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The ISO 11929 standard on characteristic limits in measurements of ionizing radiation extends the concept of the detection limit, originally formalized by Currie, to incorporate the effects of uncertainties in the instrument calibration. The mathematical formulation presented in the standard is known to fail when those uncertainties are too large, however, making it unsuitable for applications where large calibration uncertainties are common. The most recent revision of the standard allows alternative calculation methods to evaluate the detection limit in such circumstances. Results from two such alternative methods are compared here: an approximation combining Gaussian treatment of counting uncertainties with a log-normal representation of the calibration uncertainties, and a numerical evaluation of a statistical modeling approach that accurately represents Poisson counting statistics and accommodates a variety of realistic distributions to describe the calibration uncertainty. Multiple choices of calibration uncertainty distribution for the statistical modeling method are demonstrated and discussed. [ABSTRACT FROM AUTHOR]

Background: Color imaging is a tried and true method for the evaluation of cosmetic and dermatological effects, but it fails to capture all the information in a scene's spectral reflectance. For this reason, there has been in recent years increasing interest in the use of imaging spectrometers for clinical studies and product evaluation. Material and methods: We developed a novel HyperSpectral Imager (HSI) able to take in vivo full‐face format images as a next generation instrument for skin color measurement and beyond. Here, we report part of the results of our first full‐scale validation test of the HSI. We replicated a make‐up foundation screening test by applying three products to a panel of 9 models and evaluated the product L∗, a∗, b∗, and ∆E effect immediately after application relative to the bare skin condition. We repeated this test twice in order to study the repeatability of the HSI as an evaluation instrument and during each test two different operators duplicated the data acquisition so we can assess the reproducibility of the measurements. Results: We find that the measurements from the HSI provide repeatability and reproducibility as good or better than those of our previous benchmark devices. Conclusion: From these results, we conclude that not only is the HSI suitable for use in color evaluation studies, but also that it gives operational advantages over the previous generation of evaluation instruments, as it provides a spectral measurement combined with good spatial resolution. This allows for analysis of color over an area and post hoc selection of study regions and so opens new possibilities for studies of complex in vivo phenomena which neither non‐imaging spectrometers nor conventional cameras can pursue. This study also raises points for future work concerning proper inclusion of instrument uncertainty in comparisons of results between instruments and handling of systematic uncertainties from analyses based on a single area. [ABSTRACT FROM AUTHOR]

The shear strength of rock joints is an important property required in order to analyze the stability of rock slopes and tunnels. However, estimation of the shear strength of rock joints for in situ conditions is a complex task due to various influencing factors present in the field. Among these factors, the shear velocity or the shear displacement rate along the rock joints are important parameters which are relatively less studied since their effect is considered to be of second order compared to other factors. However, some recent studies in the literature suggest that shear velocity has a significant influence on the shear strength of rock joints, and hence the shear strength of joints estimated at low shear velocities in laboratories cannot be used under in situ conditions where the possibility of higher shear velocities exist due to the presence of different factors, such as blasting, excavation, and thermal and seismic loads. In this paper, we have addressed these issues in three steps. In the first step, an experimental study on jointed rock specimens is presented to investigate the influence of the displacement rate on the shear strength of rock joints. In the second step, a probabilistic method is developed based on the experimental results and the compiled data from the literature to estimate the in situ shear strength of joints under higher displacement rate conditions, i.e., blasting, excavation, and seismic loads from laboratory-estimated shear strength at the International Society for Rock Mechanics suggested low displacement rates. In the third step, a case study of a Himalayan rock tunnel was used to demonstrate the described approach. It was observed that the shear strength of discontinuities reduced with ncreasing shear velocity and that the rate dependency was higher for low-density rocks and under high confining stress. Further, a considerable effect was observed on the probability of failure of the rock tunnel when the effect of shear velocity was considered in the stability analysis. [ABSTRACT FROM AUTHOR]

Empirical correlations between different geotechnical parameters are frequently sought after by exploiting multivariate databases. An example are estimations of total unit weight from cone penetration tests (CPTu), which are very useful in earlier design stages. If the underlying soil database includes data from many different soils a single correlation may lack precision. Precision is gained when the underlying database is narrowed down to some specific soil type, but the applicability of a soil-specific correlation is also limited. A way out of this dilemma is to apply clustering techniques to a general database before developing separate correlations for different clusters. Projecting the clustered data back to a convenient classification space (e.g., one spanned by normalized CPTu metrics) new data can be easily assigned to different clusters and the appropriate correlation used. This idea is illustrated here using Bayesian Mixture Analysis (BMA) to identify hidden soil classes within a general geotechnical database that supports correlations between soil total unit weight and CPTu readings. It is shown that BMA supported clustering improves the accuracy of previous regressions, and, more importantly, facilitates the formulation of novel and more accurate regressions. A simple discriminant criterion is developed to facilitate application of cluster-based regressions to new sites. The good performance of the method is illustrated with application to a deltaic site. • Clustering technique to treat global geotechnical dataset. • Bayesian Mixture Analysis for clusters identification. • Novel cluster-based regressions for total unit weight prediction from CPTu readings. • Improvement of accuracy and precision of total unit weight estimates from CPTu. • Novel criterion for cluster-based correlations application. [ABSTRACT FROM AUTHOR]

A hybrid approach by combining the past performance and the envelope methods has been proposed for the selection of an ensemble of general circulation models (GCMs) of Couple Model Intercomparison phase 5 (CMIP5) for the projection of spatiotemporal changes in annual and seasonal temperatures of Iraq for four representative concentration pathways (RCP) scenarios. A filter known as Symmetrical Uncertainty (SU) was used to rank the GCMs considering their ability to simulate monthly average of daily maximum and minimum temperature for the historical period (1961–2005). The highest rank GCMs that represents the widest range of projection was then selected for the projection of temperature through statistical downscaling. A linear bias correction approach was used for the downscaling of temperature, a random forest regression was used to generate multi-model ensemble (MME) mean of projections and a quantile regression (QR) was used to assess the trends in projections at 95% level of confidence. Four GCMs namely, HadGEM2-AO, HadGEM2-ES, MIROC5 and MIROC-ESM were found most suitable for projection of temperature of Iraq. Ensemble mean of the selected GCMs revealed increases in minimum temperatures in the range of 1.5–2.4 °C, 1.6–3.6 °C, 1.2–4.2 °C, and 1.3–6.2 °C and maximum temperatures in the range of 1.7–2.9 °C, 1.8–4.4 °C, 1.5–4.9 °C, and 1.7–6.8 °C under RCP2.6, RCP4.5, RCP6.0 and RCP8.5 scenarios, respectively during 2070–2099. Higher increases in temperatures were projected in the north and northeast of Iraq where the average temperature is usually low, which indicates that spatial distribution of temperature would be more homogeneous in future compared to base years. The maximum temperature was found to increase more in winter while the minimum in summer. The quantile regression revealed that average summer maximum temperature may reach almost 50 °C, while the sub-zero temperature will gradually become rare during winter. [ABSTRACT FROM AUTHOR]

Purpose The aim of this study is to provide a practical method to quantify the dosimetric effects on organs-at-risk ( OARs) due to systematic uncertainties in linear accelerator treatment delivery in order to aid external beam treatment planning and raise warnings about additional risks to critical OARs. Methods A dose approximation method, based on geometrical transformations, was developed to automatically estimate perturbations to dose volumes arising from five important potential uncertainties at the time of treatment delivery, including (a) systematic isocenter misalignment between image guidance and beam delivery systems, and systematic errors in, (b) collimator, (c) gantry, (d) couch table, and (e) multi-leaf collimator ( MLC) leaf bank positions. The agreement between the estimated dose volume using the dose approximation method and the re-calculated dose volume obtained from the treatment planning system ( TPS) was verified using a dose difference test (2% threshold and 0 mm distance-to-agreement). For each type of uncertainty, the worst-case maximal dose values to the most critical OARs (brainstem, chiasm, optic nerves, and spinal cord) were quantitatively evaluated, and compared with the maximal dose values to the corresponding OARs from clinical plans. Results Six brain and six T-spine IMRT plans were used for evaluation. The average passing rates of 2% dose difference test were calculated to be 98.9% ± 1.3% for the uncertainties considered in this paper. The average time per patient to automatically analyze the dosimetric effects of all systematic uncertainties is 5.8 s. The worst-case scenarios for each plan, i.e., the largest changes in maximal doses to the OARs, were identified and confirmed to be in agreement with those calculated using the TPS. Conclusion For a given external beam plan, the proposed dose approximation method allows efficient evaluation of the dosimetric effects of potential patient positioning uncertainties and systematic machine delivery errors on maximal dose to critical OARs. While the same uncertainties can be manually analyzed using the TPS, the proposed method is automatic and computationally inexpensive, and therefore significantly more practical. The proposed method could be useful to provide insights about otherwise unquantified risks and plan robustness during the stage of treatment planning. [ABSTRACT FROM AUTHOR]

We reopen Erwin Schrödinger's thought experiment involving a cat in an informationally impenetrable box. A common view is that the cat enters a superposition of alive/ dead because of a lack of observation, leading to uncertainty about the state of the cat. We, on the other hand, argue that the cat only enters a superposition if everything about the cat is known prior to the box being closed. The superposition results from a lack of uncertainty inside the box. Rather than interpreting this state of affairs as a live and dead cat interacting with each other, we suggest that the more natural interpretation is that of an inability to precisely position events within spacetime due to the lack of uncertainty. We clarify how stable measurement depends on a diversified portfolio of statistical uncertainty, and how the lack of such uncertainty in Schrödinger's box precludes stabilization. [ABSTRACT FROM AUTHOR]

Coincidence detection of multiple particles emitted during an experiment can yield a new depth of understanding of the underlying process under study. However, the probability of detecting particles that are generated from the same physical event within a given coincidence time window is generally much lower than that of detecting particles that appear in the same coincidence time window, but were not created from the same physical event, and are therefore detected randomly in coincidence with each other. Thus, accurate and precise methods of measuring this random-coincidence background are essential for a wide variety of fields of science. A method to determine this background directly using the data themselves without any additional experimental run time or fake signals introduced in the data was recently established (O'Donnell, 2016). This method yields a statistical uncertainty on the random-coincidence background that is orders of magnitude smaller than that of the true coincidence data, though the potential for systematic errors of backgrounds from this method was never explored. In this work, we discuss common varieties of correlated and uncorrelated changes in the detection rates of each particle detected in an experiment. We demonstrate here that a correlation between particle detection rates from, for example, an incident particle beam that initiates a physical process of interest, creates systematic errors in the random-coincidence background measurement. We also discuss the impact of a variety of other realistic scenarios for rate changes in experiments. Lastly, a method is introduced to correct for errors in the random-coincidence background from any source, yielding an optimization between statistical precision and eliminating potential lingering systematic errors. [ABSTRACT FROM AUTHOR]

In methodologies for nuclear data (ND) uncertainty assessment and propagation based on random sampling, likelihood weights can be used to infer experimental information into the distributions for the ND. As the included number of correlated experimental points grows large, the computational time for the matrix inversion involved in obtaining the likelihood can become a practical problem. There are also other problems related to the conventional computation of the likelihood, e.g. , the assumption that all experimental uncertainties are Gaussian. In this study, a way to estimate the likelihood which avoids matrix inversion is investigated; instead, the experimental correlations are included by sampling of systematic errors. It is shown that the model underlying the sampling methodology (using univariate normal distributions for random and systematic errors) implies a multivariate Gaussian for the experimental points ( i.e. , the conventional model). It is also shown that the likelihood estimates obtained through sampling of systematic errors approach the likelihood obtained with matrix inversion as the sample size for the systematic errors grows large. In studied practical cases, it is seen that the estimates for the likelihood weights converge impractically slowly with the sample size, compared to matrix inversion. The computational time is estimated to be greater than for matrix inversion in cases with more experimental points, too. Hence, the sampling of systematic errors has little potential to compete with matrix inversion in cases where the latter is applicable. Nevertheless, the underlying model and the likelihood estimates can be easier to intuitively interpret than the conventional model and the likelihood function involving the inverted covariance matrix. Therefore, this work can both have pedagogical value and be used to help motivating the conventional assumption of a multivariate Gaussian for experimental data. The sampling of systematic errors could also be used in cases where the experimental uncertainties are not Gaussian, and for other purposes than to compute the likelihood, e.g. , to produce random experimental data sets for a more direct use in ND evaluation. [ABSTRACT FROM AUTHOR]

A new method for measuring chance-coincidence backgrounds during the collection of coincidence data is presented. The method relies on acquiring data with near-zero dead time, which is now realistic due to the increasing deployment of flash electronic-digitizer (waveform digitizer) techniques. An experiment designed to use this new method is capable of acquiring more coincidence data, and a much reduced statistical fluctuation of the measured background. A statistical analysis is presented, and used to derive a figure of merit for the new method. Factors of four improvement over other analyzes are realistic. The technique is illustrated with preliminary data taken as part of a program to make new measurements of the prompt fission neutron spectra at Los Alamos Neutron Science Center. It is expected that the these measurements will occur in a regime where the maximum figure of merit will be exploited. [ABSTRACT FROM AUTHOR]

Excavation is a complex multistage problem, where field responses of soil properties such as deflections at one stage of the operation depend on responses at the preceding stage. In order to help asset managers make better decisions and thus improve safety, soil properties should be accurately identified using sensor-data collected at the current stage. This task is not easy to accomplish, mainly because of its intrinsic ambiguity. Sensors usually only measure effects (e.g., field responses) but not causes (e.g., soil parameter values). A strategy that helps meet this challenge is to perform inverse analysis to validate soil parameter values. Error-Domain Model Falsification (EDMF) is a methodology that achieves this goal. More precisely, EDMF helps identify good behavior models of excavation by falsifying soil parameter values for which the predictions of the corresponding behavior models cannot explain field-response measurements collected by sensors. However, a remaining challenge is the identification of soil parameter values that are not falsified by EDMF, especially when the computation of the predictions is time-consuming. This paper proposes a new framework that combines EDMF and an optimization algorithm for efficient identification of soil parameter values. Results on a full-scale excavation site in Singapore show that the new framework is robust and accurate, and it has the potential to improve current practice, which relies primarily on surrogate models without uncertainty. [ABSTRACT FROM AUTHOR]

Research on the development of the optical frequency standard based on trapped and cold Ca with the 4s S-3d D clock transition at 729 nm is reported. A single calcium ion was trapped and laser cooled in the Paul trap and stay in trap for more than 15 days. The linewidth of a 729 nm laser was reduced to less than 10 Hz by locking to a cavity for longer than 50 hours uninterruptedly. The overall systematic uncertainty of the clock transition has been characterized to be better than 6.5×10. The absolute frequency of the clock transition was measured at 10 level using an optical frequency comb referenced to a Hydrogen maser, which was calibrated to the SI second through the global positioning system (GPS). The frequency value was 411042129776393.0(1.6) Hz after the correction of the systematic shifts. [ABSTRACT FROM AUTHOR]

Abstract: To calculate the upper limit for the Poisson observable at given confidence level with inclusion of systematic uncertainties in background expectation and signal efficiency, formulations have been established along the line of Bayesian approach. A FORTRAN program, BPULE, has been developed to implement the upper limit calculation. [Copyright &y& Elsevier]

Monte-Carlo nuclear reaction and transport codes are widely used to devise accelerator-based nuclear physics experiments; at the same time, many experiments are performed to validate the Monte-Carlo codes, which can be used for the design of full-scale nuclear power applications or the design of new benchmark experiments. Dedicated model benchmark studies investigate a broad range of nuclear reactions and quantities. Examples of these include isotope formation or secondary particle fluxes that result from the interactions of GeV-range hadrons with monoisotopic targets, which can be used to assess the respective systematic uncertainty of models. Such benchmark studies, as well as many nuclear application experiments and simulations carried out by various groups over the last few decades, enable us to draw methodological lessons. In this work, model uncertainty determined based on available experimental data allow us to identify the effects of practitioner expertise as well as the design of codes (user access to micro-scale parameters) on the range of uncertainties. We found that in cases when simulations are performed by code developers or users that are very experienced in performing simulations, the model to experiment quantity ratios generally agree with the limits determined by dedicated benchmark studies. In other cases, the ratios generally tend to be either smaller (underestimation of model error) or larger (overestimation of model error). A plausible explanation of the aforementioned effects is suggested. [ABSTRACT FROM AUTHOR]

This paper presents a new set-membership based hybrid Kalman filter (SM-HKF) by combining the Kalman filtering (KF) framework with the set-membership concept for nonlinear state estimation under systematic uncertainty consisted of both stochastic error and unknown but bounded (UBB) error. Upon the linearization of the nonlinear system model via a Taylor series expansion, this method introduces a new UBB error term by combining the linearization error with systematic UBB error through the Minkowski sum. Subsequently, an optimal Kalman gain is derived to minimize the mean squared error of the state estimate in the KF framework by taking both stochastic and UBB errors into account. The proposed SM-HKF handles the systematic UBB error, stochastic error as well as the linearization error simultaneously, thus overcoming the limitations of the extended Kalman filter (EKF). The effectiveness and superiority of the proposed SM-HKF have been verified through simulations and comparison analysis with EKF. It is shown that the SM-HKF outperforms EKF for nonlinear state estimation with systematic UBB error and stochastic error. [ABSTRACT FROM AUTHOR]