Estimation Methodology Research Articles

Hierarchical Bayesian modeling for Inverse Uncertainty Quantification of system thermal-hydraulics code using critical flow experimental data

The best estimate plus uncertainty methodology in nuclear system thermal-hydraulic studies necessitates a comprehensive understanding of uncertainties in system code predictions. The forward uncertainty quantification (UQ) process involves the propagation of input uncertainties through the computational models to obtain uncertainties in the outputs. To this end, achieving an accurate estimation of input uncertainties is important, which is the focus of inverse UQ (IUQ). Traditionally, research in Bayesian IUQ within the nuclear engineering domain has largely relied on single-level Bayesian inference. While being effective for relatively small datasets, this approach encounters limitations for cases with large datasets. The use of a single-level model may prove inefficient, as the resultant posterior distributions can significantly differ when distinct subsets of data are employed. To address this issue, we employ an hierarchical Bayesian model for IUQ. This approach involves organizing observations into different groups based on the test conditions, thereby accommodating varying calibration parameters across these distinct groups. In this study, we developed and implemented a hierarchical Bayesian IUQ method to consider the grouping effect of critical flow measurement data from various geometries. Comparing the outcomes of IUQ under different selections of test data using hierarchical Bayesian IUQ against those obtained from single-level Bayesian IUQ, the forward propagation of hierarchical Bayesian IUQ results demonstrates a notably improved agreement with the experimental data.

The odyssey of women in university education: stochastic growth models for studying access and graduation process

This research aims to explore the evolution of statistics related to the proportion of women in university student populations (focusing on bachelor’s or equivalent degree programs) over an extensive period, spanning from the founding of the first university until recent times. This is done by utilizing empirical insights derived from comprehensive data series, presented here for the first time and primarily encompassing Greece, UK, Canada and USA. Motivated by the distinctive properties of these series and their sigmoid pattern, we delve into a general family of stochastic growth models for data analysis. A notable contribution of this paper lies in offering a novel interpretation of this family within a competing cause scenario, similar to those used in the theory of event history analysis. We emphasize on key common elements within the two statistical domains of research, thereby highlighting potential future research directions; besides, the interpretability of model parameters stands out as an advantage compared to other approaches such as those based on regression model theory. For assessing the proposed parameter estimation methodology under realistic conditions a simulation study is also conducted. The application and results are aimed at making a significant contribution to research in higher and university education by presenting comprehensive sets of consistent data and statistical tools to capture their fundamental characteristics and trends. Additionally, they can serve as valuable resources for education policy-makers and human resources planners alike.

Mass transfer estimation in gas–liquid systems through integration of hydrodynamic model and computer vision algorithms

AbstractA novel methodology for estimation of mass transfer in bubbly flows is investigated. The key advantage of the research is integration of the developed theoretical model of absorption based on Levich’s theory and computer vision techniques. This methodology demonstrates high accuracy in predicting mass transfer rates, which is verified by comparisons with empirical data from experimental jet stream fermenter. Using high-speed video recordings and advanced image processing algorithms, our approach overcomes the limitations of many direct methods for mass transfer measurement: offering real-time, dynamic estimation of mass transfer intensity, scale-up flexibility and low costs. The integration of computer vision with analytical modeling represents a significant advancement in the field of liquid–gas media estimation, paving the way for the development of more sophisticated and effective measurement tools. The proposed methods have high adaptability and can be useful to handle complex bubble dynamics both in industrial tasks and scientific research.

Estimation of Joint Kinetics During Manual Material Handling Using Inertial Motion Capture: A Follow-Up Study.

Musculoskeletal models based on inertial motion capture (IMC) and ground reaction force (GRF) prediction hold great potential for field-based risk assessment of manual material handling (MMH). However, previous evaluations have identified inaccuracies in the methodology's estimation of spinal forces, while the accuracy of other key outcome variables is currently unclear. This study evaluated knee, shoulder, and L5-S1 joint reaction forces (JRFs) derived from a musculoskeletal model based on inertial motion capture and GRF prediction against a model based on simultaneously collected optical motion capture (OMC) and force plate measurements. Data from 19 healthy subjects performing lifts with various horizontal locations (HLs), deposit heights (DHs), and asymmetry angles (AAs) were analyzed, and the consistency and absolute agreement of the model estimates statistically compared. Despite varying levels of agreement across tasks and variables, considerable absolute differences were identified for the L5-S1 axial compression (AC) (root-mean-square error (RMSE) = 63.0-94.2%BW) and anteroposterior (AP) shear forces (RMSE = 40.9-80.6%BW) as well as the bilateral knee JRFs (RMSE = 78.9-117%BW). Glenohumeral JRFs and vertical GRFs exhibited the highest overall consistency (r = 0.33-0.91, median 0.78) and absolute agreement (RMSE = 7.63-34.9%BW), while the L5-S1 axial compression forces also showed decent consistency (r = 0.04-0.89, median 0.80). The findings generally align with prior evaluations, indicating persistent challenges with the accuracy of key outcome variables. While the modeling framework shows promise, further development of the methodology is encouraged to enhance its applicability in ergonomic evaluations.

The Click-Based MNL Model: A Framework for Modeling Click Data in Assortment Optimization

We introduce the click-based MNL choice model, a framework for capturing customer purchasing decisions in e-commerce settings. Specifically, we augment the classical Multinomial Logit choice model by assuming that customers only consider the items they have clicked on before they proceed to compare their random utilities. In this context, we study the resulting assortment optimization problem, where the objective is to select a subset of products, made available for purchase, to maximize the expected revenue. Our main algorithmic contribution comes in the form of a polynomial-time approximation scheme (PTAS) for this problem, showing that the optimal expected revenue can be efficiently approached within any degree of accuracy. To establish this result, we develop several technical ideas, including enumeration schemes and stochastic inequalities, which may be of broader interest. Using data from Alibaba’s online marketplace, we fit click-based MNL and latent class MNL models to historical sales and click data in a setting where the online platform recommends a personalized six-product display to each user. We propose an estimation methodology for the click-based MNL model that leverages clickstream data and machine learning classification algorithms. Our numerical results suggest that clickstream data are valuable for predicting choices and that the click-based MNL model can outperform standard logit-based models in certain settings. This paper was accepted by Chung Piaw Teo, optimization. Funding: D. Segev was supported by the Israel Science Foundation [Grant 1407/20]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2021.00281 .

Estimating the daily mean blue-sky land surface albedo on the Tibetan Plateau using convolutional neural network

ABSTRACT Surface albedo is a critical indicator of Earth’s ability to reflect solar radiation, influencing energy balance and climate dynamics. Research on blue-sky albedo on the Tibetan Plateau has often focused on clear-sky conditions, overlooking seasonal snow cover and variable weather. This paper uses convolutional neural networks (CNNs) to estimate blue-sky surface albedo on the plateau. The residual neural network 18 (ResNet18) model outperforms other deep learning architectures, such as long short-term memory (LSTM) networks, significantly improving the correlation between in situ observations and remote sensing data. Albedo simulation accuracy is notably higher than moderate resolution imaging spectroradiometer (MODIS) products, especially in areas with homogeneous surface properties. Surface albedo variation rates show that albedo changes have not been significant, influenced by factors such as summer vegetation greenness, high-altitude glacier mass balance reduction, and winter snow cover fluctuations. These results highlight regional differences in albedo change trends on the Tibetan Plateau. This study demonstrates the effectiveness of CNNs in simulating surface albedo, offering valuable insights into albedo changes and establishing a framework for refining albedo estimation methodologies.

Mission profile oriented energy efficiency and CO2 footprint assessment of naval vessels at conceptual design phase: A comparative case study of conventional and hybrid energy systems

CO2 is the main green house gas (GHG) produced in fossil fuel burning engines on ships. The growth of activities of sea-going vessels, have been increasing interest and concerns over ship-born CO2 emissions. Consequently, numerous studies have been introduced for emission estimation. Each estimation approach has its own assumptions, thus uncertainty with data of vessel activities, vessel attributes, and calculating methods. The conceptual design phase is the first phase of ship design. One of the issues addressed at this phase is the assessment of energy system alternatives of relevant ship. A ship energy system provides propulsion and electrical power. The assessment of energy system alternatives for a naval vessel is a very complicated business that has a significant impact and interaction on overall vessel integration. Naval vessels and their energy systems constitute a noteworthy part of the total world ship fleet. The present study aims to present a realistic methodology for estimating efficiency, hence CO2 emissions, for candidate energy systems of surface vessels at conceptual design phase. A frigate is selected as the case (baseline) vessel. Three energy systems, namely CODAD, CODOG, and CODLOG-CC, are proposed as alternatives for the case vessel. Instead of general emission estimation approaches, the present study proposes a mission profile-oriented estimation methodology which provides more precise and accurate results. The Energy Efficiency Design Index (EEDI) defines efficiency and sets limitation during design and construction of specific merchant vessels, but since this index does not comply with unique characteristics of mission profile of surface vessels, the novel efficiency design index defined as integral warship EEDI (IWEEDI) is implemented for the case vessel. According to the analysis, CODAD is found to be the most efficient alternative, followed by CODLOG-CC and CODOG, respectively.

A new global carbon flux estimation methodology by assimilation of both in situ and satellite CO2 observations

Accurate estimation of carbon removal by terrestrial ecosystems and oceans is crucial to the success of global carbon mitigation initiatives. The emergence of multi-source CO2 observations offers prospects for an improved assessment of carbon fluxes. However, the utility of these diverse observations has been limited by their heterogeneity, leading to much variation in estimated carbon fluxes. To harvest the diverse data types, this paper develops a multi-observation carbon assimilation system (MCAS), which simultaneously integrates both satellite and ground-based observations. MCAS modifies the ensemble Kalman filter to apply different inflation factors to different types of observation errors, addressing the heterogeneity between satellite and in situ data. In commonly used independent validation datasets, the carbon flux derived from MCAS outperformed those obtained from a single source, demonstrating a 20% reduction in error compared to existing carbon flux products. We use MCAS to conduct ecosystem and ocean carbon flux inversion for the period of 2016–2020, which reveals that the 5-year average global net terrestrial and ocean sink was 1.84 ± 0.60 and 2.74 ± 0.49 petagrams, absorbing approximately 47% of human-caused CO2 emissions together, which were consistent with the global carbon project estimates of 1.82 and 2.66 petagrams. All these facts suggest MCAS is a better methodology than those for assimilating single-source observation only.

Effective Market Power and Welfare Loss

ABSTRACTThis paper introduces a straightforward and effective estimation methodology for quantifying firm‐level welfare losses attributable to market power in monopolistic competition markets. Our analysis underscores that welfare losses are primarily contingent on effective market power, expressed as the product of the Lerner index and market share. Furthermore, our study demonstrates that welfare loss computed using the Hicksian measurement is smaller than that obtained through the Marshallian measurement. Finally, we apply this methodology to estimate firm‐level market power and welfare losses in the Chinese carbonated beverage industry.

Internal Temperature Estimation for Lithium-Ion Cells Based on a Layered Electro-Thermal Equivalent Circuit Model

In the domain of Battery Management System (BMS) research, the precise acquisition and estimation of internal temperature distribution within lithium-ion cells is a significant challenge. The commercial viability precludes the use of internal temperature sensors, and existing methodologies for online estimation of internal temperatures under various electrical loads are constrained by computational limitations and model accuracy. This study presents a layered electro-thermal equivalent circuit model (LETECM), developed by integrating a layered second-order fractional equivalent circuit model with a layered thermal equivalent circuit model. A lithium-ion battery divided into three layers was employed to illustrate the development of this LETECM. The model’s precision was validated against a 3D Newman Finite Element Model (3DNFEM), constructed using actual battery parameters. Given that the thermal gradient inside the battery is usually more pronounced under high load conditions, a 10C direct current discharge for 60 s followed by a rest period of 240 s was adopted as the test condition in the simulation. The results indicate that at the end of the DC discharge, the temperature difference between the inner layer and the surface of the battery was the largest and the maximum temperature difference predicted by the LETECM was 3.58 °C, while the 3DNFEM exhibited a temperature difference of 3.74 °C. The trends in each layer temperature and battery surface temperature obtained by the two models are highly consistent. The proposed model offers computational efficiency and maintains notable accuracy, suggesting its potential integration into BMS for real-time online applications. This advancement could provide critical internal temperature data for refining battery charging and discharging performance assessments and lifespan predictions, thereby optimizing battery management strategies.

Application of DSP2 for biological sex estimation in a Spanish sample: analysis based on sex and side.

Applying existing sexing methodologies to different populations, and reporting these findings is important to enhance their applicability and accuracy in real cases across the world. DSP was published in 2005 (Murail et al., 2005) and updated as a DSP2 in 2017 (Brůžek et al., 2017) based on a database of 10 pelvic measurements from 2040 individuals worldwide. These tools have been applied subsequently to various populations, however, its applicability to a dry Spanish population is lacking. 303 hipbones belonging to 157 individuals from the School of Legal Medicine from the University Complutense of Madrid (Spain), of which 140 individuals were documented, were analyzed to investigate the reliability, applicability and accuracy of the DSP2 sex estimation methodology, examining side and sex-based potential differences for the first time. In most of the DSP variables, intra-rater reliability showed excellent results and % applicability was higher than 85.0%. Overall % accuracy was higher than 94.0% regardless of the number or discriminant power of the utilized DSP variables. However, % sexing decreased when less variables or less discriminant ones were used for estimations, reaching 45.51% (left) and 43.31% (right). Regarding sexual dimorphism, females' results of % applicability, % sexing and % accuracy were higher compared to males. In addition, left os coxae achieved better outcomes (aforementioned percentages) in most of the cases in the sex-pooled sample. Decreasing the mandatory posterior probability by 10% yielded an increase in the % sexing but reduced % accuracy, and thus, does not seem to enhance the approach's performance. The present study validates the applicability and reliability of DSP for sexing a Spanish population. Future investigations will attempt to assess its applicability within virtual anthropology.

Research on passenger flow control at metro transfer stations based on real-time flow calculation of streamlines

PurposeThe volume of passenger traffic at metro transfer stations serves as a pivotal metric for the orchestration of crowd flow management. Given the intricacies of crowd dynamics within these stations and the recurrent instances of substantial passenger influxes, a methodology predicated on stochastic processes and the principle of user equilibrium is introduced to facilitate real-time traffic flow estimation within transfer station streamlines.Design/methodology/approachThe synthesis of stochastic process theory with streamline analysis engenders a probabilistic model of intra-station pedestrian traffic dynamics. Leveraging real-time passenger flow data procured from monitoring systems within the transfer station, a gradient descent optimization technique is employed to minimize the cost function, thereby deducing the dynamic distribution of categorized passenger flows. Subsequently, adhering to the tenets of user equilibrium, the Frank–Wolfe algorithm is implemented to allocate the intra-station categorized passenger flows across various streamlines, ascertaining the traffic volume for each.FindingsUtilizing the Xiaozhai Station of the Xi’an Metro as a case study, the Anylogic simulation software is engaged to emulate the intra-station crowd dynamics, thereby substantiating the efficacy of the proposed passenger flow estimation model. The derived solutions are instrumental in formulating a crowd control strategy for Xiaozhai Station during the peak interval from 17:30 to 18:00 on a designated day, yielding crowd management interventions that offer insights for the orchestration of passenger flow and operational governance within metro stations.Originality/valueThe construction of an estimation methodology for the real-time streamline traffic flow augments the model’s dataset, supplanting estimated values derived from surveys or historical datasets with real-time computed traffic data, thereby enhancing the precision and immediacy of crowd flow management within metro stations.

An algorithm for multi-damage size estimation of composite laminates

Localization and size estimation of composite damage are challenging but essential for composite performance evaluation. This paper proposes a new methodology for the size estimation of multi-damage in composite laminates using Lamb wave technology. The pure A0 modal of Lamb wave is excited to avoid dispersion and multi-modal effects of Lamb wave. An extraction algorithm is introduced to obtain the first wave packet and time-of-flight. According to the results obtained by the extraction algorithm, the Bayesian-hybrid localization algorithm based on the reconstruction algorithm for probabilistic inspection of damage and modified delay-and-sum (MDAS) is performed to localize damages. The damage boundaries are obtained through convex enveloping a series of damage boundary points identified by MDAS. An adaptive Gaussian mixture model based on Akaike’s Information Criterion and Bayesian Information Criterion is designed to remove abnormal boundary points. The proposed method is numerically investigated and validated through multi-damage experiments. The results demonstrate that it can accurately estimate the locations and boundaries of multi-damage in composite laminates.

Количественная оценка потерь почвы вследствие водной эрозии на сильновыпаханных почвах бассейна верхней Оки с использованием географических информационных систем

The paper presents some results of studying soil losses appearing due to water erosion in an agricultural field with highly degraded plowed soils. The research was carried out on the arable slope of the southern exposure in an experimental area located in the Oryol district of the Oryol Region (upper Oka basin). A set of methods was used: morphometric analysis of the topography relief, radiocaesium method, soil-morphological method, method for determining easily decomposable organic matter (labile organic matter) designed according to Ganzhara and Borisov, agrochemical methods. The study was based upon the authors’ in-situ data of 2016–2023. Maps are compiled to show the caesium-137, humus and labile organic matter spatial distribution, and of the soil degradation degree (in points on the Ganzhara and Borisov scale) in the arable horizon of 0–25 cm. Analysis of the caesium-137 radioactivity spatial distribution made it possible to identify two sectors in an area of highly degraded plowed soil, which differ in surface slope values and in nature of the caesium-137 radioactivity interdependences with catchment area and sign of profile curvature. Developed semi-empirical computational dependences for the delineated sectors allow to compile a gridded map of the soil runoff intensity (in t/ha/year). The intensity of soil runoff in the area with highly degraded plowed soils is ranged from 5 to more than 20 t/ha/year. The algorithm for the intensity of soil delivery outside of the studied area was developed on the basis of layer-by-layer soil sampling data collected in the mouth of the ravine, removing soil from the highly degraded plowed soil area. The algorithm showed that 4.7–6.5 tons of soil per year are removed from 1 hectare of the catchment area basin. The rest of the outwashed soil material is deposited in accumulation areas within the catchment area basin. The authors recommend verifying the findings in other areas of the agricultural field. The proposed methodology for soil loss estimation, developed for highly degraded plowed soil areas in an agricultural field, requires clarification by enlarging the scale of the study.

DREAMx: A Data-Driven Error Estimation Methodology for Adders Composed of Cascaded Approximate Units

DREAMx: A Data-Driven Error Estimation Methodology for Adders Composed of Cascaded Approximate Units

Realistic mathematics education and mathematical literacy: a meta-analysis conducted on studies in Indonesia

A considerable body of literature exists pertaining to realistic mathematics education (RME) and its correlation with mathematical literacy, with numerous studies demonstrating incongruent results. The principal objective of this meta-analysis is to systematically investigate the overarching influence of RME on mathematical literacy within the context of Indonesia. The collection of documents comprises a total of seventeen publications that were released between the years 2014 and 2023. The estimation methodologies utilized in this study were grounded on a random-effects model, and statistical computations were conducted utilizing the comprehensive meta-analysis (CMA) software in academic writing. The equation proffered by Hedges was employed for the quantification of effect magnitude. The outcomes of the investigation reveal that the implementation of RME learning yielded a noteworthy and advantageous impact (effect size = 1.031; p 0.05) on the adeptness of students in the domain of mathematical literacy. Moreover, many moderating factors, including class capacity, educational level, geographical location, content of the Programme for International Student Assessment (PISA), and the combination of learning, did not significantly impact students' diverse mathematical literacy proficiency. This study proposes that mathematics educators should consider utilizing the RME as a means of improving students' proficiency in mathematical literacy.

State of Charge Estimation for Lithium-ion Battery Using Long Short-Term Memory Networks

Abstract Accurate estimation of the State of Charge (SOC) in lithium-ion batteries is crucial for enhancing performance and extending battery life, especially in applications like electric vehicles and energy storage systems. This study introduces a novel method for SOC estimation that utilizes Long Short-Term Memory (LSTM) neural networks. To evaluate the LSTM model’s effectiveness, we compared its performance with that of Backpropagation (BP) neural networks and Recurrent Neural Networks (RNN) using the Root Mean Square Error (RMSE) as the evaluation metric. The findings reveal that the LSTM model achieved a significantly lower RMSE of 0.0107, surpassing the performance of the BP neural network and RNN, which had RMSEs of 0.015 and 0.0149, respectively. These results highlight the LSTM network’s exceptional capability to capture the temporal dynamics inherent in battery data, resulting in more accurate SOC predictions. This research underscores the substantial potential of LSTM networks in battery management systems and provides valuable insights into advancing SOC estimation methodologies.

Root canal widths of mandibular molars in predicting the legal age threshold 18 years in a sample of juveniles and sub-adults of south-indian origin: an orthopantomographic study.

Pursuing a proficient age estimation methodology introducing novel radiographic methods remains an ongoing and demanding aspect of forensic and medicolegal research. In 2017, Roberts GJ et al. (J Forensic Sci 62(2):351-4, 2017) described a new radiographic method, i.e., root canal width (RCW) patterns to assign a subject to above 18-year-age threshold. Since then, few researchers have investigated the validity of this radiographic method in other populations. The present study aimed to test the usefulness of these RCW patterns in predicting 18 years in a sample of South Indian juveniles and sub-adults aged between 16 and 23. Descriptive analysis revealed that pattern-A was initially observed at a minimum age of 16.08 and 16.22 years in males and females. Pattern-B at 16.31 years in males and 16.22 years in females, while pattern-C was initially recorded at 18.73 years in males and 19.01 years in females, respectively. In summary, if an individual, regardless of sex, exhibits a fully-formed (apex closed) mandibular first, second, and third molars and concurrently displays RCW-C, there is a strong likelihood that the person has exceeded the legally relevant age of 18 years. However, due to higher rate of technically unacceptable errors (adults wrongly identified as individuals below 18 years), reliance on this method alone should be restricted, and it is advisable to combine it with other methods.

Estimation of Malondialdehyde Content in Medicago truncatula under Salt Stress Based on Multi-Order Spectral Transformation Characteristics

Salt stress is a significant abiotic factor affecting the growth and development of alfalfa. Malondialdehyde (MDA) serves as a critical biomarker for assessing alfalfa’s salt tolerance. Traditional methods for measuring MDA are often time-consuming and labor-intensive. Recent advances in remote sensing technology have made non-destructive estimation of metabolites feasible, positioning the accurate estimation of MDA content in alfalfa as a key focus in intelligent breeding. To address the challenge of detecting subtle changes in MDA content, this study developed a partial least squares regression (PLSR) model specifically for Medicago truncatula. This study utilized leaf reflectance hyperspectral data across the visible near-infrared–shortwave infrared (VIR-NIR-SWIR) spectrum, applying multi-order spectral transformation methods, including continuous wavelet transform (CWT), fractional differential (FD), and multi-granularity spectral segmentation (MGSS). Feature selection techniques, such as sequential forward selection (SFS), Least-Squares Boosting (LSBoost), and feature selection using neighborhood component analysis for regression (FSRNCA), were employed to enhance the efficiency of the MDA estimation. The findings revealed that the optimal PLSR model for MDA estimation was achieved by integrating CWT features across orders 1–30 with the SFS method. This model demonstrated robust estimation capabilities under varying salt stress conditions, significantly outperforming the original spectral data (R2 = 0.654, RMSE = 22.567 vs. R2 = 0.242, RMSE = 33.411). A comparative analysis of feature selection methods confirmed that SFS was the most effective for estimating MDA content in alfalfa. These results provide valuable insights and methodologies for MDA estimation and evaluating salt tolerance in alfalfa.

The impact of physiological state and environmental stress on bacterial load estimation methodologies for Mycobacterium tuberculosis

When processed in solid or liquid medium, tuberculosis patient samples yield different proportions of a heterogenous bacterial community over the duration of treatment. We aimed to derive a relationship between methodologies for bacterial load determination and assess the effect of the growth phase of the parent culture and its exposure to stress on the results. Mycobacterium tuberculosis H37Rv was grown with and without antibiotic (isoniazid or rifampicin) and sampled on day 0, 3, 11 and 21 of growth in broth culture. The bacterial load was estimated by colony counts and the BD BACTEC MGIT system. Linear and nonlinear mixed-effects models were used to describe the relationship between time-to-positivity (TTP) and time-to-growth (TTG) versus colony forming units (CFU), and growth units (GU) versus incubation time in MGIT. For samples with the same CFU, antibiotic-treated and stationary phase cells had a shorter TTP than antibiotic-free controls and early-logarithmic phase cells, respectively. Similarly, stationary phase samples reached higher GUs and had shorter TTG than early-log phase ones. This suggests that there is a population of bacterial cells that can be differentially recovered in liquid medium, giving us insight into the physiological states of the original culture, aiding the interpretation of clinical trial outputs.