[The formation of trihalomethanes precursor in anionic surfactant biodegradation].
Trihalomethanes (THMs) formed by chlorination of precursor substances in raw water, are found in drinking water. They are found at concentration levels higher than those of other organic contaminants. In the present study, the relationship between the formation of THM precursors and anionic surfectants biodegradation was investigated. Three anionic surfectants (LAS, AS and soap), dissolved in river
Biodegradation of α-Olefin Sulfonates (AOS) and Other Surfactants
To follow the progress of biodegradation of anionic surfactants, the methylene blue method is the most widely employed for quantitative analysis. But, there is unaccountably the case in which the activity to methylene blue disappeared even before surfactant has not yet completely been biodegraded into inorganic compounds. And so, we examined the biodegradability of several surfactants by measuring the residual carbonaceous materials in comparison with the colorimetric analysis.Sodium α-olefin sulfonates (AOS) and other ten anionic and three nonionic surfactants were taken as test materials. In the shake culture test method, it was found that surfactants containing straight alkyl chain such as AOS, alcohol sulfate and soap were degraded in shorter period necessary for the disappearance of the carbonaceous materials during biodegradation. However, surfactants having benzene ring or ethoxylate chain in their molecules were slow in decrease of the concentration of organic carbon during biodegradation, and there was a large discrepancy between the biodegradation rates obtained by colorimetric analysis and measurement of organic carbon.
An automated retinal image quality grading algorithm
This paper introduces an algorithm for the automated assessment of retinal fundus image quality grade. Retinal image quality grading assesses whether the quality of the image is sufficient to allow diagnostic procedures to be applied. Automated quality analysis is an important preprocessing step in algorithmic diagnosis, as it is necessary to ensure that images are sufficiently clear to allow pathologies to be visible. The algorithm is based on standard recommendations for quality analysis by human screeners, examining the clarity of retinal vessels within the macula region. An evaluation against a reference standard data-set is given; it is shown that the algorithm's performance correlates closely with that of clinicians manually grading image quality.