The article presents data on the prevalence of
colibacillosis of pigs in farms of the Krasnodar region
in the period 2010-2016. Colibacillosis is widespread
in pig farms of the Krasnodar region. In different
years, it was recorded at 32.6 - 55.6% of bacterial
infectious diseases of pigs. And only in 2013-2014, in
farms of the region colibacillosis was not registered.
Among the bacterial pathology, colibacillosis in pigs
in the farms of the Krasnodar region is in the first
place after staphyloccocus (3 - 15 %), streptoccocus
(2-13,7 %), and diseases caused by conditionally
pathogenic microflora (17,9-20 %). In the Krasnodar
region, we annually allocate different serotypes of E.
Coli that vary depending on areas and farms, however,
regularly in pigs in the Krasnodar region there are the
following serotypes: A8, О20, О119, О26, О86 in
Bryukhovetskiy, Dinskoy, Kalininskiy, Korenovskiy,
Kurganinskiy, Kushchevskaiy, Labinskiy regions of
the Krasnodar territory. According to the reports of
outbreaks, colibacillosis in pigs for several years were
recorded in the Central, Korenovskiy, Kushchevskaya,
Labinskiy, Primorsko-Akhtarskiy, Seversky,
Slavyanskiy, Tbilisskiy, Timashevskiy, Ust-Labinskiy
districts of the Krasnodar region and in the city of
Krasnodar. After 2013-2014, after the total absence of
the disease in the region, there were reported outbreaks
in 2015 in some farms in the Timashevskiy district,
and in 2016 – in Kurganinskiy
14 January 2019 at the website of the higher attestation Commission of the Russian Federation http://vak.ed.gov.ru/87 the information appeared: "About refining of scientific specialties and their respective fields of science where publications are included in the List of peer-reviewed scientific publications, where basic scientific results of dissertations on competition of a scientific degree of candidate of Sciences, on competition of a scientific degree of the doctor of Sciences must be published ". It is reported that according to the recommendation of the HAC for other publications included in the List of groups of scientific specialties, the work on refining scientific specialties and branches of science will be continued in 2019. This work is a continuation of the author's series of works on cognitive linguistics. It offers innovative intelligent technology to automate the solution of the problem formulated by the higher attestation Commission of the Russian Federation above. With the use of the automated system-cognitive analysis (ASC-analysis) and its programmatic toolkit which is intellectual system called "Eidos" directly on the basis of official texts of passports of scientific specialties of the higher attestation Commission of the Russian Federation, there were established their semantic kernels, and then, implemented the automatic classification of scientific texts (articles, monographs, textbooks, etc.) on the specialties and groups of specialties of the higher attestation Commission of the Russian Federation. Traditionally, this task is solved by dissertation councils, as well as editorial boards of scientific publications, i.e. by experts, on the basis of expert assessments, in an informal way, on the basis of experience, intuition and professional competence. However, the traditional approach has a number of serious drawbacks that impose significant limitations on the quality and volume of analysis. Therefore, the efforts of researchers and developers to overcome these limitations are relevant. Currently, there are all grounds to consider these restrictions as unacceptable, because they are not only necessary, but also quite possible to overcome. Thus, there is a problem, the solution of which is the subject of consideration in this article. A detailed numerical example of solving the problem on real data is given as well
This prospective study was conducted on 10 commercial dairy herds, over one year on milk urea monitoring, determination of diets characteristics effects on MU concentration and on assessment of MU concentrations as a predictor of N utilization and urinary N excretion. Milk samples were collected twice every month and analyzed for urea concentration using a colorimetric procedure. Representative feed samples were also collected on the same day of milk collection. Feed samples were characterized and their concentrations of protein digestible in the intestine and net energy for lactation were calculated according to the French system as well as PDI requirements. Average of milk urea concentrations range is 25.0 - 32.0 mg/dl. A significant positive association (p