Estimation Method Research Articles

An improved GNSS ambiguity best integer equivariant estimation method with Laplacian distribution for urban low-cost RTK positioning

An improved GNSS ambiguity best integer equivariant estimation method with Laplacian distribution for urban low-cost RTK positioning

Quantitative blood loss measurement methods for early detection of primary postpartum haemorrhage following vaginal birth: A scoping review.

To map the commonly used quantitative blood loss measurement methods in clinical practice and provide a solid foundation for future studies. This study adhered to the JBI methodology for scoping reviews and preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews. We conducted a literature search using five databases to retrieve articles published between January 2012 and September 2022. The search was repeated on 29 February 2024. Data extraction and verification were carried out by two independent researchers using a self-designed data extraction form. Ultimately, 26 studies published between 2012 and 2024 were considered eligible for inclusion. Six categories of methods were identified from the 26 articles. Among the included studies, only two involved randomized controlled trials, with the majority being observational studies. The World Health Organization (2012) version of the postpartum haemorrhage diagnostic criteria was predominantly used in most studies. Gravimetric and volumetric methods emerged as the most commonly used methods for quantifying postpartum haemorrhages. The timing of blood collection was inconsistent among the included studies. Only 12 studies mentioned measures for the management of amniotic fluid. This scoping review supports the replacement of the visual estimation of blood loss with quantitative assessment methods. Supporting a specific assessment approach is not feasible due to the variability of the study. Future research should focus on establishing the best practices for specific quantitative methods to standardize the management of postpartum haemorrhage and reduce the incidence of postpartum haemorrhage-related adverse outcomes. Healthcare professionals need to acknowledge the low accuracy of visual estimation methods and implement quantitative methods to assess postpartum blood loss. Given the limitations inherent in each assessment method, quantification of blood loss should be combined with assessment of maternal vital signs, physiologic indicators and other factors.

A Systematized Review of Quantitative Ultrasound Based on First-Order Speckle Statistics.

Since the late 1970s, the speckle interference patterns ubiquitous in pulse-echo ultrasound images have been used to characterize sub-resolution tissue structure. During this time, new models, estimation methods, and processing techniques have proliferated, offering a wealth of recommendations for the task of tissue characterization. A literature review was performed to draw attention to these various methods and to critically track assumptions and gaps in knowledge. A total of 388 articles were collected from a systematic search for first-order speckle statistics in diagnostic ultrasound in the NIH PubMed database and Elsevier's Scopus database. Articles were grouped by basic characteristics and evaluated for addressing fundamental assumptions. A sampling of models and methods is presented in order to reveal the state of the art in speckle statistics as well as sources of measurement error and other important considerations. While this body of literature emphasizes the value of speckle analysis in diagnostic ultrasound, it is shown that relatively little attention is devoted to basic assumptions such as the linearity of system response and scatterer geometry. Additionally, several areas of investigation are available to improve upon speckle statistics analysis, potentially leading to the advancement of this unique tool.

The tightness of the measure-based method to estimate quantum coherence

With the advancement of quantum information science, the understanding of quantum coherence has become increasingly important. In this paper, we investigate the effects of different channels on quantum states and analyze the evolution process of quantum states during channel transmission. Our findings reveal variations in the impact of different channels on quantum coherence. Additionally, we explore the estimation of lower bounds on coherence after passing through these diverse channels. We observe that channel transmission has a certain influence on the tightness of lower bounds on quantum coherence. We conduct a detailed analysis of this influence and propose improvement method to enhance the tightness of the lower bound estimation. Based on our research results, we draw conclusions that unveil the characteristics of quantum coherence under different channel conditions. Furthermore, we provide an effective estimation method and improvement strategies to accurately assess the coherence of quantum states. This research holds significant implications for further advancements in quantum information processing and quantum communication.

Genetic association between gut microbiome and blood pressure and blood cell count as mediator: A two-step Mendelian randomization analysis.

Previous studies have established a genetic link between gut microbiota and hypertension, but whether blood cell count plays a mediating role in this remains unknown. This study aims to explore genetic associations and causal factors involving the gut microbiome, peripheral blood cell count, and blood pressure. We utilized summary statistics derived from genome-wide association studies to conduct a two-sample mediation Mendelian randomization analysis (https://gwas.mrcieu.ac.uk/). We applied inverse variance weighted (IVW) estimation method as the primary method, along with MR Egger, Weighted median, Simple mode and Weighted mode as complementary methods. To ensure the robustness of the results, several sensitivity analyses were conducted. Genetic variants significantly associated with the microbiome, blood pressure, or peripheral blood cell counts were selected as instrumental variables. Fourteen microbial taxa were found to have suggestive associations with diastolic blood pressure (DBP), while fifteen microbial taxa showed suggestive associations with systolic blood pressure (SBP). Meanwhile, red blood cell count, lymphocyte count, and platelet count were identified to mediate the influence of the gut microbiome on blood pressure. Specifically, red cell count was identified to mediate the effects of the phylum Cyanobacteria on DBP (mediated proportion: 8.262 %). Lymphocyte count was found mediate the effects of the genus Subdoligranulum (mediated proportion: 2.642 %) and genus Collinsella (mediated proportion: 2.749 %) on SBP. Additionally, platelet count was found to mediate the relationship between the genus Eubacterium ventriosum group and SBP, explaining 3.421 % of the mediated proportion. Our findings highlighted that gut microbiota may have causal influence on the blood pressure by modulating blood cell counts, which sheds new light on the pathogenesis and potential clinical interventions through the intricate axis of gut microbiome, blood cell counts, and blood pressure.

Defining Overweight and Obesity by Percent Body Fat instead of Body Mass Index.

Thresholds for overweight and obesity are currently defined by body mass index (BMI), a poor surrogate marker of actual adiposity (percent body fat, %BF). Practical modern technologies provide estimates of %BF but medical providers need outcome-based %BF thresholds to guide patients. This analysis determines %BF thresholds based on key obesity-related comorbidities, exhibited as metabolic syndrome (MetSyn). These limits were compared to existing BMI thresholds of overweight and obesity. Correlational analysis of data from cross sectional sampling of 16,918 adults (8,734 men and 8,184 women) from the US population, accessed by the National Health and Nutrition Examination Survey (NHANES) public use datasets. Individuals measured by BMI as overweight (BMI>25 kg/m2) and with obesity (BMI>30 kg/m2) included 5% and 35% of individuals with MetSyn, respectively. For men, there were no cases of MetSyn below 18%BF, %BF equivalence to "overweight" (i.e., 5% of MetSyn individuals) occurred at 25%BF, and "obesity" (i.e., 35% of MetSyn individuals) corresponded to 30%BF. For women, there were no cases of MetSyn below 30%BF, "overweight" occurred at 36%BF, and "obesity" corresponded to 42%BF. Comparison of BMI to %BF illustrates the wide range of variability in BMI prediction of %BF, highlighting the potential importance of using more direct measures of adiposity to manage obesity-related disease. Practical methods of body composition estimation can now replace the indirect BMI assessment for obesity management, using threshold values provided from this study. Clinically relevant "overweight" can be defined as 25 and 36% BF for men and women, respectively, and "obesity" is defined as 30 and 42% BF for men and women.

Estimation of fetal weight at term by clinical and sonographic assessment and its correlation with the birth weight – A prospective cohort study in a tertiary care hospital

Estimation of fetal weight is necessary to decide on the time and route of delivery. Different clinical and ultrasound methods for estimating fetal weight are followed by different institutions. Antenatal assessment of fetal weight is necessary to achieve better feto-maternal outcomes. The objectives of this study were: (a) fetal weight estimation by ultrasound (Hadlock’s formula) and clinical methods (Johnson’s formula and Dare’s formula) and (b) to compare them with the actual weight of the baby after birth. A prospective cohort study was conducted in a tertiary care centre at Puducherry, India including all singleton, term pregnant women with vertex presentation, normal AFI and BMI <35 kg/m who delivered within the next 48 hours of assessment. The agreement between the different methods of fetal weight estimation and actual birth weight were calculated using intraclass correlation coefficient. Bland and Altman plot was done to identify the intervals of agreements. A total of 400 term antenatal women with age range of 17 to 44 years participated in the study. Intraclass correlation coefficient for Johnson’s formula was 0.816, Dare’s formula was 0.672 and Hadlock’s formula was 0.912. All the three methods had statistically significant correlation with the actual birth weight. Ultrasound estimation of fetal weight is the best out of the three methods and it correlated well with the actual birth weight. Johnson’s formula also gave results close to actual birth weight. With a correlation coefficient comparable to that of ultrasound estimation, Johnson’s formula can be used in low resource settings where ultrasound facilities are not available.

MRI-only based material mass density and relative stopping power estimation via deep learning for proton therapy: a preliminary study

Magnetic Resonance Imaging (MRI) is increasingly being used in treatment planning due to its superior soft tissue contrast, which is useful for tumor and soft tissue delineation compared to computed tomography (CT). However, MRI cannot directly provide mass density or relative stopping power (RSP) maps, which are required for calculating proton radiotherapy doses. Therefore, the integration of artificial intelligence (AI) into MRI-based treatment planning to estimate mass density and RSP directly from MRI has generated significant interest. A deep learning (DL) based framework was developed to establish a voxel-wise correlation between MR images and mass density as well as RSP. To facilitate the study, five tissue substitute phantoms were created, representing different tissues such as skin, muscle, adipose tissue, 45% hydroxyapatite (HA), and spongiosa bone. The composition of these phantoms was based on information from ICRP reports. Additionally, two animal tissue phantoms, simulating pig brain and liver, were prepared for DL training purposes. The phantom study involved the development of two DL models. The first model utilized clinical T1 and T2 MRI scans as input, while the second model incorporated zero echo time (ZTE) MRI scans. In the patient application study, two more DL models were trained: one using T1 and T2 MRI scans as input, and another model incorporating synthetic dual-energy computed tomography (sDECT) images to provide accurate bone tissue information. The DECT empirical model was used as a reference to evaluate the proposed models in both phantom and patient application studies. The DECT empirical model was selected as the reference for evaluating the proposed models in both phantom and patient application studies. In the phantom study, the DL model based on T1, and T2 MRI scans demonstrated higher accuracy in estimating mass density and RSP for skin, muscle, adipose tissue, brain, and liver. The mean absolute percentage errors (MAPE) were 0.42%, 0.14%, 0.19%, 0.78%, and 0.26% for mass density, and 0.30%, 0.11%, 0.16%, 0.61%, and 0.23% for RSP, respectively. The DL model incorporating ZTE MRI further improved the accuracy of mass density and RSP estimation for 45% HA and spongiosa bone, with MAPE values of 0.23% and 0.09% for mass density, and 0.19% and 0.07% for RSP, respectively. These results demonstrate the feasibility of using an MRI-only approach combined with DL methods for mass density and RSP estimation in proton therapy treatment planning. By employing this approach, it is possible to obtain the necessary information for proton radiotherapy directly from MRI scans, eliminating the need for additional imaging modalities.

Online updating mode learning for streaming datasets

In the case of limited computer memory, it is worth discussing how to regression the big data stream and solve the outlier problem reasonably. To solve the above problems, this article proposes an online updatable modal linear regression algorithm, which is robust to heavy-tailed distribution, heterogeneous errors and outliers. However, due to streaming data, the traditional static statistical methods are facing new challenges. In addition, existing inference tools in online learning cannot be used directly for modal linear regression. A major challenge is that the data is one-pass and the final amount of data is not even known, which results in the inability to obtain optimal bandwidth, resulting in poor theoretical performance of the estimator. In particular, the estimation and inference methods are proposed. The process only passes the data once. Finally, the finite sample performance of the proposed method is verified by simulation and real data analysis.

Adaptive dual-domain filtering for random seismic noise removal

Random noise significantly reduces the signal-to-noise ratio (S/N) of seismic data and affects the accuracy of seismic interpretation. Traditional denoising methods typically require manual parameter tuning to increase the robustness and accuracy across various random noise levels. In this study, based on the statistical definition of random noise, we used the variance of random noise as the level of random noise and proposed an adaptive dual-domain filter (ADDF). The ADDF method estimates the random noise variance in the seismic data and uses this estimation to effectively denoise the seismic data. First, we employ a difference operator in two directions to remove useful structures from the seismic data. The processed data are then used to estimate the global random noise variance through iterative statistical processing. In the denoising process of the ADDF, seismic data are masked by a bilateral filter in the spatial domain, followed by a short-time Fourier transform with wavelet shrinkage in the frequency domain, both controlled by the adaptively estimated random noise variance. The dual-domain filter is applied iteratively for the best performance. Synthetic experiments demonstrate the robustness of the ADDF in accurately estimating the noise variance without tuning parameters, and its superior denoising performance is evident in both synthetic examples and field data when compared to two typical denoising methods: f-x deconvolution and curvelet domain thresholding. As an adaptive random noise estimation and removal method, the ADDF relies only on seismic data, making denoising random noise more objective and accurate without manual adjustment.

Calibration and pose measurement of a combined vision sensor system for industrial robot grasping of brackets

Recently, visual sensing measurement and its application in industrial robot operations have been widely researched, promoting the development of instrumentation and automation. This study proposes a combined vision sensor system for robot grasping, focusing on combined sensor system calibration and bracket pose measurements. The system configuration and working strategy of the combined vision system are introduced. Thereafter, the calibration of the combined vision coordinate systems is presented, wherein a global vision system acts as the external measuring equipment for accurately calibrating the local vision. Furthermore, a pose estimation method using a local vision system (LVS) is proposed, including morphology-based image enhancement and principal component analysis based corner recognition methods. Verification experiments, including combined calibration and bracket pose measurements, were performed to validate the effectiveness and accuracy of the proposed combined vision measurement strategy. The results demonstrated that the proposed system applies to industrial robot grasping of brackets. In addition, the proposed robot-sensor calibration method improves calibration accuracy. Finally, the proposed corner detection method is effective and accurate for different bracket detection applications. This study provides a system that improves robot grasping results by considering key factors, such as vision measurement accuracy, and calibration methods.

NanoMUD: Profiling of pseudouridine and N1-methylpseudouridine using Oxford Nanopore direct RNA sequencing.

Nanopore direct RNA sequencing provided a promising solution for unraveling the landscapes of modifications on single RNA molecules. Here, we proposed NanoMUD, a computational framework for predicting the RNA pseudouridine modification (Ψ) and its methylated analog N1-methylpseudouridine (m1Ψ), which have critical application in mRNA vaccination, at single-base and single-molecule resolution from direct RNA sequencing data. Electric signal features were fed into a bidirectional LSTM neural network to achieve improved accuracy and predictive capabilities. Motif-specific models (NNUNN, N = A, C, U or G) were trained based on features extracted from designed dataset and achieved superior performance on molecule-level modification prediction (Ψ models: min AUC = 0.86, max AUC = 0.99; m1Ψ models: min AUC = 0.87, max AUC = 0.99). We then aggregated read-level predictions for site stoichiometry estimation. Given the observed sequence-dependent bias in model performance, we trained regression models based on the distribution of modification probabilities for sites with known stoichiometry. The distribution-based site stoichiometry estimation method allows unbiased comparison between different contexts. To demonstrate the feasibility of our work, three case studies on both in vitro and in vivo transcribed RNAs were presented. NanoMUD will make a powerful tool to facilitate the research on modified therapeutic IVT RNAs and provides useful insight to the landscape and stoichiometry of pseudouridine and N1-pseudouridine on in vivo transcribed RNA species.

Comparative analysis of C n2 estimation methods for sonic anemometer data

Wind speed and sonic temperature measured with ultrasonic anemometers are often utilized to estimate the refractive index structure parameter C n 2, a vital parameter for optical propagation. In this work, we compare four methods to estimate C n 2 from C T 2, using the same temporal sonic temperature data streams for two separated sonic anemometers on a homogenous path. Values of C n 2 obtained with these four methods using field trial data are compared to those from a commercial scintillometer and from the differential image motion method using a grid of light sources positioned at the end of a common path. In addition to the comparison between the methods, we also consider appropriate error bars for C n 2 based on sonic temperature considering only the errors from having a finite number of turbulent samples. The Bayesian and power spectral methods were found to give adequate estimates for strong turbulence levels but consistently overestimated the C n 2 for weak turbulence. The nearest neighbors and structure function methods performed well under all turbulence strengths tested.

Resource‐driven changes in wild boar movement and their consequences for the spread of African Swine Fever in the Russian Far East

Knowledge of animal movement patterns is invaluable to understanding the spread of diseases among wildlife populations. One example is the recent African swine fever (ASF) outbreak among wild boar Sus scrofa populations across East Asia, where there is a lack of information on movements of this species. During a wild boar tracking project to inform abundance estimation methods in the Russian Far East's Sikhote‐Alin Biosphere Zapovednik, the combination of high variability in pulsed resources of acorns and pine nuts between fall 2019 and fall 2020, and the outbreak of ASF during the latter year, offered the unique opportunity to investigate the relationship between wild boar movements to exploit pulsed resources and the potential for disease spread. We analyzed relocation data from GPS‐collared wild boar in fall 2019 and 2020 and compared them to reference data in Belgium, representative of western Europe. We found remarkable differences in movement patterns, with Far East wild boar travelling large distances in fall 2020 (maximum observed of 77 km in four days) when the availability of acorns was low. In our resource selection analysis, we found clear selection for mast‐producing forest types that corresponded with the species of greater mast production (oak or pine) for that year. Comparing the displacement of individual wild boar along a moving window of 1–7 days (time between infection and the onset ASF symptoms) highlighted the potential of rapid ASF spread over long distances when wild boar are in search of pulsed resources. This work demonstrates the capacity of wild boar to move long distances to exploit resources and emphasizes the need to consider resource availability when predicting the speed and extent to which diseases such as ASF can spread.

The Impact of Non-Tariff Measures on the Exporting of Agricultural Products of Cambodia

The production and exporting of agricultural products were considered one of the most important roles of the Cambodian people. In the free trade world, tariff measures were not determined to be the main obstacle to moving products from one country to worldwide, but non-tariff measures, NTMs, were defined to be the main problem nowadays. An empirical investigation using the gravity model with four classes of NTMs: SPS, TBT, PSI, and NTB was carried out to assess the impact of Cambodia’s exported agricultural products abroad. In order to generate robust standard errors for hypothesis testing, the Ordinary Least Square (OLS) estimation method was combined with the bootstrapping technique. The results of this research supported the gravity model hypothesis, however, SPS and PSI were negatively affecting Cambodia’s exporting at significant levels of 10 percent and 1 percent, respectively, while the TBT measure had a positive influence on trade at a level of significant of 1 percent. In contrast, the NTB class had no significant impact on exports.

Open-Circuit Voltage Variation in LiCoO2 Battery Cycled in Different States of Charge Regions

Open-Circuit Voltage Variation in LiCoO2 Battery Cycled in Different States of Charge Regions

Novel Battery State of Health Estimation and Lifetime Prediction Method Based on the Catboost Model

Novel Battery State of Health Estimation and Lifetime Prediction Method Based on the Catboost Model

Adaptive Fuzzy Fixed-Time Control for Nonlinear Systems with Unmodeled Dynamics

This article concentrates on the problem of fixed-time tracking control for a certain class of nonlinear systems with unmodeled dynamics. Unmodeled dynamics are prevalent in practical engineering systems, such as axially symmetric systems like robotic arms, spacecraft, and missiles. In this paper, the fuzzy-logic systems (FLSs) are implemented to address the challenge of accurately approximating the unknown nonlinear terms that arise during the derived control algorithm process. By employing fixed-time command filters (FTCF), the “explosion of complexity” issues encountered in traditional backstepping methods will be effectively resolved. Moreover, error compensation mechanisms are derived to effectively mitigate the filtering errors that may arise from the FTCFs. The computational burden associated with FLSs is reduced through the utilization of the weight vector estimation method based on the maximal norm and an adaptive approach. A fixed-time adaptive fuzzy tracking controller is developed within the backstepping control framework to ensure the boundedness of all signals and achieve fixed-time convergence of the tracking error for the controlled system. Illustrative examples are conducted to illustrate the viability of the derived controller.

On the Maximum Likelihood Estimators’ Uniqueness and Existence for Two Unitary Distributions: Analytically and Graphically, with Application

Unit distributions, exhibiting inherent symmetrical properties, have been extensively studied across various fields. A significant challenge in these studies, particularly evident in parameter estimations, is the existence and uniqueness of estimators. Often, it is challenging to demonstrate the existence of a unique estimator. The major issue with maximum likelihood and other estimator-finding methods that use iterative methods is that they need an initial value to reach the solution. This dependency on initial values can lead to local extremes that fail to represent the global extremities, highlighting a lack of symmetry in solution robustness. This study applies a very simple, and unique, estimation method for unit Weibull and unit Burr XII distributions that both attain the global maximum value. Therefore, we can conclude that the findings from the obtained propositions demonstrate that both the maximum likelihood and graphical methods are symmetrically similar. In addition, three real-world data applications are made to show that the method works efficiently.

Extreme Value Statistics in Semi-Supervised Models

We consider extreme value analysis in a semi-supervised setting, where we observe, next to the n data on the target variable, n + m data on one or more covariates. This is called the semi-supervised model with n labeled and m unlabeled data. By exploiting the tail dependence between the target variable and the covariates, we derive estimators for the extreme value index and extreme quantiles of the target variable in this setting and establish their asymptotic behavior. Our estimators substantially improve the univariate estimators, based on only the n target variable data, in terms of asymptotic variances whereas the asymptotic biases remain unchanged. A simulation study confirms the substantially improved behavior of both estimators. Finally the estimation method is applied to rainfall data in France. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.