Understanding the many-body expansion for large systems. II. Accuracy considerations

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

Lao, Ka Un; Liu, Kuan-Yu; Richard, Ryan M.

2016-04-28

To complement our study of the role of finite precision in electronic structure calculations based on a truncated many-body expansion (MBE, or “n-body expansion”), we examine the accuracy of such methods in the present work. Accuracy may be defined either with respect to a supersystem calculation computed at the same level of theory as the n-body calculations, or alternatively with respect to high-quality benchmarks. Both metrics are considered here. In applications to a sequence of water clusters, (H{sub 2}O){sub N=6−55} described at the B3LYP/cc-pVDZ level, we obtain mean absolute errors (MAEs) per H{sub 2}O monomer of ∼1.0 kcal/mol for two-bodymore » expansions, where the benchmark is a B3LYP/cc-pVDZ calculation on the entire cluster. Three- and four-body expansions exhibit MAEs of 0.5 and 0.1 kcal/mol/monomer, respectively, without resort to charge embedding. A generalized many-body expansion truncated at two-body terms [GMBE(2)], using 3–4 H{sub 2}O molecules per fragment, outperforms all of these methods and affords a MAE of ∼0.02 kcal/mol/monomer, also without charge embedding. GMBE(2) requires significantly fewer (although somewhat larger) subsystem calculations as compared to MBE(4), reducing problems associated with floating-point roundoff errors. When compared to high-quality benchmarks, we find that error cancellation often plays a critical role in the success of MBE(n) calculations, even at the four-body level, as basis-set superposition error can compensate for higher-order polarization interactions. A many-body counterpoise correction is introduced for the GMBE, and its two-body truncation [GMBCP(2)] is found to afford good results without error cancellation. Together with a method such as ωB97X-V/aug-cc-pVTZ that can describe both covalent and non-covalent interactions, the GMBE(2)+GMBCP(2) approach provides an accurate, stable, and tractable approach for large systems.« less

Daily Reservoir Inflow Forecasting using Deep Learning with Downscaled Multi-General Circulation Models (GCMs) Platform

NASA Astrophysics Data System (ADS)

Li, D.; Fang, N. Z.

2017-12-01

Dallas-Fort Worth Metroplex (DFW) has a population of over 7 million depending on many water supply reservoirs. The reservoir inflow plays a vital role in water supply decision making process and long-term strategic planning for the region. This paper demonstrates a method of utilizing deep learning algorithms and multi-general circulation model (GCM) platform to forecast reservoir inflow for three reservoirs within the DFW: Eagle Mountain Lake, Lake Benbrook and Lake Arlington. Ensemble empirical mode decomposition was firstly employed to extract the features, which were then represented by the deep belief networks (DBNs). The first 75 years of the historical data (1940 -2015) were used to train the model, while the last 2 years of the data (2016-2017) were used for the model validation. The weights of each DBN gained from the training process were then applied to establish a neural network (NN) that was able to forecast reservoir inflow. Feature predictors used for the forecasting model were generated from weather forecast results of the downscaled multi-GCM platform for the North Texas region. By comparing root mean square error (RMSE) and mean bias error (MBE) with the observed data, the authors found that the deep learning with downscaled multi-GCM platform is an effective approach in the reservoir inflow forecasting.

Implications of drying temperature and humidity on the drying kinetics of seaweed

NASA Astrophysics Data System (ADS)

Ali, Majid Khan Majahar; Fudholi, Ahmad; Muthuvalu, M. S.; Sulaiman, Jumat; Yasir, Suhaimi Md

2017-11-01

A Low Temperature and Humidity Chamber Test tested in the Solar Energy Laboratory, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Selangor, Malaysia. Experiments are attempted to study the effect of drying air temperature and humidity on the drying kinetics of seaweed Kappaphycus species Striatum besides to develop a model to estimate the drying curves. Simple method using a excel software is used in the analysis of raw data obtained from the drying experiment. The values of the parameters a, n and the constant k for the models are determined using a plot of curve drying models. Three different drying models are compared with experiment data seaweed drying at 30, 40, 50 and 60°C and relative humidity 20, 30 and 40% for seaweed. The higher drying temperatures and low relative humidity effects the moisture content that will be rapidly reduced. The most suitable model is selected to best describe the drying behavior of seaweed. The values of the coefficient of determination (R2), mean bias error (MBE) and root mean square error (RMSE) are used to determine the goodness or the quality of the fit. The Page model is showed a better fit to drying seaweed. The results from this study crucial for solar dryer development on pilot scale in Malaysia.

Climatological Modeling of Monthly Air Temperature and Precipitation in Egypt through GIS Techniques

NASA Astrophysics Data System (ADS)

El Kenawy, A.

2009-09-01

This paper describes a method for modeling and mapping four climatic variables (maximum temperature, minimum temperature, mean temperature and total precipitation) in Egypt using a multiple regression approach implemented in a GIS environment. In this model, a set of variables including latitude, longitude, elevation within a distance of 5, 10 and 15 km, slope, aspect, distance to the Mediterranean Sea, distance to the Red Sea, distance to the Nile, ratio between land and water masses within a radius of 5, 10, 15 km, the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), the Normalized Difference Temperature Index (NDTI) and reflectance are included as independent variables. These variables were integrated as raster layers in MiraMon software at a spatial resolution of 1 km. Climatic variables were considered as dependent variables and averaged from quality controlled and homogenized 39 series distributing across the entire country during the period of (1957-2006). For each climatic variable, digital and objective maps were finally obtained using the multiple regression coefficients at monthly, seasonal and annual timescale. The accuracy of these maps were assessed through cross-validation between predicted and observed values using a set of statistics including coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), mean bias Error (MBE) and D Willmott statistic. These maps are valuable in the sense of spatial resolution as well as the number of observatories involved in the current analysis.

Validation and Spatiotemporal Analysis of CERES Surface Net Radiation Product

DOE PAGES

Jia, Aolin; Jiang, Bo; Liang, Shunlin; ...

2016-01-23

The Clouds and the Earth’s Radiant Energy System (CERES) generates one of the few global satellite radiation products. The CERES ARM Validation Experiment (CAVE) has been providing long-term in situ observations for the validation of the CERES products. However, the number of these sites is low and their distribution is globally sparse, and particularly the surface net radiation product has not been rigorously validated yet. Therefore, additional validation efforts are highly required to determine the accuracy of the CERES radiation products. In this study, global land surface measurements were comprehensively collected for use in the validation of the CERES netmore » radiation (R n) product on a daily (340 sites) and a monthly (260 sites) basis, respectively. The validation results demonstrated that the CERES R n product was, overall, highly accurate. The daily validations had a Mean Bias Error (MBE) of 3.43 W·m −2, Root Mean Square Error (RMSE) of 33.56 W·m −2, and R 2 of 0.79, and the monthly validations had an MBE of 3.40 W·m −2, RMSE of 25.57 W·m −2, and R 2 of 0.84. The accuracy was slightly lower for the high latitudes. Following the validation, the monthly CERES R n product, from March 2000 to July 2014, was used for a further analysis. We analysed the global spatiotemporal variation of the R n, which occurred during the measurement period. In addition, two hot spot regions, the southern Great Plains and south-central Africa, were then selected for use in determining the driving factors or attribution of the R n variation. We determined that R n over the southern Great Plains decreased by −0.33 W·m −2 per year, which was mainly driven by changes in surface green vegetation and precipitation. In south-central Africa, R n decreased at a rate of −0.63 W·m −2 per year, the major driving factor of which was surface green vegetation.« less

Validation and Spatiotemporal Analysis of CERES Surface Net Radiation Product

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

Jia, Aolin; Jiang, Bo; Liang, Shunlin

The Clouds and the Earth’s Radiant Energy System (CERES) generates one of the few global satellite radiation products. The CERES ARM Validation Experiment (CAVE) has been providing long-term in situ observations for the validation of the CERES products. However, the number of these sites is low and their distribution is globally sparse, and particularly the surface net radiation product has not been rigorously validated yet. Therefore, additional validation efforts are highly required to determine the accuracy of the CERES radiation products. In this study, global land surface measurements were comprehensively collected for use in the validation of the CERES netmore » radiation (R n) product on a daily (340 sites) and a monthly (260 sites) basis, respectively. The validation results demonstrated that the CERES R n product was, overall, highly accurate. The daily validations had a Mean Bias Error (MBE) of 3.43 W·m −2, Root Mean Square Error (RMSE) of 33.56 W·m −2, and R 2 of 0.79, and the monthly validations had an MBE of 3.40 W·m −2, RMSE of 25.57 W·m −2, and R 2 of 0.84. The accuracy was slightly lower for the high latitudes. Following the validation, the monthly CERES R n product, from March 2000 to July 2014, was used for a further analysis. We analysed the global spatiotemporal variation of the R n, which occurred during the measurement period. In addition, two hot spot regions, the southern Great Plains and south-central Africa, were then selected for use in determining the driving factors or attribution of the R n variation. We determined that R n over the southern Great Plains decreased by −0.33 W·m −2 per year, which was mainly driven by changes in surface green vegetation and precipitation. In south-central Africa, R n decreased at a rate of −0.63 W·m −2 per year, the major driving factor of which was surface green vegetation.« less

40 CFR 33.203 - How does an entity qualify as an MBE or WBE under EPA's 10% statute?

Code of Federal Regulations, 2011 CFR

2011-07-01

... have been impeded in developing a business concern as a result of racial or ethnic discrimination. (f... subjected to racial or ethnic prejudice or cultural bias because of his or her identity as a member of a.... Nothing in this section shall prohibit any member of a racial or ethnic group that is not designated as...

40 CFR 33.203 - How does an entity qualify as an MBE or WBE under EPA's 10% statute?

Code of Federal Regulations, 2014 CFR

2014-07-01

... have been impeded in developing a business concern as a result of racial or ethnic discrimination. (f... subjected to racial or ethnic prejudice or cultural bias because of his or her identity as a member of a.... Nothing in this section shall prohibit any member of a racial or ethnic group that is not designated as...

40 CFR 33.203 - How does an entity qualify as an MBE or WBE under EPA's 10% statute?

Code of Federal Regulations, 2012 CFR

2012-07-01

... have been impeded in developing a business concern as a result of racial or ethnic discrimination. (f... subjected to racial or ethnic prejudice or cultural bias because of his or her identity as a member of a.... Nothing in this section shall prohibit any member of a racial or ethnic group that is not designated as...

40 CFR 33.203 - How does an entity qualify as an MBE or WBE under EPA's 10% statute?

Code of Federal Regulations, 2013 CFR

2013-07-01

... have been impeded in developing a business concern as a result of racial or ethnic discrimination. (f... subjected to racial or ethnic prejudice or cultural bias because of his or her identity as a member of a.... Nothing in this section shall prohibit any member of a racial or ethnic group that is not designated as...

A Substantive Process Analysis of Responses to Items from the Multistate Bar Examination

ERIC Educational Resources Information Center

Bonner, Sarah M.; D'Agostino, Jerome V.

2012-01-01

We investigated examinees' cognitive processes while they solved selected items from the Multistate Bar Exam (MBE), a high-stakes professional certification examination. We focused on ascertaining those mental processes most frequently used by examinees, and the most common types of errors in their thinking. We compared the relationships between…

Ultralow threshold graded-index separate-confinement heterostructure single quantum well (Al, Ga) As lasers

NASA Technical Reports Server (NTRS)

Derry, P. L.; Chen, H. Z.; Morkoc, H.; Yariv, A.; Lau, K. Y.

1988-01-01

Broad area graded-index separate-confinement heterostructure single quantum well lasers grown by molecular-beam epitaxy (MBE) with threshold current density as low as 93 A/sq cm (520 microns long) have been fabricated. Buried lasers formed from similarly structured MBE material with liquid phase epitaxy regrowth had threshold currents at submilliampere levels when high reflectivity coatings were applied to the end facets. A CW threshold current of 0.55 mA was obtained for a laser with facet reflectivities of about 80 percent, a cavity length of 120 micron, and an active region stripe width of 1 micron. These devices driven directly with logic level signals have switch-on delays less than 50 ps without any current prebias. Such lasers permit fully on-off switching while at the same time obviating the need for bias monitoring and feedback control.

Direct correlation and strong reduction of native point defects and microwave dielectric loss in air-annealed (Ba,Sr)TiO{sub 3}

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

Zeng, Z. Q.; Podpirka, A.; Kirchoefer, S. W.

2015-05-04

We report on the native defect and microwave properties of 1 μm thick Ba{sub 0.50}Sr{sub 0.50}TiO{sub 3} (BST) films grown on MgO (100) substrates by molecular beam epitaxy (MBE). Depth-resolved cathodoluminescence spectroscopy (DRCLS) showed high densities of native point defects in as-deposited BST films, causing strong subgap emission between 2.0 eV and 3.0 eV due to mixed cation V{sub C} and oxygen Vo vacancies. Post growth air anneals reduce these defects with 2.2, 2.65, and 3.0 eV V{sub O} and 2.4 eV V{sub C} intensities decreasing with increasing anneal temperature and by nearly two orders of magnitude after 950 °C annealing. These low-defect annealed BSTmore » films exhibited high quality microwave properties, including room temperature interdigitated capacitor tunability of 13% under an electric bias of 40 V and tan δ of 0.002 at 10 GHz and 40 V bias. The results provide a feasible route to grow high quality BST films by MBE through post-air annealing guided by DRCLS.« less

A Comparison Between Heliosat-2 and Artificial Neural Network Methods for Global Horizontal Irradiance Retrievals over Desert Environments

NASA Astrophysics Data System (ADS)

Ghedira, H.; Eissa, Y.

2012-12-01

Global horizontal irradiance (GHI) retrievals at the surface of any given location could be used for preliminary solar resource assessments. More accurately, the direct normal irradiance (DNI) and diffuse horizontal irradiance (DHI) are also required to estimate the global tilt irradiance, mainly used for fixed flat plate collectors. Two different satellite-based models for solar irradiance retrievals have been applied over the desert environment of the United Arab Emirates (UAE). Both models employ channels of the SEVIRI instrument, onboard the geostationary satellite Meteosat Second Generation, as their main inputs. The satellite images used in this study have a temporal resolution of 15-min and a spatial resolution of 3-km. The objective of this study is to compare between the GHI retrieved using the Heliosat-2 method and an artificial neural network (ANN) ensemble method over the UAE. The high-resolution visible channel of SEVIRI is used in the Heliosat-2 method to derive the cloud index. The cloud index is then used to compute the cloud transmission, while the cloud-free GHI is computed from the Linke turbidity factor. The product of the cloud transmission and the cloud-free GHI denotes the estimated GHI. A constant underestimation is observed in the estimated GHI over the dataset available in the UAE. Therefore, the cloud-free DHI equation in the model was recalibrated to fix the bias. After recalibration, results over the UAE show a root mean square error (RMSE) value of 10.1% and a mean bias error (MBE) of -0.5%. As for the ANN approach, six thermal channels of SEVIRI were used to estimate the DHI and the total optical depth of the atmosphere (δ). An ensemble approach is employed to obtain a better generalizability of the results, as opposed to using one single weak network. The DNI is then computed from the estimated δ using the Beer-Bouguer-Lambert law. The GHI is computed from the DNI and DHI estimates. The RMSE for the estimated GHI obtained over an independent dataset over the UAE is 7.2% and the MBE is +1.9%. The results obtained by the two methods have shown that both the recalibrated Heliosat-2 and the ANN ensemble methods estimate the GHI at a 15-min resolution with high accuracy. The advantage of the ANN ensemble approach is that it derives the GHI from accurate DNI and DHI estimates. The DNI and DHI estimates are valuable when computing the global tilt irradiance. Also, accurate DNI estimates are beneficial for preliminary site selection for concentrating solar powered plants.

Generation of common coefficients to estimate global solar radiation over different locations of India

NASA Astrophysics Data System (ADS)

Samanta, Suman; Patra, Pulak Kumar; Banerjee, Saon; Narsimhaiah, Lakshmi; Sarath Chandran, M. A.; Vijaya Kumar, P.; Bandyopadhyay, Sanjib

2018-06-01

In developing countries like India, global solar radiation (GSR) is measured at very few locations due to non-availability of radiation measuring instruments. To overcome the inadequacy of GSR measurements, scientists developed many empirical models to estimate location-wise GSR. In the present study, three simple forms of Angstrom equation [Angstrom-Prescott (A-P), Ogelman, and Bahel] were used to estimate GSR at six geographically and climatologically different locations across India with an objective to find out a set of common constants usable for whole country. Results showed that GSR values varied from 9.86 to 24.85 MJ m-2 day-1 for different stations. It was also observed that A-P model showed smaller errors than Ogelman and Bahel models. All the models well estimated GSR, as the 1:1 line between measured and estimated values showed Nash-Sutcliffe efficiency (NSE) values ≥ 0.81 for all locations. Measured data of GSR pooled over six selected locations was analyzed to obtain a new set of constants for A-P equation which can be applicable throughout the country. The set of constants (a = 0.29 and b = 0.40) was named as "One India One Constant (OIOC)," and the model was named as "MOIOC." Furthermore, the developed constants are validated statistically for another six locations of India and produce close estimation. High R 2 values (≥ 76%) along with low mean bias error (MBE) ranging from - 0.64 to 0.05 MJ m-2 day-1 revealed that the new constants are able to predict GSR with lesser percentage of error.

MBE Regrowth of a Laterally-biased Double Quantum Well Tunable Detector

DTIC Science & Technology

2010-06-01

with 9 sccm of Ar, 9 sccm of  SiCl4  and with a power of 107 W. With these parameters, DC Bias  of 340 V was obtained and the pressure during the etching...regrowth of a laterally‐biased double quantum well tunable detector– Final Report  2010  29    The etching can be performed using only  SiCl4 , but by...following AFM images show GaAs surfaces after an etching of 500nm:                125 W, 1,5 sccm Ar, 15 sccm  SiCl4   MBE regrowth of a laterally‐biased

Ideal Channel Field Effect Transistors

DTIC Science & Technology

2010-03-01

well as on /?-GaAs/w-GaAs homojunctions grown by molecular beam epitaxy (MBE). The diode I-Vs at reverse bias are plotted below. The measured breakdown...transistors and composite channel InAlAs/InGaAs/lnP/InAlAs high electron mobility transistors ( HEMTs ), which have taken the full advantage of the matched...result in a large number of dislocations in GaAs films epitaxially grown on wurtzite GaN. In this work, we have successfully integrated GaAs with GaN

Thalassospira alkalitolerans sp. nov. and Thalassospira mesophila sp. nov., isolated from a decaying bamboo sunken in the marine environment, and emended description of the genus Thalassospira.

PubMed

Tsubouchi, Taishi; Ohta, Yukari; Haga, Takuma; Usui, Keiko; Shimane, Yasuhiro; Mori, Kozue; Tanizaki, Akiko; Adachi, Akiko; Kobayashi, Kiwa; Yukawa, Kiyotaka; Takagi, Emiko; Tame, Akihiro; Uematsu, Katsuyuki; Maruyama, Tadashi; Hatada, Yuji

2014-01-01

Two marine bacteria, designated strains MBE#61(T) and MBE#74(T), were isolated from a piece of sunken bamboo in the marine environment in Japan. Both of these strains were Gram-stain-negative, but had different cell shapes: MBE#61(T) was spiral, whereas MBE#74(T) was rod-shaped. The temperature, pH and salt concentration ranges for growth of strain MBE#61(T) were 4-38 °C (optimal at 32 °C), pH 4.5-11.0 (optimal at pH 7.0-8.0) and 1-11 % (optimal at 2 %) NaCl, whereas those of strain MBE#74(T) were 4-36 °C (optimal at 30 °C), pH 4.0-10.5 (optimal at pH 7.0-8.0) and 1-12 % (optimal at 4 %) NaCl. Phylogenetic analysis based on partial 16S rRNA gene sequences revealed that both strains belong to the genus Thalassospira within the class Alphaproteobacteria. Similarity between the 16S rRNA gene sequence of strain MBE#61(T) and those of the type strains of species of the genus Thalassospira was 97.5-99.0 %, and that of strain MBE#74(T) was 96.9-98.6 %; these two isolates were most closely related to Thalassospira lucentensis QMT2(T). However, the DNA-DNA hybridization values between T. lucentensis QMT2(T) and strain MBE#61(T) or MBE#74(T) were only 16.0 % and 7.1 %, respectively. The DNA G+C content of strain MBE#61(T) was 54.4 mol%, and that of strain MBE#74(T) was 55.9 mol%. The predominant isoprenoid quinone of the two strains was Q-10 (MBE#61(T), 97.3 %; MBE#74(T), 93.5 %). The major cellular fatty acids of strain MBE#61(T) were C18 : 1ω7c (31.1 %), summed feature 3 comprising C16 : 0ω7c/iso-C15 : 0 2-OH (26.1 %) and C16 : 0 (20.9 %); those of strain MBE#74(T) were C16 : 0 (26.2 %), C17 : 0 cyclo (19.9 %) and C18 : 1ω7c (12.1 %). On the basis of these results, strain MBE#61(T) and strain MBE#74(T) are considered to represent novel species of the genus Thalassospira, for which names Thalassospira alkalitolerans sp. nov. and Thalassospira mesophila sp. nov. are proposed. The type strains are MBE#61(T) ( = JCM 18968(T) = CECT 8273(T)) and MBE#74(T) ( = JCM 18969(T) = CECT 8274(T)), respectively. An emended description of the genus Thalassospira is also proposed.

Effect of different stages of tensile deformation on micromagnetic parameters in high-strength, low-alloy steel

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

Vaidyanathan, S.; Moorthy, V.; Kalyanasundaram, P.

The influence of tensile deformation on the magnetic Barkhausen emissions (MBE) and hysteresis loop has been studied in a high-strength, low-alloy steel (HSLA) and its weldment. The magnetic measurements were made both in loaded and unloaded conditions for different stress levels. The root-mean-square (RMS) voltage of the MBE has been used for analysis. This study shows that the preyield and postyield deformation can be identified from the change in the MBE profile. The initial elastic deformation showed a linear increase in the MBE level in the loaded condition, and the MBE level remained constant in the unloaded condition. The microplasticmore » yielding, well below the macroyield stress, significantly reduces the MBE, indicating the operation of grain-boundary dislocation sources below the macroyield stress. This is indicated by the slow increase in the MBE level in the loaded condition and the decrease in the MBE level in the unloaded condition. The macroyielding resulted in a significant increase in the MBE level in the loaded condition and, more clearly, in the unloaded condition. The increase in the MBE level during macroyielding has been attributed to the grain rotation phenomenon, in order to maintain the boundary integrity between adjacent grains, which would preferentially align the magnetic domains along the stress direction. This study shows that MBE during tensile deformation can be classified into four stages: (1) perfectly elastic, (2) microplastic yielding, (3) macroyielding, and (4) progressive plastic deformation. A multimagnetic parameter approach, combining the hysteresis loop and MBE, has been suggested to evaluate the residual stresses.« less

Errors in causal inference: an organizational schema for systematic error and random error.

PubMed

Suzuki, Etsuji; Tsuda, Toshihide; Mitsuhashi, Toshiharu; Mansournia, Mohammad Ali; Yamamoto, Eiji

2016-11-01

To provide an organizational schema for systematic error and random error in estimating causal measures, aimed at clarifying the concept of errors from the perspective of causal inference. We propose to divide systematic error into structural error and analytic error. With regard to random error, our schema shows its four major sources: nondeterministic counterfactuals, sampling variability, a mechanism that generates exposure events and measurement variability. Structural error is defined from the perspective of counterfactual reasoning and divided into nonexchangeability bias (which comprises confounding bias and selection bias) and measurement bias. Directed acyclic graphs are useful to illustrate this kind of error. Nonexchangeability bias implies a lack of "exchangeability" between the selected exposed and unexposed groups. A lack of exchangeability is not a primary concern of measurement bias, justifying its separation from confounding bias and selection bias. Many forms of analytic errors result from the small-sample properties of the estimator used and vanish asymptotically. Analytic error also results from wrong (misspecified) statistical models and inappropriate statistical methods. Our organizational schema is helpful for understanding the relationship between systematic error and random error from a previously less investigated aspect, enabling us to better understand the relationship between accuracy, validity, and precision. Copyright © 2016 Elsevier Inc. All rights reserved.

MBE Growth of Ferromagnetic Metal/Compound Semiconductor Heterostructures for Spintronics

ScienceCinema

Palmstrom, Chris [University of California, Santa Barbara, California, United States

2017-12-09

Electrical transport and spin-dependent transport across ferromagnet/semiconductor contacts is crucial in the realization of spintronic devices. Interfacial reactions, the formation of non-magnetic interlayers, and conductivity mismatch have been attributed to low spin injection efficiency. MBE has been used to grow epitaxial ferromagnetic metal/GA(1-x)AL(x)As heterostructures with the aim of controlling the interfacial structural, electronic, and magnetic properties. In situ, STM, XPS, RHEED and LEED, and ex situ XRD, RBS, TEM, magnetotransport, and magnetic characterization have been used to develop ferromagnetic elemental and metallic compound/compound semiconductor tunneling contacts for spin injection. The efficiency of the spin polarized current injected from the ferromagnetic contact has been determined by measuring the electroluminescence polarization of the light emitted from/GA(1-x)AL(x)As light-emitting diodes as a function of applied magnetic field and temperature. Interfacial reactions during MBE growth and post-growth anneal, as well as the semiconductor device band structure, were found to have a dramatic influence on the measured spin injection, including sign reversal. Lateral spin-transport devices with epitaxial ferromagnetic metal source and drain tunnel barrier contacts have been fabricated with the demonstration of electrical detection and the bias dependence of spin-polarized electron injection and accumulation at the contacts. This talk emphasizes the progress and achievements in the epitaxial growth of a number of ferromagnetic compounds/III-V semiconductor heterostructures and the progress towards spintronic devices.

Growth of BaSi2 continuous films on Ge(111) by molecular beam epitaxy and fabrication of p-BaSi2/n-Ge heterojunction solar cells

NASA Astrophysics Data System (ADS)

Takabe, Ryota; Yachi, Suguru; Tsukahara, Daichi; Toko, Kaoru; Suemasu, Takashi

2017-05-01

We grew BaSi2 films on Ge(111) substrates by various growth methods based on molecular beam epitaxy (MBE). First, we attempted to form BaSi2 films directly on Ge(111) by MBE without templates. We next formed BaSi2 films using BaGe2 templates as commonly used for MBE growth of BaSi2 on Si substrates. Contrary to our prediction, the lateral growth of BaSi2 was not promoted by these two methods; BaSi2 formed not into a continuous film but into islands. Although streaky patterns of reflection high-energy electron diffraction were observed inside the growth chamber, no X-ray diffraction lines of BaSi2 were observed in samples taken out from the growth chamber. Such BaSi2 islands were easily to get oxidized. We finally attempted to form a continuous BaSi2 template layer on Ge(111) by solid phase epitaxy, that is, the deposition of amorphous Ba-Si layers onto MBE-grown BaSi2 epitaxial islands, followed by post annealing. We achieved the formation of an approximately 5-nm-thick BaSi2 continuous layer by this method. Using this BaSi2 layer as a template, we succeeded in forming a-axis-oriented 520-nm-thick BaSi2 epitaxial films on Ge substrates, although (111)-oriented Si grains were included in the grown layer. We next formed a B-doped p-BaSi2(20 nm)/n-Ge(111) heterojunction solar cell. A wide-spectrum response from 400 to 2000 nm was achieved. At an external bias voltage of 1 V, the external quantum efficiency reached as high as 60%, demonstrating the great potential of BaSi2/Ge combination. However, the efficiency of a solar cell under AM1.5 illumination was quite low (0.1%). The origin of such a low efficiency was examined.

Hypothesis Testing Using Factor Score Regression

PubMed Central

Devlieger, Ines; Mayer, Axel; Rosseel, Yves

2015-01-01

In this article, an overview is given of four methods to perform factor score regression (FSR), namely regression FSR, Bartlett FSR, the bias avoiding method of Skrondal and Laake, and the bias correcting method of Croon. The bias correcting method is extended to include a reliable standard error. The four methods are compared with each other and with structural equation modeling (SEM) by using analytic calculations and two Monte Carlo simulation studies to examine their finite sample characteristics. Several performance criteria are used, such as the bias using the unstandardized and standardized parameterization, efficiency, mean square error, standard error bias, type I error rate, and power. The results show that the bias correcting method, with the newly developed standard error, is the only suitable alternative for SEM. While it has a higher standard error bias than SEM, it has a comparable bias, efficiency, mean square error, power, and type I error rate. PMID:29795886

Establishing the need and identifying goals for a curriculum in medical business ethics: a survey of students and residents at two medical centers in Missouri.

PubMed

Kraus, Elena M; Bakanas, Erin; Gursahani, Kamal; DuBois, James M

2014-10-09

In recent years, issues in medical business ethics (MBE), such as conflicts of interest (COI), Medicare fraud and abuse, and the structure and functioning of reimbursement systems, have received significant attention from the media and professional associations in the United States. As a result of highly publicized instances of financial interests altering physician decision-making, major professional organizations and government bodies have produced reports and guidelines to encourage self-regulation and impose rules to limit physician relationships with for-profit entities. Nevertheless, no published curricula exist in the area of MBE. This study aimed to establish a baseline level of knowledge and the educational goals medical students and residents prioritize in the area of MBE. 732 medical students and 380 residents at two academic medical centers in the state of Missouri, USA, completed a brief survey indicating their awareness of major MBE guidance documents, knowledge of key MBE research, beliefs about the goals of an education in MBE, and the areas of MBE they were most interested in learning more about. Medical students and residents had little awareness of recent and major reports on MBE topics, and had minimal knowledge of basic MBE facts. Residents scored statistically better than medical students in both of these areas. Medical students and residents were in close agreement regarding the goals of an MBE curriculum. Both groups showed significant interest in learning more about MBE topics with an emphasis on background topics such as "the business aspects of medicine" and "health care delivery systems". The content of major reports by professional associations and expert bodies has not trickled down to medical students and residents, yet both groups are interested in learning more about MBE topics. Our survey suggests potentially beneficial ways to frame and embed MBE topics into the larger framework of medical education.

Bias in the Wagner-Nelson estimate of the fraction of drug absorbed.

PubMed

Wang, Yibin; Nedelman, Jerry

2002-04-01

To examine and quantify bias in the Wagner-Nelson estimate of the fraction of drug absorbed resulting from the estimation error of the elimination rate constant (k), measurement error of the drug concentration, and the truncation error in the area under the curve. Bias in the Wagner-Nelson estimate was derived as a function of post-dosing time (t), k, ratio of absorption rate constant to k (r), and the coefficient of variation for estimates of k (CVk), or CV% for the observed concentration, by assuming a one-compartment model and using an independent estimate of k. The derived functions were used for evaluating the bias with r = 0.5, 3, or 6; k = 0.1 or 0.2; CV, = 0.2 or 0.4; and CV, =0.2 or 0.4; for t = 0 to 30 or 60. Estimation error of k resulted in an upward bias in the Wagner-Nelson estimate that could lead to the estimate of the fraction absorbed being greater than unity. The bias resulting from the estimation error of k inflates the fraction of absorption vs. time profiles mainly in the early post-dosing period. The magnitude of the bias in the Wagner-Nelson estimate resulting from estimation error of k was mainly determined by CV,. The bias in the Wagner-Nelson estimate resulting from to estimation error in k can be dramatically reduced by use of the mean of several independent estimates of k, as in studies for development of an in vivo-in vitro correlation. The truncation error in the area under the curve can introduce a negative bias in the Wagner-Nelson estimate. This can partially offset the bias resulting from estimation error of k in the early post-dosing period. Measurement error of concentration does not introduce bias in the Wagner-Nelson estimate. Estimation error of k results in an upward bias in the Wagner-Nelson estimate, mainly in the early drug absorption phase. The truncation error in AUC can result in a downward bias, which may partially offset the upward bias due to estimation error of k in the early absorption phase. Measurement error of concentration does not introduce bias. The joint effect of estimation error of k and truncation error in AUC can result in a non-monotonic fraction-of-drug-absorbed-vs-time profile. However, only estimation error of k can lead to the Wagner-Nelson estimate of fraction of drug absorbed greater than unity.

Thermoelectric properties of epitaxial β-FeSi2 thin films grown on Si(111) substrates with various film qualities

NASA Astrophysics Data System (ADS)

Watanabe, Kentaro; Taniguchi, Tatsuhiko; Sakane, Shunya; Aoki, Shunsuke; Suzuki, Takeyuki; Fujita, Takeshi; Nakamura, Yoshiaki

2017-05-01

Si-based epitaxial β-FeSi2 thin films are attractive as materials for on-chip thermoelectric power generators. We investigated the structure, crystallinity, and thermoelectric properties of β-FeSi2 thin films epitaxially grown on Si(111) substrates by using three different techniques: conventional reactive deposition epitaxy followed by molecular beam epitaxy (RDE+MBE), solid phase epitaxy (SPE) based on codeposition of Fe and Si presented previously, and SPE followed by MBE (SPE+MBE) presented newly by this work. Their epitaxial growth temperatures were fixed at 530 °C for comparison. RDE+MBE thin films exhibited high crystalline quality, but rough surfaces and rugged β-FeSi2/Si(111) interfaces. On the other hand, SPE thin films showed flat surfaces and abrupt β-FeSi2/Si(111) interfaces but low crystallinity. We found that SPE+MBE thin films realized crystallinity higher than SPE thin films, and also had flatter surfaces and sharper interfaces than RDE+MBE thin films. In SPE+MBE thin film growth, due to the initial SPE process with low temperature codeposition, thermal interdiffusion of Fe and Si was suppressed, resulting in the surface flatness and abrupt interface. Second high temperature MBE process improved the crystallinity. We also investigated thermoelectric properties of these β-FeSi2 thin films. Structural factors affecting the thermoelectric properties of RDE+MBE, SPE, and SPE+MBE thin films were investigated.

40 CFR 33.503 - How does a recipient calculate MBE and WBE participation for reporting purposes?

Code of Federal Regulations, 2011 CFR

2011-07-01

... attributable to the MBE or WBE. If an MBE's or WBE's risk of loss, control or management responsibilities is... ENTERPRISES IN UNITED STATES ENVIRONMENTAL PROTECTION AGENCY PROGRAMS Recordkeeping and Reporting § 33.503 How... performing a commercially useful function: (1) The MBE or WBE must be responsible for the management and...

40 CFR 33.503 - How does a recipient calculate MBE and WBE participation for reporting purposes?

Code of Federal Regulations, 2010 CFR

2010-07-01

... attributable to the MBE or WBE. If an MBE's or WBE's risk of loss, control or management responsibilities is... ENTERPRISES IN UNITED STATES ENVIRONMENTAL PROTECTION AGENCY PROGRAMS Recordkeeping and Reporting § 33.503 How... performing a commercially useful function: (1) The MBE or WBE must be responsible for the management and...

Do explicit memory manipulations affect the memory blocking effect?

PubMed

Landau, Joshua D; Leynes, P Andrew

2006-01-01

The memory blocking effect (MBE) occurs when people are prevented from completing word fragments because they studied orthographically similar words. Across 3 experiments, we investigated how manipulations that influence explicit memory tasks would influence the MBE. Although a significant MBE was observed in all 3 experiments, manipulating depth of processing (Experiment 1), time to complete the fragments (Experiment 2), and awareness of the MBE (Experiment 3) did not change the magnitude of the MBE. We discuss these results in the context of a suppression mechanism involved in retrieval-induced forgetting.

A procedure for removing the effect of response bias errors from waterfowl hunter questionnaire responses

USGS Publications Warehouse

Atwood, E.L.

1958-01-01

Response bias errors are studied by comparing questionnaire responses from waterfowl hunters using four large public hunting areas with actual hunting data from these areas during two hunting seasons. To the extent that the data permit, the sources of the error in the responses were studied and the contribution of each type to the total error was measured. Response bias errors, including both prestige and memory bias, were found to be very large as compared to non-response and sampling errors. Good fits were obtained with the seasonal kill distribution of the actual hunting data and the negative binomial distribution and a good fit was obtained with the distribution of total season hunting activity and the semi-logarithmic curve. A comparison of the actual seasonal distributions with the questionnaire response distributions revealed that the prestige and memory bias errors are both positive. The comparisons also revealed the tendency for memory bias errors to occur at digit frequencies divisible by five and for prestige bias errors to occur at frequencies which are multiples of the legal daily bag limit. A graphical adjustment of the response distributions was carried out by developing a smooth curve from those frequency classes not included in the predictable biased frequency classes referred to above. Group averages were used in constructing the curve, as suggested by Ezekiel [1950]. The efficiency of the technique described for reducing response bias errors in hunter questionnaire responses on seasonal waterfowl kill is high in large samples. The graphical method is not as efficient in removing response bias errors in hunter questionnaire responses on seasonal hunting activity where an average of 60 percent was removed.

On the Limitations of Variational Bias Correction

NASA Technical Reports Server (NTRS)

Moradi, Isaac; Mccarty, Will; Gelaro, Ronald

2018-01-01

Satellite radiances are the largest dataset assimilated into Numerical Weather Prediction (NWP) models, however the data are subject to errors and uncertainties that need to be accounted for before assimilating into the NWP models. Variational bias correction uses the time series of observation minus background to estimate the observations bias. This technique does not distinguish between the background error, forward operator error, and observations error so that all these errors are summed up together and counted as observation error. We identify some sources of observations errors (e.g., antenna emissivity, non-linearity in the calibration, and antenna pattern) and show the limitations of variational bias corrections on estimating these errors.

Semi-empirical model for retrieval of soil moisture using RISAT-1 C-Band SAR data over a sub-tropical semi-arid area of Rewari district, Haryana (India)

NASA Astrophysics Data System (ADS)

Rawat, Kishan Singh; Sehgal, Vinay Kumar; Pradhan, Sanatan; Ray, Shibendu S.

2018-03-01

We have estimated soil moisture (SM) by using circular horizontal polarization backscattering coefficient (σ o_{RH}), differences of circular vertical and horizontal σ o (σ o_{RV} {-} σ o_{RH}) from FRS-1 data of Radar Imaging Satellite (RISAT-1) and surface roughness in terms of RMS height ({RMS}_{height}). We examined the performance of FRS-1 in retrieving SM under wheat crop at tillering stage. Results revealed that it is possible to develop a good semi-empirical model (SEM) to estimate SM of the upper soil layer using RISAT-1 SAR data rather than using existing empirical model based on only single parameter, i.e., σ o. Near surface SM measurements were related to σ o_{RH}, σ o_{RV} {-} σ o_{RH} derived using 5.35 GHz (C-band) image of RISAT-1 and {RMS}_{height}. The roughness component derived in terms of {RMS}_{height} showed a good positive correlation with σ o_{RV} {-} σ o_{RH} (R2 = 0.65). By considering all the major influencing factors (σ o_{RH}, σ o_{RV} {-} σ o_{RH}, and {RMS}_{height}), an SEM was developed where SM (volumetric) predicted values depend on σ o_{RH}, σ o_{RV} {-} σ o_{RH}, and {RMS}_{height}. This SEM showed R2 of 0.87 and adjusted R2 of 0.85, multiple R=0.94 and with standard error of 0.05 at 95% confidence level. Validation of the SM derived from semi-empirical model with observed measurement ({SM}_{Observed}) showed root mean square error (RMSE) = 0.06, relative-RMSE (R-RMSE) = 0.18, mean absolute error (MAE) = 0.04, normalized RMSE (NRMSE) = 0.17, Nash-Sutcliffe efficiency (NSE) = 0.91 ({≈ } 1), index of agreement (d) = 1, coefficient of determination (R2) = 0.87, mean bias error (MBE) = 0.04, standard error of estimate (SEE) = 0.10, volume error (VE) = 0.15, variance of the distribution of differences ({S}d2) = 0.004. The developed SEM showed better performance in estimating SM than Topp empirical model which is based only on σ o. By using the developed SEM, top soil SM can be estimated with low mean absolute percent error (MAPE) = 1.39 and can be used for operational applications.

Sun compass error model

NASA Technical Reports Server (NTRS)

Blucker, T. J.; Ferry, W. W.

1971-01-01

An error model is described for the Apollo 15 sun compass, a contingency navigational device. Field test data are presented along with significant results of the test. The errors reported include a random error resulting from tilt in leveling the sun compass, a random error because of observer sighting inaccuracies, a bias error because of mean tilt in compass leveling, a bias error in the sun compass itself, and a bias error because the device is leveled to the local terrain slope.

Design and fabrication of low power GaAs/AlAs resonant tunneling diodes

NASA Astrophysics Data System (ADS)

Md Zawawi, Mohamad Adzhar; Missous, Mohamed

2017-12-01

A very low peak voltage GaAs/AlAs resonant tunneling diode (RTD) grown by molecular beam epitaxy (MBE) has been studied in detail. Excellent growth control with atomic-layer precision resulted in a peak voltage of merely 0.28 V (0.53 V) in forward (reverse) direction. The peak current density in forward bias is around 15.4 kA/cm2 with variation of within 7%. As for reverse bias, the peak current density is around 22.8 kA/cm2 with 4% variation which implies excellent scalability. In this work, we have successfully demonstrated the fabrication of a GaAs/AlAs RTD by using a conventional optical lithography and chemical wet-etching with very low peak voltage suitable for application in low dc input power RTD-based sub-millimetre wave oscillators.

Comparison of AlGaAs Oxidation in MBE and MOCVD Grown Samples

DTIC Science & Technology

2002-01-01

vertical cavity surface emitting lasers ( VCSELs ) [1, 2, 3]. They are also being... molecular beam epitaxy ( MBE ) [5, 6] or metal organic chemical vapor deposition (MOCVD) [7, 8]. The MBE -grown A1GaAs layers are sometimes pseudo or digital...Simultaneous wet-thermal oxidation of MBE and MOCVD grown AlxGal_xAs layers (x = 0.1 to 1.0) showed that the epitaxial growth method does not

Complete Systematic Error Model of SSR for Sensor Registration in ATC Surveillance Networks

PubMed Central

Besada, Juan A.

2017-01-01

In this paper, a complete and rigorous mathematical model for secondary surveillance radar systematic errors (biases) is developed. The model takes into account the physical effects systematically affecting the measurement processes. The azimuth biases are calculated from the physical error of the antenna calibration and the errors of the angle determination dispositive. Distance bias is calculated from the delay of the signal produced by the refractivity index of the atmosphere, and from clock errors, while the altitude bias is calculated taking into account the atmosphere conditions (pressure and temperature). It will be shown, using simulated and real data, that adapting a classical bias estimation process to use the complete parametrized model results in improved accuracy in the bias estimation. PMID:28934157

Millimeterwave and digital applications of InP-based MBE grown HEMTs and HBTs

NASA Astrophysics Data System (ADS)

Greiling, Paul

1997-05-01

Microwave and millimeterwave devices grown by MBE have significantly advanced the state of the art for RF device performance with respect to noise figure, power output, power added efficiency and extended the clock frequency of digital circuits into the millimeterwave regime. Ober the last 10-15 years, military systems have greatly benefited from the superior performance of MBE grown devices. In order to have a similar impact on the commercial marketplace, MBE growers will have to focus their efforts on a different set of performance criteria; i.e. cost, uniformity and reproducibility. This paper discusses outstanding performance achieved by MBE grown devices and outlines the criteria for commercial applications.

Climatic warming in China during 1901–2015 based on an extended dataset of instrumental temperature records

DOE PAGES

Cao, Lijuan; Yan, Zhongwei; Zhao, Ping; ...

2017-05-26

Monthly mean instrumental surface air temperature (SAT) observations back to the nineteenth century in China are synthesized from different sources via specific quality-control, interpolation, and homogenization. Compared with the first homogenized long-term SAT dataset for China which contained 18 stations mainly located in the middle and eastern part of China, the present dataset includes homogenized monthly SAT series at 32 stations, with an extended coverage especially towards western China. Missing values are interpolated by using observations at nearby stations, including those from neighboring countries. Cross validation shows that the mean bias error (MBE) is generally small and falls between 0.45more » °C and –0.35 °C. Multiple homogenization methods and available metadata are applied to assess the consistency of the time series and to adjust inhomogeneity biases. The homogenized annual mean SAT series shows a range of trends between 1.1 °C and 4.0 °C/century in northeastern China, between 0.4 °C and 1.9 °C/century in southeastern China, and between 1.4 °C and 3.7 °C/century in western China to the west of 105 E (from the initial years of the stations to 2015). The unadjusted data include unusually warm records during the 1940s and hence tend to underestimate the warming trends at a number of stations. As a result, the mean SAT series for China based on the climate anomaly method shows a warming trend of 1.56 °C/century during 1901–2015, larger than those based on other currently available datasets.« less

Climatic warming in China during 1901–2015 based on an extended dataset of instrumental temperature records

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

Cao, Lijuan; Yan, Zhongwei; Zhao, Ping

Monthly mean instrumental surface air temperature (SAT) observations back to the nineteenth century in China are synthesized from different sources via specific quality-control, interpolation, and homogenization. Compared with the first homogenized long-term SAT dataset for China which contained 18 stations mainly located in the middle and eastern part of China, the present dataset includes homogenized monthly SAT series at 32 stations, with an extended coverage especially towards western China. Missing values are interpolated by using observations at nearby stations, including those from neighboring countries. Cross validation shows that the mean bias error (MBE) is generally small and falls between 0.45more » °C and –0.35 °C. Multiple homogenization methods and available metadata are applied to assess the consistency of the time series and to adjust inhomogeneity biases. The homogenized annual mean SAT series shows a range of trends between 1.1 °C and 4.0 °C/century in northeastern China, between 0.4 °C and 1.9 °C/century in southeastern China, and between 1.4 °C and 3.7 °C/century in western China to the west of 105 E (from the initial years of the stations to 2015). The unadjusted data include unusually warm records during the 1940s and hence tend to underestimate the warming trends at a number of stations. As a result, the mean SAT series for China based on the climate anomaly method shows a warming trend of 1.56 °C/century during 1901–2015, larger than those based on other currently available datasets.« less

LncRNA-uc002mbe.2 Interacting with hnRNPA2B1 Mediates AKT Deactivation and p21 Up-Regulation Induced by Trichostatin in Liver Cancer Cells.

PubMed

Chen, Ting; Gu, Chengxin; Xue, Cailin; Yang, Tao; Zhong, Yun; Liu, Shiming; Nie, Yuqiang; Yang, Hui

2017-01-01

Long non-coding RNAs (lncRNAs) have been implicated in liver carcinogenesis. We previously showed that the induction of lncRNA-uc002mbe.2 is positively associated with the apoptotic effect of trichostatin A (TSA) in hepatocellular carcinoma (HCC) cells. The current study further analyzed the role of uc002mbe.2 in TSA-induced liver cancer cell death. The level of uc002mbe.2 was markedly increased by TSA in the cytoplasm of HCC cells. Knockdown of uc002mbe.2 prohibited TSA-induced G2/M cell cycle arrest, p21 induction, and apoptosis of Huh7 cells and reversed the TSA-mediated decrease in p-AKT. RNA pull-down and RNA-binding protein immunoprecipitation (RIP) assays revealed that TSA induced an interaction between uc002mbe.2 and heterogeneous nuclear ribonucleoprotein A2B1 (hnRNPA2B1) in Huh7 cells. This interaction mediated AKT deactivation and p21 induction in liver cancer cells. In an athymic xenograft mouse model, knockdown of uc002mbe.2 significantly prohibited the TSA-mediated reduction in tumor size and weight. In addition, the ability of TSA to reduce hnRNPA2B1 and p-AKT levels and induce p21 in the xenograft tumors was prevented by uc002mbe.2 knockdown. Therefore, the interaction of uc002mbe.2 and hnRNPA2B1 in mediating AKT deactivation and p21 induction is involved in the cytostatic effect of trichostatin in liver cancer cells.

Technical note: Intercomparison of three AATSR Level 2 (L2) AOD products over China

NASA Astrophysics Data System (ADS)

Che, Yahui; Xue, Yong; Mei, Linlu; Guang, Jie; She, Lu; Guo, Jianping; Hu, Yincui; Xu, Hui; He, Xingwei; Di, Aojie; Fan, Cheng

2016-08-01

One of four main focus areas of the PEEX initiative is to establish and sustain long-term, continuous, and comprehensive ground-based, airborne, and seaborne observation infrastructure together with satellite data. The Advanced Along-Track Scanning Radiometer (AATSR) aboard ENVISAT is used to observe the Earth in dual view. The AATSR data can be used to retrieve aerosol optical depth (AOD) over both land and ocean, which is an important parameter in the characterization of aerosol properties. In recent years, aerosol retrieval algorithms have been developed both over land and ocean, taking advantage of the features of dual view, which can help eliminate the contribution of Earth's surface to top-of-atmosphere (TOA) reflectance. The Aerosol_cci project, as a part of the Climate Change Initiative (CCI), provides users with three AOD retrieval algorithms for AATSR data, including the Swansea algorithm (SU), the ATSR-2ATSR dual-view aerosol retrieval algorithm (ADV), and the Oxford-RAL Retrieval of Aerosol and Cloud algorithm (ORAC). The validation team of the Aerosol-CCI project has validated AOD (both Level 2 and Level 3 products) and AE (Ångström Exponent) (Level 2 product only) against the AERONET data in a round-robin evaluation using the validation tool of the AeroCOM (Aerosol Comparison between Observations and Models) project. For the purpose of evaluating different performances of these three algorithms in calculating AODs over mainland China, we introduce ground-based data from CARSNET (China Aerosol Remote Sensing Network), which was designed for aerosol observations in China. Because China is vast in territory and has great differences in terms of land surfaces, the combination of the AERONET and CARSNET data can validate the L2 AOD products more comprehensively. The validation results show different performances of these products in 2007, 2008, and 2010. The SU algorithm performs very well over sites with different surface conditions in mainland China from March to October, but it slightly underestimates AOD over barren or sparsely vegetated surfaces in western China, with mean bias error (MBE) ranging from 0.05 to 0.10. The ADV product has the same precision with a low root mean square error (RMSE) smaller than 0.2 over most sites and the same error distribution as the SU product. The main limits of the ADV algorithm are underestimation and applicability; underestimation is particularly obvious over the sites of Datong, Lanzhou, and Urumchi, where the dominant land cover is grassland, with an MBE larger than 0.2, and the main aerosol sources are coal combustion and dust. The ORAC algorithm has the ability to retrieve AOD at different ranges, including high AOD (larger than 1.0); however, the stability deceases significantly with increasing AOD, especially when AOD > 1.0. In addition, the ORAC product is consistent with the CARSNET product in winter (December, January, and February), whereas other validation results lack matches during winter.

The Causes of Errors in Clinical Reasoning: Cognitive Biases, Knowledge Deficits, and Dual Process Thinking.

PubMed

Norman, Geoffrey R; Monteiro, Sandra D; Sherbino, Jonathan; Ilgen, Jonathan S; Schmidt, Henk G; Mamede, Silvia

2017-01-01

Contemporary theories of clinical reasoning espouse a dual processing model, which consists of a rapid, intuitive component (Type 1) and a slower, logical and analytical component (Type 2). Although the general consensus is that this dual processing model is a valid representation of clinical reasoning, the causes of diagnostic errors remain unclear. Cognitive theories about human memory propose that such errors may arise from both Type 1 and Type 2 reasoning. Errors in Type 1 reasoning may be a consequence of the associative nature of memory, which can lead to cognitive biases. However, the literature indicates that, with increasing expertise (and knowledge), the likelihood of errors decreases. Errors in Type 2 reasoning may result from the limited capacity of working memory, which constrains computational processes. In this article, the authors review the medical literature to answer two substantial questions that arise from this work: (1) To what extent do diagnostic errors originate in Type 1 (intuitive) processes versus in Type 2 (analytical) processes? (2) To what extent are errors a consequence of cognitive biases versus a consequence of knowledge deficits?The literature suggests that both Type 1 and Type 2 processes contribute to errors. Although it is possible to experimentally induce cognitive biases, particularly availability bias, the extent to which these biases actually contribute to diagnostic errors is not well established. Educational strategies directed at the recognition of biases are ineffective in reducing errors; conversely, strategies focused on the reorganization of knowledge to reduce errors have small but consistent benefits.

MBE development of dilute nitrides for commercial long-wavelength laser applications

NASA Astrophysics Data System (ADS)

Malis, O.; Liu, W. K.; Gmachl, C.; Fastenau, J. M.; Joel, A.; Gong, P.; Bland, S. W.; Moshegov, N.

2003-04-01

InGaAsN-based materials are being developed at IQE, Inc. for 1.3 μm laser applications. Both MBE and MOCVD growth technology are employed and under investigation for commercial viability. The MBE effort focuses on optimizing the process for the large-volume manufacturing environment. The PL efficiencies of InGaAsN QWs grown with different nitrogen sources on single and multi-wafer MBE platforms are compared. The effect of various annealing treatments on the PL intensity and wavelength uniformity is also discussed in detail. The PL intensity of MBE-grown InGaAsN QWs is inferior to the efficiency of MOCVD samples emitting below 1.29 μm. MOCVD samples, however, exhibit a faster decay of the PL intensity with increasing wavelength, and loose their advantage above 1.29 μm. Deep and shallow ridge-waveguide lasers emitting at 1.28 μm were processed from the MBE material and the laser characteristics are discussed.

Retrieving high-resolution surface solar radiation with cloud parameters derived by combining MODIS and MTSAT data

NASA Astrophysics Data System (ADS)

Tang, Wenjun; Qin, Jun; Yang, Kun; Liu, Shaomin; Lu, Ning; Niu, Xiaolei

2016-03-01

Cloud parameters (cloud mask, effective particle radius, and liquid/ice water path) are the important inputs in estimating surface solar radiation (SSR). These parameters can be derived from MODIS with high accuracy, but their temporal resolution is too low to obtain high-temporal-resolution SSR retrievals. In order to obtain hourly cloud parameters, an artificial neural network (ANN) is applied in this study to directly construct a functional relationship between MODIS cloud products and Multifunctional Transport Satellite (MTSAT) geostationary satellite signals. In addition, an efficient parameterization model for SSR retrieval is introduced and, when driven with MODIS atmospheric and land products, its root mean square error (RMSE) is about 100 W m-2 for 44 Baseline Surface Radiation Network (BSRN) stations. Once the estimated cloud parameters and other information (such as aerosol, precipitable water, ozone) are input to the model, we can derive SSR at high spatiotemporal resolution. The retrieved SSR is first evaluated against hourly radiation data at three experimental stations in the Haihe River basin of China. The mean bias error (MBE) and RMSE in hourly SSR estimate are 12.0 W m-2 (or 3.5 %) and 98.5 W m-2 (or 28.9 %), respectively. The retrieved SSR is also evaluated against daily radiation data at 90 China Meteorological Administration (CMA) stations. The MBEs are 9.8 W m-2 (or 5.4 %); the RMSEs in daily and monthly mean SSR estimates are 34.2 W m-2 (or 19.1 %) and 22.1 W m-2 (or 12.3 %), respectively. The accuracy is comparable to or even higher than two other radiation products (GLASS and ISCCP-FD), and the present method is more computationally efficient and can produce hourly SSR data at a spatial resolution of 5 km.

Retrieving high-resolution surface solar radiation with cloud parameters derived by combining MODIS and MTSAT data

NASA Astrophysics Data System (ADS)

Tang, W.; Qin, J.; Yang, K.; Liu, S.; Lu, N.; Niu, X.

2015-12-01

Cloud parameters (cloud mask, effective particle radius and liquid/ice water path) are the important inputs in determining surface solar radiation (SSR). These parameters can be derived from MODIS with high accuracy but their temporal resolution is too low to obtain high temporal resolution SSR retrievals. In order to obtain hourly cloud parameters, the Artificial Neural Network (ANN) is applied in this study to directly construct a functional relationship between MODIS cloud products and Multi-functional Transport Satellite (MTSAT) geostationary satellite signals. Meanwhile, an efficient parameterization model for SSR retrieval is introduced and, when driven with MODIS atmospheric and land products, its root mean square error (RMSE) is about 100 W m-2 for 44 Baseline Surface Radiation Network (BSRN) stations. Once the estimated cloud parameters and other information (such as aerosol, precipitable water, ozone and so on) are input to the model, we can derive SSR at high spatio-temporal resolution. The retrieved SSR is first evaluated against hourly radiation data at three experimental stations in the Haihe River Basin of China. The mean bias error (MBE) and RMSE in hourly SSR estimate are 12.0 W m-2 (or 3.5 %) and 98.5 W m-2 (or 28.9 %), respectively. The retrieved SSR is also evaluated against daily radiation data at 90 China Meteorological Administration (CMA) stations. The MBEs are 9.8 W m-2 (5.4 %); the RMSEs in daily and monthly-mean SSR estimates are 34.2 W m-2 (19.1 %) and 22.1 W m-2 (12.3 %), respectively. The accuracy is comparable or even higher than other two radiation products (GLASS and ISCCP-FD), and the present method is more computationally efficient and can produce hourly SSR data at a spatial resolution of 5 km.

Estimating Bias Error Distributions

NASA Technical Reports Server (NTRS)

Liu, Tian-Shu; Finley, Tom D.

2001-01-01

This paper formulates the general methodology for estimating the bias error distribution of a device in a measuring domain from less accurate measurements when a minimal number of standard values (typically two values) are available. A new perspective is that the bias error distribution can be found as a solution of an intrinsic functional equation in a domain. Based on this theory, the scaling- and translation-based methods for determining the bias error distribution arc developed. These methods are virtually applicable to any device as long as the bias error distribution of the device can be sufficiently described by a power series (a polynomial) or a Fourier series in a domain. These methods have been validated through computational simulations and laboratory calibration experiments for a number of different devices.

CAUSES: On the Role of Surface Energy Budget Errors to the Warm Surface Air Temperature Error Over the Central United States

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

Ma, H. -Y.; Klein, S. A.; Xie, S.

Many weather forecasting and climate models simulate a warm surface air temperature (T2m) bias over mid-latitude continents during the summertime, especially over the Great Plains. We present here one of a series of papers from a multi-model intercomparison project (CAUSES: Cloud Above the United States and Errors at the Surface), which aims to evaluate the role of cloud, radiation, and precipitation biases in contributing to T2m bias using a short-term hindcast approach with observations mainly from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site during the period of April to August 2011. The present study examines the contributionmore » of surface energy budget errors to the bias. All participating models simulate higher net shortwave and longwave radiative fluxes at the surface but there is no consistency on signs of biases in latent and sensible heat fluxes over the Central U.S. and ARM SGP. Nevertheless, biases in net shortwave and downward longwave fluxes, as well as surface evaporative fraction (EF) are the main contributors to T2m bias. Radiation biases are largely affected by cloud simulations, while EF is affected by soil moisture modulated by seasonal accumulated precipitation and evaporation. An approximate equation is derived to further quantify the magnitudes of radiation and EF contributions to T2m bias. Our analysis suggests that radiation errors are always an important source of T2m error for long-term climate runs with EF errors either of equal or lesser importance. However, for the short-term hindcasts, EF errors are more important provided a model has a substantial EF bias.« less

Biased interpretation and memory in children with varying levels of spider fear.

PubMed

Klein, Anke M; Titulaer, Geraldine; Simons, Carlijn; Allart, Esther; de Gier, Erwin; Bögels, Susan M; Becker, Eni S; Rinck, Mike

2014-01-01

This study investigated multiple cognitive biases in children simultaneously, to investigate whether spider-fearful children display an interpretation bias, a recall bias, and source monitoring errors, and whether these biases are specific for spider-related materials. Furthermore, the independent ability of these biases to predict spider fear was investigated. A total of 121 children filled out the Spider Anxiety and Disgust Screening for Children (SADS-C), and they performed an interpretation task, a memory task, and a Behavioural Assessment Test (BAT). As expected, a specific interpretation bias was found: Spider-fearful children showed more negative interpretations of ambiguous spider-related scenarios, but not of other scenarios. We also found specific source monitoring errors: Spider-fearful children made more fear-related source monitoring errors for the spider-related scenarios, but not for the other scenarios. Only limited support was found for a recall bias. Finally, interpretation bias, recall bias, and source monitoring errors predicted unique variance components of spider fear.

Associations among selective attention, memory bias, cognitive errors and symptoms of anxiety in youth.

PubMed

Watts, Sarah E; Weems, Carl F

2006-12-01

The purpose of this study was to examine the linkages among selective attention, memory bias, cognitive errors, and anxiety problems by testing a model of the interrelations among these cognitive variables and childhood anxiety disorder symptoms. A community sample of 81 youth (38 females and 43 males) aged 9-17 years and their parents completed measures of the child's anxiety disorder symptoms. Youth completed assessments measuring selective attention, memory bias, and cognitive errors. Results indicated that selective attention, memory bias, and cognitive errors were each correlated with childhood anxiety problems and provide support for a cognitive model of anxiety which posits that these three biases are associated with childhood anxiety problems. Only limited support for significant interrelations among selective attention, memory bias, and cognitive errors was found. Finally, results point towards an effective strategy for moving the assessment of selective attention to younger and community samples of youth.

Modal Correction Method For Dynamically Induced Errors In Wind-Tunnel Model Attitude Measurements

NASA Technical Reports Server (NTRS)

Buehrle, R. D.; Young, C. P., Jr.

1995-01-01

This paper describes a method for correcting the dynamically induced bias errors in wind tunnel model attitude measurements using measured modal properties of the model system. At NASA Langley Research Center, the predominant instrumentation used to measure model attitude is a servo-accelerometer device that senses the model attitude with respect to the local vertical. Under smooth wind tunnel operating conditions, this inertial device can measure the model attitude with an accuracy of 0.01 degree. During wind tunnel tests when the model is responding at high dynamic amplitudes, the inertial device also senses the centrifugal acceleration associated with model vibration. This centrifugal acceleration results in a bias error in the model attitude measurement. A study of the response of a cantilevered model system to a simulated dynamic environment shows significant bias error in the model attitude measurement can occur and is vibration mode and amplitude dependent. For each vibration mode contributing to the bias error, the error is estimated from the measured modal properties and tangential accelerations at the model attitude device. Linear superposition is used to combine the bias estimates for individual modes to determine the overall bias error as a function of time. The modal correction model predicts the bias error to a high degree of accuracy for the vibration modes characterized in the simulated dynamic environment.

Twenty years of molecular beam epitaxy

NASA Astrophysics Data System (ADS)

Cho, A. Y.

1995-05-01

The term "molecular beam epitaxy" (MBE) was first used in one of our crystal growth papers in 1970, after having conducted extensive surface physics studies in the late 1960's of the interaction of atomic and molecular beams with solid surfaces. The unique feature of MBE is the ability to prepare single crystal layers with atomic dimensional precision. MBE sets the standard for epitaxial growth and has made possible semiconductor structures that could not be fabricated with either naturally existing materials or by other crystal growth techniques. MBE led the crystal growth technologies when it prepared the first semiconductor quantum well and superlattice structures that gave unexpected and exciting electrical and optical properties. For example, the discovery of the fractional quantized Hall effect. It brought experimental quantum physics to the classroom, and practically all major universities throughout the world are now equipped with MBE systems. The fundamental principles demonstrated by the MBE growth of III-V compound semiconductors have also been applied to the growth of group IV, II-VI, metal, and insulating materials. For manufacturing, the most important criteria are uniformity, precise control of the device structure, and reproducibility. MBE has produced more lasers (3 to 5 million per month for compact disc application) than any other crystal growth technique in the world. New directions for MBE are to incorporate in-situ, real-time monitoring capabilities so that complex structures can be precisely "engineered". In the future, as environmental concerns increase, the use of toxic arsine and phosphine may be limited. Successful use of valved cracker cells for solid arsenic and phosphorus has already produced InP based injection lasers.

Differential sea-state bias: A case study using TOPEX/POSEIDON data

NASA Technical Reports Server (NTRS)

Stewart, Robert H.; Devalla, B.

1994-01-01

We used selected data from the NASA altimeter TOPEX/POSEIDON to calculate differences in range measured by the C and Ku-band altimeters when the satellite overflew 5 to 15 m waves late at night. The range difference is due to free electrons in the ionosphere and to errors in sea-state bias. For the selected data the ionospheric influence on Ku range is less than 2 cm. Any difference in range over short horizontal distances is due only to a small along-track variability of the ionosphere and to errors in calculating the differential sea-state bias. We find that there is a barely detectable error in the bias in the geophysical data records. The wave-induced error in the ionospheric correction is less than 0.2% of significant wave height. The equivalent error in differential range is less than 1% of wave height. Errors in the differential sea-state bias calculations appear to be small even for extreme wave heights that greatly exceed the conditions on which the bias is based. The results also improved our confidence in the sea-state bias correction used for calculating the geophysical data records. Any error in the correction must influence Ku and C-band ranges almost equally.

Considerations for analysis of time-to-event outcomes measured with error: Bias and correction with SIMEX.

PubMed

Oh, Eric J; Shepherd, Bryan E; Lumley, Thomas; Shaw, Pamela A

2018-04-15

For time-to-event outcomes, a rich literature exists on the bias introduced by covariate measurement error in regression models, such as the Cox model, and methods of analysis to address this bias. By comparison, less attention has been given to understanding the impact or addressing errors in the failure time outcome. For many diseases, the timing of an event of interest (such as progression-free survival or time to AIDS progression) can be difficult to assess or reliant on self-report and therefore prone to measurement error. For linear models, it is well known that random errors in the outcome variable do not bias regression estimates. With nonlinear models, however, even random error or misclassification can introduce bias into estimated parameters. We compare the performance of 2 common regression models, the Cox and Weibull models, in the setting of measurement error in the failure time outcome. We introduce an extension of the SIMEX method to correct for bias in hazard ratio estimates from the Cox model and discuss other analysis options to address measurement error in the response. A formula to estimate the bias induced into the hazard ratio by classical measurement error in the event time for a log-linear survival model is presented. Detailed numerical studies are presented to examine the performance of the proposed SIMEX method under varying levels and parametric forms of the error in the outcome. We further illustrate the method with observational data on HIV outcomes from the Vanderbilt Comprehensive Care Clinic. Copyright © 2017 John Wiley & Sons, Ltd.

Electronic Transport in Ultrathin Heterostructures.

DTIC Science & Technology

1981-10-01

heterostructures, superlattices, diffusion-enhanced disorder, transport properties, molecular beam epitaxy (MBE), photoluminescence, optical absorption...tion of single and multilayer GatlAs/GaAs heterostructures by metalorganic chemical vapor deposition (MJCVD) and molecular beam epitaxy (MBE) has...fundamental nature of these clusters and their relevance to other epitaxial techniques such as molecular beam epitaxy (MBE). To further varify or

Estimating Climatological Bias Errors for the Global Precipitation Climatology Project (GPCP)

NASA Technical Reports Server (NTRS)

Adler, Robert; Gu, Guojun; Huffman, George

2012-01-01

A procedure is described to estimate bias errors for mean precipitation by using multiple estimates from different algorithms, satellite sources, and merged products. The Global Precipitation Climatology Project (GPCP) monthly product is used as a base precipitation estimate, with other input products included when they are within +/- 50% of the GPCP estimates on a zonal-mean basis (ocean and land separately). The standard deviation s of the included products is then taken to be the estimated systematic, or bias, error. The results allow one to examine monthly climatologies and the annual climatology, producing maps of estimated bias errors, zonal-mean errors, and estimated errors over large areas such as ocean and land for both the tropics and the globe. For ocean areas, where there is the largest question as to absolute magnitude of precipitation, the analysis shows spatial variations in the estimated bias errors, indicating areas where one should have more or less confidence in the mean precipitation estimates. In the tropics, relative bias error estimates (s/m, where m is the mean precipitation) over the eastern Pacific Ocean are as large as 20%, as compared with 10%-15% in the western Pacific part of the ITCZ. An examination of latitudinal differences over ocean clearly shows an increase in estimated bias error at higher latitudes, reaching up to 50%. Over land, the error estimates also locate regions of potential problems in the tropics and larger cold-season errors at high latitudes that are due to snow. An empirical technique to area average the gridded errors (s) is described that allows one to make error estimates for arbitrary areas and for the tropics and the globe (land and ocean separately, and combined). Over the tropics this calculation leads to a relative error estimate for tropical land and ocean combined of 7%, which is considered to be an upper bound because of the lack of sign-of-the-error canceling when integrating over different areas with a different number of input products. For the globe the calculated relative error estimate from this study is about 9%, which is also probably a slight overestimate. These tropical and global estimated bias errors provide one estimate of the current state of knowledge of the planet's mean precipitation.

Classification based upon gene expression data: bias and precision of error rates.

PubMed

Wood, Ian A; Visscher, Peter M; Mengersen, Kerrie L

2007-06-01

Gene expression data offer a large number of potentially useful predictors for the classification of tissue samples into classes, such as diseased and non-diseased. The predictive error rate of classifiers can be estimated using methods such as cross-validation. We have investigated issues of interpretation and potential bias in the reporting of error rate estimates. The issues considered here are optimization and selection biases, sampling effects, measures of misclassification rate, baseline error rates, two-level external cross-validation and a novel proposal for detection of bias using the permutation mean. Reporting an optimal estimated error rate incurs an optimization bias. Downward bias of 3-5% was found in an existing study of classification based on gene expression data and may be endemic in similar studies. Using a simulated non-informative dataset and two example datasets from existing studies, we show how bias can be detected through the use of label permutations and avoided using two-level external cross-validation. Some studies avoid optimization bias by using single-level cross-validation and a test set, but error rates can be more accurately estimated via two-level cross-validation. In addition to estimating the simple overall error rate, we recommend reporting class error rates plus where possible the conditional risk incorporating prior class probabilities and a misclassification cost matrix. We also describe baseline error rates derived from three trivial classifiers which ignore the predictors. R code which implements two-level external cross-validation with the PAMR package, experiment code, dataset details and additional figures are freely available for non-commercial use from http://www.maths.qut.edu.au/profiles/wood/permr.jsp

Systematic Error Modeling and Bias Estimation

PubMed Central

Zhang, Feihu; Knoll, Alois

2016-01-01

This paper analyzes the statistic properties of the systematic error in terms of range and bearing during the transformation process. Furthermore, we rely on a weighted nonlinear least square method to calculate the biases based on the proposed models. The results show the high performance of the proposed approach for error modeling and bias estimation. PMID:27213386

Quantitative RHEED Studies of MBE Growth of 3-5 Compounds

DTIC Science & Technology

1991-06-03

Vertical - Cavity Surface - Emitting Laser Using Molecular Beam Epitaxial ...Growth of Vertical Cavity Surface - emitting Lasers Our work under this ARO contract on the control of MBE growth has enhanced our ability to grow...pattern about the surface structure of nearly perfect crystals prepared by Molecular Beam Epitaxy ( MBE ) and to use these techniques

Selective Weighted Least Squares Method for Fourier Transform Infrared Quantitative Analysis.

PubMed

Wang, Xin; Li, Yan; Wei, Haoyun; Chen, Xia

2017-06-01

Classical least squares (CLS) regression is a popular multivariate statistical method used frequently for quantitative analysis using Fourier transform infrared (FT-IR) spectrometry. Classical least squares provides the best unbiased estimator for uncorrelated residual errors with zero mean and equal variance. However, the noise in FT-IR spectra, which accounts for a large portion of the residual errors, is heteroscedastic. Thus, if this noise with zero mean dominates in the residual errors, the weighted least squares (WLS) regression method described in this paper is a better estimator than CLS. However, if bias errors, such as the residual baseline error, are significant, WLS may perform worse than CLS. In this paper, we compare the effect of noise and bias error in using CLS and WLS in quantitative analysis. Results indicated that for wavenumbers with low absorbance, the bias error significantly affected the error, such that the performance of CLS is better than that of WLS. However, for wavenumbers with high absorbance, the noise significantly affected the error, and WLS proves to be better than CLS. Thus, we propose a selective weighted least squares (SWLS) regression that processes data with different wavenumbers using either CLS or WLS based on a selection criterion, i.e., lower or higher than an absorbance threshold. The effects of various factors on the optimal threshold value (OTV) for SWLS have been studied through numerical simulations. These studies reported that: (1) the concentration and the analyte type had minimal effect on OTV; and (2) the major factor that influences OTV is the ratio between the bias error and the standard deviation of the noise. The last part of this paper is dedicated to quantitative analysis of methane gas spectra, and methane/toluene mixtures gas spectra as measured using FT-IR spectrometry and CLS, WLS, and SWLS. The standard error of prediction (SEP), bias of prediction (bias), and the residual sum of squares of the errors (RSS) from the three quantitative analyses were compared. In methane gas analysis, SWLS yielded the lowest SEP and RSS among the three methods. In methane/toluene mixture gas analysis, a modification of the SWLS has been presented to tackle the bias error from other components. The SWLS without modification presents the lowest SEP in all cases but not bias and RSS. The modification of SWLS reduced the bias, which showed a lower RSS than CLS, especially for small components.

Perceptions and Practices of Mass Bat Exposure Events in the Setting of Rabies Among U.S. Public Health Agencies

PubMed Central

Hsu, C. H.; Brown, C. M.; Murphy, J. M.; Haskell, M. G.; Williams, C.; Feldman, K.; Mitchell, K.; Blanton, J. D.; Petersen, B. W.; Wallace, R. M.

2017-01-01

Summary Current guidelines in the setting of exposures to potentially rabid bats established by the Advisory Committee on Immunization Practices (ACIP) address post-exposure prophylaxis (PEP) administration in situations where a person may not be aware that a bite or direct contact has occurred and the bat is not available for diagnostic testing. These include instances when a bat is discovered in a room where a person awakens from sleep, is a child without an adult witness, has a mental disability or is intoxicated. The current ACIP guidelines, however, do not address PEP in the setting of multiple persons exposed to a bat or a bat colony, otherwise known as mass bat exposure (MBE) events. Due to a dearth of recommendations for response to these events, the reported reactions by public health agencies have varied widely. To address this perceived limitation, a survey of 45 state public health agencies was conducted to characterize prior experiences with MBE and practices to mitigate the public health risks. In general, most states (69% of the respondents) felt current ACIP guidelines were unclear in MBE scenarios. Thirty-three of the 45 states reported prior experience with MBE, receiving an average of 16.9 MBE calls per year and an investment of 106.7 person-hours annually on MBE investigations. PEP criteria, investigation methods and the experts recruited in MBE investigations varied between states. These dissimilarities could reflect differences in experience, scenario and resources. The lack of consistency in state responses to potential mass exposures to a highly fatal disease along with the large contingent of states dissatisfied with current ACIP guidance warrants the development of national guidelines in MBE settings. PMID:27389926

Response of the carbon cycle in an intermediate complexity model to the different climate configurations of the last nine interglacials

NASA Astrophysics Data System (ADS)

Bouttes, Nathaelle; Swingedouw, Didier; Roche, Didier M.; Sanchez-Goni, Maria F.; Crosta, Xavier

2018-03-01

Atmospheric CO2 levels during interglacials prior to the Mid-Brunhes Event (MBE, ˜ 430 ka BP) were around 40 ppm lower than after the MBE. The reasons for this difference remain unclear. A recent hypothesis proposed that changes in oceanic circulation, in response to different external forcings before and after the MBE, might have increased the ocean carbon storage in pre-MBE interglacials, thus lowering atmospheric CO2. Nevertheless, no quantitative estimate of this hypothesis has been produced up to now. Here we use an intermediate complexity model including the carbon cycle to evaluate the response of the carbon reservoirs in the atmosphere, ocean and land in response to the changes of orbital forcings, ice sheet configurations and atmospheric CO2 concentrations over the last nine interglacials. We show that the ocean takes up more carbon during pre-MBE interglacials in agreement with data, but the impact on atmospheric CO2 is limited to a few parts per million. Terrestrial biosphere is simulated to be less developed in pre-MBE interglacials, which reduces the storage of carbon on land and increases atmospheric CO2. Accounting for different simulated ice sheet extents modifies the vegetation cover and temperature, and thus the carbon reservoir distribution. Overall, atmospheric CO2 levels are lower during these pre-MBE simulated interglacials including all these effects, but the magnitude is still far too small. These results suggest a possible misrepresentation of some key processes in the model, such as the magnitude of ocean circulation changes, or the lack of crucial mechanisms or internal feedbacks, such as those related to permafrost, to fully account for the lower atmospheric CO2 concentrations during pre-MBE interglacials.

Temperature Dependence of Faraday Effect-Induced Bias Error in a Fiber Optic Gyroscope

PubMed Central

Li, Xuyou; Guang, Xingxing; Xu, Zhenlong; Li, Guangchun

2017-01-01

Improving the performance of interferometric fiber optic gyroscope (IFOG) in harsh environments, such as magnetic field and temperature field variation, is necessary for its practical applications. This paper presents an investigation of Faraday effect-induced bias error of IFOG under varying temperature. Jones matrix method is utilized to formulize the temperature dependence of Faraday effect-induced bias error. Theoretical results show that the Faraday effect-induced bias error changes with the temperature in the non-skeleton polarization maintaining (PM) fiber coil. This phenomenon is caused by the temperature dependence of linear birefringence and Verdet constant of PM fiber. Particularly, Faraday effect-induced bias errors of two polarizations always have opposite signs that can be compensated optically regardless of the changes of the temperature. Two experiments with a 1000 m non-skeleton PM fiber coil are performed, and the experimental results support these theoretical predictions. This study is promising for improving the bias stability of IFOG. PMID:28880203

Temperature Dependence of Faraday Effect-Induced Bias Error in a Fiber Optic Gyroscope.

PubMed

Li, Xuyou; Liu, Pan; Guang, Xingxing; Xu, Zhenlong; Guan, Lianwu; Li, Guangchun

2017-09-07

Improving the performance of interferometric fiber optic gyroscope (IFOG) in harsh environments, such as magnetic field and temperature field variation, is necessary for its practical applications. This paper presents an investigation of Faraday effect-induced bias error of IFOG under varying temperature. Jones matrix method is utilized to formulize the temperature dependence of Faraday effect-induced bias error. Theoretical results show that the Faraday effect-induced bias error changes with the temperature in the non-skeleton polarization maintaining (PM) fiber coil. This phenomenon is caused by the temperature dependence of linear birefringence and Verdet constant of PM fiber. Particularly, Faraday effect-induced bias errors of two polarizations always have opposite signs that can be compensated optically regardless of the changes of the temperature. Two experiments with a 1000 m non-skeleton PM fiber coil are performed, and the experimental results support these theoretical predictions. This study is promising for improving the bias stability of IFOG.

An analysis of input errors in precipitation-runoff models using regression with errors in the independent variables

USGS Publications Warehouse

Troutman, Brent M.

1982-01-01

Errors in runoff prediction caused by input data errors are analyzed by treating precipitation-runoff models as regression (conditional expectation) models. Independent variables of the regression consist of precipitation and other input measurements; the dependent variable is runoff. In models using erroneous input data, prediction errors are inflated and estimates of expected storm runoff for given observed input variables are biased. This bias in expected runoff estimation results in biased parameter estimates if these parameter estimates are obtained by a least squares fit of predicted to observed runoff values. The problems of error inflation and bias are examined in detail for a simple linear regression of runoff on rainfall and for a nonlinear U.S. Geological Survey precipitation-runoff model. Some implications for flood frequency analysis are considered. A case study using a set of data from Turtle Creek near Dallas, Texas illustrates the problems of model input errors.

Wavelength-scale Microlasers based on VCSEL-Photonic Crystal Architecture

DTIC Science & Technology

2015-01-20

molecular beam epitaxy , MBE). We will also assume the triangular lattice of air...Abbreviations, and Acronyms InP: indium phosphide InGaAsP: indium gallium arsenide phosphide MBE: molecular beam epiitaxy VCSEL : vertical cavity...substrates and were grown by MBE. Electron beam lithography and reactive ion etching was used to deep‐etch the holes of the PhC‐ VCSELS ,

Publisher Correction: Mutations in Vps15 perturb neuronal migration in mice and are associated with neurodevelopmental disease in humans.

PubMed

Gstrein, Thomas; Edwards, Andrew; Přistoupilová, Anna; Leca, Ines; Breuss, Martin; Pilat-Carotta, Sandra; Hansen, Andi H; Tripathy, Ratna; Traunbauer, Anna K; Hochstoeger, Tobias; Rosoklija, Gavril; Repic, Marco; Landler, Lukas; Stránecký, Viktor; Dürnberger, Gerhard; Keane, Thomas M; Zuber, Johannes; Adams, David J; Flint, Jonathan; Honzik, Tomas; Gut, Marta; Beltran, Sergi; Mechtler, Karl; Sherr, Elliott; Kmoch, Stanislav; Gut, Ivo; Keays, David A

2018-06-06

In the supplementary information PDF originally posted, there were discrepancies from the integrated supplementary information that appeared in the HTML; the former has been corrected as follows. In the legend to Supplementary Fig. 2c, "major organs of the mouse" has been changed to "major organs of the adult mouse." In the legend to Supplementary Fig. 6d,h, "At E14.5 Mbe/Mbe mutants have a smaller percentage of Brdu positive cells in bin 3" has been changed to "At E14.5 Mbe/Mbe mutants have a higher percentage of Brdu positive cells in bin 3."

Accounting for measurement error in log regression models with applications to accelerated testing.

PubMed

Richardson, Robert; Tolley, H Dennis; Evenson, William E; Lunt, Barry M

2018-01-01

In regression settings, parameter estimates will be biased when the explanatory variables are measured with error. This bias can significantly affect modeling goals. In particular, accelerated lifetime testing involves an extrapolation of the fitted model, and a small amount of bias in parameter estimates may result in a significant increase in the bias of the extrapolated predictions. Additionally, bias may arise when the stochastic component of a log regression model is assumed to be multiplicative when the actual underlying stochastic component is additive. To account for these possible sources of bias, a log regression model with measurement error and additive error is approximated by a weighted regression model which can be estimated using Iteratively Re-weighted Least Squares. Using the reduced Eyring equation in an accelerated testing setting, the model is compared to previously accepted approaches to modeling accelerated testing data with both simulations and real data.

The cost of adherence mismeasurement in serious mental illness: a claims-based analysis.

PubMed

Shafrin, Jason; Forma, Felicia; Scherer, Ethan; Hatch, Ainslie; Vytlacil, Edward; Lakdawalla, Darius

2017-05-01

To quantify how adherence mismeasurement affects the estimated impact of adherence on inpatient costs among patients with serious mental illness (SMI). Proportion of days covered (PDC) is a common claims-based measure of medication adherence. Because PDC does not measure medication ingestion, however, it may inaccurately measure adherence. We derived a formula to correct the bias that occurs in adherence-utilization studies resulting from errors in claims-based measures of adherence. We conducted a literature review to identify the correlation between gold-standard and claims-based adherence measures. We derived a bias-correction methodology to address claims-based medication adherence measurement error. We then applied this methodology to a case study of patients with SMI who initiated atypical antipsychotics in 2 large claims databases. Our literature review identified 6 studies of interest. The 4 most relevant ones measured correlations between 0.38 and 0.91. Our preferred estimate implies that the effect of adherence on inpatient spending estimated from claims data would understate the true effect by a factor of 5.3, if there were no other sources of bias. Although our procedure corrects for measurement error, such error also may amplify or mitigate other potential biases. For instance, if adherent patients are healthier than nonadherent ones, measurement error makes the resulting bias worse. On the other hand, if adherent patients are sicker, measurement error mitigates the other bias. Measurement error due to claims-based adherence measures is worth addressing, alongside other more widely emphasized sources of bias in inference.

Automated detection of heuristics and biases among pathologists in a computer-based system.

PubMed

Crowley, Rebecca S; Legowski, Elizabeth; Medvedeva, Olga; Reitmeyer, Kayse; Tseytlin, Eugene; Castine, Melissa; Jukic, Drazen; Mello-Thoms, Claudia

2013-08-01

The purpose of this study is threefold: (1) to develop an automated, computer-based method to detect heuristics and biases as pathologists examine virtual slide cases, (2) to measure the frequency and distribution of heuristics and errors across three levels of training, and (3) to examine relationships of heuristics to biases, and biases to diagnostic errors. The authors conducted the study using a computer-based system to view and diagnose virtual slide cases. The software recorded participant responses throughout the diagnostic process, and automatically classified participant actions based on definitions of eight common heuristics and/or biases. The authors measured frequency of heuristic use and bias across three levels of training. Biases studied were detected at varying frequencies, with availability and search satisficing observed most frequently. There were few significant differences by level of training. For representativeness and anchoring, the heuristic was used appropriately as often or more often than it was used in biased judgment. Approximately half of the diagnostic errors were associated with one or more biases. We conclude that heuristic use and biases were observed among physicians at all levels of training using the virtual slide system, although their frequencies varied. The system can be employed to detect heuristic use and to test methods for decreasing diagnostic errors resulting from cognitive biases.

Simultaneous Estimation of Model State Variables and Observation and Forecast Biases Using a Two-Stage Hybrid Kalman Filter

NASA Technical Reports Server (NTRS)

Pauwels, V. R. N.; DeLannoy, G. J. M.; Hendricks Franssen, H.-J.; Vereecken, H.

2013-01-01

In this paper, we present a two-stage hybrid Kalman filter to estimate both observation and forecast bias in hydrologic models, in addition to state variables. The biases are estimated using the discrete Kalman filter, and the state variables using the ensemble Kalman filter. A key issue in this multi-component assimilation scheme is the exact partitioning of the difference between observation and forecasts into state, forecast bias and observation bias updates. Here, the error covariances of the forecast bias and the unbiased states are calculated as constant fractions of the biased state error covariance, and the observation bias error covariance is a function of the observation prediction error covariance. In a series of synthetic experiments, focusing on the assimilation of discharge into a rainfall-runoff model, it is shown that both static and dynamic observation and forecast biases can be successfully estimated. The results indicate a strong improvement in the estimation of the state variables and resulting discharge as opposed to the use of a bias-unaware ensemble Kalman filter. Furthermore, minimal code modification in existing data assimilation software is needed to implement the method. The results suggest that a better performance of data assimilation methods should be possible if both forecast and observation biases are taken into account.

Height-selective etching for regrowth of self-aligned contacts using MBE

NASA Astrophysics Data System (ADS)

Burek, G. J.; Wistey, M. A.; Singisetti, U.; Nelson, A.; Thibeault, B. J.; Bank, S. R.; Rodwell, M. J. W.; Gossard, A. C.

2009-03-01

Advanced III-V transistors require unprecedented low-resistance contacts in order to simultaneously scale bandwidth, fmax and ft with the physical active region [M.J.W. Rodwell, M. Le, B. Brar, in: Proceedings of the IEEE, 96, 2008, p. 748]. Low-resistance contacts have been previously demonstrated using molecular beam epitaxy (MBE), which provides active doping above 4×10 19 cm -3 and permits in-situ metal deposition for the lowest resistances [U. Singisetti, M.A. Wistey, J.D. Zimmerman, B.J. Thibeault, M.J.W. Rodwell, A.C. Gossard, S.R. Bank, Appl. Phys. Lett., submitted]. But MBE is a blanket deposition technique, and applying MBE regrowth to deep-submicron lateral device dimensions is difficult even with advanced lithography techniques. We present a simple method for selectively etching undesired regrowth from the gate or mesa of a III-V MOSFET or laser, resulting in self-aligned source/drain contacts regardless of the device dimensions. This turns MBE into an effectively selective area growth technique.

Enhancing the far-ultraviolet sensitivity of silicon complementary metal oxide semiconductor imaging arrays

NASA Astrophysics Data System (ADS)

Retherford, Kurt D.; Bai, Yibin; Ryu, Kevin K.; Gregory, James A.; Welander, Paul B.; Davis, Michael W.; Greathouse, Thomas K.; Winters, Gregory S.; Suntharalingam, Vyshnavi; Beletic, James W.

2015-10-01

We report our progress toward optimizing backside-illuminated silicon P-type intrinsic N-type complementary metal oxide semiconductor devices developed by Teledyne Imaging Sensors (TIS) for far-ultraviolet (UV) planetary science applications. This project was motivated by initial measurements at Southwest Research Institute of the far-UV responsivity of backside-illuminated silicon PIN photodiode test structures, which revealed a promising QE in the 100 to 200 nm range. Our effort to advance the capabilities of thinned silicon wafers capitalizes on recent innovations in molecular beam epitaxy (MBE) doping processes. Key achievements to date include the following: (1) representative silicon test wafers were fabricated by TIS, and set up for MBE processing at MIT Lincoln Laboratory; (2) preliminary far-UV detector QE simulation runs were completed to aid MBE layer design; (3) detector fabrication was completed through the pre-MBE step; and (4) initial testing of the MBE doping process was performed on monitoring wafers, with detailed quality assessments.

Detection and evaluation of embedded mild steel can material into 18 Cr-oxide dispersion strengthened steel tubes by magnetic Barkhausen emission

NASA Astrophysics Data System (ADS)

Kishore, G. V. K.; Kumar, Anish; Rajkumar, K. V.; Purnachandra Rao, B.; Pramanik, Debabrata; Kapoor, Komal; Jha, Sanjay Kumar

2017-12-01

The paper presents a new methodology for detection and evaluation of mild steel (MS) can material embedded into oxide dispersion strengthened (ODS) steel tubes by magnetic Barkhausen emission (MBE) technique. The high frequency MBE measurements (125 Hz sweep frequency and 70-200 kHz analyzing frequency) are found to be very sensitive for detection of presence of MS on the surface of the ODS steel tube. However, due to a shallow depth of information from the high frequency MBE measurements, it cannot be used for evaluation of the thickness of the embedded MS. The low frequency MBE measurements (0.5 Hz sweep frequency and 2-20 kHz analyzing frequency) indicate presence of two MBE RMS voltage peaks corresponding to the MS and the ODS steel. The ratio of the two peaks changes with the thickness of the MS and hence, can be used for measurement of the thickness of the MS layer.

Heuristics and Cognitive Error in Medical Imaging.

PubMed

Itri, Jason N; Patel, Sohil H

2018-05-01

The field of cognitive science has provided important insights into mental processes underlying the interpretation of imaging examinations. Despite these insights, diagnostic error remains a major obstacle in the goal to improve quality in radiology. In this article, we describe several types of cognitive bias that lead to diagnostic errors in imaging and discuss approaches to mitigate cognitive biases and diagnostic error. Radiologists rely on heuristic principles to reduce complex tasks of assessing probabilities and predicting values into simpler judgmental operations. These mental shortcuts allow rapid problem solving based on assumptions and past experiences. Heuristics used in the interpretation of imaging studies are generally helpful but can sometimes result in cognitive biases that lead to significant errors. An understanding of the causes of cognitive biases can lead to the development of educational content and systematic improvements that mitigate errors and improve the quality of care provided by radiologists.

Radar error statistics for the space shuttle

NASA Technical Reports Server (NTRS)

Lear, W. M.

1979-01-01

Radar error statistics of C-band and S-band that are recommended for use with the groundtracking programs to process space shuttle tracking data are presented. The statistics are divided into two parts: bias error statistics, using the subscript B, and high frequency error statistics, using the subscript q. Bias errors may be slowly varying to constant. High frequency random errors (noise) are rapidly varying and may or may not be correlated from sample to sample. Bias errors were mainly due to hardware defects and to errors in correction for atmospheric refraction effects. High frequency noise was mainly due to hardware and due to atmospheric scintillation. Three types of atmospheric scintillation were identified: horizontal, vertical, and line of sight. This was the first time that horizontal and line of sight scintillations were identified.

Effect of tensile deformation on micromagnetic parameters in 0.2% carbon steel and 2.25Cr-1Mo steel

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

Moorthy, V.; Vaidyanathan, S.; Jayakumar, T.

The influence of prior tensile deformation on the magnetic Barkhausen emission (MBE) and the hysteresis (B-H) curve has been studied in 0.2% carbon steel and 2.25Cr-1Mo steel under different tempered conditions. This study shows that the micromagnetic parameters can be used to identify the four stages of deformation, namely (1) perfectly elastic, (2) microplastic yielding, (3) macroyielding and (4) progressive plastic deformation. However, it is observed that the MBE profile shows more distinct changes at different stages of tensile deformation than the hysteresis curve. It has been established that the beginning of microplastic yielding and macroyielding can be identified frommore » the MBE profile which is not possible from the stress-strain plot. The onset of microplastic yielding can be identified from the decrease in the MBE peak height. The macroyielding can be identified from the merging of the initially present two-peak MBE profile into a single central peak with relatively higher peak height and narrow profile width. The difference between the variation of MBE and hysteresis curve parameters with strain beyond macroyielding indicates the difference in the deformation state of the surface and bulk of the sample.« less

A Comparative Study on Phytochemical Profiles and Biological Activities of Sclerocarya birrea (A.Rich.) Hochst Leaf and Bark Extracts

PubMed Central

Russo, Daniela; Miglionico, Rocchina; Carmosino, Monica; Bisaccia, Faustino; Armentano, Maria Francesca

2018-01-01

Sclerocarya birrea (A.Rich.) Hochst (Anacardiaceae) is a savannah tree that has long been used in sub-Saharan Africa as a medicinal remedy for numerous ailments. The purpose of this study was to increase the scientific knowledge about this plant by evaluating the total content of polyphenols, flavonoids, and tannins in the methanol extracts of the leaves and bark (MLE and MBE, respectively), as well as the in vitro antioxidant activity and biological activities of these extracts. Reported results show that MLE is rich in flavonoids (132.7 ± 10.4 mg of quercetin equivalents/g), whereas MBE has the highest content of tannins (949.5 ± 29.7 mg of tannic acid equivalents/g). The antioxidant activity was measured using four different in vitro tests: β-carotene bleaching (BCB), 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), O2−•, and nitric oxide (NO•) assays. In all cases, MBE was the most active compared to MLE and the standards used (Trolox and ascorbic acid). Furthermore, MBE and MLE were tested to evaluate their activity in HepG2 and fibroblast cell lines. A higher cytotoxic activity of MBE was evidenced and confirmed by more pronounced alterations in cell morphology. MBE induced cell death, triggering the intrinsic apoptotic pathway by reactive oxygen species (ROS) generation, which led to a loss of mitochondrial membrane potential with subsequent cytochrome c release from the mitochondria into the cytosol. Moreover, MBE showed lower cytotoxicity in normal human dermal fibroblasts, suggesting its potential as a selective anticancer agent. PMID:29316691

A Comparative Study on Phytochemical Profiles and Biological Activities of Sclerocarya birrea (A.Rich.) Hochst Leaf and Bark Extracts.

PubMed

Russo, Daniela; Miglionico, Rocchina; Carmosino, Monica; Bisaccia, Faustino; Andrade, Paula B; Valentão, Patrícia; Milella, Luigi; Armentano, Maria Francesca

2018-01-08

Sclerocarya birrea (A.Rich.) Hochst (Anacardiaceae) is a savannah tree that has long been used in sub-Saharan Africa as a medicinal remedy for numerous ailments. The purpose of this study was to increase the scientific knowledge about this plant by evaluating the total content of polyphenols, flavonoids, and tannins in the methanol extracts of the leaves and bark (MLE and MBE, respectively), as well as the in vitro antioxidant activity and biological activities of these extracts. Reported results show that MLE is rich in flavonoids (132.7 ± 10.4 mg of quercetin equivalents/g), whereas MBE has the highest content of tannins (949.5 ± 29.7 mg of tannic acid equivalents/g). The antioxidant activity was measured using four different in vitro tests: β-carotene bleaching (BCB), 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), O₂ -• , and nitric oxide (NO • ) assays. In all cases, MBE was the most active compared to MLE and the standards used (Trolox and ascorbic acid). Furthermore, MBE and MLE were tested to evaluate their activity in HepG2 and fibroblast cell lines. A higher cytotoxic activity of MBE was evidenced and confirmed by more pronounced alterations in cell morphology. MBE induced cell death, triggering the intrinsic apoptotic pathway by reactive oxygen species (ROS) generation, which led to a loss of mitochondrial membrane potential with subsequent cytochrome c release from the mitochondria into the cytosol. Moreover, MBE showed lower cytotoxicity in normal human dermal fibroblasts, suggesting its potential as a selective anticancer agent.

Multipollutant measurement error in air pollution epidemiology studies arising from predicting exposures with penalized regression splines

PubMed Central

Bergen, Silas; Sheppard, Lianne; Kaufman, Joel D.; Szpiro, Adam A.

2016-01-01

Summary Air pollution epidemiology studies are trending towards a multi-pollutant approach. In these studies, exposures at subject locations are unobserved and must be predicted using observed exposures at misaligned monitoring locations. This induces measurement error, which can bias the estimated health effects and affect standard error estimates. We characterize this measurement error and develop an analytic bias correction when using penalized regression splines to predict exposure. Our simulations show bias from multi-pollutant measurement error can be severe, and in opposite directions or simultaneously positive or negative. Our analytic bias correction combined with a non-parametric bootstrap yields accurate coverage of 95% confidence intervals. We apply our methodology to analyze the association of systolic blood pressure with PM2.5 and NO2 in the NIEHS Sister Study. We find that NO2 confounds the association of systolic blood pressure with PM2.5 and vice versa. Elevated systolic blood pressure was significantly associated with increased PM2.5 and decreased NO2. Correcting for measurement error bias strengthened these associations and widened 95% confidence intervals. PMID:27789915

Accounting for independent nondifferential misclassification does not increase certainty that an observed association is in the correct direction.

PubMed

Greenland, Sander; Gustafson, Paul

2006-07-01

Researchers sometimes argue that their exposure-measurement errors are independent of other errors and are nondifferential with respect to disease, resulting in estimation bias toward the null. Among well-known problems with such arguments are that independence and nondifferentiality are harder to satisfy than ordinarily appreciated (e.g., because of correlation of errors in questionnaire items, and because of uncontrolled covariate effects on error rates); small violations of independence or nondifferentiality may lead to bias away from the null; and, if exposure is polytomous, the bias produced by independent nondifferential error is not always toward the null. The authors add to this list by showing that, in a 2 x 2 table (for which independent nondifferential error produces bias toward the null), accounting for independent nondifferential error does not reduce the p value even though it increases the point estimate. Thus, such accounting should not increase certainty that an association is present.

Status of the MBE technology at leti LIR for the manufacturing of HgCdTe focal plane arrays

NASA Astrophysics Data System (ADS)

Ferret, P.; Zanatta, J. P.; Hamelin, R.; Cremer, S.; Million, A.; Wolny, M.; Destefanis, G.

2000-06-01

This paper presents recent developments that have been made in Leti Infrared Laboratory in the field of molecular beam epitaxy (MBE) growth and fabrication of medium wavelength and long wavelength infrared (MWIR and LWIR) HgCdTe devices. The techniques that lead to growth temperature and flux control are presented. Run to run composition reproducibility is investigated on runs of more than 15 consecutively grown layers. Etch pit density in the low 105 cm-2 and void density lower than 103 cm-2 are obtained routinely on CdZnTe substrates. The samples exhibit low n-type carrier concentration in the 1014 to 1015 cm-3 range and mobility in excess of 105 cm2/Vs at 77 K for epilayers with 9.5 µm cut-off wavelength. LWIR diodes, fabricated with an-on-p homojunction process present dynamic resistance area products which reach values of 8 103 Ωcm2 for a biased voltage of -50 mV and a cutoff wavelength of 9.5 µm at 77 K. A 320 × 240 plane array with a 30 µm pitch operating at 77 K in the MWIR range has been developed using HgCdTe and CdTe layers MBE grown on a Germanium substrate. Mean NEDT value of 8.8 mK together with an operability of 99.94% is obtained. We fabricated MWIR two-color detectors by the superposition of layers of HgCdTe with different compositions and a mixed MESA and planar technology. These detectors are spatially coherent and can be independently addressed. Current voltage curves of 60 × 60 µm2 photodiodes have breakdown voltage exceeding 800 mV for each diode. The cutoff wavelength at 77 K is 3.1 µm for the MWIR-1 and 5 µm for the MWIR-2.

Magnetic Nanoparticle Thermometer: An Investigation of Minimum Error Transmission Path and AC Bias Error

PubMed Central

Du, Zhongzhou; Su, Rijian; Liu, Wenzhong; Huang, Zhixing

2015-01-01

The signal transmission module of a magnetic nanoparticle thermometer (MNPT) was established in this study to analyze the error sources introduced during the signal flow in the hardware system. The underlying error sources that significantly affected the precision of the MNPT were determined through mathematical modeling and simulation. A transfer module path with the minimum error in the hardware system was then proposed through the analysis of the variations of the system error caused by the significant error sources when the signal flew through the signal transmission module. In addition, a system parameter, named the signal-to-AC bias ratio (i.e., the ratio between the signal and AC bias), was identified as a direct determinant of the precision of the measured temperature. The temperature error was below 0.1 K when the signal-to-AC bias ratio was higher than 80 dB, and other system errors were not considered. The temperature error was below 0.1 K in the experiments with a commercial magnetic fluid (Sample SOR-10, Ocean Nanotechnology, Springdale, AR, USA) when the hardware system of the MNPT was designed with the aforementioned method. PMID:25875188

A Systematic Error Correction Method for TOVS Radiances

NASA Technical Reports Server (NTRS)

Joiner, Joanna; Rokke, Laurie; Einaudi, Franco (Technical Monitor)

2000-01-01

Treatment of systematic errors is crucial for the successful use of satellite data in a data assimilation system. Systematic errors in TOVS radiance measurements and radiative transfer calculations can be as large or larger than random instrument errors. The usual assumption in data assimilation is that observational errors are unbiased. If biases are not effectively removed prior to assimilation, the impact of satellite data will be lessened and can even be detrimental. Treatment of systematic errors is important for short-term forecast skill as well as the creation of climate data sets. A systematic error correction algorithm has been developed as part of a 1D radiance assimilation. This scheme corrects for spectroscopic errors, errors in the instrument response function, and other biases in the forward radiance calculation for TOVS. Such algorithms are often referred to as tuning of the radiances. The scheme is able to account for the complex, air-mass dependent biases that are seen in the differences between TOVS radiance observations and forward model calculations. We will show results of systematic error correction applied to the NOAA 15 Advanced TOVS as well as its predecessors. We will also discuss the ramifications of inter-instrument bias with a focus on stratospheric measurements.

Perceptions and Practices of Mass Bat Exposure Events in the Setting of Rabies Among U.S. Public Health Agencies.

PubMed

Hsu, C H; Brown, C M; Murphy, J M; Haskell, M G; Williams, C; Feldman, K; Mitchell, K; Blanton, J D; Petersen, B W; Wallace, R M

2017-03-01

Current guidelines in the setting of exposures to potentially rabid bats established by the Advisory Committee on Immunization Practices (ACIP) address post-exposure prophylaxis (PEP) administration in situations where a person may not be aware that a bite or direct contact has occurred and the bat is not available for diagnostic testing. These include instances when a bat is discovered in a room where a person awakens from sleep, is a child without an adult witness, has a mental disability or is intoxicated. The current ACIP guidelines, however, do not address PEP in the setting of multiple persons exposed to a bat or a bat colony, otherwise known as mass bat exposure (MBE) events. Due to a dearth of recommendations for response to these events, the reported reactions by public health agencies have varied widely. To address this perceived limitation, a survey of 45 state public health agencies was conducted to characterize prior experiences with MBE and practices to mitigate the public health risks. In general, most states (69% of the respondents) felt current ACIP guidelines were unclear in MBE scenarios. Thirty-three of the 45 states reported prior experience with MBE, receiving an average of 16.9 MBE calls per year and an investment of 106.7 person-hours annually on MBE investigations. PEP criteria, investigation methods and the experts recruited in MBE investigations varied between states. These dissimilarities could reflect differences in experience, scenario and resources. The lack of consistency in state responses to potential mass exposures to a highly fatal disease along with the large contingent of states dissatisfied with current ACIP guidance warrants the development of national guidelines in MBE settings. Published 2016. This article is a U.S. Government work and is in the public domain in the USA. Zoonoses and Public Health published by Blackwell Verlag GmbH.

Determination of Shift/Bias in Digital Aerial Triangulation of UAV Imagery Sequences

NASA Astrophysics Data System (ADS)

Wierzbicki, Damian

2017-12-01

Currently UAV Photogrammetry is characterized a largely automated and efficient data processing. Depicting from the low altitude more often gains on the meaning in the uses of applications as: cities mapping, corridor mapping, road and pipeline inspections or mapping of large areas e.g. forests. Additionally, high-resolution video image (HD and bigger) is more often use for depicting from the low altitude from one side it lets deliver a lot of details and characteristics of ground surfaces features, and from the other side is presenting new challenges in the data processing. Therefore, determination of elements of external orientation plays a substantial role the detail of Digital Terrain Models and artefact-free ortophoto generation. Parallel a research on the quality of acquired images from UAV and above the quality of products e.g. orthophotos are conducted. Despite so fast development UAV photogrammetry still exists the necessity of accomplishment Automatic Aerial Triangulation (AAT) on the basis of the observations GPS/INS and via ground control points. During low altitude photogrammetric flight, the approximate elements of external orientation registered by UAV are burdened with the influence of some shift/bias errors. In this article, methods of determination shift/bias error are presented. In the process of the digital aerial triangulation two solutions are applied. In the first method shift/bias error was determined together with the drift/bias error, elements of external orientation and coordinates of ground control points. In the second method shift/bias error was determined together with the elements of external orientation, coordinates of ground control points and drift/bias error equals 0. When two methods were compared the difference for shift/bias error is more than ±0.01 m for all terrain coordinates XYZ.

Validity of mail survey data on bagged waterfowl

USGS Publications Warehouse

Atwood, E.L.

1956-01-01

Knowledge of the pattern of occurrence and characteristics of response errors obtained during an investigation of the validity of post-season surveys of hunters was used to advantage to devise a two-step method for removing the response-bias errors from the raw survey data. The method was tested on data with known errors and found to have a high efficiency in reducing the effect of response-bias errors. The development of this method for removing the effect of the response-bias errors, and its application to post-season hunter-take survey data, increased the reliability of the data from below the point of practical management significance up to the approximate reliability limits corresponding to the sampling errors.

Ion-induced crystal damage during plasma-assisted MBE growth of GaN layers

NASA Astrophysics Data System (ADS)

Kirchner, V.; Heinke, H.; Birkle, U.; Einfeldt, S.; Hommel, D.; Selke, H.; Ryder, P. L.

1998-12-01

Gallium nitride layers were grown by plasma-assisted molecular-beam epitaxy on (0001)-oriented sapphire substrates using an electron cyclotron resonance (ECR) and a radio frequency (rf) plasma source. An applied substrate bias was varied from -200 to +250 V, resulting in a change of the density and energy of nitrogen ions impinging the growth surface. The layers were investigated by high-resolution x-ray diffractometry and high-resolution transmission electron microscopy (HRTEM). Applying a negative bias during growth has a marked detrimental effect on the crystal perfection of the layers grown with an ECR plasma source. This is indicated by a change in shape and width of (0002) and (202¯5) reciprocal lattice points as monitored by triple axis x-ray measurements. In HRTEM images, isolated basal plane stacking faults were found, which probably result from precipitation of interstitial atoms. The crystal damage in layers grown with a highly negative substrate bias is comparable to that observed for ion implantation processes at orders of magnitude larger ion energies. This is attributed to the impact of ions on the growing surface. None of the described phenomena was observed for the samples grown with the rf plasma source.

Bias-field equalizer for bubble memories

NASA Technical Reports Server (NTRS)

Keefe, G. E.

1977-01-01

Magnetoresistive Perm-alloy sensor monitors bias field required to maintain bubble memory. Sensor provides error signal that, in turn, corrects magnitude of bias field. Error signal from sensor can be used to control magnitude of bias field in either auxiliary set of bias-field coils around permanent magnet field, or current in small coils used to remagnetize permanent magnet by infrequent, short, high-current pulse or short sequence of pulses.

Perceptual Bias in Speech Error Data Collection: Insights from Spanish Speech Errors

ERIC Educational Resources Information Center

Perez, Elvira; Santiago, Julio; Palma, Alfonso; O'Seaghdha, Padraig G.

2007-01-01

This paper studies the reliability and validity of naturalistic speech errors as a tool for language production research. Possible biases when collecting naturalistic speech errors are identified and specific predictions derived. These patterns are then contrasted with published reports from Germanic languages (English, German and Dutch) and one…

Introduction to CAUSES: Description of Weather and Climate Models and Their Near-Surface Temperature Errors in 5 day Hindcasts Near the Southern Great Plains

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

Morcrette, C. J.; Van Weverberg, K.; Ma, H. -Y.

The Clouds Above the United States and Errors at the Surface (CAUSES) project is aimed at gaining a better understanding of the physical processes that are leading to the creation of warm screen-temperature biases over the American Midwest, which are seen in many numerical models. Here in Part 1, a series of 5-day hindcasts, each initialised from re-analyses and performed by 11 different models, are evaluated against screen-temperature observations. All the models have a warm bias over parts of the Midwest. Several ways of quantifying the impact of the initial conditions on the evolution of the simulations are presented, showingmore » that within a day or so all models have produced a warm bias that is representative of their bias after 5 days, and not closely tied to the conditions at the initial time. Although the surface temperature biases sometimes coincide with locations where the re-analyses themselves have a bias, there are many regions in each of the models where biases grow over the course of 5 days or are larger than the biases present in the reanalyses. At the Southern Great Plains site, the model biases are shown to not be confined to the surface, but extend several kilometres into the atmosphere. In most of the models, there is a strong diurnal cycle in the screen-temperature bias and in some models the biases are largest around midday, while in the others it is largest during the night. While the different physical processes that are contributing to a given model having a screen-temperature error will be discussed in more detail in the companion papers (Parts 2 and 3) the fact that there is a spatial coherence in the phase of the diurnal cycle of the error across wide regions and that there are numerous locations across the Midwest where the diurnal cycle of the error is highly correlated with the diurnal cycle of the error at SGP suggest that the detailed evaluations of the role of different processes in contributing to errors at SGP will be representative of errors that are prevalent over a much larger spatial scale.« less

Introduction to CAUSES: Description of weather and climate models and their near-surface temperature errors in 5-day hindcasts near the Southern Great Plains

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

Morcrette, Cyril J.; Van Weverberg, Kwinten; Ma, H

2018-02-16

The Clouds Above the United States and Errors at the Surface (CAUSES) project is aimed at gaining a better understanding of the physical processes that are leading to the creation of warm screen-temperature biases over the American Midwest, which are seen in many numerical models. Here in Part 1, a series of 5-day hindcasts, each initialised from re-analyses and performed by 11 different models, are evaluated against screen-temperature observations. All the models have a warm bias over parts of the Midwest. Several ways of quantifying the impact of the initial conditions on the evolution of the simulations are presented, showingmore » that within a day or so all models have produced a warm bias that is representative of their bias after 5 days, and not closely tied to the conditions at the initial time. Although the surface temperature biases sometimes coincide with locations where the re-analyses themselves have a bias, there are many regions in each of the models where biases grow over the course of 5 days or are larger than the biases present in the reanalyses. At the Southern Great Plains site, the model biases are shown to not be confined to the surface, but extend several kilometres into the atmosphere. In most of the models, there is a strong diurnal cycle in the screen-temperature bias and in some models the biases are largest around midday, while in the others it is largest during the night. While the different physical processes that are contributing to a given model having a screen-temperature error will be discussed in more detail in the companion papers (Parts 2 and 3) the fact that there is a spatial coherence in the phase of the diurnal cycle of the error across wide regions and that there are numerous locations across the Midwest where the diurnal cycle of the error is highly correlated with the diurnal cycle of the error at SGP suggest that the detailed evaluations of the role of different processes in contributing to errors at SGP will be representative of errors that are prevalent over a much larger spatial scale.« less

Peak-locking centroid bias in Shack-Hartmann wavefront sensing

NASA Astrophysics Data System (ADS)

Anugu, Narsireddy; Garcia, Paulo J. V.; Correia, Carlos M.

2018-05-01

Shack-Hartmann wavefront sensing relies on accurate spot centre measurement. Several algorithms were developed with this aim, mostly focused on precision, i.e. minimizing random errors. In the solar and extended scene community, the importance of the accuracy (bias error due to peak-locking, quantization, or sampling) of the centroid determination was identified and solutions proposed. But these solutions only allow partial bias corrections. To date, no systematic study of the bias error was conducted. This article bridges the gap by quantifying the bias error for different correlation peak-finding algorithms and types of sub-aperture images and by proposing a practical solution to minimize its effects. Four classes of sub-aperture images (point source, elongated laser guide star, crowded field, and solar extended scene) together with five types of peak-finding algorithms (1D parabola, the centre of gravity, Gaussian, 2D quadratic polynomial, and pyramid) are considered, in a variety of signal-to-noise conditions. The best performing peak-finding algorithm depends on the sub-aperture image type, but none is satisfactory to both bias and random errors. A practical solution is proposed that relies on the antisymmetric response of the bias to the sub-pixel position of the true centre. The solution decreases the bias by a factor of ˜7 to values of ≲ 0.02 pix. The computational cost is typically twice of current cross-correlation algorithms.

Bias estimation for the Landsat 8 operational land imager

USGS Publications Warehouse

Morfitt, Ron; Vanderwerff, Kelly

2011-01-01

The Operational Land Imager (OLI) is a pushbroom sensor that will be a part of the Landsat Data Continuity Mission (LDCM). This instrument is the latest in the line of Landsat imagers, and will continue to expand the archive of calibrated earth imagery. An important step in producing a calibrated image from instrument data is accurately accounting for the bias of the imaging detectors. Bias variability is one factor that contributes to error in bias estimation for OLI. Typically, the bias is simply estimated by averaging dark data on a per-detector basis. However, data acquired during OLI pre-launch testing exhibited bias variation that correlated well with the variation in concurrently collected data from a special set of detectors on the focal plane. These detectors are sensitive to certain electronic effects but not directly to incoming electromagnetic radiation. A method of using data from these special detectors to estimate the bias of the imaging detectors was developed, but found not to be beneficial at typical radiance levels as the detectors respond slightly when the focal plane is illuminated. In addition to bias variability, a systematic bias error is introduced by the truncation performed by the spacecraft of the 14-bit instrument data to 12-bit integers. This systematic error can be estimated and removed on average, but the per pixel quantization error remains. This paper describes the variability of the bias, the effectiveness of a new approach to estimate and compensate for it, as well as the errors due to truncation and how they are reduced.

CAUSES: On the Role of Surface Energy Budget Errors to the Warm Surface Air Temperature Error Over the Central United States

DOE PAGES

Ma, H. -Y.; Klein, S. A.; Xie, S.; ...

2018-02-27

Many weather forecast and climate models simulate warm surface air temperature (T 2m) biases over midlatitude continents during the summertime, especially over the Great Plains. We present here one of a series of papers from a multimodel intercomparison project (CAUSES: Cloud Above the United States and Errors at the Surface), which aims to evaluate the role of cloud, radiation, and precipitation biases in contributing to the T 2m bias using a short-term hindcast approach during the spring and summer of 2011. Observations are mainly from the Atmospheric Radiation Measurement Southern Great Plains sites. The present study examines the contributions ofmore » surface energy budget errors. All participating models simulate too much net shortwave and longwave fluxes at the surface but with no consistent mean bias sign in turbulent fluxes over the Central United States and Southern Great Plains. Nevertheless, biases in the net shortwave and downward longwave fluxes as well as surface evaporative fraction (EF) are contributors to T 2m bias. Radiation biases are largely affected by cloud simulations, while EF bias is largely affected by soil moisture modulated by seasonal accumulated precipitation and evaporation. An approximate equation based upon the surface energy budget is derived to further quantify the magnitudes of radiation and EF contributions to T 2m bias. Our analysis ascribes that a large EF underestimate is the dominant source of error in all models with a large positive temperature bias, whereas an EF overestimate compensates for an excess of absorbed shortwave radiation in nearly all the models with the smallest temperature bias.« less

CAUSES: On the Role of Surface Energy Budget Errors to the Warm Surface Air Temperature Error Over the Central United States

NASA Astrophysics Data System (ADS)

Ma, H.-Y.; Klein, S. A.; Xie, S.; Zhang, C.; Tang, S.; Tang, Q.; Morcrette, C. J.; Van Weverberg, K.; Petch, J.; Ahlgrimm, M.; Berg, L. K.; Cheruy, F.; Cole, J.; Forbes, R.; Gustafson, W. I.; Huang, M.; Liu, Y.; Merryfield, W.; Qian, Y.; Roehrig, R.; Wang, Y.-C.

2018-03-01

Many weather forecast and climate models simulate warm surface air temperature (T2m) biases over midlatitude continents during the summertime, especially over the Great Plains. We present here one of a series of papers from a multimodel intercomparison project (CAUSES: Cloud Above the United States and Errors at the Surface), which aims to evaluate the role of cloud, radiation, and precipitation biases in contributing to the T2m bias using a short-term hindcast approach during the spring and summer of 2011. Observations are mainly from the Atmospheric Radiation Measurement Southern Great Plains sites. The present study examines the contributions of surface energy budget errors. All participating models simulate too much net shortwave and longwave fluxes at the surface but with no consistent mean bias sign in turbulent fluxes over the Central United States and Southern Great Plains. Nevertheless, biases in the net shortwave and downward longwave fluxes as well as surface evaporative fraction (EF) are contributors to T2m bias. Radiation biases are largely affected by cloud simulations, while EF bias is largely affected by soil moisture modulated by seasonal accumulated precipitation and evaporation. An approximate equation based upon the surface energy budget is derived to further quantify the magnitudes of radiation and EF contributions to T2m bias. Our analysis ascribes that a large EF underestimate is the dominant source of error in all models with a large positive temperature bias, whereas an EF overestimate compensates for an excess of absorbed shortwave radiation in nearly all the models with the smallest temperature bias.

CAUSES: On the Role of Surface Energy Budget Errors to the Warm Surface Air Temperature Error Over the Central United States

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

Ma, H. -Y.; Klein, S. A.; Xie, S.

Many weather forecast and climate models simulate warm surface air temperature (T 2m) biases over midlatitude continents during the summertime, especially over the Great Plains. We present here one of a series of papers from a multimodel intercomparison project (CAUSES: Cloud Above the United States and Errors at the Surface), which aims to evaluate the role of cloud, radiation, and precipitation biases in contributing to the T 2m bias using a short-term hindcast approach during the spring and summer of 2011. Observations are mainly from the Atmospheric Radiation Measurement Southern Great Plains sites. The present study examines the contributions ofmore » surface energy budget errors. All participating models simulate too much net shortwave and longwave fluxes at the surface but with no consistent mean bias sign in turbulent fluxes over the Central United States and Southern Great Plains. Nevertheless, biases in the net shortwave and downward longwave fluxes as well as surface evaporative fraction (EF) are contributors to T 2m bias. Radiation biases are largely affected by cloud simulations, while EF bias is largely affected by soil moisture modulated by seasonal accumulated precipitation and evaporation. An approximate equation based upon the surface energy budget is derived to further quantify the magnitudes of radiation and EF contributions to T 2m bias. Our analysis ascribes that a large EF underestimate is the dominant source of error in all models with a large positive temperature bias, whereas an EF overestimate compensates for an excess of absorbed shortwave radiation in nearly all the models with the smallest temperature bias.« less

Investigating Surface Bias Errors in the Weather Research and Forecasting (WRF) Model using a Geographic Information System (GIS)

DTIC Science & Technology

2015-02-01

WRF ) Model using a Geographic Information System (GIS) by Jeffrey A Smith, Theresa A Foley, John W Raby, and Brian Reen...ARL-TR-7212 ● FEB 2015 US Army Research Laboratory Investigating Surface Bias Errors in the Weather Research and Forecasting ( WRF ) Model...SUBTITLE Investigating surface bias errors in the Weather Research and Forecasting ( WRF ) Model using a Geographic Information System (GIS) 5a

Mismeasurement and the resonance of strong confounders: uncorrelated errors.

PubMed

Marshall, J R; Hastrup, J L

1996-05-15

Greenland first documented (Am J Epidemiol 1980; 112:564-9) that error in the measurement of a confounder could resonate--that it could bias estimates of other study variables, and that the bias could persist even with statistical adjustment for the confounder as measured. An important question is raised by this finding: can such bias be more than trivial within the bounds of realistic data configurations? The authors examine several situations involving dichotomous and continuous data in which a confounder and a null variable are measured with error, and they assess the extent of resultant bias in estimates of the effect of the null variable. They show that, with continuous variables, measurement error amounting to 40% of observed variance in the confounder could cause the observed impact of the null study variable to appear to alter risk by as much as 30%. Similarly, they show, with dichotomous independent variables, that 15% measurement error in the form of misclassification could lead the null study variable to appear to alter risk by as much as 50%. Such bias would result only from strong confounding. Measurement error would obscure the evidence that strong confounding is a likely problem. These results support the need for every epidemiologic inquiry to include evaluations of measurement error in each variable considered.

Effects of vegetation heterogeneity and surface topography on spatial scaling of net primary productivity

NASA Astrophysics Data System (ADS)

Chen, J. M.; Chen, X.; Ju, W.

2013-03-01

Due to the heterogeneous nature of the land surface, spatial scaling is an inevitable issue in the development of land models coupled with low-resolution Earth system models (ESMs) for predicting land-atmosphere interactions and carbon-climate feedbacks. In this study, a simple spatial scaling algorithm is developed to correct errors in net primary productivity (NPP) estimates made at a coarse spatial resolution based on sub-pixel information of vegetation heterogeneity and surface topography. An eco-hydrological model BEPS-TerrainLab, which considers both vegetation and topographical effects on the vertical and lateral water flows and the carbon cycle, is used to simulate NPP at 30 m and 1 km resolutions for a 5700 km2 watershed with an elevation range from 518 m to 3767 m in the Qinling Mountain, Shaanxi Province, China. Assuming that the NPP simulated at 30 m resolution represents the reality and that at 1 km resolution is subject to errors due to sub-pixel heterogeneity, a spatial scaling index (SSI) is developed to correct the coarse resolution NPP values pixel by pixel. The agreement between the NPP values at these two resolutions is improved considerably from R2 = 0.782 to R2 = 0.884 after the correction. The mean bias error (MBE) in NPP modeled at the 1 km resolution is reduced from 14.8 g C m-2 yr-1 to 4.8 g C m-2 yr-1 in comparison with NPP modeled at 30 m resolution, where the mean NPP is 668 g C m-2 yr-1. The range of spatial variations of NPP at 30 m resolution is larger than that at 1 km resolution. Land cover fraction is the most important vegetation factor to be considered in NPP spatial scaling, and slope is the most important topographical factor for NPP spatial scaling especially in mountainous areas, because of its influence on the lateral water redistribution, affecting water table, soil moisture and plant growth. Other factors including leaf area index (LAI), elevation and aspect have small and additive effects on improving the spatial scaling between these two resolutions.

Effects of vegetation heterogeneity and surface topography on spatial scaling of net primary productivity

NASA Astrophysics Data System (ADS)

Chen, J. M.; Chen, X.; Ju, W.

2013-07-01

Due to the heterogeneous nature of the land surface, spatial scaling is an inevitable issue in the development of land models coupled with low-resolution Earth system models (ESMs) for predicting land-atmosphere interactions and carbon-climate feedbacks. In this study, a simple spatial scaling algorithm is developed to correct errors in net primary productivity (NPP) estimates made at a coarse spatial resolution based on sub-pixel information of vegetation heterogeneity and surface topography. An eco-hydrological model BEPS-TerrainLab, which considers both vegetation and topographical effects on the vertical and lateral water flows and the carbon cycle, is used to simulate NPP at 30 m and 1 km resolutions for a 5700 km2 watershed with an elevation range from 518 m to 3767 m in the Qinling Mountain, Shanxi Province, China. Assuming that the NPP simulated at 30 m resolution represents the reality and that at 1 km resolution is subject to errors due to sub-pixel heterogeneity, a spatial scaling index (SSI) is developed to correct the coarse resolution NPP values pixel by pixel. The agreement between the NPP values at these two resolutions is improved considerably from R2 = 0.782 to R2 = 0.884 after the correction. The mean bias error (MBE) in NPP modelled at the 1 km resolution is reduced from 14.8 g C m-2 yr-1 to 4.8 g C m-2 yr-1 in comparison with NPP modelled at 30 m resolution, where the mean NPP is 668 g C m-2 yr-1. The range of spatial variations of NPP at 30 m resolution is larger than that at 1 km resolution. Land cover fraction is the most important vegetation factor to be considered in NPP spatial scaling, and slope is the most important topographical factor for NPP spatial scaling especially in mountainous areas, because of its influence on the lateral water redistribution, affecting water table, soil moisture and plant growth. Other factors including leaf area index (LAI) and elevation have small and additive effects on improving the spatial scaling between these two resolutions.

Study of thin film growth kinetics of homoepitaxy by molecular beam epitaxy and pulsed laser deposition

NASA Astrophysics Data System (ADS)

Shin, Byungha

This thesis presents an extensive study of the growth kinetics during low temperature homoepitaxy by Molecular Beam Epitaxy (MBE) and Pulsed Laser Deposition (PLD) of our model system Ge(001). The range of the study covers from the sub-monolayer (sub-ML) regime to the later stage where film thickness amounts to a few thousand MLs; it also covers epitaxial breakdown in which epitaxial growth is no longer sustained and the growing phase becomes amorphous. First, we have conducted a systematic investigation of the phase shift of the RHEED intensity oscillations during Ge(001) homoepitaxy MBE for a wide range of diffraction conditions. We conclude that the phase shift is caused by the overlap of the specular spot and the Kikuchi features, in contrast to models involving dynamical scattering theory for the phase shift. We have studied the sub-ML growth of Ge(001) homoepitaxy by MBE at low temperatures using RHEED intensity oscillations obtained for a range of low incidence angles where the influence of the dynamical nature of electron scattering such as the Kikuchi features is minimized. We have developed a new model for RHEED specular intensity that includes the diffuse scattering off surface steps and the layer interference between terraces of different heights using the kinematic approximation. By using the model to interpret the measured RHEED intensity, we find the evolution of the coverage of the first 2--3 layers, from which we infer the ES barrier height to be 0.077 +/- 0.014 eV. Finally, using a dual MBE-PLD UHV chamber, we have conducted experiments under identical thermal, background, and surface preparation conditions to compare Ge(001) homoepitaxial growth morphology in PLD and MBE at low temperatures. To isolate the effect of kinetic energy of depositing species during PLD, we varied the average kinetic energy: ˜450 eV in PLD-HKE, ˜300 eV in PLD-LKE, and <1 eV in PLD-TH. At 150°C, we find that in PLD-LKE and in MBE the film morphology evolves in a similar fashion: initially irregularly shaped mounds form, followed by pyramidal mounds with edges of the square-base along <100> directions. The areal feature density is higher for PLD films than for MBE films grown at the same average growth rate and temperature. Furthermore, the dependence upon film thickness of roughness and feature separation differ for PLD and MBE. The thicknesses at which epitaxial breakdown occurs are ranked in the order PLD-HKE > PLD-LKE > MBE. At 100°C, PLD-LKE and MBE follow the same morphology evolution as at 150°C. The epitaxial thicknesses are ranked in the order PLD-LKE > MBE > PLD-TH; additionally, the surface is smoother in PLD-LKE than in MBE. Together, these results convincingly demonstrate that the enhancement of epitaxial growth---the reduction in roughness and the delay of epitaxial breakdown---are due to the kinetic energy of depositing species in PLD. To study the relaxation behavior, we varied the repetition rate from 5 Hz to 20 Hz in PLD-LKE at 100°C. However, we find no systematic effect on surface roughness by varying the repetition rate. This result is consistent with an investigation on the sub-ML growth regime of PLD-LKE by monitoring the intensity variations of the RHEED specular spot.

Errors in the estimation of approximate entropy and other recurrence-plot-derived indices due to the finite resolution of RR time series.

PubMed

García-González, Miguel A; Fernández-Chimeno, Mireya; Ramos-Castro, Juan

2009-02-01

An analysis of the errors due to the finite resolution of RR time series in the estimation of the approximate entropy (ApEn) is described. The quantification errors in the discrete RR time series produce considerable errors in the ApEn estimation (bias and variance) when the signal variability or the sampling frequency is low. Similar errors can be found in indices related to the quantification of recurrence plots. An easy way to calculate a figure of merit [the signal to resolution of the neighborhood ratio (SRN)] is proposed in order to predict when the bias in the indices could be high. When SRN is close to an integer value n, the bias is higher than when near n - 1/2 or n + 1/2. Moreover, if SRN is close to an integer value, the lower this value, the greater the bias is.

Comparison between satellite precipitation product and observation rain gauges in the Red-Thai Binh River Basin

NASA Astrophysics Data System (ADS)

Lakshmi, V.; Le, M. H.; Sutton, J. R. P.; Bui, D. D.; Bolten, J. D.

2017-12-01

The Red-ThaiBinh River is the second largest river in Vietnam in terms of economic impact and is home to around 29 million people. The river has been facing challenges for water resources allocation, which require reliable and routine hydrological assessments. However, hydrological analysis is difficult due to insufficient spatial coverage by rain gauges. Satellite-based precipitation estimates are a promising alternative with high-resolution in both time and space. This study aims at investigating the uncertainties in satellite-based precipitation product TRMM 3B42 v7.0 by comparing them against in-situ measurements over the Red-ThaiBinh River basin. The TRMM 3B42 v7.0 are assessed in terms of seasonal, monthly and daily variations over a 17-year period (1998 - 2014). Preliminary results indicate that at a daily scale, except for low Mean Bias Error (MBE), satellite based rainfall product has weak relationship with ground observation data, indicating by average performance of 0.326 and -0.485 for correlation coefficient and Nash Sutcliffe Efficiency (NSE), respectively. At monthly scale, we observe that the TRMM 3B42 v7.0 has higher correlation with the correlation increased significantly to 0.863 and NSE of 0.522. By analyzing wet season (May - October) and dry season (November - April) separately we find that the correlation between the TRMM 3B42 v7.0 with ground observations were higher for wet season than the dry season.

A DoD/DESAT Phase I Final Report,

DTIC Science & Technology

1982-06-30

19-22, 1982 in Albuquerque, New Mexico: 1) Spatially Correlated Redistribution of Mn and Ge in Inl.x Gax As MBE layers, E. Silberg , T.Y. Chang, and...Urbana-Champaign. 2) Spatially correlated redistribution of Mn and Ge in InGaAs MBE layers in conjunction with E. Silberg , T.Y. Chang and E.A. Caridi at...AlGaAs MBE layers. 2) A group headed by Ors. T. Chang and E. Silberg of Bell Laboratories in Holmdel, New Jersey, have been involved in growing Mn and

Ultrahigh Error Threshold for Surface Codes with Biased Noise

NASA Astrophysics Data System (ADS)

Tuckett, David K.; Bartlett, Stephen D.; Flammia, Steven T.

2018-02-01

We show that a simple modification of the surface code can exhibit an enormous gain in the error correction threshold for a noise model in which Pauli Z errors occur more frequently than X or Y errors. Such biased noise, where dephasing dominates, is ubiquitous in many quantum architectures. In the limit of pure dephasing noise we find a threshold of 43.7(1)% using a tensor network decoder proposed by Bravyi, Suchara, and Vargo. The threshold remains surprisingly large in the regime of realistic noise bias ratios, for example 28.2(2)% at a bias of 10. The performance is, in fact, at or near the hashing bound for all values of the bias. The modified surface code still uses only weight-4 stabilizers on a square lattice, but merely requires measuring products of Y instead of Z around the faces, as this doubles the number of useful syndrome bits associated with the dominant Z errors. Our results demonstrate that large efficiency gains can be found by appropriately tailoring codes and decoders to realistic noise models, even under the locality constraints of topological codes.

Increasing skepticism toward potential liars: effects of existential threat on veracity judgments and the moderating role of honesty norm activation

PubMed Central

Schindler, Simon; Reinhard, Marc-André

2015-01-01

With the present research, we investigated effects of existential threat on veracity judgments. According to several meta-analyses, people judge potentially deceptive messages of other people as true rather than as false (so-called truth bias). This judgmental bias has been shown to depend on how people weigh the error of judging a true message as a lie (error 1) and the error of judging a lie as a true message (error 2). The weight of these errors has been further shown to be affected by situational variables. Given that research on terror management theory has found evidence that mortality salience (MS) increases the sensitivity toward the compliance of cultural norms, especially when they are of focal attention, we assumed that when the honesty norm is activated, MS affects judgmental error weighing and, consequently, judgmental biases. Specifically, activating the norm of honesty should decrease the weight of error 1 (the error of judging a true message as a lie) and increase the weight of error 2 (the error of judging a lie as a true message) when mortality is salient. In a first study, we found initial evidence for this assumption. Furthermore, the change in error weighing should reduce the truth bias, automatically resulting in better detection accuracy of actual lies and worse accuracy of actual true statements. In two further studies, we manipulated MS and honesty norm activation before participants judged several videos containing actual truths or lies. Results revealed evidence for our prediction. Moreover, in Study 3, the truth bias was increased after MS when group solidarity was previously emphasized. PMID:26388815

A method for sensitivity analysis to assess the effects of measurement error in multiple exposure variables using external validation data.

PubMed

Agogo, George O; van der Voet, Hilko; van 't Veer, Pieter; Ferrari, Pietro; Muller, David C; Sánchez-Cantalejo, Emilio; Bamia, Christina; Braaten, Tonje; Knüppel, Sven; Johansson, Ingegerd; van Eeuwijk, Fred A; Boshuizen, Hendriek C

2016-10-13

Measurement error in self-reported dietary intakes is known to bias the association between dietary intake and a health outcome of interest such as risk of a disease. The association can be distorted further by mismeasured confounders, leading to invalid results and conclusions. It is, however, difficult to adjust for the bias in the association when there is no internal validation data. We proposed a method to adjust for the bias in the diet-disease association (hereafter, association), due to measurement error in dietary intake and a mismeasured confounder, when there is no internal validation data. The method combines prior information on the validity of the self-report instrument with the observed data to adjust for the bias in the association. We compared the proposed method with the method that ignores the confounder effect, and with the method that ignores measurement errors completely. We assessed the sensitivity of the estimates to various magnitudes of measurement error, error correlations and uncertainty in the literature-reported validation data. We applied the methods to fruits and vegetables (FV) intakes, cigarette smoking (confounder) and all-cause mortality data from the European Prospective Investigation into Cancer and Nutrition study. Using the proposed method resulted in about four times increase in the strength of association between FV intake and mortality. For weakly correlated errors, measurement error in the confounder minimally affected the hazard ratio estimate for FV intake. The effect was more pronounced for strong error correlations. The proposed method permits sensitivity analysis on measurement error structures and accounts for uncertainties in the reported validity coefficients. The method is useful in assessing the direction and quantifying the magnitude of bias in the association due to measurement errors in the confounders.

A method of bias correction for maximal reliability with dichotomous measures.

PubMed

Penev, Spiridon; Raykov, Tenko

2010-02-01

This paper is concerned with the reliability of weighted combinations of a given set of dichotomous measures. Maximal reliability for such measures has been discussed in the past, but the pertinent estimator exhibits a considerable bias and mean squared error for moderate sample sizes. We examine this bias, propose a procedure for bias correction, and develop a more accurate asymptotic confidence interval for the resulting estimator. In most empirically relevant cases, the bias correction and mean squared error correction can be performed simultaneously. We propose an approximate (asymptotic) confidence interval for the maximal reliability coefficient, discuss the implementation of this estimator, and investigate the mean squared error of the associated asymptotic approximation. We illustrate the proposed methods using a numerical example.

Effects of vibration on inertial wind-tunnel model attitude measurement devices

NASA Technical Reports Server (NTRS)

Young, Clarence P., Jr.; Buehrle, Ralph D.; Balakrishna, S.; Kilgore, W. Allen

1994-01-01

Results of an experimental study of a wind tunnel model inertial angle-of-attack sensor response to a simulated dynamic environment are presented. The inertial device cannot distinguish between the gravity vector and the centrifugal accelerations associated with wind tunnel model vibration, this situation results in a model attitude measurement bias error. Significant bias error in model attitude measurement was found for the model system tested. The model attitude bias error was found to be vibration mode and amplitude dependent. A first order correction model was developed and used for estimating attitude measurement bias error due to dynamic motion. A method for correcting the output of the model attitude inertial sensor in the presence of model dynamics during on-line wind tunnel operation is proposed.

Error Biases in Inner and Overt Speech: Evidence from Tongue Twisters

ERIC Educational Resources Information Center

Corley, Martin; Brocklehurst, Paul H.; Moat, H. Susannah

2011-01-01

To compare the properties of inner and overt speech, Oppenheim and Dell (2008) counted participants' self-reported speech errors when reciting tongue twisters either overtly or silently and found a bias toward substituting phonemes that resulted in words in both conditions, but a bias toward substituting similar phonemes only when speech was…

Effects of substrate orientation on the growth of InSb nanostructures by molecular beam epitaxy

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

Chou, C. Y.; Torfi, A.; Pei, C.

2016-05-09

In this work, the effects of substrate orientation on InSb quantum structure growth by molecular beam epitaxy (MBE) are presented. Motivated by the observation that (411) evolves naturally as a stable facet during MBE crystal growth, comparison studies have been carried out to investigate the effects of the crystal orientation of the underlying GaSb substrate on the growth of InSb by MBE. By depositing InSb on a number of different substrate orientations, namely: (100), (311), (411), and (511), a higher nanostructure density was observed on the (411) surface compared with the other orientations. This result suggests that the (411) orientationmore » presents a superior surface in MBE growth to develop a super-flat GaSb buffer surface, naturally favorable for nanostructure growth.« less

Low-Temperature Growth and Doping of Mercury-Based II-Vi Multiple Quantum Well Structures by Molecular Beam Epitaxy

NASA Astrophysics Data System (ADS)

Lansari, Yamina

The growth of Hg-based single layers and multiple quantum well structures by conventional molecular beam epitaxy (MBE) and photoassisted MBE was studied. The use of photoassisted MBE, an epitaxial growth technique developed at NCSU, has resulted in a substantial reduction of the film growth temperature. Indeed, substrate temperatures 50 to 100^circC lower than those customarily used by others for conventional MBE growth of Hg-based layers were successfully employed. Photoassisted MBE allowed the preparation of excellent structural quality HgTe layers (FWHM for the (400) diffraction peak ~ 40 arcsec), HgCdTe layers (FWHM for the (400) diffraction peak ~ 14 arcsec), and HgTeCdTe superlattices (FWHM for the (400) diffraction peak ~ 28 arcsec). In addition, n-type and p-type modulation-doping of Hg-based multilayers was accomplished by photoassisted MBE. This technique has been shown to have a significant effect on the growth process kinetics as well as on the desorption rates of the film species, thereby affecting dopant incorporation mechanisms and allowing for the successful substitutional doping of the multilayer structures. Finally, surface morphology studies were completed using scanning electron microscopy (SEM) and Nomarsky optical microscopy to study the effects of substrate surface preparation, growth initiation, and growth parameters on the density of pyramidal hillocks, a common growth defect plaguing the Hg-based layers grown in the (100) direction. Conditions which minimize the hillock density for (100) film growth have been determined.

CCD image sensor induced error in PIV applications

NASA Astrophysics Data System (ADS)

Legrand, M.; Nogueira, J.; Vargas, A. A.; Ventas, R.; Rodríguez-Hidalgo, M. C.

2014-06-01

The readout procedure of charge-coupled device (CCD) cameras is known to generate some image degradation in different scientific imaging fields, especially in astrophysics. In the particular field of particle image velocimetry (PIV), widely extended in the scientific community, the readout procedure of the interline CCD sensor induces a bias in the registered position of particle images. This work proposes simple procedures to predict the magnitude of the associated measurement error. Generally, there are differences in the position bias for the different images of a certain particle at each PIV frame. This leads to a substantial bias error in the PIV velocity measurement (˜0.1 pixels). This is the order of magnitude that other typical PIV errors such as peak-locking may reach. Based on modern CCD technology and architecture, this work offers a description of the readout phenomenon and proposes a modeling for the CCD readout bias error magnitude. This bias, in turn, generates a velocity measurement bias error when there is an illumination difference between two successive PIV exposures. The model predictions match the experiments performed with two 12-bit-depth interline CCD cameras (MegaPlus ES 4.0/E incorporating the Kodak KAI-4000M CCD sensor with 4 megapixels). For different cameras, only two constant values are needed to fit the proposed calibration model and predict the error from the readout procedure. Tests by different researchers using different cameras would allow verification of the model, that can be used to optimize acquisition setups. Simple procedures to obtain these two calibration values are also described.

Big Data and Large Sample Size: A Cautionary Note on the Potential for Bias

PubMed Central

Chambers, David A.; Glasgow, Russell E.

2014-01-01

Abstract A number of commentaries have suggested that large studies are more reliable than smaller studies and there is a growing interest in the analysis of “big data” that integrates information from many thousands of persons and/or different data sources. We consider a variety of biases that are likely in the era of big data, including sampling error, measurement error, multiple comparisons errors, aggregation error, and errors associated with the systematic exclusion of information. Using examples from epidemiology, health services research, studies on determinants of health, and clinical trials, we conclude that it is necessary to exercise greater caution to be sure that big sample size does not lead to big inferential errors. Despite the advantages of big studies, large sample size can magnify the bias associated with error resulting from sampling or study design. Clin Trans Sci 2014; Volume #: 1–5 PMID:25043853

Publication bias was not a good reason to discourage trials with low power.

PubMed

Borm, George F; den Heijer, Martin; Zielhuis, Gerhard A

2009-01-01

The objective was to investigate whether it is justified to discourage trials with less than 80% power. Trials with low power are unlikely to produce conclusive results, but their findings can be used by pooling then in a meta-analysis. However, such an analysis may be biased, because trials with low power are likely to have a nonsignificant result and are less likely to be published than trials with a statistically significant outcome. We simulated several series of studies with varying degrees of publication bias and then calculated the "real" one-sided type I error and the bias of meta-analyses with a "nominal" error rate (significance level) of 2.5%. In single trials, in which heterogeneity was set at zero, low, and high, the error rates were 2.3%, 4.7%, and 16.5%, respectively. In multiple trials with 80%-90% power and a publication rate of 90% when the results were nonsignificant, the error rates could be as high as 5.1%. When the power was 50% and the publication rate of non-significant results was 60%, the error rates did not exceed 5.3%, whereas the bias was at most 15% of the difference used in the power calculation. The impact of publication bias does not warrant the exclusion of trials with 50% power.

Using Audit Information to Adjust Parameter Estimates for Data Errors in Clinical Trials

PubMed Central

Shepherd, Bryan E.; Shaw, Pamela A.; Dodd, Lori E.

2013-01-01

Background Audits are often performed to assess the quality of clinical trial data, but beyond detecting fraud or sloppiness, the audit data is generally ignored. In earlier work using data from a non-randomized study, Shepherd and Yu (2011) developed statistical methods to incorporate audit results into study estimates, and demonstrated that audit data could be used to eliminate bias. Purpose In this manuscript we examine the usefulness of audit-based error-correction methods in clinical trial settings where a continuous outcome is of primary interest. Methods We demonstrate the bias of multiple linear regression estimates in general settings with an outcome that may have errors and a set of covariates for which some may have errors and others, including treatment assignment, are recorded correctly for all subjects. We study this bias under different assumptions including independence between treatment assignment, covariates, and data errors (conceivable in a double-blinded randomized trial) and independence between treatment assignment and covariates but not data errors (possible in an unblinded randomized trial). We review moment-based estimators to incorporate the audit data and propose new multiple imputation estimators. The performance of estimators is studied in simulations. Results When treatment is randomized and unrelated to data errors, estimates of the treatment effect using the original error-prone data (i.e., ignoring the audit results) are unbiased. In this setting, both moment and multiple imputation estimators incorporating audit data are more variable than standard analyses using the original data. In contrast, in settings where treatment is randomized but correlated with data errors and in settings where treatment is not randomized, standard treatment effect estimates will be biased. And in all settings, parameter estimates for the original, error-prone covariates will be biased. Treatment and covariate effect estimates can be corrected by incorporating audit data using either the multiple imputation or moment-based approaches. Bias, precision, and coverage of confidence intervals improve as the audit size increases. Limitations The extent of bias and the performance of methods depend on the extent and nature of the error as well as the size of the audit. This work only considers methods for the linear model. Settings much different than those considered here need further study. Conclusions In randomized trials with continuous outcomes and treatment assignment independent of data errors, standard analyses of treatment effects will be unbiased and are recommended. However, if treatment assignment is correlated with data errors or other covariates, naive analyses may be biased. In these settings, and when covariate effects are of interest, approaches for incorporating audit results should be considered. PMID:22848072

Physical Validation of TRMM TMI and PR Monthly Rain Products Over Oklahoma

NASA Technical Reports Server (NTRS)

Fisher, Brad L.

2004-01-01

The Tropical Rainfall Measuring Mission (TRMM) provides monthly rainfall estimates using data collected by the TRMM satellite. These estimates cover a substantial fraction of the earth's surface. The physical validation of TRMM estimates involves corroborating the accuracy of spaceborne estimates of areal rainfall by inferring errors and biases from ground-based rain estimates. The TRMM error budget consists of two major sources of error: retrieval and sampling. Sampling errors are intrinsic to the process of estimating monthly rainfall and occur because the satellite extrapolates monthly rainfall from a small subset of measurements collected only during satellite overpasses. Retrieval errors, on the other hand, are related to the process of collecting measurements while the satellite is overhead. One of the big challenges confronting the TRMM validation effort is how to best estimate these two main components of the TRMM error budget, which are not easily decoupled. This four-year study computed bulk sampling and retrieval errors for the TRMM microwave imager (TMI) and the precipitation radar (PR) by applying a technique that sub-samples gauge data at TRMM overpass times. Gridded monthly rain estimates are then computed from the monthly bulk statistics of the collected samples, providing a sensor-dependent gauge rain estimate that is assumed to include a TRMM equivalent sampling error. The sub-sampled gauge rain estimates are then used in conjunction with the monthly satellite and gauge (without sub- sampling) estimates to decouple retrieval and sampling errors. The computed mean sampling errors for the TMI and PR were 5.9% and 7.796, respectively, in good agreement with theoretical predictions. The PR year-to-year retrieval biases exceeded corresponding TMI biases, but it was found that these differences were partially due to negative TMI biases during cold months and positive TMI biases during warm months.

Accelerated carrier recombination by grain boundary/edge defects in MBE grown transition metal dichalcogenides

NASA Astrophysics Data System (ADS)

Chen, Ke; Roy, Anupam; Rai, Amritesh; Movva, Hema C. P.; Meng, Xianghai; He, Feng; Banerjee, Sanjay K.; Wang, Yaguo

2018-05-01

Defect-carrier interaction in transition metal dichalcogenides (TMDs) plays important roles in carrier relaxation dynamics and carrier transport, which determines the performance of electronic devices. With femtosecond laser time-resolved spectroscopy, we investigated the effect of grain boundary/edge defects on the ultrafast dynamics of photoexcited carrier in molecular beam epitaxy (MBE)-grown MoTe2 and MoSe2. We found that, comparing with exfoliated samples, the carrier recombination rate in MBE-grown samples accelerates by about 50 times. We attribute this striking difference to the existence of abundant grain boundary/edge defects in MBE-grown samples, which can serve as effective recombination centers for the photoexcited carriers. We also observed coherent acoustic phonons in both exfoliated and MBE-grown MoTe2, indicating strong electron-phonon coupling in this materials. Our measured sound velocity agrees well with the previously reported result of theoretical calculation. Our findings provide a useful reference for the fundamental parameters: carrier lifetime and sound velocity and reveal the undiscovered carrier recombination effect of grain boundary/edge defects, both of which will facilitate the defect engineering in TMD materials for high speed opto-electronics.

Temporal and Spatial Simulation of Atmospheric Pollutant PM2.5 Changes and Risk Assessment of Population Exposure to Pollution Using Optimization Algorithms of the Back Propagation-Artificial Neural Network Model and GIS

PubMed Central

Zhang, Ping; Hong, Bo; He, Liang; Cheng, Fei; Zhao, Peng; Wei, Cailiang; Liu, Yunhui

2015-01-01

PM2.5 pollution has become of increasing public concern because of its relative importance and sensitivity to population health risks. Accurate predictions of PM2.5 pollution and population exposure risks are crucial to developing effective air pollution control strategies. We simulated and predicted the temporal and spatial changes of PM2.5 concentration and population exposure risks, by coupling optimization algorithms of the Back Propagation-Artificial Neural Network (BP-ANN) model and a geographical information system (GIS) in Xi’an, China, for 2013, 2020, and 2025. Results indicated that PM2.5 concentration was positively correlated with GDP, SO2, and NO2, while it was negatively correlated with population density, average temperature, precipitation, and wind speed. Principal component analysis of the PM2.5 concentration and its influencing factors’ variables extracted four components that accounted for 86.39% of the total variance. Correlation coefficients of the Levenberg-Marquardt (trainlm) and elastic (trainrp) algorithms were more than 0.8, the index of agreement (IA) ranged from 0.541 to 0.863 and from 0.502 to 0.803 by trainrp and trainlm algorithms, respectively; mean bias error (MBE) and Root Mean Square Error (RMSE) indicated that the predicted values were very close to the observed values, and the accuracy of trainlm algorithm was better than the trainrp. Compared to 2013, temporal and spatial variation of PM2.5 concentration and risk of population exposure to pollution decreased in 2020 and 2025. The high-risk areas of population exposure to PM2.5 were mainly distributed in the northern region, where there is downtown traffic, abundant commercial activity, and more exhaust emissions. A moderate risk zone was located in the southern region associated with some industrial pollution sources, and there were mainly low-risk areas in the western and eastern regions, which are predominantly residential and educational areas. PMID:26426030

Temporal and Spatial Simulation of Atmospheric Pollutant PM2.5 Changes and Risk Assessment of Population Exposure to Pollution Using Optimization Algorithms of the Back Propagation-Artificial Neural Network Model and GIS.

PubMed

Zhang, Ping; Hong, Bo; He, Liang; Cheng, Fei; Zhao, Peng; Wei, Cailiang; Liu, Yunhui

2015-09-29

PM2.5 pollution has become of increasing public concern because of its relative importance and sensitivity to population health risks. Accurate predictions of PM2.5 pollution and population exposure risks are crucial to developing effective air pollution control strategies. We simulated and predicted the temporal and spatial changes of PM2.5 concentration and population exposure risks, by coupling optimization algorithms of the Back Propagation-Artificial Neural Network (BP-ANN) model and a geographical information system (GIS) in Xi'an, China, for 2013, 2020, and 2025. Results indicated that PM2.5 concentration was positively correlated with GDP, SO₂, and NO₂, while it was negatively correlated with population density, average temperature, precipitation, and wind speed. Principal component analysis of the PM2.5 concentration and its influencing factors' variables extracted four components that accounted for 86.39% of the total variance. Correlation coefficients of the Levenberg-Marquardt (trainlm) and elastic (trainrp) algorithms were more than 0.8, the index of agreement (IA) ranged from 0.541 to 0.863 and from 0.502 to 0.803 by trainrp and trainlm algorithms, respectively; mean bias error (MBE) and Root Mean Square Error (RMSE) indicated that the predicted values were very close to the observed values, and the accuracy of trainlm algorithm was better than the trainrp. Compared to 2013, temporal and spatial variation of PM2.5 concentration and risk of population exposure to pollution decreased in 2020 and 2025. The high-risk areas of population exposure to PM2.5 were mainly distributed in the northern region, where there is downtown traffic, abundant commercial activity, and more exhaust emissions. A moderate risk zone was located in the southern region associated with some industrial pollution sources, and there were mainly low-risk areas in the western and eastern regions, which are predominantly residential and educational areas.

44. VIEW TO SOUTHWEST; MBE BUILDING, THIRD FLOOR, CONDUCTORS' LOCKER ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

44. VIEW TO SOUTHWEST; MBE BUILDING, THIRD FLOOR, CONDUCTORS' LOCKER ROOM LAVATORY (Dobson) - Los Angeles Union Passenger Terminal, Mail, Baggage, & Express Building, 800 North Alameda Street, Los Angeles, Los Angeles County, CA

42. VIEW TO SOUTHEAST; MBE BUILDING, THIRD FLOOR, CONDUCTORS' LOCKER ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

42. VIEW TO SOUTHEAST; MBE BUILDING, THIRD FLOOR, CONDUCTORS' LOCKER ROOM INTERIOR (Dobson) - Los Angeles Union Passenger Terminal, Mail, Baggage, & Express Building, 800 North Alameda Street, Los Angeles, Los Angeles County, CA

43. VIEW TO NORTHEAST; MBE BUILDING, THIRD FLOOR, CONDUCTORS' LOCKER ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

43. VIEW TO NORTHEAST; MBE BUILDING, THIRD FLOOR, CONDUCTORS' LOCKER ROOM INTERIOR (Dobson) - Los Angeles Union Passenger Terminal, Mail, Baggage, & Express Building, 800 North Alameda Street, Los Angeles, Los Angeles County, CA

Evaluation of normalization methods for cDNA microarray data by k-NN classification

PubMed Central

Wu, Wei; Xing, Eric P; Myers, Connie; Mian, I Saira; Bissell, Mina J

2005-01-01

Background Non-biological factors give rise to unwanted variations in cDNA microarray data. There are many normalization methods designed to remove such variations. However, to date there have been few published systematic evaluations of these techniques for removing variations arising from dye biases in the context of downstream, higher-order analytical tasks such as classification. Results Ten location normalization methods that adjust spatial- and/or intensity-dependent dye biases, and three scale methods that adjust scale differences were applied, individually and in combination, to five distinct, published, cancer biology-related cDNA microarray data sets. Leave-one-out cross-validation (LOOCV) classification error was employed as the quantitative end-point for assessing the effectiveness of a normalization method. In particular, a known classifier, k-nearest neighbor (k-NN), was estimated from data normalized using a given technique, and the LOOCV error rate of the ensuing model was computed. We found that k-NN classifiers are sensitive to dye biases in the data. Using NONRM and GMEDIAN as baseline methods, our results show that single-bias-removal techniques which remove either spatial-dependent dye bias (referred later as spatial effect) or intensity-dependent dye bias (referred later as intensity effect) moderately reduce LOOCV classification errors; whereas double-bias-removal techniques which remove both spatial- and intensity effect reduce LOOCV classification errors even further. Of the 41 different strategies examined, three two-step processes, IGLOESS-SLFILTERW7, ISTSPLINE-SLLOESS and IGLOESS-SLLOESS, all of which removed intensity effect globally and spatial effect locally, appear to reduce LOOCV classification errors most consistently and effectively across all data sets. We also found that the investigated scale normalization methods do not reduce LOOCV classification error. Conclusion Using LOOCV error of k-NNs as the evaluation criterion, three double-bias-removal normalization strategies, IGLOESS-SLFILTERW7, ISTSPLINE-SLLOESS and IGLOESS-SLLOESS, outperform other strategies for removing spatial effect, intensity effect and scale differences from cDNA microarray data. The apparent sensitivity of k-NN LOOCV classification error to dye biases suggests that this criterion provides an informative measure for evaluating normalization methods. All the computational tools used in this study were implemented using the R language for statistical computing and graphics. PMID:16045803

Evaluation of normalization methods for cDNA microarray data by k-NN classification.

PubMed

Wu, Wei; Xing, Eric P; Myers, Connie; Mian, I Saira; Bissell, Mina J

2005-07-26

Non-biological factors give rise to unwanted variations in cDNA microarray data. There are many normalization methods designed to remove such variations. However, to date there have been few published systematic evaluations of these techniques for removing variations arising from dye biases in the context of downstream, higher-order analytical tasks such as classification. Ten location normalization methods that adjust spatial- and/or intensity-dependent dye biases, and three scale methods that adjust scale differences were applied, individually and in combination, to five distinct, published, cancer biology-related cDNA microarray data sets. Leave-one-out cross-validation (LOOCV) classification error was employed as the quantitative end-point for assessing the effectiveness of a normalization method. In particular, a known classifier, k-nearest neighbor (k-NN), was estimated from data normalized using a given technique, and the LOOCV error rate of the ensuing model was computed. We found that k-NN classifiers are sensitive to dye biases in the data. Using NONRM and GMEDIAN as baseline methods, our results show that single-bias-removal techniques which remove either spatial-dependent dye bias (referred later as spatial effect) or intensity-dependent dye bias (referred later as intensity effect) moderately reduce LOOCV classification errors; whereas double-bias-removal techniques which remove both spatial- and intensity effect reduce LOOCV classification errors even further. Of the 41 different strategies examined, three two-step processes, IGLOESS-SLFILTERW7, ISTSPLINE-SLLOESS and IGLOESS-SLLOESS, all of which removed intensity effect globally and spatial effect locally, appear to reduce LOOCV classification errors most consistently and effectively across all data sets. We also found that the investigated scale normalization methods do not reduce LOOCV classification error. Using LOOCV error of k-NNs as the evaluation criterion, three double-bias-removal normalization strategies, IGLOESS-SLFILTERW7, ISTSPLINE-SLLOESS and IGLOESS-SLLOESS, outperform other strategies for removing spatial effect, intensity effect and scale differences from cDNA microarray data. The apparent sensitivity of k-NN LOOCV classification error to dye biases suggests that this criterion provides an informative measure for evaluating normalization methods. All the computational tools used in this study were implemented using the R language for statistical computing and graphics.

Systematic Biases in Parameter Estimation of Binary Black-Hole Mergers

NASA Technical Reports Server (NTRS)

Littenberg, Tyson B.; Baker, John G.; Buonanno, Alessandra; Kelly, Bernard J.

2012-01-01

Parameter estimation of binary-black-hole merger events in gravitational-wave data relies on matched filtering techniques, which, in turn, depend on accurate model waveforms. Here we characterize the systematic biases introduced in measuring astrophysical parameters of binary black holes by applying the currently most accurate effective-one-body templates to simulated data containing non-spinning numerical-relativity waveforms. For advanced ground-based detectors, we find that the systematic biases are well within the statistical error for realistic signal-to-noise ratios (SNR). These biases grow to be comparable to the statistical errors at high signal-to-noise ratios for ground-based instruments (SNR approximately 50) but never dominate the error budget. At the much larger signal-to-noise ratios expected for space-based detectors, these biases will become large compared to the statistical errors but are small enough (at most a few percent in the black-hole masses) that we expect they should not affect broad astrophysical conclusions that may be drawn from the data.

Calibration of remotely sensed proportion or area estimates for misclassification error

Treesearch

Raymond L. Czaplewski; Glenn P. Catts

1992-01-01

Classifications of remotely sensed data contain misclassification errors that bias areal estimates. Monte Carlo techniques were used to compare two statistical methods that correct or calibrate remotely sensed areal estimates for misclassification bias using reference data from an error matrix. The inverse calibration estimator was consistently superior to the...

Correction of stream quality trends for the effects of laboratory measurement bias

USGS Publications Warehouse

Alexander, Richard B.; Smith, Richard A.; Schwarz, Gregory E.

1993-01-01

We present a statistical model relating measurements of water quality to associated errors in laboratory methods. Estimation of the model allows us to correct trends in water quality for long-term and short-term variations in laboratory measurement errors. An illustration of the bias correction method for a large national set of stream water quality and quality assurance data shows that reductions in the bias of estimates of water quality trend slopes are achieved at the expense of increases in the variance of these estimates. Slight improvements occur in the precision of estimates of trend in bias by using correlative information on bias and water quality to estimate random variations in measurement bias. The results of this investigation stress the need for reliable, long-term quality assurance data and efficient statistical methods to assess the effects of measurement errors on the detection of water quality trends.

Are phonological influences on lexical (mis)selection the result of a monitoring bias?

PubMed Central

Ratinckx, Elie; Ferreira, Victor S.; Hartsuiker, Robert J.

2009-01-01

A monitoring bias account is often used to explain speech error patterns that seem to be the result of an interactive language production system, like phonological influences on lexical selection errors. A biased monitor is suggested to detect and covertly correct certain errors more often than others. For instance, this account predicts that errors which are phonologically similar to intended words are harder to detect than ones that are phonologically dissimilar. To test this, we tried to elicit phonological errors under the same conditions that show other kinds of lexical selection errors. In five experiments, we presented participants with high cloze probability sentence fragments followed by a picture that was either semantically related, a homophone of a semantically related word, or phonologically related to the (implicit) last word of the sentence. All experiments elicited semantic completions or homophones of semantic completions, but none elicited phonological completions. This finding is hard to reconcile with a monitoring bias account and is better explained with an interactive production system. Additionally, this finding constrains the amount of bottom-up information flow in interactive models. PMID:18942035

Mathematical analysis study for radar data processing and enchancement. Part 2: Modeling of propagation path errors

NASA Technical Reports Server (NTRS)

James, R.; Brownlow, J. D.

1985-01-01

A study is performed under NASA contract to evaluate data from an AN/FPS-16 radar installed for support of flight programs at Dryden Flight Research Facility of NASA Ames Research Center. The purpose of this study is to provide information necessary for improving post-flight data reduction and knowledge of accuracy of derived radar quantities. Tracking data from six flights are analyzed. Noise and bias errors in raw tracking data are determined for each of the flights. A discussion of an altitude bias error during all of the tracking missions is included. This bias error is defined by utilizing pressure altitude measurements made during survey flights. Four separate filtering methods, representative of the most widely used optimal estimation techniques for enhancement of radar tracking data, are analyzed for suitability in processing both real-time and post-mission data. Additional information regarding the radar and its measurements, including typical noise and bias errors in the range and angle measurements, is also presented. This report is in two parts. This is part 2, a discussion of the modeling of propagation path errors.

A Nonlinear Adaptive Filter for Gyro Thermal Bias Error Cancellation

NASA Technical Reports Server (NTRS)

Galante, Joseph M.; Sanner, Robert M.

2012-01-01

Deterministic errors in angular rate gyros, such as thermal biases, can have a significant impact on spacecraft attitude knowledge. In particular, thermal biases are often the dominant error source in MEMS gyros after calibration. Filters, such as J\\,fEKFs, are commonly used to mitigate the impact of gyro errors and gyro noise on spacecraft closed loop pointing accuracy, but often have difficulty in rapidly changing thermal environments and can be computationally expensive. In this report an existing nonlinear adaptive filter is used as the basis for a new nonlinear adaptive filter designed to estimate and cancel thermal bias effects. A description of the filter is presented along with an implementation suitable for discrete-time applications. A simulation analysis demonstrates the performance of the filter in the presence of noisy measurements and provides a comparison with existing techniques.

Photoluminescence Mapping and Angle-Resolved Photoluminescence of MBE-Grown InGaAs/GaAs RC LED and VCSEL Structures

DTIC Science & Technology

2002-06-03

resonant-cavity light-emitting diodes (RC LEDs) and vertical-cavity surface-emitting lasers ( VCSELs )] fabricated from molecular beam epitaxy (MBE)-grown...grown 8470-631. by molecular beam epitaxy (MBE) using a Riber 32P E-mail address: muszal@ite.waw.pl (0. Muszalski). reactor. Details of the growth can be... molecular beams hit the center of a rotating sion features of RC LED and VCSEL structures, as well sample. However, due to the transversal distribution of as

Understanding the many-body expansion for large systems. III. Critical role of four-body terms, counterpoise corrections, and cutoffs.

PubMed

Liu, Kuan-Yu; Herbert, John M

2017-10-28

Papers I and II in this series [R. M. Richard et al., J. Chem. Phys. 141, 014108 (2014); K. U. Lao et al., ibid. 144, 164105 (2016)] have attempted to shed light on precision and accuracy issues affecting the many-body expansion (MBE), which only manifest in larger systems and thus have received scant attention in the literature. Many-body counterpoise (CP) corrections are shown to accelerate convergence of the MBE, which otherwise suffers from a mismatch between how basis-set superposition error affects subsystem versus supersystem calculations. In water clusters ranging in size up to (H 2 O) 37 , four-body terms prove necessary to achieve accurate results for both total interaction energies and relative isomer energies, but the sheer number of tetramers makes the use of cutoff schemes essential. To predict relative energies of (H 2 O) 20 isomers, two approximations based on a lower level of theory are introduced and an ONIOM-type procedure is found to be very well converged with respect to the appropriate MBE benchmark, namely, a CP-corrected supersystem calculation at the same level of theory. Results using an energy-based cutoff scheme suggest that if reasonable approximations to the subsystem energies are available (based on classical multipoles, say), then the number of requisite subsystem calculations can be reduced even more dramatically than when distance-based thresholds are employed. The end result is several accurate four-body methods that do not require charge embedding, and which are stable in large basis sets such as aug-cc-pVTZ that have sometimes proven problematic for fragment-based quantum chemistry methods. Even with aggressive thresholding, however, the four-body approach at the self-consistent field level still requires roughly ten times more processors to outmatch the performance of the corresponding supersystem calculation, in test cases involving 1500-1800 basis functions.

Understanding the many-body expansion for large systems. III. Critical role of four-body terms, counterpoise corrections, and cutoffs

NASA Astrophysics Data System (ADS)

Liu, Kuan-Yu; Herbert, John M.

2017-10-01

Papers I and II in this series [R. M. Richard et al., J. Chem. Phys. 141, 014108 (2014); K. U. Lao et al., ibid. 144, 164105 (2016)] have attempted to shed light on precision and accuracy issues affecting the many-body expansion (MBE), which only manifest in larger systems and thus have received scant attention in the literature. Many-body counterpoise (CP) corrections are shown to accelerate convergence of the MBE, which otherwise suffers from a mismatch between how basis-set superposition error affects subsystem versus supersystem calculations. In water clusters ranging in size up to (H2O)37, four-body terms prove necessary to achieve accurate results for both total interaction energies and relative isomer energies, but the sheer number of tetramers makes the use of cutoff schemes essential. To predict relative energies of (H2O)20 isomers, two approximations based on a lower level of theory are introduced and an ONIOM-type procedure is found to be very well converged with respect to the appropriate MBE benchmark, namely, a CP-corrected supersystem calculation at the same level of theory. Results using an energy-based cutoff scheme suggest that if reasonable approximations to the subsystem energies are available (based on classical multipoles, say), then the number of requisite subsystem calculations can be reduced even more dramatically than when distance-based thresholds are employed. The end result is several accurate four-body methods that do not require charge embedding, and which are stable in large basis sets such as aug-cc-pVTZ that have sometimes proven problematic for fragment-based quantum chemistry methods. Even with aggressive thresholding, however, the four-body approach at the self-consistent field level still requires roughly ten times more processors to outmatch the performance of the corresponding supersystem calculation, in test cases involving 1500-1800 basis functions.

15. VIEW TO SOUTHWEST; EAST BACK MBE BUILDING, THIRD AND ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

15. VIEW TO SOUTHWEST; EAST BACK MBE BUILDING, THIRD AND SECOND FLOORS; GASOLINE PUMPS CENTER (Dobson) - Los Angeles Union Passenger Terminal, Mail, Baggage, & Express Building, 800 North Alameda Street, Los Angeles, Los Angeles County, CA

Self-calibration of photometric redshift scatter in weak-lensing surveys

DOE PAGES

Zhang, Pengjie; Pen, Ue -Li; Bernstein, Gary

2010-06-11

Photo-z errors, especially catastrophic errors, are a major uncertainty for precision weak lensing cosmology. We find that the shear-(galaxy number) density and density-density cross correlation measurements between photo-z bins, available from the same lensing surveys, contain valuable information for self-calibration of the scattering probabilities between the true-z and photo-z bins. The self-calibration technique we propose does not rely on cosmological priors nor parameterization of the photo-z probability distribution function, and preserves all of the cosmological information available from shear-shear measurement. We estimate the calibration accuracy through the Fisher matrix formalism. We find that, for advanced lensing surveys such as themore » planned stage IV surveys, the rate of photo-z outliers can be determined with statistical uncertainties of 0.01-1% for z < 2 galaxies. Among the several sources of calibration error that we identify and investigate, the galaxy distribution bias is likely the most dominant systematic error, whereby photo-z outliers have different redshift distributions and/or bias than non-outliers from the same bin. This bias affects all photo-z calibration techniques based on correlation measurements. As a result, galaxy bias variations of O(0.1) produce biases in photo-z outlier rates similar to the statistical errors of our method, so this galaxy distribution bias may bias the reconstructed scatters at several-σ level, but is unlikely to completely invalidate the self-calibration technique.« less

Self-consistent expansion for the molecular beam epitaxy equation

NASA Astrophysics Data System (ADS)

Katzav, Eytan

2002-03-01

Motivated by a controversy over the correct results derived from the dynamic renormalization group (DRG) analysis of the nonlinear molecular beam epitaxy (MBE) equation, a self-consistent expansion for the nonlinear MBE theory is considered. The scaling exponents are obtained for spatially correlated noise of the general form D(r-->-r',t-t')=2D0\\|r-->- r'\\|2ρ-dδ(t-t'). I find a lower critical dimension dc(ρ)=4+2ρ, above which the linear MBE solution appears. Below the lower critical dimension a ρ-dependent strong-coupling solution is found. These results help to resolve the controversy over the correct exponents that describe nonlinear MBE, using a reliable method that proved itself in the past by giving reasonable results for the strong-coupling regime of the Kardar-Parisi-Zhang system (for d>1), where DRG failed to do so.

Self-consistent expansion for the molecular beam epitaxy equation.

PubMed

Katzav, Eytan

2002-03-01

Motivated by a controversy over the correct results derived from the dynamic renormalization group (DRG) analysis of the nonlinear molecular beam epitaxy (MBE) equation, a self-consistent expansion for the nonlinear MBE theory is considered. The scaling exponents are obtained for spatially correlated noise of the general form D(r-r('),t-t('))=2D(0)[r-->-r(')](2rho-d)delta(t-t(')). I find a lower critical dimension d(c)(rho)=4+2rho, above which the linear MBE solution appears. Below the lower critical dimension a rho-dependent strong-coupling solution is found. These results help to resolve the controversy over the correct exponents that describe nonlinear MBE, using a reliable method that proved itself in the past by giving reasonable results for the strong-coupling regime of the Kardar-Parisi-Zhang system (for d>1), where DRG failed to do so.

Demonstration of Nonlinearity Bias in the Measurement of the Apparent Diffusion Coefficient in Multicenter Trials

PubMed Central

Malyarenko, Dariya; Newitt, David; Wilmes, Lisa; Tudorica, Alina; Helmer, Karl G.; Arlinghaus, Lori R.; Jacobs, Michael A.; Jajamovich, Guido; Taouli, Bachir; Yankeelov, Thomas E.; Huang, Wei; Chenevert, Thomas L.

2015-01-01

Purpose Characterize system-specific bias across common magnetic resonance imaging (MRI) platforms for quantitative diffusion measurements in multicenter trials. Methods Diffusion weighted imaging (DWI) was performed on an ice-water phantom along the superior-inferior (SI) and right-left (RL) orientations spanning ±150 mm. The same scanning protocol was implemented on 14 MRI systems at seven imaging centers. The bias was estimated as a deviation of measured from known apparent diffusion coefficient (ADC) along individual DWI directions. The relative contributions of gradient nonlinearity, shim errors, imaging gradients and eddy currents were assessed independently. The observed bias errors were compared to numerical models. Results The measured systematic ADC errors scaled quadratically with offset from isocenter, and ranged between −55% (SI) and 25% (RL). Nonlinearity bias was dependent on system design and diffusion gradient direction. Consistent with numerical models, minor ADC errors (±5%) due to shim, imaging and eddy currents were mitigated by double echo DWI and image co-registration of individual gradient directions. Conclusion The analysis confirms gradient nonlinearity as a major source of spatial DW bias and variability in off-center ADC measurements across MRI platforms, with minor contributions from shim, imaging gradients and eddy currents. The developed protocol enables empiric description of systematic bias in multicenter quantitative DWI studies. PMID:25940607

Demonstration of nonlinearity bias in the measurement of the apparent diffusion coefficient in multicenter trials.

PubMed

Malyarenko, Dariya I; Newitt, David; J Wilmes, Lisa; Tudorica, Alina; Helmer, Karl G; Arlinghaus, Lori R; Jacobs, Michael A; Jajamovich, Guido; Taouli, Bachir; Yankeelov, Thomas E; Huang, Wei; Chenevert, Thomas L

2016-03-01

Characterize system-specific bias across common magnetic resonance imaging (MRI) platforms for quantitative diffusion measurements in multicenter trials. Diffusion weighted imaging (DWI) was performed on an ice-water phantom along the superior-inferior (SI) and right-left (RL) orientations spanning ± 150 mm. The same scanning protocol was implemented on 14 MRI systems at seven imaging centers. The bias was estimated as a deviation of measured from known apparent diffusion coefficient (ADC) along individual DWI directions. The relative contributions of gradient nonlinearity, shim errors, imaging gradients, and eddy currents were assessed independently. The observed bias errors were compared with numerical models. The measured systematic ADC errors scaled quadratically with offset from isocenter, and ranged between -55% (SI) and 25% (RL). Nonlinearity bias was dependent on system design and diffusion gradient direction. Consistent with numerical models, minor ADC errors (± 5%) due to shim, imaging and eddy currents were mitigated by double echo DWI and image coregistration of individual gradient directions. The analysis confirms gradient nonlinearity as a major source of spatial DW bias and variability in off-center ADC measurements across MRI platforms, with minor contributions from shim, imaging gradients and eddy currents. The developed protocol enables empiric description of systematic bias in multicenter quantitative DWI studies. © 2015 Wiley Periodicals, Inc.

Safety and Toxicology of Magnolol and Honokiol.

PubMed

Sarrica, Andrea; Kirika, Natalja; Romeo, Margherita; Salmona, Mario; Diomede, Luisa

2018-06-20

Magnolia officinalis and Magnolia obovata bark extracts have been used for thousands of years in Chinese and Japanese traditional medicines and are still widely employed as herbal preparations for their sedative, antioxidant, anti-inflammatory, antibiotic, and antispastic effects. Neolignans, particularly magnolol and honokiol, are the main substances responsible for the beneficial properties of the magnolia bark extract (MBE). The content of magnolol and honokiol in MBE depends on different factors, including the Magnolia plant species, the area of origin, the part of the plant employed, and the method used to prepare the extract. The biological and pharmacological activities of magnolol and honokiol have been extensively investigated. Here we review the safety and toxicological properties of magnolol and honokiol as pure substances or as components of concentrated MBE, including the potential side-effects in humans after oral intake. In vitro and in vivo genotoxicity studies indicated that concentrated MBE has no mutagenic and genotoxic potential, while a subchronic study performed according to OECD (Organisation for Economic Co-operation and Development) guidelines established a no adverse effect level for concentrated MBE > 240 mg/kg b.w/d. Similar to other dietary polyphenols, magnolol and honokiol are subject to glucuronidation, and despite a relatively quick clearance, an interaction with pharmaceutical active principles or other herbal constituents cannot be excluded. However, intervention trials employing concentrated MBE for up to 1 y did not report adverse effects. In conclusion, over the recent years different food safety authorities evaluated magnolol and honokiol and considered them safe. Georg Thieme Verlag KG Stuttgart · New York.

Sonographic estimation of fetal weight: comparison of bias, precision and consistency using 12 different formulae.

PubMed

Anderson, N G; Jolley, I J; Wells, J E

2007-08-01

To determine the major sources of error in ultrasonographic assessment of fetal weight and whether they have changed over the last decade. We performed a prospective observational study in 1991 and again in 2000 of a mixed-risk pregnancy population, estimating fetal weight within 7 days of delivery. In 1991, the Rose and McCallum formula was used for 72 deliveries. Inter- and intraobserver agreement was assessed within this group. Bland-Altman measures of agreement from log data were calculated as ratios. We repeated the study in 2000 in 208 consecutive deliveries, comparing predicted and actual weights for 12 published equations using Bland-Altman and percentage error methods. We compared bias (mean percentage error), precision (SD percentage error), and their consistency across the weight ranges. 95% limits of agreement ranged from - 4.4% to + 3.3% for inter- and intraobserver estimates, but were - 18.0% to 24.0% for estimated and actual birth weight. There was no improvement in accuracy between 1991 and 2000. In 2000 only six of the 12 published formulae had overall bias within 7% and precision within 15%. There was greater bias and poorer precision in nearly all equations if the birth weight was < 1,000 g. Observer error is a relatively minor component of the error in estimating fetal weight; error due to the equation is a larger source of error. Improvements in ultrasound technology have not improved the accuracy of estimating fetal weight. Comparison of methods of estimating fetal weight requires statistical methods that can separate out bias, precision and consistency. Estimating fetal weight in the very low birth weight infant is subject to much greater error than it is in larger babies. Copyright (c) 2007 ISUOG. Published by John Wiley & Sons, Ltd.

Bias correction by use of errors-in-variables regression models in studies with K-X-ray fluorescence bone lead measurements.

PubMed

Lamadrid-Figueroa, Héctor; Téllez-Rojo, Martha M; Angeles, Gustavo; Hernández-Ávila, Mauricio; Hu, Howard

2011-01-01

In-vivo measurement of bone lead by means of K-X-ray fluorescence (KXRF) is the preferred biological marker of chronic exposure to lead. Unfortunately, considerable measurement error associated with KXRF estimations can introduce bias in estimates of the effect of bone lead when this variable is included as the exposure in a regression model. Estimates of uncertainty reported by the KXRF instrument reflect the variance of the measurement error and, although they can be used to correct the measurement error bias, they are seldom used in epidemiological statistical analyzes. Errors-in-variables regression (EIV) allows for correction of bias caused by measurement error in predictor variables, based on the knowledge of the reliability of such variables. The authors propose a way to obtain reliability coefficients for bone lead measurements from uncertainty data reported by the KXRF instrument and compare, by the use of Monte Carlo simulations, results obtained using EIV regression models vs. those obtained by the standard procedures. Results of the simulations show that Ordinary Least Square (OLS) regression models provide severely biased estimates of effect, and that EIV provides nearly unbiased estimates. Although EIV effect estimates are more imprecise, their mean squared error is much smaller than that of OLS estimates. In conclusion, EIV is a better alternative than OLS to estimate the effect of bone lead when measured by KXRF. Copyright © 2010 Elsevier Inc. All rights reserved.

Complacency and Automation Bias in the Use of Imperfect Automation.

PubMed

Wickens, Christopher D; Clegg, Benjamin A; Vieane, Alex Z; Sebok, Angelia L

2015-08-01

We examine the effects of two different kinds of decision-aiding automation errors on human-automation interaction (HAI), occurring at the first failure following repeated exposure to correctly functioning automation. The two errors are incorrect advice, triggering the automation bias, and missing advice, reflecting complacency. Contrasts between analogous automation errors in alerting systems, rather than decision aiding, have revealed that alerting false alarms are more problematic to HAI than alerting misses are. Prior research in decision aiding, although contrasting the two aiding errors (incorrect vs. missing), has confounded error expectancy. Participants performed an environmental process control simulation with and without decision aiding. For those with the aid, automation dependence was created through several trials of perfect aiding performance, and an unexpected automation error was then imposed in which automation was either gone (one group) or wrong (a second group). A control group received no automation support. The correct aid supported faster and more accurate diagnosis and lower workload. The aid failure degraded all three variables, but "automation wrong" had a much greater effect on accuracy, reflecting the automation bias, than did "automation gone," reflecting the impact of complacency. Some complacency was manifested for automation gone, by a longer latency and more modest reduction in accuracy. Automation wrong, creating the automation bias, appears to be a more problematic form of automation error than automation gone, reflecting complacency. Decision-aiding automation should indicate its lower degree of confidence in uncertain environments to avoid the automation bias. © 2015, Human Factors and Ergonomics Society.

Characterizing Satellite Rainfall Errors based on Land Use and Land Cover and Tracing Error Source in Hydrologic Model Simulation

NASA Astrophysics Data System (ADS)

Gebregiorgis, A. S.; Peters-Lidard, C. D.; Tian, Y.; Hossain, F.

2011-12-01

Hydrologic modeling has benefited from operational production of high resolution satellite rainfall products. The global coverage, near-real time availability, spatial and temporal sampling resolutions have advanced the application of physically based semi-distributed and distributed hydrologic models for wide range of environmental decision making processes. Despite these successes, the existence of uncertainties due to indirect way of satellite rainfall estimates and hydrologic models themselves remain a challenge in making meaningful and more evocative predictions. This study comprises breaking down of total satellite rainfall error into three independent components (hit bias, missed precipitation and false alarm), characterizing them as function of land use and land cover (LULC), and tracing back the source of simulated soil moisture and runoff error in physically based distributed hydrologic model. Here, we asked "on what way the three independent total bias components, hit bias, missed, and false precipitation, affect the estimation of soil moisture and runoff in physically based hydrologic models?" To understand the clear picture of the outlined question above, we implemented a systematic approach by characterizing and decomposing the total satellite rainfall error as a function of land use and land cover in Mississippi basin. This will help us to understand the major source of soil moisture and runoff errors in hydrologic model simulation and trace back the information to algorithm development and sensor type which ultimately helps to improve algorithms better and will improve application and data assimilation in future for GPM. For forest and woodland and human land use system, the soil moisture was mainly dictated by the total bias for 3B42-RT, CMORPH, and PERSIANN products. On the other side, runoff error was largely dominated by hit bias than the total bias. This difference occurred due to the presence of missed precipitation which is a major contributor to the total bias both during the summer and winter seasons. Missed precipitation, most likely light rain and rain over snow cover, has significant effect on soil moisture and are less capable of producing runoff that results runoff dependency on the hit bias only.

Minority Business Enterprise/Women's Business Enterprise (MBE/WBE) overview

EPA Pesticide Factsheets

The data base allows Minority Business Enterprise/Women's Business Enterprise (MBE/WBE) Coordinators to input fair share goals negotiated by EPA and the recipient. This system also provides to all users the ability to see recipient fair share goals.

50. VIEW TO EAST; SOUTH END OF MBE BUILDING, FIRST ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

50. VIEW TO EAST; SOUTH END OF MBE BUILDING, FIRST FLOOR; SAFE, DOOR OPEN ELECTRONIC FLASH INTERIOR ILLUMINATION (Andersen) - Los Angeles Union Passenger Terminal, Mail, Baggage, & Express Building, 800 North Alameda Street, Los Angeles, Los Angeles County, CA

39. VIEW TO NORTHEAST; WEST FRONT MBE BUILDING, FIRST FLOOR, ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

39. VIEW TO NORTHEAST; WEST FRONT MBE BUILDING, FIRST FLOOR, FRED HARVEY NEWSSTAND STOREROOM (AREA BURNED BY VANDALS) (Dobson) - Los Angeles Union Passenger Terminal, Mail, Baggage, & Express Building, 800 North Alameda Street, Los Angeles, Los Angeles County, CA

MBE growth of vertical-cavity surface-emitting laser structure without real-time monitoring

NASA Astrophysics Data System (ADS)

Wu, C. Z.; Tsou, Y.; Tsai, C. M.

1999-05-01

Evaluation of producing a vertical-cavity surface-emitting laser (VCSEL) epitaxial structure by molecular beam epitaxy (MBE) without resorting to any real-time monitoring technique is reported. Continuous grading of Al xGa 1- xAs between x=0.12 to x=0.92 was simply achieved by changing the Al and Ga cell temperatures in no more than three steps per DBR period. Highly uniform DBR and VCSEL structures were demonstrated with a multi-wafer MBE system. Run-to-run standard deviation of reflectance spectrum center wavelength was 0.5% and 1.4% for VCSEL etalon wavelength.

The Ciprofloxacin Impact on Biofilm Formation by Proteus Mirabilis and P. Vulgaris Strains

PubMed Central

Kwiecinska-Pirog, Joanna; Skowron, Krzysztof; Bartczak, Wojciech; Gospodarek-Komkowska, Eugenia

2016-01-01

Background Proteus spp. bacilli belong to opportunistic human pathogens, which are primarily responsible for urinary tract and wound infections. An important virulence factor is their ability to form biofilms that greatly reduce the effectiveness of antibiotics in the site of infection. Objectives The aim of this study was to determine the value of the minimum concentration of ciprofloxacin that eradicates a biofilm of Proteus spp. strains. Materials and Methods A biofilm formation of 20 strains of P. mirabilis and 20 strains of P. vulgaris were evaluated by a spectrophotometric method using 0.1% 2, 3, 5-Triphenyl-tetrazolium chloride solution (TTC, AVANTORTM). On the basis of the results of the absorbance of the formazan, a degree of reduction of biofilm and minimum biofilm eradication (MBE) values of MBE50 and MBE90 were determined. Results All tested strains formed a biofilm. A value of 1.0 μg/mL ciprofloxacin is MBE50 for the strains of both tested species. An MBE90 value of ciprofloxacin for isolates of P. vulgaris was 2 μg/mL and for P. mirabilis was 512 μg/mL. Conclusions Minimum biofilm eradication values of ciprofloxacin obtained in the study are close to the values of the minimal inhibition concentration (MIC). PMID:27303616

Enhancing the far-UV sensitivity of silicon CMOS imaging arrays

NASA Astrophysics Data System (ADS)

Retherford, K. D.; Bai, Yibin; Ryu, Kevin K.; Gregory, J. A.; Welander, Paul B.; Davis, Michael W.; Greathouse, Thomas K.; Winter, Gregory S.; Suntharalingam, Vyshnavi; Beletic, James W.

2014-07-01

We report our progress toward optimizing backside-illuminated silicon PIN CMOS devices developed by Teledyne Imaging Sensors (TIS) for far-UV planetary science applications. This project was motivated by initial measurements at Southwest Research Institute (SwRI) of the far-UV responsivity of backside-illuminated silicon PIN photodiode test structures described in Bai et al., SPIE, 2008, which revealed a promising QE in the 100-200 nm range as reported in Davis et al., SPIE, 2012. Our effort to advance the capabilities of thinned silicon wafers capitalizes on recent innovations in molecular beam epitaxy (MBE) doping processes. Key achievements to date include: 1) Representative silicon test wafers were fabricated by TIS, and set up for MBE processing at MIT Lincoln Laboratory (LL); 2) Preliminary far-UV detector QE simulation runs were completed to aid MBE layer design; 3) Detector fabrication was completed through the pre-MBE step; and 4) Initial testing of the MBE doping process was performed on monitoring wafers, with detailed quality assessments. Early results suggest that potential challenges in optimizing the UV-sensitivity of silicon PIN type CMOS devices, compared with similar UV enhancement methods established for CCDs, have been mitigated through our newly developed methods. We will discuss the potential advantages of our approach and briefly describe future development steps.

MBE HgCdTe for HDVIP Devices: Horizontal Integration in the US HgCdTe FPA Industry

NASA Astrophysics Data System (ADS)

Aqariden, F.; Elsworth, J.; Zhao, J.; Grein, C. H.; Sivananthan, S.

2012-10-01

Molecular beam epitaxy (MBE) growth of HgCdTe offers the possibility of fabricating multilayer device structures with an almost unlimited choice of infrared sensor designs for focal-plane array (FPA) fabrication. HgCdTe offers two major advantages that explain its dominance in the infrared photon detector marketplace. The thermal generation rate per unit volume of the material is lower and the quantum efficiency for photon absorption in the infrared is higher in HgCdTe than in any competing material—it yields devices with quantum efficiencies as high as 0.99. Recently, EPIR Technologies and DRS Infrared Technologies agreed to collaborate and examine: (i) the feasibility of employing MBE HgCdTe in the fabrication of high-density vertically interconnected photodiodes (HDVIPs), which are usually fabricated with liquid-phase epitaxy material, and (ii) the potential benefits of horizontal integration, with EPIR supplying the MBE materials to DRS for device and array fabrication. The team designed and developed passivation-absorber-passivation structures that are heavily used by DRS. This paper provides an overview of the characteristics of HDVIP devices and arrays fabricated from MBE HgCdTe and the anticipated advantages of horizontal integration in the industry. Material growth, device fabrication, and test results are presented.

Real-Time PPP Based on the Coupling Estimation of Clock Bias and Orbit Error with Broadcast Ephemeris.

PubMed

Pan, Shuguo; Chen, Weirong; Jin, Xiaodong; Shi, Xiaofei; He, Fan

2015-07-22

Satellite orbit error and clock bias are the keys to precise point positioning (PPP). The traditional PPP algorithm requires precise satellite products based on worldwide permanent reference stations. Such an algorithm requires considerable work and hardly achieves real-time performance. However, real-time positioning service will be the dominant mode in the future. IGS is providing such an operational service (RTS) and there are also commercial systems like Trimble RTX in operation. On the basis of the regional Continuous Operational Reference System (CORS), a real-time PPP algorithm is proposed to apply the coupling estimation of clock bias and orbit error. The projection of orbit error onto the satellite-receiver range has the same effects on positioning accuracy with clock bias. Therefore, in satellite clock estimation, part of the orbit error can be absorbed by the clock bias and the effects of residual orbit error on positioning accuracy can be weakened by the evenly distributed satellite geometry. In consideration of the simple structure of pseudorange equations and the high precision of carrier-phase equations, the clock bias estimation method coupled with orbit error is also improved. Rovers obtain PPP results by receiving broadcast ephemeris and real-time satellite clock bias coupled with orbit error. By applying the proposed algorithm, the precise orbit products provided by GNSS analysis centers are rendered no longer necessary. On the basis of previous theoretical analysis, a real-time PPP system was developed. Some experiments were then designed to verify this algorithm. Experimental results show that the newly proposed approach performs better than the traditional PPP based on International GNSS Service (IGS) real-time products. The positioning accuracies of the rovers inside and outside the network are improved by 38.8% and 36.1%, respectively. The PPP convergence speeds are improved by up to 61.4% and 65.9%. The new approach can change the traditional PPP mode because of its advantages of independence, high positioning precision, and real-time performance. It could be an alternative solution for regional positioning service before global PPP service comes into operation.

Real-Time PPP Based on the Coupling Estimation of Clock Bias and Orbit Error with Broadcast Ephemeris

PubMed Central

Pan, Shuguo; Chen, Weirong; Jin, Xiaodong; Shi, Xiaofei; He, Fan

2015-01-01

Satellite orbit error and clock bias are the keys to precise point positioning (PPP). The traditional PPP algorithm requires precise satellite products based on worldwide permanent reference stations. Such an algorithm requires considerable work and hardly achieves real-time performance. However, real-time positioning service will be the dominant mode in the future. IGS is providing such an operational service (RTS) and there are also commercial systems like Trimble RTX in operation. On the basis of the regional Continuous Operational Reference System (CORS), a real-time PPP algorithm is proposed to apply the coupling estimation of clock bias and orbit error. The projection of orbit error onto the satellite-receiver range has the same effects on positioning accuracy with clock bias. Therefore, in satellite clock estimation, part of the orbit error can be absorbed by the clock bias and the effects of residual orbit error on positioning accuracy can be weakened by the evenly distributed satellite geometry. In consideration of the simple structure of pseudorange equations and the high precision of carrier-phase equations, the clock bias estimation method coupled with orbit error is also improved. Rovers obtain PPP results by receiving broadcast ephemeris and real-time satellite clock bias coupled with orbit error. By applying the proposed algorithm, the precise orbit products provided by GNSS analysis centers are rendered no longer necessary. On the basis of previous theoretical analysis, a real-time PPP system was developed. Some experiments were then designed to verify this algorithm. Experimental results show that the newly proposed approach performs better than the traditional PPP based on International GNSS Service (IGS) real-time products. The positioning accuracies of the rovers inside and outside the network are improved by 38.8% and 36.1%, respectively. The PPP convergence speeds are improved by up to 61.4% and 65.9%. The new approach can change the traditional PPP mode because of its advantages of independence, high positioning precision, and real-time performance. It could be an alternative solution for regional positioning service before global PPP service comes into operation. PMID:26205276

Using Analysis Increments (AI) to Estimate and Correct Systematic Errors in the Global Forecast System (GFS) Online

NASA Astrophysics Data System (ADS)

Bhargava, K.; Kalnay, E.; Carton, J.; Yang, F.

2017-12-01

Systematic forecast errors, arising from model deficiencies, form a significant portion of the total forecast error in weather prediction models like the Global Forecast System (GFS). While much effort has been expended to improve models, substantial model error remains. The aim here is to (i) estimate the model deficiencies in the GFS that lead to systematic forecast errors, (ii) implement an online correction (i.e., within the model) scheme to correct GFS following the methodology of Danforth et al. [2007] and Danforth and Kalnay [2008, GRL]. Analysis Increments represent the corrections that new observations make on, in this case, the 6-hr forecast in the analysis cycle. Model bias corrections are estimated from the time average of the analysis increments divided by 6-hr, assuming that initial model errors grow linearly and first ignoring the impact of observation bias. During 2012-2016, seasonal means of the 6-hr model bias are generally robust despite changes in model resolution and data assimilation systems, and their broad continental scales explain their insensitivity to model resolution. The daily bias dominates the sub-monthly analysis increments and consists primarily of diurnal and semidiurnal components, also requiring a low dimensional correction. Analysis increments in 2015 and 2016 are reduced over oceans, which is attributed to improvements in the specification of the SSTs. These results encourage application of online correction, as suggested by Danforth and Kalnay, for mean, seasonal and diurnal and semidiurnal model biases in GFS to reduce both systematic and random errors. As the error growth in the short-term is still linear, estimated model bias corrections can be added as a forcing term in the model tendency equation to correct online. Preliminary experiments with GFS, correcting temperature and specific humidity online show reduction in model bias in 6-hr forecast. This approach can then be used to guide and optimize the design of sub-grid scale physical parameterizations, more accurate discretization of the model dynamics, boundary conditions, radiative transfer codes, and other potential model improvements which can then replace the empirical correction scheme. The analysis increments also provide guidance in testing new physical parameterizations.

Use of the Magnetic Field for Improving Gyroscopes’ Biases Estimation

PubMed Central

Munoz Diaz, Estefania; de Ponte Müller, Fabian; García Domínguez, Juan Jesús

2017-01-01

An accurate orientation is crucial to a satisfactory position in pedestrian navigation. The orientation estimation, however, is greatly affected by errors like the biases of gyroscopes. In order to minimize the error in the orientation, the biases of gyroscopes must be estimated and subtracted. In the state of the art it has been proposed, but not proved, that the estimation of the biases can be accomplished using magnetic field measurements. The objective of this work is to evaluate the effectiveness of using magnetic field measurements to estimate the biases of medium-cost micro-electromechanical sensors (MEMS) gyroscopes. We carry out the evaluation with experiments that cover both, quasi-error-free turn rate and magnetic measurements and medium-cost MEMS turn rate and magnetic measurements. The impact of different homogeneous magnetic field distributions and magnetically perturbed environments is analyzed. Additionally, the effect of the successful biases subtraction on the orientation and the estimated trajectory is detailed. Our results show that the use of magnetic field measurements is beneficial to the correct biases estimation. Further, we show that different magnetic field distributions affect differently the biases estimation process. Moreover, the biases are likewise correctly estimated under perturbed magnetic fields. However, for indoor and urban scenarios the biases estimation process is very slow. PMID:28398232

Catastrophic photometric redshift errors: Weak-lensing survey requirements

DOE PAGES

Bernstein, Gary; Huterer, Dragan

2010-01-11

We study the sensitivity of weak lensing surveys to the effects of catastrophic redshift errors - cases where the true redshift is misestimated by a significant amount. To compute the biases in cosmological parameters, we adopt an efficient linearized analysis where the redshift errors are directly related to shifts in the weak lensing convergence power spectra. We estimate the number N spec of unbiased spectroscopic redshifts needed to determine the catastrophic error rate well enough that biases in cosmological parameters are below statistical errors of weak lensing tomography. While the straightforward estimate of N spec is ~10 6 we findmore » that using only the photometric redshifts with z ≤ 2.5 leads to a drastic reduction in N spec to ~ 30,000 while negligibly increasing statistical errors in dark energy parameters. Therefore, the size of spectroscopic survey needed to control catastrophic errors is similar to that previously deemed necessary to constrain the core of the z s – z p distribution. We also study the efficacy of the recent proposal to measure redshift errors by cross-correlation between the photo-z and spectroscopic samples. We find that this method requires ~ 10% a priori knowledge of the bias and stochasticity of the outlier population, and is also easily confounded by lensing magnification bias. In conclusion, the cross-correlation method is therefore unlikely to supplant the need for a complete spectroscopic redshift survey of the source population.« less

GPS measurement error gives rise to spurious 180 degree turning angles and strong directional biases in animal movement data.

PubMed

Hurford, Amy

2009-05-20

Movement data are frequently collected using Global Positioning System (GPS) receivers, but recorded GPS locations are subject to errors. While past studies have suggested methods to improve location accuracy, mechanistic movement models utilize distributions of turning angles and directional biases and these data present a new challenge in recognizing and reducing the effect of measurement error. I collected locations from a stationary GPS collar, analyzed a probabilistic model and used Monte Carlo simulations to understand how measurement error affects measured turning angles and directional biases. Results from each of the three methods were in complete agreement: measurement error gives rise to a systematic bias where a stationary animal is most likely to be measured as turning 180 degrees or moving towards a fixed point in space. These spurious effects occur in GPS data when the measured distance between locations is <20 meters. Measurement error must be considered as a possible cause of 180 degree turning angles in GPS data. Consequences of failing to account for measurement error are predicting overly tortuous movement, numerous returns to previously visited locations, inaccurately predicting species range, core areas, and the frequency of crossing linear features. By understanding the effect of GPS measurement error, ecologists are able to disregard false signals to more accurately design conservation plans for endangered wildlife.

Reducing Modeling Error of Graphical Methods for Estimating Volume of Distribution Measurements in PIB-PET study

PubMed Central

Guo, Hongbin; Renaut, Rosemary A; Chen, Kewei; Reiman, Eric M

2010-01-01

Graphical analysis methods are widely used in positron emission tomography quantification because of their simplicity and model independence. But they may, particularly for reversible kinetics, lead to bias in the estimated parameters. The source of the bias is commonly attributed to noise in the data. Assuming a two-tissue compartmental model, we investigate the bias that originates from modeling error. This bias is an intrinsic property of the simplified linear models used for limited scan durations, and it is exaggerated by random noise and numerical quadrature error. Conditions are derived under which Logan's graphical method either over- or under-estimates the distribution volume in the noise-free case. The bias caused by modeling error is quantified analytically. The presented analysis shows that the bias of graphical methods is inversely proportional to the dissociation rate. Furthermore, visual examination of the linearity of the Logan plot is not sufficient for guaranteeing that equilibrium has been reached. A new model which retains the elegant properties of graphical analysis methods is presented, along with a numerical algorithm for its solution. We perform simulations with the fibrillar amyloid β radioligand [11C] benzothiazole-aniline using published data from the University of Pittsburgh and Rotterdam groups. The results show that the proposed method significantly reduces the bias due to modeling error. Moreover, the results for data acquired over a 70 minutes scan duration are at least as good as those obtained using existing methods for data acquired over a 90 minutes scan duration. PMID:20493196

Experiences from the testing of a theory for modelling groundwater flow in heterogeneous media

USGS Publications Warehouse

Christensen, S.; Cooley, R.L.

2002-01-01

Usually, small-scale model error is present in groundwater modelling because the model only represents average system characteristics having the same form as the drift and small-scale variability is neglected. These errors cause the true errors of a regression model to be correlated. Theory and an example show that the errors also contribute to bias in the estimates of model parameters. This bias originates from model nonlinearity. In spite of this bias, predictions of hydraulic head are nearly unbiased if the model intrinsic nonlinearity is small. Individual confidence and prediction intervals are accurate if the t-statistic is multiplied by a correction factor. The correction factor can be computed from the true error second moment matrix, which can be determined when the stochastic properties of the system characteristics are known.

Experience gained in testing a theory for modelling groundwater flow in heterogeneous media

USGS Publications Warehouse

Christensen, S.; Cooley, R.L.

2002-01-01

Usually, small-scale model error is present in groundwater modelling because the model only represents average system characteristics having the same form as the drift, and small-scale variability is neglected. These errors cause the true errors of a regression model to be correlated. Theory and an example show that the errors also contribute to bias in the estimates of model parameters. This bias originates from model nonlinearity. In spite of this bias, predictions of hydraulic head are nearly unbiased if the model intrinsic nonlinearity is small. Individual confidence and prediction intervals are accurate if the t-statistic is multiplied by a correction factor. The correction factor can be computed from the true error second moment matrix, which can be determined when the stochastic properties of the system characteristics are known.

Diagnosing Crime and Diagnosing Disease: Bias Reduction Strategies in the Forensic and Clinical Sciences.

PubMed

Lockhart, Joseph J; Satya-Murti, Saty

2017-11-01

Cognitive effort is an essential part of both forensic and clinical decision-making. Errors occur in both fields because the cognitive process is complex and prone to bias. We performed a selective review of full-text English language literature on cognitive bias leading to diagnostic and forensic errors. Earlier work (1970-2000) concentrated on classifying and raising bias awareness. Recently (2000-2016), the emphasis has shifted toward strategies for "debiasing." While the forensic sciences have focused on the control of misleading contextual cues, clinical debiasing efforts have relied on checklists and hypothetical scenarios. No single generally applicable and effective bias reduction strategy has emerged so far. Generalized attempts at bias elimination have not been particularly successful. It is time to shift focus to the study of errors within specific domains, and how to best communicate uncertainty in order to improve decision making on the part of both the expert and the trier-of-fact. © 2017 American Academy of Forensic Sciences.

52. VIEW TO EAST; SOUTH END OF MBE BUILDING, SECOND ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

52. VIEW TO EAST; SOUTH END OF MBE BUILDING, SECOND FLOOR; HIGHLY ALTERED INTERIOR OFFICE SPACE, FORMERLY REGIONAL OFFICES OF REA (Andersen) - Los Angeles Union Passenger Terminal, Mail, Baggage, & Express Building, 800 North Alameda Street, Los Angeles, Los Angeles County, CA

Measurement of the Errors of Service Altimeter Installations During Landing-Approach and Take-Off Operations

NASA Technical Reports Server (NTRS)

Gracey, William; Jewel, Joseph W., Jr.; Carpenter, Gene T.

1960-01-01

The overall errors of the service altimeter installations of a variety of civil transport, military, and general-aviation airplanes have been experimentally determined during normal landing-approach and take-off operations. The average height above the runway at which the data were obtained was about 280 feet for the landings and about 440 feet for the take-offs. An analysis of the data obtained from 196 airplanes during 415 landing approaches and from 70 airplanes during 152 take-offs showed that: 1. The overall error of the altimeter installations in the landing- approach condition had a probable value (50 percent probability) of +/- 36 feet and a maximum probable value (99.7 percent probability) of +/- 159 feet with a bias of +10 feet. 2. The overall error in the take-off condition had a probable value of +/- 47 feet and a maximum probable value of +/- 207 feet with a bias of -33 feet. 3. The overall errors of the military airplanes were generally larger than those of the civil transports in both the landing-approach and take-off conditions. In the landing-approach condition the probable error and the maximum probable error of the military airplanes were +/- 43 and +/- 189 feet, respectively, with a bias of +15 feet, whereas those for the civil transports were +/- 22 and +/- 96 feet, respectively, with a bias of +1 foot. 4. The bias values of the error distributions (+10 feet for the landings and -33 feet for the take-offs) appear to represent a measure of the hysteresis characteristics (after effect and recovery) and friction of the instrument and the pressure lag of the tubing-instrument system.

Bias and heteroscedastic memory error in self-reported health behavior: an investigation using covariance structure analysis

PubMed Central

Kupek, Emil

2002-01-01

Background Frequent use of self-reports for investigating recent and past behavior in medical research requires statistical techniques capable of analyzing complex sources of bias associated with this methodology. In particular, although decreasing accuracy of recalling more distant past events is commonplace, the bias due to differential in memory errors resulting from it has rarely been modeled statistically. Methods Covariance structure analysis was used to estimate the recall error of self-reported number of sexual partners for past periods of varying duration and its implication for the bias. Results Results indicated increasing levels of inaccuracy for reports about more distant past. Considerable positive bias was found for a small fraction of respondents who reported ten or more partners in the last year, last two years and last five years. This is consistent with the effect of heteroscedastic random error where the majority of partners had been acquired in the more distant past and therefore were recalled less accurately than the partners acquired more recently to the time of interviewing. Conclusions Memory errors of this type depend on the salience of the events recalled and are likely to be present in many areas of health research based on self-reported behavior. PMID:12435276

Confirmation bias in human reinforcement learning: Evidence from counterfactual feedback processing

PubMed Central

Lefebvre, Germain; Blakemore, Sarah-Jayne

2017-01-01

Previous studies suggest that factual learning, that is, learning from obtained outcomes, is biased, such that participants preferentially take into account positive, as compared to negative, prediction errors. However, whether or not the prediction error valence also affects counterfactual learning, that is, learning from forgone outcomes, is unknown. To address this question, we analysed the performance of two groups of participants on reinforcement learning tasks using a computational model that was adapted to test if prediction error valence influences learning. We carried out two experiments: in the factual learning experiment, participants learned from partial feedback (i.e., the outcome of the chosen option only); in the counterfactual learning experiment, participants learned from complete feedback information (i.e., the outcomes of both the chosen and unchosen option were displayed). In the factual learning experiment, we replicated previous findings of a valence-induced bias, whereby participants learned preferentially from positive, relative to negative, prediction errors. In contrast, for counterfactual learning, we found the opposite valence-induced bias: negative prediction errors were preferentially taken into account, relative to positive ones. When considering valence-induced bias in the context of both factual and counterfactual learning, it appears that people tend to preferentially take into account information that confirms their current choice. PMID:28800597

Confirmation bias in human reinforcement learning: Evidence from counterfactual feedback processing.

PubMed

Palminteri, Stefano; Lefebvre, Germain; Kilford, Emma J; Blakemore, Sarah-Jayne

2017-08-01

Previous studies suggest that factual learning, that is, learning from obtained outcomes, is biased, such that participants preferentially take into account positive, as compared to negative, prediction errors. However, whether or not the prediction error valence also affects counterfactual learning, that is, learning from forgone outcomes, is unknown. To address this question, we analysed the performance of two groups of participants on reinforcement learning tasks using a computational model that was adapted to test if prediction error valence influences learning. We carried out two experiments: in the factual learning experiment, participants learned from partial feedback (i.e., the outcome of the chosen option only); in the counterfactual learning experiment, participants learned from complete feedback information (i.e., the outcomes of both the chosen and unchosen option were displayed). In the factual learning experiment, we replicated previous findings of a valence-induced bias, whereby participants learned preferentially from positive, relative to negative, prediction errors. In contrast, for counterfactual learning, we found the opposite valence-induced bias: negative prediction errors were preferentially taken into account, relative to positive ones. When considering valence-induced bias in the context of both factual and counterfactual learning, it appears that people tend to preferentially take into account information that confirms their current choice.

A misleading review of response bias: comment on McGrath, Mitchell, Kim, and Hough (2010).

PubMed

Rohling, Martin L; Larrabee, Glenn J; Greiffenstein, Manfred F; Ben-Porath, Yossef S; Lees-Haley, Paul; Green, Paul; Greve, Kevin W

2011-07-01

In the May 2010 issue of Psychological Bulletin, R. E. McGrath, M. Mitchell, B. H. Kim, and L. Hough published an article entitled "Evidence for Response Bias as a Source of Error Variance in Applied Assessment" (pp. 450-470). They argued that response bias indicators used in a variety of settings typically have insufficient data to support such use in everyday clinical practice. Furthermore, they claimed that despite 100 years of research into the use of response bias indicators, "a sufficient justification for [their] use… in applied settings remains elusive" (p. 450). We disagree with McGrath et al.'s conclusions. In fact, we assert that the relevant and voluminous literature that has addressed the issues of response bias substantiates validity of these indicators. In addition, we believe that response bias measures should be used in clinical and research settings on a regular basis. Finally, the empirical evidence for the use of response bias measures is strongest in clinical neuropsychology. We argue that McGrath et al.'s erroneous perspective on response bias measures is a result of 3 errors in their research methodology: (a) inclusion criteria for relevant studies that are too narrow; (b) errors in interpreting results of the empirical research they did include; (c) evidence of a confirmatory bias in selectively citing the literature, as evidence of moderation appears to have been overlooked. Finally, their acknowledging experts in the field who might have highlighted these errors prior to publication may have prevented critiques during the review process.

Cognitive bias in clinical practice - nurturing healthy skepticism among medical students.

PubMed

Bhatti, Alysha

2018-01-01

Errors in clinical reasoning, known as cognitive biases, are implicated in a significant proportion of diagnostic errors. Despite this knowledge, little emphasis is currently placed on teaching cognitive psychology in the undergraduate medical curriculum. Understanding the origin of these biases and their impact on clinical decision making helps stimulate reflective practice. This article outlines some of the common types of cognitive biases encountered in the clinical setting as well as cognitive debiasing strategies. Medical educators should nurture healthy skepticism among medical students by raising awareness of cognitive biases and equipping them with robust tools to circumvent such biases. This will enable tomorrow's doctors to improve the quality of care delivered, thus optimizing patient outcomes.

CAUSES: Diagnosis of the Summertime Warm Bias in CMIP5 Climate Models at the ARM Southern Great Plains Site

NASA Astrophysics Data System (ADS)

Zhang, Chengzhu; Xie, Shaocheng; Klein, Stephen A.; Ma, Hsi-yen; Tang, Shuaiqi; Van Weverberg, Kwinten; Morcrette, Cyril J.; Petch, Jon

2018-03-01

All the weather and climate models participating in the Clouds Above the United States and Errors at the Surface project show a summertime surface air temperature (T2 m) warm bias in the region of the central United States. To understand the warm bias in long-term climate simulations, we assess the Atmospheric Model Intercomparison Project simulations from the Coupled Model Intercomparison Project Phase 5, with long-term observations mainly from the Atmospheric Radiation Measurement program Southern Great Plains site. Quantities related to the surface energy and water budget, and large-scale circulation are analyzed to identify possible factors and plausible links involved in the warm bias. The systematic warm season bias is characterized by an overestimation of T2 m and underestimation of surface humidity, precipitation, and precipitable water. Accompanying the warm bias is an overestimation of absorbed solar radiation at the surface, which is due to a combination of insufficient cloud reflection and clear-sky shortwave absorption by water vapor and an underestimation in surface albedo. The bias in cloud is shown to contribute most to the radiation bias. The surface layer soil moisture impacts T2 m through its control on evaporative fraction. The error in evaporative fraction is another important contributor to T2 m. Similar sources of error are found in hindcast from other Clouds Above the United States and Errors at the Surface studies. In Atmospheric Model Intercomparison Project simulations, biases in meridional wind velocity associated with the low-level jet and the 500 hPa vertical velocity may also relate to T2 m bias through their control on the surface energy and water budget.

A simulation test of the effectiveness of several methods for error-checking non-invasive genetic data

USGS Publications Warehouse

Roon, David A.; Waits, L.P.; Kendall, K.C.

2005-01-01

Non-invasive genetic sampling (NGS) is becoming a popular tool for population estimation. However, multiple NGS studies have demonstrated that polymerase chain reaction (PCR) genotyping errors can bias demographic estimates. These errors can be detected by comprehensive data filters such as the multiple-tubes approach, but this approach is expensive and time consuming as it requires three to eight PCR replicates per locus. Thus, researchers have attempted to correct PCR errors in NGS datasets using non-comprehensive error checking methods, but these approaches have not been evaluated for reliability. We simulated NGS studies with and without PCR error and 'filtered' datasets using non-comprehensive approaches derived from published studies and calculated mark-recapture estimates using CAPTURE. In the absence of data-filtering, simulated error resulted in serious inflations in CAPTURE estimates; some estimates exceeded N by ??? 200%. When data filters were used, CAPTURE estimate reliability varied with per-locus error (E??). At E?? = 0.01, CAPTURE estimates from filtered data displayed < 5% deviance from error-free estimates. When E?? was 0.05 or 0.09, some CAPTURE estimates from filtered data displayed biases in excess of 10%. Biases were positive at high sampling intensities; negative biases were observed at low sampling intensities. We caution researchers against using non-comprehensive data filters in NGS studies, unless they can achieve baseline per-locus error rates below 0.05 and, ideally, near 0.01. However, we suggest that data filters can be combined with careful technique and thoughtful NGS study design to yield accurate demographic information. ?? 2005 The Zoological Society of London.

High breakdown single-crystal GaN p-n diodes by molecular beam epitaxy

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

Qi, Meng; Zhao, Yuning; Yan, Xiaodong

2015-12-07

Molecular beam epitaxy grown GaN p-n vertical diodes are demonstrated on single-crystal GaN substrates. A low leakage current <3 nA/cm{sup 2} is obtained with reverse bias voltage up to −20 V. With a 400 nm thick n-drift region, an on-resistance of 0.23 mΩ cm{sup 2} is achieved, with a breakdown voltage corresponding to a peak electric field of ∼3.1 MV/cm in GaN. Single-crystal GaN substrates with very low dislocation densities enable the low leakage current and the high breakdown field in the diodes, showing significant potential for MBE growth to attain near-intrinsic performance when the density of dislocations is low.

47. VIEW TO WEST; SOUTH END OF MBE BUILDING, FIRST ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

47. VIEW TO WEST; SOUTH END OF MBE BUILDING, FIRST FLOOR; FORMER PACKAGE HANDLING AREA ADJACENT TO FORMER PACIFIC ELECTRIC RAILWAY TERMINAL (Andersen) - Los Angeles Union Passenger Terminal, Mail, Baggage, & Express Building, 800 North Alameda Street, Los Angeles, Los Angeles County, CA

Applying CLIPS to control of molecular beam epitaxy processing

NASA Technical Reports Server (NTRS)

Rabeau, Arthur A.; Bensaoula, Abdelhak; Jamison, Keith D.; Horton, Charles; Ignatiev, Alex; Glover, John R.

1990-01-01

A key element of U.S. industrial competitiveness in the 1990's will be the exploitation of advanced technologies which involve low-volume, high-profit manufacturing. The demands of such manufacture limit participation to a few major entities in the U.S. and elsewhere, and offset the lower manufacturing costs of other countries which have, for example, captured much of the consumer electronics market. One such technology is thin-film epitaxy, a technology which encompasses several techniques such as Molecular Beam Epitaxy (MBE), Chemical Beam Epitaxy (CBE), and Vapor-Phase Epitaxy (VPE). Molecular Beam Epitaxy (MBE) is a technology for creating a variety of electronic and electro-optical materials. Compared to standard microelectronic production techniques (including gaseous diffusion, ion implantation, and chemical vapor deposition), MBE is much more exact, though much slower. Although newer than the standard technologies, MBE is the technology of choice for fabrication of ultraprecise materials for cutting-edge microelectronic devices and for research into the properties of new materials.

Selective area growth of N-polar GaN nanorods by plasma-assisted MBE on micro-cone-patterned c-sapphire substrates

NASA Astrophysics Data System (ADS)

Jmerik, V. N.; Kuznetsova, N. V.; Nechaev, D. V.; Shubina, T. V.; Kirilenko, D. A.; Troshkov, S. I.; Davydov, V. Yu.; Smirnov, A. N.; Ivanov, S. V.

2017-11-01

The site-controlled selective area growth of N-polar GaN nanorods (NR) was developed by plasma-assisted MBE (PA MBE) on micro-cone-patterned sapphire substrates (μ-CPSS) by using a two-stage growth process. A GaN nucleation layer grown by migration enhanced epitaxy provides the best selectivity for nucleation of NRs on the apexes of 3.5-μm-diameter cones, whereas the subsequent growth of 1-μm-high NRs with a constant diameter of about 100 nm proceeds by standard high-temperature PA MBE at nitrogen-rich conditions. These results are explained by anisotropy of the surface energy for GaN of different polarity and crystal orientation. The InGaN single quantum wells inserted in the GaN NRs grown on the μ-CPSS demonstrate photoluminescence at 510 nm with a spatially periodic variation of its intensity with a period of ∼6 μm equal to that of the substrate patterning profile.

Commercial production of QWIP wafers by molecular beam epitaxy

NASA Astrophysics Data System (ADS)

Fastenau, J. M.; Liu, W. K.; Fang, X. M.; Lubyshev, D. I.; Pelzel, R. I.; Yurasits, T. R.; Stewart, T. R.; Lee, J. H.; Li, S. S.; Tidrow, M. Z.

2001-06-01

As the performance of quantum well infrared photodetectors (QWIPs) and QWIP-based imaging systems continues to improve, their demand will undoubtedly grow. This points to the importance of a reliable commercial supplier of semiconductor QWIP material on three inch and, in the near future, four-inch substrates. Molecular beam epitaxy (MBE) is the preferred technique for growing the demanding QWIP structure, as tight control is required over the material composition and layer thickness. We report the current status of MBE-grown GaAs-based QWIP structures in a commercial production environment at IQE. Uniformity data and run-to-run reproducibility on both three-inch and four-inch GaAs substrates are quantified using alloy composition and QW thickness. Initial results on growth technology transfer to a multi-wafer MBE reactor are also presented. High-resolution X-ray diffraction measurements demonstrate GaAs QW thickness variations and AlGaAs barrier compositions changes to be less than 4% and 1% Al, respectively, across four-inch QWIP wafers from both single- and multiple-wafer MBE platforms.

LPE growth of crack-free PbSe layers on Si(100) using MBE-Grown PbSe/BaF2CaF2 buffer layers

NASA Astrophysics Data System (ADS)

Strecker, B. N.; McCann, P. J.; Fang, X. M.; Hauenstein, R. J.; O'Steen, M.; Johnson, M. B.

1997-05-01

Crack-free PbSe on (100)-oriented Si has been obtained by a combination of liquid phase epitaxy (LPE) and molecular beam epitaxy (MBE) techniques. MBE is employed first to grow a PbSe/BaF2/CaF2 buffer structure on the (100)-oriented Si. A 2.5 μm thick PbSe layer is then grown by LPE. The LPE-grown PbSe displays excellent surface morphology and is continuous over the entire 8×8 mm2 area of growth. This result is surprising because of the large mismatch in thermal expansion coefficients between PbSe and Si. Previous attempts to grow crack-free PbSe by MBE alone using similar buffer structures on (100)-oriented Si have been unsuccessful. It is speculated that the large concentration of Se vacancies in the LPE-grown PbSe layer may allow dislocation climb along higher order slip planes, providing strain relaxation.

Electronic, structural and chemical properties of GaAs/ZnSe heterovalent interfaces as dependent on MBE growth conditions and ex situ annealing

NASA Astrophysics Data System (ADS)

Komissarova, T. A.; Lebedev, M. V.; Sorokin, S. V.; Klimko, G. V.; Sedova, I. V.; Gronin, S. V.; Komissarov, K. A.; Calvet, W.; Drozdov, M. N.; Ivanov, S. V.

2017-04-01

A study of electronic, structural and chemical properties of GaAs/ZnSe heterovalent interfaces (HI) in dependence on molecular beam epitaxy (MBE) growth conditions and post-growth annealing was performed. Initial GaAs surface reconstructions ((2 × 4)As or c(4 × 4)As) and ZnSe growth mode (MBE or migration-enhanced epitaxy (MEE)) were varied for different undoped and n-doped heterovalent structures. Although all the structures have low extended defect density (less than 106 cm-2) and rather small (less than 5 nm) atomic interdiffusion at the HI, the structural, chemical and electronic properties of the near-interface area (short-distance interdiffusion effects, dominant chemical bonds, and valence band offset values) as well as electrical properties of the n-GaAs/n-ZnSe heterovalent structures were found to be influenced strongly by the MBE growth conditions and post-growth annealing.

The Extended HANDS Characterization and Analysis of Metric Biases

NASA Astrophysics Data System (ADS)

Kelecy, T.; Knox, R.; Cognion, R.

The Extended High Accuracy Network Determination System (Extended HANDS) consists of a network of low cost, high accuracy optical telescopes designed to support space surveillance and development of space object characterization technologies. Comprising off-the-shelf components, the telescopes are designed to provide sub arc-second astrometric accuracy. The design and analysis team are in the process of characterizing the system through development of an error allocation tree whose assessment is supported by simulation, data analysis, and calibration tests. The metric calibration process has revealed 1-2 arc-second biases in the right ascension and declination measurements of reference satellite position, and these have been observed to have fairly distinct characteristics that appear to have some dependence on orbit geometry and tracking rates. The work presented here outlines error models developed to aid in development of the system error budget, and examines characteristic errors (biases, time dependence, etc.) that might be present in each of the relevant system elements used in the data collection and processing, including the metric calibration processing. The relevant reference frames are identified, and include the sensor (CCD camera) reference frame, Earth-fixed topocentric frame, topocentric inertial reference frame, and the geocentric inertial reference frame. The errors modeled in each of these reference frames, when mapped into the topocentric inertial measurement frame, reveal how errors might manifest themselves through the calibration process. The error analysis results that are presented use satellite-sensor geometries taken from periods where actual measurements were collected, and reveal how modeled errors manifest themselves over those specific time periods. These results are compared to the real calibration metric data (right ascension and declination residuals), and sources of the bias are hypothesized. In turn, the actual right ascension and declination calibration residuals are also mapped to other relevant reference frames in an attempt to validate the source of the bias errors. These results will serve as the basis for more focused investigation into specific components embedded in the system and system processes that might contain the source of the observed biases.

First Impressions of CARTOSAT-1

NASA Technical Reports Server (NTRS)

Lutes, James

2007-01-01

CARTOSAT-1 RPCs need special handling. Absolute accuracy of uncontrolled scenes is poor (biases > 300 m). Noticeable cross-track scale error (+/- 3-4 m across stereo pair). Most errors are either biases or linear in line/sample (These are easier to correct with ground control).

All MBE grown InAs/GaAs quantum dot lasers on on-axis Si (001).

PubMed

Kwoen, Jinkwan; Jang, Bongyong; Lee, Joohang; Kageyama, Takeo; Watanabe, Katsuyuki; Arakawa, Yasuhiko

2018-04-30

Directly grown III-V quantum dot (QD) laser on on-axis Si (001) is a good candidate for achieving monolithically integrated Si photonics light source. Nowadays, laser structures containing high quality InAs / GaAs QD are generally grown by molecular beam epitaxy (MBE). However, the buffer layer between the on-axis Si (001) substrate and the laser structure are usually grown by metal-organic chemical vapor deposition (MOCVD). In this paper, we demonstrate all MBE grown high-quality InAs/GaAs QD lasers on on-axis Si (001) substrates without using patterning and intermediate layers of foreign material.

Analysis of Soft Error Rates in 65- and 28-nm FD-SOI Processes Depending on BOX Region Thickness and Body Bias by Monte-Carlo Based Simulations

NASA Astrophysics Data System (ADS)

Zhang, Kuiyuan; Umehara, Shigehiro; Yamaguchi, Junki; Furuta, Jun; Kobayashi, Kazutoshi

2016-08-01

This paper analyzes how body bias and BOX region thickness affect soft error rates in 65-nm SOTB (Silicon on Thin BOX) and 28-nm UTBB (Ultra Thin Body and BOX) FD-SOI processes. Soft errors are induced by alpha-particle and neutron irradiation and the results are then analyzed by Monte Carlo based simulation using PHITS-TCAD. The alpha-particle-induced single event upset (SEU) cross-section and neutron-induced soft error rate (SER) obtained by simulation are consistent with measurement results. We clarify that SERs decreased in response to an increase in the BOX thickness for SOTB while SERs in UTBB are independent of BOX thickness. We also discover SOTB develops a higher tolerance to soft errors when reverse body bias is applied while UTBB become more susceptible.

Measurement error in environmental epidemiology and the shape of exposure-response curves.

PubMed

Rhomberg, Lorenz R; Chandalia, Juhi K; Long, Christopher M; Goodman, Julie E

2011-09-01

Both classical and Berkson exposure measurement errors as encountered in environmental epidemiology data can result in biases in fitted exposure-response relationships that are large enough to affect the interpretation and use of the apparent exposure-response shapes in risk assessment applications. A variety of sources of potential measurement error exist in the process of estimating individual exposures to environmental contaminants, and the authors review the evaluation in the literature of the magnitudes and patterns of exposure measurement errors that prevail in actual practice. It is well known among statisticians that random errors in the values of independent variables (such as exposure in exposure-response curves) may tend to bias regression results. For increasing curves, this effect tends to flatten and apparently linearize what is in truth a steeper and perhaps more curvilinear or even threshold-bearing relationship. The degree of bias is tied to the magnitude of the measurement error in the independent variables. It has been shown that the degree of bias known to apply to actual studies is sufficient to produce a false linear result, and that although nonparametric smoothing and other error-mitigating techniques may assist in identifying a threshold, they do not guarantee detection of a threshold. The consequences of this could be great, as it could lead to a misallocation of resources towards regulations that do not offer any benefit to public health.

Skin-deep diagnosis: affective bias and zebra retreat complicating the diagnosis of systemic sclerosis.

PubMed

Miller, Chad S

2013-01-01

Nearly half of medical errors can be attributed to an error of clinical reasoning or decision making. It is estimated that the correct diagnosis is missed or delayed in between 5% and 14% of acute hospital admissions. Through understanding why and how physicians make these errors, it is hoped that strategies can be developed to decrease the number of these errors. In the present case, a patient presented with dyspnea, gastrointestinal symptoms and weight loss; the diagnosis was initially missed when the treating physicians took mental short cuts and used heuristics as in this case. Heuristics have an inherent bias that can lead to faulty reasoning or conclusions, especially in complex or difficult cases. Affective bias, which is the overinvolvement of emotion in clinical decision making, limited the available information for diagnosis because of the hesitancy to acquire a full history and perform a complete physical examination in this patient. Zebra retreat, another type of bias, is when a rare diagnosis figures prominently on the differential diagnosis but the physician retreats for various reasons. Zebra retreat also factored in the delayed diagnosis. Through the description of these clinical reasoning errors in an actual case, it is hoped that future errors can be prevented or inspiration for additional research in this area will develop.

Evaluation and error apportionment of an ensemble of ...

EPA Pesticide Factsheets

Through the comparison of several regional-scale chemistry transport modelling systems that simulate meteorology and air quality over the European and American continents, this study aims at i) apportioning the error to the responsible processes using time-scale analysis, ii) helping to detect causes of models error, and iii) identifying the processes and scales most urgently requiring dedicated investigations. The analysis is conducted within the framework of the third phase of the Air Quality Model Evaluation International Initiative (AQMEII) and tackles model performance gauging through measurement-to-model comparison, error decomposition and time series analysis of the models biases for several fields (ozone, CO, SO2, NO, NO2, PM10, PM2.5, wind speed, and temperature). The operational metrics (magnitude of the error, sign of the bias, associativity) provide an overall sense of model strengths and deficiencies, while apportioning the error to its constituent parts (bias, variance and covariance) can help to assess the nature and quality of the error. Each of the error components is analysed independently and apportioned to specific processes based on the corresponding timescale (long scale, synoptic, diurnal, and intra-day) using the error apportionment technique devised in the former phases of AQMEII.The application of the error apportionment method to the AQMEII Phase 3 simulations provides several key insights. In addition to reaffirming the strong impact

CAUSES: Attribution of Surface Radiation Biases in NWP and Climate Models near the U.S. Southern Great Plains

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

Van Weverberg, K.; Morcrette, C. J.; Petch, J.

Many numerical weather prediction (NWP) and climate models exhibit too warm lower tropospheres near the mid-latitude continents. This warm bias has been extensively studied before, but evidence about its origin remains inconclusive. Some studies point to deficiencies in the deep convective or low clouds. Other studies found an important contribution from errors in the land surface properties. The warm bias has been shown to coincide with important surface radiation biases that likely play a critical role in the inception or the growth of the warm bias. Documenting these radiation errors is hence an important step towards understanding and alleviating themore » warm bias. This paper presents an attribution study to quantify the net radiation biases in 9 model simulations, performed in the framework of the CAUSES project (Clouds Above the United States and Errors at the Surface). Contributions from deficiencies in the surface properties, clouds, integrated water vapor (IWV) and aerosols are quantified, using an array of radiation measurement stations near the ARM SGP site. Furthermore, an in depth-analysis is shown to attribute the radiation errors to specific cloud regimes. The net surface SW radiation is overestimated (LW underestimated) in all models throughout most of the simulation period. Cloud errors are shown to contribute most to this overestimation in all but one model, which has a dominant albedo issue. Using a cloud regime analysis, it was shown that missing deep cloud events and/or simulating deep clouds with too weak cloud-radiative effects account for most of these cloud-related radiation errors. Some models have compensating errors between excessive occurrence of deep cloud, but largely underestimating their radiative effect, while other models miss deep cloud events altogether. Surprisingly however, even the latter models tend to produce too much and too frequent afternoon surface precipitation. This suggests that rather than issues with the triggering of deep convection, the deep cloud problem in many models could be related to too weak convective cloud detrainment and too large precipitation efficiencies. This does not rule out that previously documented issues with the evaporative fraction contribute to the warm bias as well, since the majority of the models underestimate the surface rain rates overall, as they miss the observed large nocturnal precipitation peak.« less

Bipolar Cascade Vertical-Cavity Surface-Emitting Lasers for RF Photonic Link Applications

DTIC Science & Technology

2007-09-01

6 IV Current versus Voltage . . . . . . . . . . . . . . . . . . . . . 7 MBE Molecular Beam Epitaxy ...of carrying maximum photocur- rent. Numerous material parameters have been studied. Growth parameters for molecular beam epitaxy (MBE), metal-organic...12 MOCVD Metal-Organic Chemical Vapor Deposition . . . . . . . . . . 12 CBE Chemical Beam Epitaxy . . . . . . . . . . . . . . . . . . . . 12 LPE

Threat engagement, disengagement, and sensitivity bias in worry-prone individuals as measured by an emotional go/no-go task.

PubMed

Gole, Markus; Köchel, Angelika; Schäfer, Axel; Schienle, Anne

2012-03-01

The goal of the present study was to investigate a threat engagement, disengagement, and sensitivity bias in individuals suffering from pathological worry. Twenty participants high in worry proneness and 16 control participants low in worry proneness completed an emotional go/no-go task with worry-related threat words and neutral words. Shorter reaction times (i.e., threat engagement bias), smaller omission error rates (i.e., threat sensitivity bias), and larger commission error rates (i.e., threat disengagement bias) emerged only in the high worry group when worry-related words constituted the go-stimuli and neutral words the no-go stimuli. Also, smaller omission error rates as well as larger commission error rates were observed in the high worry group relative to the low worry group when worry-related go stimuli and neutral no-go stimuli were used. The obtained results await further replication within a generalized anxiety disorder sample. Also, further samples should include men as well. Our data suggest that worry-prone individuals are threat-sensitive, engage more rapidly with aversion, and disengage harder. Copyright © 2011 Elsevier Ltd. All rights reserved.

Bootstrap Estimates of Standard Errors in Generalizability Theory

ERIC Educational Resources Information Center

Tong, Ye; Brennan, Robert L.

2007-01-01

Estimating standard errors of estimated variance components has long been a challenging task in generalizability theory. Researchers have speculated about the potential applicability of the bootstrap for obtaining such estimates, but they have identified problems (especially bias) in using the bootstrap. Using Brennan's bias-correcting procedures…

Molecular Beam Epitaxial Growth and Characterization of Graphene and Hexagonal Boron Nitride Two-Dimensional Layers

NASA Astrophysics Data System (ADS)

Zheng, Renjing

Van der Waals (vdW) materials (also called as two-dimensional (2D) material in some literature) systems have received extensive attention recently due to their potential applications in next-generation electronics platform. Exciting properties have been discovered in this field, however, the performance and properties of the systems rely on the materials' quality and interface significantly, leading to the urgent need for scalable synthesis of high-quality vdW crystals and heterostructures. Toward this direction, this dissertation is devoted on the study of Molecular Beam Epitaxy (MBE) growth and various characterization of vdW materials and heterostructures, especially graphene and hexagonal boron nitride (h-BN). The goal is to achieve high-quality vdW materials and related heterostructures. There are mainly four projects discussed in this dissertation. The first project (Chapter 2) is about MBE growth of large-area h-BN on copper foil. After the growth, the film was transferred onto SiO2 substrate for characterization. It is observed that as-grown film gives evident h-BN Raman spectrum; what's more, h-BN peak intensity and position is dependent on film thickness. N-1s and B-1s XPS peaks further suggest the formation of h-BN. AFM and SEM images show the film is flat and continuous over large area. Our synthesis method shows it's possible to use MBE to achieve h-BN growth and could also pave a way for some unique structure, such as h-BN/graphene heterostructures and doped h-BN films by MBE. The second project (Chapter 3) is focused on establishment of grapehene/h-BN heterostructure on cobalt (Co) film. In-situ epitaxial growth of graphene/h-BN heterostructures on Co film substrate was achieved by using plasma-assisted MBE. The direct graphene/h-BN vertical stacking structures were demonstrated and further confirmed by various characterizations, such as Raman spectroscopy, SEM, XPS and TEM. Large area heterostructures consisting of single- /bilayer graphene and multilayer h-BN were achieved. The mismatch angle between graphene and h-BN is below 1º. The third project (Chapter 4) is about graphene growth on Fe by MBE at low temperature. Temperature-dependent growth of graphene on Fe using MBE is studied. Two-dimensional (2D), large-area graphene samples were grown on Fe thin films, and characterized by Raman, X-ray photoelectron spectroscopy, X-ray diffraction, optical microscopy, transmission electron microscopy and atomic force microscopy. Graphene is achieved on Fe at a wide growth temperature range and as low as 400 °C. The growth mechanism is studied and shows graphene growth is associated with formation and decomposition of iron carbide. The forth part is about a convenient way to produce vdW heterostructures: graphene growth of exfoliated h-BN on Co. We demonstrated graphene/h-BN heterostructures by growing graphene onto the substrates which consist of exfoliated h-BN on Co thin film using MBE. The heterostructure samples grown at different temperatures and growth durations were characterized by Raman, optical microscopy, atomic force microscopy, microwave impedance microscopy and scanning tunneling microscopy. It is found that the graphene/h-BN heterostructures were formed by the formation of graphene underneath rather than on top of the h-BN flakes. The growth mechanism is discussed. In summary, we develop and optimize growth of vdW materials (h-BN and graphene), and vdW heterostructures by MBE. Various characterization has been carried out to evaluate properties of the films in structural, optical and electrical aspects. Our results reveal that MBE can provide an excellent alternative way for reliable growth of high-quality and large-size vdW materials and related heterostructures, which will attract more attention for the utilization of MBE in vdW materials research.

Comparing interval estimates for small sample ordinal CFA models

PubMed Central

Natesan, Prathiba

2015-01-01

Robust maximum likelihood (RML) and asymptotically generalized least squares (AGLS) methods have been recommended for fitting ordinal structural equation models. Studies show that some of these methods underestimate standard errors. However, these studies have not investigated the coverage and bias of interval estimates. An estimate with a reasonable standard error could still be severely biased. This can only be known by systematically investigating the interval estimates. The present study compares Bayesian, RML, and AGLS interval estimates of factor correlations in ordinal confirmatory factor analysis models (CFA) for small sample data. Six sample sizes, 3 factor correlations, and 2 factor score distributions (multivariate normal and multivariate mildly skewed) were studied. Two Bayesian prior specifications, informative and relatively less informative were studied. Undercoverage of confidence intervals and underestimation of standard errors was common in non-Bayesian methods. Underestimated standard errors may lead to inflated Type-I error rates. Non-Bayesian intervals were more positive biased than negatively biased, that is, most intervals that did not contain the true value were greater than the true value. Some non-Bayesian methods had non-converging and inadmissible solutions for small samples and non-normal data. Bayesian empirical standard error estimates for informative and relatively less informative priors were closer to the average standard errors of the estimates. The coverage of Bayesian credibility intervals was closer to what was expected with overcoverage in a few cases. Although some Bayesian credibility intervals were wider, they reflected the nature of statistical uncertainty that comes with the data (e.g., small sample). Bayesian point estimates were also more accurate than non-Bayesian estimates. The results illustrate the importance of analyzing coverage and bias of interval estimates, and how ignoring interval estimates can be misleading. Therefore, editors and policymakers should continue to emphasize the inclusion of interval estimates in research. PMID:26579002

Comparing interval estimates for small sample ordinal CFA models.

PubMed

Natesan, Prathiba

2015-01-01

Robust maximum likelihood (RML) and asymptotically generalized least squares (AGLS) methods have been recommended for fitting ordinal structural equation models. Studies show that some of these methods underestimate standard errors. However, these studies have not investigated the coverage and bias of interval estimates. An estimate with a reasonable standard error could still be severely biased. This can only be known by systematically investigating the interval estimates. The present study compares Bayesian, RML, and AGLS interval estimates of factor correlations in ordinal confirmatory factor analysis models (CFA) for small sample data. Six sample sizes, 3 factor correlations, and 2 factor score distributions (multivariate normal and multivariate mildly skewed) were studied. Two Bayesian prior specifications, informative and relatively less informative were studied. Undercoverage of confidence intervals and underestimation of standard errors was common in non-Bayesian methods. Underestimated standard errors may lead to inflated Type-I error rates. Non-Bayesian intervals were more positive biased than negatively biased, that is, most intervals that did not contain the true value were greater than the true value. Some non-Bayesian methods had non-converging and inadmissible solutions for small samples and non-normal data. Bayesian empirical standard error estimates for informative and relatively less informative priors were closer to the average standard errors of the estimates. The coverage of Bayesian credibility intervals was closer to what was expected with overcoverage in a few cases. Although some Bayesian credibility intervals were wider, they reflected the nature of statistical uncertainty that comes with the data (e.g., small sample). Bayesian point estimates were also more accurate than non-Bayesian estimates. The results illustrate the importance of analyzing coverage and bias of interval estimates, and how ignoring interval estimates can be misleading. Therefore, editors and policymakers should continue to emphasize the inclusion of interval estimates in research.

The effects of recall errors and of selection bias in epidemiologic studies of mobile phone use and cancer risk.

PubMed

Vrijheid, Martine; Deltour, Isabelle; Krewski, Daniel; Sanchez, Marie; Cardis, Elisabeth

2006-07-01

This paper examines the effects of systematic and random errors in recall and of selection bias in case-control studies of mobile phone use and cancer. These sensitivity analyses are based on Monte-Carlo computer simulations and were carried out within the INTERPHONE Study, an international collaborative case-control study in 13 countries. Recall error scenarios simulated plausible values of random and systematic, non-differential and differential recall errors in amount of mobile phone use reported by study subjects. Plausible values for the recall error were obtained from validation studies. Selection bias scenarios assumed varying selection probabilities for cases and controls, mobile phone users, and non-users. Where possible these selection probabilities were based on existing information from non-respondents in INTERPHONE. Simulations used exposure distributions based on existing INTERPHONE data and assumed varying levels of the true risk of brain cancer related to mobile phone use. Results suggest that random recall errors of plausible levels can lead to a large underestimation in the risk of brain cancer associated with mobile phone use. Random errors were found to have larger impact than plausible systematic errors. Differential errors in recall had very little additional impact in the presence of large random errors. Selection bias resulting from underselection of unexposed controls led to J-shaped exposure-response patterns, with risk apparently decreasing at low to moderate exposure levels. The present results, in conjunction with those of the validation studies conducted within the INTERPHONE study, will play an important role in the interpretation of existing and future case-control studies of mobile phone use and cancer risk, including the INTERPHONE study.

Status of two-color and large format HgCdTe FPA technology at Raytheon Vision Systems

NASA Astrophysics Data System (ADS)

Smith, E. P. G.; Bornfreund, R. E.; Kasai, I.; Pham, L. T.; Patten, E. A.; Peterson, J. M.; Roth, J. A.; Nosho, B. Z.; De Lyon, T. J.; Jensen, J. E.; Bangs, J. W.; Johnson, S. M.; Radford, W. A.

2006-02-01

Raytheon Vision Systems (RVS) is developing two-color and large format single color FPAs fabricated from molecular beam epitaxy (MBE) grown HgCdTe triple layer heterojunction (TLHJ) wafers on CdZnTe substrates and double layer heterojunction (DLHJ) wafers on Si substrates, respectively. MBE material growth development has resulted in scaling TLHJ growth on CdZnTe substrates from 10cm2 to 50cm2, long-wavelength infrared (LWIR) DLHJ growth on 4-inch Si substrates and the first demonstration of mid-wavelength infrared (MWIR) DLHJ growth on 6-inch Si substrates with low defect density (<1000cm -2) and excellent uniformity (composition<0.1%, cut-off wavelength Δcenter-edge<0.1μm). Advanced FPA fabrication techniques such as inductively coupled plasma (ICP) etching are being used to achieve high aspect ratio mesa delineation of individual detector elements with benefits to detector performance. Recent two-color detectors with MWIR and LWIR cut-off wavelengths of 5.5μm and 10.5μm, respectively, exhibit significant improvement in 78K LW performance with >70% quantum efficiency, diffusion limited reverse bias dark currents below 300pA and RA products (zero field-of-view, +150mV bias) in excess of 1×103 Ωcm2. Two-color 20μm unit-cell 1280×720 MWIR/LWIR FPAs with pixel response operability approaching 99% have been produced and high quality simultaneous imaging of the spectral bands has been achieved by mating the FPA to a readout integrated circuit (ROIC) with Time Division Multiplexed Integration (TDMI). Large format mega pixel 20μm unit-cell 2048×2048 and 25μm unit-cell 2560×512 FPAs have been demonstrated using DLHJ HgCdTe growth on Si substrates in the short wavelength infrared (SWIR) and MWIR spectral range. Recent imaging of 30μm unit-cell 256×256 LWIR FPAs with 10.0-10.7μm 78K cut-off wavelength and pixel response operability as high as 99.7% show the potential for extending HgCdTe/Si technology to LWIR wavelengths.

Investigating Perceptual Biases, Data Reliability, and Data Discovery in a Methodology for Collecting Speech Errors From Audio Recordings.

PubMed

Alderete, John; Davies, Monica

2018-04-01

This work describes a methodology of collecting speech errors from audio recordings and investigates how some of its assumptions affect data quality and composition. Speech errors of all types (sound, lexical, syntactic, etc.) were collected by eight data collectors from audio recordings of unscripted English speech. Analysis of these errors showed that: (i) different listeners find different errors in the same audio recordings, but (ii) the frequencies of error patterns are similar across listeners; (iii) errors collected "online" using on the spot observational techniques are more likely to be affected by perceptual biases than "offline" errors collected from audio recordings; and (iv) datasets built from audio recordings can be explored and extended in a number of ways that traditional corpus studies cannot be.

A minimalist approach to bias estimation for passive sensor measurements with targets of opportunity

NASA Astrophysics Data System (ADS)

Belfadel, Djedjiga; Osborne, Richard W.; Bar-Shalom, Yaakov

2013-09-01

In order to carry out data fusion, registration error correction is crucial in multisensor systems. This requires estimation of the sensor measurement biases. It is important to correct for these bias errors so that the multiple sensor measurements and/or tracks can be referenced as accurately as possible to a common tracking coordinate system. This paper provides a solution for bias estimation for the minimum number of passive sensors (two), when only targets of opportunity are available. The sensor measurements are assumed time-coincident (synchronous) and perfectly associated. Since these sensors provide only line of sight (LOS) measurements, the formation of a single composite Cartesian measurement obtained from fusing the LOS measurements from different sensors is needed to avoid the need for nonlinear filtering. We evaluate the Cramer-Rao Lower Bound (CRLB) on the covariance of the bias estimate, i.e., the quantification of the available information about the biases. Statistical tests on the results of simulations show that this method is statistically efficient, even for small sample sizes (as few as two sensors and six points on the trajectory of a single target of opportunity). We also show that the RMS position error is significantly improved with bias estimation compared with the target position estimation using the original biased measurements.

The Effect of Amplifier Bias Drift on Differential Magnitude Estimation in Multiple-Star Systems

NASA Astrophysics Data System (ADS)

Tyler, David W.; Muralimanohar, Hariharan; Borelli, Kathy J.

2007-02-01

We show how the temporal drift of CCD amplifier bias can cause significant relative magnitude estimation error in speckle interferometric observations of multiple-star systems. When amplifier bias varies over time, the estimation error arises if the time between acquisition of dark-frame calibration data and science data is long relative to the timescale over which the bias changes. Using analysis, we show that while detector-temperature drift over time causes a variation in accumulated dark current and a residual bias in calibrated imagery, only amplifier bias variations cause a residual bias in the estimated energy spectrum. We then use telescope data taken specifically to investigate this phenomenon to show that for the detector used, temporal bias drift can cause residual energy spectrum bias as large or larger than the mean value of the noise energy spectrum. Finally, we use a computer simulation to demonstrate the effect of residual bias on differential magnitude estimation. A supplemental calibration technique is described in the appendices.

Cognitive Abilities, Monitoring Confidence, and Control Thresholds Explain Individual Differences in Heuristics and Biases

PubMed Central

Jackson, Simon A.; Kleitman, Sabina; Howie, Pauline; Stankov, Lazar

2016-01-01

In this paper, we investigate whether individual differences in performance on heuristic and biases tasks can be explained by cognitive abilities, monitoring confidence, and control thresholds. Current theories explain individual differences in these tasks by the ability to detect errors and override automatic but biased judgments, and deliberative cognitive abilities that help to construct the correct response. Here we retain cognitive abilities but disentangle error detection, proposing that lower monitoring confidence and higher control thresholds promote error checking. Participants (N = 250) completed tasks assessing their fluid reasoning abilities, stable monitoring confidence levels, and the control threshold they impose on their decisions. They also completed seven typical heuristic and biases tasks such as the cognitive reflection test and Resistance to Framing. Using structural equation modeling, we found that individuals with higher reasoning abilities, lower monitoring confidence, and higher control threshold performed significantly and, at times, substantially better on the heuristic and biases tasks. Individuals with higher control thresholds also showed lower preferences for risky alternatives in a gambling task. Furthermore, residual correlations among the heuristic and biases tasks were reduced to null, indicating that cognitive abilities, monitoring confidence, and control thresholds accounted for their shared variance. Implications include the proposal that the capacity to detect errors does not differ between individuals. Rather, individuals might adopt varied strategies that promote error checking to different degrees, regardless of whether they have made a mistake or not. The results support growing evidence that decision-making involves cognitive abilities that construct actions and monitoring and control processes that manage their initiation. PMID:27790170

Cognitive Abilities, Monitoring Confidence, and Control Thresholds Explain Individual Differences in Heuristics and Biases.

PubMed

Jackson, Simon A; Kleitman, Sabina; Howie, Pauline; Stankov, Lazar

2016-01-01

In this paper, we investigate whether individual differences in performance on heuristic and biases tasks can be explained by cognitive abilities, monitoring confidence, and control thresholds. Current theories explain individual differences in these tasks by the ability to detect errors and override automatic but biased judgments, and deliberative cognitive abilities that help to construct the correct response. Here we retain cognitive abilities but disentangle error detection, proposing that lower monitoring confidence and higher control thresholds promote error checking. Participants ( N = 250) completed tasks assessing their fluid reasoning abilities, stable monitoring confidence levels, and the control threshold they impose on their decisions. They also completed seven typical heuristic and biases tasks such as the cognitive reflection test and Resistance to Framing. Using structural equation modeling, we found that individuals with higher reasoning abilities, lower monitoring confidence, and higher control threshold performed significantly and, at times, substantially better on the heuristic and biases tasks. Individuals with higher control thresholds also showed lower preferences for risky alternatives in a gambling task. Furthermore, residual correlations among the heuristic and biases tasks were reduced to null, indicating that cognitive abilities, monitoring confidence, and control thresholds accounted for their shared variance. Implications include the proposal that the capacity to detect errors does not differ between individuals. Rather, individuals might adopt varied strategies that promote error checking to different degrees, regardless of whether they have made a mistake or not. The results support growing evidence that decision-making involves cognitive abilities that construct actions and monitoring and control processes that manage their initiation.

Outbreak Column 16: Cognitive errors in outbreak decision making.

PubMed

Curran, Evonne T

2015-01-01

During outbreaks, decisions must be made without all the required information. People, including infection prevention and control teams (IPCTs), who have to make decisions during uncertainty use heuristics to fill the missing data gaps. Heuristics are mental model short cuts that by-and-large enable us to make good decisions quickly. However, these heuristics contain biases and effects that at times lead to cognitive (thinking) errors. These cognitive errors are not made to deliberately misrepresent any given situation; we are subject to heuristic biases when we are trying to perform optimally. The science of decision making is large; there are over 100 different biases recognised and described. Outbreak Column 16 discusses and relates these heuristics and biases to decision making during outbreak prevention, preparedness and management. Insights as to how we might recognise and avoid them are offered.

Bias Reduction and Filter Convergence for Long Range Stereo

NASA Technical Reports Server (NTRS)

Sibley, Gabe; Matthies, Larry; Sukhatme, Gaurav

2005-01-01

We are concerned here with improving long range stereo by filtering image sequences. Traditionally, measurement errors from stereo camera systems have been approximated as 3-D Gaussians, where the mean is derived by triangulation and the covariance by linearized error propagation. However, there are two problems that arise when filtering such 3-D measurements. First, stereo triangulation suffers from a range dependent statistical bias; when filtering this leads to over-estimating the true range. Second, filtering 3-D measurements derived via linearized error propagation leads to apparent filter divergence; the estimator is biased to under-estimate range. To address the first issue, we examine the statistical behavior of stereo triangulation and show how to remove the bias by series expansion. The solution to the second problem is to filter with image coordinates as measurements instead of triangulated 3-D coordinates.

Number-counts slope estimation in the presence of Poisson noise

NASA Technical Reports Server (NTRS)

Schmitt, Juergen H. M. M.; Maccacaro, Tommaso

1986-01-01

The slope determination of a power-law number flux relationship in the case of photon-limited sampling. This case is important for high-sensitivity X-ray surveys with imaging telescopes, where the error in an individual source measurement depends on integrated flux and is Poisson, rather than Gaussian, distributed. A bias-free method of slope estimation is developed that takes into account the exact error distribution, the influence of background noise, and the effects of varying limiting sensitivities. It is shown that the resulting bias corrections are quite insensitive to the bias correction procedures applied, as long as only sources with signal-to-noise ratio five or greater are considered. However, if sources with signal-to-noise ratio five or less are included, the derived bias corrections depend sensitively on the shape of the error distribution.

INCREASING THE ACCURACY OF MAYFIELD ESTIMATES USING KNOWLEDGE OF NEST AGE

EPA Science Inventory

This presentation will focus on the error introduced in nest-survival modeling when nest-cycles are assumed to be of constant length. I will present the types of error that may occur, including biases resulting from incorrect estimates of expected values, as well as biases that o...

Generalized approach for using unbiased symmetric metrics with negative values: normalized mean bias factor and normalized mean absolute error factor

EPA Science Inventory

Unbiased symmetric metrics provide a useful measure to quickly compare two datasets, with similar interpretations for both under and overestimations. Two examples include the normalized mean bias factor and normalized mean absolute error factor. However, the original formulations...

Model Errors in Simulating Precipitation and Radiation fields in the NARCCAP Hindcast Experiment

NASA Astrophysics Data System (ADS)

Kim, J.; Waliser, D. E.; Mearns, L. O.; Mattmann, C. A.; McGinnis, S. A.; Goodale, C. E.; Hart, A. F.; Crichton, D. J.

2012-12-01

The relationship between the model errors in simulating precipitation and radiation fields including the surface insolation and OLR, is examined from the multi-RCM NARCCAP hindcast experiment for the conterminous U.S. region. Findings in this study suggest that the RCM biases in simulating precipitation are related with those in simulating radiation fields. For a majority of RCMs participated in the NARCCAP hindcast experiment as well as their ensemble, the spatial pattern of the insolation bias is negatively correlated with that of the precipitation bias, suggesting that the biases in precipitation and surface insolation are systematically related, most likely via the cloud fields. The relationship varies according to seasons as well with stronger relationship between the simulated precipitation and surface insolation during winter. This suggests that the RCM biases in precipitation and radiation are related via cloud fields. Additional analysis on the RCM errors in OLR is underway to examine more details of this relationship.

Experimental measurement and theoretical modeling of microwave scattering and the structure of the sea surface influencing radar observations from space

NASA Technical Reports Server (NTRS)

Arnold, David; Kong, J. A.

1992-01-01

The electromagnetic bias is an error present in radar altimetry of the ocean due to the non-uniform reflection from wave troughs and crests. A study of the electromagnetic bias became necessary to permit error reduction in mean sea level measurements of satellite radar altimeters. Satellite radar altimeters have been used to find the upper and lower bounds for the electromagnetic bias. This report will present a theory using physical optics scattering and an empirical model of the short wave modulation to predict the electromagnetic bias. The predicted electromagnetic bias will be compared to measurements at C and Ku bands.

Introduction to CAUSES: Description of Weather and Climate Models and Their Near-Surface Temperature Errors in 5 day Hindcasts Near the Southern Great Plains

DOE PAGES

Morcrette, C. J.; Van Weverberg, K.; Ma, H. -Y.; ...

2018-02-16

We introduce the Clouds Above the United States and Errors at the Surface (CAUSES) project with its aim of better understanding the physical processes leading to warm screen temperature biases over the American Midwest in many numerical models. In this first of four companion papers, 11 different models, from nine institutes, perform a series of 5 day hindcasts, each initialized from reanalyses. After describing the common experimental protocol and detailing each model configuration, a gridded temperature data set is derived from observations and used to show that all the models have a warm bias over parts of the Midwest. Additionally,more » a strong diurnal cycle in the screen temperature bias is found in most models. In some models the bias is largest around midday, while in others it is largest during the night. At the Department of Energy Atmospheric Radiation Measurement Southern Great Plains (SGP) site, the model biases are shown to extend several kilometers into the atmosphere. Finally, to provide context for the companion papers, in which observations from the SGP site are used to evaluate the different processes contributing to errors there, it is shown that there are numerous locations across the Midwest where the diurnal cycle of the error is highly correlated with the diurnal cycle of the error at SGP. This suggests that conclusions drawn from detailed evaluation of models using instruments located at SGP will be representative of errors that are prevalent over a larger spatial scale.« less

Introduction to CAUSES: Description of Weather and Climate Models and Their Near-Surface Temperature Errors in 5 day Hindcasts Near the Southern Great Plains

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

Morcrette, C. J.; Van Weverberg, K.; Ma, H. -Y.

We introduce the Clouds Above the United States and Errors at the Surface (CAUSES) project with its aim of better understanding the physical processes leading to warm screen temperature biases over the American Midwest in many numerical models. In this first of four companion papers, 11 different models, from nine institutes, perform a series of 5 day hindcasts, each initialized from reanalyses. After describing the common experimental protocol and detailing each model configuration, a gridded temperature data set is derived from observations and used to show that all the models have a warm bias over parts of the Midwest. Additionally,more » a strong diurnal cycle in the screen temperature bias is found in most models. In some models the bias is largest around midday, while in others it is largest during the night. At the Department of Energy Atmospheric Radiation Measurement Southern Great Plains (SGP) site, the model biases are shown to extend several kilometers into the atmosphere. Finally, to provide context for the companion papers, in which observations from the SGP site are used to evaluate the different processes contributing to errors there, it is shown that there are numerous locations across the Midwest where the diurnal cycle of the error is highly correlated with the diurnal cycle of the error at SGP. This suggests that conclusions drawn from detailed evaluation of models using instruments located at SGP will be representative of errors that are prevalent over a larger spatial scale.« less

Introduction to CAUSES: Description of Weather and Climate Models and Their Near-Surface Temperature Errors in 5 day Hindcasts Near the Southern Great Plains

NASA Astrophysics Data System (ADS)

Morcrette, C. J.; Van Weverberg, K.; Ma, H.-Y.; Ahlgrimm, M.; Bazile, E.; Berg, L. K.; Cheng, A.; Cheruy, F.; Cole, J.; Forbes, R.; Gustafson, W. I.; Huang, M.; Lee, W.-S.; Liu, Y.; Mellul, L.; Merryfield, W. J.; Qian, Y.; Roehrig, R.; Wang, Y.-C.; Xie, S.; Xu, K.-M.; Zhang, C.; Klein, S.; Petch, J.

2018-03-01

We introduce the Clouds Above the United States and Errors at the Surface (CAUSES) project with its aim of better understanding the physical processes leading to warm screen temperature biases over the American Midwest in many numerical models. In this first of four companion papers, 11 different models, from nine institutes, perform a series of 5 day hindcasts, each initialized from reanalyses. After describing the common experimental protocol and detailing each model configuration, a gridded temperature data set is derived from observations and used to show that all the models have a warm bias over parts of the Midwest. Additionally, a strong diurnal cycle in the screen temperature bias is found in most models. In some models the bias is largest around midday, while in others it is largest during the night. At the Department of Energy Atmospheric Radiation Measurement Southern Great Plains (SGP) site, the model biases are shown to extend several kilometers into the atmosphere. Finally, to provide context for the companion papers, in which observations from the SGP site are used to evaluate the different processes contributing to errors there, it is shown that there are numerous locations across the Midwest where the diurnal cycle of the error is highly correlated with the diurnal cycle of the error at SGP. This suggests that conclusions drawn from detailed evaluation of models using instruments located at SGP will be representative of errors that are prevalent over a larger spatial scale.

Using regime analysis to identify the contribution of clouds to surface temperature errors in weather and climate models

DOE PAGES

Van Weverberg, Kwinten; Morcrette, Cyril J.; Ma, Hsi -Yen; ...

2015-06-17

Many global circulation models (GCMs) exhibit a persistent bias in the 2 m temperature over the midlatitude continents, present in short-range forecasts as well as long-term climate simulations. A number of hypotheses have been proposed, revolving around deficiencies in the soil–vegetation–atmosphere energy exchange, poorly resolved low-level boundary-layer clouds or misrepresentations of deep-convective storms. A common approach to evaluating model biases focuses on the model-mean state. However, this makes difficult an unambiguous interpretation of the origins of a bias, given that biases are the result of the superposition of impacts of clouds and land-surface deficiencies over multiple time steps. This articlemore » presents a new methodology to objectively detect the role of clouds in the creation of a surface warm bias. A unique feature of this study is its focus on temperature-error growth at the time-step level. It is shown that compositing the temperature-error growth by the coinciding bias in total downwelling radiation provides unambiguous evidence for the role that clouds play in the creation of the surface warm bias during certain portions of the day. Furthermore, the application of an objective cloud-regime classification allows for the detection of the specific cloud regimes that matter most for the creation of the bias. We applied this method to two state-of-the-art GCMs that exhibit a distinct warm bias over the Southern Great Plains of the USA. Our analysis highlights that, in one GCM, biases in deep-convective and low-level clouds contribute most to the temperature-error growth in the afternoon and evening respectively. In the second GCM, deep clouds persist too long in the evening, leading to a growth of the temperature bias. In conclusion, the reduction of the temperature bias in both models in the morning and the growth of the bias in the second GCM in the afternoon could not be assigned to a cloud issue, but are more likely caused by a land-surface deficiency.« less

Mixtures of Berkson and classical covariate measurement error in the linear mixed model: Bias analysis and application to a study on ultrafine particles.

PubMed

Deffner, Veronika; Küchenhoff, Helmut; Breitner, Susanne; Schneider, Alexandra; Cyrys, Josef; Peters, Annette

2018-05-01

The ultrafine particle measurements in the Augsburger Umweltstudie, a panel study conducted in Augsburg, Germany, exhibit measurement error from various sources. Measurements of mobile devices show classical possibly individual-specific measurement error; Berkson-type error, which may also vary individually, occurs, if measurements of fixed monitoring stations are used. The combination of fixed site and individual exposure measurements results in a mixture of the two error types. We extended existing bias analysis approaches to linear mixed models with a complex error structure including individual-specific error components, autocorrelated errors, and a mixture of classical and Berkson error. Theoretical considerations and simulation results show, that autocorrelation may severely change the attenuation of the effect estimations. Furthermore, unbalanced designs and the inclusion of confounding variables influence the degree of attenuation. Bias correction with the method of moments using data with mixture measurement error partially yielded better results compared to the usage of incomplete data with classical error. Confidence intervals (CIs) based on the delta method achieved better coverage probabilities than those based on Bootstrap samples. Moreover, we present the application of these new methods to heart rate measurements within the Augsburger Umweltstudie: the corrected effect estimates were slightly higher than their naive equivalents. The substantial measurement error of ultrafine particle measurements has little impact on the results. The developed methodology is generally applicable to longitudinal data with measurement error. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Regression dilution bias: tools for correction methods and sample size calculation.

PubMed

Berglund, Lars

2012-08-01

Random errors in measurement of a risk factor will introduce downward bias of an estimated association to a disease or a disease marker. This phenomenon is called regression dilution bias. A bias correction may be made with data from a validity study or a reliability study. In this article we give a non-technical description of designs of reliability studies with emphasis on selection of individuals for a repeated measurement, assumptions of measurement error models, and correction methods for the slope in a simple linear regression model where the dependent variable is a continuous variable. Also, we describe situations where correction for regression dilution bias is not appropriate. The methods are illustrated with the association between insulin sensitivity measured with the euglycaemic insulin clamp technique and fasting insulin, where measurement of the latter variable carries noticeable random error. We provide software tools for estimation of a corrected slope in a simple linear regression model assuming data for a continuous dependent variable and a continuous risk factor from a main study and an additional measurement of the risk factor in a reliability study. Also, we supply programs for estimation of the number of individuals needed in the reliability study and for choice of its design. Our conclusion is that correction for regression dilution bias is seldom applied in epidemiological studies. This may cause important effects of risk factors with large measurement errors to be neglected.

A Multivariate Generalizability Analysis of the Multistate Bar Examination

ERIC Educational Resources Information Center

Yin, Ping

2005-01-01

The main purpose of this study is to examine the content structure of the Multistate Bar Examination (MBE) using the "table of specifications" model from the perspective of multivariate generalizability theory. Specifically, using MBE data collected over different years (six administrations: three from the February test and three from July test),…

Recent progress in MBE grown HgCdTe materials and devices at UWA

NASA Astrophysics Data System (ADS)

Gu, R.; Lei, W.; Antoszewski, J.; Madni, I.; Umana-Menbreno, G.; Faraone, L.

2016-05-01

HgCdTe has dominated the high performance end of the IR detector market for decades. At present, the fabrication costs of HgCdTe based advanced infrared devices is relatively high, due to the low yield associated with lattice matched CdZnTe substrates and a complicated cooling system. One approach to ease this problem is to use a cost effective alternative substrate, such as Si or GaAs. Recently, GaSb has emerged as a new alternative with better lattice matching. In addition, implementation of MBE-grown unipolar n-type/barrier/n-type detector structures in the HgCdTe material system has been recently proposed and studied intensively to enhance the detector operating temperature. The unipolar nBn photodetector structure can be used to substantially reduce dark current and noise without impeding photocurrent flow. In this paper, recent progress in MBE growth of HgCdTe infrared material at the University of Western Australia (UWA) is reported, including MBE growth of HgCdTe on GaSb alternative substrates and growth of HgCdTe nBn structures.

Kinetic modeling of microscopic processes during electron cyclotron resonance microwave plasma-assisted molecular beam epitaxial growth of GaN/GaAs-based heterostructures

NASA Astrophysics Data System (ADS)

Bandić, Z. Z.; Hauenstein, R. J.; O'Steen, M. L.; McGill, T. C.

1996-03-01

Microscopic growth processes associated with GaN/GaAs molecular beam epitaxy (MBE) are examined through the introduction of a first-order kinetic model. The model is applied to the electron cyclotron resonance microwave plasma-assisted MBE (ECR-MBE) growth of a set of δ-GaNyAs1-y/GaAs strained-layer superlattices that consist of nitrided GaAs monolayers separated by GaAs spacers, and that exhibit a strong decrease of y with increasing T over the range 540-580 °C. This y(T) dependence is quantitatively explained in terms of microscopic anion exchange, and thermally activated N surface-desorption and surface-segregation processes. N surface segregation is found to be significant during GaAs overgrowth of GaNyAs1-y layers at typical GaN ECR-MBE growth temperatures, with an estimated activation energy Es˜0.9 eV. The observed y(T) dependence is shown to result from a combination of N surface segregation/desorption processes.

Estimation of attitude sensor timetag biases

NASA Technical Reports Server (NTRS)

Sedlak, J.

1995-01-01

This paper presents an extended Kalman filter for estimating attitude sensor timing errors. Spacecraft attitude is determined by finding the mean rotation from a set of reference vectors in inertial space to the corresponding observed vectors in the body frame. Any timing errors in the observations can lead to attitude errors if either the spacecraft is rotating or the reference vectors themselves vary with time. The state vector here consists of the attitude quaternion, timetag biases, and, optionally, gyro drift rate biases. The filter models the timetags as random walk processes: their expectation values propagate as constants and white noise contributes to their covariance. Thus, this filter is applicable to cases where the true timing errors are constant or slowly varying. The observability of the state vector is studied first through an examination of the algebraic observability condition and then through several examples with simulated star tracker timing errors. The examples use both simulated and actual flight data from the Extreme Ultraviolet Explorer (EUVE). The flight data come from times when EUVE had a constant rotation rate, while the simulated data feature large angle attitude maneuvers. The tests include cases with timetag errors on one or two sensors, both constant and time-varying, and with and without gyro bias errors. Due to EUVE's sensor geometry, the observability of the state vector is severely limited when the spacecraft rotation rate is constant. In the absence of attitude maneuvers, the state elements are highly correlated, and the state estimate is unreliable. The estimates are particularly sensitive to filter mistuning in this case. The EUVE geometry, though, is a degenerate case having coplanar sensors and rotation vector. Observability is much improved and the filter performs well when the rate is either varying or noncoplanar with the sensors, as during a slew. Even with bad geometry and constant rates, if gyro biases are independently known, the timetag error for a single sensor can be accurately estimated as long as its boresight is not too close to the spacecraft rotation axis.

The accuracy of self-reported pregnancy-related weight: a systematic review.

PubMed

Headen, I; Cohen, A K; Mujahid, M; Abrams, B

2017-03-01

Self-reported maternal weight is error-prone, and the context of pregnancy may impact error distributions. This systematic review summarizes error in self-reported weight across pregnancy and assesses implications for bias in associations between pregnancy-related weight and birth outcomes. We searched PubMed and Google Scholar through November 2015 for peer-reviewed articles reporting accuracy of self-reported, pregnancy-related weight at four time points: prepregnancy, delivery, over gestation and postpartum. Included studies compared maternal self-report to anthropometric measurement or medical report of weights. Sixty-two studies met inclusion criteria. We extracted data on magnitude of error and misclassification. We assessed impact of reporting error on bias in associations between pregnancy-related weight and birth outcomes. Women underreported prepregnancy (PPW: -2.94 to -0.29 kg) and delivery weight (DW: -1.28 to 0.07 kg), and over-reported gestational weight gain (GWG: 0.33 to 3 kg). Magnitude of error was small, ranged widely, and varied by prepregnancy weight class and race/ethnicity. Misclassification was moderate (PPW: 0-48.3%; DW: 39.0-49.0%; GWG: 16.7-59.1%), and overestimated some estimates of population prevalence. However, reporting error did not largely bias associations between pregnancy-related weight and birth outcomes. Although measured weight is preferable, self-report is a cost-effective and practical measurement approach. Future researchers should develop bias correction techniques for self-reported pregnancy-related weight. © 2017 World Obesity Federation.

Clinical decision-making: heuristics and cognitive biases for the ophthalmologist.

PubMed

Hussain, Ahsen; Oestreicher, James

Diagnostic errors have a significant impact on health care outcomes and patient care. The underlying causes and development of diagnostic error are complex with flaws in health care systems, as well as human error, playing a role. Cognitive biases and a failure of decision-making shortcuts (heuristics) are human factors that can compromise the diagnostic process. We describe these mechanisms, their role with the clinician, and provide clinical scenarios to highlight the various points at which biases may emerge. We discuss strategies to modify the development and influence of these processes and the vulnerability of heuristics to provide insight and improve clinical outcomes. Copyright © 2017 Elsevier Inc. All rights reserved.

Working memory and the memory distortion component of hindsight bias.

PubMed

Calvillo, Dustin P

2012-01-01

One component of hindsight bias is memory distortion: Individuals' recollections of their predictions are biased towards known outcomes. The present study examined the role of working memory in the memory distortion component of hindsight bias. Participants answered almanac-like questions, completed a measure of working memory capacity, were provided with the correct answers, and attempted to recollect their original judgements in two conditions: with and without a concurrent working memory load. Participants' recalled judgements were more biased by feedback when they recalled these judgements with a concurrent memory load and working memory capacity was negatively correlated with memory distortion. These findings are consistent with reconstruction accounts of the memory distortion component of hindsight bias and, more generally, with dual process theories of cognition. These results also relate the memory distortion component of hindsight bias with other cognitive errors, such as source monitoring errors, the belief bias in syllogistic reasoning and anchoring effects. Implications for the separate components view of hindsight bias are discussed.

Molecular Beam Epitaxy Growth of Transition Metal Dichalcogenides

NASA Astrophysics Data System (ADS)

Yue, Ruoyu

The exponential growth of Si-based technology has finally reached its limit, and a new generation of devices must be developed to continue scaling. A unique class of materials, transition metal dichalcogenides (TMD), have attracted great attention due to their remarkable optical and electronic properties at the atomic thickness scale. Over the past decade, enormous efforts have been put into TMD research for application in low-power devices. Among these studies, a high-quality TMD synthesis method is essential. Molecular beam epitaxy (MBE) can enable high-quality TMD growth by combining high purity elemental sources and an ultra-high vacuum growth environment, together with the back-end-of-line compatible growth temperatures. Although many TMD candidates have been grown by MBE with promising microstructure, the limited grain size (< 200 nm) for the MBE-grown TMDs reported in the literature thus far is unsuitable for high-performance device applications. In this dissertation, the synthesis of TMDs by MBE and their implementation in device structures were investigated. van der Waals epitaxial growth of these TMDs (HfSe2, WTe2, WSe2, WTex Se2-x), due to the relaxed interactions at the interface, have been demonstrated on large lattice-mismatched substrates without strain and misfit dislocations. The fundamental nucleation and growth behavior of WSe2 was investigated through a detailed experimental design, combined with on-lattice, diffusion-based first principles kinetic modeling. Over one order of magnitude improvement in grain size was achieved through this study. Results from both experiment and simulation showed that reducing the growth rate, enabled by high growth temperature and low metal flux, is vital to nucleation density control. Meanwhile, providing a chalcogen-rich growth environment will promote larger grain lateral growth by suppressing vertical growth. Applying the knowledge learned from the nucleation study, we sucessfully integrated the MBE-grown WSe2 into Si complementary metal-oxide-semiconductor (CMOS) compatible field-effect transistors (FETs). Excellent transport properties, such as field effect hole mobilities (40 cm 2/V·s) with orders of magnitude improvement over the reported values of MBE-grown TMDs, are shown. These studies provide a comprehensive understanding of the MBE synthesis of TMDs and devices, indicating the great potential of integrating TMDs into CMOS process flows for the future electronics.

Bias estimation for moving optical sensor measurements with targets of opportunity

NASA Astrophysics Data System (ADS)

Belfadel, Djedjiga; Osborne, Richard W.; Bar-Shalom, Yaakov

2014-06-01

Integration of space based sensors into a Ballistic Missile Defense System (BMDS) allows for detection and tracking of threats over a larger area than ground based sensors [1]. This paper examines the effect of sensor bias error on the tracking quality of a Space Tracking and Surveillance System (STSS) for the highly non-linear problem of tracking a ballistic missile. The STSS constellation consists of two or more satellites (on known trajectories) for tracking ballistic targets. Each satellite is equipped with an IR sensor that provides azimuth and elevation to the target. The tracking problem is made more difficult due to a constant or slowly varying bias error present in each sensor's line of sight measurements. It is important to correct for these bias errors so that the multiple sensor measurements and/or tracks can be referenced as accurately as possible to a common tracking coordinate system. The measurements provided by these sensors are assumed time-coincident (synchronous) and perfectly associated. The line of sight (LOS) measurements from the sensors can be fused into measurements which are the Cartesian target position, i.e., linear in the target state. We evaluate the Cramér-Rao Lower Bound (CRLB) on the covariance of the bias estimates, which serves as a quantification of the available information about the biases. Statistical tests on the results of simulations show that this method is statistically efficient, even for small sample sizes (as few as two sensors and six points on the (unknown) trajectory of a single target of opportunity). We also show that the RMS position error is significantly improved with bias estimation compared with the target position estimation using the original biased measurements.

Modeling error in experimental assays using the bootstrap principle: Understanding discrepancies between assays using different dispensing technologies

PubMed Central

Hanson, Sonya M.; Ekins, Sean; Chodera, John D.

2015-01-01

All experimental assay data contains error, but the magnitude, type, and primary origin of this error is often not obvious. Here, we describe a simple set of assay modeling techniques based on the bootstrap principle that allow sources of error and bias to be simulated and propagated into assay results. We demonstrate how deceptively simple operations—such as the creation of a dilution series with a robotic liquid handler—can significantly amplify imprecision and even contribute substantially to bias. To illustrate these techniques, we review an example of how the choice of dispensing technology can impact assay measurements, and show how large contributions to discrepancies between assays can be easily understood and potentially corrected for. These simple modeling techniques—illustrated with an accompanying IPython notebook—can allow modelers to understand the expected error and bias in experimental datasets, and even help experimentalists design assays to more effectively reach accuracy and imprecision goals. PMID:26678597

U.S. Maternally Linked Birth Records May Be Biased for Hispanics and Other Population Groups

PubMed Central

LEISS, JACK K.; GILES, DENISE; SULLIVAN, KRISTIN M.; MATHEWS, RAHEL; SENTELLE, GLENDA; TOMASHEK, KAY M.

2010-01-01

Purpose To advance understanding of linkage error in U.S. maternally linked datasets, and how the error may affect results of studies based on the linked data. Methods North Carolina birth and fetal death records for 1988-1997 were maternally linked (n=1,030,029). The maternal set probability, defined as the probability that all records assigned to the same maternal set do in fact represent events to the same woman, was used to assess differential maternal linkage error across race/ethnic groups. Results Maternal set probabilities were lower for records specifying Asian or Hispanic race/ethnicity, suggesting greater maternal linkage error. The lower probabilities for Hispanics were concentrated in women of Mexican origin who were not born in the United States. Conclusions Differential maternal linkage error may be a source of bias in studies using U.S. maternally linked datasets to make comparisons between Hispanics and other groups or among Hispanic subgroups. Methods to quantify and adjust for this potential bias are needed. PMID:20006273

Estimating the Autocorrelated Error Model with Trended Data: Further Results,

DTIC Science & Technology

1979-11-01

Perhaps the most serious deficiency of OLS in the presence of autocorrelation is not inefficiency but bias in its estimated standard errors--a bias...k for all t has variance var(b) = o2/ Tk2 2This refutes Maeshiro’s (1976) conjecture that "an estimator utilizing relevant extraneous information

Biases and Standard Errors of Standardized Regression Coefficients

ERIC Educational Resources Information Center

Yuan, Ke-Hai; Chan, Wai

2011-01-01

The paper obtains consistent standard errors (SE) and biases of order O(1/n) for the sample standardized regression coefficients with both random and given predictors. Analytical results indicate that the formulas for SEs given in popular text books are consistent only when the population value of the regression coefficient is zero. The sample…

Mathematical analysis study for radar data processing and enhancement. Part 1: Radar data analysis

NASA Technical Reports Server (NTRS)

James, R.; Brownlow, J. D.

1985-01-01

A study is performed under NASA contract to evaluate data from an AN/FPS-16 radar installed for support of flight programs at Dryden Flight Research Facility of NASA Ames Research Center. The purpose of this study is to provide information necessary for improving post-flight data reduction and knowledge of accuracy of derived radar quantities. Tracking data from six flights are analyzed. Noise and bias errors in raw tracking data are determined for each of the flights. A discussion of an altiude bias error during all of the tracking missions is included. This bias error is defined by utilizing pressure altitude measurements made during survey flights. Four separate filtering methods, representative of the most widely used optimal estimation techniques for enhancement of radar tracking data, are analyzed for suitability in processing both real-time and post-mission data. Additional information regarding the radar and its measurements, including typical noise and bias errors in the range and angle measurements, is also presented. This is in two parts. This is part 1, an analysis of radar data.

A regret-induced status-quo bias

PubMed Central

Nicolle, A.; Fleming, S.M.; Bach, D.R.; Driver, J.; Dolan, R. J.

2011-01-01

A suboptimal bias towards accepting the ‘status-quo’ option in decision-making is well established behaviorally, but the underlying neural mechanisms are less clear. Behavioral evidence suggests the emotion of regret is higher when errors arise from rejection rather than acceptance of a status-quo option. Such asymmetry in the genesis of regret might drive the status-quo bias on subsequent decisions, if indeed erroneous status-quo rejections have a greater neuronal impact than erroneous status-quo acceptances. To test this, we acquired human fMRI data during a difficult perceptual decision task that incorporated a trial-to-trial intrinsic status-quo option, with explicit signaling of outcomes (error or correct). Behaviorally, experienced regret was higher after an erroneous status-quo rejection compared to acceptance. Anterior insula and medial prefrontal cortex showed increased BOLD signal after such status-quo rejection errors. In line with our hypothesis, a similar pattern of signal change predicted acceptance of the status-quo on a subsequent trial. Thus, our data link a regret-induced status-quo bias to error-related activity on the preceding trial. PMID:21368043

On electrical and interfacial properties of iron and platinum Schottky barrier diodes on (111) n-type Si0.65Ge0.35

NASA Astrophysics Data System (ADS)

Hamri, D.; Teffahi, A.; Djeghlouf, A.; Chalabi, D.; Saidane, A.

2018-04-01

Current-voltage (I-V), capacitance-voltage-frequency (C-V-f) and conductance-voltage-frequency (G/ω-V-f) characteristics of Molecular Beam Epitaxy (MBE)-deposited Fe/n-Si0.65Ge0.35 (FM1) and Pt/n-Si0.65Ge0.35(PM2) (111) orientated Schottky barrier diodes (SBDs) have been investigated at room-temperature. Barrier height (ΦB0), ideality factor (n) and series resistance (RS) were extracted. Dominant current conduction mechanisms were determined. They revealed that Poole-Frenkel-type conduction mechanism dominated reverse current. Differences in shunt resistance confirmed the difference found in leakage current. Under forward bias, quasi-ohmic conduction is found at low voltage regions and space charge-limited conduction (SCLC) at higher voltage regions for both SBDs. Density of interface states (NSS) indicated a difference in interface reactivity. Distribution profiles of series resistance (RS) with bias gives a peak in depletion region at low-frequencies that disappears with increasing frequencies. These results show that interface states density and series resistance of Schottky diodes are important parameters that strongly influence electrical properties of FM1 and PM2 structures.

Importance of understanding landscape biases in USGS gage locations: Implications and solutions for managers

USGS Publications Warehouse

Wagner, Tyler; DeWeber, Jefferson Tyrell; Tsang, Yin-Phan; Krueger, Damon; Whittier, Joanna B.; Infante, Dana M.; Whelan, Gary

2014-01-01

Flow and water temperature are fundamental properties of stream ecosystems upon which many freshwater resource management decisions are based. U.S. Geological Survey (USGS) gages are the most important source of streamflow and water temperature data available nationwide, but the degree to which gages represent landscape attributes of the larger population of streams has not been thoroughly evaluated. We identified substantial biases for seven landscape attributes in one or more regions across the conterminous United States. Streams with small watersheds (<10 km2) and at high elevations were often underrepresented, and biases were greater for water temperature gages and in arid regions. Biases can fundamentally alter management decisions and at a minimum this potential for error must be acknowledged accurately and transparently. We highlight three strategies that seek to reduce bias or limit errors arising from bias and illustrate how one strategy, supplementing USGS data, can greatly reduce bias.

Correcting surface solar radiation of two data assimilation systems against FLUXNET observations in North America

NASA Astrophysics Data System (ADS)

Zhao, Lei; Lee, Xuhui; Liu, Shoudong

2013-09-01

Solar radiation at the Earth's surface is an important driver of meteorological and ecological processes. The objective of this study is to evaluate the accuracy of the reanalysis solar radiation produced by NARR (North American Regional Reanalysis) and MERRA (Modern-Era Retrospective Analysis for Research and Applications) against the FLUXNET measurements in North America. We found that both assimilation systems systematically overestimated the surface solar radiation flux on the monthly and annual scale, with an average bias error of +37.2 Wm-2 for NARR and of +20.2 Wm-2 for MERRA. The bias errors were larger under cloudy skies than under clear skies. A postreanalysis algorithm consisting of empirical relationships between model bias, a clearness index, and site elevation was proposed to correct the model errors. Results show that the algorithm can remove the systematic bias errors for both FLUXNET calibration sites (sites used to establish the algorithm) and independent validation sites. After correction, the average annual mean bias errors were reduced to +1.3 Wm-2 for NARR and +2.7 Wm-2 for MERRA. Applying the correction algorithm to the global domain of MERRA brought the global mean surface incoming shortwave radiation down by 17.3 W m-2 to 175.5 W m-2. Under the constraint of the energy balance, other radiation and energy balance terms at the Earth's surface, estimated from independent global data products, also support the need for a downward adjustment of the MERRA surface solar radiation.

Alternatives to accuracy and bias metrics based on percentage errors for radiation belt modeling applications

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

Morley, Steven Karl

This report reviews existing literature describing forecast accuracy metrics, concentrating on those based on relative errors and percentage errors. We then review how the most common of these metrics, the mean absolute percentage error (MAPE), has been applied in recent radiation belt modeling literature. Finally, we describe metrics based on the ratios of predicted to observed values (the accuracy ratio) that address the drawbacks inherent in using MAPE. Specifically, we define and recommend the median log accuracy ratio as a measure of bias and the median symmetric accuracy as a measure of accuracy.

Orbit error characteristic and distribution of TLE using CHAMP orbit data

NASA Astrophysics Data System (ADS)

Xu, Xiao-li; Xiong, Yong-qing

2018-02-01

Space object orbital covariance data is required for collision risk assessments, but publicly accessible two line element (TLE) data does not provide orbital error information. This paper compared historical TLE data and GPS precision ephemerides of CHAMP to assess TLE orbit accuracy from 2002 to 2008, inclusive. TLE error spatial variations with longitude and latitude were calculated to analyze error characteristics and distribution. The results indicate that TLE orbit data are systematically biased from the limited SGP4 model. The biases can reach the level of kilometers, and the sign and magnitude are correlate significantly with longitude.

Electrical properties of surface and interface layers of the N- and In-polar undoped and Mg-doped InN layers grown by PA MBE

NASA Astrophysics Data System (ADS)

Komissarova, T. A.; Kampert, E.; Law, J.; Jmerik, V. N.; Paturi, P.; Wang, X.; Yoshikawa, A.; Ivanov, S. V.

2018-01-01

Electrical properties of N-polar undoped and Mg-doped InN layers and In-polar undoped InN layers grown by plasma-assisted molecular beam epitaxy (PA MBE) were studied. Transport parameters of the surface and interface layers were determined from the measurements of the Hall coefficient and resistivity as well as the Shubnikov-de Haas oscillations at magnetic fields up to 60 T. Contributions of the 2D surface, 3D near-interface, and 2D interface layers to the total conductivity of the InN films were defined and discussed to be dependent on InN surface polarity, Mg doping, and PA MBE growth conditions.

AQMEII3: the EU and NA regional scale program of the ...

EPA Pesticide Factsheets

The presentation builds on the work presented last year at the 14th CMAS meeting and it is applied to the work performed in the context of the AQMEII-HTAP collaboration. The analysis is conducted within the framework of the third phase of AQMEII (Air Quality Model Evaluation International Initiative) and encompasses the gauging of model performance through measurement-to-model comparison, error decomposition and time series analysis of the models biases. Through the comparison of several regional-scale chemistry transport modelling systems applied to simulate meteorology and air quality over two continental areas, this study aims at i) apportioning the error to the responsible processes through time-scale analysis, and ii) help detecting causes of models error, and iii) identify the processes and scales most urgently requiring dedicated investigations. The operational metrics (magnitude of the error, sign of the bias, associativity) provide an overall sense of model strengths and deficiencies, while the apportioning of the error into its constituent parts (bias, variance and covariance) can help assess the nature and quality of the error. Each of the error components is analysed independently and apportioned to specific processes based on the corresponding timescale (long scale, synoptic, diurnal, and intra-day) using the error apportionment technique devised in the previous phases of AQMEII. The National Exposure Research Laboratory (NERL) Computational Exposur

Counteracting estimation bias and social influence to improve the wisdom of crowds.

PubMed

Kao, Albert B; Berdahl, Andrew M; Hartnett, Andrew T; Lutz, Matthew J; Bak-Coleman, Joseph B; Ioannou, Christos C; Giam, Xingli; Couzin, Iain D

2018-04-01

Aggregating multiple non-expert opinions into a collective estimate can improve accuracy across many contexts. However, two sources of error can diminish collective wisdom: individual estimation biases and information sharing between individuals. Here, we measure individual biases and social influence rules in multiple experiments involving hundreds of individuals performing a classic numerosity estimation task. We first investigate how existing aggregation methods, such as calculating the arithmetic mean or the median, are influenced by these sources of error. We show that the mean tends to overestimate, and the median underestimate, the true value for a wide range of numerosities. Quantifying estimation bias, and mapping individual bias to collective bias, allows us to develop and validate three new aggregation measures that effectively counter sources of collective estimation error. In addition, we present results from a further experiment that quantifies the social influence rules that individuals employ when incorporating personal estimates with social information. We show that the corrected mean is remarkably robust to social influence, retaining high accuracy in the presence or absence of social influence, across numerosities and across different methods for averaging social information. Using knowledge of estimation biases and social influence rules may therefore be an inexpensive and general strategy to improve the wisdom of crowds. © 2018 The Author(s).

Competitive action video game players display rightward error bias during on-line video game play.

PubMed

Roebuck, Andrew J; Dubnyk, Aurora J B; Cochran, David; Mandryk, Regan L; Howland, John G; Harms, Victoria

2017-09-12

Research in asymmetrical visuospatial attention has identified a leftward bias in the general population across a variety of measures including visual attention and line-bisection tasks. In addition, increases in rightward collisions, or bumping, during visuospatial navigation tasks have been demonstrated in real world and virtual environments. However, little research has investigated these biases beyond the laboratory. The present study uses a semi-naturalistic approach and the online video game streaming service Twitch to examine navigational errors and assaults as skilled action video game players (n = 60) compete in Counter Strike: Global Offensive. This study showed a significant rightward bias in both fatal assaults and navigational errors. Analysis using the in-game ranking system as a measure of skill failed to show a relationship between bias and skill. These results suggest that a leftward visuospatial bias may exist in skilled players during online video game play. However, the present study was unable to account for some factors such as environmental symmetry and player handedness. In conclusion, video game streaming is a promising method for behavioural research in the future, however further study is required before one can determine whether these results are an artefact of the method applied, or representative of a genuine rightward bias.

Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework

NASA Astrophysics Data System (ADS)

Raleigh, M. S.; Lundquist, J. D.; Clark, M. P.

2015-07-01

Physically based models provide insights into key hydrologic processes but are associated with uncertainties due to deficiencies in forcing data, model parameters, and model structure. Forcing uncertainty is enhanced in snow-affected catchments, where weather stations are scarce and prone to measurement errors, and meteorological variables exhibit high variability. Hence, there is limited understanding of how forcing error characteristics affect simulations of cold region hydrology and which error characteristics are most important. Here we employ global sensitivity analysis to explore how (1) different error types (i.e., bias, random errors), (2) different error probability distributions, and (3) different error magnitudes influence physically based simulations of four snow variables (snow water equivalent, ablation rates, snow disappearance, and sublimation). We use the Sobol' global sensitivity analysis, which is typically used for model parameters but adapted here for testing model sensitivity to coexisting errors in all forcings. We quantify the Utah Energy Balance model's sensitivity to forcing errors with 1 840 000 Monte Carlo simulations across four sites and five different scenarios. Model outputs were (1) consistently more sensitive to forcing biases than random errors, (2) generally less sensitive to forcing error distributions, and (3) critically sensitive to different forcings depending on the relative magnitude of errors. For typical error magnitudes found in areas with drifting snow, precipitation bias was the most important factor for snow water equivalent, ablation rates, and snow disappearance timing, but other forcings had a more dominant impact when precipitation uncertainty was due solely to gauge undercatch. Additionally, the relative importance of forcing errors depended on the model output of interest. Sensitivity analysis can reveal which forcing error characteristics matter most for hydrologic modeling.

Evaluation of Bias Correction Method for Satellite-Based Rainfall Data

PubMed Central

Bhatti, Haris Akram; Rientjes, Tom; Haile, Alemseged Tamiru; Habib, Emad; Verhoef, Wouter

2016-01-01

With the advances in remote sensing technology, satellite-based rainfall estimates are gaining attraction in the field of hydrology, particularly in rainfall-runoff modeling. Since estimates are affected by errors correction is required. In this study, we tested the high resolution National Oceanic and Atmospheric Administration’s (NOAA) Climate Prediction Centre (CPC) morphing technique (CMORPH) satellite rainfall product (CMORPH) in the Gilgel Abbey catchment, Ethiopia. CMORPH data at 8 km-30 min resolution is aggregated to daily to match in-situ observations for the period 2003–2010. Study objectives are to assess bias of the satellite estimates, to identify optimum window size for application of bias correction and to test effectiveness of bias correction. Bias correction factors are calculated for moving window (MW) sizes and for sequential windows (SW’s) of 3, 5, 7, 9, …, 31 days with the aim to assess error distribution between the in-situ observations and CMORPH estimates. We tested forward, central and backward window (FW, CW and BW) schemes to assess the effect of time integration on accumulated rainfall. Accuracy of cumulative rainfall depth is assessed by Root Mean Squared Error (RMSE). To systematically correct all CMORPH estimates, station based bias factors are spatially interpolated to yield a bias factor map. Reliability of interpolation is assessed by cross validation. The uncorrected CMORPH rainfall images are multiplied by the interpolated bias map to result in bias corrected CMORPH estimates. Findings are evaluated by RMSE, correlation coefficient (r) and standard deviation (SD). Results showed existence of bias in the CMORPH rainfall. It is found that the 7 days SW approach performs best for bias correction of CMORPH rainfall. The outcome of this study showed the efficiency of our bias correction approach. PMID:27314363

Evaluation of Bias Correction Method for Satellite-Based Rainfall Data.

PubMed

Bhatti, Haris Akram; Rientjes, Tom; Haile, Alemseged Tamiru; Habib, Emad; Verhoef, Wouter

2016-06-15

With the advances in remote sensing technology, satellite-based rainfall estimates are gaining attraction in the field of hydrology, particularly in rainfall-runoff modeling. Since estimates are affected by errors correction is required. In this study, we tested the high resolution National Oceanic and Atmospheric Administration's (NOAA) Climate Prediction Centre (CPC) morphing technique (CMORPH) satellite rainfall product (CMORPH) in the Gilgel Abbey catchment, Ethiopia. CMORPH data at 8 km-30 min resolution is aggregated to daily to match in-situ observations for the period 2003-2010. Study objectives are to assess bias of the satellite estimates, to identify optimum window size for application of bias correction and to test effectiveness of bias correction. Bias correction factors are calculated for moving window (MW) sizes and for sequential windows (SW's) of 3, 5, 7, 9, …, 31 days with the aim to assess error distribution between the in-situ observations and CMORPH estimates. We tested forward, central and backward window (FW, CW and BW) schemes to assess the effect of time integration on accumulated rainfall. Accuracy of cumulative rainfall depth is assessed by Root Mean Squared Error (RMSE). To systematically correct all CMORPH estimates, station based bias factors are spatially interpolated to yield a bias factor map. Reliability of interpolation is assessed by cross validation. The uncorrected CMORPH rainfall images are multiplied by the interpolated bias map to result in bias corrected CMORPH estimates. Findings are evaluated by RMSE, correlation coefficient (r) and standard deviation (SD). Results showed existence of bias in the CMORPH rainfall. It is found that the 7 days SW approach performs best for bias correction of CMORPH rainfall. The outcome of this study showed the efficiency of our bias correction approach.

Atmospheric Multiple Scattering Effects on GLAS Altimetry. Part 2; Analysis of Expected Errors in Antarctic Altitude Measurements

NASA Technical Reports Server (NTRS)

Mahesh, Ashwin; Spinhirne, James D.; Duda, David P.; Eloranta, Edwin W.; Starr, David O'C (Technical Monitor)

2001-01-01

The altimetry bias in GLAS (Geoscience Laser Altimeter System) or other laser altimeters resulting from atmospheric multiple scattering is studied in relationship to current knowledge of cloud properties over the Antarctic Plateau. Estimates of seasonal and interannual changes in the bias are presented. Results show the bias in altitude from multiple scattering in clouds would be a significant error source without correction. The selective use of low optical depth clouds or cloudfree observations, as well as improved analysis of the return pulse such as by the Gaussian method used here, are necessary to minimize the surface altitude errors. The magnitude of the bias is affected by variations in cloud height, cloud effective particle size and optical depth. Interannual variations in these properties as well as in cloud cover fraction could lead to significant year-to-year variations in the altitude bias. Although cloud-free observations reduce biases in surface elevation measurements from space, over Antarctica these may often include near-surface blowing snow, also a source of scattering-induced delay. With careful selection and analysis of data, laser altimetry specifications can be met.

Evidence for Response Bias as a Source of Error Variance in Applied Assessment

ERIC Educational Resources Information Center

McGrath, Robert E.; Mitchell, Matthew; Kim, Brian H.; Hough, Leaetta

2010-01-01

After 100 years of discussion, response bias remains a controversial topic in psychological measurement. The use of bias indicators in applied assessment is predicated on the assumptions that (a) response bias suppresses or moderates the criterion-related validity of substantive psychological indicators and (b) bias indicators are capable of…

Bias Correction and Random Error Characterization for the Assimilation of HRDI Line-of-Sight Wind Measurements

NASA Technical Reports Server (NTRS)

Tangborn, Andrew; Menard, Richard; Ortland, David; Einaudi, Franco (Technical Monitor)

2001-01-01

A new approach to the analysis of systematic and random observation errors is presented in which the error statistics are obtained using forecast data rather than observations from a different instrument type. The analysis is carried out at an intermediate retrieval level, instead of the more typical state variable space. This method is carried out on measurements made by the High Resolution Doppler Imager (HRDI) on board the Upper Atmosphere Research Satellite (UARS). HRDI, a limb sounder, is the only satellite instrument measuring winds in the stratosphere, and the only instrument of any kind making global wind measurements in the upper atmosphere. HRDI measures doppler shifts in the two different O2 absorption bands (alpha and B) and the retrieved products are tangent point Line-of-Sight wind component (level 2 retrieval) and UV winds (level 3 retrieval). This analysis is carried out on a level 1.9 retrieval, in which the contributions from different points along the line-of-sight have not been removed. Biases are calculated from O-F (observed minus forecast) LOS wind components and are separated into a measurement parameter space consisting of 16 different values. The bias dependence on these parameters (plus an altitude dependence) is used to create a bias correction scheme carried out on the level 1.9 retrieval. The random error component is analyzed by separating the gamma and B band observations and locating observation pairs where both bands are very nearly looking at the same location at the same time. It is shown that the two observation streams are uncorrelated and that this allows the forecast error variance to be estimated. The bias correction is found to cut the effective observation error variance in half.

Reliability and Validity Assessment of a Linear Position Transducer

PubMed Central

Garnacho-Castaño, Manuel V.; López-Lastra, Silvia; Maté-Muñoz, José L.

2015-01-01

The objectives of the study were to determine the validity and reliability of peak velocity (PV), average velocity (AV), peak power (PP) and average power (AP) measurements were made using a linear position transducer. Validity was assessed by comparing measurements simultaneously obtained using the Tendo Weightlifting Analyzer Systemi and T-Force Dynamic Measurement Systemr (Ergotech, Murcia, Spain) during two resistance exercises, bench press (BP) and full back squat (BS), performed by 71 trained male subjects. For the reliability study, a further 32 men completed both lifts using the Tendo Weightlifting Analyzer Systemz in two identical testing sessions one week apart (session 1 vs. session 2). Intraclass correlation coefficients (ICCs) indicating the validity of the Tendo Weightlifting Analyzer Systemi were high, with values ranging from 0.853 to 0.989. Systematic biases and random errors were low to moderate for almost all variables, being higher in the case of PP (bias ±157.56 W; error ±131.84 W). Proportional biases were identified for almost all variables. Test-retest reliability was strong with ICCs ranging from 0.922 to 0.988. Reliability results also showed minimal systematic biases and random errors, which were only significant for PP (bias -19.19 W; error ±67.57 W). Only PV recorded in the BS showed no significant proportional bias. The Tendo Weightlifting Analyzer Systemi emerged as a reliable system for measuring movement velocity and estimating power in resistance exercises. The low biases and random errors observed here (mainly AV, AP) make this device a useful tool for monitoring resistance training. Key points This study determined the validity and reliability of peak velocity, average velocity, peak power and average power measurements made using a linear position transducer The Tendo Weight-lifting Analyzer Systemi emerged as a reliable system for measuring movement velocity and power. PMID:25729300

AQMEII3 evaluation of regional NA/EU simulations and ...

EPA Pesticide Factsheets

Through the comparison of several regional-scale chemistry transport modelling systems that simulate meteorology and air quality over the European and American continents, this study aims at i) apportioning the error to the responsible processes using time-scale analysis, ii) helping to detect causes of models error, and iii) identifying the processes and scales most urgently requiring dedicated investigations. The analysis is conducted within the framework of the third phase of the Air Quality Model Evaluation International Initiative (AQMEII) and tackles model performance gauging through measurement-to-model comparison, error decomposition and time series analysis of the models biases for several fields (ozone, CO, SO2, NO, NO2, PM10, PM2.5, wind speed, and temperature). The operational metrics (magnitude of the error, sign of the bias, associativity) provide an overall sense of model strengths and deficiencies, while apportioning the error to its constituent parts (bias, variance and covariance) can help to assess the nature and quality of the error. Each of the error components is analysed independently and apportioned to specific processes based on the corresponding timescale (long scale, synoptic, diurnal, and intra-day) using the error apportionment technique devised in the former phases of AQMEII. The application of the error apportionment method to the AQMEII Phase 3 simulations provides several key insights. In addition to reaffirming the strong impac

Mean Bias in Seasonal Forecast Model and ENSO Prediction Error.

PubMed

Kim, Seon Tae; Jeong, Hye-In; Jin, Fei-Fei

2017-07-20

This study uses retrospective forecasts made using an APEC Climate Center seasonal forecast model to investigate the cause of errors in predicting the amplitude of El Niño Southern Oscillation (ENSO)-driven sea surface temperature variability. When utilizing Bjerknes coupled stability (BJ) index analysis, enhanced errors in ENSO amplitude with forecast lead times are found to be well represented by those in the growth rate estimated by the BJ index. ENSO amplitude forecast errors are most strongly associated with the errors in both the thermocline slope response and surface wind response to forcing over the tropical Pacific, leading to errors in thermocline feedback. This study concludes that upper ocean temperature bias in the equatorial Pacific, which becomes more intense with increasing lead times, is a possible cause of forecast errors in the thermocline feedback and thus in ENSO amplitude.

Perceptions of Randomness: Why Three Heads Are Better than Four

ERIC Educational Resources Information Center

Hahn, Ulrike; Warren, Paul A.

2009-01-01

A long tradition of psychological research has lamented the systematic errors and biases in people's perception of the characteristics of sequences generated by a random mechanism such as a coin toss. It is proposed that once the likely nature of people's actual experience of such processes is taken into account, these "errors" and "biases"…

Reducing Bias and Error in the Correlation Coefficient Due to Nonnormality

ERIC Educational Resources Information Center

Bishara, Anthony J.; Hittner, James B.

2015-01-01

It is more common for educational and psychological data to be nonnormal than to be approximately normal. This tendency may lead to bias and error in point estimates of the Pearson correlation coefficient. In a series of Monte Carlo simulations, the Pearson correlation was examined under conditions of normal and nonnormal data, and it was compared…

Testing Error Management Theory: Exploring the Commitment Skepticism Bias and the Sexual Overperception Bias

ERIC Educational Resources Information Center

Henningsen, David Dryden; Henningsen, Mary Lynn Miller

2010-01-01

Research on error management theory indicates that men tend to overestimate women's sexual interest and women underestimate men's interest in committed relationships (Haselton & Buss, 2000). We test the assumptions of the theory in face-to-face, stranger interactions with 111 man-woman dyads. Support for the theory emerges, but potential boundary…

The Weak Spots in Contemporary Science (and How to Fix Them)

PubMed Central

2017-01-01

Simple Summary Several fraud cases, widespread failure to replicate or reproduce seminal findings, and pervasive error in the scientific literature have led to a crisis of confidence in the biomedical, behavioral, and social sciences. In this review, the author discusses some of the core findings that point at weak spots in contemporary science and considers the human factors that underlie them. He delves into the human tendencies that create errors and biases in data collection, analyses, and reporting of research results. He presents several solutions to deal with observer bias, publication bias, the researcher’s tendency to exploit degrees of freedom in their analysis of data, low statistical power, and errors in the reporting of results, with a focus on the specific challenges in animal welfare research. Abstract In this review, the author discusses several of the weak spots in contemporary science, including scientific misconduct, the problems of post hoc hypothesizing (HARKing), outcome switching, theoretical bloopers in formulating research questions and hypotheses, selective reading of the literature, selective citing of previous results, improper blinding and other design failures, p-hacking or researchers’ tendency to analyze data in many different ways to find positive (typically significant) results, errors and biases in the reporting of results, and publication bias. The author presents some empirical results highlighting problems that lower the trustworthiness of reported results in scientific literatures, including that of animal welfare studies. Some of the underlying causes of these biases are discussed based on the notion that researchers are only human and hence are not immune to confirmation bias, hindsight bias, and minor ethical transgressions. The author discusses solutions in the form of enhanced transparency, sharing of data and materials, (post-publication) peer review, pre-registration, registered reports, improved training, reporting guidelines, replication, dealing with publication bias, alternative inferential techniques, power, and other statistical tools. PMID:29186879

New MBE buffer for micron- and quarter-micron-gateGaAs MESFETs

NASA Technical Reports Server (NTRS)

1988-01-01

A new buffer layer has been developed that eliminates backgating in GaAs MESFETs and substantially reduces short-channel effects in GaAs MESFETs with 0.27-micron-long gates. The new buffer is grown by molecular beam epitaxy (MBE) at a substrate temperature of 200 C using Ga and As sub 4 beam fluxes. The buffer is crystalline, highly resistive, optically inactive, and can be overgrown with high quality GaAs. GaAs MESFETs with a gate length of 0.27 microns that incorporate the new buffer show improved dc and RF properties in comparison with a similar MESFET with a thin undoped GaAs buffer. To demonstrate the backgating performance improvement afforded by the new buffer, MESFETs were fabricated using a number of different buffer layers and structures. A schematic cross section of the MESFET structure used in this study is shown. The measured gate length, gate width, and source-drain spacing of this device are 2,98, and 5.5 microns, respectively. An ohmic contact, isolated from the MESFET by mesa etching, served as the sidegate. The MESFETs were fabricated in MBE n-GaAs layers grown on the new buffer and also in MBE n-GaAs layers grown on buffer layers of undoped GaAs, AlGaAs, and GaAs/AlGaAs superlattices. All the buffer layers were grown by MBE and are 2 microns thick. The active layer is doped to approximately 2 x 10 to the 17th/cu cm with silicon and is 0.3 microns thick.

Investigations of Optical Properties of Active Regions in Vertical Cavity Surface Emitting Lasers Grown by MBE

DTIC Science & Technology

2002-06-03

Molecular beam epitaxy ; Planar microcavities; Vertical cavity surface emitting lasers 1... Vertical Cavity Surface Emitting Lasers Grown by MBE DISTRIBUTION: Approved for public release, distribution unlimited This paper is part of the...S-581 83 Linkiping, Sweden Abstract The design of the vertical cavity surface emitting lasers ( VCSELs ) needs proper tuning of many

TRMM On-Orbit Performance Re-Accessed After Control Change

NASA Technical Reports Server (NTRS)

Bilanow, Steve

2006-01-01

The Tropical Rainfall Measuring Mission (TRMM) spacecraft, a joint mission between the U.S. and Japan, launched onboard an HI1 rocket on November 27,1997 and transitioned in August, 2001 from an average operating altitude of 350 kilometers to 402.5 kilometers. Due to problems using the Earth Sensor Assembly (ESA) at the higher altitude, TRMM switched to a backup attitude control mode. Prior to the orbit boost TRMM controlled pitch and roll to the local vertical using ESA measurements while using gyro data to propagate yaw attitude between yaw updates from the Sun sensors. After the orbit boost, a Kalman filter used 3-axis gyro data with Sun sensor and magnetometers to estimate onboard attitude. While originally intended to meet a degraded attitude accuracy of 0.7 degrees, the new control mode met the original 0.2 degree attitude accuracy requirement after improving onboard ephemeris prediction and adjusting the magnetometer calibration onboard. Independent roll attitude checks using a science instrument, the Precipitation Radar (PR) which was built in Japan, provided a novel insight into the pointing performance. The PR data helped identify the pointing errors after the orbit boost, track the performance improvements, and show subtle effects from ephemeris errors and gyro bias errors. It also helped identify average bias trends throughout the mission. Roll errors tracked by the PR from sample orbits pre-boost and post-boost are shown in Figure 1. Prior to the orbit boost the largest attitude errors were due to occasional interference in the ESA. These errors were sometime larger than 0.2 degrees in pitch and roll, but usually less, as estimated from a comprehensive review of the attitude excursions using gyro data. Sudden jumps in the onboard roll show up as spikes in the reported attitude since the control responds within tens of seconds to null the pointing error. The PR estimated roll tracks well with an estimate of the roll history propagated using gyro data. After the orbit boost, the attitude errors shown by the PR roll have a smooth sine-wave type signal because of the way that attitude errors propagate with the use of gyro data. Yaw errors couple at orbit period to roll with '/4 orbit lag. By tracking the amplitude, phase, and bias of the sinusoidal PR roll error signal, it was shown that the average pitch rotation axis tends to be offset from orbit normal in a direction perpendicular to the Sun direction, as shown in Figure 2 for a 200 day period following the orbit boost. This is a result of the higher accuracy and stability of the Sun sensor measurements relative to the magnetometer measurements used in the Kalman filter. In November, 2001 a magnetometer calibration adjustment was uploaded which improved the pointing performance, keeping the roll and yaw amplitudes within about 0.1 degrees. After the boost, onboard ephemeris errors had a direct effect on the pitch pointing, being used to compute the Earth pointing reference frame. Improvements after the orbit boost have kept the the onboard ephemeris errors generally below 20 kilometers. Ephemeris errors have secondary effects on roll and yaw, especially during high beta angle when pitch effects can couple into roll and yaw. This is illustrated in figure 3. The onboard roll bias trends as measured by PR data show correlations with the Kalman filter's gyro bias error. This particularly shows up after yaw turns (every 2 to 4 weeks) as shown in Figure 3, when a slight roll bias is observed while the onboard computed gyro biases settle to new values. As for longer term trends, the PR data shows that the roll bias was influenced by Earth horizon radiance effects prior to the boost, changing values at yaw turns, and indicated a long term drift as shown in Figure 4. After the boost, the bias variations were smaller and showed some possible correlation with solar beta angle, probably due to sun sensor misalignment effects.

Influence of MBE growth modes and conditions on spontaneous formation of metallic In nanoparticles and electrical properties of InN matrix

NASA Astrophysics Data System (ADS)

Komissarova, T. A.; Wang, P.; Paturi, P.; Wang, X.; Ivanov, S. V.

2017-11-01

Influence of the molecular beam epitaxy (MBE) growth conditions on the electrical properties of the InN epilayers in terms of minimization of the effect of spontaneously formed In nanoparticles was studied. A three-step growth sequence was used, including direct MBE growth of an InN nucleation layer, migration enhanced epitaxy (MEE) of an InN buffer layer, and In-rich MBE growth of the main InN layer, utilizing the droplet elimination by radical-beam irradiation (DERI) technique. The three-step growth regime was found to lead to decreasing the relative amount of In nanoparticles to 4.8% and 3.8% in In-rich and near-stoichiometric conditions, respectively, whereas the transport properties are better for the In-rich growth. Further reduction of the metallic indium inclusions in the InN films, while keeping simultaneously satisfactory transport parameters, is hardly possible due to fundamental processes of InN thermal decomposition and formation of the nitrogen vacancy conglomerates in the InN matrix. The In inclusions are shown to dominate the electrical conductivity of the InN films even at their minimum amount.

Testing the Digital Thread in Support of Model-Based Manufacturing and Inspection

PubMed Central

Hedberg, Thomas; Lubell, Joshua; Fischer, Lyle; Maggiano, Larry; Feeney, Allison Barnard

2016-01-01

A number of manufacturing companies have reported anecdotal evidence describing the benefits of Model-Based Enterprise (MBE). Based on this evidence, major players in industry have embraced a vision to deploy MBE. In our view, the best chance of realizing this vision is the creation of a single “digital thread.” Under MBE, there exists a Model-Based Definition (MBD), created by the Engineering function, that downstream functions reuse to complete Model-Based Manufacturing and Model-Based Inspection activities. The ensemble of data that enables the combination of model-based definition, manufacturing, and inspection defines this digital thread. Such a digital thread would enable real-time design and analysis, collaborative process-flow development, automated artifact creation, and full-process traceability in a seamless real-time collaborative development among project participants. This paper documents the strengths and weaknesses in the current, industry strategies for implementing MBE. It also identifies gaps in the transition and/or exchange of data between various manufacturing processes. Lastly, this paper presents measured results from a study of model-based processes compared to drawing-based processes and provides evidence to support the anecdotal evidence and vision made by industry. PMID:27325911

Constraints on a scale-dependent bias from galaxy clustering

NASA Astrophysics Data System (ADS)

Amendola, L.; Menegoni, E.; Di Porto, C.; Corsi, M.; Branchini, E.

2017-01-01

We forecast the future constraints on scale-dependent parametrizations of galaxy bias and their impact on the estimate of cosmological parameters from the power spectrum of galaxies measured in a spectroscopic redshift survey. For the latter we assume a wide survey at relatively large redshifts, similar to the planned Euclid survey, as the baseline for future experiments. To assess the impact of the bias we perform a Fisher matrix analysis, and we adopt two different parametrizations of scale-dependent bias. The fiducial models for galaxy bias are calibrated using mock catalogs of H α emitting galaxies mimicking the expected properties of the objects that will be targeted by the Euclid survey. In our analysis we have obtained two main results. First of all, allowing for a scale-dependent bias does not significantly increase the errors on the other cosmological parameters apart from the rms amplitude of density fluctuations, σ8 , and the growth index γ , whose uncertainties increase by a factor up to 2, depending on the bias model adopted. Second, we find that the accuracy in the linear bias parameter b0 can be estimated to within 1%-2% at various redshifts regardless of the fiducial model. The nonlinear bias parameters have significantly large errors that depend on the model adopted. Despite this, in the more realistic scenarios departures from the simple linear bias prescription can be detected with a ˜2 σ significance at each redshift explored. Finally, we use the Fisher matrix formalism to assess the impact od assuming an incorrect bias model and find that the systematic errors induced on the cosmological parameters are similar or even larger than the statistical ones.

Comparison and modeling of effects of normal and reduced precipitation supply in field experiment with spring barley

NASA Astrophysics Data System (ADS)

Pohanková, Eva; Orság, Matěj; Fischer, Milan; Hlavinka, Petr

2015-04-01

This paper evaluates two-year (2013 and 2014) results of field experiments with spring barley (cultivar Bojos) under reduced precipitation supply. The field experiments were carried out at the experimental station in Domanínek (Czech Republic; 49°31,470'N, 16°14,400'E, altitude 530 m a.s.l.) and conducted by Institute of Agrosystems and bioclimatology at Mendel Univerzity in Brno in cooperation with Global Change Research Centre AS CR. The field experiments consisted of small plots in two variants and three repetitions. The first variant was uncovered the second was partially covered to exclude rain through out the whole vegetation season. For the partial covering of the plot, a material which transmits solar radiation and diverts rainwater away from the percentage coverage of the plots was used. In 2013, the covered area of the experimental plot was 30%, and in 2014, it was 70%. The main aim was to determine whether there are any differences in the spring barley's development, growth and yield in the uncovered and the partially covered plots, and a comparison of the results. Firstly, differences of key parameters (seasonal dynamics of the leaf area index and above ground biomass, soil water content, yield components and yields) compared; secondly, the results of the field experiments served as input data for the crop growth model DAISY. Subsequently, the crop growth model' ability to simulate crop growth and crop development which were affected by the drought stress was explored. The results were assessed using the following statistical indexes: root mean square error (RMSE) and mean bias error (MBE). This study was funded by project "Building up a multidisciplinary scientific team focused on drought" No. CZ.1.07/2.3.00/20.0248, NAZV-JPI - project supported by Czech National Agency of Agricultural Research No. QJ1310123 "Crop modelling as a tool for increasing the production potential and food security of the Czech Republic under Climate Change" and project LD13030 supporting ES1106 COST Action.

Utilization of downscaled microwave satellite data and GRACE Total Water Storage anomalies for improving streamflow prediction in the Lower Mekong Basin

NASA Astrophysics Data System (ADS)

Lakshmi, V.; Gupta, M.; Bolten, J. D.

2016-12-01

The Mekong river is the world's eighth largest in discharge with draining an area of 795,000 km² from the Eastern watershed of the Tibetan Plateau to the Mekong Delta including, Myanmar, Laos PDR, Thailand, Cambodia, Vietnam and three provinces of China. The populations in these countries are highly dependent on the Mekong River and they are vulnerable to the availability and quality of the water resources within the Mekong River Basin. Soil moisture is one of the most important hydrological cycle variables and is available from passive microwave satellite sensors (such as AMSR-E, SMOS and SMAP), but their spatial resolution is frequently too coarse for effective use by land managers and decision makers. The merging of satellite observations with numerical models has led to improved land surface predictions. Although performance of the models have been continuously improving, the laboratory methods for determining key hydraulic parameters are time consuming and expensive. The present study assesses a method to determine the effective soil hydraulic parameters using a downscaled microwave remote sensing soil moisture product based on the NASA Advanced Microwave Scanning Radiometer (AMSR-E). The soil moisture downscaling algorithm is based on a regression relationship between 1-km MODIS land surface temperature and 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) to produce an enhanced spatial resolution ASMR-E-based soil moisture product. Since the optimized parameters are based on the near surface soil moisture information, further constraints are applied during the numerical simulation through the assimilation of GRACE Total Water Storage (TWS) within the land surface model. This work improves the hydrological fluxes and the state variables are optimized and the optimal parameter values are then transferred for retrieving hydrological fluxes. To evaluate the performance of the system in helping improve simulation accuracy and whether they can be used to obtain soil moisture profiles at poorly gauged catchments the root mean square error (RMSE) and Mean Bias error (MBE) are used to measure the performance of the simulations.

An evaluation of SEBAL algorithm using high resolution aircraft data acquired during BEAREX07

NASA Astrophysics Data System (ADS)

Paul, G.; Gowda, P. H.; Prasad, V. P.; Howell, T. A.; Staggenborg, S.

2010-12-01

Surface Energy Balance Algorithm for Land (SEBAL) computes spatially distributed surface energy fluxes and evapotranspiration (ET) rates using a combination of empirical and deterministic equations executed in a strictly hierarchical sequence. Over the past decade SEBAL has been tested over various regions and has found its application in solving water resources and irrigation problems. This research combines high resolution remote sensing data and field measurements of the surface radiation and agro-meteorological variables to review various SEBAL steps for mapping ET in the Texas High Plains (THP). High resolution aircraft images (0.5-1.8 m) acquired during the Bushland Evapotranspiration and Agricultural Remote Sensing Experiment 2007 (BEAREX07) conducted at the USDA-ARS Conservation and Production Research Laboratory in Bushland, Texas, was utilized to evaluate the SEBAL. Accuracy of individual relationships and predicted ET were investigated using observed hourly ET rates from 4 large weighing lysimeters, each located at the center of 4.7 ha field. The uniqueness and the strength of this study come from the fact that it evaluates the SEBAL for irrigated and dryland conditions simultaneously with each lysimeter field planted to irrigated forage sorghum, irrigated forage corn, dryland clumped grain sorghum, and dryland row sorghum. Improved coefficients for the local conditions were developed for the computation of roughness length for momentum transport. The decision involved in selection of dry and wet pixels, which essentially determines the partitioning of the available energy between sensible (H) and latent (LE) heat fluxes has been discussed. The difference in roughness length referred to as the kB-1 parameter was modified in the current study. Performance of the SEBAL was evaluated using mean bias error (MBE) and root mean square error (RMSE). An RMSE of ±37.68 W m-2 and ±0.11 mm h-1 was observed for the net radiation and hourly actual ET, respectively. Application of SEBAL over THP shows promising prospects for water management, however, locally derived relation, careful selection of dry and wet pixel and calibration is required for good performance.

Synergistic Utilization of Microwave Satellite Data and GRACE-Total Water Storage Anomaly for Improving Available Water Capacity Prediction in Lower Mekong Basin

NASA Astrophysics Data System (ADS)

Gupta, M.; Bolten, J. D.; Lakshmi, V.

2015-12-01

The Mekong River is the longest river in Southeast Asia and the world's eighth largest in discharge with draining an area of 795,000 km² from the eastern watershed of the Tibetan Plateau to the Mekong Delta including three provinces of China, Myanmar, Lao PDR, Thailand, Cambodia and Viet Nam. This makes the life of people highly vulnerable to availability of the water resources as soil moisture is one of the major fundamental variables in global hydrological cycles. The day-to-day variability in soil moisture on field to global scales is an important quantity for early warning systems for events like flooding and drought. In addition to the extreme situations the accurate soil moisture retrieval are important for agricultural irrigation scheduling and water resource management. The present study proposes a method to determine the effective soil hydraulic parameters directly from information available for the soil moisture state from the recently launched SMAP (L-band) microwave remote sensing observations. Since the optimized parameters are based on the near surface soil moisture information, further constraints are applied during the numerical simulation through the assimilation of GRACE Total Water Storage (TWS) within the physically based land surface model. This work addresses the improvement of available water capacity as the soil hydraulic parameters are optimized through the utilization of satellite-retrieved near surface soil moisture. The initial ranges of soil hydraulic parameters are taken in correspondence with the values available from the literature based on FAO. The optimization process is divided into two steps: the state variable are optimized and the optimal parameter values are then transferred for retrieving soil moisture and streamflow. A homogeneous soil system is considered as the soil moisture from sensors such as AMSR-E/SMAP can only be retrieved for the top few centimeters of soil. To evaluate the performance of the system in helping improve simulation accuracy and whether they can be used to obtain soil moisture profiles at poorly gauged catchments the root mean square error (RMSE) and Mean Bias error (MBE) are used to measure the performance of the simulations.

Individual differences in conflict detection during reasoning.

PubMed

Frey, Darren; Johnson, Eric D; De Neys, Wim

2018-05-01

Decades of reasoning and decision-making research have established that human judgment is often biased by intuitive heuristics. Recent "error" or bias detection studies have focused on reasoners' abilities to detect whether their heuristic answer conflicts with logical or probabilistic principles. A key open question is whether there are individual differences in this bias detection efficiency. Here we present three studies in which co-registration of different error detection measures (confidence, response time and confidence response time) allowed us to assess bias detection sensitivity at the individual participant level in a range of reasoning tasks. The results indicate that although most individuals show robust bias detection, as indexed by increased latencies and decreased confidence, there is a subgroup of reasoners who consistently fail to do so. We discuss theoretical and practical implications for the field.

Data Assimilation in the Presence of Forecast Bias: The GEOS Moisture Analysis

NASA Technical Reports Server (NTRS)

Dee, Dick P.; Todling, Ricardo

1999-01-01

We describe the application of the unbiased sequential analysis algorithm developed by Dee and da Silva (1998) to the GEOS DAS moisture analysis. The algorithm estimates the persistent component of model error using rawinsonde observations and adjusts the first-guess moisture field accordingly. Results of two seasonal data assimilation cycles show that moisture analysis bias is almost completely eliminated in all observed regions. The improved analyses cause a sizable reduction in the 6h-forecast bias and a marginal improvement in the error standard deviations.

The controlled growth of perovskite thin films: Opportunities, challenges, and synthesis

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

Schlom, D.G.; Theis, C.D.; Hawley, M.E.

1997-10-01

The broad spectrum of electronic and optical properties exhibited by perovskites offers tremendous opportunities for microelectronic devices, especially when a combination of properties in a single device is desired. Molecular beam epitaxy (MBE) has achieved unparalleled control in the integration of semiconductors at the monolayer-level; its use for the integration of perovskites with similar nanoscale customization appears promising. Composition control and oxidation are often significant challenges to the growth of perovskites by MBE, but we show that these can be met through the use of purified ozone as an oxidant and real-time atomic absorption composition control. The opportunities, challenges, andmore » synthesis of oxide heterostructures by reactive MBE are described, with examples taken from the growth of oxide superconductors and oxide ferroelectrics.« less

Doping of free-standing zinc-blende GaN layers grown by molecular beam epitaxy

NASA Astrophysics Data System (ADS)

Novikov, S. V.; Powell, R. E. L.; Staddon, C. R.; Kent, A. J.; Foxon, C. T.

2014-10-01

Currently there is high level of interest in developing of vertical device structures based on the group III nitrides. We have studied n- and p-doping of free-standing zinc-blende GaN grown by plasma-assisted molecular beam epitaxy (PA-MBE). Si was used as the n-dopant and Mg as the p-dopant for zinc-blende GaN. Controllable levels of doping with Si and Mg in free-standing zinc-blende GaN have been achieved by PA-MBE. The Si and Mg doping depth uniformity through the zinc-blende GaN layers have been confirmed by secondary ion mass spectrometry (SIMS). Controllable Si and Mg doping makes PA-MBE a promising method for the growth of conducting group III-nitrides bulk crystals.

Synthesis science of SrRuO3 and CaRuO3 epitaxial films with high residual resistivity ratios

NASA Astrophysics Data System (ADS)

Nair, Hari P.; Liu, Yang; Ruf, Jacob P.; Schreiber, Nathaniel J.; Shang, Shun-Li; Baek, David J.; Goodge, Berit H.; Kourkoutis, Lena F.; Liu, Zi-Kui; Shen, Kyle M.; Schlom, Darrell G.

2018-04-01

Epitaxial SrRuO3 and CaRuO3 films were grown under an excess flux of elemental ruthenium in an adsorption-controlled regime by molecular-beam epitaxy (MBE), where the excess volatile RuOx (x = 2 or 3) desorbs from the growth front leaving behind a single-phase film. By growing in this regime, we were able to achieve SrRuO3 and CaRuO3 films with residual resistivity ratios (ρ300 K/ρ4 K) of 76 and 75, respectively. A combined phase stability diagram based on the thermodynamics of MBE (TOMBE) growth, termed a TOMBE diagram, is employed to provide improved guidance for the growth of complex materials by MBE.

Comparative study of LaNiO3/LaAlO3 heterostructures grown by pulsed laser deposition and oxide molecular beam epitaxy

NASA Astrophysics Data System (ADS)

Wrobel, F.; Mark, A. F.; Christiani, G.; Sigle, W.; Habermeier, H.-U.; van Aken, P. A.; Logvenov, G.; Keimer, B.; Benckiser, E.

2017-01-01

Variations in growth conditions associated with different deposition techniques can greatly affect the phase stability and defect structure of complex oxide heterostructures. We synthesized superlattices of the paramagnetic metal LaNiO3 and the large band gap insulator LaAlO3 by atomic layer-by-layer molecular beam epitaxy (MBE) and pulsed laser deposition (PLD) and compared their crystallinity and microstructure as revealed by high-resolution transmission electron microscopy images and resistivity. The MBE samples show a higher density of stacking faults but smoother interfaces and generally higher electrical conductivity. Our study identifies the opportunities and challenges of MBE and PLD growth and serves as a general guide for the choice of the deposition technique for perovskite oxides.

Explanation of Two Anomalous Results in Statistical Mediation Analysis.

PubMed

Fritz, Matthew S; Taylor, Aaron B; Mackinnon, David P

2012-01-01

Previous studies of different methods of testing mediation models have consistently found two anomalous results. The first result is elevated Type I error rates for the bias-corrected and accelerated bias-corrected bootstrap tests not found in nonresampling tests or in resampling tests that did not include a bias correction. This is of special concern as the bias-corrected bootstrap is often recommended and used due to its higher statistical power compared with other tests. The second result is statistical power reaching an asymptote far below 1.0 and in some conditions even declining slightly as the size of the relationship between X and M , a , increased. Two computer simulations were conducted to examine these findings in greater detail. Results from the first simulation found that the increased Type I error rates for the bias-corrected and accelerated bias-corrected bootstrap are a function of an interaction between the size of the individual paths making up the mediated effect and the sample size, such that elevated Type I error rates occur when the sample size is small and the effect size of the nonzero path is medium or larger. Results from the second simulation found that stagnation and decreases in statistical power as a function of the effect size of the a path occurred primarily when the path between M and Y , b , was small. Two empirical mediation examples are provided using data from a steroid prevention and health promotion program aimed at high school football players (Athletes Training and Learning to Avoid Steroids; Goldberg et al., 1996), one to illustrate a possible Type I error for the bias-corrected bootstrap test and a second to illustrate a loss in power related to the size of a . Implications of these findings are discussed.

Statistical approaches to account for false-positive errors in environmental DNA samples.

PubMed

Lahoz-Monfort, José J; Guillera-Arroita, Gurutzeta; Tingley, Reid

2016-05-01

Environmental DNA (eDNA) sampling is prone to both false-positive and false-negative errors. We review statistical methods to account for such errors in the analysis of eDNA data and use simulations to compare the performance of different modelling approaches. Our simulations illustrate that even low false-positive rates can produce biased estimates of occupancy and detectability. We further show that removing or classifying single PCR detections in an ad hoc manner under the suspicion that such records represent false positives, as sometimes advocated in the eDNA literature, also results in biased estimation of occupancy, detectability and false-positive rates. We advocate alternative approaches to account for false-positive errors that rely on prior information, or the collection of ancillary detection data at a subset of sites using a sampling method that is not prone to false-positive errors. We illustrate the advantages of these approaches over ad hoc classifications of detections and provide practical advice and code for fitting these models in maximum likelihood and Bayesian frameworks. Given the severe bias induced by false-negative and false-positive errors, the methods presented here should be more routinely adopted in eDNA studies. © 2015 John Wiley & Sons Ltd.

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

Morley, Steven

The PyForecastTools package provides Python routines for calculating metrics for model validation, forecast verification and model comparison. For continuous predictands the package provides functions for calculating bias (mean error, mean percentage error, median log accuracy, symmetric signed bias), and for calculating accuracy (mean squared error, mean absolute error, mean absolute scaled error, normalized RMSE, median symmetric accuracy). Convenience routines to calculate the component parts (e.g. forecast error, scaled error) of each metric are also provided. To compare models the package provides: generic skill score; percent better. Robust measures of scale including median absolute deviation, robust standard deviation, robust coefficient ofmore » variation and the Sn estimator are all provided by the package. Finally, the package implements Python classes for NxN contingency tables. In the case of a multi-class prediction, accuracy and skill metrics such as proportion correct and the Heidke and Peirce skill scores are provided as object methods. The special case of a 2x2 contingency table inherits from the NxN class and provides many additional metrics for binary classification: probability of detection, probability of false detection, false alarm ration, threat score, equitable threat score, bias. Confidence intervals for many of these quantities can be calculated using either the Wald method or Agresti-Coull intervals.« less

CAUSES: Attribution of Surface Radiation Biases in NWP and Climate Models near the U.S. Southern Great Plains

DOE PAGES

Van Weverberg, K.; Morcrette, C. J.; Petch, J.; ...

2018-02-28

Many Numerical Weather Prediction (NWP) and climate models exhibit too warm lower tropospheres near the midlatitude continents. The warm bias has been shown to coincide with important surface radiation biases that likely play a critical role in the inception or the growth of the warm bias. This paper presents an attribution study on the net radiation biases in nine model simulations, performed in the framework of the CAUSES project (Clouds Above the United States and Errors at the Surface). Contributions from deficiencies in the surface properties, clouds, water vapor, and aerosols are quantified, using an array of radiation measurement stationsmore » near the Atmospheric Radiation Measurement Southern Great Plains site. Furthermore, an in-depth analysis is shown to attribute the radiation errors to specific cloud regimes. The net surface shortwave radiation is overestimated in all models throughout most of the simulation period. Cloud errors are shown to contribute most to this overestimation, although nonnegligible contributions from the surface albedo exist in most models. Missing deep cloud events and/or simulating deep clouds with too weak cloud radiative effects dominate in the cloud-related radiation errors. Some models have compensating errors between excessive occurrence of deep cloud but largely underestimating their radiative effect, while other models miss deep cloud events altogether. Surprisingly, even the latter models tend to produce too much and too frequent afternoon surface precipitation. This suggests that rather than issues with the triggering of deep convection, cloud radiative deficiencies are related to too weak convective cloud detrainment and too large precipitation efficiencies.« less

CAUSES: Attribution of Surface Radiation Biases in NWP and Climate Models near the U.S. Southern Great Plains

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

Van Weverberg, K.; Morcrette, C. J.; Petch, J.

Many Numerical Weather Prediction (NWP) and climate models exhibit too warm lower tropospheres near the midlatitude continents. The warm bias has been shown to coincide with important surface radiation biases that likely play a critical role in the inception or the growth of the warm bias. This paper presents an attribution study on the net radiation biases in nine model simulations, performed in the framework of the CAUSES project (Clouds Above the United States and Errors at the Surface). Contributions from deficiencies in the surface properties, clouds, water vapor, and aerosols are quantified, using an array of radiation measurement stationsmore » near the Atmospheric Radiation Measurement Southern Great Plains site. Furthermore, an in-depth analysis is shown to attribute the radiation errors to specific cloud regimes. The net surface shortwave radiation is overestimated in all models throughout most of the simulation period. Cloud errors are shown to contribute most to this overestimation, although nonnegligible contributions from the surface albedo exist in most models. Missing deep cloud events and/or simulating deep clouds with too weak cloud radiative effects dominate in the cloud-related radiation errors. Some models have compensating errors between excessive occurrence of deep cloud but largely underestimating their radiative effect, while other models miss deep cloud events altogether. Surprisingly, even the latter models tend to produce too much and too frequent afternoon surface precipitation. This suggests that rather than issues with the triggering of deep convection, cloud radiative deficiencies are related to too weak convective cloud detrainment and too large precipitation efficiencies.« less

CAUSES: Attribution of Surface Radiation Biases in NWP and Climate Models near the U.S. Southern Great Plains

NASA Astrophysics Data System (ADS)

Van Weverberg, K.; Morcrette, C. J.; Petch, J.; Klein, S. A.; Ma, H.-Y.; Zhang, C.; Xie, S.; Tang, Q.; Gustafson, W. I.; Qian, Y.; Berg, L. K.; Liu, Y.; Huang, M.; Ahlgrimm, M.; Forbes, R.; Bazile, E.; Roehrig, R.; Cole, J.; Merryfield, W.; Lee, W.-S.; Cheruy, F.; Mellul, L.; Wang, Y.-C.; Johnson, K.; Thieman, M. M.

2018-04-01

Many Numerical Weather Prediction (NWP) and climate models exhibit too warm lower tropospheres near the midlatitude continents. The warm bias has been shown to coincide with important surface radiation biases that likely play a critical role in the inception or the growth of the warm bias. This paper presents an attribution study on the net radiation biases in nine model simulations, performed in the framework of the CAUSES project (Clouds Above the United States and Errors at the Surface). Contributions from deficiencies in the surface properties, clouds, water vapor, and aerosols are quantified, using an array of radiation measurement stations near the Atmospheric Radiation Measurement Southern Great Plains site. Furthermore, an in-depth analysis is shown to attribute the radiation errors to specific cloud regimes. The net surface shortwave radiation is overestimated in all models throughout most of the simulation period. Cloud errors are shown to contribute most to this overestimation, although nonnegligible contributions from the surface albedo exist in most models. Missing deep cloud events and/or simulating deep clouds with too weak cloud radiative effects dominate in the cloud-related radiation errors. Some models have compensating errors between excessive occurrence of deep cloud but largely underestimating their radiative effect, while other models miss deep cloud events altogether. Surprisingly, even the latter models tend to produce too much and too frequent afternoon surface precipitation. This suggests that rather than issues with the triggering of deep convection, cloud radiative deficiencies are related to too weak convective cloud detrainment and too large precipitation efficiencies.

Associations among Selective Attention, Memory Bias, Cognitive Errors and Symptoms of Anxiety in Youth

ERIC Educational Resources Information Center

Watts, Sarah E.; Weems, Carl F.

2006-01-01

The purpose of this study was to examine the linkages among selective attention, memory bias, cognitive errors, and anxiety problems by testing a model of the interrelations among these cognitive variables and childhood anxiety disorder symptoms. A community sample of 81 youth (38 females and 43 males) aged 9-17 years and their parents completed…

Is the Intergenerational Transmission of High Cultural Activities Biased by the Retrospective Measurement of Parental High Cultural Activities?

ERIC Educational Resources Information Center

de Vries, Jannes; de Graaf, Paul M.

2008-01-01

In this article we study the bias caused by the conventional retrospective measurement of parental high cultural activities in the effects of parental high cultural activities and educational attainment on son's or daughter's high cultural activities. Multi-informant data show that there is both random measurement error and correlated error in the…

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

ERIC Educational Resources Information Center

Kane, Michael T.

2017-01-01

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

Radiation Tests on 2Gb NAND Flash Memories

NASA Technical Reports Server (NTRS)

Nguyen, Duc N.; Guertin, Steven M.; Patterson, J. D.

2006-01-01

We report on SEE and TID tests of highly scaled Samsung 2Gbits flash memories. Both in-situ and biased interval irradiations were used to characterize the response of the total accumulated dose failures. The radiation-induced failures can be categorized as followings: single event upset (SEU) read errors in biased and unbiased modes, write errors, and single-event-functional-interrupt (SEFI) failures.

#2 - An Empirical Assessment of Exposure Measurement Error and Effect Attenuation in Bi-Pollutant Epidemiologic Models

EPA Science Inventory

Background• Differing degrees of exposure error acrosspollutants• Previous focus on quantifying and accounting forexposure error in single-pollutant models• Examine exposure errors for multiple pollutantsand provide insights on the potential for bias andattenuation...

Measuring food intake in studies of obesity.

PubMed

Lissner, Lauren

2002-12-01

The problem of how to measure habitual food intake in studies of obesity remains an enigma in nutritional research. The existence of obesity-specific underreporting was rather controversial until the advent of the doubly labelled water technique gave credence to previously anecdotal evidence that such a bias does in fact exist. This paper reviews a number of issues relevant to interpreting dietary data in studies involving obesity. Topics covered include: participation biases, normative biases,importance of matching method to study, selective underreporting, and a brief discussion of the potential implications of generalised and selective underreporting in analytical epidemiology. It is concluded that selective underreporting of certain food types by obese individuals would produce consequences in analytical epidemiological studies that are both unpredictable and complex. Since it is becoming increasingly acknowledged that selective reporting error does occur, it is important to emphasise that correction for energy intake is not sufficient to eliminate the biases from this type of error. This is true both for obesity-related selective reporting errors and more universal types of selective underreporting, e.g. foods of low social desirability. Additional research is urgently required to examine the consequences of this type of error.

The contribution of natural variability to GCM bias: Can we effectively bias-correct climate projections?

NASA Astrophysics Data System (ADS)

McAfee, S. A.; DeLaFrance, A.

2017-12-01

Investigating the impacts of climate change often entails using projections from inherently imperfect general circulation models (GCMs) to drive models that simulate biophysical or societal systems in great detail. Error or bias in the GCM output is often assessed in relation to observations, and the projections are adjusted so that the output from impacts models can be compared to historical or observed conditions. Uncertainty in the projections is typically accommodated by running more than one future climate trajectory to account for differing emissions scenarios, model simulations, and natural variability. The current methods for dealing with error and uncertainty treat them as separate problems. In places where observed and/or simulated natural variability is large, however, it may not be possible to identify a consistent degree of bias in mean climate, blurring the lines between model error and projection uncertainty. Here we demonstrate substantial instability in mean monthly temperature bias across a suite of GCMs used in CMIP5. This instability is greatest in the highest latitudes during the cool season, where shifts from average temperatures below to above freezing could have profound impacts. In models with the greatest degree of bias instability, the timing of regional shifts from below to above average normal temperatures in a single climate projection can vary by about three decades, depending solely on the degree of bias assessed. This suggests that current bias correction methods based on comparison to 20- or 30-year normals may be inappropriate, particularly in the polar regions.

Cirrus Cloud Retrieval Using Infrared Sounding Data: Multilevel Cloud Errors.

NASA Astrophysics Data System (ADS)

Baum, Bryan A.; Wielicki, Bruce A.

1994-01-01

In this study we perform an error analysis for cloud-top pressure retrieval using the High-Resolution Infrared Radiometric Sounder (HIRS/2) 15-µm CO2 channels for the two-layer case of transmissive cirrus overlying an overcast, opaque stratiform cloud. This analysis includes standard deviation and bias error due to instrument noise and the presence of two cloud layers, the lower of which is opaque. Instantaneous cloud pressure retrieval errors are determined for a range of cloud amounts (0.1 1.0) and cloud-top pressures (850250 mb). Large cloud-top pressure retrieval errors are found to occur when a lower opaque layer is present underneath an upper transmissive cloud layer in the satellite field of view (FOV). Errors tend to increase with decreasing upper-cloud elective cloud amount and with decreasing cloud height (increasing pressure). Errors in retrieved upper-cloud pressure result in corresponding errors in derived effective cloud amount. For the case in which a HIRS FOV has two distinct cloud layers, the difference between the retrieved and actual cloud-top pressure is positive in all casts, meaning that the retrieved upper-cloud height is lower than the actual upper-cloud height. In addition, errors in retrieved cloud pressure are found to depend upon the lapse rate between the low-level cloud top and the surface. We examined which sounder channel combinations would minimize the total errors in derived cirrus cloud height caused by instrument noise and by the presence of a lower-level cloud. We find that while the sounding channels that peak between 700 and 1000 mb minimize random errors, the sounding channels that peak at 300—500 mb minimize bias errors. For a cloud climatology, the bias errors are most critical.

Accommodating Sensor Bias in MRAC for State Tracking

NASA Technical Reports Server (NTRS)

Patre, Parag; Joshi, Suresh M.

2011-01-01

The problem of accommodating unknown sensor bias is considered in a direct model reference adaptive control (MRAC) setting for state tracking using state feedback. Sensor faults can occur during operation, and if the biased state measurements are directly used with a standard MRAC control law, neither closed-loop signal boundedness, nor asymptotic tracking can be guaranteed and the resulting tracking errors may be unbounded or unacceptably large. A modified MRAC law is proposed, which combines a bias estimator with control gain adaptation, and it is shown that signal boundedness can be accomplished, although the tracking error may not go to zero. Further, for the case wherein an asymptotically stable sensor bias estimator is available, an MRAC control law is proposed to accomplish asymptotic tracking and signal boundedness. Such a sensor bias estimator can be designed if additional sensor measurements are available, as illustrated for the case wherein bias is present in the rate gyro and airspeed measurements. Numerical example results are presented to illustrate each of the schemes.

Fluorescence XAS using Ge PAD: Application to High-Temperature Superconducting Thin Film Single Crystals

NASA Astrophysics Data System (ADS)

Oyanagi, H.; Tsukada, A.; Naito, M.; Saini, N. L.; Zhang, C.

2007-02-01

A Ge pixel array detector (PAD) with 100 segments was used in fluorescence x-ray absorption spectroscopy (XAS) study, probing local structure of high temperature superconducting thin film single crystals. Independent monitoring of individual pixel outputs allows real-time inspection of interference of substrates which has long been a major source of systematic error. By optimizing grazing-incidence angle and azimuthal orientation, smooth extended x-ray absorption fine structure (EXAFS) oscillations were obtained, demonstrating that strain effects can be studied using high-quality data for thin film single crystals grown by molecular beam epitaxy (MBE). The results of (La,Sr)2CuO4 thin film single crystals under strain are related to the strain dependence of the critical temperature of superconductivity.

Fabrications and application of single crystalline GaN for high-performance deep UV photodetectors

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

Velazquez, R.; Rivera, M.; Feng, P., E-mail: p.feng@upr.edu

2016-08-15

High-quality single crystalline Gallium Nitride (GaN) semiconductor has been synthesized using molecule beam epitaxy (MBE) technique for development of high-performance deep ultraviolet (UV) photodetectors. Thickness of the films was estimated by using surface profile meter and scanning electron microscope. Electronic states and elemental composition of the films were obtained using Raman scattering spectroscopy. The orientation, crystal structure and phase purity of the films were examined using a Siemens x-ray diffractometer radiation. The surface microstructure was studied using high resolution scanning electron microscopy (SEM). Two types of metal pairs: Al-Al, Al-Cu or Cu-Cu were used for interdigital electrodes on GaN filmmore » in order to examine the Schottky properties of the GaN based photodetector. The characterizations of the fabricated prototype include the stability, responsivity, response and recovery times. Typical time dependent photoresponsivity by switching different UV light source on and off five times for each 240 seconds at a bias of 2V, respectively, have been obtained. The detector appears to be highly sensitive to various UV wavelengths of light with very stable baseline and repeatability. The obtained photoresponsivity was up to 354 mA/W at the bias 2V. Higher photoresponsivity could be obtained if higher bias was applied but it would unavoidably result in a higher dark current. Thermal effect on the fabricated GaN based prototype was discussed.« less

Advanced Shutter Control for a Molecular Beam Epitaxy Reactor

DTIC Science & Technology

An open-source hardware and software-based shutter controller solution was developed that communicates over Ethernet with our original equipment...manufacturer (OEM) molecular beam epitaxy (MBE) reactor control software. An Arduino Mega microcontroller is the used for the brain of the shutter... controller , while a custom-designed circuit board distributes 24-V power to each of the 16 shutter solenoids available on the MBE. Using Ethernet

Moisture Forecast Bias Correction in GEOS DAS

NASA Technical Reports Server (NTRS)

Dee, D.

1999-01-01

Data assimilation methods rely on numerous assumptions about the errors involved in measuring and forecasting atmospheric fields. One of the more disturbing of these is that short-term model forecasts are assumed to be unbiased. In case of atmospheric moisture, for example, observational evidence shows that the systematic component of errors in forecasts and analyses is often of the same order of magnitude as the random component. we have implemented a sequential algorithm for estimating forecast moisture bias from rawinsonde data in the Goddard Earth Observing System Data Assimilation System (GEOS DAS). The algorithm is designed to remove the systematic component of analysis errors and can be easily incorporated in an existing statistical data assimilation system. We will present results of initial experiments that show a significant reduction of bias in the GEOS DAS moisture analyses.

The role of bias in simulation of the Indian monsoon and its relationship to predictability

NASA Astrophysics Data System (ADS)

Kelly, P.

2016-12-01

Confidence in future projections of how climate change will affect the Indian monsoon is currently limited by- among other things-model biases. That is, the systematic error in simulating the mean present day climate. An important priority question in seamless prediction involves the role of the mean state. How much of the prediction error in imperfect models stems from a biased mean state (itself a result of many interacting process errors), and how much stems from the flow dependence of processes during an oscillation or variation we are trying to predict? Using simple but effective nudging techniques, we are able to address this question in a clean and incisive framework that teases apart the roles of the mean state vs. transient flow dependence in constraining predictability. The role of bias in model fidelity of simulations of the Indian monsoon is investigated in CAM5, and the relationship to predictability in remote regions in the "free" (non-nudged) domain is explored.

Reducing Bias and Error in the Correlation Coefficient Due to Nonnormality.

PubMed

Bishara, Anthony J; Hittner, James B

2015-10-01

It is more common for educational and psychological data to be nonnormal than to be approximately normal. This tendency may lead to bias and error in point estimates of the Pearson correlation coefficient. In a series of Monte Carlo simulations, the Pearson correlation was examined under conditions of normal and nonnormal data, and it was compared with its major alternatives, including the Spearman rank-order correlation, the bootstrap estimate, the Box-Cox transformation family, and a general normalizing transformation (i.e., rankit), as well as to various bias adjustments. Nonnormality caused the correlation coefficient to be inflated by up to +.14, particularly when the nonnormality involved heavy-tailed distributions. Traditional bias adjustments worsened this problem, further inflating the estimate. The Spearman and rankit correlations eliminated this inflation and provided conservative estimates. Rankit also minimized random error for most sample sizes, except for the smallest samples ( n = 10), where bootstrapping was more effective. Overall, results justify the use of carefully chosen alternatives to the Pearson correlation when normality is violated.

Reducing Bias and Error in the Correlation Coefficient Due to Nonnormality

PubMed Central

Hittner, James B.

2014-01-01

It is more common for educational and psychological data to be nonnormal than to be approximately normal. This tendency may lead to bias and error in point estimates of the Pearson correlation coefficient. In a series of Monte Carlo simulations, the Pearson correlation was examined under conditions of normal and nonnormal data, and it was compared with its major alternatives, including the Spearman rank-order correlation, the bootstrap estimate, the Box–Cox transformation family, and a general normalizing transformation (i.e., rankit), as well as to various bias adjustments. Nonnormality caused the correlation coefficient to be inflated by up to +.14, particularly when the nonnormality involved heavy-tailed distributions. Traditional bias adjustments worsened this problem, further inflating the estimate. The Spearman and rankit correlations eliminated this inflation and provided conservative estimates. Rankit also minimized random error for most sample sizes, except for the smallest samples (n = 10), where bootstrapping was more effective. Overall, results justify the use of carefully chosen alternatives to the Pearson correlation when normality is violated. PMID:29795841

Dual Systems for Spatial Updating in Immediate and Retrieved Environments: Evidence from Bias Analysis.

PubMed

Liu, Chuanjun; Xiao, Chengli

2018-01-01

The spatial updating and memory systems are employed during updating in both the immediate and retrieved environments. However, these dual systems seem to work differently, as the difference of pointing latency and absolute error between the two systems vary across environments. To verify this issue, the present study employed the bias analysis of signed errors based on the hypothesis that the transformed representation will bias toward the original one. Participants learned a spatial layout and then either stayed in the learning location or were transferred to a neighboring room directly or after being disoriented. After that, they performed spatial judgments from perspectives aligned with the learning direction, aligned with the direction they faced during the test, or a novel direction misaligned with the two above-mentioned directions. The patterns of signed error bias were consistent across environments. Responses for memory aligned perspectives were unbiased, whereas responses for sensorimotor aligned perspectives were biased away from the memory aligned perspective, and responses for misaligned perspectives were biased toward sensorimotor aligned perspectives. These findings indicate that the spatial updating system is consistently independent of the spatial memory system regardless of the environments, but the updating system becomes less accessible as the environment changes from immediate to a retrieved one.

Dual Systems for Spatial Updating in Immediate and Retrieved Environments: Evidence from Bias Analysis

PubMed Central

Liu, Chuanjun; Xiao, Chengli

2018-01-01

The spatial updating and memory systems are employed during updating in both the immediate and retrieved environments. However, these dual systems seem to work differently, as the difference of pointing latency and absolute error between the two systems vary across environments. To verify this issue, the present study employed the bias analysis of signed errors based on the hypothesis that the transformed representation will bias toward the original one. Participants learned a spatial layout and then either stayed in the learning location or were transferred to a neighboring room directly or after being disoriented. After that, they performed spatial judgments from perspectives aligned with the learning direction, aligned with the direction they faced during the test, or a novel direction misaligned with the two above-mentioned directions. The patterns of signed error bias were consistent across environments. Responses for memory aligned perspectives were unbiased, whereas responses for sensorimotor aligned perspectives were biased away from the memory aligned perspective, and responses for misaligned perspectives were biased toward sensorimotor aligned perspectives. These findings indicate that the spatial updating system is consistently independent of the spatial memory system regardless of the environments, but the updating system becomes less accessible as the environment changes from immediate to a retrieved one. PMID:29467698

Cognitive aspect of diagnostic errors.

PubMed

Phua, Dong Haur; Tan, Nigel C K

2013-01-01

Diagnostic errors can result in tangible harm to patients. Despite our advances in medicine, the mental processes required to make a diagnosis exhibits shortcomings, causing diagnostic errors. Cognitive factors are found to be an important cause of diagnostic errors. With new understanding from psychology and social sciences, clinical medicine is now beginning to appreciate that our clinical reasoning can take the form of analytical reasoning or heuristics. Different factors like cognitive biases and affective influences can also impel unwary clinicians to make diagnostic errors. Various strategies have been proposed to reduce the effect of cognitive biases and affective influences when clinicians make diagnoses; however evidence for the efficacy of these methods is still sparse. This paper aims to introduce the reader to the cognitive aspect of diagnostic errors, in the hope that clinicians can use this knowledge to improve diagnostic accuracy and patient outcomes.

CdHgTe heterostructures for new-generation IR photodetectors operating at elevated temperatures

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

Varavin, V. S.; Vasilyev, V. V.; Guzev, A. A.

2016-12-15

The parameters of multilayer Cd{sub x}Hg{sub 1–x}Te heterostructures for photodetectors operating at wavelengths of up to 5 μm, grown by molecular-beam epitaxy (MBE) on silicon substrates, are studied. The passivating properties of thin CdTe layers on the surface of these structures are analyzed by measuring the C–V characteristics. The temperature dependences of the minority carrier lifetime in the photoabsorption layer after growth and thermal annealing are investigated. Samples of p{sup +}–n-type photodiodes are fabricated by the implantation of arsenic ions into n-type layers, doped with In to a concentration of (1–5) × 10{sup 15} cm{sup –3}. The temperature dependences ofmore » the reverse currents are measured at several bias voltages; these currents turn out to be almost two orders of magnitude lower than those for n{sup +}–p-type diodes.« less

Optimization and Experimentation of Dual-Mass MEMS Gyroscope Quadrature Error Correction Methods

PubMed Central

Cao, Huiliang; Li, Hongsheng; Kou, Zhiwei; Shi, Yunbo; Tang, Jun; Ma, Zongmin; Shen, Chong; Liu, Jun

2016-01-01

This paper focuses on an optimal quadrature error correction method for the dual-mass MEMS gyroscope, in order to reduce the long term bias drift. It is known that the coupling stiffness and demodulation error are important elements causing bias drift. The coupling stiffness in dual-mass structures is analyzed. The experiment proves that the left and right masses’ quadrature errors are different, and the quadrature correction system should be arranged independently. The process leading to quadrature error is proposed, and the Charge Injecting Correction (CIC), Quadrature Force Correction (QFC) and Coupling Stiffness Correction (CSC) methods are introduced. The correction objects of these three methods are the quadrature error signal, force and the coupling stiffness, respectively. The three methods are investigated through control theory analysis, model simulation and circuit experiments, and the results support the theoretical analysis. The bias stability results based on CIC, QFC and CSC are 48 °/h, 9.9 °/h and 3.7 °/h, respectively, and this value is 38 °/h before quadrature error correction. The CSC method is proved to be the better method for quadrature correction, and it improves the Angle Random Walking (ARW) value, increasing it from 0.66 °/√h to 0.21 °/√h. The CSC system general test results show that it works well across the full temperature range, and the bias stabilities of the six groups’ output data are 3.8 °/h, 3.6 °/h, 3.4 °/h, 3.1 °/h, 3.0 °/h and 4.2 °/h, respectively, which proves the system has excellent repeatability. PMID:26751455

Optimization and Experimentation of Dual-Mass MEMS Gyroscope Quadrature Error Correction Methods.

PubMed

Cao, Huiliang; Li, Hongsheng; Kou, Zhiwei; Shi, Yunbo; Tang, Jun; Ma, Zongmin; Shen, Chong; Liu, Jun

2016-01-07

This paper focuses on an optimal quadrature error correction method for the dual-mass MEMS gyroscope, in order to reduce the long term bias drift. It is known that the coupling stiffness and demodulation error are important elements causing bias drift. The coupling stiffness in dual-mass structures is analyzed. The experiment proves that the left and right masses' quadrature errors are different, and the quadrature correction system should be arranged independently. The process leading to quadrature error is proposed, and the Charge Injecting Correction (CIC), Quadrature Force Correction (QFC) and Coupling Stiffness Correction (CSC) methods are introduced. The correction objects of these three methods are the quadrature error signal, force and the coupling stiffness, respectively. The three methods are investigated through control theory analysis, model simulation and circuit experiments, and the results support the theoretical analysis. The bias stability results based on CIC, QFC and CSC are 48 °/h, 9.9 °/h and 3.7 °/h, respectively, and this value is 38 °/h before quadrature error correction. The CSC method is proved to be the better method for quadrature correction, and it improves the Angle Random Walking (ARW) value, increasing it from 0.66 °/√h to 0.21 °/√h. The CSC system general test results show that it works well across the full temperature range, and the bias stabilities of the six groups' output data are 3.8 °/h, 3.6 °/h, 3.4 °/h, 3.1 °/h, 3.0 °/h and 4.2 °/h, respectively, which proves the system has excellent repeatability.

Multivariate Statistics Applied to Seismic Phase Picking

NASA Astrophysics Data System (ADS)

Velasco, A. A.; Zeiler, C. P.; Anderson, D.; Pingitore, N. E.

2008-12-01

The initial effort of the Seismogram Picking Error from Analyst Review (SPEAR) project has been to establish a common set of seismograms to be picked by the seismological community. Currently we have 13 analysts from 4 institutions that have provided picks on the set of 26 seismograms. In comparing the picks thus far, we have identified consistent biases between picks from different institutions; effects of the experience of analysts; and the impact of signal-to-noise on picks. The institutional bias in picks brings up the important concern that picks will not be the same between different catalogs. This difference means less precision and accuracy when combing picks from multiple institutions. We also note that depending on the experience level of the analyst making picks for a catalog the error could fluctuate dramatically. However, the experience level is based off of number of years in picking seismograms and this may not be an appropriate criterion for determining an analyst's precision. The common data set of seismograms provides a means to test an analyst's level of precision and biases. The analyst is also limited by the quality of the signal and we show that the signal-to-noise ratio and pick error are correlated to the location, size and distance of the event. This makes the standard estimate of picking error based on SNR more complex because additional constraints are needed to accurately constrain the measurement error. We propose to extend the current measurement of error by adding the additional constraints of institutional bias and event characteristics to the standard SNR measurement. We use multivariate statistics to model the data and provide constraints to accurately assess earthquake location and measurement errors.

Magnolia officinalis L. Fortified Gum Improves Resistance of Oral Epithelial Cells Against Inflammation.

PubMed

Walker, Jessica; Imboeck, Julia Maria; Walker, Joel Michael; Maitra, Amarnath; Haririan, Hady; Rausch-Fan, Xiaohui; Dodds, Michael; Inui, Taichi; Somoza, Veronika

2016-01-01

Inflammatory diseases of the periodontal tissues are known health problems worldwide. Therefore, anti-inflammatory active compounds are used in oral care products to reduce long-term inflammation. In addition to inducing inflammation, pathogen attack leads to an increased production of reactive oxygen species (ROS), which may lead to oxidative damage of macromolecules. Magnolia officinalis L. bark extract (MBE) has been shown to possess antioxidant and anti-inflammatory potential in vitro. In the present study, the influence of MBE-fortified chewing gum on the resistance against lipopolysaccharide (LPS)-induced inflammation and oxidative stress of oral epithelial cells was investigated in a four-armed parallel designed human intervention trial with 40 healthy volunteers. Ex vivo stimulation of oral epithelial cells with LPS from Porphyromonas gingivalis for 6[Formula: see text]h increased the mRNA expression and release of the pro-inflammatory cytokines IL-1[Formula: see text], IL-[Formula: see text], IL-8, MIP-1[Formula: see text], and TNF[Formula: see text]. Chewing MBE-fortified gum for 10[Formula: see text]min reduced the ex vivo LPS-induced increase of IL-8 release by 43.8 [Formula: see text] 17.1% at the beginning of the intervention. In addition, after the two-week intervention with MBE-fortified chewing gum, LPS-stimulated TNF[Formula: see text] release was attenuated by 73.4 [Formula: see text] 12.0% compared to chewing regular control gum. This increased resistance against LPS-induced inflammation suggests that MBE possesses anti-inflammatory activity in vivo when added to chewing gum. In contrast, the conditions used to stimulate an immune response of oral epithelial cells failed to induce oxidative stress, measured by catalase activity, or oxidative DNA damage.

Insolation-induced mid-Brunhes transition in Southern Ocean ventilation and deep-ocean temperature.

PubMed

Yin, Qiuzhen

2013-02-14

Glacial-interglacial cycles characterized by long cold periods interrupted by short periods of warmth are the dominant feature of Pleistocene climate, with the relative intensity and duration of past and future interglacials being of particular interest for civilization. The interglacials after 430,000 years ago were characterized by warmer climates and higher atmospheric concentrations of carbon dioxide than the interglacials before, but the cause of this climatic transition (the so-called mid-Brunhes event (MBE)) is unknown. Here I show, on the basis of model simulations, that in response to insolation changes only, feedbacks between sea ice, temperature, evaporation and salinity caused vigorous pre-MBE Antarctic bottom water formation and Southern Ocean ventilation. My results also show that strong westerlies increased the pre-MBE overturning in the Southern Ocean via an increased latitudinal insolation gradient created by changes in eccentricity during austral winter and by changes in obliquity during austral summer. The stronger bottom water formation led to a cooler deep ocean during the older interglacials. These insolation-induced differences in the deep-sea temperature and in the Southern Ocean ventilation between the more recent interglacials and the older ones were not expected, because there is no straightforward systematic difference in the astronomical parameters between the interglacials before and after 430,000 years ago. Rather than being a real 'event', the apparent MBE seems to have resulted from a series of individual interglacial responses--including notable exceptions to the general pattern--to various combinations of insolation conditions. Consequently, assuming no anthropogenic interference, future interglacials may have pre- or post-MBE characteristics without there being a systematic change in forcings. These findings are a first step towards understanding the magnitude change of the interglacial carbon dioxide concentration around 430,000 years ago.

Impact of MBE deposition conditions on InAs/GaInSb superlattices for very long wavelength infrared detection

NASA Astrophysics Data System (ADS)

Brown, G. J.; Haugan, H. J.; Mahalingam, K.; Grazulis, L.; Elhamri, S.

2015-01-01

The objective of this work is to establish molecular beam epitaxy (MBE) growth processes that can produce high quality InAs/GaInSb superlattice (SL) materials specifically tailored for very long wavelength infrared (VLWIR) detection. To accomplish this goal, several series of MBE growth optimization studies, using a SL structure of 47.0 Å InAs/21.5 Å Ga0.75In0.25Sb, were performed to refine the MBE growth process and optimize growth parameters. Experimental results demonstrated that our "slow" MBE growth process can consistently produce an energy gap near 50 meV. This is an important factor in narrow band gap SLs. However, there are other growth factors that also impact the electrical and optical properties of the SL materials. The SL layers are particularly sensitive to the anion incorporation condition formed during the surface reconstruction process. Since antisite defects are potentially responsible for the inherent residual carrier concentrations and short carrier lifetimes, the optimization of anion incorporation conditions, by manipulating anion fluxes, anion species, and deposition temperature, was systematically studied. Optimization results are reported in the context of comparative studies on the influence of the growth temperature on the crystal structural quality and surface roughness performed under a designed set of deposition conditions. The optimized SL samples produced an overall strong photoresponse signal with a relatively sharp band edge that is essential for developing VLWIR detectors. A quantitative analysis of the lattice strain, performed at the atomic scale by aberration corrected transmission electron microscopy, provided valuable information about the strain distribution at the GaInSb-on-InAs interface and in the InAs layers, which was important for optimizing the anion conditions.

Cognitions and emotions in eating disorders.

PubMed

Siep, Nicolette; Jansen, Anita; Havermans, Remco; Roefs, Anne

2011-01-01

The cognitive model of eating disorders (EDs) states that the processing of external and internal stimuli might be biased in mental disorders. These biases, or cognitive errors, systematically distort the individual's experiences and, in that way, maintains the eating disorder. This chapter presents an updated literature review of experimental studies investigating these cognitive biases. Results indicate that ED patients show biases in attention, interpretation, and memory when it comes to the processing of food-, weight-, and body shape-related cues. Some recent studies show that they also demonstrate errors in general cognitive abilities such as set shifting, central coherence, and decision making. A future challenge is whether cognitive biases and processes can be manipulated. Few preliminary studies suggest that an attention retraining and training in the cognitive modulation of food reward processing might be effective strategies to change body satisfaction, food cravings, and eating behavior.

Bias in the Counseling Process: How to Recognize and Avoid It.

ERIC Educational Resources Information Center

Morrow, Kelly A.; Deidan, Cecilia T.

1992-01-01

Notes that counselors' vulnerability to inferential bias during counseling process may result in misdiagnosis and improper interventions. Discusses these inferential biases: availability and representativeness heuristics; fundamental attribution error; anchoring, prior knowledge, and labeling; confirmatory hypothesis testing; and reconstructive…

Geolocation and Pointing Accuracy Analysis for the WindSat Sensor

NASA Technical Reports Server (NTRS)

Meissner, Thomas; Wentz, Frank J.; Purdy, William E.; Gaiser, Peter W.; Poe, Gene; Uliana, Enzo A.

2006-01-01

Geolocation and pointing accuracy analyses of the WindSat flight data are presented. The two topics were intertwined in the flight data analysis and will be addressed together. WindSat has no unusual geolocation requirements relative to other sensors, but its beam pointing knowledge accuracy is especially critical to support accurate polarimetric radiometry. Pointing accuracy was improved and verified using geolocation analysis in conjunction with scan bias analysis. nvo methods were needed to properly identify and differentiate between data time tagging and pointing knowledge errors. Matchups comparing coastlines indicated in imagery data with their known geographic locations were used to identify geolocation errors. These coastline matchups showed possible pointing errors with ambiguities as to the true source of the errors. Scan bias analysis of U, the third Stokes parameter, and of vertical and horizontal polarizations provided measurement of pointing offsets resolving ambiguities in the coastline matchup analysis. Several geolocation and pointing bias sources were incfementally eliminated resulting in pointing knowledge and geolocation accuracy that met all design requirements.

Life on the edge: squirrel-cage fringe fields and their effects in the MBE-4 combiner experiment

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

Fawley, W.M.

1996-02-01

The MBE-4 combiner experiment employs an electrostatic combined-function focusing/bending element, the so-called ``squirrel-cage`` just before the actual merging region. There has been concern that non-linear fields, primarily in the fringe regions at the beginning and end of the cage, may be strong enough to lead to significant emittance degradation. This note present the results of numerical calculations which determined the anharmonic, non-linear components of the 3D fields in the cage and the resultant, orbit-integrated effects upon the MBE-4 beamlets. We find that while the anharmonic effects are small compared to the dipole deflection, the resultant transverse emittance growth is significantmore » when compared to the expected value of the initial emittance of the individual beamlets.« less

Photoluminescence and Band Alignment of Strained GaAsSb/GaAs QW Structures Grown by MBE on GaAs

PubMed Central

Sadofyev, Yuri G.; Samal, Nigamananda

2010-01-01

An in-depth optimization of growth conditions and investigation of optical properties including discussions on band alignment of GaAsSb/GaAs quantum well (QW) on GaAs by molecular beam epitaxy (MBE) are reported. Optimal MBE growth temperature of GaAsSb QW is found to be 470 ± 10 °C. GaAsSb/GaAs QW with Sb content ~0.36 has a weak type-II band alignment with valence band offset ratio QV ~1.06. A full width at half maximum (FWHM) of ~60 meV in room temperature (RT) photoluminescence (PL) indicates fluctuation in electrostatic potential to be less than 20 meV. Samples grown under optimal conditions do not exhibit any blue shift of peak in RT PL spectra under varying excitation.

Bias in error estimation when using cross-validation for model selection.

PubMed

Varma, Sudhir; Simon, Richard

2006-02-23

Cross-validation (CV) is an effective method for estimating the prediction error of a classifier. Some recent articles have proposed methods for optimizing classifiers by choosing classifier parameter values that minimize the CV error estimate. We have evaluated the validity of using the CV error estimate of the optimized classifier as an estimate of the true error expected on independent data. We used CV to optimize the classification parameters for two kinds of classifiers; Shrunken Centroids and Support Vector Machines (SVM). Random training datasets were created, with no difference in the distribution of the features between the two classes. Using these "null" datasets, we selected classifier parameter values that minimized the CV error estimate. 10-fold CV was used for Shrunken Centroids while Leave-One-Out-CV (LOOCV) was used for the SVM. Independent test data was created to estimate the true error. With "null" and "non null" (with differential expression between the classes) data, we also tested a nested CV procedure, where an inner CV loop is used to perform the tuning of the parameters while an outer CV is used to compute an estimate of the error. The CV error estimate for the classifier with the optimal parameters was found to be a substantially biased estimate of the true error that the classifier would incur on independent data. Even though there is no real difference between the two classes for the "null" datasets, the CV error estimate for the Shrunken Centroid with the optimal parameters was less than 30% on 18.5% of simulated training data-sets. For SVM with optimal parameters the estimated error rate was less than 30% on 38% of "null" data-sets. Performance of the optimized classifiers on the independent test set was no better than chance. The nested CV procedure reduces the bias considerably and gives an estimate of the error that is very close to that obtained on the independent testing set for both Shrunken Centroids and SVM classifiers for "null" and "non-null" data distributions. We show that using CV to compute an error estimate for a classifier that has itself been tuned using CV gives a significantly biased estimate of the true error. Proper use of CV for estimating true error of a classifier developed using a well defined algorithm requires that all steps of the algorithm, including classifier parameter tuning, be repeated in each CV loop. A nested CV procedure provides an almost unbiased estimate of the true error.

Attention and memory bias to facial emotions underlying negative symptoms of schizophrenia.

PubMed

Jang, Seon-Kyeong; Park, Seon-Cheol; Lee, Seung-Hwan; Cho, Yang Seok; Choi, Kee-Hong

2016-01-01

This study assessed bias in selective attention to facial emotions in negative symptoms of schizophrenia and its influence on subsequent memory for facial emotions. Thirty people with schizophrenia who had high and low levels of negative symptoms (n = 15, respectively) and 21 healthy controls completed a visual probe detection task investigating selective attention bias (happy, sad, and angry faces randomly presented for 50, 500, or 1000 ms). A yes/no incidental facial memory task was then completed. Attention bias scores and recognition errors were calculated. Those with high negative symptoms exhibited reduced attention to emotional faces relative to neutral faces; those with low negative symptoms showed the opposite pattern when faces were presented for 500 ms regardless of the valence. Compared to healthy controls, those with high negative symptoms made more errors for happy faces in the memory task. Reduced attention to emotional faces in the probe detection task was significantly associated with less pleasure and motivation and more recognition errors for happy faces in schizophrenia group only. Attention bias away from emotional information relatively early in the attentional process and associated diminished positive memory may relate to pathological mechanisms for negative symptoms.

ERRATUM: 'MAPPING THE GAS TURBULENCE IN THE COMA CLUSTER: PREDICTIONS FOR ASTRO-H'

NASA Technical Reports Server (NTRS)

Zuhone, J. A.; Markevitch, M.; Zhuravleva, I.

2016-01-01

The published version of this paper contained an error in Figure 5. This figure is intended to show the effect on the structure function of subtracting the bias induced by the statistical and systematic errors on the line shift. The filled circles show the bias-subtracted structure function. The positions of these points in the left panel of the original figure were calculated incorrectly. The figure is reproduced below (with the original caption) with the correct values for the bias-subtracted structure function. No other computations or figures in the original manuscript are affected.

Reduction in the write error rate of voltage-induced dynamic magnetization switching using the reverse bias method

NASA Astrophysics Data System (ADS)

Ikeura, Takuro; Nozaki, Takayuki; Shiota, Yoichi; Yamamoto, Tatsuya; Imamura, Hiroshi; Kubota, Hitoshi; Fukushima, Akio; Suzuki, Yoshishige; Yuasa, Shinji

2018-04-01

Using macro-spin modeling, we studied the reduction in the write error rate (WER) of voltage-induced dynamic magnetization switching by enhancing the effective thermal stability of the free layer using a voltage-controlled magnetic anisotropy change. Marked reductions in WER can be achieved by introducing reverse bias voltage pulses both before and after the write pulse. This procedure suppresses the thermal fluctuations of magnetization in the initial and final states. The proposed reverse bias method can offer a new way of improving the writing stability of voltage-driven spintronic devices.

Fabrication of precision high quality facets on molecular beam epitaxy material

DOEpatents

Petersen, Holly E.; Goward, William D.; Dijaili, Sol P.

2001-01-01

Fabricating mirrored vertical surfaces on semiconductor layered material grown by molecular beam epitaxy (MBE). Low energy chemically assisted ion beam etching (CAIBE) is employed to prepare mirrored vertical surfaces on MBE-grown III-V materials under unusually low concentrations of oxygen in evacuated etching atmospheres of chlorine and xenon ion beams. UV-stabilized smooth-surfaced photoresist materials contribute to highly vertical, high quality mirrored surfaces during the etching.

Tools to Study Interfaces for Superconducting, Thermoelectric, and Magnetic Materials at the University of Houston

DTIC Science & Technology

2016-09-01

The MBE system, which grows crystalline thin films in ultrahigh vacuum (UHV) with precise control of thickness, composition, and morphology, will...used on our sputtering system to fabricate thin films with interfaces. - The electronic structures of these materials will be investigated using the...magnetization/transport measurements. The MBE system, which grows crystalline thin films in ultrahigh vacuum (UHV) with precise control of thickness, composition

Gallium Nitride (GaN) High Power Electronics (FY11)

DTIC Science & Technology

2012-01-01

GaN films grown by metal-organic chemical vapor deposition (MOCVD) and ~1010 in films grown by molecular beam epitaxy (MBE) when they are deposited...inductively coupled plasma I-V current-voltage L-HVPE low doped HVPE MBE molecular beam epitaxy MOCVD metal-organic chemical vapor deposition...figure of merit HEMT high electron mobility transistor H-HVPE high doped HVPE HPE high power electronics HVPE hydride vapor phase epitaxy ICP

AlGaSb Buffer Layers for Sb-Based Transistors

DTIC Science & Technology

2010-01-01

transistor ( HEMT ), molecular beam epitaxy (MBE), field-effect transistor (FET), buffer layer INTRODUCTION High-electron-mobility transistors ( HEMTs ) with InAs...monolayers/s. The use of thinner buffer layers reduces molecular beam epitaxial growth time and source consumption. The buffer layers also exhibit...source. In addition, some of the flux from an Sb cell in a molecular beam epitaxy (MBE) system will deposit near the mouth of the cell, eventually

Avalanche Photoconductive Switching

DTIC Science & Technology

1989-06-01

implantation and by MBE growth , and p-type material was created by MBE growth of a Be doped layer. Ion implantation creates a heavily doped layer...which is used commonly for GaAs integrated circuits. We plan to use Ti-Pt-Au for p-type contacts in the future. Experimental Results Test Confi...optical wavelenght does not significantly affect the switching process. Another feature of this mode of operation is that there is a threshold

JSEP Fellowship

DTIC Science & Technology

1993-06-28

entitled "MBE Grown Microcavities for Optoelectronic Devices." In the dissertation work,1 the precision of molecular - beam epitaxy (MBE) is taken...Layers For Surface Normal Optoelectronic Devices," North American Conference on Molecular Beam Epitaxy , Ottawa, Canada, October 12-14, 1992, to be...8. C. Lei, T. J. Rogers, D. G. Deppe, and B. G. Streetman, "InGaAs-GaAs Quantum Well Vertical-Cavity Surface-Emitting Laser Using Molecular Beam

Overcoming bias and systematic errors in next generation sequencing data.

PubMed

Taub, Margaret A; Corrada Bravo, Hector; Irizarry, Rafael A

2010-12-10

Considerable time and effort has been spent in developing analysis and quality assessment methods to allow the use of microarrays in a clinical setting. As is the case for microarrays and other high-throughput technologies, data from new high-throughput sequencing technologies are subject to technological and biological biases and systematic errors that can impact downstream analyses. Only when these issues can be readily identified and reliably adjusted for will clinical applications of these new technologies be feasible. Although much work remains to be done in this area, we describe consistently observed biases that should be taken into account when analyzing high-throughput sequencing data. In this article, we review current knowledge about these biases, discuss their impact on analysis results, and propose solutions.

Hindsight Bias and Developing Theories of Mind

PubMed Central

Bernstein, Daniel M.; Atance, Cristina; Meltzoff, Andrew N.; Loftus, Geoffrey R.

2013-01-01

Although hindsight bias (the “I knew it all along” phenomenon) has been documented in adults, its development has not been investigated. This is despite the fact that hindsight bias errors closely resemble the errors children make on theory of mind (ToM) tasks. Two main goals of the present work were to (a) create a battery of hindsight tasks for preschoolers, and (b) assess the relation between children’s performance on these and ToM tasks. In two experiments involving 144 preschoolers, 3-, 4-, and 5-year olds exhibited strong hindsight bias. Performance on hindsight and ToM tasks was significantly correlated independent of age, language ability, and inhibitory control. These findings contribute to a more comprehensive account of perspective taking across the lifespan. PMID:17650144

The Impact of Atmospheric Modeling Errors on GRACE Estimates of Mass Loss in Greenland and Antarctica

NASA Astrophysics Data System (ADS)

Hardy, Ryan A.; Nerem, R. Steven; Wiese, David N.

2017-12-01

Systematic errors in Gravity Recovery and Climate Experiment (GRACE) monthly mass estimates over the Greenland and Antarctic ice sheets can originate from low-frequency biases in the European Centre for Medium-Range Weather Forecasts (ECMWF) Operational Analysis model, the atmospheric component of the Atmospheric and Ocean Dealising Level-1B (AOD1B) product used to forward model atmospheric and ocean gravity signals in GRACE processing. These biases are revealed in differences in surface pressure between the ECMWF Operational Analysis model, state-of-the-art reanalyses, and in situ surface pressure measurements. While some of these errors are attributable to well-understood discrete model changes and have published corrections, we examine errors these corrections do not address. We compare multiple models and in situ data in Antarctica and Greenland to determine which models have the most skill relative to monthly averages of the dealiasing model. We also evaluate linear combinations of these models and synthetic pressure fields generated from direct interpolation of pressure observations. These models consistently reveal drifts in the dealiasing model that cause the acceleration of Antarctica's mass loss between April 2002 and August 2016 to be underestimated by approximately 4 Gt yr-2. We find similar results after attempting to solve the inverse problem, recovering pressure biases directly from the GRACE Jet Propulsion Laboratory RL05.1 M mascon solutions. Over Greenland, we find a 2 Gt yr-1 bias in mass trend. While our analysis focuses on errors in Release 05 of AOD1B, we also evaluate the new AOD1B RL06 product. We find that this new product mitigates some of the aforementioned biases.

Bias analysis applied to Agricultural Health Study publications to estimate non-random sources of uncertainty.

PubMed

Lash, Timothy L

2007-11-26

The associations of pesticide exposure with disease outcomes are estimated without the benefit of a randomized design. For this reason and others, these studies are susceptible to systematic errors. I analyzed studies of the associations between alachlor and glyphosate exposure and cancer incidence, both derived from the Agricultural Health Study cohort, to quantify the bias and uncertainty potentially attributable to systematic error. For each study, I identified the prominent result and important sources of systematic error that might affect it. I assigned probability distributions to the bias parameters that allow quantification of the bias, drew a value at random from each assigned distribution, and calculated the estimate of effect adjusted for the biases. By repeating the draw and adjustment process over multiple iterations, I generated a frequency distribution of adjusted results, from which I obtained a point estimate and simulation interval. These methods were applied without access to the primary record-level dataset. The conventional estimates of effect associating alachlor and glyphosate exposure with cancer incidence were likely biased away from the null and understated the uncertainty by quantifying only random error. For example, the conventional p-value for a test of trend in the alachlor study equaled 0.02, whereas fewer than 20% of the bias analysis iterations yielded a p-value of 0.02 or lower. Similarly, the conventional fully-adjusted result associating glyphosate exposure with multiple myleoma equaled 2.6 with 95% confidence interval of 0.7 to 9.4. The frequency distribution generated by the bias analysis yielded a median hazard ratio equal to 1.5 with 95% simulation interval of 0.4 to 8.9, which was 66% wider than the conventional interval. Bias analysis provides a more complete picture of true uncertainty than conventional frequentist statistical analysis accompanied by a qualitative description of study limitations. The latter approach is likely to lead to overconfidence regarding the potential for causal associations, whereas the former safeguards against such overinterpretations. Furthermore, such analyses, once programmed, allow rapid implementation of alternative assignments of probability distributions to the bias parameters, so elevate the plane of discussion regarding study bias from characterizing studies as "valid" or "invalid" to a critical and quantitative discussion of sources of uncertainty.

Omens of coupled model biases in the CMIP5 AMIP simulations

NASA Astrophysics Data System (ADS)

Găinuşă-Bogdan, Alina; Hourdin, Frédéric; Traore, Abdoul Khadre; Braconnot, Pascale

2018-02-01

Despite decades of efforts and improvements in the representation of processes as well as in model resolution, current global climate models still suffer from a set of important, systematic biases in sea surface temperature (SST), not much different from the previous generation of climate models. Many studies have looked at errors in the wind field, cloud representation or oceanic upwelling in coupled models to explain the SST errors. In this paper we highlight the relationship between latent heat flux (LH) biases in forced atmospheric simulations and the SST biases models develop in coupled mode, at the scale of the entire intertropical domain. By analyzing 22 pairs of forced atmospheric and coupled ocean-atmosphere simulations from the CMIP5 database, we show a systematic, negative correlation between the spatial patterns of these two biases. This link between forced and coupled bias patterns is also confirmed by two sets of dedicated sensitivity experiments with the IPSL-CM5A-LR model. The analysis of the sources of the atmospheric LH bias pattern reveals that the near-surface wind speed bias dominates the zonal structure of the LH bias and that the near-surface relative humidity dominates the east-west contrasts.

Estimation bias from using nonlinear Fourier plane correlators for sub-pixel image shift measurement and implications for the binary joint transform correlator

NASA Astrophysics Data System (ADS)

Grycewicz, Thomas J.; Florio, Christopher J.; Franz, Geoffrey A.; Robinson, Ross E.

2007-09-01

When using Fourier plane digital algorithms or an optical correlator to measure the correlation between digital images, interpolation by center-of-mass or quadratic estimation techniques can be used to estimate image displacement to the sub-pixel level. However, this can lead to a bias in the correlation measurement. This bias shifts the sub-pixel output measurement to be closer to the nearest pixel center than the actual location. The paper investigates the bias in the outputs of both digital and optical correlators, and proposes methods to minimize this effect. We use digital studies and optical implementations of the joint transform correlator to demonstrate optical registration with accuracies better than 0.1 pixels. We use both simulations of image shift and movies of a moving target as inputs. We demonstrate bias error for both center-of-mass and quadratic interpolation, and discuss the reasons that this bias is present. Finally, we suggest measures to reduce or eliminate the bias effects. We show that when sub-pixel bias is present, it can be eliminated by modifying the interpolation method. By removing the bias error, we improve registration accuracy by thirty percent.

Accounting for sampling error when inferring population synchrony from time-series data: a Bayesian state-space modelling approach with applications.

PubMed

Santin-Janin, Hugues; Hugueny, Bernard; Aubry, Philippe; Fouchet, David; Gimenez, Olivier; Pontier, Dominique

2014-01-01

Data collected to inform time variations in natural population size are tainted by sampling error. Ignoring sampling error in population dynamics models induces bias in parameter estimators, e.g., density-dependence. In particular, when sampling errors are independent among populations, the classical estimator of the synchrony strength (zero-lag correlation) is biased downward. However, this bias is rarely taken into account in synchrony studies although it may lead to overemphasizing the role of intrinsic factors (e.g., dispersal) with respect to extrinsic factors (the Moran effect) in generating population synchrony as well as to underestimating the extinction risk of a metapopulation. The aim of this paper was first to illustrate the extent of the bias that can be encountered in empirical studies when sampling error is neglected. Second, we presented a space-state modelling approach that explicitly accounts for sampling error when quantifying population synchrony. Third, we exemplify our approach with datasets for which sampling variance (i) has been previously estimated, and (ii) has to be jointly estimated with population synchrony. Finally, we compared our results to those of a standard approach neglecting sampling variance. We showed that ignoring sampling variance can mask a synchrony pattern whatever its true value and that the common practice of averaging few replicates of population size estimates poorly performed at decreasing the bias of the classical estimator of the synchrony strength. The state-space model used in this study provides a flexible way of accurately quantifying the strength of synchrony patterns from most population size data encountered in field studies, including over-dispersed count data. We provided a user-friendly R-program and a tutorial example to encourage further studies aiming at quantifying the strength of population synchrony to account for uncertainty in population size estimates.

Bias and uncertainty in regression-calibrated models of groundwater flow in heterogeneous media

USGS Publications Warehouse

Cooley, R.L.; Christensen, S.

2006-01-01

Groundwater models need to account for detailed but generally unknown spatial variability (heterogeneity) of the hydrogeologic model inputs. To address this problem we replace the large, m-dimensional stochastic vector ?? that reflects both small and large scales of heterogeneity in the inputs by a lumped or smoothed m-dimensional approximation ????*, where ?? is an interpolation matrix and ??* is a stochastic vector of parameters. Vector ??* has small enough dimension to allow its estimation with the available data. The consequence of the replacement is that model function f(????*) written in terms of the approximate inputs is in error with respect to the same model function written in terms of ??, ??,f(??), which is assumed to be nearly exact. The difference f(??) - f(????*), termed model error, is spatially correlated, generates prediction biases, and causes standard confidence and prediction intervals to be too small. Model error is accounted for in the weighted nonlinear regression methodology developed to estimate ??* and assess model uncertainties by incorporating the second-moment matrix of the model errors into the weight matrix. Techniques developed by statisticians to analyze classical nonlinear regression methods are extended to analyze the revised method. The analysis develops analytical expressions for bias terms reflecting the interaction of model nonlinearity and model error, for correction factors needed to adjust the sizes of confidence and prediction intervals for this interaction, and for correction factors needed to adjust the sizes of confidence and prediction intervals for possible use of a diagonal weight matrix in place of the correct one. If terms expressing the degree of intrinsic nonlinearity for f(??) and f(????*) are small, then most of the biases are small and the correction factors are reduced in magnitude. Biases, correction factors, and confidence and prediction intervals were obtained for a test problem for which model error is large to test robustness of the methodology. Numerical results conform with the theoretical analysis. ?? 2005 Elsevier Ltd. All rights reserved.

Accounting for Sampling Error When Inferring Population Synchrony from Time-Series Data: A Bayesian State-Space Modelling Approach with Applications

PubMed Central

Santin-Janin, Hugues; Hugueny, Bernard; Aubry, Philippe; Fouchet, David; Gimenez, Olivier; Pontier, Dominique

2014-01-01

Background Data collected to inform time variations in natural population size are tainted by sampling error. Ignoring sampling error in population dynamics models induces bias in parameter estimators, e.g., density-dependence. In particular, when sampling errors are independent among populations, the classical estimator of the synchrony strength (zero-lag correlation) is biased downward. However, this bias is rarely taken into account in synchrony studies although it may lead to overemphasizing the role of intrinsic factors (e.g., dispersal) with respect to extrinsic factors (the Moran effect) in generating population synchrony as well as to underestimating the extinction risk of a metapopulation. Methodology/Principal findings The aim of this paper was first to illustrate the extent of the bias that can be encountered in empirical studies when sampling error is neglected. Second, we presented a space-state modelling approach that explicitly accounts for sampling error when quantifying population synchrony. Third, we exemplify our approach with datasets for which sampling variance (i) has been previously estimated, and (ii) has to be jointly estimated with population synchrony. Finally, we compared our results to those of a standard approach neglecting sampling variance. We showed that ignoring sampling variance can mask a synchrony pattern whatever its true value and that the common practice of averaging few replicates of population size estimates poorly performed at decreasing the bias of the classical estimator of the synchrony strength. Conclusion/Significance The state-space model used in this study provides a flexible way of accurately quantifying the strength of synchrony patterns from most population size data encountered in field studies, including over-dispersed count data. We provided a user-friendly R-program and a tutorial example to encourage further studies aiming at quantifying the strength of population synchrony to account for uncertainty in population size estimates. PMID:24489839

A propensity score approach to correction for bias due to population stratification using genetic and non-genetic factors.

PubMed

Zhao, Huaqing; Rebbeck, Timothy R; Mitra, Nandita

2009-12-01

Confounding due to population stratification (PS) arises when differences in both allele and disease frequencies exist in a population of mixed racial/ethnic subpopulations. Genomic control, structured association, principal components analysis (PCA), and multidimensional scaling (MDS) approaches have been proposed to address this bias using genetic markers. However, confounding due to PS can also be due to non-genetic factors. Propensity scores are widely used to address confounding in observational studies but have not been adapted to deal with PS in genetic association studies. We propose a genomic propensity score (GPS) approach to correct for bias due to PS that considers both genetic and non-genetic factors. We compare the GPS method with PCA and MDS using simulation studies. Our results show that GPS can adequately adjust and consistently correct for bias due to PS. Under no/mild, moderate, and severe PS, GPS yielded estimated with bias close to 0 (mean=-0.0044, standard error=0.0087). Under moderate or severe PS, the GPS method consistently outperforms the PCA method in terms of bias, coverage probability (CP), and type I error. Under moderate PS, the GPS method consistently outperforms the MDS method in terms of CP. PCA maintains relatively high power compared to both MDS and GPS methods under the simulated situations. GPS and MDS are comparable in terms of statistical properties such as bias, type I error, and power. The GPS method provides a novel and robust tool for obtaining less-biased estimates of genetic associations that can consider both genetic and non-genetic factors. 2009 Wiley-Liss, Inc.

Exploring Model Error through Post-processing and an Ensemble Kalman Filter on Fire Weather Days

NASA Astrophysics Data System (ADS)

Erickson, Michael J.

The proliferation of coupling atmospheric ensemble data to models in other related fields requires a priori knowledge of atmospheric ensemble biases specific to the desired application. In that spirit, this dissertation focuses on elucidating atmospheric ensemble model bias and error through a variety of different methods specific to fire weather days (FWDs) over the Northeast United States (NEUS). Other than a handful of studies that use models to predict fire indices for single fire seasons (Molders 2008, Simpson et al. 2014), an extensive exploration of model performance specific to FWDs has not been attempted. Two unique definitions for FWDs are proposed; one that uses pre-existing fire indices (FWD1) and another from a new statistical fire weather index (FWD2) relating fire occurrence and near-surface meteorological observations. Ensemble model verification reveals FWDs to have warmer (> 1 K), moister (~ 0.4 g kg-1) and less windy (~ 1 m s-1) biases than the climatological average for both FWD1 and FWD2. These biases are not restricted to the near surface but exist through the entirety of the planetary boundary layer (PBL). Furthermore, post-processing methods are more effective when previous FWDs are incorporated into the statistical training, suggesting that model bias could be related to the synoptic flow pattern. An Ensemble Kalman Filter (EnKF) is used to explore the effectiveness of data assimilation during a period of extensive FWDs in April 2012. Model biases develop rapidly on FWDs, consistent with the FWD1 and FWD2 verification. However, the EnKF is effective at removing most biases for temperature, wind speed and specific humidity. Potential sources of error in the parameterized physics of the PBL are explored by rerunning the EnKF with simultaneous state and parameter estimation (SSPE) for two relevant parameters within the ACM2 PBL scheme. SSPE helps to reduce the cool temperature bias near the surface on FWDs, with the variability in parameter estimates exhibiting some relationship to model bias for temperature. This suggests the potential for structural model error within the ACM2 PBL scheme and could lead toward the future development of improved PBL parameterizations.

Scaled test statistics and robust standard errors for non-normal data in covariance structure analysis: a Monte Carlo study.

PubMed

Chou, C P; Bentler, P M; Satorra, A

1991-11-01

Research studying robustness of maximum likelihood (ML) statistics in covariance structure analysis has concluded that test statistics and standard errors are biased under severe non-normality. An estimation procedure known as asymptotic distribution free (ADF), making no distributional assumption, has been suggested to avoid these biases. Corrections to the normal theory statistics to yield more adequate performance have also been proposed. This study compares the performance of a scaled test statistic and robust standard errors for two models under several non-normal conditions and also compares these with the results from ML and ADF methods. Both ML and ADF test statistics performed rather well in one model and considerably worse in the other. In general, the scaled test statistic seemed to behave better than the ML test statistic and the ADF statistic performed the worst. The robust and ADF standard errors yielded more appropriate estimates of sampling variability than the ML standard errors, which were usually downward biased, in both models under most of the non-normal conditions. ML test statistics and standard errors were found to be quite robust to the violation of the normality assumption when data had either symmetric and platykurtic distributions, or non-symmetric and zero kurtotic distributions.

Development of multiple-eye PIV using mirror array

NASA Astrophysics Data System (ADS)

Maekawa, Akiyoshi; Sakakibara, Jun

2018-06-01

In order to reduce particle image velocimetry measurement error, we manufactured an ellipsoidal polyhedral mirror and placed it between a camera and flow target to capture n images of identical particles from n (=80 maximum) different directions. The 3D particle positions were determined from the ensemble average of n C2 intersecting points of a pair of line-of-sight back-projected points from a particle found in any combination of two images in the n images. The method was then applied to a rigid-body rotating flow and a turbulent pipe flow. In the former measurement, bias error and random error fell in a range of  ±0.02 pixels and 0.02–0.05 pixels, respectively; additionally, random error decreased in proportion to . In the latter measurement, in which the measured value was compared to direct numerical simulation, bias error was reduced and random error also decreased in proportion to .

Automated Detection of Heuristics and Biases among Pathologists in a Computer-Based System

ERIC Educational Resources Information Center

Crowley, Rebecca S.; Legowski, Elizabeth; Medvedeva, Olga; Reitmeyer, Kayse; Tseytlin, Eugene; Castine, Melissa; Jukic, Drazen; Mello-Thoms, Claudia

2013-01-01

The purpose of this study is threefold: (1) to develop an automated, computer-based method to detect heuristics and biases as pathologists examine virtual slide cases, (2) to measure the frequency and distribution of heuristics and errors across three levels of training, and (3) to examine relationships of heuristics to biases, and biases to…

Adaptive Registration of Varying Contrast-Weighted Images for Improved Tissue Characterization (ARCTIC): Application to T1 Mapping

PubMed Central

Roujol, Sébastien; Foppa, Murilo; Weingartner, Sebastian; Manning, Warren J.; Nezafat, Reza

2014-01-01

Purpose To propose and evaluate a novel non-rigid image registration approach for improved myocardial T1 mapping. Methods Myocardial motion is estimated as global affine motion refined by a novel local non-rigid motion estimation algorithm. A variational framework is proposed, which simultaneously estimates motion field and intensity variations, and uses an additional regularization term to constrain the deformation field using automatic feature tracking. The method was evaluated in 29 patients by measuring the DICE similarity coefficient (DSC) and the myocardial boundary error (MBE) in short axis and four chamber data. Each image series was visually assessed as “no motion” or “with motion”. Overall T1 map quality and motion artifacts were assessed in the 85 T1 maps acquired in short axis view using a 4-point scale (1-non diagnostic/severe motion artifact, 4-excellent/no motion artifact). Results Increased DSC (0.78±0.14 to 0.87±0.03, p<0.001), reduced MBE (1.29±0.72mm to 0.84±0.20mm, p<0.001), improved overall T1 map quality (2.86±1.04 to 3.49±0.77, p<0.001), and reduced T1 map motion artifacts (2.51±0.84 to 3.61±0.64, p<0.001) were obtained after motion correction of “with motion” data (~56% of data). Conclusion The proposed non-rigid registration approach reduces the respiratory-induced motion that occurs during breath-hold T1 mapping, and significantly improves T1 map quality. PMID:24798588

Eddy-covariance data with low signal-to-noise ratio: time-lag determination, uncertainties and limit of detection

NASA Astrophysics Data System (ADS)

Langford, B.; Acton, W.; Ammann, C.; Valach, A.; Nemitz, E.

2015-10-01

All eddy-covariance flux measurements are associated with random uncertainties which are a combination of sampling error due to natural variability in turbulence and sensor noise. The former is the principal error for systems where the signal-to-noise ratio of the analyser is high, as is usually the case when measuring fluxes of heat, CO2 or H2O. Where signal is limited, which is often the case for measurements of other trace gases and aerosols, instrument uncertainties dominate. Here, we are applying a consistent approach based on auto- and cross-covariance functions to quantify the total random flux error and the random error due to instrument noise separately. As with previous approaches, the random error quantification assumes that the time lag between wind and concentration measurement is known. However, if combined with commonly used automated methods that identify the individual time lag by looking for the maximum in the cross-covariance function of the two entities, analyser noise additionally leads to a systematic bias in the fluxes. Combining data sets from several analysers and using simulations, we show that the method of time-lag determination becomes increasingly important as the magnitude of the instrument error approaches that of the sampling error. The flux bias can be particularly significant for disjunct data, whereas using a prescribed time lag eliminates these effects (provided the time lag does not fluctuate unduly over time). We also demonstrate that when sampling at higher elevations, where low frequency turbulence dominates and covariance peaks are broader, both the probability and magnitude of bias are magnified. We show that the statistical significance of noisy flux data can be increased (limit of detection can be decreased) by appropriate averaging of individual fluxes, but only if systematic biases are avoided by using a prescribed time lag. Finally, we make recommendations for the analysis and reporting of data with low signal-to-noise and their associated errors.

Eddy-covariance data with low signal-to-noise ratio: time-lag determination, uncertainties and limit of detection

NASA Astrophysics Data System (ADS)

Langford, B.; Acton, W.; Ammann, C.; Valach, A.; Nemitz, E.

2015-03-01

All eddy-covariance flux measurements are associated with random uncertainties which are a combination of sampling error due to natural variability in turbulence and sensor noise. The former is the principal error for systems where the signal-to-noise ratio of the analyser is high, as is usually the case when measuring fluxes of heat, CO2 or H2O. Where signal is limited, which is often the case for measurements of other trace gases and aerosols, instrument uncertainties dominate. We are here applying a consistent approach based on auto- and cross-covariance functions to quantifying the total random flux error and the random error due to instrument noise separately. As with previous approaches, the random error quantification assumes that the time-lag between wind and concentration measurement is known. However, if combined with commonly used automated methods that identify the individual time-lag by looking for the maximum in the cross-covariance function of the two entities, analyser noise additionally leads to a systematic bias in the fluxes. Combining datasets from several analysers and using simulations we show that the method of time-lag determination becomes increasingly important as the magnitude of the instrument error approaches that of the sampling error. The flux bias can be particularly significant for disjunct data, whereas using a prescribed time-lag eliminates these effects (provided the time-lag does not fluctuate unduly over time). We also demonstrate that when sampling at higher elevations, where low frequency turbulence dominates and covariance peaks are broader, both the probability and magnitude of bias are magnified. We show that the statistical significance of noisy flux data can be increased (limit of detection can be decreased) by appropriate averaging of individual fluxes, but only if systematic biases are avoided by using a prescribed time-lag. Finally, we make recommendations for the analysis and reporting of data with low signal-to-noise and their associated errors.

Multipath calibration in GPS pseudorange measurements

NASA Technical Reports Server (NTRS)

Kee, Changdon (Inventor); Parkinson, Bradford W. (Inventor)

1998-01-01

Novel techniques are disclosed for eliminating multipath errors, including mean bias errors, in pseudorange measurements made by conventional global positioning system receivers. By correlating the multipath signals of different satellites at their cross-over points in the sky, multipath mean bias errors are effectively eliminated. By then taking advantage of the geometrical dependence of multipath, a linear combination of spherical harmonics are fit to the satellite multipath data to create a hemispherical model of the multipath. This calibration model can then be used to compensate for multipath in subsequent measurements and thereby obtain GPS positioning to centimeter accuracy.

Thirty Years of Improving the NCEP Global Forecast System

NASA Astrophysics Data System (ADS)

White, G. H.; Manikin, G.; Yang, F.

2014-12-01

Current eight day forecasts by the NCEP Global Forecast System are as accurate as five day forecasts 30 years ago. This revolution in weather forecasting reflects increases in computer power, improvements in the assimilation of observations, especially satellite data, improvements in model physics, improvements in observations and international cooperation and competition. One important component has been and is the diagnosis, evaluation and reduction of systematic errors. The effect of proposed improvements in the GFS on systematic errors is one component of the thorough testing of such improvements by the Global Climate and Weather Modeling Branch. Examples of reductions in systematic errors in zonal mean temperatures and winds and other fields will be presented. One challenge in evaluating systematic errors is uncertainty in what reality is. Model initial states can be regarded as the best overall depiction of the atmosphere, but can be misleading in areas of few observations or for fields not well observed such as humidity or precipitation over the oceans. Verification of model physics is particularly difficult. The Environmental Modeling Center emphasizes the evaluation of systematic biases against observations. Recently EMC has placed greater emphasis on synoptic evaluation and on precipitation, 2-meter temperatures and dew points and 10 meter winds. A weekly EMC map discussion reviews the performance of many models over the United States and has helped diagnose and alleviate significant systematic errors in the GFS, including a near surface summertime evening cold wet bias over the eastern US and a multi-week period when the GFS persistently developed bogus tropical storms off Central America. The GFS exhibits a wet bias for light rain and a dry bias for moderate to heavy rain over the continental United States. Significant changes to the GFS are scheduled to be implemented in the fall of 2014. These include higher resolution, improved physics and improvements to the assimilation. These changes significantly improve the tropospheric flow and reduce a tropical upper tropospheric warm bias. One important error remaining is the failure of the GFS to maintain deep convection over Indonesia and in the tropical west Pacific. This and other current systematic errors will be presented.

Insights into the use of time-lapse GPR data as observations for inverse multiphase flow simulations of DNAPL migration

USGS Publications Warehouse

Johnson, R.H.; Poeter, E.P.

2007-01-01

Perchloroethylene (PCE) saturations determined from GPR surveys were used as observations for inversion of multiphase flow simulations of a PCE injection experiment (Borden 9??m cell), allowing for the estimation of optimal bulk intrinsic permeability values. The resulting fit statistics and analysis of residuals (observed minus simulated PCE saturations) were used to improve the conceptual model. These improvements included adjustment of the elevation of a permeability contrast, use of the van Genuchten versus Brooks-Corey capillary pressure-saturation curve, and a weighting scheme to account for greater measurement error with larger saturation values. A limitation in determining PCE saturations through one-dimensional GPR modeling is non-uniqueness when multiple GPR parameters are unknown (i.e., permittivity, depth, and gain function). Site knowledge, fixing the gain function, and multiphase flow simulations assisted in evaluating non-unique conceptual models of PCE saturation, where depth and layering were reinterpreted to provide alternate conceptual models. Remaining bias in the residuals is attributed to the violation of assumptions in the one-dimensional GPR interpretation (which assumes flat, infinite, horizontal layering) resulting from multidimensional influences that were not included in the conceptual model. While the limitations and errors in using GPR data as observations for inverse multiphase flow simulations are frustrating and difficult to quantify, simulation results indicate that the error and bias in the PCE saturation values are small enough to still provide reasonable optimal permeability values. The effort to improve model fit and reduce residual bias decreases simulation error even for an inversion based on biased observations and provides insight into alternate GPR data interpretations. Thus, this effort is warranted and provides information on bias in the observation data when this bias is otherwise difficult to assess. ?? 2006 Elsevier B.V. All rights reserved.

Attitude errors arising from antenna/satellite altitude errors - Recognition and reduction

NASA Technical Reports Server (NTRS)

Godbey, T. W.; Lambert, R.; Milano, G.

1972-01-01

A review is presented of the three basic types of pulsed radar altimeter designs, as well as the source and form of altitude bias errors arising from antenna/satellite attitude errors in each design type. A quantitative comparison of the three systems was also made.

Impact of random and systematic recall errors and selection bias in case--control studies on mobile phone use and brain tumors in adolescents (CEFALO study).

PubMed

Aydin, Denis; Feychting, Maria; Schüz, Joachim; Andersen, Tina Veje; Poulsen, Aslak Harbo; Prochazka, Michaela; Klaeboe, Lars; Kuehni, Claudia E; Tynes, Tore; Röösli, Martin

2011-07-01

Whether the use of mobile phones is a risk factor for brain tumors in adolescents is currently being studied. Case--control studies investigating this possible relationship are prone to recall error and selection bias. We assessed the potential impact of random and systematic recall error and selection bias on odds ratios (ORs) by performing simulations based on real data from an ongoing case--control study of mobile phones and brain tumor risk in children and adolescents (CEFALO study). Simulations were conducted for two mobile phone exposure categories: regular and heavy use. Our choice of levels of recall error was guided by a validation study that compared objective network operator data with the self-reported amount of mobile phone use in CEFALO. In our validation study, cases overestimated their number of calls by 9% on average and controls by 34%. Cases also overestimated their duration of calls by 52% on average and controls by 163%. The participation rates in CEFALO were 83% for cases and 71% for controls. In a variety of scenarios, the combined impact of recall error and selection bias on the estimated ORs was complex. These simulations are useful for the interpretation of previous case-control studies on brain tumor and mobile phone use in adults as well as for the interpretation of future studies on adolescents. Copyright © 2011 Wiley-Liss, Inc.

Accurate and predictive antibody repertoire profiling by molecular amplification fingerprinting.

PubMed

Khan, Tarik A; Friedensohn, Simon; Gorter de Vries, Arthur R; Straszewski, Jakub; Ruscheweyh, Hans-Joachim; Reddy, Sai T

2016-03-01

High-throughput antibody repertoire sequencing (Ig-seq) provides quantitative molecular information on humoral immunity. However, Ig-seq is compromised by biases and errors introduced during library preparation and sequencing. By using synthetic antibody spike-in genes, we determined that primer bias from multiplex polymerase chain reaction (PCR) library preparation resulted in antibody frequencies with only 42 to 62% accuracy. Additionally, Ig-seq errors resulted in antibody diversity measurements being overestimated by up to 5000-fold. To rectify this, we developed molecular amplification fingerprinting (MAF), which uses unique molecular identifier (UID) tagging before and during multiplex PCR amplification, which enabled tagging of transcripts while accounting for PCR efficiency. Combined with a bioinformatic pipeline, MAF bias correction led to measurements of antibody frequencies with up to 99% accuracy. We also used MAF to correct PCR and sequencing errors, resulting in enhanced accuracy of full-length antibody diversity measurements, achieving 98 to 100% error correction. Using murine MAF-corrected data, we established a quantitative metric of recent clonal expansion-the intraclonal diversity index-which measures the number of unique transcripts associated with an antibody clone. We used this intraclonal diversity index along with antibody frequencies and somatic hypermutation to build a logistic regression model for prediction of the immunological status of clones. The model was able to predict clonal status with high confidence but only when using MAF error and bias corrected Ig-seq data. Improved accuracy by MAF provides the potential to greatly advance Ig-seq and its utility in immunology and biotechnology.

Accurate and predictive antibody repertoire profiling by molecular amplification fingerprinting

PubMed Central

Khan, Tarik A.; Friedensohn, Simon; de Vries, Arthur R. Gorter; Straszewski, Jakub; Ruscheweyh, Hans-Joachim; Reddy, Sai T.

2016-01-01

High-throughput antibody repertoire sequencing (Ig-seq) provides quantitative molecular information on humoral immunity. However, Ig-seq is compromised by biases and errors introduced during library preparation and sequencing. By using synthetic antibody spike-in genes, we determined that primer bias from multiplex polymerase chain reaction (PCR) library preparation resulted in antibody frequencies with only 42 to 62% accuracy. Additionally, Ig-seq errors resulted in antibody diversity measurements being overestimated by up to 5000-fold. To rectify this, we developed molecular amplification fingerprinting (MAF), which uses unique molecular identifier (UID) tagging before and during multiplex PCR amplification, which enabled tagging of transcripts while accounting for PCR efficiency. Combined with a bioinformatic pipeline, MAF bias correction led to measurements of antibody frequencies with up to 99% accuracy. We also used MAF to correct PCR and sequencing errors, resulting in enhanced accuracy of full-length antibody diversity measurements, achieving 98 to 100% error correction. Using murine MAF-corrected data, we established a quantitative metric of recent clonal expansion—the intraclonal diversity index—which measures the number of unique transcripts associated with an antibody clone. We used this intraclonal diversity index along with antibody frequencies and somatic hypermutation to build a logistic regression model for prediction of the immunological status of clones. The model was able to predict clonal status with high confidence but only when using MAF error and bias corrected Ig-seq data. Improved accuracy by MAF provides the potential to greatly advance Ig-seq and its utility in immunology and biotechnology. PMID:26998518

Analysis and correction of gradient nonlinearity bias in apparent diffusion coefficient measurements.

PubMed

Malyarenko, Dariya I; Ross, Brian D; Chenevert, Thomas L

2014-03-01

Gradient nonlinearity of MRI systems leads to spatially dependent b-values and consequently high non-uniformity errors (10-20%) in apparent diffusion coefficient (ADC) measurements over clinically relevant field-of-views. This work seeks practical correction procedure that effectively reduces observed ADC bias for media of arbitrary anisotropy in the fewest measurements. All-inclusive bias analysis considers spatial and time-domain cross-terms for diffusion and imaging gradients. The proposed correction is based on rotation of the gradient nonlinearity tensor into the diffusion gradient frame where spatial bias of b-matrix can be approximated by its Euclidean norm. Correction efficiency of the proposed procedure is numerically evaluated for a range of model diffusion tensor anisotropies and orientations. Spatial dependence of nonlinearity correction terms accounts for the bulk (75-95%) of ADC bias for FA = 0.3-0.9. Residual ADC non-uniformity errors are amplified for anisotropic diffusion. This approximation obviates need for full diffusion tensor measurement and diagonalization to derive a corrected ADC. Practical scenarios are outlined for implementation of the correction on clinical MRI systems. The proposed simplified correction algorithm appears sufficient to control ADC non-uniformity errors in clinical studies using three orthogonal diffusion measurements. The most efficient reduction of ADC bias for anisotropic medium is achieved with non-lab-based diffusion gradients. Copyright © 2013 Wiley Periodicals, Inc.

Space-based IR tracking bias removal using background star observations

NASA Astrophysics Data System (ADS)

Clemons, T. M., III; Chang, K. C.

2009-05-01

This paper provides the results of a proposed methodology for removing sensor bias from a space-based infrared (IR) tracking system through the use of stars detected in the background field of the tracking sensor. The tracking system consists of two satellites flying in a lead-follower formation tracking a ballistic target. Each satellite is equipped with a narrow-view IR sensor that provides azimuth and elevation to the target. The tracking problem is made more difficult due to a constant, non-varying or slowly varying bias error present in each sensor's line of sight measurements. As known stars are detected during the target tracking process, the instantaneous sensor pointing error can be calculated as the difference between star detection reading and the known position of the star. The system then utilizes a separate bias filter to estimate the bias value based on these detections and correct the target line of sight measurements to improve the target state vector. The target state vector is estimated through a Linearized Kalman Filter (LKF) for the highly non-linear problem of tracking a ballistic missile. Scenarios are created using Satellite Toolkit(C) for trajectories with associated sensor observations. Mean Square Error results are given for tracking during the period when the target is in view of the satellite IR sensors. The results of this research provide a potential solution to bias correction while simultaneously tracking a target.

Analysis and correction of gradient nonlinearity bias in ADC measurements

PubMed Central

Malyarenko, Dariya I.; Ross, Brian D.; Chenevert, Thomas L.

2013-01-01

Purpose Gradient nonlinearity of MRI systems leads to spatially-dependent b-values and consequently high non-uniformity errors (10–20%) in ADC measurements over clinically relevant field-of-views. This work seeks practical correction procedure that effectively reduces observed ADC bias for media of arbitrary anisotropy in the fewest measurements. Methods All-inclusive bias analysis considers spatial and time-domain cross-terms for diffusion and imaging gradients. The proposed correction is based on rotation of the gradient nonlinearity tensor into the diffusion gradient frame where spatial bias of b-matrix can be approximated by its Euclidean norm. Correction efficiency of the proposed procedure is numerically evaluated for a range of model diffusion tensor anisotropies and orientations. Results Spatial dependence of nonlinearity correction terms accounts for the bulk (75–95%) of ADC bias for FA = 0.3–0.9. Residual ADC non-uniformity errors are amplified for anisotropic diffusion. This approximation obviates need for full diffusion tensor measurement and diagonalization to derive a corrected ADC. Practical scenarios are outlined for implementation of the correction on clinical MRI systems. Conclusions The proposed simplified correction algorithm appears sufficient to control ADC non-uniformity errors in clinical studies using three orthogonal diffusion measurements. The most efficient reduction of ADC bias for anisotropic medium is achieved with non-lab-based diffusion gradients. PMID:23794533

Impact of exposure measurement error in air pollution epidemiology: effect of error type in time-series studies.

PubMed

Goldman, Gretchen T; Mulholland, James A; Russell, Armistead G; Strickland, Matthew J; Klein, Mitchel; Waller, Lance A; Tolbert, Paige E

2011-06-22

Two distinctly different types of measurement error are Berkson and classical. Impacts of measurement error in epidemiologic studies of ambient air pollution are expected to depend on error type. We characterize measurement error due to instrument imprecision and spatial variability as multiplicative (i.e. additive on the log scale) and model it over a range of error types to assess impacts on risk ratio estimates both on a per measurement unit basis and on a per interquartile range (IQR) basis in a time-series study in Atlanta. Daily measures of twelve ambient air pollutants were analyzed: NO2, NOx, O3, SO2, CO, PM10 mass, PM2.5 mass, and PM2.5 components sulfate, nitrate, ammonium, elemental carbon and organic carbon. Semivariogram analysis was applied to assess spatial variability. Error due to this spatial variability was added to a reference pollutant time-series on the log scale using Monte Carlo simulations. Each of these time-series was exponentiated and introduced to a Poisson generalized linear model of cardiovascular disease emergency department visits. Measurement error resulted in reduced statistical significance for the risk ratio estimates for all amounts (corresponding to different pollutants) and types of error. When modelled as classical-type error, risk ratios were attenuated, particularly for primary air pollutants, with average attenuation in risk ratios on a per unit of measurement basis ranging from 18% to 92% and on an IQR basis ranging from 18% to 86%. When modelled as Berkson-type error, risk ratios per unit of measurement were biased away from the null hypothesis by 2% to 31%, whereas risk ratios per IQR were attenuated (i.e. biased toward the null) by 5% to 34%. For CO modelled error amount, a range of error types were simulated and effects on risk ratio bias and significance were observed. For multiplicative error, both the amount and type of measurement error impact health effect estimates in air pollution epidemiology. By modelling instrument imprecision and spatial variability as different error types, we estimate direction and magnitude of the effects of error over a range of error types.

Ultra-Low Threshold Vertical-Cavity Surface-Emitting Lasers for USAF Applications

DTIC Science & Technology

2005-01-01

molecular beam epitaxy , semiconductors, finite element method, modeling and simulation, oxidation furnace 16. SECURITY CLASSIFICATION OF: 19a. NAME OF...Patterson Air Force Base). Device material growth was accomplished by means of molecular beam epitaxy (MBE) using a Varian GENII MBE system owned by the...grown by molecular beam epitaxy on a GaAs substrate. Vertical posts, with square and circular cross sections ranging in size from 5 to 40 microns

Nitrogen Plasma Optimization for High-Quality Dilute Nitrides

DTIC Science & Technology

2005-02-01

Available online 1 February 2005Abstract Growth of GaInNAs by molecular beam epitaxy (MBE) generally requires a nitrogen plasma, which complicates growth...InGaAs and InGaAsP lasers. This paper addresses several of the challenges of plasma-assisted molecular beam epitaxy (MBE) of high-quality dilute nitrides...A.L. Holmes, Using beam flux monitor as Langmuir probe for plasma-assisted molecular beam epitaxy , J. Vac. Sci. Technol. B, in press.

Growth of delta-doped layers on silicon CCD/S for enhanced ultraviolet response

NASA Technical Reports Server (NTRS)

Hoenk, Michael E. (Inventor); Grunthaner, Paula J. (Inventor); Grunthaner, Frank J. (Inventor); Terhune, Robert W. (Inventor); Hecht, Michael H. (Inventor)

1994-01-01

The backside surface potential well of a backside-illuminated CCD is confined to within about half a nanometer of the surface by using molecular beam epitaxy (MBE) to grow a delta-doped silicon layer on the back surface. Delta-doping in an MBE process is achieved by temporarily interrupting the evaporated silicon source during MBE growth without interrupting the evaporated p+ dopant source (e.g., boron). This produces an extremely sharp dopant profile in which the dopant is confined to only a few atomic layers, creating an electric field high enough to confine the backside surface potential well to within half a nanometer of the surface. Because the probability of UV-generated electrons being trapped by such a narrow potential well is low, the internal quantum efficiency of the CCD is nearly 100% throughout the UV wavelength range. Furthermore, the quantum efficiency is quite stable.

Surface stability and the selection rules of substrate orientation for optimal growth of epitaxial II-VI semiconductors

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

Yin, Wan-Jian; Department of Physics & Astronomy, and Wright Center for Photovoltaics Innovation and Commercialization, The University of Toledo, Toledo, Ohio 43606; Yang, Ji-Hui

2015-10-05

The surface structures of ionic zinc-blende CdTe (001), (110), (111), and (211) surfaces are systematically studied by first-principles density functional calculations. Based on the surface structures and surface energies, we identify the detrimental twinning appearing in molecular beam epitaxy (MBE) growth of II-VI compounds as the (111) lamellar twin boundaries. To avoid the appearance of twinning in MBE growth, we propose the following selection rules for choosing optimal substrate orientations: (1) the surface should be nonpolar so that there is no large surface reconstructions that could act as a nucleation center and promote the formation of twins; (2) the surfacemore » structure should have low symmetry so that there are no multiple equivalent directions for growth. These straightforward rules, in consistent with experimental observations, provide guidelines for selecting proper substrates for high-quality MBE growth of II-VI compounds.« less

Passivation effect on optical and electrical properties of molecular beam epitaxy-grown HgCdTe/CdTe/Si layers

NASA Astrophysics Data System (ADS)

Kiran, Rajni; Mallick, Shubhrangshu; Hahn, Suk-Ryong; Lee, T. S.; Sivananthan, Sivalingam; Ghosh, Siddhartha; Wijewarnasuriya, P. S.

2006-06-01

The effects of passivation with two different passivants, ZnS and CdTe, and two different passivation techniques, physical vapor deposition (PVD) and molecular beam epitaxy (MBE), were quantified in terms of the minority carrier lifetime and extracted surface recombination velocity on both MBE-grown medium-wavelength ir (MWIR) and long-wavelength ir HgCdTe samples. A gradual increment of the minority carrier lifetime was reported as the passivation technique was changed from PVD ZnS to PVD CdTe, and finally to MBE CdTe, especially at low temperatures. A corresponding reduction in the extracted surface recombination velocity in the same order was also reported for the first time. Initial data on the 1/ f noise values of as-grown MWIR samples showed a reduction of two orders of noise power after 1200-Å ZnS deposition.

The impact of substrate selection for the controlled growth of graphene by molecular beam epitaxy

NASA Astrophysics Data System (ADS)

Schumann, T.; Lopes, J. M. J.; Wofford, J. M.; Oliveira, M. H.; Dubslaff, M.; Hanke, M.; Jahn, U.; Geelhaar, L.; Riechert, H.

2015-09-01

We examine how substrate selection impacts the resulting film properties in graphene growth by molecular beam epitaxy (MBE). Graphene growth on metallic as well as dielectric templates was investigated. We find that MBE offers control over the number of atomic graphene layers regardless of the substrate used. High structural quality could be achieved for graphene prepared on Ni (111) films which were epitaxially grown on MgO (111). For growth either on Al2O3 (0001) or on (6√3×6√3)R30°-reconstructed SiC (0001) surfaces, graphene with a higher density of defects is obtained. Interestingly, despite their defective nature, the layers possess a well defined epitaxial relation to the underlying substrate. These results demonstrate the feasibility of MBE as a technique for realizing the scalable synthesis of this two-dimensional crystal on a variety of substrates.

Experimental and theoretical determination of sea-state bias in radar altimetry

NASA Technical Reports Server (NTRS)

Stewart, Robert H.

1991-01-01

The major unknown error in radar altimetry is due to waves on the sea surface which cause the mean radar-reflecting surface to be displaced from mean sea level. This is the electromagnetic bias. The primary motivation for the project was to understand the causes of the bias so that the error it produces in radar altimetry could be calculated and removed from altimeter measurements made from space by the Topex/Poseidon altimetric satellite. The goals of the project were: (1) observe radar scatter at vertical incidence using a simple radar on a platform for a wide variety of environmental conditions at the same time wind and wave conditions were measured; (2) calculate electromagnetic bias from the radar observations; (3) investigate the limitations of the present theory describing radar scatter at vertical incidence; (4) compare measured electromagnetic bias with bias calculated from theory using measurements of wind and waves made at the time of the radar measurements; and (5) if possible, extend the theory so bias can be calculated for a wider range of environmental conditions.

Bias correction of bounded location errors in presence-only data

USGS Publications Warehouse

Hefley, Trevor J.; Brost, Brian M.; Hooten, Mevin B.

2017-01-01

Location error occurs when the true location is different than the reported location. Because habitat characteristics at the true location may be different than those at the reported location, ignoring location error may lead to unreliable inference concerning species–habitat relationships.We explain how a transformation known in the spatial statistics literature as a change of support (COS) can be used to correct for location errors when the true locations are points with unknown coordinates contained within arbitrary shaped polygons.We illustrate the flexibility of the COS by modelling the resource selection of Whooping Cranes (Grus americana) using citizen contributed records with locations that were reported with error. We also illustrate the COS with a simulation experiment.In our analysis of Whooping Crane resource selection, we found that location error can result in up to a five-fold change in coefficient estimates. Our simulation study shows that location error can result in coefficient estimates that have the wrong sign, but a COS can efficiently correct for the bias.

Being an honest broker of hydrology: Uncovering, communicating and addressing model error in a climate change streamflow dataset

NASA Astrophysics Data System (ADS)

Chegwidden, O.; Nijssen, B.; Pytlak, E.

2017-12-01

Any model simulation has errors, including errors in meteorological data, process understanding, model structure, and model parameters. These errors may express themselves as bias, timing lags, and differences in sensitivity between the model and the physical world. The evaluation and handling of these errors can greatly affect the legitimacy, validity and usefulness of the resulting scientific product. In this presentation we will discuss a case study of handling and communicating model errors during the development of a hydrologic climate change dataset for the Pacific Northwestern United States. The dataset was the result of a four-year collaboration between the University of Washington, Oregon State University, the Bonneville Power Administration, the United States Army Corps of Engineers and the Bureau of Reclamation. Along the way, the partnership facilitated the discovery of multiple systematic errors in the streamflow dataset. Through an iterative review process, some of those errors could be resolved. For the errors that remained, honest communication of the shortcomings promoted the dataset's legitimacy. Thoroughly explaining errors also improved ways in which the dataset would be used in follow-on impact studies. Finally, we will discuss the development of the "streamflow bias-correction" step often applied to climate change datasets that will be used in impact modeling contexts. We will describe the development of a series of bias-correction techniques through close collaboration among universities and stakeholders. Through that process, both universities and stakeholders learned about the others' expectations and workflows. This mutual learning process allowed for the development of methods that accommodated the stakeholders' specific engineering requirements. The iterative revision process also produced a functional and actionable dataset while preserving its scientific merit. We will describe how encountering earlier techniques' pitfalls allowed us to develop improved methods for scientists and practitioners alike.

A New Paradigm for Diagnosing Contributions to Model Aerosol Forcing Error: Diagnosing Model Aerosol Forcing Error

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

Jones, A. L.; Feldman, D. R.; Freidenreich, S.

A new paradigm in benchmark absorption-scattering radiative transfer is presented that enables both the globally averaged and spatially resolved testing of climate model radiation parameterizations in order to uncover persistent sources of biases in the aerosol instantaneous radiative effect (IRE). A proof of concept is demonstrated with the Geophysical Fluid Dynamics Laboratory AM4 and Community Earth System Model 1.2.2 climate models. Instead of prescribing atmospheric conditions and aerosols, as in prior intercomparisons, native snapshots of the atmospheric state and aerosol optical properties from the participating models are used as inputs to an accurate radiation solver to uncover model-relevant biases. Thesemore » diagnostic results show that the models' aerosol IRE bias is of the same magnitude as the persistent range cited (~1 W/m 2) and also varies spatially and with intrinsic aerosol optical properties. The findings presented here underscore the significance of native model error analysis and its dispositive ability to diagnose global biases, confirming its fundamental value for the Radiative Forcing Model Intercomparison Project.« less

A New Paradigm for Diagnosing Contributions to Model Aerosol Forcing Error: Diagnosing Model Aerosol Forcing Error

DOE PAGES

Jones, A. L.; Feldman, D. R.; Freidenreich, S.; ...

2017-12-07

A new paradigm in benchmark absorption-scattering radiative transfer is presented that enables both the globally averaged and spatially resolved testing of climate model radiation parameterizations in order to uncover persistent sources of biases in the aerosol instantaneous radiative effect (IRE). A proof of concept is demonstrated with the Geophysical Fluid Dynamics Laboratory AM4 and Community Earth System Model 1.2.2 climate models. Instead of prescribing atmospheric conditions and aerosols, as in prior intercomparisons, native snapshots of the atmospheric state and aerosol optical properties from the participating models are used as inputs to an accurate radiation solver to uncover model-relevant biases. Thesemore » diagnostic results show that the models' aerosol IRE bias is of the same magnitude as the persistent range cited (~1 W/m 2) and also varies spatially and with intrinsic aerosol optical properties. The findings presented here underscore the significance of native model error analysis and its dispositive ability to diagnose global biases, confirming its fundamental value for the Radiative Forcing Model Intercomparison Project.« less

Dynamic response tests of inertial and optical wind-tunnel model attitude measurement devices

NASA Technical Reports Server (NTRS)

Buehrle, R. D.; Young, C. P., Jr.; Burner, A. W.; Tripp, J. S.; Tcheng, P.; Finley, T. D.; Popernack, T. G., Jr.

1995-01-01

Results are presented for an experimental study of the response of inertial and optical wind-tunnel model attitude measurement systems in a wind-off simulated dynamic environment. This study is part of an ongoing activity at the NASA Langley Research Center to develop high accuracy, advanced model attitude measurement systems that can be used in a dynamic wind-tunnel environment. This activity was prompted by the inertial model attitude sensor response observed during high levels of model vibration which results in a model attitude measurement bias error. Significant bias errors in model attitude measurement were found for the measurement using the inertial device during wind-off dynamic testing of a model system. The amount of bias present during wind-tunnel tests will depend on the amplitudes of the model dynamic response and the modal characteristics of the model system. Correction models are presented that predict the vibration-induced bias errors to a high degree of accuracy for the vibration modes characterized in the simulated dynamic environment. The optical system results were uncorrupted by model vibration in the laboratory setup.

Aggression, emotional self-regulation, attentional bias, and cognitive inhibition predict risky driving behavior.

PubMed

Sani, Susan Raouf Hadadi; Tabibi, Zahra; Fadardi, Javad Salehi; Stavrinos, Despina

2017-12-01

The present study explored whether aggression, emotional regulation, cognitive inhibition, and attentional bias towards emotional stimuli were related to risky driving behavior (driving errors, and driving violations). A total of 117 applicants for taxi driver positions (89% male, M age=36.59years, SD=9.39, age range 24-62years) participated in the study. Measures included the Ahwaz Aggression Inventory, the Difficulties in emotion regulation Questionnaire, the emotional Stroop task, the Go/No-go task, and the Driving Behavior Questionnaire. Correlation and regression analyses showed that aggression and emotional regulation predicted risky driving behavior. Difficulties in emotion regulation, the obstinacy and revengeful component of aggression, attentional bias toward emotional stimuli, and cognitive inhibition predicted driving errors. Aggression was the only significant predictive factor for driving violations. In conclusion, aggression and difficulties in regulating emotions may exacerbate risky driving behaviors. Deficits in cognitive inhibition and attentional bias toward negative emotional stimuli can increase driving errors. Predisposition to aggression has strong effect on making one vulnerable to violation of traffic rules and crashes. Copyright © 2017 Elsevier Ltd. All rights reserved.

Developmental Differences in the Effects of Repeated Interviews and Interviewer Bias on Young Children’s Event Memory and False Reports

PubMed Central

Quas, Jodi A.; Malloy, Lindsay C.; Melinder, Annika; Goodman, Gail S.; D’Mello, Michelle; Schaaf, Jennifer

2010-01-01

The present study investigated developmental differences in the effects of repeated interviews and interviewer bias on children’s memory and suggestibility. Three- and 5-year-olds were singly or repeatedly interviewed about a play event by a highly biased or control interviewer. Children interviewed once by the biased interviewer after a long delay made the most errors. Children interviewed repeatedly, regardless of interviewer bias, were more accurate and less likely to falsely claim that they played with a man. In free recall, among children questioned once after a long delay by the biased interviewer, 5-year-olds were more likely than were 3-year-olds to claim falsely that they played with a man. However, in response to direct questions, 3-year-olds were more easily manipulated into implying that they played with him. Findings suggest that interviewer bias is particularly problematic when children’s memory has weakened. In contrast, repeated interviews that occur a short time after a to-be-remembered event do not necessarily increase children’s errors, even when interviews include misleading questions and interviewer bias. Implications for developmental differences in memory and suggestibility are discussed. PMID:17605517

On the sea-state bias of the Geosat altimeter

NASA Technical Reports Server (NTRS)

Ray, Richard D.; Koblinsky, Chester J.

1991-01-01

The sea-state bias in a satellite altimeter's range measurement is caused by the influence of ocean waves on the radar return pulse; it results in an estimate of sea level that is too low according to some function of the wave height. This bias is here estimated for Geosat by correlating collinear differences of altimetric sea-surface heights with collinear differences of significant wave heights (H1/3). Corrections for satellite orbit error are estimated simultaneously with the sea-state bias. Based on twenty 17-day repeat cycles of the Geosat Exact Repeat Mission, the solution for the sea-state bias is 2.6 + or - 0.2 percent of H1/3. The least-squares residuals, however, show a correlation with wind speed U, so the traditional model of the bias has been supplemented with a second term: H1/3 + alpha-2H1/3U. This second term produces a small, but statistically significant, reduction in variance of the residuals. Both systematic and random errors in H1/3 and U tend to bias the estimates of alpha-1 and alpha-2, which complicates comparisons of the results with ground-based measurements of the sea-state bias.

On the sea-state bias of the Geosat altimeter

NASA Astrophysics Data System (ADS)

Ray, Richard D.; Koblinsky, Chester J.

1991-06-01

The sea-state bias in a satellite altimeter's range measurement is caused by the influence of ocean waves on the radar return pulse; it results in an estimate of sea level that is too low according to some function of the wave height. This bias is here estimated for Geosat by correlating collinear differences of altimetric sea-surface heights with collinear differences of significant wave heights (H1/3). Corrections for satellite orbit error are estimated simultaneously with the sea-state bias. Based on twenty 17-day repeat cycles of the Geosat Exact Repeat Mission, the solution for the sea-state bias is 2.6 + or - 0.2 percent of H1/3. The least-squares residuals, however, show a correlation with wind speed U, so the traditional model of the bias has been supplemented with a second term: H1/3 + alpha-2H1/3U. This second term produces a small, but statistically significant, reduction in variance of the residuals. Both systematic and random errors in H1/3 and U tend to bias the estimates of alpha-1 and alpha-2, which complicates comparisons of the results with ground-based measurements of the sea-state bias.

Performance of bias-correction methods for exposure measurement error using repeated measurements with and without missing data.

PubMed

Batistatou, Evridiki; McNamee, Roseanne

2012-12-10

It is known that measurement error leads to bias in assessing exposure effects, which can however, be corrected if independent replicates are available. For expensive replicates, two-stage (2S) studies that produce data 'missing by design', may be preferred over a single-stage (1S) study, because in the second stage, measurement of replicates is restricted to a sample of first-stage subjects. Motivated by an occupational study on the acute effect of carbon black exposure on respiratory morbidity, we compare the performance of several bias-correction methods for both designs in a simulation study: an instrumental variable method (EVROS IV) based on grouping strategies, which had been recommended especially when measurement error is large, the regression calibration and the simulation extrapolation methods. For the 2S design, either the problem of 'missing' data was ignored or the 'missing' data were imputed using multiple imputations. Both in 1S and 2S designs, in the case of small or moderate measurement error, regression calibration was shown to be the preferred approach in terms of root mean square error. For 2S designs, regression calibration as implemented by Stata software is not recommended in contrast to our implementation of this method; the 'problematic' implementation of regression calibration although substantially improved with use of multiple imputations. The EVROS IV method, under a good/fairly good grouping, outperforms the regression calibration approach in both design scenarios when exposure mismeasurement is severe. Both in 1S and 2S designs with moderate or large measurement error, simulation extrapolation severely failed to correct for bias. Copyright © 2012 John Wiley & Sons, Ltd.

Domain-Level Assessment of the Weather Running Estimate-Nowcast (WREN) Model

DTIC Science & Technology

2016-11-01

Added by Decreased Grid Spacing 14 4.4 Performance Comparison of 2 WRE–N Configurations 18 4.5 Performance Comparison: Dumais WRE–N with FDDA vs. the...FDDA for 2 -m-AGL TMP (K) ..................................................... 15 Fig. 11 Bias and RMSE errors for the 3 grids for Dumais and Passner...WRE–N with FDDA for 2 -m-AGL DPT (K) ...................................................... 16 Fig. 12 Bias and RMSE errors for the 3 grids for Dumais

Allicat magnetoresistive head design and performance

NASA Astrophysics Data System (ADS)

Hannon, David; Krounbi, Mohamed; Christner, Jodie

1994-03-01

The general design features of the magnetoresistive (MR) merged head are described and compared to the earlier MR piggy-back head called Corsair. Examples of static, magnetic, and error rate testing are given. Dual track profiles show the read-narrow feature of the MR head. Stability of the signal with write disturbance shows the effectiveness of the hard-bias longitudinal biasing. Error rate versus off-track position indicates the robustness of the file design.

Estimation of Carbon Dioxide Storage Capacity for Depleted Gas Reservoirs

NASA Astrophysics Data System (ADS)

Lai, Yen Ting; Shen, Chien-Hao; Tseng, Chi-Chung; Fan, Chen-Hui; Hsieh, Bieng-Zih

2015-04-01

A depleted gas reservoir is one of the best options for CO2 storage for many reasons. First of all, the storage safety or the caprock integrity has been proven because the natural gas was trapped in the formation for a very long period of time. Also the formation properties and fluid flow characteristics for the reservoir have been well studied since the discovery of the gas reservoir. Finally the surface constructions and facilities are very useful and relatively easy to convert for the use of CO2 storage. The purpose of this study was to apply an analytical approach to estimate CO2 storage capacity in a depleted gas reservoir. The analytical method we used is the material balance equation (MBE), which have been widely used in natural gas storage. We proposed a modified MBE for CO2 storage in a depleted gas reservoir by introducing the z-factors of gas, CO2 and the mixture of the two. The MBE can be derived to a linear relationship between the ratio of pressure to gas z-factor (p/z) and the cumulative term (Gp-Ginj, where Gp is the cumulative gas production and Ginj is the cumulative CO2 injection). The CO2 storage capacity can be calculated when constraints of reservoir recovery pressure are adopted. The numerical simulation was also used for the validation of the theoretical estimation of CO2 storage capacity from the MBE. We found that the quantity of CO2 stored is more than that of gas produced when the reservoir pressure is recovered from the abandon pressure to the initial pressure. This result was basically from the fact that the gas- CO2 mixture z-factors are lower than the natural gas z-factors in reservoir conditions. We also established a useful p/z plot to easily observe the pressure behavior of CO2 storage and efficiently calculate the CO2 storage capacity. The application of the MBE we proposed was demonstrated by a case study of a depleted gas reservoir in northwestern Taiwan. The estimated CO2 storage capacities from conducting reservoir simulation and using analytical equation were very consistent. The validation results showed that the modified MBE we proposed in this study can be efficiently used for the estimation of CO2 storage capacity in a depleted gas reservoir.

Verification bias an underrecognized source of error in assessing the efficacy of medical imaging.

PubMed

Petscavage, Jonelle M; Richardson, Michael L; Carr, Robert B

2011-03-01

Diagnostic tests are validated by comparison against a "gold standard" reference test. When the reference test is invasive or expensive, it may not be applied to all patients. This can result in biased estimates of the sensitivity and specificity of the diagnostic test. This type of bias is called "verification bias," and is a common problem in imaging research. The purpose of our study is to estimate the prevalence of verification bias in the recent radiology literature. All issues of the American Journal of Roentgenology (AJR), Academic Radiology, Radiology, and European Journal of Radiology (EJR) between November 2006 and October 2009 were reviewed for original research articles mentioning sensitivity or specificity as endpoints. Articles were read to determine whether verification bias was present and searched for author recognition of verification bias in the design. During 3 years, these journals published 2969 original research articles. A total of 776 articles used sensitivity or specificity as an outcome. Of these, 211 articles demonstrated potential verification bias. The fraction of articles with potential bias was respectively 36.4%, 23.4%, 29.5%, and 13.4% for AJR, Academic Radiology, Radiology, and EJR. The total fraction of papers with potential bias in which the authors acknowledged this bias was 17.1%. Verification bias is a common and frequently unacknowledged source of error in efficacy studies of diagnostic imaging. Bias can often be eliminated by proper study design. When it cannot be eliminated, it should be estimated and acknowledged. Published by Elsevier Inc.

Correcting Memory Improves Accuracy of Predicted Task Duration

ERIC Educational Resources Information Center

Roy, Michael M.; Mitten, Scott T.; Christenfeld, Nicholas J. S.

2008-01-01

People are often inaccurate in predicting task duration. The memory bias explanation holds that this error is due to people having incorrect memories of how long previous tasks have taken, and these biased memories cause biased predictions. Therefore, the authors examined the effect on increasing predictive accuracy of correcting memory through…

Mismeasurement and the resonance of strong confounders: correlated errors.

PubMed

Marshall, J R; Hastrup, J L; Ross, J S

1999-07-01

Confounding in epidemiology, and the limits of standard methods of control for an imperfectly measured confounder, have been understood for some time. However, most treatments of this problem are based on the assumption that errors of measurement in confounding and confounded variables are independent. This paper considers the situation in which a strong risk factor (confounder) and an inconsequential but suspected risk factor (confounded) are each measured with errors that are correlated; the situation appears especially likely to occur in the field of nutritional epidemiology. Error correlation appears to add little to measurement error as a source of bias in estimating the impact of a strong risk factor: it can add to, diminish, or reverse the bias induced by measurement error in estimating the impact of the inconsequential risk factor. Correlation of measurement errors can add to the difficulty involved in evaluating structures in which confounding and measurement error are present. In its presence, observed correlations among risk factors can be greater than, less than, or even opposite to the true correlations. Interpretation of multivariate epidemiologic structures in which confounding is likely requires evaluation of measurement error structures, including correlations among measurement errors.

Error Patterns with Fraction Calculations at Fourth Grade as a Function of Students' Mathematics Achievement Status.

PubMed

Schumacher, Robin F; Malone, Amelia S

2017-09-01

The goal of the present study was to describe fraction-calculation errors among 4 th -grade students and determine whether error patterns differed as a function of problem type (addition vs. subtraction; like vs. unlike denominators), orientation (horizontal vs. vertical), or mathematics-achievement status (low- vs. average- vs. high-achieving). We specifically addressed whether mathematics-achievement status was related to students' tendency to operate with whole number bias. We extended this focus by comparing low-performing students' errors in two instructional settings that focused on two different types of fraction understandings: core instruction that focused on part-whole understanding vs. small-group tutoring that focused on magnitude understanding. Results showed students across the sample were more likely to operate with whole number bias on problems with unlike denominators. Students with low or average achievement (who only participated in core instruction) were more likely to operate with whole number bias than students with low achievement who participated in small-group tutoring. We suggest instruction should emphasize magnitude understanding to sufficiently increase fraction understanding for all students in the upper elementary grades.

Error propagation in energetic carrying capacity models

USGS Publications Warehouse

Pearse, Aaron T.; Stafford, Joshua D.

2014-01-01

Conservation objectives derived from carrying capacity models have been used to inform management of landscapes for wildlife populations. Energetic carrying capacity models are particularly useful in conservation planning for wildlife; these models use estimates of food abundance and energetic requirements of wildlife to target conservation actions. We provide a general method for incorporating a foraging threshold (i.e., density of food at which foraging becomes unprofitable) when estimating food availability with energetic carrying capacity models. We use a hypothetical example to describe how past methods for adjustment of foraging thresholds biased results of energetic carrying capacity models in certain instances. Adjusting foraging thresholds at the patch level of the species of interest provides results consistent with ecological foraging theory. Presentation of two case studies suggest variation in bias which, in certain instances, created large errors in conservation objectives and may have led to inefficient allocation of limited resources. Our results also illustrate how small errors or biases in application of input parameters, when extrapolated to large spatial extents, propagate errors in conservation planning and can have negative implications for target populations.

Systematic bias in genomic classification due to contaminating non-neoplastic tissue in breast tumor samples.

PubMed

Elloumi, Fathi; Hu, Zhiyuan; Li, Yan; Parker, Joel S; Gulley, Margaret L; Amos, Keith D; Troester, Melissa A

2011-06-30

Genomic tests are available to predict breast cancer recurrence and to guide clinical decision making. These predictors provide recurrence risk scores along with a measure of uncertainty, usually a confidence interval. The confidence interval conveys random error and not systematic bias. Standard tumor sampling methods make this problematic, as it is common to have a substantial proportion (typically 30-50%) of a tumor sample comprised of histologically benign tissue. This "normal" tissue could represent a source of non-random error or systematic bias in genomic classification. To assess the performance characteristics of genomic classification to systematic error from normal contamination, we collected 55 tumor samples and paired tumor-adjacent normal tissue. Using genomic signatures from the tumor and paired normal, we evaluated how increasing normal contamination altered recurrence risk scores for various genomic predictors. Simulations of normal tissue contamination caused misclassification of tumors in all predictors evaluated, but different breast cancer predictors showed different types of vulnerability to normal tissue bias. While two predictors had unpredictable direction of bias (either higher or lower risk of relapse resulted from normal contamination), one signature showed predictable direction of normal tissue effects. Due to this predictable direction of effect, this signature (the PAM50) was adjusted for normal tissue contamination and these corrections improved sensitivity and negative predictive value. For all three assays quality control standards and/or appropriate bias adjustment strategies can be used to improve assay reliability. Normal tissue sampled concurrently with tumor is an important source of bias in breast genomic predictors. All genomic predictors show some sensitivity to normal tissue contamination and ideal strategies for mitigating this bias vary depending upon the particular genes and computational methods used in the predictor.

Alternative Regression Equations for Estimation of Annual Peak-Streamflow Frequency for Undeveloped Watersheds in Texas using PRESS Minimization

USGS Publications Warehouse

Asquith, William H.; Thompson, David B.

2008-01-01

The U.S. Geological Survey, in cooperation with the Texas Department of Transportation and in partnership with Texas Tech University, investigated a refinement of the regional regression method and developed alternative equations for estimation of peak-streamflow frequency for undeveloped watersheds in Texas. A common model for estimation of peak-streamflow frequency is based on the regional regression method. The current (2008) regional regression equations for 11 regions of Texas are based on log10 transformations of all regression variables (drainage area, main-channel slope, and watershed shape). Exclusive use of log10-transformation does not fully linearize the relations between the variables. As a result, some systematic bias remains in the current equations. The bias results in overestimation of peak streamflow for both the smallest and largest watersheds. The bias increases with increasing recurrence interval. The primary source of the bias is the discernible curvilinear relation in log10 space between peak streamflow and drainage area. Bias is demonstrated by selected residual plots with superimposed LOWESS trend lines. To address the bias, a statistical framework based on minimization of the PRESS statistic through power transformation of drainage area is described and implemented, and the resulting regression equations are reported. Compared to log10-exclusive equations, the equations derived from PRESS minimization have PRESS statistics and residual standard errors less than the log10 exclusive equations. Selected residual plots for the PRESS-minimized equations are presented to demonstrate that systematic bias in regional regression equations for peak-streamflow frequency estimation in Texas can be reduced. Because the overall error is similar to the error associated with previous equations and because the bias is reduced, the PRESS-minimized equations reported here provide alternative equations for peak-streamflow frequency estimation.

More on Systematic Error in a Boyle's Law Experiment

ERIC Educational Resources Information Center

McCall, Richard P.

2012-01-01

A recent article in "The Physics Teacher" describes a method for analyzing a systematic error in a Boyle's law laboratory activity. Systematic errors are important to consider in physics labs because they tend to bias the results of measurements. There are numerous laboratory examples and resources that discuss this common source of error.

Growth of III-V films by control of MBE growth front stoichiometry

NASA Technical Reports Server (NTRS)

Grunthaner, Frank J. (Inventor); Liu, John K. (Inventor); Hancock, Bruce R. (Inventor)

1992-01-01

For the growth of strain-layer materials and high quality single and multiple quantum wells, the instantaneous control of growth front stoichiometry is critical. The process of the invention adjusts the offset or phase of molecular beam epitaxy (MBE) control shutters to program the instantaneous arrival or flux rate of In and As4 reactants to grow InAs. The interrupted growth of first In, then As4, is also a key feature.

Optimization of the Nonradiative Lifetime of Molecular-Beam-Epitaxy (MBE)-Grown Undoped GaAs/AlGaAs Double Heterostructures (DH)

DTIC Science & Technology

2013-09-01

Optimization of the Nonradiative Lifetime of Molecular- Beam-Epitaxy (MBE)-Grown Undoped GaAs/AlGaAs Double Heterostructures (DH) by P...it to the originator. Army Research Laboratory Adelphi, MD 20783-1197 ARL-TR-6660 September 2013 Optimization of the Nonradiative ...REPORT TYPE Final 3. DATES COVERED (From - To) FY2013 4. TITLE AND SUBTITLE Optimization of the Nonradiative Lifetime of Molecular-Beam-Epitaxy

Nano Electronics on Atomically Controlled van der Waals Quantum Heterostructures

DTIC Science & Technology

2015-03-30

for the structural of the atomically sharp interface between hBN and Bi2Te3. Finally, we have developed unprecedentedly clean graphene supercoductor...crystals by MBE method. We also use transmission electron microscopy (TEM) analysis for the structural of the atomically sharp interface between hBN and...by MBE method. We also use transmission electron microscopy (TEM) analysis for the structural of the atomically sharp interface between hBN and Bi2Te3

Systematic Study of p-type Doping and Related Defects in III-Nitrides: Pathway toward a Nitride HBT

DTIC Science & Technology

2012-11-20

InGaN growth where an intermediate regime does not exist.40 Considering GaN molecular - beam epitaxy (MBE) growth phase diagrams such as those...1009 (2007). 44 S. D. Burnham, Improved Understanding and Control of Magnesium-Doped Gallium Nitride by Plasma Assisted Molecular Beam Epitaxy , in...reported using a modified form of molecular beam epitaxy (MBE) called Metal-Modulated Epitaxy (MME).11, 12 The details of this shuttered technique

Ultra-High Aggregate Bandwidth Two-Dimensional Multiple-Wavelength Diode Laser Arrays

DTIC Science & Technology

1994-04-09

surface temperature across the wafer during the growth of the cavity spacer region using the fact that the molecular beam epitaxy (MBE) growth of GaAs...substrate surface temperature across the wafer during the growth of the cavity spacer region. Using the fact that, during an molecular beam epitaxy (MBE...K. Bacher and J.S. Harris, "Periodically Induced Mode Shift in Vertical Cavity Fabry Perot Etalons Grown by Molecular Beam Epitaxy ," to be presented

Single- and two-color infrared focal plane arrays made by MBE in HgCdTe

NASA Astrophysics Data System (ADS)

Zanatta, Jean-Paul; Ferret, P.; Loyer, R.; Petroz, G.; Cremer, S.; Chamonal, Jean-Paul; Bouchut, Philippe; Million, Alain; Destefanis, Gerard L.

2000-12-01

We present here recent developments obtained at LETI infrared laboratory in the field of infrared detectors made in HgCdTe material and using the molecular beam epitaxial growth technique (MBE). We discuss the metallurgical points (growth temperature and flux control) that lead to achieve excellent quality epitaxial layers grown by MBE. We show a run-to-run reproducibility measured on growth run of more than 15 layers. The crystalline quality, surface morphology, and composition uniformity are excellent. The etch pits density (EPD) are in the low 105.cm-2 when HgCdTe grows on a CdZnTe substrate. Transport properties reveal a low n-type carrier concentration in the 1014 to 1015.cm-3 range with a carrier mobility in excess of 105 cm2/V/sec at 77K for epilayers grown with 10 micrometers cutoff wavelength. We describe the performances of several kinds of our HgCdTe- MBE devices: single color MWIR and LWIR detectors on HgCdTe/CdZnTe operating at 77K in respectively (3-5 micrometers ) and (8-12 micrometers ) wavelength range; single color MWIR detectors on HgCdTe grown on germanium heterosubstrate operating at 77K in the (3-5 micrometers ) wavelength range; two color HgCdTe detectors operating within the MWIR (3-5 micrometers ) band.

A new approach to epitaxially grow high-quality GaN films on Si substrates: the combination of MBE and PLD.

PubMed

Wang, Wenliang; Wang, Haiyan; Yang, Weijia; Zhu, Yunnong; Li, Guoqiang

2016-04-22

High-quality GaN epitaxial films have been grown on Si substrates with Al buffer layer by the combination of molecular beam epitaxy (MBE) and pulsed laser deposition (PLD) technologies. MBE is used to grow Al buffer layer at first, and then PLD is deployed to grow GaN epitaxial films on the Al buffer layer. The surface morphology, crystalline quality, and interfacial property of as-grown GaN epitaxial films on Si substrates are studied systematically. The as-grown ~300 nm-thick GaN epitaxial films grown at 850 °C with ~30 nm-thick Al buffer layer on Si substrates show high crystalline quality with the full-width at half-maximum (FWHM) for GaN(0002) and GaN(102) X-ray rocking curves of 0.45° and 0.61°, respectively; very flat GaN surface with the root-mean-square surface roughness of 2.5 nm; as well as the sharp and abrupt GaN/AlGaN/Al/Si hetero-interfaces. Furthermore, the corresponding growth mechanism of GaN epitaxial films grown on Si substrates with Al buffer layer by the combination of MBE and PLD is hence studied in depth. This work provides a novel and simple approach for the epitaxial growth of high-quality GaN epitaxial films on Si substrates.

Effects of a hyperonic many-body force on BΛ values of hypernuclei

NASA Astrophysics Data System (ADS)

Isaka, M.; Yamamoto, Y.; Rijken, Th. A.

2017-04-01

The stiff equation of state (EoS) giving the neutron-star mass of 2 M⊙ suggests the existence of strongly repulsive many-body effects (MBE) not only in nucleon channels but also in hyperonic ones. As a specific model for MBE, the repulsive multi-Pomeron exchange potential (MPP) is added to the two-body interaction together with the phenomenological three-body attraction. For various versions of the Nijmegen interaction models, the MBE parts are determined so as to reproduce the observed data of BΛ. The mass dependence of BΛ values is shown to be reproduced well by adding MBE to the strong MPP repulsion, assuring the stiff EoS of hyperon-mixed neutron-star matter, in which P -state components of the adopted interaction model lead to almost vanishing contributions. The nuclear matter Λ N G -matrix interactions are derived and used in Λ hypernuclei on the basis of the averaged-density approximation (ADA). The BΛ values of hypernuclei with 9 ≤A ≤59 are analyzed in the framework of antisymmetrized molecular dynamics with use of the two types of Λ N G -matrix interactions including strong and weak MPP repulsions. The calculated values of BΛ reproduce the experimental data well within a few hundred keV. The values of BΛ in p states also can be reproduced well, when the ADA is modified to be suitable also for weakly bound Λ states.

Growth studies of CVD-MBE by in-situ diagnostics

NASA Astrophysics Data System (ADS)

Maracas, George N.; Steimle, Timothy C.

1992-10-01

This is the final technical report for the three year DARPA-URI program 'Growth Studies of CVD-MBE by in-situ Diagnostics'. The goals of the program were to develop non-invasive, real time epitaxial growth monitoring techniques and combine them to gain an understanding of processes that occur during MBE growth from gas sources. We have adapted these techniques to a commercially designed gas source MBE system (Vacuum Generators Inc.) to facilitate technology transfer out of the laboratory into industrial environments. The in-situ measurement techniques of spectroscopic ellipsometry (SE) and laser induced fluorescence (LIF) have been successfully implemented to monitor the optical and chemical properties of the growing epitaxial film and the gas phase reactants. The ellipsometer was jointly developed with the J. Woolam Co. and has become a commercial product. The temperature dependence of group 3 and 5 desorption from GaAs and InP has been measured as well as the incident effusion cell fluxes. The temporal evolution of the growth has also been measured both by SE and LIF to show the smoothing of heterojunction surfaces during growth interruption. Complicated microcavity optical device structures have been monitored by ellipsometry in real time to improve device quality. This data has been coupled with the structural information obtained from reflection high energy electron diffraction (RHEED) to understand the growth processes in binary and ternary bulk 3-5 semiconductors and heterojunctions.

Carrier Concentration Control of GaSb/GaInAsSb System

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

Lazzari, J.-L.; Anda, F. de; Nieto, J.

2007-02-22

The residual carrier concentration of GaSb and GaSb-lattice matched Ga1-xInxAsySb1-y alloys (x = 0.12-0.26; y = 0.9x) grown by liquid phase epitaxy (LPE) and molecular beam epitaxy (MBE) was studied as a function of growth temperature, V/III ratio and alloy composition. Typical carrier concentrations p {approx} 2-3x1016 cm-3 were obtained for undoped GaSb grown by MBE at 480 deg. C, by LPE from Ga-rich melt at low temperature (400 deg. C), and by LPE from Sb-rich melts at {approx}600 deg. C. The native acceptor defect responsible of the high p-type residual doping in GaSb is reduced when the indium concentrationmore » is increased, and disappears for indium rich alloys (x = 0.23, 0.26). Tellurium compensation was used for controlled n-type doping in the (0.05-30)x1017 cm-3 range. A maximum of free carrier concentration was 1.5x1018 cm-3 for LPE layers, 2x1018 cm-3 for MBE layers grown at 1.0 {mu}m/h, 3.5x1018 cm-3 for MBE layers grown at 0.2 {mu}m/h. SIMS measurements showed Te concentrations of more than 1020 at/cm3, suggesting the formation of ternary GaSb1-xTex solid solution.« less

Responsivity drop due to conductance modulation in GaN metal-semiconductor-metal Schottky based UV photodetectors on Si(111)

NASA Astrophysics Data System (ADS)

Ravikiran, L.; Radhakrishnan, K.; Dharmarasu, N.; Agrawal, M.; Wang, Zilong; Bruno, Annalisa; Soci, Cesare; Lihuang, Tng; Kian Siong, Ang

2016-09-01

GaN Schottky metal-semiconductor-metal (MSM) UV photodetectors were fabricated on a 600 nm thick GaN layer, grown on 100 mm Si (111) substrate using an ammonia-MBE growth technique. In this report, the effect of device dimensions, applied bias and input power on the linearity of the GaN Schottky-based MSM photodetectors on Si substrate were investigated. Devices with larger interdigitated spacing, ‘S’ of 9.0 μm between the fingers resulted in good linearity and flat responsivity characteristics as a function of input power with an external quantum efficiency (EQE) of ˜33% at an applied bias of 15 V and an input power of 0.8 W m-2. With the decrease of ‘S’ to 3.0 μm, the EQE was found to increase to ˜97%. However, devices showed non linearity and drop in responsivity from flatness at higher input power. Moreover, the position of dropping from flatter responsivity was found to shift to lower powers with increased bias. The drop in the responsivity was attributed to the modulation of conductance in the MSM due to the trapping of electrons at the dislocations, resulting in the formation of depletion regions around them. In devices with lower ‘S’, both the image force reduction and the enhanced collection efficiency increased the photocurrent as well as the charging of the dislocations. This resulted in the increased depletion regions around the dislocations leading to the modulation of conductance and non-linearity.

Accurate Magnetometer/Gyroscope Attitudes Using a Filter with Correlated Sensor Noise

NASA Technical Reports Server (NTRS)

Sedlak, J.; Hashmall, J.

1997-01-01

Magnetometers and gyroscopes have been shown to provide very accurate attitudes for a variety of spacecraft. These results have been obtained, however, using a batch-least-squares algorithm and long periods of data. For use in onboard applications, attitudes are best determined using sequential estimators such as the Kalman filter. When a filter is used to determine attitudes using magnetometer and gyroscope data for input, the resulting accuracy is limited by both the sensor accuracies and errors inherent in the Earth magnetic field model. The Kalman filter accounts for the random component by modeling the magnetometer and gyroscope errors as white noise processes. However, even when these tuning parameters are physically realistic, the rate biases (included in the state vector) have been found to show systematic oscillations. These are attributed to the field model errors. If the gyroscope noise is sufficiently small, the tuned filter 'memory' will be long compared to the orbital period. In this case, the variations in the rate bias induced by field model errors are substantially reduced. Mistuning the filter to have a short memory time leads to strongly oscillating rate biases and increased attitude errors. To reduce the effect of the magnetic field model errors, these errors are estimated within the filter and used to correct the reference model. An exponentially-correlated noise model is used to represent the filter estimate of the systematic error. Results from several test cases using in-flight data from the Compton Gamma Ray Observatory are presented. These tests emphasize magnetometer errors, but the method is generally applicable to any sensor subject to a combination of random and systematic noise.

Procrustes-based geometric morphometrics on MRI images: An example of inter-operator bias in 3D landmarks and its impact on big datasets.

PubMed

Daboul, Amro; Ivanovska, Tatyana; Bülow, Robin; Biffar, Reiner; Cardini, Andrea

2018-01-01

Using 3D anatomical landmarks from adult human head MRIs, we assessed the magnitude of inter-operator differences in Procrustes-based geometric morphometric analyses. An in depth analysis of both absolute and relative error was performed in a subsample of individuals with replicated digitization by three different operators. The effect of inter-operator differences was also explored in a large sample of more than 900 individuals. Although absolute error was not unusual for MRI measurements, including bone landmarks, shape was particularly affected by differences among operators, with up to more than 30% of sample variation accounted for by this type of error. The magnitude of the bias was such that it dominated the main pattern of bone and total (all landmarks included) shape variation, largely surpassing the effect of sex differences between hundreds of men and women. In contrast, however, we found higher reproducibility in soft-tissue nasal landmarks, despite relatively larger errors in estimates of nasal size. Our study exemplifies the assessment of measurement error using geometric morphometrics on landmarks from MRIs and stresses the importance of relating it to total sample variance within the specific methodological framework being used. In summary, precise landmarks may not necessarily imply negligible errors, especially in shape data; indeed, size and shape may be differentially impacted by measurement error and different types of landmarks may have relatively larger or smaller errors. Importantly, and consistently with other recent studies using geometric morphometrics on digital images (which, however, were not specific to MRI data), this study showed that inter-operator biases can be a major source of error in the analysis of large samples, as those that are becoming increasingly common in the 'era of big data'.

Procrustes-based geometric morphometrics on MRI images: An example of inter-operator bias in 3D landmarks and its impact on big datasets

PubMed Central

Ivanovska, Tatyana; Bülow, Robin; Biffar, Reiner; Cardini, Andrea

2018-01-01

Using 3D anatomical landmarks from adult human head MRIs, we assessed the magnitude of inter-operator differences in Procrustes-based geometric morphometric analyses. An in depth analysis of both absolute and relative error was performed in a subsample of individuals with replicated digitization by three different operators. The effect of inter-operator differences was also explored in a large sample of more than 900 individuals. Although absolute error was not unusual for MRI measurements, including bone landmarks, shape was particularly affected by differences among operators, with up to more than 30% of sample variation accounted for by this type of error. The magnitude of the bias was such that it dominated the main pattern of bone and total (all landmarks included) shape variation, largely surpassing the effect of sex differences between hundreds of men and women. In contrast, however, we found higher reproducibility in soft-tissue nasal landmarks, despite relatively larger errors in estimates of nasal size. Our study exemplifies the assessment of measurement error using geometric morphometrics on landmarks from MRIs and stresses the importance of relating it to total sample variance within the specific methodological framework being used. In summary, precise landmarks may not necessarily imply negligible errors, especially in shape data; indeed, size and shape may be differentially impacted by measurement error and different types of landmarks may have relatively larger or smaller errors. Importantly, and consistently with other recent studies using geometric morphometrics on digital images (which, however, were not specific to MRI data), this study showed that inter-operator biases can be a major source of error in the analysis of large samples, as those that are becoming increasingly common in the 'era of big data'. PMID:29787586

Measurement Error and Environmental Epidemiology: A Policy Perspective

PubMed Central

Edwards, Jessie K.; Keil, Alexander P.

2017-01-01

Purpose of review Measurement error threatens public health by producing bias in estimates of the population impact of environmental exposures. Quantitative methods to account for measurement bias can improve public health decision making. Recent findings We summarize traditional and emerging methods to improve inference under a standard perspective, in which the investigator estimates an exposure response function, and a policy perspective, in which the investigator directly estimates population impact of a proposed intervention. Summary Under a policy perspective, the analysis must be sensitive to errors in measurement of factors that modify the effect of exposure on outcome, must consider whether policies operate on the true or measured exposures, and may increasingly need to account for potentially dependent measurement error of two or more exposures affected by the same policy or intervention. Incorporating approaches to account for measurement error into such a policy perspective will increase the impact of environmental epidemiology. PMID:28138941

Laser Doppler, velocimeter system for turbine stator cascade studies and analysis of statistical biasing errors

NASA Technical Reports Server (NTRS)

Seasholtz, R. G.

1977-01-01

A laser Doppler velocimeter (LDV) built for use in the Lewis Research Center's turbine stator cascade facilities is described. The signal processing and self contained data processing are based on a computing counter. A procedure is given for mode matching the laser to the probe volume. An analysis is presented of biasing errors that were observed in turbulent flow when the mean flow was not normal to the fringes.

Error biases in inner and overt speech: evidence from tongue twisters.

PubMed

Corley, Martin; Brocklehurst, Paul H; Moat, H Susannah

2011-01-01

To compare the properties of inner and overt speech, Oppenheim and Dell (2008) counted participants' self-reported speech errors when reciting tongue twisters either overtly or silently and found a bias toward substituting phonemes that resulted in words in both conditions, but a bias toward substituting similar phonemes only when speech was overt. Here, we report 3 experiments revisiting their conclusion that inner speech remains underspecified at the subphonemic level, which they simulated within an activation-feedback framework. In 2 experiments, participants recited tongue twisters that could result in the errorful substitutions of similar or dissimilar phonemes to form real words or nonwords. Both experiments included an auditory masking condition, to gauge the possible impact of loss of auditory feedback on the accuracy of self-reporting of speech errors. In Experiment 1, the stimuli were composed entirely from real words, whereas, in Experiment 2, half the tokens used were nonwords. Although masking did not have any effects, participants were more likely to report substitutions of similar phonemes in both experiments, in inner as well as overt speech. This pattern of results was confirmed in a 3rd experiment using the real-word materials from Oppenheim and Dell (in press). In addition to these findings, a lexical bias effect found in Experiments 1 and 3 disappeared in Experiment 2. Our findings support a view in which plans for inner speech are indeed specified at the feature level, even when there is no intention to articulate words overtly, and in which editing of the plan for errors is implicated. (PsycINFO Database Record (c) 2010 APA, all rights reserved).

High Quality GaAs Growth by MBE on Si Using GeSi Buffers and Prospects for Space Photovoltaics

NASA Technical Reports Server (NTRS)

Carlin, J. A.; Ringel, S. A.; Fitzgerald, E. A.; Bulsara, M.

2005-01-01

III-V solar cells on Si substrates are of interest for space photovoltaics since this would combine high performance space cells with a strong, lightweight and inexpensive substrate. However, the primary obstacles blocking III-V/Si cells from achieving high performance to date have been fundamental materials incompatabilities, namely the 4% lattice mismatch between GaAs and Si, and the large mismatch in thermal expansion coefficient. In this paper, we report on the molecular beam epitaxial (MBE) growth and properties of GaAs layers and single junction GaAs cells on Si wafers which utilize compositionally graded GeSi Intermediate buffers grown by ultra-high vacuum chemical vapor deposition (UHVCVD) to mitigate the large lattice mismatch between GaAs and Si. Ga As cell structures were found to incorporate a threading dislocation density of 0.9-1.5 x 10 (exp 6) per square centimeter, identical to the underlying relaxed Ge cap of the graded buffer, via a combination of transmission electron microscopy, electron beam induced current, and etch pit density measurements. AlGaAs/GaAs double heterostructures wre grown on the GeSi/Si substrates for time-resolved photoluminescence measurements, which revealed a bulk GaAs minority carrier lifetime in excess of 10 ns, the highest lifetime ever reported for GaAs on Si. A series of growth were performed to ass3ss the impact of a GaAs buffer to a thickness of only 0.1 micrometer. Secondary ion mass spectroscopy studies revealed that there is negligible cross diffusion of Ga, As and Ge at he III-V/Ge interface, identical to our earlier findings for GaAs grown on Ge wafers using MBE. This indicates that there is no need for a buffer to "bury" regions of high autodopjing,a nd that either pn or np configuration cells are easily accomodated by these substrates. Preliminary diodes and single junction Al Ga As heteroface cells were grown and fabricated on the Ge/GeSi/Si substrates for the first time. Diodes fabricated on GaAs, Ge and Ge/GeSi/Si substrate show nearly identical I-V characteristics in both forward and reverse bias regions. External quantum efficiencies of AlGaAs/GaAs cell structures grown on Ge/GeSi/Si and Ge substrates demonstrated nearly identical photoresponse, which indicates that high lifetimes, diffusion lengths and efficient minority carrier collection is maintained after complete cell processing.

Effects of upstream-biased third-order space correction terms on multidimensional Crowley advection schemes

NASA Technical Reports Server (NTRS)

Schlesinger, R. E.

1985-01-01

The impact of upstream-biased corrections for third-order spatial truncation error on the stability and phase error of the two-dimensional Crowley combined advective scheme with the cross-space term included is analyzed, putting primary emphasis on phase error reduction. The various versions of the Crowley scheme are formally defined, and their stability and phase error characteristics are intercompared using a linear Fourier component analysis patterned after Fromm (1968, 1969). The performances of the schemes under prototype simulation conditions are tested using time-dependent numerical experiments which advect an initially cone-shaped passive scalar distribution in each of three steady nondivergent flows. One such flow is solid rotation, while the other two are diagonal uniform flow and a strongly deformational vortex.

Analysis of case-only studies accounting for genotyping error.

PubMed

Cheng, K F

2007-03-01

The case-only design provides one approach to assess possible interactions between genetic and environmental factors. It has been shown that if these factors are conditionally independent, then a case-only analysis is not only valid but also very efficient. However, a drawback of the case-only approach is that its conclusions may be biased by genotyping errors. In this paper, our main aim is to propose a method for analysis of case-only studies when these errors occur. We show that the bias can be adjusted through the use of internal validation data, which are obtained by genotyping some sampled individuals twice. Our analysis is based on a simple and yet highly efficient conditional likelihood approach. Simulation studies considered in this paper confirm that the new method has acceptable performance under genotyping errors.

Field evaluation of distance-estimation error during wetland-dependent bird surveys

USGS Publications Warehouse

Nadeau, Christopher P.; Conway, Courtney J.

2012-01-01

Context: The most common methods to estimate detection probability during avian point-count surveys involve recording a distance between the survey point and individual birds detected during the survey period. Accurately measuring or estimating distance is an important assumption of these methods; however, this assumption is rarely tested in the context of aural avian point-count surveys. Aims: We expand on recent bird-simulation studies to document the error associated with estimating distance to calling birds in a wetland ecosystem. Methods: We used two approaches to estimate the error associated with five surveyor's distance estimates between the survey point and calling birds, and to determine the factors that affect a surveyor's ability to estimate distance. Key results: We observed biased and imprecise distance estimates when estimating distance to simulated birds in a point-count scenario (x̄error = -9 m, s.d.error = 47 m) and when estimating distances to real birds during field trials (x̄error = 39 m, s.d.error = 79 m). The amount of bias and precision in distance estimates differed among surveyors; surveyors with more training and experience were less biased and more precise when estimating distance to both real and simulated birds. Three environmental factors were important in explaining the error associated with distance estimates, including the measured distance from the bird to the surveyor, the volume of the call and the species of bird. Surveyors tended to make large overestimations to birds close to the survey point, which is an especially serious error in distance sampling. Conclusions: Our results suggest that distance-estimation error is prevalent, but surveyor training may be the easiest way to reduce distance-estimation error. Implications: The present study has demonstrated how relatively simple field trials can be used to estimate the error associated with distance estimates used to estimate detection probability during avian point-count surveys. Evaluating distance-estimation errors will allow investigators to better evaluate the accuracy of avian density and trend estimates. Moreover, investigators who evaluate distance-estimation errors could employ recently developed models to incorporate distance-estimation error into analyses. We encourage further development of such models, including the inclusion of such models into distance-analysis software.

How Angular Velocity Features and Different Gyroscope Noise Types Interact and Determine Orientation Estimation Accuracy.

PubMed

Pasciuto, Ilaria; Ligorio, Gabriele; Bergamini, Elena; Vannozzi, Giuseppe; Sabatini, Angelo Maria; Cappozzo, Aurelio

2015-09-18

In human movement analysis, 3D body segment orientation can be obtained through the numerical integration of gyroscope signals. These signals, however, are affected by errors that, for the case of micro-electro-mechanical systems, are mainly due to: constant bias, scale factor, white noise, and bias instability. The aim of this study is to assess how the orientation estimation accuracy is affected by each of these disturbances, and whether it is influenced by the angular velocity magnitude and 3D distribution across the gyroscope axes. Reference angular velocity signals, either constant or representative of human walking, were corrupted with each of the four noise types within a simulation framework. The magnitude of the angular velocity affected the error in the orientation estimation due to each noise type, except for the white noise. Additionally, the error caused by the constant bias was also influenced by the angular velocity 3D distribution. As the orientation error depends not only on the noise itself but also on the signal it is applied to, different sensor placements could enhance or mitigate the error due to each disturbance, and special attention must be paid in providing and interpreting measures of accuracy for orientation estimation algorithms.

How Angular Velocity Features and Different Gyroscope Noise Types Interact and Determine Orientation Estimation Accuracy

PubMed Central

Pasciuto, Ilaria; Ligorio, Gabriele; Bergamini, Elena; Vannozzi, Giuseppe; Sabatini, Angelo Maria; Cappozzo, Aurelio

2015-01-01

In human movement analysis, 3D body segment orientation can be obtained through the numerical integration of gyroscope signals. These signals, however, are affected by errors that, for the case of micro-electro-mechanical systems, are mainly due to: constant bias, scale factor, white noise, and bias instability. The aim of this study is to assess how the orientation estimation accuracy is affected by each of these disturbances, and whether it is influenced by the angular velocity magnitude and 3D distribution across the gyroscope axes. Reference angular velocity signals, either constant or representative of human walking, were corrupted with each of the four noise types within a simulation framework. The magnitude of the angular velocity affected the error in the orientation estimation due to each noise type, except for the white noise. Additionally, the error caused by the constant bias was also influenced by the angular velocity 3D distribution. As the orientation error depends not only on the noise itself but also on the signal it is applied to, different sensor placements could enhance or mitigate the error due to each disturbance, and special attention must be paid in providing and interpreting measures of accuracy for orientation estimation algorithms. PMID:26393606

Theoretical and material studies on thin-film electroluminescent devices

NASA Technical Reports Server (NTRS)

Summers, C. J.; Goldman, J. A.; Brennan, K.

1988-01-01

During this report period work was performed on the modeling of High Field Electronic Transport in Bulk ZnS and ZnSe, and also on the surface cleaning of Si for MBE growth. Some MBE growth runs have also been performed in the Varian GEN II System. A brief outline of the experimental work is given. A complete summary will be done at the end of the next reporting period at the completion of the investigation. The theoretical studies are included.

Photodetectors using III-V nitrides

DOEpatents

Moustakas, T.D.; Misra, M.

1997-10-14

A photodetector using a III-V nitride and having predetermined electrical properties is disclosed. The photodetector includes a substrate with interdigitated electrodes formed on its surface. The substrate has a sapphire base layer, a buffer layer formed from a III-V nitride and a single crystal III-V nitride film. The three layers are formed by electron cyclotron resonance microwave plasma-assisted molecular beam epitaxy (ECR-assisted MBE). Use of the ECR-assisted MBE process allows control and predetermination of the electrical properties of the photodetector. 24 figs.

Ultracold Field Gradient Magnetometry and Transport to Study Correlated Topological Phases

DTIC Science & Technology

2016-10-01

glove box. Note that in Fig. 1(b) baking blankets are attached to the MBE, but are removed during normal operation of the system. The manipulator...Note that in Fig. 1(b)  baking   blankets are attached to the MBE, but are removed during normal operation of the system.  The  manipulator arms are

Delta-Doping at Wafer Level for High Throughput, High Yield Fabrication of Silicon Imaging Arrays

NASA Technical Reports Server (NTRS)

Hoenk, Michael E. (Inventor); Nikzad, Shoulch (Inventor); Jones, Todd J. (Inventor); Greer, Frank (Inventor); Carver, Alexander G. (Inventor)

2014-01-01

Systems and methods for producing high quantum efficiency silicon devices. A silicon MBE has a preparation chamber that provides for cleaning silicon surfaces using an oxygen plasma to remove impurities and a gaseous (dry) NH3 + NF3 room temperature oxide removal process that leaves the silicon surface hydrogen terminated. Silicon wafers up to 8 inches in diameter have devices that can be fabricated using the cleaning procedures and MBE processing, including delta doping.

Overcoming Ehrlich-Schwöbel barrier in (1 1 1)A GaAs molecular beam epitaxy

NASA Astrophysics Data System (ADS)

Ritzmann, Julian; Schott, Rüdiger; Gross, Katherine; Reuter, Dirk; Ludwig, Arne; Wieck, Andreas D.

2018-01-01

In this work, we first study the effect of different growth parameters on the molecular beam epitaxy (MBE) growth of GaAs layers on (1 1 1)A oriented substrates. After that we present a method for the MBE growth of atomically smooth layers by sequences of growth and annealing phases. The samples exhibit low surface roughness and good electrical properties shown by atomic force microscopy (AFM), scanning electron microscopy (SEM) and van-der-Pauw Hall measurements.

Adhesion Measurements of Epitaxially Lifted MBE-Grown ZnSe

NASA Astrophysics Data System (ADS)

Mavridi, N.; Zhu, J.; Eldose, N. M.; Prior, K. A.; Moug, R. T.

2018-05-01

ZnSe layers grown by molecular beam epitaxy (MBE), after processing by epitaxial lift-off, have been analyzed using fracture mechanics and thin-film interference to determine their adhesion properties on two different substrates, viz. ZnSe and glass, yielding adhesion energy of 270 ± 60 mJ m-2 and 34 ± 4 mJ m-2, respectively. These values are considerably larger than if only van der Waals forces were present and imply that adhesion arises from chemical bonding.

Enhanced Hole Mobility and Density in GaSb Quantum Wells

DTIC Science & Technology

2013-01-01

Keywords: Molecular beam epitaxy Quantum wells Semiconducting III–V materials Field-effect transistors GaSb a b s t r a c t Modulation-doped quantum wells...QWs) of GaSb clad by AlAsSb were grown by molecular beam epitaxy on InP substrates. By virtue of quantum confinement and compressive strain of the...heterostructures studied here are grown by molecular beam epitaxy (MBE) on semi-insulating (001) InP substrates using a Riber Compact 21T MBE system. A cross

Photodetectors using III-V nitrides

DOEpatents

Moustakas, Theodore D.; Misra, Mira

1997-01-01

A photodetector using a III-V nitride and having predetermined electrical properties is disclosed. The photodetector includes a substrate with interdigitated electrodes formed on its surface. The substrate has a sapphire base layer, a buffer layer formed from a III-V nitride and a single crystal III-V nitride film. The three layers are formed by electron cyclotron resonance microwave plasma-assisted molecular beam epitaxy (ECR-assisted MBE). Use of the ECR-assisted MBE process allows control and predetermination of the electrical properties of the photodetector.

Chip-Scale Controlled Storage All-Optical Memory

DTIC Science & Technology

2007-02-01

half width at half maximum KHZ kilo Hertz KK Kramers-Kronig LH light hole MBE molecular beam epitaxy MHz mega Hertz MZI Mach-Zehnder...waveguide geometry. The sample used in experiments 1 and 2 consists of 15 GaAs (135Å)/Al0.3Ga0.7As(150 Å) QWs grown by molecular beam epitaxy (MBE...We developed the capability to grow GaAs QWs on (110)-oriented substrates using molecular beam epitaxy in a very short amount of time. The very

Enhanced Photoluminescence from Long Wavelength InAs Quantum Dots Embedded in a Graded (In,Ga)As Quantum Well

DTIC Science & Technology

2002-01-01

emitting lasers operating from 1.0 to 1.3 gim with very low threshold currents have been reported [2,3,9]; in addition, vertical - cavity surface - emitting ...grown by solid source molecular beam epitaxy ( MBE ). By modifying Indium composition profile within quantum well (QW) region, it’s found the... lasers ( VCSELs ) have also been successfully demonstrated [4]. There are currently several approaches to grow 1.3 jim (In,Ga)As quantum dots by MBE

High-Temperature Spintronic Devices and Circuits in Absence of Magnetic Field

DTIC Science & Technology

2012-04-23

non-equilibrium Green’s function (NEGF) formalism. • Molecular beam epitaxy (MBE) growth of ferromagnetic metals (Fe, MnAs) and...measured for two diode injection currents in the Faraday geometry. The quantum dot microcavity device was grown by molecular beam epitaxy with a low...channel (10 nm, lxlOl9j Mn-doped) / undoped-AlAs (1 nm) tunnel barrier / undoped-GaAs (0.5 nm) / MnAs (25 nm) were grown by molecular beam epitaxy (MBE

Bias, Confounding, and Interaction: Lions and Tigers, and Bears, Oh My!

PubMed

Vetter, Thomas R; Mascha, Edward J

2017-09-01

Epidemiologists seek to make a valid inference about the causal effect between an exposure and a disease in a specific population, using representative sample data from a specific population. Clinical researchers likewise seek to make a valid inference about the association between an intervention and outcome(s) in a specific population, based upon their randomly collected, representative sample data. Both do so by using the available data about the sample variable to make a valid estimate about its corresponding or underlying, but unknown population parameter. Random error in an experiment can be due to the natural, periodic fluctuation or variation in the accuracy or precision of virtually any data sampling technique or health measurement tool or scale. In a clinical research study, random error can be due to not only innate human variability but also purely chance. Systematic error in an experiment arises from an innate flaw in the data sampling technique or measurement instrument. In the clinical research setting, systematic error is more commonly referred to as systematic bias. The most commonly encountered types of bias in anesthesia, perioperative, critical care, and pain medicine research include recall bias, observational bias (Hawthorne effect), attrition bias, misclassification or informational bias, and selection bias. A confounding variable is a factor associated with both the exposure of interest and the outcome of interest. A confounding variable (confounding factor or confounder) is a variable that correlates (positively or negatively) with both the exposure and outcome. Confounding is typically not an issue in a randomized trial because the randomized groups are sufficiently balanced on all potential confounding variables, both observed and nonobserved. However, confounding can be a major problem with any observational (nonrandomized) study. Ignoring confounding in an observational study will often result in a "distorted" or incorrect estimate of the association or treatment effect. Interaction among variables, also known as effect modification, exists when the effect of 1 explanatory variable on the outcome depends on the particular level or value of another explanatory variable. Bias and confounding are common potential explanations for statistically significant associations between exposure and outcome when the true relationship is noncausal. Understanding interactions is vital to proper interpretation of treatment effects. These complex concepts should be consistently and appropriately considered whenever one is not only designing but also analyzing and interpreting data from a randomized trial or observational study.

The Effects and Side-Effects of Statistics Education: Psychology Students' (Mis-)Conceptions of Probability

ERIC Educational Resources Information Center

Morsanyi, Kinga; Primi, Caterina; Chiesi, Francesca; Handley, Simon

2009-01-01

In three studies we looked at two typical misconceptions of probability: the representativeness heuristic, and the equiprobability bias. The literature on statistics education predicts that some typical errors and biases (e.g., the equiprobability bias) increase with education, whereas others decrease. This is in contrast with reasoning theorists'…

Explanation of Two Anomalous Results in Statistical Mediation Analysis

ERIC Educational Resources Information Center

Fritz, Matthew S.; Taylor, Aaron B.; MacKinnon, David P.

2012-01-01

Previous studies of different methods of testing mediation models have consistently found two anomalous results. The first result is elevated Type I error rates for the bias-corrected and accelerated bias-corrected bootstrap tests not found in nonresampling tests or in resampling tests that did not include a bias correction. This is of special…

Is There a Lexical Bias Effect in Comprehension Monitoring?

ERIC Educational Resources Information Center

Severens, Els; Hartsuiker, Robert J.

2009-01-01

Event-related potentials were used to investigate if there is a lexical bias effect in comprehension monitoring. The lexical bias effect in language production (the tendency of phonological errors to result in existing words rather than nonwords) has been attributed to an internal self-monitoring system, which uses the comprehension system, and…

A Theoretical Foundation for the Study of Inferential Error in Decision-Making Groups.

ERIC Educational Resources Information Center

Gouran, Dennis S.

To provide a theoretical base for investigating the influence of inferential error on group decision making, current literature on both inferential error and decision making is reviewed and applied to the Watergate incident. Although groups tend to make fewer inferential errors because members' inferences are generally not biased in the same…

Meta-regression approximations to reduce publication selection bias.

PubMed

Stanley, T D; Doucouliagos, Hristos

2014-03-01

Publication selection bias is a serious challenge to the integrity of all empirical sciences. We derive meta-regression approximations to reduce this bias. Our approach employs Taylor polynomial approximations to the conditional mean of a truncated distribution. A quadratic approximation without a linear term, precision-effect estimate with standard error (PEESE), is shown to have the smallest bias and mean squared error in most cases and to outperform conventional meta-analysis estimators, often by a great deal. Monte Carlo simulations also demonstrate how a new hybrid estimator that conditionally combines PEESE and the Egger regression intercept can provide a practical solution to publication selection bias. PEESE is easily expanded to accommodate systematic heterogeneity along with complex and differential publication selection bias that is related to moderator variables. By providing an intuitive reason for these approximations, we can also explain why the Egger regression works so well and when it does not. These meta-regression methods are applied to several policy-relevant areas of research including antidepressant effectiveness, the value of a statistical life, the minimum wage, and nicotine replacement therapy. Copyright © 2013 John Wiley & Sons, Ltd.

Bias error reduction using ratios to baseline experiments. Heat transfer case study

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

Chakroun, W.; Taylor, R.P.; Coleman, H.W.

1993-10-01

Employing a set of experiments devoted to examining the effect of surface finish (riblets) on convective heat transfer as an example, this technical note seeks to explore the notion that precision uncertainties in experiments can be reduced by repeated trials and averaging. This scheme for bias error reduction can give considerable advantage when parametric effects are investigated experimentally. When the results of an experiment are presented as a ratio with the baseline results, a large reduction in the overall uncertainty can be achieved when all the bias limits in the variables of the experimental result are fully correlated with thosemore » of the baseline case. 4 refs.« less

Climate model biases in seasonality of continental water storage revealed by satellite gravimetry

USGS Publications Warehouse

Swenson, Sean; Milly, P.C.D.

2006-01-01

Satellite gravimetric observations of monthly changes in continental water storage are compared with outputs from five climate models. All models qualitatively reproduce the global pattern of annual storage amplitude, and the seasonal cycle of global average storage is reproduced well, consistent with earlier studies. However, global average agreements mask systematic model biases in low latitudes. Seasonal extrema of low‐latitude, hemispheric storage generally occur too early in the models, and model‐specific errors in amplitude of the low‐latitude annual variations are substantial. These errors are potentially explicable in terms of neglected or suboptimally parameterized water stores in the land models and precipitation biases in the climate models.

Component Analysis of Errors on PERSIANN Precipitation Estimates over Urmia Lake Basin, IRAN

NASA Astrophysics Data System (ADS)

Ghajarnia, N.; Daneshkar Arasteh, P.; Liaghat, A. M.; Araghinejad, S.

2016-12-01

In this study, PERSIANN daily dataset is evaluated from 2000 to 2011 in 69 pixels over Urmia Lake basin in northwest of Iran. Different analytical approaches and indexes are used to examine PERSIANN precision in detection and estimation of rainfall rate. The residuals are decomposed into Hit, Miss and FA estimation biases while continues decomposition of systematic and random error components are also analyzed seasonally and categorically. New interpretation of estimation accuracy named "reliability on PERSIANN estimations" is introduced while the changing manners of existing categorical/statistical measures and error components are also seasonally analyzed over different rainfall rate categories. This study yields new insights into the nature of PERSIANN errors over Urmia lake basin as a semi-arid region in the middle-east, including the followings: - The analyzed contingency table indexes indicate better detection precision during spring and fall. - A relatively constant level of error is generally observed among different categories. The range of precipitation estimates at different rainfall rate categories is nearly invariant as a sign for the existence of systematic error. - Low level of reliability is observed on PERSIANN estimations at different categories which are mostly associated with high level of FA error. However, it is observed that as the rate of precipitation increase, the ability and precision of PERSIANN in rainfall detection also increases. - The systematic and random error decomposition in this area shows that PERSIANN has more difficulty in modeling the system and pattern of rainfall rather than to have bias due to rainfall uncertainties. The level of systematic error also considerably increases in heavier rainfalls. It is also important to note that PERSIANN error characteristics at each season varies due to the condition and rainfall patterns of that season which shows the necessity of seasonally different approach for the calibration of this product. Overall, we believe that different error component's analysis performed in this study, can substantially help any further local studies for post-calibration and bias reduction of PERSIANN estimations.

A large-area, spatially continuous assessment of land cover map error and its impact on downstream analyses.

PubMed

Estes, Lyndon; Chen, Peng; Debats, Stephanie; Evans, Tom; Ferreira, Stefanus; Kuemmerle, Tobias; Ragazzo, Gabrielle; Sheffield, Justin; Wolf, Adam; Wood, Eric; Caylor, Kelly

2018-01-01

Land cover maps increasingly underlie research into socioeconomic and environmental patterns and processes, including global change. It is known that map errors impact our understanding of these phenomena, but quantifying these impacts is difficult because many areas lack adequate reference data. We used a highly accurate, high-resolution map of South African cropland to assess (1) the magnitude of error in several current generation land cover maps, and (2) how these errors propagate in downstream studies. We first quantified pixel-wise errors in the cropland classes of four widely used land cover maps at resolutions ranging from 1 to 100 km, and then calculated errors in several representative "downstream" (map-based) analyses, including assessments of vegetative carbon stocks, evapotranspiration, crop production, and household food security. We also evaluated maps' spatial accuracy based on how precisely they could be used to locate specific landscape features. We found that cropland maps can have substantial biases and poor accuracy at all resolutions (e.g., at 1 km resolution, up to ∼45% underestimates of cropland (bias) and nearly 50% mean absolute error (MAE, describing accuracy); at 100 km, up to 15% underestimates and nearly 20% MAE). National-scale maps derived from higher-resolution imagery were most accurate, followed by multi-map fusion products. Constraining mapped values to match survey statistics may be effective at minimizing bias (provided the statistics are accurate). Errors in downstream analyses could be substantially amplified or muted, depending on the values ascribed to cropland-adjacent covers (e.g., with forest as adjacent cover, carbon map error was 200%-500% greater than in input cropland maps, but ∼40% less for sparse cover types). The average locational error was 6 km (600%). These findings provide deeper insight into the causes and potential consequences of land cover map error, and suggest several recommendations for land cover map users. © 2017 John Wiley & Sons Ltd.

Quantum Error Correction with Biased Noise

NASA Astrophysics Data System (ADS)

Brooks, Peter

Quantum computing offers powerful new techniques for speeding up the calculation of many classically intractable problems. Quantum algorithms can allow for the efficient simulation of physical systems, with applications to basic research, chemical modeling, and drug discovery; other algorithms have important implications for cryptography and internet security. At the same time, building a quantum computer is a daunting task, requiring the coherent manipulation of systems with many quantum degrees of freedom while preventing environmental noise from interacting too strongly with the system. Fortunately, we know that, under reasonable assumptions, we can use the techniques of quantum error correction and fault tolerance to achieve an arbitrary reduction in the noise level. In this thesis, we look at how additional information about the structure of noise, or "noise bias," can improve or alter the performance of techniques in quantum error correction and fault tolerance. In Chapter 2, we explore the possibility of designing certain quantum gates to be extremely robust with respect to errors in their operation. This naturally leads to structured noise where certain gates can be implemented in a protected manner, allowing the user to focus their protection on the noisier unprotected operations. In Chapter 3, we examine how to tailor error-correcting codes and fault-tolerant quantum circuits in the presence of dephasing biased noise, where dephasing errors are far more common than bit-flip errors. By using an appropriately asymmetric code, we demonstrate the ability to improve the amount of error reduction and decrease the physical resources required for error correction. In Chapter 4, we analyze a variety of protocols for distilling magic states, which enable universal quantum computation, in the presence of faulty Clifford operations. Here again there is a hierarchy of noise levels, with a fixed error rate for faulty gates, and a second rate for errors in the distilled states which decreases as the states are distilled to better quality. The interplay of of these different rates sets limits on the achievable distillation and how quickly states converge to that limit.

Can Family Planning Service Statistics Be Used to Track Population-Level Outcomes?

PubMed

Magnani, Robert J; Ross, John; Williamson, Jessica; Weinberger, Michelle

2018-03-21

The need for annual family planning program tracking data under the Family Planning 2020 (FP2020) initiative has contributed to renewed interest in family planning service statistics as a potential data source for annual estimates of the modern contraceptive prevalence rate (mCPR). We sought to assess (1) how well a set of commonly recorded data elements in routine service statistics systems could, with some fairly simple adjustments, track key population-level outcome indicators, and (2) whether some data elements performed better than others. We used data from 22 countries in Africa and Asia to analyze 3 data elements collected from service statistics: (1) number of contraceptive commodities distributed to clients, (2) number of family planning service visits, and (3) number of current contraceptive users. Data quality was assessed via analysis of mean square errors, using the United Nations Population Division World Contraceptive Use annual mCPR estimates as the "gold standard." We also examined the magnitude of several components of measurement error: (1) variance, (2) level bias, and (3) slope (or trend) bias. Our results indicate modest levels of tracking error for data on commodities to clients (7%) and service visits (10%), and somewhat higher error rates for data on current users (19%). Variance and slope bias were relatively small for all data elements. Level bias was by far the largest contributor to tracking error. Paired comparisons of data elements in countries that collected at least 2 of the 3 data elements indicated a modest advantage of data on commodities to clients. None of the data elements considered was sufficiently accurate to be used to produce reliable stand-alone annual estimates of mCPR. However, the relatively low levels of variance and slope bias indicate that trends calculated from these 3 data elements can be productively used in conjunction with the Family Planning Estimation Tool (FPET) currently used to produce annual mCPR tracking estimates for FP2020. © Magnani et al.

Enhancement of spin-lattice coupling in nanoengineered oxide films and heterostructures by laser MBE

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

Xi, Xiaoxing

The objective of the proposed research is to investigate nanoengineered oxide films and multilayer structures that are predicted to show desirable properties. The main focus of the project is an atomic layer-by-layer laser MBE (ALL-Laser MBE ) technique that is superior to the conventional laser MBE in broadening the conditions for the synthesis of high quality nanoscale oxides and new designer materials. In ALL-Laser MBE, separate oxide targets are used instead of one compound target in the conventional laser MBE. The targets are switched back and forth in front of a UV laser beam as they are alternately ablated. Themore » oxide film is thus constructed one atomic layer at a time. The growth of each atomic layer is monitored and controlled by the reflection high energy electron diffraction (RHEED). The intensity of the diffraction spots increases or decreases depending on the chemistry of each atomic layer as well as the surface roughness. This allows us to determine whether the chemical ratio of the different elements in the films meets the desired value and whether each atomic layer is complete. ALL-Laser MBE is versatile: it works for non-polar film on non-polar substrate, polar film on polar substrate, and polar film on non-polar substrate. (In a polar material, each atomic layer is charged whereas in a non-polar material the atomic layers are charge neutral.) It allows one to push the thermodynamic boundary further in stabilizing new phases than reactive MBE and PLD, two of the most successful techniques for oxide thin films. For example, La 5Ni 4O 13, the Ruddlesden-Popper phase with n = 4, has never been reported in the literature because it needs atomic layer-by-layer growth at high oxygen pressures, not possible with other growth techniques. ALL-Laser MBE makes it possible. We have studied the interfacial 2-dimensional electron gas in the LaAlO 3/SrTiO 3 system, whose mechanism has been a subject of controversy. According to the most prevailing electronic reconstruction mechanism, a positive diverging electric potential is built up in the polar LaAlO 3 film when it is grown on a TiO 2-terminated SrTiO 3 substrate, which is non-polar. This leads to the transfer of half of an electron from the LaAlO 3 film surface to SrTiO 3 when the LaAlO 3 layer is thicker than 4 unit cells, creating a 2D electron gas at the interface with a sheet carrier density of 3.3×10 14/cm 2 for sufficiently thick LaAlO 3. A serious inconsistency with this mechanism is that the carrier densities reported experimentally are invariably lower than the expected value. The most likely reason is that the SrTiO 3 substrate is oxygen difficient due to the low oxygen pressures (< 10 mTorr) during growth, and post-growth annealing in oxygen is often used to remove the oxygen vacancies. People cannot grow the LaAlO 3 film in higher oxygen pressures - it results in insulating samples or 3D island growth. Because we grow the LaAlO 3 film one atomic layer at a time, we were able to grow conducting LaAlO 3/SrTiO 3 interfaces at a high oxygen pressure with ALL-Laser MBE, as high as 37 mTorr. The high oxygen pressure helps to prevent the possible oxygen reduction in SrTiO 3, ensure that the LaAlO 3 films are sufficiently oxygenated. Measurements of x-ray linear dichroism (XLD) and x-ray magnetic circular dichroism (XMCD) both show that the spectra of our films are similar to those of well oxygenated samples. In the LaAlO 3/SrTiO 3 interfaces grown by ALL-Laser MBE at 37 mTorr oxygen pressure, a quantitative agreement between our experimental result and the theoretical prediction was observed, which provides a strong support to the electronic reconstruction mechanism. The key differences between our result and the previous reports are the high oxygen pressure during the film growth and the high film crystallinity. The high oxygen pressure suppresses the likelihood of oxygen vacancies in SrTiO 3. Well oxygenated samples produced during film growth can avoid possible defects when sufficient oxygen is provided only after the growth by annealing. Using ALL-Laser MBE, we also synthesized high-quality singlec-rystalline CaMnO 3 films. The systematic increase of the oxygen vacancy content in CaMnO 3 as a function of applied in-plane strain is observed and confirmed experimentally using high-resolution soft x-ray XAS and hard x-ray photoemission spectroscopy (HAXPES). The relevant defect states in the densities of states are identified and the vacancy content in the films quantified using the combination of first-principles theory and core-hole multiplet calculations with holistic fitting. The strain-induced oxygen-vacancy formation and ordering are a promising avenue for designing and controlling new functionalities in complex transition-metal oxides.« less

Estimation and correction of different flavors of surface observation biases in ensemble Kalman filter

NASA Astrophysics Data System (ADS)

Lorente-Plazas, Raquel; Hacker, Josua P.; Collins, Nancy; Lee, Jared A.

2017-04-01

The impact of assimilating surface observations has been shown in several publications, for improving weather prediction inside of the boundary layer as well as the flow aloft. However, the assimilation of surface observations is often far from optimal due to the presence of both model and observation biases. The sources of these biases can be diverse: an instrumental offset, errors associated to the comparison of point-based observations and grid-cell average, etc. To overcome this challenge, a method was developed using the ensemble Kalman filter. The approach consists on representing each observation bias as a parameter. These bias parameters are added to the forward operator and they extend the state vector. As opposed to the observation bias estimation approaches most common in operational systems (e.g. for satellite radiances), the state vector and parameters are simultaneously updated by applying the Kalman filter equations to the augmented state. The method to estimate and correct the observation bias is evaluated using observing system simulation experiments (OSSEs) with the Weather Research and Forecasting (WRF) model. OSSEs are constructed for the conventional observation network including radiosondes, aircraft observations, atmospheric motion vectors, and surface observations. Three different kinds of biases are added to 2-meter temperature for synthetic METARs. From the simplest to more sophisticated, imposed biases are: (1) a spatially invariant bias, (2) a spatially varying bias proportional to topographic height differences between the model and the observations, and (3) bias that is proportional to the temperature. The target region characterized by complex terrain is the western U.S. on a domain with 30-km grid spacing. Observations are assimilated every 3 hours using an 80-member ensemble during September 2012. Results demonstrate that the approach is able to estimate and correct the bias when it is spatially invariant (experiment 1). More complex bias structure in experiments (2) and (3) are more difficult to estimate, but still possible. Estimated the parameter in experiments with unbiased observations results in spatial and temporal parameter variability about zero, and establishes a threshold on the accuracy of the parameter in further experiments. When the observations are biased, the mean parameter value is close to the true bias, but temporal and spatial variability in the parameter estimates is similar to the parameters used when estimating a zero bias in the observations. The distributions are related to other errors in the forecasts, indicating that the parameters are absorbing some of the forecast error from other sources. In this presentation we elucidate the reasons for the resulting parameter estimates, and their variability.

Comparison of Statistical Approaches for Dealing With Immortal Time Bias in Drug Effectiveness Studies

PubMed Central

Karim, Mohammad Ehsanul; Gustafson, Paul; Petkau, John; Tremlett, Helen

2016-01-01

In time-to-event analyses of observational studies of drug effectiveness, incorrect handling of the period between cohort entry and first treatment exposure during follow-up may result in immortal time bias. This bias can be eliminated by acknowledging a change in treatment exposure status with time-dependent analyses, such as fitting a time-dependent Cox model. The prescription time-distribution matching (PTDM) method has been proposed as a simpler approach for controlling immortal time bias. Using simulation studies and theoretical quantification of bias, we compared the performance of the PTDM approach with that of the time-dependent Cox model in the presence of immortal time. Both assessments revealed that the PTDM approach did not adequately address immortal time bias. Based on our simulation results, another recently proposed observational data analysis technique, the sequential Cox approach, was found to be more useful than the PTDM approach (Cox: bias = −0.002, mean squared error = 0.025; PTDM: bias = −1.411, mean squared error = 2.011). We applied these approaches to investigate the association of β-interferon treatment with delaying disability progression in a multiple sclerosis cohort in British Columbia, Canada (Long-Term Benefits and Adverse Effects of Beta-Interferon for Multiple Sclerosis (BeAMS) Study, 1995–2008). PMID:27455963

A variational regularization of Abel transform for GPS radio occultation

NASA Astrophysics Data System (ADS)

Wee, Tae-Kwon

2018-04-01

In the Global Positioning System (GPS) radio occultation (RO) technique, the inverse Abel transform of measured bending angle (Abel inversion, hereafter AI) is the standard means of deriving the refractivity. While concise and straightforward to apply, the AI accumulates and propagates the measurement error downward. The measurement error propagation is detrimental to the refractivity in lower altitudes. In particular, it builds up negative refractivity bias in the tropical lower troposphere. An alternative to AI is the numerical inversion of the forward Abel transform, which does not incur the integration of error-possessing measurement and thus precludes the error propagation. The variational regularization (VR) proposed in this study approximates the inversion of the forward Abel transform by an optimization problem in which the regularized solution describes the measurement as closely as possible within the measurement's considered accuracy. The optimization problem is then solved iteratively by means of the adjoint technique. VR is formulated with error covariance matrices, which permit a rigorous incorporation of prior information on measurement error characteristics and the solution's desired behavior into the regularization. VR holds the control variable in the measurement space to take advantage of the posterior height determination and to negate the measurement error due to the mismodeling of the refractional radius. The advantages of having the solution and the measurement in the same space are elaborated using a purposely corrupted synthetic sounding with a known true solution. The competency of VR relative to AI is validated with a large number of actual RO soundings. The comparison to nearby radiosonde observations shows that VR attains considerably smaller random and systematic errors compared to AI. A noteworthy finding is that in the heights and areas that the measurement bias is supposedly small, VR follows AI very closely in the mean refractivity deserting the first guess. In the lowest few kilometers that AI produces large negative refractivity bias, VR reduces the refractivity bias substantially with the aid of the background, which in this study is the operational forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF). It is concluded based on the results presented in this study that VR offers a definite advantage over AI in the quality of refractivity.

Nonlinear bias compensation of ZiYuan-3 satellite imagery with cubic splines

NASA Astrophysics Data System (ADS)

Cao, Jinshan; Fu, Jianhong; Yuan, Xiuxiao; Gong, Jianya

2017-11-01

Like many high-resolution satellites such as the ALOS, MOMS-2P, QuickBird, and ZiYuan1-02C satellites, the ZiYuan-3 satellite suffers from different levels of attitude oscillations. As a result of such oscillations, the rational polynomial coefficients (RPCs) obtained using a terrain-independent scenario often have nonlinear biases. In the sensor orientation of ZiYuan-3 imagery based on a rational function model (RFM), these nonlinear biases cannot be effectively compensated by an affine transformation. The sensor orientation accuracy is thereby worse than expected. In order to eliminate the influence of attitude oscillations on the RFM-based sensor orientation, a feasible nonlinear bias compensation approach for ZiYuan-3 imagery with cubic splines is proposed. In this approach, no actual ground control points (GCPs) are required to determine the cubic splines. First, the RPCs are calculated using a three-dimensional virtual control grid generated based on a physical sensor model. Second, one cubic spline is used to model the residual errors of the virtual control points in the row direction and another cubic spline is used to model the residual errors in the column direction. Then, the estimated cubic splines are used to compensate the nonlinear biases in the RPCs. Finally, the affine transformation parameters are used to compensate the residual biases in the RPCs. Three ZiYuan-3 images were tested. The experimental results showed that before the nonlinear bias compensation, the residual errors of the independent check points were nonlinearly biased. Even if the number of GCPs used to determine the affine transformation parameters was increased from 4 to 16, these nonlinear biases could not be effectively compensated. After the nonlinear bias compensation with the estimated cubic splines, the influence of the attitude oscillations could be eliminated. The RFM-based sensor orientation accuracies of the three ZiYuan-3 images reached 0.981 pixels, 0.890 pixels, and 1.093 pixels, which were respectively 42.1%, 48.3%, and 54.8% better than those achieved before the nonlinear bias compensation.

GaN nanowires with pentagon shape cross-section by ammonia-source molecular beam epitaxy

DOE PAGES

Lin, Yong; Leung, Benjamin; Li, Qiming; ...

2015-07-14

In this study, ammonia-based molecular beam epitaxy (NH 3-MBE) was used to grow catalyst-assisted GaN nanowires on (11¯02) r-plane sapphire substrates. Dislocation free [112¯0] oriented nanowires are formed with pentagon shape cross-section, instead of the usual triangular shape facet configuration. Specifically, the cross-section is the result of the additional two nonpolar {101¯0} side facets, which appear due to a decrease in relative growth rate of the {101¯0} facets to the {101¯1} and {101¯1} facets under the growth regime in NH 3-MBE. Compared to GaN nanowires grown by Ni-catalyzed metal–organic chemical vapor deposition, the NH 3-MBE grown GaN nanowires show moremore » than an order of magnitude increase in band-edge to yellow luminescence intensity ratio, as measured by cathodoluminescence, indicating improved microstructural and optical properties.« less

MBE growth of Topological Isolators based on strained semi-metallic HgCdTe layers

NASA Astrophysics Data System (ADS)

Grendysa, J.; Tomaka, G.; Sliz, P.; Becker, C. R.; Trzyna, M.; Wojnarowska-Nowak, R.; Bobko, E.; Sheregii, E. M.

2017-12-01

Particularities of Molecular Beam Epitaxial (MBE) technology for the growth of Topological Insulators (TI) based on the semi-metal Hg1-xCdx Te are presented. A series of strained layers grown on GaAs substrates with a composition close to the 3D Dirac point were studied. The composition of the layers was verified by means of the position of the E1 maximum in optical reflectivity in the visible region. The surface morphology was determined via atomic force and electron microscopy. Magneto-transport measurements show quantized Hall resistance curves and Shubnikov de Hass oscillations (up to 50 K). It has been demonstrated that a well-developed MBE technology enables one to grow strained Hg1-xCdx Te layers on GaAs/CdTe substrates with a well-defined composition near the 3D Dirac point and consequently allows one to produce a 3D topological Dirac semimetal - 3D analogy of graphene - for future applications.

Uniformity of dc and rf performance of MBE-grown AlGaN/GaN HEMTS on HVPE-grown buffers

NASA Astrophysics Data System (ADS)

Gillespie, J. K.; Fitch, R. C.; Moser, N.; Jenkins, T.; Sewell, J.; Via, D.; Crespo, A.; Dabiran, A. M.; Chow, P. P.; Osinsky, A.; Mastro, M. A.; Tsvetkov, D.; Soukhoveev, V.; Usikov, A.; Dmitriev, V.; Luo, B.; Pearton, S. J.; Ren, F.

2003-10-01

AlGaN/GaN high electron mobility transistors (HEMTs) were grown by molecular beam epitaxy (MBE) on 2 in. diameter GaN buffer layers grown by hydride vapor epitaxy (HVPE) on sapphire substrates. HEMTs with 1 μm gate length displayed excellent dc and rf performance uniformity with up to 258 separate devices measured for each parameter. The drain-source saturation current was 561 mA with a standard deviation of 1.9% over the 2 in. diameter, with a corresponding transconductance of 118 ± 3.9 mS/mm. The threshold voltage was -5.3 ± 0.07 V. The rf performance uniformity was equally good, with an fT of 8.6 ± 0.8 GHz and fmax of 12.8 ± 2.5 GHz. The results show the excellent uniformity of the MBE technique for producing AlGaN/GaN HEMTs and also the ability of HVPE to provide high quality buffers at low cost.

Use of Two-Way Time Transfer Measurements to Improve Geostationary Satellite Navigation

DTIC Science & Technology

2007-03-01

lo ck E rro r ( m et er s...clock measurement blackouts. 2 4 6 8 10 12 14 16 18 20 22 -100 -50 0 50 100 GEO Clock Bias Error Time (hours) C lo ck E rro r ( m et er s...20 GEO Clock Bias Error Time (hours) C lo ck E rro r ( m et er s) Filter-Computed Covariance 0 5 10 15 20 -20 -15 -10 -5 0 5 10 15 20 GEO

Verification of sex from harvested sea otters using DNA testing

USGS Publications Warehouse

Scribner, Kim T.; Green, Ben A.; Gorbics, Carol; Bodkin, James L.

2005-01-01

We used molecular genetic methods to determine the sex of 138 sea otters (Enhydra lutris) harvested from 3 regions of Alaska from 1994 to 1997, to assess the accuracy of post‐harvest field‐sexing. We also tested each of a series of factors associated with errors in field‐sexing of sea otters, including male or female bias, age‐class bias, regional bias, and bias associated with hunt characteristics. Blind control results indicated that sex was determined with 100% accuracy using polymerase chain reaction (PCR) amplification using primers that co‐amplify the zinc finger‐Y‐X gene, located on both the mammalian Y‐ and X‐chromosomes, and Testes Determining Factor (TDF), located on the mammalian Y‐chromosome. DNA‐based sexing revealed that 12.3% of the harvested sea otters were incorrectly sexed in the field, with most errors (13 of 17) occurring as males incorrectly reported as females. Thus, female harvest was overestimated. Using logistic regression analysis, we detected no statistical association of incorrect determination of sex in the field with age class, hunt region, or hunt type. The error in field‐sexing appears to be random, at least with respect to the variables evaluated in this study.

Uses and biases of volunteer water quality data

USGS Publications Warehouse

Loperfido, J.V.; Beyer, P.; Just, C.L.; Schnoor, J.L.

2010-01-01

State water quality monitoring has been augmented by volunteer monitoring programs throughout the United States. Although a significant effort has been put forth by volunteers, questions remain as to whether volunteer data are accurate and can be used by regulators. In this study, typical volunteer water quality measurements from laboratory and environmental samples in Iowa were analyzed for error and bias. Volunteer measurements of nitrate+nitrite were significantly lower (about 2-fold) than concentrations determined via standard methods in both laboratory-prepared and environmental samples. Total reactive phosphorus concentrations analyzed by volunteers were similar to measurements determined via standard methods in laboratory-prepared samples and environmental samples, but were statistically lower than the actual concentration in four of the five laboratory-prepared samples. Volunteer water quality measurements were successful in identifying and classifying most of the waters which violate United States Environmental Protection Agency recommended water quality criteria for total nitrogen (66%) and for total phosphorus (52%) with the accuracy improving when accounting for error and biases in the volunteer data. An understanding of the error and bias in volunteer water quality measurements can allow regulators to incorporate volunteer water quality data into total maximum daily load planning or state water quality reporting. ?? 2010 American Chemical Society.

Measurement effects of seasonal and monthly variability on pedometer-determined data.

PubMed

Kang, Minsoo; Bassett, David R; Barreira, Tiago V; Tudor-Locke, Catrine; Ainsworth, Barbara E

2012-03-01

The seasonal and monthly variability of pedometer-determined physical activity and its effects on accurate measurement have not been examined. The purpose of the study was to reduce measurement error in step-count data by controlling a) the length of the measurement period and b) the season or month of the year in which sampling was conducted. Twenty-three middle-aged adults were instructed to wear a Yamax SW-200 pedometer over 365 consecutive days. The step-count measurement periods of various lengths (eg, 2, 3, 4, 5, 6, 7 days, etc.) were randomly selected 10 times for each season and month. To determine accurate estimates of yearly step-count measurement, mean absolute percentage error (MAPE) and bias were calculated. The year-round average was considered as a criterion measure. A smaller MAPE and bias represent a better estimate. Differences in MAPE and bias among seasons were trivial; however, they varied among different months. The months in which seasonal changes occur presented the highest MAPE and bias. Targeting the data collection during certain months (eg, May) may reduce pedometer measurement error and provide more accurate estimates of year-round averages.

Suitability of Smartphone Inertial Sensors for Real-Time Biofeedback Applications.

PubMed

Kos, Anton; Tomažič, Sašo; Umek, Anton

2016-02-27

This article studies the suitability of smartphones with built-in inertial sensors for biofeedback applications. Biofeedback systems use various sensors to measure body functions and parameters. These sensor data are analyzed, and the results are communicated back to the user, who then tries to act on the feedback signals. Smartphone inertial sensors can be used to capture body movements in biomechanical biofeedback systems. These sensors exhibit various inaccuracies that induce significant angular and positional errors. We studied deterministic and random errors of smartphone accelerometers and gyroscopes, primarily focusing on their biases. Based on extensive measurements, we determined accelerometer and gyroscope noise models and bias variation ranges. Then, we compiled a table of predicted positional and angular errors under various biofeedback system operation conditions. We suggest several bias compensation options that are suitable for various examples of use in real-time biofeedback applications. Measurements within the developed experimental biofeedback application show that under certain conditions, even uncompensated sensors can be used for real-time biofeedback. For general use, especially for more demanding biofeedback applications, sensor biases should be compensated. We are convinced that real-time biofeedback systems based on smartphone inertial sensors are applicable to many similar examples in sports, healthcare, and other areas.

Suitability of Smartphone Inertial Sensors for Real-Time Biofeedback Applications

PubMed Central

Kos, Anton; Tomažič, Sašo; Umek, Anton

2016-01-01

This article studies the suitability of smartphones with built-in inertial sensors for biofeedback applications. Biofeedback systems use various sensors to measure body functions and parameters. These sensor data are analyzed, and the results are communicated back to the user, who then tries to act on the feedback signals. Smartphone inertial sensors can be used to capture body movements in biomechanical biofeedback systems. These sensors exhibit various inaccuracies that induce significant angular and positional errors. We studied deterministic and random errors of smartphone accelerometers and gyroscopes, primarily focusing on their biases. Based on extensive measurements, we determined accelerometer and gyroscope noise models and bias variation ranges. Then, we compiled a table of predicted positional and angular errors under various biofeedback system operation conditions. We suggest several bias compensation options that are suitable for various examples of use in real-time biofeedback applications. Measurements within the developed experimental biofeedback application show that under certain conditions, even uncompensated sensors can be used for real-time biofeedback. For general use, especially for more demanding biofeedback applications, sensor biases should be compensated. We are convinced that real-time biofeedback systems based on smartphone inertial sensors are applicable to many similar examples in sports, healthcare, and other areas. PMID:26927125

Measurement error in epidemiologic studies of air pollution based on land-use regression models.

PubMed

Basagaña, Xavier; Aguilera, Inmaculada; Rivera, Marcela; Agis, David; Foraster, Maria; Marrugat, Jaume; Elosua, Roberto; Künzli, Nino

2013-10-15

Land-use regression (LUR) models are increasingly used to estimate air pollution exposure in epidemiologic studies. These models use air pollution measurements taken at a small set of locations and modeling based on geographical covariates for which data are available at all study participant locations. The process of LUR model development commonly includes a variable selection procedure. When LUR model predictions are used as explanatory variables in a model for a health outcome, measurement error can lead to bias of the regression coefficients and to inflation of their variance. In previous studies dealing with spatial predictions of air pollution, bias was shown to be small while most of the effect of measurement error was on the variance. In this study, we show that in realistic cases where LUR models are applied to health data, bias in health-effect estimates can be substantial. This bias depends on the number of air pollution measurement sites, the number of available predictors for model selection, and the amount of explainable variability in the true exposure. These results should be taken into account when interpreting health effects from studies that used LUR models.

Regional Climate Simulations over North America: Interaction of Local Processes with Improved Large-Scale Flow.

NASA Astrophysics Data System (ADS)

Miguez-Macho, Gonzalo; Stenchikov, Georgiy L.; Robock, Alan

2005-04-01

The reasons for biases in regional climate simulations were investigated in an attempt to discern whether they arise from deficiencies in the model parameterizations or are due to dynamical problems. Using the Regional Atmospheric Modeling System (RAMS) forced by the National Centers for Environmental Prediction-National Center for Atmospheric Research reanalysis, the detailed climate over North America at 50-km resolution for June 2000 was simulated. First, the RAMS equations were modified to make them applicable to a large region, and its turbulence parameterization was corrected. The initial simulations showed large biases in the location of precipitation patterns and surface air temperatures. By implementing higher-resolution soil data, soil moisture and soil temperature initialization, and corrections to the Kain-Fritch convective scheme, the temperature biases and precipitation amount errors could be removed, but the precipitation location errors remained. The precipitation location biases could only be improved by implementing spectral nudging of the large-scale (wavelength of 2500 km) dynamics in RAMS. This corrected for circulation errors produced by interactions and reflection of the internal domain dynamics with the lateral boundaries where the model was forced by the reanalysis.

Space based optical staring sensor LOS determination and calibration using GCPs observation

NASA Astrophysics Data System (ADS)

Chen, Jun; An, Wei; Deng, Xinpu; Yang, Jungang; Sha, Zhichao

2016-10-01

Line of sight (LOS) attitude determination and calibration is the key prerequisite of tracking and location of targets in space based infrared (IR) surveillance systems (SBIRS) and the LOS determination and calibration of staring sensor is one of the difficulties. This paper provides a novel methodology for removing staring sensor bias through the use of Ground Control Points (GCPs) detected in the background field of the sensor. Based on researching the imaging model and characteristics of the staring sensor of SBIRS geostationary earth orbit part (GEO), the real time LOS attitude determination and calibration algorithm using landmark control point is proposed. The influential factors (including the thermal distortions error, assemble error, and so on) of staring sensor LOS attitude error are equivalent to bias angle of LOS attitude. By establishing the observation equation of GCPs and the state transition equation of bias angle, and using an extend Kalman filter (EKF), the real time estimation of bias angle and the high precision sensor LOS attitude determination and calibration are achieved. The simulation results show that the precision and timeliness of the proposed algorithm meet the request of target tracking and location process in space based infrared surveillance system.

Model studies of the beam-filling error for rain-rate retrieval with microwave radiometers

NASA Technical Reports Server (NTRS)

Ha, Eunho; North, Gerald R.

1995-01-01

Low-frequency (less than 20 GHz) single-channel microwave retrievals of rain rate encounter the problem of beam-filling error. This error stems from the fact that the relationship between microwave brightness temperature and rain rate is nonlinear, coupled with the fact that the field of view is large or comparable to important scales of variability of the rain field. This means that one may not simply insert the area average of the brightness temperature into the formula for rain rate without incurring both bias and random error. The statistical heterogeneity of the rain-rate field in the footprint of the instrument is key to determining the nature of these errors. This paper makes use of a series of random rain-rate fields to study the size of the bias and random error associated with beam filling. A number of examples are analyzed in detail: the binomially distributed field, the gamma, the Gaussian, the mixed gamma, the lognormal, and the mixed lognormal ('mixed' here means there is a finite probability of no rain rate at a point of space-time). Of particular interest are the applicability of a simple error formula due to Chiu and collaborators and a formula that might hold in the large field of view limit. It is found that the simple formula holds for Gaussian rain-rate fields but begins to fail for highly skewed fields such as the mixed lognormal. While not conclusively demonstrated here, it is suggested that the notionof climatologically adjusting the retrievals to remove the beam-filling bias is a reasonable proposition.

Per-pixel bias-variance decomposition of continuous errors in data-driven geospatial modeling: A case study in environmental remote sensing

NASA Astrophysics Data System (ADS)

Gao, Jing; Burt, James E.

2017-12-01

This study investigates the usefulness of a per-pixel bias-variance error decomposition (BVD) for understanding and improving spatially-explicit data-driven models of continuous variables in environmental remote sensing (ERS). BVD is a model evaluation method originated from machine learning and have not been examined for ERS applications. Demonstrated with a showcase regression tree model mapping land imperviousness (0-100%) using Landsat images, our results showed that BVD can reveal sources of estimation errors, map how these sources vary across space, reveal the effects of various model characteristics on estimation accuracy, and enable in-depth comparison of different error metrics. Specifically, BVD bias maps can help analysts identify and delineate model spatial non-stationarity; BVD variance maps can indicate potential effects of ensemble methods (e.g. bagging), and inform efficient training sample allocation - training samples should capture the full complexity of the modeled process, and more samples should be allocated to regions with more complex underlying processes rather than regions covering larger areas. Through examining the relationships between model characteristics and their effects on estimation accuracy revealed by BVD for both absolute and squared errors (i.e. error is the absolute or the squared value of the difference between observation and estimate), we found that the two error metrics embody different diagnostic emphases, can lead to different conclusions about the same model, and may suggest different solutions for performance improvement. We emphasize BVD's strength in revealing the connection between model characteristics and estimation accuracy, as understanding this relationship empowers analysts to effectively steer performance through model adjustments.

Validation of the ASTER Global Digital Elevation Model Version 2 over the conterminous United States

USGS Publications Warehouse

Gesch, Dean B.; Oimoen, Michael J.; Zhang, Zheng; Meyer, David J.; Danielson, Jeffrey J.

2012-01-01

The ASTER Global Digital Elevation Model Version 2 (GDEM v2) was evaluated over the conterminous United States in a manner similar to the validation conducted for the original GDEM Version 1 (v1) in 2009. The absolute vertical accuracy of GDEM v2 was calculated by comparison with more than 18,000 independent reference geodetic ground control points from the National Geodetic Survey. The root mean square error (RMSE) measured for GDEM v2 is 8.68 meters. This compares with the RMSE of 9.34 meters for GDEM v1. Another important descriptor of vertical accuracy is the mean error, or bias, which indicates if a DEM has an overall vertical offset from true ground level. The GDEM v2 mean error of -0.20 meters is a significant improvement over the GDEM v1 mean error of -3.69 meters. The absolute vertical accuracy assessment results, both mean error and RMSE, were segmented by land cover to examine the effects of cover types on measured errors. The GDEM v2 mean errors by land cover class verify that the presence of aboveground features (tree canopies and built structures) cause a positive elevation bias, as would be expected for an imaging system like ASTER. In open ground classes (little or no vegetation with significant aboveground height), GDEM v2 exhibits a negative bias on the order of 1 meter. GDEM v2 was also evaluated by differencing with the Shuttle Radar Topography Mission (SRTM) dataset. In many forested areas, GDEM v2 has elevations that are higher in the canopy than SRTM.

How does bias correction of RCM precipitation affect modelled runoff?

NASA Astrophysics Data System (ADS)

Teng, J.; Potter, N. J.; Chiew, F. H. S.; Zhang, L.; Vaze, J.; Evans, J. P.

2014-09-01

Many studies bias correct daily precipitation from climate models to match the observed precipitation statistics, and the bias corrected data are then used for various modelling applications. This paper presents a review of recent methods used to bias correct precipitation from regional climate models (RCMs). The paper then assesses four bias correction methods applied to the weather research and forecasting (WRF) model simulated precipitation, and the follow-on impact on modelled runoff for eight catchments in southeast Australia. Overall, the best results are produced by either quantile mapping or a newly proposed two-state gamma distribution mapping method. However, the difference between the tested methods is small in the modelling experiments here (and as reported in the literature), mainly because of the substantial corrections required and inconsistent errors over time (non-stationarity). The errors remaining in bias corrected precipitation are typically amplified in modelled runoff. The tested methods cannot overcome limitation of RCM in simulating precipitation sequence, which affects runoff generation. Results further show that whereas bias correction does not seem to alter change signals in precipitation means, it can introduce additional uncertainty to change signals in high precipitation amounts and, consequently, in runoff. Future climate change impact studies need to take this into account when deciding whether to use raw or bias corrected RCM results. Nevertheless, RCMs will continue to improve and will become increasingly useful for hydrological applications as the bias in RCM simulations reduces.

Decision Making In A High-Tech World: Automation Bias and Countermeasures

NASA Technical Reports Server (NTRS)

Mosier, Kathleen L.; Skitka, Linda J.; Burdick, Mark R.; Heers, Susan T.; Rosekind, Mark R. (Technical Monitor)

1996-01-01

Automated decision aids and decision support systems have become essential tools in many high-tech environments. In aviation, for example, flight management systems computers not only fly the aircraft, but also calculate fuel efficient paths, detect and diagnose system malfunctions and abnormalities, and recommend or carry out decisions. Air Traffic Controllers will soon be utilizing decision support tools to help them predict and detect potential conflicts and to generate clearances. Other fields as disparate as nuclear power plants and medical diagnostics are similarly becoming more and more automated. Ideally, the combination of human decision maker and automated decision aid should result in a high-performing team, maximizing the advantages of additional cognitive and observational power in the decision-making process. In reality, however, the presence of these aids often short-circuits the way that even very experienced decision makers have traditionally handled tasks and made decisions, and introduces opportunities for new decision heuristics and biases. Results of recent research investigating the use of automated aids have indicated the presence of automation bias, that is, errors made when decision makers rely on automated cues as a heuristic replacement for vigilant information seeking and processing. Automation commission errors, i.e., errors made when decision makers inappropriately follow an automated directive, or automation omission errors, i.e., errors made when humans fail to take action or notice a problem because an automated aid fails to inform them, can result from this tendency. Evidence of the tendency to make automation-related omission and commission errors has been found in pilot self reports, in studies using pilots in flight simulations, and in non-flight decision making contexts with student samples. Considerable research has found that increasing social accountability can successfully ameliorate a broad array of cognitive biases and resultant errors. To what extent these effects generalize to performance situations is not yet empirically established. The two studies to be presented represent concurrent efforts, with student and professional pilot samples, to determine the effects of accountability pressures on automation bias and on the verification of the accurate functioning of automated aids. Students (Experiment 1) and commercial pilots (Experiment 2) performed simulated flight tasks using automated aids. In both studies, participants who perceived themselves as accountable for their strategies of interaction with the automation were significantly more likely to verify its correctness, and committed significantly fewer automation-related errors than those who did not report this perception.

Growth and Structure of High-Temperature Superconducting Thin Films

NASA Astrophysics Data System (ADS)

Achutharaman, Vedapuram Sankar

High temperature superconducting thin films with atomic scale perfection are required for technological applications and scientific studies on the mechanism of superconductivity. Ozone assisted molecular beam epitaxy (MBE) has been shown to produce in-situ superconducting thin films. To obtain a well-controlled and reproducible process, some components such as the substrate heater and the substrate holder have to be designed to be compatible with high oxygen partial pressures. Also, to ensure precise stoichiometry and precipitate-free films, evaporation sources and temperature controllers have to be designed for better temperature stability. The investigation of the MBE process and the thin films grown by MBE are required to obtain a better understanding of the growth parameters such as the composition of the film, substrate surface structure, substrate temperature and ozone partial pressure. This can be obtained by dynamically monitoring the growth process by in-situ characterization techniques such as reflection high energy electron diffraction (RHEED). Intensity oscillations of the specular RHEED beam have been observed during the growth of RBa_2Cu_3 O_7 (R = Y,Dy) films on SrTiO _3. A model for the origin of these RHEED intensity oscillations will be proposed from extensive RHEED intensity studies. A mechanism for growth of these oxides by physical vapor deposition techniques such as MBE and pulsed laser deposition will also be developed. To verify both the models, the growth of the superconductors will be simulated by the Monte Carlo method and compared with experimental RHEED observations.

Thermoelectric Properties of Epitaxial β-FeSi2 Thin Films on Si(111) and Approach for Their Enhancement

NASA Astrophysics Data System (ADS)

Taniguchi, Tatsuhiko; Sakane, Shunya; Aoki, Shunsuke; Okuhata, Ryo; Ishibe, Takafumi; Watanabe, Kentaro; Suzuki, Takeyuki; Fujita, Takeshi; Sawano, Kentarou; Nakamura, Yoshiaki

2017-05-01

We have investigated the intrinsic thermoelectric properties of epitaxial β-FeSi2 thin films and the impact of phosphorus (P) doping. Epitaxial β-FeSi2 thin films with single phase were grown on Si(111) substrates by two different techniques in an ultrahigh-vacuum molecular beam epitaxy (MBE) system: solid-phase epitaxy (SPE), where iron silicide films formed by codeposition of Fe and Si at room temperature were recrystallized by annealing at 530°C to form epitaxial β-FeSi2 thin films on Si(111) substrates, and MBE of β-FeSi2 thin films on epitaxial β-FeSi2 templates formed on Si(111) by reactive deposition epitaxy (RDE) at 530°C (RDE + MBE). Epitaxial SPE thin films based on codeposition had a flatter surface and more abrupt β-FeSi2/Si(111) interface than epitaxial RDE + MBE thin films. We investigated the intrinsic thermoelectric properties of the epitaxial β-FeSi2 thin films on Si(111), revealing lower thermal conductivity and higher electrical conductivity compared with bulk β-FeSi2. We also investigated the impact of doping on the Seebeck coefficient of bulk and thin-film β-FeSi2. A route to enhance the thermoelectric performance of β-FeSi2 is proposed, based on (1) fabrication of thin-film structures for high electrical conductivity and low thermal conductivity, and (2) proper choice of doping for high Seebeck coefficient.

Ab initio-based approach to structural change of compound semiconductor surfaces during MBE growth

NASA Astrophysics Data System (ADS)

Ito, Tomonori; Akiyama, Toru; Nakamura, Kohji

2009-01-01

Phase diagrams of GaAs and GaN surfaces are systematically investigated by using our ab initio-based approach in conjunction with molecular beam epitaxy (MBE). The phase diagrams are obtained as a function of growth parameters such as temperature and beam equivalent pressure (BEP). The versatility of our approach is exemplified by the phase diagram calculations for GaAs(0 0 1) surfaces, where the stable phases and those phase boundaries are successfully determined as functions of temperature and As 2 and As 4 BEPs. The initial growth processes are clarified by the phase diagram calculations for GaAs(1 1 1)B-(2×2). The calculated results demonstrate that the As-trimer desorption on the GaAs(1 1 1)B-(2×2) with Ga adatoms occurs beyond 500-700 K while the desorption without Ga adatoms does beyond 800-1000 K. This self-surfactant effect induced by Ga adsorption crucially affects the initial growth of GaAs on the GaAs(1 1 1)B-(2×2). Furthermore, the phase diagram calculations for GaN(0 0 0 1) suggests that Ga adsorption or desorption during GaN MBE growth can easily change the pseudo-(1×1) to the (2×2)-Ga via newly found (1×1) and vice versa. On the basis of this finding, the possibility of ghost island formation during MBE growth is discussed.

Exceptionally large migration length of carbon and topographically-facilitated self-limiting molecular beam epitaxial growth of graphene on hexagonal boron nitride

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

Plaut, Annette S.; Wurstbauer, Ulrich; Wang, Sheng

We demonstrate growth of single-layer graphene (SLG) on hexagonal boron nitride (h-BN) by molecular beam epitaxy (MBE), only limited in area by the finite size of the h-BN flakes. Using atomic force microscopy and micro-Raman spectroscopy, we show that for growth over a wide range of temperatures (500 °C – 1000 °C) the deposited carbon atoms spill off the edge of the h-BN flakes. We attribute this spillage to the very high mobility of the carbon atoms on the BN basal plane, consistent with van der Waals MBE. The h-BN flakes vary in size from 30 μm to 100 μm,more » thus demonstrating that the migration length of carbon atoms on h-BN is greater than 100 μm. When sufficient carbon is supplied to compensate for this loss, which is largely due to this fast migration of the carbon atoms to and off the edges of the h-BN flake, we find that the best growth temperature for MBE SLG on h-BN is ~950 °C. Self-limiting graphene growth appears to be facilitated by topographic h-BN surface features: We have thereby grown MBE self-limited SLG on an h-BN ridge. This opens up future avenues for precisely tailored fabrication of nano- and hetero-structures on pre-patterned h-BN surfaces for device applications.« less

MBE growth of few-layer 2H-MoTe2 on 3D substrates

NASA Astrophysics Data System (ADS)

Vishwanath, Suresh; Sundar, Aditya; Liu, Xinyu; Azcatl, Angelica; Lochocki, Edward; Woll, Arthur R.; Rouvimov, Sergei; Hwang, Wan Sik; Lu, Ning; Peng, Xin; Lien, Huai-Hsun; Weisenberger, John; McDonnell, Stephen; Kim, Moon J.; Dobrowolska, Margaret; Furdyna, Jacek K.; Shen, Kyle; Wallace, Robert M.; Jena, Debdeep; Xing, Huili Grace

2018-01-01

MoTe2 is the least explored material in the Molybdenum-chalcogen family. Molecular beam epitaxy (MBE) provides a unique opportunity to tackle the small electronegativity difference between Mo and Te while growing layer by layer away from thermodynamic equilibrium. We find that for a few-layer MoTe2 grown at a moderate rate of ∼6 min per monolayer, a narrow window in temperature (above Te cell temperature) and Te:Mo ratio exists, where we can obtain pure phase 2H-MoTe2. This is confirmed using reflection high-energy electron diffraction (RHEED), Raman spectroscopy and X-ray photoemission spectroscopy (XPS). For growth on CaF2, Grazing incidence X-ray diffraction (GI-XRD) reveals a grain size of ∼90 Å and presence of twinned grains. In this work, we hypothesis the presence of excess Te incorporation in MBE grown few layer 2H-MoTe2. For film on CaF2, it is based on >2 Te:Mo stoichiometry using XPS as well as 'a' and 'c' lattice spacing greater than bulk 2H-MoTe2. On GaAs, its based on observations of Te crystallite formation on film surface, 2 × 2 superstructure observed in RHEED and low energy electron diffraction, larger than bulk c-lattice spacing as well as the lack of electrical conductivity modulation by field effect. Finally, thermal stability and air sensitivity of MBE 2H-MoTe2 is investigated by temperature dependent XRD and XPS, respectively.

A two-phase sampling survey for nonresponse and its paradata to correct nonresponse bias in a health surveillance survey.

PubMed

Santin, G; Bénézet, L; Geoffroy-Perez, B; Bouyer, J; Guéguen, A

2017-02-01

The decline in participation rates in surveys, including epidemiological surveillance surveys, has become a real concern since it may increase nonresponse bias. The aim of this study is to estimate the contribution of a complementary survey among a subsample of nonrespondents, and the additional contribution of paradata in correcting for nonresponse bias in an occupational health surveillance survey. In 2010, 10,000 workers were randomly selected and sent a postal questionnaire. Sociodemographic data were available for the whole sample. After data collection of the questionnaires, a complementary survey among a random subsample of 500 nonrespondents was performed using a questionnaire administered by an interviewer. Paradata were collected for the complete subsample of the complementary survey. Nonresponse bias in the initial sample and in the combined samples were assessed using variables from administrative databases available for the whole sample, not subject to differential measurement errors. Corrected prevalences by reweighting technique were estimated by first using the initial survey alone and then the initial and complementary surveys combined, under several assumptions regarding the missing data process. Results were compared by computing relative errors. The response rates of the initial and complementary surveys were 23.6% and 62.6%, respectively. For the initial and the combined surveys, the relative errors decreased after correction for nonresponse on sociodemographic variables. For the combined surveys without paradata, relative errors decreased compared with the initial survey. The contribution of the paradata was weak. When a complex descriptive survey has a low response rate, a short complementary survey among nonrespondents with a protocol which aims to maximize the response rates, is useful. The contribution of sociodemographic variables in correcting for nonresponse bias is important whereas the additional contribution of paradata in correcting for nonresponse bias is questionable. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Validation and Expected Error Estimation of Suomi-NNP VIIRS Aerosol Optical Thickness and Angstrom Exponent with AERONET

NASA Technical Reports Server (NTRS)

Huang, Jingfeng; Kondragunta, Shobha; Laszlo, Istvan; Liu, Hongqing; Remer, Lorraine A.; Zhang, Hai; Superczynski, Stephen; Ciren, Pubu; Holben, Brent N.; Petrenko, Maksym

2016-01-01

The new-generation polar-orbiting operational environmental sensor, the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership (S-NPP) satellite, provides critical daily global aerosol observations. As older satellite sensors age out, the VIIRS aerosol product will become the primary observational source for global assessments of aerosol emission and transport, aerosol meteorological and climatic effects, air quality monitoring, and public health. To prove their validity and to assess their maturity level, the VIIRS aerosol products were compared to the spatiotemporally matched Aerosol Robotic Network (AERONET)measurements. Over land, the VIIRS aerosol optical thickness (AOT) environmental data record (EDR) exhibits an overall global bias against AERONET of 0.0008 with root-mean-square error(RMSE) of the biases as 0.12. Over ocean, the mean bias of VIIRS AOT EDR is 0.02 with RMSE of the biases as 0.06.The mean bias of VIIRS Ocean Angstrom Exponent (AE) EDR is 0.12 with RMSE of the biases as 0.57. The matchups between each product and its AERONET counterpart allow estimates of expected error in each case. Increased uncertainty in the VIIRS AOT and AE products is linked to specific regions, seasons, surface characteristics, and aerosol types, suggesting opportunity for future modifications as understanding of algorithm assumptions improves. Based on the assessment, the VIIRS AOT EDR over land reached Validated maturity beginning 23 January 2013; the AOT EDR and AE EDR over ocean reached Validated maturity beginning 2 May 2012, excluding the processing error period 15 October to 27 November 2012. These findings demonstrate the integrity and usefulness of the VIIRS aerosol products that will transition from S-NPP to future polar-orbiting environmental satellites in the decades to come and become the standard global aerosol data set as the previous generations missions come to an end.

Accounting for unknown foster dams in the genetic evaluation of embryo transfer progeny.

PubMed

Suárez, M J; Munilla, S; Cantet, R J C

2015-02-01

Animals born by embryo transfer (ET) are usually not included in the genetic evaluation of beef cattle for preweaning growth if the recipient dam is unknown. This is primarily to avoid potential bias in the estimation of the unknown age of dam. We present a method that allows including records of calves with unknown age of dam. Assumptions are as follows: (i) foster cows belong to the same breed being evaluated, (ii) there is no correlation between the breeding value (BV) of the calf and the maternal BV of the recipient cow, and (iii) cows of all ages are used as recipients. We examine the issue of bias for the fixed level of unknown age of dam (AOD) and propose an estimator of the effect based on classical measurement error theory (MEM) and a Bayesian approach. Using stochastic simulation under random mating or selection, the MEM estimating equations were compared with BLUP in two situations as follows: (i) full information (FI); (ii) missing AOD information on some dams. Predictions of breeding value (PBV) from the FI situation had the smallest empirical average bias followed by PBV obtained without taking measurement error into account. In turn, MEM displayed the highest bias, although the differences were small. On the other hand, MEM showed the smallest MSEP, for either random mating or selection, followed by FI, whereas ignoring measurement error produced the largest MSEP. As a consequence from the smallest MSEP with a relatively small bias, empirical accuracies of PBV were larger for MEM than those for full information, which in turn showed larger accuracies than the situation ignoring measurement error. It is concluded that MEM equations are a useful alternative for analysing weaning weight data when recipient cows are unknown, as it mitigates the effects of bias in AOD by decreasing MSEP. © 2014 Blackwell Verlag GmbH.

How large are the consequences of covariate imbalance in cluster randomized trials: a simulation study with a continuous outcome and a binary covariate at the cluster level.

PubMed

Moerbeek, Mirjam; van Schie, Sander

2016-07-11

The number of clusters in a cluster randomized trial is often low. It is therefore likely random assignment of clusters to treatment conditions results in covariate imbalance. There are no studies that quantify the consequences of covariate imbalance in cluster randomized trials on parameter and standard error bias and on power to detect treatment effects. The consequences of covariance imbalance in unadjusted and adjusted linear mixed models are investigated by means of a simulation study. The factors in this study are the degree of imbalance, the covariate effect size, the cluster size and the intraclass correlation coefficient. The covariate is binary and measured at the cluster level; the outcome is continuous and measured at the individual level. The results show covariate imbalance results in negligible parameter bias and small standard error bias in adjusted linear mixed models. Ignoring the possibility of covariate imbalance while calculating the sample size at the cluster level may result in a loss in power of at most 25 % in the adjusted linear mixed model. The results are more severe for the unadjusted linear mixed model: parameter biases up to 100 % and standard error biases up to 200 % may be observed. Power levels based on the unadjusted linear mixed model are often too low. The consequences are most severe for large clusters and/or small intraclass correlation coefficients since then the required number of clusters to achieve a desired power level is smallest. The possibility of covariate imbalance should be taken into account while calculating the sample size of a cluster randomized trial. Otherwise more sophisticated methods to randomize clusters to treatments should be used, such as stratification or balance algorithms. All relevant covariates should be carefully identified, be actually measured and included in the statistical model to avoid severe levels of parameter and standard error bias and insufficient power levels.

Test-to-Test Repeatability of Results From a Subsonic Wing-Body Configuration in the National Transonic Facility

NASA Technical Reports Server (NTRS)

Mineck, Raymond E.; Pendergraft, Odis C., Jr.

2000-01-01

Results from three wind tunnel tests in the National Transonic Facility of a model of an advanced-technology, subsonic-transport wing-body configuration have been analyzed to assess the test-to-test repeatability of several aerodynamic parameters. The scatter, as measured by the prediction interval, in the longitudinal force and moment coefficients increases as the Mach number increases. Residual errors with and without the ESP tubes installed suggest a bias leading to lower drag with the tubes installed. Residual errors as well as average values of the longitudinal force and moment coefficients show that there are small bias errors between the different tests.

Acute anxiety and social inference: An experimental manipulation with 7.5% carbon dioxide inhalation

PubMed Central

Button, Katherine S; Karwatowska, Lucy; Kounali, Daphne; Munafò, Marcus R; Attwood, Angela S

2016-01-01

Background: Positive self-bias is thought to be protective for mental health. We previously found that the degree of positive bias when learning self-referential social evaluation decreases with increasing social anxiety. It is unclear whether this reduction is driven by differences in state or trait anxiety, as both are elevated in social anxiety; therefore, we examined the effects on the state of anxiety induced by the 7.5% carbon dioxide (CO2) inhalation model of generalised anxiety disorder (GAD) on social evaluation learning. Methods: For our study, 48 (24 of female gender) healthy volunteers took two inhalations (medical air and 7.5% CO2, counterbalanced) whilst learning social rules (self-like, self-dislike, other-like and other-dislike) in an instrumental social evaluation learning task. We analysed the outcomes (number of positive responses and errors to criterion) using the random effects Poisson regression. Results: Participants made fewer and more positive responses when breathing 7.5% CO2 in the other-like and other-dislike rules, respectively (gas × condition × rule interaction p = 0.03). Individuals made fewer errors learning self-like than self-dislike, and this positive self-bias was unaffected by CO2. Breathing 7.5% CO2 increased errors, but only in the other-referential rules (gas × condition × rule interaction p = 0.003). Conclusions: Positive self-bias (i.e. fewer errors learning self-like than self-dislike) seemed robust to changes in state anxiety. In contrast, learning other-referential evaluation was impaired as state anxiety increased. This suggested that the previously observed variations in self-bias arise due to trait, rather than state, characteristics. PMID:27380750

Memory Errors Reveal a Bias to Spontaneously Generalize to Categories

PubMed Central

Sutherland, Shelbie L.; Cimpian, Andrei; Leslie, Sarah-Jane; Gelman, Susan A.

2014-01-01

Much evidence suggests that, from a young age, humans are able to generalize information learned about a subset of a category to the category itself. Here, we propose that—beyond simply being able to perform such generalizations—people are biased to generalize to categories, such that they routinely make spontaneous, implicit category generalizations from information that licenses such generalizations. To demonstrate the existence of this bias, we asked participants to perform a task in which category generalizations would distract from the main goal of the task, leading to a characteristic pattern of errors. Specifically, participants were asked to memorize two types of novel facts: quantified facts about sets of kind members (e.g., facts about all or many stups) and generic facts about entire kinds (e.g., facts about zorbs as a kind). Moreover, half of the facts concerned properties that are typically generalizable to an animal kind (e.g., eating fruits and vegetables), and half concerned properties that are typically more idiosyncratic (e.g., getting mud in their hair). We predicted that—because of the hypothesized bias—participants would spontaneously generalize the quantified facts to the corresponding kinds, and would do so more frequently for the facts about generalizable (rather than idiosyncratic) properties. In turn, these generalizations would lead to a higher rate of quantified-to-generic memory errors for the generalizable properties. The results of four experiments (N = 449) supported this prediction. Moreover, the same generalizable-versus-idiosyncratic difference in memory errors occurred even under cognitive load, which suggests that the hypothesized bias operates unnoticed in the background, requiring few cognitive resources. In sum, this evidence suggests the presence of a powerful bias to draw generalizations about kinds. PMID:25327964

Acute anxiety and social inference: An experimental manipulation with 7.5% carbon dioxide inhalation.

PubMed

Button, Katherine S; Karwatowska, Lucy; Kounali, Daphne; Munafò, Marcus R; Attwood, Angela S

2016-10-01

Positive self-bias is thought to be protective for mental health. We previously found that the degree of positive bias when learning self-referential social evaluation decreases with increasing social anxiety. It is unclear whether this reduction is driven by differences in state or trait anxiety, as both are elevated in social anxiety; therefore, we examined the effects on the state of anxiety induced by the 7.5% carbon dioxide (CO2) inhalation model of generalised anxiety disorder (GAD) on social evaluation learning. For our study, 48 (24 of female gender) healthy volunteers took two inhalations (medical air and 7.5% CO2, counterbalanced) whilst learning social rules (self-like, self-dislike, other-like and other-dislike) in an instrumental social evaluation learning task. We analysed the outcomes (number of positive responses and errors to criterion) using the random effects Poisson regression. Participants made fewer and more positive responses when breathing 7.5% CO2 in the other-like and other-dislike rules, respectively (gas × condition × rule interaction p = 0.03). Individuals made fewer errors learning self-like than self-dislike, and this positive self-bias was unaffected by CO2. Breathing 7.5% CO2 increased errors, but only in the other-referential rules (gas × condition × rule interaction p = 0.003). Positive self-bias (i.e. fewer errors learning self-like than self-dislike) seemed robust to changes in state anxiety. In contrast, learning other-referential evaluation was impaired as state anxiety increased. This suggested that the previously observed variations in self-bias arise due to trait, rather than state, characteristics. © The Author(s) 2016.

Distortion Representation of Forecast Errors for Model Skill Assessment and Objective Analysis

NASA Technical Reports Server (NTRS)

Hoffman, Ross N.; Nehrkorn, Thomas; Grassotti, Christopher

1996-01-01

We study a novel characterization of errors for numerical weather predictions. In its simplest form we decompose the error into a part attributable to phase errors and a remainder. The phase error is represented in the same fashion as a velocity field and will be required to vary slowly and smoothly with position. A general distortion representation allows for the displacement and a bias correction of forecast anomalies. In brief, the distortion is determined by minimizing the objective function by varying the displacement and bias correction fields. In the present project we use a global or hemispheric domain, and spherical harmonics to represent these fields. In this project we are initially focusing on the assessment application, restricted to a realistic but univariate 2-dimensional situation. Specifically we study the forecast errors of the 500 hPa geopotential height field for forecasts of the short and medium range. The forecasts are those of the Goddard Earth Observing System data assimilation system. Results presented show that the methodology works, that a large part of the total error may be explained by a distortion limited to triangular truncation at wavenumber 10, and that the remaining residual error contains mostly small spatial scales.

The inference of atmospheric ozone using satellite horizon measurements in the 1042 per cm band.

NASA Technical Reports Server (NTRS)

Russell, J. M., III; Drayson, S. R.

1972-01-01

Description of a method for inferring atmospheric ozone information using infrared horizon radiance measurements in the 1042 per cm band. An analysis based on this method proves the feasibility of the horizon experiment for determining ozone information and shows that the ozone partial pressure can be determined in the altitude range from 50 down to 25 km. A comprehensive error study is conducted which considers effects of individual errors as well as the effect of all error sources acting simultaneously. The results show that in the absence of a temperature profile bias error, it should be possible to determine the ozone partial pressure to within an rms value of 15 to 20%. It may be possible to reduce this rms error to 5% by smoothing the solution profile. These results would be seriously degraded by an atmospheric temperature bias error of only 3 K; thus, great care should be taken to minimize this source of error in an experiment. It is probable, in view of recent technological developments, that these errors will be much smaller in future flight experiments and the altitude range will widen to include from about 60 km down to the tropopause region.

The Influence of Improper Sets of Information on Judgment: How Irrelevant Information Can Bias Judged Probability

ERIC Educational Resources Information Center

Dougherty, Michael R.; Sprenger, Amber

2006-01-01

This article introduces 2 new sources of bias in probability judgment, discrimination failure and inhibition failure, which are conceptualized as arising from an interaction between error prone memory processes and a support theory like comparison process. Both sources of bias stem from the influence of irrelevant information on participants'…

Cultural and Ethnic Bias in Teacher Ratings of Behavior: A Criterion-Focused Review

ERIC Educational Resources Information Center

Mason, Benjamin A.; Gunersel, Adalet Baris; Ney, Emilie A.

2014-01-01

Behavior rating scales are indirect measures of emotional and social functioning used for assessment purposes. Rater bias is systematic error that may compromise the validity of behavior rating scale scores. Teacher bias in ratings of behavior has been investigated in multiple studies, but not yet assessed in a research synthesis that focuses on…

Electrocortical measures of information processing biases in social anxiety disorder: A review.

PubMed

Harrewijn, Anita; Schmidt, Louis A; Westenberg, P Michiel; Tang, Alva; van der Molen, Melle J W

2017-10-01

Social anxiety disorder (SAD) is characterized by information processing biases, however, their underlying neural mechanisms remain poorly understood. The goal of this review was to give a comprehensive overview of the most frequently studied EEG spectral and event-related potential (ERP) measures in social anxiety during rest, anticipation, stimulus processing, and recovery. A Web of Science search yielded 35 studies reporting on electrocortical measures in individuals with social anxiety or related constructs. Social anxiety was related to increased delta-beta cross-frequency correlation during anticipation and recovery, and information processing biases during early processing of faces (P1) and errors (error-related negativity). These electrocortical measures are discussed in relation to the persistent cycle of information processing biases maintaining SAD. Future research should further investigate the mechanisms of this persistent cycle and study the utility of electrocortical measures in early detection, prevention, treatment and endophenotype research. Copyright © 2017 Elsevier B.V. All rights reserved.

An Uncertainty Data Set for Passive Microwave Satellite Observations of Warm Cloud Liquid Water Path

NASA Astrophysics Data System (ADS)

Greenwald, Thomas J.; Bennartz, Ralf; Lebsock, Matthew; Teixeira, João.

2018-04-01

The first extended comprehensive data set of the retrieval uncertainties in passive microwave observations of cloud liquid water path (CLWP) for warm oceanic clouds has been created for practical use in climate applications. Four major sources of systematic errors were considered over the 9-year record of the Advanced Microwave Scanning Radiometer-EOS (AMSR-E): clear-sky bias, cloud-rain partition (CRP) bias, cloud-fraction-dependent bias, and cloud temperature bias. Errors were estimated using a unique merged AMSR-E/Moderate resolution Imaging Spectroradiometer Level 2 data set as well as observations from the Cloud-Aerosol Lidar with Orthogonal Polarization and the CloudSat Cloud Profiling Radar. To quantify the CRP bias more accurately, a new parameterization was developed to improve the inference of CLWP in warm rain. The cloud-fraction-dependent bias was found to be a combination of the CRP bias, an in-cloud bias, and an adjacent precipitation bias. Globally, the mean net bias was 0.012 kg/m2, dominated by the CRP and in-cloud biases, but with considerable regional and seasonal variation. Good qualitative agreement between a bias-corrected AMSR-E CLWP climatology and ship observations in the Northeast Pacific suggests that the bias estimates are reasonable. However, a possible underestimation of the net bias in certain conditions may be due in part to the crude method used in classifying precipitation, underscoring the need for an independent method of detecting rain in warm clouds. This study demonstrates the importance of combining visible-infrared imager data and passive microwave CLWP observations for estimating uncertainties and improving the accuracy of these observations.

Spatial measurement error and correction by spatial SIMEX in linear regression models when using predicted air pollution exposures.

PubMed

Alexeeff, Stacey E; Carroll, Raymond J; Coull, Brent

2016-04-01

Spatial modeling of air pollution exposures is widespread in air pollution epidemiology research as a way to improve exposure assessment. However, there are key sources of exposure model uncertainty when air pollution is modeled, including estimation error and model misspecification. We examine the use of predicted air pollution levels in linear health effect models under a measurement error framework. For the prediction of air pollution exposures, we consider a universal Kriging framework, which may include land-use regression terms in the mean function and a spatial covariance structure for the residuals. We derive the bias induced by estimation error and by model misspecification in the exposure model, and we find that a misspecified exposure model can induce asymptotic bias in the effect estimate of air pollution on health. We propose a new spatial simulation extrapolation (SIMEX) procedure, and we demonstrate that the procedure has good performance in correcting this asymptotic bias. We illustrate spatial SIMEX in a study of air pollution and birthweight in Massachusetts. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Evaluation and Applications of the Prediction of Intensity Model Error (PRIME) Model

NASA Astrophysics Data System (ADS)

Bhatia, K. T.; Nolan, D. S.; Demaria, M.; Schumacher, A.

2015-12-01

Forecasters and end users of tropical cyclone (TC) intensity forecasts would greatly benefit from a reliable expectation of model error to counteract the lack of consistency in TC intensity forecast performance. As a first step towards producing error predictions to accompany each TC intensity forecast, Bhatia and Nolan (2013) studied the relationship between synoptic parameters, TC attributes, and forecast errors. In this study, we build on previous results of Bhatia and Nolan (2013) by testing the ability of the Prediction of Intensity Model Error (PRIME) model to forecast the absolute error and bias of four leading intensity models available for guidance in the Atlantic basin. PRIME forecasts are independently evaluated at each 12-hour interval from 12 to 120 hours during the 2007-2014 Atlantic hurricane seasons. The absolute error and bias predictions of PRIME are compared to their respective climatologies to determine their skill. In addition to these results, we will present the performance of the operational version of PRIME run during the 2015 hurricane season. PRIME verification results show that it can reliably anticipate situations where particular models excel, and therefore could lead to a more informed protocol for hurricane evacuations and storm preparations. These positive conclusions suggest that PRIME forecasts also have the potential to lower the error in the original intensity forecasts of each model. As a result, two techniques are proposed to develop a post-processing procedure for a multimodel ensemble based on PRIME. The first approach is to inverse-weight models using PRIME absolute error predictions (higher predicted absolute error corresponds to lower weights). The second multimodel ensemble applies PRIME bias predictions to each model's intensity forecast and the mean of the corrected models is evaluated. The forecasts of both of these experimental ensembles are compared to those of the equal-weight ICON ensemble, which currently provides the most reliable forecasts in the Atlantic basin.

Time determination for spacecraft users of the Navstar Global Positioning System /GPS/

NASA Technical Reports Server (NTRS)

Grenchik, T. J.; Fang, B. T.

1977-01-01

Global Positioning System (GPS) navigation is performed by time measurements. A description is presented of a two body model of spacecraft motion. Orbit determination is the process of inferring the position, velocity, and clock offset of the user from measurements made of the user motion in the Newtonian coordinate system. To illustrate the effect of clock errors and the accuracy with which the user spacecraft time and orbit may be determined, a low-earth-orbit spacecraft (Seasat) as tracked by six Phase I GPS space vehicles is considered. The obtained results indicate that in the absence of unmodeled dynamic parameter errors clock biases may be determined to the nanosecond level. There is, however, a high correlation between the clock bias and the uncertainty in the gravitational parameter GM, i.e., the product of the universal gravitational constant and the total mass of the earth. It is, therefore, not possible to determine clock bias to better than 25 nanosecond accuracy in the presence of a gravitational error of one part per million.

Alternative mechanisms for regulating racial responses according to internal vs external cues.

PubMed

Amodio, David M; Kubota, Jennifer T; Harmon-Jones, Eddie; Devine, Patricia G

2006-06-01

Personal (internal) and normative (external) impetuses for regulating racially biased behaviour are well-documented, yet the extent to which internally and externally driven regulatory processes arise from the same mechanism is unknown. Whereas the regulation of race bias according to internal cues has been associated with conflict-monitoring processes and activation of the dorsal anterior cingulate cortex (dACC), we proposed that responses regulated according to external cues to respond without prejudice involves mechanisms of error-perception, a process associated with rostral anterior cingulate cortex (rACC) activity. We recruited low-prejudice participants who reported high or low sensitivity to non-prejudiced norms, and participants completed a stereotype inhibition task in private or public while electroencephalography was recorded. Analysis of event-related potentials revealed that the error-related negativity component, linked to dACC activity, predicted behavioural control of bias across conditions, whereas the error-perception component, linked to rACC activity, predicted control only in public among participants sensitive to external pressures to respond without prejudice.

A zero-augmented generalized gamma regression calibration to adjust for covariate measurement error: A case of an episodically consumed dietary intake

PubMed Central

Agogo, George O.

2017-01-01

Measurement error in exposure variables is a serious impediment in epidemiological studies that relate exposures to health outcomes. In nutritional studies, interest could be in the association between long-term dietary intake and disease occurrence. Long-term intake is usually assessed with food frequency questionnaire (FFQ), which is prone to recall bias. Measurement error in FFQ-reported intakes leads to bias in parameter estimate that quantifies the association. To adjust for bias in the association, a calibration study is required to obtain unbiased intake measurements using a short-term instrument such as 24-hour recall (24HR). The 24HR intakes are used as response in regression calibration to adjust for bias in the association. For foods not consumed daily, 24HR-reported intakes are usually characterized by excess zeroes, right skewness, and heteroscedasticity posing serious challenge in regression calibration modeling. We proposed a zero-augmented calibration model to adjust for measurement error in reported intake, while handling excess zeroes, skewness, and heteroscedasticity simultaneously without transforming 24HR intake values. We compared the proposed calibration method with the standard method and with methods that ignore measurement error by estimating long-term intake with 24HR and FFQ-reported intakes. The comparison was done in real and simulated datasets. With the 24HR, the mean increase in mercury level per ounce fish intake was about 0.4; with the FFQ intake, the increase was about 1.2. With both calibration methods, the mean increase was about 2.0. Similar trend was observed in the simulation study. In conclusion, the proposed calibration method performs at least as good as the standard method. PMID:27704599

Bias correction for selecting the minimal-error classifier from many machine learning models.

PubMed

Ding, Ying; Tang, Shaowu; Liao, Serena G; Jia, Jia; Oesterreich, Steffi; Lin, Yan; Tseng, George C

2014-11-15

Supervised machine learning is commonly applied in genomic research to construct a classifier from the training data that is generalizable to predict independent testing data. When test datasets are not available, cross-validation is commonly used to estimate the error rate. Many machine learning methods are available, and it is well known that no universally best method exists in general. It has been a common practice to apply many machine learning methods and report the method that produces the smallest cross-validation error rate. Theoretically, such a procedure produces a selection bias. Consequently, many clinical studies with moderate sample sizes (e.g. n = 30-60) risk reporting a falsely small cross-validation error rate that could not be validated later in independent cohorts. In this article, we illustrated the probabilistic framework of the problem and explored the statistical and asymptotic properties. We proposed a new bias correction method based on learning curve fitting by inverse power law (IPL) and compared it with three existing methods: nested cross-validation, weighted mean correction and Tibshirani-Tibshirani procedure. All methods were compared in simulation datasets, five moderate size real datasets and two large breast cancer datasets. The result showed that IPL outperforms the other methods in bias correction with smaller variance, and it has an additional advantage to extrapolate error estimates for larger sample sizes, a practical feature to recommend whether more samples should be recruited to improve the classifier and accuracy. An R package 'MLbias' and all source files are publicly available. tsenglab.biostat.pitt.edu/software.htm. ctseng@pitt.edu Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Estimation and correction of visibility bias in aerial surveys of wintering ducks

USGS Publications Warehouse

Pearse, A.T.; Gerard, P.D.; Dinsmore, S.J.; Kaminski, R.M.; Reinecke, K.J.

2008-01-01

Incomplete detection of all individuals leading to negative bias in abundance estimates is a pervasive source of error in aerial surveys of wildlife, and correcting that bias is a critical step in improving surveys. We conducted experiments using duck decoys as surrogates for live ducks to estimate bias associated with surveys of wintering ducks in Mississippi, USA. We found detection of decoy groups was related to wetland cover type (open vs. forested), group size (1?100 decoys), and interaction of these variables. Observers who detected decoy groups reported counts that averaged 78% of the decoys actually present, and this counting bias was not influenced by either covariate cited above. We integrated this sightability model into estimation procedures for our sample surveys with weight adjustments derived from probabilities of group detection (estimated by logistic regression) and count bias. To estimate variances of abundance estimates, we used bootstrap resampling of transects included in aerial surveys and data from the bias-correction experiment. When we implemented bias correction procedures on data from a field survey conducted in January 2004, we found bias-corrected estimates of abundance increased 36?42%, and associated standard errors increased 38?55%, depending on species or group estimated. We deemed our method successful for integrating correction of visibility bias in an existing sample survey design for wintering ducks in Mississippi, and we believe this procedure could be implemented in a variety of sampling problems for other locations and species.

Improving soil moisture simulation to support Agricultural Water Resource Management using Satellite-based water cycle observations

NASA Astrophysics Data System (ADS)

Gupta, Manika; Bolten, John; Lakshmi, Venkat

2016-04-01

Efficient and sustainable irrigation systems require optimization of operational parameters such as irrigation amount which are dependent on the soil hydraulic parameters that affect the model's accuracy in simulating soil water content. However, it is a scientific challenge to provide reliable estimates of soil hydraulic parameters and irrigation estimates, given the absence of continuously operating soil moisture and rain gauge network. For agricultural water resource management, the in-situ measurements of soil moisture are currently limited to discrete measurements at specific locations, and such point-based measurements do not represent the spatial distribution at a larger scale accurately, as soil moisture is highly variable both spatially and temporally (Wang and Qu 2009). In the current study, flood irrigation scheme within the land surface model is triggered when the root-zone soil moisture deficit reaches below a threshold of 25%, 50% and 75% with respect to the maximum available water capacity (difference between field capacity and wilting point) and applied until the top layer is saturated. An additional important criterion needed to activate the irrigation scheme is to ensure that it is irrigation season by assuming that the greenness vegetation fraction (GVF) of the pixel exceed 0.40 of the climatological annual range of GVF (Ozdogan et al. 2010). The main hypothesis used in this study is that near-surface remote sensing soil moisture data contain useful information that can describe the effective hydrological conditions of the basin such that when appropriately inverted, it would provide field capacity and wilting point soil moisture, which may be representative of that basin. Thus, genetic algorithm inverse method is employed to derive the effective parameters and derive the soil moisture deficit for the root zone by coupling of AMSR-E soil moisture with the physically based hydrological model. Model performance is evaluated using MODIS-evapotranspiration (ET) and MODIS land surface temperature (LST) products. The soil moisture estimates for the root zone are also validated with the in-situ field data, for three sites (2- irrigated and 1- rainfed) located at the University of Nebraska Agricultural Research and Development Center near Mead, NE and monitored by three AmeriFlux installations (Verma et al., 2005) by evaluating the root mean square error (RMSE) and Mean Bias error (MBE).

Contributions of the atmosphere-land and ocean-sea ice model components to the tropical Atlantic SST bias in CESM1

NASA Astrophysics Data System (ADS)

Song, Z.; Lee, S. K.; Wang, C.; Kirtman, B. P.; Qiao, F.

2016-02-01

In order to identify and quantify intrinsic errors in the atmosphere-land and ocean-sea ice model components of the Community Earth System Model version 1 (CESM1) and their contributions to the tropical Atlantic sea surface temperature (SST) bias in CESM1, we propose a new method of diagnosis and apply it to a set of CESM1 simulations. Our analyses of the model simulations indicate that both the atmosphere-land and ocean-sea ice model components of CESM1 contain large errors in the tropical Atlantic. When the two model components are fully coupled, the intrinsic errors in the two components emerge quickly within a year with strong seasonality in their growth rates. In particular, the ocean-sea ice model contributes significantly in forcing the eastern equatorial Atlantic warm SST bias in early boreal summer. Further analysis shows that the upper thermocline water underneath the eastern equatorial Atlantic surface mixed layer is too warm in a stand-alone ocean-sea ice simulation of CESM1 forced with observed surface flux fields, suggesting that the mixed layer cooling associated with the entrainment of upper thermocline water is too weak in early boreal summer. Therefore, although we acknowledge the potential importance of the westerly wind bias in the western equatorial Atlantic and the low-level stratus cloud bias in the southeastern tropical Atlantic, both of which originate from the atmosphere-land model, we emphasize here that solving those problems in the atmosphere-land model alone does not resolve the equatorial Atlantic warm bias in CESM1.

Insights on the impact of systematic model errors on data assimilation performance in changing catchments

NASA Astrophysics Data System (ADS)

Pathiraja, S.; Anghileri, D.; Burlando, P.; Sharma, A.; Marshall, L.; Moradkhani, H.

2018-03-01

The global prevalence of rapid and extensive land use change necessitates hydrologic modelling methodologies capable of handling non-stationarity. This is particularly true in the context of Hydrologic Forecasting using Data Assimilation. Data Assimilation has been shown to dramatically improve forecast skill in hydrologic and meteorological applications, although such improvements are conditional on using bias-free observations and model simulations. A hydrologic model calibrated to a particular set of land cover conditions has the potential to produce biased simulations when the catchment is disturbed. This paper sheds new light on the impacts of bias or systematic errors in hydrologic data assimilation, in the context of forecasting in catchments with changing land surface conditions and a model calibrated to pre-change conditions. We posit that in such cases, the impact of systematic model errors on assimilation or forecast quality is dependent on the inherent prediction uncertainty that persists even in pre-change conditions. Through experiments on a range of catchments, we develop a conceptual relationship between total prediction uncertainty and the impacts of land cover changes on the hydrologic regime to demonstrate how forecast quality is affected when using state estimation Data Assimilation with no modifications to account for land cover changes. This work shows that systematic model errors as a result of changing or changed catchment conditions do not always necessitate adjustments to the modelling or assimilation methodology, for instance through re-calibration of the hydrologic model, time varying model parameters or revised offline/online bias estimation.

Improvement of VLBI EOP Accuracy and Precision

NASA Technical Reports Server (NTRS)

MacMillan, Daniel; Ma, Chopo

2000-01-01

In the CORE program, EOP measurements will be made with several different networks, each operating on a different day. It is essential that systematic differences between EOP derived by the different networks be minimized. Observed biases between the simultaneous CORE-A and NEOS-A sessions are about 60-130 micro(as) for PM, UT1 and nutation parameters. After removing biases, the observed rms differences are consistent with an increase in the formal precision of the measurements by factors ranging from 1.05 to 1.4. We discuss the possible sources of unmodeled error that account for these factors and the biases and the sensitivities of the network differences to modeling errors. We also discuss differences between VLBI and GPS PM measurements.

Diagnostic Reasoning and Cognitive Biases of Nurse Practitioners.

PubMed

Lawson, Thomas N

2018-04-01

Diagnostic reasoning is often used colloquially to describe the process by which nurse practitioners and physicians come to the correct diagnosis, but a rich definition and description of this process has been lacking in the nursing literature. A literature review was conducted with theoretical sampling seeking conceptual insight into diagnostic reasoning. Four common themes emerged: Cognitive Biases and Debiasing Strategies, the Dual Process Theory, Diagnostic Error, and Patient Harm. Relevant cognitive biases are discussed, followed by debiasing strategies and application of the dual process theory to reduce diagnostic error and harm. The accuracy of diagnostic reasoning of nurse practitioners may be improved by incorporating these items into nurse practitioner education and practice. [J Nurs Educ. 2018;57(4):203-208.]. Copyright 2018, SLACK Incorporated.

Phase Error Correction in Time-Averaged 3D Phase Contrast Magnetic Resonance Imaging of the Cerebral Vasculature

PubMed Central

MacDonald, M. Ethan; Forkert, Nils D.; Pike, G. Bruce; Frayne, Richard

2016-01-01

Purpose Volume flow rate (VFR) measurements based on phase contrast (PC)-magnetic resonance (MR) imaging datasets have spatially varying bias due to eddy current induced phase errors. The purpose of this study was to assess the impact of phase errors in time averaged PC-MR imaging of the cerebral vasculature and explore the effects of three common correction schemes (local bias correction (LBC), local polynomial correction (LPC), and whole brain polynomial correction (WBPC)). Methods Measurements of the eddy current induced phase error from a static phantom were first obtained. In thirty healthy human subjects, the methods were then assessed in background tissue to determine if local phase offsets could be removed. Finally, the techniques were used to correct VFR measurements in cerebral vessels and compared statistically. Results In the phantom, phase error was measured to be <2.1 ml/s per pixel and the bias was reduced with the correction schemes. In background tissue, the bias was significantly reduced, by 65.6% (LBC), 58.4% (LPC) and 47.7% (WBPC) (p < 0.001 across all schemes). Correction did not lead to significantly different VFR measurements in the vessels (p = 0.997). In the vessel measurements, the three correction schemes led to flow measurement differences of -0.04 ± 0.05 ml/s, 0.09 ± 0.16 ml/s, and -0.02 ± 0.06 ml/s. Although there was an improvement in background measurements with correction, there was no statistical difference between the three correction schemes (p = 0.242 in background and p = 0.738 in vessels). Conclusions While eddy current induced phase errors can vary between hardware and sequence configurations, our results showed that the impact is small in a typical brain PC-MR protocol and does not have a significant effect on VFR measurements in cerebral vessels. PMID:26910600

Error Model and Compensation of Bell-Shaped Vibratory Gyro

PubMed Central

Su, Zhong; Liu, Ning; Li, Qing

2015-01-01

A bell-shaped vibratory angular velocity gyro (BVG), inspired by the Chinese traditional bell, is a type of axisymmetric shell resonator gyroscope. This paper focuses on development of an error model and compensation of the BVG. A dynamic equation is firstly established, based on a study of the BVG working mechanism. This equation is then used to evaluate the relationship between the angular rate output signal and bell-shaped resonator character, analyze the influence of the main error sources and set up an error model for the BVG. The error sources are classified from the error propagation characteristics, and the compensation method is presented based on the error model. Finally, using the error model and compensation method, the BVG is calibrated experimentally including rough compensation, temperature and bias compensation, scale factor compensation and noise filter. The experimentally obtained bias instability is from 20.5°/h to 4.7°/h, the random walk is from 2.8°/h1/2 to 0.7°/h1/2 and the nonlinearity is from 0.2% to 0.03%. Based on the error compensation, it is shown that there is a good linear relationship between the sensing signal and the angular velocity, suggesting that the BVG is a good candidate for the field of low and medium rotational speed measurement. PMID:26393593

An Uncertainty Data Set for Passive Microwave Satellite Observations of Warm Cloud Liquid Water Path

PubMed Central

Bennartz, Ralf; Lebsock, Matthew; Teixeira, João

2018-01-01

Abstract The first extended comprehensive data set of the retrieval uncertainties in passive microwave observations of cloud liquid water path (CLWP) for warm oceanic clouds has been created for practical use in climate applications. Four major sources of systematic errors were considered over the 9‐year record of the Advanced Microwave Scanning Radiometer‐EOS (AMSR‐E): clear‐sky bias, cloud‐rain partition (CRP) bias, cloud‐fraction‐dependent bias, and cloud temperature bias. Errors were estimated using a unique merged AMSR‐E/Moderate resolution Imaging Spectroradiometer Level 2 data set as well as observations from the Cloud‐Aerosol Lidar with Orthogonal Polarization and the CloudSat Cloud Profiling Radar. To quantify the CRP bias more accurately, a new parameterization was developed to improve the inference of CLWP in warm rain. The cloud‐fraction‐dependent bias was found to be a combination of the CRP bias, an in‐cloud bias, and an adjacent precipitation bias. Globally, the mean net bias was 0.012 kg/m2, dominated by the CRP and in‐cloud biases, but with considerable regional and seasonal variation. Good qualitative agreement between a bias‐corrected AMSR‐E CLWP climatology and ship observations in the Northeast Pacific suggests that the bias estimates are reasonable. However, a possible underestimation of the net bias in certain conditions may be due in part to the crude method used in classifying precipitation, underscoring the need for an independent method of detecting rain in warm clouds. This study demonstrates the importance of combining visible‐infrared imager data and passive microwave CLWP observations for estimating uncertainties and improving the accuracy of these observations. PMID:29938146

Application of the ALE and MBE Methods to the Growth of Layered Hg sub x Cd sub 1-x Te Films.

DTIC Science & Technology

1986-09-26

films / We have studied the applicability of the Atomic Layer Epitaxy (ALE, vee Ref. -1pand Molecular Beam Epitaxy (MBE) ito growth of Hg2 Cdi- ,Te...thin- films throughout the composition range 0 x $ 0.8. The progress of the Contract has been reported periodically in five interim reports. This final...I separate sources) yielded films with high x values. On the grounds of these observations we do not find ALE suitable for growth of HgCdTe. 2) ALE

MBE Growth, Characterization and Electronic Device Processing of HgCdTe, HgZnTe, Related Heterojunctions and HgCdTe-CdTe Superlattices

DTIC Science & Technology

1987-06-30

metal lattice sites using the liquid phase epitaxy. However, group V elements have not been successfully Incorporated Into MBE grown HgCdTe layer as...narrow-gap side was first Both groups used the liquid pweepitaxy (LPE) growth made with a thicknem of 2 to 3/pm before the growth condi- technique and...higher quasiequilibrium pressure than with the shutter opened. This study shows that with the particular geometry 27 used the time constant required

Sb-Based n- and p-Channel Heterostructure FETs for High-Speed, Low-Power Applications

DTIC Science & Technology

2008-07-01

Laboratory are presented. 2. InAlSb/InAs HEMTs The HEMT material was grown by solid-source molecu- lar beam epitaxy (MBE) on a semi-insulating (100) GaAs...and S.Y. Lin, “Strained quantum well modulation-doped InGaSb/AlGaSb struc- tures grown by molecular beam epitaxy ,” J. Electron. Mater., vol.22, no.3...where he majored in solid state physics and researched growth by molecular - beam epitaxy (MBE) of certain compound semiconductor ma- terials. Since

Estimations of ABL fluxes and other turbulence parameters from Doppler lidar data

NASA Technical Reports Server (NTRS)

Gal-Chen, Tzvi; Xu, Mei; Eberhard, Wynn

1989-01-01

Techniques for extraction boundary layer parameters from measurements of a short-pulse CO2 Doppler lidar are described. The measurements are those collected during the First International Satellites Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE). By continuously operating the lidar for about an hour, stable statistics of the radial velocities can be extracted. Assuming that the turbulence is horizontally homogeneous, the mean wind, its standard deviations, and the momentum fluxes were estimated. Spectral analysis of the radial velocities is also performed from which, by examining the amplitude of the power spectrum at the inertial range, the kinetic energy dissipation was deduced. Finally, using the statistical form of the Navier-Stokes equations, the surface heat flux is derived as the residual balance between the vertical gradient of the third moment of the vertical velocity and the kinetic energy dissipation. Combining many measurements would normally reduce the error provided that, it is unbiased and uncorrelated. The nature of some of the algorithms however, is such that, biased and correlated errors may be generated even though the raw measurements are not. Data processing procedures were developed that eliminate bias and minimize error correlation. Once bias and error correlations are accounted for, the large sample size is shown to reduce the errors substantially. The principal features of the derived turbulence statistics for two case studied are presented.

Multiple-rule bias in the comparison of classification rules

PubMed Central

Yousefi, Mohammadmahdi R.; Hua, Jianping; Dougherty, Edward R.

2011-01-01

Motivation: There is growing discussion in the bioinformatics community concerning overoptimism of reported results. Two approaches contributing to overoptimism in classification are (i) the reporting of results on datasets for which a proposed classification rule performs well and (ii) the comparison of multiple classification rules on a single dataset that purports to show the advantage of a certain rule. Results: This article provides a careful probabilistic analysis of the second issue and the ‘multiple-rule bias’, resulting from choosing a classification rule having minimum estimated error on the dataset. It quantifies this bias corresponding to estimating the expected true error of the classification rule possessing minimum estimated error and it characterizes the bias from estimating the true comparative advantage of the chosen classification rule relative to the others by the estimated comparative advantage on the dataset. The analysis is applied to both synthetic and real data using a number of classification rules and error estimators. Availability: We have implemented in C code the synthetic data distribution model, classification rules, feature selection routines and error estimation methods. The code for multiple-rule analysis is implemented in MATLAB. The source code is available at http://gsp.tamu.edu/Publications/supplementary/yousefi11a/. Supplementary simulation results are also included. Contact: edward@ece.tamu.edu Supplementary Information: Supplementary data are available at Bioinformatics online. PMID:21546390

Seasonal prediction of Indian summer monsoon rainfall in NCEP CFSv2: forecast and predictability error

NASA Astrophysics Data System (ADS)

Pokhrel, Samir; Saha, Subodh Kumar; Dhakate, Ashish; Rahman, Hasibur; Chaudhari, Hemantkumar S.; Salunke, Kiran; Hazra, Anupam; Sujith, K.; Sikka, D. R.

2016-04-01

A detailed analysis of sensitivity to the initial condition for the simulation of the Indian summer monsoon using retrospective forecast by the latest version of the Climate Forecast System version-2 (CFSv2) is carried out. This study primarily focuses on the tropical region of Indian and Pacific Ocean basin, with special emphasis on the Indian land region. The simulated seasonal mean and the inter-annual standard deviations of rainfall, upper and lower level atmospheric circulations and Sea Surface Temperature (SST) tend to be more skillful as the lead forecast time decreases (5 month lead to 0 month lead time i.e. L5-L0). In general spatial correlation (bias) increases (decreases) as forecast lead time decreases. This is further substantiated by their averaged value over the selected study regions over the Indian and Pacific Ocean basins. The tendency of increase (decrease) of model bias with increasing (decreasing) forecast lead time also indicates the dynamical drift of the model. Large scale lower level circulation (850 hPa) shows enhancement of anomalous westerlies (easterlies) over the tropical region of the Indian Ocean (Western Pacific Ocean), which indicates the enhancement of model error with the decrease in lead time. At the upper level circulation (200 hPa) biases in both tropical easterly jet and subtropical westerlies jet tend to decrease as the lead time decreases. Despite enhancement of the prediction skill, mean SST bias seems to be insensitive to the initialization. All these biases are significant and together they make CFSv2 vulnerable to seasonal uncertainties in all the lead times. Overall the zeroth lead (L0) seems to have the best skill, however, in case of Indian summer monsoon rainfall (ISMR), the 3 month lead forecast time (L3) has the maximum ISMR prediction skill. This is valid using different independent datasets, wherein these maximum skill scores are 0.64, 0.42 and 0.57 with respect to the Global Precipitation Climatology Project, CPC Merged Analysis of Precipitation and the India Meteorological Department precipitation dataset respectively for L3. Despite significant El-Niño Southern Oscillation (ENSO) spring predictability barrier at L3, the ISMR skill score is highest at L3. Further, large scale zonal wind shear (Webster-Yang index) and SST over Niño3.4 region is best at L1 and L0. This implies that predictability aspect of ISMR is controlled by factors other than ENSO and Indian Ocean Dipole. Also, the model error (forecast error) outruns the error acquired by the inadequacies in the initial conditions (predictability error). Thus model deficiency is having more serious consequences as compared to the initial condition error for the seasonal forecast. All the model parameters show the increase in the predictability error as the lead decreases over the equatorial eastern Pacific basin and peaks at L2, then it further decreases. The dynamical consistency of both the forecast and the predictability error among all the variables indicates that these biases are purely systematic in nature and improvement of the physical processes in the CFSv2 may enhance the overall predictability.

Measures of model performance based on the log accuracy ratio

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

Morley, Steven Karl; Brito, Thiago Vasconcelos; Welling, Daniel T.

Quantitative assessment of modeling and forecasting of continuous quantities uses a variety of approaches. We review existing literature describing metrics for forecast accuracy and bias, concentrating on those based on relative errors and percentage errors. Of these accuracy metrics, the mean absolute percentage error (MAPE) is one of the most common across many fields and has been widely applied in recent space science literature and we highlight the benefits and drawbacks of MAPE and proposed alternatives. We then introduce the log accuracy ratio, and derive from it two metrics: the median symmetric accuracy; and the symmetric signed percentage bias. Robustmore » methods for estimating the spread of a multiplicative linear model using the log accuracy ratio are also presented. The developed metrics are shown to be easy to interpret, robust, and to mitigate the key drawbacks of their more widely-used counterparts based on relative errors and percentage errors. Their use is illustrated with radiation belt electron flux modeling examples.« less

Measures of model performance based on the log accuracy ratio

DOE PAGES

Morley, Steven Karl; Brito, Thiago Vasconcelos; Welling, Daniel T.

2018-01-03

Quantitative assessment of modeling and forecasting of continuous quantities uses a variety of approaches. We review existing literature describing metrics for forecast accuracy and bias, concentrating on those based on relative errors and percentage errors. Of these accuracy metrics, the mean absolute percentage error (MAPE) is one of the most common across many fields and has been widely applied in recent space science literature and we highlight the benefits and drawbacks of MAPE and proposed alternatives. We then introduce the log accuracy ratio, and derive from it two metrics: the median symmetric accuracy; and the symmetric signed percentage bias. Robustmore » methods for estimating the spread of a multiplicative linear model using the log accuracy ratio are also presented. The developed metrics are shown to be easy to interpret, robust, and to mitigate the key drawbacks of their more widely-used counterparts based on relative errors and percentage errors. Their use is illustrated with radiation belt electron flux modeling examples.« less

The assessment of cognitive errors using an observer-rated method.

PubMed

Drapeau, Martin

2014-01-01

Cognitive Errors (CEs) are a key construct in cognitive behavioral therapy (CBT). Integral to CBT is that individuals with depression process information in an overly negative or biased way, and that this bias is reflected in specific depressotypic CEs which are distinct from normal information processing. Despite the importance of this construct in CBT theory, practice, and research, few methods are available to researchers and clinicians to reliably identify CEs as they occur. In this paper, the author presents a rating system, the Cognitive Error Rating Scale, which can be used by trained observers to identify and assess the cognitive errors of patients or research participants in vivo, i.e., as they are used or reported by the patients or participants. The method is described, including some of the more important rating conventions to be considered when using the method. This paper also describes the 15 cognitive errors assessed, and the different summary scores, including valence of the CEs, that can be derived from the method.

Estimation of an Occupational Choice Model when Occupations Are Misclassified

ERIC Educational Resources Information Center

Sullivan, Paul

2009-01-01

This paper develops an empirical occupational choice model that corrects for misclassification in occupational choices and measurement error in occupation-specific work experience. The model is used to estimate the extent of measurement error in occupation data and quantify the bias that results from ignoring measurement error in occupation codes…