New Method for Rapid Determination of Carboxyhemoglobin by Use of Double-Wavelength Spectrophotometry

Abstract We have developed a simple method for rapid determination of carboxyhemoglobin concentration in blood, by use of double-wavelength spectrophotometry. The absorbance of both oxyhemoglobin and reduced hemoglobin are instrumentally nulled out in this procedure, so that the absorption signal reflects HbCO and only a single spectrophotometric reading is required. This procedure is accurate in the range encountered in ambulatory persons as well as in the "toxic" range. Results correlated well with the dithionite reduction procedure. The stability of carboxyhemoglobin in solution was investigated and concentrations of carbon monoxide in groups of smokers and nonsmokers were determined.

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Determination of Objective Weights Using a New Method Based on the Removal Effects of Criteria (MEREC)

Symmetry ◽

10.3390/sym13040525 ◽

2021 ◽

Vol 13 (4) ◽

pp. 525 ◽

Cited By ~ 2

Author(s):

Mehdi Keshavarz-Ghorabaee ◽

Maghsoud Amiri ◽

Edmundas Kazimieras Zavadskas ◽

Zenonas Turskis ◽

Jurgita Antucheviciene

Keyword(s):

Essential Elements ◽

New Method ◽

Multi Criteria Decision Making ◽

Analysis Of Means ◽

Simulation Based ◽

Criteria Weights ◽

The Stability ◽

Computational Analyses ◽

Weighting Methods

The weights of criteria in multi-criteria decision-making (MCDM) problems are essential elements that can significantly affect the results. Accordingly, researchers developed and presented several methods to determine criteria weights. Weighting methods could be objective, subjective, and integrated. This study introduces a new method, called MEREC (MEthod based on the Removal Effects of Criteria), to determine criteria’ objective weights. This method uses a novel idea for weighting criteria. After systematically introducing the method, we present some computational analyses to confirm the efficiency of the MEREC. Firstly, an illustrative example demonstrates the procedure of the MEREC for calculation of the weights of criteria. Secondly, a comparative analysis is presented through an example for validation of the introduced method’s results. Additionally, we perform a simulation-based analysis to verify the reliability of MEREC and the stability of its results. The data of the MCDM problems generated for making this analysis follow a prevalent symmetric distribution (normal distribution). We compare the results of the MEREC with some other objective weighting methods in this analysis, and the analysis of means (ANOM) for variances shows the stability of its results. The conducted analyses demonstrate that the MEREC is efficient to determine objective weights of criteria.

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The rapid determination of carcass fat by the Foss-Let specific gravity technique

British Journal Of Nutrition ◽

10.1079/bjn19760110 ◽

1976 ◽

Vol 36 (3) ◽

pp. 567-570 ◽

Cited By ~ 7

Author(s):

C. J. H. Woodward ◽

P. Trayhurn ◽

W. P. T. James

Keyword(s):

Specific Gravity ◽

Rapid Determination ◽

Reference Method ◽

New Method ◽

Storage Lipid ◽

Structural Lipid ◽

Nutritional Studies

1. Carcass fat was determined by extraction with tetrachloroethylene and measurement of the solvent's change in density. The results were comparable in precision to those of a reference method; the new method extracted storage lipid but little structural lipid.2. The technique is simple, rapid and appropriate for many nutritional studies.

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Transformer Fault Diagnosis Based on BP-Adaboost and PNN Series Connection

Mathematical Problems in Engineering ◽

10.1155/2019/1019845 ◽

2019 ◽

Vol 2019 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Chun Yan ◽

Meixuan Li ◽

Wei Liu

Keyword(s):

Neural Network ◽

Bp Neural Network ◽

Rapid Determination ◽

Oil Analysis ◽

Dissolved Gas ◽

Adaboost Algorithm ◽

Diagnosis Model ◽

The Stability ◽

Transformer Fault

Dissolved gas-in-oil analysis (DGA) is a powerful method to diagnose and detect transformer faults. It is of profound significance for the accurate and rapid determination of the fault of the transformer and the stability of the power. In different transformer faults, the concentration of dissolved gases in oil is also inconsistent. Commonly used gases include hydrogen (H2), methane (CH4), acetylene (C2H2), ethane (C2H6), and ethylene (C2H4). This paper first combines BP neural network with improved Adaboost algorithm, then combines PNN neural network to form a series diagnosis model for transformer fault, and finally combines dissolved gas-in-oil analysis to diagnose transformer fault. The experimental results show that the accuracy of the series diagnosis model proposed in this paper is greatly improved compared with BP neural network, GA-BP neural network, PNN neural network, and BP-Adaboost.

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