Precise Estimates Research Articles

A vision model for automated frozen tuna processing.

Accurate and rapid segmentation of key parts of frozen tuna, along with precise pose estimation, is crucial for automated processing. However, challenges such as size differences and indistinct features of tuna parts, as well as the complexity of determining fish poses in multi-fish scenarios, hinder this process. To address these issues, this paper introduces TunaVision, a vision model based on YOLOv8 designed for automated tuna processing. TunaVision incorporates enhancements in instance segmentation through YOLOv8m-FusionSeg, improving the segmentation of small and complex targets by increasing channel depth and optimizing feature fusion. Additionally, the YOLOv8s RSF model improves feature extraction speed and accuracy, ensuring each fish is correctly identified and localized before segmentation and pose estimation. Furthermore, TunaVision employs a vector-based approach for pose estimation, utilizing detection and segmentation results to determine fish posture and orientation. Experiments show that YOLOv8m-FusionSeg achieves an mAP@0.5 of 93.3%, while YOLOv8s RSF achieves an mAP@0.5 of 96.1%, with a mean absolute error (MAE) of 1.81 degrees in angle estimation, significantly outperforming other methods. These findings highlight TunaVision's effectiveness in segmenting, detecting, and estimating poses of frozen tuna, offering valuable insights for the development of automated processing systems.

Incorporating Community Case Management in Risk-Based Surveillance for Malaria Elimination in the Dominican Republic.

As countries strive for malaria elimination, it is crucial to gather sufficient evidence to confirm the absence of transmission. Routine surveillance data often lack the sensitivity to detect community transmission at low levels. In the Dominican Republic, community health workers (CHWs) have been deployed in malaria foci to perform active case detection. This study aimed to assess the added value of CHWs in enhancing the health system's malaria detection capabilities. Freedom from infection (FFI) is a statistical framework designed to demonstrate the absence of malaria by using routinely collected health data. We adapted this framework to include CHW data, estimating their contribution to the health system's malaria detection ability. The model was applied to facility and CHW data from 33 facilities across nine provinces in the Dominican Republic, covering the period from January 2018 to April 2022. The likelihood that a facility's catchment population is free from malaria infection (Pfree) was achieved in 52% of facilities by using only routine data, sustained for an average of 13 months. With the addition of CHW data, 88% of facilities reached Pfree, sustained for an average of 37 months. Incorporating CHW data enhanced the precision of model estimates by over 500-fold. The study demonstrated the near absence of malaria in several facility catchment populations. It highlighted the importance of community case management in supplementing routine surveillance, thereby improving the precision of malaria transmission estimates. These findings support the further application of the FFI framework to accelerate progress toward malaria elimination in the Dominican Republic.

Evaluating genome-wide and targeted forensic sequencing approaches to kinship determination.

Kinship determination is a valuable tool in forensic genetics, with applications including familial searching, disaster victim identification, and investigative genetic genealogy. Conventional typing of small numbers of autosomal short tandem repeats (STRs) confidently identifies only first-degree relatives. Massively parallel sequencing (MPS) can access more STRs and resolve alleles identical by length but differing in sequence (isoalleles), which may increase the power of kinship estimation, particularly when combined with additional sequenced single nucleotide polymorphism (SNP) loci, as in the ForenSeq DNA Signature Prep kit. MPS sequencing of ∼10,000 SNPs is available in the ForenSeq Kintelligence kit, promising detection of more distant kin, while SNP chips carrying hundreds of thousands of markers increase resolution still further. Here we evaluate these different resolutions in a set of pedigrees, and via simulations. As expected, the key factor influencing the precision of kinship estimation is the number of markers analysed and MPS-based analysis of STRs increases resolution, with the full set of ForenSeq DNA Signature Prep kit markers allowing detection of third-degree relatives. Since SNP chips include non-autosomal (X- and Y-chromosomal, and mitochondrial [mtDNA]) markers, we ask how these perform within the pedigrees, cross-referencing to Y-STR sequence data. We highlight the importance of understanding haplogroup resolutions in the increasingly complex Y and mtDNA phylogenies, to avoid false exclusions. Incorporation of X-SNPs allows tracing of X-chromosome segments within families. These different approaches can add value to kinship estimation, but some require simpler bioinformatic interfaces to make them more widely accessible in practice, and also access to appropriate allele frequency data to avoid problems associated with ancestry mis-specification.

Advancing Soft Robot Proprioception Through 6D Strain Sensors Embedding.

Soft robots and bioinspired systems have revolutionized robot design by incorporating flexibility and deformable materials inspired by nature's ingenious designs. Similar to many robotic applications, sensing and perception are paramount to enable soft robots to adeptly navigate the unpredictable real world, ensuring safe interactions with both humans and the environment. Despite recent progress, soft robot sensorization still faces significant challenges due to the virtual infinite degrees of freedom of the system and the need for efficient computational models capable of estimating valuable information from sensor data. In this article, we present a new model-based proprioceptive system for slender soft robots based on strain sensing and a strain-based modeling approach called Geometric Variable-Strain (GVS). We develop a flexible 2-Plate 6D strain sensor (Flex-2P6D) capable of measuring the 6 dimensions (6D) strain at specific points of the soft robot with an accuracy higher than 95%. Coupled with the GVS approach, the proposed methodology is able to directly measure the configuration variables and reconstruct complex robot shapes with very high accuracy, even in very challenging conditions. The sensors are embedded inside the soft body, which makes them also suitable for underwater operation and physical interaction with the environment. Something that we also demonstrate experimentally. We believe that our approach has the potential to be applied across a wide variety of applications, including observation and exploration missions, as well as human-robot interaction, where the states of the system are required for implementing precise closed-loop control and estimation methods.

Decoding Palaeolithic Hand Stencils: Age and Sex Identification Through Geometric Morphometrics

This study pioneers the application of geometric morphometrics to Palaeolithic hand stencils, offering new insights into the problematics of classic rock art using innovative methodology. Employing a triple approach encompassing contemporary, experimental, and archaeological populations, the research achieves a precise estimation of age and biological sex through hand morphometry, enhancing our anthropometric understanding of the individuals whose hands were stencilled. The results of the analysis of the archaeological sample reveals a notable presence of sub-adults, indicating children’s participation in the creation of hand stencils. The findings also suggest a balanced, mixed participation between sexes across age groups and cave locations, reflecting cohesive social behaviour among Upper Palaeolithic human groups. Statistical analysis indicates that finger width in the Palaeolithic population is larger than in the modern sample, possibly due to a more active lifestyle. Most importantly, the study demonstrates the methodology’s repeatability across various chronologies and geographies, especially in well-preserved samples, underscoring its applicability worldwide. Overall, this research represents a significant methodological advancement in our ability to understand Palaeolithic rock art, shedding light on lifestyle and cultural dynamics of hunter-gatherer societies.

Neighborhood opportunity and residential instability: associations with mental health in middle childhood.

Neighborhood quality may contribute to child mental health, but families with young children often move, and residential instability has also been tied to adverse mental health. This study's primary goal was to disentangle the effects of neighborhood quality from those of residential instability on mental health in middle childhood. 1,946 children from 1,652 families in the Upstate KIDS cohort from New York state, US, were followed prospectively from birth to age 10. Residential addresses were linked at the census tract level to the Child Opportunity Index 2.0, a multidimensional indicator of neighborhood quality. The number of different addresses reported from birth to age 10 was counted to indicate residential instability, and the change in COI quintile indicated social mobility. Parents completed three assessments of attention-deficit/hyperactivity disorder, problematic behavior, and internalizing psychopathology symptoms at ages 7, 8, and 10. Child and family covariates were selected a priori to adjust sample characteristics, increase estimate precision, and account for potential confounding. In unadjusted models, higher neighborhood quality at birth was associated with fewer psychopathology symptoms in middle childhood, but associations were largely mediated by residential instability. In adjusted models, residential instability was associated with more psychopathology symptoms, even accounting for social mobility. Neighborhood quality at birth had indirect effects on child mental health symptoms through residential instability. Children born into lower-quality neighborhoods moved more, and moving more was associated with higher psychopathology symptoms. Findings were similar across different timings of residential moves, for girls and boys, and for children who did not experience a major life event. Additional research is needed to better understand which aspects of moving are most disruptive to young children.

Properties and Applications of Neutrosophic Burr XII Distribution

Primarily, when the hazards function has intricate structures, the BrXII distribution is an established framework for lifetime data analysis. However, classical probability models are limited in the sense that they cannot measure or record the data in exactness fueling the notion of indeterminacy in data collection process. This study addresses this issue by proposing the idea of neutrosophic BrXII (NeS-BrXII) distribution. The primary objective is to study the statistical properties of the proposed model by providing explicit expressions of reliability properties, the expression of moments and generating function, expression of order statistics, mean residual life, mean inactivity time, stochastic ordering, income inequality measures, and entropy in neutrosophic realm. In addition, the neutrosophic model parameters are estimated using the principle of maximum-likelihood estimation. Further, the precision of these model estimates is verified via a simulation study of the proposed model. Applying the model on two real-world material sciences data sets reinforces its efficacy with the NeS-BrXII distribution proving to be more suitable for managing anomalies in neutrosophic surface analysis among other models.

Precision of body composition estimation from commercial bioelectrical impedance analysis devices in male Mexican soccer players

Precision of body composition estimation from commercial bioelectrical impedance analysis devices in male Mexican soccer players

I need a vacation: A meta-analysis of vacation and employee well-being.

Previous meta-analytic research concluded that the well-being benefits of vacation are small and fade away quickly, suggesting that vacation may not be that effective of a recovery opportunity for improving employee well-being. Since the time of this initial meta-analysis, however, the number of vacation studies has increased, providing an opportunity to estimate more precise meta-analytic estimates and increase our understanding of the different factors that play a role in this vacation-well-being relationship. As such, we conduct a meta-analysis using 32 studies that include 256 effect sizes to examine how employee well-being levels change due to vacation. Our results reveal that vacation has a large effect on well-being that does not fade out as quickly as previously thought. In terms of moderators, our results suggest that vacation length, national culture, and number of nationally mandated vacation days moderate this relationship, but the role of vacation location (i.e., away from home, at home, or a mix of both) remains unclear. Finally, we examine how types of activities and specific recovery experiences during vacation correlate with well-being during and after vacation using a meta-analysis of eight studies that include 69 effect sizes. Our findings suggest that psychological detachment and physical activities during vacation may be the most beneficial for improving employee well-being. Overall, this meta-analysis provides evidence that vacation is a more effective recovery opportunity for improving employee well-being than previous work suggests. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Adjustment of Truncation Effect in First Birth Interval using Current Status Data Technique

Introduction: Estimating the First Birth Interval (FBI) from cross-sectional data often presents challenges related to truncation effects. These challenges stem from the data’s inability to capture the enough exposure for an event, resulting in potential biases and inaccuracies in FBI estimates. Recognizing and addressing truncation effects is essential for obtaining more precise and meaningful fertility parameter estimates in a cross-sectional survey. This study seeks to mitigate truncation effects in the estimation of the FBI by utilizing the Current Status Data technique. This approach focuses on women with specific marital durations, providing a means to counteract the bias caused by truncation and thereby yielding more accurate and reliable FBI estimates. Methods: Data from the National Family Health Survey (NFHS-IV) are employed for this study. The Current Status Data Technique is applied to the dataset, considering exclusively those women with marital durations less than 120 months. This methodology enables the adjustment of truncation effects and facilitates a more precise estimation of the FBI. Statistical analysis is conducted to determine the FBI distribution and ascertain the necessary sample size. Results: The estimated First Birth Interval (FBI) without accounting for truncation is 27.85 months, while the estimate considering truncation is 31.70 months. When applying the Current Status Data technique, the estimated FBI is 30.70 months. To obtain reliable estimates of the FBI using Current Status techniques, a minimum sample size of over 5,000 observations is necessary. Conclusion: The truncation effect in FBI is addressed, and some non-parametric adjustments are used for estimating the duration of FBI. The Current Status Data technique emerges as a valuable tool for mitigating these effects and enhancing the precision of FBI estimates. This research contributes to an improved understanding of fertility dynamics and provides valuable insights for future studies on the First Birth Interva

Federated Adaptive Causal Estimation (FACE) of Target Treatment Effects

Federated learning of causal estimands may greatly improve estimation efficiency by leveraging data from multiple study sites, but robustness to heterogeneity and model misspecifications is vital for ensuring validity. We develop a Federated Adaptive Causal Estimation (FACE) framework to incorporate heterogeneous data from multiple sites to provide treatment effect estimation and inference for a flexibly specified target population of interest. FACE accounts for site-level heterogeneity in the distribution of covariates through density ratio weighting. To safely incorporate source sites and avoid negative transfer, we introduce an adaptive weighting procedure via a penalized regression, which achieves both consistency and optimal efficiency. Our strategy is communication-efficient and privacy-preserving, allowing participating sites to share summary statistics only once with other sites. We conduct both theoretical and numerical evaluations of FACE and apply it to conduct a comparative effectiveness study of BNT162b2 (Pfizer) and mRNA-1273 (Moderna) vaccines on COVID-19 outcomes in U.S. veterans using electronic health records from five VA regional sites. We show that compared to traditional methods, FACE meaningfully increases the precision of treatment effect estimates, with reductions in standard errors ranging from 26 % to 67 % .

An extended state repetitive observer formulation for periodic/polynomial signal rejection in feedback control schemes

Repetitive control is a renowned method for managing periodic disturbances and references in control systems. Despite its effectiveness, performance issues arise when confronting disturbances like polynomial or sinusoidal signals. To enhance its capability, we introduce an extended state repetitive observer by incorporating internal models of periodic, sinusoidal, and polynomial signals into a novel observer framework. This design empowers the observer to adeptly reject and track both periodic and combined sinusoidal+polynomial signals. Beyond addressing the limitations of traditional repetitive control, our observer offers precise disturbance estimation for further analysis and feedback implementation. The observer’s performance is demonstrated through experimental validation within a control setup across various scenarios, focusing on its ability to reject periodic disturbances and track periodic signals effectively.

Predefined-Time Enhanced Antidisturbance Attitude Control for Rigid–Liquid Coupled Launch Vehicles

High-precision and fast-response attitude control of launch vehicles plays an increasingly important role in numerous space launch missions, ensuring the efficiency of space transportation. However, disturbances caused by liquid sloshing and external environments degrade the control performances seriously. Because of the complicated coupling with control inputs and angular acceleration, the dynamics of liquid sloshing disturbance are highly uncertain. Therefore, it is urgent to develop novel disturbance estimation methods for launch vehicles by thoroughly analyzing disturbance dynamics. In this paper, an observer-based predefined-time attitude control method aimed at achieving precise and rapid estimation of liquid sloshing disturbance and other external disturbances is proposed. More specifically, a prescribed-time reduced-order state observer is proposed for estimating the liquid sloshing disturbance and the unmeasurable system state, leveraging the specific characteristics and inherent coupling relations of liquid sloshing disturbance. Additionally, a prescribed-time extended state observer is introduced for estimating external disturbances and nonlinear terms. Subsequently, by employing a predefined-time output feedback controller, a composite control law is proposed to enhance the antidisturbance capabilities of rigid–liquid coupled launch vehicles. Finally, simulation examples are presented to illustrate the effectiveness of the proposed method.

A solution to the pervasive problem of response bias in self-reports

Self-reports are used ubiquitously to probe people's thoughts, feelings, and behaviors and inform medical decisions, enterprise operations, and government policy and legislation. Despite their pervasive use, self-report measures such as Likert scales have a profound problem: Standard analytic approaches do not control for the confounding effects of idiosyncratic response biases. Here, we present a model-based solution to this problem. Our model disentangles response bias from latent constructs of interest to obtain less biased scores of the latent states of respondents. Inspired by Thurstonian approaches in the psychophysics literature, the model requires nothing further than standard Likert scale design assumptions. The model uses a data-driven approach to control for response biases, without the need to prespecify bias types or response strategies. We demonstrate the model's ability to uncover more precise estimates of latent state associations, outperforming bias-affected standard scoring techniques, and garner insights into previously undetected codependencies between certain latent states and particular forms of response bias. The model is thus a tool which outperforms standard scoring methods and generates insights into, and controls for, the potentially confounding effects of response bias on self-report Likert scale data.

Twofold Auxiliary Information Under Two-Phase Sampling: An Improved Family of Double-Transformed Variance Estimators

Outlier values and rankings are important for emphasizing data distribution variability, which improves the accuracy and effectiveness of variance estimations. To enhance the estimation of finite population variance in a two-phase sampling framework, this study presents an improved class of double exponential-type estimators by utilizing the outlier values and ranks of an auxiliary variable. A theoretical analysis is conducted to derive the biases and mean squared errors (MSEs) of these estimators using first-order approximations. A comprehensive simulation study is then performed to analyze the performance of the proposed estimators. The results clearly show that the new estimators provide more precise estimates, achieving a higher percentage relative efficiency (PRE) across all simulated scenarios. Furthermore, three data sets are analyzed to further confirm the efficiency of the proposed estimators as compared to other existing estimators. These results emphasize the potential of the proposed class of estimators to optimize variance estimation techniques, making it a more cost-effective and accurate choice for researchers using two-phase sampling in a variety of domains.

A Combination of Classification Robust Adaptive Kalman Filter with PPP-RTK to Improve Fault Detection for Integrity Monitoring of Autonomous Vehicles

Real-time integrity monitoring (IM) is essential for autonomous vehicle positioning, requiring high availability and manageable computational load. This research proposes using precise point positioning real-time kinematic (PPP-RTK) as the positioning method, combined with an improved classification adaptive Kalman filter (CAKF) for processing. PPP-RTK enhances IM availability by allowing undifferenced and uncombined observations, enabling individual observation exclusion during fault detection and exclusion (FDE). The CAKF reduces FDE computational load by using a robustness test instead of traditional FDE methods, improving precision and availability in protection level estimation. Epoch-wise weighting adjustments in the robustness test create a more accurate stochastic model, aided by an adaptive unit weight variance (UWV) calculated with a sliding window, achieving a 7–28% UWV reduction. Three test scenarios with up to four simultaneous faults in code and phase observations, ranging from 1 to 200 m and 0.4 to 20 m, respectively, demonstrated successful identification and de-weighting of faults, resulting in maximum positioning errors of 6 mm (horizontal) and 11 mm (vertical). The method reduced FDE computational load by 50–99.999% compared to other approaches.

Mapping the Washington Group on Disability Statistics disability measure to the Health Utilities Index Mark 3: Development and validation of a predictive multivariable model in a general population sample.

Statistics Canada routinely collects information on functional health and related concepts. Recently, the Washington Group on Disability Statistics (WG) measure of disability has been introduced to the Canadian Community Health Survey (CCHS). The WG measure is used as a tool for developing internationally comparable data on disability. In alternate cycles of the CCHS, it replaces the Health Utilities Index Mark 3 (HUI3), a generic preference-based measure of health-related quality of life. The HUI3 is used to derive evaluative health measures common in population health and economic evaluations. Since the WG measure is not preference-based, it is unable to derive these measures. To address resulting data gaps, this study empirically maps the health state utility values of the HUI3 score from the WG measure. Empirical mapping used a "head-to-head" subsample of the 2017 CCHS where WG and HUI3 measures were collected from the same respondents aged 40 and over. Empirical mapping used regression models to estimate the statistical relationship between WG and HUI3 measures in addition to health and demographic variables. Out-of-sample predictive performance was assessed through descriptive statistics, mean absolute error, and other measures of predictive accuracy. The preferred estimation strategy resulted in reasonably precise estimates of the HUI3 score corresponding to trends across health and demographic characteristics and reflecting distributional properties of the HUI3 score. Inclusion of different components of the WG measure influenced predictive accuracy. Empirical mapping offers a potential method to estimate health state utility scores from the WG measure and addresses data gaps in health-related quality of life measures in the CCHS when HUI3 is not collected.

A Purely Real-Valued Fast Estimator of Dynamic Harmonics for Application in Embedded Monitoring Devices in Power-Electronic Grids

Dynamic harmonic estimation is important for the monitoring and control of power-electronic grids. But the high-precision dynamic harmonic estimation algorithms usually have a heavy computational burden and occupy a large memory space, making them difficult to implement in the embedded platform. Thus, the motivation of this paper lies in providing an estimator with low computational complexity and less storage space consumption. A purely real-valued fast dynamic harmonics estimator is proposed. Firstly, a purely real-valued estimation model is established based on the Taylor series expansion on the time-varying amplitude and phase angle. Secondly, the estimation filter bank is computed in the least-squares sense, and the corresponding estimation error is theoretically analyzed. Finally, the purely real-valued fast dynamic harmonics estimator is designed. The advantage includes significantly reducing the computational complexity and memory space consumption while maintaining high-precision estimation. The testing results show that the proposed estimator can achieve the highest harmonics estimation precision under dynamic conditions. The frequency error, magnitude error, and phase angle error are less than 5 × 10−2 Hz, 7 × 10−1%, and 8 × 10−2 degrees, respectively, which verifies the advantage of high-precision estimation. The proposed estimator achieves a computational speed-up of approximately 430, 396, and 330 times compared to the Prony method, ESPRIT method, and iterative Taylor Fourier transform method, respectively. The computational load rate for executing the proposed estimator on the embedded prototype using C6748 DSP for estimating 50 harmonics is approximately only 2.05%, which verifies the advantage of a low computational load rate.

Estimating realized relatedness in free-ranging macaques by inferring identity-by-descent segments

Biological relatedness is a key consideration in studies of behavior, population structure, and trait evolution. Except for parent-offspring dyads, pedigrees capture relatedness imperfectly. The number and length of identical-by-descent DNA segments (IBD) yield the most precise relatedness estimates. Here, we leverage different methods for estimating IBD segments from low-depth whole genome resequencing data to demonstrate the feasibility and value of resolving fine-scaled gradients of relatedness in free-living animals. Using primarily 4 to 6× depth data from a rhesus macaque (Macaca mulatta) population with long-term pedigree data, we show that we can infer the number and length of IBD segments across the genome with high accuracy even at 0.5× sequencing depth. In line with expectations based on simulation, the resulting estimates demonstrate substantial variation in genetic relatedness within kin classes, leading to overlapping distributions between kin classes. By comparing the IBD-based estimates with pedigree and short tandem repeat-based methods, we show that IBD estimates are more reliable and provide more detailed information on kinship. The inferred IBD segments also identify cryptic genetic relatives not represented in the pedigree and reveal elevated recombination rates in females relative to males, which enables the majority of close maternal and paternal kin to be distinguished with genotype data alone. Our findings represent a breakthrough in the ability to study the predictors and consequences of genetic relatedness in natural populations, contributing to our understanding of a fundamental component of population structure in the wild.

A high‐altitude thermal infrared method for estimating moose abundance and demography in Rocky Mountain National Park, USA

Resource managers require accurate estimates of large herbivore abundance and demography to maintain ecological integrity. Common methods to count these species, including observations from low altitude helicopter flights, may conflict with other protected area management objectives and struggle to produce precise estimates for more cryptic species. To address these issues, we evaluated thermal infrared (TIR) assisted counts of Shiras moose Alces alces shirasi in a temperate montane ecosystem of Colorado, USA. We binned the 694.7 km2 study area into two strata, depicting higher and lower moose habitat suitability. Sixty transects (each 536 m wide by 5 km long) were randomly selected and flown from fixed wing aircraft flying at altitudes centered around ~ 610 m (2000 ft) above ground level (AGL) in July 2019 and again in July 2020. We determined abundance and demographic information using double‐observer line distance sampling with estimates produced using n‐mixture models. Detection probability of groups on the line, and of individuals within a group, conditional on the group being observed, were high. Mean moose density estimates across the study area were consistent from year to year, with reasonable confidence. We estimated 0.215 moose km‐2 (HDI 0.145, 0.286) in 2019 and 0.207 moose km‐2 (HDI 0.144, 0.276) in 2020. Within‐stratum estimates varied from year to year, likely an effect of the definition of suitable habitat and transect classifcation. Estimated ratios of bulls per cow and calves per cow fell within expected ranges for Shiras moose in Colorado but did vary across years. Thermal clutter created some impacts to TIR video quality. Our study indicates that this approach can reliably estimate moose densities in forested and topographically complex environments, while maintaining greater aircraft altitude above ground level when compared with traditional aerial survey methods. This reduced disturbance to wildlife, impacts to wilderness and improved aviator and aircraft passenger safety.