This purpose of this study was to assess the relationship between the presence of classroom components of positive behavior support and student behavior outcomes. Data were collected using the Tool for Assessing Classroom Level-Positive Behavior Support (TACL-PBS) developed by Ern (2004) to assess the presence or absence of critical components of positive behavior support at the classroom level. Descriptive data on the instrument including the internal consistency, interrater agreement, and concurrent validity were analyzed and are included in this report. Forty classrooms from ten diverse elementary schools in three school districts were selected to participate in this study. Office discipline referral (ODR) information and rates of on-task behavior were collected for each classroom and were correlated with the presence of the classroom components. Data were collected using teacher interview, student interview, and direct observation methods. In all, 40 teacher intervie ws, 116 student interviews, and 39 classroom observations were conducted as part of this study and the information was used to provide evidence of the presence or absence of classroom PBS components. Given that the TACL-PBS uses three independent methods for data collection, the study was also interested in the usefulness of each method. The data revealed that the correlational structure of the instrument is strongest when scores from all three methods are combined. Results indicated low to moderate correlations between the components assessed by the TACL-PBS and student outcomes (i.e., discipline referrals, rates of on-task behavior). A significant, negative correlation was found between the consistent use of classroom management practices by teachers and ODR's. Results also indicated that as the teacher use of classroom management practices (as reported by students) increased, rates of student on-task behavior increased. The presence of preplanned and sequential procedures for respon ding to behavioral violations (i.e., consequence system) had the second highest relation to the numbers of discipline referrals in a classroom. The study also found that the fidelity with which school-wide PBS was being implemented at the building-level did not significantly relate to implementation at the classroom-level. Discussion focuses on theoretical and practical implications of the current results, limitations, and directions for future research.

I tested influence of a body size and overal colouration in feeders experiments on recognition of sparrowhawk (Accipiter nisus) by chosen species of passerine. Experiments were conducted in years 2015 ̶ 2018. I used a plush dummies which carried a specific featuress of raptor (hooked beak, claws) and specific features of genus Accipiter (yellow eyes, respectively yellow eyes and overal colouration). As a control, dummy of harmless pigeon was used. Smaller dummies (size of a great tit) were not recognized as a raptor regardless of colouration. Large dummies with colouration of great tit and pigeon were not recognized as a raptor as well. On a contrary, large dummy with colouration of robin was recognized as a raptor, respectively sparrowhawk. Most birds who were flying to the feeders, at first payed attention to global features (size, overal colouration). If these features belong to well-known harmless bird, then local features (particular) which characterize raptor or sparrowhawk had no effect. Birds did not pay attention to them. Therefore it depends on a context, in which potential local key features occure.

This master’s thesis is about automatic digital modulation recognition using artificial neural networks. The paper briefly describes the issue and existing algorithms for solving the problem of modulation recognition. It was found that the best results are achieved when using the feature-recognition methods and artificial neural networks. The digital modulations that were chosen for recognition are described theoretically and they are ASK, FSK, BPSK, QPSK and 16QAM. These modulations are most commonly used today. Later was briefly described theory of neural networks. In another part was given to the characteristic features of modulation for modulation recognition using artificial neural networks. The penultimate part describes the parameters for signal simulation in Matlab, how to create the key features in Matlab and results after experimental simulation. The last part contains neural network optimization experiments.

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