needed to perform external validation of the risk model. Completeness of reporting in genetic risk prediction studies is adequate for general elements of study design but is suboptimal for several aspects that characterize genetic risk prediction studies such as description of the model construction. Improvements in the transparency of reporting of these aspects would facilitate the identification, replication, and application of genetic risk prediction models. Copyright © 2014 Elsevier Inc. All rights reserved.

A Comparison of a Machine Learning Model with EuroSCORE II in Predicting Mortality after Elective Cardiac Surgery: A Decision Curve Analysis

PubMed Central

Allyn, Jérôme; Allou, Nicolas; Augustin, Pascal; Philip, Ivan; Martinet, Olivier; Belghiti, Myriem; Provenchere, Sophie; Montravers, Philippe; Ferdynus, Cyril

2017-01-01

Background The benefits of cardiac surgery are sometimes difficult to predict and the decision to operate on a given individual is complex. Machine Learning and Decision Curve Analysis (DCA) are recent methods developed to create and evaluate prediction models. Methods and finding We conducted a retrospective cohort study using a prospective collected database from December 2005 to December 2012, from a cardiac surgical center at University Hospital. The different models of prediction of mortality in-hospital after elective cardiac surgery, including EuroSCORE II, a logistic regression model and a machine learning model, were compared by ROC and DCA. Of the 6,520 patients having elective cardiac surgery with cardiopulmonary bypass, 6.3% died. Mean age was 63.4 years old (standard deviation 14.4), and mean EuroSCORE II was 3.7 (4.8) %. The area under ROC curve (IC95%) for the machine learning model (0.795 (0.755–0.834)) was significantly higher than EuroSCORE II or the logistic regression model (respectively, 0.737 (0.691–0.783) and 0.742 (0.698–0.785), p < 0.0001). Decision Curve Analysis showed that the machine learning model, in this monocentric study, has a greater benefit whatever the probability threshold. Conclusions According to ROC and DCA, machine learning model is more accurate in predicting mortality after elective cardiac surgery than EuroSCORE II. These results confirm the use of machine learning methods in the field of medical prediction. PMID:28060903

A modeling framework for investment planning in interdependent infrastructures in multi-hazard environments.

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

Brown, Nathanael J. K.; Gearhart, Jared Lee; Jones, Dean A.

Currently, much of protection planning is conducted separately for each infrastructure and hazard. Limited funding requires a balance of expenditures between terrorism and natural hazards based on potential impacts. This report documents the results of a Laboratory Directed Research & Development (LDRD) project that created a modeling framework for investment planning in interdependent infrastructures focused on multiple hazards, including terrorism. To develop this framework, three modeling elements were integrated: natural hazards, terrorism, and interdependent infrastructures. For natural hazards, a methodology was created for specifying events consistent with regional hazards. For terrorism, we modeled the terrorists actions based on assumptions regardingmore » their knowledge, goals, and target identification strategy. For infrastructures, we focused on predicting post-event performance due to specific terrorist attacks and natural hazard events, tempered by appropriate infrastructure investments. We demonstrate the utility of this framework with various examples, including protection of electric power, roadway, and hospital networks.« less

Modeling and numerical analysis of a magneto-inertial fusion concept with the target created through FRC merging

NASA Astrophysics Data System (ADS)

Li, Chenguang; Yang, Xianjun

2016-10-01

The Magnetized Plasma Fusion Reactor concept is proposed as a magneto-inertial fusion approach based on the target plasma created through the collision merging of two oppositely translating field reversed configuration plasmas, which is then compressed by the imploding liner driven by the pulsed-power driver. The target creation process is described by a two-dimensional magnetohydrodynamics model, resulting in the typical target parameters. The implosion process and the fusion reaction are modeled by a simple zero-dimensional model, taking into account the alpha particle heating and the bremsstrahlung radiation loss. The compression on the target can be 2D cylindrical or 2.4D with the additive axial contraction taken into account. The dynamics of the liner compression and fusion burning are simulated and the optimum fusion gain and the associated target parameters are predicted. The scientific breakeven could be achieved at the optimized conditions.

United Space Alliance LLC Parachute Refurbishment Facility Model

NASA Technical Reports Server (NTRS)

Esser, Valerie; Pessaro, Martha; Young, Angela

2007-01-01

The Parachute Refurbishment Facility Model was created to reflect the flow of hardware through the facility using anticipated start and delivery times from a project level IV schedule. Distributions for task times were built using historical build data for SFOC work and new data generated for CLV/ARES task times. The model currently processes 633 line items from 14 SFOC builds for flight readiness, 16 SFOC builds returning from flight for defoul, wash, and dry operations, 12 builds for CLV manufacturing operations, and 1 ARES 1X build. Modeling the planned workflow through the PRF is providing a reliable way to predict the capability of the facility as well as the manpower resource need. Creating a real world process allows for real world problems to be identified and potential workarounds to be implemented in a safe, simulated world before taking it to the next step, implementation in the real world.

Towards Improved Forecasts of Atmospheric and Oceanic Circulations over the Complex Terrain of the Eastern Mediterranean

NASA Technical Reports Server (NTRS)

Chronis, Themis; Case, Jonathan L.; Papadopoulos, Anastasios; Anagnostou, Emmanouil N.; Mecikalski, John R.; Haines, Stephanie L.

2008-01-01

Forecasting atmospheric and oceanic circulations accurately over the Eastern Mediterranean has proved to be an exceptional challenge. The existence of fine-scale topographic variability (land/sea coverage) and seasonal dynamics variations can create strong spatial gradients in temperature, wind and other state variables, which numerical models may have difficulty capturing. The Hellenic Center for Marine Research (HCMR) is one of the main operational centers for wave forecasting in the eastern Mediterranean. Currently, HCMR's operational numerical weather/ocean prediction model is based on the coupled Eta/Princeton Ocean Model (POM). Since 1999, HCMR has also operated the POSEIDON floating buoys as a means of state-of-the-art, real-time observations of several oceanic and surface atmospheric variables. This study attempts a first assessment at improving both atmospheric and oceanic prediction by initializing a regional Numerical Weather Prediction (NWP) model with high-resolution sea surface temperatures (SST) from remotely sensed platforms in order to capture the small-scale characteristics.

Preliminary Empirical Models for Predicting Shrinkage, Part Geometry and Metallurgical Aspects of Ti-6Al-4V Shaped Metal Deposition Builds

NASA Astrophysics Data System (ADS)

Escobar-Palafox, Gustavo; Gault, Rosemary; Ridgway, Keith

2011-12-01

Shaped Metal Deposition (SMD) is an additive manufacturing process which creates parts layer by layer by weld depositions. In this work, empirical models that predict part geometry (wall thickness and outer diameter) and some metallurgical aspects (i.e. surface texture, portion of finer Widmanstätten microstructure) for the SMD process were developed. The models are based on an orthogonal fractional factorial design of experiments with four factors at two levels. The factors considered were energy level (a relationship between heat source power and the rate of raw material input.), step size, programmed diameter and travel speed. The models were validated using previous builds; the prediction error for part geometry was under 11%. Several relationships between the factors and responses were identified. Current had a significant effect on wall thickness; thickness increases with increasing current. Programmed diameter had a significant effect on percentage of shrinkage; this decreased with increasing component size. Surface finish decreased with decreasing step size and current.

Automated Performance Prediction of Message-Passing Parallel Programs

NASA Technical Reports Server (NTRS)

Block, Robert J.; Sarukkai, Sekhar; Mehra, Pankaj; Woodrow, Thomas S. (Technical Monitor)

1995-01-01

The increasing use of massively parallel supercomputers to solve large-scale scientific problems has generated a need for tools that can predict scalability trends of applications written for these machines. Much work has been done to create simple models that represent important characteristics of parallel programs, such as latency, network contention, and communication volume. But many of these methods still require substantial manual effort to represent an application in the model's format. The NIK toolkit described in this paper is the result of an on-going effort to automate the formation of analytic expressions of program execution time, with a minimum of programmer assistance. In this paper we demonstrate the feasibility of our approach, by extending previous work to detect and model communication patterns automatically, with and without overlapped computations. The predictions derived from these models agree, within reasonable limits, with execution times of programs measured on the Intel iPSC/860 and Paragon. Further, we demonstrate the use of MK in selecting optimal computational grain size and studying various scalability metrics.

A Probabilistic Approach to Predict Thermal Fatigue Life for Ball Grid Array Solder Joints

NASA Astrophysics Data System (ADS)

Wei, Helin; Wang, Kuisheng

2011-11-01

Numerous studies of the reliability of solder joints have been performed. Most life prediction models are limited to a deterministic approach. However, manufacturing induces uncertainty in the geometry parameters of solder joints, and the environmental temperature varies widely due to end-user diversity, creating uncertainties in the reliability of solder joints. In this study, a methodology for accounting for variation in the lifetime prediction for lead-free solder joints of ball grid array packages (PBGA) is demonstrated. The key aspects of the solder joint parameters and the cyclic temperature range related to reliability are involved. Probabilistic solutions of the inelastic strain range and thermal fatigue life based on the Engelmaier model are developed to determine the probability of solder joint failure. The results indicate that the standard deviation increases significantly when more random variations are involved. Using the probabilistic method, the influence of each variable on the thermal fatigue life is quantified. This information can be used to optimize product design and process validation acceptance criteria. The probabilistic approach creates the opportunity to identify the root causes of failed samples from product fatigue tests and field returns. The method can be applied to better understand how variation affects parameters of interest in an electronic package design with area array interconnections.

Frequency Response of Synthetic Vocal Fold Models with Linear and Nonlinear Material Properties

PubMed Central

Shaw, Stephanie M.; Thomson, Scott L.; Dromey, Christopher; Smith, Simeon

2014-01-01

Purpose The purpose of this study was to create synthetic vocal fold models with nonlinear stress-strain properties and to investigate the effect of linear versus nonlinear material properties on fundamental frequency during anterior-posterior stretching. Method Three materially linear and three materially nonlinear models were created and stretched up to 10 mm in 1 mm increments. Phonation onset pressure (Pon) and fundamental frequency (F0) at Pon were recorded for each length. Measurements were repeated as the models were relaxed in 1 mm increments back to their resting lengths, and tensile tests were conducted to determine the stress-strain responses of linear versus nonlinear models. Results Nonlinear models demonstrated a more substantial frequency response than did linear models and a more predictable pattern of F0 increase with respect to increasing length (although range was inconsistent across models). Pon generally increased with increasing vocal fold length for nonlinear models, whereas for linear models, Pon decreased with increasing length. Conclusions Nonlinear synthetic models appear to more accurately represent the human vocal folds than linear models, especially with respect to F0 response. PMID:22271874

Frequency response of synthetic vocal fold models with linear and nonlinear material properties.

PubMed

Shaw, Stephanie M; Thomson, Scott L; Dromey, Christopher; Smith, Simeon

2012-10-01

The purpose of this study was to create synthetic vocal fold models with nonlinear stress-strain properties and to investigate the effect of linear versus nonlinear material properties on fundamental frequency (F0) during anterior-posterior stretching. Three materially linear and 3 materially nonlinear models were created and stretched up to 10 mm in 1-mm increments. Phonation onset pressure (Pon) and F0 at Pon were recorded for each length. Measurements were repeated as the models were relaxed in 1-mm increments back to their resting lengths, and tensile tests were conducted to determine the stress-strain responses of linear versus nonlinear models. Nonlinear models demonstrated a more substantial frequency response than did linear models and a more predictable pattern of F0 increase with respect to increasing length (although range was inconsistent across models). Pon generally increased with increasing vocal fold length for nonlinear models, whereas for linear models, Pon decreased with increasing length. Nonlinear synthetic models appear to more accurately represent the human vocal folds than do linear models, especially with respect to F0 response.

Development and validation of a computational model of the knee joint for the evaluation of surgical treatments for osteoarthritis

PubMed Central

Mootanah, R.; Imhauser, C.W.; Reisse, F.; Carpanen, D.; Walker, R.W.; Koff, M.F.; Lenhoff, M.W.; Rozbruch, S.R.; Fragomen, A.T.; Dewan, Z.; Kirane, Y.M.; Cheah, Pamela A.; Dowell, J.K.; Hillstrom, H.J.

2014-01-01

A three-dimensional (3D) knee joint computational model was developed and validated to predict knee joint contact forces and pressures for different degrees of malalignment. A 3D computational knee model was created from high-resolution radiological images to emulate passive sagittal rotation (full-extension to 65°-flexion) and weight acceptance. A cadaveric knee mounted on a six-degree-of-freedom robot was subjected to matching boundary and loading conditions. A ligament-tuning process minimised kinematic differences between the robotically loaded cadaver specimen and the finite element (FE) model. The model was validated by measured intra-articular force and pressure measurements. Percent full scale error between EE-predicted and in vitro-measured values in the medial and lateral compartments were 6.67% and 5.94%, respectively, for normalised peak pressure values, and 7.56% and 4.48%, respectively, for normalised force values. The knee model can accurately predict normalised intra-articular pressure and forces for different loading conditions and could be further developed for subject-specific surgical planning. PMID:24786914

Development and validation of a computational model of the knee joint for the evaluation of surgical treatments for osteoarthritis.

PubMed

Mootanah, R; Imhauser, C W; Reisse, F; Carpanen, D; Walker, R W; Koff, M F; Lenhoff, M W; Rozbruch, S R; Fragomen, A T; Dewan, Z; Kirane, Y M; Cheah, K; Dowell, J K; Hillstrom, H J

2014-01-01

A three-dimensional (3D) knee joint computational model was developed and validated to predict knee joint contact forces and pressures for different degrees of malalignment. A 3D computational knee model was created from high-resolution radiological images to emulate passive sagittal rotation (full-extension to 65°-flexion) and weight acceptance. A cadaveric knee mounted on a six-degree-of-freedom robot was subjected to matching boundary and loading conditions. A ligament-tuning process minimised kinematic differences between the robotically loaded cadaver specimen and the finite element (FE) model. The model was validated by measured intra-articular force and pressure measurements. Percent full scale error between FE-predicted and in vitro-measured values in the medial and lateral compartments were 6.67% and 5.94%, respectively, for normalised peak pressure values, and 7.56% and 4.48%, respectively, for normalised force values. The knee model can accurately predict normalised intra-articular pressure and forces for different loading conditions and could be further developed for subject-specific surgical planning.

Systematic review and retrospective validation of prediction models for weight loss after bariatric surgery.

PubMed

Sharples, Alistair J; Mahawar, Kamal; Cheruvu, Chandra V N

2017-11-01

Patients often have less than realistic expectations of the weight loss they are likely to achieve after bariatric surgery. It would be useful to have a well-validated prediction tool that could give patients a realistic estimate of their expected weight loss. To perform a systematic review of the literature to identify existing prediction models and attempt to validate these models. University hospital, United Kingdom. A systematic review was performed. All English language studies were included if they used data to create a prediction model for postoperative weight loss after bariatric surgery. These models were then tested on patients undergoing bariatric surgery between January 1, 2013 and December 31, 2014 within our unit. An initial literature search produced 446 results, of which only 4 were included in the final review. Our study population included 317 patients. Mean preoperative body mass index was 46.1 ± 7.1. For 257 (81.1%) patients, 12-month follow-up was available, and mean body mass index and percentage excess weight loss at 12 months was 33.0 ± 6.7 and 66.1% ± 23.7%, respectively. All 4 of the prediction models significantly overestimated the amount of weight loss achieved by patients. The best performing prediction model in our series produced a correlation coefficient (R 2 ) of .61 and an area under the curve of .71 on receiver operating curve analysis. All prediction models overestimated weight loss after bariatric surgery in our cohort. There is a need to develop better procedures and patient-specific models for better patient counselling. Copyright © 2017 American Society for Bariatric Surgery. Published by Elsevier Inc. All rights reserved.

Multipolar Electrostatic Energy Prediction for all 20 Natural Amino Acids Using Kriging Machine Learning.

PubMed

Fletcher, Timothy L; Popelier, Paul L A

2016-06-14

A machine learning method called kriging is applied to the set of all 20 naturally occurring amino acids. Kriging models are built that predict electrostatic multipole moments for all topological atoms in any amino acid based on molecular geometry only. These models then predict molecular electrostatic interaction energies. On the basis of 200 unseen test geometries for each amino acid, no amino acid shows a mean prediction error above 5.3 kJ mol(-1), while the lowest error observed is 2.8 kJ mol(-1). The mean error across the entire set is only 4.2 kJ mol(-1) (or 1 kcal mol(-1)). Charged systems are created by protonating or deprotonating selected amino acids, and these show no significant deviation in prediction error over their neutral counterparts. Similarly, the proposed methodology can also handle amino acids with aromatic side chains, without the need for modification. Thus, we present a generic method capable of accurately capturing multipolar polarizable electrostatics in amino acids.

A novel QSAR model of Salmonella mutagenicity and its application in the safety assessment of drug impurities

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

Valencia, Antoni; Prous, Josep; Mora, Oscar

As indicated in ICH M7 draft guidance, in silico predictive tools including statistically-based QSARs and expert analysis may be used as a computational assessment for bacterial mutagenicity for the qualification of impurities in pharmaceuticals. To address this need, we developed and validated a QSAR model to predict Salmonella t. mutagenicity (Ames assay outcome) of pharmaceutical impurities using Prous Institute's Symmetry℠, a new in silico solution for drug discovery and toxicity screening, and the Mold2 molecular descriptor package (FDA/NCTR). Data was sourced from public benchmark databases with known Ames assay mutagenicity outcomes for 7300 chemicals (57% mutagens). Of these data, 90%more » was used to train the model and the remaining 10% was set aside as a holdout set for validation. The model's applicability to drug impurities was tested using a FDA/CDER database of 951 structures, of which 94% were found within the model's applicability domain. The predictive performance of the model is acceptable for supporting regulatory decision-making with 84 ± 1% sensitivity, 81 ± 1% specificity, 83 ± 1% concordance and 79 ± 1% negative predictivity based on internal cross-validation, while the holdout dataset yielded 83% sensitivity, 77% specificity, 80% concordance and 78% negative predictivity. Given the importance of having confidence in negative predictions, an additional external validation of the model was also carried out, using marketed drugs known to be Ames-negative, and obtained 98% coverage and 81% specificity. Additionally, Ames mutagenicity data from FDA/CFSAN was used to create another data set of 1535 chemicals for external validation of the model, yielding 98% coverage, 73% sensitivity, 86% specificity, 81% concordance and 84% negative predictivity. - Highlights: • A new in silico QSAR model to predict Ames mutagenicity is described. • The model is extensively validated with chemicals from the FDA and the public domain. • Validation tests show desirable high sensitivity and high negative predictivity. • The model predicted 14 reportedly difficult to predict drug impurities with accuracy. • The model is suitable to support risk evaluation of potentially mutagenic compounds.« less

Disease prevention versus data privacy: using landcover maps to inform spatial epidemic models.

PubMed

Tildesley, Michael J; Ryan, Sadie J

2012-01-01

The availability of epidemiological data in the early stages of an outbreak of an infectious disease is vital for modelers to make accurate predictions regarding the likely spread of disease and preferred intervention strategies. However, in some countries, the necessary demographic data are only available at an aggregate scale. We investigated the ability of models of livestock infectious diseases to predict epidemic spread and obtain optimal control policies in the event of imperfect, aggregated data. Taking a geographic information approach, we used land cover data to predict UK farm locations and investigated the influence of using these synthetic location data sets upon epidemiological predictions in the event of an outbreak of foot-and-mouth disease. When broadly classified land cover data were used to create synthetic farm locations, model predictions deviated significantly from those simulated on true data. However, when more resolved subclass land use data were used, moderate to highly accurate predictions of epidemic size, duration and optimal vaccination and ring culling strategies were obtained. This suggests that a geographic information approach may be useful where individual farm-level data are not available, to allow predictive analyses to be carried out regarding the likely spread of disease. This method can also be used for contingency planning in collaboration with policy makers to determine preferred control strategies in the event of a future outbreak of infectious disease in livestock.

Disease Prevention versus Data Privacy: Using Landcover Maps to Inform Spatial Epidemic Models

PubMed Central

Tildesley, Michael J.; Ryan, Sadie J.

2012-01-01

The availability of epidemiological data in the early stages of an outbreak of an infectious disease is vital for modelers to make accurate predictions regarding the likely spread of disease and preferred intervention strategies. However, in some countries, the necessary demographic data are only available at an aggregate scale. We investigated the ability of models of livestock infectious diseases to predict epidemic spread and obtain optimal control policies in the event of imperfect, aggregated data. Taking a geographic information approach, we used land cover data to predict UK farm locations and investigated the influence of using these synthetic location data sets upon epidemiological predictions in the event of an outbreak of foot-and-mouth disease. When broadly classified land cover data were used to create synthetic farm locations, model predictions deviated significantly from those simulated on true data. However, when more resolved subclass land use data were used, moderate to highly accurate predictions of epidemic size, duration and optimal vaccination and ring culling strategies were obtained. This suggests that a geographic information approach may be useful where individual farm-level data are not available, to allow predictive analyses to be carried out regarding the likely spread of disease. This method can also be used for contingency planning in collaboration with policy makers to determine preferred control strategies in the event of a future outbreak of infectious disease in livestock. PMID:23133352

Cerebrospinal Fluid Cortisol Mediates Brain-Derived Neurotrophic Factor Relationships to Mortality after Severe TBI: A Prospective Cohort Study

PubMed Central

Munoz, Miranda J.; Kumar, Raj G.; Oh, Byung-Mo; Conley, Yvette P.; Wang, Zhensheng; Failla, Michelle D.; Wagner, Amy K.

2017-01-01

Distinct regulatory signaling mechanisms exist between cortisol and brain derived neurotrophic factor (BDNF) that may influence secondary injury cascades associated with traumatic brain injury (TBI) and predict outcome. We investigated concurrent CSF BDNF and cortisol relationships in 117 patients sampled days 0–6 after severe TBI while accounting for BDNF genetics and age. We also determined associations between CSF BDNF and cortisol with 6-month mortality. BDNF variants, rs6265 and rs7124442, were used to create a gene risk score (GRS) in reference to previously published hypothesized risk for mortality in “younger patients” (<48 years) and hypothesized BDNF production/secretion capacity with these variants. Group based trajectory analysis (TRAJ) was used to create two cortisol groups (high and low trajectories). A Bayesian estimation approach informed the mediation models. Results show CSF BDNF predicted patient cortisol TRAJ group (P = 0.001). Also, GRS moderated BDNF associations with cortisol TRAJ group. Additionally, cortisol TRAJ predicted 6-month mortality (P = 0.001). In a mediation analysis, BDNF predicted mortality, with cortisol acting as the mediator (P = 0.011), yielding a mediation percentage of 29.92%. Mediation effects increased to 45.45% among younger patients. A BDNF*GRS interaction predicted mortality in younger patients (P = 0.004). Thus, we conclude 6-month mortality after severe TBI can be predicted through a mediation model with CSF cortisol and BDNF, suggesting a regulatory role for cortisol with BDNF's contribution to TBI pathophysiology and mortality, particularly among younger individuals with severe TBI. Based on the literature, cortisol modulated BDNF effects on mortality after TBI may be related to known hormone and neurotrophin relationships to neurological injury severity and autonomic nervous system imbalance. PMID:28337122

Two-phase model for prediction of cell-free layer width in blood flow

PubMed Central

Namgung, Bumseok; Ju, Meongkeun; Cabrales, Pedro; Kim, Sangho

2014-01-01

This study aimed to develop a numerical model capable of predicting changes in the cell-free layer (CFL) width in narrow tubes with consideration of red blood cell aggregation effects. The model development integrates to empirical relations for relative viscosity (ratio of apparent viscosity to medium viscosity) and core viscosity measured on independent blood samples to create a continuum model that includes these two regions. The constitutive relations were derived from in vitro experiments performed with three different glass-capillary tubes (inner diameter = 30, 50 and 100 μm) over a wide range of pseudoshear rates (5-300 s−1). The aggregation tendency of the blood samples was also varied by adding Dextran 500 kDa. Our model predicted that the CFL width was strongly modulated by the relative viscosity function. Aggregation increased the width of CFL, and this effect became more pronounced at low shear rates. The CFL widths predicted in the present study at high shear conditions were in agreement with those reported in previous studies. However, unlike previous multi-particle models, our model did not require a high computing cost, and it was capable of reproducing results for a thicker CFL width at low shear conditions, depending on aggregating tendency of the blood. PMID:23116701

Pharmacokinetic modeling of gentamicin in treatment of infective endocarditis: Model development and validation of existing models.

PubMed

Gomes, Anna; van der Wijk, Lars; Proost, Johannes H; Sinha, Bhanu; Touw, Daan J

2017-01-01

Gentamicin shows large variations in half-life and volume of distribution (Vd) within and between individuals. Thus, monitoring and accurately predicting serum levels are required to optimize effectiveness and minimize toxicity. Currently, two population pharmacokinetic models are applied for predicting gentamicin doses in adults. For endocarditis patients the optimal model is unknown. We aimed at: 1) creating an optimal model for endocarditis patients; and 2) assessing whether the endocarditis and existing models can accurately predict serum levels. We performed a retrospective observational two-cohort study: one cohort to parameterize the endocarditis model by iterative two-stage Bayesian analysis, and a second cohort to validate and compare all three models. The Akaike Information Criterion and the weighted sum of squares of the residuals divided by the degrees of freedom were used to select the endocarditis model. Median Prediction Error (MDPE) and Median Absolute Prediction Error (MDAPE) were used to test all models with the validation dataset. We built the endocarditis model based on data from the modeling cohort (65 patients) with a fixed 0.277 L/h/70kg metabolic clearance, 0.698 (±0.358) renal clearance as fraction of creatinine clearance, and Vd 0.312 (±0.076) L/kg corrected lean body mass. External validation with data from 14 validation cohort patients showed a similar predictive power of the endocarditis model (MDPE -1.77%, MDAPE 4.68%) as compared to the intensive-care (MDPE -1.33%, MDAPE 4.37%) and standard (MDPE -0.90%, MDAPE 4.82%) models. All models acceptably predicted pharmacokinetic parameters for gentamicin in endocarditis patients. However, these patients appear to have an increased Vd, similar to intensive care patients. Vd mainly determines the height of peak serum levels, which in turn correlate with bactericidal activity. In order to maintain simplicity, we advise to use the existing intensive-care model in clinical practice to avoid potential underdosing of gentamicin in endocarditis patients.

Pharmacokinetic modeling of gentamicin in treatment of infective endocarditis: Model development and validation of existing models

PubMed Central

van der Wijk, Lars; Proost, Johannes H.; Sinha, Bhanu; Touw, Daan J.

2017-01-01

Gentamicin shows large variations in half-life and volume of distribution (Vd) within and between individuals. Thus, monitoring and accurately predicting serum levels are required to optimize effectiveness and minimize toxicity. Currently, two population pharmacokinetic models are applied for predicting gentamicin doses in adults. For endocarditis patients the optimal model is unknown. We aimed at: 1) creating an optimal model for endocarditis patients; and 2) assessing whether the endocarditis and existing models can accurately predict serum levels. We performed a retrospective observational two-cohort study: one cohort to parameterize the endocarditis model by iterative two-stage Bayesian analysis, and a second cohort to validate and compare all three models. The Akaike Information Criterion and the weighted sum of squares of the residuals divided by the degrees of freedom were used to select the endocarditis model. Median Prediction Error (MDPE) and Median Absolute Prediction Error (MDAPE) were used to test all models with the validation dataset. We built the endocarditis model based on data from the modeling cohort (65 patients) with a fixed 0.277 L/h/70kg metabolic clearance, 0.698 (±0.358) renal clearance as fraction of creatinine clearance, and Vd 0.312 (±0.076) L/kg corrected lean body mass. External validation with data from 14 validation cohort patients showed a similar predictive power of the endocarditis model (MDPE -1.77%, MDAPE 4.68%) as compared to the intensive-care (MDPE -1.33%, MDAPE 4.37%) and standard (MDPE -0.90%, MDAPE 4.82%) models. All models acceptably predicted pharmacokinetic parameters for gentamicin in endocarditis patients. However, these patients appear to have an increased Vd, similar to intensive care patients. Vd mainly determines the height of peak serum levels, which in turn correlate with bactericidal activity. In order to maintain simplicity, we advise to use the existing intensive-care model in clinical practice to avoid potential underdosing of gentamicin in endocarditis patients. PMID:28475651

Predicting survival of Escherichia coli O157:H7 in dry fermented sausage using artificial neural networks.

PubMed

Palanichamy, A; Jayas, D S; Holley, R A

2008-01-01

The Canadian Food Inspection Agency required the meat industry to ensure Escherichia coli O157:H7 does not survive (experiences > or = 5 log CFU/g reduction) in dry fermented sausage (salami) during processing after a series of foodborne illness outbreaks resulting from this pathogenic bacterium occurred. The industry is in need of an effective technique like predictive modeling for estimating bacterial viability, because traditional microbiological enumeration is a time-consuming and laborious method. The accuracy and speed of artificial neural networks (ANNs) for this purpose is an attractive alternative (developed from predictive microbiology), especially for on-line processing in industry. Data from a study of interactive effects of different levels of pH, water activity, and the concentrations of allyl isothiocyanate at various times during sausage manufacture in reducing numbers of E. coli O157:H7 were collected. Data were used to develop predictive models using a general regression neural network (GRNN), a form of ANN, and a statistical linear polynomial regression technique. Both models were compared for their predictive error, using various statistical indices. GRNN predictions for training and test data sets had less serious errors when compared with the statistical model predictions. GRNN models were better and slightly better for training and test sets, respectively, than was the statistical model. Also, GRNN accurately predicted the level of allyl isothiocyanate required, ensuring a 5-log reduction, when an appropriate production set was created by interpolation. Because they are simple to generate, fast, and accurate, ANN models may be of value for industrial use in dry fermented sausage manufacture to reduce the hazard associated with E. coli O157:H7 in fresh beef and permit production of consistently safe products from this raw material.

Using Analog Ensemble to generate spatially downscaled probabilistic wind power forecasts

NASA Astrophysics Data System (ADS)

Delle Monache, L.; Shahriari, M.; Cervone, G.

2017-12-01

We use the Analog Ensemble (AnEn) method to generate probabilistic 80-m wind power forecasts. We use data from the NCEP GFS ( 28 km resolution) and NCEP NAM (12 km resolution). We use forecasts data from NAM and GFS, and analysis data from NAM which enables us to: 1) use a lower-resolution model to create higher-resolution forecasts, and 2) use a higher-resolution model to create higher-resolution forecasts. The former essentially increases computing speed and the latter increases forecast accuracy. An aggregated model of the former can be compared against the latter to measure the accuracy of the AnEn spatial downscaling. The AnEn works by taking a deterministic future forecast and comparing it with past forecasts. The model searches for the best matching estimates within the past forecasts and selects the predictand value corresponding to these past forecasts as the ensemble prediction for the future forecast. Our study is based on predicting wind speed and air density at more than 13,000 grid points in the continental US. We run the AnEn model twice: 1) estimating 80-m wind speed by using predictor variables such as temperature, pressure, geopotential height, U-component and V-component of wind, 2) estimating air density by using predictors such as temperature, pressure, and relative humidity. We use the air density values to correct the standard wind power curves for different values of air density. The standard deviation of the ensemble members (i.e. ensemble spread) will be used as the degree of difficulty to predict wind power at different locations. The value of the correlation coefficient between the ensemble spread and the forecast error determines the appropriateness of this measure. This measure is prominent for wind farm developers as building wind farms in regions with higher predictability will reduce the real-time risks of operating in the electricity markets.

Finite Element Modeling of the NASA Langley Aluminum Testbed Cylinder

NASA Technical Reports Server (NTRS)

Grosveld, Ferdinand W.; Pritchard, Joselyn I.; Buehrle, Ralph D.; Pappa, Richard S.

2002-01-01

The NASA Langley Aluminum Testbed Cylinder (ATC) was designed to serve as a universal structure for evaluating structural acoustic codes, modeling techniques and optimization methods used in the prediction of aircraft interior noise. Finite element models were developed for the components of the ATC based on the geometric, structural and material properties of the physical test structure. Numerically predicted modal frequencies for the longitudinal stringer, ring frame and dome component models, and six assembled ATC configurations were compared with experimental modal survey data. The finite element models were updated and refined, using physical parameters, to increase correlation with the measured modal data. Excellent agreement, within an average 1.5% to 2.9%, was obtained between the predicted and measured modal frequencies of the stringer, frame and dome components. The predictions for the modal frequencies of the assembled component Configurations I through V were within an average 2.9% and 9.1%. Finite element modal analyses were performed for comparison with 3 psi and 6 psi internal pressurization conditions in Configuration VI. The modal frequencies were predicted by applying differential stiffness to the elements with pressure loading and creating reduced matrices for beam elements with offsets inside external superelements. The average disagreement between the measured and predicted differences for the 0 psi and 6 psi internal pressure conditions was less than 0.5%. Comparably good agreement was obtained for the differences between the 0 psi and 3 psi measured and predicted internal pressure conditions.

Modelling of capillary-driven flow for closed paper-based microfluidic channels

NASA Astrophysics Data System (ADS)

Songok, Joel; Toivakka, Martti

2017-06-01

Paper-based microfluidics is an emerging field focused on creating inexpensive devices, with simple fabrication methods for applications in various fields including healthcare, environmental monitoring and veterinary medicine. Understanding the flow of liquid is important in achieving consistent operation of the devices. This paper proposes capillary models to predict flow in paper-based microfluidic channels, which include a flow accelerating hydrophobic top cover. The models, which consider both non-absorbing and absorbing substrates, are in good agreement with the experimental results.

EXPERIMENTAL MODELLING OF AORTIC ANEURYSMS

PubMed Central

Doyle, Barry J; Corbett, Timothy J; Cloonan, Aidan J; O’Donnell, Michael R; Walsh, Michael T; Vorp, David A; McGloughlin, Timothy M

2009-01-01

A range of silicone rubbers were created based on existing commercially available materials. These silicones were designed to be visually different from one another and have distinct material properties, in particular, ultimate tensile strengths and tear strengths. In total, eleven silicone rubbers were manufactured, with the materials designed to have a range of increasing tensile strengths from approximately 2-4MPa, and increasing tear strengths from approximately 0.45-0.7N/mm. The variations in silicones were detected using a standard colour analysis technique. Calibration curves were then created relating colour intensity to individual material properties. All eleven materials were characterised and a 1st order Ogden strain energy function applied. Material coefficients were determined and examined for effectiveness. Six idealised abdominal aortic aneurysm models were also created using the two base materials of the study, with a further model created using a new mixing technique to create a rubber model with randomly assigned material properties. These models were then examined using videoextensometry and compared to numerical results. Colour analysis revealed a statistically significant linear relationship (p<0.0009) with both tensile strength and tear strength, allowing material strength to be determined using a non-destructive experimental technique. The effectiveness of this technique was assessed by comparing predicted material properties to experimentally measured methods, with good agreement in the results. Videoextensometry and numerical modelling revealed minor percentage differences, with all results achieving significance (p<0.0009). This study has successfully designed and developed a range of silicone rubbers that have unique colour intensities and material strengths. Strengths can be readily determined using a non-destructive analysis technique with proven effectiveness. These silicones may further aid towards an improved understanding of the biomechanical behaviour of aneurysms using experimental techniques. PMID:19595622

Single- and two-phase flow in microfluidic porous media analogs based on Voronoi tessellation

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

Wu, Mengjie; Xiao, Feng; Johnson-Paben, Rebecca

2012-01-01

The objective of this study was to create a microfluidic model of complex porous media for studying single and multiphase flows. Most experimental porous media models consist of periodic geometries that lend themselves to comparison with well-developed theoretical predictions. However, most real porous media such as geological formations and biological tissues contain a degree of randomness and complexity that is not adequately represented in periodic geometries. To design an experimental tool to study these complex geometries, we created microfluidic models of random homogeneous and heterogeneous networks based on Voronoi tessellations. These networks consisted of approximately 600 grains separated by amore » highly connected network of channels with an overall porosity of 0.11 0.20. We found that introducing heterogeneities in the form of large cavities within the network changed the permeability in a way that cannot be predicted by the classical porosity-permeability relationship known as the Kozeny equation. The values of permeability found in experiments were in excellent agreement with those calculated from three-dimensional lattice Boltzmann simulations. In two-phase flow experiments of oil displacement with water we found that the surface energy of channel walls determined the pattern of water invasion, while the network topology determined the residual oil saturation. These results suggest that complex network topologies lead to fluid flow behavior that is difficult to predict based solely on porosity. The microfluidic models developed in this study using a novel geometry generation algorithm based on Voronoi tessellation are a new experimental tool for studying fluid and solute transport problems within complex porous media.« less

Creation of Woven Structures Impacting Self-cleaning Superoleophobicity

NASA Astrophysics Data System (ADS)

Lim, Jihye

For protection of human life from harmful or toxic liquids in working areas, solid surface resistance to liquid with low surface tension (e.g. oil) should be achieved in the outermost layer of protective clothing. Based on the literature review, multiscale structures were emphasized because they can increase roughness on a solid surface and create more void spaces of different sizes. The roughness and void spaces contribute to creating a liquid-vapor interface and reducing the liquid contact area to the solid surface. Woven fabric inherently consists of multiscale structures by its construction: microscale in a yarn structure and macroscale in a fabric structure. When the solid surface tension is low relative to oil, creating an appropriate structural geometry will become a critical way to obtain a superoleophobic surface for oil-resistance. Theoretical modeling and experiments with actual fabric samples were utilized to predict and prove the highest performing structural geometry in woven fabric, respectively. The theoretical geometric modeling accounted for the different weave structures, the yarn compression by the yarn flattening factor, e, and the void space by the void space ratio to the fiber or yarn diameter, T, impacting the liquid apparent contact angle on a fabric surface. The Cassie-Baxter equations were developed using Young's contact angle, thetae, thetae and e, or thetae, e, and T, to predict the liquid apparent contact angle for different geometries. In addition, to prevent a liquid's penetration into a solid structure, the ranges of the protuberance height (>> h2) and distance (< 4ℓ 2 cap) were predicted by the definition of the Laplace pressure, the capillary pressure, and the sagging phenomenon. Those predictions were in strong agreement with the results from the empirical experiment using the actual woven fabric samples. This study identified the impact of the geometries in yarn and woven fabric structures on the fabric resistance against oil through theoretical modeling and experiments. The results suggest particular weave structures, the range of the void space (or the protuberance distance) and the protuberance height in the yarn and fabric structures for the highest performing self-cleaning superoleophobic woven fabric surface.

Tissue-engineered microenvironment systems for modeling human vasculature.

PubMed

Tourovskaia, Anna; Fauver, Mark; Kramer, Gregory; Simonson, Sara; Neumann, Thomas

2014-09-01

The high attrition rate of drug candidates late in the development process has led to an increasing demand for test assays that predict clinical outcome better than conventional 2D cell culture systems and animal models. Government agencies, the military, and the pharmaceutical industry have started initiatives for the development of novel in-vitro systems that recapitulate functional units of human tissues and organs. There is growing evidence that 3D cell arrangement, co-culture of different cell types, and physico-chemical cues lead to improved predictive power. A key element of all tissue microenvironments is the vasculature. Beyond transporting blood the microvasculature assumes important organ-specific functions. It is also involved in pathologic conditions, such as inflammation, tumor growth, metastasis, and degenerative diseases. To provide a tool for modeling this important feature of human tissue microenvironments, we developed a microfluidic chip for creating tissue-engineered microenvironment systems (TEMS) composed of tubular cell structures. Our chip design encompasses a small chamber that is filled with an extracellular matrix (ECM) surrounding one or more tubular channels. Endothelial cells (ECs) seeded into the channels adhere to the ECM walls and grow into perfusable tubular tissue structures that are fluidically connected to upstream and downstream fluid channels in the chip. Using these chips we created models of angiogenesis, the blood-brain barrier (BBB), and tumor-cell extravasation. Our angiogenesis model recapitulates true angiogenesis, in which sprouting occurs from a "parent" vessel in response to a gradient of growth factors. Our BBB model is composed of a microvessel generated from brain-specific ECs within an ECM populated with astrocytes and pericytes. Our tumor-cell extravasation model can be utilized to visualize and measure tumor-cell migration through vessel walls into the surrounding matrix. The described technology can be used to create TEMS that recapitulate structural, functional, and physico-chemical elements of vascularized human tissue microenvironments in vitro. © 2014 by the Society for Experimental Biology and Medicine.

An analysis of the North Rainier Elk Herd area, Washington: Change detection and habitat modeling with remote sensing and GIS

NASA Astrophysics Data System (ADS)

Benton, Joshua J.

The North Rainier Elk Herd (NREH) is one of ten designated herds in Washington State, all managed by the Washington Department of Fish and Wildlife (WDFW). To aid in the management of the herd, the WDFW has decided to implement a spatial ecosystem analysis. This thesis partially undertakes this analysis through the use of a suite of software tools, the Westside Elk Nutrition and Habitat Use Models (WENHUM). This model analyzes four covariates that have a strong correlation to elk habitat selection: dietary digestible energy (DDE); distance to roads open to the public; mean slope; and distance to cover-forage edge and returns areas of likely elk habitation or use. This thesis includes an update of the base vegetation layer from 2006 data to 2011, a series of clear cuts were identified as areas of change and fed into the WENHUM models. The addition of these clear cuts created improvements in the higher quality DDE levels and when the updated data is compared to the original, predictions of elk use are higher. The presence of open or closed roads was simulated by creating an area of possible closures, selecting candidate roads within that area and then modeling them as either "all open" or "all closed". The simulation of the road closures produced increases in the higher levels of predicted use.

Electrical and chemical properties of XeCl*(308 nm) exciplex lamp created by a dielectric barrier discharge

NASA Astrophysics Data System (ADS)

Baadj, S.; Harrache, Z.; Belasri, A.

2013-12-01

The aim of this work is to highlight, through numerical modeling, the chemical and the electrical characteristics of xenon chloride mixture in XeCl* (308 nm) excimer lamp created by a dielectric barrier discharge. A temporal model, based on the Xe/Cl2 mixture chemistry, the circuit and the Boltzmann equations, is constructed. The effects of operating voltage, Cl2 percentage in the Xe/Cl2 gas mixture, dielectric capacitance, as well as gas pressure on the 308-nm photon generation, under typical experimental operating conditions, have been investigated and discussed. The importance of charged and excited species, including the major electronic and ionic processes, is also demonstrated. The present calculations show clearly that the model predicts the optimal operating conditions and describes the electrical and chemical properties of the XeCl* exciplex lamp.

The ascendance of microphysiological systems to solve the drug testing dilemma

PubMed Central

Dehne, Eva-Maria; Hasenberg, Tobias; Marx, Uwe

2017-01-01

The development of drugs is a process obstructed with manifold security and efficacy concerns. Although animal models are still widely used to meet the diligence required, they are regarded as outdated tools with limited predictability. Novel microphysiological systems intend to create systemic models of human biology. Their ability to host 3D organoid constructs in a controlled microenvironment with mechanical and electrophysiological stimuli enables them to create and maintain homeostasis. These platforms are, thus, envisioned to be superior tools for testing and developing substances such as drugs, cosmetics and chemicals. We will present reasons why microphysiological systems are required for the emerging demands, highlight current technological and regulatory obstacles, and depict possible solutions from state-of-the-art platforms from major contributors. PMID:28670475

CARE 3 user-friendly interface user's guide

NASA Technical Reports Server (NTRS)

Martensen, A. L.

1987-01-01

CARE 3 predicts the unreliability of highly reliable reconfigurable fault-tolerant systems that include redundant computers or computer systems. CARE3MENU is a user-friendly interface used to create an input for the CARE 3 program. The CARE3MENU interface has been designed to minimize user input errors. Although a CARE3MENU session may be successfully completed and all parameters may be within specified limits or ranges, the CARE 3 program is not guaranteed to produce meaningful results if the user incorrectly interprets the CARE 3 stochastic model. The CARE3MENU User Guide provides complete information on how to create a CARE 3 model with the interface. The CARE3MENU interface runs under the VAX/VMS operating system.

The ascendance of microphysiological systems to solve the drug testing dilemma.

PubMed

Dehne, Eva-Maria; Hasenberg, Tobias; Marx, Uwe

2017-06-01

The development of drugs is a process obstructed with manifold security and efficacy concerns. Although animal models are still widely used to meet the diligence required, they are regarded as outdated tools with limited predictability. Novel microphysiological systems intend to create systemic models of human biology. Their ability to host 3D organoid constructs in a controlled microenvironment with mechanical and electrophysiological stimuli enables them to create and maintain homeostasis. These platforms are, thus, envisioned to be superior tools for testing and developing substances such as drugs, cosmetics and chemicals. We will present reasons why microphysiological systems are required for the emerging demands, highlight current technological and regulatory obstacles, and depict possible solutions from state-of-the-art platforms from major contributors.

Predicting coronary artery disease using different artificial neural network models.

PubMed

Colak, M Cengiz; Colak, Cemil; Kocatürk, Hasan; Sağiroğlu, Seref; Barutçu, Irfan

2008-08-01

Eight different learning algorithms used for creating artificial neural network (ANN) models and the different ANN models in the prediction of coronary artery disease (CAD) are introduced. This work was carried out as a retrospective case-control study. Overall, 124 consecutive patients who had been diagnosed with CAD by coronary angiography (at least 1 coronary stenosis > 50% in major epicardial arteries) were enrolled in the work. Angiographically, the 113 people (group 2) with normal coronary arteries were taken as control subjects. Multi-layered perceptrons ANN architecture were applied. The ANN models trained with different learning algorithms were performed in 237 records, divided into training (n=171) and testing (n=66) data sets. The performance of prediction was evaluated by sensitivity, specificity and accuracy values based on standard definitions. The results have demonstrated that ANN models trained with eight different learning algorithms are promising because of high (greater than 71%) sensitivity, specificity and accuracy values in the prediction of CAD. Accuracy, sensitivity and specificity values varied between 83.63%-100%, 86.46%-100% and 74.67%-100% for training, respectively. For testing, the values were more than 71% for sensitivity, 76% for specificity and 81% for accuracy. It may be proposed that the use of different learning algorithms other than backpropagation and larger sample sizes can improve the performance of prediction. The proposed ANN models trained with these learning algorithms could be used a promising approach for predicting CAD without the need for invasive diagnostic methods and could help in the prognostic clinical decision.

Multinomial Logistic Regression Predicted Probability Map To Visualize The Influence Of Socio-Economic Factors On Breast Cancer Occurrence in Southern Karnataka

NASA Astrophysics Data System (ADS)

Madhu, B.; Ashok, N. C.; Balasubramanian, S.

2014-11-01

Multinomial logistic regression analysis was used to develop statistical model that can predict the probability of breast cancer in Southern Karnataka using the breast cancer occurrence data during 2007-2011. Independent socio-economic variables describing the breast cancer occurrence like age, education, occupation, parity, type of family, health insurance coverage, residential locality and socioeconomic status of each case was obtained. The models were developed as follows: i) Spatial visualization of the Urban- rural distribution of breast cancer cases that were obtained from the Bharat Hospital and Institute of Oncology. ii) Socio-economic risk factors describing the breast cancer occurrences were complied for each case. These data were then analysed using multinomial logistic regression analysis in a SPSS statistical software and relations between the occurrence of breast cancer across the socio-economic status and the influence of other socio-economic variables were evaluated and multinomial logistic regression models were constructed. iii) the model that best predicted the occurrence of breast cancer were identified. This multivariate logistic regression model has been entered into a geographic information system and maps showing the predicted probability of breast cancer occurrence in Southern Karnataka was created. This study demonstrates that Multinomial logistic regression is a valuable tool for developing models that predict the probability of breast cancer Occurrence in Southern Karnataka.

A Path to an Instructional Science: Data-Generated vs. Postulated Models

ERIC Educational Resources Information Center

Gropper, George L.

2016-01-01

Psychological testing can serve as a prototype on which to base a data-generated approach to instructional design. In "testing batteries" tests are used to predict achievement. In the proposed approach batteries of prescriptions would be used to produce achievement. In creating "test batteries" tests are selected for their…

Predicting Performance on a Firefighter's Ability Test from Fitness Parameters

ERIC Educational Resources Information Center

Michaelides, Marcos A.; Parpa, Koulla M.; Thompson, Jerald; Brown, Barry

2008-01-01

The purpose of this project was to identify the relationships between various fitness parameters such as upper body muscular endurance, upper and lower body strength, flexibility, body composition and performance on an ability test (AT) that included simulated firefighting tasks. A second intent was to create a regression model that would predict…

Prediction of properties and elemental composition of biomass pyrolysis oils by NMR and partial least squares analysis

USDA-ARS?s Scientific Manuscript database

Several partial least squares (PLS) models were created correlating various properties and chemical composition measurements with the 1H and 13C NMR spectra of 73 different of pyrolysis bio-oil samples from various biomass sources (crude and intermediate products), finished oils and small molecule s...

An experimental study on the manufacture and characterization of in-plane fibre-waviness defects in composites

PubMed Central

DiazDelaO, F. A.; Atherton, K.

2018-01-01

A new method has been developed for creating localized in-plane fibre waviness in composite coupons and used to create a large batch of specimens. This method could be used by manufacturers to experimentally explore the effect of fibre waviness on composite structures both directly and indirectly to develop and validate computational models. The specimens were assessed using ultrasound, digital image correlation and a novel inspection technique capable of measuring residual strain fields. To explore how the defect affects the performance of composite structures, the specimens were then loaded to failure. Predictions of remnant strength were made using a simple ultrasound damage metric and a new residual strain-based damage metric. The predictions made using residual strain measurements were found to be substantially more effective at characterizing ultimate strength than ultrasound measurements. This suggests that residual strains have a significant effect on the failure of laminates containing fibre waviness and that these strains could be incorporated into computational models to improve their ability to simulate the defect. PMID:29892446

A fuzzy set preference model for market share analysis

NASA Technical Reports Server (NTRS)

Turksen, I. B.; Willson, Ian A.

1992-01-01

Consumer preference models are widely used in new product design, marketing management, pricing, and market segmentation. The success of new products depends on accurate market share prediction and design decisions based on consumer preferences. The vague linguistic nature of consumer preferences and product attributes, combined with the substantial differences between individuals, creates a formidable challenge to marketing models. The most widely used methodology is conjoint analysis. Conjoint models, as currently implemented, represent linguistic preferences as ratio or interval-scaled numbers, use only numeric product attributes, and require aggregation of individuals for estimation purposes. It is not surprising that these models are costly to implement, are inflexible, and have a predictive validity that is not substantially better than chance. This affects the accuracy of market share estimates. A fuzzy set preference model can easily represent linguistic variables either in consumer preferences or product attributes with minimal measurement requirements (ordinal scales), while still estimating overall preferences suitable for market share prediction. This approach results in flexible individual-level conjoint models which can provide more accurate market share estimates from a smaller number of more meaningful consumer ratings. Fuzzy sets can be incorporated within existing preference model structures, such as a linear combination, using the techniques developed for conjoint analysis and market share estimation. The purpose of this article is to develop and fully test a fuzzy set preference model which can represent linguistic variables in individual-level models implemented in parallel with existing conjoint models. The potential improvements in market share prediction and predictive validity can substantially improve management decisions about what to make (product design), for whom to make it (market segmentation), and how much to make (market share prediction).

The derivation and validation of a simple model for predicting in-hospital mortality of acutely admitted patients to internal medicine wards.

PubMed

Sakhnini, Ali; Saliba, Walid; Schwartz, Naama; Bisharat, Naiel

2017-06-01

Limited information is available about clinical predictors of in-hospital mortality in acute unselected medical admissions. Such information could assist medical decision-making.To develop a clinical model for predicting in-hospital mortality in unselected acute medical admissions and to test the impact of secondary conditions on hospital mortality.This is an analysis of the medical records of patients admitted to internal medicine wards at one university-affiliated hospital. Data obtained from the years 2013 to 2014 were used as a derivation dataset for creating a prediction model, while data from 2015 was used as a validation dataset to test the performance of the model. For each admission, a set of clinical and epidemiological variables was obtained. The main diagnosis at hospitalization was recorded, and all additional or secondary conditions that coexisted at hospital admission or that developed during hospital stay were considered secondary conditions.The derivation and validation datasets included 7268 and 7843 patients, respectively. The in-hospital mortality rate averaged 7.2%. The following variables entered the final model; age, body mass index, mean arterial pressure on admission, prior admission within 3 months, background morbidity of heart failure and active malignancy, and chronic use of statins and antiplatelet agents. The c-statistic (ROC-AUC) of the prediction model was 80.5% without adjustment for main or secondary conditions, 84.5%, with adjustment for the main diagnosis, and 89.5% with adjustment for the main diagnosis and secondary conditions. The accuracy of the predictive model reached 81% on the validation dataset.A prediction model based on clinical data with adjustment for secondary conditions exhibited a high degree of prediction accuracy. We provide a proof of concept that there is an added value for incorporating secondary conditions while predicting probabilities of in-hospital mortality. Further improvement of the model performance and validation in other cohorts are needed to aid hospitalists in predicting health outcomes.

Predicting tumor hypoxia in non-small cell lung cancer by combining CT, FDG PET and dynamic contrast-enhanced CT.

PubMed

Even, Aniek J G; Reymen, Bart; La Fontaine, Matthew D; Das, Marco; Jochems, Arthur; Mottaghy, Felix M; Belderbos, José S A; De Ruysscher, Dirk; Lambin, Philippe; van Elmpt, Wouter

2017-11-01

Most solid tumors contain inadequately oxygenated (i.e., hypoxic) regions, which tend to be more aggressive and treatment resistant. Hypoxia PET allows visualization of hypoxia and may enable treatment adaptation. However, hypoxia PET imaging is expensive, time-consuming and not widely available. We aimed to predict hypoxia levels in non-small cell lung cancer (NSCLC) using more easily available imaging modalities: FDG-PET/CT and dynamic contrast-enhanced CT (DCE-CT). For 34 NSCLC patients, included in two clinical trials, hypoxia HX4-PET/CT, planning FDG-PET/CT and DCE-CT scans were acquired before radiotherapy. Scans were non-rigidly registered to the planning CT. Tumor blood flow (BF) and blood volume (BV) were calculated by kinetic analysis of DCE-CT images. Within the gross tumor volume, independent clusters, i.e., supervoxels, were created based on FDG-PET/CT. For each supervoxel, tumor-to-background ratios (TBR) were calculated (median SUV/aorta SUV mean ) for HX4-PET/CT and supervoxel features (median, SD, entropy) for the other modalities. Two random forest models (cross-validated: 10 folds, five repeats) were trained to predict the hypoxia TBR; one based on CT, FDG, BF and BV, and one with only CT and FDG features. Patients were split in a training (trial NCT01024829) and independent test set (trial NCT01210378). For each patient, predicted, and observed hypoxic volumes (HV) (TBR > 1.2) were compared. Fifteen patients (3291 supervoxels) were used for training and 19 patients (1502 supervoxels) for testing. The model with all features (RMSE training: 0.19 ± 0.01, test: 0.27) outperformed the model with only CT and FDG-PET features (RMSE training: 0.20 ± 0.01, test: 0.29). All tumors of the test set were correctly classified as normoxic or hypoxic (HV > 1 cm 3 ) by the best performing model. We created a data-driven methodology to predict hypoxia levels and hypoxia spatial patterns using CT, FDG-PET and DCE-CT features in NSCLC. The model correctly classifies all tumors, and could therefore, aid tumor hypoxia classification and patient stratification.

Characterization and validation of an in silico toxicology model to predict the mutagenic potential of drug impurities*

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

Valerio, Luis G., E-mail: luis.valerio@fda.hhs.gov; Cross, Kevin P.

Control and minimization of human exposure to potential genotoxic impurities found in drug substances and products is an important part of preclinical safety assessments of new drug products. The FDA's 2008 draft guidance on genotoxic and carcinogenic impurities in drug substances and products allows use of computational quantitative structure–activity relationships (QSAR) to identify structural alerts for known and expected impurities present at levels below qualified thresholds. This study provides the information necessary to establish the practical use of a new in silico toxicology model for predicting Salmonella t. mutagenicity (Ames assay outcome) of drug impurities and other chemicals. We describemore » the model's chemical content and toxicity fingerprint in terms of compound space, molecular and structural toxicophores, and have rigorously tested its predictive power using both cross-validation and external validation experiments, as well as case studies. Consistent with desired regulatory use, the model performs with high sensitivity (81%) and high negative predictivity (81%) based on external validation with 2368 compounds foreign to the model and having known mutagenicity. A database of drug impurities was created from proprietary FDA submissions and the public literature which found significant overlap between the structural features of drug impurities and training set chemicals in the QSAR model. Overall, the model's predictive performance was found to be acceptable for screening drug impurities for Salmonella mutagenicity. -- Highlights: ► We characterize a new in silico model to predict mutagenicity of drug impurities. ► The model predicts Salmonella mutagenicity and will be useful for safety assessment. ► We examine toxicity fingerprints and toxicophores of this Ames assay model. ► We compare these attributes to those found in drug impurities known to FDA/CDER. ► We validate the model and find it has a desired predictive performance.« less

Predicting the mortality from asbestos-related diseases based on the amount of asbestos used and the effects of slate buildings in Korea.

PubMed

Kim, Su-Young; Kim, Young-Chan; Kim, Yongku; Hong, Won-Hwa

2016-01-15

Asbestos has been used since ancient times, owing to its heat-resistant, rot-proof, and insulating qualities, and its usage rapidly increased after the industrial revolution. In Korea, all slates were previously manufactured in a mixture of about 90% cement and 10% chrysotile (white asbestos). This study used a Generalized Poisson regression (GPR) model after creating databases of the mortality from asbestos-related diseases and of the amount of asbestos used in Korea as a means to predict the future mortality of asbestos-related diseases and mesothelioma in Korea. Moreover, to predict the future mortality according to the effects of slate buildings, a comparative analysis based on the result of the GPR model was conducted after creating databases of the amount of asbestos used in Korea and of the amount of asbestos used in making slates. We predicted the mortality from asbestos-related diseases by year, from 2014 to 2036, according to the amount of asbestos used. As a result, it was predicted that a total of 1942 people (maximum, 3476) will die by 2036. Moreover, based on the comparative analysis according to the influence index, it was predicted that a maximum of 555 people will die from asbestos-related diseases by 2031 as a result of the effects of asbestos-containing slate buildings, and the mortality was predicted to peak in 2021, with 53 cases. Although mesothelioma and pulmonary asbestosis were considered as asbestos-related diseases, these are not the only two diseases caused by asbestos. However the results of this study are highly important and relevant, as, for the first time in Korea, the future mortality from asbestos-related diseases was predicted. These findings are expected to contribute greatly to the Korean government's policies related to the compensation for asbestos victims. Copyright © 2015 Elsevier B.V. All rights reserved.

Connecting clinical and actuarial prediction with rule-based methods.

PubMed

Fokkema, Marjolein; Smits, Niels; Kelderman, Henk; Penninx, Brenda W J H

2015-06-01

Meta-analyses comparing the accuracy of clinical versus actuarial prediction have shown actuarial methods to outperform clinical methods, on average. However, actuarial methods are still not widely used in clinical practice, and there has been a call for the development of actuarial prediction methods for clinical practice. We argue that rule-based methods may be more useful than the linear main effect models usually employed in prediction studies, from a data and decision analytic as well as a practical perspective. In addition, decision rules derived with rule-based methods can be represented as fast and frugal trees, which, unlike main effects models, can be used in a sequential fashion, reducing the number of cues that have to be evaluated before making a prediction. We illustrate the usability of rule-based methods by applying RuleFit, an algorithm for deriving decision rules for classification and regression problems, to a dataset on prediction of the course of depressive and anxiety disorders from Penninx et al. (2011). The RuleFit algorithm provided a model consisting of 2 simple decision rules, requiring evaluation of only 2 to 4 cues. Predictive accuracy of the 2-rule model was very similar to that of a logistic regression model incorporating 20 predictor variables, originally applied to the dataset. In addition, the 2-rule model required, on average, evaluation of only 3 cues. Therefore, the RuleFit algorithm appears to be a promising method for creating decision tools that are less time consuming and easier to apply in psychological practice, and with accuracy comparable to traditional actuarial methods. (c) 2015 APA, all rights reserved).

The influence of API concentration on the roller compaction process: modeling and prediction of the post compacted ribbon, granule and tablet properties using multivariate data analysis.

PubMed

Boersen, Nathan; Carvajal, M Teresa; Morris, Kenneth R; Peck, Garnet E; Pinal, Rodolfo

2015-01-01

While previous research has demonstrated roller compaction operating parameters strongly influence the properties of the final product, a greater emphasis might be placed on the raw material attributes of the formulation. There were two main objectives to this study. First, to assess the effects of different process variables on the properties of the obtained ribbons and downstream granules produced from the rolled compacted ribbons. Second, was to establish if models obtained with formulations of one active pharmaceutical ingredient (API) could predict the properties of similar formulations in terms of the excipients used, but with a different API. Tolmetin and acetaminophen, chosen for their different compaction properties, were roller compacted on Fitzpatrick roller compactor using the same formulation. Models created using tolmetin and tested using acetaminophen. The physical properties of the blends, ribbon, granule and tablet were characterized. Multivariate analysis using partial least squares was used to analyze all data. Multivariate models showed that the operating parameters and raw material attributes were essential in the prediction of ribbon porosity and post-milled particle size. The post compacted ribbon and granule attributes also significantly contributed to the prediction of the tablet tensile strength. Models derived using tolmetin could reasonably predict the ribbon porosity of a second API. After further processing, the post-milled ribbon and granules properties, rather than the physical attributes of the formulation were needed to predict downstream tablet properties. An understanding of the percolation threshold of the formulation significantly improved the predictive ability of the models.

A data-driven SVR model for long-term runoff prediction and uncertainty analysis based on the Bayesian framework

NASA Astrophysics Data System (ADS)

Liang, Zhongmin; Li, Yujie; Hu, Yiming; Li, Binquan; Wang, Jun

2017-06-01

Accurate and reliable long-term forecasting plays an important role in water resources management and utilization. In this paper, a hybrid model called SVR-HUP is presented to predict long-term runoff and quantify the prediction uncertainty. The model is created based on three steps. First, appropriate predictors are selected according to the correlations between meteorological factors and runoff. Second, a support vector regression (SVR) model is structured and optimized based on the LibSVM toolbox and a genetic algorithm. Finally, using forecasted and observed runoff, a hydrologic uncertainty processor (HUP) based on a Bayesian framework is used to estimate the posterior probability distribution of the simulated values, and the associated uncertainty of prediction was quantitatively analyzed. Six precision evaluation indexes, including the correlation coefficient (CC), relative root mean square error (RRMSE), relative error (RE), mean absolute percentage error (MAPE), Nash-Sutcliffe efficiency (NSE), and qualification rate (QR), are used to measure the prediction accuracy. As a case study, the proposed approach is applied in the Han River basin, South Central China. Three types of SVR models are established to forecast the monthly, flood season and annual runoff volumes. The results indicate that SVR yields satisfactory accuracy and reliability at all three scales. In addition, the results suggest that the HUP cannot only quantify the uncertainty of prediction based on a confidence interval but also provide a more accurate single value prediction than the initial SVR forecasting result. Thus, the SVR-HUP model provides an alternative method for long-term runoff forecasting.

Usability Prediction & Ranking of SDLC Models Using Fuzzy Hierarchical Usability Model

NASA Astrophysics Data System (ADS)

Gupta, Deepak; Ahlawat, Anil K.; Sagar, Kalpna

2017-06-01

Evaluation of software quality is an important aspect for controlling and managing the software. By such evaluation, improvements in software process can be made. The software quality is significantly dependent on software usability. Many researchers have proposed numbers of usability models. Each model considers a set of usability factors but do not cover all the usability aspects. Practical implementation of these models is still missing, as there is a lack of precise definition of usability. Also, it is very difficult to integrate these models into current software engineering practices. In order to overcome these challenges, this paper aims to define the term `usability' using the proposed hierarchical usability model with its detailed taxonomy. The taxonomy considers generic evaluation criteria for identifying the quality components, which brings together factors, attributes and characteristics defined in various HCI and software models. For the first time, the usability model is also implemented to predict more accurate usability values. The proposed system is named as fuzzy hierarchical usability model that can be easily integrated into the current software engineering practices. In order to validate the work, a dataset of six software development life cycle models is created and employed. These models are ranked according to their predicted usability values. This research also focuses on the detailed comparison of proposed model with the existing usability models.

QoS prediction for web services based on user-trust propagation model

NASA Astrophysics Data System (ADS)

Thinh, Le-Van; Tu, Truong-Dinh

2017-10-01

There is an important online role for Web service providers and users; however, the rapidly growing number of service providers and users, it can create some similar functions among web services. This is an exciting area for research, and researchers seek to to propose solutions for the best service to users. Collaborative filtering (CF) algorithms are widely used in recommendation systems, although these are less effective for cold-start users. Recently, some recommender systems have been developed based on social network models, and the results show that social network models have better performance in terms of CF, especially for cold-start users. However, most social network-based recommendations do not consider the user's mood. This is a hidden source of information, and is very useful in improving prediction efficiency. In this paper, we introduce a new model called User-Trust Propagation (UTP). The model uses a combination of trust and the mood of users to predict the QoS value and matrix factorisation (MF), which is used to train the model. The experimental results show that the proposed model gives better accuracy than other models, especially for the cold-start problem.

Modeling Water-Surface Elevations and Virtual Shorelines for the Colorado River in Grand Canyon, Arizona

USGS Publications Warehouse

Magirl, Christopher S.; Breedlove, Michael J.; Webb, Robert H.; Griffiths, Peter G.

2008-01-01

Using widely-available software intended for modeling rivers, a new one-dimensional hydraulic model was developed for the Colorado River through Grand Canyon from Lees Ferry to Diamond Creek. Solving one-dimensional equations of energy and continuity, the model predicts stage for a known steady-state discharge at specific locations, or cross sections, along the river corridor. This model uses 2,680 cross sections built with high-resolution digital topography of ground locations away from the river flowing at a discharge of 227 m3/s; synthetic bathymetry was created for topography submerged below the 227 m3/s water surface. The synthetic bathymetry was created by adjusting the water depth at each cross section up or down until the model?s predicted water-surface elevation closely matched a known water surface. This approach is unorthodox and offers a technique to construct one-dimensional hydraulic models of bedrock-controlled rivers where bathymetric data have not been collected. An analysis of this modeling approach shows that while effective in enabling a useful model, the synthetic bathymetry can differ from the actual bathymetry. The known water-surface profile was measured using elevation data collected in 2000 and 2002, and the model can simulate discharges up to 5,900 m3/s. In addition to the hydraulic model, GIS-based techniques were used to estimate virtual shorelines and construct inundation maps. The error of the hydraulic model in predicting stage is within 0.4 m for discharges less than 1,300 m3/s. Between 1,300-2,500 m3/s, the model accuracy is about 1.0 m, and for discharges between 2,500-5,900 m3/s, the model accuracy is on the order of 1.5 m. In the absence of large floods on the flow-regulated Colorado River in Grand Canyon, the new hydraulic model and the accompanying inundation maps are a useful resource for researchers interested in water depths, shorelines, and stage-discharge curves for flows within the river corridor with 2002 topographic conditions.

Paint Pavement Marking Performance Prediction Model That Includes the Impacts of Snow Removal Operations

DTIC Science & Technology

2011-03-01

Hypothesized that snow plows wear down mountain road pavement markings. 2007 Craig et al. -Edge lines degrade slower than center/skip lines 2007...retroreflectivity to create the models. They discovered that paint pavement markings last 80% longer on Portland Cement Concrete than Asphalt Concrete at low AADT...retroreflectivity, while yellow markings lost 21%. Lu and Barter attributed the sizable degradation to snow removal, sand application, and studded

Annotated Bibliography: Value of Environmental Protection and Restoration

DTIC Science & Technology

1993-02-01

approach. Ecological Economics, 3, 1-24. Key Words: wetlands, ecotechnology A simulation model is developed to predict the efficiency and economics of an...application of ecotechnology using a created wetland to receive and treat coal mine drainage. The model examines the role of loading rates of iron on...shows that the use of ecotechnology such as wetland trt- "’nt systems can provide low-cost solutions to some expensive pollution problems. Wetland

Virtual reconstruction of glenoid bone defects using a statistical shape model.

PubMed

Plessers, Katrien; Vanden Berghe, Peter; Van Dijck, Christophe; Wirix-Speetjens, Roel; Debeer, Philippe; Jonkers, Ilse; Vander Sloten, Jos

2018-01-01

Description of the native shape of a glenoid helps surgeons to preoperatively plan the position of a shoulder implant. A statistical shape model (SSM) can be used to virtually reconstruct a glenoid bone defect and to predict the inclination, version, and center position of the native glenoid. An SSM-based reconstruction method has already been developed for acetabular bone reconstruction. The goal of this study was to evaluate the SSM-based method for the reconstruction of glenoid bone defects and the prediction of native anatomic parameters. First, an SSM was created on the basis of 66 healthy scapulae. Then, artificial bone defects were created in all scapulae and reconstructed using the SSM-based reconstruction method. For each bone defect, the reconstructed surface was compared with the original surface. Furthermore, the inclination, version, and glenoid center point of the reconstructed surface were compared with the original parameters of each scapula. For small glenoid bone defects, the healthy surface of the glenoid was reconstructed with a root mean square error of 1.2 ± 0.4 mm. Inclination, version, and glenoid center point were predicted with an accuracy of 2.4° ± 2.1°, 2.9° ± 2.2°, and 1.8 ± 0.8 mm, respectively. The SSM-based reconstruction method is able to accurately reconstruct the native glenoid surface and to predict the native anatomic parameters. Based on this outcome, statistical shape modeling can be considered a successful technique for use in the preoperative planning of shoulder arthroplasty. Copyright © 2017 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Elsevier Inc. All rights reserved.

Effectiveness of various isometric exercises at improving bone strength in cortical regions prone to distal tibial stress fractures.

PubMed

Florio, C S

2018-06-01

A computational model was used to compare the local bone strengthening effectiveness of various isometric exercises that may reduce the likelihood of distal tibial stress fractures. The developed model predicts local endosteal and periosteal cortical accretion and resorption based on relative local and global measures of the tibial stress state and its surface variation. Using a multisegment 3-dimensional leg model, tibia shape adaptations due to 33 combinations of hip, knee, and ankle joint angles and the direction of a single or sequential series of generated isometric resultant forces were predicted. The maximum stress at a common fracture-prone region in each optimized geometry was compared under likely stress fracture-inducing midstance jogging conditions. No direct correlations were found between stress reductions over an initially uniform circular hollow cylindrical geometry under these critical design conditions and the exercise-based sets of active muscles, joint angles, or individual muscle force and local stress magnitudes. Additionally, typically favorable increases in cross-sectional geometric measures did not guarantee stress decreases at these locations. Instead, tibial stress distributions under the exercise conditions best predicted strengthening ability. Exercises producing larger anterior distal stresses created optimized tibia shapes that better resisted the high midstance jogging bending stresses. Bent leg configurations generating anteriorly directed or inferiorly directed resultant forces created favorable adaptations. None of the studied loads produced by a straight leg was significantly advantageous. These predictions and the insight gained can provide preliminary guidance in the screening and development of targeted bone strengthening techniques for those susceptible to distal tibial stress fractures. Copyright © 2018 John Wiley & Sons, Ltd.

Study of journal bearing dynamics using 3-dimensional motion picture graphics

NASA Technical Reports Server (NTRS)

Brewe, D. E.; Sosoka, D. J.

1985-01-01

Computer generated motion pictures of three dimensional graphics are being used to analyze journal bearings under dynamically loaded conditions. The motion pictures simultaneously present the motion of the journal and the pressures predicted within the fluid film of the bearing as they evolve in time. The correct prediction of these fluid film pressures can be complicated by the development of cavitation within the fluid. The numerical model that is used predicts the formation of the cavitation bubble and its growth, downstream movement, and subsequent collapse. A complete physical picture is created in the motion picture as the journal traverses through the entire dynamic cycle.

Developing a topographic model to predict the northern hardwood forest type within Carolina northern flying squirrel (Glaucomys sabrinus coloratus) recovery areas of the southern Appalachians

USGS Publications Warehouse

Evans, Andrew; Odom, Richard H.; Resler, Lynn M.; Ford, W. Mark; Prisley, Stephen

2014-01-01

The northern hardwood forest type is an important habitat component for the endangered Carolina northern flying squirrel (CNFS; Glaucomys sabrinus coloratus) for den sites and corridor habitats between boreo-montane conifer patches foraging areas. Our study related terrain data to presence of northern hardwood forest type in the recovery areas of CNFS in the southern Appalachian Mountains of western North Carolina, eastern Tennessee, and southwestern Virginia. We recorded overstory species composition and terrain variables at 338 points, to construct a robust, spatially predictive model. Terrain variables analyzed included elevation, aspect, slope gradient, site curvature, and topographic exposure. We used an information-theoretic approach to assess seven models based on associations noted in existing literature as well as an inclusive global model. Our results indicate that, on a regional scale, elevation, aspect, and topographic exposure index (TEI) are significant predictors of the presence of the northern hardwood forest type in the southern Appalachians. Our elevation + TEI model was the best approximating model (the lowest AICc score) for predicting northern hardwood forest type correctly classifying approximately 78% of our sample points. We then used these data to create region-wide predictive maps of the distribution of the northern hardwood forest type within CNFS recovery areas.

5D Modelling: An Efficient Approach for Creating Spatiotemporal Predictive 3D Maps of Large-Scale Cultural Resources

NASA Astrophysics Data System (ADS)

Doulamis, A.; Doulamis, N.; Ioannidis, C.; Chrysouli, C.; Grammalidis, N.; Dimitropoulos, K.; Potsiou, C.; Stathopoulou, E.-K.; Ioannides, M.

2015-08-01

Outdoor large-scale cultural sites are mostly sensitive to environmental, natural and human made factors, implying an imminent need for a spatio-temporal assessment to identify regions of potential cultural interest (material degradation, structuring, conservation). On the other hand, in Cultural Heritage research quite different actors are involved (archaeologists, curators, conservators, simple users) each of diverse needs. All these statements advocate that a 5D modelling (3D geometry plus time plus levels of details) is ideally required for preservation and assessment of outdoor large scale cultural sites, which is currently implemented as a simple aggregation of 3D digital models at different time and levels of details. The main bottleneck of such an approach is its complexity, making 5D modelling impossible to be validated in real life conditions. In this paper, a cost effective and affordable framework for 5D modelling is proposed based on a spatial-temporal dependent aggregation of 3D digital models, by incorporating a predictive assessment procedure to indicate which regions (surfaces) of an object should be reconstructed at higher levels of details at next time instances and which at lower ones. In this way, dynamic change history maps are created, indicating spatial probabilities of regions needed further 3D modelling at forthcoming instances. Using these maps, predictive assessment can be made, that is, to localize surfaces within the objects where a high accuracy reconstruction process needs to be activated at the forthcoming time instances. The proposed 5D Digital Cultural Heritage Model (5D-DCHM) is implemented using open interoperable standards based on the CityGML framework, which also allows the description of additional semantic metadata information. Visualization aspects are also supported to allow easy manipulation, interaction and representation of the 5D-DCHM geometry and the respective semantic information. The open source 3DCityDB incorporating a PostgreSQL geo-database is used to manage and manipulate 3D data and their semantics.

A Spatial Modeling Approach to Predicting the Secondary Spread of Invasive Species Due to Ballast Water Discharge

PubMed Central

Sieracki, Jennifer L.; Bossenbroek, Jonathan M.; Chadderton, W. Lindsay

2014-01-01

Ballast water in ships is an important contributor to the secondary spread of invasive species in the Laurentian Great Lakes. Here, we use a model previously created to determine the role ballast water management has played in the secondary spread of viral hemorrhagic septicemia virus (VHSV) to identify the future spread of one current and two potential invasive species in the Great Lakes, the Eurasian Ruffe (Gymnocephalus cernuus), killer shrimp (Dikerogammarus villosus), and golden mussel (Limnoperna fortunei), respectively. Model predictions for Eurasian Ruffe have been used to direct surveillance efforts within the Great Lakes and DNA evidence of ruffe presence was recently reported from one of three high risk port localities identified by our model. Predictions made for killer shrimp and golden mussel suggest that these two species have the potential to become rapidly widespread if introduced to the Great Lakes, reinforcing the need for proactive ballast water management. The model used here is flexible enough to be applied to any species capable of being spread by ballast water in marine or freshwater ecosystems. PMID:25470822

Combining inferred regulatory and reconstructed metabolic networks enhances phenotype prediction in yeast.

PubMed

Wang, Zhuo; Danziger, Samuel A; Heavner, Benjamin D; Ma, Shuyi; Smith, Jennifer J; Li, Song; Herricks, Thurston; Simeonidis, Evangelos; Baliga, Nitin S; Aitchison, John D; Price, Nathan D

2017-05-01

Gene regulatory and metabolic network models have been used successfully in many organisms, but inherent differences between them make networks difficult to integrate. Probabilistic Regulation Of Metabolism (PROM) provides a partial solution, but it does not incorporate network inference and underperforms in eukaryotes. We present an Integrated Deduced And Metabolism (IDREAM) method that combines statistically inferred Environment and Gene Regulatory Influence Network (EGRIN) models with the PROM framework to create enhanced metabolic-regulatory network models. We used IDREAM to predict phenotypes and genetic interactions between transcription factors and genes encoding metabolic activities in the eukaryote, Saccharomyces cerevisiae. IDREAM models contain many fewer interactions than PROM and yet produce significantly more accurate growth predictions. IDREAM consistently outperformed PROM using any of three popular yeast metabolic models and across three experimental growth conditions. Importantly, IDREAM's enhanced accuracy makes it possible to identify subtle synthetic growth defects. With experimental validation, these novel genetic interactions involving the pyruvate dehydrogenase complex suggested a new role for fatty acid-responsive factor Oaf1 in regulating acetyl-CoA production in glucose grown cells.

Rank Order Entropy: why one metric is not enough

PubMed Central

McLellan, Margaret R.; Ryan, M. Dominic; Breneman, Curt M.

2011-01-01

The use of Quantitative Structure-Activity Relationship models to address problems in drug discovery has a mixed history, generally resulting from the mis-application of QSAR models that were either poorly constructed or used outside of their domains of applicability. This situation has motivated the development of a variety of model performance metrics (r2, PRESS r2, F-tests, etc) designed to increase user confidence in the validity of QSAR predictions. In a typical workflow scenario, QSAR models are created and validated on training sets of molecules using metrics such as Leave-One-Out or many-fold cross-validation methods that attempt to assess their internal consistency. However, few current validation methods are designed to directly address the stability of QSAR predictions in response to changes in the information content of the training set. Since the main purpose of QSAR is to quickly and accurately estimate a property of interest for an untested set of molecules, it makes sense to have a means at hand to correctly set user expectations of model performance. In fact, the numerical value of a molecular prediction is often less important to the end user than knowing the rank order of that set of molecules according to their predicted endpoint values. Consequently, a means for characterizing the stability of predicted rank order is an important component of predictive QSAR. Unfortunately, none of the many validation metrics currently available directly measure the stability of rank order prediction, making the development of an additional metric that can quantify model stability a high priority. To address this need, this work examines the stabilities of QSAR rank order models created from representative data sets, descriptor sets, and modeling methods that were then assessed using Kendall Tau as a rank order metric, upon which the Shannon Entropy was evaluated as a means of quantifying rank-order stability. Random removal of data from the training set, also known as Data Truncation Analysis (DTA), was used as a means for systematically reducing the information content of each training set while examining both rank order performance and rank order stability in the face of training set data loss. The premise for DTA ROE model evaluation is that the response of a model to incremental loss of training information will be indicative of the quality and sufficiency of its training set, learning method, and descriptor types to cover a particular domain of applicability. This process is termed a “rank order entropy” evaluation, or ROE. By analogy with information theory, an unstable rank order model displays a high level of implicit entropy, while a QSAR rank order model which remains nearly unchanged during training set reductions would show low entropy. In this work, the ROE metric was applied to 71 data sets of different sizes, and was found to reveal more information about the behavior of the models than traditional metrics alone. Stable, or consistently performing models, did not necessarily predict rank order well. Models that performed well in rank order did not necessarily perform well in traditional metrics. In the end, it was shown that ROE metrics suggested that some QSAR models that are typically used should be discarded. ROE evaluation helps to discern which combinations of data set, descriptor set, and modeling methods lead to usable models in prioritization schemes, and provides confidence in the use of a particular model within a specific domain of applicability. PMID:21875058

Design of artificial neural networks using a genetic algorithm to predict collection efficiency in venturi scrubbers.

PubMed

Taheri, Mahboobeh; Mohebbi, Ali

2008-08-30

In this study, a new approach for the auto-design of neural networks, based on a genetic algorithm (GA), has been used to predict collection efficiency in venturi scrubbers. The experimental input data, including particle diameter, throat gas velocity, liquid to gas flow rate ratio, throat hydraulic diameter, pressure drop across the venturi scrubber and collection efficiency as an output, have been used to create a GA-artificial neural network (ANN) model. The testing results from the model are in good agreement with the experimental data. Comparison of the results of the GA optimized ANN model with the results from the trial-and-error calibrated ANN model indicates that the GA-ANN model is more efficient. Finally, the effects of operating parameters such as liquid to gas flow rate ratio, throat gas velocity, and particle diameter on collection efficiency were determined.

Parameterized reduced-order models using hyper-dual numbers.

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

Fike, Jeffrey A.; Brake, Matthew Robert

2013-10-01

The goal of most computational simulations is to accurately predict the behavior of a real, physical system. Accurate predictions often require very computationally expensive analyses and so reduced order models (ROMs) are commonly used. ROMs aim to reduce the computational cost of the simulations while still providing accurate results by including all of the salient physics of the real system in the ROM. However, real, physical systems often deviate from the idealized models used in simulations due to variations in manufacturing or other factors. One approach to this issue is to create a parameterized model in order to characterize themore » effect of perturbations from the nominal model on the behavior of the system. This report presents a methodology for developing parameterized ROMs, which is based on Craig-Bampton component mode synthesis and the use of hyper-dual numbers to calculate the derivatives necessary for the parameterization.« less

A transient model of the RL10A-3-3A rocket engine

NASA Technical Reports Server (NTRS)

Binder, Michael P.

1995-01-01

RL10A-3-3A rocket engines have served as the main propulsion system for Centaur upper stage vehicles since the early 1980's. This hydrogen/oxygen expander cycle engine continues to play a major role in the American launch industry. The Space Propulsion Technology Division at the NASA Lewis Research Center has created a computer model of the RL10 engine, based on detailed component analyses and available test data. This RL10 engine model can predict the performance of the engine over a wide range of operating conditions. The model may also be used to predict the effects of any proposed design changes and anticipated failure scenarios. In this paper, the results of the component analyses are discussed. Simulation results from the new system model are compared with engine test and flight data, including the start and shut-down transient characteristics.

Optimizing Chemotherapy Dose and Schedule by Norton-Simon Mathematical Modeling

PubMed Central

Traina, Tiffany A.; Dugan, Ute; Higgins, Brian; Kolinsky, Kenneth; Theodoulou, Maria; Hudis, Clifford A.; Norton, Larry

2011-01-01

Background To hasten and improve anticancer drug development, we created a novel approach to generating and analyzing preclinical dose-scheduling data so as to optimize benefit-to-toxicity ratios. Methods We applied mathematical methods based upon Norton-Simon growth kinetic modeling to tumor-volume data from breast cancer xenografts treated with capecitabine (Xeloda®, Roche) at the conventional schedule of 14 days of treatment followed by a 7-day rest (14 - 7). Results The model predicted that 7 days of treatment followed by a 7-day rest (7 - 7) would be superior. Subsequent preclinical studies demonstrated that this biweekly capecitabine schedule allowed for safe delivery of higher daily doses, improved tumor response, and prolonged animal survival. Conclusions We demonstrated that the application of Norton-Simon modeling to the design and analysis of preclinical data predicts an improved capecitabine dosing schedule in xenograft models. This method warrants further investigation and application in clinical drug development. PMID:20519801

Coupling continuous damage and debris fragmentation for energy absorption prediction by cfrp structures during crushing

NASA Astrophysics Data System (ADS)

Espinosa, Christine; Lachaud, Frédéric; Limido, Jérome; Lacome, Jean-Luc; Bisson, Antoine; Charlotte, Miguel

2015-05-01

Energy absorption during crushing is evaluated using a thermodynamic based continuum damage model inspired from the Matzenmiller-Lubliner-Taylors model. It was found that for crash-worthiness applications, it is necessary to couple the progressive ruin of the material to a representation of the matter openings and debris generation. Element kill technique (erosion) and/or cohesive elements are efficient but not predictive. A technique switching finite elements into discrete particles at rupture is used to create debris and accumulated mater during the crushing of the structure. Switching criteria are evaluated using the contribution of the different ruin modes in the damage evolution, energy absorption, and reaction force generation.

Use of a Machine-learning Method for Predicting Highly Cited Articles Within General Radiology Journals.

PubMed

Rosenkrantz, Andrew B; Doshi, Ankur M; Ginocchio, Luke A; Aphinyanaphongs, Yindalon

2016-12-01

This study aimed to assess the performance of a text classification machine-learning model in predicting highly cited articles within the recent radiological literature and to identify the model's most influential article features. We downloaded from PubMed the title, abstract, and medical subject heading terms for 10,065 articles published in 25 general radiology journals in 2012 and 2013. Three machine-learning models were applied to predict the top 10% of included articles in terms of the number of citations to the article in 2014 (reflecting the 2-year time window in conventional impact factor calculations). The model having the highest area under the curve was selected to derive a list of article features (words) predicting high citation volume, which was iteratively reduced to identify the smallest possible core feature list maintaining predictive power. Overall themes were qualitatively assigned to the core features. The regularized logistic regression (Bayesian binary regression) model had highest performance, achieving an area under the curve of 0.814 in predicting articles in the top 10% of citation volume. We reduced the initial 14,083 features to 210 features that maintain predictivity. These features corresponded with topics relating to various imaging techniques (eg, diffusion-weighted magnetic resonance imaging, hyperpolarized magnetic resonance imaging, dual-energy computed tomography, computed tomography reconstruction algorithms, tomosynthesis, elastography, and computer-aided diagnosis), particular pathologies (prostate cancer; thyroid nodules; hepatic adenoma, hepatocellular carcinoma, non-alcoholic fatty liver disease), and other topics (radiation dose, electroporation, education, general oncology, gadolinium, statistics). Machine learning can be successfully applied to create specific feature-based models for predicting articles likely to achieve high influence within the radiological literature. Copyright © 2016 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Effects of Uncertainties in Hydrological Modelling. A Case Study of a Mountainous Catchment in Southern Norway

NASA Astrophysics Data System (ADS)

Engeland, Kolbjorn; Steinsland, Ingelin

2016-04-01

The aim of this study is to investigate how the inclusion of uncertainties in inputs and observed streamflow influence the parameter estimation, streamflow predictions and model evaluation. In particular we wanted to answer the following research questions: • What is the effect of including a random error in the precipitation and temperature inputs? • What is the effect of decreased information about precipitation by excluding the nearest precipitation station? • What is the effect of the uncertainty in streamflow observations? • What is the effect of reduced information about the true streamflow by using a rating curve where the measurement of the highest and lowest streamflow is excluded when estimating the rating curve? To answer these questions, we designed a set of calibration experiments and evaluation strategies. We used the elevation distributed HBV model operating on daily time steps combined with a Bayesian formulation and the MCMC routine Dream for parameter inference. The uncertainties in inputs was represented by creating ensembles of precipitation and temperature. The precipitation ensemble were created using a meta-gaussian random field approach. The temperature ensembles were created using a 3D Bayesian kriging with random sampling of the temperature laps rate. The streamflow ensembles were generated by a Bayesian multi-segment rating curve model. Precipitation and temperatures were randomly sampled for every day, whereas the streamflow ensembles were generated from rating curve ensembles, and the same rating curve was always used for the whole time series in a calibration or evaluation run. We chose a catchment with a meteorological station measuring precipitation and temperature, and a rating curve of relatively high quality. This allowed us to investigate and further test the effect of having less information on precipitation and streamflow during model calibration, predictions and evaluation. The results showed that including uncertainty in the precipitation and temperature input has a negligible effect on the posterior distribution of parameters and for the Nash-Sutcliffe (NS) efficiency for the predicted flows, while the reliability and the continuous rank probability score (CRPS) improves. Reduced information in precipitation input resulted in a and a shift in the water balance parameter Pcorr, a model producing smoother streamflow predictions giving poorer NS and CRPS, but higher reliability. The effect of calibrating the hydrological model using wrong rating curves is mainly seen as variability in the water balance parameter Pcorr. When evaluating predictions obtained using a wrong rating curve, the evaluation scores varies depending on the true rating curve. Generally, the best evaluation scores were not achieved for the rating curve used for calibration, but for a rating curves giving low variance in streamflow observations. Reduced information in streamflow influenced the water balance parameter Pcorr, and increased the spread in evaluation scores giving both better and worse scores. This case study shows that estimating the water balance is challenging since both precipitation inputs and streamflow observations have pronounced systematic component in their uncertainties.

Predicting in-hospital mortality after redo cardiac operations: development of a preoperative scorecard.

PubMed

Launcelott, Sebastian; Ouzounian, Maral; Buth, Karen J; Légaré, Jean-Francois

2012-09-01

The present study generated a risk model and an easy-to-use scorecard for the preoperative prediction of in-hospital mortality for patients undergoing redo cardiac operations. All patients who underwent redo cardiac operations in which the initial and subsequent procedures were performed through a median sternotomy were included. A logistic regression model was created to identify independent preoperative predictors of in-hospital mortality. The results were then used to create a scorecard predicting operative risk. A total of 1,521 patients underwent redo procedures between 1995 and 2010 at a single institution. Coronary bypass procedures were the most common previous (58%) or planned operations (54%). The unadjusted in-hospital mortality for all redo cases was higher than for first-time procedures (9.7% vs. 3.4%; p<0.001). Independent predictors of in-hospital mortality were a composite urgency variable (odds ratio [OR], 3.47), older age (70-79 years, OR, 2.74; ≥80 years, OR, 3.32), more than 2 previous sternotomies (OR, 2.69), current procedure other than isolated coronary or valve operation (OR, 2.64), preoperative renal failure (OR, 1.89), and peripheral vascular disease (PVD) (OR, 1.55); all p<0.05. A scorecard was generated using these independent predictors, stratifying patients undergoing redo cardiac operations into 6 risk categories of in-hospital mortality ranging from <5% risk to >40%. Reoperation represents a significant proportion of modern cardiac surgical procedures and is often associated with significantly higher mortality than first-time operations. We created an easy-to-use scorecard to assist clinicians in estimating operative mortality to ensure optimal decision making in the care of patients facing redo cardiac operations. Copyright © 2012 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

Human Splice-Site Prediction with Deep Neural Networks.

PubMed

Naito, Tatsuhiko

2018-04-18

Accurate splice-site prediction is essential to delineate gene structures from sequence data. Several computational techniques have been applied to create a system to predict canonical splice sites. For classification tasks, deep neural networks (DNNs) have achieved record-breaking results and often outperformed other supervised learning techniques. In this study, a new method of splice-site prediction using DNNs was proposed. The proposed system receives an input sequence data and returns an answer as to whether it is splice site. The length of input is 140 nucleotides, with the consensus sequence (i.e., "GT" and "AG" for the donor and acceptor sites, respectively) in the middle. Each input sequence model is applied to the pretrained DNN model that determines the probability that an input is a splice site. The model consists of convolutional layers and bidirectional long short-term memory network layers. The pretraining and validation were conducted using the data set tested in previously reported methods. The performance evaluation results showed that the proposed method can outperform the previous methods. In addition, the pattern learned by the DNNs was visualized as position frequency matrices (PFMs). Some of PFMs were very similar to the consensus sequence. The trained DNN model and the brief source code for the prediction system are uploaded. Further improvement will be achieved following the further development of DNNs.

Online EEG-Based Workload Adaptation of an Arithmetic Learning Environment.

PubMed

Walter, Carina; Rosenstiel, Wolfgang; Bogdan, Martin; Gerjets, Peter; Spüler, Martin

2017-01-01

In this paper, we demonstrate a closed-loop EEG-based learning environment, that adapts instructional learning material online, to improve learning success in students during arithmetic learning. The amount of cognitive workload during learning is crucial for successful learning and should be held in the optimal range for each learner. Based on EEG data from 10 subjects, we created a prediction model that estimates the learner's workload to obtain an unobtrusive workload measure. Furthermore, we developed an interactive learning environment that uses the prediction model to estimate the learner's workload online based on the EEG data and adapt the difficulty of the learning material to keep the learner's workload in an optimal range. The EEG-based learning environment was used by 13 subjects to learn arithmetic addition in the octal number system, leading to a significant learning effect. The results suggest that it is feasible to use EEG as an unobtrusive measure of cognitive workload to adapt the learning content. Further it demonstrates that a promptly workload prediction is possible using a generalized prediction model without the need for a user-specific calibration.

Longitudinal Study-Based Dementia Prediction for Public Health

PubMed Central

Kim, HeeChel; Chun, Hong-Woo; Kim, Seonho; Coh, Byoung-Youl; Kwon, Oh-Jin; Moon, Yeong-Ho

2017-01-01

The issue of public health in Korea has attracted significant attention given the aging of the country’s population, which has created many types of social problems. The approach proposed in this article aims to address dementia, one of the most significant symptoms of aging and a public health care issue in Korea. The Korean National Health Insurance Service Senior Cohort Database contains personal medical data of every citizen in Korea. There are many different medical history patterns between individuals with dementia and normal controls. The approach used in this study involved examination of personal medical history features from personal disease history, sociodemographic data, and personal health examinations to develop a prediction model. The prediction model used a support-vector machine learning technique to perform a 10-fold cross-validation analysis. The experimental results demonstrated promising performance (80.9% F-measure). The proposed approach supported the significant influence of personal medical history features during an optimal observation period. It is anticipated that a biomedical “big data”-based disease prediction model may assist the diagnosis of any disease more correctly. PMID:28867810

Generalized Polynomial Chaos Based Uncertainty Quantification for Planning MRgLITT Procedures

PubMed Central

Fahrenholtz, S.; Stafford, R. J.; Maier, F.; Hazle, J. D.; Fuentes, D.

2014-01-01

Purpose A generalized polynomial chaos (gPC) method is used to incorporate constitutive parameter uncertainties within the Pennes representation of bioheat transfer phenomena. The stochastic temperature predictions of the mathematical model are critically evaluated against MR thermometry data for planning MR-guided Laser Induced Thermal Therapies (MRgLITT). Methods Pennes bioheat transfer model coupled with a diffusion theory approximation of laser tissue interaction was implemented as the underlying deterministic kernel. A probabilistic sensitivity study was used to identify parameters that provide the most variance in temperature output. Confidence intervals of the temperature predictions are compared to MR temperature imaging (MRTI) obtained during phantom and in vivo canine (n=4) MRgLITT experiments. The gPC predictions were quantitatively compared to MRTI data using probabilistic linear and temporal profiles as well as 2-D 60 °C isotherms. Results Within the range of physically meaningful constitutive values relevant to the ablative temperature regime of MRgLITT, the sensitivity study indicated that the optical parameters, particularly the anisotropy factor, created the most variance in the stochastic model's output temperature prediction. Further, within the statistical sense considered, a nonlinear model of the temperature and damage dependent perfusion, absorption, and scattering is captured within the confidence intervals of the linear gPC method. Multivariate stochastic model predictions using parameters with the dominant sensitivities show good agreement with experimental MRTI data. Conclusions Given parameter uncertainties and mathematical modeling approximations of the Pennes bioheat model, the statistical framework demonstrates conservative estimates of the therapeutic heating and has potential for use as a computational prediction tool for thermal therapy planning. PMID:23692295

In-Class Robot Flyby of an Endoplanet

NASA Astrophysics Data System (ADS)

Chadwick, A. J.; Capaldi, T.; Aurnou, J. M.

2013-12-01

For our Introduction to Computing class, we have developed a miniature robotic spacecraft mission that performs a flyby of an in-class 'endoplanet.' Our constructed endoplanet contains an internal dipole magnet, tilted with a dip angle that is unknown a priori. The spacecraft analog is a remotely controlled LEGO MINDSTORMS robot programmed using LabVIEW. Students acquire magnetic field data via a first spacecraft flyby past the endoplanet. This dataset is then imported into MATLAB, and is inverted to create a model of the magnet's orientation and dipole moment. Students use their models to predict the magnetic field profile along a different flyby path. They then test the accuracy of their models, comparing their predictions against the data acquired from this secondary flyby. We will be demonstrating this device at our poster in the Moscone Center.

Creating and parameterizing patient-specific deep brain stimulation pathway-activation models using the hyperdirect pathway as an example

PubMed Central

Gunalan, Kabilar; Chaturvedi, Ashutosh; Howell, Bryan; Duchin, Yuval; Lempka, Scott F.; Patriat, Remi; Sapiro, Guillermo; Harel, Noam; McIntyre, Cameron C.

2017-01-01

Background Deep brain stimulation (DBS) is an established clinical therapy and computational models have played an important role in advancing the technology. Patient-specific DBS models are now common tools in both academic and industrial research, as well as clinical software systems. However, the exact methodology for creating patient-specific DBS models can vary substantially and important technical details are often missing from published reports. Objective Provide a detailed description of the assembly workflow and parameterization of a patient-specific DBS pathway-activation model (PAM) and predict the response of the hyperdirect pathway to clinical stimulation. Methods Integration of multiple software tools (e.g. COMSOL, MATLAB, FSL, NEURON, Python) enables the creation and visualization of a DBS PAM. An example DBS PAM was developed using 7T magnetic resonance imaging data from a single unilaterally implanted patient with Parkinson’s disease (PD). This detailed description implements our best computational practices and most elaborate parameterization steps, as defined from over a decade of technical evolution. Results Pathway recruitment curves and strength-duration relationships highlight the non-linear response of axons to changes in the DBS parameter settings. Conclusion Parameterization of patient-specific DBS models can be highly detailed and constrained, thereby providing confidence in the simulation predictions, but at the expense of time demanding technical implementation steps. DBS PAMs represent new tools for investigating possible correlations between brain pathway activation patterns and clinical symptom modulation. PMID:28441410

Comparison of different models for predicting soil bulk density. Case study - Slovakian agricultural soils

NASA Astrophysics Data System (ADS)

Makovníková, Jarmila; Širáň, Miloš; Houšková, Beata; Pálka, Boris; Jones, Arwyn

2017-10-01

Soil bulk density is one of the main direct indicators of soil health, and is an important aspect of models for determining agroecosystem services potential. By way of applying multi-regression methods, we have created a distributed prediction of soil bulk density used subsequently for topsoil carbon stock estimation. The soil data used for this study were from the Slovakian partial monitoring system-soil database. In our work, two models of soil bulk density in an equilibrium state, with different combinations of input parameters (soil particle size distribution and soil organic carbon content in %), have been created, and subsequently validated using a data set from 15 principal sampling sites of Slovakian partial monitoring system-soil, that were different from those used to generate the bulk density equations. We have made a comparison of measured bulk density data and data calculated by the pedotransfer equations against soil bulk density calculated according to equations recommended by Joint Research Centre Sustainable Resources for Europe. The differences between measured soil bulk density and the model values vary from -0.144 to 0.135 g cm-3 in the verification data set. Furthermore, all models based on pedotransfer functions give moderately lower values. The soil bulk density model was then applied to generate a first approximation of soil bulk density map for Slovakia using texture information from 17 523 sampling sites, and was subsequently utilised for topsoil organic carbon estimation.

Validation of attenuation models for ground motion applications in central and eastern North America

DOE PAGES

Pasyanos, Michael E.

2015-11-01

Recently developed attenuation models are incorporated into standard one-dimensional (1-D) ground motion prediction equations (GMPEs), effectively making them two-dimensional (2-D) and eliminating the need to create different GMPEs for an increasing number of sub-regions. The model is tested against a data set of over 10,000 recordings from 81 earthquakes in North America. The use of attenuation models in GMPEs improves our ability to fit observed ground motions and should be incorporated into future national hazard maps. The improvement is most significant at higher frequencies and longer distances which have a greater number of wave cycles. This has implications for themore » rare high-magnitude earthquakes, which produce potentially damaging ground motions over wide areas, and drive the seismic hazards. Furthermore, the attenuation models can be created using weak ground motions, they could be developed for regions of low seismicity where empirical recordings of ground motions are uncommon and do not span the full range of magnitudes and distances.« less

An articulated predictive model for fluid-free artificial basilar membrane as broadband frequency sensor

NASA Astrophysics Data System (ADS)

Ahmed, Riaz; Banerjee, Sourav

2018-02-01

In this article, an extremely versatile predictive model for a newly developed Basilar meta-Membrane (BM2) sensors is reported with variable engineering parameters that contribute to it's frequency selection capabilities. The predictive model reported herein is for advancement over existing method by incorporating versatile and nonhomogeneous (e.g. functionally graded) model parameters that could not only exploit the possibilities of creating complex combinations of broadband frequency sensors but also explain the unique unexplained physical phenomenon that prevails in BM2, e.g. tailgating waves. In recent years, few notable attempts were made to fabricate the artificial basilar membrane, mimicking the mechanics of the human cochlea within a very short range of frequencies. To explain the operation of these sensors a few models were proposed. But, we fundamentally argue the "fabrication to explanation" approach and proposed the model driven predictive design process for the design any (BM2) as broadband sensors. Inspired by the physics of basilar membrane, frequency domain predictive model is proposed where both the material and geometrical parameters can be arbitrarily varied. Broadband frequency is applicable in many fields of science, engineering and technology, such as, sensors for chemical, biological and acoustic applications. With the proposed model, which is three times faster than its FEM counterpart, it is possible to alter the attributes of the selected length of the designed sensor using complex combinations of model parameters, based on target frequency applications. Finally, the tailgating wave peaks in the artificial basilar membranes that prevails in the previously reported experimental studies are also explained using the proposed model.

Verilog-A Device Models for Cryogenic Temperature Operation of Bulk Silicon CMOS Devices

NASA Technical Reports Server (NTRS)

Akturk, Akin; Potbhare, Siddharth; Goldsman, Neil; Holloway, Michael

2012-01-01

Verilog-A based cryogenic bulk CMOS (complementary metal oxide semiconductor) compact models are built for state-of-the-art silicon CMOS processes. These models accurately predict device operation at cryogenic temperatures down to 4 K. The models are compatible with commercial circuit simulators. The models extend the standard BSIM4 [Berkeley Short-channel IGFET (insulated-gate field-effect transistor ) Model] type compact models by re-parameterizing existing equations, as well as adding new equations that capture the physics of device operation at cryogenic temperatures. These models will allow circuit designers to create optimized, reliable, and robust circuits operating at cryogenic temperatures.

How Obstacles Perturb Population Fronts and Alter Their Genetic Structure.

PubMed

Möbius, Wolfram; Murray, Andrew W; Nelson, David R

2015-12-01

As populations spread into new territory, environmental heterogeneities can shape the population front and genetic composition. We focus here on the effects of an important building block of heterogeneous environments, isolated obstacles. With a combination of experiments, theory, and simulation, we show how isolated obstacles both create long-lived distortions of the front shape and amplify the effect of genetic drift. A system of bacteriophage T7 spreading on a spatially heterogeneous Escherichia coli lawn serves as an experimental model system to study population expansions. Using an inkjet printer, we create well-defined replicates of the lawn and quantitatively study the population expansion of phage T7. The transient perturbations of the population front found in the experiments are well described by a model in which the front moves with constant speed. Independent of the precise details of the expansion, we show that obstacles create a kink in the front that persists over large distances and is insensitive to the details of the obstacle's shape. The small deviations between experimental findings and the predictions of the constant speed model can be understood with a more general reaction-diffusion model, which reduces to the constant speed model when the obstacle size is large compared to the front width. Using this framework, we demonstrate that frontier genotypes just grazing the side of an isolated obstacle increase in abundance, a phenomenon we call 'geometry-enhanced genetic drift', complementary to the founder effect associated with spatial bottlenecks. Bacterial range expansions around nutrient-poor barriers and stochastic simulations confirm this prediction. The effect of the obstacle on the genealogy of individuals at the front is characterized by simulations and rationalized using the constant speed model. Lastly, we consider the effect of two obstacles on front shape and genetic composition of the population illuminating the effects expected from complex environments with many obstacles.

How Obstacles Perturb Population Fronts and Alter Their Genetic Structure

PubMed Central

Möbius, Wolfram; Murray, Andrew W.; Nelson, David R.

2015-01-01

As populations spread into new territory, environmental heterogeneities can shape the population front and genetic composition. We focus here on the effects of an important building block of heterogeneous environments, isolated obstacles. With a combination of experiments, theory, and simulation, we show how isolated obstacles both create long-lived distortions of the front shape and amplify the effect of genetic drift. A system of bacteriophage T7 spreading on a spatially heterogeneous Escherichia coli lawn serves as an experimental model system to study population expansions. Using an inkjet printer, we create well-defined replicates of the lawn and quantitatively study the population expansion of phage T7. The transient perturbations of the population front found in the experiments are well described by a model in which the front moves with constant speed. Independent of the precise details of the expansion, we show that obstacles create a kink in the front that persists over large distances and is insensitive to the details of the obstacle’s shape. The small deviations between experimental findings and the predictions of the constant speed model can be understood with a more general reaction-diffusion model, which reduces to the constant speed model when the obstacle size is large compared to the front width. Using this framework, we demonstrate that frontier genotypes just grazing the side of an isolated obstacle increase in abundance, a phenomenon we call ‘geometry-enhanced genetic drift’, complementary to the founder effect associated with spatial bottlenecks. Bacterial range expansions around nutrient-poor barriers and stochastic simulations confirm this prediction. The effect of the obstacle on the genealogy of individuals at the front is characterized by simulations and rationalized using the constant speed model. Lastly, we consider the effect of two obstacles on front shape and genetic composition of the population illuminating the effects expected from complex environments with many obstacles. PMID:26696601

PP087. Multicenter external validation and recalibration of a model for preconceptional prediction of recurrent early-onset preeclampsia.

PubMed

van Kuijk, Sander; Delahaije, Denise; Dirksen, Carmen; Scheepers, Hubertina C J; Spaanderman, Marc; Ganzevoort, W; Duvekot, Hans; Oudijk, M A; van Pampus, M G; Dadelszen, Peter von; Peeters, Louis L; Smiths, Luc

2013-04-01

In an earlier paper we reported on the development of a model aimed at the prediction of preeclampsia recurrence, based on variables obtained before the next pregnancy (fasting glucose, BMI, previous birth of a small-for-gestational-age infant, duration of the previous pregnancy, and the presence of hypertension). To externally validate and recalibrate the prediction model for the risk of recurrence of early-onset preeclampsia. We collected data about course and outcome of the next ongoing pregnancy in 229 women with a history of early-onset preeclampsia. Recurrence was defined as preeclampsia requiring delivery before 34 weeks. We computed risk of recurrence and assessed model performance. In addition, we constructed a table comparing sensitivity, specificity, and predictive values for different suggested risk-thresholds. Early-onset preeclampsia recurred in 6.6% of women. The model systematically underestimated recurrence risk. The model's discriminative ability was modest, the area under the receiver operating characteristic curve was 58.9% (95% CI: 45.1 - 72.7). Using relevant risk-thresholds, the model created groups that were only moderately different in terms of their average risk of recurrent preeclampsia (Table 1). Compared to an AUC of 65% in the development cohort, the discriminate ability of the model was diminished. It had inadequate performance to classify women into clinically relevant risk groups. Copyright © 2013. Published by Elsevier B.V.

Artificial neural networks applied to flow prediction scenarios in Tomebamba River - Paute watershed, for flood and water quality control and management at City of Cuenca Ecuador

NASA Astrophysics Data System (ADS)

Cisneros, Felipe; Veintimilla, Jaime

2013-04-01

The main aim of this research is to create a model of Artificial Neural Networks (ANN) that allows predicting the flow in Tomebamba River both, at real time and in a certain day of year. As inputs we are using information of rainfall and flow of the stations along of the river. This information is organized in scenarios and each scenario is prepared to a specific area. The information is acquired from the hydrological stations placed in the watershed using an electronic system developed at real time and it supports any kind or brands of this type of sensors. The prediction works very good three days in advance This research includes two ANN models: Back propagation and a hybrid model between back propagation and OWO-HWO. These last two models have been tested in a preliminary research. To validate the results we are using some error indicators such as: MSE, RMSE, EF, CD and BIAS. The results of this research reached high levels of reliability and the level of error are minimal. These predictions are useful for flood and water quality control and management at City of Cuenca Ecuador

Prediction of mechanical and wear properties of 6026 aluminum alloy waste to be used in prosthetics limbs

NASA Astrophysics Data System (ADS)

Arbilei, Marwan N.

2018-05-01

This paper aimed to recycle high power electrical wires west in prosthetics limbs manufacturing. The effect of grain size on mechanical properties (Hardness and Tensile Strength), and wear resistance of commercial 6026 T9 Aluminum alloys that used in electrical industry have been modeled to be predicted. Six sets of samples were prepared with different annealing heat treatment parameters, (300,350 and 400)°C with (1 and 2) hours. Each treatment gained different grain sizes (23-71) μm and evenly HV (61-169) values. The grain size that produced from heat treatments was ranged from. Tensile properties regarding HV have been reviewed and all data haven collected to create a mathematical model showing the relation between Tensile strength and Hardness. The Sliding wear tests applied with (3 and 8) N with five periods (20-100) minutes. Multiple regression model prepared for predicting the values of weight loss for wear process. The model was tested and validated for the properties. The main purpose of this research is to provide an effective and accurate way to predict weight loose rate in wear process.

Deriving the polarization behavior of many-layer mirror coatings

NASA Astrophysics Data System (ADS)

White, Amanda J.; Harrington, David M.; Sueoka, Stacey R.

2018-06-01

End-to-end models of astronomical instrument performance are becoming commonplace to demonstrate feasibility and guarantee performance at large observatories. Astronomical techniques like adaptive optics and high contrast imaging have made great strides towards making detailed performance predictions, however, for polarimetric techniques, fundamental tools for predicting performance do not exist. One big missing piece is predicting the wavelength and field of view dependence of a many-mirror articulated optical system particularly with complex protected metal coatings. Predicting polarization performance of instruments requires combining metrology of mirror coatings, tools to create mirror coating models, and optical modeling software for polarized beam propagation. The inability to predict instrument induced polarization or to define polarization performance expectations has far reaching implications for up and coming major observatories, such as the Daniel K. Inouye Solar Telescope (DKIST), that aim to take polarization measurements at unprecedented sensitivity and resolution.Here we present a method for modelling the wavelength dependent refractive index of an optic using Berreman calculus - a mathematical formalism that describes how an electromagnetic field propagates through a birefringent medium. From Berreman calculus, we can better predict the Mueller matrix, diattenuation, and retardance of an arbitrary thicknesses of amorphous many-layer coatings as well as stacks of birefringent crystals from laboratory measurements. This will allow for the wavelength dependent refractive index to be accurately determined and the polarization behavior to be derived for a given optic.

Multivariate Radiological-Based Models for the Prediction of Future Knee Pain: Data from the OAI

PubMed Central

Galván-Tejada, Jorge I.; Celaya-Padilla, José M.; Treviño, Victor; Tamez-Peña, José G.

2015-01-01

In this work, the potential of X-ray based multivariate prognostic models to predict the onset of chronic knee pain is presented. Using X-rays quantitative image assessments of joint-space-width (JSW) and paired semiquantitative central X-ray scores from the Osteoarthritis Initiative (OAI), a case-control study is presented. The pain assessments of the right knee at the baseline and the 60-month visits were used to screen for case/control subjects. Scores were analyzed at the time of pain incidence (T-0), the year prior incidence (T-1), and two years before pain incidence (T-2). Multivariate models were created by a cross validated elastic-net regularized generalized linear models feature selection tool. Univariate differences between cases and controls were reported by AUC, C-statistics, and ODDs ratios. Univariate analysis indicated that the medial osteophytes were significantly more prevalent in cases than controls: C-stat 0.62, 0.62, and 0.61, at T-0, T-1, and T-2, respectively. The multivariate JSW models significantly predicted pain: AUC = 0.695, 0.623, and 0.620, at T-0, T-1, and T-2, respectively. Semiquantitative multivariate models predicted paint with C-stat = 0.671, 0.648, and 0.645 at T-0, T-1, and T-2, respectively. Multivariate models derived from plain X-ray radiography assessments may be used to predict subjects that are at risk of developing knee pain. PMID:26504490

SU-F-T-348: The Impact of Model Library Population On RapidPlan Based Dose-Volume Histograms (DVHs) Prediction for Rectal Cancer Patients Treated with Volumetric-Modulated Radiotherapy (VMAT)

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

Li, K; Zhou, L; Chen, Z

Purpose: RapidPlan uses a library consisting of expert plans from different patients to create a model that can predict achievable dose-volume histograms (DVHs) for new patients. The goal of this study is to investigate the impacts of model library population (plan numbers) on the DVH prediction for rectal cancer patients treated with volumetric-modulated radiotherapy (VMAT) Methods: Ninety clinically accepted rectal cancer patients’ VMAT plans were selected to establish 3 models, named as Model30, Model60 and Model90, with 30,60, and 90 plans in the model training. All plans had sufficient target coverage and bladder and femora sparings. Additional 10 patients weremore » enrolled to test the DVH prediction differences with these 3 models. The predicted DVHs from these 3 models were compared and analyzed. Results: Predicted V40 (Vx, percent of volume that received x Gy for the organs at risk) and Dmean (mean dose, cGy) of the bladder were 39.84±13.38 and 2029.4±141.6 for the Model30,37.52±16.00 and 2012.5±152.2 for the Model60, and 36.33±18.35 and 2066.5±174.3 for the Model90. Predicted V30 and Dmean of the left femur were 23.33±9.96 and 1443.3±114.5 for the Model30, 21.83±5.75 and 1436.6±61.9 for the Model60, and 20.31±4.6 and 1415.0±52.4 for the Model90.There were no significant differences among the 3 models for the bladder and left femur predictions. Predicted V40 and Dmean of the right femur were 19.86±10.00 and 1403.6±115.6 (Model30),18.97±6.19 and 1401.9±68.78 (Model60), and 21.08±7.82 and 1424.0±85.3 (Model90). Although a slight lower DVH prediction of the right femur was found on the Model60, the mean differences for V30 and mean dose were less than 2% and 1%, respectively. Conclusion: There were no significant differences among Model30, Model60 and Model90 for predicting DVHs on rectal patients treated with VMAT. The impact of plan numbers for model library might be limited for cancers with similar target shape.« less

Construction and evaluation of FiND, a fall risk prediction model of inpatients from nursing data.

PubMed

Yokota, Shinichiroh; Ohe, Kazuhiko

2016-04-01

To construct and evaluate an easy-to-use fall risk prediction model based on the daily condition of inpatients from secondary use electronic medical record system data. The present authors scrutinized electronic medical record system data and created a dataset for analysis by including inpatient fall report data and Intensity of Nursing Care Needs data. The authors divided the analysis dataset into training data and testing data, then constructed the fall risk prediction model FiND from the training data, and tested the model using the testing data. The dataset for analysis contained 1,230,604 records from 46,241 patients. The sensitivity of the model constructed from the training data was 71.3% and the specificity was 66.0%. The verification result from the testing dataset was almost equivalent to the theoretical value. Although the model's accuracy did not surpass that of models developed in previous research, the authors believe FiND will be useful in medical institutions all over Japan because it is composed of few variables (only age, sex, and the Intensity of Nursing Care Needs items), and the accuracy for unknown data was clear. © 2016 Japan Academy of Nursing Science.

Modelling endurance and resumption times for repetitive one-hand pushing.

PubMed

Rose, Linda M; Beauchemin, Catherine A A; Neumann, W Patrick

2018-07-01

This study's objective was to develop models of endurance time (ET), as a function of load level (LL), and of resumption time (RT) after loading as a function of both LL and loading time (LT) for repeated loadings. Ten male participants with experience in construction work each performed 15 different one-handed repetaed pushing tasks at shoulder height with varied exerted force and duration. These data were used to create regression models predicting ET and RT. It is concluded that power law relationships are most appropriate to use when modelling ET and RT. While the data the equations are based on are limited regarding number of participants, gender, postures, magnitude and type of exerted force, the paper suggests how this kind of modelling can be used in job design and in further research. Practitioner Summary: Adequate muscular recovery during work-shifts is important to create sustainable jobs. This paper describes mathematical modelling and presents models for endurance times and resumption times (an aspect of recovery need), based on data from an empirical study. The models can be used to help manage fatigue levels in job design.

ISING MODEL OF CHORIOCAPILLARIS FLOW.

PubMed

Spaide, Richard F

2018-01-01

To develop a mathematical model of local blood flow in the choriocapillaris using an Ising model. A JavaScript Ising model was used to create images that emulated the development of signal voids as would be seen in optical coherence tomography angiography of the choriocapillaris. The model was produced by holding the temperature near criticality and varying the field strength. Individual frames were evaluated, and a movie video was created to show the hypothetical development of flow-related signal voids over a lifetime. Much the same as actual choriocapillaris images in humans, the model of flow-related signal voids followed a power-law distribution. The slope and intercept both decreased with age, as is seen in human subjects. This model is a working hypothesis, and as such can help predict system characteristics, evaluate conclusions drawn from studies, suggest new research questions, and provide a way of obtaining an estimate of behavior in which experimental data are not yet available. It may be possible to understand choriocapillaris blood flow in health and disease states by determining by observing deviations from an expected model.

Multisite external validation of a risk prediction model for the diagnosis of blood stream infections in febrile pediatric oncology patients without severe neutropenia.

PubMed

Esbenshade, Adam J; Zhao, Zhiguo; Aftandilian, Catherine; Saab, Raya; Wattier, Rachel L; Beauchemin, Melissa; Miller, Tamara P; Wilkes, Jennifer J; Kelly, Michael J; Fernbach, Alison; Jeng, Michael; Schwartz, Cindy L; Dvorak, Christopher C; Shyr, Yu; Moons, Karl G M; Sulis, Maria-Luisa; Friedman, Debra L

2017-10-01

Pediatric oncology patients are at an increased risk of invasive bacterial infection due to immunosuppression. The risk of such infection in the absence of severe neutropenia (absolute neutrophil count ≥ 500/μL) is not well established and a validated prediction model for blood stream infection (BSI) risk offers clinical usefulness. A 6-site retrospective external validation was conducted using a previously published risk prediction model for BSI in febrile pediatric oncology patients without severe neutropenia: the Esbenshade/Vanderbilt (EsVan) model. A reduced model (EsVan2) excluding 2 less clinically reliable variables also was created using the initial EsVan model derivative cohort, and was validated using all 5 external validation cohorts. One data set was used only in sensitivity analyses due to missing some variables. From the 5 primary data sets, there were a total of 1197 febrile episodes and 76 episodes of bacteremia. The overall C statistic for predicting bacteremia was 0.695, with a calibration slope of 0.50 for the original model and a calibration slope of 1.0 when recalibration was applied to the model. The model performed better in predicting high-risk bacteremia (gram-negative or Staphylococcus aureus infection) versus BSI alone, with a C statistic of 0.801 and a calibration slope of 0.65. The EsVan2 model outperformed the EsVan model across data sets with a C statistic of 0.733 for predicting BSI and a C statistic of 0.841 for high-risk BSI. The results of this external validation demonstrated that the EsVan and EsVan2 models are able to predict BSI across multiple performance sites and, once validated and implemented prospectively, could assist in decision making in clinical practice. Cancer 2017;123:3781-3790. © 2017 American Cancer Society. © 2017 American Cancer Society.

SU-E-J-234: Application of a Breathing Motion Model to ViewRay Cine MR Images

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

O’Connell, D. P.; Thomas, D. H.; Dou, T. H.

2015-06-15

Purpose: A respiratory motion model previously used to generate breathing-gated CT images was used with cine MR images. Accuracy and predictive ability of the in-plane models were evaluated. Methods: Sagittalplane cine MR images of a patient undergoing treatment on a ViewRay MRI/radiotherapy system were acquired before and during treatment. Images were acquired at 4 frames/second with 3.5 × 3.5 mm resolution and a slice thickness of 5 mm. The first cine frame was deformably registered to following frames. Superior/inferior component of the tumor centroid position was used as a breathing surrogate. Deformation vectors and surrogate measurements were used to determinemore » motion model parameters. Model error was evaluated and subsequent treatment cines were predicted from breathing surrogate data. A simulated CT cine was created by generating breathing-gated volumetric images at 0.25 second intervals along the measured breathing trace, selecting a sagittal slice and downsampling to the resolution of the MR cines. A motion model was built using the first half of the simulated cine data. Model accuracy and error in predicting the remaining frames of the cine were evaluated. Results: Mean difference between model predicted and deformably registered lung tissue positions for the 28 second preview MR cine acquired before treatment was 0.81 +/− 0.30 mm. The model was used to predict two minutes of the subsequent treatment cine with a mean accuracy of 1.59 +/− 0.63 mm. Conclusion: Inplane motion models were built using MR cine images and evaluated for accuracy and ability to predict future respiratory motion from breathing surrogate measurements. Examination of long term predictive ability is ongoing. The technique was applied to simulated CT cines for further validation, and the authors are currently investigating use of in-plane models to update pre-existing volumetric motion models used for generation of breathing-gated CT planning images.« less

Model-based functional neuroimaging using dynamic neural fields: An integrative cognitive neuroscience approach

PubMed Central

Wijeakumar, Sobanawartiny; Ambrose, Joseph P.; Spencer, John P.; Curtu, Rodica

2017-01-01

A fundamental challenge in cognitive neuroscience is to develop theoretical frameworks that effectively span the gap between brain and behavior, between neuroscience and psychology. Here, we attempt to bridge this divide by formalizing an integrative cognitive neuroscience approach using dynamic field theory (DFT). We begin by providing an overview of how DFT seeks to understand the neural population dynamics that underlie cognitive processes through previous applications and comparisons to other modeling approaches. We then use previously published behavioral and neural data from a response selection Go/Nogo task as a case study for model simulations. Results from this study served as the ‘standard’ for comparisons with a model-based fMRI approach using dynamic neural fields (DNF). The tutorial explains the rationale and hypotheses involved in the process of creating the DNF architecture and fitting model parameters. Two DNF models, with similar structure and parameter sets, are then compared. Both models effectively simulated reaction times from the task as we varied the number of stimulus-response mappings and the proportion of Go trials. Next, we directly simulated hemodynamic predictions from the neural activation patterns from each model. These predictions were tested using general linear models (GLMs). Results showed that the DNF model that was created by tuning parameters to capture simultaneously trends in neural activation and behavioral data quantitatively outperformed a Standard GLM analysis of the same dataset. Further, by using the GLM results to assign functional roles to particular clusters in the brain, we illustrate how DNF models shed new light on the neural populations’ dynamics within particular brain regions. Thus, the present study illustrates how an interactive cognitive neuroscience model can be used in practice to bridge the gap between brain and behavior. PMID:29118459

Learning a Continuous-Time Streaming Video QoE Model.

PubMed

Ghadiyaram, Deepti; Pan, Janice; Bovik, Alan C

2018-05-01

Over-the-top adaptive video streaming services are frequently impacted by fluctuating network conditions that can lead to rebuffering events (stalling events) and sudden bitrate changes. These events visually impact video consumers' quality of experience (QoE) and can lead to consumer churn. The development of models that can accurately predict viewers' instantaneous subjective QoE under such volatile network conditions could potentially enable the more efficient design of quality-control protocols for media-driven services, such as YouTube, Amazon, Netflix, and so on. However, most existing models only predict a single overall QoE score on a given video and are based on simple global video features, without accounting for relevant aspects of human perception and behavior. We have created a QoE evaluator, called the time-varying QoE Indexer, that accounts for interactions between stalling events, analyzes the spatial and temporal content of a video, predicts the perceptual video quality, models the state of the client-side data buffer, and consequently predicts continuous-time quality scores that agree quite well with human opinion scores. The new QoE predictor also embeds the impact of relevant human cognitive factors, such as memory and recency, and their complex interactions with the video content being viewed. We evaluated the proposed model on three different video databases and attained standout QoE prediction performance.

Improving simulations of precipitation phase and snowpack at a site subject to cold air intrusions: Snoqualmie Pass, WA

NASA Astrophysics Data System (ADS)

Wayand, Nicholas E.; Stimberis, John; Zagrodnik, Joseph P.; Mass, Clifford F.; Lundquist, Jessica D.

2016-09-01

Low-level cold air from eastern Washington often flows westward through mountain passes in the Washington Cascades, creating localized inversions and locally reducing climatological temperatures. The persistence of this inversion during a frontal passage can result in complex patterns of snow and rain that are difficult to predict. Yet these predictions are critical to support highway avalanche control, ski resort operations, and modeling of headwater snowpack storage. In this study we used observations of precipitation phase from a disdrometer and snow depth sensors across Snoqualmie Pass, WA, to evaluate surface-air-temperature-based and mesoscale-model-based predictions of precipitation phase during the anomalously warm 2014-2015 winter. Correlations of phase between surface-based methods and observations were greatly improved (r2 from 0.45 to 0.66) and frozen precipitation biases reduced (+36% to -6% of accumulated snow water equivalent) by using air temperature from a nearby higher-elevation station, which was less impacted by low-level inversions. Alternatively, we found a hybrid method that combines surface-based predictions with output from the Weather Research and Forecasting mesoscale model to have improved skill (r2 = 0.61) over both parent models (r2 = 0.42 and 0.55). These results suggest that prediction of precipitation phase in mountain passes can be improved by incorporating observations or models from above the surface layer.

a Probability Model for Drought Prediction Using Fusion of Markov Chain and SAX Methods

NASA Astrophysics Data System (ADS)

Jouybari-Moghaddam, Y.; Saradjian, M. R.; Forati, A. M.

2017-09-01

Drought is one of the most powerful natural disasters which are affected on different aspects of the environment. Most of the time this phenomenon is immense in the arid and semi-arid area. Monitoring and prediction the severity of the drought can be useful in the management of the natural disaster caused by drought. Many indices were used in predicting droughts such as SPI, VCI, and TVX. In this paper, based on three data sets (rainfall, NDVI, and land surface temperature) which are acquired from MODIS satellite imagery, time series of SPI, VCI, and TVX in time limited between winters 2000 to summer 2015 for the east region of Isfahan province were created. Using these indices and fusion of symbolic aggregation approximation and hidden Markov chain drought was predicted for fall 2015. For this purpose, at first, each time series was transformed into the set of quality data based on the state of drought (5 group) by using SAX algorithm then the probability matrix for the future state was created by using Markov hidden chain. The fall drought severity was predicted by fusion the probability matrix and state of drought severity in summer 2015. The prediction based on the likelihood for each state of drought includes severe drought, middle drought, normal drought, severe wet and middle wet. The analysis and experimental result from proposed algorithm show that the product of this algorithm is acceptable and the proposed algorithm is appropriate and efficient for predicting drought using remote sensor data.

A Predictive Model for Medical Events Based on Contextual Embedding of Temporal Sequences

PubMed Central

Wang, Zhimu; Huang, Yingxiang; Wang, Shuang; Wang, Fei; Jiang, Xiaoqian

2016-01-01

Background Medical concepts are inherently ambiguous and error-prone due to human fallibility, which makes it hard for them to be fully used by classical machine learning methods (eg, for tasks like early stage disease prediction). Objective Our work was to create a new machine-friendly representation that resembles the semantics of medical concepts. We then developed a sequential predictive model for medical events based on this new representation. Methods We developed novel contextual embedding techniques to combine different medical events (eg, diagnoses, prescriptions, and labs tests). Each medical event is converted into a numerical vector that resembles its “semantics,” via which the similarity between medical events can be easily measured. We developed simple and effective predictive models based on these vectors to predict novel diagnoses. Results We evaluated our sequential prediction model (and standard learning methods) in estimating the risk of potential diseases based on our contextual embedding representation. Our model achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.79 on chronic systolic heart failure and an average AUC of 0.67 (over the 80 most common diagnoses) using the Medical Information Mart for Intensive Care III (MIMIC-III) dataset. Conclusions We propose a general early prognosis predictor for 80 different diagnoses. Our method computes numeric representation for each medical event to uncover the potential meaning of those events. Our results demonstrate the efficiency of the proposed method, which will benefit patients and physicians by offering more accurate diagnosis. PMID:27888170

Prediction of North Pacific Height Anomalies During Strong Madden-Julian Oscillation Events

NASA Astrophysics Data System (ADS)

Kai-Chih, T.; Barnes, E. A.; Maloney, E. D.

2017-12-01

The Madden Julian Oscillation (MJO) creates strong variations in extratropical atmospheric circulations that have important implications for subseasonal-to-seasonal prediction. In particular, certain MJO phases are characterized by a consistent modulation of geopotential height in the North Pacific and adjacent regions across different MJO events. Until recently, only limited research has examined the relationship between these robust MJO tropical-extratropical teleconnections and model prediction skill. In this study, reanalysis data (MERRA and ERA-Interim) and ECMWF ensemble hindcasts are used to demonstrate that robust teleconnections in specific MJO phases and time lags are also characterized by excellent agreement in the prediction of geopotential height anoma- lies across model ensemble members at forecast leads of up to 3 weeks. These periods of enhanced prediction capabilities extend the possibility for skillful extratropical weather prediction beyond traditional 10-13 day limits. Furthermore, we also examine the phase dependency of teleconnection robustness by using Linear Baroclinic Model (LBM) and the result is consistent with the ensemble hindcasts : the anomalous heating of MJO phase 2 (phase 6) can consistently generate positive (negative) geopotential height anomalies around the extratropical Pacific with a lead of 15-20 days, while other phases are more sensitive to the variaion of the mean state.

Experimental design based 3-D QSAR analysis of steroid-protein interactions: Application to human CBG complexes

NASA Astrophysics Data System (ADS)

Norinder, Ulf

1990-12-01

An experimental design based 3-D QSAR analysis using a combination of principal component and PLS analysis is presented and applied to human corticosteroid-binding globulin complexes. The predictive capability of the created model is good. The technique can also be used as guidance when selecting new compounds to be investigated.

Predictive habitat models derived from nest-box occupancy for the endangered Carolina northern flying squirrel in the southern Appalachians

Treesearch

W. Mark Ford; Andrew M. Evans; Richard H. Odom; Jane L. Rodrigue; Christine A. Kelly; Nicole Abaid; Corinne A. Diggins; Douglas Newcomb

2015-01-01

In the southern Appalachians, artificial nest-boxes are used to survey for the endangered Carolina northern flying squirrel (CNFS; Glaucomys sabrinus coloratus), a disjunct subspecies associated with high elevation (>1385 m) forests. Using environmental parameters diagnostic of squirrel habitat, we created 35 a priori occupancy...

Predictive Modeling of K-12 Academic Outcomes: A Primer for Researchers Working with Education Data

ERIC Educational Resources Information Center

Porter, Kristin E.; Balu, Rekha

2016-01-01

Education systems are increasingly creating rich, longitudinal data sets with frequent, and even real-time, data updates of many student measures, including daily attendance, homework submissions, and exam scores. These data sets provide an opportunity for district and school staff members to move beyond an indicators-based approach and instead…

Delinquency Level Classification Via the HEW Community Program Youth Impact Scales.

ERIC Educational Resources Information Center

Truckenmiller, James L.

The former HEW National Strategy for Youth Development (NSYD) model was created as a community-based planning and procedural tool to promote youth development and prevent delinquency. To assess the predictive power of NSYD Impact Scales in classifying youths into low, medium, and high delinquency levels, male and female students aged 10-19 years…

New Approach to Synthesis of Powder and Composite Materials by Electron Beam. Part 1. Technological Features of the Process

NASA Astrophysics Data System (ADS)

Rudskoy, A. I.; Kondrat'ev, S. Yu.; Sokolov, Yu. A.

2016-05-01

Possibilities of electron beam synthesis of structural and tool composite materials are considered. It is shown that a novel process involving mathematical modeling of each individual operation makes it possible to create materials with programmable structure and predictable properties from granules of various specified chemical compositions and sizes.

The effects of forest fragmentation on forest stand attributes

Treesearch

Ronald E. McRoberts; Greg C. Liknes

2002-01-01

For two study areas in Minnesota, USA, one heavily forested and one sparsely forested, maps of predicted proportion forest area were created using Landsat Thematic Mapper imagery, forest inventory plot data, and a logistic regression model. The maps were used to estimate quantitative indices of forest fragmentation. Correlations between the values of the indices and...

The Relationship between Adolescents' Levels of Hopelessness and Cyberbullying: The Role of Values

ERIC Educational Resources Information Center

Dilmaç, Bülent

2017-01-01

The purpose of this research is to present the relationship of teenagers' values with their levels of cyberbullying and hopelessness, as well as to test the created model in terms of these relations. This research analyzes the predictive relationships among adolescents' values, cyberbullying, and hopelessness through the program AMOS 19 in…

Artificial Intelligence Systems as Prognostic and Predictive Tools in Ovarian Cancer.

PubMed

Enshaei, A; Robson, C N; Edmondson, R J

2015-11-01

The ability to provide accurate prognostic and predictive information to patients is becoming increasingly important as clinicians enter an era of personalized medicine. For a disease as heterogeneous as epithelial ovarian cancer, conventional algorithms become too complex for routine clinical use. This study therefore investigated the potential for an artificial intelligence model to provide this information and compared it with conventional statistical approaches. The authors created a database comprising 668 cases of epithelial ovarian cancer during a 10-year period and collected data routinely available in a clinical environment. They also collected survival data for all the patients, then constructed an artificial intelligence model capable of comparing a variety of algorithms and classifiers alongside conventional statistical approaches such as logistic regression. The model was used to predict overall survival and demonstrated that an artificial neural network (ANN) algorithm was capable of predicting survival with high accuracy (93 %) and an area under the curve (AUC) of 0.74 and that this outperformed logistic regression. The model also was used to predict the outcome of surgery and again showed that ANN could predict outcome (complete/optimal cytoreduction vs. suboptimal cytoreduction) with 77 % accuracy and an AUC of 0.73. These data are encouraging and demonstrate that artificial intelligence systems may have a role in providing prognostic and predictive data for patients. The performance of these systems likely will improve with increasing data set size, and this needs further investigation.

Presence of indicator plant species as a predictor of wetland vegetation integrity

USGS Publications Warehouse

Stapanian, Martin A.; Adams, Jean V.; Gara, Brian

2013-01-01

We fit regression and classification tree models to vegetation data collected from Ohio (USA) wetlands to determine (1) which species best predict Ohio vegetation index of biotic integrity (OVIBI) score and (2) which species best predict high-quality wetlands (OVIBI score >75). The simplest regression tree model predicted OVIBI score based on the occurrence of three plant species: skunk-cabbage (Symplocarpus foetidus), cinnamon fern (Osmunda cinnamomea), and swamp rose (Rosa palustris). The lowest OVIBI scores were best predicted by the absence of the selected plant species rather than by the presence of other species. The simplest classification tree model predicted high-quality wetlands based on the occurrence of two plant species: skunk-cabbage and marsh-fern (Thelypteris palustris). The overall misclassification rate from this tree was 13 %. Again, low-quality wetlands were better predicted than high-quality wetlands by the absence of selected species rather than the presence of other species using the classification tree model. Our results suggest that a species’ wetland status classification and coefficient of conservatism are of little use in predicting wetland quality. A simple, statistically derived species checklist such as the one created in this study could be used by field biologists to quickly and efficiently identify wetland sites likely to be regulated as high-quality, and requiring more intensive field assessments. Alternatively, it can be used for advanced determinations of low-quality wetlands. Agencies can save considerable money by screening wetlands for the presence/absence of such “indicator” species before issuing permits.

[Simulation and data mining model for identifying and prediction budget changes in the care of patients with hypertension].

PubMed

Joyanes-Aguilar, Luis; Castaño, Néstor J; Osorio, José H

2015-10-01

Objective To present a simulation model that establishes the economic impact to the health care system produced by the diagnostic evolution of patients suffering from arterial hypertension. Methodology The information used corresponds to that available in Individual Health Records (RIPs, in Spanish). A statistical characterization was carried out and a model for matrix storage in MATLAB was proposed. Data mining was used to create predictors. Finally, a simulation environment was built to determine the economic cost of diagnostic evolution. Results 5.7 % of the population progresses from the diagnosis, and the cost overrun associated with it is 43.2 %. Conclusions Results shows the applicability and possibility of focussing research on establishing diagnosis relationships using all the information reported in the RIPS in order to create econometric indicators that can determine which diagnostic evolutions are most relevant to budget allocation.

The Kyoto protocol and payments for tropical forest: An interdisciplinary method for estimating carbon-offset supply and increasing the feasibility of a carbon market under the CDM

USGS Publications Warehouse

Pfaff, Alexander S.P.; Kerr, Suzi; Hughes, R. Flint; Liu, Shuguang; Sanchez-Azofeifa, G. Arturo; Schimel, David; Tosi, Joseph; Watson, Vicente

2000-01-01

Protecting tropical forests under the Clean Development Mechanism (CDM) could reduce the cost of emissions limitations set in Kyoto. However, while society must soon decide whether or not to use tropical forest-based offsets, evidence regarding tropical carbon sinks is sparse. This paper presents a general method for constructing an integrated model (based on detailed historical, remote sensing and field data) that can produce land-use and carbon baselines, predict carbon sequestration supply to a carbon-offsets market and also help to evaluate optimal market rules. Creating such integrated models requires close collaboration between social and natural scientists. Our project combines varied disciplinary expertise (in economics, ecology and geography) with local knowledge in order to create high-quality, empirically grounded, integrated models for Costa Rica.

Electrical and chemical properties of XeCl*(308 nm) exciplex lamp created by a dielectric barrier discharge

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

Baadj, S.; Harrache, Z., E-mail: zharrache@yahoo.com; Belasri, A.

2013-12-15

The aim of this work is to highlight, through numerical modeling, the chemical and the electrical characteristics of xenon chloride mixture in XeCl* (308 nm) excimer lamp created by a dielectric barrier discharge. A temporal model, based on the Xe/Cl{sub 2} mixture chemistry, the circuit and the Boltzmann equations, is constructed. The effects of operating voltage, Cl{sub 2} percentage in the Xe/Cl{sub 2} gas mixture, dielectric capacitance, as well as gas pressure on the 308-nm photon generation, under typical experimental operating conditions, have been investigated and discussed. The importance of charged and excited species, including the major electronic and ionicmore » processes, is also demonstrated. The present calculations show clearly that the model predicts the optimal operating conditions and describes the electrical and chemical properties of the XeCl* exciplex lamp.« less

Preoperative predictive model of recovery of urinary continence after radical prostatectomy.

PubMed

Matsushita, Kazuhito; Kent, Matthew T; Vickers, Andrew J; von Bodman, Christian; Bernstein, Melanie; Touijer, Karim A; Coleman, Jonathan A; Laudone, Vincent T; Scardino, Peter T; Eastham, James A; Akin, Oguz; Sandhu, Jaspreet S

2015-10-01

To build a predictive model of urinary continence recovery after radical prostatectomy (RP) that incorporates magnetic resonance imaging (MRI) parameters and clinical data. We conducted a retrospective review of data from 2,849 patients who underwent pelvic staging MRI before RP from November 2001 to June 2010. We used logistic regression to evaluate the association between each MRI variable and continence at 6 or 12 months, adjusting for age, body mass index (BMI) and American Society of Anesthesiologists (ASA) score, and then used multivariable logistic regression to create our model. A nomogram was constructed using the multivariable logistic regression models. In all, 68% (1,742/2,559) and 82% (2,205/2,689) regained function at 6 and 12 months, respectively. In the base model, age, BMI and ASA score were significant predictors of continence at 6 or 12 months on univariate analysis (P < 0.005). Among the preoperative MRI measurements, membranous urethral length, which showed great significance, was incorporated into the base model to create the full model. For continence recovery at 6 months, the addition of membranous urethral length increased the area under the curve (AUC) to 0.664 for the validation set, an increase of 0.064 over the base model. For continence recovery at 12 months, the AUC was 0.674, an increase of 0.085 over the base model. Using our model, the likelihood of continence recovery increases with membranous urethral length and decreases with age, BMI and ASA score. This model could be used for patient counselling and for the identification of patients at high risk for urinary incontinence in whom to study changes in operative technique that improve urinary function after RP. © 2015 The Authors BJU International © 2015 BJU International Published by John Wiley & Sons Ltd.

Method of Testing and Predicting Failures of Electronic Mechanical Systems

NASA Technical Reports Server (NTRS)

Iverson, David L.; Patterson-Hine, Frances A.

1996-01-01

A method employing a knowledge base of human expertise comprising a reliability model analysis implemented for diagnostic routines is disclosed. The reliability analysis comprises digraph models that determine target events created by hardware failures human actions, and other factors affecting the system operation. The reliability analysis contains a wealth of human expertise information that is used to build automatic diagnostic routines and which provides a knowledge base that can be used to solve other artificial intelligence problems.

Selective sweeps in growing microbial colonies

NASA Astrophysics Data System (ADS)

Korolev, Kirill S.; Müller, Melanie J. I.; Karahan, Nilay; Murray, Andrew W.; Hallatschek, Oskar; Nelson, David R.

2012-04-01

Evolutionary experiments with microbes are a powerful tool to study mutations and natural selection. These experiments, however, are often limited to the well-mixed environments of a test tube or a chemostat. Since spatial organization can significantly affect evolutionary dynamics, the need is growing for evolutionary experiments in spatially structured environments. The surface of a Petri dish provides such an environment, but a more detailed understanding of microbial growth on Petri dishes is necessary to interpret such experiments. We formulate a simple deterministic reaction-diffusion model, which successfully predicts the spatial patterns created by two competing species during colony expansion. We also derive the shape of these patterns analytically without relying on microscopic details of the model. In particular, we find that the relative fitness of two microbial strains can be estimated from the logarithmic spirals created by selective sweeps. The theory is tested with strains of the budding yeast Saccharomyces cerevisiae for spatial competitions with different initial conditions and for a range of relative fitnesses. The reaction-diffusion model also connects the microscopic parameters like growth rates and diffusion constants with macroscopic spatial patterns and predicts the relationship between fitness in liquid cultures and on Petri dishes, which we confirmed experimentally. Spatial sector patterns therefore provide an alternative fitness assay to the commonly used liquid culture fitness assays.

Three-link Swimming in Sand

NASA Astrophysics Data System (ADS)

Hatton, R. L.; Ding, Yang; Masse, Andrew; Choset, Howie; Goldman, Daniel

2011-11-01

Many animals move within in granular media such as desert sand. Recent biological experiments have revealed that the sandfish lizard uses an undulatory gait to swim within sand. Models reveal that swimming occurs in a frictional fluid in which inertial effects are small and kinematics dominate. To understand the fundamental mechanics of swimming in granular media (GM), we examine a model system that has been well-studied in Newtonian fluids: the three-link swimmer. We create a physical model driven by two servo-motors, and a discrete element simulation of the swimmer. To predict optimal gaits we use a recent geometric mechanics theory combined with empirically determined resistive force laws for GM. We develop a kinematic relationship between the swimmer's shape and position velocities and construct connection vector field and constraint curvature function visualizations of the system dynamics. From these we predict optimal gaits for forward, lateral and rotational motion. Experiment and simulation are in accord with the theoretical predictions; thus geometric tools can be used to study locomotion in GM.

Predicting the risk of suicide by analyzing the text of clinical notes.

PubMed

Poulin, Chris; Shiner, Brian; Thompson, Paul; Vepstas, Linas; Young-Xu, Yinong; Goertzel, Benjamin; Watts, Bradley; Flashman, Laura; McAllister, Thomas

2014-01-01

We developed linguistics-driven prediction models to estimate the risk of suicide. These models were generated from unstructured clinical notes taken from a national sample of U.S. Veterans Administration (VA) medical records. We created three matched cohorts: veterans who committed suicide, veterans who used mental health services and did not commit suicide, and veterans who did not use mental health services and did not commit suicide during the observation period (n = 70 in each group). From the clinical notes, we generated datasets of single keywords and multi-word phrases, and constructed prediction models using a machine-learning algorithm based on a genetic programming framework. The resulting inference accuracy was consistently 65% or more. Our data therefore suggests that computerized text analytics can be applied to unstructured medical records to estimate the risk of suicide. The resulting system could allow clinicians to potentially screen seemingly healthy patients at the primary care level, and to continuously evaluate the suicide risk among psychiatric patients.

Two-Speed Gearbox Dynamic Simulation Predictions and Test Validation

NASA Technical Reports Server (NTRS)

Lewicki, David G.; DeSmidt, Hans; Smith, Edward C.; Bauman, Steven W.

2010-01-01

Dynamic simulations and experimental validation tests were performed on a two-stage, two-speed gearbox as part of the drive system research activities of the NASA Fundamental Aeronautics Subsonics Rotary Wing Project. The gearbox was driven by two electromagnetic motors and had two electromagnetic, multi-disk clutches to control output speed. A dynamic model of the system was created which included a direct current electric motor with proportional-integral-derivative (PID) speed control, a two-speed gearbox with dual electromagnetically actuated clutches, and an eddy current dynamometer. A six degree-of-freedom model of the gearbox accounted for the system torsional dynamics and included gear, clutch, shaft, and load inertias as well as shaft flexibilities and a dry clutch stick-slip friction model. Experimental validation tests were performed on the gearbox in the NASA Glenn gear noise test facility. Gearbox output speed and torque as well as drive motor speed and current were compared to those from the analytical predictions. The experiments correlate very well with the predictions, thus validating the dynamic simulation methodologies.

Predicting the Risk of Suicide by Analyzing the Text of Clinical Notes

PubMed Central

Thompson, Paul; Vepstas, Linas; Young-Xu, Yinong; Goertzel, Benjamin; Watts, Bradley; Flashman, Laura; McAllister, Thomas

2014-01-01

We developed linguistics-driven prediction models to estimate the risk of suicide. These models were generated from unstructured clinical notes taken from a national sample of U.S. Veterans Administration (VA) medical records. We created three matched cohorts: veterans who committed suicide, veterans who used mental health services and did not commit suicide, and veterans who did not use mental health services and did not commit suicide during the observation period (n = 70 in each group). From the clinical notes, we generated datasets of single keywords and multi-word phrases, and constructed prediction models using a machine-learning algorithm based on a genetic programming framework. The resulting inference accuracy was consistently 65% or more. Our data therefore suggests that computerized text analytics can be applied to unstructured medical records to estimate the risk of suicide. The resulting system could allow clinicians to potentially screen seemingly healthy patients at the primary care level, and to continuously evaluate the suicide risk among psychiatric patients. PMID:24489669

Predicting the planform configuration of the braided Toklat River, AK with a suite of rule-based models

USGS Publications Warehouse

Podolak, Charles J.

2013-01-01

An ensemble of rule-based models was constructed to assess possible future braided river planform configurations for the Toklat River in Denali National Park and Preserve, Alaska. This approach combined an analysis of large-scale influences on stability with several reduced-complexity models to produce the predictions at a practical level for managers concerned about the persistence of bank erosion while acknowledging the great uncertainty in any landscape prediction. First, a model of confluence angles reproduced observed angles of a major confluence, but showed limited susceptibility to a major rearrangement of the channel planform downstream. Second, a probabilistic map of channel locations was created with a two-parameter channel avulsion model. The predicted channel belt location was concentrated in the same area as the current channel belt. Finally, a suite of valley-scale channel and braid plain characteristics were extracted from a light detection and ranging (LiDAR)-derived surface. The characteristics demonstrated large-scale stabilizing topographic influences on channel planform. The combination of independent analyses increased confidence in the conclusion that the Toklat River braided planform is a dynamically stable system due to large and persistent valley-scale influences, and that a range of avulsive perturbations are likely to result in a relatively unchanged planform configuration in the short term.

Image-based modeling and characterization of RF ablation lesions in cardiac arrhythmia therapy

NASA Astrophysics Data System (ADS)

Linte, Cristian A.; Camp, Jon J.; Rettmann, Maryam E.; Holmes, David R.; Robb, Richard A.

2013-03-01

In spite of significant efforts to enhance guidance for catheter navigation, limited research has been conducted to consider the changes that occur in the tissue during ablation as means to provide useful feedback on the progression of therapy delivery. We propose a technique to visualize lesion progression and monitor the effects of the RF energy delivery using a surrogate thermal ablation model. The model incorporates both physical and physiological tissue parameters, and uses heat transfer principles to estimate temperature distribution in the tissue and geometry of the generated lesion in near real time. The ablation model has been calibrated and evaluated using ex vivo beef muscle tissue in a clinically relevant ablation protocol. To validate the model, the predicted temperature distribution was assessed against that measured directly using fiberoptic temperature probes inserted in the tissue. Moreover, the model-predicted lesions were compared to the lesions observed in the post-ablation digital images. Results showed an agreement within 5°C between the model-predicted and experimentally measured tissue temperatures, as well as comparable predicted and observed lesion characteristics and geometry. These results suggest that the proposed technique is capable of providing reasonably accurate and sufficiently fast representations of the created RF ablation lesions, to generate lesion maps in near real time. These maps can be used to guide the placement of successive lesions to ensure continuous and enduring suppression of the arrhythmic pathway.

Predicting Mouse Liver Microsomal Stability with “Pruned” Machine Learning Models and Public Data

PubMed Central

Perryman, Alexander L.; Stratton, Thomas P.; Ekins, Sean; Freundlich, Joel S.

2015-01-01

Purpose Mouse efficacy studies are a critical hurdle to advance translational research of potential therapeutic compounds for many diseases. Although mouse liver microsomal (MLM) stability studies are not a perfect surrogate for in vivo studies of metabolic clearance, they are the initial model system used to assess metabolic stability. Consequently, we explored the development of machine learning models that can enhance the probability of identifying compounds possessing MLM stability. Methods Published assays on MLM half-life values were identified in PubChem, reformatted, and curated to create a training set with 894 unique small molecules. These data were used to construct machine learning models assessed with internal cross-validation, external tests with a published set of antitubercular compounds, and independent validation with an additional diverse set of 571 compounds (PubChem data on percent metabolism). Results “Pruning” out the moderately unstable/moderately stable compounds from the training set produced models with superior predictive power. Bayesian models displayed the best predictive power for identifying compounds with a half-life ≥1 hour. Conclusions Our results suggest the pruning strategy may be of general benefit to improve test set enrichment and provide machine learning models with enhanced predictive value for the MLM stability of small organic molecules. This study represents the most exhaustive study to date of using machine learning approaches with MLM data from public sources. PMID:26415647

Predicting Mouse Liver Microsomal Stability with "Pruned" Machine Learning Models and Public Data.

PubMed

Perryman, Alexander L; Stratton, Thomas P; Ekins, Sean; Freundlich, Joel S

2016-02-01

Mouse efficacy studies are a critical hurdle to advance translational research of potential therapeutic compounds for many diseases. Although mouse liver microsomal (MLM) stability studies are not a perfect surrogate for in vivo studies of metabolic clearance, they are the initial model system used to assess metabolic stability. Consequently, we explored the development of machine learning models that can enhance the probability of identifying compounds possessing MLM stability. Published assays on MLM half-life values were identified in PubChem, reformatted, and curated to create a training set with 894 unique small molecules. These data were used to construct machine learning models assessed with internal cross-validation, external tests with a published set of antitubercular compounds, and independent validation with an additional diverse set of 571 compounds (PubChem data on percent metabolism). "Pruning" out the moderately unstable / moderately stable compounds from the training set produced models with superior predictive power. Bayesian models displayed the best predictive power for identifying compounds with a half-life ≥1 h. Our results suggest the pruning strategy may be of general benefit to improve test set enrichment and provide machine learning models with enhanced predictive value for the MLM stability of small organic molecules. This study represents the most exhaustive study to date of using machine learning approaches with MLM data from public sources.

Generation of fluoroscopic 3D images with a respiratory motion model based on an external surrogate signal

NASA Astrophysics Data System (ADS)

Hurwitz, Martina; Williams, Christopher L.; Mishra, Pankaj; Rottmann, Joerg; Dhou, Salam; Wagar, Matthew; Mannarino, Edward G.; Mak, Raymond H.; Lewis, John H.

2015-01-01

Respiratory motion during radiotherapy can cause uncertainties in definition of the target volume and in estimation of the dose delivered to the target and healthy tissue. In this paper, we generate volumetric images of the internal patient anatomy during treatment using only the motion of a surrogate signal. Pre-treatment four-dimensional CT imaging is used to create a patient-specific model correlating internal respiratory motion with the trajectory of an external surrogate placed on the chest. The performance of this model is assessed with digital and physical phantoms reproducing measured irregular patient breathing patterns. Ten patient breathing patterns are incorporated in a digital phantom. For each patient breathing pattern, the model is used to generate images over the course of thirty seconds. The tumor position predicted by the model is compared to ground truth information from the digital phantom. Over the ten patient breathing patterns, the average absolute error in the tumor centroid position predicted by the motion model is 1.4 mm. The corresponding error for one patient breathing pattern implemented in an anthropomorphic physical phantom was 0.6 mm. The global voxel intensity error was used to compare the full image to the ground truth and demonstrates good agreement between predicted and true images. The model also generates accurate predictions for breathing patterns with irregular phases or amplitudes.

Ontological Modeling for Integrated Spacecraft Analysis

NASA Technical Reports Server (NTRS)

Wicks, Erica

2011-01-01

Current spacecraft work as a cooperative group of a number of subsystems. Each of these requiresmodeling software for development, testing, and prediction. It is the goal of my team to create anoverarching software architecture called the Integrated Spacecraft Analysis (ISCA) to aid in deploying the discrete subsystems' models. Such a plan has been attempted in the past, and has failed due to the excessive scope of the project. Our goal in this version of ISCA is to use new resources to reduce the scope of the project, including using ontological models to help link the internal interfaces of subsystems' models with the ISCA architecture.I have created an ontology of functions specific to the modeling system of the navigation system of a spacecraft. The resulting ontology not only links, at an architectural level, language specificinstantiations of the modeling system's code, but also is web-viewable and can act as a documentation standard. This ontology is proof of the concept that ontological modeling can aid in the integration necessary for ISCA to work, and can act as the prototype for future ISCA ontologies.

Real-time predictive seasonal influenza model in Catalonia, Spain

PubMed Central

Basile, Luca; Oviedo de la Fuente, Manuel; Torner, Nuria; Martínez, Ana; Jané, Mireia

2018-01-01

Influenza surveillance is critical to monitoring the situation during epidemic seasons and predictive mathematic models may aid the early detection of epidemic patterns. The objective of this study was to design a real-time spatial predictive model of ILI (Influenza Like Illness) incidence rate in Catalonia using one- and two-week forecasts. The available data sources used to select explanatory variables to include in the model were the statutory reporting disease system and the sentinel surveillance system in Catalonia for influenza incidence rates, the official climate service in Catalonia for meteorological data, laboratory data and Google Flu Trend. Time series for every explanatory variable with data from the last 4 seasons (from 2010–2011 to 2013–2014) was created. A pilot test was conducted during the 2014–2015 season to select the explanatory variables to be included in the model and the type of model to be applied. During the 2015–2016 season a real-time model was applied weekly, obtaining the intensity level and predicted incidence rates with 95% confidence levels one and two weeks away for each health region. At the end of the season, the confidence interval success rate (CISR) and intensity level success rate (ILSR) were analysed. For the 2015–2016 season a CISR of 85.3% at one week and 87.1% at two weeks and an ILSR of 82.9% and 82% were observed, respectively. The model described is a useful tool although it is hard to evaluate due to uncertainty. The accuracy of prediction at one and two weeks was above 80% globally, but was lower during the peak epidemic period. In order to improve the predictive power, new explanatory variables should be included. PMID:29513710

Coupling of EIT with computational lung modeling for predicting patient-specific ventilatory responses.

PubMed

Roth, Christian J; Becher, Tobias; Frerichs, Inéz; Weiler, Norbert; Wall, Wolfgang A

2017-04-01

Providing optimal personalized mechanical ventilation for patients with acute or chronic respiratory failure is still a challenge within a clinical setting for each case anew. In this article, we integrate electrical impedance tomography (EIT) monitoring into a powerful patient-specific computational lung model to create an approach for personalizing protective ventilatory treatment. The underlying computational lung model is based on a single computed tomography scan and able to predict global airflow quantities, as well as local tissue aeration and strains for any ventilation maneuver. For validation, a novel "virtual EIT" module is added to our computational lung model, allowing to simulate EIT images based on the patient's thorax geometry and the results of our numerically predicted tissue aeration. Clinically measured EIT images are not used to calibrate the computational model. Thus they provide an independent method to validate the computational predictions at high temporal resolution. The performance of this coupling approach has been tested in an example patient with acute respiratory distress syndrome. The method shows good agreement between computationally predicted and clinically measured airflow data and EIT images. These results imply that the proposed framework can be used for numerical prediction of patient-specific responses to certain therapeutic measures before applying them to an actual patient. In the long run, definition of patient-specific optimal ventilation protocols might be assisted by computational modeling. NEW & NOTEWORTHY In this work, we present a patient-specific computational lung model that is able to predict global and local ventilatory quantities for a given patient and any selected ventilation protocol. For the first time, such a predictive lung model is equipped with a virtual electrical impedance tomography module allowing real-time validation of the computed results with the patient measurements. First promising results obtained in an acute respiratory distress syndrome patient show the potential of this approach for personalized computationally guided optimization of mechanical ventilation in future. Copyright © 2017 the American Physiological Society.

Simplified subsurface modelling: data assimilation and violated model assumptions

NASA Astrophysics Data System (ADS)

Erdal, Daniel; Lange, Natascha; Neuweiler, Insa

2017-04-01

Integrated models are gaining more and more attention in hydrological modelling as they can better represent the interaction between different compartments. Naturally, these models come along with larger numbers of unknowns and requirements on computational resources compared to stand-alone models. If large model domains are to be represented, e.g. on catchment scale, the resolution of the numerical grid needs to be reduced or the model itself needs to be simplified. Both approaches lead to a reduced ability to reproduce the present processes. This lack of model accuracy may be compensated by using data assimilation methods. In these methods observations are used to update the model states, and optionally model parameters as well, in order to reduce the model error induced by the imposed simplifications. What is unclear is whether these methods combined with strongly simplified models result in completely data-driven models or if they can even be used to make adequate predictions of the model state for times when no observations are available. In the current work we consider the combined groundwater and unsaturated zone, which can be modelled in a physically consistent way using 3D-models solving the Richards equation. For use in simple predictions, however, simpler approaches may be considered. The question investigated here is whether a simpler model, in which the groundwater is modelled as a horizontal 2D-model and the unsaturated zones as a few sparse 1D-columns, can be used within an Ensemble Kalman filter to give predictions of groundwater levels and unsaturated fluxes. This is tested under conditions where the feedback between the two model-compartments are large (e.g. shallow groundwater table) and the simplification assumptions are clearly violated. Such a case may be a steep hill-slope or pumping wells, creating lateral fluxes in the unsaturated zone, or strong heterogeneous structures creating unaccounted flows in both the saturated and unsaturated compartments. Under such circumstances, direct modelling using a simplified model will not provide good results. However, a more data driven (e.g. grey box) approach, driven by the filter, may still provide an improved understanding of the system. Comparisons between full 3D simulations and simplified filter driven models will be shown and the resulting benefits and drawbacks will be discussed.

Column Testing and 1D Reactive Transport Modeling to Evaluate Uranium Plume Persistence Processes

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

Johnson, Raymond H.; Morrison, Stan; Morris, Sarah

Motivation for Study: Natural flushing of contaminants at various U.S. Department of Energy Office of Legacy Management sites is not proceeding as quickly as predicted (plume persistence) Objectives: Help determine natural flushing rates using column tests. Use 1D reactive transport modeling to better understand the major processes that are creating plume persistence Approach: Core samples from under a former mill tailings area Tailings have been removed. Column leaching using lab-prepared water similar to nearby Gunnison River water. 1D reactive transport modeling to evaluate processes

Validity of one-repetition maximum predictive equations in men with spinal cord injury.

PubMed

Ribeiro Neto, F; Guanais, P; Dornelas, E; Coutinho, A C B; Costa, R R G

2017-10-01

Cross-sectional study. The study aimed (a) to test the cross-validation of current one-repetition maximum (1RM) predictive equations in men with spinal cord injury (SCI); (b) to compare the current 1RM predictive equations to a newly developed equation based on the 4- to 12-repetition maximum test (4-12RM). SARAH Rehabilitation Hospital Network, Brasilia, Brazil. Forty-five men aged 28.0 years with SCI between C6 and L2 causing complete motor impairment were enrolled in the study. Volunteers were tested, in a random order, in 1RM test or 4-12RM with 2-3 interval days. Multiple regression analysis was used to generate an equation for predicting 1RM. There were no significant differences between 1RM test and the current predictive equations. ICC values were significant and were classified as excellent for all current predictive equations. The predictive equation of Lombardi presented the best Bland-Altman results (0.5 kg and 12.8 kg for mean difference and interval range around the differences, respectively). The two created equation models for 1RM demonstrated the same and a high adjusted R 2 (0.971, P<0.01), but different SEE of measured 1RM (2.88 kg or 5.4% and 2.90 kg or 5.5%). All 1RM predictive equations are accurate to assess individuals with SCI at the bench press exercise. However, the predictive equation of Lombardi presented the best associated cross-validity results. A specific 1RM prediction equation was also elaborated for individuals with SCI. The created equation should be tested in order to verify whether it presents better accuracy than the current ones.

Verification of predicted specimen-specific natural and implanted patellofemoral kinematics during simulated deep knee bend.

PubMed

Baldwin, Mark A; Clary, Chadd; Maletsky, Lorin P; Rullkoetter, Paul J

2009-10-16

Verified computational models represent an efficient method for studying the relationship between articular geometry, soft-tissue constraint, and patellofemoral (PF) mechanics. The current study was performed to evaluate an explicit finite element (FE) modeling approach for predicting PF kinematics in the natural and implanted knee. Experimental three-dimensional kinematic data were collected on four healthy cadaver specimens in their natural state and after total knee replacement in the Kansas knee simulator during a simulated deep knee bend activity. Specimen-specific FE models were created from medical images and CAD implant geometry, and included soft-tissue structures representing medial-lateral PF ligaments and the quadriceps tendon. Measured quadriceps loads and prescribed tibiofemoral kinematics were used to predict dynamic kinematics of an isolated PF joint between 10 degrees and 110 degrees femoral flexion. Model sensitivity analyses were performed to determine the effect of rigid or deformable patellar representations and perturbed PF ligament mechanical properties (pre-tension and stiffness) on model predictions and computational efficiency. Predicted PF kinematics from the deformable analyses showed average root mean square (RMS) differences for the natural and implanted states of less than 3.1 degrees and 1.7 mm for all rotations and translations. Kinematic predictions with rigid bodies increased average RMS values slightly to 3.7 degrees and 1.9 mm with a five-fold decrease in computational time. Two-fold increases and decreases in PF ligament initial strain and linear stiffness were found to most adversely affect kinematic predictions for flexion, internal-external tilt and inferior-superior translation in both natural and implanted states. The verified models could be used to further investigate the effects of component alignment or soft-tissue variability on natural and implant PF mechanics.

Prospects of Genomic Prediction in the USDA Soybean Germplasm Collection: Historical Data Creates Robust Models for Enhancing Selection of Accessions.

PubMed

Jarquin, Diego; Specht, James; Lorenz, Aaron

2016-08-09

The identification and mobilization of useful genetic variation from germplasm banks for use in breeding programs is critical for future genetic gain and protection against crop pests. Plummeting costs of next-generation sequencing and genotyping is revolutionizing the way in which researchers and breeders interface with plant germplasm collections. An example of this is the high density genotyping of the entire USDA Soybean Germplasm Collection. We assessed the usefulness of 50K single nucleotide polymorphism data collected on 18,480 domesticated soybean (Glycine max) accessions and vast historical phenotypic data for developing genomic prediction models for protein, oil, and yield. Resulting genomic prediction models explained an appreciable amount of the variation in accession performance in independent validation trials, with correlations between predicted and observed reaching up to 0.92 for oil and protein and 0.79 for yield. The optimization of training set design was explored using a series of cross-validation schemes. It was found that the target population and environment need to be well represented in the training set. Second, genomic prediction training sets appear to be robust to the presence of data from diverse geographical locations and genetic clusters. This finding, however, depends on the influence of shattering and lodging, and may be specific to soybean with its presence of maturity groups. The distribution of 7608 nonphenotyped accessions was examined through the application of genomic prediction models. The distribution of predictions of phenotyped accessions was representative of the distribution of predictions for nonphenotyped accessions, with no nonphenotyped accessions being predicted to fall far outside the range of predictions of phenotyped accessions. Copyright © 2016 Jarquin et al.

Analysis of the M-shell spectra emitted by a short-pulse laser-created tantalum plasma

PubMed

Busquet; Jiang; Coinsertion Markte CY; Kieffer; Klapisch; Bar-Shalom; Bauche-Arnoult; Bachelier

2000-01-01

The spectrum of tantalum emitted by a subpicosecond laser-created plasma, was recorded in the regions of the 3d-5f, 3d-4f, and 3d-4p transitions. The main difference with a nanosecond laser-created plasma spectrum is a broad understructure appearing under the 3d-5f transitions. An interpretation of this feature as a density effect is proposed. The supertransition array model is used for interpreting the spectrum, assuming local thermodynamic equilibrium (LTE) at some effective temperature. An interpretation of the 3d-4f spectrum using the more detailed unresolved transition array formalism, which does not assume LTE, is also proposed. Fitted contributions of the different ionic species differ slightly from the LTE-predicted values.

A contemporary risk model for predicting 30-day mortality following percutaneous coronary intervention in England and Wales.

PubMed

McAllister, Katherine S L; Ludman, Peter F; Hulme, William; de Belder, Mark A; Stables, Rodney; Chowdhary, Saqib; Mamas, Mamas A; Sperrin, Matthew; Buchan, Iain E

2016-05-01

The current risk model for percutaneous coronary intervention (PCI) in the UK is based on outcomes of patients treated in a different era of interventional cardiology. This study aimed to create a new model, based on a contemporary cohort of PCI treated patients, which would: predict 30 day mortality; provide good discrimination; and be well calibrated across a broad risk-spectrum. The model was derived from a training dataset of 336,433 PCI cases carried out between 2007 and 2011 in England and Wales, with 30 day mortality provided by record linkage. Candidate variables were selected on the basis of clinical consensus and data quality. Procedures in 2012 were used to perform temporal validation of the model. The strongest predictors of 30-day mortality were: cardiogenic shock; dialysis; and the indication for PCI and the degree of urgency with which it was performed. The model had an area under the receiver operator characteristic curve of 0.85 on the training data and 0.86 on validation. Calibration plots indicated a good model fit on development which was maintained on validation. We have created a contemporary model for PCI that encompasses a range of clinical risk, from stable elective PCI to emergency primary PCI and cardiogenic shock. The model is easy to apply and based on data reported in national registries. It has a high degree of discrimination and is well calibrated across the risk spectrum. The examination of key outcomes in PCI audit can be improved with this risk-adjusted model. Copyright © 2016 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

IL-8 predicts pediatric oncology patients with febrile neutropenia at low risk for bacteremia.

PubMed

Cost, Carrye R; Stegner, Martha M; Leonard, David; Leavey, Patrick

2013-04-01

Despite a low bacteremia rate, pediatric oncology patients are frequently admitted for febrile neutropenia. A pediatric risk prediction model with high sensitivity to identify patients at low risk for bacteremia is not available. We performed a single-institution prospective cohort study of pediatric oncology patients with febrile neutropenia to create a risk prediction model using clinical factors, respiratory viral infection, and cytokine expression. Pediatric oncology patients with febrile neutropenia were enrolled between March 30, 2010 and April 1, 2011 and managed per institutional protocol. Blood samples for C-reactive protein and cytokine expression and nasopharyngeal swabs for respiratory viral testing were obtained. Medical records were reviewed for clinical data. Statistical analysis utilized mixed multiple logistic regression modeling. During the 12-month period, 195 febrile neutropenia episodes were enrolled. There were 24 (12%) episodes of bacteremia. Univariate analysis revealed several factors predictive for bacteremia, and interleukin (IL)-8 was the most predictive variable in the multivariate stepwise logistic regression. Low serum IL-8 predicted patients at low risk for bacteremia with a sensitivity of 0.9 and negative predictive value of 0.98. IL-8 is a highly sensitive predictor for patients at low risk for bacteremia. IL-8 should be utilized in a multi-institution prospective trial to assign risk stratification to pediatric patients admitted with febrile neutropenia.

Computational Approaches to Chemical Hazard Assessment

PubMed Central

Luechtefeld, Thomas; Hartung, Thomas

2018-01-01

Summary Computational prediction of toxicity has reached new heights as a result of decades of growth in the magnitude and diversity of biological data. Public packages for statistics and machine learning make model creation faster. New theory in machine learning and cheminformatics enables integration of chemical structure, toxicogenomics, simulated and physical data in the prediction of chemical health hazards, and other toxicological information. Our earlier publications have characterized a toxicological dataset of unprecedented scale resulting from the European REACH legislation (Registration Evaluation Authorisation and Restriction of Chemicals). These publications dove into potential use cases for regulatory data and some models for exploiting this data. This article analyzes the options for the identification and categorization of chemicals, moves on to the derivation of descriptive features for chemicals, discusses different kinds of targets modeled in computational toxicology, and ends with a high-level perspective of the algorithms used to create computational toxicology models. PMID:29101769

Bulalo field, Philippines: Reservoir modeling for prediction of limits to sustainable generation

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

Strobel, Calvin J.

1993-01-28

The Bulalo geothermal field, located in Laguna province, Philippines, supplies 12% of the electricity on the island of Luzon. The first 110 MWe power plant was on line May 1979; current 330 MWe (gross) installed capacity was reached in 1984. Since then, the field has operated at an average plant factor of 76%. The National Power Corporation plans to add 40 MWe base load and 40 MWe standby in 1995. A numerical simulation model for the Bulalo field has been created that matches historic pressure changes, enthalpy and steam flash trends and cumulative steam production. Gravity modeling provided independent verificationmore » of mass balances and time rate of change of liquid desaturation in the rock matrix. Gravity modeling, in conjunction with reservoir simulation provides a means of predicting matrix dry out and the time to limiting conditions for sustainable levelized steam deliverability and power generation.« less

Approximating high-dimensional dynamics by barycentric coordinates with linear programming

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

Hirata, Yoshito, E-mail: yoshito@sat.t.u-tokyo.ac.jp; Aihara, Kazuyuki; Suzuki, Hideyuki

The increasing development of novel methods and techniques facilitates the measurement of high-dimensional time series but challenges our ability for accurate modeling and predictions. The use of a general mathematical model requires the inclusion of many parameters, which are difficult to be fitted for relatively short high-dimensional time series observed. Here, we propose a novel method to accurately model a high-dimensional time series. Our method extends the barycentric coordinates to high-dimensional phase space by employing linear programming, and allowing the approximation errors explicitly. The extension helps to produce free-running time-series predictions that preserve typical topological, dynamical, and/or geometric characteristics ofmore » the underlying attractors more accurately than the radial basis function model that is widely used. The method can be broadly applied, from helping to improve weather forecasting, to creating electronic instruments that sound more natural, and to comprehensively understanding complex biological data.« less

Approximating high-dimensional dynamics by barycentric coordinates with linear programming.

PubMed

Hirata, Yoshito; Shiro, Masanori; Takahashi, Nozomu; Aihara, Kazuyuki; Suzuki, Hideyuki; Mas, Paloma

2015-01-01

The increasing development of novel methods and techniques facilitates the measurement of high-dimensional time series but challenges our ability for accurate modeling and predictions. The use of a general mathematical model requires the inclusion of many parameters, which are difficult to be fitted for relatively short high-dimensional time series observed. Here, we propose a novel method to accurately model a high-dimensional time series. Our method extends the barycentric coordinates to high-dimensional phase space by employing linear programming, and allowing the approximation errors explicitly. The extension helps to produce free-running time-series predictions that preserve typical topological, dynamical, and/or geometric characteristics of the underlying attractors more accurately than the radial basis function model that is widely used. The method can be broadly applied, from helping to improve weather forecasting, to creating electronic instruments that sound more natural, and to comprehensively understanding complex biological data.

Status of Computational Aerodynamic Modeling Tools for Aircraft Loss-of-Control

NASA Technical Reports Server (NTRS)

Frink, Neal T.; Murphy, Patrick C.; Atkins, Harold L.; Viken, Sally A.; Petrilli, Justin L.; Gopalarathnam, Ashok; Paul, Ryan C.

2016-01-01

A concerted effort has been underway over the past several years to evolve computational capabilities for modeling aircraft loss-of-control under the NASA Aviation Safety Program. A principal goal has been to develop reliable computational tools for predicting and analyzing the non-linear stability & control characteristics of aircraft near stall boundaries affecting safe flight, and for utilizing those predictions for creating augmented flight simulation models that improve pilot training. Pursuing such an ambitious task with limited resources required the forging of close collaborative relationships with a diverse body of computational aerodynamicists and flight simulation experts to leverage their respective research efforts into the creation of NASA tools to meet this goal. Considerable progress has been made and work remains to be done. This paper summarizes the status of the NASA effort to establish computational capabilities for modeling aircraft loss-of-control and offers recommendations for future work.