Recursive Decomposition of Electromyographic Signals With a Varying Number of Active Sources: Bayesian Modeling and Filtering

2020 ◽  
Vol 67 (2) ◽  
pp. 428-440 ◽  
Author(s):  
Tianyi Yu ◽  
Konstantin Akhmadeev ◽  
Eric Le Carpentier ◽  
Yannick Aoustin ◽  
Raphael Gross ◽  
...  
Keyword(s):  
Bayesian Modeling ◽  
Recursive Decomposition ◽  
Electromyographic Signals

Evaluation of the impact of public investment programs on social and economic performance of territories

2019 ◽  
pp. 109-123
Author(s):  
I. E. Limonov ◽  
M. V. Nesena
Keyword(s):  
Bayesian Modeling ◽  
Positive Impact ◽  
Public Investment ◽  
Difference In Differences ◽  
The Public ◽  
Export Activity ◽  
Regional Investment ◽  
The Difference ◽  
The Impact ◽  
Development Institution

The purpose of this study is to evaluate the impact of public investment programs on the socio-economic development of territories. As a case, the federal target programs for the development of regions and investment programs of the financial development institution — Vnesheconombank, designed to solve the problems of regional development are considered. The impact of the public interventions were evaluated by the “difference in differences” method using Bayesian modeling. The results of the evaluation suggest the positive impact of federal target programs on the total factor productivity of regions and on innovation; and that regional investment programs of Vnesheconombank are improving the export activity. All of the investments considered are likely to have contributed to the reduction of unemployment, but their implementation has been accompanied by an increase in social inequality.

Synthesis of digital twins with use of multidimensional recursive decomposition

2019 ◽  
pp. 26-37
Author(s):  
V. A. Minaev ◽  
A. V. Mazin ◽  
K. B. Zdiruk ◽  
L. S. Kulikov
Keyword(s):  
Educational Institution ◽  
Subject Area ◽  
Information Storage ◽  
Information Space ◽  
Practical Implementation ◽  
Cognitive Approach ◽  
Recursive Decomposition ◽  
Digital Twins ◽  
Secondary Information ◽  
Integrated Information

The article presents the scientific and methodological issues of formation of digital twins collections based on the use of the multi-aspect recursive decomposition algorithm of the subject area. The general approaches to the solution of topical issues of the modern stage of artificial intelligence are considered. The terminology is concretized in the interrelated areas of knowledge – information – data and its relation with the term of «digital twins» as information containers of knowledge is discussed. The structure, power estimation and metrizability of the information space presented as a recursively defined ordered set of elements – a collection of digital twins (DT-collections) are considered. It is shown that the practical implementation of this approach and its application as part of automated control systems involves maintaining the life cycle of the creation and operation of digital twins in the Integrated information storage, implementing a two-circuit scheme (model) of management. A new cognitive approach to assess the completeness of the knowledge measure in the information space is proposed. The model of the integrated information storage realizing accumulation of knowledge in data banks of primary and secondary information is considered. As an example, a recursive decomposition of a subset of engineering systems of an educational institution is performed.

scholarly journals Bayesian modeling of inconsistent plastic response due to material variability

2019 ◽  
Vol 353 ◽  
pp. 183-200 ◽  
Author(s):  
F. Rizzi ◽  
M. Khalil ◽  
R.E. Jones ◽  
J.A. Templeton ◽  
J.T. Ostien ◽  
...  
Keyword(s):  
Bayesian Modeling ◽  
Plastic Response

scholarly journals 1326. Vancomycin Area Under the Concentration-Time Curve (AUC) Estimation Using a Bayesian Approach Versus First-Order Pharmacokinetic Equations: A Pilot Study

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S673-S673
Author(s):  
Jeffrey Pearson ◽  
Yazed S Alsowaida ◽  
B S Pharm ◽  
David W Kubiak ◽  
Mary P Kovacevic ◽  
...  
Keyword(s):  
Pilot Study ◽  
Median Time ◽  
Bayesian Modeling ◽  
Surgical Prophylaxis ◽  
Time Curve ◽  
Team Members ◽  
First Order ◽  
Concentration Time ◽  
Study Team ◽  
Auc Estimation

Abstract Background Current guidelines endorse area under the concentration-time curve (AUC)-based monitoring over trough-only monitoring for systemic vancomycin. Vancomycin AUC can be estimated using either Bayesian modeling software or first-order pharmacokinetic (PK) calculations. The objective of this pilot study was to evaluate and compare the efficiency and feasibility of these two approaches for calculating the estimated vancomycin AUC. Methods A single-center crossover study was conducted in four medical/surgical units at Brigham and Women’s Hospital over a 3-month time period. All adult patients who received vancomycin were included. Patients were excluded if they were receiving vancomycin for surgical prophylaxis, were on hemodialysis, if vancomycin was being dosed by level, or if vancomycin levels were never drawn. The primary endpoint was the amount of time study team members spent calculating the estimated AUC and determining regimen adjustments with Bayesian modeling compared to first-order PK calculations. Secondary endpoints included the number of vancomycin levels drawn and the percent of those drawn that were usable for AUC calculations. Results One hundred twenty-four patients received vancomycin during the study, of whom 47 met inclusion criteria. The most likely reasons for exclusion were receiving vancomycin for surgical prophylaxis (n=40) or never having vancomycin levels drawn (n=32). The median time taken to assess levels in the Bayesian arm was 9.3 minutes [interquartile range (IQR) 7.8-12.4] versus 6.8 minutes (IQR 4.8-8.0) in the 2-level PK arm (p=0.004). However, if Bayesian software is integrated into the electronic health record (EHR), the median time to assess levels was 3.8 minutes (IQR 2.3-6.8, p=0.019). In the Bayesian arm, 30 of 34 vancomycin levels (88.2%) were usable for AUC calculations, compared to 28 of 58 (48.3%) in the 2-level PK arm. Conclusion With EHR integration, the use of Bayesian software to calculate the AUC was more efficient than first-order PK calculations. Additionally, vancomycin levels were more likely to be usable in the Bayesian arm, thereby avoiding delays in estimating the vancomycin AUC. Disclosures All Authors: No reported disclosures

scholarly journals scMET: Bayesian modeling of DNA methylation heterogeneity at single-cell resolution

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Chantriolnt-Andreas Kapourani ◽  
Ricard Argelaguet ◽  
Guido Sanguinetti ◽  
Catalina A. Vallejos
Keyword(s):  
Single Cell ◽  
Bayesian Modeling ◽  
Bayesian Model ◽  
Regulation Of Gene Expression ◽  
Differential Methylation ◽  
Hierarchical Bayesian Model ◽  
Genomic Features ◽  
Distinct Cell ◽  
Biological Heterogeneity

AbstractHigh-throughput single-cell measurements of DNA methylomes can quantify methylation heterogeneity and uncover its role in gene regulation. However, technical limitations and sparse coverage can preclude this task. scMET is a hierarchical Bayesian model which overcomes sparsity, sharing information across cells and genomic features to robustly quantify genuine biological heterogeneity. scMET can identify highly variable features that drive epigenetic heterogeneity, and perform differential methylation and variability analyses. We illustrate how scMET facilitates the characterization of epigenetically distinct cell populations and how it enables the formulation of novel hypotheses on the epigenetic regulation of gene expression. scMET is available at https://github.com/andreaskapou/scMET.

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