Accident Rates, Phase of Operations, and Injury Severity for Solo Students in Pursuit of Private Pilot Certification (1994–2013)

Flight training accidents constitute 14% of general aviation accidents. Herein we determined the rates, injury severity, and phase of flight for primary student solo accidents/incidents (mishaps) in Cessna 172 aircraft. Mishaps over the period spanning 1994–2013 were identified from the NTSB database. Student population data were from the FAA. Statistics employed proportion tests, Poisson distribution, and Mann-Whitney tests. Across the study period, 598 mishaps were identified. While the mishap rate increased nearly two-fold between 1994/1997 and 2002/ 2005, a 35% decline was evident thereafter. Nevertheless, no statistical difference in mishap rates was evident between the initial and current periods. Over 90% of mishaps resulted in no or minor injuries. However, 97% of involved aircraft incurred substantial damage and no incidents were reported. While the percentage of takeoff accidents climbed two-fold, landing accidents accounted for .70% of all mishaps and remained unchanged over the 20 years. Over one-third of landing accidents were related to excess speed. Landing speed computation for a solo flight with an average weight trainee indicated an 11 knot lower V-ref than that for a Cessna 172S at maximum weight. No statistical difference was evident between the two genders for most phases of operation, although females were overrepresented for excess speed landing accidents. Landing accidents, one-third of which relate to excess speed, continue to challenge primary students. The importance of landing speed control, in the context of reduced aircraft weight, should receive additional emphasis in flight instruction

Improving Assessments and Evaluations of Pilots in Collegiate Aviation Programs

The purpose of the proposed study is to improve assessments and evaluation techniques of student pilots in collegiate aviation programs. Proper evaluation and assessment of student pilot performance in collegiate aviation programs is important to the success and credibility of the training program. Students in pilot training programs are scrutinized unlike other students in other academic programs. There exists well-defined program evaluation cycles, specific skills and knowledge to be tested, objective criteria on which to judge performance, tight restrictions on evaluator qualifications, and a well-defined curriculum for creating an evaluation context. This researcher would look at three reasons student pilots are evaluated. The first is to decide whether an individual pilot is proficient to fly. Decisions involving the student pilots ability to proceed in the degree to graduation warrant the highest quality performance evaluation. Second, instructors need to assess their trainees\u27 knowledge and skill to offer appropriate feedback and remediation. The quality of pilot training depends on the evaluators being capable of making accurate judgments of performance. Third, student pilot evaluations should guide the development and modification of training programs. This researcher seeks feedback from the members of the National Training Aircraft Symposium as to the development of a methodology for evaluator training and collaboration sessions. These kind of sessions are relatively new and have arisen primarily as a result of airline training programs. Many of the major carriers have some sort of evaluator training, in which collegiate aviation programs might benefit by establishing like programs

Deep image prior for 3D magnetic particle imaging: A quantitative comparison of regularization techniques on Open MPI dataset

Magnetic particle imaging (MPI) is an imaging modality exploiting the nonlinear magnetization behavior of (super-)paramagnetic nanoparticles to obtain a space- and often also time-dependent concentration of a tracer consisting of these nanoparticles. MPI has a continuously increasing number of potential medical applications. One prerequisite for successful performance in these applications is a proper solution to the image reconstruction problem. More classical methods from inverse problems theory, as well as novel approaches from the field of machine learning, have the potential to deliver high-quality reconstructions in MPI. We investigate a novel reconstruction approach based on a deep image prior, which builds on representing the solution by a deep neural network. Novel approaches, as well as variational and iterative regularization techniques, are compared quantitatively in terms of peak signal-to-noise ratios and structural similarity indices on the publicly available Open MPI dataset