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Bullying is a prelude to murder. Victims of bullying have resorted to extreme acts of violence in order to escape the torment that bullies have created in victims' lives. Research on empathy supports the idea that teacher empathy could ameliorate bullying in schools. This study investigated whether or not teacher empathy had an impact on decreasing bullying in schools. Data were gathered from teachers regarding their level of empathy, as well as student data on bullying. Variables included overall incidents of bullying, bullying by the students' gender and race. The participants were students and teachers in two middle schools in an urban school district in the mid-south. The teacher data were collected using the Interpersonal Reactivity Index, consisting of four subscales, to find the mean empathy level for the teachers in the two participating schools. The four empathy subscales were the "Perspective Taking" subscale, "Fantasy" subscale, "Empathic Concern" subscale and "Personal Distress" subscale. Student data were gathered from the Olweus Standard School Report that identified the overall number of incidents of student bullying, as well as by students' gender and race. Teacher data were analyzed using t -tests to determine whether or not statistically significant differences on teacher empathy were found between the two schools. Chi-Square tests were administered to determine whether or not statistically significant differences in students' perception of being bullied were found between the two schools. Results of the data analysis revealed the school that scored significantly higher in three of the four teacher empathy subscales also had significantly higher incidents of bullying. This positive relationship between teacher empathy level and the number of incidents of bullying within the same school leads the researcher to conclude that teacher empathy had no impact on decreasing the number of bullying incidents. Another significant finding was that white students had the highest incidents of bullying in each of the two schools, as compared to the black and Hispanic students. For further research, it is recommended that a large-scale correlational study be conducted in order to determine whether or not statistically significant relationships exist between bullying incidents in schools and students' race. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

High performance control of permanent magnet machines (PMSM) requires accurate knowledge of the parameters that describe their mathematical models. This parameter information enables the controller to optimize the drive performance and efficiency, and to react to possible changes in the machine model. Several methods have been tested in order to get motor parameter estimates. Most of them are based on off-line measurements or estimates, which are then stored in the controller. Other methods published are compatible with on-line implementation, but these are usually restricted to a subset of the machine parameters. This dissertation proposes a solution to the problem of on-line estimation of PMSM stator resistance, torque constant and d-q inductances. An analysis of the machine parameters and their effects on motor drive performance that motivates the development of a new parameter estimation algorithm is presented. This algorithm combines two instances of the recursive least squares method, which interact in order to account for different machine parameter dynamics. As a consequence, the presented method is able to provide the controller with parameter estimates even when sudden changes in operation take place. The effectiveness of the proposed parameter estimation algorithm is validated using both a computer simulation and an experimental motor drive. This simulation model was developed specifically for this project and includes accurate inverter and controller modeling, in addition to a machine model that features parameter variation. The simulation model is used in both the algorithm development stages and its validation. The experimental setup provides additional verification of the effectiveness of the proposed algorithm. It is based on the use of a digital signal processor for the controller algorithm implementation, and includes motor drive classical algorithms as well as the proposed parameter estimation program. Both simulation and experimental results demonstrate the performance of the parameter estimation algorithm, both in transient and steady state operations.