Anticipating Future Extreme Climate Events for Alaska Using Dynamical Downscaling and Quantile Mapping

NASA Astrophysics Data System (ADS)

Lader, R.; Walsh, J. E.

2016-12-01

Alaska is projected to experience major changes in extreme climate during the 21st century, due to greenhouse warming and exacerbated by polar amplification, wherein the Arctic is warming at twice the rate compared to the Northern Hemisphere. Given its complex topography, Alaska displays extreme gradients of temperature and precipitation. However, global climate models (GCMs), which typically have a spatial resolution on the order of 100km, struggle to replicate these extremes. To help resolve this issue, this study employs dynamically downscaled regional climate simulations and quantile-mapping methodologies to provide a full suite of daily model variables at 20 km spatial resolution for Alaska, from 1970 to 2100. These data include downscaled products of the: ERA-Interim reanalysis from 1979 to 2015, GFDL-CM3 historical from 1970 to 2005, and GFDL-CM3 RCP 8.5 from 2006 to 2100. Due to the limited nature of long-term observations and high-resolution modeling in Alaska, these data enable a broad expansion of extremes analysis. This study uses these data to highlight a subset of the 27 climate extremes indices, previously defined by the Expert Team on Climate Change Detection and Indices, as they pertain to climate change in Alaska. These indices are based on the statistical distributions of daily surface temperature and precipitation and focus on threshold exceedance, and percentiles. For example, the annual number of days with a daily maximum temperature greater than 25°C is anticipated to triple in many locations in Alaska by the end of the century. Climate extremes can also refer to long duration events, such as the record-setting warmth that defined the 2015-16 cold season in Alaska. The downscaled climate model simulations indicate that this past winter will be considered normal by as early as the mid-2040s, if we continue to warm according to the business-as-usual RCP 8.5 emissions scenario. This represents an accelerated warming as compared to projections

Percentile-Based ETCCDI Temperature Extremes Indices for CMIP5 Model Output: New Results through Semiparametric Quantile Regression Approach

NASA Astrophysics Data System (ADS)

Li, L.; Yang, C.

2017-12-01

Climate extremes often manifest as rare events in terms of surface air temperature and precipitation with an annual reoccurrence period. In order to represent the manifold characteristics of climate extremes for monitoring and analysis, the Expert Team on Climate Change Detection and Indices (ETCCDI) had worked out a set of 27 core indices based on daily temperature and precipitation data, describing extreme weather and climate events on an annual basis. The CLIMDEX project (http://www.climdex.org) had produced public domain datasets of such indices for data from a variety of sources, including output from global climate models (GCM) participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5). Among the 27 ETCCDI indices, there are six percentile-based temperature extremes indices that may fall into two groups: exceedance rates (ER) (TN10p, TN90p, TX10p and TX90p) and durations (CSDI and WSDI). Percentiles must be estimated prior to the calculation of the indices, and could more or less be biased by the adopted algorithm. Such biases will in turn be propagated to the final results of indices. The CLIMDEX used an empirical quantile estimator combined with a bootstrap resampling procedure to reduce the inhomogeneity in the annual series of the ER indices. However, there are still some problems remained in the CLIMDEX datasets, namely the overestimated climate variability due to unaccounted autocorrelation in the daily temperature data, seasonally varying biases and inconsistency between algorithms applied to the ER indices and to the duration indices. We now present new results of the six indices through a semiparametric quantile regression approach for the CMIP5 model output. By using the base-period data as a whole and taking seasonality and autocorrelation into account, this approach successfully addressed the aforementioned issues and came out with consistent results. The new datasets cover the historical and three projected (RCP2.6, RCP4.5 and RCP

Multi-element stochastic spectral projection for high quantile estimation

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

Ko, Jordan, E-mail: jordan.ko@mac.com; Garnier, Josselin

2013-06-15

We investigate quantile estimation by multi-element generalized Polynomial Chaos (gPC) metamodel where the exact numerical model is approximated by complementary metamodels in overlapping domains that mimic the model’s exact response. The gPC metamodel is constructed by the non-intrusive stochastic spectral projection approach and function evaluation on the gPC metamodel can be considered as essentially free. Thus, large number of Monte Carlo samples from the metamodel can be used to estimate α-quantile, for moderate values of α. As the gPC metamodel is an expansion about the means of the inputs, its accuracy may worsen away from these mean values where themore » extreme events may occur. By increasing the approximation accuracy of the metamodel, we may eventually improve accuracy of quantile estimation but it is very expensive. A multi-element approach is therefore proposed by combining a global metamodel in the standard normal space with supplementary local metamodels constructed in bounded domains about the design points corresponding to the extreme events. To improve the accuracy and to minimize the sampling cost, sparse-tensor and anisotropic-tensor quadratures are tested in addition to the full-tensor Gauss quadrature in the construction of local metamodels; different bounds of the gPC expansion are also examined. The global and local metamodels are combined in the multi-element gPC (MEgPC) approach and it is shown that MEgPC can be more accurate than Monte Carlo or importance sampling methods for high quantile estimations for input dimensions roughly below N=8, a limit that is very much case- and α-dependent.« less

Censored quantile regression with recursive partitioning-based weights

PubMed Central

Wey, Andrew; Wang, Lan; Rudser, Kyle

2014-01-01

Censored quantile regression provides a useful alternative to the Cox proportional hazards model for analyzing survival data. It directly models the conditional quantile of the survival time and hence is easy to interpret. Moreover, it relaxes the proportionality constraint on the hazard function associated with the popular Cox model and is natural for modeling heterogeneity of the data. Recently, Wang and Wang (2009. Locally weighted censored quantile regression. Journal of the American Statistical Association 103, 1117–1128) proposed a locally weighted censored quantile regression approach that allows for covariate-dependent censoring and is less restrictive than other censored quantile regression methods. However, their kernel smoothing-based weighting scheme requires all covariates to be continuous and encounters practical difficulty with even a moderate number of covariates. We propose a new weighting approach that uses recursive partitioning, e.g. survival trees, that offers greater flexibility in handling covariate-dependent censoring in moderately high dimensions and can incorporate both continuous and discrete covariates. We prove that this new weighting scheme leads to consistent estimation of the quantile regression coefficients and demonstrate its effectiveness via Monte Carlo simulations. We also illustrate the new method using a widely recognized data set from a clinical trial on primary biliary cirrhosis. PMID:23975800

A perturbation approach for assessing trends in precipitation extremes across Iran

NASA Astrophysics Data System (ADS)

Tabari, Hossein; AghaKouchak, Amir; Willems, Patrick

2014-11-01

Extreme precipitation events have attracted a great deal of attention among the scientific community because of their devastating consequences on human livelihood and socio-economic development. To assess changes in precipitation extremes in a given region, it is essential to analyze decadal oscillations in precipitation extremes. This study examines temporal oscillations in precipitation data in several sub-regions of Iran using a novel quantile perturbation method during 1980-2010. Precipitation data from NASA's Modern-Era Retrospective Analysis for Research and Applications-Land (MERRA-Land) are used in this study. The results indicate significant anomalies in precipitation extremes in the northwest and southeast regions of Iran. Analysis of extreme precipitation perturbations reveals that perturbations for the monthly aggregation level are generally lower than the annual perturbations. Furthermore, high-oscillation and low-oscillation periods are found in extreme precipitation quantiles across different seasons. In all selected regions, a significant anomaly (i.e., extreme wet/dry conditions) in precipitation extremes is observed during spring.

Variable screening via quantile partial correlation

PubMed Central

Ma, Shujie; Tsai, Chih-Ling

2016-01-01

In quantile linear regression with ultra-high dimensional data, we propose an algorithm for screening all candidate variables and subsequently selecting relevant predictors. Specifically, we first employ quantile partial correlation for screening, and then we apply the extended Bayesian information criterion (EBIC) for best subset selection. Our proposed method can successfully select predictors when the variables are highly correlated, and it can also identify variables that make a contribution to the conditional quantiles but are marginally uncorrelated or weakly correlated with the response. Theoretical results show that the proposed algorithm can yield the sure screening set. By controlling the false selection rate, model selection consistency can be achieved theoretically. In practice, we proposed using EBIC for best subset selection so that the resulting model is screening consistent. Simulation studies demonstrate that the proposed algorithm performs well, and an empirical example is presented. PMID:28943683

Generation of multivariate near shore extreme wave conditions based on an extreme value copula for offshore boundary conditions.

NASA Astrophysics Data System (ADS)

Leyssen, Gert; Mercelis, Peter; De Schoesitter, Philippe; Blanckaert, Joris

2013-04-01

Near shore extreme wave conditions, used as input for numerical wave agitation simulations and for the dimensioning of coastal defense structures, need to be determined at a harbour entrance situated at the French North Sea coast. To obtain significant wave heights, the numerical wave model SWAN has been used. A multivariate approach was used to account for the joint probabilities. Considered variables are: wind velocity and direction, water level and significant offshore wave height and wave period. In a first step a univariate extreme value distribution has been determined for the main variables. By means of a technique based on the mean excess function, an appropriate member of the GPD is selected. An optimal threshold for peak over threshold selection is determined by maximum likelihood optimization. Next, the joint dependency structure for the primary random variables is modeled by an extreme value copula. Eventually the multivariate domain of variables was stratified in different classes, each of which representing a combination of variable quantiles with a joint probability, which are used for model simulation. The main variable is the wind velocity, as in the area of concern extreme wave conditions are wind driven. The analysis is repeated for 9 different wind directions. The secondary variable is water level. In shallow waters extreme waves will be directly affected by water depth. Hence the joint probability of occurrence for water level and wave height is of major importance for design of coastal defense structures. Wind velocity and water levels are only dependent for some wind directions (wind induced setup). Dependent directions are detected using a Kendall and Spearman test and appeared to be those with the longest fetch. For these directions, wind velocity and water level extreme value distributions are multivariately linked through a Gumbel Copula. These distributions are stratified into classes of which the frequency of occurrence can be

Simultaneous multiple non-crossing quantile regression estimation using kernel constraints

PubMed Central

Liu, Yufeng; Wu, Yichao

2011-01-01

Quantile regression (QR) is a very useful statistical tool for learning the relationship between the response variable and covariates. For many applications, one often needs to estimate multiple conditional quantile functions of the response variable given covariates. Although one can estimate multiple quantiles separately, it is of great interest to estimate them simultaneously. One advantage of simultaneous estimation is that multiple quantiles can share strength among them to gain better estimation accuracy than individually estimated quantile functions. Another important advantage of joint estimation is the feasibility of incorporating simultaneous non-crossing constraints of QR functions. In this paper, we propose a new kernel-based multiple QR estimation technique, namely simultaneous non-crossing quantile regression (SNQR). We use kernel representations for QR functions and apply constraints on the kernel coefficients to avoid crossing. Both unregularised and regularised SNQR techniques are considered. Asymptotic properties such as asymptotic normality of linear SNQR and oracle properties of the sparse linear SNQR are developed. Our numerical results demonstrate the competitive performance of our SNQR over the original individual QR estimation. PMID:22190842

Nonuniform sampling by quantiles

NASA Astrophysics Data System (ADS)

Craft, D. Levi; Sonstrom, Reilly E.; Rovnyak, Virginia G.; Rovnyak, David

2018-03-01

A flexible strategy for choosing samples nonuniformly from a Nyquist grid using the concept of statistical quantiles is presented for broad classes of NMR experimentation. Quantile-directed scheduling is intuitive and flexible for any weighting function, promotes reproducibility and seed independence, and is generalizable to multiple dimensions. In brief, weighting functions are divided into regions of equal probability, which define the samples to be acquired. Quantile scheduling therefore achieves close adherence to a probability distribution function, thereby minimizing gaps for any given degree of subsampling of the Nyquist grid. A characteristic of quantile scheduling is that one-dimensional, weighted NUS schedules are deterministic, however higher dimensional schedules are similar within a user-specified jittering parameter. To develop unweighted sampling, we investigated the minimum jitter needed to disrupt subharmonic tracts, and show that this criterion can be met in many cases by jittering within 25-50% of the subharmonic gap. For nD-NUS, three supplemental components to choosing samples by quantiles are proposed in this work: (i) forcing the corner samples to ensure sampling to specified maximum values in indirect evolution times, (ii) providing an option to triangular backfill sampling schedules to promote dense/uniform tracts at the beginning of signal evolution periods, and (iii) providing an option to force the edges of nD-NUS schedules to be identical to the 1D quantiles. Quantile-directed scheduling meets the diverse needs of current NUS experimentation, but can also be used for future NUS implementations such as off-grid NUS and more. A computer program implementing these principles (a.k.a. QSched) in 1D- and 2D-NUS is available under the general public license.

Consistent model identification of varying coefficient quantile regression with BIC tuning parameter selection

PubMed Central

Zheng, Qi; Peng, Limin

2016-01-01

Quantile regression provides a flexible platform for evaluating covariate effects on different segments of the conditional distribution of response. As the effects of covariates may change with quantile level, contemporaneously examining a spectrum of quantiles is expected to have a better capacity to identify variables with either partial or full effects on the response distribution, as compared to focusing on a single quantile. Under this motivation, we study a general adaptively weighted LASSO penalization strategy in the quantile regression setting, where a continuum of quantile index is considered and coefficients are allowed to vary with quantile index. We establish the oracle properties of the resulting estimator of coefficient function. Furthermore, we formally investigate a BIC-type uniform tuning parameter selector and show that it can ensure consistent model selection. Our numerical studies confirm the theoretical findings and illustrate an application of the new variable selection procedure. PMID:28008212

Improving Global Forecast System of extreme precipitation events with regional statistical model: Application of quantile-based probabilistic forecasts

NASA Astrophysics Data System (ADS)

Shastri, Hiteshri; Ghosh, Subimal; Karmakar, Subhankar

2017-02-01

Forecasting of extreme precipitation events at a regional scale is of high importance due to their severe impacts on society. The impacts are stronger in urban regions due to high flood potential as well high population density leading to high vulnerability. Although significant scientific improvements took place in the global models for weather forecasting, they are still not adequate at a regional scale (e.g., for an urban region) with high false alarms and low detection. There has been a need to improve the weather forecast skill at a local scale with probabilistic outcome. Here we develop a methodology with quantile regression, where the reliably simulated variables from Global Forecast System are used as predictors and different quantiles of rainfall are generated corresponding to that set of predictors. We apply this method to a flood-prone coastal city of India, Mumbai, which has experienced severe floods in recent years. We find significant improvements in the forecast with high detection and skill scores. We apply the methodology to 10 ensemble members of Global Ensemble Forecast System and find a reduction in ensemble uncertainty of precipitation across realizations with respect to that of original precipitation forecasts. We validate our model for the monsoon season of 2006 and 2007, which are independent of the training/calibration data set used in the study. We find promising results and emphasize to implement such data-driven methods for a better probabilistic forecast at an urban scale primarily for an early flood warning.

Nonuniform sampling by quantiles.

PubMed

Craft, D Levi; Sonstrom, Reilly E; Rovnyak, Virginia G; Rovnyak, David

2018-03-01

A flexible strategy for choosing samples nonuniformly from a Nyquist grid using the concept of statistical quantiles is presented for broad classes of NMR experimentation. Quantile-directed scheduling is intuitive and flexible for any weighting function, promotes reproducibility and seed independence, and is generalizable to multiple dimensions. In brief, weighting functions are divided into regions of equal probability, which define the samples to be acquired. Quantile scheduling therefore achieves close adherence to a probability distribution function, thereby minimizing gaps for any given degree of subsampling of the Nyquist grid. A characteristic of quantile scheduling is that one-dimensional, weighted NUS schedules are deterministic, however higher dimensional schedules are similar within a user-specified jittering parameter. To develop unweighted sampling, we investigated the minimum jitter needed to disrupt subharmonic tracts, and show that this criterion can be met in many cases by jittering within 25-50% of the subharmonic gap. For nD-NUS, three supplemental components to choosing samples by quantiles are proposed in this work: (i) forcing the corner samples to ensure sampling to specified maximum values in indirect evolution times, (ii) providing an option to triangular backfill sampling schedules to promote dense/uniform tracts at the beginning of signal evolution periods, and (iii) providing an option to force the edges of nD-NUS schedules to be identical to the 1D quantiles. Quantile-directed scheduling meets the diverse needs of current NUS experimentation, but can also be used for future NUS implementations such as off-grid NUS and more. A computer program implementing these principles (a.k.a. QSched) in 1D- and 2D-NUS is available under the general public license. Copyright © 2018 Elsevier Inc. All rights reserved.

Modeling distributional changes in winter precipitation of Canada using Bayesian spatiotemporal quantile regression subjected to different teleconnections

NASA Astrophysics Data System (ADS)

Tan, Xuezhi; Gan, Thian Yew; Chen, Shu; Liu, Bingjun

2018-05-01

Climate change and large-scale climate patterns may result in changes in probability distributions of climate variables that are associated with changes in the mean and variability, and severity of extreme climate events. In this paper, we applied a flexible framework based on the Bayesian spatiotemporal quantile (BSTQR) model to identify climate changes at different quantile levels and their teleconnections to large-scale climate patterns such as El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO) and Pacific-North American (PNA). Using the BSTQR model with time (year) as a covariate, we estimated changes in Canadian winter precipitation and their uncertainties at different quantile levels. There were some stations in eastern Canada showing distributional changes in winter precipitation such as an increase in low quantiles but a decrease in high quantiles. Because quantile functions in the BSTQR model vary with space and time and assimilate spatiotemporal precipitation data, the BSTQR model produced much spatially smoother and less uncertain quantile changes than the classic regression without considering spatiotemporal correlations. Using the BSTQR model with five teleconnection indices (i.e., SOI, PDO, PNA, NP and NAO) as covariates, we investigated effects of large-scale climate patterns on Canadian winter precipitation at different quantile levels. Winter precipitation responses to these five teleconnections were found to occur differently at different quantile levels. Effects of five teleconnections on Canadian winter precipitation were stronger at low and high than at medium quantile levels.

Estimating geographic variation on allometric growth and body condition of Blue Suckers with quantile regression

USGS Publications Warehouse

Cade, B.S.; Terrell, J.W.; Neely, B.C.

2011-01-01

Increasing our understanding of how environmental factors affect fish body condition and improving its utility as a metric of aquatic system health require reliable estimates of spatial variation in condition (weight at length). We used three statistical approaches that varied in how they accounted for heterogeneity in allometric growth to estimate differences in body condition of blue suckers Cycleptus elongatus across 19 large-river locations in the central USA. Quantile regression of an expanded allometric growth model provided the most comprehensive estimates, including variation in exponents within and among locations (range = 2.88–4.24). Blue suckers from more-southerly locations had the largest exponents. Mixed-effects mean regression of a similar expanded allometric growth model allowed exponents to vary among locations (range = 3.03–3.60). Mean relative weights compared across selected intervals of total length (TL = 510–594 and 594–692 mm) in a multiplicative model involved the implicit assumption that allometric exponents within and among locations were similar to the exponent (3.46) for the standard weight equation. Proportionate differences in the quantiles of weight at length for adult blue suckers (TL = 510, 594, 644, and 692 mm) compared with their average across locations ranged from 1.08 to 1.30 for southern locations (Texas, Mississippi) and from 0.84 to 1.00 for northern locations (Montana, North Dakota); proportionate differences for mean weight ranged from 1.13 to 1.17 and from 0.87 to 0.95, respectively, and those for mean relative weight ranged from 1.10 to 1.18 and from 0.86 to 0.98, respectively. Weights for fish at longer lengths varied by 600–700 g within a location and by as much as 2,000 g among southern and northern locations. Estimates for the Wabash River, Indiana (0.96–1.07 times the average; greatest increases for lower weights at shorter TLs), and for the Missouri River from Blair, Nebraska, to Sioux City, Iowa (0.90�

A gentle introduction to quantile regression for ecologists

USGS Publications Warehouse

Cade, B.S.; Noon, B.R.

2003-01-01

Quantile regression is a way to estimate the conditional quantiles of a response variable distribution in the linear model that provides a more complete view of possible causal relationships between variables in ecological processes. Typically, all the factors that affect ecological processes are not measured and included in the statistical models used to investigate relationships between variables associated with those processes. As a consequence, there may be a weak or no predictive relationship between the mean of the response variable (y) distribution and the measured predictive factors (X). Yet there may be stronger, useful predictive relationships with other parts of the response variable distribution. This primer relates quantile regression estimates to prediction intervals in parametric error distribution regression models (eg least squares), and discusses the ordering characteristics, interval nature, sampling variation, weighting, and interpretation of the estimates for homogeneous and heterogeneous regression models.

Forecasting peak asthma admissions in London: an application of quantile regression models.

PubMed

Soyiri, Ireneous N; Reidpath, Daniel D; Sarran, Christophe

2013-07-01

Asthma is a chronic condition of great public health concern globally. The associated morbidity, mortality and healthcare utilisation place an enormous burden on healthcare infrastructure and services. This study demonstrates a multistage quantile regression approach to predicting excess demand for health care services in the form of asthma daily admissions in London, using retrospective data from the Hospital Episode Statistics, weather and air quality. Trivariate quantile regression models (QRM) of asthma daily admissions were fitted to a 14-day range of lags of environmental factors, accounting for seasonality in a hold-in sample of the data. Representative lags were pooled to form multivariate predictive models, selected through a systematic backward stepwise reduction approach. Models were cross-validated using a hold-out sample of the data, and their respective root mean square error measures, sensitivity, specificity and predictive values compared. Two of the predictive models were able to detect extreme number of daily asthma admissions at sensitivity levels of 76 % and 62 %, as well as specificities of 66 % and 76 %. Their positive predictive values were slightly higher for the hold-out sample (29 % and 28 %) than for the hold-in model development sample (16 % and 18 %). QRMs can be used in multistage to select suitable variables to forecast extreme asthma events. The associations between asthma and environmental factors, including temperature, ozone and carbon monoxide can be exploited in predicting future events using QRMs.

Forecasting peak asthma admissions in London: an application of quantile regression models

NASA Astrophysics Data System (ADS)

Soyiri, Ireneous N.; Reidpath, Daniel D.; Sarran, Christophe

2013-07-01

Asthma is a chronic condition of great public health concern globally. The associated morbidity, mortality and healthcare utilisation place an enormous burden on healthcare infrastructure and services. This study demonstrates a multistage quantile regression approach to predicting excess demand for health care services in the form of asthma daily admissions in London, using retrospective data from the Hospital Episode Statistics, weather and air quality. Trivariate quantile regression models (QRM) of asthma daily admissions were fitted to a 14-day range of lags of environmental factors, accounting for seasonality in a hold-in sample of the data. Representative lags were pooled to form multivariate predictive models, selected through a systematic backward stepwise reduction approach. Models were cross-validated using a hold-out sample of the data, and their respective root mean square error measures, sensitivity, specificity and predictive values compared. Two of the predictive models were able to detect extreme number of daily asthma admissions at sensitivity levels of 76 % and 62 %, as well as specificities of 66 % and 76 %. Their positive predictive values were slightly higher for the hold-out sample (29 % and 28 %) than for the hold-in model development sample (16 % and 18 %). QRMs can be used in multistage to select suitable variables to forecast extreme asthma events. The associations between asthma and environmental factors, including temperature, ozone and carbon monoxide can be exploited in predicting future events using QRMs.

Estimating effects of limiting factors with regression quantiles

USGS Publications Warehouse

Cade, B.S.; Terrell, J.W.; Schroeder, R.L.

1999-01-01

In a recent Concepts paper in Ecology, Thomson et al. emphasized that assumptions of conventional correlation and regression analyses fundamentally conflict with the ecological concept of limiting factors, and they called for new statistical procedures to address this problem. The analytical issue is that unmeasured factors may be the active limiting constraint and may induce a pattern of unequal variation in the biological response variable through an interaction with the measured factors. Consequently, changes near the maxima, rather than at the center of response distributions, are better estimates of the effects expected when the observed factor is the active limiting constraint. Regression quantiles provide estimates for linear models fit to any part of a response distribution, including near the upper bounds, and require minimal assumptions about the form of the error distribution. Regression quantiles extend the concept of one-sample quantiles to the linear model by solving an optimization problem of minimizing an asymmetric function of absolute errors. Rank-score tests for regression quantiles provide tests of hypotheses and confidence intervals for parameters in linear models with heteroscedastic errors, conditions likely to occur in models of limiting ecological relations. We used selected regression quantiles (e.g., 5th, 10th, ..., 95th) and confidence intervals to test hypotheses that parameters equal zero for estimated changes in average annual acorn biomass due to forest canopy cover of oak (Quercus spp.) and oak species diversity. Regression quantiles also were used to estimate changes in glacier lily (Erythronium grandiflorum) seedling numbers as a function of lily flower numbers, rockiness, and pocket gopher (Thomomys talpoides fossor) activity, data that motivated the query by Thomson et al. for new statistical procedures. Both example applications showed that effects of limiting factors estimated by changes in some upper regression quantile (e

Quantile regression analyses of associated factors for body mass index in Korean adolescents.

PubMed

Kim, T H; Lee, E K; Han, E

2015-05-01

This study examined the influence of home and school environments, and individual health-risk behaviours on body weight outcomes in Korean adolescents. This was a cross-sectional observational study. Quantile regression models to explore heterogeneity in the association of specific factors with body mass index (BMI) over the entire conditional BMI distribution was used. A nationally representative web-based survey for youths was used. Paternal education level of college or more education was associated with lower BMI for girls, whereas college or more education of mothers was associated with higher BMI for boys; for both, the magnitude of association became larger at the upper quantiles of the conditional BMI distribution. Girls with good family economic status were more likely to have higher BMIs than those with average family economic status, particularly at the upper quantile of the conditional BMI distribution. Attending a co-ed school was associated with lower BMI for both genders with a larger association at the upper quantiles. Substantial screen time for TV watching, video games, or internet surfing was associated with a higher BMI with a larger association at the upper quantiles for both girls and boys. Dental prevention was negatively associated with BMI, whereas suicide consideration was positively associated with BMIs of both genders with a larger association at a higher quantile. These findings suggest that interventions aimed at behavioural changes and positive parental roles are needed to effectively address high adolescent BMI. Copyright © 2015 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Multiple imputation for cure rate quantile regression with censored data.

PubMed

Wu, Yuanshan; Yin, Guosheng

2017-03-01

The main challenge in the context of cure rate analysis is that one never knows whether censored subjects are cured or uncured, or whether they are susceptible or insusceptible to the event of interest. Considering the susceptible indicator as missing data, we propose a multiple imputation approach to cure rate quantile regression for censored data with a survival fraction. We develop an iterative algorithm to estimate the conditionally uncured probability for each subject. By utilizing this estimated probability and Bernoulli sample imputation, we can classify each subject as cured or uncured, and then employ the locally weighted method to estimate the quantile regression coefficients with only the uncured subjects. Repeating the imputation procedure multiple times and taking an average over the resultant estimators, we obtain consistent estimators for the quantile regression coefficients. Our approach relaxes the usual global linearity assumption, so that we can apply quantile regression to any particular quantile of interest. We establish asymptotic properties for the proposed estimators, including both consistency and asymptotic normality. We conduct simulation studies to assess the finite-sample performance of the proposed multiple imputation method and apply it to a lung cancer study as an illustration. © 2016, The International Biometric Society.

GLOBALLY ADAPTIVE QUANTILE REGRESSION WITH ULTRA-HIGH DIMENSIONAL DATA

PubMed Central

Zheng, Qi; Peng, Limin; He, Xuming

2015-01-01

Quantile regression has become a valuable tool to analyze heterogeneous covaraite-response associations that are often encountered in practice. The development of quantile regression methodology for high dimensional covariates primarily focuses on examination of model sparsity at a single or multiple quantile levels, which are typically prespecified ad hoc by the users. The resulting models may be sensitive to the specific choices of the quantile levels, leading to difficulties in interpretation and erosion of confidence in the results. In this article, we propose a new penalization framework for quantile regression in the high dimensional setting. We employ adaptive L1 penalties, and more importantly, propose a uniform selector of the tuning parameter for a set of quantile levels to avoid some of the potential problems with model selection at individual quantile levels. Our proposed approach achieves consistent shrinkage of regression quantile estimates across a continuous range of quantiles levels, enhancing the flexibility and robustness of the existing penalized quantile regression methods. Our theoretical results include the oracle rate of uniform convergence and weak convergence of the parameter estimators. We also use numerical studies to confirm our theoretical findings and illustrate the practical utility of our proposal. PMID:26604424

Probabilistic forecasting for extreme NO2 pollution episodes.

PubMed

Aznarte, José L

2017-10-01

In this study, we investigate the convenience of quantile regression to predict extreme concentrations of NO 2 . Contrarily to the usual point-forecasting, where a single value is forecast for each horizon, probabilistic forecasting through quantile regression allows for the prediction of the full probability distribution, which in turn allows to build models specifically fit for the tails of this distribution. Using data from the city of Madrid, including NO 2 concentrations as well as meteorological measures, we build models that predict extreme NO 2 concentrations, outperforming point-forecasting alternatives, and we prove that the predictions are accurate, reliable and sharp. Besides, we study the relative importance of the independent variables involved, and show how the important variables for the median quantile are different than those important for the upper quantiles. Furthermore, we present a method to compute the probability of exceedance of thresholds, which is a simple and comprehensible manner to present probabilistic forecasts maximizing their usefulness. Copyright © 2017 Elsevier Ltd. All rights reserved.

A quantile count model of water depth constraints on Cape Sable seaside sparrows

USGS Publications Warehouse

Cade, B.S.; Dong, Q.

2008-01-01

1. A quantile regression model for counts of breeding Cape Sable seaside sparrows Ammodramus maritimus mirabilis (L.) as a function of water depth and previous year abundance was developed based on extensive surveys, 1992-2005, in the Florida Everglades. The quantile count model extends linear quantile regression methods to discrete response variables, providing a flexible alternative to discrete parametric distributional models, e.g. Poisson, negative binomial and their zero-inflated counterparts. 2. Estimates from our multiplicative model demonstrated that negative effects of increasing water depth in breeding habitat on sparrow numbers were dependent on recent occupation history. Upper 10th percentiles of counts (one to three sparrows) decreased with increasing water depth from 0 to 30 cm when sites were not occupied in previous years. However, upper 40th percentiles of counts (one to six sparrows) decreased with increasing water depth for sites occupied in previous years. 3. Greatest decreases (-50% to -83%) in upper quantiles of sparrow counts occurred as water depths increased from 0 to 15 cm when previous year counts were 1, but a small proportion of sites (5-10%) held at least one sparrow even as water depths increased to 20 or 30 cm. 4. A zero-inflated Poisson regression model provided estimates of conditional means that also decreased with increasing water depth but rates of change were lower and decreased with increasing previous year counts compared to the quantile count model. Quantiles computed for the zero-inflated Poisson model enhanced interpretation of this model but had greater lack-of-fit for water depths > 0 cm and previous year counts 1, conditions where the negative effect of water depths were readily apparent and fitted better with the quantile count model.

Groundwater depth prediction in a shallow aquifer in north China by a quantile regression model

NASA Astrophysics Data System (ADS)

Li, Fawen; Wei, Wan; Zhao, Yong; Qiao, Jiale

2017-01-01

There is a close relationship between groundwater level in a shallow aquifer and the surface ecological environment; hence, it is important to accurately simulate and predict the groundwater level in eco-environmental construction projects. The multiple linear regression (MLR) model is one of the most useful methods to predict groundwater level (depth); however, the predicted values by this model only reflect the mean distribution of the observations and cannot effectively fit the extreme distribution data (outliers). The study reported here builds a prediction model of groundwater-depth dynamics in a shallow aquifer using the quantile regression (QR) method on the basis of the observed data of groundwater depth and related factors. The proposed approach was applied to five sites in Tianjin city, north China, and the groundwater depth was calculated in different quantiles, from which the optimal quantile was screened out according to the box plot method and compared to the values predicted by the MLR model. The results showed that the related factors in the five sites did not follow the standard normal distribution and that there were outliers in the precipitation and last-month (initial state) groundwater-depth factors because the basic assumptions of the MLR model could not be achieved, thereby causing errors. Nevertheless, these conditions had no effect on the QR model, as it could more effectively describe the distribution of original data and had a higher precision in fitting the outliers.

Quantile regression applied to spectral distance decay

USGS Publications Warehouse

Rocchini, D.; Cade, B.S.

2008-01-01

Remotely sensed imagery has long been recognized as a powerful support for characterizing and estimating biodiversity. Spectral distance among sites has proven to be a powerful approach for detecting species composition variability. Regression analysis of species similarity versus spectral distance allows us to quantitatively estimate the amount of turnover in species composition with respect to spectral and ecological variability. In classical regression analysis, the residual sum of squares is minimized for the mean of the dependent variable distribution. However, many ecological data sets are characterized by a high number of zeroes that add noise to the regression model. Quantile regressions can be used to evaluate trend in the upper quantiles rather than a mean trend across the whole distribution of the dependent variable. In this letter, we used ordinary least squares (OLS) and quantile regressions to estimate the decay of species similarity versus spectral distance. The achieved decay rates were statistically nonzero (p < 0.01), considering both OLS and quantile regressions. Nonetheless, the OLS regression estimate of the mean decay rate was only half the decay rate indicated by the upper quantiles. Moreover, the intercept value, representing the similarity reached when the spectral distance approaches zero, was very low compared with the intercepts of the upper quantiles, which detected high species similarity when habitats are more similar. In this letter, we demonstrated the power of using quantile regressions applied to spectral distance decay to reveal species diversity patterns otherwise lost or underestimated by OLS regression. ?? 2008 IEEE.

Quantile Regression Models for Current Status Data

PubMed Central

Ou, Fang-Shu; Zeng, Donglin; Cai, Jianwen

2016-01-01

Current status data arise frequently in demography, epidemiology, and econometrics where the exact failure time cannot be determined but is only known to have occurred before or after a known observation time. We propose a quantile regression model to analyze current status data, because it does not require distributional assumptions and the coefficients can be interpreted as direct regression effects on the distribution of failure time in the original time scale. Our model assumes that the conditional quantile of failure time is a linear function of covariates. We assume conditional independence between the failure time and observation time. An M-estimator is developed for parameter estimation which is computed using the concave-convex procedure and its confidence intervals are constructed using a subsampling method. Asymptotic properties for the estimator are derived and proven using modern empirical process theory. The small sample performance of the proposed method is demonstrated via simulation studies. Finally, we apply the proposed method to analyze data from the Mayo Clinic Study of Aging. PMID:27994307

Contrasting OLS and Quantile Regression Approaches to Student "Growth" Percentiles

ERIC Educational Resources Information Center

Castellano, Katherine Elizabeth; Ho, Andrew Dean

2013-01-01

Regression methods can locate student test scores in a conditional distribution, given past scores. This article contrasts and clarifies two approaches to describing these locations in terms of readily interpretable percentile ranks or "conditional status percentile ranks." The first is Betebenner's quantile regression approach that results in…

Relationship between Urbanization and Cancer Incidence in Iran Using Quantile Regression.

PubMed

Momenyan, Somayeh; Sadeghifar, Majid; Sarvi, Fatemeh; Khodadost, Mahmoud; Mosavi-Jarrahi, Alireza; Ghaffari, Mohammad Ebrahim; Sekhavati, Eghbal

2016-01-01

Quantile regression is an efficient method for predicting and estimating the relationship between explanatory variables and percentile points of the response distribution, particularly for extreme percentiles of the distribution. To study the relationship between urbanization and cancer morbidity, we here applied quantile regression. This cross-sectional study was conducted for 9 cancers in 345 cities in 2007 in Iran. Data were obtained from the Ministry of Health and Medical Education and the relationship between urbanization and cancer morbidity was investigated using quantile regression and least square regression. Fitting models were compared using AIC criteria. R (3.0.1) software and the Quantreg package were used for statistical analysis. With the quantile regression model all percentiles for breast, colorectal, prostate, lung and pancreas cancers demonstrated increasing incidence rate with urbanization. The maximum increase for breast cancer was in the 90th percentile (β=0.13, p-value<0.001), for colorectal cancer was in the 75th percentile (β=0.048, p-value<0.001), for prostate cancer the 95th percentile (β=0.55, p-value<0.001), for lung cancer was in 95th percentile (β=0.52, p-value=0.006), for pancreas cancer was in 10th percentile (β=0.011, p-value<0.001). For gastric, esophageal and skin cancers, with increasing urbanization, the incidence rate was decreased. The maximum decrease for gastric cancer was in the 90th percentile(β=0.003, p-value<0.001), for esophageal cancer the 95th (β=0.04, p-value=0.4) and for skin cancer also the 95th (β=0.145, p-value=0.071). The AIC showed that for upper percentiles, the fitting of quantile regression was better than least square regression. According to the results of this study, the significant impact of urbanization on cancer morbidity requirs more effort and planning by policymakers and administrators in order to reduce risk factors such as pollution in urban areas and ensure proper nutrition

Ensuring the consistancy of Flow Direction Curve reconstructions: the 'quantile solidarity' approach

NASA Astrophysics Data System (ADS)

Poncelet, Carine; Andreassian, Vazken; Oudin, Ludovic

2015-04-01

Flow Duration Curves (FDCs) are a hydrologic tool describing the distribution of streamflows at a catchment outlet. FDCs are usually used for calibration of hydrological models, managing water quality and classifying catchments, among others. For gauged catchments, empirical FDCs can be computed from streamflow records. For ungauged catchments, on the other hand, FDCs cannot be obtained from streamflow records and must therefore be obtained in another manner, for example through reconstructions. Regression-based reconstructions are methods relying on the evaluation of quantiles separately from catchments' attributes (climatic or physical features).The advantage of this category of methods is that it is informative about the processes and it is non-parametric. However, the large number of parameters required can cause unwanted artifacts, typically reconstructions that do not always produce increasing quantiles. In this paper we propose a new approach named Quantile Solidarity (QS), which is applied under strict proxy-basin test conditions (Klemes, 1986) to a set of 600 French catchments. Half of the catchments are considered as gauged and used to calibrate the regression and compute residuals of the regression. The QS approach consists in a three-step regionalization scheme, which first links quantile values to physical descriptors, then reduces the number of regression parameters and finally exploits the spatial correlation of the residuals. The innovation is the utilisation of the parameters continuity across the quantiles to dramatically reduce the number of parameters. The second half of catchment is used as an independent validation set over which we show that the QS approach ensures strictly growing FDC reconstructions in ungauged conditions. Reference: V. KLEMEŠ (1986) Operational testing of hydrological simulation models, Hydrological Sciences Journal, 31:1, 13-24

Quantile based Tsallis entropy in residual lifetime

NASA Astrophysics Data System (ADS)

Khammar, A. H.; Jahanshahi, S. M. A.

2018-02-01

Tsallis entropy is a generalization of type α of the Shannon entropy, that is a nonadditive entropy unlike the Shannon entropy. Shannon entropy may be negative for some distributions, but Tsallis entropy can always be made nonnegative by choosing appropriate value of α. In this paper, we derive the quantile form of this nonadditive's entropy function in the residual lifetime, namely the residual quantile Tsallis entropy (RQTE) and get the bounds for it, depending on the Renyi's residual quantile entropy. Also, we obtain relationship between RQTE and concept of proportional hazards model in the quantile setup. Based on the new measure, we propose a stochastic order and aging classes, and study its properties. Finally, we prove characterizations theorems for some well known lifetime distributions. It is shown that RQTE uniquely determines the parent distribution unlike the residual Tsallis entropy.

Variable Selection for Nonparametric Quantile Regression via Smoothing Spline AN OVA

PubMed Central

Lin, Chen-Yen; Bondell, Howard; Zhang, Hao Helen; Zou, Hui

2014-01-01

Quantile regression provides a more thorough view of the effect of covariates on a response. Nonparametric quantile regression has become a viable alternative to avoid restrictive parametric assumption. The problem of variable selection for quantile regression is challenging, since important variables can influence various quantiles in different ways. We tackle the problem via regularization in the context of smoothing spline ANOVA models. The proposed sparse nonparametric quantile regression (SNQR) can identify important variables and provide flexible estimates for quantiles. Our numerical study suggests the promising performance of the new procedure in variable selection and function estimation. Supplementary materials for this article are available online. PMID:24554792

Comparing least-squares and quantile regression approaches to analyzing median hospital charges.

PubMed

Olsen, Cody S; Clark, Amy E; Thomas, Andrea M; Cook, Lawrence J

2012-07-01

Emergency department (ED) and hospital charges obtained from administrative data sets are useful descriptors of injury severity and the burden to EDs and the health care system. However, charges are typically positively skewed due to costly procedures, long hospital stays, and complicated or prolonged treatment for few patients. The median is not affected by extreme observations and is useful in describing and comparing distributions of hospital charges. A least-squares analysis employing a log transformation is one approach for estimating median hospital charges, corresponding confidence intervals (CIs), and differences between groups; however, this method requires certain distributional properties. An alternate method is quantile regression, which allows estimation and inference related to the median without making distributional assumptions. The objective was to compare the log-transformation least-squares method to the quantile regression approach for estimating median hospital charges, differences in median charges between groups, and associated CIs. The authors performed simulations using repeated sampling of observed statewide ED and hospital charges and charges randomly generated from a hypothetical lognormal distribution. The median and 95% CI and the multiplicative difference between the median charges of two groups were estimated using both least-squares and quantile regression methods. Performance of the two methods was evaluated. In contrast to least squares, quantile regression produced estimates that were unbiased and had smaller mean square errors in simulations of observed ED and hospital charges. Both methods performed well in simulations of hypothetical charges that met least-squares method assumptions. When the data did not follow the assumed distribution, least-squares estimates were often biased, and the associated CIs had lower than expected coverage as sample size increased. Quantile regression analyses of hospital charges provide unbiased

Shrinkage Estimation of Varying Covariate Effects Based On Quantile Regression

PubMed Central

Peng, Limin; Xu, Jinfeng; Kutner, Nancy

2013-01-01

Varying covariate effects often manifest meaningful heterogeneity in covariate-response associations. In this paper, we adopt a quantile regression model that assumes linearity at a continuous range of quantile levels as a tool to explore such data dynamics. The consideration of potential non-constancy of covariate effects necessitates a new perspective for variable selection, which, under the assumed quantile regression model, is to retain variables that have effects on all quantiles of interest as well as those that influence only part of quantiles considered. Current work on l1-penalized quantile regression either does not concern varying covariate effects or may not produce consistent variable selection in the presence of covariates with partial effects, a practical scenario of interest. In this work, we propose a shrinkage approach by adopting a novel uniform adaptive LASSO penalty. The new approach enjoys easy implementation without requiring smoothing. Moreover, it can consistently identify the true model (uniformly across quantiles) and achieve the oracle estimation efficiency. We further extend the proposed shrinkage method to the case where responses are subject to random right censoring. Numerical studies confirm the theoretical results and support the utility of our proposals. PMID:25332515

Quantile regression via vector generalized additive models.

PubMed

Yee, Thomas W

2004-07-30

One of the most popular methods for quantile regression is the LMS method of Cole and Green. The method naturally falls within a penalized likelihood framework, and consequently allows for considerable flexible because all three parameters may be modelled by cubic smoothing splines. The model is also very understandable: for a given value of the covariate, the LMS method applies a Box-Cox transformation to the response in order to transform it to standard normality; to obtain the quantiles, an inverse Box-Cox transformation is applied to the quantiles of the standard normal distribution. The purposes of this article are three-fold. Firstly, LMS quantile regression is presented within the framework of the class of vector generalized additive models. This confers a number of advantages such as a unifying theory and estimation process. Secondly, a new LMS method based on the Yeo-Johnson transformation is proposed, which has the advantage that the response is not restricted to be positive. Lastly, this paper describes a software implementation of three LMS quantile regression methods in the S language. This includes the LMS-Yeo-Johnson method, which is estimated efficiently by a new numerical integration scheme. The LMS-Yeo-Johnson method is illustrated by way of a large cross-sectional data set from a New Zealand working population. Copyright 2004 John Wiley & Sons, Ltd.

Estimating risks to aquatic life using quantile regression

USGS Publications Warehouse

Schmidt, Travis S.; Clements, William H.; Cade, Brian S.

2012-01-01

One of the primary goals of biological assessment is to assess whether contaminants or other stressors limit the ecological potential of running waters. It is important to interpret responses to contaminants relative to other environmental factors, but necessity or convenience limit quantification of all factors that influence ecological potential. In these situations, the concept of limiting factors is useful for data interpretation. We used quantile regression to measure risks to aquatic life exposed to metals by including all regression quantiles (τ  =  0.05–0.95, by increments of 0.05), not just the upper limit of density (e.g., 90th quantile). We measured population densities (individuals/0.1 m2) of 2 mayflies (Rhithrogena spp., Drunella spp.) and a caddisfly (Arctopsyche grandis), aqueous metal mixtures (Cd, Cu, Zn), and other limiting factors (basin area, site elevation, discharge, temperature) at 125 streams in Colorado. We used a model selection procedure to test which factor was most limiting to density. Arctopsyche grandis was limited by other factors, whereas metals limited most quantiles of density for the 2 mayflies. Metals reduced mayfly densities most at sites where other factors were not limiting. Where other factors were limiting, low mayfly densities were observed despite metal concentrations. Metals affected mayfly densities most at quantiles above the mean and not just at the upper limit of density. Risk models developed from quantile regression showed that mayfly densities observed at background metal concentrations are improbable when metal mixtures are at US Environmental Protection Agency criterion continuous concentrations. We conclude that metals limit potential density, not realized average density. The most obvious effects on mayfly populations were at upper quantiles and not mean density. Therefore, we suggest that policy developed from mean-based measures of effects may not be as useful as policy based on the concept of

Regional estimation of extreme suspended sediment concentrations using watershed characteristics

NASA Astrophysics Data System (ADS)

Tramblay, Yves; Ouarda, Taha B. M. J.; St-Hilaire, André; Poulin, Jimmy

2010-01-01

SummaryThe number of stations monitoring daily suspended sediment concentration (SSC) has been decreasing since the 1980s in North America while suspended sediment is considered as a key variable for water quality. The objective of this study is to test the feasibility of regionalising extreme SSC, i.e. estimating SSC extremes values for ungauged basins. Annual maximum SSC for 72 rivers in Canada and USA were modelled with probability distributions in order to estimate quantiles corresponding to different return periods. Regionalisation techniques, originally developed for flood prediction in ungauged basins, were tested using the climatic, topographic, land cover and soils attributes of the watersheds. Two approaches were compared, using either physiographic characteristics or seasonality of extreme SSC to delineate the regions. Multiple regression models to estimate SSC quantiles as a function of watershed characteristics were built in each region, and compared to a global model including all sites. Regional estimates of SSC quantiles were compared with the local values. Results show that regional estimation of extreme SSC is more efficient than a global regression model including all sites. Groups/regions of stations have been identified, using either the watershed characteristics or the seasonality of occurrence for extreme SSC values providing a method to better describe the extreme events of SSC. The most important variables for predicting extreme SSC are the percentage of clay in the soils, precipitation intensity and forest cover.

The effectiveness of drinking and driving policies for different alcohol-related fatalities: a quantile regression analysis.

PubMed

Ying, Yung-Hsiang; Wu, Chin-Chih; Chang, Koyin

2013-09-27

To understand the impact of drinking and driving laws on drinking and driving fatality rates, this study explored the different effects these laws have on areas with varying severity rates for drinking and driving. Unlike previous studies, this study employed quantile regression analysis. Empirical results showed that policies based on local conditions must be used to effectively reduce drinking and driving fatality rates; that is, different measures should be adopted to target the specific conditions in various regions. For areas with low fatality rates (low quantiles), people's habits and attitudes toward alcohol should be emphasized instead of transportation safety laws because "preemptive regulations" are more effective. For areas with high fatality rates (or high quantiles), "ex-post regulations" are more effective, and impact these areas approximately 0.01% to 0.05% more than they do areas with low fatality rates.

Estimation of peak discharge quantiles for selected annual exceedance probabilities in northeastern Illinois

USGS Publications Warehouse

Over, Thomas M.; Saito, Riki J.; Veilleux, Andrea G.; Sharpe, Jennifer B.; Soong, David T.; Ishii, Audrey L.

2016-06-28

This report provides two sets of equations for estimating peak discharge quantiles at annual exceedance probabilities (AEPs) of 0.50, 0.20, 0.10, 0.04, 0.02, 0.01, 0.005, and 0.002 (recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, respectively) for watersheds in Illinois based on annual maximum peak discharge data from 117 watersheds in and near northeastern Illinois. One set of equations was developed through a temporal analysis with a two-step least squares-quantile regression technique that measures the average effect of changes in the urbanization of the watersheds used in the study. The resulting equations can be used to adjust rural peak discharge quantiles for the effect of urbanization, and in this study the equations also were used to adjust the annual maximum peak discharges from the study watersheds to 2010 urbanization conditions.The other set of equations was developed by a spatial analysis. This analysis used generalized least-squares regression to fit the peak discharge quantiles computed from the urbanization-adjusted annual maximum peak discharges from the study watersheds to drainage-basin characteristics. The peak discharge quantiles were computed by using the Expected Moments Algorithm following the removal of potentially influential low floods defined by a multiple Grubbs-Beck test. To improve the quantile estimates, regional skew coefficients were obtained from a newly developed regional skew model in which the skew increases with the urbanized land use fraction. The drainage-basin characteristics used as explanatory variables in the spatial analysis include drainage area, the fraction of developed land, the fraction of land with poorly drained soils or likely water, and the basin slope estimated as the ratio of the basin relief to basin perimeter.This report also provides the following: (1) examples to illustrate the use of the spatial and urbanization-adjustment equations for estimating peak discharge quantiles at ungaged

The Effectiveness of Drinking and Driving Policies for Different Alcohol-Related Fatalities: A Quantile Regression Analysis

PubMed Central

Ying, Yung-Hsiang; Wu, Chin-Chih; Chang, Koyin

2013-01-01

To understand the impact of drinking and driving laws on drinking and driving fatality rates, this study explored the different effects these laws have on areas with varying severity rates for drinking and driving. Unlike previous studies, this study employed quantile regression analysis. Empirical results showed that policies based on local conditions must be used to effectively reduce drinking and driving fatality rates; that is, different measures should be adopted to target the specific conditions in various regions. For areas with low fatality rates (low quantiles), people’s habits and attitudes toward alcohol should be emphasized instead of transportation safety laws because “preemptive regulations” are more effective. For areas with high fatality rates (or high quantiles), “ex-post regulations” are more effective, and impact these areas approximately 0.01% to 0.05% more than they do areas with low fatality rates. PMID:24084673

Use of Flood Seasonality in Pooling-Group Formation and Quantile Estimation: An Application in Great Britain

NASA Astrophysics Data System (ADS)

Formetta, Giuseppe; Bell, Victoria; Stewart, Elizabeth

2018-02-01

Regional flood frequency analysis is one of the most commonly applied methods for estimating extreme flood events at ungauged sites or locations with short measurement records. It is based on: (i) the definition of a homogeneous group (pooling-group) of catchments, and on (ii) the use of the pooling-group data to estimate flood quantiles. Although many methods to define a pooling-group (pooling schemes, PS) are based on catchment physiographic similarity measures, in the last decade methods based on flood seasonality similarity have been contemplated. In this paper, two seasonality-based PS are proposed and tested both in terms of the homogeneity of the pooling-groups they generate and in terms of the accuracy in estimating extreme flood events. The method has been applied in 420 catchments in Great Britain (considered as both gauged and ungauged) and compared against the current Flood Estimation Handbook (FEH) PS. Results for gauged sites show that, compared to the current PS, the seasonality-based PS performs better both in terms of homogeneity of the pooling-group and in terms of the accuracy of flood quantile estimates. For ungauged locations, a national-scale hydrological model has been used for the first time to quantify flood seasonality. Results show that in 75% of the tested locations the seasonality-based PS provides an improvement in the accuracy of the flood quantile estimates. The remaining 25% were located in highly urbanized, groundwater-dependent catchments. The promising results support the aspiration that large-scale hydrological models complement traditional methods for estimating design floods.

Effects of environmental variables on invasive amphibian activity: Using model selection on quantiles for counts

USGS Publications Warehouse

Muller, Benjamin J.; Cade, Brian S.; Schwarzkoph, Lin

2018-01-01

Many different factors influence animal activity. Often, the value of an environmental variable may influence significantly the upper or lower tails of the activity distribution. For describing relationships with heterogeneous boundaries, quantile regressions predict a quantile of the conditional distribution of the dependent variable. A quantile count model extends linear quantile regression methods to discrete response variables, and is useful if activity is quantified by trapping, where there may be many tied (equal) values in the activity distribution, over a small range of discrete values. Additionally, different environmental variables in combination may have synergistic or antagonistic effects on activity, so examining their effects together, in a modeling framework, is a useful approach. Thus, model selection on quantile counts can be used to determine the relative importance of different variables in determining activity, across the entire distribution of capture results. We conducted model selection on quantile count models to describe the factors affecting activity (numbers of captures) of cane toads (Rhinella marina) in response to several environmental variables (humidity, temperature, rainfall, wind speed, and moon luminosity) over eleven months of trapping. Environmental effects on activity are understudied in this pest animal. In the dry season, model selection on quantile count models suggested that rainfall positively affected activity, especially near the lower tails of the activity distribution. In the wet season, wind speed limited activity near the maximum of the distribution, while minimum activity increased with minimum temperature. This statistical methodology allowed us to explore, in depth, how environmental factors influenced activity across the entire distribution, and is applicable to any survey or trapping regime, in which environmental variables affect activity.

Predicting Word Reading Ability: A Quantile Regression Study

ERIC Educational Resources Information Center

McIlraith, Autumn L.

2018-01-01

Predictors of early word reading are well established. However, it is unclear if these predictors hold for readers across a range of word reading abilities. This study used quantile regression to investigate predictive relationships at different points in the distribution of word reading. Quantile regression analyses used preschool and…

Quantile regression reveals hidden bias and uncertainty in habitat models

Treesearch

Brian S. Cade; Barry R. Noon; Curtis H. Flather

2005-01-01

We simulated the effects of missing information on statistical distributions of animal response that covaried with measured predictors of habitat to evaluate the utility and performance of quantile regression for providing more useful intervals of uncertainty in habitat relationships. These procedures were evaulated for conditions in which heterogeneity and hidden bias...

Quantile uncertainty and value-at-risk model risk.

PubMed

Alexander, Carol; Sarabia, José María

2012-08-01

This article develops a methodology for quantifying model risk in quantile risk estimates. The application of quantile estimates to risk assessment has become common practice in many disciplines, including hydrology, climate change, statistical process control, insurance and actuarial science, and the uncertainty surrounding these estimates has long been recognized. Our work is particularly important in finance, where quantile estimates (called Value-at-Risk) have been the cornerstone of banking risk management since the mid 1980s. A recent amendment to the Basel II Accord recommends additional market risk capital to cover all sources of "model risk" in the estimation of these quantiles. We provide a novel and elegant framework whereby quantile estimates are adjusted for model risk, relative to a benchmark which represents the state of knowledge of the authority that is responsible for model risk. A simulation experiment in which the degree of model risk is controlled illustrates how to quantify Value-at-Risk model risk and compute the required regulatory capital add-on for banks. An empirical example based on real data shows how the methodology can be put into practice, using only two time series (daily Value-at-Risk and daily profit and loss) from a large bank. We conclude with a discussion of potential applications to nonfinancial risks. © 2012 Society for Risk Analysis.

Simulating Quantile Models with Applications to Economics and Management

NASA Astrophysics Data System (ADS)

Machado, José A. F.

2010-05-01

The massive increase in the speed of computers over the past forty years changed the way that social scientists, applied economists and statisticians approach their trades and also the very nature of the problems that they could feasibly tackle. The new methods that use intensively computer power go by the names of "computer-intensive" or "simulation". My lecture will start with bird's eye view of the uses of simulation in Economics and Statistics. Then I will turn out to my own research on uses of computer- intensive methods. From a methodological point of view the question I address is how to infer marginal distributions having estimated a conditional quantile process, (Counterfactual Decomposition of Changes in Wage Distributions using Quantile Regression," Journal of Applied Econometrics 20, 2005). Illustrations will be provided of the use of the method to perform counterfactual analysis in several different areas of knowledge.

Modeling energy expenditure in children and adolescents using quantile regression

PubMed Central

Yang, Yunwen; Adolph, Anne L.; Puyau, Maurice R.; Vohra, Firoz A.; Zakeri, Issa F.

2013-01-01

Advanced mathematical models have the potential to capture the complex metabolic and physiological processes that result in energy expenditure (EE). Study objective is to apply quantile regression (QR) to predict EE and determine quantile-dependent variation in covariate effects in nonobese and obese children. First, QR models will be developed to predict minute-by-minute awake EE at different quantile levels based on heart rate (HR) and physical activity (PA) accelerometry counts, and child characteristics of age, sex, weight, and height. Second, the QR models will be used to evaluate the covariate effects of weight, PA, and HR across the conditional EE distribution. QR and ordinary least squares (OLS) regressions are estimated in 109 children, aged 5–18 yr. QR modeling of EE outperformed OLS regression for both nonobese and obese populations. Average prediction errors for QR compared with OLS were not only smaller at the median τ = 0.5 (18.6 vs. 21.4%), but also substantially smaller at the tails of the distribution (10.2 vs. 39.2% at τ = 0.1 and 8.7 vs. 19.8% at τ = 0.9). Covariate effects of weight, PA, and HR on EE for the nonobese and obese children differed across quantiles (P < 0.05). The associations (linear and quadratic) between PA and HR with EE were stronger for the obese than nonobese population (P < 0.05). In conclusion, QR provided more accurate predictions of EE compared with conventional OLS regression, especially at the tails of the distribution, and revealed substantially different covariate effects of weight, PA, and HR on EE in nonobese and obese children. PMID:23640591

Spectral distance decay: Assessing species beta-diversity by quantile regression

USGS Publications Warehouse

Rocchinl, D.; Nagendra, H.; Ghate, R.; Cade, B.S.

2009-01-01

Remotely sensed data represents key information for characterizing and estimating biodiversity. Spectral distance among sites has proven to be a powerful approach for detecting species composition variability. Regression analysis of species similarity versus spectral distance may allow us to quantitatively estimate how beta-diversity in species changes with respect to spectral and ecological variability. In classical regression analysis, the residual sum of squares is minimized for the mean of the dependent variable distribution. However, many ecological datasets are characterized by a high number of zeroes that can add noise to the regression model. Quantile regression can be used to evaluate trend in the upper quantiles rather than a mean trend across the whole distribution of the dependent variable. In this paper, we used ordinary least square (ols) and quantile regression to estimate the decay of species similarity versus spectral distance. The achieved decay rates were statistically nonzero (p < 0.05) considering both ols and quantile regression. Nonetheless, ols regression estimate of mean decay rate was only half the decay rate indicated by the upper quantiles. Moreover, the intercept value, representing the similarity reached when spectral distance approaches zero, was very low compared with the intercepts of upper quantiles, which detected high species similarity when habitats are more similar. In this paper we demonstrated the power of using quantile regressions applied to spectral distance decay in order to reveal species diversity patterns otherwise lost or underestimated by ordinary least square regression. ?? 2009 American Society for Photogrammetry and Remote Sensing.

A hierarchical Bayesian GEV model for improving local and regional flood quantile estimates

NASA Astrophysics Data System (ADS)

Lima, Carlos H. R.; Lall, Upmanu; Troy, Tara; Devineni, Naresh

2016-10-01

We estimate local and regional Generalized Extreme Value (GEV) distribution parameters for flood frequency analysis in a multilevel, hierarchical Bayesian framework, to explicitly model and reduce uncertainties. As prior information for the model, we assume that the GEV location and scale parameters for each site come from independent log-normal distributions, whose mean parameter scales with the drainage area. From empirical and theoretical arguments, the shape parameter for each site is shrunk towards a common mean. Non-informative prior distributions are assumed for the hyperparameters and the MCMC method is used to sample from the joint posterior distribution. The model is tested using annual maximum series from 20 streamflow gauges located in an 83,000 km2 flood prone basin in Southeast Brazil. The results show a significant reduction of uncertainty estimates of flood quantile estimates over the traditional GEV model, particularly for sites with shorter records. For return periods within the range of the data (around 50 years), the Bayesian credible intervals for the flood quantiles tend to be narrower than the classical confidence limits based on the delta method. As the return period increases beyond the range of the data, the confidence limits from the delta method become unreliable and the Bayesian credible intervals provide a way to estimate satisfactory confidence bands for the flood quantiles considering parameter uncertainties and regional information. In order to evaluate the applicability of the proposed hierarchical Bayesian model for regional flood frequency analysis, we estimate flood quantiles for three randomly chosen out-of-sample sites and compare with classical estimates using the index flood method. The posterior distributions of the scaling law coefficients are used to define the predictive distributions of the GEV location and scale parameters for the out-of-sample sites given only their drainage areas and the posterior distribution of the

Composite marginal quantile regression analysis for longitudinal adolescent body mass index data.

PubMed

Yang, Chi-Chuan; Chen, Yi-Hau; Chang, Hsing-Yi

2017-09-20

Childhood and adolescenthood overweight or obesity, which may be quantified through the body mass index (BMI), is strongly associated with adult obesity and other health problems. Motivated by the child and adolescent behaviors in long-term evolution (CABLE) study, we are interested in individual, family, and school factors associated with marginal quantiles of longitudinal adolescent BMI values. We propose a new method for composite marginal quantile regression analysis for longitudinal outcome data, which performs marginal quantile regressions at multiple quantile levels simultaneously. The proposed method extends the quantile regression coefficient modeling method introduced by Frumento and Bottai (Biometrics 2016; 72:74-84) to longitudinal data accounting suitably for the correlation structure in longitudinal observations. A goodness-of-fit test for the proposed modeling is also developed. Simulation results show that the proposed method can be much more efficient than the analysis without taking correlation into account and the analysis performing separate quantile regressions at different quantile levels. The application to the longitudinal adolescent BMI data from the CABLE study demonstrates the practical utility of our proposal. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Quantile Regression in the Study of Developmental Sciences

PubMed Central

Petscher, Yaacov; Logan, Jessica A. R.

2014-01-01

Linear regression analysis is one of the most common techniques applied in developmental research, but only allows for an estimate of the average relations between the predictor(s) and the outcome. This study describes quantile regression, which provides estimates of the relations between the predictor(s) and outcome, but across multiple points of the outcome’s distribution. Using data from the High School and Beyond and U.S. Sustained Effects Study databases, quantile regression is demonstrated and contrasted with linear regression when considering models with: (a) one continuous predictor, (b) one dichotomous predictor, (c) a continuous and a dichotomous predictor, and (d) a longitudinal application. Results from each example exhibited the differential inferences which may be drawn using linear or quantile regression. PMID:24329596

Quantile Regression for Analyzing Heterogeneity in Ultra-high Dimension

PubMed Central

Wang, Lan; Wu, Yichao

2012-01-01

Ultra-high dimensional data often display heterogeneity due to either heteroscedastic variance or other forms of non-location-scale covariate effects. To accommodate heterogeneity, we advocate a more general interpretation of sparsity which assumes that only a small number of covariates influence the conditional distribution of the response variable given all candidate covariates; however, the sets of relevant covariates may differ when we consider different segments of the conditional distribution. In this framework, we investigate the methodology and theory of nonconvex penalized quantile regression in ultra-high dimension. The proposed approach has two distinctive features: (1) it enables us to explore the entire conditional distribution of the response variable given the ultra-high dimensional covariates and provides a more realistic picture of the sparsity pattern; (2) it requires substantially weaker conditions compared with alternative methods in the literature; thus, it greatly alleviates the difficulty of model checking in the ultra-high dimension. In theoretic development, it is challenging to deal with both the nonsmooth loss function and the nonconvex penalty function in ultra-high dimensional parameter space. We introduce a novel sufficient optimality condition which relies on a convex differencing representation of the penalized loss function and the subdifferential calculus. Exploring this optimality condition enables us to establish the oracle property for sparse quantile regression in the ultra-high dimension under relaxed conditions. The proposed method greatly enhances existing tools for ultra-high dimensional data analysis. Monte Carlo simulations demonstrate the usefulness of the proposed procedure. The real data example we analyzed demonstrates that the new approach reveals substantially more information compared with alternative methods. PMID:23082036

(When and where) Do extreme climate events trigger extreme ecosystem responses? - Development and initial results of a holistic analysis framework

NASA Astrophysics Data System (ADS)

Hauber, Eva K.; Donner, Reik V.

2015-04-01

In the context of ongoing climate change, extremes are likely to increase in magnitude and frequency. One of the most important consequences of these changes is that the associated ecological risks and impacts are potentially rising as well. In order to better anticipate and understand these impacts, it therefore becomes more and more crucial to understand the general connection between climate extremes and the response and functionality of ecosystems. Among other region of the world, Europe presents an excellent test case for studies concerning the interaction between climate and biosphere, since it lies in the transition region between cold polar and warm tropical air masses and thus covers a great variety of different climatic zones and associated terrestrial ecosystems. The large temperature differences across the continent make this region particularly interesting for investigating the effects of climate change on biosphere-climate interactions. However, previously used methods for defining an extreme event typically disregard the necessity of taking seasonality as well as seasonal variance appropriately into account. Furthermore, most studies have focused on the impacts of individual extreme events instead of considering a whole inventory of extremes with their respective spatio-temporal extents. In order to overcome the aforementioned research gaps, this work introduces a new approach to studying climate-biosphere interactions associated with extreme events, which comprises three consecutive steps: (1) Since Europe exhibits climatic conditions characterized by marked seasonality, a novel method is developed to define extreme events taking into account the seasonality in all quantiles of the probability distribution of the respective variable of interest. This is achieved by considering kernel density estimates individually for each observation date during the year, including the properly weighted information from adjacent dates. By this procedure, we obtain

Analysis of the labor productivity of enterprises via quantile regression

NASA Astrophysics Data System (ADS)

Türkan, Semra

2017-07-01

In this study, we have analyzed the factors that affect the performance of Turkey's Top 500 Industrial Enterprises using quantile regression. The variable about labor productivity of enterprises is considered as dependent variable, the variableabout assets is considered as independent variable. The distribution of labor productivity of enterprises is right-skewed. If the dependent distribution is skewed, linear regression could not catch important aspects of the relationships between the dependent variable and its predictors due to modeling only the conditional mean. Hence, the quantile regression, which allows modelingany quantilesof the dependent distribution, including the median,appears to be useful. It examines whether relationships between dependent and independent variables are different for low, medium, and high percentiles. As a result of analyzing data, the effect of total assets is relatively constant over the entire distribution, except the upper tail. It hasa moderately stronger effect in the upper tail.

Quantile equivalence to evaluate compliance with habitat management objectives

USGS Publications Warehouse

Cade, Brian S.; Johnson, Pamela R.

2011-01-01

Equivalence estimated with linear quantile regression was used to evaluate compliance with habitat management objectives at Arapaho National Wildlife Refuge based on monitoring data collected in upland (5,781 ha; n = 511 transects) and riparian and meadow (2,856 ha, n = 389 transects) habitats from 2005 to 2008. Quantiles were used because the management objectives specified proportions of the habitat area that needed to comply with vegetation criteria. The linear model was used to obtain estimates that were averaged across 4 y. The equivalence testing framework allowed us to interpret confidence intervals for estimated proportions with respect to intervals of vegetative criteria (equivalence regions) in either a liberal, benefit-of-doubt or conservative, fail-safe approach associated with minimizing alternative risks. Simple Boolean conditional arguments were used to combine the quantile equivalence results for individual vegetation components into a joint statement for the multivariable management objectives. For example, management objective 2A required at least 809 ha of upland habitat with a shrub composition ≥0.70 sagebrush (Artemisia spp.), 20–30% canopy cover of sagebrush ≥25 cm in height, ≥20% canopy cover of grasses, and ≥10% canopy cover of forbs on average over 4 y. Shrub composition and canopy cover of grass each were readily met on >3,000 ha under either conservative or liberal interpretations of sampling variability. However, there were only 809–1,214 ha (conservative to liberal) with ≥10% forb canopy cover and 405–1,098 ha with 20–30%canopy cover of sagebrush ≥25 cm in height. Only 91–180 ha of uplands simultaneously met criteria for all four components, primarily because canopy cover of sagebrush and forbs was inversely related when considered at the spatial scale (30 m) of a sample transect. We demonstrate how the quantile equivalence analyses also can help refine the numerical specification of habitat objectives and explore

Extreme climatic events drive mammal irruptions: regression analysis of 100-year trends in desert rainfall and temperature

PubMed Central

Greenville, Aaron C; Wardle, Glenda M; Dickman, Chris R

2012-01-01

Extreme climatic events, such as flooding rains, extended decadal droughts and heat waves have been identified increasingly as important regulators of natural populations. Climate models predict that global warming will drive changes in rainfall and increase the frequency and severity of extreme events. Consequently, to anticipate how organisms will respond we need to document how changes in extremes of temperature and rainfall compare to trends in the mean values of these variables and over what spatial scales the patterns are consistent. Using the longest historical weather records available for central Australia – 100 years – and quantile regression methods, we investigate if extreme climate events have changed at similar rates to median events, if annual rainfall has increased in variability, and if the frequency of large rainfall events has increased over this period. Specifically, we compared local (individual weather stations) and regional (Simpson Desert) spatial scales, and quantified trends in median (50th quantile) and extreme weather values (5th, 10th, 90th, and 95th quantiles). We found that median and extreme annual minimum and maximum temperatures have increased at both spatial scales over the past century. Rainfall changes have been inconsistent across the Simpson Desert; individual weather stations showed increases in annual rainfall, increased frequency of large rainfall events or more prolonged droughts, depending on the location. In contrast to our prediction, we found no evidence that intra-annual rainfall had become more variable over time. Using long-term live-trapping records (22 years) of desert small mammals as a case study, we demonstrate that irruptive events are driven by extreme rainfalls (>95th quantile) and that increases in the magnitude and frequency of extreme rainfall events are likely to drive changes in the populations of these species through direct and indirect changes in predation pressure and wildfires. PMID:23170202

Modeling energy expenditure in children and adolescents using quantile regression

USDA-ARS?s Scientific Manuscript database

Advanced mathematical models have the potential to capture the complex metabolic and physiological processes that result in energy expenditure (EE). Study objective is to apply quantile regression (QR) to predict EE and determine quantile-dependent variation in covariate effects in nonobese and obes...

Estimating equivalence with quantile regression

USGS Publications Warehouse

Cade, B.S.

2011-01-01

Equivalence testing and corresponding confidence interval estimates are used to provide more enlightened statistical statements about parameter estimates by relating them to intervals of effect sizes deemed to be of scientific or practical importance rather than just to an effect size of zero. Equivalence tests and confidence interval estimates are based on a null hypothesis that a parameter estimate is either outside (inequivalence hypothesis) or inside (equivalence hypothesis) an equivalence region, depending on the question of interest and assignment of risk. The former approach, often referred to as bioequivalence testing, is often used in regulatory settings because it reverses the burden of proof compared to a standard test of significance, following a precautionary principle for environmental protection. Unfortunately, many applications of equivalence testing focus on establishing average equivalence by estimating differences in means of distributions that do not have homogeneous variances. I discuss how to compare equivalence across quantiles of distributions using confidence intervals on quantile regression estimates that detect differences in heterogeneous distributions missed by focusing on means. I used one-tailed confidence intervals based on inequivalence hypotheses in a two-group treatment-control design for estimating bioequivalence of arsenic concentrations in soils at an old ammunition testing site and bioequivalence of vegetation biomass at a reclaimed mining site. Two-tailed confidence intervals based both on inequivalence and equivalence hypotheses were used to examine quantile equivalence for negligible trends over time for a continuous exponential model of amphibian abundance. ?? 2011 by the Ecological Society of America.

Regularized quantile regression for SNP marker estimation of pig growth curves.

PubMed

Barroso, L M A; Nascimento, M; Nascimento, A C C; Silva, F F; Serão, N V L; Cruz, C D; Resende, M D V; Silva, F L; Azevedo, C F; Lopes, P S; Guimarães, S E F

2017-01-01

Genomic growth curves are generally defined only in terms of population mean; an alternative approach that has not yet been exploited in genomic analyses of growth curves is the Quantile Regression (QR). This methodology allows for the estimation of marker effects at different levels of the variable of interest. We aimed to propose and evaluate a regularized quantile regression for SNP marker effect estimation of pig growth curves, as well as to identify the chromosome regions of the most relevant markers and to estimate the genetic individual weight trajectory over time (genomic growth curve) under different quantiles (levels). The regularized quantile regression (RQR) enabled the discovery, at different levels of interest (quantiles), of the most relevant markers allowing for the identification of QTL regions. We found the same relevant markers simultaneously affecting different growth curve parameters (mature weight and maturity rate): two (ALGA0096701 and ALGA0029483) for RQR(0.2), one (ALGA0096701) for RQR(0.5), and one (ALGA0003761) for RQR(0.8). Three average genomic growth curves were obtained and the behavior was explained by the curve in quantile 0.2, which differed from the others. RQR allowed for the construction of genomic growth curves, which is the key to identifying and selecting the most desirable animals for breeding purposes. Furthermore, the proposed model enabled us to find, at different levels of interest (quantiles), the most relevant markers for each trait (growth curve parameter estimates) and their respective chromosomal positions (identification of new QTL regions for growth curves in pigs). These markers can be exploited under the context of marker assisted selection while aiming to change the shape of pig growth curves.

[Spatial heterogeneity in body condition of small yellow croaker in Yellow Sea and East China Sea based on mixed-effects model and quantile regression analysis].

PubMed

Liu, Zun-Lei; Yuan, Xing-Wei; Yan, Li-Ping; Yang, Lin-Lin; Cheng, Jia-Hua

2013-09-01

By using the 2008-2010 investigation data about the body condition of small yellow croaker in the offshore waters of southern Yellow Sea (SYS), open waters of northern East China Sea (NECS), and offshore waters of middle East China Sea (MECS), this paper analyzed the spatial heterogeneity of body length-body mass of juvenile and adult small yellow croakers by the statistical approaches of mean regression model and quantile regression model. The results showed that the residual standard errors from the analysis of covariance (ANCOVA) and the linear mixed-effects model were similar, and those from the simple linear regression were the highest. For the juvenile small yellow croakers, their mean body mass in SYS and NECS estimated by the mixed-effects mean regression model was higher than the overall average mass across the three regions, while the mean body mass in MECS was below the overall average. For the adult small yellow croakers, their mean body mass in NECS was higher than the overall average, while the mean body mass in SYS and MECS was below the overall average. The results from quantile regression indicated the substantial differences in the allometric relationships of juvenile small yellow croakers between SYS, NECS, and MECS, with the estimated mean exponent of the allometric relationship in SYS being 2.85, and the interquartile range being from 2.63 to 2.96, which indicated the heterogeneity of body form. The results from ANCOVA showed that the allometric body length-body mass relationships were significantly different between the 25th and 75th percentile exponent values (F=6.38, df=1737, P<0.01) and the 25th percentile and median exponent values (F=2.35, df=1737, P=0.039). The relationship was marginally different between the median and 75th percentile exponent values (F=2.21, df=1737, P=0.051). The estimated body length-body mass exponent of adult small yellow croakers in SYS was 3.01 (10th and 95th percentiles = 2.77 and 3.1, respectively). The

Early Home Activities and Oral Language Skills in Middle Childhood: A Quantile Analysis

ERIC Educational Resources Information Center

Law, James; Rush, Robert; King, Tom; Westrupp, Elizabeth; Reilly, Sheena

2018-01-01

Oral language development is a key outcome of elementary school, and it is important to identify factors that predict it most effectively. Commonly researchers use ordinary least squares regression with conclusions restricted to average performance conditional on relevant covariates. Quantile regression offers a more sophisticated alternative.…

Extreme Conditions Modeling Workshop Report

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

Coe, R. G.; Neary, V. S.; Lawson, M. J.

2014-07-01

Sandia National Laboratories (SNL) and the National Renewable Energy Laboratory (NREL) hosted the Wave Energy Converter (WEC) Extreme Conditions Modeling (ECM) Workshop in Albuquerque, NM on May 13th-14th, 2014. The objective of the workshop was to review the current state of knowledge on how to model WECs in extreme conditions (e.g. hurricanes and other large storms) and to suggest how U.S. Department of Energy (DOE) and national laboratory resources could be used to improve ECM methods for the benefit of the wave energy industry.

Boosting structured additive quantile regression for longitudinal childhood obesity data.

PubMed

Fenske, Nora; Fahrmeir, Ludwig; Hothorn, Torsten; Rzehak, Peter; Höhle, Michael

2013-07-25

Childhood obesity and the investigation of its risk factors has become an important public health issue. Our work is based on and motivated by a German longitudinal study including 2,226 children with up to ten measurements on their body mass index (BMI) and risk factors from birth to the age of 10 years. We introduce boosting of structured additive quantile regression as a novel distribution-free approach for longitudinal quantile regression. The quantile-specific predictors of our model include conventional linear population effects, smooth nonlinear functional effects, varying-coefficient terms, and individual-specific effects, such as intercepts and slopes. Estimation is based on boosting, a computer intensive inference method for highly complex models. We propose a component-wise functional gradient descent boosting algorithm that allows for penalized estimation of the large variety of different effects, particularly leading to individual-specific effects shrunken toward zero. This concept allows us to flexibly estimate the nonlinear age curves of upper quantiles of the BMI distribution, both on population and on individual-specific level, adjusted for further risk factors and to detect age-varying effects of categorical risk factors. Our model approach can be regarded as the quantile regression analog of Gaussian additive mixed models (or structured additive mean regression models), and we compare both model classes with respect to our obesity data.

Heritability Across the Distribution: An Application of Quantile Regression

PubMed Central

Petrill, Stephen A.; Hart, Sara A.; Schatschneider, Christopher; Thompson, Lee A.; Deater-Deckard, Kirby; DeThorne, Laura S.; Bartlett, Christopher

2016-01-01

We introduce a new method for analyzing twin data called quantile regression. Through the application presented here, quantile regression is able to assess the genetic and environmental etiology of any skill or ability, at multiple points in the distribution of that skill or ability. This method is compared to the Cherny et al. (Behav Genet 22:153–162, 1992) method in an application to four different reading-related outcomes in 304 pairs of first-grade same sex twins enrolled in the Western Reserve Reading Project. Findings across the two methods were similar; both indicated some variation across the distribution of the genetic and shared environmental influences on non-word reading. However, quantile regression provides more details about the location and size of the measured effect. Applications of the technique are discussed. PMID:21877231

Intersection of All Top Quantile

EPA Pesticide Factsheets

This layer combines the Top quantiles of the CES, CEVA, and EJSM layers so that viewers can see the overlap of 00e2??hot spots00e2?? for each method. This layer was created by James Sadd of Occidental College of Los Angeles

Scaling of Precipitation Extremes Modelled by Generalized Pareto Distribution

NASA Astrophysics Data System (ADS)

Rajulapati, C. R.; Mujumdar, P. P.

2017-12-01

Precipitation extremes are often modelled with data from annual maximum series or peaks over threshold series. The Generalized Pareto Distribution (GPD) is commonly used to fit the peaks over threshold series. Scaling of precipitation extremes from larger time scales to smaller time scales when the extremes are modelled with the GPD is burdened with difficulties arising from varying thresholds for different durations. In this study, the scale invariance theory is used to develop a disaggregation model for precipitation extremes exceeding specified thresholds. A scaling relationship is developed for a range of thresholds obtained from a set of quantiles of non-zero precipitation of different durations. The GPD parameters and exceedance rate parameters are modelled by the Bayesian approach and the uncertainty in scaling exponent is quantified. A quantile based modification in the scaling relationship is proposed for obtaining the varying thresholds and exceedance rate parameters for shorter durations. The disaggregation model is applied to precipitation datasets of Berlin City, Germany and Bangalore City, India. From both the applications, it is observed that the uncertainty in the scaling exponent has a considerable effect on uncertainty in scaled parameters and return levels of shorter durations.

Quantile Regression for Recurrent Gap Time Data

PubMed Central

Luo, Xianghua; Huang, Chiung-Yu; Wang, Lan

2014-01-01

Summary Evaluating covariate effects on gap times between successive recurrent events is of interest in many medical and public health studies. While most existing methods for recurrent gap time analysis focus on modeling the hazard function of gap times, a direct interpretation of the covariate effects on the gap times is not available through these methods. In this article, we consider quantile regression that can provide direct assessment of covariate effects on the quantiles of the gap time distribution. Following the spirit of the weighted risk-set method by Luo and Huang (2011, Statistics in Medicine 30, 301–311), we extend the martingale-based estimating equation method considered by Peng and Huang (2008, Journal of the American Statistical Association 103, 637–649) for univariate survival data to analyze recurrent gap time data. The proposed estimation procedure can be easily implemented in existing software for univariate censored quantile regression. Uniform consistency and weak convergence of the proposed estimators are established. Monte Carlo studies demonstrate the effectiveness of the proposed method. An application to data from the Danish Psychiatric Central Register is presented to illustrate the methods developed in this article. PMID:23489055

Quantile Regression with Censored Data

ERIC Educational Resources Information Center

Lin, Guixian

2009-01-01

The Cox proportional hazards model and the accelerated failure time model are frequently used in survival data analysis. They are powerful, yet have limitation due to their model assumptions. Quantile regression offers a semiparametric approach to model data with possible heterogeneity. It is particularly powerful for censored responses, where the…

Non-inferiority tests for anti-infective drugs using control group quantiles.

PubMed

Fay, Michael P; Follmann, Dean A

2016-12-01

In testing for non-inferiority of anti-infective drugs, the primary endpoint is often the difference in the proportion of failures between the test and control group at a landmark time. The landmark time is chosen to approximately correspond to the qth historic quantile of the control group, and the non-inferiority margin is selected to be reasonable for the target level q. For designing these studies, a troubling issue is that the landmark time must be pre-specified, but there is no guarantee that the proportion of control failures at the landmark time will be close to the target level q. If the landmark time is far from the target control quantile, then the pre-specified non-inferiority margin may not longer be reasonable. Exact variable margin tests have been developed by Röhmel and Kieser to address this problem, but these tests can have poor power if the observed control failure rate at the landmark time is far from its historic value. We develop a new variable margin non-inferiority test where we continue sampling until a pre-specified proportion of failures, q, have occurred in the control group, where q is the target quantile level. The test does not require any assumptions on the failure time distributions, and hence, no knowledge of the true [Formula: see text] control quantile for the study is needed. Our new test is exact and has power comparable to (or greater than) its competitors when the true control quantile from the study equals (or differs moderately from) its historic value. Our nivm R package performs the test and gives confidence intervals on the difference in failure rates at the true target control quantile. The tests can be applied to time to cure or other numeric variables as well. A substantial proportion of new anti-infective drugs being developed use non-inferiority tests in their development, and typically, a pre-specified landmark time and its associated difference margin are set at the design stage to match a specific target control

Impacts of urbanization on Indian summer monsoon rainfall extremes

NASA Astrophysics Data System (ADS)

Shastri, Hiteshri; Paul, Supantha; Ghosh, Subimal; Karmakar, Subhankar

2015-01-01

areas have different climatology with respect to their rural surroundings. Though urbanization is a worldwide phenomenon, it is especially prevalent in India, where urban areas have experienced an unprecedented rate of growth over the last 30 years. Here we take up an observational study to understand the influence of urbanization on the characteristics of precipitation (specifically extremes) in India. We identify 42 urban regions and compare their extreme rainfall characteristics with those of surrounding rural areas. We observe that, on an overall scale, the urban signatures on extreme rainfall are not prominently and consistently visible, but they are spatially nonuniform. Zonal analysis reveals significant impacts of urbanization on extreme rainfall in central and western regions of India. An additional examination, to understand the influences of urbanization on heavy rainfall climatology, is carried with station level data using a statistical method, quantile regression. This is performed for the most populated city of India, Mumbai, in pair with a nearby nonurban area, Alibaug; both having similar geographic location. The derived extreme rainfall regression quantiles reveal the sensitivity of extreme rainfall events to the increased urbanization. Overall the study identifies the climatological zones in India, where increased urbanization affects regional rainfall pattern and extremes, with a detailed case study of Mumbai. This also calls attention to the need of further experimental investigation, for the identification of the key climatological processes, in different regions of India, affected by increased urbanization.

Birthweight Related Factors in Northwestern Iran: Using Quantile Regression Method.

PubMed

Fallah, Ramazan; Kazemnejad, Anoshirvan; Zayeri, Farid; Shoghli, Alireza

2015-11-18

Birthweight is one of the most important predicting indicators of the health status in adulthood. Having a balanced birthweight is one of the priorities of the health system in most of the industrial and developed countries. This indicator is used to assess the growth and health status of the infants. The aim of this study was to assess the birthweight of the neonates by using quantile regression in Zanjan province. This analytical descriptive study was carried out using pre-registered (March 2010 - March 2012) data of neonates in urban/rural health centers of Zanjan province using multiple-stage cluster sampling. Data were analyzed using multiple linear regressions andquantile regression method and SAS 9.2 statistical software. From 8456 newborn baby, 4146 (49%) were female. The mean age of the mothers was 27.1±5.4 years. The mean birthweight of the neonates was 3104 ± 431 grams. Five hundred and seventy-three patients (6.8%) of the neonates were less than 2500 grams. In all quantiles, gestational age of neonates (p<0.05), weight and educational level of the mothers (p<0.05) showed a linear significant relationship with the i of the neonates. However, sex and birth rank of the neonates, mothers age, place of residence (urban/rural) and career were not significant in all quantiles (p>0.05). This study revealed the results of multiple linear regression and quantile regression were not identical. We strictly recommend the use of quantile regression when an asymmetric response variable or data with outliers is available.

Birthweight Related Factors in Northwestern Iran: Using Quantile Regression Method

PubMed Central

Fallah, Ramazan; Kazemnejad, Anoshirvan; Zayeri, Farid; Shoghli, Alireza

2016-01-01

Introduction: Birthweight is one of the most important predicting indicators of the health status in adulthood. Having a balanced birthweight is one of the priorities of the health system in most of the industrial and developed countries. This indicator is used to assess the growth and health status of the infants. The aim of this study was to assess the birthweight of the neonates by using quantile regression in Zanjan province. Methods: This analytical descriptive study was carried out using pre-registered (March 2010 - March 2012) data of neonates in urban/rural health centers of Zanjan province using multiple-stage cluster sampling. Data were analyzed using multiple linear regressions andquantile regression method and SAS 9.2 statistical software. Results: From 8456 newborn baby, 4146 (49%) were female. The mean age of the mothers was 27.1±5.4 years. The mean birthweight of the neonates was 3104 ± 431 grams. Five hundred and seventy-three patients (6.8%) of the neonates were less than 2500 grams. In all quantiles, gestational age of neonates (p<0.05), weight and educational level of the mothers (p<0.05) showed a linear significant relationship with the i of the neonates. However, sex and birth rank of the neonates, mothers age, place of residence (urban/rural) and career were not significant in all quantiles (p>0.05). Conclusion: This study revealed the results of multiple linear regression and quantile regression were not identical. We strictly recommend the use of quantile regression when an asymmetric response variable or data with outliers is available. PMID:26925889

Estimating normative limits of Heidelberg Retina Tomograph optic disc rim area with quantile regression.

PubMed

Artes, Paul H; Crabb, David P

2010-01-01

To investigate why the specificity of the Moorfields Regression Analysis (MRA) of the Heidelberg Retina Tomograph (HRT) varies with disc size, and to derive accurate normative limits for neuroretinal rim area to address this problem. Two datasets from healthy subjects (Manchester, UK, n = 88; Halifax, Nova Scotia, Canada, n = 75) were used to investigate the physiological relationship between the optic disc and neuroretinal rim area. Normative limits for rim area were derived by quantile regression (QR) and compared with those of the MRA (derived by linear regression). Logistic regression analyses were performed to quantify the association between disc size and positive classifications with the MRA, as well as with the QR-derived normative limits. In both datasets, the specificity of the MRA depended on optic disc size. The odds of observing a borderline or outside-normal-limits classification increased by approximately 10% for each 0.1 mm(2) increase in disc area (P < 0.1). The lower specificity of the MRA with large optic discs could be explained by the failure of linear regression to model the extremes of the rim area distribution (observations far from the mean). In comparison, the normative limits predicted by QR were larger for smaller discs (less specific, more sensitive), and smaller for larger discs, such that false-positive rates became independent of optic disc size. Normative limits derived by quantile regression appear to remove the size-dependence of specificity with the MRA. Because quantile regression does not rely on the restrictive assumptions of standard linear regression, it may be a more appropriate method for establishing normative limits in other clinical applications where the underlying distributions are nonnormal or have nonconstant variance.

Spatio-temporal characteristics of the extreme precipitation by L-moment-based index-flood method in the Yangtze River Delta region, China

NASA Astrophysics Data System (ADS)

Yin, Yixing; Chen, Haishan; Xu, Chong-Yu; Xu, Wucheng; Chen, Changchun; Sun, Shanlei

2016-05-01

The regionalization methods, which "trade space for time" by pooling information from different locations in the frequency analysis, are efficient tools to enhance the reliability of extreme quantile estimates. This paper aims at improving the understanding of the regional frequency of extreme precipitation by using regionalization methods, and providing scientific background and practical assistance in formulating the regional development strategies for water resources management in one of the most developed and flood-prone regions in China, the Yangtze River Delta (YRD) region. To achieve the main goals, L-moment-based index-flood (LMIF) method, one of the most popular regionalization methods, is used in the regional frequency analysis of extreme precipitation with special attention paid to inter-site dependence and its influence on the accuracy of quantile estimates, which has not been considered by most of the studies using LMIF method. Extensive data screening of stationarity, serial dependence, and inter-site dependence was carried out first. The entire YRD region was then categorized into four homogeneous regions through cluster analysis and homogenous analysis. Based on goodness-of-fit statistic and L-moment ratio diagrams, generalized extreme-value (GEV) and generalized normal (GNO) distributions were identified as the best fitted distributions for most of the sub-regions, and estimated quantiles for each region were obtained. Monte Carlo simulation was used to evaluate the accuracy of the quantile estimates taking inter-site dependence into consideration. The results showed that the root-mean-square errors (RMSEs) were bigger and the 90 % error bounds were wider with inter-site dependence than those without inter-site dependence for both the regional growth curve and quantile curve. The spatial patterns of extreme precipitation with a return period of 100 years were finally obtained which indicated that there are two regions with highest precipitation

Observed and predicted sensitivities of extreme surface ozone to meteorological drivers in three US cities

NASA Astrophysics Data System (ADS)

Fix, Miranda J.; Cooley, Daniel; Hodzic, Alma; Gilleland, Eric; Russell, Brook T.; Porter, William C.; Pfister, Gabriele G.

2018-03-01

We conduct a case study of observed and simulated maximum daily 8-h average (MDA8) ozone (O3) in three US cities for summers during 1996-2005. The purpose of this study is to evaluate the ability of a high resolution atmospheric chemistry model to reproduce observed relationships between meteorology and high or extreme O3. We employ regional coupled chemistry-transport model simulations to make three types of comparisons between simulated and observational data, comparing (1) tails of the O3 response variable, (2) distributions of meteorological predictor variables, and (3) sensitivities of high and extreme O3 to meteorological predictors. This last comparison is made using two methods: quantile regression, for the 0.95 quantile of O3, and tail dependence optimization, which is used to investigate even higher O3 extremes. Across all three locations, we find substantial differences between simulations and observational data in both meteorology and meteorological sensitivities of high and extreme O3.

Intersection of Screening Methods High Quantile

EPA Pesticide Factsheets

This layer combines the high quantiles of the CES, CEVA, and EJSM layers so that viewers can see the overlap of 00e2??hot spots00e2?? for each method. This layer was created by James Sadd of Occidental College of Los Angeles

Extreme value modelling of Ghana stock exchange index.

PubMed

Nortey, Ezekiel N N; Asare, Kwabena; Mettle, Felix Okoe

2015-01-01

Modelling of extreme events has always been of interest in fields such as hydrology and meteorology. However, after the recent global financial crises, appropriate models for modelling of such rare events leading to these crises have become quite essential in the finance and risk management fields. This paper models the extreme values of the Ghana stock exchange all-shares index (2000-2010) by applying the extreme value theory (EVT) to fit a model to the tails of the daily stock returns data. A conditional approach of the EVT was preferred and hence an ARMA-GARCH model was fitted to the data to correct for the effects of autocorrelation and conditional heteroscedastic terms present in the returns series, before the EVT method was applied. The Peak Over Threshold approach of the EVT, which fits a Generalized Pareto Distribution (GPD) model to excesses above a certain selected threshold, was employed. Maximum likelihood estimates of the model parameters were obtained and the model's goodness of fit was assessed graphically using Q-Q, P-P and density plots. The findings indicate that the GPD provides an adequate fit to the data of excesses. The size of the extreme daily Ghanaian stock market movements were then computed using the value at risk and expected shortfall risk measures at some high quantiles, based on the fitted GPD model.

Extreme Conditions Modeling Workshop Report

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

Coe, Ryan Geoffrey; Neary, Vincent Sinclair; Lawon, Michael J.

2014-07-01

Sandia National Laboratories (SNL) and the National Renewable Energy Laboratory (NREL) hosted the Wave Energy Converter (WEC) Extreme Conditions Modeling (ECM) Workshop in Albuquerque, New Mexico on May 13–14, 2014. The objective of the workshop was to review the current state of knowledge on how to numerically and experimentally model WECs in extreme conditions (e.g. large ocean storms) and to suggest how national laboratory resources could be used to improve ECM methods for the benefit of the wave energy industry. More than 30 U.S. and European WEC experts from industry, academia, and national research institutes attended the workshop, which consistedmore » of presentations from W EC developers, invited keynote presentations from subject matter experts, breakout sessions, and a final plenary session .« less

Data quantile-quantile plots: quantifying the time evolution of space climatology

NASA Astrophysics Data System (ADS)

Tindale, Elizabeth; Chapman, Sandra

2017-04-01

The solar wind is inherently variable across a wide range of spatio-temporal scales; embedded in the flow are the signatures of distinct non-linear physical processes from evolving turbulence to the dynamical solar corona. In-situ satellite observations of solar wind magnetic field and velocity are at minute and below time resolution and now extend over several solar cycles. Each solar cycle is unique, and the space climatology challenge is to quantify how solar wind variability changes within, and across, each distinct solar cycle, and how this in turn drives space weather at earth. We will demonstrate a novel statistical method, that of data-data quantile-quantile (DQQ) plots, which quantifies how the underlying statistical distribution of a given observable is changing in time. Importantly this method does not require any assumptions concerning the underlying functional form of the distribution and can identify multi-component behaviour that is changing in time. This can be used to determine when a sub-range of a given observable is undergoing a change in statistical distribution, or where the moments of the distribution only are changing and the functional form of the underlying distribution is not changing in time. The method is quite general; for this application we use data from the WIND satellite to compare the solar wind across the minima and maxima of solar cycles 23 and 24 [1], and how these changes are manifest in parameters that quantify coupling to the earth's magnetosphere. [1] Tindale, E., and S.C. Chapman (2016), Geophys. Res. Lett., 43(11), doi: 10.1002/2016GL068920.

Quantiles for Finite Mixtures of Normal Distributions

ERIC Educational Resources Information Center

Rahman, Mezbahur; Rahman, Rumanur; Pearson, Larry M.

2006-01-01

Quantiles for finite mixtures of normal distributions are computed. The difference between a linear combination of independent normal random variables and a linear combination of independent normal densities is emphasized. (Contains 3 tables and 1 figure.)

An application of quantile random forests for predictive mapping of forest attributes

Treesearch

E.A. Freeman; G.G. Moisen

2015-01-01

Increasingly, random forest models are used in predictive mapping of forest attributes. Traditional random forests output the mean prediction from the random trees. Quantile regression forests (QRF) is an extension of random forests developed by Nicolai Meinshausen that provides non-parametric estimates of the median predicted value as well as prediction quantiles. It...

[Socioeconomic factors conditioning obesity in adults. Evidence based on quantile regression and panel data].

PubMed

Temporelli, Karina L; Viego, Valentina N

2016-08-01

Objective To measure the effect of socioeconomic variables on the prevalence of obesity. Factors such as income level, urbanization, incorporation of women into the labor market and access to unhealthy foods are considered in this paper. Method Econometric estimates of the proportion of obese men and women by country were calculated using models based on panel data and quantile regressions, with data from 192 countries for the period 2002-2005.Levels of per capita income, urbanization, income/big mac ratio price and labor indicators for female population were considered as explanatory variables. Results Factors that have influence over obesity in adults differ between men and women; accessibility to fast food is related to male obesity, while the employment mode causes higher rates in women. The underlying socioeconomic factors for obesity are also different depending on the magnitude of this problem in each country; in countries with low prevalence, a greater level of income favor the transition to obesogenic habits, while a higher income level mitigates the problem in those countries with high rates of obesity. Discussion Identifying the socio-economic causes of the significant increase in the prevalence of obesity is essential for the implementation of effective strategies for prevention, since this condition not only affects the quality of life of those who suffer from it but also puts pressure on health systems due to the treatment costs of associated diseases.

Spatio-temporal analysis of the extreme precipitation by the L-moment-based index-flood method in the Yangtze River Delta region, China

NASA Astrophysics Data System (ADS)

Yin, Yixing; Chen, Haishan; Xu, Chongyu; Xu, Wucheng; Chen, Changchun

2014-05-01

The regionalization methods which 'trade space for time' by including several at-site data records in the frequency analysis are an efficient tool to improve the reliability of extreme quantile estimates. With the main aims of improving the understanding of the regional frequency of extreme precipitation and providing scientific and practical background and assistance in formulating the regional development strategies for water resources management in one of the most developed and flood-prone regions in China, the Yangtze River Delta (YRD) region, in this paper, L-moment-based index-flood (LMIF) method, one of the popular regionalization methods, is used in the regional frequency analysis of extreme precipitation; attention was paid to inter-site dependence and its influence on the accuracy of quantile estimates, which hasn't been considered for most of the studies using LMIF method. Extensive data screening of stationarity, serial dependence and inter-site dependence was carried out first. The entire YRD region was then categorized into four homogeneous regions through cluster analysis and homogenous analysis. Based on goodness-of-fit statistic and L-moment ratio diagrams, Generalized extreme-value (GEV) and Generalized Normal (GNO) distributions were identified as the best-fit distributions for most of the sub regions. Estimated quantiles for each region were further obtained. Monte-Carlo simulation was used to evaluate the accuracy of the quantile estimates taking inter-site dependence into consideration. The results showed that the root mean square errors (RMSEs) were bigger and the 90% error bounds were wider with inter-site dependence than those with no inter-site dependence for both the regional growth curve and quantile curve. The spatial patterns of extreme precipitation with return period of 100 years were obtained which indicated that there are two regions with the highest precipitation extremes (southeastern coastal area of Zhejiang Province and the

Simulation of extreme rainfall and projection of future changes using the GLIMCLIM model

NASA Astrophysics Data System (ADS)

Rashid, Md. Mamunur; Beecham, Simon; Chowdhury, Rezaul Kabir

2017-10-01

In this study, the performance of the Generalized LInear Modelling of daily CLImate sequence (GLIMCLIM) statistical downscaling model was assessed to simulate extreme rainfall indices and annual maximum daily rainfall (AMDR) when downscaled daily rainfall from National Centers for Environmental Prediction (NCEP) reanalysis and Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCM) (four GCMs and two scenarios) output datasets and then their changes were estimated for the future period 2041-2060. The model was able to reproduce the monthly variations in the extreme rainfall indices reasonably well when forced by the NCEP reanalysis datasets. Frequency Adapted Quantile Mapping (FAQM) was used to remove bias in the simulated daily rainfall when forced by CMIP5 GCMs, which reduced the discrepancy between observed and simulated extreme rainfall indices. Although the observed AMDR were within the 2.5th and 97.5th percentiles of the simulated AMDR, the model consistently under-predicted the inter-annual variability of AMDR. A non-stationary model was developed using the generalized linear model for local, shape and scale to estimate the AMDR with an annual exceedance probability of 0.01. The study shows that in general, AMDR is likely to decrease in the future. The Onkaparinga catchment will also experience drier conditions due to an increase in consecutive dry days coinciding with decreases in heavy (>long term 90th percentile) rainfall days, empirical 90th quantile of rainfall and maximum 5-day consecutive total rainfall for the future period (2041-2060) compared to the base period (1961-2000).

Spline methods for approximating quantile functions and generating random samples

NASA Technical Reports Server (NTRS)

Schiess, J. R.; Matthews, C. G.

1985-01-01

Two cubic spline formulations are presented for representing the quantile function (inverse cumulative distribution function) of a random sample of data. Both B-spline and rational spline approximations are compared with analytic representations of the quantile function. It is also shown how these representations can be used to generate random samples for use in simulation studies. Comparisons are made on samples generated from known distributions and a sample of experimental data. The spline representations are more accurate for multimodal and skewed samples and to require much less time to generate samples than the analytic representation.

Matching a Distribution by Matching Quantiles Estimation

PubMed Central

Sgouropoulos, Nikolaos; Yao, Qiwei; Yastremiz, Claudia

2015-01-01

Motivated by the problem of selecting representative portfolios for backtesting counterparty credit risks, we propose a matching quantiles estimation (MQE) method for matching a target distribution by that of a linear combination of a set of random variables. An iterative procedure based on the ordinary least-squares estimation (OLS) is proposed to compute MQE. MQE can be easily modified by adding a LASSO penalty term if a sparse representation is desired, or by restricting the matching within certain range of quantiles to match a part of the target distribution. The convergence of the algorithm and the asymptotic properties of the estimation, both with or without LASSO, are established. A measure and an associated statistical test are proposed to assess the goodness-of-match. The finite sample properties are illustrated by simulation. An application in selecting a counterparty representative portfolio with a real dataset is reported. The proposed MQE also finds applications in portfolio tracking, which demonstrates the usefulness of combining MQE with LASSO. PMID:26692592

Quantile rank maps: a new tool for understanding individual brain development.

PubMed

Chen, Huaihou; Kelly, Clare; Castellanos, F Xavier; He, Ye; Zuo, Xi-Nian; Reiss, Philip T

2015-05-01

We propose a novel method for neurodevelopmental brain mapping that displays how an individual's values for a quantity of interest compare with age-specific norms. By estimating smoothly age-varying distributions at a set of brain regions of interest, we derive age-dependent region-wise quantile ranks for a given individual, which can be presented in the form of a brain map. Such quantile rank maps could potentially be used for clinical screening. Bootstrap-based confidence intervals are proposed for the quantile rank estimates. We also propose a recalibrated Kolmogorov-Smirnov test for detecting group differences in the age-varying distribution. This test is shown to be more robust to model misspecification than a linear regression-based test. The proposed methods are applied to brain imaging data from the Nathan Kline Institute Rockland Sample and from the Autism Brain Imaging Data Exchange (ABIDE) sample. Copyright © 2015 Elsevier Inc. All rights reserved.

Confidence intervals for expected moments algorithm flood quantile estimates

USGS Publications Warehouse

Cohn, Timothy A.; Lane, William L.; Stedinger, Jery R.

2001-01-01

Historical and paleoflood information can substantially improve flood frequency estimates if appropriate statistical procedures are properly applied. However, the Federal guidelines for flood frequency analysis, set forth in Bulletin 17B, rely on an inefficient “weighting” procedure that fails to take advantage of historical and paleoflood information. This has led researchers to propose several more efficient alternatives including the Expected Moments Algorithm (EMA), which is attractive because it retains Bulletin 17B's statistical structure (method of moments with the Log Pearson Type 3 distribution) and thus can be easily integrated into flood analyses employing the rest of the Bulletin 17B approach. The practical utility of EMA, however, has been limited because no closed‐form method has been available for quantifying the uncertainty of EMA‐based flood quantile estimates. This paper addresses that concern by providing analytical expressions for the asymptotic variance of EMA flood‐quantile estimators and confidence intervals for flood quantile estimates. Monte Carlo simulations demonstrate the properties of such confidence intervals for sites where a 25‐ to 100‐year streamgage record is augmented by 50 to 150 years of historical information. The experiments show that the confidence intervals, though not exact, should be acceptable for most purposes.

Flood quantile estimation at ungauged sites by Bayesian networks

NASA Astrophysics Data System (ADS)

Mediero, L.; Santillán, D.; Garrote, L.

2012-04-01

Estimating flood quantiles at a site for which no observed measurements are available is essential for water resources planning and management. Ungauged sites have no observations about the magnitude of floods, but some site and basin characteristics are known. The most common technique used is the multiple regression analysis, which relates physical and climatic basin characteristic to flood quantiles. Regression equations are fitted from flood frequency data and basin characteristics at gauged sites. Regression equations are a rigid technique that assumes linear relationships between variables and cannot take the measurement errors into account. In addition, the prediction intervals are estimated in a very simplistic way from the variance of the residuals in the estimated model. Bayesian networks are a probabilistic computational structure taken from the field of Artificial Intelligence, which have been widely and successfully applied to many scientific fields like medicine and informatics, but application to the field of hydrology is recent. Bayesian networks infer the joint probability distribution of several related variables from observations through nodes, which represent random variables, and links, which represent causal dependencies between them. A Bayesian network is more flexible than regression equations, as they capture non-linear relationships between variables. In addition, the probabilistic nature of Bayesian networks allows taking the different sources of estimation uncertainty into account, as they give a probability distribution as result. A homogeneous region in the Tagus Basin was selected as case study. A regression equation was fitted taking the basin area, the annual maximum 24-hour rainfall for a given recurrence interval and the mean height as explanatory variables. Flood quantiles at ungauged sites were estimated by Bayesian networks. Bayesian networks need to be learnt from a huge enough data set. As observational data are reduced, a

The Applicability of Confidence Intervals of Quantiles for the Generalized Logistic Distribution

NASA Astrophysics Data System (ADS)

Shin, H.; Heo, J.; Kim, T.; Jung, Y.

2007-12-01

The generalized logistic (GL) distribution has been widely used for frequency analysis. However, there is a little study related to the confidence intervals that indicate the prediction accuracy of distribution for the GL distribution. In this paper, the estimation of the confidence intervals of quantiles for the GL distribution is presented based on the method of moments (MOM), maximum likelihood (ML), and probability weighted moments (PWM) and the asymptotic variances of each quantile estimator are derived as functions of the sample sizes, return periods, and parameters. Monte Carlo simulation experiments are also performed to verify the applicability of the derived confidence intervals of quantile. As the results, the relative bias (RBIAS) and relative root mean square error (RRMSE) of the confidence intervals generally increase as return period increases and reverse as sample size increases. And PWM for estimating the confidence intervals performs better than the other methods in terms of RRMSE when the data is almost symmetric while ML shows the smallest RBIAS and RRMSE when the data is more skewed and sample size is moderately large. The GL model was applied to fit the distribution of annual maximum rainfall data. The results show that there are little differences in the estimated quantiles between ML and PWM while distinct differences in MOM.

Alternative configurations of Quantile Regression for estimating predictive uncertainty in water level forecasts for the Upper Severn River: a comparison

NASA Astrophysics Data System (ADS)

Lopez, Patricia; Verkade, Jan; Weerts, Albrecht; Solomatine, Dimitri

2014-05-01

Hydrological forecasting is subject to many sources of uncertainty, including those originating in initial state, boundary conditions, model structure and model parameters. Although uncertainty can be reduced, it can never be fully eliminated. Statistical post-processing techniques constitute an often used approach to estimate the hydrological predictive uncertainty, where a model of forecast error is built using a historical record of past forecasts and observations. The present study focuses on the use of the Quantile Regression (QR) technique as a hydrological post-processor. It estimates the predictive distribution of water levels using deterministic water level forecasts as predictors. This work aims to thoroughly verify uncertainty estimates using the implementation of QR that was applied in an operational setting in the UK National Flood Forecasting System, and to inter-compare forecast quality and skill in various, differing configurations of QR. These configurations are (i) 'classical' QR, (ii) QR constrained by a requirement that quantiles do not cross, (iii) QR derived on time series that have been transformed into the Normal domain (Normal Quantile Transformation - NQT), and (iv) a piecewise linear derivation of QR models. The QR configurations are applied to fourteen hydrological stations on the Upper Severn River with different catchments characteristics. Results of each QR configuration are conditionally verified for progressively higher flood levels, in terms of commonly used verification metrics and skill scores. These include Brier's probability score (BS), the continuous ranked probability score (CRPS) and corresponding skill scores as well as the Relative Operating Characteristic score (ROCS). Reliability diagrams are also presented and analysed. The results indicate that none of the four Quantile Regression configurations clearly outperforms the others.

Communities that thrive in extreme conditions captured from a freshwater lake.

PubMed

Low-Décarie, Etienne; Fussmann, Gregor F; Dumbrell, Alex J; Bell, Graham

2016-09-01

Organisms that can grow in extreme conditions would be expected to be confined to extreme environments. However, we were able to capture highly productive communities of algae and bacteria capable of growing in acidic (pH 2), basic (pH 12) and saline (40 ppt) conditions from an ordinary freshwater lake. Microbial communities may thus include taxa that are highly productive in conditions that are far outside the range of conditions experienced in their host ecosystem. The organisms we captured were not obligate extremophiles, but were capable of growing in both extreme and benign conditions. The ability to grow in extreme conditions may thus be a common functional attribute in microbial communities. © 2016 The Author(s).

Quality of life in breast cancer patients--a quantile regression analysis.

PubMed

Pourhoseingholi, Mohamad Amin; Safaee, Azadeh; Moghimi-Dehkordi, Bijan; Zeighami, Bahram; Faghihzadeh, Soghrat; Tabatabaee, Hamid Reza; Pourhoseingholi, Asma

2008-01-01

Quality of life study has an important role in health care especially in chronic diseases, in clinical judgment and in medical resources supplying. Statistical tools like linear regression are widely used to assess the predictors of quality of life. But when the response is not normal the results are misleading. The aim of this study is to determine the predictors of quality of life in breast cancer patients, using quantile regression model and compare to linear regression. A cross-sectional study conducted on 119 breast cancer patients that admitted and treated in chemotherapy ward of Namazi hospital in Shiraz. We used QLQ-C30 questionnaire to assessment quality of life in these patients. A quantile regression was employed to assess the assocciated factors and the results were compared to linear regression. All analysis carried out using SAS. The mean score for the global health status for breast cancer patients was 64.92+/-11.42. Linear regression showed that only grade of tumor, occupational status, menopausal status, financial difficulties and dyspnea were statistically significant. In spite of linear regression, financial difficulties were not significant in quantile regression analysis and dyspnea was only significant for first quartile. Also emotion functioning and duration of disease statistically predicted the QOL score in the third quartile. The results have demonstrated that using quantile regression leads to better interpretation and richer inference about predictors of the breast cancer patient quality of life.

Response of Simple, Model Systems to Extreme Conditions

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

Ewing, Rodney C.; Lang, Maik

2015-07-30

The focus of the research was on the application of high-pressure/high-temperature techniques, together with intense energetic ion beams, to the study of the behavior of simple oxide systems (e.g., SiO 2, GeO 2, CeO 2, TiO 2, HfO 2, SnO 2, ZnO and ZrO 2) under extreme conditions. These simple stoichiometries provide unique model systems for the analysis of structural responses to pressure up to and above 1 Mbar, temperatures of up to several thousands of kelvin, and the extreme energy density generated by energetic heavy ions (tens of keV/atom). The investigations included systematic studies of radiation- and pressure-induced amorphizationmore » of high P-T polymorphs. By studying the response of simple stoichiometries that have multiple structural “outcomes”, we have established the basic knowledge required for the prediction of the response of more complex structures to extreme conditions. We especially focused on the amorphous state and characterized the different non-crystalline structure-types that result from the interplay of radiation and pressure. For such experiments, we made use of recent technological developments, such as the perforated diamond-anvil cell and in situ investigation using synchrotron x-ray sources. We have been particularly interested in using extreme pressures to alter the electronic structure of a solid prior to irradiation. We expected that the effects of modified band structure would be evident in the track structure and morphology, information which is much needed to describe theoretically the fundamental physics of track-formation. Finally, we investigated the behavior of different simple-oxide, composite nanomaterials (e.g., uncoated nanoparticles vs. core/shell systems) under coupled, extreme conditions. This provided insight into surface and boundary effects on phase stability under extreme conditions.« less

Influences of spatial and temporal variation on fish-habitat relationships defined by regression quantiles

Treesearch

Jason B. Dunham; Brian S. Cade; James W. Terrell

2002-01-01

We used regression quantiles to model potentially limiting relationships between the standing crop of cutthroat trout Oncorhynchus clarki and measures of stream channel morphology. Regression quantile models indicated that variation in fish density was inversely related to the width:depth ratio of streams but not to stream width or depth alone. The...

Comparison of different hydrological similarity measures to estimate flow quantiles

NASA Astrophysics Data System (ADS)

Rianna, M.; Ridolfi, E.; Napolitano, F.

2017-07-01

This paper aims to evaluate the influence of hydrological similarity measures on the definition of homogeneous regions. To this end, several attribute sets have been analyzed in the context of the Region of Influence (ROI) procedure. Several combinations of geomorphological, climatological, and geographical characteristics are also used to cluster potentially homogeneous regions. To verify the goodness of the resulting pooled sites, homogeneity tests arecarried out. Through a Monte Carlo simulation and a jack-knife procedure, flow quantiles areestimated for the regions effectively resulting as homogeneous. The analysis areperformed in both the so-called gauged and ungauged scenarios to analyze the effect of hydrological measures on flow quantiles estimation.

Quantile regression of microgeographic variation in population characteristics of an invasive vertebrate predator

USGS Publications Warehouse

Siers, Shane R.; Savidge, Julie A.; Reed, Robert

2017-01-01

Localized ecological conditions have the potential to induce variation in population characteristics such as size distributions and body conditions. The ability to generalize the influence of ecological characteristics on such population traits may be particularly meaningful when those traits influence prospects for successful management interventions. To characterize variability in invasive Brown Treesnake population attributes within and among habitat types, we conducted systematic and seasonally-balanced surveys, collecting 100 snakes from each of 18 sites: three replicates within each of six major habitat types comprising 95% of Guam’s geographic expanse. Our study constitutes one of the most comprehensive and controlled samplings of any published snake study. Quantile regression on snake size and body condition indicated significant ecological heterogeneity, with a general trend of relative consistency of size classes and body conditions within and among scrub and Leucaena forest habitat types and more heterogeneity among ravine forest, savanna, and urban residential sites. Larger and more robust snakes were found within some savanna and urban habitat replicates, likely due to relative availability of larger prey. Compared to more homogeneous samples in the wet season, variability in size distributions and body conditions was greater during the dry season. Although there is evidence of habitat influencing Brown Treesnake populations at localized scales (e.g., the higher prevalence of larger snakes—particularly males—in savanna and urban sites), the level of variability among sites within habitat types indicates little ability to make meaningful predictions about these traits at unsampled locations. Seasonal variability within sites and habitats indicates that localized population characterization should include sampling in both wet and dry seasons. Extreme values at single replicates occasionally influenced overall habitat patterns, while pooling

Quantile regression of microgeographic variation in population characteristics of an invasive vertebrate predator

PubMed Central

Siers, Shane R.; Savidge, Julie A.; Reed, Robert N.

2017-01-01

Localized ecological conditions have the potential to induce variation in population characteristics such as size distributions and body conditions. The ability to generalize the influence of ecological characteristics on such population traits may be particularly meaningful when those traits influence prospects for successful management interventions. To characterize variability in invasive Brown Treesnake population attributes within and among habitat types, we conducted systematic and seasonally-balanced surveys, collecting 100 snakes from each of 18 sites: three replicates within each of six major habitat types comprising 95% of Guam’s geographic expanse. Our study constitutes one of the most comprehensive and controlled samplings of any published snake study. Quantile regression on snake size and body condition indicated significant ecological heterogeneity, with a general trend of relative consistency of size classes and body conditions within and among scrub and Leucaena forest habitat types and more heterogeneity among ravine forest, savanna, and urban residential sites. Larger and more robust snakes were found within some savanna and urban habitat replicates, likely due to relative availability of larger prey. Compared to more homogeneous samples in the wet season, variability in size distributions and body conditions was greater during the dry season. Although there is evidence of habitat influencing Brown Treesnake populations at localized scales (e.g., the higher prevalence of larger snakes—particularly males—in savanna and urban sites), the level of variability among sites within habitat types indicates little ability to make meaningful predictions about these traits at unsampled locations. Seasonal variability within sites and habitats indicates that localized population characterization should include sampling in both wet and dry seasons. Extreme values at single replicates occasionally influenced overall habitat patterns, while pooling

Quantile regression of microgeographic variation in population characteristics of an invasive vertebrate predator.

PubMed

Siers, Shane R; Savidge, Julie A; Reed, Robert N

2017-01-01

Localized ecological conditions have the potential to induce variation in population characteristics such as size distributions and body conditions. The ability to generalize the influence of ecological characteristics on such population traits may be particularly meaningful when those traits influence prospects for successful management interventions. To characterize variability in invasive Brown Treesnake population attributes within and among habitat types, we conducted systematic and seasonally-balanced surveys, collecting 100 snakes from each of 18 sites: three replicates within each of six major habitat types comprising 95% of Guam's geographic expanse. Our study constitutes one of the most comprehensive and controlled samplings of any published snake study. Quantile regression on snake size and body condition indicated significant ecological heterogeneity, with a general trend of relative consistency of size classes and body conditions within and among scrub and Leucaena forest habitat types and more heterogeneity among ravine forest, savanna, and urban residential sites. Larger and more robust snakes were found within some savanna and urban habitat replicates, likely due to relative availability of larger prey. Compared to more homogeneous samples in the wet season, variability in size distributions and body conditions was greater during the dry season. Although there is evidence of habitat influencing Brown Treesnake populations at localized scales (e.g., the higher prevalence of larger snakes-particularly males-in savanna and urban sites), the level of variability among sites within habitat types indicates little ability to make meaningful predictions about these traits at unsampled locations. Seasonal variability within sites and habitats indicates that localized population characterization should include sampling in both wet and dry seasons. Extreme values at single replicates occasionally influenced overall habitat patterns, while pooling replicates

Spatial quantile regression using INLA with applications to childhood overweight in Malawi.

PubMed

Mtambo, Owen P L; Masangwi, Salule J; Kazembe, Lawrence N M

2015-04-01

Analyses of childhood overweight have mainly used mean regression. However, using quantile regression is more appropriate as it provides flexibility to analyse the determinants of overweight corresponding to quantiles of interest. The main objective of this study was to fit a Bayesian additive quantile regression model with structured spatial effects for childhood overweight in Malawi using the 2010 Malawi DHS data. Inference was fully Bayesian using R-INLA package. The significant determinants of childhood overweight ranged from socio-demographic factors such as type of residence to child and maternal factors such as child age and maternal BMI. We observed significant positive structured spatial effects on childhood overweight in some districts of Malawi. We recommended that the childhood malnutrition policy makers should consider timely interventions based on risk factors as identified in this paper including spatial targets of interventions. Copyright © 2015 Elsevier Ltd. All rights reserved.

Conditional probability of rainfall extremes across multiple durations

NASA Astrophysics Data System (ADS)

Le, Phuong Dong; Leonard, Michael; Westra, Seth

2017-04-01

The conditional probability that extreme rainfall will occur at one location given that it is occurring at another location is critical in engineering design and management circumstances including planning of evacuation routes and the sitting of emergency infrastructure. A challenge with this conditional simulation is that in many situations the interest is not so much the conditional distributions of rainfall of the same duration at two locations, but rather the conditional distribution of flooding in two neighbouring catchments, which may be influenced by rainfall of different critical durations. To deal with this challenge, a model that can consider both spatial and duration dependence of extremes is required. The aim of this research is to develop a model that can take account both spatial dependence and duration dependence into the dependence structure of extreme rainfalls. To achieve this aim, this study is a first attempt at combining extreme rainfall for multiple durations within a spatial extreme model framework based on max-stable process theory. Max-stable processes provide a general framework for modelling multivariate extremes with spatial dependence for just a single duration extreme rainfall. To achieve dependence across multiple timescales, this study proposes a new approach that includes addition elements representing duration dependence of extremes to the covariance matrix of max-stable model. To improve the efficiency of calculation, a re-parameterization proposed by Koutsoyiannis et al. (1998) is used to reduce the number of parameters necessary to be estimated. This re-parameterization enables the GEV parameters to be represented as a function of timescale. A stepwise framework has been adopted to achieve the overall aims of this research. Firstly, the re-parameterization is used to define a new set of common parameters for marginal distribution across multiple durations. Secondly, spatial interpolation of the new parameter set is used to

Principles of Quantile Regression and an Application

ERIC Educational Resources Information Center

Chen, Fang; Chalhoub-Deville, Micheline

2014-01-01

Newer statistical procedures are typically introduced to help address the limitations of those already in practice or to deal with emerging research needs. Quantile regression (QR) is introduced in this paper as a relatively new methodology, which is intended to overcome some of the limitations of least squares mean regression (LMR). QR is more…

Hospital charges associated with motorcycle crash factors: a quantile regression analysis.

PubMed

Olsen, Cody S; Thomas, Andrea M; Cook, Lawrence J

2014-08-01

Previous studies of motorcycle crash (MC) related hospital charges use trauma registries and hospital records, and do not adjust for the number of motorcyclists not requiring medical attention. This may lead to conservative estimates of helmet use effectiveness. MC records were probabilistically linked with emergency department and hospital records to obtain total hospital charges. Missing data were imputed. Multivariable quantile regression estimated reductions in hospital charges associated with helmet use and other crash factors. Motorcycle helmets were associated with reduced median hospital charges of $256 (42% reduction) and reduced 98th percentile of $32,390 (33% reduction). After adjusting for other factors, helmets were associated with reductions in charges in all upper percentiles studied. Quantile regression models described homogenous and heterogeneous associations between other crash factors and charges. Quantile regression comprehensively describes associations between crash factors and hospital charges. Helmet use among motorcyclists is associated with decreased hospital charges. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Robust and efficient estimation with weighted composite quantile regression

NASA Astrophysics Data System (ADS)

Jiang, Xuejun; Li, Jingzhi; Xia, Tian; Yan, Wanfeng

2016-09-01

In this paper we introduce a weighted composite quantile regression (CQR) estimation approach and study its application in nonlinear models such as exponential models and ARCH-type models. The weighted CQR is augmented by using a data-driven weighting scheme. With the error distribution unspecified, the proposed estimators share robustness from quantile regression and achieve nearly the same efficiency as the oracle maximum likelihood estimator (MLE) for a variety of error distributions including the normal, mixed-normal, Student's t, Cauchy distributions, etc. We also suggest an algorithm for the fast implementation of the proposed methodology. Simulations are carried out to compare the performance of different estimators, and the proposed approach is used to analyze the daily S&P 500 Composite index, which verifies the effectiveness and efficiency of our theoretical results.

A quantile regression model for failure-time data with time-dependent covariates

PubMed Central

Gorfine, Malka; Goldberg, Yair; Ritov, Ya’acov

2017-01-01

Summary Since survival data occur over time, often important covariates that we wish to consider also change over time. Such covariates are referred as time-dependent covariates. Quantile regression offers flexible modeling of survival data by allowing the covariates to vary with quantiles. This article provides a novel quantile regression model accommodating time-dependent covariates, for analyzing survival data subject to right censoring. Our simple estimation technique assumes the existence of instrumental variables. In addition, we present a doubly-robust estimator in the sense of Robins and Rotnitzky (1992, Recovery of information and adjustment for dependent censoring using surrogate markers. In: Jewell, N. P., Dietz, K. and Farewell, V. T. (editors), AIDS Epidemiology. Boston: Birkhaäuser, pp. 297–331.). The asymptotic properties of the estimators are rigorously studied. Finite-sample properties are demonstrated by a simulation study. The utility of the proposed methodology is demonstrated using the Stanford heart transplant dataset. PMID:27485534

Focus issue on the Study of Matter at Extreme Conditions

NASA Astrophysics Data System (ADS)

Saini, Naurang L.; Saxena, Surendra K.; Bansil, Arun

2015-09-01

Study of matter at extreme conditions encompasses many different approaches for understanding the physics, chemistry and materials science underlying processes, products and technologies important for society. Although extreme conditions have been associated traditionally with research in areas of geology, mineral and earth sciences, the field has expanded in the recent years to include work on energy related materials and quantum functional materials from hard to soft matter. With the motivation to engage a large number of scientists with various disciplinary interests, ranging from physics, chemistry, geophysics to materials science, the study of matter at extreme conditions has been the theme of a series of conferences hosted by the High Pressure Science Society of America (HiPSSA) and the Center for the Study of Matter at Extreme Conditions (CeSMEC) of Florida International University (FIU), Miami. These SMEC (Study of Matter at Extreme Conditions) conferences are aimed at providing a unique platform for leading researchers to meet and share cutting-edge developments, and to bridge established fields under this interdisciplinary umbrella for research on materials. The seventh meeting in the SMEC series was held during March 23-30, 2013, while sailing from Miami to the Caribbean Islands, and concluded with great enthusiasm.

Censored Quantile Instrumental Variable Estimates of the Price Elasticity of Expenditure on Medical Care.

PubMed

Kowalski, Amanda

2016-01-02

Efforts to control medical care costs depend critically on how individuals respond to prices. I estimate the price elasticity of expenditure on medical care using a censored quantile instrumental variable (CQIV) estimator. CQIV allows estimates to vary across the conditional expenditure distribution, relaxes traditional censored model assumptions, and addresses endogeneity with an instrumental variable. My instrumental variable strategy uses a family member's injury to induce variation in an individual's own price. Across the conditional deciles of the expenditure distribution, I find elasticities that vary from -0.76 to -1.49, which are an order of magnitude larger than previous estimates.

Analysis of the Influence of Quantile Regression Model on Mainland Tourists' Service Satisfaction Performance

PubMed Central

Wang, Wen-Cheng; Cho, Wen-Chien; Chen, Yin-Jen

2014-01-01

It is estimated that mainland Chinese tourists travelling to Taiwan can bring annual revenues of 400 billion NTD to the Taiwan economy. Thus, how the Taiwanese Government formulates relevant measures to satisfy both sides is the focus of most concern. Taiwan must improve the facilities and service quality of its tourism industry so as to attract more mainland tourists. This paper conducted a questionnaire survey of mainland tourists and used grey relational analysis in grey mathematics to analyze the satisfaction performance of all satisfaction question items. The first eight satisfaction items were used as independent variables, and the overall satisfaction performance was used as a dependent variable for quantile regression model analysis to discuss the relationship between the dependent variable under different quantiles and independent variables. Finally, this study further discussed the predictive accuracy of the least mean regression model and each quantile regression model, as a reference for research personnel. The analysis results showed that other variables could also affect the overall satisfaction performance of mainland tourists, in addition to occupation and age. The overall predictive accuracy of quantile regression model Q0.25 was higher than that of the other three models. PMID:24574916

Analysis of the influence of quantile regression model on mainland tourists' service satisfaction performance.

PubMed

Wang, Wen-Cheng; Cho, Wen-Chien; Chen, Yin-Jen

2014-01-01

It is estimated that mainland Chinese tourists travelling to Taiwan can bring annual revenues of 400 billion NTD to the Taiwan economy. Thus, how the Taiwanese Government formulates relevant measures to satisfy both sides is the focus of most concern. Taiwan must improve the facilities and service quality of its tourism industry so as to attract more mainland tourists. This paper conducted a questionnaire survey of mainland tourists and used grey relational analysis in grey mathematics to analyze the satisfaction performance of all satisfaction question items. The first eight satisfaction items were used as independent variables, and the overall satisfaction performance was used as a dependent variable for quantile regression model analysis to discuss the relationship between the dependent variable under different quantiles and independent variables. Finally, this study further discussed the predictive accuracy of the least mean regression model and each quantile regression model, as a reference for research personnel. The analysis results showed that other variables could also affect the overall satisfaction performance of mainland tourists, in addition to occupation and age. The overall predictive accuracy of quantile regression model Q0.25 was higher than that of the other three models.

The critical role of uncertainty in projections of hydrological extremes

NASA Astrophysics Data System (ADS)

Meresa, Hadush K.; Romanowicz, Renata J.

2017-08-01

This paper aims to quantify the uncertainty in projections of future hydrological extremes in the Biala Tarnowska River at Koszyce gauging station, south Poland. The approach followed is based on several climate projections obtained from the EURO-CORDEX initiative, raw and bias-corrected realizations of catchment precipitation, and flow simulations derived using multiple hydrological model parameter sets. The projections cover the 21st century. Three sources of uncertainty are considered: one related to climate projection ensemble spread, the second related to the uncertainty in hydrological model parameters and the third related to the error in fitting theoretical distribution models to annual extreme flow series. The uncertainty of projected extreme indices related to hydrological model parameters was conditioned on flow observations from the reference period using the generalized likelihood uncertainty estimation (GLUE) approach, with separate criteria for high- and low-flow extremes. Extreme (low and high) flow quantiles were estimated using the generalized extreme value (GEV) distribution at different return periods and were based on two different lengths of the flow time series. A sensitivity analysis based on the analysis of variance (ANOVA) shows that the uncertainty introduced by the hydrological model parameters can be larger than the climate model variability and the distribution fit uncertainty for the low-flow extremes whilst for the high-flow extremes higher uncertainty is observed from climate models than from hydrological parameter and distribution fit uncertainties. This implies that ignoring one of the three uncertainty sources may cause great risk to future hydrological extreme adaptations and water resource planning and management.

A quantile regression approach can reveal the effect of fruit and vegetable consumption on plasma homocysteine levels.

PubMed

Verly, Eliseu; Steluti, Josiane; Fisberg, Regina Mara; Marchioni, Dirce Maria Lobo

2014-01-01

A reduction in homocysteine concentration due to the use of supplemental folic acid is well recognized, although evidence of the same effect for natural folate sources, such as fruits and vegetables (FV), is lacking. The traditional statistical analysis approaches do not provide further information. As an alternative, quantile regression allows for the exploration of the effects of covariates through percentiles of the conditional distribution of the dependent variable. To investigate how the associations of FV intake with plasma total homocysteine (tHcy) differ through percentiles in the distribution using quantile regression. A cross-sectional population-based survey was conducted among 499 residents of Sao Paulo City, Brazil. The participants provided food intake and fasting blood samples. Fruit and vegetable intake was predicted by adjusting for day-to-day variation using a proper measurement error model. We performed a quantile regression to verify the association between tHcy and the predicted FV intake. The predicted values of tHcy for each percentile model were calculated considering an increase of 200 g in the FV intake for each percentile. The results showed that tHcy was inversely associated with FV intake when assessed by linear regression whereas, the association was different when using quantile regression. The relationship with FV consumption was inverse and significant for almost all percentiles of tHcy. The coefficients increased as the percentile of tHcy increased. A simulated increase of 200 g in the FV intake could decrease the tHcy levels in the overall percentiles, but the higher percentiles of tHcy benefited more. This study confirms that the effect of FV intake on lowering the tHcy levels is dependent on the level of tHcy using an innovative statistical approach. From a public health point of view, encouraging people to increase FV intake would benefit people with high levels of tHcy.

Quantile regression in the presence of monotone missingness with sensitivity analysis

PubMed Central

Liu, Minzhao; Daniels, Michael J.; Perri, Michael G.

2016-01-01

In this paper, we develop methods for longitudinal quantile regression when there is monotone missingness. In particular, we propose pattern mixture models with a constraint that provides a straightforward interpretation of the marginal quantile regression parameters. Our approach allows sensitivity analysis which is an essential component in inference for incomplete data. To facilitate computation of the likelihood, we propose a novel way to obtain analytic forms for the required integrals. We conduct simulations to examine the robustness of our approach to modeling assumptions and compare its performance to competing approaches. The model is applied to data from a recent clinical trial on weight management. PMID:26041008

A simulation study of nonparametric total deviation index as a measure of agreement based on quantile regression.

PubMed

Lin, Lawrence; Pan, Yi; Hedayat, A S; Barnhart, Huiman X; Haber, Michael

2016-01-01

Total deviation index (TDI) captures a prespecified quantile of the absolute deviation of paired observations from raters, observers, methods, assays, instruments, etc. We compare the performance of TDI using nonparametric quantile regression to the TDI assuming normality (Lin, 2000). This simulation study considers three distributions: normal, Poisson, and uniform at quantile levels of 0.8 and 0.9 for cases with and without contamination. Study endpoints include the bias of TDI estimates (compared with their respective theoretical values), standard error of TDI estimates (compared with their true simulated standard errors), and test size (compared with 0.05), and power. Nonparametric TDI using quantile regression, although it slightly underestimates and delivers slightly less power for data without contamination, works satisfactorily under all simulated cases even for moderate (say, ≥40) sample sizes. The performance of the TDI based on a quantile of 0.8 is in general superior to that of 0.9. The performances of nonparametric and parametric TDI methods are compared with a real data example. Nonparametric TDI can be very useful when the underlying distribution on the difference is not normal, especially when it has a heavy tail.

Logistic quantile regression provides improved estimates for bounded avian counts: A case study of California Spotted Owl fledgling production

USGS Publications Warehouse

Cade, Brian S.; Noon, Barry R.; Scherer, Rick D.; Keane, John J.

2017-01-01

Counts of avian fledglings, nestlings, or clutch size that are bounded below by zero and above by some small integer form a discrete random variable distribution that is not approximated well by conventional parametric count distributions such as the Poisson or negative binomial. We developed a logistic quantile regression model to provide estimates of the empirical conditional distribution of a bounded discrete random variable. The logistic quantile regression model requires that counts are randomly jittered to a continuous random variable, logit transformed to bound them between specified lower and upper values, then estimated in conventional linear quantile regression, repeating the 3 steps and averaging estimates. Back-transformation to the original discrete scale relies on the fact that quantiles are equivariant to monotonic transformations. We demonstrate this statistical procedure by modeling 20 years of California Spotted Owl fledgling production (0−3 per territory) on the Lassen National Forest, California, USA, as related to climate, demographic, and landscape habitat characteristics at territories. Spotted Owl fledgling counts increased nonlinearly with decreasing precipitation in the early nesting period, in the winter prior to nesting, and in the prior growing season; with increasing minimum temperatures in the early nesting period; with adult compared to subadult parents; when there was no fledgling production in the prior year; and when percentage of the landscape surrounding nesting sites (202 ha) with trees ≥25 m height increased. Changes in production were primarily driven by changes in the proportion of territories with 2 or 3 fledglings. Average variances of the discrete cumulative distributions of the estimated fledgling counts indicated that temporal changes in climate and parent age class explained 18% of the annual variance in owl fledgling production, which was 34% of the total variance. Prior fledgling production explained as much of

Quantile Regression in the Study of Developmental Sciences

ERIC Educational Resources Information Center

Petscher, Yaacov; Logan, Jessica A. R.

2014-01-01

Linear regression analysis is one of the most common techniques applied in developmental research, but only allows for an estimate of the average relations between the predictor(s) and the outcome. This study describes quantile regression, which provides estimates of the relations between the predictor(s) and outcome, but across multiple points of…

Goodness of Fit and Misspecification in Quantile Regressions

ERIC Educational Resources Information Center

Furno, Marilena

2011-01-01

The article considers a test of specification for quantile regressions. The test relies on the increase of the objective function and the worsening of the fit when unnecessary constraints are imposed. It compares the objective functions of restricted and unrestricted models and, in its different formulations, it verifies (a) forecast ability, (b)…

Quantile regression for the statistical analysis of immunological data with many non-detects.

PubMed

Eilers, Paul H C; Röder, Esther; Savelkoul, Huub F J; van Wijk, Roy Gerth

2012-07-07

Immunological parameters are hard to measure. A well-known problem is the occurrence of values below the detection limit, the non-detects. Non-detects are a nuisance, because classical statistical analyses, like ANOVA and regression, cannot be applied. The more advanced statistical techniques currently available for the analysis of datasets with non-detects can only be used if a small percentage of the data are non-detects. Quantile regression, a generalization of percentiles to regression models, models the median or higher percentiles and tolerates very high numbers of non-detects. We present a non-technical introduction and illustrate it with an implementation to real data from a clinical trial. We show that by using quantile regression, groups can be compared and that meaningful linear trends can be computed, even if more than half of the data consists of non-detects. Quantile regression is a valuable addition to the statistical methods that can be used for the analysis of immunological datasets with non-detects.

Quantile-Specific Penetrance of Genes Affecting Lipoproteins, Adiposity and Height

PubMed Central

Williams, Paul T.

2012-01-01

Quantile-dependent penetrance is proposed to occur when the phenotypic expression of a SNP depends upon the population percentile of the phenotype. To illustrate the phenomenon, quantiles of height, body mass index (BMI), and plasma lipids and lipoproteins were compared to genetic risk scores (GRS) derived from single nucleotide polymorphisms (SNP)s having established genome-wide significance: 180 SNPs for height, 32 for BMI, 37 for low-density lipoprotein (LDL)-cholesterol, 47 for high-density lipoprotein (HDL)-cholesterol, 52 for total cholesterol, and 31 for triglycerides in 1930 subjects. Both phenotypes and GRSs were adjusted for sex, age, study, and smoking status. Quantile regression showed that the slope of the genotype-phenotype relationships increased with the percentile of BMI (P = 0.002), LDL-cholesterol (P = 3×10−8), HDL-cholesterol (P = 5×10−6), total cholesterol (P = 2.5×10−6), and triglyceride distribution (P = 7.5×10−6), but not height (P = 0.09). Compared to a GRS's phenotypic effect at the 10th population percentile, its effect at the 90th percentile was 4.2-fold greater for BMI, 4.9-fold greater for LDL-cholesterol, 1.9-fold greater for HDL-cholesterol, 3.1-fold greater for total cholesterol, and 3.3-fold greater for triglycerides. Moreover, the effect of the rs1558902 (FTO) risk allele was 6.7-fold greater at the 90th than the 10th percentile of the BMI distribution, and that of the rs3764261 (CETP) risk allele was 2.4-fold greater at the 90th than the 10th percentile of the HDL-cholesterol distribution. Conceptually, it maybe useful to distinguish environmental effects on the phenotype that in turn alters a gene's phenotypic expression (quantile-dependent penetrance) from environmental effects affecting the gene's phenotypic expression directly (gene-environment interaction). PMID:22235250

Temporal development of extreme precipitation in Germany projected by EURO-CORDEX simulations

NASA Astrophysics Data System (ADS)

Brendel, Christoph; Deutschländer, Thomas

2017-04-01

A sustainable operation of transport infrastructure requires an enhanced resilience to the increasing impacts of climate change and related extreme meteorological events. To meet this challenge, the German Federal Ministry of Transport and Digital Infrastructure (BMVI) commenced a comprehensive national research program on safe and sustainable transport in Germany. A network of departmental research institutes addresses the "Adaptation of the German transport infrastructure towards climate change and extreme events". Various studies already have identified an increase in the average global precipitation for the 20th century. There is some indication that these increases are most visible in a rising frequency of precipitation extremes. However, the changes are highly variable between regions and seasons. With a further increase of atmospheric greenhouse gas concentrations in the 21st century, the likelihood of occurrence of such extreme events will continue to rise. A kernel estimator has been used in order to obtain a robust estimate of the temporal development of extreme precipitation events projected by an ensemble of EURO-CORDEX simulations. The kernel estimator measures the intensity of the poisson point process indicating temporal changes in the frequency of extreme events. Extreme precipitation events were selected using the peaks over threshold (POT) method with the 90th, 95th and 99th quantile of daily precipitation sums as thresholds. Application of this non-parametric approach with relative thresholds renders the use of a bias correction non-mandatory. In addition, in comparison to fitting an extreme value theory (EVT) distribution, the method is completely unsusceptible to outliers. First results show an overall increase of extreme precipitation events for Germany until the end of the 21st century. However, major differences between seasons, quantiles and the three different Representative Concentration Pathways (RCP 2.6, 4.5, and 8.5) have been

Censored Quantile Instrumental Variable Estimates of the Price Elasticity of Expenditure on Medical Care

PubMed Central

Kowalski, Amanda

2015-01-01

Efforts to control medical care costs depend critically on how individuals respond to prices. I estimate the price elasticity of expenditure on medical care using a censored quantile instrumental variable (CQIV) estimator. CQIV allows estimates to vary across the conditional expenditure distribution, relaxes traditional censored model assumptions, and addresses endogeneity with an instrumental variable. My instrumental variable strategy uses a family member’s injury to induce variation in an individual’s own price. Across the conditional deciles of the expenditure distribution, I find elasticities that vary from −0.76 to −1.49, which are an order of magnitude larger than previous estimates. PMID:26977117

A comparative assessment of statistical methods for extreme weather analysis

NASA Astrophysics Data System (ADS)

Schlögl, Matthias; Laaha, Gregor

2017-04-01

Extreme weather exposure assessment is of major importance for scientists and practitioners alike. We compare different extreme value approaches and fitting methods with respect to their value for assessing extreme precipitation and temperature impacts. Based on an Austrian data set from 25 meteorological stations representing diverse meteorological conditions, we assess the added value of partial duration series over the standardly used annual maxima series in order to give recommendations for performing extreme value statistics of meteorological hazards. Results show the merits of the robust L-moment estimation, which yielded better results than maximum likelihood estimation in 62 % of all cases. At the same time, results question the general assumption of the threshold excess approach (employing partial duration series, PDS) being superior to the block maxima approach (employing annual maxima series, AMS) due to information gain. For low return periods (non-extreme events) the PDS approach tends to overestimate return levels as compared to the AMS approach, whereas an opposite behavior was found for high return levels (extreme events). In extreme cases, an inappropriate threshold was shown to lead to considerable biases that may outperform the possible gain of information from including additional extreme events by far. This effect was neither visible from the square-root criterion, nor from standardly used graphical diagnosis (mean residual life plot), but from a direct comparison of AMS and PDS in synoptic quantile plots. We therefore recommend performing AMS and PDS approaches simultaneously in order to select the best suited approach. This will make the analyses more robust, in cases where threshold selection and dependency introduces biases to the PDS approach, but also in cases where the AMS contains non-extreme events that may introduce similar biases. For assessing the performance of extreme events we recommend conditional performance measures that focus

Projected changes in snowfall extremes and interannual variability of snowfall in the western United States

NASA Astrophysics Data System (ADS)

Lute, A. C.; Abatzoglou, J. T.; Hegewisch, K. C.

2015-02-01

Projected warming will have significant impacts on snowfall accumulation and melt, with implications for water availability and management in snow-dominated regions. Changes in snowfall extremes are confounded by projected increases in precipitation extremes. Downscaled climate projections from 20 global climate models were bias-corrected to montane Snowpack Telemetry stations across the western United States to assess mid-21st century changes in the mean and variability of annual snowfall water equivalent (SFE) and extreme snowfall events, defined by the 90th percentile of cumulative 3 day SFE amounts. Declines in annual SFE and number of snowfall days were projected for all stations. Changes in the magnitude of snowfall event quantiles were sensitive to historical winter temperature. At climatologically cooler locations, such as in the Rocky Mountains, changes in the magnitude of snowfall events mirrored changes in the distribution of precipitation events, with increases in extremes and less change in more moderate events. By contrast, declines in snowfall event magnitudes were found for all quantiles in warmer locations. Common to both warmer and colder sites was a relative increase in the magnitude of snowfall extremes compared to annual SFE and a larger fraction of annual SFE from snowfall extremes. The coefficient of variation of annual SFE increased up to 80% in warmer montane regions due to projected declines in snowfall days and the increased contribution of snowfall extremes to annual SFE. In addition to declines in mean annual SFE, more frequent low-snowfall years and less frequent high-snowfall years were projected for every station.

An assessment of temporal effect on extreme rainfall estimates

NASA Astrophysics Data System (ADS)

Das, Samiran; Zhu, Dehua; Chi-Han, Cheng

2018-06-01

This study assesses the temporal behaviour in terms of inter-decadal variability of extreme daily rainfall of stated return period relevant for hydrologic risk analysis using a novel regional parametric approach. The assessment is carried out based on annual maximum daily rainfall series of 180 meteorological stations of Yangtze River Basin over a 50-year period (1961-2010). The outcomes of the analysis reveal that while there were effects present indicating higher quantile values when estimated from data of the 1990s, it is found not to be noteworthy to exclude the data of any decade from the extreme rainfall estimation process for hydrologic risk analysis.

Modeling the human development index and the percentage of poor people using quantile smoothing splines

NASA Astrophysics Data System (ADS)

Mulyani, Sri; Andriyana, Yudhie; Sudartianto

2017-03-01

Mean regression is a statistical method to explain the relationship between the response variable and the predictor variable based on the central tendency of the data (mean) of the response variable. The parameter estimation in mean regression (with Ordinary Least Square or OLS) generates a problem if we apply it to the data with a symmetric, fat-tailed, or containing outlier. Hence, an alternative method is necessary to be used to that kind of data, for example quantile regression method. The quantile regression is a robust technique to the outlier. This model can explain the relationship between the response variable and the predictor variable, not only on the central tendency of the data (median) but also on various quantile, in order to obtain complete information about that relationship. In this study, a quantile regression is developed with a nonparametric approach such as smoothing spline. Nonparametric approach is used if the prespecification model is difficult to determine, the relation between two variables follow the unknown function. We will apply that proposed method to poverty data. Here, we want to estimate the Percentage of Poor People as the response variable involving the Human Development Index (HDI) as the predictor variable.

Managing more than the mean: Using quantile regression to identify factors related to large elk groups

USGS Publications Warehouse

Brennan, Angela K.; Cross, Paul C.; Creely, Scott

2015-01-01

Synthesis and applications. Our analysis of elk group size distributions using quantile regression suggests that private land, irrigation, open habitat, elk density and wolf abundance can affect large elk group sizes. Thus, to manage larger groups by removal or dispersal of individuals, we recommend incentivizing hunting on private land (particularly if irrigated) during the regular and late hunting seasons, promoting tolerance of wolves on private land (if elk aggregate in these areas to avoid wolves) and creating more winter range and varied habitats. Relationships to the variables of interest also differed by quantile, highlighting the importance of using quantile regression to examine response variables more completely to uncover relationships important to conservation and management.

Asymmetric impact of rainfall on India's food grain production: evidence from quantile autoregressive distributed lag model

NASA Astrophysics Data System (ADS)

Pal, Debdatta; Mitra, Subrata Kumar

2018-01-01

This study used a quantile autoregressive distributed lag (QARDL) model to capture asymmetric impact of rainfall on food production in India. It was found that the coefficient corresponding to the rainfall in the QARDL increased till the 75th quantile and started decreasing thereafter, though it remained in the positive territory. Another interesting finding is that at the 90th quantile and above the coefficients of rainfall though remained positive was not statistically significant and therefore, the benefit of high rainfall on crop production was not conclusive. However, the impact of other determinants, such as fertilizer and pesticide consumption, is quite uniform over the whole range of the distribution of food grain production.

Understanding the Impact of Extreme Temperature on Crop Production in Karnataka in India

NASA Astrophysics Data System (ADS)

Mahato, S.; Murari, K. K.; Jayaraman, T.

2017-12-01

The impact of extreme temperature on crop yield is seldom explored in work around climate change impact on agriculture. Further, these studies are restricted mainly to crops such as wheat and maize. Since different agro-climatic zones bear different crops and cropping patterns, it is important to explore the nature of the impact of changes in climate variables in agricultural systems under differential conditions. The study explores the effects of temperature rise on the major crops paddy, jowar, ragi and tur in the state of Karnataka of southern India. The choice of the unit of study to understand impact of climate variability on crop yields is largely restricted to availability of data for the unit. While, previous studies have dealt with this issue by replacing yield with NDVI at finer resolution, the use of an index in place of yield data has its limitations and may not reflect the true estimates. For this study, the unit considered is taluk, i.e. sub-district level. The crop yield for taluk is obtained between the year the 1995 to 2011 by aggregating point yield data from crop cutting experiments for each year across the taluks. The long term temperature data shows significantly increasing trend that ranges between 0.6 to 0.75 C across Karnataka. Further, the analysis suggests a warming trend in seasonal average temperature for Kharif and Rabi seasons across districts. The study also found that many districts exhibit the tendency of occurrence of extreme temperature days, which is of particular concern in terms of crop yield, since exposure of crops to extreme temperature has negative consequences for crop production and productivity. Using growing degree days GDD, extreme degree days EDD and total season rainfall as predictor variables, the fixed effect model shows that EDD is a more influential parameter as compared to GDD and rainfall. Also it has a statistically significant negative effect in most cases. Further, quantile regression was used to evaluate

Influences of spatial and temporal variation on fish-habitat relationships defined by regression quantiles

USGS Publications Warehouse

Dunham, J.B.; Cade, B.S.; Terrell, J.W.

2002-01-01

We used regression quantiles to model potentially limiting relationships between the standing crop of cutthroat trout Oncorhynchus clarki and measures of stream channel morphology. Regression quantile models indicated that variation in fish density was inversely related to the width:depth ratio of streams but not to stream width or depth alone. The spatial and temporal stability of model predictions were examined across years and streams, respectively. Variation in fish density with width:depth ratio (10th-90th regression quantiles) modeled for streams sampled in 1993-1997 predicted the variation observed in 1998-1999, indicating similar habitat relationships across years. Both linear and nonlinear models described the limiting relationships well, the latter performing slightly better. Although estimated relationships were transferable in time, results were strongly dependent on the influence of spatial variation in fish density among streams. Density changes with width:depth ratio in a single stream were responsible for the significant (P < 0.10) negative slopes estimated for the higher quantiles (>80th). This suggests that stream-scale factors other than width:depth ratio play a more direct role in determining population density. Much of the variation in densities of cutthroat trout among streams was attributed to the occurrence of nonnative brook trout Salvelinus fontinalis (a possible competitor) or connectivity to migratory habitats. Regression quantiles can be useful for estimating the effects of limiting factors when ecological responses are highly variable, but our results indicate that spatiotemporal variability in the data should be explicitly considered. In this study, data from individual streams and stream-specific characteristics (e.g., the occurrence of nonnative species and habitat connectivity) strongly affected our interpretation of the relationship between width:depth ratio and fish density.

The quantile regression approach to efficiency measurement: insights from Monte Carlo simulations.

PubMed

Liu, Chunping; Laporte, Audrey; Ferguson, Brian S

2008-09-01

In the health economics literature there is an ongoing debate over approaches used to estimate the efficiency of health systems at various levels, from the level of the individual hospital - or nursing home - up to that of the health system as a whole. The two most widely used approaches to evaluating the efficiency with which various units deliver care are non-parametric data envelopment analysis (DEA) and parametric stochastic frontier analysis (SFA). Productivity researchers tend to have very strong preferences over which methodology to use for efficiency estimation. In this paper, we use Monte Carlo simulation to compare the performance of DEA and SFA in terms of their ability to accurately estimate efficiency. We also evaluate quantile regression as a potential alternative approach. A Cobb-Douglas production function, random error terms and a technical inefficiency term with different distributions are used to calculate the observed output. The results, based on these experiments, suggest that neither DEA nor SFA can be regarded as clearly dominant, and that, depending on the quantile estimated, the quantile regression approach may be a useful addition to the armamentarium of methods for estimating technical efficiency.

Regionalisation of a distributed method for flood quantiles estimation: Revaluation of local calibration hypothesis to enhance the spatial structure of the optimised parameter

NASA Astrophysics Data System (ADS)

Odry, Jean; Arnaud, Patrick

2016-04-01

here is to develop a SHYREG evaluation scheme focusing on both local and regional performances. Indeed, it is necessary to maintain the accuracy of at site flood quantiles estimation while identifying a configuration leading to a satisfactory spatial pattern of the calibrated parameter. This ability to be regionalised can be appraised by the association of common regionalisation techniques and split sample validation tests on a set of around 1,500 catchments representing the whole diversity of France physiography. Also, the presence of many nested catchments and a size-based split sample validation make possible to assess the relevance of the calibrated parameter spatial structure inside the largest catchments. The application of this multi-objective evaluation leads to the selection of a version of SHYREG more suitable for regionalisation. References: Arnaud, P., Cantet, P., Aubert, Y., 2015. Relevance of an at-site flood frequency analysis method for extreme events based on stochastic simulation of hourly rainfall. Hydrological Sciences Journal: on press. DOI:10.1080/02626667.2014.965174 Aubert, Y., Arnaud, P., Ribstein, P., Fine, J.A., 2014. The SHYREG flow method-application to 1605 basins in metropolitan France. Hydrological Sciences Journal, 59(5): 993-1005. DOI:10.1080/02626667.2014.902061

Synoptic and meteorological drivers of extreme ozone concentrations over Europe

NASA Astrophysics Data System (ADS)

Otero, Noelia Felipe; Sillmann, Jana; Schnell, Jordan L.; Rust, Henning W.; Butler, Tim

2016-04-01

The present work assesses the relationship between local and synoptic meteorological conditions and surface ozone concentration over Europe in spring and summer months, during the period 1998-2012 using a new interpolated data set of observed surface ozone concentrations over the European domain. Along with local meteorological conditions, the influence of large-scale atmospheric circulation on surface ozone is addressed through a set of airflow indices computed with a novel implementation of a grid-by-grid weather type classification across Europe. Drivers of surface ozone over the full distribution of maximum daily 8-hour average values are investigated, along with drivers of the extreme high percentiles and exceedances or air quality guideline thresholds. Three different regression techniques are applied: multiple linear regression to assess the drivers of maximum daily ozone, logistic regression to assess the probability of threshold exceedances and quantile regression to estimate the meteorological influence on extreme values, as represented by the 95th percentile. The relative importance of the input parameters (predictors) is assessed by a backward stepwise regression procedure that allows the identification of the most important predictors in each model. Spatial patterns of model performance exhibit distinct variations between regions. The inclusion of the ozone persistence is particularly relevant over Southern Europe. In general, the best model performance is found over Central Europe, where the maximum temperature plays an important role as a driver of maximum daily ozone as well as its extreme values, especially during warmer months.

Injuries in an Extreme Conditioning Program.

PubMed

Aune, Kyle T; Powers, Joseph M

2016-10-19

Extreme conditioning programs (ECPs) are fitness training regimens relying on aerobic, plyometric, and resistance training exercises, often with high levels of intensity for a short duration of time. These programs have grown rapidly in popularity in recent years, but science describing the safety profile of these programs is lacking. The rate of injury in the extreme conditioning program is greater than the injury rate of weightlifting and the majority of injuries occur to the shoulder and back. Cross-sectional study. Level 4. This is a retrospective survey of injuries reported by athletes participating in an ECP. An injury survey was sent to 1100 members of Iron Tribe Fitness, a gym franchise with 5 locations across Birmingham, Alabama, that employs exercises consistent with an ECP in this study. An injury was defined as a physical condition resulting from ECP participation that caused the athlete to either seek medical treatment, take time off from exercising, or make modifications to his or her technique to continue. A total of 247 athletes (22%) completed the survey. The majority (57%) of athletes were male (n = 139), and 94% of athletes were white (n = 227). The mean age of athletes was 38.9 years (±8.9 years). Athletes reported participation in the ECP for, on average, 3.6 hours per week (± 1.2 hours). Eighty-five athletes (34%) reported that they had sustained an injury while participating in the ECP. A total of 132 injuries were recorded, yielding an estimated incidence of 2.71 per 1000 hours. The shoulder or upper arm was the most commonly injured body site, accounting for 38 injuries (15% of athletes). Athletes with a previous shoulder injury were 8.1 times as likely to injure their shoulder in the ECP compared with athletes with healthy shoulders. The trunk, back, head, or neck (n = 29, 12%) and the leg or knee (n = 29, 12%) were the second most commonly injured sites. The injury incidence rate among athletes with < 6 months of experience in the ECP

Use of historical information in extreme storm surges frequency analysis

NASA Astrophysics Data System (ADS)

Hamdi, Yasser; Duluc, Claire-Marie; Deville, Yves; Bardet, Lise; Rebour, Vincent

2013-04-01

The prevention of storm surge flood risks is critical for protection and design of coastal facilities to very low probabilities of failure. The effective protection requires the use of a statistical analysis approach having a solid theoretical motivation. Relating extreme storm surges to their frequency of occurrence using probability distributions has been a common issue since 1950s. The engineer needs to determine the storm surge of a given return period, i.e., the storm surge quantile or design storm surge. Traditional methods for determining such a quantile have been generally based on data from the systematic record alone. However, the statistical extrapolation, to estimate storm surges corresponding to high return periods, is seriously contaminated by sampling and model uncertainty if data are available for a relatively limited period. This has motivated the development of approaches to enlarge the sample extreme values beyond the systematic period. The nonsystematic data occurred before the systematic period is called historical information. During the last three decades, the value of using historical information as a nonsystematic data in frequency analysis has been recognized by several authors. The basic hypothesis in statistical modeling of historical information is that a perception threshold exists and that during a giving historical period preceding the period of tide gauging, all exceedances of this threshold have been recorded. Historical information prior to the systematic records may arise from high-sea water marks left by extreme surges on the coastal areas. It can also be retrieved from archives, old books, earliest newspapers, damage reports, unpublished written records and interviews with local residents. A plotting position formula, to compute empirical probabilities based on systematic and historical data, is used in this communication paper. The objective of the present work is to examine the potential gain in estimation accuracy with the

Application of Electro Chemical Machining for materials used in extreme conditions

NASA Astrophysics Data System (ADS)

Pandilov, Z.

2018-03-01

Electro-Chemical Machining (ECM) is the generic term for a variety of electrochemical processes. ECM is used to machine work pieces from metal and metal alloys irrespective of their hardness, strength or thermal properties, through the anodic dissolution, in aerospace, automotive, construction, medical equipment, micro-systems and power supply industries. The Electro Chemical Machining is extremely suitable for machining of materials used in extreme conditions. General overview of the Electro-Chemical Machining and its application for different materials used in extreme conditions is presented.

Robust small area estimation of poverty indicators using M-quantile approach (Case study: Sub-district level in Bogor district)

NASA Astrophysics Data System (ADS)

Girinoto, Sadik, Kusman; Indahwati

2017-03-01

The National Socio-Economic Survey samples are designed to produce estimates of parameters of planned domains (provinces and districts). The estimation of unplanned domains (sub-districts and villages) has its limitation to obtain reliable direct estimates. One of the possible solutions to overcome this problem is employing small area estimation techniques. The popular choice of small area estimation is based on linear mixed models. However, such models need strong distributional assumptions and do not easy allow for outlier-robust estimation. As an alternative approach for this purpose, M-quantile regression approach to small area estimation based on modeling specific M-quantile coefficients of conditional distribution of study variable given auxiliary covariates. It obtained outlier-robust estimation from influence function of M-estimator type and also no need strong distributional assumptions. In this paper, the aim of study is to estimate the poverty indicator at sub-district level in Bogor District-West Java using M-quantile models for small area estimation. Using data taken from National Socioeconomic Survey and Villages Potential Statistics, the results provide a detailed description of pattern of incidence and intensity of poverty within Bogor district. We also compare the results with direct estimates. The results showed the framework may be preferable when direct estimate having no incidence of poverty at all in the small area.

A Quantile Regression Approach to Understanding the Relations Between Morphological Awareness, Vocabulary, and Reading Comprehension in Adult Basic Education Students

PubMed Central

Tighe, Elizabeth L.; Schatschneider, Christopher

2015-01-01

The purpose of this study was to investigate the joint and unique contributions of morphological awareness and vocabulary knowledge at five reading comprehension levels in Adult Basic Education (ABE) students. We introduce the statistical technique of multiple quantile regression, which enabled us to assess the predictive utility of morphological awareness and vocabulary knowledge at multiple points (quantiles) along the continuous distribution of reading comprehension. To demonstrate the efficacy of our multiple quantile regression analysis, we compared and contrasted our results with a traditional multiple regression analytic approach. Our results indicated that morphological awareness and vocabulary knowledge accounted for a large portion of the variance (82-95%) in reading comprehension skills across all quantiles. Morphological awareness exhibited the greatest unique predictive ability at lower levels of reading comprehension whereas vocabulary knowledge exhibited the greatest unique predictive ability at higher levels of reading comprehension. These results indicate the utility of using multiple quantile regression to assess trajectories of component skills across multiple levels of reading comprehension. The implications of our findings for ABE programs are discussed. PMID:25351773

A Quantile Regression Approach to Understanding the Relations Among Morphological Awareness, Vocabulary, and Reading Comprehension in Adult Basic Education Students.

PubMed

Tighe, Elizabeth L; Schatschneider, Christopher

2016-07-01

The purpose of this study was to investigate the joint and unique contributions of morphological awareness and vocabulary knowledge at five reading comprehension levels in adult basic education (ABE) students. We introduce the statistical technique of multiple quantile regression, which enabled us to assess the predictive utility of morphological awareness and vocabulary knowledge at multiple points (quantiles) along the continuous distribution of reading comprehension. To demonstrate the efficacy of our multiple quantile regression analysis, we compared and contrasted our results with a traditional multiple regression analytic approach. Our results indicated that morphological awareness and vocabulary knowledge accounted for a large portion of the variance (82%-95%) in reading comprehension skills across all quantiles. Morphological awareness exhibited the greatest unique predictive ability at lower levels of reading comprehension whereas vocabulary knowledge exhibited the greatest unique predictive ability at higher levels of reading comprehension. These results indicate the utility of using multiple quantile regression to assess trajectories of component skills across multiple levels of reading comprehension. The implications of our findings for ABE programs are discussed. © Hammill Institute on Disabilities 2014.

Environmental determinants of different blood lead levels in children: a quantile analysis from a nationwide survey.

PubMed

Etchevers, Anne; Le Tertre, Alain; Lucas, Jean-Paul; Bretin, Philippe; Oulhote, Youssef; Le Bot, Barbara; Glorennec, Philippe

2015-01-01

Blood lead levels (BLLs) have substantially decreased in recent decades in children in France. However, further reducing exposure is a public health goal because there is no clear toxicological threshold. The identification of the environmental determinants of BLLs as well as risk factors associated with high BLLs is important to update prevention strategies. We aimed to estimate the contribution of environmental sources of lead to different BLLs in children in France. We enrolled 484 children aged from 6months to 6years, in a nationwide cross-sectional survey in 2008-2009. We measured lead concentrations in blood and environmental samples (water, soils, household settled dusts, paints, cosmetics and traditional cookware). We performed two models: a multivariate generalized additive model on the geometric mean (GM), and a quantile regression model on the 10th, 25th, 50th, 75th and 90th quantile of BLLs. The GM of BLLs was 13.8μg/L (=1.38μg/dL) (95% confidence intervals (CI): 12.7-14.9) and the 90th quantile was 25.7μg/L (CI: 24.2-29.5). Household and common area dust, tap water, interior paint, ceramic cookware, traditional cosmetics, playground soil and dust, and environmental tobacco smoke were associated with the GM of BLLs. Household dust and tap water made the largest contributions to both the GM and the 90th quantile of BLLs. The concentration of lead in dust was positively correlated with all quantiles of BLLs even at low concentrations. Lead concentrations in tap water above 5μg/L were also positively correlated with the GM, 75th and 90th quantiles of BLLs in children drinking tap water. Preventative actions must target household settled dust and tap water to reduce the BLLs of children in France. The use of traditional cosmetics should be avoided whereas ceramic cookware should be limited to decorative purposes. Copyright © 2014 Elsevier Ltd. All rights reserved.

L-statistics for Repeated Measurements Data With Application to Trimmed Means, Quantiles and Tolerance Intervals.

PubMed

Assaad, Houssein I; Choudhary, Pankaj K

2013-01-01

The L -statistics form an important class of estimators in nonparametric statistics. Its members include trimmed means and sample quantiles and functions thereof. This article is devoted to theory and applications of L -statistics for repeated measurements data, wherein the measurements on the same subject are dependent and the measurements from different subjects are independent. This article has three main goals: (a) Show that the L -statistics are asymptotically normal for repeated measurements data. (b) Present three statistical applications of this result, namely, location estimation using trimmed means, quantile estimation and construction of tolerance intervals. (c) Obtain a Bahadur representation for sample quantiles. These results are generalizations of similar results for independently and identically distributed data. The practical usefulness of these results is illustrated by analyzing a real data set involving measurement of systolic blood pressure. The properties of the proposed point and interval estimators are examined via simulation.

Using the Quantile Mapping to improve a weather generator

NASA Astrophysics Data System (ADS)

Chen, Y.; Themessl, M.; Gobiet, A.

2012-04-01

We developed a weather generator (WG) by using statistical and stochastic methods, among them are quantile mapping (QM), Monte-Carlo, auto-regression, empirical orthogonal function (EOF). One of the important steps in the WG is using QM, through which all the variables, no matter what distribution they originally are, are transformed into normal distributed variables. Therefore, the WG can work on normally distributed variables, which greatly facilitates the treatment of random numbers in the WG. Monte-Carlo and auto-regression are used to generate the realization; EOFs are employed for preserving spatial relationships and the relationships between different meteorological variables. We have established a complete model named WGQM (weather generator and quantile mapping), which can be applied flexibly to generate daily or hourly time series. For example, with 30-year daily (hourly) data and 100-year monthly (daily) data as input, the 100-year daily (hourly) data would be relatively reasonably produced. Some evaluation experiments with WGQM have been carried out in the area of Austria and the evaluation results will be presented.

Injuries in an Extreme Conditioning Program

PubMed Central

Aune, Kyle T.; Powers, Joseph M.

2016-01-01

Background: Extreme conditioning programs (ECPs) are fitness training regimens relying on aerobic, plyometric, and resistance training exercises, often with high levels of intensity for a short duration of time. These programs have grown rapidly in popularity in recent years, but science describing the safety profile of these programs is lacking. Hypothesis: The rate of injury in the extreme conditioning program is greater than the injury rate of weightlifting and the majority of injuries occur to the shoulder and back. Study Design: Cross-sectional study. Level of Evidence: Level 4. Methods: This is a retrospective survey of injuries reported by athletes participating in an ECP. An injury survey was sent to 1100 members of Iron Tribe Fitness, a gym franchise with 5 locations across Birmingham, Alabama, that employs exercises consistent with an ECP in this study. An injury was defined as a physical condition resulting from ECP participation that caused the athlete to either seek medical treatment, take time off from exercising, or make modifications to his or her technique to continue. Results: A total of 247 athletes (22%) completed the survey. The majority (57%) of athletes were male (n = 139), and 94% of athletes were white (n = 227). The mean age of athletes was 38.9 years (±8.9 years). Athletes reported participation in the ECP for, on average, 3.6 hours per week (± 1.2 hours). Eighty-five athletes (34%) reported that they had sustained an injury while participating in the ECP. A total of 132 injuries were recorded, yielding an estimated incidence of 2.71 per 1000 hours. The shoulder or upper arm was the most commonly injured body site, accounting for 38 injuries (15% of athletes). Athletes with a previous shoulder injury were 8.1 times as likely to injure their shoulder in the ECP compared with athletes with healthy shoulders. The trunk, back, head, or neck (n = 29, 12%) and the leg or knee (n = 29, 12%) were the second most commonly injured sites. The

Analysis and trends of precipitation lapse rate and extreme indices over north Sikkim eastern Himalayas under CMIP5ESM-2M RCPs experiments

NASA Astrophysics Data System (ADS)

Singh, Vishal; Goyal, Manish Kumar

2016-01-01

This paper draws attention to highlight the spatial and temporal variability in precipitation lapse rate (PLR) and precipitation extreme indices (PEIs) through the mesoscale characterization of Teesta river catchment, which corresponds to north Sikkim eastern Himalayas. A PLR rate is an important variable for the snowmelt runoff models. In a mountainous region, the PLR could be varied from lower elevation parts to high elevation parts. In this study, a PLR was computed by accounting elevation differences, which varies from around 1500 m to 7000 m. A precipitation variability and extremity were analysed using multiple mathematical functions viz. quantile regression, spatial mean, spatial standard deviation, Mann-Kendall test and Sen's estimation. For this reason, a daily precipitation, in the historical (years 1980-2005) as measured/observed gridded points and projected experiments for the 21st century (years 2006-2100) simulated by CMIP5 ESM-2 M model (Coupled Model Intercomparison Project Phase 5 Earth System Model 2) employing three different radiative forcing scenarios (Representative Concentration Pathways), utilized for the research work. The outcomes of this study suggest that a PLR is significantly varied from lower elevation to high elevation parts. The PEI based analysis showed that the extreme high intensity events have been increased significantly, especially after 2040s. The PEI based observations also showed that the numbers of wet days are increased for all the RCPs. The quantile regression plots showed significant increments in the upper and lower quantiles of the various extreme indices. The Mann-Kendall test and Sen's estimation tests clearly indicated significant changing patterns in the frequency and intensity of the precipitation indices across all the sub-basins and RCP scenario in an intra-decadal time series domain. The RCP8.5 showed extremity of the projected outcomes.

Extreme river flow dependence in Northern Scotland

NASA Astrophysics Data System (ADS)

Villoria, M. Franco; Scott, M.; Hoey, T.; Fischbacher-Smith, D.

2012-04-01

predominantly impermeable bedrock, with the Ewe's one being very wet. The Lossie(216km2) and Dulnain (272.2km2) both contain significant areas of glacial deposits. River flow in the Dulnain is usually affected by snowmelt. In all cases, the conditional probability of each of the three rivers (Dulnain, Lossie, Ewe) decreases as the event in the conditioning river (Ness) becomes more extreme. The Ewe, despite being the furthest of the three sites from the Ness shows the strongest dependence, with relatively high (>0.4) conditional probabilities even for very extreme events (>0.995). Although the Lossie is closer geographically to the Ness than the Ewe, it shows relatively low conditional probabilities and can be considered independent of the Ness for very extreme events (> 0.990). The conditional probabilities seem to reflect the different catchment characteristics and dominant precipitation generating events, with the Ewe being more similar to the Ness than the other two rivers. This interpretation suggests that the conditional method may yield improved estimates of extreme events, but the approach is time consuming. An alternative model that is easier to implement, using a spatial quantile regression, is currently being investigated, which would also allow the introduction of further covariates, essential as the effects of climate change are incorporated into estimation procedures.

More green space is related to less antidepressant prescription rates in the Netherlands: A Bayesian geoadditive quantile regression approach.

PubMed

Helbich, Marco; Klein, Nadja; Roberts, Hannah; Hagedoorn, Paulien; Groenewegen, Peter P

2018-06-20

Exposure to green space seems to be beneficial for self-reported mental health. In this study we used an objective health indicator, namely antidepressant prescription rates. Current studies rely exclusively upon mean regression models assuming linear associations. It is, however, plausible that the presence of green space is non-linearly related with different quantiles of the outcome antidepressant prescription rates. These restrictions may contribute to inconsistent findings. Our aim was: a) to assess antidepressant prescription rates in relation to green space, and b) to analyze how the relationship varies non-linearly across different quantiles of antidepressant prescription rates. We used cross-sectional data for the year 2014 at a municipality level in the Netherlands. Ecological Bayesian geoadditive quantile regressions were fitted for the 15%, 50%, and 85% quantiles to estimate green space-prescription rate correlations, controlling for physical activity levels, socio-demographics, urbanicity, etc. RESULTS: The results suggested that green space was overall inversely and non-linearly associated with antidepressant prescription rates. More important, the associations differed across the quantiles, although the variation was modest. Significant non-linearities were apparent: The associations were slightly positive in the lower quantile and strongly negative in the upper one. Our findings imply that an increased availability of green space within a municipality may contribute to a reduction in the number of antidepressant prescriptions dispensed. Green space is thus a central health and community asset, whilst a minimum level of 28% needs to be established for health gains. The highest effectiveness occurred at a municipality surface percentage higher than 79%. This inverse dose-dependent relation has important implications for setting future community-level health and planning policies. Copyright © 2018 Elsevier Inc. All rights reserved.

Quantile regression and clustering analysis of standardized precipitation index in the Tarim River Basin, Xinjiang, China

NASA Astrophysics Data System (ADS)

Yang, Peng; Xia, Jun; Zhang, Yongyong; Han, Jian; Wu, Xia

2017-11-01

Because drought is a very common and widespread natural disaster, it has attracted a great deal of academic interest. Based on 12-month time scale standardized precipitation indices (SPI12) calculated from precipitation data recorded between 1960 and 2015 at 22 weather stations in the Tarim River Basin (TRB), this study aims to identify the trends of SPI and drought duration, severity, and frequency at various quantiles and to perform cluster analysis of drought events in the TRB. The results indicated that (1) both precipitation and temperature at most stations in the TRB exhibited significant positive trends during 1960-2015; (2) multiple scales of SPIs changed significantly around 1986; (3) based on quantile regression analysis of temporal drought changes, the positive SPI slopes indicated less severe and less frequent droughts at lower quantiles, but clear variation was detected in the drought frequency; and (4) significantly different trends were found in drought frequency probably between severe droughts and drought frequency.

Technical note: Combining quantile forecasts and predictive distributions of streamflows

NASA Astrophysics Data System (ADS)

Bogner, Konrad; Liechti, Katharina; Zappa, Massimiliano

2017-11-01

The enhanced availability of many different hydro-meteorological modelling and forecasting systems raises the issue of how to optimally combine this great deal of information. Especially the usage of deterministic and probabilistic forecasts with sometimes widely divergent predicted future streamflow values makes it even more complicated for decision makers to sift out the relevant information. In this study multiple streamflow forecast information will be aggregated based on several different predictive distributions, and quantile forecasts. For this combination the Bayesian model averaging (BMA) approach, the non-homogeneous Gaussian regression (NGR), also known as the ensemble model output statistic (EMOS) techniques, and a novel method called Beta-transformed linear pooling (BLP) will be applied. By the help of the quantile score (QS) and the continuous ranked probability score (CRPS), the combination results for the Sihl River in Switzerland with about 5 years of forecast data will be compared and the differences between the raw and optimally combined forecasts will be highlighted. The results demonstrate the importance of applying proper forecast combination methods for decision makers in the field of flood and water resource management.

Forecasting conditional climate-change using a hybrid approach

USGS Publications Warehouse

Esfahani, Akbar Akbari; Friedel, Michael J.

2014-01-01

A novel approach is proposed to forecast the likelihood of climate-change across spatial landscape gradients. This hybrid approach involves reconstructing past precipitation and temperature using the self-organizing map technique; determining quantile trends in the climate-change variables by quantile regression modeling; and computing conditional forecasts of climate-change variables based on self-similarity in quantile trends using the fractionally differenced auto-regressive integrated moving average technique. The proposed modeling approach is applied to states (Arizona, California, Colorado, Nevada, New Mexico, and Utah) in the southwestern U.S., where conditional forecasts of climate-change variables are evaluated against recent (2012) observations, evaluated at a future time period (2030), and evaluated as future trends (2009–2059). These results have broad economic, political, and social implications because they quantify uncertainty in climate-change forecasts affecting various sectors of society. Another benefit of the proposed hybrid approach is that it can be extended to any spatiotemporal scale providing self-similarity exists.

Estimating extreme losses for the Florida Public Hurricane Model—part II

NASA Astrophysics Data System (ADS)

Gulati, Sneh; George, Florence; Hamid, Shahid

2018-02-01

Rising global temperatures are leading to an increase in the number of extreme events and losses (http://www.epa.gov/climatechange/science/indicators/). Accurate estimation of these extreme losses with the intention of protecting themselves against them is critical to insurance companies. In a previous paper, Gulati et al. (2014) discussed probable maximum loss (PML) estimation for the Florida Public Hurricane Loss Model (FPHLM) using parametric and nonparametric methods. In this paper, we investigate the use of semi-parametric methods to do the same. Detailed analysis of the data shows that the annual losses from FPHLM do not tend to be very heavy tailed, and therefore, neither the popular Hill's method nor the moment's estimator work well. However, Pickand's estimator with threshold around the 84th percentile provides a good fit for the extreme quantiles for the losses.

Heterogeneous effects of oil shocks on exchange rates: evidence from a quantile regression approach.

PubMed

Su, Xianfang; Zhu, Huiming; You, Wanhai; Ren, Yinghua

2016-01-01

The determinants of exchange rates have attracted considerable attention among researchers over the past several decades. Most studies, however, ignore the possibility that the impact of oil shocks on exchange rates could vary across the exchange rate returns distribution. We employ a quantile regression approach to address this issue. Our results indicate that the effect of oil shocks on exchange rates is heterogeneous across quantiles. A large US depreciation or appreciation tends to heighten the effects of oil shocks on exchange rate returns. Positive oil demand shocks lead to appreciation pressures in oil-exporting countries and this result is robust across lower and upper return distributions. These results offer rich and useful information for investors and decision-makers.

Synoptic Conditions and Moisture Sources Actuating Extreme Precipitation in Nepal

NASA Astrophysics Data System (ADS)

Bohlinger, Patrik; Sorteberg, Asgeir; Sodemann, Harald

2017-12-01

Despite the vast literature on heavy-precipitation events in South Asia, synoptic conditions and moisture sources related to extreme precipitation in Nepal have not been addressed systematically. We investigate two types of synoptic conditions—low-pressure systems and midlevel troughs—and moisture sources related to extreme precipitation events. To account for the high spatial variability in rainfall, we cluster station-based daily precipitation measurements resulting in three well-separated geographic regions: west, central, and east Nepal. For each region, composite analysis of extreme events shows that atmospheric circulation is directed against the Himalayas during an extreme event. The direction of the flow is regulated by midtropospheric troughs and low-pressure systems traveling toward the respective region. Extreme precipitation events feature anomalous high abundance of total column moisture. Quantitative Lagrangian moisture source diagnostic reveals that the largest direct contribution stems from land (approximately 75%), where, in particular, over the Indo-Gangetic Plain moisture uptake was increased. Precipitation events occurring in this region before the extreme event likely provided additional moisture.

Estimation of local extreme suspended sediment concentrations in California Rivers.

PubMed

Tramblay, Yves; Saint-Hilaire, André; Ouarda, Taha B M J; Moatar, Florentina; Hecht, Barry

2010-09-01

The total amount of suspended sediment load carried by a stream during a year is usually transported during one or several extreme events related to high river flow and intense rainfall, leading to very high suspended sediment concentrations (SSCs). In this study quantiles of SSC derived from annual maximums and the 99th percentile of SSC series are considered to be estimated locally in a site-specific approach using regional information. Analyses of relationships between physiographic characteristics and the selected indicators were undertaken using the localities of 5-km radius draining of each sampling site. Multiple regression models were built to test the regional estimation for these indicators of suspended sediment transport. To assess the accuracy of the estimates, a Jack-Knife re-sampling procedure was used to compute the relative bias and root mean square error of the models. Results show that for the 19 stations considered in California, the extreme SSCs can be estimated with 40-60% uncertainty, depending on the presence of flow regulation in the basin. This modelling approach is likely to prove functional in other Mediterranean climate watersheds since they appear useful in California, where geologic, climatic, physiographic, and land-use conditions are highly variable. Copyright 2010 Elsevier B.V. All rights reserved.

Early Warning Signals of Financial Crises with Multi-Scale Quantile Regressions of Log-Periodic Power Law Singularities.

PubMed

Zhang, Qun; Zhang, Qunzhi; Sornette, Didier

2016-01-01

We augment the existing literature using the Log-Periodic Power Law Singular (LPPLS) structures in the log-price dynamics to diagnose financial bubbles by providing three main innovations. First, we introduce the quantile regression to the LPPLS detection problem. This allows us to disentangle (at least partially) the genuine LPPLS signal and the a priori unknown complicated residuals. Second, we propose to combine the many quantile regressions with a multi-scale analysis, which aggregates and consolidates the obtained ensembles of scenarios. Third, we define and implement the so-called DS LPPLS Confidence™ and Trust™ indicators that enrich considerably the diagnostic of bubbles. Using a detailed study of the "S&P 500 1987" bubble and presenting analyses of 16 historical bubbles, we show that the quantile regression of LPPLS signals contributes useful early warning signals. The comparison between the constructed signals and the price development in these 16 historical bubbles demonstrates their significant predictive ability around the real critical time when the burst/rally occurs.

The matter in extreme conditions instrument at the Linac Coherent Light Source

DOE PAGES

Nagler, Bob; Arnold, Brice; Bouchard, Gary; ...

2015-04-21

The LCLS beam provides revolutionary capabilities for studying the transient behavior of matter in extreme conditions. The particular strength of the Matter in Extreme Conditions instrument is that it combines the unique LCLS beam with high-power optical laser beams, and a suite of dedicated diagnostics tailored for this field of science. In this paper an overview of the beamline, the capabilities of the instrumentation, and selected highlights of experiments and commissioning results are presented.

Evaluation of uncertainties in mean and extreme precipitation under climate change for northwestern Mediterranean watersheds from high-resolution Med and Euro-CORDEX ensembles

NASA Astrophysics Data System (ADS)

Colmet-Daage, Antoine; Sanchez-Gomez, Emilia; Ricci, Sophie; Llovel, Cécile; Borrell Estupina, Valérie; Quintana-Seguí, Pere; Llasat, Maria Carmen; Servat, Eric

2018-01-01

The climate change impact on mean and extreme precipitation events in the northern Mediterranean region is assessed using high-resolution EuroCORDEX and MedCORDEX simulations. The focus is made on three regions, Lez and Aude located in France, and Muga located in northeastern Spain, and eight pairs of global and regional climate models are analyzed with respect to the SAFRAN product. First the model skills are evaluated in terms of bias for the precipitation annual cycle over historical period. Then future changes in extreme precipitation, under two emission scenarios, are estimated through the computation of past/future change coefficients of quantile-ranked model precipitation outputs. Over the 1981-2010 period, the cumulative precipitation is overestimated for most models over the mountainous regions and underestimated over the coastal regions in autumn and higher-order quantile. The ensemble mean and the spread for future period remain unchanged under RCP4.5 scenario and decrease under RCP8.5 scenario. Extreme precipitation events are intensified over the three catchments with a smaller ensemble spread under RCP8.5 revealing more evident changes, especially in the later part of the 21st century.

Using Gamma and Quantile Regressions to Explore the Association between Job Strain and Adiposity in the ELSA-Brasil Study: Does Gender Matter?

PubMed

Fonseca, Maria de Jesus Mendes da; Juvanhol, Leidjaira Lopes; Rotenberg, Lúcia; Nobre, Aline Araújo; Griep, Rosane Härter; Alves, Márcia Guimarães de Mello; Cardoso, Letícia de Oliveira; Giatti, Luana; Nunes, Maria Angélica; Aquino, Estela M L; Chor, Dóra

2017-11-17

This paper explores the association between job strain and adiposity, using two statistical analysis approaches and considering the role of gender. The research evaluated 11,960 active baseline participants (2008-2010) in the ELSA-Brasil study. Job strain was evaluated through a demand-control questionnaire, while body mass index (BMI) and waist circumference (WC) were evaluated in continuous form. The associations were estimated using gamma regression models with an identity link function. Quantile regression models were also estimated from the final set of co-variables established by gamma regression. The relationship that was found varied by analytical approach and gender. Among the women, no association was observed between job strain and adiposity in the fitted gamma models. In the quantile models, a pattern of increasing effects of high strain was observed at higher BMI and WC distribution quantiles. Among the men, high strain was associated with adiposity in the gamma regression models. However, when quantile regression was used, that association was found not to be homogeneous across outcome distributions. In addition, in the quantile models an association was observed between active jobs and BMI. Our results point to an association between job strain and adiposity, which follows a heterogeneous pattern. Modelling strategies can produce different results and should, accordingly, be used to complement one another.

The Matter in Extreme Conditions instrument at the Linac Coherent Light Source

PubMed Central

Nagler, Bob; Arnold, Brice; Bouchard, Gary; Boyce, Richard F.; Boyce, Richard M.; Callen, Alice; Campell, Marc; Curiel, Ruben; Galtier, Eric; Garofoli, Justin; Granados, Eduardo; Hastings, Jerry; Hays, Greg; Heimann, Philip; Lee, Richard W.; Milathianaki, Despina; Plummer, Lori; Schropp, Andreas; Wallace, Alex; Welch, Marc; White, William; Xing, Zhou; Yin, Jing; Young, James; Zastrau, Ulf; Lee, Hae Ja

2015-01-01

The LCLS beam provides revolutionary capabilities for studying the transient behavior of matter in extreme conditions. The particular strength of the Matter in Extreme Conditions instrument is that it combines the unique LCLS beam with high-power optical laser beams, and a suite of dedicated diagnostics tailored for this field of science. In this paper an overview of the beamline, the capabilities of the instrumentation, and selected highlights of experiments and commissioning results are presented. PMID:25931063

Extreme weather exposure identification for road networks - a comparative assessment of statistical methods

NASA Astrophysics Data System (ADS)

Schlögl, Matthias; Laaha, Gregor

2017-04-01

The assessment of road infrastructure exposure to extreme weather events is of major importance for scientists and practitioners alike. In this study, we compare the different extreme value approaches and fitting methods with respect to their value for assessing the exposure of transport networks to extreme precipitation and temperature impacts. Based on an Austrian data set from 25 meteorological stations representing diverse meteorological conditions, we assess the added value of partial duration series (PDS) over the standardly used annual maxima series (AMS) in order to give recommendations for performing extreme value statistics of meteorological hazards. Results show the merits of the robust L-moment estimation, which yielded better results than maximum likelihood estimation in 62 % of all cases. At the same time, results question the general assumption of the threshold excess approach (employing PDS) being superior to the block maxima approach (employing AMS) due to information gain. For low return periods (non-extreme events) the PDS approach tends to overestimate return levels as compared to the AMS approach, whereas an opposite behavior was found for high return levels (extreme events). In extreme cases, an inappropriate threshold was shown to lead to considerable biases that may outperform the possible gain of information from including additional extreme events by far. This effect was visible from neither the square-root criterion nor standardly used graphical diagnosis (mean residual life plot) but rather from a direct comparison of AMS and PDS in combined quantile plots. We therefore recommend performing AMS and PDS approaches simultaneously in order to select the best-suited approach. This will make the analyses more robust, not only in cases where threshold selection and dependency introduces biases to the PDS approach but also in cases where the AMS contains non-extreme events that may introduce similar biases. For assessing the performance of

Growth curves of preschool children in the northeast of iran: a population based study using quantile regression approach.

PubMed

Payande, Abolfazl; Tabesh, Hamed; Shakeri, Mohammad Taghi; Saki, Azadeh; Safarian, Mohammad

2013-01-14

Growth charts are widely used to assess children's growth status and can provide a trajectory of growth during early important months of life. The objectives of this study are going to construct growth charts and normal values of weight-for-age for children aged 0 to 5 years using a powerful and applicable methodology. The results compare with the World Health Organization (WHO) references and semi-parametric LMS method of Cole and Green. A total of 70737 apparently healthy boys and girls aged 0 to 5 years were recruited in July 2004 for 20 days from those attending community clinics for routine health checks as a part of a national survey. Anthropometric measurements were done by trained health staff using WHO methodology. The nonparametric quantile regression method obtained by local constant kernel estimation of conditional quantiles curves using for estimation of curves and normal values. The weight-for-age growth curves for boys and girls aged from 0 to 5 years were derived utilizing a population of children living in the northeast of Iran. The results were similar to the ones obtained by the semi-parametric LMS method in the same data. Among all age groups from 0 to 5 years, the median values of children's weight living in the northeast of Iran were lower than the corresponding values in WHO reference data. The weight curves of boys were higher than those of girls in all age groups. The differences between growth patterns of children living in the northeast of Iran versus international ones necessitate using local and regional growth charts. International normal values may not properly recognize the populations at risk for growth problems in Iranian children. Quantile regression (QR) as a flexible method which doesn't require restricted assumptions, proposed for estimation reference curves and normal values.

Growth Curves of Preschool Children in the Northeast of Iran: A Population Based Study Using Quantile Regression Approach

PubMed Central

Payande, Abolfazl; Tabesh, Hamed; Shakeri, Mohammad Taghi; Saki, Azadeh; Safarian, Mohammad

2013-01-01

Introduction: Growth charts are widely used to assess children’s growth status and can provide a trajectory of growth during early important months of life. The objectives of this study are going to construct growth charts and normal values of weight-for-age for children aged 0 to 5 years using a powerful and applicable methodology. The results compare with the World Health Organization (WHO) references and semi-parametric LMS method of Cole and Green. Methods: A total of 70737 apparently healthy boys and girls aged 0 to 5 years were recruited in July 2004 for 20 days from those attending community clinics for routine health checks as a part of a national survey. Anthropometric measurements were done by trained health staff using WHO methodology. The nonparametric quantile regression method obtained by local constant kernel estimation of conditional quantiles curves using for estimation of curves and normal values. Results: The weight-for-age growth curves for boys and girls aged from 0 to 5 years were derived utilizing a population of children living in the northeast of Iran. The results were similar to the ones obtained by the semi-parametric LMS method in the same data. Among all age groups from 0 to 5 years, the median values of children’s weight living in the northeast of Iran were lower than the corresponding values in WHO reference data. The weight curves of boys were higher than those of girls in all age groups. Conclusion: The differences between growth patterns of children living in the northeast of Iran versus international ones necessitate using local and regional growth charts. International normal values may not properly recognize the populations at risk for growth problems in Iranian children. Quantile regression (QR) as a flexible method which doesn’t require restricted assumptions, proposed for estimation reference curves and normal values. PMID:23618470

Using nonlinear quantile regression to estimate the self-thinning boundary curve

Treesearch

Quang V. Cao; Thomas J. Dean

2015-01-01

The relationship between tree size (quadratic mean diameter) and tree density (number of trees per unit area) has been a topic of research and discussion for many decades. Starting with Reineke in 1933, the maximum size-density relationship, on a log-log scale, has been assumed to be linear. Several techniques, including linear quantile regression, have been employed...

11. DETAIL OF EXTREMELY DETERIORATED CONDITION OF ORIGINAL STONE DAM ...

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

11. DETAIL OF EXTREMELY DETERIORATED CONDITION OF ORIGINAL STONE DAM ABUTMENT AND REASON FOR ENCASING ABUTMENT IN CONCRETE, c. 1918. - Dam No. 5 Hydroelectric Plant, On Potomac River, Hedgesville, Berkeley County, WV

Extreme mechanical properties of materials under extreme pressure and temperature conditions (Invited)

NASA Astrophysics Data System (ADS)

Kavner, A.; Armentrout, M. M.; Xie, M.; Weinberger, M.; Kaner, R. B.; Tolbert, S. H.

2010-12-01

A strong synergy ties together the high-pressure subfields of mineral physics, solid-state physics, and materials engineering. The catalog of studies measuring the mechanical properties of materials subjected to large differential stresses in the diamond anvil cell demonstrates a significant pressure-enhancement of strength across many classes of materials, including elemental solids, salts, oxides, silicates, and borides and nitrides. High pressure techniques—both radial diffraction and laser heating in the diamond anvil cell—can be used to characterize the behavior of ultrahard materials under extreme conditions, and help test hypotheses about how composition, structure, and bonding work together to govern the mechanical properties of materials. The principles that are elucidated by these studies can then be used to help design engineering materials to encourage desired properties. Understanding Earth and planetary interiors requires measuring equations of state of relevant materials, including oxides, silicates, and metals under extreme conditions. If these minerals in the diamond anvil cell have any ability to support a differential stress, the assumption of quasi-hydrostaticity no longer applies, with a resulting non-salubrious effect on attempts to measure equation of state. We illustrate these applications with the results of variety of studies from our laboratory and others’ that have used high-pressure radial diffraction techniques and also laser heating in the diamond anvil cell to characterize the mechanical properties of a variety of ultrahard materials, especially osmium metal, osmium diboride, rhenium diboride, and tungsten tetraboride. We compare ambient condition strength studies such as hardness testing with high-pressure studies, especially radial diffraction under differential stress. In addition, we outline criteria for evaluating mechanical properties of materials at combination high pressures and temperatures. Finally, we synthesize our

Spatially Modeling the Effects of Meteorological Drivers of PM2.5 in the Eastern United States via a Local Linear Penalized Quantile Regression Estimator.

PubMed

Russell, Brook T; Wang, Dewei; McMahan, Christopher S

2017-08-01

Fine particulate matter (PM 2.5 ) poses a significant risk to human health, with long-term exposure being linked to conditions such as asthma, chronic bronchitis, lung cancer, atherosclerosis, etc. In order to improve current pollution control strategies and to better shape public policy, the development of a more comprehensive understanding of this air pollutant is necessary. To this end, this work attempts to quantify the relationship between certain meteorological drivers and the levels of PM 2.5 . It is expected that the set of important meteorological drivers will vary both spatially and within the conditional distribution of PM 2.5 levels. To account for these characteristics, a new local linear penalized quantile regression methodology is developed. The proposed estimator uniquely selects the set of important drivers at every spatial location and for each quantile of the conditional distribution of PM 2.5 levels. The performance of the proposed methodology is illustrated through simulation, and it is then used to determine the association between several meteorological drivers and PM 2.5 over the Eastern United States (US). This analysis suggests that the primary drivers throughout much of the Eastern US tend to differ based on season and geographic location, with similarities existing between "typical" and "high" PM 2.5 levels.

Strategies to take into account variations in extreme rainfall events for design storms in urban area: an example over Naples (Southern Italy)

NASA Astrophysics Data System (ADS)

Mercogliano, P.; Rianna, G.

2017-12-01

Eminent works highlighted how available observations display ongoing increases in extreme rainfall events while climate models assess them for future. Although the constraints in rainfall networks observations and uncertainties in climate modelling currently affect in significant way investigations, the huge impacts potentially induced by climate changes (CC) suggest adopting effective adaptation measures in order to take proper precautions. In this regard, design storms are used by engineers to size hydraulic infrastructures potentially affected by direct (e.g. pluvial/urban flooding) and indirect (e.g. river flooding) effects of extreme rainfall events. Usually they are expressed as IDF curves, mathematical relationships between rainfall Intensity, Duration, and the return period (frequency, F). They are estimated interpreting through Extreme Theories Statistical Theories (ETST) past rainfall records under the assumption of steady conditions resulting then unsuitable under climate change. In this work, a methodology to estimate future variations in IDF curves is presented and carried out for the city of Naples (Southern Italy). In this regard, the Equidistance Quantile Matching Approach proposed by Sivrastav et al. (2014) is adopted. According it, daily-subdaily maximum precipitation observations [a] and the analogous daily data provided by climate projections on current [b] and future time spans [c] are interpreted in IDF terms through Generalized Extreme Value (GEV) approach. After, quantile based mapping approach is used to establish a statistical relationship between cumulative distribution functions resulting by GEV of [a] and [b] (spatial downscaling) and [b] and [c] functions (temporal downscaling). Coupling so-obtained relations permits generating IDF curves under CC assumption. To account for uncertainties in future projections, all climate simulations available for the area in Euro-Cordex multimodel ensemble at 0.11° (about 12 km) are considered under

Applying quantile regression for modeling equivalent property damage only crashes to identify accident blackspots.

PubMed

Washington, Simon; Haque, Md Mazharul; Oh, Jutaek; Lee, Dongmin

2014-05-01

Hot spot identification (HSID) aims to identify potential sites-roadway segments, intersections, crosswalks, interchanges, ramps, etc.-with disproportionately high crash risk relative to similar sites. An inefficient HSID methodology might result in either identifying a safe site as high risk (false positive) or a high risk site as safe (false negative), and consequently lead to the misuse the available public funds, to poor investment decisions, and to inefficient risk management practice. Current HSID methods suffer from issues like underreporting of minor injury and property damage only (PDO) crashes, challenges of accounting for crash severity into the methodology, and selection of a proper safety performance function to model crash data that is often heavily skewed by a preponderance of zeros. Addressing these challenges, this paper proposes a combination of a PDO equivalency calculation and quantile regression technique to identify hot spots in a transportation network. In particular, issues related to underreporting and crash severity are tackled by incorporating equivalent PDO crashes, whilst the concerns related to the non-count nature of equivalent PDO crashes and the skewness of crash data are addressed by the non-parametric quantile regression technique. The proposed method identifies covariate effects on various quantiles of a population, rather than the population mean like most methods in practice, which more closely corresponds with how black spots are identified in practice. The proposed methodology is illustrated using rural road segment data from Korea and compared against the traditional EB method with negative binomial regression. Application of a quantile regression model on equivalent PDO crashes enables identification of a set of high-risk sites that reflect the true safety costs to the society, simultaneously reduces the influence of under-reported PDO and minor injury crashes, and overcomes the limitation of traditional NB model in dealing

Physiological and psychological fatigue in extreme conditions: the military example.

PubMed

Weeks, Sharon R; McAuliffe, Caitlin L; Durussel, David; Pasquina, Paul F

2010-05-01

The extreme conditions causing fatigue in military service members in combat and combat training deserve special consideration. The collective effects of severe exertion, limited caloric intake, and sleep deprivation, combined with the inherent stressors of combat, lead to both physiological and psychological fatigue that may significantly impair performance. Studies of combat training have revealed a myriad of endocrine, cognitive, and neurological changes that occur as a result of exposure to extreme conditions. Further contributory effects of multiple military deployments, post-traumatic stress disorder, and traumatic brain injury may also influence both the susceptibility to and expression of fatigue states. Further research is needed to explore these effects to enhance military readiness and performance as well as prevent injuries. Copyright (c) 2010 American Academy of Physical Medicine and Rehabilitation. Published by Elsevier Inc. All rights reserved.

Trait Mindfulness as a Limiting Factor for Residual Depressive Symptoms: An Explorative Study Using Quantile Regression

PubMed Central

Radford, Sholto; Eames, Catrin; Brennan, Kate; Lambert, Gwladys; Crane, Catherine; Williams, J. Mark G.; Duggan, Danielle S.; Barnhofer, Thorsten

2014-01-01

Mindfulness has been suggested to be an important protective factor for emotional health. However, this effect might vary with regard to context. This study applied a novel statistical approach, quantile regression, in order to investigate the relation between trait mindfulness and residual depressive symptoms in individuals with a history of recurrent depression, while taking into account symptom severity and number of episodes as contextual factors. Rather than fitting to a single indicator of central tendency, quantile regression allows exploration of relations across the entire range of the response variable. Analysis of self-report data from 274 participants with a history of three or more previous episodes of depression showed that relatively higher levels of mindfulness were associated with relatively lower levels of residual depressive symptoms. This relationship was most pronounced near the upper end of the response distribution and moderated by the number of previous episodes of depression at the higher quantiles. The findings suggest that with lower levels of mindfulness, residual symptoms are less constrained and more likely to be influenced by other factors. Further, the limiting effect of mindfulness on residual symptoms is most salient in those with higher numbers of episodes. PMID:24988072

Bayesian quantile regression-based partially linear mixed-effects joint models for longitudinal data with multiple features.

PubMed

Zhang, Hanze; Huang, Yangxin; Wang, Wei; Chen, Henian; Langland-Orban, Barbara

2017-01-01

In longitudinal AIDS studies, it is of interest to investigate the relationship between HIV viral load and CD4 cell counts, as well as the complicated time effect. Most of common models to analyze such complex longitudinal data are based on mean-regression, which fails to provide efficient estimates due to outliers and/or heavy tails. Quantile regression-based partially linear mixed-effects models, a special case of semiparametric models enjoying benefits of both parametric and nonparametric models, have the flexibility to monitor the viral dynamics nonparametrically and detect the varying CD4 effects parametrically at different quantiles of viral load. Meanwhile, it is critical to consider various data features of repeated measurements, including left-censoring due to a limit of detection, covariate measurement error, and asymmetric distribution. In this research, we first establish a Bayesian joint models that accounts for all these data features simultaneously in the framework of quantile regression-based partially linear mixed-effects models. The proposed models are applied to analyze the Multicenter AIDS Cohort Study (MACS) data. Simulation studies are also conducted to assess the performance of the proposed methods under different scenarios.

Early Warning Signals of Financial Crises with Multi-Scale Quantile Regressions of Log-Periodic Power Law Singularities

PubMed Central

Zhang, Qun; Zhang, Qunzhi; Sornette, Didier

2016-01-01

We augment the existing literature using the Log-Periodic Power Law Singular (LPPLS) structures in the log-price dynamics to diagnose financial bubbles by providing three main innovations. First, we introduce the quantile regression to the LPPLS detection problem. This allows us to disentangle (at least partially) the genuine LPPLS signal and the a priori unknown complicated residuals. Second, we propose to combine the many quantile regressions with a multi-scale analysis, which aggregates and consolidates the obtained ensembles of scenarios. Third, we define and implement the so-called DS LPPLS Confidence™ and Trust™ indicators that enrich considerably the diagnostic of bubbles. Using a detailed study of the “S&P 500 1987” bubble and presenting analyses of 16 historical bubbles, we show that the quantile regression of LPPLS signals contributes useful early warning signals. The comparison between the constructed signals and the price development in these 16 historical bubbles demonstrates their significant predictive ability around the real critical time when the burst/rally occurs. PMID:27806093

Future extreme water levels and floodplains in Gironde Estuary considering climate change

NASA Astrophysics Data System (ADS)

Laborie, V.; Hissel, F.; Sergent, P.

2012-04-01

Within THESEUS European project, an overflowing model of Gironde Estuary has been used to evaluate future surge levels at Le Verdon and future water levels at 6 specific sites of the estuary : le Verdon, Richard, Laména, Pauillac, Le Marquis and Bordeaux. It was then used to study the evolution of floodplains' location and areas towards 2100 in the entire Estuary. In this study, no breaching and no modification in the elevation of the dike was considered. The model was fed by several data sources : wind fields at Royan and Mérignac interpolated from the grid of the European Climatolologic Model CLM/SGA, a tide signal at Le Verdon, the discharges of Garonne (at La Réole), the Dordogne (at Pessac) and Isle (at Libourne). A simplified mathematical model of surge levels has been adjusted at Le Verdon with 10 surge storms and by using wind and pressure fields given by CLM/SGA. This adjustment was led so that the statistical analysis of the global signal at Le Verdon gives the same quantiles as the same analysis driven on maregraphic observations for the period [1960 ; 2000]. The assumption used for sea level rise was the pessimistic one of the French national institute for climate change: 60 cm in 2100. The model was then used to study the evolution of extreme water levels towards 2100. The analysis of surge levels at Le Verdon shows a decrease in quantiles which is coherent with the analysis of climatologic fields. The analysis of water levels shows that the increase in mean water levels quantiles represents only a part of sea level rise in Gironde Estuary. Moreover this effect seems to decrease from the maritime limit of the model towards upstream. Concerning floodplains, those corresponding to return periods from 2 to 100 years for present conditions and 3 slices [2010; 2039], [2040; 2069] and [2070; 2099] have been mapped for 3 areas in Gironde Estuary : around Le Verdon, at the confluence between Garonne and Dordogne, and near Bordeaux. Concerning the evolution

Local Composite Quantile Regression Smoothing for Harris Recurrent Markov Processes

PubMed Central

Li, Degui; Li, Runze

2016-01-01

In this paper, we study the local polynomial composite quantile regression (CQR) smoothing method for the nonlinear and nonparametric models under the Harris recurrent Markov chain framework. The local polynomial CQR regression method is a robust alternative to the widely-used local polynomial method, and has been well studied in stationary time series. In this paper, we relax the stationarity restriction on the model, and allow that the regressors are generated by a general Harris recurrent Markov process which includes both the stationary (positive recurrent) and nonstationary (null recurrent) cases. Under some mild conditions, we establish the asymptotic theory for the proposed local polynomial CQR estimator of the mean regression function, and show that the convergence rate for the estimator in nonstationary case is slower than that in stationary case. Furthermore, a weighted type local polynomial CQR estimator is provided to improve the estimation efficiency, and a data-driven bandwidth selection is introduced to choose the optimal bandwidth involved in the nonparametric estimators. Finally, we give some numerical studies to examine the finite sample performance of the developed methodology and theory. PMID:27667894

Assessment of extreme value distributions for maximum temperature in the Mediterranean area

NASA Astrophysics Data System (ADS)

Beck, Alexander; Hertig, Elke; Jacobeit, Jucundus

2015-04-01

Extreme maximum temperatures highly affect the natural as well as the societal environment Heat stress has great effects on flora, fauna and humans and culminates in heat related morbidity and mortality. Agriculture and different industries are severely affected by extreme air temperatures. Even more under climate change conditions, it is necessary to detect potential hazards which arise from changes in the distributional parameters of extreme values, and this is especially relevant for the Mediterranean region which is characterized as a climate change hot spot. Therefore statistical approaches are developed to estimate these parameters with a focus on non-stationarities emerging in the relationship between regional climate variables and their large-scale predictors like sea level pressure, geopotential heights, atmospheric temperatures and relative humidity. Gridded maximum temperature data from the daily E-OBS dataset (Haylock et al., 2008) with a spatial resolution of 0.25° x 0.25° from January 1950 until December 2012 are the predictands for the present analyses. A s-mode principal component analysis (PCA) has been performed in order to reduce data dimension and to retain different regions of similar maximum temperature variability. The grid box with the highest PC-loading represents the corresponding principal component. A central part of the analyses is the model development for temperature extremes under the use of extreme value statistics. A combined model is derived consisting of a Generalized Pareto Distribution (GPD) model and a quantile regression (QR) model which determines the GPD location parameters. The QR model as well as the scale parameters of the GPD model are conditioned by various large-scale predictor variables. In order to account for potential non-stationarities in the predictors-temperature relationships, a special calibration and validation scheme is applied, respectively. Haylock, M. R., N. Hofstra, A. M. G. Klein Tank, E. J. Klok, P

Incremental Treatment Costs Attributable to Overweight and Obesity in Patients with Diabetes: Quantile Regression Approach.

PubMed

Lee, Seung-Mi; Choi, In-Sun; Han, Euna; Suh, David; Shin, Eun-Kyung; Je, Seyunghe; Lee, Sung Su; Suh, Dong-Churl

2018-01-01

This study aimed to estimate treatment costs attributable to overweight and obesity in patients with diabetes who were less than 65 years of age in the United States. This study used data from the Medical Expenditure Panel Survey from 2001 to 2013. Patients with diabetes were identified by using the International Classification of Diseases, Ninth Revision, Clinical Modification code (250), clinical classification codes (049 and 050), or self-reported physician diagnoses. Total treatment costs attributable to overweight and obesity were calculated as the differences in the adjusted costs compared with individuals with diabetes and normal weight. Adjusted costs were estimated by using generalized linear models or unconditional quantile regression models. The mean annual treatment costs attributable to obesity were $1,852 higher than those attributable to normal weight, while costs attributable to overweight were $133 higher. The unconditional quantile regression results indicated that the impact of obesity on total treatment costs gradually became more significant as treatment costs approached the upper quantile. Among patients with diabetes who were less than 65 years of age, patients with diabetes and obesity have significantly higher treatment costs than patients with diabetes and normal weight. The economic burden of diabetes to society will continue to increase unless more proactive preventive measures are taken to effectively treat patients with overweight or obesity. © 2017 The Obesity Society.

Estimation of peak discharge quantiles for selected annual exceedance probabilities in Northeastern Illinois.

DOT National Transportation Integrated Search

2016-06-01

This report provides two sets of equations for estimating peak discharge quantiles at annual exceedance probabilities (AEPs) of 0.50, 0.20, 0.10, : 0.04, 0.02, 0.01, 0.005, and 0.002 (recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years,...

Bias and Variance Approximations for Estimators of Extreme Quantiles

DTIC Science & Technology

1988-11-01

r u - g(u). The errors of these approximations are, respectively, O ...The conditions required for this are yrci, yr+ypci. Taking the special cases r -1, r -1 and the limit r -) O , we deduce Jelog g(Y) 6 2folog g(Y) ~ e( 3+2y...a 2 (log g(TipL, o , o )) - I + I- exp-a" a a r - (- + Z - Ze - Z + (Z 2 - z~eZ + Z3 e - Z) + 0(y 2 )) 2 18 and using the formula E[Zre- sz1 - (_-) r r ( r

Mechanical characterization of alloys in extreme conditions of high strain rates and high temperature

NASA Astrophysics Data System (ADS)

Cadoni, Ezio

2018-03-01

The aim of this paper is the description of the mechanical characterization of alloys under extreme conditions of temperature and loading. In fact, in the frame of the Cost Action CA15102 “Solutions for Critical Raw Materials Under Extreme Conditions (CRM-EXTREME)” this aspect is crucial and many industrial applications have to consider the dynamic response of materials. Indeed, for a reduction and substitution of CRMs in alloys is necessary to design the materials and understand if the new materials behave better or if the substitution or reduction badly affect their performance. For this reason, a deep knowledge of the mechanical behaviour at high strain-rates of considered materials is required. In general, machinery manufacturing industry or transport industry as well as energy industry have important dynamic phenomena that are simultaneously affected by extended strain, high strain-rate, damage and pressure, as well as conspicuous temperature gradients. The experimental results in extreme conditions of high strain rate and high temperature of an austenitic stainless steel as well as a high-chromium tempered martensitic reduced activation steel Eurofer97 are presented.

Attempting to physically explain space-time correlation of extremes

NASA Astrophysics Data System (ADS)

Bernardara, Pietro; Gailhard, Joel

2010-05-01

Spatial and temporal clustering of hydro-meteorological extreme events is scientific evidence. Moreover, the statistical parameters characterizing their local frequencies of occurrence show clear spatial patterns. Thus, in order to robustly assess the hydro-meteorological hazard, statistical models need to be able to take into account spatial and temporal dependencies. Statistical models considering long term correlation for quantifying and qualifying temporal and spatial dependencies are available, such as multifractal approach. Furthermore, the development of regional frequency analysis techniques allows estimating the frequency of occurrence of extreme events taking into account spatial patterns on the extreme quantiles behaviour. However, in order to understand the origin of spatio-temporal clustering, an attempt to find physical explanation should be done. Here, some statistical evidences of spatio-temporal correlation and spatial patterns of extreme behaviour are given on a large database of more than 400 rainfall and discharge series in France. In particular, the spatial distribution of multifractal and Generalized Pareto distribution parameters shows evident correlation patterns in the behaviour of frequency of occurrence of extremes. It is then shown that the identification of atmospheric circulation pattern (weather types) can physically explain the temporal clustering of extreme rainfall events (seasonality) and the spatial pattern of the frequency of occurrence. Moreover, coupling this information with the hydrological modelization of a watershed (as in the Schadex approach) an explanation of spatio-temporal distribution of extreme discharge can also be provided. We finally show that a hydro-meteorological approach (as the Schadex approach) can explain and take into account space and time dependencies of hydro-meteorological extreme events.

Changes in seasonal streamflow extremes experienced in rivers of Northwestern South America (Colombia)

NASA Astrophysics Data System (ADS)

Pierini, J. O.; Restrepo, J. C.; Aguirre, J.; Bustamante, A. M.; Velásquez, G. J.

2017-04-01

A measure of the variability in seasonal extreme streamflow was estimated for the Colombian Caribbean coast, using monthly time series of freshwater discharge from ten watersheds. The aim was to detect modifications in the streamflow monthly distribution, seasonal trends, variance and extreme monthly values. A 20-year length time moving window, with 1-year successive shiftments, was applied to the monthly series to analyze the seasonal variability of streamflow. The seasonal-windowed data were statistically fitted through the Gamma distribution function. Scale and shape parameters were computed using the Maximum Likelihood Estimation (MLE) and the bootstrap method for 1000 resample. A trend analysis was performed for each windowed-serie, allowing to detect the window of maximum absolute values for trends. Significant temporal shifts in seasonal streamflow distribution and quantiles (QT), were obtained for different frequencies. Wet and dry extremes periods increased significantly in the last decades. Such increase did not occur simultaneously through the region. Some locations exhibited continuous increases only at minimum QT.

A method to preserve trends in quantile mapping bias correction of climate modeled temperature

NASA Astrophysics Data System (ADS)

Grillakis, Manolis G.; Koutroulis, Aristeidis G.; Daliakopoulos, Ioannis N.; Tsanis, Ioannis K.

2017-09-01

Bias correction of climate variables is a standard practice in climate change impact (CCI) studies. Various methodologies have been developed within the framework of quantile mapping. However, it is well known that quantile mapping may significantly modify the long-term statistics due to the time dependency of the temperature bias. Here, a method to overcome this issue without compromising the day-to-day correction statistics is presented. The methodology separates the modeled temperature signal into a normalized and a residual component relative to the modeled reference period climatology, in order to adjust the biases only for the former and preserve the signal of the later. The results show that this method allows for the preservation of the originally modeled long-term signal in the mean, the standard deviation and higher and lower percentiles of temperature. To illustrate the improvements, the methodology is tested on daily time series obtained from five Euro CORDEX regional climate models (RCMs).

Wireless pilot monitoring system for extreme race conditions.

PubMed

Pino, Esteban J; Arias, Diego E; Aqueveque, Pablo; Melin, Pedro; Curtis, Dorothy W

2012-01-01

This paper presents the design and implementation of an assistive device to monitor car drivers under extreme conditions. In particular, this system is designed in preparation for the 2012 Atacama Solar Challenge to be held in the Chilean desert. Actual preliminary results show the feasibility of such a project including physiological and ambient sensors, real-time processing algorithms, wireless data transmission and a remote monitoring station. Implementation details and field results are shown along with a discussion of the main problems found in real-life telemetry monitoring.

The evolution of extreme precipitations in high resolution scenarios over France

NASA Astrophysics Data System (ADS)

Colin, J.; Déqué, M.; Somot, S.

2009-09-01

Over the past years, improving the modelling of extreme events and their variability at climatic time scales has become one of the challenging issue raised in the regional climate research field. This study shows the results of a high resolution (12 km) scenario run over France with the limited area model (LAM) ALADIN-Climat, regarding the representation of extreme precipitations. The runs were conducted in the framework of the ANR-SCAMPEI national project on high resolution scenarios over French mountains. As a first step, we attempt to quantify one of the uncertainties implied by the use of LAM : the size of the area on which the model is run. In particular, we address the issue of whether a relatively small domain allows the model to create its small scale process. Indeed, high resolution scenarios cannot be run on large domains because of the computation time. Therefore one needs to answer this preliminary question before producing and analyzing such scenarios. To do so, we worked in the framework of a « big brother » experiment. We performed a 23-year long global simulation in present-day climate (1979-2001) with the ARPEGE-Climat GCM, at a resolution of approximately 50 km over Europe (stretched grid). This first simulation, named ARP50, constitutes the « big brother » reference of our experiment. It has been validated in comparison with the CRU climatology. Then we filtered the short waves (up to 200 km) from ARP50 in order to obtain the equivalent of coarse resolution lateral boundary conditions (LBC). We have carried out three ALADIN-Climat simulations at a 50 km resolution with these LBC, using different configurations of the model : * FRA50, run over a small domain (2000 x 2000 km, centered over France), * EUR50, run over a larger domain (5000 x 5000 km, centered over France as well), * EUR50-SN, run over the large domain (using spectral nudging). Considering the facts that ARPEGE-Climat and ALADIN-Climat models share the same physics and dynamics

Modelling hydrological extremes under non-stationary conditions using climate covariates

NASA Astrophysics Data System (ADS)

Vasiliades, Lampros; Galiatsatou, Panagiota; Loukas, Athanasios

2013-04-01

Extreme value theory is a probabilistic theory that can interpret the future probabilities of occurrence of extreme events (e.g. extreme precipitation and streamflow) using past observed records. Traditionally, extreme value theory requires the assumption of temporal stationarity. This assumption implies that the historical patterns of recurrence of extreme events are static over time. However, the hydroclimatic system is nonstationary on time scales that are relevant to extreme value analysis, due to human-mediated and natural environmental change. In this study the generalized extreme value (GEV) distribution is used to assess nonstationarity in annual maximum daily rainfall and streamflow timeseries at selected meteorological and hydrometric stations in Greece and Cyprus. The GEV distribution parameters (location, scale, and shape) are specified as functions of time-varying covariates and estimated using the conditional density network (CDN) as proposed by Cannon (2010). The CDN is a probabilistic extension of the multilayer perceptron neural network. Model parameters are estimated via the generalized maximum likelihood (GML) approach using the quasi-Newton BFGS optimization algorithm, and the appropriate GEV-CDN model architecture for the selected meteorological and hydrometric stations is selected by fitting increasingly complicated models and choosing the one that minimizes the Akaike information criterion with small sample size correction. For all case studies in Greece and Cyprus different formulations are tested with combinational cases of stationary and nonstationary parameters of the GEV distribution, linear and non-linear architecture of the CDN and combinations of the input climatic covariates. Climatic indices such as the Southern Oscillation Index (SOI), which describes atmospheric circulation in the eastern tropical pacific related to El Niño Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO) index that varies on an interdecadal

Extreme Drought Conditions in the Rio Grande/Bravo Basin

NASA Astrophysics Data System (ADS)

Gutiérrez, F.; Dracup, J. A.

2001-12-01

The Treaty of February 3, 1944 entitled "Utilization of Waters of the Colorado and Tijuana Rivers and of the Rio Grande" between the U.S. and Mexico regulates the distribution of flows of the rivers between these two countries. The treaty is based on hydrological data available up to 1944. Using new (historical and paleoclimatological) data, the water balance presented in the Treaty is re-examinated and the 431,721,000 m3/year allocation for USA during "extreme drought conditions" is re-evaluated. The authors define "extreme drought conditions" for this basin and a hydrological drought analysis is carried out using a streamflow simulation model. The analysis is complemented with an analysis of the effects of the El Niño - Southern Oscillation and the Pacific Decadal Oscillation on precipitation and streamflow. The results of this research will be applicable to potential changes in the current water resources management policies on the basin. Given the social, economical and political importance of this basin, the findings of this research potentially will have significant impacts. This research is founded by the NSF fund SAHRA (Science and Technology Center to study and promote the "Sustainability of Water Resources in Semi-Arid Regions" at the University of Arizona).

Survival and Energy Producing Strategies of Alkane Degraders Under Extreme Conditions and Their Biotechnological Potential.

PubMed

Park, Chulwoo; Park, Woojun

2018-01-01

Many petroleum-polluted areas are considered as extreme environments because of co-occurrence of low and high temperatures, high salt, and acidic and anaerobic conditions. Alkanes, which are major constituents of crude oils, can be degraded under extreme conditions, both aerobically and anaerobically by bacteria and archaea of different phyla. Alkane degraders possess exclusive metabolic pathways and survival strategies, which involve the use of protein and RNA chaperones, compatible solutes, biosurfactants, and exopolysaccharide production for self-protection during harsh environmental conditions such as oxidative and osmotic stress, and ionic nutrient-shortage. Recent findings suggest that the thermophilic sulfate-reducing archaeon Archaeoglobus fulgidus uses a novel alkylsuccinate synthase for long-chain alkane degradation, and the thermophilic Candidatus Syntrophoarchaeum butanivorans anaerobically oxidizes butane via alkyl-coenzyme M formation. In addition, gene expression data suggest that extremophiles produce energy via the glyoxylate shunt and the Pta-AckA pathway when grown on a diverse range of alkanes under stress conditions. Alkane degraders possess biotechnological potential for bioremediation because of their unusual characteristics. This review will provide genomic and molecular insights on alkane degraders under extreme conditions.

Understanding Child Stunting in India: A Comprehensive Analysis of Socio-Economic, Nutritional and Environmental Determinants Using Additive Quantile Regression

PubMed Central

Fenske, Nora; Burns, Jacob; Hothorn, Torsten; Rehfuess, Eva A.

2013-01-01

Background Most attempts to address undernutrition, responsible for one third of global child deaths, have fallen behind expectations. This suggests that the assumptions underlying current modelling and intervention practices should be revisited. Objective We undertook a comprehensive analysis of the determinants of child stunting in India, and explored whether the established focus on linear effects of single risks is appropriate. Design Using cross-sectional data for children aged 0–24 months from the Indian National Family Health Survey for 2005/2006, we populated an evidence-based diagram of immediate, intermediate and underlying determinants of stunting. We modelled linear, non-linear, spatial and age-varying effects of these determinants using additive quantile regression for four quantiles of the Z-score of standardized height-for-age and logistic regression for stunting and severe stunting. Results At least one variable within each of eleven groups of determinants was significantly associated with height-for-age in the 35% Z-score quantile regression. The non-modifiable risk factors child age and sex, and the protective factors household wealth, maternal education and BMI showed the largest effects. Being a twin or multiple birth was associated with dramatically decreased height-for-age. Maternal age, maternal BMI, birth order and number of antenatal visits influenced child stunting in non-linear ways. Findings across the four quantile and two logistic regression models were largely comparable. Conclusions Our analysis confirms the multifactorial nature of child stunting. It emphasizes the need to pursue a systems-based approach and to consider non-linear effects, and suggests that differential effects across the height-for-age distribution do not play a major role. PMID:24223839

Understanding child stunting in India: a comprehensive analysis of socio-economic, nutritional and environmental determinants using additive quantile regression.

PubMed

Fenske, Nora; Burns, Jacob; Hothorn, Torsten; Rehfuess, Eva A

2013-01-01

Most attempts to address undernutrition, responsible for one third of global child deaths, have fallen behind expectations. This suggests that the assumptions underlying current modelling and intervention practices should be revisited. We undertook a comprehensive analysis of the determinants of child stunting in India, and explored whether the established focus on linear effects of single risks is appropriate. Using cross-sectional data for children aged 0-24 months from the Indian National Family Health Survey for 2005/2006, we populated an evidence-based diagram of immediate, intermediate and underlying determinants of stunting. We modelled linear, non-linear, spatial and age-varying effects of these determinants using additive quantile regression for four quantiles of the Z-score of standardized height-for-age and logistic regression for stunting and severe stunting. At least one variable within each of eleven groups of determinants was significantly associated with height-for-age in the 35% Z-score quantile regression. The non-modifiable risk factors child age and sex, and the protective factors household wealth, maternal education and BMI showed the largest effects. Being a twin or multiple birth was associated with dramatically decreased height-for-age. Maternal age, maternal BMI, birth order and number of antenatal visits influenced child stunting in non-linear ways. Findings across the four quantile and two logistic regression models were largely comparable. Our analysis confirms the multifactorial nature of child stunting. It emphasizes the need to pursue a systems-based approach and to consider non-linear effects, and suggests that differential effects across the height-for-age distribution do not play a major role.

Addressing the mischaracterization of extreme rainfall in regional climate model simulations - A synoptic pattern based bias correction approach

NASA Astrophysics Data System (ADS)

Li, Jingwan; Sharma, Ashish; Evans, Jason; Johnson, Fiona

2018-01-01

Addressing systematic biases in regional climate model simulations of extreme rainfall is a necessary first step before assessing changes in future rainfall extremes. Commonly used bias correction methods are designed to match statistics of the overall simulated rainfall with observations. This assumes that change in the mix of different types of extreme rainfall events (i.e. convective and non-convective) in a warmer climate is of little relevance in the estimation of overall change, an assumption that is not supported by empirical or physical evidence. This study proposes an alternative approach to account for the potential change of alternate rainfall types, characterized here by synoptic weather patterns (SPs) using self-organizing maps classification. The objective of this study is to evaluate the added influence of SPs on the bias correction, which is achieved by comparing the corrected distribution of future extreme rainfall with that using conventional quantile mapping. A comprehensive synthetic experiment is first defined to investigate the conditions under which the additional information of SPs makes a significant difference to the bias correction. Using over 600,000 synthetic cases, statistically significant differences are found to be present in 46% cases. This is followed by a case study over the Sydney region using a high-resolution run of the Weather Research and Forecasting (WRF) regional climate model, which indicates a small change in the proportions of the SPs and a statistically significant change in the extreme rainfall over the region, although the differences between the changes obtained from the two bias correction methods are not statistically significant.

Accelerating Approximate Bayesian Computation with Quantile Regression: application to cosmological redshift distributions

NASA Astrophysics Data System (ADS)

Kacprzak, T.; Herbel, J.; Amara, A.; Réfrégier, A.

2018-02-01

Approximate Bayesian Computation (ABC) is a method to obtain a posterior distribution without a likelihood function, using simulations and a set of distance metrics. For that reason, it has recently been gaining popularity as an analysis tool in cosmology and astrophysics. Its drawback, however, is a slow convergence rate. We propose a novel method, which we call qABC, to accelerate ABC with Quantile Regression. In this method, we create a model of quantiles of distance measure as a function of input parameters. This model is trained on a small number of simulations and estimates which regions of the prior space are likely to be accepted into the posterior. Other regions are then immediately rejected. This procedure is then repeated as more simulations are available. We apply it to the practical problem of estimation of redshift distribution of cosmological samples, using forward modelling developed in previous work. The qABC method converges to nearly same posterior as the basic ABC. It uses, however, only 20% of the number of simulations compared to basic ABC, achieving a fivefold gain in execution time for our problem. For other problems the acceleration rate may vary; it depends on how close the prior is to the final posterior. We discuss possible improvements and extensions to this method.

Bumps in river profiles: uncertainty assessment and smoothing using quantile regression techniques

NASA Astrophysics Data System (ADS)

Schwanghart, Wolfgang; Scherler, Dirk

2017-12-01

The analysis of longitudinal river profiles is an important tool for studying landscape evolution. However, characterizing river profiles based on digital elevation models (DEMs) suffers from errors and artifacts that particularly prevail along valley bottoms. The aim of this study is to characterize uncertainties that arise from the analysis of river profiles derived from different, near-globally available DEMs. We devised new algorithms - quantile carving and the CRS algorithm - that rely on quantile regression to enable hydrological correction and the uncertainty quantification of river profiles. We find that globally available DEMs commonly overestimate river elevations in steep topography. The distributions of elevation errors become increasingly wider and right skewed if adjacent hillslope gradients are steep. Our analysis indicates that the AW3D DEM has the highest precision and lowest bias for the analysis of river profiles in mountainous topography. The new 12 m resolution TanDEM-X DEM has a very low precision, most likely due to the combined effect of steep valley walls and the presence of water surfaces in valley bottoms. Compared to the conventional approaches of carving and filling, we find that our new approach is able to reduce the elevation bias and errors in longitudinal river profiles.

Preparation of monolithic silica-chitin composite under extreme biomimetic conditions.

PubMed

Bazhenov, Vasilii V; Wysokowski, Marcin; Petrenko, Iaroslav; Stawski, Dawid; Sapozhnikov, Philipp; Born, René; Stelling, Allison L; Kaiser, Sabine; Jesionowski, Teofil

2015-05-01

Chitin is a widespread renewable biopolymer that is extensively distributed in the natural world. The high thermal stability of chitin provides an opportunity to develop novel inorganic-organic composites under hydrothermal synthesis conditions in vitro. For the first time, in this work we prepared monolithic silica-chitin composite under extreme biomimetic conditions (80°C and pH 1.5) using three dimensional chitinous matrices isolated from the marine sponge Aplysina cauliformis. The resulting material was studied using light and fluorescence microscopy, scanning electron microscopy, Fourier transform infrared spectroscopy. A mechanism for the silica-chitin interaction after exposure to these hydrothermal conditions is proposed and discussed. Copyright © 2015 Elsevier B.V. All rights reserved.

Frequency analysis and its spatiotemporal characteristics of precipitation extreme events in China during 1951-2010

NASA Astrophysics Data System (ADS)

Shao, Yuehong; Wu, Junmei; Ye, Jinyin; Liu, Yonghe

2015-08-01

This study investigates frequency analysis and its spatiotemporal characteristics of precipitation extremes based on annual maximum of daily precipitation (AMP) data of 753 observation stations in China during the period 1951-2010. Several statistical methods including L-moments, Mann-Kendall test (MK test), Student's t test ( t test) and analysis of variance ( F-test) are used to study different statistical properties related to frequency and spatiotemporal characteristics of precipitation extremes. The results indicate that the AMP series of most sites have no linear trends at 90 % confidence level, but there is a distinctive decrease trend in Beijing-Tianjin-Tangshan region. The analysis of abrupt changes shows that there are no significant changes in most sites, and no distinctive regional patterns within the mutation sites either. An important innovation different from the previous studies is the shift in the mean and the variance which are also studied in this paper in order to further analyze the changes of strong and weak precipitation extreme events. The shift analysis shows that we should pay more attention to the drought in North China and to the flood control and drought in South China, especially to those regions that have no clear trend and have a significant shift in the variance. More important, this study conducts the comprehensive analysis of a complete set of quantile estimates and its spatiotemporal characteristic in China. Spatial distribution of quantile estimation based on the AMP series demonstrated that the values gradually increased from the Northwest to the Southeast with the increment of duration and return period, while the increasing rate of estimation is smooth in the arid and semiarid region and is rapid in humid region. Frequency estimates of 50-year return period are in agreement with the maximum observations of AMP series in the most stations, which can provide more quantitative and scientific basis for decision making.

Examining Predictive Validity of Oral Reading Fluency Slope in Upper Elementary Grades Using Quantile Regression.

PubMed

Cho, Eunsoo; Capin, Philip; Roberts, Greg; Vaughn, Sharon

2017-07-01

Within multitiered instructional delivery models, progress monitoring is a key mechanism for determining whether a child demonstrates an adequate response to instruction. One measure commonly used to monitor the reading progress of students is oral reading fluency (ORF). This study examined the extent to which ORF slope predicts reading comprehension outcomes for fifth-grade struggling readers ( n = 102) participating in an intensive reading intervention. Quantile regression models showed that ORF slope significantly predicted performance on a sentence-level fluency and comprehension assessment, regardless of the students' reading skills, controlling for initial ORF performance. However, ORF slope was differentially predictive of a passage-level comprehension assessment based on students' reading skills when controlling for initial ORF status. Results showed that ORF explained unique variance for struggling readers whose posttest performance was at the upper quantiles at the end of the reading intervention, but slope was not a significant predictor of passage-level comprehension for students whose reading problems were the most difficult to remediate.

Return levels of temperature extremes in southern Pakistan

NASA Astrophysics Data System (ADS)

Zahid, Maida; Blender, Richard; Lucarini, Valerio; Caterina Bramati, Maria

2017-12-01

Southern Pakistan (Sindh) is one of the hottest regions in the world and is highly vulnerable to temperature extremes. In order to improve rural and urban planning, it is useful to gather information about the recurrence of temperature extremes. In this work, return levels of the daily maximum temperature Tmax are estimated, as well as the daily maximum wet-bulb temperature TWmax extremes. We adopt the peaks over threshold (POT) method, which has not yet been used for similar studies in this region. Two main datasets are analyzed: temperatures observed at nine meteorological stations in southern Pakistan from 1980 to 2013, and the ERA-Interim (ECMWF reanalysis) data for the nearest corresponding locations. The analysis provides the 2-, 5-, 10-, 25-, 50-, and 100-year return levels (RLs) of temperature extremes. The 90 % quantile is found to be a suitable threshold for all stations. We find that the RLs of the observed Tmax are above 50 °C at northern stations and above 45 °C at the southern stations. The RLs of the observed TWmax exceed 35 °C in the region, which is considered as a limit of survivability. The RLs estimated from the ERA-Interim data are lower by 3 to 5 °C than the RLs assessed for the nine meteorological stations. A simple bias correction applied to ERA-Interim data improves the RLs remarkably, yet discrepancies are still present. The results have potential implications for the risk assessment of extreme temperatures in Sindh.

Quantifying variability in fast and slow solar wind: From turbulence to extremes

NASA Astrophysics Data System (ADS)

Tindale, E.; Chapman, S. C.; Moloney, N.; Watkins, N. W.

2017-12-01

Fast and slow solar wind exhibit variability across a wide range of spatiotemporal scales, with evolving turbulence producing fluctuations on sub-hour timescales and the irregular solar cycle modulating the system over many years. Here, we apply the data quantile-quantile (DQQ) method [Tindale and Chapman 2016, 2017] to over 20 years of Wind data, to study the time evolution of the statistical distribution of plasma parameters in fast and slow solar wind. This model-independent method allows us to simultaneously explore the evolution of fluctuations across all scales. We find a two-part functional form for the statistical distributions of the interplanetary magnetic field (IMF) magnitude and its components, with each region of the distribution evolving separately over the solar cycle. Up to a value of 8nT, turbulent fluctuations dominate the distribution of the IMF, generating the approximately lognormal shape found by Burlaga [2001]. The mean of this core-turbulence region tracks solar cycle activity, while its variance remains constant, independent of the fast or slow state of the solar wind. However, when we test the lognormality of this core-turbulence component over time, we find the model provides a poor description of the data at solar maximum, where sharp peaks in the distribution dominate over the lognormal shape. At IMF values higher than 8nT, we find a separate, extremal distribution component, whose moments are sensitive to solar cycle phase, the peak activity of the cycle and the solar wind state. We further investigate these `extremal' values using burst analysis, where a burst is defined as a continuous period of exceedance over a predefined threshold. This form of extreme value statistics allows us to study the stochastic process underlying the time series, potentially supporting a probabilistic forecast of high-energy events. Tindale, E., and S.C. Chapman (2016), Geophys. Res. Lett., 43(11) Tindale, E., and S.C. Chapman (2017), submitted Burlaga, L

Determinants of Academic Attainment in the United States: A Quantile Regression Analysis of Test Scores

ERIC Educational Resources Information Center

Haile, Getinet Astatike; Nguyen, Anh Ngoc

2008-01-01

We investigate the determinants of high school students' academic attainment in mathematics, reading and science in the United States; focusing particularly on possible differential impacts of ethnicity and family background across the distribution of test scores. Using data from the NELS2000 and employing quantile regression, we find two…

The use of historical information for regional frequency analysis of extreme skew surge

NASA Astrophysics Data System (ADS)

Frau, Roberto; Andreewsky, Marc; Bernardara, Pietro

2018-03-01

The design of effective coastal protections requires an adequate estimation of the annual occurrence probability of rare events associated with a return period up to 103 years. Regional frequency analysis (RFA) has been proven to be an applicable way to estimate extreme events by sorting regional data into large and spatially distributed datasets. Nowadays, historical data are available to provide new insight on past event estimation. The utilisation of historical information would increase the precision and the reliability of regional extreme's quantile estimation. However, historical data are from significant extreme events that are not recorded by tide gauge. They usually look like isolated data and they are different from continuous data from systematic measurements of tide gauges. This makes the definition of the duration of our observations period complicated. However, the duration of the observation period is crucial for the frequency estimation of extreme occurrences. For this reason, we introduced here the concept of credible duration. The proposed RFA method (hereinafter referenced as FAB, from the name of the authors) allows the use of historical data together with systematic data, which is a result of the use of the credible duration concept.

Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables

NASA Astrophysics Data System (ADS)

Cannon, Alex J.

2018-01-01

Most bias correction algorithms used in climatology, for example quantile mapping, are applied to univariate time series. They neglect the dependence between different variables. Those that are multivariate often correct only limited measures of joint dependence, such as Pearson or Spearman rank correlation. Here, an image processing technique designed to transfer colour information from one image to another—the N-dimensional probability density function transform—is adapted for use as a multivariate bias correction algorithm (MBCn) for climate model projections/predictions of multiple climate variables. MBCn is a multivariate generalization of quantile mapping that transfers all aspects of an observed continuous multivariate distribution to the corresponding multivariate distribution of variables from a climate model. When applied to climate model projections, changes in quantiles of each variable between the historical and projection period are also preserved. The MBCn algorithm is demonstrated on three case studies. First, the method is applied to an image processing example with characteristics that mimic a climate projection problem. Second, MBCn is used to correct a suite of 3-hourly surface meteorological variables from the Canadian Centre for Climate Modelling and Analysis Regional Climate Model (CanRCM4) across a North American domain. Components of the Canadian Forest Fire Weather Index (FWI) System, a complicated set of multivariate indices that characterizes the risk of wildfire, are then calculated and verified against observed values. Third, MBCn is used to correct biases in the spatial dependence structure of CanRCM4 precipitation fields. Results are compared against a univariate quantile mapping algorithm, which neglects the dependence between variables, and two multivariate bias correction algorithms, each of which corrects a different form of inter-variable correlation structure. MBCn outperforms these alternatives, often by a large margin

Identifying the Safety Factors over Traffic Signs in State Roads using a Panel Quantile Regression Approach.

PubMed

Šarić, Željko; Xu, Xuecai; Duan, Li; Babić, Darko

2018-06-20

This study intended to investigate the interactions between accident rate and traffic signs in state roads located in Croatia, and accommodate the heterogeneity attributed to unobserved factors. The data from 130 state roads between 2012 and 2016 were collected from Traffic Accident Database System maintained by the Republic of Croatia Ministry of the Interior. To address the heterogeneity, a panel quantile regression model was proposed, in which quantile regression model offers a more complete view and a highly comprehensive analysis of the relationship between accident rate and traffic signs, while the panel data model accommodates the heterogeneity attributed to unobserved factors. Results revealed that (1) low visibility of material damage (MD) and death or injured (DI) increased the accident rate; (2) the number of mandatory signs and the number of warning signs were more likely to reduce the accident rate; (3)average speed limit and the number of invalid traffic signs per km exhibited a high accident rate. To our knowledge, it's the first attempt to analyze the interactions between accident consequences and traffic signs by employing a panel quantile regression model; by involving the visibility, the present study demonstrates that the low visibility causes a relatively higher risk of MD and DI; It is noteworthy that average speed limit corresponds with accident rate positively; The number of mandatory signs and the number of warning signs are more likely to reduce the accident rate; The number of invalid traffic signs per km are significant for accident rate, thus regular maintenance should be kept for a safer roadway environment.

Gender Gaps in Mathematics, Science and Reading Achievements in Muslim Countries: A Quantile Regression Approach

ERIC Educational Resources Information Center

Shafiq, M. Najeeb

2013-01-01

Using quantile regression analyses, this study examines gender gaps in mathematics, science, and reading in Azerbaijan, Indonesia, Jordan, the Kyrgyz Republic, Qatar, Tunisia, and Turkey among 15-year-old students. The analyses show that girls in Azerbaijan achieve as well as boys in mathematics and science and overachieve in reading. In Jordan,…

Simulation of climate characteristics and extremes of the Volta Basin using CCLM and RCA regional climate models

NASA Astrophysics Data System (ADS)

Darko, Deborah; Adjei, Kwaku A.; Appiah-Adjei, Emmanuel K.; Odai, Samuel N.; Obuobie, Emmanuel; Asmah, Ruby

2018-06-01

The extent to which statistical bias-adjusted outputs of two regional climate models alter the projected change signals for the mean (and extreme) rainfall and temperature over the Volta Basin is evaluated. The outputs from two regional climate models in the Coordinated Regional Climate Downscaling Experiment for Africa (CORDEX-Africa) are bias adjusted using the quantile mapping technique. Annual maxima rainfall and temperature with their 10- and 20-year return values for the present (1981-2010) and future (2051-2080) climates are estimated using extreme value analyses. Moderate extremes are evaluated using extreme indices (viz. percentile-based, duration-based, and intensity-based). Bias adjustment of the original (bias-unadjusted) models improves the reproduction of mean rainfall and temperature for the present climate. However, the bias-adjusted models poorly reproduce the 10- and 20-year return values for rainfall and maximum temperature whereas the extreme indices are reproduced satisfactorily for the present climate. Consequently, projected changes in rainfall and temperature extremes were weak. The bias adjustment results in the reduction of the change signals for the mean rainfall while the mean temperature signals are rather magnified. The projected changes for the original mean climate and extremes are not conserved after bias adjustment with the exception of duration-based extreme indices.

Improving multisensor estimation of heavy-to-extreme precipitation via conditional bias-penalized optimal estimation

NASA Astrophysics Data System (ADS)

Kim, Beomgeun; Seo, Dong-Jun; Noh, Seong Jin; Prat, Olivier P.; Nelson, Brian R.

2018-01-01

A new technique for merging radar precipitation estimates and rain gauge data is developed and evaluated to improve multisensor quantitative precipitation estimation (QPE), in particular, of heavy-to-extreme precipitation. Unlike the conventional cokriging methods which are susceptible to conditional bias (CB), the proposed technique, referred to herein as conditional bias-penalized cokriging (CBPCK), explicitly minimizes Type-II CB for improved quantitative estimation of heavy-to-extreme precipitation. CBPCK is a bivariate version of extended conditional bias-penalized kriging (ECBPK) developed for gauge-only analysis. To evaluate CBPCK, cross validation and visual examination are carried out using multi-year hourly radar and gauge data in the North Central Texas region in which CBPCK is compared with the variant of the ordinary cokriging (OCK) algorithm used operationally in the National Weather Service Multisensor Precipitation Estimator. The results show that CBPCK significantly reduces Type-II CB for estimation of heavy-to-extreme precipitation, and that the margin of improvement over OCK is larger in areas of higher fractional coverage (FC) of precipitation. When FC > 0.9 and hourly gauge precipitation is > 60 mm, the reduction in root mean squared error (RMSE) by CBPCK over radar-only (RO) is about 12 mm while the reduction in RMSE by OCK over RO is about 7 mm. CBPCK may be used in real-time analysis or in reanalysis of multisensor precipitation for which accurate estimation of heavy-to-extreme precipitation is of particular importance.

Atmospheric conditions and weather regimes associated with extreme winter dry spells over the Mediterranean basin

NASA Astrophysics Data System (ADS)

Raymond, Florian; Ullmann, Albin; Camberlin, Pierre; Oueslati, Boutheina; Drobinski, Philippe

2018-06-01

Very long dry spell events occurring during winter are natural hazards to which the Mediterranean region is extremely vulnerable, because they can lead numerous impacts for environment and society. Four dry spell patterns have been identified in a previous work. Identifying the main associated atmospheric conditions controlling the dry spell patterns is key to better understand their dynamics and their evolution in a changing climate. Except for the Levant region, the dry spells are generally associated with anticyclonic blocking conditions located about 1000 km to the Northwest of the affected area. These anticyclonic conditions are favourable to dry spell occurrence as they are associated with subsidence of cold and dry air coming from boreal latitudes which bring low amount of water vapour and non saturated air masses, leading to clear sky and absence of precipitation. These extreme dry spells are also partly related to the classical four Euro-Atlantic weather regimes are: the two phases of the North Atlantic Oscillation, the Scandinavian "blocking" or "East-Atlantic", and the "Atlantic ridge". Only the The "East-Atlantic", "Atlantic ridge" and the positive phase of the North Atlantic Oscillation are frequently associated with extremes dry spells over the Mediterranean basin but they do not impact the four dry spell patterns equally. Finally long sequences of those weather regimes are more favourable to extreme dry spells than short sequences. These long sequences are associated with the favourable prolonged and reinforced anticyclonic conditions

Evolution of precipitation extremes in two large ensembles of climate simulations

NASA Astrophysics Data System (ADS)

Martel, Jean-Luc; Mailhot, Alain; Talbot, Guillaume; Brissette, François; Ludwig, Ralf; Frigon, Anne; Leduc, Martin; Turcotte, Richard

2017-04-01

Recent studies project significant changes in the future distribution of precipitation extremes due to global warming. It is likely that extreme precipitation intensity will increase in a future climate and that extreme events will be more frequent. In this work, annual maxima daily precipitation series from the Canadian Earth System Model (CanESM2) 50-member large ensemble (spatial resolution of 2.8°x2.8°) and the Community Earth System Model (CESM1) 40-member large ensemble (spatial resolution of 1°x1°) are used to investigate extreme precipitation over the historical (1980-2010) and future (2070-2100) periods. The use of these ensembles results in respectively 1 500 (30 years x 50 members) and 1200 (30 years x 40 members) simulated years over both the historical and future periods. These large datasets allow the computation of empirical daily extreme precipitation quantiles for large return periods. Using the CanESM2 and CESM1 large ensembles, extreme daily precipitation with return periods ranging from 2 to 100 years are computed in historical and future periods to assess the impact of climate change. Results indicate that daily precipitation extremes generally increase in the future over most land grid points and that these increases will also impact the 100-year extreme daily precipitation. Considering that many public infrastructures have lifespans exceeding 75 years, the increase in extremes has important implications on service levels of water infrastructures and public safety. Estimated increases in precipitation associated to very extreme precipitation events (e.g. 100 years) will drastically change the likelihood of flooding and their extent in future climate. These results, although interesting, need to be extended to sub-daily durations, relevant for urban flooding protection and urban infrastructure design (e.g. sewer networks, culverts). Models and simulations at finer spatial and temporal resolution are therefore needed.

A global analysis of the asymmetric effect of ENSO on extreme precipitation

NASA Astrophysics Data System (ADS)

Sun, Xun; Renard, Benjamin; Thyer, Mark; Westra, Seth; Lang, Michel

2015-11-01

The global and regional influence of the El Niño-Southern Oscillation (ENSO) phenomenon on extreme precipitation was analyzed using a global database comprising over 7000 high quality observation sites. To better quantify possible changes in relatively rare design-relevant precipitation quantiles (e.g. the 1 in 10 year event), a Bayesian regional extreme value model was used, which employed the Southern Oscillation Index (SOI) - a measure of ENSO - as a covariate. Regions found to be influenced by ENSO include parts of North and South America, southern and eastern Asia, South Africa, Australia and Europe. The season experiencing the greatest ENSO effect varies regionally, but in most of the ENSO-affected regions the strongest effect happens in boreal winter, during which time the 10-year precipitation for |SOI| = 20 (corresponding to either a strong El Niño or La Niña episode) can be up to 50% higher or lower than for SOI = 0 (a neutral phase). Importantly, the effect of ENSO on extreme precipitation is asymmetric, with most parts of the world experiencing a significant effect only for a single ENSO phase. This finding has important implications on the current understanding of how ENSO influences extreme precipitation, and will enable a more rigorous theoretical foundation for providing quantitative extreme precipitation intensity predictions at seasonal timescales. We anticipate that incorporating asymmetric impacts of ENSO on extreme precipitation will help lead to better-informed climate-adaptive design of flood-sensitive infrastructure.

Decision Support System for hydrological extremes

NASA Astrophysics Data System (ADS)

Bobée, Bernard; El Adlouni, Salaheddine

2014-05-01

The study of the tail behaviour of extreme event distributions is important in several applied statistical fields such as hydrology, finance, and telecommunications. For example in hydrology, it is important to estimate adequately extreme quantiles in order to build and manage safe and effective hydraulic structures (dams, for example). Two main classes of distributions are used in hydrological frequency analysis: the class D of sub-exponential (Gamma (G2), Gumbel, Halphen type A (HA), Halphen type B (HB)…) and the class C of regularly varying distributions (Fréchet, Log-Pearson, Halphen type IB …) with a heavier tail. A Decision Support System (DSS) based on the characterization of the right tail, corresponding low probability of excedence p (high return period T=1/p, in hydrology), has been developed. The DSS allows discriminating between the class C and D and in its last version, a new prior step is added in order to test Lognormality. Indeed, the right tail of the Lognormal distribution (LN) is between the tails of distributions of the classes C and D; studies indicated difficulty with the discrimination between LN and distributions of the classes C and D. Other tools are useful to discriminate between distributions of the same class D (HA, HB and G2; see other communication). Some numerical illustrations show that, the DSS allows discriminating between Lognormal, regularly varying and sub-exponential distributions; and lead to coherent conclusions. Key words: Regularly varying distributions, subexponential distributions, Decision Support System, Heavy tailed distribution, Extreme value theory

On the distortion of elevation dependent warming signals by quantile mapping

NASA Astrophysics Data System (ADS)

Jury, Martin W.; Mendlik, Thomas; Maraun, Douglas

2017-04-01

Elevation dependent warming (EDW), the amplification of warming under climate change with elevation, is likely to accelerate changes in e.g. cryospheric and hydrological systems. Responsible for EDW is a mixture of processes including snow albedo feedback, cloud formations or the location of aerosols. The degree of incorporation of this processes varies across state of the art climate models. In a recent study we were preparing bias corrected model output of CMIP5 GCMs and CORDEX RCMs over the Himalayan region for the glacier modelling community. In a first attempt we used quantile mapping (QM) to generate this data. A beforehand model evaluation showed that more than two third of the 49 included climate models were able to reproduce positive trend differences between areas of higher and lower elevations in winter, clearly visible in all of our five observational datasets used. Regrettably, we noticed that height dependent trend signals provided by models were distorted, most of the time in the direction of less EDW, sometimes even reversing EDW signals present in the models before the bias correction. As a consequence, we refrained from using quantile mapping for our task, as EDW poses one important factor influencing the climate in high altitudes for the nearer and more distant future, and used a climate change signal preserving bias correction approach. Here we present our findings of the distortion of the EDW temperature change by QM and discuss the influence of QM on different statistical properties as well as their modifications.

Customized Fetal Growth Charts for Parents' Characteristics, Race, and Parity by Quantile Regression Analysis: A Cross-sectional Multicenter Italian Study.

PubMed

Ghi, Tullio; Cariello, Luisa; Rizzo, Ludovica; Ferrazzi, Enrico; Periti, Enrico; Prefumo, Federico; Stampalija, Tamara; Viora, Elsa; Verrotti, Carla; Rizzo, Giuseppe

2016-01-01

The purpose of this study was to construct fetal biometric charts between 16 and 40 weeks' gestation that were customized for parental characteristics, race, and parity, using quantile regression analysis. In a multicenter cross-sectional study, 8070 sonographic examinations from low-risk pregnancies between 16 and 40 weeks' gestation were analyzed. The fetal measurements obtained were biparietal diameter, head circumference, abdominal circumference, and femur diaphysis length. Quantile regression was used to examine the impact of parental height and weight, parity, and race across biometric percentiles for the fetal measurements considered. Paternal and maternal height were significant covariates for all of the measurements considered (P < .05). Maternal weight significantly influenced head circumference, abdominal circumference, and femur diaphysis length. Parity was significantly associated with biparietal diameter and head circumference. Central African race was associated with head circumference and femur diaphysis length, whereas North African race was only associated with femur diaphysis length. In this study we constructed customized biometric growth charts using quantile regression in a large cohort of low-risk pregnancies. These charts offer the advantage of defining individualized normal ranges of fetal biometric parameters at each specific percentile corrected for parental height and weight, parity, and race. This study supports the importance of including these variables in routine sonographic screening for fetal growth abnormalities.

Multidecadal oscillations in rainfall and hydrological extremes

NASA Astrophysics Data System (ADS)

Willems, Patrick

2013-04-01

Many studies have anticipated a worldwide increase in the frequency and intensity of precipitation extremes and floods since the last decade(s). Natural variability by climate oscillations partly determines the observed evolution of precipitation extremes. Based on a technique for the identification and analysis of changes in extreme quantiles, it is shown that hydrological extremes have oscillatory behaviour at multidecadal time scales. Results are based on nearly independent extremes extracted from long-term historical time series of precipitation intensities and river flows. Study regions include Belgium - The Netherlands (Meuse basin), Ethiopia (Blue Nile basin) and Ecuador (Paute basin). For Belgium - The Netherlands, the past 100 years showed larger and more hydrological extremes around the 1910s, 1950-1960s, and more recently during the 1990-2000s. Interestingly, the oscillations for southwestern Europe are anti-correlated with these of northwestern Europe, thus with oscillation highs in the 1930-1940s and 1970s. The precipitation oscillation peaks are explained by persistence in atmospheric circulation patterns over the North Atlantic during periods of 10 to 15 years. References: Ntegeka V., Willems P. (2008), 'Trends and multidecadal oscillations in rainfall extremes, based on a more than 100 years time series of 10 minutes rainfall intensities at Uccle, Belgium', Water Resources Research, 44, W07402, doi:10.1029/2007WR006471 Mora, D., Willems, P. (2012), 'Decadal oscillations in rainfall and air temperature in the Paute River Basin - Southern Andes of Ecuador', Theoretical and Applied Climatology, 108(1), 267-282, doi:0.1007/s00704-011-0527-4 Taye, M.T., Willems, P. (2011). 'Influence of climate variability on representative QDF predictions of the upper Blue Nile Basin', Journal of Hydrology, 411, 355-365, doi:10.1016/j.jhydrol.2011.10.019 Taye, M.T., Willems, P. (2012). 'Temporal variability of hydro-climatic extremes in the Blue Nile basin', Water

Ordinary Least Squares and Quantile Regression: An Inquiry-Based Learning Approach to a Comparison of Regression Methods

ERIC Educational Resources Information Center

Helmreich, James E.; Krog, K. Peter

2018-01-01

We present a short, inquiry-based learning course on concepts and methods underlying ordinary least squares (OLS), least absolute deviation (LAD), and quantile regression (QR). Students investigate squared, absolute, and weighted absolute distance functions (metrics) as location measures. Using differential calculus and properties of convex…

Life under Multiple Extreme Conditions: Diversity and Physiology of the Halophilic Alkalithermophiles

PubMed Central

Wiegel, Juergen

2012-01-01

Around the world, there are numerous alkaline, hypersaline environments that are heated either geothermally or through intense solar radiation. It was once thought that such harsh environments were inhospitable and incapable of supporting a variety of life. However, numerous culture-dependent and -independent studies revealed the presence of an extensive diversity of aerobic and anaerobic bacteria and archaea that survive and grow under these multiple harsh conditions. This diversity includes the halophilic alkalithermophiles, a novel group of polyextremophiles that require for growth and proliferation the multiple extremes of high salinity, alkaline pH, and elevated temperature. Life under these conditions undoubtedly involves the development of unique physiological characteristics, phenotypic properties, and adaptive mechanisms that enable control of membrane permeability, control of intracellular osmotic balance, and stability of the cell wall, intracellular proteins, and other cellular constituents. This minireview highlights the ecology and growth characteristics of the extremely halophilic alkalithermophiles that have been isolated thus far. Biochemical, metabolic, and physiological properties of the extremely halophilic alkalithermophiles are described, and their roles in resistance to the combined stressors of high salinity, alkaline pH, and high temperature are discussed. The isolation of halophilic alkalithermophiles broadens the physicochemical boundaries for life and extends the boundaries for the combinations of the maximum salinity, pH, and temperature that can support microbial growth. PMID:22492435

"On-off-on" switchable sensor: a fluorescent spiropyran responds to extreme pH conditions and its bioimaging applications.

PubMed

Wan, Shulin; Zheng, Yang; Shen, Jie; Yang, Wantai; Yin, Meizhen

2014-11-26

A novel spiropyran that responds to both extreme acid and extreme alkali and has an "on-off-on" switch is reported. Benzoic acid at the indole N-position and carboxyl group at the indole 6-position contribute to the extreme acid response. The ionizations of carboxyl and phenolic hydroxyl groups cause the extreme alkali response. Moreover, the fluorescent imaging in bacterial cells under extreme pH conditions supports the mechanism of pH response.

Gender Gaps in Mathematics, Science and Reading Achievements in Muslim Countries: Evidence from Quantile Regression Analyses

ERIC Educational Resources Information Center

Shafiq, M. Najeeb

2011-01-01

Using quantile regression analyses, this study examines gender gaps in mathematics, science, and reading in Azerbaijan, Indonesia, Jordan, the Kyrgyz Republic, Qatar, Tunisia, and Turkey among 15 year-old students. The analyses show that girls in Azerbaijan achieve as well as boys in mathematics and science and overachieve in reading. In Jordan,…

Extreme fog events in Poland with respect to circulation conditions

NASA Astrophysics Data System (ADS)

Ustrnul, Z.; Czekierda, D.; Wypych, A.

2010-09-01

Fog is a phenomenon which belongs to a group of so-called hydrometeorites and, according to the different dictionaries, it is a suspension of water droplets or ice crystals in the ground layer of the air that impairs visibility in the horizontal direction below 1 km. The phenomenon of fog, although much less dynamic or violent than other extreme phenomena, such as thunderstorms or hail, is equally dangerous and brings about huge social and economic complications. Land and air transportation suffer and fog may sometimes leads to a complete crippling of the whole economy in an area where fog occurs. The main objective of the study is determination of the circulation types bringing extreme fog events in Poland. The duration of fog at each meteorological station was considered as the main input data originated from 54 synoptic stations located across the country. The mentioned data series cover the period of 56 years (1951-2006). The occurrence of fog depends on meteorological conditions caused to a large extent by a given synoptic situation and local terrain conditions. In this study, according to its objectives, only circulation conditions are taken into consideration. These have been described by 5 different circulation classifications (Grosswetterlagen, Litynski, Osuchowska-Klein, Niedzwiedz and Ustrnul). Situations when this phenomenon occurred across a large part of the country were taken into detailed consideration. Special attention was paid to fog coverage during 24-hour periods. In this work, in light of certain doubts about the homogeneity of the observation material available, the intensity of fog was not included, as it requires additional and very tedious analysis. In the first step all cases of fog during the 1966-2006 study period which lasted 24 hours at more than 10 of the considered weather stations, i.e: at least 5 stations have been considered. As expected, in most cases, either a centre of a classical high pressure system or a high pressure wedge

Modeling soil organic carbon with Quantile Regression: Dissecting predictors' effects on carbon stocks

NASA Astrophysics Data System (ADS)

Lombardo, Luigi; Saia, Sergio; Schillaci, Calogero; Mai, P. Martin; Huser, Raphaël

2018-05-01

Soil Organic Carbon (SOC) estimation is crucial to manage both natural and anthropic ecosystems and has recently been put under the magnifying glass after the Paris agreement 2016 due to its relationship with greenhouse gas. Statistical applications have dominated the SOC stock mapping at regional scale so far. However, the community has hardly ever attempted to implement Quantile Regression (QR) to spatially predict the SOC distribution. In this contribution, we test QR to estimate SOC stock (0-30 $cm$ depth) in the agricultural areas of a highly variable semi-arid region (Sicily, Italy, around 25,000 $km2$) by using topographic and remotely sensed predictors. We also compare the results with those from available SOC stock measurement. The QR models produced robust performances and allowed to recognize dominant effects among the predictors with respect to the considered quantile. This information, currently lacking, suggests that QR can discern predictor influences on SOC stock at specific sub-domains of each predictors. In this work, the predictive map generated at the median shows lower errors than those of the Joint Research Centre and International Soil Reference, and Information Centre benchmarks. The results suggest the use of QR as a comprehensive and effective method to map SOC using legacy data in agro-ecosystems. The R code scripted in this study for QR is included.

Predictability of the atmospheric conditions leading to extreme weather events in the Western Mediterranean Region in comparison with the seasonal mean conditions

NASA Astrophysics Data System (ADS)

Khodayar, Samiro; Kalthoff, Norbert

2013-04-01

Among all severe convective weather situations, fall season heavy rainfall represents the most threatening phenomenon in the western Mediterranean region. Devastating flash floods occur every year somewhere in eastern Spain, southern France, Italy, or North Africa, being responsible for a great proportion of the fatalities, property losses, and destruction of infrastructure caused by natural hazards. Investigations in the area have shown that most of the heavy rainfall events in this region can be attributed to mesoscale convective systems. The main goal of this investigation is to understand and identify the atmospheric conditions that favor the initiation and development of such systems. Insight of the involved processes and conditions will improve their predictability and help preventing some of the fatal consequences related with the occurrence of these weather phenomena. The HyMeX (Hydrological cycle in the Mediterranean eXperiment) provides a unique framework to investigate this issue. Making use of high-resolution seasonal simulations with the COSMO-CLM model the mean atmospheric conditions of the fall season, September, October and November, are investigated in different western Mediterranean regions such as eastern Spain, Southern France, northern Africa and Italy. The precipitation distribution, its daily cycle, and probability distribution function are evaluated to ascertain the similarities and differences between the regions of interest, as well as the spatial distribution of extreme events. Additionally, the regional differences of the boundary layer and mid-tropospheric conditions, atmospheric stability and inhibition, and low-level triggering are presented. Selected high impact weather HyMeX episodes' are analyzed with special focus on the atmospheric pre-conditions leading to the extreme weather situations. These pre-conditions are then compared to the mean seasonal conditions to identify and point out possible anomalies in the atmospheric

Observational Simulation of Icing in Extreme Weather Conditions

NASA Astrophysics Data System (ADS)

Gultepe, Ismail; Heymsfield, Andrew; Agelin-Chaab, Martin; Komar, John; Elfstrom, Garry; Baumgardner, Darrel

2017-04-01

Observations and prediction of icing in extreme weather conditions are important for aviation, transportation, and shipping applications, and icing adversely affects the economy. Icing environments can be studied either in the outdoor atmosphere or in the laboratory. There have been several aircraft based in-situ studies related to weather conditions affecting aviation operations, transportation, and marine shipping that includes icing, wind, and turbulence. However, studying severe weather conditions from aircraft observations are limited due to safety and sampling issues, instrumental uncertainties, and even the possibility of aircraft producing its own physical and dynamical effects. Remote sensing based techniques (e.g. retrieval techniques) for studying severe weather conditions represent usually a volume that cannot characterize the important scales and also represents indirect observations. Therefore, laboratory simulations of atmospheric processes can help us better understand the interactions among microphysical and dynamical processes. The Climatic Wind Tunnel (CWT) in ACE at the University of Ontario Institute of Technology (UOIT) has a large semi-open jet test chamber with flow area 7-13 m2 that can precisely control temperatures down to -40°C, and up to 250 km hr-1 wind speeds, for heavy or dry snow conditions with low visibility, similar to ones observed in the Arctic and cold climate regions, or at high altitude aeronautical conditions. In this study, the ACE CWT employed a spray nozzle array suspended in its settling chamber and fed by pressurized water, creating various particle sizes from a few microns up to mm size range. This array, together with cold temperature and high wind speed, enabled simulation of severe weather conditions, including icing, visibility, strong wind and turbulence, ice fog and frost, freezing fog, heavy snow and blizzard conditions. In this study, the test results will be summarized, and their application to aircraft

Numerical Analysis of Flood modeling of upper Citarum River under Extreme Flood Condition

NASA Astrophysics Data System (ADS)

Siregar, R. I.

2018-02-01

This paper focuses on how to approach the numerical method and computation to analyse flood parameters. Water level and flood discharge are the flood parameters solved by numerical methods approach. Numerical method performed on this paper for unsteady flow conditions have strengths and weaknesses, among others easily applied to the following cases in which the boundary irregular flow. The study area is in upper Citarum Watershed, Bandung, West Java. This paper uses computation approach with Force2 programming and HEC-RAS to solve the flow problem in upper Citarum River, to investigate and forecast extreme flood condition. Numerical analysis based on extreme flood events that have occurred in the upper Citarum watershed. The result of water level parameter modeling and extreme flood discharge compared with measurement data to analyse validation. The inundation area about flood that happened in 2010 is about 75.26 square kilometres. Comparing two-method show that the FEM analysis with Force2 programs has the best approach to validation data with Nash Index is 0.84 and HEC-RAS that is 0.76 for water level. For discharge data Nash Index obtained the result analysis use Force2 is 0.80 and with use HEC-RAS is 0.79.

Solutions for Critical Raw Materials under Extreme Conditions: A Review

PubMed Central

Grilli, Maria Luisa; Bellezze, Tiziano; Gamsjäger, Ernst; Rinaldi, Antonio; Novak, Pavel; Balos, Sebastian; Piticescu, Radu Robert; Ruello, Maria Letizia

2017-01-01

In Europe, many technologies with high socio-economic benefits face materials requirements that are often affected by demand-supply disruption. This paper offers an overview of critical raw materials in high value alloys and metal-matrix composites used in critical applications, such as energy, transportation and machinery manufacturing associated with extreme working conditions in terms of temperature, loading, friction, wear and corrosion. The goal is to provide perspectives about the reduction and/or substitution of selected critical raw materials: Co, W, Cr, Nb and Mg. PMID:28772645

Numerical tools to predict the environmental loads for offshore structures under extreme weather conditions

NASA Astrophysics Data System (ADS)

Wu, Yanling

2018-05-01

In this paper, the extreme waves were generated using the open source computational fluid dynamic (CFD) tools — OpenFOAM and Waves2FOAM — using linear and nonlinear NewWave input. They were used to conduct the numerical simulation of the wave impact process. Numerical tools based on first-order (with and without stretching) and second-order NewWave are investigated. The simulation to predict force loading for the offshore platform under the extreme weather condition is implemented and compared.

Role of Water in the Selection of Stable Proteins at Ambient and Extreme Thermodynamic Conditions

NASA Astrophysics Data System (ADS)

Bianco, Valentino; Franzese, Giancarlo; Dellago, Christoph; Coluzza, Ivan

2017-04-01

Proteins that are functional at ambient conditions do not necessarily work at extreme conditions of temperature T and pressure P . Furthermore, there are limits of T and P above which no protein has a stable functional state. Here, we show that these limits and the selection mechanisms for working proteins depend on how the properties of the surrounding water change with T and P . We find that proteins selected at high T are superstable and are characterized by a nonextreme segregation of a hydrophilic surface and a hydrophobic core. Surprisingly, a larger segregation reduces the stability range in T and P . Our computer simulations, based on a new protein design protocol, explain the hydropathy profile of proteins as a consequence of a selection process influenced by water. Our results, potentially useful for engineering proteins and drugs working far from ambient conditions, offer an alternative rationale to the evolutionary action exerted by the environment in extreme conditions.

Lower Extremity Overuse Conditions Affecting Figure Skaters During Daily Training

PubMed Central

Campanelli, Valentina; Piscitelli, Francesco; Verardi, Luciano; Maillard, Pauline; Sbarbati, Andrea

2015-01-01

Background Most ice figure skaters train and compete with ongoing issues in the lower extremities, which are often overlooked by the skaters and considered injuries only when they prevent the athletes from skating. Although not severe, these conditions impair the quality of daily training and compromise the skaters’ state of mind and performances. Purpose (1) To determine the point prevalence of the ongoing lower extremity overuse conditions in a population of ice figure skaters of all ages and levels and (2) to identify the risk factors contributing to the development of the most common ongoing conditions. Study Design Cross-sectional study; Level of evidence, 3. Methods A total of 95 skaters of all ages and skating levels were evaluated in a single examination in the middle of the competitive season. Data collection consisted of a questionnaire, clinical examination, and measurement of the skaters’ characteristics and the equipment used. Results Retrocalcaneal bursitis was the most common problem, affecting at least 1 foot in 34% of the skaters evaluated, followed by posterior heel skin calluses and superficial calcaneal bursitis, which affected 29% and 28% of skaters, respectively. The prevalence of the majority of these conditions was 10% to 32% higher in elite skaters than in nonelite skaters. Higher boot–foot length difference was associated with greater risk of superficial calcaneal bursitis in the landing foot of elite skaters, while higher body weight and greater in-skate ankle flexibility were associated with the development of retrocalcaneal bursitis in nonelite skaters. Only 30 skaters (32%) wore the appropriate boot size, while 57 skaters (51%) could not dorsiflex their ankles properly while wearing skates. Conclusion The heel represents a major area of concern for the high prevalence of calcaneal bursitis and calluses in proximity of the Achilles tendon, suggesting that improvements on the boot heel cup design should take priority. The

The Microbial Sulfur Cycle at Extremely Haloalkaline Conditions of Soda Lakes

PubMed Central

Sorokin, Dimitry Y.; Kuenen, J. Gijs; Muyzer, Gerard

2011-01-01

Soda lakes represent a unique ecosystem with extremely high pH (up to 11) and salinity (up to saturation) due to the presence of high concentrations of sodium carbonate in brines. Despite these double extreme conditions, most of the lakes are highly productive and contain a fully functional microbial system. The microbial sulfur cycle is among the most active in soda lakes. One of the explanations for that is high-energy efficiency of dissimilatory conversions of inorganic sulfur compounds, both oxidative and reductive, sufficient to cope with costly life at double extreme conditions. The oxidative part of the sulfur cycle is driven by chemolithoautotrophic haloalkaliphilic sulfur-oxidizing bacteria (SOB), which are unique for soda lakes. The haloalkaliphilic SOB are present in the surface sediment layer of various soda lakes at high numbers of up to 106 viable cells/cm3. The culturable forms are so far represented by four novel genera within the Gammaproteobacteria, including the genera Thioalkalivibrio, Thioalkalimicrobium, Thioalkalispira, and Thioalkalibacter. The latter two were only found occasionally and each includes a single species, while the former two are widely distributed in various soda lakes over the world. The genus Thioalkalivibrio is the most physiologically diverse and covers the whole spectrum of salt/pH conditions present in soda lakes. Most importantly, the dominant subgroup of this genus is able to grow in saturated soda brines containing 4 M total Na+ – a so far unique property for any known aerobic chemolithoautotroph. Furthermore, some species can use thiocyanate as a sole energy source and three out of nine species can grow anaerobically with nitrogen oxides as electron acceptor. The reductive part of the sulfur cycle is active in the anoxic layers of the sediments of soda lakes. The in situ measurements of sulfate reduction rates and laboratory experiments with sediment slurries using sulfate, thiosulfate, or elemental sulfur as

Longitudinal analysis of the strengths and difficulties questionnaire scores of the Millennium Cohort Study children in England using M-quantile random-effects regression.

PubMed

Tzavidis, Nikos; Salvati, Nicola; Schmid, Timo; Flouri, Eirini; Midouhas, Emily

2016-02-01

Multilevel modelling is a popular approach for longitudinal data analysis. Statistical models conventionally target a parameter at the centre of a distribution. However, when the distribution of the data is asymmetric, modelling other location parameters, e.g. percentiles, may be more informative. We present a new approach, M -quantile random-effects regression, for modelling multilevel data. The proposed method is used for modelling location parameters of the distribution of the strengths and difficulties questionnaire scores of children in England who participate in the Millennium Cohort Study. Quantile mixed models are also considered. The analyses offer insights to child psychologists about the differential effects of risk factors on children's outcomes.

Spatiotemporal variation and statistical characteristic of extreme precipitation in the middle reaches of the Yellow River Basin during 1960-2013

NASA Astrophysics Data System (ADS)

Zhang, Yin; Xia, Jun; She, Dunxian

2018-01-01

In recent decades, extreme precipitation events have been a research hotspot worldwide. Based on 12 extreme precipitation indices, the spatiotemporal variation and statistical characteristic of precipitation extremes in the middle reaches of the Yellow River Basin (MRYRB) during 1960-2013 were investigated. The results showed that the values of most extreme precipitation indices (except consecutive dry days (CDD)) increased from the northwest to the southeast of the MRYRB, reflecting that the southeast was the wettest region in the study area. Temporally, the precipitation extremes presented a drying trend with less frequent precipitation events. Generalized extreme value (GEV) distribution was selected to fit the time series of all indices, and the quantiles values under the 50-year return period showed a similar spatial extent with the corresponding precipitation extreme indices during 1960-2013, indicating a higher risk of extreme precipitation in the southeast of the MRYRB. Furthermore, the changes in probability distribution functions of indices for the period of 1960-1986 and 1987-2013 revealed a drying tendency in our study area. Both El Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) were proved to have a strong influence on precipitation extremes in the MRYRB. The results of this study are useful to master the change rule of local precipitation extremes, which will help to prevent natural hazards caused.

Numerical analysis of the accuracy of bivariate quantile distributions utilizing copulas compared to the GUM supplement 2 for oil pressure balance uncertainties

NASA Astrophysics Data System (ADS)

Ramnath, Vishal

2017-11-01

In the field of pressure metrology the effective area is Ae = A0 (1 + λP) where A0 is the zero-pressure area and λ is the distortion coefficient and the conventional practise is to construct univariate probability density functions (PDFs) for A0 and λ. As a result analytical generalized non-Gaussian bivariate joint PDFs has not featured prominently in pressure metrology. Recently extended lambda distribution based quantile functions have been successfully utilized for summarizing univariate arbitrary PDF distributions of gas pressure balances. Motivated by this development we investigate the feasibility and utility of extending and applying quantile functions to systems which naturally exhibit bivariate PDFs. Our approach is to utilize the GUM Supplement 1 methodology to solve and generate Monte Carlo based multivariate uncertainty data for an oil based pressure balance laboratory standard that is used to generate known high pressures, and which are in turn cross-floated against another pressure balance transfer standard in order to deduce the transfer standard's respective area. We then numerically analyse the uncertainty data by formulating and constructing an approximate bivariate quantile distribution that directly couples A0 and λ in order to compare and contrast its accuracy to an exact GUM Supplement 2 based uncertainty quantification analysis.

Environmental influence on mussel (Mytilus edulis) growth - A quantile regression approach

NASA Astrophysics Data System (ADS)

Bergström, Per; Lindegarth, Mats

2016-03-01

The need for methods for sustainable management and use of coastal ecosystems has increased in the last century. A key aspect for obtaining ecologically and economically sustainable aquaculture in threatened coastal areas is the requirement of geographic information of growth and potential production capacity. Growth varies over time and space and depends on a complex pattern of interactions between the bivalve and a diverse range of environmental factors (e.g. temperature, salinity, food availability). Understanding these processes and modelling the environmental control of bivalve growth has been central in aquaculture. In contrast to the most conventional modelling techniques, quantile regression can handle cases where not all factors are measured and provide the possibility to estimate the effect at different levels of the response distribution and give therefore a more complete picture of the relationship between environmental factors and biological response. Observation of the relationships between environmental factors and growth of the bivalve Mytilus edulis revealed relationships that varied both among level of growth rate and within the range of environmental variables along the Swedish west coast. The strongest patterns were found for water oxygen concentration level which had a negative effect on growth for all oxygen levels and growth levels. However, these patterns coincided with differences in growth among periods and very little of the remaining variability within periods could be explained indicating that interactive processes masked the importance of the individual variables. By using quantile regression and local regression (LOESS) this study was able to provide valuable information on environmental factors influencing the growth of M. edulis and important insight for the development of ecosystem based management tools of aquaculture activities, its use in mitigation efforts and successful management of human use of coastal areas.

Soil heating and evaporation under extreme conditions: Forest fires and slash pile burns

NASA Astrophysics Data System (ADS)

Massman, W. J.

2011-12-01

Heating any soil during a sufficiently intense wild fire or prescribed burn can alter soil irreversibly, resulting in many significant and well known, long term biological, chemical, and hydrological effects. To better understand how fire impacts soil, especially considering the increasing probability of wildfires that is being driven by climate change and the increasing use of prescribe burns by land managers, it is important to better understand the dynamics of the coupled heat and moisture transport in soil during these extreme heating events. Furthermore, improving understanding of heat and mass transport during such extreme conditions should also provide insights into the associated transport mechanisms under more normal conditions as well. Here I describe the development of a new model designed to simulate soil heat and moisture transport during fires where the surface heating often ranges between 10,000 and 100,000 Wm-2 for several minutes to several hours. Model performance is tested against laboratory measurements of soil temperature and moisture changes at several depths during controlled heating events created with an extremely intense radiant heater. The laboratory tests employed well described soils with well known physical properties. The model, on the other hand, is somewhat unusual in that it employs formulations for temperature dependencies of the soil specific heat, thermal conductivity, and the water retention curve (relation between soil moisture and soil moisture potential). It also employs a new formulation for the surface evaporation rate as a component of the upper boundary condition, as well as the Newton-Raphson method and the generalized Thomas algorithm for inverting block tri-diagonal matrices to solve for soil temperature and soil moisture potential. Model results show rapid evaporation rates with significant vapor transfer not only to the free atmosphere above the soil, but to lower depths of the soil, where the vapor re

Integration of modern statistical tools for the analysis of climate extremes into the web-GIS “CLIMATE”

NASA Astrophysics Data System (ADS)

Ryazanova, A. A.; Okladnikov, I. G.; Gordov, E. P.

2017-11-01

The frequency of occurrence and magnitude of precipitation and temperature extreme events show positive trends in several geographical regions. These events must be analyzed and studied in order to better understand their impact on the environment, predict their occurrences, and mitigate their effects. For this purpose, we augmented web-GIS called “CLIMATE” to include a dedicated statistical package developed in the R language. The web-GIS “CLIMATE” is a software platform for cloud storage processing and visualization of distributed archives of spatial datasets. It is based on a combined use of web and GIS technologies with reliable procedures for searching, extracting, processing, and visualizing the spatial data archives. The system provides a set of thematic online tools for the complex analysis of current and future climate changes and their effects on the environment. The package includes new powerful methods of time-dependent statistics of extremes, quantile regression and copula approach for the detailed analysis of various climate extreme events. Specifically, the very promising copula approach allows obtaining the structural connections between the extremes and the various environmental characteristics. The new statistical methods integrated into the web-GIS “CLIMATE” can significantly facilitate and accelerate the complex analysis of climate extremes using only a desktop PC connected to the Internet.

Thermoregulatory value of cracking-clay soil shelters for small vertebrates during extreme desert conditions.

PubMed

Waudby, Helen P; Petit, Sophie

2017-05-01

Deserts exhibit extreme climatic conditions. Small desert-dwelling vertebrates have physiological and behavioral adaptations to cope with these conditions, including the ability to seek shelter. We investigated the temperature (T) and relative humidity (RH) regulating properties of the soil cracks that characterize the extensive cracking-clay landscapes of arid Australia, and the extent of their use by 2 small marsupial species: fat-tailed and stripe-faced dunnarts (Sminthopsis crassicaudata and Sminthopsis macroura). We measured hourly (over 24-h periods) the T and RH of randomly-selected soil cracks compared to outside conditions, during 2 summers and 2 winters. We tracked 17 dunnarts (8 Sminthopsis crassicaudata and 9 Sminthopsis macroura) to quantify their use of cracks. Cracks consistently moderated microclimate, providing more stable conditions than available from non-crack points, which often displayed comparatively dramatic fluctuations in T and RH. Both dunnart species used crack shelters extensively. Cracks constitute important shelter for small animals during extreme conditions by providing a stable microclimate, which is typically cooler than outside conditions in summer and warmer in winter. Cracks likely play a fundamental sheltering role by sustaining the physiological needs of small mammal populations. Globally, cracking-clay areas are dominated by agricultural land uses, including livestock grazing. Management of these systems should focus not only on vegetation condition, but also on soil integrity, to maintain shelter resources for ground-dwelling fauna. © 2016 International Society of Zoological Sciences, Institute of Zoology/Chinese Academy of Sciences and John Wiley & Sons Australia, Ltd.

Reduced CO2 fertilization effect in temperate C3 grasslands under more extreme weather conditions

NASA Astrophysics Data System (ADS)

Obermeier, W. A.; Lehnert, L. W.; Kammann, C. I.; Müller, C.; Grünhage, L.; Luterbacher, J.; Erbs, M.; Moser, G.; Seibert, R.; Yuan, N.; Bendix, J.

2017-02-01

The increase in atmospheric greenhouse gas concentrations from anthropogenic activities is the major driver of recent global climate change. The stimulation of plant photosynthesis due to rising atmospheric carbon dioxide concentrations ([CO2]) is widely assumed to increase the net primary productivity (NPP) of C3 plants--the CO2 fertilization effect (CFE). However, the magnitude and persistence of the CFE under future climates, including more frequent weather extremes, are controversial. Here we use data from 16 years of temperate grassland grown under `free-air carbon dioxide enrichment’ conditions to show that the CFE on above-ground biomass is strongest under local average environmental conditions. The observed CFE was reduced or disappeared under wetter, drier and/or hotter conditions when the forcing variable exceeded its intermediate regime. This is in contrast to predictions of an increased CO2 fertilization effect under drier and warmer conditions. Such extreme weather conditions are projected to occur more intensely and frequently under future climate scenarios. Consequently, current biogeochemical models might overestimate the future NPP sink capacity of temperate C3 grasslands and hence underestimate future atmospheric [CO2] increase.

Regional Frequency and Uncertainty Analysis of Extreme Precipitation in Bangladesh

NASA Astrophysics Data System (ADS)

Mortuza, M. R.; Demissie, Y.; Li, H. Y.

2014-12-01

Increased frequency of extreme precipitations, especially those with multiday durations, are responsible for recent urban floods and associated significant losses of lives and infrastructures in Bangladesh. Reliable and routinely updated estimation of the frequency of occurrence of such extreme precipitation events are thus important for developing up-to-date hydraulic structures and stormwater drainage system that can effectively minimize future risk from similar events. In this study, we have updated the intensity-duration-frequency (IDF) curves for Bangladesh using daily precipitation data from 1961 to 2010 and quantified associated uncertainties. Regional frequency analysis based on L-moments is applied on 1-day, 2-day and 5-day annual maximum precipitation series due to its advantages over at-site estimation. The regional frequency approach pools the information from climatologically similar sites to make reliable estimates of quantiles given that the pooling group is homogeneous and of reasonable size. We have used Region of influence (ROI) approach along with homogeneity measure based on L-moments to identify the homogenous pooling groups for each site. Five 3-parameter distributions (i.e., Generalized Logistic, Generalized Extreme value, Generalized Normal, Pearson Type Three, and Generalized Pareto) are used for a thorough selection of appropriate models that fit the sample data. Uncertainties related to the selection of the distributions and historical data are quantified using the Bayesian Model Averaging and Balanced Bootstrap approaches respectively. The results from this study can be used to update the current design and management of hydraulic structures as well as in exploring spatio-temporal variations of extreme precipitation and associated risk.

Analysis of extreme summers and prior late winter/spring conditions in central Europe

NASA Astrophysics Data System (ADS)

Träger-Chatterjee, C.; Müller, R. W.; Bendix, J.

2013-05-01

Drought and heat waves during summer in mid-latitudes are a serious threat to human health and agriculture and have negative impacts on the infrastructure, such as problems in energy supply. The appearance of such extreme events is expected to increase with the progress of global warming. A better understanding of the development of extremely hot and dry summers and the identification of possible precursors could help improve existing seasonal forecasts in this regard, and could possibly lead to the development of early warning methods. The development of extremely hot and dry summer seasons in central Europe is attributed to a combined effect of the dominance of anticyclonic weather regimes and soil moisture-atmosphere interactions. The atmospheric circulation largely determines the amount of solar irradiation and the amount of precipitation in an area. These two variables are themselves major factors controlling the soil moisture. Thus, solar irradiation and precipitation are used as proxies to analyse extreme sunny and dry late winter/spring and summer seasons for the period 1958-2011 in Germany and adjacent areas. For this purpose, solar irradiation data from the European Center for Medium Range Weather Forecast 40-yr and interim re-analysis dataset, as well as remote sensing data are used. Precipitation data are taken from the Global Precipitation Climatology Project. To analyse the atmospheric circulation geopotential data at 850 hPa are also taken from the European Center for Medium Range Weather Forecast 40-yr and interim re-analysis datasets. For the years in which extreme summers in terms of high solar irradiation and low precipitation are identified, the previous late winter/spring conditions of solar irradiation and precipitation in Germany and adjacent areas are analysed. Results show that if the El Niño-Southern Oscillation (ENSO) is not very intensely developed, extremely high solar irradiation amounts, together with extremely low precipitation

Quantile Mapping Bias correction for daily precipitation over Vietnam in a regional climate model

NASA Astrophysics Data System (ADS)

Trinh, L. T.; Matsumoto, J.; Ngo-Duc, T.

2017-12-01

In the past decades, Regional Climate Models (RCMs) have been developed significantly, allowing climate simulation to be conducted at a higher resolution. However, RCMs often contained biases when comparing with observations. Therefore, statistical correction methods were commonly employed to reduce/minimize the model biases. In this study, outputs of the Regional Climate Model (RegCM) version 4.3 driven by the CNRM-CM5 global products were evaluated with and without the Quantile Mapping (QM) bias correction method. The model domain covered the area from 90oE to 145oE and from 15oS to 40oN with a horizontal resolution of 25km. The QM bias correction processes were implemented by using the Vietnam Gridded precipitation dataset (VnGP) and the outputs of RegCM historical run in the period 1986-1995 and then validated for the period 1996-2005. Based on the statistical quantity of spatial correlation and intensity distributions, the QM method showed a significant improvement in rainfall compared to the non-bias correction method. The improvements both in time and space were recognized in all seasons and all climatic sub-regions of Vietnam. Moreover, not only the rainfall amount but also some extreme indices such as R10m, R20mm, R50m, CDD, CWD, R95pTOT, R99pTOT were much better after the correction. The results suggested that the QM correction method should be taken into practice for the projections of the future precipitation over Vietnam.

Seasonal Forecasts of Extreme Conditions for Wildland Fire Management in Alaska using NMME

NASA Astrophysics Data System (ADS)

Bhatt, U. S.; Bieniek, P.; Thoman, R.; York, A.; Ziel, R.

2016-12-01

The summer of 2015 was the second largest Alaska fire season since 1950 where approximately the land area of Massachusetts burned. The record fire year of 2004 resulted in 6.5 million acres burned and was costly from property loss (> 35M) and emergency personnel (> 17M). In addition to requiring significant resources, wildfire smoke impacts air quality in Alaska and downstream into North America. Fires in Alaska result from lightning strikes coupled with persistent (extreme) dry warm conditions in remote areas with limited fire management and the seasonal climate/weather determine the extent of the fire season in Alaska. Fire managers rely on weather/climate outlooks for allocating staff and resources from days to a season in advance. Though currently few tested products are available at the seasonal scale. Probabilistic forecasts of the expected seasonal climate/weather would aid tremendously in the planning process. Advanced knowledge of both lightning and fuel conditions would assist managers in planning resource allocation for the upcoming season. For fuel conditions, the Canadian Forest Fire Weather Index System (CFFWIS) has been used since 1992 because it better suits the Alaska fire regime than the standard US National Fire Danger Rating System (NFDRS). This CFFWIS is based on early afternoon values of 2-m air temperature, relative humidity, and 10-m winds and daily total precipitation. Extremes of these indices and the variables are used to calculate these indices will be defined in reference to fire weather for the boreal forest. The CFFWIS will be applied and evaluated for the NMME hindcasts. This study will evaluate the quality of the forecasts comparing the hindcast NMME CFFWIS to acres burned in Alaska. Spatial synoptic patterns in the NMME related to fire weather extremes will be constructed using self-organized maps and probabilities of occurrence will be evaluated against acres burned.

Molecular-dynamics simulation of Richtmyer-Meshkov instability on a Li-H2 interface at extreme compressing conditions

NASA Astrophysics Data System (ADS)

Huang, Shenghong; Wang, Weirong; Luo, Xisheng

2018-06-01

The new characteristics of Richtmyer-Meshkov instability (RMI) under extreme shock conditions are numerically studied by using molecular dynamics simulation incorporated with the electron force field model. The emphasis is placed on the ionization effects caused by different impacting speeds (6-30 km/s) on the microscale RMI on a Li-H2 interface. The linear region of the amplitude growth rate of the shocked interface under extreme shock conditions is observed to be much longer than that at the ordinary impact, which is in good accord with experimental results obtained with a Nova laser. It is also found that the amplitude of the nonlinear region is larger than the ordinary counterpart or the prediction by theory without considering the ionization effect. The two new characteristics are attributed to the ambipolar acceleration induced by the extra electric field due to the electron/ion separation under extreme shock conditions. These new findings may shed new light on the very complex physical process of the inertial confinement fusion on nanoscales.

[Experimental evaluation of actoprotective activity of nitrogen-containing heterocyclic compounds derivatives in extreme conditions].

PubMed

Tsublova, E G; Ivanova, T G; Ivanova, T N; Iasnetsov, V V

2013-07-01

In experiments on nonlinear male mice the ability of new derivatives of nitrogen-containing heterocyclic compounds to increase the physical working capacity in conditions of hyperthermia, hypothermia and acute normobaric hypoxia and hypercapnia has been investigated. It is established, that pyridine derivative IBHF-11 has more expressed positive action in the said conditions. It provided increase of the working capacity of animals at all kinds of extreme influence, and the value of positive action was comparable, and in conditions of acute normobaric hypoxia and hypercapnia exceeded those at the reference products bemitil and bromantan.

Investigating the Effects of Simulated Space conditions on Novel Extremely Halophilic Archaea: Halovarius Luteus gen. nov., sp. nov.

NASA Astrophysics Data System (ADS)

Feshangsaz, Niloofar; Van Loon, ing.. Jack J. W. A.; Nazmi, Kamran; Semsarha, Farid

2016-07-01

Studying halophiles from different environments of Earth provide new insights into our search for life in the universe. Haloarchaea show some unique characteristics and physiological adaptations like acidic proteins against harsh environments such as natural brine with salt concentration approaching saturation (5 M) and regions with low active water. These properties make haloarchaea interesting candidate for astrobiological studies. Halovarius luteus gen. nov., sp. nov. a novel extremely halophilic archaeon from Urmia salt lake, in Iran has been chosen to explore its resistance against a series of extreme conditions. The aim of this study is to assess the resistance of strain DA50T under the effects of simulated space conditions like simulated microgravity, hypergravity, and desiccation. In this paper we will discuss the results of these studies where we specifically focus on changes in carotenoid pigments production and whole cell proteome. This is the first report of very novel Iranian archaea in response to extreme space conditions. The pigments were extracted by acetone and methanol. Pigments were analyzed by scanning the absorbance spectrum in the UV-VIS spectrophotometer. And they were separated by TLC. Whole protein from cell lysate supernatant was extracted after lysis with Bacterial Protein Extraction Reagent and fractionated by RP-HPLC using C18 column. Proteome analyzed by electrophoresis (SDS-PAGE), and MALDI-TOF. Carotenoid pigments are formed under different extreme conditions such as dry environment and gravitational changes. Also the protein composition exhibits alterations after exposure to the same conditions. Our conclusion is that pigments and proteins formation depend on the growth circumstances. Halophiles use this as an adaptation to survive under different environmental conditions.

Removing Batch Effects from Longitudinal Gene Expression - Quantile Normalization Plus ComBat as Best Approach for Microarray Transcriptome Data

PubMed Central

Müller, Christian; Schillert, Arne; Röthemeier, Caroline; Trégouët, David-Alexandre; Proust, Carole; Binder, Harald; Pfeiffer, Norbert; Beutel, Manfred; Lackner, Karl J.; Schnabel, Renate B.; Tiret, Laurence; Wild, Philipp S.; Blankenberg, Stefan

2016-01-01

Technical variation plays an important role in microarray-based gene expression studies, and batch effects explain a large proportion of this noise. It is therefore mandatory to eliminate technical variation while maintaining biological variability. Several strategies have been proposed for the removal of batch effects, although they have not been evaluated in large-scale longitudinal gene expression data. In this study, we aimed at identifying a suitable method for batch effect removal in a large study of microarray-based longitudinal gene expression. Monocytic gene expression was measured in 1092 participants of the Gutenberg Health Study at baseline and 5-year follow up. Replicates of selected samples were measured at both time points to identify technical variability. Deming regression, Passing-Bablok regression, linear mixed models, non-linear models as well as ReplicateRUV and ComBat were applied to eliminate batch effects between replicates. In a second step, quantile normalization prior to batch effect correction was performed for each method. Technical variation between batches was evaluated by principal component analysis. Associations between body mass index and transcriptomes were calculated before and after batch removal. Results from association analyses were compared to evaluate maintenance of biological variability. Quantile normalization, separately performed in each batch, combined with ComBat successfully reduced batch effects and maintained biological variability. ReplicateRUV performed perfectly in the replicate data subset of the study, but failed when applied to all samples. All other methods did not substantially reduce batch effects in the replicate data subset. Quantile normalization plus ComBat appears to be a valuable approach for batch correction in longitudinal gene expression data. PMID:27272489

Extreme Quantile Estimation in Binary Response Models

DTIC Science & Technology

1990-03-01

in Cancer Research," Biometria , VoL 66, pp. 307-316. Hsi, B.P. [1969], ’The Multiple Sample Up-and-Down Method in Bioassay," Journal of the American...New Method of Estimation," Biometria , VoL 53, pp. 439-454. Wetherill, G.B. [1976], Sequential Methods in Statistics, London: Chapman and Hall. Wu, C.FJ

Extreme Sea Conditions in Shallow Water: Estimation based on in-situ measurements

NASA Astrophysics Data System (ADS)

Le Crom, Izan; Saulnier, Jean-Baptiste

2013-04-01

The design of marine renewable energy devices and components is based, among others, on the assessment of the environmental extreme conditions (winds, currents, waves, and water level) that must be combined together in order to evaluate the maximal loads on a floating/fixed structure, and on the anchoring system over a determined return period. Measuring devices are generally deployed at sea over relatively short durations (a few months to a few years), typically when describing water free surface elevation, and extrapolation methods based on hindcast data (and therefore on wave simulation models) have to be used. How to combine, in a realistic way, the action of the different loads (winds and waves for instance) and which correlation of return periods should be used are highly topical issues. However, the assessment of the extreme condition itself remains a not-fully-solved, crucial, and sensitive task. Above all in shallow water, extreme wave height, Hmax, is the most significant contribution in the dimensioning process of EMR devices. As a case study, existing methodologies for deep water have been applied to SEMREV, the French marine energy test site. The interest of this study, especially at this location, goes beyond the simple application to SEMREV's WEC and floating wind turbines deployment as it could also be extended to the Banc de Guérande offshore wind farm that are planned close by. More generally to pipes and communication cables as it is a redundant problematic. The paper will first present the existing measurements (wave and wind on site), the prediction chain that has been developed via wave models, the extrapolation methods applied to hindcast data, and will try to formulate recommendations for improving this assessment in shallow water.

Streamflow distribution maps for the Cannon River drainage basin, southeast Minnesota, and the St. Louis River drainage basin, northeast Minnesota

USGS Publications Warehouse

Smith, Erik A.; Sanocki, Chris A.; Lorenz, David L.; Jacobsen, Katrin E.

2017-12-27

Streamflow distribution maps for the Cannon River and St. Louis River drainage basins were developed by the U.S. Geological Survey, in cooperation with the Legislative-Citizen Commission on Minnesota Resources, to illustrate relative and cumulative streamflow distributions. The Cannon River was selected to provide baseline data to assess the effects of potential surficial sand mining, and the St. Louis River was selected to determine the effects of ongoing Mesabi Iron Range mining. Each drainage basin (Cannon, St. Louis) was subdivided into nested drainage basins: the Cannon River was subdivided into 152 nested drainage basins, and the St. Louis River was subdivided into 353 nested drainage basins. For each smaller drainage basin, the estimated volumes of groundwater discharge (as base flow) and surface runoff flowing into all surface-water features were displayed under the following conditions: (1) extreme low-flow conditions, comparable to an exceedance-probability quantile of 0.95; (2) low-flow conditions, comparable to an exceedance-probability quantile of 0.90; (3) a median condition, comparable to an exceedance-probability quantile of 0.50; and (4) a high-flow condition, comparable to an exceedance-probability quantile of 0.02.Streamflow distribution maps were developed using flow-duration curve exceedance-probability quantiles in conjunction with Soil-Water-Balance model outputs; both the flow-duration curve and Soil-Water-Balance models were built upon previously published U.S. Geological Survey reports. The selected streamflow distribution maps provide a proactive water management tool for State cooperators by illustrating flow rates during a range of hydraulic conditions. Furthermore, after the nested drainage basins are highlighted in terms of surface-water flows, the streamflows can be evaluated in the context of meeting specific ecological flows under different flow regimes and potentially assist with decisions regarding groundwater and surface

Typical meteorological conditions associated with extreme nitrogen dioxide (NO2) pollution events over Scandinavia

NASA Astrophysics Data System (ADS)

Thomas, Manu Anna; Devasthale, Abhay

2017-10-01

Characterizing typical meteorological conditions associated with extreme pollution events helps to better understand the role of local meteorology in governing the transport and distribution of pollutants in the atmosphere. The knowledge of their co-variability could further help to evaluate and constrain chemistry transport models. Hence, in this study, we investigate the statistical linkages between extreme nitrogen dioxide (NO2) pollution events and meteorology over Scandinavia using observational and reanalysis data. It is observed that the south-westerly winds dominated during extreme events, accounting for 50-65 % of the total events depending on the season, while the second largest annual occurrence was from south-easterly winds, accounting for 17 % of total events. The specific humidity anomalies showed an influx of warmer and moisture-laden air masses over Scandinavia in the free troposphere. Two distinct modes in the persistency of circulation patterns are observed. The first mode lasts for 1-2 days, dominated by south-easterly winds that prevailed during 78 % of total extreme events in that mode, while the second mode lasted for 3-5 days, dominated by south-westerly winds that prevailed during 86 % of the events. The combined analysis of circulation patterns, their persistency, and associated changes in humidity and clouds suggests that NO2 extreme events over Scandinavia occur mainly due to long-range transport from the southern latitudes.

Normal and Extreme Wind Conditions for Power at Coastal Locations in China.

PubMed

Gao, Meng; Ning, Jicai; Wu, Xiaoqing

2015-01-01

In this paper, the normal and extreme wind conditions for power at 12 coastal locations along China's coastline were investigated. For this purpose, the daily meteorological data measured at the standard 10-m height above ground for periods of 40-62 years are statistically analyzed. The East Asian Monsoon that affects almost China's entire coastal region is considered as the leading factor determining wind energy resources. For most stations, the mean wind speed is higher in winter and lower in summer. Meanwhile, the wind direction analysis indicates that the prevalent winds in summer are southerly, while those in winter are northerly. The air densities at different coastal locations differ significantly, resulting in the difference in wind power density. The Weibull and lognormal distributions are applied to fit the yearly wind speeds. The lognormal distribution performs better than the Weibull distribution at 8 coastal stations according to two judgement criteria, the Kolmogorov-Smirnov test and absolute error (AE). Regarding the annual maximum extreme wind speed, the generalized extreme value (GEV) distribution performs better than the commonly-used Gumbel distribution. At these southeastern coastal locations, strong winds usually occur in typhoon season. These 4 coastal provinces, that is, Guangdong, Fujian, Hainan, and Zhejiang, which have abundant wind resources, are also prone to typhoon disasters.

Variability of temperature sensitivity of extreme precipitation from a regional-to-local impact scale perspective

NASA Astrophysics Data System (ADS)

Schroeer, K.; Kirchengast, G.

2016-12-01

Relating precipitation intensity to temperature is a popular approach to assess potential changes of extreme events in a warming climate. Potential increases in extreme rainfall induced hazards, such as flash flooding, serve as motivation. It has not been addressed whether the temperature-precipitation scaling approach is meaningful on a regional to local level, where the risk of climate and weather impact is dealt with. Substantial variability of temperature sensitivity of extreme precipitation has been found that results from differing methodological assumptions as well as from varying climatological settings of the study domains. Two aspects are consistently found: First, temperature sensitivities beyond the expected consistency with the Clausius-Clapeyron (CC) equation are a feature of short-duration, convective, sub-daily to sub-hourly high-percentile rainfall intensities at mid-latitudes. Second, exponential growth ceases or reverts at threshold temperatures that vary from region to region, as moisture supply becomes limited. Analyses of pooled data, or of single or dispersed stations over large areas make it difficult to estimate the consequences in terms of local climate risk. In this study we test the meaningfulness of the scaling approach from an impact scale perspective. Temperature sensitivities are assessed using quantile regression on hourly and sub-hourly precipitation data from 189 stations in the Austrian south-eastern Alpine region. The observed scaling rates vary substantially, but distinct regional and seasonal patterns emerge. High sensitivity exceeding CC-scaling is seen on the 10-minute scale more than on the hourly scale, in storms shorter than 2 hours duration, and in shoulder seasons, but it is not necessarily a significant feature of the extremes. To be impact relevant, change rates need to be linked to absolute rainfall amounts. We show that high scaling rates occur in lower temperature conditions and thus have smaller effect on absolute

Modeling soil heating and moisture transport under extreme conditions: Forest fires and slash pile burns

NASA Astrophysics Data System (ADS)

Massman, W. J.

2012-10-01

Heating any soil during a sufficiently intense wildfire or prescribed burn can alter it irreversibly, causing many significant, long-term biological, chemical, and hydrological effects. Given the climate-change-driven increasing probability of wildfires and the increasing use of prescribed burns by land managers, it is important to better understand the dynamics of the coupled heat and moisture transport in soil during these extreme heating events. Furthermore, improved understanding and modeling of heat and mass transport during extreme conditions should provide insights into the associated transport mechanisms under more normal conditions. The present study describes a numerical model developed to simulate soil heat and moisture transport during fires where the surface heating often ranges between 10,000 and 100,000 W m-2 for several minutes to several hours. Basically, the model extends methods commonly used to model coupled heat flow and moisture evaporation at ambient conditions into regions of extreme dryness and heat. But it also incorporates some infrequently used formulations for temperature dependencies of the soil specific heat, thermal conductivity, and the water retention curve, as well as advective effects due to the large changes in volume that occur when liquid water is rapidly volatilized. Model performance is tested against laboratory measurements of soil temperature and moisture changes at several depths during controlled heating events. Qualitatively, the model agrees with the laboratory observations, namely, it simulates an increase in soil moisture ahead of the drying front (due to the condensation of evaporated soil water at the front) and a hiatus in the soil temperature rise during the strongly evaporative stage of the soil drying. Nevertheless, it is shown that the model is incapable of producing a physically realistic solution because it does not (and, in fact, cannot) represent the relationship between soil water potential and soil

A Quantile Regression Approach to Understanding the Relations among Morphological Awareness, Vocabulary, and Reading Comprehension in Adult Basic Education Students

ERIC Educational Resources Information Center

Tighe, Elizabeth L.; Schatschneider, Christopher

2016-01-01

The purpose of this study was to investigate the joint and unique contributions of morphological awareness and vocabulary knowledge at five reading comprehension levels in adult basic education (ABE) students. We introduce the statistical technique of multiple quantile regression, which enabled us to assess the predictive utility of morphological…

Pi2 Pulsations During Extremely Quiet Geomagnetic Condition: Van Allen Probe Observations

NASA Astrophysics Data System (ADS)

Ghamry, Essam

2017-06-01

A ultra low frequency (ULF) wave, Pi2, has been reported to occur during periods of extremely quiet magnetospheric and solar wind conditions. And no statistical study on the Pi2 has been performed during extremely quiet conditions, using satellite observations to the author’s knowledge. Also Pi2 pulsations in the space fluxgate magnetometers near perigee failed to attract scientist’s attention previously. In this paper, Pi2 pulsations detected by the Van Allen probe satellites (VAP-A & VAP-B) were investigated statistically. During the period from October 2012 to December 2014, ninety six Pi2 events were identified using VAP when Kp = 0 while using Kakioka (KAK, L = 1.23) as a reference ground station. Seventy five events had high coherence between VAP-Bz and H components at KAK station. As a result, it was found that 77 % of the events had power spectra between 5 and 12 mHz, which differs from the regular Pi2 band range of from 6.7 to 25 mHz. In addition, it was shown that it is possible to observe Pi2 pulsations from space fluxgate magnetometers near perigee. Twenty two clean Pi2 pulsations were found where L < 4 and four examples of Pi2 oscillations at different L shells are presented in this paper.

Straw Mulching Reduces the Harmful Effects of Extreme Hydrological and Temperature Conditions in Citrus Orchards

PubMed Central

Liu, Yi; Wang, Jing; Liu, Dongbi; Li, Zhiguo; Zhang, Guoshi; Tao, Yong; Xie, Juan; Pan, Junfeng; Chen, Fang

2014-01-01

Extreme weather conditions with negative impacts can strongly affect agricultural production. In the Danjiangkou reservoir area, citrus yields were greatly influenced by cold weather conditions and drought stress in 2011. Soil straw mulching (SM) practices have a major effect on soil water and thermal regimes. A two-year field experiment was conducted to evaluate whether the SM practices can help achieve favorable citrus fruit yields. Results showed that the annual total runoff was significantly (P<0.05) reduced with SM as compared to the control (CK). Correspondingly, mean soil water storage in the top 100 cm of the soil profile was increased in the SM as compared to the CK treatment. However, this result was significant only in the dry season (Jan to Mar), and not in the wet season (Jul to Sep) for both years. Interestingly, the SM treatment did not significantly increase citrus fruit yield in 2010 but did so in 2011, when the citrus crop was completely destroyed (zero fruit yield) in the CK treatment plot due to extremely low temperatures during the citrus overwintering stage. The mulch probably acted as an insulator, resulting in smaller fluctuations in soil temperature in the SM than in the CK treatment. The results suggested that the small effects on soil water and temperature changes created by surface mulch had limited impact on citrus fruit yield in a normal year (e.g., in 2010). However, SM practices can positively impact citrus fruit yield in extreme weather conditions. PMID:24489844

Finite-sample and asymptotic sign-based tests for parameters of non-linear quantile regression with Markov noise

NASA Astrophysics Data System (ADS)

Sirenko, M. A.; Tarasenko, P. F.; Pushkarev, M. I.

2017-01-01

One of the most noticeable features of sign-based statistical procedures is an opportunity to build an exact test for simple hypothesis testing of parameters in a regression model. In this article, we expanded a sing-based approach to the nonlinear case with dependent noise. The examined model is a multi-quantile regression, which makes it possible to test hypothesis not only of regression parameters, but of noise parameters as well.

Quantile regression analysis of body mass and wages.

PubMed

Johar, Meliyanni; Katayama, Hajime

2012-05-01

Using the National Longitudinal Survey of Youth 1979, we explore the relationship between body mass and wages. We use quantile regression to provide a broad description of the relationship across the wage distribution. We also allow the relationship to vary by the degree of social skills involved in different jobs. Our results find that for female workers body mass and wages are negatively correlated at all points in their wage distribution. The strength of the relationship is larger at higher-wage levels. For male workers, the relationship is relatively constant across wage distribution but heterogeneous across ethnic groups. When controlling for the endogeneity of body mass, we find that additional body mass has a negative causal impact on the wages of white females earning more than the median wages and of white males around the median wages. Among these workers, the wage penalties are larger for those employed in jobs that require extensive social skills. These findings may suggest that labor markets reward white workers for good physical shape differently, depending on the level of wages and the type of job a worker has. Copyright © 2011 John Wiley & Sons, Ltd.

Porous materials for thermal management under extreme conditions.

PubMed

Clyne, T W; Golosnoy, I O; Tan, J C; Markaki, A E

2006-01-15

A brief analysis is presented of how heat transfer takes place in porous materials of various types. The emphasis is on materials able to withstand extremes of temperature, gas pressure, irradiation, etc. i.e. metals and ceramics, rather than polymers. A primary aim is commonly to maximize either the thermal resistance (i.e. provide insulation) or the rate of thermal equilibration between the material and a fluid passing through it (i.e. to facilitate heat exchange). The main structural characteristics concern porosity (void content), anisotropy, pore connectivity and scale. The effect of scale is complex, since the permeability decreases as the structure is refined, but the interfacial area for fluid-solid heat exchange is, thereby, raised. The durability of the pore structure may also be an issue, with a possible disadvantage of finer scale structures being poor microstructural stability under service conditions. Finally, good mechanical properties may be required, since the development of thermal gradients, high fluid fluxes, etc. can generate substantial levels of stress. There are, thus, some complex interplays between service conditions, pore architecture/scale, fluid permeation characteristics, convective heat flow, thermal conduction and radiative heat transfer. Such interplays are illustrated with reference to three examples: (i) a thermal barrier coating in a gas turbine engine; (ii) a Space Shuttle tile; and (iii) a Stirling engine heat exchanger. Highly porous, permeable materials are often made by bonding fibres together into a network structure and much of the analysis presented here is oriented towards such materials.

Matter Under Extreme Conditions: The Early Years

NASA Astrophysics Data System (ADS)

Keeler, R. Norris; Gibson, Carl H.

2012-03-01

Extreme conditions in natural flows are examined, starting with a turbulent big bang. A hydro-gravitational-dynamics cosmology model is adopted. Planck-Kerr turbulence instability causes Planck-particle turbulent combustion. Inertial-vortex forces induce a non-turbulent ki- netic energy cascade to Planck-Kolmogorov scales where vorticity is produced, overcoming 10113 Pa Planck-Fortov pressures. The spinning, expanding fireball has a slight deficit of Planck antiparticles. Space and mass-energy powered by gluon viscous stresses expand exponentially at speeds >1025 c. Turbulent temperature and spin fluctuations fossilize at scales larger than ct, where c is light speed and t is time. Because "dark-energy" antigravity forces vanish when infla- tion ceases, and because turbulence produces entropy, the universe is closed and will collapse and rebound. Density and spin fossils of big bang turbulent mixing trigger structure formation in the plasma epoch. Fragmenting protosuperclustervoids and protoclustervoids produce weak tur- bulence until the plasma-gas transition give chains of protogalaxies with the morphology of tur- bulence. Chain galaxy clusters observed at large redshifts ~8.6 support this interpretation. Pro- togalaxies fragment into clumps, each with a trillion Earth-mass H-He gas planets. These make stars, supernovae, the first chemicals, the first oceans and the first life soon after the cosmologi- cal event.

Extraordinary survival of nanobacteria under extreme conditions

NASA Astrophysics Data System (ADS)

Bjorklund, Michael; Ciftcioglu, Neva; Kajander, E. Olavi

1998-07-01

Nanobacteria show high resistance to gamma irradiation. To further examine their survival in extreme conditions several disinfecting and sterilizing chemicals as well as autoclaving, UV light, microwaves, heating and drying treatments were carried out. The effect of antibiotics used in cell culture were also evaluated. Two forms of nanobacteria were used in the tests: nanobacteria cultured in serum containing medium, and nanobacteria cultured in serum-free medium, the latter being more mineralized. Nanobacteria, having various amounts of apatite on their surfaces, were used to analyze the degree of protection given by the mineral. The chemicals tested included ethanol, glutaraldehyde, formalin, hypochlorite, hydrogen peroxide, hydrochloric acid, sodium hydroxide, detergents, and commercial disinfectants at concentrations generally used for disinfection. After chemical and physical treatments for various times, the nanobacteria were subcultered to detect their survival. The results show unique and wide resistance of nanobacteria to common agents used in disinfection. It can also be seen that the mineralization of the nanobacterial surface furthermore increases the resistance. Survival of nanobacteria is unique among living bacteria, but it can be compared with that observed in spores. Interestingly, nanobacteria have metabolic rate as slow as bacterial spores. A slow metabolic rate and protective structures, like mineral, biofilm and impermeable cell wall, can thus explain the observations made.

Normal and Extreme Wind Conditions for Power at Coastal Locations in China

PubMed Central

Gao, Meng; Ning, Jicai; Wu, Xiaoqing

2015-01-01

In this paper, the normal and extreme wind conditions for power at 12 coastal locations along China’s coastline were investigated. For this purpose, the daily meteorological data measured at the standard 10-m height above ground for periods of 40–62 years are statistically analyzed. The East Asian Monsoon that affects almost China’s entire coastal region is considered as the leading factor determining wind energy resources. For most stations, the mean wind speed is higher in winter and lower in summer. Meanwhile, the wind direction analysis indicates that the prevalent winds in summer are southerly, while those in winter are northerly. The air densities at different coastal locations differ significantly, resulting in the difference in wind power density. The Weibull and lognormal distributions are applied to fit the yearly wind speeds. The lognormal distribution performs better than the Weibull distribution at 8 coastal stations according to two judgement criteria, the Kolmogorov–Smirnov test and absolute error (AE). Regarding the annual maximum extreme wind speed, the generalized extreme value (GEV) distribution performs better than the commonly-used Gumbel distribution. At these southeastern coastal locations, strong winds usually occur in typhoon season. These 4 coastal provinces, that is, Guangdong, Fujian, Hainan, and Zhejiang, which have abundant wind resources, are also prone to typhoon disasters. PMID:26313256

Microarray image analysis: background estimation using quantile and morphological filters.

PubMed

Bengtsson, Anders; Bengtsson, Henrik

2006-02-28

In a microarray experiment the difference in expression between genes on the same slide is up to 103 fold or more. At low expression, even a small error in the estimate will have great influence on the final test and reference ratios. In addition to the true spot intensity the scanned signal consists of different kinds of noise referred to as background. In order to assess the true spot intensity background must be subtracted. The standard approach to estimate background intensities is to assume they are equal to the intensity levels between spots. In the literature, morphological opening is suggested to be one of the best methods for estimating background this way. This paper examines fundamental properties of rank and quantile filters, which include morphological filters at the extremes, with focus on their ability to estimate between-spot intensity levels. The bias and variance of these filter estimates are driven by the number of background pixels used and their distributions. A new rank-filter algorithm is implemented and compared to methods available in Spot by CSIRO and GenePix Pro by Axon Instruments. Spot's morphological opening has a mean bias between -47 and -248 compared to a bias between 2 and -2 for the rank filter and the variability of the morphological opening estimate is 3 times higher than for the rank filter. The mean bias of Spot's second method, morph.close.open, is between -5 and -16 and the variability is approximately the same as for morphological opening. The variability of GenePix Pro's region-based estimate is more than ten times higher than the variability of the rank-filter estimate and with slightly more bias. The large variability is because the size of the background window changes with spot size. To overcome this, a non-adaptive region-based method is implemented. Its bias and variability are comparable to that of the rank filter. The performance of more advanced rank filters is equal to the best region-based methods. However, in

Extreme summer temperatures in Iberia: health impacts and associated synoptic conditions

NASA Astrophysics Data System (ADS)

García-Herrera, R.; Díaz, J.; Trigo, R. M.; Hernández, E.

2005-02-01

This paper examines the effect of extreme summer temperatures on daily mortality in two large cities of Iberia: Lisbon (Portugal) and Madrid (Spain). Daily mortality and meteorological variables are analysed using the same methodology based on Box-Jenkins models. Results reveal that in both cases there is a triggering effect on mortality when maximum daily temperature exceeds a given threshold (34°C in Lisbon and 36°C in Madrid). The impact of most intense heat events is very similar for both cities, with significant mortality values occurring up to 3 days after the temperature threshold has been surpassed. This impact is measured as the percentual increase of mortality associated to a 1°C increase above the threshold temperature. In this respect, Lisbon shows a higher impact, 31%, as compared with Madrid at 21%. The difference can be attributed to demographic and socio-economic factors. Furthermore, the longer life span of Iberian women is critical to explain why, in both cities, females are more susceptible than males to heat effects, with an almost double mortality impact value. The analysis of Sea Level Pressure (SLP), 500hPa geopotential height and temperature fields reveals that, despite being relatively close to each other, Lisbon and Madrid have relatively different synoptic circulation anomalies associated with their respective extreme summer temperature days. The SLP field reveals higher anomalies for Lisbon, but extending over a smaller area. Extreme values in Madrid seem to require a more western location of the Azores High, embracing a greater area over Europe, even if it is not as deep as for Lisbon. The origin of the hot and dry air masses that usually lead to extreme heat days in both cities is located in Northern Africa. However, while Madrid maxima require wind blowing directly from the south, transporting heat from Southern Spain and Northern Africa, Lisbon maxima occur under more easterly conditions, when Northern African air flows over the

Predicting birth weight with conditionally linear transformation models.

PubMed

Möst, Lisa; Schmid, Matthias; Faschingbauer, Florian; Hothorn, Torsten

2016-12-01

Low and high birth weight (BW) are important risk factors for neonatal morbidity and mortality. Gynecologists must therefore accurately predict BW before delivery. Most prediction formulas for BW are based on prenatal ultrasound measurements carried out within one week prior to birth. Although successfully used in clinical practice, these formulas focus on point predictions of BW but do not systematically quantify uncertainty of the predictions, i.e. they result in estimates of the conditional mean of BW but do not deliver prediction intervals. To overcome this problem, we introduce conditionally linear transformation models (CLTMs) to predict BW. Instead of focusing only on the conditional mean, CLTMs model the whole conditional distribution function of BW given prenatal ultrasound parameters. Consequently, the CLTM approach delivers both point predictions of BW and fetus-specific prediction intervals. Prediction intervals constitute an easy-to-interpret measure of prediction accuracy and allow identification of fetuses subject to high prediction uncertainty. Using a data set of 8712 deliveries at the Perinatal Centre at the University Clinic Erlangen (Germany), we analyzed variants of CLTMs and compared them to standard linear regression estimation techniques used in the past and to quantile regression approaches. The best-performing CLTM variant was competitive with quantile regression and linear regression approaches in terms of conditional coverage and average length of the prediction intervals. We propose that CLTMs be used because they are able to account for possible heteroscedasticity, kurtosis, and skewness of the distribution of BWs. © The Author(s) 2014.

Assessing the impact of local meteorological variables on surface ozone in Hong Kong during 2000-2015 using quantile and multiple line regression models

NASA Astrophysics Data System (ADS)

Zhao, Wei; Fan, Shaojia; Guo, Hai; Gao, Bo; Sun, Jiaren; Chen, Laiguo

2016-11-01

The quantile regression (QR) method has been increasingly introduced to atmospheric environmental studies to explore the non-linear relationship between local meteorological conditions and ozone mixing ratios. In this study, we applied QR for the first time, together with multiple linear regression (MLR), to analyze the dominant meteorological parameters influencing the mean, 10th percentile, 90th percentile and 99th percentile of maximum daily 8-h average (MDA8) ozone concentrations in 2000-2015 in Hong Kong. The dominance analysis (DA) was used to assess the relative importance of meteorological variables in the regression models. Results showed that the MLR models worked better at suburban and rural sites than at urban sites, and worked better in winter than in summer. QR models performed better in summer for 99th and 90th percentiles and performed better in autumn and winter for 10th percentile. And QR models also performed better in suburban and rural areas for 10th percentile. The top 3 dominant variables associated with MDA8 ozone concentrations, changing with seasons and regions, were frequently associated with the six meteorological parameters: boundary layer height, humidity, wind direction, surface solar radiation, total cloud cover and sea level pressure. Temperature rarely became a significant variable in any season, which could partly explain the peak of monthly average ozone concentrations in October in Hong Kong. And we found the effect of solar radiation would be enhanced during extremely ozone pollution episodes (i.e., the 99th percentile). Finally, meteorological effects on MDA8 ozone had no significant changes before and after the 2010 Asian Games.

Analyzing phenological extreme events over the past five decades in Germany

NASA Astrophysics Data System (ADS)

Schleip, Christoph; Menzel, Annette; Estrella, Nicole; Graeser, Philipp

2010-05-01

As climate change may alter the frequency and intensity of extreme temperatures, we analysed whether warming of the last 5 decades has already changed the statistics of phenological extreme events. In this context, two extreme value statistical concepts are discussed and applied to existing phenological datasets of German Weather Service (DWD) in order to derive probabilities of occurrence for extreme early or late phenological events. We analyse four phenological groups; "begin of flowering, "leaf foliation", "fruit ripening" and "leaf colouring" as well as DWD indicator phases of the "phenological year". Additionally we put an emphasis on a between-species analysis; a comparison of differences in extreme onsets between three common northern conifers. Furthermore we conducted a within-species analysis with different phases of horse chestnut throughout a year. The first statistical approach fits data to a Gaussian model using traditional statistical techniques, and then analyses the extreme quantile. The key point of this approach is the adoption of an appropriate probability density function (PDF) to the observed data and the assessment of the PDF parameters change in time. The full analytical description in terms of the estimated PDF for defined time steps of the observation period allows probability assessments of extreme values for e.g. annual or decadal time steps. Related with this approach is the possibility of counting out the onsets which fall in our defined extreme percentiles. The estimation of the probability of extreme events on the basis of the whole data set is in contrast to analyses with the generalized extreme value distribution (GEV). The second approach deals with the extreme PDFs itself and fits the GEV distribution to annual minima of phenological series to provide useful estimates about return levels. For flowering and leaf unfolding phases exceptionally early extremes are seen since the mid 1980s and especially for the single years 1961

Towards validated chemistry at extreme conditions: reactive MD simulations of shocked Polyvinyl Nitrate and Nitromethane

NASA Astrophysics Data System (ADS)

Islam, Md Mahbubul; Strachan, Alejandro

A detailed atomistic-level understanding of the ultrafast chemistry of detonation processes of high energy materials is crucial to understand their performance and safety. Recent advances in laser shocks and ultra-fast spectroscopy is yielding the first direct experimental evidence of chemistry at extreme conditions. At the same time, reactive molecular dynamics (MD) in current high-performance computing platforms enable an atomic description of shock-induced chemistry with length and timescales approaching those of experiments. We use MD simulations with the reactive force field ReaxFF to investigate the shock-induced chemical decomposition mechanisms of polyvinyl nitrate (PVN) and nitromethane (NM). The effect of shock pressure on chemical reaction mechanisms and kinetics of both the materials are investigated. For direct comparison of our simulation results with experimentally derived IR absorption data, we performed spectral analysis using atomistic velocity at various shock conditions. The combination of reactive MD simulations and ultrafast spectroscopy enables both the validation of ReaxFF at extreme conditions and contributes to the interpretation of the experimental data relating changes in spectral features to atomic processes. Office of Naval Research MURI program.

Concrete Condition Assessment Using Impact-Echo Method and Extreme Learning Machines

PubMed Central

Zhang, Jing-Kui; Yan, Weizhong; Cui, De-Mi

2016-01-01

The impact-echo (IE) method is a popular non-destructive testing (NDT) technique widely used for measuring the thickness of plate-like structures and for detecting certain defects inside concrete elements or structures. However, the IE method is not effective for full condition assessment (i.e., defect detection, defect diagnosis, defect sizing and location), because the simple frequency spectrum analysis involved in the existing IE method is not sufficient to capture the IE signal patterns associated with different conditions. In this paper, we attempt to enhance the IE technique and enable it for full condition assessment of concrete elements by introducing advanced machine learning techniques for performing comprehensive analysis and pattern recognition of IE signals. Specifically, we use wavelet decomposition for extracting signatures or features out of the raw IE signals and apply extreme learning machine, one of the recently developed machine learning techniques, as classification models for full condition assessment. To validate the capabilities of the proposed method, we build a number of specimens with various types, sizes, and locations of defects and perform IE testing on these specimens in a lab environment. Based on analysis of the collected IE signals using the proposed machine learning based IE method, we demonstrate that the proposed method is effective in performing full condition assessment of concrete elements or structures. PMID:27023563

Application of empirical mode decomposition with local linear quantile regression in financial time series forecasting.

PubMed

Jaber, Abobaker M; Ismail, Mohd Tahir; Altaher, Alsaidi M

2014-01-01

This paper mainly forecasts the daily closing price of stock markets. We propose a two-stage technique that combines the empirical mode decomposition (EMD) with nonparametric methods of local linear quantile (LLQ). We use the proposed technique, EMD-LLQ, to forecast two stock index time series. Detailed experiments are implemented for the proposed method, in which EMD-LPQ, EMD, and Holt-Winter methods are compared. The proposed EMD-LPQ model is determined to be superior to the EMD and Holt-Winter methods in predicting the stock closing prices.

Log Pearson type 3 quantile estimators with regional skew information and low outlier adjustments

USGS Publications Warehouse

Griffis, V.W.; Stedinger, Jery R.; Cohn, T.A.

2004-01-01

The recently developed expected moments algorithm (EMA) [Cohn et al., 1997] does as well as maximum likelihood estimations at estimating log‐Pearson type 3 (LP3) flood quantiles using systematic and historical flood information. Needed extensions include use of a regional skewness estimator and its precision to be consistent with Bulletin 17B. Another issue addressed by Bulletin 17B is the treatment of low outliers. A Monte Carlo study compares the performance of Bulletin 17B using the entire sample with and without regional skew with estimators that use regional skew and censor low outliers, including an extended EMA estimator, the conditional probability adjustment (CPA) from Bulletin 17B, and an estimator that uses probability plot regression (PPR) to compute substitute values for low outliers. Estimators that neglect regional skew information do much worse than estimators that use an informative regional skewness estimator. For LP3 data the low outlier rejection procedure generally results in no loss of overall accuracy, and the differences between the MSEs of the estimators that used an informative regional skew are generally modest in the skewness range of real interest. Samples contaminated to model actual flood data demonstrate that estimators which give special treatment to low outliers significantly outperform estimators that make no such adjustment.

Log Pearson type 3 quantile estimators with regional skew information and low outlier adjustments

NASA Astrophysics Data System (ADS)

Griffis, V. W.; Stedinger, J. R.; Cohn, T. A.

2004-07-01

The recently developed expected moments algorithm (EMA) [, 1997] does as well as maximum likelihood estimations at estimating log-Pearson type 3 (LP3) flood quantiles using systematic and historical flood information. Needed extensions include use of a regional skewness estimator and its precision to be consistent with Bulletin 17B. Another issue addressed by Bulletin 17B is the treatment of low outliers. A Monte Carlo study compares the performance of Bulletin 17B using the entire sample with and without regional skew with estimators that use regional skew and censor low outliers, including an extended EMA estimator, the conditional probability adjustment (CPA) from Bulletin 17B, and an estimator that uses probability plot regression (PPR) to compute substitute values for low outliers. Estimators that neglect regional skew information do much worse than estimators that use an informative regional skewness estimator. For LP3 data the low outlier rejection procedure generally results in no loss of overall accuracy, and the differences between the MSEs of the estimators that used an informative regional skew are generally modest in the skewness range of real interest. Samples contaminated to model actual flood data demonstrate that estimators which give special treatment to low outliers significantly outperform estimators that make no such adjustment.

Use of Quantile Regression to Determine the Impact on Total Health Care Costs of Surgical Site Infections Following Common Ambulatory Procedures

PubMed Central

Olsen, Margaret A.; Tian, Fang; Wallace, Anna E.; Nickel, Katelin B.; Warren, David K.; Fraser, Victoria J.; Selvam, Nandini; Hamilton, Barton H.

2017-01-01

Objective To determine the impact of surgical site infections (SSIs) on healthcare costs following common ambulatory surgical procedures throughout the cost distribution. Background Data on costs of SSIs following ambulatory surgery are sparse, particularly variation beyond just mean costs. Methods We performed a retrospective cohort study of persons undergoing cholecystectomy, breast-conserving surgery (BCS), anterior cruciate ligament reconstruction (ACL), and hernia repair from 12/31/2004–12/31/2010 using commercial insurer claims data. SSIs within 90 days post-procedure were identified; infections during a hospitalization or requiring surgery were considered serious. We used quantile regression, controlling for patient, operative, and postoperative factors to examine the impact of SSIs on 180-day healthcare costs throughout the cost distribution. Results The incidence of serious and non-serious SSIs were 0.8% and 0.2% after 21,062 ACL, 0.5% and 0.3% after 57,750 cholecystectomy, 0.6% and 0.5% after 60,681 hernia, and 0.8% and 0.8% after 42,489 BCS procedures. Serious SSIs were associated with significantly higher costs than non-serious SSIs for all 4 procedures throughout the cost distribution. The attributable cost of serious SSIs increased for both cholecystectomy and hernia repair as the quantile of total costs increased ($38,410 for cholecystectomy with serious SSI vs. no SSI at the 70th percentile of costs, up to $89,371 at the 90th percentile). Conclusions SSIs, particularly serious infections resulting in hospitalization or surgical treatment, were associated with significantly increased healthcare costs after 4 common surgical procedures. Quantile regression illustrated the differential effect of serious SSIs on healthcare costs at the upper end of the cost distribution. PMID:28059961

Use of Quantile Regression to Determine the Impact on Total Health Care Costs of Surgical Site Infections Following Common Ambulatory Procedures.

PubMed

Olsen, Margaret A; Tian, Fang; Wallace, Anna E; Nickel, Katelin B; Warren, David K; Fraser, Victoria J; Selvam, Nandini; Hamilton, Barton H

2017-02-01

To determine the impact of surgical site infections (SSIs) on health care costs following common ambulatory surgical procedures throughout the cost distribution. Data on costs of SSIs following ambulatory surgery are sparse, particularly variation beyond just mean costs. We performed a retrospective cohort study of persons undergoing cholecystectomy, breast-conserving surgery, anterior cruciate ligament reconstruction, and hernia repair from December 31, 2004 to December 31, 2010 using commercial insurer claims data. SSIs within 90 days post-procedure were identified; infections during a hospitalization or requiring surgery were considered serious. We used quantile regression, controlling for patient, operative, and postoperative factors to examine the impact of SSIs on 180-day health care costs throughout the cost distribution. The incidence of serious and nonserious SSIs was 0.8% and 0.2%, respectively, after 21,062 anterior cruciate ligament reconstruction, 0.5% and 0.3% after 57,750 cholecystectomy, 0.6% and 0.5% after 60,681 hernia, and 0.8% and 0.8% after 42,489 breast-conserving surgery procedures. Serious SSIs were associated with significantly higher costs than nonserious SSIs for all 4 procedures throughout the cost distribution. The attributable cost of serious SSIs increased for both cholecystectomy and hernia repair as the quantile of total costs increased ($38,410 for cholecystectomy with serious SSI vs no SSI at the 70th percentile of costs, up to $89,371 at the 90th percentile). SSIs, particularly serious infections resulting in hospitalization or surgical treatment, were associated with significantly increased health care costs after 4 common surgical procedures. Quantile regression illustrated the differential effect of serious SSIs on health care costs at the upper end of the cost distribution.

Multiple extreme environmental conditions of intermittent soda pans in the Carpathian Basin (Central Europe).

PubMed

Boros, Emil; Katalin, V-Balogh; Vörös, Lajos; Horváth, Zsófia

2017-01-01

Soda lakes and pans represent saline ecosystems with unique chemical composition, occurring on all continents. The purpose of this study was to identify and characterise the main environmental gradients and trophic state that prevail in the soda pans (n=84) of the Carpathian Basin in Central Europe. Underwater light conditions, dissolved organic matter, phosphorus and chlorophyll a were investigated in 84 pans during 2009-2010. Besides, water temperature was measured hourly with an automatic sensor throughout one year in a selected pan. The pans were very shallow (median depth: 15 cm), and their extremely high turbidity (Secchi depth median: 3 cm, min: 0.5 cm) was caused by high concentrations of inorganic suspended solids (median: 0.4 g L -1 , max: 16 g L -1 ), which was the dominant (>50%) contributing factor to the vertical attenuation coefficient in 67 pans (80%). All pans were polyhumic (median DOC: 47 mg L -1 ), and total phosphorus concentration was also extremely high (median: 2 mg L -1 , max: 32 mg L -1 ). The daily water temperature maximum (44 °C) and fluctuation maximum (28 °C) were extremely high during summertime. The combination of environmental boundaries: shallowness, daily water temperature fluctuation, intermittent hydroperiod, high turbidity, polyhumic organic carbon concentration, high alkalinity and hypertrophy represent a unique extreme aquatic ecosystem.

Multiple extreme environmental conditions of intermittent soda pans in the Carpathian Basin (Central Europe)

PubMed Central

Boros, Emil; Katalin, V.-Balogh; Vörös, Lajos; Horváth, Zsófia

2017-01-01

Soda lakes and pans represent saline ecosystems with unique chemical composition, occurring on all continents. The purpose of this study was to identify and characterise the main environmental gradients and trophic state that prevail in the soda pans (n=84) of the Carpathian Basin in Central Europe. Underwater light conditions, dissolved organic matter, phosphorus and chlorophyll a were investigated in 84 pans during 2009–2010. Besides, water temperature was measured hourly with an automatic sensor throughout one year in a selected pan. The pans were very shallow (median depth: 15 cm), and their extremely high turbidity (Secchi depth median: 3 cm, min: 0.5 cm) was caused by high concentrations of inorganic suspended solids (median: 0.4 g L–1, max: 16 g L–1), which was the dominant (>50%) contributing factor to the vertical attenuation coefficient in 67 pans (80%). All pans were polyhumic (median DOC: 47 mg L–1), and total phosphorus concentration was also extremely high (median: 2 mg L–1, max: 32 mg L–1). The daily water temperature maximum (44 °C) and fluctuation maximum (28 °C) were extremely high during summertime. The combination of environmental boundaries: shallowness, daily water temperature fluctuation, intermittent hydroperiod, high turbidity, polyhumic organic carbon concentration, high alkalinity and hypertrophy represent a unique extreme aquatic ecosystem. PMID:28572691

Nonempirical Semilocal Free-Energy Density Functional for Matter under Extreme Conditions.

PubMed

Karasiev, Valentin V; Dufty, James W; Trickey, S B

2018-02-16

Realizing the potential for predictive density functional calculations of matter under extreme conditions depends crucially upon having an exchange-correlation (XC) free-energy functional accurate over a wide range of state conditions. Unlike the ground-state case, no such functional exists. We remedy that with systematic construction of a generalized gradient approximation XC free-energy functional based on rigorous constraints, including the free-energy gradient expansion. The new functional provides the correct temperature dependence in the slowly varying regime and the correct zero-T, high-T, and homogeneous electron gas limits. Its accuracy in the warm dense matter regime is attested by excellent agreement of the calculated deuterium equation of state with reference path integral Monte Carlo results at intermediate and elevated T. Pressure shifts for hot electrons in compressed static fcc Al and for low-density Al demonstrate the combined magnitude of thermal and gradient effects handled well by this functional over a wide T range.

Extreme Wave-Induced Oscillation in Paradip Port Under the Resonance Conditions

NASA Astrophysics Data System (ADS)

Kumar, Prashant; Gulshan

2017-12-01

A mathematical model is constructed to analyze the long wave-induced oscillation in Paradip Port, Odisha, India under the resonance conditions to avert any extreme wave hazards. Boundary element method (BEM) with corner contribution is utilized to solve the Helmholtz equation under the partial reflection boundary conditions. Furthermore, convergence analysis is also performed for the boundary element scheme with uniform and non-uniform discretization of the boundary. The numerical scheme is also validated with analytic approximation and existing studies based on harbor resonance. Then, the amplification factor is estimated at six key record stations in the Paradip Port with multidirectional incident waves and resonance modes are also estimated at the boundary of the port. Ocean surface wave field is predicted in the interior of Paradip Port for the different directional incident wave at various resonance modes. Moreover, the safe locations in the port have been identified for loading and unloading of moored ship with different resonance modes and directional incident waves.

Nonempirical Semilocal Free-Energy Density Functional for Matter under Extreme Conditions

NASA Astrophysics Data System (ADS)

Karasiev, Valentin V.; Dufty, James W.; Trickey, S. B.

2018-02-01

Realizing the potential for predictive density functional calculations of matter under extreme conditions depends crucially upon having an exchange-correlation (X C ) free-energy functional accurate over a wide range of state conditions. Unlike the ground-state case, no such functional exists. We remedy that with systematic construction of a generalized gradient approximation X C free-energy functional based on rigorous constraints, including the free-energy gradient expansion. The new functional provides the correct temperature dependence in the slowly varying regime and the correct zero-T , high-T , and homogeneous electron gas limits. Its accuracy in the warm dense matter regime is attested by excellent agreement of the calculated deuterium equation of state with reference path integral Monte Carlo results at intermediate and elevated T . Pressure shifts for hot electrons in compressed static fcc Al and for low-density Al demonstrate the combined magnitude of thermal and gradient effects handled well by this functional over a wide T range.

Extreme groundwater levels caused by extreme weather conditions - the highest ever measured groundwater levels in Middle Germany and their management

NASA Astrophysics Data System (ADS)

Reinstorf, F.; Kramer, S.; Koch, T.; Pfützner, B.

2017-12-01

Extreme weather conditions during the years 2009 - 2011 in combination with changes in the regional water management led to maximum groundwater levels in large areas of Germany in 2011. This resulted in extensive water logging, with problems especially in urban areas near rivers, where water logging produced huge problems for buildings and infrastructure. The acute situation still exists in many areas and requires the development of solution concepts. Taken the example of the Elbe-Saale-Region in the Federal State of Saxony-Anhalt, were a pilot research project was carried out, the analytical situation, the development of a management tool and the implementation of a groundwater management concept are shown. The central tool is a coupled water budget - groundwater flow model. In combination with sophisticated multi-scale parameter estimation, a high-resolution groundwater level simulation was carried out. A decision support process with an intensive stakeholder interaction combined with high-resolution simulations enables the development of a management concept for extreme groundwater situations in consideration of sustainable and environmentally sound solutions mainly on the base of passive measures.

Bivariate extreme value distributions

NASA Technical Reports Server (NTRS)

Elshamy, M.

1992-01-01

In certain engineering applications, such as those occurring in the analyses of ascent structural loads for the Space Transportation System (STS), some of the load variables have a lower bound of zero. Thus, the need for practical models of bivariate extreme value probability distribution functions with lower limits was identified. We discuss the Gumbel models and present practical forms of bivariate extreme probability distributions of Weibull and Frechet types with two parameters. Bivariate extreme value probability distribution functions can be expressed in terms of the marginal extremel distributions and a 'dependence' function subject to certain analytical conditions. Properties of such bivariate extreme distributions, sums and differences of paired extremals, as well as the corresponding forms of conditional distributions, are discussed. Practical estimation techniques are also given.

Aluminum/water reactions under extreme conditions

NASA Astrophysics Data System (ADS)

Hooper, Joseph

2013-03-01

We discuss mechanisms that may control the reaction of aluminum and water under extreme conditions. We are particularly interested in the high-temperature, high-strain regime where the native oxide layer is destroyed and fresh aluminum is initially in direct contact with liquid or supercritical water. Disparate experimental data over the years have suggested rapid oxidation of aluminum is possible in such situations, but no coherent picture has emerged as to the basic oxidation mechanism or the physical processes that govern the extent of reaction. We present theoretical and computational analysis of traditional metal/water reaction mechanisms that treat diffusion through a dynamic oxide layer or reaction limited by surface kinetics. Diffusion through a fresh solid oxide layer is shown to be far too slow to have any effect on the millisecond timescale (even at high temperatures). Quantum molecular dynamics simulations of liquid Al and water surface reactions show rapid water decomposition at the interface, catalyzed by adjacent water molecules in a Grotthus-like relay mechanism. The surface reaction barriers are far too low for this to be rate-limiting in any way. With these straightforward mechanisms ruled out, we investigate two more complex possibilities for the rate-limiting factor; first, we explore the possibility that newly formed oxide remains a metastable liquid well below its freezing point, allowing for diffusion-limited reactions through the oxide shell but on a much faster timescale. The extent of reaction would then be controlled by the solidification kinetics of alumina. Second, we discuss preliminary analysis on surface erosion and turbulent mixing, which may play a prominent role during hypervelocity penetration of solid aluminum projectiles into water.

Hydrologic extremes - an intercomparison of multiple gridded statistical downscaling methods

NASA Astrophysics Data System (ADS)

Werner, A. T.; Cannon, A. J.

2015-06-01

Gridded statistical downscaling methods are the main means of preparing climate model data to drive distributed hydrological models. Past work on the validation of climate downscaling methods has focused on temperature and precipitation, with less attention paid to the ultimate outputs from hydrological models. Also, as attention shifts towards projections of extreme events, downscaling comparisons now commonly assess methods in terms of climate extremes, but hydrologic extremes are less well explored. Here, we test the ability of gridded downscaling models to replicate historical properties of climate and hydrologic extremes, as measured in terms of temporal sequencing (i.e., correlation tests) and distributional properties (i.e., tests for equality of probability distributions). Outputs from seven downscaling methods - bias correction constructed analogues (BCCA), double BCCA (DBCCA), BCCA with quantile mapping reordering (BCCAQ), bias correction spatial disaggregation (BCSD), BCSD using minimum/maximum temperature (BCSDX), climate imprint delta method (CI), and bias corrected CI (BCCI) - are used to drive the Variable Infiltration Capacity (VIC) model over the snow-dominated Peace River basin, British Columbia. Outputs are tested using split-sample validation on 26 climate extremes indices (ClimDEX) and two hydrologic extremes indices (3 day peak flow and 7 day peak flow). To characterize observational uncertainty, four atmospheric reanalyses are used as climate model surrogates and two gridded observational datasets are used as downscaling target data. The skill of the downscaling methods generally depended on reanalysis and gridded observational dataset. However, CI failed to reproduce the distribution and BCSD and BCSDX the timing of winter 7 day low flow events, regardless of reanalysis or observational dataset. Overall, DBCCA passed the greatest number of tests for the ClimDEX indices, while BCCAQ, which is designed to more accurately resolve event

Hydrologic extremes - an intercomparison of multiple gridded statistical downscaling methods

NASA Astrophysics Data System (ADS)

Werner, Arelia T.; Cannon, Alex J.

2016-04-01

Gridded statistical downscaling methods are the main means of preparing climate model data to drive distributed hydrological models. Past work on the validation of climate downscaling methods has focused on temperature and precipitation, with less attention paid to the ultimate outputs from hydrological models. Also, as attention shifts towards projections of extreme events, downscaling comparisons now commonly assess methods in terms of climate extremes, but hydrologic extremes are less well explored. Here, we test the ability of gridded downscaling models to replicate historical properties of climate and hydrologic extremes, as measured in terms of temporal sequencing (i.e. correlation tests) and distributional properties (i.e. tests for equality of probability distributions). Outputs from seven downscaling methods - bias correction constructed analogues (BCCA), double BCCA (DBCCA), BCCA with quantile mapping reordering (BCCAQ), bias correction spatial disaggregation (BCSD), BCSD using minimum/maximum temperature (BCSDX), the climate imprint delta method (CI), and bias corrected CI (BCCI) - are used to drive the Variable Infiltration Capacity (VIC) model over the snow-dominated Peace River basin, British Columbia. Outputs are tested using split-sample validation on 26 climate extremes indices (ClimDEX) and two hydrologic extremes indices (3-day peak flow and 7-day peak flow). To characterize observational uncertainty, four atmospheric reanalyses are used as climate model surrogates and two gridded observational data sets are used as downscaling target data. The skill of the downscaling methods generally depended on reanalysis and gridded observational data set. However, CI failed to reproduce the distribution and BCSD and BCSDX the timing of winter 7-day low-flow events, regardless of reanalysis or observational data set. Overall, DBCCA passed the greatest number of tests for the ClimDEX indices, while BCCAQ, which is designed to more accurately resolve event

Comparison of Extreme Pressure Additive Treat Rates in Soybean and Mineral Oils Under Boundary Lubrication Conditions

USDA-ARS?s Scientific Manuscript database

Traditionally, it is considered that, under boundary lubrication conditions, the reduction in friction and wear is mostly dependent on Extreme Pressure (EP) additives, rather than the basestock. However, several studies indicate that vegetable oils also contribute to the lubricity under this regime...

Logistic quantile regression provides improved estimates for bounded avian counts: a case study of California Spotted Owl fledgling production

Treesearch

Brian S. Cade; Barry R. Noon; Rick D. Scherer; John J. Keane

2017-01-01

Counts of avian fledglings, nestlings, or clutch size that are bounded below by zero and above by some small integer form a discrete random variable distribution that is not approximated well by conventional parametric count distributions such as the Poisson or negative binomial. We developed a logistic quantile regression model to provide estimates of the empirical...

Factors associated with the income distribution of full-time physicians: a quantile regression approach.

PubMed

Shih, Ya-Chen Tina; Konrad, Thomas R

2007-10-01

Physician income is generally high, but quite variable; hence, physicians have divergent perspectives regarding health policy initiatives and market reforms that could affect their incomes. We investigated factors underlying the distribution of income within the physician population. Full-time physicians (N=10,777) from the restricted version of the 1996-1997 Community Tracking Study Physician Survey (CTS-PS), 1996 Area Resource File, and 1996 health maintenance organization penetration data. We conducted separate analyses for primary care physicians (PCPs) and specialists. We employed least square and quantile regression models to examine factors associated with physician incomes at the mean and at various points of the income distribution, respectively. We accounted for the complex survey design for the CTS-PS data using appropriate weighted procedures and explored endogeneity using an instrumental variables method. We detected widespread and subtle effects of many variables on physician incomes at different points (10th, 25th, 75th, and 90th percentiles) in the distribution that were undetected when employing regression estimations focusing on only the means or medians. Our findings show that the effects of managed care penetration are demonstrable at the mean of specialist incomes, but are more pronounced at higher levels. Conversely, a gender gap in earnings occurs at all levels of income of both PCPs and specialists, but is more pronounced at lower income levels. The quantile regression technique offers an analytical tool to evaluate policy effects beyond the means. A longitudinal application of this approach may enable health policy makers to identify winners and losers among segments of the physician workforce and assess how market dynamics and health policy initiatives affect the overall physician income distribution over various time intervals.

Quantile-based Bayesian maximum entropy approach for spatiotemporal modeling of ambient air quality levels.

PubMed

Yu, Hwa-Lung; Wang, Chih-Hsin

2013-02-05

Understanding the daily changes in ambient air quality concentrations is important to the assessing human exposure and environmental health. However, the fine temporal scales (e.g., hourly) involved in this assessment often lead to high variability in air quality concentrations. This is because of the complex short-term physical and chemical mechanisms among the pollutants. Consequently, high heterogeneity is usually present in not only the averaged pollution levels, but also the intraday variance levels of the daily observations of ambient concentration across space and time. This characteristic decreases the estimation performance of common techniques. This study proposes a novel quantile-based Bayesian maximum entropy (QBME) method to account for the nonstationary and nonhomogeneous characteristics of ambient air pollution dynamics. The QBME method characterizes the spatiotemporal dependence among the ambient air quality levels based on their location-specific quantiles and accounts for spatiotemporal variations using a local weighted smoothing technique. The epistemic framework of the QBME method can allow researchers to further consider the uncertainty of space-time observations. This study presents the spatiotemporal modeling of daily CO and PM10 concentrations across Taiwan from 1998 to 2009 using the QBME method. Results show that the QBME method can effectively improve estimation accuracy in terms of lower mean absolute errors and standard deviations over space and time, especially for pollutants with strong nonhomogeneous variances across space. In addition, the epistemic framework can allow researchers to assimilate the site-specific secondary information where the observations are absent because of the common preferential sampling issues of environmental data. The proposed QBME method provides a practical and powerful framework for the spatiotemporal modeling of ambient pollutants.

The role of ensemble post-processing for modeling the ensemble tail

NASA Astrophysics Data System (ADS)

Van De Vyver, Hans; Van Schaeybroeck, Bert; Vannitsem, Stéphane

2016-04-01

The past decades the numerical weather prediction community has witnessed a paradigm shift from deterministic to probabilistic forecast and state estimation (Buizza and Leutbecher, 2015; Buizza et al., 2008), in an attempt to quantify the uncertainties associated with initial-condition and model errors. An important benefit of a probabilistic framework is the improved prediction of extreme events. However, one may ask to what extent such model estimates contain information on the occurrence probability of extreme events and how this information can be optimally extracted. Different approaches have been proposed and applied on real-world systems which, based on extreme value theory, allow the estimation of extreme-event probabilities conditional on forecasts and state estimates (Ferro, 2007; Friederichs, 2010). Using ensemble predictions generated with a model of low dimensionality, a thorough investigation is presented quantifying the change of predictability of extreme events associated with ensemble post-processing and other influencing factors including the finite ensemble size, lead time and model assumption and the use of different covariates (ensemble mean, maximum, spread...) for modeling the tail distribution. Tail modeling is performed by deriving extreme-quantile estimates using peak-over-threshold representation (generalized Pareto distribution) or quantile regression. Common ensemble post-processing methods aim to improve mostly the ensemble mean and spread of a raw forecast (Van Schaeybroeck and Vannitsem, 2015). Conditional tail modeling, on the other hand, is a post-processing in itself, focusing on the tails only. Therefore, it is unclear how applying ensemble post-processing prior to conditional tail modeling impacts the skill of extreme-event predictions. This work is investigating this question in details. Buizza, Leutbecher, and Isaksen, 2008: Potential use of an ensemble of analyses in the ECMWF Ensemble Prediction System, Q. J. R. Meteorol

Charge transfer in TATB and HMX under extreme conditions.

PubMed

Zhang, Chaoyang; Ma, Yu; Jiang, Daojian

2012-11-01

Charge transfer is usually accompanied by structural changes in materials under different conditions. However, the charge transfer in energetic materials that are subjected to extreme conditions has seldom been explored by researchers. In the work described here, the charge transfer in single molecules and unit cells of the explosives TATB and HMX under high temperatures and high pressures was investigated by performing static and dynamic calculations using three DFT methods, including the PWC functional of LDA, and the BLYP and PBE functionals of GGA. The results showed that negative charge is transferred from the nitro groups of molecular or crystalline TATB and HMX when they are heated. All DFT calculations for the compressed TATB unit cell indicate that, generally, negative charge transfer occurs to its nitro groups as the compression increases. PWC and PBE calculations for crystalline HMX show that negative charge is first transferred to the nitro groups but, as the compression increases, the negative charge is transferred from the nitro groups. However, the BLYP calculations indicated that there was gradual negative charge transfer to the nitro groups of HMX, similar to the case for TATB. The unrelaxed state of the uniformly compressed TATB causes negative charge to be transferred from its nitro groups, in contrast to what is seen in the relaxed state. Charge transfer in TATB is predicted to occur much more easily than in HMX.

Extreme Environments Rig

NASA Image and Video Library

2013-08-13

The Glenn Extreme Environment Chamber (GEER) simulates the extreme conditions found in space and tests many devices that will explore Venus to see if they can withstand the punishing environment and temperatures over 800 degrees F.

Optimal regionalization of extreme value distributions for flood estimation

NASA Astrophysics Data System (ADS)

Asadi, Peiman; Engelke, Sebastian; Davison, Anthony C.

2018-01-01

Regionalization methods have long been used to estimate high return levels of river discharges at ungauged locations on a river network. In these methods, discharge measurements from a homogeneous group of similar, gauged, stations are used to estimate high quantiles at a target location that has no observations. The similarity of this group to the ungauged location is measured in terms of a hydrological distance measuring differences in physical and meteorological catchment attributes. We develop a statistical method for estimation of high return levels based on regionalizing the parameters of a generalized extreme value distribution. The group of stations is chosen by optimizing over the attribute weights of the hydrological distance, ensuring similarity and in-group homogeneity. Our method is applied to discharge data from the Rhine basin in Switzerland, and its performance at ungauged locations is compared to that of other regionalization methods. For gauged locations we show how our approach improves the estimation uncertainty for long return periods by combining local measurements with those from the chosen group.

Extreme ambient temperatures and cardiorespiratory emergency room visits: assessing risk by comorbid health conditions in a time series study

PubMed Central

2014-01-01

Background Extreme ambient temperatures are an increasing public health concern. The aim of this study was to assess if persons with comorbid health conditions were at increased risk of adverse cardiorespiratory morbidity during temperature extremes. Methods A time series study design was applied to 292,666 and 562,738 emergency room (ER) visits for cardiovascular and respiratory diseases, respectively, that occurred in Toronto area hospitals between April 1st 2002 and March 31st 2010. Subgroups of persons with comorbid health conditions were identified. Relative risks (RRs) and their corresponding 95% confidence intervals (CIs) were estimated using a Poisson regression model with distributed lag non-linear model, and were adjusted for the confounding influence of seasonality, relative humidity, day-of-the-week, outdoor air pollutants and daily influenza ER visits. Effect modification by comorbid health conditions was tested using the relative effect modification (REM) index. Results Stronger associations of cardiovascular disease ER visits were observed for persons with diabetes compared to persons without diabetes (REM = 1.12; 95% CI: 1.01 – 1.27) with exposure to the cumulative short term effect of extreme hot temperatures (i.e. 99th percentile of temperature distribution vs. 75th percentile). Effect modification was also found for comorbid respiratory disease (REM = 1.17; 95% CI: 1.02 – 1.44) and cancer (REM = 1.20; 95% CI: 1.02 – 1.49) on respiratory disease ER visits during short term hot temperature episodes. The effect of extreme cold temperatures (i.e. 1st percentile of temperature distribution vs. 25th percentile) on cardiovascular disease ER visits were stronger for individuals with comorbid cardiac diseases (REM = 1.47; 95% CI: 1.06 – 2.23) and kidney diseases (REM = 2.43; 95% CI: 1.59 – 8.83) compared to those without these conditions when cumulated over a two-week period. Conclusions The identification of those most

The fate of carbon dioxide in water-rich fluids under extreme conditions

PubMed Central

Pan, Ding; Galli, Giulia

2016-01-01

Investigating the fate of dissolved carbon dioxide under extreme conditions is critical to understanding the deep carbon cycle in Earth, a process that ultimately influences global climate change. We used first-principles molecular dynamics simulations to study carbonates and carbon dioxide dissolved in water at pressures (P) and temperatures (T) approximating the conditions of Earth’s upper mantle. Contrary to popular geochemical models assuming that molecular CO2(aq) is the major carbon species present in water under deep Earth conditions, we found that at 11 GPa and 1000 K, carbon exists almost entirely in the forms of solvated carbonate (CO32−) and bicarbonate (HCO3−) ions and that even carbonic acid [H2CO3(aq)] is more abundant than CO2(aq). Furthermore, our simulations revealed that ion pairing between Na+ and CO32−/HCO3− is greatly affected by P-T conditions, decreasing with increasing pressure at 800 to 1000 K. Our results suggest that in Earth’s upper mantle, water-rich geofluids transport a majority of carbon in the form of rapidly interconverting CO32− and HCO3− ions, not solvated CO2(aq) molecules. PMID:27757424

The fate of carbon dioxide in water-rich fluids under extreme conditions.

PubMed

Pan, Ding; Galli, Giulia

2016-10-01

Investigating the fate of dissolved carbon dioxide under extreme conditions is critical to understanding the deep carbon cycle in Earth, a process that ultimately influences global climate change. We used first-principles molecular dynamics simulations to study carbonates and carbon dioxide dissolved in water at pressures ( P ) and temperatures ( T ) approximating the conditions of Earth's upper mantle. Contrary to popular geochemical models assuming that molecular CO 2 (aq) is the major carbon species present in water under deep Earth conditions, we found that at 11 GPa and 1000 K, carbon exists almost entirely in the forms of solvated carbonate ([Formula: see text]) and bicarbonate ([Formula: see text]) ions and that even carbonic acid [H 2 CO 3 (aq)] is more abundant than CO 2 (aq). Furthermore, our simulations revealed that ion pairing between Na + and [Formula: see text]/[Formula: see text] is greatly affected by P - T conditions, decreasing with increasing pressure at 800 to 1000 K. Our results suggest that in Earth's upper mantle, water-rich geofluids transport a majority of carbon in the form of rapidly interconverting [Formula: see text] and [Formula: see text] ions, not solvated CO 2 (aq) molecules.

Isolation and Characterization of Bacteria Capable of Tolerating the Extreme Conditions of Clean Room Environments▿

PubMed Central

La Duc, Myron T.; Dekas, Anne; Osman, Shariff; Moissl, Christine; Newcombe, David; Venkateswaran, Kasthuri

2007-01-01

In assessing the bacterial populations present in spacecraft assembly, spacecraft test, and launch preparation facilities, extremophilic bacteria (requiring severe conditions for growth) and extremotolerant bacteria (tolerant to extreme conditions) were isolated. Several cultivation approaches were employed to select for and identify bacteria that not only survive the nutrient-limiting conditions of clean room environments but can also withstand even more inhospitable environmental stresses. Due to their proximity to spacefaring objects, these bacteria pose a considerable risk for forward contamination of extraterrestrial sites. Samples collected from four geographically distinct National Aeronautics and Space Administration clean rooms were challenged with UV-C irradiation, 5% hydrogen peroxide, heat shock, pH extremes (pH 3.0 and 11.0), temperature extremes (4°C to 65°C), and hypersalinity (25% NaCl) prior to and/or during cultivation as a means of selecting for extremotolerant bacteria. Culture-independent approaches were employed to measure viable microbial (ATP-based) and total bacterial (quantitative PCR-based) burdens. Intracellular ATP concentrations suggested a viable microbial presence ranging from below detection limits to 106 cells/m2. However, only 0.1 to 55% of these viable cells were able to grow on defined culture medium. Isolated members of the Bacillaceae family were more physiologically diverse than those reported in previous studies, including thermophiles (Geobacillus), obligate anaerobes (Paenibacillus), and halotolerant, alkalophilic species (Oceanobacillus and Exiguobacterium). Non-spore-forming microbes (α- and β-proteobacteria and actinobacteria) exhibiting tolerance to the selected stresses were also encountered. The multiassay cultivation approach employed herein enhances the current understanding of the physiological diversity of bacteria housed in these clean rooms and leads us to ponder the origin and means of translocation of

Isolation and characterization of bacteria capable of tolerating the extreme conditions of clean room environments.

PubMed

La Duc, Myron T; Dekas, Anne; Osman, Shariff; Moissl, Christine; Newcombe, David; Venkateswaran, Kasthuri

2007-04-01

In assessing the bacterial populations present in spacecraft assembly, spacecraft test, and launch preparation facilities, extremophilic bacteria (requiring severe conditions for growth) and extremotolerant bacteria (tolerant to extreme conditions) were isolated. Several cultivation approaches were employed to select for and identify bacteria that not only survive the nutrient-limiting conditions of clean room environments but can also withstand even more inhospitable environmental stresses. Due to their proximity to spacefaring objects, these bacteria pose a considerable risk for forward contamination of extraterrestrial sites. Samples collected from four geographically distinct National Aeronautics and Space Administration clean rooms were challenged with UV-C irradiation, 5% hydrogen peroxide, heat shock, pH extremes (pH 3.0 and 11.0), temperature extremes (4 degrees C to 65 degrees C), and hypersalinity (25% NaCl) prior to and/or during cultivation as a means of selecting for extremotolerant bacteria. Culture-independent approaches were employed to measure viable microbial (ATP-based) and total bacterial (quantitative PCR-based) burdens. Intracellular ATP concentrations suggested a viable microbial presence ranging from below detection limits to 10(6) cells/m(2). However, only 0.1 to 55% of these viable cells were able to grow on defined culture medium. Isolated members of the Bacillaceae family were more physiologically diverse than those reported in previous studies, including thermophiles (Geobacillus), obligate anaerobes (Paenibacillus), and halotolerant, alkalophilic species (Oceanobacillus and Exiguobacterium). Non-spore-forming microbes (alpha- and beta-proteobacteria and actinobacteria) exhibiting tolerance to the selected stresses were also encountered. The multiassay cultivation approach employed herein enhances the current understanding of the physiological diversity of bacteria housed in these clean rooms and leads us to ponder the origin and means

Extreme anthropogenic loads and the northern ecosystem condition

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

Kryuckkov, V.V.

1993-11-01

In the extreme North, the polar region of siberian Russia, the largest mining and processing enterprises for metallic and nonmetallic ores, coal, oil, and gas are situated. The extremely vulnerable boreal and polar ecosystems of the north are responding adversely to the impact of these activities, and are in danger of collapse because of them. The mechanisms of such impacts, their formation, continuous extension, and merger have been studied. The deforested and destroyed areas of former forest-tundra and taiga ecosystems resemble the Arctic zones of a much harsher environment more than the typical Arctic zones where they occur. 5 refs.,more » 3 figs., 2 tabs.« less

Extreme hydrothermal conditions at an active plate-bounding fault.

PubMed

Sutherland, Rupert; Townend, John; Toy, Virginia; Upton, Phaedra; Coussens, Jamie; Allen, Michael; Baratin, Laura-May; Barth, Nicolas; Becroft, Leeza; Boese, Carolin; Boles, Austin; Boulton, Carolyn; Broderick, Neil G R; Janku-Capova, Lucie; Carpenter, Brett M; Célérier, Bernard; Chamberlain, Calum; Cooper, Alan; Coutts, Ashley; Cox, Simon; Craw, Lisa; Doan, Mai-Linh; Eccles, Jennifer; Faulkner, Dan; Grieve, Jason; Grochowski, Julia; Gulley, Anton; Hartog, Arthur; Howarth, Jamie; Jacobs, Katrina; Jeppson, Tamara; Kato, Naoki; Keys, Steven; Kirilova, Martina; Kometani, Yusuke; Langridge, Rob; Lin, Weiren; Little, Timothy; Lukacs, Adrienn; Mallyon, Deirdre; Mariani, Elisabetta; Massiot, Cécile; Mathewson, Loren; Melosh, Ben; Menzies, Catriona; Moore, Jo; Morales, Luiz; Morgan, Chance; Mori, Hiroshi; Niemeijer, Andre; Nishikawa, Osamu; Prior, David; Sauer, Katrina; Savage, Martha; Schleicher, Anja; Schmitt, Douglas R; Shigematsu, Norio; Taylor-Offord, Sam; Teagle, Damon; Tobin, Harold; Valdez, Robert; Weaver, Konrad; Wiersberg, Thomas; Williams, Jack; Woodman, Nick; Zimmer, Martin

2017-06-01

Temperature and fluid pressure conditions control rock deformation and mineralization on geological faults, and hence the distribution of earthquakes. Typical intraplate continental crust has hydrostatic fluid pressure and a near-surface thermal gradient of 31 ± 15 degrees Celsius per kilometre. At temperatures above 300-450 degrees Celsius, usually found at depths greater than 10-15 kilometres, the intra-crystalline plasticity of quartz and feldspar relieves stress by aseismic creep and earthquakes are infrequent. Hydrothermal conditions control the stability of mineral phases and hence frictional-mechanical processes associated with earthquake rupture cycles, but there are few temperature and fluid pressure data from active plate-bounding faults. Here we report results from a borehole drilled into the upper part of the Alpine Fault, which is late in its cycle of stress accumulation and expected to rupture in a magnitude 8 earthquake in the coming decades. The borehole (depth 893 metres) revealed a pore fluid pressure gradient exceeding 9 ± 1 per cent above hydrostatic levels and an average geothermal gradient of 125 ± 55 degrees Celsius per kilometre within the hanging wall of the fault. These extreme hydrothermal conditions result from rapid fault movement, which transports rock and heat from depth, and topographically driven fluid movement that concentrates heat into valleys. Shear heating may occur within the fault but is not required to explain our observations. Our data and models show that highly anomalous fluid pressure and temperature gradients in the upper part of the seismogenic zone can be created by positive feedbacks between processes of fault slip, rock fracturing and alteration, and landscape development at plate-bounding faults.

Extreme hydrothermal conditions at an active plate-bounding fault

NASA Astrophysics Data System (ADS)

Sutherland, Rupert; Townend, John; Toy, Virginia; Upton, Phaedra; Coussens, Jamie; Allen, Michael; Baratin, Laura-May; Barth, Nicolas; Becroft, Leeza; Boese, Carolin; Boles, Austin; Boulton, Carolyn; Broderick, Neil G. R.; Janku-Capova, Lucie; Carpenter, Brett M.; Célérier, Bernard; Chamberlain, Calum; Cooper, Alan; Coutts, Ashley; Cox, Simon; Craw, Lisa; Doan, Mai-Linh; Eccles, Jennifer; Faulkner, Dan; Grieve, Jason; Grochowski, Julia; Gulley, Anton; Hartog, Arthur; Howarth, Jamie; Jacobs, Katrina; Jeppson, Tamara; Kato, Naoki; Keys, Steven; Kirilova, Martina; Kometani, Yusuke; Langridge, Rob; Lin, Weiren; Little, Timothy; Lukacs, Adrienn; Mallyon, Deirdre; Mariani, Elisabetta; Massiot, Cécile; Mathewson, Loren; Melosh, Ben; Menzies, Catriona; Moore, Jo; Morales, Luiz; Morgan, Chance; Mori, Hiroshi; Niemeijer, Andre; Nishikawa, Osamu; Prior, David; Sauer, Katrina; Savage, Martha; Schleicher, Anja; Schmitt, Douglas R.; Shigematsu, Norio; Taylor-Offord, Sam; Teagle, Damon; Tobin, Harold; Valdez, Robert; Weaver, Konrad; Wiersberg, Thomas; Williams, Jack; Woodman, Nick; Zimmer, Martin

2017-06-01

Temperature and fluid pressure conditions control rock deformation and mineralization on geological faults, and hence the distribution of earthquakes. Typical intraplate continental crust has hydrostatic fluid pressure and a near-surface thermal gradient of 31 ± 15 degrees Celsius per kilometre. At temperatures above 300-450 degrees Celsius, usually found at depths greater than 10-15 kilometres, the intra-crystalline plasticity of quartz and feldspar relieves stress by aseismic creep and earthquakes are infrequent. Hydrothermal conditions control the stability of mineral phases and hence frictional-mechanical processes associated with earthquake rupture cycles, but there are few temperature and fluid pressure data from active plate-bounding faults. Here we report results from a borehole drilled into the upper part of the Alpine Fault, which is late in its cycle of stress accumulation and expected to rupture in a magnitude 8 earthquake in the coming decades. The borehole (depth 893 metres) revealed a pore fluid pressure gradient exceeding 9 ± 1 per cent above hydrostatic levels and an average geothermal gradient of 125 ± 55 degrees Celsius per kilometre within the hanging wall of the fault. These extreme hydrothermal conditions result from rapid fault movement, which transports rock and heat from depth, and topographically driven fluid movement that concentrates heat into valleys. Shear heating may occur within the fault but is not required to explain our observations. Our data and models show that highly anomalous fluid pressure and temperature gradients in the upper part of the seismogenic zone can be created by positive feedbacks between processes of fault slip, rock fracturing and alteration, and landscape development at plate-bounding faults.

Attribution of Extreme Rainfall Events in the South of France Using EURO-CORDEX Simulations

NASA Astrophysics Data System (ADS)

Luu, L. N.; Vautard, R.; Yiou, P.

2017-12-01

The Mediterranean region regularly undergoes episodes of intense precipitation in the fall season that exceed 300mm a day. This study focuses on the role of climate change on the dynamics of the events that occur in the South of France. We used an ensemble of 10 EURO-CORDEX model simulations with two horizontal resolutions (EUR-11: 0.11° and EUR-44: 0.44°) for the attribution of extreme rainfall in the fall in the Cevennes mountain range (South of France). The biases of the simulations were corrected with simple scaling adjustment and a quantile correction (CDFt). This produces five datasets including EUR-44 and EUR-11 with and without scaling adjustment and CDFt-EUR-11, on which we test the impact of resolution and bias correction on the extremes. Those datasets, after pooling all of models together, are fitted by a stationary Generalized Extreme Value distribution for several periods to estimate a climate change signal in the tail of distribution of extreme rainfall in the Cévenne region. Those changes are then interpreted by a scaling model that links extreme rainfall with mean and maximum daily temperature. The results show that higher-resolution simulations with bias adjustment provide a robust and confident increase of intensity and likelihood of occurrence of autumn extreme rainfall in the area in current climate in comparison with historical climate. The probability (exceedance probability) of 1-in-1000-year event in historical climate may increase by a factor of 1.8 under current climate with a confident interval of 0.4 to 5.3 following the CDFt bias-adjusted EUR-11. The change of magnitude appears to follow the Clausius-Clapeyron relation that indicates a 7% increase in rainfall per 1oC increase in temperature.

Factors Associated with Adherence to Adjuvant Endocrine Therapy Among Privately Insured and Newly Diagnosed Breast Cancer Patients: A Quantile Regression Analysis.

PubMed

Farias, Albert J; Hansen, Ryan N; Zeliadt, Steven B; Ornelas, India J; Li, Christopher I; Thompson, Beti

2016-08-01

Adherence to adjuvant endocrine therapy (AET) for estrogen receptor-positive breast cancer remains suboptimal, which suggests that women are not getting the full benefit of the treatment to reduce breast cancer recurrence and mortality. The majority of studies on adherence to AET focus on identifying factors among those women at the highest levels of adherence and provide little insight on factors that influence medication use across the distribution of adherence. To understand how factors influence adherence among women across low and high levels of adherence. A retrospective evaluation was conducted using the Truven Health MarketScan Commercial Claims and Encounters Database from 2007-2011. Privately insured women aged 18-64 years who were recently diagnosed and treated for breast cancer and who initiated AET within 12 months of primary treatment were assessed. Adherence was measured as the proportion of days covered (PDC) over a 12-month period. Simultaneous multivariable quantile regression was used to assess the association between treatment and demographic factors, use of mail order pharmacies, medication switching, and out-of-pocket costs and adherence. The effect of each variable was examined at the 40th, 60th, 80th, and 95th quantiles. Among the 6,863 women in the cohort, mail order pharmacies had the greatest influence on adherence at the 40th quantile, associated with a 29.6% (95% CI = 22.2-37.0) higher PDC compared with retail pharmacies. Out-of-pocket cost for a 30-day supply of AET greater than $20 was associated with an 8.6% (95% CI = 2.8-14.4) lower PDC versus $0-$9.99. The main factors that influenced adherence at the 95th quantile were mail order pharmacies, associated with a 4.4% higher PDC (95% CI = 3.8-5.0) versus retail pharmacies, and switching AET medication 2 or more times, associated with a 5.6% lower PDC versus not switching (95% CI = 2.3-9.0). Factors associated with adherence differed across quantiles. Addressing the use of mail order

Extreme groundwater levels caused by extreme weather conditions - the highest ever measured groundwater levels in Middle Germany and their management

NASA Astrophysics Data System (ADS)

Reinstorf, F.

2016-12-01

Extreme weather conditions during the years 2009 - 2011 in combination with changes in the regional water management and possible impacts of climate change led to maximum groundwater levels in large areas of Germany in 2011. This resulted in extensive water logging, with problems especially in urban areas near rivers, where water logging produced huge problems for buildings and infrastructure. The acute situation still exists in many areas and requires the development of solution concepts. Taken the example of the Elbe-Saale-Region in the Federal State of Saxony-Anhalt, were a pilot research project was carried out, the analytical situation, the development of a management tool and the implementation of a groundwater management concept are shown. The central tool is a coupled water budget - groundwater flow model. In combination with sophisticated multi-scale parameter estimation, a high resolution groundwater level simulation was carried out. A decision support process with a very intensive stakeholder interaction combined with high resolution simulations enables the development of a management concept for extreme groundwater situations in consideration of sustainable and environmentally sound solutions mainly on the base of passive measures.

Extreme groundwater levels caused by extreme weather conditions - the highest ever measured groundwater levels in Middle Germany and their management

NASA Astrophysics Data System (ADS)

Reinstorf, Frido; Kramer, Stefanie; Koch, Thomas; Seifert, Sven; Monninkhoff, Bertram; Pfützner, Bernd

2017-04-01

Extreme weather conditions during the years 2009 - 2011 in combination with changes in the regional water management and possible impacts of climate change led to maximum groundwater levels in large areas of Germany in 2011. This resulted in extensive water logging, with problems especially in urban areas near rivers, where water logging produced huge problems for buildings and infrastructure. The acute situation still exists in many areas and requires the development of solution concepts. Taken the example of the Elbe-Saale-Region in the Federal State of Saxony-Anhalt, were a pilot research project was carried out, the analytical situation, the development of a management tool and the implementation of a groundwater management concept are shown. The central tool is a coupled water budget - groundwater flow model. In combination with sophisticated multi-scale parameter estimation, a high resolution groundwater level simulation was carried out. A decision support process with a very intensive stakeholder interaction combined with high resolution simulations enables the development of a management concept for extreme groundwater situations in consideration of sustainable and environmentally sound solutions mainly on the base of passive measures.

Measuring racial/ethnic disparities across the distribution of health care expenditures.

PubMed

Cook, Benjamin Lê; Manning, Willard G

2009-10-01

To assess whether black-white and Hispanic-white disparities increase or abate in the upper quantiles of total health care expenditure, conditional on covariates. Nationally representative adult population of non-Hispanic whites, African Americans, and Hispanics from the 2001-2005 Medical Expenditure Panel Surveys. We examine unadjusted racial/ethnic differences across the distribution of expenditures. We apply quantile regression to measure disparities at the median, 75th, 90th, and 95th quantiles, testing for differences over the distribution of health care expenditures and across income and education categories. We test the sensitivity of the results to comparisons based only on health status and estimate a two-part model to ensure that results are not driven by an extremely skewed distribution of expenditures with a large zero mass. Black-white and Hispanic-white disparities diminish in the upper quantiles of expenditure, but expenditures for blacks and Hispanics remain significantly lower than for whites throughout the distribution. For most education and income categories, disparities exist at the median and decline, but remain significant even with increased education and income. Blacks and Hispanics receive significantly disparate care at high expenditure levels, suggesting prioritization of improved access to quality care among minorities with critical health issues.

Nonempirical Semilocal Free-Energy Density Functional for Matter under Extreme Conditions

DOE PAGES

Karasiev, Valentin V.; Dufty, James W.; Trickey, S. B.

2018-02-14

The potential for density functional calculations to predict the properties of matter under extreme conditions depends crucially upon having a non-empirical approximate free energy functional valid over a wide range of state conditions. Unlike the ground-state case, no such free-energy exchange- correlation (XC) functional exists. We remedy that with systematic construction of a generalized gradient approximation XC free-energy functional based on rigorous constraints, including the free energy gradient expansion. The new functional provides the correct temperature dependence in the slowly varying regime and the correct zero-T, high-T, and homogeneous electron gas limits. Application in Kohn-Sham calculations for hot electrons inmore » a static fcc Aluminum lattice demon- strates the combined magnitude of thermal and gradient effects handled by this functional. Its accuracy in the increasingly important warm dense matter regime is attested by excellent agreement of the calculated deuterium equation of state with reference path integral Monte Carlo results at intermediate and elevated temperatures and by low density Al calculations over a wide T range.« less

Nonempirical Semilocal Free-Energy Density Functional for Matter under Extreme Conditions

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

Karasiev, Valentin V.; Dufty, James W.; Trickey, S. B.

The potential for density functional calculations to predict the properties of matter under extreme conditions depends crucially upon having a non-empirical approximate free energy functional valid over a wide range of state conditions. Unlike the ground-state case, no such free-energy exchange- correlation (XC) functional exists. We remedy that with systematic construction of a generalized gradient approximation XC free-energy functional based on rigorous constraints, including the free energy gradient expansion. The new functional provides the correct temperature dependence in the slowly varying regime and the correct zero-T, high-T, and homogeneous electron gas limits. Application in Kohn-Sham calculations for hot electrons inmore » a static fcc Aluminum lattice demon- strates the combined magnitude of thermal and gradient effects handled by this functional. Its accuracy in the increasingly important warm dense matter regime is attested by excellent agreement of the calculated deuterium equation of state with reference path integral Monte Carlo results at intermediate and elevated temperatures and by low density Al calculations over a wide T range.« less

Factors Associated with the Income Distribution of Full-Time Physicians: A Quantile Regression Approach

PubMed Central

Shih, Ya-Chen Tina; Konrad, Thomas R

2007-01-01

Objective Physician income is generally high, but quite variable; hence, physicians have divergent perspectives regarding health policy initiatives and market reforms that could affect their incomes. We investigated factors underlying the distribution of income within the physician population. Data Sources Full-time physicians (N=10,777) from the restricted version of the 1996–1997 Community Tracking Study Physician Survey (CTS-PS), 1996 Area Resource File, and 1996 health maintenance organization penetration data. Study Design We conducted separate analyses for primary care physicians (PCPs) and specialists. We employed least square and quantile regression models to examine factors associated with physician incomes at the mean and at various points of the income distribution, respectively. We accounted for the complex survey design for the CTS-PS data using appropriate weighted procedures and explored endogeneity using an instrumental variables method. Principal Findings We detected widespread and subtle effects of many variables on physician incomes at different points (10th, 25th, 75th, and 90th percentiles) in the distribution that were undetected when employing regression estimations focusing on only the means or medians. Our findings show that the effects of managed care penetration are demonstrable at the mean of specialist incomes, but are more pronounced at higher levels. Conversely, a gender gap in earnings occurs at all levels of income of both PCPs and specialists, but is more pronounced at lower income levels. Conclusions The quantile regression technique offers an analytical tool to evaluate policy effects beyond the means. A longitudinal application of this approach may enable health policy makers to identify winners and losers among segments of the physician workforce and assess how market dynamics and health policy initiatives affect the overall physician income distribution over various time intervals. PMID:17850525

On the Performance of Carbon Nanotubes in Extreme Conditions and in the Presence of Microwaves

DTIC Science & Technology

2013-01-01

been considered for use as transparent conductors include: transparent conducting oxides (TCOs), intrinsically conducting polymers (ICPs), graphene ...optical transmission properties, but are extremely sensitive to environmental conditions (such as temperature and humidity). Graphene has recently...during the dicing procedure, silver paint was applied to the sample to serve as improvised contact/probe-landing points. Figure 1 shows the CNT thin

Assessment of Weighted Quantile Sum Regression for Modeling Chemical Mixtures and Cancer Risk

PubMed Central

Czarnota, Jenna; Gennings, Chris; Wheeler, David C

2015-01-01

In evaluation of cancer risk related to environmental chemical exposures, the effect of many chemicals on disease is ultimately of interest. However, because of potentially strong correlations among chemicals that occur together, traditional regression methods suffer from collinearity effects, including regression coefficient sign reversal and variance inflation. In addition, penalized regression methods designed to remediate collinearity may have limitations in selecting the truly bad actors among many correlated components. The recently proposed method of weighted quantile sum (WQS) regression attempts to overcome these problems by estimating a body burden index, which identifies important chemicals in a mixture of correlated environmental chemicals. Our focus was on assessing through simulation studies the accuracy of WQS regression in detecting subsets of chemicals associated with health outcomes (binary and continuous) in site-specific analyses and in non-site-specific analyses. We also evaluated the performance of the penalized regression methods of lasso, adaptive lasso, and elastic net in correctly classifying chemicals as bad actors or unrelated to the outcome. We based the simulation study on data from the National Cancer Institute Surveillance Epidemiology and End Results Program (NCI-SEER) case–control study of non-Hodgkin lymphoma (NHL) to achieve realistic exposure situations. Our results showed that WQS regression had good sensitivity and specificity across a variety of conditions considered in this study. The shrinkage methods had a tendency to incorrectly identify a large number of components, especially in the case of strong association with the outcome. PMID:26005323

Assessment of weighted quantile sum regression for modeling chemical mixtures and cancer risk.

PubMed

Czarnota, Jenna; Gennings, Chris; Wheeler, David C

2015-01-01

In evaluation of cancer risk related to environmental chemical exposures, the effect of many chemicals on disease is ultimately of interest. However, because of potentially strong correlations among chemicals that occur together, traditional regression methods suffer from collinearity effects, including regression coefficient sign reversal and variance inflation. In addition, penalized regression methods designed to remediate collinearity may have limitations in selecting the truly bad actors among many correlated components. The recently proposed method of weighted quantile sum (WQS) regression attempts to overcome these problems by estimating a body burden index, which identifies important chemicals in a mixture of correlated environmental chemicals. Our focus was on assessing through simulation studies the accuracy of WQS regression in detecting subsets of chemicals associated with health outcomes (binary and continuous) in site-specific analyses and in non-site-specific analyses. We also evaluated the performance of the penalized regression methods of lasso, adaptive lasso, and elastic net in correctly classifying chemicals as bad actors or unrelated to the outcome. We based the simulation study on data from the National Cancer Institute Surveillance Epidemiology and End Results Program (NCI-SEER) case-control study of non-Hodgkin lymphoma (NHL) to achieve realistic exposure situations. Our results showed that WQS regression had good sensitivity and specificity across a variety of conditions considered in this study. The shrinkage methods had a tendency to incorrectly identify a large number of components, especially in the case of strong association with the outcome.

The Extreme Male Brain Theory and Gender Role Behaviour in Persons with an Autism Spectrum Condition

ERIC Educational Resources Information Center

Stauder, J. E. A.; Cornet, L. J. M.; Ponds, R. W. H. M.

2011-01-01

According to the Extreme Male Brain theory persons with autism possess masculinised cognitive traits. In this study masculinisation of gender role behaviour is evaluated in 25 persons with an autism spectrum condition (ASC) and matched controls with gender role behaviour as part of a shortened version of the Minnesota Multiphasic Personality…

Fluid Fe(1 - x)Hx under extreme conditions

NASA Astrophysics Data System (ADS)

Seclaman, Alexandra; Wilson, Hugh F.; Cohen, Ronald E.

We study the fluid Fe-H binary system using first principles molecular dynamics (FPMD) and a new FPMD-based method, CATS, in order to compute efficiently and accurately the equation of state of Fe-H fluids up to 5 TPa and 30,000K. We constructed GRBV-type LDA pseudopotentials for Fe and H with small rcuts in order to avoid pseudo-core overlap. In the liquid Fe regime we find good agreement with previous works, up to the pressures where data is available. In the high density regime of pure H we also find good agreement with previous results. Previous work has been focused on low Fe concentrations in metallic liquid H. We extend previous studies by investigating several intermediate Fe(1 - x)Hx liquid compositions, as well as metallic liquid H and Fe. Preliminary results indicate extreme compositional pressure effects under isothermic and isochoric conditions, 3.9 TPa difference between Fe and H at 20,000K. Thermal pressure effects are comparatively small, 0.12-0.15 TPa per 10,000K for H and Fe, respectively. Equations of state will be presented and fluid immiscibility will be discussed. This work has been supported by the ERC Advanced Grant ToMCaT and NSF and the Carnegie Institution.

Detecting Long-term Trend of Water Quality Indices of Dong-gang River, Taiwan Using Quantile Regression

NASA Astrophysics Data System (ADS)

Yang, D.; Shiau, J.

2013-12-01

ABSTRACT BODY: Abstract Surface water quality is an essential issue in water-supply for human uses and sustaining healthy ecosystem of rivers. However, water quality of rivers is easily influenced by anthropogenic activities such as urban development and wastewater disposal. Long-term monitoring of water quality can assess whether water quality of rivers deteriorates or not. Taiwan is a population-dense area and heavily depends on surface water for domestic, industrial, and agricultural uses. Dong-gang River is one of major resources in southern Taiwan for agricultural requirements. The water-quality data of four monitoring stations of the Dong-gang River for the period of 2000-2012 are selected for trend analysis. The parameters used to characterize water quality of rivers include biochemical oxygen demand (BOD), dissolved oxygen (DO), suspended solids (SS), and ammonia nitrogen (NH3-N). These four water-quality parameters are integrated into an index called river pollution index (RPI) to indicate the pollution level of rivers. Although widely used non-parametric Mann-Kendall test and linear regression exhibit computational efficiency to identify trends of water-quality indices, limitations of such approaches include sensitive to outliers and estimations of conditional mean only. Quantile regression, capable of identifying changes over time of any percentile values, is employed in this study to detect long-term trend of water-quality indices for the Dong-gang River located in southern Taiwan. The results show that Dong-gang River 4 stations from 2000 to 2012 monthly long-term trends in water quality.To analyze s Dong-gang River long-term water quality trends and pollution characteristics. The results showed that the bridge measuring ammonia Long-dong, BOD5 measure in that station on a downward trend, DO, and SS is on the rise, River Pollution Index (RPI) on a downward trend. The results form Chau-Jhou station also ahowed simialar trends .more and more near the

Influenza transmission during extreme indoor conditions in a low-resource tropical setting

NASA Astrophysics Data System (ADS)

Tamerius, James; Ojeda, Sergio; Uejio, Christopher K.; Shaman, Jeffrey; Lopez, Brenda; Sanchez, Nery; Gordon, Aubree

2017-04-01

Influenza transmission occurs throughout the planet across wide-ranging environmental conditions. However, our understanding of the environmental factors mediating transmission is evaluated using outdoor environmental measurements, which may not be representative of the indoor conditions where influenza is transmitted. In this study, we examined the relationship between indoor environment and influenza transmission in a low-resource tropical population. We used a case-based ascertainment design to enroll 34 households with a suspected influenza case and then monitored households for influenza, while recording indoor temperature and humidity data in each household. We show that the indoor environment is not commensurate with outdoor conditions and that the relationship between indoor and outdoor conditions varies significantly across homes. We also show evidence of influenza transmission in extreme indoor environments. Specifically, our data suggests that indoor environments averaged 29 °C, 18 g/kg specific humidity, and 68 % relative humidity across 15 transmission events observed. These indoor settings also exhibited significant temporal variability with temperatures as high as 39 °C and specific and relative humidity increasing to 22 g/kg and 85 %, respectively, during some transmission events. However, we were unable to detect differences in the transmission efficiency by indoor temperature or humidity conditions. Overall, these results indicate that laboratory studies investigating influenza transmission and virus survival should increase the range of environmental conditions that they assess and that observational studies investigating the relationship between environment and influenza activity should use caution using outdoor environmental measurements since they can be imprecise estimates of the conditions that mediate transmission indoors.

Influenza transmission during extreme indoor conditions in a low-resource tropical setting.

PubMed

Tamerius, James; Ojeda, Sergio; Uejio, Christopher K; Shaman, Jeffrey; Lopez, Brenda; Sanchez, Nery; Gordon, Aubree

2017-04-01

Influenza transmission occurs throughout the planet across wide-ranging environmental conditions. However, our understanding of the environmental factors mediating transmission is evaluated using outdoor environmental measurements, which may not be representative of the indoor conditions where influenza is transmitted. In this study, we examined the relationship between indoor environment and influenza transmission in a low-resource tropical population. We used a case-based ascertainment design to enroll 34 households with a suspected influenza case and then monitored households for influenza, while recording indoor temperature and humidity data in each household. We show that the indoor environment is not commensurate with outdoor conditions and that the relationship between indoor and outdoor conditions varies significantly across homes. We also show evidence of influenza transmission in extreme indoor environments. Specifically, our data suggests that indoor environments averaged 29 °C, 18 g/kg specific humidity, and 68 % relative humidity across 15 transmission events observed. These indoor settings also exhibited significant temporal variability with temperatures as high as 39 °C and specific and relative humidity increasing to 22 g/kg and 85 %, respectively, during some transmission events. However, we were unable to detect differences in the transmission efficiency by indoor temperature or humidity conditions. Overall, these results indicate that laboratory studies investigating influenza transmission and virus survival should increase the range of environmental conditions that they assess and that observational studies investigating the relationship between environment and influenza activity should use caution using outdoor environmental measurements since they can be imprecise estimates of the conditions that mediate transmission indoors.

Detectors in Extreme Conditions

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

Blaj, G.; Carini, G.; Carron, S.

2015-08-06

Free Electron Lasers opened a new window on imaging the motion of atoms and molecules. At SLAC, FEL experiments are performed at LCLS using 120Hz pulses with 10 12 - 10 13 photons in 10 femtoseconds (billions of times brighter than the most powerful synchrotrons). This extreme detection environment raises unique challenges, from obvious to surprising. Radiation damage is a constant threat due to accidental exposure to insufficiently attenuated beam, focused beam and formation of ice crystals reflecting the beam onto the detector. Often high power optical lasers are also used (e.g., 25TW), increasing the risk of damage or impedingmore » data acquisition through electromagnetic pulses (EMP). The sample can contaminate the detector surface or even produce shrapnel damage. Some experiments require ultra high vacuum (UHV) with strict design, surface contamination and cooling requirements - also for detectors. The setup is often changed between or during experiments with short turnaround times, risking mechanical and ESD damage, requiring work planning, training of operators and sometimes continuous participation of the LCLS Detector Group in the experiments. The detectors used most often at LCLS are CSPAD cameras for hard x-rays and pnCCDs for soft x-rays.« less

The N-shaped environmental Kuznets curve: an empirical evaluation using a panel quantile regression approach.

PubMed

Allard, Alexandra; Takman, Johanna; Uddin, Gazi Salah; Ahmed, Ali

2018-02-01

We evaluate the N-shaped environmental Kuznets curve (EKC) using panel quantile regression analysis. We investigate the relationship between CO 2 emissions and GDP per capita for 74 countries over the period of 1994-2012. We include additional explanatory variables, such as renewable energy consumption, technological development, trade, and institutional quality. We find evidence for the N-shaped EKC in all income groups, except for the upper-middle-income countries. Heterogeneous characteristics are, however, observed over the N-shaped EKC. Finally, we find a negative relationship between renewable energy consumption and CO 2 emissions, which highlights the importance of promoting greener energy in order to combat global warming.

Extreme weather conditions reduce the CO2 fertilization effect in temperate C3 grasslands

NASA Astrophysics Data System (ADS)

Obermeier, Wolfgang; Lehnert, Lukas; Kammann, Claudia; Müller, Christoph; Grünhage, Ludger; Luterbacher, Jürg; Erbs, Martin; Yuan, Naiming; Bendix, Jörg

2016-04-01

The increase in atmospheric greenhouse gas concentrations from anthropogenic activities is the major driver of global climate change. The rising atmospheric carbon dioxide (CO2) concentrations may stimulate plant photosynthesis and, thus, cause a net sink effect in the global carbon cycle. As a consequence of an enhanced photosynthesis, an increase in the net primary productivity (NPP) of C3 plants (termed CO2 fertilization) is widely assumed. This process is associated with a reduced stomatal conductance of leaves as the carbon demand of photosynthesis is met earlier. This causes a higher water-use efficiency and, hence, may reduce water stress in plants exposed to elevated CO2 concentrations ([eCO2]). However, the magnitude and persistence of the CO2 fertilization effect under a future climate including more frequent weather extremes are controversial. To test the CO2 fertilization effect for Central European grasslands, a data set comprising 16 years of biomass samples and environmental variables such as local weather and soil conditions was analysed by means of a novel approach. The data set was recorded on a "Free Air Carbon dioxide Enrichment" (FACE) experimental site which allows to quantify the CO2 fertilization effect under naturally occurring climate variations. The results indicate that the CO2 fertilization effect on the aboveground biomass is strongest under local average environmental conditions. Such intermediate regimes were defined by the mean +/- 1 standard deviation of the long-term average in the respective variable three months before harvest. The observed CO2 fertilization effect was reduced or vanished under drier, wetter and hotter conditions when the respective variable exceeded the bounds of the intermediate regimes. Comparable conditions, characterized by a higher frequency of more extreme weather conditions, are predicted for the future by climate projections. Consequently, biogeochemical models may overestimate the future NPP sink

Selection of extreme environmental conditions, albedo coefficient and Earth infrared radiation, for polar summer Long Duration Balloon missions

NASA Astrophysics Data System (ADS)

González-Llana, Arturo; González-Bárcena, David; Pérez-Grande, Isabel; Sanz-Andrés, Ángel

2018-07-01

The selection of the extreme thermal environmental conditions -albedo coefficient and Earth infrared radiation- for the thermal design of stratospheric balloon missions is usually based on the methodologies applied in space missions. However, the particularities of stratospheric balloon missions, such as the much higher residence time of the balloon payload over a determined area, make necessary an approach centered in the actual environment the balloon is going to find, in terms of geographic area and season of flight. In this sense, this work is focussed on stratospheric balloon missions circumnavigating the North Pole during the summer period. Pairs of albedo and Earth infrared radiation satellite data restricted to this area and season of interest have been treated statistically. Furthermore, the environmental conditions leading to the extreme temperatures of the payload depend in turn on the surface finish, and more particularly on the ratio between the solar absorptance and the infrared emissivity α/ε. A simple but representative thermal model of a balloon and its payload has been set up in order to identify the pairs of albedo coefficient and Earth infrared radiation leading to extreme temperatures for each value of α/ε.

Impact of urban WWTP and CSO fluxes on river peak flow extremes under current and future climate conditions.

PubMed

Keupers, Ingrid; Willems, Patrick

2013-01-01

The impact of urban water fluxes on the river system outflow of the Grote Nete catchment (Belgium) was studied. First the impact of the Waste Water Treatment Plant (WWTP) and the Combined Sewer Overflow (CSO) outflows on the river system for the current climatic conditions was determined by simulating the urban fluxes as point sources in a detailed, hydrodynamic river model. Comparison was made of the simulation results on peak flow extremes with and without the urban point sources. In a second step, the impact of climate change scenarios on the urban fluxes and the consequent impacts on the river flow extremes were studied. It is shown that the change in the 10-year return period hourly peak flow discharge due to climate change (-14% to +45%) was in the same order of magnitude as the change due to the urban fluxes (+5%) in current climate conditions. Different climate change scenarios do not change the impact of the urban fluxes much except for the climate scenario that involves a strong increase in rainfall extremes in summer. This scenario leads to a strong increase of the impact of the urban fluxes on the river system.

[Sports and extreme conditions. Cardiovascular incidence in long term exertion and extreme temperatures (heat, cold)].

PubMed

Melin, B; Savourey, G

2001-06-30

During ultra-endurance exercise, both increase in body temperature and dehydration due to sweat losses, lead to a decrease in central blood volume. The heart rate drift allows maintaining appropriate cardiac output, in order to satisfy both muscle perfusion and heat transfer requirements by increasing skin blood flow. The resulting dehydration can impair thermal regulation and increase the risks of serious accidents as heat stroke. Endurance events, lasting more than 8 hours, result in large sweat sodium chloride losses. Thus, ingestion of large amounts of water with poor salt intake can induce symptomatic hyponatremia (plasma sodium < 130 mEq/L) which is also a serious accident. Heat environment increases the thermal constraint and when the air humidity is high, evaporation of sweat is compromise. Thus, thermal stress becomes uncompensable which increases the risk of cardiovascular collapse. Cold exposure induces physiological responses to maintain internal temperature by both limiting thermal losses and increasing metabolic heat production. Cold can induce accidental hypothermia and local frost-bites; moreover, it increases the risk of arrhythmia during exercise. Some guidelines (cardiovascular fitness, water and electrolyte intakes, protective clothing) are given for each extreme condition.

Force Field Accelerated Density Functional Theory Molecular Dynamics for Simulation of Reactive Systems at Extreme Conditions

NASA Astrophysics Data System (ADS)

Lindsey, Rebecca; Goldman, Nir; Fried, Laurence

2017-06-01

Atomistic modeling of chemistry at extreme conditions remains a challenge, despite continuing advances in computing resources and simulation tools. While first principles methods provide a powerful predictive tool, the time and length scales associated with chemistry at extreme conditions (ns and μm, respectively) largely preclude extension of such models to molecular dynamics. In this work, we develop a simulation approach that retains the accuracy of density functional theory (DFT) while decreasing computational effort by several orders of magnitude. We generate n-body descriptions for atomic interactions by mapping forces arising from short density functional theory (DFT) trajectories on to simple Chebyshev polynomial series. We examine the importance of including greater than 2-body interactions, model transferability to different state points, and discuss approaches to ensure smooth and reasonable model shape outside of the distance domain sampled by the DFT training set. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Force Field Accelerated Density Functional Theory Molecular Dynamics for Simulation of Reactive Systems at Extreme Conditions

NASA Astrophysics Data System (ADS)

Lindsey, Rebecca; Goldman, Nir; Fried, Laurence

Understanding chemistry at extreme conditions is crucial in fields including geochemistry, astrobiology, and alternative energy. First principles methods can provide valuable microscopic insights into such systems while circumventing the risks of physical experiments, however the time and length scales associated with chemistry at extreme conditions (ns and μm, respectively) largely preclude extension of such models to molecular dynamics. In this work, we develop a simulation approach that retains the accuracy of density functional theory (DFT) while decreasing computational effort by several orders of magnitude. We generate n-body descriptions for atomic interactions by mapping forces arising from short density functional theory (DFT) trajectories on to simple Chebyshev polynomial series. We examine the importance of including greater than 2-body interactions, model transferability to different state points, and discuss approaches to ensure smooth and reasonable model shape outside of the distance domain sampled by the DFT training set. This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Three responses of wetland conditions to climatic extremes in the Prairie Pothole Region

USGS Publications Warehouse

Cressey, Ryann L.; Austin, Jane; Stafford, Joshua D.

2016-01-01

Wetlands in central North Dakota were revisited after 50 years to assess changes following extreme drought and a prolonged wet period. We compared data collected during 1961–1966 to current (2013–2014) wetland conditions. We revisited 80 wetlands in 2013 and 2014 across three study areas and measured wetland area, ponded-water depth, and specific conductance. Wetlands at the three study areas responded to prolonged wet conditions in one of three ways. Wetlands at Crystal Springs became larger, and had deeper ponds of lower specific conductance in 2013–14 compared to the 1960s. Wetlands at Cottonwood were larger with deeper ponds of slightly higher specific conductance in 2013–2014. Wetlands at Mt. Moriah had only subtle changes in size, pond depth, and specific conductance between periods. Prolonged wet conditions led to merging of most wetlands (defined as the outer edge of wet-meadow vegetation) at Crystal Springs and a few wetlands at Cottonwood. Low topographic relief at Crystal Springs and Cottonwood contributed to storage of excess water in wetlands with associated responses to prolonged wet conditions. In contrast, higher topographic relief and natural outlets into two intermittent streams at Mt. Moriah resulted in wetlands being less impacted by prolonged wet conditions.

Process-conditioned bias correction for seasonal forecasting: a case-study with ENSO in Peru

NASA Astrophysics Data System (ADS)

Manzanas, R.; Gutiérrez, J. M.

2018-05-01

This work assesses the suitability of a first simple attempt for process-conditioned bias correction in the context of seasonal forecasting. To do this, we focus on the northwestern part of Peru and bias correct 1- and 4-month lead seasonal predictions of boreal winter (DJF) precipitation from the ECMWF System4 forecasting system for the period 1981-2010. In order to include information about the underlying large-scale circulation which may help to discriminate between precipitation affected by different processes, we introduce here an empirical quantile-quantile mapping method which runs conditioned on the state of the Southern Oscillation Index (SOI), which is accurately predicted by System4 and is known to affect the local climate. Beyond the reduction of model biases, our results show that the SOI-conditioned method yields better ROC skill scores and reliability than the raw model output over the entire region of study, whereas the standard unconditioned implementation provides no added value for any of these metrics. This suggests that conditioning the bias correction on simple but well-simulated large-scale processes relevant to the local climate may be a suitable approach for seasonal forecasting. Yet, further research on the suitability of the application of similar approaches to the one considered here for other regions, seasons and/or variables is needed.

Probability modeling of high flow extremes in Yingluoxia watershed, the upper reaches of Heihe River basin

NASA Astrophysics Data System (ADS)

Li, Zhanling; Li, Zhanjie; Li, Chengcheng

2014-05-01

Probability modeling of hydrological extremes is one of the major research areas in hydrological science. Most basins in humid and semi-humid south and east of China are concerned for probability modeling analysis of high flow extremes. While, for the inland river basin which occupies about 35% of the country area, there is a limited presence of such studies partly due to the limited data availability and a relatively low mean annual flow. The objective of this study is to carry out probability modeling of high flow extremes in the upper reach of Heihe River basin, the second largest inland river basin in China, by using the peak over threshold (POT) method and Generalized Pareto Distribution (GPD), in which the selection of threshold and inherent assumptions for POT series are elaborated in details. For comparison, other widely used probability distributions including generalized extreme value (GEV), Lognormal, Log-logistic and Gamma are employed as well. Maximum likelihood estimate is used for parameter estimations. Daily flow data at Yingluoxia station from 1978 to 2008 are used. Results show that, synthesizing the approaches of mean excess plot, stability features of model parameters, return level plot and the inherent independence assumption of POT series, an optimum threshold of 340m3/s is finally determined for high flow extremes in Yingluoxia watershed. The resulting POT series is proved to be stationary and independent based on Mann-Kendall test, Pettitt test and autocorrelation test. In terms of Kolmogorov-Smirnov test, Anderson-Darling test and several graphical diagnostics such as quantile and cumulative density function plots, GPD provides the best fit to high flow extremes in the study area. The estimated high flows for long return periods demonstrate that, as the return period increasing, the return level estimates are probably more uncertain. The frequency of high flow extremes exhibits a very slight but not significant decreasing trend from 1978 to

The myoglobin of Emperor penguin (Aptenodytes forsteri): amino acid sequence and functional adaptation to extreme conditions.

PubMed

Tamburrini, M; Romano, M; Giardina, B; di Prisco, G

1999-02-01

In the framework of a study on molecular adaptations of the oxygen-transport and storage systems to extreme conditions in Antarctic marine organisms, we have investigated the structure/function relationship in Emperor penguin (Aptenodytes forsteri) myoglobin, in search of correlation with the bird life style. In contrast with previous reports, the revised amino acid sequence contains one additional residue and 15 differences. The oxygen-binding parameters seem well adapted to the diving behaviour of the penguin and to the environmental conditions of the Antarctic habitat. Addition of lactate has no major effect on myoglobin oxygenation over a large temperature range. Therefore, metabolic acidosis does not impair myoglobin function under conditions of prolonged physical effort, such as diving.

Sensitivity to change of mobility measures in musculoskeletal conditions on lower extremities in outpatient rehabilitation settings.

PubMed

Navarro-Pujalte, Esther; Gacto-Sánchez, Mariano; Montilla-Herrador, Joaquina; Escolar-Reina, Pilar; Ángeles Franco-Sierra, María; Medina-Mirapeix, Francesc

2018-01-12

Prospective longitudinal study. To examine the sensitivity of the Mobility Activities Measure for lower extremities and to compare it to the sensitivity of the Physical Functioning Scale (PF-10) and the Patient-Specific Functional Scale (PSFS) at week 4 and week 8 post-hospitalization in outpatient rehabilitation settings. Mobility Activities Measure is a set of short mobility measures to track outpatient rehabilitation progress: its scales have shown good properties but its sensitivity to change has not been reported. Patients with musculoskeletal conditions were recruited at admission in three outpatient rehabilitation settings in Spain. Data were collected at admission, week 4 and week 8 from an initial sample of 236 patients (mean age ± SD = 36.7 ± 11.1). Mobility Activities Measure scales for lower extremity; PF-10; and PSFS. All the Mobility Activities Measure scales were sensitive to both positive and negative changes (the Standardized Response Means (SRMs) ranged between 1.05 and 1.53 at week 4, and between 0.63 and 1.47 at week 8). The summary measure encompassing the three Mobility Activities Measure scales detected a higher proportion of participants who had improved beyond the minimal detectable change (MDC) than detected by the PSFS and the PF-10 both at week 4 (86.64% vs. 69.81% and 42.23%, respectively) and week 8 (71.14% vs. 55.65% and 60.81%, respectively). The three Mobility Activities Measure scales assessing the lower extremity can be used across outpatient rehabilitation settings to provide consistent and sensitive measures of changes in patients' mobility. Implications for rehabilitation All the scales of the Mobility Activities Measure for the lower extremity were sensitive to both positive and negative change across the follow-up periods. Overall, the summary measure encompassing the three Mobility Activities Measure scales for the lower extremity appeared more sensitive to positive changes than the Physical Functioning Scale

Stochastic variability in stress, sleep duration, and sleep quality across the distribution of body mass index: insights from quantile regression.

PubMed

Yang, Tse-Chuan; Matthews, Stephen A; Chen, Vivian Y-J

2014-04-01

Obesity has become a problem in the USA and identifying modifiable factors at the individual level may help to address this public health concern. A burgeoning literature has suggested that sleep and stress may be associated with obesity; however, little is know about whether these two factors moderate each other and even less is known about whether their impacts on obesity differ by gender. This study investigates whether sleep and stress are associated with body mass index (BMI) respectively, explores whether the combination of stress and sleep is also related to BMI, and demonstrates how these associations vary across the distribution of BMI values. We analyze the data from 3,318 men and 6,689 women in the Philadelphia area using quantile regression (QR) to evaluate the relationships between sleep, stress, and obesity by gender. Our substantive findings include: (1) high and/or extreme stress were related to roughly an increase of 1.2 in BMI after accounting for other covariates; (2) the pathways linking sleep and BMI differed by gender, with BMI for men increasing by 0.77-1 units with reduced sleep duration and BMI for women declining by 0.12 unit with 1 unit increase in sleep quality; (3) stress- and sleep-related variables were confounded, but there was little evidence for moderation between these two; (4) the QR results demonstrate that the association between high and/or extreme stress to BMI varied stochastically across the distribution of BMI values, with an upward trend, suggesting that stress played a more important role among adults with higher BMI (i.e., BMI > 26 for both genders); and (5) the QR plots of sleep-related variables show similar patterns, with stronger effects on BMI at the upper end of BMI distribution. Our findings suggested that sleep and stress were two seemingly independent predictors for BMI and their relationships with BMI were not constant across the BMI distribution.

heterogeneous mixture distributions for multi-source extreme rainfall

NASA Astrophysics Data System (ADS)

Ouarda, T.; Shin, J.; Lee, T. S.

2013-12-01

Mixture distributions have been used to model hydro-meteorological variables showing mixture distributional characteristics, e.g. bimodality. Homogeneous mixture (HOM) distributions (e.g. Normal-Normal and Gumbel-Gumbel) have been traditionally applied to hydro-meteorological variables. However, there is no reason to restrict the mixture distribution as the combination of one identical type. It might be beneficial to characterize the statistical behavior of hydro-meteorological variables from the application of heterogeneous mixture (HTM) distributions such as Normal-Gamma. In the present work, we focus on assessing the suitability of HTM distributions for the frequency analysis of hydro-meteorological variables. In the present work, in order to estimate the parameters of HTM distributions, the meta-heuristic algorithm (Genetic Algorithm) is employed to maximize the likelihood function. In the present study, a number of distributions are compared, including the Gamma-Extreme value type-one (EV1) HTM distribution, the EV1-EV1 HOM distribution, and EV1 distribution. The proposed distribution models are applied to the annual maximum precipitation data in South Korea. The Akaike Information Criterion (AIC), the root mean squared errors (RMSE) and the log-likelihood are used as measures of goodness-of-fit of the tested distributions. Results indicate that the HTM distribution (Gamma-EV1) presents the best fitness. The HTM distribution shows significant improvement in the estimation of quantiles corresponding to the 20-year return period. It is shown that extreme rainfall in the coastal region of South Korea presents strong heterogeneous mixture distributional characteristics. Results indicate that HTM distributions are a good alternative for the frequency analysis of hydro-meteorological variables when disparate statistical characteristics are presented.

On the distributions of annual and seasonal daily rainfall extremes in central Arizona and their spatial variability

NASA Astrophysics Data System (ADS)

Mascaro, Giuseppe

2018-04-01

This study uses daily rainfall records of a dense network of 240 gauges in central Arizona to gain insights on (i) the variability of the seasonal distributions of rainfall extremes; (ii) how the seasonal distributions affect the shape of the annual distribution; and (iii) the presence of spatial patterns and orographic control for these distributions. For this aim, recent methodological advancements in peak-over-threshold analysis and application of the Generalized Pareto Distribution (GPD) were used to assess the suitability of the GPD hypothesis and improve the estimation of its parameters, while limiting the effect of short sample sizes. The distribution of daily rainfall extremes was found to be heavy-tailed (i.e., GPD shape parameter ξ > 0) during the summer season, dominated by convective monsoonal thunderstorms. The exponential distribution (a special case of GPD with ξ = 0) was instead showed to be appropriate for modeling wintertime daily rainfall extremes, mainly caused by cold fronts transported by westerly flow. The annual distribution exhibited a mixed behavior, with lighter upper tails than those found in summer. A hybrid model mixing the two seasonal distributions was demonstrated capable of reproducing the annual distribution. Organized spatial patterns, mainly controlled by elevation, were observed for the GPD scale parameter, while ξ did not show any clear control of location or orography. The quantiles returned by the GPD were found to be very similar to those provided by the National Oceanic and Atmospheric Administration (NOAA) Atlas 14, which used the Generalized Extreme Value (GEV) distribution. Results of this work are useful to improve statistical modeling of daily rainfall extremes at high spatial resolution and provide diagnostic tools for assessing the ability of climate models to simulate extreme events.

Does red noise increase or decrease extinction risk? Single extreme events versus series of unfavorable conditions.

PubMed

Schwager, Monika; Johst, Karin; Jeltsch, Florian

2006-06-01

Recent theoretical studies have shown contrasting effects of temporal correlation of environmental fluctuations (red noise) on the risk of population extinction. It is still debated whether and under which conditions red noise increases or decreases extinction risk compared with uncorrelated (white) noise. Here, we explain the opposing effects by introducing two features of red noise time series. On the one hand, positive autocorrelation increases the probability of series of poor environmental conditions, implying increasing extinction risk. On the other hand, for a given time period, the probability of at least one extremely bad year ("catastrophe") is reduced compared with white noise, implying decreasing extinction risk. Which of these two features determines extinction risk depends on the strength of environmental fluctuations and the sensitivity of population dynamics to these fluctuations. If extreme (catastrophic) events can occur (strong noise) or sensitivity is high (overcompensatory density dependence), then temporal correlation decreases extinction risk; otherwise, it increases it. Thus, our results provide a simple explanation for the contrasting previous findings and are a crucial step toward a general understanding of the effect of noise color on extinction risk.

The National Ignition Facility: an experimental platform for studying behavior of matter under extreme conditions

NASA Astrophysics Data System (ADS)

Moses, Edward

2011-11-01

The National Ignition Facility (NIF), a 192-beam Nd-glass laser facility capable of producing 1.8 MJ and 500 TW of ultraviolet light, is now operational at Lawrence Livermore National Laboratory (LLNL). As the world's largest and most energetic laser system, NIF serves as the national center for the U.S. Department of Energy (DOE) and National Nuclear Security Administration to achieve thermonuclear burn in the laboratory and to explore the behavior of matter at extreme temperatures and energy densities. By concentrating the energy from all of its 192 extremely energetic laser beams into a mm3-sized target, NIF can reach the conditions required to initiate fusion reactions. NIF can also provide access to extreme scientific environments: temperatures about 100 million K, densities of 1,000 g/cm3, and pressures 100 billion times atmospheric pressure. These conditions have never been created before in a laboratory and exist naturally only in interiors of the planetary and stellar environments as well as in nuclear weapons. Since August 2009, the NIF team has been conducting experiments in support of the National Ignition Campaign (NIC)—a partnership among LLNL, Los Alamos National Laboratory, General Atomics, the University of Rochester, Sandia National Laboratories, as well as a number of universities and international collaborators. The results from these initial experiments show promise for the relatively near-term achievement of ignition. Capsule implosion experiments at energies up to 1.2 MJ have demonstrated laser energetics, radiation temperatures, and symmetry control that scale to ignition conditions. Of particular importance is the demonstration of peak hohlraum temperatures near 300 eV with overall backscatter less than 10%. Cryogenic target capability and additional diagnostics are being installed in preparation for layered target deuterium-tritium implosions to be conducted later in 2010. Important national security and basic science experiments have

Efficacy of a prehospital self-expanding polyurethane foam for noncompressible hemorrhage under extreme operational conditions.

PubMed

Rago, Adam P; Larentzakis, Andreas; Marini, John; Picard, Abby; Duggan, Michael J; Busold, Rany; Helmick, Marc; Zugates, Greg; Beagle, John; Sharma, Upma; King, David R

2015-02-01

Noncompressible abdominal hemorrhage is a significant cause of battlefield and civilian mortality. We developed a self-expanding polyurethane foam intended to provide temporary hemorrhage control and enable evacuation to a definitive surgical capability, for casualties who would otherwise die. We hypothesized that foam treatment would be efficacious over a wide range of out-of-hospital operational conditions. The foam was tested in an established lethal, closed-cavity hepatoportal injury model in four groups as follows. Group 1 involved baseline conditions, wherein foam was deployed from a pneumatically driven, first-generation delivery device at room temperature (n = 6). Group 2 involved foam deployment from a field-relevant, handheld delivery prototype (n = 12). Group 3 involved foam components that were conditioned to simulate 1-year shelf-life (n = 6). Group 4 involved foam that was conditioned to a range of temperatures (10 °C and 50 °C; n = 6 per group). In all studies, survival was monitored for up to 180 minutes and compared with an ongoing and accumulating control group with no intervention (n = 14). In Group 1 with a first-generation delivery system, foam treatment resulted in a significant survival advantage relative to the control group (p < 0.001), confirming previous results. In Group 2 with a handheld delivery system, survival was also improved, 83% at 3 hours, compared with 7% in the control group (p < 0.001). In Group 3, survival was 83% at 3 hours (p = 0.002). In Group 4 at temperature extremes, 3-hour survival was 83% (p = 0.002) and 67% (p = 0.014) in the low- and high-temperature groups, respectively. Temperature extremes did not result in hypothermia, hyperthermia, or thermal injury. In all studies, the bleeding rate in foam groups was significantly lower than in the control group (p < 0.05). Under a range of military operational conditions, foam treatment resulted in a survival advantage relative to the control group. This supports the

Cyclone-induced rapid creation of extreme Antarctic sea ice conditions

PubMed Central

Wang, Zhaomin; Turner, John; Sun, Bo; Li, Bingrui; Liu, Chengyan

2014-01-01

Two polar vessels, Akademik Shokalskiy and Xuelong, were trapped by thick sea ice in the Antarctic coastal region just to the west of 144°E and between 66.5°S and 67°S in late December 2013. This event demonstrated the rapid establishment of extreme Antarctic sea ice conditions on synoptic time scales. The event was associated with cyclones that developed at lower latitudes. Near the event site, cyclone-enhanced strong southeasterly katabatic winds drove large westward drifts of ice floes. In addition, the cyclones also gave southward ice drift. The arrival and grounding of Iceberg B9B in Commonwealth Bay in March 2011 led to the growth of fast ice around it, forming a northward protruding barrier. This barrier blocked the westward ice drift and hence aided sea ice consolidation on its eastern side. Similar cyclone-induced events have occurred at this site in the past after the grounding of Iceberg B9B. Future events may be predictable on synoptic time scales, if cyclone-induced strong wind events can be predicted. PMID:24937550

A quantile-based scenario analysis approach to biomass supply chain optimization under uncertainty

DOE PAGES

Zamar, David S.; Gopaluni, Bhushan; Sokhansanj, Shahab; ...

2016-11-21

Supply chain optimization for biomass-based power plants is an important research area due to greater emphasis on renewable power energy sources. Biomass supply chain design and operational planning models are often formulated and studied using deterministic mathematical models. While these models are beneficial for making decisions, their applicability to real world problems may be limited because they do not capture all the complexities in the supply chain, including uncertainties in the parameters. This study develops a statistically robust quantile-based approach for stochastic optimization under uncertainty, which builds upon scenario analysis. We apply and evaluate the performance of our approach tomore » address the problem of analyzing competing biomass supply chains subject to stochastic demand and supply. Finally, the proposed approach was found to outperform alternative methods in terms of computational efficiency and ability to meet the stochastic problem requirements.« less

A quantile-based scenario analysis approach to biomass supply chain optimization under uncertainty

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

Zamar, David S.; Gopaluni, Bhushan; Sokhansanj, Shahab

Supply chain optimization for biomass-based power plants is an important research area due to greater emphasis on renewable power energy sources. Biomass supply chain design and operational planning models are often formulated and studied using deterministic mathematical models. While these models are beneficial for making decisions, their applicability to real world problems may be limited because they do not capture all the complexities in the supply chain, including uncertainties in the parameters. This study develops a statistically robust quantile-based approach for stochastic optimization under uncertainty, which builds upon scenario analysis. We apply and evaluate the performance of our approach tomore » address the problem of analyzing competing biomass supply chains subject to stochastic demand and supply. Finally, the proposed approach was found to outperform alternative methods in terms of computational efficiency and ability to meet the stochastic problem requirements.« less

Parameter uncertainty and nonstationarity in regional extreme rainfall frequency analysis in Qu River Basin, East China

NASA Astrophysics Data System (ADS)

Zhu, Q.; Xu, Y. P.; Gu, H.

2014-12-01

Traditionally, regional frequency analysis methods were developed for stationary environmental conditions. Nevertheless, recent studies have identified significant changes in hydrological records, leading to the 'death' of stationarity. Besides, uncertainty in hydrological frequency analysis is persistent. This study aims to investigate the impact of one of the most important uncertainty sources, parameter uncertainty, together with nonstationarity, on design rainfall depth in Qu River Basin, East China. A spatial bootstrap is first proposed to analyze the uncertainty of design rainfall depth estimated by regional frequency analysis based on L-moments and estimated on at-site scale. Meanwhile, a method combining the generalized additive models with 30-year moving window is employed to analyze non-stationarity existed in the extreme rainfall regime. The results show that the uncertainties of design rainfall depth with 100-year return period under stationary conditions estimated by regional spatial bootstrap can reach 15.07% and 12.22% with GEV and PE3 respectively. On at-site scale, the uncertainties can reach 17.18% and 15.44% with GEV and PE3 respectively. In non-stationary conditions, the uncertainties of maximum rainfall depth (corresponding to design rainfall depth) with 0.01 annual exceedance probability (corresponding to 100-year return period) are 23.09% and 13.83% with GEV and PE3 respectively. Comparing the 90% confidence interval, the uncertainty of design rainfall depth resulted from parameter uncertainty is less than that from non-stationarity frequency analysis with GEV, however, slightly larger with PE3. This study indicates that the spatial bootstrap can be successfully applied to analyze the uncertainty of design rainfall depth on both regional and at-site scales. And the non-stationary analysis shows that the differences between non-stationary quantiles and their stationary equivalents are important for decision makes of water resources management

Microbial diversity of extreme habitats in human homes.

PubMed

Savage, Amy M; Hills, Justin; Driscoll, Katherine; Fergus, Daniel J; Grunden, Amy M; Dunn, Robert R

2016-01-01

High-throughput sequencing techniques have opened up the world of microbial diversity to scientists, and a flurry of studies in the most remote and extreme habitats on earth have begun to elucidate the key roles of microbes in ecosystems with extreme conditions. These same environmental extremes can also be found closer to humans, even in our homes. Here, we used high-throughput sequencing techniques to assess bacterial and archaeal diversity in the extreme environments inside human homes (e.g., dishwashers, hot water heaters, washing machine bleach reservoirs, etc.). We focused on habitats in the home with extreme temperature, pH, and chemical environmental conditions. We found a lower diversity of microbes in these extreme home environments compared to less extreme habitats in the home. However, we were nonetheless able to detect sequences from a relatively diverse array of bacteria and archaea. Habitats with extreme temperatures alone appeared to be able to support a greater diversity of microbes than habitats with extreme pH or extreme chemical environments alone. Microbial diversity was lowest when habitats had both extreme temperature and one of these other extremes. In habitats with both extreme temperatures and extreme pH, taxa with known associations with extreme conditions dominated. Our findings highlight the importance of examining interactive effects of multiple environmental extremes on microbial communities. Inasmuch as taxa from extreme environments can be both beneficial and harmful to humans, our findings also suggest future work to understand both the threats and opportunities posed by the life in these habitats.

Survival in Extreme Conditions.

ERIC Educational Resources Information Center

Bloom, Martin; Halsema, James

1983-01-01

Explores the psychosocial and environmental configurations involved in the survival of 500 civilians in a Japanese internment camp in the Philippines during World War II. Although conditions were very harsh, the survival rate of this group was better than expected. Discusses available demographic, social organizational, and cultural information.…

The heterogeneous effects of urbanization and income inequality on CO2 emissions in BRICS economies: evidence from panel quantile regression.

PubMed

Zhu, Huiming; Xia, Hang; Guo, Yawei; Peng, Cheng

2018-04-12

This paper empirically examines the effects of urbanization and income inequality on CO 2 emissions in the BRICS economies (i.e., Brazil, Russia, India, China, and South Africa) during the periods 1994-2013. The method we used is the panel quantile regression, which takes into account the unobserved individual heterogeneity and distributional heterogeneity. Our empirical results indicate that urbanization has a significant and negative impact on carbon emissions, except in the 80 th , 90 th , and 95 th quantiles. We also quantitatively investigate the direct and indirect effect of urbanization on carbon emissions, and the results show that we may underestimate urbanization's effect on carbon emissions if we ignore its indirect effect. In addition, in middle- and high-emission countries, income inequality has a significant and positive impact on carbon emissions. The results of our study indicate that in the BRICS economies, there is an inverted U-shaped environmental Kuznets curve (EKC) between the GDP per capita and carbon emissions. The conclusions of this study have important policy implications for policymakers. Policymakers should try to narrow the income gap between the rich and the poor to improve environmental quality; the BRICS economies can speed up urbanization to reduce carbon emissions, but they must improve energy efficiency and use clean energy to the greatest extent in the process.

High-fidelity numerical modeling of the Upper Mississippi River under extreme flood condition

NASA Astrophysics Data System (ADS)

Khosronejad, Ali; Le, Trung; DeWall, Petra; Bartelt, Nicole; Woldeamlak, Solomon; Yang, Xiaolei; Sotiropoulos, Fotis

2016-12-01

We present data-driven numerical simulations of extreme flooding in a large-scale river coupling coherent-structure resolving hydrodynamics with bed morphodynamics under live-bed conditions. The study area is a ∼ 3.2 km long and ∼ 300 m wide reach of the Upper Mississippi River, near Minneapolis MN, which contains several natural islands and man-made hydraulic structures. We employ the large-eddy simulation (LES) and bed-morphodynamic modules of the Virtual Flow Simulator (VFS-Rivers) model, a recently developed in-house code, to investigate the flow and bed evolution of the river during a 100-year flood event. The coupling of the two modules is carried out via a fluid-structure interaction approach using a nested domain approach to enhance the resolution of bridge scour predictions. We integrate data from airborne Light Detection and Ranging (LiDAR), sub-aqueous sonar apparatus on-board a boat and in-situ laser scanners to construct a digital elevation model of the river bathymetry and surrounding flood plain, including islands and bridge piers. A field campaign under base-flow condition is also carried out to collect mean flow measurements via Acoustic Doppler Current Profiler (ADCP) to validate the hydrodynamic module of the VFS-Rivers model. Our simulation results for the bed evolution of the river under the 100-year flood reveal complex sediment transport dynamics near the bridge piers consisting of both scour and refilling events due to the continuous passage of sand dunes. We find that the scour depth near the bridge piers can reach to a maximum of ∼ 9 m. The data-driven simulation strategy we present in this work exemplifies a practical simulation-based-engineering-approach to investigate the resilience of infrastructures to extreme flood events in intricate field-scale riverine systems.

Quantifying the relationship between extreme air pollution events and extreme weather events

NASA Astrophysics Data System (ADS)

Zhang, Henian; Wang, Yuhang; Park, Tae-Won; Deng, Yi

2017-05-01

Extreme weather events can strongly affect surface air quality, which has become a major environmental factor to affect human health. Here, we examined the relationship between extreme ozone and PM2.5 (particular matter with an aerodynamic diameter less than 2.5 μm) events and the representative meteorological parameters such as daily maximum temperature (Tmax), minimum relative humidity (RHmin), and minimum wind speed (Vmin), using the location-specific 95th or 5th percentile threshold derived from historical reanalysis data (30 years for ozone and 10 years for PM2.5). We found that ozone and PM2.5 extremes were decreasing over the years, reflecting EPA's tightened standards and effort on reducing the corresponding precursor's emissions. Annual ozone and PM2.5 extreme days were highly correlated with Tmax and RHmin, especially in the eastern U.S. They were positively (negatively) correlated with Vmin in urban (rural and suburban) stations. The overlapping ratios of ozone extreme days with Tmax were fairly constant, about 32%, and tended to be high in fall and low in winter. Ozone extreme days were most sensitive to Tmax, then RHmin, and least sensitive to Vmin. The majority of ozone extremes occurred when Tmax was between 300 K and 320 K, RHmin was less than 40%, and Vmin was less than 3 m/s. The number of annual extreme PM2.5 days was highly positively correlated with the extreme RHmin/Tmax days, with correlation coefficient between PM2.5/RHmin highest in urban and suburban regions and the correlation coefficient between PM2.5/Tmax highest in rural area. Tmax has more impact on PM2.5 extreme over the eastern U.S. Extreme PM2.5 days were more likely to occur at low RH conditions in the central and southeastern U.S., especially during spring time, and at high RH conditions in the northern U.S. and the Great Plains. Most extreme PM2.5 events occurred when Tmax was between 300 K and 320 K and RHmin was between 10% and 50%. Extreme PM2.5 days usually occurred when

Dynamic extreme values modeling and monitoring by means of sea shores water quality biomarkers and valvometry.

PubMed

Durrieu, Gilles; Pham, Quang-Khoai; Foltête, Anne-Sophie; Maxime, Valérie; Grama, Ion; Tilly, Véronique Le; Duval, Hélène; Tricot, Jean-Marie; Naceur, Chiraz Ben; Sire, Olivier

2016-07-01

Water quality can be evaluated using biomarkers such as tissular enzymatic activities of endemic species. Measurement of molluscs bivalves activity at high frequency (e.g., valvometry) during a long time period is another way to record the animal behavior and to evaluate perturbations of the water quality in real time. As the pollution affects the activity of oysters, we consider the valves opening and closing velocities to monitor the water quality assessment. We propose to model the huge volume of velocity data collected in the framework of valvometry using a new nonparametric extreme values statistical model. The objective is to estimate the tail probabilities and the extreme quantiles of the distribution of valve closing velocity. The tail of the distribution function of valve closing velocity is modeled by a Pareto distribution with parameter t,τ , beyond a threshold τ according to the time t of the experiment. Our modeling approach reveals the dependence between the specific activity of two enzymatic biomarkers (Glutathione-S-transferase and acetylcholinesterase) and the continuous recording of oyster valve velocity, proving the suitability of this tool for water quality assessment. Thus, valvometry allows in real-time in situ analysis of the bivalves behavior and appears as an effective early warning tool in ecological risk assessment and marine environment monitoring.

The phase-contrast imaging instrument at the matter in extreme conditions endstation at LCLS

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

Nagler, Bob; Schropp, Andreas; Galtier, Eric C.

2016-10-07

Here, we describe the phase-contrast imaging instrument at the Matter in Extreme Conditions (MEC) endstation of the Linac Coherent Light Source. The instrument can image phenomena with a spatial resolution of a few hundreds of nanometers and at the same time reveal the atomic structure through X-ray diffraction, with a temporal resolution better than 100 fs. It was specifically designed for studies relevant to high-energy-density science and can monitor, e.g., shock fronts, phase transitions, or void collapses. This versatile instrument was commissioned last year and is now available to the MEC user community.

Patient characteristics associated with differences in radiation exposure from pediatric abdomen-pelvis CT scans: a quantile regression analysis.

PubMed

Cooper, Jennifer N; Lodwick, Daniel L; Adler, Brent; Lee, Choonsik; Minneci, Peter C; Deans, Katherine J

2017-06-01

Computed tomography (CT) is a widely used diagnostic tool in pediatric medicine. However, due to concerns regarding radiation exposure, it is essential to identify patient characteristics associated with higher radiation burden from CT imaging, in order to more effectively target efforts towards dose reduction. Our objective was to identify the effects of various demographic and clinical patient characteristics on radiation exposure from single abdomen/pelvis CT scans in children. CT scans performed at our institution between January 2013 and August 2015 in patients under 16 years of age were processed using a software tool that estimates patient-specific organ and effective doses and merges these estimates with data from the electronic health record and billing record. Quantile regression models at the 50th, 75th, and 90th percentiles were used to estimate the effects of patients' demographic and clinical characteristics on effective dose. 2390 abdomen/pelvis CT scans (median effective dose 1.52mSv) were included. Of all characteristics examined, only older age, female gender, higher BMI, and whether the scan was a multiphase exam or an exam that required repeating for movement were significant predictors of higher effective dose at each quantile examined (all p<0.05). The effects of obesity and multiphase or repeat scanning on effective dose were magnified in higher dose scans. Older age, female gender, obesity, and multiphase or repeat scanning are all associated with increased effective dose from abdomen/pelvis CT. Targeted efforts to reduce dose from abdominal CT in these groups should be undertaken. Copyright © 2017 Elsevier Ltd. All rights reserved.

Evolution of Precipitation Extremes in Three Large Ensembles of Climate Simulations - Impact of Spatial and Temporal Resolutions

NASA Astrophysics Data System (ADS)

Martel, J. L.; Brissette, F.; Mailhot, A.; Wood, R. R.; Ludwig, R.; Frigon, A.; Leduc, M.; Turcotte, R.

2017-12-01

Recent studies indicate that the frequency and intensity of extreme precipitation will increase in future climate due to global warming. In this study, we compare annual maxima precipitation series from three large ensembles of climate simulations at various spatial and temporal resolutions. The first two are at the global scale: the Canadian Earth System Model (CanESM2) 50-member large ensemble (CanESM2-LE) at a 2.8° resolution and the Community Earth System Model (CESM1) 40-member large ensemble (CESM1-LE) at a 1° resolution. The third ensemble is at the regional scale over both Eastern North America and Europe: the Canadian Regional Climate Model (CRCM5) 50-member large ensemble (CRCM5-LE) at a 0.11° resolution, driven at its boundaries by the CanESM-LE. The CRCM5-LE is a new ensemble issued from the ClimEx project (http://www.climex-project.org), a Québec-Bavaria collaboration. Using these three large ensembles, change in extreme precipitations over the historical (1980-2010) and future (2070-2100) periods are investigated. This results in 1 500 (30 years x 50 members for CanESM2-LE and CRCM5-LE) and 1200 (30 years x 40 members for CESM1-LE) simulated years over both the historical and future periods. Using these large datasets, the empirical daily (and sub-daily for CRCM5-LE) extreme precipitation quantiles for large return periods ranging from 2 to 100 years are computed. Results indicate that daily extreme precipitations generally will increase over most land grid points of both domains according to the three large ensembles. Regarding the CRCM5-LE, the increase in sub-daily extreme precipitations will be even more important than the one observed for daily extreme precipitations. Considering that many public infrastructures have lifespans exceeding 75 years, the increase in extremes has important implications on service levels of water infrastructures and public safety.

Solar Wind Interaction with the Martian Upper Atmosphere at Early Mars/Extreme Solar Conditions

NASA Astrophysics Data System (ADS)

Dong, C.; Bougher, S. W.; Ma, Y.; Toth, G.; Lee, Y.; Nagy, A. F.; Tenishev, V.; Pawlowski, D. J.; Combi, M. R.

2014-12-01

The investigation of ion escape fluxes from Mars, resulting from the solar wind interaction with its upper atmosphere/ionosphere, is important due to its potential impact on the long-term evolution of Mars atmosphere (e.g., loss of water) over its history. In the present work, we adopt the 3-D Mars cold neutral atmosphere profiles (0 ~ 300 km) from the newly developed and validated Mars Global Ionosphere Thermosphere Model (M-GITM) and the 3-D hot oxygen profiles (100 km ~ 5 RM) from the exosphere Monte Carlo model Adaptive Mesh Particle Simulator (AMPS). We apply these 3-D model output fields into the 3-D BATS-R-US Mars multi-fluid MHD (MF-MHD) model (100 km ~ 20 RM) that can simulate the interplay between Mars upper atmosphere and solar wind by considering the dynamics of individual ion species. The multi-fluid MHD model solves separate continuity, momentum and energy equations for each ion species (H+, O+, O2+, CO2+). The M-GITM model together with the AMPS exosphere model take into account the effects of solar cycle and seasonal variations on both cold and hot neutral atmospheres. This feature allows us to investigate the corresponding effects on the Mars upper atmosphere ion escape by using a one-way coupling approach, i.e., both the M-GITM and AMPS model output fields are used as the input for the multi-fluid MHD model and the M-GITM is used as input into the AMPS exosphere model. In this study, we present M-GITM, AMPS, and MF-MHD calculations (1-way coupled) for 2.5 GYA conditions and/or extreme solar conditions for present day Mars (high solar wind velocities, high solar wind dynamic pressure, and high solar irradiance conditions, etc.). Present day extreme conditions may result in MF-MHD outputs that are similar to 2.5 GYA cases. The crustal field orientations are also considered in this study. By comparing estimates of past ion escape rates with the current ion loss rates to be returned by the MAVEN spacecraft (2013-2016), we can better constrain the

Establishment and performance of an experimental green roof under extreme climatic conditions.

PubMed

Klein, Petra M; Coffman, Reid

2015-04-15

Green roofs alter the surface energy balance and can help in mitigating urban heat islands. However, the cooling of green roofs due to evapotranspiration strongly depends on the climatic conditions, and vegetation type and density. In the Southern Central Plains of the United States, extreme weather events, such as high winds, heat waves and drought conditions pose challenges for successful implementation of green roofs, and likely alter their standard performance. The National Weather Center Experimental Green Roof, an interdisciplinary research site established in 2010 in Norman, OK, aimed to investigate the ecological performance and surface energy balance of green roof systems. Starting in May 2010, 26 months of vegetation studies were conducted and the radiation balance, air temperature, relative humidity, and buoyancy fluxes were monitored at two meteorological stations during April-October 2011. The establishment of a vegetative community trended towards prairie plant dominance. High mortality of succulents and low germination of grasses and herbaceous plants contributed to low vegetative coverage. In this condition succulent diversity declined. Bouteloua gracilis and Delosperma cooperi showed typological dominance in harsh climatic conditions, while Sedum species experienced high mortality. The plant community diversified through volunteers such as Euphorbia maculate and Portulaca maculate. Net radiation measured at a green-roof meteorological station was higher than at a control station over the original, light-colored roofing material. These findings indicate that the albedo of the green roof was lower than the albedo of the original roofing material. The low vegetative coverage during the heat and drought conditions in 2011, which resulted in the dark substrate used in the green roof containers being exposed, likely contributed to the low albedo values. Nevertheless, air temperatures and buoyancy fluxes were often lower over the green roof indicating

The use of quantile regression to forecast higher than expected respiratory deaths in a daily time series: a study of New York City data 1987-2000.

PubMed

Soyiri, Ireneous N; Reidpath, Daniel D

2013-01-01

Forecasting higher than expected numbers of health events provides potentially valuable insights in its own right, and may contribute to health services management and syndromic surveillance. This study investigates the use of quantile regression to predict higher than expected respiratory deaths. Data taken from 70,830 deaths occurring in New York were used. Temporal, weather and air quality measures were fitted using quantile regression at the 90th-percentile with half the data (in-sample). Four QR models were fitted: an unconditional model predicting the 90th-percentile of deaths (Model 1), a seasonal/temporal (Model 2), a seasonal, temporal plus lags of weather and air quality (Model 3), and a seasonal, temporal model with 7-day moving averages of weather and air quality. Models were cross-validated with the out of sample data. Performance was measured as proportionate reduction in weighted sum of absolute deviations by a conditional, over unconditional models; i.e., the coefficient of determination (R1). The coefficient of determination showed an improvement over the unconditional model between 0.16 and 0.19. The greatest improvement in predictive and forecasting accuracy of daily mortality was associated with the inclusion of seasonal and temporal predictors (Model 2). No gains were made in the predictive models with the addition of weather and air quality predictors (Models 3 and 4). However, forecasting models that included weather and air quality predictors performed slightly better than the seasonal and temporal model alone (i.e., Model 3 > Model 4 > Model 2) This study provided a new approach to predict higher than expected numbers of respiratory related-deaths. The approach, while promising, has limitations and should be treated at this stage as a proof of concept.

The Use of Quantile Regression to Forecast Higher Than Expected Respiratory Deaths in a Daily Time Series: A Study of New York City Data 1987-2000

PubMed Central

Soyiri, Ireneous N.; Reidpath, Daniel D.

2013-01-01

Forecasting higher than expected numbers of health events provides potentially valuable insights in its own right, and may contribute to health services management and syndromic surveillance. This study investigates the use of quantile regression to predict higher than expected respiratory deaths. Data taken from 70,830 deaths occurring in New York were used. Temporal, weather and air quality measures were fitted using quantile regression at the 90th-percentile with half the data (in-sample). Four QR models were fitted: an unconditional model predicting the 90th-percentile of deaths (Model 1), a seasonal / temporal (Model 2), a seasonal, temporal plus lags of weather and air quality (Model 3), and a seasonal, temporal model with 7-day moving averages of weather and air quality. Models were cross-validated with the out of sample data. Performance was measured as proportionate reduction in weighted sum of absolute deviations by a conditional, over unconditional models; i.e., the coefficient of determination (R1). The coefficient of determination showed an improvement over the unconditional model between 0.16 and 0.19. The greatest improvement in predictive and forecasting accuracy of daily mortality was associated with the inclusion of seasonal and temporal predictors (Model 2). No gains were made in the predictive models with the addition of weather and air quality predictors (Models 3 and 4). However, forecasting models that included weather and air quality predictors performed slightly better than the seasonal and temporal model alone (i.e., Model 3 > Model 4 > Model 2) This study provided a new approach to predict higher than expected numbers of respiratory related-deaths. The approach, while promising, has limitations and should be treated at this stage as a proof of concept. PMID:24147122

Multi-catchment rainfall-runoff simulation for extreme flood estimation

NASA Astrophysics Data System (ADS)

Paquet, Emmanuel

2017-04-01

The SCHADEX method (Paquet et al., 2013) is a reference method in France for the estimation of extreme flood for dam design. The method is based on a semi-continuous rainfall-runoff simulation process: hundreds of different rainy events, randomly drawn up to extreme values, are simulated independently in the hydrological conditions of each day when a rainy event has been actually observed. This allows generating an exhaustive set of crossings between precipitation and soil saturation hazards, and to build a complete distribution of flood discharges up to extreme quantiles. The hydrological model used within SCHADEX, the MORDOR model (Garçon, 1996), is a lumped model, which implies that hydrological processes, e.g. rainfall and soil saturation, are supposed to be homogeneous throughout the catchment. Snow processes are nevertheless represented in relation with altitude. This hypothesis of homogeneity is questionable especially as the size of the catchment increases, or in areas of highly contrasted climatology (like mountainous areas). Conversely, modeling the catchment with a fully distributed approach would cause different problems, in particular distributing the rainfall-runoff model parameters trough space, and within the SCHADEX stochastic framework, generating extreme rain fields with credible spatio-temporal features. An intermediate solution is presented here. It provides a better representation of the hydro-climatic diversity of the studied catchment (especially regarding flood processes) while keeping the SCHADEX simulation framework. It consists in dividing the catchment in several, more homogeneous sub-catchments. Rainfall-runoff models are parameterized individually for each of them, using local discharge data if available. A first SCHADEX simulation is done at the global scale, which allows assigning a probability to each simulated event, mainly based on the global areal rainfall drawn for the event (see Paquet el al., 2013 for details). Then the

Combining Radar and Daily Precipitation Data to Estimate Meaningful Sub-daily Precipitation Extremes

NASA Astrophysics Data System (ADS)

Pegram, G. G. S.; Bardossy, A.

2016-12-01

Short duration extreme rainfalls are important for design. The purpose of this presentation is not to improve the day by day estimation of precipitation, but to obtain reasonable statistics for the subdaily extremes at gauge locations. We are interested specifically in daily and sub-daily extreme values of precipitation at gauge locations. We do not employ the common procedure of using time series of control station to determine the missing data values in a target. We are interested in individual rare events, not sequences. The idea is to use radar to disaggregate daily totals to sub-daily amounts. In South Arica, an S-band radar operated relatively continuously at Bethlehem from 1998 to 2003, whose scan at 1.5 km above ground [CAPPI] overlapped a dense (10 km spacing) set of 45 pluviometers recording in the same 6-year period. Using this valuable set of data, we are only interested in rare extremes, therefore small to medium values of rainfall depth were neglected, leaving 12 days of ranked daily maxima in each set per year, whose sum typically comprised about 50% of each annual rainfall total. The method presented here uses radar for disaggregating daily gauge totals in subdaily intervals down to 15 minutes in order to extract the maxima of sub-hourly through to daily rainfall at each of 37 selected radar pixels [1 km square in plan] which contained one of the 45 pluviometers not masked out by the radar foot-print. The pluviometer data were aggregated to daily totals, to act as if they were daily read gauges; their only other task was to help in the cross-validation exercise. The extrema were obtained as quantiles by ordering the 12 daily maxima of each interval per year. The unusual and novel goal was not to obtain the reproduction of the precipitation matching in space and time, but to obtain frequency distributions of the gauge and radar extremes, by matching their ranks, which we found to be stable and meaningful in cross-validation tests. We provide and

Identification of Extreme Events Under Climate Change Conditions Over Europe and The Northwest-atlantic Region: Spatial Patterns and Time Series Characteristics

NASA Astrophysics Data System (ADS)

Leckebusch, G.; Ulbrich, U.; Speth, P.

In the context of climate change and the resulting possible impacts on socio-economic conditions for human activities it seems that due to a changed occurrence of extreme events more severe consequences have to be expected than from changes in the mean climate. These extreme events like floods, excessive heats and droughts or windstorms possess impacts on human social and economic life in different categories such as forestry, agriculture, energy use, tourism and the reinsurance business. Reinsurances are affected by nearly 70% of all insured damages over Europe in the case of wind- storms. Especially the December 1999 French windstorms caused damages about 10 billion. A new EU-founded project (MICE = Modelling the Impact of Climate Ex- tremes) will focus on these impacts caused by changed occurrences of extreme events over Europe. Based upon the output of general circulation models as well as regional climate models, investigations are carried out with regard to time series characteristics as well as the spatial patterns of extremes under climate changed conditions. After the definition of specific thresholds for climate extremes, in this talk we will focus on the results of the analysis for the different data sets (HadCM3 and CGCMII GCM's and RCM's, re-analyses, observations) with regard to windstorm events. At first the results of model outputs are validated against re-analyses and observations. Especially a comparison of the stormtrack (2.5 to 8 day bandpass filtered 500 hPa geopotential height), cyclone track, cyclone frequency and intensity is presented. Highly relevant to damages is the extreme wind near the ground level, so the 10 m wind speed will be investigated additionally. of special interest to possible impacts is the changed spatial occurrence of windspeed maxima under 2xCO2-induced climate change.

Soda pans of the Pannonian steppe harbor unique bacterial communities adapted to multiple extreme conditions.

PubMed

Szabó, Attila; Korponai, Kristóf; Kerepesi, Csaba; Somogyi, Boglárka; Vörös, Lajos; Bartha, Dániel; Márialigeti, Károly; Felföldi, Tamás

2017-05-01

Soda pans of the Pannonian steppe are unique environments regarding their physical and chemical characteristics: shallowness, high turbidity, intermittent character, alkaline pH, polyhumic organic carbon concentration, hypertrophic condition, moderately high salinity, sodium and carbonate ion dominance. The pans are highly productive environments with picophytoplankton predominance. Little is known about the planktonic bacterial communities inhabiting these aquatic habitats; therefore, amplicon sequencing and shotgun metagenomics were applied to reveal their composition and functional properties. Results showed a taxonomically complex bacterial community which was distinct from other soda lakes regarding its composition, e.g. the dominance of class Alphaproteobacteria was observed within phylum Proteobacteria. The shotgun metagenomic analysis revealed several functional gene components related to the harsh and at the same time hypertrophic environmental conditions, e.g. proteins involved in stress response, transport and hydrolase systems targeting phytoplankton-derived organic matter. This is the first detailed report on the indigenous planktonic bacterial communities coping with the multiple extreme conditions present in the unique soda pans of the Pannonian steppe.

Socio-demographic, clinical characteristics and utilization of mental health care services associated with SF-6D utility scores in patients with mental disorders: contributions of the quantile regression.

PubMed

Prigent, Amélie; Kamendje-Tchokobou, Blaise; Chevreul, Karine

2017-11-01

Health-related quality of life (HRQoL) is a widely used concept in the assessment of health care. Some generic HRQoL instruments, based on specific algorithms, can generate utility scores which reflect the preferences of the general population for the different health states described by the instrument. This study aimed to investigate the relationships between utility scores and potentially associated factors in patients with mental disorders followed in inpatient and/or outpatient care settings using two statistical methods. Patients were recruited in four psychiatric sectors in France. Patient responses to the SF-36 generic HRQoL instrument were used to calculate SF-6D utility scores. The relationships between utility scores and patient socio-demographic, clinical characteristics, and mental health care utilization, considered as potentially associated factors, were studied using OLS and quantile regressions. One hundred and seventy six patients were included. Women, severely ill patients and those hospitalized full-time tended to report lower utility scores, whereas psychotic disorders (as opposed to mood disorders) and part-time care were associated with higher scores. The quantile regression highlighted that the size of the associations between the utility scores and some patient characteristics varied along with the utility score distribution, and provided more accurate estimated values than OLS regression. The quantile regression may constitute a relevant complement for the analysis of factors associated with utility scores. For policy decision-making, the association of full-time hospitalization with lower utility scores while part-time care was associated with higher scores supports the further development of alternatives to full-time hospitalizations.

Structured Additive Quantile Regression for Assessing the Determinants of Childhood Anemia in Rwanda.

PubMed

Habyarimana, Faustin; Zewotir, Temesgen; Ramroop, Shaun

2017-06-17

Childhood anemia is among the most significant health problems faced by public health departments in developing countries. This study aims at assessing the determinants and possible spatial effects associated with childhood anemia in Rwanda. The 2014/2015 Rwanda Demographic and Health Survey (RDHS) data was used. The analysis was done using the structured spatial additive quantile regression model. The findings of this study revealed that the child's age; the duration of breastfeeding; gender of the child; the nutritional status of the child (whether underweight and/or wasting); whether the child had a fever; had a cough in the two weeks prior to the survey or not; whether the child received vitamin A supplementation in the six weeks before the survey or not; the household wealth index; literacy of the mother; mother's anemia status; mother's age at the birth are all significant factors associated with childhood anemia in Rwanda. Furthermore, significant structured spatial location effects on childhood anemia was found.

Focusing adaptive-optics for neutron spectroscopy at extreme conditions

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

Simeoni, G. G., E-mail: ggsimeoni@outlook.com; Physics Department E13, Technical University of Munich, D-85748 Garching; Valicu, R. G.

2015-12-14

Neutron Spectroscopy employing extreme-conditions sample environments is nowadays a crucial tool for the understanding of fundamental scientific questions as well as for the investigation of materials and chemical-physical properties. For all these kinds of studies, an increased neutron flux over a small sample area is needed. The prototype of a focusing neutron guide component, developed and produced completely at the neutron source FRM II in Garching (Germany), has been installed at the time-of-flight (TOF) disc-chopper neutron spectrometer TOFTOF and came into routine-operation. The design is based on the compressed Archimedes' mirror concept for finite-size divergent sources. It represents a uniquemore » device combining the supermirror technology with Adaptive Optics, suitable for broad-bandwidth thermal-cold TOF neutron spectroscopy (here optimized for 1.4–10 Å). It is able to squeeze the beam cross section down to a square centimeter, with a more than doubled signal-to-background ratio, increased efficiency at high scattering angles, and improved symmetry of the elastic resolution function. We present a comparison between the simulated and measured beam cross sections, as well as the performance of the instrument within real experiments. This work intends to show the unprecedented opportunities achievable at already existing instruments, along with useful guidelines for the design and construction of next-generation neutron spectrometers.« less

Super Clausius-Clapeyron scaling of extreme hourly precipitation and its relation to large-scale atmospheric conditions

NASA Astrophysics Data System (ADS)

Lenderink, Geert; Barbero, Renaud; Loriaux, Jessica; Fowler, Hayley

2017-04-01

Present-day precipitation-temperature scaling relations indicate that hourly precipitation extremes may have a response to warming exceeding the Clausius-Clapeyron (CC) relation; for The Netherlands the dependency on surface dew point temperature follows two times the CC relation corresponding to 14 % per degree. Our hypothesis - as supported by a simple physical argument presented here - is that this 2CC behaviour arises from the physics of convective clouds. So, we think that this response is due to local feedbacks related to the convective activity, while other large scale atmospheric forcing conditions remain similar except for the higher temperature (approximately uniform warming with height) and absolute humidity (corresponding to the assumption of unchanged relative humidity). To test this hypothesis, we analysed the large-scale atmospheric conditions accompanying summertime afternoon precipitation events using surface observations combined with a regional re-analysis for the data in The Netherlands. Events are precipitation measurements clustered in time and space derived from approximately 30 automatic weather stations. The hourly peak intensities of these events again reveal a 2CC scaling with the surface dew point temperature. The temperature excess of moist updrafts initialized at the surface and the maximum cloud depth are clear functions of surface dew point temperature, confirming the key role of surface humidity on convective activity. Almost no differences in relative humidity and the dry temperature lapse rate were found across the dew point temperature range, supporting our theory that 2CC scaling is mainly due to the response of convection to increases in near surface humidity, while other atmospheric conditions remain similar. Additionally, hourly precipitation extremes are on average accompanied by substantial large-scale upward motions and therefore large-scale moisture convergence, which appears to accelerate with surface dew point. This

Glenn Extreme Environment Rig (GEER)

NASA Image and Video Library

2017-01-17

NASA Glenn research engineers prepare our extreme environments chamber (GEER) for a test. GEER, which simulates the extreme conditions found in space, tests many devices that will explore Venus to see if they can withstand the punishing environment and temperatures over 800˚F.

Fitness to work of astronauts in conditions of action of the extreme emotional factors

NASA Astrophysics Data System (ADS)

Prisniakova, L. M.

2004-01-01

The theoretical model for the quantitative determination of influence of a level of emotional exertion on the success of human activity is presented. The learning curves of fixed words in the groups with a different level of the emotional exertion are analyzed. The obtained magnitudes of time constant T depending on a type of the emotional exertion are a quantitative measure of the emotional exertion. Time constants could also be of use for a prediction of the characteristic of fitness to work of an astronaut in conditions of extreme factors. The inverse of the sign of influencing on efficiency of activity of the man is detected. The paper offers a mathematical model of the relation between successful activity and motivations or the emotional exertion (Yerkes-Dodson law). Proposed models can serve by the theoretical basis of the quantitative characteristics of an estimation of activity of astronauts in conditions of the emotional factors at a phase of their selection.

Fitness to work of astronauts in conditions of action of the extreme emotional factors.

PubMed

Prisniakova, L M

2004-01-01

The theoretical model for the quantitative determination of influence of a level of emotional exertion on the success of human activity is presented. The learning curves of fixed words in the groups with a different level of the emotional exertion are analyzed. The obtained magnitudes of time constant T depending on a type of the emotional exertion are a quantitative measure of the emotional exertion. Time constants could also be of use for a prediction of the characteristic of fitness to work of an astronaut in conditions of extreme factors. The inverse of the sign of influencing on efficiency of activity of the man is detected. The paper offers a mathematical model of the relation between successful activity and motivations or the emotional exertion (Yerkes-Dodson law). Proposed models can serve by the theoretical basis of the quantitative characteristics of an estimation of activity of astronauts in conditions of the emotional factors at a phase of their selection. Published by Elsevier Ltd on behalf of COSPAR.

Regional metamorphism at extreme conditions: Implications for orogeny at convergent plate margins

NASA Astrophysics Data System (ADS)

Zheng, Yong-Fei; Chen, Ren-Xu

2017-09-01

Regional metamorphism at extreme conditions refers either to Alpine-type metamorphism at low geothermal gradients of <10 °C/km, or to Buchan-type metamorphism at high geothermal gradients of >30 °C/km. Extreme pressures refer to those above the polymorphic transition of quartz to coesite, so that ultrahigh-pressure (UHP) eclogite-facies metamorphism occurs at mantle depths of >80 km. Extreme temperatures refer to those higher than 900 °C at crustal depths of ≤80 km, so that ultrahigh-temperature (UHT) granulite-facies metamorphism occurs at medium to high pressures. While crustal subduction at the low geothermal gradients results in blueschist-eclogite facies series without arc volcanism, heating of the thinned orogenic lithosphere brings about the high geothermal gradients for amphibolite-granulite facies series with abundant magmatism. Therefore, UHP metamorphic rocks result from cold lithospheric subduction to the mantle depths, whereas UHT metamorphic rocks are produced by hot underplating of the asthenospheric mantle at the crustal depths. Active continental rifting is developed on the thinned lithosphere in response to asthenospheric upwelling, and this tectonism is suggested as a feasible mechanism for regional granulite-facies metamorphism, with the maximum temperature depending on the extent to which the mantle lithosphere is thinned prior to the rifting. While lithospheric compression is associated with subduction metamorphism in accretionary and collisional orogens, the thinned orogenic lithosphere undergoes extension due to the asthenospheric upwelling to result in orogen-parallel rifting metamorphism and magmatism. Thus, the rifting metamorphism provides a complement to the subduction metamorphism and its operation marks the asthenospheric heating of the orogenic lithosphere. Because of the partial melting and melt extraction of the lower continental crust, contemporaneous granite-migmatite-granulite associations may serve as a petrological

Beyond Traditional Extreme Value Theory Through a Metastatistical Approach: Lessons Learned from Precipitation, Hurricanes, and Storm Surges

NASA Astrophysics Data System (ADS)

Marani, M.; Zorzetto, E.; Hosseini, S. R.; Miniussi, A.; Scaioni, M.

2017-12-01

The Generalized Extreme Value (GEV) distribution is widely adopted irrespective of the properties of the stochastic process generating the extreme events. However, GEV presents several limitations, both theoretical (asymptotic validity for a large number of events/year or hypothesis of Poisson occurrences of Generalized Pareto events), and practical (fitting uses just yearly maxima or a few values above a high threshold). Here we describe the Metastatistical Extreme Value Distribution (MEVD, Marani & Ignaccolo, 2015), which relaxes asymptotic or Poisson/GPD assumptions and makes use of all available observations. We then illustrate the flexibility of the MEVD by applying it to daily precipitation, hurricane intensity, and storm surge magnitude. Application to daily rainfall from a global raingauge network shows that MEVD estimates are 50% more accurate than those from GEV when the recurrence interval of interest is much greater than the observational period. This makes MEVD suited for application to satellite rainfall observations ( 20 yrs length). Use of MEVD on TRMM data yields extreme event patterns that are in better agreement with surface observations than corresponding GEV estimates.Applied to the HURDAT2 Atlantic hurricane intensity dataset, MEVD significantly outperforms GEV estimates of extreme hurricanes. Interestingly, the Generalized Pareto distribution used for "ordinary" hurricane intensity points to the existence of a maximum limit wind speed that is significantly smaller than corresponding physically-based estimates. Finally, we applied the MEVD approach to water levels generated by tidal fluctuations and storm surges at a set of coastal sites spanning different storm-surge regimes. MEVD yields accurate estimates of large quantiles and inferences on tail thickness (fat vs. thin) of the underlying distribution of "ordinary" surges. In summary, the MEVD approach presents a number of theoretical and practical advantages, and outperforms traditional

Rainfall Downscaling Conditional on Upper-air Atmospheric Predictors: Improved Assessment of Rainfall Statistics in a Changing Climate

NASA Astrophysics Data System (ADS)

Langousis, Andreas; Mamalakis, Antonis; Deidda, Roberto; Marrocu, Marino

2015-04-01

To improve the level skill of Global Climate Models (GCMs) and Regional Climate Models (RCMs) in reproducing the statistics of rainfall at a basin level and at hydrologically relevant temporal scales (e.g. daily), two types of statistical approaches have been suggested. One is the statistical correction of climate model rainfall outputs using historical series of precipitation. The other is the use of stochastic models of rainfall to conditionally simulate precipitation series, based on large-scale atmospheric predictors produced by climate models (e.g. geopotential height, relative vorticity, divergence, mean sea level pressure). The latter approach, usually referred to as statistical rainfall downscaling, aims at reproducing the statistical character of rainfall, while accounting for the effects of large-scale atmospheric circulation (and, therefore, climate forcing) on rainfall statistics. While promising, statistical rainfall downscaling has not attracted much attention in recent years, since the suggested approaches involved complex (i.e. subjective or computationally intense) identification procedures of the local weather, in addition to demonstrating limited success in reproducing several statistical features of rainfall, such as seasonal variations, the distributions of dry and wet spell lengths, the distribution of the mean rainfall intensity inside wet periods, and the distribution of rainfall extremes. In an effort to remedy those shortcomings, Langousis and Kaleris (2014) developed a statistical framework for simulation of daily rainfall intensities conditional on upper air variables, which accurately reproduces the statistical character of rainfall at multiple time-scales. Here, we study the relative performance of: a) quantile-quantile (Q-Q) correction of climate model rainfall products, and b) the statistical downscaling scheme of Langousis and Kaleris (2014), in reproducing the statistical structure of rainfall, as well as rainfall extremes, at a

Acclimatization to extreme heat

NASA Astrophysics Data System (ADS)

Warner, M. E.; Ganguly, A. R.; Bhatia, U.

2017-12-01

Heat extremes throughout the globe, as well as in the United States, are expected to increase. These heat extremes have been shown to impact human health, resulting in some of the highest levels of lives lost as compared with similar natural disasters. But in order to inform decision makers and best understand future mortality and morbidity, adaptation and mitigation must be considered. Defined as the ability for individuals or society to change behavior and/or adapt physiologically, acclimatization encompasses the gradual adaptation that occurs over time. Therefore, this research aims to account for acclimatization to extreme heat by using a hybrid methodology that incorporates future air conditioning use and installation patterns with future temperature-related time series data. While previous studies have not accounted for energy usage patterns and market saturation scenarios, we integrate such factors to compare the impact of air conditioning as a tool for acclimatization, with a particular emphasis on mortality within vulnerable communities.

Electronics for Extreme Environments

NASA Astrophysics Data System (ADS)

Patel, J. U.; Cressler, J.; Li, Y.; Niu, G.

2001-01-01

Most of the NASA missions involve extreme environments comprising radiation and low or high temperatures. Current practice of providing friendly ambient operating environment to electronics costs considerable power and mass (for shielding). Immediate missions such as the Europa orbiter and lander and Mars landers require the electronics to perform reliably in extreme conditions during the most critical part of the mission. Some other missions planned in the future also involve substantial surface activity in terms of measurements, sample collection, penetration through ice and crust and the analysis of samples. Thus it is extremely critical to develop electronics that could reliably operate under extreme space environments. Silicon On Insulator (SOI) technology is an extremely attractive candidate for NASA's future low power and high speed electronic systems because it offers increased transconductance, decreased sub-threshold slope, reduced short channel effects, elimination of kink effect, enhanced low field mobility, and immunity from radiation induced latch-up. A common belief that semiconductor devices function better at low temperatures is generally true for bulk devices but it does not hold true for deep sub-micron SOI CMOS devices with microscopic device features of 0.25 micrometers and smaller. Various temperature sensitive device parameters and device characteristics have recently been reported in the literature. Behavior of state of the art technology devices under such conditions needs to be evaluated in order to determine possible modifications in the device design for better performance and survivability under extreme environments. Here, we present a unique approach of developing electronics for extreme environments to benefit future NASA missions as described above. This will also benefit other long transit/life time missions such as the solar sail and planetary outposts in which electronics is out open in the unshielded space at the ambient space

Technology development of protein rich concentrates for nutrition in extreme conditions using soybean and meat by-products.

PubMed

Kalenik, Tatiana K; Costa, Rui; Motkina, Elena V; Kosenko, Tamara A; Skripko, Olga V; Kadnikova, Irina A

2017-01-01

There is a need to develop new foods for participants of expeditions in extreme conditions, which must be self-sufficient. These foods should be light to carry, with a long shelf life, tasty and with  high nutrient density. Currently, protein sources are limited mainly to dried and canned meat. In this work, a protein-rich dried concentrate suitable for extreme expeditions was developed using soya, tomato, milk whey and meat by-products. Protein concentrates were developed using minced beef liver and heart, dehydrated and mixed with a soya protein-lycopene coagulate (SPLC) obtained from a solution prepared with germi- nated soybeans and mixed with tomato paste in milk whey, and finally dried. The technological parameters of pressing SPLC and of drying the protein concentrate were optimized using response surface methodology. The optimized technological parameters to prepare the protein concentrates were obtained, with 70:30 being the ideal ratio of minced meat to SPLC. The developed protein concentrates are characterized by a high calorific value of 376 kcal/100 g of dry product, with a water content of 98 g·kg-1, and 641-644 g·kg-1 of proteins. The essential amino acid indices are 100, with minimum essential amino acid content constitut- ing 100-128% of the FAO standard, depending on the raw meat used. These concentrates are also rich in micronutrients such as β-carotene and vitamin C. Analysis of the nutrient content showed that these non-perishable concentrates present a high nutritional value and complement other widely available vegetable concentrates to prepare a two-course meal. The soups and porridges prepared with these concentrates can be classified as functional foods, and comply with army requirements applicable to food products for extreme conditions.

Extreme Conditioning Programs: Potential Benefits and Potential Risks.

PubMed

Knapik, Joseph J

2015-01-01

CrossFit, Insanity, Gym Jones, and P90X are examples of extreme conditioning programs (ECPs). ECPs typically involve high-volume and high-intensity physical activities with short rest periods between movements and use of multiple joint exercises. Data on changes in fitness with ECPs are limited to CrossFit investigations that demonstrated improvements in muscle strength, muscular endurance, aerobic fitness, and body composition. However, no study has directly compared CrossFit or other ECPs to other more traditional forms of aerobic and resistance training within the same investigation. These direct comparisons are needed to more adequately evaluate the effectiveness of ECPs. Until these studies emerge, the comparisons with available literature suggest that improvements in CrossFit, in terms of muscular endurance (push-ups, sit-ups), strength, and aerobic capacity, appear to be similar to those seen in more traditional training programs. Investigations of injuries in ECPs are limited to two observational studies that suggest that the overall injury rate is similar to that seen in other exercise programs. Several cases of rhabdomyolysis and cervical carotid artery dissections have been reported during CrossFit training. The symptoms, diagnosis, and treatment of these are reviewed here. Until more data on ECPs emerge, physical training should be aligned with US Army doctrine. If ECPs are included in exercise programs, trainers should (1) have appropriate training certifications, (2) inspect exercise equipment regularly to assure safety, (3) introduce ECPs to new participants, (4) ensure medical clearance of Soldiers with special health problems before participation in ECPs, (4) tailor ECPs to the individual Soldier, (5) adjust rest periods to optimize recovery and reduce fatigue, (6) monitor Soldiers for signs of overtraining, rhabdomyolysis, and other problems, and (7) coordinate exercise programs with other unit training activities to eliminate redundant activities

Unique Nature of the Quality of Life in the Context of Extreme Climatic, Geographical and Specific Socio-Cultural Living Conditions

ERIC Educational Resources Information Center

Kulik, Anastasia; Neyaskina, Yuliya; Frizen, Marina; Shiryaeva, Olga; Surikova, Yana

2016-01-01

This article presents the results of a detailed empirical research, aimed at studying the quality of life in the context of extreme climatic, geographical and specific sociocultural living conditions. Our research is based on the methodological approach including social, economical, ecological and psychological characteristics and reflecting…

Using Quantile and Asymmetric Least Squares Regression for Optimal Risk Adjustment.

PubMed

Lorenz, Normann

2017-06-01

In this paper, we analyze optimal risk adjustment for direct risk selection (DRS). Integrating insurers' activities for risk selection into a discrete choice model of individuals' health insurance choice shows that DRS has the structure of a contest. For the contest success function (csf) used in most of the contest literature (the Tullock-csf), optimal transfers for a risk adjustment scheme have to be determined by means of a restricted quantile regression, irrespective of whether insurers are primarily engaged in positive DRS (attracting low risks) or negative DRS (repelling high risks). This is at odds with the common practice of determining transfers by means of a least squares regression. However, this common practice can be rationalized for a new csf, but only if positive and negative DRSs are equally important; if they are not, optimal transfers have to be calculated by means of a restricted asymmetric least squares regression. Using data from German and Swiss health insurers, we find considerable differences between the three types of regressions. Optimal transfers therefore critically depend on which csf represents insurers' incentives for DRS and, if it is not the Tullock-csf, whether insurers are primarily engaged in positive or negative DRS. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Using instant messaging to enhance the interpersonal relationships of Taiwanese adolescents: evidence from quantile regression analysis.

PubMed

Lee, Yueh-Chiang; Sun, Ya Chung

2009-01-01

Even though use of the internet by adolescents has grown exponentially, little is known about the correlation between their interaction via Instant Messaging (IM) and the evolution of their interpersonal relationships in real life. In the present study, 369 junior high school students in Taiwan responded to questions regarding their IM usage and their dispositional measures of real-life interpersonal relationships. Descriptive statistics, factor analysis, and quantile regression methods were used to analyze the data. Results indicate that (1) IM helps define adolescents' self-identity (forming and maintaining individual friendships) and social-identity (belonging to a peer group), and (2) how development of an interpersonal relationship is impacted by the use of IM since it appears that adolescents use IM to improve their interpersonal relationships in real life.

Influence of climate variability versus change at multi-decadal time scales on hydrological extremes

NASA Astrophysics Data System (ADS)

Willems, Patrick

2014-05-01

Recent studies have shown that rainfall and hydrological extremes do not randomly occur in time, but are subject to multidecadal oscillations. In addition to these oscillations, there are temporal trends due to climate change. Design statistics, such as intensity-duration-frequency (IDF) for extreme rainfall or flow-duration-frequency (QDF) relationships, are affected by both types of temporal changes (short term and long term). This presentation discusses these changes, how they influence water engineering design and decision making, and how this influence can be assessed and taken into account in practice. The multidecadal oscillations in rainfall and hydrological extremes were studied based on a technique for the identification and analysis of changes in extreme quantiles. The statistical significance of the oscillations was evaluated by means of a non-parametric bootstrapping method. Oscillations in large scale atmospheric circulation were identified as the main drivers for the temporal oscillations in rainfall and hydrological extremes. They also explain why spatial phase shifts (e.g. north-south variations in Europe) exist between the oscillation highs and lows. Next to the multidecadal climate oscillations, several stations show trends during the most recent decades, which may be attributed to climate change as a result of anthropogenic global warming. Such attribution to anthropogenic global warming is, however, uncertain. It can be done based on simulation results with climate models, but it is shown that the climate model results are too uncertain to enable a clear attribution. Water engineering design statistics, such as extreme rainfall IDF or peak or low flow QDF statistics, obviously are influenced by these temporal variations (oscillations, trends). It is shown in the paper, based on the Brussels 10-minutes rainfall data, that rainfall design values may be about 20% biased or different when based on short rainfall series of 10 to 15 years length, and

On Quantile Regression in Reproducing Kernel Hilbert Spaces with Data Sparsity Constraint

PubMed Central

Zhang, Chong; Liu, Yufeng; Wu, Yichao

2015-01-01

For spline regressions, it is well known that the choice of knots is crucial for the performance of the estimator. As a general learning framework covering the smoothing splines, learning in a Reproducing Kernel Hilbert Space (RKHS) has a similar issue. However, the selection of training data points for kernel functions in the RKHS representation has not been carefully studied in the literature. In this paper we study quantile regression as an example of learning in a RKHS. In this case, the regular squared norm penalty does not perform training data selection. We propose a data sparsity constraint that imposes thresholding on the kernel function coefficients to achieve a sparse kernel function representation. We demonstrate that the proposed data sparsity method can have competitive prediction performance for certain situations, and have comparable performance in other cases compared to that of the traditional squared norm penalty. Therefore, the data sparsity method can serve as a competitive alternative to the squared norm penalty method. Some theoretical properties of our proposed method using the data sparsity constraint are obtained. Both simulated and real data sets are used to demonstrate the usefulness of our data sparsity constraint. PMID:27134575

Modeling short duration extreme precipitation patterns using copula and generalized maximum pseudo-likelihood estimation with censoring

NASA Astrophysics Data System (ADS)

Bargaoui, Zoubeida Kebaili; Bardossy, Andràs

2015-10-01

The paper aims to develop researches on the spatial variability of heavy rainfall events estimation using spatial copula analysis. To demonstrate the methodology, short time resolution rainfall time series from Stuttgart region are analyzed. They are constituted by rainfall observations on continuous 30 min time scale recorded over a network composed by 17 raingages for the period July 1989-July 2004. The analysis is performed aggregating the observations from 30 min up to 24 h. Two parametric bivariate extreme copula models, the Husler-Reiss model and the Gumbel model are investigated. Both involve a single parameter to be estimated. Thus, model fitting is operated for every pair of stations for a giving time resolution. A rainfall threshold value representing a fixed rainfall quantile is adopted for model inference. Generalized maximum pseudo-likelihood estimation is adopted with censoring by analogy with methods of univariate estimation combining historical and paleoflood information with systematic data. Only pairs of observations greater than the threshold are assumed as systematic data. Using the estimated copula parameter, a synthetic copula field is randomly generated and helps evaluating model adequacy which is achieved using Kolmogorov Smirnov distance test. In order to assess dependence or independence in the upper tail, the extremal coefficient which characterises the tail of the joint bivariate distribution is adopted. Hence, the extremal coefficient is reported as a function of the interdistance between stations. If it is less than 1.7, stations are interpreted as dependent in the extremes. The analysis of the fitted extremal coefficients with respect to stations inter distance highlights two regimes with different dependence structures: a short spatial extent regime linked to short duration intervals (from 30 min to 6 h) with an extent of about 8 km and a large spatial extent regime related to longer rainfall intervals (from 12 h to 24 h) with an

Kinetics of Materials at Extreme Conditions: Understanding the Time Dependent Approach to Equilibrium at MaRIE

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

Kraus, R. G.; Mcnabb, D.; Kumar, M.

The National Nuclear Security Agency has recently recognized that a long-term need exists to establish a stronger scientific basis for the assessment and qualification of materials and manufacturing processes for the nuclear stockpile and other national security applications. These materials may have undergone substantial changes with age, or may represent new materials that are being introduced because of difficulties associated with reusing or recreating materials used in original stockpile components. Also, with advancements in manufacturing methods, the NNSA anticipates opportunities for an enhanced range of control over fabricated components, an enhanced pace of materials development, and enhanced functionality. The developmentmore » of qualification standards for these new materials will require the ability to understand and control material characteristics that affect both mechanical and dynamic performance. A unique aspect for NNSA is that the performance requirements for materials are often set by system hydrodynamics, and these materials must perform in extreme environments and loading conditions. Thus, the scientific motivation is to understand “Matter-Radiation Interactions in Extremes (MaRIE).”« less

Research progress of extreme climate and its vegetation response

NASA Astrophysics Data System (ADS)

Cui, Xiaolin; Wei, Xiaoqing; Wang, Tao

2017-08-01

The IPCC’s fifth assessment report indicates that climate warming is unquestionable, the frequency and intensity of extreme weather events may increase, and extreme weather events can destroy the growth conditions of vegetation that is otherwise in a stable condition. Therefore, it is essential to research the formation of extreme weather events and its ecological response, both in terms scientific development and the needs of societal development. This paper mainly examines these issues from the following aspects: (1) the definition of extreme climate events and the methods of studying the associated response of vegetation; (2) the research progress on extreme climate events and their vegetation response; and (3) the future direction of research on extreme climate and its vegetation response.

Modeling Spatial Dependence of Rainfall Extremes Across Multiple Durations

NASA Astrophysics Data System (ADS)

Le, Phuong Dong; Leonard, Michael; Westra, Seth

2018-03-01

Determining the probability of a flood event in a catchment given that another flood has occurred in a nearby catchment is useful in the design of infrastructure such as road networks that have multiple river crossings. These conditional flood probabilities can be estimated by calculating conditional probabilities of extreme rainfall and then transforming rainfall to runoff through a hydrologic model. Each catchment's hydrological response times are unlikely to be the same, so in order to estimate these conditional probabilities one must consider the dependence of extreme rainfall both across space and across critical storm durations. To represent these types of dependence, this study proposes a new approach for combining extreme rainfall across different durations within a spatial extreme value model using max-stable process theory. This is achieved in a stepwise manner. The first step defines a set of common parameters for the marginal distributions across multiple durations. The parameters are then spatially interpolated to develop a spatial field. Storm-level dependence is represented through the max-stable process for rainfall extremes across different durations. The dependence model shows a reasonable fit between the observed pairwise extremal coefficients and the theoretical pairwise extremal coefficient function across all durations. The study demonstrates how the approach can be applied to develop conditional maps of the return period and return level across different durations.

Seasonal Prediction of Hydro-Climatic Extremes in the Greater Horn of Africa Under Evolving Climate Conditions to Support Adaptation Strategies

NASA Astrophysics Data System (ADS)

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

2014-12-01

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

Extreme Metal Music and Anger Processing

PubMed Central

Sharman, Leah; Dingle, Genevieve A.

2015-01-01

The claim that listening to extreme music causes anger, and expressions of anger such as aggression and delinquency have yet to be substantiated using controlled experimental methods. In this study, 39 extreme music listeners aged 18–34 years were subjected to an anger induction, followed by random assignment to 10 min of listening to extreme music from their own playlist, or 10 min silence (control). Measures of emotion included heart rate and subjective ratings on the Positive and Negative Affect Scale (PANAS). Results showed that ratings of PANAS hostility, irritability, and stress increased during the anger induction, and decreased after the music or silence. Heart rate increased during the anger induction and was sustained (not increased) in the music condition, and decreased in the silence condition. PANAS active and inspired ratings increased during music listening, an effect that was not seen in controls. The findings indicate that extreme music did not make angry participants angrier; rather, it appeared to match their physiological arousal and result in an increase in positive emotions. Listening to extreme music may represent a healthy way of processing anger for these listeners. PMID:26052277

Association of Perceived Stress with Stressful Life Events, Lifestyle and Sociodemographic Factors: A Large-Scale Community-Based Study Using Logistic Quantile Regression

PubMed Central

Feizi, Awat; Aliyari, Roqayeh; Roohafza, Hamidreza

2012-01-01

Objective. The present paper aimed at investigating the association between perceived stress and major life events stressors in Iranian general population. Methods. In a cross-sectional large-scale community-based study, 4583 people aged 19 and older, living in Isfahan, Iran, were investigated. Logistic quantile regression was used for modeling perceived stress, measured by GHQ questionnaire, as the bounded outcome (dependent), variable, and as a function of most important stressful life events, as the predictor variables, controlling for major lifestyle and sociodemographic factors. This model provides empirical evidence of the predictors' effects heterogeneity depending on individual location on the distribution of perceived stress. Results. The results showed that among four stressful life events, family conflicts and social problems were more correlated with level of perceived stress. Higher levels of education were negatively associated with perceived stress and its coefficients monotonically decrease beyond the 30th percentile. Also, higher levels of physical activity were associated with perception of low levels of stress. The pattern of gender's coefficient over the majority of quantiles implied that females are more affected by stressors. Also high perceived stress was associated with low or middle levels of income. Conclusions. The results of current research suggested that in a developing society with high prevalence of stress, interventions targeted toward promoting financial and social equalities, social skills training, and healthy lifestyle may have the potential benefits for large parts of the population, most notably female and lower educated people. PMID:23091560

Extreme weather events and infectious disease outbreaks.

PubMed

McMichael, Anthony J

2015-01-01

Human-driven climatic changes will fundamentally influence patterns of human health, including infectious disease clusters and epidemics following extreme weather events. Extreme weather events are projected to increase further with the advance of human-driven climate change. Both recent and historical experiences indicate that infectious disease outbreaks very often follow extreme weather events, as microbes, vectors and reservoir animal hosts exploit the disrupted social and environmental conditions of extreme weather events. This review article examines infectious disease risks associated with extreme weather events; it draws on recent experiences including Hurricane Katrina in 2005 and the 2010 Pakistan mega-floods, and historical examples from previous centuries of epidemics and 'pestilence' associated with extreme weather disasters and climatic changes. A fuller understanding of climatic change, the precursors and triggers of extreme weather events and health consequences is needed in order to anticipate and respond to the infectious disease risks associated with human-driven climate change. Post-event risks to human health can be constrained, nonetheless, by reducing background rates of persistent infection, preparatory action such as coordinated disease surveillance and vaccination coverage, and strengthened disaster response. In the face of changing climate and weather conditions, it is critically important to think in ecological terms about the determinants of health, disease and death in human populations.

Seasonal effects of wind conditions on migration patterns of soaring American white pelican.

PubMed

Gutierrez Illan, Javier; Wang, Guiming; Cunningham, Fred L; King, D Tommy

2017-01-01

Energy and time expenditures are determinants of bird migration strategies. Soaring birds have developed migration strategies to minimize these costs, optimizing the use of all the available resources to facilitate their displacement. We analysed the effects of different wind factors (tailwind, turbulence, vertical updrafts) on the migratory flying strategies adopted by 24 satellite-tracked American white pelicans (Pelecanus erythrorhynchos) throughout spring and autumn in North America. We hypothesize that different wind conditions encountered along migration routes between spring and autumn induce pelicans to adopt different flying strategies and use of these wind resources. Using quantile regression and fine-scale atmospheric data, we found that the pelicans optimized the use of available wind resources, flying faster and more direct routes in spring than in autumn. They actively selected tailwinds in both spring and autumn displacements but relied on available updrafts predominantly in their spring migration, when they needed to arrive at the breeding regions. These effects varied depending on the flying speed of the pelicans. We found significant directional correlations between the pelican migration flights and wind direction. In light of our results, we suggest plasticity of migratory flight strategies by pelicans is likely to enhance their ability to cope with the effects of ongoing climate change and the alteration of wind regimes. Here, we also demonstrate the usefulness and applicability of quantile regression techniques to investigate complex ecological processes such as variable effects of atmospheric conditions on soaring migration.

Seasonal effects of wind conditions on migration patterns of soaring American white pelican

PubMed Central

Wang, Guiming; Cunningham, Fred L.; King, D. Tommy

2017-01-01

Energy and time expenditures are determinants of bird migration strategies. Soaring birds have developed migration strategies to minimize these costs, optimizing the use of all the available resources to facilitate their displacement. We analysed the effects of different wind factors (tailwind, turbulence, vertical updrafts) on the migratory flying strategies adopted by 24 satellite-tracked American white pelicans (Pelecanus erythrorhynchos) throughout spring and autumn in North America. We hypothesize that different wind conditions encountered along migration routes between spring and autumn induce pelicans to adopt different flying strategies and use of these wind resources. Using quantile regression and fine-scale atmospheric data, we found that the pelicans optimized the use of available wind resources, flying faster and more direct routes in spring than in autumn. They actively selected tailwinds in both spring and autumn displacements but relied on available updrafts predominantly in their spring migration, when they needed to arrive at the breeding regions. These effects varied depending on the flying speed of the pelicans. We found significant directional correlations between the pelican migration flights and wind direction. In light of our results, we suggest plasticity of migratory flight strategies by pelicans is likely to enhance their ability to cope with the effects of ongoing climate change and the alteration of wind regimes. Here, we also demonstrate the usefulness and applicability of quantile regression techniques to investigate complex ecological processes such as variable effects of atmospheric conditions on soaring migration. PMID:29065188

Selection criteria for wear resistant powder coatings under extreme erosive wear conditions

NASA Astrophysics Data System (ADS)

Kulu, P.; Pihl, T.

2002-12-01

Wear-resistant thermal spray coatings for sliding wear are hard but brittle (such as carbide and oxide based coatings), which makes them useless under impact loading conditions and sensitive to fatigue. Under extreme conditions of erosive wear (impact loading, high hardness of abrasives, and high velocity of abradant particles), composite coatings ensure optimal properties of hardness and toughness. The article describes tungsten carbide-cobalt (WC-Co) systems and self-fluxing alloys, containing tungsten carbide based hardmetal particles [NiCrSiB-(WC-Co)] deposited by the detonation gun, continuous detonation spraying, and spray fusion processes. Different powder compositions and processes were studied, and the effect of the coating structure and wear parameters on the wear resistance of coatings are evaluated. The dependence of the wear resistance of sprayed and fused coatings on their hardness is discussed, and hardness criteria for coating selection are proposed. The so-called “double cemented” structure of WC-Co based hardmetal or metal matrix composite coatings, as compared with a simple cobalt matrix containing particles of WC, was found optimal. Structural criteria for coating selection are provided. To assist the end user in selecting an optimal deposition method and materials, coating selection diagrams of wear resistance versus hardness are given. This paper also discusses the cost-effectiveness of coatings in the application areas that are more sensitive to cost, and composite coatings based on recycled materials are offered.

Lack of correlation of desiccation and radiation tolerance in microorganisms from diverse extreme environments tested under anoxic conditions

PubMed Central

Bohmeier, Maria; Perras, Alexandra K; Schwendner, Petra; Rabbow, Elke; Moissl-Eichinger, Christine; Cockell, Charles S; Vannier, Pauline; Marteinsson, Viggo T; Monaghan, Euan P; Ehrenfreund, Pascale; Garcia-Descalzo, Laura; Gómez, Felipe; Malki, Moustafa; Amils, Ricardo; Gaboyer, Frédéric; Westall, Frances; Cabezas, Patricia; Walter, Nicolas; Rettberg, Petra

2018-01-01

Abstract Four facultative anaerobic and two obligate anaerobic bacteria were isolated from extreme environments (deep subsurface halite mine, sulfidic anoxic spring, mineral-rich river) in the frame MASE (Mars Analogues for Space Exploration) project. The isolates were investigated under anoxic conditions for their survivability after desiccation up to 6 months and their tolerance to ionizing radiation up to 3000 Gy. The results indicated that tolerances to both stresses are strain-specific features. Yersinia intermedia MASE-LG-1 showed a high desiccation tolerance but its radiation tolerance was very low. The most radiation-tolerant strains were Buttiauxella sp. MASE-IM-9 and Halanaerobium sp. MASE-BB-1. In both cases, cultivable cells were detectable after an exposure to 3 kGy of ionizing radiation, but cells only survived desiccation for 90 and 30 days, respectively. Although a correlation between desiccation and ionizing radiation resistance has been hypothesized for some aerobic microorganisms, our data showed that there was no correlation between tolerance to desiccation and ionizing radiation, suggesting that the physiological basis of both forms of tolerances is not necessarily linked. In addition, these results indicated that facultative and obligate anaerobic organisms living in extreme environments possess varied species-specific tolerances to extremes. PMID:29474542

The effect of extreme spring weather on body condition and stress physiology in Lapland longspurs and white-crowned sparrows breeding in the Arctic.

PubMed

Krause, Jesse S; Pérez, Jonathan H; Chmura, Helen E; Sweet, Shannan K; Meddle, Simone L; Hunt, Kathleen E; Gough, Laura; Boelman, Natalie; Wingfield, John C

2016-10-01

Climate change is causing rapid shifts in temperature while also increasing the frequency, duration, and intensity of extreme weather. In the northern hemisphere, the spring of 2013 was characterized as extreme due to record high snow cover and low temperatures. Studies that describe the effects of extreme weather on phenology across taxa are limited while morphological and physiological responses remain poorly understood. Stress physiology, as measured through baseline and stress-induced concentrations of cortisol or corticosterone, has often been studied to understand how organisms respond to environmental stressors. We compared body condition and stress physiology of two long-distance migrants breeding in low arctic Alaska - the white-crowned sparrow (Zonotrichia leucophrys) and Lapland longspur (Calcarius lapponicus) - in 2013, an extreme weather year, with three more typical years (2011, 2012, and 2014). The extended snow cover in spring 2013 caused measureable changes in phenology, body condition and physiology. Arrival timing for both species was delayed 4-5days compared to the other three years. Lapland longspurs had reduced fat stores, pectoralis muscle profiles, body mass, and hematocrit levels, while stress-induced concentrations of corticosterone were increased. Similarly, white-crowned sparrows had reduced pectoralis muscle profiles and hematocrit levels, but in contrast to Lapland longspurs, had elevated fat stores and no difference in mass or stress physiology relative to other study years. An understanding of physiological mechanisms that regulate coping strategies is of critical importance for predicting how species will respond to the occurrence of extreme events in the future due to global climate change. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Palliative Care for Extremely Premature Infants and Their Families

ERIC Educational Resources Information Center

Boss, Renee D.

2010-01-01

Extremely premature infants face multiple acute and chronic life-threatening conditions. In addition, the treatments to ameliorate or cure these conditions often entail pain and discomfort. Integrating palliative care from the moment that extremely premature labor is diagnosed offers families and clinicians support through the process of defining…

Stable pelagic vertebrate community structure through extreme Paleogene greenhouse conditions

NASA Astrophysics Data System (ADS)

Sibert, E. C.; Friedman, M.; Hull, P. M.; Hunt, G.; Norris, R. D.

2016-02-01

The species composition (structure) and energy transfer (function) of an ecosystem is reflected by the presence and type of consumers that it supports. Here we use ichthyoliths, microfossil fish teeth and shark denticles, to assess the ecological variability of the pelagic fish community structure and composition from the Late Cretaceous to the middle Eocene from a drill core in the South Pacific gyre (DSDP Site 596). We find that the overall vertebrate community structure, as measured by the relative abundance of sharks to ray-finned fishes, has a punctuated change at the Cretaceous/Paleogene mass extinction. The vertebrate community structure remained stable throughout the Paleogene despite a five-fold increase in overall abundance of ichthyoliths during the extreme greenhouse of the Early Eocene. Further, we use a novel system to quantify the morphological variation in fish teeth. We find that the morphospace occupied by the tooth assemblage is conserved throughout the interval, with a slight expansion following the Cretaceous-Paleogene mass extinction, and the evolution of a distinct morphotype-group around the Paleocene-Eocene boundary. While there are elevated rates of morphotype origination and extinction following the Cretaceous-Paleogene mass extinction, the extreme greenhouse warming of the Early Eocene and associated increase in fish production produce near-zero origination and extinction rates. The relative stability in composition of the pelagic vertebrate community during intervals of extreme climate change and across large ranges of total fish accumulation, suggests that pelagic ecosystem structure is robust to climate events, and that the overall structure of the pelagic fish community may be decoupled from both climate and ecosystem function.

[Injury mechanisms in extreme violence settings].

PubMed

Arcaute-Velazquez, Fernando Federico; García-Núñez, Luis Manuel; Noyola-Vilallobos, Héctor Faustino; Espinoza-Mercado, Fernando; Rodríguez-Vega, Carlos Eynar

2016-01-01

Extreme violence events are consequence of current world-wide economic, political and social conditions. Injury patterns found among victims of extreme violence events are very complex, obeying several high-energy injury mechanisms. In this article, we present the basic concepts of trauma kinematics that regulate the clinical approach to victims of extreme violence events, in the hope that clinicians increase their theoretical armamentarium, and reflecting on obtaining better outcomes. Copyright © 2016. Published by Masson Doyma México S.A.

Effects of anthropogenic heat due to air-conditioning systems on an extreme high temperature event in Hong Kong

NASA Astrophysics Data System (ADS)

Wang, Y.; Li, Y.; Di Sabatino, S.; Martilli, A.; Chan, P. W.

2018-03-01

Anthropogenic heat flux is the heat generated by human activities in the urban canopy layer, which is considered the main contributor to the urban heat island (UHI). The UHI can in turn increase the use and energy consumption of air-conditioning systems. In this study, two effective methods for water-cooling air-conditioning systems in non-domestic areas, including the direct cooling system and central piped cooling towers (CPCTs), are physically based, parameterized, and implemented in a weather research and forecasting model at the city scale of Hong Kong. An extreme high temperature event (June 23-28, 2016) in the urban areas was examined, and we assessed the effects on the surface thermal environment, the interaction of sea-land breeze circulation and urban heat island circulation, boundary layer dynamics, and a possible reduction of energy consumption. The results showed that both water-cooled air-conditioning systems could reduce the 2 m air temperature by around 0.5 °C-0.8 °C during the daytime, and around 1.5 °C around 7:00-8:00 pm when the planetary boundary layer (PBL) height was confined to a few hundred meters. The CPCT contributed around 80%-90% latent heat flux and significantly increased the water vapor mixing ratio in the atmosphere by around 0.29 g kg-1 on average. The implementation of the two alternative air-conditioning systems could modify the heat and momentum of turbulence, which inhibited the evolution of the PBL height (a reduction of 100-150 m), reduced the vertical mixing, presented lower horizontal wind speed and buoyant production of turbulent kinetic energy, and reduced the strength of sea breeze and UHI circulation, which in turn affected the removal of air pollutants. Moreover, the two alternative air-conditioning systems could significantly reduce the energy consumption by around 30% during extreme high temperature events. The results of this study suggest potential UHI mitigation strategies and can be extended to

Using quantile regression to examine health care expenditures during the Great Recession.

PubMed

Chen, Jie; Vargas-Bustamante, Arturo; Mortensen, Karoline; Thomas, Stephen B

2014-04-01

To examine the association between the Great Recession of 2007-2009 and health care expenditures along the health care spending distribution, with a focus on racial/ethnic disparities. Secondary data analyses of the Medical Expenditure Panel Survey (2005-2006 and 2008-2009). Quantile multivariate regressions are employed to measure the different associations between the economic recession of 2007-2009 and health care spending. Race/ethnicity and interaction terms between race/ethnicity and a recession indicator are controlled to examine whether minorities encountered disproportionately lower health spending during the economic recession. The Great Recession was significantly associated with reductions in health care expenditures at the 10th-50th percentiles of the distribution, but not at the 75th-90th percentiles. Racial and ethnic disparities were more substantial at the lower end of the health expenditure distribution; however, on average the reduction in expenditures was similar for all race/ethnic groups. The Great Recession was also positively associated with spending on emergency department visits. This study shows that the relationship between the Great Recession and health care spending varied along the health expenditure distribution. More variability was observed in the lower end of the health spending distribution compared to the higher end. © Health Research and Educational Trust.

Automatic coronary artery segmentation based on multi-domains remapping and quantile regression in angiographies.

PubMed

Li, Zhixun; Zhang, Yingtao; Gong, Huiling; Li, Weimin; Tang, Xianglong

2016-12-01

Coronary artery disease has become the most dangerous diseases to human life. And coronary artery segmentation is the basis of computer aided diagnosis and analysis. Existing segmentation methods are difficult to handle the complex vascular texture due to the projective nature in conventional coronary angiography. Due to large amount of data and complex vascular shapes, any manual annotation has become increasingly unrealistic. A fully automatic segmentation method is necessary in clinic practice. In this work, we study a method based on reliable boundaries via multi-domains remapping and robust discrepancy correction via distance balance and quantile regression for automatic coronary artery segmentation of angiography images. The proposed method can not only segment overlapping vascular structures robustly, but also achieve good performance in low contrast regions. The effectiveness of our approach is demonstrated on a variety of coronary blood vessels compared with the existing methods. The overall segmentation performances si, fnvf, fvpf and tpvf were 95.135%, 3.733%, 6.113%, 96.268%, respectively. Copyright © 2016 Elsevier Ltd. All rights reserved.

Obesity inequality in Malaysia: decomposing differences by gender and ethnicity using quantile regression.

PubMed

Dunn, Richard A; Tan, Andrew K G; Nayga, Rodolfo M

2012-01-01

Obesity prevalence is unequally distributed across gender and ethnic group in Malaysia. In this paper, we examine the role of socioeconomic inequality in explaining these disparities. The body mass index (BMI) distributions of Malays and Chinese, the two largest ethnic groups in Malaysia, are estimated through the use of quantile regression. The differences in the BMI distributions are then decomposed into two parts: attributable to differences in socioeconomic endowments and attributable to differences in responses to endowments. For both males and females, the BMI distribution of Malays is shifted toward the right of the distribution of Chinese, i.e., Malays exhibit higher obesity rates. In the lower 75% of the distribution, differences in socioeconomic endowments explain none of this difference. At the 90th percentile, differences in socioeconomic endowments account for no more than 30% of the difference in BMI between ethnic groups. Our results demonstrate that the higher levels of income and education that accrue with economic development will likely not eliminate obesity inequality. This leads us to conclude that reduction of obesity inequality, as well the overall level of obesity, requires increased efforts to alter the lifestyle behaviors of Malaysians.

Hospital ownership and drug utilization under a global budget: a quantile regression analysis.

PubMed

Zhang, Jing Hua; Chou, Shin-Yi; Deily, Mary E; Lien, Hsien-Ming

2014-03-01

A global budgeting system helps control the growth of healthcare spending by setting expenditure ceilings. However, the hospital global budget implemented in Taiwan in 2002 included a special provision: drug expenditures are reimbursed at face value, while other expenditures are subject to discounting. That gives hospitals, particularly those that are for-profit, an incentive to increase drug expenditures in treating patients. We calculated monthly drug expenditures by hospital departments from January 1997 to June 2006, using a sample of 348 193 patient claims to Taiwan National Health Insurance. To allow for variation among responses by departments with differing reliance on drugs and among hospitals of different ownerships, we used quantile regression to identify the effect of the hospital global budget on drug expenditures. Although drug expenditure increased in all hospital departments after the enactment of the hospital global budget, departments in for-profit hospitals that rely more heavily on drug treatments increased drug spending more, relative to public hospitals. Our findings suggest that a global budgeting system with special reimbursement provisions for certain treatment categories may alter treatment decisions and may undermine cost-containment goals, particularly among for-profit hospitals.

Probabilistic forecasting of extreme weather events based on extreme value theory

NASA Astrophysics Data System (ADS)

Van De Vyver, Hans; Van Schaeybroeck, Bert

2016-04-01

Extreme events in weather and climate such as high wind gusts, heavy precipitation or extreme temperatures are commonly associated with high impacts on both environment and society. Forecasting extreme weather events is difficult, and very high-resolution models are needed to describe explicitly extreme weather phenomena. A prediction system for such events should therefore preferably be probabilistic in nature. Probabilistic forecasts and state estimations are nowadays common in the numerical weather prediction community. In this work, we develop a new probabilistic framework based on extreme value theory that aims to provide early warnings up to several days in advance. We consider the combined events when an observation variable Y (for instance wind speed) exceeds a high threshold y and its corresponding deterministic forecasts X also exceeds a high forecast threshold y. More specifically two problems are addressed:} We consider pairs (X,Y) of extreme events where X represents a deterministic forecast, and Y the observation variable (for instance wind speed). More specifically two problems are addressed: Given a high forecast X=x_0, what is the probability that Y>y? In other words: provide inference on the conditional probability: [ Pr{Y>y|X=x_0}. ] Given a probabilistic model for Problem 1, what is the impact on the verification analysis of extreme events. These problems can be solved with bivariate extremes (Coles, 2001), and the verification analysis in (Ferro, 2007). We apply the Ramos and Ledford (2009) parametric model for bivariate tail estimation of the pair (X,Y). The model accommodates different types of extremal dependence and asymmetry within a parsimonious representation. Results are presented using the ensemble reforecast system of the European Centre of Weather Forecasts (Hagedorn, 2008). Coles, S. (2001) An Introduction to Statistical modelling of Extreme Values. Springer-Verlag.Ferro, C.A.T. (2007) A probability model for verifying deterministic

Inferring river bathymetry via Image-to-Depth Quantile Transformation (IDQT)

USGS Publications Warehouse

Legleiter, Carl

2016-01-01

Conventional, regression-based methods of inferring depth from passive optical image data undermine the advantages of remote sensing for characterizing river systems. This study introduces and evaluates a more flexible framework, Image-to-Depth Quantile Transformation (IDQT), that involves linking the frequency distribution of pixel values to that of depth. In addition, a new image processing workflow involving deep water correction and Minimum Noise Fraction (MNF) transformation can reduce a hyperspectral data set to a single variable related to depth and thus suitable for input to IDQT. Applied to a gravel bed river, IDQT avoided negative depth estimates along channel margins and underpredictions of pool depth. Depth retrieval accuracy (R25 0.79) and precision (0.27 m) were comparable to an established band ratio-based method, although a small shallow bias (0.04 m) was observed. Several ways of specifying distributions of pixel values and depths were evaluated but had negligible impact on the resulting depth estimates, implying that IDQT was robust to these implementation details. In essence, IDQT uses frequency distributions of pixel values and depths to achieve an aspatial calibration; the image itself provides information on the spatial distribution of depths. The approach thus reduces sensitivity to misalignment between field and image data sets and allows greater flexibility in the timing of field data collection relative to image acquisition, a significant advantage in dynamic channels. IDQT also creates new possibilities for depth retrieval in the absence of field data if a model could be used to predict the distribution of depths within a reach.

Differential effects of dietary diversity and maternal characteristics on linear growth of children aged 6-59 months in sub-Saharan Africa: a multi-country analysis.

PubMed

Amugsi, Dickson A; Dimbuene, Zacharie T; Kimani-Murage, Elizabeth W; Mberu, Blessing; Ezeh, Alex C

2017-04-01

To investigate the differential effects of dietary diversity (DD) and maternal characteristics on child linear growth at different points of the conditional distribution of height-for-age Z-score (HAZ) in sub-Saharan Africa. Secondary analysis of data from nationally representative cross-sectional samples of singleton children aged 0-59 months, born to mothers aged 15-49 years. The outcome variable was child HAZ. Quantile regression was used to perform the multivariate analysis. The most recent Demographic and Health Surveys from Ghana, Nigeria, Kenya, Mozambique and Democratic Republic of Congo (DRC). The present analysis was restricted to children aged 6-59 months (n 31 604). DD was associated positively with HAZ in the first four quantiles (5th, 10th, 25th and 50th) and the highest quantile (90th) in Nigeria. The largest effect occurred at the very bottom (5th quantile) and the very top (90th quantile) of the conditional HAZ distribution. In DRC, DD was significantly and positively associated with HAZ in the two lower quantiles (5th, 10th). The largest effects of maternal education occurred at the lower end of the conditional HAZ distribution in Ghana, Nigeria and DRC. Maternal BMI and height also had positive effects on HAZ at different points of the conditional distribution of HAZ. Our analysis shows that the association between DD and maternal factors and HAZ differs along the conditional HAZ distribution. Intervention measures need to take into account the heterogeneous effect of the determinants of child nutritional status along the different percentiles of the HAZ distribution.

Correlation between structural and semiconductor-metal changes and extreme conditions materials chemistry in Ge-Sn.

PubMed

Guillaume, Christophe L; Serghiou, George; Thomson, Andrew; Morniroli, Jean-Paul; Frost, Dan J; Odling, Nicholas; Jeffree, Chris E

2010-09-20

High pressure and temperature experiments on Ge-Sn mixtures to 24 GPa and 2000 K reveal segregation of Sn from Ge below 10 GPa whereas Ge-Sn agglomerates persist above 10 GPa regardless of heat treatment. At 10 GPa Ge reacts with Sn to form a tetragonal P4(3)2(1)2 Ge(0.9)Sn(0.1) solid solution on recovery, of interest for optoelectronic applications. Using electron diffraction and scanning electron microscopy measurements in conjunction with a series of tailored experiments promoting equilibrium and kinetically hindered synthetic conditions, we provide a step by step correlation between the semiconductor-metal and structural changes of the solid and liquid states of the two elements, and whether they segregate, mix or react upon compression. We identify depletion zones as an effective monitor for whether the process is moving toward reaction or segregation. This work hence also serves as a reference for interpretation of complex agglomerates and for developing successful synthesis conditions for new materials using extremes of pressure and temperature.

Quantification of Uncertainty in the Flood Frequency Analysis

NASA Astrophysics Data System (ADS)

Kasiapillai Sudalaimuthu, K.; He, J.; Swami, D.

2017-12-01

Flood frequency analysis (FFA) is usually carried out for planning and designing of water resources and hydraulic structures. Owing to the existence of variability in sample representation, selection of distribution and estimation of distribution parameters, the estimation of flood quantile has been always uncertain. Hence, suitable approaches must be developed to quantify the uncertainty in the form of prediction interval as an alternate to deterministic approach. The developed framework in the present study to include uncertainty in the FFA discusses a multi-objective optimization approach to construct the prediction interval using ensemble of flood quantile. Through this approach, an optimal variability of distribution parameters is identified to carry out FFA. To demonstrate the proposed approach, annual maximum flow data from two gauge stations (Bow river at Calgary and Banff, Canada) are used. The major focus of the present study was to evaluate the changes in magnitude of flood quantiles due to the recent extreme flood event occurred during the year 2013. In addition, the efficacy of the proposed method was further verified using standard bootstrap based sampling approaches and found that the proposed method is reliable in modeling extreme floods as compared to the bootstrap methods.

Development of synchrotron X-ray micro-tomography under extreme conditions of pressure and temperature.

PubMed

Álvarez-Murga, M; Perrillat, J P; Le Godec, Y; Bergame, F; Philippe, J; King, A; Guignot, N; Mezouar, M; Hodeau, J L

2017-01-01

X-ray tomography is a non-destructive three-dimensional imaging/microanalysis technique selective to a wide range of properties such as density, chemical composition, chemical states and crystallographic structure with extremely high sensitivity and spatial resolution. Here the development of in situ high-pressure high-temperature micro-tomography using a rotating module for the Paris-Edinburgh cell combined with synchrotron radiation is described. By rotating the sample chamber by 360°, the limited angular aperture of ordinary high-pressure cells is surmounted. Such a non-destructive high-resolution probe provides three-dimensional insight on the morphological and structural evolution of crystalline as well as amorphous phases during high pressure and temperature treatment. To demonstrate the potentials of this new experimental technique the compression behavior of a basalt glass is investigated by X-ray absorption tomography, and diffraction/scattering tomography imaging of the structural changes during the polymerization of C 60 molecules under pressure is performed. Small size and weight of the loading frame and rotating module means that this apparatus is portable, and can be readily installed on most synchrotron facilities to take advantage of the diversity of three-dimensional imaging techniques available at beamlines. This experimental breakthrough should open new ways for in situ imaging of materials under extreme pressure-temperature-stress conditions, impacting diverse areas in physics, chemistry, geology or materials sciences.

Approximating Long-Term Statistics Early in the Global Precipitation Measurement Era

NASA Technical Reports Server (NTRS)

Stanley, Thomas; Kirschbaum, Dalia B.; Huffman, George J.; Adler, Robert F.

2017-01-01

Long-term precipitation records are vital to many applications, especially the study of extreme events. The Tropical Rainfall Measuring Mission (TRMM) has served this need, but TRMMs successor mission, Global Precipitation Measurement (GPM), does not yet provide a long-term record. Quantile mapping, the conversion of values across paired empirical distributions, offers a simple, established means to approximate such long-term statistics, but only within appropriately defined domains. This method was applied to a case study in Central America, demonstrating that quantile mapping between TRMM and GPM data maintains the performance of a real-time landslide model. Use of quantile mapping could bring the benefits of the latest satellite-based precipitation dataset to existing user communities such as those for hazard assessment, crop forecasting, numerical weather prediction, and disease tracking.

Physical Activity and Pediatric Obesity: A Quantile Regression Analysis

PubMed Central

Mitchell, Jonathan A.; Dowda, Marsha; Pate, Russell R.; Kordas, Katarzyna; Froberg, Karsten; Sardinha, Luís B.; Kolle, Elin; Page, Angela

2016-01-01

Purpose We aimed to determine if moderate-to-vigorous physical activity (MVPA) and sedentary behavior (SB) were independently associated with body mass index (BMI) and waist circumference (WC) in children and adolescents. Methods Data from the International Children’s Accelerometry Database (ICAD) were used to address our objectives (N=11,115; 6-18y; 51% female). We calculated age and gender specific body mass index (BMI) and waist circumference (WC) Z-scores and used accelerometry to estimate MVPA and total SB. Self-reported television viewing was used as a measure of leisure time SB. Quantile regression was used to analyze the data. Results MVPA and total SB were associated with lower and higher BMI and WC Z-scores, respectively. These associations were strongest at the higher percentiles of the Z-score distributions. After including MVPA and total SB in the same model the MVPA associations remained, but the SB associations were no longer present. For example, each additional hour per day of MVPA was not associated with BMI Z-score at the 10th percentile (b=-0.02, P=0.170), but was associated with lower BMI Z-score at the 50th (b=-0.19, P<0.001) and 90th percentiles (b=-0.41, P<0.001). More television viewing was associated with higher BMI and WC and the associations were strongest at the higher percentiles of the Z-score distributions, with adjustment for MVPA and total SB. Conclusions Our observation of stronger associations at the higher percentiles indicate that increasing MVPA and decreasing television viewing at the population-level could shift the upper tails of the BMI and WC frequency distributions to lower values, thereby lowering the number of children and adolescents classified as obese. PMID:27755284

Quantile-based permutation thresholds for quantitative trait loci hotspots.

PubMed

Neto, Elias Chaibub; Keller, Mark P; Broman, Andrew F; Attie, Alan D; Jansen, Ritsert C; Broman, Karl W; Yandell, Brian S

2012-08-01

Quantitative trait loci (QTL) hotspots (genomic locations affecting many traits) are a common feature in genetical genomics studies and are biologically interesting since they may harbor critical regulators. Therefore, statistical procedures to assess the significance of hotspots are of key importance. One approach, randomly allocating observed QTL across the genomic locations separately by trait, implicitly assumes all traits are uncorrelated. Recently, an empirical test for QTL hotspots was proposed on the basis of the number of traits that exceed a predetermined LOD value, such as the standard permutation LOD threshold. The permutation null distribution of the maximum number of traits across all genomic locations preserves the correlation structure among the phenotypes, avoiding the detection of spurious hotspots due to nongenetic correlation induced by uncontrolled environmental factors and unmeasured variables. However, by considering only the number of traits above a threshold, without accounting for the magnitude of the LOD scores, relevant information is lost. In particular, biologically interesting hotspots composed of a moderate to small number of traits with strong LOD scores may be neglected as nonsignificant. In this article we propose a quantile-based permutation approach that simultaneously accounts for the number and the LOD scores of traits within the hotspots. By considering a sliding scale of mapping thresholds, our method can assess the statistical significance of both small and large hotspots. Although the proposed approach can be applied to any type of heritable high-volume "omic" data set, we restrict our attention to expression (e)QTL analysis. We assess and compare the performances of these three methods in simulations and we illustrate how our approach can effectively assess the significance of moderate and small hotspots with strong LOD scores in a yeast expression data set.

A Coupled Approach with Stochastic Rainfall-Runoff Simulation and Hydraulic Modeling for Extreme Flood Estimation on Large Watersheds

NASA Astrophysics Data System (ADS)

Paquet, E.

2015-12-01

The SCHADEX method aims at estimating the distribution of peak and daily discharges up to extreme quantiles. It couples a precipitation probabilistic model based on weather patterns, with a stochastic rainfall-runoff simulation process using a conceptual lumped model. It allows exploring an exhaustive set of hydrological conditions and watershed responses to intense rainfall events. Since 2006, it has been widely applied in France to about one hundred watersheds for dam spillway design, and also aboard (Norway, Canada and central Europe among others). However, its application to large watersheds (above 10 000 km²) faces some significant issues: spatial heterogeneity of rainfall and hydrological processes and flood peak damping due to hydraulic effects (flood plains, natural or man-made embankment) being the more important. This led to the development of an extreme flood simulation framework for large and heterogeneous watersheds, based on the SCHADEX method. Its main features are: Division of the large (or main) watershed into several smaller sub-watersheds, where the spatial homogeneity of the hydro-meteorological processes can reasonably be assumed, and where the hydraulic effects can be neglected. Identification of pilot watersheds where discharge data are available, thus where rainfall-runoff models can be calibrated. They will be parameters donors to non-gauged watersheds. Spatially coherent stochastic simulations for all the sub-watersheds at the daily time step. Identification of a selection of simulated events for a given return period (according to the distribution of runoff volumes at the scale of the main watershed). Generation of the complete hourly hydrographs at each of the sub-watersheds outlets. Routing to the main outlet with hydraulic 1D or 2D models. The presentation will be illustrated with the case-study of the Isère watershed (9981 km), a French snow-driven watershed. The main novelties of this method will be underlined, as well as its

How extreme are extremes?

NASA Astrophysics Data System (ADS)

Cucchi, Marco; Petitta, Marcello; Calmanti, Sandro

2016-04-01

High temperatures have an impact on the energy balance of any living organism and on the operational capabilities of critical infrastructures. Heat-wave indicators have been mainly developed with the aim of capturing the potential impacts on specific sectors (agriculture, health, wildfires, transport, power generation and distribution). However, the ability to capture the occurrence of extreme temperature events is an essential property of a multi-hazard extreme climate indicator. Aim of this study is to develop a standardized heat-wave indicator, that can be combined with other indices in order to describe multiple hazards in a single indicator. The proposed approach can be used in order to have a quantified indicator of the strenght of a certain extreme. As a matter of fact, extremes are usually distributed in exponential or exponential-exponential functions and it is difficult to quickly asses how strong was an extreme events considering only its magnitude. The proposed approach simplify the quantitative and qualitative communication of extreme magnitude

[The heart in extreme sports: hyperbaric activity and microgravity].

PubMed

Berrettini, Umberto; Landolfi, Angelo; Patteri, Giovanna

2008-10-01

The study of the cardiovascular and respiratory modifications in extreme environments could be useful for the understanding of the adaptive mechanisms of the body in particular conditions. The knowledge of how different environmental conditions in terms of extreme pressure, temperature and gravity modify the neurovegetative and cardiovascular system could be useful in daily practice for hypobaric and hyperbaric sports.

Quantile regression and Bayesian cluster detection to identify radon prone areas.

PubMed

Sarra, Annalina; Fontanella, Lara; Valentini, Pasquale; Palermi, Sergio

2016-11-01

Albeit the dominant source of radon in indoor environments is the geology of the territory, many studies have demonstrated that indoor radon concentrations also depend on dwelling-specific characteristics. Following a stepwise analysis, in this study we propose a combined approach to delineate radon prone areas. We first investigate the impact of various building covariates on indoor radon concentrations. To achieve a more complete picture of this association, we exploit the flexible formulation of a Bayesian spatial quantile regression, which is also equipped with parameters that controls the spatial dependence across data. The quantitative knowledge of the influence of each significant building-specific factor on the measured radon levels is employed to predict the radon concentrations that would have been found if the sampled buildings had possessed standard characteristics. Those normalised radon measures should reflect the geogenic radon potential of the underlying ground, which is a quantity directly related to the geological environment. The second stage of the analysis is aimed at identifying radon prone areas, and to this end, we adopt a Bayesian model for spatial cluster detection using as reference unit the building with standard characteristics. The case study is based on a data set of more than 2000 indoor radon measures, available for the Abruzzo region (Central Italy) and collected by the Agency of Environmental Protection of Abruzzo, during several indoor radon monitoring surveys. Copyright © 2016 Elsevier Ltd. All rights reserved.

Superquantile/CVaR Risk Measures: Second-Order Theory

DTIC Science & Technology

2014-07-17

order version of quantile regression . Keywords: superquantiles, conditional value-at-risk, second-order superquantiles, mixed superquan- tiles... quantile regression . 15. SUBJECT TERMS 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT Same as Report (SAR) 18. NUMBER OF PAGES 26 19a...second-order superquantiles is in the domain of generalized regression . We laid out in [16] a parallel methodology to that of quantile regression

Exploring Mbar shock conditions and isochorically heated aluminum at the Matter in Extreme Conditions end station of the Linac Coherent Light Source

DOE PAGES

Doppner, T.; LePape, S.; Ma, T.; ...

2014-08-11

Recent experiments performed at the Matter in Extreme Conditions end station of the Linac Coherent Light Source (LCLS) have demonstrated the first spectrally resolved measurements of plasmons from isochorically heated aluminum. The experiments have been performed using a seeded 8-keV x-ray laser beam as a pump and probe to both volumetrically heat and scatterx-rays from aluminum. Collective x-ray Thomson scattering spectra show a well-resolved plasmon feature that is down-shifted in energy by 19 eV. In addition, Mbar shock pressures from laser-compressed aluminum foils using velocity interferometer system for any reflector have been measured. Furthermore, the combination of experiments fully demonstratesmore » the possibility to perform warm dense matter studies at the LCLS with unprecedented accuracy and precision.« less

Distributional changes in rainfall and river flow in Sarawak, Malaysia

NASA Astrophysics Data System (ADS)

Sa'adi, Zulfaqar; Shahid, Shamsuddin; Ismail, Tarmizi; Chung, Eun-Sung; Wang, Xiao-Jun

2017-11-01

Climate change may not change the rainfall mean, but the variability and extremes. Therefore, it is required to explore the possible distributional changes of rainfall characteristics over time. The objective of present study is to assess the distributional changes in annual and northeast monsoon rainfall (November-January) and river flow in Sarawak where small changes in rainfall or river flow variability/distribution may have severe implications on ecology and agriculture. A quantile regression-based approach was used to assess the changes of scale and location of empirical probability density function over the period 1980-2014 at 31 observational stations. The results indicate that diverse variation patterns exist at all stations for annual rainfall but mainly increasing quantile trend at the lowers, and higher quantiles for the month of January and December. The significant increase in annual rainfall is found mostly in the north and central-coastal region and monsoon month rainfalls in the interior and north of Sarawak. Trends in river flow data show that changes in rainfall distribution have affected higher quantiles of river flow in monsoon months at some of the basins and therefore more flooding. The study reveals that quantile trend can provide more information of rainfall change which may be useful for climate change mitigation and adaptation planning.

Base Oil-Extreme Pressure Additive Synergy in Lubricants

USDA-ARS?s Scientific Manuscript database

Extreme pressure (EP) additives are those containing reactive elements such as sulfur, phosphorus, and chlorine. In lubrication processes that occur under extremely severe conditions (e.g., high pressure and/or slow speed), these elements undergo chemical reactions generating new materials (tribofi...

Global Weirding? - Using Very Large Ensembles and Extreme Value Theory to assess Changes in Extreme Weather Events Today

NASA Astrophysics Data System (ADS)

Otto, F. E. L.; Mitchell, D.; Sippel, S.; Black, M. T.; Dittus, A. J.; Harrington, L. J.; Mohd Saleh, N. H.

2014-12-01

A shift in the distribution of socially-relevant climate variables such as daily minimum winter temperatures and daily precipitation extremes, has been attributed to anthropogenic climate change for various mid-latitude regions. However, while there are many process-based arguments suggesting also a change in the shape of these distributions, attribution studies demonstrating this have not currently been undertaken. Here we use a very large initial condition ensemble of ~40,000 members simulating the European winter 2013/2014 using the distributed computing infrastructure under the weather@home project. Two separate scenarios are used:1. current climate conditions, and 2. a counterfactual scenario of "world that might have been" without anthropogenic forcing. Specifically focusing on extreme events, we assess how the estimated parameters of the Generalized Extreme Value (GEV) distribution vary depending on variable-type, sampling frequency (daily, monthly, …) and geographical region. We find that the location parameter changes for most variables but, depending on the region and variables, we also find significant changes in scale and shape parameters. The very large ensemble allows, furthermore, to assess whether such findings in the fitted GEV distributions are consistent with an empirical analysis of the model data, and whether the most extreme data still follow a known underlying distribution that in a small sample size might otherwise be thought of as an out-lier. The ~40,000 member ensemble is simulated using 12 different SST patterns (1 'observed', and 11 best guesses of SSTs with no anthropogenic warming). The range in SSTs, along with the corresponding changings in the NAO and high-latitude blocking inform on the dynamics governing some of these extreme events. While strong tele-connection patterns are not found in this particular experiment, the high number of simulated extreme events allows for a more thorough analysis of the dynamics than has been

Decrease in hydroclimatic conditions generating floods in the southeast of Belgium over the last 50 years resulting from changes in seasonal snow cover and extreme precipitation events

NASA Astrophysics Data System (ADS)

Wyard, Coraline; Fettweis, Xavier

2016-04-01

As a consequence of climate change, several studies concluded that winter flood occurrence could increase in the future in many rivers of northern and western Europe in response to an increase in extreme precipitation events. This study aims to determine if trends in extreme hydroclimatic events generating floods can already be detected over the last century. In particular, we focus on the Ourthe River (southeast of Belgium) which is one of the main tributaries of the Meuse River with a catchment area of 3500 km². In this river, most of the floods occur during winter and about 50% of them are due to rainfall events associated with the melting of the snow which covers the Ardennes during winter. In this study, hydroclimatic conditions favorable to flooding were reconstructed over the 20th century using the regional climate model MAR ("Modèle Atmosphérique Régional") forced by the following reanalyses: the ERA-20C, the ERA-Interim and the NCEP/NCAR-v1. The use of the MAR model allows to compute precipitation, snow depth and run-off resulting from precipitation events and snow melting in any part of the Ourthe river catchment area. Therefore, extreme hydroclimatic events, namely extreme run-off events, which could potentially generate floods, can be reconstructed using the MAR model. As validation, the MAR results were compared to weather station-based data. A trend analysis was then performed in order to study the evolution of conditions favorable to flooding in the Ourthe River catchment. The results show that the MAR model allows the detection of more than 95% of the hydroclimatic conditions which effectively generated observed floods in the Ourthe River over the 1974-2014 period. Conditions favorable to flooding present a negative trend over the last 50 years as a result of a decrease in snow accumulation and in extreme precipitation events. However, significance of these trends depends on the reanalysis used to force the regional climate model as well as the

An influence of extremal edges on boundary extension.

PubMed

Hale, Ralph G; Brown, James M; McDunn, Benjamin A; Siddiqui, Aisha P

2015-08-01

Studies have shown that people consistently remember seeing more of a studied scene than was physically present (e.g., Intraub & Richardson Journal of Experimental Psychology: Learning, Memory, and Cognition, 15, 179-187, 1989). This scene memory error, known as boundary extension, has been suggested to occur due to an observer's failure to differentiate between the contributing sources of information, including the sensory input, amodal continuation beyond the view boundaries, and contextual associations with the main objects and depicted scene locations (Intraub, 2010). Here, "scenes" made of abstract shapes on random-dot backgrounds, previously shown to elicit boundary extension (McDunn, Siddiqui, & Brown Psychonomic Bulletin & Review, 21, 370-375, 2014), were compared with versions made with extremal edges (Palmer & Ghose Psychological Science, 19, 77-84, 2008) added to their borders, in order to examine how boundary extension is influenced when amodal continuation at the borders' view boundaries is manipulated in this way. Extremal edges were expected to reduce boundary extension as compared to the same scenes without them, because extremal edge boundaries explicitly indicate an image's end (i.e., they do not continue past the view boundary). A large and a small difference (16 % and 40 %) between the close and wide-angle views shown during the experiment were tested to examine the effects of both boundary extension and normalization with and without extremal edges. Images without extremal edges elicited typical boundary extension for the 16 % size change condition, whereas the 40 % condition showed signs of normalization. With extremal edges, a reduced amount of boundary extension occurred for the 16 % condition, and only normalization was found for the 40 % condition. Our findings support and highlight the importance of amodal continuation at the view boundaries as a component of boundary extension.

Nutritional condition of Pacific Black Brant wintering at the extremes of their range

USGS Publications Warehouse

Mason, D.D.; Barboza, P.S.; Ward, D.H.

2006-01-01

Endogenous stores of energy allow birds to survive periods of severe weather and food shortage during winter. We documented changes in lipid, protein, moisture, and ash in body tissues of adult female Pacific Black Brant (Branta bernicla nigricans) and modeled the energetic costs of wintering. Birds were collected at the extremes of their winter range, in Alaska and Baja California, Mexico. Body lipids decreased over winter for birds in Alaska but increased for those in Baja California. Conversely, body protein increased over winter for Brant in Alaska and remained stable for birds in Baja California. Lipid stores likely fuel migration for Brant wintering in Baja California and ensure winter survival for those in Alaska. Increases in body protein may support earlier reproduction for Brant in Alaska. Predicted energy demands were similar between sites during late winter but avenues of expenditure were different. Birds in Baja California spent more energy on lipid synthesis while those in Alaska incurred higher thermoregulatory costs. Estimated daily intake rates of eelgrass were similar between sites in early winter; however, feeding time was more constrained in Alaska because of high tides and short photoperiods. Despite differences in energetic costs and foraging time, Brant wintering at both sites appeared to be in good condition. We suggest that wintering in Alaska may be more advantageous than long-distance migration if winter survival is similar between sites and constraints on foraging time do not impair body condition. ?? The Cooper Ornithological Society 2006.

Using Quantile Regression to Examine Health Care Expenditures during the Great Recession

PubMed Central

Chen, Jie; Vargas-Bustamante, Arturo; Mortensen, Karoline; Thomas, Stephen B

2014-01-01

Objective To examine the association between the Great Recession of 2007–2009 and health care expenditures along the health care spending distribution, with a focus on racial/ethnic disparities. Data Sources/Study Setting Secondary data analyses of the Medical Expenditure Panel Survey (2005–2006 and 2008–2009). Study Design Quantile multivariate regressions are employed to measure the different associations between the economic recession of 2007–2009 and health care spending. Race/ethnicity and interaction terms between race/ethnicity and a recession indicator are controlled to examine whether minorities encountered disproportionately lower health spending during the economic recession. Principal Findings The Great Recession was significantly associated with reductions in health care expenditures at the 10th–50th percentiles of the distribution, but not at the 75th–90th percentiles. Racial and ethnic disparities were more substantial at the lower end of the health expenditure distribution; however, on average the reduction in expenditures was similar for all race/ethnic groups. The Great Recession was also positively associated with spending on emergency department visits. Conclusion This study shows that the relationship between the Great Recession and health care spending varied along the health expenditure distribution. More variability was observed in the lower end of the health spending distribution compared to the higher end. PMID:24134797

Spatial extreme value analysis to project extremes of large-scale indicators for severe weather

PubMed Central

Gilleland, Eric; Brown, Barbara G; Ammann, Caspar M

2013-01-01

Concurrently high values of the maximum potential wind speed of updrafts (Wmax) and 0–6 km wind shear (Shear) have been found to represent conducive environments for severe weather, which subsequently provides a way to study severe weather in future climates. Here, we employ a model for the product of these variables (WmSh) from the National Center for Atmospheric Research/United States National Center for Environmental Prediction reanalysis over North America conditioned on their having extreme energy in the spatial field in order to project the predominant spatial patterns of WmSh. The approach is based on the Heffernan and Tawn conditional extreme value model. Results suggest that this technique estimates the spatial behavior of WmSh well, which allows for exploring possible changes in the patterns over time. While the model enables a method for inferring the uncertainty in the patterns, such analysis is difficult with the currently available inference approach. A variation of the method is also explored to investigate how this type of model might be used to qualitatively understand how the spatial patterns of WmSh correspond to extreme river flow events. A case study for river flows from three rivers in northwestern Tennessee is studied, and it is found that advection of WmSh from the Gulf of Mexico prevails while elsewhere, WmSh is generally very low during such extreme events. © 2013 The Authors. Environmetrics published by JohnWiley & Sons, Ltd. PMID:24223482

Predicting the solubility of gases in Nitrile Butadiene Rubber in extreme conditions using molecular simulation

NASA Astrophysics Data System (ADS)

Khawaja, Musab; Molinari, Nicola; Sutton, Adrian; Mostofi, Arash

In the oil and gas industry, elastomer seals play an important role in protecting sensitive monitoring equipment from contamination by gases - a problem that is exacerbated by the high pressures and temperatures found down-hole. The ability to predict and prevent such permeative failure has proved elusive to-date. Nitrile butadiene rubber (NBR) is a common choice of elastomer for seals due to its resistance to heat and fuels. In the conditions found in the well it readily absorbs small molecular weight gases. How this behaviour changes quantitatively for different gases as a function of temperature and pressure is not well-understood. In this work a series of fully atomistic simulations are performed to understand the effect of extreme conditions on gas solubility in NBR. Widom particle insertion is used to compute solubilities. The importance of sampling and allowing structural relaxation upon compression are highlighted, and qualitatively reasonable trends reproduced. Finally, while at STP it has previously been shown that the solubility of CO2 is higher than that of He in NBR, we observe that under the right circumstances it is possible to reverse this trend.

Viscosity models for pure hydrocarbons at extreme conditions: A review and comparative study

DOE PAGES

Baled, Hseen O.; Gamwo, Isaac K.; Enick, Robert M.; ...

2018-01-12

Here, viscosity is a critical fundamental property required in many applications in the chemical and oil industries. In this review the performance of seven select viscosity models, representative of various predictive and correlative approaches, is discussed and evaluated by comparison to experimental data of 52 pure hydrocarbons including straight-chain alkanes, branched alkanes, cycloalkanes, and aromatics. This analysis considers viscosity data to extremely high-temperature, high-pressure conditions up to 573 K and 300 MPa. Unsatisfactory results are found, particularly at high pressures, with the Chung-Ajlan-Lee-Starling, Pedersen-Fredenslund, and Lohrenz-Bray-Clark models commonly used for oil reservoir simulation. If sufficient experimental viscosity data are readilymore » available to determine model-specific parameters, the free volume theory and the expanded fluid theory models provide generally comparable results that are superior to those obtained with the friction theory, particularly at pressures higher than 100 MPa. Otherwise, the entropy scaling method by Lötgering-Lin and Gross is recommended as the best predictive model.« less

Viscosity models for pure hydrocarbons at extreme conditions: A review and comparative study

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

Baled, Hseen O.; Gamwo, Isaac K.; Enick, Robert M.

Here, viscosity is a critical fundamental property required in many applications in the chemical and oil industries. In this review the performance of seven select viscosity models, representative of various predictive and correlative approaches, is discussed and evaluated by comparison to experimental data of 52 pure hydrocarbons including straight-chain alkanes, branched alkanes, cycloalkanes, and aromatics. This analysis considers viscosity data to extremely high-temperature, high-pressure conditions up to 573 K and 300 MPa. Unsatisfactory results are found, particularly at high pressures, with the Chung-Ajlan-Lee-Starling, Pedersen-Fredenslund, and Lohrenz-Bray-Clark models commonly used for oil reservoir simulation. If sufficient experimental viscosity data are readilymore » available to determine model-specific parameters, the free volume theory and the expanded fluid theory models provide generally comparable results that are superior to those obtained with the friction theory, particularly at pressures higher than 100 MPa. Otherwise, the entropy scaling method by Lötgering-Lin and Gross is recommended as the best predictive model.« less

Metabolism and antioxidant defense in the larval chironomid Tanytarsus minutipalpus: adjustments to diel variations in the extreme conditions of Lake Magadi

PubMed Central

Wood, Chris M.; Bergman, Harold L.; Johannsson, Ora E.; Laurent, Pierre; Chevalier, Claudine; Kisipan, Mosiany L.; Kavembe, Geraldine D.; Papah, Michael B.; Brix, Kevin V.; De Boeck, Gudrun; Maina, John N.; Ojoo, Rodi O.; Bianchini, Adalto

2017-01-01

ABSTRACT Insect larvae are reported to be a major component of the simple but highly productive trophic web found in Lake Magadi (Kenya, Africa), which is considered to be one of the most extreme aquatic environments on Earth. Previous studies show that fish must display biochemical and physiological adjustments to thrive under the extreme conditions of the lake. However, information for invertebrates is lacking. In the present study, the occurrence of the larval chironomid Tanytarsus minutipalpus is reported in Lake Magadi for the first time. Additionally, changes in larval metabolism and antioxidant defense correlated with diel variations in the extremely hostile environmental conditions of the lake are described. Wide variations in water temperature (20.2-29.3°C) and dissolved oxygen content (3.2-18.6 mg O2 l−1) were observed at different times of day, without significant change in water pH (10.0±0.03). Temperature and dissolved oxygen were higher at 13:00 h (29.3±0.4°C and 18.6±1.0 mg O2 l−1) and 19:00 h (29.3±0.8°C and 16.2±1.6 mg O2 l−1) and lower at 01:00 h (21.1±0.1°C and 10.7±0.03 mg O2 l−1) and 07:00 h (20.2±0.4°C and 3.2±0.7 mg O2 l−1). Significant and parallel increases in parameters related to metabolism (cholinesterase, glucose, cholesterol, urea, creatinine and hemoglobin) and the antioxidant system (SOD, GPx, GR, GSH and GSSG) were observed in larvae collected at 13:00 h. In contrast, no significant changes were observed in pro-oxidants (ROS and NO), TOSC and oxidative damage parameters (LPO and DNA damage). Therefore, the observed increases in temperature and dissolved O2 content in Lake Magadi were associated with changes in the antioxidant system of T. minutipalpus larvae. Adjustments performed by the chironomid larvae were efficient in maintaining body homeostasis, as well as protecting biomolecules against oxidative damage, so that oxidative stress did not occur. GSH-GSSG and GPx-GR systems appeared to

Realistic sampling of anisotropic correlogram parameters for conditional simulation of daily rainfields

NASA Astrophysics Data System (ADS)

Gyasi-Agyei, Yeboah

2018-01-01

This paper has established a link between the spatial structure of radar rainfall, which more robustly describes the spatial structure, and gauge rainfall for improved daily rainfield simulation conditioned on the limited gauged data for regions with or without radar records. A two-dimensional anisotropic exponential function that has parameters of major and minor axes lengths, and direction, is used to describe the correlogram (spatial structure) of daily rainfall in the Gaussian domain. The link is a copula-based joint distribution of the radar-derived correlogram parameters that uses the gauge-derived correlogram parameters and maximum daily temperature as covariates of the Box-Cox power exponential margins and Gumbel copula. While the gauge-derived, radar-derived and the copula-derived correlogram parameters reproduced the mean estimates similarly using leave-one-out cross-validation of ordinary kriging, the gauge-derived parameters yielded higher standard deviation (SD) of the Gaussian quantile which reflects uncertainty in over 90% of cases. However, the distribution of the SD generated by the radar-derived and the copula-derived parameters could not be distinguished. For the validation case, the percentage of cases of higher SD by the gauge-derived parameter sets decreased to 81.2% and 86.6% for the non-calibration and the calibration periods, respectively. It has been observed that 1% reduction in the Gaussian quantile SD can cause over 39% reduction in the SD of the median rainfall estimate, actual reduction being dependent on the distribution of rainfall of the day. Hence the main advantage of using the most correct radar correlogram parameters is to reduce the uncertainty associated with conditional simulations that rely on SD through kriging.

The near-term prediction of drought and flooding conditions in the northeastern United States based on extreme phases of AMO and NAO

NASA Astrophysics Data System (ADS)

Berton, Rouzbeh; Driscoll, Charles T.; Adamowski, Jan F.

2017-10-01

frequency of wet, average, and dry discharge conditions with regards to the extreme phases of AMO and NAO. While the function was decaying, the tail asymptotically merged into and stabilized at the theoretical probability of the event. As the basin scale increased, the probability of wet, average, and dry discharge conditions decreased. The Merrimack River watershed will most likely experience greater than average discharge as its extreme condition, therefore development should be avoided on flood plains. Furthermore, the current reservoir storage capacity in the Merrimack should be improved in order to accommodate excess water input and minimize flood damage. Future research should target changes in the magnitude and timing of high discharge events in order to develop adaptation strategies for aging hydraulic infrastructure in the region.

Effect of elevation on extreme precipitation of short durations: evidences of orographic signature on the parameters of Depth-Duration-Frequency curves

NASA Astrophysics Data System (ADS)

Avanzi, Francesco; De Michele, Carlo; Gabriele, Salvatore; Ghezzi, Antonio; Rosso, Renzo

2015-04-01

Here, we show how atmospheric circulation and topography rule the variability of depth-duration-frequency (DDF) curves parameters, and we discuss how this variability has physical implications on the formation of extreme precipitations at high elevations. A DDF is a curve ruling the value of the maximum annual precipitation H as a function of duration D and the level of probability F. We consider around 1500 stations over the Italian territory, with at least 20 years of data of maximum annual precipitation depth at different durations. We estimated the DDF parameters at each location by using the asymptotic distribution of extreme values, i.e. the so-called Generalized Extreme Value (GEV) distribution, and considering a statistical simple scale invariance hypothesis. Consequently, a DDF curve depends on five different parameters. A first set relates H with the duration (namely, the mean value of annual maximum precipitation depth for unit duration and the scaling exponent), while a second set links H to F (namely, a scale, position and shape parameter). The value of the shape parameter has consequences on the type of random variable (unbounded, upper or lower bounded). This extensive analysis shows that the variability of the mean value of annual maximum precipitation depth for unit duration obeys to the coupled effect of topography and modal direction of moisture flux during extreme events. Median values of this parameter decrease with elevation. We called this phenomenon "reverse orographic effect" on extreme precipitation of short durations, since it is in contrast with general knowledge about the orographic effect on mean precipitation. Moreover, the scaling exponent is mainly driven by topography alone (with increasing values of this parameter at increasing elevations). Therefore, the quantiles of H(D,F) at durations greater than unit turn to be more variable at high elevations than at low elevations. Additionally, the analysis of the variability of the shape

Arctic sea ice, Eurasia snow, and extreme winter haze in China.

PubMed

Zou, Yufei; Wang, Yuhang; Zhang, Yuzhong; Koo, Ja-Ho

2017-03-01

The East China Plains (ECP) region experienced the worst haze pollution on record for January in 2013. We show that the unprecedented haze event is due to the extremely poor ventilation conditions, which had not been seen in the preceding three decades. Statistical analysis suggests that the extremely poor ventilation conditions are linked to Arctic sea ice loss in the preceding autumn and extensive boreal snowfall in the earlier winter. We identify the regional circulation mode that leads to extremely poor ventilation over the ECP region. Climate model simulations indicate that boreal cryospheric forcing enhances the regional circulation mode of poor ventilation in the ECP region and provides conducive conditions for extreme haze such as that of 2013. Consequently, extreme haze events in winter will likely occur at a higher frequency in China as a result of the changing boreal cryosphere, posing difficult challenges for winter haze mitigation but providing a strong incentive for greenhouse gas emission reduction.

Arctic sea ice, Eurasia snow, and extreme winter haze in China

PubMed Central

Zou, Yufei; Wang, Yuhang; Zhang, Yuzhong; Koo, Ja-Ho

2017-01-01

The East China Plains (ECP) region experienced the worst haze pollution on record for January in 2013. We show that the unprecedented haze event is due to the extremely poor ventilation conditions, which had not been seen in the preceding three decades. Statistical analysis suggests that the extremely poor ventilation conditions are linked to Arctic sea ice loss in the preceding autumn and extensive boreal snowfall in the earlier winter. We identify the regional circulation mode that leads to extremely poor ventilation over the ECP region. Climate model simulations indicate that boreal cryospheric forcing enhances the regional circulation mode of poor ventilation in the ECP region and provides conducive conditions for extreme haze such as that of 2013. Consequently, extreme haze events in winter will likely occur at a higher frequency in China as a result of the changing boreal cryosphere, posing difficult challenges for winter haze mitigation but providing a strong incentive for greenhouse gas emission reduction. PMID:28345056

Insertion sequences enrichment in extreme Red sea brine pool vent.

PubMed

Elbehery, Ali H A; Aziz, Ramy K; Siam, Rania

2017-03-01

Mobile genetic elements are major agents of genome diversification and evolution. Limited studies addressed their characteristics, including abundance, and role in extreme habitats. One of the rare natural habitats exposed to multiple-extreme conditions, including high temperature, salinity and concentration of heavy metals, are the Red Sea brine pools. We assessed the abundance and distribution of different mobile genetic elements in four Red Sea brine pools including the world's largest known multiple-extreme deep-sea environment, the Red Sea Atlantis II Deep. We report a gradient in the abundance of mobile genetic elements, dramatically increasing in the harshest environment of the pool. Additionally, we identified a strong association between the abundance of insertion sequences and extreme conditions, being highest in the harshest and deepest layer of the Red Sea Atlantis II Deep. Our comparative analyses of mobile genetic elements in secluded, extreme and relatively non-extreme environments, suggest that insertion sequences predominantly contribute to polyextremophiles genome plasticity.

Extreme flood estimation by the SCHADEX method in a snow-driven catchment: application to Atnasjø (Norway)

NASA Astrophysics Data System (ADS)

Paquet, Emmanuel; Lawrence, Deborah

2013-04-01

The SCHADEX method for extreme flood estimation was developed by Paquet et al. (2006, 2013), and since 2008, it is the reference method used by Electricité de France (EDF) for dam spillway design. SCHADEX is a so-called "semi-continuous" stochastic simulation method in that flood events are simulated on an event basis and are superimposed on a continuous simulation of the catchment saturation hazard usingrainfall-runoff modelling. The MORDOR hydrological model (Garçon, 1999) has thus far been used for the rainfall-runoff modelling. MORDOR is a conceptual, lumped, reservoir model with daily areal rainfall and air temperature as the driving input data. The principal hydrological processes represented are evapotranspiration, direct and indirect runoff, ground water, snow accumulation and melt, and routing. The model has been intensively used at EDF for more than 15 years, in particular for inflow forecasts for French mountainous catchments. SCHADEX has now also been applied to the Atnasjø catchment (463 km²), a well-documented inland catchment in south-central Norway, dominated by snowmelt flooding during spring/early summer. To support this application, a weather pattern classification based on extreme rainfall was first established for Norway (Fleig, 2012). This classification scheme was then used to build a Multi-Exponential Weather Pattern distribution (MEWP), as introduced by Garavaglia et al. (2010) for extreme rainfall estimation. The MORDOR model was then calibrated relative to daily discharge data for Atnasjø. Finally, a SCHADEX simulation was run to build a daily discharge distribution with a sufficient number of simulations for assessing the extreme quantiles. Detailed results are used to illustrate how SCHADEX handles the complex and interacting hydrological processes driving flood generation in this snow driven catchment. Seasonal and monthly distributions, as well as statistics for several thousand simulated events reaching a 1000 years return level

Assessment extreme hydrometeorological conditions in the Gulf of Bothnia, the Baltic Sea

NASA Astrophysics Data System (ADS)

Dvornikov, Anton; Martyanov, Stanislav; Ryabchenko, Vladimir; Eremina, Tatjana; Isaev, Alexey; Sein, Dmitry

2017-04-01

Extreme hydrometeorological conditions in the Gulf of Bothnia, the Baltic Sea, are estimated paying a special attention to the area of the future construction of nuclear power plant (NPP) "Hanhikivi-1" (24° 16' E, 64° 32' N). To produce these estimates, long-term observations and results from numerical models of water and ice circulation and wind waves are used. It is estimated that the average annual air temperature in the vicinity of the station is +3° C, summer and winter extreme temperature is equal to 33.3° C and -41.5° C, respectively. Model calculations of wind waves have shown that the most dangerous (in terms of the generation of wind waves in the NPP area) is a north-west wind with the direction of 310°. The maximum height of the waves in the Gulf of Bothnia near the NPP for this wind direction with wind velocity of 10 m/s is 1.2-1.4 m. According to the model estimates, the highest possible level of the sea near the NPP is 248 cm, the minimum level, -151 cm, respectively for the western and eastern winds. These estimates are in good agreement with observations on the sea level for the period 1922-2015 at the nearest hydrometeorological station Raahe (Finland). In order to assess the likely impact of the NPP on the marine environment numerical experiments for the cold (2010) and warm year (2014) have been carried out. These calculations have shown that permanent release of heat into the marine environment from the operating NPP for the cold year (2010) will increase the temperature in the upper layer of 0-250m zone by 10°C in winter - spring and by 8°C in summer - early autumn, and in the bottom layer of 0-250m zone by 5°C in winter - spring and 3°C in summer - early autumn. For the warm year (2014), these temperature changes are smaller. Ice cover in both cases will disappear in two - kilometer vicinity of the NPP. These effects should be taken into account when assessing local climate changes in the future

Estimating earnings losses due to mental illness: a quantile regression approach.

PubMed

Marcotte, Dave E; Wilcox-Gök, Virginia

2003-09-01

The ability of workers to remain productive and sustain earnings when afflicted with mental illness depends importantly on access to appropriate treatment and on flexibility and support from employers. In the United States there is substantial variation in access to health care and sick leave and other employment flexibilities across the earnings distribution. Consequently, a worker's ability to work and how much his/her earnings are impeded likely depend upon his/her position in the earnings distribution. Because of this, focusing on average earnings losses may provide insufficient information on the impact of mental illness in the labor market. In this paper, we examine the effects of mental illness on earnings by recognizing that effects could vary across the distribution of earnings. Using data from the National Comorbidity Survey, we employ a quantile regression estimator to identify the effects at key points in the earnings distribution. We find that earnings effects vary importantly across the distribution. While average effects are often not large, mental illness more commonly imposes earnings losses at the lower tail of the distribution, especially for women. In only one case do we find an illness to have negative effects across the distribution. Mental illness can have larger negative impacts on economic outcomes than previously estimated, even if those effects are not uniform. Consequently, researchers and policy makers alike should not be placated by findings that mean earnings effects are relatively small. Such estimates miss important features of how and where mental illness is associated with real economic losses for the ill.

Gender differences in French GPs' activity: the contribution of quantile regressions.

PubMed

Dumontet, Magali; Franc, Carine

2015-05-01

In any fee-for-service system, doctors may be encouraged to increase the number of services (private activity) they provide to receive a higher income. Studying private activity determinants helps to predict doctors' provision of care. In the context of strong feminization and heterogeneity in general practitioners' (GP) behavior, we first aim to measure the effects of the determinants of private activity. Second, we study the evolution of these effects along the private activity distribution. Third, we examine the differences between male and female GPs. From an exhaustive database of French GPs working in private practice in 2008, we performed an ordinary least squares (OLS) regression and quantile regressions (QR) on the GPs' private activity. Among other determinants, we examined the trade-offs within the GPs' household considering his/her marital status, spousal income, and children. While the OLS results showed that female GPs had less private activity than male GPs (-13%), the QR results emphasized a private activity gender gap that increased significantly in the upper tail of the distribution. We also find gender differences in the private activity determinants, including family structure, practice characteristics, and case-mix variables. For instance, having a youngest child under 12 years old had a positive effect on the level of private activity for male GPs and a negative effect for female GPs. The results allow us to understand to what extent the supply of care differs between male and female GPs. In the context of strong feminization, this is essential to consider for organizing and forecasting the GPs' supply of care.

Role of absorbing aerosols on hot extremes in India in a GCM

NASA Astrophysics Data System (ADS)

Mondal, A.; Sah, N.; Venkataraman, C.; Patil, N.

2017-12-01

Temperature extremes and heat waves in North-Central India during the summer months of March through June are known for causing significant impact in terms of human health, productivity and mortality. While greenhouse gas-induced global warming is generally believed to intensify the magnitude and frequency of such extremes, aerosols are usually associated with an overall cooling, by virtue of their dominant radiation scattering nature, in most world regions. Recently, large-scale atmospheric conditions leading to heat wave and extreme temperature conditions have been analysed for the North-Central Indian region. However, the role of absorbing aerosols, including black carbon and dust, is still not well understood, in mediating hot extremes in the region. In this study, we use 30-year simulations from a chemistry-coupled atmosphere-only General Circulation Model (GCM), ECHAM6-HAM2, forced with evolving aerosol emissions in an interactive aerosol module, along with observed sea surface temperatures, to examine large-scale and mesoscale conditions during hot extremes in India. The model is first validated with observed gridded temperature and reanalysis data, and is found to represent observed variations in temperature in the North-Central region and concurrent large-scale atmospheric conditions during high temperature extremes realistically. During these extreme events, changes in near surface properties include a reduction in single scattering albedo and enhancement in short-wave solar heating rate, compared to climatological conditions. This is accompanied by positive anomalies of black carbon and dust aerosol optical depths. We conclude that the large-scale atmospheric conditions such as the presence of anticyclones and clear skies, conducive to heat waves and high temperature extremes, are exacerbated by absorbing aerosols in North-Central India. Future air quality regulations are expected to reduce sulfate particles and their masking of GHG warming. It is

Climatic Extremes and Food Grain Production in India

NASA Astrophysics Data System (ADS)

A, A.; Mishra, V.

2015-12-01

Climate change is likely to affect food and water security in India. India has witnessed tremendous growth in its food production after the green revolution. However, during the recent decades the food grain yields were significantly affected by the extreme climate and weather events. Air temperature and associated extreme events (number of hot days and hot nights, heat waves) increased significantly during the last 50 years in the majority of India. More remarkably, a substantial increase in mean and extreme temperatures was observed during the winter season in India. On the other hand, India witnessed extreme flood and drought events that have become frequent during the past few decades. Extreme rainfall during the non-monsoon season adversely affected the food grain yields and results in tremendous losses in several parts of the country. Here we evaluate the changes in hydroclimatic extremes and its linkage with the food grain production in India. We use observed food grain yield data for the period of 1980-2012 at district level. We understand the linkages between food grain yield and crop phenology obtained from the high resolution leaf area index and NDVI datasets from satellites. We used long-term observed data of daily precipitation and maximum and minimum temperatures to evaluate changes in the extreme events. We use statistical models to develop relationships between crop yields, mean and extreme temperatures for various crops to understand the sensitivity of these crops towards changing climatic conditions. We find that some of the major crop types and predominant crop growing areas have shown a significant sensitivity towards changes in extreme climatic conditions in India.

The impact of extreme weather conditions on the life of settlers in the Central Russia in X - XVI centuries

NASA Astrophysics Data System (ADS)

Graves, Irina; Nizovtsev, Viacheslav; Erman, Natalia

2017-04-01

A special place in the reconstruction of climate dynamics takes an analysis of extraordinary meteorological phenomena. These extreme weather events in the first place impact the functioning of, the rhythm and dynamics of the landscapes and determine not only the features of economy, but also certain aspects of historical development. In the analysis of primary chronicles and published data, along with the direct climatic characteristics (hot, warm, cold, wet, dry, etc.) a lot of attention was paid to abnormal (extreme) natural phenomena and indirect indications of climate variability (floods, crop failures, hunger years, epidemics, etc.). As a result, tables were compiled reflecting climatic basic characteristics and extremes for each year since 900 BC. X-XI centuries was a period of minor climatic optimum - the climate was warmer and drier than the modern one. In addition to higher temperatures (up to 1-3C above than mordern), during this period there were no severe winters. A small amount of summer rainfall has led to a reduction in the number of small water reservoirs and flooding rivers. This is evidenced by Slavic settlements on floodplains of a number of rivers in the Moscow region. It is in this favorable climatic time the way "from the Vikings to the Greeks" was open. Catastrophic natural events had a minimum repeatability. For example, during the X century the Russian chronicles mentioned 41 extreme event, but for the XIII century - 102. Most of the villages and towns were located on the low floodplain terraces of rivers. The main farmland was concentrated there as well. In the "period of contrasts" (XIII - XIV centuries) there was an increase of intra-seasonal climate variability, humidity and widespread reduction in summer temperatures by 1-2C. The number of extreme weather events increased: cold prolonged winters, long rains in summers, cold weather returns in the early summer, early frosts in late summer - early autumn. Such conditions often

A single pH fluorescent probe for biosensing and imaging of extreme acidity and extreme alkalinity.

PubMed

Chao, Jian-Bin; Wang, Hui-Juan; Zhang, Yong-Bin; Li, Zhi-Qing; Liu, Yu-Hong; Huo, Fang-Jun; Yin, Cai-Xia; Shi, Ya-Wei; Wang, Juan-Juan

2017-07-04

A simple tailor-made pH fluorescent probe 2-benzothiazole (N-ethylcarbazole-3-yl) hydrazone (Probe) is facilely synthesized by the condensation reaction of 2-hydrazinobenzothiazole with N-ethylcarbazole-3-formaldehyde, which is a useful fluorescent probe for monitoring extremely acidic and alkaline pH, quantitatively. The pH titrations indicate that Probe displays a remarkable emission enhancement with a pK a of 2.73 and responds linearly to minor pH fluctuations within the extremely acidic range of 2.21-3.30. Interestingly, Probe also exhibits strong pH-dependent characteristics with pK a 11.28 and linear response to extreme-alkalinity range of 10.41-12.43. In addition, Probe shows a large Stokes shift of 84 nm under extremely acidic and alkaline conditions, high selectivity, excellent sensitivity, good water-solubility and fine stability, all of which are favorable for intracellular pH imaging. The probe is further successfully applied to image extremely acidic and alkaline pH values fluctuations in E. coli cells. Copyright © 2017 Elsevier B.V. All rights reserved.

Enhancing the Extreme Climate Index (ECI) to monitor climate extremes for an index-based insurance scheme across Africa

NASA Astrophysics Data System (ADS)

Helmschrot, J.; Malherbe, J.; Chamunorwa, M.; Muthige, M.; Petitta, M.; Calmanti, S.; Cucchi, M.; Syroka, J.; Iyahen, E.; Engelbrecht, F.

2017-12-01

Climate services are a key component of National Adaptation Plan (NAP) processes, which require the analysis of current climate conditions, future climate change scenarios and the identification of adaptation strategies, including the capacity to finance and implement effective adaptation options. The Extreme Climate Facility (XCF) proposed by the African Risk Capacity (ARC) developed a climate index insurance scheme, which is based on the Extreme Climate Index (ECI): an objective, multi-hazard index capable of tracking changes in the frequency or magnitude of extreme weather events, thus indicating possible shifts to a new climate regime in various regions. The main hazards covered by ECI are extreme dry, wet and heat events, with the possibility of adding other region-specific risk events. The ECI is standardized across broad geographical regions, so that extreme events occurring under different climatic regimes in Africa can be compared. Initially developed by an Italian company specialized in Climate Services, research is now conducted at the CSIR and SASSCAL, to verify and further develop the ECI for application in southern African countries, through a project initiated by the World Food Programme (WFP) and ARC. The paper will present findings on the most appropriate definitions of extremely wet and dry conditions in Africa, in terms of their impact across a multitude of sub-regional climates of the African continent. Findings of a verification analysis of the ECI, as determined through vegetation monitoring data and the SASSCAL weather station network will be discussed. Changes in the ECI under climate change will subsequently be projected, using detailed regional projections generated by the CSIR and through the Coordinated Regional Downscaling Experiment (CORDEX). This work will be concluded by the development of a web-based climate service informing African Stakeholders on climate extremes.

Stationarity is undead: Uncertainty dominates the distribution of extremes

NASA Astrophysics Data System (ADS)

Serinaldi, Francesco; Kilsby, Chris G.

2015-03-01

The increasing effort to develop and apply nonstationary models in hydrologic frequency analyses under changing environmental conditions can be frustrated when the additional uncertainty related to the model complexity is accounted for along with the sampling uncertainty. In order to show the practical implications and possible problems of using nonstationary models and provide critical guidelines, in this study we review the main tools developed in this field (such as nonstationary distribution functions, return periods, and risk of failure) highlighting advantages and disadvantages. The discussion is supported by three case studies that revise three illustrative examples reported in the scientific and technical literature referring to the Little Sugar Creek (at Charlotte, North Carolina), Red River of the North (North Dakota/Minnesota), and the Assunpink Creek (at Trenton, New Jersey). The uncertainty of the results is assessed by complementing point estimates with confidence intervals (CIs) and emphasizing critical aspects such as the subjectivity affecting the choice of the models' structure. Our results show that (1) nonstationary frequency analyses should not only be based on at-site time series but require additional information and detailed exploratory data analyses (EDA); (2) as nonstationary models imply that the time-varying model structure holds true for the entire future design life period, an appropriate modeling strategy requires that EDA identifies a well-defined deterministic mechanism leading the examined process; (3) when the model structure cannot be inferred in a deductive manner and nonstationary models are fitted by inductive inference, model structure introduces an additional source of uncertainty so that the resulting nonstationary models can provide no practical enhancement of the credibility and accuracy of the predicted extreme quantiles, whereas possible model misspecification can easily lead to physically inconsistent results; (4) when

Temperature sensitivity of extreme precipitation events in the south-eastern Alpine forelands

NASA Astrophysics Data System (ADS)

Schroeer, Katharina; Kirchengast, Gottfried

2016-04-01

factors leads to the urgent questions of what we might expect from future heavy precipitation, particularly summertime convective storms, and how the associated risks will change if the observed trends persist. Working on an event basis allows us to consider a robust diversity of indicators such as storm duration, total sums, and peak intensities of the individual rainfall events in our analysis. First results suggest that the temperature sensitivity of precipitation events in the study region generally rises in accordance with the CC rate, but rates diverge dependent on the spatio-temporal properties of the sampling. At high temperatures above about 25 °C, the heaviest events do not show increases beyond the CC rate, as have been reported in some other studies for temperatures below 25°C. This is likely due to limitations of moisture availability in hot summer conditions. Observations of relative humidity available for 77 out of the 188 stations used support this hypothesis. When events where humidity is well below saturation are excluded from the sample, quantile regression results show higher scaling rates. The preliminary findings underline the need for a more sophisticated analysis of the temperature-precipitation relationship especially in heterogeneous regions with complex terrain.

Ecoclimatic indicators to study crop suitability in present and future climatic conditionsTIC CONDITIONS

NASA Astrophysics Data System (ADS)

Caubel, Julie; Garcia de Cortazar Atauri, Inaki; Huard, Frédéric; Launay, Marie; Ripoche, Dominique; Gouache, David; Bancal, Marie-Odile; Graux, Anne-Isabelle; De Noblet, Nathalie

2013-04-01

Climate change is expected to affect both regional and global food production through changes in overall agroclimatic conditions. It is therefore necessary to develop simple tools of crop suitability diagnosis in a given area so that stakeholders can envisage land use adaptations under climate change conditions. The most common way to investigate potential impacts of climate on the evolution of agrosystems is to make use of an array of agroclimatic indicators, which provide synthetic information derived from climatic variables and calculated within fixed periods (i.e. January first - 31th July). However, the information obtained during these periods does not enable to take account of the plant response to climate. In this work, we present some results of the research program ORACLE (Opportunities and Risks of Agrosystems & forests in response to CLimate, socio-economic and policy changEs in France (and Europe). We proposed a suite of relevant ecoclimatic indicators, based on temperature and rainfall, in order to evaluate crop suitability for both present and new climatic conditions. Ecoclimatic indicators are agroclimatic indicators (e.g., grain heat stress) calculated during specific phenological phases so as to take account of the plant response to climate (e.g., the grain filling period, flowering- harvest). These indicators are linked with the ecophysiological processes they characterize (for e.g., the grain filling). To represent this methodology, we studied the suitability of winter wheat in future climatic conditions through three distinct French sites, Toulouse, Dijon and Versailles. Indicators have been calculated using climatic data from 1950 to 2100 simulated by the global climate model ARPEGE forced by a greenhouse effect corresponding to the SRES A1B scenario. The Quantile-Quantile downscaling method was applied to obtain data for the three locations. Phenological stages (emergence, ear 1 cm, flowering, beginning of grain filling and harvest) have been

Modelling probabilities of heavy precipitation by regional approaches

NASA Astrophysics Data System (ADS)

Gaal, L.; Kysely, J.

2009-09-01

Extreme precipitation events are associated with large negative consequences for human society, mainly as they may trigger floods and landslides. The recent series of flash floods in central Europe (affecting several isolated areas) on June 24-28, 2009, the worst one over several decades in the Czech Republic as to the number of persons killed and the extent of damage to buildings and infrastructure, is an example. Estimates of growth curves and design values (corresponding e.g. to 50-yr and 100-yr return periods) of precipitation amounts, together with their uncertainty, are important in hydrological modelling and other applications. The interest in high quantiles of precipitation distributions is also related to possible climate change effects, as climate model simulations tend to project increased severity of precipitation extremes in a warmer climate. The present study compares - in terms of Monte Carlo simulation experiments - several methods to modelling probabilities of precipitation extremes that make use of ‘regional approaches’: the estimation of distributions of extremes takes into account data in a ‘region’ (‘pooling group’), in which one may assume that the distributions at individual sites are identical apart from a site-specific scaling factor (the condition is referred to as ‘regional homogeneity’). In other words, all data in a region - often weighted in some way - are taken into account when estimating the probability distribution of extremes at a given site. The advantage is that sampling variations in the estimates of model parameters and high quantiles are to a large extent reduced compared to the single-site analysis. We focus on the ‘region-of-influence’ (ROI) method which is based on the identification of unique pooling groups (forming the database for the estimation) for each site under study. The similarity of sites is evaluated in terms of a set of site attributes related to the distributions of extremes. The issue of

Irrigation mitigates against heat extremes

NASA Astrophysics Data System (ADS)

Thiery, Wim; Fischer, Erich; Visser, Auke; Hirsch, Annette L.; Davin, Edouard L.; Lawrence, Dave; Hauser, Mathias; Seneviratne, Sonia I.

2017-04-01

Irrigation is an essential practice for sustaining global food production and many regional economies. Emerging scientific evidence indicates that irrigation substantially affects mean climate conditions in different regions of the world. Yet how this practice influences climate extremes is currently unknown. Here we use gridded observations and ensemble simulations with the Community Earth System Model to assess the impacts of irrigation on climate extremes. While the influence of irrigation on annual mean temperatures is limited, we find a large impact on temperature extremes, with a particularly strong cooling during the hottest day of the year (-0.78 K averaged over irrigated land). The strong influence on hot extremes stems from the timing of irrigation and its influence on land-atmosphere coupling strength. Together these effects result in asymmetric temperature responses, with a more pronounced cooling during hot and/or dry periods. The influence of irrigation is even more pronounced when considering subgrid-scale model output, suggesting that local effects of land management are far more important than previously thought. Finally we find that present-day irrigation is partly masking GHG-induced warming of extreme temperatures, with particularly strong effects in South Asia. Our results overall underline that irrigation substantially reduces our exposure to hot temperature extremes and highlight the need to account for irrigation in future climate projections.

Liquid Water Restricts Habitability in Extreme Deserts

NASA Astrophysics Data System (ADS)

Cockell, Charles S.; Brown, Sarah; Landenmark, Hanna; Samuels, Toby; Siddall, Rebecca; Wadsworth, Jennifer

2017-04-01

Liquid water is a requirement for biochemistry, yet under some circumstances it is deleterious to life. Here, we show that liquid water reduces the upper temperature survival limit for two extremophilic photosynthetic microorganisms (Gloeocapsa and Chroococcidiopsis spp.) by greater than 40°C under hydrated conditions compared to desiccated conditions. Under hydrated conditions, thermal stress causes protein inactivation as shown by the fluorescein diacetate assay. The presence of water was also found to enhance the deleterious effects of freeze-thaw in Chroococcidiopsis sp. In the presence of water, short-wavelength UV radiation more effectively kills Gloeocapsa sp. colonies, which we hypothesize is caused by factors including the greater penetration of UV radiation into hydrated colonies compared to desiccated colonies. The data predict that deserts where maximum thermal stress or irradiation occurs in conjunction with the presence of liquid water may be less habitable to some organisms than more extreme arid deserts where organisms can dehydrate prior to being exposed to these extremes, thus minimizing thermal and radiation damage. Life in extreme deserts is poised between the deleterious effects of the presence and the lack of liquid water.

Climate teleconnections, weather extremes, and vector-borne disease outbreaks

USDA-ARS?s Scientific Manuscript database

Fluctuations in climate lead to extremes in temperature, rainfall, flooding, and droughts. These climate extremes create ideal ecological conditions that promote mosquito-borne disease transmission that impact global human and animal health. One well known driver of such global scale climate fluctua...

Variability of Extreme Precipitation Events in Tijuana, Mexico During ENSO Years

NASA Astrophysics Data System (ADS)

Cavazos, T.; Rivas, D.

2007-05-01

We present the variability of daily precipitation extremes (top 10 percecnt) in Tijuana, Mexico during 1950-2000. Interannual rainfall variability is significantly modulated by El Nino/Southern Oscillation. The interannual precipitation variability exhibits a large change with a relatively wet period and more variability during 1976- 2000. The wettest years and the largest frequency of daily extremes occurred after 1976-1977, with 6 out of 8 wet years characterized by El Nino episodes and 2 by neutral conditions. However, more than half of the daily extremes during 1950-2000 occurred in non-ENSO years, evidencing that neutral conditions also contribute significantly to extreme climatic variability in the region. Extreme events that occur in neutral (strong El Nino) conditions are associated with a pineapple express and a neutral PNA (negative TNH) teleconnection pattern that links an anomalous tropical convective forcing west (east) of the date line with a strong subtropical jet over the study area. At regional scale, both types of extremes are characterized by a trough in the subtropical jet over California/Baja California, which is further intensified by thermal interaction with an anomalous warm California Current off Baja California, low-level moisture advection from the subtropical warm sea-surface region, intense convective activity over the study area and extreme rainfall from southern California to Baja California.

XBeach-G: a tool for predicting gravel barrier response to extreme storm conditions

NASA Astrophysics Data System (ADS)

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

2014-05-01

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

Extreme Magnetic Storms: Their Characteristics and Possible Consequences for Humanity

NASA Astrophysics Data System (ADS)

Falkowski, B. J.; Tsurutani, B.; Lakhina, G. S.; Deng, Y.; Mannucci, A. J.

2015-12-01

The solar and interplanetary conditions necessary to create an extreme magnetic storm will be discussed. The Carrington 1859 event was not the largest possible. It will be shown that different facets of fast ICMEs/extreme magnetic storms will have different limitations. Some possible adverse effects of such extreme space weather events on society will be addressed.

Operational early warning platform for extreme meteorological events

NASA Astrophysics Data System (ADS)

Mühr, Bernhard; Kunz, Michael

2015-04-01

Operational early warning platform for extreme meteorological events Most natural disasters are related to extreme weather events (e.g. typhoons); weather conditions, however, are also highly relevant for humanitarian and disaster relief operations during and after other natural disaster like earthquakes. The internet service "Wettergefahren-Frühwarnung" (WF) provides various information on extreme weather events, especially when these events are associated with a high potential for large damage. The main focus of the platform is on Central Europe, but major events are also monitored worldwide on a daily routine. WF provides high-resolution forecast maps for many weather parameters which allow detailed and reliable predictions about weather conditions during the next days in the affected areas. The WF service became operational in February 2004 and is part of the Center for Disaster Management and Risk Reduction Technology (CEDIM) since 2007. At the end of 2011, CEDIM embarked a new type of interdisciplinary disaster research termed as forensic disaster analysis (FDA) in near real time. In case of an imminent extreme weather event WF plays an important role in CEDIM's FDA group. It provides early and precise information which are always available and updated several times during a day and gives advice and assists with articles and reports on extreme events.

Structural and Mechanical Properties of Intermediate Filaments under Extreme Conditions and Disease

NASA Astrophysics Data System (ADS)

Qin, Zhao

Intermediate filaments are one of the three major components of the cytoskeleton in eukaryotic cells. It was discovered during the recent decades that intermediate filament proteins play key roles to reinforce cells subjected to large-deformation as well as participate in signal transduction. However, it is still poorly understood how the nanoscopic structure, as well as the biochemical properties of these protein molecules contribute to their biomechanical functions. In this research we investigate the material function of intermediate filaments under various extreme mechanical conditions as well as disease states. We use a full atomistic model and study its response to mechanical stresses. Learning from the mechanical response obtained from atomistic simulations, we build mesoscopic models following the finer-trains-coarser principles. By using this multiple-scale model, we present a detailed analysis of the mechanical properties and associated deformation mechanisms of intermediate filament network. We reveal the mechanism of a transition from alpha-helices to beta-sheets with subsequent intermolecular sliding under mechanical force, which has been inferred previously from experimental results. This nanoscale mechanism results in a characteristic nonlinear force-extension curve, which leads to a delocalization of mechanical energy and prevents catastrophic fracture. This explains how intermediate filament can withstand extreme mechanical deformation of > 1 00% strain despite the presence of structural defects. We combine computational and experimental techniques to investigate the molecular mechanism of Hutchinson-Gilford progeria syndrome, a premature aging disease. We find that the mutated lamin tail .domain is more compact and stable than the normal one. This altered structure and stability may enhance the association of intermediate filaments with the nuclear membrane, providing a molecular mechanism of the disease. We study the nuclear membrane association

Gravo-Aeroelastic Scaling for Extreme-Scale Wind Turbines

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

Fingersh, Lee J; Loth, Eric; Kaminski, Meghan

2017-06-09

A scaling methodology is described in the present paper for extreme-scale wind turbines (rated at 10 MW or more) that allow their sub-scale turbines to capture their key blade dynamics and aeroelastic deflections. For extreme-scale turbines, such deflections and dynamics can be substantial and are primarily driven by centrifugal, thrust and gravity forces as well as the net torque. Each of these are in turn a function of various wind conditions, including turbulence levels that cause shear, veer, and gust loads. The 13.2 MW rated SNL100-03 rotor design, having a blade length of 100-meters, is herein scaled to the CART3more » wind turbine at NREL using 25% geometric scaling and blade mass and wind speed scaled by gravo-aeroelastic constraints. In order to mimic the ultralight structure on the advanced concept extreme-scale design the scaling results indicate that the gravo-aeroelastically scaled blades for the CART3 are be three times lighter and 25% longer than the current CART3 blades. A benefit of this scaling approach is that the scaled wind speeds needed for testing are reduced (in this case by a factor of two), allowing testing under extreme gust conditions to be much more easily achieved. Most importantly, this scaling approach can investigate extreme-scale concepts including dynamic behaviors and aeroelastic deflections (including flutter) at an extremely small fraction of the full-scale cost.« less

Chest Ultrasonography in Modern Day Extreme Settings: From Military Setting and Natural Disasters to Space Flights and Extreme Sports

PubMed Central

Mucci, Viviana

2018-01-01

Chest ultrasonography (CU) is a noninvasive imaging technique able to provide an immediate diagnosis of the underlying aetiology of acute respiratory failure and traumatic chest injuries. Given the great technologies, it is now possible to perform accurate CU in remote and adverse environments including the combat field, extreme sport settings, and environmental disasters, as well as during space missions. Today, the usage of CU in the extreme emergency setting is more likely to occur, as this technique proved to be a fast diagnostic tool to assist resuscitation manoeuvres and interventional procedures in many cases. A scientific literature review is presented here. This was based on a systematic search of published literature, on the following online databases: PubMed and Scopus. The following words were used: “chest sonography,” “ thoracic ultrasound,” and “lung sonography,” in different combinations with “extreme sport,” “extreme environment,” “wilderness,” “catastrophe,” and “extreme conditions.” This manuscript reports the most relevant usages of CU in the extreme setting as well as technological improvements and current limitations. CU application in the extreme setting is further encouraged here. PMID:29736195

Meteorological Drivers of Extreme Air Pollution Events

NASA Astrophysics Data System (ADS)

Horton, D. E.; Schnell, J.; Callahan, C. W.; Suo, Y.

2017-12-01

The accumulation of pollutants in the near-surface atmosphere has been shown to have deleterious consequences for public health, agricultural productivity, and economic vitality. Natural and anthropogenic emissions of ozone and particulate matter can accumulate to hazardous concentrations when atmospheric conditions are favorable, and can reach extreme levels when such conditions persist. Favorable atmospheric conditions for pollutant accumulation include optimal temperatures for photochemical reaction rates, circulation patterns conducive to pollutant advection, and a lack of ventilation, dispersion, and scavenging in the local environment. Given our changing climate system and the dual ingredients of poor air quality - pollutants and the atmospheric conditions favorable to their accumulation - it is important to characterize recent changes in favorable meteorological conditions, and quantify their potential contribution to recent extreme air pollution events. To facilitate our characterization, this study employs the recently updated Schnell et al (2015) 1°×1° gridded observed surface ozone and particulate matter datasets for the period of 1998 to 2015, in conjunction with reanalysis and climate model simulation data. We identify extreme air pollution episodes in the observational record and assess the meteorological factors of primary support at local and synoptic scales. We then assess (i) the contribution of observed meteorological trends (if extant) to the magnitude of the event, (ii) the return interval of the meteorological event in the observational record, simulated historical climate, and simulated pre-industrial climate, as well as (iii) the probability of the observed meteorological trend in historical and pre-industrial climates.