Chapter 20: Freeze-Thaw Process Data Analysis and Mechanistic Modeling: Simplified Lumped Capacitance Analysis for Small Fill Volumes

2020 ◽  
pp. 487-498
Author(s):  
Alina A. Alexeenko ◽  
Laura Mozdzen ◽  
Sherwin Shang ◽  
Michelle A. Long ◽  
Grace Kim ◽  
...  
Keyword(s):  
Data Analysis ◽  
Mechanistic Modeling ◽  
Process Data ◽  
Freeze Thaw

scholarly journals A framework for data-driven design in a product innovation process: data analysis and visualisation for model-based decision making

2020 ◽  
Vol 24 (1) ◽  
pp. 68
Author(s):  
Rob Vingerhoeds ◽  
Claude Baron ◽  
Alessandro Bertoni ◽  
Xin Yi ◽  
Phillippe Esteban
Keyword(s):  
Decision Making ◽  
Data Analysis ◽  
Product Innovation ◽  
Innovation Process ◽  
Data Driven ◽  
Process Data ◽  
Model Based

A systematic view on data descriptors for the visual analysis of tabular data

2016 ◽  
Vol 16 (3) ◽  
pp. 232-256 ◽  
Author(s):  
Hans-Jörg Schulz ◽  
Thomas Nocke ◽  
Magnus Heitzler ◽  
Heidrun Schumann
Keyword(s):  
Data Analysis ◽  
Visual Analysis ◽  
Data Gathering ◽  
Climate Impact ◽  
General Concept ◽  
Basic Knowledge ◽  
Data Types ◽  
Process Data ◽  
Tabular Data ◽  
Analysis Process

Visualization has become an important ingredient of data analysis, supporting users in exploring data and confirming hypotheses. At the beginning of a visual data analysis process, data characteristics are often assessed in an initial data profiling step. These include, for example, statistical properties of the data and information on the data’s well-formedness, which can be used during the subsequent analysis to adequately parametrize views and to highlight or exclude data items. We term this information data descriptors, which can span such diverse aspects as the data’s provenance, its storage schema, or its uncertainties. Gathered descriptors encapsulate basic knowledge about the data and can thus be used as objective starting points for the visual analysis process. In this article, we bring together these different aspects in a systematic form that describes the data itself (e.g. its content and context) and its relation to the larger data gathering and visual analysis process (e.g. its provenance and its utility). Once established in general, we further detail the concept of data descriptors specifically for tabular data as the most common form of structured data today. Finally, we utilize these data descriptors for tabular data to capture domain-specific data characteristics in the field of climate impact research. This procedure from the general concept via the concrete data type to the specific application domain effectively provides a blueprint for instantiating data descriptors for other data types and domains in the future.

Process Data Analysis and Interpretation

1999 ◽  
pp. 1-103 ◽  
Author(s):  
J.F. Davis ◽  
M.J. Piovoso ◽  
K.A. Hoo ◽  
B.R. Bakshi
Keyword(s):  
Data Analysis ◽  
Process Data

Mechanistic Modeling and Simulation for Process Data Generation

2019 ◽  
Vol 58 (38) ◽  
pp. 17871-17884
Author(s):  
Natércia C. P. Fernandes ◽  
Andrey Romanenko ◽  
Marco S. Reis
Keyword(s):  
Modeling And Simulation ◽  
Mechanistic Modeling ◽  
Data Generation ◽  
Process Data

scholarly journals UAV Monitoring of Excavation Works

2020 ◽  
Author(s):  
Roman Shults ◽  
Khaini-Kamal Kassymkanova ◽  
Shugyla Burlibayeva ◽  
Daria Skopinova ◽  
Roman Demianenko ◽  
...  
Keyword(s):  
Data Analysis ◽  
3D Models ◽  
High Accuracy ◽  
Process Data ◽  
Total Station ◽  
Depth Data ◽  
Data Source ◽  
Geodetic Control ◽  
Construction Works

The first stage of any construction is carrying out excavation works. These works are high-priced and timeconsuming. Mostly, for geodetic control of the works, the surveyors are using total stations and GNSS equipment. Last decade, UAV technology was a breakthrough in the geodetic technologies market. One of the possible applications of UAV is the monitoring of excavation works. In the article, the opportunities and accuracy of UAV data while performing the excavation works were studied. The surveying of earth volume in the middle of construction works was made using DJI Phantom 4 UAV. The data were being processed using two photogrammetric software: Agisoft Metashape and PhotoModeler Premium. For comparison, the surveying also was made using a conventional total station. For each data source, the 3D models were generated. The obtained models were compared with each other in CloudCompare software. The comparison revealed the high accuracy of UAV data that satisfies customer’s requirements. For the case of two software comparing, it is better to process data using PhotoModeler. The PhotoModeler software allows performing in-depth data analysis and blunders searching.

scholarly journals SAS ENTERPRISE GUIDE 6.1 FOR PHYSICIANS: MULTIPLE COMPARISONS.

2020 ◽  
Author(s):  
Nikolai S. Bunenkov ◽  
Gulnara F. Bunenkova ◽  
Vladimir V. Komok ◽  
Oleg A. Grinenko ◽  
Alexander S. Nemkov
Keyword(s):  
Clinical Trial ◽  
Data Analysis ◽  
Randomized Clinical Trial ◽  
Clinical Laboratory ◽  
Multiple Comparisons ◽  
Wilcoxon Test ◽  
Process Data ◽  
Bonferroni Adjustment ◽  
Efficient Data ◽  
Medical University

Objective: to develop algorithm of multiple comparisons data of prospective non-randomized clinical trial AMIRI CABG (ClinicalTrials.gov Identifier: NCT03050489) using SAS Enterprise Guide 6.1. Materials and methods. Data collection was performed according prospective non-randomized clinical trial AMIRI CABG (ClinicalTrials.gov Identifier: NCT03050489) in 1Pavlov First St. Petersburg State Medical University, Saint-Petersburg, Russia between 2016-2019 years with 336 patients. There is database with clinical, laboratory and instrumental data. Multiple comparisons test was performed with SAS Enterprise Guide 6.1. Results. There was developed algorithm of multiple comparisons data of prospective non-randomized clinical trial AMIRI CABG (ClinicalTrials.gov Identifier: NCT03050489). This algorithm could be useful for physicians and researchers for data analysis. Conclusion. Presented algorithm of data analysis could make easier and improve efficient data analysis. SAS Enterprise Guide 6.1 allows fast and accurate process data Key words: SAS Enterprise Guide 6.1, statistical analysis, clinical trials, multiple comparisons, Bonferroni adjustment, Kruskal-Wallis test, Wilcoxon test, Tukey test.

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