Estimation Process Research Articles

Macromorphoscopic evaluation of the mastoid process for sex estimation in contemporary Brazilian skulls: an accuracy study

This study aimed to analyze the accuracy of the macromorphoscopic evaluation of the mastoid process of the temporal bone in identified human skeletons for estimating sex. A total of 832 mastoid processes from individuals of both sexes, aged at least 20 years, were examined. All the mastoid processes were analyzed concerning their anatomical characteristics and classified according to the types established by Buikstra and Ubelaker (1994), assigning scores in which the lowest values (1 and 2) correspond to females, higher values (4 and 5) to males and value 3 represents a zone of indeterminate sex. For the total sample, score 3 was predominant, while score 1 was less frequent for both mastoid processes. In addition, men showed more occurrences of score 4, while women showed more classifications in score 2, both on the right and left, with a statistically significant relationship between sex and mastoid process. Overall accuracy was 76%, with males showing a higher percentage of correct observations than females (89.7% and 61.1%, respectively). The right mastoid process showed greater accuracy in predicting sex (p<0.001). Despite the greater number of studies on metric methods in the literature, morphoscopic methods are important in forensic anthropology, especially for sexual diagnosis, given their easy applicability and potential for good results.

Advancing river flow monitoring with small uncrewed aircraft and simulation-driven development

Current streamgaging processes for river flow rate estimation are typically slow and often hazardous, leading to inadequate coverage across national waterways. This paper presents a semi-autonomous aerial monitoring system that is designed for rapid river flow gaging, building upon a recently developed sensor package that is mounted beneath a small uncrewed aerial vehicle. This package consists of, among other instruments, a mid-wave infrared camera that can be used to detect minute thermal variations in the water surface, from which a particle image velocimetry algorithm is used to extract flow estimation. The design and testing of this sensor package and velocimetry algorithm for field evaluation are discussed, and a simulation environment facilitating the development of algorithms for automatic a priori and live-adaptive vehicle trajectory planning is presented. The simulation environment captures a physically based approximation of vehicle flight characteristics, contains digital terrain models of field test sites, and incorporates water surface flow maps generated from numerical flow simulation data and real-world measurements. Field and simulation results are presented validating the design of the sensor package and the use of simulation as a digital twin for aerial streamgaging development. This framework and the lessons learned to date lay the foundation for accelerated improvements in waterway measurement for both routine and disaster response purposes requiring rapid deployment in novel locations.

Quantification of total uncertainty in the physics-informed reconstruction of CVSim-6 physiology.

When predicting physical phenomena through simulation, quantification of the total uncertainty due to multiple sources is as crucial as making sure the underlying numerical model is accurate. Possible sources include irreducible aleatoric uncertainty due to noise in the data, epistemic uncertainty induced by insufficient data or inadequate parameterization and model-form uncertainty related to the use of misspecified model equations. In addition, recently proposed approaches provide flexible ways to combine information from data with full or partial satisfaction of equations that typically encode physical principles. Physics-based regularization interacts in non-trivial ways with aleatoric, epistemic and model-form uncertainty and their combination, and a better understanding of this interaction is needed to improve the predictive performance of physics-informed digital twins that operate under real conditions. To better understand this interaction, with a specific focus on biological and physiological models, this study investigates the decomposition of total uncertainty in the estimation of states and parameters of a differential system simulated with MC X-TFC, a new physics-informed approach for uncertainty quantification based on random projections and Monte Carlo sampling. After an introductory comparison between approaches for physics-informed estimation, MC X-TFC is applied to a six-compartment stiff ODE system, the CVSim-6 model, developed in the context of human physiology. The system is first analysed by progressively removing data while estimating an increasing number of parameters, and subsequently by investigating total uncertainty under model-form misspecification of nonlinear resistance in the pulmonary compartment. In particular, we focus on the interaction between the formulation of the discrepancy term and quantification of model-form uncertainty, and show how additional physics can help in the estimation process. The method demonstrates robustness and efficiency in estimating unknown states and parameters, even with limited, sparse and noisy data. It also offers great flexibility in integrating data with physics for improved estimation, even in cases of model misspecification.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 1)'.

Assessment of estimation performance for activities’ cost and time in construction projects using statistical process control

PurposeThe cost and duration estimation process is important to be monitored and controlled in construction projects. Estimation variation from actuals presents a problem when attempting to complete a project on planned time and budget. Various studies cover project performance monitoring with different cost and time performance indices. Nevertheless, project monitoring techniques do not take advantage of the available data to assess the performance and accuracy of estimates developed by the estimation team.Design/methodology/approachThis research proposes using statistical process control (SPC) to assess the consistency and stability of the estimation of activities’ costs and durations. The proposed system calculates the deviation of the estimated costs and durations from the actual values. These calculations are the activities’ indices that are used to plot the control chart. The process capability analysis (PCA) is used to determine the accuracy deviation of the estimations from the organization’s targets.FindingsResults of the proposed system application to a real project determine the activities that have inaccurate cost and duration estimations. This result helps the estimation departments to analyze reasons for inaccurate estimations. The proposed system is an easy, effective tool for continuous improvement to the performance of the estimation department.Originality/valueSome projects are classified as troubled projects when calculating the status of the project with reference to estimations, while the estimations themselves are troubled and need to be corrected. The proposed system of this paper is considered a novel approach by using SPC techniques such as control charts and process capability analysis for continuous monitoring and assessing of cost and duration estimation process performance to improve process accuracy and increase the credibility of estimation teams or departments.

Progress and Limitations in Forest Carbon Stock Estimation Using Remote Sensing Technologies: A Comprehensive Review

Forests play a key role in carbon sequestration and oxygen production. They significantly contribute to carbon peaking and carbon neutrality goals. Accurate estimation of forest carbon stocks is essential for a precise understanding of the carbon sequestration capacity of forest ecosystems. Remote sensing technology, with its wide observational coverage, strong timeliness, and low cost, is essential for carbon stock estimation research. However, challenges in data acquisition and processing include data variability, signal saturation in dense forests, and environmental limitations. These factors hinder accurate carbon stock estimation. This review summarizes the current state of research on forest carbon stock estimation from two aspects, namely remote sensing data and estimation methods, highlighting both the advantages and the limitations of various data sources and models. It also explores technological innovations and cutting-edge research in the field, focusing on deep learning techniques, optical vegetation thickness estimation methods, and the impact of forest–climate interactions on carbon stock estimation. Finally, the review discusses the current challenges in the field, including issues related to remote sensing data quality, model adaptability, forest stand complexity, and uncertainties in the estimation process. Based on these challenges, the paper looks ahead to future trends, proposing potential technological breakthroughs and pathways. The aim of this study is to provide theoretical support and methodological guidance for researchers in related fields.

Gintreg: Generalized interval regression

Many important research questions involve regression models in which the dependent variable is censored or reported in intervals rather than as a numerical value. A common approach to treating these problems is to assume that the data correspond to a certain distribution (for example, a normal distribution) and then apply maximum likelihood estimation. While this method is widely used in the literature, it can yield inconsistent estimators in the presence of either heteroskedasticity or distributional misspecification. The gintreg command is a partially adaptive maximum-likelihood estimation procedure that 1) generalizes the intreg command by relaxing the normality assumption and 2) draws from a library of fexible distributional forms. The treatment of heteroskedasticity is expanded to account for possible skewness and kurtosis. Additional options provide interaction with the estimation process, informative metrics, and visualizations. Right- and left-censored, interval, grouped, and point data can be accommodatec with this method.

A land use regression model to explain the spatial variation of nitrate concentration - A regional scale case study in the north-west of Ireland.

Regional-scale groundwater contamination estimation is crucial for sustainable water management. The primary obstacles in evaluating groundwater include limited data availability, small sample sizes, and difficulties in linking concentration levels to land use patterns. Linear regression identifies the relationship between measured concentrations and both natural and human-influenced factors. However, the primary difficulty with this method lies in choosing a group of regressors that meet all necessary criteria for the model when multiple potential regressors exist. This study introduces a buffer-based land-use linear regression method to develop a catchment-scale model for predicting nitrate concentrations in groundwater. The model successfully captures 85% of the spatial variability in nitrate across the study area, as indicated by the validation results from 32 training sites. The model's prediction capability and ability to capture the spatial variability of nitrate concentration were found to be good in the model development (R2=0.89) and validation (R2=0.80) steps. The model performed well in the accuracy assessment and error estimation processes (RMSE=0.025 and MAE=0.020). In future, this LUR model can be reparameterised with the latest available time series datasets to capture climate change scenarios. While this study focused on a small sub-catchment of the Bonet River, the methodology has the potential to be applied in a border study area. Future studies with a more robust methodology and more accurate predictor variables to explain the influence of the contamination sources, transport and attenuation processes can improve the buffer-based LUR technique for better model adaptation and applicability to other study areas.

Improved precision and accuracy of electron energy-loss spectroscopy quantification via fine structure fitting with constrained optimization.

Improved precision and accuracy of electron energy-loss spectroscopy quantification via fine structure fitting with constrained optimization.

Research on pose estimation algorithm of non-cooperative target tracked vehicles based on PnP model

To address the pose estimation problem of non-cooperative tracked vehicles, this study proposes a non-iterative Perspective-n-Point (PnP) method. In particular, the method leverages the large contact area between the vehicle’s track and the ground, thereby ensuring that the pitch angle between the camera coordinate system and the object coordinate system remains largely consistent. Based on this constraint, the rotation matrix is simplified by fixing the pitch angle, which reduces the degrees of freedom in pose estimation. To further enhance robustness, the proposed framework employs the RANSAC algorithm to eliminate outliers, thereby reducing iterative errors associated with traditional PnP algorithms and filtering out mismatched feature points. Finally, the pose equation is solved by finding the local minimum of the cost function through algebraic optimization, thus avoiding convergence issues in iterative optimization. Experimental validation is conducted using an optoelectronic reconnaissance unmanned platform. The results demonstrate that the proposed approach not only simplifies the pose estimation process for tracked vehicles but also enhances computational efficiency. Notably, it achieves an improved computation rate while maintaining accuracy, thereby enabling real-time pose estimation. This advancement holds significant potential for applications in autonomous navigation and target tracking systems requiring rapid and reliable state estimation.

Hybrid A*-Guided Model Predictive Path Integral Control for Robust Navigation in Rough Terrains

Navigating rough terrains requires a robust path planning algorithm that accounts for the physical properties of the environment to maintain stability and ensure safety. This article proposes the Hybrid A*-guided Model Predictive Path Integral (MPPI) algorithm augmented with traversability estimation to address the challenges of path planning on uneven terrains. The traversability estimation process quantifies surface characteristics, such as slope and roughness to identify traversable regions. Using this information, the Hybrid A* algorithm computes paths that minimize surface irregularities and prioritize regions with lower gradients, thereby enhancing stability and reducing dynamic disturbances. These computed paths are then used to define the mean control input for the MPPI algorithm, which performs localized optimization while adhering to the terrain-aware trajectory. By integrating terrain-aware guidance through the Hybrid A* algorithm with the MPPI, the proposed methodology automates the selection of the appropriate mean control input and enhances control performance by explicitly incorporating terrain properties into the planning process. Experimental results demonstrate the ability of the algorithm to navigate complex terrains with reduced roll and pitch motions, contributing to improved stability and performance.

Two-Dimensional DOA Estimation for Coprime Planar Arrays: From Array Structure Design to Dimensionality-Reduction Root MUSIC Algorithm.

This paper proposes a novel sparse array design and an efficient algorithm for two-dimensional direction-of-arrival (2D-DOA) estimation. By analyzing the hole distribution in coprime arrays and introducing supplementary elements, we design a Complementary Coprime Planar Array (CCPA) that strategically fills key holes in the virtual array. This design enhances the array's continuous Degrees Of Freedom (DOFs) and virtual aperture, achieving improved performance in 2D-DOA estimation with fewer physical elements. The virtualization of the array further increases the available DOFs, while the hole-filling strategy ensures better spatial coverage and continuity. On the algorithmic side, we introduce a dimensionality-reduction root MUSIC algorithm tailored for uniform planar arrays after virtualization. By decomposing the two-dimensional spectral peak search into two one-dimensional polynomial root-finding problems, the proposed method significantly reduces computational complexity while maintaining high estimation accuracy. This approach effectively mitigates the challenges of 2D peak search, making it computationally efficient without sacrificing precision. Extensive simulations demonstrate the advantages of the proposed array and algorithm, including higher DOFs, reduced complexity, and superior estimation performance compared to existing methods. These results validate the effectiveness of the proposed framework in advancing sparse array design and signal processing for 2D-DOA estimation.

Novel Statistical Modelling and Optimization Techniques of Fading Channel Coefficients for 5G Network Performance

The channel estimation process (CE) is a pivotal element in wireless communication employing orthogonal frequency division multiplexing (OFDM) modulation. The derived coefficient can represent the channel characterization. A newly proposed model derived for fifth-generation (5G) network and future communication systems two encounter the fading effects of the channel. The proposed model improves the channel estimation accuracy by incorporating precise measurements of channel coefficients, enhancing the overall performance. The model uses least square (LS) and minimum mean squared error (MMSE) optimization algorithms for the channel coefficient extraction process. The effectiveness of the proposed model has been experimentally confirmed using different fading channel scenarios in the literature. The mean square error (MSE) and bit error rate (BER) test criteria are used for performance measures. The test was generalized using a 64, 256 and 1024 quadrature amplitude modulation (QAM) scheme, demonstrating the model’s applicability to various communication scenarios. The proposed model was evaluated alongside several state-of-the-art statistical channel estimation methods to demonstrate its performance across various tests. When compared to pilot-based estimation, the model consistently exhibits superior accuracy and adaptability, ensuring dependable performance in a wide range of communication scenarios. Additionally, the proposed model is better suited to handle the complexity of non-line-of-sight (NLOS) and multipath environments, offering a more nuanced approach compared to pilot signals, which often rely on idealized assumptions of channel linearity.

A Multi-Dimensional Assessment of Pocket Park Landscapes: Insights from Scenic Beauty Estimation and Analytic Hierarchy Process in Dadukou District, Chongqing

Pocket parks are small green spaces that significantly enhance urban livability, particularly in densely populated cities. To leverage this advantage, Chongqing has committed to establishing 100 pocket parks by 2024. This study assesses the landscape quality of six pocket parks in Chongqing’s Dadukou District, employing Scenic Beauty Estimation (SBE) and Analytic Hierarchy Process (AHP). The results of the SBE method identify four main factors—spatial hierarchy, plant diversity, landscape harmony, and color richness—that show a strong correlation with scenic beauty (R = 0.947, R2 = 0.897). AHP analysis reveals that landscape function is the dominant criterion, accounting for 66.86% of the total weight, with ecological function and service function contributing 21.44% and 8.82%, respectively. Notably, plant color richness emerges as the most significant factor, with a comprehensive weight of 0.1509, emphasizing its critical role in enhancing visual appeal. Based on these findings, this study recommends increasing plant diversity, optimizing color design to improve visual appeal, and implementing sustainable, low-maintenance strategies. This research integrates both quantitative and qualitative approaches, offering strategies to refine pocket park designs and establish a framework to enhance green spaces in densely populated urban areas, not only in China but potentially globally, promoting environmental sustainability and user satisfaction.

Nonparametric multi-task regression under group sparsity and low-rank structures

Regression models find extensive applications across various domains. When multiple output variables are involved, they constitute a multivariate regression model. The objective of such models is to estimate regression functions for each output variable. Classical approaches, such as least squares or maximum likelihood methods, treat each function independently, leading to challenges with parameter estimation as the number of functions increases. Multi-task learning emerges as an alternative approach by leveraging information across related tasks. Reduced-rank regression, which aligns with the multi-task learning idea, addresses this challenge by imposing low-rank constraints on the coefficient matrix to incorporate the low-dimensional structure in the estimation process. The group lasso approach, on the other hand, extends the concept of sparsity-inducing penalization to select important variable subsets. Combining group sparsity and low-rank regularization approaches is expected to offer promising possibilities in multi-task regression. However, no work has explored their combined application within the nonparametric regression framework. In this study, we propose a nonparametric multi-task regression estimator that jointly incorporates low-rank and group sparsity structures. We implement this method using the alternating direction method of multipliers algorithm and conduct numerical studies to demonstrate its effectiveness across various datasets, including the ozone concentration and nitrogen oxide data.

Discrete spectrum nonlinear frequency division multiplexing system fused digital frequency offset loading with neural network-based receiver

The amplified spontaneous emission (ASE) noise in fiber links severely destroys the integrability of channel transmission equations in discrete spectrum nonlinear frequency division multiplexing (DS-NFDM) systems. Additionally, the processing noise caused by the limited accuracy of nonlinear Fourier transform (NFT) algorithm, approximation error of the NFDM model, and truncation error of waveform design aggravates the signal distortion of DS-NFDM in high-power transmission. To address the abovementioned issues, we innovatively propose a DS-NFDM system fused digital frequency offset (DFO) loading technique with a neural network (NN)-based receiver, which is abbreviated as a DFO-NN system. Firstly, based on the one-to-one mapping relationship between the information bit and the eigenvalue offset, it adopts the DFO loading technique on the transmitter to mitigate ASE noise. Subsequently, an optimized NN-based receiver is ingeniously utilized to learn the periodicity of DS-NFDM time-domain waveforms after DFO loading, which can not only replace the classical NFT algorithm and frequency offset estimation (FOE) process but also effectively suppress the processing noise. The effectiveness of the DFO-NN system has been verified by 1 GBaud and 2 GBaud DS-NFDM numerical and experimental systems. The results demonstrate that its maximum frequency offset (FO) tolerance spans from −5.8 GHz to +5.3 GHz. Moreover, compared with the traditional b-NFT and DFO-NFT systems, the optical signal-to-noise ratio (OSNR) penalties of the DFO-NN system are reduced by 6.0 dB and 2.0 dB, respectively. In addition, the maximum transmission distance of this system is extended 473 km longer than that of the DFO-NFT system. Importantly, the complexity of this NN demodulation scheme is O(M2), which is on the same order of magnitude as that of the NFT-based scheme.

Bias Estimation for Low-Cost IMU Including X- and Y-Axis Accelerometers in INS/GPS/Gyrocompass.

Inertial navigation systems (INSs) provide autonomous position estimation capabilities independent of global navigation satellite systems (GNSSs). However, the high cost of traditional sensors, such as fiber-optic gyroscopes (FOGs), limits their widespread adoption. In contrast, micro-electromechanical system (MEMS)-based inertial measurement units (IMUs) offer a low-cost alternative; however, their lower accuracy and sensor bias issues, particularly in maritime environments, remain considerable obstacles. This study proposes an improved method for bias estimation by comparing the estimated values from a trajectory generator (TG)-based acceleration and angular-velocity estimation system with actual measurements. Additionally, for X- and Y-axis accelerations, we introduce a method that leverages the correlation between altitude differences derived from an INS/GNSS/gyrocompass (IGG) and those obtained during the TG estimation process to estimate the bias. Simulation datasets from experimental voyages validate the proposed method by evaluating the mean, median, normalized cross-correlation, least squares, and fast Fourier transform (FFT). The Butterworth filter achieved the smallest angular-velocity bias estimation error. For X- and Y-axis acceleration bias, altitude-based estimation achieved differences of 1.2 × 10-2 m/s2 and 1.0 × 10-4 m/s2, respectively, by comparing the input bias using 30 min data. These methods enhance the positioning and attitude estimation accuracy of low-cost IMUs, providing a cost-effective maritime navigation solution.

Cure Models with Modified Log-Logistic Distribution: An Application to Oncology Studies

The log-logistic distribution has been widely used in survival analysis, particularly in modeling survival times and event data in healthcare and biological studies. This study investigates the parameter estimation of the Log-Logistic Tangent (LLT) distribution using Maximum Likelihood Estimation (MLE), focusing on the consistency, bias, and precision of the estimated parameters. The simulation study results reveal that the estimated values of the parameters α and β deviate from their true values, indicating some bias in the estimation process. The mean values of the estimated parameters are found to be 0.623 for α and 1.433 for β, with respective standard deviations of 0.072 and 0.147, highlighting the variability across iterations. Further analysis of the asymptotic properties of the LLT model shows that the parameter estimates converge to stable values as sample size increases, demonstrating consistency in the estimation process. Additionally, asymptotic normality is confirmed through the calculation of the observed Fisher information matrix and derived standard errors. The LLT model was successfully applied to real-life data, yielding survival probability estimates, which were further validated through statistical testing. The study concludes that while the LLT model is effective in capturing survival patterns, improvements can be made to reduce bias, refine optimization techniques, and explore alternative estimation methods. Recommendations for future research include expanding the model to handle covariates and time-varying effects, thereby enhancing its applicability in diverse fields such as healthcare, finance, and engineering.

Neutrosophic developments in Horvitz-Thompson type estimators

ABSTRACT In the realm of unequal probability sampling and Horvitz-Thompson type technique, addressing the challenge of estimating the mean in the presence of ambiguous data is one of the most important topics that has not yet been thoroughly discussed. The presence of ambiguous data not only complicates the estimation process but also affects its sensitivity, particularly when dealing with variables of varying significance. Leveraging auxiliary information becomes crucial to mitigate these challenges. This study presents a pioneering method that combines neutrosophic statistics, Horvitz-Thompson type estimators employing unequal probability sampling for mean estimation, and the integration of auxiliary information. Neutrosophic set theory, extending classical set theory to manage indeterminacy, offers a framework for addressing inherent uncertainties within the data. By merging neutrosophic statistics with the Horvitz-Thompson technique, the study develops innovative mean estimators adept at effectively managing ambiguous data within unequal probability sampling. To assess the effectiveness of this approach, the study conducts a numerical analysis utilizing real-world data. Findings from this analysis showcase the superior efficiency of the proposed methodology.

Interpretable Machine Learning for Age‐at‐Death Estimation From the Pubic Symphysis

ABSTRACTAge‐at‐death estimation is an arduous task in human identification based on characteristics such as appearance, morphology or ossification patterns in skeletal remains. This process is performed manually, although in recent years there have been several studies that attempt to automate it. One of the most recent approaches involves considering interpretable machine learning methods, obtaining simple and easily understandable models. The ultimate goal is not to fully automate the task but to obtain an accurate model supporting the forensic anthropologists in the age‐at‐death estimation process. We propose a semi‐automatic method for age‐at‐death estimation based on nine pubic symphysis traits identified from Todd's pioneering method. Genetic programming is used to learn simple mathematical expressions following a symbolic regression process, also developing feature selection. Our method follows a component‐scoring approach where the values of the different traits are evaluated by the expert and aggregated by the corresponding mathematical expression to directly estimate the numeric age‐at‐death value. Oversampling methods are considered to deal with the strongly imbalanced nature of the problem. State‐of‐the‐art performance is achieved thanks to an interpretable model structure that allows us to both validate existing knowledge and extract some new insights in the discipline.

Optimizing Resource Estimation for Scientific Workflows in HPC Environments: A Layered‐Bucket Heuristic Approach

ABSTRACTAs computational simulations become complex and the amount of processed data grows, executing scientific workflows in High‐Performance Computing (HPC) environments is increasingly essential. However, accurately estimating the required computational resources for such executions presents a significant challenge, requiring a thorough examination of the workflow structure and the characteristics of the computational environment. This manuscript introduces the GraspCC‐LB heuristic, based on the Greedy Randomized Adaptive Search Procedure (GRASP), for estimating the necessary resources for executing scientific workflows in HPC environments. Unlike existing methods, GraspCC‐LB incorporates the layered structure of workflows into its estimation process. The proposed approach was evaluated using real traces of workflows from the fields of bioinformatics and astronomy. The resource estimations produced by GraspCC‐LB were compared against the actual resource usage in a real‐world HPC environment to evaluate its effectiveness. The results demonstrate the effectiveness of GraspCC‐LB as a robust approach for resource optimization in the context of large‐scale scientific workflows that require HPC capabilities.