Reference methods and their wider applicability to measurements of roundness profiles

1998 ◽  
Vol 115 (4) ◽  
pp. 197-202
Author(s):  
S. Adamczak ◽  
D. Janecki
Keyword(s):  
Reference Methods

scholarly journals Comparative evaluation ofTrichosporon asahiisusceptibility using ASTY colorimetric microdilution and CLSI M27-A2 broth microdilution reference methods

2008 ◽  
Vol 52 (9) ◽  
pp. 435-439 ◽  
Author(s):  
Ayse Kalkanci ◽  
Nanthawan Mekha ◽  
Natteewan Poonwan ◽  
Koichi Makimura ◽  
Takashi Sugita
Keyword(s):  
Comparative Evaluation ◽  
Broth Microdilution ◽  
Reference Methods

scholarly journals Simultaneous determination of iron and copper in children's sera by FAAS

2011 ◽  
Vol 61 (1) ◽  
pp. 93-102 ◽  
Author(s):  
Svjetlana Luterotti ◽  
Tončica Kordić ◽  
Slavica Dodig
Keyword(s):  
Atomic Absorption ◽  
Simultaneous Determination ◽  
Sample Pretreatment ◽  
Single Step ◽  
Design Experiment ◽  
New Approach ◽  
Flame Atomic Absorption ◽  
Reference Methods ◽  
Atomic Absorption Spectrometric

Simultaneous determination of iron and copper in children's sera by FAASA new and simple flame atomic-absorption spectrometric (FAAS) method is proposed for simultaneous determination of iron and copper in children's sera. It is based on single-step sample pretreatment (deproteinization with 3 mol L-1HCl, ratio 1:1) and single-step calibration using 1.5 mol L-1HCl standard. During method's optimization a short multifactorial design experiment was used. The proposed method assures accuracy, sensitivity and precision comparable to that of the reference methods. The new approach is simple and time-, labour- and serum-saving, the latter being especially important in pediatric diagnostics.

A Quantum-Inspired Genetic Algorithm for Extractive Text Summarization

2021 ◽  
Vol 10 (2) ◽  
pp. 42-60
Author(s):  
Khadidja Chettah ◽  
Amer Draa
Keyword(s):  
Genetic Algorithm ◽  
State Of The Art ◽  
Text Summarization ◽  
Automated System ◽  
Evaluation Metrics ◽  
Document Summarization ◽  
Automatic Text Summarization ◽  
Reference Methods ◽  
Textual Data ◽  
Automatic Text

Automatic text summarization has recently become a key instrument for reducing the huge quantity of textual data. In this paper, the authors propose a quantum-inspired genetic algorithm (QGA) for extractive single-document summarization. The QGA is used inside a totally automated system as an optimizer to search for the best combination of sentences to be put in the final summary. The presented approach is compared with 11 reference methods including supervised and unsupervised summarization techniques. They have evaluated the performances of the proposed approach on the DUC 2001 and DUC 2002 datasets using the ROUGE-1 and ROUGE-2 evaluation metrics. The obtained results show that the proposal can compete with other state-of-the-art methods. It is ranked first out of 12, outperforming all other algorithms.

scholarly journals European and International validation of 15 main reference methods in the microbiology of the food chain

2019 ◽  
Vol 288 ◽  
pp. 1-2
Author(s):  
Alexandre Leclercq ◽  
Gwenola Hardouin ◽  
Bertrand Lombard
Keyword(s):  
Food Chain ◽  
Reference Methods

Development of a RIA for clonidine and its comparison with the reference methods

1981 ◽  
Vol 6 (4) ◽  
pp. 295-307 ◽  
Author(s):  
D. Arndts ◽  
H. Stähle ◽  
H.-J. Förster
Keyword(s):  
Reference Methods

Cefditoren in vitro activity and spectrum: a review of international studies using reference methods

2001 ◽  
Vol 41 (1-2) ◽  
pp. 1-14 ◽  
Author(s):  
Ronald N. Jones ◽  
Michael A. Pfaller ◽  
Michael R. Jacobs ◽  
Peter C. Appelbaum ◽  
Peter C. Fuchs
Keyword(s):  
Vitro Activity ◽  
International Studies ◽  
In Vitro Activity ◽  
Reference Methods

scholarly journals A comparative analysis of non­reference assessment methods

Connectivity ◽  
2020 ◽  
Vol 148 (6) ◽  
Author(s):  
V. V. Grebenyuk ◽  
◽  
O. A. Dibrivnyy ◽  
O. V. Nehodenko
Keyword(s):  
Comparative Analysis ◽  
Image Quality ◽  
Image Quality Assessment ◽  
Excellent Result ◽  
Video Quality ◽  
Mean Value ◽  
Performance Comparison ◽  
Quality Level ◽  
Reference Methods

A comparative analysis of functions to assess image quality in the absence of a sample: no-reference (NR) measure or NR-type methods. The availability of NR-methods is very important for assessing the quality of streaming video such as television, game streaming, online conferences, web-chatting, etc. (because on the side of the recipient of the video there is no standard for quality comparison) and assessing the results of transformations aimed at improving video, and choosing the parameters of these transformations (brightness change, semitone and others). The human visual system (HVS) is able to visually assessing video quality, but If required to visually assess the quality of dozens or hundreds of videos or ranking them by quality level it will be needed a huge amount of time. Six types of experiments were performed to analyze the correlation of calculated quantitative estimates with visual assessments of the quality of the tested video files. Three of them are fundamentally new: comparing video after gamma correction and changing the contrast with different parameters, as well as blurring, which may be the result of defocusing the camcorder. A hybrid method (or reduced-reference (RR) measure) and a full-reference (FR) measure or FR-type method were also added for comparison. It has been experimentally shown that none of the studied non-reference methods of image quality assessment is universal, and the calculated assessment cannot be converted into a quality scale without taking into account the factors influencing the distortion of image quality. Moreover, all NR-type methods could not cope with the experiment of changing the contrast, believing that the best result is the most contrasting image but the original. Instead, the reference methods showed an excellent result (except one, which showed partial ineffectiveness). Also, it has been shown performance comparison between methods. It is shown that most of the studied methods calculate local estimates for each frame, and their arithmetic mean value is an estimate of the quality of the entire video file. If the video is dominated by large areas of uniform evaluation, methods of this type may give incorrect quality evaluations that do not coincide with the visual evaluations.

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