Indiana University-Purdue University Indianapolis (IUPUI) / Madeline Heilman’s (1983) Lack of Fit Model, which postulates why discrimination occurs in the selection of sex-typed jobs, has been applied to the interaction of applicant attractiveness. Yet recent research suggests that other appearance variables, namely sex-typed facial features, may be associated with perceptions of fit. Building upon Heilman’s 1983 model, the current study evaluated how sex-typed facial features relate to applicant selection for sex-typed fields. Undergraduate students were recruited for participation during the spring academic semester (n = 413) and data were analyzed using a 2x2x2 ANOVA. Results indicated that selection is significantly impacted by the three-way interaction of applicant sex, facial feature-type, and sex type of the applying field. Further, masculine-featured females and feminine-featured males were significantly less favored for selection within the feminine sex-typed field. Implications of these findings and the differential evaluation of male and female applicants in a feminine field are discussed.

Indiana University-Purdue University Indianapolis (IUPUI) / This thesis presents a converter topology for photovoltaic panels. This topology minimizes the number of switching devices used, thereby reducing power losses that arise from high frequency switching operations. The control strategy is implemented using a simple micro-controller that implements the proportional plus integral control. All the control loops are closed feedback loops hence minimizing error instantaneously and adjusting efficiently to system variations. The energy management between three components, namely, the photovoltaic panel, a battery and a DC link for a microgrid, is shown distributed over three modes. These modes are dependent on the irradiance from the sunlight. All three modes are simulated. The maximum power point tracking of the system plays a crucial role in this configuration, as it is one of the main challenges tackled by the control system. Various methods of MPPT are discussed, and the Perturb and Observe method is employed and is described in detail. Experimental results are shown for the maximum power point tracking of this system with a scaled down version of the panel's actual capability.

Indiana University-Purdue University Indianapolis (IUPUI) / Identifying spatial and temporal patterns of disease occurrence by mapping the residential locations of affected people can provide information that informs response by public health practitioners and improves understanding in epidemiological research. A common method of locating patients at the individual level is geocoding residential addresses stored in electronic medical records (EMRs) using address matching procedures in a geographic information system (GIS). While the process of geocoding is becoming more common in public health studies, few researchers take the time to examine the effects of using different address databases on match rate and positional accuracy of the geocoded results. This research examined and compared accuracy and match rate resulting from four commonly-used geocoding databases applied to sample of 59,341 subjects residing in and around Marion County/ Indianapolis, IN. The results are intended to inform researchers on the benefits and downsides to their selection of a database to geocode patient addresses in EMRs.

Indiana University-Purdue University Indianapolis (IUPUI) / Over the past two decades, northern parts of the United States have experienced extreme heat conditions. Some of the notable heat wave impacts have occurred in Chicago in 1995 with over 600 reported deaths and in Philadelphia in 1993 with over 180 reported deaths. The distribution of extreme heat events in Indianapolis has varied since the year 2000. The Urban Heat Island effect has caused the temperatures to rise unusually high during the summer months. Although the number of reported deaths in Indianapolis is smaller when compared to Chicago and Philadelphia, the heat wave in the year 2010 affected primarily the vulnerable population comprised of the elderly and the lower socio-economic groups. Studying the spatial distribution of high temperatures in the vulnerable areas helps determine not only the extent of the heat affected areas, but also to devise strategies and methods to plan, mitigate, and tackle extreme heat. In addition, examining spatial patterns of vulnerability can aid in development of a heat warning system to alert the populations at risk during extreme heat events. This study focuses on the qualitative and quantitative methods used to measure extreme heat events. Land surface temperatures obtained from the Landsat TM images provide useful means by which the spatial distribution of temperatures can be studied in relation to the temporal changes and socioeconomic vulnerability. The percentile method used, helps to determine the vulnerable areas and their extents. The maximum temperatures measured using LST conversion of the original digital number values of the Landsat TM images is reliable in terms of identifying the heat-affected regions.

Indiana University-Purdue University Indianapolis (IUPUI) / Environmental planning is very cumbersome work for environmentalists, government agencies like USDA and NRCS, and farmers. There are a number of conflicts and issues involved in such a decision making process. This research is based on the work to provide a common platform for environmental planning called WRESTORE (Watershed Restoration using Spatio-Temporal Optimization of Resources). We have designed a system that can be used to provide the best management practices for environmental planning. A distributed system was designed to combine high performance computing power of clusters/supercomputers in running various environmental model simulations. The system is designed to be a multi-user system just like a multi-user operating system. A number of stakeholders can log-on and run environmental model simulations simultaneously, seamlessly collaborate, and make collective judgments by visualizing their landscapes. In the research, we identified challenges in running such a system and proposed various solutions. One challenge was the lack of fast optimization algorithm. In our research, several algorithms are utilized such as Genetic Algorithm (GA) and Learning Automaton (LA). However, the criticism is that LA has a slow rate of convergence and that both LA and GA have the problem of getting stuck in local optima. We tried to solve the multi-objective problems using LA in batch mode to make the learning faster and accurate. The problems where the evaluation of the fitness functions for optimization is a bottleneck, like running environmental model simulation, evaluation of a number of such models in parallel can give considerable speed-up. In the multi-objective LA, different weight pair solutions were evaluated independently. We created their parallel versions to make them practically faster in computation. Additionally, we extended the parallelism concept with the batch mode learning. Another challenge we faced was in User Modeling. There are a number of User Modeling techniques available. Selection of the best user modeling technique is a hard problem. In this research, we modeled user's preferences and search criteria using an ANN (Artificial Neural Network). Training an ANN with limited data is not always feasible. There are many situations where a simple modeling technique works better if the learning data set is small. We formulated ways to fine tune the ANN in case of limited data and also introduced the concept of Deep Learning in User Modeling for environmental planning system.