Robert Oaks

The gene responsible for Friedreich's ataxia, a disease characterized by neurodegeneration and cardiomyopathy, has recently been cloned and its product designated frataxin. A gene in Saccharomyces cerevisiae was characterized whose predicted protein product has high sequence similarity to the human frataxin protein. The yeast gene (yeast frataxin homolog, YFH1) encodes a mitochondrial protein involved in iron homeostasis and respiratory function. Human frataxin also was shown to be a mitochondrial protein. Characterizing the mechanism by which YFH1 regulates iron homeostasis in yeast may help to define the pathologic process leading to cell damage in Friedreich's ataxia.

At the heart of every air traffic decision support tool’s functionality is its trajectory prediction, where a trajectory is defined as the 4-dimensional path of an aircraft. This paper presents a comprehensive implementation for measuring the accuracy of a trajectory prediction in support of a validation methodology. The process includes four main processing areas: (1) parsing and checking the actual positional data of an aircraft (i.e., the aircraft’s actual trajectory), (2) parsing the trajectory predictions, (3) comparing the actual and predicted aircraft trajectory by sampling and measuring, and (4) analyzing the results. This paper presents detailed descriptions of the sampling process and metrics used to measure the accuracy of a predicted trajectory. Several aspects of the analysis and implementation are provided as well, such as inferential statistical approaches and graphical user interfaces to examine individual flights.

A methodology is presented for generating conflict scenarios that can be used as test cases to estimate the operational performance of a conflict probe. Recorded air traffic data is time shifted to create traffic scenarios featuring conflicts with characteristic properties similar to those encountered in typical air traffic operations. First, a reference set of conflicts is obtained from trajectories that are computed using birth points and nominal flight plans extracted from recorded traffic data. Distributions are obtained for several primary properties (e.g., encounter angle) that are most likely to affect the performance of a conflict probe. A genetic algorithm is then utilized to determine the values of time shifts for the recorded track data so that the primary properties of conflicts generated by the time shifted data match those of the reference set. This methodology is successfully demonstrated using recorded traffic data for the Memphis Air Route Traffic Control Center; a key result is that the required time shifts are less than 5 min for 99% of the tracks. It is also observed that close matching of the primary properties used in this study additionally provides a good match for some other secondary properties.

This paper presents two algorithms that determine an aircraft's phase of flight state in post-analysis using tracked radar surveillance data. These algorithms determine the horizontal phase of flight (whether the aircraft was flying straight or turning) and the vertical phase of flight (whether the aircraft was flying level or transitioning; i.e., ascending or descending). The paper presents the algorithms and identifies their input parameters. It then discusses the design of experiment analysis that was used to determine the algorithms' optimum performance using Global Positional Satellite position data. Examples clarifying the algorithms' application are provided along with a comparison of the new algorithms and the legacy version that it replaces.