Precise Estimates Research Articles

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.

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.

State of Charge Estimation for Lithium Battery in Shipboard DC Power Grid Based on Differential Evolutionary Algorithm

More and more attention has been paid to ships with a DC power grid. State-of-charge (SOC) estimation is a pivotal and challenging assignment for lithium-ion batteries in such ships. However, the precision of SOC estimation is strongly connected with the system parameters. To better identify these parameters in lithium-ion batteries, a differential evolution (DE) algorithm was introduced into this paper as the optimizer. Initially, a first-order RC equivalent circuit model (ECM) was created to characterize the battery’s dynamic behavior. Following this, to estimate open-circuit voltage (OCV) throughout the entire dynamic process, a math model of optimization was established to minimize inaccuracies between the real and estimated terminal voltages. Moreover, estimated SOC values were obtained through OCV-SOC mappings and were contrasted against the true SOC values. The findings manifested the efficacy of the presented structure and technique in comparison with various frequently-cited DE variants.

A State-of-Health Estimation Method of a Lithium-Ion Power Battery for Swapping Stations Based on a Transformer Framework

Against the backdrop of automobile electrification, an increasing number of battery-swapping stations for electric vehicles have been launched to address the issue of slow battery charging under cold temperature conditions. However, due to the separation of the discharging and charging processes for lithium-ion batteries (LIBs) at swapping stations, and the circulation of batteries across different vehicles and stations, the operating data become fragmented, making it difficult to accurately identify the battery state-of-health (SOH). This study proposes a BiLSTM-Transformer framework that extracts the Constant Voltage Time (CVT) feature using only charging data, enabling the precise estimation of battery capacity degradation. Validation experiments conducted on battery samples under different operating temperatures showed that the model achieved a normalized RMSE of less than 1.6%. In ideal conditions, the normalized RMSE of the estimation reached as low as 0.11%. This model enables SOH estimation without relying on discharge data, contributing to the efficient and safe operation of battery swapping stations.

Comparative Study on Hyperelastic Constitutive Models for the Static and Dynamic Behavior of Resilient Mounts

Resilient mounts play a vital role in anti-vibration and shock-absorption systems, making precise estimation of their static and dynamic stiffness essential for ensuring optimal mechanical performance and effective design. This study investigates the behavior of resilient mounts by analyzing their static and dynamic stiffness characteristics through the application of various hyperelastic constitutive models. Seven hyperelastic models were reviewed and systematically compared using numerical simulations, experimental data, and analytical solutions. The model parameters were calibrated and optimized based on experimental results obtained from quasi-static loading tests, including tension, compression, and shear conditions. Additionally, frequency response simulations were employed to evaluate the dynamic behavior of the resilient mounts under varying preload scenarios. The results provide practical insights into the performance of hyperelastic models and offer a comprehensive guideline for selecting suitable models for resilient mount applications, contributing to improved accuracy in both static and dynamic performance predictions.

Improving spatial disaggregation of vehicular emission inventories

ABSTRACT Precise estimates of vehicular emissions at fine spatial scales are essential for effective emission reduction strategies. Achieving high-resolution vehicular emission inventories necessitates detailed data on traffic flow, driving patterns, and vehicle speeds for each road network segment. However, in developing countries, the lack of comprehensive traffic data, limited infrastructure, and insufficient monitoring systems constrains the development of high-resolution inventories. This gap poses significant challenges for accurately quantify emissions in regions that often experience rapid urbanisation and traffic growth. Here, we propose a novel method to enhance the spatial disaggregation of large-scale vehicular emission inventories. By analysing road-level emissions data from 63 Brazilian municipalities, we developed a model that predicts weighting factors to disaggregate vehicular emissions into a gridded format, based on the proportion of primary road lengths. Our findings indicate that the predicted weighting factors significantly improve the spatial disaggregation of vehicular emissions compared to the traditional road density method by reasonably increasing the emissions in high vehicular activity areas. This approach not only provides more accurate representations of vehicular emissions for urban planning in Brazil but also offers a solution that can be adapted to enhance top-down vehicular emissions inventories globally. Our study offers a valuable tool that can be tailored to various regions, enabling more precise urban planning and policy-making for air quality management worldwide.

Design, optimization, and inference of biphasic decay of infectious virus particles.

Virus population dynamics are driven by counter-balancing forces of production and loss. Whereas viral production arises from complex interactions with susceptible hosts, the loss of infectious virus particles is often approximated as a first-order kinetic process. As such, experimental protocols to measure infectious virus loss are not typically designed to identify non-exponential decay processes. Here, we propose methods to evaluate if an experimental design is adequate to identify multiphasic virus particle decay and to optimize the sampling times of decay experiments, accounting for uncertainties in viral kinetics. First, we evaluate synthetic scenarios of biphasic decays, with varying decay rates and initial proportions of subpopulations. We show that robust inference of multiphasic decay is more likely when the faster decaying subpopulation predominates insofar as early samples are taken to resolve the faster decay rate. Moreover, design optimization involving non-equal spacing between observations increases the precision of estimation while reducing the number of samples. We then apply these methods to infer multiple decay rates associated with the decay of bacteriophage ('phage') ΦD9, an evolved isolate derived from phage Φ21. A pilot experiment confirmed that ΦD9 decay is multiphasic, but was unable to resolve the rate or proportion of the fast decaying subpopulation(s). We then applied a Fisher information matrix-based design optimization method to propose non-equally spaced sampling times. Using this strategy, we were able to robustly estimate multiple decay rates and the size of the respective subpopulations. Notably, we conclude that the vast majority (94%) of the phage ΦD9 population decays at a rate 16-fold higher than the slow decaying population. Altogether, these results provide both a rationale and a practical approach to quantitatively estimate heterogeneity in viral decay.

Quantifying sighting dominance using on-display projections of monocular and binocular views.

Sighting dominance is an important behavioral property which has been difficult to measure quantitatively with high precision. We developed a measurement method that is grounded in a two-camera model that satisfies these aims. Using a simple alignment task, this method quantifies sighting ocular dominance during binocular viewing, identifying each eye's relative contribution to binocular vision. The method involves placing a physical target between the viewer and a display. The viewer indicates the perceived target's projection on the display with both eyes open and with only one eye open. The relative location of the binocular projection in relation to the two monocular projections is the index of dominance. The method produces a continuous variable with robust test-retest reliability (ICC = 0.96). The unit of measurement for the computed quantity is physiologically grounded: it is proportional to the distance between the monocular projections, which we show is predictable from interpupillary distance and phoria. Comparisons with the classic 'hole in card' sighting dominance test show good agreement, but also hint at potential bias for determining right-eye dominance in the latter. Interestingly, we find that some individuals systematically demonstrate nearly balanced vision, a phenomenon previously construed as mixed dominance or noisy responses. We also present ways to quantify and mitigate sources of random noise in this measurement. Overall, this new method allows for precise estimation of sighting dominance during binocular viewing. We expect it will allow a more effective understanding of the neural basis of dominance and improved effectiveness when using sighting dominance as a covariate in more complex analyses.

Leveraging Unlabeled Data for Superior ROC Curve Estimation via a Semiparametric Approach

The receiver operating characteristic (ROC) curve is a widely utilized tool in various fields, including economics, medicine, and machine learning, for evaluating classification performance and comparing treatment effect. The absence of clear and readily labels is a frequent phenomenon in estimating ROC owing to various reasons like labeling cost, time constraints, data privacy and information asymmetry. Traditional supervised estimators commonly rely solely on labeled data, where each sample is associated with a fully-observed response variable. We propose a new set of semi-supervised (SS) estimators to exploit available unlabeled data (samples lack of observations for responses) to enhance the estimation precision under the semi-parametric setting assuming that the distribution of the response variable for one group is known up to unknown parameters. The newly proposed SS estimators have attractive properties such as adaptability and efficiency by leveraging the flexibility of kernel smoothing method. We establish the large sample properties of the SS estimators, which demonstrate that the SS estimators outperform the supervised estimator consistently under mild assumptions. Numeric experiments provide empirical evidence to support our theoretical findings. Finally, we showcase the practical applicability of our proposed methodology by applying it to two real datasets.

Analysis of the effect of cypermethrin on the development of Lucilia sericata (Diptera: Calliphoridae)

ABSTRACT Forensic entomotoxicology examines the impact of toxicants on insect development, their presence in carcass-feeding insects, and their bioaccumulation to determine the post-mortem interval (PMI). Incorporate environmental elements to obtain precise estimates of the post-mortem interval. This study examined how cypermethrin affected the fly, Lucilia sericata, throughout various developmental stages. Larvae were grown on pork tissues (liver and muscle) spiked with cypermethrin at various temperatures (20, 25, and 30°C). The study further explored how cypermethrin affected the development rate of L. sericata. The findings revealed that cypermethrin in pork liver and muscle accelerates the growth of Lucilia sericata compared to control samples. This change was constant across all temperature regimes. At 30°C, the average duration from 1st instar to pupae was 172 ± 1.54 hours, at 25°C it was 261.1 ± 1.9 hours, and at 20°C it was 366.2 ± 0.75 hours in pork liver. The average duration from 1st instar to pupae for cypermethrin-treated pork muscle was 169.7 ± 1.45 hours, 261.5 ± 0.65 hours, and 359.7 ± 0.55 hours at 30°C, 25°C, and 20°C, respectively. PMI estimates were impacted by differences in L. sericata larval stage: cypermethrin changed the morphology and the larvae could remain at 30°C for up to 7 hours in pork liver and 4 hours in pork muscle.

Advances in Whole Genome Sequencing: Methods, Tools, and Applications in Population Genomics.

With the rapid advancement of high-throughput sequencing technologies, whole genome sequencing (WGS) has emerged as a crucial tool for studying genetic variation and population structure. Utilizing population genomics tools to analyze resequencing data allows for the effective integration of selection signals with population history, precise estimation of effective population size, historical population trends, and structural insights, along with the identification of specific genetic loci and variations. This paper reviews current whole genome sequencing technologies, detailing primary research methods, relevant software, and their advantages and limitations within population genomics. The goal is to examine the application and progress of resequencing technologies in this field and to consider future developments, including deep learning models and machine learning algorithms, which promise to enhance analytical methodologies and drive further advancements in population genomics.

On the decay of solutions of the wave equation with logarithmic damping

We obtain a precise decay estimate of the energy of solutions to the initial boundary value problem for the wave equation with logarithmic damping. This is the first decay result for the wave equation with a logarithmic damped term.

The influence of intervention fidelity on treatment effect estimates in clinical trials of complex interventions: A meta-epidemiological study

BackgroundRandomized clinical trials (RCT) provide the most reliable estimates of treatment effectiveness for therapeutic interventions. However, flaws in their design and conduct may bias treatment effect estimates, leading to over or underestimation of the true intervention effect. This is especially relevant for complex interventions, such as those in rehabilitation, which are multifaceted and tailored for individual patients or providers, leading to variations in delivery and treatment effects. ObjectiveTo assess whether poor intervention fidelity, the faithfulness of the intervention delivered in an RCT to what was intended in the trial protocol, influences (biases) estimates of treatment effects derived from meta-analysis of rehabilitation RCTs. MethodsIn this meta-epidemiological study of 19 meta-analyses and 204 RCTs published between 2010–2020, we evaluated the difference in intervention effects between RCTs in which intervention fidelity was monitored and those in which it was absent. We also conducted random-effects meta-regression to measure associations between intervention fidelity, risk of bias (ROB), study sample size, and treatment effect estimates. ResultsThere was a linear relationship between fidelity and treatment effect sizes across RCTs, even after adjusting for ROB and study sample size. Higher degrees of fidelity were associated with smaller but more precise treatment effect estimates (d= -0.23 95% CI: -0.38, -0.74). Lower or absent fidelity was associated with larger, less precise estimates. Adjusting for fidelity reduced pooled treatment effect estimates in 4 meta-analyses from moderate to small or from small to no negligible or no effect, highlighting how poor fidelity can bias meta-analyses’ results. ConclusionPoor or absent intervention fidelity in RCTs may lead to overestimation of observed treatment effects, skewing the conclusions from individuals studies and systematic reviews with meta-analyses when pooled. Caution is needed when interpreting the results of complex intervention RCTs when fidelity is not monitored or is monitored but not reported.

Advancing Forensic Investigations based on TiO2/Bi2O3 Electrochemical Sensor for Precise Potassium Estimation in Vitreous Humor

Advancing Forensic Investigations based on TiO2/Bi2O3 Electrochemical Sensor for Precise Potassium Estimation in Vitreous Humor

Estimating Per-Class Statistics for Label Noise Learning.

Real-world data may contain a considerable amount of noisily labeled examples, which usually mislead the training algorithm and result in degraded classification performance on test data. Therefore, Label Noise Learning (LNL) was proposed, of which one popular research trend focused on estimating the critical statistics (e.g., sample mean and sample covariance), to recover the clean data distribution. However, existing methods may suffer from the unreliable sample selection process or can hardly be applied to multi-class cases. Inspired by the centroid estimation theory, we propose Per-Class Statistic Estimation (PCSE), which establishes the quantitative relationship between the clean (first-order and second-order) statistics and the corresponding noisy statistics for every class. This relationship is further utilized to induce a generative classifier for model inference. Unlike existing methods, our approach does not require sample selection from the instance level. Moreover, our PCSE can serve as a general post-processing strategy applicable to various popular networks pre-trained on the noisy dataset for boosting their classification performance. Theoretically, we prove that the estimated statistics converge to their ground-truth values as the sample size increases, even if the label transition matrix is biased. Empirically, we conducted intensive experiments on various binary and multi-class datasets, and the results demonstrate that PCSE achieves more precise statistic estimation as well as higher classification accuracy when compared with state-of-the-art methods in LNL.

Advanced Pedestrian Distance Estimation for ADAS with Canny Edge Detection and Stereo Vision

Pedestrian detection is a vital aspect of Advanced Driver Assistance Systems (ADAS), crucial for ensuring driving safety and minimizing collision risks. While detecting pedestrians is important, it must be paired with precise distance estimation to create a robust safety solution. Stereovision cameras are well-regarded for their effectiveness and affordability in measuring depth through disparity between two images. Despite this, research on pedestrian distance estimation using only stereovision remains sparse, with many studies relying on computationally heavy dense depth maps. This paper proposes an innovative method for computing object-level disparity specifically for pedestrian detection using stereo cameras. The approach integrates Canny edge detection with ORB (Oriented FAST and Rotated BRIEF) feature matching to efficiently identify and track keypoints within pedestrian bounding boxes. This method not only improves the accuracy of distance estimation but also reduces computational demands, making it suitable for real-time applications. The approach was thoroughly tested on a Raspberry Pi 4, a resource-constrained device, and achieved promising results, demonstrating its potential for practical use in ADAS.

High-Gain Observer in Fuel Cell and Cascaded DC-DC Boost Converter System

In this study, we propose using a high-gain observer (HGO) in fuel cell systems and the FC-cascade boost converter to achieve precise state estimation and improve control accuracy. The HGO offers significant advantages, including robustness to disturbances, simplicity of design, and reduced dependency on costly sensors, thereby enhancing system performance and reducing operational costs. Our results demonstrate the superior accuracy and reliability of this approach, confirming its potential as a promising solution for fuel cell applications.