Publications by authors named "K Musayeva"

The study aimed to investigate the effect of low-frequency oscillations on the cow udder, milk parameters, and animal welfare during the automated milking process. The study's objective was to investigate the impact of low-frequency oscillations on the udder and teats' blood circulation by creating a mathematical model of mammary glands, using milkers and vibrators to analyze the theoretical dynamics of oscillations. The mechanical vibration device developed and tested in the study was mounted on a DeLaval automatic milking machine, which excited the udder with low-frequency oscillations, allowing the analysis of input parameters (temperature, oscillation amplitude) and using feedback data, changing the device parameters such as vibration frequency and duration.

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Exogenous fibrolytic enzymes (EFE) and yeast are feed supplements that improve forage digestion in rumen, but their influences on physical reticulorumen parameters are not well studied. This study was designed to evaluate the effect of the EFE:endo-β-xylanase (37x104 U/cow/day), endocellulase (45x104 U/cow/day), endo-β-glucanase (12x104U/cow/day), and active yeast - Saccharomyces cerevisiae CNCM-1077 (10x109CFU/cow/day) supplements on reticulorumen pH (RpH) and temperature (RT) in dairy cows. Nine Lithuanian Red cows were allocated into three groups (3 cows/group): control group (C) - farm diet without supplementation, enzyme group (E) - farm diet supplemented with EFE, enzyme and active yeast group (EY) - farm diet supplemented with EFE and active yeast.

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The aim of this study was to analyze an effect of udder health status, somatic cell count (SCC), stage and number of lactations, and different seasons on the concentration of lactoferrin (LF) and immunoglobulin G (IgG) in quarter milk samples (n=120) from crossbreed (Lithuanian Black-and-White & Holstein) dairy cows. Quarter health status was based on SCC and microbiological analysis. The highest mean value of LF and IgG were observed in quarters with subclinical mastitis 0.

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Background: A well-known problem in cluster analysis is finding an optimal number of clusters reflecting the inherent structure of the data. PFClust is a partitioning-based clustering algorithm capable, unlike many widely-used clustering algorithms, of automatically proposing an optimal number of clusters for the data.

Results: The results of tests on various types of data showed that PFClust can discover clusters of arbitrary shapes, sizes and densities.

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