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The growth and fertility rates of past populations can be estimated by analyzing the age-at-death distribution of skeletal samples. The procedure involves regressing growth or fertility rate on the age-at-death ratio, which is a proxy that captures the number of skeletons in two broad age-at-death categories (e.g., D5+/D20+). Galeta and Pankowská (2023, doi: 10.1371/journal.pone.0286580) recently developed a new prediction algorithm. They proposed to estimate growth and fertility rates using a unique prediction formula for each skeletal sample. Each formula is based on a unique reference set of simulated skeletal samples that match the size of the target real skeletal sample. The simulated skeletal samples are generated from populations with similar mortality levels to those assumed in the time period represented by the target skeletal sample. A correct setting of the sample size and the level of mortality increases the accuracy of the estimate. The approach, however, is computationally intensive because it involves generating many simulated reference skeletal samples. In this paper, we present the demrat package, written in the R programming language, which automates the simulation. The functions of the package provide a complete workflow from a real skeletal sample to the prediction of demographic rates. In addition, we offer a web application that allows non-R users to deploy predictions using the demrat package with a user-friendly, point-and-click graphical interface. Although the demrat package allows for estimating demographic rates for a single skeletal sample, we recommend predicting demographic rates in a larger set of skeletal samples and producing smoothed general demographic trends over large areas and/or long periods of time. [ABSTRACT FROM AUTHOR]

Abstract In any visual search task in the lab or in the world, observers will make errors. Those errors can be categorized as “deterministic”: If you miss this target in this display once, you will definitely miss it again. Alternatively, errors can be “stochastic”, occurring randomly with some probability from trial to trial. Researchers and practitioners have sought to reduce errors in visual search, but different types of errors might require different techniques for mitigation. To empirically categorize errors in a simple search task, our observers searched for the letter “T” among “L” distractors, with each display presented twice. When the letters were clearly visible (white letters on a gray background), the errors were almost completely stochastic (Exp 1). An error made on the first appearance of a display did not predict that an error would be made on the second appearance. When the visibility of the letters was manipulated (letters of different gray levels on a noisy background), the errors became a mix of stochastic and deterministic. Unsurprisingly, lower contrast targets produced more deterministic errors. (Exp 2). Using the stimuli of Exp 2, we tested whether errors could be reduced using cues that guided attention around the display but knew nothing about the content of that display (Exp3a, b). This had no effect, but cueing all item locations did succeed in reducing deterministic errors (Exp3c).

In any visual search task in the lab or in the world, observers will make errors. Those errors can be categorized as "deterministic": If you miss this target in this display once, you will definitely miss it again. Alternatively, errors can be "stochastic", occurring randomly with some probability from trial to trial. Researchers and practitioners have sought to reduce errors in visual search, but different types of errors might require different techniques for mitigation. To empirically categorize errors in a simple search task, our observers searched for the letter "T" among "L" distractors, with each display presented twice. When the letters were clearly visible (white letters on a gray background), the errors were almost completely stochastic (Exp 1). An error made on the first appearance of a display did not predict that an error would be made on the second appearance. When the visibility of the letters was manipulated (letters of different gray levels on a noisy background), the errors became a mix of stochastic and deterministic. Unsurprisingly, lower contrast targets produced more deterministic errors. (Exp 2). Using the stimuli of Exp 2, we tested whether errors could be reduced using cues that guided attention around the display but knew nothing about the content of that display (Exp3a, b). This had no effect, but cueing all item locations did succeed in reducing deterministic errors (Exp3c). [ABSTRACT FROM AUTHOR]

Regardless of whether the global navigation satellite system (GNSS)/inertial navigation system (INS) is integrated or the INS operates independently during GNSS outages, the stochastic error of the inertial sensor has an important impact on the navigation performance. The structure of stochastic error in low-cost inertial sensors is quite complex; therefore, it is difficult to identify and separate errors in the spectral domain using classical stochastic error methods such as the Allan variance (AV) method and power spectral density (PSD) analysis. However, a recently proposed estimation, based on generalized wavelet moment estimation (GMWM), is applied to the stochastic error modeling of inertial sensors, giving significant advantages. Focusing on the online implementation of GMWM and its integration within a general navigation filter, this paper proposes an algorithm for online stochastic error calibration of inertial sensors in urban cities. We further develop the autonomous stochastic error model by constructing a complete stochastic error model and determining model ranking criterion. Then, a detecting module is designed to work together with the autonomous stochastic error model as feedback for the INS/GNSS integration. Finally, two experiments are conducted to compare the positioning performance of this algorithm with other classical methods. The results validate the capability of this algorithm to improve navigation accuracy and achieve the online realization of complex stochastic models. [ABSTRACT FROM AUTHOR]

• The analytical stochastic errors of power spectral density functions for dimension-reduction representation methods are given. • The Gaussianity of sample processes is discussed from the from the view of the first four statistical moments. • The Type I orthogonal function set is recommended to be utilized in practice. The simulation of random vector process is of great importance, particularly for the nonlinear dynamic analysis and reliability evaluation of large-scale structures. Combining the probability density evolution method, which provides an efficient way to perform the structural dynamic reliability analysis, reducing the randomness degree of the simulation method becomes a new challenge task. In order to address this issue, dimension-reduction representation methods for simulating stationary vector processes were proposed recently. However, the features regarding the stochastic error of power spectral density functions and the Gaussianity of vector processes generated by the dimension-reduction representation methods remain unclear. In this paper, the analytical stochastic errors of power spectral density functions for dimension-reduction representation methods will be given, and be proved through the numerical example. Moreover, the Gaussianity of the generated vector processes will be discussed from the view of the first four statistical moments. [ABSTRACT FROM AUTHOR]

ABSTRACT Viral metagenomics is the most powerful tool to profile viromic composition for a given sample. Different viromic methods, including amplification-free ones, have been developed, but choosing them for different purposes requires comprehensive benchmarks. Here, we assessed the performance of four routinely used methods, i.e., multiple displacement amplification (MDA), direct metagenomic sequencing (MTG), sequence-independent single-primer amplification (SIA), and metatranscriptomic sequencing (MTT), using marmot rectal samples as the templates spiked with five known viruses of different genome types. The obtained clean data were differently contaminated by host and bacterial genomes, resulting in MDA having the most, with ~72.1%, but MTT had only ~7.5% data, useful for follow-up viromic analysis. MDA showed a broader spectrum with higher efficiency to profile the DNA virome, and MTT captured almost all RNA viruses with extraordinary sensitivity; hence, they are advisable in richness-based viromic studies. MTG was weak in capturing single-stranded DNA viruses, and SIA could detect both RNA and DNA viruses but with high randomness. Due to biases to certain types of viruses, the four methods caused different alterations to species abundance compared to the initial virus composition. SIA and MDA introduced greater stochastic errors to relative abundances of species, genus, and family taxa, whereas the two amplification-free methods were more tolerant toward such errors and thus are recommendable in abundance-based analyses. In addition, genus taxon is a compromising analytic level that ensures technically supported and biologically and/or ecologically meaningful viromic conclusions. IMPORTANCE Viral metagenomics can be roughly divided into species richness-based studies and species abundance-based analyses. Viromic methods with different principles have been developed, but rational selection of these techniques according to different purposes requires comprehensive understanding of their properties. By assessing the four most widely used methods using template samples, we found that multiple displacement amplification (MDA) and metatranscriptomic sequencing (MTT) are advisable for species richness-based viromic studies, as they show excellent efficiency to detect DNA and RNA viruses. Meanwhile, metagenomic sequencing (MTG) and MTT are more compatible with stochastic errors of methods introduced into relative abundance of viromic taxa and hence are rational choices in species abundance-based analyses. This study also highlights that MTG needs to tackle host genome contamination and ameliorate the capacity to detect single-stranded DNA viruses in the future, and the MTT method requires an improvement in bacterial rRNA depletion prior to library preparation.

Navigators have been taught for centuries to estimate the location of their craft on a map from three lines of position, for redundancy. The three lines typically form a triangle, called a cocked hat. How is the location of the craft related to the triangle? For more than 80 years navigators have also been taught that, if each line of position is equally likely to pass to the right and to the left of the true location, then the likelihood that the craft is in the triangle is exactly 1/4. This is stated in numerous reputable sources, but was never stated or proved in a mathematically formal and rigorous fashion. In this paper we prove that the likelihood is indeed 1/4 if we assume that the lines of position always intersect pairwise. We also show that the result does not hold under weaker (and more reasonable) assumptions, and we prove a generalisation to $n$ lines. [ABSTRACT FROM AUTHOR]

The task of inertial sensor calibration has required the development of various techniques to take into account the sources of measurement error coming from such devices. The calibration of the stochastic errors of these sensors has been the focus of increasing amount of research in which the method of reference has been the so-called “Allan variance (AV) slope method” which, in addition to not having appropriate statistical properties, requires a subjective input which makes it prone to mistakes. To overcome this, recent research has started proposing “automatic” approaches where the parameters of the probabilistic models underlying the error signals are estimated by matching functions of the AV or wavelet variance with their model-implied counterparts. However, given the increased use of such techniques, there has been no study or clear direction for practitioners on which approach is optimal for the purpose of sensor calibration. This article, for the first time, formally defines the class of estimators based on this technique and puts forward theoretical and applied results that, comparing with estimators in this class, suggest the use of the Generalized method of Wavelet moments (GMWM) as an optimal choice. In addition to analytical proofs, experiment-driven Monte Carlo simulations demonstrated the superior performance of this estimator. Further analysis of the error signal from a gyroscope was also provided to further motivate performing such analyses, as real-world observed error signals may show significant deviation from manufacturer-provided error models. [ABSTRACT FROM AUTHOR]

A unified model is developed in this paper to characterize multiple stochastic errors in fiber optic gyro (FOG). Differential equations to characterize different stochastic errors in FOG are derived and an equivalent relationship is proved. The unified model of the multiple stochastic errors in FOG is derived by using the differential equations of individual stochastic errors and the equivalent relationship. The derived stochastic error model is used in the initial alignment of a self-designed FOG inertial navigation system (INS). The experimental test results are compared with those of using the normal White-Noise model and Gauss-Markov model, which has shown the better performances of the proposed modeling approach. [ABSTRACT FROM AUTHOR]

• Confounders of panel sequencing based tumor mutational burden (psTMB) were analyzed. • Analysis of panel size, germline filtering, biological and technical variance. • Stochastic error connected to panel size was the largest of all error contributors. • Quality control, optimized laboratory workflows and bioinformatics are essential. • Analysis framework is applicable to other complex biomarkers such as HRD. Retrospective data including subgroup analyses in clinical studies have sparked strong interest in developing tumor mutational burden (TMB) as a predictive biomarker for immune checkpoint blockade. While individual factors influencing panel sequencing based measurement of TMB (psTMB) have been discussed in the recent literature, an integrative study quantifying, comparing and combining all potential confounders is still missing. We separated different potential confounders of psTMB measurement including "panel size", "germline mutation filtering", "biological variance" and "technical variance" and developed a specific error model for each of these factors. Published experimental psTMB data were fitted to the error models to quantify the contribution of each of the confounders. The total psTMB variance was obtained as sum over the variance contributions of each of the confounders. Using a typical large panel (size 1–1.5 Mbp) total errors of 57 %, 42 %, 34 % and 28 % were observed for tumors with psTMB of 5, 10, 20 and 40 muts/Mbp. Even for large panels, the stochastic error connected to the panel size represented the largest of all contributions to the total psTMB variance, especially for tumors with TMB up to 20 muts/Mbp. Other sources of psTMB variability could be kept under control, but rigorous quality control, best practice laboratory workflows and optimized bioinformatics pipelines are essential. A statistical framework for the analysis of complex, genomic biomarkers was developed and applied to the analysis of psTMB variability. The methods developed here can support the analysis of other quantitative biomarkers and their implementation in clinical practice. [ABSTRACT FROM AUTHOR]

Denoising of hyperspectral images is an essential step to remove the visual artifacts and improve the quality of an image. There are various sources of noise such as dark current, thermal and read noise produced due to detectors, stochastic error of photo‐counting and so on which leads to variability of noise both in spatial and spectral domains. In this study, author proposes a novel denoising method based on concept of Hilbert vibration decomposition (HVD). Being iterative in nature it segregates initial amplitude composition into various components which are composed of slow varying wavelength. Any hyperspectral image is captured by the sensor over contiguous wavelengths. Thus, variation in intensities over the spectral dimension is less. HVD separates pixels in decreasing order of their intensity and results in denoising of the image. To evaluate method, various noise conditions have been tested on three real datasets: Washington DC mall, Urban and Pavia University. The validation is done both visually and quantitatively. The denoising with almost 100% mean structural similarity index confirms superiority of the designed method. Clustering and spectral analysis of various denoised images have also been reported. Clustering accuracy of 65% is achieved by the HVD as compared to other methods. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering for Thermal Energy & Power / Reneng Dongli Gongcheng is the property of Journal of Engineering for Thermal Energy & Power 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.)

Fiber Optic Gyroscope (FOG) under the complicated environment exhibits nonstationary dynamic characteristics over time, and the traditional Allan variance is difficult to indicate and determine the stochastic errors. To have a better evaluation of the instability of the signal, a novel hybrid algorithm based on Overlap Nonstationary Dynamic Allan Variance (DAVAR) is proposed considering the FOG time-varying nature. The overlap sampling could make full use of entire data in every cluster and reveal the transition during the dynamic process. The estimation accuracy of the different sampling methods-based Allan Deviations are compared by simulation data and the advantages of the introduced method are verified. To track the variation of FOG error coefficients, the frequency of output signal is analyzed to determine the window length of DAVAR. Furthermore, a fast algorithm in Overlap Nonstationary DAVAR is introduced to improve calculation efficiency in processing long cluster time data. In the FOG vibration experiment, the vibration characteristics are analysed by the variations of primary error coefficients like Quantization noise. The 3-D diagrams of Overlap Nonstationary DAVAR are capable of identifying and distinguishing the dynamic stochastic error items. In conclusion, the developed algorithm has better estimation accuracy and could be utilized to analyze FOG nonstationary dynamic stochastic error systematically. [ABSTRACT FROM AUTHOR]

A new cross section adjustment method has been derived in which systematic errors in measured data and calculated results of neutronics characteristics are estimated and removed in the adjustment. Bias factors which are the ratio between measured data and calculated results are used to estimate systematic errors. The difference of the bias factors from unity is caused generally by systematic errors and stochastic errors. Therefore by determining whether the difference is within the total stochastic errors of measurements and calculations, systematic errors are estimated. Since stochastic errors are determined for individual confidence levels, systematic errors are also dependent to the confidence levels. The method has been applied to cross section adjustments using 589 measured data obtained from fast critical assemblies and fast reactors. The adjustments results are compared with those of the conventional adjustment method. Also the effect of the confidence level to the adjusted cross sections is discussed. [ABSTRACT FROM AUTHOR]

In plenty of researches and real systems, barometer and accelerometer which can provide altitude and acceleration measurements, respectively, are utilised in altitude estimation of small unmanned aerial vehicle. These sensors can draw on their merits and improve the accuracy of altitude estimation. The altitude measurement of barometer has the drift problem as one of its disadvantages which could deteriorate the performance of altitude estimation. In addition, barometer and accelerometer both have stochastic error which reduce the accuracy of altitude estimation. A novel scenario is proposed by utilising differential altitude measurement and complementary filter to deal with above issues. It is accurate and suitable for real‐time application. In this scenario, altitude drift is reduced by using differential altitude measurement, and the stochastic error of altitude is minimised by using complementary filter algorithm. Stair and flight experiments are implemented to test this scenario. The experimental results indicate that it can provide good dynamic performance and flexibility for altitude estimation. [ABSTRACT FROM AUTHOR]

Fiber optic current transducer (FOCT) is a new type of current measurement instrument with high accuracy, large dynamic range, wide frequency range and excellent insulating property. Thus, it has been highly concerned for using in high voltage grid. Although these FOCTs have such crucial advantages, the stochastic errors, mainly including random walk, bias instability, quantization noise, may suddenly degrade the system performance in a short period of time. To evaluate the stochastic error characteristics of fiber optic current transducer (FOCT), traditional solution adopts Allan variance method to plot the curve on the log–log scale. Considering the deficiency of severe shock in Allan variance, total variance methodology is proposed to analyze the stochastic error characteristics of FOCT with the aim of overcoming the deficiency. Total variance method transforms time-offset data to frequency-offset data and extends the data to be a virtual sequence nearly as three times long as the initial data by mapping. Additionally, the least square fitting (LSF) is applied to identify and obtain each coefficient of FOCT stochastic error, which helps to evaluate the stochastic error characteristics of FOCT excellently. Simulation results demonstrate that both Allan variance result and total variance result are nearly equivalent, when correlation time is short. However, when correlation time exceeds half total duration of the measurement, Allan variance estimation values tend to shock increasingly severely and total variance curve always extends steadily. Besides, every stochastic error coefficient is decreased effectively by total variance method, comparing with Allan variance method. Total variance methodology overcomes the deficiency of severe shock in Allan variance successfully, and then optimizes evaluation method of FOCT stochastic error characteristics. [ABSTRACT FROM AUTHOR]

A dual-axis rotational Inertial Navigation System (INS) has received wide attention in recent years because of high performance and low cost. However, some errors of inertial sensors such as stochastic errors are not averaged out automatically during navigation. Therefore a Twice Position-fix Reset (TPR) method is provided to enhance accuracy of a dual-axis rotational INS by compensating stochastic errors. According to characteristics of an azimuth error introduced by stochastic errors of an inertial sensor in the dual-axis rotational INS, both an azimuth error and a radial-position error are much better corrected by the TPR method based on an optimised error propagation equation. As a result, accuracy of the dual-axis rotational INS is prominently enhanced by the TPR method, as is verified by simulations and field tests. [ABSTRACT FROM PUBLISHER]

Activity-based models of travel demand have received considerable attention in transportation planning and forecasting in recent years. However, in most cases they use a micro-simulation approach, thereby inevitably including a stochastic error that is caused by the statistical distributions of random components. As a consequence, running a transport micro-simulation model several times with the same input will generate different outputs, which baffles practitioners in applying such a model and in interpreting the results. A common approach is therefore to run the model multiple times and to use the average value of the results. The question then becomes: what is the minimum number of model runs required to reach a stable result? In this paper, systematic experiments are carried out using Forecasting Evolutionary Activity-Travel of Households and their Environmental RepercussionS (FEATHERS), an activity-based micro-simulation modelling framework currently implemented for the Flanders region of Belgium. Six levels of geographic detail are taken into account. Three travel indices – average daily activities per person, average daily trips per person and average daily distance travelled per person, as well as their corresponding segmentations – are calculated by running the model 100 times. The results show that the more disaggregated the level, the larger the number of model runs is needed to ensure confidence. Furthermore, based on the time-dependent origin-destination table derived from the model output, traffic assignment is performed by loading it onto the Flemish road network, and the total vehicle kilometres travelled in the whole Flanders are subsequently computed. The stable results at the Flanders level provides model users with confidence that application of FEATHERS at an aggregated level requires only limited model runs. [ABSTRACT FROM AUTHOR]

Random processes, such as the wind velocity field and spatially varying ground motions, are usually simulated as N-variate stochastic processes by the conditional-simulation method. However, the temporal power spectral density (PSD) function of a single, simulated sample process may be different from the target PSD. Such differences can usually be assessed by the statistical errors (i.e., the bias and stochastic errors). Therefore, this paper investigates the bias errors and the stochastic errors of the PSD functions produced by the conditionalsimulation method. For the bias errors, it was found that the conditional-simulation method might produce the nonzero bias error of the PSD functions in some cases. However, this usually does not occur in the unconditional simulation, and it should be avoided. To avoid the nonzero bias error of the PSD functions, a modified conditional-simulation method was proposed. It was verified using both the theoretical derivation and the numerical example. For the stochastic errors, the closed-form solutions for the PSD functions' stochastic errors produced by a conditional simulation were given by the theoretical derivation and verified by numerical examples. Finally, the PSD functions' stochastic errors produced by a conditional simulation were compared with those produced by an unconditional simulation using the spectral-representation method. [ABSTRACT FROM AUTHOR]

The spectral representation method (SRM) is widely used when simulating spatially variable ground motions. It has mainly two formulas, i.e., the random amplitudes and the random phases formulas. There exist three methods for decomposing the cross spectral density matrix: Cholesky decomposition, eigen decomposition, and root decomposition. Therefore, there are six forms with respect to the different combinations of the simulation formulas and the decomposition methods. To provide researchers and engineers with the guidance on choosing simulation method, the six forms are systematically investigated from five aspects: the power intensity, response spectra, and stochastic error of auto/cross spectral density, Fourier spectra, and difference indexes for Fourier amplitudes and phases. Finally, we give the following advice: the characteristics of the ground motions simulated by the random amplitudes formula are independent of the decomposition method, while the characteristics of the ground motions simulated by random phases formula are dependent of the decomposition method. Furthermore, the root decomposition is strongly recommended when utilizing the random phases formula. [ABSTRACT FROM PUBLISHER]

This correspondence is a companion paper to [J. Dong, M. Verhaegen, and F. Gustafsson, “Robust Fault Detection With Statistical Uncertainty in Identified Parameters,” IEEE Trans. Signal Process., vol. 60, no. 10, Oct. 2012], extending it to fault isolation. Also, here, use is made of a linear in the parameters model representation of the input-output behavior of the nominal system (i.e. fault-free). The projection of the residual onto directions only sensitive to individual faults is robustified against the stochastic errors of the estimated model parameters. The correspondence considers additive error sequences to the input and output quantities that represent failures like drift, biased, stuck, or saturated sensors/actuators. [ABSTRACT FROM PUBLISHER]

Abstract: Impedance measurements were conducted to gain insight into flooding of a single polymer electrolyte membrane (PEM) fuel cell. The stochastic character of the formation of water droplets and subsequent removal by gas flow is demonstrated to increase the standard deviation of impedance measurements, yielding a sensitive manner to detect onset of flooding. The increase in stochastic noise associated with flooding was more apparent at low frequencies, due to the closer match to the characteristic frequency associated with the growth and removal of water droplets. The onset of flooding was sensitive to the design of the gas diffusion layer. [Copyright &y& Elsevier]

Using basic probability theory, we show that there is a substantial likelihood that even in the presence of strong purifying selection, there will be a number of codons in which the number of synonymous nucleotide substitutions per site ( d S) exceeds the number of non-synonymous nucleotide substitutions per site ( d N). In an empirical study, we examined the numbers of synonymous ( b S) and non-synonymous substitutions ( b N) along branches of the phylogenies of 69 single-copy orthologous genes from seven species of mammals. A pattern of b N > b S was most commonly seen in the shortest branches of the tree and was associated with a high coefficient of variation in both b N and b S, suggesting that high stochastic error in b N and b S on short branches, rather than positive Darwinian selection, is the explanation of most cases where b N is greater than b S on a given branch. The branch-site method of Zhang et al. (Zhang, Nielsen, Yang, Mol Biol Evol, 22:2472–2479, ) identified 117 codons on 35 branches as “positively selected,” but a majority of these codons lacked synonymous substitutions, while in the others, synonymous and non-synonymous differences per site occurred in approximately equal frequencies. Thus, it was impossible to rule out the hypothesis that chance variation in the pattern of mutation across sites, rather than positive selection, accounted for the observed pattern. Our results showed that b N/ b S was consistently elevated in immune system genes, but neither the search for branches with b N > b S nor the branch-site method revealed this trend. [ABSTRACT FROM AUTHOR]