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Purpose: The purpose of this paper is to identify the key influencing factors of aviation accidents and to predict the aviation accidents caused by the factors. Design/methodology/approach: This paper proposes an improved gray correlation analysis (IGCA) theory to make the relational analysis of aviation accidents and influencing factors and find out the critical causes of aviation accidents. The optimal varying weight combination model (OVW-CM) is constructed based on gradient boosted regression tree (GBRT), extreme gradient boosting (XGBoost) and support vector regression (SVR) to predict aviation accidents due to critical factors. Findings: The global aviation accident data from 1919 to 2020 is selected as the experimental data. The airplane, takeoff/landing and unexpected results are the leading causes of the aviation accidents based on IGCA. Then GBRT, XGBoost, SVR, equal-weight combination model (EQ-CM), variance-covariance combination model (VCW-CM) and OVW-CM are used to predict aviation accidents caused by airplane, takeoff/landing and unexpected results, respectively. The experimental results show that OVW-CM has a better prediction effect, and the prediction accuracy and stability are higher than other models. Originality/value: Unlike the traditional gray correlation analysis (GCA), IGCA weights the sample by distance analysis to more objectively reflect the degree of influence of different factors on aviation accidents. OVW-CM is built by minimizing the combined prediction error at sample points and assigns different weights to different individual models at different moments, which can make full use of the advantages of each model and has higher prediction accuracy. And the model parameters of GBRT, XGBoost and SVR are optimized by the particle swarm algorithm. The study can guide the analysis and prediction of aviation accidents and provide a scientific basis for aviation safety management. [ABSTRACT FROM AUTHOR]

Flow‐dependent background error covariances estimated from short‐term ensemble forecasts suffer from sampling errors due to limited ensemble sizes. Covariance localization is often used to mitigate the sampling errors, especially for high dimensional geophysical applications. Most applied localization methods, empirical or adaptive ones, multiply the Kalman gain or background error covariances by a distance‐dependent parameter, which is a simple linear filtering model. Here two localization methods based on convolutional neural networks (CNNs) learning from paired data sets are proposed. The CNN‐based localization function (CLF) aims to minimize the sampling error of the estimated Kalman gain, and the CNN‐based empirical localization function (CELF) aims to minimize the posterior error of state variables. These two CNN‐based localization methods can provide localization functions that are nonlinear, spatially and temporally adaptive, and non‐symmetric with respect to displacement, without requiring any prior assumptions for the localization functions. Results using the Lorenz05 model show that CLF and CELF can better capture the structures of the Kalman gain than the best Gaspari and Cohn (GC) localization function and the adaptive reference localization method. For both perfect‐ and imperfect‐model experiments, CLF produces smaller errors of the Kalman gain, prior and posterior than the best GC and reference localization, especially for spatially averaged observations. Without model error, CELF has smaller prior and posterior errors than the best GC and reference localization for spatially averaged observations, while with model error, CELF has smaller prior and posterior errors than the best GC and reference localization for single‐point observations. Plain Language Summary: Ensemble Kalman filters have been widely used for high‐dimensional geophysical applications, with the advantages to provide flow‐dependent background error covariances based on short‐term ensemble forecasts. Due to the massive computational costs for advancing ensemble simulations, limited ensemble members are commonly adopted, which results in sample‐estimated background error covariances contaminated by spurious noisy correlations. To remedy the sampling error, covariance localization that tapers the observation impact on state variables with distance, is commonly used. There are pre‐defined localization functions with tuning parameters and adaptive localization functions that are often based on correlation statistics. Here two purely data‐driven localization methods based on convolutional neural networks are proposed. These newly proposed localization functions are spatially and temporally adaptive, non‐symmetric with respect to displacement, with better captured structures of the Kalman gain than the empirical and adaptive localization methods. When they are applied in cycling assimilation, the localization methods based on the convolutional neural networks can produce improved analyses and forecasts. Key Points: Two CNN‐based localization methods are proposed to minimize sampling errors of estimated Kalman gain or posterior errors of state variablesCNN‐based localizations are adaptive in space and time, non‐symmetric with respect to displacement, and able to fit nonlinear functionsCNN‐based localizations effectively represent the Kalman gain, and lead to improved analyses and forecasts in cycling assimilations [ABSTRACT FROM AUTHOR]

In this work a novel application for multivariable forecasting is presented, applied to hydrological variables and based on a multivariable NARX model. The proposed approach is designed for two hydrological stations located at the Atrato River in Colombia where the variables of water level, water flow and water precipitation are correlated by using the NARX model based on a neural network structure. The structure of the NARX-based neural network is designed in order to consider the complex dynamics of hydrological variables and their corresponding cross-correlations. A short-term water level forecasting is designed based on the NARX model, to be used as an early warning flood system. The validation of the proposed approach is performed by comparing the estimation error with an ARX dynamic model. As a result, it is shown that a NARX model structure is more suitable for water level forecasting than simplified structures. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The ratio, product, difference estimators, and unbiased estimator under systematic sampling scheme has been studied in the presence of measurement error. The methods of estimation have been proposed for the estimation of the finite population mean. To exhibit the effect of measurement error, the study variable and auxiliary variable are supposed to be observed with measurement error. The properties of estimators' viz. bias and MSE have been obtained. The simulation study has been conducted to compute the effect of measurement error on the MSE for the different levels of correlation coefficient and different levels of error variance. [ABSTRACT FROM AUTHOR]

• A new statistic metric is proposed to measure the difference between the nuclear-data samples and the population. • Extending the concept of FOM to the field of uncertainty analysis to evaluate the quality of the sampled nuclear data. • FOM is an attribute of the sampling method and is approximately constant. • FOM can comprehensively evaluate the efficiency of sampling methods and save computing resources. In recent years, various tools for nuclear data sampling have been developed to quantify the uncertainty of reactor modeling and simulation (M&S) results. Since M&S consumes numerous computing resources, some advanced sampling methods have been proposed to reduce the sample size. Against this backdrop, this paper extends the concept of FOM to the field of uncertainty analysis to evaluate the quality of the sampled nuclear data. And the rationality is validated based on the numerical results of various sampling methods. The statistic metric thus developed and designed works in three steps. First, four sampling methods are employed and their performance is evaluated based on conventional statistic metrics, such as mean, standard deviation, correlation coefficient, and normality. Then, the sampling error is defined as the difference between the nuclear-data samples and the population. Finally, FOM, a novel statistic metric based on the sampling error and sample size, is proposed for assessing the sampling procedures. Results of our numerical experiments show that FOM can comprehensively evaluate the efficiency of sampling methods and save computing resources by evaluating the moments of nuclear-data samples rather than the results of uncertainty quantification (UQ). [ABSTRACT FROM AUTHOR]

Multilevel structural equation modeling (MSEM) allows researchers to model latent factor structures at multiple levels simultaneously by decomposing within- and between-group variation. Yet the extent to which the sampling ratio (i.e., proportion of cases sampled from each group) influences the results of MSEM models remains unknown. This article explores how variation in the sampling ratio in MSEM affects the measurement of Level 2 (L2) latent constructs. Specifically, we investigated whether the sampling ratio is related to bias and variability in aggregated L2 construct measurement and estimation in the context of doubly latent MSEM models utilizing a two-step Monte Carlo simulation study. Findings suggest that while lower sampling ratios were related to increased bias, standard errors, and root mean square error, the overall size of these errors was negligible, making the doubly latent model an appealing choice for researchers. An applied example using empirical survey data is further provided to illustrate the application and interpretation of the model. We conclude by considering the implications of various sampling ratios on the design of MSEM studies, with a particular focus on educational research. [ABSTRACT FROM AUTHOR]

The degradation of image quality caused by satellite jitter has drawn attention, and many researches have illustrated the importance of the jitter compensation. As the essential component of jitter compensation, image matching involves in the determination of jitter processing accuracy. Hence, the impact of imaging matching methods on jitter compensation is explored in this paper. At first, a framework based on imaging matching is built for jitter compensation. Two typical sub-pixel accuracy matching methods (i.e. correlation coefficient and least squares matching, as well as phase correlation matching) could then be served for the framework. The experiments are designed by using multispectral images of GF-1 satellite, and quantitative evaluations show that compared with correlation coefficient and least squares matching, phase matching method makes the accuracy of obtained jitter curve increase with the amplitude error decreasing by more than 0.012 pixel as well as phase error decreasing by more than 0.0119 rad, and makes the quality of restored images improved on both geometry and radiation. It indicates that phase matching method has better performance with respect to the framework of jitter compensation in this paper. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Henan Agricultural Sciences is the property of Editorial Board of Journal of Henan Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The present study was conducted to determine the extent of observation of patients' privacy by physicians and nurses and its relationship with patient satisfaction. This descriptive correlational study was conducted on 600 patients selected by convenience sampling. Based on the patients' points of view, the level of observation of patients' privacy and its dimensions, especially the psychosocial dimension, were reported to be higher in nurses than in physicians. Pearson's correlation coefficient showed a direct and significant relationship between the observation of privacy by the nurses and physicians and different dimensions of patient satisfaction. [ABSTRACT FROM AUTHOR]

The current study aimed to estimate the prevalence of depression, anxiety, and suicidality and their correlated factors among high school students in Jordan. A descriptive cross-sectional correlational research design was used. Data were collected using self-reported questionnaires completed by students attending high schools in Jordan. Data show that anxiety and depression are prevalent among adolescents and are associated with higher risk of suicide and disease prevalence. Twenty-seven percent of the variance in suicidality is explained by anxiety and depression. This finding indicates that the most significant predictor of suicidality is anxiety and depression among high school students. Results show that mental health issues are a genuine general health issue among high school students. Health care professionals should routinely screen for mental health problems among young people. Mental health and well-being advancement programs should be coordinated and directed by all parties involved in youth mental health. [Journal of Psychosocial Nursing and Mental Health Services, 59(8), 43–51.] [ABSTRACT FROM AUTHOR]

This study develops a theoretical model for the costs of an exam as a function of its duration. Two kind of costs are distinguished: (1) the costs of measurement errors and (2) the costs of the measurement. Both costs are expressed in time of the student. Based on a classical test theory model, enriched with assumptions on the context, the costs of the exam can be expressed as a function of various parameters, including the duration of the exam. It is shown that these costs can be minimized in time. Applied in a real example with reliability.80, the outcome is that the optimal exam time would be much shorter and would have reliability.675. The consequences of the model are investigated and discussed. One of the consequences is that optimal exam duration depends on the study load of the course, all other things being equal. It is argued that it is worthwhile to investigate empirically how much time students spend on preparing for resits. Six variants of the model are distinguished, which differ in their weights of the errors and in the way grades affect how much time students study for the resit. [ABSTRACT FROM AUTHOR]

Introduction: The promotion of a positive birth experience has been a main goal of the World Health Organization's (WHO) recent work on improving maternity care. The purpose of this study was to assess the cesarean rates, the prevalence of birth practices, perinatal outcomes, and maternal satisfaction, in women involved with the respectful maternity care (RMC) support groups in Sao Paulo, Brazil. Methods: This was a cross‐sectional study of women with low‐risk pregnancies who were assisted by professionals recommended by the RMC groups. An online questionnaire was administered. Variables to assess birth practices were classified as positive, negative, or unspecified according to the WHO guidelines. The Pearson chi‐square tests and odds ratios (ORs) with their corresponding 95% confidence intervals (CIs) were computed to assess differences between the groups. Results: Five‐hundred and eighty women completed the questionnaire. The cesarean rate was 14.7%, and the operative vaginal birth rate was 9.5%. The VBAC rate was 87.1%, and there was no significant difference in risk for cesarean between women with or without a prior cesarean. Of all women, 83.1% had a midwife's assistance and 75.5% hired a doula; 81.4% gave birth in a nonlithotomic position. The practices of enema, fasting and episiotomy were all under 2%. All 5‐minute Apgar scores were ≥7. Most (83.1%) women reported having a positive birth experience. Conclusions: Woman's engagement with the birth support groups and a transdisciplinary team focused on RMC are key elements to achieve positive perinatal outcomes and high women's satisfaction. [ABSTRACT FROM AUTHOR]

[Display omitted] • An approach is proposed for features contribution analysis to estimate ash content. • The dataset is formed with five particle sizes coal under four magnifications. • Ensemble learning algorithm is used to effectively estimate ash content online. • Color features play a dominant role in ash content estimation. • Using color-texture mixed features can achieve better estimation results. Online estimation of ash content in coal based on machine vision has been paid more attention to by academia and industry. Existing research has mainly focused on feature extraction and model design for estimating ash content, but the exploration of the feature's contribution to the model is rarely reported. Therefore, this paper presented a method to analyze features' contribution to microscopic images. Expressly, ensemble learning could explicitly point out the assistance of different features to ash content estimation according to the results of weak regressors, which possessed good interpretability. Firstly, we explored the relationship among particle size, magnification, and precision to determine Adaboost as the optimal regressor. Secondly, we discussed the contribution of different features to the regressor quantitatively and qualitatively on the 0.5–0.25 (mm) particle size of datasets under magnification of 0.3, which we called the optimal condition. Ultimately, the experimental conclusions were validated by feature importance analysis and correlation analysis. The experimental results show that the magnification significantly influenced the precision of ash content estimation. Still, coal microscopic images' 0.5–0.25 (mm) particle size estimation accuracy was optimal under all magnifications. When magnification was 0.3 and particle size was 0.5–0.25 (mm), there was the best performance to ash content estimation (In the testing set, 98% of the samples had an error less than 0.5% between ash content estimated value and ash content value). Color features played a dominant role in ash content estimation in the optimal regressors. Only the building color feature regressor could achieve that 96.4% of samples had an estimated error of less than 0.5%. In contrast, only 68% of the samples using the texture feature regressors had an estimated error of less than 0.5%. Moreover, the building mixture of color and texture regressor possessed mutually reinforcing effects under specific conditions. The summarized features' contribution was essential for model building and design. [ABSTRACT FROM AUTHOR]

This pilot study was conducted using a sample of 374 Portuguese occupational therapists to evaluate burnout levels and prevalence among occupational therapists as well as to relate those levels to practitioner age, gender, client age, years of professional activity, and area of practice. The Copenhagen Burnout Inventory questionnaire (Portuguese version) and a sociodemographic questionnaire was used. Results showed that ∼23%, 44%, and 45% of occupational therapists presented with client-related burnout, personal burnout (PB), and work-related burnout, respectively. Higher rates of burnout were observed across all dimensions as the age of the professionals increased, with more years of working increased the client-rated rate, and client age increased the PB rate. Outcomes suggest a need to implement intervention programs to promote personal and organizational strategies to prevent burnout. [ABSTRACT FROM AUTHOR]

Purpose: Multishot interleaved echo‐planar imaging (iEPI) can achieve higher image resolution than single‐shot EPI for diffusion tensor imaging (DTI), but its application is limited by the prolonged acquisition time. To reduce the acquisition time, a novel model‐based reconstruction for simultaneous multislice (SMS) and parallel imaging (PI) accelerated iEPI DTI is proposed. Materials and methods: DTI datasets acquired by iEPI with SMS and PI acceleration can be regarded as 3D k‐space data, which is undersampled along both the slice and phase encoding directions. Instead of reconstruction of individual diffusion‐weighted image, diffusion tensors are directly estimated by the joint reconstruction of undersampled 3D k‐space from all diffusion‐encoding directions using a model‐based formulation to exploit the correlation across different directions. DTI simulation and in vivo acquisition were used to demonstrate the superior performance of the proposed method. Results: The proposed method reduced the estimation errors and artifacts than traditional parallel imaging reconstruction in DTI simulation. In the in vivo DTI experiment, the acquisition time of 4‐shot iEPI was reduced from 11 min 7 s to 3 min 53 s with an acceleration factor of 4, and the image quality and precision of quantitative parameters were comparable with the fully sampled acquisition. Conclusions: The proposed model‐based reconstruction for iEPI DTI with SMS and PI can achieve fourfold acceleration while maintaining high accuracy for tensor measurements. [ABSTRACT FROM AUTHOR]

We consider two nonindependent random fields Ψ and Φ defined on a countable set Z. For instance, Z = Zd or Z = Zd X I, where I denotes a finite set of possible "internal degrees of freedom" such as spin.We prove that, if the cumulants of Ψ and Φenjoy a certain decay property, then all joint cumulants between Ψ and Φ are ℓ2-summable in the precise sense described in the text. The decay assumption for the cumulants of Ψ and Φ is a restricted ℓ1 summability condition called ℓ1-clustering property. One immediate application of the results is given by a stochastic process Ψt (x) whose state is ℓΨ = Ψt and Φ= Ψ0 and we obtain uniform in t estimates for the summability of time-correlations of the field. The above clustering assumption is obviously satisfied by any ℓ1-clustering stationary state of the process and our original motivation for the control of the summability of time-correlations comes from a quest for a rigorous control of the Green-Kubo correlation function in such a system. A key role in the proof is played by the properties of non-GaussianWick polynomials and their connection to cumulants. [ABSTRACT FROM AUTHOR]

Purpose: In this investigation, we argue for why and how available intraclass correlation coefficients and other types of reliability estimates can be employed as sample-based reliability estimates within primary and meta-analytic studies when relationships between group-level phenomena are of interest. Design/Methodology/Approach: Group-level correlations and reliability estimates were obtained from 46 studies examining organizational climate-performance relationships. We illustrate how the group-level reliability estimates can be used to correct correlations for predictor and criterion unreliability. Procedures are presented for computing the sampling variances of individually corrected correlations that account for sampling error in the group-level reliability estimates. Findings: Support was found for the conservative nature of meta-analytic parameter estimates when group-level reliability information is sample-based as opposed to assumed population values. In addition, our analyses indicated that conclusions about substantive relationships between group-level variables can change based on availability of sample-based reliabilities within both primary and meta-analytic studies. Implications: Results from this study suggest that researchers should rely on sample-based meta-analytic procedures when examining the generalizability of group-level relationships. This study also demonstrates the importance of using all available reliability information and accounting for sampling error in the reliability estimates when conducting meta-analyses at the group level of analysis. Originality/Value: This study breaks ground by systematically examining the use of intraclass correlation coefficients as reliability estimates within group-level meta-analytic studies. Furthermore, illustrative analyses provide guidance to primary and meta-analytic researchers in regard to how to correct group-level correlations for unreliability in the predictor, criterion, or both whenever and in whatever proportions the artifact information is available. [ABSTRACT FROM AUTHOR]

In this study, seven random combination sampling strategies were applied to investigate the uncertainties in estimating the hillslope mean soil water content (SWC) and correlation coefficients between the SWC and soil/terrain properties on a tea + bamboo hillslope. One of the sampling strategies is the global random sampling and the other six are the stratified random sampling on the top, middle, toe, top + mid, top + toe and mid + toe slope positions. When each sampling strategy was applied, sample sizes were gradually reduced and each sampling size contained 3000 replicates. Under each sampling size of each sampling strategy, the relative errors (REs) and coefficients of variation (CVs) of the estimated hillslope mean SWC and correlation coefficients between the SWC and soil/terrain properties were calculated to quantify the accuracy and uncertainty. The results showed that the uncertainty of the estimations decreased as the sampling size increasing. However, larger sample sizes were required to reduce the uncertainty in correlation coefficient estimation than in hillslope mean SWC estimation. Under global random sampling, 12 randomly sampled sites on this hillslope were adequate to estimate the hillslope mean SWC with RE and CV ≤10%. However, at least 72 randomly sampled sites were needed to ensure the estimated correlation coefficients with REs and CVs ≤10%. Comparing with all sampling strategies, reducing sampling sites on the middle slope had the least influence on the estimation of hillslope mean SWC and correlation coefficients. Under this strategy, 60 sites (10 on the middle slope and 50 on the top and toe slopes) were enough to ensure the estimated correlation coefficients with REs and CVs ≤10%. This suggested that when designing the SWC sampling, the proportion of sites on the middle slope can be reduced to 16.7% of the total number of sites. Findings of this study will be useful for the optimal SWC sampling design. [ABSTRACT FROM AUTHOR]

Positive relationship between size of local populations and their stability is a central concept in conservation biology, utilised in its tools including metapopulation models and population viability analysis, although most empirical studies found this relationship rather weak. In the present study I evaluated the impact of the precision of population estimates on the strength of the relationship between population sizes, or patch areas used as their proxy, and the extent of their temporal variability. Based on the data provided by 13-year monitoring of three species of Maculinea (= Phengaris ) butterflies, I quantified the variability of their population time-series with the coefficients of variation (CVs), using both their overall temporal variances as well as the variances corrected for sampling variance, which reflects the precision (sampling error) of seasonal population size estimates. The extent of population variability, expressed with the CVs uncorrected for sampling variance, correlated negatively with both population size and habitat patch area, but these relationships were relatively weak, especially in M. teleius and M. nausithous ( R 2 ~ 0.1). However, sampling error increased considerably with population size and patch area, due to smaller fractions of individuals being sampled. Consequently, in the case of the CVs based on the variances corrected for sampling variance, the negative correlations with population size and patch area were substantially stronger ( R 2 reaching 0.3–0.6). My results highlight the advantage of monitoring methods that allow quantifying sampling error, which is essential for assessing species extinction risk. [ABSTRACT FROM AUTHOR]

Background Health inequalities are to a substantial degree due to socioeconomic status (SES) related differences in health behaviors such as physical activity. However, little is known about the role SES plays in the self-regulation of physical activity. Purpose This systematic review with meta-analysis examines whether a comprehensive set of indicators of SES (income, education, occupational status) impacts on the behavioral self-regulation by moderating the relationships between social cognitions in the Theory of Planned Behavior (TPB) and physical activity. Methods A systematic literature search identified 99 studies from 88 articles that provided information on sample SES and correlations between TPB variables and physical activity. Random-effects meta-analyses were used to pool correlations corrected for sampling and measurement error. Random-effects meta-regression was used to examine moderating effects of study-level SES on these correlations. Results Education moderated the relationship between intentions and physical activity, such that studies with better educated samples reported stronger intention-physical activity relationships. Conclusions These results suggest that education might play a major role in the self-regulation of physical activity, with better educated samples more likely to translate intentions into behavior. This can both help to explain heterogeneity in the relation between intentions and physical activity as well as support the development of more effective interventions targeting intentions and physical activity. [ABSTRACT FROM AUTHOR]

Outputs from a data assimilation system may be used to diagnose observation- and background-error statistics, as has been demonstrated by previous researchers. In this study, that technique is extended to diagnose model error statistics using a weak-constraint data assimilation. It deals with a set of observations over a time window and uses the temporal distribution to separate model errors from errors in the background forecast. In a set of idealized tests, this method is shown to be able to distinguish successfully between model and background errors. The success of this method depends on the prior assumptions included in the weak-constraint data assimilation and how well these describe the true nature of the system being modelled. Other authors have demonstrated that it can be challenging to separate observation and background errors in diagnosis schemes. Some separation of these errors is possible within the context of four-dimensional assimilation, provided that observation errors are known to be uncorrelated in time. [ABSTRACT FROM AUTHOR]

Reliable measurements are key to social science research. Multiple measures of reliability of the total score have been developed, including coefficient alpha, coefficient omega, the greatest lower bound reliability, and others. Among these, the coefficient alpha has been most widely used, and it is reported in nearly every study involving the measure of a construct through multiple items in social and behavioral research. However, it is known that coefficient alpha underestimates the true reliability unless the items are tau-equivalent, and coefficient omega is deemed as a practical alternative to coefficient alpha in estimating measurement reliability of the total score. However, many researchers noticed that the difference between alpha and omega is minor in applications. Since the observed differences in alpha and omega can be due to sampling errors, the purpose of the present study, therefore, is to propose a method to evaluate the difference of coefficient alpha ( α ^ ) and omega ( ω ^ ) statistically. In particular, the current article develops a procedure to estimate the SE of ( ω ^ − α ^ ) and consequently the confidence interval (CI) for ( ω − α ). This procedure allows us to test whether the observed difference ( ω ^ − α ^ ) is due to sample error or ω ^ is significantly greater than α ^ . The developed procedure is then applied to multiple real data sets from well-known scales to empirically verify the values of ( ω ^ − α ^ ) in practice. Results showed that in most of the comparisons the differences are significantly above zero but cases also exist where the CIs contain zero. An R program for calculating ω ^ , α ^ , and the SE of ( ω ^ − α ^ ) is also included in the present study so that the developed procedure is easily accessible to applied researchers. [ABSTRACT FROM AUTHOR]

Estimating observation error covariance matrix properly is a key step towards successful seismic history matching. Typically, observation errors of seismic data are spatially correlated; therefore, the observation error covariance matrix is non-diagonal. Estimating such a non-diagonal covariance matrix is the focus of the current study. We decompose the estimation into two steps: (1) estimate observation errors and (2) construct covariance matrix based on the estimated observation errors. Our focus is on step (1), whereas at step (2) we use a procedure similar to that in Aanonsen et al. 2003. In Aanonsen et al. 2003, step (1) is carried out using a local moving average algorithm. By treating seismic data as an image, this algorithm can be interpreted as a discrete convolution between an image and a rectangular window function. Following the perspective of image processing, we consider three types of image denoising methods, namely, local moving average with different window functions (as an extension of the method in Aanonsen et al. 2003), non-local means denoising and wavelet denoising. The performance of these three algorithms is compared using both synthetic and field seismic data. It is found that, in our investigated cases, the wavelet denoising method leads to the best performance in most of the time. [ABSTRACT FROM AUTHOR]

Elemental compositions of biomass are essential for designing energy conversion systems. Only a few correlations to estimate the elemental compositions using the proximate analysis of raw biomass have been published so far. Recently researches on biomass torrefaction have been increasing significantly, which require performing an elemental analysis of the torrefied biomass. Torrefaction affects both the proximate and elemental analyses of biomass. Therefore, this study examines if the existing correlations can be deployed or not for estimating carbon, hydrogen, and oxygen compositions of the torrefied biomass. For this, estimation errors were calculated for the existing correlations using data from the torrefied biomass. Results suggest that existing correlations were not suitable for predicting the elemental compositions of the torrefied biomass. New correlations were proposed using a wide range biomass, including both raw and torrefied biomass (447 samples). New correlations C = - 35.9972 + 0.7698 VM + 1.3269 FC + 0.3250 ASH , H = 55.3678 - 0.4830 VM - 0.5319 FC - 0.5600 ASH , and O = 223.6805 - 1.7226 VM - 2.2296 FC - 2.2463 ASH were selected for future use. These correlations have the MAE of 2.58%, 0.41%, 2.60%, the AAE of 5.23%, 9.94%, 8.79%, the ABE of 0.45%, 2.82%, 2.01%, and the R 2 of 0.83, 0.70, 0.84 corresponding to the measured values of C , H , and O , respectively. The selected correlations were also validated and compared with the existing correlations using another set of data that includes raw, washed, torrefied, and carbonized biomass. Selected new correlations could be used for predicting carbon, hydrogen, and oxygen compositions in the raw and torrefied biomass, especially those biomasses which have negligible nitrogen and sulfur contents. [ABSTRACT FROM AUTHOR]

This article compares the mean-squared error (or ℓ2risk) of ordinary least squares (OLS), James–Stein, and least absolute shrinkage and selection operator (Lasso) shrinkage estimators in simple linear regression where the number of regressors is smaller than the sample size. We compare and contrast the known risk bounds for these estimators, which shows that neither James–Stein nor Lasso uniformly dominates the other. We investigate the finite sample risk using a simple simulation experiment. We find that the risk of Lasso estimation is particularly sensitive to coefficient parameterization, and for a significant portion of the parameter space Lasso has higher mean-squared error than OLS. This investigation suggests that there are potential pitfalls arising with Lasso estimation, and simulation studies need to be more attentive to careful exploration of the parameter space. [ABSTRACT FROM AUTHOR]

Ensemble Kalman filters are widely used for data assimilation in large geophysical models. Good results with affordable ensemble sizes require enhancements to the basic algorithms to deal with insufficient ensemble variance and spurious ensemble correlations between observations and state variables. These challenges are often dealt with by using inflation and localization algorithms. A new method for understanding and reducing some ensemble filter errors is introduced and tested. The method assumes that sampling error due to small ensemble size is the primary source of error. Sampling error in the ensemble correlations between observations and state variables is reduced by estimating the distribution of correlations as part of the ensemble filter algorithm. This correlation error reduction (CER) algorithm can produce high-quality ensemble assimilations in low-order models without using any a priori localization like a specified localization function. The method is also applied in an observing system simulation experiment with a very coarse resolution dry atmospheric general circulation model. This demonstrates that the algorithm provides insight into the need for localization in large geophysical applications, suggesting that sampling error may be a primary cause in some cases. [ABSTRACT FROM AUTHOR]

Background:The use of the borderline regression method (BRM) is a widely accepted standard setting method for OSCEs. However, it is unclear whether this method is appropriate for use with small cohorts (e.g. specialist post-graduate examinations). Aims and methods:This work uses an innovative application of resampling methods applied to four pre-existing OSCE data sets (number of stations between 17 and 21) from two institutions to investigate how the robustness of the BRM changes as the cohort size varies. Using a variety of metrics, the ‘quality’ of an OSCE is evaluated for cohorts of approximatelyn = 300 down ton = 15. Estimates of the standard error in station-level and overall pass marks,R2coefficient, and Cronbach’s alpha are all calculated as cohort size varies. Results and conclusion: For larger cohorts (n > 200), the standard error in the overall pass mark is small (less than 0.5%), and for individual stations is of the order of 1–2%. These errors grow as the sample size reduces, with cohorts of less than 50 candidates showing unacceptably large standard error. Alpha andR2also become unstable for small cohorts. The resampling methodology is shown to be robust and has the potential to be more widely applied in standard setting and medical assessment quality assurance and research. [ABSTRACT FROM PUBLISHER]

Dual-satellite crossover data from the Jason-2 and Cryosat-2 altimeter missions are used in a novel approach to quantify stationary and nonstationary tides from time-lagged mean square sea surface height (SSH) differences, computed for lags from 1 to 1440 h (60 days). The approach is made feasible by removing independent estimates of the stationary tide and mesoscale SSH variance, which greatly reduces the sampling error of the SSH statistics. For the semidiurnal tidal band, the stationary tidal variance is approximately 0.73 cm2, and the nonstationary variance is about 0.33 cm2, or 30% of the total. The temporal correlation of the nonstationary tide is examined by complex demodulation and found to be oscillatory with first 0 crossing at 400 h (17 days). Because a significant fraction of the time-variable mesoscale signal is resolved at time scales of roughly 150 h by the present constellation of satellite altimeters, the results suggest that it may be feasible to predict the nonstationary tide from modulations of the resolved mesoscale, thus enhancing the efficacy of tidal corrections for planned wide-swath altimeters such as the Surface Water and Ocean Topography (SWOT) mission. [ABSTRACT FROM AUTHOR]

Tett, Hundley, and Christiansen (2017) describe two main sources of uncertainty that are usually reported in a meta-analysis: uncertainty about the value of the underlying mean correlation (which they describe as SE(rxy)) and uncertainty about the individual values of rho that arise from the random-effects variance (the square root of which they describe as SD(rho)). They proceed to recommend descriptions of small, medium, and large values of each uncertainty that meta-analysts should report and consider for interpretation. However, there exists a simpler solution to expressing and interpreting such uncertainty. [ABSTRACT FROM AUTHOR]

All practical ensemble-based atmospheric data assimilation (DA) systems use localisation to reduce the damaging impact of spurious long-range correlations arising from the finite ensemble size. However, the form of the localisation function is generally ad-hoc, and requires expensive tuning to optimise the system. For the case of a single observation and known true background error correlation, we derive an expression for the localisation factor that minimises the expected root-mean-square (RMS) analysis error. Idealised tests show this formulation performs well for multiple observations provided their density is not too high. The width of the optimal localisation function scales with the width of the underlying correlation, but does not have the same shape. The optimal observation-space localisation for a single spatially integrating observation depends on the observation-to-gridpoint background error correlation, making it broader than the optimal localisation for point observations and potentially competitive with model-space localisation. A new form of hybrid DA is proposed in which localisation damps the sample correlations towards their climatological mean rather than zero, reducing the RMS error and potentially improving the dynamic balance of the analysis. The presence of variance errors causes the optimal localisation factor to depend on the ratio of observation to background error variance, and raises the possibility that a small amount of variance damping may be beneficial. For dense observations, a more elaborate theory is required, which will almost certainly depend on the observation network. We present some preliminary analysis of the features of the multi-observation problem, which for instance suggests that the optimal solution may involve different localisation factors in the numerator and denominator of the Kalman filter equation. We note that even optimal localisation gives an expected RMS error which exceeds that of perfect DA, contrary to the assumption made by 'deterministic' ensemble filters. [ABSTRACT FROM AUTHOR]

A method is presented for mapping sea surface salinity (SSS) from Aquarius level-2 along-track data in order to improve the utility of the SSS fields at short length [ O(150 km)] and time [ O(1 week)] scales. The method is based on optimal interpolation (OI) and derives an SSS estimate at a grid point as a weighted sum of nearby satellite observations. The weights are optimized to minimize the estimation error variance. As an initial demonstration, the method is applied to Aquarius data in the North Atlantic. The key element of the method is that it takes into account the so-called long-wavelength errors (by analogy with altimeter applications), referred to here as interbeam and ascending/descending biases, which appear to correlate over long distances along the satellite tracks. The developed technique also includes filtering of along-track SSS data prior to OI and the use of realistic correlation scales of mesoscale SSS anomalies. All these features are shown to result in more accurate SSS maps, free from spurious structures. A trial SSS analysis is produced in the North Atlantic on a uniform grid with 0.25° resolution and a temporal resolution of one week, encompassing the period from September 2011 through August 2013. A brief statistical description, based on the comparison between SSS maps and concurrent in situ data, is used to demonstrate the utility of the OI analysis and the potential of Aquarius SSS products to document salinity structure at ~150-km length and weekly time scales. [ABSTRACT FROM AUTHOR]

This paper considers the robust estimation fusion problem for distributed multisensor systems 8/9with uncertain correlations of local estimation errors. For an uncertain class characterized by the Kullback-Leibler (KL) divergence from the actual model to nominal model of local estimation error covariance, the robust estimation fusion problem is formulated to find a linear minimum variance unbiased estimator for the least favorable model. It is proved that the optimal fuser under nominal correlation model is robust while the estimation error has a relative entropy uncertainty. [ABSTRACT FROM AUTHOR]

In this paper, theoretical lower bounds on performance of linear least-squares (LLS) position estimators are obtained, and performance differences between LLS and nonlinear least-squares (NLS) position estimators are quantified. In addition, two techniques are proposed in order to improve the performance of the LLS approach. First, a reference selection algorithm is proposed to optimally select the measurement that is used for linearizing the other measurements in an LLS estimator. Then, a maximum likelihood approach is proposed, which takes correlations between different measurements into account in order to reduce average position estimation errors. Simulations are performed to evaluate the theoretical limits and to compare performance of various LLS estimators. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A novel Huber M-estimator based proportionate affine projection algorithm (APA) is proposed for echo cancellation. The Huber objective function is minimised as the cost function and results in Huber M-estimator based APA. Moreover, the cutoff value of Huber objective function is updated according to the correlation between the error signal and the far-end input signal. It is shown that the proposed algorithm can achieve faster convergence and better robustness against double-talk than conventional robust algorithms. [ABSTRACT FROM AUTHOR]

The authors express their views on an article on bootstrap-based methods for statistical estimation, that is published in this issue. Topics discussed include correlation coefficients in a linear regression model; estimation of confidence intervals for adaptive bootstrap methods; and normalized estimation error in regression analysis.

We propose a sensor placement method for spatio-temporal field estimation based on a kriged Kalman filter (KKF) using a network of static or mobile sensors. The developed framework dynamically designs the optimal constellation to place the sensors. We combine the estimation error (for the stationary as well as non-stationary component of the field) minimization problem with a sparsity-enforcing penalty to design the optimal sensor constellation in an economic manner. The developed sensor placement method can be directly used for a general class of covariance matrices (ill-conditioned or well-conditioned) modelling the spatial variability of the stationary component of the field, which acts as a correlated observation noise, while estimating the non-stationary component of the field. Finally, a KKF estimator is used to estimate the field using the measurements from the selected sensing locations. Numerical results are provided to exhibit the feasibility of the proposed dynamic sensor placement followed by the KKF estimation method. [ABSTRACT FROM AUTHOR]

This guide offers suggestions/insights on uncertainty quantification of nuclear structure models. We discuss a simple approach to statistical-error estimates, strategies to assess systematic errors, and show how to uncover inter-dependences by correlation analysis. The basic concepts are illustrated through simple examples. By providing theoretical error bars on predicted quantities and using statistical methods to study correlations between observables, theory can significantly enhance the feedback between experiment and nuclear modeling. [ABSTRACT FROM AUTHOR]