Estimation Of Function Research Articles

Global existence of the strong solution to the climate dynamics model with topography effects and phase transformation of water vapor

This study investigates a climate dynamics model that incorporates topographical effects and the phase transformation of water vapor. The system comprises the Navier–Stokes equations, the temperature equation, the specific humidity equation, and the water content equation, all adhering to principles of energy conservation. Applying energy estimation methods, the Helmholtz–Weyl decomposition theorem, and the Brezis–Wainger inequality, we derive high-order a priori estimates for state functions. Subsequently, based on the initial data assumptions V0 ∈ H4(Ω), T0′,q0,mw0∈H2(Ω), we can prove that a strong solution to this system exists globally in time and establish the uniqueness of the global strong solution.

УЛЬТРАЗВУКОВОЕ ИССЛЕДОВАНИЕ ДИАФРАГМЫ КАК ПРЕДИКТОР УСПЕШНОГО ОТЛУЧЕНИЯ ПАЦИЕНТОВ ОТ АППАРАТА ИСКУССТВЕННОЙ ВЕНТИЛЯЦИИ ЛЕГКИХ

The relevance of the survey is linked with the persistent problem of weaning patients who have been on mechanical ventilation for a long time. The objective. To conduct a literature analysis in order to summarize the results of modern researches devoted to the study of sonographic parameters that allow estimation of diaphragmatic functions for predicting successful weaning of patients from artificial lung ventilation. Material and methods. In total 33 Russian-language and English-language sources were analyzed. Results. The following ultrasound parameters of the diaphragm were analyzed in detail: diaphragm excursion, diaphragm thickening fraction, diaphragmatic rapid shallow breathing index, and the time to peak inspiratory amplitude of the diaphragm. Conclusions. The ability to timely diagnose diaphragm dysfunction using ultrasound minimizes the risk of respiratory and cardiac failure. Currently there are methods of the diaphragm muscle stimulation to improve the recovery of lost functions.

Sustainable banking and trust in the global South

AbstractTrust in banking plays a significant role in promoting financial inclusion. Multinational banks (MNBs) have the potential to enhance trust by adopting sustainable banking practices. We investigate the impact of MNBs' adoption of ESG (Environmental, Social and Governance) practices on trust in banking in 38 developing countries. Using an instrumental variable approach and control function estimation, our findings indicate that sustainable practices by commercial MNBs are positively and significantly associated with increased trust in banking. The results remain consistent across different samples, lending robustness to our findings. By demonstrating the importance of sustainable banking in fostering trust, this study contributes to the limited literature on trust in banking in the global South.

Wage Function Estimation of Estonia and Latvia

Abstract The study of wage determinants goes back to the beginning of economic science, with theoretical and empirical foundations providing the basis for the specification of a model of the wage function. Using the OLS method, we study the determinants of real wages for Estonia between 2006Q3 and 2022Q3 and Latvia between 2004Q1 and 2022Q3. The lagged dependent variable exerts the most considerable impact on real wages, i.e., real wages in the preceding quarter. We find that unemployment has a relatively larger impact on real wage dynamics than real productivity in the Latvian than in Estonian model. In the Estonian model, real productivity has a relatively stronger impact than unemployment in explaining real wages. In both countries, changes in real productivity impact real wages with a one-quarter lag. The findings on the relative influence of real wage determinants guide economic policymakers in targeting measures that could increase real wages in both countries.

A collaborative siege method of multiple unmanned vehicles based on reinforcement learning

A method based on multi-agent reinforcement learning is proposed to tackle the challenges to capture escaping Target by Unmanned Ground Vehicles (UGVs). Initially, this study introduces environment and motion models tailored for cooperative UGV capture, along with clearly defined success criteria for direct capture. An attention mechanism integrated into the Soft Actor-Critic (SAC) is leveraged, directing focus towards pivotal state features pertinent to the task while effectively managing less relevant aspects. This allows capturing agents to concentrate on the whereabouts and activities of the target agent, thereby enhancing coordination and collaboration during pursuit. This focus on the target agent aids in refining the capture process and ensures precise estimation of value functions. The reduction in superfluous activities and unproductive scenarios amplifies efficiency and robustness. Furthermore, the attention weights dynamically adapt to environmental shifts. To address constrained incentives arising in scenarios with multiple vehicles capturing targets, the study introduces a revamped reward system. It divides the reward function into individual and cooperative components, thereby optimizing both global and localized incentives. By facilitating cooperative collaboration among capturing UGVs, this approach curtails the action space of the target UGV, leading to successful capture outcomes. The proposed technique demonstrates enhanced capture success compared to previous SAC algorithms. Simulation trials and comparisons with alternative learning methodologies validate the effectiveness of the algorithm and the design approach of the reward function.

On the limits of 16S rRNA gene-based metagenome prediction and functional profiling.

Molecular profiling techniques such as metagenomics, metatranscriptomics or metabolomics offer important insights into the functional diversity of the microbiome. In contrast, 16S rRNA gene sequencing, a widespread and cost-effective technique to measure microbial diversity, only allows for indirect estimation of microbial function. To mitigate this, tools such as PICRUSt2, Tax4Fun2, PanFP and MetGEM infer functional profiles from 16S rRNA gene sequencing data using different algorithms. Prior studies have cast doubts on the quality of these predictions, motivating us to systematically evaluate these tools using matched 16S rRNA gene sequencing, metagenomic datasets, and simulated data. Our contribution is threefold: (i) using simulated data, we investigate if technical biases could explain the discordance between inferred and expected results; (ii) considering human cohorts for type two diabetes, colorectal cancer and obesity, we test if health-related differential abundance measures of functional categories are concordant between 16S rRNA gene-inferred and metagenome-derived profiles and; (iii) since 16S rRNA gene copy number is an important confounder in functional profiles inference, we investigate if a customised copy number normalisation with the rrnDB database could improve the results. Our results show that 16S rRNA gene-based functional inference tools generally do not have the necessary sensitivity to delineate health-related functional changes in the microbiome and should thus be used with care. Furthermore, we outline important differences in the individual tools tested and offer recommendations for tool selection.

Correcting Optical Aberration via Depth-Aware Point Spread Functions.

Optical aberration is a ubiquitous degeneration in realistic lens-based imaging systems. Optical aberrations are caused by the differences in the optical path length when light travels through different regions of the camera lens with different incident angles. The blur and chromatic aberrations manifest significant discrepancies when the optical system changes. This work designs a transferable and effective image simulation system of simple lenses via multi-wavelength, depth-aware, spatially-variant four-dimensional point spread functions (4D-PSFs) estimation by changing a small amount of lens-dependent parameters. The image simulation system can alleviate the overhead of dataset collecting and exploiting the principle of computational imaging for effective optical aberration correction. With the guidance of domain knowledge about the image formation model provided by the 4D-PSFs, we establish a multi-scale optical aberration correction network for degraded image reconstruction, which consists of a scene depth estimation branch and an image restoration branch. Specifically, we propose to predict adaptive filters with the depth-aware PSFs and carry out dynamic convolutions, which facilitate the model's generalization in various scenes. We also employ convolution and self-attention mechanisms for global and local feature extraction and realize a spatially-variant restoration. The multi-scale feature extraction complements the features across different scales and provides fine details and contextual features. Extensive experiments demonstrate that our proposed algorithm performs favorably against state-of-the-art restoration methods. The source code and trained models are available to the public.

Toward Equitable Kidney Function Estimation in Critical Care Practice: Guidance From the Society of Critical Care Medicine's Diversity, Equity, and Inclusion in Renal Clinical Practice Task Force.

Accurate glomerular filtration rate (GFR) assessment is essential in critically ill patients. GFR is often estimated using creatinine-based equations, which require surrogates for muscle mass such as age and sex. Race has also been included in GFR equations, based on the assumption that Black individuals have genetically determined higher muscle mass. However, race-based GFR estimation has been questioned with the recognition that race is a poor surrogate for genetic ancestry, and racial health disparities are driven largely by socioeconomic factors. The American Society of Nephrology and the National Kidney Foundation (ASN/NKF) recommend widespread adoption of new "race-free" creatinine equations, and increased use of cystatin C as a race-agnostic GFR biomarker. Literature review and expert consensus. English language publications evaluating GFR assessment and racial disparities. We provide an overview of the ASN/NKF recommendations. We then apply an Implementation science methodology to identify facilitators and barriers to implementation of the ASN/NKF recommendations into critical care settings and identify evidence-based implementation strategies. Last, we highlight research priorities for advancing GFR estimation in critically ill patients. Implementation of the new creatinine-based GFR equation is facilitated by low cost and relative ease of incorporation into electronic health records. The key barrier to implementation is a lack of direct evidence in critically ill patients. Additional barriers to implementing cystatin C-based GFR estimation include higher cost and lack of test availability in most laboratories. Further, cystatin C concentrations are influenced by inflammation, which complicates interpretation. The lack of direct evidence in critically ill patients is a key barrier to broad implementation of newly developed "race-free" GFR equations. Additional research evaluating GFR equations in critically ill patients and novel approaches to dynamic kidney function estimation is required to advance equitable GFR assessment in this vulnerable population.

Differential Impact of the UNOS Simultaneous Liver-kidney Transplant Policy Change Among Patients With Sustained Acute Kidney Injury.

Simultaneous liver-kidney transplant (SLK) allocation policy in the United States was revised in August 2017, reducing access for liver transplant candidates with sustained acute kidney injury (sAKI) and potentially adversely impacting vulnerable populations whose true renal function is overestimated by commonly used estimation equations. We examined national transplant registry data containing information for all liver transplant recipients from June 2013 to December 2021 to assess the impact of this policy change using instrumental variable estimation based on date of listing. Posttransplant survival was compared for propensity-matched patients with sAKI who were only eligible for liver transplant alone (LTA_post; n = 638) after the policy change but would have been SLK-eligible before August 2017, with similar patients who were previously able to receive an SLK (SLK; n = 319). Overall posttransplant patient survival was similar at 3 y (81% versus 80%; P = 0.9). However, receiving an SLK versus LTA increased survival among African Americans (87% versus 61% at 3 y; P = 0.029). A trend toward survival benefit from SLK versus LTA, especially later in the follow-up period, was observed in recipients ≥ age 60 (3-y survival: 84% versus 76%; P = 0.2) and women (86% versus 80%; P = 0.2). The 2017 United Network for Organ Sharing SLK Allocation Policy was associated with reduced survival of African Americans with end-stage liver disease and sAKI and, potentially, older patients and women. Our study suggested the use of race-neutral estimation of renal function would ameliorate racial disparities in the SLK arena; however, further studies are needed to reduce disparity in posttransplant outcomes among patients with liver and kidney failure.

Estimation of the incubation time distribution in the singly and doubly interval censored model

We analyze nonparametric estimators for the distribution function of the incubation time in the singly and doubly interval censoring model. The classical approach is to use parametric families like Weibull, log‐normal or gamma distributions in the estimation procedure. We propose nonparametric estimates for functions of the observations, which stay closer to the data than the classical parametric methods. We also give explicit limit distributions for discrete versions of the models and apply this to compute confidence intervals. The methods complement the analysis of the continuous model in Groeneboom (2021, 2023). R scripts for computation of the estimates are provided in Groeneboom (2020).

Ridge regularized estimation of VAR models for inference

Ridge regression is a popular method for dense least squares regularization. In this article, ridge regression is studied in the context of VAR model estimation and inference. The implications of anisotropic penalization are discussed, and a comparison is made with Bayesian ridge‐type estimators. The asymptotic distribution and the properties of cross‐validation techniques are analyzed. Finally, the estimation of impulse response functions is evaluated with Monte Carlo simulations and ridge regression is compared with a number of similar and competing methods.

Unique continuation problem on RCD Spaces. I

In this note we establish the weak unique continuation theorem for caloric functions on compact RCD(K, 2) spaces and show that there exists an RCD(K, 4) space on which there exist non-trivial eigenfunctions of the Laplacian and non-stationary solutions of the heat equation which vanish up to infinite order at one point . We also establish frequency estimates for eigenfunctions and caloric functions on the metric horn. In particular, this gives a strong unique continuation type result on the metric horn for harmonic functions with a high rate of decay at the horn tip, where it is known that the standard strong unique continuation property fails.

The effect of correlated errors on the performance of local linear estimation of regression function based on random functional design

The effect of correlated errors on the performance of local linear estimation of regression function based on random functional design

A Framework for Leveraging Machine Learning Tools to Estimate Personalized Survival Curves

The conditional survival function of a time-to-event outcome subject to censoring and truncation is a common target of estimation in survival analysis. This parameter may be of scientific interest and also often appears as a nuisance in nonparametric and semiparametric problems. In addition to classical parametric and semiparametric methods (e.g., based on the Cox proportional hazards model), flexible machine learning approaches have been developed to estimate the conditional survival function. However, many of these methods are either implicitly or explicitly targeted toward risk stratification rather than overall survival function estimation. Others apply only to discrete-time settings or require inverse probability of censoring weights, which can be as difficult to estimate as the outcome survival function itself. Here, we employ a decomposition of the conditional survival function in terms of observable regression models in which censoring and truncation play no role. This allows application of an array of flexible regression and classification methods rather than only approaches that explicitly handle the complexities inherent to survival data. We outline estimation procedures based on this decomposition, empirically assess their performance, and demonstrate their use on data from an HIV vaccine trial. Supplementary materials for this article are available online.

Single-loop cross-entropy-based directional importance sampling for efficient estimation of failure probability function

Failure probability function (FPF) can not only reflect the effect of random input distribution parameter (DP) on structural safety degree, but also decouple reliability based design optimization. Nowadays, it still desires the efficient algorithms for FPF. Thus, this paper proposes an efficient and novel single-loop cross-entropy-based directional importance sampling method (SL-CE-DIS). The proposed SL-CE-DIS has two innovations. The first is constructing the transformation relationship (Tr-Re) of the samples on the limit state surface (LSS) in the standard normal space corresponding to different DP realizations. This Tr-Re is established by the property of the LSS in original physical space independent of DP, and it makes the LSS sample explored by single CE-DIS at a given DP realization can be shared by the CE-DIS at other DP realizations. The second is designing an inverse updating rule to reconstruct the quasi-optimal DIS density (DIS-D) corresponding to other DP realization, which is in the adjacent region of the given DP realization, based on CE principle, and this designed inverse updating rule can easily estimate the FPF values corresponding to other DP realization by matching the transformed LSS samples. Based on the designed LSS sample sharing strategy and inverse updating rule of DIS-D, the proposed SL-CE-DIS can use the LSS information explored by single CE-DIS at the given DP realization to estimate the FPF corresponding to the DP over the adjacent of the given DP realization, and the presented examples fully verify the superiority of the SL-CE-DIS over the other existing single-loop sampling methods for FPF.

The hedonic price model for the wine market: A systematic and comparative review of the literature

AbstractThis paper carries out a thorough review of the literature on the estimation of hedonic price functions in the wine market, compiling and carefully documenting all research work on the subject. The review analyses the main methodological decisions taken by the different authors, as well as the typology of the available databases: identification of the relevant market, specification of the price function, sources and types of prices, econometric methodology, and type of publication. The variance decomposition analysis of the Adjusted‐R‐squared values from the estimated hedonic price functions suggests that attribute selection, the definition of the product market, the characteristics of information sources, and the implemented econometric procedures are the most relevant factors in explaining the models’ explanatory power.

Estimating lung function from computed tomography at the patient and lobe level using machine learning.

Automated estimation of Pulmonary function test (PFT) results from Computed Tomography (CT) could advance the use of CT in screening, diagnosis, and staging of restrictive pulmonary diseases. Estimating lung function per lobe, which cannot be done with PFTs, would be helpful for risk assessment for pulmonary resection surgery and bronchoscopic lung volume reduction. To automatically estimate PFT results from CT and furthermore disentangle the individual contribution of pulmonary lobes to a patient's lung function. We propose I3Dr, a deep learning architecture for estimating global measures from an image that can also estimate the contributions of individual parts of the image to this global measure. We apply it to estimate the separate contributions of each pulmonary lobe to a patient's total lung function from CT, while requiring only CT scans and patient level lung function measurements for training. I3Dr consists of a lobe-level and a patient-level model. The lobe-level model extracts all anatomical pulmonary lobes from a CT scan and processes them in parallel to produce lobe level lung function estimates that sum up to a patient level estimate. The patient-level model directly estimates patient level lung function from a CT scan and is used to re-scale the output of the lobe-level model to increase performance. After demonstrating the viability of the proposed approach, the I3Dr model is trained and evaluated for PFT result estimation using a large data set of 8433 CT volumes for training, 1775 CT volumes for validation, and 1873 CT volumes fortesting. First, we demonstrate the viability of our approach by showing that a model trained with a collection of digit images to estimate their sum implicitly learns to assign correct values to individual digits. Next, we show that our models can estimate lobe-level quantities, such as COVID-19 severity scores, pulmonary volume (PV), and functional pulmonary volume (FPV) from CT while only provided with patient-level quantities during training. Lastly, we train and evaluate models for producing spirometry and diffusion capacity of carbon mono-oxide (DLCO) estimates at the patient and lobe level. For producing Forced Expiratory Volume in one second (FEV1), Forced Vital Capacity (FVC), and DLCO estimates, I3Dr obtains mean absolute errors (MAE) of 0.377L, 0.297L, and 2.800mL/min/mm Hg respectively. We release the resulting algorithms for lung function estimation to the research community at https://grand-challenge.org/algorithms/lobe-wise-lung-function-estimation/ CONCLUSIONS: I3Dr can estimate global measures from an image, as well as the contributions of individual parts of the image to this global measure. It offers a promising approach for estimating PFT results from CT scans and disentangling the individual contribution of pulmonary lobes to a patient's lung function. The findings presented in this work may advance the use of CT in screening, diagnosis, and staging of restrictive pulmonary diseases as well as in risk assessment for pulmonary resection surgery and bronchoscopic lung volumereduction.

Truncated composite quantile regression with covariates measurement errors

Truncated data and measurement error data are often encountered in practice. In this paper, we study two classes of truncated composite quantile regression with covariates measured with errors. We propose weighted composite quantile regression estimators of the slope parameters based on weights which are random quantities and determined by the product-limit estimates of the distribution function of truncated variable. However, the estimators are often biased when one treats mismeasured covariates as error-free. To eliminate the bias and obtain consistent estimators, a debiasing technique is proposed. The resulting estimates are proved to be asymptotically normal and root n consistent under some mild conditions. Simulation studies and a real data example show that the proposed methods work promisingly.

Deep survival analysis model for incident clearance time prediction

Incident clearance time prediction is a key task for traffic incident management. A hazard-based duration model is a prevalent approach for predicting and analyzing the incident clearance time, which considers “duration dependence” of which the probability of an incident clearance ending depends on the time the clearance has lasted. However, the performance is limited due to its model assumptions for clearance time distribution, linear relationship, and the time-invariant effects of influential factors. This study proposes a deep survival analysis model that relaxes the assumptions of the hazard-based duration model while considering duration dependence based on a multi-task deep neural network (MTDNN). The MTDNN can consider the duration dependence when predicting incident clearance time by simultaneously estimating the survival function based on the concept of multi-task learning. The effects of influential factors on the prediction of MTDNN are also investigated using a post-analysis method. The proposed model is evaluated by its predictive performance and the estimated effects of influential factors using the freeway incident data collected in Korea from 2014 to 2019. These evaluations show that, compared to the baseline hazard-based duration model, the proposed MTDNN improves the predictive performance by 29.7% in terms of mean absolute percent error, and outperforms all statistical and machine learning models for both incident clearance time prediction and the survival function estimation. The analysis of the influential factors reveals that the hazard-based duration model and MTDNN had major influencing factors in common, but the impact of some factors is considerably different.

A Class of Non-Bazilevic Functions Subordinate to Gegenbauer Polynomials

In this paper, we introduce and investigate a class non-Bazilevic functions that associated by Gegenbauer Polynomials. The coefficient estimates of functions belonging to this class are derived. Moreover, we obtain the classical Fekete-Szegö inequality of functions belonging to this class.