WOMEN, CYCLING, AND THE PUBLIC SPHERE: HOW DISCURSIVE AND COMMUNITY PRACTICES AFFECT ENGAGEMENT

This thesis considers the impact that discursive and community practices have on women’s access to the public sphere by examining female cyclists and a cycling community in Miami, Florida via interviews and observation. In the interviews, female cyclists frequently reported fears for their safety, including concern over harassment, when riding in public space. I interviewed participants of the cycling community and observed Emerge Miami’s meetings and events, where publicly organized cycling excursions were a major component. Using the theoretical and methodological lenses of Feminist Critical Discourse Analysis and Communities of Practice, I examined the interviews to understand how participants discursively framed and contextualized gender-based harassment. I found two meta-discourse frames in operation: a normative frame (that essentially accepted the status quo) and a feminist frame (that challenged the “naturalness” of women’s harassment as just what one had to live with). The feminist frame offered a pathway for women to exert control over their experiences and alter the cultural understanding of harassment’s meaning and effect. The local community practices of Emerge Miami also challenged the normative frames that often silence women, employing explicitly invitational practices, which demonstrates how local discursive and social activity can impact and increase women’s involvement by creating a more accessible space for women to engage with their local cycling community.

Space trajectories optimization using variable-chromosome-length genetic algorithms

The problem of optimal design of a multi-gravity-assist space trajectories, with free number of deep space maneuvers (MGADSM) poses multi-modal cost functions. In the general form of the problem, the number of design variables is solution dependent. To handle global optimization problems where the number of design variables varies from one solution to another, two novel genetic-based techniques are introduced: hidden genes genetic algorithm (HGGA) and dynamic-size multiple population genetic algorithm (DSMPGA). In HGGA, a fixed length for the design variables is assigned for all solutions. Independent variables of each solution are divided into effective and ineffective (hidden) genes. Hidden genes are excluded in cost function evaluations. Full-length solutions undergo standard genetic operations. In DSMPGA, sub-populations of fixed size design spaces are randomly initialized. Standard genetic operations are carried out for a stage of generations. A new population is then created by reproduction from all members based on their relative fitness. The resulting sub-populations have different sizes from their initial sizes. The process repeats, leading to increasing the size of sub-populations of more fit solutions. Both techniques are applied to several MGADSM problems. They have the capability to determine the number of swing-bys, the planets to swing by, launch and arrival dates, and the number of deep space maneuvers as well as their locations, magnitudes, and directions in an optimal sense. The results show that solutions obtained using the developed tools match known solutions for complex case studies. The HGGA is also used to obtain the asteroids sequence and the mission structure in the global trajectory optimization competition (GTOC) problem. As an application of GA optimization to Earth orbits, the problem of visiting a set of ground sites within a constrained time frame is solved. The J2 perturbation and zonal coverage are considered to design repeated Sun-synchronous orbits. Finally, a new set of orbits, the repeated shadow track orbits (RSTO), is introduced. The orbit parameters are optimized such that the shadow of a spacecraft on the Earth visits the same locations periodically every desired number of days.