Estimation Accuracy Research Articles

Effects of experimental, network-based social circle norm feedback on studying behavior and alcohol consumption.

Misrepresentation of peer behavior has often been observed in college students and may lead to over-expression of alcohol consumption and under-expression of studying. While social norm feedback approaches have had mixed success in addressing these misrepresentations and altering behavior, they may have been too unspecific to be effective and did not directly assess individual perception accuracy. We thus investigated how specific, one-time feedback on the behavioral distribution of alcohol consumption or study time of a clearly defined, individually-adjusted social circle would affect the respective norm estimations and behavior of a class of Psychology students (n = 89 in January) across their first year of study. Students overestimated alcohol consumption and partially underestimated studying norms. While social circle feedback on alcohol consumption did not clearly affect both individual estimation accuracy and alcohol consumption, feedback on peers' studying time increased studying with no clear effect on estimation accuracy. This indicates that social circle norm feedback may be suitable to evoke behavioral effects. The correction of the detected inaccuracies did not appear to be a precondition for the feedback to be effective.

An autonomous robotic arm for efficient rock collection in uncharted territories

The autonomous rock collector using robotic arm for exploration of unknown territories (ARCAxUT) is introduced as an innovative solution for the efficient retrieval of rock samples in unexplored space regions. Traditional, human-reliant methods are costly and hazardous, prompting the development of ARCAxUT. Equipped with a smart robotic arm, an RGB-D camera, and NUC computer, the system autonomously detects and estimates the mass of various rock samples. Validated in simulated and real-world environments, the algorithm ensures precise gripper control, achieving an impressive 95.4% accuracy in rock size estimation. This breakthrough offers transformative capabilities for space missions, revolutionizing celestial body sample collection and advancing broader societal implications in space exploration technologies.

Determining the residual volume in peritoneal dialysis using low molecular weight markers.

Variation in residual volume between peritoneal dialysis dwells creates uncertainty in ultrafiltration determination, dialysis efficiency, and poses a risk of overfill if the residual volume is large. Measuring the dilution of a marker molecule during fluid fill offers a convenient approach, however, estimation accuracy depends on the choice of dilution marker. We here evaluate the feasibility of creatinine and urea as dilution markers compared to albumin-based residual volumes and three-pore model estimations. This clinical, retrospective analysis comprises 56 residual volume estimations from 20 individuals, based on the dilution of pre-fill dialysate creatinine, urea and albumin concentrations during the dialysis fluid fill phase. Outcomes were compared individually. Bias induced by ultrafiltration, marker molecule mass-transfer and influence of fluid glucose contents was quantified using the three-pore model. Linear regression established conversion factors enabling conversion between the various marker molecules. Creatinine-based calculations overestimated residual volumes by 115 mL (IQR 89-149) in 1.5% dwells and 252 mL (IQR 179-313) in 4.25% glucose dwells. In hypertonic dwells, ultrafiltration was 52 mL (IQR 38-66), while intraperitoneal creatinine mass increased by 67% during fluid fill, being the leading cause of overestimation. Albumin-based volumes conformed strongly with three-pore model estimates. Correction factors effectively enabled marker molecule interchangeability. Mass-transfer of low molecular weight marker molecules is associated with residual volume overestimation. However, by applying correction factors, creatinine and urea dilution can still provide reasonable estimates, particularly when the purpose is to exclude the presence of a very large residual volume.

Diagnostic Accuracy of an Integrated AI Tool to Estimate Gestational Age From Blind Ultrasound Sweeps.

Accurate assessment of gestational age (GA) is essential to good pregnancy care but often requires ultrasonography, which may not be available in low-resource settings. This study developed a deep learning artificial intelligence (AI) model to estimate GA from blind ultrasonography sweeps and incorporated it into the software of a low-cost, battery-powered device. To evaluate GA estimation accuracy of an AI-enabled ultrasonography tool when used by novice users with no prior training in sonography. This prospective diagnostic accuracy study enrolled 400 individuals with viable, single, nonanomalous, first-trimester pregnancies in Lusaka, Zambia, and Chapel Hill, North Carolina. Credentialed sonographers established the "ground truth" GA via transvaginal crown-rump length measurement. At random follow-up visits throughout gestation, including a primary evaluation window from 14 0/7 weeks' to 27 6/7 weeks' gestation, novice users obtained blind sweeps of the maternal abdomen using the AI-enabled device (index test) and credentialed sonographers performed fetal biometry with a high-specification machine (study standard). The primary outcome was the mean absolute error (MAE) of the index test and study standard, which was calculated by comparing each method's estimate to the previously established GA and considered equivalent if the difference fell within a prespecified margin of ±2 days. In the primary evaluation window, the AI-enabled device met criteria for equivalence to the study standard, with an MAE (SE) of 3.2 (0.1) days vs 3.0 (0.1) days (difference, 0.2 days [95% CI, -0.1 to 0.5]). Additionally, the percentage of assessments within 7 days of the ground truth GA was comparable (90.7% for the index test vs 92.5% for the study standard). Performance was consistent in prespecified subgroups, including the Zambia and North Carolina cohorts and those with high body mass index. Between 14 and 27 weeks' gestation, novice users with no prior training in ultrasonography estimated GA as accurately with the low-cost, point-of-care AI tool as credentialed sonographers performing standard biometry on high-specification machines. These findings have immediate implications for obstetrical care in low-resource settings, advancing the World Health Organization goal of ultrasonography estimation of GA for all pregnant people. ClinicalTrials.gov Identifier: NCT05433519.

Estimation methods based on ranked set sampling for the power logarithmic distribution

The sample strategy employed in statistical parameter estimation issues has a major impact on the accuracy of the parameter estimates. Ranked set sampling (RSS) is a highly helpful technique for gathering data when it is difficult or impossible to quantify the units in a population. A bounded power logarithmic distribution (PLD) has been proposed recently, and it may be used to describe many real-world bounded data sets. In the current work, the three parameters of the PLD are estimated using the RSS technique. A number of conventional estimators using maximum likelihood, minimum spacing absolute log-distance, minimum spacing square distance, Anderson-Darling, minimum spacing absolute distance, maximum product of spacings, least squares, Cramer-von-Mises, minimum spacing square log distance, and minimum spacing Linex distance are investigated. The different estimates via RSS are compared with their simple random sampling (SRS) counterparts. We found that the maximum product spacing estimate appears to be the best option based on our simulation results for the SRS and RSS data sets. Estimates generated from SRS data sets are less efficient than those derived from RSS data sets. The usefulness of the RSS estimators is also investigated by means of a real data example.

Continuous mapping of forest canopy height using ICESat-2 data and a weighted kernel integration of multi-temporal multi-source remote sensing data aided by Google Earth Engine.

Forest Canopy Height (FCH) is a crucial parameter that offers valuable insights into forest structure. Spaceborne LiDAR missions provide accurate FCH measurements, but a significant challenge is their point-based measurements lacking spatial continuity. This study integrated ICESat-2's ATL08-derived FCH values with multi-temporal and multi-source remote sensing (RS) datasets to generate continuous FCH maps for northern forests in Iran. Sentinel-1/2, ALOS-2 PALSAR-2, and FABDEM datasets were prepared in Google Earth Engine (GEE) for FCH mapping, each possessing unique spatial and geometrical characteristics that differ from those of the ATL08 product. Given the importance of accurately representing the geometrical characteristics of the ATL08 segments in modeling FCH, a novel Weighted Kernel (WK) approach was proposed in this paper. The WK approach could better represent the RS datasets within the ATL08 ground segments compared to other commonly used resampling approaches. The correlation between all RS data features improved by approximately 6% compared to previously employed approaches, indicating that the RS data features derived after convolving the WK approach are more predictive of FCH values. Furthermore, the WK approach demonstrated superior performance among machine learning models, with random forests outperforming other models, achieving a coefficient of determination (R2) of 0.71, root mean square error (RMSE) of 4.92m, and mean absolute percentage error (MAPE) of 29.95%. Furthermore, in contrast to previous studies using only summer datasets, this study included spring and autumn data from Sentinel-1/2, resulting in a 6% increase in R2 and a 0.5-m decrease in RMSE. The proposed methodology filled the research gaps and improved the accuracy of FCH estimations.

Point cloud-based crown volume improves tree biomass estimation: Evaluating different crown volume extraction algorithms

Accurately estimating aboveground biomass (AGB) is increasingly crucial for a better understanding of terrestrial ecosystem carbon balance and its relationship with global climate change. Terrestrial laser scanning (TLS) enables detailed reconstruction of tree crown three-dimensional structures, such as tree crown volume (CV), which has been proven to have a strong correlation with AGB. In this study, a coupled algorithm framework for extracting “optimal biomass” CV to improve AGB estimation using allometric models is proposed. The coupled algorithm framework was compared with commonly used tree CV extraction algorithms (model prediction, convex hull, alpha-shape, and voxel) based on point cloud data from 19 artificial Korean pine plots. The results reveal that the alpha-shape coupled with the voxel algorithm provides the closest approximation to the “optimal biomass” CV, exhibiting the highest correlation with AGB (r = 0.7905). α values and voxel size significantly influence the accuracy of CV extraction, while small variations in point cloud density have minimal impact on the extracted CV. Furthermore, the explanatory power of the “optimal biomass” CV for tree AGB was tested using 30 destructively harvested sampled trees. It was observed that the allometric model incorporating both “optimal biomass” volume and DBH (R2 = 0.966, RMSE = 15.321 kg) outperformed the allometric model incorporating both H and DBH (R2 = 0.953, RMSE = 16.131 kg) and the DBH-only model (R2 = 0.948, RMSE = 16.615 kg), particularly in reducing underestimation of AGB for larger trees. These findings demonstrate that the proposed alpha-shape coupled with the voxel algorithm provides an effective solution for extracting “optimal biomass” CV, significantly improving the accuracy of tree-level AGB estimation.

Precise and low-complexity method for underwater Doppler estimation based on acoustic frequency comb waveforms

Ocean observation has advanced rapidly in recent decades due to its crucial role in resource exploration and scientific research, with the Doppler factor being widely utilized. However, the precision of Doppler estimation is frequently constrained by frequency resolution. Traditional frequency estimation methods using single-tone signals face considerable challenges with low accuracy and poor robustness. In response, this paper introduces a novel Doppler-sensitive Acoustic Frequency Comb (AFC) for estimating the Doppler factor, enabling multiple measurements with a single transmission and reception of the signal. The proposed Combined Uneven Uncertainty (CUU) method based on AFC achieves a bias of less than 1.1×10-5, significantly surpassing the optimal result of 3.2×10-5 attained by other frequency estimation methods in the absence of noise. Compared to traditional single-tone methods, the AFC approach improves spectral leakage performance and enhances estimation accuracy without increasing computational complexity. Experimental results demonstrate that the CUU method realizes a difference performance of less than 3.4×10-6, notably lower than that of 3.2×10-5 induced by coherent spectral leakage in fast Fourier Transform (FFT).

Linear-quadratic Tobit regression model with a change point due to a covariate threshold

This paper considers a linear-quadratic Tobit regression model, which is developed for modelling the mixture structure with a line segment and a quadratic segment intersecting at an unknown change point. Due to the smoothness of such model structure, the regression coefficients and the change point can be obtained by directly maximising the likelihood function. The proposed estimator has rapid convergence rate and high estimation accuracy, without other computation burden. The asymptotic properties for the proposed estimator are derived by using empirical process theory. A sup-likelihood ratio test procedure is developed for testing the existence of a change point, and its limiting distributions are derived. The good finite sample performances of the proposed estimator are illustrated by numerical studies and empirical applications to the MGUS and GDP data.

An Improved Wiener Path Integral Approach for Stochastic Response Estimation of Nonlinear Systems Under Non-White Excitation

Abstract An improved Wiener path integral (WPI) approach is developed for predicting the stochastic response probability density functions (PDFs) of nonlinear systems under non-white excitation. Specifically, the excitation process is modeled as the output of a filter whose input is Gaussian white noise, the joint response PDF of the nonlinear system is expressed as a functional integral of the stochastic action over the space of all possible trajectories between the initial and final states, and the second-order variation of the stochastic action is recast to a quadratic form and taken into account in the estimation of the joint response PDF. Compared to the standard WPI approach where the second-order variation of the stochastic action is regarded as a constant, the improved WPI approach developed herein considers the fluctuations of the second-order variation of the stochastic action in the computational domain, thus improving the accuracy of the stochastic response estimation. Two numerical examples are illustrated, and the nonstationary response PDFs estimated by the improved WPI approach agree well with the Monte Carlo simulation results.

A method for estimating the rotation errors of BDS broadcast ephemeris using global satellite laser ranging data

Abstract The BeiDou-3 Navigation Satellite System (BDS-3) generates navigation messages from a regional ground tracking network and inter-satellite links (ISL), relying on predicted station coordinates and Earth rotation parameters (ERPs). These procedures, however, may lead to orientation errors in the broadcast ephemeris, termed constellation rotation errors.
Traditionally, these errors are monitored through Helmert transformation analysis comparing broadcast and post-processed orbits, a method that highlights discrepancies between the
GNSS Terrestrial Reference Frame (TRF) and the International Terrestrial Reference Frame
(ITRF) but suffers from a two-week evaluation delay. This study proposes a near real-time approach for estimating constellation rotation errors using high precision Satellite Laser-Ranging (SLR) data, representing the difference between GNSS TRF and ITRF through the
International Laser Ranging Service (ILRS) TRF. The impact of SLR data quantity and distribution on rotation estimation accuracy is investigated, and the effectiveness of this method on BDS-3 orbits with ERP-induced orientation deficiencies is validated. Results show that the root mean square (RMS) of broadcast orbit errors and the orbit user ranging error
(URE) reduce from 0.8/0.7/0.2 m to 0.1/0.1/0.1 m, respectively. This demonstrates the method’s capability for real-time monitoring and correcting orientation errors within BDS-3 navigation messages.

Pinball boosting of regression quantiles

An algorithm for boosting regression quantiles using asymmetric least absolute deviations, better known as pinball loss, is proposed. Existing approaches for boosting regression quantiles are essentially equal to least squares boosting of regression means with the single difference that their working residuals are based on pinball loss. All steps of our boosting algorithm are embedded in the well-established framework of quantile regression, and its main components – sequential base learning, fitting, and updating – are based on consistent scoring rules for regression quantiles. The Monte Carlo simulations performed indicate that the pinball boosting algorithm is competitive with existing approaches for boosting regression quantiles in terms of estimation accuracy and variable selection, and that its application to the study of regression quantiles of hedonic price functions allows the estimation of previously infeasible high-dimensional specifications.

A method for estimating the bulk density and particle density of granite residual soil based on the construction of pedotransfer functions

AbstractParticle density (ρs) and bulk density (ρb) are key factors in the calculation of total soil porosity. However, direct measurements of ρs and ρb are labour‐intensive, time‐consuming, and sometimes impractical. Pedotransfer functions (PTFs) provide alternative methods for indirect estimation of ρs and ρb. In this paper, the accuracy of typical 12 ρs and 9 ρb PTFs was evaluated using easily measurable soil properties (sand, silt, clay, and soil organic matter (SOM) content) from granitic residual soils collected from six study areas in subtropical China, and the accuracy of PTFs constructed based on multiple linear stepwise regression (MSR) and machine‐learned algorithms (backpropagation neural network, k‐nearest neighbour algorithms, random forests, support vector machines, and gradient boosted decision trees) was compared to determine the accuracy of PTFs. The results show that typical PTFs have poor accuracy (R2adjusted < 0.020) and are not applicable to the indirect estimation of ρs and ρb in granitic residual soils. The PTFs constructed by machine learning algorithms all performed better than MSR, with the highest estimation accuracy of the PTFs constructed by the random forest algorithm, with R2adjusted values of 0.923 and 0.933 for the ρs and ρb PTFs, respectively, and root‐mean‐square error of 0.020 g·cm−3 and 0.023 g·cm−3, respectively. Compared with MSR, the random forest algorithm has greater accuracy and eliminates the restriction of PTFs on predictors, which provides support for understanding the changing rules of ρs and ρb in granite residual soils in subtropical regions, evaluating soil quality and improving soil structure.

Investigating the Multifaceted Characteristics of Ba2FeWO6 Double Perovskite: Insights from Density Functional Theory

This study undertook a comprehensive examination of the double perovskite complex Ba2FeWO6, investigating its structural, electrical, magnetic, thermal and elastic characteristics. The study used density functional theory (DFT), specifically the full potential linearized augmented plane wave (FP-LAPW) method. It also used different approximations, including the generalized gradient approximation (GGA) and the modified Trans-Blaha (TB-mBJ) approach, to improve the accuracy of the band gap estimation more accurate. Additionlly, the GGA+U approach, incorporating the the Hubbard correction term (U), was utilized. Our findings indicate that Ba2FeWO6 exhibits indirect half-metallic band gaps in the (L-X) direction, with value of 0.91 eV and a net magnetic moment of 4 μB, predominatly influenced by the iron atom. The compound demonstrated exceptional characteristics suitable for thermoelectric applications, particularly at lower temperatures. Furthermore, the elasticity analysis revealed low brittleness, facilitates its manipulation in manufacturing procedures.

Carbohydrate Estimation Accuracy of Two Commercially Available Smartphone Applications vs Estimation by Individuals With Type 1 Diabetes: A Comparative Study.

Despite remarkable progress in diabetes technology, most systems still require estimating meal carbohydrate (CHO) content for meal-time insulin delivery. Emerging smartphone applications may obviate this need, but performance data in relation to patient estimates remain scarce. The objective is to assess the accuracy of two commercial CHO estimation applications, SNAQ and Calorie Mama, and compare their performance with the estimation accuracy of people with type 1 diabetes (T1D). Carbohydrate estimates of 53 individuals with T1D (aged ≥16 years) were compared with those of SNAQ (food recognition + quantification) and Calorie Mama (food recognition + adjustable standard portion size). Twenty-six cooked meals were prepared at the hospital kitchen. Each participant estimated the CHO content of two meals in three different sizes without assistance. Participants then used SNAQ for CHO quantification in one meal and Calorie Mama for the other (all three sizes). Accuracy was the estimate's deviation from ground-truth CHO content (weight multiplied by nutritional facts from recipe database). Furthermore, the applications were rated using the Mars-G questionnaire. Participants' mean ± standard deviation (SD) absolute error was 21 ± 21.5 g (71 ± 72.7%). Calorie Mama had a mean absolute error of 24 ± 36.5 g (81.2 ± 123.4%). With a mean absolute error of 13.1 ± 11.3 g (44.3 ± 38.2%), SNAQ outperformed the estimation accuracy of patients and Calorie Mama (both P > .05). Error consistency (quantified by the within-participant SD) did not significantly differ between the methods. SNAQ may provide effective CHO estimation support for people with T1D, particularly those with large or inconsistent CHO estimation errors. Its impact on glucose control remains to be evaluated.

Comparison of KF-Based Vehicle Sideslip Estimation Logics with Increasing Complexity for a Passenger Car

Nowadays, control is pervasive in vehicles, and a full and accurate knowledge of vehicle states is crucial to guarantee safety levels and support the development of Advanced Driver-Assistance Systems (ADASs). In this scenario, real-time monitoring of the vehicle sideslip angle becomes fundamental, and various virtual sensing techniques based on both vehicle dynamics models and data-driven methods are widely presented in the literature. Given the need for on-board embedded device solutions in autonomous vehicles, it is mandatory to find the correct balance between estimation accuracy and the computational burden required. This work mainly presents different physical KF-based methodologies and proposes both mathematical and graphical analysis to explore the effectiveness of these solutions, all employing equal tire and vehicle simplified models. For this purpose, results are compared with accurate sensor acquisition provided by the on-track campaign on passenger vehicles; moreover, to truthfully represent the possibility of using such virtual sensing techniques in real-world scenarios, the vehicle is also equipped with low-end sensors that provide information to all the employed observers.

Time series-based Project Cost Forecasting Framework

Current inflation rates have significantly been increasing construction project costs globally. Thus, it is crucial to consider potential inflationary trends when estimating the cost at completion of prolonged projects. However, the absence of literature providing guidance on analyzing inflation trends and adjusting the ongoing project cost performance factor is a gap that needs filling. This study proposes a framework for estimating ongoing project costs based on trend and seasonality analysis of the project cost performance through the Holt-Winters method. The proposed framework also incorporates the estimation of project completion time and is applied to a real case study to assess its efficacy. The results are compared with the ones obtained through pre-existing index-based methods. This paper seeks to improve the accuracy of project cost estimation and management in challenging and uncertain economic environments by providing an alternative to the traditional cost at completion forecasting approach.

Estimating nonlinear effects of random slopes: A comparison of multilevel structural equation modeling with a two-step, a single-indicator, and a plausible values approach.

Multilevel structural equation modeling (MSEM) is a statistical framework of major relevance for research concerned with people's intrapersonal dynamics. An application domain that is rapidly gaining relevance is the study of individual differences in the within-person association (WPA) of variables that fluctuate over time. For instance, an individual's social reactivity - their emotional response to social situations - can be represented as the association between repeated measurements of the individual's social interaction quantity and momentary well-being. MSEM allows researchers to investigate the associations between WPAs and person-level outcome variables (e.g., life satisfaction) by specifying the WPAs as random slopes in the structural equation on level1 and using the latent representations of the slopes to predict outcomes on level2. Here, we are concerned with the case in which a researcher is interested in nonlinear effects of WPAs on person-level outcomes - a U-shaped effect of a WPA, a moderation effect of two WPAs, or an effect of congruence between two WPAs - such that the corresponding MSEM includes latent interactions between random slopes. We evaluate the nonlinear MSEM approach for the three classes of nonlinear effects (U-shaped, moderation, congruence) and compare it with three simpler approaches: a simple two-step approach, a single-indicator approach, and a plausible values approach. We use a simulation study to compare the approaches on accuracy of parameter estimates and inference. We derive recommendations for practice and provide code templates and an illustrative example to help researchers implement the approaches.

Analysis of the lifetime performance index in Gompertz distribution based on k-record values

The lifetime performance index plays a crucial role in manufacturing procedures as it provides valuable insights into the overall quality of a product. Considering the Gompertz distribution as a widely applicable statistical distribution, this index to emphasize the effect in analyzing lifetime data. This paper investigates the inference techniques associated with the lifetime performance index for the Gompertz distribution when k-record values are available. For this problem, two innovative methods are introduced utilizing the concept of the generalized pivotal variable. These methods are not only applicable to hypothesis tests but also enable the construction of confidence intervals. To further enhance the accuracy of estimation, the study proposes Bayesian estimation techniques based on the Metropolis–Hastings algorithms. To assess the efficacy of these methods, a simulation study is conducted. The results demonstrate that all three procedures exhibit exceptional performance, even when confronted with datasets consisting of small k-record values. Moreover, a dataset is utilized to demonstrate the implementation of these three methods.

Mortality Surveillance for the COVID-19 Pandemic: Review of the Centers for Disease Control and Prevention's Multiple System Strategy.

Mortality surveillance systems can have limitations, including reporting delays, incomplete reporting, missing data, and insufficient detail on important risk or sociodemographic factors that can impact the accuracy of estimates of current trends, disease severity, and related disparities across subpopulations. The Centers for Disease Control and Prevention used multiple data systems during the COVID-19 emergency response-line-level case‒death surveillance, aggregate death surveillance, and the National Vital Statistics System-to collectively provide more comprehensive and timely information on COVID-19‒associated mortality necessary for informed decisions. This article will review in detail the line-level, aggregate, and National Vital Statistics System surveillance systems and the purpose and use of each. This retrospective review of the hybrid surveillance systems strategy may serve as an example for adaptive informational approaches needed over the course of future public health emergencies. (Am J Public Health. Published online ahead of print July 25, 2024:e1-e10. https://doi.org/10.2105/AJPH.2024.307743).