An automated system for data processing in the metabolic balance laboratory

1971 ◽  
Vol 4 (1-2) ◽  
pp. 181-196 ◽  
Author(s):  
Jerome P. Kassirer ◽  
David H. Brand ◽  
William B. Schwartz
Keyword(s):  
Data Processing ◽  
Automated System ◽  
Metabolic Balance

scholarly journals TreeNet–The Biological Drought and Growth Indicator Network

2021 ◽  
Vol 4 ◽  
Author(s):  
Roman Zweifel ◽  
Sophia Etzold ◽  
David Basler ◽  
Reinhard Bischoff ◽  
Sabine Braun ◽  
...  
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Environmental Conditions ◽  
Forest Growth ◽  
Water Dynamics ◽  
Automated System ◽  
Indicator Network ◽  
High Temporal Resolution ◽  
Stem Size ◽  
Wide Range

The TreeNet research and monitoring network has been continuously collecting data from point dendrometers and air and soil microclimate using an automated system since 2011. The goal of TreeNet is to generate high temporal resolution datasets of tree growth and tree water dynamics for research and to provide near real-time indicators of forest growth performance and drought stress to a wide audience. This paper explains the key working steps from the installation of sensors in the field to data acquisition, data transmission, data processing, and online visualization. Moreover, we discuss the underlying premises to convert dynamic stem size changes into relevant biological information. Every 10 min, the stem radii of about 420 trees from 13 species at 61 sites in Switzerland are measured electronically with micrometer precision, in parallel with the environmental conditions above and below ground. The data are automatically transmitted, processed and stored on a central server. Automated data processing (R-based functions) includes screening of outliers, interpolation of data gaps, and extraction of radial stem growth and water deficit for each tree. These long-term data are used for scientific investigations as well as to calculate and display daily indicators of growth trends and drought levels in Switzerland based on historical and current data. The current collection of over 100 million data points forms the basis for identifying dynamics of tree-, site- and species-specific processes along environmental gradients. TreeNet is one of the few forest networks capable of tracking the diurnal and seasonal cycles of tree physiology in near real-time, covering a wide range of temperate forest species and their respective environmental conditions.

scholarly journals Automated system for data collection and data processing using microcrystals

2015 ◽  
Vol 71 (a1) ◽  
pp. s187-s187
Author(s):  
Keitaro Yamashita ◽  
Kunio Hirata ◽  
Yoshiaki Kawano ◽  
Go Ueno ◽  
Kazuya Hasegawa ◽  
...  
Keyword(s):  
Data Collection ◽  
Data Processing ◽  
Automated System

Automated system for medical data processing

1970 ◽  
Vol 1 (3) ◽  
pp. 193-209 ◽  
Author(s):  
A.A. Popov ◽  
V.M. Yanenko ◽  
N.G. Zaitsev ◽  
S.Ja. Zaslavsky
Keyword(s):  
Data Processing ◽  
Medical Data ◽  
Automated System ◽  
Medical Data Processing

A systems approach to fluorescent immunoassay: general principles and representative applications.

1979 ◽  
Vol 25 (9) ◽  
pp. 1591-1595 ◽  
Author(s):  
R E Curry ◽  
H Heitzman ◽  
D H Riege ◽  
R V Sweet ◽  
M G Simonsen
Keyword(s):  
Data Processing ◽  
Fluorescent Dyes ◽  
Systems Approach ◽  
Competitive Binding ◽  
Automated System ◽  
Fluorescence Immunoassay ◽  
Molecular Fluorescence ◽  
Processing Module ◽  
Precision And Accuracy ◽  
Automated Sampling

Abstract We have developed an automated system for the immunoassay of subnanogram quantities of clinically interesting compounds by molecular fluorescence. The system includes all the necessary reagents and an automated fluorometer. The microprocessor-based instrument consists of a measurement and data-processing module and an automated sampling unit. With use of 10 pmol/L amounts of fluorescent dyes such as fluorescein, measurements with precision and accuracy of 1--3% are attained. In a competitive-binding fluorescence immunoassay, antigen labeled with a fluorescent dye competes with antigen in the sample or standard for a limited amount of antibody immobilized on a polyacrylamide bead 2--5 micrometers in diameter. After separating antibody-bound from free tracer, we measure the amount of fluorescence bound to the beads. In representative example assays, correlation of fluorescence immunoassay (y) with a reference radioimmunoassay (x) of thyroxine was y = 1.01x + 13 nmol/L, r = 0.98. Correlation of fluorescence immunoassay (y) with a reference radioimmunoassay (x) of triiodothyronine was y = 0.99x + 0.004 nmol/L, r = 0.96.

Microcomputer data processing in a TLD automated system for personnel dosimetry

1986 ◽  
Vol 59 (1-2) ◽  
pp. 213-216
Author(s):  
M. Prokić ◽  
S. Glodić ◽  
D. Hadžić
Keyword(s):  
Data Processing ◽  
Automated System

Geodynamic Monitoring in Area of Nuclear Power Plant

2014 ◽  
Vol 492 ◽  
pp. 556-560 ◽  
Author(s):  
Ruslan V. Sharapov ◽  
Oleg R. Kuzichkin
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Data Processing ◽  
Nuclear Power ◽  
Object Oriented ◽  
Automated System ◽  
Geodynamic Monitoring ◽  
Electromagnetic Control

In article the technique of construction of the automated system for data processing is considered at the electromagnetic control of geodynamic objects. The structure of interrelations for the object-oriented and serving subsystems realizing methodical, technical and a supply with information of processes of registration and data processing of the geodynamic control is determined.

scholarly journals Deep Learning Automated System for Thermal Defectometry of Multilayer Materials

2021 ◽  
Vol 12 (2) ◽  
pp. 98-107
Author(s):  
A. S. Momot ◽  
R. M. Galagan ◽  
V. Yu. Gluhovskii
Keyword(s):  
Deep Learning ◽  
Data Processing ◽  
Relative Error ◽  
Automated System ◽  
Thermal Testing ◽  
Defect Depth ◽  
Traditional Methods ◽  
Testing Data ◽  
Multilayer Materials

Currently, along with growth in industrial production, the requirements for product quality testing are also increasing. In the tasks of defectoscopy and defectometry of multilayer materials, the use of thermal nondestructive testing method is promising. At the same time, interpretation of thermal testing data is complicated by a number of factors, which makes the use of traditional methods of data processing ineffective. Therefore, an urgent task is to search for new methods of thermal testing that will automate the diagnostic process and increase information content of obtained results. The purpose of article is to use the advances in deep learning for processing results of active thermal testing of products made of multilayer materials and development of an automated system for thermal defectoscopy and defectometry of such products. The proposed system consists of a heating source, an infrared camera for recording sequences of thermograms and a digital information processing unit. Three neural network modules are used for automated data processing, each of which performs one of the tasks: defects detection and classification, determination of the defect depth and thickness. The software algorithms and user interface for interacting with system are programmed in the NI LabVIEW development environment.Experimental studies on samples made of multilayer fiberglass have shown a significant advantage of the developed system over using traditional methods for analyzing thermal testing data. The defect classification (determining the type) error on the test dataset was 15.7 %. Developed system ensured determination of defect depth with a relative error of 3.2 %, as well as the defect thickness with a relative error of 3.5 %.

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