The theory of n-scales

Dündar, Furkan Semih

doi:10.1063/1.5020463

Cited by 2 publications

(4 citation statements)

References 7 publications

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“…An automated QC app based on the R Shiny framework can be an agile, versatile, and convenient solution to facilitate QC processes. In order to do so, such an app should perform the following typical tasks: Matching the dataset to the data specification file 3 , 4 , 12 , 13 Checking for missing or unavailable (NA) data entries: Confirming that the variables have correct data types and units Ensuring number of unique data values and their derivation matches data specification Data visualization to detect outliers and/or missing values 3 , 4 , 12 , 14 : Comparing protocol‐defined nominal times (NTs) with actual observation or event times relative to the first drug administration to detect aberrations Reviewing individual profiles to detect missing or anomalous data entries Checking covariate distributions and correlations Reviewing tabulated summaries to examine attributes of the dataset, such as number of participants per study, amount of missing or unquantifiable samples, and the central tendencies of covariates. …”

Section: Requirements For a Shiny Qc Appmentioning

confidence: 99%

“…Data visualization to detect outliers and/or missing values 3 , 4 , 12 , 14 : Comparing protocol‐defined nominal times (NTs) with actual observation or event times relative to the first drug administration to detect aberrations Reviewing individual profiles to detect missing or anomalous data entries Checking covariate distributions and correlations Reviewing tabulated summaries to examine attributes of the dataset, such as number of participants per study, amount of missing or unquantifiable samples, and the central tendencies of covariates. …”

Section: Requirements For a Shiny Qc Appmentioning

confidence: 99%

“…In the scope of pharmacometric data preparation, this includes various checks, such as identifying missing records, validating formatting and units, and adhering to the data specifications. 2 , 3 , 4 …”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improving pharmacometrics analysis efficiency using DataCheQC: An interactive, Shiny‐based app for quality control of pharmacometrics datasets

Dotan,

Radivojevic,

Singh

2023

CPT Pharmacom & Syst Pharma

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DataCheQC is an interactive application based on the R Shiny framework developed for the purposes of performing quality control (QC) checks on pharmacometric datasets, and thereby supporting the implementation of model‐informed drug development. Features include visual inspection of variables and data entries for errors and/or anomalies, and ensuring structural integrity through comparison with a dataset specification file. The app, which requires no programming knowledge to operate, allows the user to collect all findings into a summary report downloadable directly from the app itself. The source code for the app is freely available on GitHub under an open‐source license (https://github.com/DotanOr/DataCheQC) and can also be accessed online (https://dotanor.shinyapps.io/DataCheQC/).

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Section: Requirements For a Shiny Qc Appmentioning

confidence: 99%

Section: Requirements For a Shiny Qc Appmentioning

confidence: 99%

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Improving pharmacometrics analysis efficiency using DataCheQC: An interactive, Shiny‐based app for quality control of pharmacometrics datasets

Dotan,

Radivojevic,

Singh

2023

CPT Pharmacom & Syst Pharma

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“…The theory of time scales calculus has also provided considerable development in recent years [14][15][16][17][18]. The first work on the geometric interpretation of the theory [19] provided the introduction of the concept of partial dynamic derivatives on time scales [20] and various geometric studies are introduced [21][22][23][24]28].…”

Section: Introductionmentioning

confidence: 99%

Discrete Normal Vector Field Approximation via Time Scale Calculus

Akgandüller

Atmaca

2020

Applied Mathematics and Nonlinear Sciences

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The theory of time scales calculus have long been a subject to many researchers from different disciplines. Beside the unification and the extension aspects of the theory, it emerge as a powerful tool for mimetic discretization process. In this study, we present a framework to find normal vector fields of discrete point sets in ℝ3 by using symmetric differential on time scales. A surface parameterized by the tensor product of two time scales can be analogously expressed as the vertex set of non-regular rectangular grids. If the time scales are dense, then the discrete grid structure vanishes. If the time scales are isolated, then the further geometric analysis can be executed by using symmetric dynamic differential. Moreover, we present an algorithmic procedure to determine the symmetric dynamic differential structure on the neighborhood of points in surfaces. Our results indicate that the method we present has good approximation to unit normal vector fields of parameterized surfaces rather than the Delaunay triangulation for some points.

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The theory of n-scales

Cited by 2 publications

References 7 publications

Improving pharmacometrics analysis efficiency using DataCheQC: An interactive, Shiny‐based app for quality control of pharmacometrics datasets

Improving pharmacometrics analysis efficiency using DataCheQC: An interactive, Shiny‐based app for quality control of pharmacometrics datasets

Discrete Normal Vector Field Approximation via Time Scale Calculus

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