Estimation Method Research Articles

A Novel Lifetime Estimation Method and Structural Optimization Design for Film Capacitors in EVs Considering Material Aging and Power Losses

A Novel Lifetime Estimation Method and Structural Optimization Design for Film Capacitors in EVs Considering Material Aging and Power Losses

Stress-strain curve estimation from micropillar compression with transverse contraction effect

The Focused Ion Beam (FIB) technique and the associated compression testing capabilities provide versatile access to understanding the microstructural behaviour of materials. Among many objectives, determining the stress-strain relationship from experimental or numerical force-displacement data remains a fundamental challenge. However, for micropillers under compression, the inside deformation constraint is complex like in components and the transformation of a measured force-displacement curve into an accurate uniaxial stress-strain curve is not straightforward. In this context, in a previous numerical based analysis for perfect volume constancy or incompressibility the determination of reliable and accurate stress-strain curves from nonlinear load-deformation curves of micropillers was already verified. In the case of transverse contractions, however, numerical simulations of micropillers provide unexpected force-displacement behaviour, i.e. their force-displacement curves are almost superimposing and are very close to the force-displacements for incompressibility. This effect implies, that transverse contraction would have no contributions in micropillar compression. In contrast, independent simulations using a single finite element accurately explore the true uniaxial stress-strain behaviour for the whole range of transverse contractions. These are considered as the reference behaviour and are also treated by an analytical expression. With the presented new stress-strain estimation approach, including transverse contraction, any force-displacement curve from a micropillar under compression can be stepwise corrected. The first step of the new stress-strain curve estimation method, including transverse contraction, is carried out in the same way as in the preceding analysis for incompressibility, followed by further steps to obtain the final true stress-strain curve. Although the presented numerical analysis is exemplarily performed for the behaviour of fused silica, but the stress-strain approach is in the same manner generally applicable for a material behaviour with other stress-strain curves. The assumption behind is isotropic and nonlinear material behaviour.

Modeling and Control of Heating and Heat Circulation in Direct Air Capture System

Direct air capture (DAC) is a critical technology for mitigating climate change. However, the high heat consumption of temperature vacuum swing adsorption (TVSA)-based DAC processes hinders its widespread deployment. This study focuses on developing a control strategy to optimize the energy efficiency of the TVSA heating phase. A novel adsorbent temperature estimation method, validated through experimental data, was integrated into a cascaded PI controller with a fuzzy gain scheduler (FGS). Experimental results demonstrate that the proposed control strategy effectively regulates the heating process, achieving a potential energy saving of up to 14%. This work contributes to enhancing the feasibility and sustainability of DAC technologies.

Adaptive momentum equation method for overcoming singularities of dispersed phases

The singularity issue arising from the phase fraction approaching zero in multiphase flow can significantly intensify the solution difficulty and lead to nonphysical results. By employing the conservative form of momentum equations in high-phase-fraction and discontinuity regions and the phase-intensive form of momentum equations in low-phase-fraction regions, computational reliability can be assured while avoiding the singularity issue. Regarding the proposed adaptive momentum equation method, the form of momentum equations for each cell is determined by a conversion bound and a phase fraction discontinuity detector. A comparative analysis is conducted on this method and other singularity-free methods. For discontinuities of dispersed phases, an error estimation method of the conversion bound is presented through theoretical analysis. Computational results demonstrate that the discontinuity detector accurately captures discontinuities in high-phase-fraction regions while disregarding pseudo-discontinuities in low-phase-fraction regions. Compared to the conservative form corrected by the terminal velocity method, the method yields higher-quality flow fields and potentially exhibits an efficiency improvement of over 10 times. Compared to the phase-intensive form, the method benefits from the physical quantity conservation, providing higher computational reliability. When encountering discontinuities, the expected error from the error estimation method aligns well with the actual error, indicating its effectiveness. When the conversion bound is below 1/10 000 of the inlet phase fraction, the errors of the adaptive method are essentially negligible.

The Effects of Takeover Laws on Corporate Financing Policies: Global Evidence

Using a cutting-edge estimation method to address heterogenous treatment effects arising from differential treatment timing, we examine the effect of international takeover laws on corporate financing. We find that firms reduce their equity financing in response to takeover laws. Further analyses indicate that takeover laws’ negative effect on equity financing is more pronounced for firms with higher financial constraints and for firms in countries with strong investor protections.

Signal Quality-Aware Frequency Demodulation-Based ECG-Derived Respiration Rate Estimation Method With Reduced False Alarms

Signal Quality-Aware Frequency Demodulation-Based ECG-Derived Respiration Rate Estimation Method With Reduced False Alarms

Advanced data-driven techniques in AI for predicting lithium-ion battery remaining useful life: a comprehensive review

As artificial intelligence (AI) technology evolves, data-driven approaches are gaining attention in predicting lithium-ion battery's remaining useful life (RUL). Indeed, accurate RUL prediction is challenging, primarily because of the complex nature of the work and dynamic shifts in model parameters. To address these challenges, this article comprehensively explores five significant publicly accessible lithium-ion battery datasets, encompassing diverse usage conditions and battery types, offering researchers a rich repository of experimental data. In particular, we not only provide detailed information and access addresses for each dataset, but also present, for the first time, four innovative methods for battery aging health factor extraction. These methods, based on advanced AI techniques, are able to effectively identify and quantify key indicators of battery performance degradation, thereby enhancing the precision and dependability of RUL prediction. Additionally, the article identifies major challenges faced by current predictive techniques, including data quality, model generalization capabilities, and computational cost, highlighting the need for research focused on dataset diversity, multiple algorithm fusion, and hybrid physical-data-driven models to enhance prediction accuracy. We believe that this review will help researchers gain a comprehensive understanding of RUL estimation methods, and promote the development of AI in battery.

Petrophysical parameter estimation using a rock-physics model and collaborative sparse representation

The estimation of petrophysical parameters is key in the identification of underground reservoirs. Current petrophysical parameter estimation methods are typically constrained by the choice of particular rock-physics models, necessitating the use of distinct models for various reservoirs. Furthermore, the inherent pronounced nonlinearity of these models presents significant challenges to the solution process in reservoir parameter inversion. Although linearized petrophysical inversion methods can simplify the solving process, they can introduce errors due to linearization. To address these limitations, we develop a petrophysical parameter estimation method driven by a rock-physics model and collaborative sparse representation (CSR). In this approach, the rock-physics model governs the fundamental pattern of the petrophysical parameters, with the CSR introducing perturbations to minimize model errors. Our method maximizes the use of well-log data and the rock-physics model, thereby reducing errors associated with linearized rock-physics (LRP) models and enhancing the method’s adaptability. We use the joint dictionary method to learn features and correlations among multiple parameters from the existing well-log data. This learned dictionary is then used as a CSR regularization constraint and applied to refine the LRP inversion objective function. Finally, the objective function is minimized using the block coordinate descent method to predict the petrophysical parameters. The effectiveness of this method in enhancing the adaptability and accuracy of petrophysical parameter estimation is confirmed through synthetic and field data tests.

APPLICATION OF INTDOSKIT TOOL FOR ASSESSMENT OF UNCERTAINTIES ON DOSE COEFFICIENTS FOR INGESTION OF URANIUM BY WORKERS.

The International Commission on Radiological Protection (ICRP) has developed the Human Alimentary Tract Model (HATM) to calculate radiation doses from the ingestion of radionuclides for the protection of workers and the public. In parallel, the ICRP's Occupational Intake of Radionuclides (OIR) series provides biokinetic models and dose coefficients based on a reference human, primarily for regulatory purposes. Although these coefficients are not usually checked for uncertainties, the investigation of such uncertainties is crucial to ensure their reliability in radiation protection. This study uses INTDOSKIT, a software tool developed using the R programming language and RStudio as the Integrated Development Environment (IDE), to calculate doses and explore uncertainties following ingestion of U-238 by workers. Two scenarios were investigated: Case I, using the latest HATM model and the systemic uranium model from ICRP publications, and Case II, based on data from Puncher and Burt (2013), using older ICRP models. In both cases, uptake of U-238 in its soluble form (Type F) was modelled and the results were validated using the ICRP OIR dataviewer software. Following validation, uncertainty and sensitivity analyses were performed. In Case I, uncertainties were assigned to particle transport parameters in the HATM and uranium uptake fraction (fA) into the blood, while in Case II they were limited to the uptake fraction (f1) and the systemic uranium model. A Monte Carlo simulation of 60,000 runs was performed for both cases, sampling model parameters from their respective probability distributions to generate dose distributions. The influence of each parameter on these distributions was also analyzed. Probability distributions were inferred to the calculated dose values using the maximum likelihood estimation method and the Kolmogorov-Smirnov goodness-of-fit. The results showed that for both cases the committed effective dose coefficient, e (50), followed a lognormal distribution. Case I had a geometric mean of 3.2E-08 Sv/Bq and GSD of 2.0, while case II had a slightly lower geometric mean of 3.1E-08 Sv/Bq and GSD of 1.9. Sensitivity analysis showed that the main contributor to dose uncertainty was the fraction of uranium absorbed from the small intestine into the blood. Both cases showed similar trends, with slightly higher results in case I. Overall, this study demonstrates the effectiveness of INTDOSKIT in calculating dose coefficients and analyzing uncertainties. It suggests that while the ICRP reference values remain useful for protection, the incorporation of additional statistical measures and distribution characteristics could further enhance radiation protection strategies.

Blood on the drapes: A multispecialty comparison of blood spill estimates.

Estimated blood loss (EBL) is an important part of the perioperative process. This project aims to determine the accuracy of perioperative team members to estimate blood volume on drapes and the operating room floor. Aliquots of unused human blood were used to create surgical scenarios, and standardized pictures and videos were taken. Physicians, residents, nurses, medical students, and surgical technicians were surveyed and asked to estimate the blood volume for each series. Accuracy and consistency of responses was analyzed. One hundred and forty five responses were recorded: 57 attending physicians, 36 residents, 27 registered nurses, 17 medical students, and seven circulating surgical techs. Median percent error (PE) for all cases was 211.11%, demonstrating a global overestimation of blood volume. PE for the 150mL images was statistically significantly lower than that of the 50 and 100mL images. Circulating Surgical Technicians were the most accurate group, with a median PE of 125%, followed closely by Medical Students (PE=158.33%). The most accurate specialty was Orthopedics (PE=168.06%). The least accurate groups were Attending Physicians (PE=286.11%) and General surgery (GSGY) (PE=327.78%). The most accurate orthopedic surgery and GSGY subspecialties were Hand (PE=237.64%) and Vascular (PE=108.33%), respectively. Statistical analyses showed no significant differences by clinical role, surgical specialty, or subspecialty. This study demonstrates a global overestimation of blood volume when using the visual method, with improved accuracy at higher volumes. Our findings highlight the limitations of visual estimation methods for EBL.

Bioanalytical method for the simultaneous estimation of atoltivimab, maftivimab and odesivimab in rat plasma by LCMS/MS and its application to a pharmacokinetic study

Bioanalytical method for the simultaneous estimation of atoltivimab, maftivimab and odesivimab in rat plasma by LCMS/MS and its application to a pharmacokinetic study

Kidney function assessment for eligibility in clinical cancer trials – Data from the European Organisation for Research and Treatment of Cancer

PurposeThere is no consensus on how to estimate kidney function for the assessment of eligibility in clinical cancer trials. Patients and methodsWe recalculated the creatinine clearance (CrCl)/glomerular filtration rate (GFR) at baseline in a total of 1768 patients enrolled in twelve clinical trials using the Cockcroft-Gault (CG), Modification of Diet in Renal Disease (MDRD), 2021 Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI 2021) and European Kidney Function Consortium (EKFC) formulas. Patients were classified as having renal impairment (RI; CrCl/GFR <60 mL/min) or no renal impairment (NRI; CrCl/GFR ≥60 mL/min) with each of the four formulas, respectively. Furthermore, we analyzed the number of adverse events (AE) per month under study treatment using measures of central tendency, variability and regression models. ResultsUsing CG, EKFC, MDRD and CKD-EPI 2021, 152 (8 %), 140 (8 %), 110 (6 %), and 61 (4 %) patients had RI respectively. Indeed, 47 (3 %) patients had RI using all 4 formulas, while 158 (9 %) had RI by at least one but not all four methods. CG showed the broadest variability and inconsistencies with other methods. All calculation methods performed similarly for excluding patients at risk of severe AE. EKFC demonstrated superior predictive ability for excluding patients at risk of renal and urinary tract AE. ConclusionThis post hoc analysis highlights the importance of choosing accurate and representative methods for kidney function estimation in clinical cancer trials. CG should be replaced by newer methods. While CKD-EPI 2021 may maximize trial accrual, EKFC should be considered for treatment affecting kidney function.

Research on Dynamic Characteristics Estimation Method for Predicting Ship Structural Health

Research on Dynamic Characteristics Estimation Method for Predicting Ship Structural Health

Estimating vehicle sideslip angle through kinematic and dynamic contributions: Theory and experimental results

Vehicle lateral stability plays an important role within vehicle passenger safety. The study of lateral stability is typically related to investigating the dynamics of relevant vehicle states: among these, the vehicle sideslip angle ([Formula: see text]) emerges as a prominent candidate. Sideslip angle measurement is expensive and impractical, hence estimation techniques are often used, typically based on Kalman filters or neural networks, both with their issues. This work presents an alternative estimation method based on the idea of splitting sideslip angle into kinematic and dynamic contributions, and by observing that the kinematic contribution is straightforward to estimate. Therefore, efforts are devoted into estimating dynamic sideslip angle, which is herein obtained through a parametric interpolation harnessing lateral acceleration. Only data available from traditional vehicle onboard sensors are used in the process. Experimental results are presented along several manoeuvres on a full-scale vehicle, with the estimator running online within a dSPACE unit, ultimately supporting the efficacy and real-time feasibility of the proposed approach.

Simultaneous interval estimation of actuator fault and state for a class of nonlinear systems by zonotope analysis

In this paper, an actuator fault and state interval estimation method for a class of nonlinear systems is proposed by integrating observer design and zonotope analysis. For the considered systems, we present a novel unknown input observer structure with broad applications. The design procedure is based on H∞ method to decrease the influence of unknown but bounded process disturbances and measurement noise. Moreover, a novel interval estimation method is presented based on zonotope analysis to obtain tighter intervals. Numerical simulations of a quadruple-tank system are conducted to assess the performance of the proposed approach.

Assessment of foetal dose and occupational exposure for pregnant workers in nuclear medicine using the Taiwanese pregnancy phantom.

For pregnant workers in nuclear medicine, radiation doses can pose a risk to their foetus. However, foetal radiation doses cannot be measured directly. In this study, a method of estimating foetal radiation doses was developed through simulations and measurements of phantoms of pregnant women in the three trimesters. The uterus and abdominal surface doses for monoenergetic photons (137Cs) and medical diagnostic X-rays were measured, and uterine dose conversion coefficients (UDCCs) were calculated. The accuracy of the UDCC estimates were validated for measurements from thermoluminescent dosemeter (TLD) chips and TLD badges on the abdomen or chest. The foetal effective dose could be estimated using TLD chips and TLD badges on the abdomen or chest, or through literature estimation method. The proposed method can be used to easily and accurately estimate foetal effective doses from chest-worn TLD badges, ensuring accurate estimation in the early stage of pregnancy when a worker may not yet be wearing an abdominal badge. A flowchart for applying the UDCC method to approximate a foetal dose is also provided to ensure that total doses remain below the maximum of 1mSv recommended in the International Commission on Radiological Protection 103 guidelines.

Adaptive continual reassessment method: A maximum tolerated dose estimation method in phase I clinical trial

Adaptive continual reassessment method: A maximum tolerated dose estimation method in phase I clinical trial

Nakagami Distribution for Modeling Monthly Precipitations in Van, Türkiye

Precipitation patterns are intricately influenced by geographic factors and local environmental conditions. Statistical distributions are one of the methods that help investigate precipitation characteristics at different sites. Van is a province that ranks among the provinces with the lowest precipitation in the Eastern Anatolia region of Türkiye, receiving an annual rainfall of around 400 mm. In this study, 63 years of monthly average precipitation data from Van, are modeled employing various well-known statistical distributions including the Nakagami distribution. The Nakagami distribution is one of the flexible distributions used in describing data from various fields. In estimating the parameters of the considered distributions maximum likelihood estimation method is utilized. Comparisons are made using various goodness of fit criteria including root mean squared error, coefficient of determination, and Kolmogorov-Smirnov test. According to the results, the Nakagami distribution is found to be the most suitable statistical distribution for modeling precipitations in Van province. Additionally, precipitation values for 10, 25, 50, and 100-year return periods are obtained.

문화예술부문에 대한 재정지출이 온실가스배출에 미치는 영향분석

As the culture and arts sector develops alongside economic growth, the increase in greenhouse gas emissions highlights the importance of environmental considerations in the context of climate change. This study employs a two-step estimation method using a dynamic panel model to analyze the impact of fiscal expenditure for the culture and arts sector on greenhouse gas emissions, distinguishing between direct and indirect effects. The first-stage analysis reveals that increased fiscal expenditure in the culture and arts sector positively impacts regional economic growth. The second-stage analysis indicates the presence of an inverted U-shaped curve between per-capita GRDP and greenhouse gas emissions, supporting the Environmental Kuznets Curve hypothesis. While fiscal expenditure for the culture and arts sector directly increases greenhouse gas emissions, it indirectly reduces them through regional economic growth. However, the direct effect is greater and more significant than the indirect effect, resulting in a net increase in greenhouse gas emissions. These findings suggest that for effective greenhouse gas reduction policies at the local government level, it is necessary to improve energy efficiency in cultural and artistic activities and promote the use of eco-friendly energy sources. Additionally, there is a need for support that can raise environmental awareness among cultural and artistic professionals and induce changes in their energy consumption patterns.

Cybersecurity strategies for protecting big data in business intelligence systems: Implication for operational efficiency and profitability

The alarming increase in consumers goods and services with concurrent devaluations of naira have necessitated the need to examine the effects of NUSX(Nigerian naira/1 US dollar), NFX (Nigerian naira/1 France franc) and NCX (Nigerian naira/1 Chinese yuan) rates on inflation(measured by CPI) and its volatility in Nigeria using data sets spanning from 2008 to 2022. The auxiliary autoregressive AAR(3) order of integration test specify that all the variables are stationary at first difference. Adopting multiple regression model with least square method of estimation and generalized autoregressive conditional heteroskedasticity (GARCH(1, 1)) as measure of volatility, the results indicate that NCX (Nigerian naira/1 Chinese yuan), NFX (Nigerian naira/1 France franc) and NUSX(Nigerian naira/1 US dollar) have no influence on both inflation and its volatility in Nigeria. This implies that variation in exchange rates system is not sufficient to explain the sudden increase in the prices of goods and services in Nigeria. Therefore, the government should look beyond exchange rates in finding solution to rising inflation phenomenon in Nigeria’s economic space.