The purpose of this applied project was to research potential methods for conducting performance and evaluation observations on users of Positive Train Control (PTC) and recommend the most effective measures of performance (MOPs) and measures of efficiency (MOEs) of those users. I conducted a study to collect and analyze what data could be observed and examined most effectively to produce causal explanations of behaviors when utilizing the PTC system. This study was done through literature review, interviews of PTC users and trainers, and through direct observations as I rode on trains watching crews interact with the system. Additionally, I researched several studies on human computer interface (HCI) usability studies of various software applications. Based upon the results, I recommend that direct-participant observations be employed and apply both the system and individual MOPs and MOEs identified in the report to track user’s proficiency. The data collected from these observations can be centralized and used to identify behavioral trends, drive corrective actions, create future policies as well as training content. These observations will address the need to have structured observations which allow observers to focus undistracted on the specific behaviors that affect train operations. This database would also identify employees that may need additional or refresher training.

Background:
Drosophila gene expression pattern images document the spatiotemporal dynamics of gene expression during embryogenesis. A comparative analysis of these images could provide a fundamentally important way for studying the regulatory networks governing development. To facilitate pattern comparison and searching, groups of images in the Berkeley Drosophila Genome Project (BDGP) high-throughput study were annotated with a variable number of anatomical terms manually using a controlled vocabulary. Considering that the number of available images is rapidly increasing, it is imperative to design computational methods to automate this task.

Results:
We present a computational method to annotate gene expression pattern images automatically. The proposed method uses the bag-of-words scheme to utilize the existing information on pattern annotation and annotates images using a model that exploits correlations among terms. The proposed method can annotate images individually or in groups (e.g., according to the developmental stage). In addition, the proposed method can integrate information from different two-dimensional views of embryos. Results on embryonic patterns from BDGP data demonstrate that our method significantly outperforms other methods.

Conclusion:
The proposed bag-of-words scheme is effective in representing a set of annotations assigned to a group of images, and the model employed to annotate images successfully captures the correlations among different controlled vocabulary terms. The integration of existing annotation information from multiple embryonic views improves annotation performance.