Evaluating Upper-Body Strength and Power From a Single Test: The Ballistic Push-up.

PubMed

Wang, Ran; Hoffman, Jay R; Sadres, Eliahu; Bartolomei, Sandro; Muddle, Tyler W D; Fukuda, David H; Stout, Jeffrey R

2017-05-01

Wang, R, Hoffman, JR, Sadres, E, Bartolomei, S, Muddle, TWD, Fukuda, DH, and Stout, JR. Evaluating upper-body strength and power from a single test: the ballistic push-up. J Strength Cond Res 31(5): 1338-1345, 2017-The purpose of this study was to examine the reliability of the ballistic push-up (BPU) exercise and to develop a prediction model for both maximal strength (1 repetition maximum [1RM]) in the bench press exercise and upper-body power. Sixty recreationally active men completed a 1RM bench press and 2 BPU assessments in 3 separate testing sessions. Peak and mean force, peak and mean rate of force development, net impulse, peak velocity, flight time, and peak and mean power were determined. Intraclass correlation coefficients were used to examine the reliability of the BPU. Stepwise linear regression was used to develop 1RM bench press and power prediction equations. Intraclass correlation coefficient's ranged from 0.849 to 0.971 for the BPU measurements. Multiple regression analysis provided the following 1RM bench press prediction equation: 1RM = 0.31 × Mean Force - 1.64 × Body Mass + 0.70 (R = 0.837, standard error of the estimate [SEE] = 11 kg); time-based power prediction equation: Peak Power = 11.0 × Body Mass + 2012.3 × Flight Time - 338.0 (R = 0.658, SEE = 150 W), Mean Power = 6.7 × Body Mass + 1004.4 × Flight Time - 224.6 (R = 0.664, SEE = 82 W); and velocity-based power prediction equation: Peak Power = 8.1 × Body Mass + 818.6 × Peak Velocity - 762.0 (R = 0.797, SEE = 115 W); Mean Power = 5.2 × Body Mass + 435.9 × Peak Velocity - 467.7 (R = 0.838, SEE = 57 W). The BPU is a reliable test for both upper-body strength and power. Results indicate that the mean force generated from the BPU can be used to predict 1RM bench press, whereas peak velocity and flight time measured during the BPU can be used to predict upper-body power. These findings support the potential use of the BPU as a valid method to evaluate upper-body strength and power.

Distinct plasma lipids profiles of recurrent ovarian cancer by liquid chromatography-mass spectrometry

PubMed Central

Li, Ang; Cheng, Jinlong; Yang, Kai; Wang, Jingtao; Wang, Wenjie; Zhang, Fan; Li, Zhenzi; Dhillon, Harman S.; Openkova, Margarita S; Zhou, Xiaohua; Li, Kang; Hou, Yan

2017-01-01

Epithelial ovarian cancer (EOC) is the most deadly gynecologic malignancy worldwide due to its high recurrence rate after surgery and chemotherapy. There is a critical need for discovery of novel biomarkers for EOC recurrence providing higher prediction power than that of the present ones. Lipids have been reported to associate with development and progression of cancer. In the current study, we aim to identify and validate the lipids which were relevant to the ovarian cancer recurrence based on plasma lipidomics performed by ultra-performance liquid chromatography coupled with mass spectrometry. In order to fulfill this objective, plasma from 70 EOC patients with follow up information was obtained. The results revealed that patients with and without recurrence could be clearly distinguished based on their lipid profiles. Thirty-one lipid metabolites were identified as potential biomarkers for EOC recurrence. The AUC value of these metabolite combinations for predicting EOC recurrence was 0.897. In terms of clinical applicability, LysoPG(20:5) arose as a potential EOC recurrence predictive biomarker to increase the predictive power of clinical predictors from AUC value 0.739 to 0.875. Additionally, we still found that individuals with early relapses (< 6 months) had a distinctive metabolomic pattern compared with late EOC and non-EOC recurrence subjects. Interestingly, decreased levels of triglycerides (TGs) were found to be a specific metabolic feature foreshadowing an early relapse. In conclusion, plasma lipidomics study could be used for predicting EOC recurrences, as well as early and late recurrent cases. The lipid biomarker research improves the predictive power of clinical predictors and the identified biomarkers are of great prognostic and therapeutic potential. PMID:27564116

Cortical oscillatory activity and the induction of plasticity in the human motor cortex.

PubMed

McAllister, Suzanne M; Rothwell, John C; Ridding, Michael C

2011-05-01

Repetitive transcranial magnetic stimulation paradigms such as continuous theta burst stimulation (cTBS) induce long-term potentiation- and long-term depression-like plasticity in the human motor cortex. However, responses to cTBS are highly variable and may depend on the activity of the cortex at the time of stimulation. We investigated whether power in different electroencephalogram (EEG) frequency bands predicted the response to subsequent cTBS, and conversely whether cTBS had after-effects on the EEG. cTBS may utilize similar mechanisms of plasticity to motor learning; thus, we conducted a parallel set of experiments to test whether ongoing electroencephalography could predict performance of a visuomotor training task, and whether training itself had effects on the EEG. Motor evoked potentials (MEPs) provided an index of cortical excitability pre- and post-intervention. The EEG was recorded over the motor cortex pre- and post-intervention, and power spectra were computed. cTBS reduced MEP amplitudes; however, baseline power in the delta, theta, alpha or beta frequencies did not predict responses to cTBS or learning of the visuomotor training task. cTBS had no effect on delta, theta, alpha or beta power. In contrast, there was an increase in alpha power following visuomotor training that was positively correlated with changes in MEP amplitude post-training. The results suggest that the EEG is not a useful state-marker for predicting responses to plasticity-inducing paradigms. The correlation between alpha power and changes in corticospinal excitability following visuomotor training requires further investigation, but may be related to disengagement of the somatosensory system important for motor memory consolidation. © 2011 The Authors. European Journal of Neuroscience © 2011 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

Effects of Power on Mental Rotation and Emotion Recognition in Women.

PubMed

Nissan, Tali; Shapira, Oren; Liberman, Nira

2015-10-01

Based on construal-level theory (CLT) and its view of power as an instance of social distance, we predicted that high, relative to low power would enhance women's mental-rotation performance and impede their emotion-recognition performance. The predicted effects of power emerged both when it was manipulated via a recall priming task (Study 1) and environmental cues (Studies 2 and 3). Studies 3 and 4 found evidence for mediation by construal level of the effect of power on emotion recognition but not on mental rotation. We discuss potential mediating mechanisms for these effects based on both the social distance/construal level and the approach/inhibition views of power. We also discuss implications for optimizing performance on mental rotation and emotion recognition in everyday life. © 2015 by the Society for Personality and Social Psychology, Inc.

Improving Power Density of Free-Piston Stirling Engines

NASA Technical Reports Server (NTRS)

Briggs, Maxwell H.; Prahl, Joseph M.; Loparo, Kenneth A.

2016-01-01

Analyses and experiments demonstrate the potential benefits of optimizing piston and displacer motion in a free-piston Stirling Engine. Isothermal analysis shows the theoretical limits of power density improvement due to ideal motion in ideal Stirling engines. More realistic models based on nodal analysis show that ideal piston and displacer waveforms are not optimal, often producing less power than engines that use sinusoidal piston and displacer motion. Constrained optimization using nodal analysis predicts that Stirling engine power density can be increased by as much as 58 percent using optimized higher harmonic piston and displacer motion. An experiment is conducted in which an engine designed for sinusoidal motion is forced to operate with both second and third harmonics, resulting in a piston power increase of as much as 14 percent. Analytical predictions are compared to experimental data and show close agreement with indirect thermodynamic power calculations, but poor agreement with direct electrical power measurements.

Improving Power Density of Free-Piston Stirling Engines

NASA Technical Reports Server (NTRS)

Briggs, Maxwell H.; Prahl, Joseph; Loparo, Kenneth

2016-01-01

Analyses and experiments demonstrate the potential benefits of optimizing piston and displacer motion in a free piston Stirling Engine. Isothermal analysis shows the theoretical limits of power density improvement due to ideal motion in ideal Stirling engines. More realistic models based on nodal analysis show that ideal piston and displacer waveforms are not optimal, often producing less power than engines that use sinusoidal piston and displacer motion. Constrained optimization using nodal analysis predicts that Stirling engine power density can be increased by as much as 58 using optimized higher harmonic piston and displacer motion. An experiment is conducted in which an engine designed for sinusoidal motion is forced to operate with both second and third harmonics, resulting in a maximum piston power increase of 14. Analytical predictions are compared to experimental data showing close agreement with indirect thermodynamic power calculations, but poor agreement with direct electrical power measurements.

Improving Free-Piston Stirling Engine Power Density

NASA Technical Reports Server (NTRS)

Briggs, Maxwell H.

2016-01-01

Analyses and experiments demonstrate the potential benefits of optimizing piston and displacer motion in a free piston Stirling Engine. Isothermal analysis shows the theoretical limits of power density improvement due to ideal motion in ideal Stirling engines. More realistic models based on nodal analysis show that ideal piston and displacer waveforms are not optimal, often producing less power than engines that use sinusoidal piston and displacer motion. Constrained optimization using nodal analysis predicts that Stirling engine power density can be increased by as much as 58% using optimized higher harmonic piston and displacer motion. An experiment is conducted in which an engine designed for sinusoidal motion is forced to operate with both second and third harmonics, resulting in a maximum piston power increase of 14%. Analytical predictions are compared to experimental data showing close agreement with indirect thermodynamic power calculations, but poor agreement with direct electrical power measurements.

Different slopes for different folks: alpha and delta EEG power predict subsequent video game learning rate and improvements in cognitive control tasks.

PubMed

Mathewson, Kyle E; Basak, Chandramallika; Maclin, Edward L; Low, Kathy A; Boot, Walter R; Kramer, Arthur F; Fabiani, Monica; Gratton, Gabriele

2012-12-01

We hypothesized that control processes, as measured using electrophysiological (EEG) variables, influence the rate of learning of complex tasks. Specifically, we measured alpha power, event-related spectral perturbations (ERSPs), and event-related brain potentials during early training of the Space Fortress task, and correlated these measures with subsequent learning rate and performance in transfer tasks. Once initial score was partialled out, the best predictors were frontal alpha power and alpha and delta ERSPs, but not P300. By combining these predictors, we could explain about 50% of the learning rate variance and 10%-20% of the variance in transfer to other tasks using only pretraining EEG measures. Thus, control processes, as indexed by alpha and delta EEG oscillations, can predict learning and skill improvements. The results are of potential use to optimize training regimes. Copyright © 2012 Society for Psychophysiological Research.

Simple Emergent Power Spectra from Complex Inflationary Physics

NASA Astrophysics Data System (ADS)

Dias, Mafalda; Frazer, Jonathan; Marsh, M. C. David

2016-09-01

We construct ensembles of random scalar potentials for Nf-interacting scalar fields using nonequilibrium random matrix theory, and use these to study the generation of observables during small-field inflation. For Nf=O (few ), these heavily featured scalar potentials give rise to power spectra that are highly nonlinear, at odds with observations. For Nf≫1 , the superhorizon evolution of the perturbations is generically substantial, yet the power spectra simplify considerably and become more predictive, with most realizations being well approximated by a linear power spectrum. This provides proof of principle that complex inflationary physics can give rise to simple emergent power spectra. We explain how these results can be understood in terms of large Nf universality of random matrix theory.

Simple Emergent Power Spectra from Complex Inflationary Physics.

PubMed

Dias, Mafalda; Frazer, Jonathan; Marsh, M C David

2016-09-30

We construct ensembles of random scalar potentials for N_{f}-interacting scalar fields using nonequilibrium random matrix theory, and use these to study the generation of observables during small-field inflation. For N_{f}=O(few), these heavily featured scalar potentials give rise to power spectra that are highly nonlinear, at odds with observations. For N_{f}≫1, the superhorizon evolution of the perturbations is generically substantial, yet the power spectra simplify considerably and become more predictive, with most realizations being well approximated by a linear power spectrum. This provides proof of principle that complex inflationary physics can give rise to simple emergent power spectra. We explain how these results can be understood in terms of large N_{f} universality of random matrix theory.

Customization of Discriminant Function Analysis for Prediction of Solar Flares

DTIC Science & Technology

2005-03-01

lives such as telecommunication, commercial airlines, electrical power , wireless services, and terrestrial weather tracking and forecasting...the 1800’s can wreak havoc on today’s power , fuel, and telecommunication lines and finds its origin in solar activity. Enormous amounts of solar...inducing potential differences across large areas of the surface. Earth-bound power , fuel, and telecommunication lines grounded to the Earth provide an

Heatpipe power system and heatpipe bimodal system design and development options

NASA Technical Reports Server (NTRS)

Houts, M. G.; Poston, D. I.; Emrich, W. J., Jr.

1997-01-01

The Heatpipe Power System (HPS) is a potential, near-term, low-cost space fission power system. The Heatpipe Bimodal System (HBS) is a potential, near-term, low-cost space fission power and/or propulsion system. Both systems will be composed of independent modules, and all components operate within the existing databases. The HPS and HBS have relatively few system integration issues; thus, the successful development of a module is a significant step toward verifying system feasibility and performance estimates. A prototypic HPS module is being fabricated, and testing is scheduled to begin in November 1996. A successful test will provide high confidence that the HPS can achieve its predicted performance.

Multivariable Time Series Prediction for the Icing Process on Overhead Power Transmission Line

PubMed Central

Li, Peng; Zhao, Na; Zhou, Donghua; Cao, Min; Li, Jingjie; Shi, Xinling

2014-01-01

The design of monitoring and predictive alarm systems is necessary for successful overhead power transmission line icing. Given the characteristics of complexity, nonlinearity, and fitfulness in the line icing process, a model based on a multivariable time series is presented here to predict the icing load of a transmission line. In this model, the time effects of micrometeorology parameters for the icing process have been analyzed. The phase-space reconstruction theory and machine learning method were then applied to establish the prediction model, which fully utilized the history of multivariable time series data in local monitoring systems to represent the mapping relationship between icing load and micrometeorology factors. Relevant to the characteristic of fitfulness in line icing, the simulations were carried out during the same icing process or different process to test the model's prediction precision and robustness. According to the simulation results for the Tao-Luo-Xiong Transmission Line, this model demonstrates a good accuracy of prediction in different process, if the prediction length is less than two hours, and would be helpful for power grid departments when deciding to take action in advance to address potential icing disasters. PMID:25136653

Investigation of hydraulic transmission noise sources

NASA Astrophysics Data System (ADS)

Klop, Richard J.

Advanced hydrostatic transmissions and hydraulic hybrids show potential in new market segments such as commercial vehicles and passenger cars. Such new applications regard low noise generation as a high priority, thus, demanding new quiet hydrostatic transmission designs. In this thesis, the aim is to investigate noise sources of hydrostatic transmissions to discover strategies for designing compact and quiet solutions. A model has been developed to capture the interaction of a pump and motor working in a hydrostatic transmission and to predict overall noise sources. This model allows a designer to compare noise sources for various configurations and to design compact and inherently quiet solutions. The model describes dynamics of the system by coupling lumped parameter pump and motor models with a one-dimensional unsteady compressible transmission line model. The model has been verified with dynamic pressure measurements in the line over a wide operating range for several system structures. Simulation studies were performed illustrating sensitivities of several design variables and the potential of the model to design transmissions with minimal noise sources. A semi-anechoic chamber has been designed and constructed suitable for sound intensity measurements that can be used to derive sound power. Measurements proved the potential to reduce audible noise by predicting and reducing both noise sources. Sound power measurements were conducted on a series hybrid transmission test bench to validate the model and compare predicted noise sources with sound power.

Small hydropower spot prediction using SWAT and a diversion algorithm, case study: Upper Citarum Basin

NASA Astrophysics Data System (ADS)

Kardhana, Hadi; Arya, Doni Khaira; Hadihardaja, Iwan K.; Widyaningtyas, Riawan, Edi; Lubis, Atika

2017-11-01

Small-Scale Hydropower (SHP) had been important electric energy power source in Indonesia. Indonesia is vast countries, consists of more than 17.000 islands. It has large fresh water resource about 3 m of rainfall and 2 m of runoff. Much of its topography is mountainous, remote but abundant with potential energy. Millions of people do not have sufficient access to electricity, some live in the remote places. Recently, SHP development was encouraged for energy supply of the places. Development of global hydrology data provides opportunity to predict distribution of hydropower potential. In this paper, we demonstrate run-of-river type SHP spot prediction tool using SWAT and a river diversion algorithm. The use of Soil and Water Assessment Tool (SWAT) with input of CFSR (Climate Forecast System Re-analysis) of 10 years period had been implemented to predict spatially distributed flow cumulative distribution function (CDF). A simple algorithm to maximize potential head of a location by a river diversion expressing head race and penstock had been applied. Firm flow and power of the SHP were estimated from the CDF and the algorithm. The tool applied to Upper Citarum River Basin and three out of four existing hydropower locations had been well predicted. The result implies that this tool is able to support acceleration of SHP development at earlier phase.

Geographic information system (GIS) simulation of emergency power production from disaster debris in a combined heat and power (CHP) system

NASA Astrophysics Data System (ADS)

Ryals, Christopher Shannon

The objective of this study is to determine a predicted energy capacity of disaster debris for the production of emergency power using a combined heat and power (CHP) unit. A prediction simulation using geographic information systems (GIS) will use data from past storms to calculate an estimated amount of debris along with an estimated energy potential of said debris. Rather than the expense and burden of transporting woody debris such as downed trees and wood framing materials offsite, they can be processed (sorting and chipping) to provide an onsite energy source to provide power to emergency management facilities such as shelters in schools and hospitals. A CHP unit can simultaneously produce heat, cooling effects and electrical power using various biomass sources. This study surveys the quantity and composition of debris produced for a given classification of disaster and location. A comparison of power efficiency estimates for various disasters is conducted.

Modelling for Prediction vs. Modelling for Understanding: Commentary on Musso et al. (2013)

ERIC Educational Resources Information Center

Edelsbrunner, Peter; Schneider, Michael

2013-01-01

Musso et al. (2013) predict students' academic achievement with high accuracy one year in advance from cognitive and demographic variables, using artificial neural networks (ANNs). They conclude that ANNs have high potential for theoretical and practical improvements in learning sciences. ANNs are powerful statistical modelling tools but they can…

PPSP: prediction of PK-specific phosphorylation site with Bayesian decision theory.

PubMed

Xue, Yu; Li, Ao; Wang, Lirong; Feng, Huanqing; Yao, Xuebiao

2006-03-20

As a reversible and dynamic post-translational modification (PTM) of proteins, phosphorylation plays essential regulatory roles in a broad spectrum of the biological processes. Although many studies have been contributed on the molecular mechanism of phosphorylation dynamics, the intrinsic feature of substrates specificity is still elusive and remains to be delineated. In this work, we present a novel, versatile and comprehensive program, PPSP (Prediction of PK-specific Phosphorylation site), deployed with approach of Bayesian decision theory (BDT). PPSP could predict the potential phosphorylation sites accurately for approximately 70 PK (Protein Kinase) groups. Compared with four existing tools Scansite, NetPhosK, KinasePhos and GPS, PPSP is more accurate and powerful than these tools. Moreover, PPSP also provides the prediction for many novel PKs, say, TRK, mTOR, SyK and MET/RON, etc. The accuracy of these novel PKs are also satisfying. Taken together, we propose that PPSP could be a potentially powerful tool for the experimentalists who are focusing on phosphorylation substrates with their PK-specific sites identification. Moreover, the BDT strategy could also be a ubiquitous approach for PTMs, such as sumoylation and ubiquitination, etc.

Does NASA SMAP Improve the Accuracy of Power Outage Models?

NASA Astrophysics Data System (ADS)

Quiring, S. M.; McRoberts, D. B.; Toy, B.; Alvarado, B.

2016-12-01

Electric power utilities make critical decisions in the days prior to hurricane landfall that are primarily based on the estimated impact to their service area. For example, utilities must determine how many repair crews to request from other utilities, the amount of material and equipment they will need to make repairs, and where in their geographically expansive service area to station crews and materials. Accurate forecasts of the impact of an approaching hurricane within their service area are critical for utilities in balancing the costs and benefits of different levels of resources. The Hurricane Outage Prediction Model (HOPM) are a family of statistical models that utilize predictions of tropical cyclone windspeed and duration of strong winds, along with power system and environmental variables (e.g., soil moisture, long-term precipitation), to forecast the number and location of power outages. This project assesses whether using NASA SMAP soil moisture improves the accuracy of power outage forecasts as compared to using model-derived soil moisture from NLDAS-2. A sensitivity analysis is employed since there have been very few tropical cyclones making landfall in the United States since SMAP was launched. The HOPM is used to predict power outages for 13 historical tropical cyclones and the model is run using twice, once with NLDAS soil moisture and once with SMAP soil moisture. Our results demonstrate that using SMAP soil moisture can have a significant impact on power outage predictions. SMAP has the potential to enhance the accuracy of power outage forecasts. Improved outage forecasts reduce the duration of power outages which reduces economic losses and accelerates recovery.

Enhanced charging kinetics of porous electrodes: surface conduction as a short-circuit mechanism.

PubMed

Mirzadeh, Mohammad; Gibou, Frederic; Squires, Todd M

2014-08-29

We use direct numerical simulations of the Poisson-Nernst-Planck equations to study the charging kinetics of porous electrodes and to evaluate the predictive capabilities of effective circuit models, both linear and nonlinear. The classic transmission line theory of de Levie holds for general electrode morphologies, but only at low applied potentials. Charging dynamics are slowed appreciably at high potentials, yet not as significantly as predicted by the nonlinear transmission line model of Biesheuvel and Bazant. We identify surface conduction as a mechanism which can effectively "short circuit" the high-resistance electrolyte in the bulk of the pores, thus accelerating the charging dynamics and boosting power densities. Notably, the boost in power density holds only for electrode morphologies with continuous conducting surfaces in the charging direction.

Geographic potential of disease caused by Ebola and Marburg viruses in Africa.

PubMed

Peterson, A Townsend; Samy, Abdallah M

2016-10-01

Filoviruses represent a significant public health threat worldwide. West Africa recently experienced the largest-scale and most complex filovirus outbreak yet known, which underlines the need for a predictive understanding of the geographic distribution and potential for transmission to humans of these viruses. Here, we used ecological niche modeling techniques to understand the relationship between known filovirus occurrences and environmental characteristics. Our study derived a picture of the potential transmission geography of Ebola virus species and Marburg, paired with views of the spatial uncertainty associated with model-to-model variation in our predictions. We found that filovirus species have diverged ecologically, but only three species are sufficiently well known that models could be developed with significant predictive power. We quantified uncertainty in predictions, assessed potential for outbreaks outside of known transmission areas, and highlighted the Ethiopian Highlands and scattered areas across East Africa as additional potentially unrecognized transmission areas. Copyright © 2016 Elsevier B.V. All rights reserved.

Predictability of depression severity based on posterior alpha oscillations.

PubMed

Jiang, H; Popov, T; Jylänki, P; Bi, K; Yao, Z; Lu, Q; Jensen, O; van Gerven, M A J

2016-04-01

We aimed to integrate neural data and an advanced machine learning technique to predict individual major depressive disorder (MDD) patient severity. MEG data was acquired from 22 MDD patients and 22 healthy controls (HC) resting awake with eyes closed. Individual power spectra were calculated by a Fourier transform. Sources were reconstructed via beamforming technique. Bayesian linear regression was applied to predict depression severity based on the spatial distribution of oscillatory power. In MDD patients, decreased theta (4-8 Hz) and alpha (8-14 Hz) power was observed in fronto-central and posterior areas respectively, whereas increased beta (14-30 Hz) power was observed in fronto-central regions. In particular, posterior alpha power was negatively related to depression severity. The Bayesian linear regression model showed significant depression severity prediction performance based on the spatial distribution of both alpha (r=0.68, p=0.0005) and beta power (r=0.56, p=0.007) respectively. Our findings point to a specific alteration of oscillatory brain activity in MDD patients during rest as characterized from MEG data in terms of spectral and spatial distribution. The proposed model yielded a quantitative and objective estimation for the depression severity, which in turn has a potential for diagnosis and monitoring of the recovery process. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Wind Power predictability a risk factor in the design, construction and operation of Wind Generation Turbines

NASA Astrophysics Data System (ADS)

Thiesen, J.; Gulstad, L.; Ristic, I.; Maric, T.

2010-09-01

Summit: The wind power predictability is often a forgotten decision and planning factor for most major wind parks, both onshore and offshore. The results of the predictability are presented after having examined a number of European offshore and offshore parks power predictability by using three(3) mesoscale model IRIE_GFS and IRIE_EC and WRF. Full description: It is well known that the potential wind production is changing with latitude and complexity in terrain, but how big are the changes in the predictability and the economic impacts on a project? The concept of meteorological predictability has hitherto to some degree been neglected as a risk factor in the design, construction and operation of wind power plants. Wind power plants are generally built in places where the wind resources are high, but these are often also sites where the predictability of the wind and other weather parameters is comparatively low. This presentation addresses the question of whether higher predictability can outweigh lower average wind speeds with regard to the overall economy of a wind power project. Low predictability also tends to reduce the value of the energy produced. If it is difficult to forecast the wind on a site, it will also be difficult to predict the power production. This, in turn, leads to increased balance costs and a less reduced carbon emission from the renewable source. By investigating the output from three(3) mesoscale models IRIE and WRF, using ECMWF and GFS as boundary data over a forecasting period of 3 months for 25 offshore and onshore wind parks in Europe, the predictability are mapped. Three operational mesoscale models with two different boundary data have been chosen in order to eliminate the uncertainty with one mesoscale model. All mesoscale models are running in a 10 km horizontal resolution. The model output are converted into "day a head" wind turbine generation forecasts by using a well proven advanced physical wind power model. The power models are using a number of weather parameters like wind speed in different heights, friction velocity and DTHV. The 25 wind sites are scattered around in Europe and contains 4 offshore parks and 21 onshore parks in various terrain complexity. The "day a head" forecasts are compared with production data and predictability for the period February 2010-April 2010 are given in Mean Absolute Errors (MAE) and Root Mean Squared Errors (RMSE). The power predictability results are mapped for each turbine giving a clear picture of the predictability in Europe. . Finally a economic analysis are shown for each wind parks in different regimes of predictability will be compared with regard to the balance costs that result from errors in the wind power prediction. Analysis shows that it may very well be profitable to place wind parks in regions of lower, but more predictable wind ressource. Authors: Ivan Ristic, CTO Weather2Umberlla D.O.O Tomislav Maric, Meteorologist at Global Flow Solutions Vestas Wind Technology R&D Line Gulstad, Manager Global Flow Solutions Vestas Wind Technology R&D Jesper Thiesen, CEO ConWx ApS

Internal mechanisms underlying anticipatory language processing: Evidence from event-related-potentials and neural oscillations.

PubMed

Li, Xiaoqing; Zhang, Yuping; Xia, Jinyan; Swaab, Tamara Y

2017-07-28

Although numerous studies have demonstrated that the language processing system can predict upcoming content during comprehension, there is still no clear picture of the anticipatory stage of predictive processing. This electroencephalograph study examined the cognitive and neural oscillatory mechanisms underlying anticipatory processing during language comprehension, and the consequences of this prediction for bottom-up processing of predicted/unpredicted content. Participants read Mandarin Chinese sentences that were either strongly or weakly constraining and that contained critical nouns that were congruent or incongruent with the sentence contexts. We examined the effects of semantic predictability on anticipatory processing prior to the onset of the critical nouns and on integration of the critical nouns. The results revealed that, at the integration stage, the strong-constraint condition (compared to the weak-constraint condition) elicited a reduced N400 and reduced theta activity (4-7Hz) for the congruent nouns, but induced beta (13-18Hz) and theta (4-7Hz) power decreases for the incongruent nouns, indicating benefits of confirmed predictions and potential costs of disconfirmed predictions. More importantly, at the anticipatory stage, the strongly constraining context elicited an enhanced sustained anterior negativity and beta power decrease (19-25Hz), which indicates that strong prediction places a higher processing load on the anticipatory stage of processing. The differences (in the ease of processing and the underlying neural oscillatory activities) between anticipatory and integration stages of lexical processing were discussed with regard to predictive processing models. Copyright © 2017 Elsevier Ltd. All rights reserved.

An Implanted, Stimulated Muscle Powered Piezoelectric Generator

NASA Technical Reports Server (NTRS)

Lewandowski, Beth; Gustafson, Kenneth; Kilgore, Kevin

2007-01-01

A totally implantable piezoelectric generator system able to harness power from electrically activated muscle could be used to augment the power systems of implanted medical devices, such as neural prostheses, by reducing the number of battery replacement surgeries or by allowing periods of untethered functionality. The features of our generator design are no moving parts and the use of a portion of the generated power for system operation and regulation. A software model of the system has been developed and simulations have been performed to predict the output power as the system parameters were varied within their constraints. Mechanical forces that mimic muscle forces have been experimentally applied to a piezoelectric generator to verify the accuracy of the simulations and to explore losses due to mechanical coupling. Depending on the selection of system parameters, software simulations predict that this generator concept can generate up to approximately 700 W of power, which is greater than the power necessary to drive the generator, conservatively estimated to be 50 W. These results suggest that this concept has the potential to be an implantable, self-replenishing power source and further investigation is underway.

A large-eddy simulation based power estimation capability for wind farms over complex terrain

NASA Astrophysics Data System (ADS)

Senocak, I.; Sandusky, M.; Deleon, R.

2017-12-01

There has been an increasing interest in predicting wind fields over complex terrain at the micro-scale for resource assessment, turbine siting, and power forecasting. These capabilities are made possible by advancements in computational speed from a new generation of computing hardware, numerical methods and physics modelling. The micro-scale wind prediction model presented in this work is based on the large-eddy simulation paradigm with surface-stress parameterization. The complex terrain is represented using an immersed-boundary method that takes into account the parameterization of the surface stresses. Governing equations of incompressible fluid flow are solved using a projection method with second-order accurate schemes in space and time. We use actuator disk models with rotation to simulate the influence of turbines on the wind field. Data regarding power production from individual turbines are mostly restricted because of proprietary nature of the wind energy business. Most studies report percentage drop of power relative to power from the first row. There have been different approaches to predict power production. Some studies simply report available wind power in the upstream, some studies estimate power production using power curves available from turbine manufacturers, and some studies estimate power as torque multiplied by rotational speed. In the present work, we propose a black-box approach that considers a control volume around a turbine and estimate the power extracted from the turbine based on the conservation of energy principle. We applied our wind power prediction capability to wind farms over flat terrain such as the wind farm over Mower County, Minnesota and the Horns Rev offshore wind farm in Denmark. The results from these simulations are in good agreement with published data. We also estimate power production from a hypothetical wind farm in complex terrain region and identify potential zones suitable for wind power production.

Predictive Power Estimation Algorithm (PPEA) - A New Algorithm to Reduce Overfitting for Genomic Biomarker Discovery

PubMed Central

Liu, Jiangang; Jolly, Robert A.; Smith, Aaron T.; Searfoss, George H.; Goldstein, Keith M.; Uversky, Vladimir N.; Dunker, Keith; Li, Shuyu; Thomas, Craig E.; Wei, Tao

2011-01-01

Toxicogenomics promises to aid in predicting adverse effects, understanding the mechanisms of drug action or toxicity, and uncovering unexpected or secondary pharmacology. However, modeling adverse effects using high dimensional and high noise genomic data is prone to over-fitting. Models constructed from such data sets often consist of a large number of genes with no obvious functional relevance to the biological effect the model intends to predict that can make it challenging to interpret the modeling results. To address these issues, we developed a novel algorithm, Predictive Power Estimation Algorithm (PPEA), which estimates the predictive power of each individual transcript through an iterative two-way bootstrapping procedure. By repeatedly enforcing that the sample number is larger than the transcript number, in each iteration of modeling and testing, PPEA reduces the potential risk of overfitting. We show with three different cases studies that: (1) PPEA can quickly derive a reliable rank order of predictive power of individual transcripts in a relatively small number of iterations, (2) the top ranked transcripts tend to be functionally related to the phenotype they are intended to predict, (3) using only the most predictive top ranked transcripts greatly facilitates development of multiplex assay such as qRT-PCR as a biomarker, and (4) more importantly, we were able to demonstrate that a small number of genes identified from the top-ranked transcripts are highly predictive of phenotype as their expression changes distinguished adverse from nonadverse effects of compounds in completely independent tests. Thus, we believe that the PPEA model effectively addresses the over-fitting problem and can be used to facilitate genomic biomarker discovery for predictive toxicology and drug responses. PMID:21935387

Applicability of advanced automotive heat engines to solar thermal power

NASA Technical Reports Server (NTRS)

Beremand, D. G.; Evans, D. G.; Alger, D. L.

1981-01-01

The requirements of a solar thermal power system are reviewed and compared with the predicted characteristics of automobile engines under development. A good match is found in terms of power level and efficiency when the automobile engines, designed for maximum powers of 65-100 kW (87 to 133 hp) are operated to the nominal 20-40 kW electric output requirement of the solar thermal application. At these reduced power levels it appears that the automotive gas turbine and Stirling engines have the potential to deliver the 40+ percent efficiency goal of the solar thermal program.

Applicability of advanced automotive heat engines to solar thermal power

NASA Astrophysics Data System (ADS)

Beremand, D. G.; Evans, D. G.; Alger, D. L.

The requirements of a solar thermal power system are reviewed and compared with the predicted characteristics of automobile engines under development. A good match is found in terms of power level and efficiency when the automobile engines, designed for maximum powers of 65-100 kW (87 to 133 hp) are operated to the nominal 20-40 kW electric output requirement of the solar thermal application. At these reduced power levels it appears that the automotive gas turbine and Stirling engines have the potential to deliver the 40+ percent efficiency goal of the solar thermal program.

Cerenkov Maser and Cerenkov Laser Devices.

DTIC Science & Technology

1982-12-01

The principle goal of the work was the development of high power Cerenkov sources in the lower mm wavelength range. It was demonstrated that a...it is • Subject catecory name: approximately one kw. At the present-time the-beam i-s High Power icr ave collected on a mirror set at a 450 angle to...differences in the boundary-scat- This process shows potential as a tunable source of fared phonon conductivity are predicted along the prim- highs power

Soil vulnerability for cesium transfer.

PubMed

Vandenhove, Hildegarde; Sweeck, Lieve

2011-07-01

The recent events at the Fukushima Daiichi nuclear power plant in Japan have raised questions about the accumulation of radionuclides in soils and the possible impacts on agriculture surrounding nuclear power plants. This article summarizes the knowledge gained after the nuclear power plant accident in Chernobyl, Ukraine, on how soil parameters influence soil vulnerability for radiocesium bioavailability, discusses some potential agrochemical countermeasures, and presents some predictions of radiocesium crop concentrations for areas affected by the Fukushima accident. Copyright © 2011 SETAC.

Potential of laser for SPS power transmission

NASA Technical Reports Server (NTRS)

Bain, C. N.

1978-01-01

Research on the feasibility of using a laser subsystem as an additional option for the transmission of the satellite power system (STS) power is presented. Current laser work and predictions for future laser performance provide a level of confidence that the development of a laser power transmission system is technologically feasible in the time frame required to develop the SBS. There are significant economic advantages in lower ground distribution costs and a reduction of more than two orders of magnitude in real estate requirements for ground based receiving/conversion sites.

Complete set of deuteron analyzing powers from d ⃗p elastic scattering at 190 MeV/nucleon

NASA Astrophysics Data System (ADS)

Sekiguchi, K.; Witała, H.; Akieda, T.; Eto, D.; Kon, H.; Wada, Y.; Watanabe, A.; Chebotaryov, S.; Dozono, M.; Golak, J.; Kamada, H.; Kawakami, S.; Kubota, Y.; Maeda, Y.; Miki, K.; Milman, E.; Ohkura, A.; Sakai, H.; Sakaguchi, S.; Sakamoto, N.; Sasano, M.; Shindo, Y.; Skibiński, R.; Suzuki, H.; Tabata, M.; Uesaka, T.; Wakasa, T.; Yako, K.; Yamamoto, T.; Yanagisawa, Y.; Yasuda, J.

2017-12-01

All deuteron analyzing powers for elastic deuteron-proton (d p ) scattering have been measured with a polarized deuteron beam at 186.6 MeV/nucleon. They are compared with results of three-nucleon Faddeev calculations based on the standard, high-precision nucleon-nucleon (N N ) potentials alone or combined with commonly used three-nucleon force (3 N F ) models such as the Tucson-Melbourne '99 or the Urbana IX. Predicted 3 N F effects localized at backward angles are supported only partially by the data. The data are also compared to predictions based on locally regularized chiral N N potentials. An estimation of theoretical truncation uncertainties in the consecutive orders of chiral expansion suggests that the observed discrepancies between this modern theory and the data could probably be explained by including chiral 3 N F 's in future calculations. A systematic comparison to the deuteron analyzing power data previously taken at incident energies from 70 to 294 MeV/nucleon clearly shows that not only the cross section but also the analyzing powers reveal growing 3 N F effects when the three-nucleon system energy is increased.

On Explaining Language Shift: Sociology or Social Psychology of Language?

ERIC Educational Resources Information Center

Maitz, Peter

2011-01-01

This study investigates the potentials and limits of sociolinguistic research on language shift. Starting from a position that the ultimate goal of the research must be to create a general theory of language shift of predictive power, the author examines the explanatory potential of current mainstream research methodology now regarded as canonical…

Normative and descriptive accounts of the influence of power and contingency on causal judgement.

PubMed

Perales, José C; Shanks, David R

2003-08-01

The power PC theory (Cheng, 1997) is a normative account of causal inference, which predicts that causal judgements are based on the power p of a potential cause, where p is the cause-effect contingency normalized by the base rate of the effect. In three experiments we demonstrate that both cause-effect contingency and effect base-rate independently affect estimates in causal learning tasks. In Experiment 1, causal strength judgements were directly related to power p in a task in which the effect base-rate was manipulated across two positive and two negative contingency conditions. In Experiments 2 and 3 contingency manipulations affected causal estimates in several situations in which power p was held constant, contrary to the power PC theory's predictions. This latter effect cannot be explained by participants' conflation of reliability and causal strength, as Experiment 3 demonstrated independence of causal judgements and confidence. From a descriptive point of view, the data are compatible with Pearce's (1987) model, as well as with several other judgement rules, but not with the Rescorla-Wagner (Rescorla & Wagner, 1972) or power PC models.

Plasma sheath structure surrounding a large powered spacecraft

NASA Technical Reports Server (NTRS)

Mandell, M. J.; Jongeward, G. A.; Katz, I.

1984-01-01

Various factors determining the floating potential of a highly biased (about 4-kV) spacecraft in low earth orbit are discussed. While the common rule of thumb (90 percent negative; 10 percent positive) is usually a good guide, different biasing and grounding patterns can lead to high positive potentials. The NASCAP/LEO code can be used to predict spacecraft floating potential for complex three-dimensional spacecraft.

The potential distribution of Phlebotomus papatasi (Diptera: Psychodidae) in Libya based on ecological niche model.

PubMed

Abdel-Dayem, M S; Annajar, B B; Hanafi, H A; Obenauer, P J

2012-05-01

The increased cases of cutaneous leishmaniasis vectored by Phlebotomus papatasi (Scopoli) in Libya have driven considerable effort to develop a predictive model for the potential geographical distribution of this disease. We collected adult P. papatasi from 17 sites in Musrata and Yefern regions of Libya using four different attraction traps. Our trap results and literature records describing the distribution of P. papatasi were incorporated into a MaxEnt algorithm prediction model that used 22 environmental variables. The model showed a high performance (AUC = 0.992 and 0.990 for training and test data, respectively). High suitability for P. papatasi was predicted to be largely confined to the coast at altitudes <600 m. Regions south of 300 degrees N latitude were calculated as unsuitable for this species. Jackknife analysis identified precipitation as having the most significant predictive power, while temperature and elevation variables were less influential. The National Leishmaniasis Control Program in Libya may find this information useful in their efforts to control zoonotic cutaneous leishmaniasis. Existing records are strongly biased toward a few geographical regions, and therefore, further sand fly collections are warranted that should include documentation of such factors as soil texture and humidity, land cover, and normalized difference vegetation index (NDVI) data to increase the model's predictive power.

Impacts of past and future climate change on wind energy resources in the United States

NASA Astrophysics Data System (ADS)

McCaa, J. R.; Wood, A.; Eichelberger, S.; Westrick, K.

2009-12-01

The links between climate change and trends in wind energy resources have important potential implications for the wind energy industry, and have received significant attention in recent studies. We have conducted two studies that provide insights into the potential for climate change to affect future wind power production. In one experiment, we projected changes in power capacity for a hypothetical wind farm located near Kennewick, Washington, due to greenhouse gas-induced climate change, estimated using a set of regional climate model simulations. Our results show that the annual wind farm power capacity is projected to decrease 1.3% by 2050. In a wider study focusing on wind speed instead of power, we analyzed projected changes in wind speed from 14 different climate simulations that were performed in support of the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4). Our results show that the predicted ensemble mean changes in annual mean wind speeds are expected to be modest. However, seasonal changes and changes predicted by individual models are large enough to affect the profitability of existing and future wind projects. The majority of the model simulations reveal that near-surface wind speed values are expected to shift poleward in response to the IPCC A2 emission scenario, particularly during the winter season. In the United States, most models agree that the mean annual wind speed values will increase in a region extending from the Great Lakes southward across the Midwest and into Texas. Decreased values, though, are predicted across most of the western United States. However, these predicted changes have a strong seasonal dependence, with wind speed increases over most of the United States during the winter and decreases over the northern United States during the summer.

Tissue Chips to aid drug development and modeling for rare diseases

PubMed Central

Low, Lucie A.; Tagle, Danilo A.

2016-01-01

Introduction The technologies used to design, create and use microphysiological systems (MPS, “tissue chips” or “organs-on-chips”) have progressed rapidly in the last 5 years, and validation studies of the functional relevance of these platforms to human physiology, and response to drugs for individual model organ systems, are well underway. These studies are paving the way for integrated multi-organ systems that can model diseases and predict drug efficacy and toxicology of multiple organs in real-time, improving the potential for diagnostics and development of novel treatments of rare diseases in the future. Areas covered This review will briefly summarize the current state of tissue chip research and highlight model systems where these microfabricated (or bioengineered) devices are already being used to screen therapeutics, model disease states, and provide potential treatments in addition to helping elucidate the basic molecular and cellular phenotypes of rare diseases. Expert opinion Microphysiological systems hold great promise and potential for modeling rare disorders, as well as for their potential use to enhance the predictive power of new drug therapeutics, plus potentially increase the statistical power of clinical trials while removing the inherent risks of these trials in rare disease populations. PMID:28626620

Predicting therapy success for treatment as usual and blended treatment in the domain of depression.

PubMed

van Breda, Ward; Bremer, Vincent; Becker, Dennis; Hoogendoorn, Mark; Funk, Burkhardt; Ruwaard, Jeroen; Riper, Heleen

2018-06-01

In this paper, we explore the potential of predicting therapy success for patients in mental health care. Such predictions can eventually improve the process of matching effective therapy types to individuals. In the EU project E-COMPARED, a variety of information is gathered about patients suffering from depression. We use this data, where 276 patients received treatment as usual and 227 received blended treatment, to investigate to what extent we are able to predict therapy success. We utilize different encoding strategies for preprocessing, varying feature selection techniques, and different statistical procedures for this purpose. Significant predictive power is found with average AUC values up to 0.7628 for treatment as usual and 0.7765 for blended treatment. Adding daily assessment data for blended treatment does currently not add predictive accuracy. Cost effectiveness analysis is needed to determine the added potential for real-world applications.

Wind Plant Preconstruction Energy Estimates. Current Practice and Opportunities

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

Clifton, Andrew; Smith, Aaron; Fields, Michael

2016-04-19

Understanding the amount of energy that will be harvested by a wind power plant each year and the variability of that energy is essential to assessing and potentially improving the financial viability of that power plant. The preconstruction energy estimate process predicts the amount of energy--with uncertainty estimates--that a wind power plant will deliver to the point of revenue. This report describes the preconstruction energy estimate process from a technical perspective and seeks to provide insight into the financial implications associated with each step.

On the predictive potential of Pc5 ULF waves to forecast relativistic electrons based on their relationships over two solar cycles

NASA Astrophysics Data System (ADS)

Lam, Hing-Lan

2017-01-01

A statistical study of relativistic electron (>2 MeV) fluence derived from geosynchronous satellites and Pc5 ultralow frequency (ULF) wave power computed from a ground magnetic observatory data located in Canada's auroral zone has been carried out. The ground observations were made near the foot points of field lines passing through the GOESs from 1987 to 2009 (cycles 22 and 23). We determine statistical relationships between the two quantities for different phases of a solar cycle and validate these relationships in two different cycles. There is a positive linear relationship between log fluence and log Pc5 power for all solar phases; however, the power law indices vary for different phases of the cycle. High index values existed during the descending phase. The Pearson's cross correlation between electron fluence and Pc5 power indicates fluence enhancement 2-3 days after strong Pc5 wave activity for all solar phases. The lag between the two quantities is shorter for extremely high fluence (due to high Pc5 power), which tends to occur during the declining phases of both cycles. Most occurrences of extremely low fluence were observed during the extended solar minimum of cycle 23. The precursory attribute of Pc5 power with respect to fluence and the enhancement of fluence due to rising Pc5 power both support the notion of an electron acceleration mechanism by Pc5 ULF waves. This precursor behavior establishes the potential of using Pc5 power to predict relativistic electron fluence.

Predicting the remaining service life of concrete

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

Clifton, J.F.

1991-11-01

Nuclear power plants are providing, currently, about 17 percent of the U.S. electricity and many of these plants are approaching their licensed life of 40 years. The U.S. Nuclear Regulatory Commission and the Department of Energy`s Oak Ridge National Laboratory are carrying out a program to develop a methodology for assessing the remaining safe-life of the concrete components and structures in nuclear power plants. This program has the overall objective of identifying potential structural safety issues, as well as acceptance criteria, for use in evaluations of nuclear power plants for continued service. The National Institute of Standards and Technology (NIST)more » is contributing to this program by identifying and analyzing methods for predicting the remaining life of in-service concrete materials. This report examines the basis for predicting the remaining service lives of concrete materials of nuclear power facilities. Methods for predicting the service life of new and in-service concrete materials are analyzed. These methods include (1) estimates based on experience, (2) comparison of performance, (3) accelerated testing, (4) stochastic methods, and (5) mathematical modeling. New approaches for predicting the remaining service lives of concrete materials are proposed and recommendations for their further development given. Degradation processes are discussed based on considerations of their mechanisms, likelihood of occurrence, manifestations, and detection. They include corrosion, sulfate attack, alkali-aggregate reactions, frost attack, leaching, radiation, salt crystallization, and microbiological attack.« less

A summary of wind power prediction methods

NASA Astrophysics Data System (ADS)

Wang, Yuqi

2018-06-01

The deterministic prediction of wind power, the probability prediction and the prediction of wind power ramp events are introduced in this paper. Deterministic prediction includes the prediction of statistical learning based on histor ical data and the prediction of physical models based on NWP data. Due to the great impact of wind power ramp events on the power system, this paper also introduces the prediction of wind power ramp events. At last, the evaluation indicators of all kinds of prediction are given. The prediction of wind power can be a good solution to the adverse effects of wind power on the power system due to the abrupt, intermittent and undulation of wind power.

Potential for natural evaporation as a reliable renewable energy resource.

PubMed

Cavusoglu, Ahmet-Hamdi; Chen, Xi; Gentine, Pierre; Sahin, Ozgur

2017-09-26

About 50% of the solar energy absorbed at the Earth's surface drives evaporation, fueling the water cycle that affects various renewable energy resources, such as wind and hydropower. Recent advances demonstrate our nascent ability to convert evaporation energy into work, yet there is little understanding about the potential of this resource. Here we study the energy available from natural evaporation to predict the potential of this ubiquitous resource. We find that natural evaporation from open water surfaces could provide power densities comparable to current wind and solar technologies while cutting evaporative water losses by nearly half. We estimate up to 325 GW of power is potentially available in the United States. Strikingly, water's large heat capacity is sufficient to control power output by storing excess energy when demand is low, thus reducing intermittency and improving reliability. Our findings motivate the improvement of materials and devices that convert energy from evaporation.The evaporation of water represents an alternative source of renewable energy. Building on previous models of evaporation, Cavusoglu et al. show that the power available from this natural resource is comparable to wind and solar power, yet it does not suffer as much from varying weather conditions.

Analysis of electromagnetic interference from power system processing and transmission components for Space Station Freedom

NASA Technical Reports Server (NTRS)

Barber, Peter W.; Demerdash, Nabeel A. O.; Wang, R.; Hurysz, B.; Luo, Z.

1991-01-01

The goal is to analyze the potential effects of electromagnetic interference (EMI) originating from power system processing and transmission components for Space Station Freedom.The approach consists of four steps: (1) develop analytical tools (models and computer programs); (2) conduct parameterization studies; (3) predict the global space station EMI environment; and (4) provide a basis for modification of EMI standards.

Prediction by data mining, of suicide attempts in Korean adolescents: a national study

PubMed Central

Bae, Sung Man; Lee, Seung A; Lee, Seung-Hwan

2015-01-01

Objective This study aimed to develop a prediction model for suicide attempts in Korean adolescents. Methods We conducted a decision tree analysis of 2,754 middle and high school students nationwide. We fixed suicide attempt as the dependent variable and eleven sociodemographic, intrapersonal, and extrapersonal variables as independent variables. Results The rate of suicide attempts of the total sample was 9.5%, and severity of depression was the strongest variable to predict suicide attempt. The rates of suicide attempts in the depression and potential depression groups were 5.4 and 2.8 times higher than that of the non-depression group. In the depression group, the most powerful factor to predict a suicide attempt was delinquency, and the rate of suicide attempts in those in the depression group with higher delinquency was two times higher than in those in the depression group with lower delinquency. Of special note, the rate of suicide attempts in the depressed females with higher delinquency was the highest. Interestingly, in the potential depression group, the most impactful factor to predict a suicide attempt was intimacy with family, and the rate of suicide attempts of those in the potential depression group with lower intimacy with family was 2.4 times higher than that of those in the potential depression group with higher intimacy with family. And, among the potential depression group, middle school students with lower intimacy with family had a 2.5-times higher rate of suicide attempts than high school students with lower intimacy with family. Finally, in the non-depression group, stress level was the most powerful factor to predict a suicide attempt. Among the non-depression group, students who reported high levels of stress showed an 8.3-times higher rate of suicide attempts than students who reported average levels of stress. Discussion Based on the results, we especially need to pay attention to depressed females with higher delinquency and those with potential depression with lower intimacy with family to prevent suicide attempts in teenagers. PMID:26396521

New algorithm for toric intraocular lens power calculation considering the posterior corneal astigmatism.

PubMed

Canovas, Carmen; Alarcon, Aixa; Rosén, Robert; Kasthurirangan, Sanjeev; Ma, Joseph J K; Koch, Douglas D; Piers, Patricia

2018-02-01

To assess the accuracy of toric intraocular lens (IOL) power calculations of a new algorithm that incorporates the effect of posterior corneal astigmatism (PCA). Abbott Medical Optics, Inc., Groningen, the Netherlands. Retrospective case report. In eyes implanted with toric IOLs, the exact vergence formula of the Tecnis toric calculator was used to predict refractive astigmatism from preoperative biometry, surgeon-estimated surgically induced astigmatism (SIA), and implanted IOL power, with and without including the new PCA algorithm. For each calculation method, the error in predicted refractive astigmatism was calculated as the vector difference between the prediction and the actual refraction. Calculations were also made using postoperative keratometry (K) values to eliminate the potential effect of incorrect SIA estimates. The study comprised 274 eyes. The PCA algorithm significantly reduced the centroid error in predicted refractive astigmatism (P < .001). With the PCA algorithm, the centroid error reduced from 0.50 @ 1 to 0.19 @ 3 when using preoperative K values and from 0.30 @ 0 to 0.02 @ 84 when using postoperative K values. Patients who had anterior corneal against-the-rule, with-the-rule, and oblique astigmatism had improvement with the PCA algorithm. In addition, the PCA algorithm reduced the median absolute error in all groups (P < .001). The use of the new PCA algorithm decreased the error in the prediction of residual refractive astigmatism in eyes implanted with toric IOLs. Therefore, the new PCA algorithm, in combination with an exact vergence IOL power calculation formula, led to an increased predictability of toric IOL power. Copyright © 2018 ASCRS and ESCRS. Published by Elsevier Inc. All rights reserved.

Price dynamics in political prediction markets

PubMed Central

Majumder, Saikat Ray; Diermeier, Daniel; Rietz, Thomas A.; Amaral, Luís A. Nunes

2009-01-01

Prediction markets, in which contract prices are used to forecast future events, are increasingly applied to various domains ranging from political contests to scientific breakthroughs. However, the dynamics of such markets are not well understood. Here, we study the return dynamics of the oldest, most data-rich prediction markets, the Iowa Electronic Presidential Election “winner-takes-all” markets. As with other financial markets, we find uncorrelated returns, power-law decaying volatility correlations, and, usually, power-law decaying distributions of returns. However, unlike other financial markets, we find conditional diverging volatilities as the contract settlement date approaches. We propose a dynamic binary option model that captures all features of the empirical data and can potentially provide a tool with which one may extract true information events from a price time series. PMID:19155442

Preliminary analysis of the PreFlexMS molten salt once-through steam generator dynamics and control strategy

NASA Astrophysics Data System (ADS)

Trabucchi, Stefano; Casella, Francesco; Maioli, Tommaso; Elsido, Cristina; Franzini, Davide; Ramond, Mathieu

2017-06-01

Concentrated Solar Power plants (CSP) coupled with thermal storage have the potential to guarantee both flexible and continuous energy production, thus being competitive with conventional fossil fuel and hydro power plants, in terms of dispatchability and provision of ancillary services. Hence, the plant equipment and control design have to be focused on flexible operation on one hand, and on plant safety concerning the molten salt freezing on the other hand. The PreFlexMS European project aims to introduce a molten salt Once-Through Steam Generator (OTSG) within a Rankine cycle based power unit, a technology that has greater flexibility potential if compared to steam drum boilers, currently used in CSP plants. The dynamic modelling and simulation from the early design stages is, thus, of paramount importance, to assess the plant dynamic behavior and controllability, and to predict the achievable closed-loop dynamic performance, potentially saving money and time during the detailed design, construction and commissioning phases. The present paper reports the main results of the analysis carried out during the first part of the project, regarding the system analysis and control design. In particular, two different control systems have been studied and tested with the plant dynamic model: a decentralized control strategy based on PI controllers and a Linear Model Predictive Control (LMPC).

Design, Fabrication, and Testing of Emissive Probes to Determine the Plasma Potential of the Plumes of Various Electric Thrusters

NASA Technical Reports Server (NTRS)

Chen, Erinna M.

2005-01-01

A significant problem in the use of electric thrusters in spacecraft is the formation of low-energy ions in the thruster plume. Low-energy ions are formed in the plume via random collisions between high-velocity ions ejected from the thruster and slow-moving neutral atoms of propellant effusing from the engine. The sputtering of spacecraft materials due to interactions with low-energy ions may result in erosion or contamination of the spacecraft. The trajectory of these ions is determined primarily by the plasma potential of the plume. Thus, accurate characterization of the plasma potential is essential to predicting low-energy ion contamination. Emissive probes were utilized to determine the plasma potential. When the ion and electron currents to the probe are balanced, the potential of such probes float to the plasma potential. Two emissive probes were fabricated; one utilizing a DC power supply, another utilizing a rectified AC power source. Labview programs were written to coordinate and automate probe motion in the thruster plume. Employing handshaking interaction, these motion programs were synchronized to various data acquisition programs to ensure precision and accuracy of the measurements. Comparing these experimental values to values from theoretical models will allow for a more accurate prediction of low-energy ion interaction.

Unconventional Rotor Power Response to Yaw Error Variations

DOE PAGES

Schreck, S. J.; Schepers, J. G.

2014-12-16

Continued inquiry into rotor and blade aerodynamics remains crucial for achieving accurate, reliable prediction of wind turbine power performance under yawed conditions. To exploit key advantages conferred by controlled inflow conditions, we used EU-JOULE DATA Project and UAE Phase VI experimental data to characterize rotor power production under yawed conditions. Anomalies in rotor power variation with yaw error were observed, and the underlying fluid dynamic interactions were isolated. Unlike currently recognized influences caused by angled inflow and skewed wake, which may be considered potential flow interactions, these anomalies were linked to pronounced viscous and unsteady effects.

Toward an operant model of power in organizations

PubMed Central

Goltz, Sonia M.

2003-01-01

The purpose of this paper is to suggest that behavior analysis can help to explain social power. In this approach, an individual's potential for influence is thought to be partially a function of his or her access to stimuli that can be used as consequences. This access can occur either through direct authority or indirectly through social networks and exchanges. Social power is also thought to be a function of an individual's skill in delivering the stimuli in ways that will have the most impact on behavior. A number of predictions about power based on an operant approach are offered. PMID:22478398

Incident Energy Focused Design and Validation for the Floating Potential Probe

NASA Technical Reports Server (NTRS)

Fincannon, James

2002-01-01

Utilizing the spacecraft shadowing and incident energy analysis capabilities of the NASA Glenn Research Center Power and Propulsion Office's SPACE System Power Analysis for Capability Evaluation) computer code, this paper documents the analyses for various International Space Station (ISS) Floating Potential Probe (EPP) preliminary design options. These options include various solar panel orientations and configurations as well as deployment locations on the ISS. The incident energy for the final selected option is characterized. A good correlation between the predicted data and on-orbit operational telemetry is demonstrated. Minor deviations are postulated to be induced by degradation or sensor drift.

SRG110 Stirling Generator Dynamic Simulator Vibration Test Results and Analysis Correlation

NASA Technical Reports Server (NTRS)

Lewandowski, Edward J.; Suarez, Vicente J.; Goodnight, Thomas W.; Callahan, John

2007-01-01

The U.S. Department of Energy (DOE), Lockheed Martin (LM), and NASA Glenn Research Center (GRC) have been developing the Stirling Radioisotope Generator (SRG110) for use as a power system for space science missions. The launch environment enveloping potential missions results in a random input spectrum that is significantly higher than historical radioisotope power system (RPS) launch levels and is a challenge for designers. Analysis presented in prior work predicted that tailoring the compliance at the generator-spacecraft interface reduced the dynamic response of the system thereby allowing higher launch load input levels and expanding the range of potential generator missions. To confirm analytical predictions, a dynamic simulator representing the generator structure, Stirling convertors and heat sources were designed and built for testing with and without a compliant interface. Finite element analysis was performed to guide the generator simulator and compliant interface design so that test modes and frequencies were representative of the SRG110 generator. This paper presents the dynamic simulator design, the test setup and methodology, test article modes and frequencies and dynamic responses, and post-test analysis results. With the compliant interface, component responses to an input environment exceeding the SRG110 qualification level spectrum were all within design allowables. Post-test analysis included finite element model tuning to match test frequencies and random response analysis using the test input spectrum. Analytical results were in good overall agreement with the test results and confirmed previous predictions that the SRG110 power system may be considered for a broad range of potential missions, including those with demanding launch environments.

Tidal current and tidal energy changes imposed by a dynamic tidal power system in the Taiwan Strait, China

NASA Astrophysics Data System (ADS)

Dai, Peng; Zhang, Jisheng; Zheng, Jinhai

2017-12-01

The Taiwan Strait has recently been proposed as a promising site for dynamic tidal power systems because of its shallow depth and strong tides. Dynamic tidal power is a new concept for extracting tidal potential energy in which a coast-perpendicular dike is used to create water head and generate electricity via turbines inserted in the dike. Before starting such a project, the potential power output and hydrodynamic impacts of the dike must be assessed. In this study, a two-dimensional numerical model based on the Delft3D-FLOW module is established to simulate tides in China. A dike module is developed to account for turbine processes and estimate power output by integrating a special algorithm into the model. The domain decomposition technique is used to divide the computational zone into two subdomains with grid refinement near the dike. The hydrodynamic processes predicted by the model, both with and without the proposed construction, are examined in detail, including tidal currents and tidal energy flux. The predicted time-averaged power yields with various opening ratios are presented. The results show that time-averaged power yield peaks at an 8% opening ratio. For semidiurnal tides, the flow velocity increases in front of the head of the dike and decreases on either side. For diurnal tides, these changes are complicated by the oblique incidence of tidal currents with respect to the dike as well as by bathymetric features. The dike itself blocks the propagation of tidal energy flux.

Predictive & Prognostic Controller for Wide Band Gap (Silicon Carbide) Power Conversion (Preprint)

DTIC Science & Technology

2006-11-01

that is required for them to achieve their full potential, or other elements that are different from the traditional practice of Power Conditioning...symptoms, medical clinicians rely strongly on family history, individual history, environmental conditions or exposures and lifestyle to ascertain the...the model and possibly related models of the particular converter design. The lifestyle , the stress and the exposure that is considered in human

Experimental Evaluation of a Water Shield for a Surface Power Reactor

NASA Technical Reports Server (NTRS)

Pearson, J. Boise; Reid, Robert S.

2006-01-01

As part of the Vision for Space Exploration the end of the next decade will bring man back to the surface of the moon. One of the most critical issues for the establishment of human presence on the moon will be the availability of compact power sources. The establishment of man on the moon will require power from greater than 10's of kWt's in follow on years. Nuclear reactors are extremely we11 suited to meet the needs for power generation on the lunar or Martian surface. reactor system. Several competing concepts exist for lightweight, safe, robust shielding systems such as a water shield, lithium hydride (LiH), Boron Carbide, and others. Water offers several potential advantages, including reduced cost, reduced technical risk, and reduced mass. Water has not typically been considered for space reactor applications because of the need for gravity to remove the potential for radiation streaming paths. The water shield concept relies on predictions of passive circulation of the shield water by natural convection to adequately cool the shield. This prediction needs to be experimentally evaluated, especially for shields with complex geometries. MSFC has developed the experience and fac necessary to do this evaluation in the Early Flight Fission - Test Facility (EFF-TF).

Potential for natural evaporation as a reliable renewable energy resource

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

Cavusoglu, Ahmet-Hamdi; Chen, Xi; Gentine, Pierre

About 50% of the solar energy absorbed at the Earth’s surface drives evaporation, fueling the water cycle that affects various renewable energy resources, such as wind and hydropower. Recent advances demonstrate our nascent ability to convert evaporation energy into work, yet there is little understanding about the potential of this resource. Here in this paper we study the energy available from natural evaporation to predict the potential of this ubiquitous resource. We find that natural evaporation from open water surfaces could provide power densities comparable to current wind and solar technologies while cutting evaporative water losses by nearly half. Wemore » estimate up to 325 GW of power is potentially available in the United States. Strikingly, water’s large heat capacity is sufficient to control power output by storing excess energy when demand is low, thus reducing intermittency and improving reliability. Our findings motivate the improvement of materials and devices that convert energy from evaporation.« less

Potential for natural evaporation as a reliable renewable energy resource

DOE PAGES

Cavusoglu, Ahmet-Hamdi; Chen, Xi; Gentine, Pierre; ...

2017-09-26

About 50% of the solar energy absorbed at the Earth’s surface drives evaporation, fueling the water cycle that affects various renewable energy resources, such as wind and hydropower. Recent advances demonstrate our nascent ability to convert evaporation energy into work, yet there is little understanding about the potential of this resource. Here in this paper we study the energy available from natural evaporation to predict the potential of this ubiquitous resource. We find that natural evaporation from open water surfaces could provide power densities comparable to current wind and solar technologies while cutting evaporative water losses by nearly half. Wemore » estimate up to 325 GW of power is potentially available in the United States. Strikingly, water’s large heat capacity is sufficient to control power output by storing excess energy when demand is low, thus reducing intermittency and improving reliability. Our findings motivate the improvement of materials and devices that convert energy from evaporation.« less

Electrical Generation for More-Electric Aircraft Using Solid Oxide Fuel Cells

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

Whyatt, Greg A.; Chick, Lawrence A.

This report examines the potential for Solid-Oxide Fuel Cells (SOFC) to provide electrical generation on-board commercial aircraft. Unlike a turbine-based auxiliary power unit (APU) a solid oxide fuel cell power unit (SOFCPU) would be more efficient than using the main engine generators to generate electricity and would operate continuously during flight. The focus of this study is on more-electric aircraft which minimize bleed air extraction from the engines and instead use electrical power obtained from generators driven by the main engines to satisfy all major loads. The increased electrical generation increases the potential fuel savings obtainable through more efficient electricalmore » generation using a SOFCPU. However, the weight added to the aircraft by the SOFCPU impacts the main engine fuel consumption which reduces the potential fuel savings. To investigate these relationships the Boeing 787­8 was used as a case study. The potential performance of the SOFCPU was determined by coupling flowsheet modeling using ChemCAD software with a stack performance algorithm. For a given stack operating condition (cell voltage, anode utilization, stack pressure, target cell exit temperature), ChemCAD software was used to determine the cathode air rate to provide stack thermal balance, the heat exchanger duties, the gross power output for a given fuel rate, the parasitic power for the anode recycle blower and net power obtained from (or required by) the compressor/expander. The SOFC is based on the Gen4 Delphi planar SOFC with assumed modifications to tailor it to this application. The size of the stack needed to satisfy the specified condition was assessed using an empirically-based algorithm. The algorithm predicts stack power density based on the pressure, inlet temperature, cell voltage and anode and cathode inlet flows and compositions. The algorithm was developed by enhancing a model for a well-established material set operating at atmospheric pressure to reflect the effect of elevated pressure and to represent the expected enhancement obtained using a promising cell material set which has been tested in button cells but not yet used to produce full-scale stacks. The predictions for the effect of pressure on stack performance were based on literature. As part of this study, additional data were obtained on button cells at elevated pressure to confirm the validity of the predictions. The impact of adding weight to the 787-8 fuel consumption was determined as a function of flight distance using a PianoX model. A conceptual design for a SOFC power system for the Boeing 787 is developed and the weight estimated. The results indicate that the power density of the stacks must increase by at least a factor of 2 to begin saving fuel on the 787 aircraft. However, the conceptual design of the power system may still be useful for other applications which are less weight sensitive.« less

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

Schreck, S. J.; Schepers, J. G.

Continued inquiry into rotor and blade aerodynamics remains crucial for achieving accurate, reliable prediction of wind turbine power performance under yawed conditions. To exploit key advantages conferred by controlled inflow conditions, we used EU-JOULE DATA Project and UAE Phase VI experimental data to characterize rotor power production under yawed conditions. Anomalies in rotor power variation with yaw error were observed, and the underlying fluid dynamic interactions were isolated. Unlike currently recognized influences caused by angled inflow and skewed wake, which may be considered potential flow interactions, these anomalies were linked to pronounced viscous and unsteady effects.

Optimizing Wind Power Generation while Minimizing Wildlife Impacts in an Urban Area

PubMed Central

Bohrer, Gil; Zhu, Kunpeng; Jones, Robert L.; Curtis, Peter S.

2013-01-01

The location of a wind turbine is critical to its power output, which is strongly affected by the local wind field. Turbine operators typically seek locations with the best wind at the lowest level above ground since turbine height affects installation costs. In many urban applications, such as small-scale turbines owned by local communities or organizations, turbine placement is challenging because of limited available space and because the turbine often must be added without removing existing infrastructure, including buildings and trees. The need to minimize turbine hazard to wildlife compounds the challenge. We used an exclusion zone approach for turbine-placement optimization that incorporates spatially detailed maps of wind distribution and wildlife densities with power output predictions for the Ohio State University campus. We processed public GIS records and airborne lidar point-cloud data to develop a 3D map of all campus buildings and trees. High resolution large-eddy simulations and long-term wind climatology were combined to provide land-surface-affected 3D wind fields and the corresponding wind-power generation potential. This power prediction map was then combined with bird survey data. Our assessment predicts that exclusion of areas where bird numbers are highest will have modest effects on the availability of locations for power generation. The exclusion zone approach allows the incorporation of wildlife hazard in wind turbine siting and power output considerations in complex urban environments even when the quantitative interaction between wildlife behavior and turbine activity is unknown. PMID:23409117

Optimizing wind power generation while minimizing wildlife impacts in an urban area.

PubMed

Bohrer, Gil; Zhu, Kunpeng; Jones, Robert L; Curtis, Peter S

2013-01-01

The location of a wind turbine is critical to its power output, which is strongly affected by the local wind field. Turbine operators typically seek locations with the best wind at the lowest level above ground since turbine height affects installation costs. In many urban applications, such as small-scale turbines owned by local communities or organizations, turbine placement is challenging because of limited available space and because the turbine often must be added without removing existing infrastructure, including buildings and trees. The need to minimize turbine hazard to wildlife compounds the challenge. We used an exclusion zone approach for turbine-placement optimization that incorporates spatially detailed maps of wind distribution and wildlife densities with power output predictions for the Ohio State University campus. We processed public GIS records and airborne lidar point-cloud data to develop a 3D map of all campus buildings and trees. High resolution large-eddy simulations and long-term wind climatology were combined to provide land-surface-affected 3D wind fields and the corresponding wind-power generation potential. This power prediction map was then combined with bird survey data. Our assessment predicts that exclusion of areas where bird numbers are highest will have modest effects on the availability of locations for power generation. The exclusion zone approach allows the incorporation of wildlife hazard in wind turbine siting and power output considerations in complex urban environments even when the quantitative interaction between wildlife behavior and turbine activity is unknown.

The wind power prediction research based on mind evolutionary algorithm

NASA Astrophysics Data System (ADS)

Zhuang, Ling; Zhao, Xinjian; Ji, Tianming; Miao, Jingwen; Cui, Haina

2018-04-01

When the wind power is connected to the power grid, its characteristics of fluctuation, intermittent and randomness will affect the stability of the power system. The wind power prediction can guarantee the power quality and reduce the operating cost of power system. There were some limitations in several traditional wind power prediction methods. On the basis, the wind power prediction method based on Mind Evolutionary Algorithm (MEA) is put forward and a prediction model is provided. The experimental results demonstrate that MEA performs efficiently in term of the wind power prediction. The MEA method has broad prospect of engineering application.

Simplified APC for Space Shuttle applications. [Adaptive Predictive Coding for speech transmission

NASA Technical Reports Server (NTRS)

Hutchins, S. E.; Batson, B. H.

1975-01-01

This paper describes an 8 kbps adaptive predictive digital speech transmission system which was designed for potential use in the Space Shuttle Program. The system was designed to provide good voice quality in the presence of both cabin noise on board the Shuttle and the anticipated bursty channel. Minimal increase in size, weight, and power over the current high data rate system was also a design objective.

High-Density Signal Interface Electromagnetic Radiation Prediction for Electromagnetic Compatibility Evaluation.

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

Halligan, Matthew

Radiated power calculation approaches for practical scenarios of incomplete high- density interface characterization information and incomplete incident power information are presented. The suggested approaches build upon a method that characterizes power losses through the definition of power loss constant matrices. Potential radiated power estimates include using total power loss information, partial radiated power loss information, worst case analysis, and statistical bounding analysis. A method is also proposed to calculate radiated power when incident power information is not fully known for non-periodic signals at the interface. Incident data signals are modeled from a two-state Markov chain where bit state probabilities aremore » derived. The total spectrum for windowed signals is postulated as the superposition of spectra from individual pulses in a data sequence. Statistical bounding methods are proposed as a basis for the radiated power calculation due to the statistical calculation complexity to find a radiated power probability density function.« less

Hard sphere perturbation theory for fluids with soft-repulsive-core potentials

NASA Astrophysics Data System (ADS)

Ben-Amotz, Dor; Stell, George

2004-03-01

The thermodynamic properties of fluids with very soft repulsive-core potentials, resembling those of some liquid metals, are predicted with unprecedented accuracy using a new first-order thermodynamic perturbation theory. This theory is an extension of Mansoori-Canfield/Rasaiah-Stell (MCRS) perturbation theory, obtained by including a configuration integral correction recently identified by Mon, who evaluated it by computer simulation. In this work we derive an analytic expression for Mon's correction in terms of the radial distribution function of the soft-core fluid, g0(r), approximated using Lado's self-consistent extension of Weeks-Chandler-Andersen (WCA) theory. Comparisons with WCA and MCRS predictions show that our new extended-MCRS theory outperforms other first-order theories when applied to fluids with very soft inverse-power potentials (n⩽6), and predicts free energies that are within 0.3kT of simulation results up to the fluid freezing point.

Mobility Fuels for the Navy.

DTIC Science & Technology

1982-01-01

aircraft powered by gas turbines through the year 2000. The study also predicted that some types of diesels and steam powerplants could potentially...figure 3. Diesels are more efficient even at high- power settings. CHANGES TO FUELS Interrelationships between the price and quality of fuel have...emaIcal Physics 20 (1979) pp. 6446- PP 252 899), AD) A061 938 Nunn, Wolter R.. -Poeltion Finding with Prior Kowledge of .Eonrience Parameters," 5 pp., Jun

Analysis of electromagnetic interference from power system processing and transmission components for Space Station Freedom

NASA Technical Reports Server (NTRS)

Barber, Peter W.; Demerdash, Nabeel A. O.; Hurysz, B.; Luo, Z.; Denny, Hugh W.; Millard, David P.; Herkert, R.; Wang, R.

1992-01-01

The goal of this research project was to analyze the potential effects of electromagnetic interference (EMI) originating from power system processing and transmission components for Space Station Freedom. The approach consists of four steps: (1) developing analytical tools (models and computer programs); (2) conducting parameterization (what if?) studies; (3) predicting the global space station EMI environment; and (4) providing a basis for modification of EMI standards.

Flexible piezoelectric energy harvesting from jaw movements

NASA Astrophysics Data System (ADS)

Delnavaz, Aidin; Voix, Jérémie

2014-10-01

Piezoelectric fiber composites (PFC) represent an interesting subset of smart materials that can function as sensor, actuator and energy converter. Despite their excellent potential for energy harvesting, very few PFC mechanisms have been developed to capture the human body power and convert it into an electric current to power wearable electronic devices. This paper provides a proof of concept for a head-mounted device with a PFC chin strap capable of harvesting energy from jaw movements. An electromechanical model based on the bond graph method is developed to predict the power output of the energy harvesting system. The optimum resistance value of the load and the best stretch ratio in the strap are also determined. A prototype was developed and tested and its performances were compared to the analytical model predictions. The proposed piezoelectric strap mechanism can be added to all types of head-mounted devices to power small-scale electronic devices such as hearing aids, electronic hearing protectors and communication earpieces.

Analytical modeling of helium turbomachinery using FORTRAN 77

NASA Astrophysics Data System (ADS)

Balaji, Purushotham

Advanced Generation IV modular reactors, including Very High Temperature Reactors (VHTRs), utilize helium as the working fluid, with a potential for high efficiency power production utilizing helium turbomachinery. Helium is chemically inert and nonradioactive which makes the gas ideal for a nuclear power-plant environment where radioactive leaks are a high concern. These properties of helium gas helps to increase the safety features as well as to decrease the aging process of plant components. The lack of sufficient helium turbomachinery data has made it difficult to study the vital role played by the gas turbine components of these VHTR powered cycles. Therefore, this research work focuses on predicting the performance of helium compressors. A FORTRAN77 program is developed to simulate helium compressor operation, including surge line prediction. The resulting design point and off design performance data can be used to develop compressor map files readable by Numerical Propulsion Simulation Software (NPSS). This multi-physics simulation software that was developed for propulsion system analysis has found applications in simulating power-plant cycles.

Relationship of physical fitness test results and hockey playing potential in elite-level ice hockey players.

PubMed

Burr, Jaime F; Jamnik, Roni K; Baker, Joseph; Macpherson, Alison; Gledhill, Norman; McGuire, E J

2008-09-01

The primary purpose of this study was to determine the fitness variables with the highest capability for predicting hockey playing potential at the elite level as determined by entry draft selection order. We also examined the differences associated with the predictive abilities of the test components among playing positions. The secondary purpose of this study was to update the physiological profile of contemporary hockey players including positional differences. Fitness test results conducted by our laboratory at the National Hockey League Entry Draft combine were compared with draft selection order on a total of 853 players. Regression models revealed peak anaerobic power output to be important for higher draft round selection in all positions; however, the degree of importance of this measurement varied with playing position. The body index, which is a composite score of height, lean mass, and muscular development, was similarly important in all models, with differing influence by position. Removal of the goalies' data increased predictive capacity, suggesting that talent identification using physical fitness testing of this sort may be more appropriate for skating players. Standing long jump was identified as a significant predictor variable for forwards and defense and could be a useful surrogate for assessing overall hockey potential. Significant differences exist between the physiological profiles of current players based on playing position. There are also positional differences in the relative importance of anthropometric and fitness measures of off-ice hockey tests in relation to draft order. Physical fitness measures and anthropometric data are valuable in helping predict hockey playing potential. Emphasis on anthropometry should be used when comparing elite-level forwards, whereas peak anaerobic power and fatigue rate are more useful for differentiating between defense.

Experimental Investigation and Modeling of Scale Effects in Micro Jet Pumps

NASA Astrophysics Data System (ADS)

Gardner, William Geoffrey

2011-12-01

Since the mid-1990s there has been an active effort to develop hydrocarbon-fueled power generation and propulsion systems on the scale of centimeters or smaller. This effort led to the creation and expansion of a field of research focused around the design and reduction to practice of Power MEMS (microelectromechanical systems) devices, beginning first with microscale jet engines and a generation later more broadly encompassing MEMS devices which generate power or pump heat. Due to small device scale and fabrication techniques, design constraints are highly coupled and conventional solutions for device requirements may not be practicable. This thesis describes the experimental investigation, modeling and potential applications for two classes of microscale jet pumps: jet ejectors and jet injectors. These components pump fluids with no moving parts and can be integrated into Power MEMS devices to satisfy pumping requirements by supplementing or replacing existing solutions. This thesis presents models developed from first principles which predict losses experienced at small length scales and agree well with experimental results. The models further predict maximum achievable power densities at the onset of detrimental viscous losses.

Multi-Temporal Decomposed Wind and Load Power Models for Electric Energy Systems

NASA Astrophysics Data System (ADS)

Abdel-Karim, Noha

This thesis is motivated by the recognition that sources of uncertainties in electric power systems are multifold and may have potentially far-reaching effects. In the past, only system load forecast was considered to be the main challenge. More recently, however, the uncertain price of electricity and hard-to-predict power produced by renewable resources, such as wind and solar, are making the operating and planning environment much more challenging. The near-real-time power imbalances are compensated by means of frequency regulation and generally require fast-responding costly resources. Because of this, a more accurate forecast and look-ahead scheduling would result in a reduced need for expensive power balancing. Similarly, long-term planning and seasonal maintenance need to take into account long-term demand forecast as well as how the short-term generation scheduling is done. The better the demand forecast, the more efficient planning will be as well. Moreover, computer algorithms for scheduling and planning are essential in helping the system operators decide what to schedule and planners what to build. This is needed given the overall complexity created by different abilities to adjust the power output of generation technologies, demand uncertainties and by the network delivery constraints. Given the growing presence of major uncertainties, it is likely that the main control applications will use more probabilistic approaches. Today's predominantly deterministic methods will be replaced by methods which account for key uncertainties as decisions are made. It is well-understood that although demand and wind power cannot be predicted at very high accuracy, taking into consideration predictions and scheduling in a look-ahead way over several time horizons generally results in more efficient and reliable utilization, than when decisions are made assuming deterministic, often worst-case scenarios. This change is in approach is going to ultimately require new electricity market rules capable of providing the right incentives to manage uncertainties and of differentiating various technologies according to the rate at which they can respond to ever changing conditions. Given the overall need for modeling uncertainties in electric energy systems, we consider in this thesis the problem of multi-temporal modeling of wind and demand power, in particular. Historic data is used to derive prediction models for several future time horizons. Short-term prediction models derived can be used for look-ahead economic dispatch and unit commitment, while the long-term annual predictive models can be used for investment planning. As expected, the accuracy of such predictive models depends on the time horizons over which the predictions are made, as well as on the nature of uncertain signals. It is shown that predictive models obtained using the same general modeling approaches result in different accuracy for wind than for demand power. In what follows, we introduce several models which have qualitatively different patterns, ranging from hourly to annual. We first transform historic time-stamped data into the Fourier Transform (Fr) representation. The frequency domain data representation is used to decompose the wind and load power signals and to derive predictive models relevant for short-term and long-term predictions using extracted spectral techniques. The short-term results are interpreted next as a Linear Prediction Coding Model (LPC) and its accuracy is analyzed. Next, a new Markov-Based Sensitivity Model (MBSM) for short term prediction has been proposed and the dispatched costs of uncertainties for different predictive models with comparisons have been developed. Moreover, the Discrete Markov Process (DMP) representation is applied to help assess probabilities of most likely short-, medium- and long-term states and the related multi-temporal risks. In addition, this thesis discusses operational impacts of wind power integration in different scenario levels by performing more than 9,000 AC Optimal Power Flow runs. The effects of both wind and load variations on system constraints and costs are presented. The limitations of DC Optimal Power Flow (DCOPF) vs. ACOPF are emphasized by means of system convergence problems due to the effect of wind power on changing line flows and net power injections. By studying the effect of having wind power on line flows, we found that the divergence problem applies in areas with high wind and hydro generation capacity share (cheap generations). (Abstract shortened by UMI.).

Predictive information processing is a fundamental learning mechanism present in early development: evidence from infants.

PubMed

Trainor, Laurel J

2012-02-01

Evidence is presented that predictive coding is fundamental to brain function and present in early infancy. Indeed, mismatch responses to unexpected auditory stimuli are among the earliest robust cortical event-related potential responses, and have been measured in young infants in response to many types of deviation, including in pitch, timing, and melodic pattern. Furthermore, mismatch responses change quickly with specific experience, suggesting that predictive coding reflects a powerful, early-developing learning mechanism. Copyright © 2011 Elsevier B.V. All rights reserved.

Heat conduction in a chain of colliding particles with a stiff repulsive potential

NASA Astrophysics Data System (ADS)

Gendelman, Oleg V.; Savin, Alexander V.

2016-11-01

One-dimensional billiards, i.e., a chain of colliding particles with equal masses, is a well-known example of a completely integrable system. Billiards with different particle masses is generically not integrable, but it still exhibits divergence of a heat conduction coefficient (HCC) in the thermodynamic limit. Traditional billiards models imply instantaneous (zero-time) collisions between the particles. We relax this condition of instantaneous impact and consider heat transport in a chain of stiff colliding particles with the power-law potential of the nearest-neighbor interaction. The instantaneous collisions correspond to the limit of infinite power in the interaction potential; for finite powers, the interactions take nonzero time. This modification of the model leads to a profound physical consequence—the probability of multiple (in particular triple) -particle collisions becomes nonzero. Contrary to the integrable billiards of equal particles, the modified model exhibits saturation of the heat conduction coefficient for a large system size. Moreover, the identification of scattering events with triple-particle collisions leads to a simple definition of the characteristic mean free path and a kinetic description of heat transport. This approach allows us to predict both the temperature and density dependencies for the HCC limit values. The latter dependence is quite counterintuitive—the HCC is inversely proportional to the particle density in the chain. Both predictions are confirmed by direct numerical simulations.

Anodic microbial community diversity as a predictor of the power output of microbial fuel cells.

PubMed

Stratford, James P; Beecroft, Nelli J; Slade, Robert C T; Grüning, André; Avignone-Rossa, Claudio

2014-03-01

The relationship between the diversity of mixed-species microbial consortia and their electrogenic potential in the anodes of microbial fuel cells was examined using different diversity measures as predictors. Identical microbial fuel cells were sampled at multiple time-points. Biofilm and suspension communities were analysed by denaturing gradient gel electrophoresis to calculate the number and relative abundance of species. Shannon and Simpson indices and richness were examined for association with power using bivariate and multiple linear regression, with biofilm DNA as an additional variable. In simple bivariate regressions, the correlation of Shannon diversity of the biofilm and power is stronger (r=0.65, p=0.001) than between power and richness (r=0.39, p=0.076), or between power and the Simpson index (r=0.5, p=0.018). Using Shannon diversity and biofilm DNA as predictors of power, a regression model can be constructed (r=0.73, p<0.001). Ecological parameters such as the Shannon index are predictive of the electrogenic potential of microbial communities. Copyright © 2014 Elsevier Ltd. All rights reserved.

Predicting rates of interspecific interaction from phylogenetic trees.

PubMed

Nuismer, Scott L; Harmon, Luke J

2015-01-01

Integrating phylogenetic information can potentially improve our ability to explain species' traits, patterns of community assembly, the network structure of communities, and ecosystem function. In this study, we use mathematical models to explore the ecological and evolutionary factors that modulate the explanatory power of phylogenetic information for communities of species that interact within a single trophic level. We find that phylogenetic relationships among species can influence trait evolution and rates of interaction among species, but only under particular models of species interaction. For example, when interactions within communities are mediated by a mechanism of phenotype matching, phylogenetic trees make specific predictions about trait evolution and rates of interaction. In contrast, if interactions within a community depend on a mechanism of phenotype differences, phylogenetic information has little, if any, predictive power for trait evolution and interaction rate. Together, these results make clear and testable predictions for when and how evolutionary history is expected to influence contemporary rates of species interaction. © 2014 John Wiley & Sons Ltd/CNRS.

Breast magnetic resonance elastography: a review of clinical work and future perspectives.

PubMed

Bohte, A E; Nelissen, J L; Runge, J H; Holub, O; Lambert, S A; de Graaf, L; Kolkman, S; van der Meij, S; Stoker, J; Strijkers, G J; Nederveen, A J; Sinkus, R

2018-05-30

This review on magnetic resonance elastography (MRE) of the breast provides an overview of available literature and describes current developments in the field of breast MRE, including new transducer technology for data acquisition and multi-frequency-derived power-law behaviour of tissue. Moreover, we discuss the future potential of breast MRE, which goes beyond its original application as an additional tool in differentiating benign from malignant breast lesions. These areas of ongoing and future research include MRE for pre-operative tumour delineation, staging, monitoring and predicting response to treatment, as well as prediction of the metastatic potential of primary tumours. Copyright © 2018 John Wiley & Sons, Ltd.

Realistic Specific Power Expectations for Advanced Radioisotope Power Systems

NASA Technical Reports Server (NTRS)

Mason, Lee S.

2006-01-01

Radioisotope Power Systems (RPS) are being considered for a wide range of future NASA space science and exploration missions. Generally, RPS offer the advantages of high reliability, long life, and predictable power production regardless of operating environment. Previous RPS, in the form of Radioisotope Thermoelectric Generators (RTG), have been used successfully on many NASA missions including Apollo, Viking, Voyager, and Galileo. NASA is currently evaluating design options for the next generation of RPS. Of particular interest is the use of advanced, higher efficiency power conversion to replace the previous thermoelectric devices. Higher efficiency reduces the quantity of radioisotope fuel and potentially improves the RPS specific power (watts per kilogram). Power conversion options include Segmented Thermoelectric (STE), Stirling, Brayton, and Thermophotovoltaic (TPV). This paper offers an analysis of the advanced 100 watt-class RPS options and provides credible projections for specific power. Based on the analysis presented, RPS specific power values greater than 10 W/kg appear unlikely.

Utilizing Dental Electronic Health Records Data to Predict Risk for Periodontal Disease.

PubMed

Thyvalikakath, Thankam P; Padman, Rema; Vyawahare, Karnali; Darade, Pratiksha; Paranjape, Rhucha

2015-01-01

Periodontal disease is a major cause for tooth loss and adversely affects individuals' oral health and quality of life. Research shows its potential association with systemic diseases like diabetes and cardiovascular disease, and social habits such as smoking. This study explores mining potential risk factors from dental electronic health records to predict and display patients' contextualized risk for periodontal disease. We retrieved relevant risk factors from structured and unstructured data on 2,370 patients who underwent comprehensive oral examinations at the Indiana University School of Dentistry, Indianapolis, IN, USA. Predicting overall risk and displaying relationships between risk factors and their influence on the patient's oral and general health can be a powerful educational and disease management tool for patients and clinicians at the point of care.

Implications of the difference between true and predicted breeding values for the study of natural selection and micro-evolution.

PubMed

Postma, E

2006-03-01

The ability to predict individual breeding values in natural populations with known pedigrees has provided a powerful tool to separate phenotypic values into their genetic and environmental components in a nonexperimental setting. This has allowed sophisticated analyses of selection, as well as powerful tests of evolutionary change and differentiation. To date, there has, however, been no evaluation of the reliability or potential limitations of the approach. In this article, I address these gaps. In particular, I emphasize the differences between true and predicted breeding values (PBVs), which as yet have largely been ignored. These differences do, however, have important implications for the interpretation of, firstly, the relationship between PBVs and fitness, and secondly, patterns in PBVs over time. I subsequently present guidelines I believe to be essential in the formulation of the questions addressed in studies using PBVs, and I discuss possibilities for future research.

Spatiotemporal property and predictability of large-scale human mobility

NASA Astrophysics Data System (ADS)

Zhang, Hai-Tao; Zhu, Tao; Fu, Dongfei; Xu, Bowen; Han, Xiao-Pu; Chen, Duxin

2018-04-01

Spatiotemporal characteristics of human mobility emerging from complexity on individual scale have been extensively studied due to the application potential on human behavior prediction and recommendation, and control of epidemic spreading. We collect and investigate a comprehensive data set of human activities on large geographical scales, including both websites browse and mobile towers visit. Numerical results show that the degree of activity decays as a power law, indicating that human behaviors are reminiscent of scale-free random walks known as Lévy flight. More significantly, this study suggests that human activities on large geographical scales have specific non-Markovian characteristics, such as a two-segment power-law distribution of dwelling time and a high possibility for prediction. Furthermore, a scale-free featured mobility model with two essential ingredients, i.e., preferential return and exploration, and a Gaussian distribution assumption on the exploration tendency parameter is proposed, which outperforms existing human mobility models under scenarios of large geographical scales.

A novel high-density power energy harvesting methodology for transmission line online monitoring devices.

PubMed

Liu, Yadong; Xie, Xiaolei; Hu, Yue; Qian, Yong; Sheng, Gehao; Jiang, Xiuchen; Liu, Yilu

2016-07-01

This paper presents a novel energy-harvesting model which takes the primary current, secondary turns, dimension, the magnitude of magnetic flux density B, and the core loss resistance into consideration systematically. The relationship among the potential maximum output power, the dimension of energy harvesting coil (EHC), the load type of EHC, and the secondary turns is predicted by theoretical analysis and further verified by experiments. A high power density harvester is also developed and tested. It is shown that the power density of this novel harvester is 0.7 mW/g at 10 A, which is more than 2 times powerful than the traditional ones. Hence, it could lighten the half weight of the harvester at the same conditions.

The Use of Statistical Downscaling to Project Regional Climate Changes as they Relate to Future Energy Production

NASA Astrophysics Data System (ADS)

Werth, D. W.; O'Steen, L.; Chen, K.; Altinakar, M. S.; Garrett, A.; Aleman, S.; Ramalingam, V.

2010-12-01

Global climate change has the potential for profound impacts on society, and poses significant challenges to government and industry in the areas of energy security and sustainability. Given that the ability to exploit energy resources often depends on the climate, the possibility of climate change means we cannot simply assume that the untapped potential of today will still exist in the future. Predictions of future climate are generally based on global climate models (GCMs) which, due to computational limitations, are run at spatial resolutions of hundreds of kilometers. While the results from these models can predict climatic trends averaged over large spatial and temporal scales, their ability to describe the effects of atmospheric phenomena that affect weather on regional to local scales is inadequate. We propose the use of several optimized statistical downscaling techniques that can infer climate change at the local scale from coarse resolution GCM predictions, and apply the results to assess future sustainability for two sources of energy production dependent on adequate water resources: nuclear power (through the dissipation of waste heat from cooling towers, ponds, etc.) and hydroelectric power. All methods will be trained with 20th century data, and applied to data from the years 2040-2049 to get the local-scale changes. Models of cooling tower operation and hydropower potential will then use the downscaled data to predict the possible changes in energy production, and the implications of climate change on plant siting, design, and contribution to the future energy grid can then be examined.

Short-term load and wind power forecasting using neural network-based prediction intervals.

PubMed

Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

2014-02-01

Electrical power systems are evolving from today's centralized bulk systems to more decentralized systems. Penetrations of renewable energies, such as wind and solar power, significantly increase the level of uncertainty in power systems. Accurate load forecasting becomes more complex, yet more important for management of power systems. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in system operations. To quantify potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for the construction of prediction intervals (PIs). A newly introduced method, called lower upper bound estimation (LUBE), is applied and extended to develop PIs using NN models. A new problem formulation is proposed, which translates the primary multiobjective problem into a constrained single-objective problem. Compared with the cost function, this new formulation is closer to the primary problem and has fewer parameters. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Electrical demands from Singapore and New South Wales (Australia), as well as wind power generation from Capital Wind Farm, are used to validate the PSO-based LUBE method. Comparative results show that the proposed method can construct higher quality PIs for load and wind power generation forecasts in a short time.

GPS-ARM: Computational Analysis of the APC/C Recognition Motif by Predicting D-Boxes and KEN-Boxes

PubMed Central

Ren, Jian; Cao, Jun; Zhou, Yanhong; Yang, Qing; Xue, Yu

2012-01-01

Anaphase-promoting complex/cyclosome (APC/C), an E3 ubiquitin ligase incorporated with Cdh1 and/or Cdc20 recognizes and interacts with specific substrates, and faithfully orchestrates the proper cell cycle events by targeting proteins for proteasomal degradation. Experimental identification of APC/C substrates is largely dependent on the discovery of APC/C recognition motifs, e.g., the D-box and KEN-box. Although a number of either stringent or loosely defined motifs proposed, these motif patterns are only of limited use due to their insufficient powers of prediction. We report the development of a novel GPS-ARM software package which is useful for the prediction of D-boxes and KEN-boxes in proteins. Using experimentally identified D-boxes and KEN-boxes as the training data sets, a previously developed GPS (Group-based Prediction System) algorithm was adopted. By extensive evaluation and comparison, the GPS-ARM performance was found to be much better than the one using simple motifs. With this powerful tool, we predicted 4,841 potential D-boxes in 3,832 proteins and 1,632 potential KEN-boxes in 1,403 proteins from H. sapiens, while further statistical analysis suggested that both the D-box and KEN-box proteins are involved in a broad spectrum of biological processes beyond the cell cycle. In addition, with the co-localization information, we predicted hundreds of mitosis-specific APC/C substrates with high confidence. As the first computational tool for the prediction of APC/C-mediated degradation, GPS-ARM is a useful tool for information to be used in further experimental investigations. The GPS-ARM is freely accessible for academic researchers at: http://arm.biocuckoo.org. PMID:22479614

Development of self-powered wireless high temperature electrochemical sensor for in situ corrosion monitoring of coal-fired power plant.

PubMed

Aung, Naing Naing; Crowe, Edward; Liu, Xingbo

2015-03-01

Reliable wireless high temperature electrochemical sensor technology is needed to provide in situ corrosion information for optimal predictive maintenance to ensure a high level of operational effectiveness under the harsh conditions present in coal-fired power generation systems. This research highlights the effectiveness of our novel high temperature electrochemical sensor for in situ coal ash hot corrosion monitoring in combination with the application of wireless communication and an energy harvesting thermoelectric generator (TEG). This self-powered sensor demonstrates the successful wireless transmission of both corrosion potential and corrosion current signals to a simulated control room environment. Copyright © 2014 ISA. All rights reserved.

Predictive probability methods for interim monitoring in clinical trials with longitudinal outcomes.

PubMed

Zhou, Ming; Tang, Qi; Lang, Lixin; Xing, Jun; Tatsuoka, Kay

2018-04-17

In clinical research and development, interim monitoring is critical for better decision-making and minimizing the risk of exposing patients to possible ineffective therapies. For interim futility or efficacy monitoring, predictive probability methods are widely adopted in practice. Those methods have been well studied for univariate variables. However, for longitudinal studies, predictive probability methods using univariate information from only completers may not be most efficient, and data from on-going subjects can be utilized to improve efficiency. On the other hand, leveraging information from on-going subjects could allow an interim analysis to be potentially conducted once a sufficient number of subjects reach an earlier time point. For longitudinal outcomes, we derive closed-form formulas for predictive probabilities, including Bayesian predictive probability, predictive power, and conditional power and also give closed-form solutions for predictive probability of success in a future trial and the predictive probability of success of the best dose. When predictive probabilities are used for interim monitoring, we study their distributions and discuss their analytical cutoff values or stopping boundaries that have desired operating characteristics. We show that predictive probabilities utilizing all longitudinal information are more efficient for interim monitoring than that using information from completers only. To illustrate their practical application for longitudinal data, we analyze 2 real data examples from clinical trials. Copyright © 2018 John Wiley & Sons, Ltd.

Plasma cytokines eotaxin, MIP-1α, MCP-4, and vascular endothelial growth factor in acute lower respiratory tract infection.

PubMed

Relster, Mette Marie; Holm, Anette; Pedersen, Court

2017-02-01

Major overlaps of clinical characteristics and the limitations of conventional diagnostic tests render the initial diagnosis and clinical management of pulmonary disorders difficult. In this pilot study, we analyzed the predictive value of eotaxin, macrophage inflammatory protein 1 alpha (MIP-1α), monocyte chemoattractant protein 4 (MCP-4), and vascular endothelial growth factor (VEGF) in 40 patients hospitalized with acute lower respiratory tract infections (LRTI). The cytokines contribute to the pathogenesis of several inflammatory respiratory diseases, indicating a potential as markers for LRTI. Patients were stratified according to etiology and severity of LRTI, based on baseline C-reactive protein and CURB-65 scores. Using a multiplex immunoassay of plasma, levels of eotaxin and MCP-4 were shown to increase from baseline until day 6 after admission to hospital. The four cytokines were unable to predict the etiology and severity. Eotaxin and MCP-4 were significantly lower in patients with C-reactive protein ≥100, and MIP-1α was significantly higher in the patients with CURB-65 > 3, but the predictive power was low. In conclusion, further evaluation, including more patients, is required to assess the full potential of eotaxin, MCP-4, MIP-1α, and VEGF as biomarkers for LRTI because of their low predictive power and a high interindividual variation of cytokine levels. © 2016 APMIS. Published by John Wiley & Sons Ltd.

PREDICTIVE MODELING OF ACOUSTIC SIGNALS FROM THERMOACOUSTIC POWER SENSORS (TAPS)

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

Dumm, Christopher M.; Vipperman, Jeffrey S.

2016-06-30

Thermoacoustic Power Sensor (TAPS) technology offers the potential for self-powered, wireless measurement of nuclear reactor core operating conditions. TAPS are based on thermoacoustic engines, which harness thermal energy from fission reactions to generate acoustic waves by virtue of gas motion through a porous stack of thermally nonconductive material. TAPS can be placed in the core, where they generate acoustic waves whose frequency and amplitude are proportional to the local temperature and radiation flux, respectively. TAPS acoustic signals are not measured directly at the TAPS; rather, they propagate wirelessly from an individual TAPS through the reactor, and ultimately to a low-powermore » receiver network on the vessel’s exterior. In order to rely on TAPS as primary instrumentation, reactor-specific models which account for geometric/acoustic complexities in the signal propagation environment must be used to predict the amplitude and frequency of TAPS signals at receiver locations. The reactor state may then be derived by comparing receiver signals to the reference levels established by predictive modeling. In this paper, we develop and experimentally benchmark a methodology for predictive modeling of the signals generated by a TAPS system, with the intent of subsequently extending these efforts to modeling of TAPS in a liquid sodium environmen« less

Ensemble Data Assimilation of Wind and Photovoltaic Power Information in the Convection-permitting High-Resolution Model COSMO-DE

NASA Astrophysics Data System (ADS)

Declair, Stefan; Saint-Drenan, Yves-Marie; Potthast, Roland

2016-04-01

Determining the amount of weather dependent renewable energy is a demanding task for transmission system operators (TSOs) and wind and photovoltaic (PV) prediction errors require the use of reserve power, which generate costs and can - in extreme cases - endanger the security of supply. In the project EWeLiNE funded by the German government, the German Weather Service and the Fraunhofer Institute on Wind Energy and Energy System Technology develop innovative weather- and power forecasting models and tools for grid integration of weather dependent renewable energy. The key part in energy prediction process chains is the numerical weather prediction (NWP) system. Wind speed and irradiation forecast from NWP system are however subject to several sources of error. The quality of the wind power prediction is mainly penalized by forecast error of the NWP model in the planetary boundary layer (PBL), which is characterized by high spatial and temporal fluctuations of the wind speed. For PV power prediction, weaknesses of the NWP model to correctly forecast i.e. low stratus, the absorption of condensed water or aerosol optical depth are the main sources of errors. Inaccurate radiation schemes (i.e. the two-stream parametrization) are also known as a deficit of NWP systems with regard to irradiation forecast. To mitigate errors like these, NWP model data can be corrected by post-processing techniques such as model output statistics and calibration using historical observational data. Additionally, latest observations can be used in a pre-processing technique called data assimilation (DA). In DA, not only the initial fields are provided, but the model is also synchronized with reality - the observations - and hence the model error is reduced in the forecast. Besides conventional observation networks like radiosondes, synoptic observations or air reports of wind, pressure and humidity, the number of observations measuring meteorological information indirectly such as satellite radiances, radar reflectivities or GPS slant delays strongly increases. The numerous wind farm and PV plants installed in Germany potentially represent a dense meteorological network assessing irradiation and wind speed through their power measurements. The accuracy of the NWP data may thus be enhanced by extending the observations in the assimilation by this new source of information. Wind power data can serve as indirect measurements of wind speed at hub height. The impact on the NWP model is potentially interesting since conventional observation network lacks measurements in this part of the PBL. Photovoltaic power plants can provide information on clouds, aerosol optical depth or low stratus in terms of remote sensing: the power output is strongly dependent on perturbations along the slant between sun position and PV panel. Additionally, since the latter kind of data is not limited to the vertical column above or below the detector. It may thus complement satellite data and compensate weaknesses in the radiation scheme. In this contribution, the DA method (Local Ensemble Transform Kalman Filter, LETKF) is shortly sketched. Furthermore, the computation of the model power equivalents is described and first assimilation results are presented and discussed.

Active optimal control strategies for increasing the efficiency of photovoltaic cells

NASA Astrophysics Data System (ADS)

Aljoaba, Sharif Zidan Ahmad

Energy consumption has increased drastically during the last century. Currently, the worldwide energy consumption is about 17.4 TW and is predicted to reach 25 TW by 2035. Solar energy has emerged as one of the potential renewable energy sources. Since its first physical recognition in 1887 by Adams and Day till nowadays, research in solar energy is continuously developing. This has lead to many achievements and milestones that introduced it as one of the most reliable and sustainable energy sources. Recently, the International Energy Agency declared that solar energy is predicted to be one of the major electricity production energy sources by 2035. Enhancing the efficiency and lifecycle of photovoltaic (PV) modules leads to significant cost reduction. Reducing the temperature of the PV module improves its efficiency and enhances its lifecycle. To better understand the PV module performance, it is important to study the interaction between the output power and the temperature. A model that is capable of predicting the PV module temperature and its effects on the output power considering the individual contribution of the solar spectrum wavelengths significantly advances the PV module edsigns toward higher efficiency. In this work, a thermoelectrical model is developed to predict the effects of the solar spectrum wavelengths on the PV module performance. The model is characterized and validated under real meteorological conditions where experimental temperature and output power of the PV module measurements are shown to agree with the predicted results. The model is used to validate the concept of active optical filtering. Since this model is wavelength-based, it is used to design an active optical filter for PV applications. Applying this filter to the PV module is expected to increase the output power of the module by filtering the spectrum wavelengths. The active filter performance is optimized, where different cutoff wavelengths are used to maximize the module output power. It is predicted that if the optimized active optical filter is applied to the PV module, the module efficiency is predicted to increase by about 1%. Different technologies are considered for physical implementation of the active optical filter.

Accelerated Testing and Modeling of Potential-Induced Degradation as a Function of Temperature and Relative Humidity

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

Hacke, Peter; Spataru, Sergiu; Terwilliger, Kent

2015-06-14

An acceleration model based on the Peck equation was applied to power performance of crystalline silicon cell modules as a function of time and of temperature and humidity, the two main environmental stress factors that promote potential-induced degradation. This model was derived from module power degradation data obtained semi-continuously and statistically by in-situ dark current-voltage measurements in an environmental chamber. The modeling enables prediction of degradation rates and times as functions of temperature and humidity. Power degradation could be modeled linearly as a function of time to the second power; additionally, we found that coulombs transferred from the active cellmore » circuit to ground during the stress test is approximately linear with time. Therefore, the power loss could be linearized as a function of coulombs squared. With this result, we observed that when the module face was completely grounded with a condensed phase conductor, leakage current exceeded the anticipated corresponding degradation rate relative to the other tests performed in damp heat.« less

Very-short-term wind power prediction by a hybrid model with single- and multi-step approaches

NASA Astrophysics Data System (ADS)

Mohammed, E.; Wang, S.; Yu, J.

2017-05-01

Very-short-term wind power prediction (VSTWPP) has played an essential role for the operation of electric power systems. This paper aims at improving and applying a hybrid method of VSTWPP based on historical data. The hybrid method is combined by multiple linear regressions and least square (MLR&LS), which is intended for reducing prediction errors. The predicted values are obtained through two sub-processes:1) transform the time-series data of actual wind power into the power ratio, and then predict the power ratio;2) use the predicted power ratio to predict the wind power. Besides, the proposed method can include two prediction approaches: single-step prediction (SSP) and multi-step prediction (MSP). WPP is tested comparatively by auto-regressive moving average (ARMA) model from the predicted values and errors. The validity of the proposed hybrid method is confirmed in terms of error analysis by using probability density function (PDF), mean absolute percent error (MAPE) and means square error (MSE). Meanwhile, comparison of the correlation coefficients between the actual values and the predicted values for different prediction times and window has confirmed that MSP approach by using the hybrid model is the most accurate while comparing to SSP approach and ARMA. The MLR&LS is accurate and promising for solving problems in WPP.

Understanding redshift space distortions in density-weighted peculiar velocity

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

Sugiyama, Naonori S.; Okumura, Teppei; Spergel, David N., E-mail: nao.s.sugiyama@gmail.com, E-mail: teppei.oku@gmail.com, E-mail: dns@astro.princeton.edu

2016-07-01

Observations of the kinetic Sunyaev-Zel'dovich (kSZ) effect measure the density-weighted velocity field, a potentially powerful cosmological probe. This paper presents an analytical method to predict the power spectrum and two-point correlation function of the density-weighted velocity in redshift space, the direct observables in kSZ surveys. We show a simple relation between the density power spectrum and the density-weighted velocity power spectrum that holds for both dark matter and halos. Using this relation, we can then extend familiar perturbation expansion techniques to the kSZ power spectrum. One of the most important features of density-weighted velocity statistics in redshift space is themore » change in sign of the cross-correlation between the density and density-weighted velocity at mildly small scales due to nonlinear redshift space distortions. Our model can explain this characteristic feature without any free parameters. As a result, our results can precisely predict the non-linear behavior of the density-weighted velocity field in redshift space up to ∼ 30 h {sup -1} Mpc for dark matter particles at the redshifts of z =0.0, 0.5, and 1.0.« less

The status of lightweight photovoltaic space array technology based on amorphous silicon solar cells

NASA Astrophysics Data System (ADS)

Hanak, J. J.; Kaschmitter, J. L.

1991-05-01

An ultralight, flexible photovoltaic (PV) array of amorphous silicon (a-Si) has been identified as a potential low-cost power source for small satellites. We have conducted a survey of the status of the a-Si PV array technology with respect to present and future performance, availability, cost and risks. For existing, experimental array 'blankets' made of commercial cell material, utilizing metal foil substrates, the BOL performance at AM0 and 35 C includes total power up to 200 W, power per area of 64 W/sq m and power per weight of 258 W/kg. Doubling of power per weight occurs when polyimide substrates are used. Estimated EOL power output after 10 years in a nominal low-earth orbit would be 80 percent of BOL, the degradation being due to largely light-induced effects (minus 10 to minus 15 percent) and in part (minus 5 percent) to space radiation. Predictions for the year 1995 for flexible PV arrays, made on the basis of published results for rigid a-Si modules, indicate EOL power output per area and per weight of 105 W/sq m and 400 W/kg, respectively, while predictions for the late 1990s based on existing US national PV program goals indicate EOL values of 157 W/sq m and 600 W/kg. cost estimates by vendors for 200 W ultralight arrays in volume of over 1000 units range from $100/watt to $125/watt. Identified risks include the lack of flexible, space compatible encapsulant, the lack of space qualification effort, recent partial or full acquisitions of US manufacturers of a-Si cells by foreign firms, and the absence of a national commitment for a long-range development program toward developing of this important power source for space. One new US developer has emerged as a future potential supplier of a-Si PV devices on thin, polyimide substrates.

Space power systems technology

NASA Technical Reports Server (NTRS)

Coulman, George A.

1994-01-01

Reported here is a series of studies which examine several potential catalysts and electrodes for some fuel cell systems, some materials for space applications, and mathematical modeling and performance predictions for some solid oxide fuel cells and electrolyzers. The fuel cell systems have a potential for terrestrial applications in addition to solar energy conversion in space applications. Catalysts and electrodes for phosphoric acid fuel cell systems and for polymer electrolyte membrane (PEM) fuel cell and electrolyzer systems were examined.

Microscopic optical model potential based on a Dirac Brueckner Hartree Fock approach and the relevant uncertainty analysis

NASA Astrophysics Data System (ADS)

Xu, Ruirui; Ma, Zhongyu; Muether, Herbert; van Dalen, E. N. E.; Liu, Tinjin; Zhang, Yue; Zhang, Zhi; Tian, Yuan

2017-09-01

A relativistic microscopic optical model potential, named CTOM, for nucleon-nucleus scattering is investigated in the framework of Dirac-Brueckner-Hartree-Fock approach. The microscopic feature of CTOM is guaranteed through rigorously adopting the isospin dependent DBHF calculation within the subtracted T matrix scheme. In order to verify its prediction power, a global study n, p+ A scattering are carried out. The predicted scattering observables coincide with experimental data within a good accuracy over a broad range of targets and a large region of energies only with two free items, namely the free-range factor t in the applied improved local density approximation and minor adjustments of the scalar and vector potentials in the low-density region. In addition, to estimate the uncertainty of the theoretical results, the deterministic simple least square approach is preliminarily employed to derive the covariance of predicted angular distributions, which is also briefly contained in this paper.

The COMMAND trial of cognitive therapy to prevent harmful compliance with command hallucinations: predictors of outcome and mediators of change.

PubMed

Birchwood, Max; Dunn, Graham; Meaden, Alan; Tarrier, Nicholas; Lewis, Shon; Wykes, Til; Davies, Linda; Michail, Maria; Peters, Emmanuelle

2017-12-05

Acting on harmful command hallucinations is a major clinical concern. Our COMMAND CBT trial approximately halved the rate of harmful compliance (OR = 0.45, 95% CI 0.23-0.88, p = 0.021). The focus of the therapy was a single mechanism, the power dimension of voice appraisal, was also significantly reduced. We hypothesised that voice power differential (between voice and voice hearer) was the mediator of the treatment effect. The trial sample (n = 197) was used. A logistic regression model predicting 18-month compliance was used to identify predictors, and an exploratory principal component analysis (PCA) of baseline variables used as potential predictors (confounders) in their own right. Stata's paramed command used to obtain estimates of the direct, indirect and total effects of treatment. Voice omnipotence was the best predictor although the PCA identified a highly predictive cognitive-affective dimension comprising: voices' power, childhood trauma, depression and self-harm. In the mediation analysis, the indirect effect of treatment was fully explained by its effect on the hypothesised mediator: voice power differential. Voice power and treatment allocation were the best predictors of harmful compliance up to 18 months; post-treatment, voice power differential measured at nine months was the mediator of the effect of treatment on compliance at 18 months.

The Potential Wind Power Resource in Australia: A New Perspective

PubMed Central

Hallgren, Willow; Gunturu, Udaya Bhaskar; Schlosser, Adam

2014-01-01

Australia’s wind resource is considered to be very good, and the utilization of this renewable energy resource is increasing rapidly: wind power installed capacity increased by 35% from 2006 to 2011 and is predicted to account for over 12% of Australia’s electricity generation in 2030. Due to this growth in the utilization of the wind resource and the increasing importance of wind power in Australia’s energy mix, this study sets out to analyze and interpret the nature of Australia’s wind resources using robust metrics of the abundance, variability and intermittency of wind power density, and analyzes the variation of these characteristics with current and potential wind turbine hub heights. We also assess the extent to which wind intermittency, on hourly or greater timescales, can potentially be mitigated by the aggregation of geographically dispersed wind farms, and in so doing, lessen the severe impact on wind power economic viability of long lulls in wind and power generated. Our results suggest that over much of Australia, areas that have high wind intermittency coincide with large expanses in which the aggregation of turbine output does not mitigate variability. These areas are also geographically remote, some are disconnected from the east coast’s electricity grid and large population centers, which are factors that could decrease the potential economic viability of wind farms in these locations. However, on the eastern seaboard, even though the wind resource is weaker, it is less variable, much closer to large population centers, and there exists more potential to mitigate it’s intermittency through aggregation. This study forms a necessary precursor to the analysis of the impact of large-scale circulations and oscillations on the wind resource at the mesoscale. PMID:24988222

The potential wind power resource in Australia: a new perspective.

PubMed

Hallgren, Willow; Gunturu, Udaya Bhaskar; Schlosser, Adam

2014-01-01

Australia's wind resource is considered to be very good, and the utilization of this renewable energy resource is increasing rapidly: wind power installed capacity increased by 35% from 2006 to 2011 and is predicted to account for over 12% of Australia's electricity generation in 2030. Due to this growth in the utilization of the wind resource and the increasing importance of wind power in Australia's energy mix, this study sets out to analyze and interpret the nature of Australia's wind resources using robust metrics of the abundance, variability and intermittency of wind power density, and analyzes the variation of these characteristics with current and potential wind turbine hub heights. We also assess the extent to which wind intermittency, on hourly or greater timescales, can potentially be mitigated by the aggregation of geographically dispersed wind farms, and in so doing, lessen the severe impact on wind power economic viability of long lulls in wind and power generated. Our results suggest that over much of Australia, areas that have high wind intermittency coincide with large expanses in which the aggregation of turbine output does not mitigate variability. These areas are also geographically remote, some are disconnected from the east coast's electricity grid and large population centers, which are factors that could decrease the potential economic viability of wind farms in these locations. However, on the eastern seaboard, even though the wind resource is weaker, it is less variable, much closer to large population centers, and there exists more potential to mitigate it's intermittency through aggregation. This study forms a necessary precursor to the analysis of the impact of large-scale circulations and oscillations on the wind resource at the mesoscale.

Development of Northeast Asia Nuclear Power Plant Accident Simulator.

PubMed

Kim, Juyub; Kim, Juyoul; Po, Li-Chi Cliff

2017-06-15

A conclusion from the lessons learned after the March 2011 Fukushima Daiichi accident was that Korea needs a tool to estimate consequences from a major accident that could occur at a nuclear power plant located in a neighboring country. This paper describes a suite of computer-based codes to be used by Korea's nuclear emergency response staff for training and potentially operational support in Korea's national emergency preparedness and response program. The systems of codes, Northeast Asia Nuclear Accident Simulator (NANAS), consist of three modules: source-term estimation, atmospheric dispersion prediction and dose assessment. To quickly assess potential doses to the public in Korea, NANAS includes specific reactor data from the nuclear power plants in China, Japan and Taiwan. The completed simulator is demonstrated using data for a hypothetical release. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Nomogram Method as Means for Resource Potential Efficiency Predicative Aid of Petrothermal Energy

NASA Astrophysics Data System (ADS)

Gabdrakhmanova, K. F.; Izmailova, G. R.; Larin, P. A.; Vasilyeva, E. R.; Madjidov, M. A.; Marupov, S. R.

2018-05-01

The article describes the innovative approach when predicting the resource potential efficiency of petrothermal energy. Various geothermal gradients representative of Bashkortostan and Tatarstan republics regions were considered. With the help of nomograms, the authors analysed fluid temperature dependency graphs at the outlet and the thermal power versus fluid velocity along the wellbore. From the family of graphs plotted by us, velocities corresponding to specific temperature were found. Then, according to thermal power versus velocity curve, power levels corresponding to these velocities relative to the selected fluid temperature were found. On the basis of two dependencies obtained, nomograms were plotted. The result of determining the petrothermal energy production efficiency is a family of isocline lines that enables one to select the optimum temperature and injection rate to obtain the required amount of heat for a particular depth and geothermal gradient.

On the nature of the fragment environment created by the range destruction or random failure of solid rocket motor casings

NASA Technical Reports Server (NTRS)

Eck, M.; Mukunda, M.

1988-01-01

Given here are predictions of fragment velocities and azimuths resulting from the Space Transportation System Solid Rocket Motor range destruct, or random failure occurring at any time during the 120 seconds of Solid Rocket Motor burn. Results obtained using the analytical methods described showed good agreement between predictions and observations for two specific events. It was shown that these methods have good potential for use in predicting the fragmentation process of a number of generically similar casing systems. It was concluded that coupled Eulerian-Lagrangian calculational methods of the type described here provide a powerful tool for predicting Solid Rocket Motor response.

Classical Mathematical Models for Description and Prediction of Experimental Tumor Growth

PubMed Central

Benzekry, Sébastien; Lamont, Clare; Beheshti, Afshin; Tracz, Amanda; Ebos, John M. L.; Hlatky, Lynn; Hahnfeldt, Philip

2014-01-01

Despite internal complexity, tumor growth kinetics follow relatively simple laws that can be expressed as mathematical models. To explore this further, quantitative analysis of the most classical of these were performed. The models were assessed against data from two in vivo experimental systems: an ectopic syngeneic tumor (Lewis lung carcinoma) and an orthotopically xenografted human breast carcinoma. The goals were threefold: 1) to determine a statistical model for description of the measurement error, 2) to establish the descriptive power of each model, using several goodness-of-fit metrics and a study of parametric identifiability, and 3) to assess the models' ability to forecast future tumor growth. The models included in the study comprised the exponential, exponential-linear, power law, Gompertz, logistic, generalized logistic, von Bertalanffy and a model with dynamic carrying capacity. For the breast data, the dynamics were best captured by the Gompertz and exponential-linear models. The latter also exhibited the highest predictive power, with excellent prediction scores (≥80%) extending out as far as 12 days in the future. For the lung data, the Gompertz and power law models provided the most parsimonious and parametrically identifiable description. However, not one of the models was able to achieve a substantial prediction rate (≥70%) beyond the next day data point. In this context, adjunction of a priori information on the parameter distribution led to considerable improvement. For instance, forecast success rates went from 14.9% to 62.7% when using the power law model to predict the full future tumor growth curves, using just three data points. These results not only have important implications for biological theories of tumor growth and the use of mathematical modeling in preclinical anti-cancer drug investigations, but also may assist in defining how mathematical models could serve as potential prognostic tools in the clinic. PMID:25167199

Classical mathematical models for description and prediction of experimental tumor growth.

PubMed

Benzekry, Sébastien; Lamont, Clare; Beheshti, Afshin; Tracz, Amanda; Ebos, John M L; Hlatky, Lynn; Hahnfeldt, Philip

2014-08-01

Despite internal complexity, tumor growth kinetics follow relatively simple laws that can be expressed as mathematical models. To explore this further, quantitative analysis of the most classical of these were performed. The models were assessed against data from two in vivo experimental systems: an ectopic syngeneic tumor (Lewis lung carcinoma) and an orthotopically xenografted human breast carcinoma. The goals were threefold: 1) to determine a statistical model for description of the measurement error, 2) to establish the descriptive power of each model, using several goodness-of-fit metrics and a study of parametric identifiability, and 3) to assess the models' ability to forecast future tumor growth. The models included in the study comprised the exponential, exponential-linear, power law, Gompertz, logistic, generalized logistic, von Bertalanffy and a model with dynamic carrying capacity. For the breast data, the dynamics were best captured by the Gompertz and exponential-linear models. The latter also exhibited the highest predictive power, with excellent prediction scores (≥80%) extending out as far as 12 days in the future. For the lung data, the Gompertz and power law models provided the most parsimonious and parametrically identifiable description. However, not one of the models was able to achieve a substantial prediction rate (≥70%) beyond the next day data point. In this context, adjunction of a priori information on the parameter distribution led to considerable improvement. For instance, forecast success rates went from 14.9% to 62.7% when using the power law model to predict the full future tumor growth curves, using just three data points. These results not only have important implications for biological theories of tumor growth and the use of mathematical modeling in preclinical anti-cancer drug investigations, but also may assist in defining how mathematical models could serve as potential prognostic tools in the clinic.

Predictive analysis of beer quality by correlating sensory evaluation with higher alcohol and ester production using multivariate statistics methods.

PubMed

Dong, Jian-Jun; Li, Qing-Liang; Yin, Hua; Zhong, Cheng; Hao, Jun-Guang; Yang, Pan-Fei; Tian, Yu-Hong; Jia, Shi-Ru

2014-10-15

Sensory evaluation is regarded as a necessary procedure to ensure a reproducible quality of beer. Meanwhile, high-throughput analytical methods provide a powerful tool to analyse various flavour compounds, such as higher alcohol and ester. In this study, the relationship between flavour compounds and sensory evaluation was established by non-linear models such as partial least squares (PLS), genetic algorithm back-propagation neural network (GA-BP), support vector machine (SVM). It was shown that SVM with a Radial Basis Function (RBF) had a better performance of prediction accuracy for both calibration set (94.3%) and validation set (96.2%) than other models. Relatively lower prediction abilities were observed for GA-BP (52.1%) and PLS (31.7%). In addition, the kernel function of SVM played an essential role of model training when the prediction accuracy of SVM with polynomial kernel function was 32.9%. As a powerful multivariate statistics method, SVM holds great potential to assess beer quality. Copyright © 2014 Elsevier Ltd. All rights reserved.

Predicting the ethanol potential of wheat straw using near-infrared spectroscopy and chemometrics: The challenge of inherently intercorrelated response functions

DOE PAGES

Rinnan, Asmund; Bruun, Sander; Lindedam, Jane; ...

2017-02-07

Here, the combination of NIR spectroscopy and chemometrics is a powerful correlation method for predicting the chemical constituents in biological matrices, such as the glucose and xylose content of straw. However, difficulties arise when it comes to predicting enzymatic glucose and xylose release potential, which is matrix dependent. Further complications are caused by xylose and glucose release potential being highly intercorrelated. This study emphasizes the importance of understanding the causal relationship between the model and the constituent of interest. It investigates the possibility of using near-infrared spectroscopy to evaluate the ethanol potential of wheat straw by analyzing more than 1000more » samples from different wheat varieties and growth conditions. During the calibration model development, the prime emphasis was to investigate the correlation structure between the two major quality traits for saccharification of wheat straw: glucose and xylose release. The large sample set enabled a versatile and robust calibration model to be developed, showing that the prediction model for xylose release is based on a causal relationship with the NIR spectral data. In contrast, the prediction of glucose release was found to be highly dependent on the intercorrelation with xylose release. If this correlation is broken, the model performance breaks down. A simple method was devised for avoiding this breakdown and can be applied to any large dataset for investigating the causality or lack of causality of a prediction model.« less

Predicting the ethanol potential of wheat straw using near-infrared spectroscopy and chemometrics: The challenge of inherently intercorrelated response functions

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

Rinnan, Asmund; Bruun, Sander; Lindedam, Jane

Here, the combination of NIR spectroscopy and chemometrics is a powerful correlation method for predicting the chemical constituents in biological matrices, such as the glucose and xylose content of straw. However, difficulties arise when it comes to predicting enzymatic glucose and xylose release potential, which is matrix dependent. Further complications are caused by xylose and glucose release potential being highly intercorrelated. This study emphasizes the importance of understanding the causal relationship between the model and the constituent of interest. It investigates the possibility of using near-infrared spectroscopy to evaluate the ethanol potential of wheat straw by analyzing more than 1000more » samples from different wheat varieties and growth conditions. During the calibration model development, the prime emphasis was to investigate the correlation structure between the two major quality traits for saccharification of wheat straw: glucose and xylose release. The large sample set enabled a versatile and robust calibration model to be developed, showing that the prediction model for xylose release is based on a causal relationship with the NIR spectral data. In contrast, the prediction of glucose release was found to be highly dependent on the intercorrelation with xylose release. If this correlation is broken, the model performance breaks down. A simple method was devised for avoiding this breakdown and can be applied to any large dataset for investigating the causality or lack of causality of a prediction model.« less

Using Predictive Analytics to Predict Power Outages from Severe Weather

NASA Astrophysics Data System (ADS)

Wanik, D. W.; Anagnostou, E. N.; Hartman, B.; Frediani, M. E.; Astitha, M.

2015-12-01

The distribution of reliable power is essential to businesses, public services, and our daily lives. With the growing abundance of data being collected and created by industry (i.e. outage data), government agencies (i.e. land cover), and academia (i.e. weather forecasts), we can begin to tackle problems that previously seemed too complex to solve. In this session, we will present newly developed tools to aid decision-support challenges at electric distribution utilities that must mitigate, prepare for, respond to and recover from severe weather. We will show a performance evaluation of outage predictive models built for Eversource Energy (formerly Connecticut Light & Power) for storms of all types (i.e. blizzards, thunderstorms and hurricanes) and magnitudes (from 20 to >15,000 outages). High resolution weather simulations (simulated with the Weather and Research Forecast Model) were joined with utility outage data to calibrate four types of models: a decision tree (DT), random forest (RF), boosted gradient tree (BT) and an ensemble (ENS) decision tree regression that combined predictions from DT, RF and BT. The study shows that the ENS model forced with weather, infrastructure and land cover data was superior to the other models we evaluated, especially in terms of predicting the spatial distribution of outages. This research has the potential to be used for other critical infrastructure systems (such as telecommunications, drinking water and gas distribution networks), and can be readily expanded to the entire New England region to facilitate better planning and coordination among decision-makers when severe weather strikes.

The role of the mitochondrial ribosome in human disease: searching for mutations in 12S mitochondrial rRNA with high disruptive potential

PubMed Central

Smith, Paul M.; Elson, Joanna L.; Greaves, Laura C.; Wortmann, Saskia B.; Rodenburg, Richard J.T.; Lightowlers, Robert N.; Chrzanowska-Lightowlers, Zofia M.A.; Taylor, Robert W.; Vila-Sanjurjo, Antón

2014-01-01

Mutations of mitochondrial DNA are linked to many human diseases. Despite the identification of a large number of variants in the mitochondrially encoded rRNA (mt-rRNA) genes, the evidence supporting their pathogenicity is, at best, circumstantial. Establishing the pathogenicity of these variations is of major diagnostic importance. Here, we aim to estimate the disruptive effect of mt-rRNA variations on the function of the mitochondrial ribosome. In the absence of direct biochemical methods to study the effect of mt-rRNA variations, we relied on the universal conservation of the rRNA fold to infer their disruptive potential. Our method, named heterologous inferential analysis or HIA, combines conservational information with functional and structural data obtained from heterologous ribosomal sources. Thus, HIA's predictive power is superior to the traditional reliance on simple conservation indexes. By using HIA, we have been able to evaluate the disruptive potential for a subset of uncharacterized 12S mt-rRNA variations. Our analysis revealed the existence of variations in the rRNA component of the human mitoribosome with different degrees of disruptive power. In cases where sufficient information regarding the genetic and pathological manifestation of the mitochondrial phenotype is available, HIA data can be used to predict the pathogenicity of mt-rRNA mutations. In other cases, HIA analysis will allow the prioritization of variants for additional investigation. Eventually, HIA-inspired analysis of potentially pathogenic mt-rRNA variations, in the context of a scoring system specifically designed for these variants, could lead to a powerful diagnostic tool. PMID:24092330

Can nutrient status of four woody plant species be predicted using field spectrometry?

NASA Astrophysics Data System (ADS)

Ferwerda, Jelle G.; Skidmore, Andrew K.

This paper demonstrates the potential of hyperspectral remote sensing to predict the chemical composition (i.e., nitrogen, phosphorous, calcium, potassium, sodium, and magnesium) of three tree species (i.e., willow, mopane and olive) and one shrub species (i.e., heather). Reflectance spectra, derivative spectra and continuum-removed spectra were compared in terms of predictive power. Results showed that the best predictions for nitrogen, phosphorous, and magnesium occur when using derivative spectra, and the best predictions for sodium, potassium, and calcium occur when using continuum-removed data. To test whether a general model for multiple species is also valid for individual species, a bootstrapping routine was applied. Prediction accuracies for the individual species were lower then prediction accuracies obtained for the combined dataset for all except one element/species combination, indicating that indices with high prediction accuracies at the landscape scale are less appropriate to detect the chemical content of individual species.

Energy in a Planetary Context

NASA Technical Reports Server (NTRS)

Hoehler, Tori M.

2017-01-01

The potential present day habitability of solar system bodies beyond Earth is limited to subsurface environments, where the availability of energy in biologically useful form is a paramount consideration. Energy availability is commonly quantified in terms of molar Gibbs energy changes for metabolisms of interest, but this can provide an incomplete and even misleading picture. A second aspect of life's requirement for energy - the rate of delivery, or power - strongly influences habitability, biomass abundance, growth rates, and, ultimately, rates of evolution. We are developing an approach to quantify metabolic power, using a cell-scale reactive transport model in which physical and chemical environmental parameters are varied. Simultaneously, we evaluate cell-specific energy flux requirements and their dependence on environmental "extremes". Comparison of metabolic power supply and demand provides a constraint on how biomass abundance varies across a range of environmental parameters, and thereby a prediction of the relative habitability of different environments. We are evaluating the predictive capability of this approach through comparison to observed distributions of microbial abundance in a range of subsurface (predominantly serpentinizing) systems.

Validation of finite element and boundary element methods for predicting structural vibration and radiated noise

NASA Technical Reports Server (NTRS)

Seybert, A. F.; Wu, X. F.; Oswald, Fred B.

1992-01-01

Analytical and experimental validation of methods to predict structural vibration and radiated noise are presented. A rectangular box excited by a mechanical shaker was used as a vibrating structure. Combined finite element method (FEM) and boundary element method (BEM) models of the apparatus were used to predict the noise radiated from the box. The FEM was used to predict the vibration, and the surface vibration was used as input to the BEM to predict the sound intensity and sound power. Vibration predicted by the FEM model was validated by experimental modal analysis. Noise predicted by the BEM was validated by sound intensity measurements. Three types of results are presented for the total radiated sound power: (1) sound power predicted by the BEM modeling using vibration data measured on the surface of the box; (2) sound power predicted by the FEM/BEM model; and (3) sound power measured by a sound intensity scan. The sound power predicted from the BEM model using measured vibration data yields an excellent prediction of radiated noise. The sound power predicted by the combined FEM/BEM model also gives a good prediction of radiated noise except for a shift of the natural frequencies that are due to limitations in the FEM model.

Possible Potentials Responsible for Stable Circular Relativistic Orbits

ERIC Educational Resources Information Center

Kumar, Prashant; Bhattacharya, Kaushik

2011-01-01

Bertrand's theorem in classical mechanics of the central force fields attracts us because of its predictive power. It categorically proves that there can only be two types of forces which can produce stable, circular orbits. In this paper an attempt has been made to generalize Bertrand's theorem to the central force problem of relativistic…

An analytical method for designing low noise helicopter transmissions

NASA Technical Reports Server (NTRS)

Bossler, R. B., Jr.; Bowes, M. A.; Royal, A. C.

1978-01-01

The development and experimental validation of a method for analytically modeling the noise mechanism in the helicopter geared power transmission systems is described. This method can be used within the design process to predict interior noise levels and to investigate the noise reducing potential of alternative transmission design details. Examples are discussed.

Univariate Time Series Prediction of Solar Power Using a Hybrid Wavelet-ARMA-NARX Prediction Method

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

Nazaripouya, Hamidreza; Wang, Yubo; Chu, Chi-Cheng

This paper proposes a new hybrid method for super short-term solar power prediction. Solar output power usually has a complex, nonstationary, and nonlinear characteristic due to intermittent and time varying behavior of solar radiance. In addition, solar power dynamics is fast and is inertia less. An accurate super short-time prediction is required to compensate for the fluctuations and reduce the impact of solar power penetration on the power system. The objective is to predict one step-ahead solar power generation based only on historical solar power time series data. The proposed method incorporates discrete wavelet transform (DWT), Auto-Regressive Moving Average (ARMA)more » models, and Recurrent Neural Networks (RNN), while the RNN architecture is based on Nonlinear Auto-Regressive models with eXogenous inputs (NARX). The wavelet transform is utilized to decompose the solar power time series into a set of richer-behaved forming series for prediction. ARMA model is employed as a linear predictor while NARX is used as a nonlinear pattern recognition tool to estimate and compensate the error of wavelet-ARMA prediction. The proposed method is applied to the data captured from UCLA solar PV panels and the results are compared with some of the common and most recent solar power prediction methods. The results validate the effectiveness of the proposed approach and show a considerable improvement in the prediction precision.« less

Comparison of the economic impact of different wind power forecast systems for producers

NASA Astrophysics Data System (ADS)

Alessandrini, S.; Davò, F.; Sperati, S.; Benini, M.; Delle Monache, L.

2014-05-01

Deterministic forecasts of wind production for the next 72 h at a single wind farm or at the regional level are among the main end-users requirement. However, for an optimal management of wind power production and distribution it is important to provide, together with a deterministic prediction, a probabilistic one. A deterministic forecast consists of a single value for each time in the future for the variable to be predicted, while probabilistic forecasting informs on probabilities for potential future events. This means providing information about uncertainty (i.e. a forecast of the PDF of power) in addition to the commonly provided single-valued power prediction. A significant probabilistic application is related to the trading of energy in day-ahead electricity markets. It has been shown that, when trading future wind energy production, using probabilistic wind power predictions can lead to higher benefits than those obtained by using deterministic forecasts alone. In fact, by using probabilistic forecasting it is possible to solve economic model equations trying to optimize the revenue for the producer depending, for example, on the specific penalties for forecast errors valid in that market. In this work we have applied a probabilistic wind power forecast systems based on the "analog ensemble" method for bidding wind energy during the day-ahead market in the case of a wind farm located in Italy. The actual hourly income for the plant is computed considering the actual selling energy prices and penalties proportional to the unbalancing, defined as the difference between the day-ahead offered energy and the actual production. The economic benefit of using a probabilistic approach for the day-ahead energy bidding are evaluated, resulting in an increase of 23% of the annual income for a wind farm owner in the case of knowing "a priori" the future energy prices. The uncertainty on price forecasting partly reduces the economic benefit gained by using a probabilistic energy forecast system.

Metabolic pathways for the whole community.

PubMed

Hanson, Niels W; Konwar, Kishori M; Hawley, Alyse K; Altman, Tomer; Karp, Peter D; Hallam, Steven J

2014-07-22

A convergence of high-throughput sequencing and computational power is transforming biology into information science. Despite these technological advances, converting bits and bytes of sequence information into meaningful insights remains a challenging enterprise. Biological systems operate on multiple hierarchical levels from genomes to biomes. Holistic understanding of biological systems requires agile software tools that permit comparative analyses across multiple information levels (DNA, RNA, protein, and metabolites) to identify emergent properties, diagnose system states, or predict responses to environmental change. Here we adopt the MetaPathways annotation and analysis pipeline and Pathway Tools to construct environmental pathway/genome databases (ePGDBs) that describe microbial community metabolism using MetaCyc, a highly curated database of metabolic pathways and components covering all domains of life. We evaluate Pathway Tools' performance on three datasets with different complexity and coding potential, including simulated metagenomes, a symbiotic system, and the Hawaii Ocean Time-series. We define accuracy and sensitivity relationships between read length, coverage and pathway recovery and evaluate the impact of taxonomic pruning on ePGDB construction and interpretation. Resulting ePGDBs provide interactive metabolic maps, predict emergent metabolic pathways associated with biosynthesis and energy production and differentiate between genomic potential and phenotypic expression across defined environmental gradients. This multi-tiered analysis provides the user community with specific operating guidelines, performance metrics and prediction hazards for more reliable ePGDB construction and interpretation. Moreover, it demonstrates the power of Pathway Tools in predicting metabolic interactions in natural and engineered ecosystems.

Evaluation and prediction of solar radiation for energy management based on neural networks

NASA Astrophysics Data System (ADS)

Aldoshina, O. V.; Van Tai, Dinh

2017-08-01

Currently, there is a high rate of distribution of renewable energy sources and distributed power generation based on intelligent networks; therefore, meteorological forecasts are particularly useful for planning and managing the energy system in order to increase its overall efficiency and productivity. The application of artificial neural networks (ANN) in the field of photovoltaic energy is presented in this article. Implemented in this study, two periodically repeating dynamic ANS, that are the concentration of the time delay of a neural network (CTDNN) and the non-linear autoregression of a network with exogenous inputs of the NAEI, are used in the development of a model for estimating and daily forecasting of solar radiation. ANN show good productivity, as reliable and accurate models of daily solar radiation are obtained. This allows to successfully predict the photovoltaic output power for this installation. The potential of the proposed method for controlling the energy of the electrical network is shown using the example of the application of the NAEI network for predicting the electric load.

Assessing Probabilistic Risk Assessment Approaches for Insect Biological Control Introductions.

PubMed

Kaufman, Leyla V; Wright, Mark G

2017-07-07

The introduction of biological control agents to new environments requires host specificity tests to estimate potential non-target impacts of a prospective agent. Currently, the approach is conservative, and is based on physiological host ranges determined under captive rearing conditions, without consideration for ecological factors that may influence realized host range. We use historical data and current field data from introduced parasitoids that attack an endemic Lepidoptera species in Hawaii to validate a probabilistic risk assessment (PRA) procedure for non-target impacts. We use data on known host range and habitat use in the place of origin of the parasitoids to determine whether contemporary levels of non-target parasitism could have been predicted using PRA. Our results show that reasonable predictions of potential non-target impacts may be made if comprehensive data are available from places of origin of biological control agents, but scant data produce poor predictions. Using apparent mortality data rather than marginal attack rate estimates in PRA resulted in over-estimates of predicted non-target impact. Incorporating ecological data into PRA models improved the predictive power of the risk assessments.

Assessing Probabilistic Risk Assessment Approaches for Insect Biological Control Introductions

PubMed Central

Kaufman, Leyla V.; Wright, Mark G.

2017-01-01

The introduction of biological control agents to new environments requires host specificity tests to estimate potential non-target impacts of a prospective agent. Currently, the approach is conservative, and is based on physiological host ranges determined under captive rearing conditions, without consideration for ecological factors that may influence realized host range. We use historical data and current field data from introduced parasitoids that attack an endemic Lepidoptera species in Hawaii to validate a probabilistic risk assessment (PRA) procedure for non-target impacts. We use data on known host range and habitat use in the place of origin of the parasitoids to determine whether contemporary levels of non-target parasitism could have been predicted using PRA. Our results show that reasonable predictions of potential non-target impacts may be made if comprehensive data are available from places of origin of biological control agents, but scant data produce poor predictions. Using apparent mortality data rather than marginal attack rate estimates in PRA resulted in over-estimates of predicted non-target impact. Incorporating ecological data into PRA models improved the predictive power of the risk assessments. PMID:28686180

Alpha decay studies on Po isotopes using different versions of nuclear potentials

NASA Astrophysics Data System (ADS)

Santhosh, K. P.; Sukumaran, Indu

2017-12-01

The alpha decays from 186-224Po isotopes have been studied using 25 different versions of nuclear potentials so as to select a suitable nuclear potential for alpha decay studies. The computed standard deviation of the calculated half-lives in comparison with the experimental data suggested that proximity 2003-I is the apt form of nuclear potential for alpha decay studies as it possesses the least standard deviation, σ =0.620 . Among the different proximity potentials, proximity 1966 ( σ =0.630 and proximity 1977 ( σ =0.636 , are also found to work well in alpha decay studies with low deviation. Among other versions of nuclear potentials (other than proximity potentials), Bass 1980 is suggested to be a significant form of nuclear potential because of its good predictive power. However, while the other forms of potentials are able to reproduce the experimental data to some extent, these potentials cannot be considered as apposite potentials for alpha decay studies in their present form. Since the experimental correlation of the models is noticed to be satisfying, the alpha decay half-lives of certain Po isotopes that are not detected experimentally yet have been predicted.

Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction.

PubMed

Gao, Xiang-Ming; Yang, Shi-Feng; Pan, San-Bo

2017-01-01

Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.

Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction

PubMed Central

2017-01-01

Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization. PMID:28912803

Computing organic stereoselectivity - from concepts to quantitative calculations and predictions.

PubMed

Peng, Qian; Duarte, Fernanda; Paton, Robert S

2016-11-07

Advances in theory and processing power have established computation as a valuable interpretative and predictive tool in the discovery of new asymmetric catalysts. This tutorial review outlines the theory and practice of modeling stereoselective reactions. Recent examples illustrate how an understanding of the fundamental principles and the application of state-of-the-art computational methods may be used to gain mechanistic insight into organic and organometallic reactions. We highlight the emerging potential of this computational tool-box in providing meaningful predictions for the rational design of asymmetric catalysts. We present an accessible account of the field to encourage future synergy between computation and experiment.

Selenide isotope generator for the Galileo mission. SIG/Galileo contract compliance power prediction technique

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

Hammel, T.E.; Srinivas, V.

1978-11-01

This initial definition of the power degradation prediction technique outlines a model for predicting SIG/Galileo mean EOM power using component test data and data from a module power degradation demonstration test program. (LCL)

Assessment of the present and future offshore wind power potential: a case study in a target territory of the Baltic Sea near the Latvian coast.

PubMed

Lizuma, Lita; Avotniece, Zanita; Rupainis, Sergejs; Teilans, Artis

2013-01-01

Offshore wind energy development promises to be a significant domestic renewable energy source in Latvia. The reliable prediction of present and future wind resources at offshore sites is crucial for planning and selecting the location for wind farms. The overall goal of this paper is the assessment of offshore wind power potential in a target territory of the Baltic Sea near the Latvian coast as well as the identification of a trend in the future wind energy potential for the study territory. The regional climate model CLM and High Resolution Limited Area Model (Hirlam) simulations were used to obtain the wind climatology data for the study area. The results indicated that offshore wind energy is promising for expanding the national electricity generation and will continue to be a stable resource for electricity generation in the region over the 21st century.

Market inefficiency identified by both single and multiple currency trends

NASA Astrophysics Data System (ADS)

Tokár, T.; Horváth, D.

2012-11-01

Many studies have shown that there are good reasons to claim very low predictability of currency returns; nevertheless, the deviations from true randomness exist which have potential predictive and prognostic power [J. James, Simple trend-following strategies in currency trading, Quantitative finance 3 (2003) C75-C77]. We analyze the local trends which are of the main focus of the technical analysis. In this article we introduced various statistical quantities examining role of single temporal discretized trend or multitude of grouped trends corresponding to different time delays. Our specific analysis based predominantly on Euro-dollar currency pair data at the one minute frequency suggests the importance of cumulative nonrandom effect of trends on the potential forecasting performance.

The Effect of Sex on Heart Rate Variability at High Altitude.

PubMed

Boos, Christopher John; Vincent, Emma; Mellor, Adrian; O'Hara, John; Newman, Caroline; Cruttenden, Richard; Scott, Phylip; Cooke, Mark; Matu, Jamie; Woods, David Richard

2017-12-01

There is evidence suggesting that high altitude (HA) exposure leads to a fall in heart rate variability (HRV) that is linked to the development of acute mountain sickness (AMS). The effects of sex on changes in HRV at HA and its relationship to AMS are unknown. HRV (5-min single-lead ECG) was measured in 63 healthy adults (41 men and 22 women) 18-56 yr of age at sea level (SL) and during a HA trek at 3619, 4600, and 5140 m, respectively. The main effects of altitude (SL, 3619 m, 4600 m, and 5140 m) and sex (men vs women) and their potential interaction were assessed using a factorial repeated-measures ANOVA. Logistic regression analyses were performed to assess the ability of HRV to predict AMS. Men and women were of similar age (31.2 ± 9.3 vs 31.7 ± 7.5 yr), ethnicity, and body and mass index. There was main effect for altitude on heart rate, SD of normal-to-normal (NN) intervals (SDNN), root mean square of successive differences (RMSSD), number of pairs of successive NN differing by >50 ms (NN50), NN50/total number of NN, very low-frequency power, low-frequency (LF) power, high-frequency (HF) power, and total power (TP). The most consistent effect on post hoc analysis was reduction in these HRV measures between 3619 and 5140 m at HA. Heart rate was significantly lower and SDNN, RMSSD, LF power, HF power, and TP were higher in men compared with women at HA. There was no interaction between sex and altitude for any of the HRV indices measured. HRV was not predictive of AMS development. Increasing HA leads to a reduction in HRV. Significant differences between men and women emerge at HA. HRV was not predictive of AMS.

Significant SNPs have limited prediction ability for thyroid cancer

PubMed Central

Guo, Shicheng; Wang, Yu-Long; Li, Yi; Jin, Li; Xiong, Momiao; Ji, Qing-Hai; Wang, Jiucun

2014-01-01

Recently, five thyroid cancer significantly associated genetic variants (rs965513, rs944289, rs116909374, rs966423, and rs2439302) have been discovered and validated in two independent GWAS and numerous case–control studies, which were conducted in different populations. We genotyped the above five single nucleotide polymorphisms (SNPs) in Han Chinese populations and performed thyroid cancer-risk predictions with nine machine learning methods. We found that four SNPs were significantly associated with thyroid cancer in Han Chinese population, while no polymorphism was observed for rs116909374. Small familial relative risks (1.02–1.05) and limited power to predict thyroid cancer (AUCs: 0.54–0.60) indicate limited clinical potential. Four significant SNPs have limited prediction ability for thyroid cancer. PMID:24591304

Revising the predictions of inflation for the cosmic microwave background anisotropies.

PubMed

Agulló, Iván; Navarro-Salas, José; Olmo, Gonzalo J; Parker, Leonard

2009-08-07

We point out that, if quantum field renormalization is taken into account and the counterterms are evaluated at the Hubble-radius crossing time or few e-foldings after it, the predictions of slow-roll inflation for both the scalar and the tensorial power spectrum change significantly. This leads to a change in the consistency condition that relates the tensor-to-scalar amplitude ratio with spectral indices. A reexamination of the potentials varphi;{2} and varphi;{4} shows that both are compatible with five-year WMAP data. Only when the counterterms are evaluated at much larger times beyond the end of inflation does one recover the standard predictions. The alternative predictions presented here may soon come within the range of measurement of near-future experiments.

Harnessing the hybrid power supply systems of utility grid and photovoltaic panels at retrofit residential single family building in Medan

NASA Astrophysics Data System (ADS)

Pangaribuan, A. B.; Rahmat, R. F.; Lidya, M. S.; Zálešák, M.

2017-01-01

The paper describes improvisation mode of energy supply source by collaboration between national utility grid as represented by fossil fuels and PV as independent renewable power resource in order to aim the energy consumptions efficiently in retrofit single family house. In this case, one existing single family house model in Medan, Indonesia was observed for the possibility of future refurbishment. The eco-design version of the house model and prediction analyses regarding nearby potential renewable energy resource (solar system) had been made using Autodesk Revit MEP 2015, Climate Consultant 6.0 and Green Building Studio Analysis. Economical evaluation of using hybrid power supply is discussed as well.

Potential efficiencies of open- and closed-cycle CO, supersonic, electric-discharge lasers

NASA Technical Reports Server (NTRS)

Monson, D. J.

1976-01-01

Computed open- and closed-cycle system efficiencies (laser power output divided by electrical power input) are presented for a CW carbon monoxide, supersonic, electric-discharge laser. Closed-system results include the compressor power required to overcome stagnation pressure losses due to supersonic heat addition and a supersonic diffuser. The paper shows the effect on the system efficiencies of varying several important parameters. These parameters include: gas mixture, gas temperature, gas total temperature, gas density, total discharge energy loading, discharge efficiency, saturated gain coefficient, optical cavity size and location with respect to the discharge, and supersonic diffuser efficiency. Maximum open-cycle efficiency of 80-90% is predicted; the best closed-cycle result is 60-70%.

Mathematical modeling of a thermovoltaic cell

NASA Technical Reports Server (NTRS)

White, Ralph E.; Kawanami, Makoto

1992-01-01

A new type of battery named 'Vaporvolt' cell is in the early stage of its development. A mathematical model of a CuO/Cu 'Vaporvolt' cell is presented that can be used to predict the potential and the transport behavior of the cell during discharge. A sensitivity analysis of the various transport and electrokinetic parameters indicates which parameters have the most influence on the predicted energy and power density of the 'Vaporvolt' cell. This information can be used to decide which parameters should be optimized or determined more accurately through further modeling or experimental studies. The optimal thicknesses of electrodes and separator, the concentration of the electrolyte, and the current density are determined by maximizing the power density. These parameter sensitivities and optimal design parameter values will help in the development of a better CuO/Cu 'Vaporvolt' cell.

Energy-harvesting potential of automobile suspension

NASA Astrophysics Data System (ADS)

Múčka, Peter

2016-12-01

This study is aimed quantify dissipated power in a damper of automobile suspension to predict energy harvesting potential of a passenger car more accurately. Field measurements of power dissipation in a regenerative damper are still rare. The novelty is in using the broad database of real road profiles, a 9 degrees-of-freedom full-car model with real parameters, and a tyre-enveloping contact model. Results were presented as a function of road surface type, velocity and road roughness characterised by International Roughness Index. Results were calculated for 1600 test sections of a total length about 253.5 km. Root mean square of a dissipated power was calculated from 19 to 46 W for all four suspension dampers and velocity 60 km/h and from 24 to 58 W for velocity 90 km/h. Results were compared for a full-car model with a tyre-enveloping road contact, full-car and quarter-car models with a tyre-road point contact. Mean difference among three models in calculated power was a few per cent.

Hypersonic Experimental and Computational Capability, Improvement and Validation. Volume 2. (l’Hypersonique experimentale et de calcul - capacite, ameliorafion et validation)

DTIC Science & Technology

1998-12-01

Soft Sphere Molecular Model for Inverse-Power-Law or Lennard Jones Potentials , Physics of Fluids A, Vol. 3, No. 10, pp. 2459-2465. 42. Legge, H...information; — Providing assistance to member nations for the purpose of increasing their scientific and technical potential ; — Rendering scientific and...nal, 34:756-763, 1996. [22] W. Jones and B. Launder. The Prediction of Laminarization with a Two-Equation Model of Turbulence. Int. Journal of Heat

The useful potential of using existing data to uniquely identify predictable wind events and regimes, part 1

NASA Technical Reports Server (NTRS)

Trettel, D. W.; Aquino, J. T.; Piazza, T. R.; Taylor, L. E.; Trask, D. C.

1982-01-01

Correlations between standard meteorological data and wind power generation potential were developed. Combined with appropriate wind forecasts, these correlations can be useful to load dispatchers to supplement conventional energy sources. Hourly wind data were analyzed for four sites, each exhibiting a unique physiography. These sites are Amarillo, Texas; Ludington, Michigan; Montauk Point, New York; and San Gorgonio, California. Synoptic weather maps and tables are presented to illustrate various wind 'regimes' at these sites.

Multisensory stimuli elicit altered oscillatory brain responses at gamma frequencies in patients with schizophrenia

PubMed Central

Stone, David B.; Coffman, Brian A.; Bustillo, Juan R.; Aine, Cheryl J.; Stephen, Julia M.

2014-01-01

Deficits in auditory and visual unisensory responses are well documented in patients with schizophrenia; however, potential abnormalities elicited from multisensory audio-visual stimuli are less understood. Further, schizophrenia patients have shown abnormal patterns in task-related and task-independent oscillatory brain activity, particularly in the gamma frequency band. We examined oscillatory responses to basic unisensory and multisensory stimuli in schizophrenia patients (N = 46) and healthy controls (N = 57) using magnetoencephalography (MEG). Time-frequency decomposition was performed to determine regions of significant changes in gamma band power by group in response to unisensory and multisensory stimuli relative to baseline levels. Results showed significant behavioral differences between groups in response to unisensory and multisensory stimuli. In addition, time-frequency analysis revealed significant decreases and increases in gamma-band power in schizophrenia patients relative to healthy controls, which emerged both early and late over both sensory and frontal regions in response to unisensory and multisensory stimuli. Unisensory gamma-band power predicted multisensory gamma-band power differently by group. Furthermore, gamma-band power in these regions predicted performance in select measures of the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) test battery differently by group. These results reveal a unique pattern of task-related gamma-band power in schizophrenia patients relative to controls that may indicate reduced inhibition in combination with impaired oscillatory mechanisms in patients with schizophrenia. PMID:25414652

Little pigeons can carry great messages: potential distribution and ecology of Uranotaenia (Pseudoficalbia) unguiculata Edwards, 1913 (Diptera: Culicidae), a lesser-known mosquito species from the Western Palaearctic.

PubMed

Filatov, Serhii

2017-10-10

Uranotaenia unguiculata is a Palaearctic mosquito species with poorly known distribution and ecology. This study is aimed at filling the gap in our understanding of the species potential distribution and its environmental requirements through a species distribution modelling (SDM) exercise. Furthermore, aspects of the mosquito ecology that may be relevant to the epidemiology of certain zoonotic vector-borne diseases in Europe are discussed. A maximum entropy (Maxent) modelling approach has been applied to predict the potential distribution of Ur. unguiculata in the Western Palaearctic. Along with the high accuracy and predictive power, the model reflects well the known species distribution and predicts as highly suitable some areas where the occurrence of the species is hitherto unknown. To our knowledge, the potential distribution of a mosquito species from the genus Uranotaenia is modelled for the first time. Provided that Ur. unguiculata is a widely-distributed species, and some pathogens of zoonotic concern have been detected in this mosquito on several occasions, the question regarding its host associations and possible epidemiological role warrants further investigation.

Power load prediction based on GM (1,1)

NASA Astrophysics Data System (ADS)

Wu, Di

2017-05-01

Currently, Chinese power load prediction is highly focused; the paper deeply studies grey prediction and applies it to Chinese electricity consumption during the recent 14 years; through after-test test, it obtains grey prediction which has good adaptability to medium and long-term power load.

How animals move: comparative lessons on animal locomotion.

PubMed

Schaeffer, Paul J; Lindstedt, Stan L

2013-01-01

Comparative physiology often provides unique insights in animal structure and function. It is specifically through this lens that we discuss the fundamental properties of skeletal muscle and animal locomotion, incorporating variation in body size and evolved difference among species. For example, muscle frequencies in vivo are highly constrained by body size, which apparently tunes muscle use to maximize recovery of elastic recoil potential energy. Secondary to this constraint, there is an expected linking of skeletal muscle structural and functional properties. Muscle is relatively simple structurally, but by changing proportions of the few muscle components, a diverse range of functional outputs is possible. Thus, there is a consistent and predictable relation between muscle function and myocyte composition that illuminates animal locomotion. When animals move, the mechanical properties of muscle diverge from the static textbook force-velocity relations described by A. V. Hill, as recovery of elastic potential energy together with force and power enhancement with activation during stretch combine to modulate performance. These relations are best understood through the tool of work loops. Also, when animals move, locomotion is often conveniently categorized energetically. Burst locomotion is typified by high-power outputs and short durations while sustained, cyclic, locomotion engages a smaller fraction of the muscle tissue, yielding lower force and power. However, closer examination reveals that rather than a dichotomy, energetics of locomotion is a continuum. There is a remarkably predictable relationship between duration of activity and peak sustainable performance.

Two methods for estimating limits to large-scale wind power generation

PubMed Central

Miller, Lee M.; Brunsell, Nathaniel A.; Mechem, David B.; Gans, Fabian; Monaghan, Andrew J.; Vautard, Robert; Keith, David W.; Kleidon, Axel

2015-01-01

Wind turbines remove kinetic energy from the atmospheric flow, which reduces wind speeds and limits generation rates of large wind farms. These interactions can be approximated using a vertical kinetic energy (VKE) flux method, which predicts that the maximum power generation potential is 26% of the instantaneous downward transport of kinetic energy using the preturbine climatology. We compare the energy flux method to the Weather Research and Forecasting (WRF) regional atmospheric model equipped with a wind turbine parameterization over a 105 km2 region in the central United States. The WRF simulations yield a maximum generation of 1.1 We⋅m−2, whereas the VKE method predicts the time series while underestimating the maximum generation rate by about 50%. Because VKE derives the generation limit from the preturbine climatology, potential changes in the vertical kinetic energy flux from the free atmosphere are not considered. Such changes are important at night when WRF estimates are about twice the VKE value because wind turbines interact with the decoupled nocturnal low-level jet in this region. Daytime estimates agree better to 20% because the wind turbines induce comparatively small changes to the downward kinetic energy flux. This combination of downward transport limits and wind speed reductions explains why large-scale wind power generation in windy regions is limited to about 1 We⋅m−2, with VKE capturing this combination in a comparatively simple way. PMID:26305925

Effect of accuracy of wind power prediction on power system operator

NASA Technical Reports Server (NTRS)

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

1985-01-01

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

SRG110 Stirling Generator Dynamic Simulator Vibration Test Results and Analysis Correlation

NASA Technical Reports Server (NTRS)

Suarez, Vicente J.; Lewandowski, Edward J.; Callahan, John

2006-01-01

The U.S. Department of Energy (DOE), Lockheed Martin (LM), and NASA Glenn Research Center (GRC) have been developing the Stirling Radioisotope Generator (SRG110) for use as a power system for space science missions. The launch environment enveloping potential missions results in a random input spectrum that is significantly higher than historical RPS launch levels and is a challenge for designers. Analysis presented in prior work predicted that tailoring the compliance at the generator-spacecraft interface reduced the dynamic response of the system thereby allowing higher launch load input levels and expanding the range of potential generator missions. To confirm analytical predictions, a dynamic simulator representing the generator structure, Stirling convertors and heat sources was designed and built for testing with and without a compliant interface. Finite element analysis was performed to guide the generator simulator and compliant interface design so that test modes and frequencies were representative of the SRG110 generator. This paper presents the dynamic simulator design, the test setup and methodology, test article modes and frequencies and dynamic responses, and post-test analysis results. With the compliant interface, component responses to an input environment exceeding the SRG110 qualification level spectrum were all within design allowables. Post-test analysis included finite element model tuning to match test frequencies and random response analysis using the test input spectrum. Analytical results were in good overall agreement with the test results and confirmed previous predictions that the SRG110 power system may be considered for a broad range of potential missions, including those with demanding launch environments.

Lack of predictive power of trait fear and anxiety for conditioned pain modulation (CPM).

PubMed

Horn-Hofmann, Claudia; Priebe, Janosch A; Schaller, Jörg; Görlitz, Rüdiger; Lautenbacher, Stefan

2016-12-01

In recent years the association of conditioned pain modulation (CPM) with trait fear and anxiety has become a hot topic in pain research due to the assumption that such variables may explain the low CPM efficiency in some individuals. However, empirical evidence concerning this association is still equivocal. Our study is the first to investigate the predictive power of fear and anxiety for CPM by using a well-established psycho-physiological measure of trait fear, i.e. startle potentiation, in addition to two self-report measures of pain-related trait anxiety. Forty healthy, pain-free participants (female: N = 20; age: M = 23.62 years) underwent two experimental blocks in counter-balanced order: (1) a startle paradigm with affective picture presentation and (2) a CPM procedure with hot water as conditioning stimulus (CS) and contact heat as test stimulus (TS). At the end of the experimental session, pain catastrophizing (PCS) and pain anxiety (PASS) were assessed. PCS score, PASS score and startle potentiation to threatening pictures were entered as predictors in a linear regression model with CPM magnitude as criterion. We were able to show an inhibitory CPM effect in our sample: pain ratings of the heat stimuli were significantly reduced during hot water immersion. However, CPM was neither predicted by self-report of pain-related anxiety nor by startle potentiation as psycho-physiological measure of trait fear. These results corroborate previous negative findings concerning the association between trait fear/anxiety and CPM efficiency and suggest that shifting the focus from trait to state measures might be promising.

Childhood and Adolescent Animal Cruelty Methods and Their Possible Link to Adult Violent Crimes

ERIC Educational Resources Information Center

Hensley, Christopher; Tallichet, Suzanne E.

2009-01-01

Few researchers have investigated the potentially predictive power of childhood and adolescent animal cruelty methods as they are associated with subsequent interpersonal violence in adulthood. Based on a sample of 261 inmates at medium- and maximum-security prisons in a southern state, the present study examines the relationship between several…

Potential for travertine formation: Fossil Creek, Arizona

Treesearch

John Malusa; Steven T. Overby; Roderic A. Parnell

2003-01-01

Chemical analyses of water emanating from Fossil Springs in Central Arizona were conducted to predict changes in travertine deposition related to changes in stream discharge caused by diversion for hydroelectric power generation. During spring of 1996, water was sampled at 15 locations during normal seepage flow in a 6.7 km reach below Fossil Springs and at full...

The Content, Predictive Power, and Potential Bias in Five Widely Used Teacher Observation Instruments. REL 2017-191

ERIC Educational Resources Information Center

Gill, Brian; Shoji, Megan; Coen, Thomas; Place, Kate

2016-01-01

School districts and states across the Regional Educational Laboratory Mid-Atlantic Region and the country as a whole have been modifying their teacher evaluation systems to identify more effective and less effective teachers and provide better feedback to improve instructional practice. The new systems typically include components related to…

Predicting High-Power Performance in Professional Cyclists.

PubMed

Sanders, Dajo; Heijboer, Mathieu; Akubat, Ibrahim; Meijer, Kenneth; Hesselink, Matthijs K

2017-03-01

To assess if short-duration (5 to ~300 s) high-power performance can accurately be predicted using the anaerobic power reserve (APR) model in professional cyclists. Data from 4 professional cyclists from a World Tour cycling team were used. Using the maximal aerobic power, sprint peak power output, and an exponential constant describing the decrement in power over time, a power-duration relationship was established for each participant. To test the predictive accuracy of the model, several all-out field trials of different durations were performed by each cyclist. The power output achieved during the all-out trials was compared with the predicted power output by the APR model. The power output predicted by the model showed very large to nearly perfect correlations to the actual power output obtained during the all-out trials for each cyclist (r = .88 ± .21, .92 ± .17, .95 ± .13, and .97 ± .09). Power output during the all-out trials remained within an average of 6.6% (53 W) of the predicted power output by the model. This preliminary pilot study presents 4 case studies on the applicability of the APR model in professional cyclists using a field-based approach. The decrement in all-out performance during high-intensity exercise seems to conform to a general relationship with a single exponential-decay model describing the decrement in power vs increasing duration. These results are in line with previous studies using the APR model to predict performance during brief all-out trials. Future research should evaluate the APR model with a larger sample size of elite cyclists.

Exonic Splicing Mutations Are More Prevalent than Currently Estimated and Can Be Predicted by Using In Silico Tools

PubMed Central

Soukarieh, Omar; Gaildrat, Pascaline; Hamieh, Mohamad; Drouet, Aurélie; Baert-Desurmont, Stéphanie; Frébourg, Thierry; Tosi, Mario; Martins, Alexandra

2016-01-01

The identification of a causal mutation is essential for molecular diagnosis and clinical management of many genetic disorders. However, even if next-generation exome sequencing has greatly improved the detection of nucleotide changes, the biological interpretation of most exonic variants remains challenging. Moreover, particular attention is typically given to protein-coding changes often neglecting the potential impact of exonic variants on RNA splicing. Here, we used the exon 10 of MLH1, a gene implicated in hereditary cancer, as a model system to assess the prevalence of RNA splicing mutations among all single-nucleotide variants identified in a given exon. We performed comprehensive minigene assays and analyzed patient’s RNA when available. Our study revealed a staggering number of splicing mutations in MLH1 exon 10 (77% of the 22 analyzed variants), including mutations directly affecting splice sites and, particularly, mutations altering potential splicing regulatory elements (ESRs). We then used this thoroughly characterized dataset, together with experimental data derived from previous studies on BRCA1, BRCA2, CFTR and NF1, to evaluate the predictive power of 3 in silico approaches recently described as promising tools for pinpointing ESR-mutations. Our results indicate that ΔtESRseq and ΔHZEI-based approaches not only discriminate which variants affect splicing, but also predict the direction and severity of the induced splicing defects. In contrast, the ΔΨ-based approach did not show a compelling predictive power. Our data indicates that exonic splicing mutations are more prevalent than currently appreciated and that they can now be predicted by using bioinformatics methods. These findings have implications for all genetically-caused diseases. PMID:26761715

Computational prediction and biochemical characterization of novel RNA aptamers to Rift Valley fever virus nucleocapsid protein.

PubMed

Ellenbecker, Mary; St Goddard, Jeremy; Sundet, Alec; Lanchy, Jean-Marc; Raiford, Douglas; Lodmell, J Stephen

2015-10-01

Rift Valley fever virus (RVFV) is a potent human and livestock pathogen endemic to sub-Saharan Africa and the Arabian Peninsula that has potential to spread to other parts of the world. Although there is no proven effective and safe treatment for RVFV infections, a potential therapeutic target is the virally encoded nucleocapsid protein (N). During the course of infection, N binds to viral RNA, and perturbation of this interaction can inhibit viral replication. To gain insight into how N recognizes viral RNA specifically, we designed an algorithm that uses a distance matrix and multidimensional scaling to compare the predicted secondary structures of known N-binding RNAs, or aptamers, that were isolated and characterized in previous in vitro evolution experiment. These aptamers did not exhibit overt sequence or predicted structure similarity, so we employed bioinformatic methods to propose novel aptamers based on analysis and clustering of secondary structures. We screened and scored the predicted secondary structures of novel randomly generated RNA sequences in silico and selected several of these putative N-binding RNAs whose secondary structures were similar to those of known N-binding RNAs. We found that overall the in silico generated RNA sequences bound well to N in vitro. Furthermore, introduction of these RNAs into cells prior to infection with RVFV inhibited viral replication in cell culture. This proof of concept study demonstrates how the predictive power of bioinformatics and the empirical power of biochemistry can be jointly harnessed to discover, synthesize, and test new RNA sequences that bind tightly to RVFV N protein. The approach would be easily generalizable to other applications. Copyright © 2015 Elsevier Ltd. All rights reserved.

RTOD- RADIAL TURBINE OFF-DESIGN PERFORMANCE ANALYSIS

NASA Technical Reports Server (NTRS)

Glassman, A. J.

1994-01-01

The RTOD program was developed to accurately predict radial turbine off-design performance. The radial turbine has been used extensively in automotive turbochargers and aircraft auxiliary power units. It is now being given serious consideration for primary powerplant applications. In applications where the turbine will operate over a wide range of power settings, accurate off-design performance prediction is essential for a successful design. RTOD predictions have already illustrated a potential improvement in off-design performance offered by rotor back-sweep for high-work-factor radial turbines. RTOD can be used to analyze other potential performance enhancing design features. RTOD predicts the performance of a radial turbine (with or without rotor blade sweep) as a function of pressure ratio, speed, and stator setting. The program models the flow with the following: 1) stator viscous and trailing edge losses; 2) a vaneless space loss between the stator and the rotor; and 3) rotor incidence, viscous, trailing-edge, clearance, and disk friction losses. The stator and rotor viscous losses each represent the combined effects of profile, endwall, and secondary flow losses. The stator inlet and exit and the rotor inlet flows are modeled by a mean-line analysis, but a sector analysis is used at the rotor exit. The leakage flow through the clearance gap in a pivoting stator is also considered. User input includes gas properties, turbine geometry, and the stator and rotor viscous losses at a reference performance point. RTOD output includes predicted turbine performance over a specified operating range and any user selected flow parameters. The RTOD program is written in FORTRAN IV for batch execution and has been implemented on an IBM 370 series computer with a central memory requirement of approximately 100K of 8 bit bytes. The RTOD program was developed in 1983.

Effective equilibrium states in mixtures of active particles driven by colored noise

NASA Astrophysics Data System (ADS)

Wittmann, René; Brader, J. M.; Sharma, A.; Marconi, U. Marini Bettolo

2018-01-01

We consider the steady-state behavior of pairs of active particles having different persistence times and diffusivities. To this purpose we employ the active Ornstein-Uhlenbeck model, where the particles are driven by colored noises with exponential correlation functions whose intensities and correlation times vary from species to species. By extending Fox's theory to many components, we derive by functional calculus an approximate Fokker-Planck equation for the configurational distribution function of the system. After illustrating the predicted distribution in the solvable case of two particles interacting via a harmonic potential, we consider systems of particles repelling through inverse power-law potentials. We compare the analytic predictions to computer simulations for such soft-repulsive interactions in one dimension and show that at linear order in the persistence times the theory is satisfactory. This work provides the toolbox to qualitatively describe many-body phenomena, such as demixing and depletion, by means of effective pair potentials.

On the gravitational potential and field anomalies due to thin mass layers

NASA Technical Reports Server (NTRS)

Ockendon, J. R.; Turcotte, D. L.

1977-01-01

The gravitational potential and field anomalies for thin mass layers are derived using the technique of matched asymptotic expansions. An inner solution is obtained using an expansion in powers of the thickness and it is shown that the outer solution is given by a surface distribution of mass sources and dipoles. Coefficients are evaluated by matching the inner expansion of the outer solution with the outer expansion of the inner solution. The leading term in the inner expansion for the normal gravitational field gives the Bouguer formula. The leading term in the expansion for the gravitational potential gives an expression for the perturbation to the geoid. The predictions given by this term are compared with measurements by satellite altimetry. The second-order terms in the expansion for the gravitational field are required to predict the gravity anomaly at a continental margin. The results are compared with observations.

Selection of nontarget arthropod taxa for field research on transgenic insecticidal crops: using empirical data and statistical power.

PubMed

Prasifka, J R; Hellmich, R L; Dively, G P; Higgins, L S; Dixon, P M; Duan, J J

2008-02-01

One of the possible adverse effects of transgenic insecticidal crops is the unintended decline in the abundance of nontarget arthropods. Field trials designed to evaluate potential nontarget effects can be more complex than expected because decisions to conduct field trials and the selection of taxa to include are not always guided by the results of laboratory tests. Also, recent studies emphasize the potential for indirect effects (adverse impacts to nontarget arthropods without feeding directly on plant tissues), which are difficult to predict because of interactions among nontarget arthropods, target pests, and transgenic crops. As a consequence, field studies may attempt to monitor expansive lists of arthropod taxa, making the design of such broad studies more difficult and reducing the likelihood of detecting any negative effects that might be present. To improve the taxonomic focus and statistical rigor of future studies, existing field data and corresponding power analysis may provide useful guidance. Analysis of control data from several nontarget field trials using repeated-measures designs suggests that while detection of small effects may require considerable increases in replication, there are taxa from different ecological roles that are sampled effectively using standard methods. The use of statistical power to guide selection of taxa for nontarget trials reflects scientists' inability to predict the complex interactions among arthropod taxa, particularly when laboratory trials fail to provide guidance on which groups are more likely to be affected. However, scientists still may exercise judgment, including taxa that are not included in or supported by power analyses.

Machine learning molecular dynamics for the simulation of infrared spectra.

PubMed

Gastegger, Michael; Behler, Jörg; Marquetand, Philipp

2017-10-01

Machine learning has emerged as an invaluable tool in many research areas. In the present work, we harness this power to predict highly accurate molecular infrared spectra with unprecedented computational efficiency. To account for vibrational anharmonic and dynamical effects - typically neglected by conventional quantum chemistry approaches - we base our machine learning strategy on ab initio molecular dynamics simulations. While these simulations are usually extremely time consuming even for small molecules, we overcome these limitations by leveraging the power of a variety of machine learning techniques, not only accelerating simulations by several orders of magnitude, but also greatly extending the size of systems that can be treated. To this end, we develop a molecular dipole moment model based on environment dependent neural network charges and combine it with the neural network potential approach of Behler and Parrinello. Contrary to the prevalent big data philosophy, we are able to obtain very accurate machine learning models for the prediction of infrared spectra based on only a few hundreds of electronic structure reference points. This is made possible through the use of molecular forces during neural network potential training and the introduction of a fully automated sampling scheme. We demonstrate the power of our machine learning approach by applying it to model the infrared spectra of a methanol molecule, n -alkanes containing up to 200 atoms and the protonated alanine tripeptide, which at the same time represents the first application of machine learning techniques to simulate the dynamics of a peptide. In all of these case studies we find an excellent agreement between the infrared spectra predicted via machine learning models and the respective theoretical and experimental spectra.

Piezoelectric monolayers as nonlinear energy harvesters.

PubMed

López-Suárez, Miquel; Pruneda, Miguel; Abadal, Gabriel; Rurali, Riccardo

2014-05-02

We study the dynamics of h-BN monolayers by first performing ab-initio calculations of the deformation potential energy and then solving numerically a Langevine-type equation to explore their use in nonlinear vibration energy harvesting devices. An applied compressive strain is used to drive the system into a nonlinear bistable regime, where quasi-harmonic vibrations are combined with low-frequency swings between the minima of a double-well potential. Due to its intrinsic piezoelectric response, the nonlinear mechanical harvester naturally provides an electrical power that is readily available or can be stored by simply contacting the monolayer at its ends. Engineering the induced nonlinearity, a 20 nm2 device is predicted to harvest an electrical power of up to 0.18 pW for a noisy vibration of 5 pN.

Mechanical work for step-to-step transitions is a major determinant of the metabolic cost of human walking.

PubMed

Donelan, J Maxwell; Kram, Rodger; Kuo, Arthur D

2002-12-01

In the single stance phase of walking, center of mass motion resembles that of an inverted pendulum. Theoretically, mechanical work is not necessary for producing the pendular motion, but work is needed to redirect the center of mass velocity from one pendular arc to the next during the transition between steps. A collision model predicts a rate of negative work proportional to the fourth power of step length. Positive work is required to restore the energy lost, potentially exacting a proportional metabolic cost. We tested these predictions with humans (N=9) walking over a range of step lengths (0.4-1.1 m) while keeping step frequency fixed at 1.8 Hz. We measured individual limb external mechanical work using force plates, and metabolic rate using indirect calorimetry. As predicted, average negative and positive external mechanical work rates increased with the fourth power of step length (from 1 W to 38 W; r(2)=0.96). Metabolic rate also increased with the fourth power of step length (from 7 W to 379 W; r(2)=0.95), and linearly with mechanical work rate. Mechanical work for step-to-step transitions, rather than pendular motion itself, appears to be a major determinant of the metabolic cost of walking.

Rainfall prediction using fuzzy inference system for preliminary micro-hydro power plant planning

NASA Astrophysics Data System (ADS)

Suprapty, B.; Malani, R.; Minardi, J.

2018-04-01

East Kalimantan is a very rich area with water sources, in the form of river streams that branch to the remote areas. The conditions of natural potency like this become alternative solution for area that has not been reached by the availability of electric energy from State Electricity Company. The river water in selected location (catchment area) which is channelled to the canal, pipeline or penstock can be used to drive the waterwheel or turbine. The amount of power obtained depends on the volume/water discharge and headwater (the effective height between the reservoir and the turbine). The water discharge is strongly influenced by the amount of rainfall. Rainfall is the amount of water falling on the flat surface for a certain period measured, in units of mm3, above the horizontal surface in the absence of evaporation, run-off and infiltration. In this study, the prediction of rainfall is done in the area of East Kalimantan which has 13 watersheds which, in principle, have the potential for the construction of Micro Hydro Power Plant. Rainfall time series data is modelled by using AR (Auto Regressive) Model based on FIS (Fuzzy Inference System). The FIS structure of the training results is then used to predict the next two years rainfall.

Power Management for Fuel Cell and Battery Hybrid Unmanned Aerial Vehicle Applications

NASA Astrophysics Data System (ADS)

Stein, Jared Robert

As electric powered unmanned aerial vehicles enter a new age of commercial viability, market opportunities in the small UAV sector are expanding. Extending UAV flight time through a combination of fuel cell and battery technologies enhance the scope of potential applications. A brief survey of UAV history provides context and examples of modern day UAVs powered by fuel cells are given. Conventional hybrid power system management employs DC-to-DC converters to control the power split between battery and fuel cell. In this study, a transistor replaces the DC-to-DC converter which lowers weight and cost. Simulation models of a lithium ion battery and a proton exchange membrane fuel cell are developed and integrated into a UAV power system model. Flight simulations demonstrate the operation of the transistor-based power management scheme and quantify the amount of hydrogen consumed by a 5.5 kg fixed wing UAV during a six hour flight. Battery power assists the fuel cell during high throttle periods but may also augment fuel cell power during cruise flight. Simulations demonstrate a 60 liter reduction in hydrogen consumption when battery power assists the fuel cell during cruise flight. Over the full duration of the flight, averaged efficiency of the power system exceeds 98%. For scenarios where inflight battery recharge is desirable, a constant current battery charger is integrated into the UAV power system. Simulation of inflight battery recharge is performed. Design of UAV hybrid power systems must consider power system weight against potential flight time. Data from the flight simulations are used to identify a simple formula that predicts flight time as a function of energy stored onboard the modeled UAV. A small selection of commercially available batteries, fuel cells, and compressed air storage tanks are listed to characterize the weight of possible systems. The formula is then used in conjunction with the weight data to generate a graph of power system weight versus potential flight times. Combinations of the listed batteries, fuel cells, and storage tanks are plotted on the graph to evaluate various hybrid power system configurations.

DR2DI: a powerful computational tool for predicting novel drug-disease associations

NASA Astrophysics Data System (ADS)

Lu, Lu; Yu, Hua

2018-05-01

Finding the new related candidate diseases for known drugs provides an effective method for fast-speed and low-risk drug development. However, experimental identification of drug-disease associations is expensive and time-consuming. This motivates the need for developing in silico computational methods that can infer true drug-disease pairs with high confidence. In this study, we presented a novel and powerful computational tool, DR2DI, for accurately uncovering the potential associations between drugs and diseases using high-dimensional and heterogeneous omics data as information sources. Based on a unified and extended similarity kernel framework, DR2DI inferred the unknown relationships between drugs and diseases using Regularized Kernel Classifier. Importantly, DR2DI employed a semi-supervised and global learning algorithm which can be applied to uncover the diseases (drugs) associated with known and novel drugs (diseases). In silico global validation experiments showed that DR2DI significantly outperforms recent two approaches for predicting drug-disease associations. Detailed case studies further demonstrated that the therapeutic indications and side effects of drugs predicted by DR2DI could be validated by existing database records and literature, suggesting that DR2DI can be served as a useful bioinformatic tool for identifying the potential drug-disease associations and guiding drug repositioning. Our software and comparison codes are freely available at https://github.com/huayu1111/DR2DI.

DR2DI: a powerful computational tool for predicting novel drug-disease associations

NASA Astrophysics Data System (ADS)

Lu, Lu; Yu, Hua

2018-04-01

Finding the new related candidate diseases for known drugs provides an effective method for fast-speed and low-risk drug development. However, experimental identification of drug-disease associations is expensive and time-consuming. This motivates the need for developing in silico computational methods that can infer true drug-disease pairs with high confidence. In this study, we presented a novel and powerful computational tool, DR2DI, for accurately uncovering the potential associations between drugs and diseases using high-dimensional and heterogeneous omics data as information sources. Based on a unified and extended similarity kernel framework, DR2DI inferred the unknown relationships between drugs and diseases using Regularized Kernel Classifier. Importantly, DR2DI employed a semi-supervised and global learning algorithm which can be applied to uncover the diseases (drugs) associated with known and novel drugs (diseases). In silico global validation experiments showed that DR2DI significantly outperforms recent two approaches for predicting drug-disease associations. Detailed case studies further demonstrated that the therapeutic indications and side effects of drugs predicted by DR2DI could be validated by existing database records and literature, suggesting that DR2DI can be served as a useful bioinformatic tool for identifying the potential drug-disease associations and guiding drug repositioning. Our software and comparison codes are freely available at https://github.com/huayu1111/DR2DI.

High-Efficiency Nested Hall Thrusters for Robotic Solar System Exploration

NASA Technical Reports Server (NTRS)

Hofer, Richard R.

2013-01-01

This work describes the scaling and design attributes of Nested Hall Thrusters (NHT) with extremely large operational envelopes, including a wide range of throttleability in power and specific impulse at high efficiency (>50%). NHTs have the potential to provide the game changing performance, powerprocessing capabilities, and cost effectiveness required to enable missions that cannot otherwise be accomplished. NHTs were first identified in the electric propulsion community as a path to 100- kW class thrusters for human missions. This study aimed to identify the performance capabilities NHTs can provide for NASA robotic and human missions, with an emphasis on 10-kW class thrusters well-suited for robotic exploration. A key outcome of this work has been the identification of NHTs as nearly constant-efficiency devices over large power throttling ratios, especially in direct-drive power systems. NHT systems sized for robotic solar system exploration are predicted to be capable of high-efficiency operation over nearly their entire power throttling range. A traditional Annular Hall Thruster (AHT) consists of a single annular discharge chamber where the propellant is ionized and accelerated. In an NHT, multiple annular channels are concentrically stacked. The channels can be operated in unison or individually depending on the available power or required performance. When throttling an AHT, performance must be sacrificed since a single channel cannot satisfy the diverse design attributes needed to maintain high thrust efficiency. NHTs can satisfy these requirements by varying which channels are operated and thereby offer significant benefits in terms of thruster performance, especially under deep power throttling conditions where the efficiency of an AHT suffers since a single channel can only operate efficiently (>50%) over a narrow power throttling ratio (3:1). Designs for 10-kW class NHTs were developed and compared with AHT systems. Power processing systems were considered using either traditional Power Processing Units (PPU) or Direct Drive Units (DDU). In a PPU-based system, power from the solar arrays is transformed from the low voltage of the arrays to the high voltage needed by the thruster. In a DDU-based system, power from the solar arrays is fed to the thruster without conversion. DDU-based systems are attractive for their simplicity since they eliminate the most complex and expensive part of the propulsion system. The results point to the strong potential of NHTs operating with either PPUs or DDUs to benefit robotic and human missions through their unprecedented power and specific impulse throttling capabilities. NHTs coupled to traditional PPUs are predicted to offer high-efficiency (>50%) power throttling ratios 320% greater than present capabilities, while NHTs with direct-drive power systems (DDU) could exceed existing capabilities by 340%. Because the NHT-DDU approach is implicitly low-cost, NHT-DDU technology has the potential to radically reduce the cost of SEP-enabled NASA missions while simultaneously enabling unprecedented performance capability.

Stochastic Short-term High-resolution Prediction of Solar Irradiance and Photovoltaic Power Output

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

Melin, Alexander M.; Olama, Mohammed M.; Dong, Jin

The increased penetration of solar photovoltaic (PV) energy sources into electric grids has increased the need for accurate modeling and prediction of solar irradiance and power production. Existing modeling and prediction techniques focus on long-term low-resolution prediction over minutes to years. This paper examines the stochastic modeling and short-term high-resolution prediction of solar irradiance and PV power output. We propose a stochastic state-space model to characterize the behaviors of solar irradiance and PV power output. This prediction model is suitable for the development of optimal power controllers for PV sources. A filter-based expectation-maximization and Kalman filtering mechanism is employed tomore » estimate the parameters and states in the state-space model. The mechanism results in a finite dimensional filter which only uses the first and second order statistics. The structure of the scheme contributes to a direct prediction of the solar irradiance and PV power output without any linearization process or simplifying assumptions of the signal’s model. This enables the system to accurately predict small as well as large fluctuations of the solar signals. The mechanism is recursive allowing the solar irradiance and PV power to be predicted online from measurements. The mechanism is tested using solar irradiance and PV power measurement data collected locally in our lab.« less

Numerical simulations of electric potential field for alternating current potential drop associated with surface cracks in low-alloy steel nuclear material

NASA Astrophysics Data System (ADS)

Yeh, Chun-Ping; Huang, Jiunn-Yuan

2018-04-01

Low-alloy steels used as structural materials in nuclear power plants are subjected to cyclic stresses during power plant operations. As a result, cracks may develop and propagate through the material. The alternating current potential drop technique is used to measure the lengths of cracks in metallic components. The depth of the penetration of the alternating current is assumed to be small compared to the crack length. This assumption allows the adoption of the unfolding technique to simplify the problem to a surface Laplacian field. The numerical modelling of the electric potential and current density distribution prediction model for a compact tension specimen and the unfolded crack model are presented in this paper. The goal of this work is to conduct numerical simulations to reduce deviations occurring in the crack length measurements. Numerical simulations were conducted on AISI 4340 low-alloy steel with different crack lengths to evaluate the electric potential distribution. From the simulated results, an optimised position for voltage measurements in the crack region was proposed.

Wireless Medical Devices for MRI-Guided Interventions

NASA Astrophysics Data System (ADS)

Venkateswaran, Madhav

Wireless techniques can play an important role in next-generation, image-guided surgical techniques with integration strategies being the key. We present our investigations on three wireless applications. First, we validate a position and orientation independent method to noninvasively monitor wireless power delivery using current perturbation measurements of switched load modulation of the RF carrier. This is important for safe and efficient powering without using bulky batteries or invasive cables. Use of MRI transmit RF pulses for simultaneous powering is investigated in the second part. We develop system models for the MRI transmit chain, wireless powering circuits and a typical load. Detailed analysis and validation of nonlinear and cascaded modeling strategies are performed, useful for decoupled optimization of the harvester coil and RF-DC converter. MRI pulse sequences are investigated for suitability for simultaneous powering. Simulations indicate that a 1.8V, 2 mA load can be powered with a 100% duty cycle using a 30° fGRE sequence, despite the RF duty cycle being 44 mW for a 30° flip angle, consistent with model predictions. Investigations on imaging artifacts indicates that distortion is mostly restricted to within the physical span of the harvester coil in the imaging volume, with the homogeneous B1+ transmit field providing positioning flexibility to minimize this for simultaneous powering. The models are potentially valuable in designing wireless powering solutions for implantable devices with simultaneous real-time imaging in MRI-guided surgical suites. Finally in the last section, we model endovascular MRI coil coupling during RF transmit. FEM models for a series-resonant multimode coil and quadrature birdcage coil fields are developed and computationally efficient, circuit and full-wave simulations are used to model inductive coupling. The Bloch Siegert B1 mapping sequence is used for validating at 24, 28 and 34 microT background excitation. Quantitative performance metrics are successfully predicted and the role of simulation in geometric optimization is demonstrated. In a pig study, we demonstrate navigation of a catheter, with tip-tracking and high-resolution intravascular imaging, through the vasculature into the heart, followed by contextual visualization. A potentially significant application is in MRI-guided cardiac ablation procedures.

Computing general-relativistic effects from Newtonian N-body simulations: Frame dragging in the post-Friedmann approach

NASA Astrophysics Data System (ADS)

Bruni, Marco; Thomas, Daniel B.; Wands, David

2014-02-01

We present the first calculation of an intrinsically relativistic quantity, the leading-order correction to Newtonian theory, in fully nonlinear cosmological large-scale structure studies. Traditionally, nonlinear structure formation in standard ΛCDM cosmology is studied using N-body simulations, based on Newtonian gravitational dynamics on an expanding background. When one derives the Newtonian regime in a way that is a consistent approximation to the Einstein equations, the first relativistic correction to the usual Newtonian scalar potential is a gravitomagnetic vector potential, giving rise to frame dragging. At leading order, this vector potential does not affect the matter dynamics, thus it can be computed from Newtonian N-body simulations. We explain how we compute the vector potential from simulations in ΛCDM and examine its magnitude relative to the scalar potential, finding that the power spectrum of the vector potential is of the order 10-5 times the scalar power spectrum over the range of nonlinear scales we consider. On these scales the vector potential is up to two orders of magnitudes larger than the value predicted by second-order perturbation theory extrapolated to the same scales. We also discuss some possible observable effects and future developments.

Analysis of Application Power and Schedule Composition in a High Performance Computing Environment

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

Elmore, Ryan; Gruchalla, Kenny; Phillips, Caleb

As the capacity of high performance computing (HPC) systems continues to grow, small changes in energy management have the potential to produce significant energy savings. In this paper, we employ an extensive informatics system for aggregating and analyzing real-time performance and power use data to evaluate energy footprints of jobs running in an HPC data center. We look at the effects of algorithmic choices for a given job on the resulting energy footprints, and analyze application-specific power consumption, and summarize average power use in the aggregate. All of these views reveal meaningful power variance between classes of applications as wellmore » as chosen methods for a given job. Using these data, we discuss energy-aware cost-saving strategies based on reordering the HPC job schedule. Using historical job and power data, we present a hypothetical job schedule reordering that: (1) reduces the facility's peak power draw and (2) manages power in conjunction with a large-scale photovoltaic array. Lastly, we leverage this data to understand the practical limits on predicting key power use metrics at the time of submission.« less

Hexatic smectic phase with algebraically decaying bond-orientational order

NASA Astrophysics Data System (ADS)

Agosta, Lorenzo; Metere, Alfredo; Dzugutov, Mikhail

2018-05-01

The hexatic phase predicted by the theories of two-dimensional melting is characterized by the power-law decay of the orientational correlations, whereas the in-layer bond orientational order in all the hexatic smectic phases observed so far was found to be long range. We report a hexatic smectic phase where the in-layer bond orientational correlations decay algebraically, in quantitative agreement with the hexatic ordering predicted by the theory for two dimensions. The phase was formed in a molecular dynamics simulation of a one-component system of particles interacting via a spherically symmetric potential. The present results thus demonstrate that the theoretically predicted two-dimensional hexatic order can exist in a three-dimensional system.

Weighted Feature Significance: A Simple, Interpretable Model of Compound Toxicity Based on the Statistical Enrichment of Structural Features

PubMed Central

Huang, Ruili; Southall, Noel; Xia, Menghang; Cho, Ming-Hsuang; Jadhav, Ajit; Nguyen, Dac-Trung; Inglese, James; Tice, Raymond R.; Austin, Christopher P.

2009-01-01

In support of the U.S. Tox21 program, we have developed a simple and chemically intuitive model we call weighted feature significance (WFS) to predict the toxicological activity of compounds, based on the statistical enrichment of structural features in toxic compounds. We trained and tested the model on the following: (1) data from quantitative high–throughput screening cytotoxicity and caspase activation assays conducted at the National Institutes of Health Chemical Genomics Center, (2) data from Salmonella typhimurium reverse mutagenicity assays conducted by the U.S. National Toxicology Program, and (3) hepatotoxicity data published in the Registry of Toxic Effects of Chemical Substances. Enrichments of structural features in toxic compounds are evaluated for their statistical significance and compiled into a simple additive model of toxicity and then used to score new compounds for potential toxicity. The predictive power of the model for cytotoxicity was validated using an independent set of compounds from the U.S. Environmental Protection Agency tested also at the National Institutes of Health Chemical Genomics Center. We compared the performance of our WFS approach with classical classification methods such as Naive Bayesian clustering and support vector machines. In most test cases, WFS showed similar or slightly better predictive power, especially in the prediction of hepatotoxic compounds, where WFS appeared to have the best performance among the three methods. The new algorithm has the important advantages of simplicity, power, interpretability, and ease of implementation. PMID:19805409

Neuronal synchrony and the relation between the blood-oxygen-level dependent response and the local field potential

PubMed Central

Nguyen, Mai; Winawer, Jonathan

2017-01-01

The most widespread measures of human brain activity are the blood-oxygen-level dependent (BOLD) signal and surface field potential. Prior studies report a variety of relationships between these signals. To develop an understanding of how to interpret these signals and the relationship between them, we developed a model of (a) neuronal population responses and (b) transformations from neuronal responses into the functional magnetic resonance imaging (fMRI) BOLD signal and electrocorticographic (ECoG) field potential. Rather than seeking a transformation between the two measures directly, this approach interprets each measure with respect to the underlying neuronal population responses. This model accounts for the relationship between BOLD and ECoG data from human visual cortex in V1, V2, and V3, with the model predictions and data matching in three ways: across stimuli, the BOLD amplitude and ECoG broadband power were positively correlated, the BOLD amplitude and alpha power (8–13 Hz) were negatively correlated, and the BOLD amplitude and narrowband gamma power (30–80 Hz) were uncorrelated. The two measures provide complementary information about human brain activity, and we infer that features of the field potential that are uncorrelated with BOLD arise largely from changes in synchrony, rather than level, of neuronal activity. PMID:28742093

Predictive models of alcohol use based on attitudes and individual values.

PubMed

García del Castillo Rodríguez, José A; López-Sánchez, Carmen; Quiles Soler, M Carmen; García del Castillo-López, Alvaro; Gázquez Pertusa, Mónica; Marzo Campos, Juan Carlos; Inglés, Candido J

2013-01-01

Two predictive models are developed in this article: the first is designed to predict people's attitudes to alcoholic drinks, while the second sets out to predict the use of alcohol in relation to selected individual values. University students (N = 1,500) were recruited through stratified sampling based on sex and academic discipline. The questionnaire used obtained information on participants' alcohol use, attitudes and personal values. The results show that the attitudes model correctly classifies 76.3% of cases. Likewise, the model for level of alcohol use correctly classifies 82% of cases. According to our results, we can conclude that there are a series of individual values that influence drinking and attitudes to alcohol use, which therefore provides us with a potentially powerful instrument for developing preventive intervention programs.

Bisubstrate inhibition: Theory and application to N-acetyltransferases.

PubMed

Yu, Michael; Magalhães, Maria L B; Cook, Paul F; Blanchard, John S

2006-12-12

Bisubstrate inhibitors represent a potentially powerful group of compounds that have found significant therapeutic utility. Although these compounds have been synthesized and tested against a number of enzymes that catalyze sequential bireactant reactions, the detailed theory for predicting the expected patterns of inhibition against the two substrates for various bireactant kinetic mechanisms has, heretofore, not been presented. We have derived the rate equations for all likely sequential bireactant mechanisms and provide two examples in which bisubstrate inhibitors allow the kinetic mechanism to be determined. Bisubstrate inhibitor kinetics is a powerful diagnostic for the determination of kinetic mechanisms.

Allometric scaling in-vitro

NASA Astrophysics Data System (ADS)

Ahluwalia, Arti

2017-02-01

About two decades ago, West and coworkers established a model which predicts that metabolic rate follows a three quarter power relationship with the mass of an organism, based on the premise that tissues are supplied nutrients through a fractal distribution network. Quarter power scaling is widely considered a universal law of biology and it is generally accepted that were in-vitro cultures to obey allometric metabolic scaling, they would have more predictive potential and could, for instance, provide a viable substitute for animals in research. This paper outlines a theoretical and computational framework for establishing quarter power scaling in three-dimensional spherical constructs in-vitro, starting where fractal distribution ends. Allometric scaling in non-vascular spherical tissue constructs was assessed using models of Michaelis Menten oxygen consumption and diffusion. The models demonstrate that physiological scaling is maintained when about 5 to 60% of the construct is exposed to oxygen concentrations less than the Michaelis Menten constant, with a significant concentration gradient in the sphere. The results have important implications for the design of downscaled in-vitro systems with physiological relevance.

Prediction of Francis Turbine Prototype Part Load Pressure and Output Power Fluctuations with Hydroelectric Model

NASA Astrophysics Data System (ADS)

Alligné, S.; Nicolet, C.; Béguin, A.; Landry, C.; Gomes, J.; Avellan, F.

2017-04-01

The prediction of pressure and output power fluctuations amplitudes on Francis turbine prototype is a challenge for hydro-equipment industry since it is subjected to guarantees to ensure smooth and reliable operation of the hydro units. The European FP7 research project Hyperbole aims to setup a methodology to transpose the pressure fluctuations induced by the cavitation vortex rope from the reduced scale model to the prototype generating units. A Francis turbine unit of 444MW with a specific speed value of ν = 0.29, is considered as case study. A SIMSEN model of the power station including electrical system, controllers, rotating train and hydraulic system with transposed draft tube excitation sources is setup. Based on this model, a frequency analysis of the hydroelectric system is performed for all technologies to analyse potential interactions between hydraulic excitation sources and electrical components. Three technologies have been compared: the classical fixed speed configuration with Synchronous Machine (SM) and the two variable speed technologies which are Doubly Fed Induction Machine (DFIM) and Full Size Frequency Converter (FSFC).

Study of Hydrokinetic Turbine Arrays with Large Eddy Simulation

NASA Astrophysics Data System (ADS)

Sale, Danny; Aliseda, Alberto

2014-11-01

Marine renewable energy is advancing towards commercialization, including electrical power generation from ocean, river, and tidal currents. The focus of this work is to develop numerical simulations capable of predicting the power generation potential of hydrokinetic turbine arrays-this includes analysis of unsteady and averaged flow fields, turbulence statistics, and unsteady loadings on turbine rotors and support structures due to interaction with rotor wakes and ambient turbulence. The governing equations of large-eddy-simulation (LES) are solved using a finite-volume method, and the presence of turbine blades are approximated by the actuator-line method in which hydrodynamic forces are projected to the flow field as a body force. The actuator-line approach captures helical wake formation including vortex shedding from individual blades, and the effects of drag and vorticity generation from the rough seabed surface are accounted for by wall-models. This LES framework was used to replicate a previous flume experiment consisting of three hydrokinetic turbines tested under various operating conditions and array layouts. Predictions of the power generation, velocity deficit and turbulence statistics in the wakes are compared between the LES and experimental datasets.

Ensemble Data Assimilation of Photovoltaic Power Information in the Convection-permitting High-Resolution Model COSMO-DE

NASA Astrophysics Data System (ADS)

Declair, Stefan; Saint-Drenan, Yves-Marie; Potthast, Roland

2017-04-01

Determining the amount of weather dependent renewable energy is a demanding task for transmission system operators (TSOs) and wind and photovoltaic (PV) prediction errors require the use of reserve power, which generate costs and can - in extreme cases - endanger the security of supply. In the project EWeLiNE funded by the German government, the German Weather Service and the Fraunhofer Institute on Wind Energy and Energy System Technology develop innovative weather- and power forecasting models and tools for grid integration of weather dependent renewable energy. The key part in energy prediction process chains is the numerical weather prediction (NWP) system. Irradiation forecasts from NWP systems are however subject to several sources of error. For PV power prediction, weaknesses of the NWP model to correctly forecast i.e. low stratus, absorption of condensed water or aerosol optical depths are the main sources of errors. Inaccurate radiation schemes (i.e. the two-stream parametrization) are also known as a deficit of NWP systems with regard to irradiation forecast. To mitigate errors like these, latest observations can be used in a pre-processing technique called data assimilation (DA). In DA, not only the initial fields are provided, but the model is also synchronized with reality - the observations - and hence forecast errors are reduced. Besides conventional observation networks like radiosondes, synoptic observations or air reports of wind, pressure and humidity, the number of observations measuring meteorological information indirectly by means of remote sensing such as satellite radiances, radar reflectivities or GPS slant delays strongly increases. Numerous PV plants installed in Germany potentially represent a dense meteorological network assessing irradiation through their power measurements. Forecast accuracy may thus be enhanced by extending the observations in the assimilation by this new source of information. PV power plants can provide information on clouds, aerosol optical depth or low stratus in terms of remote sensing: the power output is strongly dependent on perturbations along the slant between sun position and PV panel. Since these data are not limited to the vertical column above or below the detector, it may thus complement satellite data and compensate weaknesses in the radiation scheme. In this contribution, the used DA technique (Local Ensemble Transform Kalman Filter, LETKF) is shortly sketched. Furthermore, the computation of the model power equivalents is described and first results are presented and discussed.

Ion acceleration in a helicon source due to the self-bias effect

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

Wiebold, Matt; Sung, Yung-Ta; Scharer, John E.

2012-05-15

Time-averaged plasma potential differences up to 165 V over several hundred Debye lengths are observed in low pressure (p{sub n} < 1 mTorr) expanding argon plasmas in the Madison Helicon eXperiment (MadHeX). The potential gradient leads to ion acceleration greater than that predicted by ambipolar expansion, exceeding E{sub i} Almost-Equal-To 7 kT{sub e} in some cases. RF power up to 500 W at 13.56 MHz is supplied to a half-turn, double-helix antenna in the presence of a nozzle magnetic field, adjustable up to 1 kG. A retarding potential analyzer (RPA) measures the ion energy distribution function (IEDF) and a sweptmore » emissive probe measures the plasma potential. Single and double probes measure the electron density and temperature. Two distinct mode hops, the capacitive-inductive (E-H) and inductive-helicon (H-W) transitions, are identified by jumps in density as RF power is increased. In the capacitive (E) mode, large fluctuations of the plasma potential (V{sub p-p} Greater-Than-Or-Equivalent-To 140V, V{sub p-p}/V{sub p} Almost-Equal-To 150%) exist at the RF frequency and its harmonics. The more mobile electrons can easily respond to RF-timescale gradients in the plasma potential whereas the inertially constrained ions cannot, leading to an initial flux imbalance and formation of a self-bias voltage between the source and expansion chambers. In the capacitive mode, the ion acceleration is not well described by an ambipolar relation, while in the inductive and helicon modes the ion acceleration more closely follows an ambipolar relation. The scaling of the potential gradient with the argon flow rate and RF power are investigated, with the largest potential gradients observed for the lowest flow rates in the capacitive mode. The magnitude of the self-bias voltage agrees with that predicted for RF self-bias at a wall. Rapid fluctuations in the plasma potential result in a time-dependent axial electron flux that acts to 'neutralize' the accelerated ion population, resulting in a zero net time-averaged current through the acceleration region when an insulating upstream boundary condition is enforced. Grounding the upstream endplate increases the self-bias voltage compared to a floating endplate.« less

The effect of radio-frequency self bias on ion acceleration in expanding argon plasmas in helicon sources

NASA Astrophysics Data System (ADS)

Wiebold, Matthew D.

Time-averaged plasma potential differences up to ˜ 165 V over several hundred Debye lengths are observed in low pressure (pn < 1 mTorr) expanding argon plasmas in the Madison Helicon Experiment. The potential gradient leads to ion acceleration exceeding Ei ≈ 7 kTe in some cases. Up to 1 kW of 13.56 MHz RF power is supplied to a half-turn, double-helix antenna in the presence of a nozzle magnetic field up to 1 kG. An RPA measures the IEDF and an emissive probe measures the plasma potential. Single and double probes measure the electron density and temperature. Two distinct mode hops, the capacitive-inductive (E-H) and inductive-helicon (H-W) transitions, are identified by jumps in electron density as RF power is increased. In the capacitive mode, large fluctuations of the plasma potential (Vp--p ≳ 140 V, Vp--p/Vp ≈ 150%) exist at the RF frequency, leading to formation of a self-bias voltage. The mobile electrons can flow from the upstream region during an RF cycle whereas ions cannot, leading to an initial imbalance of flux, and the self-bias voltage builds as a result. The plasma potential in the expansion chamber is held near the floating potential for argon (Vp ≈ 5kTe/e). In the capacitive mode, the ion acceleration is not well described by an ambipolar relation. The accelerated population decay is consistent with that predicted by charge-exchange collisions. Grounding the upstream endplate increases the self-bias voltage compared to a floating endplate. In the inductive and helicon modes, the ion acceleration more closely follows an ambipolar relation, a result of decreased capacitive coupling due to the decreased RF skin depth. The scaling of the potential gradient with the argon flow rate, magnetic field and RF power are investigated, with the highest potential gradients observed for the lowest flow rates in the capacitive mode. The magnitude of the self-bias voltage agrees well with that predicted for RF sheaths. Use of the self-bias effect in a plasma thruster is explored, possibly for a low thrust, high specific impulse mode in a multi-mode helicon thruster. This work could also explain similar potential gradients in expanding helicon plasmas that are ascribed to double layer formation in the literature.

Radioisotope thermal photovoltaic application of the GaSb solar cell

NASA Technical Reports Server (NTRS)

Morgan, M. D.; Horne, W. E.; Day, A. C.

1991-01-01

An examination of a RTVP (radioisotopic thermophotovoltaic) conceptual design has shown a high potential for power densities well above those achievable with radioisotopic thermoelectric generator (RTG) systems. An efficiency of 14.4 percent and system specific power of 9.25 watts/kg were predicted for a system with sixteen GPHS (general purpose heat source) sources operating at 1100 C. The models also showed a 500 watt system power by the strontium-90 isotope at 1200 C at an efficiency of 17.0 percent and a system specific power of 11.8 watts/kg. The key to this level of performance is a high-quality photovoltaic cell with narrow bandgap and a reflective rear contact. Recent work at Boeing on GaSb cells and transparent back GaAs cells indicate that such a cell is well within reach.

SINGLE NEURON ACTIVITY AND THETA MODULATION IN POSTRHINAL CORTEX DURING VISUAL OBJECT DISCRIMINATION

PubMed Central

Furtak, Sharon C.; Ahmed, Omar J.; Burwell, Rebecca D.

2012-01-01

Postrhinal cortex, the rodent homolog of the primate parahippocampal cortex, processes spatial and contextual information. Our hypothesis of postrhinal function is that it serves to encode context, in part, by forming representations that link objects to places. We recorded postrhinal neuronal activity and local field potentials (LFPs) in rats trained on a two-choice, visual discrimination task. As predicted, a large proportion of postrhinal neurons signaled object-location conjunctions. In addition, postrhinal LFPs exhibited strong oscillatory rhythms in the theta band, and many postrhinal neurons were phase locked to theta. Although correlated with running speed, theta power was lower than predicted by speed alone immediately before and after choice. However, theta power was significantly increased following incorrect decisions, suggesting a role in signaling error. These findings provide evidence that postrhinal cortex encodes representations that link objects to places and suggest that postrhinal theta modulation extends to cognitive as well as spatial functions. PMID:23217745

Enhanced thermoelectric performance of graphene nanoribbon-based devices

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

Hossain, Md Sharafat, E-mail: hossain@student.unimelb.edu.au; Huynh, Duc Hau; Nguyen, Phuong Duc

There have been numerous theoretical studies on exciting thermoelectric properties of graphene nano-ribbons (GNRs); however, most of these studies are mainly based on simulations. In this work, we measure and characterize the thermoelectric properties of GNRs and compare the results with theoretical predictions. Our experimental results verify that nano-structuring and patterning graphene into nano-ribbons significantly enhance its thermoelectric power, confirming previous predictions. Although patterning results in lower conductance (G), the overall power factor (S{sup 2}G) increases for nanoribbons. We demonstrate that edge roughness plays an important role in achieving such an enhanced performance and support it through first principles simulations.more » We show that uncontrolled edge roughness, which is considered detrimental in GNR-based electronic devices, leads to enhanced thermoelectric performance of GNR-based thermoelectric devices. The result validates previously reported theoretical studies of GNRs and demonstrates the potential of GNRs for the realization of highly efficient thermoelectric devices.« less

Topological defect formation and spontaneous symmetry breaking in ion Coulomb crystals.

PubMed

Pyka, K; Keller, J; Partner, H L; Nigmatullin, R; Burgermeister, T; Meier, D M; Kuhlmann, K; Retzker, A; Plenio, M B; Zurek, W H; del Campo, A; Mehlstäubler, T E

2013-01-01

Symmetry breaking phase transitions play an important role in nature. When a system traverses such a transition at a finite rate, its causally disconnected regions choose the new broken symmetry state independently. Where such local choices are incompatible, topological defects can form. The Kibble-Zurek mechanism predicts the defect densities to follow a power law that scales with the rate of the transition. Owing to its ubiquitous nature, this theory finds application in a wide field of systems ranging from cosmology to condensed matter. Here we present the successful creation of defects in ion Coulomb crystals by a controlled quench of the confining potential, and observe an enhanced power law scaling in accordance with numerical simulations and recent predictions. This simple system with well-defined critical exponents opens up ways to investigate the physics of non-equilibrium dynamics from the classical to the quantum regime.

Predictability of Landslide Timing From Quasi-Periodic Precursory Earthquakes

NASA Astrophysics Data System (ADS)

Bell, Andrew F.

2018-02-01

Accelerating rates of geophysical signals are observed before a range of material failure phenomena. They provide insights into the physical processes controlling failure and the basis for failure forecasts. However, examples of accelerating seismicity before landslides are rare, and their behavior and forecasting potential are largely unknown. Here I use a Bayesian methodology to apply a novel gamma point process model to investigate a sequence of quasiperiodic repeating earthquakes preceding a large landslide at Nuugaatsiaq in Greenland in June 2017. The evolution in earthquake rate is best explained by an inverse power law increase with time toward failure, as predicted by material failure theory. However, the commonly accepted power law exponent value of 1.0 is inconsistent with the data. Instead, the mean posterior value of 0.71 indicates a particularly rapid acceleration toward failure and suggests that only relatively short warning times may be possible for similar landslides in future.

Causal inference in economics and marketing.

PubMed

Varian, Hal R

2016-07-05

This is an elementary introduction to causal inference in economics written for readers familiar with machine learning methods. The critical step in any causal analysis is estimating the counterfactual-a prediction of what would have happened in the absence of the treatment. The powerful techniques used in machine learning may be useful for developing better estimates of the counterfactual, potentially improving causal inference.

Causal inference in economics and marketing

PubMed Central

Varian, Hal R.

2016-01-01

This is an elementary introduction to causal inference in economics written for readers familiar with machine learning methods. The critical step in any causal analysis is estimating the counterfactual—a prediction of what would have happened in the absence of the treatment. The powerful techniques used in machine learning may be useful for developing better estimates of the counterfactual, potentially improving causal inference. PMID:27382144

Modified aging of elite athletes revealed by analysis of epigenetic age markers

PubMed Central

Spólnicka, Magdalena; Pośpiech, Ewelina; Adamczyk, Jakub Grzegorz; Freire-Aradas, Ana; Pepłońska, Beata; Zbieć-Piekarska, Renata; Makowska, Żanetta; Pięta, Anna; Lareu, Maria Victoria; Phillips, Christopher; Płoski, Rafał; Żekanowski, Cezary

2018-01-01

Recent progress in epigenomics has led to the development of prediction systems that enable accurate age estimation from DNA methylation data. Our objective was to track responses to intense physical exercise of individual age-correlated DNA methylation markers and to infer their potential impact on the aging processes. The study showed accelerated DNA hypermethylation for two CpG sites in TRIM59 and KLF14. Both markers predicted the investigated elite athletes to be several years older than controls and this effect was more substantial in subjects involved in power sports. Accordingly, the complete 5-CpG model revealed age acceleration of elite athletes (P=1.503x10-7) and the result was more significant amongst power athletes (P=1.051x10-9). The modified methylation of TRIM59 and KLF14 in top athletes may be accounted for by the biological roles played by these genes. Their known anti-tumour and anti-inflammatory activities suggests that intense physical training has a complex influence on aging and potentially launches signalling networks that contribute to the observed lower risk of elite athletes to develop cardiovascular disease and cancer. PMID:29466246

Modified aging of elite athletes revealed by analysis of epigenetic age markers.

PubMed

Spólnicka, Magdalena; Pośpiech, Ewelina; Adamczyk, Jakub Grzegorz; Freire-Aradas, Ana; Pepłońska, Beata; Zbieć-Piekarska, Renata; Makowska, Żanetta; Pięta, Anna; Lareu, Maria Victoria; Phillips, Christopher; Płoski, Rafał; Żekanowski, Cezary; Branicki, Wojciech

2018-02-15

Recent progress in epigenomics has led to the development of prediction systems that enable accurate age estimation from DNA methylation data. Our objective was to track responses to intense physical exercise of individual age-correlated DNA methylation markers and to infer their potential impact on the aging processes. The study showed accelerated DNA hypermethylation for two CpG sites in TRIM59 and KLF14 . Both markers predicted the investigated elite athletes to be several years older than controls and this effect was more substantial in subjects involved in power sports. Accordingly, the complete 5-CpG model revealed age acceleration of elite athletes ( P =1.503x10 -7 ) and the result was more significant amongst power athletes (P=1.051x10 -9 ). The modified methylation of TRIM59 and KLF14 in top athletes may be accounted for by the biological roles played by these genes. Their known anti-tumour and anti-inflammatory activities suggests that intense physical training has a complex influence on aging and potentially launches signalling networks that contribute to the observed lower risk of elite athletes to develop cardiovascular disease and cancer.

Prediction of Wind Energy Resources (PoWER) Users Guide

DTIC Science & Technology

2016-01-01

ARL-TR-7573● JAN 2016 US Army Research Laboratory Prediction of Wind Energy Resources (PoWER) User’s Guide by David P Sauter...not return it to the originator. ARL-TR-7573 ● JAN 2016 US Army Research Laboratory Prediction of Wind Energy Resources (PoWER...2016 2. REPORT TYPE Final 3. DATES COVERED (From - To) 09/2015–11/2015 4. TITLE AND SUBTITLE Prediction of Wind Energy Resources (PoWER) User’s

Analysis and experimental evaluation of shunt active power filter for power quality improvement based on predictive direct power control.

PubMed

Aissa, Oualid; Moulahoum, Samir; Colak, Ilhami; Babes, Badreddine; Kabache, Nadir

2017-10-12

This paper discusses the use of the concept of classical and predictive direct power control for shunt active power filter function. These strategies are used to improve the active power filter performance by compensation of the reactive power and the elimination of the harmonic currents drawn by non-linear loads. A theoretical analysis followed by a simulation using MATLAB/Simulink software for the studied techniques has been established. Moreover, two test benches have been carried out using the dSPACE card 1104 for the classic and predictive DPC control to evaluate the studied methods in real time. Obtained results are presented and compared in this paper to confirm the superiority of the predictive technique. To overcome the pollution problems caused by the consumption of fossil fuels, renewable energies are the alternatives recommended to ensure green energy. In the same context, the tested predictive filter can easily be supplied by a renewable energy source that will give its impact to enhance the power quality.

Predicting and mapping potential Whooping Crane stopover habitat to guide site selection for wind energy projects.

PubMed

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

2014-04-01

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

New Secondary Batteries Utilizing Electronically Conductive Polypyrrole Cathode. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Yeu, Taewhan

1991-01-01

To gain a better understanding of the dynamic behavior in electronically conducting polypyrroles and to provide guidance toward designs of new secondary batteries based on these polymers, two mathematical models are developed; one for the potentiostatically controlled switching behavior of polypyrrole film, and one for the galvanostatically controlled charge/discharge behavior of lithium/polypyrrole secondary battery cell. The first model is used to predict the profiles of electrolyte concentrations, charge states, and electrochemical potentials within the thin polypyrrole film during switching process as functions of applied potential and position. Thus, the detailed mechanisms of charge transport and electrochemical reaction can be understood. Sensitivity analysis is performed for independent parameters, describing the physical and electrochemical characteristic of polypyrrole film, to verify their influences on the model performance. The values of independent parameters are estimated by comparing model predictions with experimental data obtained from identical conditions. The second model is used to predict the profiles of electrolyte concentrations, charge state, and electrochemical potentials within the battery system during charge and discharge processes as functions of time and position. Energy and power densities are estimated from model predictions and compared with existing battery systems. The independent design criteria on the charge and discharge performance of the cell are provided by studying the effects of design parameters.

Resting-state sensorimotor rhythm (SMR) power predicts the ability to up-regulate SMR in an EEG-instrumental conditioning paradigm.

PubMed

Reichert, Johanna Louise; Kober, Silvia Erika; Neuper, Christa; Wood, Guilherme

2015-11-01

Instrumental conditioning of EEG activity (EEG-IC) is a promising method for improvement and rehabilitation of cognitive functions. However, it has been found that even healthy adults are not always able to learn how to regulate their brain activity during EEG-IC. In the present study, the role of a neurophysiological predictor of EEG-IC learning performance, the resting-state power of sensorimotor rhythm (rs-SMR, 12-15Hz), was investigated. Eyes-open and eyes-closed rs-SMR power was assessed before N=28 healthy adults underwent 10 training sessions of instrumental SMR conditioning (ISC), in which participants should learn to voluntarily increase their SMR power by means of audio-visual feedback. A control group of N=19 participants received gamma (40-43Hz) or sham EEG-IC. N=19 of the ISC participants could be classified as "responders" as they were able to increase SMR power during training sessions, while N=9 participants ("non-responders") were not able to increase SMR power. Rs-SMR power in responders before start of ISC was higher in widespread parieto-occipital areas than in non-responders. A discriminant analysis indicated that eyes-open rs-SMR power in a central brain region specifically predicted later ISC performance, but not an increase of SMR in the control group. Together, these findings indicate that rs-SMR power is a specific and easy-to-measure predictor of later ISC learning performance. The assessment of factors that influence the ability to regulate brain activity is of high relevance, as it could be used to avoid potentially frustrating and expensive EEG-IC training sessions for participants who have a low chance of success. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Electrostatic protection of the solar power satellite and rectenna. Part 1: Protection of the solar power satellite

NASA Technical Reports Server (NTRS)

1980-01-01

Several features of the interactions of the Solar Power Satellite (SPS) with its space environment are examined theoretically. The voltages produced at various surfaces due to space plasmas and the plasma leakage currents through the kapton and sapphire solar cell blankets are calculated. At geosynchronous orbit, this parasitic power loss is only 0.7%, and is easily compensated by oversizing. At low Earth orbit, the power loss is potentially much larger (3%), and anomalous arcing is expected for the EOTV high voltage negative surfaces. Preliminary results of a three dimensional self consistent plasma and electric field computer program are presented, confirming the validity of the predictions made from the one dimensional models. Lastly, magnetic shielding of the satellite is considered to reduce the power drain and to protect the solar cells from energetic electron and plasma ion bombardment. It is concluded that minor modifications can allow the SPS to operate safely and efficiently in its space environment. Subsequent design changes will substantially alter the basic conclusions.

Lightweight Damage Tolerant Radiators for In-Space Nuclear Electric Power and Propulsion

NASA Technical Reports Server (NTRS)

Craven, Paul; SanSoucie, Michael P.; Tomboulian, Briana; Rogers, Jan; Hyers, Robert

2014-01-01

Nuclear electric propulsion (NEP) is a promising option for high-speed in-space travel due to the high energy density of nuclear power sources and efficient electric thrusters. Advanced power conversion technologies for converting thermal energy from the reactor to electrical energy at high operating temperatures would benefit from lightweight, high temperature radiator materials. Radiator performance dictates power output for nuclear electric propulsion systems. Pitch-based carbon fiber materials have the potential to offer significant improvements in operating temperature and mass. An effort at the NASA Marshall Space Flight Center to show that woven high thermal conductivity carbon fiber mats can be used to replace standard metal and composite radiator fins to dissipate waste heat from NEP systems is ongoing. The goals of this effort are to demonstrate a proof of concept, to show that a significant improvement of specific power (power/mass) can be achieved, and to develop a thermal model with predictive capabilities. A description of this effort is presented.

AMTEC powered residential furnace and auxiliary power

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

Ivanenok, J.F. III; Sievers, R.K.

1996-12-31

Residential gas furnaces normally rely on utility grid electric power to operate the fans and/or the pumps used to circulate conditioned air or water and they are thus vulnerable to interruptions of utility grid service. Experience has shown that such interruptions can occur during the heating season, and can lead to serious consequences. A gas furnace coupled to an AMTEC conversion system retains the potential to produce heat and electricity (gas lines are seldom interrupted during power outages), and can save approximately $47/heating season compared to a conventional gas furnace. The key to designing a power system is understanding, andmore » predicting, the cell performance characteristics. The three main processes that must be understood and modeled to fully characterize an AMTEC cell are the electro-chemical, sodium vapor flow, and heat transfer. This paper will show the results of the most recent attempt to model the heat transfer in a multi-tube AMTEC cell and then discusses the conceptual design of a self-powered residential furnace.« less

Range prediction for tissue mixtures based on dual-energy CT

NASA Astrophysics Data System (ADS)

Möhler, Christian; Wohlfahrt, Patrick; Richter, Christian; Greilich, Steffen

2016-06-01

The use of dual-energy CT (DECT) potentially decreases range uncertainties in proton and ion therapy treatment planning via determination of the involved physical target quantities. For eventual clinical application, the correct treatment of tissue mixtures and heterogeneities is an essential feature, as they naturally occur within a patient’s CT. Here, we present how existing methods for DECT-based ion-range prediction can be modified in order to incorporate proper mixing behavior on several structural levels. Our approach is based on the factorization of the stopping-power ratio into the relative electron density and the relative stopping number. The latter is confined for tissue between about 0.95 and 1.02 at a therapeutic beam energy of 200 MeV u-1 and depends on the I-value. We show that convenient mixing and averaging properties arise by relating the relative stopping number to the relative cross section obtained by DECT. From this, a maximum uncertainty of the stopping-power ratio prediction below 1% is suggested for arbitrary mixtures of human body tissues.

Reduced frontal theta oscillations indicate altered crossmodal prediction error processing in schizophrenia

PubMed Central

Keil, Julian; Balz, Johanna; Gallinat, Jürgen; Senkowski, Daniel

2016-01-01

Our brain generates predictions about forthcoming stimuli and compares predicted with incoming input. Failures in predicting events might contribute to hallucinations and delusions in schizophrenia (SZ). When a stimulus violates prediction, neural activity that reflects prediction error (PE) processing is found. While PE processing deficits have been reported in unisensory paradigms, it is unknown whether SZ patients (SZP) show altered crossmodal PE processing. We measured high-density electroencephalography and applied source estimation approaches to investigate crossmodal PE processing generated by audiovisual speech. In SZP and healthy control participants (HC), we used an established paradigm in which high- and low-predictive visual syllables were paired with congruent or incongruent auditory syllables. We examined crossmodal PE processing in SZP and HC by comparing differences in event-related potentials and neural oscillations between incongruent and congruent high- and low-predictive audiovisual syllables. In both groups event-related potentials between 206 and 250 ms were larger in high- compared with low-predictive syllables, suggesting intact audiovisual incongruence detection in the auditory cortex of SZP. The analysis of oscillatory responses revealed theta-band (4–7 Hz) power enhancement in high- compared with low-predictive syllables between 230 and 370 ms in the frontal cortex of HC but not SZP. Thus aberrant frontal theta-band oscillations reflect crossmodal PE processing deficits in SZ. The present study suggests a top-down multisensory processing deficit and highlights the role of dysfunctional frontal oscillations for the SZ psychopathology. PMID:27358314

The aerodynamic cost of flight in the short-tailed fruit bat (Carollia perspicillata): comparing theory with measurement

PubMed Central

von Busse, Rhea; Waldman, Rye M.; Swartz, Sharon M.; Voigt, Christian C.; Breuer, Kenneth S.

2014-01-01

Aerodynamic theory has long been used to predict the power required for animal flight, but widely used models contain many simplifications. It has been difficult to ascertain how closely biological reality matches model predictions, largely because of the technical challenges of accurately measuring the power expended when an animal flies. We designed a study to measure flight speed-dependent aerodynamic power directly from the kinetic energy contained in the wake of bats flying in a wind tunnel. We compared these measurements with two theoretical predictions that have been used for several decades in diverse fields of vertebrate biology and to metabolic measurements from a previous study using the same individuals. A high-accuracy displaced laser sheet stereo particle image velocimetry experimental design measured the wake velocities in the Trefftz plane behind four bats flying over a range of speeds (3–7 m s−1). We computed the aerodynamic power contained in the wake using a novel interpolation method and compared these results with the power predicted by Pennycuick's and Rayner's models. The measured aerodynamic power falls between the two theoretical predictions, demonstrating that the models effectively predict the appropriate range of flight power, but the models do not accurately predict minimum power or maximum range speeds. Mechanical efficiency—the ratio of aerodynamic power output to metabolic power input—varied from 5.9% to 9.8% for the same individuals, changing with flight speed. PMID:24718450

Is the Spatial Distribution of Mankind's Most Basic Economic Traits Determined by Climate and Soil Alone?

PubMed Central

Beck, Jan; Sieber, Andrea

2010-01-01

Background Several authors, most prominently Jared Diamond (1997, Guns, Germs and Steel), have investigated biogeographic determinants of human history and civilization. The timing of the transition to an agricultural lifestyle, associated with steep population growth and consequent societal change, has been suggested to be affected by the availability of suitable organisms for domestication. These factors were shown to quantitatively explain some of the current global inequalities of economy and political power. Here, we advance this approach one step further by looking at climate and soil as sole determining factors. Methodology/Principal Findings As a simplistic ‘null model’, we assume that only climate and soil conditions affect the suitability of four basic landuse types – agriculture, sedentary animal husbandry, nomadic pastoralism and hunting-and-gathering. Using ecological niche modelling (ENM), we derive spatial predictions of the suitability for these four landuse traits and apply these to the Old World and Australia. We explore two aspects of the properties of these predictions, conflict potential and population density. In a calculation of overlap of landuse suitability, we map regions of potential conflict between landuse types. Results are congruent with a number of real, present or historical, regions of conflict between ethnic groups associated with different landuse traditions. Furthermore, we found that our model of agricultural suitability explains a considerable portion of population density variability. We mapped residuals from this correlation, finding geographically highly structured deviations that invite further investigation. We also found that ENM of agricultural suitability correlates with a metric of local wealth generation (Gross Domestic Product, Purchasing Power Parity). Conclusions/Significance From simplified assumptions on the links between climate, soil and landuse we are able to provide good predictions on complex features of human geography. The spatial distribution of deviations from ENM predictions identifies those regions requiring further investigation of potential explanations. Our findings and methodological approaches may be of applied interest, e.g., in the context of climate change. PMID:20463959

Is the spatial distribution of mankind's most basic economic traits determined by climate and soil alone?

PubMed

Beck, Jan; Sieber, Andrea

2010-05-05

Several authors, most prominently Jared Diamond (1997, Guns, Germs and Steel), have investigated biogeographic determinants of human history and civilization. The timing of the transition to an agricultural lifestyle, associated with steep population growth and consequent societal change, has been suggested to be affected by the availability of suitable organisms for domestication. These factors were shown to quantitatively explain some of the current global inequalities of economy and political power. Here, we advance this approach one step further by looking at climate and soil as sole determining factors. As a simplistic 'null model', we assume that only climate and soil conditions affect the suitability of four basic landuse types - agriculture, sedentary animal husbandry, nomadic pastoralism and hunting-and-gathering. Using ecological niche modelling (ENM), we derive spatial predictions of the suitability for these four landuse traits and apply these to the Old World and Australia. We explore two aspects of the properties of these predictions, conflict potential and population density. In a calculation of overlap of landuse suitability, we map regions of potential conflict between landuse types. Results are congruent with a number of real, present or historical, regions of conflict between ethnic groups associated with different landuse traditions. Furthermore, we found that our model of agricultural suitability explains a considerable portion of population density variability. We mapped residuals from this correlation, finding geographically highly structured deviations that invite further investigation. We also found that ENM of agricultural suitability correlates with a metric of local wealth generation (Gross Domestic Product, Purchasing Power Parity). From simplified assumptions on the links between climate, soil and landuse we are able to provide good predictions on complex features of human geography. The spatial distribution of deviations from ENM predictions identifies those regions requiring further investigation of potential explanations. Our findings and methodological approaches may be of applied interest, e.g., in the context of climate change.

Prediction of anaerobic power values from an abbreviated WAnT protocol.

PubMed

Stickley, Christopher D; Hetzler, Ronald K; Kimura, Iris F

2008-05-01

The traditional 30-second Wingate anaerobic test (WAnT) is a widely used anaerobic power assessment protocol. An abbreviated protocol has been shown to decrease the mild to severe physical discomfort often associated with the WAnT. Therefore, the purpose of this study was to determine whether a 20-second WAnT protocol could be used to accurately predict power values of a standard 30-second WAnT. In 96 college females, anaerobic power variables were assessed using a standard 30-second WAnT protocol. Maximum power values as well as instantaneous power at 10, 15, and 20 seconds were recorded. Based on these results, stepwise regression analysis was performed to determine the accuracy with which mean power, minimum power, 30-second power, and percentage of fatigue for a standard 30-second WAnT could be predicted from values obtained during the first 20 seconds of testing. Mean power values showed the highest level of predictability (R2 = 0.99) from the 20-second values. Minimum power, 30-second power, and percentage of fatigue also showed high levels of predictability (R2 = 0.91, 0.84, and 0.84, respectively) using only values obtained during the first 20 seconds of the protocol. An abbreviated (20-second) WAnT protocol appears to effectively predict results of a standard 30-second WAnT in college-age females, allowing for comparison of data to published norms. A shortened test may allow for a decrease in unwanted side effects associated with the traditional WAnT protocol.

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

O'Driscoll, M.

Power capacity additions in Asia will at least triple by 2010, and Arthur D. Little Inc. predicts natural gas can pick up a good 15 percent of that market. The study predicts Asia potentially will need 720 gigawatts of new power generation by 2010, of which 15 percent may be gas-based. This represents a market three times the size of the US market in the same period, and would require more than $1 trillion in investment to finance the power generation projects alone. Six forces are driving new market opportunities for natural gas in Asia, and have set the stagemore » for major investments in Asian gas-based power generation. They are: New technologies; growing environmental pressures; privatization; alternative energy pricing; gas availability; and continued economic growth. Japan, South Korea and Taiwan already have large, well-established markets for both gas and power that provide minimal opportunities for foreign investment. But the rest of Asia - specifically, India, Pakistan, the Philippines, Vietnam, Indonesia, Malaysia, the People's Republic of China, Thailand, Bangladesh and Myanmar - is still relatively undeveloped, the study said, and gas is emerging as an energy import substitute or export earner. The study found those countries will turn increased environmental awareness and concern into legislation as their economic prosperity grows, leading to a higher future value for natural gas relative to other fuels. Stricter emissions standards will favor gas over diesel, fuel oil and coal.« less

Coexistence of collapse and stable spatiotemporal solitons in multimode fibers

NASA Astrophysics Data System (ADS)

Shtyrina, Olga V.; Fedoruk, Mikhail P.; Kivshar, Yuri S.; Turitsyn, Sergei K.

2018-01-01

We analyze spatiotemporal solitons in multimode optical fibers and demonstrate the existence of stable solitons, in a sharp contrast to earlier predictions of collapse of multidimensional solitons in three-dimensional media. We discuss the coexistence of blow-up solutions and collapse stabilization by a low-dimensional external potential in graded-index media, and also predict the existence of stable higher-order nonlinear waves such as dipole-mode spatiotemporal solitons. To support the main conclusions of our numerical studies we employ a variational approach and derive analytically the stability criterion for input powers for the collapse stabilization.

Limited change in dune mobility in response to a large decrease in wind power in semi-arid northern China since the 1970s

USGS Publications Warehouse

Mason, J.A.; Swinehart, J.B.; Lu, H.; Miao, X.; Cha, P.; Zhou, Y.

2008-01-01

The climatic controls on dune mobility, especially the relative importance of wind strength, remain incompletely understood. This is a key research problem in semi-arid northern China, both for interpreting past dune activity as evidence of paleoclimate and for predicting future environmental change. Potential eolian sand transport, which is approximately proportional to wind power above the threshold for sand entrainment, has decreased across much of northern China since the 1970s. Over the same period, effective moisture (ratio of precipitation to potential evapotranspiration) has not changed significantly. This "natural experiment" provides insight on the relative importance of wind power as a control on dune mobility in three dunefields of northern China (Mu Us, Otindag, and Horqin), although poorly understood and potentially large effects of human land use complicate interpretation. Dune forms in these three regions are consistent with sand transport vectors inferred from weather station data, suggesting that wind directions have remained stable and the stations adequately represent winds that shaped the dunes. The predicted effect of weaker winds since the 1970s would be dune stabilization, with lower sand transport rates allowing vegetation cover to expand. Large portions of all three dunefields remained stabilized by vegetation in the 1970s despite high wind power. Since the 1970s, trends in remotely sensed vegetation greenness and change in mobile dune area inferred from sequential Landsat images do indicate widespread dune stabilization in the eastern Mu Us region. On the other hand, expansion of active dunes took place farther west in the Mu Us dunefield and especially in the central Otindag dunefield, with little overall change in two parts of the Horqin dunes. Better ground truth is needed to validate the remote sensing analyses, but results presented here place limits on the relative importance of wind strength as a control on dune mobility in the study areas. High wind power alone does not completely destabilize these dunes. A large decrease in wind power either has little short-term effect on the dunes, or more likely its effect is sufficiently small that it is obscured by human impacts on dune stability in many parts of the study areas. ?? 2008 Elsevier B.V. All rights reserved.

Modeling photovoltaic performance in periodic patterned colloidal quantum dot solar cells.

PubMed

Fu, Yulan; Dinku, Abay G; Hara, Yukihiro; Miller, Christopher W; Vrouwenvelder, Kristina T; Lopez, Rene

2015-07-27

Colloidal quantum dot (CQD) solar cells have attracted tremendous attention mostly due to their wide absorption spectrum window and potentially low processability cost. The ultimate efficiency of CQD solar cells is highly limited by their high trap state density. Here we show that the overall device power conversion efficiency could be improved by employing photonic structures that enhance both charge generation and collection efficiencies. By employing a two-dimensional numerical model, we have calculated the characteristics of patterned CQD solar cells based of a simple grating structure. Our calculation predicts a power conversion efficiency as high as 11.2%, with a short circuit current density of 35.2 mA/cm2, a value nearly 1.5 times larger than the conventional flat design, showing the great potential value of patterned quantum dot solar cells.

New Computational Methods for the Prediction and Analysis of Helicopter Noise

NASA Technical Reports Server (NTRS)

Strawn, Roger C.; Oliker, Leonid; Biswas, Rupak

1996-01-01

This paper describes several new methods to predict and analyze rotorcraft noise. These methods are: 1) a combined computational fluid dynamics and Kirchhoff scheme for far-field noise predictions, 2) parallel computer implementation of the Kirchhoff integrations, 3) audio and visual rendering of the computed acoustic predictions over large far-field regions, and 4) acoustic tracebacks to the Kirchhoff surface to pinpoint the sources of the rotor noise. The paper describes each method and presents sample results for three test cases. The first case consists of in-plane high-speed impulsive noise and the other two cases show idealized parallel and oblique blade-vortex interactions. The computed results show good agreement with available experimental data but convey much more information about the far-field noise propagation. When taken together, these new analysis methods exploit the power of new computer technologies and offer the potential to significantly improve our prediction and understanding of rotorcraft noise.

Diagnostic potential of endotoxin scattering photometry for sepsis and septic shock.

PubMed

Shimizu, Tomoharu; Obata, Toru; Sonoda, Hiromichi; Akabori, Hiroya; Miyake, Tohru; Yamamoto, Hiroshi; Tabata, Takahisa; Eguchi, Yutaka; Tani, Tohru

2013-12-01

Endotoxin scattering photometry (ESP) is a novel Limulus amebocyte lysate (LAL) assay that uses a laser light-scattering particle-counting method. In the present study, we compared ESP, standard turbidimetric LAL assay, and procalcitonin assay for the evaluation of sepsis after emergency gastrointestinal surgery. A total of 174 samples were collected from 40 adult patients undergoing emergency gastrointestinal surgery and 10 patients with colorectal cancer undergoing elective surgery as nonseptic controls. Plasma endotoxin levels were measured with ESP and turbidimetric LAL assay, and plasma procalcitonin levels were assessed with a standard procalcitonin assay. Plasma endotoxin and procalcitonin levels increased corresponding to the degree of sepsis. Endotoxin scattering photometry significantly discriminated between patients with or without septic shock: sensitivity, 81.1%; specificity, 76.6%; positive predictive value, 48.4%; negative predictive value, 93.8%; and accuracy, 77.6%. The area under the receiver operating characteristic curve for septic shock with the ESP assay (endotoxin cutoff value, 23.8 pg/mL) was 0.8532 ± 0.0301 (95% confidence interval, 0.7841-0.9030; P < 0.0001). The predictive power of ESP was superior to that of turbidimetric assay (difference, 0.1965 ± 0.0588; 95% confidence interval, 0.0812-0.3117; P = 0.0008). There was no significant difference in predictive power between ESP and procalcitonin assay. Endotoxin scattering photometry also discriminated between patients with and without sepsis. Area under the receiver operating characteristic curve analysis showed that ESP had the best predictive power for diagnosing sepsis. In conclusion, compared with turbidimetric LAL assay, ESP more sensitively detected plasma endotoxin and significantly discriminated between sepsis and septic shock in patients undergoing gastrointestinal emergency surgery.

Exploring the Effects of Low Power Schemas in Mothers.

ERIC Educational Resources Information Center

Mills, Rosemary S. L.

1999-01-01

Assessed whether low perceived maternal power and temperamentally fearful preschool-aged daughters predicted subsequent maternal overcontrol and internalizing symptoms in daughters 2 years later. Found that low perceived maternal power predicted subsequent maternal overcontrol with initially fearful daughters but did not predict subsequent…

Pump-probe imaging of pigmented cutaneous melanoma primary lesions gives insight into metastatic potential

PubMed Central

Robles, Francisco E.; Deb, Sanghamitra; Wilson, Jesse W.; Gainey, Christina S.; Selim, M. Angelica; Mosca, Paul J.; Tyler, Douglas S.; Fischer, Martin C.; Warren, Warren S.

2015-01-01

Metastatic melanoma is associated with a poor prognosis, but no method reliably predicts which melanomas of a given stage will ultimately metastasize and which will not. While sentinel lymph node biopsy (SLNB) has emerged as the most powerful predictor of metastatic disease, the majority of people dying from metastatic melanoma still have a negative SLNB. Here we analyze pump-probe microscopy images of thin biopsy slides of primary melanomas to assess their metastatic potential. Pump-probe microscopy reveals detailed chemical information of melanin with subcellular spatial resolution. Quantification of the molecular signatures without reference standards is achieved using a geometrical representation of principal component analysis. Melanin structure is analyzed in unison with the chemical information by applying principles of mathematical morphology. Results show that melanin in metastatic primary lesions has lower chemical diversity than non-metastatic primary lesions, and contains two distinct phenotypes that are indicative of aggressive disease. Further, the mathematical morphology analysis reveals melanin in metastatic primary lesions has a distinct “dusty” quality. Finally, a statistical analysis shows that the combination of the chemical information with spatial structures predicts metastatic potential with much better sensitivity than SLNB and high specificity, suggesting pump-probe microscopy can be an important tool to help predict the metastatic potential of melanomas. PMID:26417529

Inflation model selection meets dark radiation

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

Tram, Thomas; Vallance, Robert; Vennin, Vincent, E-mail: thomas.tram@port.ac.uk, E-mail: robert.vallance@student.manchester.ac.uk, E-mail: vincent.vennin@port.ac.uk

2017-01-01

We investigate how inflation model selection is affected by the presence of additional free-streaming relativistic degrees of freedom, i.e. dark radiation. We perform a full Bayesian analysis of both inflation parameters and cosmological parameters taking reheating into account self-consistently. We compute the Bayesian evidence for a few representative inflation scenarios in both the standard ΛCDM model and an extension including dark radiation parametrised by its effective number of relativistic species N {sub eff}. Using a minimal dataset (Planck low-ℓ polarisation, temperature power spectrum and lensing reconstruction), we find that the observational status of most inflationary models is unchanged. The exceptionsmore » are potentials such as power-law inflation that predict large values for the scalar spectral index that can only be realised when N {sub eff} is allowed to vary. Adding baryon acoustic oscillations data and the B-mode data from BICEP2/Keck makes power-law inflation disfavoured, while adding local measurements of the Hubble constant H {sub 0} makes power-law inflation slightly favoured compared to the best single-field plateau potentials. This illustrates how the dark radiation solution to the H {sub 0} tension would have deep consequences for inflation model selection.« less

Predicting the ultimate potential of natural gas SOFC power cycles with CO2 capture - Part A: Methodology and reference cases

NASA Astrophysics Data System (ADS)

Campanari, Stefano; Mastropasqua, Luca; Gazzani, Matteo; Chiesa, Paolo; Romano, Matteo C.

2016-08-01

Driven by the search for the highest theoretical efficiency, in the latest years several studies investigated the integration of high temperature fuel cells in natural gas fired power plants, where fuel cells are integrated with simple or modified Brayton cycles and/or with additional bottoming cycles, and CO2 can be separated via chemical or physical separation, oxy-combustion and cryogenic methods. Focusing on Solid Oxide Fuel Cells (SOFC) and following a comprehensive review and analysis of possible plant configurations, this work investigates their theoretical potential efficiency and proposes two ultra-high efficiency plant configurations based on advanced intermediate-temperature SOFCs integrated with a steam turbine or gas turbine cycle. The SOFC works at atmospheric or pressurized conditions and the resulting power plant exceeds 78% LHV efficiency without CO2 capture (as discussed in part A of the work) and 70% LHV efficiency with substantial CO2 capture (part B). The power plants are simulated at the 100 MW scale with a complete set of realistic assumptions about fuel cell (FC) performance, plant components and auxiliaries, presenting detailed energy and material balances together with a second law analysis.

Assessment of the Present and Future Offshore Wind Power Potential: A Case Study in a Target Territory of the Baltic Sea Near the Latvian Coast

PubMed Central

Teilans, Artis

2013-01-01

Offshore wind energy development promises to be a significant domestic renewable energy source in Latvia. The reliable prediction of present and future wind resources at offshore sites is crucial for planning and selecting the location for wind farms. The overall goal of this paper is the assessment of offshore wind power potential in a target territory of the Baltic Sea near the Latvian coast as well as the identification of a trend in the future wind energy potential for the study territory. The regional climate model CLM and High Resolution Limited Area Model (Hirlam) simulations were used to obtain the wind climatology data for the study area. The results indicated that offshore wind energy is promising for expanding the national electricity generation and will continue to be a stable resource for electricity generation in the region over the 21st century. PMID:23983619

Preliminary Evaluation of Convective Heat Transfer in a Water Shield for a Surface Power Reactor

NASA Technical Reports Server (NTRS)

Pearson J. Boise; Reid, Robert S.

2007-01-01

As part of the Vision for Space Exploration, the end of the next decade will bring man back to the surface of the moon. A crucial issue for the establishment of human presence on the moon will be the availability of compact power sources. This presence could require greater than 10's of kWt's in follow on years. Nuclear reactors are well suited to meet the needs for power generation on the lunar or Martian surface. Radiation shielding is a key component of any surface power reactor system. Several competing concepts exist for lightweight, safe, robust shielding systems such as a water shield, lithium hydride (LiH), and boron carbide. Water offers several potential advantages, including reduced cost, reduced technical risk, and reduced mass. Water has not typically been considered for space reactor applications because of the need for gravity to fix the location of any vapor that could form radiation streaming paths. The water shield concept relies on the predictions of passive circulation of the shield water by natural convection to adequately cool the shield. This prediction needs to be experimentally evaluated, especially for shields with complex geometries. NASA Marshall Space Flight Center has developed the experience and facilities necessary to do this evaluation in its Early Flight Fission - Test Facility (EFF-TF).

Model predictive direct power control for active power decoupled single-phase quasi- Z -source inverter

DOE PAGES

Liu, Yushan; Ge, Baoming; Abu-Rub, Haitham; ...

2016-06-14

In this study, the active power filter (APF) that consists of a half-bridge leg and an ac capacitor is integrated in the single-phase quasi-Z-source inverter (qZSI) in this paper to avoid the second harmonic power flowing into the dc side. The capacitor of APF buffers the second harmonic power of the load, and the ac capacitor allows highly pulsating ac voltage, so that the capacitances of both dc and ac sides can be small. A model predictive direct power control (DPC) is further proposed to achieve the purpose of this newtopology through predicting the capacitor voltage of APF at eachmore » sampling period and ensuring the APF power to track the second harmonic power of single-phase qZSI. Simulation and experimental results verify the model predictive DPC for the APF-integrated single-phase qZSI.« less

Model predictive direct power control for active power decoupled single-phase quasi- Z -source inverter

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

Liu, Yushan; Ge, Baoming; Abu-Rub, Haitham

In this study, the active power filter (APF) that consists of a half-bridge leg and an ac capacitor is integrated in the single-phase quasi-Z-source inverter (qZSI) in this paper to avoid the second harmonic power flowing into the dc side. The capacitor of APF buffers the second harmonic power of the load, and the ac capacitor allows highly pulsating ac voltage, so that the capacitances of both dc and ac sides can be small. A model predictive direct power control (DPC) is further proposed to achieve the purpose of this newtopology through predicting the capacitor voltage of APF at eachmore » sampling period and ensuring the APF power to track the second harmonic power of single-phase qZSI. Simulation and experimental results verify the model predictive DPC for the APF-integrated single-phase qZSI.« less

Bayesian predictive power: choice of prior and some recommendations for its use as probability of success in drug development.

PubMed

Rufibach, Kaspar; Burger, Hans Ulrich; Abt, Markus

2016-09-01

Bayesian predictive power, the expectation of the power function with respect to a prior distribution for the true underlying effect size, is routinely used in drug development to quantify the probability of success of a clinical trial. Choosing the prior is crucial for the properties and interpretability of Bayesian predictive power. We review recommendations on the choice of prior for Bayesian predictive power and explore its features as a function of the prior. The density of power values induced by a given prior is derived analytically and its shape characterized. We find that for a typical clinical trial scenario, this density has a u-shape very similar, but not equal, to a β-distribution. Alternative priors are discussed, and practical recommendations to assess the sensitivity of Bayesian predictive power to its input parameters are provided. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Spontaneous Decoding of the Timing and Content of Human Object Perception from Cortical Surface Recordings Reveals Complementary Information in the Event-Related Potential and Broadband Spectral Change

PubMed Central

Miller, Kai J.; Schalk, Gerwin; Hermes, Dora; Ojemann, Jeffrey G.; Rao, Rajesh P. N.

2016-01-01

The link between object perception and neural activity in visual cortical areas is a problem of fundamental importance in neuroscience. Here we show that electrical potentials from the ventral temporal cortical surface in humans contain sufficient information for spontaneous and near-instantaneous identification of a subject’s perceptual state. Electrocorticographic (ECoG) arrays were placed on the subtemporal cortical surface of seven epilepsy patients. Grayscale images of faces and houses were displayed rapidly in random sequence. We developed a template projection approach to decode the continuous ECoG data stream spontaneously, predicting the occurrence, timing and type of visual stimulus. In this setting, we evaluated the independent and joint use of two well-studied features of brain signals, broadband changes in the frequency power spectrum of the potential and deflections in the raw potential trace (event-related potential; ERP). Our ability to predict both the timing of stimulus onset and the type of image was best when we used a combination of both the broadband response and ERP, suggesting that they capture different and complementary aspects of the subject’s perceptual state. Specifically, we were able to predict the timing and type of 96% of all stimuli, with less than 5% false positive rate and a ~20ms error in timing. PMID:26820899

Accurate Predictions of Mean Geomagnetic Dipole Excursion and Reversal Frequencies, Mean Paleomagnetic Field Intensity, and the Radius of Earth's Core Using McLeod's Rule

NASA Technical Reports Server (NTRS)

Voorhies, Coerte V.; Conrad, Joy

1996-01-01

The geomagnetic spatial power spectrum R(sub n)(r) is the mean square magnetic induction represented by degree n spherical harmonic coefficients of the internal scalar potential averaged over the geocentric sphere of radius r. McLeod's Rule for the magnetic field generated by Earth's core geodynamo says that the expected core surface power spectrum (R(sub nc)(c)) is inversely proportional to (2n + 1) for 1 less than n less than or equal to N(sub E). McLeod's Rule is verified by locating Earth's core with main field models of Magsat data; the estimated core radius of 3485 kn is close to the seismologic value for c of 3480 km. McLeod's Rule and similar forms are then calibrated with the model values of R(sub n) for 3 less than or = n less than or = 12. Extrapolation to the degree 1 dipole predicts the expectation value of Earth's dipole moment to be about 5.89 x 10(exp 22) Am(exp 2)rms (74.5% of the 1980 value) and the expected geomagnetic intensity to be about 35.6 (mu)T rms at Earth's surface. Archeo- and paleomagnetic field intensity data show these and related predictions to be reasonably accurate. The probability distribution chi(exp 2) with 2n+1 degrees of freedom is assigned to (2n + 1)R(sub nc)/(R(sub nc). Extending this to the dipole implies that an exceptionally weak absolute dipole moment (less than or = 20% of the 1980 value) will exist during 2.5% of geologic time. The mean duration for such major geomagnetic dipole power excursions, one quarter of which feature durable axial dipole reversal, is estimated from the modern dipole power time-scale and the statistical model of excursions. The resulting mean excursion duration of 2767 years forces us to predict an average of 9.04 excursions per million years, 2.26 axial dipole reversals per million years, and a mean reversal duration of 5533 years. Paleomagnetic data show these predictions to be quite accurate. McLeod's Rule led to accurate predictions of Earth's core radius, mean paleomagnetic field intensity, and mean geomagnetic dipole power excursion and axial dipole reversal frequencies. We conclude that McLeod's Rule helps unify geo-paleomagnetism, correctly relates theoretically predictable statistical properties of the core geodynamo to magnetic observation, and provides a priori information required for stochastic inversion of paleo-, archeo-, and/or historical geomagnetic measurements.

The status of lightweight photovoltaic space array technology based on amorphous silicon solar cells

NASA Technical Reports Server (NTRS)

Hanak, Joseph J.; Kaschmitter, Jim

1991-01-01

Ultralight, flexible photovoltaic (PV) array of amorphous silicon (a-Si) was identified as a potential low cost power source for small satellites. A survey was conducted of the status of the a-Si PV array technology with respect to present and future performance, availability, cost, and risks. For existing, experimental array blankets made of commercial cell material, utilizing metal foil substrates, the Beginning of Life (BOL) performance at Air Mass Zero (AM0) and 35 C includes total power up to 200 W, power per area of 64 W/sq m and power per weight of 258 W/kg. Doubling of power per weight occurs when polyimide substrates are used. Estimated End of Life (EOL) power output after 10 years in a nominal low earth orbit would be 80 pct. of BOL, the degradation being due to largely light induced effects (-10 to -15 pct.) and in part (-5 pct.) to space radiation. Predictions for the year 1995 for flexible PV arrays, made on the basis of published results for rigid a-Si modules, indicate EOL power output per area and per weight of 105 W/sq m and 400 W/kg, respectively, while predictions for the late 1990s based on existing U.S. national PV program goals indicate EOL values of 157 W/sq m and 600 W/kg. Cost estimates by vendors for 200 W ultralight arrays in volume of over 1000 units range from $100/watt to $125/watt. Identified risks include the lack of flexible, space compatible encapsulant, the lack of space qualification effort, recent partial or full acquisitions of US manufacturers of a-Si cells by foreign firms, and the absence of a national commitment for a long range development program toward developing of this important power source for space.

Multi-scale Mexican spotted owl (Strix occidentalis lucida) nest/roost habitat selection in Arizona and a comparison with single-scale modeling results

Treesearch

Brad C. Timm; Kevin McGarigal; Samuel A. Cushman; Joseph L. Ganey

2016-01-01

Efficacy of future habitat selection studies will benefit by taking a multi-scale approach. In addition to potentially providing increased explanatory power and predictive capacity, multi-scale habitat models enhance our understanding of the scales at which species respond to their environment, which is critical knowledge required to implement effective...

The relationship of plasma decoy receptor 3 and coronary collateral circulation in patients with coronary artery disease.

PubMed

Yan, Youyou; Song, Dandan; Liu, Lulu; Meng, Xiuping; Qi, Chao; Wang, Junnan

2017-11-15

Previously, decoy receptor 3 (DcR3) was found to be a potential angiogenetic factor, while the relationship of DcR3 with coronary collateral circulation formation has not been investigated. In this study, we aimed to investigate whether plasma decoy receptor 3 levels was associated with CCC formation and evaluate its predictive power for CCC status in patients with coronary artery disease. Among patients who underwent coronary angiography with coronary artery disease and had a stenosis of ≥90% were included in our study. Collateral degree was graded according to Rentrope Cohen classification. Patients with grade 2 or 3 collateral degree were enrolled in good CCC group and patients with grade 0 or 1 collateral degree were enrolled in poor CCC group. Plasma DcR3 level was significantly higher in good CCC group (328.00±230.82 vs 194.84±130.63ng/l, p<0.01) and positively correlated with Rentrope grade (p<0.01). In addition, plasma DcR3 was also positively correlated with VEGF-A. Both ROC (receiver operating characteristic curve) and multinomial logistical regression analysis showed that plasma DcR3 displayed potent predictive power for CCC status. Higher plasma DcR3 level was related to better CCC formation and displayed potent predictive power for CCC status. Copyright © 2017. Published by Elsevier Inc.

Improving accuracy and power with transfer learning using a meta-analytic database.

PubMed

Schwartz, Yannick; Varoquaux, Gaël; Pallier, Christophe; Pinel, Philippe; Poline, Jean-Baptiste; Thirion, Bertrand

2012-01-01

Typical cohorts in brain imaging studies are not large enough for systematic testing of all the information contained in the images. To build testable working hypotheses, investigators thus rely on analysis of previous work, sometimes formalized in a so-called meta-analysis. In brain imaging, this approach underlies the specification of regions of interest (ROIs) that are usually selected on the basis of the coordinates of previously detected effects. In this paper, we propose to use a database of images, rather than coordinates, and frame the problem as transfer learning: learning a discriminant model on a reference task to apply it to a different but related new task. To facilitate statistical analysis of small cohorts, we use a sparse discriminant model that selects predictive voxels on the reference task and thus provides a principled procedure to define ROIs. The benefits of our approach are twofold. First it uses the reference database for prediction, i.e., to provide potential biomarkers in a clinical setting. Second it increases statistical power on the new task. We demonstrate on a set of 18 pairs of functional MRI experimental conditions that our approach gives good prediction. In addition, on a specific transfer situation involving different scanners at different locations, we show that voxel selection based on transfer learning leads to higher detection power on small cohorts.

Power maximization of a point absorber wave energy converter using improved model predictive control

NASA Astrophysics Data System (ADS)

Milani, Farideh; Moghaddam, Reihaneh Kardehi

2017-08-01

This paper considers controlling and maximizing the absorbed power of wave energy converters for irregular waves. With respect to physical constraints of the system, a model predictive control is applied. Irregular waves' behavior is predicted by Kalman filter method. Owing to the great influence of controller parameters on the absorbed power, these parameters are optimized by imperialist competitive algorithm. The results illustrate the method's efficiency in maximizing the extracted power in the presence of unknown excitation force which should be predicted by Kalman filter.

Photovoltaics: Reviewing the European Feed-in-Tariffs and Changing PV Efficiencies and Costs

PubMed Central

Zhang, H. L.; Van Gerven, T.; Baeyens, J.; Degrève, J.

2014-01-01

Feed-in-Tariff (FiT) mechanisms have been important in boosting renewable energy, by providing a long-term guaranteed subsidy of the kWh-price, thus mitigating investment risks and enhancing the contribution of sustainable electricity. By ongoing PV development, the contribution of solar power increases exponentially. Within this significant potential, it is important for investors, operators, and scientists alike to provide answers to different questions related to subsidies, PV efficiencies and costs. The present paper therefore (i) briefly reviews the mechanisms, advantages, and evolution of FiT; (ii) describes the developments of PV, (iii) applies a comprehensive literature-based model for the solar irradiation to predict the PV solar energy potential in some target European countries, whilst comparing output predictions with the monthly measured electricity generation of a 57 m² photovoltaic system (Belgium); and finally (iv) predicts the levelized cost of energy (LCOE) in terms of investment and efficiency, providing LCOE values between 0.149 and 0.313 €/kWh, as function of the overall process efficiency and cost. The findings clearly demonstrate the potential of PV energy in Europe, where FiT can be considerably reduced or even be eliminated in the near future. PMID:24959614

Pairwise contact energy statistical potentials can help to find probability of point mutations.

PubMed

Saravanan, K M; Suvaithenamudhan, S; Parthasarathy, S; Selvaraj, S

2017-01-01

To adopt a particular fold, a protein requires several interactions between its amino acid residues. The energetic contribution of these residue-residue interactions can be approximated by extracting statistical potentials from known high resolution structures. Several methods based on statistical potentials extracted from unrelated proteins are found to make a better prediction of probability of point mutations. We postulate that the statistical potentials extracted from known structures of similar folds with varying sequence identity can be a powerful tool to examine probability of point mutation. By keeping this in mind, we have derived pairwise residue and atomic contact energy potentials for the different functional families that adopt the (α/β) 8 TIM-Barrel fold. We carried out computational point mutations at various conserved residue positions in yeast Triose phosphate isomerase enzyme for which experimental results are already reported. We have also performed molecular dynamics simulations on a subset of point mutants to make a comparative study. The difference in pairwise residue and atomic contact energy of wildtype and various point mutations reveals probability of mutations at a particular position. Interestingly, we found that our computational prediction agrees with the experimental studies of Silverman et al. (Proc Natl Acad Sci 2001;98:3092-3097) and perform better prediction than i Mutant and Cologne University Protein Stability Analysis Tool. The present work thus suggests deriving pairwise contact energy potentials and molecular dynamics simulations of functionally important folds could help us to predict probability of point mutations which may ultimately reduce the time and cost of mutation experiments. Proteins 2016; 85:54-64. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Optimization of power and energy densities in supercapacitors

NASA Astrophysics Data System (ADS)

Robinson, David B.

Supercapacitors use nanoporous electrodes to store large amounts of charge on their high surface areas, and use the ions in electrolytes to carry charge into the pores. Their high power density makes them a potentially useful complement to batteries. However, ion transport through long, narrow channels still limits power and efficiency in these devices. Proper design can mitigate this. Current collector geometry must also be considered once this is done. Here, De Levie's model for porous electrodes is applied to quantitatively predict device performance and to propose optimal device designs for given specifications. Effects unique to nanoscale pores are considered, including that pores may not have enough salt to fully charge. Supercapacitors are of value for electric vehicles, portable electronics, and power conditioning in electrical grids with distributed renewable sources, and that value will increase as new device fabrication methods are developed and proper design accommodates those improvements. Example design outlines for vehicle applications are proposed and compared.

Design and evaluation of fluidized bed heat recovery for diesel engine systems

NASA Technical Reports Server (NTRS)

Hamm, J. R.; Newby, R. A.; Vidt, E. J.; Lippert, T. E.

1985-01-01

The potential of utilizing fluidized bed heat exchangers in place of conventional counter-flow heat exchangers for heat recovery from adiabatic diesel engine exhaust gas streams was studied. Fluidized bed heat recovery systems were evaluated in three different heavy duty transport applications: (1) heavy duty diesel truck; (2) diesel locomotives; and (3) diesel marine pushboat. The three applications are characterized by differences in overall power output and annual utilization. For each application, the exhaust gas source is a turbocharged-adiabatic diesel core. Representative subposed exhaust gas heat utilization power cycles were selected for conceptual design efforts including design layouts and performance estimates for the fluidized bed heat recovery heat exchangers. The selected power cycles were: organic rankine with RC-1 working fluid, turbocompound power turbine with steam injection, and stirling engine. Fuel economy improvement predictions are used in conjunction with capital cost estimates and fuel price data to determine payback times for the various cases.

Is laughter a better vocal change detector than a growl?

PubMed

Pinheiro, Ana P; Barros, Carla; Vasconcelos, Margarida; Obermeier, Christian; Kotz, Sonja A

2017-07-01

The capacity to predict what should happen next and to minimize any discrepancy between an expected and an actual sensory input (prediction error) is a central aspect of perception. Particularly in vocal communication, the effective prediction of an auditory input that informs the listener about the emotionality of a speaker is critical. What is currently unknown is how the perceived valence of an emotional vocalization affects the capacity to predict and detect a change in the auditory input. This question was probed in a combined event-related potential (ERP) and time-frequency analysis approach. Specifically, we examined the brain response to standards (Repetition Positivity) and to deviants (Mismatch Negativity - MMN), as well as the anticipatory response to the vocal sounds (pre-stimulus beta oscillatory power). Short neutral, happy (laughter), and angry (growls) vocalizations were presented both as standard and deviant stimuli in a passive oddball listening task while participants watched a silent movie and were instructed to ignore the vocalizations. MMN amplitude was increased for happy compared to neutral and angry vocalizations. The Repetition Positivity was enhanced for happy standard vocalizations. Induced pre-stimulus upper beta power was increased for happy vocalizations, and predicted the modulation of the standard Repetition Positivity. These findings indicate enhanced sensory prediction for positive vocalizations such as laughter. Together, the results suggest that positive vocalizations are more effective predictors in social communication than angry and neutral ones, possibly due to their high social significance. Copyright © 2017 Elsevier Ltd. All rights reserved.

Comparison of RNA-seq and microarray-based models for clinical endpoint prediction.

PubMed

Zhang, Wenqian; Yu, Ying; Hertwig, Falk; Thierry-Mieg, Jean; Zhang, Wenwei; Thierry-Mieg, Danielle; Wang, Jian; Furlanello, Cesare; Devanarayan, Viswanath; Cheng, Jie; Deng, Youping; Hero, Barbara; Hong, Huixiao; Jia, Meiwen; Li, Li; Lin, Simon M; Nikolsky, Yuri; Oberthuer, André; Qing, Tao; Su, Zhenqiang; Volland, Ruth; Wang, Charles; Wang, May D; Ai, Junmei; Albanese, Davide; Asgharzadeh, Shahab; Avigad, Smadar; Bao, Wenjun; Bessarabova, Marina; Brilliant, Murray H; Brors, Benedikt; Chierici, Marco; Chu, Tzu-Ming; Zhang, Jibin; Grundy, Richard G; He, Min Max; Hebbring, Scott; Kaufman, Howard L; Lababidi, Samir; Lancashire, Lee J; Li, Yan; Lu, Xin X; Luo, Heng; Ma, Xiwen; Ning, Baitang; Noguera, Rosa; Peifer, Martin; Phan, John H; Roels, Frederik; Rosswog, Carolina; Shao, Susan; Shen, Jie; Theissen, Jessica; Tonini, Gian Paolo; Vandesompele, Jo; Wu, Po-Yen; Xiao, Wenzhong; Xu, Joshua; Xu, Weihong; Xuan, Jiekun; Yang, Yong; Ye, Zhan; Dong, Zirui; Zhang, Ke K; Yin, Ye; Zhao, Chen; Zheng, Yuanting; Wolfinger, Russell D; Shi, Tieliu; Malkas, Linda H; Berthold, Frank; Wang, Jun; Tong, Weida; Shi, Leming; Peng, Zhiyu; Fischer, Matthias

2015-06-25

Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model. We generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models. We demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.

Changing the approach to treatment choice in epilepsy using big data.

PubMed

Devinsky, Orrin; Dilley, Cynthia; Ozery-Flato, Michal; Aharonov, Ranit; Goldschmidt, Ya'ara; Rosen-Zvi, Michal; Clark, Chris; Fritz, Patty

2016-03-01

A UCB-IBM collaboration explored the application of machine learning to large claims databases to construct an algorithm for antiepileptic drug (AED) choice for individual patients. Claims data were collected between January 2006 and September 2011 for patients with epilepsy > 16 years of age. A subset of patient claims with a valid index date of AED treatment change (new, add, or switch) were used to train the AED prediction model by retrospectively evaluating an index date treatment for subsequent treatment change. Based on the trained model, a model-predicted AED regimen with the lowest likelihood of treatment change was assigned to each patient in the group of test claims, and outcomes were evaluated to test model validity. The model had 72% area under receiver operator characteristic curve, indicating good predictive power. Patients who were given the model-predicted AED regimen had significantly longer survival rates (time until a treatment change event) and lower expected health resource utilization on average than those who received another treatment. The actual prescribed AED regimen at the index date matched the model-predicted AED regimen in only 13% of cases; there were large discrepancies in the frequency of use of certain AEDs/combinations between model-predicted AED regimens and those actually prescribed. Chances of treatment success were improved if patients received the model-predicted treatment. Using the model's prediction system may enable personalized, evidence-based epilepsy care, accelerating the match between patients and their ideal therapy, thereby delivering significantly better health outcomes for patients and providing health-care savings by applying resources more efficiently. Our goal will be to strengthen the predictive power of the model by integrating diverse data sets and potentially moving to prospective data collection. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.

Feature Extraction of Event-Related Potentials Using Wavelets: An Application to Human Performance Monitoring

NASA Technical Reports Server (NTRS)

Trejo, Leonard J.; Shensa, Mark J.; Remington, Roger W. (Technical Monitor)

1998-01-01

This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWT was compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models developed to predict a composite measure of human signal detection performance. Linear regression models based on coefficients of the decimated DWT predicted signal detection performance with half as many f ree parameters as comparable models based on PCA scores. In addition, the DWT-based models were more resistant to model degradation due to over-fitting than PCA-based models. Feed-forward neural networks were trained using the backpropagation,-, algorithm to predict signal detection performance based on raw ERPs, PCA scores, or high-power coefficients of the DWT. Neural networks based on high-power DWT coefficients trained with fewer iterations, generalized to new data better, and were more resistant to overfitting than networks based on raw ERPs. Networks based on PCA scores did not generalize to new data as well as either the DWT network or the raw ERP network. The results show that wavelet expansions represent the ERP efficiently and extract behaviorally important features for use in linear regression or neural network models of human performance. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance.

Feature extraction of event-related potentials using wavelets: an application to human performance monitoring

NASA Technical Reports Server (NTRS)

Trejo, L. J.; Shensa, M. J.

1999-01-01

This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWT was compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models developed to predict a composite measure of human signal detection performance. Linear regression models based on coefficients of the decimated DWT predicted signal detection performance with half as many free parameters as comparable models based on PCA scores. In addition, the DWT-based models were more resistant to model degradation due to over-fitting than PCA-based models. Feed-forward neural networks were trained using the backpropagation algorithm to predict signal detection performance based on raw ERPs, PCA scores, or high-power coefficients of the DWT. Neural networks based on high-power DWT coefficients trained with fewer iterations, generalized to new data better, and were more resistant to overfitting than networks based on raw ERPs. Networks based on PCA scores did not generalize to new data as well as either the DWT network or the raw ERP network. The results show that wavelet expansions represent the ERP efficiently and extract behaviorally important features for use in linear regression or neural network models of human performance. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance. Copyright 1999 Academic Press.

Water and energy and hydrological limitations of whole-plant conductance along a subalpine forested hillslope

NASA Astrophysics Data System (ADS)

Beverly, D.; Speckman, H. N.; Klatt, A. L.; Ewers, B. E.

2016-12-01

Whole-plant hydraulic conductance is now used in many processed-based ecohydrological models running at the plot to regional scales. Many models, such as Dynamic Global Vegetation Model (DGVM), predict entire ecosystem evapotranspiration (ET) based on a single unvarying plant conductance parameter that assumes no variation in plant traits. However, whole-plant conductance varies in space, time, and with topography. Understanding this variation increases model predictive power for stand and ecosystem level estimates of ET, ultimately reducing uncertainty in predictions of the water balance. We hypothesize that whole-plant conductance (Kw) is water limited in the up-slope stands due to water flow paths and energy limited in down-slope stands due to shading. To test this hypothesis in two adjacent stands in the Medicine Bow Mountains of southern Wyoming. Both mixed conifer stands were south-facing, with the upper stand being 300 m above the down-slope stand. Whole-plant conductance was quantified measuring sapflow (Js) and leaf water potential (WPL) throughout the growing season. To quantify Js, each stand was instrumented with 30 Granier-type sapflow sensors. Leaf-water potentials were measured in monthly 48-hour campaigns sampling every 3 hours. The upper slope stand exhibited significantly lower Kw (approximately 35% lower in spruce and pine) and decreased throughout the growing season, driven by drying soils resulting in lower predawn WPL. In contrast, the down-slope stand Kw peaked in July before decreasing for rest of the summer. Down-slope predawn WPL maintained a consistent predawn WPL until October reflecting consistent water input from the upper slopes and ground water. Including this topographical variation in whole-plant conductance will increase the predictive power of models simulating evapotranspiration at the watershed scale.

Internal exposure dynamics drive the Adverse Outcome Pathways of synthetic glucocorticoids in fish

NASA Astrophysics Data System (ADS)

Margiotta-Casaluci, Luigi; Owen, Stewart F.; Huerta, Belinda; Rodríguez-Mozaz, Sara; Kugathas, Subramanian; Barceló, Damià; Rand-Weaver, Mariann; Sumpter, John P.

2016-02-01

The Adverse Outcome Pathway (AOP) framework represents a valuable conceptual tool to systematically integrate existing toxicological knowledge from a mechanistic perspective to facilitate predictions of chemical-induced effects across species. However, its application for decision-making requires the transition from qualitative to quantitative AOP (qAOP). Here we used a fish model and the synthetic glucocorticoid beclomethasone dipropionate (BDP) to investigate the role of chemical-specific properties, pharmacokinetics, and internal exposure dynamics in the development of qAOPs. We generated a qAOP network based on drug plasma concentrations and focused on immunodepression, skin androgenisation, disruption of gluconeogenesis and reproductive performance. We showed that internal exposure dynamics and chemical-specific properties influence the development of qAOPs and their predictive power. Comparing the effects of two different glucocorticoids, we highlight how relatively similar in vitro hazard-based indicators can lead to different in vivo risk. This discrepancy can be predicted by their different uptake potential, pharmacokinetic (PK) and pharmacodynamic (PD) profiles. We recommend that the development phase of qAOPs should include the application of species-species uptake and physiologically-based PK/PD models. This integration will significantly enhance the predictive power, enabling a more accurate assessment of the risk and the reliable transferability of qAOPs across chemicals.

Development and design of photovoltaic power prediction system

NASA Astrophysics Data System (ADS)

Wang, Zhijia; Zhou, Hai; Cheng, Xu

2018-02-01

In order to reduce the impact of power grid safety caused by volatility and randomness of the energy produced in photovoltaic power plants, this paper puts forward a construction scheme on photovoltaic power generation prediction system, introducing the technical requirements, system configuration and function of each module, and discussing the main technical features of the platform software development. The scheme has been applied in many PV power plants in the northwest of China. It shows that the system can produce reasonable prediction results, providing a right guidance for dispatching and efficient running for PV power plant.

Temperature-dependent thermal conductivities of one-dimensional nonlinear Klein-Gordon lattices with a soft on-site potential

NASA Astrophysics Data System (ADS)

Yang, Linlin; Li, Nianbei; Li, Baowen

2014-12-01

The temperature-dependent thermal conductivities of one-dimensional nonlinear Klein-Gordon lattices with soft on-site potential (soft-KG) are investigated systematically. Similarly to the previously studied hard-KG lattices, the existence of renormalized phonons is also confirmed in soft-KG lattices. In particular, the temperature dependence of the renormalized phonon frequency predicted by a classical field theory is verified by detailed numerical simulations. However, the thermal conductivities of soft-KG lattices exhibit the opposite trend in temperature dependence in comparison with those of hard-KG lattices. The interesting thing is that the temperature-dependent thermal conductivities of both soft- and hard-KG lattices can be interpreted in the same framework of effective phonon theory. According to the effective phonon theory, the exponents of the power-law dependence of the thermal conductivities as a function of temperature are only determined by the exponents of the soft or hard on-site potentials. These theoretical predictions are consistently verified very well by extensive numerical simulations.

A relation to predict the failure of materials and potential application to volcanic eruptions and landslides.

PubMed

Hao, Shengwang; Liu, Chao; Lu, Chunsheng; Elsworth, Derek

2016-06-16

A theoretical explanation of a time-to-failure relation is presented, with this relationship then used to describe the failure of materials. This provides the potential to predict timing (tf - t) immediately before failure by extrapolating the trajectory as it asymptotes to zero with no need to fit unknown exponents as previously proposed in critical power law behaviors. This generalized relation is verified by comparison with approaches to criticality for volcanic eruptions and creep failure. A new relation based on changes with stress is proposed as an alternative expression of Voight's relation, which is widely used to describe the accelerating precursory signals before material failure and broadly applied to volcanic eruptions, landslides and other phenomena. The new generalized relation reduces to Voight's relation if stress is limited to increase at a constant rate with time. This implies that the time-derivatives in Voight's analysis may be a subset of a more general expression connecting stress derivatives, and thus provides a potential method for forecasting these events.

Development scenario for laser fusion

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

Maniscalco, J.A.; Hovingh, J.; Buntzen, R.R.

1976-03-30

This scenario proposes establishment of test and engineering facilities to (1) investigate the technological problems associated with laser fusion, (2) demonstrate fissile fuel production, and (3) demonstrate competitive electrical power production. Such facilities would be major milestones along the road to a laser-fusion power economy. The relevant engineering and economic aspects of each of these research and development facilities are discussed. Pellet design and gain predictions corresponding to the most promising laser systems are presented for each plant. The results show that laser fusion has the potential to make a significant contribution to our energy needs. Beginning in the earlymore » 1990's, this new technology could be used to produce fissile fuel, and after the turn of the century it could be used to generate electrical power.« less

Overview and Meteorological Validation of the Wind Integration National Dataset toolkit

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

Draxl, C.; Hodge, B. M.; Clifton, A.

2015-04-13

The Wind Integration National Dataset (WIND) Toolkit described in this report fulfills these requirements, and constitutes a state-of-the-art national wind resource data set covering the contiguous United States from 2007 to 2013 for use in a variety of next-generation wind integration analyses and wind power planning. The toolkit is a wind resource data set, wind forecast data set, and wind power production and forecast data set derived from the Weather Research and Forecasting (WRF) numerical weather prediction model. WIND Toolkit data are available online for over 116,000 land-based and 10,000 offshore sites representing existing and potential wind facilities.

Evaluation of the Frequencies for Canister Inspections for SCC

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

Stockman, Christine; Bryan, Charles R.

2016-02-02

This report fulfills the M3 milestone M3FT-15SN0802042, “Evaluate the Frequencies for Canister Inspections for SCC” under Work Package FT-15SN080204, “ST Field Demonstration Support – SNL”. It reviews the current state of knowledge on the potential for stress corrosion cracking (SCC) of dry storage canisters and evaluates the implications of this state of knowledge on the establishment of an SCC inspection frequency. Models for the prediction of SCC by the Japanese Central Research Institute of Electric Power Industry (CRIEPI), the United States (U.S.) Electric Power Research Institute (EPRI), and Sandia National Laboratories (SNL) are summarized, and their limitations discussed.

Nonlinear damping for vibration isolation of microsystems using shear thickening fluid

NASA Astrophysics Data System (ADS)

Iyer, S. S.; Vedad-Ghavami, R.; Lee, H.; Liger, M.; Kavehpour, H. P.; Candler, R. N.

2013-06-01

This work reports the measurement and analysis of nonlinear damping of micro-scale actuators immersed in shear thickening fluids (STFs). A power-law damping term is added to the linear second-order model to account for the shear-dependent viscosity of the fluid. This nonlinear model is substantiated by measurements of oscillatory motion of a torsional microactuator. At high actuation forces, the vibration velocity amplitude saturates. The model accurately predicts the nonlinear damping characteristics of the STF using a power-law index extracted from independent rheology experiments. This result reveals the potential to use STFs as adaptive, passive dampers for vibration isolation of microelectromechanical systems.

Managing PV Power on Mars - MER Rovers

NASA Technical Reports Server (NTRS)

Stella, Paul M.; Chin, Keith; Wood, Eric; Herman, Jennifer; Ewell, Richard

2009-01-01

The MER Rovers have recently completed over 5 years of operation! This is a remarkable demonstration of the capabilities of PV power on the Martian surface. The extended mission required the development of an efficient process to predict the power available to the rovers on a day-to-day basis. The performance of the MER solar arrays is quite unlike that of any other Space array and perhaps more akin to Terrestrial PV operation, although even severe by that comparison. The impact of unpredictable factors, such as atmospheric conditions and dust accumulation (and removal) on the panels limits the accurate prediction of array power to short time spans. Based on the above, it is clear that long term power predictions are not sufficiently accurate to allow for detailed long term planning. Instead, the power assessment is essentially a daily activity, effectively resetting the boundary points for the overall predictive power model. A typical analysis begins with the importing of the telemetry from each rover's previous day's power subsystem activities. This includes the array power generated, battery state-of-charge, rover power loads, and rover orientation, all as functions of time. The predicted performance for that day is compared to the actual performance to identify the extent of any differences. The model is then corrected for these changes. Details of JPL's MER power analysis procedure are presented, including the description of steps needed to provide the final prediction for the mission planners. A dust cleaning event of the solar array is also highlighted to illustrate the impact of Martian weather on solar array performance

NASA's Prediction Of Worldwide Energy Resource (POWER) Project Unveils a New Geospatial Data Portal

Atmospheric Science Data Center

2018-03-16

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

Communal and Agentic Interpersonal and Intergroup Motives Predict Preferences for Status Versus Power.

PubMed

Locke, Kenneth D; Heller, Sonja

2017-01-01

Seven studies involving 1,343 participants showed how circumplex models of social motives can help explain individual differences in preferences for status (having others' admiration) versus power (controlling valuable resources). Studies 1 to 3 and 7 concerned interpersonal motives in workplace contexts, and found that stronger communal motives (to have mutual trust, support, and cooperation) predicted being more attracted to status (but not power) and achieving more workplace status, while stronger agentic motives (to be firm, decisive, and influential) predicted being more attracted to and achieving more workplace power, and experiencing a stronger connection between workplace power and job satisfaction. Studies 4 to 6 found similar effects for intergroup motives: Stronger communal motives predicted wanting one's ingroup (e.g., country) to have status-but not power-relative to other groups. Finally, most people preferred status over power, and this was especially true for women, which was partially explained by women having stronger communal motives.

Drivers and seasonal predictability of extreme wind speeds in the ECMWF System 4 and a statistical model

NASA Astrophysics Data System (ADS)

Walz, M. A.; Donat, M.; Leckebusch, G. C.

2017-12-01

As extreme wind speeds are responsible for large socio-economic losses in Europe, a skillful prediction would be of great benefit for disaster prevention as well as for the actuarial community. Here we evaluate patterns of large-scale atmospheric variability and the seasonal predictability of extreme wind speeds (e.g. >95th percentile) in the European domain in the dynamical seasonal forecast system ECMWF System 4, and compare to the predictability based on a statistical prediction model. The dominant patterns of atmospheric variability show distinct differences between reanalysis and ECMWF System 4, with most patterns in System 4 extended downstream in comparison to ERA-Interim. The dissimilar manifestations of the patterns within the two models lead to substantially different drivers associated with the occurrence of extreme winds in the respective model. While the ECMWF System 4 is shown to provide some predictive power over Scandinavia and the eastern Atlantic, only very few grid cells in the European domain have significant correlations for extreme wind speeds in System 4 compared to ERA-Interim. In contrast, a statistical model predicts extreme wind speeds during boreal winter in better agreement with the observations. Our results suggest that System 4 does not seem to capture the potential predictability of extreme winds that exists in the real world, and therefore fails to provide reliable seasonal predictions for lead months 2-4. This is likely related to the unrealistic representation of large-scale patterns of atmospheric variability. Hence our study points to potential improvements of dynamical prediction skill by improving the simulation of large-scale atmospheric dynamics.

The predictive power of local properties of financial networks

NASA Astrophysics Data System (ADS)

Caraiani, Petre

2017-01-01

The literature on analyzing the dynamics of financial networks has focused so far on the predictive power of global measures of networks like entropy or index cohesive force. In this paper, I show that the local network properties have similar predictive power. I focus on key network measures like average path length, average degree or cluster coefficient, and also consider the diameter and the s-metric. Using Granger causality tests, I show that some of these measures have statistically significant prediction power with respect to the dynamics of aggregate stock market. Average path length is most robust relative to the frequency of data used or specification (index or growth rate). Most measures are found to have predictive power only for monthly frequency. Further evidences that support this view are provided through a simple regression model.

Predicting energy expenditure through hand rim propulsion power output in individuals who use wheelchairs.

PubMed

Conger, Scott A; Scott, Stacy N; Bassett, David R

2014-07-01

To examine the relationship between hand rim propulsion power and energy expenditure (EE) during wheelchair wheeling and to investigate whether adding other variables to the model could improve on the prediction of EE. Individuals who use manual wheelchairs (n=14) performed five different wheeling activities in a wheelchair with a PowerTap power meter hub built into the right rear wheel. Activities included wheeling on a smooth, level surface at three different speeds (4.5, 5.5 and 6.5 km/h), wheeling on a rubberised track at one speed (5.5 km/h) and wheeling on a sidewalk course that included uphill and downhill segments at a self-selected speed. EE was measured using a portable indirect calorimetry system. Stepwise linear regression was performed to predict EE from power output variables. A repeated-measures analysis of variance was used to compare the measured EE to the estimates from the power models. Bland-Altman plots were used to assess the agreement between the criterion values and the predicted values. EE and power were significantly correlated (r=0.694, p<0.001). Regression analysis yielded three significant prediction models utilising measured power; measured power and speed; and measured power, speed and heart rate. No significant differences were found between measured EE and any of the prediction models. EE can be accurately and precisely estimated based on hand rim propulsion power. These results indicate that power could be used as a method to assess EE in individuals who use wheelchairs. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Neutron-deuteron analyzing power data at En=22.5 MeV

NASA Astrophysics Data System (ADS)

Weisel, G. J.; Tornow, W.; Crowell, A. S.; Esterline, J. H.; Hale, G. M.; Howell, C. R.; O'Malley, P. D.; Tompkins, J. R.; Witała, H.

2014-05-01

We present measurements of n-d analyzing power, Ay(θ), at En=22.5 MeV. The experiment uses a shielded neutron source which produced polarized neutrons via the 2H(d⃗,n⃗)3He reaction. It also uses a deuterated liquid-scintillator center detector and six pairs of liquid-scintillator neutron side detectors. Elastic neutron scattering events are identified by using time-of-flight techniques and by setting a window in the center detector pulse-height spectrum. The beam polarization is monitored by using a high-pressure helium gas cell and an additional pair of liquid-scintillator side detectors. The n-d Ay(θ) data were corrected for finite-geometry and multiple-scattering effects using a Monte Carlo simulation of the experiment. The 22.5-MeV data demonstrate that the three-nucleon analyzing power puzzle also exists at this energy. They show a significant discrepancy with predictions of high-precision nucleon-nucleon potentials alone or combined with Tucscon-Melbourne or Urbana IX three-nucleon forces, as well as currently available effective-field theory based potentials of next-to-next-to-next-to-leading order.

Ground state energies from converging and diverging power series expansions

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

Lisowski, C.; Norris, S.; Pelphrey, R.

2016-10-15

It is often assumed that bound states of quantum mechanical systems are intrinsically non-perturbative in nature and therefore any power series expansion methods should be inapplicable to predict the energies for attractive potentials. However, if the spatial domain of the Schrödinger Hamiltonian for attractive one-dimensional potentials is confined to a finite length L, the usual Rayleigh–Schrödinger perturbation theory can converge rapidly and is perfectly accurate in the weak-binding region where the ground state’s spatial extension is comparable to L. Once the binding strength is so strong that the ground state’s extension is less than L, the power expansion becomes divergent,more » consistent with the expectation that bound states are non-perturbative. However, we propose a new truncated Borel-like summation technique that can recover the bound state energy from the diverging sum. We also show that perturbation theory becomes divergent in the vicinity of an avoided-level crossing. Here the same numerical summation technique can be applied to reproduce the energies from the diverging perturbative sums.« less

Trace elements in coal. Environmental and health significance

USGS Publications Warehouse

Finkelman, R.B.

1999-01-01

Trace elements can have profound adverse effects on the health of people burning coal in homes or living near coal deposits, coal mines, and coal- burning power plants. Trace elements such as arsenic emitted from coal- burning power plants in Europe and Asia have been shown to cause severe health problems. Perhaps the most widespread health problems are caused by domestic coal combustion in developing countries where millions of people suffer from fluorosis and thousands from arsenism. Better knowledge of coal quality characteristics may help to reduce some of these health problems. For example, information on concentrations and distributions of potentially toxic elements in coal may help delineate areas of a coal deposit to be avoided. Information on the modes of occurrence of these elements and the textural relations of the minerals in coal may help to predict the behavior of the potentially toxic trace metals during coal cleaning, combustion, weathering, and leaching.

Conditional power and predictive power based on right censored data with supplementary auxiliary information.

PubMed

Sun, Libo; Wan, Ying

2018-04-22

Conditional power and predictive power provide estimates of the probability of success at the end of the trial based on the information from the interim analysis. The observed value of the time to event endpoint at the interim analysis could be biased for the true treatment effect due to early censoring, leading to a biased estimate of conditional power and predictive power. In such cases, the estimates and inference for this right censored primary endpoint are enhanced by incorporating a fully observed auxiliary variable. We assume a bivariate normal distribution of the transformed primary variable and a correlated auxiliary variable. Simulation studies are conducted that not only shows enhanced conditional power and predictive power but also can provide the framework for a more efficient futility interim analysis in terms of an improved accuracy in estimator, a smaller inflation in type II error and an optimal timing for such analysis. We also illustrated the new approach by a real clinical trial example. Copyright © 2018 John Wiley & Sons, Ltd.

The predictive power of Japanese candlestick charting in Chinese stock market

NASA Astrophysics Data System (ADS)

Chen, Shi; Bao, Si; Zhou, Yu

2016-09-01

This paper studies the predictive power of 4 popular pairs of two-day bullish and bearish Japanese candlestick patterns in Chinese stock market. Based on Morris' study, we give the quantitative details of definition of long candlestick, which is important in two-day candlestick pattern recognition but ignored by several previous researches, and we further give the quantitative definitions of these four pairs of two-day candlestick patterns. To test the predictive power of candlestick patterns on short-term price movement, we propose the definition of daily average return to alleviate the impact of correlation among stocks' overlap-time returns in statistical tests. To show the robustness of our result, two methods of trend definition are used for both the medium-market-value and large-market-value sample sets. We use Step-SPA test to correct for data snooping bias. Statistical results show that the predictive power differs from pattern to pattern, three of the eight patterns provide both short-term and relatively long-term prediction, another one pair only provide significant forecasting power within very short-term period, while the rest three patterns present contradictory results for different market value groups. For all the four pairs, the predictive power drops as predicting time increases, and forecasting power is stronger for stocks with medium market value than those with large market value.

Phylogenomic detection and functional prediction of genes potentially important for plant meiosis.

PubMed

Zhang, Luoyan; Kong, Hongzhi; Ma, Hong; Yang, Ji

2018-02-15

Meiosis is a specialized type of cell division necessary for sexual reproduction in eukaryotes. A better understanding of the cytological procedures of meiosis has been achieved by comprehensive cytogenetic studies in plants, while the genetic mechanisms regulating meiotic progression remain incompletely understood. The increasing accumulation of complete genome sequences and large-scale gene expression datasets has provided a powerful resource for phylogenomic inference and unsupervised identification of genes involved in plant meiosis. By integrating sequence homology and expression data, 164, 131, 124 and 162 genes potentially important for meiosis were identified in the genomes of Arabidopsis thaliana, Oryza sativa, Selaginella moellendorffii and Pogonatum aloides, respectively. The predicted genes were assigned to 45 meiotic GO terms, and their functions were related to different processes occurring during meiosis in various organisms. Most of the predicted meiotic genes underwent lineage-specific duplication events during plant evolution, with about 30% of the predicted genes retaining only a single copy in higher plant genomes. The results of this study provided clues to design experiments for better functional characterization of meiotic genes in plants, promoting the phylogenomic approach to the evolutionary dynamics of the plant meiotic machineries. Copyright © 2017 Elsevier B.V. All rights reserved.

Systems-based biological concordance and predictive reproducibility of gene set discovery methods in cardiovascular disease.

PubMed

Azuaje, Francisco; Zheng, Huiru; Camargo, Anyela; Wang, Haiying

2011-08-01

The discovery of novel disease biomarkers is a crucial challenge for translational bioinformatics. Demonstration of both their classification power and reproducibility across independent datasets are essential requirements to assess their potential clinical relevance. Small datasets and multiplicity of putative biomarker sets may explain lack of predictive reproducibility. Studies based on pathway-driven discovery approaches have suggested that, despite such discrepancies, the resulting putative biomarkers tend to be implicated in common biological processes. Investigations of this problem have been mainly focused on datasets derived from cancer research. We investigated the predictive and functional concordance of five methods for discovering putative biomarkers in four independently-generated datasets from the cardiovascular disease domain. A diversity of biosignatures was identified by the different methods. However, we found strong biological process concordance between them, especially in the case of methods based on gene set analysis. With a few exceptions, we observed lack of classification reproducibility using independent datasets. Partial overlaps between our putative sets of biomarkers and the primary studies exist. Despite the observed limitations, pathway-driven or gene set analysis can predict potentially novel biomarkers and can jointly point to biomedically-relevant underlying molecular mechanisms. Copyright © 2011 Elsevier Inc. All rights reserved.

The prediction of the impact of climatic factors on short-term electric power load based on the big data of smart city

NASA Astrophysics Data System (ADS)

Qiu, Yunfei; Li, Xizhong; Zheng, Wei; Hu, Qinghe; Wei, Zhanmeng; Yue, Yaqin

2017-08-01

The climate changes have great impact on the residents’ electricity consumption, so the study on the impact of climatic factors on electric power load is of significance. In this paper, the effects of the data of temperature, rainfall and wind of smart city on short-term power load is studied to predict power load. The authors studied the relation between power load and daily temperature, rainfall and wind in the 31 days of January of one year. In the research, the authors used the Matlab neural network toolbox to establish the combinational forecasting model. The authors trained the original input data continuously to get the internal rules inside the data and used the rules to predict the daily power load in the next January. The prediction method relies on the accuracy of weather forecasting. If the weather forecasting is different from the actual weather, we need to correct the climatic factors to ensure accurate prediction.

Forecasting influenza-like illness dynamics for military populations using neural networks and social media

DOE PAGES

Volkova, Svitlana; Ayton, Ellyn; Porterfield, Katherine; ...

2017-12-15

This work is the first to take advantage of recurrent neural networks to predict influenza-like-illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data [1, 2] and the state-of-the-art machine learning models [3, 4], we build and evaluate the predictive power of Long Short Term Memory (LSTMs) architectures capable of nowcasting (predicting in \\real-time") and forecasting (predicting the future) ILI dynamics in the 2011 { 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, stylistic and syntactic patterns,more » emotions and opinions, and communication behavior. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks. Finally, we combine ILI and social media signals to build joint neural network models for ILI dynamics prediction. Unlike the majority of the existing work, we specifically focus on developing models for local rather than national ILI surveillance [1], specifically for military rather than general populations [3] in 26 U.S. and six international locations. Our approach demonstrates several advantages: (a) Neural network models learned from social media data yield the best performance compared to previously used regression models. (b) Previously under-explored language and communication behavior features are more predictive of ILI dynamics than syntactic and stylistic signals expressed in social media. (c) Neural network models learned exclusively from social media signals yield comparable or better performance to the models learned from ILI historical data, thus, signals from social media can be potentially used to accurately forecast ILI dynamics for the regions where ILI historical data is not available. (d) Neural network models learned from combined ILI and social media signals significantly outperform models that rely solely on ILI historical data, which adds to a great potential of alternative public sources for ILI dynamics prediction. (e) Location-specific models outperform previously used location-independent models e.g., U.S. only. (f) Prediction results significantly vary across geolocations depending on the amount of social media data available and ILI activity patterns.« less

Forecasting influenza-like illness dynamics for military populations using neural networks and social media

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

Volkova, Svitlana; Ayton, Ellyn; Porterfield, Katherine

This work is the first to take advantage of recurrent neural networks to predict influenza-like-illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data [1, 2] and the state-of-the-art machine learning models [3, 4], we build and evaluate the predictive power of Long Short Term Memory (LSTMs) architectures capable of nowcasting (predicting in \\real-time") and forecasting (predicting the future) ILI dynamics in the 2011 { 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, stylistic and syntactic patterns,more » emotions and opinions, and communication behavior. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks. Finally, we combine ILI and social media signals to build joint neural network models for ILI dynamics prediction. Unlike the majority of the existing work, we specifically focus on developing models for local rather than national ILI surveillance [1], specifically for military rather than general populations [3] in 26 U.S. and six international locations. Our approach demonstrates several advantages: (a) Neural network models learned from social media data yield the best performance compared to previously used regression models. (b) Previously under-explored language and communication behavior features are more predictive of ILI dynamics than syntactic and stylistic signals expressed in social media. (c) Neural network models learned exclusively from social media signals yield comparable or better performance to the models learned from ILI historical data, thus, signals from social media can be potentially used to accurately forecast ILI dynamics for the regions where ILI historical data is not available. (d) Neural network models learned from combined ILI and social media signals significantly outperform models that rely solely on ILI historical data, which adds to a great potential of alternative public sources for ILI dynamics prediction. (e) Location-specific models outperform previously used location-independent models e.g., U.S. only. (f) Prediction results significantly vary across geolocations depending on the amount of social media data available and ILI activity patterns.« less

Development & experimental validation of a SINDA/FLUINT thermal/fluid/electrical model of a multi-tube AMTEC cell

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

Hendricks, T.J.; Borkowski, C.A.; Huang, C.

1998-01-01

AMTEC (Alkali Metal Thermal-to-Electric Conversion) cell development has received increased attention and funding in the space power community because of several desirable performance characteristics compared to current radioisotope thermoelectric generation and solar photovoltaic (PV) power generation. AMTEC cell development is critically dependent upon the ability to predict thermal, fluid dynamic and electrical performance of an AMTEC cell which has many complex thermal, fluid dynamic and electrical processes and interactions occurring simultaneously. Development of predictive capability is critical to understanding the complex processes and interactions within the AMTEC cell, and thereby creating the ability to design high-performance, cost-effective AMTEC cells. Amore » flexible, sophisticated thermal/fluid/electrical model of an operating AMTEC cell has been developed using the SINDA/FLUINT analysis software. This model can accurately simulate AMTEC cell performance at any hot side and cold side temperature combination desired, for any voltage and current conditions, and for a broad range of cell design parameters involving the cell dimensions, current collector and electrode design, electrode performance parameters, and cell wall and thermal shield emissivity. The model simulates the thermal radiation network within the AMTEC cell using RadCAD thermal radiation analysis; hot side, cold side and cell wall conductive and radiative coupling; BASE (Beta Alumina Solid Electrode) tube electrochemistry, including electrode over-potentials; the fluid dynamics of the low-pressure sodium vapor flow to the condenser and liquid sodium flow in the wick; sodium condensation at the condenser; and high-temperature sodium evaporation in the wick. The model predicts the temperature profiles within the AMTEC cell walls, the BASE tube temperature profiles, the sodium temperature profile in the artery return, temperature profiles in the evaporator, thermal energy flows throughout the AMTEC cell, all sodium pressure drops from hot BASE tubes to the condenser, the current, voltage, and power output from the cell, and the cell efficiency. This AMTEC cell model is so powerful and flexible that it is used in radioisotope AMTEC power system design, solar AMTEC power system design, and combustion-driven power system design on several projects at Advanced Modular Power Systems, Inc. (AMPS). The model has been successfully validated against actual cell experimental data and its performance predictions agree very well with experimental data on PX-5B cells and other test cells at AMPS. {copyright} {ital 1998 American Institute of Physics.}« less

The Scientific Foundations of Forecasting Magnetospheric Space Weather

NASA Astrophysics Data System (ADS)

Eastwood, J. P.; Nakamura, R.; Turc, L.; Mejnertsen, L.; Hesse, M.

2017-11-01

The magnetosphere is the lens through which solar space weather phenomena are focused and directed towards the Earth. In particular, the non-linear interaction of the solar wind with the Earth's magnetic field leads to the formation of highly inhomogenous electrical currents in the ionosphere which can ultimately result in damage to and problems with the operation of power distribution networks. Since electric power is the fundamental cornerstone of modern life, the interruption of power is the primary pathway by which space weather has impact on human activity and technology. Consequently, in the context of space weather, it is the ability to predict geomagnetic activity that is of key importance. This is usually stated in terms of geomagnetic storms, but we argue that in fact it is the substorm phenomenon which contains the crucial physics, and therefore prediction of substorm occurrence, severity and duration, either within the context of a longer-lasting geomagnetic storm, but potentially also as an isolated event, is of critical importance. Here we review the physics of the magnetosphere in the frame of space weather forecasting, focusing on recent results, current understanding, and an assessment of probable future developments.

Analysis of 2D Transport and Performance Characteristics for Lateral Power Devices Based on AlGaN Alloys

DOE PAGES

Coltrin, Michael E.; Baca, Albert G.; Kaplar, Robert J.

2017-10-26

In this paper, predicted lateral power device performance as a function of alloy composition is characterized by a standard lateral device figure-of-merit (LFOM) that depends on mobility, critical electric field, and sheet carrier density. The paper presents calculations of AlGaN electron mobility in lateral devices such as HEMTs across the entire alloy composition range. Alloy scattering and optical polar phonon scattering are the dominant mechanisms limiting carrier mobility. Due to the significant degradation of mobility from alloy scattering, at room temperature Al fractions greater than about 85% are required for improved LFOM relative to GaN using a conservative sheet chargemore » density of 1 × 10 13 cm –2. However, at higher temperatures at which AlGaN power devices are anticipated to operate, this “breakeven” composition decreases to about 65% at 500 K, for example. For high-frequency applications, the Johnson figure-of-merit (JFOM) is the relevant metric to compare potential device performance across materials platforms. At room temperature, the JFOM for AlGaN alloys is predicted to surpass that of GaN for Al fractions greater than about 40%.« less

Analysis of 2D Transport and Performance Characteristics for Lateral Power Devices Based on AlGaN Alloys

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

Coltrin, Michael E.; Baca, Albert G.; Kaplar, Robert J.

In this paper, predicted lateral power device performance as a function of alloy composition is characterized by a standard lateral device figure-of-merit (LFOM) that depends on mobility, critical electric field, and sheet carrier density. The paper presents calculations of AlGaN electron mobility in lateral devices such as HEMTs across the entire alloy composition range. Alloy scattering and optical polar phonon scattering are the dominant mechanisms limiting carrier mobility. Due to the significant degradation of mobility from alloy scattering, at room temperature Al fractions greater than about 85% are required for improved LFOM relative to GaN using a conservative sheet chargemore » density of 1 × 10 13 cm –2. However, at higher temperatures at which AlGaN power devices are anticipated to operate, this “breakeven” composition decreases to about 65% at 500 K, for example. For high-frequency applications, the Johnson figure-of-merit (JFOM) is the relevant metric to compare potential device performance across materials platforms. At room temperature, the JFOM for AlGaN alloys is predicted to surpass that of GaN for Al fractions greater than about 40%.« less

Integrating in Silico and in Vitro Approaches To Predict Drug Accessibility to the Central Nervous System.

PubMed

Zhang, Yan-Yan; Liu, Houfu; Summerfield, Scott G; Luscombe, Christopher N; Sahi, Jasminder

2016-05-02

Estimation of uptake across the blood-brain barrier (BBB) is key to designing central nervous system (CNS) therapeutics. In silico approaches ranging from physicochemical rules to quantitative structure-activity relationship (QSAR) models are utilized to predict potential for CNS penetration of new chemical entities. However, there are still gaps in our knowledge of (1) the relationship between marketed human drug derived CNS-accessible chemical space and preclinical neuropharmacokinetic (neuroPK) data, (2) interpretability of the selected physicochemical descriptors, and (3) correlation of the in vitro human P-glycoprotein (P-gp) efflux ratio (ER) and in vivo rodent unbound brain-to-blood ratio (Kp,uu), as these are assays routinely used to predict clinical CNS exposure, during drug discovery. To close these gaps, we explored the CNS druglike property boundaries of 920 market oral drugs (315 CNS and 605 non-CNS) and 846 compounds (54 CNS drugs and 792 proprietary GlaxoSmithKline compounds) with available rat Kp,uu data. The exact permeability coefficient (Pexact) and P-gp ER were determined for 176 compounds from the rat Kp,uu data set. Receiver operating characteristic curves were performed to evaluate the predictive power of human P-gp ER for rat Kp,uu. Our data demonstrates that simple physicochemical rules (most acidic pKa ≥ 9.5 and TPSA < 100) in combination with P-gp ER < 1.5 provide mechanistic insights for filtering BBB permeable compounds. For comparison, six classification modeling methods were investigated using multiple sets of in silico molecular descriptors. We present a random forest model with excellent predictive power (∼0.75 overall accuracy) using the rat neuroPK data set. We also observed good concordance between the structural interpretation results and physicochemical descriptor importance from the Kp,uu classification QSAR model. In summary, we propose a novel, hybrid in silico/in vitro approach and an in silico screening model for the effective development of chemical series with the potential to achieve optimal CNS exposure.

Thermoelectric Power Factor Limit of a 1D Nanowire

NASA Astrophysics Data System (ADS)

Chen, I.-Ju; Burke, Adam; Svilans, Artis; Linke, Heiner; Thelander, Claes

2018-04-01

In the past decade, there has been significant interest in the potentially advantageous thermoelectric properties of one-dimensional (1D) nanowires, but it has been challenging to find high thermoelectric power factors based on 1D effects in practice. Here we point out that there is an upper limit to the thermoelectric power factor of nonballistic 1D nanowires, as a consequence of the recently established quantum bound of thermoelectric power output. We experimentally test this limit in quasiballistic InAs nanowires by extracting the maximum power factor of the first 1D subband through I -V characterization, finding that the measured maximum power factors conform to the theoretical limit. The established limit allows the prediction of the achievable power factor of a specific nanowire material system with 1D electronic transport based on the nanowire dimension and mean free path. The power factor of state-of-the-art semiconductor nanowires with small cross section and high crystal quality can be expected to be highly competitive (on the order of mW /m K2 ) at low temperatures. However, they have no clear advantage over bulk materials at, or above, room temperature.

Thermoelectric Power Factor Limit of a 1D Nanowire.

PubMed

Chen, I-Ju; Burke, Adam; Svilans, Artis; Linke, Heiner; Thelander, Claes

2018-04-27

In the past decade, there has been significant interest in the potentially advantageous thermoelectric properties of one-dimensional (1D) nanowires, but it has been challenging to find high thermoelectric power factors based on 1D effects in practice. Here we point out that there is an upper limit to the thermoelectric power factor of nonballistic 1D nanowires, as a consequence of the recently established quantum bound of thermoelectric power output. We experimentally test this limit in quasiballistic InAs nanowires by extracting the maximum power factor of the first 1D subband through I-V characterization, finding that the measured maximum power factors conform to the theoretical limit. The established limit allows the prediction of the achievable power factor of a specific nanowire material system with 1D electronic transport based on the nanowire dimension and mean free path. The power factor of state-of-the-art semiconductor nanowires with small cross section and high crystal quality can be expected to be highly competitive (on the order of mW/m K^{2}) at low temperatures. However, they have no clear advantage over bulk materials at, or above, room temperature.

Attractors, universality, and inflation

NASA Astrophysics Data System (ADS)

Downes, Sean; Dutta, Bhaskar; Sinha, Kuver

2012-11-01

Studies of the initial conditions for inflation have conflicting predictions from exponential suppression to inevitability. At the level of phase space, this conflict arises from the competing intuitions of CPT invariance and thermodynamics. After reviewing this conflict, we enlarge the ensemble beyond phase space to include scalar potential data. We show how this leads to an important contribution from inflection point inflation, enhancing the likelihood of inflation to a power law, 1/Ne3. In the process, we emphasize the attractor dynamics of the gravity-scalar system and the existence of universality classes from inflection point inflation. Finally, we comment on the predictivity of inflation in light of these results.

Prediction of force coefficients for labyrinth seals

NASA Technical Reports Server (NTRS)

Lee, O. W. K.; Martinez-Sanchez, M.; Czajkowski, E.

1984-01-01

The development of a linear model for the prediction of labyrinth seal forces and on its comparison to available stiffness data is presented. A discussion of the relevance of fluid damping forces and the preliminary stages of a program to obtain data on these forces are examined. Fluid-dynamic forces arising from nonuniform pressure patterns in labyrinth seal glands are known to be potentially destablizing in high power turbomachinery. A well documented case in point is that of the space Shuttle Main Engine turbopumps. Seal forces are also an important factor for the stability of shrouded turbines, acting in that case in conjunction with the effects of blade-tip clearance variations.

Performance Prediction and Validation: Data, Frameworks, and Considerations

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

Tinnesand, Heidi

2017-05-19

Improving the predictability and reliability of wind power generation and operations will reduce costs and potentially establish a framework to attract new capital into the distributed wind sector, a key cost reduction requirement highlighted in results from the distributed wind future market assessment conducted with dWind. Quantifying and refining the accuracy of project performance estimates will also directly address several of the key challenges identified by industry stakeholders in 2015 as part of the distributed wind resource assessment workshop and be cross-cutting for several other facets of the distributed wind portfolio. This presentation covers the efforts undertaken in 2016 tomore » address these topics.« less

The Impact of Human Factors on Decision Making in Combat

DTIC Science & Technology

1990-06-01

positive attitude and intellectual reach speak volumes for the aggressive approach necessary for progress in human factors research. * Professor Glenn...stimuli, while the third focuses on the change in combat potential/combat power resulting from stimuli on factors related to either the individual...predict the behavior of a member of this society, all possible combina- tions of variables must be tested. At the point where every variable combination

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

Mandage, Revati S.; McAteer, R. T. James, E-mail: mcateer@nmsu.edu

A magnetic power spectral analysis is performed on 53 solar active regions, observed from 2011 August to 2012 July. Magnetic field data obtained from the Helioseismic and Magnetic Imager, inverted as Active Region Patches, are used to study the evolution of the magnetic power index as each region rotates across the solar disk. Active regions are classified based on the numbers and sizes of solar flares they produce in order to study the relationship between flare productivity and the magnetic power index. The choice of window size and inertial range plays a key role in determining the correct magnetic powermore » index. The overall distribution of magnetic power indices has a range of 1.0–2.5. Flare-quiet regions peak at a value of 1.6. However, flare-productive regions peak at a value of 2.2. Overall, the histogram of the distribution of power indices of flare-productive active regions is well separated from flare-quiet active regions. Only 12% of flare-quiet regions exhibit an index greater than 2, whereas 90% of flare-productive regions exhibit an index greater than 2. Flare-quiet regions exhibit a high temporal variance (i.e., the index fluctuates between high and low values), whereas flare-productive regions maintain an index greater than 2 for several days. This shows the importance of including the temporal evolution of active regions in flare prediction studies, and highlights the potential of a 2–3 day prediction window for space weather applications.« less

Characterization of the Optical Properties of Turbid Media by Supervised Learning of Scattering Patterns.

PubMed

Hassaninia, Iman; Bostanabad, Ramin; Chen, Wei; Mohseni, Hooman

2017-11-10

Fabricated tissue phantoms are instrumental in optical in-vitro investigations concerning cancer diagnosis, therapeutic applications, and drug efficacy tests. We present a simple non-invasive computational technique that, when coupled with experiments, has the potential for characterization of a wide range of biological tissues. The fundamental idea of our approach is to find a supervised learner that links the scattering pattern of a turbid sample to its thickness and scattering parameters. Once found, this supervised learner is employed in an inverse optimization problem for estimating the scattering parameters of a sample given its thickness and scattering pattern. Multi-response Gaussian processes are used for the supervised learning task and a simple setup is introduced to obtain the scattering pattern of a tissue sample. To increase the predictive power of the supervised learner, the scattering patterns are filtered, enriched by a regressor, and finally characterized with two parameters, namely, transmitted power and scaled Gaussian width. We computationally illustrate that our approach achieves errors of roughly 5% in predicting the scattering properties of many biological tissues. Our method has the potential to facilitate the characterization of tissues and fabrication of phantoms used for diagnostic and therapeutic purposes over a wide range of optical spectrum.

Metabolome of human gut microbiome is predictive of host dysbiosis.

PubMed

Larsen, Peter E; Dai, Yang

2015-01-01

Humans live in constant and vital symbiosis with a closely linked bacterial ecosystem called the microbiome, which influences many aspects of human health. When this microbial ecosystem becomes disrupted, the health of the human host can suffer; a condition called dysbiosis. However, the community compositions of human microbiomes also vary dramatically from individual to individual, and over time, making it difficult to uncover the underlying mechanisms linking the microbiome to human health. We propose that a microbiome's interaction with its human host is not necessarily dependent upon the presence or absence of particular bacterial species, but instead is dependent on its community metabolome; an emergent property of the microbiome. Using data from a previously published, longitudinal study of microbiome populations of the human gut, we extrapolated information about microbiome community enzyme profiles and metabolome models. Using machine learning techniques, we demonstrated that the aggregate predicted community enzyme function profiles and modeled metabolomes of a microbiome are more predictive of dysbiosis than either observed microbiome community composition or predicted enzyme function profiles. Specific enzyme functions and metabolites predictive of dysbiosis provide insights into the molecular mechanisms of microbiome-host interactions. The ability to use machine learning to predict dysbiosis from microbiome community interaction data provides a potentially powerful tool for understanding the links between the human microbiome and human health, pointing to potential microbiome-based diagnostics and therapeutic interventions.

Metabolome of human gut microbiome is predictive of host dysbiosis

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

Larsen, Peter E.; Dai, Yang

Background: Humans live in constant and vital symbiosis with a closely linked bacterial ecosystem called the microbiome, which influences many aspects of human health. When this microbial ecosystem becomes disrupted, the health of the human host can suffer; a condition called dysbiosis. The community compositions of human microbiomes also vary dramatically from individual to individual, and over time, making it difficult to uncover the underlying mechanisms linking the microbiome to human health. We propose that a microbiome’s interaction with its human host is not necessarily dependent upon the presence or absence of particular bacterial species, but instead is dependent onmore » its community metabolome; an emergent property of the microbiome. Results: Using data from a previously published, longitudinal study of microbiome populations of the human gut, we extrapolated information about microbiome community enzyme profiles and metabolome models. Using machine learning techniques, we demonstrated that the aggregate predicted community enzyme function profiles and modeled metabolomes of a microbiome are more predictive of dysbiosis than either observed microbiome community composition or predicted enzyme function profiles. Conclusions: Specific enzyme functions and metabolites predictive of dysbiosis provide insights into the molecular mechanisms of microbiome–host interactions. The ability to use machine learning to predict dysbiosis from microbiome community interaction data provides a potentially powerful tool for understanding the links between the human microbiome and human health, pointing to potential microbiome-based diagnostics and therapeutic interventions.« less

Metabolome of human gut microbiome is predictive of host dysbiosis

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

Larsen, Peter E.; Dai, Yang

Background: Humans live in constant and vital symbiosis with a closely linked bacterial ecosystem called the microbiome, which influences many aspects of human health. When this microbial ecosystem becomes disrupted, the health of the human host can suffer; a condition called dysbiosis. However, the community compositions of human microbiomes also vary dramatically from individual to individual, and over time, making it difficult to uncover the underlying mechanisms linking the microbiome to human health. We propose that a microbiome’s interaction with its human host is not necessarily dependent upon the presence or absence of particular bacterial species, but instead is dependentmore » on its community metabolome; an emergent property of the microbiome. Results: Using data from a previously published, longitudinal study of microbiome populations of the human gut, we extrapolated information about microbiome community enzyme profiles and metabolome models. Using machine learning techniques, we demonstrated that the aggregate predicted community enzyme function profiles and modeled metabolomes of a microbiome are more predictive of dysbiosis than either observed microbiome community composition or predicted enzyme function profiles. Conclusions: Specific enzyme functions and metabolites predictive of dysbiosis provide insights into the molecular mechanisms of microbiome–host interactions. The ability to use machine learning to predict dysbiosis from microbiome community interaction data provides a potentially powerful tool for understanding the links between the human microbiome and human health, pointing to potential microbiome-based diagnostics and therapeutic interventions.« less

Metabolome of human gut microbiome is predictive of host dysbiosis

DOE PAGES

Larsen, Peter E.; Dai, Yang

2015-09-14

Background: Humans live in constant and vital symbiosis with a closely linked bacterial ecosystem called the microbiome, which influences many aspects of human health. When this microbial ecosystem becomes disrupted, the health of the human host can suffer; a condition called dysbiosis. The community compositions of human microbiomes also vary dramatically from individual to individual, and over time, making it difficult to uncover the underlying mechanisms linking the microbiome to human health. We propose that a microbiome’s interaction with its human host is not necessarily dependent upon the presence or absence of particular bacterial species, but instead is dependent onmore » its community metabolome; an emergent property of the microbiome. Results: Using data from a previously published, longitudinal study of microbiome populations of the human gut, we extrapolated information about microbiome community enzyme profiles and metabolome models. Using machine learning techniques, we demonstrated that the aggregate predicted community enzyme function profiles and modeled metabolomes of a microbiome are more predictive of dysbiosis than either observed microbiome community composition or predicted enzyme function profiles. Conclusions: Specific enzyme functions and metabolites predictive of dysbiosis provide insights into the molecular mechanisms of microbiome–host interactions. The ability to use machine learning to predict dysbiosis from microbiome community interaction data provides a potentially powerful tool for understanding the links between the human microbiome and human health, pointing to potential microbiome-based diagnostics and therapeutic interventions.« less

A Python Analytical Pipeline to Identify Prohormone Precursors and Predict Prohormone Cleavage Sites

PubMed Central

Southey, Bruce R.; Sweedler, Jonathan V.; Rodriguez-Zas, Sandra L.

2008-01-01

Neuropeptides and hormones are signaling molecules that support cell–cell communication in the central nervous system. Experimentally characterizing neuropeptides requires significant efforts because of the complex and variable processing of prohormone precursor proteins into neuropeptides and hormones. We demonstrate the power and flexibility of the Python language to develop components of an bioinformatic analytical pipeline to identify precursors from genomic data and to predict cleavage as these precursors are en route to the final bioactive peptides. We identified 75 precursors in the rhesus genome, predicted cleavage sites using support vector machines and compared the rhesus predictions to putative assignments based on homology to human sequences. The correct classification rate of cleavage using the support vector machines was over 97% for both human and rhesus data sets. The functionality of Python has been important to develop and maintain NeuroPred (http://neuroproteomics.scs.uiuc.edu/neuropred.html), a user-centered web application for the neuroscience community that provides cleavage site prediction from a wide range of models, precision and accuracy statistics, post-translational modifications, and the molecular mass of potential peptides. The combined results illustrate the suitability of the Python language to implement an all-inclusive bioinformatics approach to predict neuropeptides that encompasses a large number of interdependent steps, from scanning genomes for precursor genes to identification of potential bioactive neuropeptides. PMID:19169350

Utilization of Model Predictive Control to Balance Power Absorption Against Load Accumulation

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

Abbas, Nikhar; Tom, Nathan M

2017-06-03

Wave energy converter (WEC) control strategies have been primarily focused on maximizing power absorption. The use of model predictive control strategies allows for a finite-horizon, multiterm objective function to be solved. This work utilizes a multiterm objective function to maximize power absorption while minimizing the structural loads on the WEC system. Furthermore, a Kalman filter and autoregressive model were used to estimate and forecast the wave exciting force and predict the future dynamics of the WEC. The WEC's power-take-off time-averaged power and structural loads under a perfect forecast assumption in irregular waves were compared against results obtained from the Kalmanmore » filter and autoregressive model to evaluate model predictive control performance.« less

Utilization of Model Predictive Control to Balance Power Absorption Against Load Accumulation: Preprint

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

Abbas, Nikhar; Tom, Nathan

Wave energy converter (WEC) control strategies have been primarily focused on maximizing power absorption. The use of model predictive control strategies allows for a finite-horizon, multiterm objective function to be solved. This work utilizes a multiterm objective function to maximize power absorption while minimizing the structural loads on the WEC system. Furthermore, a Kalman filter and autoregressive model were used to estimate and forecast the wave exciting force and predict the future dynamics of the WEC. The WEC's power-take-off time-averaged power and structural loads under a perfect forecast assumption in irregular waves were compared against results obtained from the Kalmanmore » filter and autoregressive model to evaluate model predictive control performance.« less

Upper Limits for Power Yield in Thermal, Chemical, and Electrochemical Systems

NASA Astrophysics Data System (ADS)

Sieniutycz, Stanislaw

2010-03-01

We consider modeling and power optimization of energy converters, such as thermal, solar and chemical engines and fuel cells. Thermodynamic principles lead to expressions for converter's efficiency and generated power. Efficiency equations serve to solve the problems of upgrading or downgrading a resource. Power yield is a cumulative effect in a system consisting of a resource, engines, and an infinite bath. While optimization of steady state systems requires using the differential calculus and Lagrange multipliers, dynamic optimization involves variational calculus and dynamic programming. The primary result of static optimization is the upper limit of power, whereas that of dynamic optimization is a finite-rate counterpart of classical reversible work (exergy). The latter quantity depends on the end state coordinates and a dissipation index, h, which is the Hamiltonian of the problem of minimum entropy production. In reacting systems, an active part of chemical affinity constitutes a major component of the overall efficiency. The theory is also applied to fuel cells regarded as electrochemical flow engines. Enhanced bounds on power yield follow, which are stronger than those predicted by the reversible work potential.

Nonlinear dielectric response and transient current: An effective potential for ferroelectric domain wall displacement

NASA Astrophysics Data System (ADS)

Placeres Jiménez, Rolando; Pedro Rino, José; Marino Gonçalves, André; Antonio Eiras, José

2013-09-01

Ferroelectric domain walls are modeled as rigid bodies moving under the action of a potential field in a dissipative medium. Assuming that the dielectric permittivity follows the dependence ɛ '∝1/(α+βE2), it obtained the exact expression for the effective potential. Simulations of polarization current correctly predict a power law. Such results could be valuable in the study of domain wall kinetic and ultrafast polarization processes. The model is extended to poled samples allowing the study of nonlinear dielectric permittivity under subswitching electric fields. Experimental nonlinear data from PZT 20/80 thin films and Fe+3 doped PZT 40/60 ceramic are reproduced.

Ultra-Short-Term Wind Power Prediction Using a Hybrid Model

NASA Astrophysics Data System (ADS)

Mohammed, E.; Wang, S.; Yu, J.

2017-05-01

This paper aims to develop and apply a hybrid model of two data analytical methods, multiple linear regressions and least square (MLR&LS), for ultra-short-term wind power prediction (WPP), for example taking, Northeast China electricity demand. The data was obtained from the historical records of wind power from an offshore region, and from a wind farm of the wind power plant in the areas. The WPP achieved in two stages: first, the ratios of wind power were forecasted using the proposed hybrid method, and then the transformation of these ratios of wind power to obtain forecasted values. The hybrid model combines the persistence methods, MLR and LS. The proposed method included two prediction types, multi-point prediction and single-point prediction. WPP is tested by applying different models such as autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA) and artificial neural network (ANN). By comparing results of the above models, the validity of the proposed hybrid model is confirmed in terms of error and correlation coefficient. Comparison of results confirmed that the proposed method works effectively. Additional, forecasting errors were also computed and compared, to improve understanding of how to depict highly variable WPP and the correlations between actual and predicted wind power.

Experimental validation of boundary element methods for noise prediction

NASA Technical Reports Server (NTRS)

Seybert, A. F.; Oswald, Fred B.

1992-01-01

Experimental validation of methods to predict radiated noise is presented. A combined finite element and boundary element model was used to predict the vibration and noise of a rectangular box excited by a mechanical shaker. The predicted noise was compared to sound power measured by the acoustic intensity method. Inaccuracies in the finite element model shifted the resonance frequencies by about 5 percent. The predicted and measured sound power levels agree within about 2.5 dB. In a second experiment, measured vibration data was used with a boundary element model to predict noise radiation from the top of an operating gearbox. The predicted and measured sound power for the gearbox agree within about 3 dB.

Predicting Rediated Noise With Power Flow Finite Element Analysis

DTIC Science & Technology

2007-02-01

Defence R&D Canada – Atlantic DEFENCE DÉFENSE & Predicting Rediated Noise With Power Flow Finite Element Analysis D. Brennan T.S. Koko L. Jiang J...PREDICTING RADIATED NOISE WITH POWER FLOW FINITE ELEMENT ANALYSIS D.P. Brennan T.S. Koko L. Jiang J.C. Wallace Martec Limited Martec Limited...model- or full-scale data before it is available for general use. Brennan, D.P., Koko , T.S., Jiang, L., Wallace, J.C. 2007. Predicting Radiated

Dynamic Modeling and Very Short-term Prediction of Wind Power Output Using Box-Cox Transformation

NASA Astrophysics Data System (ADS)

Urata, Kengo; Inoue, Masaki; Murayama, Dai; Adachi, Shuichi

2016-09-01

We propose a statistical modeling method of wind power output for very short-term prediction. The modeling method with a nonlinear model has cascade structure composed of two parts. One is a linear dynamic part that is driven by a Gaussian white noise and described by an autoregressive model. The other is a nonlinear static part that is driven by the output of the linear part. This nonlinear part is designed for output distribution matching: we shape the distribution of the model output to match with that of the wind power output. The constructed model is utilized for one-step ahead prediction of the wind power output. Furthermore, we study the relation between the prediction accuracy and the prediction horizon.

Social motives and cognitive power-sex associations: predictors of aggressive sexual behavior.

PubMed

Zurbriggen, E L

2000-03-01

The present study investigated whether implicit social motives and cognitive power-sex associations would predict self-reports of aggressive sexual behavior. Participants wrote stories in response to Thematic Apperception Test pictures, which were scored for power and affiliation-intimacy motives. They also completed a lexical-decision priming task that provided an index of the strength of the cognitive association between the concepts of "power" and "sexuality." For men, high levels of power motivation and strong power-sex associations predicted more frequent aggression. There was also an interaction: Power motivation was unrelated to aggression for men with the weakest power-sex associations. For women, high levels of affiliation-intimacy motivation were associated with more frequent aggression. Strong power-sex associations were also predictive for women but only when affiliation-intimacy motivation was high.

Prognostics for Microgrid Components

NASA Technical Reports Server (NTRS)

Saxena, Abhinav

2012-01-01

Prognostics is the science of predicting future performance and potential failures based on targeted condition monitoring. Moving away from the traditional reliability centric view, prognostics aims at detecting and quantifying the time to impending failures. This advance warning provides the opportunity to take actions that can preserve uptime, reduce cost of damage, or extend the life of the component. The talk will focus on the concepts and basics of prognostics from the viewpoint of condition-based systems health management. Differences with other techniques used in systems health management and philosophies of prognostics used in other domains will be shown. Examples relevant to micro grid systems and subsystems will be used to illustrate various types of prediction scenarios and the resources it take to set up a desired prognostic system. Specifically, the implementation results for power storage and power semiconductor components will demonstrate specific solution approaches of prognostics. The role of constituent elements of prognostics, such as model, prediction algorithms, failure threshold, run-to-failure data, requirements and specifications, and post-prognostic reasoning will be explained. A discussion on performance evaluation and performance metrics will conclude the technical discussion followed by general comments on open research problems and challenges in prognostics.

Internal health locus of control predicts willingness to track health behaviors online and with smartphone applications.

PubMed

Bennett, Brooke L; Goldstein, Carly M; Gathright, Emily C; Hughes, Joel W; Latner, Janet D

2017-12-01

Given rising technology use across all demographic groups, digital interventions offer a potential strategy for increasing access to health information and care. Research is lacking on identifying individual differences that impact willingness to use digital interventions, which may affect patient engagement. Health locus of control, the amount of control an individual believes they have over their own health, may predict willingness to use mobile health (mHealth) applications ('apps') and online trackers. A cross-sectional study (n = 276) was conducted to assess college students' health locus of control beliefs and willingness to use health apps and online trackers. Internal and powerful other health locus of control beliefs predicted willingness to use health apps and online trackers while chance health locus of control beliefs did not. Individuals with internal and powerful other health locus of control beliefs are more willing than those with chance health locus of control beliefs to utilize a form of technology to monitor or change health behaviors. Health locus of control is an easy-to-assess patient characteristic providers can measure to identify which patients are more likely to utilize mHealth apps and online trackers.

Sparse network-based models for patient classification using fMRI

PubMed Central

Rosa, Maria J.; Portugal, Liana; Hahn, Tim; Fallgatter, Andreas J.; Garrido, Marta I.; Shawe-Taylor, John; Mourao-Miranda, Janaina

2015-01-01

Pattern recognition applied to whole-brain neuroimaging data, such as functional Magnetic Resonance Imaging (fMRI), has proved successful at discriminating psychiatric patients from healthy participants. However, predictive patterns obtained from whole-brain voxel-based features are difficult to interpret in terms of the underlying neurobiology. Many psychiatric disorders, such as depression and schizophrenia, are thought to be brain connectivity disorders. Therefore, pattern recognition based on network models might provide deeper insights and potentially more powerful predictions than whole-brain voxel-based approaches. Here, we build a novel sparse network-based discriminative modeling framework, based on Gaussian graphical models and L1-norm regularized linear Support Vector Machines (SVM). In addition, the proposed framework is optimized in terms of both predictive power and reproducibility/stability of the patterns. Our approach aims to provide better pattern interpretation than voxel-based whole-brain approaches by yielding stable brain connectivity patterns that underlie discriminative changes in brain function between the groups. We illustrate our technique by classifying patients with major depressive disorder (MDD) and healthy participants, in two (event- and block-related) fMRI datasets acquired while participants performed a gender discrimination and emotional task, respectively, during the visualization of emotional valent faces. PMID:25463459

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

Glucose determination in human aqueous humor with Raman spectroscopy

NASA Technical Reports Server (NTRS)

Lambert, James L.; Pelletier, Christine C.; Borchert, Mark

2005-01-01

It has been suggested that spectroscopic analysis of the aqueous humor of the eye could be used to indirectly predict blood glucose levels in diabetics noninvasively. We have been investigating this potential using Raman spectroscopy in combination with partial least squares (PLS) analysis. We have determined that glucose at clinically relevant concentrations can be accurately predicted in human aqueous humor in vitro using a PLS model based on artificial aqueous humor. We have further determined that with proper instrument design, the light energy necessary to achieve clinically acceptable prediction of glucose does not damage the retinas of rabbits and can be delivered at powers below internationally acceptable safety limits. Herein we summarize our current results and address our strategies to improve instrument design. 2005 Society of Photo-Optical Instrumentation Engineers.

What is interesting? Exploring the appraisal structure of interest.

PubMed

Silvia, Paul J

2005-03-01

Relative to other emotions, interest is poorly understood. On the basis of theories of appraisal process and structure, it was predicted that interest consists of appraisals of novelty (factors related to unfamiliarity and complexity) and appraisals of coping potential (the ability to understand the new, complex thing). Four experiments, using in vivo rather than retrospective methods, supported this appraisal structure. The findings were general across measured and manipulated appraisals, interesting stimuli (random polygons, visual art, poetry), and measures of interest (self-reports, forced-choice, behavioral measures). Furthermore, the appraisal structure was specific to interest (it did not predict enjoyment, a related positive emotion), and appraisals predicted interest beyond relevant traits (curiosity, openness). The appraisal perspective offers a powerful way of construing the causes of interest. Copyright 2005 APA, all rights reserved.

Two-field analysis of no-scale supergravity inflation

DOE PAGES

Ellis, John; Garcia, Marcos A. G.; Nanopoulos, Dimitri V.; ...

2015-01-08

Since the building-blocks of supersymmetric models include chiral superfields containing pairs of effective scalar fields, a two-field approach is particularly appropriate for models of inflation based on supergravity. In this paper, we generalize the two-field analysis of the inflationary power spectrum to supergravity models with arbitrary Kähler potential. We show how two-field effects in the context of no-scale supergravity can alter the model predictions for the scalar spectral index n s and the tensor-to-scalar ratio r, yielding results that interpolate between the Planck-friendly Starobinsky model and BICEP2-friendly predictions. In particular, we show that two-field effects in a chaotic no-scale inflationmore » model with a quadratic potential are capable of reducing r to very small values << 0.1. Here, we also calculate the non-Gaussianity measure f NL, finding that is well below the current experimental sensitivity.« less

A new phenomenological /τ-/α interaction

NASA Astrophysics Data System (ADS)

Heiberg-Andersen, H.; Mackintosh, R. S.; Vaagen, J. S.

2003-01-01

We present a potential model, with distinctive features, reproducing angular distributions and analyzing power data for τ- α scattering from 20 to 30 MeV τ energy with regular variation of the parameters. The distinctive features are: (1) a spin-orbit term which incorporates the influence of central depression in the α nucleus, and, (2) central terms which are strongly parity dependent. The parity dependence of the real central term is such that the odd-parity component has both a greater rms radius and greater volume integral than the even-parity component. These parity dependence characteristics had been predicted by the inversion of the RGM S-matrix. Our result supports a considerable contribution from three-nucleon exchange processes. The predicted 1/2 - level of 7Be is shifted 3 MeV relative to a previous one-level R-matrix formula fit, and depends strongly on the geometry of the spin-orbit potential.

Potential climate impact of black carbon emitted by rockets

NASA Astrophysics Data System (ADS)

Ross, Martin; Mills, Michael; Toohey, Darin

2010-12-01

A new type of hydrocarbon rocket engine is expected to power a fleet of suborbital rockets for commercial and scientific purposes in coming decades. A global climate model predicts that emissions from a fleet of 1000 launches per year of suborbital rockets would create a persistent layer of black carbon particles in the northern stratosphere that could cause potentially significant changes in the global atmospheric circulation and distributions of ozone and temperature. Tropical stratospheric ozone abundances are predicted to change as much as 1%, while polar ozone changes by up to 6%. Polar surface temperatures change as much as one degree K regionally with significant impacts on polar sea ice fractions. After one decade of continuous launches, globally averaged radiative forcing from the black carbon would exceed the forcing from the emitted CO2 by a factor of about 105 and would be comparable to the radiative forcing estimated from current subsonic aviation.

Operator performance and localized muscle fatigue in a simulated space vehicle control task

NASA Technical Reports Server (NTRS)

Lewis, J. L., Jr.

1979-01-01

Fourier transforms in a special purpose computer were utilized to obtain power spectral density functions from electromyograms of the biceps brachii, triceps brachii, brachioradialis, flexor carpi ulnaris, brachialis, and pronator teres in eight subjects performing isometric tracking tasks in two directions utilizing a prototype spacecraft rotational hand controller. Analysis of these spectra in general purpose computers aided in defining muscles involved in performing the task, and yielded a derived measure potentially useful in predicting task termination. The triceps was the only muscle to show significant differences in all possible tests for simple effects in both tasks and, overall, was the most consistently involved of the six muscles. The total power monitored for triceps, biceps, and brachialis dropped to minimal levels across all subjects earlier than for other muscles. However, smaller variances existed for the biceps, brachioradialis, brachialis, and flexor carpi ulnaris muscles and could provide longer predictive times due to smaller standard deviations for a greater population range.

Worldwide impact of aerosol's time scale on the predicted long-term concentrating solar power potential.

PubMed

Ruiz-Arias, Jose A; Gueymard, Christian A; Santos-Alamillos, Francisco J; Pozo-Vázquez, David

2016-08-10

Concentrating solar technologies, which are fuelled by the direct normal component of solar irradiance (DNI), are among the most promising solar technologies. Currently, the state-of the-art methods for DNI evaluation use datasets of aerosol optical depth (AOD) with only coarse (typically monthly) temporal resolution. Using daily AOD data from both site-specific observations at ground stations as well as gridded model estimates, a methodology is developed to evaluate how the calculated long-term DNI resource is affected by using AOD data averaged over periods from 1 to 30 days. It is demonstrated here that the use of monthly representations of AOD leads to systematic underestimations of the predicted long-term DNI up to 10% in some areas with high solar resource, which may result in detrimental consequences for the bankability of concentrating solar power projects. Recommendations for the use of either daily or monthly AOD data are provided on a geographical basis.

Worldwide impact of aerosol’s time scale on the predicted long-term concentrating solar power potential

PubMed Central

Ruiz-Arias, Jose A.; Gueymard, Christian A.; Santos-Alamillos, Francisco J.; Pozo-Vázquez, David

2016-01-01

Concentrating solar technologies, which are fuelled by the direct normal component of solar irradiance (DNI), are among the most promising solar technologies. Currently, the state-of the-art methods for DNI evaluation use datasets of aerosol optical depth (AOD) with only coarse (typically monthly) temporal resolution. Using daily AOD data from both site-specific observations at ground stations as well as gridded model estimates, a methodology is developed to evaluate how the calculated long-term DNI resource is affected by using AOD data averaged over periods from 1 to 30 days. It is demonstrated here that the use of monthly representations of AOD leads to systematic underestimations of the predicted long-term DNI up to 10% in some areas with high solar resource, which may result in detrimental consequences for the bankability of concentrating solar power projects. Recommendations for the use of either daily or monthly AOD data are provided on a geographical basis. PMID:27507711

Potential climatic impacts and reliability of large-scale offshore wind farms

NASA Astrophysics Data System (ADS)

Wang, Chien; Prinn, Ronald G.

2011-04-01

The vast availability of wind power has fueled substantial interest in this renewable energy source as a potential near-zero greenhouse gas emission technology for meeting future world energy needs while addressing the climate change issue. However, in order to provide even a fraction of the estimated future energy needs, a large-scale deployment of wind turbines (several million) is required. The consequent environmental impacts, and the inherent reliability of such a large-scale usage of intermittent wind power would have to be carefully assessed, in addition to the need to lower the high current unit wind power costs. Our previous study (Wang and Prinn 2010 Atmos. Chem. Phys. 10 2053) using a three-dimensional climate model suggested that a large deployment of wind turbines over land to meet about 10% of predicted world energy needs in 2100 could lead to a significant temperature increase in the lower atmosphere over the installed regions. A global-scale perturbation to the general circulation patterns as well as to the cloud and precipitation distribution was also predicted. In the later study reported here, we conducted a set of six additional model simulations using an improved climate model to further address the potential environmental and intermittency issues of large-scale deployment of offshore wind turbines for differing installation areas and spatial densities. In contrast to the previous land installation results, the offshore wind turbine installations are found to cause a surface cooling over the installed offshore regions. This cooling is due principally to the enhanced latent heat flux from the sea surface to lower atmosphere, driven by an increase in turbulent mixing caused by the wind turbines which was not entirely offset by the concurrent reduction of mean wind kinetic energy. We found that the perturbation of the large-scale deployment of offshore wind turbines to the global climate is relatively small compared to the case of land-based installations. However, the intermittency caused by the significant seasonal wind variations over several major offshore sites is substantial, and demands further options to ensure the reliability of large-scale offshore wind power. The method that we used to simulate the offshore wind turbine effect on the lower atmosphere involved simply increasing the ocean surface drag coefficient. While this method is consistent with several detailed fine-scale simulations of wind turbines, it still needs further study to ensure its validity. New field observations of actual wind turbine arrays are definitely required to provide ultimate validation of the model predictions presented here.

Analysis on Potential of Electric Energy Market based on Large Industrial Consumer

NASA Astrophysics Data System (ADS)

Lin, Jingyi; Zhu, Xinzhi; Yang, Shuo; Xia, Huaijian; Yang, Di; Li, Hao; Lin, Haiying

2018-01-01

The implementation of electric energy substitution by enterprises plays an important role in promoting the development of energy conservation and emission reduction in china. In order to explore alternative energy potential of industrial enterprises, to simulate and analyze the process of industrial enterprises, identify high energy consumption process and equipment, give priority to alternative energy technologies, and determine the enterprise electric energy substitution potential predictive value, this paper constructs the evaluation model of the influence factors of the electric energy substitution potential of industrial enterprises, and uses the combined weight method to determine the weight value of the evaluation factors to calculate the target value of the electric energy substitution potential. Taking the iron and steel industry as an example, this method is used to excavate the potential. The results show that the method can effectively tap the potential of the electric power industry

The G8 screening tool detects relevant geriatric impairments and predicts survival in elderly patients with a haematological malignancy.

PubMed

Hamaker, Marije E; Mitrovic, M; Stauder, R

2014-06-01

The G8 screening tool was developed to separate fit older cancer patients who were able to receive standard treatment from those that should undergo a geriatric assessment to guide tailoring of therapy. We set out to determine the discriminative power and prognostic value of the G8 in older patients with a haematological malignancy. Between September 2009 and May 2013, a multi-dimensional geriatric assessment was performed in consecutive patients aged ≥67 years diagnosed with blood cancer at the Innsbruck University Hospital. The assessment included (instrumental) activities of daily living, cognition, mood, nutritional status, mobility, polypharmacy and social support. In parallel, the G8 was also administered (cut-off ≤ 14). Using a cut-off of ≥2 impaired domains, 70 % of the 108 included patients were considered as having an impaired geriatric assessment while 61 % had an impaired G8. The G8 lacked discriminative power for impairments on full geriatric assessment: sensitivity 69, specificity 79, positive predictive value 89 and negative predictive value 50 %. However, G8 was an independent predictor of mortality within the first year after inclusion (hazard ratio 3.93; 95 % confidence interval 1.67-9.22, p < 0.001). Remarkably, patients with impaired G8 fared poorly, irrespective of treatment choices (p < 0.001). This is the first report on the clinical and prognostic relevance of G8 in elderly patients with haematological malignancies. Although the G8 lacked discriminative power for outcome of multi-dimensional geriatric assessment, this score appears to be a powerful prognosticator and could potentially represent a useful tool in treatment decisions. This novel finding certainly deserves further exploration.

POWER Web Access Data

Atmospheric Science Data Center

2018-05-27

Description:  Obtain Prediction of Worldwide Energy Resource (POWER) data The Prediction of Worldwide Energy ... (POWER) project was initiated to improve upon the current renewable energy data set and to create new data sets from new satellite ...

Recent sheath physics studies on DIII-D

NASA Astrophysics Data System (ADS)

Watkins, J. G.; Labombard, B.; Stangeby, P. C.; Lasnier, C. J.; McLean, A. G.; Nygren, R. E.; Boedo, J. A.; Leonard, A. W.; Rudakov, D. L.

2015-08-01

A study to examine some current issues in the physics of the plasma sheath has been recently carried out in DIII-D low power Ohmic plasmas using both flush and domed Langmuir probes, divertor Thomson scattering (DTS), an infrared camera (IRTV), and a new calorimeter triple probe assembly mounted on the Divertor Materials Evaluation System (DIMES). The sheath power transmission factor was found to be consistent with the theoretically predicted value of 7 (±2) for low power plasmas. Using this factor, the three heat flux profiles derived from the LP, DTS, and calorimeter diagnostic measurements agree. Comparison of flush and domed Langmuir probes and divertor Thomson scattering indicates that proper interpretation of flush probe data to get target plate density and temperature is feasible and could potentially yield accurate measurements of target plate conditions where the probes are located.

Detection of the power spectrum of cosmic microwave background lensing by the Atacama Cosmology Telescope.

PubMed

Das, Sudeep; Sherwin, Blake D; Aguirre, Paula; Appel, John W; Bond, J Richard; Carvalho, C Sofia; Devlin, Mark J; Dunkley, Joanna; Dünner, Rolando; Essinger-Hileman, Thomas; Fowler, Joseph W; Hajian, Amir; Halpern, Mark; Hasselfield, Matthew; Hincks, Adam D; Hlozek, Renée; Huffenberger, Kevin M; Hughes, John P; Irwin, Kent D; Klein, Jeff; Kosowsky, Arthur; Lupton, Robert H; Marriage, Tobias A; Marsden, Danica; Menanteau, Felipe; Moodley, Kavilan; Niemack, Michael D; Nolta, Michael R; Page, Lyman A; Parker, Lucas; Reese, Erik D; Schmitt, Benjamin L; Sehgal, Neelima; Sievers, Jon; Spergel, David N; Staggs, Suzanne T; Swetz, Daniel S; Switzer, Eric R; Thornton, Robert; Visnjic, Katerina; Wollack, Ed

2011-07-08

We report the first detection of the gravitational lensing of the cosmic microwave background through a measurement of the four-point correlation function in the temperature maps made by the Atacama Cosmology Telescope. We verify our detection by calculating the levels of potential contaminants and performing a number of null tests. The resulting convergence power spectrum at 2° angular scales measures the amplitude of matter density fluctuations on comoving length scales of around 100 Mpc at redshifts around 0.5 to 3. The measured amplitude of the signal agrees with Lambda cold dark matter cosmology predictions. Since the amplitude of the convergence power spectrum scales as the square of the amplitude of the density fluctuations, the 4σ detection of the lensing signal measures the amplitude of density fluctuations to 12%.

Development of VIS/NIR spectroscopic system for real-time prediction of fresh pork quality

NASA Astrophysics Data System (ADS)

Zhang, Haiyun; Peng, Yankun; Zhao, Songwei; Sasao, Akira

2013-05-01

Quality attributes of fresh meat will influence nutritional value and consumers' purchasing power. The aim of the research was to develop a prototype for real-time detection of quality in meat. It consisted of hardware system and software system. A VIS/NIR spectrograph in the range of 350 to 1100 nm was used to collect the spectral data. In order to acquire more potential information of the sample, optical fiber multiplexer was used. A conveyable and cylindrical device was designed and fabricated to hold optical fibers from multiplexer. High power halogen tungsten lamp was collected as the light source. The spectral data were obtained with the exposure time of 2.17ms from the surface of the sample by press down the trigger switch on the self-developed system. The system could automatically acquire, process, display and save the data. Moreover the quality could be predicted on-line. A total of 55 fresh pork samples were used to develop prediction model for real time detection. The spectral data were pretreated with standard normalized variant (SNV) and partial least squares regression (PLSR) was used to develop prediction model. The correlation coefficient and root mean square error of the validation set for water content and pH were 0.810, 0.653, and 0.803, 0.098 respectively. The research shows that the real-time non-destructive detection system based on VIS/NIR spectroscopy can be efficient to predict the quality of fresh meat.

Applying a new mammographic imaging marker to predict breast cancer risk

NASA Astrophysics Data System (ADS)

Aghaei, Faranak; Danala, Gopichandh; Hollingsworth, Alan B.; Stoug, Rebecca G.; Pearce, Melanie; Liu, Hong; Zheng, Bin

2018-02-01

Identifying and developing new mammographic imaging markers to assist prediction of breast cancer risk has been attracting extensive research interest recently. Although mammographic density is considered an important breast cancer risk, its discriminatory power is lower for predicting short-term breast cancer risk, which is a prerequisite to establish a more effective personalized breast cancer screening paradigm. In this study, we presented a new interactive computer-aided detection (CAD) scheme to generate a new quantitative mammographic imaging marker based on the bilateral mammographic tissue density asymmetry to predict risk of cancer detection in the next subsequent mammography screening. An image database involving 1,397 women was retrospectively assembled and tested. Each woman had two digital mammography screenings namely, the "current" and "prior" screenings with a time interval from 365 to 600 days. All "prior" images were originally interpreted negative. In "current" screenings, these cases were divided into 3 groups, which include 402 positive, 643 negative, and 352 biopsy-proved benign cases, respectively. There is no significant difference of BIRADS based mammographic density ratings between 3 case groups (p < 0.6). When applying the CAD-generated imaging marker or risk model to classify between 402 positive and 643 negative cases using "prior" negative mammograms, the area under a ROC curve is 0.70+/-0.02 and the adjusted odds ratios show an increasing trend from 1.0 to 8.13 to predict the risk of cancer detection in the "current" screening. Study demonstrated that this new imaging marker had potential to yield significantly higher discriminatory power to predict short-term breast cancer risk.

Local Fields in Human Subthalamic Nucleus Track the Lead-up to Impulsive Choices.

PubMed

Pearson, John M; Hickey, Patrick T; Lad, Shivanand P; Platt, Michael L; Turner, Dennis A

2017-01-01

The ability to adaptively minimize not only motor but cognitive symptoms of neurological diseases, such as Parkinson's Disease (PD) and obsessive-compulsive disorder (OCD), is a primary goal of next-generation deep brain stimulation (DBS) devices. On the basis of studies demonstrating a link between beta-band synchronization and severity of motor symptoms in PD, the minimization of beta band activity has been proposed as a potential training target for closed-loop DBS. At present, no comparable signal is known for the impulsive side effects of PD, though multiple studies have implicated theta band activity within the subthalamic nucleus (STN), the site of DBS treatment, in processes of conflict monitoring and countermanding. Here, we address this challenge by recording from multiple independent channels within the STN in a self-paced decision task to test whether these signals carry information sufficient to predict stopping behavior on a trial-by-trial basis. As in previous studies, we found that local field potentials (LFPs) exhibited modulations preceding self-initiated movements, with power ramping across multiple frequencies during the deliberation period. In addition, signals showed phasic changes in power around the time of decision. However, a prospective model that attempted to use these signals to predict decision times showed effects of risk level did not improve with the addition of LFPs as regressors. These findings suggest information tracking the lead-up to impulsive choices is distributed across multiple frequency scales in STN, though current techniques may not possess sufficient signal-to-noise ratios to predict-and thus curb-impulsive behavior on a moment-to-moment basis.

Biomarkers are used to predict quantitative metabolite concentration profiles in human red blood cells

DOE PAGES

Yurkovich, James T.; Yang, Laurence; Palsson, Bernhard O.; ...

2017-03-06

Deep-coverage metabolomic profiling has revealed a well-defined development of metabolic decay in human red blood cells (RBCs) under cold storage conditions. A set of extracellular biomarkers has been recently identified that reliably defines the qualitative state of the metabolic network throughout this metabolic decay process. Here, we extend the utility of these biomarkers by using them to quantitatively predict the concentrations of other metabolites in the red blood cell. We are able to accurately predict the concentration profile of 84 of the 91 (92%) measured metabolites ( p < 0.05) in RBC metabolism using only measurements of these five biomarkers.more » The median of prediction errors (symmetric mean absolute percent error) across all metabolites was 13%. Furthermore, the ability to predict numerous metabolite concentrations from a simple set of biomarkers offers the potential for the development of a powerful workflow that could be used to evaluate the metabolic state of a biological system using a minimal set of measurements.« less

Biomarkers are used to predict quantitative metabolite concentration profiles in human red blood cells

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

Yurkovich, James T.; Yang, Laurence; Palsson, Bernhard O.

Deep-coverage metabolomic profiling has revealed a well-defined development of metabolic decay in human red blood cells (RBCs) under cold storage conditions. A set of extracellular biomarkers has been recently identified that reliably defines the qualitative state of the metabolic network throughout this metabolic decay process. Here, we extend the utility of these biomarkers by using them to quantitatively predict the concentrations of other metabolites in the red blood cell. We are able to accurately predict the concentration profile of 84 of the 91 (92%) measured metabolites ( p < 0.05) in RBC metabolism using only measurements of these five biomarkers.more » The median of prediction errors (symmetric mean absolute percent error) across all metabolites was 13%. Furthermore, the ability to predict numerous metabolite concentrations from a simple set of biomarkers offers the potential for the development of a powerful workflow that could be used to evaluate the metabolic state of a biological system using a minimal set of measurements.« less

Neurophysiologic predictors of response to atomoxetine in young adults with attention deficit hyperactivity disorder: a pilot project.

PubMed

Leuchter, Andrew F; McGough, James J; Korb, Alexander S; Hunter, Aimee M; Glaser, Paul E A; Deldar, Ahmed; Durell, Todd M; Cook, Ian A

2014-07-01

Atomoxetine is a non-stimulant medication with sustained benefit throughout the day, and is a useful pharmacologic treatment option for young adults with Attention-Deficit/Hyperactivity Disorder (ADHD). It is difficult to determine, however, those patients for whom atomoxetine will be both effective and advantageous. Patients may need to take the medication for several weeks before therapeutic benefit is apparent, so a biomarker that could predict atomoxetine effectiveness early in the course of treatment could be clinically useful. There has been increased interest in the study of thalamocortical oscillatory activity using quantitative electroencephalography (qEEG) as a biomarker in ADHD. In this study, we investigated qEEG absolute power, relative power, and cordance, which have been shown to predict response to reuptake inhibitor antidepressants in Major Depressive Disorder (MDD), as potential predictors of response to atomoxetine. Forty-four young adults with ADHD (ages 18-30) enrolled in a multi-site, double-blind placebo-controlled study of the effectiveness of atomoxetine and underwent serial qEEG recordings at pretreatment baseline and one week after the start of medication. qEEG measures were calculated from a subset of the sample (N = 29) that provided useable qEEG recordings. Left temporoparietal cordance in the theta frequency band after one week of treatment was associated with ADHD symptom improvement and quality of life measured at 12 weeks in atomoxetine-treated subjects, but not in those treated with placebo. Neither absolute nor relative power measures selectively predicted improvement in medication-treated subjects. Measuring theta cordance after one week of treatment could be useful in predicting atomoxetine treatment response in adult ADHD. Copyright © 2014 Elsevier Ltd. All rights reserved.

Solar-based navigation for robotic explorers

NASA Astrophysics Data System (ADS)

Shillcutt, Kimberly Jo

2000-12-01

This thesis introduces the application of solar position and shadowing information to robotic exploration. Power is a critical resource for robots with remote, long-term missions, so this research focuses on the power generation capabilities of robotic explorers during navigational tasks, in addition to power consumption. Solar power is primarily considered, with the possibility of wind power also contemplated. Information about the environment, including the solar ephemeris, terrain features, time of day, and surface location, is incorporated into a planning structure, allowing robots to accurately predict shadowing and thus potential costs and gains during navigational tasks. By evaluating its potential to generate and expend power, a robot can extend its lifetime and accomplishments. The primary tasks studied are coverage patterns, with a variety of plans developed for this research. The use of sun, terrain and temporal information also enables new capabilities of identifying and following sun-synchronous and sun-seeking paths. Digital elevation maps are combined with an ephemeris algorithm to calculate the altitude and azimuth of the sun from surface locations, and to identify and map shadows. Solar navigation path simulators use this information to perform searches through two-dimensional space, while considering temporal changes. Step by step simulations of coverage patterns also incorporate time in addition to location. Evaluations of solar and wind power generation, power consumption, area coverage, area overlap, and time are generated for sets of coverage patterns, with on-board environmental information linked to the simulations. This research is implemented on the Nomad robot for the Robotic Antarctic Meteorite Search. Simulators have been developed for coverage pattern tests, as well as for sun-synchronous and sun-seeking path searches. Results of field work and simulations are reported and analyzed, with demonstrated improvements in efficiency, productivity and lifetime of robotic explorers, along with new solar navigation abilities.

GIMDA: Graphlet interaction-based MiRNA-disease association prediction.

PubMed

Chen, Xing; Guan, Na-Na; Li, Jian-Qiang; Yan, Gui-Ying

2018-03-01

MicroRNAs (miRNAs) have been confirmed to be closely related to various human complex diseases by many experimental studies. It is necessary and valuable to develop powerful and effective computational models to predict potential associations between miRNAs and diseases. In this work, we presented a prediction model of Graphlet Interaction for MiRNA-Disease Association prediction (GIMDA) by integrating the disease semantic similarity, miRNA functional similarity, Gaussian interaction profile kernel similarity and the experimentally confirmed miRNA-disease associations. The related score of a miRNA to a disease was calculated by measuring the graphlet interactions between two miRNAs or two diseases. The novelty of GIMDA lies in that we used graphlet interaction to analyse the complex relationships between two nodes in a graph. The AUCs of GIMDA in global and local leave-one-out cross-validation (LOOCV) turned out to be 0.9006 and 0.8455, respectively. The average result of five-fold cross-validation reached to 0.8927 ± 0.0012. In case study for colon neoplasms, kidney neoplasms and prostate neoplasms based on the database of HMDD V2.0, 45, 45, 41 of the top 50 potential miRNAs predicted by GIMDA were validated by dbDEMC and miR2Disease. Additionally, in the case study of new diseases without any known associated miRNAs and the case study of predicting potential miRNA-disease associations using HMDD V1.0, there were also high percentages of top 50 miRNAs verified by the experimental literatures. © 2017 The Authors. Journal of Cellular and Molecular Medicine published by John Wiley & Sons Ltd and Foundation for Cellular and Molecular Medicine.

Climate and pH predict the potential range of the invasive apple snail (Pomacea insularum) in the southeastern United States.

PubMed

Byers, James E; McDowell, William G; Dodd, Shelley R; Haynie, Rebecca S; Pintor, Lauren M; Wilde, Susan B

2013-01-01

Predicting the potential range of invasive species is essential for risk assessment, monitoring, and management, and it can also inform us about a species' overall potential invasiveness. However, modeling the distribution of invasive species that have not reached their equilibrium distribution can be problematic for many predictive approaches. We apply the modeling approach of maximum entropy (MaxEnt) that is effective with incomplete, presence-only datasets to predict the distribution of the invasive island apple snail, Pomacea insularum. This freshwater snail is native to South America and has been spreading in the USA over the last decade from its initial introductions in Texas and Florida. It has now been documented throughout eight southeastern states. The snail's extensive consumption of aquatic vegetation and ability to accumulate and transmit algal toxins through the food web heighten concerns about its spread. Our model shows that under current climate conditions the snail should remain mostly confined to the coastal plain of the southeastern USA where it is limited by minimum temperature in the coldest month and precipitation in the warmest quarter. Furthermore, low pH waters (pH <5.5) are detrimental to the snail's survival and persistence. Of particular note are low-pH blackwater swamps, especially Okefenokee Swamp in southern Georgia (with a pH below 4 in many areas), which are predicted to preclude the snail's establishment even though many of these areas are well matched climatically. Our results elucidate the factors that affect the regional distribution of P. insularum, while simultaneously presenting a spatial basis for the prediction of its future spread. Furthermore, the model for this species exemplifies that combining climatic and habitat variables is a powerful way to model distributions of invasive species.

POWER/SSE Web Access Data

Atmospheric Science Data Center

2018-06-25

Description:  Obtain Prediction of Worldwide Energy Resource (POWER) data The Prediction of Worldwide Energy ... (POWER) project was initiated to improve upon the current renewable energy data set and to create new data sets from new satellite ...

Research on prediction of agricultural machinery total power based on grey model optimized by genetic algorithm

NASA Astrophysics Data System (ADS)

Xie, Yan; Li, Mu; Zhou, Jin; Zheng, Chang-zheng

2009-07-01

Agricultural machinery total power is an important index to reflex and evaluate the level of agricultural mechanization. It is the power source of agricultural production, and is the main factors to enhance the comprehensive agricultural production capacity expand production scale and increase the income of the farmers. Its demand is affected by natural, economic, technological and social and other "grey" factors. Therefore, grey system theory can be used to analyze the development of agricultural machinery total power. A method based on genetic algorithm optimizing grey modeling process is introduced in this paper. This method makes full use of the advantages of the grey prediction model and characteristics of genetic algorithm to find global optimization. So the prediction model is more accurate. According to data from a province, the GM (1, 1) model for predicting agricultural machinery total power was given based on the grey system theories and genetic algorithm. The result indicates that the model can be used as agricultural machinery total power an effective tool for prediction.

Stirling convertor performance mapping test results

NASA Astrophysics Data System (ADS)

Qiu, Songgang; Peterson, Allen A.; White, Maurice A.; Faultersack, Franklyn; Redinger, Darin L.; Petersen, Stephen L.

2002-01-01

The Department of Energy (DOE) has selected Free-Piston Stirling Convertors as a technology for future advanced radioisotope space power systems. In August 2000, DOE awarded competitive Phase I, Stirling Radioisotope Generator (SRG) power system integration contracts to three major aerospace contractors, resulting in SRG conceptual designs in February 2001. All three contractors based their designs on the Technology Demonstration Convertor (TDC) developed by Stirling Technology Company (STC) for DOE. The contract award to a single system integration contractor for Phases II and III of the SRG program is anticipated in late 2001. The first potential SRG mission is targeted for a Mars rover. Recent TDC performance data are provided in this paper, together with predictions from Stirling simulation models. .

Postoperative air leak grading is useful to predict prolonged air leak after pulmonary lobectomy.

PubMed

Oh, Sang Gi; Jung, Yochun; Jheon, Sanghoon; Choi, Yunhee; Yun, Ju Sik; Na, Kook Joo; Ahn, Byoung Hee

2017-01-23

Results of studies to predict prolonged air leak (PAL; air leak longer than 5 days) after pulmonary lobectomy have been inconsistent and are of limited use. We developed a new scale representing the amount of early postoperative air leak and determined its correlation with air leak duration and its potential as a predictor of PAL. We grade postoperative air leak using a 5-grade scale. All 779 lobectomies from January 2005 to December 2009 with available medical records were reviewed retrospectively. We devised six 'SUM' variables using air leak grades in the initial 72 h postoperatively. Excluding unrecorded cases and postoperative broncho-pleural fistulas, there were 720 lobectomies. PAL occurred in 135 cases (18.8%). Correlation analyses showed each SUM variable highly correlated with air leak duration, and the SUM 4to9 , which was the sum of six consecutive values of air leak grades for every 8 h record on postoperative days 2 and 3, was proved to be the most powerful predictor of PAL; PAL could be predicted with 75.7% and 77.7% positive and negative predictive value, respectively, when SUM 4to9  ≥ 16. When 4 predictors derived from multivariable logistic regression of perioperative variables were combined with SUM 4to9 , there was no significant increase in predictability compared with SUM 4to9 alone. This simple new method to predict PAL using SUM 4to9 showed that the amount of early postoperative air leak is the most powerful predictor of PAL, therefore, grading air leak after pulmonary lobectomy is a useful method to predict PAL.

LRSSLMDA: Laplacian Regularized Sparse Subspace Learning for MiRNA-Disease Association prediction

PubMed Central

Huang, Li

2017-01-01

Predicting novel microRNA (miRNA)-disease associations is clinically significant due to miRNAs’ potential roles of diagnostic biomarkers and therapeutic targets for various human diseases. Previous studies have demonstrated the viability of utilizing different types of biological data to computationally infer new disease-related miRNAs. Yet researchers face the challenge of how to effectively integrate diverse datasets and make reliable predictions. In this study, we presented a computational model named Laplacian Regularized Sparse Subspace Learning for MiRNA-Disease Association prediction (LRSSLMDA), which projected miRNAs/diseases’ statistical feature profile and graph theoretical feature profile to a common subspace. It used Laplacian regularization to preserve the local structures of the training data and a L1-norm constraint to select important miRNA/disease features for prediction. The strength of dimensionality reduction enabled the model to be easily extended to much higher dimensional datasets than those exploited in this study. Experimental results showed that LRSSLMDA outperformed ten previous models: the AUC of 0.9178 in global leave-one-out cross validation (LOOCV) and the AUC of 0.8418 in local LOOCV indicated the model’s superior prediction accuracy; and the average AUC of 0.9181+/-0.0004 in 5-fold cross validation justified its accuracy and stability. In addition, three types of case studies further demonstrated its predictive power. Potential miRNAs related to Colon Neoplasms, Lymphoma, Kidney Neoplasms, Esophageal Neoplasms and Breast Neoplasms were predicted by LRSSLMDA. Respectively, 98%, 88%, 96%, 98% and 98% out of the top 50 predictions were validated by experimental evidences. Therefore, we conclude that LRSSLMDA would be a valuable computational tool for miRNA-disease association prediction. PMID:29253885

V/STOL Aerodynamics

DTIC Science & Technology

1974-10-01

jet exhaust, m (ft) Ro radius of engine exhaust, m (ft) 1. INTRODUCTION free deg S wing area, m2 (ft2) t time, see T Thrust, N (lb) u...dimensional potential flow method to lift prediction for a wing with internally blown flaps is described. INTRODUCTION The objectives of this paper are...twofold. The first is to provide an introduction to this session on research into the aerodynamics of powered high lift systems. This will be

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%.

Performance of Machine Learning Algorithms for Qualitative and Quantitative Prediction Drug Blockade of hERG1 channel.

PubMed

Wacker, Soren; Noskov, Sergei Yu

2018-05-01

Drug-induced abnormal heart rhythm known as Torsades de Pointes (TdP) is a potential lethal ventricular tachycardia found in many patients. Even newly released anti-arrhythmic drugs, like ivabradine with HCN channel as a primary target, block the hERG potassium current in overlapping concentration interval. Promiscuous drug block to hERG channel may potentially lead to perturbation of the action potential duration (APD) and TdP, especially when with combined with polypharmacy and/or electrolyte disturbances. The example of novel anti-arrhythmic ivabradine illustrates clinically important and ongoing deficit in drug design and warrants for better screening methods. There is an urgent need to develop new approaches for rapid and accurate assessment of how drugs with complex interactions and multiple subcellular targets can predispose or protect from drug-induced TdP. One of the unexpected outcomes of compulsory hERG screening implemented in USA and European Union resulted in large datasets of IC 50 values for various molecules entering the market. The abundant data allows now to construct predictive machine-learning (ML) models. Novel ML algorithms and techniques promise better accuracy in determining IC 50 values of hERG blockade that is comparable or surpassing that of the earlier QSAR or molecular modeling technique. To test the performance of modern ML techniques, we have developed a computational platform integrating various workflows for quantitative structure activity relationship (QSAR) models using data from the ChEMBL database. To establish predictive powers of ML-based algorithms we computed IC 50 values for large dataset of molecules and compared it to automated patch clamp system for a large dataset of hERG blocking and non-blocking drugs, an industry gold standard in studies of cardiotoxicity. The optimal protocol with high sensitivity and predictive power is based on the novel eXtreme gradient boosting (XGBoost) algorithm. The ML-platform with XGBoost displays excellent performance with a coefficient of determination of up to R 2 ~0.8 for pIC 50 values in evaluation datasets, surpassing other metrics and approaches available in literature. Ultimately, the ML-based platform developed in our work is a scalable framework with automation potential to interact with other developing technologies in cardiotoxicity field, including high-throughput electrophysiology measurements delivering large datasets of profiled drugs, rapid synthesis and drug development via progress in synthetic biology.

Method for predicting water demand for crop uses in New Jersey in 1990, 2000, 2010, and 2020, and for estimating water use for livestock and selected sectors of the food-processing industry in New Jersey in 1987

USGS Publications Warehouse

Clawges, R.M.; Titus, E.O.

1993-01-01

A method was developed to predict water demand for crop uses in New Jersey. A separate method was developed to estimate water use for livestock and selected sectors of the food-processing industry in 1987. Predictions of water demand for field- grown crops in New Jersey were made for 1990, 2000, 2010, and 2020 under three climatological scenarios: (1) wet year, (2) average year, and (3) drought year. These estimates ranged from 4.10 times 10 to the 9th power to 16.82 times 10 to the 9th power gal (gallons). Irrigation amounts calculated for the three climatological scenarios by using a daily water-balance model were multiplied by predicted numbers of irrigated acreage. Irrigated acreage was predicted from historical crop-irrigation data and from predictions of harvested acreage produced by using a statistical model relating population to harvested acreage. Predictions of water demand for cranberries and container-grown nursery crops also were made for 1990, 2000, 2010, and 2020. Predictions of water demand under the three climatological scenarios were made for container- grown nursery crops, but not for cranberries, because water demand for cranberries varies little in response to climatological factors. Water demand for cranberries was predicted to remain constant at 4.43 times 10 to the 9th power gal through the year 2020. Predictions of water demand for container-grown nursery crops ranged from 1.89 times 10 to the 9th power to 3.63 times 10 to the 9th power gal. Water-use for livestock in 1987 was estimated to be 0.78 times 10 to the 9th power gal, and water use for selected sectors of the food-processing industry was estimated to be 3.75 times 10 to the 9th power gal.

Microinverter Thermal Performance in the Real-World: Measurements and Modeling

PubMed Central

Hossain, Mohammad Akram; Xu, Yifan; Peshek, Timothy J.; Ji, Liang; Abramson, Alexis R.; French, Roger H.

2015-01-01

Real-world performance, durability and reliability of microinverters are critical concerns for microinverter-equipped photovoltaic systems. We conducted a data-driven study of the thermal performance of 24 new microinverters (Enphase M215) connected to 8 different brands of PV modules on dual-axis trackers at the Solar Durability and Lifetime Extension (SDLE) SunFarm at Case Western Reserve University, based on minute by minute power and thermal data from the microinverters and PV modules along with insolation and environmental data from July through October 2013. The analysis shows the strengths of the associations of microinverter temperature with ambient temperature, PV module temperature, irradiance and AC power of the PV systems. The importance of the covariates are rank ordered. A multiple regression model was developed and tested based on stable solar noon-time data, which gives both an overall function that predicts the temperature of microinverters under typical local conditions, and coefficients adjustments reecting refined prediction of the microinverter temperature connected to the 8 brands of PV modules in the study. The model allows for prediction of internal temperature for the Enphase M215 given similar climatic condition and can be expanded to predict microinverter temperature in fixed-rack and roof-top PV systems. This study is foundational in that similar models built on later stage data in the life of a device could reveal potential influencing factors in performance degradation. PMID:26147339

Predicting Mouse Liver Microsomal Stability with “Pruned” Machine Learning Models and Public Data

PubMed Central

Perryman, Alexander L.; Stratton, Thomas P.; Ekins, Sean; Freundlich, Joel S.

2015-01-01

Purpose Mouse efficacy studies are a critical hurdle to advance translational research of potential therapeutic compounds for many diseases. Although mouse liver microsomal (MLM) stability studies are not a perfect surrogate for in vivo studies of metabolic clearance, they are the initial model system used to assess metabolic stability. Consequently, we explored the development of machine learning models that can enhance the probability of identifying compounds possessing MLM stability. Methods Published assays on MLM half-life values were identified in PubChem, reformatted, and curated to create a training set with 894 unique small molecules. These data were used to construct machine learning models assessed with internal cross-validation, external tests with a published set of antitubercular compounds, and independent validation with an additional diverse set of 571 compounds (PubChem data on percent metabolism). Results “Pruning” out the moderately unstable/moderately stable compounds from the training set produced models with superior predictive power. Bayesian models displayed the best predictive power for identifying compounds with a half-life ≥1 hour. Conclusions Our results suggest the pruning strategy may be of general benefit to improve test set enrichment and provide machine learning models with enhanced predictive value for the MLM stability of small organic molecules. This study represents the most exhaustive study to date of using machine learning approaches with MLM data from public sources. PMID:26415647

Predicting Mouse Liver Microsomal Stability with "Pruned" Machine Learning Models and Public Data.

PubMed

Perryman, Alexander L; Stratton, Thomas P; Ekins, Sean; Freundlich, Joel S

2016-02-01

Mouse efficacy studies are a critical hurdle to advance translational research of potential therapeutic compounds for many diseases. Although mouse liver microsomal (MLM) stability studies are not a perfect surrogate for in vivo studies of metabolic clearance, they are the initial model system used to assess metabolic stability. Consequently, we explored the development of machine learning models that can enhance the probability of identifying compounds possessing MLM stability. Published assays on MLM half-life values were identified in PubChem, reformatted, and curated to create a training set with 894 unique small molecules. These data were used to construct machine learning models assessed with internal cross-validation, external tests with a published set of antitubercular compounds, and independent validation with an additional diverse set of 571 compounds (PubChem data on percent metabolism). "Pruning" out the moderately unstable / moderately stable compounds from the training set produced models with superior predictive power. Bayesian models displayed the best predictive power for identifying compounds with a half-life ≥1 h. Our results suggest the pruning strategy may be of general benefit to improve test set enrichment and provide machine learning models with enhanced predictive value for the MLM stability of small organic molecules. This study represents the most exhaustive study to date of using machine learning approaches with MLM data from public sources.

Experimental verification of stopping-power prediction from single- and dual-energy computed tomography in biological tissues

NASA Astrophysics Data System (ADS)

Möhler, Christian; Russ, Tom; Wohlfahrt, Patrick; Elter, Alina; Runz, Armin; Richter, Christian; Greilich, Steffen

2018-01-01

An experimental setup for consecutive measurement of ion and x-ray absorption in tissue or other materials is introduced. With this setup using a 3D-printed sample container, the reference stopping-power ratio (SPR) of materials can be measured with an uncertainty of below 0.1%. A total of 65 porcine and bovine tissue samples were prepared for measurement, comprising five samples each of 13 tissue types representing about 80% of the total body mass (three different muscle and fatty tissues, liver, kidney, brain, heart, blood, lung and bone). Using a standard stoichiometric calibration for single-energy CT (SECT) as well as a state-of-the-art dual-energy CT (DECT) approach, SPR was predicted for all tissues and then compared to the measured reference. With the SECT approach, the SPRs of all tissues were predicted with a mean error of (-0.84  ±  0.12)% and a mean absolute error of (1.27  ±  0.12)%. In contrast, the DECT-based SPR predictions were overall consistent with the measured reference with a mean error of (-0.02  ±  0.15)% and a mean absolute error of (0.10  ±  0.15)%. Thus, in this study, the potential of DECT to decrease range uncertainty could be confirmed in biological tissue.

The Role of Minority Stressors in Lesbian Relationship Commitment and Persistence over Time.

PubMed

Barrantes, Renzo J; Eaton, Asia A; Veldhuis, Cindy B; Hughes, Tonda L

2017-06-01

The Investment Model of relationship commitment uses interpersonal investment, relationship satisfaction, quality of alternatives, and commitment to predict relationship longevity (Rusbult, 1980, 1983). Although ample support for the Investment Model has been found in heterosexual couples, it appears to be less powerful in predicting stability in same-sex relationships (Beals, Impett, & Peplau, 2002), potentially because the model does not account for factors unique to same-sex relationships, such as anti-gay discrimination. However, no research has tested the nature and power of sexual minority stress factors in predicting same-sex relationship stability over time. Using secondary, longitudinal data collected from a diverse sample of lesbian women in relationships ( N = 211), we examined how internalized homonegativity, sexual identity disclosure, and workplace discrimination affected the Investment Model antecedents of relationship persistence: satisfaction, quality of alternatives, and investment. We tested the influence of sexual minority stressors on Investment Model processes using structural equations modeling and found that sexual identity disclosure was positively associated with satisfaction and investment, internalized homonegativity was only negatively associated with satisfaction and investment, while workplace discrimination was negatively associated with alternatives. Moreover, both relationship satisfaction and investment influenced commitment which predicted persistence in these relationships over about seven years' time, demonstrating support for the Investment Model. Our findings support the addition of sexual minority stress variables to the Investment Model when examining same-sex relationships and implications are discussed.

Rational selection of training and test sets for the development of validated QSAR models

NASA Astrophysics Data System (ADS)

Golbraikh, Alexander; Shen, Min; Xiao, Zhiyan; Xiao, Yun-De; Lee, Kuo-Hsiung; Tropsha, Alexander

2003-02-01

Quantitative Structure-Activity Relationship (QSAR) models are used increasingly to screen chemical databases and/or virtual chemical libraries for potentially bioactive molecules. These developments emphasize the importance of rigorous model validation to ensure that the models have acceptable predictive power. Using k nearest neighbors ( kNN) variable selection QSAR method for the analysis of several datasets, we have demonstrated recently that the widely accepted leave-one-out (LOO) cross-validated R2 (q2) is an inadequate characteristic to assess the predictive ability of the models [Golbraikh, A., Tropsha, A. Beware of q2! J. Mol. Graphics Mod. 20, 269-276, (2002)]. Herein, we provide additional evidence that there exists no correlation between the values of q 2 for the training set and accuracy of prediction ( R 2) for the test set and argue that this observation is a general property of any QSAR model developed with LOO cross-validation. We suggest that external validation using rationally selected training and test sets provides a means to establish a reliable QSAR model. We propose several approaches to the division of experimental datasets into training and test sets and apply them in QSAR studies of 48 functionalized amino acid anticonvulsants and a series of 157 epipodophyllotoxin derivatives with antitumor activity. We formulate a set of general criteria for the evaluation of predictive power of QSAR models.

The Role of Minority Stressors in Lesbian Relationship Commitment and Persistence over Time

PubMed Central

Barrantes, Renzo J.; Eaton, Asia A.; Veldhuis, Cindy B.; Hughes, Tonda L.

2017-01-01

The Investment Model of relationship commitment uses interpersonal investment, relationship satisfaction, quality of alternatives, and commitment to predict relationship longevity (Rusbult, 1980, 1983). Although ample support for the Investment Model has been found in heterosexual couples, it appears to be less powerful in predicting stability in same-sex relationships (Beals, Impett, & Peplau, 2002), potentially because the model does not account for factors unique to same-sex relationships, such as anti-gay discrimination. However, no research has tested the nature and power of sexual minority stress factors in predicting same-sex relationship stability over time. Using secondary, longitudinal data collected from a diverse sample of lesbian women in relationships (N = 211), we examined how internalized homonegativity, sexual identity disclosure, and workplace discrimination affected the Investment Model antecedents of relationship persistence: satisfaction, quality of alternatives, and investment. We tested the influence of sexual minority stressors on Investment Model processes using structural equations modeling and found that sexual identity disclosure was positively associated with satisfaction and investment, internalized homonegativity was only negatively associated with satisfaction and investment, while workplace discrimination was negatively associated with alternatives. Moreover, both relationship satisfaction and investment influenced commitment which predicted persistence in these relationships over about seven years’ time, demonstrating support for the Investment Model. Our findings support the addition of sexual minority stress variables to the Investment Model when examining same-sex relationships and implications are discussed. PMID:28695154

Application of clustering analysis in the prediction of photovoltaic power generation based on neural network

NASA Astrophysics Data System (ADS)

Cheng, K.; Guo, L. M.; Wang, Y. K.; Zafar, M. T.

2017-11-01

In order to select effective samples in the large number of data of PV power generation years and improve the accuracy of PV power generation forecasting model, this paper studies the application of clustering analysis in this field and establishes forecasting model based on neural network. Based on three different types of weather on sunny, cloudy and rainy days, this research screens samples of historical data by the clustering analysis method. After screening, it establishes BP neural network prediction models using screened data as training data. Then, compare the six types of photovoltaic power generation prediction models before and after the data screening. Results show that the prediction model combining with clustering analysis and BP neural networks is an effective method to improve the precision of photovoltaic power generation.

Integrated modelling of H-mode pedestal and confinement in JET-ILW

NASA Astrophysics Data System (ADS)

Saarelma, S.; Challis, C. D.; Garzotti, L.; Frassinetti, L.; Maggi, C. F.; Romanelli, M.; Stokes, C.; Contributors, JET

2018-01-01

A pedestal prediction model Europed is built on the existing EPED1 model by coupling it with core transport simulation using a Bohm-gyroBohm transport model to self-consistently predict JET-ILW power scan for hybrid plasmas that display weaker power degradation than the IPB98(y, 2) scaling of the energy confinement time. The weak power degradation is reproduced in the coupled core-pedestal simulation. The coupled core-pedestal model is further tested for a 3.0 MA plasma with the highest stored energy achieved in JET-ILW so far, giving a prediction of the stored plasma energy within the error margins of the measured experimental value. A pedestal density prediction model based on the neutral penetration is tested on a JET-ILW database giving a prediction with an average error of 17% from the experimental data when a parameter taking into account the fuelling rate is added into the model. However the model fails to reproduce the power dependence of the pedestal density implying missing transport physics in the model. The future JET-ILW deuterium campaign with increased heating power is predicted to reach plasma energy of 11 MJ, which would correspond to 11-13 MW of fusion power in equivalent deuterium-tritium plasma but with isotope effects on pedestal stability and core transport ignored.

Regression to fuzziness method for estimation of remaining useful life in power plant components

NASA Astrophysics Data System (ADS)

Alamaniotis, Miltiadis; Grelle, Austin; Tsoukalas, Lefteri H.

2014-10-01

Mitigation of severe accidents in power plants requires the reliable operation of all systems and the on-time replacement of mechanical components. Therefore, the continuous surveillance of power systems is a crucial concern for the overall safety, cost control, and on-time maintenance of a power plant. In this paper a methodology called regression to fuzziness is presented that estimates the remaining useful life (RUL) of power plant components. The RUL is defined as the difference between the time that a measurement was taken and the estimated failure time of that component. The methodology aims to compensate for a potential lack of historical data by modeling an expert's operational experience and expertise applied to the system. It initially identifies critical degradation parameters and their associated value range. Once completed, the operator's experience is modeled through fuzzy sets which span the entire parameter range. This model is then synergistically used with linear regression and a component's failure point to estimate the RUL. The proposed methodology is tested on estimating the RUL of a turbine (the basic electrical generating component of a power plant) in three different cases. Results demonstrate the benefits of the methodology for components for which operational data is not readily available and emphasize the significance of the selection of fuzzy sets and the effect of knowledge representation on the predicted output. To verify the effectiveness of the methodology, it was benchmarked against the data-based simple linear regression model used for predictions which was shown to perform equal or worse than the presented methodology. Furthermore, methodology comparison highlighted the improvement in estimation offered by the adoption of appropriate of fuzzy sets for parameter representation.

Effects of 31 FDA approved small-molecule kinase inhibitors on isolated rat liver mitochondria.

PubMed

Zhang, Jun; Salminen, Alec; Yang, Xi; Luo, Yong; Wu, Qiangen; White, Matthew; Greenhaw, James; Ren, Lijun; Bryant, Matthew; Salminen, William; Papoian, Thomas; Mattes, William; Shi, Qiang

2017-08-01

The FDA has approved 31 small-molecule kinase inhibitors (KIs) for human use as of November 2016, with six having black box warnings for hepatotoxicity (BBW-H) in product labeling. The precise mechanisms and risk factors for KI-induced hepatotoxicity are poorly understood. Here, the 31 KIs were tested in isolated rat liver mitochondria, an in vitro system recently proposed to be a useful tool to predict drug-induced hepatotoxicity in humans. The KIs were incubated with mitochondria or submitochondrial particles at concentrations ranging from therapeutic maximal blood concentrations (Cmax) levels to 100-fold Cmax levels. Ten endpoints were measured, including oxygen consumption rate, inner membrane potential, cytochrome c release, swelling, reactive oxygen species, and individual respiratory chain complex (I-V) activities. Of the 31 KIs examined only three including sorafenib, regorafenib and pazopanib, all of which are hepatotoxic, caused significant mitochondrial toxicity at concentrations equal to the Cmax, indicating that mitochondrial toxicity likely contributes to the pathogenesis of hepatotoxicity associated with these KIs. At concentrations equal to 100-fold Cmax, 18 KIs were found to be toxic to mitochondria, and among six KIs with BBW-H, mitochondrial injury was induced by regorafenib, lapatinib, idelalisib, and pazopanib, but not ponatinib, or sunitinib. Mitochondrial liability at 100-fold Cmax had a positive predictive power (PPV) of 72% and negative predictive power (NPV) of 33% in predicting human KI hepatotoxicity as defined by product labeling, with the sensitivity and specificity being 62% and 44%, respectively. Similar predictive power was obtained using the criterion of Cmax ≥1.1 µM or daily dose ≥100 mg. Mitochondrial liability at 1-2.5-fold Cmax showed a 100% PPV and specificity, though the NPV and sensitivity were 32% and 14%, respectively. These data provide novel mechanistic insights into KI hepatotoxicity and indicate that mitochondrial toxicity at therapeutic levels can help identify hepatotoxic KIs.

Maximum predictive power and the superposition principle

NASA Technical Reports Server (NTRS)

Summhammer, Johann

1994-01-01

In quantum physics the direct observables are probabilities of events. We ask how observed probabilities must be combined to achieve what we call maximum predictive power. According to this concept the accuracy of a prediction must only depend on the number of runs whose data serve as input for the prediction. We transform each probability to an associated variable whose uncertainty interval depends only on the amount of data and strictly decreases with it. We find that for a probability which is a function of two other probabilities maximum predictive power is achieved when linearly summing their associated variables and transforming back to a probability. This recovers the quantum mechanical superposition principle.

Exceptional power density and stability at intermediate temperatures in protonic ceramic fuel cells

NASA Astrophysics Data System (ADS)

Choi, Sihyuk; Kucharczyk, Chris J.; Liang, Yangang; Zhang, Xiaohang; Takeuchi, Ichiro; Ji, Ho-Il; Haile, Sossina M.

2018-03-01

Over the past several years, important strides have been made in demonstrating protonic ceramic fuel cells (PCFCs). Such fuel cells offer the potential of environmentally sustainable and cost-effective electric power generation. However, their power outputs have lagged behind predictions based on their high electrolyte conductivities. Here we overcome PCFC performance and stability challenges by employing a high-activity cathode, PrBa0.5Sr0.5Co1.5Fe0.5O5+δ (PBSCF), in combination with a chemically stable electrolyte, BaZr0.4Ce0.4Y0.1Yb0.1O3 (BZCYYb4411). We deposit a thin dense interlayer film of the cathode material onto the electrolyte surface to mitigate contact resistance, an approach which is made possible by the proton permeability of PBSCF. The peak power densities of the resulting fuel cells exceed 500 mW cm-2 at 500 °C, while also offering exceptional, long-term stability under CO2.

SP-100 multimegawatt scaleup to meet electric propulsion mission requirements

NASA Astrophysics Data System (ADS)

Newkirk, D. W.; Salamah, S. A.; Stewart, S. L.; Pluta, P. R.

The SP-100 nuclear heat source technology, utilizing uranium nitride fuel clad in PWC-11 in a fast reactor with lithium coolant circulated by an electromagnetic pump, is shown to be directly extrapolatable to thermal power levels that meet NASA nuclear electric propulsion requirements using different power conversion techniques. The SP-100 nuclear technology can be applied to missions with NEP (nuclear electric propulsion) requirements as low as tens of kWe to tens of MWe. It is pointed out that the SP-100 heat source has a great advantage of very long lifetime capability, since it utilizes very rugged refractory metal fuel pins and is independent of the power conversion scheme chosen for a given mission. The only moving parts in the nuclear subsystems are the control rods moved to compensate for fuel enrichment degradation due to fission and for power shutdown. Lowest alpha values in the range of interest for potential NASA missions are predicted for the dynamic Rankine and static HYTEC conversion systems.

Novel high-frequency, high-power, pulsed oscillator based on a transmission line transformer.

PubMed

Burdt, R; Curry, R D

2007-07-01

Recent analysis and experiments have demonstrated the potential for transmission line transformers to be employed as compact, high-frequency, high-power, pulsed oscillators with variable rise time, high output impedance, and high operating efficiency. A prototype system was fabricated and tested that generates a damped sinusoidal wave form at a center frequency of 4 MHz into a 200 Omega load, with operating efficiency above 90% and peak power on the order of 10 MW. The initial rise time of the pulse is variable and two experiments were conducted to demonstrate initial rise times of 12 and 3 ns, corresponding to a spectral content from 4-30 and from 4-100 MHz, respectively. A SPICE model has been developed to accurately predict the circuit behavior and scaling laws have been identified to allow for circuit design at higher frequencies and higher peak power. The applications, circuit analysis, test stand, experimental results, circuit modeling, and design of future systems are all discussed.

Accurate classical short-range forces for the study of collision cascades in Fe–Ni–Cr

DOE PAGES

Béland, Laurent Karim; Tamm, Artur; Mu, Sai; ...

2017-05-10

The predictive power of a classical molecular dynamics simulation is largely determined by the physical validity of its underlying empirical potential. In the case of high-energy collision cascades, it was recently shown that correctly modeling interactions at short distances is necessary to accurately predict primary damage production. An ab initio based framework is introduced for modifying an existing embedded-atom method FeNiCr potential to handle these short-range interactions. Density functional theory is used to calculate the energetics of two atoms approaching each other, embedded in the alloy, and to calculate the equation of state of the alloy as it is compressed.more » The pairwise terms and the embedding terms of the potential are modi ed in accordance with the ab initio results. Using this reparametrized potential, collision cascades are performed in Ni 50Fe 50, Ni 80Cr 20 and Ni 33Fe 33Cr 33. The simulations reveal that alloying Ni and NiCr to Fe reduces primary damage production, in agreement with some previous calculations. Alloying Ni and NiFe to Cr does not reduce primary damage production, in contradiction with previous calculations.« less

Prediction of brain tissue temperature using near-infrared spectroscopy.

PubMed

Holper, Lisa; Mitra, Subhabrata; Bale, Gemma; Robertson, Nicola; Tachtsidis, Ilias

2017-04-01

Broadband near-infrared spectroscopy (NIRS) can provide an endogenous indicator of tissue temperature based on the temperature dependence of the water absorption spectrum. We describe a first evaluation of the calibration and prediction of brain tissue temperature obtained during hypothermia in newborn piglets (animal dataset) and rewarming in newborn infants (human dataset) based on measured body (rectal) temperature. The calibration using partial least squares regression proved to be a reliable method to predict brain tissue temperature with respect to core body temperature in the wavelength interval of 720 to 880 nm with a strong mean predictive power of [Formula: see text] (animal dataset) and [Formula: see text] (human dataset). In addition, we applied regression receiver operating characteristic curves for the first time to evaluate the temperature prediction, which provided an overall mean error bias between NIRS predicted brain temperature and body temperature of [Formula: see text] (animal dataset) and [Formula: see text] (human dataset). We discuss main methodological aspects, particularly the well-known aspect of over- versus underestimation between brain and body temperature, which is relevant for potential clinical applications.

Prediction of Knee Joint Contact Forces From External Measures Using Principal Component Prediction and Reconstruction.

PubMed

Saliba, Christopher M; Clouthier, Allison L; Brandon, Scott C E; Rainbow, Michael J; Deluzio, Kevin J

2018-05-29

Abnormal loading of the knee joint contributes to the pathogenesis of knee osteoarthritis. Gait retraining is a non-invasive intervention that aims to reduce knee loads by providing audible, visual, or haptic feedback of gait parameters. The computational expense of joint contact force prediction has limited real-time feedback to surrogate measures of the contact force, such as the knee adduction moment. We developed a method to predict knee joint contact forces using motion analysis and a statistical regression model that can be implemented in near real-time. Gait waveform variables were deconstructed using principal component analysis and a linear regression was used to predict the principal component scores of the contact force waveforms. Knee joint contact force waveforms were reconstructed using the predicted scores. We tested our method using a heterogenous population of asymptomatic controls and subjects with knee osteoarthritis. The reconstructed contact force waveforms had mean (SD) RMS differences of 0.17 (0.05) bodyweight compared to the contact forces predicted by a musculoskeletal model. Our method successfully predicted subject-specific shape features of contact force waveforms and is a potentially powerful tool in biofeedback and clinical gait analysis.

An evaluation of HEMT potential for millimeter-wave signal sources using interpolation and harmonic balance techniques

NASA Technical Reports Server (NTRS)

Kwon, Youngwoo; Pavlidis, Dimitris; Tutt, Marcel N.

1991-01-01

A large-signal analysis method based on an harmonic balance technique and a 2-D cubic spline interpolation function has been developed and applied to the prediction of InP-based HEMT oscillator performance for frequencies extending up to the submillimeter-wave range. The large-signal analysis method uses a limited number of DC and small-signal S-parameter data and allows the accurate characterization of HEMT large-signal behavior. The method has been validated experimentally using load-pull measurement. Oscillation frequency, power performance, and load requirements are discussed, with an operation capability of 300 GHz predicted using state-of-the-art devices (fmax is approximately equal to 450 GHz).

Decoding genes with coexpression networks and metabolomics - 'majority report by precogs'.

PubMed

Saito, Kazuki; Hirai, Masami Y; Yonekura-Sakakibara, Keiko

2008-01-01

Following the sequencing of whole genomes of model plants, high-throughput decoding of gene function is a major challenge in modern plant biology. In view of remarkable technical advances in transcriptomics and metabolomics, integrated analysis of these 'omics' by data-mining informatics is an excellent tool for prediction and identification of gene function, particularly for genes involved in complicated metabolic pathways. The availability of Arabidopsis public transcriptome datasets containing data of >1000 microarrays reinforces the potential for prediction of gene function by transcriptome coexpression analysis. Here, we review the strategy of combining transcriptome and metabolome as a powerful technology for studying the functional genomics of model plants and also crop and medicinal plants.

The identification of high potential archers based on relative psychological coping skills variables: A Support Vector Machine approach

NASA Astrophysics Data System (ADS)

Taha, Zahari; Muazu Musa, Rabiu; Majeed, A. P. P. Abdul; Razali Abdullah, Mohamad; Aizzat Zakaria, Muhammad; Muaz Alim, Muhammad; Arif Mat Jizat, Jessnor; Fauzi Ibrahim, Mohamad

2018-03-01

Support Vector Machine (SVM) has been revealed to be a powerful learning algorithm for classification and prediction. However, the use of SVM for prediction and classification in sport is at its inception. The present study classified and predicted high and low potential archers from a collection of psychological coping skills variables trained on different SVMs. 50 youth archers with the average age and standard deviation of (17.0 ±.056) gathered from various archery programmes completed a one end shooting score test. Psychological coping skills inventory which evaluates the archers level of related coping skills were filled out by the archers prior to their shooting tests. k-means cluster analysis was applied to cluster the archers based on their scores on variables assessed. SVM models, i.e. linear and fine radial basis function (RBF) kernel functions, were trained on the psychological variables. The k-means clustered the archers into high psychologically prepared archers (HPPA) and low psychologically prepared archers (LPPA), respectively. It was demonstrated that the linear SVM exhibited good accuracy and precision throughout the exercise with an accuracy of 92% and considerably fewer error rate for the prediction of the HPPA and the LPPA as compared to the fine RBF SVM. The findings of this investigation can be valuable to coaches and sports managers to recognise high potential athletes from the selected psychological coping skills variables examined which would consequently save time and energy during talent identification and development programme.

Space radioisotope power source requirements update and technology status

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

Mondt, J.F.

1998-07-01

The requirements for a space advanced radioisotope power source are based on potential deep space missions being investigated for the NASA Advanced Space Systems Development Program. Since deep space missions have not been approved, updating requirements is a continuos parallel process of designing the spacecraft and the science instruments to accomplish the potential missions and developing the power source technology to meet changing requirements. There are at least two potential missions, Pluto/Kuiper Express and Europa Orbiter, which may require space advanced radioisotope power sources. The Europa Orbiter has been selected as the preferred first potential mission. However the final decisionmore » will depend on the technology readiness of all the subsystems and the project must be able to switch to Pluto Kuiper Express as the first mission as late as the beginning of fiscal year 2000. Therefore the requirements for the power source will cover both potential missions. As the deep space spacecraft design evolves to meet the science requirements and the Alkali Metal Thermal to Electric (AMTEC) technology matures the advanced radioisotope power source design requirements are updated The AMTEC technology developed to date uses stainless steel for the sodium containment material. The higher efficiency required for the space power system dictates that the AMTEC technology must operate at a higher temperature than possible with stainless steel. Therefore refractory materials have been selected as the baseline material for the AMTEC cell. These refractory materials are Nb1Zr for the hot side and Nb1Zr or Nb10Hf1Ti for the cold side. These materials were selected so the AMTEC cell can operate at 1150K to 1350K hot side temperature and 600K to 700K cold side temperature and meet the present power and mass requirements using four to six general purpose heat source modules as the heat source. The new containment materials and brazes will be evaluated as to lifetime, compatibility and performance with the AMTEC beta prime Alumina, the TiN electrodes, the sodium and the molybdenum current collectors. AMTEC cell components and cells will be built with the baseline containment materials and brazes and tested to determine the performance as a function of temperature. These containment materials will be also be tested with all the other AMTEC components to determine acceleration factors needed to predict AMTEC performance degradation and failure as a function of operating time at temperature.« less

Simulation-Based Height of Burst Map for Asteroid Airburst Damage Prediction

NASA Technical Reports Server (NTRS)

Aftosmis, Michael J.; Mathias, Donovan L.; Tarano, Ana M.

2017-01-01

Entry and breakup models predict that airburst in the Earth's atmosphere is likely for asteroids up to approximately 200 meters in diameter. Objects of this size can deposit over 250 megatons of energy into the atmosphere. Fast-running ground damage prediction codes for such events rely heavily upon methods developed from nuclear weapons research to estimate the damage potential for an airburst at altitude. (Collins, 2005; Mathias, 2017; Hills and Goda, 1993). In particular, these tools rely upon the powerful yield scaling laws developed for point-source blasts that are used in conjunction with a Height of Burst (HOB) map to predict ground damage for an airburst of a specific energy at a given altitude. While this approach works extremely well for yields as large as tens of megatons, it becomes less accurate as yields increase to the hundreds of megatons potentially released by larger airburst events. This study revisits the assumptions underlying this approach and shows how atmospheric buoyancy becomes important as yield increases beyond a few megatons. We then use large-scale three-dimensional simulations to construct numerically generated height of burst maps that are appropriate at the higher energy levels associated with the entry of asteroids with diameters of hundreds of meters. These numerically generated HOB maps can then be incorporated into engineering methods for damage prediction, significantly improving their accuracy for asteroids with diameters greater than 80-100 m.

Semiempirical prediction of protein folds

NASA Astrophysics Data System (ADS)

Fernández, Ariel; Colubri, Andrés; Appignanesi, Gustavo

2001-08-01

We introduce a semiempirical approach to predict ab initio expeditious pathways and native backbone geometries of proteins that fold under in vitro renaturation conditions. The algorithm is engineered to incorporate a discrete codification of local steric hindrances that constrain the movements of the peptide backbone throughout the folding process. Thus, the torsional state of the chain is assumed to be conditioned by the fact that hopping from one basin of attraction to another in the Ramachandran map (local potential energy surface) of each residue is energetically more costly than the search for a specific (Φ, Ψ) torsional state within a single basin. A combinatorial procedure is introduced to evaluate coarsely defined torsional states of the chain defined ``modulo basins'' and translate them into meaningful patterns of long range interactions. Thus, an algorithm for structure prediction is designed based on the fact that local contributions to the potential energy may be subsumed into time-evolving conformational constraints defining sets of restricted backbone geometries whereupon the patterns of nonbonded interactions are constructed. The predictive power of the algorithm is assessed by (a) computing ab initio folding pathways for mammalian ubiquitin that ultimately yield a stable structural pattern reproducing all of its native features, (b) determining the nucleating event that triggers the hydrophobic collapse of the chain, and (c) comparing coarse predictions of the stable folds of moderately large proteins (N~100) with structural information extracted from the protein data bank.

Molecular inspired models for prediction and control of directional FSO/RF wireless networks

NASA Astrophysics Data System (ADS)

Llorca, Jaime; Milner, Stuart D.; Davis, Christopher C.

2010-08-01

Directional wireless networks using FSO and RF transmissions provide wireless backbone support for mobile communications in dynamic environments. The heterogeneous and dynamic nature of such networks challenges their robustness and requires self-organization mechanisms to assure end-to-end broadband connectivity. We developed a framework based on the definition of a potential energy function to characterize robustness in communication networks and the study of first and second order variations of the potential energy to provide prediction and control strategies for network performance optimization. In this paper, we present non-convex molecular potentials such as the Morse Potential, used to describe the potential energy of bonds within molecules, for the characterization of communication links in the presence of physical constraints such as the power available at the network nodes. The inclusion of the Morse Potential translates into adaptive control strategies where forces on network nodes drive the release, retention or reconfiguration of communication links for network performance optimization. Simulation results show the effectiveness of our self-organized control mechanism, where the physical topology reorganizes to maximize the number of source to destination communicating pairs. Molecular Normal Mode Analysis (NMA) techniques for assessing network performance degradation in dynamic networks are also presented. Preliminary results show correlation between peaks in the eigenvalues of the Hessian of the network potential and network degradation.

The potential diagnostic power of circulating tumor cell analysis for non-small-cell lung cancer.

PubMed

Ross, Kirsty; Pailler, Emma; Faugeroux, Vincent; Taylor, Melissa; Oulhen, Marianne; Auger, Nathalie; Planchard, David; Soria, Jean-Charles; Lindsay, Colin R; Besse, Benjamin; Vielh, Philippe; Farace, Françoise

2015-01-01

In non-small-cell lung cancer (NSCLC), genotyping tumor biopsies for targetable somatic alterations has become routine practice. However, serial biopsies have limitations: they may be technically difficult or impossible and could incur serious risks to patients. Circulating tumor cells (CTCs) offer an alternative source for tumor analysis that is easily accessible and presents the potential to identify predictive biomarkers to tailor therapies on a personalized basis. Examined here is our current knowledge of CTC detection and characterization in NSCLC and their potential role in EGFR-mutant, ALK-rearranged and ROS1-rearranged patients. This is followed by discussion of the ongoing issues such as the question of CTC partnership as diagnostic tools in NSCLC.

Gamma rays from dark matter subhalos revisited: Refining the predictions and constraints

DOE PAGES

Hooper, Dan; Witte, Samuel J.

2017-04-11

Utilizing data from the ELVIS and Via Lactea-II simulations, we characterize the local dark matter subhalo population, and use this information to refine the predictions for the gamma-ray fluxes arising from annihilating dark matter in this class of objects. We find that the shapes of nearby subhalos are significantly altered by tidal effects, and are generally not well described by NFW density profiles, instead prefering power-law profiles with an exponential cutoff. From the subhalo candidates detected by the Fermi Gamma-Ray Space Telescope, we place limits on the dark matter annihilation cross section that are only modestly weaker than those basedmore » on observations of dwarf galaxies. Furthermore, we also calculate the fraction of observable subhalos that are predicted to be spatially extended at a level potentially discernible to Fermi.« less

Gamma rays from dark matter subhalos revisited: refining the predictions and constraints

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

Hooper, Dan; Witte, Samuel J., E-mail: dhooper@fnal.gov, E-mail: switte@physics.ucla.edu

2017-04-01

Utilizing data from the ELVIS and Via Lactea-II simulations, we characterize the local dark matter subhalo population, and use this information to refine the predictions for the gamma-ray fluxes arising from annihilating dark matter in this class of objects. We find that the shapes of nearby subhalos are significantly altered by tidal effects, and are generally not well described by NFW density profiles, instead prefering power-law profiles with an exponential cutoff. From the subhalo candidates detected by the Fermi Gamma-Ray Space Telescope, we place limits on the dark matter annihilation cross section that are only modestly weaker than those basedmore » on observations of dwarf galaxies. We also calculate the fraction of observable subhalos that are predicted to be spatially extended at a level potentially discernible to Fermi.« less

Gamma rays from dark matter subhalos revisited: Refining the predictions and constraints

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

Hooper, Dan; Witte, Samuel J.

Utilizing data from the ELVIS and Via Lactea-II simulations, we characterize the local dark matter subhalo population, and use this information to refine the predictions for the gamma-ray fluxes arising from annihilating dark matter in this class of objects. We find that the shapes of nearby subhalos are significantly altered by tidal effects, and are generally not well described by NFW density profiles, instead prefering power-law profiles with an exponential cutoff. From the subhalo candidates detected by the Fermi Gamma-Ray Space Telescope, we place limits on the dark matter annihilation cross section that are only modestly weaker than those basedmore » on observations of dwarf galaxies. Furthermore, we also calculate the fraction of observable subhalos that are predicted to be spatially extended at a level potentially discernible to Fermi.« less

Electrostatic protection of the Solar Power Satellite and rectenna

NASA Technical Reports Server (NTRS)

Freeman, J. W.; Few, A. A., Jr.; Reiff, P. H.; Cooke, D.; Bohannon, J.; Haymes, B.

1979-01-01

Several features of the interactions of the solar power satellite (SPS) with its space environment were examined theoretically. The voltages produced at various surfaces due to space plasmas and the plasma leakage currents through the kapton and sapphire solar cell blankets were calculated. At geosynchronous orbit, this parasitic power loss is only 0.7%, and is easily compensated by oversizing. At low-Earth orbit, the power loss is potentially much larger (3%), and anomalous arcing is expected for the EOTV high voltage negative surfaces. Preliminary results of a three dimensional self-consistent plasma and electric field computer program are presented, confirming the validity of the predictions made from the one dimensional models. Magnetic shielding of the satellite, to reduce the power drain and to protect the solar cells from energetic electron and plasma ion bombardment is considered. It is concluded that minor modifications can allow the SPS to operate safely and efficiently in its space environment. The SPS design employed in this study is the 1978 MSFC baseline design utilizing GaAs solar cells at CR-2 and an aluminum structure.

Lightweight Radiator for in Space Nuclear Electric Propulsion

NASA Technical Reports Server (NTRS)

Craven, Paul; Tomboulian, Briana; SanSoucie, Michael

2014-01-01

Nuclear electric propulsion (NEP) is a promising option for high-speed in-space travel due to the high energy density of nuclear fission power sources and efficient electric thrusters. Advanced power conversion technologies may require high operating temperatures and would benefit from lightweight radiator materials. Radiator performance dictates power output for nuclear electric propulsion systems. Game-changing propulsion systems are often enabled by novel designs using advanced materials. Pitch-based carbon fiber materials have the potential to offer significant improvements in operating temperature, thermal conductivity, and mass. These properties combine to allow advances in operational efficiency and high temperature feasibility. An effort at the NASA Marshall Space Flight Center to show that woven high thermal conductivity carbon fiber mats can be used to replace standard metal and composite radiator fins to dissipate waste heat from NEP systems is ongoing. The goals of this effort are to demonstrate a proof of concept, to show that a significant improvement of specific power (power/mass) can be achieved, and to develop a thermal model with predictive capabilities making use of constrained input parameter space. A description of this effort is presented.

A relation to predict the failure of materials and potential application to volcanic eruptions and landslides

PubMed Central

Hao, Shengwang; Liu, Chao; Lu, Chunsheng; Elsworth, Derek

2016-01-01

A theoretical explanation of a time-to-failure relation is presented, with this relationship then used to describe the failure of materials. This provides the potential to predict timing (tf − t) immediately before failure by extrapolating the trajectory as it asymptotes to zero with no need to fit unknown exponents as previously proposed in critical power law behaviors. This generalized relation is verified by comparison with approaches to criticality for volcanic eruptions and creep failure. A new relation based on changes with stress is proposed as an alternative expression of Voight’s relation, which is widely used to describe the accelerating precursory signals before material failure and broadly applied to volcanic eruptions, landslides and other phenomena. The new generalized relation reduces to Voight’s relation if stress is limited to increase at a constant rate with time. This implies that the time-derivatives in Voight’s analysis may be a subset of a more general expression connecting stress derivatives, and thus provides a potential method for forecasting these events. PMID:27306851

Identification and Management of Pump Thrombus in the HeartWare Left Ventricular Assist Device System: A Novel Approach Using Log File Analysis.

PubMed

Jorde, Ulrich P; Aaronson, Keith D; Najjar, Samer S; Pagani, Francis D; Hayward, Christopher; Zimpfer, Daniel; Schlöglhofer, Thomas; Pham, Duc T; Goldstein, Daniel J; Leadley, Katrin; Chow, Ming-Jay; Brown, Michael C; Uriel, Nir

2015-11-01

The study sought to characterize patterns in the HeartWare (HeartWare Inc., Framingham, Massachusetts) ventricular assist device (HVAD) log files associated with successful medical treatment of device thrombosis. Device thrombosis is a serious adverse event for mechanical circulatory support devices and is often preceded by increased power consumption. Log files of the pump power are easily accessible on the bedside monitor of HVAD patients and may allow early diagnosis of device thrombosis. Furthermore, analysis of the log files may be able to predict the success rate of thrombolysis or the need for pump exchange. The log files of 15 ADVANCE trial patients (algorithm derivation cohort) with 16 pump thrombus events treated with tissue plasminogen activator (tPA) were assessed for changes in the absolute and rate of increase in power consumption. Successful thrombolysis was defined as a clinical resolution of pump thrombus including normalization of power consumption and improvement in biochemical markers of hemolysis. Significant differences in log file patterns between successful and unsuccessful thrombolysis treatments were verified in 43 patients with 53 pump thrombus events implanted outside of clinical trials (validation cohort). The overall success rate of tPA therapy was 57%. Successful treatments had significantly lower measures of percent of expected power (130.9% vs. 196.1%, p = 0.016) and rate of increase in power (0.61 vs. 2.87, p < 0.0001). Medical therapy was successful in 77.7% of the algorithm development cohort and 81.3% of the validation cohort when the rate of power increase and percent of expected power values were <1.25% and 200%, respectively. Log file parameters can potentially predict the likelihood of successful tPA treatments and if validated prospectively, could substantially alter the approach to thrombus management. Copyright © 2015 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Size effects and charge transport in metals: Quantum theory of the resistivity of nanometric metallic structures arising from electron scattering by grain boundaries and by rough surfaces

NASA Astrophysics Data System (ADS)

Munoz, Raul C.; Arenas, Claudio

2017-03-01

We discuss recent progress regarding size effects and their incidence upon the coefficients describing charge transport (resistivity, magnetoresistance, and Hall effect) induced by electron scattering from disordered grain boundaries and from rough surfaces on metallic nanostructures; we review recent measurements of the magneto transport coefficients that elucidate the electron scattering mechanisms at work. We review as well theoretical developments regarding quantum transport theories that allow calculating the increase in resistivity induced by electron-rough surface scattering (in the absence of grain boundaries) from first principles—from the parameters that describe the surface roughness that can be measured with a Scanning Tunnelling Microscope (STM). We evaluate the predicting power of the quantum version of the Fuchs-Sondheimer theory and of the model proposed by Calecki, abandoning the method of parameter fitting used for decades, but comparing instead theoretical predictions with resistivity measured in thin films where surface roughness has also been measured with a STM, and where electron-grain boundary scattering can be neglected. We also review the theory of Mayadas and Shatzkes (MS) [Phys. Rev. B 1, 1382 (1970)] used for decades, and discuss its severe conceptual difficulties that arise out of the fact that: (i) MS employed plane waves to describe the electronic states within the metal sample having periodic grain boundaries, rather than the Bloch states known since the thirties to be the solutions of the Schrödinger equation describing electrons propagating through a Krönig-Penney [Proc. R. Soc. London Ser. A 130, 499 (1931)] periodic potential; (ii) MS ignored the fact that the wave functions describing electrons propagating through a 1-D disordered potential are expected to decay exponentially with increasing distance, a fact known since the work of Anderson [Phys. Rev. 109, 1492 (1958)] in 1958 for which he was awarded the Nobel Prize in 1977; (iii) The current in the sample should be proportional to TN, the probability that an electron traverses N consecutive (disordered) grains found along a mean free path; MS assumed that TN = 1. We review unpublished details of a quantum transport theory based upon a model of diffusive transport and Kubo's linear response formalism recently published [Arenas et al., Appl. Surf. Sci. 329, 184 (2015)], which permits estimating the increase in resistivity of a metallic specimen (over the bulk resistivity) under the combined effects of electron scattering by phonons, impurities, disordered grain boundaries, and rough surfaces limiting the sample. We evaluate the predicting power of both the MS theory and of the new quantum model on samples where the temperature dependence of the resistivity has been measured between 4 K and 300 K, and where surface roughness and grain size distribution has been measured on each sample via independent experiments. We find that the quantum theory does exhibit a predicting power, whereas the predicting power of the MS model as well as the significance and reliability of its fitting parameters seems questionable. We explore the power of the new theory by comparing, for the first time, the resistivity predicted and measured on nanometric Cu wires of (approximately) rectangular cross section employed in building integrated circuits, based upon a quantum description of electron motion.

Theoretical insights on the antioxidant activity of edaravone free radical scavengers derivatives

NASA Astrophysics Data System (ADS)

Cerón-Carrasco, José P.; Roy, Hélène M.; Cerezo, Javier; Jacquemin, Denis; Laurent, Adèle D.

2014-04-01

The prediction of antioxidant properties is not straightforward due to the complexity of the in vivo systems. Here, we use theoretical descriptors, including the potential of ionization, the electrodonating power and the spin density distribution, to characterize the antioxidant capacity of edaravone (EDV) derivatives. Our computations reveal the relationship between these parameters and their potential bioactivity as free radical scavengers. We conclude that more efficient antioxidants could be synthesized by tuning the R1 and R2 positions of the EDV structure, rather than modifying the R3 group. Such modifications might improve the antioxidant activity in neutral and deprotonated forms.

Does Spontaneous Favorability to Power (vs. Universalism) Values Predict Spontaneous Prejudice and Discrimination?

PubMed

Souchon, Nicolas; Maio, Gregory R; Hanel, Paul H P; Bardin, Brigitte

2017-10-01

We conducted five studies testing whether an implicit measure of favorability toward power over universalism values predicts spontaneous prejudice and discrimination. Studies 1 (N = 192) and 2 (N = 86) examined correlations between spontaneous favorability toward power (vs. universalism) values, achievement (vs. benevolence) values, and a spontaneous measure of prejudice toward ethnic minorities. Study 3 (N = 159) tested whether conditioning participants to associate power values with positive adjectives and universalism values with negative adjectives (or inversely) affects spontaneous prejudice. Study 4 (N = 95) tested whether decision bias toward female handball players could be predicted by spontaneous attitude toward power (vs. universalism) values. Study 5 (N = 123) examined correlations between spontaneous attitude toward power (vs. universalism) values, spontaneous importance toward power (vs. universalism) values, and spontaneous prejudice toward Black African people. Spontaneous positivity toward power (vs. universalism) values was associated with spontaneous negativity toward minorities and predicted gender bias in a decision task, whereas the explicit measures did not. These results indicate that the implicit assessment of evaluative responses attached to human values helps to model value-attitude-behavior relations. © 2016 The Authors. Journal of Personality Published by Wiley Periodicals, Inc.

On the Linearly-Balanced Kinetic Energy Spectrum

NASA Technical Reports Server (NTRS)

Lu, Huei,-Iin; Robertson, F. R.

1999-01-01

It is well known that the earth's atmospheric motion can generally be characterized by the two dimensional quasi-geostrophic approximation, in which the constraints on global integrals of kinetic energy, entrophy and potential vorticity play very important roles in redistributing the wave energy among different scales of motion. Assuming the hypothesis of Kolmogrov's local isotropy, derived a -3 power law of the equilibrium two-dimensional kinetic energy spectrum that entails constant vorticity and zero energy flows from the energy-containing wave number up to the viscous cutoff. In his three dimensional quasi-geostrophic theory, showed that the spectrum function of the vertical scale turbulence - expressible in terms of the available potential energy - possesses the same power law as the two dimensional kinetic energy spectrum. As the slope of kinetic energy spectrum in the inertial range is theoretically related to the predictability of the synoptic scales (Lorenz, 1969), many general circulation models includes a horizontal diffusion to provide reasonable kinetic energy spectra, although the actual power law exhibited in the atmospheric general circulation is controversial. Note that in either the atmospheric modeling or the observational analyses, the proper choice of wave number Index to represent the turbulence scale Is the degree of the Legendre polynomial.

Performance Estimation for Two-Dimensional Brownian Rotary Ratchet Systems

NASA Astrophysics Data System (ADS)

Tutu, Hiroki; Horita, Takehiko; Ouchi, Katsuya

2015-04-01

Within the context of the Brownian ratchet model, a molecular rotary system that can perform unidirectional rotations induced by linearly polarized ac fields and produce positive work under loads was studied. The model is based on the Langevin equation for a particle in a two-dimensional (2D) three-tooth ratchet potential of threefold symmetry. The performance of the system is characterized by the coercive torque, i.e., the strength of the load competing with the torque induced by the ac driving field, and the energy efficiency in force conversion from the driving field to the torque. We propose a master equation for coarse-grained states, which takes into account the boundary motion between states, and develop a kinetic description to estimate the mean angular momentum (MAM) and powers relevant to the energy balance equation. The framework of analysis incorporates several 2D characteristics and is applicable to a wide class of models of smooth 2D ratchet potential. We confirm that the obtained expressions for MAM, power, and efficiency of the model can enable us to predict qualitative behaviors. We also discuss the usefulness of the torque/power relationship for experimental analyses, and propose a characteristic for 2D ratchet systems.

Deconvolution of the vestibular evoked myogenic potential using the power spectrum of the electromyogram.

PubMed

Lütkenhöner, Bernd

2015-10-06

The vestibular evoked myogenic potential (VEMP) can be modelled reasonably well by convolving two functions: one representing an average motor unit action potential (MUAP), the other representing the temporal modulation of the MUAP rate (rate modulation). It is the latter which contains the information of interest, and so it would be desirable to be able to estimate this function from a combination of the VEMP with some other data. As the VEMP is simply a stimulus-triggered average of the electromyogram (EMG), a supplementary, easily accessible source of information is the EMG power spectrum, which can be shown to be roughly proportional to the squared modulus of the Fourier transform of the MUAP. But no phase information is available for the MUAP so that a straightforward deconvolution is not possible. To get around the problem of incomplete information, the rate modulation is described by a thoughtfully chosen function with just a few adjustable parameters. The convolution model is then used to make predictions as to the energy spectral density of the VEMP, and the parameters are optimized using a cost function that quantifies the difference between model prediction and data. The workability of the proposed approach is demonstrated by analysing Monte Carlo simulated data and exemplary data from patients who underwent VEMP testing as part of a clinical evaluation of their dizziness symptoms. The approach is suited, for example, to estimate the duration of the inhibition causing the VEMP or to disentangle a VEMP consisting of more than one component.

Potential of electric bicycles to improve the health of people with Type 2 diabetes: a feasibility study.

PubMed

Cooper, A R; Tibbitts, B; England, C; Procter, D; Searle, A; Sebire, S J; Ranger, E; Page, A S

2018-05-08

To explore in a feasibility study whether 'e-cycling' was acceptable to, and could potentially improve the health of, people with Type 2 diabetes. Twenty people with Type 2 diabetes were recruited and provided with an electric bicycle for 20 weeks. Participants completed a submaximal fitness test at baseline and follow-up to measure predicted maximal aerobic power, and semi-structured interviews were conducted to assess the acceptability of using an electric bicycle. Participants wore a heart rate monitor and a Global Positioning System (GPS) receiver in the first week of electric bicycle use to measure their heart-rate during e-cycling. Eighteen participants completed the study, cycling a median (interquartile range) of 21.4 (5.5-37.7) km per week Predicted maximal aerobic power increased by 10.9%. Heart rate during electric bicycle journeys was 74.7% of maximum, compared with 64.3% of maximum when walking. Participants used the electric bicycles for commuting, shopping and recreation, and expressed how the electric bicycle helped them to overcome barriers to active travel/cycling, such as hills. Fourteen participants purchased an electric bicycle on study completion. There was evidence that e-cycling was acceptable, could increase fitness and elicited a heart rate that may lead to improvements in cardiometabolic risk factors in this population. Electric bicycles have potential as a health-improving intervention in people with Type 2 diabetes. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Plasma Interaction with International Space Station High Voltage Solar Arrays

NASA Technical Reports Server (NTRS)

Heard, John W.

2002-01-01

The International Space Station (ISS) is presently being assembled in low-earth orbit (LEO) operating high voltage solar arrays (-160 V max, -140 V typical with respect to the ambient atmosphere). At the station's present altitude, there exists substantial ambient plasma that can interact with the solar arrays. The biasing of an object to an electric potential immersed in plasma creates a plasma "sheath" or non-equilibrium plasma around the object to mask out the electric fields. A positively biased object can collect electrons from the plasma sheath and the sheath will draw a current from the surrounding plasma. This parasitic current can enter the solar cells and effectively "short out" the potential across the cells, reducing the power that can be generated by the panels. Predictions of collected current based on previous high voltage experiments (SAMPIE (Solar Array Module Plasma Interactions Experiment), PASP+ (Photovoltaic Array Space Power) were on the order of amperes of current. However, present measurements of parasitic current are on the order of several milliamperes, and the current collection mainly occurs during an "eclipse exit" event, i.e., when the space station comes out of darkness. This collection also has a time scale, t approx. 1000 s, that is much slower than any known plasma interaction time scales. The reason for the discrepancy between predictions and present electron collection is not understood and is under investigation by the PCU (Plasma Contactor Unit) "Tiger" team. This paper will examine the potential structure within and around the solar arrays, and the possible causes and reasons for the electron collection of the array.

Electric Power Engineering Cost Predicting Model Based on the PCA-GA-BP

NASA Astrophysics Data System (ADS)

Wen, Lei; Yu, Jiake; Zhao, Xin

2017-10-01

In this paper a hybrid prediction algorithm: PCA-GA-BP model is proposed. PCA algorithm is established to reduce the correlation between indicators of original data and decrease difficulty of BP neural network in complex dimensional calculation. The BP neural network is established to estimate the cost of power transmission project. The results show that PCA-GA-BP algorithm can improve result of prediction of electric power engineering cost.

Evaluation of Data-Driven Models for Predicting Solar Photovoltaics Power Output

DOE PAGES

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

2017-09-10

This research was undertaken to evaluate different inverse models for predicting power output of solar photovoltaic (PV) systems under different practical scenarios. In particular, we have investigated whether PV power output prediction accuracy can be improved if module/cell temperature was measured in addition to climatic variables, and also the extent to which prediction accuracy degrades if solar irradiation is not measured on the plane of array but only on a horizontal surface. We have also investigated the significance of different independent or regressor variables, such as wind velocity and incident angle modifier in predicting PV power output and cell temperature.more » The inverse regression model forms have been evaluated both in terms of their goodness-of-fit, and their accuracy and robustness in terms of their predictive performance. Given the accuracy of the measurements, expected CV-RMSE of hourly power output prediction over the year varies between 3.2% and 8.6% when only climatic data are used. Depending on what type of measured climatic and PV performance data is available, different scenarios have been identified and the corresponding appropriate modeling pathways have been proposed. The corresponding models are to be implemented on a controller platform for optimum operational planning of microgrids and integrated energy systems.« less

Improved techniques for predicting spacecraft power

NASA Technical Reports Server (NTRS)

Chmielewski, A. B.

1987-01-01

Radioisotope Thermoelectric Generators (RTGs) are going to supply power for the NASA Galileo and Ulysses spacecraft now scheduled to be launched in 1989 and 1990. The duration of the Galileo mission is expected to be over 8 years. This brings the total RTG lifetime to 13 years. In 13 years, the RTG power drops more than 20 percent leaving a very small power margin over what is consumed by the spacecraft. Thus it is very important to accurately predict the RTG performance and be able to assess the magnitude of errors involved. The paper lists all the error sources involved in the RTG power predictions and describes a statistical method for calculating the tolerance.

ESB-based Sensor Web integration for the prediction of electric power supply system vulnerability.

PubMed

Stoimenov, Leonid; Bogdanovic, Milos; Bogdanovic-Dinic, Sanja

2013-08-15

Electric power supply companies increasingly rely on enterprise IT systems to provide them with a comprehensive view of the state of the distribution network. Within a utility-wide network, enterprise IT systems collect data from various metering devices. Such data can be effectively used for the prediction of power supply network vulnerability. The purpose of this paper is to present the Enterprise Service Bus (ESB)-based Sensor Web integration solution that we have developed with the purpose of enabling prediction of power supply network vulnerability, in terms of a prediction of defect probability for a particular network element. We will give an example of its usage and demonstrate our vulnerability prediction model on data collected from two different power supply companies. The proposed solution is an extension of the GinisSense Sensor Web-based architecture for collecting, processing, analyzing, decision making and alerting based on the data received from heterogeneous data sources. In this case, GinisSense has been upgraded to be capable of operating in an ESB environment and combine Sensor Web and GIS technologies to enable prediction of electric power supply system vulnerability. Aside from electrical values, the proposed solution gathers ambient values from additional sensors installed in the existing power supply network infrastructure. GinisSense aggregates gathered data according to an adapted Omnibus data fusion model and applies decision-making logic on the aggregated data. Detected vulnerabilities are visualized to end-users through means of a specialized Web GIS application.

ESB-Based Sensor Web Integration for the Prediction of Electric Power Supply System Vulnerability

PubMed Central

Stoimenov, Leonid; Bogdanovic, Milos; Bogdanovic-Dinic, Sanja

2013-01-01

Electric power supply companies increasingly rely on enterprise IT systems to provide them with a comprehensive view of the state of the distribution network. Within a utility-wide network, enterprise IT systems collect data from various metering devices. Such data can be effectively used for the prediction of power supply network vulnerability. The purpose of this paper is to present the Enterprise Service Bus (ESB)-based Sensor Web integration solution that we have developed with the purpose of enabling prediction of power supply network vulnerability, in terms of a prediction of defect probability for a particular network element. We will give an example of its usage and demonstrate our vulnerability prediction model on data collected from two different power supply companies. The proposed solution is an extension of the GinisSense Sensor Web-based architecture for collecting, processing, analyzing, decision making and alerting based on the data received from heterogeneous data sources. In this case, GinisSense has been upgraded to be capable of operating in an ESB environment and combine Sensor Web and GIS technologies to enable prediction of electric power supply system vulnerability. Aside from electrical values, the proposed solution gathers ambient values from additional sensors installed in the existing power supply network infrastructure. GinisSense aggregates gathered data according to an adapted Omnibus data fusion model and applies decision-making logic on the aggregated data. Detected vulnerabilities are visualized to end-users through means of a specialized Web GIS application. PMID:23955435

Assessment of arrays of in-stream tidal turbines in the Bay of Fundy.

PubMed

Karsten, Richard; Swan, Amanda; Culina, Joel

2013-02-28

Theories of in-stream turbines are adapted to analyse the potential electricity generation and impact of turbine arrays deployed in Minas Passage, Bay of Fundy. Linear momentum actuator disc theory (LMADT) is combined with a theory that calculates the flux through the passage to determine both the turbine power and the impact of rows of turbine fences. For realistically small blockage ratios, the theory predicts that extracting 2000-2500 MW of turbine power will result in a reduction in the flow of less than 5 per cent. The theory also suggests that there is little reason to tune the turbines if the blockage ratio remains small. A turbine array model is derived that extends LMADT by using the velocity field from a numerical simulation of the flow through Minas Passage and modelling the turbine wakes. The model calculates the resulting speed of the flow through and around a turbine array, allowing for the sequential positioning of turbines in regions of strongest flow. The model estimates that over 2000 MW of power is possible with only a 2.5 per cent reduction in the flow. If turbines are restricted to depths less than 50 m, the potential power generation is reduced substantially, down to 300 MW. For large turbine arrays, the blockage ratios remain small and the turbines can produce maximum power with a drag coefficient equal to the Betz-limit value.

Characterising the spatial variability of the tidal stream energy resource from floating turbines

NASA Astrophysics Data System (ADS)

Ward, Sophie; Neill, Simon; Robins, Peter

2017-04-01

The shelf seas, in particular the northwest European shelf seas surrounding the UK, contain significant tidal power potential. Tidal stream energy is both predictable and reliable providing that sites are well-selected based upon the hydrodynamic regime and the device specifics. In this high resolution three-dimensional tidal modelling study, we investigate how the tidal stream resource around the Welsh coast (UK) varies with water depth and location, with particular focus on the Pembrokeshire region. The potential extractable energy for a floating tidal stream energy converter is compared with that for a bottom-fixed device, highlighting the need to vary the resource characterisation criteria based on device specifics. We demonstrate how small variations in the tidal current speeds - with hub depth or due to tidal asymmetry - can lead to substantial variations in potential power output. Further, the results indicate that power generation from floating tidal energy converters will be more significantly influenced by tidal elevations in regions characterised by a lower tidal range (more progressive waves) than regions that experience a high tidal range (standing waves). As numerical modelling capacity improves and tidal stream energy converter technologies develop, ongoing improved quantification of the tidal resource is needed, as well as consideration of the possible feedbacks of the devices and energy extraction on the hydrodynamic regime and the surrounding area.

Convexity of Energy-Like Functions: Theoretical Results and Applications to Power System Operations

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

Dvijotham, Krishnamurthy; Low, Steven; Chertkov, Michael

2015-01-12

Power systems are undergoing unprecedented transformations with increased adoption of renewables and distributed generation, as well as the adoption of demand response programs. All of these changes, while making the grid more responsive and potentially more efficient, pose significant challenges for power systems operators. Conventional operational paradigms are no longer sufficient as the power system may no longer have big dispatchable generators with sufficient positive and negative reserves. This increases the need for tools and algorithms that can efficiently predict safe regions of operation of the power system. In this paper, we study energy functions as a tool to designmore » algorithms for various operational problems in power systems. These have a long history in power systems and have been primarily applied to transient stability problems. In this paper, we take a new look at power systems, focusing on an aspect that has previously received little attention: Convexity. We characterize the domain of voltage magnitudes and phases within which the energy function is convex in these variables. We show that this corresponds naturally with standard operational constraints imposed in power systems. We show that power of equations can be solved using this approach, as long as the solution lies within the convexity domain. We outline various desirable properties of solutions in the convexity domain and present simple numerical illustrations supporting our results.« less

Summary of the Manufacture, Testing and Model Validation of a Full-Scale Radiator for Fission Surface Power Applications

NASA Technical Reports Server (NTRS)

Ellis, David L.; Calder, James; Siamidis, John

2011-01-01

A full-scale radiator for a lunar fission surface power application was manufactured by Material innovations, Inc., for the NASA Glenn Research Center. The radiator was designed to reject 6 kWt with an inlet water temperature of 400 K and a water mass flow rate of 0.5 kg/s. While not flight hardware, the radiator incorporated many potential design features and manufacturing techniques for future flight hardware. The radiator was tested at NASA Glenn Research Center for heat rejection performance. The results showed that the radiator design was capable of rejecting over 6 kWt when operating at the design conditions. The actual performance of the radiator as a function of operational manifolds, inlet water temperature and facility sink temperature was compared to the predictive model developed by NASA Glenn Research Center. The results showed excellent agreement with the model with the actual average face sheet temperature being within 1% of the predicted value. The results will be used in the design and production of NASA s next generation fission power heat rejection systems. The NASA Glenn Research Center s Technology Demonstration Unit will be the first project to take advantage of the newly developed manufacturing techniques and analytical models.

SIPSim: A Modeling Toolkit to Predict Accuracy and Aid Design of DNA-SIP Experiments.

PubMed

Youngblut, Nicholas D; Barnett, Samuel E; Buckley, Daniel H

2018-01-01

DNA Stable isotope probing (DNA-SIP) is a powerful method that links identity to function within microbial communities. The combination of DNA-SIP with multiplexed high throughput DNA sequencing enables simultaneous mapping of in situ assimilation dynamics for thousands of microbial taxonomic units. Hence, high throughput sequencing enabled SIP has enormous potential to reveal patterns of carbon and nitrogen exchange within microbial food webs. There are several different methods for analyzing DNA-SIP data and despite the power of SIP experiments, it remains difficult to comprehensively evaluate method accuracy across a wide range of experimental parameters. We have developed a toolset (SIPSim) that simulates DNA-SIP data, and we use this toolset to systematically evaluate different methods for analyzing DNA-SIP data. Specifically, we employ SIPSim to evaluate the effects that key experimental parameters (e.g., level of isotopic enrichment, number of labeled taxa, relative abundance of labeled taxa, community richness, community evenness, and beta-diversity) have on the specificity, sensitivity, and balanced accuracy (defined as the product of specificity and sensitivity) of DNA-SIP analyses. Furthermore, SIPSim can predict analytical accuracy and power as a function of experimental design and community characteristics, and thus should be of great use in the design and interpretation of DNA-SIP experiments.

SIPSim: A Modeling Toolkit to Predict Accuracy and Aid Design of DNA-SIP Experiments

PubMed Central

Youngblut, Nicholas D.; Barnett, Samuel E.; Buckley, Daniel H.

2018-01-01

DNA Stable isotope probing (DNA-SIP) is a powerful method that links identity to function within microbial communities. The combination of DNA-SIP with multiplexed high throughput DNA sequencing enables simultaneous mapping of in situ assimilation dynamics for thousands of microbial taxonomic units. Hence, high throughput sequencing enabled SIP has enormous potential to reveal patterns of carbon and nitrogen exchange within microbial food webs. There are several different methods for analyzing DNA-SIP data and despite the power of SIP experiments, it remains difficult to comprehensively evaluate method accuracy across a wide range of experimental parameters. We have developed a toolset (SIPSim) that simulates DNA-SIP data, and we use this toolset to systematically evaluate different methods for analyzing DNA-SIP data. Specifically, we employ SIPSim to evaluate the effects that key experimental parameters (e.g., level of isotopic enrichment, number of labeled taxa, relative abundance of labeled taxa, community richness, community evenness, and beta-diversity) have on the specificity, sensitivity, and balanced accuracy (defined as the product of specificity and sensitivity) of DNA-SIP analyses. Furthermore, SIPSim can predict analytical accuracy and power as a function of experimental design and community characteristics, and thus should be of great use in the design and interpretation of DNA-SIP experiments. PMID:29643843

Dense and Sparse Matrix Operations on the Cell Processor

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

Williams, Samuel W.; Shalf, John; Oliker, Leonid

2005-05-01

The slowing pace of commodity microprocessor performance improvements combined with ever-increasing chip power demands has become of utmost concern to computational scientists. Therefore, the high performance computing community is examining alternative architectures that address the limitations of modern superscalar designs. In this work, we examine STI's forthcoming Cell processor: a novel, low-power architecture that combines a PowerPC core with eight independent SIMD processing units coupled with a software-controlled memory to offer high FLOP/s/Watt. Since neither Cell hardware nor cycle-accurate simulators are currently publicly available, we develop an analytic framework to predict Cell performance on dense and sparse matrix operations, usingmore » a variety of algorithmic approaches. Results demonstrate Cell's potential to deliver more than an order of magnitude better GFLOP/s per watt performance, when compared with the Intel Itanium2 and Cray X1 processors.« less

Wireless energy transfer: Dielectric lens antennas for beam shaping in wireless power-transfer applications

NASA Astrophysics Data System (ADS)

Gonçalves, Ricardo; Carvalho, Nuno B.; Pinho, Pedro

2017-02-01

In the current contest of wireless systems, the last frontier remains the cut of the power cord. In that sense, the interest over wireless energy transfer technologies in the past years has grown exponentially. However, there are still many challenges to be overcome in order to enable wireless energy transfer full potential. One of the focus in the development of such systems is the design of very-high-gain, highly efficient, antennas that can compensate for the propagation loss of radio signals over the air. In this paper, we explore the design and manufacturing process of dielectric lenses, fabricated using a professional-grade desktop 3D printer. Lens antennas are used in order to increase beam efficiency and therefore maximize the efficiency of a wireless power-transfer system operating at microwave frequencies in the Ku band. Measurements of two fabricated prototypes showcase a large directivity, as predicted with simulations. xml:lang="fr"

Study of parametric instability in gravitational wave detectors with silicon test masses

NASA Astrophysics Data System (ADS)

Zhang, Jue; Zhao, Chunnong; Ju, Li; Blair, David

2017-03-01

Parametric instability is an intrinsic risk in high power laser interferometer gravitational wave detectors, in which the optical cavity modes interact with the acoustic modes of the mirrors, leading to exponential growth of the acoustic vibration. In this paper, we investigate the potential parametric instability for a proposed next generation gravitational wave detector, the LIGO Voyager blue design, with cooled silicon test masses of size 45 cm in diameter and 55 cm in thickness. It is shown that there would be about two unstable modes per test mass at an arm cavity power of 3 MW, with the highest parametric gain of  ∼76. While this is less than the predicted number of unstable modes for Advanced LIGO (∼40 modes with max gain of  ∼32 at the designed operating power of 830 kW), the importance of developing suitable instability suppression schemes is emphasized.

Cross-timescale Interference and Rainfall Extreme Events in South Eastern South America

NASA Astrophysics Data System (ADS)

Munoz, Angel G.

The physical mechanisms and predictability associated with extreme daily rainfall in South East South America (SESA) are investigated for the December-February season. Through a k-mean analysis, a robust set of daily circulation regimes is identified and then it is used to link the frequency of rainfall extreme events with large-scale potential predictors at subseasonal-to-seasonal scales. This basic set of daily circulation regimes is related to the continental and oceanic phases of the South Atlantic Convergence Zone (SACZ) and wave train patterns superimposed on the Southern Hemisphere Polar Jet. Some of these recurrent synoptic circulation types are conducive to extreme rainfall events in the region through synoptic control of different meso-scale physical features and, at the same time, are influenced by climate phenomena that could be used as sources of potential predictability. Extremely high rainfall (as measured by the 95th- and 99th-percentiles) is preferentially associated with two of these weather types, which are characterized by moisture advection intrusions from lower latitudes and the Pacific; another three weather types, characterized by above-normal moisture advection toward lower latitudes or the Andes, are preferentially associated with dry days (days with no rain). The analysis permits the identification of several subseasonal-to-seasonal scale potential predictors that modulate the occurrence of circulation regimes conducive to extreme rainfall events in SESA. It is conjectured that a cross-timescale interference between the different climate drivers improves the predictive skill of extreme precipitation in the region. The potential and real predictive skill of the frequency of extreme rainfall is then evaluated, finding evidence indicating that mechanisms of climate variability at one timescale contribute to the predictability at another scale, i.e., taking into account the interference of different potential sources of predictability at different timescales increases the predictive skill. This fact is in agreement with the Cross-timescale Interference Conjecture proposed in the first part of the thesis. At seasonal scale, a combination of those weather types tends to outperform all the other potential predictors explored, i.e., sea surface temperature patterns, phases of the Madden-Julian Oscillation, and combinations of both. Spatially averaged Kendall’s τ improvements of 43% for the potential predictability and 23% for realtime predictions are attained with respect to standard models considering sea-surface temperature fields alone. A new subseasonal-to-seasonal predictive methodology for extreme rainfall events is proposed, based on probability forecasts of seasonal sequences of these weather types. The cross-validated realtime skill of the new probabilistic approach, as measured by the Hit Score and the Heidke Skill Score, is on the order of twice that associated with climatological values. The approach is designed to offer useful subseasonal-to-seasonal climate information to decision-makers interested not only in how many extreme events will happen in the season, but also in how, when and where those events will probably occur. In order to gain further understanding about how the cross-timescale interference occurs, an externally-forced Lorenz model is used to explore the impact of different kind of forcings, at inter-annual and decadal scales, in the establishment of constructive interactions associated with the simulated “extreme events”. Using a wavelet analysis, it is shown that this simple model is capable of reproducing the same kind of cross-timescale structures observed in the wavelet power spectrum of the Nino3.4 index only when it is externally forced by both inter-annual and decadal signals: the annual cycle and a decadal forcing associated with the natural solar variability. The nature of this interaction is non-linear, and it impacts both mean and extreme values in the time series. No predictive power was found when using metrics like standard deviation and auto-correlation. Nonetheless, it was proposed that an early warning signal for occurrence of extreme rainfall in SESA may be possible via a continuous monitoring of relative phases between the cross-timescale leading components.

Predicting power-optimal kinematics of avian wings

PubMed Central

Parslew, Ben

2015-01-01

A theoretical model of avian flight is developed which simulates wing motion through a class of methods known as predictive simulation. This approach uses numerical optimization to predict power-optimal kinematics of avian wings in hover, cruise, climb and descent. The wing dynamics capture both aerodynamic and inertial loads. The model is used to simulate the flight of the pigeon, Columba livia, and the results are compared with previous experimental measurements. In cruise, the model unearths a vast range of kinematic modes that are capable of generating the required forces for flight. The most efficient mode uses a near-vertical stroke–plane and a flexed-wing upstroke, similar to kinematics recorded experimentally. In hover, the model predicts that the power-optimal mode uses an extended-wing upstroke, similar to hummingbirds. In flexing their wings, pigeons are predicted to consume 20% more power than if they kept their wings full extended, implying that the typical kinematics used by pigeons in hover are suboptimal. Predictions of climbing flight suggest that the most energy-efficient way to reach a given altitude is to climb as steeply as possible, subjected to the availability of power. PMID:25392398

Performance of comorbidity, risk adjustment, and functional status measures in expenditure prediction for patients with diabetes.

PubMed

Maciejewski, Matthew L; Liu, Chuan-Fen; Fihn, Stephan D

2009-01-01

To compare the ability of generic comorbidity and risk adjustment measures, a diabetes-specific measure, and a self-reported functional status measure to explain variation in health care expenditures for individuals with diabetes. This study included a retrospective cohort of 3,092 diabetic veterans participating in a multisite trial. Two comorbidity measures, four risk adjusters, a functional status measure, a diabetes complication count, and baseline expenditures were constructed from administrative and survey data. Outpatient, inpatient, and total expenditure models were estimated using ordinary least squares regression. Adjusted R(2) statistics and predictive ratios were compared across measures to assess overall explanatory power and explanatory power of low- and high-cost subgroups. Administrative data-based risk adjusters performed better than the comorbidity, functional status, and diabetes-specific measures in all expenditure models. The diagnostic cost groups (DCGs) measure had the greatest predictive power overall and for the low- and high-cost subgroups, while the diabetes-specific measure had the lowest predictive power. A model with DCGs and the diabetes-specific measure modestly improved predictive power. Existing generic measures can be useful for diabetes-specific research and policy applications, but more predictive diabetes-specific measures are needed.

Applications and limitations of radiomics

NASA Astrophysics Data System (ADS)

Yip, Stephen S. F.; Aerts, Hugo J. W. L.

2016-07-01

Radiomics is an emerging field in quantitative imaging that uses advanced imaging features to objectively and quantitatively describe tumour phenotypes. Radiomic features have recently drawn considerable interest due to its potential predictive power for treatment outcomes and cancer genetics, which may have important applications in personalized medicine. In this technical review, we describe applications and challenges of the radiomic field. We will review radiomic application areas and technical issues, as well as proper practices for the designs of radiomic studies.

Model Specification for Rework of Aircraft Engine, Power Transmission, and Accessory/Auxiliary Ball and Roller Bearings

NASA Technical Reports Server (NTRS)

Zaretsky, Erwin V.; Branzai, Emanuel V.

2007-01-01

This document provides a model specification for the rework and/or repair of bearings used in aircraft engines, helicopter main power train transmissions, and auxiliary bearings determined to be critical by virtue of performance, function, or availability. The rolling-element bearings to be processed under the provisions of this model specification may be used bearings removed after service, unused bearings returned from the field, or certain rejected bearings returned for reinspection and salvage. In commercial and military aircraft application, it has been a practice that rolling-element bearings removed at maintenance or overhaul be reworked and returned to service. Depending on the extent of rework and based upon theoretical analysis, representative life factors (LF) for bearings subject to rework ranged from 0.87 to 0.99 the lives of new bearings. Based on bearing endurance data, 92 percent of the bearing sets that would be subject to rework would result in L(sub 10) lives equaling and/or exceeding that predicted for new bearings. The remaining 8 percent of the bearings have the potential to achieve the analytically predicted life of new bearings when one of the rings is replaced at rework. The potential savings from bearing rework varies from 53 to 82 percent of that of new bearings depending on the cost, size, and complexity of the bearing

Prediction of light aircraft interior sound pressure level using the room equation

NASA Technical Reports Server (NTRS)

Atwal, M.; Bernhard, R.

1984-01-01

The room equation is investigated for predicting interior sound level. The method makes use of an acoustic power balance, by equating net power flow into the cabin volume to power dissipated within the cabin using the room equation. The sound power level transmitted through the panels was calculated by multiplying the measured space averaged transmitted intensity for each panel by its surface area. The sound pressure level was obtained by summing the mean square sound pressures radiated from each panel. The data obtained supported the room equation model in predicting the cabin interior sound pressure level.

Potential and limits to unravel the genetic architecture and predict the variation of Fusarium head blight resistance in European winter wheat (Triticum aestivum L.).

PubMed

Jiang, Y; Zhao, Y; Rodemann, B; Plieske, J; Kollers, S; Korzun, V; Ebmeyer, E; Argillier, O; Hinze, M; Ling, J; Röder, M S; Ganal, M W; Mette, M F; Reif, J C

2015-03-01

Genome-wide mapping approaches in diverse populations are powerful tools to unravel the genetic architecture of complex traits. The main goals of our study were to investigate the potential and limits to unravel the genetic architecture and to identify the factors determining the accuracy of prediction of the genotypic variation of Fusarium head blight (FHB) resistance in wheat (Triticum aestivum L.) based on data collected with a diverse panel of 372 European varieties. The wheat lines were phenotyped in multi-location field trials for FHB resistance and genotyped with 782 simple sequence repeat (SSR) markers, and 9k and 90k single-nucleotide polymorphism (SNP) arrays. We applied genome-wide association mapping in combination with fivefold cross-validations and observed surprisingly high accuracies of prediction for marker-assisted selection based on the detected quantitative trait loci (QTLs). Using a random sample of markers not selected for marker-trait associations revealed only a slight decrease in prediction accuracy compared with marker-based selection exploiting the QTL information. The same picture was confirmed in a simulation study, suggesting that relatedness is a main driver of the accuracy of prediction in marker-assisted selection of FHB resistance. When the accuracy of prediction of three genomic selection models was contrasted for the three marker data sets, no significant differences in accuracies among marker platforms and genomic selection models were observed. Marker density impacted the accuracy of prediction only marginally. Consequently, genomic selection of FHB resistance can be implemented most cost-efficiently based on low- to medium-density SNP arrays.

The effects of load on system and lower-body joint kinetics during jump squats.

PubMed

Moir, Gavin L; Gollie, Jared M; Davis, Shala E; Guers, John J; Witmer, Chad A

2012-11-01

To investigate the effects of different loads on system and lower-body kinetics during jump squats, 12 resistance-trained men performed jumps under different loading conditions: 0%, 12%, 27%, 42%, 56%, 71%, and 85% of 1-repetition maximum (1-RM). System power output was calculated as the product of the vertical component of the ground reaction force and the vertical velocity of the bar during its ascent. Joint power output was calculated during bar ascent for the hip, knee, and ankle joints, and was also summed across the joints. System power output and joint power at knee and ankle joints were maximized at 0% 1-RM (p < 0.001) and followed the linear trends (p < 0.001) caused by power output decreasing as the load increased. Power output at the hip was maximized at 42% 1-RM (p = 0.016) and followed a quadratic trend (p = 0.030). Summed joint power could be predicted from system power (p < 0.05), while system power could predict power at the knee and ankle joints under some of the loading conditions. Power at the hip could not be predicted from system power. System power during loaded jumps reflects the power at the knee and ankle, while power at the hip does not correspond to system power.

Increasing power generation in horizontal axis wind turbines using optimized flow control

NASA Astrophysics Data System (ADS)

Cooney, John A., Jr.

In order to effectively realize future goals for wind energy, the efficiency of wind turbines must increase beyond existing technology. One direct method for achieving increased efficiency is by improving the individual power generation characteristics of horizontal axis wind turbines. The potential for additional improvement by traditional approaches is diminishing rapidly however. As a result, a research program was undertaken to assess the potential of using distributed flow control to increase power generation. The overall objective was the development of validated aerodynamic simulations and flow control approaches to improve wind turbine power generation characteristics. BEM analysis was conducted for a general set of wind turbine models encompassing last, current, and next generation designs. This analysis indicated that rotor lift control applied in Region II of the turbine power curve would produce a notable increase in annual power generated. This was achieved by optimizing induction factors along the rotor blade for maximum power generation. In order to demonstrate this approach and other advanced concepts, the University of Notre Dame established the Laboratory for Enhanced Wind Energy Design (eWiND). This initiative includes a fully instrumented meteorological tower and two pitch-controlled wind turbines. The wind turbines are representative in their design and operation to larger multi-megawatt turbines, but of a scale that allows rotors to be easily instrumented and replaced to explore new design concepts. Baseline data detailing typical site conditions and turbine operation is presented. To realize optimized performance, lift control systems were designed and evaluated in CFD simulations coupled with shape optimization tools. These were integrated into a systematic design methodology involving BEM simulations, CFD simulations and shape optimization, and selected experimental validation. To refine and illustrate the proposed design methodology, a complete design cycle was performed for the turbine model incorporated in the wind energy lab. Enhanced power generation was obtained through passive trailing edge shaping aimed at reaching lift and lift-to-drag goals predicted to optimize performance. These targets were determined by BEM analysis to improve power generation characteristics and annual energy production (AEP) for the wind turbine. A preliminary design was validated in wind tunnel experiments on a 2D rotor section in preparation for testing in the full atmospheric environment of the eWiND Laboratory. These tests were performed for the full-scale geometry and atmospheric conditions. Upon making additional improvements to the shape optimization tools, a series of trailing edge additions were designed to optimize power generation. The trailing edge additions were predicted to increase the AEP by up to 4.2% at the White Field site. The pieces were rapid-prototyped and installed on the wind turbine in March, 2014. Field tests are ongoing.

Prediction of Thorough QT study results using action potential simulations based on ion channel screens.

PubMed

Mirams, Gary R; Davies, Mark R; Brough, Stephen J; Bridgland-Taylor, Matthew H; Cui, Yi; Gavaghan, David J; Abi-Gerges, Najah

2014-01-01

Detection of drug-induced pro-arrhythmic risk is a primary concern for pharmaceutical companies and regulators. Increased risk is linked to prolongation of the QT interval on the body surface ECG. Recent studies have shown that multiple ion channel interactions can be required to predict changes in ventricular repolarisation and therefore QT intervals. In this study we attempt to predict the result of the human clinical Thorough QT (TQT) study, using multiple ion channel screening which is available early in drug development. Ion current reduction was measured, in the presence of marketed drugs which have had a TQT study, for channels encoded by hERG, CaV1.2, NaV1.5, KCNQ1/MinK, and Kv4.3/KChIP2.2. The screen was performed on two platforms - IonWorks Quattro (all 5 channels, 34 compounds), and IonWorks Barracuda (hERG & CaV1.2, 26 compounds). Concentration-effect curves were fitted to the resulting data, and used to calculate a percentage reduction in each current at a given concentration. Action potential simulations were then performed using the ten Tusscher and Panfilov (2006), Grandi et al. (2010) and O'Hara et al. (2011) human ventricular action potential models, pacing at 1Hz and running to steady state, for a range of concentrations. We compared simulated action potential duration predictions with the QT prolongation observed in the TQT studies. At the estimated concentrations, simulations tended to underestimate any observed QT prolongation. When considering a wider range of concentrations, and conventional patch clamp rather than screening data for hERG, prolongation of ≥5ms was predicted with up to 79% sensitivity and 100% specificity. This study provides a proof-of-principle for the prediction of human TQT study results using data available early in drug development. We highlight a number of areas that need refinement to improve the method's predictive power, but the results suggest that such approaches will provide a useful tool in cardiac safety assessment. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Wind farms production: Control and prediction

NASA Astrophysics Data System (ADS)

El-Fouly, Tarek Hussein Mostafa

Wind energy resources, unlike dispatchable central station generation, produce power dependable on external irregular source and that is the incident wind speed which does not always blow when electricity is needed. This results in the variability, unpredictability, and uncertainty of wind resources. Therefore, the integration of wind facilities to utility electrical grid presents a major challenge to power system operator. Such integration has significant impact on the optimum power flow, transmission congestion, power quality issues, system stability, load dispatch, and economic analysis. Due to the irregular nature of wind power production, accurate prediction represents the major challenge to power system operators. Therefore, in this thesis two novel models are proposed for wind speed and wind power prediction. One proposed model is dedicated to short-term prediction (one-hour ahead) and the other involves medium term prediction (one-day ahead). The accuracy of the proposed models is revealed by comparing their results with the corresponding values of a reference prediction model referred to as the persistent model. Utility grid operation is not only impacted by the uncertainty of the future production of wind farms, but also by the variability of their current production and how the active and reactive power exchange with the grid is controlled. To address this particular task, a control technique for wind turbines, driven by doubly-fed induction generators (DFIGs), is developed to regulate the terminal voltage by equally sharing the generated/absorbed reactive power between the rotor-side and the gridside converters. To highlight the impact of the new developed technique in reducing the power loss in the generator set, an economic analysis is carried out. Moreover, a new aggregated model for wind farms is proposed that accounts for the irregularity of the incident wind distribution throughout the farm layout. Specifically, this model includes the wake effect and the time delay of the incident wind speed of the different turbines on the farm, and to simulate the fluctuation in the generated power more accurately and more closer to real-time operation. Recently, wind farms with considerable output power ratings have been installed. Their integrating into the utility grid will substantially affect the electricity markets. This thesis investigates the possible impact of wind power variability, wind farm control strategy, wind energy penetration level, wind farm location, and wind power prediction accuracy on the total generation costs and close to real time electricity market prices. These issues are addressed by developing a single auction market model for determining the real-time electricity market prices.

Forecasting influenza-like illness dynamics for military populations using neural networks and social media

PubMed Central

Ayton, Ellyn; Porterfield, Katherine; Corley, Courtney D.

2017-01-01

This work is the first to take advantage of recurrent neural networks to predict influenza-like illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data and the state-of-the-art machine learning models, we build and evaluate the predictive power of neural network architectures based on Long Short Term Memory (LSTMs) units capable of nowcasting (predicting in “real-time”) and forecasting (predicting the future) ILI dynamics in the 2011 – 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, embeddings, word ngrams, stylistic patterns, and communication behavior using hashtags and mentions. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks using a diverse set of evaluation metrics. Finally, we combine ILI and social media signals to build a joint neural network model for ILI dynamics prediction. Unlike the majority of the existing work, we specifically focus on developing models for local rather than national ILI surveillance, specifically for military rather than general populations in 26 U.S. and six international locations., and analyze how model performance depends on the amount of social media data available per location. Our approach demonstrates several advantages: (a) Neural network architectures that rely on LSTM units trained on social media data yield the best performance compared to previously used regression models. (b) Previously under-explored language and communication behavior features are more predictive of ILI dynamics than stylistic and topic signals expressed in social media. (c) Neural network models learned exclusively from social media signals yield comparable or better performance to the models learned from ILI historical data, thus, signals from social media can be potentially used to accurately forecast ILI dynamics for the regions where ILI historical data is not available. (d) Neural network models learned from combined ILI and social media signals significantly outperform models that rely solely on ILI historical data, which adds to a great potential of alternative public sources for ILI dynamics prediction. (e) Location-specific models outperform previously used location-independent models e.g., U.S. only. (f) Prediction results significantly vary across geolocations depending on the amount of social media data available and ILI activity patterns. (g) Model performance improves with more tweets available per geo-location e.g., the error gets lower and the Pearson score gets higher for locations with more tweets. PMID:29244814

Forecasting influenza-like illness dynamics for military populations using neural networks and social media.

PubMed

Volkova, Svitlana; Ayton, Ellyn; Porterfield, Katherine; Corley, Courtney D

2017-01-01

This work is the first to take advantage of recurrent neural networks to predict influenza-like illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data and the state-of-the-art machine learning models, we build and evaluate the predictive power of neural network architectures based on Long Short Term Memory (LSTMs) units capable of nowcasting (predicting in "real-time") and forecasting (predicting the future) ILI dynamics in the 2011 - 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, embeddings, word ngrams, stylistic patterns, and communication behavior using hashtags and mentions. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks using a diverse set of evaluation metrics. Finally, we combine ILI and social media signals to build a joint neural network model for ILI dynamics prediction. Unlike the majority of the existing work, we specifically focus on developing models for local rather than national ILI surveillance, specifically for military rather than general populations in 26 U.S. and six international locations., and analyze how model performance depends on the amount of social media data available per location. Our approach demonstrates several advantages: (a) Neural network architectures that rely on LSTM units trained on social media data yield the best performance compared to previously used regression models. (b) Previously under-explored language and communication behavior features are more predictive of ILI dynamics than stylistic and topic signals expressed in social media. (c) Neural network models learned exclusively from social media signals yield comparable or better performance to the models learned from ILI historical data, thus, signals from social media can be potentially used to accurately forecast ILI dynamics for the regions where ILI historical data is not available. (d) Neural network models learned from combined ILI and social media signals significantly outperform models that rely solely on ILI historical data, which adds to a great potential of alternative public sources for ILI dynamics prediction. (e) Location-specific models outperform previously used location-independent models e.g., U.S. only. (f) Prediction results significantly vary across geolocations depending on the amount of social media data available and ILI activity patterns. (g) Model performance improves with more tweets available per geo-location e.g., the error gets lower and the Pearson score gets higher for locations with more tweets.

Replacement of SSE (Release 6) with NASA's Prediction of Worldwide Energy Resource (POWER) Project GIS-enabled Web Data Portal:

Atmospheric Science Data Center

2018-03-15

... effort has been developed under the Prediction Of Worldwide Energy Resource (POWER) Project funded largely by NASA Earth Applied Sciences ... to NASA's satellite and modeling analysis for Renewable Energy, Sustainable Buildings and Agroclimatology applications.  A new POWER ...

POWER/SSE

Atmospheric Science Data Center

2018-06-15

... The Prediction of Worldwide Energy Resource (POWER) project was initiated to improve upon the current SSE ... continue to be focussed on the solar and wind Renewable Energy industry. New data sets will target Sustainable Buildings ... The Prediction of Worldwide Energy Resource (POWER) project was initiated to improve upon the current SSE ...

Tutorial: Neural networks and their potential application in nuclear power plants

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

Uhrig, R.E.

A neural network is a data processing system consisting of a number of simple, highly interconnected processing elements in an architecture inspired by the structure of the cerebral cortex portion of the brain. Hence, neural networks are often capable of doing things which humans or animals do well but which conventional computers often do poorly. Neural networks have emerged in the past few years as an area of unusual opportunity for research, development and application to a variety of real world problems. Indeed, neural networks exhibit characteristics and capabilities not provided by any other technology. Examples include reading Japanese Kanjimore » characters and human handwriting, reading a typewritten manuscript aloud, compensating for alignment errors in robots, interpreting very noise'' signals (e.g. electroencephalograms), modeling complex systems that cannot be modelled mathematically, and predicting whether proposed loans will be good or fail. This paper presents a brief tutorial on neural networks and describes research on the potential applications to nuclear power plants.« less

Cardiovascular risk

PubMed Central

Payne, Rupert A

2012-01-01

Cardiovascular disease is a major, growing, worldwide problem. It is important that individuals at risk of developing cardiovascular disease can be effectively identified and appropriately stratified according to risk. This review examines what we understand by the term risk, traditional and novel risk factors, clinical scoring systems, and the use of risk for informing prescribing decisions. Many different cardiovascular risk factors have been identified. Established, traditional factors such as ageing are powerful predictors of adverse outcome, and in the case of hypertension and dyslipidaemia are the major targets for therapeutic intervention. Numerous novel biomarkers have also been described, such as inflammatory and genetic markers. These have yet to be shown to be of value in improving risk prediction, but may represent potential therapeutic targets and facilitate more targeted use of existing therapies. Risk factors have been incorporated into several cardiovascular disease prediction algorithms, such as the Framingham equation, SCORE and QRISK. These have relatively poor predictive power, and uncertainties remain with regards to aspects such as choice of equation, different risk thresholds and the roles of relative risk, lifetime risk and reversible factors in identifying and treating at-risk individuals. Nonetheless, such scores provide objective and transparent means of quantifying risk and their integration into therapeutic guidelines enables equitable and cost-effective distribution of health service resources and improves the consistency and quality of clinical decision making. PMID:22348281

Quantifying the influence of sediment source area sampling on detrital thermochronometer data

NASA Astrophysics Data System (ADS)

Whipp, D. M., Jr.; Ehlers, T. A.; Coutand, I.; Bookhagen, B.

2014-12-01

Detrital thermochronology offers a unique advantage over traditional bedrock thermochronology because of its sensitivity to sediment production and transportation to sample sites. In mountainous regions, modern fluvial sediment is often collected and dated to determine the past (105 to >107 year) exhumation history of the upstream drainage area. Though potentially powerful, the interpretation of detrital thermochronometer data derived from modern fluvial sediment is challenging because of spatial and temporal variations in sediment production and transport, and target mineral concentrations. Thermochronometer age prediction models provide a quantitative basis for data interpretation, but it can be difficult to separate variations in catchment bedrock ages from the effects of variable basin denudation and sediment transport. We present two examples of quantitative data interpretation using detrital thermochronometer data from the Himalaya, focusing on the influence of spatial and temporal variations in basin denudation on predicted age distributions. We combine age predictions from the 3D thermokinematic numerical model Pecube with simple models for sediment sampling in the upstream drainage basin area to assess the influence of variations in sediment production by different geomorphic processes or scaled by topographic metrics. We first consider a small catchment from the central Himalaya where bedrock landsliding appears to have affected the observed muscovite 40Ar/39Ar age distributions. Using a simple model of random landsliding with a power-law landslide frequency-area relationship we find that the sediment residence time in the catchment has a major influence on predicted age distributions. In the second case, we compare observed detrital apatite fission-track age distributions from 16 catchments in the Bhutan Himalaya to ages predicted using Pecube and scaled by various topographic metrics. Preliminary results suggest that predicted age distributions scaled by the rock uplift rate in Pecube are statistically equivalent to the observed age distributions for ~75% of the catchments, but may improve when scaled by local relief or specific stream power weighted by satellite-derived precipitation. Ongoing work is exploring the effect of scaling by other topographic metrics.

Critical Power: An Important Fatigue Threshold in Exercise Physiology

PubMed Central

Poole, David C.; Burnley, Mark; Vanhatalo, Anni; Rossiter, Harry B.; Jones, Andrew M.

2016-01-01

The hyperbolic form of the power-duration relationship is rigorous and highly conserved across species, forms of exercise and individual muscles/muscle groups. For modalities such as cycling, the relationship resolves to two parameters, the asymptote for power (critical power, CP) and the so-called W′ (work doable above CP), which together predict the tolerable duration of exercise above CP. Crucially, the CP concept integrates sentinel physiological profiles - respiratory, metabolic and contractile - within a coherent framework that has great scientific and practical utility. Rather than calibrating equivalent exercise intensities relative to metabolically distant parameters such as the lactate threshold or V̇O2 max, setting the exercise intensity relative to CP unifies the profile of systemic and intramuscular responses and, if greater than CP, predicts the tolerable duration of exercise until W′ is expended, V̇O2 max is attained, and intolerance is manifested. CP may be regarded as a ‘fatigue threshold’ in the sense that it separates exercise intensity domains within which the physiological responses to exercise can (CP) be stabilized. The CP concept therefore enables important insights into 1) the principal loci of fatigue development (central vs. peripheral) at different intensities of exercise, and 2) mechanisms of cardiovascular and metabolic control and their modulation by factors such as O2 delivery. Practically, the CP concept has great potential application in optimizing athletic training programs and performance as well as improving the life quality for individuals enduring chronic disease. PMID:27031742

Critical Power: An Important Fatigue Threshold in Exercise Physiology.

PubMed

Poole, David C; Burnley, Mark; Vanhatalo, Anni; Rossiter, Harry B; Jones, Andrew M

2016-11-01

: The hyperbolic form of the power-duration relationship is rigorous and highly conserved across species, forms of exercise, and individual muscles/muscle groups. For modalities such as cycling, the relationship resolves to two parameters, the asymptote for power (critical power [CP]) and the so-called W' (work doable above CP), which together predict the tolerable duration of exercise above CP. Crucially, the CP concept integrates sentinel physiological profiles-respiratory, metabolic, and contractile-within a coherent framework that has great scientific and practical utility. Rather than calibrating equivalent exercise intensities relative to metabolically distant parameters such as the lactate threshold or V˙O2max, setting the exercise intensity relative to CP unifies the profile of systemic and intramuscular responses and, if greater than CP, predicts the tolerable duration of exercise until W' is expended, V˙O2max is attained, and intolerance is manifested. CP may be regarded as a "fatigue threshold" in the sense that it separates exercise intensity domains within which the physiological responses to exercise can (CP) be stabilized. The CP concept therefore enables important insights into 1) the principal loci of fatigue development (central vs. peripheral) at different intensities of exercise and 2) mechanisms of cardiovascular and metabolic control and their modulation by factors such as O2 delivery. Practically, the CP concept has great potential application in optimizing athletic training programs and performance as well as improving the life quality for individuals enduring chronic disease.

Using prediction markets to estimate the reproducibility of scientific research.

PubMed

Dreber, Anna; Pfeiffer, Thomas; Almenberg, Johan; Isaksson, Siri; Wilson, Brad; Chen, Yiling; Nosek, Brian A; Johannesson, Magnus

2015-12-15

Concerns about a lack of reproducibility of statistically significant results have recently been raised in many fields, and it has been argued that this lack comes at substantial economic costs. We here report the results from prediction markets set up to quantify the reproducibility of 44 studies published in prominent psychology journals and replicated in the Reproducibility Project: Psychology. The prediction markets predict the outcomes of the replications well and outperform a survey of market participants' individual forecasts. This shows that prediction markets are a promising tool for assessing the reproducibility of published scientific results. The prediction markets also allow us to estimate probabilities for the hypotheses being true at different testing stages, which provides valuable information regarding the temporal dynamics of scientific discovery. We find that the hypotheses being tested in psychology typically have low prior probabilities of being true (median, 9%) and that a "statistically significant" finding needs to be confirmed in a well-powered replication to have a high probability of being true. We argue that prediction markets could be used to obtain speedy information about reproducibility at low cost and could potentially even be used to determine which studies to replicate to optimally allocate limited resources into replications.

Using prediction markets to estimate the reproducibility of scientific research

PubMed Central

Dreber, Anna; Pfeiffer, Thomas; Almenberg, Johan; Isaksson, Siri; Wilson, Brad; Chen, Yiling; Nosek, Brian A.; Johannesson, Magnus

2015-01-01

Concerns about a lack of reproducibility of statistically significant results have recently been raised in many fields, and it has been argued that this lack comes at substantial economic costs. We here report the results from prediction markets set up to quantify the reproducibility of 44 studies published in prominent psychology journals and replicated in the Reproducibility Project: Psychology. The prediction markets predict the outcomes of the replications well and outperform a survey of market participants’ individual forecasts. This shows that prediction markets are a promising tool for assessing the reproducibility of published scientific results. The prediction markets also allow us to estimate probabilities for the hypotheses being true at different testing stages, which provides valuable information regarding the temporal dynamics of scientific discovery. We find that the hypotheses being tested in psychology typically have low prior probabilities of being true (median, 9%) and that a “statistically significant” finding needs to be confirmed in a well-powered replication to have a high probability of being true. We argue that prediction markets could be used to obtain speedy information about reproducibility at low cost and could potentially even be used to determine which studies to replicate to optimally allocate limited resources into replications. PMID:26553988

Flex-gear electrical power transmission

NASA Technical Reports Server (NTRS)

Vranish, John; Peritt, Jonathan

1993-01-01

This study was conducted to develop an alternative way of transferring electricity across a continuously rotating joint, with little wear and the potential for low electrical noise. The problems with wires, slip rings, electromagnetic couplings, and recently invented roll-rings are discussed. Flex-gears, an improvement of roll-rings, are described. An entire class of flexgear devices is developed. Finally, the preferred flex-gear device is optimized for maximum electrical contact and analyzed for average mechanical power loss and maximum stress. For a device diameter of six inches, the preferred device is predicted to have a total electrical contact area of 0.066 square inches. In the preferred device, a small amount of internal sliding produces a 0.003 inch-pound torque that resists the motion of the device.

Electrical Transport Properties of Liquid Al-Cu Alloys

NASA Astrophysics Data System (ADS)

Thakore, B. Y.; Khambholja, S. G.; Suthar, P. H.; Jani, A. R.

2010-06-01

Electrical transport properties viz. electrical resistivity, thermoelectric power and thermal conductivity of liquid Al-Cu alloys as a function of Cu concentration have been studied in the present paper. Ashcroft empty core model potential has been used to incorporate the ion-electron interaction. To incorporate the exchange and correlation effects, five different forms of local field correction functions viz. Hartree, Taylor, Ichimaru et al., Farid et al. and Sarkar et al. have been used. The transport properties of binary system have been studied using Faber-Ziman formulation combined with Ashcroft-Langreth (AL) partial structure factor. The computed values of electrical resistivity are compared with experimental data and for low Cu concentration, good agreement has been observed. Further, thermoelectric power and thermal conductivity have also been predicted.

Electrical Transport Properties of Liquid Sn-Sb Binary Alloys

NASA Astrophysics Data System (ADS)

Thakore, B. Y.; Suthar, P. H.; Khambholja, S. G.; Jani, A. R.

2010-06-01

The study of electrical transport properties viz. electrical resistivity, thermo electrical power and thermal conductivity of liquid Sn-Sb binary alloys have been made by our well recognized single parametric model potential. In the present work, screening functions due to Hartree, Taylor, Ichimaru et al.. Farid et al.. and Sarkar et al.. have been employed to incorporate the exchange and correlation effects. The liquid alloy is studied as a function of its composition at temperature 823 K according to the Faber-Ziman model. Further, thermoelectric power and thermal conductivity have been predicted. The values of electrical resistivity of binary alloys computed with Ichimaru et al. and Farid et al.. screening function are in good agreement with the experimental data.

Effect of the equivalent refractive index on intraocular lens power prediction with ray tracing after myopic laser in situ keratomileusis.

PubMed

Canovas, Carmen; van der Mooren, Marrie; Rosén, Robert; Piers, Patricia A; Wang, Li; Koch, Douglas D; Artal, Pablo

2015-05-01

To determine the impact of the equivalent refractive index (ERI) on intraocular lens (IOL) power prediction for eyes with previous myopic laser in situ keratomileusis (LASIK) using custom ray tracing. AMO B.V., Groningen, the Netherlands, and the Department of Ophthalmology, Baylor College of Medicine, Houston, Texas, USA. Retrospective data analysis. The ERI was calculated individually from the post-LASIK total corneal power. Two methods to account for the posterior corneal surface were tested; that is, calculation from pre-LASIK data or from post-LASIK data only. Four IOL power predictions were generated using a computer-based ray-tracing technique, including individual ERI results from both calculation methods, a mean ERI over the whole population, and the ERI for normal patients. For each patient, IOL power results calculated from the four predictions as well as those obtained with the Haigis-L were compared with the optimum IOL power calculated after cataract surgery. The study evaluated 25 patients. The mean and range of ERI values determined using post-LASIK data were similar to those determined from pre-LASIK data. Introducing individual or an average ERI in the ray-tracing IOL power calculation procedure resulted in mean IOL power errors that were not significantly different from zero. The ray-tracing procedure that includes an average ERI gave a greater percentage of eyes with an IOL power prediction error within ±0.5 diopter than the Haigis-L (84% versus 52%). For IOL power determination in post-LASIK patients, custom ray tracing including a modified ERI was an accurate procedure that exceeded the current standards for normal eyes. Copyright © 2015 ASCRS and ESCRS. Published by Elsevier Inc. All rights reserved.

Prediction of subjective ratings of emotional pictures by EEG features

NASA Astrophysics Data System (ADS)

McFarland, Dennis J.; Parvaz, Muhammad A.; Sarnacki, William A.; Goldstein, Rita Z.; Wolpaw, Jonathan R.

2017-02-01

Objective. Emotion dysregulation is an important aspect of many psychiatric disorders. Brain-computer interface (BCI) technology could be a powerful new approach to facilitating therapeutic self-regulation of emotions. One possible BCI method would be to provide stimulus-specific feedback based on subject-specific electroencephalographic (EEG) responses to emotion-eliciting stimuli. Approach. To assess the feasibility of this approach, we studied the relationships between emotional valence/arousal and three EEG features: amplitude of alpha activity over frontal cortex; amplitude of theta activity over frontal midline cortex; and the late positive potential over central and posterior mid-line areas. For each feature, we evaluated its ability to predict emotional valence/arousal on both an individual and a group basis. Twenty healthy participants (9 men, 11 women; ages 22-68) rated each of 192 pictures from the IAPS collection in terms of valence and arousal twice (96 pictures on each of 4 d over 2 weeks). EEG was collected simultaneously and used to develop models based on canonical correlation to predict subject-specific single-trial ratings. Separate models were evaluated for the three EEG features: frontal alpha activity; frontal midline theta; and the late positive potential. In each case, these features were used to simultaneously predict both the normed ratings and the subject-specific ratings. Main results. Models using each of the three EEG features with data from individual subjects were generally successful at predicting subjective ratings on training data, but generalization to test data was less successful. Sparse models performed better than models without regularization. Significance. The results suggest that the frontal midline theta is a better candidate than frontal alpha activity or the late positive potential for use in a BCI-based paradigm designed to modify emotional reactions.

Temperature Distribution Within a Defect-Free Silicon Carbide Diode Predicted by a Computational Model

NASA Technical Reports Server (NTRS)

Kuczmarski, Maria A.; Neudeck, Philip G.

2000-01-01

Most solid-state electronic devices diodes, transistors, and integrated circuits are based on silicon. Although this material works well for many applications, its properties limit its ability to function under extreme high-temperature or high-power operating conditions. Silicon carbide (SiC), with its desirable physical properties, could someday replace silicon for these types of applications. A major roadblock to realizing this potential is the quality of SiC material that can currently be produced. Semiconductors require very uniform, high-quality material, and commercially available SiC tends to suffer from defects in the crystalline structure that have largely been eliminated in silicon. In some power circuits, these defects can focus energy into an extremely small area, leading to overheating that can damage the device. In an effort to better understand the way that these defects affect the electrical performance and reliability of an SiC device in a power circuit, the NASA Glenn Research Center at Lewis Field began an in-house three-dimensional computational modeling effort. The goal is to predict the temperature distributions within a SiC diode structure subjected to the various transient overvoltage breakdown stresses that occur in power management circuits. A commercial computational fluid dynamics computer program (FLUENT-Fluent, Inc., Lebanon, New Hampshire) was used to build a model of a defect-free SiC diode and generate a computational mesh. A typical breakdown power density was applied over 0.5 msec in a heated layer at the junction between the p-type SiC and n-type SiC, and the temperature distribution throughout the diode was then calculated. The peak temperature extracted from the computational model agreed well (within 6 percent) with previous first-order calculations of the maximum expected temperature at the end of the breakdown pulse. This level of agreement is excellent for a model of this type and indicates that three-dimensional computational modeling can provide useful predictions for this class of problem. The model is now being extended to include the effects of crystal defects. The model will provide unique insights into how high the temperature rises in the vicinity of the defects in a diode at various power densities and pulse durations. This information also will help researchers in understanding and designing SiC devices for safe and reliable operation in high-power circuits.

Forecasting Electric Power Generation of Photovoltaic Power System for Energy Network

NASA Astrophysics Data System (ADS)

Kudo, Mitsuru; Takeuchi, Akira; Nozaki, Yousuke; Endo, Hisahito; Sumita, Jiro

Recently, there has been an increase in concern about the global environment. Interest is growing in developing an energy network by which new energy systems such as photovoltaic and fuel cells generate power locally and electric power and heat are controlled with a communications network. We developed the power generation forecast method for photovoltaic power systems in an energy network. The method makes use of weather information and regression analysis. We carried out forecasting power output of the photovoltaic power system installed in Expo 2005, Aichi Japan. As a result of comparing measurements with a prediction values, the average prediction error per day was about 26% of the measured power.

Universal predictability of mobility patterns in cities

PubMed Central

Yan, Xiao-Yong; Zhao, Chen; Fan, Ying; Di, Zengru; Wang, Wen-Xu

2014-01-01

Despite the long history of modelling human mobility, we continue to lack a highly accurate approach with low data requirements for predicting mobility patterns in cities. Here, we present a population-weighted opportunities model without any adjustable parameters to capture the underlying driving force accounting for human mobility patterns at the city scale. We use various mobility data collected from a number of cities with different characteristics to demonstrate the predictive power of our model. We find that insofar as the spatial distribution of population is available, our model offers universal prediction of mobility patterns in good agreement with real observations, including distance distribution, destination travel constraints and flux. By contrast, the models that succeed in modelling mobility patterns in countries are not applicable in cities, which suggests that there is a diversity of human mobility at different spatial scales. Our model has potential applications in many fields relevant to mobility behaviour in cities, without relying on previous mobility measurements. PMID:25232053

Predicting New Materials for Hydrogen Storage Application

PubMed Central

Vajeeston, Ponniah; Ravindran, Ponniah; Fjellvåg, Helmer

2009-01-01

Knowledge about the ground-state crystal structure is a prerequisite for the rational understanding of solid-state properties of new materials. To act as an efficient energy carrier, hydrogen should be absorbed and desorbed in materials easily and in high quantities. Owing to the complexity in structural arrangements and difficulties involved in establishing hydrogen positions by x-ray diffraction methods, the structural information of hydrides are very limited compared to other classes of materials (like oxides, intermetallics, etc.). This can be overcome by conducting computational simulations combined with selected experimental study which can save environment, money, and man power. The predicting capability of first-principles density functional theory (DFT) is already well recognized and in many cases structural and thermodynamic properties of single/multi component system are predicted. This review will focus on possible new classes of materials those have high hydrogen content, demonstrate the ability of DFT to predict crystal structure, and search for potential meta-stable phases. Stabilization of such meta-stable phases is also discussed.

A statistical framework to predict functional non-coding regions in the human genome through integrated analysis of annotation data.

PubMed

Lu, Qiongshi; Hu, Yiming; Sun, Jiehuan; Cheng, Yuwei; Cheung, Kei-Hoi; Zhao, Hongyu

2015-05-27

Identifying functional regions in the human genome is a major goal in human genetics. Great efforts have been made to functionally annotate the human genome either through computational predictions, such as genomic conservation, or high-throughput experiments, such as the ENCODE project. These efforts have resulted in a rich collection of functional annotation data of diverse types that need to be jointly analyzed for integrated interpretation and annotation. Here we present GenoCanyon, a whole-genome annotation method that performs unsupervised statistical learning using 22 computational and experimental annotations thereby inferring the functional potential of each position in the human genome. With GenoCanyon, we are able to predict many of the known functional regions. The ability of predicting functional regions as well as its generalizable statistical framework makes GenoCanyon a unique and powerful tool for whole-genome annotation. The GenoCanyon web server is available at http://genocanyon.med.yale.edu.

A Task-Optimized Neural Network Replicates Human Auditory Behavior, Predicts Brain Responses, and Reveals a Cortical Processing Hierarchy.

PubMed

Kell, Alexander J E; Yamins, Daniel L K; Shook, Erica N; Norman-Haignere, Sam V; McDermott, Josh H

2018-05-02

A core goal of auditory neuroscience is to build quantitative models that predict cortical responses to natural sounds. Reasoning that a complete model of auditory cortex must solve ecologically relevant tasks, we optimized hierarchical neural networks for speech and music recognition. The best-performing network contained separate music and speech pathways following early shared processing, potentially replicating human cortical organization. The network performed both tasks as well as humans and exhibited human-like errors despite not being optimized to do so, suggesting common constraints on network and human performance. The network predicted fMRI voxel responses substantially better than traditional spectrotemporal filter models throughout auditory cortex. It also provided a quantitative signature of cortical representational hierarchy-primary and non-primary responses were best predicted by intermediate and late network layers, respectively. The results suggest that task optimization provides a powerful set of tools for modeling sensory systems. Copyright © 2018 Elsevier Inc. All rights reserved.

Assessment of Power Potential of Tidal Currents and Impacts of Power Extraction on Flow Speeds in Indonesia

NASA Astrophysics Data System (ADS)

Orhan, K.; Mayerle, R.

2016-12-01

A methodology comprising of the estimates of power yield, evaluation of the effects of power extraction on flow conditions, and near-field investigations to deliver wake characteritics, recovery and interactions is described and applied to several straits in Indonesia. Site selection is done with high-resolution, three-dimensional flow models providing sufficient spatiotemporal coverage. Much attention has been given to the meteorological forcing, and conditions at the open sea boundaries to adequately capture the density gradients and flow fields. Model verification using tidal records shows excellent agreement. Sites with adequate depth for the energy conversion using horizontal axis tidal turbines, average kinetic power density greater than 0.5 kW/m2, and surface area larger than 0.5km2 are defined as energy hotspots. Spatial variation of the average extractable electric power is determined, and annual tidal energy resource is estimated for the straits in question. The results showed that the potential for tidal power generation in Indonesia is likely to exceed previous predictions reaching around 4,800MW. To assess the impact of the devices, flexible mesh models with higher resolutions have been developed. Effects on flow conditions, and near-field turbine wakes are resolved in greater detail with triangular horizontal grids. The energy is assumed to be removed uniformly by sub-grid scale arrays of turbines, and calculations are made based on velocities at the hub heights of the devices. An additional drag force resulting in dissipation of the pre-existing kinetic power from %10 to %60 within a flow cross-section is introduced to capture the impacts. It was found that the effect of power extraction on water levels and flow speeds in adjacent areas is not significant. Results show the effectivess of the method to capture wake characteritics and recovery reasonably well with low computational cost.

Improved accuracy of intraocular lens power calculation with the Zeiss IOLMaster.

PubMed

Olsen, Thomas

2007-02-01

This study aimed to demonstrate how the level of accuracy in intraocular lens (IOL) power calculation can be improved with optical biometry using partial optical coherence interferometry (PCI) (Zeiss IOLMaster) and current anterior chamber depth (ACD) prediction algorithms. Intraocular lens power in 461 consecutive cataract operations was calculated using both PCI and ultrasound and the accuracy of the results of each technique were compared. To illustrate the importance of ACD prediction per se, predictions were calculated using both a recently published 5-variable method and the Haigis 2-variable method and the results compared. All calculations were optimized in retrospect to account for systematic errors, including IOL constants and other off-set errors. The average absolute IOL prediction error (observed minus expected refraction) was 0.65 dioptres with ultrasound and 0.43 D with PCI using the 5-variable ACD prediction method (p < 0.00001). The number of predictions within +/- 0.5 D, +/- 1.0 D and +/- 2.0 D of the expected outcome was 62.5%, 92.4% and 99.9% with PCI, compared with 45.5%, 77.3% and 98.4% with ultrasound, respectively (p < 0.00001). The 2-variable ACD method resulted in an average error in PCI predictions of 0.46 D, which was significantly higher than the error in the 5-variable method (p < 0.001). The accuracy of IOL power calculation can be significantly improved using calibrated axial length readings obtained with PCI and modern IOL power calculation formulas incorporating the latest generation ACD prediction algorithms.

Applicability of internet search index for asthma admission forecast using machine learning.

PubMed

Luo, Li; Liao, Chengcheng; Zhang, Fengyi; Zhang, Wei; Li, Chunyang; Qiu, Zhixin; Huang, Debin

2018-04-15

This study aimed to determine whether a search index could provide insight into trends in asthma admission in China. An Internet search index is a powerful tool to monitor and predict epidemic outbreaks. However, whether using an internet search index can significantly improve asthma admissions forecasts remains unknown. The long-term goal is to develop a surveillance system to help early detection and interventions for asthma and to avoid asthma health care resource shortages in advance. In this study, we used a search index combined with air pollution data, weather data, and historical admissions data to forecast asthma admissions using machine learning. Results demonstrated that the best area under the curve in the test set that can be achieved is 0.832, using all predictors mentioned earlier. A search index is a powerful predictor in asthma admissions forecast, and a recent search index can reflect current asthma admissions with a lag-effect to a certain extent. The addition of a real-time, easily accessible search index improves forecasting capabilities and demonstrates the predictive potential of search index. Copyright © 2018 John Wiley & Sons, Ltd.

Optimal Concentrations in Transport Networks

NASA Astrophysics Data System (ADS)

Jensen, Kaare; Savage, Jessica; Kim, Wonjung; Bush, John; Holbrook, N. Michele

2013-03-01

Biological and man-made systems rely on effective transport networks for distribution of material and energy. Mass flow in these networks is determined by the flow rate and the concentration of material. While the most concentrated solution offers the greatest potential for mass flow, impedance grows with concentration and thus makes it the most difficult to transport. The concentration at which mass flow is optimal depends on specific physical and physiological properties of the system. We derive a simple model which is able to predict optimal concentrations observed in blood flows, sugar transport in plants, and nectar feeding animals. Our model predicts that the viscosity at the optimal concentration μopt =2nμ0 is an integer power of two times the viscosity of the pure carrier medium μ0. We show how the observed powers 1 <= n <= 6 agree well with theory and discuss how n depends on biological constraints imposed on the transport process. The model provides a universal framework for studying flows impeded by concentration and provides hints of how to optimize engineered flow systems, such as congestion in traffic flows.

Freezing of soft spheres: A critical test for weighted-density-functional theories

NASA Astrophysics Data System (ADS)

Laird, Brian B.; Kroll, D. M.

1990-10-01

We study the freezing properties of systems with inverse-power and Yukawa interactions (soft spheres), using recently developed weighted-density-functional theories. We find that the modified weighted-density-functional approximation (MWDA) of Denton and Ashcroft yields results for the liquid to face-centered-cubic (fcc) structure transition that represent a significant improvement over those of earlier ``second-order'' density-functional freezing theories; however, this theory, like the earlier ones, fails to predict any liquid to body-centered-cubic (bcc) transition, even under conditions where the computer simulations indicate that this should be the equilibrium solid structure. In addition, we show that both the modified effective-liquid approximation (MELA) of Baus [J. Phys. Condens. Matter 2, 2111 (1990)] and the generalized effective-liquid approximation of Lutsko and Baus [Phys. Rev. Lett. 64, 761 (1990)], while giving excellent results for the freezing of hard spheres, fail completely to predict freezing into either fcc or bcc solid phases for soft inverse-power potentials. We also give an alternate derivation of the MWDA that makes clearer its connection to earlier theories.

Nuclear Fission: from more phenomenology and adjusted parameters to more fundamental theory and increased predictive power

NASA Astrophysics Data System (ADS)

Bulgac, Aurel; Jin, Shi; Magierski, Piotr; Roche, Kenneth; Schunck, Nicolas; Stetcu, Ionel

2017-11-01

Two major recent developments in theory and computational resources created the favorable conditions for achieving a microscopic description of fission dynamics in classically allowed regions of the collective potential energy surface, almost eighty years after its discovery in 1939 by Hahn and Strassmann [1]. The first major development was in theory, the extension of the Time-Dependent Density Functional Theory (TDDFT) [2-5] to superfluid fermion systems [6]. The second development was in computing, the emergence of powerful enough supercomputers capable of solving the complex systems of equations describing the time evolution in three dimensions without any restrictions of hundreds of strongly interacting nucleons. Thus the conditions have been created to renounce phenomenological models and incomplete microscopic treatments with uncontrollable approximations and/or assumptions in the description of the complex dynamics of fission. Even though the available nuclear energy density functionals (NEDFs) are phenomenological still, their accuracy is improving steadily and the prospects of being able to perform calculations of the nuclear fission dynamics and to predict many properties of the fission fragments, otherwise not possible to extract from experiments.

Theory of energy harvesting from heartbeat including the effects of pleural cavity and respiration.

PubMed

Zhang, Yangyang; Lu, Bingwei; Lü, Chaofeng; Feng, Xue

2017-11-01

Self-powered implantable devices with flexible energy harvesters are of significant interest due to their potential to solve the problem of limited battery life and surgical replacement. The flexible electronic devices made of piezoelectric materials have been employed to harvest energy from the motion of biological organs. Experimental measurements show that the output voltage of the device mounted on porcine left ventricle in chest closed environment decreases significantly compared to the case of chest open. A restricted-space deformation model is proposed to predict the impeding effect of pleural cavity, surrounding tissues, as well as respiration on the efficiency of energy harvesting from heartbeat using flexible piezoelectric devices. The analytical solution is verified by comparing theoretical predictions to experimental measurements. A simple scaling law is established to analyse the intrinsic correlations between the normalized output power and the combined system parameters, i.e. the normalized permitted space and normalized electrical load. The results may provide guidelines for optimization of in vivo energy harvesting from heartbeat or the motions of other biological organs using flexible piezoelectric energy harvesters.

Theory of energy harvesting from heartbeat including the effects of pleural cavity and respiration

NASA Astrophysics Data System (ADS)

Zhang, Yangyang; Lu, Bingwei; Lü, Chaofeng; Feng, Xue

2017-11-01

Self-powered implantable devices with flexible energy harvesters are of significant interest due to their potential to solve the problem of limited battery life and surgical replacement. The flexible electronic devices made of piezoelectric materials have been employed to harvest energy from the motion of biological organs. Experimental measurements show that the output voltage of the device mounted on porcine left ventricle in chest closed environment decreases significantly compared to the case of chest open. A restricted-space deformation model is proposed to predict the impeding effect of pleural cavity, surrounding tissues, as well as respiration on the efficiency of energy harvesting from heartbeat using flexible piezoelectric devices. The analytical solution is verified by comparing theoretical predictions to experimental measurements. A simple scaling law is established to analyse the intrinsic correlations between the normalized output power and the combined system parameters, i.e. the normalized permitted space and normalized electrical load. The results may provide guidelines for optimization of in vivo energy harvesting from heartbeat or the motions of other biological organs using flexible piezoelectric energy harvesters.

Effects of global atmospheric perturbations on forest ecosystems: Predictions of seasonal and cumulative effects

NASA Technical Reports Server (NTRS)

Tinus, R. W.; Roddy, D. J.

1988-01-01

The physical effects of certain large events, such as giant impacts, explosive volcanism, or combined nuclear explosions, have the potential of inducing global catastrophes in our terrestrial environment. Such highly energetic events can inject substantial quantities of material into the atmosphere. In turn, this changes the amount of sunlight reaching the Earth's surface and modifies atmospheric temperatures to produce a wide range of global effects. One consequence is the introduction of serious stresses in both plants and animals throughout the Earth's biosphere. For example, recent studies predict that forest lands, crop lands, and range lands would suffer specific physical and biological degradations if major physical and chemical disruptions occurred in our atmosphere. Forests, which cover over 4 times 10 to the 9th power hectares (4 times 10 to the 7th power sq km) of our planet, or about 3 times the area now cultivated for crops, are critical to many processes in the biosphere. Forests contribute heavily to the production of atmospheric oxygen, supply the major volume of biomass, and provide a significant percentage of plant and animal habitats.

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

PubMed

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

2018-04-01

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

Hybrid robust predictive optimization method of power system dispatch

DOEpatents

Chandra, Ramu Sharat [Niskayuna, NY; Liu, Yan [Ballston Lake, NY; Bose, Sumit [Niskayuna, NY; de Bedout, Juan Manuel [West Glenville, NY

2011-08-02

A method of power system dispatch control solves power system dispatch problems by integrating a larger variety of generation, load and storage assets, including without limitation, combined heat and power (CHP) units, renewable generation with forecasting, controllable loads, electric, thermal and water energy storage. The method employs a predictive algorithm to dynamically schedule different assets in order to achieve global optimization and maintain the system normal operation.

Power capability evaluation for lithium iron phosphate batteries based on multi-parameter constraints estimation

NASA Astrophysics Data System (ADS)

Wang, Yujie; Pan, Rui; Liu, Chang; Chen, Zonghai; Ling, Qiang

2018-01-01

The battery power capability is intimately correlated with the climbing, braking and accelerating performance of the electric vehicles. Accurate power capability prediction can not only guarantee the safety but also regulate driving behavior and optimize battery energy usage. However, the nonlinearity of the battery model is very complex especially for the lithium iron phosphate batteries. Besides, the hysteresis loop in the open-circuit voltage curve is easy to cause large error in model prediction. In this work, a multi-parameter constraints dynamic estimation method is proposed to predict the battery continuous period power capability. A high-fidelity battery model which considers the battery polarization and hysteresis phenomenon is presented to approximate the high nonlinearity of the lithium iron phosphate battery. Explicit analyses of power capability with multiple constraints are elaborated, specifically the state-of-energy is considered in power capability assessment. Furthermore, to solve the problem of nonlinear system state estimation, and suppress noise interference, the UKF based state observer is employed for power capability prediction. The performance of the proposed methodology is demonstrated by experiments under different dynamic characterization schedules. The charge and discharge power capabilities of the lithium iron phosphate batteries are quantitatively assessed under different time scales and temperatures.

Evaluation of Several Two-Step Scoring Functions Based on Linear Interaction Energy, Effective Ligand Size, and Empirical Pair Potentials for Prediction of Protein-Ligand Binding Geometry and Free Energy

PubMed Central

Rahaman, Obaidur; Estrada, Trilce P.; Doren, Douglas J.; Taufer, Michela; Brooks, Charles L.; Armen, Roger S.

2011-01-01

The performance of several two-step scoring approaches for molecular docking were assessed for their ability to predict binding geometries and free energies. Two new scoring functions designed for “step 2 discrimination” were proposed and compared to our CHARMM implementation of the linear interaction energy (LIE) approach using the Generalized-Born with Molecular Volume (GBMV) implicit solvation model. A scoring function S1 was proposed by considering only “interacting” ligand atoms as the “effective size” of the ligand, and extended to an empirical regression-based pair potential S2. The S1 and S2 scoring schemes were trained and five-fold cross validated on a diverse set of 259 protein-ligand complexes from the Ligand Protein Database (LPDB). The regression-based parameters for S1 and S2 also demonstrated reasonable transferability in the CSARdock 2010 benchmark using a new dataset (NRC HiQ) of diverse protein-ligand complexes. The ability of the scoring functions to accurately predict ligand geometry was evaluated by calculating the discriminative power (DP) of the scoring functions to identify native poses. The parameters for the LIE scoring function with the optimal discriminative power (DP) for geometry (step 1 discrimination) were found to be very similar to the best-fit parameters for binding free energy over a large number of protein-ligand complexes (step 2 discrimination). Reasonable performance of the scoring functions in enrichment of active compounds in four different protein target classes established that the parameters for S1 and S2 provided reasonable accuracy and transferability. Additional analysis was performed to definitively separate scoring function performance from molecular weight effects. This analysis included the prediction of ligand binding efficiencies for a subset of the CSARdock NRC HiQ dataset where the number of ligand heavy atoms ranged from 17 to 35. This range of ligand heavy atoms is where improved accuracy of predicted ligand efficiencies is most relevant to real-world drug design efforts. PMID:21644546

Evaluation of several two-step scoring functions based on linear interaction energy, effective ligand size, and empirical pair potentials for prediction of protein-ligand binding geometry and free energy.

PubMed

Rahaman, Obaidur; Estrada, Trilce P; Doren, Douglas J; Taufer, Michela; Brooks, Charles L; Armen, Roger S

2011-09-26

The performances of several two-step scoring approaches for molecular docking were assessed for their ability to predict binding geometries and free energies. Two new scoring functions designed for "step 2 discrimination" were proposed and compared to our CHARMM implementation of the linear interaction energy (LIE) approach using the Generalized-Born with Molecular Volume (GBMV) implicit solvation model. A scoring function S1 was proposed by considering only "interacting" ligand atoms as the "effective size" of the ligand and extended to an empirical regression-based pair potential S2. The S1 and S2 scoring schemes were trained and 5-fold cross-validated on a diverse set of 259 protein-ligand complexes from the Ligand Protein Database (LPDB). The regression-based parameters for S1 and S2 also demonstrated reasonable transferability in the CSARdock 2010 benchmark using a new data set (NRC HiQ) of diverse protein-ligand complexes. The ability of the scoring functions to accurately predict ligand geometry was evaluated by calculating the discriminative power (DP) of the scoring functions to identify native poses. The parameters for the LIE scoring function with the optimal discriminative power (DP) for geometry (step 1 discrimination) were found to be very similar to the best-fit parameters for binding free energy over a large number of protein-ligand complexes (step 2 discrimination). Reasonable performance of the scoring functions in enrichment of active compounds in four different protein target classes established that the parameters for S1 and S2 provided reasonable accuracy and transferability. Additional analysis was performed to definitively separate scoring function performance from molecular weight effects. This analysis included the prediction of ligand binding efficiencies for a subset of the CSARdock NRC HiQ data set where the number of ligand heavy atoms ranged from 17 to 35. This range of ligand heavy atoms is where improved accuracy of predicted ligand efficiencies is most relevant to real-world drug design efforts.

Balancing computation and communication power in power constrained clusters

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

Piga, Leonardo; Paul, Indrani; Huang, Wei

Systems, apparatuses, and methods for balancing computation and communication power in power constrained environments. A data processing cluster with a plurality of compute nodes may perform parallel processing of a workload in a power constrained environment. Nodes that finish tasks early may be power-gated based on one or more conditions. In some scenarios, a node may predict a wait duration and go into a reduced power consumption state if the wait duration is predicted to be greater than a threshold. The power saved by power-gating one or more nodes may be reassigned for use by other nodes. A cluster agentmore » may be configured to reassign the unused power to the active nodes to expedite workload processing.« less

Modeling of a resonant heat engine

NASA Astrophysics Data System (ADS)

Preetham, B. S.; Anderson, M.; Richards, C.

2012-12-01

A resonant heat engine in which the piston assembly is replaced by a sealed elastic cavity is modeled and analyzed. A nondimensional lumped-parameter model is derived and used to investigate the factors that control the performance of the engine. The thermal efficiency predicted by the model agrees with that predicted from the relation for the Otto cycle based on compression ratio. The predictions show that for a fixed mechanical load, increasing the heat input results in increased efficiency. The output power and power density are shown to depend on the loading for a given heat input. The loading condition for maximum output power is different from that required for maximum power density.

Synchrophasor-Assisted Prediction of Stability/Instability of a Power System

NASA Astrophysics Data System (ADS)

Saha Roy, Biman Kumar; Sinha, Avinash Kumar; Pradhan, Ashok Kumar

2013-05-01

This paper presents a technique for real-time prediction of stability/instability of a power system based on synchrophasor measurements obtained from phasor measurement units (PMUs) at generator buses. For stability assessment the technique makes use of system severity indices developed using bus voltage magnitude obtained from PMUs and generator electrical power. Generator power is computed using system information and PMU information like voltage and current phasors obtained from PMU. System stability/instability is predicted when the indices exceeds a threshold value. A case study is carried out on New England 10-generator, 39-bus system to validate the performance of the technique.

Prediction of the effects of propeller operation on the static longitudinal stability of single-engine tractor monoplanes with flaps retracted

NASA Technical Reports Server (NTRS)

Weil, Joseph; Sleeman, William C , Jr

1949-01-01

The effects of propeller operation on the static longitudinal stability of single-engine tractor monoplanes are analyzed, and a simple method is presented for computing power-on pitching-moment curves for flap-retracted flight conditions. The methods evolved are based on the results of powered-model wind-tunnel investigations of 28 model configurations. Correlation curves are presented from which the effects of power on the downwash over the tail and the stabilizer effectiveness can be rapidly predicted. The procedures developed enable prediction of power-on longitudinal stability characteristics that are generally in very good agreement with experiment.

An Operating Method Using Prediction of Photovoltaic Power for a Photovoltaic-Diesel Hybrid Power Generation System

NASA Astrophysics Data System (ADS)

Yamamoto, Shigehiro; Sumi, Kazuyoshi; Nishikawa, Eiichi; Hashimoto, Takeshi

This paper describes a novel operating method using prediction of photovoltaic (PV) power for a photovoltaic-diesel hybrid power generation system. The system is composed of a PV array, a storage battery, a bi-directional inverter and a diesel engine generator (DG). The proposed method enables the system to save fuel consumption by using PV energy effectively, reducing charge and discharge energy of the storage battery, and avoiding low-load operation of the DG. The PV power is simply predicted from a theoretical equation of solar radiation and the observed PV energy for a constant time before the prediction. The amount of fuel consumption of the proposed method is compared with that of other methods by a simulation based on measurement data of the PV power at an actual PV generation system for one year. The simulation results indicate that the amount of fuel consumption of the proposed method is smaller than that of any other methods, and is close to that of the ideal operation of the DG.

A Comparison between Bench Press Throw and Ballistic Push-Up tests to assess upper-body power in trained individuals.

PubMed

Bartolomei, Sandro; Nigro, Federico; Ruggeri, Sandro; Lanzoni, Ivan Malagoli; Ciacci, Simone; Merni, Franco; Sadres, Eliahu; Hoffman, Jay R; Semprini, Gabriele

2018-03-06

The purpose of the present study was to validate the ballistic push-up test performed with hands on a force plate (BPU) as a method to measure upper-body power. Twenty-eight experienced resistance trained men (age = 25.4 ± 5.2 y; body mass = 78.5 ± 9.0 kg; body height = 179.6 ± 7.8 cm) performed, two days apart, a bench press 1RM test and upper-body power tests. Mean power and peak power were assessed using the bench press throw test (BT) and the BPU test performed in randomized order. The area under the force/power curve (AUC) obtained at BT was also calculated. Power expressed at BPU was estimated using a time-based prediction equation. Mean force and the participant's body weight were used to predict the bench press 1RM. Pearson product moment correlations were used to examine relationships between the power assessment methods and between the predicted 1RM bench and the actual value. Large correlations (0.79; p < 0.001) were found between AUC and mean power expressed at BPU. Large correlations were also detected between mean power and peak power expressed at BT and BPU (0.75; p < 0.001 and 0.74; p < 0.001, respectively). Very large correlations (0.87; p < 0.001) were found between the 1RM bench and the 1RM predicted by the BPU. Results of the present study indicate that BPU represents a valid and reliable method to estimate the upper-body power in resistance-trained individuals.

Studying the Power of the Integrative Weaning Index in Predicting the Success Rate of the Spontaneous Breathing Trial in Patients under Mechanical Ventilation.

PubMed

Ebrahimabadi, Sahar; Moghadam, Ahmad Bagheri; Vakili, Mohammadali; Modanloo, Mahnaz; Khoddam, Homeira

2017-08-01

The use of weaning predictive indicators can avoid early extubation and wrongful prolonged mechanical ventilation. This study aimed to determine the power of the integrative weaning index (IWI) in predicting the success rate of the spontaneous breathing trial (SBT) in patients under mechanical ventilation. In this prospective study, 105 patients undergoing mechanical ventilation for over 48 h were enrolled. Before weaning initiation, the IWI was calculated and based on the defined cutoff point (≥25), the success rate of the SBT was predicted. In case of weaning from the device, 2-h SBT was performed and the physiologic and respiratory indices were continuously studied while being intubated. If they were in the normal range besides the patient's tolerance, the test was considered as a success. The result was then compared with the IWI and further analyzed. The SBT was successful in 90 (85.7%) and unsuccessful in 15 (14.3%) cases. The difference between the true patient outcome after SBT, and the IWI prediction was 0.143 according to the Kappa agreement coefficient ( P < 0.001). Moreover, regarding the predictive power, IWI had high sensitivity (95.6%), specificity (40%), positive and negative predictive values (90.5% and 60), positive and negative likelihood ratios (1.59 and 0.11), and accuracy (86.7%). The IWI as a more objective indicator has acceptable accuracy and power for predicting the 2-h SBT result. Therefore, in addition to the reliable prediction of the final weaning outcome, it has favorable power to predict if the patient is ready to breathe spontaneously as the first step to weaning.

Studying the Power of the Integrative Weaning Index in Predicting the Success Rate of the Spontaneous Breathing Trial in Patients under Mechanical Ventilation

PubMed Central

Ebrahimabadi, Sahar; Moghadam, Ahmad Bagheri; Vakili, Mohammadali; Modanloo, Mahnaz; Khoddam, Homeira

2017-01-01

Background and Aims: The use of weaning predictive indicators can avoid early extubation and wrongful prolonged mechanical ventilation. This study aimed to determine the power of the integrative weaning index (IWI) in predicting the success rate of the spontaneous breathing trial (SBT) in patients under mechanical ventilation. Materials and Methods: In this prospective study, 105 patients undergoing mechanical ventilation for over 48 h were enrolled. Before weaning initiation, the IWI was calculated and based on the defined cutoff point (≥25), the success rate of the SBT was predicted. In case of weaning from the device, 2-h SBT was performed and the physiologic and respiratory indices were continuously studied while being intubated. If they were in the normal range besides the patient's tolerance, the test was considered as a success. The result was then compared with the IWI and further analyzed. Results: The SBT was successful in 90 (85.7%) and unsuccessful in 15 (14.3%) cases. The difference between the true patient outcome after SBT, and the IWI prediction was 0.143 according to the Kappa agreement coefficient (P < 0.001). Moreover, regarding the predictive power, IWI had high sensitivity (95.6%), specificity (40%), positive and negative predictive values (90.5% and 60), positive and negative likelihood ratios (1.59 and 0.11), and accuracy (86.7%). Conclusion: The IWI as a more objective indicator has acceptable accuracy and power for predicting the 2-h SBT result. Therefore, in addition to the reliable prediction of the final weaning outcome, it has favorable power to predict if the patient is ready to breathe spontaneously as the first step to weaning. PMID:28904477

Electronic stopping powers for heavy ions in SiC and SiO{sub 2}

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

Jin, K.; Xue, H.; Zhang, Y., E-mail: Zhangy1@ornl.gov

2014-01-28

Accurate information on electronic stopping power is fundamental for broad advances in materials science, electronic industry, space exploration, and sustainable energy technologies. In the case of slow heavy ions in light targets, current codes and models provide significantly inconsistent predictions, among which the Stopping and Range of Ions in Matter (SRIM) code is the most commonly used one. Experimental evidence, however, has demonstrated considerable errors in the predicted ion and damage profiles based on SRIM stopping powers. In this work, electronic stopping powers for Cl, Br, I, and Au ions are experimentally determined in two important functional materials, SiC andmore » SiO{sub 2}, based on a single ion technique, and new electronic stopping power values are derived over the energy regime from 0 to 15 MeV, where large deviations from the SRIM predictions are observed. As an experimental validation, Rutherford backscattering spectrometry (RBS) and secondary ion mass spectrometry (SIMS) are utilized to measure the depth profiles of implanted Au ions in SiC for energies from 700 keV to 15 MeV. The measured ion distributions by both RBS and SIMS are considerably deeper than the SRIM predictions, but agree well with predictions based on our derived stopping powers.« less

Electronic Stopping Powers For Heavy Ions In SiC And SiO2

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

Jin, Ke; Zhang, Y.; Zhu, Zihua

2014-01-24

Accurate information on electronic stopping power is fundamental for broad advances in materials science, electronic industry, space exploration, and sustainable energy technologies. In the case of slow heavy ions in light targets, current codes and models provide significantly inconsistent predictions, among which the Stopping and Range of Ions in Matter (SRIM) code is the most commonly used one. Experimental evidence, however, has demonstrated considerable errors in the predicted ion and damage profiles based on SRIM stopping powers. In this work, electronic stopping powers for Cl, Br, I, and Au ions are experimentally determined in two important functional materials, SiC andmore » SiO2, based on a single ion technique, and new electronic stopping power values are derived over the energy regime from 0 to 15 MeV, where large deviations from the SRIM predictions are observed. As an experimental validation, Rutherford backscattering spectrometry (RBS) and secondary ion mass spectrometry (SIMS) are utilized to measure the depth profiles of implanted Au ions in SiC for energies from 700 keV to 15MeV. The measured ion distributions by both RBS and SIMS are considerably deeper than the SRIM predictions, but agree well with predictions based on our derived stopping powers.« less

Toward more environmentally resistant gas turbines: Progress in NASA-Lewis programs

NASA Technical Reports Server (NTRS)

Lowell, C. E.; Grisaffe, S. J.; Levine, S. R.

1976-01-01

A wide range of programs are being conducted for improving the environmental resistance to oxidation and hot corrosion of gas turbine and power system materials. They range from fundamental efforts to delineate attack mechanisms, allow attack modeling and permit life prediction, to more applied efforts to develop potentially more resistant alloys and coatings. Oxidation life prediction efforts have resulted in a computer program which provides an initial method for predicting long time metal loss using short time oxidation data by means of a paralinear attack model. Efforts in alloy development have centered on oxide-dispersion strengthened alloys based on the Ni-Cr-Al system. Compositions have been identified which are compromises between oxidation and thermal fatigue resistance. Fundamental studies of hot corrosion mechanisms include thermodynamic studies of sodium sulfate formation during turbine combustion. Information concerning species formed during the vaporization of Na2SO4 has been developed using high temperature mass spectrometry.

Memory processes of flight situation awareness: interactive roles of working memory capacity, long-term working memory, and expertise.

PubMed

Sohn, Young Woo; Doane, Stephanie M

2004-01-01

This research examined the role of working memory (WM) capacity and long-term working memory (LT-WM) in flight situation awareness (SA). We developed spatial and verbal measures of WM capacity and LT-WM skill and then determined the ability of these measures to predict pilot performance on SA tasks. Although both spatial measures of WM capacity and LT-WM skills were important predictors of SA performance, their importance varied as a function of pilot expertise. Spatial WM capacity was most predictive of SA performance for novices, whereas spatial LT-WM skill based on configurations of control flight elements (attitude and power) was most predictive for experts. Furthermore, evidence for an interactive role of WM and LT-WM mechanisms was indicated. Actual or potential applications of this research include cognitive analysis of pilot expertise and aviation training.

COMSAC: Computational Methods for Stability and Control. Part 2

NASA Technical Reports Server (NTRS)

Fremaux, C. Michael (Compiler); Hall, Robert M. (Compiler)

2004-01-01

The unprecedented advances being made in computational fluid dynamic (CFD) technology have demonstrated the powerful capabilities of codes in applications to civil and military aircraft. Used in conjunction with wind-tunnel and flight investigations, many codes are now routinely used by designers in diverse applications such as aerodynamic performance predictions and propulsion integration. Typically, these codes are most reliable for attached, steady, and predominantly turbulent flows. As a result of increasing reliability and confidence in CFD, wind-tunnel testing for some new configurations has been substantially reduced in key areas, such as wing trade studies for mission performance guarantees. Interest is now growing in the application of computational methods to other critical design challenges. One of the most important disciplinary elements for civil and military aircraft is prediction of stability and control characteristics. CFD offers the potential for significantly increasing the basic understanding, prediction, and control of flow phenomena associated with requirements for satisfactory aircraft handling characteristics.

Hyper-X Mach 7 Scramjet Design, Ground Test and Flight Results

NASA Technical Reports Server (NTRS)

Ferlemann, Shelly M.; McClinton, Charles R.; Rock, Ken E.; Voland, Randy T.

2005-01-01

The successful Mach 7 flight test of the Hyper-X (X-43) research vehicle has provided the major, essential demonstration of the capability of the airframe integrated scramjet engine. This flight was a crucial first step toward realizing the potential for airbreathing hypersonic propulsion for application to space launch vehicles. However, it is not sufficient to have just achieved a successful flight. The more useful knowledge gained from the flight is how well the prediction methods matched the actual test results in order to have confidence that these methods can be applied to the design of other scramjet engines and powered vehicles. The propulsion predictions for the Mach 7 flight test were calculated using the computer code, SRGULL, with input from computational fluid dynamics (CFD) and wind tunnel tests. This paper will discuss the evolution of the Mach 7 Hyper-X engine, ground wind tunnel experiments, propulsion prediction methodology, flight results and validation of design methods.

Efficient Modeling and Active Learning Discovery of Biological Responses

PubMed Central

Naik, Armaghan W.; Kangas, Joshua D.; Langmead, Christopher J.; Murphy, Robert F.

2013-01-01

High throughput and high content screening involve determination of the effect of many compounds on a given target. As currently practiced, screening for each new target typically makes little use of information from screens of prior targets. Further, choices of compounds to advance to drug development are made without significant screening against off-target effects. The overall drug development process could be made more effective, as well as less expensive and time consuming, if potential effects of all compounds on all possible targets could be considered, yet the cost of such full experimentation would be prohibitive. In this paper, we describe a potential solution: probabilistic models that can be used to predict results for unmeasured combinations, and active learning algorithms for efficiently selecting which experiments to perform in order to build those models and determining when to stop. Using simulated and experimental data, we show that our approaches can produce powerful predictive models without exhaustive experimentation and can learn them much faster than by selecting experiments at random. PMID:24358322

Office work and complaints of the arms, neck and shoulders: the role of job characteristics, muscular tension and need for recovery.

PubMed

Gawke, Jason C; Gorgievski, Marjan J; van der Linden, Dimitri

2012-01-01

This study investigated physical, psychological and social job characteristics as potential risk factors for complaints of the arms, neck and shoulders (CANS) and mediating effects of muscular tension and need for recovery. Data were collected among 105 computer workers using questionnaires and electromyography (EMG), and were analyzed with linear regression analyses. Task interdependence, information processing and lower social support predicted more CANS. Physical job demands had no predictive power over and above psychological and social Stressors. Both muscular tension and need for recovery partially mediated the job characteristics-CANS relationships. Occupational health professionals should not neglect psychological and social job characteristics as potentially important predictors of CANS in specific occupational groups, such as office workers. Our findings imply that CANS interventions should not be restricted to ergonomic improvements, but should be accompanied by improvement of the job design from a psychological and social perspective and reactive intervention aimed at decreasing short-term physical strain (muscular tension) and mental strain (need for recovery).

Dynamic Modeling, Controls, and Testing for Electrified Aircraft

NASA Technical Reports Server (NTRS)

Connolly, Joseph; Stalcup, Erik

2017-01-01

Electrified aircraft have the potential to provide significant benefits for efficiency and emissions reductions. To assess these potential benefits, modeling tools are needed to provide rapid evaluation of diverse concepts and to ensure safe operability and peak performance over the mission. The modeling challenge for these vehicles is the ability to show significant benefits over the current highly refined aircraft systems. The STARC-ABL (single-aisle turbo-electric aircraft with an aft boundary layer propulsor) is a new test proposal that builds upon previous N3-X team hybrid designs. This presentation describes the STARC-ABL concept, the NASA Electric Aircraft Testbed (NEAT) which will allow testing of the STARC-ABL powertrain, and the related modeling and simulation efforts to date. Modeling and simulation includes a turbofan simulation, Numeric Propulsion System Simulation (NPSS), which has been integrated with NEAT; and a power systems and control model for predicting testbed performance and evaluating control schemes. Model predictions provide good comparisons with testbed data for an NPSS-integrated test of the single-string configuration of NEAT.

Early warnings of hazardous thunderstorms over Lake Victoria

NASA Astrophysics Data System (ADS)

Thiery, Wim; Gudmundsson, Lukas; Bedka, Kristopher; Semazzi, Fredrick H. M.; Lhermitte, Stef; Willems, Patrick; van Lipzig, Nicole P. M.; Seneviratne, Sonia I.

2017-07-01

Weather extremes have harmful impacts on communities around Lake Victoria in East Africa. Every year, intense nighttime thunderstorms cause numerous boating accidents on the lake, resulting in thousands of deaths among fishermen. Operational storm warning systems are therefore crucial. Here we complement ongoing early warning efforts based on numerical weather prediction, by presenting a new satellite data-driven storm prediction system, the prototype Lake Victoria Intense storm Early Warning System (VIEWS). VIEWS derives predictability from the correlation between afternoon land storm activity and nighttime storm intensity on Lake Victoria, and relies on logistic regression techniques to forecast extreme thunderstorms from satellite observations. Evaluation of the statistical model reveals that predictive power is high and independent of the type of input dataset. We then optimise the configuration and show that false alarms also contain valuable information. Our results suggest that regression-based models that are motivated through process understanding have the potential to reduce the vulnerability of local fishing communities around Lake Victoria. The experimental prediction system is publicly available under the MIT licence at http://github.com/wthiery/VIEWS.

Prediction of Central Nervous System Side Effects Through Drug Permeability to Blood-Brain Barrier and Recommendation Algorithm.

PubMed

Fan, Jun; Yang, Jing; Jiang, Zhenran

2018-04-01

Drug side effects are one of the public health concerns. Using powerful machine-learning methods to predict potential side effects before the drugs reach the clinical stages is of great importance to reduce time consumption and protect the security of patients. Recently, researchers have proved that the central nervous system (CNS) side effects of a drug are closely related to its permeability to the blood-brain barrier (BBB). Inspired by this, we proposed an extended neighborhood-based recommendation method to predict CNS side effects using drug permeability to the BBB and other known features of drug. To the best of our knowledge, this is the first attempt to predict CNS side effects considering drug permeability to the BBB. Computational experiments demonstrated that drug permeability to the BBB is an important factor in CNS side effects prediction. Moreover, we built an ensemble recommendation model and obtained higher AUC score (area under the receiver operating characteristic curve) and AUPR score (area under the precision-recall curve) on the data set of CNS side effects by integrating various features of drug.

Prediction of Backbreak in Open Pit Blasting by Adaptive Neuro-Fuzzy Inference System / Prognozowanie Spękań Skał Przy Pracach Strzałowych W Kopalniach Odkrywkowych Przy Użyciu Metod Neuronowych I Wnioskowania Rozmytego (Anfis) Zastosowanych W Modelu Adaptywnym

NASA Astrophysics Data System (ADS)

Bazzazi, Abbas Aghajani; Esmaeili, Mohammad

2012-12-01

Adaptive neuro-fuzzy inference system (ANFIS) is powerful model in solving complex problems. Since ANFIS has the potential of solving nonlinear problem and can easily achieve the input-output mapping, it is perfect to be used for solving the predicting problem. Backbreak is one of the undesirable effects of blasting operations causing instability in mine walls, falling down the machinery, improper fragmentation and reduction in efficiency of drilling. In this paper, ANFIS was applied to predict backbreak in Sangan iron mine of Iran. The performance of the model was assessed through the root mean squared error (RMSE), the variance account for (VAF) and the correlation coefficient (R2) computed from the measured of backbreak and model-predicted values of the dependent variables. The RMSE, VAF, R2 indices were calculated 0.6, 0.94 and 0.95 for ANFIS model. As results, these indices revealed that the ANFIS model has very good prediction performance.

A Prognostic Gene Expression Profile That Predicts Circulating Tumor Cell Presence in Breast Cancer Patients

PubMed Central

Molloy, Timothy J.; Roepman, Paul; Naume, Bjørn; van't Veer, Laura J.

2012-01-01

The detection of circulating tumor cells (CTCs) in the peripheral blood and microarray gene expression profiling of the primary tumor are two promising new technologies able to provide valuable prognostic data for patients with breast cancer. Meta-analyses of several established prognostic breast cancer gene expression profiles in large patient cohorts have demonstrated that despite sharing few genes, their delineation of patients into “good prognosis” or “poor prognosis” are frequently very highly correlated, and combining prognostic profiles does not increase prognostic power. In the current study, we aimed to develop a novel profile which provided independent prognostic data by building a signature predictive of CTC status rather than outcome. Microarray gene expression data from an initial training cohort of 72 breast cancer patients for which CTC status had been determined in a previous study using a multimarker QPCR-based assay was used to develop a CTC-predictive profile. The generated profile was validated in two independent datasets of 49 and 123 patients and confirmed to be both predictive of CTC status, and independently prognostic. Importantly, the “CTC profile” also provided prognostic information independent of the well-established and powerful ‘70-gene’ prognostic breast cancer signature. This profile therefore has the potential to not only add prognostic information to currently-available microarray tests but in some circumstances even replace blood-based prognostic CTC tests at time of diagnosis for those patients already undergoing testing by multigene assays. PMID:22384245

Potential of extended airbreathing operation of a two-stage launch vehicle by scramjet propulsion

NASA Astrophysics Data System (ADS)

Schoettle, U. M.; Hillesheimer, M.; Rahn, M.

This paper examines the application of scramjet propulsion to extend the ramjet operation of an airbreathing two-stage launch designed for horizontal takeoff and landing. Performance comparisons are made for two alternative propulsion concepts. The mission performance predictions presented are obtained from a multistep optimization procedure employing both trajectory optimization and vehicle design steps to achieve maximum payload capabilities. The simulation results are shown to offer an attractive payload advantage of the scramjet variant over the ramjet powered vehicle.

Results from Numerical General Relativity

NASA Technical Reports Server (NTRS)

Baker, John G.

2011-01-01

For several years numerical simulations have been revealing the details of general relativity's predictions for the dynamical interactions of merging black holes. I will review what has been learned of the rich phenomenology of these mergers and the resulting gravitational wave signatures. These wave forms provide a potentially observable record of the powerful astronomical events, a central target of gravitational wave astronomy. Asymmetric radiation can produce a thrust on the system which may accelerate the single black hole resulting from the merger to high relative velocity.

Comparison and validation of statistical methods for predicting power outage durations in the event of hurricanes.

PubMed

Nateghi, Roshanak; Guikema, Seth D; Quiring, Steven M

2011-12-01

This article compares statistical methods for modeling power outage durations during hurricanes and examines the predictive accuracy of these methods. Being able to make accurate predictions of power outage durations is valuable because the information can be used by utility companies to plan their restoration efforts more efficiently. This information can also help inform customers and public agencies of the expected outage times, enabling better collective response planning, and coordination of restoration efforts for other critical infrastructures that depend on electricity. In the long run, outage duration estimates for future storm scenarios may help utilities and public agencies better allocate risk management resources to balance the disruption from hurricanes with the cost of hardening power systems. We compare the out-of-sample predictive accuracy of five distinct statistical models for estimating power outage duration times caused by Hurricane Ivan in 2004. The methods compared include both regression models (accelerated failure time (AFT) and Cox proportional hazard models (Cox PH)) and data mining techniques (regression trees, Bayesian additive regression trees (BART), and multivariate additive regression splines). We then validate our models against two other hurricanes. Our results indicate that BART yields the best prediction accuracy and that it is possible to predict outage durations with reasonable accuracy. © 2011 Society for Risk Analysis.

Performance of Comorbidity, Risk Adjustment, and Functional Status Measures in Expenditure Prediction for Patients With Diabetes

PubMed Central

Maciejewski, Matthew L.; Liu, Chuan-Fen; Fihn, Stephan D.

2009-01-01

OBJECTIVE—To compare the ability of generic comorbidity and risk adjustment measures, a diabetes-specific measure, and a self-reported functional status measure to explain variation in health care expenditures for individuals with diabetes. RESEARCH DESIGN AND METHODS—This study included a retrospective cohort of 3,092 diabetic veterans participating in a multisite trial. Two comorbidity measures, four risk adjusters, a functional status measure, a diabetes complication count, and baseline expenditures were constructed from administrative and survey data. Outpatient, inpatient, and total expenditure models were estimated using ordinary least squares regression. Adjusted R2 statistics and predictive ratios were compared across measures to assess overall explanatory power and explanatory power of low- and high-cost subgroups. RESULTS—Administrative data–based risk adjusters performed better than the comorbidity, functional status, and diabetes-specific measures in all expenditure models. The diagnostic cost groups (DCGs) measure had the greatest predictive power overall and for the low- and high-cost subgroups, while the diabetes-specific measure had the lowest predictive power. A model with DCGs and the diabetes-specific measure modestly improved predictive power. CONCLUSIONS—Existing generic measures can be useful for diabetes-specific research and policy applications, but more predictive diabetes-specific measures are needed. PMID:18945927

Off-Ice Anaerobic Power Does Not Predict On-Ice Repeated Shift Performance in Hockey.

PubMed

Peterson, Ben J; Fitzgerald, John S; Dietz, Calvin C; Ziegler, Kevin S; Baker, Sarah E; Snyder, Eric M

2016-09-01

Peterson, BJ, Fitzgerald, JS, Dietz, CC, Ziegler, KS, Baker, SE, and Snyder, EM. Off-ice anaerobic power does not predict on-ice repeated shift performance in hockey. J Strength Cond Res 30(9): 2375-2381, 2016-Anaerobic power is a significant predictor of acceleration and top speed in team sport athletes. Historically, these findings have been applied to ice hockey although recent research has brought their validity for this sport into question. As ice hockey emphasizes the ability to repeatedly produce power, single bout anaerobic power tests should be examined to determine their ability to predict on-ice performance. We tested whether conventional off-ice anaerobic power tests could predict on-ice acceleration, top speed, and repeated shift performance. Forty-five hockey players, aged 18-24 years, completed anthropometric, off-ice, and on-ice tests. Anthropometric and off-ice testing included height, weight, body composition, vertical jump, and Wingate tests. On-ice testing consisted of acceleration, top speed, and repeated shift fatigue tests. Vertical jump (VJ) (r = -0.42; r = -0.58), Wingate relative peak power (WRPP) (r = -0.32; r = -0.43), and relative mean power (WRMP) (r = -0.34; r = -0.48) were significantly correlated (p ≤ 0.05) to on-ice acceleration and top speed, respectively. Conversely, none of the off-ice tests correlated with on-ice repeated shift performance, as measured by first gate, second gate, or total course fatigue; VJ (r = 0.06; r = 0.13; r = 0.09), WRPP (r = 0.06; r = 0.14; r = 0.10), or WRMP (r = -0.10; r = -0.01; r = -0.01). Although conventional off-ice anaerobic power tests predict single bout on-ice acceleration and top speed, they neither predict the repeated shift ability of the player, nor are good markers for performance in ice hockey.

Computationally Driven Two-Dimensional Materials Design: What Is Next?

DOE PAGES

Pan, Jie; Lany, Stephan; Qi, Yue

2017-07-17

Two-dimensional (2D) materials offer many key advantages to innovative applications, such as spintronics and quantum information processing. Theoretical computations have accelerated 2D materials design. In this issue of ACS Nano, Kumar et al. report that ferromagnetism can be achieved in functionalized nitride MXene based on first-principles calculations. Their computational results shed light on a potentially vast group of materials for the realization of 2D magnets. In this Perspective, we briefly summarize the promising properties of 2D materials and the role theory has played in predicting these properties. Additionally, we discuss challenges and opportunities to boost the power of computation formore » the prediction of the 'structure-property-process (synthesizability)' relationship of 2D materials.« less

Continuum Electrostatics Approaches to Calculating pKas and Ems in Proteins

PubMed Central

Gunner, MR; Baker, Nathan A.

2017-01-01

Proteins change their charge state through protonation and redox reactions as well as through binding charged ligands. The free energy of these reactions are dominated by solvation and electrostatic energies and modulated by protein conformational relaxation in response to the ionization state changes. Although computational methods for calculating these interactions can provide very powerful tools for predicting protein charge states, they include several critical approximations of which users should be aware. This chapter discusses the strengths, weaknesses, and approximations of popular computational methods for predicting charge states and understanding their underlying electrostatic interactions. The goal of this chapter is to inform users about applications and potential caveats of these methods as well as outline directions for future theoretical and computational research. PMID:27497160

Organs-on-chips at the frontiers of drug discovery

PubMed Central

Esch, Eric W.; Bahinski, Anthony; Huh, Dongeun

2016-01-01

Improving the effectiveness of preclinical predictions of human drug responses is critical to reducing costly failures in clinical trials. Recent advances in cell biology, microfabrication and microfluidics have enabled the development of microengineered models of the functional units of human organs — known as organs-on-chips — that could provide the basis for preclinical assays with greater predictive power. Here, we examine the new opportunities for the application of organ-on-chip technologies in a range of areas in preclinical drug discovery, such as target identification and validation, target-based screening, and phenotypic screening. We also discuss emerging drug discovery opportunities enabled by organs-on-chips, as well as important challenges in realizing the full potential of this technology. PMID:25792263

Quantum decay model with exact explicit analytical solution

NASA Astrophysics Data System (ADS)

Marchewka, Avi; Granot, Er'El

2009-01-01

A simple decay model is introduced. The model comprises a point potential well, which experiences an abrupt change. Due to the temporal variation, the initial quantum state can either escape from the well or stay localized as a new bound state. The model allows for an exact analytical solution while having the necessary features of a decay process. The results show that the decay is never exponential, as classical dynamics predicts. Moreover, at short times the decay has a fractional power law, which differs from perturbation quantum method predictions. At long times the decay includes oscillations with an envelope that decays algebraically. This is a model where the final state can be either continuous or localized, and that has an exact analytical solution.

Offshore Wind Power Integration in severely fluctuating Wind Conditions

NASA Astrophysics Data System (ADS)

von Bremen, L.

2010-09-01

Strong power fluctuations from offshore wind farms that are induced by wind speed fluctuations pose a severe problem to the save integration of offshore wind power into the power supply system. Experience at the first large-scale offshore wind farm Horns Rev showed that spatial smoothing of power fluctuations within a single wind farm is significantly smaller than onshore results suggest when distributed wind farms of 160 MW altogether are connected to a single point of common-coupling. Wind power gradients larger than 10% of the rated capacity within 5 minutes require large amount of regulation power that is very expensive for the grid operator. It must be noted that a wind speed change of only 0.5m/s result in a wind power change of 10% (within the range of 9-11 m/s where the wind power curve is steepest). Hence, it is very important for the grid operator to know if strong fluctuations are likely or not. Observed weather conditions at the German wind energy research platform FINO1 in the German bight are used to quantify wind fluctuations. With a standard power curve these wind fluctuations are transfered to wind power. The aim is to predict the probability of exceedence of certain wind power gradients that occur in a time interval of e.g. 12 hours. During 2006 and 2009 the distribution of wind power fluctuations looks very similar giving hope that distinct atmospheric processes can be determined that act as a trigger. Most often high wind power fluctuations occur in a range of wind speeds between 9-12 m/s as can be expected from the shape of the wind power curve. A cluster analysis of the 500 hPa geopotential height to detect predominant weather regimes shows that high fluctuations are more likely in north-western flow. It is shown that most often high fluctuations occur in non-stable atmospheric stratification. The description of stratification by means of the vertical gradient of the virtual potential temperature is chosen to be indicative for convection, i.e. it can be assumed that a negative gradient indicates convection which leads to strong wind fluctuations in the updraft and downdraft of the cloud. Neural Networks are used to determine the probability of exceedence of wind power gradients from a set of atmospheric parameters that are taken from Numerical Weather Prediction Models. Parameters describing atmospheric stability, that are related to convection (e.g. rain rate) and that forecast wind gusts tend to carry most information to estimate expected wind power fluctuations.

Measured and predicted rotor performance for the SERI advanced wind turbine blades

NASA Astrophysics Data System (ADS)

Tangler, J.; Smith, B.; Kelley, N.; Jager, D.

1992-02-01

Measured and predicted rotor performance for the Solar Energy Research Institute (SERI) advanced wind turbine blades were compared to assess the accuracy of predictions and to identify the sources of error affecting both predictions and measurements. An awareness of these sources of error contributes to improved prediction and measurement methods that will ultimately benefit future rotor design efforts. Propeller/vane anemometers were found to underestimate the wind speed in turbulent environments such as the San Gorgonio Pass wind farm area. Using sonic or cup anemometers, good agreement was achieved between predicted and measured power output for wind speeds up to 8 m/sec. At higher wind speeds an optimistic predicted power output and the occurrence of peak power at wind speeds lower than measurements resulted from the omission of turbulence and yaw error. In addition, accurate two-dimensional (2-D) airfoil data prior to stall and a post stall airfoil data synthesization method that reflects three-dimensional (3-D) effects were found to be essential for accurate performance prediction.

NASA Lewis Stirling engine computer code evaluation

NASA Technical Reports Server (NTRS)

Sullivan, Timothy J.

1989-01-01

In support of the U.S. Department of Energy's Stirling Engine Highway Vehicle Systems program, the NASA Lewis Stirling engine performance code was evaluated by comparing code predictions without engine-specific calibration factors to GPU-3, P-40, and RE-1000 Stirling engine test data. The error in predicting power output was -11 percent for the P-40 and 12 percent for the Re-1000 at design conditions and 16 percent for the GPU-3 at near-design conditions (2000 rpm engine speed versus 3000 rpm at design). The efficiency and heat input predictions showed better agreement with engine test data than did the power predictions. Concerning all data points, the error in predicting the GPU-3 brake power was significantly larger than for the other engines and was mainly a result of inaccuracy in predicting the pressure phase angle. Analysis into this pressure phase angle prediction error suggested that improvements to the cylinder hysteresis loss model could have a significant effect on overall Stirling engine performance predictions.

Magnetic storm effects in electric power systems and prediction needs

NASA Technical Reports Server (NTRS)

Albertson, V. D.; Kappenman, J. G.

1979-01-01

Geomagnetic field fluctuations produce spurious currents in electric power systems. These currents enter and exit through points remote from each other. The fundamental period of these currents is on the order of several minutes which is quasi-dc compared to the normal 60 Hz or 50 Hz power system frequency. Nearly all of the power systems problems caused by the geomagnetically induced currents result from the half-cycle saturation of power transformers due to simultaneous ac and dc excitation. The effects produced in power systems are presented, current research activity is discussed, and magnetic storm prediction needs of the power industry are listed.

A variable capacitance based modeling and power capability predicting method for ultracapacitor

NASA Astrophysics Data System (ADS)

Liu, Chang; Wang, Yujie; Chen, Zonghai; Ling, Qiang

2018-01-01

Methods of accurate modeling and power capability predicting for ultracapacitors are of great significance in management and application of lithium-ion battery/ultracapacitor hybrid energy storage system. To overcome the simulation error coming from constant capacitance model, an improved ultracapacitor model based on variable capacitance is proposed, where the main capacitance varies with voltage according to a piecewise linear function. A novel state-of-charge calculation approach is developed accordingly. After that, a multi-constraint power capability prediction is developed for ultracapacitor, in which a Kalman-filter-based state observer is designed for tracking ultracapacitor's real-time behavior. Finally, experimental results verify the proposed methods. The accuracy of the proposed model is verified by terminal voltage simulating results under different temperatures, and the effectiveness of the designed observer is proved by various test conditions. Additionally, the power capability prediction results of different time scales and temperatures are compared, to study their effects on ultracapacitor's power capability.

The aerodynamic cost of flight in bats--comparing theory with measurement

NASA Astrophysics Data System (ADS)

von Busse, Rhea; Waldman, Rye M.; Swartz, Sharon M.; Breuer, Kenneth S.

2012-11-01

Aerodynamic theory has long been used to predict the aerodynamic power required for animal flight. However, even though the actuator disk model does not account for the flapping motion of a wing, it is used for lack of any better model. The question remains: how close are these predictions to reality? We designed a study to compare predicted aerodynamic power to measured power from the kinetic energy contained in the wake shed behind a bat flying in a wind tunnel. A high-accuracy displaced light-sheet stereo PIV system was used in the Trefftz plane to capture the wake behind four bats flown over a range of flight speeds (1-6m/s). The total power in the wake was computed from the wake vorticity and these estimates were compared with the power predicted using Pennycuick's model for bird flight as well as estimates derived from measurements of the metabolic cost of flight, previously acquired from the same individuals.

A new method of power load prediction in electrification railway

NASA Astrophysics Data System (ADS)

Dun, Xiaohong

2018-04-01

Aiming at the character of electrification railway, the paper mainly studies the problem of load prediction in electrification railway. After the preprocessing of data, and the similar days are separated on the basis of its statistical characteristics. Meanwhile the accuracy of different methods is analyzed. The paper provides a new thought of prediction and a new method of accuracy of judgment for the load prediction of power system.

A neural network based computational model to predict the output power of different types of photovoltaic cells.

PubMed

Xiao, WenBo; Nazario, Gina; Wu, HuaMing; Zhang, HuaMing; Cheng, Feng

2017-01-01

In this article, we introduced an artificial neural network (ANN) based computational model to predict the output power of three types of photovoltaic cells, mono-crystalline (mono-), multi-crystalline (multi-), and amorphous (amor-) crystalline. The prediction results are very close to the experimental data, and were also influenced by numbers of hidden neurons. The order of the solar generation power output influenced by the external conditions from smallest to biggest is: multi-, mono-, and amor- crystalline silicon cells. In addition, the dependences of power prediction on the number of hidden neurons were studied. For multi- and amorphous crystalline cell, three or four hidden layer units resulted in the high correlation coefficient and low MSEs. For mono-crystalline cell, the best results were achieved at the hidden layer unit of 8.

A Case for Application Oblivious Energy-Efficient MPI Runtime

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

Venkatesh, Akshay; Vishnu, Abhinav; Hamidouche, Khaled

Power has become the major impediment in designing large scale high-end systems. Message Passing Interface (MPI) is the {\\em de facto} communication interface used as the back-end for designing applications, programming models and runtime for these systems. Slack --- the time spent by an MPI process in a single MPI call --- provides a potential for energy and power savings, if an appropriate power reduction technique such as core-idling/Dynamic Voltage and Frequency Scaling (DVFS) can be applied without perturbing application's execution time. Existing techniques that exploit slack for power savings assume that application behavior repeats across iterations/executions. However, an increasingmore » use of adaptive, data-dependent workloads combined with system factors (OS noise, congestion) makes this assumption invalid. This paper proposes and implements Energy Aware MPI (EAM) --- an application-oblivious energy-efficient MPI runtime. EAM uses a combination of communication models of common MPI primitives (point-to-point, collective, progress, blocking/non-blocking) and an online observation of slack for maximizing energy efficiency. Each power lever incurs time overhead, which must be amortized over slack to minimize degradation. When predicted communication time exceeds a lever overhead, the lever is used {\\em as soon as possible} --- to maximize energy efficiency. When mis-prediction occurs, the lever(s) are used automatically at specific intervals for amortization. We implement EAM using MVAPICH2 and evaluate it on ten applications using up to 4096 processes. Our performance evaluation on an InfiniBand cluster indicates that EAM can reduce energy consumption by 5--41\\% in comparison to the default approach, with negligible (less than 4\\% in all cases) performance loss.« less

Wilkinson Microwave Anisotropy Probe (WMAP) First Year Observations: TE Polarization

NASA Technical Reports Server (NTRS)

Kogut, A.; Spergel, D. N.; Barnes, C.; Bennett, C. L.; Halpern, M.; Hinshaw, G.; Jarosik, N.; Limon, M.; Meyer, S. S.; Page, L.;

Air quality impacts of distributed energy resources implemented in the northeastern United States.

PubMed

Carreras-Sospedra, Marc; Dabdub, Donald; Brouwer, Jacob; Knipping, Eladio; Kumar, Naresh; Darrow, Ken; Hampson, Anne; Hedman, Bruce

2008-07-01

Emissions from the potential installation of distributed energy resources (DER) in the place of current utility-scale power generators have been introduced into an emissions inventory of the northeastern United States. A methodology for predicting future market penetration of DER that considers economics and emission factors was used to estimate the most likely implementation of DER. The methodology results in spatially and temporally resolved emission profiles of criteria pollutants that are subsequently introduced into a detailed atmospheric chemistry and transport model of the region. The DER technology determined by the methodology includes 62% reciprocating engines, 34% gas turbines, and 4% fuel cells and other emerging technologies. The introduction of DER leads to retirement of 2625 MW of existing power plants for which emissions are removed from the inventory. The air quality model predicts maximum differences in air pollutant concentrations that are located downwind from the central power plants that were removed from the domain. Maximum decreases in hourly peak ozone concentrations due to DER use are 10 ppb and are located over the state of New Jersey. Maximum decreases in 24-hr average fine particulate matter (PM2.5) concentrations reach 3 microg/m3 and are located off the coast of New Jersey and New York. The main contribution to decreased PM2.5 is the reduction of sulfate levels due to significant reductions in direct emissions of sulfur oxides (SO(x)) from the DER compared with the central power plants removed. The scenario presented here represents an accelerated DER penetration case with aggressive emission reductions due to removal of highly emitting power plants. Such scenario provides an upper bound for air quality benefits of DER implementation scenarios.

Initial comparison of single cylinder Stirling engine computer model predictions with test results

NASA Technical Reports Server (NTRS)

Tew, R. C., Jr.; Thieme, L. G.; Miao, D.

1979-01-01

A NASA developed digital computer code for a Stirling engine, modelling the performance of a single cylinder rhombic drive ground performance unit (GPU), is presented and its predictions are compared to test results. The GPU engine incorporates eight regenerator/cooler units and the engine working space is modelled by thirteen control volumes. The model calculates indicated power and efficiency for a given engine speed, mean pressure, heater and expansion space metal temperatures and cooler water inlet temperature and flow rate. Comparison of predicted and observed powers implies that the reference pressure drop calculations underestimate actual pressure drop, possibly due to oil contamination in the regenerator/cooler units, methane contamination in the working gas or the underestimation of mechanical loss. For a working gas of hydrogen, the predicted values of brake power are from 0 to 6% higher than experimental values, and brake efficiency is 6 to 16% higher, while for helium the predicted brake power and efficiency are 2 to 15% higher than the experimental.

The impact of three recent coal-fired power plant closings on Pittsburgh air quality: A natural experiment.

PubMed

Russell, Marie C; Belle, Jessica H; Liu, Yang

2017-01-01

Relative to the rest of the United States, the region of southwestern Pennsylvania, including metropolitan Pittsburgh, experiences high ambient concentrations of fine particulate matter (PM 2.5 ), which is known to be associated with adverse respiratory and cardiovascular health impacts. This study evaluates whether the closing of three coal-fired power plants within the southwestern Pennsylvania region resulted in a significant decrease in PM 2.5 concentration. Both PM 2.5 data obtained from EPA ground stations in the study region and aerosol optical depth (AOD) data retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments onboard the Terra and Aqua satellites were used to investigate regional air quality from January 2011 through December 2014. The impact of the plant closings on PM 2.5 concentration and AOD was evaluated using a series of generalized additive models. The model results show that monthly fuel consumption of the Elrama plant, which closed in October of 2012, and monthly fuel consumption of both the Mitchell and Hatfield's Ferry plants, which closed in October of 2013, were significant predictors of both PM 2.5 concentration and AOD at EPA ground stations in the study region, after controlling for multiple meteorological factors and long-term, region-wide air quality improvements. The model's power to predict PM 2.5 concentration increased from an adjusted R 2 of 0.61 to 0.68 after excluding data from ground stations with higher uncertainty due to recent increases in unconventional natural gas extraction activities. After preliminary analyses of mean PM 2.5 concentration and AOD showed a downward trend following each power plant shutdown, results from a series of generalized additive models confirmed that the activity of the three plants that closed, measured by monthly fuel consumption, was highly significant in predicting both AOD and PM 2.5 at 12 EPA ground stations; further research on PM 2.5 emissions from unconventional natural gas extraction is needed. With many coal-fired power plants scheduled to close across the United States in the coming years, there is interest in the potential impact on regional PM 2.5 concentrations. In southwestern Pennsylvania, recent coal-fired power plant closings were coupled with a boom in unconventional natural gas extraction. Natural gas is currently seen as an economically viable bridge fuel between coal and renewable energy. This study provides policymakers with more information on the potential ambient concentration changes associated with coal-fired power plant closings as the nation's energy reliance shifts toward natural gas.

Pinning time statistics for vortex lines in disordered environments.

PubMed

Dobramysl, Ulrich; Pleimling, Michel; Täuber, Uwe C

2014-12-01

We study the pinning dynamics of magnetic flux (vortex) lines in a disordered type-II superconductor. Using numerical simulations of a directed elastic line model, we extract the pinning time distributions of vortex line segments. We compare different model implementations for the disorder in the surrounding medium: discrete, localized pinning potential wells that are either attractive and repulsive or purely attractive, and whose strengths are drawn from a Gaussian distribution; as well as continuous Gaussian random potential landscapes. We find that both schemes yield power-law distributions in the pinned phase as predicted by extreme-event statistics, yet they differ significantly in their effective scaling exponents and their short-time behavior.

Ultra-Low Carbon Emissions from Coal-Fired Power Plants through Bio-Oil Co-Firing and Biochar Sequestration.

PubMed

Dang, Qi; Mba Wright, Mark; Brown, Robert C

2015-12-15

This study investigates a novel strategy of reducing carbon emissions from coal-fired power plants through co-firing bio-oil and sequestering biochar in agricultural lands. The heavy end fraction of bio-oil recovered from corn stover fast pyrolysis is blended and co-fired with bituminous coal to form a bio-oil co-firing fuel (BCF). Life-cycle greenhouse gas (GHG) emissions per kWh electricity produced vary from 1.02 to 0.26 kg CO2-eq among different cases, with BCF heavy end fractions ranging from 10% to 60%, which corresponds to a GHG emissions reduction of 2.9% to 74.9% compared with that from traditional bituminous coal power plants. We found a heavy end fraction between 34.8% and 37.3% is required to meet the Clean Power Plan's emission regulation for new coal-fired power plants. The minimum electricity selling prices are predicted to increase from 8.8 to 14.9 cents/kWh, with heavy end fractions ranging from 30% to 60%. A minimum carbon price of $67.4 ± 13 per metric ton of CO2-eq was estimated to make BCF power commercially viable for the base case. These results suggest that BCF co-firing is an attractive pathway for clean power generation in existing power plants with a potential for significant reductions in carbon emissions.

A phenomenological model of muscle fatigue and the power-endurance relationship.

PubMed

James, A; Green, S

2012-11-01

The relationship between power output and the time that it can be sustained during exercise (i.e., endurance) at high intensities is curvilinear. Although fatigue is implicit in this relationship, there is little evidence pertaining to it. To address this, we developed a phenomenological model that predicts the temporal response of muscle power during submaximal and maximal exercise and which was based on the type, contractile properties (e.g., fatiguability), and recruitment of motor units (MUs) during exercise. The model was first used to predict power outputs during all-out exercise when fatigue is clearly manifest and for several distributions of MU type. The model was then used to predict times that different submaximal power outputs could be sustained for several MU distributions, from which several power-endurance curves were obtained. The model was simultaneously fitted to two sets of human data pertaining to all-out exercise (power-time profile) and submaximal exercise (power-endurance relationship), yielding a high goodness of fit (R(2) = 0.96-0.97). This suggested that this simple model provides an accurate description of human power output during submaximal and maximal exercise and that fatigue-related processes inherent in it account for the curvilinearity of the power-endurance relationship.

Application and verification of ECMWF seasonal forecast for wind energy

NASA Astrophysics Data System (ADS)

Žagar, Mark; Marić, Tomislav; Qvist, Martin; Gulstad, Line

2015-04-01

A good understanding of long-term annual energy production (AEP) is crucial when assessing the business case of investing in green energy like wind power. The art of wind-resource assessment has emerged into a scientific discipline on its own, which has advanced at high pace over the last decade. This has resulted in continuous improvement of the AEP accuracy and, therefore, increase in business case certainty. Harvesting the full potential output of a wind farm or a portfolio of wind farms depends heavily on optimizing operation and management strategy. The necessary information for short-term planning (up to 14 days) is provided by standard weather and power forecasting services, and the long-term plans are based on climatology. However, the wind-power industry is lacking quality information on intermediate scales of the expected variability in seasonal and intra-annual variations and their geographical distribution. The seasonal power forecast presented here is designed to bridge this gap. The seasonal power production forecast is based on the ECMWF seasonal weather forecast and the Vestas' high-resolution, mesoscale weather library. The seasonal weather forecast is enriched through a layer of statistical post-processing added to relate large-scale wind speed anomalies to mesoscale climatology. The resulting predicted energy production anomalies, thus, include mesoscale effects not captured by the global forecasting systems. The turbine power output is non-linearly related to the wind speed, which has important implications for the wind power forecast. In theory, the wind power is proportional to the cube of wind speed. However, due to the nature of turbine design, this exponent is close to 3 only at low wind speeds, becomes smaller as the wind speed increases, and above 11-13 m/s the power output remains constant, called the rated power. The non-linear relationship between wind speed and the power output generally increases sensitivity of the forecasted power to the wind speed anomalies. On the other hand, in some cases and areas where turbines operate close to, or above the rated power, the sensitivity of power forecast is reduced. Thus, the seasonal power forecasting system requires good knowledge of the changes in frequency of events with sufficient wind speeds to have acceptable skill. The scientific background for the Vestas seasonal power forecasting system is described and the relationship between predicted monthly wind speed anomalies and observed wind energy production are investigated for a number of operating wind farms in different climate zones. Current challenges will be discussed and some future research and development areas identified.

Estimation of Power Consumption in the Circular Sawing of Stone Based on Tangential Force Distribution

NASA Astrophysics Data System (ADS)

Huang, Guoqin; Zhang, Meiqin; Huang, Hui; Guo, Hua; Xu, Xipeng

2018-04-01

Circular sawing is an important method for the processing of natural stone. The ability to predict sawing power is important in the optimisation, monitoring and control of the sawing process. In this paper, a predictive model (PFD) of sawing power, which is based on the tangential force distribution at the sawing contact zone, was proposed, experimentally validated and modified. With regard to the influence of sawing speed on tangential force distribution, the modified PFD (MPFD) performed with high predictive accuracy across a wide range of sawing parameters, including sawing speed. The mean maximum absolute error rate was within 6.78%, and the maximum absolute error rate was within 11.7%. The practicability of predicting sawing power by the MPFD with few initial experimental samples was proved in case studies. On the premise of high sample measurement accuracy, only two samples are required for a fixed sawing speed. The feasibility of applying the MPFD to optimise sawing parameters while lowering the energy consumption of the sawing system was validated. The case study shows that energy use was reduced 28% by optimising the sawing parameters. The MPFD model can be used to predict sawing power, optimise sawing parameters and control energy.

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

Jin, Ke; Zhang, Yanwen; Zhu, Zihua

Accurate information of electronic stopping power is fundamental for broad advances in electronic industry, space exploration, national security, and sustainable energy technologies. The Stopping and Range of Ions in Matter (SRIM) code has been widely applied to predict stopping powers and ion distributions for decades. Recent experimental results have, however, shown considerable errors in the SRIM predictions for stopping of heavy ions in compounds containing light elements, indicating an urgent need to improve current stopping power models. The electronic stopping powers of 35Cl, 80Br, 127I, and 197Au ions are experimentally determined in two important functional materials, SiC and SiO2, frommore » tens to hundreds keV/u based on a single ion technique. By combining with the reciprocity theory, new electronic stopping powers are suggested in a region from 0 to 15 MeV, where large deviations from SRIM predictions are observed. For independent experimental validation of the electronic stopping powers we determined, Rutherford backscattering spectrometry (RBS) and secondary ion mass spectrometry (SIMS) are utilized to measure the depth profiles of implanted Au ions in SiC with energies from 700 keV to 15 MeV. The measured ion distributions from both RBS and SIMS are considerably deeper (up to ~30%) than the predictions from the commercial SRIM code. In comparison, the new electronic stopping power values are utilized in a modified TRIM-85 (the original version of the SRIM) code, M-TRIM, to predict ion distributions, and the results are in good agreement with the experimentally measured ion distributions.« less

Modelling Influence and Opinion Evolution in Online Collective Behaviour

PubMed Central

Gend, Pascal; Rentfrow, Peter J.; Hendrickx, Julien M.; Blondel, Vincent D.

2016-01-01

Opinion evolution and judgment revision are mediated through social influence. Based on a large crowdsourced in vitro experiment (n = 861), it is shown how a consensus model can be used to predict opinion evolution in online collective behaviour. It is the first time the predictive power of a quantitative model of opinion dynamics is tested against a real dataset. Unlike previous research on the topic, the model was validated on data which did not serve to calibrate it. This avoids to favor more complex models over more simple ones and prevents overfitting. The model is parametrized by the influenceability of each individual, a factor representing to what extent individuals incorporate external judgments. The prediction accuracy depends on prior knowledge on the participants’ past behaviour. Several situations reflecting data availability are compared. When the data is scarce, the data from previous participants is used to predict how a new participant will behave. Judgment revision includes unpredictable variations which limit the potential for prediction. A first measure of unpredictability is proposed. The measure is based on a specific control experiment. More than two thirds of the prediction errors are found to occur due to unpredictability of the human judgment revision process rather than to model imperfection. PMID:27336834

Prediction of brain tissue temperature using near-infrared spectroscopy

PubMed Central

Holper, Lisa; Mitra, Subhabrata; Bale, Gemma; Robertson, Nicola; Tachtsidis, Ilias

2017-01-01

Abstract. Broadband near-infrared spectroscopy (NIRS) can provide an endogenous indicator of tissue temperature based on the temperature dependence of the water absorption spectrum. We describe a first evaluation of the calibration and prediction of brain tissue temperature obtained during hypothermia in newborn piglets (animal dataset) and rewarming in newborn infants (human dataset) based on measured body (rectal) temperature. The calibration using partial least squares regression proved to be a reliable method to predict brain tissue temperature with respect to core body temperature in the wavelength interval of 720 to 880 nm with a strong mean predictive power of R2=0.713±0.157 (animal dataset) and R2=0.798±0.087 (human dataset). In addition, we applied regression receiver operating characteristic curves for the first time to evaluate the temperature prediction, which provided an overall mean error bias between NIRS predicted brain temperature and body temperature of 0.436±0.283°C (animal dataset) and 0.162±0.149°C (human dataset). We discuss main methodological aspects, particularly the well-known aspect of over- versus underestimation between brain and body temperature, which is relevant for potential clinical applications. PMID:28630878

NASA's Prediction Of Worldwide Energy Resource (POWER) Project Unveils a New Geospatial Data Portal

Atmospheric Science Data Center

2018-03-01

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

Cosmic shear as a probe of galaxy formation physics

DOE PAGES

Foreman, Simon; Becker, Matthew R.; Wechsler, Risa H.

2016-09-01

Here, we evaluate the potential for current and future cosmic shear measurements from large galaxy surveys to constrain the impact of baryonic physics on the matter power spectrum. We do so using a model-independent parametrization that describes deviations of the matter power spectrum from the dark-matter-only case as a set of principal components that are localized in wavenumber and redshift. We perform forecasts for a variety of current and future data sets, and find that at least ~90 per cent of the constraining power of these data sets is contained in no more than nine principal components. The constraining powermore » of different surveys can be quantified using a figure of merit defined relative to currently available surveys. With this metric, we find that the final Dark Energy Survey data set (DES Y5) and the Hyper Suprime-Cam Survey will be roughly an order of magnitude more powerful than existing data in constraining baryonic effects. Upcoming Stage IV surveys (Large Synoptic Survey Telescope, Euclid, and Wide Field Infrared Survey Telescope) will improve upon this by a further factor of a few. We show that this conclusion is robust to marginalization over several key systematics. The ultimate power of cosmic shear to constrain galaxy formation is dependent on understanding systematics in the shear measurements at small (sub-arcminute) scales. Lastly, if these systematics can be sufficiently controlled, cosmic shear measurements from DES Y5 and other future surveys have the potential to provide a very clean probe of galaxy formation and to strongly constrain a wide range of predictions from modern hydrodynamical simulations.« less

Optimal surveys for weak-lensing tomography

NASA Astrophysics Data System (ADS)

Amara, Adam; Réfrégier, Alexandre

2007-11-01

Weak-lensing surveys provide a powerful probe of dark energy through the measurement of the mass distribution of the local Universe. A number of ground-based and space-based surveys are being planned for this purpose. Here, we study the optimal strategy for these future surveys using the joint constraints on the equation-of-state parameter wn and its evolution wa as a figure of merit by considering power spectrum tomography. For this purpose, we first consider an `ideal' survey which is both wide and deep and exempt from systematics. We find that such a survey has great potential for dark energy studies, reaching 1σ precisions of 1 and 10 per cent on the two parameters, respectively. We then study the relative impact of various limitations by degrading this ideal survey. In particular, we consider the effect of sky coverage, survey depth, shape measurement systematics, photometric redshift systematics and uncertainties in the non-linear power spectrum predictions. We find that, for a given observing time, it is always advantageous to choose a wide rather than a deep survey geometry. We also find that the dark energy constraints from power spectrum tomography are robust to photometric redshift errors and catastrophic failures, if a spectroscopic calibration sample of 104-105 galaxies are available. The impact of these systematics is small compared to the limitations that come from potential uncertainties in the power spectrum, due to shear measurement and theoretical errors. To help the planning of future surveys, we summarize our results with comprehensive scaling relations which avoid the need for full Fisher matrix calculations.