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Perennial groundcover (PGC) has promise as a scalable approach to generating natural resource benefits and sustainable biofuel feedstock while preserving the high yields of annual row crop production. Partnering row crops with temporally and spatially complementary low-growing, shallow-rooted perennials, such as Kentucky bluegrass (KBG) (Poa pratensis L.), is one example of an emerging PGC system. PGC's ecosystem benefits can only be fully realized if commercial-scale adoption occurs, which hinges on its economic feasibility. This paper utilizes an enterprise budget framework to detail and compare the expected cost and revenue of establishing and maintaining PGC in row crop systems with standard continuous corn (SCC) (Zea mays L.) production, including stover harvest, but excluding economic incentives for ecosystem services. Optimistic and pessimistic assumptions were used, along with Monte Carlo simulation, to characterize the uncertainty in results. In the optimistic stover market scenario, Year 1 net returns for PGC averaged USD 84/ac less than for SCC; Year 2+ net returns averaged USD 83/ac more, meaning that cost parity with SCC occurs by the second PGC system year. Without stover revenue, parity is achieved after five years. These results affirm that PGC's economic viability is critically impacted by a groundcover's lifespan, the yield parity with SCC, and the availability of a stover market. [ABSTRACT FROM AUTHOR]

This column presents ideas and techniques to enhance science teaching. This month's issue describes preservice elementary teachers learning engineering principles from engineers. Few elementary teachers have experience with implementing engineering into the classroom. While engineering professional development opportunities for inservice teachers are becoming more numerous, engineering education is rarely required or even offered in elementary teacher-preparation programs. To prepare future elementary teachers to teach engineering, a collaborative partnership was formed between professors in Iowa State University's College of Engineering (CoE) and School of Education (SoE). The partnership included teacher education faculty in science and mathematics education and three engineering faculty who provided perspectives on content, knowledge, and skills foundational to engineering. Members of the partnership worked together to co-plan and co-implement engineering experiences across a teacher education program. These experiences included building engineering content knowledge through a Saturday short course, inclusion of engineering in methods courses, and a summer workshop that preceded a partnership with an engineering graduate student. This article describes the Saturday short course provided to prospective elementary teachers by three members of the engineering faculty and two from the teacher education faculty.

Summer research opportunities for undergraduates, such as those supported by the National Science Foundation's Research Experience for Undergraduates (REU) program, can be critical experiences that help persuade students to pursue research through graduate studies. Studies analyzing the key actions of successful mentors are scarce. The goal of this study was to explore how hypothesized "key actions" of mentors correlated with student perceptions of mentoring and of overall program quality, students' scholarly output resulting from the REU, and the influence of the REU on students' decisions to pursue graduate school. Students who participated in 11 REU programs at a large Midwestern US university in summer 2012 were surveyed in spring 2013 about their experiences in the program and with their primary research mentor. Results suggest that the key factors hypothesized to be associated with good mentoring correlated with students' ratings of their relationship with their mentor, students' overall program ratings, students' scholarly output resulting from the REU, and the influence of the REU on students' decisions to pursue graduate school. The six "key actions" hypothesized to be associated with good mentoring were significantly correlated with student experience in an REU program. Although none of the six actions is particularly complex, taking these actions is crucial and non-trivial. Prospective REU mentors could be informed about these key actions during mentor training activities. Future research could examine these six dimensions across postsecondary institutions and in mentoring relationships outside of REU programs.

Abstract Background The availability and low cost of lignocellulosic biomass has caused tremendous interest in the bioconversion of this feedstock into liquid fuels. One measure of the economic viability of the bioconversion process is the ease with which a particular feedstock is hydrolyzed and fermented. Because monitoring the analytes in hydrolysis and fermentation experiments is time consuming, the objective of this study was to develop a rapid fluorescence-based method to monitor sugar production during biomass hydrolysis, and to demonstrate its application in monitoring corn stover hydrolysis. Results Hydrolytic enzymes were used in conjunction with Escherichia coli strain CA8404 (a hexose and pentose-consuming strain), modified to produce green fluorescent protein (GFP). The combination of hydrolytic enzymes and a sugar-consuming organism minimizes feedback inhibition of the hydrolytic enzymes. We observed that culture growth rate as measured by change in culture turbidity is proportional to GFP fluorescence and total growth and growth rate depends upon how much sugar is present at inoculation. Furthermore, it was possible to monitor the course of enzymatic hydrolysis in near real-time, though there are instrumentation challenges in doing this. Conclusion We found that instantaneous fluorescence is proportional to the bacterial growth rate. As growth rate is limited by the availability of sugar, the integral of fluorescence is proportional to the amount of sugar consumed by the microbe. We demonstrate that corn stover varieties can be differentiated based on sugar yields in enzymatic hydrolysis reactions using post-hydrolysis fluorescence measurements. Also, it may be possible to monitor fluorescence in real-time during hydrolysis to compare different hydrolysis protocols.

Many experiences in engineering education boast positive gains to students' learning and achievement. However, current literature is less clear on the economic costs associated with these efforts, or methods for performing said analyses. To address this gap, we proposed a structured approach to analyzing the incremental costs associated with an experience in engineering education. This method was modeled after those found in medicine and early childhood education. We illustrated our methodology using marginal (above baseline) time and cost ingredients that were collected during the development, pilot, and steady-state phases of a mechatronic experience in a first-year undergraduate engineering technology course. Specifically, our method included descriptive analysis, Pareto analysis, and cost per capacity estimate analysis, the latter of which has received limited discussion in current cost analysis literature. The purpose of our illustrated explanation was to provide a clear method for incremental cost analyses of experiences in engineering education. We found that the development, pilot, and steady-state phases cost just over $17.Ik (~S 12.4k for personnel and ~$4.7k for equipment), based on 2015 US$ and an enrollment capacity of 121 students. Cost vs. capacity scaled at a factor of - 0.64 (y = 3,121x-0.64, R² = 0.99), which was within the 95% interval for personnel and capital commonly observed in the chemical processing industry. Based on a four-year operational life and a range of 20-400 students per year, we estimated per seat total costs to range from roughly $70-5470, with our mechatronic experience averaging just under $150 per seat. Notably, the development phase cost, as well as the robot chassis and microcontroller capital cost were the primary cost terms of this intervention. [ABSTRACT FROM AUTHOR]

In our review we examined the primary and secondary influences of mechatronic experiences on student engagement in fundamental engineering courses. Using a systematic review methodology, we collected 402 articles with publication dates ranging from 1990-2014. Screening on title and abstract information reduced our included sources to 137, from which we mapped six parent and 22 child codes. Appraising 17 of these articles we identified eight high quality studies as the focus of our synthesis, which identified five primary influences (Student Motivation, Self-Efficacy, Course Rigor, Learning Retention, and Gender) and two secondary influences (Accreditation and Ease-of-lmplementation). In these influences we found evidence that mechatronic experiences can increase student motivation, self-efficacy, and course rigor. Also, positive effects on learning retention, gender diversity, accreditation efforts, and ease of course content implementation were identified. Future research is needed to clarify: (1) if mechatronic experiences truly increase student motivation and self-efficacy more than lecture-based strategies, (2) how the positive short-term impacts of these experiences translate to subjective academic success (i.e., future course and career goals), (3) how implementation logistics are influenced by experience type (i.e., open-ended projects verse contests), class size, institution and industry support, etc., and (4) to what degree the factors of gender, underrepresented student groups, course curricular placement, and activity type influence student engagement. [ABSTRACT FROM AUTHOR]

Historical data from multiple institutions show that students who achieve a first-semester grade point average (GPA) below 2.0 are at substantially greater risk of leaving engineering programs before graduating with a degree than are those who achieved above 2.0. Identifying these “at risk” students prior to the start of their first semester could enable improved strategies to enhance their academic success and likelihood of graduation. This study used two distinct modeling approaches to predict first-term GPA group (low-risk: GPA ≥ 2.0; at-risk: GPA < 2.0) based upon data available prior to the student’s first pre-enrollment advising session. In the case of one of the approaches—which allowed a differential weighting of Type I to Type II errors—we explore how these weightings influences the prediction accuracy. The models used academic and demographic data for first-year engineering students from 2010 to 2012 from a single large public research-active institution. The two model types employed to build predictive models were (1) ordinary least squares multiple linear regression (MLR), and (2) classification and regression trees (CART). For both MLR and CART models, high school GPA and math placement exam scores were found to be significant predictors of first-term GPA. Increasing the cost of missing at-risk students in the CART models improves at-risk prediction accuracy but also increases the rate of false positives (incorrectly identifying a low-risk student as at-risk). The relative simplicity of the CART models, as well as the ease with which error-types can be weighted to reflect institutional values, encourages their use in this type of modeling effort. [ABSTRACT FROM AUTHOR]