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Detrital zircon (DZ) geochronology is based on a multistage, hierarchical sampling process in which sampling errors (statistical variance) and non-sampling errors (sampling bias) are endemic. Most of these errors are related to 1) initial collection of a non-random grab sample (typically from outcrop); 2) processing the grab sample to obtain a non-random grain-size sample of restricted grain size(s) (effectively a processed population); 3) randomly and/or non-randomly selecting DZs (often based on their size, shape, and colour) from the grain-size sample to obtain a hand-picked sample (effectively an analysed population); and 4) randomly and/or non-randomly radiometrically dating only certain DZs (due to limitations of instrument spot size and grain imperfections) from the hand-picked sample to obtain an analysed sample. Claims that hand-picked samples and analysed samples are randomly chosen are questionable, since these samples likely represent haphazard sampling attempting to emulate randomness. Non-randomness has major implications for calculations that determine sample size and inter-sample 'sameness' comparison, as both techniques generally depend on randomly chosen samples. The use of inter-sample comparison is particularly troublesome in that sameness is determined for the analysed sample, which is not representative of the initial grab sample. Yet more bias occurs with a 'leap of inference', the generalization of DZ ages from the analysed sample to the target population (the strata of interest) or to a general population (DZs derived from ultimate and proximate sources). Sampling bias can be reduced by unambiguously defining the sampled population (the population that inferential statistics apply), selecting representative samples (if possible, randomization should be used at every stage in the sampling process), and preserving the representativeness of subsamples by using correct sampling devices such as a microsplitter. It may be timely for standardized sampling, interpreting, and reporting guidelines so that detrital geochronology data are scientifically meaningful and replicable. [ABSTRACT FROM AUTHOR]

Abstract The atmospheric CO2 growth rate is a fundamental measure of climate forcing. NOAA's growth rate estimates, derived from in situ observations at the marine boundary layer (MBL), serve as the benchmark in policy and science. However, NOAA's MBL‐based method encounters challenges in accurately estimating the whole‐atmosphere CO2 growth rate at sub‐annual scales. Here we introduce the Growth Rate from Satellite Observations (GRESO) method as a complementary approach to estimate the whole‐atmosphere CO2 growth rate utilizing satellite data. Satellite CO2 observations offer extensive atmospheric coverage that extends the capability of the current NOAA benchmark. We assess the sampling errors of the GRESO and NOAA methods using 10 atmospheric transport model simulations. The simulations generate synthetic OCO‐2 satellite and NOAA MBL data for calculating CO2 growth rates, which are compared against the global sum of carbon fluxes used as model inputs. We find good performance for the NOAA method (R = 0.93, RMSE = 0.12 ppm year−1 or 0.25 PgC year−1). GRESO demonstrates lower sampling errors (R = 1.00; RMSE = 0.04 ppm year−1 or 0.09 PgC year−1). Additionally, GRESO shows better performance at monthly scales than the NOAA method (R = 0.76 vs. 0.47, respectively). Due to CO2's atmospheric longevity, the NOAA method accurately captures growth rates over 5‐year intervals. GRESO's robustness across partial coverage configurations (ocean or land data) shows that satellites can be promising tools for low‐latency CO2 growth rate information, provided the systematic biases are minimized using in situ observations. Along with accurate and calibrated NOAA in situ data, satellite‐derived growth rates can provide information about the global carbon cycle at sub‐annual scales.

Abstract This paper proposes a new calibration estimator for population variance within a stratified two-phase sampling design. It takes into account random non-response and measurement errors, specifically applying this method to estimate the variance in Gas turbine exhaust pressure data. The study integrates additional information from two highly positively correlated auxiliary variables to develop a general class of estimators tailored for the stratified two-phase sampling scheme. The properties of these estimators, in terms of their biases and mean square errors, have been thoroughly examined and extensively analyzed through numerical and simulation studies. Furthermore, the calibrated weights of the strata are derived. The proposed estimators outperform the natural estimator of population variance. Finally, suitable recommendations have been made for survey statisticians intending to apply these findings to real-life problems.

The accurate measurement of PM2.5 and its inorganic matters (IMs) is crucial for compliance monitoring and understanding particle formation. However, semi-volatile IMs (SVIMs) like NH4+, NO3-, and Cl- tend to evaporate from particles, causing sampling artifacts. The evaporation loss occurs due to many factors making the quantitative prediction difficult. This study aimed to investigate the evaporation loss of SVIMs in PM2.5 under different sampling conditions. In the field tests, when a normal single Teflon filter (STF) sampler, which is like a Federal Reference Method (FRM) sampler, was used to sample PM2.5 at ambient conditions, a significant SVIM evaporation loss was observed, resulting in negative biases for total IMs (-25.68 ± 3.25%) and PM2.5 concentrations (-9.87 ± 4.27%). But if PM2.5 was sampled by a chilled Teflon filter sampler (CTF) at 4 °C following aerosol dehumidification so that relative humidity (RH) was controlled to within the 10-20% range (RHd), evaporation loss was minimized with a bias of <±10% for both total IMs and PM2.5based on the reference data. When RHd is below 10%, both IMs and PM2.5 are under-measured, but only PM2.5 is over-measured when RHd is >20%. A model considering predictable saturation ratios for NH4+, NO3-, and Cl- under various pressure drop, temperature and RH conditions was developed to predict accurately the actual concentrations of PM2.5 and its SVIMs for the STF. Additionally, the ISORROPIA-II model predicted SVIMs effectively for the CTF. In summary, using the CTF at optimized sampling conditions can achieve accurate measurement of both SVIMs and PM2.5 concentrations simultaneously. [ABSTRACT FROM AUTHOR]

Localization is the key component to the successful application of ensemble data assimilation (DA) to high-dimensional problems in the geosciences. We study the impact of sampling error and its amelioration through localization using both analytical development and numerical experiments. Specifically, we show how sampling error in covariance estimates accumulates and spreads throughout the entire domain during the computation of the Kalman gain. This results in a bias, which is the dominant issue in unlocalized ensemble DA, and, surprisingly, we find that it depends directly on the number of independent observations but only indirectly on the state dimension. Our derivations and experiments further make it clear that an important aspect of localization is a significant reduction of bias in the Kalman gain, which in turn leads to an increased accuracy of ensemble DA. We illustrate our findings on a variety of simplified linear and nonlinear test problems, including a cycling ensemble Kalman filter applied to the Lorenz-96 model. Significance Statement: The dampening of long-range correlations has been the key to the success of ensemble data assimilation in global numerical weather prediction. In this paper, we show how noise in covariance estimates propagates through the state estimation process and corrupts state estimates. We show that this noise results in a bias and that this bias depends on the number of observations and not, as might be expected, on the state dimension. We go on to show how dampening long-range covariances through a process referred to as "localization" helps to mitigate the detrimental effects of this sampling noise. [ABSTRACT FROM AUTHOR]

Evaluating and validating satellite sea surface salinity (SSS) measurements is fundamental. There are two types of errors in satellite SSS: measurement error due to the instrument's inaccuracy and problems in retrieval, and sampling error due to unrepresentativeness in the way that the sea surface is sampled in time and space by the instrument. In this study, we focus on sampling errors, which impact both satellite and in situ products. We estimate the sampling errors of Level 3 satellite SSS products from Aquarius, SMOS and SMAP, and in situ gridded products. To do that, we use simulated L2 and L3 Aquarius, SMAP and SMOS SSS data, individual Argo observations and gridded Argo products derived from a 12-month high-resolution 1/48° ocean model. The use of the simulated data allows us to quantify the sampling error and eliminate the measurement error. We found that the sampling errors are high in regions of high SSS variability and are globally about 0.02/0.03 psu at weekly time scales and 0.01/0.02 psu at monthly time scales for satellite products. The in situ-based product sampling error is significantly higher than that of the three satellite products at monthly scales (0.085 psu) indicating the need to be cautious when using in situ-based gridded products to validate satellite products. Similar results are found using a Correlated Triple Collocation method that quantifies the standard deviation of products' errors acquired with different instruments. By improving our understanding and quantifying the effect of sampling errors on satellite-in situ SSS consistency over various spatial and temporal scales, this study will help to improve the validation of SSS, the robustness of scientific applications and the design of future salinity missions. [ABSTRACT FROM AUTHOR]

The internal audits carried out in the first half of 2019 in water laboratories as part of quality accreditation in accordance with ISO/IEC 17025:2017 showed a high frequency of adverse events in connection with sampling. These faults can be a consequence of a wide range of causes, and in some cases, the information about them can be insufficient or unclear. Considering that sampling has a major influence on the quality of the analytical results provided by water laboratories, this work presents a system for reporting and learning adverse events. Its aim is to record nonconformities, errors, and adverse events, making possible automatic data analysis aiming to ensure continuous improvement in operational sampling. The system is based on the Eindhoven Classification Model and enables automatic data analysis and reporting to identify the main causes of failure. Logic programming is used to represent knowledge and support the reasoning mechanisms to model the universe of discourse in scenarios of incomplete, contradicting, or even unknown information. In addition to suggesting solutions to the problem, the system provides formal evidence of the solutions presented, which will help to continuously improve drinking water quality and promote public health. HIGHLIGHTS An adverse event reporting and learning system for water sampling is described.; The Eindhoven Classification Model is extended and adapted for water sampling.; Logic programming is used for knowledge representation.; The proposed system can deal with insufficient or ambiguous information.; The system allows to identify the relevant issues behind the errors that may occur.;

The ensemble-based Kalman filter requires at least a considerable ensemble (e.g., 10,000 members) to identify relevant error covariance at great distances for multidimensional geophysical systems. However, increasing numerous ensemble sizes will enlarge sampling errors. This study proposes a modified Cholesky decomposition based on the covariance localization (CL) scheme, namely a covariance localization scheme with modified Cholesky decomposition (CL-MC). Our main idea utilizes a modified Cholesky (MC) decomposition technique for estimating the background error covariance matrix; meanwhile, we employ the tunable singular value decomposition method on the background error covariance to improve the ensemble increment and avoid the imbalance of the system. To verify if the proposed method can effectively mitigate the sampling errors, numerical experiments are conducted on the Lorenz-96 model and large-scale model (SPEEDY model). The results show that the CL-MC method outperforms the CL method for different data assimilation parameters (ensemble sizes and localizations). Furthermore, by performing one year of assimilation experiments on the SPEEDY model, it is found that the 1-day forecast RMSEs obtained by the CL are approximately equal to the 5-day forecast RMSEs of CL-MC. So, the CL-MC method has potential advantages for long-term forecasting. Maybe the proposed CL-MC method achieves good prospects for widespread application in atmospheric general circulation models. [ABSTRACT FROM AUTHOR]

The current sampling errors in dual-motor group (DMG) cause more adverse effects on system performance than that in a single motor system. To solve this problem, in this article, a synergistic correction strategy for the abovementioned problems in DMG fed by double inverters is proposed. First, an orthogonal pulsewidth modulation carriers strategy is proposed to imitate the “shutdown” status of motor subsystems to obtain ascertainable current relations. Then, the current values with sampling errors in DMG at several “fixed” sampling points are derived, based on which the sampling errors in DMG are corrected. Meanwhile, the rules for the action time of active vectors and output voltage range in each inverter are analyzed for each “fixed” sampling point. Finally, the proposed strategy is verified by experimental results on a DMG system, which show that the sampling errors can be precisely estimated with the proposed strategy. [ABSTRACT FROM AUTHOR]

Evaluating and validating satellite sea surface salinity (SSS) measurements is fundamental. There are two types of errors in satellite SSS: measurement error due to the instrument’s inaccuracy and problems in retrieval, and sampling error due to unrepresentativeness in the way that the sea surface is sampled in time and space by the instrument. In this study, we focus on sampling errors, which impact both satellite and in situ products. We estimate the sampling errors of Level 3 satellite SSS products from Aquarius, SMOS and SMAP, and in situ gridded products. To do that, we use simulated L2 and L3 Aquarius, SMAP and SMOS SSS data, individual Argo observations and gridded Argo products derived from a 12-month high-resolution 1/48° ocean model. The use of the simulated data allows us to quantify the sampling error and eliminate the measurement error. We found that the sampling errors are high in regions of high SSS variability and are globally about 0.02/0.03 psu at weekly time scales and 0.01/0.02 psu at monthly time scales for satellite products. The in situ-based product sampling error is significantly higher than that of the three satellite products at monthly scales (0.085 psu) indicating the need to be cautious when using in situ-based gridded products to validate satellite products. Similar results are found using a Correlated Triple Collocation method that quantifies the standard deviation of products’ errors acquired with different instruments. By improving our understanding and quantifying the effect of sampling errors on satellite-in situ SSS consistency over various spatial and temporal scales, this study will help to improve the validation of SSS, the robustness of scientific applications and the design of future salinity missions.

When sampling data of specific classes (i.e., known classes) for a scientific task, collectors may encounter unknown classes (i.e., novel classes). Since these novel classes might be valuable for future research, collectors will also sample them and assign them to several clusters with the help of known-class data. This assigning process is known as novel class discovery (NCD). However, category confusion is common in the sampling process and may make the NCD unreliable. To tackle this problem, this paper introduces a new and more realistic setting, where collectors may misidentify known classes and even confuse known classes with novel classes—we name it NCD under unreliable sampling (NUSA). We find that NUSA will empirically degrade existing NCD methods if taking no care of sampling errors. To handle NUSA, we propose an effective solution, named hidden-prototype-based discovery network (HPDN): (1) we try to obtain relatively clean data representations even with the confusedly sampled data; (2) we propose a mini-batch K-means variant for robust clustering, alleviating the negative impact of residual errors embedded in the representations by detaching the noisy supervision timely. Experiments demonstrate that, under NUSA, HPDN significantly outperforms competitive baselines (e.g., 6 % more than the best baseline on CIFAR-10) and remains robust when encountering serious sampling errors. [ABSTRACT FROM AUTHOR]

This paper proposes a new calibration estimator for population variance within a stratified two-phase sampling design. It takes into account random non-response and measurement errors, specifically applying this method to estimate the variance in Gas turbine exhaust pressure data. The study integrates additional information from two highly positively correlated auxiliary variables to develop a general class of estimators tailored for the stratified two-phase sampling scheme. The properties of these estimators, in terms of their biases and mean square errors, have been thoroughly examined and extensively analyzed through numerical and simulation studies. Furthermore, the calibrated weights of the strata are derived. The proposed estimators outperform the natural estimator of population variance. Finally, suitable recommendations have been made for survey statisticians intending to apply these findings to real-life problems., (© 2024. The Author(s).)

In this article, we present an event‐triggered control law for systems with an unknown control direction, where the adaptation dynamics of the Nussbaum gain are sampled. Specifically, we employ the emulation method and introduce a specific set of sampling errors. This approach enables the design of an event‐triggered law without encountering Zeno behavior. Additionally, we introduce a lemma to address the non‐integrability issue of the Nussbaum gain dynamics during the event‐triggered law design. We apply this design philosophy to co‐design both the continuous‐time controller and the event‐triggered law, aiming to achieve the global stabilization of systems with an unknown control direction. Finally, we validate the theoretical results through a numerical example. [ABSTRACT FROM AUTHOR]

Stereotactic needle biopsy stands as a crucial method for diagnosing intracranial lesions unsuitable for surgical intervention. Nonetheless, the potential for sampling errors lead to innovative approaches to enhance diagnostic precision. This study contrasts the outcomes of patients undergoing fluorescein-assisted frameless stereotactic needle biopsy with those receiving traditional biopsies to evaluate the impact on diagnostic accuracy and safety. This study included patients with contrast-enhancing intracranial lesions, comprising a prospective group undergoing fluorescein-assisted biopsies and a retrospective group undergoing conventional biopsies at the same institution. We've collected data on demographics, procedural specifics, diagnostic outcomes, and postoperative events. A comparative analysis involved 43 patients who received fluorescein-assisted biopsies against 77 patients who underwent conventional biopsies. The average age was 60.5 years. The fluorescein group exhibited a 93% success rate in diagnosis, markedly higher than the 70.1% in the non-fluorescein group (OR = 5.67; 95%IC: 1.59–20.24; p < 0.01). The rate of complications was statistically similar across both cohorts. Despite its established value, stereotactic needle biopsy is susceptible to inaccuracies and complications. The application of fluorescence-based adjuncts like 5-ALA and fluorescein has been investigated to improve diagnostic fidelity and reduce risks. These technologies potentially minimize the necessity for multiple biopsies, decrease surgical duration, and provide immediate verification of tumor presence. Fluorescein-assisted stereotactic biopsy emerges as an effective, secure alternative to conventional methods. [ABSTRACT FROM AUTHOR]

AbstractIn genomic research, identifying the exon regions in eukaryotes is the most cumbersome task. This article introduces a new promising model-independent method based on short-time discrete Fourier transform (ST-DFT) and fine-tuned variational mode decomposition (FTVMD) for identifying exon regions. The proposed method uses the N/3 periodicity property of the eukaryotic genes to detect the exon regions using the ST-DFT. However, background noise is present in the spectrum of ST-DFT since the sliding rectangular window produces spectral leakage. To overcome this, FTVMD is proposed in this work. VMD is more resilient to noise and sampling errors than other decomposition techniques because it utilizes the generalization of the Wiener filter into several adaptive bands. The performance of VMD is affected due to the improper selection of the penalty factor (α), and the number of modes (K). Therefore, in fine-tuned VMD, the parameters of VMD (K and α) are optimized by maximum kurtosis value. The main objective of this article is to enhance the accuracy in the identification of exon regions in a DNA sequence. At last, a comparative study demonstrates that the proposed technique is superior to its counterparts. [ABSTRACT FROM AUTHOR]

The rapid progression of emerging technologies like the Internet of Things (IoT) and Big Data analytics for manufacturing has driven innovation across various industries worldwide. Production data are utilized to construct a model for quality evaluation and analysis applicable to components processed by machine tools, ensuring process quality for critical components and final product quality for the machine tools. Machine tool parts often encompass several quality characteristics concurrently, categorized into three types: smaller-the-better, larger-the-better, and nominal-the-better. In this paper, an evaluation index for the nominal-the-better quality characteristic was segmented into two single-sided Six Sigma quality indexes. Furthermore, the process quality of the entire component product was assessed by n single-sided Six Sigma quality indexes. According to numerous studies, machine tool manufacturers conventionally base their decisions on small sample sizes (n), considering timeliness and costs. However, this often leads to inconsistent evaluation results due to significant sampling errors. Therefore, this paper established fuzzy testing rules using the confidence intervals of the q single-sided Six Sigma quality indices, serving as the fuzzy quality evaluation model for components of machine tools. [ABSTRACT FROM AUTHOR]

Because of the time-sharing sampling logic, the three-phase currents reconstructed by single dc-link shunt present significant errors in the permanent magnet synchronous motor drives. A traceback-prediction current correction method for sampled currents is proposed in this article to eliminate the reconstruction errors. According to the analysis of time-sharing sampling errors, the concept of equivalent current change rate during one pulsewidth modulation (PWM) period is introduced, and the phase current characteristic model based on state observer is established. Both the equivalent and instantaneous current change rates can be obtained by the observer-based calculation method. In order to improve the current reconstruction precision, the ultimate values of three-phase currents can be predicted by adopting the proposed traceback-prediction current correction method, which does not require major computation and is easy to implement with no restrictions on the PWM strategy. The effectiveness of the proposed method is verified by experimental results. [ABSTRACT FROM AUTHOR]