In this paper, six univariate and two multivariate best linear unbiased prediction (BLUP) models were tested for the estimation of breeding values (BV) in Holstein Friesian cattle in Serbia. Two univariate models were formed using the numerator relationship matrix (NRM), four using the genomic relationship matrix (GRM). Multivariate models contained only an NRM. Two cases were studied, the first when only first lactations were observed, and the second when all lactations were observed using a repeatability model. A total of 6041 animals were included, and of them, 2565 had data on milk yield (MY), milk fat yield (FY), milk fat content (FC), milk protein yield (PY) and milk protein content (PC). Finally, out of those 2565 cows, 1491 were genotyped. A higher accuracy of BV was obtained when using a combination of NRM and GRM compared to NRM alone in univariate analysis, while multivariate analysis with repeated measures gave the highest accuracy with all 6041 animals. When only genotyped animals were observed, the highest accuracy of the estimated BV was calculated by the ssGBLUPp model, and the lowest by the univariate BLUP model. In conclusion, the current breeding programs in Serbia should be changed to use multivariate analysis with repeated measurements until the optimal size of the reference population, which must include genotyping data on both bulls and cows, is reached.

Summary: This research aims to estimate heritability and repeatability based on the data on milk production traits (MY – milk yield; FY – milk fat yield; FC – milk fat content; PY – milk protein yield and PC – milk protein content) as well as pedigree and genomic information. A total of 6,041 animals were included in the research, while 2,565 of them had data for milk production traits. In order to form a genomic relationship matrix, 58K SNP data were used for a total of 1,491 cows. Several software tools were used in the preparation and analysis of data, which were provided by the Central Breeding Organization, Department of Animal Science, Faculty of Agriculture, University of Novi Sad. PreGSF90, in combination with RENUMF90, was used for quality control of genomic information. Genetic analysis was performed in WOMBAT software by the REML using standard repeatability univariate analysis (BLUPpe) and repeatability models for genomic prediction (GBLUPpe and ssGBLUPpe). In all three analyses, the highest heritability (0.410, 0.378 and 0.389, respectively) and repeatability (0.449, 0.429 and 0.440, respectively) were calculated for FC. Heritability estimates for all other traits were lower. Heritability ranged from 0.158 to 0.185 for MY, from 0.166 to 0.178 for FY, from 0.141 to 0.154 for PY and from 0.135 to 0.221 for PC. Heritability estimates indicate that it is possible to achieve genetic improvement but it is necessary to introduce the best model for prediction of breeding values of cow.

This paper presents an effort to incorporate assessment of manually assessed aspects in an automated code assessment process. Fully automated code assessment has its drawbacks which makes manual assessment of some aspects of student solutions a necessity. In most situations during the assessment of code written by students the most appropriate approach would thus be splitting the process in two phases - automatic plus manual. This might result in two completely distinct procedures for each of the phases. The solution proposed in this paper tries to join these procedures into one uniform process keeping the benefits from each.

Software maintenance takes up a disproportionately large amount of time in the modern software life cycle. One of the common problems is understanding the original code that is being restructured and improved and this is especially true with low-level code. This paper investigates the results and properties of an automated process that can raise the abstraction level of code from low-level operations to high-level structures. The process is made of independent components and can be adapted to different scenarios. The automated improvements implementation relies on the program transformation system FermaT and its catalogue of semantics-preserving transformations. The process uses hill climbing and a metric for the fitness function of the programs. This component was made to work on general inputs, without explicit knowledge of the type of origin of the program. The paper explores how different inputs are actually handled by the system, what are the properties and how these can be used for further improvements. Two main types of inputs are shown, x86 assembly and MicroJava bytecode. These two have many operational differences, and the translator tools introduce some more, but nonetheless, the same process can handle all of these and, on average, improve the Structure metric (a good approximation of the complexity of the code) by around 85%.