Division of Engineering Education & Centers

This manuscript provides insight into a new approach to chemically-based soil improvement with organosilanes (OS). In particular, OS creates a hydrophobic surface on virtually any silica-based material through covalent bonding. In contrast to ion exchange techniques, grafting OS on soils results in near permanent modification. As an illustration, laboratory testing was conducted to evaluate the influence of OS modification on the compaction, strength, swell, erosive and hydraulic properties of several soils. OS modification resulted in modest changes to strength and swell potential and a dramatic reduction in infiltration capacity. Likewise, use of OS on a 2H:1V slope reduced the mass of eroded soil by a factor of nearly 50. Overall, these results suggest that OS modification may have wide application in geotechnical and geoenvironmental engineering.

The goal of this session is to increase the participants' knowledge of current funding opportunities at the National Science Foundation (NSF) to support projects with potential significant impacts on science, technology, engineering, and mathematics (STEM) education. In particular, the discussion will focus on new and current funding opportunities in the Division of Undergraduate Education (DUE) in the Directorate of Education and Human Resources (EHR) and the Division of Engineering Education & Centers (EEC) in the Directorate of Engineering. During the session, we will provide examples of project activities that support STEM education research opportunities. The session will use a highly interactive format (i.e., team-based activities and discussion) to engage the participants, to clarify misconceptions, and to potentially initiate and share new ideas pertinent to engineering education research and innovations in classroom implementations. This session facilitates idea sharing and interaction amongst peers.

The goal of this session is to increase the participants' knowledge of current funding opportunities at the National Science Foundation (NSF) to support projects with potential significant impacts on science, technology, engineering, and mathematics (STEM) education. In particular, the discussion will focus on new and current funding opportunities in the Division of Undergraduate Education (DUE) in the Directorate of Education and Human Resources (EHR) and the Division of Engineering Education & Centers (EEC) in the Directorate of Engineering. During the session, we will provide examples of project activities that support STEM education research opportunities. The session will use a highly interactive format (i.e., team-based activities and discussion) to engage the participants, to clarify misconceptions, and to potentially initiate and share new ideas pertinent to engineering education research and innovations in classroom implementations. This session facilitates idea sharing and interaction amongst peers.

In 1997, the National Science Foundation (NSF) funded three Earthquake Engineering Research Centers (EERCs) to focus on earthquake hazard mitigation through systems-oriented research, education and outreach, and industrial/practitioner/end user collaboration and technology transfer. Collectively receiving about one third of the total annual funding in NSF's earthquake engineering research portfolio, the three EERCs enable cross-disciplinary, cross-institutional teams of faculty and students to work collaboratively, in partnership with industry, practitioners and other end users, over a long-term period, on science, engineering, and societal research issues related to earthquake hazard mitigation. This paper presents an overview of the key features of the EERCs and their individual and collaborative efforts.

We revisit the complexity analysis of the recursive version of the randomized greedy algorithm for computing a maximal independent set (MIS), originally analyzed by Yoshida, Yamamoto, and Ito (2009). They showed that, on average per vertex, the expected number of recursive calls made by this algorithm is upperbounded by the average degree of the input graph. While their analysis is clever and intricate, we provide a significantly simpler alternative that achieves the same guarantee.