Direct Estimation Research Articles

Estimating soil strength using ultra high resolution seismic: A case study from a shallow water windfarm

The increasing global reliance on offshore wind farms as a sustainable energy source necessitates precise soil assessment for foundation design, due to their high foundation costs and complex integration into marine environments. This study explores the utilization of ultra-high-resolution seismic data in conjunction with cone penetration test data for soil strength estimation in offshore wind farm development. We propose a convolutional neural network-based approach that leverages ultra-high-resolution seismic data for direct soil strength estimation, aiming to address the challenges of limited cone penetration test measurements and potential overfitting in complex geological settings. Our methodology involves preprocessing ultra-high-resolution seismic and cone penetration test data, interpreting and integrating geological unit information, and applying repeated k-fold cross-validation to evaluate model performance. The field data results demonstrate the model's efficacy in accurately predicting cone penetration test curve trends, albeit with some amplitude discrepancies. We highlight the significance of geological interpretation in predicting soil strength and identify the dependency on valid data within each geological unit as a limitation.

Modeling and Estimation of Adaptive Optics-Corrected long-exposure Point Spread Function Using Gaussian Sum Approximation

Abstract Adaptive optics (AO) enhances astronomical images by correcting perturbations in the optical system, resulting in complex point spread function (PSF) shapes, which provide critical information for optical design, calibration, and diagnostics. This paper presents a parametric model for an AO-corrected long-exposure PSF that adapts to complex shapes and various seeing conditions. A complementary estimation method is also introduced, addressing the inverse problem of estimating the atmospheric PSF or power spectral density (PSD) from observed PSF data. The model, based on Gaussian sum approximation (GSA), was tested using simulated PSF data from the OOMAO toolbox for Fried parameter (r0) values ranging from 0.10 to 0.20 m and on-sky data from the Very Large Telescope’s MUSE instrument. Two approaches were analyzed: (i) direct PSF estimation using GSA, and (ii) PSF estimation derived from atmospheric PSD estimation. Both methods were evaluated using root mean square error (RMSE). While approach (i) provided the best performance, approach (ii) produced more accurate atmospheric PSF estimates compared to the Moffat approximation and the Fetick method. Additionally, approach (ii) enabled the estimation of key physical parameters, such as the Fried parameter (r0) and residual AO variance ($\sigma _{AO}^2$). The proposed methods effectively estimate atmospheric PSF and PSD, even for complex PSF shapes influenced by spiders, central obstructions, or static aberrations.

Reevaluating antibiotic prophylaxis: insights from a network meta-analysis on dry socket and surgical site infections.

Three databases (MEDLINE, Cochrane Library, and Scopus) were searched in December 2021 for 16 Randomised Clinical Trials (RCTs). Three reviewers reviewed the articles on oral antibiotic prophylaxis (ABP) for the prevention of surgical site infection (SSI) and dry socket (DS) after lower third molar (L3M) extraction using the PICO framework. From 1999 to 2021, RCTs involving healthy patients undergoing L3M extraction with ABP, placebo, or no therapy were included. Adverse effects (AEs) associated with antibiotic usage, along with the main outcomes (DS and SSI), were also documented. Three independent investigators selected articles based on pre-established inclusion criteria, with any disagreements resolved by consensus or additional researchers. PRISMA guidelines were followed, involving initial title and abstract screening, followed by full-text evaluation. Exclusion reasons were documented, and the most recent report was included when multiple reports on the same patients were found, with no language restrictions applied. Two investigators evaluated studies quality and quality of evidence respectively using the Cochrane Collaboration tool and GRADEpro GDT. They independently extracted data, focusing on the type of extraction and the number of extracted L3M. They also detailed the use of antibiotics, including dosage, dosage regimen, timing, and duration. Among 16 articles, 15 used a parallel arm design, while one used a crossover design. The antibiotics studied included Amoxicillin+Clavulanic acid (7 articles), Amoxicillin (6), Metronidazole (2), Azithromycin (1), and Clindamycin (2), all compared with no treatment or placebo. A pairwise meta-analysis was used to combine studies with equivalent treatment (direct estimation), and a network meta-analysis compared outcome variables across different treatments (indirect comparison). Two included articles had a low risk of bias and the level of evidence was low according to GRADE. Pooled results supported the use of antibiotics to reduce DS and SSI following L3M extraction with a number needed to treat 25 and 18, respectively. Despite the fact that ABP reduces the risk of DS and SSI, it is recommended to consider systemic conditions and individual patient risk factors before prescribing antibiotics, due to global health threat.

Effects of different types of intermittent fasting on metabolic outcomes: an umbrella review and network meta-analysis.

Intermittent fasting (IF) holds promise for enhancing metabolic health. However, the optimum IF forms and their superiority over continuous energy restriction (CER) remain unclear due to disconnected findings. We systematically searched PubMed, Embase, and the Cochrane databases for meta-analyses of randomized controlled trials (RCTs) investigating the association between IF and metabolic health outcomes. Subsequently, we performed an umbrella review and network meta-analysis (NMA) to evaluate the efficacy of different forms of IF (time-restricted eating (TRE), alternate-day fasting (ADF), and 5:2 diet (regular eating for 5days and energy restriction for 2days per week)) compared to CER and usual diets on metabolic health outcomes. To assess the certainty of both direct and indirect estimates, we employed the Confidence in Network Meta-Analysis (CINeMA) approach. Additionally, we calculated the surface under the cumulative ranking curve (SUCRA) for each dietary strategy to determine their ranking in terms of metabolic health benefits. Ten of the best and non-redundant meta-analysis studies, involving 153 original studies and 9846 participants, were included. When considering direct evidence only, all IF forms significantly reduced body weight compared to usual diets. In NMAincorporating indirect evidence, all IF regimens alsosignificantly reduced body weight compared to usual diets. In the SUCRA of NMA, IF ranked higher than usual diets or CER in 85.4% and 56.1% of the outcomes, respectively. ADF had the highest overall ranking for improving metabolic health (ranked first: 64.3%, ranked second: 14.3%). Overall, all IF forms demonstrate potentials to improve metabolic health, with ADF appearing to produce better outcomes across investigated outcomes. Further high-quality trials are warranted to confirm the (relative) efficacy of IF on metabolic health. PROSPERO (record no: CRD42022302690).

A new method for estimating recent adult mortality from summary sibling histories.

In low- and middle-income countries with limited death registration statistics, adult mortality rates are commonly estimated through sibling survival histories (SSH). In full SSH, respondents are asked about either the age, or the age and time of death, of each of their siblings in turn. Full SSH allow direct mortality estimation but can be time-consuming to collect. In this study, we introduce a new indirect estimation method using summary SSH, requiring only a limited set of questions to produce recent mortality estimates. We developed a set of 192 microsimulations representing a wide range of fertility and mortality patterns, and reconstructed summary SSH within these simulations as if they had been collected from adults aged 15-49. For each age group of respondents, we calculated coefficients that convert the proportion of adult siblings who died in the previous 5 years into age-specific mortality rates. We then evaluated the performance of this new method with real data, using 154 Demographic and Health Surveys. The new indirect method provides mortality rates that are consistent with direct estimates from full SSH. Across all DHS, the mean absolute percentage error in the risk of dying in adulthood (ages 15-49) is 6% for both men and women. In all but one survey, 95% confidence intervals around the direct and indirect estimates overlap. As with direct estimates of adult mortality from SSH, the indirect estimates remain, however, lower than those of the Population Division of the United Nations. Summary questions on sibling survival can be included in censuses and rapid turn-around surveys for the measurement of recent adult mortality.

Issues Associated With the Formulation of a Small Area Model for Estimation of State-Level Crime Victimization Rates

Subnational estimation is an important and challenging problem in the context of the National Crime Victimization Survey (NCVS). Direct estimates for subnational domains are often unreliable due to small sample sizes. Model-based procedures have potential to improve upon direct estimators. However, model-based estimation presents several new challenges. One must identify suitable covariates, and an appropriate model form must be specified. This article discusses issues associated with the formulation of a small area model for production of state-level estimates of crime victimization rates. An analysis of direct estimates and covariates motivates the development of a Bayesian multivariate model. A model in the original scale is compared to a model in the log scale. Efficiency gains from the model relative to the direct estimators are examined. One challenge is that direct estimates can equal zero. Two ways of handling zero direct estimates are discussed.

π-Conjugation as a Direct Estimate of Lewis Acidity.

A computational approach to directly estimate the relative acidity of a given Lewis acid is presented. This approach is based on the strength of the π-conjugation in trans-crotonaldehyde-Lewis acid complexes, the species used in the well-known Childs' Lewis acidity scale. It is found that the π-conjugative strength values given by the Energy Decomposition Analysis - Natural Orbital for Chemical Valence method strongly correlate not only with the variation of the bond lengths in the system, which are greatly affected by the nature of the Lewis acid but also with the downfield shifts experienced by the different nuclei of the conjugated system upon binding to the Lewis acid. These strong correlations indicate that the (easy-to-compute) π-conjugation energies can be used as an alternative Lewis acidity scale.

A Food Intake Estimation System Using an Artificial Intelligence-Based Model for Estimating Leftover Hospital Liquid Food in Clinical Environments: Development and Validation Study.

Medical staff often conduct assessments, such as food intake and nutrient sufficiency ratios, to accurately evaluate patients' food consumption. However, visual estimations to measure food intake are difficult to perform with numerous patients. Hence, the clinical environment requires a simple and accurate method to measure dietary intake. This study aims to develop a food intake estimation system through an artificial intelligence (AI) model to estimate leftover food. The accuracy of the AI's estimation was compared with that of visual estimation for liquid foods served to hospitalized patients. The estimations were evaluated by a dietitian who looked at the food photo (image visual estimation) and visual measurement evaluation was carried out by a nurse who looked directly at the food (direct visual estimation) based on actual measurements. In total, 300 dishes of liquid food (100 dishes of thin rice gruel, 100 of vegetable soup, 31 of fermented milk, and 18, 12, 13, and 26 of peach, grape, orange, and mixed juices, respectively) were used. The root-mean-square error (RMSE) and coefficient of determination (R2) were used as metrics to determine the accuracy of the evaluation process. Corresponding t tests and Spearman rank correlation coefficients were used to verify the accuracy of the measurements by each estimation method with the weighing method. The RMSE obtained by the AI estimation approach was 8.12 for energy. This tended to be smaller and larger than that obtained by the image visual estimation approach (8.49) and direct visual estimation approach (4.34), respectively. In addition, the R2 value for the AI estimation tended to be larger and smaller than the image and direct visual estimations, respectively. There was no difference between the AI estimation (mean 71.7, SD 23.9 kcal, P=.82) and actual values with the weighing method. However, the mean nutrient intake from the image visual estimation (mean 75.5, SD 23.2 kcal, P<.001) and direct visual estimation (mean 73.1, SD 26.4 kcal, P=.007) were significantly different from the actual values. Spearman rank correlation coefficients were high for energy (ρ=0.89-0.97), protein (ρ=0.94-0.97), fat (ρ=0.91-0.94), and carbohydrate (ρ=0.89-0.97). The measurement from the food intake estimation system by an AI-based model to estimate leftover liquid food intake in patients showed a high correlation with the actual values with the weighing method. Furthermore, it also showed a higher accuracy than the image visual estimation. The errors of the AI estimation method were within the acceptable range of the weighing method, which indicated that the AI-based food intake estimation system could be applied in clinical environments. However, its lower accuracy than that of direct visual estimation was still an issue.

Estimating the Greenhouse Gas Emission Potential Due to Construction Work on the 225 kV Segou - Bamako High Voltage BI-Ternal Electric Line in Mali in The Context of Climate Change

The aim of this study is to assess the carbon emissions resulting from the construction of the power line linking Bamako to Ségou. It focuses on direct emissions, more specifically those linked to the degradation and desctruction of the vegetation cover in the project right-of-way. To collect the data, surveys were set up on the project right-of-way, through which 3302 trees were inventoried in situ at the three sites. Circumference was measured at 1.30 m from the ground surface. Allometric equations for direct biomass estimation were used as a predictor o f tree diameter. Emission factors were then used to estimate the potential greenhouse gas emissions resulting from tree felling during power line construction. The standard error associated with the carbon fraction in dry biomass was calculated. In the Ségou, Koulikoro and Dioïla project areas, there are a number of plant species that provide local populations with various ecosystem services, including micro-climate regulation through carbon sequestration or absorption of atmospheric CO2. The implementation of the project will undoubtedly result in the felling of these trees. This felling will generate emissions of 22.11 t.eqCO2/ha in Ségou, 16.56 t.eqCO2/ha in Koulikoro and 15.43 t.eqCO2/ha in Dioïla. Across Ségou, Koulikoro and Dioïla, the trees in the project right-of-way represent a reservoir of 34.04 t/ha of above-ground woody biomass. This biomass represents a carbon stock of around 16.58 t.C/ha. This carbon stock will be transformed into a carbon source with emissions of 60.78 t.eqCO2/ha if the trees in the power line right-of-way are systematically felled. Mitigation measures are therefore urgently needed, with compensatory reforestation areas planted with species with high carbon sequestration potential.

Bayesian Evaluation of Growth Rates and Kleiber's Ratios in Harnali Sheep: Dissecting Maternal and Additive Genetic Contributions.

Understanding the genetic basis of growth and metabolic traits in sheep is crucial for improving production efficiency and sustainability. The current study aimed to estimate the genetic influences, both direct and maternal, on growth rate and Kleiber's ratio traits in Harnali sheep using pedigree data under Bayesian inference. The data pertained to 2404 animals spanned over 24 years (1998-2021). Fixed factors such as birth period, lamb sex and dam's weight at lambing were considered. The traits studied included average daily gains (ADGs) categorised into ADG1 (birth to weaning age), ADG2 (weaning to 6 months of age) and ADG3 (6-12 months of age), as well as corresponding Kleiber's ratios (KR1, KR2 and KR3). Six single-trait animal models were employed to estimate covariance components and heritabilities, integrating direct additive and maternal effects alongside significant fixed factors using THRGIBBS1F90 and POSTGIBBSF90 programmes. Direct heritability estimates were obtained for ADG1 (0.11 ± 0.05), ADG2 (0.06 ± 0.03), ADG3 (0.03 ± 0.03), KR1 (0.07 ± 0.03), KR2 (0.06 ± 0.03) and KR3 (0.05 ± 0.03). Maternal genetic effects have contributed significant particularly to pre-weaning traits. The study identified an antagonistic relationship between direct additive and maternal genetic effects. Positive genetic and phenotypic correlations emphasised the intricate relationship between growth and metabolic efficiency in Harnali sheep. The current study offers critical insights into the genetic basis of growth and metabolic traits in Harnali sheep, ultimately contributing to more efficient and sustainable sheep production systems.

Estimation of Earthquake-Induced Direct Economic Losses of Portfolio Buildings Based on Seismic Fragility Surface

Regional seismic loss assessment is crucial for developing earthquake emergency plans, which can significantly reduce casualties and socioeconomic losses in urban communities. Due to the complex types of building structures in a region, it is not possible to accurately measure the damage state of building structures using a single scalar ground motion intensity measure (IM). To more accurately reflect the responses of structures under earthquake excitations, the seismic fragility surface models for 4 typical RC frame structures are developed based on the vector ground motion IMs. The results indicate that the fragility surface model offers a superior level of precision in capturing the inherent uncertainty of ground motion and subsequently determines a more precise probability of structural failure, outperforming traditional single scalar ground motion IM fragility curve models. This enhanced accuracy holds significant implications for regional loss assessment. To fully consider the spatial cross-correlation of vector ground motion IMs, based on the established ground motion database of 8778 records, principal component analysis (PCA) and geostatistical analysis methods are used to construct a spatial cross-correlation influence field that simultaneously considers multiple ground motion IMs to simulate more realistic earthquake scenarios. Considering the impact of uncertainty in loss ratio on earthquake-induced direct economic loss estimation results, the maximum entropy technique is used in conjunction with an improved Latin Hypercube Sampling (LHS) to achieve spatially uniform sampling of loss ratio (LR). This study proposes an assessment framework for regional seismic direct economic loss for portfolio buildings based on the seismic fragility surface model considering spatial cross-correlation and uncertainty of loss ratio. The proposed framework is verified by numerical examples, and it is proven that the regional portfolio buildings considering multiple ground motion IMs is more reliable and robust than those merely considering a single ground motion IM, and when spatial cross-correlation is not considered, the probability of serious economic losses will be underestimated.

Lagrangian analysis of submesoscale flows from sparse data using Gaussian Process Regression for field reconstruction

Lagrangian analyses of oceanic flows provide insight into the various transport pathways in the ocean. This analysis typically relies on a dense set of trajectories that can be computed using high-resolution velocity fields, which are often not available during field experiments. Instruments like drifters and floats are often employed to overcome the limitations imposed by satellite- and radar-based velocity fields, to understand the transport pathways in the ocean. However, the sparsity in available drifter-trajectory data proves prohibitive to obtaining a comprehensive map of the Lagrangian characteristics of the underlying flow. To circumvent these issues, we use Gaussian Process Regression (GPR) to obtain velocity fields from sparse drifter data to generate synthetic trajectories and subsequently estimate two Lagrangian metrics, FTLE and dilation rate. A detailed error analysis is performed for drifter clusters deployed within various dynamical regions in the analytic Bickley jet system. The uncertainties in velocity reconstruction obtained from the GPR method, averaged along particle trajectories, locate Lagrangian confidence regions that are applicable both to synthetic trajectories and the dilation rate field. A sensitivity analysis reveals the role played by factors such as the spatial sampling density and temporal resolution of the drifter data, as well as the effect of position uncertainty as a result of GPS inaccuracy. The method is then applied to the drifter data from the Lagrangian Submesoscale Experiment in 2016 to locate convergent filaments. The results present a marked improvement over direct estimation of area-averaged dilation rates using drifter clusters.

An Adaptive Combined Method for Lithium‐Ion Battery State of Charge Estimation Using Long Short‐Term Memory Network and Unscented Kalman Filter Considering Battery Aging

AbstractThe accurate estimation of battery state of charge (SOC) enables the reliable and safe operation of lithium‐ion batteries. Data‐driven SOC estimation is considered an emerging and effective solution. However, existing data‐driven SOC estimation methods typically involve direct estimation and lack effective feedback correction. Moreover, battery degradation poses additional challenges to accurate SOC estimation. Therefore, this study proposes an adaptive combined method for battery SOC estimation based on a long short‐term memory (LSTM) network and unscented Kalman filter (UKF) algorithm considering battery aging status. First, an LSTM model is constructed to characterize the battery's dynamic performance instead of traditional battery models. Then, the UKF algorithm is employed to perform SOC estimation through the feedback of terminal voltage prediction. To enhance estimation accuracy under different aging statuses, a proportional‐integral‐derivative controller is employed to correct the capacity fading during the SOC estimation process. Validation results indicate that the terminal voltage prediction model demonstrates exceptional robustness against interference from current and voltage noise. Compared to the traditional estimation method combining the deep learning model and Kalman filter algorithm, the proposed method demonstrates superior estimation accuracy under various complex operating conditions. Furthermore, the proposed method outperforms the traditional method in estimation performance during battery aging.

Deep Learning for Opportunistic Rain Estimation via Satellite Microwave Links.

Accurate precipitation measurement is critical for managing flood and drought risks. Traditional meteorological tools, such as rain gauges and remote sensors, have limitations in resolution, coverage, and cost-effectiveness. Recently, the opportunistic use of microwave communication signals has been explored to improve precipitation estimation. While there is growing interest in using satellite-to-earth microwave links (SMLs) for machine learning-based precipitation estimation, direct rainfall estimation from raw signal-to-noise ratio (SNR) data via deep learning remains underexplored. This study investigates a range of machine learning (ML) approaches, including deep learning (DL) models and traditional methods like gradient boosting machine (GBM), for estimating rainfall rates from SNR data collected by interactive satellite receivers. We develop real-time models for rainfall detection and estimation using downlink SNR signals from satellites to user terminals. By leveraging a year-long dataset from multiple locations-including SNR measurements paired with disdrometer and rain-gauge data-we explore and evaluate various ML models. Our final models include ensemble approaches for both rainfall detection and cumulative rainfall estimation. The proposed models provide a reliable solution for estimating precipitation using Earth-satellite microwave links, potentially improving precipitation monitoring. Compared to the state-of-the-art power-law-based models applied to similar datasets reported in the literature, our ML models achieve a 46% reduction in the root mean squared error (RMSE) for event-based cumulative precipitation predictions.

Addressing bias due to measurement error of an outcome with unknown sensitivity in database epidemiological studies.

In epidemiological database studies, the occurrence of an event is measured with error through an indicator whose specificity is often maximised, at the expense of sensitivity. However, if the indicator has low sensitivity, measures of occurrence are underestimated. In association studies, risk difference is biased, and risk ratio may be biased as well, in either direction, if the sensitivity is differential across exposure groups. In this work, we show that if an auxiliary screening indicator can be defined to complement the main indicator, estimates of the positive predictive value of both indicators provide tools to estimate the sensitivity of the primary indicator, or a lower bound thereof. This mitigates bias in estimating the number of cases, prevalence, cumulative incidence, rate (particularly when the event is rare), and in association studies, risk ratio and risk difference. They also allow testing for non-differential sensitivity. While direct estimation of sensitivity is often infeasible, this novel methodology improves evidence based on data obtained from re-use of existing databases, which may prove critical for regulatory and public health decisions.

Point of care ultrasound measurement of paralumbar caudal vena cava diameter and caudal vena cava to aortic ratio in hypovolemic dogs.

Accurate assessment of intravascular volume is critical for precise fluid prescription. In people, bedside or point of care ultrasound is used to measure the inferior vena cava, with or without paired aortic measurement, to estimate intravascular volume. To determine if point of care ultrasound measurement of the caudal vena cava (CVC) diameter or the CVC diameter to the abdominal aorta (Ao) diameter (CVC:Ao) at the paralumbar view are associated with changes in intravascular volume, mean arterial pressure (MAP), or cardiac output in normovolemic and hypovolemic dogs. 8 purpose-bred dogs. Pressure-targeted hemorrhagic shock was induced in purpose-bred dogs under general anesthesia. Dogs were exsanguinated to a mean arterial pressure of 40 mmHg, or a maximum 60% blood volume lost, then auto-transfused shed blood. At a left paralumbar view, longitudinal plane measurements of the abdominal CVC diameter and aortic diameter were obtained. Measurements were performed at 4 timepoints: baseline under anesthesia (TP1), after hemorrhagic shock was induced (TP2), after ½ of shed blood had been re-transfused (TP3), and post-resuscitation with completed re-transfusion (TP4). Additional variables collected included cardiac output using thermodilution and arterial blood pressure. CVC:Ao was not significantly different between timepoints and was not associated with changes in CO (p = 0.28) or MAP (p = 0.50). CVC diameter was significantly different between baseline (TP1) and hemorrhagic shock (TP2). CVC diameter was significantly different at TP2 compared to TP1 after controlling for the effect of CO (p = 0.03) and MAP (p = 0.001). Aortic diameter was also significantly different at TP2 (p = 0.002, p = 0.001) and TP3 (p = 0.023, p = 0.017) compared to TP1 after controlling for CO and MAP. Obtaining point of care ultrasound images for CVC:Ao measurement was feasible. With a marked decrease in intravascular volume, both CVC and Ao diameter decreased, resulting in an unchanged CVC:Ao. Despite changes in CVC and Ao diameters, these changes were not associated with measured changes in CO, emphasizing that CO is not a direct estimate of intravascular volume and is affected by many compensatory mechanisms. Additional studies are needed to determine the most accurate method for bedside measurement of intravascular volume status.

Towards a Better Uncertainty Quantification in Automated Valuation Models

AbstractThis study introduces a novel framework for quantifying prediction uncertainty in automated valuation models (AVMs), crucial tools in modern real estate finance. While non-linear AVMs excel in predictive performance, their limited methods for assessing prediction uncertainty reduces reliability and practical utility. We address this gap by proposing an approach for quantifying the uncertainty associated with predicted house prices and by introducing a model-specific AVM uncertainty estimate (AVMU) for AVM comparisons. Using a dataset of 51,747 historical apartment transactions in Oslo, Norway, we train three AVMs (XGBoost, random forest, support vector machine) to predict sales prices. Thereafter, we develop three base uncertainty estimators (direct loss estimation, bootstrap ensemble, quantile regression) and three meta estimators (average regressor, voting regressor, stacked generalization) for uncertainty quantification. Conformal calibration aligns the outputted uncertainty estimates from the six estimators with standard deviations of corresponding prediction errors. Having strong positive correlations with observed absolute prediction errors, the calibrated uncertainty estimators are shown to effectively capture prediction uncertainty. While the direct loss estimation excels among base estimators, the voting regressor and stacked generalization meta estimators consistently outperform it. Furthermore, by using the AVMU estimate from the stacked generalization meta estimator we can successfully identify the best-performing AVM for three separate apartment portfolios without knowing true sales prices. This alignment of the mean estimated prediction uncertainty with observed deviations underlines the utility of pre-factual AVMU estimates for model comparisons. In conclusion, our framework helps bridge prediction accuracy and uncertainty for AVMs, enhancing their reliability and supporting informed decision making for stakeholders.

PET mapping of receptor occupancy using joint direct parametric reconstruction.

In this work, we evaluate a novel joint direct parametric reconstruction framework to directly estimate distributions of RO and other kinetic parameters in the brain from a pair of baseline and postdrug injection dynamic PET scans. The proposed method combines the use of regularization on RO maps with alternating optimization to enable estimation of occupancy even in low binding regions. Simulation results demonstrate the quantitative improvement of this method over conventional approaches in terms of accuracy and precision of occupancy. The proposed method is also evaluated in preclinical in-vivo experiments using 11C-MK6884 and a muscarinic acetylcholine receptor 4 positive allosteric modulator drug, showing improved estimation of receptor occupancy as compared to traditional estimators. The proposed joint direct estimation framework improves RO estimation compared to conventional methods, especially in intermediate to low-binding regions. This work could potentially facilitate the evaluation of new drug candidates targeting the CNS.

Approximation results of Phillips type operators including exponential function

&lt;p&gt;The current article deals with a study on some moderation of the Phillips operators, including constant and exponential functions. Here, we derive the moments applying the notion of moment-generating function for the well-known Phillips operators. The authors also establish uniform convergence estimates for the improved form of these operators. Additionally, some direct estimates involving the asymptotic-type results are discussed.&lt;/p&gt;

Direct Estimation for Commonly Used Pattern-Mixture Models in Clinical Trials

Pattern-mixture models have received increasing attention as they are commonly used to assess treatment effects in primary or sensitivity analyses for clinical trials with nonignorable missing data. Pattern-mixture models have traditionally been implemented using multiple imputation, where the variance estimation may be a challenge because the Rubin’s approach of combining between- and within-imputation variance may not provide consistent variance estimation while bootstrap methods may be time-consuming. Direct likelihood-based approaches have been proposed in the literature and implemented for some pattern-mixture models, but the assumptions are sometimes restrictive and the theoretical framework is fragile. In this article, we propose an analytical framework for an efficient direct likelihood estimation method for commonly used pattern-mixture models corresponding to return-to-baseline, jump-to-reference, placebo washout, and retrieved dropout imputations. A parsimonious tipping point analysis is also discussed and implemented. Results from simulation studies demonstrate that the proposed methods provide consistent estimators. We further illustrate the utility of the proposed methods using data from a clinical trial evaluating a treatment for type 2 diabetes.