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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]

This systematic review and meta‐analysis assesses independently the acute effects of anodal and cathodal motor cortex transcranial direct current stimulation (tDCS) on athletic performance in healthy adults. Besides, it evaluates the unique and conjoint effects of potential moderators (i.e., stimulation parameters, exercise type, subjects' training status and risk of bias). Online database search was performed from inception until March 18th 2024 (PROSPERO: CRD42023355461). Forty‐three controlled trials were included in the systematic review, 40 in the anodal tDCS meta‐analysis (68 effects), and 9 (11 effects) in the cathodal tDCS meta‐analysis. Performance enhancement between pre‐ and post‐stimulation was the main outcome measure considered. The anodal tDCS effects on physical performance were small to moderate (g =.29, 95%CI [.18,.40], PI = −.64 to 1.23, I2 = 64.0%). Exercise type, training status and use of commercial tDCS were significant moderators of the results. The cathodal tDCS effects were null (g =.04, 95%CI [−.05,.12], PI = −.14 to.23, I2 = 0%), with a small to moderate heterogeneity entirely due to sampling error, thus impairing further moderator analysis. These findings hold significant implications for the field of brain stimulation and physical performance, as they not only demonstrate a small to moderate effect of acute tDCS but also identify specific categories of individuals, devices and activities that are more susceptible to improvements. By addressing the multidimensional factors influencing the mechanisms of tDCS, we also provide suggestions for future research. Results indicate a small favouring effect of anodal tDCS on athletic performance with large heterogeneity. Exercise type, training status and use of commercial tDCS significantly moderate anodal tDCS effects. Cathodal tDCS shows null effects, with small to moderate heterogeneity entirely due to sampling error. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the variability in urinary stone composition analysis due to sampling and suggest potential solutions. Materials and Methods: We collected 1,135 stone fragments from 149 instances that had undergone a stone removal at Hanoi Medical University Hospital from January 2022 to August 2022. Each fragment was ground into fine powder and divided into separate specimens if the amount was abundant. For composition analyzing every specimen, Fourier transform infrared spectroscopy was performed. The composition of a given fragment was the average of its belonging specimens. The variability in composition was assessed on the fragment level (i.e., between fragments of an instance). We defined an instance as "significantly variable" if the maximum difference in any composition across its belonging fragments was equal to or greater than a given threshold. Results: On average, there were 7.6±3.3 stone fragments per instance and 2.3±0.5 specimens per fragment. We found that the variability could be substantial on the fragment level. Eighty-nine (69.5%) and 70 (54.7%) out of 128 multiple-component instances were significantly variable if the threshold was set at 20% and 30%, respectively. The variability of an instance on the fragment level was correlated with the size of fragment and the number of components. Conclusions: Our study demonstrated the significant variability in urinary stone composition and showed that it correlated with the size and the impurity of samples. Mapping denotation while sampling and analyzing as well as reporting the composition of individual fragments could be valuable to reduce potential variability. [ABSTRACT FROM AUTHOR]

This paper is devoted to the event-triggered consensus control for a class of uncertain multiple Euler-Lagrange (EL) systems with actuator faults. Different from the related works where strict conditions are imposed on system uncertainties and the measurements of the leader's output, more serious uncertainties are involved since all the system dynamic matrices are unknown while both actuator faults and external disturbance are considered; and moreover, fewer measurements of the leader's output are required since its time derivatives are not necessarily available for feedback. Mainly because of these, the consensus problem is hard to solve by straightforwardly extending the existing results. To solve the control problem, a dynamic gain with a smart choice of its updating law is introduced to overcome the serious uncertainties and the sampling error of the control signal. By incorporating the dynamic gain into the vectorial backstepping procedure, an adaptive consensus controller joined with an event-triggered mechanism is designed for each follower to ensure the consensus of the multi-agent system in the sense that all the states of the closed-loop system are bounded while the output of each follower tracks the leader's output. Finally, the effectiveness of the proposed method is verified by a simulation example. [ABSTRACT FROM AUTHOR]

Predicting the rate of penetration (ROP) is important for optimizing drilling parameters, improving drilling efficiency, and optimizing economic benefits throughout the drilling process. The current prediction model of ROP based on machine learning algorithms does not consider the interference of outliers. Therefore, in this study, we propose a method to predict ROP based on fuzzy support vector regression (FSVR). First, appropriate input parameters were selected from the controllable parameters. Second, based on the local outlier factor, a fuzzy membership degree was assigned to each sample. Finally, the sample with the fuzzy membership value was input into the model for ROP prediction. The results demonstrated that the goodness of fit (R2) of the improved FSVR model is 0.9634, and the mean absolute error is 0.1974. Compared with standard SVR and other models, the improved FSVR model has a stronger anti-interference ability, smaller prediction error for normal samples, and higher accuracy. [ABSTRACT FROM AUTHOR]

The primary focus of this research paper is to explore the realm of dynamic learning in sampled‐data strict‐feedback nonlinear systems (SFNSs) by leveraging the capabilities of radial basis function (RBF) neural networks (NNs) under the framework of adaptive control. First, the exact discrete‐time model of the continuous‐time system is expressed as an Euler strict‐feedback model with a sampling approximation error. We provide the consistency condition that establishes the relationship between the exact model and the Euler model with meticulous detail. Meanwhile, a novel lemma is derived to show the stability condition of a digital first‐order filter. To address the non‐causality issues of SFNSs with sampling approximation error and the input data dimension explosion of NNs, the auxiliary digital first‐order filter and backstepping technology are combined to propose an adaptive neural dynamic surface control (ANDSC) scheme. Such a scheme avoids the n$$ n $$‐step time delays associated with the existing NN updating laws derived by the common n$$ n $$‐step predictor technology. A rigorous recursion method is employed to provide a comprehensive verification of the stability, guaranteeing its overall performance and dependability. Following that, the NN weight error systems are systematically decomposed into a sequence of linear time‐varying subsystems, allowing for a more detailed analysis and understanding. In order to ensure the recurrent nature of the input variables, a recursive design is employed, thereby satisfying the partial persistent excitation condition specifically designed for the RBF NNs. Meanwhile, it can verify that the NN estimated weights converge to their ideal values. Compared with the common n$$ n $$‐step predictor technology, there is no need to redesign the learning rules due to the designed NN weight updating laws without time delays. Subsequently, after capturing and storing the convergence weights, a novel neural learning dynamic surface control (NLDSC) scheme is specifically formulated by leveraging the acquired knowledge. The introduced methodology reduces computational complexity and facilitates practical implementation. Finally, empirical evidence obtained from simulation experiments validates the efficacy and viability of the proposed methodology. [ABSTRACT FROM AUTHOR]

Accurately determining if the sample parameters from a natural gas pipeline's sampling system reflect the fluid characteristics of the main pipe has been a significant industry concern for many years. In this paper, samples of natural gas in a horizontal pipeline are investigated. CFD is used in this work and the turbulence is considered in the simulation. Firstly, the critical diameter for particles affected by gravity within such pipeline is determined. And then, the effects of the operation pressure and velocity of sampling branches on sample parameters, and the influence of particle density on these sample parameters, are analyzed. Finally, four different structures of sample branches for natural gas in a horizontal pipeline are compared. It is found that 100 μm is the critical diameter at which particles are affected by gravity; the operating pressure of the sampling branch has a significant impact on the particle mass concentration. The particle density has little impact on the sampling system. Overall, the design of the sampling branches does not cause significant sampling errors. This study provides guidance for optimal sampling in existing natural gas pipelines and enables effective monitoring of particle impurity content and properties in natural gas. [ABSTRACT FROM AUTHOR]

Objective: To assess the cost-effectiveness of using cheaper-but-noisier outcome measures, such as a short questionnaire, for large simple clinical trials. Background: To detect associations reliably, trials must avoid bias and random error. To reduce random error, we can increase the size of the trial and increase the accuracy of the outcome measurement process. However, with fixed resources, there is a trade-off between the number of participants a trial can enrol and the amount of information that can be collected on each participant during data collection. Methods: To consider the effect on measurement error of using outcome scales with varying numbers of categories, we define and calculate the variance from categorisation that would be expected from using a category midpoint; define the analytic conditions under which such a measure is cost-effective; use meta-regression to estimate the impact of participant burden, defined as questionnaire length, on response rates; and develop an interactive web-app to allow researchers to explore the cost-effectiveness of using such a measure under plausible assumptions. Results: An outcome scale with only a few categories greatly reduced the variance of non-measurement. For example, a scale with five categories reduced the variance of non-measurement by 96% for a uniform distribution. We show that a simple measure will be more cost-effective than a gold-standard measure if the relative increase in variance due to using it is less than the relative increase in cost from the gold standard, assuming it does not introduce bias in the measurement. We found an inverse power law relationship between participant burden and response rates such that a doubling the burden on participants reduces the response rate by around one third. Finally, we created an interactive web-app (https://benjiwoolf.shinyapps.io/cheapbutnoisymeasures/) to allow exploration of when using a cheap-but-noisy measure will be more cost-effective using realistic parameters. Conclusion: Cheaper-but-noisier questionnaires containing just a few questions can be a cost-effective way of maximising power. However, their use requires a judgement on the trade-off between the potential increase in risk of information bias and the reduction in the potential of selection bias due to the expected higher response rates. Key messages: A cheaper-but-noisier outcome measure, like a short form questionnaire, is a more cost-effective method of maximising power in large simple clinical trials than an error free gold standard measure when the percentage increase in noise from using the cheaper-but-noisier measure is less than the relative difference in the cost of administering the two measures. We have created an R-shiny app to facilitate the exploration of when this condition is met at https://benjiwoolf.shinyapps.io/cheapbutnoisymeasures/ Cheaper-but-noisier outcome measures are more likely to introduce information bias than a gold standard but may reduce selection bias because they reduce loss-to-follow-up. Researchers therefore need to form a judgement about the relative increase or decrease in bias before using a cheap-but-noisy measure. We encourage the development and validation of short form questionnaires to enable the use of high quality cheaper-but-noisier outcome measures in randomised controlled trials. [ABSTRACT FROM AUTHOR]

Objective Methods Results Conclusion The policy implemented in Israel with the purpose of eradicating the COVID‐19 pandemic restricted Israeli citizens’ individual freedom. During this period, Israel was suffering from government instability that led to recurring election campaigns within a short period. In addition, the prime minister was under legal investigation for suspicions of government corruption. This study examines the legitimization of government by the Israeli public during the COVID‐19 pandemic.The research findings are based on a representative attitude survey conducted by a survey company among Israeli citizens (men and women older than 18), including 604 respondents from the Jewish population and others and another 135 respondents from the Arab sector, that is, a total of 739 respondents, where the sampling error was 4.4 percent.The findings indicate that right‐wing voters tended to support governmental legitimization at a higher rate compared to voters from opposing blocs. Correspondingly, they tended to oppose claims attacking governmental legitimization at a lower rate in comparison to coalition party voters.The conclusion indicates that the party identification of the respondents is a mediating variable in their perception of governmental legitimacy vis‐à‐vis each of the three variables examined, thereby strengthening the perceptual screening. [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]

Target localization for distributed radars requires precise knowledge of the calibration errors in time, frequency, and space. Previous studies focus on the sensor location error and sensor directional error in space. This paper concentrates on the system errors in time and frequency which directly affect the targets estimation of range and velocity. Based on linear frequency modulation waveform, the impact of the system errors including sample interval, pulse repetition interval, and centre frequency are analysed. An alternating optimization algorithm is proposed to achieve the target localization, velocity estimation and system errors estimation simultaneously. Simulation shows the effectiveness of the algorithm and the influence on localization due to varying number of targets and radars. [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]

This article provides a precise, quantitative description of the sampling error on burn counts in Monte-Carlo wildfire simulations - that is, the prediction variability introduced by the fact that the set of simulated fires is random and finite. We show that the marginal burn counts are (very nearly) Poisson-distributed in typical settings and infer through Bayesian updating that Gamma distributions are suitable summaries of the remaining uncertainty. In particular, the coefficient of variation of the burn count is equal to the inverse square root of its expected value, and this expected value is proportional to the number of simulated fires multiplied by the asymptotic burn probability. From these results, we derive practical guidelines for choosing the number of simulated fires and estimating the sampling error. Notably, the required number of simulated years is expressed as a power law. Such findings promise to relieve fire modelers of resource-consuming iterative experiments for sizing simulations and assessing their convergence: statistical theory provides better answers, faster. [ABSTRACT FROM AUTHOR]

Medical datasets may be imbalanced and contain errors due to subjective test results and clinical variability. The poor quality of original data affects classification accuracy and reliability. Hence, detecting abnormal samples in the dataset can help clinicians make better decisions. In this study, we propose an unsupervised error detection method using patterns discovered by the Pattern Discovery and Disentanglement (PDD) model, developed in our earlier work. Applied to the large data, the eICU Collaborative Research Database for sepsis risk assessment, the proposed algorithm can effectively discover statistically significant association patterns, generate an interpretable knowledge base for interpretability, cluster samples in an unsupervised learning manner, and detect abnormal samples from the dataset. As shown in the experimental result, our method outperformed K-Means by 38 % on the full dataset and 47 % on the reduced dataset for unsupervised clustering. Multiple supervised classifiers improve accuracy by an average of 4 % after removing abnormal samples by the proposed error detection approach. Therefore, the proposed algorithm provides a robust and practical solution for unsupervised clustering and error detection in healthcare data. [ABSTRACT FROM AUTHOR]

This document is a correction notice for a published publication on the taxonomy of blenniid fishes. It addresses errors in the identification of samples and localities for GenBank sequences. The corrected sequences are provided, and changes to specimen identification and sampling locality are explained. The authors state that these corrections do not affect the scientific conclusions of the study. The document also includes a figure and a table that have been updated to reflect the corrections. The table lists various species of fish, their scientific names, previous classification, and the locations where they were found. The authors of the document are Luca Vecchioni, Andrew C. Ching, Federico Marrone, Marco Arculeo, Peter J. Hundt, and Andrew M. Simons. [Extracted from the article]

Egocentric networks represent a popular research design for network research. However, to what extent and under what conditions egocentric network centrality can serve as reasonable substitutes for their sociocentric counterparts are important questions to study. The answers to these questions are uncertain simply because of the large variety of networks. Hence, this paper aims to provide exploratory answers to these questions by analyzing both empirical and simulated data. Through analyses of various empirical networks (including some classic albeit small ones), this paper shows that egocentric betweenness approximates sociocentric betweenness quite well (the correlation is high across almost all the networks being examined) while egocentric closeness approximates sociocentric closeness only reasonably well (the correlation is a bit lower on average with a larger variance across networks). Simulations also confirm this finding. Analyses further show that egocentric approximations of betweenness and closeness seem to work well in different types of networks (as featured by network size, density, centralization, reciprocity, transitivity, and geodistance). Lastly, the paper briefly presents three ideas to help improve egocentric approximations of centrality measures. [ABSTRACT FROM AUTHOR]

The network scale-up method (NSUM) is a survey-based method for estimating the number of individuals in a hidden or hard-to-reach subgroup of a general population. In NSUM surveys, sampled individuals report how many others they know in the subpopulation of interest (e.g. "How many sex workers do you know?") and how many others they know in subpopulations of the general population (e.g. "How many bus drivers do you know?"). NSUM is widely used to estimate the size of important sociological and epidemiological risk groups, including men who have sex with men, sex workers, HIV+ individuals, and drug users. Unlike several other methods for population size estimation, NSUM requires only a single random sample and the estimator has a conveniently simple form. Despite its popularity, there are no published guidelines for the minimum sample size calculation to achieve a desired statistical precision. Here, we provide a sample size formula that can be employed in any NSUM survey. We show analytically and by simulation that the sample size controls error at the nominal rate and is robust to some forms of network model mis-specification. We apply this methodology to study the minimum sample size and relative error properties of several published NSUM surveys. [ABSTRACT FROM AUTHOR]

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]

Copyright of Journal of Soil & Water Conservation (1009-2242) is the property of Institute of Soil & Water Conservation 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.)

Background: In global health crises, there is a heightened risk to patient and professional safety. Several studies have evaluated the safety climate, revealing different perceptions among healthcare professionals, often influenced by demographic characteristics. This study aimed to assess the percentage of problematic responses (PPR) for the patient safety climate dimensions and... [ABSTRACT FROM AUTHOR]

This article addresses the global regulation by periodic event‐triggered output feedback control for a class of feedforward nonlinear systems. Particularly, the considered systems suffer from parameter uncertainties and unknown measurement sensitivity, which entail integrating advanced compensation strategies within the event‐triggered framework. To this end, a time‐varying gain is devised to tackle uncertain nonlinearities as well as the sampling error. Subsequently, a novel periodic event‐triggered output feedback controller is constructed based on the delicate choice of design parameters. Notably, the proposed control scheme is able to avoid the continuous monitoring of the system behavior, and guarantee that all closed‐loop system states are globally bounded and ultimately converge to zero. Finally, a practical example is given to illustrate the main results. [ABSTRACT FROM AUTHOR]

Organizations must employ big data analytics to maintain sustained innovation in the highly dynamic and evolving business landscape. Even though BDA has a transformative power to revolutionize how businesses do things and engage with their customers' adopting BDA has faced significant challenges, especially in developing countries. This research aims to create a theoretical framework to understand how organizational readiness for BDA can influence sustainable innovation performance. Sampling errors were mitigated through a time-lagged study design, and the data was collected in three phases. The test results using Partial Least Squares Structural Equation Modeling show that organizational readiness is a critical mediator, establishing a robust chain between BDA skills and sustainable innovation performance. The results of this study imply the need for organizational foundation and alignment, which are critical to the compelling strategic deployment of BDA for sustainability innovation performance. Thus, this study can offer a valuable contribution to this topic in the future and a profound implication of the phenomenon at receptive stages. [ABSTRACT FROM AUTHOR]

This study is concerned with the stabilization analysis and controller design for networked systems with stochastic sampling and two‐channel deception attacks. First, we give a general matrix decomposition approach which is applicable to scenarios where the system matrix A$A$ contains complex‐value eigenvalues. Then, a discrete stochastic framework is established for a class of networked systems which considers the joint effects of sampling errors and two‐channel deception attacks. Utilizing the matrix decomposition approach introduced in this study, it becomes feasible to decouple the expectation operations for specific coupling matrices characterized by substantial nonlinearity and randomness. Based on this, a stabilization controller is constructed that ensures the exponential mean‐square stability of the resulting discrete stochastic system. Finally, three simulation examples are provided to validate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The echo signal of ultrasonic internal inspection is highly sensitive to the incident deflection angle between the probe and the measured surface of the pipeline, which is the key factor affecting the accuracy of ultrasonic inspection. In practical engineering, the echo signal of ultrasonic inspection is easy to be interfered by the external environment, which makes it difficult to identify the deflection angle. The data of defect echo signal under different circumferential declination were collected by the pipeline ultrasonic internal inspection system, and the characteristics of signal variation in time domain under different circumferential declination were analysed. The results show that the existence of circumferential declination reduced the absolute value of the overall amplitude of ultrasonic echo signal. A signal processing method based on amplitude reduction is proposed to reduce the sampling random error. Comparative analysis shows that the circumferential declination can be identified based on amplitude reduction method, and the accuracy of ultrasonic recognition can be improved. By extracting the characteristic value of waveform, the empirical formula is obtained. Within 2°, the maximum deviation is 0.137°. The research results have certain guiding significance for the identification of circumferential declination in practical engineering. [ABSTRACT FROM AUTHOR]

Questions: We asked how interobserver error in sampling vegetation (excluding trees) varied over time, space and habitat type; determined whether there were any obvious correlates of observer error; and evaluated evidence of bias among observers. Location: Nine national park units in the Midwestern USA. Methods: We quantified observer error in the context of a long‐term monitoring program employing three observers, evaluating interobserver error across 11 locations in the Midwestern USA over five years. The vegetation (excluding trees) was sampled independently by two teams of observers at prairie and woodland locations (n = 94 plots total). Results: Total pseudoturnover ranged between 20.2% and 22.1% at prairie locations, and between 16.8% and 28.6% at woodland locations. The overlooking component of pseudoturnover accounted for 75% or more of total pseudoturnover, with misidentification and cautious components each contributing 19% or less of the total, depending on location. The percentage of comparisons in which both observers recorded the same cover class ranged from 71.3% to 78.5% at the prairie locations and 56.9% to 85.6% at woodland locations. When observers did not agree on cover class, they were off by more than one class less than 6% of the time. Overlooking error was more likely to occur for species with less cover, while estimation error was more likely to occur for species with greater cover. A bias existed among observers, as the least experienced observer recorded 6.2%–11.8% more species than the other two observers. Interobserver bias also existed for rates of estimation error, as one observer consistently recorded higher cover classes. Conclusions: Observer error is a pervasive aspect of vegetation sampling. Continued training and experience yielded limited increases in precision. Elements of the sampling design probably reduced observer error to a certain degree, although some level of interobserver error in vegetation surveys is unavoidable. [ABSTRACT FROM AUTHOR]

Significant errors often arise when measuring streamflow during high flows and flood events. Such errors conflated by short records of observations may induce bias in the flood frequency estimates, leading to costly engineering design mistakes. This work illustrates how observational (measurement) errors affect the uncertainty of flood frequency estimation. The study used the Bulletin 17 C (US standard) method to estimate flood frequencies of historical peak flows modified to represent the measurement limitations. To perform the modifications, we explored, via Monte Carlo simulation, four hypothetical scenarios that mimic measurement errors, sample size limitations, and their combination. We used a multiplicative noise from a log-normal distribution to simulate the measurement errors and implemented a bootstrap approach to represent the sampling error. Then, we randomly selected M samples from the total N records of the observed peak flows of four gauging stations in Iowa in central USA. The observed data record ranges between 76 and 119 years for watersheds with drainage areas between 500 and 16,000 km2. According to the results, measurement errors lead to more significant differences than sampling limitations. The scenarios exhibited differences with median magnitudes of up to 50%, with some cases reaching differences up to 100% for return periods above 50 years. The results raise a red flag regarding flood frequency estimation that warrants looking for further research on observational errors. [ABSTRACT FROM AUTHOR]

Many hypotheses in ecology and evolution rely on indices measuring abstract phenomena, usually multifaceted or noisy (e.g., diversity indices may capture richness or evenness, and the data used can have large sampling error). Hence, different indices often provide uncorrelated values. Indices, experiments, and statistical methods all help to control for confounding factors, such as missing variables; still, this variety of approaches limits cross-study comparisons and may increase the odds of a researcher accepting hypotheses that fit their intuition. We propose guidelines to link hypotheses, study design, and index selection to help mitigate these challenges. Repositories of hypotheses and indices, building on existing collaborative tools and databases, could help researchers navigate and clarify complex and often obscure methodological decisions in hypothesis testing. Hypothesis testing requires meaningful ways to quantify biological phenomena and account for alternative mechanisms that could explain the same pattern. Researchers combine experiments, statistics, and indices to account for these confounding mechanisms. Key concepts in ecology and evolution, such as niche breadth (NB) or fitness, can be represented by several indices, which often provide uncorrelated estimates. Is this because the indices use different types of noisy data or because the targeted phenomenon is complex and multidimensional? We discuss implications of these scenarios and propose five steps to aid researchers in identifying and combining indices, experiments, and statistics. Building on prior efforts to construct databases of hypotheses and indices and document assumptions, these steps help provide a formal strategy to reduce self-confirmatory bias. [ABSTRACT FROM AUTHOR]

Specification of the observation‐error covariance matrix for data assimilation systems affects the observation information content retained by the analysis, particularly for observations known to have correlated observation errors (e.g., geostationary satellite and Doppler radar data). A widely adopted approach for estimating observation‐error covariance matrices uses observation‐minus‐background and observation‐minus‐analysis residuals, which are routinely produced by most data assimilation systems. Although this approach is known to produce biased and noisy estimates, due to sampling and misspecification errors, there has been no systematic study of sampling errors with this approach to date. Furthermore, the eigenspectrum of the estimated observation‐error covariance matrix is known to influence the analysis information content and numerical convergence of variational assimilation schemes. In this work, we provide new theorems for the sampling error and eigenvalues of the estimated observation‐error covariance matrices with this approach. We also conduct numerical experiments to illustrate our theoretical results. We find that this method produces large sampling errors if the true observation‐error standard deviation is large, while the other error characteristics, including the true background‐error standard deviation and observation‐ and background‐error correlation length‐scales, have a relatively small effect. We also find that the smallest eigenvalues of the estimated matrices may be smaller or larger than the true eigenvalues, depending on the assumed and true observation‐ and background‐error statistics. These results may provide insights for practical applications: for example, in deciding on appropriate sample sizes and choosing parameters for matrix reconditioning techniques. [ABSTRACT FROM AUTHOR]

As global warming becomes increasingly serious, humans start to consider how to coexist with the natural environment. People become more and more aware of environmental protection and sustainable development. Therefore, in the pursuit of economic growth, it has become a consensus that enterprises should be responsible for the social and ecological environment. Regarding the manufacturing of electronic devices, as long as both component production quality and assembly quality are ensured, consumers can be provided with high-quality, safe, and efficient products. In light of this trend, enhancing product availability and reliability can reduce costs and carbon emissions resulting from repairing or replacing components, thus becoming a vital factor for corporate and environmental sustainability. Accordingly, enterprises enhance their economic benefits as well as have the effects of energy conservation and waste reduction by extending products' service lifetime and increasing their added value. According to several studies, it takes a long time to retrieve electronic products' lifetime data. Moreover, acquiring complete samples is often challenging. Consequently, when analyzing real cases, samples are usually collected using censoring techniques. The type-I right censoring data is suitable for industrial processes. Thus, this study utilized type-I right censoring sample data to estimate the lifetime performance index. It usually takes a large amount of time to access lifetime data for electronic products and it is often impossible to obtain complete samples since the size of the sample is usually small. Hence, to avoid misjudgment caused by sampling errors, this study followed suggestions from existing research and applied fuzzy tests built on confidence intervals to establish a fuzzy evaluation model for the lifetime performance index. This model helps relevant electronic industries not only evaluate the lifetime of their electronic components but also instantly seize opportunities for improvement. [ABSTRACT FROM AUTHOR]

Ranked set sampling is widely acknowledged for its superior efficiency compared with simple random sampling. Only a small amount of work has been conducted using ranked set sampling when measurement errors are present. This study introduces innovative estimators utilizing ranked set sampling to assess the population mean when faced with both correlated and uncorrelated measurement errors. The expressions for the bias and mean squared error of the proposed estimators are derived up to the first-order approximation, revealing their superior performance compared to the other examined estimators. The efficacy of the suggested estimators in handling measurement errors was demonstrated through numerical illustration and simulation study investigations. The recommended estimators are further compared to the existing ones using the percentage relative efficiency and mean squared error, and the impact of measurement errors on the results is highlighted through the percentage computation of measurement errors. The innovative estimators suggested were formulated by judiciously incorporating ratio, exponential, and log estimators. Numerical examples involving expenditure and income, as well as simulated data generated from a normal population using R software, affirm the superior performance of the proposed estimators over existing ones such as the usual mean estimator and those proposed by Vishwakarma and Singh (2022), as evidenced by the higher percent relative efficiency and lower mean squared error. The evaluation of the percentage contribution of measurement error values confirms the impact of measurement errors on the properties of the estimators. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. 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.)

With the intensification of ocean exploration and development in recent years, the navigation equipment in the marine environment has become increasingly diversified, making the marine environment more complex. Traditional methods for underwater target recognition are gradually becoming less applicable and unable to achieve better results. With the application of deep learning in underwater target recognition, the robustness of deep learning models used for underwater target recognition is crucial due to the significant environmental interference in underwater target data and the susceptibility of deep learning models to adversarial samples. This paper proposes an error sample feature compensation method for improving the robustness of deep learning models for underwater target recognition, focusing on the problem of the significant impact of sample data quality on the robustness of deep learning models for underwater target recognition. The method innovatively divides error samples into difficult-to-improve samples and easy-to-improve samples and proposes an adversarial training method combined with classification conditions. At the same time, the method uses a weighted index of model accuracy to combine adversarial training models with feature compensation methods, further improving the robustness of deep learning models for underwater target recognition tasks. Finally, the method is validated on an underwater dataset, and the results show that the proposed method improves the robustness of deep learning models used for underwater target recognition. [ABSTRACT FROM AUTHOR]

Deep transfer learning is an effective method for unsupervised fault diagnosis of rolling bearings. In some works, the pseudo-label of target domain prediction is used to improve the ability of target domain prediction in transfer learning. However, its validity depends on the quality of pseudo-label generated by the network itself, which is easy to cause the misclassification of the samples. Aiming to this, a dual sample screening (DSS) method based on the information of predicted label changes is proposed in the article, and it is applied to the fault diagnosis of rolling bearings with variable working conditions. DSS combines pre-screening and real-time screening and uses the continuous output of prediction label change information in the training process to improve the network training. It owes to eliminating part of the target domain samples with prediction errors in the stage of network training with pseudo-label. The proposed method improves the stability of the pseudo-label involved in the training and alleviates the negative effects caused by the pseudo-label. The experimental results on Paderborn University dataset show that, compare with the deep transfer learning fault diagnosis method based on pseudo-label cross-entropy, the average diagnostic accuracy of the six transfer tasks using DSS is increased by 5.97%, which effectively improves the fault diagnosis accuracy of rolling bearings. [ABSTRACT FROM AUTHOR]

This paper presents the creation and realization of a precision time base (PTB) for mitigating jitter and measuring time interval error (TIE) in digital sampling oscilloscopes (DSO), achieved through the utilization of a field programmable gate array (FPGA). Our proposed method focuses on mitigating jitter through PTB, which involves the sampling of two reference channels having a phase shift of approximately 90∘ (at quadrature) for an accurate timebase correction. Through extensive experimentation and analysis, we were able to achieve a reduction in root mean square (RMS) jitter, minimizing it to around 260 fs. This outcome demonstrates the effectiveness of our approach in enhancing the accuracy and reliability of DSO measurements. Expanding on the application of our previously proposed technique, we demonstrate that the same sampling error correction approach can be utilized for TIE measurement. By sampling a large amount of data at either the rising or falling edge of a sinusoidal signal, TIE can be accurately evaluated. This extension enhances the versatility of our FPGA-based implementation by enabling comprehensive TIE analysis alongside PTB compensation in DSOs. [ABSTRACT FROM AUTHOR]

Employing a large dataset (at most, the order of n = 106), this study attempts enhance the literature on the comparison between regression and machine learning-based rent price prediction models by adding new empirical evidence and considering the spatial dependence of the observations. The regression-based approach incorporates the nearest neighbor Gaussian processes (NNGP) model, enabling the application of kriging to large datasets. In contrast, the machine learning-based approach utilizes typical models: extreme gradient boosting (XGBoost), random forest (RF), and deep neural network (DNN). The out-of-sample prediction accuracy of these models was compared using Japanese apartment rent data, with a varying order of sample sizes (i.e., n = 104, 105, 106). The results showed that, as the sample size increased, XGBoost and RF outperformed NNGP with higher out-of-sample prediction accuracy. XGBoost achieved the highest prediction accuracy for all sample sizes and error measures in both logarithmic and real scales and for all price bands if the distribution of rents is similar in training and test data. A comparison of several methods to account for the spatial dependence in RF showed that simply adding spatial coordinates to the explanatory variables may be sufficient. [ABSTRACT FROM AUTHOR]

The impact of an episodic river flood is intimately linked to its duration. Yet it is still unclear how often should a river be observed to accurately determine the occurrence and duration of extreme events. Here we assess flow statistics along with peak flow event detection and duration as a function of the discharge sampling period for large tributaries of the Mississippi basin using hourly gages over 2010–2022. Median event durations above high quantiles spatially vary from around 2 days upstream to 30 days downstream. Discharge mean, standard deviation, and quantiles can all be estimated within 2.5% error for sampling periods up to 8 days. A minimum temporal sampling 4× (2×) finer than peak flow event median duration is required to detect 95 ± 3% (85 ± 5%) of events and to estimate their duration within 90 ± 5% (75 ± 10%) median accuracy. Our findings have direct implications for future satellite missions concerned with capturing flood events. Plain Language Summary: High flow events and their duration in rivers are key aspects of flood studies. Yet, it is still unclear how often a river should be observed to detect floods and determine their durations. Analyzing hourly stream gage data in the Mississippi River basin from 2010 to 2022, we calculate that flood events vary widely in duration from upstream (2 days) to downstream (30 days). We show that observing large rivers every few days is sufficient to compute common long‐term river flow statistics within 2.5% error. However, capturing most floods and accurately assessing their duration (within 10% error) requires monitoring at least four times more frequently than the typical duration of these events. This means that if a major flood typically lasts 10 days, the river data should be collected at least every 2–3 days. Our findings have implications for the design of monitoring systems concerned with the accurate capture of floods, including those using Earth orbiting satellites. Key Points: Peak flow events in the Mississippi basin range from 2 days upstream to 30 days downstream, hence exhibiting acute spatial variabilityDecadal mean, standard deviation and quantiles of hourly discharge can all be computed within 2.5% error up to an 8‐day sampling periodSampling periods must be a factor of 4 finer than median peak flow event durations to estimate durations within 90 ± 5% median accuracy [ABSTRACT FROM AUTHOR]

A too‐weak eddy feedback in models has been proposed to explain the signal‐to‐noise paradox in seasonal‐to‐decadal forecasts of the winter Northern Hemisphere. We show that the "eddy feedback parameter" (EFP) used in previous studies is sensitive to sampling and multidecadal variability. When these uncertainties are accounted for, the EFP diagnosed from CMIP6 historical simulations generally falls within the reanalysis uncertainty. We find the EFP is not independent of the sampled North Atlantic Oscillation (NAO). Within the same dataset, a sample containing larger NAO variability will show a larger EFP, suggesting that the link between eddy feedbacks and the signal‐to‐noise paradox could be due to sampling effects with the EFP. An alternative measure of eddy feedback, the barotropic energy generation rate, is less sensitive to sampling errors and delineates CMIP6 models that have weak, strong, or unbiased eddy feedbacks, but shows little relation to NAO variability. Plain Language Summary: Model forecasts on seasonal‐to‐decadal timescales have recently been shown to have significant skill in predicting the North Atlantic Oscillation (NAO, a large‐scale pattern of variability). However, these forecasts are undermined by signal‐to‐noise ratios that are lower than expected given the skill, meaning the models are underconfident. This problem is known as the "signal‐to‐noise paradox". Previous work has shown that models underestimate the strength of feedback from atmospheric eddies onto the midlatitude circulation, but models with a stronger eddy feedback suffer less from the signal‐to‐noise paradox. However, we find that the "eddy feedback parameter" (EFP) used in these studies exhibits large sampling uncertainty that has not previously been taken into account. When accounting for this sampling uncertainty, the EFP in models is generally consistent with reanalysis data. Furthermore, across samples, the EFP correlates with the variability of the NAO, meaning they are not independent, which makes the EFP problematic for understanding the causes of the signal‐to‐noise paradox. Samples with larger NAO variability are diagnosed with a larger EFP, even within the same dataset. An alternative measure of eddy feedback is less sensitive to sampling and better identifies models which have weak, strong, or unbiased eddy feedbacks. Key Points: The "eddy feedback parameter" is a highly non‐stationary quantity, making reanalysis and model comparisons problematic on short time periodsSampling uncertainty in the eddy feedback parameter from reanalysis data is comparable to the intermodel spreadBarotropic energy generation rate is a more stable quantity, but does not explain model spread in North Atlantic climate variability [ABSTRACT FROM AUTHOR]

The one-sample log-rank test is the preferred method for analysing the outcome of single-arm survival trials. It compares the survival distribution of patients with a prefixed reference survival curve that usually represents the expected outcome under standard of care. However, classical one-sample log-rank tests assume that the reference curve is known, ignoring that it is frequently estimated from historical data and therefore susceptible to sampling error. Neglecting the variability of the reference curve can lead to an inflated type I error rate, as shown in a previous paper. Here, we propose a new survival test that allows to account for the sampling error of the reference curve without knowledge of the full underlying historical survival time data. Our new test allows to perform a valid historical comparison of patient survival times when only a historical survival curve rather than the full historic data is available. It thus applies in settings where the two-sample log-rank test is not applicable as method of choice due to non-availability of historic individual patient survival time data. We develop sample size calculation formulas, give an example application and study the performance of the new test in a simulation study. [ABSTRACT FROM AUTHOR]

Google Trends is a popular data source that has been utilized in hundreds of studies across various fields, including information technology, business, economics, healthcare, and political science. While several previous research has addressed sampling error issues, this article focuses on the measurement errors resulting from changes in Google Trends' data collection method. By examining key phrases such as 'covid-19,' 'decoupling China,' and 'debt trap diplomacy,' this article reveals that, after 2022 when the data collection method was updated, Google Trends generates search results for terms that were not yet coined or yields too few results. These 'strange' search results indicate the presence of measurement errors in Google Trends data. Scholars are advised to validate the contents of Google Trends data through additional steps. In particular, authors in fields like business, economics, and healthcare are encouraged to replicate their research conducted before 2022 to assess the forecasting errors arising from the new data sets retrieved after that period. [ABSTRACT FROM AUTHOR]

Ecological meta‐analyses usually exhibit high relative heterogeneity of effect size: most among‐study variation in effect size represents true variation in mean effect size, rather than sampling error. This heterogeneity arises from both methodological and ecological sources. Methodological heterogeneity is a nuisance that complicates the interpretation of data syntheses. One way to reduce methodological heterogeneity is via coordinated distributed experiments, in which investigators conduct the same experiment at different sites, using the same methods. We tested whether coordinated distributed experiments in ecology exhibit 1) low heterogeneity in effect size, and 2) lower heterogeneity than meta‐analyses, using data on 17 effects from eight coordinated distributed experiments, and 406 meta‐analyses. Consistent with our expectations, among‐site heterogeneity typically comprised <50% of the variance in effect size in distributed experiments. In contrast, heterogeneity within and among studies typically comprised >90% of the variance in effect size in meta‐analyses. However, this difference largely reflected the small size of most coordinated distributed experiments, and was no longer significant after controlling for size (number of studies or sites). These results are consistent with the hypothesis that methodological heterogeneity rarely comprises a substantial fraction of variance in effect size in ecology. We also conducted pairwise comparisons of absolute heterogeneity between coordinated distributed experiments and meta‐analyses on the same topics. Coordinated distributed experiments did not consistently exhibit lower absolute heterogeneity in effect size than meta‐analyses on the same topics. Our findings suggest that coordinated distributed experiments rarely increase uniformity of results by reducing methodological heterogeneity. Our results help refine the numerous distinct reasons for conducting coordinated distributed experiments. [ABSTRACT FROM AUTHOR]

The sex ratio is nearly equal in most organisms, while several ecological and evolutionary factors skew the sex ratio. We evaluated the sex ratios of 12 species in the freshwater snail genus Semisulcospira in and around Lake Biwa. We found highly female-biased sex ratios in all 12 species. The sex ratios were nearly equal or slightly female-biased in the smaller shell-size classes, while they were highly female-biased in the larger size classes. We also found that females were significantly larger in most of these species. Further, the species with a larger size difference between the sexes tended to exhibit a more female-biased sex ratio. Considering the general correlation between shell size and age, this pattern can be explained by longer lifespans in females, shaping the observed female-biased sex ratios. We also found that some Semisulcospira species showed female-biased sex ratios in all shell size classes. This pattern suggests that the distinct longevity hypothesis can not solely explain the observed biased sex ratios. We discussed the possible contributions of the sampling errors and selfish genetic elements to the observed biased sex ratios. This study provides essential information to disentangle the mechanisms of the biased sex ratio in the Semisulcospira snails. [ABSTRACT FROM AUTHOR]

Bootstrap is a widely applicable computational statistical method. The focus of this paper lies in developing a bootstrap method for linear mixed models under homoscedasticity violation (heteroscedasticity) in variance of errors. We assume that the form of heteroscedasticity is unknown. To generate bootstrap response data as close as possible to the actual response data, we transform the marginal residuals to ensure that the variance of the modified marginal residuals in bootstrap samples is an unbiased estimator for the variance of the response variable in the linear mixed models. The consistency is proved, implying that the parameter estimators by the proposed method are asymptotically unbiased. The simulations are conducted with varying error terms and sample sizes to show improvement of the proposed method in parameter estimation. The simulation results are compared to demonstrate that the proposed method outperforms the other considered approaches including the wild method in the heteroscedastic linear mixed models under small samples and performs competitively with the parametric method and both methods outperform the other considered methods in homoscedastic linear mixed models. The proposed bootstrap method is applied to squid data for illustration of its effectiveness. [ABSTRACT FROM AUTHOR]

In this comprehensive study, we have delved into the intricate realm of lightning forecasting, a captivating yet challenging pursuit due to the elusive nature of storm electrification. Our focus rested on evaluating the skill of lightning forecasts of NCMRWF Regional Ensemble Prediction System (NEPS-R), based on the Met Office Global and Regional Ensemble Prediction System (MOGREPS). Based on the lightning hotspots in the observed seasonal mean lightning flash count, study regions were identified over East-Northeast India (ENEI) and Peninsular India (PI) during pre-monsoon season and Central and East-Northeast India (CENEI) for monsoon season during the year 2022. Probabilistic and deterministic skill of NEPS-R in lightning prediction using rank histogram, Relative Operating Characteristic (ROC), Brier Skill Score (BSS), Continuous Ranked Probability Skill Score (CRPSS), reliability diagrams, Probability of Detection (POD), False Alarm ratio (FAR), Equitable Threat Score (ETS), and Fractions Skill Score (FSS) were investigated in the present study. Rank histograms brought out the negative bias, which increases with increasing lead time over all the study regions. While ROC and BSS indicate that forecast is skillful on day-1 over ENEI for all the thresholds (> 1, > 5 and > 10) and over PI on day-2 and day-3, CRPSS has a contrasting trend with most skillful forecast over CENEI region. Reliability diagrams indicate under-forecasting for lower probabilities and over-forecasting for higher probabilities for all the thresholds for all the study areas. The overconfidence in the forecast could be at least partially attributed to the sampling error caused by a small ensemble size of NEPS-R, which also displayed similar trend of skill as BSS. Higher POD values in the CENEI region have surpassed those in ENEI and PI. Lower FAR and higher ETS are found over PI region compared to ENEI and CENEI. Furthermore, FSS (threshold > 1) indicates that CENEI region attains better skill (> 0.5) at a neighborhood size of 36 km in day-1 forecast compared to ENEI (68 km) and PI (84 km). The skill scores do emphasize the model's ability in predicting the dominant large scale features of monsoon. This is reflected in the better skill of CENEI region during monsoon as compared to ENEI and PI regions during the pre-monsoon season, where the model is falling short in predicting the prominent local scale features. Also, the model's inadequacy in proper representation of microphysics and kinematics does affect the lightning prediction capability of the model which could be addressed with improved model initial conditions and parameterization schemes and finer horizontal resolution. [ABSTRACT FROM AUTHOR]

Ensemble data assimilation methods, such as the Ensemble Kalman Filter (EnKF), are well suited for climate reanalysis because they feature flow‐dependent covariance. However, because Earth System Models are heavy computationally, the method uses a few tens of members. Sampling error in the covariance matrix can introduce biases in the deep ocean, which may cause a drift in the reanalysis and in the predictions. Here, we assess the potential of the hybrid covariance approach (EnKF‐OI) to counteract sampling error. The EnKF‐OI combines the flow‐dependent covariance computed from a dynamical ensemble with another covariance matrix that is static but less prone to sampling error. We test the method within the Norwegian Climate Prediction Model, which combines the Norwegian Earth System Model and the EnKF. We test the performance of the reanalyzes in an idealized twin experiment, where we assimilate synthetic sea surface temperature observations monthly over 1980–2010. The dynamical and static ensembles consist respectively of 30 members and 315 seasonal members sampled from a pre‐industrial run. We compare the performance of the EnKF to an EnKF‐OI with a global hybrid coefficient, referred to as standard hybrid, and an EnKF‐OI with adaptive hybrid coefficients estimated in space and time. Both hybrid covariance methods cure the bias introduced by the EnKF at intermediate and deep water. The adaptive EnKF‐OI performs best overall by addressing sampling noise and rank deficiencies issues and can sustain low analysis errors by doing smaller updates than the standard hybrid version. Plain Language Summary: Data assimilation is a statistical method that reduces uncertainty in a model, based on observations. Because of their ease of implementation, the ensemble data assimilation methods, that rely on the statistics of a finite ensemble of realizations of the model, are popular for climate reanalysis and prediction. However, observations are sparse—mostly near the surface—and the sampling error from data assimilation method introduces a deterioration in the deep ocean. We use a method that complements this ensemble with a pre‐existing database of model states to reduce sampling error. We show that the approach substantially reduces error at the intermediate and deep ocean. The method typically requires the tunning of a parameter, but we show that it can be estimated online, achieving the best performance. Key Points: Hybrid covariance handles sampling error and improves the update of deep water masses when assimilating surface observation with an Ensemble Kalman FilterThe method is well suited to provide a long coupled reanalysis of the past centuryHybrid covariance with adaptive hybrid coefficients explicitly estimated in space and time achieved the best performance [ABSTRACT FROM AUTHOR]

Misidentification errors in tumor marker tests can lead to serious diagnostic and treatment errors. This study aims to develop a method for detecting these errors using a machine learning (ML)-based delta check approach, overcoming limitations of conventional methods. We analyzed five tumor marker test results: alpha-fetoprotein (AFP), cancer antigen 19-9 (CA19-9), cancer antigen 125 (CA125), carcinoembryonic antigen (CEA), and prostate-specific antigen (PSA). A total of 246,261 records were used in the analysis. Of these, 179,929 records were used for model training and 66,332 records for performance evaluation. We developed a misidentification error detection model based on the random forest (RF) and deep neural network (DNN) methods. We performed an in silico simulation with 1 % random sample shuffling. The performance of the developed models was evaluated and compared to conventional delta check methods such as delta percent change (DPC), absolute DPC (absDPC), and reference change values (RCV). The DNN model outperformed the RF, DPC, absDPC, and RCV methods in detecting sample misidentification errors. It achieved balanced accuracies of 0.828, 0.842, 0.792, 0.818, and 0.833 for AFP, CA19-9, CA125, CEA, and PSA, respectively. Although the RF method performed better than DPC and absDPC, it showed similar or lower performance compared to RCV. Our research results demonstrate that an ML-based delta check method can more effectively detect sample misidentification errors compared to conventional delta check methods. In particular, the DNN model demonstrated superior and stable detection performance compared to the RF, DPC, absDPC, and RCV methods. [ABSTRACT FROM AUTHOR]

When CO2 saline aquifer storage is carried out, the heterogeneity of reservoir rock is an important factor affecting CO2 transport, and the reservoir heterogeneity in numerical simulations is mainly manifested as the heterogeneity of the parameter field. Since the parameter distributions across the reservoir are not available with the existing probes, the stochastic finite element method is combined with a two‐phase flow model to establish an unconditional random field of permeability, and computations are performed using the Monte Carlo method. The permeability, CO2 maximum migration distance (Md) and CO2 sweep area (Sa) were analyzed for mutual correlation. The permeability correlation area affecting Md and Sa was obtained, and the changes in the correlation area under the coefficient of variation (Cv) and correlation length (λx) of the permeability field in the different reservoirs were analyzed. The kriging superposition approach (KSA) was subsequently used to estimate both the Md and Sa of the target reservoir by establishing conditional random fields based on the sampling parameters in regions with different correlations, resulting in errors of 0.66% for Md and 0.96% for Sa in the high correlation region and 4.86% and 3.12% for Md and Sa in the low correlation region, which suggested that the sampling results from the high correlation region were less biased in the estimation. Under limited sampling conditions, it is recommended that samples be collected in regions with high correlations to reduce the uncertainty of CO2 transport analysis due to unknown heterogeneity. © 2024 Society of Chemical Industry and John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Copyright of China Rubber Industry is the property of Editorial Office of China Rubber Industry 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.)

In statistics, samples are drawn from a population in a data‐generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence‐generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer‐review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants. [ABSTRACT FROM AUTHOR]

Many constructs in management studies, such as perceptions, personalities, attitudes, and behavioral intentions, are not directly observable. Typically, empirical studies measure such constructs using established scales with multiple indicators. When the scales are used in a different population, the items are translated into other languages or revised to adapt to other populations, it is essential for researchers to report the quality of measurement scales before using them to test hypotheses. Researchers commonly report the quality of these measurement scales based on Cronbach's alpha and confirmatory factor analysis results. However, these results are usually inadequate and sometimes inappropriate. Moreover, researchers rarely consider sampling errors for these psychometric quality measures. In this best practice paper, we first critically review the most frequently-used approaches in empirical studies to evaluate the quality of measurement scales when using structural equation modeling. Next, we recommend best practices in assessing reliability, convergent and discriminant validity based on multiple criteria and taking sampling errors into consideration. Then, we illustrate with numerical examples the application of a specifically-developed R package, measureQ, that provides a one-stop solution for implementing the recommended best practices and a template for reporting the results. measureQ is easy to implement, even for those new to R. Our overall aim is to provide a best-practice reference for future authors, reviewers, and editors in reporting and reviewing the quality of measurement scales in empirical management studies. [ABSTRACT FROM AUTHOR]