Search

Point estimates in survey sampling only provide a single value for the parameter being studied and are consequently vulnerable to changes caused by sampling error. In order to cope with ambiguity, indeterminacy, and uncertainty in data, Florentin Smarandache's neutrosophic technique, which generates interval estimates with high probability, offers a helpful solution. To estimate the neutrosophic population mean of the studied variable, this research provides new neutrosophic factor type exponential estimators using well-known neutrosophic auxiliary parameters. For the first-degree of approximation, the study derives the bias and Mean Squared Error (MSE) of the proposed estimators. Characterising constants have neutrosophic optimal values, and for these optimum values, the least value of the neutrosophic MSE is obtained. Notably, the proposed neutrosophic estimators outperform the corresponding adapted classical estimators since their estimated interval falls under the minimal MSE and lies within the estimated interval of the proposed neutrosophic estimators. The theoretical results are supported by empirical data from real data sets acquired by the "Ministry of Earth Sciences" and the "India Meteorological Department (IMD), Pune, India," as well as simulated data sets produced via Neutrosophic Normal Distribution. The estimator with the lowest MSE is suggested for practical applications across many domains, providing greater accuracy and reliability in parameter estimation when utilising the neutrosophic methodology. [ABSTRACT FROM AUTHOR]

A multi-disciplinary approach to estimate discharge is proposed that is ideal for field conditions and also addresses the shortcomings of rating curve method. First, the discharge is defined as a function of a section's hydraulic characteristics, and it is assumed that an approximate convective-diffusive wave governs the flood wave that passes through the section under an unsteady flow situation. Only the stage data are needed by the generated model to predict discharge. Second, an IoT (Internet of Things)-based gauging station was established to collect stage data. The near-real-time stage data is captured using an algorithm designed to minimize the sampling error. Therefore, the multidisciplinary solution has addressed important issues for researchers and field engineers. Additionally, it may be used to estimate discharge in near-real-time at the chosen temporal frequency. The suitability and accuracy of the model were tested at a USGS (United States Geological Survey) gauging station considering three flooding events. The applicability evaluated using six evaluation criteria indicated an accuracy of 98.075% on average. Finally, it was employed over an un-gauged river site at Om-Chhu to estimate discharge in near-near-real-time at a temporal frequency of 5-min intervals. [ABSTRACT FROM AUTHOR]

In this article, we focus on solving a sequence of linear systems that have identical (or similar) coefficient matrices. For this type of problem, we investigate subspace correction (SC) and deflation methods, which use an auxiliary matrix (subspace) to accelerate the convergence of the iterative method. In practical simulations, these acceleration methods typically work well when the range of the auxiliary matrix contains eigenspaces corresponding to small eigenvalues of the coefficient matrix. We develop a new algebraic auxiliary matrix construction method based on error vector sampling in which eigenvectors with small eigenvalues are efficiently identified in the solution process. We use the generated auxiliary matrix for convergence acceleration in the following solution step. Numerical tests confirm that both SC and deflation methods with the auxiliary matrix can accelerate the solution process of the iterative solver. Furthermore, we examine the applicability of our technique to the estimation of the condition number of the coefficient matrix. We also present the algorithm of the preconditioned conjugate gradient method with condition number estimation. [ABSTRACT FROM AUTHOR]

Proper sample selection is based on the study purpose, research question(s), and study design. Investigators must use care to select a sample population that is representative of the source and target populations. Welldefined inclusion and exclusion criteria serve as guidance when screening potential candidates for eligibility for participation in a study. Sampling and non-sampling errors may influence study outcomes and generalizability of results. The purpose of this short report is to review common sampling errors made when designing a study and when reporting study outcomes. [ABSTRACT FROM AUTHOR]

Log-normal, Weibull, and log-logistic distributions are widely used in modeling nonnegative skewed data. We develop sequential methodologies to discriminate between any two of these three distributions as well as to discriminate among these three distributions. These methods are extended to discriminate Mð 2Þ distributions from location-scale, log-location-scale and regular families of distributions. Discriminating three or more distributions having similar shapes often requires large sample size. Sequential procedures allow early stopping which in turn reduce the sample size needed for discrimination. Proposed methods yield high probabilities of correct selection that are shown to converge to 1 asymptotically. Asymptotic behavior of expected sample size and error probabilities are studied as stopping boundaries tend to infinity. Extensive simulation study validates finite sample performances of the proposed procedures requiring significantly fewer samples on average. These methods are applied to three benchmark datasets on cancer trials and are shown to select the correct model with high probability [ABSTRACT FROM AUTHOR]

This article proposes a variable-sample method for the computation of stochastic stable Nash equilibrium, in which the objective functions are approximated, in each iteration, by the sample average approximation with different sample sizes. We start by investigating the contraction mapping properties under the variable-sample framework. Under some moderate conditions, it is shown that the accumulation points attained from the algorithm satisfy the first-order equilibrium conditions with probability one. Moreover, we use the asymptotic unbiasedness condition to prove the convergence of the accumulation points of the algorithm into the set of fixed points and prove the finite termination property of the algorithm. We also verify that the algorithm converges to the equilibrium even if the optimization problems in each iteration are solved inexactly. In the numerical tests, we comparatively analyze the accuracy error and the precision error of the estimators with different sample size schedules with respect to the sampling loads and the computational times. The results validate the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Data deficiencies among rare or cryptic species preclude assessment of community‐level processes using many existing approaches, limiting our understanding of the trends and stressors for large numbers of species. Yet evaluating the dynamics of whole communities, not just common or charismatic species, is critical to understanding and the responses of biodiversity to ongoing environmental pressures.A recent surge in both public science and government‐funded data collection efforts has led to a wealth of biodiversity data. However, these data collection programmes use a wide range of sampling protocols (from unstructured, opportunistic observations of wildlife to well‐structured, design‐based programmes) and record information at a variety of spatiotemporal scales. As a result, available biodiversity data vary substantially in quantity and information content, which must be carefully reconciled for meaningful ecological analysis.Hierarchical modelling, including single‐species integrated models and hierarchical community models, has improved our ability to assess and predict biodiversity trends and processes. Here, we highlight the emerging 'integrated community modelling' framework that combines both data integration and community modelling to improve inferences on species‐ and community‐level dynamics.We illustrate the framework with a series of worked examples. Our three case studies demonstrate how integrated community models can be used to extend the geographic scope when evaluating species distributions and community‐level richness patterns; discern population and community trends over time; and estimate demographic rates and population growth for communities of sympatric species. We implemented these worked examples using multiple software methods through the R platform via packages with formula‐based interfaces and through development of custom code in JAGS, NIMBLE and Stan.Integrated community models provide an exciting approach to model biological and observational processes for multiple species using multiple data types and sources simultaneously, thus accounting for uncertainty and sampling error within a unified framework. By leveraging the combined benefits of both data integration and community modelling, integrated community models can produce valuable information about both common and rare species as well as community‐level dynamics, allowing for holistic evaluation of the effects of global change on biodiversity. [ABSTRACT FROM AUTHOR]

Objectives: Benign category of Bethesda classification is generally well known to carry a false-negative rate of 0--3%. The current study was designed to investigate the rate of falsenegative cytology in patients who underwent thyroidectomy for presumably benign thyroid diseases. Predictive risk factors for false results and malignancy were evaluated along with cytology--histology discrepant cases. Materials and methods: Females who underwent thyroidectomy between May 2014 and December 2022 were included. Demographics, ultrasound (US) features, fine-needle aspiration (FNA) diagnosis, surgical indications and outcomes, final histology reports, risk factors, and malignancy rate were recorded. Cytology--histology discrepant cases were further evaluated for interpretation errors and risk factors. Statistical analyses were performed using Fisher's exact and Mann--Whitney U tests. Results: Of 581 women with a benign thyroid disease who underwent thyroidectomy, 91 was diagnosed as incidental carcinoma (15.6%) and most was T1a (4.9 ± 2.7 mm, 95.6%). Final histology reports revealed mostly papillary carcinoma (93.4%). Predictors of malignancy such as age, family history, previous radiation exposure, and iodine-deficient diet did not help in risk stratification (p > 0.05, for each). However, FNA taken during pregnancy was determined as a risk factor (n = 7, 7.6%, p < 0.05) since it may cause a delay in diagnosis. Cytology--histology discrepant cases were seen to be mostly due to sampling errors (45%, p < 0.05), followed by misinterpretations (37.3%, p < 0.05). There was no reason for discrepancy in 17.5%, and this was linked to inherent nature of thyroid nodule with overlapping cytologic features. Best identifiable risk factor for misinterpretation was pregnancy as well (n = 5, 14.7%, p < 0.05). Conclusions: Risk of malignancy in a presumably benign thyroid disease should not be ignored. Radiology--cytology correlation by an experienced dedicated team may help in decreasing sampling errors. Physiologic changes caused by pregnancy may shade malignant transformation in thyrocytes, and it would be appropriate to be cautious about benign FNA taken during this period. [ABSTRACT FROM AUTHOR]

The purpose of this review is to present a comprehensive overview of the theory of ensemble Kalman–Bucy filtering for continuous-time, linear-Gaussian signal and observation models. We present a system of equations that describe the flow of individual particles and the flow of the sample covariance and the sample mean in continuous-time ensemble filtering. We consider these equations and their characteristics in a number of popular ensemble Kalman filtering variants. Given these equations, we study their asymptotic convergence to the optimal Bayesian filter. We also study in detail some non-asymptotic time-uniform fluctuation, stability, and contraction results on the sample covariance and sample mean (or sample error track). We focus on testable signal/observation model conditions, and we accommodate fully unstable (latent) signal models. We discuss the relevance and importance of these results in characterising the filter's behaviour, e.g. it is signal tracking performance, and we contrast these results with those in classical studies of stability in Kalman–Bucy filtering. We also provide a novel (and negative) result proving that the bootstrap particle filter cannot track even the most basic unstable latent signal, in contrast with the ensemble Kalman filter (and the optimal filter). We provide intuition for how the main results extend to nonlinear signal models and comment on their consequence on some typical filter behaviours seen in practice, e.g. catastrophic divergence. [ABSTRACT FROM AUTHOR]

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]

We study the problem of detecting and locating change points in high-dimensional Vector Autoregressive (VAR) models, whose transition matrices exhibit low rank plus sparse structure. We first address the problem of detecting a single change point using an exhaustive search algorithm and establish a finite sample error bound for its accuracy. Next, we extend the results to the case of multiple change points that can grow as a function of the sample size. Their detection is based on a two-step algorithm, wherein the first step, an exhaustive search for a candidate change point is employed for overlapping windows, and subsequently a backward elimination procedure is used to screen out redundant candidates. The two-step strategy yields consistent estimates of the number and the locations of the change points. To reduce computation cost, we also investigate conditions under which a surrogate VAR model with a weakly sparse transition matrix can accurately estimate the change points and their locations for data generated by the original model. This work also addresses and resolves a number of novel technical challenges posed by the nature of the VAR models under consideration. The effectiveness of the proposed algorithms and methodology is illustrated on both synthetic and two real datasets. for this article are available online. [ABSTRACT FROM AUTHOR]

A low-power SAR ADC with capacitor-splitting energy-efficient switching scheme is proposed for wearable biosensor applications. Based on capacitor-splitting, additional reference voltage Vcm, and common-mode techniques, the proposed switching scheme achieves 93.76% less switching energy compared to the conventional scheme with common-mode voltage shift in one LSB. With the switching scheme, the proposed SAR ADC can lower the dependency on the accuracy of Vcm and the complexity of digital control logic and DAC driver circuits. Furthermore, the SAR ADC employs low-noise and low-power dynamic comparators utilizing multi-clock control, low sampling error sampling switches based on the bootstrap technique, and dynamic SAR logic. The simulation results demonstrate that the ADC achieves a 61.77 dB SNDR and a 78.06 dB SFDR and consumes 4.45 μW of power in a 180 nm process with a 1 V power supply, a full-swing input signal frequency of 93.33 kHz, and a sampling rate of 200 kS/s. [ABSTRACT FROM AUTHOR]

Although it is widely known that observations of aeolian sediment transport are susceptible to large sampling errors, sample designs are frequently used that do not sufficiently reduce the measurement uncertainties inherent in the study of aeolian processes. Here, we examine the influence of sample size (n) and sampling location on uncertainty in models of aeolian sediment transport responses to vegetation cover change. We compare measurements from a stratified random array of 27 horizontal sediment mass flux samplers to vegetative cover data collected at a 1 ha site over a period of nearly 6 years. To assess the sensitivity of modeled relationships between aeolian transport and vegetative cover to sample design, we analyze statistical regressions for all possible combinations of sample size and sampler locations. We show that at least 17 randomly located samplers are needed to consistently capture the sediment mass flux response to vegetative cover change. We found that multiple statistically significant models can describe the sediment flux‐vegetative cover relationship when using smaller sample sizes, demonstrating the risks of inferring sediment transport response from an underpowered sample design. Across vegetative functional groups, we found that woody cover generally influenced aeolian sediment transport rates more than herbaceous cover, while model uncertainty at large sample sizes (n > 17) showed the limitation of using vegetative cover as an indicator of aeolian sediment transport. Our results suggest an evaluation of sampling practices in aeolian sediment transport studies may be needed to avoid inferential errors that are likely pervasive in this field of study. Plain Language Summary: Studies of aeolian sediment transport are prone to sampling errors, largely due to the many factors that control the transport process. However, researchers often look to simplify sample designs and monitoring methods as monitoring is time consuming and expensive. To that end, vegetative cover is an easily collected and interpreted environmental indicator that affects the rates of aeolian sediment transport. However, little consideration has been given to how many samples are needed to confidently model the relationship between changes in vegetative cover and sediment transport. We investigate how changes in sample size, modeling approaches, and vegetative cover may affect inferences made about aeolian sediment transport. We show that using traditional approaches in aeolian research (using small sample sizes and relatively simple environmental data) could risk false conclusions being drawn about aeolian transport processes, even with models that appear to be accurate. If sampling does not adequately describe the sediment transport "population" response, variability in field measurements can be so large that it undermines our understanding of how and why aeolian sediment transport rates change over time. These findings may require a re‐evaluation of typical sediment transport sampling practices. Key Points: Heterogeneity in soil, vegetation, and wind cause aeolian sediment flux to have large spatiotemporal variabilityModels describing change in aeolian sediment fluxes with ground cover are highly sensitive to sampling errorLarge sample sizes and a variety of environmental indicators are needed to produce consistent models that can inform wind erosion management [ABSTRACT FROM AUTHOR]

Root fractal analysis is instrumental in comprehending the intricate structures of plant root systems, offering insights into root morphology, branching patterns, and resource acquisition efficiency. We conducted a field experiment on paddy–wheat root systems under varying nitrogen fertilizer strategies to address the need for quantitative standardization in root fractal analysis. The study evaluated the impact of nitrogen fertilizer heterogeneity on root length and number. We established functional relationships and correlations among root fractal characteristics and root length across different box dimension scales and various root placement angles at 2.5, 5, 10, 20, 40, and 80 box dimension scales. Results indicated that nitrogen fertilizer had a limited impact on paddy–wheat axile roots, with a coefficient of variation below 0.35 among samples. Box dimension scale influenced 3D fractal dimension (FD) and fractal abundance (FA), with strong correlations (>78%) among 3D fractal features and low sample errors (<6%). The linear correlation coefficient exceeded 72% between 3D FA and root length and 50% between FA and FD. Different axile root placement angles significantly impacted planar fractal results, particularly at a 10° angle. This stability was maintained throughout the sampling period, with high correlation coefficients (>0.76 for FA and >0.5 for FD) and low sample errors (<1.5% for FA and <4.5% for FD). In conclusion, for calculating the 3D fractal characteristics of paddy–wheat axile roots during the seedling stage, box dimension scales of 2.5, 5, 10, 20, 40, and 80, as well as 2, 5, 10, 20, 50 and 100 and 3, 6, 12, 24, 48 and 96, were suitable. When computing the planar fractal characteristics of paddy–wheat axile roots during this stage, a 10° placement angle between axile root systems yields lower errors. These findings enhance root quantification methods, standardize root analysis, and promote the comparability of crop root system fractal data across different varieties and regions, thereby advancing root-related research. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the traditional pulse vibration high-frequency voltage injection method is unable to identify the rotor initial position of the surface-mounted permanent magnet synchronous motor due to the small convex polarity of the motor and the error in the sampling circuit, an improved pulse vibration high-frequency voltage injection method is proposed. The estimated D-axis is oriented to inject high-frequency sinusoidal voltage signals at three position angles of 0, 2π/3, and --2π/3 rad, and the magnitude of the high-frequency response current on the Q-axis is estimated by comparing the three times of the injected high-frequency voltage signals, which is then used to determine the initial position angle corresponding to the rotor location area. Then the estimated D-axis is directed to the initial position angle to continue to inject pulsating high-frequency voltage, closed-loop control, using the PI regulator to make the estimated Q-axis high-frequency response current convergence to zero, and finally through the polarity of the initial position of the rotor. Verification experiments are also carried out using the experimental platform with DSP 28379D as the control core, and the experimental results show that the method has a high position recognition accuracy and can effectively recognize the rotor position. [ABSTRACT FROM AUTHOR]

The object of the study is the selective coefficient of variation of dangerous parameters of the gas environment during the ignition of materials. The measure of the sample coefficient of variation of an arbitrary hazardous parameter of the gas environment observed at an arbitrary time interval is substantiated. The representativeness error of the measure of the sampling coefficient of variation, which depends on the value of the measure and the sample size, was determined. The measure allows you to numerically determine its value for an arbitrary observation interval. The difference in the measure at the intervals corresponding to the reliable absence and occurrence of ignition allows to detect the occurrence of ignition of the material. According to the results of laboratory studies, the measures of the sample coefficient of variation for carbon monoxide concentration, smoke density, and temperature of the gas medium in the laboratory chamber at intervals of absence and appearance of ignition of alcohol, paper, wood, and textiles were determined. It was established that the dangerous parameters of the gas environment at the intervals of absence and presence of ignition are characterized by different values of the increase in the measure of the sample coefficient of variation. For example, it is determined that the ignition of alcohol causes the maximum increase in the measure for carbon monoxide concentration from 0.135 to 0.441, for smoke density from 0.629 to 0.805, and for temperature from 0.001 to 0.115. When paper catches fire, the measure for carbon monoxide concentration and temperature increases from 0.0026 to 0.140 and from 0.0019 to 0.05, respectively. When burning wood, the measure for carbon monoxide concentration and temperature increases from 0.0072 to 0.177 and from 0.0067 to 0.016, respectively. The obtained results, provided that the hazardous parameters of the gas environment in the premises are measured and the sample coefficient of variation is calculated in practice, make it possible to use them in the creation of early fire detection systems. [ABSTRACT FROM AUTHOR]

We propose a parametric sampling strategy for reduction of large scale PDE systems with multidimensional input parametric spaces by leveraging models of different fidelity. The design of this methodology allows a user to adaptively sample points ad hoc from a discrete training set with no prior requirement of error estimators. It is achieved by exploiting low‐fidelity models throughout the parametric space to sample points using an efficient sampling strategy, and at the sampled parametric points, high‐fidelity models are evaluated to recover the reduced basis functions. The low‐fidelity models are then adapted with the reduced order models built by projection onto the subspace spanned by the recovered basis functions. The process continues until the low‐fidelity model can represent the high‐fidelity model adequately for all the parameters in the parametric space. Since the proposed methodology leverages the use of low‐fidelity models to assimilate the solution database, it significantly reduces the computational cost in the offline stage. The highlight of this article is to present the construction of the initial low‐fidelity model, and a sampling strategy based on the discrete empirical interpolation method. We test this approach on a 2D steady‐state heat conduction problem for two different input parameters and make a qualitative comparison with the classical greedy reduced basis method and with random selection of points. Further, we test the efficacy of the proposed method on a 9‐dimensional parametric non‐coercive elliptic problem and analyze the computational performance based on different tuning of greedy selection of points. [ABSTRACT FROM AUTHOR]

Robot vision technology based on binocular vision holds tremendous potential for development in various fields, including 3D scene reconstruction, target detection, and autonomous driving. However, current binocular vision methods used in robotics engineering have limitations such as high costs, complex algorithms, and low reliability of the generated disparity map in different scenes. To overcome these challenges, a cross-domain stereo matching algorithm for binocular vision based on transfer learning was proposed in this paper, named Cross-Domain Adaptation and Transfer Learning Network (Ct-Net), which has shown valuable results in multiple robot scenes. First, this paper introduces a General Feature Extractor to extract rich general feature information for domain adaptive stereo matching tasks. Then, a feature adapter is used to adapt the general features to the stereo matching network. Furthermore, a Domain Adaptive Cost Optimization Module is designed to optimize the matching cost. A disparity score prediction module was also embedded to adaptively adjust the search range of disparity and optimize the cost distribution. The overall framework was trained using a phased strategy, and ablation experiments were conducted to verify the effectiveness of the training strategy. Compared with the prototype PSMNet, on KITTI 2015 benchmark, the 3PE-fg of Ct-Net in all regions and non-occluded regions decreased by 19.3 and 21.1% respectively, meanwhile, on the Middlebury dataset, the proposed algorithm improves the sample error rate at least 28.4%, which is the Staircase sample. The quantitative and qualitative results obtained from Middlebury, Apollo, and other datasets demonstrate that Ct-Net significantly improves the cross-domain performance of stereo matching. Stereo matching experiments in real-world scenes have shown that it can effectively address visual tasks in multiple scenes. [ABSTRACT FROM AUTHOR]

Sampling for prevalence estimation of infection is subject to bias by both oversampling of symptomatic individuals and error‐prone tests. This results in naïve estimators of prevalence (ie, proportion of observed infected individuals in the sample) that can be very far from the true proportion of infected. In this work, we present a method of prevalence estimation that reduces both the effect of bias due to testing errors and oversampling of symptomatic individuals, eliminating it altogether in some scenarios. Moreover, this procedure considers stratified errors in which tests have different error rate profiles for symptomatic and asymptomatic individuals. This results in easily implementable algorithms, for which code is provided, that produce better prevalence estimates than other methods (in terms of reducing and/or removing bias), as demonstrated by formal results, simulations, and on COVID‐19 data from the Israeli Ministry of Health. [ABSTRACT FROM AUTHOR]

Rigorous evaluation of surrogate endpoints is performed in a trial‐level analysis in which the strength of the association between treatment effects on the clinical and surrogate endpoints is quantified across a collection of previously conducted trials. To reduce bias in measures of the performance of the surrogate, the statistical model must account for the sampling error in each trial's estimated treatment effects and their potential correlation. Unfortunately, these within‐study correlations can be difficult to obtain, especially for meta‐analysis of published trial results where individual patient data is not available. As such, these terms are frequently partially or completely missing in the analysis. We show that improper handling of these missing terms can meaningfully alter the perceived quality of the surrogate and we introduce novel strategies to handle the missingness. [ABSTRACT FROM AUTHOR]

Based on some studies, there are many important parts of tool machines, all of which have some essential smaller-the-better-type quality characteristics. The six sigma quality index of the smaller-the-better type offers accurate measurement of the process yield and the six sigma quality level. In this paper, we first proposed a six sigma product index by integrating all evaluation indicators for products that contain several quality characteristics of the smaller-the-better type. Next, we derived the confidence interval of this six sigma product index and developed an evaluation model for product quality. When a product passes the evaluation of this model, not only can it be guaranteed that the product reaches the required quality level, but also a high rate of product yield can be ensured. In addition, we also created a product improvement testing model, which can avoid missing opportunities for improvement in the process to ensure improvement effects. This complete evaluation and improvement model is applicable to the entire machine tool industry chain. It can not only increase the product value of the machine tool industry chain but also decrease environmental pollution caused by rework or scrap, which is beneficial to companies to enhance their image of fulfilling social responsibilities. Apart from the above advantages, the model formed in this paper is based on confidence intervals, thereby reducing the chance of misjudgment resulting from sampling error. [ABSTRACT FROM AUTHOR]

Global climate models are susceptible to drift, causing spurious trends in output variables. Drift is often corrected using data from a control simulation. However, internal climate variability within the control simulation introduces uncertainty to the drift correction process. To quantify this drift uncertainty, we develop a probabilistic technique: Monte Carlo drift correction (MCDC). MCDC samples the standard error associated with drift in the control time series. We apply MCDC to an ensemble of global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6). We find that drift correction partially addresses a problem related to drift: energy leakage. Nevertheless, the energy balance of several models remains suspect. We quantify the drift uncertainty of global quantities associated with the Earth's energy balance and thermal expansion of the ocean. When correcting drift in a cumulatively integrated energy flux, we find that it is preferable to integrate the flux before correcting the drift: an alternative method would be to correct the bias before integrating the flux, but this alternative method amplifies the drift uncertainty. Assuming that drift is linear likely leads to an underestimation of drift uncertainty. Time series with weak trends may be especially susceptible to drift uncertainty: for historical thermosteric sea level rise since the 1850s, the drift uncertainty can range from 3 to 24 mm , which is of comparable magnitude to the impact of omitting volcanic forcing in control simulations. Derived coefficients – such as the ocean's expansion efficiency of heat – can also be susceptible to drift uncertainty. When evaluating and analysing global climate model data that are susceptible to drift, researchers should consider drift uncertainty. [ABSTRACT FROM AUTHOR]

In genetic association analysis of complex traits, permutation testing can be a valuable tool for assessing significance when the distribution of the test statistic is unknown or not well-approximated. This commonly arises, e.g, in tests of gene-set, pathway or genome-wide significance, or when the statistic is formed by machine learning or data adaptive methods. Existing applications include eQTL mapping, association testing with rare variants, inclusion of admixed individuals in genetic association analysis, and epistasis detection among many others. For genetic association testing in samples with population structure and/or relatedness, use of naive permutation can lead to inflated type 1 error. To address this in quantitative traits, the MVNpermute method was developed. However, for association mapping of a binary trait, the relationship between the mean and variance makes both naive permutation and the MVNpermute method invalid. We propose BRASS, a permutation method for binary traits, for use in association mapping in structured samples. In addition to modeling structure in the sample, BRASS allows for covariates, ascertainment and simultaneous testing of multiple markers, and it accommodates a wide range of test statistics. In simulation studies, we compare BRASS to other permutation and resampling-based methods in a range of scenarios that include population structure, familial relatedness, ascertainment and phenotype model misspecification. In these settings, we demonstrate the superior control of type 1 error by BRASS compared to the other 6 methods considered. We apply BRASS to assess genome-wide significance for association analyses in domestic dog for elbow dysplasia (ED) and idiopathic epilepsy (IE). For both traits we detect previously identified associations, and in addition, for ED, we detect significant association with a SNP on chromosome 35 that was not detected by previous analyses, demonstrating the potential of the method. Author summary: To determine whether genetic association with a trait is significant, permutation methods are an attractive and popular approach when analytic methods based on distributional assumptions are not available, e.g., when applying machine learning or data adaptive methods, or when performing a multiple testing correction, e.g., to assess region-wide or genome-wide significance in association mapping studies. Existing applications include eQTL mapping, association testing with rare variants, inclusion of admixed individuals in genetic association analysis, and detection of genetic interaction among many others. However, when there is population structure in the sample, naive permutation of the data can lead to inflated significance of the association results. For continuous traits, linear mixed-model based approaches have been proposed for permutation-based tests that can also adjust for sample structure; however, these do not remain valid when applied to binary traits, as key features of binary data are not well accounted for. We propose BRASS, a permutation-based testing method for binary data that incorporates important characteristics of binary data in the trait model, can accommodate relevant covariates and ascertainment, and adjusts for the presence of structure in the sample. In simulations, we demonstrate the superior control of type 1 error by BRASS compared to other methods, and we apply BRASS in the context of correcting for multiple testing in two genome-wide association studies in domestic dog: one for elbow dysplasia and one for idiopathic epilepsy. [ABSTRACT FROM AUTHOR]

When the indicators of the HT ht do not have predefined metrics, we can rewrite equation (8) as 9 HT ht where the HT ht are arbitrary positive numbers. [Extracted from the article]

Minimum error entropy with fiducial points (MEEF) has received a lot of attention, due to its outstanding performance to curb the negative influence caused by non-Gaussian noises in the fields of machine learning and signal processing. However, the estimate of the information potential of MEEF involves a double summation operator based on all available error samples, which can result in large computational burden in many practical scenarios. In this paper, an efficient quantization method is therefore adopted to represent the primary set of error samples with a smaller subset, generating a quantized MEEF (QMEEF). Some basic properties of QMEEF are presented and proved from theoretical perspectives. In addition, we have applied this new criterion to train a class of linear-in-parameters models, including the commonly used linear regression model, random vector functional link network, and broad learning system as special cases. Experimental results on various datasets are reported to demonstrate the desirable performance of the proposed methods to perform regression tasks with contaminated data. [ABSTRACT FROM AUTHOR]

Aim: Communities contain more individuals of small species and fewer individuals of large species. According to the 'metabolic theory of ecology', the relationship of log mean abundance with log mean body size across communities should exhibit a slope of −3/4 that is invariant across environmental conditions. Here, we investigate whether this slope is indeed invariant or changes systematically across gradients in temperature, resource availability and predation pressure. Location: 1048 lakes across the USA. Time Period: 2012. Major Taxa Studied: Phytoplankton. Results: We found that the size–abundance relationship across all sampled phytoplankton communities was significantly lower than −3/4 and near −1 overall. More importantly, we found strong evidence that the environment affects the slope: it varies between −0.33 and −0.93 across interacting gradients of temperature, resource (phosphorus) supply and zooplankton predation pressure. Therefore, phytoplankton communities have orders of magnitude more small or large cells depending on environmental conditions across geographical locations. Conclusion: Our results emphasise the importance of the environmental factors' effect on macroecological patterns that arise through physiological and ecological processes. An investigation of the mechanisms underlying the link between individual energetics constrain and macroecological patterns would allow to predict how global warming and changes in nutrients will alter large‐scale ecological patterns in the future. [ABSTRACT FROM AUTHOR]

We study a statistical framework for replicability based on a recently proposed quantitative measure of replication success, the sceptical p$$ p $$‐value. A recalibration is proposed to obtain exact overall Type‐I error control if the effect is null in both studies and additional bounds on the partial and conditional Type‐I error rate, which represent the case where only one study has a null effect. The approach avoids the double dichotomization for significance of the two‐trials rule and has larger project power to detect existing effects over both studies in combination. It can also be used for power calculations and requires a smaller replication sample size than the two‐trials rule for already convincing original studies. We illustrate the performance of the proposed methodology in an application to data from the Experimental Economics Replication Project. [ABSTRACT FROM AUTHOR]

We consider a Markov chain Monte Carlo method, known as Hamiltonian Monte Carlo (HMC), on compact manifolds in Euclidean space. It utilizes Hamiltonian dynamics to generate samples approximating a target distribution in high dimensions efficiently. The efficiency of HMC is characterized by its convergence property, called geometric ergodicity. This property is important to generate low-correlated samples. It also plays a crucial role in establishing the error estimate for the quadrature of bounded functions by HMC sampling, referred to as the Hoeffding-type inequality. While the geometric ergodicity has been proved for HMC on Euclidean space, it has not been established on manifolds. In this paper, we prove the geometric ergodicity for HMC on compact manifolds. As an example to confirm the efficiency of the proposed HMC method, we consider a sampling problem associated with the N-vortex problem on the unit sphere, which is a statistical model of two-dimensional turbulence. We apply HMC to approximate the statistical quantities with respect to the invariant measure of the N-vortex problem, called the Gibbs measure. We observe the organization of large vortex structures as seen in two-dimensional turbulence. [ABSTRACT FROM AUTHOR]

We propose a demand estimation method that allows for a large number of zero‐ sale observations, rich unobserved heterogeneity, and endogenous prices. We do so by modeling small market sizes through Poisson arrivals. Each of these arriving consumers solves a standard discrete choice problem. We present a Bayesian IV estimation approach that addresses sampling error in product shares and scales well to rich data environments. The data requirements are traditional market‐level data as well as a measure of market sizes or consumer arrivals. After presenting simulation studies, we demonstrate the method in an empirical application of air travel demand. [ABSTRACT FROM AUTHOR]

Molecular gene-expression datasets consist of samples with tens of thousands of measured quantities (i.e., high dimensional data). However, lower-dimensional representations that retain the useful biological information do exist. We present a novel algorithm for such dimensionality reduction called Pathway Activity Score Learning (PASL). The major novelty of PASL is that the constructed features directly correspond to known molecular pathways (genesets in general) and can be interpreted as pathway activity scores. Hence, unlike PCA and similar methods, PASL's latent space has a fairly straightforward biological interpretation. PASL is shown to outperform in predictive performance the state-of-the-art method (PLIER) on two collections of breast cancer and leukemia gene expression datasets. PASL is also trained on a large corpus of 50000 gene expression samples to construct a universal dictionary of features across different tissues and pathologies. The dictionary validated on 35643 held-out samples for reconstruction error. It is then applied on 165 held-out datasets spanning a diverse range of diseases. The AutoML tool JADBio is employed to show that the predictive information in the PASL-created feature space is retained after the transformation. The code is available at https://github.com/mensxmachina/PASL. [ABSTRACT FROM AUTHOR]

Since ocean mobile observation equipment and numerical models have achieved remarkable results, the combination of the two has become an influential topic. A numerical model provides auxiliary information for the arrangement of observation equipment. As feedback, observations help produce more accurate initial conditions when incorporated into data assimilation. However, it is still worth investigating the ways to select the most valuable observation sites within the computational domain and the ways to design the observation scheme of the mobile platform. To improve the efficiency of observation, researchers attempt to select the best observation region in the vast ocean. The approach of deploying additional observing assets in selected regions is referred to as targeted observation. By combining the features of the targeted observation and the mobile observing platform, we propose a design approach for the observation scheme. First, based on a model, we estimate the initial perturbation that causes the greatest change in the sea surface temperature in the future. Then, according to the spatial component of the perturbation, we divide the experimental regions into sensitive regions and non-sensitive regions. Observing system simulation experiments are carried out to verify that samples in sensitive regions are more helpful to improve model prediction. Afterward, considering the variation of the perturbation with time, we propose a hybrid sampling scheme design method for an underwater unmanned vehicle combining Q-learning and particle swarm optimization algorithm. Finally, the effectiveness of the hybrid algorithm is verified by comparing the sampling schemes designed in static environment. This approach provides a dynamic basis for the path planning of mobile observing platforms. [ABSTRACT FROM AUTHOR]

This study proposes a closed-form direction-of-arrival (DoA) solution derived from multilateration equations for microphone arrays of co-centered and orthogonal pairs. The generalized cross-correlation phase transform (GCC-Phat) algorithm is used to obtain the time-difference-of-arrival (TDoA) values. Simulation studies have shown the success of our proposed method compared to existing DoA methods in the literature by varying the sampling frequency of the sound signal, inter-microphone distances, and the source distance. The results from the simulation are validated by the measurements from our experiments. Our proposed solution gives better results than the far-field solution against the angle error, which is more pronounced at incidence angles smaller than 15°. These angle errors, which approach 3° using the far-field method, are reduced to less than 0.5 degrees using our proposed solution. Our solution also gives more stable results against TDoA measurement errors. Our proposed solution achieves a 66% improvement for azimuth angle and 5.88% improvement for elevation angle compared to the simulation results in the absence of TDoA measurement error, outperforming the far-field approach. When normally distributed sampling error is added to TDoA measurements, with a standard deviation of three samples, our proposed solution achieves a 41% improvement for azimuth angle and a 5.44% improvement for elevation angle. In our field measurements, an absolute mean error of 0.94 degrees was observed with our proposed method for azimuth angle. It is shown to be a more stable and faster solution method for real-time applications. [ABSTRACT FROM AUTHOR]

In industry, for the quality inspection processes, acceptance sampling plans proved to be economically viable, but the unpredictability of the plan's characteristics made the use of conventional acceptance sampling plans less reliable. The generalized fuzzy multiple deferred state sampling plan (GFMDSSP) is suggested in this study for qualities that consider the difficulty in calculating the precise value of the percentage of defectives in a batch. The strategy is created with a minimal average sample size in mind and the performance measures have already been determined. An analysis of the current fuzzy acceptance sampling plans for characteristics is conducted, and an important conclusion is drawn regarding the effectiveness of the proposed scheme. Analysis of the impact of inspection errors on the sampling process reveals a decline in plan acceptance standards that is correlated with escalating inspection errors. Finally, some numerical examples are provided to support the findings. [ABSTRACT FROM AUTHOR]

Background & Objective: HER2, a molecular biomarker, is routinely evaluated in breast cancer patients to guide therapeutic decisions. However, the concordance of HER2 status between coreneedle biopsy (CNB) and surgical specimen (SS) samples is not always high, potentially affecting the accuracy of diagnosis and treatment. This study aims to review recent studies assessing the agreement of HER2 status between CNB and SS samples in breast cancer patients. Materials & Methods: A literature search was conducted in PubMed, Scopus, and Google Scholar databases from January 2018 to August 2023 using the keywords: concordance, core needle biopsy, resection, HER2 status. Ten articles meeting the inclusion criteria were selected for this review. Results: The results demonstrated variable concordance rates of HER2 status between CNB and SS samples, ranging from 83.3% to 99.5%. The primary factors influencing discordance were tumor heterogeneity, preoperative treatment, sampling error, and differing testing methods. Discordance was more prevalent in HER2-negative and HER2-low tumors compared to HER2-positive tumors. Conclusion: The concordance of HER2 status between CNB and SS samples is generally high but not perfect. Therefore, retesting HER2 status on SS samples is recommended to ensure optimal treatment decisions for breast cancer patients. [ABSTRACT FROM AUTHOR]

Convex clustering has recently garnered increasing interest due to its attractive theoretical and computational properties, but its merits become limited in the face of high-dimensional data. In such settings, pairwise affinity terms that rely on k-nearest neighbors become poorly specified and Euclidean measures of fit provide weaker discriminating power. To surmount these issues, we propose to modify the convex clustering objective so that feature weights are optimized jointly with the centroids. The resulting problem becomes biconvex, and as such remains well-behaved statistically and algorithmically. In particular, we derive a fast algorithm with closed form updates and convergence guarantees, and establish finite-sample bounds on its prediction error. Under interpretable regularity conditions, the error bound analysis implies consistency of the proposed estimator. Biconvex clustering performs feature selection throughout the clustering task: as the learned weights change the effective feature representation, pairwise affinities can be updated adaptively across iterations rather than precomputed within a dubious feature space. We validate the contributions on real and simulated data, showing that our method effectively addresses the challenges of dimensionality while reducing dependence on carefully tuned heuristics typical of existing approaches. for this article are available online. [ABSTRACT FROM AUTHOR]

The statistical supervised learning framework assumes an input–output set with a joint probability distribution that is reliably represented by the training dataset. The learning system is then required to output a prediction rule learned from the training dataset's input–output pairs. In this work, we investigate the relationship between the sample complexity, the empirical risk and the generalization error based on the asymptotic equipartition property (AEP) (Shannon, 1948). We provide theoretical guarantees for reliable learning under the information-theoretic AEP, with respect to the generalization error and the sample size in different settings. [ABSTRACT FROM AUTHOR]

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

A new formula is derived for the random error of sample central moments from correlated data which does not assume an underlying distribution and is accurate to leading order in the number of sample elements. Central moments, being important quantities in turbulence research, require accurate error estimation. Many approaches have been followed in the past for estimating the random errors of central moments from correlated data. These include: simple extensions of the formula for independent data, using the formula for the random error of generic averages, assuming an underlying normal distribution, and using block bootstraps. All of these approaches are compared with the present formula using datasets from a turbulent boundary layer, freestream grid turbulence, and a turbulent round jet. For even-order sample central moments, many of the existing approaches perform well with differences of <15%. However, for odd-order sample central moments, only the block bootstrap methodology performs similarly well. For the same sample central moments, the other methods differ by as much as 200–1000%. [ABSTRACT FROM AUTHOR]

Populations suffer two types of stochasticity: demographic stochasticity, from sampling error in offspring number, and environmental stochasticity, from temporal variation in the growth rate. By modelling evolution through phenotypic selection following an abrupt environmental change, we investigate how genetic and demographic dynamics, as well as effects on population survival of the genetic variance and of the strength of stabilizing selection, differ under the two types of stochasticity. We show that population survival probability declines sharply with stronger stabilizing selection under demographic stochasticity, but declines more continuously when environmental stochasticity is strengthened. However, the genetic variance that confers the highest population survival probability differs little under demographic and environmental stochasticity. Since the influence of demographic stochasticity is stronger when population size is smaller, a slow initial decline of genetic variance, which allows quicker evolution, is important for population persistence. In contrast, the influence of environmental stochasticity is population‐size‐independent, so higher initial fitness becomes important for survival under strong environmental stochasticity. The two types of stochasticity interact in a more than multiplicative way in reducing the population survival probability. Our work suggests the importance of explicitly distinguishing and measuring the forms of stochasticity during evolutionary rescue. [ABSTRACT FROM AUTHOR]

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

Psychology has witnessed a dramatic increase in the use of intensive longitudinal data (ILD) to study within-person processes, accompanied by a growing number of indices used to capture individual differences in within-person dynamics (WPD). The reliability of WPD indices is rarely investigated and reported in empirical studies. Unreliability in these indices can bias parameter estimates and yield erroneous conclusions. We propose an approach to (a) estimate the reliability and (b) correct for sampling error of WPD indices using "Level-1 variance-known" (V-known) multilevel models (Raudenbush & Bryk, 2002). When WPD indices are calculated for each individual, the sampling variance of the observed WPD scores is typically falsely assumed to be zero. V-known models replace this "zero" with an approximate sampling variance fixed at Level 1 to estimate the true variance of the index at Level 2, following random effects meta-analysis principles. We demonstrate how V-known models can be applied to a broad range of emotion dynamics commonly derived from ILD, including indices of the average level (mean), variability (intraindividual standard deviation), instability (probability of acute change), bipolarity (correlation), differentiation (intraclass correlation), inertia (autocorrelation), and relative variability (relative standard deviation) of emotions. A simulation study shows the usefulness of V-known models to recover the true reliability of these indices. Using a 21-day diary study, we illustrate the implementation of the proposed approach to obtain reliability estimates and to correct for unreliability of WPD indices in real data. The techniques may facilitate psychometrically sound inferences from WPD indices in this burgeoning research area. [ABSTRACT FROM AUTHOR]

Considering the burden of refractive error, clinical-based research methods are often used as epidemiological tools. This study aimed to generate evidence on the prevalence and distribution of refractive error in Portugal. A cross-sectional retrospective study was designed to review optometric records from Portuguese practices during July 2021. 348 optometric records were analysed. Subjects had a mean age of 44.2 ± 19.2 years (range 6–81) and 58.4% were female. The mean spherical equivalent was myopic, −0.65 ± 2.38 Diopters (D), varying from a minimum of −13.63 to a maximum of 6.25 D. According to sex, the mean spherical equivalent was −0.76 ± 2.29 D for female and −0.49 ± 2.49 D for male, with no significant difference between them (p =.307). The distribution of the spherical equivalent mean across the age groups, linearly varies from a myopic −1.62 ± 1.74 D in the age group of [6 – 29]; −1.58 ± 2.80 D in [30 – 44]; −0.09 ± 2.40 in [45 – 59] to a hyperopic 0.67 ± 1.61 D in the group of [60 – 81]. High myopia had a prevalence of 2.7% in the sample. Myopia was the most prevalent refractive error in the sample representing 41.3%. In the age group [6–29], myopia had a prevalence of 69.3%. Hyperopia had a prevalence in the sample of 29.7%. Myopia represents the most prevalent refractive error within the sample and is the prevalence is higher in the younger age groups, demonstrating a shift towards an increase of myopia in the next years. [ABSTRACT FROM AUTHOR]

Multi-parameter modulated signal can improve the anti-interception ability yet makes the range extraction of the target inconvenient. To solve the problem, a novel scaling transformation combined with the non-uniform short-time fractional Fourier transformation is proposed to effectively acquire the target range information. Unlike the traditional methods, the influence of the varied carrier frequency is firstly mitigated by the proposed scaling transformation. Then, the error caused by the period-varying is relieved by the sampling conversion in the digital domain. Further, the corresponding dictionary is constructed to accomplish the ranging information searching and extracting. Simulation results show that the proposed method can mitigate the impact of multiple varied parameters on signal processing. The detection error could be reduced by 67.9% after using the proposed method. • The non-uniform short-time fractional Fourier transformation is applied to deal with ranging problem of the. • A novel scaling transformation is proposed to mitigate the influence of the varied frequency. • Uniform sampling and the non-uniform sampling are investigated to reduce the ranging error caused by the varied period. • The dictionary is constructed in the sparse domain for the multi-parameter modulated signal to extract the ranging information. [ABSTRACT FROM AUTHOR]

It is essential to define more convincing and applicable classifiers for small datasets. In this paper, a minimum reconstruction error-based K-nearest neighbors (K-NN) classifier is proposed. We propose a new neighbor selection method. In the proposed neighbor selection method, a subset of data that minimize the reconstruction error is assigned as the neighbors of the query sample. Also, there is not any constraint on the distance of the neighbors from the query sample. An l 0 -based sparse representation problem is introduced for selecting the neighbors. These neighbors are assigned as neighbors of the minimum reconstruction error-based K-NN classifiers. Three l 0 -based minimum reconstruction error-based K-NN classifiers are introduced. These classifiers are less sensitive to the reconstruction coefficients in minimum reconstruction error-based K-NN classifiers and reconstruct the query sample with less error. The results on UCI machine learning repository, UCR time-series archive datasets, and a small subset (16%) of MNIST handwritten digit database demonstrate the suitable performance of the proposed method. The recognition precision increases up to more than 3% in some evaluations. [ABSTRACT FROM AUTHOR]

In this paper, the signals of interest reside in a reproducing kernel space with mixed norm defined on metric measure spaces. We consider three sampling schemes of the signals of interest depending on the accessible sampling domains that are the whole metric measure spaces or part thereof. We propose iterative algorithms that reconstruct or approximate the original signals from the sampling data. When one can do sampling on the whole metric measure spaces, the proposed algorithm can perfectly reconstruct the original signals if the sampling size is large enough. When one cannot access the whole metric measure spaces, we show that the proposed algorithms can approximate the original concentrated signals with the approximation error being a multiple of the concentration ratio if the sampling set has sufficient density. [ABSTRACT FROM AUTHOR]

Robust estimation of a mean vector, a topic regarded as obsolete in the traditional robust statistics community, has recently surged in machine learning literature in the last decade. The latest focus is on the sub-Gaussian performance and computability of the estimators in a non-asymptotic setting. Numerous traditional robust estimators are computationally intractable, which partly contributes to the renewal of the interest in the robust mean estimation. Robust centrality estimators, however, include the trimmed mean and the sample median. The latter has the best robustness but suffers a low efficiency drawback. Trimmed mean and median of means, achieving sub-Gaussian performance have been proposed and studied in the literature. This article investigates the robustness of leading sub-Gaussian estimators of mean and reveals that none of them can resist greater than 25 % contamination in data and consequently introduces an outlyingness induced winsorized mean which has the best possible robustness (can resist up to 50 % contamination without breakdown) meanwhile achieving high efficiency. Furthermore, it has a sub-Gaussian performance for uncontaminated samples and a bounded estimation error for contaminated samples at a given confidence level in a finite sample setting. It can be computed in linear time. [ABSTRACT FROM AUTHOR]

Statistical tests like t test are used to test the hypothesis for comparison of mean between two groups. The result from a t test is used to determine if there a significant difference between the two samples mean, which cannot be attributed to sampling error or to random occurrence. A t test is considered a parametric test, meaning test samples should meet assumptions of normality, equal variances and independence. The article aims to calculate the power of unpaired t test using simulation in Microsoft Excel. [ABSTRACT FROM AUTHOR]

When comparison among groups is of major importance, it is necessary to ensure that the measuring tool exhibits measurement invariance. This means that it measures the same construct in the same way for all groups. In the opposite case, the test results in measurement error and bias toward a particular group of respondents. In this study, a new approach to examine measurement invariance was applied, which was appropriately designed when a large number of group comparisons are involved: the alignment approach. We used this approach to examine whether the factor structure of a cognitive ability test exhibited measurement invariance across the 26 universities of the Kingdom of Saudi Arabia. The results indicated that the P-GAT subscales were invariant across the 26 universities. Moreover, the aligned factor mean values were estimated, and factor mean comparisons of every group's mean with all the other group means were conducted. The findings from this study showed that the alignment procedure is a valuable method to assess measurement invariance and latent mean differences when a large number of groups are involved. This technique provides an unbiased statistical estimation of group means, with significance tests between group pairs that adjust for sampling errors and missing data. [ABSTRACT FROM AUTHOR]

Purpose: In an oncological set up the role of frozen section biopsy is undeniable. They serve as an important tool for surgeon's intraoperative decision making but the diagnostic reliability of intraoperative frozen section may vary from institute to institute. The surgeon should be well aware of the accuracy of the frozen section reports in their setup to enable them to take decisions based on the report. This is why we had conducted a retrospective study at Dr B. Borooah Cancer Institute, Guwahati, Assam, India to find out our institutional frozen section accuracy. Methods: The study was conducted from 1st January 2017 to 31st December 2022 (5 years). All gynaecology oncology patients who were operated on during the study period and had an intraoperative frozen section done were included in the study. Patients who had incomplete final histopathological report (HPR) or no final HPR were excluded from the study. Frozen section and final histopathology report were compared and analysed and discordant cases were analysed based on the degree of discordancy. Results: For benign ovarian disease, the IFS accuracy, sensitivity and specificity are 96.7%, 100% and 93%, respectively. For borderline ovarian disease the IFS accuracy, sensitivity and specificity are 96.7%, 80% and 97.6%, respectively. For malignant ovarian disease the IFS accuracy, sensitivity and specificity are 95.4%, 89.1% and 100%, respectively. Sampling error was the most common cause of discordancy. Conclusion: Intraoperative frozen section may not have 100% diagnostic accuracy but still it is the running horse of our oncological institute. [ABSTRACT FROM AUTHOR]

Compressors are one of the three major components of gas turbines, and their characteristic curves are used to analyze off-design performance. How to infer the characteristic curve based on different data is an important research topic. In this paper, PG9351FA gas turbine is taken as the research object. Two methods, artificial neural network and parameter estimation, are used to predict its characteristic curve, and the prediction accuracy and application conditions of the two methods are discussed. This article compares the two methods from the perspectives of known speed characteristic curve regression and unknown speed characteristic curve inference, analyzes the impact of sample size and sample error on their inference results, and quantitatively analyzes the error through statistical methods such as calculating the mean square deviation of the data. The application scope and conditions of different methods are provided. The research results show that the method based on neural network to infer the characteristic curve has high accuracy and is suitable for the prediction of known and unknown speed characteristic curves under sufficient data, but not for the prediction of unknown side curves. The elliptic equation fitting method based on parameter estimation has a slightly lower accuracy in processing the nearly vertical compressor characteristic curve, but it can be used as an effective and reliable method to infer the compressor characteristic curve in the case of a small amount of data. The modulization method based on parameter estimation has high accuracy and is applicable to the estimation of complete characteristic curve from partial data of known characteristic curve. In this paper, the application scope and conditions of these two methods are determined, which can provide reference for engineering practice. [ABSTRACT FROM AUTHOR]