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Quantifying uncertainty is important to establishing the significance of comparisons, to making predictions with known confidence, and to identifying priorities for investment. However, uncertainty can be difficult to quantify correctly. While sampling error is commonly reported based on replicate measurements, the uncertainty in regression models used to estimate forest biomass from tree dimensions is commonly ignored and has sometimes been reported incorrectly, due either to lack of clarity in recommended procedures or to incentives to underestimate uncertainties. Even more rarely are the uncertainty in predicting individuals and the uncertainty in the mean both recognized for their contributions to overall uncertainty. In this paper, we demonstrate the effect of propagating these two sources of uncertainty using a simple example of calcium concentration of sugar maple foliage, which does not require regression, then the mass of foliage and calcium content of foliage, and finally an entire forest with multiple species and tissue types. The uncertainty due to predicting individuals is greater than the uncertainty in the mean for studies with few trees—up to 30 trees for foliar calcium concentration and 50 trees for foliar mass and calcium content in the data set we analyzed from the Hubbard Brook Experimental Forest. The most correct analysis will take both sources of uncertainty into account, but for practical purposes, country-level reports of uncertainty in carbon stocks can safely ignore the uncertainty in individuals, which becomes negligible with large enough numbers of trees. Ignoring the uncertainty in the mean will result in exaggerated confidence in estimates of forest biomass and carbon and nutrient contents. [ABSTRACT FROM AUTHOR]

In typical machine learning frameworks, model selection is of fundamental importance: commonly, multiple models have to be trained and compared in order to identify the one with the best predictive performances. The aim of this study is to provide a new tool to improve the model selection process, allowing the user to identify the algorithm which significantly outperforms the other candidates. It proposes a robust model selection procedure based on a multi‐aspect permutation test which makes it possible to detect differences in both location and variability between two paired samples of prediction errors. A new extension of the nonparametric combination (NPC) methodology is therefore introduced and is integrated with an appropriate ranking procedure in order to deal with the comparison of C≥2 candidate models. A simulation study is conducted to evaluate the performances of this testing procedure in 2‐sample and C‐sample problems, by generating data from various well‐known distributions and simulating several possible null and alternative scenarios. The adoption of the proposed technique in machine learning model selection problems is then discussed by means of multiple real data applications. These applications confirm what emerges from the simulation study: the introduced NPC‐based approach performs well under several different scenarios and represents a valuable tool for robust machine learning model selection. [ABSTRACT FROM AUTHOR]

Field Oriented Control (FOC) effectively realizes independent control of flux linkage and torque, and is widely used in application of Permanent Magnet Synchronous Motor (PMSM). However, it is necessary to detect the phase current information of the motor to realize the current closed-loop control. The phase current detection method based on a sampling resistor will cause a measurement error due to the influence of parasitic parameters of the sampling resistor, which will lead to the decrease in PMSM control performance. This paper reveals the formation mechanism of the current sampling error caused by parasitic inductance and capacitance of the sampling resistor, and further confirms that the above error will lead to the fluctuation of the electromagnetic torque output by simulation. Moreover, we propose an approach for online observation and compensation of the current sampling error based on PI-type observer to suppresses the torque pulsation of PMSM. The phase current sampling error is estimated by the proportional and integral (PI) observer, and the deviation value of current sampling is obtained by low-pass filter (LPF). The above deviation value is further injected into the phase current close-loop for error compensation. The PI observer continues to work to keep the current sampling error close to zero. The simulation platform of Matlab/Simulink (Version: R2021b) is established to verify the effectiveness of online error observation and compensation. Further experiments also prove that the proposed method can effectively improve the torque fluctuation of the PMSM and enhance its control accuracy performance of rotation speed. [ABSTRACT FROM AUTHOR]

Generative models have the potential to revolutionize 3D extended reality. A primary obstacle is that augmented and virtual reality need real-time computing. Current state-of-the-art point cloud random generation methods are not fast enough for these applications. We introduce a vector-quantized variational autoencoder model (VQVAE) that can synthesize high-quality point clouds in milliseconds. Unlike previous work in VQVAEs, our model offers a compact sample representation suitable for conditional generation and data exploration with potential applications in rapid prototyping. We achieve this result by combining architectural improvements with an innovative approach for probabilistic random generation. First, we rethink current parallel point cloud autoencoder structures, and we propose several solutions to improve robustness, efficiency and reconstruction quality. Notable contributions in the decoder architecture include an innovative computation layer to process the shape semantic information, an attention mechanism that helps the model focus on different areas and a filter to cover possible sampling errors. Secondly, we introduce a parallel sampling strategy for VQVAE models consisting of a double encoding system, where a variational autoencoder learns how to generate the complex discrete distribution of the VQVAE, not only allowing quick inference but also describing the shape with a few global variables. We compare the proposed decoder and our VQVAE model with established and concurrent work, and we prove, one by one, the validity of the single contributions. [ABSTRACT FROM AUTHOR]

The Performance Evaluation Matrix (PEM) is an excellent decision-making tool for assessment and resource management. Satisfaction Index and Importance Index are two important evaluation indicators of construction and PEM. Managers can decide whether the service item needs to be improved based on the Satisfaction Index of the service item. When resources are limited, managers can determine the priority of improving the service item based on the Importance Index. In order to avoid the risk of misjudgment caused by sample errors and meet the needs of enterprises' rapid decision-making, this study proposed a fuzzy test built on the confidence intervals of the above two key indicators to decide whether essential service items should be improved and determine the priority of improvement. Since the fuzzy test was relatively complex, this study further came up with fuzzy evaluation values and fuzzy evaluation critical values of service items following fuzzy testing rules. Besides, evaluation rules were established to facilitate industrial applications. This approach can be completed with any common word processing software, so it is relatively convenient in application and easy to manage. Finally, an application example was presented in this paper to explain the applicability of the proposed approach. [ABSTRACT FROM AUTHOR]

This document discusses the estimation of random mobility models (RMMs) using the expectation-maximization (EM) method. RMMs are important for evaluating mobile wireless networks, particularly in the context of unmanned aerial vehicles (UAVs) as platforms for airborne communication networks. The study focuses on RMMs formulated as random switching systems and develops a generic method based on EM to estimate the parameters in the random variables. The method is shown to outperform existing estimation methods and is applied to real UAV trajectories. The research was supported by the Army Research Office and the National Science Foundation. [Extracted from the article]

This year (2023) marks 140 years since the first publication of a facial soft tissue thickness (FSTT) study. Since 1883, a total of 139 studies have been published, collectively tallying > 220,000 tissue thickness measurements of > 19,500 adults. In just the last 5-years, 33 FSTT studies have been conducted. Herein, we add these data (plus an additional 20 studies) to the 2018 T-Table to provide an update of > 81,000 new datapoints to the global tallied facial soft tissue depths table. In contrast to the original 2008 T-Table, some notable changes are as follows: increased FSTTs by 3 mm at infra second molar (ecm2–iM2ʹ), 2.5 mm at gonion (go–goʹ), 2 mm at mid-ramus (mr–mrʹ), and 1.5 mm at zygion (zy–zyʹ). Rolling grand means indicate that stable values have been attained for all nine median FSTT landmarks, while six out of nine bilateral landmarks continue to show ongoing fluctuations, indicating further data collection at these landmarks holds value. When used as point estimators for individuals with known values across 24 landmarks (i.e., C-Table data), the updated grand means produce slightly less estimation error than the 2018 T-Table means (3.5 mm versus 3.6 mm, respectively). Future efforts to produce less noisy datasets (i.e., reduce measurement and sampling errors as much as possible between studies) would be useful. [ABSTRACT FROM AUTHOR]

For discrete time nonlinear networked control systems, a novel self-triggered adaptive model predictive control (MPC) strategy is developed. Different from the existing self-triggered MPC methods that determine the triggering instants based on the difference between the optimal and real states at one single instant, the proposed approach updates the MPC system according to the differential form of the state error of two consecutive sampling moments to effectively reduce the computation and communication burden while maintaining the ideal control performance. In addition, this paper introduces a new adaptive prediction horizon mechanism to the self-triggered MPC, so that the amplitude of prediction horizon contraction is sufficiently large to further reduce the computational burden of the MPC method. Finally, the recursive feasibility and robust stability of this proposed strategy are proved strictly by theoretical analysis, and the simulation comparison results are shown to verify the proposed framework. [ABSTRACT FROM AUTHOR]

The process capability index is a tool for quality measurement and analysis widely used in the industry. It is also a good tool for the sales department to communicate with customers. Although the value of the process capability index can be affected by the accuracy and precision of the process, the index itself cannot be differentiated. Therefore, the process incapability index is directly divided into two items, accuracy and precision, based on the expected value of the Taguchi process loss function. In fact, accuracy and precision are two important reference items for improving the manufacturing process. Thus, the process incapability index is good for evaluating process quality. The process incapability index contains two unknown parameters, so it needs to be estimated with sample data. Since point estimates are subject to misjudgment incurred by the inaccuracy of sampling, and since modern businesses are in the era of rapid response, the size of sampling usually tends to be small. A number of studies have suggested that a fuzzy testing method built on the confidence interval be adopted at this time because it integrates experts and the experience accumulated in the past. In addition to a decrease in the possibility of misjudgment resulting from sampling error, this method can improve the test accuracy. Therefore, based on the confidence interval of the process incapability index, we proposed the fuzzy testing method to assess whether the process capability can attain a necessary level of quality. If the quality level fails to meet the requirement, then an improvement must be made. If the quality level exceeds the requirement, then it is equivalent to excess quality, and a resource transfer must be considered to reduce costs. [ABSTRACT FROM AUTHOR]

This study hybridizes the background error covariance (BEC) of the hourly atmospheric three-dimensional variational data assimilation (3DVar) in Local Analysis (LA) operated at Japan Meteorological Agency using the flow-dependent BEC derived from the singular vector-based Mesoscale Ensemble Prediction System (MEPS) and the static BEC. The impact of introducing the hybrid BEC into the 3DVar is examined, along with its sensitivities to various factors like the ensemble size that is augmented by using lagged ensemble forecasts, the weight given to the ensemble-based component of BEC, the localization scales, and the use (or not) of the cross-variable correlation. This hybrid 3DVar system can be operated with small additional computational cost because it has no coupling with another ensemble data assimilation system. In sensitivity experiments, this hybrid 3DVar is shown to yield smaller forecast root mean square errors than the pure 3DVar, especially for surface variables. Moreover, the hybrid 3DVar shows a better equitable threat score for strong precipitation. These improvements were greater in the experiments with larger ensemble sizes that were increased by using lagged ensemble forecasts because of the reduced sampling errors in the ensemble-based BEC. These results were sensitive to the weight given to the ensemble-based BEC and the horizontal localization scale, whose optimal values were found to be approximately 0.5 and 100 km, respectively. The longer vertical correlation scale and the cross-variable correlation were also found important to create dynamically-balanced analysis, which is especially true for heavy rain cases. [ABSTRACT FROM AUTHOR]

Aim: Spatial sampling bias (SSB) is a feature of opportunistically sampled species records. Species distribution models (SDMs) built using these data (i.e. presence‐background models) can produce biased predictions of suitability across geographic space, confounding species occurrence with the distribution of sampling effort. A wide range of SSB correction methods have been developed but simulations suggest effects on predictive performance are highly variable. Here, we aim to identify the SSB correction methods that have the highest likelihood of improving true predictive performance and evaluation strategies that provide a reliable indicator of model performance when independent test data are unavailable. Location: Global, simulation. Time Period: Current, simulation. Methods: A meta‐analysis was used to evaluate the performance of SSB correction methods in studies where there were direct comparisons between corrected and uncorrected SDMs. A simulation model was then developed to test evaluation strategies against a known truth using four common SSB correction methods. Results: Effect sizes from published studies suggest some support for small positive effects of SSB correction on predictive performance when assessed using independent test data, but this was not evident using internal cross‐validation and no single method stood out as consistently effective. Simulations support these findings and show that evaluation using internal test data was generally a poor indicator of the true effect of SSB correction. Methods that adjust models relative to a known driver of SSB produced the largest performance gains, but were also the most inconsistent. Main Conclusions: Correcting SSB in presence‐background SDMs without independent test data to evaluate the effect on model performance requires careful implementation. We recommend clearer documentation of SSB correction effects on SDMs, presenting results from models with and without correction, evaluating effects of different assumptions of SSB implementation on predictions, as well as greater efforts to collect independent test datasets to validate model predictions. [ABSTRACT FROM AUTHOR]

This article presents recommendations and corresponding detailed procedures for the assessment of a geolocation system’s accuracy, as well as the specification of accuracy requirements and their subsequent validation when they are available. Applicable metrics and related processing are based on samples of corresponding geolocation errors. This article also presents similar recommendations for the predicted accuracy of a geolocation system, based on samples of geolocation error, as well as corresponding predicted error covariance matrices associated with the geolocations. Reliable error covariance matrices enable optimal use of a geolocation system’s products, such as the optimal fusion of multiple geolocations or multiple products for higher confidence and increased accuracy. The recommendations presented in this article enable reliable estimates of accuracy and reliable predicted accuracies, both of which are critical to many geolocation-based applications. The recommendations associated with predicted accuracy are also relatively new and innovative. [ABSTRACT FROM AUTHOR]

Background: Glioma grading is a critical procedure for selecting the most effective treatment policy. Biopsy result is the gold standard method for glioma grading, but inherent sampling errors in the biopsy procedure could lead to tumor misclassification. Aim: This study evaluated grading performances of a more comprehensive collection of the physiological indices quantified using an optimized dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) paradigm for glioma grading. Methods: Thirty-five patients with glioma underwent DCE-MR imaging to evaluate the grading performances of DCE-MRI-derived physiological indices. The statistical differences in the physiological indices between the different grades of gliomas were studied, and the grading performances of these parameters were evaluated using the leave-one-out cross-validation method. Results: There were significant statistical differences in DCE-MRI-derived physiological indices between the different grades of gliomas. The mean rCBVs for grade II (low-grade glioma, LGG), grade III, grade IV, and high-grade (HGG) gliomas were 2.03 ± 0.78, 3.61 ± 1.64, 7.14 ± 3.19, and 5.28 ± 3.02, respectively. The mean rCBFs of 1.94 ± 0.97, 2.67 ± 0.96, 4.57 ± 1.77, and 3.57 ± 1.68 were, respectively, quantified for grade II (LGG), grade III, grade IV, and high-grade gliomas. The leave-one-out cross-validation method indicates that the grades of glioma tumors could be determined based on a specific threshold for each physiological index; for example, the optimal cutoff values for rCBF, rCBV, Ktrans, Kep, and Vp indices to distinguish between HGGs and LGGs were 2.11, 2.80, 0.025 mL/g min, 0.29 min−1, and 0.065 mL/g, respectively. Conclusions: From the results, it could be concluded that glioma grades could be determined using DCE-MRI-derived physiological indices with an acceptable agreement with histopathological results. [ABSTRACT FROM AUTHOR]

Introduction/Objectives: The fecal immunochemical test (FIT) helps triage primary care patients at risk of colorectal cancer (CRC). Improving FIT returns has received recent attention, however uncertainty exists regarding the accurate completion of samples provided for laboratory analysis. This study aims to identify the rejection rate of returned FIT samples and determine rejection causes. Methods: FIT samples from symptomatic patients within South Yorkshire, Bassetlaw, and North Derbyshire are processed at a central laboratory. Tests requests are made from 225 GP practices, which serve an estimated 2 million population. This study describes a retrospective review of FIT samples received in the central laboratory between 01/09/19 and 31/12/22. Locally held data was interrogated in March 2023 to determine the number of FIT samples received and rejected during the study period. Documented reasons for rejection were explored to identify common themes. Results: Total FIT specimens received during the study period was 126 422. Of these, 5190 (4.1%) were rejected. Monthly rejection rates fell from 17.4% in September 2019 to 1.3% in December 2022 (P <.001). Sampling errors were the most frequent cause for FIT rejection (2151/5190), with other causes including: expired specimen; no sample collection date/ time, no request form, incomplete patient information and illegible handwriting. Conclusions: This is the first study exploring FIT rejection rates in symptomatic primary care patients, which shows improvements in rejection rates over time. Targeted interventions could improve rejection rates further, thereby reducing NHS resource use and costs and diagnostic delays. [ABSTRACT FROM AUTHOR]

The problem of domain generalization is to learn, given data from different source distributions, a model that can be expected to generalize well on new target distributions which are only seen through unlabeled samples. In this paper, we study domain generalization as a problem of functional regression. Our concept leads to a new algorithm for learning a linear operator from marginal distributions of inputs to the corresponding conditional distributions of outputs given inputs. Our algorithm allows a source distribution-dependent construction of reproducing kernel Hilbert spaces for prediction, and, satisfies finite sample error bounds for the idealized risk. Numerical implementations and source code are available1. [ABSTRACT FROM AUTHOR]

Non-response situations are inevitable in sample survey work, and this is also true in dual sampling frame. In view of this, this study proposes alternative imputation techniques to solve the problem of missing data and further suggests an estimate of the population total in dual sampling frame. The properties of the suggested estimator are analyzed, and its empirical results are compared to the entire response situation. Based on the results obtained, appropriate suggestions have been proposed to survey practitioners and researchers for their practical applications. [ABSTRACT FROM AUTHOR]

To improve the pulse width modulation (PWM) harmonic suppression effect of the three‐phase VSI‐fed PMSM system, this paper proposed a novel random switching period (RSP)‐Space Vector Pulse Width Modulation (SVPWM) technique for the three‐phase power converter. First, the proposed technique replaces the 10‐bit linear feedback shift register pseudorandom number generator (LFSR‐PRNG) with a Tiny Mersenne Twister (MT)‐PRNG to quickly generate high‐quality pseudorandom number sequences. Then, to reduce the sampling error caused by the current ripple, this paper analyzes the sampling timing under different modulation techniques. Based on the interrupted update mechanism of the Infineon AURIX‐TC264, the variable frequency sampling of the RSP‐SVPWM technique is implemented to ensure the best sampling of the motor phase current. Finally, the simulation and experimental results show that the proposed technique can effectively disperse PWM harmonic energy. Meanwhile, the RSP‐SVPWM technique based on variable sampling frequency effectively improves the phase current quality of the PMSM system. The proposed technique shows great potential for suppressing PWM harmonics in integrated PMSM systems. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The percentage of and factors associated with the regression of Barrett's esophagus (BE) or its characteristic intestinal metaplasia (IM) remain unclear, and conflicting results have been reported because of diverse regression and sampling error definitions. Thus, we investigated the rates of IM regression, sampling error, and associated factors. Forty-two patients with proven short-segment BE with IM who underwent two follow-up endoscopies with biopsies of Barrett's mucosa were retrospectively analyzed. Additional Alcian blue and MUC2 staining were done on the biopsy specimens without IM in hematoxylin–eosin staining. Only patients with negative hematoxylin–eosin, Alcian blue, and MUC2 staining for IM in both follow-up endoscopies were considered to have true regression. When all three stains were negative for IM in the first, but positive in the second follow-up endoscopy, we considered IM persisting and declared sampling error. Among the 18 patients without IM at the first follow-up endoscopy, only five (11.9%) were judged to have true regression. Prolonged proton-pump inhibitor use was significantly associated with regression. Limited experience of the endoscopist, and insufficient biopsy number were significantly related to sampling error. Receiver operating characteristic (ROC) curve analysis showed the best cut-off value of the biopsy number/maximal-length (cm) ratio to predict sampling error was 2.25. In our patients with short-segment BE, 11.9% experienced regression of IM. Prolonged proton-pump inhibitors treatment was associated with regression. An insufficient biopsy number was related to a missed IM, which may be eliminated by maintaining biopsy number/maximal-length (cm) ratio ≥2.25. [ABSTRACT FROM AUTHOR]

Self-interested customers' form of reasoning and its consequences for system performance affect the planning decisions of service providers. We study procedurally rational customers—customers who make decisions based on a sample containing anecdotes of the system times experienced by other customers. Specifically, we consider procedurally rational customers in two-station service networks with open routing, that is, customers can choose the order in which to visit the stations. Because some actions may be less represented in the population, a given customer may not succeed in obtaining anecdotes about all possible actions. We introduce a novel sampling framework that extends the procedurally rational framework to incorporate the possibility that a customer may not receive any anecdotes for one of the actions; in this case, the customer uses a prior point estimate in lieu of the missing anecdotes. Under this framework, we study the procedurally rational equilibrium in open routing. We show first that as the sample size grows large, customers' estimates become more accurate, and the procedurally rational equilibrium converges to the fully rational equilibrium (which is also socially optimal). We then uncover two main findings. First, we obtain bounds on the distance between the procedurally rational and fully rational equilibrium, aiding operational planning and showing the rate of convergence to the fully rational outcome as the sample size of anecdotes of each individual customer grows. Second, if customers obtain anecdotes of both actions with high probability, then the equilibrium will approximate the fully rational outcome, despite the sampling error inherent to procedural rationality. [ABSTRACT FROM AUTHOR]

Policy makers, firms, and researchers often choose among multiple options based on estimates. Sampling error in the estimates used to guide choice leads to a winner's curse, since we are more likely to select a given option precisely when we overestimate its effectiveness. This winner's curse biases our estimates for selected options upward and can invalidate conventional confidence intervals. This article develops estimators and confidence intervals that eliminate this winner's curse. We illustrate our results by studying selection of job-training programs based on estimated earnings effects and selection of neighborhoods based on estimated economic opportunity. We find that our winner's curse corrections can make an economically significant difference to conclusions but still allow informative inference. [ABSTRACT FROM AUTHOR]

Weather radar is a crucial tool in rainfall estimation, providing high-resolution estimates in both space and time. Despite this, radar rainfall estimates are subject to many error sources – including attenuation, ground clutter, beam blockage and the drop-size distribution – with the true rainfall field unknown. A flexible stochastic model for simulating errors relating to the radar rainfall estimation process is implemented, inverting standard weather radar processing methods, imposing path-integrated attenuation effects, a stochastic drop-size distribution field, along with sampling and random errors. This can provide realistic weather radar images, of which we know the true rainfall field, and the corrected 'best guess' rainfall field which would be obtained if they were observed in the real-world case. The structure of these errors is then investigated, with a focus on frequency and behaviour of 'rainfall shadows'. Half of simulated weather radar images have at least 3 % of significant rainfall rates shadowed and 25 % had at least 45 km2 containing rainfall shadows, resulting in underestimation of potential impacts of flooding. A model framework for investigating the behaviour of errors relating to the radar rainfall estimation process is demonstrated, with the flexible and efficient tool performing well at generating realistic weather radar images visually, for a large range of event types. [ABSTRACT FROM AUTHOR]

Resonator networks are ubiquitous in natural and engineered systems, such as solid-state materials, electrical circuits, quantum processors, and even neural tissue. To understand and manipulate these networks it is essential to characterize their building blocks, which include the mechanical analogs of mass, elasticity, damping, and coupling of each resonator element. While these mechanical parameters are typically obtained from response spectra using least-squares fitting, this approach requires a priori knowledge of all parameters and is susceptible to large error due to convergence to local minima. Here we validate an alternative algebraic means to characterize resonator networks with no or minimal a priori knowledge. Our approach recasts the equations of motion of the network into a linear homogeneous algebraic equation and solves the equation with a set of discrete measured network response vectors. For validation, we employ our approach on noisy simulated data from a single resonator and a coupled resonator pair, and we characterize the accuracy of the recovered parameters using high-dimension factorial simulations. Generally, we find that the error is inversely proportional to the signal-to-noise ratio, that measurements at two frequencies are sufficient to recover all parameters, and that sampling near the resonant peaks is optimal. Our simple, powerful tool will enable future efforts to ascertain network properties and control resonator networks in diverse physical domains. [ABSTRACT FROM AUTHOR]

Quantitative analysis relies on pure-substance primary calibrators with known mass fractions of impurity. Here, label-free quantification (LFQ) is being evaluated as a readily available, reliable method for determining the mass fraction of host cell proteins (HCPs) in bioengineered proteins which are intended for use as protein calibration standards. In this study a purified hemoglobin-A2 (HbA2) protein, obtained through its overexpression in E. coli, was used. Two different materials were produced: natural and U15N-labeled HbA2. For the quantification of impurities, precursor ion (MS1-) intensities were integrated over all E. coli proteins identified and divided by the intensities obtained for HbA2. This ratio was calibrated against the corresponding results for an E. coli cell lysate, which had been spiked at known mass ratios to pure HbA2. To demonstrate the universal applicability of LFQ, further proteomes (yeast and human K562) were then alternatively used for calibration and found to produce comparable results. Valid results were also obtained when the complexity of the calibrator was reduced to a mix of just nine proteins, and a minimum of five proteins was estimated to be sufficient to keep the sampling error below 15%. For the studied materials, HbA2 mass fractions (or purities) of 923 and 928 mg(HbA2)/g(total protein) were found with expanded uncertainties (U) of 2.8 and 1.3%, resp. Value assignment by LFQ thus contributes up to about 3% of the overall uncertainty of HbA2 quantification when these materials are used as calibrators. Further purification of the natural HbA2 yielded a mass fraction of 999.1 mg/g, with a negligible uncertainty (U = 0.02%), though at a significant loss of material. If an overall uncertainty of 5% is acceptable for protein quantification, working with the original materials would therefore definitely be viable, circumventing the need of further purification. [ABSTRACT FROM AUTHOR]

High quality long-term observational records are essential to ensure appropriate and reliable trend detection of tropospheric ozone. However, the necessity for maintaining high sampling frequency, in addition to continuity, is often under-appreciated. A common assumption is that so long as long-term records (e.g., span of a few decades) are available, (1) the estimated trends are accurate and precise, and (2) the impact of small-scale variability (e.g., weather) can be eliminated. In this study we show that the undercoverage bias (e.g., a type of sampling error resulting from statistical inference based on sparse or insufficient samples, such as once-per-week sampling frequency) can persistently reduce the trend accuracy of free tropospheric ozone, even if multi-decadal time series are considered. We use over 40 years of nighttime ozone observations measured at Mauna Loa, Hawaii (representative of the lower free troposphere) to make this demonstration, and quantify the bias in monthly means and trends under different sampling strategies. We also show that short-term meteorological variability remains a cause for an inflated long-term trend uncertainty. To improve the trend precision and accuracy due to sampling bias, two remedies are proposed: (1) a data variability attribution of colocated meteorological influence can efficiently reduce estimation uncertainty and moderately reduce the impact of sparse sampling; and (2) an adaptive sampling strategy based on anomaly detection enables us to greatly reduce the sampling bias, and produce more accurate trends using fewer samples compared to an intense regular sampling strategy. [ABSTRACT FROM AUTHOR]

The Southern Ocean plays a crucial role in the global carbon cycle. Recently, the utilization of biogeochemical (BGC) Argo float data has provided valuable insights into the uptake and release of carbon dioxide (CO2) by this region. However, significant uncertainty remains regarding the accuracy of p CO2 (partial pressure of CO2) values derived from float data. In this study, we compared p CO2 estimates obtained from float pH data with those from ship-collected data across the Southern Ocean, employing p CO2-depth, p CO2-O2 and CO2-O2 vs saturation plots to assess the degree of agreement between these two datasets. Our findings reveal significant systematic differences. A preliminary analysis, ignoring other factors, found that the float data is consistently higher, on average, than the ship data at equivalent depths and oxygen levels. We tested the hypothesis that inaccurate float pH data or float p CO2 correction process is the main cause of the p CO2 difference, by quantifying other factors that could produce systematic differences, including: (i) spatial sampling bias, (ii) seasonal bias, (iii) errors in estimated alkalinity, (iv) errors in carbonate system constants, and (v) higher levels of anthropogenic CO2 in float data. However, none of the other factors were found to be able to fully account for the discrepancies, suggesting issues with float pH data quality and/or the float p CO2 correction process. Additional analysis included refinements to ship-based and float-based p CO2 before intercomparison. Overall, we estimate that, in the Southern Ocean, surface p CO2 from floats is biased high by, on average, at least 10 μatm. [ABSTRACT FROM AUTHOR]

The Southern Ocean plays a crucial role in the global carbon cycle. Recently, the utilization of biogeochemical BGC) Argo float data has provided valuable insights into the uptake and release of carbon dioxide (CO2) by this region. However, significant uncertainty remains regarding the accuracy of pCO2 (partial pressure of CO2) values derived from float data. In this study, we compared pCO2 estimates obtained from float pH data with those from ship-collected data across the Southern Ocean, employing pCO2-depth, pCO2-O2 and CO2-O2 vs saturation plots to assess the degree of agreement between these two datasets. Our findings reveal significant systematic differences. A preliminary analysis, ignoring other factors, found that the float data is consistently higher, on average, than the ship data at equivalent depths and oxygen levels. We tested the hypothesis that inaccurate float pH data or float pCO2 correction process is the main cause of the pCO2 difference, by quantifying other factors that could produce systematic differences, including: (i) spatial sampling bias, (ii) seasonal bias, iii) errors in estimated alkalinity, (iv) errors in carbonate system constants, and (v) higher levels of anthropogenic CO2 in float data. However, none of the other factors were found to be able to fully account for the discrepancies, suggesting issues with float pH data quality and/or the float pCO2 correction process. Additional analysis included refinements to ship-based and float-based pCO2 before intercomparison. Overall, we estimate that, in the Southern Ocean, surface pCO2 from floats is biased high by, on average, at least 10 μatm. [ABSTRACT FROM AUTHOR]

Barrett's esophagus (BE) is the precursor to esophageal adenocarcinoma (EAC), and is caused by chronic gastroesophageal reflux. BE can progress over time from metaplasia to dysplasia, and eventually to EAC. EAC is associated with a poor prognosis, often due to advanced disease at the time of diagnosis. However, if BE is diagnosed early, pharmacologic and endoscopic treatments can prevent progression to EAC. The current standard of care for BE surveillance utilizes the Seattle protocol. Unfortunately, a sizable proportion of early EAC and BE-related high-grade dysplasia (HGD) are missed due to poor adherence to the Seattle protocol and sampling errors. New modalities using artificial intelligence (AI) have been proposed to improve the detection of early EAC and BE-related HGD. This review will focus on AI technology and its application to various endoscopic modalities such as high-definition white light endoscopy, narrow-band imaging, and volumetric laser endomicroscopy. [ABSTRACT FROM AUTHOR]

This paper devotes the optimal design problem for comparing two regression curves estimated from two samples with asymmetric errors. The μ p -optimality criterion is developed under the framework of second-order least squares estimation, and the associated design theory, including the general equivalence theorems and efficiency bounds, is established. The particle swarm optimization (PSO) is used to generate μ ∞ -optimal designs. A simulation study shows that the use of the μ p -optimal designs yields a substantial reduction on part of the design space in terms of the width of confidence bands. Some extensions of the μ p -optimal designs are also addressed. • The μ p -optimality criterion is developed under the second-order least squares estimation. • The general equivalence theorems and efficiency bounds for the μ p -criterion are established. • Some extensions of the μ p -optimal designs are addressed. • Numerical results for comparing Michaelis–Menten models are presented. [ABSTRACT FROM AUTHOR]

The Goddard Profiling algorithm (GPROF) converts radiometer observations from Global Precipitation Measurement (GPM) constellation satellites into precipitation estimates. Typically, high-quality ground-based estimates serve as reference to evaluate GPROF's performance. To provide a fair comparison, the ground-based estimates are often spatially aligned to GPROF. However, GPROF combines observations from various sensors and channels, each associated with a distinct footprint. Consequently, uncertainties related to the representativeness of the sampled areas are introduced in addition to the uncertainty when converting brightness temperatures into precipitation intensities. The exact contribution of resampling precipitation estimates, required to spatially and temporally align different resolutions when combining or comparing precipitation observations, to the overall uncertainty remains unknown. Here, we analyze the current performance of GPROF over the Netherlands during a 4-year period (2017–2020) while investigating the uncertainty related to sampling. The latter is done by simulating the reference precipitation as satellite footprints that vary in size, geometry, and applied weighting technique. Only GPROF estimates based on observations from the conical-scanning radiometers of the GPM constellation are used. The reference estimates are gauge-adjusted radar precipitation estimates from two ground-based weather radars from the Royal Netherlands Meteorological Institute (KNMI). Echo top heights (ETHs) retrieved from the same radars are used to classify the precipitation as shallow, medium, or deep. Spatial averaging methods (Gaussian weighting vs. arithmetic mean) minimally affect the magnitude of the precipitation estimates. Footprint size has a higher impact but cannot explain all discrepancies between the ground- and satellite-based estimates. Additionally, the discrepancies between GPROF and the reference are largest for low ETHs, while the relative bias between the different footprint sizes and implemented weighting methods increase with increasing ETHs. Lastly, our results do not show a clear difference between coastal and land simulations. We conclude that the uncertainty introduced by merging different channels and sensors cannot fully explain the discrepancies between satellite- and ground-based precipitation estimates. Hence, uncertainties related to the retrieval algorithm and environmental conditions are found to be more prominent than resampling uncertainties, in particular for shallow and light precipitation. [ABSTRACT FROM AUTHOR]

Background and Objectives: Specimen labeling errors along with incorrect patient identification have been described as among the most complex and demanding occurrences in laboratory diagnostics. The study aimed to describe the rates of sample identification and labeling errors in the blood center of a multispecialty hospital and identify possible measures to reduce them. Methods: A retrospective document review and analysis of sample identification and labeling errors were conducted in a Blood Bank Laboratory in Saudi Arabia for 4 years. The quality assurance database from the laboratory information system was used to access and review all relevant information. The one-way analysis of variance was employed to check the statistical differences between the laboratory errors and other variables in the study. The significance level was set at P < 0.05. Results: Overall, 74,279 samples and laboratory requests were received, with over 3107 canceled requests and rejected samples. An error rate of 0.43% was due to incorrect identification and labeling. This accounts for 10.2% of the total canceled tests. The annual labeling error ranges from 0.26% to 0.73%. Primarily, the errors were due to incomplete data (0.37%) and were noted as the leading type of error in all the departments. More than half (51.3%) of the errors happened in type and screen tests, whereas a significant proportion was also noted in crossmatch red blood cells (31.9%). Conclusions: The rate of sample identification and labeling errors in this study was comparable to established data and was primarily due to incomplete data or labels. Identification and labeling errors are most common in type and screen testing and crossmatch. Carefully monitoring specimen labeling quality continually can lower specimen labeling errors and determine improvements. [ABSTRACT FROM AUTHOR]

The issue of the actually achieved measurement temperature accuracy of samples of materials used for fabricating printed circuit board bases using the contact method is considered. The purpose of the work is to estimate the error in measuring the temperature of the sample surface caused by the temperature field distortion when a thermocouple probe is brought into contact with the surface. Numerical simulation of the temperature distribution in the sample before and after its contact with the thermocouple probe is carried out. The effect of the thermal conductivity of a sample and the contact area of a thermocouple with the sample on the temperature measurement error is studied. The possibility of increasing the accuracy of temperature measurements by the contact method by introducing a thermal interface in the form of a heat-conducting paste into the contact area is discussed. [ABSTRACT FROM AUTHOR]

For Paleoclimate data assimilation (PDA), a hybrid gain analog offline ensemble Kalman filter (HGAOEnKF) is proposed. It keeps the benefits of the analog offline ensemble Kalman filter (AOEnKF) that constructs analog ensembles from existing climate simulations with joint information of the proxies. The analog ensembles can provide more accurate prior ensemble mean and "flow‐dependent" error covariances than randomly sampled ensembles. HGAOEnKF further incorporates the benefits of static prior error covariances that better capture large‐scale error correlations and mitigate sampling errors than the sample prior error covariances, through a hybrid gain approach within an ensemble framework. Observing system simulation experiments are conducted for various data assimilation methods, using ensemble simulations from the Community Earth System Model‐Last Millennium Ensemble Project. Results show that using the static prior error covariances estimated from a sufficiently large sample set is beneficial for the traditional offline ensemble Kalman filter (OEnKF) and AOEnKF. HGAOEnKF method is superior to the OEnKF and AOEnKF with and without static prior error covariances, especially for the reconstruction of extreme events. The advantages of HGAOEnKF over OEnKF and AOEnKF with and without static prior error covariances are persistent with varying sample sizes and presence of model errors. Plain Language Summary: Paleoclimate data assimilation (PDA) combines information from the climate simulations and proxy data, aiming to provide an optimal estimate of past climates. The relatively low predictive skill of the climate model and the noisy and sparse paleoclimate proxies have imposed great challenges on PDA. Previous studies have shown that offline ensemble assimilation methods are much more computationally efficient than online ensemble assimilation ones, and the offline analog ensemble can also provide "flow‐dependent" prior error covariances. However, the sample prior error covariances estimated from an ensemble with limited members can be contaminated by sampling errors. Hybridization of the "flow‐dependent" prior error covariances with the static ones can remedy the sampling error, and also infuse better representation of large‐scale error correlations from the static ones. Thus a hybrid gain analog offline ensemble Kalman filter (EnKF) is proposed, which retains the advantages of the analog ensemble and further adapts the benefits of static prior error covariances. The hybrid gain analog offline EnKF is superior to the traditional offline EnKF and the offline EnKF with analog ensembles. Key Points: Static B using sufficiently large samples can better capture large‐scale error correlations and mitigate sampling errors than the sampled BHybrid gain analog offline ensemble Kalman filter (HGAOEnKF) uses the ensemble mean prior from the analog ensemble and hybrid gains combining the static B and "flow‐dependent" BHGAOEnKF has advantages over OEnKF and analog offline ensemble Kalman filter with varying sample sizes and presence of model errors, especially for extreme years [ABSTRACT FROM AUTHOR]

The method to estimate contemporary effective population size (Ne) based on patterns of linkage disequilibrium (LD) at unlinked loci has been widely applied to natural and managed populations. The underlying model makes many simplifying assumptions, most of which have been evaluated in numerous studies published over the last two decades. Here, these performance evaluations are reviewed and summarized, with a focus on information that facilitates practical application to real populations in nature. Potential sources of bias that are discussed include calculation of r2 (a measure of LD), adjustments for sampling error, physical linkage, age structure, migration and spatial structure, mutation and selection, mating systems, changes in abundance, rare alleles, missing data, genotyping errors, data filtering choices and methods for combining multiple Ne estimates. Factors that affect precision are reviewed, including pseudoreplication that limits the information gained from large genomics datasets, constraints imposed by small samples of individuals, and the challenges in obtaining robust estimates for large populations. Topics that merit further research include the potential to weight r2 values by allele frequency, lump samples of individuals, use genotypic likelihoods rather than called genotypes, prune large LD values and apply the method to species practising partial monogamy. [ABSTRACT FROM AUTHOR]

We derive the optimal energy error estimate for a multiscale finite element method with oversampling technique applied to an elliptic system with rapidly oscillating periodic coefficients under the assumption that the coefficients are bounded and measurable, which may admit rough microstructures. As a byproduct of the energy error estimate, we derive the rate of convergence in the Ltd-1) -norm. [ABSTRACT FROM AUTHOR]

This research combines various previous research instruments with the aim of obtaining the best results in knowing the extent of the influence of the perceptions of MSME entrepreneurs, accounting understanding and socialization of sak emkm on the application of sak emkm in the bojongsoang area In this research, quantitative techniques with descriptive and verification approaches are used to investigate the relationship between three independent variables and one dependent variable. The data for the study were separated into two categories: primary data from respondent questionnaires and secondary data from books, papers, and other relevant literature. Slovin's formula is used in determining the minimum sample size of a limited population, as well as avoiding sampling errors that may occur. In this instance, the sample was chosen using a purposive sampling strategy in conjunction with a non-probability sampling technique. 141 MSME entrepreneurs in Bojongloa Kidul sub-district were selected from a total population of 1,641 MSME entrepreneurs utilizing a purposive sampling strategy and non-probability sampling method. These entrepreneurs have met the criteria of implementing SAK EMKM which is considered to represent the entire population The results show that the way MSME entrepreneurs perceive SAK EMKM, their accounting knowledge, and their familiarity with SAK EMKM through socialization greatly affect their ability to implement it well. Therefore, efforts to improve the application of SAK EMKM should concentrate on improving MSME entrepreneurs' understanding and awareness of the accounting principles and specific requirements of SAK EMKM. [ABSTRACT FROM AUTHOR]

When preparing a pure state with a quantum circuit, there is an unavoidable approximation error due to the compilation error in fault-tolerant implementation. A recently proposed approach called probabilistic state synthesis, where the circuit is probabilistically sampled, is able to reduce the approximation error compared to conventional deterministic synthesis. In this paper, we demonstrate that the optimal probabilistic synthesis quadratically reduces the approximation error. Moreover, we show that a deterministic synthesis algorithm can be efficiently converted into a probabilistic one that achieves this quadratic error reduction. We also numerically demonstrate how this conversion reduces the T-count and analytically prove that this conversion halves an information-theoretic lower bound on the circuit size. In order to derive these results, we prove general theorems about the optimal convex approximation of a quantum state. Furthermore, we demonstrate that this theorem can be used to analyze an entanglement measure. [ABSTRACT FROM AUTHOR]

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Copyright of Brazilian Business Law Journal / Administração de Empresas em Revista is the property of Administracao de Empresas em Revista 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.)

Context. Accurately estimating wildlife roadkill is necessary to compare differentroads, periods, and species, and to plan and assess mitigation effectiveness. We must account for the two main sources of errors associated with carcass sampling – carcass detection and persistence. Open-population models are used to estimate abundance, survival probabilities, and recruitment in living animal populations, accounting for imperfect detection, and they can be used in the context of animal fatalities. Aims. The aim of this study was to explore an open-population approach to estimate comparable roadkill rates from carcass capture–recapture data, accounting for carcass detection and persistence. Methods. We surveyed carcasses of the white-eared opossum (Didelphis albiventris) and blackand-white tegu lizard (Salvator merianae) on four road stretches using two sampling designs with different number of visits and sampling sessions. Carcasses were marked to be recaptured over visits within the same sampling session,resulting in a capture history for each carcass. Encounter history data were modelled using the superpopulation formulation of the open-population capture–recapture model under Bayesian inference for different datasets. A daily roadkill rate per kilometre was derived from the model entry probability estimate. Key results. We estimated a daily roadkillrate with 1501 captures from 447 opossum carcasses and 511 captures from 218 tegu carcasses. For full data, mean carcass detection overthe sessionsranged from 0.49 to 0.85 forthe opossum and from 0.27 to 0.80 for the tegu, and mean carcass persistence ranged from 0.60 to 0.94 forthe opossum and from 0.64 to 0.91 for the tegu. Scenarios with more occasions and captures increased precision of roadkill rates. Conclusions. We were able to explicitly estimate roadkill rates using an open-population capture– recapture model under a Bayesian framework. It provides accurate roadkill numbers for a known time frame and road extension, accounting for imperfect detection and its associated uncertainty. Under scenarios of few carcasses, users should consider a higher number of occasions. Implications. Not addressing carcass sampling errors or simplistically addressing them (e.g. only once during the study period) could mislead mitigation efforts. The approach used here can be used to estimate fatalities in other locations, such as windfarms and powerlines, for which repeated observations of marked carcasses are an option. [ABSTRACT FROM AUTHOR]

Analysts' perspectives on economic risk are a crucial element in the investor decision-making process. Several researchers have immersed themselves in analyst forecasts and stock prices. However, it is imperative to examine the consistency of findings from previous studies in order to draw general conclusions. This analysis is necessary to establish whether analysts' forecasts actually influence stock market returns. Consistency between studies was assessed using a meta-analysis approach, facilitated by Comprehensive Meta-Analysis (CMA) software. Metaanalysis provides an effect size estimate to assess the relationship between the independent variable (analysts' forecasts) and the dependent variable (stock market returns). The results of the heterogeneity test indicate an Isquared value in excess of 50%, signifying substantial variation. Similarly, the correlation analysis reveals a p-value of 0.004, suggesting heterogeneity in the studies of analyst forecasts and stock market returns. Heterogeneity refers to disparities in data within or between studies, attributable to varying research locations and economic conditions. Consequently, the results of this study are heterogeneous due to significant sampling error arising from various research locations and economic circumstances. Thus, it is demonstrated that analysts' forecasts have an impact on stock market returns. [ABSTRACT FROM AUTHOR]

Meta-analysis is a statistical procedure that combines the results of multiple scientific studies on the same topic. It allows for a more precise estimation of effects and identification of general trends in research. However, there are common errors that researchers make when conducting meta-analyses. These include misinterpreting the I-squared value, not properly examining outliers, using the wrong measures for effect size, not accounting for correlated observations, including studies with small sample sizes, and failing to conduct sensitivity and asymmetry analyses. These errors can lead to incorrect conclusions and have serious consequences in scientific research and clinical practice. It is recommended to work with a biostatistician to ensure the validity and appropriateness of statistical analysis methods used in meta-analyses. [Extracted from the article]

"Statistical Methods in Health Disparity Research" by J. Sunil Rao is a comprehensive book that explores statistical methods for estimating health disparities. The book covers various approaches, including non-model and linear-based models, as well as the application of machine learning. It emphasizes the importance of validating findings and discusses the advantages and limitations of each method. The book is divided into seven chapters, which cover topics such as basic concepts of health disparities, estimation of health disparities, domain-specific estimates, causality, moderation, and mediation, machine learning-based approaches, health disparity estimation under a precision medicine paradigm, and extended topics. While the book has some structural inconsistencies, it offers a diverse range of topics and provides in-depth explanations of complex concepts. It is recommended for students, professionals in public health or health policy, researchers, and practitioners working with vulnerable populations. [Extracted from the article]

Interest has grown in synthesizing participant level data of a study with relevant external aggregate information. Several efficient and flexible procedures have been developed under the assumption that the internal study and the external sources concern the same population. This homogeneity condition, albeit commonly being imposed, is hard to check due to limitedly available external information in aggregate data forms. Bias may be introduced when the assumption is violated. In this article, we propose a penalized likelihood approach that avoids undesirable bias by simultaneously selecting and synthesizing consistent external aggregate information. The proposed approach provides a general framework which incorporate consistent external information from heterogeneous study populations as long as the conditional distribution of the dependent variable under investigation is same and differences in the independent variable distributions are properly accounted for via a semi‐parametric density ratio model. The proposed approach also properly accounts for the sampling errors in the external information. A two‐step estimator and an optimization algorithm are proposed for computation. We establish the selection and estimation consistency and the asymptotic normality of the two‐step estimator. The proposed approach is illustrated with an analysis of gestational weight gain management studies. [ABSTRACT FROM AUTHOR]

We examined the discussion processes through which two independent consensus-based review panels detected errors in the same sample of items from an international test translated from English to Chinese. The discussion processes were defined according to four events: (1) identifying a potential error; and (2) agreeing with, (3) disagreeing with, and (4) elaborating an opinion expressed by other panelists. We found that, while the two panels had similar error detection rates, only half of the errors detected by the two panels altogether were detected by both panels. In addition, of the errors detected by the two panels, more than half were detected by the panels through different discussion processes. No discussion process occurred substantially more frequently or less frequently for any translation error dimension. We conclude that the unique combination of backgrounds, skills, and communication styles of panel members and the unique combination of textual features in each item shape which errors each panel is capable of detecting. While panels can be highly effective in detecting errors, one single panel may not be sufficient to detect all possible errors in a given set of translated items. Consensus-based translation error review panels should not be assumed to be exchangeable. [ABSTRACT FROM AUTHOR]

The problem of registering point clouds in scenarios with low overlap is explored in this study. Previous methodologies depended on having a sufficient number of repeatable keypoints to extract correspondences, making them less effective in partially overlapping environments. In this paper, a novel learning network is proposed to optimize correspondences in sparse keypoints. Firstly, a multi-layer channel sampling mechanism is suggested to enhance the information in point clouds, and keypoints were filtered and fused at multi-layer resolutions to form patches through feature weight filtering. Moreover, a template matching module is devised, comprising a self-attention mapping convolutional neural network and a cross-attention network. This module aims to match contextual features and refine the correspondence in overlapping areas of patches, ultimately enhancing correspondence accuracy. Experimental results demonstrate the robustness of our model across various datasets, including ModelNet40, 3DMatch, 3DLoMatch, and KITTI. Notably, our method excels in low-overlap scenarios, showcasing superior performance. [ABSTRACT FROM AUTHOR]

A data driven method based on generalized additive modelling (GAM) has been developed for homogenizing daily minimum and maximum temperature (TN, TX) series using parallel measurements and covariates. The method is applied to two coastal and two inland stations in the Netherlands. Between 1950 and 1972, these stations were relocated from cities to airports, accompanied by parallel measurement of at least 5 years at the old and new sites. Separating these parallel measurements in training and test data, the method compares numerous models involving covariates like the wind vector, cloudiness, specific humidity and sea surface temperature, and selects a model for each station. The resulting models offer an improvement compared to models based on temperature and season only: seasonal dependence is largely replaced by dependence on physical quantities. However, quantitatively, the impact is not large in the cases studied. One of the reasons might be that some covariates have only been measured at specific times not coinciding with the occurrences of the temperature minima or maxima. Additional benefits of the method are robustness and estimation of the sampling error variance of the daily homogenized daily temperature values. [ABSTRACT FROM AUTHOR]

Simple Summary: A diagnosis of Barrett's esophagus (BE) requires the macroscopic visualization of gastric-appearing mucosa in the esophagus and the identification of intestinal metaplasia on histologic examination. Histologic diagnosis of BE dysplasia can be challenging due to sampling error, pathologists' experience, interobserver variation, and difficulty in histologic interpretation: all these problems complicate patient management. In intestinal metaplasia, which occurs because of chronic gastresophageal reflux disease (GERD), the squamous epithelium converts to columnar epithelium, which is initially of the cardia type and devoid of goblet cells; it later develops goblet cell metaplasia and eventually dysplasia, which develops and progresses to adenocarcinoma because of the accumulation of multiple genetic and epigenetic alterations. Therefore, this review aims to provide an up-to-date and clear diagnostic approach to Barrett's esophagus and an overview of dysplasia's development's pathogenetic and molecular mechanisms. Barrett's esophagus (BE) was initially defined in the 1950s as the visualization of gastric-like mucosa in the esophagus. Over time, the definition has evolved to include the identification of goblet cells, which confirm the presence of intestinal metaplasia within the esophagus. Chronic gastro-esophageal reflux disease (GERD) is a significant risk factor for adenocarcinoma of the esophagus, as intestinal metaplasia can develop due to GERD. The development of adenocarcinomas related to BE progresses in sequence from inflammation to metaplasia, dysplasia, and ultimately carcinoma. In the presence of GERD, the squamous epithelium changes to columnar epithelium, which initially lacks goblet cells, but later develops goblet cell metaplasia and eventually dysplasia. The accumulation of multiple genetic and epigenetic alterations leads to the development and progression of dysplasia. The diagnosis of BE requires the identification of intestinal metaplasia on histologic examination, which has thus become an essential tool both in the diagnosis and in the assessment of dysplasia's presence and degree. The histologic diagnosis of BE dysplasia can be challenging due to sampling error, pathologists' experience, interobserver variation, and difficulty in histologic interpretation: all these problems complicate patient management. The development and progression of Barrett's esophagus (BE) depend on various molecular events that involve changes in cell-cycle regulatory genes, apoptosis, cell signaling, and adhesion pathways. In advanced stages, there are widespread genomic abnormalities with losses and gains in chromosome function, and DNA instability. This review aims to provide an updated and comprehensible diagnostic approach to BE based on the most recent guidelines available in the literature, and an overview of the pathogenetic and molecular mechanisms of its development. [ABSTRACT FROM AUTHOR]

In the Three Gorges reservoir area, the overhead upright pier is the primary structural form. For intelligent monitoring of existing terminals, this research chooses Chongqing Xintian Port as the study object and proposes a support vector machine (SVM) damage-inducing factor (DIF) inversion model based on particle swarm optimization (PSO). To apply the finite element method to analyze the stress distribution characteristics of quay pile groups under three main DIFs, including the stacking effect, ship impact load effect, and bank slope effect. After characterizing the stress data, it becomes evident that there exists a correlation between stress and each DIF parameter. Before generating the training sample set, principal component analysis is employed to reduce dimensionality and eliminate a substantial amount of redundant data. The model has an accuracy of 0.999 for the identification of the type of DIF and 0.975 for the identification of the location of the action of the DIF with F1 coefficients of 0.999 and 0.978, respectively. For the strength of DIF predictions, MAE and MSE were 4.871 and 1.202, respectively, R2 was 0.986, NSE was 0.986, WI was 0.996, and PBIAS was 0.095. After extracting every sample, the relative error for the ship impact load effect is 0.05, and the highest relative error for the bank slope effect is 0.02; the error for the stacking effect is limited to 0.08. The results suggest that the damage inducement inversion model of the SVM optimized by the PSO algorithm can effectively identify the DIF of the overhead upright pier. [ABSTRACT FROM AUTHOR]

Currently, the use of microseismic detection technology for crack detection and localization in rock masses has great potential in detecting structural damage. As engineering safety has always been a very important issue, this study investigated the problem of multi-crack identification in rock masses within the environment of track tunnels using transient waves. A tunnel rock was modeled using MIDAS GTS NX software (2019.v1.2) and a crack transient wave model in the frequency domain was obtained through data analysis and simulation. Then, this was combined with compressive sensing techniques to locate and detect multiple cracks in tunnel rock. The performance of the proposed approach was validated through experimental simulations, which included experiments on differences in the number of cracks, as well as spatial samples. The experimental results indicate that the technique performs well for single-crack localization in tunnel rock mass, where the average localization error is 4 m. Meanwhile, the localization error is larger in multi-crack localization, and the number of spatial sample points set using compressive sensing also has a large impact on the experimental results. [ABSTRACT FROM AUTHOR]

Introduction: Oblique Target-rotation in the context of exploratory factor analysis is a relevant method for the investigation of the oblique simple structure. It was argued that minimizing single cross-loadings by means of target rotation may lead to large effects of sampling error on the target rotated factor solutions. Method: In order to minimize effects of sampling error on results of Targetrotation we propose to compute the mean cross-loadings for each block of salient loadings of the independent clusters model and to perform Target-rotation for the block-wise mean cross-loadings. The resulting transformation-matrix is than applied to the complete unrotated loading matrix in order to produce mean Target-rotated factors. Results: A simulation study based on correlated independent clusters model and zero-mean cross-loading models revealed that mean oblique Target-rotation resulted in smaller bias of factor inter-correlations than conventional Targetrotation based on single loadings, especially when sample size was small and when the number of factors was large. An empirical example revealed that the similarity of Target-rotated factors computed for small subsamples with Targetrotated factors of the total sample was more pronounced for mean Targetrotation than for conventional Target-rotation. Discussion: Mean Target-rotation can be recommended in the context of oblique factor models based on simple structure, especially for small samples. An R-script and an SPSS-script for this form of Target-rotation are provided in the Supplementary Material. [ABSTRACT FROM AUTHOR]