Correlation Function Analysis

1950 ◽  
Vol 22 (6) ◽  
pp. 762-764 ◽  
Author(s):  
L. G. Kraft
Keyword(s):  
Correlation Function ◽  
Function Analysis

scholarly journals A marked correlation function analysis of halo formation times in the Millennium Simulation

2006 ◽  
Vol 367 (3) ◽  
pp. 1039-1049 ◽  
Author(s):  
G. Harker ◽  
S. Cole ◽  
J. Helly ◽  
C. Frenk ◽  
A. Jenkins
Keyword(s):  
Correlation Function ◽  
Function Analysis ◽  
Halo Formation

From random Brownian motion of particles to high automation laboratory: a brief history of correlation time (Preprint)

2019 ◽  
Author(s):  
Enrico Di Stasio ◽  
Federico Berruti ◽  
Alessandro Arcovito ◽  
Federica Romitelli ◽  
Mirca Antenucci ◽  
...  
Keyword(s):  
Correlation Function ◽  
Time Course ◽  
Function Analysis ◽  
Turnaround Time ◽  
System Capacity ◽  
Quality Level ◽  
Laboratory Automation ◽  
Production Chain ◽  
Cycle Times ◽  
Software Algorithms

BACKGROUND Laboratory automation is the actual frontier for the increase of productivity and reduction of samples turnaround time (TAT), in turn used as a key indicator of laboratory performance. However, due to the statistical distribution of TAT values, classical parameters (mean, standard deviation, percentiles) fail to describe each single sample processing “story”. The driving idea of the present work is to assimilate the samples flow in an automation laboratory to the movement of molecules in solution by means of Dynamic Light Scattering Correlation Function analysis expansion. OBJECTIVE The aim of the approach is the increase of productivity and the reduction of laboratory process cycle times thus improving data quality level. The most widely known application of laboratory automation technology is robotics, based on many different automated laboratory instruments, devices (the most common being autosamplers), software algorithms and methodologies assembled together to form an unique production chain starting from the arrival of the biological sample in the lab to the output of clinical useful final results. METHODS TAT values from 10000 samples were used to build a correlation function. Through a time course, each sample perfectly correlates with its initial status (no results available) until its specific TAT value is reached and assumes a value of 1; after the TAT is reached (produced results) it no more correlates and its status value becomes 0. The generated correlation function is simply the normalized progressive timing sum of all analyzed samples status conditions at each specific time. RESULTS By correlation function analysis, several parameters to describe the general performance of the system as well as each individual sample status are derived and applied to monitor the efficiency of the automation chain in real time mode. CONCLUSIONS Our original approach to laboratory automation leads to the possibility of determining measurable criteria able to describe the entire system capacity to buffer and reduce problems both on the full performance or on spot samples, consequently developing a new tool to evaluate different or improved performing systems CLINICALTRIAL none

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