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.

Multirobot Tethering for Localization and Control

Particle filtering has proven to be an effective localization method for wheeled autonomous vehicles. For a given map, a sensor model, and observations, occasions arise where the vehicle could equally likely be in many locations of the map. Because particle filtering algorithms may generate low confidence pose estimates under these conditions, more robust localization strategies are required to produce reliable pose estimates. This becomes more critical if the state estimate is an integral part of system control. We investigate the use of particle filter estimation techniques on a hovercraft vehicle. The marginally stable dynamics of a hovercraft require reliable state estimates for proper stability and control. We use the Monte Carlo localization method, which implements a particle filter in a recursive state estimate algorithm. An H-infinity controller, designed to accommodate the latency inherent in our state estimation, provides stability and controllability to the hovercraft. In order to eliminate the low confidence estimates produced in certain environments, a multirobot system is designed to introduce mobile environment features. By tracking and controlling the secondary robot, we can position the mobile feature throughout the environment to ensure a high confidence estimate, thus maintaining stability in the system. A laser rangefinder is the sensor the hovercraft uses to track the secondary robot, observe the environment, and facilitate successful localization and stability in motion.