The University of Pittsburgh's Department of Biomedical Informatics and Division of Pathology Informatics created a Science, Technology, Engineering, and Mathematics (STEM) pipeline in 2011 dedicated to providing cutting-edge informatics research and career preparatory experiences to a diverse group of highly motivated high-school students. In this third editorial installment describing the program, we provide a brief overview of the pipeline, report on achievements of the past scholars, and present results from self-reported assessments by the 2015 cohort of scholars. The pipeline continues to expand with the 2015 addition of the innovation internship, and the introduction of a program in 2016 aimed at offering first-time research experiences to undergraduates who are underrepresented in pathology and biomedical informatics. Achievements of program scholars include authorship of journal articles, symposium and summit presentations, and attendance at top 25 universities. All of our alumni matriculated into higher education and 90% remain in STEM majors. The 2015 high-school program had ten participating scholars who self-reported gains in confidence in their research abilities and understanding of what it means to be a scientist.

The classification of nonstationary signals in a noisy environment is a difficult task. In this paper a modified version of S-Transform technique has been proposed for classification of power signal disturbances. The S-Transform is a signal processing technique which is used for visual localization, detection, pattern classification. S-Transform has good ability in gathering high frequency signals and suppressing the lower frequency signal. The S-Transform has been used to extract features from the nonstationary power disturbance signals. The extracted features are fed as the input support vector machine classifier for power signal disturbance pattern classification. To enhance the pattern classification accuracy the extreme learning