No description available.

Students with specific learning disabilities (SLDs) account for approximately 40% of all students receiving special education services. Debate among professionals regarding the causes of SLDs and the most appropriate methods used to identify SLDs persists. This debate may be related to the increase in prevalence of SLDs since the implementation of special education law in 1975. There are three prominent theories regarding the cause of SLDs: (a) environmental theory, (b) biological theory, and (c) interactional theory. The Individuals with Disabilities Education Act (IDEA) allows school districts to implement the following SLD identification procedures: (a) the IQAchievement discrepancy method, (b) response-to-intervention (RtI), and/or (c) alternative research-based methods, such as personal strengths and weaknesses (PSW). This study employed survey methodology to evaluate the intersection between school psychologists’ beliefs about the cause of SLDs, their preferred practices, their actual practices, and their job satisfaction associated with assessment. School psychologists are one member of a multidisciplinary team aimed toward identifying children with SLDs and are estimated to spend nearly half their time in special education decision making. This study also evaluated the influence alignment between school psychologists preferred and actual practices have on their job satisfaction associated with assessment. Findings showed that, similar to other professionals, school psychologists’ had varying beliefs about the causes of SLDs. Environmental beliefs were significantly correlated with a preference for RtI for SLD identification, while biological beliefs were significantly correlated with preferences for the IQ-Achievement discrepancy method and alternative research based procedures for SLD identification. Preferred methods of identification impacted all three identification methods, and beliefs about the cause of SLDs impacted actual PSW practices, above and beyond individual and school characteristics. Finally, greater alignment between preferred SLD identification practices and actual SLD identification practices was associated with higher levels of job satisfaction related to assessment. Implications and directions for future research are discussed.

<p>In this Master's thesis, Maximum Likelihood-based parametric identification methods for discrete-time SISO Hammerstein models from perturbed observations on both input and output, are investigated. </p><p>Hammerstein models, consisting of a static nonlinear block followed by a dynamic linear one, are widely applied to modeling nonlinear dynamic systems, i.e., dynamic systems having nonlinearity at its input. </p><p>Two identification methods are proposed. The first one assumes a Hammerstein model where the input signal is noise-free and the output signal is perturbed with colored noise. The second assumes, however, white noises added to the input and output of the nonlinearity and to the output of the whole considered Hammerstein model. Both methods operate directly in the time domain and their properties are illustrated by a number of simulated examples. It should be observed that attention is focused on derivation, numerical calculation, and simulation corresponding to the first identification method mentioned above.</p>

In this Master's thesis, Maximum Likelihood-based parametric identification methods for discrete-time SISO Hammerstein models from perturbed observations on both input and output, are investigated. Hammerstein models, consisting of a static nonlinear block followed by a dynamic linear one, are widely applied to modeling nonlinear dynamic systems, i.e., dynamic systems having nonlinearity at its input. Two identification methods are proposed. The first one assumes a Hammerstein model where the input signal is noise-free and the output signal is perturbed with colored noise. The second assumes, however, white noises added to the input and output of the nonlinearity and to the output of the whole considered Hammerstein model. Both methods operate directly in the time domain and their properties are illustrated by a number of simulated examples. It should be observed that attention is focused on derivation, numerical calculation, and simulation corresponding to the first identification method mentioned above.

The thesis deals with methods of identification of dynamic systems in time and frequency domain. Nonparametric and parametric methods of identification are analyzed. Selected methods are implemented in Matlab & Simulink sotware. Finally, the user guide for the created Python application is introduced.

This text deals with the issue of identification of stakeholders in the evaluation. The concept of stakeholders is defined first, and the characteristics of the evaluation approaches are presented. Later is presented the position of the world evaluation societies on the stakeholders. There are four methods of stakeholder identification: 1) Brainstorming, 2) General list, 3) Questions and 4) The snowball method. Own research focuses on methods of identification of stakeholders in evaluations in the Czech Republic. Data sources are evaluations of OP and ROP programs. Secondary and content data analysis was used to analyse the data. The result of the research is that specific methods of stakeholder identification have not been applied in OP and ROP evaluations. Based on the results of the research, a new procedure for identification of stakeholders in the evaluation was formulated.

Apresenta uma nova técnica de identificação estocástica para estruturas sujeitas a excitação ambiente utilizando um método não recursivo, a análise estatística e a transformada wavelet. A análise estatística contribui para a seleção da melhor ordem para o sistema e automação dos procedimentos computacionais. Em geral há dificuldade para a identificação de baixas freqüências. A transformada wavelet é uma ferramenta fundamental para a compressão de dados e torna possível a identificação completa incluindo baixas freqüências e modos acoplados, além de melhorar significativamente a eficiência computacional do método. É apresentado o estudo de três sistemas simulados e os resultados obtidos são comparados aos parâmetros modais teóricos. / A new form to carry out stochastic identification of structures in operational conditions using a non recursive method, the statistic analysis and the wavelet transform, is presented. The statistic analysis contributed to select the best system order and to automation of computational procedures. In general the identification of low frequencies is a difficult task. The wavelet transform is an essential tool for compression of data making possible the complete identification including low frequencies. In addition it improves the computational efficiency. The study of three simulated system is presented and the results are compared with the analytical modal parameters.

Apresenta uma nova técnica de identificação estocástica para estruturas sujeitas a excitação ambiente utilizando um método não recursivo, a análise estatística e a transformada wavelet. A análise estatística contribui para a seleção da melhor ordem para o sistema e automação dos procedimentos computacionais. Em geral há dificuldade para a identificação de baixas freqüências. A transformada wavelet é uma ferramenta fundamental para a compressão de dados e torna possível a identificação completa incluindo baixas freqüências e modos acoplados, além de melhorar significativamente a eficiência computacional do método. É apresentado o estudo de três sistemas simulados e os resultados obtidos são comparados aos parâmetros modais teóricos. / A new form to carry out stochastic identification of structures in operational conditions using a non recursive method, the statistic analysis and the wavelet transform, is presented. The statistic analysis contributed to select the best system order and to automation of computational procedures. In general the identification of low frequencies is a difficult task. The wavelet transform is an essential tool for compression of data making possible the complete identification including low frequencies. In addition it improves the computational efficiency. The study of three simulated system is presented and the results are compared with the analytical modal parameters.

Establishing group structure in complex networks is potentially very useful since nodes belonging to the same module can often be related by commonalities in their biological function. However, module detection in complex networks poses a challenging problem and has sparked a great deal of interest in various disciplines in recent years [5]. In real networks, which can be quite complex, we have no idea about the true number of modules that exist. Furthermore, the structure of the modules may be hierarchical meaning they may be further divided into sub-modules and so forth. Many attempts have been made to deal with these problems and because the involved methods vary considerably they have been difficult to compare [5]. The objectives of this thesis are (i) to create and implement a new algorithm that will identify modules in complex networks and reconstruct the network in such a way so as to maximize modularity, (ii) to evaluate the performance of a new method, and compare it to a popular method based on a simulated annealing algorithm, and (iii) to apply the new method, and a comparator method, to analyze the metabolic network of the bacterial genus Listeria, an important pathogen in both agricultural and human clinical settings.

This master's thesis deals with the identification of the material flow curve from record of tensile test of smooth cylindrical specimen. First, necessary theory background is presented. Basic terms of incremental theory of plasticity, tensile test procedure and processing its outputs are described. Furthermore, possibilities of mathematical expression of the elastic-plastic material constitutive law, thus mathematical expression of the material flow curve itself. Mechanism of ductile damage of material is explained briefly as well. Overview of recent methods of the flow curve identification is given, focused on cases, when the stress distribution in a specimen is not uniaxial. That is either kind of analytic correction of basic formulas derived for uniaxial stress state, or application of mathematical optimization techniques combined with numerical simulation of the tensile test. Also unusual method of neural network is mentioned. For 8 given materials, the flow curve identification was performed using different methods. Namely by analytic correction, optimization, sequential identification and neural network. Algorithms of the last two methods were modified. Based on assessment of obtained results, application field and adjusting the parameters of single algorithms was recommended. It showed up, that an effective way to the accurate and credible results is the combination of different methods during flow curve identification procedure.