Rethinking the STEM fields: the importance of definitions in examining women's participation and success in the sciences

This study uses longitudinal data of undergraduate students from five public land-grant universities to better understand undergraduate students’ persistence in and switching of majors, with particular attention given to women’s participation in Science, Technology, Engineering, and Mathematics (STEM) fields. Specifically, the study examines patterns of behavior of women and minorities in relation to initial choice of college major and major field persistence, as well as what majors students switched to upon changing majors. Factors that impact major field persistence are also examined, as well as how switching majors affects students’ time-to-degree. Using a broad definition of STEM, data from nearly 17,000 undergraduate students was analyzed with descriptive statistics, cross tabulations, and binary logistic regressions. The results highlight women’s high levels of participation and success in the sciences, challenging common notions of underrepresentation in the STEM fields. The study calls for researchers to use a comprehensive definition of STEM and broad measurements of persistence when investigating students’ participation in the STEM fields.

Dynamic-Data Driven Real-Time Identification for Electric Power Systems

Power system engineers face a double challenge: to operate electric power systems within narrow stability and security margins, and to maintain high reliability. There is an acute need to better understand the dynamic nature of power systems in order to be prepared for critical situations as they arise. Innovative measurement tools, such as phasor measurement units, can capture not only the slow variation of the voltages and currents but also the underlying oscillations in a power system. Such dynamic data accessibility provides us a strong motivation and a useful tool to explore dynamic-data driven applications in power systems. To fulfill this goal, this dissertation focuses on the following three areas: Developing accurate dynamic load models and updating variable parameters based on the measurement data, applying advanced nonlinear filtering concepts and technologies to real-time identification of power system models, and addressing computational issues by implementing the balanced truncation method. By obtaining more realistic system models, together with timely updated parameters and stochastic influence consideration, we can have an accurate portrait of the ongoing phenomena in an electrical power system. Hence we can further improve state estimation, stability analysis and real-time operation.