Comparison of predictive estimates of high-latitude electrodynamics with observations of global-scale Birkeland currents

NASA Astrophysics Data System (ADS)

Anderson, Brian J.; Korth, Haje; Welling, Daniel T.; Merkin, Viacheslav G.; Wiltberger, Michael J.; Raeder, Joachim; Barnes, Robin J.; Waters, Colin L.; Pulkkinen, Antti A.; Rastaetter, Lutz

2017-02-01

Two of the geomagnetic storms for the Space Weather Prediction Center Geospace Environment Modeling challenge occurred after data were first acquired by the Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE). We compare Birkeland currents from AMPERE with predictions from four models for the 4-5 April 2010 and 5-6 August 2011 storms. The four models are the Weimer (2005b) field-aligned current statistical model, the Lyon-Fedder-Mobarry magnetohydrodynamic (MHD) simulation, the Open Global Geospace Circulation Model MHD simulation, and the Space Weather Modeling Framework MHD simulation. The MHD simulations were run as described in Pulkkinen et al. (2013) and the results obtained from the Community Coordinated Modeling Center. The total radial Birkeland current, ITotal, and the distribution of radial current density, Jr, for all models are compared with AMPERE results. While the total currents are well correlated, the quantitative agreement varies considerably. The Jr distributions reveal discrepancies between the models and observations related to the latitude distribution, morphologies, and lack of nightside current systems in the models. The results motivate enhancing the simulations first by increasing the simulation resolution and then by examining the relative merits of implementing more sophisticated ionospheric conductance models, including ionospheric outflows or other omitted physical processes. Some aspects of the system, including substorm timing and location, may remain challenging to simulate, implying a continuing need for real-time specification.

Predictive Performance of Physiologically Based Pharmacokinetic Models for the Effect of Food on Oral Drug Absorption: Current Status

PubMed Central

Zhao, Ping; Pan, Yuzhuo; Wagner, Christian

2017-01-01

A comprehensive search in literature and published US Food and Drug Administration reviews was conducted to assess whether physiologically based pharmacokinetic (PBPK) modeling could be prospectively used to predict clinical food effect on oral drug absorption. Among the 48 resulted food effect predictions, ∼50% were predicted within 1.25‐fold of observed, and 75% within 2‐fold. Dissolution rate and precipitation time were commonly optimized parameters when PBPK modeling was not able to capture the food effect. The current work presents a knowledgebase for documenting PBPK experience to predict food effect. PMID:29168611

Model for Predicting Passage of Invasive Fish Species Through Culverts

NASA Astrophysics Data System (ADS)

Neary, V.

2010-12-01

Conservation efforts to promote or inhibit fish passage include the application of simple fish passage models to determine whether an open channel flow allows passage of a given fish species. Derivations of simple fish passage models for uniform and nonuniform flow conditions are presented. For uniform flow conditions, a model equation is developed that predicts the mean-current velocity threshold in a fishway, or velocity barrier, which causes exhaustion at a given maximum distance of ascent. The derivation of a simple expression for this exhaustion-threshold (ET) passage model is presented using kinematic principles coupled with fatigue curves for threatened and endangered fish species. Mean current velocities at or above the threshold predict failure to pass. Mean current velocities below the threshold predict successful passage. The model is therefore intuitive and easily applied to predict passage or exclusion. The ET model’s simplicity comes with limitations, however, including its application only to uniform flow, which is rarely found in the field. This limitation is addressed by deriving a model that accounts for nonuniform conditions, including backwater profiles and drawdown curves. Comparison of these models with experimental data from volitional swimming studies of fish indicates reasonable performance, but limitations are still present due to the difficulty in predicting fish behavior and passage strategies that can vary among individuals and different fish species.

Remaining dischargeable time prediction for lithium-ion batteries using unscented Kalman filter

NASA Astrophysics Data System (ADS)

Dong, Guangzhong; Wei, Jingwen; Chen, Zonghai; Sun, Han; Yu, Xiaowei

2017-10-01

To overcome the range anxiety, one of the important strategies is to accurately predict the range or dischargeable time of the battery system. To accurately predict the remaining dischargeable time (RDT) of a battery, a RDT prediction framework based on accurate battery modeling and state estimation is presented in this paper. Firstly, a simplified linearized equivalent-circuit-model is developed to simulate the dynamic characteristics of a battery. Then, an online recursive least-square-algorithm method and unscented-Kalman-filter are employed to estimate the system matrices and SOC at every prediction point. Besides, a discrete wavelet transform technique is employed to capture the statistical information of past dynamics of input currents, which are utilized to predict the future battery currents. Finally, the RDT can be predicted based on the battery model, SOC estimation results and predicted future battery currents. The performance of the proposed methodology has been verified by a lithium-ion battery cell. Experimental results indicate that the proposed method can provide an accurate SOC and parameter estimation and the predicted RDT can solve the range anxiety issues.

A Semianalytical Ion Current Model for Radio Frequency Driven Collisionless Sheaths

NASA Technical Reports Server (NTRS)

Bose, Deepak; Govindan, T. R.; Meyyappan, M.; Arnold, Jim (Technical Monitor)

2001-01-01

We propose a semianalytical ion dynamics model for a collisionless radio frequency biased sheath. The model uses bulk plasma conditions and electrode boundary condition to predict ion impact energy distribution and electrical properties of the sheath. The proposed model accounts for ion inertia and ion current modulation at bias frequencies that are of the same order of magnitude as the ion plasma frequency. A relaxation equation for ion current oscillations is derived which is coupled with a damped potential equation in order to model ion inertia effects. We find that inclusion of ion current modulation in the sheath model shows marked improvements in the predictions of sheath electrical properties and ion energy distribution function.

Study on model current predictive control method of PV grid- connected inverters systems with voltage sag

NASA Astrophysics Data System (ADS)

Jin, N.; Yang, F.; Shang, S. Y.; Tao, T.; Liu, J. S.

2016-08-01

According to the limitations of the LVRT technology of traditional photovoltaic inverter existed, this paper proposes a low voltage ride through (LVRT) control method based on model current predictive control (MCPC). This method can effectively improve the photovoltaic inverter output characteristics and response speed. The MCPC method of photovoltaic grid-connected inverter designed, the sum of the absolute value of the predictive current and the given current error is adopted as the cost function with the model predictive control method. According to the MCPC, the optimal space voltage vector is selected. Photovoltaic inverter has achieved automatically switches of priority active or reactive power control of two control modes according to the different operating states, which effectively improve the inverter capability of LVRT. The simulation and experimental results proves that the proposed method is correct and effective.

Mental models accurately predict emotion transitions.

PubMed

Thornton, Mark A; Tamir, Diana I

2017-06-06

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

Three-dimensional effects for radio frequency antenna modeling

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

Carter, M.D.; Batchelor, D.B.; Stallings, D.C.

1994-10-15

Electromagnetic field calculations for radio frequency (rf) antennas in two dimensions (2-D) neglect finite antenna length effects as well as the feeders leading to the main current strap. The 2-D calculations predict that the return currents in the sidewalls of the antenna structure depend strongly on the plasma parameters, but this prediction is suspect because of experimental evidence. To study the validity of the 2-D approximation, the Multiple Antenna Implementation System (MAntIS) has been used to perform three-dimensional (3-D) modeling of the power spectrum, plasma loading, and inductance for a relevant loop antenna design. Effects on antenna performance caused bymore » feeders to the main current strap and conducting sidewalls are considered. The modeling shows that the feeders affect the launched power spectrum in an indirect way by forcing the driven rf current to return in the antenna structure rather than the plasma, as in the 2-D model. It has also been found that poloidal dependencies in the plasma impedance matrix can reduce the loading predicted from that predicted in the 2-D model. For some plasma parameters, the combined 3-D effects can lead to a reduction in the predicted loading by as much as a factor of 2 from that given by the 2-D model, even with end-effect corrections for the 2-D model.« less

Three-dimensional effects for radio frequency antenna modeling

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

Carter, M.D.; Batchelor, D.B.; Stallings, D.C.

1993-12-31

Electromagnetic field calculations for radio frequency (rf) antennas in two dimensions (2-D) neglect finite antenna length effects as well as the feeders leading to the main current strap. The 2-D calculations predict that the return currents in the sidewalls of the antenna structure depend strongly on the plasma parameters, but this prediction is suspect because of experimental evidence. To study the validity of the 2-D approximation, the Multiple Antenna Implementation System (MAntIS) has been used to perform three-dimensional (3-D) modeling of the power spectrum, plasma loading, and inductance for a relevant loop antenna design. Effects on antenna performance caused bymore » feeders to the main current strap and conducting sidewalls are considered. The modeling shows that the feeders affect the launched power spectrum in an indirect way by forcing the driven rf current to return in the antenna structure rather than the plasma, as in the 2-D model. It has also been found that poloidal dependencies in the plasma impedance matrix can reduce the loading predicted from that predicted in the 2-D model. For some plasma parameters, the combined 3-D effects can lead to a reduction in the predicted loading by as much as a factor of 2 from that given by the 2-D model, even with end-effect corrections for the 2-D model.« less

Operational prediction of rip currents using numerical model and nearshore bathymetry from video images

NASA Astrophysics Data System (ADS)

Sembiring, L.; Van Ormondt, M.; Van Dongeren, A. R.; Roelvink, J. A.

2017-07-01

Rip currents are one of the most dangerous coastal hazards for swimmers. In order to minimize the risk, a coastal operational-process based-model system can be utilized in order to provide forecast of nearshore waves and currents that may endanger beach goers. In this paper, an operational model for rip current prediction by utilizing nearshore bathymetry obtained from video image technique is demonstrated. For the nearshore scale model, XBeach1 is used with which tidal currents, wave induced currents (including the effect of the wave groups) can be simulated simultaneously. Up-to-date bathymetry will be obtained using video images technique, cBathy 2. The system will be tested for the Egmond aan Zee beach, located in the northern part of the Dutch coastline. This paper will test the applicability of bathymetry obtained from video technique to be used as input for the numerical modelling system by comparing simulation results using surveyed bathymetry and model results using video bathymetry. Results show that the video technique is able to produce bathymetry converging towards the ground truth observations. This bathymetry validation will be followed by an example of operational forecasting type of simulation on predicting rip currents. Rip currents flow fields simulated over measured and modeled bathymetries are compared in order to assess the performance of the proposed forecast system.

Using genetic algorithms to optimise current and future health planning--the example of ambulance locations.

PubMed

Sasaki, Satoshi; Comber, Alexis J; Suzuki, Hiroshi; Brunsdon, Chris

2010-01-28

Ambulance response time is a crucial factor in patient survival. The number of emergency cases (EMS cases) requiring an ambulance is increasing due to changes in population demographics. This is decreasing ambulance response times to the emergency scene. This paper predicts EMS cases for 5-year intervals from 2020, to 2050 by correlating current EMS cases with demographic factors at the level of the census area and predicted population changes. It then applies a modified grouping genetic algorithm to compare current and future optimal locations and numbers of ambulances. Sets of potential locations were evaluated in terms of the (current and predicted) EMS case distances to those locations. Future EMS demands were predicted to increase by 2030 using the model (R2 = 0.71). The optimal locations of ambulances based on future EMS cases were compared with current locations and with optimal locations modelled on current EMS case data. Optimising the location of ambulance stations locations reduced the average response times by 57 seconds. Current and predicted future EMS demand at modelled locations were calculated and compared. The reallocation of ambulances to optimal locations improved response times and could contribute to higher survival rates from life-threatening medical events. Modelling EMS case 'demand' over census areas allows the data to be correlated to population characteristics and optimal 'supply' locations to be identified. Comparing current and future optimal scenarios allows more nuanced planning decisions to be made. This is a generic methodology that could be used to provide evidence in support of public health planning and decision making.

Assessing allometric models to predict vegetative growth of mango (Mangifera indica; Anacardiaceae) at the current-year branch scale.

PubMed

Normand, Frédéric; Lauri, Pierre-Éric

2012-03-01

Accurate and reliable predictive models are necessary to estimate nondestructively key variables for plant growth studies such as leaf area and leaf, stem, and total biomass. Predictive models are lacking at the current-year branch scale despite the importance of this scale in plant science. We calibrated allometric models to estimate leaf area and stem and branch (leaves + stem) mass of current-year branches, i.e., branches several months old studied at the end of the vegetative growth season, of four mango cultivars on the basis of their basal cross-sectional area. The effects of year, site, and cultivar were tested. Models were validated with independent data and prediction accuracy was evaluated with the appropriate statistics. Models revealed a positive allometry between dependent and independent variables, whose y-intercept but not the slope, was affected by the cultivar. The effects of year and site were negligible. For each branch characteristic, cultivar-specific models were more accurate than common models built with pooled data from the four cultivars. Prediction quality was satisfactory but with data dispersion around the models, particularly for large values. Leaf area and stem and branch mass of mango current-year branches could be satisfactorily estimated on the basis of branch basal cross-sectional area with cultivar-specific allometric models. The results suggested that, in addition to the heteroscedastic behavior of the variables studied, model accuracy was probably related to the functional plasticity of branches in relation to the light environment and/or to the number of growth units composing the branches.

Analytic Guided-Search Model of Human Performance Accuracy in Target- Localization Search Tasks

NASA Technical Reports Server (NTRS)

Eckstein, Miguel P.; Beutter, Brent R.; Stone, Leland S.

2000-01-01

Current models of human visual search have extended the traditional serial/parallel search dichotomy. Two successful models for predicting human visual search are the Guided Search model and the Signal Detection Theory model. Although these models are inherently different, it has been difficult to compare them because the Guided Search model is designed to predict response time, while Signal Detection Theory models are designed to predict performance accuracy. Moreover, current implementations of the Guided Search model require the use of Monte-Carlo simulations, a method that makes fitting the model's performance quantitatively to human data more computationally time consuming. We have extended the Guided Search model to predict human accuracy in target-localization search tasks. We have also developed analytic expressions that simplify simulation of the model to the evaluation of a small set of equations using only three free parameters. This new implementation and extension of the Guided Search model will enable direct quantitative comparisons with human performance in target-localization search experiments and with the predictions of Signal Detection Theory and other search accuracy models.

In situ Observations of Heliospheric Current Sheets Evolution

NASA Astrophysics Data System (ADS)

Liu, Yong; Peng, Jun; Huang, Jia; Klecker, Berndt

2017-04-01

We investigate the Heliospheric current sheet observation time difference of the spacecraft using the STEREO, ACE and WIND data. The observations are first compared to a simple theory in which the time difference is only determined by the radial and longitudinal separation between the spacecraft. The predictions fit well with the observations except for a few events. Then the time delay caused by the latitudinal separation is taken in consideration. The latitude of each spacecraft is calculated based on the PFSS model assuming that heliospheric current sheets propagate at the solar wind speed without changing their shapes from the origin to spacecraft near 1AU. However, including the latitudinal effects does not improve the prediction, possibly because that the PFSS model may not locate the current sheets accurately enough. A new latitudinal delay is predicted based on the time delay using the observations on ACE data. The new method improved the prediction on the time lag between spacecraft; however, further study is needed to predict the location of the heliospheric current sheet more accurately.

Predictive models in urology.

PubMed

Cestari, Andrea

2013-01-01

Predictive modeling is emerging as an important knowledge-based technology in healthcare. The interest in the use of predictive modeling reflects advances on different fronts such as the availability of health information from increasingly complex databases and electronic health records, a better understanding of causal or statistical predictors of health, disease processes and multifactorial models of ill-health and developments in nonlinear computer models using artificial intelligence or neural networks. These new computer-based forms of modeling are increasingly able to establish technical credibility in clinical contexts. The current state of knowledge is still quite young in understanding the likely future direction of how this so-called 'machine intelligence' will evolve and therefore how current relatively sophisticated predictive models will evolve in response to improvements in technology, which is advancing along a wide front. Predictive models in urology are gaining progressive popularity not only for academic and scientific purposes but also into the clinical practice with the introduction of several nomograms dealing with the main fields of onco-urology.

The North American Multi-Model Ensemble (NMME): Phase-1 Seasonal to Interannual Prediction, Phase-2 Toward Developing Intra-Seasonal Prediction

NASA Technical Reports Server (NTRS)

Kirtman, Ben P.; Min, Dughong; Infanti, Johnna M.; Kinter, James L., III; Paolino, Daniel A.; Zhang, Qin; vandenDool, Huug; Saha, Suranjana; Mendez, Malaquias Pena; Becker, Emily;

Bringing modeling to the masses: A web based system to predict potential species distributions

USGS Publications Warehouse

Graham, Jim; Newman, Greg; Kumar, Sunil; Jarnevich, Catherine S.; Young, Nick; Crall, Alycia W.; Stohlgren, Thomas J.; Evangelista, Paul

2010-01-01

Predicting current and potential species distributions and abundance is critical for managing invasive species, preserving threatened and endangered species, and conserving native species and habitats. Accurate predictive models are needed at local, regional, and national scales to guide field surveys, improve monitoring, and set priorities for conservation and restoration. Modeling capabilities, however, are often limited by access to software and environmental data required for predictions. To address these needs, we built a comprehensive web-based system that: (1) maintains a large database of field data; (2) provides access to field data and a wealth of environmental data; (3) accesses values in rasters representing environmental characteristics; (4) runs statistical spatial models; and (5) creates maps that predict the potential species distribution. The system is available online at www.niiss.org, and provides web-based tools for stakeholders to create potential species distribution models and maps under current and future climate scenarios.

Mental models accurately predict emotion transitions

PubMed Central

Thornton, Mark A.; Tamir, Diana I.

2017-01-01

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

Investigation and Modeling of Cranberry Weather Stress.

NASA Astrophysics Data System (ADS)

Croft, Paul Joseph

Cranberry bog weather conditions and weather-related stress were investigated for development of crop yield prediction models and models to predict daily weather conditions in the bog. Field investigations and data gathering were completed at the Rutgers University Blueberry/Cranberry Research Center experimental bogs in Chatsworth, New Jersey. Study indicated that although cranberries generally exhibit little or no stomatal response to changing atmospheric conditions, the evaluation of weather-related stress could be accomplished via use of micrometeorological data. Definition of weather -related stress was made by establishing critical thresholds of the frequencies of occurrence, and magnitudes of, temperature and precipitation in the bog based on values determined by a review of the literature and a grower questionnaire. Stress frequencies were correlated with cranberry yield to develop predictive models based on the previous season's yield, prior season data, prior and current season data, current season data; and prior and current season data through July 31 of the current season. The predictive ability of the prior season models was best and could be used in crop planning and production. Further examination of bog micrometeorological data permitted the isolation of those weather conditions conducive to cranberry scald and allowed for the institution of a pilot scald advisory program during the 1991 season. The micrometeorological data from the bog was also used to develop models to predict daily canopy temperature and precipitation, based on upper air data, for grower use. Models were developed for each month for maximum and minimum temperatures and for precipitation and generally performed well. The modeling of bog weather conditions is an important first step toward daily prediction of cranberry weather-related stress.

HB06 : Field Validation of Realtime Predictions of Surfzone Waves and Currents

NASA Astrophysics Data System (ADS)

Guza, R. T.; O'Reilly, W. C.; Feddersen, F.

2006-12-01

California shorelines can be contaminated by the discharge of polluted streams and rivers onto the beach face or into the surf zone. Management decisions (for example, beach closures) can be assisted by accurate characterization of the waves and currents that transport and mix these pollutants. A real-time, operational waves and alongshore current model, developed for a 5 km alongshore reach at Huntington Beach (http://cdip.ucsd.edu/hb06/), will be tested for a month during Fall 2006 as part of the HB06 field experiment. The model has two components: prediction of incident waves immediately seaward of the surf zone, and the transformation of breaking waves across the surf zone. The California Safe Boating Network Model (O'Reilly et al., California World Ocean Conference, 2006) is used to estimate incident wave properties. This regional wave model accounts for blocking and refraction by offshore islands and shoals, and variation of the shoreline orientation. At Huntington Beach, the network model uses four buoys exposed to the deep ocean to estimate swell, and four nearby buoys to estimate locally generated seas. The model predictions will be compared with directional wave buoy observations in 22 m depth, 1 km from the shore. The computationally fast model for surfzone waves and breaking-wave driven alongshore currents, appropriate for random waves on beaches with simple bathymetry, is based on concepts developed and tested by Ed Thornton and his colleagues over the last 30 years. Modeled alongshore currents at Huntington Beach, with incident waves predicted by the Network model, will be compared with waves and currents observed during HB06 along a transect extending from 4 m depth to the shoreline. Support from the California Coastal Conservancy, NOAA, and ONR is gratefully acknowledged.

An Improved Model Predictive Current Controller of Switched Reluctance Machines Using Time-Multiplexed Current Sensor

PubMed Central

Li, Bingchu; Ling, Xiao; Huang, Yixiang; Gong, Liang; Liu, Chengliang

2017-01-01

This paper presents a fixed-switching-frequency model predictive current controller using multiplexed current sensor for switched reluctance machine (SRM) drives. The converter was modified to distinguish currents from simultaneously excited phases during the sampling period. The only current sensor installed in the converter was time division multiplexing for phase current sampling. During the commutation stage, the control steps of adjacent phases were shifted so that sampling time was staggered. The maximum and minimum duty ratio of pulse width modulation (PWM) was limited to keep enough sampling time for analog-to-digital (A/D) conversion. Current sensor multiplexing was realized without complex adjustment of either driver circuit nor control algorithms, while it helps to reduce the cost and errors introduced in current sampling due to inconsistency between sensors. The proposed controller is validated by both simulation and experimental results with a 1.5 kW three-phase 12/8 SRM. Satisfied current sampling is received with little difference compared with independent phase current sensors for each phase. The proposed controller tracks the reference current profile as accurately as the model predictive current controller with independent phase current sensors, while having minor tracking errors compared with a hysteresis current controller. PMID:28513554

PREDICTING ATTENUATION OF VIRUSES DURING PERCOLATION IN SOILS: 2. USER'S GUIDE TO THE VIRULO 1.0 COMPUTER MODEL

EPA Science Inventory

In the EPA document Predicting Attenuation of Viruses During Percolation in Soils 1. Probabilistic Model the conceptual, theoretical, and mathematical foundations for a predictive screening model were presented. In this current volume we present a User's Guide for the computer mo...

Modeling and Prediction of Fan Noise

NASA Technical Reports Server (NTRS)

Envia, Ed

2008-01-01

Fan noise is a significant contributor to the total noise signature of a modern high bypass ratio aircraft engine and with the advent of ultra high bypass ratio engines like the geared turbofan, it is likely to remain so in the future. As such, accurate modeling and prediction of the basic characteristics of fan noise are necessary ingredients in designing quieter aircraft engines in order to ensure compliance with ever more stringent aviation noise regulations. In this paper, results from a comprehensive study aimed at establishing the utility of current tools for modeling and predicting fan noise will be summarized. It should be emphasized that these tools exemplify present state of the practice and embody what is currently used at NASA and Industry for predicting fan noise. The ability of these tools to model and predict fan noise is assessed against a set of benchmark fan noise databases obtained for a range of representative fan cycles and operating conditions. Detailed comparisons between the predicted and measured narrowband spectral and directivity characteristics of fan nose will be presented in the full paper. General conclusions regarding the utility of current tools and recommendations for future improvements will also be given.

Dispersal and extrapolation on the accuracy of temporal predictions from distribution models for the Darwin's frog.

PubMed

Uribe-Rivera, David E; Soto-Azat, Claudio; Valenzuela-Sánchez, Andrés; Bizama, Gustavo; Simonetti, Javier A; Pliscoff, Patricio

2017-07-01

Climate change is a major threat to biodiversity; the development of models that reliably predict its effects on species distributions is a priority for conservation biogeography. Two of the main issues for accurate temporal predictions from Species Distribution Models (SDM) are model extrapolation and unrealistic dispersal scenarios. We assessed the consequences of these issues on the accuracy of climate-driven SDM predictions for the dispersal-limited Darwin's frog Rhinoderma darwinii in South America. We calibrated models using historical data (1950-1975) and projected them across 40 yr to predict distribution under current climatic conditions, assessing predictive accuracy through the area under the ROC curve (AUC) and True Skill Statistics (TSS), contrasting binary model predictions against temporal-independent validation data set (i.e., current presences/absences). To assess the effects of incorporating dispersal processes we compared the predictive accuracy of dispersal constrained models with no dispersal limited SDMs; and to assess the effects of model extrapolation on the predictive accuracy of SDMs, we compared this between extrapolated and no extrapolated areas. The incorporation of dispersal processes enhanced predictive accuracy, mainly due to a decrease in the false presence rate of model predictions, which is consistent with discrimination of suitable but inaccessible habitat. This also had consequences on range size changes over time, which is the most used proxy for extinction risk from climate change. The area of current climatic conditions that was absent in the baseline conditions (i.e., extrapolated areas) represents 39% of the study area, leading to a significant decrease in predictive accuracy of model predictions for those areas. Our results highlight (1) incorporating dispersal processes can improve predictive accuracy of temporal transference of SDMs and reduce uncertainties of extinction risk assessments from global change; (2) as geographical areas subjected to novel climates are expected to arise, they must be reported as they show less accurate predictions under future climate scenarios. Consequently, environmental extrapolation and dispersal processes should be explicitly incorporated to report and reduce uncertainties in temporal predictions of SDMs, respectively. Doing so, we expect to improve the reliability of the information we provide for conservation decision makers under future climate change scenarios. © 2017 by the Ecological Society of America.

The Current State of Predicting Furrow Irrigation Erosion

USDA-ARS?s Scientific Manuscript database

There continues to be a need to predict furrow irrigation erosion to estimate on- and off-site impacts of irrigation management. The objective of this paper is to review the current state of furrow erosion prediction technology considering four models: SISL, WEPP, WinSRFR and APEX. SISL is an empiri...

Real Patient and its Virtual Twin: Application of Quantitative Systems Toxicology Modelling in the Cardiac Safety Assessment of Citalopram.

PubMed

Patel, Nikunjkumar; Wiśniowska, Barbara; Jamei, Masoud; Polak, Sebastian

2017-11-27

A quantitative systems toxicology (QST) model for citalopram was established to simulate, in silico, a 'virtual twin' of a real patient to predict the occurrence of cardiotoxic events previously reported in patients under various clinical conditions. The QST model considers the effects of citalopram and its most notable electrophysiologically active primary (desmethylcitalopram) and secondary (didesmethylcitalopram) metabolites, on cardiac electrophysiology. The in vitro cardiac ion channel current inhibition data was coupled with the biophysically detailed model of human cardiac electrophysiology to investigate the impact of (i) the inhibition of multiple ion currents (I Kr , I Ks , I CaL ); (ii) the inclusion of metabolites in the QST model; and (iii) unbound or total plasma as the operating drug concentration, in predicting clinically observed QT prolongation. The inclusion of multiple ion channel current inhibition and metabolites in the simulation with unbound plasma citalopram concentration provided the lowest prediction error. The predictive performance of the model was verified with three additional therapeutic and supra-therapeutic drug exposure clinical cases. The results indicate that considering only the hERG ion channel inhibition of only the parent drug is potentially misleading, and the inclusion of active metabolite data and the influence of other ion channel currents should be considered to improve the prediction of potential cardiac toxicity. Mechanistic modelling can help bridge the gaps existing in the quantitative translation from preclinical cardiac safety assessment to clinical toxicology. Moreover, this study shows that the QST models, in combination with appropriate drug and systems parameters, can pave the way towards personalised safety assessment.

Predicting Grizzly Bear Density in Western North America

PubMed Central

Mowat, Garth; Heard, Douglas C.; Schwarz, Carl J.

2013-01-01

Conservation of grizzly bears (Ursus arctos) is often controversial and the disagreement often is focused on the estimates of density used to calculate allowable kill. Many recent estimates of grizzly bear density are now available but field-based estimates will never be available for more than a small portion of hunted populations. Current methods of predicting density in areas of management interest are subjective and untested. Objective methods have been proposed, but these statistical models are so dependent on results from individual study areas that the models do not generalize well. We built regression models to relate grizzly bear density to ultimate measures of ecosystem productivity and mortality for interior and coastal ecosystems in North America. We used 90 measures of grizzly bear density in interior ecosystems, of which 14 were currently known to be unoccupied by grizzly bears. In coastal areas, we used 17 measures of density including 2 unoccupied areas. Our best model for coastal areas included a negative relationship with tree cover and positive relationships with the proportion of salmon in the diet and topographic ruggedness, which was correlated with precipitation. Our best interior model included 3 variables that indexed terrestrial productivity, 1 describing vegetation cover, 2 indices of human use of the landscape and, an index of topographic ruggedness. We used our models to predict current population sizes across Canada and present these as alternatives to current population estimates. Our models predict fewer grizzly bears in British Columbia but more bears in Canada than in the latest status review. These predictions can be used to assess population status, set limits for total human-caused mortality, and for conservation planning, but because our predictions are static, they cannot be used to assess population trend. PMID:24367552

Predicting grizzly bear density in western North America.

PubMed

Mowat, Garth; Heard, Douglas C; Schwarz, Carl J

2013-01-01

Conservation of grizzly bears (Ursus arctos) is often controversial and the disagreement often is focused on the estimates of density used to calculate allowable kill. Many recent estimates of grizzly bear density are now available but field-based estimates will never be available for more than a small portion of hunted populations. Current methods of predicting density in areas of management interest are subjective and untested. Objective methods have been proposed, but these statistical models are so dependent on results from individual study areas that the models do not generalize well. We built regression models to relate grizzly bear density to ultimate measures of ecosystem productivity and mortality for interior and coastal ecosystems in North America. We used 90 measures of grizzly bear density in interior ecosystems, of which 14 were currently known to be unoccupied by grizzly bears. In coastal areas, we used 17 measures of density including 2 unoccupied areas. Our best model for coastal areas included a negative relationship with tree cover and positive relationships with the proportion of salmon in the diet and topographic ruggedness, which was correlated with precipitation. Our best interior model included 3 variables that indexed terrestrial productivity, 1 describing vegetation cover, 2 indices of human use of the landscape and, an index of topographic ruggedness. We used our models to predict current population sizes across Canada and present these as alternatives to current population estimates. Our models predict fewer grizzly bears in British Columbia but more bears in Canada than in the latest status review. These predictions can be used to assess population status, set limits for total human-caused mortality, and for conservation planning, but because our predictions are static, they cannot be used to assess population trend.

Current State and Future Perspectives in QSAR Models to Predict Blood- Brain Barrier Penetration in Central Nervous System Drug R&D.

PubMed

Morales, Juan F; Montoto, Sebastian Scioli; Fagiolino, Pietro; Ruiz, Maria E

2017-01-01

The Blood-Brain Barrier (BBB) is a physical and biochemical barrier that restricts the entry of certain drugs to the Central Nervous System (CNS), while allowing the passage of others. The ability to predict the permeability of a given molecule through the BBB is a key aspect in CNS drug discovery and development, since neurotherapeutic agents with molecular targets in the CNS should be able to cross the BBB, whereas peripherally acting agents should not, to minimize the risk of CNS adverse effects. In this review we examine and discuss QSAR approaches and current availability of experimental data for the construction of BBB permeability predictive models, focusing on the modeling of the biorelevant parameter unbound partitioning coefficient (Kp,uu). Emphasis is made on two possible strategies to overcome the current limitations of in silico models: considering the prediction of brain penetration as a multifactorial problem, and increasing experimental datasets through accurate and standardized experimental techniques.

A new lifetime estimation model for a quicker LED reliability prediction

NASA Astrophysics Data System (ADS)

Hamon, B. H.; Mendizabal, L.; Feuillet, G.; Gasse, A.; Bataillou, B.

2014-09-01

LED reliability and lifetime prediction is a key point for Solid State Lighting adoption. For this purpose, one hundred and fifty LEDs have been aged for a reliability analysis. LEDs have been grouped following nine current-temperature stress conditions. Stress driving current was fixed between 350mA and 1A and ambient temperature between 85C and 120°C. Using integrating sphere and I(V) measurements, a cross study of the evolution of electrical and optical characteristics has been done. Results show two main failure mechanisms regarding lumen maintenance. The first one is the typically observed lumen depreciation and the second one is a much more quicker depreciation related to an increase of the leakage and non radiative currents. Models of the typical lumen depreciation and leakage resistance depreciation have been made using electrical and optical measurements during the aging tests. The combination of those models allows a new method toward a quicker LED lifetime prediction. These two models have been used for lifetime predictions for LEDs.

Predicting tidal currents in San Francisco Bay using a spectral model

USGS Publications Warehouse

Burau, Jon R.; Cheng, Ralph T.

1988-01-01

This paper describes the formulation of a spectral (or frequency based) model which solves the linearized shallow water equations. To account for highly variable basin bathymetry, spectral solutions are obtained using the finite element method which allows the strategic placement of the computation points in the specific areas of interest or in areas where the gradients of the dependent variables are expected to be large. Model results are compared with data using simple statistics to judge overall model performance in the San Francisco Bay estuary. Once the model is calibrated and verified, prediction of the tides and tidal currents in San Francisco Bay is accomplished by applying astronomical tides (harmonic constants deduced from field data) at the prediction time along the model boundaries.

Where the wild things are: Predicting hotspots of seabird aggregations in the California Current System

USGS Publications Warehouse

Nur, N.; Jahncke, J.; Herzog, M.P.; Howar, J.; Hyrenbach, K.D.; Zamon, J.E.; Ainley, D.G.; Wiens, J.A.; Morgan, K.; Balance, L.T.; Stralberg, D.

2011-01-01

Marine Protected Areas (MPAs) provide an important tool for conservation of marine ecosystems. To be most effective, these areas should be strategically located in a manner that supports ecosystem function. To inform marine spatial planning and support strategic establishment of MPAs within the California Current System, we identified areas predicted to support multispecies aggregations of seabirds ("hotspot????). We developed habitat-association models for 16 species using information from at-sea observations collected over an 11-year period (1997-2008), bathymetric data, and remotely sensed oceanographic data for an area from north of Vancouver Island, Canada, to the USA/Mexico border and seaward 600 km from the coast. This approach enabled us to predict distribution and abundance of seabirds even in areas of few or no surveys. We developed single-species predictive models using a machine-learning algorithm: bagged decision trees. Single-species predictions were then combined to identify potential hotspots of seabird aggregation, using three criteria: (1) overall abundance among species, (2) importance of specific areas ("core area????) to individual species, and (3) predicted persistence of hotspots across years. Model predictions were applied to the entire California Current for four seasons (represented by February, May, July, and October) in each of 11 years. Overall, bathymetric variables were often important predictive variables, whereas oceanographic variables derived from remotely sensed data were generally less important. Predicted hotspots often aligned with currently protected areas (e.g., National Marine Sanctuaries), but we also identified potential hotspots in Northern California/Southern Oregon (from Cape Mendocino to Heceta Bank), Southern California (adjacent to the Channel Islands), and adjacent to Vancouver Island, British Columbia, that are not currently included in protected areas. Prioritization and identification of multispecies hotspots will depend on which group of species is of highest management priority. Modeling hotspots at a broad spatial scale can contribute to MPA site selection, particularly if complemented by fine-scale information for focal areas. ?? 2011 by the Ecological Society of America.

Prediction of Metabolism of Drugs using Artificial Intelligence: How far have we reached?

PubMed

Kumar, Rajnish; Sharma, Anju; Siddiqui, Mohammed Haris; Tiwari, Rajesh Kumar

2016-01-01

Information about drug metabolism is an essential component of drug development. Modeling the drug metabolism requires identification of the involved enzymes, rate and extent of metabolism, the sites of metabolism etc. There has been continuous attempts in the prediction of metabolism of drugs using artificial intelligence in effort to reduce the attrition rate of drug candidates entering to preclinical and clinical trials. Currently, there are number of predictive models available for metabolism using Support vector machines, Artificial neural networks, Bayesian classifiers etc. There is an urgent need to review their progress so far and address the existing challenges in prediction of metabolism. In this attempt, we are presenting the currently available literature models and some of the critical issues regarding prediction of drug metabolism.

Real-time assessments of water quality: expanding nowcasting throughout the Great Lakes

USGS Publications Warehouse

,

2013-01-01

Nowcasts are systems that inform the public of current bacterial water-quality conditions at beaches on the basis of predictive models. During 2010–12, the U.S. Geological Survey (USGS) worked with 23 local and State agencies to improve existing operational beach nowcast systems at 4 beaches and expand the use of predictive models in nowcasts at an additional 45 beaches throughout the Great Lakes. The predictive models were specific to each beach, and the best model for each beach was based on a unique combination of environmental and water-quality explanatory variables. The variables used most often in models to predict Escherichia coli (E. coli) concentrations or the probability of exceeding a State recreational water-quality standard included turbidity, day of the year, wave height, wind direction and speed, antecedent rainfall for various time periods, and change in lake level over 24 hours. During validation of 42 beach models during 2012, the models performed better than the current method to assess recreational water quality (previous day's E. coli concentration). The USGS will continue to work with local agencies to improve nowcast predictions, enable technology transfer of predictive model development procedures, and implement more operational systems during 2013 and beyond.

Are prediction models for Lynch syndrome valid for probands with endometrial cancer?

PubMed

Backes, Floor J; Hampel, Heather; Backes, Katherine A; Vaccarello, Luis; Lewandowski, George; Bell, Jeffrey A; Reid, Gary C; Copeland, Larry J; Fowler, Jeffrey M; Cohn, David E

2009-01-01

Currently, three prediction models are used to predict a patient's risk of having Lynch syndrome (LS). These models have been validated in probands with colorectal cancer (CRC), but not in probands presenting with endometrial cancer (EMC). Thus, the aim was to determine the performance of these prediction models in women with LS presenting with EMC. Probands with EMC and LS were identified. Personal and family history was entered into three prediction models, PREMM(1,2), MMRpro, and MMRpredict. Probabilities of mutations in the mismatch repair genes were recorded. Accurate prediction was defined as a model predicting at least a 5% chance of a proband carrying a mutation. From 562 patients prospectively enrolled in a clinical trial of patients with EMC, 13 (2.2%) were shown to have LS. Nine patients had a mutation in MSH6, three in MSH2, and one in MLH1. MMRpro predicted that 3 of 9 patients with an MSH6, 3 of 3 with an MSH2, and 1 of 1 patient with an MLH1 mutation could have LS. For MMRpredict, EMC coded as "proximal CRC" predicted 5 of 5, and as "distal CRC" three of five. PREMM(1,2) predicted that 4 of 4 with an MLH1 or MSH2 could have LS. Prediction of LS in probands presenting with EMC using current models for probands with CRC works reasonably well. Further studies are needed to develop models that include questions specific to patients with EMC with a greater age range, as well as placing increased emphasis on prediction of LS in probands with MSH6 mutations.

Time series analysis of malaria in Afghanistan: using ARIMA models to predict future trends in incidence.

PubMed

Anwar, Mohammad Y; Lewnard, Joseph A; Parikh, Sunil; Pitzer, Virginia E

2016-11-22

Malaria remains endemic in Afghanistan. National control and prevention strategies would be greatly enhanced through a better ability to forecast future trends in disease incidence. It is, therefore, of interest to develop a predictive tool for malaria patterns based on the current passive and affordable surveillance system in this resource-limited region. This study employs data from Ministry of Public Health monthly reports from January 2005 to September 2015. Malaria incidence in Afghanistan was forecasted using autoregressive integrated moving average (ARIMA) models in order to build a predictive tool for malaria surveillance. Environmental and climate data were incorporated to assess whether they improve predictive power of models. Two models were identified, each appropriate for different time horizons. For near-term forecasts, malaria incidence can be predicted based on the number of cases in the four previous months and 12 months prior (Model 1); for longer-term prediction, malaria incidence can be predicted using the rates 1 and 12 months prior (Model 2). Next, climate and environmental variables were incorporated to assess whether the predictive power of proposed models could be improved. Enhanced vegetation index was found to have increased the predictive accuracy of longer-term forecasts. Results indicate ARIMA models can be applied to forecast malaria patterns in Afghanistan, complementing current surveillance systems. The models provide a means to better understand malaria dynamics in a resource-limited context with minimal data input, yielding forecasts that can be used for public health planning at the national level.

Do We Know the Actual Magnetopause Position for Typical Solar Wind Conditions?

NASA Technical Reports Server (NTRS)

Samsonov, A. A.; Gordeev, E.; Tsyganenko, N. A.; Safrankova, J.; Nemecek, Z.; Simunek, J.; Sibeck, D. G.; Toth, G.; Merkin, V. G.; Raeder, J.

2016-01-01

We compare predicted magnetopause positions at the subsolar point and four reference points in the terminator plane obtained from several empirical and numerical MHD (magnetohydrodynamics) models. Empirical models using various sets of magnetopause crossings and making different assumptions about the magnetopause shape predict significantly different magnetopause positions (with a scatter greater than 1 Earth radius (R (sub E)) even at the subsolar point. Axisymmetric magnetopause models cannot reproduce the cusp indentations or the changes related to the dipole tilt effect, and most of them predict the magnetopause closer to the Earth than non axisymmetric models for typical solar wind conditions and zero tilt angle. Predictions of two global non axisymmetric models do not match each other, and the models need additional verification. MHD models often predict the magnetopause closer to the Earth than the non axisymmetric empirical models, but the predictions of MHD simulations may need corrections for the ring current effect and decreases of the solar wind pressure that occur in the foreshock. Comparing MHD models in which the ring current magnetic field is taken into account with the empirical Lin et al. model, we find that the differences in the reference point positions predicted by these models are relatively small for B (sub z) equals 0 (note: B (sub z) is when the Earth's magnetic field points north versus Sun's magnetic field pointing south). Therefore, we assume that these predictions indicate the actual magnetopause position, but future investigations are still needed.

A way forward for fire-caused tree mortality prediction: Modeling a physiological consequence of fire

Treesearch

Kathleen L. Kavanaugh; Matthew B. Dickinson; Anthony S. Bova

2010-01-01

Current operational methods for predicting tree mortality from fire injury are regression-based models that only indirectly consider underlying causes and, thus, have limited generality. A better understanding of the physiological consequences of tree heating and injury are needed to develop biophysical process models that can make predictions under changing or novel...

Nowcasting Ground Magnetic Perturbations with the Space Weather Modeling Framework

NASA Astrophysics Data System (ADS)

Welling, D. T.; Toth, G.; Singer, H. J.; Millward, G. H.; Gombosi, T. I.

2015-12-01

Predicting ground-based magnetic perturbations is a critical step towards specifying and predicting geomagnetically induced currents (GICs) in high voltage transmission lines. Currently, the Space Weather Modeling Framework (SWMF), a flexible modeling framework for simulating the multi-scale space environment, is being transitioned from research to operational use (R2O) by NOAA's Space Weather Prediction Center. Upon completion of this transition, the SWMF will provide localized B/t predictions using real-time solar wind observations from L1 and the F10.7 proxy for EUV as model input. This presentation describes the operational SWMF setup and summarizes the changes made to the code to enable R2O progress. The framework's algorithm for calculating ground-based magnetometer observations will be reviewed. Metrics from data-model comparisons will be reviewed to illustrate predictive capabilities. Early data products, such as regional-K index and grids of virtual magnetometer stations, will be presented. Finally, early successes will be shared, including the code's ability to reproduce the recent March 2015 St. Patrick's Day Storm.

Plant water potential improves prediction of empirical stomatal models.

PubMed

Anderegg, William R L; Wolf, Adam; Arango-Velez, Adriana; Choat, Brendan; Chmura, Daniel J; Jansen, Steven; Kolb, Thomas; Li, Shan; Meinzer, Frederick; Pita, Pilar; Resco de Dios, Víctor; Sperry, John S; Wolfe, Brett T; Pacala, Stephen

2017-01-01

Climate change is expected to lead to increases in drought frequency and severity, with deleterious effects on many ecosystems. Stomatal responses to changing environmental conditions form the backbone of all ecosystem models, but are based on empirical relationships and are not well-tested during drought conditions. Here, we use a dataset of 34 woody plant species spanning global forest biomes to examine the effect of leaf water potential on stomatal conductance and test the predictive accuracy of three major stomatal models and a recently proposed model. We find that current leaf-level empirical models have consistent biases of over-prediction of stomatal conductance during dry conditions, particularly at low soil water potentials. Furthermore, the recently proposed stomatal conductance model yields increases in predictive capability compared to current models, and with particular improvement during drought conditions. Our results reveal that including stomatal sensitivity to declining water potential and consequent impairment of plant water transport will improve predictions during drought conditions and show that many biomes contain a diversity of plant stomatal strategies that range from risky to conservative stomatal regulation during water stress. Such improvements in stomatal simulation are greatly needed to help unravel and predict the response of ecosystems to future climate extremes.

Changing head model extent affects finite element predictions of transcranial direct current stimulation distributions

NASA Astrophysics Data System (ADS)

Indahlastari, Aprinda; Chauhan, Munish; Schwartz, Benjamin; Sadleir, Rosalind J.

2016-12-01

Objective. In this study, we determined efficient head model sizes relative to predicted current densities in transcranial direct current stimulation (tDCS). Approach. Efficiency measures were defined based on a finite element (FE) simulations performed using nine human head models derived from a single MRI data set, having extents varying from 60%-100% of the original axial range. Eleven tissue types, including anisotropic white matter, and three electrode montages (T7-T8, F3-right supraorbital, Cz-Oz) were used in the models. Main results. Reducing head volume extent from 100% to 60%, that is, varying the model’s axial range from between the apex and C3 vertebra to one encompassing only apex to the superior cerebellum, was found to decrease the total modeling time by up to half. Differences between current density predictions in each model were quantified by using a relative difference measure (RDM). Our simulation results showed that {RDM} was the least affected (a maximum of 10% error) for head volumes modeled from the apex to the base of the skull (60%-75% volume). Significance. This finding suggested that the bone could act as a bioelectricity boundary and thus performing FE simulations of tDCS on the human head with models extending beyond the inferior skull may not be necessary in most cases to obtain reasonable precision in current density results.

Does the Current Minimum Validate (or Invalidate) Cycle Prediction Methods?

NASA Technical Reports Server (NTRS)

Hathaway, David H.

2010-01-01

This deep, extended solar minimum and the slow start to Cycle 24 strongly suggest that Cycle 24 will be a small cycle. A wide array of solar cycle prediction techniques have been applied to predicting the amplitude of Cycle 24 with widely different results. Current conditions and new observations indicate that some highly regarded techniques now appear to have doubtful utility. Geomagnetic precursors have been reliable in the past and can be tested with 12 cycles of data. Of the three primary geomagnetic precursors only one (the minimum level of geomagnetic activity) suggests a small cycle. The Sun's polar field strength has also been used to successfully predict the last three cycles. The current weak polar fields are indicative of a small cycle. For the first time, dynamo models have been used to predict the size of a solar cycle but with opposite predictions depending on the model and the data assimilation. However, new measurements of the surface meridional flow indicate that the flow was substantially faster on the approach to Cycle 24 minimum than at Cycle 23 minimum. In both dynamo predictions a faster meridional flow should have given a shorter cycle 23 with stronger polar fields. This suggests that these dynamo models are not yet ready for solar cycle prediction.

A Novel Modelling Approach for Predicting Forest Growth and Yield under Climate Change.

PubMed

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

2015-01-01

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

A Novel Modelling Approach for Predicting Forest Growth and Yield under Climate Change

PubMed Central

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

2015-01-01

Global climate is changing due to increasing anthropogenic emissions of greenhouse gases. Forest managers need growth and yield models that can be used to predict future forest dynamics during the transition period of present-day forests under a changing climatic regime. In this study, we developed a forest growth and yield model that can be used to predict individual-tree growth under current and projected future climatic conditions. The model was constructed by integrating historical tree growth records with predictions from an ecological process-based model using neural networks. The new model predicts basal area (BA) and volume growth for individual trees in pure or mixed species forests. For model development, tree-growth data under current climatic conditions were obtained using over 3000 permanent sample plots from the Province of Nova Scotia, Canada. Data to reflect tree growth under a changing climatic regime were projected with JABOWA-3 (an ecological process-based model). Model validation with designated data produced model efficiencies of 0.82 and 0.89 in predicting individual-tree BA and volume growth. Model efficiency is a relative index of model performance, where 1 indicates an ideal fit, while values lower than zero means the predictions are no better than the average of the observations. Overall mean prediction error (BIAS) of basal area and volume growth predictions was nominal (i.e., for BA: -0.0177 cm2 5-year-1 and volume: 0.0008 m3 5-year-1). Model variability described by root mean squared error (RMSE) in basal area prediction was 40.53 cm2 5-year-1 and 0.0393 m3 5-year-1 in volume prediction. The new modelling approach has potential to reduce uncertainties in growth and yield predictions under different climate change scenarios. This novel approach provides an avenue for forest managers to generate required information for the management of forests in transitional periods of climate change. Artificial intelligence technology has substantial potential in forest modelling. PMID:26173081

To predict the niche, model colonization and extinction

Treesearch

Charles B. Yackulic; James D. Nichols; Janice Reid; Ricky Der

2015-01-01

Ecologists frequently try to predict the future geographic distributions of species. Most studies assume that the current distribution of a species reflects its environmental requirements (i.e., the species’ niche). However, the current distributions of many species are unlikely to be at equilibrium with the current distribution of environmental conditions, both...

ANALYTICAL MODELING OF ELECTRON BACK-BOMBARDMENT INDUCED CURRENT INCREASE IN UN-GATED THERMIONIC CATHODE RF GUNS

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

Edelen, J. P.; Sun, Y.; Harris, J. R.

In this paper we derive analytical expressions for the output current of an un-gated thermionic cathode RF gun in the presence of back-bombardment heating. We provide a brief overview of back-bombardment theory and discuss comparisons between the analytical back-bombardment predictions and simulation models. We then derive an expression for the output current as a function of the RF repetition rate and discuss relationships between back-bombardment, fieldenhancement, and output current. We discuss in detail the relevant approximations and then provide predictions about how the output current should vary as a function of repetition rate for some given system configurations.

POPULATION EXPOSURES TO PARTICULATE MATTER: A COMPARISON OF EXPOSURE MODEL PREDICTIONS AND MEASUREMENT DATA

EPA Science Inventory

The US EPA National Exposure Research Laboratory (NERL) is currently developing an integrated human exposure source-to-dose modeling system (HES2D). This modeling system will incorporate models that use a probabilistic approach to predict population exposures to environmental ...

Toward a Time-Domain Fractal Lightning Simulation

NASA Astrophysics Data System (ADS)

Liang, C.; Carlson, B. E.; Lehtinen, N. G.; Cohen, M.; Lauben, D.; Inan, U. S.

2010-12-01

Electromagnetic simulations of lightning are useful for prediction of lightning properties and exploration of the underlying physical behavior. Fractal lightning models predict the spatial structure of the discharge, but thus far do not provide much information about discharge behavior in time and therefore cannot predict electromagnetic wave emissions or current characteristics. Here we develop a time-domain fractal lightning simulation from Maxwell's equations, the method of moments with the thin wire approximation, an adaptive time-stepping scheme, and a simplified electrical model of the lightning channel. The model predicts current pulse structure and electromagnetic wave emissions and can be used to simulate the entire duration of a lightning discharge. The model can be used to explore the electrical characteristics of the lightning channel, the temporal development of the discharge, and the effects of these characteristics on observable electromagnetic wave emissions.

Enhancing seasonal climate prediction capacity for the Pacific countries

NASA Astrophysics Data System (ADS)

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

2012-04-01

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

3D analysis of eddy current loss in the permanent magnet coupling.

PubMed

Zhu, Zina; Meng, Zhuo

2016-07-01

This paper first presents a 3D analytical model for analyzing the radial air-gap magnetic field between the inner and outer magnetic rotors of the permanent magnet couplings by using the Amperian current model. Based on the air-gap field analysis, the eddy current loss in the isolation cover is predicted according to the Maxwell's equations. A 3D finite element analysis model is constructed to analyze the magnetic field spatial distributions and vector eddy currents, and then the simulation results obtained are analyzed and compared with the analytical method. Finally, the current losses of two types of practical magnet couplings are measured in the experiment to compare with the theoretical results. It is concluded that the 3D analytical method of eddy current loss in the magnet coupling is viable and could be used for the eddy current loss prediction of magnet couplings.

Assessing forest vulnerability and the potential distribution of pine beetles under current and future climate scenarios in the Interior West of the US

USGS Publications Warehouse

Evangelista, P.H.; Kumar, S.; Stohlgren, T.J.; Young, N.E.

2011-01-01

The aim of our study was to estimate forest vulnerability and potential distribution of three bark beetles (Curculionidae: Scolytinae) under current and projected climate conditions for 2020 and 2050. Our study focused on the mountain pine beetle (Dendroctonus ponderosae), western pine beetle (Dendroctonus brevicomis), and pine engraver (Ips pini). This study was conducted across eight states in the Interior West of the US covering approximately 2.2millionkm2 and encompassing about 95% of the Rocky Mountains in the contiguous US. Our analyses relied on aerial surveys of bark beetle outbreaks that occurred between 1991 and 2008. Occurrence points for each species were generated within polygons created from the aerial surveys. Current and projected climate scenarios were acquired from the WorldClim database and represented by 19 bioclimatic variables. We used Maxent modeling technique fit with occurrence points and current climate data to model potential beetle distributions and forest vulnerability. Three available climate models, each having two emission scenarios, were modeled independently and results averaged to produce two predictions for 2020 and two predictions for 2050 for each analysis. Environmental parameters defined by current climate models were then used to predict conditions under future climate scenarios, and changes in different species' ranges were calculated. Our results suggested that the potential distribution for bark beetles under current climate conditions is extensive, which coincides with infestation trends observed in the last decade. Our results predicted that suitable habitats for the mountain pine beetle and pine engraver beetle will stabilize or decrease under future climate conditions, while habitat for the western pine beetle will continue to increase over time. The greatest increase in habitat area was for the western pine beetle, where one climate model predicted a 27% increase by 2050. In contrast, the predicted habitat of the mountain pine beetle from another climate model suggested a decrease in habitat areas as great as 46% by 2050. Generally, 2020 and 2050 models that tested the three climate scenarios independently had similar trends, though one climate scenario for the western pine beetle produced contrasting results. Ranges for all three species of bark beetles shifted considerably geographically suggesting that some host species may become more vulnerable to beetle attack in the future, while others may have a reduced risk over time. ?? 2011 Elsevier B.V.

Physics-of-Failure Approach to Prognostics

NASA Technical Reports Server (NTRS)

Kulkarni, Chetan S.

2017-01-01

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in accurate prediction of the electrical components present in the system. In case of electric vehicles, computing remaining battery charge is safety-critical. In order to tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operations of the vehicle. In this presentation our approach to develop a system level health monitoring safety indicator for different electronic components is presented which runs estimation and prediction algorithms to determine state-of-charge and estimate remaining useful life of respective components. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making.

Predicting Student Performance in a Collaborative Learning Environment

ERIC Educational Resources Information Center

Olsen, Jennifer K.; Aleven, Vincent; Rummel, Nikol

2015-01-01

Student models for adaptive systems may not model collaborative learning optimally. Past research has either focused on modeling individual learning or for collaboration, has focused on group dynamics or group processes without predicting learning. In the current paper, we adjust the Additive Factors Model (AFM), a standard logistic regression…

Activity Patterns in Response to Symptoms in Patients Being Treated for Chronic Fatigue Syndrome: An Experience Sampling Methodology Study

PubMed Central

2016-01-01

Objective: Cognitive–behavioral models of chronic fatigue syndrome (CFS) propose that patients respond to symptoms with 2 predominant activity patterns—activity limitation and all-or-nothing behaviors—both of which may contribute to illness persistence. The current study investigated whether activity patterns occurred at the same time as, or followed on from, patient symptom experience and affect. Method: Twenty-three adults with CFS were recruited from U.K. CFS services. Experience sampling methodology (ESM) was used to assess fluctuations in patient symptom experience, affect, and activity management patterns over 10 assessments per day for a total of 6 days. Assessments were conducted within patients’ daily life and were delivered through an app on touchscreen Android mobile phones. Multilevel model analyses were conducted to examine the role of self-reported patient fatigue, pain, and affect as predictors of change in activity patterns at the same and subsequent assessment. Results: Current experience of fatigue-related symptoms and pain predicted higher patient activity limitation at the current and subsequent assessments whereas subjective wellness predicted higher all-or-nothing behavior at both times. Current pain predicted less all-or-nothing behavior at the subsequent assessment. In contrast to hypotheses, current positive affect was predictive of current activity limitation whereas current negative affect was predictive of current all-or-nothing behavior. Both activity patterns varied at the momentary level. Conclusions: Patient symptom experiences appear to be driving patient activity management patterns in line with the cognitive–behavioral model of CFS. ESM offers a useful method for examining multiple interacting variables within the context of patients’ daily life. PMID:27819461

Is the future already here? The impact of climate change on the distribution of the eastern coral snake (Micrurus fulvius).

PubMed

Archis, Jennifer N; Akcali, Christopher; Stuart, Bryan L; Kikuchi, David; Chunco, Amanda J

2018-01-01

Anthropogenic climate change is a significant global driver of species distribution change. Although many species have undergone range expansion at their poleward limits, data on several taxonomic groups are still lacking. A common method for studying range shifts is using species distribution models to evaluate current, and predict future, distributions. Notably, many sources of 'current' climate data used in species distribution modeling use the years 1950-2000 to calculate climatic averages. However, this does not account for recent (post 2000) climate change. This study examines the influence of climate change on the eastern coral snake ( Micrurus fulvius ). Specifically, we: (1) identified the current range and suitable environment of M. fulvius in the Southeastern United States, (2) investigated the potential impacts of climate change on the distribution of M. fulvius , and (3) evaluated the utility of future models in predicting recent (2001-2015) records. We used the species distribution modeling program Maxent and compared both current (1950-2000) and future (2050) climate conditions. Future climate models showed a shift in the distribution of suitable habitat across a significant portion of the range; however, results also suggest that much of the Southeastern United States will be outside the range of current conditions, suggesting that there may be no-analog environments in the future. Most strikingly, future models were more effective than the current models at predicting recent records, suggesting that range shifts may already be occurring. These results have implications for both M. fulvius and its Batesian mimics. More broadly, we recommend future Maxent studies consider using future climate data along with current data to better estimate the current distribution.

Modeling micro-droplet formation in near-field electrohydrodynamic jet printing

NASA Astrophysics Data System (ADS)

Popell, George Colin

Near-field electrohydrodynamic jet (E-jet) printing has recently gained significant interest within the manufacturing research community because of its ability to produce micro/sub-micron-scale droplets using a wide variety of inks and substrates. However, the process currently operates in open-loop and as a result suffers from unpredictable printing quality. The use of physics-based, control-oriented process models is expected to enable closed-loop control of this printing technique. The objective of this research is to perform a fundamental study of the substrate-side droplet shape-evolution in near-field E-jet printing and to develop a physics-based model of the same that links input parameters such as voltage magnitude and ink properties to the height and diameter of the printed droplet. In order to achieve this objective, a synchronized high-speed imaging and substrate-side current-detection system was used implemented to enable a correlation between the droplet shape parameters and the measured current signal. The experimental data reveals characteristic process signatures and droplet spreading regimes. The results of these studies are then used as the basis for a model that predicts the droplet diameter and height using the measured current signal as the input. A unique scaling factor based on the measured current signal is used in this model instead of relying on empirical scaling laws found in literature. For each of the three inks tested in this study, the average absolute error in the model predictions is under 4.6% for diameter predictions and under 10.6% for height predictions of the steady-state droplet. While printing under non-conducive ambient conditions of low humidity and high temperatures, the use of the environmental correction factor in the model is seen to result in average absolute errors of 10.35% and 12.5% for diameter and height predictions.

Improving orbit prediction accuracy through supervised machine learning

NASA Astrophysics Data System (ADS)

Peng, Hao; Bai, Xiaoli

2018-05-01

Due to the lack of information such as the space environment condition and resident space objects' (RSOs') body characteristics, current orbit predictions that are solely grounded on physics-based models may fail to achieve required accuracy for collision avoidance and have led to satellite collisions already. This paper presents a methodology to predict RSOs' trajectories with higher accuracy than that of the current methods. Inspired by the machine learning (ML) theory through which the models are learned based on large amounts of observed data and the prediction is conducted without explicitly modeling space objects and space environment, the proposed ML approach integrates physics-based orbit prediction algorithms with a learning-based process that focuses on reducing the prediction errors. Using a simulation-based space catalog environment as the test bed, the paper demonstrates three types of generalization capability for the proposed ML approach: (1) the ML model can be used to improve the same RSO's orbit information that is not available during the learning process but shares the same time interval as the training data; (2) the ML model can be used to improve predictions of the same RSO at future epochs; and (3) the ML model based on a RSO can be applied to other RSOs that share some common features.

Modelling fatigue and the use of fatigue models in work settings.

PubMed

Dawson, Drew; Ian Noy, Y; Härmä, Mikko; Akerstedt, Torbjorn; Belenky, Gregory

2011-03-01

In recent years, theoretical models of the sleep and circadian system developed in laboratory settings have been adapted to predict fatigue and, by inference, performance. This is typically done using the timing of prior sleep and waking or working hours as the primary input and the time course of the predicted variables as the primary output. The aim of these models is to provide employers, unions and regulators with quantitative information on the likely average level of fatigue, or risk, associated with a given pattern of work and sleep with the goal of better managing the risk of fatigue-related errors and accidents/incidents. The first part of this review summarises the variables known to influence workplace fatigue and draws attention to the considerable variability attributable to individual and task variables not included in current models. The second part reviews the current fatigue models described in the scientific and technical literature and classifies them according to whether they predict fatigue directly by using the timing of prior sleep and wake (one-step models) or indirectly by using work schedules to infer an average sleep-wake pattern that is then used to predict fatigue (two-step models). The third part of the review looks at the current use of fatigue models in field settings by organizations and regulators. Given their limitations it is suggested that the current generation of models may be appropriate for use as one element in a fatigue risk management system. The final section of the review looks at the future of these models and recommends a standardised approach for their use as an element of the 'defenses-in-depth' approach to fatigue risk management. Copyright © 2010 Elsevier Ltd. All rights reserved.

Climate-Induced Boreal Forest Change: Predictions versus Current Observations

NASA Technical Reports Server (NTRS)

Soja, Amber J.; Tchebakova, Nadezda M.; French, Nancy H. F.; Flannigan, Michael D.; Shugart, Herman H.; Stocks, Brian J.; Sukhinin, Anatoly I.; Parfenova, E. I.; Chapin, F. Stuart, III; Stackhouse, Paul W., Jr.

2007-01-01

For about three decades, there have been many predictions of the potential ecological response in boreal regions to the currently warmer conditions. In essence, a widespread, naturally occurring experiment has been conducted over time. In this paper, we describe previously modeled predictions of ecological change in boreal Alaska, Canada and Russia, and then we investigate potential evidence of current climate-induced change. For instance, ecological models have suggested that warming will induce the northern and upslope migration of the treeline and an alteration in the current mosaic structure of boreal forests. We present evidence of the migration of keystone ecosystems in the upland and lowland treeline of mountainous regions across southern Siberia. Ecological models have also predicted a moisture-stress-related dieback in white spruce trees in Alaska, and current investigations show that as temperatures increase, white spruce tree growth is declining. Additionally, it was suggested that increases in infestation and wildfire disturbance would be catalysts that precipitate the alteration of the current mosaic forest composition. In Siberia, five of the last seven years have resulted in extreme fire seasons, and extreme fire years have also been more frequent in both Alaska and Canada. In addition, Alaska has experienced extreme and geographically expansive multi-year outbreaks of the spruce beetle, which had been previously limited by the cold, moist environment. We suggest that there is substantial evidence throughout the circumboreal region to conclude that the biosphere within the boreal terrestrial environment has already responded to the transient effects of climate change. Additionally, temperature increases and warming-induced change are progressing faster than had been predicted in some regions, suggesting a potential non-linear rapid response to changes in climate, as opposed to the predicted slow linear response to climate change.

Verification by remote sensing of an oil slick movement prediction model. [Delaware Bay

NASA Technical Reports Server (NTRS)

Klemas, V. (Principal Investigator); Davis, G.; Wang, H.

1976-01-01

The author has identified the following significant results. LANDSAT, aircraft, ships, and air-dropped current drogues were deployed to determine current circulation and to track oil slick movement on four different dates in Delaware Bay. The results were used to verify a predictive model for oil slicks given their size, location, tidal stage (current), weather (wind), and nature of crude. Both LANDSAT satellites provided valuable data on gross circulation patterns and convergent coastal fronts which by capturing oil slicks significantly influence their movement and dispersion.

Testing an algebraic model of self-reflexion.

PubMed

Grice, James W; McDaniel, Brenda L; Thompsen, Dana

2005-06-01

Self-reflexion is the conscious process of taking the position of an observer in relation to one's own thoughts, feelings, and experiences. Building on the work of Lefebvre, Lefebvre, and Adams-Webber, we used a formal algebraic model of self-reflexion to derive several predictions regarding the frequencies with which individuals would rate themselves and others positively on bipolar scales anchored by adjective terms. The current results from 108 participants (41 men, 67 women; M age= 20.2 yr.) confirmed two predictions derived from the model. Three other predictions, however, were not supported even though the observed frequencies were close to the predicted values. Although not as promising as results reported by Lefebvre, et al., these mixed findings were interpreted as encouraging support for the validity of Lefebvre's algebraic model of self-reflexion. Differences between the current methods and those from previous investigations were also examined, and methodological implications for further studies were discussed.

DATA ASSIMILATION APPROACH FOR FORECAST OF SOLAR ACTIVITY CYCLES

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

Kitiashvili, Irina N., E-mail: irina.n.kitiashvili@nasa.gov

Numerous attempts to predict future solar cycles are mostly based on empirical relations derived from observations of previous cycles, and they yield a wide range of predicted strengths and durations of the cycles. Results obtained with current dynamo models also deviate strongly from each other, thus raising questions about criteria to quantify the reliability of such predictions. The primary difficulties in modeling future solar activity are shortcomings of both the dynamo models and observations that do not allow us to determine the current and past states of the global solar magnetic structure and its dynamics. Data assimilation is a relativelymore » new approach to develop physics-based predictions and estimate their uncertainties in situations where the physical properties of a system are not well-known. This paper presents an application of the ensemble Kalman filter method for modeling and prediction of solar cycles through use of a low-order nonlinear dynamo model that includes the essential physics and can describe general properties of the sunspot cycles. Despite the simplicity of this model, the data assimilation approach provides reasonable estimates for the strengths of future solar cycles. In particular, the prediction of Cycle 24 calculated and published in 2008 is so far holding up quite well. In this paper, I will present my first attempt to predict Cycle 25 using the data assimilation approach, and discuss the uncertainties of that prediction.« less

Efficient prediction of terahertz quantum cascade laser dynamics from steady-state simulations

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

Agnew, G.; Lim, Y. L.; Nikolić, M.

2015-04-20

Terahertz-frequency quantum cascade lasers (THz QCLs) based on bound-to-continuum active regions are difficult to model owing to their large number of quantum states. We present a computationally efficient reduced rate equation (RE) model that reproduces the experimentally observed variation of THz power with respect to drive current and heat-sink temperature. We also present dynamic (time-domain) simulations under a range of drive currents and predict an increase in modulation bandwidth as the current approaches the peak of the light–current curve, as observed experimentally in mid-infrared QCLs. We account for temperature and bias dependence of the carrier lifetimes, gain, and injection efficiency,more » calculated from a full rate equation model. The temperature dependence of the simulated threshold current, emitted power, and cut-off current are thus all reproduced accurately with only one fitting parameter, the interface roughness, in the full REs. We propose that the model could therefore be used for rapid dynamical simulation of QCL designs.« less

On Verifying Currents and Other Features in the Hawaiian Islands Region Using Fully Coupled Ocean/Atmosphere Mesoscale Prediction System Compared to Global Ocean Model and Ocean Observations

NASA Astrophysics Data System (ADS)

Jessen, P. G.; Chen, S.

2014-12-01

This poster introduces and evaluates features concerning the Hawaii, USA region using the U.S. Navy's fully Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS-OS™) coupled to the Navy Coastal Ocean Model (NCOM). It also outlines some challenges in verifying ocean currents in the open ocean. The system is evaluated using in situ ocean data and initial forcing fields from the operational global Hybrid Coordinate Ocean Model (HYCOM). Verification shows difficulties in modelling downstream currents off the Hawaiian islands (Hawaii's wake). Comparing HYCOM to NCOM current fields show some displacement of small features such as eddies. Generally, there is fair agreement from HYCOM to NCOM in salinity and temperature fields. There is good agreement in SSH fields.

NEXT Ion Thruster Thermal Model

NASA Technical Reports Server (NTRS)

VanNoord, Jonathan L.

2010-01-01

As the NEXT ion thruster progresses towards higher technology readiness, it is necessary to develop the tools that will support its implementation into flight programs. An ion thruster thermal model has been developed for the latest prototype model design to aid in predicting thruster temperatures for various missions. This model is comprised of two parts. The first part predicts the heating from the discharge plasma for various throttling points based on a discharge chamber plasma model. This model shows, as expected, that the internal heating is strongly correlated with the discharge power. Typically, the internal plasma heating increases with beam current and decreases slightly with beam voltage. The second is a model based on a finite difference thermal code used to predict the thruster temperatures. Both parts of the model will be described in this paper. This model has been correlated with a thermal development test on the NEXT Prototype Model 1 thruster with most predicted component temperatures within 5 to 10 C of test temperatures. The model indicates that heating, and hence current collection, is not based purely on the footprint of the magnet rings, but follows a 0.1:1:2:1 ratio for the cathode-to-conical-to-cylindrical-to-front magnet rings. This thermal model has also been used to predict the temperatures during the worst case mission profile that is anticipated for the thruster. The model predicts ample thermal margin for all of its components except the external cable harness under the hottest anticipated mission scenario. The external cable harness will be re-rated or replaced to meet the predicted environment.

An Investment Model Analysis of Relationship Stability among Women Court-Mandated to Violence Interventions

ERIC Educational Resources Information Center

Rhatigan, Deborah L.; Moore, Todd M.; Stuart, Gregory L.

2005-01-01

This investigation examined relationship stability among 60 women court-mandated to violence interventions by applying a general model (i.e., Rusbult's 1980 Investment Model) to predict intentions to leave current relationships. As in past research, results showed that Investment Model predictions were supported such that court-mandated women who…

Evaluating the Impact of Aerosols on Numerical Weather Prediction

NASA Astrophysics Data System (ADS)

Freitas, Saulo; Silva, Arlindo; Benedetti, Angela; Grell, Georg; Members, Wgne; Zarzur, Mauricio

2015-04-01

The Working Group on Numerical Experimentation (WMO, http://www.wmo.int/pages/about/sec/rescrosscut/resdept_wgne.html) has organized an exercise to evaluate the impact of aerosols on NWP. This exercise will involve regional and global models currently used for weather forecast by the operational centers worldwide and aims at addressing the following questions: a) How important are aerosols for predicting the physical system (NWP, seasonal, climate) as distinct from predicting the aerosols themselves? b) How important is atmospheric model quality for air quality forecasting? c) What are the current capabilities of NWP models to simulate aerosol impacts on weather prediction? Toward this goal we have selected 3 strong or persistent events of aerosol pollution worldwide that could be fairly represented in current NWP models and that allowed for an evaluation of the aerosol impact on weather prediction. The selected events includes a strong dust storm that blew off the coast of Libya and over the Mediterranean, an extremely severe episode of air pollution in Beijing and surrounding areas, and an extreme case of biomass burning smoke in Brazil. The experimental design calls for simulations with and without explicitly accounting for aerosol feedbacks in the cloud and radiation parameterizations. In this presentation we will summarize the results of this study focusing on the evaluation of model performance in terms of its ability to faithfully simulate aerosol optical depth, and the assessment of the aerosol impact on the predictions of near surface wind, temperature, humidity, rainfall and the surface energy budget.

Enhanced pid vs model predictive control applied to bldc motor

NASA Astrophysics Data System (ADS)

Gaya, M. S.; Muhammad, Auwal; Aliyu Abdulkadir, Rabiu; Salim, S. N. S.; Madugu, I. S.; Tijjani, Aminu; Aminu Yusuf, Lukman; Dauda Umar, Ibrahim; Khairi, M. T. M.

2018-01-01

BrushLess Direct Current (BLDC) motor is a multivariable and highly complex nonlinear system. Variation of internal parameter values with environment or reference signal increases the difficulty in controlling the BLDC effectively. Advanced control strategies (like model predictive control) often have to be integrated to satisfy the control desires. Enhancing or proper tuning of a conventional algorithm results in achieving the desired performance. This paper presents a performance comparison of Enhanced PID and Model Predictive Control (MPC) applied to brushless direct current motor. The simulation results demonstrated that the PSO-PID is slightly better than the PID and MPC in tracking the trajectory of the reference signal. The proposed scheme could be useful algorithms for the system.

Short-term Forecasting Ground Magnetic Perturbations with the Space Weather Modeling Framework

NASA Astrophysics Data System (ADS)

Welling, Daniel; Toth, Gabor; Gombosi, Tamas; Singer, Howard; Millward, George

2016-04-01

Predicting ground-based magnetic perturbations is a critical step towards specifying and predicting geomagnetically induced currents (GICs) in high voltage transmission lines. Currently, the Space Weather Modeling Framework (SWMF), a flexible modeling framework for simulating the multi-scale space environment, is being transitioned from research to operational use (R2O) by NOAA's Space Weather Prediction Center. Upon completion of this transition, the SWMF will provide localized dB/dt predictions using real-time solar wind observations from L1 and the F10.7 proxy for EUV as model input. This presentation describes the operational SWMF setup and summarizes the changes made to the code to enable R2O progress. The framework's algorithm for calculating ground-based magnetometer observations will be reviewed. Metrics from data-model comparisons will be reviewed to illustrate predictive capabilities. Early data products, such as regional-K index and grids of virtual magnetometer stations, will be presented. Finally, early successes will be shared, including the code's ability to reproduce the recent March 2015 St. Patrick's Day Storm.

Improving Flash Flood Prediction in Multiple Environments

NASA Astrophysics Data System (ADS)

Broxton, P. D.; Troch, P. A.; Schaffner, M.; Unkrich, C.; Goodrich, D.; Wagener, T.; Yatheendradas, S.

2009-12-01

Flash flooding is a major concern in many fast responding headwater catchments . There are many efforts to model and to predict these flood events, though it is not currently possible to adequately predict the nature of flash flood events with a single model, and furthermore, many of these efforts do not even consider snow, which can, by itself, or in combination with rainfall events, cause destructive floods. The current research is aimed at broadening the applicability of flash flood modeling. Specifically, we will take a state of the art flash flood model that is designed to work with warm season precipitation in arid environments, the KINematic runoff and EROSion model (KINEROS2), and combine it with a continuous subsurface flow model and an energy balance snow model. This should improve its predictive capacity in humid environments where lateral subsurface flow significantly contributes to streamflow, and it will make possible the prediction of flooding events that involve rain-on-snow or rapid snowmelt. By modeling changes in the hydrologic state of a catchment before a flood begins, we can also better understand the factors or combination of factors that are necessary to produce large floods. Broadening the applicability of an already state of the art flash flood model, such as KINEROS2, is logical because flash floods can occur in all types of environments, and it may lead to better predictions, which are necessary to preserve life and property.

Impact of Future Climate on Radial Growth of Four Major Boreal Tree Species in the Eastern Canadian Boreal Forest

PubMed Central

Huang, Jian-Guo; Bergeron, Yves; Berninger, Frank; Zhai, Lihong; Tardif, Jacques C.; Denneler, Bernhard

2013-01-01

Immediate phenotypic variation and the lagged effect of evolutionary adaptation to climate change appear to be two key processes in tree responses to climate warming. This study examines these components in two types of growth models for predicting the 2010–2099 diameter growth change of four major boreal species Betula papyrifera, Pinus banksiana, Picea mariana, and Populus tremuloides along a broad latitudinal gradient in eastern Canada under future climate projections. Climate-growth response models for 34 stands over nine latitudes were calibrated and cross-validated. An adaptive response model (A-model), in which the climate-growth relationship varies over time, and a fixed response model (F-model), in which the relationship is constant over time, were constructed to predict future growth. For the former, we examined how future growth of stands in northern latitudes could be forecasted using growth-climate equations derived from stands currently growing in southern latitudes assuming that current climate in southern locations provide an analogue for future conditions in the north. For the latter, we tested if future growth of stands would be maximally predicted using the growth-climate equation obtained from the given local stand assuming a lagged response to climate due to genetic constraints. Both models predicted a large growth increase in northern stands due to more benign temperatures, whereas there was a minimal growth change in southern stands due to potentially warm-temperature induced drought-stress. The A-model demonstrates a changing environment whereas the F-model highlights a constant growth response to future warming. As time elapses we can predict a gradual transition between a response to climate associated with the current conditions (F-model) to a more adapted response to future climate (A-model). Our modeling approach provides a template to predict tree growth response to climate warming at mid-high latitudes of the Northern Hemisphere. PMID:23468879

Impact of future climate on radial growth of four major boreal tree species in the Eastern Canadian boreal forest.

PubMed

Huang, Jian-Guo; Bergeron, Yves; Berninger, Frank; Zhai, Lihong; Tardif, Jacques C; Denneler, Bernhard

2013-01-01

Immediate phenotypic variation and the lagged effect of evolutionary adaptation to climate change appear to be two key processes in tree responses to climate warming. This study examines these components in two types of growth models for predicting the 2010-2099 diameter growth change of four major boreal species Betula papyrifera, Pinus banksiana, Picea mariana, and Populus tremuloides along a broad latitudinal gradient in eastern Canada under future climate projections. Climate-growth response models for 34 stands over nine latitudes were calibrated and cross-validated. An adaptive response model (A-model), in which the climate-growth relationship varies over time, and a fixed response model (F-model), in which the relationship is constant over time, were constructed to predict future growth. For the former, we examined how future growth of stands in northern latitudes could be forecasted using growth-climate equations derived from stands currently growing in southern latitudes assuming that current climate in southern locations provide an analogue for future conditions in the north. For the latter, we tested if future growth of stands would be maximally predicted using the growth-climate equation obtained from the given local stand assuming a lagged response to climate due to genetic constraints. Both models predicted a large growth increase in northern stands due to more benign temperatures, whereas there was a minimal growth change in southern stands due to potentially warm-temperature induced drought-stress. The A-model demonstrates a changing environment whereas the F-model highlights a constant growth response to future warming. As time elapses we can predict a gradual transition between a response to climate associated with the current conditions (F-model) to a more adapted response to future climate (A-model). Our modeling approach provides a template to predict tree growth response to climate warming at mid-high latitudes of the Northern Hemisphere.

A Particle and Energy Balance Model of the Orificed Hollow Cathode

NASA Technical Reports Server (NTRS)

Domonkos, Matthew T.

2002-01-01

A particle and energy balance model of orificed hollow cathodes was developed to assist in cathode design. The model presented here is an ensemble of original work by the author and previous work by others. The processes in the orifice region are considered to be one of the primary drivers in determining cathode performance, since the current density was greatest in this volume (up to 1.6 x 10(exp 8) A/m2). The orifice model contains comparatively few free parameters, and its results are used to bound the free parameters for the insert model. Next, the insert region model is presented. The sensitivity of the results to the free parameters is assessed, and variation of the free parameters in the orifice dominates the calculated power consumption and plasma properties. The model predictions are compared to data from a low-current orificed hollow cathode. The predicted power consumption exceeds the experimental results. Estimates of the plasma properties in the insert region overlap Langmuir probe data, and the predicted orifice plasma suggests the presence of one or more double layers. Finally, the model is used to examine the operation of higher current cathodes.

Predictive models for Escherichia coli concentrations at inland lake beaches and relationship of model variables to pathogen detection

EPA Science Inventory

Methods are needed improve the timeliness and accuracy of recreational water‐quality assessments. Traditional culture methods require 18–24 h to obtain results and may not reflect current conditions. Predictive models, based on environmental and water quality variables, have been...

At the Crossroads of Nanotoxicology: Past Achievements and Current Challenges

DTIC Science & Technology

2015-01-01

rates of ionic dissolution, improving in vitro to in vivo predictive efficiencies, and establishing safety exposure limits. This Review will discuss...Oberdörster et al., 2005a), which drove the focus of in vitro and in vivo model selection to accommodate these areas of higher NM exposure. Most...Accordingly, a current challenge is the design of simple, in vitro models that reliably predict in vivo effects following a NM challenge. In order

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.

Identifying and Assessing Gaps in Subseasonal to Seasonal Prediction Skill using the North American Multi-model Ensemble

NASA Astrophysics Data System (ADS)

Pegion, K.; DelSole, T. M.; Becker, E.; Cicerone, T.

2016-12-01

Predictability represents the upper limit of prediction skill if we had an infinite member ensemble and a perfect model. It is an intrinsic limit of the climate system associated with the chaotic nature of the atmosphere. Producing a forecast system that can make predictions very near to this limit is the ultimate goal of forecast system development. Estimates of predictability together with calculations of current prediction skill are often used to define the gaps in our prediction capabilities on subseasonal to seasonal timescales and to inform the scientific issues that must be addressed to build the next forecast system. Quantification of the predictability is also important for providing a scientific basis for relaying to stakeholders what kind of climate information can be provided to inform decision-making and what kind of information is not possible given the intrinsic predictability of the climate system. One challenge with predictability estimates is that different prediction systems can give different estimates of the upper limit of skill. How do we know which estimate of predictability is most representative of the true predictability of the climate system? Previous studies have used the spread-error relationship and the autocorrelation to evaluate the fidelity of the signal and noise estimates. Using a multi-model ensemble prediction system, we can quantify whether these metrics accurately indicate an individual model's ability to properly estimate the signal, noise, and predictability. We use this information to identify the best estimates of predictability for 2-meter temperature, precipitation, and sea surface temperature from the North American Multi-model Ensemble and compare with current skill to indicate the regions with potential for improving skill.

Validation and optimization of SST k-ω turbulence model for pollutant dispersion within a building array

NASA Astrophysics Data System (ADS)

Yu, Hesheng; Thé, Jesse

2016-11-01

The prediction of the dispersion of air pollutants in urban areas is of great importance to public health, homeland security, and environmental protection. Computational Fluid Dynamics (CFD) emerges as an effective tool for pollutant dispersion modelling. This paper reports and quantitatively validates the shear stress transport (SST) k-ω turbulence closure model and its transitional variant for pollutant dispersion under complex urban environment for the first time. Sensitivity analysis is performed to establish recommendation for the proper use of turbulence models in urban settings. The current SST k-ω simulation is validated rigorously by extensive experimental data using hit rate for velocity components, and the "factor of two" of observations (FAC2) and fractional bias (FB) for concentration field. The simulation results show that current SST k-ω model can predict flow field nicely with an overall hit rate of 0.870, and concentration dispersion with FAC2 = 0.721 and FB = 0.045. The flow simulation of the current SST k-ω model is slightly inferior to that of a detached eddy simulation (DES), but better than that of standard k-ε model. However, the current study is the best among these three model approaches, when validated against measurements of pollutant dispersion in the atmosphere. This work aims to provide recommendation for proper use of CFD to predict pollutant dispersion in urban environment.

Non-linear behaviour of electrical parameters in porous, water-saturated rocks: a model to predict pore size distribution

NASA Astrophysics Data System (ADS)

Hallbauer-Zadorozhnaya, Valeriya; Santarato, Giovanni; Abu Zeid, Nasser

2015-08-01

In this paper, two separate but related goals are tackled. The first one is to demonstrate that in some saturated rock textures the non-linear behaviour of induced polarization (IP) and the violation of Ohm's law not only are real phenomena, but they can also be satisfactorily predicted by a suitable physical-mathematical model, which is our second goal. This model is based on Fick's second law. As the model links the specific dependence of resistivity and chargeability of a laboratory sample to the injected current and this in turn to its pore size distribution, it is able to predict pore size distribution from laboratory measurements, in good agreement with mercury injection capillary pressure test results. This fact opens up the possibility for hydrogeophysical applications on a macro scale. Mathematical modelling shows that the chargeability acquired in the field under normal conditions, that is at low current, will always be very small and approximately proportional to the applied current. A suitable field test site for demonstrating the possible reliance of both resistivity and chargeability on current was selected and a specific measuring strategy was established. Two data sets were acquired using different injected current strengths, while keeping the charging time constant. Observed variations of resistivity and chargeability are in agreement with those predicted by the mathematical model. These field test data should however be considered preliminary. If confirmed by further evidence, these facts may lead to changing the procedure of acquiring field measurements in future, and perhaps may encourage the design and building of a new specific geo-resistivity meter. This paper also shows that the well-known Marshall and Madden's equations based on Fick's law cannot be solved without specific boundary conditions.

Effect of injection current and temperature on signal strength in a laser diode optical feedback interferometer.

PubMed

Al Roumy, Jalal; Perchoux, Julien; Lim, Yah Leng; Taimre, Thomas; Rakić, Aleksandar D; Bosch, Thierry

2015-01-10

We present a simple analytical model that describes the injection current and temperature dependence of optical feedback interferometry signal strength for a single-mode laser diode. The model is derived from the Lang and Kobayashi rate equations, and is developed both for signals acquired from the monitoring photodiode (proportional to the variations in optical power) and for those obtained by amplification of the corresponding variations in laser voltage. The model shows that both the photodiode and the voltage signal strengths are dependent on the laser slope efficiency, which itself is a function of the injection current and the temperature. Moreover, the model predicts that the photodiode and voltage signal strengths depend differently on injection current and temperature. This important model prediction was proven experimentally for a near-infrared distributed feedback laser by measuring both types of signals over a wide range of injection currents and temperatures. Therefore, this simple model provides important insight into the radically different biasing strategies required to achieve optimal sensor sensitivity for both interferometric signal acquisition schemes.

Evaluating simplistic methods to understand current distributions and forecast distribution changes under climate change scenarios: An example with coypu (Myocastor coypus)

USGS Publications Warehouse

Jarnevich, Catherine S.; Young, Nicholas E; Sheffels, Trevor R.; Carter, Jacoby; Systma, Mark D.; Talbert, Colin

2017-01-01

Invasive species provide a unique opportunity to evaluate factors controlling biogeographic distributions; we can consider introduction success as an experiment testing suitability of environmental conditions. Predicting potential distributions of spreading species is not easy, and forecasting potential distributions with changing climate is even more difficult. Using the globally invasive coypu (Myocastor coypus [Molina, 1782]), we evaluate and compare the utility of a simplistic ecophysiological based model and a correlative model to predict current and future distribution. The ecophysiological model was based on winter temperature relationships with nutria survival. We developed correlative statistical models using the Software for Assisted Habitat Modeling and biologically relevant climate data with a global extent. We applied the ecophysiological based model to several global circulation model (GCM) predictions for mid-century. We used global coypu introduction data to evaluate these models and to explore a hypothesized physiological limitation, finding general agreement with known coypu distribution locally and globally and support for an upper thermal tolerance threshold. Global circulation model based model results showed variability in coypu predicted distribution among GCMs, but had general agreement of increasing suitable area in the USA. Our methods highlighted the dynamic nature of the edges of the coypu distribution due to climate non-equilibrium, and uncertainty associated with forecasting future distributions. Areas deemed suitable habitat, especially those on the edge of the current known range, could be used for early detection of the spread of coypu populations for management purposes. Combining approaches can be beneficial to predicting potential distributions of invasive species now and in the future and in exploring hypotheses of factors controlling distributions.

The Roles of Social Support and Coping Strategies in Predicting Breast Cancer Patients’ Emotional Well-being

PubMed Central

KIM, JUNGHYUN; HAN, JEONG YEOB; SHAW, BRET; MCTAVISH, FIONA; GUSTAFSON, DAVID

2011-01-01

The goal of the current study was to examine how social support and coping strategies are related in predicting emotional well-being of women with breast cancer. In achieving this goal, we examined two hypothesized models: (1) a moderation model where social support and coping strategies interact with each other in affecting psychological well-being; and (2) a mediation model where the level of social support influences choices of coping strategies between self-blame and positive reframing. In general, the data from the current study were more consistent with the mediation model than the moderation model. PMID:20460411

Modeling regulation of cardiac KATP and L-type Ca2+ currents by ATP, ADP, and Mg2+.

PubMed

Michailova, Anushka; Saucerman, Jeffrey; Belik, Mary Ellen; McCulloch, Andrew D

2005-03-01

Changes in cytosolic free Mg(2+) and adenosine nucleotide phosphates affect cardiac excitability and contractility. To investigate how modulation by Mg(2+), ATP, and ADP of K(ATP) and L-type Ca(2+) channels influences excitation-contraction coupling, we incorporated equations for intracellular ATP and MgADP regulation of the K(ATP) current and MgATP regulation of the L-type Ca(2+) current in an ionic-metabolic model of the canine ventricular myocyte. The new model: 1), quantitatively reproduces a dose-response relationship for the effects of changes in ATP on K(ATP) current, 2), simulates effects of ADP in modulating ATP sensitivity of K(ATP) channel, 3), predicts activation of Ca(2+) current during rapid increase in MgATP, and 4), demonstrates that decreased ATP/ADP ratio with normal total Mg(2+) or increased free Mg(2+) with normal ATP and ADP activate K(ATP) current, shorten action potential, and alter ionic currents and intracellular Ca(2+) signals. The model predictions are in agreement with experimental data measured under normal and a variety of pathological conditions.

Modeling regulation of cardiac KATP and L-type Ca2+ currents by ATP, ADP, and Mg2+

NASA Technical Reports Server (NTRS)

Michailova, Anushka; Saucerman, Jeffrey; Belik, Mary Ellen; McCulloch, Andrew D.

2005-01-01

Changes in cytosolic free Mg(2+) and adenosine nucleotide phosphates affect cardiac excitability and contractility. To investigate how modulation by Mg(2+), ATP, and ADP of K(ATP) and L-type Ca(2+) channels influences excitation-contraction coupling, we incorporated equations for intracellular ATP and MgADP regulation of the K(ATP) current and MgATP regulation of the L-type Ca(2+) current in an ionic-metabolic model of the canine ventricular myocyte. The new model: 1), quantitatively reproduces a dose-response relationship for the effects of changes in ATP on K(ATP) current, 2), simulates effects of ADP in modulating ATP sensitivity of K(ATP) channel, 3), predicts activation of Ca(2+) current during rapid increase in MgATP, and 4), demonstrates that decreased ATP/ADP ratio with normal total Mg(2+) or increased free Mg(2+) with normal ATP and ADP activate K(ATP) current, shorten action potential, and alter ionic currents and intracellular Ca(2+) signals. The model predictions are in agreement with experimental data measured under normal and a variety of pathological conditions.

Individual differences in transcranial electrical stimulation current density

PubMed Central

Russell, Michael J; Goodman, Theodore; Pierson, Ronald; Shepherd, Shane; Wang, Qiang; Groshong, Bennett; Wiley, David F

2013-01-01

Transcranial electrical stimulation (TCES) is effective in treating many conditions, but it has not been possible to accurately forecast current density within the complex anatomy of a given subject's head. We sought to predict and verify TCES current densities and determine the variability of these current distributions in patient-specific models based on magnetic resonance imaging (MRI) data. Two experiments were performed. The first experiment estimated conductivity from MRIs and compared the current density results against actual measurements from the scalp surface of 3 subjects. In the second experiment, virtual electrodes were placed on the scalps of 18 subjects to model simulated current densities with 2 mA of virtually applied stimulation. This procedure was repeated for 4 electrode locations. Current densities were then calculated for 75 brain regions. Comparison of modeled and measured external current in experiment 1 yielded a correlation of r = .93. In experiment 2, modeled individual differences were greatest near the electrodes (ten-fold differences were common), but simulated current was found in all regions of the brain. Sites that were distant from the electrodes (e.g. hypothalamus) typically showed two-fold individual differences. MRI-based modeling can effectively predict current densities in individual brains. Significant variation occurs between subjects with the same applied electrode configuration. Individualized MRI-based modeling should be considered in place of the 10-20 system when accurate TCES is needed. PMID:24285948

Direct model-based predictive control scheme without cost function for voltage source inverters with reduced common-mode voltage

NASA Astrophysics Data System (ADS)

Kim, Jae-Chang; Moon, Sung-Ki; Kwak, Sangshin

2018-04-01

This paper presents a direct model-based predictive control scheme for voltage source inverters (VSIs) with reduced common-mode voltages (CMVs). The developed method directly finds optimal vectors without using repetitive calculation of a cost function. To adjust output currents with the CMVs in the range of -Vdc/6 to +Vdc/6, the developed method uses voltage vectors, as finite control resources, excluding zero voltage vectors which produce the CMVs in the VSI within ±Vdc/2. In a model-based predictive control (MPC), not using zero voltage vectors increases the output current ripples and the current errors. To alleviate these problems, the developed method uses two non-zero voltage vectors in one sampling step. In addition, the voltage vectors scheduled to be used are directly selected at every sampling step once the developed method calculates the future reference voltage vector, saving the efforts of repeatedly calculating the cost function. And the two non-zero voltage vectors are optimally allocated to make the output current approach the reference current as close as possible. Thus, low CMV, rapid current-following capability and sufficient output current ripple performance are attained by the developed method. The results of a simulation and an experiment verify the effectiveness of the developed method.

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.

LDEF satellite radiation study

NASA Technical Reports Server (NTRS)

Armstrong, T. W.; Colborn, B. L.

1994-01-01

Some early results are summarized from a program under way to utilize LDEF satellite data for evaluating and improving current models of the space radiation environment in low earth orbit. Reported here are predictions and comparisons with some of the LDEF dose and induced radioactivity data, which are used to check the accuracy of current models describing the magnitude and directionality of the trapped proton environment. Preliminary findings are that the environment models underestimate both dose and activation from trapped protons by a factor of about two, and the observed anisotropy is higher than predicted.

Predicting the High Redshift Galaxy Population for JWST

NASA Astrophysics Data System (ADS)

Flynn, Zoey; Benson, Andrew

2017-01-01

The James Webb Space Telescope will be launched in Oct 2018 with the goal of observing galaxies in the redshift range of z = 10 - 15. As redshift increases, the age of the Universe decreases, allowing us to study objects formed only a few hundred million years after the Big Bang. This will provide a valuable opportunity to test and improve current galaxy formation theory by comparing predictions for mass, luminosity, and number density to the observed data. We have made testable predictions with the semi-analytical galaxy formation model Galacticus. The code uses Markov Chain Monte Carlo methods to determine viable sets of model parameters that match current astronomical data. The resulting constrained model was then set to match the specifications of the JWST Ultra Deep Field Imaging Survey. Predictions utilizing up to 100 viable parameter sets were calculated, allowing us to assess the uncertainty in current theoretical expectations. We predict that the planned UDF will be able to observe a significant number of objects past redshift z > 9 but nothing at redshift z > 11. In order to detect these faint objects at redshifts z = 11-15 we need to increase exposure time by at least a factor of 1.66.

Current status and future needs of the BehavePlus Fire Modeling System

Treesearch

Patricia L. Andrews

2014-01-01

The BehavePlus Fire Modeling System is among the most widely used systems for wildland fire prediction. It is designed for use in a range of tasks including wildfire behaviour prediction, prescribed fire planning, fire investigation, fuel hazard assessment, fire model understanding, communication and research. BehavePlus is based on mathematical models for fire...

Evaluating Antarctic sea ice predictability at seasonal to interannual timescales in global climate models

NASA Astrophysics Data System (ADS)

Marchi, Sylvain; Fichefet, Thierry; Goosse, Hugues; Zunz, Violette; Tietsche, Steffen; Day, Jonny; Hawkins, Ed

2016-04-01

Unlike the rapid sea ice losses reported in the Arctic, satellite observations show an overall increase in Antarctic sea ice extent over recent decades. Although many processes have already been suggested to explain this positive trend, it remains the subject of current investigations. Understanding the evolution of the Antarctic sea ice turns out to be more complicated than for the Arctic for two reasons: the lack of observations and the well-known biases of climate models in the Southern Ocean. Irrespective of those issues, another one is to determine whether the positive trend in sea ice extent would have been predictable if adequate observations and models were available some decades ago. This study of Antarctic sea ice predictability is carried out using 6 global climate models (HadGEM1.2, MPI-ESM-LR, GFDL CM3, EC-Earth V2, MIROC 5.2 and ECHAM 6-FESOM) which are all part of the APPOSITE project. These models are used to perform hindcast simulations in a perfect model approach. The predictive skill is estimated thanks to the PPP (Potential Prognostic Predictability) and the ACC (Anomaly Correlation Coefficient). The former is a measure of the uncertainty of the ensemble while the latter assesses the accuracy of the prediction. These two indicators are applied to different variables related to sea ice, in particular the total sea ice extent and the ice edge location. This first model intercomparison study about sea ice predictability in the Southern Ocean aims at giving a general overview of Antarctic sea ice predictability in current global climate models.

Characterization of YBa2Cu3O7, including critical current density Jc, by trapped magnetic field

NASA Technical Reports Server (NTRS)

Chen, In-Gann; Liu, Jianxiong; Weinstein, Roy; Lau, Kwong

1992-01-01

Spatial distributions of persistent magnetic field trapped by sintered and melt-textured ceramic-type high-temperature superconductor (HTS) samples have been studied. The trapped field can be reproduced by a model of the current consisting of two components: (1) a surface current Js and (2) a uniform volume current Jv. This Js + Jv model gives a satisfactory account of the spatial distribution of the magnetic field trapped by different types of HTS samples. The magnetic moment can be calculated, based on the Js + Jv model, and the result agrees well with that measured by standard vibrating sample magnetometer (VSM). As a consequence, Jc predicted by VSM methods agrees with Jc predicted from the Js + Jv model. The field mapping method described is also useful to reveal the granular structure of large HTS samples and regions of weak links.

Markovian prediction of future values for food grains in the economic survey

NASA Astrophysics Data System (ADS)

Sathish, S.; Khadar Babu, S. K.

2017-11-01

Now-a-days prediction and forecasting are plays a vital role in research. For prediction, regression is useful to predict the future value and current value on production process. In this paper, we assume food grain production exhibit Markov chain dependency and time homogeneity. The economic generative performance evaluation the balance time artificial fertilization different level in Estrusdetection using a daily Markov chain model. Finally, Markov process prediction gives better performance compare with Regression model.

Integrated Wind Power Planning Tool

NASA Astrophysics Data System (ADS)

Rosgaard, Martin; Giebel, Gregor; Skov Nielsen, Torben; Hahmann, Andrea; Sørensen, Poul; Madsen, Henrik

2013-04-01

This poster presents the current state of the public service obligation (PSO) funded project PSO 10464, with the title "Integrated Wind Power Planning Tool". The goal is to integrate a mesoscale numerical weather prediction (NWP) model with purely statistical tools in order to assess wind power fluctuations, with focus on long term power system planning for future wind farms as well as short term forecasting for existing wind farms. Currently, wind power fluctuation models are either purely statistical or integrated with NWP models of limited resolution. Using the state-of-the-art mesoscale NWP model Weather Research & Forecasting model (WRF) the forecast error is sought quantified in dependence of the time scale involved. This task constitutes a preparative study for later implementation of features accounting for NWP forecast errors in the DTU Wind Energy maintained Corwind code - a long term wind power planning tool. Within the framework of PSO 10464 research related to operational short term wind power prediction will be carried out, including a comparison of forecast quality at different mesoscale NWP model resolutions and development of a statistical wind power prediction tool taking input from WRF. The short term prediction part of the project is carried out in collaboration with ENFOR A/S; a Danish company that specialises in forecasting and optimisation for the energy sector. The integrated prediction model will allow for the description of the expected variability in wind power production in the coming hours to days, accounting for its spatio-temporal dependencies, and depending on the prevailing weather conditions defined by the WRF output. The output from the integrated short term prediction tool constitutes scenario forecasts for the coming period, which can then be fed into any type of system model or decision making problem to be solved. The high resolution of the WRF results loaded into the integrated prediction model will ensure a high accuracy data basis is available for use in the decision making process of the Danish transmission system operator. The need for high accuracy predictions will only increase over the next decade as Denmark approaches the goal of 50% wind power based electricity in 2025 from the current 20%.

Implicit cognitive aggression among young male prisoners: Association with dispositional and current aggression.

PubMed

Ireland, Jane L; Adams, Christine

2015-01-01

The current study explores associations between implicit and explicit aggression in young adult male prisoners, seeking to apply the Reflection-Impulsive Model and indicate parity with elements of the General Aggression Model and social cognition. Implicit cognitive aggressive processing is not an area that has been examined among prisoners. Two hundred and sixty two prisoners completed an implicit cognitive aggression measure (Puzzle Test) and explicit aggression measures, covering current behaviour (DIPC-R) and aggression disposition (AQ). It was predicted that dispositional aggression would be predicted by implicit cognitive aggression, and that implicit cognitive aggression would predict current engagement in aggressive behaviour. It was also predicted that more impulsive implicit cognitive processing would associate with aggressive behaviour whereas cognitively effortful implicit cognitive processing would not. Implicit aggressive cognitive processing was associated with increased dispositional aggression but not current reports of aggressive behaviour. Impulsive implicit cognitive processing of an aggressive nature predicted increased dispositional aggression whereas more cognitively effortful implicit cognitive aggression did not. The article concludes by outlining the importance of accounting for implicit cognitive processing among prisoners and the need to separate such processing into facets (i.e. impulsive vs. cognitively effortful). Implications for future research and practice in this novel area of study are indicated. Copyright © 2015 Elsevier Ltd. All rights reserved.

Taking Wave Prediction to New Levels: Wavewatch 3

DTIC Science & Technology

2016-01-01

features such as surf and rip currents , conditions that affect special operations, amphibious assaults, and logistics over the shore. Changes in...The Navy’s current version of WAVEWATCH Ill features the capability of operating with gridded domains of multiple resolution simultaneously, ranging...Netherlands. Its current form, WAVEWATCH Ill, was developed at NOAA’s National Center for Environmental Prediction. The model is free and open source

Inflationary tensor fossils in large-scale structure

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

Dimastrogiovanni, Emanuela; Fasiello, Matteo; Jeong, Donghui

Inflation models make specific predictions for a tensor-scalar-scalar three-point correlation, or bispectrum, between one gravitational-wave (tensor) mode and two density-perturbation (scalar) modes. This tensor-scalar-scalar correlation leads to a local power quadrupole, an apparent departure from statistical isotropy in our Universe, as well as characteristic four-point correlations in the current mass distribution in the Universe. So far, the predictions for these observables have been worked out only for single-clock models in which certain consistency conditions between the tensor-scalar-scalar correlation and tensor and scalar power spectra are satisfied. Here we review the requirements on inflation models for these consistency conditions to bemore » satisfied. We then consider several examples of inflation models, such as non-attractor and solid-inflation models, in which these conditions are put to the test. In solid inflation the simplest consistency conditions are already violated whilst in the non-attractor model we find that, contrary to the standard scenario, the tensor-scalar-scalar correlator probes directly relevant model-dependent information. We work out the predictions for observables in these models. For non-attractor inflation we find an apparent local quadrupolar departure from statistical isotropy in large-scale structure but that this power quadrupole decreases very rapidly at smaller scales. The consistency of the CMB quadrupole with statistical isotropy then constrains the distance scale that corresponds to the transition from the non-attractor to attractor phase of inflation to be larger than the currently observable horizon. Solid inflation predicts clustering fossils signatures in the current galaxy distribution that may be large enough to be detectable with forthcoming, and possibly even current, galaxy surveys.« less

A Critical Review for Developing Accurate and Dynamic Predictive Models Using Machine Learning Methods in Medicine and Health Care.

PubMed

Alanazi, Hamdan O; Abdullah, Abdul Hanan; Qureshi, Kashif Naseer

2017-04-01

Recently, Artificial Intelligence (AI) has been used widely in medicine and health care sector. In machine learning, the classification or prediction is a major field of AI. Today, the study of existing predictive models based on machine learning methods is extremely active. Doctors need accurate predictions for the outcomes of their patients' diseases. In addition, for accurate predictions, timing is another significant factor that influences treatment decisions. In this paper, existing predictive models in medicine and health care have critically reviewed. Furthermore, the most famous machine learning methods have explained, and the confusion between a statistical approach and machine learning has clarified. A review of related literature reveals that the predictions of existing predictive models differ even when the same dataset is used. Therefore, existing predictive models are essential, and current methods must be improved.

Constrains on the South Atlantic Anomaly from Réunion Island

NASA Astrophysics Data System (ADS)

Béguin, A.; de Groot, L. V.

2017-12-01

The South Atlantic Anomaly (SAA) is a region where the geomagnetic field intensity is about half as strong as would be expected from the current geomagnetic dipole moment that arises from geomagnetic field models. Those field models predict a westward movement of the SAA and predicts its origin East of Africa around 1500 AD. The onset and evolution of the SAA, however, are poorly constrained due to a lack of full-vector paleomagnetic data from Africa and the Indian Ocean for the past centuries. Here we present a full-vector paleosecular variation (PSV) curve for Réunion Island (21°S, 55°E) located East the African continent, in the region that currently shows the fastest increase in geomagnetic field strength in contrast to the average global decay. We sampled 27 sites covering the last 700 years, and subjected them to a directional and multi-method paleointensity study. The obtained directional records reveal shallower inclinations and less variation in the declination compared to current geomagnetic field model predictions. Scrutinizing the IZZI-Thellier, Multispecimen, and calibrated pseudo-Thellier results produces a coherent paleointensity record. The predicted intensity trend from the geomagnetic field models generally agrees with the trend in our data, however, the high paleointensities are higher than the models predict, and the low paleointensities are lower than the models. This illustrates the inevitable smoothing inherent to geomagnetic field modelling. We will discuss the constraints on the onset of the SAA that arise from the new full-vector PSV curve for Réunion that we present and the implications for the past and future evolution of this geomagnetic phenomenon.

Risk prediction model: Statistical and artificial neural network approach

NASA Astrophysics Data System (ADS)

Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim

2017-04-01

Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.

An Engineered Membrane to Measure Electroporation: Effect of Tethers and Bioelectronic Interface

PubMed Central

Hoiles, William; Krishnamurthy, Vikram; Cranfield, Charles G.; Cornell, Bruce

2014-01-01

This article reports on the construction and predictive models for a platform comprised of an engineered tethered membrane. The platform provides a controllable and physiologically relevant environment for the study of the electroporation process. The mixed self-assembled membrane is formed via a rapid solvent exchange technique. The membrane is tethered to the gold electrode and includes an ionic reservoir separating the membrane and gold surface. Above the membrane, there is an electrolyte solution, and a gold counterelectrode. A voltage is applied between the gold electrodes and the current measured. The current is dependent on the energy required to form aqueous pores and the conductance of each pore. A two-level predictive model, consisting of a macroscopic and a continuum model, is developed to relate the pore dynamics to the measured current. The macroscopic model consists of an equivalent circuit model of the tethered membrane, and asymptotic approximations to the Smoluchowski-Einstein equation of electroporation that is dependent on the pore conductance and the energy required to form aqueous pores. The continuum model is a generalized Poisson-Nernst-Planck (GPNP) system where an activity coefficient to account for steric effects of ions is added to the standard PNP system. The GPNP is used to evaluate the conductance of aqueous pores, and the electrical energy required to form the pores. As an outcome of the setup of the device and the two-level model, biologically important variables can be estimated from experimental measurements. To validate the accuracy of the two-level model, the predicted current is compared with experimentally measured current for different tethering densities. PMID:25229142

Gender differences in the causal direction between workplace harassment and drinking.

PubMed

Freels, Sally A; Richman, Judith A; Rospenda, Kathleen M

2005-08-01

Data from a longitudinal study of university employees across four waves is used to determine the extent to which workplace harassment predicts drinking or conversely the extent to which drinking predicts workplace harassment, and to address gender differences in these relationships. Mixed effects regression models are used to test the effects of 1) harassment at the previous wave on drinking at the current wave, adjusting for drinking at the previous wave, and 2) drinking at the previous wave on harassment at the current wave, adjusting for harassment at the previous wave. For males, drinking at the previous wave predicts sexual harassment at the current wave, whereas for females, sexual harassment at the previous wave predicts drinking at the current wave.

“Transference Ratios” to Predict Total Oxidized Sulfur and Nitrogen Deposition – Part II, Modeling Results

EPA Science Inventory

The current study examines predictions of transference ratios and related modeled parameters for oxidized sulfur and oxidized nitrogen using five years (2002-2006) of 12-km grid cell-specific annual estimates from EPA’s Community Air Quality Model (CMAQ) for five selected sub-re...

EVALUATING THE PERFORMANCE OF REGIONAL-SCALE PHOTOCHEMICAL MODELING SYSTEMS: PART I--METEOROLOGICAL PREDICTIONS. (R825260)

EPA Science Inventory

In this study, the concept of scale analysis is applied to evaluate two state-of-science meteorological models, namely MM5 and RAMS3b, currently being used to drive regional-scale air quality models. To this end, seasonal time series of observations and predictions for temperatur...

Observational support for the current sheet catastrophe model of substorm current disruption

NASA Technical Reports Server (NTRS)

Burkhart, G. R.; Lopez, R. E.; Dusenbery, P. B.; Speiser, T. W.

1992-01-01

The principles of the current sheet catastrophe models are briefly reviewed, and observations of some of the signatures predicted by the theory are presented. The data considered here include AMPTE/CCE observations of fifteen current sheet disruption events. According to the model proposed here, the root cause of the current disruption is some process, as yet unknown, that leads to an increase in the k sub A parameter. Possible causes for the increase in k sub A are discussed.

Statistical Prediction of Sea Ice Concentration over Arctic

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

Predicting the Overall Spatial Quality of Automotive Audio Systems

NASA Astrophysics Data System (ADS)

Koya, Daisuke

The spatial quality of automotive audio systems is often compromised due to their unideal listening environments. Automotive audio systems need to be developed quickly due to industry demands. A suitable perceptual model could evaluate the spatial quality of automotive audio systems with similar reliability to formal listening tests but take less time. Such a model is developed in this research project by adapting an existing model of spatial quality for automotive audio use. The requirements for the adaptation were investigated in a literature review. A perceptual model called QESTRAL was reviewed, which predicts the overall spatial quality of domestic multichannel audio systems. It was determined that automotive audio systems are likely to be impaired in terms of the spatial attributes that were not considered in developing the QESTRAL model, but metrics are available that might predict these attributes. To establish whether the QESTRAL model in its current form can accurately predict the overall spatial quality of automotive audio systems, MUSHRA listening tests using headphone auralisation with head tracking were conducted to collect results to be compared against predictions by the model. Based on guideline criteria, the model in its current form could not accurately predict the overall spatial quality of automotive audio systems. To improve prediction performance, the QESTRAL model was recalibrated and modified using existing metrics of the model, those that were proposed from the literature review, and newly developed metrics. The most important metrics for predicting the overall spatial quality of automotive audio systems included those that were interaural cross-correlation (IACC) based, relate to localisation of the frontal audio scene, and account for the perceived scene width in front of the listener. Modifying the model for automotive audio systems did not invalidate its use for domestic audio systems. The resulting model predicts the overall spatial quality of 2- and 5-channel automotive audio systems with a cross-validation performance of R. 2 = 0.85 and root-mean-squareerror (RMSE) = 11.03%.

Dendritic trafficking faces physiologically critical speed-precision tradeoffs

DOE PAGES

Williams, Alex H.; O'Donnell, Cian; Sejnowski, Terrence J.; ...

2016-12-30

Nervous system function requires intracellular transport of channels, receptors, mRNAs, and other cargo throughout complex neuronal morphologies. Local signals such as synaptic input can regulate cargo trafficking, motivating the leading conceptual model of neuron-wide transport, sometimes called the ‘sushi-belt model’. Current theories and experiments are based on this model, yet its predictions are not rigorously understood. We formalized the sushi belt model mathematically, and show that it can achieve arbitrarily complex spatial distributions of cargo in reconstructed morphologies. However, the model also predicts an unavoidable, morphology dependent tradeoff between speed, precision and metabolic efficiency of cargo transport. With experimental estimatesmore » of trafficking kinetics, the model predicts delays of many hours or days for modestly accurate and efficient cargo delivery throughout a dendritic tree. In conclusion, these findings challenge current understanding of the efficacy of nucleus-to-synapse trafficking and may explain the prevalence of local biosynthesis in neurons.« less

Dendritic trafficking faces physiologically critical speed-precision tradeoffs

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

Williams, Alex H.; O'Donnell, Cian; Sejnowski, Terrence J.

Nervous system function requires intracellular transport of channels, receptors, mRNAs, and other cargo throughout complex neuronal morphologies. Local signals such as synaptic input can regulate cargo trafficking, motivating the leading conceptual model of neuron-wide transport, sometimes called the ‘sushi-belt model’. Current theories and experiments are based on this model, yet its predictions are not rigorously understood. We formalized the sushi belt model mathematically, and show that it can achieve arbitrarily complex spatial distributions of cargo in reconstructed morphologies. However, the model also predicts an unavoidable, morphology dependent tradeoff between speed, precision and metabolic efficiency of cargo transport. With experimental estimatesmore » of trafficking kinetics, the model predicts delays of many hours or days for modestly accurate and efficient cargo delivery throughout a dendritic tree. In conclusion, these findings challenge current understanding of the efficacy of nucleus-to-synapse trafficking and may explain the prevalence of local biosynthesis in neurons.« less

(Q)SARs to predict environmental toxicities: current status and future needs.

PubMed

Cronin, Mark T D

2017-03-22

The current state of the art of (Quantitative) Structure-Activity Relationships ((Q)SARs) to predict environmental toxicity is assessed along with recommendations to develop these models further. The acute toxicity of compounds acting by the non-polar narcotic mechanism of action can be well predicted, however other approaches, including read-across, may be required for compounds acting by specific mechanisms of action. The chronic toxicity of compounds to environmental species is more difficult to predict from (Q)SARs, with robust data sets and more mechanistic information required. In addition, the toxicity of mixtures is little addressed by (Q)SAR approaches. Developments in environmental toxicology including Adverse Outcome Pathways (AOPs) and omics responses should be utilised to develop better, more mechanistically relevant, (Q)SAR models.

Biodiversity in environmental assessment-current practice and tools for prediction

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

Gontier, Mikael; Balfors, Berit; Moertberg, Ulla

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

River Discharge and Bathymetry Estimation from Hydraulic Inversion of Surface Currents and Water Surface Elevation Observations

NASA Astrophysics Data System (ADS)

Simeonov, J.; Holland, K. T.

2015-12-01

We developed an inversion model for river bathymetry and discharge estimation based on measurements of surface currents, water surface elevation and shoreline coordinates. The model uses a simplification of the 2D depth-averaged steady shallow water equations based on a streamline following system of coordinates and assumes spatially uniform bed friction coefficient and eddy viscosity. The spatial resolution of the predicted bathymetry is related to the resolution of the surface currents measurements. The discharge is determined by minimizing the difference between the predicted and the measured streamwise variation of the total head. The inversion model was tested using in situ and remote sensing measurements of the Kootenai River east of Bonners Ferry, ID. The measurements were obtained in August 2010 when the discharge was about 223 m3/s and the maximum river depth was about 6.5 m. Surface currents covering a 10 km reach with 8 m spatial resolution were estimated from airborne infrared video and were converted to depth-averaged currents using acoustic Doppler current profiler (ADCP) measurements along eight cross-stream transects. The streamwise profile of the water surface elevation was measured using real-time kinematic GPS from a drifting platform. The value of the friction coefficient was obtained from forward calibration simulations that minimized the difference between the predicted and measured velocity and water level along the river thalweg. The predicted along/cross-channel water depth variation was compared to the depth measured with a multibeam echo sounder. The rms error between the measured and predicted depth along the thalweg was found to be about 60cm and the estimated discharge was 5% smaller than the discharge measured by the ADCP.

Numerical Test of the Additivity Principle in Anomalous Transport

NASA Astrophysics Data System (ADS)

Tamaki, Shuji

2017-10-01

The additivity principle (AP) is one of the remarkable predictions that systematically generates all information on current fluctuations once the value of average current in the linear response regime is input. However, conditions to justify the AP are still ambiguous. We hence consider three tractable models, and discuss possible conditions. The models include the harmonic chain (HC), momentum exchange (ME) model, and momentum flip (MF) model, which respectively show ballistic, anomalous, and diffusive transport. We compare the heat current cumulants predicted by the AP with exact numerical data obtained for these models. The HC does not show the AP, whereas the MF model satisfies it, as expected, since the AP was originally proposed for diffusive systems. Surprisingly, the ME model also shows the AP. The ME model is known to show the anomalous transport similar to that shown in nonlinear systems such as the Fermi-Pasta-Ulam model. Our finding indicates that general nonlinear systems may satisfy the AP. Possible conditions for satisfying the AP are discussed.

Theory and observations of upward field-aligned currents at the magnetopause boundary layer.

PubMed

Wing, Simon; Johnson, Jay R

2015-11-16

The dependence of the upward field-aligned current density ( J ‖ ) at the dayside magnetopause boundary layer is well described by a simple analytic model based on a velocity shear generator. A previous observational survey confirmed that the scaling properties predicted by the analytical model are applicable between 11 and 17 MLT. We utilize the analytic model to predict field-aligned currents using solar wind and ionospheric parameters and compare with direct observations. The calculated and observed parallel currents are in excellent agreement, suggesting that the model may be useful to infer boundary layer structures. However, near noon, where velocity shear is small, the kinetic pressure gradients and thermal currents, which are not included in the model, could make a small but significant contribution to J ‖ . Excluding data from noon, our least squares fit returns log( J ‖,max_cal ) = (0.96 ± 0.04) log( J ‖_obs ) + (0.03 ± 0.01) where J ‖,max_cal = calculated J ‖,max and J ‖_obs = observed J ‖ .

Human In Silico Drug Trials Demonstrate Higher Accuracy than Animal Models in Predicting Clinical Pro-Arrhythmic Cardiotoxicity.

PubMed

Passini, Elisa; Britton, Oliver J; Lu, Hua Rong; Rohrbacher, Jutta; Hermans, An N; Gallacher, David J; Greig, Robert J H; Bueno-Orovio, Alfonso; Rodriguez, Blanca

2017-01-01

Early prediction of cardiotoxicity is critical for drug development. Current animal models raise ethical and translational questions, and have limited accuracy in clinical risk prediction. Human-based computer models constitute a fast, cheap and potentially effective alternative to experimental assays, also facilitating translation to human. Key challenges include consideration of inter-cellular variability in drug responses and integration of computational and experimental methods in safety pharmacology. Our aim is to evaluate the ability of in silico drug trials in populations of human action potential (AP) models to predict clinical risk of drug-induced arrhythmias based on ion channel information, and to compare simulation results against experimental assays commonly used for drug testing. A control population of 1,213 human ventricular AP models in agreement with experimental recordings was constructed. In silico drug trials were performed for 62 reference compounds at multiple concentrations, using pore-block drug models (IC 50 /Hill coefficient). Drug-induced changes in AP biomarkers were quantified, together with occurrence of repolarization/depolarization abnormalities. Simulation results were used to predict clinical risk based on reports of Torsade de Pointes arrhythmias, and further evaluated in a subset of compounds through comparison with electrocardiograms from rabbit wedge preparations and Ca 2+ -transient recordings in human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs). Drug-induced changes in silico vary in magnitude depending on the specific ionic profile of each model in the population, thus allowing to identify cell sub-populations at higher risk of developing abnormal AP phenotypes. Models with low repolarization reserve (increased Ca 2+ /late Na + currents and Na + /Ca 2+ -exchanger, reduced Na + /K + -pump) are highly vulnerable to drug-induced repolarization abnormalities, while those with reduced inward current density (fast/late Na + and Ca 2+ currents) exhibit high susceptibility to depolarization abnormalities. Repolarization abnormalities in silico predict clinical risk for all compounds with 89% accuracy. Drug-induced changes in biomarkers are in overall agreement across different assays: in silico AP duration changes reflect the ones observed in rabbit QT interval and hiPS-CMs Ca 2+ -transient, and simulated upstroke velocity captures variations in rabbit QRS complex. Our results demonstrate that human in silico drug trials constitute a powerful methodology for prediction of clinical pro-arrhythmic cardiotoxicity, ready for integration in the existing drug safety assessment pipelines.

Examination of Solar Cycle Statistical Model and New Prediction of Solar Cycle 23

NASA Technical Reports Server (NTRS)

Kim, Myung-Hee Y.; Wilson, John W.

2000-01-01

Sunspot numbers in the current solar cycle 23 were estimated by using a statistical model with the accumulating cycle sunspot data based on the odd-even behavior of historical sunspot cycles from 1 to 22. Since cycle 23 has progressed and the accurate solar minimum occurrence has been defined, the statistical model is validated by comparing the previous prediction with the new measured sunspot number; the improved sunspot projection in short range of future time is made accordingly. The current cycle is expected to have a moderate level of activity. Errors of this model are shown to be self-correcting as cycle observations become available.

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.

Antecedents of eating disorders and muscle dysmorphia in a non-clinical sample.

PubMed

Lamanna, J; Grieve, F G; Derryberry, W Pitt; Hakman, M; McClure, A

2010-01-01

Muscle Dysmorphia (MD) has recently been conceptualized as the male form of Eating Disorders (ED); although, it is not currently classified as an ED. The current study compares etiological models of MD symptomatology and ED symptomatology. It was hypothesized that sociocultural influences on appearance (SIA) would predict body dissatisfaction (BD), and that this relationship would be mediated by self-esteem (SE) and perfectionism (P); that BD would predict negative affect (NA); and that NA would predict MD and ED symptomatology. Two-hundred-forty-seven female and 101 male college students at a midsouth university completed the study. All participants completed measures assessing each of the constructs, and multiple regression analyses were conducted to test each model's fit. In both models, most predictor paths were significant. These results suggest similarity in symptomatology and etiological models between ED and MD.

Emerging approaches in predictive toxicology.

PubMed

Zhang, Luoping; McHale, Cliona M; Greene, Nigel; Snyder, Ronald D; Rich, Ivan N; Aardema, Marilyn J; Roy, Shambhu; Pfuhler, Stefan; Venkatactahalam, Sundaresan

2014-12-01

Predictive toxicology plays an important role in the assessment of toxicity of chemicals and the drug development process. While there are several well-established in vitro and in vivo assays that are suitable for predictive toxicology, recent advances in high-throughput analytical technologies and model systems are expected to have a major impact on the field of predictive toxicology. This commentary provides an overview of the state of the current science and a brief discussion on future perspectives for the field of predictive toxicology for human toxicity. Computational models for predictive toxicology, needs for further refinement and obstacles to expand computational models to include additional classes of chemical compounds are highlighted. Functional and comparative genomics approaches in predictive toxicology are discussed with an emphasis on successful utilization of recently developed model systems for high-throughput analysis. The advantages of three-dimensional model systems and stem cells and their use in predictive toxicology testing are also described. © 2014 Wiley Periodicals, Inc.

Emerging Approaches in Predictive Toxicology

PubMed Central

Zhang, Luoping; McHale, Cliona M.; Greene, Nigel; Snyder, Ronald D.; Rich, Ivan N.; Aardema, Marilyn J.; Roy, Shambhu; Pfuhler, Stefan; Venkatactahalam, Sundaresan

2016-01-01

Predictive toxicology plays an important role in the assessment of toxicity of chemicals and the drug development process. While there are several well-established in vitro and in vivo assays that are suitable for predictive toxicology, recent advances in high-throughput analytical technologies and model systems are expected to have a major impact on the field of predictive toxicology. This commentary provides an overview of the state of the current science and a brief discussion on future perspectives for the field of predictive toxicology for human toxicity. Computational models for predictive toxicology, needs for further refinement and obstacles to expand computational models to include additional classes of chemical compounds are highlighted. Functional and comparative genomics approaches in predictive toxicology are discussed with an emphasis on successful utilization of recently developed model systems for high-throughput analysis. The advantages of three-dimensional model systems and stem cells and their use in predictive toxicology testing are also described. PMID:25044351

Predictive Validation of an Influenza Spread Model

PubMed Central

Hyder, Ayaz; Buckeridge, David L.; Leung, Brian

2013-01-01

Background Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. Methods and Findings We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998–1999). Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type). Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. Conclusions Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers) with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve their predictive ability. PMID:23755236

Mechanisms Responsible for ω-Pore Currents in Cav Calcium Channel Voltage-Sensing Domains.

PubMed

Monteleone, Stefania; Lieb, Andreas; Pinggera, Alexandra; Negro, Giulia; Fuchs, Julian E; Hofer, Florian; Striessnig, Jörg; Tuluc, Petronel; Liedl, Klaus R

2017-10-03

Mutations of positively charged amino acids in the S4 transmembrane segment of a voltage-gated ion channel form ion-conducting pathways through the voltage-sensing domain, named ω-current. Here, we used structure modeling and MD simulations to predict pathogenic ω-currents in Ca V 1.1 and Ca V 1.3 Ca 2+ channels bearing several S4 charge mutations. Our modeling predicts that mutations of Ca V 1.1-R1 (R528H/G, R897S) or Ca V 1.1-R2 (R900S, R1239H) linked to hypokalemic periodic paralysis type 1 and of Ca V 1.3-R3 (R990H) identified in aldosterone-producing adenomas conducts ω-currents in resting state, but not during voltage-sensing domain activation. The mechanism responsible for the ω-current and its amplitude depend on the number of charges in S4, the position of the mutated S4 charge and countercharges, and the nature of the replacing amino acid. Functional characterization validates the modeling prediction showing that Ca V 1.3-R990H channels conduct ω-currents at hyperpolarizing potentials, but not upon membrane depolarization compared with wild-type channels. Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Water Erosion Prediction Project (WEPP) model status and updates

USDA-ARS?s Scientific Manuscript database

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

EOID System Model Validation, Metrics, and Synthetic Clutter Generation

DTIC Science & Technology

2003-09-30

Our long-term goal is to accurately predict the capability of the current generation of laser-based underwater imaging sensors to perform Electro ... Optic Identification (EOID) against relevant targets in a variety of realistic environmental conditions. The models will predict the impact of

Newer classification and regression tree techniques: Bagging and Random Forests for ecological prediction

Treesearch

Anantha M. Prasad; Louis R. Iverson; Andy Liaw; Andy Liaw

2006-01-01

We evaluated four statistical models - Regression Tree Analysis (RTA), Bagging Trees (BT), Random Forests (RF), and Multivariate Adaptive Regression Splines (MARS) - for predictive vegetation mapping under current and future climate scenarios according to the Canadian Climate Centre global circulation model.

An Online Prediction Platform to Support the Environmental Sciences (American Chemical Society)

EPA Science Inventory

Historical QSAR models are currently utilized across a broad range of applications within the U.S. Environmental Protection Agency (EPA). These models predict basic physicochemical properties (e.g., logP, aqueous solubility, vapor pressure), which are then incorporated into expo...

Spatial working memory capacity predicts bias in estimates of location.

PubMed

Crawford, L Elizabeth; Landy, David; Salthouse, Timothy A

2016-09-01

Spatial memory research has attributed systematic bias in location estimates to a combination of a noisy memory trace with a prior structure that people impose on the space. Little is known about intraindividual stability and interindividual variation in these patterns of bias. In the current work, we align recent empirical and theoretical work on working memory capacity limits and spatial memory bias to generate the prediction that those with lower working memory capacity will show greater bias in memory of the location of a single item. Reanalyzing data from a large study of cognitive aging, we find support for this prediction. Fitting separate models to individuals' data revealed a surprising variety of strategies. Some were consistent with Bayesian models of spatial category use, however roughly half of participants biased estimates outward in a way not predicted by current models and others seemed to combine these strategies. These analyses highlight the importance of studying individuals when developing general models of cognition. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Spatial Working Memory Capacity Predicts Bias in Estimates of Location

PubMed Central

Crawford, L. Elizabeth; Landy, David H.; Salthouse, Timothy A.

2016-01-01

Spatial memory research has attributed systematic bias in location estimates to a combination of a noisy memory trace with a prior structure that people impose on the space. Little is known about intra-individual stability and inter-individual variation in these patterns of bias. In the current work we align recent empirical and theoretical work on working memory capacity limits and spatial memory bias to generate the prediction that those with lower working memory capacity will show greater bias in memory of the location of a single item. Reanalyzing data from a large study of cognitive aging, we find support for this prediction. Fitting separate models to individuals’ data revealed a surprising variety of strategies. Some were consistent with Bayesian models of spatial category use, however roughly half of participants biased estimates outward in a way not predicted by current models and others seemed to combine these strategies. These analyses highlight the importance of studying individuals when developing general models of cognition. PMID:26900708

Predicting Time Series Outputs and Time-to-Failure for an Aircraft Controller Using Bayesian Modeling

NASA Technical Reports Server (NTRS)

He, Yuning

2015-01-01

Safety of unmanned aerial systems (UAS) is paramount, but the large number of dynamically changing controller parameters makes it hard to determine if the system is currently stable, and the time before loss of control if not. We propose a hierarchical statistical model using Treed Gaussian Processes to predict (i) whether a flight will be stable (success) or become unstable (failure), (ii) the time-to-failure if unstable, and (iii) time series outputs for flight variables. We first classify the current flight input into success or failure types, and then use separate models for each class to predict the time-to-failure and time series outputs. As different inputs may cause failures at different times, we have to model variable length output curves. We use a basis representation for curves and learn the mappings from input to basis coefficients. We demonstrate the effectiveness of our prediction methods on a NASA neuro-adaptive flight control system.

Gaussian mixture models as flux prediction method for central receivers

NASA Astrophysics Data System (ADS)

Grobler, Annemarie; Gauché, Paul; Smit, Willie

2016-05-01

Flux prediction methods are crucial to the design and operation of central receiver systems. Current methods such as the circular and elliptical (bivariate) Gaussian prediction methods are often used in field layout design and aiming strategies. For experimental or small central receiver systems, the flux profile of a single heliostat often deviates significantly from the circular and elliptical Gaussian models. Therefore a novel method of flux prediction was developed by incorporating the fitting of Gaussian mixture models onto flux profiles produced by flux measurement or ray tracing. A method was also developed to predict the Gaussian mixture model parameters of a single heliostat for a given time using image processing. Recording the predicted parameters in a database ensures that more accurate predictions are made in a shorter time frame.

Predictive models for Escherichia coli concentrations at inland lake beaches and relationship of model variables to pathogen detection

USGS Publications Warehouse

Francy, Donna S.; Stelzer, Erin A.; Duris, Joseph W.; Brady, Amie M.G.; Harrison, John H.; Johnson, Heather E.; Ware, Michael W.

2013-01-01

Predictive models, based on environmental and water quality variables, have been used to improve the timeliness and accuracy of recreational water quality assessments, but their effectiveness has not been studied in inland waters. Sampling at eight inland recreational lakes in Ohio was done in order to investigate using predictive models for Escherichia coli and to understand the links between E. coli concentrations, predictive variables, and pathogens. Based upon results from 21 beach sites, models were developed for 13 sites, and the most predictive variables were rainfall, wind direction and speed, turbidity, and water temperature. Models were not developed at sites where the E. coli standard was seldom exceeded. Models were validated at nine sites during an independent year. At three sites, the model resulted in increased correct responses, sensitivities, and specificities compared to use of the previous day's E. coli concentration (the current method). Drought conditions during the validation year precluded being able to adequately assess model performance at most of the other sites. Cryptosporidium, adenovirus, eaeA (E. coli), ipaH (Shigella), and spvC (Salmonella) were found in at least 20% of samples collected for pathogens at five sites. The presence or absence of the three bacterial genes was related to some of the model variables but was not consistently related to E. coli concentrations. Predictive models were not effective at all inland lake sites; however, their use at two lakes with high swimmer densities will provide better estimates of public health risk than current methods and will be a valuable resource for beach managers and the public.

Predictive models for Escherichia coli concentrations at inland lake beaches and relationship of model variables to pathogen detection.

PubMed

Francy, Donna S; Stelzer, Erin A; Duris, Joseph W; Brady, Amie M G; Harrison, John H; Johnson, Heather E; Ware, Michael W

2013-03-01

Predictive models, based on environmental and water quality variables, have been used to improve the timeliness and accuracy of recreational water quality assessments, but their effectiveness has not been studied in inland waters. Sampling at eight inland recreational lakes in Ohio was done in order to investigate using predictive models for Escherichia coli and to understand the links between E. coli concentrations, predictive variables, and pathogens. Based upon results from 21 beach sites, models were developed for 13 sites, and the most predictive variables were rainfall, wind direction and speed, turbidity, and water temperature. Models were not developed at sites where the E. coli standard was seldom exceeded. Models were validated at nine sites during an independent year. At three sites, the model resulted in increased correct responses, sensitivities, and specificities compared to use of the previous day's E. coli concentration (the current method). Drought conditions during the validation year precluded being able to adequately assess model performance at most of the other sites. Cryptosporidium, adenovirus, eaeA (E. coli), ipaH (Shigella), and spvC (Salmonella) were found in at least 20% of samples collected for pathogens at five sites. The presence or absence of the three bacterial genes was related to some of the model variables but was not consistently related to E. coli concentrations. Predictive models were not effective at all inland lake sites; however, their use at two lakes with high swimmer densities will provide better estimates of public health risk than current methods and will be a valuable resource for beach managers and the public.

Numerical Simulation of Multiphase Magnetohydrodynamic Flow and Deformation of Electrolyte-Metal Interface in Aluminum Electrolysis Cells

NASA Astrophysics Data System (ADS)

Hua, Jinsong; Rudshaug, Magne; Droste, Christian; Jorgensen, Robert; Giskeodegard, Nils-Haavard

2018-06-01

A computational fluid dynamics based multiphase magnetohydrodynamic (MHD) flow model for simulating the melt flow and bath-metal interface deformation in realistic aluminum reduction cells is presented. The model accounts for the complex physics of the MHD problem in aluminum reduction cells by coupling two immiscible fluids, electromagnetic field, Lorentz force, flow turbulence, and complex cell geometry with large length scale. Especially, the deformation of bath-metal interface is tracked directly in the simulation, and the condition of constant anode-cathode distance (ACD) is maintained by moving anode bottom dynamically with the deforming bath-metal interface. The metal pad deformation and melt flow predicted by the current model are compared to the predictions using a simplified model where the bath-metal interface is assumed flat. The effects of the induced electric current due to fluid flow and the magnetic field due to the interior cell current on the metal pad deformation and melt flow are investigated. The presented model extends the conventional simplified box model by including detailed cell geometry such as the ledge profile and all channels (side, central, and cross-channels). The simulations show the model sensitivity to different side ledge profiles and the cross-channel width by comparing the predicted melt flow and metal pad heaving. In addition, the model dependencies upon the reduction cell operation conditions such as ACD, current distribution on cathode surface and open/closed channel top, are discussed.

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.

Japanese earthquake predictability experiment with multiple runs before and after the 2011 Tohoku-oki earthquake

NASA Astrophysics Data System (ADS)

Hirata, N.; Tsuruoka, H.; Yokoi, S.

2011-12-01

The current Japanese national earthquake prediction program emphasizes the importance of modeling as well as monitoring for a sound scientific development of earthquake prediction research. One major focus of the current program is to move toward creating testable earthquake forecast models. For this purpose, in 2009 we joined the Collaboratory for the Study of Earthquake Predictability (CSEP) and installed, through an international collaboration, the CSEP Testing Centre, an infrastructure to encourage researchers to develop testable models for Japan. We started Japanese earthquake predictability experiment on November 1, 2009. The experiment consists of 12 categories, with 4 testing classes with different time spans (1 day, 3 months, 1 year and 3 years) and 3 testing regions called 'All Japan,' 'Mainland,' and 'Kanto.' A total of 160 models, as of August 2013, were submitted, and are currently under the CSEP official suite of tests for evaluating the performance of forecasts. We will present results of prospective forecast and testing for periods before and after the 2011 Tohoku-oki earthquake. Because a seismic activity has changed dramatically since the 2011 event, performances of models have been affected very much. In addition, as there is the problem of authorized catalogue related to the completeness magnitude, most models did not pass the CSEP consistency tests. Also, we will discuss the retrospective earthquake forecast experiments for aftershocks of the 2011 Tohoku-oki earthquake. Our aim is to describe what has turned out to be the first occasion for setting up a research environment for rigorous earthquake forecasting in Japan.

Japanese earthquake predictability experiment with multiple runs before and after the 2011 Tohoku-oki earthquake

NASA Astrophysics Data System (ADS)

Hirata, N.; Tsuruoka, H.; Yokoi, S.

2013-12-01

The current Japanese national earthquake prediction program emphasizes the importance of modeling as well as monitoring for a sound scientific development of earthquake prediction research. One major focus of the current program is to move toward creating testable earthquake forecast models. For this purpose, in 2009 we joined the Collaboratory for the Study of Earthquake Predictability (CSEP) and installed, through an international collaboration, the CSEP Testing Centre, an infrastructure to encourage researchers to develop testable models for Japan. We started Japanese earthquake predictability experiment on November 1, 2009. The experiment consists of 12 categories, with 4 testing classes with different time spans (1 day, 3 months, 1 year and 3 years) and 3 testing regions called 'All Japan,' 'Mainland,' and 'Kanto.' A total of 160 models, as of August 2013, were submitted, and are currently under the CSEP official suite of tests for evaluating the performance of forecasts. We will present results of prospective forecast and testing for periods before and after the 2011 Tohoku-oki earthquake. Because a seismic activity has changed dramatically since the 2011 event, performances of models have been affected very much. In addition, as there is the problem of authorized catalogue related to the completeness magnitude, most models did not pass the CSEP consistency tests. Also, we will discuss the retrospective earthquake forecast experiments for aftershocks of the 2011 Tohoku-oki earthquake. Our aim is to describe what has turned out to be the first occasion for setting up a research environment for rigorous earthquake forecasting in Japan.

Will high-resolution global ocean models benefit coupled predictions on short-range to climate timescales?

NASA Astrophysics Data System (ADS)

Hewitt, Helene T.; Bell, Michael J.; Chassignet, Eric P.; Czaja, Arnaud; Ferreira, David; Griffies, Stephen M.; Hyder, Pat; McClean, Julie L.; New, Adrian L.; Roberts, Malcolm J.

2017-12-01

As the importance of the ocean in the weather and climate system is increasingly recognised, operational systems are now moving towards coupled prediction not only for seasonal to climate timescales but also for short-range forecasts. A three-way tension exists between the allocation of computing resources to refine model resolution, the expansion of model complexity/capability, and the increase of ensemble size. Here we review evidence for the benefits of increased ocean resolution in global coupled models, where the ocean component explicitly represents transient mesoscale eddies and narrow boundary currents. We consider lessons learned from forced ocean/sea-ice simulations; from studies concerning the SST resolution required to impact atmospheric simulations; and from coupled predictions. Impacts of the mesoscale ocean in western boundary current regions on the large-scale atmospheric state have been identified. Understanding of air-sea feedback in western boundary currents is modifying our view of the dynamics in these key regions. It remains unclear whether variability associated with open ocean mesoscale eddies is equally important to the large-scale atmospheric state. We include a discussion of what processes can presently be parameterised in coupled models with coarse resolution non-eddying ocean models, and where parameterizations may fall short. We discuss the benefits of resolution and identify gaps in the current literature that leave important questions unanswered.

Estimating Model Prediction Error: Should You Treat Predictions as Fixed or Random?

NASA Technical Reports Server (NTRS)

Wallach, Daniel; Thorburn, Peter; Asseng, Senthold; Challinor, Andrew J.; Ewert, Frank; Jones, James W.; Rotter, Reimund; Ruane, Alexander

2016-01-01

Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. We compare two criteria of prediction error; MSEP fixed, which evaluates mean squared error of prediction for a model with fixed structure, parameters and inputs, and MSEP uncertain( X), which evaluates mean squared error averaged over the distributions of model structure, inputs and parameters. Comparison of model outputs with data can be used to estimate the former. The latter has a squared bias term, which can be estimated using hindcasts, and a model variance term, which can be estimated from a simulation experiment. The separate contributions to MSEP uncertain (X) can be estimated using a random effects ANOVA. It is argued that MSEP uncertain (X) is the more informative uncertainty criterion, because it is specific to each prediction situation.

Global Weather Prediction and High-End Computing at NASA

NASA Technical Reports Server (NTRS)

Lin, Shian-Jiann; Atlas, Robert; Yeh, Kao-San

2003-01-01

We demonstrate current capabilities of the NASA finite-volume General Circulation Model an high-resolution global weather prediction, and discuss its development path in the foreseeable future. This model can be regarded as a prototype of a future NASA Earth modeling system intended to unify development activities cutting across various disciplines within the NASA Earth Science Enterprise.

Dynamic-landscape metapopulation models predict complex response of wildlife populations to climate and landscape change

Treesearch

Thomas W. Bonnot; Frank R. Thompson; Joshua J. Millspaugh

2017-01-01

The increasing need to predict how climate change will impact wildlife species has exposed limitations in how well current approaches model important biological processes at scales at which those processes interact with climate. We used a comprehensive approach that combined recent advances in landscape and population modeling into dynamic-landscape metapopulation...

Implementation of channel-routing routines in the Water Erosion Prediction Project (WEPP) model

Treesearch

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

2010-01-01

The Water Erosion Prediction Project (WEPP) model is a process-based, continuous-simulation, watershed hydrology and erosion model. It is an important tool for water erosion simulation owing to its unique functionality in representing diverse landuse and management conditions. Its applicability is limited to relatively small watersheds since its current version does...

Mass-Gathering Medical Care in Electronic Dance Music Festivals.

PubMed

FitzGibbon, Kathleen M; Nable, Jose V; Ayd, Benjamin; Lawner, Benjamin J; Comer, Angela C; Lichenstein, Richard; Levy, Matthew J; Seaman, Kevin G; Bussey, Ian

2017-10-01

Introduction Electronic dance music (EDM) festivals represent a unique subset of mass-gathering events with limited guidance through literature or legislation to guide mass-gathering medical care at these events. Hypothesis/Problem Electronic dance music festivals pose unique challenges with increased patient encounters and heightened patient acuity under-estimated by current validated casualty predication models. This was a retrospective review of three separate EDM festivals with analysis of patient encounters and patient transport rates. Data obtained were inserted into the predictive Arbon and Hartman models to determine estimated patient presentation rate and patient transport rates. The Arbon model under-predicted the number of patient encounters and the number of patient transports for all three festivals, while the Hartman model under-predicted the number of patient encounters at one festival and over-predicted the number of encounters at the other two festivals. The Hartman model over-predicted patient transport rates for two of the three festivals. Electronic dance music festivals often involve distinct challenges and current predictive models are inaccurate for planning these events. The formation of a cohesive incident action plan will assist in addressing these challenges and lead to the collection of more uniform data metrics. FitzGibbon KM , Nable JV , Ayd B , Lawner BJ , Comer AC , Lichenstein R , Levy MJ , Seaman KG , Bussey I . Mass-gathering medical care in electronic dance music festivals. Prehosp Disaster Med. 2017;32(5):563-567.

Modeling and simulation of enhancement mode p-GaN Gate AlGaN/GaN HEMT for RF circuit switch applications

NASA Astrophysics Data System (ADS)

Panda, D. K.; Lenka, T. R.

2017-06-01

An enhancement mode p-GaN gate AlGaN/GaN HEMT is proposed and a physics based virtual source charge model with Landauer approach for electron transport has been developed using Verilog-A and simulated using Cadence Spectre, in order to predict device characteristics such as threshold voltage, drain current and gate capacitance. The drain current model incorporates important physical effects such as velocity saturation, short channel effects like DIBL (drain induced barrier lowering), channel length modulation (CLM), and mobility degradation due to self-heating. The predicted I d-V ds, I d-V gs, and C-V characteristics show an excellent agreement with the experimental data for both drain current and capacitance which validate the model. The developed model was then utilized to design and simulate a single-pole single-throw (SPST) RF switch.

A power-balance model for local helicity injection startup in a spherical tokamak

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

Barr, Jayson L.; Bongard, Michael W.; Burke, Marcus G.

A 0D circuit model for predicting I p( t) in Local Helicity Injection (LHI) discharges is developed. Analytic formulas for estimating the surface flux of finite-A plasmas developed are modified and expanded to treat highly shaped, ultralow-A tokamak geometry using a database of representative equilibria. Model predictions are compared to sample LHI discharges in the A ~ 1 Pegasus spherical tokamak, and are found to agree within 15% of experimental I p( t). High performance LHI discharges are found to follow the Taylor relaxation current limit for approximately the first half of the current ramp, or I p ≲ 75more » kA. The second half of the current ramp follows a limit imposed by power-balance as plasmas expand from high- A to ultralow- A. Here, this shape evolution generates a significant drop in external plasma inductance, effectively using the plasma’s initially high inductance to drive the current ramp and provide > 70% of the current drive V-s. Projections using this model indicate the relative influences of higher helicity input rate and injector current on the attainable total plasma current.« less

A power-balance model for local helicity injection startup in a spherical tokamak

DOE PAGES

Barr, Jayson L.; Bongard, Michael W.; Burke, Marcus G.; ...

2018-05-15

A 0D circuit model for predicting I p( t) in Local Helicity Injection (LHI) discharges is developed. Analytic formulas for estimating the surface flux of finite-A plasmas developed are modified and expanded to treat highly shaped, ultralow-A tokamak geometry using a database of representative equilibria. Model predictions are compared to sample LHI discharges in the A ~ 1 Pegasus spherical tokamak, and are found to agree within 15% of experimental I p( t). High performance LHI discharges are found to follow the Taylor relaxation current limit for approximately the first half of the current ramp, or I p ≲ 75more » kA. The second half of the current ramp follows a limit imposed by power-balance as plasmas expand from high- A to ultralow- A. Here, this shape evolution generates a significant drop in external plasma inductance, effectively using the plasma’s initially high inductance to drive the current ramp and provide > 70% of the current drive V-s. Projections using this model indicate the relative influences of higher helicity input rate and injector current on the attainable total plasma current.« less

AIR QUALITY MODELING OF HAZARDOUS POLLUTANTS: CURRENT STATUS AND FUTURE DIRECTIONS

EPA Science Inventory

The paper presents a review of current air toxics modeling applications and discusses possible advanced approaches. Many applications require the ability to predict hot spots from industrial sources or large roadways that are needed for community health and Environmental Justice...

A biophysical model of Lake Erie walleye (Sander vitreus) explains interannual variations in recruitment

USGS Publications Warehouse

Zhao, Yingming; Jones, Michael L.; Shuter, Brian J.; Roseman, Edward F.

2009-01-01

We used a three-dimensional coupled hydrodynamic-ecological model to investigate how lake currents can affect walleye (Sander vitreus) recruitment in western Lake Erie. Four years were selected based on a fall recruitment index: two high recruitment years (i.e., 1996 and 1999) and two low recruitment years (i.e., 1995 and 1998). During the low recruitment years, the model predicted that (i) walleye spawning grounds experienced destructive bottom currents capable of dislodging eggs from suitable habitats (reefs) to unsuitable habitats (i.e., muddy bottom), and (ii) the majority of newly hatched larvae were transported away from the known suitable nursery grounds at the start of their first feeding. Conversely, during two high recruitment years, predicted bottom currents at the spawning grounds were relatively weak, and the predicted movement of newly hatched larvae was toward suitable nursery grounds. Thus, low disturbance-based egg mortality and a temporal and spatial match between walleye first feeding larvae and their food resources were predicted for the two high recruitment years, and high egg mortality plus a mismatch of larvae with their food resources was predicted for the two low recruitment years. In general, mild westerly or southwesterly winds during the spawning-nursery period should favour walleye recruitment in the lake.

On the nature of low-frequency currents over a shallow area of the southern coast of the Gulf of Finland

NASA Astrophysics Data System (ADS)

Lilover, M.-J.; Pavelson, J.; Kõuts, T.

2014-01-01

This study aims to explain those factors influencing low-frequency currents in a shallow unstratified sea with complex topography. Current velocity measurements using a bottom-mounted ADCP, deployed at 8 m depth on the slope of Naissaar Bank (northern entrance to the Tallinn Bay, Gulf of Finland), were performed over five weeks in late autumn 2008. A quasi-steady current from nine sub-periods (two weeks) was relatively well correlated with wind (mean correlation coefficient of 0.70). During moderate to fresh winds, the current is veered to the right relative to the wind direction, by angles in the range of 14-38°. The flow is directed to the left, relative to the wind direction in stronger wind conditions, indicating evidence of topographic forcing. The observed current was reasonably in accordance with the flow predicted by the classical Ekman model. The modelled current speeds (wind speeds < 11 m s- 1) appear to be overestimated by 3-6 cm s- 1, whilst the observed rotation angles were mostly less than those predicted by the model. Inclusion of barotropic forcing to the Ekman model improved its performance. The discrepancies between the model and observations are discussed in terms of topographic steering, baroclinic effect and surface wave induced forcing.

Predicting the Future as Bayesian Inference: People Combine Prior Knowledge with Observations when Estimating Duration and Extent

ERIC Educational Resources Information Center

Griffiths, Thomas L.; Tenenbaum, Joshua B.

2011-01-01

Predicting the future is a basic problem that people have to solve every day and a component of planning, decision making, memory, and causal reasoning. In this article, we present 5 experiments testing a Bayesian model of predicting the duration or extent of phenomena from their current state. This Bayesian model indicates how people should…

Mathematical model for prediction of efficiency indicators of educational activity in high school

NASA Astrophysics Data System (ADS)

Tikhonova, O. M.; Kushnikov, V. A.; Fominykh, D. S.; Rezchikov, A. F.; Ivashchenko, V. A.; Bogomolov, A. S.; Filimonyuk, L. Yu; Dolinina, O. N.; Kushnikov, O. V.; Shulga, T. E.; Tverdokhlebov, V. A.

2018-05-01

The quality of high school is a current problem all over the world. The paper presents the system dedicated to predicting the accreditation indicators of technical universities based on J. Forrester mechanism of system dynamics. The mathematical model is developed for prediction of efficiency indicators of the educational activity and is based on the apparatus of nonlinear differential equations.

Predicting post-fire tree mortality for 14 conifers in the Pacific Northwest, USA: Model evaluation, development, and thresholds

Treesearch

Lindsay M. Grayson; Robert A. Progar; Sharon M. Hood

2017-01-01

Fire is a driving force in the North American landscape and predicting post-fire tree mortality is vital to land management. Post-fire tree mortality can have substantial economic and social impacts, and natural resource managers need reliable predictive methods to anticipate potential mortality following fire events. Current fire mortality models are limited to a few...

Validation of finite element model of transcranial electrical stimulation using scalp potentials: implications for clinical dose

NASA Astrophysics Data System (ADS)

Datta, Abhishek; Zhou, Xiang; Su, Yuzhou; Parra, Lucas C.; Bikson, Marom

2013-06-01

Objective. During transcranial electrical stimulation, current passage across the scalp generates voltage across the scalp surface. The goal was to characterize these scalp voltages for the purpose of validating subject-specific finite element method (FEM) models of current flow. Approach. Using a recording electrode array, we mapped skin voltages resulting from low-intensity transcranial electrical stimulation. These voltage recordings were used to compare the predictions obtained from the high-resolution model based on the subject undergoing transcranial stimulation. Main results. Each of the four stimulation electrode configurations tested resulted in a distinct distribution of scalp voltages; these spatial maps were linear with applied current amplitude (0.1 to 1 mA) over low frequencies (1 to 10 Hz). The FEM model accurately predicted the distinct voltage distributions and correlated the induced scalp voltages with current flow through cortex. Significance. Our results provide the first direct model validation for these subject-specific modeling approaches. In addition, the monitoring of scalp voltages may be used to verify electrode placement to increase transcranial electrical stimulation safety and reproducibility.

The Role of Multimodel Combination in Improving Streamflow Prediction

NASA Astrophysics Data System (ADS)

Arumugam, S.; Li, W.

2008-12-01

Model errors are the inevitable part in any prediction exercise. One approach that is currently gaining attention to reduce model errors is by optimally combining multiple models to develop improved predictions. The rationale behind this approach primarily lies on the premise that optimal weights could be derived for each model so that the developed multimodel predictions will result in improved predictability. In this study, we present a new approach to combine multiple hydrological models by evaluating their predictability contingent on the predictor state. We combine two hydrological models, 'abcd' model and Variable Infiltration Capacity (VIC) model, with each model's parameter being estimated by two different objective functions to develop multimodel streamflow predictions. The performance of multimodel predictions is compared with individual model predictions using correlation, root mean square error and Nash-Sutcliffe coefficient. To quantify precisely under what conditions the multimodel predictions result in improved predictions, we evaluate the proposed algorithm by testing it against streamflow generated from a known model ('abcd' model or VIC model) with errors being homoscedastic or heteroscedastic. Results from the study show that streamflow simulated from individual models performed better than multimodels under almost no model error. Under increased model error, the multimodel consistently performed better than the single model prediction in terms of all performance measures. The study also evaluates the proposed algorithm for streamflow predictions in two humid river basins from NC as well as in two arid basins from Arizona. Through detailed validation in these four sites, the study shows that multimodel approach better predicts the observed streamflow in comparison to the single model predictions.

Applicability of empirical data currently used in predicting solid propellant exhaust plumes

NASA Technical Reports Server (NTRS)

Tevepaugh, J. A.; Smith, S. D.; Penny, M. M.; Greenwood, T.; Roberts, B. B.

1977-01-01

Theoretical and experimental approaches to exhaust plume analysis are compared. A two-phase model is extended to include treatment of reacting gas chemistry, and thermodynamical modeling of the gaseous phase of the flow field is considered. The applicability of empirical data currently available to define particle drag coefficients, heat transfer coefficients, mean particle size, and particle size distributions is investigated. Experimental and analytical comparisons are presented for subscale solid rocket motors operating at three altitudes with attention to pitot total pressure and stagnation point heating rate measurements. The mathematical treatment input requirements are explained. The two-phase flow field solution adequately predicts gasdynamic properties in the inviscid portion of two-phase exhaust plumes. It is found that prediction of exhaust plume gas pressures requires an adequate model of flow field dynamics.

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.

Impact of data assimilation on ocean current forecasts in the Angola Basin

NASA Astrophysics Data System (ADS)

Phillipson, Luke; Toumi, Ralf

2017-06-01

The ocean current predictability in the data limited Angola Basin was investigated using the Regional Ocean Modelling System (ROMS) with four-dimensional variational data assimilation. Six experiments were undertaken comprising a baseline case of the assimilation of salinity/temperature profiles and satellite sea surface temperature, with the subsequent addition of altimetry, OSCAR (satellite-derived sea surface currents), drifters, altimetry and drifters combined, and OSCAR and drifters combined. The addition of drifters significantly improves Lagrangian predictability in comparison to the baseline case as well as the addition of either altimetry or OSCAR. OSCAR assimilation only improves Lagrangian predictability as much as altimetry assimilation. On average the assimilation of either altimetry or OSCAR with drifter velocities does not significantly improve Lagrangian predictability compared to the drifter assimilation alone, even degrading predictability in some cases. When the forecast current speed is large, it is more likely that the combination improves trajectory forecasts. Conversely, when the currents are weaker, it is more likely that the combination degrades the trajectory forecast.

Multi-temperature state-dependent equivalent circuit discharge model for lithium-sulfur batteries

NASA Astrophysics Data System (ADS)

Propp, Karsten; Marinescu, Monica; Auger, Daniel J.; O'Neill, Laura; Fotouhi, Abbas; Somasundaram, Karthik; Offer, Gregory J.; Minton, Geraint; Longo, Stefano; Wild, Mark; Knap, Vaclav

2016-10-01

Lithium-sulfur (Li-S) batteries are described extensively in the literature, but existing computational models aimed at scientific understanding are too complex for use in applications such as battery management. Computationally simple models are vital for exploitation. This paper proposes a non-linear state-of-charge dependent Li-S equivalent circuit network (ECN) model for a Li-S cell under discharge. Li-S batteries are fundamentally different to Li-ion batteries, and require chemistry-specific models. A new Li-S model is obtained using a 'behavioural' interpretation of the ECN model; as Li-S exhibits a 'steep' open-circuit voltage (OCV) profile at high states-of-charge, identification methods are designed to take into account OCV changes during current pulses. The prediction-error minimization technique is used. The model is parameterized from laboratory experiments using a mixed-size current pulse profile at four temperatures from 10 °C to 50 °C, giving linearized ECN parameters for a range of states-of-charge, currents and temperatures. These are used to create a nonlinear polynomial-based battery model suitable for use in a battery management system. When the model is used to predict the behaviour of a validation data set representing an automotive NEDC driving cycle, the terminal voltage predictions are judged accurate with a root mean square error of 32 mV.

Prognostics and Health Monitoring: Application to Electric Vehicles

NASA Technical Reports Server (NTRS)

Kulkarni, Chetan S.

2017-01-01

As more and more autonomous electric vehicles emerge in our daily operation progressively, a very critical challenge lies in accurate prediction of remaining useful life of the systemssubsystems, specifically the electrical powertrain. In case of electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. In order to tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operations of the vehicle.Our research approach is to develop a system level health monitoring safety indicator either to the pilotautopilot for the electric vehicles which runs estimation and prediction algorithms to estimate remaining useful life of the vehicle e.g. determine state-of-charge in batteries. Given models of the current and future system behavior, a general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making.

Predicting the vertical structure of tidal current and salinity in San Francisco Bay, California

USGS Publications Warehouse

Ford, Michael; Wang, Jia; Cheng, Ralph T.

1990-01-01

A two-dimensional laterally averaged numerical estuarine model is developed to study the vertical variations of tidal hydrodynamic properties in the central/north part of San Francisco Bay, California. Tidal stage data, current meter measurements, and conductivity, temperature, and depth profiling data in San Francisco Bay are used for comparison with model predictions. An extensive review of the literature is conducted to assess the success and failure of previous similar investigations and to establish a strategy for development of the present model. A σ plane transformation is used in the vertical dimension to alleviate problems associated with fixed grid model applications in the bay, where the tidal range can be as much as 20–25% of the total water depth. Model predictions of tidal stage and velocity compare favorably with the available field data, and prototype salinity stratification is qualitatively reproduced. Conclusions from this study as well as future model applications and research needs are discussed.

Geometry and mass model of ionizing radiation experiments on the LDEF satellite

NASA Technical Reports Server (NTRS)

Colborn, B. L.; Armstrong, T. W.

1992-01-01

Extensive measurements related to ionizing radiation environments and effects were made on the LDEF satellite during its mission lifetime of almost 6 years. These data, together with the opportunity they provide for evaluating predictive models and analysis methods, should allow more accurate assessments of the space radiation environment and related effects for future missions in low Earth orbit. The LDEF radiation dosimetry data is influenced to varying degrees by material shielding effects due to the dosimeter itself, nearby components and experiments, and the spacecraft structure. A geometry and mass model is generated of LDEF, incorporating sufficient detail that it can be applied in determining the influence of material shielding on ionizing radiation measurements and predictions. This model can be used as an aid in data interpretation by unfolding shielding effects from the LDEF radiation dosimeter responses. Use of the LDEF geometry/mass model, in conjunction with predictions and comparisons with LDEF dosimetry data currently underway, will also allow more definitive evaluations of current radiation models for future mission applications.

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.

Forcing and variability of nonstationary rip currents

USGS Publications Warehouse

Long, Joseph W.; H.T. Özkan-Haller,

2016-01-01

Surface wave transformation and the resulting nearshore circulation along a section of coast with strong alongshore bathymetric gradients outside the surf zone are modeled for a consecutive 4 week time period. The modeled hydrodynamics are compared to in situ measurements of waves and currents collected during the Nearshore Canyon Experiment and indicate that for the entire range of observed conditions, the model performance is similar to other studies along this stretch of coast. Strong alongshore wave height gradients generate rip currents that are observed by remote sensing data and predicted qualitatively well by the numerical model. Previous studies at this site have used idealized scenarios to link the rip current locations to undulations in the offshore bathymetry but do not explain the dichotomy between permanent offshore bathymetric features and intermittent rip current development. Model results from the month‐long simulation are used to track the formation and location of rip currents using hourly statistics, and results show that the direction of the incoming wave energy strongly controls whether rip currents form. In particular, most of the offshore wave spectra were bimodal and we find that the ratio of energy contained in each mode dictates rip current development, and the alongshore rip current position is controlled by the incident wave period. Additionally, model simulations performed with and without updating the nearshore morphology yield no significant change in the accuracy of the predicted surf zone hydrodyanmics indicating that the large‐scale offshore features (e.g., submarine canyon) predominately control the nearshore wave‐circulation system.

A Simple Analytical Model for Predicting the Detectable Ion Current in Ion Mobility Spectrometry Using Corona Discharge Ionization Sources

NASA Astrophysics Data System (ADS)

Kirk, Ansgar Thomas; Kobelt, Tim; Spehlbrink, Hauke; Zimmermann, Stefan

2018-05-01

Corona discharge ionization sources are often used in ion mobility spectrometers (IMS) when a non-radioactive ion source with high ion currents is required. Typically, the corona discharge is followed by a reaction region where analyte ions are formed from the reactant ions. In this work, we present a simple yet sufficiently accurate model for predicting the ion current available at the end of this reaction region when operating at reduced pressure as in High Kinetic Energy Ion Mobility Spectrometers (HiKE-IMS) or most IMS-MS instruments. It yields excellent qualitative agreement with measurement results and is even able to calculate the ion current within an error of 15%. Additional interesting findings of this model are the ion current at the end of the reaction region being independent from the ion current generated by the corona discharge and the ion current in High Kinetic Energy Ion Mobility Spectrometers (HiKE-IMS) growing quadratically when scaling down the length of the reaction region. [Figure not available: see fulltext.

A Simple Analytical Model for Predicting the Detectable Ion Current in Ion Mobility Spectrometry Using Corona Discharge Ionization Sources.

PubMed

Kirk, Ansgar Thomas; Kobelt, Tim; Spehlbrink, Hauke; Zimmermann, Stefan

2018-05-08

Corona discharge ionization sources are often used in ion mobility spectrometers (IMS) when a non-radioactive ion source with high ion currents is required. Typically, the corona discharge is followed by a reaction region where analyte ions are formed from the reactant ions. In this work, we present a simple yet sufficiently accurate model for predicting the ion current available at the end of this reaction region when operating at reduced pressure as in High Kinetic Energy Ion Mobility Spectrometers (HiKE-IMS) or most IMS-MS instruments. It yields excellent qualitative agreement with measurement results and is even able to calculate the ion current within an error of 15%. Additional interesting findings of this model are the ion current at the end of the reaction region being independent from the ion current generated by the corona discharge and the ion current in High Kinetic Energy Ion Mobility Spectrometers (HiKE-IMS) growing quadratically when scaling down the length of the reaction region. Graphical Abstract ᅟ.

Conduction and rectification in NbO x - and NiO-based metal-insulator-metal diodes

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

Osgood, Richard M.; Giardini, Stephen; Carlson, Joel

2016-09-01

Conduction and rectification in nanoantenna-coupled NbOx- and NiO-based metal-insulator-metal (MIM) diodes ('nanorectennas') are studied by comparing new theoretical predictions with the measured response of nanorectenna arrays. A new quantum mechanical model is reported and agrees with measurements of current-voltage (I-V) curves, over 10 orders of magnitude in current density, from [NbOx(native)-Nb2O5]- and NiO-based samples with oxide thicknesses in the range of 5-36 nm. The model, which introduces new physics and features, including temperature, electron effective mass, and image potential effects using the pseudobarrier technique, improves upon widely used earlier models, calculates the MIM diode's I-V curve, and predicts quantitatively themore » rectification responsivity of high frequency voltages generated in a coupled nanoantenna array by visible/near-infrared light. The model applies both at the higher frequencies, when high-energy photons are incident, and at lower frequencies, when the formula for classical rectification, involving derivatives of the I-V curve, may be used. The rectified low-frequency direct current is well-predicted in this work's model, but not by fitting the experimentally measured I-V curve with a polynomial or by using the older Simmons model (as shown herein). By fitting the measured I-V curves with our model, the barrier heights in Nb-(NbOx(native)-Nb2O5)-Pt and Ni-NiO-Ti/Ag diodes are found to be 0.41/0.77 and 0.38/0.39 eV, respectively, similar to literature reports, but with effective mass much lower than the free space value. The NbOx (native)-Nb2O5 dielectric properties improve, and the effective Pt-Nb2O5 barrier height increases as the oxide thickness increases. An observation of direct current of ~4 nA for normally incident, focused 514 nm continuous wave laser beams are reported, similar in magnitude to recent reports. This measured direct current is compared to the prediction for rectified direct current, given by the rectification responsivity, calculated from the I-V curve times input power.« less

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.

Improved NASA-ANOPP Noise Prediction Computer Code for Advanced Subsonic Propulsion Systems. Volume 2; Fan Suppression Model Development

NASA Technical Reports Server (NTRS)

Kontos, Karen B.; Kraft, Robert E.; Gliebe, Philip R.

1996-01-01

The Aircraft Noise Predication Program (ANOPP) is an industry-wide tool used to predict turbofan engine flyover noise in system noise optimization studies. Its goal is to provide the best currently available methods for source noise prediction. As part of a program to improve the Heidmann fan noise model, models for fan inlet and fan exhaust noise suppression estimation that are based on simple engine and acoustic geometry inputs have been developed. The models can be used to predict sound power level suppression and sound pressure level suppression at a position specified relative to the engine inlet.

Relative Chemical Binding Affinities for Trout and Human Estrogen Receptor Using Different Competitive Binding Assays

EPA Science Inventory

Rainbow trout-based assays for estrogenicity are currently being used for development of predictive models based upon quantitative structure activity relationships. A predictive model based on a single species raises the question of whether this information is valid for other spe...

Prediction of Coronary Artery Disease Risk Based on Multiple Longitudinal Biomarkers

PubMed Central

Yang, Lili; Yu, Menggang; Gao, Sujuan

2016-01-01

In the last decade, few topics in the area of cardiovascular disease (CVD) research have received as much attention as risk prediction. One of the well documented risk factors for CVD is high blood pressure (BP). Traditional CVD risk prediction models consider BP levels measured at a single time and such models form the basis for current clinical guidelines for CVD prevention. However, in clinical practice, BP levels are often observed and recorded in a longitudinal fashion. Information on BP trajectories can be powerful predictors for CVD events. We consider joint modeling of time to coronary artery disease and individual longitudinal measures of systolic and diastolic BPs in a primary care cohort with up to 20 years of follow-up. We applied novel prediction metrics to assess the predictive performance of joint models. Predictive performances of proposed joint models and other models were assessed via simulations and illustrated using the primary care cohort. PMID:26439685

Validation of International Space Station Electrical Performance Model via On-orbit Telemetry

NASA Technical Reports Server (NTRS)

Jannette, Anthony G.; Hojnicki, Jeffrey S.; McKissock, David B.; Fincannon, James; Kerslake, Thomas W.; Rodriguez, Carlos D.

2002-01-01

The first U.S. power module on International Space Station (ISS) was activated in December 2000. Comprised of solar arrays, nickel-hydrogen (NiH2) batteries, and a direct current power management and distribution (PMAD) system, the electric power system (EPS) supplies power to housekeeping and user electrical loads. Modeling EPS performance is needed for several reasons, but primarily to assess near-term planned and off-nominal operations and because the EPS configuration changes over the life of the ISS. The System Power Analysis for Capability Evaluation (SPACE) computer code is used to assess the ISS EPS performance. This paper describes the process of validating the SPACE EPS model via ISS on-orbit telemetry. To accomplish this goal, telemetry was first used to correct assumptions and component models in SPACE. Then on-orbit data was directly input to SPACE to facilitate comparing model predictions to telemetry. It will be shown that SPACE accurately predicts on-orbit component and system performance. For example, battery state-of-charge was predicted to within 0.6 percentage points over a 0 to 100 percent scale and solar array current was predicted to within a root mean square (RMS) error of 5.1 Amps out of a typical maximum of 220 Amps. First, SPACE model predictions are compared to telemetry for the ISS EPS components: solar arrays, NiH2 batteries, and the PMAD system. Second, SPACE predictions for the overall performance of the ISS EPS are compared to telemetry and again demonstrate model accuracy.

Viscous grounding lines

NASA Astrophysics Data System (ADS)

Worster, Grae; Huppert, Herbert; Robison, Rosalyn; Nandkishore, Rahul; Rajah, Luke

2008-11-01

We have used simple laboratory experiments with viscous fluids to explore the dynamics of grounding lines between Antarctic marine ice sheets and the freely floating ice shelves into which they develop. Ice sheets are shear-dominated gravity currents, while ice shelves are extensional gravity currents with zero shear to leading order. Though ice sheets have non-Newtonian rheology, fundamental aspects of their flow can be explored using Newtonian fluid mechanics. We have derived a mathematical model of this flow that incorporates a new dynamic boundary condition for the position of the grounding line, where the gravity current loses contact with the solid base. Good agreement between our theoretical predictions and our experimental measurements, made using gravity currents of syrup flowing down a rigid slope into a deep, dense salt solution, gives confidence in the fundamental assumptions of our model, which can be incorporated into shallow-ice models to make important predictions regarding the dynamical stability of marine ice sheets.

Urothelial cancer of the upper urinary tract: emerging biomarkers and integrative models for risk stratification.

PubMed

Mathieu, Romain; Vartolomei, Mihai D; Mbeutcha, Aurélie; Karakiewicz, Pierre I; Briganti, Alberto; Roupret, Morgan; Shariat, Shahrokh F

2016-08-01

The aim of this review was to provide an overview of current biomarkers and risk stratification models in urothelial cancer of the upper urinary tract (UTUC). A non-systematic Medline/PubMed literature search was performed using the terms "biomarkers", "preoperative models", "postoperative models", "risk stratification", together with "upper tract urothelial carcinoma". Original articles published between January 2003 and August 2015 were included based on their clinical relevance. Additional references were collected by cross referencing the bibliography of the selected articles. Various promising predictive and prognostic biomarkers have been identified in UTUC thanks to the increasing knowledge of the different biological pathways involved in UTUC tumorigenesis. These biomarkers may help identify tumors with aggressive biology and worse outcomes. Current tools aim at predicting muscle invasive or non-organ confined disease, renal failure after radical nephroureterectomy and survival outcomes. These models are still mainly based on imaging and clinicopathological feature and none has integrated biomarkers. Risk stratification in UTUC is still suboptimal, especially in the preoperative setting due to current limitations in staging and grading. Identification of novel biomarkers and external validation of current prognostic models may help improve risk stratification to allow evidence-based counselling for kidney-sparing approaches, perioperative chemotherapy and/or risk-based surveillance. Despite growing understanding of the biology underlying UTUC, management of this disease remains difficult due to the lack of validated biomarkers and the limitations of current predictive and prognostic tools. Further efforts and collaborations are necessaryry to allow their integration in daily practice.

Artificial neural networks in gynaecological diseases: current and potential future applications.

PubMed

Siristatidis, Charalampos S; Chrelias, Charalampos; Pouliakis, Abraham; Katsimanis, Evangelos; Kassanos, Dimitrios

2010-10-01

Current (and probably future) practice of medicine is mostly associated with prediction and accurate diagnosis. Especially in clinical practice, there is an increasing interest in constructing and using valid models of diagnosis and prediction. Artificial neural networks (ANNs) are mathematical systems being used as a prospective tool for reliable, flexible and quick assessment. They demonstrate high power in evaluating multifactorial data, assimilating information from multiple sources and detecting subtle and complex patterns. Their capability and difference from other statistical techniques lies in performing nonlinear statistical modelling. They represent a new alternative to logistic regression, which is the most commonly used method for developing predictive models for outcomes resulting from partitioning in medicine. In combination with the other non-algorithmic artificial intelligence techniques, they provide useful software engineering tools for the development of systems in quantitative medicine. Our paper first presents a brief introduction to ANNs, then, using what we consider the best available evidence through paradigms, we evaluate the ability of these networks to serve as first-line detection and prediction techniques in some of the most crucial fields in gynaecology. Finally, through the analysis of their current application, we explore their dynamics for future use.

Early experiences building a software quality prediction model

NASA Technical Reports Server (NTRS)

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

1990-01-01

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

Predicting the distributions of predator (snow leopard) and prey (blue sheep) under climate change in the Himalaya.

PubMed

Aryal, Achyut; Shrestha, Uttam Babu; Ji, Weihong; Ale, Som B; Shrestha, Sujata; Ingty, Tenzing; Maraseni, Tek; Cockfield, Geoff; Raubenheimer, David

2016-06-01

Future climate change is likely to affect distributions of species, disrupt biotic interactions, and cause spatial incongruity of predator-prey habitats. Understanding the impacts of future climate change on species distribution will help in the formulation of conservation policies to reduce the risks of future biodiversity losses. Using a species distribution modeling approach by MaxEnt, we modeled current and future distributions of snow leopard (Panthera uncia) and its common prey, blue sheep (Pseudois nayaur), and observed the changes in niche overlap in the Nepal Himalaya. Annual mean temperature is the major climatic factor responsible for the snow leopard and blue sheep distributions in the energy-deficient environments of high altitudes. Currently, about 15.32% and 15.93% area of the Nepal Himalaya are suitable for snow leopard and blue sheep habitats, respectively. The bioclimatic models show that the current suitable habitats of both snow leopard and blue sheep will be reduced under future climate change. The predicted suitable habitat of the snow leopard is decreased when blue sheep habitats is incorporated in the model. Our climate-only model shows that only 11.64% (17,190 km(2)) area of Nepal is suitable for the snow leopard under current climate and the suitable habitat reduces to 5,435 km(2) (reduced by 24.02%) after incorporating the predicted distribution of blue sheep. The predicted distribution of snow leopard reduces by 14.57% in 2030 and by 21.57% in 2050 when the predicted distribution of blue sheep is included as compared to 1.98% reduction in 2030 and 3.80% reduction in 2050 based on the climate-only model. It is predicted that future climate may alter the predator-prey spatial interaction inducing a lower degree of overlap and a higher degree of mismatch between snow leopard and blue sheep niches. This suggests increased energetic costs of finding preferred prey for snow leopards - a species already facing energetic constraints due to the limited dietary resources in its alpine habitat. Our findings provide valuable information for extension of protected areas in future.

Stratification established by peeling detrainment from gravity currents: laboratory experiments and models

NASA Astrophysics Data System (ADS)

Hogg, Charlie; Dalziel, Stuart; Huppert, Herbert; Imberger, Jorg; Department of Applied Mathematics; Theoretical Physics Team; CentreWater Research Team

2014-11-01

Dense gravity currents feed fluid into confined basins in lakes, the oceans and many industrial applications. Existing models of the circulation and mixing in such basins are often based on the currents entraining ambient fluid. However, recent observations have suggested that uni-directional entrainment into a gravity current does not fully describe the mixing in such currents. Laboratory experiments were carried out which visualised peeling detrainment from the gravity current occurring when the ambient fluid was stratified. A theoretical model of the observed peeling detrainment was developed to predict the stratification in the basin. This new model gives a better approximation of the stratification observed in the experiments than the pre-existing entraining model. The model can now be developed such that it integrates into operational models of lakes.

Assessing the Liquidity of Firms: Robust Neural Network Regression as an Alternative to the Current Ratio

NASA Astrophysics Data System (ADS)

de Andrés, Javier; Landajo, Manuel; Lorca, Pedro; Labra, Jose; Ordóñez, Patricia

Artificial neural networks have proven to be useful tools for solving financial analysis problems such as financial distress prediction and audit risk assessment. In this paper we focus on the performance of robust (least absolute deviation-based) neural networks on measuring liquidity of firms. The problem of learning the bivariate relationship between the components (namely, current liabilities and current assets) of the so-called current ratio is analyzed, and the predictive performance of several modelling paradigms (namely, linear and log-linear regressions, classical ratios and neural networks) is compared. An empirical analysis is conducted on a representative data base from the Spanish economy. Results indicate that classical ratio models are largely inadequate as a realistic description of the studied relationship, especially when used for predictive purposes. In a number of cases, especially when the analyzed firms are microenterprises, the linear specification is improved by considering the flexible non-linear structures provided by neural networks.

Non-inductive current drive and transport in high βN plasmas in JET

NASA Astrophysics Data System (ADS)

Voitsekhovitch, I.; Alper, B.; Brix, M.; Budny, R. V.; Buratti, P.; Challis, C. D.; Ferron, J.; Giroud, C.; Joffrin, E.; Laborde, L.; Luce, T. C.; McCune, D.; Menard, J.; Murakami, M.; Park, J. M.; JET-EFDA contributors

2009-05-01

A route to stationary MHD stable operation at high βN has been explored at the Joint European Torus (JET) by optimizing the current ramp-up, heating start time and the waveform of neutral beam injection (NBI) power. In these scenarios the current ramp-up has been accompanied by plasma pre-heat (or the NBI has been started before the current flat-top) and NBI power up to 22 MW has been applied during the current flat-top. In the discharges considered transient total βN ≈ 3.3 and stationary (during high power phase) βN ≈ 3 have been achieved by applying the feedback control of βN with the NBI power in configurations with monotonic or flat core safety factor profile and without an internal transport barrier (ITB). The transport and current drive in this scenario is analysed here by using the TRANSP and ASTRA codes. The interpretative analysis performed with TRANSP shows that 50-70% of current is driven non-inductively; half of this current is due to the bootstrap current which has a broad profile since an ITB was deliberately avoided. The GLF23 transport model predicts the temperature profiles within a ±22% discrepancy with the measurements over the explored parameter space. Predictive simulations with this model show that the E × B rotational shear plays an important role for thermal ion transport in this scenario, producing up to a 40% increase of the ion temperature. By applying transport and current drive models validated in self-consistent simulations of given reference scenarios in a wider parameter space, the requirements for fully non-inductive stationary operation at JET are estimated. It is shown that the strong stiffness of the temperature profiles predicted by the GLF23 model restricts the bootstrap current at larger heating power. In this situation full non-inductive operation without an ITB can be rather expensive strongly relying on the external non-inductive current drive sources.

Model Predictive Control of the Current Profile and the Internal Energy of DIII-D Plasmas

NASA Astrophysics Data System (ADS)

Lauret, M.; Wehner, W.; Schuster, E.

2015-11-01

For efficient and stable operation of tokamak plasmas it is important that the current density profile and the internal energy are jointly controlled by using the available heating and current-drive (H&CD) sources. The proposed approach is a version of nonlinear model predictive control in which the input set is restricted in size by the possible combinations of the H&CD on/off states. The controller uses real-time predictions over a receding-time horizon of both the current density profile (nonlinear partial differential equation) and the internal energy (nonlinear ordinary differential equation) evolutions. At every time instant the effect of every possible combination of H&CD sources on the current profile and internal energy is evaluated over the chosen time horizon. The combination that leads to the best result, which is assessed by a user-defined cost function, is then applied up until the next time instant. Simulations results based on a control-oriented transport code illustrate the effectiveness of the proposed control method. Supported by the US DOE under DE-FC02-04ER54698 & DE-SC0010661.

High-resolution Modeling Assisted Design of Customized and Individualized Transcranial Direct Current Stimulation Protocols

PubMed Central

Bikson, Marom; Rahman, Asif; Datta, Abhishek; Fregni, Felipe; Merabet, Lotfi

2012-01-01

Objectives Transcranial direct current stimulation (tDCS) is a neuromodulatory technique that delivers low-intensity currents facilitating or inhibiting spontaneous neuronal activity. tDCS is attractive since dose is readily adjustable by simply changing electrode number, position, size, shape, and current. In the recent past, computational models have been developed with increased precision with the goal to help customize tDCS dose. The aim of this review is to discuss the incorporation of high-resolution patient-specific computer modeling to guide and optimize tDCS. Methods In this review, we discuss the following topics: (i) The clinical motivation and rationale for models of transcranial stimulation is considered pivotal in order to leverage the flexibility of neuromodulation; (ii) The protocols and the workflow for developing high-resolution models; (iii) The technical challenges and limitations of interpreting modeling predictions, and (iv) Real cases merging modeling and clinical data illustrating the impact of computational models on the rational design of rehabilitative electrotherapy. Conclusions Though modeling for non-invasive brain stimulation is still in its development phase, it is predicted that with increased validation, dissemination, simplification and democratization of modeling tools, computational forward models of neuromodulation will become useful tools to guide the optimization of clinical electrotherapy. PMID:22780230

Life extending control for rocket engines

NASA Technical Reports Server (NTRS)

Lorenzo, C. F.; Saus, J. R.; Ray, A.; Carpino, M.; Wu, M.-K.

1992-01-01

The concept of life extending control is defined. A brief discussion of current fatigue life prediction methods is given and the need for an alternative life prediction model based on a continuous functional relationship is established. Two approaches to life extending control are considered: (1) the implicit approach which uses cyclic fatigue life prediction as a basis for control design; and (2) the continuous life prediction approach which requires a continuous damage law. Progress on an initial formulation of a continuous (in time) fatigue model is presented. Finally, nonlinear programming is used to develop initial results for life extension for a simplified rocket engine (model).

Musite, a tool for global prediction of general and kinase-specific phosphorylation sites.

PubMed

Gao, Jianjiong; Thelen, Jay J; Dunker, A Keith; Xu, Dong

2010-12-01

Reversible protein phosphorylation is one of the most pervasive post-translational modifications, regulating diverse cellular processes in various organisms. High throughput experimental studies using mass spectrometry have identified many phosphorylation sites, primarily from eukaryotes. However, the vast majority of phosphorylation sites remain undiscovered, even in well studied systems. Because mass spectrometry-based experimental approaches for identifying phosphorylation events are costly, time-consuming, and biased toward abundant proteins and proteotypic peptides, in silico prediction of phosphorylation sites is potentially a useful alternative strategy for whole proteome annotation. Because of various limitations, current phosphorylation site prediction tools were not well designed for comprehensive assessment of proteomes. Here, we present a novel software tool, Musite, specifically designed for large scale predictions of both general and kinase-specific phosphorylation sites. We collected phosphoproteomics data in multiple organisms from several reliable sources and used them to train prediction models by a comprehensive machine-learning approach that integrates local sequence similarities to known phosphorylation sites, protein disorder scores, and amino acid frequencies. Application of Musite on several proteomes yielded tens of thousands of phosphorylation site predictions at a high stringency level. Cross-validation tests show that Musite achieves some improvement over existing tools in predicting general phosphorylation sites, and it is at least comparable with those for predicting kinase-specific phosphorylation sites. In Musite V1.0, we have trained general prediction models for six organisms and kinase-specific prediction models for 13 kinases or kinase families. Although the current pretrained models were not correlated with any particular cellular conditions, Musite provides a unique functionality for training customized prediction models (including condition-specific models) from users' own data. In addition, with its easily extensible open source application programming interface, Musite is aimed at being an open platform for community-based development of machine learning-based phosphorylation site prediction applications. Musite is available at http://musite.sourceforge.net/.

Cortical and subcortical predictive dynamics and learning during perception, cognition, emotion and action

PubMed Central

Grossberg, Stephen

2009-01-01

An intimate link exists between the predictive and learning processes in the brain. Perceptual/cognitive and spatial/motor processes use complementary predictive mechanisms to learn, recognize, attend and plan about objects in the world, determine their current value, and act upon them. Recent neural models clarify these mechanisms and how they interact in cortical and subcortical brain regions. The present paper reviews and synthesizes data and models of these processes, and outlines a unified theory of predictive brain processing. PMID:19528003

Predictive Models for Escherichia coli Concentrations at Inland Lake Beaches and Relationship of Model Variables to Pathogen Detection

PubMed Central

Stelzer, Erin A.; Duris, Joseph W.; Brady, Amie M. G.; Harrison, John H.; Johnson, Heather E.; Ware, Michael W.

2013-01-01

Predictive models, based on environmental and water quality variables, have been used to improve the timeliness and accuracy of recreational water quality assessments, but their effectiveness has not been studied in inland waters. Sampling at eight inland recreational lakes in Ohio was done in order to investigate using predictive models for Escherichia coli and to understand the links between E. coli concentrations, predictive variables, and pathogens. Based upon results from 21 beach sites, models were developed for 13 sites, and the most predictive variables were rainfall, wind direction and speed, turbidity, and water temperature. Models were not developed at sites where the E. coli standard was seldom exceeded. Models were validated at nine sites during an independent year. At three sites, the model resulted in increased correct responses, sensitivities, and specificities compared to use of the previous day's E. coli concentration (the current method). Drought conditions during the validation year precluded being able to adequately assess model performance at most of the other sites. Cryptosporidium, adenovirus, eaeA (E. coli), ipaH (Shigella), and spvC (Salmonella) were found in at least 20% of samples collected for pathogens at five sites. The presence or absence of the three bacterial genes was related to some of the model variables but was not consistently related to E. coli concentrations. Predictive models were not effective at all inland lake sites; however, their use at two lakes with high swimmer densities will provide better estimates of public health risk than current methods and will be a valuable resource for beach managers and the public. PMID:23291550

Comparison of equilibrium ohmic and nonequilibrium swarm models for monitoring conduction electron evolution in high-altitude EMP calculations

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

Pusateri, Elise N.; Morris, Heidi E.; Nelson, Eric

2016-10-17

Here, atmospheric electromagnetic pulse (EMP) events are important physical phenomena that occur through both man-made and natural processes. Radiation-induced currents and voltages in EMP can couple with electrical systems, such as those found in satellites, and cause significant damage. Due to the disruptive nature of EMP, it is important to accurately predict EMP evolution and propagation with computational models. CHAP-LA (Compton High Altitude Pulse-Los Alamos) is a state-of-the-art EMP code that solves Maxwell inline images equations for gamma source-induced electromagnetic fields in the atmosphere. In EMP, low-energy, conduction electrons constitute a conduction current that limits the EMP by opposing themore » Compton current. CHAP-LA calculates the conduction current using an equilibrium ohmic model. The equilibrium model works well at low altitudes, where the electron energy equilibration time is short compared to the rise time or duration of the EMP. At high altitudes, the equilibration time increases beyond the EMP rise time and the predicted equilibrium ionization rate becomes very large. The ohmic model predicts an unphysically large production of conduction electrons which prematurely and abruptly shorts the EMP in the simulation code. An electron swarm model, which implicitly accounts for the time evolution of the conduction electron energy distribution, can be used to overcome the limitations exhibited by the equilibrium ohmic model. We have developed and validated an electron swarm model previously in Pusateri et al. (2015). Here we demonstrate EMP damping behavior caused by the ohmic model at high altitudes and show improvements on high-altitude, upward EMP modeling obtained by integrating a swarm model into CHAP-LA.« less

Comparison of equilibrium ohmic and nonequilibrium swarm models for monitoring conduction electron evolution in high-altitude EMP calculations

NASA Astrophysics Data System (ADS)

Pusateri, Elise N.; Morris, Heidi E.; Nelson, Eric; Ji, Wei

2016-10-01

Atmospheric electromagnetic pulse (EMP) events are important physical phenomena that occur through both man-made and natural processes. Radiation-induced currents and voltages in EMP can couple with electrical systems, such as those found in satellites, and cause significant damage. Due to the disruptive nature of EMP, it is important to accurately predict EMP evolution and propagation with computational models. CHAP-LA (Compton High Altitude Pulse-Los Alamos) is a state-of-the-art EMP code that solves Maxwell's equations for gamma source-induced electromagnetic fields in the atmosphere. In EMP, low-energy, conduction electrons constitute a conduction current that limits the EMP by opposing the Compton current. CHAP-LA calculates the conduction current using an equilibrium ohmic model. The equilibrium model works well at low altitudes, where the electron energy equilibration time is short compared to the rise time or duration of the EMP. At high altitudes, the equilibration time increases beyond the EMP rise time and the predicted equilibrium ionization rate becomes very large. The ohmic model predicts an unphysically large production of conduction electrons which prematurely and abruptly shorts the EMP in the simulation code. An electron swarm model, which implicitly accounts for the time evolution of the conduction electron energy distribution, can be used to overcome the limitations exhibited by the equilibrium ohmic model. We have developed and validated an electron swarm model previously in Pusateri et al. (2015). Here we demonstrate EMP damping behavior caused by the ohmic model at high altitudes and show improvements on high-altitude, upward EMP modeling obtained by integrating a swarm model into CHAP-LA.

Evaluation of wind induced currents modeling along the Southern Caspian Sea

NASA Astrophysics Data System (ADS)

Bohluly, Asghar; Esfahani, Fariba Sadat; Montazeri Namin, Masoud; Chegini, Fatemeh

2018-02-01

To improve our understanding of the Caspian Sea hydrodynamics, its circulation is simulated with special focus on wind-driven currents of its southern basin. The hydrodynamic models are forced with a newly developed fine resolution wind field to increase the accuracy of current modeling. A 2D shallow water equation model and a 3D baroclinic model are applied separately to examine the performance of each model for specific applications in the Caspian Sea. The model results are validated against recent field measurements including AWAC and temperature observations in the southern continental shelf region. Results show that the 2D model is able to well predict the depth-averaged current speed in storm conditions in narrow area of southern coasts. This finding suggests physical oceanographers apply 2D modeling as a more affordable method for extreme current speed analysis at the continental shelf region. On the other hand the 3D model demonstrates a better performance in reproducing monthly mean circulation and hence is preferable for surface circulation of Caspian Sea. Monthly sea surface circulation fields of the southern basin reveal a dipole cyclonic-anticyclonic pattern, a dominant eastward current along the southern coasts which intensifies from May to November and a dominant southward current along the eastern coasts in all months except February when the flow is northward. Monthly mean wind fields exhibit two main patterns including a north-south pattern occurring at warm months and collision of two wind fronts especially in the cold months. This collision occurs on a narrow region at the southern continental shelf regions. Due to wind field complexities, it leads to a major source of uncertainty in predicting the wind-driven currents. However, this source of uncertainty is significantly alleviated by applying a fine resolution wind field.

Risk models for post-endoscopic retrograde cholangiopancreatography pancreatitis (PEP): smoking and chronic liver disease are predictors of protection against PEP.

PubMed

DiMagno, Matthew J; Spaete, Joshua P; Ballard, Darren D; Wamsteker, Erik-Jan; Saini, Sameer D

2013-08-01

We investigated which variables independently associated with protection against or development of postendoscopic retrograde cholangiopancreatography (ERCP) pancreatitis (PEP) and severity of PEP. Subsequently, we derived predictive risk models for PEP. In a case-control design, 6505 patients had 8264 ERCPs, 211 patients had PEP, and 22 patients had severe PEP. We randomly selected 348 non-PEP controls. We examined 7 established- and 9 investigational variables. In univariate analysis, 7 variables predicted PEP: younger age, female sex, suspected sphincter of Oddi dysfunction (SOD), pancreatic sphincterotomy, moderate-difficult cannulation (MDC), pancreatic stent placement, and lower Charlson score. Protective variables were current smoking, former drinking, diabetes, and chronic liver disease (CLD, biliary/transplant complications). Multivariate analysis identified seven independent variables for PEP, three protective (current smoking, CLD-biliary, CLD-transplant/hepatectomy complications) and 4 predictive (younger age, suspected SOD, pancreatic sphincterotomy, MDC). Pre- and post-ERCP risk models of 7 variables have a C-statistic of 0.74. Removing age (seventh variable) did not significantly affect the predictive value (C-statistic of 0.73) and reduced model complexity. Severity of PEP did not associate with any variables by multivariate analysis. By using the newly identified protective variables with 3 predictive variables, we derived 2 risk models with a higher predictive value for PEP compared to prior studies.

The role of shoe design on the prediction of free torque at the shoe-surface interface using pressure insole technology.

PubMed

Weaver, Brian Thomas; Fitzsimons, Kathleen; Braman, Jerrod; Haut, Roger

2016-09-01

The goal of the current study was to expand on previous work to validate the use of pressure insole technology in conjunction with linear regression models to predict the free torque at the shoe-surface interface that is generated while wearing different athletic shoes. Three distinctly different shoe designs were utilised. The stiffness of each shoe was determined with a material's testing machine. Six participants wore each shoe that was fitted with an insole pressure measurement device and performed rotation trials on an embedded force plate. A pressure sensor mask was constructed from those sensors having a high linear correlation with free torque values. Linear regression models were developed to predict free torques from these pressure sensor data. The models were able to accurately predict their own free torque well (RMS error 3.72 ± 0.74 Nm), but not that of the other shoes (RMS error 10.43 ± 3.79 Nm). Models performing self-prediction were also able to measure differences in shoe stiffness. The results of the current study showed the need for participant-shoe specific linear regression models to insure high prediction accuracy of free torques from pressure sensor data during isolated internal and external rotations of the body with respect to a planted foot.

Integrated Wind Power Planning Tool

NASA Astrophysics Data System (ADS)

Rosgaard, M. H.; Giebel, G.; Nielsen, T. S.; Hahmann, A.; Sørensen, P.; Madsen, H.

2012-04-01

This poster presents the current state of the public service obligation (PSO) funded project PSO 10464, with the working title "Integrated Wind Power Planning Tool". The project commenced October 1, 2011, and the goal is to integrate a numerical weather prediction (NWP) model with purely statistical tools in order to assess wind power fluctuations, with focus on long term power system planning for future wind farms as well as short term forecasting for existing wind farms. Currently, wind power fluctuation models are either purely statistical or integrated with NWP models of limited resolution. With regard to the latter, one such simulation tool has been developed at the Wind Energy Division, Risø DTU, intended for long term power system planning. As part of the PSO project the inferior NWP model used at present will be replaced by the state-of-the-art Weather Research & Forecasting (WRF) model. Furthermore, the integrated simulation tool will be improved so it can handle simultaneously 10-50 times more turbines than the present ~ 300, as well as additional atmospheric parameters will be included in the model. The WRF data will also be input for a statistical short term prediction model to be developed in collaboration with ENFOR A/S; a danish company that specialises in forecasting and optimisation for the energy sector. This integrated prediction model will allow for the description of the expected variability in wind power production in the coming hours to days, accounting for its spatio-temporal dependencies, and depending on the prevailing weather conditions defined by the WRF output. The output from the integrated prediction tool constitute scenario forecasts for the coming period, which can then be fed into any type of system model or decision making problem to be solved. The high resolution of the WRF results loaded into the integrated prediction model will ensure a high accuracy data basis is available for use in the decision making process of the Danish transmission system operator, and the need for high accuracy predictions will only increase over the next decade as Denmark approaches the goal of 50% wind power based electricity in 2020, from the current 20%.

Deep learning architecture for air quality predictions.

PubMed

Li, Xiang; Peng, Ling; Hu, Yuan; Shao, Jing; Chi, Tianhe

2016-11-01

With the rapid development of urbanization and industrialization, many developing countries are suffering from heavy air pollution. Governments and citizens have expressed increasing concern regarding air pollution because it affects human health and sustainable development worldwide. Current air quality prediction methods mainly use shallow models; however, these methods produce unsatisfactory results, which inspired us to investigate methods of predicting air quality based on deep architecture models. In this paper, a novel spatiotemporal deep learning (STDL)-based air quality prediction method that inherently considers spatial and temporal correlations is proposed. A stacked autoencoder (SAE) model is used to extract inherent air quality features, and it is trained in a greedy layer-wise manner. Compared with traditional time series prediction models, our model can predict the air quality of all stations simultaneously and shows the temporal stability in all seasons. Moreover, a comparison with the spatiotemporal artificial neural network (STANN), auto regression moving average (ARMA), and support vector regression (SVR) models demonstrates that the proposed method of performing air quality predictions has a superior performance.

Efficacy of monitoring and empirical predictive modeling at improving public health protection at Chicago beaches

USGS Publications Warehouse

Nevers, Meredith B.; Whitman, Richard L.

2011-01-01

Efforts to improve public health protection in recreational swimming waters have focused on obtaining real-time estimates of water quality. Current monitoring techniques rely on the time-intensive culturing of fecal indicator bacteria (FIB) from water samples, but rapidly changing FIB concentrations result in management errors that lead to the public being exposed to high FIB concentrations (type II error) or beaches being closed despite acceptable water quality (type I error). Empirical predictive models may provide a rapid solution, but their effectiveness at improving health protection has not been adequately assessed. We sought to determine if emerging monitoring approaches could effectively reduce risk of illness exposure by minimizing management errors. We examined four monitoring approaches (inactive, current protocol, a single predictive model for all beaches, and individual models for each beach) with increasing refinement at 14 Chicago beaches using historical monitoring and hydrometeorological data and compared management outcomes using different standards for decision-making. Predictability (R2) of FIB concentration improved with model refinement at all beaches but one. Predictive models did not always reduce the number of management errors and therefore the overall illness burden. Use of a Chicago-specific single-sample standard-rather than the default 235 E. coli CFU/100 ml widely used-together with predictive modeling resulted in the greatest number of open beach days without any increase in public health risk. These results emphasize that emerging monitoring approaches such as empirical models are not equally applicable at all beaches, and combining monitoring approaches may expand beach access.

The ability of video image analysis to predict lean meat yield and EUROP score of lamb carcasses.

PubMed

Einarsson, E; Eythórsdóttir, E; Smith, C R; Jónmundsson, J V

2014-07-01

A total of 862 lamb carcasses that were evaluated by both the VIAscan® and the current EUROP classification system were deboned and the actual yield was measured. Models were derived for predicting lean meat yield of the legs (Leg%), loin (Loin%) and shoulder (Shldr%) using the best VIAscan® variables selected by stepwise regression analysis of a calibration data set (n=603). The equations were tested on validation data set (n=259). The results showed that the VIAscan® predicted lean meat yield in the leg, loin and shoulder with an R 2 of 0.60, 0.31 and 0.47, respectively, whereas the current EUROP system predicted lean yield with an R 2 of 0.57, 0.32 and 0.37, respectively, for the three carcass parts. The VIAscan® also predicted the EUROP score of the trial carcasses, using a model derived from an earlier trial. The EUROP classification from VIAscan® and the current system were compared for their ability to explain the variation in lean yield of the whole carcass (LMY%) and trimmed fat (FAT%). The predicted EUROP scores from the VIAscan® explained 36% of the variation in LMY% and 60% of the variation in FAT%, compared with the current EUROP system that explained 49% and 72%, respectively. The EUROP classification obtained by the VIAscan® was tested against a panel of three expert classifiers (n=696). The VIAscan® classification agreed with 82% of conformation and 73% of the fat classes assigned by a panel of expert classifiers. It was concluded that VIAscan® provides a technology that can directly predict LMY% of lamb carcasses with more accuracy than the current EUROP classification system. The VIAscan® is also capable of classifying lamb carcasses into EUROP classes with an accuracy that fulfils minimum demands for the Icelandic sheep industry. Although the VIAscan® prediction of the Loin% is low, it is comparable to the current EUROP system, and should not hinder the adoption of the technology to estimate the yield of Icelandic lambs as it delivered a more accurate prediction for the Leg%, Shldr% and overall LMY% with negligible prediction bias.

A test of source-surface model predictions of heliospheric current sheet inclination

NASA Technical Reports Server (NTRS)

Burton, M. E.; Crooker, N. U.; Siscoe, G. L.; Smith, E. J.

1994-01-01

The orientation of the heliospheric current sheet predicted from a source surface model is compared with the orientation determined from minimum-variance analysis of International Sun-Earth Explorer (ISEE) 3 magnetic field data at 1 AU near solar maximum. Of the 37 cases analyzed, 28 have minimum variance normals that lie orthogonal to the predicted Parker spiral direction. For these cases, the correlation coefficient between the predicted and measured inclinations is 0.6. However, for the subset of 14 cases for which transient signatures (either interplanetary shocks or bidirectional electrons) are absent, the agreement in inclinations improves dramatically, with a correlation coefficient of 0.96. These results validate not only the use of the source surface model as a predictor but also the previously questioned usefulness of minimum variance analysis across complex sector boundaries. In addition, the results imply that interplanetary dynamics have little effect on current sheet inclination at 1 AU. The dependence of the correlation on transient occurrence suggests that the leading edge of a coronal mass ejection (CME), where transient signatures are detected, disrupts the heliospheric current sheet but that the sheet re-forms between the trailing legs of the CME. In this way the global structure of the heliosphere, reflected both in the source surface maps and in the interplanetary sector structure, can be maintained even when the CME occurrence rate is high.

Analytical modeling of eddy-current losses caused by pulse-width-modulation switching in permanent-magnet brushless direct-current motors

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

Deng, F.; Nehl, T.W.

1998-09-01

Because of their high efficiency and power density the PM brushless dc motor is a strong candidate for electric and hybrid vehicle propulsion systems. An analytical approach is developed to predict the inverter high frequency pulse width modulation (PWM) switching caused eddy-current losses in a permanent magnet brushless dc motor. The model uses polar coordinates to take curvature effects into account, and is also capable of including the space harmonic effect of the stator magnetic field and the stator lamination effect on the losses. The model was applied to an existing motor design and was verified with the finite elementmore » method. Good agreement was achieved between the two approaches. Hence, the model is expected to be very helpful in predicting PWM switching losses in permanent magnet machine design.« less

Evaluation of scenario-specific modeling approaches to predict plane of array solar irradiation

DOE PAGES

Moslehi, Salim; Reddy, T. Agami; Katipamula, Srinivas

2017-12-20

Predicting thermal or electric power output from solar collectors requires knowledge of solar irradiance incident on the collector, known as plane of array irradiance. In the absence of such a measurement, plane of array irradiation can be predicted using relevant transposition models which essentially requires diffuse (or beam) radiation to be to be known along with total horizontal irradiation. The two main objectives of the current study are (1) to evaluate the extent to which the prediction of plane of array irradiance is improved when diffuse radiation is predicted using location-specific regression models developed from on-site measured data as againstmore » using generalized models; and (2) to estimate the expected uncertainties associated with plane of array irradiance predictions under different data collection scenarios likely to be encountered in practical situations. These issues have been investigated using monitored data for several U.S. locations in conjunction with the Typical Meteorological Year, version 3 database. An interesting behavior in the Typical Meteorological Year, version 3 data was also observed in correlation patterns between diffuse and total radiation taken from different years which seems to attest to a measurement problem. Furthermore, the current study was accomplished under a broader research agenda aimed at providing energy managers the necessary tools for predicting, scheduling, and controlling various sub-systems of an integrated energy system.« less

Evaluation of scenario-specific modeling approaches to predict plane of array solar irradiation

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

Moslehi, Salim; Reddy, T. Agami; Katipamula, Srinivas

Predicting thermal or electric power output from solar collectors requires knowledge of solar irradiance incident on the collector, known as plane of array irradiance. In the absence of such a measurement, plane of array irradiation can be predicted using relevant transposition models which essentially requires diffuse (or beam) radiation to be to be known along with total horizontal irradiation. The two main objectives of the current study are (1) to evaluate the extent to which the prediction of plane of array irradiance is improved when diffuse radiation is predicted using location-specific regression models developed from on-site measured data as againstmore » using generalized models; and (2) to estimate the expected uncertainties associated with plane of array irradiance predictions under different data collection scenarios likely to be encountered in practical situations. These issues have been investigated using monitored data for several U.S. locations in conjunction with the Typical Meteorological Year, version 3 database. An interesting behavior in the Typical Meteorological Year, version 3 data was also observed in correlation patterns between diffuse and total radiation taken from different years which seems to attest to a measurement problem. Furthermore, the current study was accomplished under a broader research agenda aimed at providing energy managers the necessary tools for predicting, scheduling, and controlling various sub-systems of an integrated energy system.« less

Dendritic trafficking faces physiologically critical speed-precision tradeoffs

PubMed Central

Williams, Alex H; O'Donnell, Cian; Sejnowski, Terrence J; O'Leary, Timothy

2016-01-01

Nervous system function requires intracellular transport of channels, receptors, mRNAs, and other cargo throughout complex neuronal morphologies. Local signals such as synaptic input can regulate cargo trafficking, motivating the leading conceptual model of neuron-wide transport, sometimes called the ‘sushi-belt model’ (Doyle and Kiebler, 2011). Current theories and experiments are based on this model, yet its predictions are not rigorously understood. We formalized the sushi belt model mathematically, and show that it can achieve arbitrarily complex spatial distributions of cargo in reconstructed morphologies. However, the model also predicts an unavoidable, morphology dependent tradeoff between speed, precision and metabolic efficiency of cargo transport. With experimental estimates of trafficking kinetics, the model predicts delays of many hours or days for modestly accurate and efficient cargo delivery throughout a dendritic tree. These findings challenge current understanding of the efficacy of nucleus-to-synapse trafficking and may explain the prevalence of local biosynthesis in neurons. DOI: http://dx.doi.org/10.7554/eLife.20556.001 PMID:28034367

Oscillating field current drive experiments in the Madison Symmetric Torus

NASA Astrophysics Data System (ADS)

Blair, Arthur P., Jr.

Oscillating Field Current Drive (OFCD) is an inductive current drive method for toroidal pinches. To test OFCD, two 280 Hz 2 MVA oscillators were installed in the toroidal and poloidal magnetic field circuits of the Madison Symmetric Torus (MST) Reversed Field Pinch (RFP.) Partial sustainment experiments were conducted where the two voltage oscillations were superimposed on the standard MST power supplies. Supplementary current drive of about 10% has been demonstrated, comparable to theoretical predictions. However, maximum current drive does not coincide with maximum helicity injection rate - possibly due to an observed dependence of core and edge tearing modes on the relative phase of the oscillators. A dependence of wall interactions on phase was also observed, the largest interaction coinciding with negative current drive. Experiments were conducted at 280 and 530 Hz. 530 Hz proved to be too high and yielded little or no net current drive. Experiments at 280 Hz proved more fruitful. A 1D relaxed state model was used to predict the effects of voltage amplitudes, frequencies, and waveforms on performance and to optimize the design of OFCD hardware. Predicted current drive was comparable to experimental values, though the aforementioned phase dependence was not. Comparisons were also made with a more comprehensive 3D model which proved to be a more accurate predictor of current drive. Both 1D and 3D models predicted the feasability of full sustainment via OFCD. Experiments were also conducted with only the toroidal field oscillator applied. An entrainment of the natural sawtooth frequency to our applied oscillation was observed as well as a slow modulation of the edge tearing mode amplitudes. A large modulation (20 to 80 eV) of the ion temperature was also observed that can be partially accounted for by collisional heating via magnetic pumping. Work is in progress to increase the power of the existing OFCD hardware.

A Comparison of Combustor-Noise Models

NASA Technical Reports Server (NTRS)

Hultgren, Lennart, S.

2012-01-01

The current status of combustor-noise prediction in the NASA Aircraft Noise Prediction Program (ANOPP) for current-generation (N) turbofan engines is summarized. Best methods for near-term updates are reviewed. Long-term needs and challenges for the N+1 through N+3 timeframe are discussed. This work was carried out under the NASA Fundamental Aeronautics Program, Subsonic Fixed Wing Project, Quiet Aircraft Subproject.

A model predictive current control of flux-switching permanent magnet machines for torque ripple minimization

NASA Astrophysics Data System (ADS)

Huang, Wentao; Hua, Wei; Yu, Feng

2017-05-01

Due to high airgap flux density generated by magnets and the special double salient structure, the cogging torque of the flux-switching permanent magnet (FSPM) machine is considerable, which limits the further applications. Based on the model predictive current control (MPCC) and the compensation control theory, a compensating-current MPCC (CC-MPCC) scheme is proposed and implemented to counteract the dominated components in cogging torque of an existing three-phase 12/10 FSPM prototyped machine, and thus to alleviate the influence of the cogging torque and improve the smoothness of electromagnetic torque as well as speed, where a comprehensive cost function is designed to evaluate the switching states. The simulated results indicate that the proposed CC-MPCC scheme can suppress the torque ripple significantly and offer satisfactory dynamic performances by comparisons with the conventional MPCC strategy. Finally, experimental results validate both the theoretical and simulated predictions.

Estimating current and future streamflow characteristics at ungaged sites, central and eastern Montana, with application to evaluating effects of climate change on fish populations

USGS Publications Warehouse

Sando, Roy; Chase, Katherine J.

2017-03-23

A common statistical procedure for estimating streamflow statistics at ungaged locations is to develop a relational model between streamflow and drainage basin characteristics at gaged locations using least squares regression analysis; however, least squares regression methods are parametric and make constraining assumptions about the data distribution. The random forest regression method provides an alternative nonparametric method for estimating streamflow characteristics at ungaged sites and requires that the data meet fewer statistical conditions than least squares regression methods.Random forest regression analysis was used to develop predictive models for 89 streamflow characteristics using Precipitation-Runoff Modeling System simulated streamflow data and drainage basin characteristics at 179 sites in central and eastern Montana. The predictive models were developed from streamflow data simulated for current (baseline, water years 1982–99) conditions and three future periods (water years 2021–38, 2046–63, and 2071–88) under three different climate-change scenarios. These predictive models were then used to predict streamflow characteristics for baseline conditions and three future periods at 1,707 fish sampling sites in central and eastern Montana. The average root mean square error for all predictive models was about 50 percent. When streamflow predictions at 23 fish sampling sites were compared to nearby locations with simulated data, the mean relative percent difference was about 43 percent. When predictions were compared to streamflow data recorded at 21 U.S. Geological Survey streamflow-gaging stations outside of the calibration basins, the average mean absolute percent error was about 73 percent.

Population Dynamics and Flight Phenology Model of Codling Moth Differ between Commercial and Abandoned Apple Orchard Ecosystems.

PubMed

Joshi, Neelendra K; Rajotte, Edwin G; Naithani, Kusum J; Krawczyk, Greg; Hull, Larry A

2016-01-01

Apple orchard management practices may affect development and phenology of arthropod pests, such as the codling moth (CM), Cydia pomonella (L.) (Lepidoptera: Tortricidae), which is a serious internal fruit-feeding pest of apples worldwide. Estimating population dynamics and accurately predicting the timing of CM development and phenology events (for instance, adult flight, and egg-hatch) allows growers to understand and control local populations of CM. Studies were conducted to compare the CM flight phenology in commercial and abandoned apple orchard ecosystems using a logistic function model based on degree-days accumulation. The flight models for these orchards were derived from the cumulative percent moth capture using two types of commercially available CM lure baited traps. Models from both types of orchards were also compared to another model known as PETE (prediction extension timing estimator) that was developed in 1970s to predict life cycle events for many fruit pests including CM across different fruit growing regions of the United States. We found that the flight phenology of CM was significantly different in commercial and abandoned orchards. CM male flight patterns for first and second generations as predicted by the constrained and unconstrained PCM (Pennsylvania Codling Moth) models in commercial and abandoned orchards were different than the flight patterns predicted by the currently used CM model (i.e., PETE model). In commercial orchards, during the first and second generations, the PCM unconstrained model predicted delays in moth emergence compared to current model. In addition, the flight patterns of females were different between commercial and abandoned orchards. Such differences in CM flight phenology between commercial and abandoned orchard ecosystems suggest potential impact of orchard environment and crop management practices on CM biology.

Population Dynamics and Flight Phenology Model of Codling Moth Differ between Commercial and Abandoned Apple Orchard Ecosystems

PubMed Central

Joshi, Neelendra K.; Rajotte, Edwin G.; Naithani, Kusum J.; Krawczyk, Greg; Hull, Larry A.

2016-01-01

Apple orchard management practices may affect development and phenology of arthropod pests, such as the codling moth (CM), Cydia pomonella (L.) (Lepidoptera: Tortricidae), which is a serious internal fruit-feeding pest of apples worldwide. Estimating population dynamics and accurately predicting the timing of CM development and phenology events (for instance, adult flight, and egg-hatch) allows growers to understand and control local populations of CM. Studies were conducted to compare the CM flight phenology in commercial and abandoned apple orchard ecosystems using a logistic function model based on degree-days accumulation. The flight models for these orchards were derived from the cumulative percent moth capture using two types of commercially available CM lure baited traps. Models from both types of orchards were also compared to another model known as PETE (prediction extension timing estimator) that was developed in 1970s to predict life cycle events for many fruit pests including CM across different fruit growing regions of the United States. We found that the flight phenology of CM was significantly different in commercial and abandoned orchards. CM male flight patterns for first and second generations as predicted by the constrained and unconstrained PCM (Pennsylvania Codling Moth) models in commercial and abandoned orchards were different than the flight patterns predicted by the currently used CM model (i.e., PETE model). In commercial orchards, during the first and second generations, the PCM unconstrained model predicted delays in moth emergence compared to current model. In addition, the flight patterns of females were different between commercial and abandoned orchards. Such differences in CM flight phenology between commercial and abandoned orchard ecosystems suggest potential impact of orchard environment and crop management practices on CM biology. PMID:27713702

Hot limpets: predicting body temperature in a conductance-mediated thermal system.

PubMed

Denny, Mark W; Harley, Christopher D G

2006-07-01

Living at the interface between the marine and terrestrial environments, intertidal organisms may serve as a bellwether for environmental change and a test of our ability to predict its biological consequences. However, current models do not allow us to predict the body temperature of intertidal organisms whose heat budgets are strongly affected by conduction to and from the substratum. Here, we propose a simple heat-budget model of one such animal, the limpet Lottia gigantea, and test the model against measurements made in the field. Working solely from easily measured physical and meteorological inputs, the model predicts the daily maximal body temperatures of live limpets within a fraction of a degree, suggesting that it may be a useful tool for exploring the thermal biology of limpets and for predicting effects of climate change. The model can easily be adapted to predict the temperatures of chitons, acorn barnacles, keyhole limpets, and encrusting animals and plants.

EFFECTS OF USING THE CB05 VS SAPRC 99 VS CB4 CHEMICAL MECHANISMS ON MODEL PREDICTIONS

EPA Science Inventory

In this study, we examine differences in predictions of ozone, other oxidants, and ozone precursors for 3 chemical mechanisms: the CB4, CB05 and SAPRC99 mechanism (CMAQ/Models3 version). We present results for current conditions and differences among the mechanisms with emission...

Online Bayesian Learning with Natural Sequential Prior Distribution Used for Wind Speed Prediction

NASA Astrophysics Data System (ADS)

Cheggaga, Nawal

2017-11-01

Predicting wind speed is one of the most important and critic tasks in a wind farm. All approaches, which directly describe the stochastic dynamics of the meteorological data are facing problems related to the nature of its non-Gaussian statistics and the presence of seasonal effects .In this paper, Online Bayesian learning has been successfully applied to online learning for three-layer perceptron's used for wind speed prediction. First a conventional transition model based on the squared norm of the difference between the current parameter vector and the previous parameter vector has been used. We noticed that the transition model does not adequately consider the difference between the current and the previous wind speed measurement. To adequately consider this difference, we use a natural sequential prior. The proposed transition model uses a Fisher information matrix to consider the difference between the observation models more naturally. The obtained results showed a good agreement between both series, measured and predicted. The mean relative error over the whole data set is not exceeding 5 %.

A seasonal hydrologic ensemble prediction system for water resource management

NASA Astrophysics Data System (ADS)

Luo, L.; Wood, E. F.

2006-12-01

A seasonal hydrologic ensemble prediction system, developed for the Ohio River basin, has been improved and expanded to several other regions including the Eastern U.S., Africa and East Asia. The prediction system adopts the traditional Extended Streamflow Prediction (ESP) approach, utilizing the VIC (Variable Infiltration Capacity) hydrological model as the central tool for producing ensemble prediction of soil moisture, snow and streamflow with lead times up to 6-month. VIC is forced by observed meteorology to estimate the hydrological initial condition prior to the forecast, but during the forecast period the atmospheric forcing comes from statistically downscaled, seasonal forecast from dynamic climate models. The seasonal hydrologic ensemble prediction system is currently producing realtime seasonal hydrologic forecast for these regions on a monthly basis. Using hindcasts from a 19-year period (1981-1999), during which seasonal hindcasts from NCEP Climate Forecast System (CFS) and European Union DEMETER project are available, we evaluate the performance of the forecast system over our forecast regions. The evaluation shows that the prediction system using the current forecast approach is able to produce reliable and accurate precipitation, soil moisture and streamflow predictions. The overall skill is much higher then the traditional ESP. In particular, forecasts based on multiple climate model forecast are more skillful than single model-based forecast. This emphasizes the significant need for producing seasonal climate forecast with multiple climate models for hydrologic applications. Forecast from this system is expected to provide very valuable information about future hydrologic states and associated risks for end users, including water resource management and financial sectors.

Circulation-based Modeling of Gravity Currents

NASA Astrophysics Data System (ADS)

Meiburg, E. H.; Borden, Z.

2013-05-01

Atmospheric and oceanic flows driven by predominantly horizontal density differences, such as sea breezes, thunderstorm outflows, powder snow avalanches, and turbidity currents, are frequently modeled as gravity currents. Efforts to develop simplified models of such currents date back to von Karman (1940), who considered a two-dimensional gravity current in an inviscid, irrotational and infinitely deep ambient. Benjamin (1968) presented an alternative model, focusing on the inviscid, irrotational flow past a gravity current in a finite-depth channel. More recently, Shin et al. (2004) proposed a model for gravity currents generated by partial-depth lock releases, considering a control volume that encompasses both fronts. All of the above models, in addition to the conservation of mass and horizontal momentum, invoke Bernoulli's law along some specific streamline in the flow field, in order to obtain a closed system of equations that can be solved for the front velocity as function of the current height. More recent computational investigations based on the Navier-Stokes equations, on the other hand, reproduce the dynamics of gravity currents based on the conservation of mass and momentum alone. We propose that it should therefore be possible to formulate a fundamental gravity current model without invoking Bernoulli's law. The talk will show that the front velocity of gravity currents can indeed be predicted as a function of their height from mass and momentum considerations alone, by considering the evolution of interfacial vorticity. This approach does not require information on the pressure field and therefore avoids the need for an energy closure argument such as those invoked by the earlier models. Predictions by the new theory are shown to be in close agreement with direct numerical simulation results. References Von Karman, T. 1940 The engineer grapples with nonlinear problems, Bull. Am. Math Soc. 46, 615-683. Benjamin, T.B. 1968 Gravity currents and related phenomena, J. Fluid Mech. 31, 209-248. Shin, J.O., Dalziel, S.B. and Linden, P.F. 2004 Gravity currents produced by lock exchange, J. Fluid Mech. 521, 1-34.

Model-based learning and the contribution of the orbitofrontal cortex to the model-free world

PubMed Central

McDannald, Michael A.; Takahashi, Yuji K.; Lopatina, Nina; Pietras, Brad W.; Jones, Josh L.; Schoenbaum, Geoffrey

2012-01-01

Learning is proposed to occur when there is a discrepancy between reward prediction and reward receipt. At least two separate systems are thought to exist: one in which predictions are proposed to be based on model-free or cached values; and another in which predictions are model-based. A basic neural circuit for model-free reinforcement learning has already been described. In the model-free circuit the ventral striatum (VS) is thought to supply a common-currency reward prediction to midbrain dopamine neurons that compute prediction errors and drive learning. In a model-based system, predictions can include more information about an expected reward, such as its sensory attributes or current, unique value. This detailed prediction allows for both behavioral flexibility and learning driven by changes in sensory features of rewards alone. Recent evidence from animal learning and human imaging suggests that, in addition to model-free information, the VS also signals model-based information. Further, there is evidence that the orbitofrontal cortex (OFC) signals model-based information. Here we review these data and suggest that the OFC provides model-based information to this traditional model-free circuitry and offer possibilities as to how this interaction might occur. PMID:22487030

A Bayesian Hierarchical Modeling Approach to Predicting Flow in Ungauged Basins

NASA Astrophysics Data System (ADS)

Gronewold, A.; Alameddine, I.; Anderson, R. M.

2009-12-01

Recent innovative approaches to identifying and applying regression-based relationships between land use patterns (such as increasing impervious surface area and decreasing vegetative cover) and rainfall-runoff model parameters represent novel and promising improvements to predicting flow from ungauged basins. In particular, these approaches allow for predicting flows under uncertain and potentially variable future conditions due to rapid land cover changes, variable climate conditions, and other factors. Despite the broad range of literature on estimating rainfall-runoff model parameters, however, the absence of a robust set of modeling tools for identifying and quantifying uncertainties in (and correlation between) rainfall-runoff model parameters represents a significant gap in current hydrological modeling research. Here, we build upon a series of recent publications promoting novel Bayesian and probabilistic modeling strategies for quantifying rainfall-runoff model parameter estimation uncertainty. Our approach applies alternative measures of rainfall-runoff model parameter joint likelihood (including Nash-Sutcliffe efficiency, among others) to simulate samples from the joint parameter posterior probability density function. We then use these correlated samples as response variables in a Bayesian hierarchical model with land use coverage data as predictor variables in order to develop a robust land use-based tool for forecasting flow in ungauged basins while accounting for, and explicitly acknowledging, parameter estimation uncertainty. We apply this modeling strategy to low-relief coastal watersheds of Eastern North Carolina, an area representative of coastal resource waters throughout the world because of its sensitive embayments and because of the abundant (but currently threatened) natural resources it hosts. Consequently, this area is the subject of several ongoing studies and large-scale planning initiatives, including those conducted through the United States Environmental Protection Agency (USEPA) total maximum daily load (TMDL) program, as well as those addressing coastal population dynamics and sea level rise. Our approach has several advantages, including the propagation of parameter uncertainty through a nonparametric probability distribution which avoids common pitfalls of fitting parameters and model error structure to a predetermined parametric distribution function. In addition, by explicitly acknowledging correlation between model parameters (and reflecting those correlations in our predictive model) our model yields relatively efficient prediction intervals (unlike those in the current literature which are often unnecessarily large, and may lead to overly-conservative management actions). Finally, our model helps improve understanding of the rainfall-runoff process by identifying model parameters (and associated catchment attributes) which are most sensitive to current and future land use change patterns. Disclaimer: Although this work was reviewed by EPA and approved for publication, it may not necessarily reflect official Agency policy.

High Resolution Forecasts in the Florida Straits: Predicting the Modulations of the Florida Current and Connectivity Around South Florida and Cuba

NASA Astrophysics Data System (ADS)

Kourafalou, V.; Kang, H.; Perlin, N.; Le Henaff, M.; Lamkin, J. T.

2016-02-01

Connectivity around the South Florida coastal regions and between South Florida and Cuba are largely influenced by a) local coastal processes and b) circulation in the Florida Straits, which is controlled by the larger scale Florida Current variability. Prediction of the physical connectivity is a necessary component for several activities that require ocean forecasts, such as oil spills, fisheries research, search and rescue. This requires a predictive system that can accommodate the intense coastal to offshore interactions and the linkages to the complex regional circulation. The Florida Straits, South Florida and Florida Keys Hybrid Coordinate Ocean Model is such a regional ocean predictive system, covering a large area over the Florida Straits and the adjacent land areas, representing both coastal and oceanic processes. The real-time ocean forecast system is high resolution ( 900m), embedded in larger scale predictive models. It includes detailed coastal bathymetry, high resolution/high frequency atmospheric forcing and provides 7-day forecasts, updated daily (see: http://coastalmodeling.rsmas.miami.edu/). The unprecedented high resolution and coastal details of this system provide value added on global forecasts through downscaling and allow a variety of applications. Examples will be presented, focusing on the period of a 2015 fisheries cruise around the coastal areas of Cuba, where model predictions helped guide the measurements on biophysical connectivity, under intense variability of the mesoscale eddy field and subsequent Florida Current meandering.

One-month validation of the Space Weather Modeling Framework geospace model

NASA Astrophysics Data System (ADS)

Haiducek, J. D.; Welling, D. T.; Ganushkina, N. Y.; Morley, S.; Ozturk, D. S.

2017-12-01

The Space Weather Modeling Framework (SWMF) geospace model consists of a magnetohydrodynamic (MHD) simulation coupled to an inner magnetosphere model and an ionosphere model. This provides a predictive capability for magnetopsheric dynamics, including ground-based and space-based magnetic fields, geomagnetic indices, currents and densities throughout the magnetosphere, cross-polar cap potential, and magnetopause and bow shock locations. The only inputs are solar wind parameters and F10.7 radio flux. We have conducted a rigorous validation effort consisting of a continuous simulation covering the month of January, 2005 using three different model configurations. This provides a relatively large dataset for assessment of the model's predictive capabilities. We find that the model does an excellent job of predicting the Sym-H index, and performs well at predicting Kp and CPCP during active times. Dayside magnetopause and bow shock positions are also well predicted. The model tends to over-predict Kp and CPCP during quiet times and under-predicts the magnitude of AL during disturbances. The model under-predicts the magnitude of night-side geosynchronous Bz, and over-predicts the radial distance to the flank magnetopause and bow shock. This suggests that the model over-predicts stretching of the magnetotail and the overall size of the magnetotail. With the exception of the AL index and the nightside geosynchronous magnetic field, we find the results to be insensitive to grid resolution.

Ocean Modeling and Visualization on Massively Parallel Computer

NASA Technical Reports Server (NTRS)

Chao, Yi; Li, P. Peggy; Wang, Ping; Katz, Daniel S.; Cheng, Benny N.

1997-01-01

Climate modeling is one of the grand challenges of computational science, and ocean modeling plays an important role in both understanding the current climatic conditions and predicting future climate change.

Towards predictive models of the human gut microbiome

PubMed Central

2014-01-01

The intestinal microbiota is an ecosystem susceptible to external perturbations such as dietary changes and antibiotic therapies. Mathematical models of microbial communities could be of great value in the rational design of microbiota-tailoring diets and therapies. Here, we discuss how advances in another field, engineering of microbial communities for wastewater treatment bioreactors, could inspire development of mechanistic mathematical models of the gut microbiota. We review the current state-of-the-art in bioreactor modeling and current efforts in modeling the intestinal microbiota. Mathematical modeling could benefit greatly from the deluge of data emerging from metagenomic studies, but data-driven approaches such as network inference that aim to predict microbiome dynamics without explicit mechanistic knowledge seem better suited to model these data. Finally, we discuss how the integration of microbiome shotgun sequencing and metabolic modeling approaches such as flux balance analysis may fulfill the promise of a mechanistic model of the intestinal microbiota. PMID:24727124

Can current models of accommodation and vergence predict accommodative behavior in myopic children?

PubMed

Sreenivasan, Vidhyapriya; Irving, Elizabeth L; Bobier, William R

2014-08-01

Investigations into the progression of myopia in children have long considered the role of accommodation as a cause and solution. Myopic children show high levels of accommodative adaptation, coupled with accommodative lag and high response AC/A (accommodative convergence per diopter of accommodation). This pattern differs from that predicted by current models of interaction between accommodation and vergence, where weakened reflex responses and a high AC/A would be associated with a low not high levels of accommodative adaptation. However, studies of young myopes were limited to only part of the accommodative vergence synkinesis and the reciprocal components of vergence adaptation and convergence accommodation were not studied in tandem. Accordingly, we test the hypothesis that the accommodative behavior of myopic children is not predicted by current models and whether that departure is explained by differences in the accommodative plant of the myopic child. Responses to incongruent stimuli (-2D, +2D adds, 10 prism diopter base-out prism) were investigated in 28 myopic and 25 non-myopic children aged 7-15 years. Subjects were divided into phoria groups - exo, ortho and eso based upon their near phoria. The school aged myopes showed high levels of accommodative adaptation but with reduced accommodation and high AC/A. This pattern is not explained by current adult models and could reflect a sluggish gain of the accommodative plant (ciliary muscle and lens), changes in near triad innervation or both. Further, vergence adaptation showed a predictable reciprocal relationship with the high accommodative adaptation, suggesting that departures from adult models were limited to accommodation not vergence behavior. Copyright © 2014 Elsevier Ltd. All rights reserved.

A multisensor evaluation of the asymmetric convective model, version 2, in southeast Texas.

PubMed

Kolling, Jenna S; Pleim, Jonathan E; Jeffries, Harvey E; Vizuete, William

2013-01-01

There currently exist a number of planetary boundary layer (PBL) schemes that can represent the effects of turbulence in daytime convective conditions, although these schemes remain a large source of uncertainty in meteorology and air quality model simulations. This study evaluates a recently developed combined local and nonlocal closure PBL scheme, the Asymmetric Convective Model, version 2 (ACM2), against PBL observations taken from radar wind profilers, a ground-based lidar, and multiple daytime radiosonde balloon launches. These observations were compared against predictions of PBLs from the Weather Research and Forecasting (WRF) model version 3.1 with the ACM2 PBL scheme option, and the Fifth-Generation Meteorological Model (MM5) version 3.7.3 with the Eta PBL scheme option that is currently being used to develop ozone control strategies in southeast Texas. MM5 and WRF predictions during the regulatory modeling episode were evaluated on their ability to predict the rise and fall of the PBL during daytime convective conditions across southeastern Texas. The MM5 predicted PBLs consistently underpredicted observations, and were also less than the WRF PBL predictions. The analysis reveals that the MM5 predicted a slower rising and shallower PBL not representative of the daytime urban boundary layer. Alternatively, the WRF model predicted a more accurate PBL evolution improving the root mean square error (RMSE), both temporally and spatially. The WRF model also more accurately predicted vertical profiles of temperature and moisture in the lowest 3 km of the atmosphere. Inspection of median surface temperature and moisture time-series plots revealed higher predicted surface temperatures in WRF and more surface moisture in MM5. These could not be attributed to surface heat fluxes, and thus the differences in performance of the WRF and MM5 models are likely due to the PBL schemes. An accurate depiction of the diurnal evolution of the planetary boundary layer (PBL) is necessary for realistic air quality simulations, and for formulating effective policy. The meteorological model used to support the southeast Texas 03 attainment demonstration made predictions of the PBL that were consistently less than those found in observations. The use of the Asymmetric Convective Model, version 2 (ACM2), predicted taller PBL heights and improved model predictions. A lower predicted PBL height in an air quality model would increase precursor concentrations and change the chemical production of O3 and possibly the response to control strategies.

SONOS Nonvolatile Memory Cell Programming Characteristics

NASA Technical Reports Server (NTRS)

MacLeod, Todd C.; Phillips, Thomas A.; Ho, Fat D.

2010-01-01

Silicon-oxide-nitride-oxide-silicon (SONOS) nonvolatile memory is gaining favor over conventional EEPROM FLASH memory technology. This paper characterizes the SONOS write operation using a nonquasi-static MOSFET model. This includes floating gate charge and voltage characteristics as well as tunneling current, voltage threshold and drain current characterization. The characterization of the SONOS memory cell predicted by the model closely agrees with experimental data obtained from actual SONOS memory cells. The tunnel current, drain current, threshold voltage and read drain current all closely agreed with empirical data.

Solar Atmosphere to Earth's Surface: Long Lead Time dB/dt Predictions with the Space Weather Modeling Framework

NASA Astrophysics Data System (ADS)

Welling, D. T.; Manchester, W.; Savani, N.; Sokolov, I.; van der Holst, B.; Jin, M.; Toth, G.; Liemohn, M. W.; Gombosi, T. I.

2017-12-01

The future of space weather prediction depends on the community's ability to predict L1 values from observations of the solar atmosphere, which can yield hours of lead time. While both empirical and physics-based L1 forecast methods exist, it is not yet known if this nascent capability can translate to skilled dB/dt forecasts at the Earth's surface. This paper shows results for the first forecast-quality, solar-atmosphere-to-Earth's-surface dB/dt predictions. Two methods are used to predict solar wind and IMF conditions at L1 for several real-world coronal mass ejection events. The first method is an empirical and observationally based system to estimate the plasma characteristics. The magnetic field predictions are based on the Bz4Cast system which assumes that the CME has a cylindrical flux rope geometry locally around Earth's trajectory. The remaining plasma parameters of density, temperature and velocity are estimated from white-light coronagraphs via a variety of triangulation methods and forward based modelling. The second is a first-principles-based approach that combines the Eruptive Event Generator using Gibson-Low configuration (EEGGL) model with the Alfven Wave Solar Model (AWSoM). EEGGL specifies parameters for the Gibson-Low flux rope such that it erupts, driving a CME in the coronal model that reproduces coronagraph observations and propagates to 1AU. The resulting solar wind predictions are used to drive the operational Space Weather Modeling Framework (SWMF) for geospace. Following the configuration used by NOAA's Space Weather Prediction Center, this setup couples the BATS-R-US global magnetohydromagnetic model to the Rice Convection Model (RCM) ring current model and a height-integrated ionosphere electrodynamics model. The long lead time predictions of dB/dt are compared to model results that are driven by L1 solar wind observations. Both are compared to real-world observations from surface magnetometers at a variety of geomagnetic latitudes. Metrics are calculated to examine how the simulated solar wind drivers impact forecast skill. These results illustrate the current state of long-lead-time forecasting and the promise of this technology for operational use.

Coupled assimilation for an intermediated coupled ENSO prediction model

NASA Astrophysics Data System (ADS)

Zheng, Fei; Zhu, Jiang

2010-10-01

The value of coupled assimilation is discussed using an intermediate coupled model in which the wind stress is the only atmospheric state which is slavery to model sea surface temperature (SST). In the coupled assimilation analysis, based on the coupled wind-ocean state covariance calculated from the coupled state ensemble, the ocean state is adjusted by assimilating wind data using the ensemble Kalman filter. As revealed by a series of assimilation experiments using simulated observations, the coupled assimilation of wind observations yields better results than the assimilation of SST observations. Specifically, the coupled assimilation of wind observations can help to improve the accuracy of the surface and subsurface currents because the correlation between the wind and ocean currents is stronger than that between SST and ocean currents in the equatorial Pacific. Thus, the coupled assimilation of wind data can decrease the initial condition errors in the surface/subsurface currents that can significantly contribute to SST forecast errors. The value of the coupled assimilation of wind observations is further demonstrated by comparing the prediction skills of three 12-year (1997-2008) hindcast experiments initialized by the ocean-only assimilation scheme that assimilates SST observations, the coupled assimilation scheme that assimilates wind observations, and a nudging scheme that nudges the observed wind stress data, respectively. The prediction skills of two assimilation schemes are significantly better than those of the nudging scheme. The prediction skills of assimilating wind observations are better than assimilating SST observations. Assimilating wind observations for the 2007/2008 La Niña event triggers better predictions, while assimilating SST observations fails to provide an early warning for that event.

Using Pareto points for model identification in predictive toxicology

PubMed Central

2013-01-01

Predictive toxicology is concerned with the development of models that are able to predict the toxicity of chemicals. A reliable prediction of toxic effects of chemicals in living systems is highly desirable in cosmetics, drug design or food protection to speed up the process of chemical compound discovery while reducing the need for lab tests. There is an extensive literature associated with the best practice of model generation and data integration but management and automated identification of relevant models from available collections of models is still an open problem. Currently, the decision on which model should be used for a new chemical compound is left to users. This paper intends to initiate the discussion on automated model identification. We present an algorithm, based on Pareto optimality, which mines model collections and identifies a model that offers a reliable prediction for a new chemical compound. The performance of this new approach is verified for two endpoints: IGC50 and LogP. The results show a great potential for automated model identification methods in predictive toxicology. PMID:23517649

Inclusion of an ultraviolet radiation transfer component in an urban forest effects model for predicting tree influences on potential below-canopy exposure to UVB radiation

Treesearch

Gordon M. Heisler; Richard H. Grant; David J. Nowak; Wei Gao; Daniel E. Crane; Jeffery T. Walton

2003-01-01

Evaluating the impact of ultraviolet-B radiation (UVB) on urban populations would be enhanced by improved predictions of the UVB radiation at the level of human activity. This paper reports the status of plans for incorporating a UVB prediction module into an existing Urban Forest Effects (UFORE) model. UFORE currently has modules to quantify urban forest structure,...

Predicting performance of power converters operating with switching frequencies in the vicinity of 100 kHZ

NASA Technical Reports Server (NTRS)

Bahler, D. D.; Owen, H. A., Jr.; Wilson, T. G.

1978-01-01

A model describing the turning-on period of a power switching transistor in an energy storage voltage step-up converter is presented. Comparisons between an experimental layout and the circuit model during the turning-on interval demonstrate the ability of the model to closely predict the effects of circuit topology on the performance of the converter. A phenomenon of particular importance that is observed in the experimental circuits and is predicted by the model is the deleterious feedback effect of the parasitic emitter lead inductance on the base current waveform during the turning-on interval.

Modeling of electron cyclotron resonance discharges

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

Meyyappan, M.; Govindan, T.R.

The current trend in plasma processing is the development of high density plasma sources to achieve high deposition and etch rates, uniformity over large ares, and low wafer damage. Here, is a simple model to predict the spatially-averaged plasma characteristics of electron cyclotron resonance (ECR) reactors is presented. The model consists of global conservation equations for species concentration, electron density and energy. A gas energy balance is used to predict the neutral temperature self-consistently. The model is demonstrated for an ECR argon discharge. The predicted behavior of the discharge as a function of system variables agrees well with experimental observations.

Distributional prediction of Pleistocene forearc minibasin turbidites in the NE Nankai Trough area (off central Japan)

NASA Astrophysics Data System (ADS)

Egawa, K.; Furukawa, T.; Saeki, T.; Suzuki, K.; Narita, H.

2011-12-01

Natural gas hydrate-related sequences commonly provide unclear seismic images due to bottom simulating reflector, a seismic indicator of the theoretical base of gas hydrate stability zone, which usually causes problems for fully analyzing the detailed sedimentary structures and seismic facies. Here we propose an alternative technique to predict the distributional pattern of gas hydrate-related deep-sea turbidites with special reference to a Pleistocene forearc minibasin in the northeastern Nankai Trough area, off central Japan, from the integrated 3D structural and sedimentologic modeling. Structural unfolding and stratigraphic backstripping successively modeled a simple horseshoe-shaped paleobathymetry of the targeted turbidite sequence. Based on best-fit matching of net-to-gross ratio (or sand fraction) between the model and wells, subsequent turbidity current modeling on the restored paleobathymetric surface during a single flow event demonstrated excellent prediction results showing the morphologically controlled turbidity current evolution and selective turbidite sand distribution within the modeled minibasin. Also, multiple turbidity current modeling indicated the stacking sheet turbidites with regression and proximal/distal onlaps in the minibasin due to reflections off an opposing slope, whose sedimentary features are coincident with the seismic interpretation. Such modeling works can help us better understand the depositional pattern of gas hydrate-related, unconsolidated turbidites and also can improve gas hydrate reservoir characterization. This study was financially supported by MH21 Research Consortium.

Categorical and dimensional approaches in the evaluation of the relationship between attachment and personality disorders: an empirical study.

PubMed

Chiesa, Marco; Cirasola, Antonella; Williams, Riccardo; Nassisi, Valentina; Fonagy, Peter

2017-04-01

Although several studies have highlighted the relationship between attachment states of mind and personality disorders, their findings have not been consistent, possibly due to the application of the traditional taxonomic classification model of attachment. A more recently developed dimensional classification of attachment representations, including more specific aspects of trauma-related representations, may have advantages. In this study, we compare specific associations and predictive power of the categorical attachment and dimensional models applied to 230 Adult Attachment Interview transcripts obtained from personality disordered and nonpsychiatric subjects. We also investigate the role that current levels of psychiatric distress may have in the prediction of PD. The results showed that both models predict the presence of PD, with the dimensional approach doing better in discriminating overall diagnosis of PD. However, both models are less helpful in discriminating specific PD diagnostic subtypes. Current psychiatric distress was found to be the most consistent predictor of PD capturing a large share of the variance and obscuring the role played by attachment variables. The results suggest that attachment parameters correlate with the presence of PD alone and have no specific associations with particular PD subtypes when current psychiatric distress is taken into account.

Assessment of soil organic carbon stocks under future climate and land cover changes in Europe.

PubMed

Yigini, Yusuf; Panagos, Panos

2016-07-01

Soil organic carbon plays an important role in the carbon cycling of terrestrial ecosystems, variations in soil organic carbon stocks are very important for the ecosystem. In this study, a geostatistical model was used for predicting current and future soil organic carbon (SOC) stocks in Europe. The first phase of the study predicts current soil organic carbon content by using stepwise multiple linear regression and ordinary kriging and the second phase of the study projects the soil organic carbon to the near future (2050) by using a set of environmental predictors. We demonstrate here an approach to predict present and future soil organic carbon stocks by using climate, land cover, terrain and soil data and their projections. The covariates were selected for their role in the carbon cycle and their availability for the future model. The regression-kriging as a base model is predicting current SOC stocks in Europe by using a set of covariates and dense SOC measurements coming from LUCAS Soil Database. The base model delivers coefficients for each of the covariates to the future model. The overall model produced soil organic carbon maps which reflect the present and the future predictions (2050) based on climate and land cover projections. The data of the present climate conditions (long-term average (1950-2000)) and the future projections for 2050 were obtained from WorldClim data portal. The future climate projections are the recent climate projections mentioned in the Fifth Assessment IPCC report. These projections were extracted from the global climate models (GCMs) for four representative concentration pathways (RCPs). The results suggest an overall increase in SOC stocks by 2050 in Europe (EU26) under all climate and land cover scenarios, but the extent of the increase varies between the climate model and emissions scenarios. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Radiation model predictions and validation using LDEF satellite data

NASA Technical Reports Server (NTRS)

Armstrong, T. W.; Colborn, B. L.

1993-01-01

Predictions and comparisons with the radiation dose measurements on Long Duration Exposure Facility (LDEF) by thermoluminescent dosimeters were made to evaluate the accuracy of models currently used in defining the ionizing radiation environment for low Earth orbit missions. The calculations include a detailed simulation of the radiation exposure (altitude and solar cycle variations, directional dependence) and shielding effects (three-dimensional LDEF geometry model) so that differences in the predicted and observed doses can be attributed to environment model uncertainties. The LDEF dose data are utilized to assess the accuracy of models describing the trapped proton flux, the trapped proton directionality, and the trapped electron flux.

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.

Modeling dilute pyroclastic density currents on Earth and Mars

NASA Astrophysics Data System (ADS)

Clarke, A. B.; Brand, B. D.; De'Michieli Vitturi, M.

2013-12-01

The surface of Mars has been shaped extensively by volcanic activity, including explosive eruptions that may have been heavily influenced by water- or ice-magma interaction. However, the dynamics of associated pyroclastic density currents (PDCs) under Martian atmospheric conditions and controls on deposition and runout from such currents are poorly understood. This work combines numerical modeling with terrestrial field measurements to explore the dynamics of dilute PDC dynamics on Earth and Mars, especially as they relate to deposit characteristics. We employ two numerical approaches. Model (1) consists of simulation of axi-symmetric flow and sedimentation from a steady-state, depth-averaged density current. Equations for conservation of mass, momentum, and energy are solved simultaneously, and the effects of atmospheric entrainment, particle sedimentation, basal friction, temperature changes, and variations in current thickness and density are explored. The Rouse number and Brunt-Väisälä frequency are used to estimate the wavelength of internal gravity waves in a density-stratified current, which allows us to predict deposit dune wavelengths. The model predicts realistic runout distances and bedform wavelengths for several well-documented field cases on Earth. The model results also suggest that dilute PDCs on Mars would have runout distances up to three times that of equivalent currents on Earth and would produce longer-wavelength bedforms. In both cases results are heavily dependent on source conditions, grain-size characteristics, and entrainment and friction parameters. Model (2) relaxes several key simplifications, resulting in a fully 3D, multiphase, unsteady model that captures more details of propagation, including density stratification, and depositional processes. Using this more complex approach, we focus on the role of unsteady or pulsatory vent conditions typically associated with phreatomagmatic eruptions. Runout distances from Model (2) agree reasonably well with Model (1) results, but details of deposit distribution vary between the two models. Model (2) shows that the Earth case initially outpaces the Mars case due to faster propagation velocities associated with higher gravitational acceleration. However, the Mars currents ultimately out-distance the Earth currents due to slower particle settling rates, which also largely explain the longer wavelength bedforms. Model (2) also predicts a peak in the streamwise distribution of deposits farther from the source compared to equivalent results from Model (1), and produces more complex patterns of vertical distribution of particles in the moving current, which varies significantly in time and space. This combination of modeling and deposit data results in a powerful tool for testing hypotheses related to PDCs on Mars, potentially improving our capacity to interpret Martian features on both the outcrop (e.g., Home Plate) and regional scale (e.g., Apollinaris Mons).

MicroRNAfold: pre-microRNA secondary structure prediction based on modified NCM model with thermodynamics-based scoring strategy.

PubMed

Han, Dianwei; Zhang, Jun; Tang, Guiliang

2012-01-01

An accurate prediction of the pre-microRNA secondary structure is important in miRNA informatics. Based on a recently proposed model, nucleotide cyclic motifs (NCM), to predict RNA secondary structure, we propose and implement a Modified NCM (MNCM) model with a physics-based scoring strategy to tackle the problem of pre-microRNA folding. Our microRNAfold is implemented using a global optimal algorithm based on the bottom-up local optimal solutions. Our experimental results show that microRNAfold outperforms the current leading prediction tools in terms of True Negative rate, False Negative rate, Specificity, and Matthews coefficient ratio.

On the Conditioning of Machine-Learning-Assisted Turbulence Modeling

NASA Astrophysics Data System (ADS)

Wu, Jinlong; Sun, Rui; Wang, Qiqi; Xiao, Heng

2017-11-01

Recently, several researchers have demonstrated that machine learning techniques can be used to improve the RANS modeled Reynolds stress by training on available database of high fidelity simulations. However, obtaining improved mean velocity field remains an unsolved challenge, restricting the predictive capability of current machine-learning-assisted turbulence modeling approaches. In this work we define a condition number to evaluate the model conditioning of data-driven turbulence modeling approaches, and propose a stability-oriented machine learning framework to model Reynolds stress. Two canonical flows, the flow in a square duct and the flow over periodic hills, are investigated to demonstrate the predictive capability of the proposed framework. The satisfactory prediction performance of mean velocity field for both flows demonstrates the predictive capability of the proposed framework for machine-learning-assisted turbulence modeling. With showing the capability of improving the prediction of mean flow field, the proposed stability-oriented machine learning framework bridges the gap between the existing machine-learning-assisted turbulence modeling approaches and the demand of predictive capability of turbulence models in real applications.

Predicting infant cortical surface development using a 4D varifold-based learning framework and local topography-based shape morphing.

PubMed

Rekik, Islem; Li, Gang; Lin, Weili; Shen, Dinggang

2016-02-01

Longitudinal neuroimaging analysis methods have remarkably advanced our understanding of early postnatal brain development. However, learning predictive models to trace forth the evolution trajectories of both normal and abnormal cortical shapes remains broadly absent. To fill this critical gap, we pioneered the first prediction model for longitudinal developing cortical surfaces in infants using a spatiotemporal current-based learning framework solely from the baseline cortical surface. In this paper, we detail this prediction model and even further improve its performance by introducing two key variants. First, we use the varifold metric to overcome the limitations of the current metric for surface registration that was used in our preliminary study. We also extend the conventional varifold-based surface registration model for pairwise registration to a spatiotemporal surface regression model. Second, we propose a morphing process of the baseline surface using its topographic attributes such as normal direction and principal curvature sign. Specifically, our method learns from longitudinal data both the geometric (vertices positions) and dynamic (temporal evolution trajectories) features of the infant cortical surface, comprising a training stage and a prediction stage. In the training stage, we use the proposed varifold-based shape regression model to estimate geodesic cortical shape evolution trajectories for each training subject. We then build an empirical mean spatiotemporal surface atlas. In the prediction stage, given an infant, we select the best learnt features from training subjects to simultaneously predict the cortical surface shapes at all later timepoints, based on similarity metrics between this baseline surface and the learnt baseline population average surface atlas. We used a leave-one-out cross validation method to predict the inner cortical surface shape at 3, 6, 9 and 12 months of age from the baseline cortical surface shape at birth. Our method attained a higher prediction accuracy and better captured the spatiotemporal dynamic change of the highly folded cortical surface than the previous proposed prediction method. Copyright © 2015 Elsevier B.V. All rights reserved.

Prediction Models for 30-Day Mortality and Complications After Total Knee and Hip Arthroplasties for Veteran Health Administration Patients With Osteoarthritis.

PubMed

Harris, Alex Hs; Kuo, Alfred C; Bowe, Thomas; Gupta, Shalini; Nordin, David; Giori, Nicholas J

2018-05-01

Statistical models to preoperatively predict patients' risk of death and major complications after total joint arthroplasty (TJA) could improve the quality of preoperative management and informed consent. Although risk models for TJA exist, they have limitations including poor transparency and/or unknown or poor performance. Thus, it is currently impossible to know how well currently available models predict short-term complications after TJA, or if newly developed models are more accurate. We sought to develop and conduct cross-validation of predictive risk models, and report details and performance metrics as benchmarks. Over 90 preoperative variables were used as candidate predictors of death and major complications within 30 days for Veterans Health Administration patients with osteoarthritis who underwent TJA. Data were split into 3 samples-for selection of model tuning parameters, model development, and cross-validation. C-indexes (discrimination) and calibration plots were produced. A total of 70,569 patients diagnosed with osteoarthritis who received primary TJA were included. C-statistics and bootstrapped confidence intervals for the cross-validation of the boosted regression models were highest for cardiac complications (0.75; 0.71-0.79) and 30-day mortality (0.73; 0.66-0.79) and lowest for deep vein thrombosis (0.59; 0.55-0.64) and return to the operating room (0.60; 0.57-0.63). Moderately accurate predictive models of 30-day mortality and cardiac complications after TJA in Veterans Health Administration patients were developed and internally cross-validated. By reporting model coefficients and performance metrics, other model developers can test these models on new samples and have a procedure and indication-specific benchmark to surpass. Published by Elsevier Inc.

Vertical structure of mean cross-shore currents across a barred surf zone

USGS Publications Warehouse

Haines, John W.; Sallenger, Asbury H.

1994-01-01

Mean cross-shore currents observed across a barred surf zone are compared to model predictions. The model is based on a simplified momentum balance with a turbulent boundary layer at the bed. Turbulent exchange is parameterized by an eddy viscosity formulation, with the eddy viscosity Aυ independent of time and the vertical coordinate. Mean currents result from gradients due to wave breaking and shoaling, and the presence of a mean setup of the free surface. Descriptions of the wave field are provided by the wave transformation model of Thornton and Guza [1983]. The wave transformation model adequately reproduces the observed wave heights across the surf zone. The mean current model successfully reproduces the observed cross-shore flows. Both observations and predictions show predominantly offshore flow with onshore flow restricted to a relatively thin surface layer. Successful application of the mean flow model requires an eddy viscosity which varies horizontally across the surf zone. Attempts are made to parameterize this variation with some success. The data does not discriminate between alternative parameterizations proposed. The overall variability in eddy viscosity suggested by the model fitting should be resolvable by field measurements of the turbulent stresses. Consistent shortcomings of the parameterizations, and the overall modeling effort, suggest avenues for further development and data collection.

Climate Ocean Modeling on Parallel Computers

NASA Technical Reports Server (NTRS)

Wang, P.; Cheng, B. N.; Chao, Y.

1998-01-01

Ocean modeling plays an important role in both understanding the current climatic conditions and predicting future climate change. However, modeling the ocean circulation at various spatial and temporal scales is a very challenging computational task.

Modeling Electrically Evoked Otoacoustic Emissions

NASA Astrophysics Data System (ADS)

Grosh, K.; Deo, N.; Parthasarathi, A. A.; Nuttall, A. L.; Zheng, J. F.; Ren, T. Y.

2003-02-01

Electrical evoked otoacoustic emissions (EEOAE) are used to investigate in vivo cochlear electromechanical function. Round window electrical stimulation gives rise to a broad frequency EEOAE response, from 100 Hz or below to 40 kHz in guinea pigs. Placing bipolar electrodes very close to the basilar membrane (in the scala vestibuli and scala tympani) gives rise to a much narrower frequency range of EEOAE, limited to around 20 kHz when the electrodes are placed near the 18 kHz best frequency place. Model predictions using a three dimensional fluid model in conjunction with a simple model for outer hair cell (OHC) activity are used to interpret the experimental results. The model is solved using a 2.5D finite-element formulation. Predictions show that the high-frequency limit of the excitation is determined by the spatial extent of the current stimulus (also called the current spread). The global peaks in the EEOAE spectra are interpreted as constructive interference between electrically evoked backward traveling waves and forward traveling waves reflected from the stapes. Steady-state response predictions of the model are presented.

Tailoring Mathematical Models to Stem-Cell Derived Cardiomyocyte Lines Can Improve Predictions of Drug-Induced Changes to Their Electrophysiology.

PubMed

Lei, Chon Lok; Wang, Ken; Clerx, Michael; Johnstone, Ross H; Hortigon-Vinagre, Maria P; Zamora, Victor; Allan, Andrew; Smith, Godfrey L; Gavaghan, David J; Mirams, Gary R; Polonchuk, Liudmila

2017-01-01

Human induced pluripotent stem cell derived cardiomyocytes (iPSC-CMs) have applications in disease modeling, cell therapy, drug screening and personalized medicine. Computational models can be used to interpret experimental findings in iPSC-CMs, provide mechanistic insights, and translate these findings to adult cardiomyocyte (CM) electrophysiology. However, different cell lines display different expression of ion channels, pumps and receptors, and show differences in electrophysiology. In this exploratory study, we use a mathematical model based on iPSC-CMs from Cellular Dynamic International (CDI, iCell), and compare its predictions to novel experimental recordings made with the Axiogenesis Cor.4U line. We show that tailoring this model to the specific cell line, even using limited data and a relatively simple approach, leads to improved predictions of baseline behavior and response to drugs. This demonstrates the need and the feasibility to tailor models to individual cell lines, although a more refined approach will be needed to characterize individual currents, address differences in ion current kinetics, and further improve these results.

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.

Mechanistic variables can enhance predictive models of endotherm distributions: the American pika under current, past, and future climates.

PubMed

Mathewson, Paul D; Moyer-Horner, Lucas; Beever, Erik A; Briscoe, Natalie J; Kearney, Michael; Yahn, Jeremiah M; Porter, Warren P

2017-03-01

How climate constrains species' distributions through time and space is an important question in the context of conservation planning for climate change. Despite increasing awareness of the need to incorporate mechanism into species distribution models (SDMs), mechanistic modeling of endotherm distributions remains limited in this literature. Using the American pika (Ochotona princeps) as an example, we present a framework whereby mechanism can be incorporated into endotherm SDMs. Pika distribution has repeatedly been found to be constrained by warm temperatures, so we used Niche Mapper, a mechanistic heat-balance model, to convert macroclimate data to pika-specific surface activity time in summer across the western United States. We then explored the difference between using a macroclimate predictor (summer temperature) and using a mechanistic predictor (predicted surface activity time) in SDMs. Both approaches accurately predicted pika presences in current and past climate regimes. However, the activity models predicted 8-19% less habitat loss in response to annual temperature increases of ~3-5 °C predicted in the region by 2070, suggesting that pikas may be able to buffer some climate change effects through behavioral thermoregulation that can be captured by mechanistic modeling. Incorporating mechanism added value to the modeling by providing increased confidence in areas where different modeling approaches agreed and providing a range of outcomes in areas of disagreement. It also provided a more proximate variable relating animal distribution to climate, allowing investigations into how unique habitat characteristics and intraspecific phenotypic variation may allow pikas to exist in areas outside those predicted by generic SDMs. Only a small number of easily obtainable data are required to parameterize this mechanistic model for any endotherm, and its use can improve SDM predictions by explicitly modeling a widely applicable direct physiological effect: climate-imposed restrictions on activity. This more complete understanding is necessary to inform climate adaptation actions, management strategies, and conservation plans. © 2016 John Wiley & Sons Ltd.

Mechanistic variables can enhance predictive models of endotherm distributions: The American pika under current, past, and future climates

USGS Publications Warehouse

Mathewson, Paul; Moyer-Horner, Lucas; Beever, Erik; Briscoe, Natalie; Kearney, Michael T.; Yahn, Jeremiah; Porter, Warren P.

2017-01-01

How climate constrains species’ distributions through time and space is an important question in the context of conservation planning for climate change. Despite increasing awareness of the need to incorporate mechanism into species distribution models (SDMs), mechanistic modeling of endotherm distributions remains limited in this literature. Using the American pika (Ochotona princeps) as an example, we present a framework whereby mechanism can be incorporated into endotherm SDMs. Pika distribution has repeatedly been found to be constrained by warm temperatures, so we used Niche Mapper, a mechanistic heat-balance model, to convert macroclimate data to pika-specific surface activity time in summer across the western United States. We then explored the difference between using a macroclimate predictor (summer temperature) and using a mechanistic predictor (predicted surface activity time) in SDMs. Both approaches accurately predicted pika presences in current and past climate regimes. However, the activity models predicted 8–19% less habitat loss in response to annual temperature increases of ~3–5 °C predicted in the region by 2070, suggesting that pikas may be able to buffer some climate change effects through behavioral thermoregulation that can be captured by mechanistic modeling. Incorporating mechanism added value to the modeling by providing increased confidence in areas where different modeling approaches agreed and providing a range of outcomes in areas of disagreement. It also provided a more proximate variable relating animal distribution to climate, allowing investigations into how unique habitat characteristics and intraspecific phenotypic variation may allow pikas to exist in areas outside those predicted by generic SDMs. Only a small number of easily obtainable data are required to parameterize this mechanistic model for any endotherm, and its use can improve SDM predictions by explicitly modeling a widely applicable direct physiological effect: climate-imposed restrictions on activity. This more complete understanding is necessary to inform climate adaptation actions, management strategies, and conservation plans.

An Integrated Uncertainty Analysis and Ensemble-based Data Assimilation Framework for Operational Snow Predictions

NASA Astrophysics Data System (ADS)

He, M.; Hogue, T. S.; Franz, K.; Margulis, S. A.; Vrugt, J. A.

2009-12-01

The National Weather Service (NWS), the agency responsible for short- and long-term streamflow predictions across the nation, primarily applies the SNOW17 model for operational forecasting of snow accumulation and melt. The SNOW17-forecasted snowmelt serves as an input to a rainfall-runoff model for streamflow forecasts in snow-dominated areas. The accuracy of streamflow predictions in these areas largely relies on the accuracy of snowmelt. However, no direct snowmelt measurements are available to validate the SNOW17 predictions. Instead, indirect measurements such as snow water equivalent (SWE) measurements or discharge are typically used to calibrate SNOW17 parameters. In addition, the forecast practice is inherently deterministic, lacking tools to systematically address forecasting uncertainties (e.g., uncertainties in parameters, forcing, SWE and discharge observations, etc.). The current research presents an Integrated Uncertainty analysis and Ensemble-based data Assimilation (IUEA) framework to improve predictions of snowmelt and discharge while simultaneously providing meaningful estimates of the associated uncertainty. The IUEA approach uses the recently developed DiffeRential Evolution Adaptive Metropolis (DREAM) to simultaneously estimate uncertainties in model parameters, forcing, and observations. The robustness and usefulness of the IUEA-SNOW17 framework is evaluated for snow-dominated watersheds in the northern Sierra Mountains, using the coupled IUEA-SNOW17 and an operational soil moisture accounting model (SAC-SMA). Preliminary results are promising and indicate successful performance of the coupled IUEA-SNOW17 framework. Implementation of the SNOW17 with the IUEA is straightforward and requires no major modification to the SNOW17 model structure. The IUEA-SNOW17 framework is intended to be modular and transferable and should assist the NWS in advancing the current forecasting system and reinforcing current operational forecasting skill.

Recent advances in hypersonic technology

NASA Technical Reports Server (NTRS)

Dwoyer, Douglas L.

1990-01-01

This paper will focus on recent advances in hypersonic aerodynamic prediction techniques. Current capabilities of existing numerical methods for predicting high Mach number flows will be discussed and shortcomings will be identified. Physical models available for inclusion into modern codes for predicting the effects of transition and turbulence will also be outlined and their limitations identified. Chemical reaction models appropriate to high-speed flows will be addressed, and the impact of their inclusion in computational fluid dynamics codes will be discussed. Finally, the problem of validating predictive techniques for high Mach number flows will be addressed.

Towards cleaner combustion engines through groundbreaking detailed chemical kinetic models

PubMed Central

Battin-Leclerc, Frédérique; Blurock, Edward; Bounaceur, Roda; Fournet, René; Glaude, Pierre-Alexandre; Herbinet, Olivier; Sirjean, Baptiste; Warth, V.

2013-01-01

In the context of limiting the environmental impact of transportation, this paper reviews new directions which are being followed in the development of more predictive and more accurate detailed chemical kinetic models for the combustion of fuels. In the first part, the performance of current models, especially in terms of the prediction of pollutant formation, is evaluated. In the next parts, recent methods and ways to improve these models are described. An emphasis is given on the development of detailed models based on elementary reactions, on the production of the related thermochemical and kinetic parameters, and on the experimental techniques available to produce the data necessary to evaluate model predictions under well defined conditions. PMID:21597604

Prestoration: Using species in restoration that will persist now and into the future

USGS Publications Warehouse

Butterfield, B.J.; Copeland, Stella; Munson, Seth M.; Roybal, C.M.; Wood, Troy E.

2017-01-01

Climate change presents new challenges for selecting species for restoration. If migration fails to keep pace with climate change, as models predict, the most suitable sources for restoration may not occur locally at all. To address this issue we propose a strategy of “prestoration”: utilizing species in restoration for which a site represents suitable habitat now and into the future. Using the Colorado Plateau, USA as a case study, we assess the ability of grass species currently used regionally in restoration to persist into the future using projections of ecological niche models (or climate envelope models) across a suite of climate change scenarios. We then present a technique for identifying new species that best compensate for future losses of suitable habitat by current target species. We found that the current suite of species, selected by a group of experts, is predicted to perform reasonably well in the short-term, but that losses of prestorable habitat by mid-century would approach 40%. Using an algorithm to identify additional species, we found that fewer than ten species could compensate for nearly all of the losses incurred by the current target species. This case study highlights the utility of integrating ecological niche modeling and future climate forecasts to predict the utility of species in restoring under climate change across a wide range of spatial and temporal scales.

Multivariate Modelling of the Career Intent of Air Force Personnel.

DTIC Science & Technology

1980-09-01

index (HOPP) was used as a measure of current job satisfaction . As with the Vroom and Fishbein/Graen models, two separate validations were accom...34 Organizational Behavior and Human Performance , 23: 251-267, 1979. Lewis, Logan M. "Expectancy Theory as a Predictive Model of Career Intent, Job Satisfaction ...W. Albright. "Expectancy Theory Predictions of the Satisfaction , Effort, Performance , and Retention of Naval Aviation Officers," Organizational

Improving the modelling of irradiation-induced brain activation for in vivo PET verification of proton therapy.

PubMed

Bauer, Julia; Chen, Wenjing; Nischwitz, Sebastian; Liebl, Jakob; Rieken, Stefan; Welzel, Thomas; Debus, Juergen; Parodi, Katia

2018-04-24

A reliable Monte Carlo prediction of proton-induced brain tissue activation used for comparison to particle therapy positron-emission-tomography (PT-PET) measurements is crucial for in vivo treatment verification. Major limitations of current approaches to overcome include the CT-based patient model and the description of activity washout due to tissue perfusion. Two approaches were studied to improve the activity prediction for brain irradiation: (i) a refined patient model using tissue classification based on MR information and (ii) a PT-PET data-driven refinement of washout model parameters. Improvements of the activity predictions compared to post-treatment PT-PET measurements were assessed in terms of activity profile similarity for six patients treated with a single or two almost parallel fields delivered by active proton beam scanning. The refined patient model yields a generally higher similarity for most of the patients, except in highly pathological areas leading to tissue misclassification. Using washout model parameters deduced from clinical patient data could considerably improve the activity profile similarity for all patients. Current methods used to predict proton-induced brain tissue activation can be improved with MR-based tissue classification and data-driven washout parameters, thus providing a more reliable basis for PT-PET verification. Copyright © 2018 Elsevier B.V. All rights reserved.

Preface: Current perspectives in modelling, monitoring, and predicting geophysical fluid dynamics

NASA Astrophysics Data System (ADS)

Mancho, Ana M.; Hernández-García, Emilio; López, Cristóbal; Turiel, Antonio; Wiggins, Stephen; Pérez-Muñuzuri, Vicente

2018-02-01

The third edition of the international workshop Nonlinear Processes in Oceanic and Atmospheric Flows was held at the Institute of Mathematical Sciences (ICMAT) in Madrid from 6 to 8 July 2016. The event gathered oceanographers, atmospheric scientists, physicists, and applied mathematicians sharing a common interest in the nonlinear dynamics of geophysical fluid flows. The philosophy of this meeting was to bring together researchers from a variety of backgrounds into an environment that favoured a vigorous discussion of concepts across different disciplines. The present Special Issue on Current perspectives in modelling, monitoring, and predicting geophysical fluid dynamics contains selected contributions, mainly from attendants of the workshop, providing an updated perspective on modelling aspects of geophysical flows as well as issues on prediction and assimilation of observational data and novel tools for describing transport and mixing processes in these contexts. More details on these aspects are discussed in this preface.

Displacement prediction of Baijiabao landslide based on empirical mode decomposition and long short-term memory neural network in Three Gorges area, China

NASA Astrophysics Data System (ADS)

Xu, Shiluo; Niu, Ruiqing

2018-02-01

Every year, landslides pose huge threats to thousands of people in China, especially those in the Three Gorges area. It is thus necessary to establish an early warning system to help prevent property damage and save peoples' lives. Most of the landslide displacement prediction models that have been proposed are static models. However, landslides are dynamic systems. In this paper, the total accumulative displacement of the Baijiabao landslide is divided into trend and periodic components using empirical mode decomposition. The trend component is predicted using an S-curve estimation, and the total periodic component is predicted using a long short-term memory neural network (LSTM). LSTM is a dynamic model that can remember historical information and apply it to the current output. Six triggering factors are chosen to predict the periodic term using the Pearson cross-correlation coefficient and mutual information. These factors include the cumulative precipitation during the previous month, the cumulative precipitation during a two-month period, the reservoir level during the current month, the change in the reservoir level during the previous month, the cumulative increment of the reservoir level during the current month, and the cumulative displacement during the previous month. When using one-step-ahead prediction, LSTM yields a root mean squared error (RMSE) value of 6.112 mm, while the support vector machine for regression (SVR) and the back-propagation neural network (BP) yield values of 10.686 mm and 8.237 mm, respectively. Meanwhile, the Elman network (Elman) yields an RMSE value of 6.579 mm. In addition, when using multi-step-ahead prediction, LSTM obtains an RMSE value of 8.648 mm, while SVR, BP and the Elman network obtains RSME values of 13.418 mm, 13.014 mm, and 13.370 mm. The predicted results indicate that, to some extent, the dynamic model (LSTM) achieves results that are more accurate than those of the static models (i.e., SVR and BP). LSTM even displays better performance than the Elman network, which is also a dynamic method.

Osmotic forces and gap junctions in spreading depression: a computational model

NASA Technical Reports Server (NTRS)

Shapiro, B. E.

2001-01-01

In a computational model of spreading depression (SD), ionic movement through a neuronal syncytium of cells connected by gap junctions is described electrodiffusively. Simulations predict that SD will not occur unless cells are allowed to expand in response to osmotic pressure gradients and K+ is allowed to move through gap junctions. SD waves of [K+]out approximately 25 to approximately 60 mM moving at approximately 2 to approximately 18 mm/min are predicted over the range of parametric values reported in gray matter, with extracellular space decreasing up to approximately 50%. Predicted waveform shape is qualitatively similar to laboratory reports. The delayed-rectifier, NMDA, BK, and Na+ currents are predicted to facilitate SD, while SK and A-type K+ currents and glial activity impede SD. These predictions are consonant with recent findings that gap junction poisons block SD and support the theories that cytosolic diffusion via gap junctions and osmotic forces are important mechanisms underlying SD.

Validation of the thermal transport model used for ITER startup scenario predictions with DIII-D experimental data

DOE PAGES

Casper, T. A.; Meyer, W. H.; Jackson, G. L.; ...

2010-12-08

We are exploring characteristics of ITER startup scenarios in similarity experiments conducted on the DIII-D Tokamak. In these experiments, we have validated scenarios for the ITER current ramp up to full current and developed methods to control the plasma parameters to achieve stability. Predictive simulations of ITER startup using 2D free-boundary equilibrium and 1D transport codes rely on accurate estimates of the electron and ion temperature profiles that determine the electrical conductivity and pressure profiles during the current rise. Here we present results of validation studies that apply the transport model used by the ITER team to DIII-D discharge evolutionmore » and comparisons with data from our similarity experiments.« less

The interpretation of hard X-ray polarization measurements in solar flares

NASA Technical Reports Server (NTRS)

Leach, J.; Emslie, A. G.; Petrosian, V.

1983-01-01

Observations of polarization of moderately hard X-rays in solar flares are reviewed and compared with the predictions of recent detailed modeling of hard X-ray bremsstrahlung production by non-thermal electrons. The recent advances in the complexity of the modeling lead to substantially lower predicted polarizations than in earlier models and more fully highlight how various parameters play a role in determining the polarization of the radiation field. The new predicted polarizations are comparable to those predicted by thermal modeling of solar flare hard X-ray production, and both are in agreement with the observations. In the light of these results, new polarization observations with current generation instruments are proposed which could be used to discriminate between non-thermal and thermal models of hard X-ray production in solar flares.

Free flux flow in two single crystals of V3Si with slightly different pinning strengths

NASA Astrophysics Data System (ADS)

Gafarov, O.; Gapud, A. A.; Moraes, S.; Thompson, J. R.; Christen, D. K.; Reyes, A. P.

2010-10-01

Results of recent measurements on two very clean, single-crystal samples of the A15 superconductor V3Si are presented. Magnetization and transport data already confirmed the ``clean'' quality of both samples, as manifested by: (i) high residual resistivity ratio, (ii) very low critical current densities, and (iii) a ``peak'' effect in the field dependence of critical current. The (H,T) phase line for this peak effect is shifted in the slightly ``dirtier'' sample, which consequently also has higher critical current density Jc(H). High-current Lorentz forces are applied on mixed-state vortices in order to induce the highly ordered free flux flow (FFF) phase, using the same methods as in previous work. A traditional model by Bardeen and Stephen (BS) predicts a simple field dependence of flux flow resistivity ρf(H), presuming a field-independent flux core size. A model by Kogan and Zelezhina (KZ) takes core size into account, and predict a clear deviation from BS. In this study, ρf(H) is confirmed to be consistent with predictions of KZ, as will be discussed.

Radar Cross Section Prediction for Coated Perfect Conductors with Arbitrary Geometries.

DTIC Science & Technology

1986-01-01

equivalent electric and magnetic surface currents as the desired unknowns. Triangular patch modelling is ap- plied to the boundary surfaces. The method of...matrix inversion for the unknown surface current coefficients. Huygens’ principle is again applied to calculate the scattered electric field produced...differential equations with the equivalent electric and magnetic surface currents as the desired unknowns. Triangular patch modelling is ap- plied to the

Assessment of Current Process Modeling Approaches to Determine Their Limitations, Applicability and Developments Needed for Long-Fiber Thermoplastic Injection Molded Composites

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

Nguyen, Ba Nghiep; Holbery, Jim; Smith, Mark T.

2006-11-30

This report describes the status of the current process modeling approaches to predict the behavior and flow of fiber-filled thermoplastics under injection molding conditions. Previously, models have been developed to simulate the injection molding of short-fiber thermoplastics, and an as-formed composite part or component can then be predicted that contains a microstructure resulting from the constituents’ material properties and characteristics as well as the processing parameters. Our objective is to assess these models in order to determine their capabilities and limitations, and the developments needed for long-fiber injection-molded thermoplastics (LFTs). First, the concentration regimes are summarized to facilitate the understandingmore » of different types of fiber-fiber interaction that can occur for a given fiber volume fraction. After the formulation of the fiber suspension flow problem and the simplification leading to the Hele-Shaw approach, the interaction mechanisms are discussed. Next, the establishment of the rheological constitutive equation is presented that reflects the coupled flow/orientation nature. The decoupled flow/orientation approach is also discussed which constitutes a good simplification for many applications involving flows in thin cavities. Finally, before outlining the necessary developments for LFTs, some applications of the current orientation model and the so-called modified Folgar-Tucker model are illustrated through the fiber orientation predictions for selected LFT samples.« less

Brownian motion with adaptive drift for remaining useful life prediction: Revisited

NASA Astrophysics Data System (ADS)

Wang, Dong; Tsui, Kwok-Leung

2018-01-01

Linear Brownian motion with constant drift is widely used in remaining useful life predictions because its first hitting time follows the inverse Gaussian distribution. State space modelling of linear Brownian motion was proposed to make the drift coefficient adaptive and incorporate on-line measurements into the first hitting time distribution. Here, the drift coefficient followed the Gaussian distribution, and it was iteratively estimated by using Kalman filtering once a new measurement was available. Then, to model nonlinear degradation, linear Brownian motion with adaptive drift was extended to nonlinear Brownian motion with adaptive drift. However, in previous studies, an underlying assumption used in the state space modelling was that in the update phase of Kalman filtering, the predicted drift coefficient at the current time exactly equalled the posterior drift coefficient estimated at the previous time, which caused a contradiction with the predicted drift coefficient evolution driven by an additive Gaussian process noise. In this paper, to alleviate such an underlying assumption, a new state space model is constructed. As a result, in the update phase of Kalman filtering, the predicted drift coefficient at the current time evolves from the posterior drift coefficient at the previous time. Moreover, the optimal Kalman filtering gain for iteratively estimating the posterior drift coefficient at any time is mathematically derived. A discussion that theoretically explains the main reasons why the constructed state space model can result in high remaining useful life prediction accuracies is provided. Finally, the proposed state space model and its associated Kalman filtering gain are applied to battery prognostics.

A predictive model for diagnosing bipolar disorder based on the clinical characteristics of major depressive episodes in Chinese population.

PubMed

Gan, Zhaoyu; Diao, Feici; Wei, Qinling; Wu, Xiaoli; Cheng, Minfeng; Guan, Nianhong; Zhang, Ming; Zhang, Jinbei

2011-11-01

A correct timely diagnosis of bipolar depression remains a big challenge for clinicians. This study aimed to develop a clinical characteristic based model to predict the diagnosis of bipolar disorder among patients with current major depressive episodes. A prospective study was carried out on 344 patients with current major depressive episodes, with 268 completing 1-year follow-up. Data were collected through structured interviews. Univariate binary logistic regression was conducted to select potential predictive variables among 19 initial variables, and then multivariate binary logistic regression was performed to analyze the combination of risk factors and build a predictive model. Receiver operating characteristic (ROC) curve was plotted. Of 19 initial variables, 13 variables were preliminarily selected, and then forward stepwise exercise produced a final model consisting of 6 variables: age at first onset, maximum duration of depressive episodes, somatalgia, hypersomnia, diurnal variation of mood, irritability. The correct prediction rate of this model was 78% (95%CI: 75%-86%) and the area under the ROC curve was 0.85 (95%CI: 0.80-0.90). The cut-off point for age at first onset was 28.5 years old, while the cut-off point for maximum duration of depressive episode was 7.5 months. The limitations of this study include small sample size, relatively short follow-up period and lack of treatment information. Our predictive models based on six clinical characteristics of major depressive episodes prove to be robust and can help differentiate bipolar depression from unipolar depression. Copyright © 2011 Elsevier B.V. All rights reserved.

Profile control simulations and experiments on TCV: a controller test environment and results using a model-based predictive controller

NASA Astrophysics Data System (ADS)

Maljaars, E.; Felici, F.; Blanken, T. C.; Galperti, C.; Sauter, O.; de Baar, M. R.; Carpanese, F.; Goodman, T. P.; Kim, D.; Kim, S. H.; Kong, M.; Mavkov, B.; Merle, A.; Moret, J. M.; Nouailletas, R.; Scheffer, M.; Teplukhina, A. A.; Vu, N. M. T.; The EUROfusion MST1-team; The TCV-team

2017-12-01

The successful performance of a model predictive profile controller is demonstrated in simulations and experiments on the TCV tokamak, employing a profile controller test environment. Stable high-performance tokamak operation in hybrid and advanced plasma scenarios requires control over the safety factor profile (q-profile) and kinetic plasma parameters such as the plasma beta. This demands to establish reliable profile control routines in presently operational tokamaks. We present a model predictive profile controller that controls the q-profile and plasma beta using power requests to two clusters of gyrotrons and the plasma current request. The performance of the controller is analyzed in both simulation and TCV L-mode discharges where successful tracking of the estimated inverse q-profile as well as plasma beta is demonstrated under uncertain plasma conditions and the presence of disturbances. The controller exploits the knowledge of the time-varying actuator limits in the actuator input calculation itself such that fast transitions between targets are achieved without overshoot. A software environment is employed to prepare and test this and three other profile controllers in parallel in simulations and experiments on TCV. This set of tools includes the rapid plasma transport simulator RAPTOR and various algorithms to reconstruct the plasma equilibrium and plasma profiles by merging the available measurements with model-based predictions. In this work the estimated q-profile is merely based on RAPTOR model predictions due to the absence of internal current density measurements in TCV. These results encourage to further exploit model predictive profile control in experiments on TCV and other (future) tokamaks.

Limitations Of The Current State Space Modelling Approach In Multistage Machining Processes Due To Operation Variations

NASA Astrophysics Data System (ADS)

Abellán-Nebot, J. V.; Liu, J.; Romero, F.

2009-11-01

The State Space modelling approach has been recently proposed as an engineering-driven technique for part quality prediction in Multistage Machining Processes (MMP). Current State Space models incorporate fixture and datum variations in the multi-stage variation propagation, without explicitly considering common operation variations such as machine-tool thermal distortions, cutting-tool wear, cutting-tool deflections, etc. This paper shows the limitations of the current State Space model through an experimental case study where the effect of the spindle thermal expansion, cutting-tool flank wear and locator errors are introduced. The paper also discusses the extension of the current State Space model to include operation variations and its potential benefits.

Hierarchical multi-scale approach to validation and uncertainty quantification of hyper-spectral image modeling

NASA Astrophysics Data System (ADS)

Engel, Dave W.; Reichardt, Thomas A.; Kulp, Thomas J.; Graff, David L.; Thompson, Sandra E.

2016-05-01

Validating predictive models and quantifying uncertainties inherent in the modeling process is a critical component of the HARD Solids Venture program [1]. Our current research focuses on validating physics-based models predicting the optical properties of solid materials for arbitrary surface morphologies and characterizing the uncertainties in these models. We employ a systematic and hierarchical approach by designing physical experiments and comparing the experimental results with the outputs of computational predictive models. We illustrate this approach through an example comparing a micro-scale forward model to an idealized solid-material system and then propagating the results through a system model to the sensor level. Our efforts should enhance detection reliability of the hyper-spectral imaging technique and the confidence in model utilization and model outputs by users and stakeholders.

A manpower calculus: the implications of SUO fellowship expansion on oncologic surgeon case volumes.

PubMed

See, William A

2014-01-01

Society of Urologic Oncology (SUO)-accredited fellowship programs have undergone substantial expansion. This study developed a mathematical model to estimate future changes in urologic oncologic surgeon (UOS) manpower and analyzed the effect of those changes on per-UOS case volumes. SUO fellowship program directors were queried as to the number of positions available on an annual basis. Current US UOS manpower was estimated from the SUO membership list. Future manpower was estimated on an annual basis by linear senescence of existing manpower combined with linear growth of newly trained surgeons. Case-volume estimates for the 4 surgical disease sites (prostate, kidney/renal pelvis, bladder, and testes) were obtained from the literature. The future number of major cases was determined from current volumes based upon the US population growth rates and the historic average annual change in disease incidence. Two models were used to predict future per-UOS major case volumes. Model 1 assumed the current distribution of cases between nononcologic surgeons and UOS would continue. Model 2 assumed a progressive redistribution of cases over time such that in 2043 100% of major urologic cancer cases would be performed by UOSs. Over the 30-year period to "manpower steady-state" SUO-accredited UOSs practicing in the United States have the potential to increase from approximately 600 currently to 1,650 in 2043. During this interval, case volumes are predicted to change 0.97-, 2.4-, 1.1-, and 1.5-fold for prostatectomy, nephrectomy, cystectomy, and retroperitoneal lymph node dissection, respectively. The ratio of future to current total annual case volumes is predicted to be 0.47 and 0.9 for models 1 and 2, respectively. The number of annual US practicing graduates necessary to achieve a future to current case-volume ratio greater than 1 is 25 and 49 in models 1 and 2, respectively. The current number of SUO fellowship trainees has the potential to decrease future per-UOS case volumes relative to current levels. Redistribution of existing case volume or a decrease in the annual number of trainees or both would be required to insure sufficient surgical volumes for skill maintenance and optimal patient outcomes. Published by Elsevier Inc.

Evolution of Cross-Shore Profile Models for Sustainable Coastal Design

NASA Astrophysics Data System (ADS)

Ismail, Nabil; El-Sayed, Mohamed

2014-05-01

Selection and evaluation of coastal structures are correlated with environmental wave and current parameters as well as cross shore profiles. The coupling between the environmental conditions and cross shore profiles necessitates the ability to predict reasonably the cross shore profiles. Results obtained from the validation of a cross-shore profile evolution model, Uniform Beach Sediment Transport-Time-Averaged Cross-Shore (UNIBEST-TC), were examined and further analyzed to reveal the reasons for the discrepancy between the model predictions of the field data at the surf zone of the Duck Beach in North Carolina, USA. The UNIBEST model was developed to predict the main cross shore parameters of wave height, direction, cross shore and long shore currents. However, the results of the model predictions are generally satisfactory for wave height and direction but not satisfactory for the remaining parameters. This research is focused on exploring the discrepancy between the model predictions and the field data of the Duck site, and conducting further analyses to recommend model refinements. The discrepancy is partially attributed due to the fact that the measured values, were taken close to the seabed, while the predicted values are the depth-averaged velocity. Further examination indicated that UNIBEST-TC model runs consider the RMS of the wave height spectrum with a constant gamma-value from the offshore wave spectrum at 8.0m depth. To confirm this argument, a Wavelet Analysis was applied to the time series of wave height and longshore current velocity parameters at the Duck site. The significant wave height ranged between 0.6m and 4.0m while the frequencies ranged between 0.08 to 0.2Hz at 8.0m water depth. Four cases corresponding to events of both high water level and low water level at Duck site were considered in this study. The results show that linear and non-linear interaction between wave height and long-shore current occur over the range of frequencies embracing; the low frequency band of infragravity (0.001- 0.02Hz) waves band and short incident wave band (0.05-0.10Hz). The present results highlight the necessity of incorporating interaction terms between wave - wave and wave- current in the development of cross shore and longshore model formulations. The numerical results confirm previous field observations of nearshore processes that waves in the infragravity range, shear and edge waves, play an important role on near shore hydrodynamics and beach morphology. A prime recommendation of this research work is that the UNIBEST- TC and similar models need to take into effect the interaction between waves, cross shore and longshore currents. Furthermore the models should consider the effects of long waves within the spectrum as well as the generated edge waves. Nevertheless, modeling of this wide range of processes on real beaches needs extensive field data of high spatial and temporal resolutions. Such challenging goal remains to be pursued to enhance state of art prediction of the cross-shore evolution profiles. REFERENCES Addison, P.S. (2002). "The Illustrated Wavelet Transform Handbook, Introductory Theory and Applications in Science", 349 p., Bristol, UK, Institute of Physics Publishing. Elsayed, M.A.K. (2006). "Application of a Cross-Shore Profile Evolution Model to Barred Beaches", Journal of Coastal Research, 22(3), 645-663. Elsayed, M.A.K. (2007). "Non-linear Wave-Wave Interactions in a Mistral Event". Journal of Coastal Research, 23(5), 1318-1323. Ismail, N. M., and Wiegel, R. L. (1983). "Effect of Opposing Waves on Momentum Jets Spreading Rate", Journal of Waterway, Port, Coastal and Ocean Division, ASCE, Vol.109, No.4, 465-483. Ismail, N.M. (1984). "Wave-Current Models for the Design of Marine Structures", Journal of Waterway, Port, Coastal and Ocean Engineering, ASCE, Vol. 110, No. 4, 432-446. Ismail, N.M. (2007). "Discussion of Reynolds Stresses and Velocity Distributions in a Wave-Current Coexisting Environment", Journal of Waterway, Port, Coastal and Ocean Engineering, ASCE, Vol. 133, No. 2, 168-169. Ismail, N. and J.W. Williams. ( 2013). Sea-Level Rise Implications for Coastal Protection from Southern Mediterranean to the U.S.A. Atlantic Coast, EGU,2013-13464, European Geosciences Union, General Assembly 2013,Vienna, Austria, 07 - 12 April.

Effect of pulsed current GTA welding parameters on the fusion zone microstructure of AA 6061 aluminium alloy

NASA Astrophysics Data System (ADS)

Kumar, T. Senthil; Balasubramanian, V.; Babu, S.; Sanavullah, M. Y.

2007-08-01

AA6061 aluminium alloy (Al-Mg-Si alloy) has gathered wide acceptance in the fabrication of food processing equipment, chemical containers, passenger cars, road tankers, and railway transport systems. The preferred process for welding these aluminium alloys is frequently Gas Tungsten Arc (GTA) welding due to its comparatively easy applicability and lower cost. In the case of single pass GTA welding of thinner sections of this alloy, the pulsed current has been found beneficial due to its advantages over the conventional continuous current processes. The use of pulsed current parameters has been found to improve the mechanical properties of the welds compared to those of continuous current welds of this alloy due to grain refinement occurring in the fusion zone. In this investigation, an attempt has been made to develop a mathematical model to predict the fusion zone grain diameter incorporating pulsed current welding parameters. Statistical tools such as design of experiments, analysis of variance, and regression analysis are used to develop the mathematical model. The developed model can be effectively used to predict the fusion grain diameter at a 95% confidence level for the given pulsed current parameters. The effect of pulsed current GTA welding parameters on the fusion zone grain diameter of AA 6061 aluminium alloy welds is reported in this paper.

Experimental High-Resolution Land Surface Prediction System for the Vancouver 2010 Winter Olympic Games

NASA Astrophysics Data System (ADS)

Belair, S.; Bernier, N.; Tong, L.; Mailhot, J.

2008-05-01

The 2010 Winter Olympic and Paralympic Games will take place in Vancouver, Canada, from 12 to 28 February 2010 and from 12 to 21 March 2010, respectively. In order to provide the best possible guidance achievable with current state-of-the-art science and technology, Environment Canada is currently setting up an experimental numerical prediction system for these special events. This system consists of a 1-km limited-area atmospheric model that will be integrated for 16h, twice a day, with improved microphysics compared with the system currently operational at the Canadian Meteorological Centre. In addition, several new and original tools will be used to adapt and refine predictions near and at the surface. Very high-resolution two-dimensional surface systems, with 100-m and 20-m grid size, will cover the Vancouver Olympic area. Using adaptation methods to improve the forcing from the lower-resolution atmospheric models, these 2D surface models better represent surface processes, and thus lead to better predictions of snow conditions and near-surface air temperature. Based on a similar strategy, a single-point model will be implemented to better predict surface characteristics at each station of an observing network especially installed for the 2010 events. The main advantage of this single-point system is that surface observations are used as forcing for the land surface models, and can even be assimilated (although this is not expected in the first version of this new tool) to improve initial conditions of surface variables such as snow depth and surface temperatures. Another adaptation tool, based on 2D stationnary solutions of a simple dynamical system, will be used to produce near-surface winds on the 100-m grid, coherent with the high- resolution orography. The configuration of the experimental numerical prediction system will be presented at the conference, together with preliminary results for winter 2007-2008.

Reducing hydrologic model uncertainty in monthly streamflow predictions using multimodel combination

NASA Astrophysics Data System (ADS)

Li, Weihua; Sankarasubramanian, A.

2012-12-01

Model errors are inevitable in any prediction exercise. One approach that is currently gaining attention in reducing model errors is by combining multiple models to develop improved predictions. The rationale behind this approach primarily lies on the premise that optimal weights could be derived for each model so that the developed multimodel predictions will result in improved predictions. A new dynamic approach (MM-1) to combine multiple hydrological models by evaluating their performance/skill contingent on the predictor state is proposed. We combine two hydrological models, "abcd" model and variable infiltration capacity (VIC) model, to develop multimodel streamflow predictions. To quantify precisely under what conditions the multimodel combination results in improved predictions, we compare multimodel scheme MM-1 with optimal model combination scheme (MM-O) by employing them in predicting the streamflow generated from a known hydrologic model (abcd model orVICmodel) with heteroscedastic error variance as well as from a hydrologic model that exhibits different structure than that of the candidate models (i.e., "abcd" model or VIC model). Results from the study show that streamflow estimated from single models performed better than multimodels under almost no measurement error. However, under increased measurement errors and model structural misspecification, both multimodel schemes (MM-1 and MM-O) consistently performed better than the single model prediction. Overall, MM-1 performs better than MM-O in predicting the monthly flow values as well as in predicting extreme monthly flows. Comparison of the weights obtained from each candidate model reveals that as measurement errors increase, MM-1 assigns weights equally for all the models, whereas MM-O assigns higher weights for always the best-performing candidate model under the calibration period. Applying the multimodel algorithms for predicting streamflows over four different sites revealed that MM-1 performs better than all single models and optimal model combination scheme, MM-O, in predicting the monthly flows as well as the flows during wetter months.

Modularity of Protein Folds as a Tool for Template-Free Modeling of Structures.

PubMed

Vallat, Brinda; Madrid-Aliste, Carlos; Fiser, Andras

2015-08-01

Predicting the three-dimensional structure of proteins from their amino acid sequences remains a challenging problem in molecular biology. While the current structural coverage of proteins is almost exclusively provided by template-based techniques, the modeling of the rest of the protein sequences increasingly require template-free methods. However, template-free modeling methods are much less reliable and are usually applicable for smaller proteins, leaving much space for improvement. We present here a novel computational method that uses a library of supersecondary structure fragments, known as Smotifs, to model protein structures. The library of Smotifs has saturated over time, providing a theoretical foundation for efficient modeling. The method relies on weak sequence signals from remotely related protein structures to create a library of Smotif fragments specific to the target protein sequence. This Smotif library is exploited in a fragment assembly protocol to sample decoys, which are assessed by a composite scoring function. Since the Smotif fragments are larger in size compared to the ones used in other fragment-based methods, the proposed modeling algorithm, SmotifTF, can employ an exhaustive sampling during decoy assembly. SmotifTF successfully predicts the overall fold of the target proteins in about 50% of the test cases and performs competitively when compared to other state of the art prediction methods, especially when sequence signal to remote homologs is diminishing. Smotif-based modeling is complementary to current prediction methods and provides a promising direction in addressing the structure prediction problem, especially when targeting larger proteins for modeling.

Diagnosis-Based Risk Adjustment for Medicare Capitation Payments

PubMed Central

Ellis, Randall P.; Pope, Gregory C.; Iezzoni, Lisa I.; Ayanian, John Z.; Bates, David W.; Burstin, Helen; Ash, Arlene S.

1996-01-01

Using 1991-92 data for a 5-percent Medicare sample, we develop, estimate, and evaluate risk-adjustment models that utilize diagnostic information from both inpatient and ambulatory claims to adjust payments for aged and disabled Medicare enrollees. Hierarchical coexisting conditions (HCC) models achieve greater explanatory power than diagnostic cost group (DCG) models by taking account of multiple coexisting medical conditions. Prospective models predict average costs of individuals with chronic conditions nearly as well as concurrent models. All models predict medical costs far more accurately than the current health maintenance organization (HMO) payment formula. PMID:10172666

An Approximation to the Adaptive Exponential Integrate-and-Fire Neuron Model Allows Fast and Predictive Fitting to Physiological Data.

PubMed

Hertäg, Loreen; Hass, Joachim; Golovko, Tatiana; Durstewitz, Daniel

2012-01-01

For large-scale network simulations, it is often desirable to have computationally tractable, yet in a defined sense still physiologically valid neuron models. In particular, these models should be able to reproduce physiological measurements, ideally in a predictive sense, and under different input regimes in which neurons may operate in vivo. Here we present an approach to parameter estimation for a simple spiking neuron model mainly based on standard f-I curves obtained from in vitro recordings. Such recordings are routinely obtained in standard protocols and assess a neuron's response under a wide range of mean-input currents. Our fitting procedure makes use of closed-form expressions for the firing rate derived from an approximation to the adaptive exponential integrate-and-fire (AdEx) model. The resulting fitting process is simple and about two orders of magnitude faster compared to methods based on numerical integration of the differential equations. We probe this method on different cell types recorded from rodent prefrontal cortex. After fitting to the f-I current-clamp data, the model cells are tested on completely different sets of recordings obtained by fluctuating ("in vivo-like") input currents. For a wide range of different input regimes, cell types, and cortical layers, the model could predict spike times on these test traces quite accurately within the bounds of physiological reliability, although no information from these distinct test sets was used for model fitting. Further analyses delineated some of the empirical factors constraining model fitting and the model's generalization performance. An even simpler adaptive LIF neuron was also examined in this context. Hence, we have developed a "high-throughput" model fitting procedure which is simple and fast, with good prediction performance, and which relies only on firing rate information and standard physiological data widely and easily available.

How Many Wolves (Canis lupus) Fit into Germany? The Role of Assumptions in Predictive Rule-Based Habitat Models for Habitat Generalists

PubMed Central

Fechter, Dominik; Storch, Ilse

2014-01-01

Due to legislative protection, many species, including large carnivores, are currently recolonizing Europe. To address the impending human-wildlife conflicts in advance, predictive habitat models can be used to determine potentially suitable habitat and areas likely to be recolonized. As field data are often limited, quantitative rule based models or the extrapolation of results from other studies are often the techniques of choice. Using the wolf (Canis lupus) in Germany as a model for habitat generalists, we developed a habitat model based on the location and extent of twelve existing wolf home ranges in Eastern Germany, current knowledge on wolf biology, different habitat modeling techniques and various input data to analyze ten different input parameter sets and address the following questions: (1) How do a priori assumptions and different input data or habitat modeling techniques affect the abundance and distribution of potentially suitable wolf habitat and the number of wolf packs in Germany? (2) In a synthesis across input parameter sets, what areas are predicted to be most suitable? (3) Are existing wolf pack home ranges in Eastern Germany consistent with current knowledge on wolf biology and habitat relationships? Our results indicate that depending on which assumptions on habitat relationships are applied in the model and which modeling techniques are chosen, the amount of potentially suitable habitat estimated varies greatly. Depending on a priori assumptions, Germany could accommodate between 154 and 1769 wolf packs. The locations of the existing wolf pack home ranges in Eastern Germany indicate that wolves are able to adapt to areas densely populated by humans, but are limited to areas with low road densities. Our analysis suggests that predictive habitat maps in general, should be interpreted with caution and illustrates the risk for habitat modelers to concentrate on only one selection of habitat factors or modeling technique. PMID:25029506

Optimal temperature for malaria transmission is dramaticallylower than previously predicted

USGS Publications Warehouse

Mordecai, Eerin A.; Paaijmans, Krijin P.; Johnson, Leah R.; Balzer, Christian; Ben-Horin, Tal; de Moor, Emily; McNally, Amy; Pawar, Samraat; Ryan, Sadie J.; Smith, Thomas C.; Lafferty, Kevin D.

2013-01-01

The ecology of mosquito vectors and malaria parasites affect the incidence, seasonal transmission and geographical range of malaria. Most malaria models to date assume constant or linear responses of mosquito and parasite life-history traits to temperature, predicting optimal transmission at 31 °C. These models are at odds with field observations of transmission dating back nearly a century. We build a model with more realistic ecological assumptions about the thermal physiology of insects. Our model, which includes empirically derived nonlinear thermal responses, predicts optimal malaria transmission at 25 °C (6 °C lower than previous models). Moreover, the model predicts that transmission decreases dramatically at temperatures > 28 °C, altering predictions about how climate change will affect malaria. A large data set on malaria transmission risk in Africa validates both the 25 °C optimum and the decline above 28 °C. Using these more accurate nonlinear thermal-response models will aid in understanding the effects of current and future temperature regimes on disease transmission.

Optimal temperature for malaria transmission is dramatically lower than previously predicted

USGS Publications Warehouse

Mordecai, Erin A.; Paaijmans, Krijn P.; Johnson, Leah R.; Balzer, Christian; Ben-Horin, Tal; de Moor, Emily; McNally, Amy; Pawar, Samraat; Ryan, Sadie J.; Smith, Thomas C.; Lafferty, Kevin D.

2013-01-01

The ecology of mosquito vectors and malaria parasites affect the incidence, seasonal transmission and geographical range of malaria. Most malaria models to date assume constant or linear responses of mosquito and parasite life-history traits to temperature, predicting optimal transmission at 31 °C. These models are at odds with field observations of transmission dating back nearly a century. We build a model with more realistic ecological assumptions about the thermal physiology of insects. Our model, which includes empirically derived nonlinear thermal responses, predicts optimal malaria transmission at 25 °C (6 °C lower than previous models). Moreover, the model predicts that transmission decreases dramatically at temperatures > 28 °C, altering predictions about how climate change will affect malaria. A large data set on malaria transmission risk in Africa validates both the 25 °C optimum and the decline above 28 °C. Using these more accurate nonlinear thermal-response models will aid in understanding the effects of current and future temperature regimes on disease transmission.

The value and cost of complexity in predictive modelling: role of tissue anisotropic conductivity and fibre tracts in neuromodulation

NASA Astrophysics Data System (ADS)

Salman Shahid, Syed; Bikson, Marom; Salman, Humaira; Wen, Peng; Ahfock, Tony

2014-06-01

Objectives. Computational methods are increasingly used to optimize transcranial direct current stimulation (tDCS) dose strategies and yet complexities of existing approaches limit their clinical access. Since predictive modelling indicates the relevance of subject/pathology based data and hence the need for subject specific modelling, the incremental clinical value of increasingly complex modelling methods must be balanced against the computational and clinical time and costs. For example, the incorporation of multiple tissue layers and measured diffusion tensor (DTI) based conductivity estimates increase model precision but at the cost of clinical and computational resources. Costs related to such complexities aggregate when considering individual optimization and the myriad of potential montages. Here, rather than considering if additional details change current-flow prediction, we consider when added complexities influence clinical decisions. Approach. Towards developing quantitative and qualitative metrics of value/cost associated with computational model complexity, we considered field distributions generated by two 4 × 1 high-definition montages (m1 = 4 × 1 HD montage with anode at C3 and m2 = 4 × 1 HD montage with anode at C1) and a single conventional (m3 = C3-Fp2) tDCS electrode montage. We evaluated statistical methods, including residual error (RE) and relative difference measure (RDM), to consider the clinical impact and utility of increased complexities, namely the influence of skull, muscle and brain anisotropic conductivities in a volume conductor model. Main results. Anisotropy modulated current-flow in a montage and region dependent manner. However, significant statistical changes, produced within montage by anisotropy, did not change qualitative peak and topographic comparisons across montages. Thus for the examples analysed, clinical decision on which dose to select would not be altered by the omission of anisotropic brain conductivity. Significance. Results illustrate the need to rationally balance the role of model complexity, such as anisotropy in detailed current flow analysis versus value in clinical dose design. However, when extending our analysis to include axonal polarization, the results provide presumably clinically meaningful information. Hence the importance of model complexity may be more relevant with cellular level predictions of neuromodulation.

Open issues in hadronic interactions for air showers

NASA Astrophysics Data System (ADS)

Pierog, Tanguy

2017-06-01

In detailed air shower simulations, the uncertainty in the prediction of shower observables for different primary particles and energies is currently dominated by differences between hadronic interaction models. With the results of the first run of the LHC, the difference between post-LHC model predictions has been reduced to the same level as experimental uncertainties of cosmic ray experiments. At the same time new types of air shower observables, like the muon production depth, have been measured, adding new constraints on hadronic models. Currently no model is able to consistently reproduce all mass composition measurements possible within the Pierre Auger Observatory for instance. Comparing the different models, and with LHC and cosmic ray data, we will show that the remaining open issues in hadronic interactions in air shower development are now in the pion-air interactions and in nuclear effects.

Development of an algebraic stress/two-layer model for calculating thrust chamber flow fields

NASA Technical Reports Server (NTRS)

Chen, C. P.; Shang, H. M.; Huang, J.

1993-01-01

Following the consensus of a workshop in Turbulence Modeling for Liquid Rocket Thrust Chambers, the current effort was undertaken to study the effects of second-order closure on the predictions of thermochemical flow fields. To reduce the instability and computational intensity of the full second-order Reynolds Stress Model, an Algebraic Stress Model (ASM) coupled with a two-layer near wall treatment was developed. Various test problems, including the compressible boundary layer with adiabatic and cooled walls, recirculating flows, swirling flows and the entire SSME nozzle flow were studied to assess the performance of the current model. Detailed calculations for the SSME exit wall flow around the nozzle manifold were executed. As to the overall flow predictions, the ASM removes another assumption for appropriate comparison with experimental data, to account for the non-isotropic turbulence effects.

Turbulence modelling of flow fields in thrust chambers

NASA Technical Reports Server (NTRS)

Chen, C. P.; Kim, Y. M.; Shang, H. M.

1993-01-01

Following the consensus of a workshop in Turbulence Modelling for Liquid Rocket Thrust Chambers, the current effort was undertaken to study the effects of second-order closure on the predictions of thermochemical flow fields. To reduce the instability and computational intensity of the full second-order Reynolds Stress Model, an Algebraic Stress Model (ASM) coupled with a two-layer near wall treatment was developed. Various test problems, including the compressible boundary layer with adiabatic and cooled walls, recirculating flows, swirling flows, and the entire SSME nozzle flow were studied to assess the performance of the current model. Detailed calculations for the SSME exit wall flow around the nozzle manifold were executed. As to the overall flow predictions, the ASM removes another assumption for appropriate comparison with experimental data to account for the non-isotropic turbulence effects.

Prediction of chemo-response in serous ovarian cancer.

PubMed

Gonzalez Bosquet, Jesus; Newtson, Andreea M; Chung, Rebecca K; Thiel, Kristina W; Ginader, Timothy; Goodheart, Michael J; Leslie, Kimberly K; Smith, Brian J

2016-10-19

Nearly one-third of serous ovarian cancer (OVCA) patients will not respond to initial treatment with surgery and chemotherapy and die within one year of diagnosis. If patients who are unlikely to respond to current standard therapy can be identified up front, enhanced tumor analyses and treatment regimens could potentially be offered. Using the Cancer Genome Atlas (TCGA) serous OVCA database, we previously identified a robust molecular signature of 422-genes associated with chemo-response. Our objective was to test whether this signature is an accurate and sensitive predictor of chemo-response in serous OVCA. We first constructed prediction models to predict chemo-response using our previously described 422-gene signature that was associated with response to treatment in serous OVCA. Performance of all prediction models were measured with area under the curves (AUCs, a measure of the model's accuracy) and their respective confidence intervals (CIs). To optimize the prediction process, we determined which elements of the signature most contributed to chemo-response prediction. All prediction models were replicated and validated using six publicly available independent gene expression datasets. The 422-gene signature prediction models predicted chemo-response with AUCs of ~70 %. Optimization of prediction models identified the 34 most important genes in chemo-response prediction. These 34-gene models had improved performance, with AUCs approaching 80 %. Both 422-gene and 34-gene prediction models were replicated and validated in six independent datasets. These prediction models serve as the foundation for the future development and implementation of a diagnostic tool to predict response to chemotherapy for serous OVCA patients.

Sensorless Modeling of Varying Pulse Width Modulator Resolutions in Three-Phase Induction Motors

PubMed Central

Marko, Matthew David; Shevach, Glenn

2017-01-01

A sensorless algorithm was developed to predict rotor speeds in an electric three-phase induction motor. This sensorless model requires a measurement of the stator currents and voltages, and the rotor speed is predicted accurately without any mechanical measurement of the rotor speed. A model of an electric vehicle undergoing acceleration was built, and the sensorless prediction of the simulation rotor speed was determined to be robust even in the presence of fluctuating motor parameters and significant sensor errors. Studies were conducted for varying pulse width modulator resolutions, and the sensorless model was accurate for all resolutions of sinusoidal voltage functions. PMID:28076418

Sensorless Modeling of Varying Pulse Width Modulator Resolutions in Three-Phase Induction Motors.

PubMed

Marko, Matthew David; Shevach, Glenn

2017-01-01

A sensorless algorithm was developed to predict rotor speeds in an electric three-phase induction motor. This sensorless model requires a measurement of the stator currents and voltages, and the rotor speed is predicted accurately without any mechanical measurement of the rotor speed. A model of an electric vehicle undergoing acceleration was built, and the sensorless prediction of the simulation rotor speed was determined to be robust even in the presence of fluctuating motor parameters and significant sensor errors. Studies were conducted for varying pulse width modulator resolutions, and the sensorless model was accurate for all resolutions of sinusoidal voltage functions.

Predictive risk models for proximal aortic surgery

PubMed Central

Díaz, Rocío; Pascual, Isaac; Álvarez, Rubén; Alperi, Alberto; Rozado, Jose; Morales, Carlos; Silva, Jacobo; Morís, César

2017-01-01

Predictive risk models help improve decision making, information to our patients and quality control comparing results between surgeons and between institutions. The use of these models promotes competitiveness and led to increasingly better results. All these virtues are of utmost importance when the surgical operation entails high-risk. Although proximal aortic surgery is less frequent than other cardiac surgery operations, this procedure itself is more challenging and technically demanding than other common cardiac surgery techniques. The aim of this study is to review the current status of predictive risk models for patients who undergo proximal aortic surgery, which means aortic root replacement, supracoronary ascending aortic replacement or aortic arch surgery. PMID:28616348

Predictive control strategies for wind turbine system based on permanent magnet synchronous generator.

PubMed

Maaoui-Ben Hassine, Ikram; Naouar, Mohamed Wissem; Mrabet-Bellaaj, Najiba

2016-05-01

In this paper, Model Predictive Control and Dead-beat predictive control strategies are proposed for the control of a PMSG based wind energy system. The proposed MPC considers the model of the converter-based system to forecast the possible future behavior of the controlled variables. It allows selecting the voltage vector to be applied that leads to a minimum error by minimizing a predefined cost function. The main features of the MPC are low current THD and robustness against parameters variations. The Dead-beat predictive control is based on the system model to compute the optimum voltage vector that ensures zero-steady state error. The optimum voltage vector is then applied through Space Vector Modulation (SVM) technique. The main advantages of the Dead-beat predictive control are low current THD and constant switching frequency. The proposed control techniques are presented and detailed for the control of back-to-back converter in a wind turbine system based on PMSG. Simulation results (under Matlab-Simulink software environment tool) and experimental results (under developed prototyping platform) are presented in order to show the performances of the considered control strategies. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Predicting the hydraulic forces on submerged macrophytes from current velocity, biomass and morphology.

PubMed

Schutten, J; Davy, A J

2000-06-01

Aquatic macrophytes are important in stabilising moderately eutrophic, shallow freshwater lakes in the clear-water state. The failure of macrophyte recovery in lakes with very soft, highly organic sediments that have been restored to clear water by biomanipulation (e.g. in the Norfolk Broads, UK) has suggested that the physical stability of the sediment may limit plant establishment. Hydraulic forces from water currents may be sufficient to break or remove plants. Our aim was to develop a simple model that could predict these forces from plant biomass, current velocity and plant form. We used an experimental flume to measure the hydraulic forces acting on shoots of 18 species of aquatic macrophyte of varying size and morphology. The hydraulic drag on the shoots was regressed on a theoretically derived predictor (shoot biomass × current velocity 1.5 ). Such linear regressions proved to be highly significant for most species. The slopes of these lines represent species-specific, hydraulic roughness factors that are analogous to classical drag coefficients. Shoot architecture parameters describing leaf and shoot shape had significant effects on the hydraulic roughness factor. Leaf width and shoot stiffness individually did not have a significant influence, but in combination with shoot shape they were significant. This hydraulic model was validated for a subset of species using measurements from an independent set of shoots. When measured and predicted hydraulic forces were compared, the fit was generally very good, except for two species with morphological variations. This simple model, together with the plant-specific factors, provides a basis for predicting the hydraulic forces acting on the root systems of macrophytes under field conditions. This information should allow prediction of the physical stability of individual plants, as an aid to shallow-lake management.

Acoustic emission by self-organising effects of micro-hollow cathode discharges

NASA Astrophysics Data System (ADS)

Kotschate, Daniel; Gaal, Mate; Kersten, Holger

2018-04-01

We designed micro-hollow cathode discharge prototypes under atmospheric pressure and investigated their acoustic characteristics. For the acoustic model of the discharge, we correlated the self-organisation effect of the current density distribution with the ideal model of an acoustic membrane. For validation of the obtained model, sound particle velocity spectroscopy was used to detect and analyse the acoustic emission experimentally. The results have shown a behaviour similar to the ideal acoustic membrane. Therefore, the acoustic excitation is decomposable into its eigenfrequencies and predictable. The model was unified utilising the gas exhaust velocity caused by the electrohydrodynamic force. The results may allow a contactless prediction of the current density distribution by measuring the acoustic emission or using the micro-discharge as a tunable acoustic source for specific applications as well.

Novel opportunities for computational biology and sociology in drug discovery☆

PubMed Central

Yao, Lixia; Evans, James A.; Rzhetsky, Andrey

2013-01-01

Current drug discovery is impossible without sophisticated modeling and computation. In this review we outline previous advances in computational biology and, by tracing the steps involved in pharmaceutical development, explore a range of novel, high-value opportunities for computational innovation in modeling the biological process of disease and the social process of drug discovery. These opportunities include text mining for new drug leads, modeling molecular pathways and predicting the efficacy of drug cocktails, analyzing genetic overlap between diseases and predicting alternative drug use. Computation can also be used to model research teams and innovative regions and to estimate the value of academy–industry links for scientific and human benefit. Attention to these opportunities could promise punctuated advance and will complement the well-established computational work on which drug discovery currently relies. PMID:20349528

Prevalidation of an Acute Inhalation Toxicity Test Using the EpiAirway In Vitro Human Airway Model

PubMed Central

Jackson, George R.; Maione, Anna G.; Klausner, Mitchell

2018-01-01

Abstract Introduction: Knowledge of acute inhalation toxicity potential is important for establishing safe use of chemicals and consumer products. Inhalation toxicity testing and classification procedures currently accepted within worldwide government regulatory systems rely primarily on tests conducted in animals. The goal of the current work was to develop and prevalidate a nonanimal (in vitro) test for determining acute inhalation toxicity using the EpiAirway™ in vitro human airway model as a potential alternative for currently accepted animal tests. Materials and Methods: The in vitro test method exposes EpiAirway tissues to test chemicals for 3 hours, followed by measurement of tissue viability as the test endpoint. Fifty-nine chemicals covering a broad range of toxicity classes, chemical structures, and physical properties were evaluated. The in vitro toxicity data were utilized to establish a prediction model to classify the chemicals into categories corresponding to the currently accepted Globally Harmonized System (GHS) and the Environmental Protection Agency (EPA) system. Results: The EpiAirway prediction model identified in vivo rat-based GHS Acute Inhalation Toxicity Category 1–2 and EPA Acute Inhalation Toxicity Category I–II chemicals with 100% sensitivity and specificity of 43.1% and 50.0%, for GHS and EPA acute inhalation toxicity systems, respectively. The sensitivity and specificity of the EpiAirway prediction model for identifying GHS specific target organ toxicity-single exposure (STOT-SE) Category 1 human toxicants were 75.0% and 56.5%, respectively. Corrosivity and electrophilic and oxidative reactivity appear to be the predominant mechanisms of toxicity for the most highly toxic chemicals. Conclusions: These results indicate that the EpiAirway test is a promising alternative to the currently accepted animal tests for acute inhalation toxicity. PMID:29904643

Prevalidation of an Acute Inhalation Toxicity Test Using the EpiAirway In Vitro Human Airway Model.

PubMed

Jackson, George R; Maione, Anna G; Klausner, Mitchell; Hayden, Patrick J

2018-06-01

Introduction: Knowledge of acute inhalation toxicity potential is important for establishing safe use of chemicals and consumer products. Inhalation toxicity testing and classification procedures currently accepted within worldwide government regulatory systems rely primarily on tests conducted in animals. The goal of the current work was to develop and prevalidate a nonanimal ( in vitro ) test for determining acute inhalation toxicity using the EpiAirway™ in vitro human airway model as a potential alternative for currently accepted animal tests. Materials and Methods: The in vitro test method exposes EpiAirway tissues to test chemicals for 3 hours, followed by measurement of tissue viability as the test endpoint. Fifty-nine chemicals covering a broad range of toxicity classes, chemical structures, and physical properties were evaluated. The in vitro toxicity data were utilized to establish a prediction model to classify the chemicals into categories corresponding to the currently accepted Globally Harmonized System (GHS) and the Environmental Protection Agency (EPA) system. Results: The EpiAirway prediction model identified in vivo rat-based GHS Acute Inhalation Toxicity Category 1-2 and EPA Acute Inhalation Toxicity Category I-II chemicals with 100% sensitivity and specificity of 43.1% and 50.0%, for GHS and EPA acute inhalation toxicity systems, respectively. The sensitivity and specificity of the EpiAirway prediction model for identifying GHS specific target organ toxicity-single exposure (STOT-SE) Category 1 human toxicants were 75.0% and 56.5%, respectively. Corrosivity and electrophilic and oxidative reactivity appear to be the predominant mechanisms of toxicity for the most highly toxic chemicals. Conclusions: These results indicate that the EpiAirway test is a promising alternative to the currently accepted animal tests for acute inhalation toxicity.

Predicting Student Success using Analytics in Course Learning Management Systems

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

Olama, Mohammed M; Thakur, Gautam; McNair, Wade

Educational data analytics is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from the educational context. For example, predicting college student performance is crucial for both the student and educational institutions. It can support timely intervention to prevent students from failing a course, increasing efficacy of advising functions, and improving course completion rate. In this paper, we present the efforts carried out at Oak Ridge National Laboratory (ORNL) toward conducting predictive analytics to academic data collected from 2009 through 2013 and available in one of the most commonly used learning management systems,more » called Moodle. First, we have identified the data features useful for predicting student outcomes such as students scores in homework assignments, quizzes, exams, in addition to their activities in discussion forums and their total GPA at the same term they enrolled in the course. Then, Logistic Regression and Neural Network predictive models are used to identify students as early as possible that are in danger of failing the course they are currently enrolled in. These models compute the likelihood of any given student failing (or passing) the current course. Numerical results are presented to evaluate and compare the performance of the developed models and their predictive accuracy.« less

Predicting student success using analytics in course learning management systems

NASA Astrophysics Data System (ADS)

Olama, Mohammed M.; Thakur, Gautam; McNair, Allen W.; Sukumar, Sreenivas R.

2014-05-01

Educational data analytics is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from the educational context. For example, predicting college student performance is crucial for both the student and educational institutions. It can support timely intervention to prevent students from failing a course, increasing efficacy of advising functions, and improving course completion rate. In this paper, we present the efforts carried out at Oak Ridge National Laboratory (ORNL) toward conducting predictive analytics to academic data collected from 2009 through 2013 and available in one of the most commonly used learning management systems, called Moodle. First, we have identified the data features useful for predicting student outcomes such as students' scores in homework assignments, quizzes, exams, in addition to their activities in discussion forums and their total GPA at the same term they enrolled in the course. Then, Logistic Regression and Neural Network predictive models are used to identify students as early as possible that are in danger of failing the course they are currently enrolled in. These models compute the likelihood of any given student failing (or passing) the current course. Numerical results are presented to evaluate and compare the performance of the developed models and their predictive accuracy.

Physics-driven Spatiotemporal Regularization for High-dimensional Predictive Modeling: A Novel Approach to Solve the Inverse ECG Problem

NASA Astrophysics Data System (ADS)

Yao, Bing; Yang, Hui

2016-12-01

This paper presents a novel physics-driven spatiotemporal regularization (STRE) method for high-dimensional predictive modeling in complex healthcare systems. This model not only captures the physics-based interrelationship between time-varying explanatory and response variables that are distributed in the space, but also addresses the spatial and temporal regularizations to improve the prediction performance. The STRE model is implemented to predict the time-varying distribution of electric potentials on the heart surface based on the electrocardiogram (ECG) data from the distributed sensor network placed on the body surface. The model performance is evaluated and validated in both a simulated two-sphere geometry and a realistic torso-heart geometry. Experimental results show that the STRE model significantly outperforms other regularization models that are widely used in current practice such as Tikhonov zero-order, Tikhonov first-order and L1 first-order regularization methods.

Model Predictive Control of LCL Three-level Photovoltaic Grid-connected Inverter

NASA Astrophysics Data System (ADS)

Liang, Cheng; Tian, Engang; Pang, Baobing; Li, Juan; Yang, Yang

2018-05-01

In this paper, neutral point clamped three-level inverter circuit is analyzed to establish a mathematical model of the three-level inverter in the αβ coordinate system. The causes and harms of the midpoint potential imbalance problem are described. The paper use the method of model predictive control to control the entire inverter circuit[1]. The simulation model of the inverter system is built in Matlab/Simulink software. It is convenient to control the grid-connected current, suppress the unbalance of the midpoint potential and reduce the switching frequency by changing the weight coefficient in the cost function. The superiority of the model predictive control in the control method of the inverter system is verified.

Covariant spectator theory of np scattering: Deuteron quadrupole moment

DOE PAGES

Gross, Franz

2015-01-26

The deuteron quadrupole moment is calculated using two CST model wave functions obtained from the 2007 high precision fits to np scattering data. Included in the calculation are a new class of isoscalar np interaction currents automatically generated by the nuclear force model used in these fits. The prediction for model WJC-1, with larger relativistic P-state components, is 2.5% smaller that the experiential result, in common with the inability of models prior to 2014 to predict this important quantity. However, model WJC-2, with very small P-state components, gives agreement to better than 1%, similar to the results obtained recently frommore » XEFT predictions to order N 3LO.« less

Anisotropic constitutive modeling for nickel base single crystal superalloys using a crystallographic approach

NASA Technical Reports Server (NTRS)

Stouffer, D. C.; Sheh, M. Y.

1988-01-01

A micromechanical model based on crystallographic slip theory was formulated for nickel-base single crystal superalloys. The current equations include both drag stress and back stress state variables to model the local inelastic flow. Specially designed experiments have been conducted to evaluate the effect of back stress in single crystals. The results showed that (1) the back stress is orientation dependent; and (2) the back stress state variable in the inelastic flow equation is necessary for predicting anelastic behavior of the material. The model also demonstrated improved fatigue predictive capability. Model predictions and experimental data are presented for single crystal superalloy Rene N4 at 982 C.

Measurement Error and Bias in Value-Added Models. Research Report. ETS RR-17-25

ERIC Educational Resources Information Center

Kane, Michael T.

2017-01-01

By aggregating residual gain scores (the differences between each student's current score and a predicted score based on prior performance) for a school or a teacher, value-added models (VAMs) can be used to generate estimates of school or teacher effects. It is known that random errors in the prior scores will introduce bias into predictions of…

Dynamic model predicting overweight, obesity, and extreme obesity prevalence trends.

PubMed

Thomas, Diana M; Weedermann, Marion; Fuemmeler, Bernard F; Martin, Corby K; Dhurandhar, Nikhil V; Bredlau, Carl; Heymsfield, Steven B; Ravussin, Eric; Bouchard, Claude

2014-02-01

Obesity prevalence in the United States appears to be leveling, but the reasons behind the plateau remain unknown. Mechanistic insights can be provided from a mathematical model. The objective of this study is to model known multiple population parameters associated with changes in body mass index (BMI) classes and to establish conditions under which obesity prevalence will plateau. A differential equation system was developed that predicts population-wide obesity prevalence trends. The model considers both social and nonsocial influences on weight gain, incorporates other known parameters affecting obesity trends, and allows for country specific population growth. The dynamic model predicts that: obesity prevalence is a function of birthrate and the probability of being born in an obesogenic environment; obesity prevalence will plateau independent of current prevention strategies; and the US prevalence of overweight, obesity, and extreme obesity will plateau by about 2030 at 28%, 32%, and 9% respectively. The US prevalence of obesity is stabilizing and will plateau, independent of current preventative strategies. This trend has important implications in accurately evaluating the impact of various anti-obesity strategies aimed at reducing obesity prevalence. Copyright © 2013 The Obesity Society.

Stem mortality in surface fires: Part II, experimental methods for characterizing the thermal response of tree stems to heating by fires

Treesearch

D. M. Jimenez; B. W. Butler; J. Reardon

2003-01-01

Current methods for predicting fire-induced plant mortality in shrubs and trees are largely empirical. These methods are not readily linked to duff burning, soil heating, and surface fire behavior models. In response to the need for a physics-based model of this process, a detailed model for predicting the temperature distribution through a tree stem as a function of...

Predictive Modeling of the CDRA 4BMS

NASA Technical Reports Server (NTRS)

Coker, Robert; Knox, James

2016-01-01

Fully predictive models of the Four Bed Molecular Sieve of the Carbon Dioxide Removal Assembly on the International Space Station are being developed. This virtual laboratory will be used to help reduce mass, power, and volume requirements for future missions. In this paper we describe current and planned modeling developments in the area of carbon dioxide removal to support future crewed Mars missions as well as the resolution of anomalies observed in the ISS CDRA.

Studies on a Novel Neuro-dynamic Model for Prediction Learning of Fluctuated Data Streams: Beyond Dichotomy between Probabilistic and Deterministic Models

DTIC Science & Technology

2014-11-04

learning by robots as well as video image understanding by accumulated learning of the exemplars are discussed. 15. SUBJECT TERMS Cognitive ...learning to predict perceptual streams or encountering events by acquiring internal models is indispensable for intelligent or cognitive systems because...various cognitive functions are based on this compentency including goal-directed planning, mental simulation and recognition of the current situation

Computer Models of Personality: Implications for Measurement

ERIC Educational Resources Information Center

Cranton, P. A.

1976-01-01

Current research on computer models of personality is reviewed and categorized under five headings: (1) models of belief systems; (2) models of interpersonal behavior; (3) models of decision-making processes; (4) prediction models; and (5) theory-based simulations of specific processes. The use of computer models in personality measurement is…

Monitoring apparatus and method for battery power supply

DOEpatents

Martin, Harry L.; Goodson, Raymond E.

1983-01-01

A monitoring apparatus and method are disclosed for monitoring and/or indicating energy that a battery power source has then remaining and/or can deliver for utilization purposes as, for example, to an electric vehicle. A battery mathematical model forms the basis for monitoring with a capacity prediction determined from measurement of the discharge current rate and stored battery parameters. The predicted capacity is used to provide a state-of-charge indication. Self-calibration over the life of the battery power supply is enacted through use of a feedback voltage based upon the difference between predicted and measured voltages to correct the battery mathematical model. Through use of a microprocessor with central information storage of temperature, current and voltage, system behavior is monitored, and system flexibility is enhanced.

Promises of Machine Learning Approaches in Prediction of Absorption of Compounds.

PubMed

Kumar, Rajnish; Sharma, Anju; Siddiqui, Mohammed Haris; Tiwari, Rajesh Kumar

2018-01-01

The Machine Learning (ML) is one of the fastest developing techniques in the prediction and evaluation of important pharmacokinetic properties such as absorption, distribution, metabolism and excretion. The availability of a large number of robust validation techniques for prediction models devoted to pharmacokinetics has significantly enhanced the trust and authenticity in ML approaches. There is a series of prediction models generated and used for rapid screening of compounds on the basis of absorption in last one decade. Prediction of absorption of compounds using ML models has great potential across the pharmaceutical industry as a non-animal alternative to predict absorption. However, these prediction models still have to go far ahead to develop the confidence similar to conventional experimental methods for estimation of drug absorption. Some of the general concerns are selection of appropriate ML methods and validation techniques in addition to selecting relevant descriptors and authentic data sets for the generation of prediction models. The current review explores published models of ML for the prediction of absorption using physicochemical properties as descriptors and their important conclusions. In addition, some critical challenges in acceptance of ML models for absorption are also discussed. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Multivariate methods for evaluating the efficiency of electrodialytic removal of heavy metals from polluted harbour sediments.

PubMed

Pedersen, Kristine Bondo; Kirkelund, Gunvor M; Ottosen, Lisbeth M; Jensen, Pernille E; Lejon, Tore

2015-01-01

Chemometrics was used to develop a multivariate model based on 46 previously reported electrodialytic remediation experiments (EDR) of five different harbour sediments. The model predicted final concentrations of Cd, Cu, Pb and Zn as a function of current density, remediation time, stirring rate, dry/wet sediment, cell set-up as well as sediment properties. Evaluation of the model showed that remediation time and current density had the highest comparative influence on the clean-up levels. Individual models for each heavy metal showed variance in the variable importance, indicating that the targeted heavy metals were bound to different sediment fractions. Based on the results, a PLS model was used to design five new EDR experiments of a sixth sediment to achieve specified clean-up levels of Cu and Pb. The removal efficiencies were up to 82% for Cu and 87% for Pb and the targeted clean-up levels were met in four out of five experiments. The clean-up levels were better than predicted by the model, which could hence be used for predicting an approximate remediation strategy; the modelling power will however improve with more data included. Copyright © 2014 Elsevier B.V. All rights reserved.

Computational model of chromosome aberration yield induced by high- and low-LET radiation exposures.

PubMed

Ponomarev, Artem L; George, Kerry; Cucinotta, Francis A

2012-06-01

We present a computational model for calculating the yield of radiation-induced chromosomal aberrations in human cells based on a stochastic Monte Carlo approach and calibrated using the relative frequencies and distributions of chromosomal aberrations reported in the literature. A previously developed DNA-fragmentation model for high- and low-LET radiation called the NASARadiationTrackImage model was enhanced to simulate a stochastic process of the formation of chromosomal aberrations from DNA fragments. The current version of the model gives predictions of the yields and sizes of translocations, dicentrics, rings, and more complex-type aberrations formed in the G(0)/G(1) cell cycle phase during the first cell division after irradiation. As the model can predict smaller-sized deletions and rings (<3 Mbp) that are below the resolution limits of current cytogenetic analysis techniques, we present predictions of hypothesized small deletions that may be produced as a byproduct of properly repaired DNA double-strand breaks (DSB) by nonhomologous end-joining. Additionally, the model was used to scale chromosomal exchanges in two or three chromosomes that were obtained from whole-chromosome FISH painting analysis techniques to whole-genome equivalent values.

Frequency domain model for analysis of paralleled, series-output-connected Mapham inverters

NASA Technical Reports Server (NTRS)

Brush, Andrew S.; Sundberg, Richard C.; Button, Robert M.

1989-01-01

The Mapham resonant inverter is characterized as a two-port network driven by a selected periodic voltage. The two-port model is then used to model a pair of Mapham inverters connected in series and employing phasor voltage regulation. It is shown that the model is useful for predicting power output in paralleled inverter units, and for predicting harmonic current output of inverter pairs, using standard power flow techniques. Some sample results are compared to data obtained from testing hardware inverters.

Frequency domain model for analysis of paralleled, series-output-connected Mapham inverters

NASA Technical Reports Server (NTRS)

Brush, Andrew S.; Sundberg, Richard C.; Button, Robert M.

1989-01-01

The Mapham resonant inverter is characterized as a two-port network driven by a selected periodic voltage. The two-port model is then used to model a pair of Mapham inverters connected in series and employing phasor voltage regulation. It is shown that the model is useful for predicting power output in paralleled inverter units, and for predicting harmonic current output of inverter pairs, using standard power flow techniques. Some examples are compared to data obtained from testing hardware inverters.

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

PubMed Central

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

2017-01-01

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

Electron beam induced current in the high injection regime.

PubMed

Haney, Paul M; Yoon, Heayoung P; Koirala, Prakash; Collins, Robert W; Zhitenev, Nikolai B

2015-07-24

Electron beam induced current (EBIC) is a powerful technique which measures the charge collection efficiency of photovoltaics with sub-micron spatial resolution. The exciting electron beam results in a high generation rate density of electron-hole pairs, which may drive the system into nonlinear regimes. An analytic model is presented which describes the EBIC response when the total electron-hole pair generation rate exceeds the rate at which carriers are extracted by the photovoltaic cell, and charge accumulation and screening occur. The model provides a simple estimate of the onset of the high injection regime in terms of the material resistivity and thickness, and provides a straightforward way to predict the EBIC lineshape in the high injection regime. The model is verified by comparing its predictions to numerical simulations in one- and two-dimensions. Features of the experimental data, such as the magnitude and position of maximum collection efficiency versus electron beam current, are consistent with the three-dimensional model.

Multispacecraft Observations and Modeling of the 22/23 June 2015 Geomagnetic Storm

NASA Technical Reports Server (NTRS)

Reiff, P. H.; Daou, A. G.; Sazykin, S. Y.; Nakamura, R.; Hairston, M. R.; Coffey, V.; Chandler, M. O.; Anderson, B. J.; Russell, C. T.; Welling, D.;

Response repetition biases in human perceptual decisions are explained by activity decay in competitive attractor models

PubMed Central

Bonaiuto, James J; de Berker, Archy; Bestmann, Sven

2016-01-01

Animals and humans have a tendency to repeat recent choices, a phenomenon known as choice hysteresis. The mechanism for this choice bias remains unclear. Using an established, biophysically informed model of a competitive attractor network for decision making, we found that decaying tail activity from the previous trial caused choice hysteresis, especially during difficult trials, and accurately predicted human perceptual choices. In the model, choice variability could be directionally altered through amplification or dampening of post-trial activity decay through simulated depolarizing or hyperpolarizing network stimulation. An analogous intervention using transcranial direct current stimulation (tDCS) over left dorsolateral prefrontal cortex (dlPFC) yielded a close match between model predictions and experimental results: net soma depolarizing currents increased choice hysteresis, while hyperpolarizing currents suppressed it. Residual activity in competitive attractor networks within dlPFC may thus give rise to biases in perceptual choices, which can be directionally controlled through non-invasive brain stimulation. DOI: http://dx.doi.org/10.7554/eLife.20047.001 PMID:28005007

A review of the ionospheric model for the long wave prediction capability

NASA Astrophysics Data System (ADS)

Ferguson, J. A.

1992-11-01

The Naval Command, Control, and Ocean Surveillance Center's Long Wave Prediction Capability (LWPC) has a built-in ionospheric model. The latter was defined after a review of the literature comparing measurements with calculations. Subsequent to this original specification of the ionospheric model in the LWPC, a new collection of data were obtained and analyzed. The new data were collected aboard a merchant ship named the Callaghan during a series of trans-Atlantic trips over a period of a year. This report presents a detailed analysis of the ionospheric model currently in use by the LWPC and the new model suggested by the shipboard measurements. We conclude that, although the fits to measurements are almost the same between the two models examined, the current LWPC model should be used because it is better than the new model for nighttime conditions at long ranges. This conclusion supports the primary use of the LWPC model for coverage assessment that requires a valid model at the limits of a transmitter's reception.

An improved large signal model of InP HEMTs

NASA Astrophysics Data System (ADS)

Li, Tianhao; Li, Wenjun; Liu, Jun

2018-05-01

An improved large signal model for InP HEMTs is proposed in this paper. The channel current and charge model equations are constructed based on the Angelov model equations. Both the equations for channel current and gate charge models were all continuous and high order drivable, and the proposed gate charge model satisfied the charge conservation. For the strong leakage induced barrier reduction effect of InP HEMTs, the Angelov current model equations are improved. The channel current model could fit DC performance of devices. A 2 × 25 μm × 70 nm InP HEMT device is used to demonstrate the extraction and validation of the model, in which the model has predicted the DC I–V, C–V and bias related S parameters accurately. Project supported by the National Natural Science Foundation of China (No. 61331006).

An Online Prediction Platform to Support the Environmental ...

EPA Pesticide Factsheets

Historical QSAR models are currently utilized across a broad range of applications within the U.S. Environmental Protection Agency (EPA). These models predict basic physicochemical properties (e.g., logP, aqueous solubility, vapor pressure), which are then incorporated into exposure, fate and transport models. Whereas the classical manner of publishing results in peer-reviewed journals remains appropriate, there are substantial benefits to be gained by providing enhanced, open access to the training data sets and resulting models. Benefits include improved transparency, more flexibility to expand training sets and improve model algorithms, and greater ability to independently characterize model performance both globally and in local areas of chemistry. We have developed a web-based prediction platform that uses open-source descriptors and modeling algorithms, employs modern cheminformatics technologies, and is tailored for ease of use by the toxicology and environmental regulatory community. This tool also provides web-services to meet both EPA’s projects and the modeling community at-large. The platform hosts models developed within EPA’s National Center for Computational Toxicology, as well as those developed by other EPA scientists and the outside scientific community. Recognizing that there are other on-line QSAR model platforms currently available which have additional capabilities, we connect to such services, where possible, to produce an integrated

Validation of the kinetic-turbulent-neoclassical theory for edge intrinsic rotation in DIII-D

NASA Astrophysics Data System (ADS)

Ashourvan, Arash; Grierson, B. A.; Battaglia, D. J.; Haskey, S. R.; Stoltzfus-Dueck, T.

2018-05-01

In a recent kinetic model of edge main-ion (deuterium) toroidal velocity, intrinsic rotation results from neoclassical orbits in an inhomogeneous turbulent field [T. Stoltzfus-Dueck, Phys. Rev. Lett. 108, 065002 (2012)]. This model predicts a value for the toroidal velocity that is co-current for a typical inboard X-point plasma at the core-edge boundary (ρ ˜ 0.9). Using this model, the velocity prediction is tested on the DIII-D tokamak for a database of L-mode and H-mode plasmas with nominally low neutral beam torque, including both signs of plasma current. Values for the flux-surface-averaged main-ion rotation velocity in the database are obtained from the impurity carbon rotation by analytically calculating the main-ion—impurity neoclassical offset. The deuterium rotation obtained in this manner has been validated by direct main-ion measurements for a limited number of cases. Key theoretical parameters of ion temperature and turbulent scale length are varied across a wide range in an experimental database of discharges. Using a characteristic electron temperature scale length as a proxy for a turbulent scale length, the predicted main-ion rotation velocity has a general agreement with the experimental measurements for neutral beam injection (NBI) powers in the range PNBI < 4 MW. At higher NBI power, the experimental rotation is observed to saturate and even degrade compared to theory. TRANSP-NUBEAM simulations performed for the database show that for discharges with nominally balanced—but high powered—NBI, the net injected torque through the edge can exceed 1 Nm in the counter-current direction. The theory model has been extended to compute the rotation degradation from this counter-current NBI torque by solving a reduced momentum evolution equation for the edge and found the revised velocity prediction to be in agreement with experiment. Using the theory modeled—and now tested—velocity to predict the bulk plasma rotation opens up a path to more confidently projecting the confinement and stability in ITER.

Reef-coral refugia in a rapidly changing ocean.

PubMed

Cacciapaglia, Chris; van Woesik, Robert

2015-06-01

This study sought to identify climate-change thermal-stress refugia for reef corals in the Indian and Pacific Oceans. A species distribution modeling approach was used to identify refugia for 12 coral species that differed considerably in their local response to thermal stress. We hypothesized that the local response of coral species to thermal stress might be similarly reflected as a regional response to climate change. We assessed the contemporary geographic range of each species and determined their temperature and irradiance preferences using a k-fold algorithm to randomly select training and evaluation sites. That information was applied to downscaled outputs of global climate models to predict where each species is likely to exist by the year 2100. Our model was run with and without a 1°C capacity to adapt to the rising ocean temperature. The results show a positive exponential relationship between the current area of habitat that coral species occupy and the predicted area of habitat that they will occupy by 2100. There was considerable decoupling between scales of response, however, and with further ocean warming some 'winners' at local scales will likely become 'losers' at regional scales. We predicted that nine of the 12 species examined will lose 24-50% of their current habitat. Most reductions are predicted to occur between the latitudes 5-15°, in both hemispheres. Yet when we modeled a 1°C capacity to adapt, two ubiquitous species, Acropora hyacinthus and Acropora digitifera, were predicted to retain much of their current habitat. By contrast, the thermally tolerant Porites lobata is expected to increase its current distribution by 14%, particularly southward along the east and west coasts of Australia. Five areas were identified as Indian Ocean refugia, and seven areas were identified as Pacific Ocean refugia for reef corals under climate change. All 12 of these reef-coral refugia deserve high-conservation status. © 2015 John Wiley & Sons Ltd.

Exploring predictive performance: A reanalysis of the geospace model transition challenge

NASA Astrophysics Data System (ADS)

Welling, D. T.; Anderson, B. J.; Crowley, G.; Pulkkinen, A. A.; Rastätter, L.

2017-01-01

The Pulkkinen et al. (2013) study evaluated the ability of five different geospace models to predict surface dB/dt as a function of upstream solar drivers. This was an important step in the assessment of research models for predicting and ultimately preventing the damaging effects of geomagnetically induced currents. Many questions remain concerning the capabilities of these models. This study presents a reanalysis of the Pulkkinen et al. (2013) results in an attempt to better understand the models' performance. The range of validity of the models is determined by examining the conditions corresponding to the empirical input data. It is found that the empirical conductance models on which global magnetohydrodynamic models rely are frequently used outside the limits of their input data. The prediction error for the models is sorted as a function of solar driving and geomagnetic activity. It is found that all models show a bias toward underprediction, especially during active times. These results have implications for future research aimed at improving operational forecast models.

Cranial electrotherapy stimulation and transcranial pulsed current stimulation: a computer based high-resolution modeling study.

PubMed

Datta, Abhishek; Dmochowski, Jacek P; Guleyupoglu, Berkan; Bikson, Marom; Fregni, Felipe

2013-01-15

The field of non-invasive brain stimulation has developed significantly over the last two decades. Though two techniques of noninvasive brain stimulation--transcranial direct current stimulation (tDCS) and transcranial magnetic stimulation (TMS)--are becoming established tools for research in neuroscience and for some clinical applications, related techniques that also show some promising clinical results have not been developed at the same pace. One of these related techniques is cranial electrotherapy stimulation (CES), a class of transcranial pulsed current stimulation (tPCS). In order to understand further the mechanisms of CES, we aimed to model CES using a magnetic resonance imaging (MRI)-derived finite element head model including cortical and also subcortical structures. Cortical electric field (current density) peak intensities and distributions were analyzed. We evaluated different electrode configurations of CES including in-ear and over-ear montages. Our results confirm that significant amounts of current pass the skull and reach cortical and subcortical structures. In addition, depending on the montage, induced currents at subcortical areas, such as midbrain, pons, thalamus and hypothalamus are of similar magnitude than that of cortical areas. Incremental variations of electrode position on the head surface also influence which cortical regions are modulated. The high-resolution modeling predictions suggest that details of electrode montage influence current flow through superficial and deep structures. Finally we present laptop based methods for tPCS dose design using dominant frequency and spherical models. These modeling predictions and tools are the first step to advance rational and optimized use of tPCS and CES. Copyright © 2012 Elsevier Inc. All rights reserved.

Modelling the current distribution and predicted spread of the flea species Ctenocephalides felis infesting outdoor dogs in Spain.

PubMed

Gálvez, Rosa; Musella, Vicenzo; Descalzo, Miguel A; Montoya, Ana; Checa, Rocío; Marino, Valentina; Martín, Oihane; Cringoli, Giuseppe; Rinaldi, Laura; Miró, Guadalupe

2017-09-19

The cat flea, Ctenocephalides felis, is the most prevalent flea species detected on dogs and cats in Europe and other world regions. The status of flea infestation today is an evident public health concern because of their cosmopolitan distribution and the flea-borne diseases transmission. This study determines the spatial distribution of the cat flea C. felis infesting dogs in Spain. Using geospatial tools, models were constructed based on entomological data collected from dogs during the period 2013-2015. Bioclimatic zones, covering broad climate and vegetation ranges, were surveyed in relation to their size. The models builded were obtained by negative binomial regression of several environmental variables to show impacts on C. felis infestation prevalence: land cover, bioclimatic zone, mean summer and autumn temperature, mean summer rainfall, distance to urban settlement and normalized difference vegetation index. In the face of climate change, we also simulated the future distributions of C. felis for the global climate model (GCM) "GFDL-CM3" and for the representative concentration pathway RCP45, which predicts their spread in the country. Predictive models for current climate conditions indicated the widespread distribution of C. felis throughout Spain, mainly across the central northernmost zone of the mainland. Under predicted conditions of climate change, the risk of spread was slightly greater, especially in the north and central peninsula, than for the current situation. The data provided will be useful for local veterinarians to design effective strategies against flea infestation and the pathogens transmitted by these arthropods.

Operational Dust Prediction

NASA Technical Reports Server (NTRS)

Benedetti, Angela; Baldasano, Jose M.; Basart, Sara; Benincasa, Francesco; Boucher, Olivier; Brooks, Malcolm E.; Chen, Jen-Ping; Colarco, Peter R.; Gong, Sunlin; Huneeus, Nicolas;

Comparison of measured ozone in southeastern Virginia with computer predictions from a photochemical model

NASA Technical Reports Server (NTRS)

Wakelyn, N. T.; Gregory, G. L.

1980-01-01

Data for one day of the 1977 southeastern Virginia urban plume study are compared with computer predictions from a traveling air parcel model using a contemporary photochemical mechanism with a minimal description of nonmethane hydrocarbon (NMHC) constitution and chemistry. With measured initial NOx and O3 concentrations and a current separate estimate of urban source loading input to the model, and for a variation of initial NMHC over a reasonable range, an ozone increase over the day is predicted from the photochemical simulation which is consistent with the flight path averaged airborne data.

The Linear Predictability of Sea Level: A Benchmark

NASA Astrophysics Data System (ADS)

Sonnewald, M.; Wunsch, C.; Heimbach, P.

2016-12-01

A benchmark of linear predictive skill of global sea level is presented, complimenting more complicated model studies of future predictive skill. Sea level is of great socioeconomic interest, as most of the worlds population live by the sea. Currently, the spread in model projections suggests poor predictive skill outside the seasonal cycle. We use 20 years of data from the ECCOv4 state estimate (1992-2012), assessing the variance attributable to the seasons and the linear predictability potential of the deseasoned component of sea level. The Northern Hemisphere has large regions where the seasons make up >90% of the variance, particularly in the western boundary current regions and zonal bands along the equator. The deaseasoned sea level is more dominant in the Southern Hemisphere, particularly in the Southern Ocean. We treat the deseasoned sea level as a weakly stationary random process, whose predictability is given by the covariance structure. Fitting an ARMA(n,m) model, we choose the order using the Akaike and Bayesian Information Criteria (AIC and BIC). The AIC is more appropriate, with generally higher orders chosen and offering slightly more predictive accuracy. Monthly detrended data shows skill generally of the order of a few months, with isolated regions of twelve months or more. With the trend, the predictive skill increases, particularly in the South Pacific. We assess the annually averaged data, although our time-series is too short to assess the variability. There is some predictive skill, which is enhanced if the trend is not removed. A major caveat of our approach is that we test and train our model on the same dataset due to the short duration of available data.

Capabilities of current wildfire models when simulating topographical flow

NASA Astrophysics Data System (ADS)

Kochanski, A.; Jenkins, M.; Krueger, S. K.; McDermott, R.; Mell, W.

2009-12-01

Accurate predictions of the growth, spread and suppression of wild fires rely heavily on the correct prediction of the local wind conditions and the interactions between the fire and the local ambient airflow. Resolving local flows, often strongly affected by topographical features like hills, canyons and ridges, is a prerequisite for accurate simulation and prediction of fire behaviors. In this study, we present the results of high-resolution numerical simulations of the flow over a smooth hill, performed using (1) the NIST WFDS (WUI or Wildland-Urban-Interface version of the FDS or Fire Dynamic Simulator), and (2) the LES version of the NCAR Weather Research and Forecasting (WRF-LES) model. The WFDS model is in the initial stages of development for application to wind flow and fire spread over complex terrain. The focus of the talk is to assess how well simple topographical flow is represented by WRF-LES and the current version of WFDS. If sufficient progress has been made prior to the meeting then the importance of the discrepancies between the predicted and measured winds, in terms of simulated fire behavior, will be examined.

Computational modeling of membrane proteins

PubMed Central

Leman, Julia Koehler; Ulmschneider, Martin B.; Gray, Jeffrey J.

2014-01-01

The determination of membrane protein (MP) structures has always trailed that of soluble proteins due to difficulties in their overexpression, reconstitution into membrane mimetics, and subsequent structure determination. The percentage of MP structures in the protein databank (PDB) has been at a constant 1-2% for the last decade. In contrast, over half of all drugs target MPs, only highlighting how little we understand about drug-specific effects in the human body. To reduce this gap, researchers have attempted to predict structural features of MPs even before the first structure was experimentally elucidated. In this review, we present current computational methods to predict MP structure, starting with secondary structure prediction, prediction of trans-membrane spans, and topology. Even though these methods generate reliable predictions, challenges such as predicting kinks or precise beginnings and ends of secondary structure elements are still waiting to be addressed. We describe recent developments in the prediction of 3D structures of both α-helical MPs as well as β-barrels using comparative modeling techniques, de novo methods, and molecular dynamics (MD) simulations. The increase of MP structures has (1) facilitated comparative modeling due to availability of more and better templates, and (2) improved the statistics for knowledge-based scoring functions. Moreover, de novo methods have benefitted from the use of correlated mutations as restraints. Finally, we outline current advances that will likely shape the field in the forthcoming decade. PMID:25355688

On the dynamical mechanisms of influence of synaptic currents on the neuron model with response differentiation

NASA Astrophysics Data System (ADS)

Zakharov, D. G.; Kuznetsov, A. S.

2015-08-01

The combined effect of synaptic NMDA, AMPA, and GABA currents on the neuron model with response differentiation has been considered. It has been shown that the GABA and NMDA currents can compensate the effects of each other, whereas the AMPA current not only leads to the suppression of oscillations but also significantly amplifies the high-frequency activity of the neuron induced by the NMDA current. Two bifurcation scenarios underlying these effects have been revealed. It has been predicted which scenario takes place under the combined influence of all three currents.

Prediction and assimilation of surf-zone processes using a Bayesian network: Part I: Forward models

USGS Publications Warehouse

Plant, Nathaniel G.; Holland, K. Todd

2011-01-01

Prediction of coastal processes, including waves, currents, and sediment transport, can be obtained from a variety of detailed geophysical-process models with many simulations showing significant skill. This capability supports a wide range of research and applied efforts that can benefit from accurate numerical predictions. However, the predictions are only as accurate as the data used to drive the models and, given the large temporal and spatial variability of the surf zone, inaccuracies in data are unavoidable such that useful predictions require corresponding estimates of uncertainty. We demonstrate how a Bayesian-network model can be used to provide accurate predictions of wave-height evolution in the surf zone given very sparse and/or inaccurate boundary-condition data. The approach is based on a formal treatment of a data-assimilation problem that takes advantage of significant reduction of the dimensionality of the model system. We demonstrate that predictions of a detailed geophysical model of the wave evolution are reproduced accurately using a Bayesian approach. In this surf-zone application, forward prediction skill was 83%, and uncertainties in the model inputs were accurately transferred to uncertainty in output variables. We also demonstrate that if modeling uncertainties were not conveyed to the Bayesian network (i.e., perfect data or model were assumed), then overly optimistic prediction uncertainties were computed. More consistent predictions and uncertainties were obtained by including model-parameter errors as a source of input uncertainty. Improved predictions (skill of 90%) were achieved because the Bayesian network simultaneously estimated optimal parameters while predicting wave heights.

Towards a whole-cell modeling approach for synthetic biology

NASA Astrophysics Data System (ADS)

Purcell, Oliver; Jain, Bonny; Karr, Jonathan R.; Covert, Markus W.; Lu, Timothy K.

2013-06-01

Despite rapid advances over the last decade, synthetic biology lacks the predictive tools needed to enable rational design. Unlike established engineering disciplines, the engineering of synthetic gene circuits still relies heavily on experimental trial-and-error, a time-consuming and inefficient process that slows down the biological design cycle. This reliance on experimental tuning is because current modeling approaches are unable to make reliable predictions about the in vivo behavior of synthetic circuits. A major reason for this lack of predictability is that current models view circuits in isolation, ignoring the vast number of complex cellular processes that impinge on the dynamics of the synthetic circuit and vice versa. To address this problem, we present a modeling approach for the design of synthetic circuits in the context of cellular networks. Using the recently published whole-cell model of Mycoplasma genitalium, we examined the effect of adding genes into the host genome. We also investigated how codon usage correlates with gene expression and find agreement with existing experimental results. Finally, we successfully implemented a synthetic Goodwin oscillator in the whole-cell model. We provide an updated software framework for the whole-cell model that lays the foundation for the integration of whole-cell models with synthetic gene circuit models. This software framework is made freely available to the community to enable future extensions. We envision that this approach will be critical to transforming the field of synthetic biology into a rational and predictive engineering discipline.

Estimating potential habitat for 134 eastern US tree species under six climate scenarios

Treesearch

Louis R. Iverson; Anantha M. Prasad; Stephen N. Matthews; Matthew Peters

2008-01-01

We modeled and mapped, using the predictive data mining tool Random Forests, 134 tree species from the eastern United States for potential response to several scenarios of climate change. Each species was modeled individually to show current and potential future habitats according to two emission scenarios (high emissions on current trajectory and reasonable...

Altered neural encoding of prediction errors in assault-related posttraumatic stress disorder.

PubMed

Ross, Marisa C; Lenow, Jennifer K; Kilts, Clinton D; Cisler, Josh M

2018-05-12

Posttraumatic stress disorder (PTSD) is widely associated with deficits in extinguishing learned fear responses, which relies on mechanisms of reinforcement learning (e.g., updating expectations based on prediction errors). However, the degree to which PTSD is associated with impairments in general reinforcement learning (i.e., outside of the context of fear stimuli) remains poorly understood. Here, we investigate brain and behavioral differences in general reinforcement learning between adult women with and without a current diagnosis of PTSD. 29 adult females (15 PTSD with exposure to assaultive violence, 14 controls) underwent a neutral reinforcement-learning task (i.e., two arm bandit task) during fMRI. We modeled participant behavior using different adaptations of the Rescorla-Wagner (RW) model and used Independent Component Analysis to identify timecourses for large-scale a priori brain networks. We found that an anticorrelated and risk sensitive RW model best fit participant behavior, with no differences in computational parameters between groups. Women in the PTSD group demonstrated significantly less neural encoding of prediction errors in both a ventral striatum/mPFC and anterior insula network compared to healthy controls. Weakened encoding of prediction errors in the ventral striatum/mPFC and anterior insula during a general reinforcement learning task, outside of the context of fear stimuli, suggests the possibility of a broader conceptualization of learning differences in PTSD than currently proposed in current neurocircuitry models of PTSD. Copyright © 2018 Elsevier Ltd. All rights reserved.

The prediction of the hydrodynamic performance of tidal current turbines

NASA Astrophysics Data System (ADS)

Y Xiao, B.; Zhou, L. J.; Xiao, Y. X.; Wang, Z. W.

2013-12-01

Nowadays tidal current energy is considered to be one of the most promising alternative green energy resources and tidal current turbines are used for power generation. Prediction of the open water performance around tidal turbines is important for the reason that it can give some advice on installation and array of tidal current turbines. This paper presents numerical computations of tidal current turbines by using a numerical model which is constructed to simulate an isolated turbine. This paper aims at studying the installation of marine current turbine of which the hydro-environmental impacts influence by means of numerical simulation. Such impacts include free-stream velocity magnitude, seabed and inflow direction of velocity. The results of the open water performance prediction show that the power output and efficiency of marine current turbine varies from different marine environments. The velocity distribution should be clearly and the suitable unit installation depth and direction be clearly chosen, which can ensure the most effective strategy for energy capture before installing the marine current turbine. The findings of this paper are expected to be beneficial in developing tidal current turbines and array in the future.

Comparison of two gas chromatograph models and analysis of binary data

NASA Technical Reports Server (NTRS)

Keba, P. S.; Woodrow, P. T.

1972-01-01

The overall objective of the gas chromatograph system studies is to generate fundamental design criteria and techniques to be used in the optimum design of the system. The particular tasks currently being undertaken are the comparison of two mathematical models of the chromatograph and the analysis of binary system data. The predictions of two mathematical models, an equilibrium absorption model and a non-equilibrium absorption model exhibit the same weaknesses in their inability to predict chromatogram spreading for certain systems. The analysis of binary data using the equilibrium absorption model confirms that, for the systems considered, superposition of predicted single component behaviors is a first order representation of actual binary data. Composition effects produce non-idealities which limit the rigorous validity of superposition.

Probing Supersymmetry with Neutral Current Scattering Experiments

NASA Astrophysics Data System (ADS)

Kurylov, A.; Ramsey-Musolf, M. J.; Su, S.

2004-02-01

We compute the supersymmetric contributions to the weak charges of the electron (QWe) and proton (QWp) in the framework of Minimal Supersymmetric Standard Model. We also consider the ratio of neutral current to charged current cross sections, R v and Rv¯ at v (v¯)-nucleus deep inelastic scattering, and compare the supersymmetric corrections with the deviations of these quantities from the Standard Model predictions implied by the recent NuTeV measurement.

Modeling of Elastic Collisions between High Energy and Slow Neutral Atoms

DTIC Science & Technology

2015-07-01

cylindrical test cell, and the currents on the four different electrodes-Inner Cylinder , Exit Plate, Back Aperture, and Collector Plat~were measured...Inner Cylinder electrode. Nevertheless, the neutral atom current to the Inner Cylinder electrode predicted by the VHS model is comparable to the...Figure 9. Normalized curre nt at the Inner Cylinder e lectrode. the point of collision. T he discrepancy in the Exit Plate neutral atom current is due to

A practical tool for estimating subsurface LNAPL distributions and transmissivity using current and historical fluid levels in groundwater wells: Effects of entrapped and residual LNAPL

NASA Astrophysics Data System (ADS)

Lenhard, R. J.; Rayner, J. L.; Davis, G. B.

2017-10-01

A model is presented to account for elevation-dependent residual and entrapped LNAPL above and below, respectively, the water-saturated zone when predicting subsurface LNAPL specific volume (fluid volume per unit area) and transmissivity from current and historic fluid levels in wells. Physically-based free, residual, and entrapped LNAPL saturation distributions and LNAPL relative permeabilities are integrated over a vertical slice of the subsurface to yield the LNAPL specific volumes and transmissivity. The model accounts for effects of fluctuating water tables. Hypothetical predictions are given for different porous media (loamy sand and clay loam), fluid levels in wells, and historic water-table fluctuations. It is shown the elevation range from the LNAPL-water interface in a well to the upper elevation where the free LNAPL saturation approaches zero is the same for a given LNAPL thickness in a well regardless of porous media type. Further, the LNAPL transmissivity is largely dependent on current fluid levels in wells and not historic levels. Results from the model can aid developing successful LNAPL remediation strategies and improving the design and operation of remedial activities. Results of the model also can aid in accessing the LNAPL recovery technology endpoint, based on the predicted transmissivity.

Dynamic prediction in functional concurrent regression with an application to child growth.

PubMed

Leroux, Andrew; Xiao, Luo; Crainiceanu, Ciprian; Checkley, William

2018-04-15

In many studies, it is of interest to predict the future trajectory of subjects based on their historical data, referred to as dynamic prediction. Mixed effects models have traditionally been used for dynamic prediction. However, the commonly used random intercept and slope model is often not sufficiently flexible for modeling subject-specific trajectories. In addition, there may be useful exposures/predictors of interest that are measured concurrently with the outcome, complicating dynamic prediction. To address these problems, we propose a dynamic functional concurrent regression model to handle the case where both the functional response and the functional predictors are irregularly measured. Currently, such a model cannot be fit by existing software. We apply the model to dynamically predict children's length conditional on prior length, weight, and baseline covariates. Inference on model parameters and subject-specific trajectories is conducted using the mixed effects representation of the proposed model. An extensive simulation study shows that the dynamic functional regression model provides more accurate estimation and inference than existing methods. Methods are supported by fast, flexible, open source software that uses heavily tested smoothing techniques. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Cardiovascular disease risk scores in the current practice: which to use in rheumatoid arthritis?

PubMed

Purcarea, A; Sovaila, S; Gheorghe, A; Udrea, G; Stoica, V

2014-01-01

Cardiovascular disease (CVD) is the highest prevalence disease in the general population (GP) and it accounts for 20 million deaths worldwide each year. Its prevalence is even higher in rheumatoid arthritis. Early detection of subclinical disease is critical and the use of cardiovascular risk prediction models and calculators is widely spread. The impact of such techniques in the GP was previously studied. Despite their common background and similarities, some disagreement exists between most scores and their importance in special high-risk populations like rheumatoid arthritis (RA), having a low level of evidence. The current article aims to single out those predictive models (models) that could be most useful in the care of rheumatoid arthritis patients.

Cardiovascular disease risk scores in the current practice: which to use in rheumatoid arthritis?

PubMed Central

Purcarea, A; Sovaila, S; Gheorghe, A; Udrea, G; Stoica, V

2014-01-01

Cardiovascular disease (CVD) is the highest prevalence disease in the general population (GP) and it accounts for 20 million deaths worldwide each year. Its prevalence is even higher in rheumatoid arthritis. Early detection of subclinical disease is critical and the use of cardiovascular risk prediction models and calculators is widely spread. The impact of such techniques in the GP was previously studied. Despite their common background and similarities, some disagreement exists between most scores and their importance in special high-risk populations like rheumatoid arthritis (RA), having a low level of evidence. The current article aims to single out those predictive models (models) that could be most useful in the care of rheumatoid arthritis patients. PMID:25713603

Moving beyond "sticks and stones": chronic psychological trauma predicts posttraumatic stress symptoms.

PubMed

Jeter, Whitney K; Brannon, Laura A

2014-01-01

To date, trauma research has focused on the impact of physical trauma on posttraumatic stress (PTS) symptoms. Sometimes psychological trauma is measured with instances of physical trauma; however, less is known about solely psychological trauma. The current study addresses this by examining psychological trauma and PTS symptoms using the chronic relational trauma (CRT) model. The CRT model examines physical and possible concurrent psychological childhood, peer, and intimate partner trauma; however, psychological trauma alone has yet to be tested. A total of 232 female undergraduates (M age = 18.32, SD = 1.60) completed a series of questionnaires. Structural equation modeling indicated that childhood, peer, and intimate partner psychological trauma predict current PTS symptoms. Contributions of these findings are discussed.

Predicting Ideological Prejudice

PubMed Central

Brandt, Mark J.

2017-01-01

A major shortcoming of current models of ideological prejudice is that although they can anticipate the direction of the association between participants’ ideology and their prejudice against a range of target groups, they cannot predict the size of this association. I developed and tested models that can make specific size predictions for this association. A quantitative model that used the perceived ideology of the target group as the primary predictor of the ideology-prejudice relationship was developed with a representative sample of Americans (N = 4,940) and tested against models using the perceived status of and choice to belong to the target group as predictors. In four studies (total N = 2,093), ideology-prejudice associations were estimated, and these observed estimates were compared with the models’ predictions. The model that was based only on perceived ideology was the most parsimonious with the smallest errors. PMID:28394693

A new model for predicting moisture uptake by packaged solid pharmaceuticals.

PubMed

Chen, Y; Li, Y

2003-04-14

A novel mathematical model has been developed for predicting moisture uptake by packaged solid pharmaceutical products during storage. High density polyethylene (HDPE) bottles containing the tablet products of two new chemical entities and desiccants are investigated. Permeability of the bottles is determined at different temperatures using steady-state data. Moisture sorption isotherms of the two model drug products and desiccants at the same temperatures are determined and expressed in polynomial equations. The isotherms are used for modeling the time-humidity profile in the container, which enables the prediction of the moisture content of individual component during storage. Predicted moisture contents agree well with real time stability data. The current model could serve as a guide during packaging selection for moisture protection, so as to reduce the cost and cycle time of screening study.

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.

Improving Computational Efficiency of Prediction in Model-Based Prognostics Using the Unscented Transform

NASA Technical Reports Server (NTRS)

Daigle, Matthew John; Goebel, Kai Frank

2010-01-01

Model-based prognostics captures system knowledge in the form of physics-based models of components, and how they fail, in order to obtain accurate predictions of end of life (EOL). EOL is predicted based on the estimated current state distribution of a component and expected profiles of future usage. In general, this requires simulations of the component using the underlying models. In this paper, we develop a simulation-based prediction methodology that achieves computational efficiency by performing only the minimal number of simulations needed in order to accurately approximate the mean and variance of the complete EOL distribution. This is performed through the use of the unscented transform, which predicts the means and covariances of a distribution passed through a nonlinear transformation. In this case, the EOL simulation acts as that nonlinear transformation. In this paper, we review the unscented transform, and describe how this concept is applied to efficient EOL prediction. As a case study, we develop a physics-based model of a solenoid valve, and perform simulation experiments to demonstrate improved computational efficiency without sacrificing prediction accuracy.

A Comparison of Two Models of Risky Sexual Behavior During Late Adolescence.

PubMed

Braje, Sopagna Eap; Eddy, J Mark; Hall, Gordon C N

2016-01-01

Two models of risky sexual behavior (RSB) were compared in a community sample of late adolescents (N = 223). For the traumagenic model, early negative sexual experiences were posited to lead to an association between negative affect with sexual relationships. For the cognitive escape model, depressive affect was posited to lead to engagement in RSB as a way to avoid negative emotions. The current study examined whether depression explained the relationship between sexual trauma and RSB, supporting the cognitive escape model, or whether it was sexual trauma that led specifically to RSB, supporting the traumagenic model. Physical trauma experiences were also examined to disentangle the effects of sexual trauma compared to other emotionally distressing events. The study examined whether the results would be moderated by participant sex. For males, support was found for the cognitive escape model but not the traumagenic model. Among males, physical trauma and depression predicted engagement in RSB but sexual trauma did not. For females, support was found for the traumagenic and cognitive escape model. Among females, depression and sexual trauma both uniquely predicted RSB. There was an additional suppressor effect of socioeconomic status in predicting RSB among females. Results suggest that the association of trauma type with RSB depends on participant sex. Implications of the current study for RSB prevention efforts are discussed.

Hierarchical Multi-Scale Approach To Validation and Uncertainty Quantification of Hyper-Spectral Image Modeling

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

Engel, David W.; Reichardt, Thomas A.; Kulp, Thomas J.

Validating predictive models and quantifying uncertainties inherent in the modeling process is a critical component of the HARD Solids Venture program [1]. Our current research focuses on validating physics-based models predicting the optical properties of solid materials for arbitrary surface morphologies and characterizing the uncertainties in these models. We employ a systematic and hierarchical approach by designing physical experiments and comparing the experimental results with the outputs of computational predictive models. We illustrate this approach through an example comparing a micro-scale forward model to an idealized solid-material system and then propagating the results through a system model to the sensormore » level. Our efforts should enhance detection reliability of the hyper-spectral imaging technique and the confidence in model utilization and model outputs by users and stakeholders.« less

Mammographic density, breast cancer risk and risk prediction

PubMed Central

Vachon, Celine M; van Gils, Carla H; Sellers, Thomas A; Ghosh, Karthik; Pruthi, Sandhya; Brandt, Kathleen R; Pankratz, V Shane

2007-01-01

In this review, we examine the evidence for mammographic density as an independent risk factor for breast cancer, describe the risk prediction models that have incorporated density, and discuss the current and future implications of using mammographic density in clinical practice. Mammographic density is a consistent and strong risk factor for breast cancer in several populations and across age at mammogram. Recently, this risk factor has been added to existing breast cancer risk prediction models, increasing the discriminatory accuracy with its inclusion, albeit slightly. With validation, these models may replace the existing Gail model for clinical risk assessment. However, absolute risk estimates resulting from these improved models are still limited in their ability to characterize an individual's probability of developing cancer. Promising new measures of mammographic density, including volumetric density, which can be standardized using full-field digital mammography, will likely result in a stronger risk factor and improve accuracy of risk prediction models. PMID:18190724

Numerical weather prediction model tuning via ensemble prediction system

NASA Astrophysics Data System (ADS)

Jarvinen, H.; Laine, M.; Ollinaho, P.; Solonen, A.; Haario, H.

2011-12-01

This paper discusses a novel approach to tune predictive skill of numerical weather prediction (NWP) models. NWP models contain tunable parameters which appear in parameterizations schemes of sub-grid scale physical processes. Currently, numerical values of these parameters are specified manually. In a recent dual manuscript (QJRMS, revised) we developed a new concept and method for on-line estimation of the NWP model parameters. The EPPES ("Ensemble prediction and parameter estimation system") method requires only minimal changes to the existing operational ensemble prediction infra-structure and it seems very cost-effective because practically no new computations are introduced. The approach provides an algorithmic decision making tool for model parameter optimization in operational NWP. In EPPES, statistical inference about the NWP model tunable parameters is made by (i) generating each member of the ensemble of predictions using different model parameter values, drawn from a proposal distribution, and (ii) feeding-back the relative merits of the parameter values to the proposal distribution, based on evaluation of a suitable likelihood function against verifying observations. In the presentation, the method is first illustrated in low-order numerical tests using a stochastic version of the Lorenz-95 model which effectively emulates the principal features of ensemble prediction systems. The EPPES method correctly detects the unknown and wrongly specified parameters values, and leads to an improved forecast skill. Second, results with an atmospheric general circulation model based ensemble prediction system show that the NWP model tuning capacity of EPPES scales up to realistic models and ensemble prediction systems. Finally, a global top-end NWP model tuning exercise with preliminary results is published.

Similarity-based prediction for Anatomical Therapeutic Chemical classification of drugs by integrating multiple data sources.

PubMed

Liu, Zhongyang; Guo, Feifei; Gu, Jiangyong; Wang, Yong; Li, Yang; Wang, Dan; Lu, Liang; Li, Dong; He, Fuchu

2015-06-01

Anatomical Therapeutic Chemical (ATC) classification system, widely applied in almost all drug utilization studies, is currently the most widely recognized classification system for drugs. Currently, new drug entries are added into the system only on users' requests, which leads to seriously incomplete drug coverage of the system, and bioinformatics prediction is helpful during this process. Here we propose a novel prediction model of drug-ATC code associations, using logistic regression to integrate multiple heterogeneous data sources including chemical structures, target proteins, gene expression, side-effects and chemical-chemical associations. The model obtains good performance for the prediction not only on ATC codes of unclassified drugs but also on new ATC codes of classified drugs assessed by cross-validation and independent test sets, and its efficacy exceeds previous methods. Further to facilitate the use, the model is developed into a user-friendly web service SPACE ( S: imilarity-based P: redictor of A: TC C: od E: ), which for each submitted compound, will give candidate ATC codes (ranked according to the decreasing probability_score predicted by the model) together with corresponding supporting evidence. This work not only contributes to knowing drugs' therapeutic, pharmacological and chemical properties, but also provides clues for drug repositioning and side-effect discovery. In addition, the construction of the prediction model also provides a general framework for similarity-based data integration which is suitable for other drug-related studies such as target, side-effect prediction etc. The web service SPACE is available at http://www.bprc.ac.cn/space. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Proposals for enhanced health risk assessment and stratification in an integrated care scenario

PubMed Central

Dueñas-Espín, Ivan; Vela, Emili; Pauws, Steffen; Bescos, Cristina; Cano, Isaac; Cleries, Montserrat; Contel, Joan Carles; de Manuel Keenoy, Esteban; Garcia-Aymerich, Judith; Gomez-Cabrero, David; Kaye, Rachelle; Lahr, Maarten M H; Lluch-Ariet, Magí; Moharra, Montserrat; Monterde, David; Mora, Joana; Nalin, Marco; Pavlickova, Andrea; Piera, Jordi; Ponce, Sara; Santaeugenia, Sebastià; Schonenberg, Helen; Störk, Stefan; Tegner, Jesper; Velickovski, Filip; Westerteicher, Christoph; Roca, Josep

2016-01-01

Objectives Population-based health risk assessment and stratification are considered highly relevant for large-scale implementation of integrated care by facilitating services design and case identification. The principal objective of the study was to analyse five health-risk assessment strategies and health indicators used in the five regions participating in the Advancing Care Coordination and Telehealth Deployment (ACT) programme (http://www.act-programme.eu). The second purpose was to elaborate on strategies toward enhanced health risk predictive modelling in the clinical scenario. Settings The five ACT regions: Scotland (UK), Basque Country (ES), Catalonia (ES), Lombardy (I) and Groningen (NL). Participants Responsible teams for regional data management in the five ACT regions. Primary and secondary outcome measures We characterised and compared risk assessment strategies among ACT regions by analysing operational health risk predictive modelling tools for population-based stratification, as well as available health indicators at regional level. The analysis of the risk assessment tool deployed in Catalonia in 2015 (GMAs, Adjusted Morbidity Groups) was used as a basis to propose how population-based analytics could contribute to clinical risk prediction. Results There was consensus on the need for a population health approach to generate health risk predictive modelling. However, this strategy was fully in place only in two ACT regions: Basque Country and Catalonia. We found marked differences among regions in health risk predictive modelling tools and health indicators, and identified key factors constraining their comparability. The research proposes means to overcome current limitations and the use of population-based health risk prediction for enhanced clinical risk assessment. Conclusions The results indicate the need for further efforts to improve both comparability and flexibility of current population-based health risk predictive modelling approaches. Applicability and impact of the proposals for enhanced clinical risk assessment require prospective evaluation. PMID:27084274

Configuration of the thermal landscape determines thermoregulatory performance of ectotherms

PubMed Central

Sears, Michael W.; Angilletta, Michael J.; Schuler, Matthew S.; Borchert, Jason; Dilliplane, Katherine F.; Stegman, Monica; Rusch, Travis W.; Mitchell, William A.

2016-01-01

Although most organisms thermoregulate behaviorally, biologists still cannot easily predict whether mobile animals will thermoregulate in natural environments. Current models fail because they ignore how the spatial distribution of thermal resources constrains thermoregulatory performance over space and time. To overcome this limitation, we modeled the spatially explicit movements of animals constrained by access to thermal resources. Our models predict that ectotherms thermoregulate more accurately when thermal resources are dispersed throughout space than when these resources are clumped. This prediction was supported by thermoregulatory behaviors of lizards in outdoor arenas with known distributions of environmental temperatures. Further, simulations showed how the spatial structure of the landscape qualitatively affects responses of animals to climate. Biologists will need spatially explicit models to predict impacts of climate change on local scales. PMID:27601639

The morphology of flare phenomena, magnetic fields, and electric currents in active regions. I - Introduction and methods

NASA Technical Reports Server (NTRS)

Canfield, Richard C.; De La Beaujardiere, J.-F.; Fan, Yuhong; Leka, K. D.; Mcclymont, A. N.; Metcalf, Thomas R.; Mickey, Donald L.; Wuelser, Jean-Pierre; Lites, Bruce W.

1993-01-01

Electric current systems in solar active regions and their spatial relationship to sites of electron precipitation and high-pressure in flares were studied with the purpose of providing observational evidence for or against the flare models commonly discussed in the literature. The paper describes the instrumentation, the data used, and the data analysis methods, as well as improvements made upon earlier studies. Several flare models are overviewed, and the predictions yielded by each model for the relationships of flares to the vertical current systems are discussed.

Rheumatology in the community of Madrid: current availability of rheumatologists and future needs using a predictive model.

PubMed

Lázaro y De Mercado, Pablo; Blasco Bravo, Antonio Javier; Lázaro y De Mercado, Ignacio; Castañeda, Santos; López Robledillo, Juan Carlos

2013-01-01

To: 1) describe the distribution of the public sector rheumatologists; 2) identify variables on which the workload in Rheumatology depends; and 3) build a predictive model on the need of rheumatologists for the next 10 years, in the Community of Madrid (CM). The information was obtained through structured questionnaires sent to all services/units of Rheumatology of public hospitals in the CM. The population figures, current and forecasted, were obtained from the National Statistics Institute. A predictive model was built based on information about the current and foreseeable supply, current and foreseeable demand, and the assumptions and criteria used to match supply with demand. The underlying uncertainty in the model was assessed by sensitivity analysis. In the CM in 2011 there were 150 staff rheumatologists and 49 residents in 27 centers, which is equivalent to one rheumatologist for every 33,280 inhabitants in the general population, and one for every 4,996 inhabitants over 65 years. To keep the level of assistance of 2011 in 2021 in the general population, it would be necessary to train more residents or hire more rheumatologists in scenarios of demand higher than 15%. However, to keep the level of assistance in the population over 65 years of age it would be necessary to train more residents or hire more specialists even without increased demand. The model developed may be very useful for planning, with the CM policy makers, the needs of human resources in Rheumatology in the coming years. Copyright © 2012 Elsevier España, S.L. All rights reserved.

A Gradually Varied Approach to Model Turbidity Currents in Submarine Channels

NASA Astrophysics Data System (ADS)

Bolla Pittaluga, M.; Frascati, A.; Falivene, O.

2018-01-01

We develop a one-dimensional model to describe the dynamics of turbidity current flowing in submarine channels. We consider the flow as a steady state polydisperse suspension accounting for water detrainment from the clear water-turbid interface, for spatial variations of the channel width and for water and sediment lateral overspill from the channel levees. Moreover, we account for sediment exchange with the bed extending the model to deal with situations where the current meets a nonerodible bed. Results show that when water detrainment is accounted for, the flow thickness becomes approximately constant proceeding downstream. Similarly, in the presence of channel levees, the flow tends to adjust to channel relief through the lateral loss of water and sediment. As more mud is spilled above the levees relative to sand, the flow becomes more sand rich proceeding downstream when lateral overspill is present. Velocity and flow thickness predicted by the model are then validated by showing good agreement with laboratory observations. Finally, the model is applied to the Monterey Canyon bathymetric data matching satisfactorily the December 2002 event field measurements and predicting a runout length consistent with observations.

An Additional Approach to Model Current Followers and Amplifiers with Electronically Controllable Parameters from Commercially Available ICs

NASA Astrophysics Data System (ADS)

Sotner, R.; Kartci, A.; Jerabek, J.; Herencsar, N.; Dostal, T.; Vrba, K.

2012-12-01

Several behavioral models of current active elements for experimental purposes are introduced in this paper. These models are based on commercially available devices. They are suitable for experimental tests of current- and mixed-mode filters, oscillators, and other circuits (employing current-mode active elements) frequently used in analog signal processing without necessity of onchip fabrication of proper active element. Several methods of electronic control of intrinsic resistance in the proposed behavioral models are discussed. All predictions and theoretical assumptions are supported by simulations and experiments. This contribution helps to find a cheaper and more effective way to preliminary laboratory tests without expensive on-chip fabrication of special active elements.

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

NASA Astrophysics Data System (ADS)

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

2015-12-01

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

Recent Achievements of the Collaboratory for the Study of Earthquake Predictability

NASA Astrophysics Data System (ADS)

Jordan, T. H.; Liukis, M.; Werner, M. J.; Schorlemmer, D.; Yu, J.; Maechling, P. J.; Jackson, D. D.; Rhoades, D. A.; Zechar, J. D.; Marzocchi, W.

2016-12-01

The Collaboratory for the Study of Earthquake Predictability (CSEP) supports a global program to conduct prospective earthquake forecasting experiments. CSEP testing centers are now operational in California, New Zealand, Japan, China, and Europe with 442 models under evaluation. The California testing center, started by SCEC, Sept 1, 2007, currently hosts 30-minute, 1-day, 3-month, 1-year and 5-year forecasts, both alarm-based and probabilistic, for California, the Western Pacific, and worldwide. Our tests are now based on the hypocentral locations and magnitudes of cataloged earthquakes, but we plan to test focal mechanisms, seismic hazard models, ground motion forecasts, and finite rupture forecasts as well. We have increased computational efficiency for high-resolution global experiments, such as the evaluation of the Global Earthquake Activity Rate (GEAR) model, introduced Bayesian ensemble models, and implemented support for non-Poissonian simulation-based forecasts models. We are currently developing formats and procedures to evaluate externally hosted forecasts and predictions. CSEP supports the USGS program in operational earthquake forecasting and a DHS project to register and test external forecast procedures from experts outside seismology. We found that earthquakes as small as magnitude 2.5 provide important information on subsequent earthquakes larger than magnitude 5. A retrospective experiment for the 2010-2012 Canterbury earthquake sequence showed that some physics-based and hybrid models outperform catalog-based (e.g., ETAS) models. This experiment also demonstrates the ability of the CSEP infrastructure to support retrospective forecast testing. Current CSEP development activities include adoption of the Comprehensive Earthquake Catalog (ComCat) as an authorized data source, retrospective testing of simulation-based forecasts, and support for additive ensemble methods. We describe the open-source CSEP software that is available to researchers as they develop their forecast models. We also discuss how CSEP procedures are being adapted to intensity and ground motion prediction experiments as well as hazard model testing.

Integrating in silico models to enhance predictivity for developmental toxicity.

PubMed

Marzo, Marco; Kulkarni, Sunil; Manganaro, Alberto; Roncaglioni, Alessandra; Wu, Shengde; Barton-Maclaren, Tara S; Lester, Cathy; Benfenati, Emilio

2016-08-31

Application of in silico models to predict developmental toxicity has demonstrated limited success particularly when employed as a single source of information. It is acknowledged that modelling the complex outcomes related to this endpoint is a challenge; however, such models have been developed and reported in the literature. The current study explored the possibility of integrating the selected public domain models (CAESAR, SARpy and P&G model) with the selected commercial modelling suites (Multicase, Leadscope and Derek Nexus) to assess if there is an increase in overall predictive performance. The results varied according to the data sets used to assess performance which improved upon model integration relative to individual models. Moreover, because different models are based on different specific developmental toxicity effects, integration of these models increased the applicable chemical and biological spaces. It is suggested that this approach reduces uncertainty associated with in silico predictions by achieving a consensus among a battery of models. The use of tools to assess the applicability domain also improves the interpretation of the predictions. This has been verified in the case of the software VEGA, which makes freely available QSAR models with a measurement of the applicability domain. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Multibody dynamic simulation of knee contact mechanics

PubMed Central

Bei, Yanhong; Fregly, Benjamin J.

2006-01-01

Multibody dynamic musculoskeletal models capable of predicting muscle forces and joint contact pressures simultaneously would be valuable for studying clinical issues related to knee joint degeneration and restoration. Current three-dimensional multi-body knee models are either quasi-static with deformable contact or dynamic with rigid contact. This study proposes a computationally efficient methodology for combining multibody dynamic simulation methods with a deformable contact knee model. The methodology requires preparation of the articular surface geometry, development of efficient methods to calculate distances between contact surfaces, implementation of an efficient contact solver that accounts for the unique characteristics of human joints, and specification of an application programming interface for integration with any multibody dynamic simulation environment. The current implementation accommodates natural or artificial tibiofemoral joint models, small or large strain contact models, and linear or nonlinear material models. Applications are presented for static analysis (via dynamic simulation) of a natural knee model created from MRI and CT data and dynamic simulation of an artificial knee model produced from manufacturer’s CAD data. Small and large strain natural knee static analyses required 1 min of CPU time and predicted similar contact conditions except for peak pressure, which was higher for the large strain model. Linear and nonlinear artificial knee dynamic simulations required 10 min of CPU time and predicted similar contact force and torque but different contact pressures, which were lower for the nonlinear model due to increased contact area. This methodology provides an important step toward the realization of dynamic musculoskeletal models that can predict in vivo knee joint motion and loading simultaneously. PMID:15564115

Development of a coupled hydrological - hydrodynamic model for probabilistic catchment flood inundation modelling

NASA Astrophysics Data System (ADS)

Quinn, Niall; Freer, Jim; Coxon, Gemma; Dunne, Toby; Neal, Jeff; Bates, Paul; Sampson, Chris; Smith, Andy; Parkin, Geoff

2017-04-01

Computationally efficient flood inundation modelling systems capable of representing important hydrological and hydrodynamic flood generating processes over relatively large regions are vital for those interested in flood preparation, response, and real time forecasting. However, such systems are currently not readily available. This can be particularly important where flood predictions from intense rainfall are considered as the processes leading to flooding often involve localised, non-linear spatially connected hillslope-catchment responses. Therefore, this research introduces a novel hydrological-hydraulic modelling framework for the provision of probabilistic flood inundation predictions across catchment to regional scales that explicitly account for spatial variability in rainfall-runoff and routing processes. Approaches have been developed to automate the provision of required input datasets and estimate essential catchment characteristics from freely available, national datasets. This is an essential component of the framework as when making predictions over multiple catchments or at relatively large scales, and where data is often scarce, obtaining local information and manually incorporating it into the model quickly becomes infeasible. An extreme flooding event in the town of Morpeth, NE England, in 2008 was used as a first case study evaluation of the modelling framework introduced. The results demonstrated a high degree of prediction accuracy when comparing modelled and reconstructed event characteristics for the event, while the efficiency of the modelling approach used enabled the generation of relatively large ensembles of realisations from which uncertainty within the prediction may be represented. This research supports previous literature highlighting the importance of probabilistic forecasting, particularly during extreme events, which can be often be poorly characterised or even missed by deterministic predictions due to the inherent uncertainty in any model application. Future research will aim to further evaluate the robustness of the approaches introduced by applying the modelling framework to a variety of historical flood events across UK catchments. Furthermore, the flexibility and efficiency of the framework is ideally suited to the examination of the propagation of errors through the model which will help gain a better understanding of the dominant sources of uncertainty currently impacting flood inundation predictions.

Surrogate modeling of joint flood risk across coastal watersheds

NASA Astrophysics Data System (ADS)

Bass, Benjamin; Bedient, Philip

2018-03-01

This study discusses the development and performance of a rapid prediction system capable of representing the joint rainfall-runoff and storm surge flood response of tropical cyclones (TCs) for probabilistic risk analysis. Due to the computational demand required for accurately representing storm surge with the high-fidelity ADvanced CIRCulation (ADCIRC) hydrodynamic model and its coupling with additional numerical models to represent rainfall-runoff, a surrogate or statistical model was trained to represent the relationship between hurricane wind- and pressure-field characteristics and their peak joint flood response typically determined from physics based numerical models. This builds upon past studies that have only evaluated surrogate models for predicting peak surge, and provides the first system capable of probabilistically representing joint flood levels from TCs. The utility of this joint flood prediction system is then demonstrated by improving upon probabilistic TC flood risk products, which currently account for storm surge but do not take into account TC associated rainfall-runoff. Results demonstrate the source apportionment of rainfall-runoff versus storm surge and highlight that slight increases in flood risk levels may occur due to the interaction between rainfall-runoff and storm surge as compared to the Federal Emergency Management Association's (FEMAs) current practices.

Non-parallel coevolution of sender and receiver in the acoustic communication system of treefrogs.

PubMed

Schul, Johannes; Bush, Sarah L

2002-09-07

Advertisement calls of closely related species often differ in quantitative features such as the repetition rate of signal units. These differences are important in species recognition. Current models of signal-receiver coevolution predict two possible patterns in the evolution of the mechanism used by receivers to recognize the call: (i) classical sexual selection models (Fisher process, good genes/indirect benefits, direct benefits models) predict that close relatives use qualitatively similar signal recognition mechanisms tuned to different values of a call parameter; and (ii) receiver bias models (hidden preference, pre-existing bias models) predict that if different signal recognition mechanisms are used by sibling species, evidence of an ancestral mechanism will persist in the derived species, and evidence of a pre-existing bias will be detectable in the ancestral species. We describe qualitatively different call recognition mechanisms in sibling species of treefrogs. Whereas Hyla chrysoscelis uses pulse rate to recognize male calls, Hyla versicolor uses absolute measurements of pulse duration and interval duration. We found no evidence of either hidden preferences or pre-existing biases. The results are compared with similar data from katydids (Tettigonia sp.). In both taxa, the data are not adequately explained by current models of signal-receiver coevolution.

Towards a Seamless Framework for Drought Analysis and Prediction from Seasonal to Climate Change Time Scales (Plinius Medal Lecture)

NASA Astrophysics Data System (ADS)

Sheffield, Justin

2013-04-01

Droughts arguably cause the most impacts of all natural hazards in terms of the number of people affected and the long-term economic costs and ecosystem stresses. Recent droughts worldwide have caused humanitarian and economic problems such as food insecurity across the Horn of Africa, agricultural economic losses across the central US and loss of livelihoods in rural western India. The prospect of future increases in drought severity and duration driven by projected changes in precipitation patterns and increasing temperatures is worrisome. Some evidence for climate change impacts on drought is already being seen for some regions, such as the Mediterranean and east Africa. Mitigation of the impacts of drought requires advance warning of developing conditions and enactment of drought plans to reduce vulnerability. A key element of this is a drought early warning system that at its heart is the capability to monitor evolving hydrological conditions and water resources storage, and provide reliable and robust predictions out to several months, as well as the capacity to act on this information. At longer time scales, planning and policy-making need to consider the potential impacts of climate change and its impact on drought risk, and do this within the context of natural climate variability, which is likely to dominate any climate change signal over the next few decades. There are several challenges that need to be met to advance our capability to provide both early warning at seasonal time scales and risk assessment under climate change, regionally and globally. Advancing our understanding of drought predictability and risk requires knowledge of drought at all time scales. This includes understanding of past drought occurrence, from the paleoclimate record to the recent past, and understanding of drought mechanisms, from initiation, through persistence to recovery and translation of this understanding to predictive models. Current approaches to monitoring and predicting drought are limited in many parts of the world, and especially in developing countries where national capacity is limited. Evaluation of past droughts and their mechanisms is limited by data availability and especially before the instrumental period of the last 50-100 years, for which there is reliance on incomplete spatial proxy data, such as tree rings. Seasonal predictability is currently mainly limited to tropical and sub-tropical regions through connections with sea surface temperature variations such as ENSO. Predictability in mid-latitudes is low and especially for precipitation, although dynamical model predictions appear to be edging statistical models in many aspects of seasonal prediction. This presentation describes ongoing research on evaluation of drought risk and drought mechanisms at regional to global scales with the eventual goal of developing a seamless monitoring and prediction framework at all time scales. Such a framework would allow consistent assessment of drought from historic to current conditions, and from seasonal and decadal predictions to climate change projections. At the center of the framework is an experimental global drought monitoring and seasonal forecast system that has evolved out of regional and continental systems for the US and Africa. The system is based on land surface hydrological modeling that is driven by satellite remote sensing precipitation to predict current hydrological conditions and the state of drought. Seasonal climate model forecasts are downscaled and bias-corrected to drive the land surface model to provide hydrological forecasts and drought products out 6-9 months. The system relies on historic reconstructions of drought variability over the 20th century, which forms the background climatology to which current conditions can be assessed and drought mechanisms can be diagnosed. Future drought risk is quantified based on bias-corrected and downscaled climate model projections that are used to drive the land surface models. Current research is focused on several aspects, including: 1) quantifying the uncertainties in historic drought reconstructions; 2) analysis of drought propagation through the coupled hydrological/vegetation system; 3) the utility of new data sources such as on the ground sensors and new satellite products for terrestrial hydrology and vegetation, for improved monitoring and prediction, especially in poorly observed regions; 4) advancing predictive skill for all aspects of drought occurrence through diagnosis of the driving mechanisms and feedbacks of historic droughts; and 5) quantification and reduction of uncertainties in future projections of drought under climate change. The steps towards the development of a seamless framework for analysis and prediction in the context of this research are discussed.

How well do basic models describe the turbidity currents coming down Monterey and Congo Canyon?

NASA Astrophysics Data System (ADS)

Cartigny, M.; Simmons, S.; Heerema, C.; Xu, J. P.; Azpiroz, M.; Clare, M. A.; Cooper, C.; Gales, J. A.; Maier, K. L.; Parsons, D. R.; Paull, C. K.; Sumner, E. J.; Talling, P.

2017-12-01

Turbidity currents rival rivers in their global capacity to transport sediment and organic carbon. Furthermore, turbidity currents break submarine cables that now transport >95% of our global data traffic. Accurate turbidity current models are thus needed to quantify their transport capacity and to predict the forces exerted on seafloor structures. Despite this need, existing numerical models are typically only calibrated with scaled-down laboratory measurements due to the paucity of direct measurements of field-scale turbidity currents. This lack of calibration thus leaves much uncertainty in the validity of existing models. Here we use the most detailed observations of turbidity currents yet acquired to validate one of the most fundamental models proposed for turbidity currents, the modified Chézy model. Direct measurements on which the validation is based come from two sites that feature distinctly different flow modes and grain sizes. The first are from the multi-institution Coordinated Canyon Experiment (CCE) in Monterey Canyon, California. An array of six moorings along the canyon axis captured at least 15 flow events that lasted up to hours. The second is the deep-sea Congo Canyon, where 10 finer grained flows were measured by a single mooring, each lasting several days. Moorings captured depth-resolved velocity and suspended sediment concentration at high resolution (<30 second) for each of the 25 events. We use both datasets to test the most basic model available for turbidity currents; the modified Chézy model. This basic model has been very useful for river studies over the past 200 years, as it provides a rapid estimate of how flow velocity varies with changes in river level and energy slope. Chézy-type models assume that the gravitational force of the flow equals the friction of the river-bed. Modified Chézy models have been proposed for turbidity currents. However, the absence of detailed measurements of friction and sediment concentration within full-scale turbidity currents has forced modellers to make rough assumptions for these parameters. Here we use mooring data to deduce observation-based relations that can replace the previous assumptions. This improvement will significantly enhance the model predictions and allow us to better constrain the behaviour of turbidity currents.

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.

Identifying influential data points in hydrological model calibration and their impact on streamflow predictions

NASA Astrophysics Data System (ADS)

Wright, David; Thyer, Mark; Westra, Seth

2015-04-01

Highly influential data points are those that have a disproportionately large impact on model performance, parameters and predictions. However, in current hydrological modelling practice the relative influence of individual data points on hydrological model calibration is not commonly evaluated. This presentation illustrates and evaluates several influence diagnostics tools that hydrological modellers can use to assess the relative influence of data. The feasibility and importance of including influence detection diagnostics as a standard tool in hydrological model calibration is discussed. Two classes of influence diagnostics are evaluated: (1) computationally demanding numerical "case deletion" diagnostics; and (2) computationally efficient analytical diagnostics, based on Cook's distance. These diagnostics are compared against hydrologically orientated diagnostics that describe changes in the model parameters (measured through the Mahalanobis distance), performance (objective function displacement) and predictions (mean and maximum streamflow). These influence diagnostics are applied to two case studies: a stage/discharge rating curve model, and a conceptual rainfall-runoff model (GR4J). Removing a single data point from the calibration resulted in differences to mean flow predictions of up to 6% for the rating curve model, and differences to mean and maximum flow predictions of up to 10% and 17%, respectively, for the hydrological model. When using the Nash-Sutcliffe efficiency in calibration, the computationally cheaper Cook's distance metrics produce similar results to the case-deletion metrics at a fraction of the computational cost. However, Cooks distance is adapted from linear regression with inherit assumptions on the data and is therefore less flexible than case deletion. Influential point detection diagnostics show great potential to improve current hydrological modelling practices by identifying highly influential data points. The findings of this study establish the feasibility and importance of including influential point detection diagnostics as a standard tool in hydrological model calibration. They provide the hydrologist with important information on whether model calibration is susceptible to a small number of highly influent data points. This enables the hydrologist to make a more informed decision of whether to (1) remove/retain the calibration data; (2) adjust the calibration strategy and/or hydrological model to reduce the susceptibility of model predictions to a small number of influential observations.

Comparison of four modeling tools for the prediction of potential distribution for non-indigenous weeds in the United States

USGS Publications Warehouse

Magarey, Roger; Newton, Leslie; Hong, Seung C.; Takeuchi, Yu; Christie, Dave; Jarnevich, Catherine S.; Kohl, Lisa; Damus, Martin; Higgins, Steven I.; Miller, Leah; Castro, Karen; West, Amanda; Hastings, John; Cook, Gericke; Kartesz, John; Koop, Anthony

2018-01-01

This study compares four models for predicting the potential distribution of non-indigenous weed species in the conterminous U.S. The comparison focused on evaluating modeling tools and protocols as currently used for weed risk assessment or for predicting the potential distribution of invasive weeds. We used six weed species (three highly invasive and three less invasive non-indigenous species) that have been established in the U.S. for more than 75 years. The experiment involved providing non-U. S. location data to users familiar with one of the four evaluated techniques, who then developed predictive models that were applied to the United States without knowing the identity of the species or its U.S. distribution. We compared a simple GIS climate matching technique known as Proto3, a simple climate matching tool CLIMEX Match Climates, the correlative model MaxEnt, and a process model known as the Thornley Transport Resistance (TTR) model. Two experienced users ran each modeling tool except TTR, which had one user. Models were trained with global species distribution data excluding any U.S. data, and then were evaluated using the current known U.S. distribution. The influence of weed species identity and modeling tool on prevalence and sensitivity effects was compared using a generalized linear mixed model. Each modeling tool itself had a low statistical significance, while weed species alone accounted for 69.1 and 48.5% of the variance for prevalence and sensitivity, respectively. These results suggest that simple modeling tools might perform as well as complex ones in the case of predicting potential distribution for a weed not yet present in the United States. Considerations of model accuracy should also be balanced with those of reproducibility and ease of use. More important than the choice of modeling tool is the construction of robust protocols and testing both new and experienced users under blind test conditions that approximate operational conditions.

Predicting wildfire occurrence distribution with spatial point process models and its uncertainty assessment: a case study in the Lake Tahoe Basin, USA

Treesearch

Jian Yang; Peter J. Weisberg; Thomas E. Dilts; E. Louise Loudermilk; Robert M. Scheller; Alison Stanton; Carl Skinner

2015-01-01

Strategic fire and fuel management planning benefits from detailed understanding of how wildfire occurrences are distributed spatially under current climate, and from predictive models of future wildfire occurrence given climate change scenarios. In this study, we fitted historical wildfire occurrence data from 1986 to 2009 to a suite of spatial point process (SPP)...

Wave Current Interactions and Wave-blocking Predictions Using NHWAVE Model

DTIC Science & Technology

2013-03-01

Navier-Stokes equation. In this approach, as with previous modeling techniques, there is difficulty in simulating the free surface that inhibits accurate...hydrostatic, free - surface , rotational flows in multiple dimensions. It is useful in predicting transformations of surface waves and rapidly varied...Stelling, G., and M. Zijlema, 2003: An accurate and efficient finite-differencing algorithm for non-hydrostatic free surface flow with application to

Model-based learning and the contribution of the orbitofrontal cortex to the model-free world.

PubMed

McDannald, Michael A; Takahashi, Yuji K; Lopatina, Nina; Pietras, Brad W; Jones, Josh L; Schoenbaum, Geoffrey

2012-04-01

Learning is proposed to occur when there is a discrepancy between reward prediction and reward receipt. At least two separate systems are thought to exist: one in which predictions are proposed to be based on model-free or cached values; and another in which predictions are model-based. A basic neural circuit for model-free reinforcement learning has already been described. In the model-free circuit the ventral striatum (VS) is thought to supply a common-currency reward prediction to midbrain dopamine neurons that compute prediction errors and drive learning. In a model-based system, predictions can include more information about an expected reward, such as its sensory attributes or current, unique value. This detailed prediction allows for both behavioral flexibility and learning driven by changes in sensory features of rewards alone. Recent evidence from animal learning and human imaging suggests that, in addition to model-free information, the VS also signals model-based information. Further, there is evidence that the orbitofrontal cortex (OFC) signals model-based information. Here we review these data and suggest that the OFC provides model-based information to this traditional model-free circuitry and offer possibilities as to how this interaction might occur. © 2012 The Authors. European Journal of Neuroscience © 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

To predict the niche, model colonization and extinction

USGS Publications Warehouse

Yackulic, Charles B.; Nichols, James D.; Reid, Janice; Der, Ricky

2015-01-01

Ecologists frequently try to predict the future geographic distributions of species. Most studies assume that the current distribution of a species reflects its environmental requirements (i.e., the species' niche). However, the current distributions of many species are unlikely to be at equilibrium with the current distribution of environmental conditions, both because of ongoing invasions and because the distribution of suitable environmental conditions is always changing. This mismatch between the equilibrium assumptions inherent in many analyses and the disequilibrium conditions in the real world leads to inaccurate predictions of species' geographic distributions and suggests the need for theory and analytical tools that avoid equilibrium assumptions. Here, we develop a general theory of environmental associations during periods of transient dynamics. We show that time-invariant relationships between environmental conditions and rates of local colonization and extinction can produce substantial temporal variation in occupancy–environment relationships. We then estimate occupancy–environment relationships during three avian invasions. Changes in occupancy–environment relationships over time differ among species but are predicted by dynamic occupancy models. Since estimates of the occupancy–environment relationships themselves are frequently poor predictors of future occupancy patterns, research should increasingly focus on characterizing how rates of local colonization and extinction vary with environmental conditions.

Bridging the gap between habitat-modeling research and bird conservation with dynamic landscape and population models

Treesearch

Frank R., III Thompson

2009-01-01

Habitat models are widely used in bird conservation planning to assess current habitat or populations and to evaluate management alternatives. These models include species-habitat matrix or database models, habitat suitability models, and statistical models that predict abundance. While extremely useful, these approaches have some limitations.

A Review of Auditory Prediction and Its Potential Role in Tinnitus Perception.

PubMed

Durai, Mithila; O'Keeffe, Mary G; Searchfield, Grant D

2018-06-01

The precise mechanisms underlying tinnitus perception and distress are still not fully understood. A recent proposition is that auditory prediction errors and related memory representations may play a role in driving tinnitus perception. It is of interest to further explore this. To obtain a comprehensive narrative synthesis of current research in relation to auditory prediction and its potential role in tinnitus perception and severity. A narrative review methodological framework was followed. The key words Prediction Auditory, Memory Prediction Auditory, Tinnitus AND Memory, Tinnitus AND Prediction in Article Title, Abstract, and Keywords were extensively searched on four databases: PubMed, Scopus, SpringerLink, and PsychINFO. All study types were selected from 2000-2016 (end of 2016) and had the following exclusion criteria applied: minimum age of participants <18, nonhuman participants, and article not available in English. Reference lists of articles were reviewed to identify any further relevant studies. Articles were short listed based on title relevance. After reading the abstracts and with consensus made between coauthors, a total of 114 studies were selected for charting data. The hierarchical predictive coding model based on the Bayesian brain hypothesis, attentional modulation and top-down feedback serves as the fundamental framework in current literature for how auditory prediction may occur. Predictions are integral to speech and music processing, as well as in sequential processing and identification of auditory objects during auditory streaming. Although deviant responses are observable from middle latency time ranges, the mismatch negativity (MMN) waveform is the most commonly studied electrophysiological index of auditory irregularity detection. However, limitations may apply when interpreting findings because of the debatable origin of the MMN and its restricted ability to model real-life, more complex auditory phenomenon. Cortical oscillatory band activity may act as neurophysiological substrates for auditory prediction. Tinnitus has been modeled as an auditory object which may demonstrate incomplete processing during auditory scene analysis resulting in tinnitus salience and therefore difficulty in habituation. Within the electrophysiological domain, there is currently mixed evidence regarding oscillatory band changes in tinnitus. There are theoretical proposals for a relationship between prediction error and tinnitus but few published empirical studies. American Academy of Audiology.

Modeling microbial communities: current, developing, and future technologies for predicting microbial community interaction.

PubMed

Larsen, Peter; Hamada, Yuki; Gilbert, Jack

2012-07-31

Never has there been a greater opportunity for investigating microbial communities. Not only are the profound effects of microbial ecology on every aspect of Earth's geochemical cycles beginning to be understood, but also the analytical and computational tools for investigating microbial Earth are undergoing a rapid revolution. This environmental microbial interactome, the system of interactions between the microbiome and the environment, has shaped the planet's past and will undoubtedly continue to do so in the future. We review recent approaches for modeling microbial community structures and the interactions of microbial populations with their environments. Different modeling approaches consider the environmental microbial interactome from different aspects, and each provides insights to different facets of microbial ecology. We discuss the challenges and opportunities for the future of microbial modeling and describe recent advances in microbial community modeling that are extending current descriptive technologies into a predictive science. Copyright © 2012 Elsevier B.V. All rights reserved.

Current target acquisition methodology in force on force simulations

NASA Astrophysics Data System (ADS)

Hixson, Jonathan G.; Miller, Brian; Mazz, John P.

2017-05-01

The U.S. Army RDECOM CERDEC NVESD MSD's target acquisition models have been used for many years by the military community in force on force simulations for training, testing, and analysis. There have been significant improvements to these models over the past few years. The significant improvements are the transition of ACQUIRE TTP-TAS (ACQUIRE Targeting Task Performance Target Angular Size) methodology for all imaging sensors and the development of new discrimination criteria for urban environments and humans. This paper is intended to provide an overview of the current target acquisition modeling approach and provide data for the new discrimination tasks. This paper will discuss advances and changes to the models and methodologies used to: (1) design and compare sensors' performance, (2) predict expected target acquisition performance in the field, (3) predict target acquisition performance for combat simulations, and (4) how to conduct model data validation for combat simulations.

Predicting in-patient falls in a geriatric clinic: a clinical study combining assessment data and simple sensory gait measurements.

PubMed

Marschollek, M; Nemitz, G; Gietzelt, M; Wolf, K H; Meyer Zu Schwabedissen, H; Haux, R

2009-08-01

Falls are among the predominant causes for morbidity and mortality in elderly persons and occur most often in geriatric clinics. Despite several studies that have identified parameters associated with elderly patients' fall risk, prediction models -- e.g., based on geriatric assessment data -- are currently not used on a regular basis. Furthermore, technical aids to objectively assess mobility-associated parameters are currently not used. To assess group differences in clinical as well as common geriatric assessment data and sensory gait measurements between fallers and non-fallers in a geriatric sample, and to derive and compare two prediction models based on assessment data alone (model #1) and added sensory measurement data (model #2). For a sample of n=110 geriatric in-patients (81 women, 29 men) the following fall risk-associated assessments were performed: Timed 'Up & Go' (TUG) test, STRATIFY score and Barthel index. During the TUG test the subjects wore a triaxial accelerometer, and sensory gait parameters were extracted from the data recorded. Group differences between fallers (n=26) and non-fallers (n=84) were compared using Student's t-test. Two classification tree prediction models were computed and compared. Significant differences between the two groups were found for the following parameters: time to complete the TUG test, transfer item (Barthel), recent falls (STRATIFY), pelvic sway while walking and step length. Prediction model #1 (using common assessment data only) showed a sensitivity of 38.5% and a specificity of 97.6%, prediction model #2 (assessment data plus sensory gait parameters) performed with 57.7% and 100%, respectively. Significant differences between fallers and non-fallers among geriatric in-patients can be detected for several assessment subscores as well as parameters recorded by simple accelerometric measurements during a common mobility test. Existing geriatric assessment data may be used for falls prediction on a regular basis. Adding sensory data improves the specificity of our test markedly.

Validation of Kinetic-Turbulent-Neoclassical Theory for Edge Intrinsic Rotation in DIII-D Plasmas

NASA Astrophysics Data System (ADS)

Ashourvan, Arash

2017-10-01

Recent experiments on DIII-D with low-torque neutral beam injection (NBI) have provided a validation of a new model of momentum generation in a wide range of conditions spanning L- and H-mode with direct ion and electron heating. A challenge in predicting the bulk rotation profile for ITER has been to capture the physics of momentum transport near the separatrix and steep gradient region. A recent theory has presented a model for edge momentum transport which predicts the value and direction of the main-ion intrinsic velocity at the pedestal-top, generated by the passing orbits in the inhomogeneous turbulent field. In this study, this model-predicted velocity is tested on DIII-D for a database of 44 low-torque NBI discharges comprised of bothL- and H-mode plasmas. For moderate NBI powers (PNBI<4 MW), model prediction agrees well with the experiments for both L- and H-mode. At higher NBI power the experimental rotation is observed to saturate and even degrade compared to theory. TRANSP-NUBEAM simulations performed for the database show that for discharges with nominally balanced - but high powered - NBI, the net injected torque through the edge can exceed 1 N.m in the counter-current direction. The theory model has been extended to compute the rotation degradation from this counter-current NBI torque by solving a reduced momentum evolution equation for the edge and found the revised velocity prediction to be in agreement with experiment. Projecting to the ITER baseline scenario, this model predicts a value for the pedestal-top rotation (ρ 0.9) comparable to 4 kRad/s. Using the theory modeled - and now tested - velocity to predict the bulk plasma rotation opens up a path to more confidently projecting the confinement and stability in ITER. Supported by the US DOE under DE-AC02-09CH11466 and DE-FC02-04ER54698.

EOID Model Validation and Performance Prediction

DTIC Science & Technology

2002-09-30

Our long-term goal is to accurately predict the capability of the current generation of laser-based underwater imaging sensors to perform Electro ... Optic Identification (EOID) against relevant targets in a variety of realistic environmental conditions. The two most prominent technologies in this area

Development and validation of a low-frequency modeling code for high-moment transmitter rod antennas

NASA Astrophysics Data System (ADS)

Jordan, Jared Williams; Sternberg, Ben K.; Dvorak, Steven L.

2009-12-01

The goal of this research is to develop and validate a low-frequency modeling code for high-moment transmitter rod antennas to aid in the design of future low-frequency TX antennas with high magnetic moments. To accomplish this goal, a quasi-static modeling algorithm was developed to simulate finite-length, permeable-core, rod antennas. This quasi-static analysis is applicable for low frequencies where eddy currents are negligible, and it can handle solid or hollow cores with winding insulation thickness between the antenna's windings and its core. The theory was programmed in Matlab, and the modeling code has the ability to predict the TX antenna's gain, maximum magnetic moment, saturation current, series inductance, and core series loss resistance, provided the user enters the corresponding complex permeability for the desired core magnetic flux density. In order to utilize the linear modeling code to model the effects of nonlinear core materials, it is necessary to use the correct complex permeability for a specific core magnetic flux density. In order to test the modeling code, we demonstrated that it can accurately predict changes in the electrical parameters associated with variations in the rod length and the core thickness for antennas made out of low carbon steel wire. These tests demonstrate that the modeling code was successful in predicting the changes in the rod antenna characteristics under high-current nonlinear conditions due to changes in the physical dimensions of the rod provided that the flux density in the core was held constant in order to keep the complex permeability from changing.

How much detail is needed in modeling a transcranial magnetic stimulation figure-8 coil: Measurements and brain simulations

PubMed Central

Mandija, Stefano; Sommer, Iris E. C.; van den Berg, Cornelis A. T.; Neggers, Sebastiaan F. W.

2017-01-01

Background Despite TMS wide adoption, its spatial and temporal patterns of neuronal effects are not well understood. Although progress has been made in predicting induced currents in the brain using realistic finite element models (FEM), there is little consensus on how a magnetic field of a typical TMS coil should be modeled. Empirical validation of such models is limited and subject to several limitations. Methods We evaluate and empirically validate models of a figure-of-eight TMS coil that are commonly used in published modeling studies, of increasing complexity: simple circular coil model; coil with in-plane spiral winding turns; and finally one with stacked spiral winding turns. We will assess the electric fields induced by all 3 coil models in the motor cortex using a computer FEM model. Biot-Savart models of discretized wires were used to approximate the 3 coil models of increasing complexity. We use a tailored MR based phase mapping technique to get a full 3D validation of the incident magnetic field induced in a cylindrical phantom by our TMS coil. FEM based simulations on a meshed 3D brain model consisting of five tissues types were performed, using two orthogonal coil orientations. Results Substantial differences in the induced currents are observed, both theoretically and empirically, between highly idealized coils and coils with correctly modeled spiral winding turns. Thickness of the coil winding turns affect minimally the induced electric field, and it does not influence the predicted activation. Conclusion TMS coil models used in FEM simulations should include in-plane coil geometry in order to make reliable predictions of the incident field. Modeling the in-plane coil geometry is important to correctly simulate the induced electric field and to correctly make reliable predictions of neuronal activation PMID:28640923

The need for conducting forensic analysis of decommissioned bridges.

DOT National Transportation Integrated Search

2014-01-01

A limiting factor in current bridge management programs is a lack of detailed knowledge of bridge deterioration : mechanisms and processes. The current state of the art is to predict future condition using statistical forecasting : models based upon ...

On-Line, Self-Learning, Predictive Tool for Determining Payload Thermal Response

NASA Technical Reports Server (NTRS)

Jen, Chian-Li; Tilwick, Leon

2000-01-01

This paper will present the results of a joint ManTech / Goddard R&D effort, currently under way, to develop and test a computer based, on-line, predictive simulation model for use by facility operators to predict the thermal response of a payload during thermal vacuum testing. Thermal response was identified as an area that could benefit from the algorithms developed by Dr. Jeri for complex computer simulations. Most thermal vacuum test setups are unique since no two payloads have the same thermal properties. This requires that the operators depend on their past experiences to conduct the test which requires time for them to learn how the payload responds while at the same time limiting any risk of exceeding hot or cold temperature limits. The predictive tool being developed is intended to be used with the new Thermal Vacuum Data System (TVDS) developed at Goddard for the Thermal Vacuum Test Operations group. This model can learn the thermal response of the payload by reading a few data points from the TVDS, accepting the payload's current temperature as the initial condition for prediction. The model can then be used as a predictive tool to estimate the future payload temperatures according to a predetermined shroud temperature profile. If the error of prediction is too big, the model can be asked to re-learn the new situation on-line in real-time and give a new prediction. Based on some preliminary tests, we feel this predictive model can forecast the payload temperature of the entire test cycle within 5 degrees Celsius after it has learned 3 times during the beginning of the test. The tool will allow the operator to play "what-if' experiments to decide what is his best shroud temperature set-point control strategy. This tool will save money by minimizing guess work and optimizing transitions as well as making the testing process safer and easier to conduct.

Validation of Magnetospheric Magnetohydrodynamic Models

NASA Astrophysics Data System (ADS)

Curtis, Brian

Magnetospheric magnetohydrodynamic (MHD) models are commonly used for both prediction and modeling of Earth's magnetosphere. To date, very little validation has been performed to determine their limits, uncertainties, and differences. In this work, we performed a comprehensive analysis using several commonly used validation techniques in the atmospheric sciences to MHD-based models of Earth's magnetosphere for the first time. The validation techniques of parameter variability/sensitivity analysis and comparison to other models were used on the OpenGGCM, BATS-R-US, and SWMF magnetospheric MHD models to answer several questions about how these models compare. The questions include: (1) the difference between the model's predictions prior to and following to a reversal of Bz in the upstream interplanetary field (IMF) from positive to negative, (2) the influence of the preconditioning duration, and (3) the differences between models under extreme solar wind conditions. A differencing visualization tool was developed and used to address these three questions. We find: (1) For a reversal in IMF Bz from positive to negative, the OpenGGCM magnetopause is closest to Earth as it has the weakest magnetic pressure near-Earth. The differences in magnetopause positions between BATS-R-US and SWMF are explained by the influence of the ring current, which is included in SWMF. Densities are highest for SWMF and lowest for OpenGGCM. The OpenGGCM tail currents differ significantly from BATS-R-US and SWMF; (2) A longer preconditioning time allowed the magnetosphere to relax more, giving different positions for the magnetopause with all three models before the IMF Bz reversal. There were differences greater than 100% for all three models before the IMF Bz reversal. The differences in the current sheet region for the OpenGGCM were small after the IMF Bz reversal. The BATS-R-US and SWMF differences decreased after the IMF Bz reversal to near zero; (3) For extreme conditions in the solar wind, the OpenGGCM has a large region of Earthward flow velocity (Ux) in the current sheet region that grows as time progresses in a compressed environment. BATS-R-US Bz , rho and Ux stabilize to a near constant value approximately one hour into the run under high compression conditions. Under high compression, the SWMF parameters begin to oscillate approximately 100 minutes into the run. All three models have similar magnetopause positions under low pressure conditions. The OpenGGCM current sheet velocities along the Sun-Earth line are largest under low pressure conditions. The results of this analysis indicate the need for accounting for model uncertainties and differences when comparing model predictions with data, provide error bars on model prediction in various magnetospheric regions, and show that the magnetotail is sensitive to the preconditioning time.

Pneumococcal vaccine targeting strategy for older adults: customized risk profiling.

PubMed

Balicer, Ran D; Cohen, Chandra J; Leibowitz, Morton; Feldman, Becca S; Brufman, Ilan; Roberts, Craig; Hoshen, Moshe

2014-02-12

Current pneumococcal vaccine campaigns take a broad, primarily age-based approach to immunization targeting, overlooking many clinical and administrative considerations necessary in disease prevention and resource planning for specific patient populations. We aim to demonstrate the utility of a population-specific predictive model for hospital-treated pneumonia to direct effective vaccine targeting. Data was extracted for 1,053,435 members of an Israeli HMO, age 50 and older, during the study period 2008-2010. We developed and validated a logistic regression model to predict hospital-treated pneumonia using training and test samples, including a set of standard and population-specific risk factors. The model's predictive value was tested for prospectively identifying cases of pneumonia and invasive pneumococcal disease (IPD), and was compared to the existing international paradigm for patient immunization targeting. In a multivariate regression, age, co-morbidity burden and previous pneumonia events were most strongly positively associated with hospital-treated pneumonia. The model predicting hospital-treated pneumonia yielded a c-statistic of 0.80. Utilizing the predictive model, the top 17% highest-risk within the study validation population were targeted to detect 54% of those members who were subsequently treated for hospitalized pneumonia in the follow up period. The high-risk population identified through this model included 46% of the follow-up year's IPD cases, and 27% of community-treated pneumonia cases. These outcomes were compared with international guidelines for risk for pneumococcal diseases that accurately identified only 35% of hospitalized pneumonia, 41% of IPD cases and 21% of community-treated pneumonia. We demonstrate that a customized model for vaccine targeting performs better than international guidelines, and therefore, risk modeling may allow for more precise vaccine targeting and resource allocation than current national and international guidelines. Health care managers and policy-makers may consider the strategic potential of utilizing clinical and administrative databases for creating population-specific risk prediction models to inform vaccination campaigns. Copyright © 2013 Elsevier Ltd. All rights reserved.

Strike-Slip Fault Patterns on Europa: Obliquity or Polar Wander?

NASA Technical Reports Server (NTRS)

Rhoden, Alyssa Rose; Hurford, Terry A.; Manga, Michael

2011-01-01

Variations in diurnal tidal stress due to Europa's eccentric orbit have been considered as the driver of strike-slip motion along pre-existing faults, but obliquity and physical libration have not been taken into account. The first objective of this work is to examine the effects of obliquity on the predicted global pattern of fault slip directions based on a tidal-tectonic formation model. Our second objective is to test the hypothesis that incorporating obliquity can reconcile theory and observations without requiring polar wander, which was previously invoked to explain the mismatch found between the slip directions of 192 faults on Europa and the global pattern predicted using the eccentricity-only model. We compute predictions for individual, observed faults at their current latitude, longitude, and azimuth with four different tidal models: eccentricity only, eccentricity plus obliquity, eccentricity plus physical libration, and a combination of all three effects. We then determine whether longitude migration, presumably due to non-synchronous rotation, is indicated in observed faults by repeating the comparisons with and without obliquity, this time also allowing longitude translation. We find that a tidal model including an obliquity of 1.2?, along with longitude migration, can predict the slip directions of all observed features in the survey. However, all but four faults can be fit with only 1? of obliquity so the value we find may represent the maximum departure from a lower time-averaged obliquity value. Adding physical libration to the obliquity model improves the accuracy of predictions at the current locations of the faults, but fails to predict the slip directions of six faults and requires additional degrees of freedom. The obliquity model with longitude migration is therefore our preferred model. Although the polar wander interpretation cannot be ruled out from these results alone, the obliquity model accounts for all observations with a value consistent with theoretical expectations and cycloid modeling.

Vibrational kinetics in CO electric discharge lasers - Modeling and experiments

NASA Technical Reports Server (NTRS)

Stanton, A. C.; Hanson, R. K.; Mitchner, M.

1980-01-01

A model of CO laser vibrational kinetics is developed, and predicted vibrational distributions are compared with measurements. The experimental distributions were obtained at various flow locations in a transverse CW discharge in supersonic (M = 3) flow. Good qualitative agreement is obtained in the comparisons, including the prediction of a total inversion at low discharge current densities. The major area of discrepancy is an observed loss in vibrational energy downstream of the discharge which is not predicted by the model. This discrepancy may be due to three-dimensional effects in the experiment which are not included in the model. Possible kinetic effects which may contribute to vibrational energy loss are also examined.

Paradigm of pretest risk stratification before coronary computed tomography.

PubMed

Jensen, Jesper Møller; Ovrehus, Kristian A; Nielsen, Lene H; Jensen, Jesper K; Larsen, Henrik M; Nørgaard, Bjarne L

2009-01-01

The optimal method of determining the pretest risk of coronary artery disease as a patient selection tool before coronary multidetector computed tomography (MDCT) is unknown. We investigated the ability of 3 different clinical risk scores to predict the outcome of coronary MDCT. This was a retrospective study of 551 patients consecutively referred for coronary MDCT on a suspicion of coronary artery disease. Diamond-Forrester, Duke, and Morise risk models were used to predict coronary artery stenosis (>50%) as assessed by coronary MDCT. The models were compared by receiver operating characteristic analysis. The distribution of low-, intermediate-, and high-risk persons, respectively, was established and compared for each of the 3 risk models. Overall, all risk prediction models performed equally well. However, the Duke risk model classified the low-risk patients more correctly than did the other models (P < 0.01). In patients without coronary artery calcification (CAC), the predictive value of the Duke risk model was superior to the other risk models (P < 0.05). Currently available risk prediction models seem to perform better in patients without CAC. Between the risk prediction models, there was a significant discrepancy in the distribution of patients at low, intermediate, or high risk (P < 0.01). The 3 risk prediction models perform equally well, although the Duke risk score may have advantages in subsets of patients. The choice of risk prediction model affects the referral pattern to MDCT. Copyright (c) 2009 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.

An Experimental and Computational Investigation of Oscillating Airfoil Unsteady Aerodynamics at Large Mean Incidence

NASA Technical Reports Server (NTRS)

Capece, Vincent R.; Platzer, Max F.

2003-01-01

A major challenge in the design and development of turbomachine airfoils for gas turbine engines is high cycle fatigue failures due to flutter and aerodynamically induced forced vibrations. In order to predict the aeroelastic response of gas turbine airfoils early in the design phase, accurate unsteady aerodynamic models are required. However, accurate predictions of flutter and forced vibration stress at all operating conditions have remained elusive. The overall objectives of this research program are to develop a transition model suitable for unsteady separated flow and quantify the effects of transition on airfoil steady and unsteady aerodynamics for attached and separated flow using this model. Furthermore, the capability of current state-of-the-art unsteady aerodynamic models to predict the oscillating airfoil response of compressor airfoils over a range of realistic reduced frequencies, Mach numbers, and loading levels will be evaluated through correlation with benchmark data. This comprehensive evaluation will assess the assumptions used in unsteady aerodynamic models. The results of this evaluation can be used to direct improvement of current models and the development of future models. The transition modeling effort will also make strides in improving predictions of steady flow performance of fan and compressor blades at off-design conditions. This report summarizes the progress and results obtained in the first year of this program. These include: installation and verification of the operation of the parallel version of TURBO; the grid generation and initiation of steady flow simulations of the NASA/Pratt&Whitney airfoil at a Mach number of 0.5 and chordal incidence angles of 0 and 10 deg.; and the investigation of the prediction of laminar separation bubbles on a NACA 0012 airfoil.

Validation of variants in SLC28A3 and UGT1A6 as genetic markers predictive of anthracycline-induced cardiotoxicity in children.

PubMed

Visscher, H; Ross, C J D; Rassekh, S R; Sandor, G S S; Caron, H N; van Dalen, E C; Kremer, L C; van der Pal, H J; Rogers, P C; Rieder, M J; Carleton, B C; Hayden, M R

2013-08-01

The use of anthracyclines as effective antineoplastic drugs is limited by the occurrence of cardiotoxicity. Multiple genetic variants predictive of anthracycline-induced cardiotoxicity (ACT) in children were recently identified. The current study was aimed to assess replication of these findings in an independent cohort of children. . Twenty-three variants were tested for association with ACT in an independent cohort of 218 patients. Predictive models including genetic and clinical risk factors were constructed in the original cohort and assessed in the current replication cohort. . We confirmed the association of rs17863783 in UGT1A6 and ACT in the replication cohort (P = 0.0062, odds ratio (OR) 7.98). Additional evidence for association of rs7853758 (P = 0.058, OR 0.46) and rs885004 (P = 0.058, OR 0.42) in SLC28A3 was found (combined P = 1.6 × 10(-5) and P = 3.0 × 10(-5), respectively). A previously constructed prediction model did not significantly improve risk prediction in the replication cohort over clinical factors alone. However, an improved prediction model constructed using replicated genetic variants as well as clinical factors discriminated significantly better between cases and controls than clinical factors alone in both original (AUC 0.77 vs. 0.68, P = 0.0031) and replication cohort (AUC 0.77 vs. 0.69, P = 0.060). . We validated genetic variants in two genes predictive of ACT in an independent cohort. A prediction model combining replicated genetic variants as well as clinical risk factors might be able to identify high- and low-risk patients who could benefit from alternative treatment options. Copyright © 2013 Wiley Periodicals, Inc.

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

NASA Astrophysics Data System (ADS)

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

2017-05-01

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

Testing predictive models of positive and negative affect with psychosocial, acculturation, and coping variables in a multiethnic undergraduate sample.

PubMed

Kuo, Ben Ch; Kwantes, Catherine T

2014-01-01

Despite the prevalence and popularity of research on positive and negative affect within the field of psychology, there is currently little research on affect involving the examination of cultural variables and with participants of diverse cultural and ethnic backgrounds. To the authors' knowledge, currently no empirical studies have comprehensively examined predictive models of positive and negative affect based specifically on multiple psychosocial, acculturation, and coping variables as predictors with any sample populations. Therefore, the purpose of the present study was to test the predictive power of perceived stress, social support, bidirectional acculturation (i.e., Canadian acculturation and heritage acculturation), religious coping and cultural coping (i.e., collective, avoidance, and engagement coping) in explaining positive and negative affect in a multiethnic sample of 301 undergraduate students in Canada. Two hierarchal multiple regressions were conducted, one for each affect as the dependent variable, with the above described predictors. The results supported the hypotheses and showed the two overall models to be significant in predicting affect of both kinds. Specifically, a higher level of positive affect was predicted by a lower level of perceived stress, less use of religious coping, and more use of engagement coping in dealing with stress by the participants. Higher level of negative affect, however, was predicted by a higher level of perceived stress and more use of avoidance coping in responding to stress. The current findings highlight the value and relevance of empirically examining the stress-coping-adaptation experiences of diverse populations from an affective conceptual framework, particularly with the inclusion of positive affect. Implications and recommendations for advancing future research and theoretical works in this area are considered and presented.

PBPK models for the prediction of in vivo performance of oral dosage forms.

PubMed

Kostewicz, Edmund S; Aarons, Leon; Bergstrand, Martin; Bolger, Michael B; Galetin, Aleksandra; Hatley, Oliver; Jamei, Masoud; Lloyd, Richard; Pepin, Xavier; Rostami-Hodjegan, Amin; Sjögren, Erik; Tannergren, Christer; Turner, David B; Wagner, Christian; Weitschies, Werner; Dressman, Jennifer

2014-06-16

Drug absorption from the gastrointestinal (GI) tract is a highly complex process dependent upon numerous factors including the physicochemical properties of the drug, characteristics of the formulation and interplay with the underlying physiological properties of the GI tract. The ability to accurately predict oral drug absorption during drug product development is becoming more relevant given the current challenges facing the pharmaceutical industry. Physiologically-based pharmacokinetic (PBPK) modeling provides an approach that enables the plasma concentration-time profiles to be predicted from preclinical in vitro and in vivo data and can thus provide a valuable resource to support decisions at various stages of the drug development process. Whilst there have been quite a few successes with PBPK models identifying key issues in the development of new drugs in vivo, there are still many aspects that need to be addressed in order to maximize the utility of the PBPK models to predict drug absorption, including improving our understanding of conditions in the lower small intestine and colon, taking the influence of disease on GI physiology into account and further exploring the reasons behind population variability. Importantly, there is also a need to create more appropriate in vitro models for testing dosage form performance and to streamline data input from these into the PBPK models. As part of the Oral Biopharmaceutical Tools (OrBiTo) project, this review provides a summary of the current status of PBPK models available. The current challenges in PBPK set-ups for oral drug absorption including the composition of GI luminal contents, transit and hydrodynamics, permeability and intestinal wall metabolism are discussed in detail. Further, the challenges regarding the appropriate integration of results from in vitro models, such as consideration of appropriate integration/estimation of solubility and the complexity of the in vitro release and precipitation data, are also highlighted as important steps to advancing the application of PBPK models in drug development. It is expected that the "innovative" integration of in vitro data from more appropriate in vitro models and the enhancement of the GI physiology component of PBPK models, arising from the OrBiTo project, will lead to a significant enhancement in the ability of PBPK models to successfully predict oral drug absorption and advance their role in preclinical and clinical development, as well as for regulatory applications. Copyright © 2013 Elsevier B.V. All rights reserved.

Replacing the nucleus pulposus of the intervertebral disk: prediction of suitable properties of a replacement material using finite element analysis.

PubMed

Meakin, J R

2001-03-01

An axisymmetric finite element model of a human lumbar disk was developed to investigate the properties required of an implant to replace the nucleus pulposus. In the intact disk, the nucleus was modeled as a fluid, and the annulus as an elastic solid. The Young's modulus of the annulus was determined empirically by matching model predictions to experimental results. The model was checked for sensitivity to the input parameter values and found to give reasonable behavior. The model predicted that removal of the nucleus would change the response of the annulus to compression. This prediction was consistent with experimental results, thus validating the model. Implants to fill the cavity produced by nucleus removal were modeled as elastic solids. The Poisson's ratio was fixed at 0.49, and the Young's modulus was varied from 0.5 to 100 MPa. Two sizes of implant were considered: full size (filling the cavity) and small size (smaller than the cavity). The model predicted that a full size implant would reverse the changes to annulus behavior, but a smaller implant would not. By comparing the stress distribution in the annulus, the ideal Young's modulus was predicted to be approximately 3 MPa. These predictions have implications for current nucleus implant designs. Copyright 2001 Kluwer Academic Publishers

Analytic model of a magnetically insulated transmission line with collisional flow electrons

NASA Astrophysics Data System (ADS)

Stygar, W. A.; Wagoner, T. C.; Ives, H. C.; Corcoran, P. A.; Cuneo, M. E.; Douglas, J. W.; Gilliland, T. L.; Mazarakis, M. G.; Ramirez, J. J.; Seamen, J. F.; Seidel, D. B.; Spielman, R. B.

2006-09-01

We have developed a relativistic-fluid model of the flow-electron plasma in a steady-state one-dimensional magnetically insulated transmission line (MITL). The model assumes that the electrons are collisional and, as a result, drift toward the anode. The model predicts that in the limit of fully developed collisional flow, the relation between the voltage Va, anode current Ia, cathode current Ik, and geometric impedance Z0 of a 1D planar MITL can be expressed as Va=IaZ0h(χ), where h(χ)≡[(χ+1)/4(χ-1)]1/2-ln⁡⌊χ+(χ2-1)1/2⌋/2χ(χ-1) and χ≡Ia/Ik. The relation is valid when Va≳1MV. In the minimally insulated limit, the anode current Ia,min⁡=1.78Va/Z0, the electron-flow current If,min⁡=1.25Va/Z0, and the flow impedance Zf,min⁡=0.588Z0. {The electron-flow current If≡Ia-Ik. Following Mendel and Rosenthal [Phys. Plasmas 2, 1332 (1995)PHPAEN1070-664X10.1063/1.871345], we define the flow impedance Zf as Va/(Ia2-Ik2)1/2.} In the well-insulated limit (i.e., when Ia≫Ia,min⁡), the electron-flow current If=9Va2/8IaZ02 and the flow impedance Zf=2Z0/3. Similar results are obtained for a 1D collisional MITL with coaxial cylindrical electrodes, when the inner conductor is at a negative potential with respect to the outer, and Z0≲40Ω. We compare the predictions of the collisional model to those of several MITL models that assume the flow electrons are collisionless. We find that at given values of Va and Z0, collisions can significantly increase both Ia,min⁡ and If,min⁡ above the values predicted by the collisionless models, and decrease Zf,min⁡. When Ia≫Ia,min⁡, we find that, at given values of Va, Z0, and Ia, collisions can significantly increase If and decrease Zf. Since the steady-state collisional model is valid only when the drift of electrons toward the anode has had sufficient time to establish fully developed collisional flow, and collisionless models assume there is no net electron drift toward the anode, we expect these two types of models to provide theoretical bounds on Ia, If, and Zf.

Application of the backstepping method to the prediction of increase or decrease of infected population.

PubMed

Kuniya, Toshikazu; Sano, Hideki

2016-05-10

In mathematical epidemiology, age-structured epidemic models have usually been formulated as the boundary-value problems of the partial differential equations. On the other hand, in engineering, the backstepping method has recently been developed and widely studied by many authors. Using the backstepping method, we obtained a boundary feedback control which plays the role of the threshold criteria for the prediction of increase or decrease of newly infected population. Under an assumption that the period of infectiousness is same for all infected individuals (that is, the recovery rate is given by the Dirac delta function multiplied by a sufficiently large positive constant), the prediction method is simplified to the comparison of the numbers of reported cases at the current and previous time steps. Our prediction method was applied to the reported cases per sentinel of influenza in Japan from 2006 to 2015 and its accuracy was 0.81 (404 correct predictions to the total 500 predictions). It was higher than that of the ARIMA models with different orders of the autoregressive part, differencing and moving-average process. In addition, a proposed method for the estimation of the number of reported cases, which is consistent with our prediction method, was better than that of the best-fitted ARIMA model ARIMA(1,1,0) in the sense of mean square error. Our prediction method based on the backstepping method can be simplified to the comparison of the numbers of reported cases of the current and previous time steps. In spite of its simplicity, it can provide a good prediction for the spread of influenza in Japan.

Conduction and rectification in NbO{sub x}- and NiO-based metal-insulator-metal diodes

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

Osgood, Richard M., E-mail: richard.m.osgood.civ@mail.mil; Giardini, Stephen; Carlson, Joel

2016-09-15

Conduction and rectification in nanoantenna-coupled NbO{sub x}- and NiO-based metal-insulator-metal (MIM) diodes (“nanorectennas”) are studied by comparing new theoretical predictions with the measured response of nanorectenna arrays. A new quantum mechanical model is reported and agrees with measurements of current–voltage (I–V) curves, over 10 orders of magnitude in current density, from [NbO{sub x}(native)-Nb{sub 2}O{sub 5}]- and NiO-based samples with oxide thicknesses in the range of 5–36 nm. The model, which introduces new physics and features, including temperature, electron effective mass, and image potential effects using the pseudobarrier technique, improves upon widely used earlier models, calculates the MIM diode's I–V curve, andmore » predicts quantitatively the rectification responsivity of high frequency voltages generated in a coupled nanoantenna array by visible/near-infrared light. The model applies both at the higher frequencies, when high-energy photons are incident, and at lower frequencies, when the formula for classical rectification, involving derivatives of the I–V curve, may be used. The rectified low-frequency direct current is well-predicted in this work's model, but not by fitting the experimentally measured I–V curve with a polynomial or by using the older Simmons model (as shown herein). By fitting the measured I–V curves with our model, the barrier heights in Nb-(NbO{sub x}(native)-Nb{sub 2}O{sub 5})-Pt and Ni-NiO-Ti/Ag diodes are found to be 0.41/0.77 and 0.38/0.39 eV, respectively, similar to literature reports, but with effective mass much lower than the free space value. The NbO{sub x} (native)-Nb{sub 2}O{sub 5} dielectric properties improve, and the effective Pt-Nb{sub 2}O{sub 5} barrier height increases as the oxide thickness increases. An observation of direct current of ∼4 nA for normally incident, focused 514 nm continuous wave laser beams are reported, similar in magnitude to recent reports. This measured direct current is compared to the prediction for rectified direct current, given by the rectification responsivity, calculated from the I–V curve times input power.« less

Mathematical modelling methodologies in predictive food microbiology: a SWOT analysis.

PubMed

Ferrer, Jordi; Prats, Clara; López, Daniel; Vives-Rego, Josep

2009-08-31

Predictive microbiology is the area of food microbiology that attempts to forecast the quantitative evolution of microbial populations over time. This is achieved to a great extent through models that include the mechanisms governing population dynamics. Traditionally, the models used in predictive microbiology are whole-system continuous models that describe population dynamics by means of equations applied to extensive or averaged variables of the whole system. Many existing models can be classified by specific criteria. We can distinguish between survival and growth models by seeing whether they tackle mortality or cell duplication. We can distinguish between empirical (phenomenological) models, which mathematically describe specific behaviour, and theoretical (mechanistic) models with a biological basis, which search for the underlying mechanisms driving already observed phenomena. We can also distinguish between primary, secondary and tertiary models, by examining their treatment of the effects of external factors and constraints on the microbial community. Recently, the use of spatially explicit Individual-based Models (IbMs) has spread through predictive microbiology, due to the current technological capacity of performing measurements on single individual cells and thanks to the consolidation of computational modelling. Spatially explicit IbMs are bottom-up approaches to microbial communities that build bridges between the description of micro-organisms at the cell level and macroscopic observations at the population level. They provide greater insight into the mesoscale phenomena that link unicellular and population levels. Every model is built in response to a particular question and with different aims. Even so, in this research we conducted a SWOT (Strength, Weaknesses, Opportunities and Threats) analysis of the different approaches (population continuous modelling and Individual-based Modelling), which we hope will be helpful for current and future researchers.

Validation of neoclassical bootstrap current models in the edge of an H-mode plasma.

PubMed

Wade, M R; Murakami, M; Politzer, P A

2004-06-11

Analysis of the parallel electric field E(parallel) evolution following an L-H transition in the DIII-D tokamak indicates the generation of a large negative pulse near the edge which propagates inward, indicative of the generation of a noninductive edge current. Modeling indicates that the observed E(parallel) evolution is consistent with a narrow current density peak generated in the plasma edge. Very good quantitative agreement is found between the measured E(parallel) evolution and that expected from neoclassical theory predictions of the bootstrap current.

Current and future assessments of soil erosion by water on the Tibetan Plateau based on RUSLE and CMIP5 climate models.

PubMed

Teng, Hongfen; Liang, Zongzheng; Chen, Songchao; Liu, Yong; Viscarra Rossel, Raphael A; Chappell, Adrian; Yu, Wu; Shi, Zhou

2018-04-18

Soil erosion by water is accelerated by a warming climate and negatively impacts water security and ecological conservation. The Tibetan Plateau (TP) has experienced warming at a rate approximately twice that observed globally, and heavy precipitation events lead to an increased risk of erosion. In this study, we assessed current erosion on the TP and predicted potential soil erosion by water in 2050. The study was conducted in three steps. During the first step, we used the Revised Universal Soil Equation (RUSLE), publicly available data, and the most recent earth observations to derive estimates of annual erosion from 2002 to 2016 on the TP at 1-km resolution. During the second step, we used a multiple linear regression (MLR) model and a set of climatic covariates to predict rainfall erosivity on the TP in 2050. The MLR was used to establish the relationship between current rainfall erosivity data and a set of current climatic and other covariates. The coefficients of the MLR were generalised with climate covariates for 2050 derived from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) models to estimate rainfall erosivity in 2050. During the third step, soil erosion by water in 2050 was predicted using rainfall erosivity in 2050 and other erosion factors. The results show that the mean annual soil erosion rate on the TP under current conditions is 2.76tha -1 y -1 , which is equivalent to an annual soil loss of 559.59×10 6 t. Our 2050 projections suggested that erosion on the TP will increase to 3.17tha -1 y -1 and 3.91tha -1 y -1 under conditions represented by RCP2.6 and RCP8.5, respectively. The current assessment and future prediction of soil erosion by water on the TP should be valuable for environment protection and soil conservation in this unique region and elsewhere. Copyright © 2018 Elsevier B.V. All rights reserved.

The Coastal Ocean Prediction Systems program: Understanding and managing our coastal ocean

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

Eden, H.F.; Mooers, C.N.K.

1990-06-01

The goal of COPS is to couple a program of regular observations to numerical models, through techniques of data assimilation, in order to provide a predictive capability for the US coastal ocean including the Great Lakes, estuaries, and the entire Exclusive Economic Zone (EEZ). The objectives of the program include: determining the predictability of the coastal ocean and the processes that govern the predictability; developing efficient prediction systems for the coastal ocean based on the assimilation of real-time observations into numerical models; and coupling the predictive systems for the physical behavior of the coastal ocean to predictive systems for biological,more » chemical, and geological processes to achieve an interdisciplinary capability. COPS will provide the basis for effective monitoring and prediction of coastal ocean conditions by optimizing the use of increased scientific understanding, improved observations, advanced computer models, and computer graphics to make the best possible estimates of sea level, currents, temperatures, salinities, and other properties of entire coastal regions.« less

SEC proton prediction model: verification and analysis.

PubMed

Balch, C C

1999-06-01

This paper describes a model that has been used at the NOAA Space Environment Center since the early 1970s as a guide for the prediction of solar energetic particle events. The algorithms for proton event probability, peak flux, and rise time are described. The predictions are compared with observations. The current model shows some ability to distinguish between proton event associated flares and flares that are not associated with proton events. The comparisons of predicted and observed peak flux show considerable scatter, with an rms error of almost an order of magnitude. Rise time comparisons also show scatter, with an rms error of approximately 28 h. The model algorithms are analyzed using historical data and improvements are suggested. Implementation of the algorithm modifications reduces the rms error in the log10 of the flux prediction by 21%, and the rise time rms error by 31%. Improvements are also realized in the probability prediction by deriving the conditional climatology for proton event occurrence given flare characteristics.

Improve SSME power balance model

NASA Technical Reports Server (NTRS)

Karr, Gerald R.

1992-01-01

Effort was dedicated to development and testing of a formal strategy for reconciling uncertain test data with physically limited computational prediction. Specific weaknesses in the logical structure of the current Power Balance Model (PBM) version are described with emphasis given to the main routing subroutines BAL and DATRED. Selected results from a variational analysis of PBM predictions are compared to Technology Test Bed (TTB) variational study results to assess PBM predictive capability. The motivation for systematic integration of uncertain test data with computational predictions based on limited physical models is provided. The theoretical foundation for the reconciliation strategy developed in this effort is presented, and results of a reconciliation analysis of the Space Shuttle Main Engine (SSME) high pressure fuel side turbopump subsystem are examined.

A 3D unstructured grid nearshore hydrodynamic model based on the vortex force formalism

NASA Astrophysics Data System (ADS)

Zheng, Peng; Li, Ming; van der A, Dominic A.; van der Zanden, Joep; Wolf, Judith; Chen, Xueen; Wang, Caixia

2017-08-01

A new three-dimensional nearshore hydrodynamic model system is developed based on the unstructured-grid version of the third generation spectral wave model SWAN (Un-SWAN) coupled with the three-dimensional ocean circulation model FVCOM to enable the full representation of the wave-current interaction in the nearshore region. A new wave-current coupling scheme is developed by adopting the vortex-force (VF) scheme to represent the wave-current interaction. The GLS turbulence model is also modified to better reproduce wave-breaking enhanced turbulence, together with a roller transport model to account for the effect of surface wave roller. This new model system is validated first against a theoretical case of obliquely incident waves on a planar beach, and then applied to three test cases: a laboratory scale experiment of normal waves on a beach with a fixed breaker bar, a field experiment of oblique incident waves on a natural, sandy barred beach (Duck'94 experiment), and a laboratory study of normal-incident waves propagating around a shore-parallel breakwater. Overall, the model predictions agree well with the available measurements in these tests, illustrating the robustness and efficiency of the present model for very different spatial scales and hydrodynamic conditions. Sensitivity tests indicate the importance of roller effects and wave energy dissipation on the mean flow (undertow) profile over the depth. These tests further suggest to adopt a spatially varying value for roller effects across the beach. In addition, the parameter values in the GLS turbulence model should be spatially inhomogeneous, which leads to better prediction of the turbulent kinetic energy and an improved prediction of the undertow velocity profile.

Mathematical modeling and computational prediction of cancer drug resistance.

PubMed

Sun, Xiaoqiang; Hu, Bin

2017-06-23

Diverse forms of resistance to anticancer drugs can lead to the failure of chemotherapy. Drug resistance is one of the most intractable issues for successfully treating cancer in current clinical practice. Effective clinical approaches that could counter drug resistance by restoring the sensitivity of tumors to the targeted agents are urgently needed. As numerous experimental results on resistance mechanisms have been obtained and a mass of high-throughput data has been accumulated, mathematical modeling and computational predictions using systematic and quantitative approaches have become increasingly important, as they can potentially provide deeper insights into resistance mechanisms, generate novel hypotheses or suggest promising treatment strategies for future testing. In this review, we first briefly summarize the current progress of experimentally revealed resistance mechanisms of targeted therapy, including genetic mechanisms, epigenetic mechanisms, posttranslational mechanisms, cellular mechanisms, microenvironmental mechanisms and pharmacokinetic mechanisms. Subsequently, we list several currently available databases and Web-based tools related to drug sensitivity and resistance. Then, we focus primarily on introducing some state-of-the-art computational methods used in drug resistance studies, including mechanism-based mathematical modeling approaches (e.g. molecular dynamics simulation, kinetic model of molecular networks, ordinary differential equation model of cellular dynamics, stochastic model, partial differential equation model, agent-based model, pharmacokinetic-pharmacodynamic model, etc.) and data-driven prediction methods (e.g. omics data-based conventional screening approach for node biomarkers, static network approach for edge biomarkers and module biomarkers, dynamic network approach for dynamic network biomarkers and dynamic module network biomarkers, etc.). Finally, we discuss several further questions and future directions for the use of computational methods for studying drug resistance, including inferring drug-induced signaling networks, multiscale modeling, drug combinations and precision medicine. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Unified Deep Learning Architecture for Modeling Biology Sequence.

PubMed

Wu, Hongjie; Cao, Chengyuan; Xia, Xiaoyan; Lu, Qiang

2017-10-09

Prediction of the spatial structure or function of biological macromolecules based on their sequence remains an important challenge in bioinformatics. When modeling biological sequences using traditional sequencing models, characteristics, such as long-range interactions between basic units, the complicated and variable output of labeled structures, and the variable length of biological sequences, usually lead to different solutions on a case-by-case basis. This study proposed the use of bidirectional recurrent neural networks based on long short-term memory or a gated recurrent unit to capture long-range interactions by designing the optional reshape operator to adapt to the diversity of the output labels and implementing a training algorithm to support the training of sequence models capable of processing variable-length sequences. Additionally, the merge and pooling operators enhanced the ability to capture short-range interactions between basic units of biological sequences. The proposed deep-learning model and its training algorithm might be capable of solving currently known biological sequence-modeling problems through the use of a unified framework. We validated our model on one of the most difficult biological sequence-modeling problems currently known, with our results indicating the ability of the model to obtain predictions of protein residue interactions that exceeded the accuracy of current popular approaches by 10% based on multiple benchmarks.

Nonlinear Recurrent Neural Network Predictive Control for Energy Distribution of a Fuel Cell Powered Robot

PubMed Central

Chen, Qihong; Long, Rong; Quan, Shuhai

2014-01-01

This paper presents a neural network predictive control strategy to optimize power distribution for a fuel cell/ultracapacitor hybrid power system of a robot. We model the nonlinear power system by employing time variant auto-regressive moving average with exogenous (ARMAX), and using recurrent neural network to represent the complicated coefficients of the ARMAX model. Because the dynamic of the system is viewed as operating- state- dependent time varying local linear behavior in this frame, a linear constrained model predictive control algorithm is developed to optimize the power splitting between the fuel cell and ultracapacitor. The proposed algorithm significantly simplifies implementation of the controller and can handle multiple constraints, such as limiting substantial fluctuation of fuel cell current. Experiment and simulation results demonstrate that the control strategy can optimally split power between the fuel cell and ultracapacitor, limit the change rate of the fuel cell current, and so as to extend the lifetime of the fuel cell. PMID:24707206

Comparison of UWCC MOX fuel measurements to MCNP-REN calculations

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

Abhold, M.; Baker, M.; Jie, R.

1998-12-31

The development of neutron coincidence counting has greatly improved the accuracy and versatility of neutron-based techniques to assay fissile materials. Today, the shift register analyzer connected to either a passive or active neutron detector is widely used by both domestic and international safeguards organizations. The continued development of these techniques and detectors makes extensive use of the predictions of detector response through the use of Monte Carlo techniques in conjunction with the point reactor model. Unfortunately, the point reactor model, as it is currently used, fails to accurately predict detector response in highly multiplying mediums such as mixed-oxide (MOX) lightmore » water reactor fuel assemblies. For this reason, efforts have been made to modify the currently used Monte Carlo codes and to develop new analytical methods so that this model is not required to predict detector response. The authors describe their efforts to modify a widely used Monte Carlo code for this purpose and also compare calculational results with experimental measurements.« less

Multi-scale Modeling of the Impact Response of a Strain Rate Sensitive High-Manganese Austenitic Steel

NASA Astrophysics Data System (ADS)

Önal, Orkun; Ozmenci, Cemre; Canadinc, Demircan

2014-09-01

A multi-scale modeling approach was applied to predict the impact response of a strain rate sensitive high-manganese austenitic steel. The roles of texture, geometry and strain rate sensitivity were successfully taken into account all at once by coupling crystal plasticity and finite element (FE) analysis. Specifically, crystal plasticity was utilized to obtain the multi-axial flow rule at different strain rates based on the experimental deformation response under uniaxial tensile loading. The equivalent stress - equivalent strain response was then incorporated into the FE model for the sake of a more representative hardening rule under impact loading. The current results demonstrate that reliable predictions can be obtained by proper coupling of crystal plasticity and FE analysis even if the experimental flow rule of the material is acquired under uniaxial loading and at moderate strain rates that are significantly slower than those attained during impact loading. Furthermore, the current findings also demonstrate the need for an experiment-based multi-scale modeling approach for the sake of reliable predictions of the impact response.

Comparison of free flux flow in two single crystals of V3Si with slightly different pinning strengths

NASA Astrophysics Data System (ADS)

Gafarov, Ozarfar; Gapud, Albert A.; Moraes, Sunhee; Thompson, James R.; Christen, David K.; Reyes, Arneil P.

2011-03-01

Results of recent measurements on two very clean, single-crystal samples of the A15 superconductor V3 Si are presented. Magnetization and transport data confirm the ``clean'' quality of both samples, as manifested by: (i) high residual resistivity ratio, (ii) low critical current densities, and (iii) a ``peak'' effect in the field dependence of critical current. The (H,T) phase line for this peak effect is shifted in the slightly ``dirtier'' sample, which also has higher critical current density Jc (H). High-current Lorentz forces are applied on mixed-state vortices in order to induce the highly ordered free flux flow (FFF) phase, using the same methods as in previous work. A traditional model by Bardeen and Stephen (BS) predicts a simple field dependence of flux flow resistivity ρf (H), presuming a field-independent flux core size. A model by Kogan and Zelezhina (KZ) takes core size into account, and predicts a deviation from BS. In this study, ρf (H) is confirmed to be consistent with predictions of KZ, as will be discussed. Funded by Research Corporation and the National Science Foundation.

Free flux flow in two single crystals of V3Si with differing pinning strengths

NASA Astrophysics Data System (ADS)

Gafarov, O.; Gapud, A. A.; Moraes, S.; Thompson, J. R.; Christen, D. K.; Reyes, A. P.

2011-10-01

Results of measurements on two very clean, single-crystal samples of the A15 superconductor V3Si are presented. Magnetization and transport data have confirmed the ``clean'' quality of both samples, as manifested by: (i) high residual electrical resistivity ratio, (ii) very low critical current densities Jc, and (iii) a ``peak'' effect in the field dependence of critical current. The (H,T) phase line for this peak effect is shifted down for the slightly ``dirtier'' sample, which consequently also has higher critical current density Jc(H). Large Lorentz forces are applied on mixed-state vortices via large currents, in order to induce the highly ordered free flux flow (FFF) phase, using experimental methods developed previously. The traditional model by Bardeen and Stephen (BS) predicts a simple field dependence of flux flow resistivity ρf(H) ˜ H/Hc2, presuming a field-independent flux core size. A model by Kogan and Zelezhina (KZ) takes into account the effects of magnetic field on core size, and predict a clear deviation from the linear BS dependence. In this study, ρf(H) is confirmed to be consistent with predictions of KZ.

Rotenone persistence model for montane streams

USGS Publications Warehouse

Brown, Peter J.; Zale, Alexander V.

2012-01-01

The efficient and effective use of rotenone is hindered by its unknown persistence in streams. Environmental conditions degrade rotenone, but current label instructions suggest fortifying the chemical along a stream based on linear distance or travel time rather than environmental conditions. Our objective was to develop models that use measurements of environmental conditions to predict rotenone persistence in streams. Detailed measurements of ultraviolet radiation, water temperature, dissolved oxygen, total dissolved solids (TDS), conductivity, pH, oxidation–reduction potential (ORP), substrate composition, amount of organic matter, channel slope, and travel time were made along stream segments located between rotenone treatment stations and cages containing bioassay fish in six streams. The amount of fine organic matter, biofilm, sand, gravel, cobble, rubble, small boulders, slope, pH, TDS, ORP, light reaching the stream, energy dissipated, discharge, and cumulative travel time were each significantly correlated with fish death. By using logistic regression, measurements of environmental conditions were paired with the responses of bioassay fish to develop a model that predicted the persistence of rotenone toxicity in streams. This model was validated with data from two additional stream treatment reaches. Rotenone persistence was predicted by a model that used travel time, rubble, and ORP. When this model predicts a probability of less than 0.95, those who apply rotenone can expect incomplete eradication and should plan on fortifying rotenone concentrations. The significance of travel time has been previously identified and is currently used to predict rotenone persistence. However, rubble substrate, which may be associated with the degradation of rotenone by adsorption and volatilization in turbulent environments, was not previously considered.

Exploring uncertainty and model predictive performance concepts via a modular snowmelt-runoff modeling framework

Treesearch

Tyler Jon Smith; Lucy Amanda Marshall

2010-01-01

Model selection is an extremely important aspect of many hydrologic modeling studies because of the complexity, variability, and uncertainty that surrounds the current understanding of watershed-scale systems. However, development and implementation of a complete precipitation-runoff modeling framework, from model selection to calibration and uncertainty analysis, are...

3-D Modeling of a Nearshore Dye Release

NASA Astrophysics Data System (ADS)

Maxwell, A. R.; Hibler, L. F.; Miller, L. M.

2006-12-01

The usage of computer modeling software in predicting the behavior of a plume discharged into deep water is well established. Nearfield plume spreading in coastal areas with complex bathymetry is less commonly studied; in addition to geometry, some of the difficulties of this environment include: tidal exchange, temperature, and salinity gradients. Although some researchers have applied complex hydrodynamic models to this problem, nearfield regions are typically modeled by calibration of an empirical or expert system model. In the present study, the 3D hydrodynamic model Delft3D-FLOW was used to predict the advective transport from a point release in Sequim Bay, Washington. A nested model approach was used, wherein a coarse model using a mesh extending to nearby tide gages (cell sizes up to 1 km) was run over several tidal cycles in order to provide boundary conditions to a smaller area. The nested mesh (cell sizes up to 30 m) was forced on two open boundaries using the water surface elevation derived from the coarse model. Initial experiments with the uncalibrated model were conducted in order to predict plume propagation based on the best available field data. Field experiments were subsequently carried out by releasing rhodamine dye into the bay at near-peak flood tidal current and near high slack tidal conditions. Surface and submerged releases were carried out from an anchored vessel. Concurrently collected data from the experiment include temperature, salinity, dye concentration, and hyperspectral imagery, collected from boats and aircraft. A REMUS autonomous underwater vehicle was used to measure current velocity and dye concentration at varying depths, as well as to acquire additional bathymetric information. Preliminary results indicate that the 3D hydrodynamic model offers a reasonable prediction of plume propagation speed and shape. A sensitivity analysis is underway to determine the significant factors in effectively using the model as a predictive tool for plume tracking in data-limited environments. The Delft-PART stochastic particle transport model is also being examined to determine its utility for the present study.

Cp Asymmetries in B0DECAYS Beyond the Standard Model

NASA Astrophysics Data System (ADS)

Dib, Claudio O.; London, David; Nir, Yosef

Of the many ingredients of the Standard Model that are relevant to the analysis of CP asymmetries in B0 decays, some are likely to hold even beyond the Standard Model while others are sensitive to new physics. Consequently, certain predictions are maintained while others may show dramatic deviations from the Standard Model. Many classes of models may show clear signatures when the asymmetries are measured: four quark generations, Z-mediated flavor-changing neutral currents, supersymmetry and “real superweak” models. On the other hand, models of left-right symmetry and multi-Higgs sectors with natural flavor conservation are unlikely to modify the Standard Model predictions.

Developing and implementing the use of predictive models for estimating water quality at Great Lakes beaches

USGS Publications Warehouse

Francy, Donna S.; Brady, Amie M.G.; Carvin, Rebecca B.; Corsi, Steven R.; Fuller, Lori M.; Harrison, John H.; Hayhurst, Brett A.; Lant, Jeremiah; Nevers, Meredith B.; Terrio, Paul J.; Zimmerman, Tammy M.

2013-01-01

Predictive models have been used at beaches to improve the timeliness and accuracy of recreational water-quality assessments over the most common current approach to water-quality monitoring, which relies on culturing fecal-indicator bacteria such as Escherichia coli (E. coli.). Beach-specific predictive models use environmental and water-quality variables that are easily and quickly measured as surrogates to estimate concentrations of fecal-indicator bacteria or to provide the probability that a State recreational water-quality standard will be exceeded. When predictive models are used for beach closure or advisory decisions, they are referred to as “nowcasts.” During the recreational seasons of 2010-12, the U.S. Geological Survey (USGS), in cooperation with 23 local and State agencies, worked to improve existing nowcasts at 4 beaches, validate predictive models at another 38 beaches, and collect data for predictive-model development at 7 beaches throughout the Great Lakes. This report summarizes efforts to collect data and develop predictive models by multiple agencies and to compile existing information on the beaches and beach-monitoring programs into one comprehensive report. Local agencies measured E. coli concentrations and variables expected to affect E. coli concentrations such as wave height, turbidity, water temperature, and numbers of birds at the time of sampling. In addition to these field measurements, equipment was installed by the USGS or local agencies at or near several beaches to collect water-quality and metrological measurements in near real time, including nearshore buoys, weather stations, and tributary staff gages and monitors. The USGS worked with local agencies to retrieve data from existing sources either manually or by use of tools designed specifically to compile and process data for predictive-model development. Predictive models were developed by use of linear regression and (or) partial least squares techniques for 42 beaches that had at least 2 years of data (2010-11 and sometimes earlier) and for 1 beach that had 1 year of data. For most models, software designed for model development by the U.S. Environmental Protection Agency (Virtual Beach) was used. The selected model for each beach was based on a combination of explanatory variables including, most commonly, turbidity, day of the year, change in lake level over 24 hours, wave height, wind direction and speed, and antecedent rainfall for various time periods. Forty-two predictive models were validated against data collected during an independent year (2012) and compared to the current method for assessing recreational water quality-using the previous day’s E. coli concentration (persistence model). Goals for good predictive-model performance were responses that were at least 5 percent greater than the persistence model and overall correct responses greater than or equal to 80 percent, sensitivities (percentage of exceedances of the bathing-water standard that were correctly predicted by the model) greater than or equal to 50 percent, and specificities (percentage of nonexceedances correctly predicted by the model) greater than or equal to 85 percent. Out of 42 predictive models, 24 models yielded over-all correct responses that were at least 5 percent greater than the use of the persistence model. Predictive-model responses met the performance goals more often than the persistence-model responses in terms of overall correctness (28 versus 17 models, respectively), sensitivity (17 versus 4 models), and specificity (34 versus 25 models). Gaining knowledge of each beach and the factors that affect E. coli concentrations is important for developing good predictive models. Collection of additional years of data with a wide range of environmental conditions may also help to improve future model performance. The USGS will continue to work with local agencies in 2013 and beyond to develop and validate predictive models at beaches and improve existing nowcasts, restructuring monitoring activities to accommodate future uncertainties in funding and resources.

Predictive Model Development for Aviation Black Carbon Mass Emissions from Alternative and Conventional Fuels at Ground and Cruise.

PubMed

Abrahamson, Joseph P; Zelina, Joseph; Andac, M Gurhan; Vander Wal, Randy L

2016-11-01

The first order approximation (FOA3) currently employed to estimate BC mass emissions underpredicts BC emissions due to inaccuracies in measuring low smoke numbers (SNs) produced by modern high bypass ratio engines. The recently developed Formation and Oxidation (FOX) method removes the need for and hence uncertainty associated with (SNs), instead relying upon engine conditions in order to predict BC mass. Using the true engine operating conditions from proprietary engine cycle data an improved FOX (ImFOX) predictive relation is developed. Still, the current methods are not optimized to estimate cruise emissions nor account for the use of alternative jet fuels with reduced aromatic content. Here improved correlations are developed to predict engine conditions and BC mass emissions at ground and cruise altitude. This new ImFOX is paired with a newly developed hydrogen relation to predict emissions from alternative fuels and fuel blends. The ImFOX is designed for rich-quench-lean style combustor technologies employed predominately in the current aviation fleet.

Interaction between polymer constituents and the structure of biopolymers

NASA Technical Reports Server (NTRS)

Rein, R.

1974-01-01

The paper reviews the current status of methods for calculating intermolecular interactions between biopolymer units. The nature of forces contributing to the various domains of intermolecular separations is investigated, and various approximations applicable in the respective regions are examined. The predictive value of current theory is tested by establishing a connection with macroscopic properties and comparing the theoretical predicted values with those derived from experimental data. This has led to the introduction of a statistical model describing DNA.

A battery power model for the EUVE spacecraft

NASA Technical Reports Server (NTRS)

Yen, Wen L.; Littlefield, Ronald G.; Mclean, David R.; Tuchman, Alan; Broseghini, Todd A.; Page, Brenda J.

1993-01-01

This paper describes a battery power model that has been developed to simulate and predict the behavior of the 50 ampere-hour nickel-cadmium battery that supports the Extreme Ultraviolet Explorer (EUVE) spacecraft in its low Earth orbit. First, for given orbit, attitude, solar array panel and spacecraft load data, the model calculates minute-by-minute values for the net power available for charging the battery for a user-specified time period (usually about two weeks). Next, the model is used to calculate minute-by-minute values for the battery voltage, current and state-of-charge for the time period. The model's calculations are explained for its three phases: sunrise charging phase, constant voltage phase, and discharge phase. A comparison of predicted model values for voltage, current and state-of-charge with telemetry data for a complete charge-discharge cycle shows good correlation. This C-based computer model will be used by the EUVE Flight Operations Team for various 'what-if' scheduling analyses.

Prediction Equation for Lower Limbs Lean Soft Tissue in Circumpubertal Boys Using Anthropometry and Biological Maturation

PubMed Central

Valente-dos-Santos, João; Coelho-e-Silva, Manuel J.; Machado-Rodrigues, Aristides M.; Elferink-Gemser, Marije T.; Malina, Robert M.; Petroski, Édio L.; Minderico, Cláudia S.; Silva, Analiza M.; Baptista, Fátima; Sardinha, Luís B.

2014-01-01

Lean soft tissue (LST), a surrogate of skeletal muscle mass, is largely limited to appendicular body regions. Simple and accurate methods to estimate lower limbs LST are often used in attempts to partition out the influence of body size on performance outputs. The aim of the current study was to develop and cross-validate a new model to predict lower limbs LST in boys aged 10–13 years, using dual-energy X-ray absorptiometry (DXA) as the reference method. Total body and segmental (lower limbs) composition were assessed with a Hologic Explorer-W QDR DXA scanner in a cross-sectional sample of 75 Portuguese boys (144.8±6.4 cm; 40.2±9.0 kg). Skinfolds were measured at the anterior and posterior mid-thigh, and medial calf. Circumferences were measured at the proximal, mid and distal thigh. Leg length was estimated as stature minus sitting height. Current stature expressed as a percentage of attained predicted mature stature (PMS) was used as an estimate of biological maturity status. Backward proportional allometric models were used to identify the model with the best statistical fit: ln (lower limbs LST)  = 0.838× ln (body mass) +0.476× ln (leg length) – 0.135× ln (mid-thigh circumference) – 0.053× ln (anterior mid-thigh skinfold) – 0.098× ln (medial calf skinfold) – 2.680+0.010× (percentage of attained PMS) (R = 0.95). The obtained equation was cross-validated using the predicted residuals sum of squares statistics (PRESS) method (R 2 PRESS = 0.90). Deming repression analysis between predicted and current lower limbs LST showed a standard error of estimation of 0.52 kg (95% limits of agreement: 0.77 to −1.27 kg). The new model accurately predicts lower limbs LST in circumpubertal boys. PMID:25229472

Modeling to Predict Escherichia coli at Presque Isle Beach 2, City of Erie, Erie County, Pennsylvania

USGS Publications Warehouse

Zimmerman, Tammy M.

2008-01-01

The Lake Erie beaches in Pennsylvania are a valuable recreational resource for Erie County. Concentrations of Escherichia coli (E. coli) at monitored beaches in Presque Isle State Park in Erie, Pa., occasionally exceed the single-sample bathing-water standard of 235 colonies per 100 milliliters resulting in potentially unsafe swimming conditions and prompting beach managers to post public advisories or to close beaches to recreation. To supplement the current method for assessing recreational water quality (E. coli concentrations from the previous day), a predictive regression model for E. coli concentrations at Presque Isle Beach 2 was developed from data collected during the 2004 and 2005 recreational seasons. Model output included predicted E. coli concentrations and exceedance probabilities--the probability that E. coli concentrations would exceed the standard. For this study, E. coli concentrations and other water-quality and environmental data were collected during the 2006 recreational season at Presque Isle Beach 2. The data from 2006, an independent year, were used to test (validate) the 2004-2005 predictive regression model and compare the model performance to the current method. Using 2006 data, the 2004-2005 model yielded more correct responses and better predicted exceedances of the standard than the use of E. coli concentrations from the previous day. The differences were not pronounced, however, and more data are needed. For example, the model correctly predicted exceedances of the standard 11 percent of the time (1 out of 9 exceedances that occurred in 2006) whereas using the E. coli concentrations from the previous day did not result in any correctly predicted exceedances. After validation, new models were developed by adding the 2006 data to the 2004-2005 dataset and by analyzing the data in 2- and 3-year combinations. Results showed that excluding the 2004 data (using 2005 and 2006 data only) yielded the best model. Explanatory variables in the 2005-2006 model were log10 turbidity, bird count, and wave height. The 2005-2006 model correctly predicted when the standard would not be exceeded (specificity) with a response of 95.2 percent (178 out of 187 nonexceedances) and correctly predicted when the standard would be exceeded (sensitivity) with a response of 64.3 percent (9 out of 14 exceedances). In all cases, the results from predictive modeling produced higher percentages of correct predictions than using E. coli concentrations from the previous day. Additional data collected each year can be used to test and possibly improve the model. The results of this study will aid beach managers in more rapidly determining when waters are not safe for recreational use and, subsequently, when to close a beach or post an advisory.

Soft Wall Ion Channel in Continuum Representation with Application to Modeling Ion Currents in α-Hemolysin

PubMed Central

Simakov, Nikolay A.

2010-01-01

A soft repulsion (SR) model of short range interactions between mobile ions and protein atoms is introduced in the framework of continuum representation of the protein and solvent. The Poisson-Nernst-Plank (PNP) theory of ion transport through biological channels is modified to incorporate this soft wall protein model. Two sets of SR parameters are introduced: the first is parameterized for all essential amino acid residues using all atom molecular dynamic simulations; the second is a truncated Lennard – Jones potential. We have further designed an energy based algorithm for the determination of the ion accessible volume, which is appropriate for a particular system discretization. The effects of these models of short-range interaction were tested by computing current-voltage characteristics of the α-hemolysin channel. The introduced SR potentials significantly improve prediction of channel selectivity. In addition, we studied the effect of choice of some space-dependent diffusion coefficient distributions on the predicted current-voltage properties. We conclude that the diffusion coefficient distributions largely affect total currents and have little effect on rectifications, selectivity or reversal potential. The PNP-SR algorithm is implemented in a new efficient parallel Poisson, Poisson-Boltzman and PNP equation solver, also incorporated in a graphical molecular modeling package HARLEM. PMID:21028776

Non-Hydrostatic Modelling of Waves and Currents over Subtle Bathymetric Features

NASA Astrophysics Data System (ADS)

Gomes, E.; Mulligan, R. P.; McNinch, J.

2014-12-01

Localized areas with high rates of shoreline erosion on beaches, referred to as erosional hotspots, can occur near clusters of relict shore-oblique sandbars. Wave transformation and wave-driven currents over these morphological features could provide an understanding of the hydrodynamic-morphologic coupling mechanism that connects them to the occurrence of erosional hotspots. To investigate this, we use the non-hydrostatic SWASH model that phase-resolves the free surface and fluid motions throughout the water column, allowing for high resolution of wave propagation and breaking processes. In this study we apply a coupled system of nested models including SWAN over a large domain of the North Carolina shelf with smaller nested SWASH domains in areas of interest to determine the hydrodynamic processes occurring over shore oblique bars. In this presentation we focus on a high resolution grid (10 vertical layers, 10 m horizontal resolution) applied to the Duck region with model validation from acoustic wave and current data, and observations from the Coastal Lidar And Radar Imaging System (CLARIS). By altering the bathymetry input for each model run based on bathymetric surveys and comparing the predicted and observed wave heights and current profiles, the effects of subtle bathymetric perturbations have on wave refraction, wave breaking, surf zone currents and vorticity are investigated. The ability to predict wave breaking and hydrodynamics with a non-hydrostatic model may improve our understanding of surf zone dynamics in relation to morphologic conditions.

Interaction Between Domperidone and Ketoconazole: Toward Prediction of Consequent QTc Prolongation Using Purely In Vitro Information

PubMed Central

Mishra, H; Polak, S; Jamei, M; Rostami-Hodjegan, A

2014-01-01

We aimed to investigate the application of combined mechanistic pharmacokinetic (PK) and pharmacodynamic (PD) modeling and simulation in predicting the domperidone (DOM) triggered pseudo-electrocardiogram modification in the presence of a CYP3A inhibitor, ketoconazole (KETO), using in vitro–in vivo extrapolation. In vitro metabolic and inhibitory data were incorporated into physiologically based pharmacokinetic (PBPK) models within Simcyp to simulate time course of plasma DOM and KETO concentrations when administered alone or in combination with KETO (DOM+KETO). Simulated DOM concentrations in plasma were used to predict changes in gender-specific QTcF (Fridericia correction) intervals within the Cardiac Safety Simulator platform taking into consideration DOM, KETO, and DOM+KETO triggered inhibition of multiple ionic currents in population. Combination of in vitro–in vivo extrapolation, PBPK, and systems pharmacology of electric currents in the heart was able to predict the direction and magnitude of PK and PD changes under coadministration of the two drugs although some disparities were detected. PMID:25116274

Minimizing the total harmonic distortion for a 3 kW, 20 kHz ac to dc converter using SPICE

NASA Technical Reports Server (NTRS)

Lollar, Louis F.; Kapustka, Robert E.

1988-01-01

This paper describes the SPICE model of a transformer-rectified-filter (TRF) circuit and the Micro-CAP (Microcomputer Circuit Analysis Program) model and their application. The models were used to develop an actual circuit with reduced input current THD. The SPICE analysis consistently predicted the THD improvements in actual circuits as various designs were attempted. In an effort to predict and verify load regulation, the incorporation of saturable inductor models significantly improved the fidelity of the TRF circuit output voltage.

A mathematical model for predicting cyclic voltammograms of electronically conductive polypyrrole

NASA Technical Reports Server (NTRS)

Yeu, Taewhan; Nguyen, Trung V.; White, Ralph E.

1988-01-01

Polypyrrole is an attractive polymer for use as a high-energy-density secondary battery because of its potential as an inexpensive, lightweight, and noncorrosive electrode material. A mathematical model to simulate cyclic voltammograms for polypyrrole is presented. The model is for a conductive porous electrode film on a rotating disk electrode (RDE) and is used to predict the spatial and time dependence of concentration, overpotential, and stored charge profiles within a polypyrrole film. The model includes both faradic and capacitance charge components in the total current density expression.

A mathematical model for predicting cyclic voltammograms of electronically conductive polypyrrole

NASA Technical Reports Server (NTRS)

Yeu, Taewhan; Nguyen, Trung V.; White, Ralph E.

1987-01-01

Polypyrrole is an attractive polymer for use as a high-energy-density secondary battery because of its potential as an inexpensive, lightweight, and noncorrosive electrode material. A mathematical model to simulate cyclic voltammograms for polypyrrole is presented. The model is for a conductive porous electrode film on a rotating disk electrode (RDE) and is used to predict the spatial and time dependence of concentration, overpotential, and stored charge profiles within a polypyrrole film. The model includes both faradic and capacitance charge components in the total current density expression.

On the Predictability of Future Impact in Science

PubMed Central

Penner, Orion; Pan, Raj K.; Petersen, Alexander M.; Kaski, Kimmo; Fortunato, Santo

2013-01-01

Correctly assessing a scientist's past research impact and potential for future impact is key in recruitment decisions and other evaluation processes. While a candidate's future impact is the main concern for these decisions, most measures only quantify the impact of previous work. Recently, it has been argued that linear regression models are capable of predicting a scientist's future impact. By applying that future impact model to 762 careers drawn from three disciplines: physics, biology, and mathematics, we identify a number of subtle, but critical, flaws in current models. Specifically, cumulative non-decreasing measures like the h-index contain intrinsic autocorrelation, resulting in significant overestimation of their “predictive power”. Moreover, the predictive power of these models depend heavily upon scientists' career age, producing least accurate estimates for young researchers. Our results place in doubt the suitability of such models, and indicate further investigation is required before they can be used in recruiting decisions. PMID:24165898

Plasmonic Light Trapping in Thin-Film Solar Cells: Impact of Modeling on Performance Prediction

PubMed Central

Micco, Alberto; Pisco, Marco; Ricciardi, Armando; Mercaldo, Lucia V.; Usatii, Iurie; La Ferrara, Vera; Delli Veneri, Paola; Cutolo, Antonello; Cusano, Andrea

2015-01-01

We present a comparative study on numerical models used to predict the absorption enhancement in thin-film solar cells due to the presence of structured back-reflectors exciting, at specific wavelengths, hybrid plasmonic-photonic resonances. To evaluate the effectiveness of the analyzed models, they have been applied in a case study: starting from a U-shaped textured glass thin-film, µc-Si:H solar cells have been successfully fabricated. The fabricated cells, with different intrinsic layer thicknesses, have been morphologically, optically and electrically characterized. The experimental results have been successively compared with the numerical predictions. We have found that, in contrast to basic models based on the underlying schematics of the cell, numerical models taking into account the real morphology of the fabricated device, are able to effectively predict the cells performances in terms of both optical absorption and short-circuit current values.

Are we near the predictability limit of tropical Indo-Pacific sea surface temperatures?

NASA Astrophysics Data System (ADS)

Newman, Matthew; Sardeshmukh, Prashant D.

2017-08-01

The predictability of seasonal anomalies worldwide rests largely on the predictability of tropical sea surface temperature (SST) anomalies. Tropical forecast skill is also a key metric of climate models. We find, however, that despite extensive model development, the tropical SST forecast skill of the operational North American Multi-Model Ensemble (NMME) of eight coupled atmosphere-ocean models remains close both regionally and temporally to that of a vastly simpler linear inverse model (LIM) derived from observed covariances of SST, sea surface height, and wind fields. The LIM clearly captures the essence of the predictable SST dynamics. The NMME and LIM skills also closely track and are only slightly lower than the potential skill estimated using the LIM's forecast signal-to-noise ratios. This suggests that the scope for further skill improvement is small in most regions, except in the western equatorial Pacific where the NMME skill is currently much lower than the LIM skill.

Prediction models for intracranial hemorrhage or major bleeding in patients on antiplatelet therapy: a systematic review and external validation study.

PubMed

Hilkens, N A; Algra, A; Greving, J P

2016-01-01

ESSENTIALS: Prediction models may help to identify patients at high risk of bleeding on antiplatelet therapy. We identified existing prediction models for bleeding and validated them in patients with cerebral ischemia. Five prediction models were identified, all of which had some methodological shortcomings. Performance in patients with cerebral ischemia was poor. Background Antiplatelet therapy is widely used in secondary prevention after a transient ischemic attack (TIA) or ischemic stroke. Bleeding is the main adverse effect of antiplatelet therapy and is potentially life threatening. Identification of patients at increased risk of bleeding may help target antiplatelet therapy. This study sought to identify existing prediction models for intracranial hemorrhage or major bleeding in patients on antiplatelet therapy and evaluate their performance in patients with cerebral ischemia. We systematically searched PubMed and Embase for existing prediction models up to December 2014. The methodological quality of the included studies was assessed with the CHARMS checklist. Prediction models were externally validated in the European Stroke Prevention Study 2, comprising 6602 patients with a TIA or ischemic stroke. We assessed discrimination and calibration of included prediction models. Five prediction models were identified, of which two were developed in patients with previous cerebral ischemia. Three studies assessed major bleeding, one studied intracerebral hemorrhage and one gastrointestinal bleeding. None of the studies met all criteria of good quality. External validation showed poor discriminative performance, with c-statistics ranging from 0.53 to 0.64 and poor calibration. A limited number of prediction models is available that predict intracranial hemorrhage or major bleeding in patients on antiplatelet therapy. The methodological quality of the models varied, but was generally low. Predictive performance in patients with cerebral ischemia was poor. In order to reliably predict the risk of bleeding in patients with cerebral ischemia, development of a prediction model according to current methodological standards is needed. © 2015 International Society on Thrombosis and Haemostasis.

Numerical modelling as a cost-reduction tool for probability of detection of bolt hole eddy current testing

NASA Astrophysics Data System (ADS)

Mandache, C.; Khan, M.; Fahr, A.; Yanishevsky, M.

2011-03-01

Probability of detection (PoD) studies are broadly used to determine the reliability of specific nondestructive inspection procedures, as well as to provide data for damage tolerance life estimations and calculation of inspection intervals for critical components. They require inspections on a large set of samples, a fact that makes these statistical assessments time- and cost-consuming. Physics-based numerical simulations of nondestructive testing inspections could be used as a cost-effective alternative to empirical investigations. They realistically predict the inspection outputs as functions of the input characteristics related to the test piece, transducer and instrument settings, which are subsequently used to partially substitute and/or complement inspection data in PoD analysis. This work focuses on the numerical modelling aspects of eddy current testing for the bolt hole inspections of wing box structures typical of the Lockheed Martin C-130 Hercules and P-3 Orion aircraft, found in the air force inventory of many countries. Boundary element-based numerical modelling software was employed to predict the eddy current signal responses when varying inspection parameters related to probe characteristics, crack geometry and test piece properties. Two demonstrator exercises were used for eddy current signal prediction when lowering the driver probe frequency and changing the material's electrical conductivity, followed by subsequent discussions and examination of the implications on using simulated data in the PoD analysis. Despite some simplifying assumptions, the modelled eddy current signals were found to provide similar results to the actual inspections. It is concluded that physics-based numerical simulations have the potential to partially substitute or complement inspection data required for PoD studies, reducing the cost, time, effort and resources necessary for a full empirical PoD assessment.

A Probabilistic Model of Meter Perception: Simulating Enculturation.

PubMed

van der Weij, Bastiaan; Pearce, Marcus T; Honing, Henkjan

2017-01-01

Enculturation is known to shape the perception of meter in music but this is not explicitly accounted for by current cognitive models of meter perception. We hypothesize that the induction of meter is a result of predictive coding: interpreting onsets in a rhythm relative to a periodic meter facilitates prediction of future onsets. Such prediction, we hypothesize, is based on previous exposure to rhythms. As such, predictive coding provides a possible explanation for the way meter perception is shaped by the cultural environment. Based on this hypothesis, we present a probabilistic model of meter perception that uses statistical properties of the relation between rhythm and meter to infer meter from quantized rhythms. We show that our model can successfully predict annotated time signatures from quantized rhythmic patterns derived from folk melodies. Furthermore, we show that by inferring meter, our model improves prediction of the onsets of future events compared to a similar probabilistic model that does not infer meter. Finally, as a proof of concept, we demonstrate how our model can be used in a simulation of enculturation. From the results of this simulation, we derive a class of rhythms that are likely to be interpreted differently by enculturated listeners with different histories of exposure to rhythms.

A Final Approach Trajectory Model for Current Operations

NASA Technical Reports Server (NTRS)

Gong, Chester; Sadovsky, Alexander

2010-01-01

Predicting accurate trajectories with limited intent information is a challenge faced by air traffic management decision support tools in operation today. One such tool is the FAA's Terminal Proximity Alert system which is intended to assist controllers in maintaining safe separation of arrival aircraft during final approach. In an effort to improve the performance of such tools, two final approach trajectory models are proposed; one based on polynomial interpolation, the other on the Fourier transform. These models were tested against actual traffic data and used to study effects of the key final approach trajectory modeling parameters of wind, aircraft type, and weight class, on trajectory prediction accuracy. Using only the limited intent data available to today's ATM system, both the polynomial interpolation and Fourier transform models showed improved trajectory prediction accuracy over a baseline dead reckoning model. Analysis of actual arrival traffic showed that this improved trajectory prediction accuracy leads to improved inter-arrival separation prediction accuracy for longer look ahead times. The difference in mean inter-arrival separation prediction error between the Fourier transform and dead reckoning models was 0.2 nmi for a look ahead time of 120 sec, a 33 percent improvement, with a corresponding 32 percent improvement in standard deviation.

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.

Statistical modelling predicts almost complete loss of major periglacial processes in Northern Europe by 2100.

PubMed

Aalto, Juha; Harrison, Stephan; Luoto, Miska

2017-09-11

The periglacial realm is a major part of the cryosphere, covering a quarter of Earth's land surface. Cryogenic land surface processes (LSPs) control landscape development, ecosystem functioning and climate through biogeochemical feedbacks, but their response to contemporary climate change is unclear. Here, by statistically modelling the current and future distributions of four major LSPs unique to periglacial regions at fine scale, we show fundamental changes in the periglacial climate realm are inevitable with future climate change. Even with the most optimistic CO 2 emissions scenario (Representative Concentration Pathway (RCP) 2.6) we predict a 72% reduction in the current periglacial climate realm by 2050 in our climatically sensitive northern Europe study area. These impacts are projected to be especially severe in high-latitude continental interiors. We further predict that by the end of the twenty-first century active periglacial LSPs will exist only at high elevations. These results forecast a future tipping point in the operation of cold-region LSP, and predict fundamental landscape-level modifications in ground conditions and related atmospheric feedbacks.Cryogenic land surface processes characterise the periglacial realm and control landscape development and ecosystem functioning. Here, via statistical modelling, the authors predict a 72% reduction of the periglacial realm in Northern Europe by 2050, and almost complete disappearance by 2100.

Image-guided preoperative prediction of pyramidal tract side effect in deep brain stimulation

NASA Astrophysics Data System (ADS)

Baumgarten, C.; Zhao, Y.; Sauleau, P.; Malrain, C.; Jannin, P.; Haegelen, C.

2016-03-01

Deep brain stimulation of the medial globus pallidus is a surgical procedure for treating patients suffering from Parkinson's disease. Its therapeutic effect may be limited by the presence of pyramidal tract side effect (PTSE). PTSE is a contraction time-locked to the stimulation when the current spreading reaches the motor fibers of the pyramidal tract within the internal capsule. The lack of side-effect predictive model leads the neurologist to secure an optimal electrode placement by iterating clinical testing on an awake patient during the surgical procedure. The objective of the study was to propose a preoperative predictive model of PTSE. A machine learning based method called PyMAN (for Pyramidal tract side effect Model based on Artificial Neural network) that accounted for the current of the stimulation, the 3D electrode coordinates and the angle of the trajectory, was designed to predict the occurrence of PTSE. Ten patients implanted in the medial globus pallidus have been tested by a clinician to create a labeled dataset of the stimulation parameters that trigger PTSE. The kappa index value between the data predicted by PyMAN and the labeled data was .78. Further evaluation studies are desirable to confirm whether PyMAN could be a reliable tool for assisting the surgeon to prevent PTSE during the preoperative planning.

[Establishment of the prediction model for ischemic cardiovascular disease of elderly male population under current health care program].

PubMed

Chen, Jin-hong; Wu, Hai-yun; He, Kun-lun; He, Yao; Qin, Yin-he

2010-10-01

To establish and verify the prediction model for ischemic cardiovascular disease (ICVD) among the elderly population who were under the current health care programs. Statistical analysis on data from physical examination, hospitalization of the past years, from questionnaire and telephone interview was carried out in May, 2003. Data was from a hospital which implementing a health care program. Baseline population with a proportion of 4:1 was randomly selected to generate both module group and verification group. Baseline data was induced to make the verification group into regression model of module group and to generate the predictive value. Distinguished ability with area under ROC curve and the predictive veracity were verified through comparing the predictive incidence rate and actual incidence rate of every deciles group by Hosmer-Lemeshow test. Predictive veracity of the prediction model at population level was verified through comparing the predictive 6-year incidence rates of ICVD with actual 6-year accumulative incidence rates of ICVD with error rate calculated. The samples included 2271 males over the age of 65 with 1817 people for modeling population and 454 for verified population. All of the samples were stratified into two layers to establish hierarchical Cox proportional hazard regression model, including one advanced age group (greater than or equal to 75 years old), and another elderly group (less than 75 years old). Data from the statically analysis showed that the risk factors in aged group were age, systolic blood pressure, serum creatinine level, fasting blood glucose level, while protective factor was high density lipoprotein;in advanced age group, the risk factors were body weight index, systolic blood pressure, serum total cholesterol level, serum creatinine level, fasting blood glucose level, while protective factor was HDL-C. The area under the ROC curve (AUC) and 95%CI were 0.723 and 0.687 - 0.759 respectively. Discriminating power was good. All individual predictive ICVD cumulative incidence and actual incidence were analyzed using Hosmer-Lemeshow test, χ(2) = 1.43, P = 0.786, showing that the predictive veracity was good. The stratified Cox Hazards Regression model was used to establish prediction model of the aged male population under a certain health care program. The common prediction factor of the two age groups were: systolic blood pressure, serum creatinine level, fasting blood glucose level and HDL-C. The area under the ROC curve of the verification group was 0.723, showing that the distinguished ability was good and the predict ability at the individual level and at the group level were also satisfactory. It was feasible to using Cox Proportional Hazards Regression Model for predicting the population groups.

Recent Achievements of the Collaboratory for the Study of Earthquake Predictability

NASA Astrophysics Data System (ADS)

Jackson, D. D.; Liukis, M.; Werner, M. J.; Schorlemmer, D.; Yu, J.; Maechling, P. J.; Zechar, J. D.; Jordan, T. H.

2015-12-01

Maria Liukis, SCEC, USC; Maximilian Werner, University of Bristol; Danijel Schorlemmer, GFZ Potsdam; John Yu, SCEC, USC; Philip Maechling, SCEC, USC; Jeremy Zechar, Swiss Seismological Service, ETH; Thomas H. Jordan, SCEC, USC, and the CSEP Working Group The Collaboratory for the Study of Earthquake Predictability (CSEP) supports a global program to conduct prospective earthquake forecasting experiments. CSEP testing centers are now operational in California, New Zealand, Japan, China, and Europe with 435 models under evaluation. The California testing center, operated by SCEC, has been operational since Sept 1, 2007, and currently hosts 30-minute, 1-day, 3-month, 1-year and 5-year forecasts, both alarm-based and probabilistic, for California, the Western Pacific, and worldwide. We have reduced testing latency, implemented prototype evaluation of M8 forecasts, and are currently developing formats and procedures to evaluate externally-hosted forecasts and predictions. These efforts are related to CSEP support of the USGS program in operational earthquake forecasting and a DHS project to register and test external forecast procedures from experts outside seismology. A retrospective experiment for the 2010-2012 Canterbury earthquake sequence has been completed, and the results indicate that some physics-based and hybrid models outperform purely statistical (e.g., ETAS) models. The experiment also demonstrates the power of the CSEP cyberinfrastructure for retrospective testing. Our current development includes evaluation strategies that increase computational efficiency for high-resolution global experiments, such as the evaluation of the Global Earthquake Activity Rate (GEAR) model. We describe the open-source CSEP software that is available to researchers as they develop their forecast models (http://northridge.usc.edu/trac/csep/wiki/MiniCSEP). We also discuss applications of CSEP infrastructure to geodetic transient detection and how CSEP procedures are being adapted to ground motion prediction experiments.

On the importance of incorporating sampling weights in occupancy model estimation

EPA Science Inventory

Occupancy models are used extensively to assess wildlife-habitat associations and to predict species distributions across large geographic regions. Occupancy models were developed as a tool to properly account for imperfect detection of a species. Current guidelines on survey des...

Stochastic Modeling and Global Warming Trend Extraction For Ocean Acoustic Travel Times.

DTIC Science & Technology

1995-01-06

consideration and that these models can not currently be relied upon by themselves to predict global warming . Experimental data is most certainly needed, not...only to measure global warming itself, but to help improve the ocean model themselves. (AN)

Experimental evaluation of a recursive model identification technique for type 1 diabetes.

PubMed

Finan, Daniel A; Doyle, Francis J; Palerm, Cesar C; Bevier, Wendy C; Zisser, Howard C; Jovanovic, Lois; Seborg, Dale E

2009-09-01

A model-based controller for an artificial beta cell requires an accurate model of the glucose-insulin dynamics in type 1 diabetes subjects. To ensure the robustness of the controller for changing conditions (e.g., changes in insulin sensitivity due to illnesses, changes in exercise habits, or changes in stress levels), the model should be able to adapt to the new conditions by means of a recursive parameter estimation technique. Such an adaptive strategy will ensure that the most accurate model is used for the current conditions, and thus the most accurate model predictions are used in model-based control calculations. In a retrospective analysis, empirical dynamic autoregressive exogenous input (ARX) models were identified from glucose-insulin data for nine type 1 diabetes subjects in ambulatory conditions. Data sets consisted of continuous (5-minute) glucose concentration measurements obtained from a continuous glucose monitor, basal insulin infusion rates and times and amounts of insulin boluses obtained from the subjects' insulin pumps, and subject-reported estimates of the times and carbohydrate content of meals. Two identification techniques were investigated: nonrecursive, or batch methods, and recursive methods. Batch models were identified from a set of training data, whereas recursively identified models were updated at each sampling instant. Both types of models were used to make predictions of new test data. For the purpose of comparison, model predictions were compared to zero-order hold (ZOH) predictions, which were made by simply holding the current glucose value constant for p steps into the future, where p is the prediction horizon. Thus, the ZOH predictions are model free and provide a base case for the prediction metrics used to quantify the accuracy of the model predictions. In theory, recursive identification techniques are needed only when there are changing conditions in the subject that require model adaptation. Thus, the identification and validation techniques were performed with both "normal" data and data collected during conditions of reduced insulin sensitivity. The latter were achieved by having the subjects self-administer a medication, prednisone, for 3 consecutive days. The recursive models were allowed to adapt to this condition of reduced insulin sensitivity, while the batch models were only identified from normal data. Data from nine type 1 diabetes subjects in ambulatory conditions were analyzed; six of these subjects also participated in the prednisone portion of the study. For normal test data, the batch ARX models produced 30-, 45-, and 60-minute-ahead predictions that had average root mean square error (RMSE) values of 26, 34, and 40 mg/dl, respectively. For test data characterized by reduced insulin sensitivity, the batch ARX models produced 30-, 60-, and 90-minute-ahead predictions with average RMSE values of 27, 46, and 59 mg/dl, respectively; the recursive ARX models demonstrated similar performance with corresponding values of 27, 45, and 61 mg/dl, respectively. The identified ARX models (batch and recursive) produced more accurate predictions than the model-free ZOH predictions, but only marginally. For test data characterized by reduced insulin sensitivity, RMSE values for the predictions of the batch ARX models were 9, 5, and 5% more accurate than the ZOH predictions for prediction horizons of 30, 60, and 90 minutes, respectively. In terms of RMSE values, the 30-, 60-, and 90-minute predictions of the recursive models were more accurate than the ZOH predictions, by 10, 5, and 2%, respectively. In this experimental study, the recursively identified ARX models resulted in predictions of test data that were similar, but not superior, to the batch models. Even for the test data characteristic of reduced insulin sensitivity, the batch and recursive models demonstrated similar prediction accuracy. The predictions of the identified ARX models were only marginally more accurate than the model-free ZOH predictions. Given the simplicity of the ARX models and the computational ease with which they are identified, however, even modest improvements may justify the use of these models in a model-based controller for an artificial beta cell. 2009 Diabetes Technology Society.

Past and ongoing shifts in Joshua tree distribution support future modeled range contraction

USGS Publications Warehouse

Cole, Kenneth L.; Ironside, Kirsten; Eischeid, Jon K.; Garfin, Gregg; Duffy, Phil; Toney, Chris

2011-01-01

The future distribution of the Joshua tree (Yucca brevifolia) is projected by combining a geostatistical analysis of 20th-century climates over its current range, future modeled climates, and paleoecological data showing its response to a past similar climate change. As climate rapidly warmed ;11 700 years ago, the range of Joshua tree contracted, leaving only the populations near what had been its northernmost limit. Its ability to spread northward into new suitable habitats after this time may have been inhibited by the somewhat earlier extinction of megafaunal dispersers, especially the Shasta ground sloth. We applied a model of climate suitability for Joshua tree, developed from its 20th-century range and climates, to future climates modeled through a set of six individual general circulation models (GCM) and one suite of 22 models for the late 21st century. All distribution data, observed climate data, and future GCM results were scaled to spatial grids of ;1 km and ;4 km in order to facilitate application within this topographically complex region. All of the models project the future elimination of Joshua tree throughout most of the southern portions of its current range. Although estimates of future monthly precipitation differ between the models, these changes are outweighed by large increases in temperature common to all the models. Only a few populations within the current range are predicted to be sustainable. Several models project significant potential future expansion into new areas beyond the current range, but the species' Historical and current rates of dispersal would seem to prevent natural expansion into these new areas. Several areas are predicted to be potential sites for relocation/ assisted migration. This project demonstrates how information from paleoecology and modern ecology can be integrated in order to understand ongoing processes and future distributions.

Composite Stress Rupture: A New Reliability Model Based on Strength Decay

NASA Technical Reports Server (NTRS)

Reeder, James R.

2012-01-01

A model is proposed to estimate reliability for stress rupture of composite overwrap pressure vessels (COPVs) and similar composite structures. This new reliability model is generated by assuming a strength degradation (or decay) over time. The model suggests that most of the strength decay occurs late in life. The strength decay model will be shown to predict a response similar to that predicted by a traditional reliability model for stress rupture based on tests at a single stress level. In addition, the model predicts that even though there is strength decay due to proof loading, a significant overall increase in reliability is gained by eliminating any weak vessels, which would fail early. The model predicts that there should be significant periods of safe life following proof loading, because time is required for the strength to decay from the proof stress level to the subsequent loading level. Suggestions for testing the strength decay reliability model have been made. If the strength decay reliability model predictions are shown through testing to be accurate, COPVs may be designed to carry a higher level of stress than is currently allowed, which will enable the production of lighter structures

Galaxy Formation At Extreme Redshifts: Semi-Analytic Model Predictions And Challenges For Observations

NASA Astrophysics Data System (ADS)

Yung, L. Y. Aaron; Somerville, Rachel S.

2017-06-01

The well-established Santa Cruz semi-analytic galaxy formation framework has been shown to be quite successful at explaining observations in the local Universe, as well as making predictions for low-redshift observations. Recently, metallicity-based gas partitioning and H2-based star formation recipes have been implemented in our model, replacing the legacy cold-gas based recipe. We then use our revised model to explore the high-redshift Universe and make predictions up to z = 15. Although our model is only calibrated to observations from the local universe, our predictions seem to match incredibly well with mid- to high-redshift observational constraints available-to-date, including rest-frame UV luminosity functions and the reionization history as constrained by CMB and IGM observations. We provide predictions for individual and statistical galaxy properties at a wide range of redshifts (z = 4 - 15), including objects that are too far or too faint to be detected with current facilities. And using our model predictions, we also provide forecasted luminosity functions and other observables for upcoming studies with JWST.

Comparing Parameter Estimation Techniques for an Electrical Power Transformer Oil Temperature Prediction Model

NASA Technical Reports Server (NTRS)

Morris, A. Terry

1999-01-01

This paper examines various sources of error in MIT's improved top oil temperature rise over ambient temperature model and estimation process. The sources of error are the current parameter estimation technique, quantization noise, and post-processing of the transformer data. Results from this paper will show that an output error parameter estimation technique should be selected to replace the current least squares estimation technique. The output error technique obtained accurate predictions of transformer behavior, revealed the best error covariance, obtained consistent parameter estimates, and provided for valid and sensible parameters. This paper will also show that the output error technique should be used to minimize errors attributed to post-processing (decimation) of the transformer data. Models used in this paper are validated using data from a large transformer in service.

Objectively-Measured Impulsivity and Attention-Deficit/Hyperactivity Disorder (ADHD): Testing Competing Predictions from the Working Memory and Behavioral Inhibition Models of ADHD

ERIC Educational Resources Information Center

Raiker, Joseph S.; Rapport, Mark D.; Kofler, Michael J.; Sarver, Dustin E.

2012-01-01

Impulsivity is a hallmark of two of the three DSM-IV ADHD subtypes and is associated with myriad adverse outcomes. Limited research, however, is available concerning the mechanisms and processes that contribute to impulsive responding by children with ADHD. The current study tested predictions from two competing models of ADHD--working memory (WM)…

Predicting paclitaxel-induced neutropenia using the DMET platform.

PubMed

Nieuweboer, Annemieke J M; Smid, Marcel; de Graan, Anne-Joy M; Elbouazzaoui, Samira; de Bruijn, Peter; Martens, John W; Mathijssen, Ron H J; van Schaik, Ron H N

2015-01-01

The use of paclitaxel in cancer treatment is limited by paclitaxel-induced neutropenia. We investigated the ability of genetic variation in drug-metabolizing enzymes and transporters to predict hematological toxicity. Using a discovery and validation approach, we identified a pharmacogenetic predictive model for neutropenia. For this, a drug-metabolizing enzymes and transporters plus DNA chip was used, which contains 1936 SNPs in 225 metabolic enzyme and drug-transporter genes. Our 10-SNP model in 279 paclitaxel-dosed patients reached 43% sensitivity in the validation cohort. Analysis in 3-weekly treated patients only resulted in improved sensitivity of 79%, with a specificity of 33%. None of our models reached statistical significance. Our drug-metabolizing enzymes and transporters-based SNP-models are currently of limited value for predicting paclitaxel-induced neutropenia in clinical practice. Original submitted 9 March 2015; Revision submitted 20 May 2015.

Predicting Barrett's Esophagus in Families: An Esophagus Translational Research Network (BETRNet) Model Fitting Clinical Data to a Familial Paradigm.

PubMed

Sun, Xiangqing; Elston, Robert C; Barnholtz-Sloan, Jill S; Falk, Gary W; Grady, William M; Faulx, Ashley; Mittal, Sumeet K; Canto, Marcia; Shaheen, Nicholas J; Wang, Jean S; Iyer, Prasad G; Abrams, Julian A; Tian, Ye D; Willis, Joseph E; Guda, Kishore; Markowitz, Sanford D; Chandar, Apoorva; Warfe, James M; Brock, Wendy; Chak, Amitabh

2016-05-01

Barrett's esophagus is often asymptomatic and only a small portion of Barrett's esophagus patients are currently diagnosed and under surveillance. Therefore, it is important to develop risk prediction models to identify high-risk individuals with Barrett's esophagus. Familial aggregation of Barrett's esophagus and esophageal adenocarcinoma, and the increased risk of esophageal adenocarcinoma for individuals with a family history, raise the necessity of including genetic factors in the prediction model. Methods to determine risk prediction models using both risk covariates and ascertained family data are not well developed. We developed a Barrett's Esophagus Translational Research Network (BETRNet) risk prediction model from 787 singly ascertained Barrett's esophagus pedigrees and 92 multiplex Barrett's esophagus pedigrees, fitting a multivariate logistic model that incorporates family history and clinical risk factors. The eight risk factors, age, sex, education level, parental status, smoking, heartburn frequency, regurgitation frequency, and use of acid suppressant, were included in the model. The prediction accuracy was evaluated on the training dataset and an independent validation dataset of 643 multiplex Barrett's esophagus pedigrees. Our results indicate family information helps to predict Barrett's esophagus risk, and predicting in families improves both prediction calibration and discrimination accuracy. Our model can predict Barrett's esophagus risk for anyone with family members known to have, or not have, had Barrett's esophagus. It can predict risk for unrelated individuals without knowing any relatives' information. Our prediction model will shed light on effectively identifying high-risk individuals for Barrett's esophagus screening and surveillance, consequently allowing intervention at an early stage, and reducing mortality from esophageal adenocarcinoma. Cancer Epidemiol Biomarkers Prev; 25(5); 727-35. ©2016 AACR. ©2016 American Association for Cancer Research.

CRCM + BATS-R-US two-way coupling

NASA Astrophysics Data System (ADS)

Glocer, A.; Fok, M.; Meng, X.; Toth, G.; Buzulukova, N.; Chen, S.; Lin, K.

2013-04-01

We present the coupling methodology and validation of a fully coupled inner and global magnetosphere code using the infrastructure provided by the Space Weather Modeling Framework (SWMF). In this model, the Comprehensive Ring Current Model (CRCM) represents the inner magnetosphere, while the Block-Adaptive-Tree Solar-Wind Roe-Type Upwind Scheme (BATS-R-US) represents the global magnetosphere. The combined model is a global magnetospheric code with a realistic ring current and consistent electric and magnetic fields. The computational performance of the model was improved to surpass real-time execution by the use of the Message Passing Interface (MPI) to parallelize the CRCM. Initial simulations under steady driving found that the coupled model resulted in a higher pressure in the inner magnetosphere and an inflated closed field-line region as compared to simulations without inner-magnetosphere coupling. Our validation effort was split into two studies. The first study examined the ability of the model to reproduce Dst for a range of events from the Geospace Environment Modeling (GEM) Dst Challenge. It also investigated the possibility of a baseline shift and compared two approaches to calculating Dst from the model. We found that the model did a reasonable job predicting Dst and Sym-H according to our two metrics of prediction efficiency and predicted yield. The second study focused on the specific case of the 22 July 2009 moderate geomagnetic storm. In this study, we directly compare model predictions and observations for Dst, THEMIS energy spectragrams, TWINS ENA images, and GOES 11 and 12 magnetometer data. The model did an adequate job reproducing trends in the data. Moreover, we found that composition can have a large effect on the result.

Numerical investigations of arc behaviour in gas metal arc welding using ANSYS CFX

NASA Astrophysics Data System (ADS)

Schnick, M.; Fuessel, U.; Hertel, M.; Spille-Kohoff, A.; Murphy, A. B.

2011-06-01

Current numerical models of gas metal arc welding (GMAW) are trying to combine magnetohydrodynamics (MHD) models of the arc and volume of fluid (VoF) models of metal transfer. They neglect vaporization and assume an argon atmosphere for the arc region, as it is common practice for models of gas tungsten arc welding. These models predict temperatures above 20 000 K and a temperature distribution similar to tungsten inert gas (TIG) arcs. However, current spectroscopic temperature measurements in GMAW arcs demonstrate much lower arc temperatures. In contrast to TIG arcs they found a central local minimum of the radial temperature distribution. The paper presents a GMAW arc model that considers metal vapour and which is in a very good agreement with experimentally observed temperatures. Furthermore, the model is able to predict the local central minimum in the radial temperature and the radial electric current density distributions for the first time. The axially symmetric model of the welding torch, the work piece, the wire and the arc (fluid domain) implements MHD as well as turbulent mixing and thermal demixing of metal vapour in argon. The mass fraction of iron vapour obtained from the simulation shows an accumulation in the arc core and another accumulation on the fringes of the arc at 2000 to 5000 K. The demixing effects lead to very low concentrations of iron between these two regions. Sensitive analyses demonstrate the influence of the transport and radiation properties of metal vapour, and the evaporation rate relative to the wire feed. Finally the model predictions are compared with the measuring results of Zielińska et al.

Real-Time Aircraft Cosmic Ray Radiation Exposure Predictions from the NAIRAS Model

NASA Astrophysics Data System (ADS)

Mertens, C. J.; Tobiska, W.; Kress, B. T.; Xu, X.

2012-12-01

The Nowcast of Atmospheric Ionizing Radiation for Aviation Safety (NAIRAS) is a prototype operational model for predicting commercial aircraft radiation exposure from galactic and solar cosmic rays. NAIRAS predictions are currently streaming live from the project's public website, and the exposure rate nowcast is also available on the SpaceWx smartphone app for iPhone, IPad, and Android. Cosmic rays are the primary source of human exposure to high linear energy transfer radiation at aircraft altitudes, which increases the risk of cancer and other adverse health effects. Thus, the NAIRAS model addresses an important national need with broad societal, public health and economic benefits. There is also interest in extending NAIRAS to the LEO environment to address radiation hazard issues for the emerging commercial spaceflight industry. The processes responsible for the variability in the solar wind, interplanetary magnetic field, solar energetic particle spectrum, and the dynamical response of the magnetosphere to these space environment inputs, strongly influence the composition and energy distribution of the atmospheric ionizing radiation field. Real-time observations are required at a variety of locations within the geospace environment. The NAIRAS model is driven by real-time input data from ground-, atmospheric-, and space-based platforms. During the development of the NAIRAS model, new science questions and observational data gaps were identified that must be addressed in order to obtain a more reliable and robust operational model of atmospheric radiation exposure. The focus of this talk is to present the current capabilities of the NAIRAS model, discuss future developments in aviation radiation modeling and instrumentation, and propose strategies and methodologies of bridging known gaps in current modeling and observational capabilities.

Dynamic Model Predicting Overweight, Obesity, and Extreme Obesity Prevalence Trends

PubMed Central

Thomas, Diana M.; Weedermann, Marion; Fuemmeler, Bernard F.; Martin, Corby K.; Dhurandhar, Nikhil V.; Bredlau, Carl; Heymsfield, Steven B.; Ravussin, Eric; Bouchard, Claude

2013-01-01

Objective Obesity prevalence in the United States (US) appears to be leveling, but the reasons behind the plateau remain unknown. Mechanistic insights can be provided from a mathematical model. The objective of this study is to model known multiple population parameters associated with changes in body mass index (BMI) classes and to establish conditions under which obesity prevalence will plateau. Design and Methods A differential equation system was developed that predicts population-wide obesity prevalence trends. The model considers both social and non-social influences on weight gain, incorporates other known parameters affecting obesity trends, and allows for country specific population growth. Results The dynamic model predicts that: obesity prevalence is a function of birth rate and the probability of being born in an obesogenic environment; obesity prevalence will plateau independent of current prevention strategies; and the US prevalence of obesity, overweight, and extreme obesity will plateau by about 2030 at 28%, 32%, and 9%, respectively. Conclusions The US prevalence of obesity is stabilizing and will plateau, independent of current preventative strategies. This trend has important implications in accurately evaluating the impact of various anti-obesity strategies aimed at reducing obesity prevalence. PMID:23804487

Automated Systematic Generation and Exploration of Flat Direction Phenomenology in Free Fermionic Heterotic String Theory

NASA Astrophysics Data System (ADS)

Greenwald, Jared

Any good physical theory must resolve current experimental data as well as offer predictions for potential searches in the future. The Standard Model of particle physics, Grand Unied Theories, Minimal Supersymmetric Models and Supergravity are all attempts to provide such a framework. However, they all lack the ability to predict many of the parameters that each of the theories utilize. String theory may yield a solution to this naturalness (or self-predictiveness) problem as well as offer a unifed theory of gravity. Studies in particle physics phenomenology based on perturbative low energy analysis of various string theories can help determine the candidacy of such models. After a review of principles and problems leading up to our current understanding of the universe, we will discuss some of the best particle physics model building techniques that have been developed using string theory. This will culminate in the introduction of a novel approach to a computational, systematic analysis of the various physical phenomena that arise from these string models. We focus on the necessary assumptions, complexity and open questions that arise while making a fully-automated at direction analysis program.

Crucial nesting habitat for gunnison sage-grouse: A spatially explicit hierarchical approach

USGS Publications Warehouse

Aldridge, Cameron L.; Saher, D.J.; Childers, T.M.; Stahlnecker, K.E.; Bowen, Z.H.

2012-01-01

Gunnison sage-grouse (Centrocercus minimus) is a species of special concern and is currently considered a candidate species under Endangered Species Act. Careful management is therefore required to ensure that suitable habitat is maintained, particularly because much of the species' current distribution is faced with exurban development pressures. We assessed hierarchical nest site selection patterns of Gunnison sage-grouse inhabiting the western portion of the Gunnison Basin, Colorado, USA, at multiple spatial scales, using logistic regression-based resource selection functions. Models were selected using Akaike Information Criterion corrected for small sample sizes (AIC c) and predictive surfaces were generated using model averaged relative probabilities. Landscape-scale factors that had the most influence on nest site selection included the proportion of sagebrush cover >5%, mean productivity, and density of 2 wheel-drive roads. The landscape-scale predictive surface captured 97% of known Gunnison sage-grouse nests within the top 5 of 10 prediction bins, implicating 57% of the basin as crucial nesting habitat. Crucial habitat identified by the landscape model was used to define the extent for patch-scale modeling efforts. Patch-scale variables that had the greatest influence on nest site selection were the proportion of big sagebrush cover >10%, distance to residential development, distance to high volume paved roads, and mean productivity. This model accurately predicted independent nest locations. The unique hierarchical structure of our models more accurately captures the nested nature of habitat selection, and allowed for increased discrimination within larger landscapes of suitable habitat. We extrapolated the landscape-scale model to the entire Gunnison Basin because of conservation concerns for this species. We believe this predictive surface is a valuable tool which can be incorporated into land use and conservation planning as well the assessment of future land-use scenarios. ?? 2011 The Wildlife Society.

Calculations on the Back of an Envelope Model: Applying Seasonal Fecundity Models to Species’ Range Limits

EPA Science Inventory

Most predictions of the effect of climate change on species’ ranges are based on correlations between climate and current species’ distributions. These so-called envelope models may be a good first approximation, but we need demographically mechanistic models to incorporate the ...

Forecasting Pell Program Applications Using Structural Aggregate Models.

ERIC Educational Resources Information Center

Cavin, Edward S.

1995-01-01

Demand for Pell Grant financial aid has become difficult to predict when using the current microsimulation model. This paper proposes an alternative model that uses aggregate data (based on individuals' microlevel decisions and macrodata on family incomes, college costs, and opportunity wages) and avoids some limitations of simple linear models.…

Exploring tropical forest vegetation dynamics using the FATES model

NASA Astrophysics Data System (ADS)

Koven, C. D.; Fisher, R.; Knox, R. G.; Chambers, J.; Kueppers, L. M.; Christoffersen, B. O.; Davies, S. J.; Dietze, M.; Holm, J.; Massoud, E. C.; Muller-Landau, H. C.; Powell, T.; Serbin, S.; Shuman, J. K.; Walker, A. P.; Wright, S. J.; Xu, C.

2017-12-01

Tropical forest vegetation dynamics represent a critical climate feedback in the Earth system, which is poorly represented in current global modeling approaches. We discuss recent progress on exploring these dynamics using the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), a demographic vegetation model for the CESM and ACME ESMs. We will discuss benchmarks of FATES predictions for forest structure against inventory sites, sensitivity of FATES predictions of size and age structure to model parameter uncertainty, and experiments using the FATES model to explore PFT competitive dynamics and the dynamics of size and age distributions in responses to changing climate and CO2.

Underwater Sound Propagation Modeling Methods for Predicting Marine Animal Exposure.

PubMed

Hamm, Craig A; McCammon, Diana F; Taillefer, Martin L

2016-01-01

The offshore exploration and production (E&P) industry requires comprehensive and accurate ocean acoustic models for determining the exposure of marine life to the high levels of sound used in seismic surveys and other E&P activities. This paper reviews the types of acoustic models most useful for predicting the propagation of undersea noise sources and describes current exposure models. The severe problems caused by model sensitivity to the uncertainty in the environment are highlighted to support the conclusion that it is vital that risk assessments include transmission loss estimates with statistical measures of confidence.

Comprehensive Analyses of Ventricular Myocyte Models Identify Targets Exhibiting Favorable Rate Dependence

PubMed Central

Bugana, Marco; Severi, Stefano; Sobie, Eric A.

2014-01-01

Reverse rate dependence is a problematic property of antiarrhythmic drugs that prolong the cardiac action potential (AP). The prolongation caused by reverse rate dependent agents is greater at slow heart rates, resulting in both reduced arrhythmia suppression at fast rates and increased arrhythmia risk at slow rates. The opposite property, forward rate dependence, would theoretically overcome these parallel problems, yet forward rate dependent (FRD) antiarrhythmics remain elusive. Moreover, there is evidence that reverse rate dependence is an intrinsic property of perturbations to the AP. We have addressed the possibility of forward rate dependence by performing a comprehensive analysis of 13 ventricular myocyte models. By simulating populations of myocytes with varying properties and analyzing population results statistically, we simultaneously predicted the rate-dependent effects of changes in multiple model parameters. An average of 40 parameters were tested in each model, and effects on AP duration were assessed at slow (0.2 Hz) and fast (2 Hz) rates. The analysis identified a variety of FRD ionic current perturbations and generated specific predictions regarding their mechanisms. For instance, an increase in L-type calcium current is FRD when this is accompanied by indirect, rate-dependent changes in slow delayed rectifier potassium current. A comparison of predictions across models identified inward rectifier potassium current and the sodium-potassium pump as the two targets most likely to produce FRD AP prolongation. Finally, a statistical analysis of results from the 13 models demonstrated that models displaying minimal rate-dependent changes in AP shape have little capacity for FRD perturbations, whereas models with large shape changes have considerable FRD potential. This can explain differences between species and between ventricular cell types. Overall, this study provides new insights, both specific and general, into the determinants of AP duration rate dependence, and illustrates a strategy for the design of potentially beneficial antiarrhythmic drugs. PMID:24675446

Comprehensive analyses of ventricular myocyte models identify targets exhibiting favorable rate dependence.

PubMed

Cummins, Megan A; Dalal, Pavan J; Bugana, Marco; Severi, Stefano; Sobie, Eric A

2014-03-01

Reverse rate dependence is a problematic property of antiarrhythmic drugs that prolong the cardiac action potential (AP). The prolongation caused by reverse rate dependent agents is greater at slow heart rates, resulting in both reduced arrhythmia suppression at fast rates and increased arrhythmia risk at slow rates. The opposite property, forward rate dependence, would theoretically overcome these parallel problems, yet forward rate dependent (FRD) antiarrhythmics remain elusive. Moreover, there is evidence that reverse rate dependence is an intrinsic property of perturbations to the AP. We have addressed the possibility of forward rate dependence by performing a comprehensive analysis of 13 ventricular myocyte models. By simulating populations of myocytes with varying properties and analyzing population results statistically, we simultaneously predicted the rate-dependent effects of changes in multiple model parameters. An average of 40 parameters were tested in each model, and effects on AP duration were assessed at slow (0.2 Hz) and fast (2 Hz) rates. The analysis identified a variety of FRD ionic current perturbations and generated specific predictions regarding their mechanisms. For instance, an increase in L-type calcium current is FRD when this is accompanied by indirect, rate-dependent changes in slow delayed rectifier potassium current. A comparison of predictions across models identified inward rectifier potassium current and the sodium-potassium pump as the two targets most likely to produce FRD AP prolongation. Finally, a statistical analysis of results from the 13 models demonstrated that models displaying minimal rate-dependent changes in AP shape have little capacity for FRD perturbations, whereas models with large shape changes have considerable FRD potential. This can explain differences between species and between ventricular cell types. Overall, this study provides new insights, both specific and general, into the determinants of AP duration rate dependence, and illustrates a strategy for the design of potentially beneficial antiarrhythmic drugs.

Integrating linear optimization with structural modeling to increase HIV neutralization breadth.

PubMed

Sevy, Alexander M; Panda, Swetasudha; Crowe, James E; Meiler, Jens; Vorobeychik, Yevgeniy

2018-02-01

Computational protein design has been successful in modeling fixed backbone proteins in a single conformation. However, when modeling large ensembles of flexible proteins, current methods in protein design have been insufficient. Large barriers in the energy landscape are difficult to traverse while redesigning a protein sequence, and as a result current design methods only sample a fraction of available sequence space. We propose a new computational approach that combines traditional structure-based modeling using the Rosetta software suite with machine learning and integer linear programming to overcome limitations in the Rosetta sampling methods. We demonstrate the effectiveness of this method, which we call BROAD, by benchmarking the performance on increasing predicted breadth of anti-HIV antibodies. We use this novel method to increase predicted breadth of naturally-occurring antibody VRC23 against a panel of 180 divergent HIV viral strains and achieve 100% predicted binding against the panel. In addition, we compare the performance of this method to state-of-the-art multistate design in Rosetta and show that we can outperform the existing method significantly. We further demonstrate that sequences recovered by this method recover known binding motifs of broadly neutralizing anti-HIV antibodies. Finally, our approach is general and can be extended easily to other protein systems. Although our modeled antibodies were not tested in vitro, we predict that these variants would have greatly increased breadth compared to the wild-type antibody.

Operationalizing the Space Weather Modeling Framework: Challenges and Resolutions

NASA Astrophysics Data System (ADS)

Welling, D. T.; Gombosi, T. I.; Toth, G.; Singer, H. J.; Millward, G. H.; Balch, C. C.; Cash, M. D.

2016-12-01

Predicting ground-based magnetic perturbations is a critical step towards specifying and predicting geomagnetically induced currents (GICs) in high voltage transmission lines. Currently, the Space Weather Modeling Framework (SWMF), a flexible modeling framework for simulating the multi-scale space environment, is being transitioned from research to operational use (R2O) by NOAA's Space Weather Prediction Center. Upon completion of this transition, the SWMF will provide localized time-varying magnetic field (dB/dt) predictions using real-time solar wind observations from L1 and the F10.7 proxy for EUV as model input. This presentation chronicles the challenges encountered during the R2O transition of the SWMF. Because operations relies on frequent calculations of global surface dB/dt, new optimizations were required to keep the model running faster than real time. Additionally, several singular situations arose during the 30-day robustness test that required immediate attention. Solutions and strategies for overcoming these issues will be presented. This includes new failsafe options for code execution, new physics and coupling parameters, and the development of an automated validation suite that allows us to monitor performance with code evolution. Finally, the operations-to-research (O2R) impact on SWMF-related research is presented. The lessons learned from this work are valuable and instructive for the space weather community as further R2O progress is made.

Gambling and the Reasoned Action Model: Predicting Past Behavior, Intentions, and Future Behavior.

PubMed

Dahl, Ethan; Tagler, Michael J; Hohman, Zachary P

2018-03-01

Gambling is a serious concern for society because it is highly addictive and is associated with a myriad of negative outcomes. The current study applied the Reasoned Action Model (RAM) to understand and predict gambling intentions and behavior. Although prior studies have taken a reasoned action approach to understand gambling, no prior study has fully applied the RAM or used the RAM to predict future gambling. Across two studies the RAM was used to predict intentions to gamble, past gambling behavior, and future gambling behavior. In study 1 the model significantly predicted intentions and past behavior in both a college student and Amazon Mechanical Turk sample. In study 2 the model predicted future gambling behavior, measured 2 weeks after initial measurement of the RAM constructs. This study stands as the first to show the utility of the RAM in predicting future gambling behavior. Across both studies, attitudes and perceived normative pressure were the strongest predictors of intentions to gamble. These findings provide increased understanding of gambling and inform the development of gambling interventions based on the RAM.

Process Optimization of Dual-Laser Beam Welding of Advanced Al-Li Alloys Through Hot Cracking Susceptibility Modeling

NASA Astrophysics Data System (ADS)

Tian, Yingtao; Robson, Joseph D.; Riekehr, Stefan; Kashaev, Nikolai; Wang, Li; Lowe, Tristan; Karanika, Alexandra

2016-07-01

Laser welding of advanced Al-Li alloys has been developed to meet the increasing demand for light-weight and high-strength aerospace structures. However, welding of high-strength Al-Li alloys can be problematic due to the tendency for hot cracking. Finding suitable welding parameters and filler material for this combination currently requires extensive and costly trial and error experimentation. The present work describes a novel coupled model to predict hot crack susceptibility (HCS) in Al-Li welds. Such a model can be used to shortcut the weld development process. The coupled model combines finite element process simulation with a two-level HCS model. The finite element process model predicts thermal field data for the subsequent HCS hot cracking prediction. The model can be used to predict the influences of filler wire composition and welding parameters on HCS. The modeling results have been validated by comparing predictions with results from fully instrumented laser welds performed under a range of process parameters and analyzed using high-resolution X-ray tomography to identify weld defects. It is shown that the model is capable of accurately predicting the thermal field around the weld and the trend of HCS as a function of process parameters.

3-D residual eddy current field characterisation: applied to diffusion weighted magnetic resonance imaging.

PubMed

O'Brien, Kieran; Daducci, Alessandro; Kickler, Nils; Lazeyras, Francois; Gruetter, Rolf; Feiweier, Thorsten; Krueger, Gunnar

2013-08-01

Clinical use of the Stejskal-Tanner diffusion weighted images is hampered by the geometric distortions that result from the large residual 3-D eddy current field induced. In this work, we aimed to predict, using linear response theory, the residual 3-D eddy current field required for geometric distortion correction based on phantom eddy current field measurements. The predicted 3-D eddy current field induced by the diffusion-weighting gradients was able to reduce the root mean square error of the residual eddy current field to ~1 Hz. The model's performance was tested on diffusion weighted images of four normal volunteers, following distortion correction, the quality of the Stejskal-Tanner diffusion-weighted images was found to have comparable quality to image registration based corrections (FSL) at low b-values. Unlike registration techniques the correction was not hindered by low SNR at high b-values, and results in improved image quality relative to FSL. Characterization of the 3-D eddy current field with linear response theory enables the prediction of the 3-D eddy current field required to correct eddy current induced geometric distortions for a wide range of clinical and high b-value protocols.

Automated Predictive Big Data Analytics Using Ontology Based Semantics.

PubMed

Nural, Mustafa V; Cotterell, Michael E; Peng, Hao; Xie, Rui; Ma, Ping; Miller, John A

2015-10-01

Predictive analytics in the big data era is taking on an ever increasingly important role. Issues related to choice on modeling technique, estimation procedure (or algorithm) and efficient execution can present significant challenges. For example, selection of appropriate and optimal models for big data analytics often requires careful investigation and considerable expertise which might not always be readily available. In this paper, we propose to use semantic technology to assist data analysts and data scientists in selecting appropriate modeling techniques and building specific models as well as the rationale for the techniques and models selected. To formally describe the modeling techniques, models and results, we developed the Analytics Ontology that supports inferencing for semi-automated model selection. The SCALATION framework, which currently supports over thirty modeling techniques for predictive big data analytics is used as a testbed for evaluating the use of semantic technology.

Automated Predictive Big Data Analytics Using Ontology Based Semantics

PubMed Central

Nural, Mustafa V.; Cotterell, Michael E.; Peng, Hao; Xie, Rui; Ma, Ping; Miller, John A.

2017-01-01

Predictive analytics in the big data era is taking on an ever increasingly important role. Issues related to choice on modeling technique, estimation procedure (or algorithm) and efficient execution can present significant challenges. For example, selection of appropriate and optimal models for big data analytics often requires careful investigation and considerable expertise which might not always be readily available. In this paper, we propose to use semantic technology to assist data analysts and data scientists in selecting appropriate modeling techniques and building specific models as well as the rationale for the techniques and models selected. To formally describe the modeling techniques, models and results, we developed the Analytics Ontology that supports inferencing for semi-automated model selection. The SCALATION framework, which currently supports over thirty modeling techniques for predictive big data analytics is used as a testbed for evaluating the use of semantic technology. PMID:29657954

Electromigration model for the prediction of lifetime based on the failure unit statistics in aluminum metallization

NASA Astrophysics Data System (ADS)

Park, Jong Ho; Ahn, Byung Tae

2003-01-01

A failure model for electromigration based on the "failure unit model" was presented for the prediction of lifetime in metal lines.The failure unit model, which consists of failure units in parallel and series, can predict both the median time to failure (MTTF) and the deviation in the time to failure (DTTF) in Al metal lines. The model can describe them only qualitatively. In our model, both the probability function of the failure unit in single grain segments and polygrain segments are considered instead of in polygrain segments alone. Based on our model, we calculated MTTF, DTTF, and activation energy for different median grain sizes, grain size distributions, linewidths, line lengths, current densities, and temperatures. Comparisons between our results and published experimental data showed good agreements and our model could explain the previously unexplained phenomena. Our advanced failure unit model might be further applied to other electromigration characteristics of metal lines.

Review and assessment of turbulence models for hypersonic flows

NASA Astrophysics Data System (ADS)

Roy, Christopher J.; Blottner, Frederick G.

2006-10-01

Accurate aerodynamic prediction is critical for the design and optimization of hypersonic vehicles. Turbulence modeling remains a major source of uncertainty in the computational prediction of aerodynamic forces and heating for these systems. The first goal of this article is to update the previous comprehensive review of hypersonic shock/turbulent boundary-layer interaction experiments published in 1991 by Settles and Dodson (Hypersonic shock/boundary-layer interaction database. NASA CR 177577, 1991). In their review, Settles and Dodson developed a methodology for assessing experiments appropriate for turbulence model validation and critically surveyed the existing hypersonic experiments. We limit the scope of our current effort by considering only two-dimensional (2D)/axisymmetric flows in the hypersonic flow regime where calorically perfect gas models are appropriate. We extend the prior database of recommended hypersonic experiments (on four 2D and two 3D shock-interaction geometries) by adding three new geometries. The first two geometries, the flat plate/cylinder and the sharp cone, are canonical, zero-pressure gradient flows which are amenable to theory-based correlations, and these correlations are discussed in detail. The third geometry added is the 2D shock impinging on a turbulent flat plate boundary layer. The current 2D hypersonic database for shock-interaction flows thus consists of nine experiments on five different geometries. The second goal of this study is to review and assess the validation usage of various turbulence models on the existing experimental database. Here we limit the scope to one- and two-equation turbulence models where integration to the wall is used (i.e., we omit studies involving wall functions). A methodology for validating turbulence models is given, followed by an extensive evaluation of the turbulence models on the current hypersonic experimental database. A total of 18 one- and two-equation turbulence models are reviewed, and results of turbulence model assessments for the six models that have been extensively applied to the hypersonic validation database are compiled and presented in graphical form. While some of the turbulence models do provide reasonable predictions for the surface pressure, the predictions for surface heat flux are generally poor, and often in error by a factor of four or more. In the vast majority of the turbulence model validation studies we review, the authors fail to adequately address the numerical accuracy of the simulations (i.e., discretization and iterative error) and the sensitivities of the model predictions to freestream turbulence quantities or near-wall y+ mesh spacing. We recommend new hypersonic experiments be conducted which (1) measure not only surface quantities but also mean and fluctuating quantities in the interaction region and (2) provide careful estimates of both random experimental uncertainties and correlated bias errors for the measured quantities and freestream conditions. For the turbulence models, we recommend that a wide-range of turbulence models (including newer models) be re-examined on the current hypersonic experimental database, including the more recent experiments. Any future turbulence model validation efforts should carefully assess the numerical accuracy and model sensitivities. In addition, model corrections (e.g., compressibility corrections) should be carefully examined for their effects on a standard, low-speed validation database. Finally, as new experiments or direct numerical simulation data become available with information on mean and fluctuating quantities, they should be used to improve the turbulence models and thus increase their predictive capability.

Review of the ionospheric model for the long wave prediction capability. Final report

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

Ferguson, J.A.

1992-11-01

The Naval Command, Control and Ocean Surveillance Center's Long Wave Prediction Capability (LWPC) has a built-in ionospheric model. The latter was defined after a review of the literature comparing measurements with calculations. Subsequent to this original specification of the ionospheric model in the LWPC, a new collection of data were obtained and analyzed. The new data were collected aboard a merchant ship named the Callaghan during a series of trans-Atlantic trips over a period of a year. This report presents a detailed analysis of the ionospheric model currently in use by the LWPC and the new model suggested by themore » shipboard measurements. We conclude that, although the fits to measurements are almost the same between the two models examined, the current LWPC model should be used because it is better than the new model for nighttime conditions at long ranges. This conclusion supports the primary use of the LWPC model for coverage assessment that requires a valid model at the limits of a transmitter's reception.... Communications, Very low frequency and low frequency, High voltage, Antennas, Measurement.« less

NASA Iced Aerodynamics and Controls Current Research

NASA Technical Reports Server (NTRS)

Addy, Gene

2009-01-01

This slide presentation reviews the state of current research in the area of aerodynamics and aircraft control with ice conditions by the Aviation Safety Program, part of the Integrated Resilient Aircraft Controls Project (IRAC). Included in the presentation is a overview of the modeling efforts. The objective of the modeling is to develop experimental and computational methods to model and predict aircraft response during adverse flight conditions, including icing. The Aircraft icing modeling efforts includes the Ice-Contaminated Aerodynamics Modeling, which examines the effects of ice contamination on aircraft aerodynamics, and CFD modeling of ice-contaminated aircraft aerodynamics, and Advanced Ice Accretion Process Modeling which examines the physics of ice accretion, and works on computational modeling of ice accretions. The IRAC testbed, a Generic Transport Model (GTM) and its use in the investigation of the effects of icing on its aerodynamics is also reviewed. This has led to a more thorough understanding and models, both theoretical and empirical of icing physics and ice accretion for airframes, advanced 3D ice accretion prediction codes, CFD methods for iced aerodynamics and better understanding of aircraft iced aerodynamics and its effects on control surface effectiveness.

Modeling hydrodynamics, water quality, and benthic processes to predict ecological effects in Narragansett Bay

EPA Science Inventory

The environmental fluid dynamics code (EFDC) was used to study the three dimensional (3D) circulation, water quality, and ecology in Narragansett Bay, RI. Predictions of the Bay hydrodynamics included the behavior of the water surface elevation, currents, salinity, and temperatur...

Office of Water Prediction

Science.gov Websites

Toggle navigation National Water Center Home (current) Visualize Map Image Viewer What's New About National Water Center National Water Model National Weather Service National Oceanic and Atmospheric Administration Welcome to the Office of Water Prediction Scientific excellence and innovation driving water

Dynamics of electrical double layer formation in room-temperature ionic liquids under constant-current charging conditions

NASA Astrophysics Data System (ADS)

Jiang, Xikai; Huang, Jingsong; Zhao, Hui; Sumpter, Bobby G.; Qiao, Rui

2014-07-01

We report detailed simulation results on the formation dynamics of an electrical double layer (EDL) inside an electrochemical cell featuring room-temperature ionic liquids (RTILs) enclosed between two planar electrodes. Under relatively small charging currents, the evolution of cell potential from molecular dynamics (MD) simulations during charging can be suitably predicted by the Landau-Ginzburg-type continuum model proposed recently (Bazant et al 2011 Phys. Rev. Lett. 106 046102). Under very large charging currents, the cell potential from MD simulations shows pronounced oscillation during the initial stage of charging, a feature not captured by the continuum model. Such oscillation originates from the sequential growth of the ionic space charge layers near the electrode surface. This allows the evolution of EDLs in RTILs with time, an atomistic process difficult to visualize experimentally, to be studied by analyzing the cell potential under constant-current charging conditions. While the continuum model cannot predict the potential oscillation under such far-from-equilibrium charging conditions, it can nevertheless qualitatively capture the growth of cell potential during the later stage of charging. Improving the continuum model by introducing frequency-dependent dielectric constant and density-dependent ion diffusion coefficients may help to further extend the applicability of the model. The evolution of ion density profiles is also compared between the MD and the continuum model, showing good agreement.

Dynamics of electrical double layer formation in room-temperature ionic liquids under constant-current charging conditions.

PubMed

Jiang, Xikai; Huang, Jingsong; Zhao, Hui; Sumpter, Bobby G; Qiao, Rui

2014-07-16

We report detailed simulation results on the formation dynamics of an electrical double layer (EDL) inside an electrochemical cell featuring room-temperature ionic liquids (RTILs) enclosed between two planar electrodes. Under relatively small charging currents, the evolution of cell potential from molecular dynamics (MD) simulations during charging can be suitably predicted by the Landau-Ginzburg-type continuum model proposed recently (Bazant et al 2011 Phys. Rev. Lett. 106 046102). Under very large charging currents, the cell potential from MD simulations shows pronounced oscillation during the initial stage of charging, a feature not captured by the continuum model. Such oscillation originates from the sequential growth of the ionic space charge layers near the electrode surface. This allows the evolution of EDLs in RTILs with time, an atomistic process difficult to visualize experimentally, to be studied by analyzing the cell potential under constant-current charging conditions. While the continuum model cannot predict the potential oscillation under such far-from-equilibrium charging conditions, it can nevertheless qualitatively capture the growth of cell potential during the later stage of charging. Improving the continuum model by introducing frequency-dependent dielectric constant and density-dependent ion diffusion coefficients may help to further extend the applicability of the model. The evolution of ion density profiles is also compared between the MD and the continuum model, showing good agreement.

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/.

Improved Model for Predicting the Free Energy Contribution of Dinucleotide Bulges to RNA Duplex Stability.

PubMed

Tomcho, Jeremy C; Tillman, Magdalena R; Znosko, Brent M

2015-09-01

Predicting the secondary structure of RNA is an intermediate in predicting RNA three-dimensional structure. Commonly, determining RNA secondary structure from sequence uses free energy minimization and nearest neighbor parameters. Current algorithms utilize a sequence-independent model to predict free energy contributions of dinucleotide bulges. To determine if a sequence-dependent model would be more accurate, short RNA duplexes containing dinucleotide bulges with different sequences and nearest neighbor combinations were optically melted to derive thermodynamic parameters. These data suggested energy contributions of dinucleotide bulges were sequence-dependent, and a sequence-dependent model was derived. This model assigns free energy penalties based on the identity of nucleotides in the bulge (3.06 kcal/mol for two purines, 2.93 kcal/mol for two pyrimidines, 2.71 kcal/mol for 5'-purine-pyrimidine-3', and 2.41 kcal/mol for 5'-pyrimidine-purine-3'). The predictive model also includes a 0.45 kcal/mol penalty for an A-U pair adjacent to the bulge and a -0.28 kcal/mol bonus for a G-U pair adjacent to the bulge. The new sequence-dependent model results in predicted values within, on average, 0.17 kcal/mol of experimental values, a significant improvement over the sequence-independent model. This model and new experimental values can be incorporated into algorithms that predict RNA stability and secondary structure from sequence.

A new theory of plant-microbe nutrient competition resolves inconsistencies between observations and model predictions.

PubMed

Zhu, Qing; Riley, William J; Tang, Jinyun

2017-04-01

Terrestrial plants assimilate anthropogenic CO 2 through photosynthesis and synthesizing new tissues. However, sustaining these processes requires plants to compete with microbes for soil nutrients, which therefore calls for an appropriate understanding and modeling of nutrient competition mechanisms in Earth System Models (ESMs). Here, we survey existing plant-microbe competition theories and their implementations in ESMs. We found no consensus regarding the representation of nutrient competition and that observational and theoretical support for current implementations are weak. To reconcile this situation, we applied the Equilibrium Chemistry Approximation (ECA) theory to plant-microbe nitrogen competition in a detailed grassland 15 N tracer study and found that competition theories in current ESMs fail to capture observed patterns and the ECA prediction simplifies the complex nature of nutrient competition and quantitatively matches the 15 N observations. Since plant carbon dynamics are strongly modulated by soil nutrient acquisition, we conclude that (1) predicted nutrient limitation effects on terrestrial carbon accumulation by existing ESMs may be biased and (2) our ECA-based approach may improve predictions by mechanistically representing plant-microbe nutrient competition. © 2016 by the Ecological Society of America.

Impact of Age on the Risk of Advanced Colorectal Neoplasia in a Young Population: An Analysis Using the Predicted Probability Model.

PubMed

Jung, Yoon Suk; Park, Chan Hyuk; Kim, Nam Hee; Lee, Mi Yeon; Park, Dong Il

2017-09-01

The incidence of colorectal cancer is decreasing in adults aged ≥50 years and increasing in those aged <50 years. We aimed to establish risk stratification model for advanced colorectal neoplasia (ACRN) in persons aged <50 years. We reviewed the records of participants who had undergone a colonoscopy as part of a health examination at two large medical examination centers in Korea. By using logistic regression analysis, we developed predicted probability models for ACRN in a population aged 30-49 years. Of 96,235 participants, 57,635 and 38,600 were included in the derivation and validation cohorts, respectively. The predicted probability model considered age, sex, body mass index, family history of colorectal cancer, and smoking habits, as follows: Y ACRN  = -8.755 + 0.080·X age  - 0.055·X male  + 0.041·X BMI  + 0.200·X family_history_of_CRC  + 0.218·X former_smoker  + 0.644·X current_smoker . The optimal cutoff value for the predicted probability of ACRN by Youden index was 1.14%. The area under the receiver-operating characteristic curve (AUROC) values of our model for ACRN were higher than those of the previously established Asia-Pacific Colorectal Screening (APCS), Korean Colorectal Screening (KCS), and Kaminski's scoring models [AUROC (95% confidence interval): model in the current study, 0.673 (0.648-0.697); vs. APCS, 0.588 (0.564-0.611), P < 0.001; vs. KCS, 0.602 (0.576-0.627), P < 0.001; and vs. Kaminski's model, 0.586 (0.560-0.612), P < 0.001]. In a young population, a predicted probability model can assess the risk of ACRN more accurately than existing models, including the APCS, KCS, and Kaminski's scoring models.

Heating of the solar middle chromosphere by large-scale electric currents

NASA Technical Reports Server (NTRS)

Goodman, M. L.

1995-01-01

A global resistive, two-dimensional, time-dependent magnetohydrodynamic (MHD) model is used to introduce and support the hypothesis that the quiet solar middle chromosphere is heated by resistive dissipation of large-scale electric currents which fill most of its volume. The scale height and maximum magnitude of the current density are 400 km and 31.3 m/sq m, respectively. The associated magnetic field is almost horizontal, has the same scale height as the current density, and has a maximum magnitude of 153 G. The current is carried by electrons flowing across magnetic field lines at 1 m/s. The resistivity is the electron contribution to the Pedersen resitivity for a weakly ionized, strongly magnetized, hydrogen gas. The model does not include a driving mechanism. Most of the physical quantities in the model decrease exponentially with time on a resistive timescale of 41.3 minutes. However, the initial values and spatial; dependence of these quantities are expected to be essentially the same as they would be if the correct driving mechanism were included in a more general model. The heating rate per unit mass is found to be 4.5 x 10(exp 9) ergs/g/s, independent of height and latitude. The electron density scale height is found to be 800 km. The model predicts that 90% of the thermal energy required to heat the middle chromosphere is deposited in the height range 300-760 km above the temperature minimum. It is shown to be consistent to assume that the radiation rate per unit volume is proportional to the magnetic energy density, and then it follows that the heating rate per unit volume is also proportional to the energy from the photosphere into the overlying chromosphere are briefly discussed as possible driving mechanisms for establishing and maintaining the current system. The case in which part of or all of the current is carried by protons and metal ions, and the contribution of electron-proton scattering to the current are also considered, with the conclusion that these effects do not change the qualitative prediction of the model, but probably change the quantitative predictions slightly, mainly by increasing the maximum magntiude of the current density and magnetic field to at most approximately 100 mA/m and approximately 484 G, respectively. The heating rate per unit mass, current density scale height, magnetic field scale height, temperatures, and pressures are unchanged or are only slightly changed by including these additional effects due to protons and ions.

A comparison of fatigue life prediction methodologies for rotorcraft

NASA Technical Reports Server (NTRS)

Everett, R. A., Jr.

1990-01-01

Because of the current U.S. Army requirement that all new rotorcraft be designed to a 'six nines' reliability on fatigue life, this study was undertaken to assess the accuracy of the current safe life philosophy using the nominal stress Palmgrem-Miner linear cumulative damage rule to predict the fatigue life of rotorcraft dynamic components. It has been shown that this methodology can predict fatigue lives that differ from test lives by more than two orders of magnitude. A further objective of this work was to compare the accuracy of this methodology to another safe life method called the local strain approach as well as to a method which predicts fatigue life based solely on crack growth data. Spectrum fatigue tests were run on notched (k(sub t) = 3.2) specimens made of 4340 steel using the Felix/28 tests fairly well, being slightly on the unconservative side of the test data. The crack growth method, which is based on 'small crack' crack growth data and a crack-closure model, also predicted the fatigue lives very well with the predicted lives being slightly longer that the mean test lives but within the experimental scatter band. The crack growth model was also able to predict the change in test lives produced by the rainflow reconstructed spectra.

Increased Drought Impacts on Temperate Rainforests from Southern South America: Results of a Process-Based, Dynamic Forest Model

PubMed Central

Gutiérrez, Alvaro G.; Armesto, Juan J.; Díaz, M. Francisca; Huth, Andreas

2014-01-01

Increased droughts due to regional shifts in temperature and rainfall regimes are likely to affect forests in temperate regions in the coming decades. To assess their consequences for forest dynamics, we need predictive tools that couple hydrologic processes, soil moisture dynamics and plant productivity. Here, we developed and tested a dynamic forest model that predicts the hydrologic balance of North Patagonian rainforests on Chiloé Island, in temperate South America (42°S). The model incorporates the dynamic linkages between changing rainfall regimes, soil moisture and individual tree growth. Declining rainfall, as predicted for the study area, should mean up to 50% less summer rain by year 2100. We analysed forest responses to increased drought using the model proposed focusing on changes in evapotranspiration, soil moisture and forest structure (above-ground biomass and basal area). We compared the responses of a young stand (YS, ca. 60 years-old) and an old-growth forest (OG, >500 years-old) in the same area. Based on detailed field measurements of water fluxes, the model provides a reliable account of the hydrologic balance of these evergreen, broad-leaved rainforests. We found higher evapotranspiration in OG than YS under current climate. Increasing drought predicted for this century can reduce evapotranspiration by 15% in the OG compared to current values. Drier climate will alter forest structure, leading to decreases in above ground biomass by 27% of the current value in OG. The model presented here can be used to assess the potential impacts of climate change on forest hydrology and other threats of global change on future forests such as fragmentation, introduction of exotic tree species, and changes in fire regimes. Our study expands the applicability of forest dynamics models in remote and hitherto overlooked regions of the world, such as southern temperate rainforests. PMID:25068869

Increased drought impacts on temperate rainforests from southern South America: results of a process-based, dynamic forest model.

PubMed

Gutiérrez, Alvaro G; Armesto, Juan J; Díaz, M Francisca; Huth, Andreas

2014-01-01

Increased droughts due to regional shifts in temperature and rainfall regimes are likely to affect forests in temperate regions in the coming decades. To assess their consequences for forest dynamics, we need predictive tools that couple hydrologic processes, soil moisture dynamics and plant productivity. Here, we developed and tested a dynamic forest model that predicts the hydrologic balance of North Patagonian rainforests on Chiloé Island, in temperate South America (42°S). The model incorporates the dynamic linkages between changing rainfall regimes, soil moisture and individual tree growth. Declining rainfall, as predicted for the study area, should mean up to 50% less summer rain by year 2100. We analysed forest responses to increased drought using the model proposed focusing on changes in evapotranspiration, soil moisture and forest structure (above-ground biomass and basal area). We compared the responses of a young stand (YS, ca. 60 years-old) and an old-growth forest (OG, >500 years-old) in the same area. Based on detailed field measurements of water fluxes, the model provides a reliable account of the hydrologic balance of these evergreen, broad-leaved rainforests. We found higher evapotranspiration in OG than YS under current climate. Increasing drought predicted for this century can reduce evapotranspiration by 15% in the OG compared to current values. Drier climate will alter forest structure, leading to decreases in above ground biomass by 27% of the current value in OG. The model presented here can be used to assess the potential impacts of climate change on forest hydrology and other threats of global change on future forests such as fragmentation, introduction of exotic tree species, and changes in fire regimes. Our study expands the applicability of forest dynamics models in remote and hitherto overlooked regions of the world, such as southern temperate rainforests.

Carcinogenicity and Mutagenicity Data: New Initiatives to Improve Access and Utility for Modeling

EPA Science Inventory

Currents models for prediction of chemical carcinogenicity and mutagenicity rely upon a relatively small number of publicly available data resources, where the data being modeled are highly summarized and aggregated representations of the actual experimental results. A number of...

Large Dataset of Acute Oral Toxicity Data Created for Testing ...

EPA Pesticide Factsheets

Acute toxicity data is a common requirement for substance registration in the US. Currently only data derived from animal tests are accepted by regulatory agencies, and the standard in vivo tests use lethality as the endpoint. Non-animal alternatives such as in silico models are being developed due to animal welfare and resource considerations. We compiled a large dataset of oral rat LD50 values to assess the predictive performance currently available in silico models. Our dataset combines LD50 values from five different sources: literature data provided by The Dow Chemical Company, REACH data from eChemportal, HSDB (Hazardous Substances Data Bank), RTECS data from Leadscope, and the training set underpinning TEST (Toxicity Estimation Software Tool). Combined these data sources yield 33848 chemical-LD50 pairs (data points), with 23475 unique data points covering 16439 compounds. The entire dataset was loaded into a chemical properties database. All of the compounds were registered in DSSTox and 59.5% have publically available structures. Compounds without a structure in DSSTox are currently having their structures registered. The structural data will be used to evaluate the predictive performance and applicable chemical domains of three QSAR models (TIMES, PROTOX, and TEST). Future work will combine the dataset with information from ToxCast assays, and using random forest modeling, assess whether ToxCast assays are useful in predicting acute oral toxicity. Pre

A practical tool for estimating subsurface LNAPL distributions and transmissivity using current and historical fluid levels in groundwater wells: Effects of entrapped and residual LNAPL.

PubMed

Lenhard, R J; Rayner, J L; Davis, G B

2017-10-01

A model is presented to account for elevation-dependent residual and entrapped LNAPL above and below, respectively, the water-saturated zone when predicting subsurface LNAPL specific volume (fluid volume per unit area) and transmissivity from current and historic fluid levels in wells. Physically-based free, residual, and entrapped LNAPL saturation distributions and LNAPL relative permeabilities are integrated over a vertical slice of the subsurface to yield the LNAPL specific volumes and transmissivity. The model accounts for effects of fluctuating water tables. Hypothetical predictions are given for different porous media (loamy sand and clay loam), fluid levels in wells, and historic water-table fluctuations. It is shown the elevation range from the LNAPL-water interface in a well to the upper elevation where the free LNAPL saturation approaches zero is the same for a given LNAPL thickness in a well regardless of porous media type. Further, the LNAPL transmissivity is largely dependent on current fluid levels in wells and not historic levels. Results from the model can aid developing successful LNAPL remediation strategies and improving the design and operation of remedial activities. Results of the model also can aid in accessing the LNAPL recovery technology endpoint, based on the predicted transmissivity. Copyright © 2017 Commonwealth Scientific and Industrial Research Organisation - Copyright 2017. Published by Elsevier B.V. All rights reserved.