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In order not to lose continuity in education during COVID-19, universities mainly found the solution in Emergency remote teaching. Student satisfaction with online learning experience is one of the measures of the excellence of learning practice. Our goal was to test the hypothesis that the predictors of students’ satisfaction with emergency remote online learning are their prior experience, attitude toward online learning, their motivation, aspects of the learning situation they value (expectations), and their digital competencies. For estimating students’ satisfaction, a survey was created and disseminated, resulting with 547 responses from students of engineering education. Results showed that all students had some experience with online learning platforms and MS Teams. Attitude toward online learning in this situation was neutral, as well as the level of motivation. Their expectations were relatively high in comparison to their satisfaction with all relevant aspects of the online learning situation, while their digital competency was rather perceived as advanced. Our results showed that the prediction of the emergency remote online learning satisfaction for engineering students can be achieved based on previous positive experience with learning platform, motivation for learning in a specific situation, the importance they put to the learning achievement, and their level of digital competency.

The aim of this study was to characterize a collection of methicillin-resistant Staphylococcus aureus (MRSA) isolates of human and animal origin from Serbia. In total, 36 MRSA isolates—30 obtained from humans and six from companion animals—were investigated by PCR for the presence of antibiotic and biocide resistance determinants and virulence genes (PVL—Panton–Valentine leukocidin, ETs—exfoliative toxins, TSST—toxic shock syndrome toxin, SEs—staphylococcal enterotoxins, and MSCRAMMs—microbial surface components recognizing adhesive matrix molecules and biofilm). Isolates were analyzed by staphylococcal cassette chromosome mec (SCCmec), spa, and dru typing, as well as by multiple locus variable number of tandem repeat analyses (MLVA), multilocus sequence typing (MLST), and subsequently, eBURST. The majority of human MRSA isolates were resistant to gentamicin, erythromycin, clindamycin, and ciprofloxacin. Different antibiotic resistance genes were detected: aac-aphD, ant(6′)-Ia, erm(A), erm(B), erm(C), tet(K), tet(M), fexA, and catpC221. All isolates were susceptible to teicoplanin and linezolid. SCCmec type III was prevalent in human isolates, while SCCmec elements in animals were mostly nontypeable. t037 was the predominant spa type in human and t242 in animal MRSA isolates. The prevalent dru type was dt11c in human and dt10a in animal MRSA isolates. MRSA isolates exhibited 27 different MLVA types. ST239 was predominant in human, while ST5 was prevalent in canine MRSA isolates. PVL was found in two, while tsst-1 was detected in three human isolates. Human-associated clones belonging to ST5, ST45, and ST239 MRSA clones were discovered in companion animals, which suggests anthropozoonotic transmission.

No abstract

In this paper, a new adaptive robustified filter algorithm of recursive weighted least squares with combined scale and variable forgetting factors for time-varying parameters estimation in non-stationary and impulsive noise environments has been proposed. To reduce the effect of impulsive noise, whether this situation is stationary or not, the proposed adaptive robustified approach extends the concept of approximate maximum likelihood robust estimation, the so-called M robust estimation, to the estimation of both filter parameters and noise variance simultaneously. The application of variable forgetting factor, calculated adaptively with respect to the robustified prediction error criterion, provides the estimation of time-varying filter parameters under a stochastic environment with possible impulsive noise. The feasibility of the proposed approach is analysed in a system identification scenario using finite impulse response (FIR) filter applications.