Estimation Of Function Research Articles

Vulnerable brain regions in adolescent major depressive disorder: A resting-state functional magnetic resonance imaging activation likelihood estimation meta-analysis.

Adolescent major depressive disorder (MDD) is a significant mental health concern that often leads to recurrent depression in adulthood. Resting-state functional magnetic resonance imaging (rs-fMRI) offers unique insights into the neural mechanisms underlying this condition. However, despite previous research, the specific vulnerable brain regions affected in adolescent MDD patients have not been fully elucidated. To identify consistent vulnerable brain regions in adolescent MDD patients using rs-fMRI and activation likelihood estimation (ALE) meta-analysis. We performed a comprehensive literature search through July 12, 2023, for studies investigating brain functional changes in adolescent MDD patients. We utilized regional homogeneity (ReHo), amplitude of low-frequency fluctuations (ALFF) and fractional ALFF (fALFF) analyses. We compared the regions of aberrant spontaneous neural activity in adolescents with MDD vs healthy controls (HCs) using ALE. Ten studies (369 adolescent MDD patients and 313 HCs) were included. Combining the ReHo and ALFF/fALFF data, the results revealed that the activity in the right cuneus and left precuneus was lower in the adolescent MDD patients than in the HCs (voxel size: 648 mm3, P < 0.05), and no brain region exhibited increased activity. Based on the ALFF data, we found decreased activity in the right cuneus and left precuneus in adolescent MDD patients (voxel size: 736 mm3, P < 0.05), with no regions exhibiting increased activity. Through ALE meta-analysis, we consistently identified the right cuneus and left precuneus as vulnerable brain regions in adolescent MDD patients, increasing our understanding of the neuropathology of affected adolescents.

Radiofrequency ablation of hepatocellular carcinoma guided by real-time physics-based ablation simulation: a prospective study

Objectives To assess the safety and efficacy of radiofrequency ablation (RFA) guidance software that incorporated patient-specific physics-based simulation of each ablation volume. Materials and methods Patients referred for curative ablation of hepatocellular carcinoma (HCC) of 2–5 cm diameter were prospectively enrolled. RFA was performed under general anesthesia. Procedure planning and intraprocedural modifications were guided by computer simulation of each ablation. The segmented target (tumor with 5 mm margin) was registered to and superimposed on subsequent 3D multiplanar images. The applied RF energy was used to calculate a simulated ablation volume which was displayed relative to the electrode and segmented target, to depict any untreated target tissue. After each additional ablation, the software updated the accumulated simulated ablation volume in relation to the target. The primary endpoints were technical efficacy and rate of local tumor progression (LTP). Results Sixty-eight tumors were ablated during 57 procedures in 52 patients (68.3 ± 9.2 years old, 78.8% male); 15 (26.3%) had multiple lesions and 23 (39.1%) had prior HCC treatment. The mean tumor diameter was 2.73 (±0.64) cm. The intraprocedural simulation directed additional overlapping ablations in 75.9% of tumors. Technical success and efficacy were 100% at 3-month contrast enhanced CT or MRI follow-up after the single treatment session. Cumulative incidence function estimates for 1- and 2-year LTP were 3.9% and 20.2%, respectively. Conclusion This prospective study found computer-assisted guidance that simulated each ablation was both safe and efficacious. The low rate of LTP was similar to studies that employed stereotactic guidance and ablation confirmation, without requiring a second contrast enhanced study.

Abstract P329: Lifetime Risk of Dementia: The Atherosclerosis Risk in Communities (ARIC) Study

Introduction: Prior studies suggest that 15-20% of adults will develop dementia after midlife. However, current lifetime risk estimates are based on limited dementia ascertainment, potentially resulting in underestimation. Methods: We conducted a prospective cohort analysis using data from the Atherosclerosis Risk in Communities Study (ARIC) (baseline 1990-1992). We used non-parametric cumulative incidence function estimation to calculate lifetime risk of dementia while accounting for the competing risk of death, overall and stratified by demographics and APOE ε 4 status. Dementia was ascertained until December 31, 2019 by: (1) cognitive testing at study visits, with diagnoses generated using a standardized computer algorithm and confirmed by a panel of experts; (2) semiannual telephone cognitive testing and Clinical Dementia Rating interviews with informants; and (3) ICD codes from hospital records and death certificates. Results: We included 13,358 participants (mean age: 57 years; 25% of Black race; 55% female sex; 31% carried at least one APOE ε 4 allele). Over a median follow-up of 24 (IQR: 20, 29) years, there were 2,812 incident cases of dementia. At age 55, the lifetime risk of dementia (up to age 95) was 43% (Fig A) . Adults with two copies of the APOE ε 4 allele had substantially higher lifetime risk (61%) of dementia compared to those with one copy (48%) and those none (40%), with differences beginning at age ~70 years (Fig B) . Lifetime risk was higher in women and Black adults (Fig C-D) . Conclusion: More than 4 in 10 adults will develop dementia after midlife. Comprehensive capture of dementia in the ARIC study results in lifetime risk estimate substantially exceeding prior research.

A Decoupling Algorithm for Efficient Estimation of Failure Probability Function Based on Statistical Moment Functions

Abstract Failure probability function (FPF) is an important index that reflects the influence of designable distribution parameters on the safety degree of a structure, and it can be used for decoupling reliability optimization models. Thus, its efficient solution is expected. A decoupling algorithm based on statistical moment functions (SMFs) of performance function is proposed to solve the FPF efficiently in this paper. The proposed algorithm first constructs an extended density weight function (EDWF), which can cover the interested region of the distribution parameters and is independent of the distribution parameters so that the statistical moment integrals corresponding to different realizations of the distribution parameters can share the same EDWF. Then, using the same EDWF, a strategy is dexterously designed to estimate the SMFs by sharing a set of integral characteristic nodes. Finally, the FPF is approximated by the SMFs, which varies with the distribution parameters in the interested design region. In addition, the proposed algorithm introduces the Box–Cox transformation of the performance function to guide the high accuracy of FPF approximated by the SMFs. The main contribution of the proposed algorithm is constructing the EDWF to decouple the dependence of solving SMFs on the realizations of the distribution parameters over the interested region and designing the strategy of estimating the SMFs by sharing the same integral characteristic nodes. Since the proposed algorithm employs a point estimation method to evaluate the FPF, it has higher efficiency than the competitive methods. Numerical and engineering examples demonstrate the superiority of the proposed algorithm.

Parameters Estimation of a Proposed Non-Homogeneous Poisson Process and Estimation of the Reliability Function Using the Gompertz Process: A Comparative Analysis of Artificially Intelligent and Traditional Methods

This work presents a novel approach to enhancing the rate of occurrence of non-homogeneousPoisson processes (NHPP) by utilizing the Gompertz distribution as the rate of occurrence. The primary objectiveof this study is to determine the parameters of the new process using both traditional methods and intelligenttechnology, specifically particle swarm optimization (PSO). Additionally, the study aims to estimate the reliabilityfunction of the process. The suggested model is simulated to achieve these goals, and the results are comparedamong various estimation techniques to identify the most accurate estimator. The study demonstrates that whenpredicting the time rate of occurrence of the proposed Gompertz process and its reliability function, the PSOalgorithm outperforms other approaches. Furthermore, this research showcases a practical application utilizing realdata from the Mosul power facility. Specifically, the data pertains to the stoppage times of two consecutive units ofthe Mosul Dam power stations from January 1st, 2021 to January 1st, 2022. Overall, this study introduces a novelprocess based on the Gompertz distribution to improve the rate of occurrence of NHPP. It employs particle swarmoptimization to calculate the process parameters and estimate the reliability function. The superiority of the PSOalgorithm is demonstrated through comprehensive comparisons. The practical application using data from theMosul power facility further validates the effectiveness of the proposed approach.

Region-based cascading impact analysis in critical infrastructure systems

ABSTRACT Critical Infrastructure System (CIS) disruption cascades into all the dependent CISs, even the regions, over the internal system where the disruption occurs. This study develops a cascading impact analysis with a region-based path-flow estimation approach based on a complex network and provides a local-scale demonstration of the interdependent electric power-water supply system in Incheon, South Korea. The analytical framework facilitates the modeling of CISs considering three major structural relationships – CIS, CIS-CIS, and CIS-Region- with functional interdependency and quantifying cascading impacts on regions via a continuous service flow. The results demonstrate more practical cascading ranges and levels caused by each CIS disruption, highlighting that the potential impacts increase in an integrated network, which a single CIS network cannot capture. It emphasizes the need to consider functional interdependent CIS structures and estimate cascading impacts from a regional perspective. This study provides multiangle insights into how regions enhance resilience through infrastructure disaster management.

On approximating dependence function and its derivatives

Bivariate extreme value distributions can be used to model dependence of observations from random variables in extreme levels. There is no finite dimensional parametric family for these distributions, but they can be characterized by a certain one-dimensional function which is known as Pickands dependence function. In many applications the general approach is to estimate the dependence function with a non-parametric method and then conduct further analysis based on the estimate. Although this approach is flexible in the sense that it does not impose any special structure on the dependence function, its main drawback is that the estimate is not available in a closed form. This paper provides some theoretical results which can be used to find a closed form approximation for an exact or an estimate of a twice differentiable dependence function and its derivatives. We demonstrate the methodology by calculating approximations for symmetric and asymmetric logistic dependence functions and their second derivatives. We show that the theory can be even applied to approximating a non-parametric estimate of dependence function using a convex optimization algorithm. Other discussed applications include a procedure for testing whether an estimate of dependence function can be assumed to be symmetric and estimation of the concordance measures of a bivariate extreme value distribution. Finally, an Australian annual maximum temperature dataset is used to illustrate how the theory can be used to build semi-infinite and compact predictions regions.

Calibration estimation of distribution function based on multidimensional scaling of auxiliary information

The distribution function is a functional parameter of great interest in many research areas, such as medicine or economics. Among other properties, it facilitates the estimation of parameters such as quantiles. Accordingly, techniques are needed to estimate this function efficiently. Survey statisticians have access to large, high-dimension databases and use them to optimise the estimates obtained. One way to incorporate auxiliary information in the estimation stage is through the calibration method, which was initially designed to estimate totals and means and consists of adjusting new sample weights in order to reduce the variance of estimators. However, calibration techniques may be subject to over-calibration, i.e. the loss of efficiency when high-dimension auxiliary data sets are incorporated. Although alternative approaches have been proposed, in which the calibration method incorporates auxiliary information in the estimation of the distribution function, these alternatives do not seek to incorporate qualitative auxiliary information, which must be introduced in the usual way through dummy variables. However, this workaround can greatly increase the dimension of the auxiliary information, producing either over-calibration or even incompatible calibration constraints. In this article, we propose adapting the calibration method through multidimensional scaling, in order to incorporate quantitative and qualitative information, thus avoiding the negative consequences of over-calibration in the estimation of the distribution function.

A quantitative second order estimate for (weighted) p-harmonic functions in manifolds under curvature-dimension condition

We build up a quantitative second-order Sobolev estimate of ln⁡w for positive p-harmonic functions w in Riemannian manifolds under Ricci curvature bounded from below and also for positive weighted p-harmonic functions w in weighted manifolds under the Bakry-Émery curvature-dimension condition.

Capacitary maximal inequalities and applications

In this paper we introduce capacitary analogues of the Hardy-Littlewood maximal function,MCf(x):=supr>0⁡1C(B(x,r))∫B(x,r)|f|dC, for C= the Hausdorff content or a Riesz capacity. For these maximal functions, we prove a strong-type (p,p) bound for 1<p≤+∞ on the capacitary integration spaces Lp(C) and a weak-type (1,1) bound on the capacitary integration space L1(C). We show how these estimates clarify and improve the existing literature concerning maximal function estimates on capacitary integration spaces. As a consequence, we deduce correspondingly stronger differentiation theorems of Lebesgue-type, which in turn, by classical capacitary inequalities, yield more precise estimates concerning Lebesgue points for functions in Sobolev spaces.

Advancing objective functions in hydrological modelling: Integrating knowable moments for improved simulation accuracy

Runoff prediction is a crucial aspect of water resource management and risk mitigation. Despite hydrological modelling plays a vital role in accurately representing catchment behaviour, calibration still poses a significant challenge. This study explores the use of knowable moments (KMoments), a category of high-order statistical moments, as part of the core of objective functions in hydrological model calibration. Traditional objective functions, such as Nash-Sutcliffe Efficiency (NSE) and Kling-Gupta Efficiency (KGE), often make assumptions about data distribution and are sensitive to outliers. KMoments offer a promising alternative by enabling reliable estimation and effective description of high-order statistics from typical hydrological samples and therefore, reducing uncertainty in their estimation and computation of the objective functions in question. Three daily lumped hydrological models (GR4J, VIC, and HYMOD) were employed to test the performance of different calibration strategies using KMoments-based objective functions and compare them with conventional approaches. The hydrological consistency of the simulations was also assessed through 27 hydrological signatures. Our findings highlight the advantages of using KMoments, including improved performance metrics and enhanced hydrological signature reproduction. The findings contribute to advancing hydrological modelling techniques and provide valuable insights for researchers and practitioners seeking to enhance simulation accuracy and reliability.

Inferring brain causal and temporal-lag networks for recognizing abnormal patterns of dementia

Brain functional network analysis has become a popular method to explore the laws of brain organization and identify biomarkers of neurological diseases. However, it is still a challenging task to construct an ideal brain network due to the limited understanding of the human brain. Existing methods often ignore the impact of temporal-lag on the results of brain network modeling, which may lead to some unreliable conclusions. To overcome this issue, we propose a novel brain functional network estimation method, which can simultaneously infer the causal mechanisms and temporal-lag values among brain regions. Specifically, our method converts the lag learning into an instantaneous effect estimation problem, and further embeds the search objectives into a deep neural network model as parameters to be learned. To verify the effectiveness of the proposed estimation method, we perform experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database by comparing the proposed model with several existing methods, including correlation-based and causality-based methods. The experimental results show that our brain networks constructed by the proposed estimation method can not only achieve promising classification performance, but also exhibit some characteristics of physiological mechanisms. Our approach provides a new perspective for understanding the pathogenesis of brain diseases. The source code is released at https://github.com/NJUSTxiazw/CTLN.

Fabrication uncertainty guided design optimization of a photonic crystal cavity by using Gaussian processes

We present a fabrication uncertainty aware and robust design optimization approach that can be used to obtain robust design estimates for nonlinear, nonconvex, and expensive model functions. It is founded on Gaussian processes and a Monte Carlo sampling procedure, and assumes knowledge about the uncertainties associated with a manufacturing process. The approach itself is iterative. First, a large parameter domain is sampled in a coarse fashion. This coarse sampling is used primarily to determine smaller candidate regions to investigate in a second, more refined sampling pass. This finer step is used to obtain an estimate of the expected performance of the found design parameter under the assumed manufacturing uncertainties. We apply the presented approach to the robust optimization of the Purcell enhancement of a photonic crystal nanobeam cavity. We obtain a predicted robust Purcell enhancement of F¯P≈3.6. For comparison we also perform an optimization without robustness. We find that an unrobust optimum of FP≈256.5 dwindles to only F¯P≈0.7 when fabrication uncertainties are taken into account. We thus demonstrate that the presented approach is able to find designs of significantly higher performance than those obtained with conventional optimization.

Polynomial approaches in improving accuracy of probability distribution estimation using the method of moments

AbstractBACKGROUNDDetermination of a probability density function (PDF) is an area of active research in engineering sciences as it can improve process systems. A previously developed polynomial method‐of‐moments‐based PDF estimation model has been applied in the research to produce accurate approximations to both standard and more complex PDF. A model with a different polynomial basis than a monomial is still to be developed and evaluated. This is the work that is undertaken in this study.RESULTSA set of standard PDF (Normal, Weibull, Log Normal and Bimodal) and more complex distributions (solutions to the Smoluchowski coagulation equation and Population Balance equation) were approximated by the method‐of‐moments using Chebyshev, Hermite and Lagrange polynomial‐based density functions. Results show that Lagrange polynomial‐based models improve the fit compared to monomial based‐modeling in terms of RMSE and Kolmogorov–Smirnov test statistic estimates. The Kolmogorov–Smirnov test‐statistics decreased by 19% and the RMSE values were improved by around 85% compared to the standard monomial basis when using Lagrange polynomial basis.CONCLUSIONThis study indicates that the procedure using Lagrange polynomials with method‐of‐moments is a more reliable reconstruction procedure that calculates the approximate distribution using lesser number of moments, which is desirable. © 2024 The Authors. Journal of Chemical Technology and Biotechnology published by John Wiley &amp; Sons Ltd on behalf of Society of Chemical Industry (SCI).

Smooth estimation of conditional quantile function with mixed covariates using Bernstein polynomials

ABSTRACT The conditional quantile function is a fundamental tool for understanding the relationship between covariates and the response variable in data analysis. In this study, we propose a novel estimator based on the Nadaraya–Watson discrete conditional distribution estimator, which uses Bernstein polynomials for smoothing. The consistency and asymptotic normality of the proposed estimator are established. Also, a simulation study to compare its performance with other existing estimators is carried out.

Estimation of renal function: Different markers for different outcomes?

Estimation of renal function: Different markers for different outcomes?

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.