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Board 438: Year Two of Developing a New Dataset for Analyzing Engineering Curricula
Get PDFThis paper discusses the developments during Year 2 for a project concerned with analyzing the curricula of engineering programs in the United States to understand the structural barriers embedded in degree requirements that could push out diverse groups of students. We are using an emerging method for quantifying the complexity of these programs called Curricular Analytics. This method involves treating the prerequisite relationships between courses as a network and applying graph theoretic measures to calculate a curriculum’s structure complexity. In Year 1, we collected 497 plans of study representing five engineering disciplines (i.e., Mechanical, Civil, Electrical, Chemical, and Industrial) across 13 institutions - spanning a decade. To ensure the dataset is as useful as possible to engineering education researchers, we have intentionally aligned our data collection with institutions available in the Multiple Institution Database for Investigating Engineering Longitudinal Development (MIDFIELD).
One of the outputs of this project is an R package that will enable researchers and practitioners to explore and leverage the dataset in their work by enabling the calculations to be completed at scale. With the efforts in Year 1, the package has the required functionality to compute the necessary metrics for Curricular Analytics. During Year 2, we have been building functions to manipulate course-taking trajectories of actual student data such that they can be compared to one another using association analysis. Association analysis will enable us to mine common course-taking patterns disaggregated by strata like institution, discipline, first-generation-status, and transfer-status and reconstruct them as networks to complement the plan of study data. Moreover, after sharing this work in preliminary forms with faculty, there was a desire for more customized functions. Thus, we are currently conducting a systematic literature review of how Curricular Analytics has been applied and extended to search for usable metrics to add to our package.
Much of Year 2 has been spent verifying the data and correcting errors that would impact the results of any analysis, whether quantitative or qualitative, by exploring the dataset using a combination of descriptive statistics and visualizations like histograms, boxplots, and longitudinal plots. As the data currently exists, the mean structural complexity of all engineering programs we considered (n = 497) is 313, and the median is 294. Chemical engineering has the highest mean structural complexity of 430, followed by mechanical engineering with a structural complexity of 369. The remaining disciplines were more tightly clustered together: electrical with 287, industrial with 248, and civil with 232. Although we are finalizing corrections to these data, it is not expected that the results will change significantly. We are currently sampling cases at the distribution\u27s tails in the box plots of structural complexity to explore the extreme cases in our dataset and jumpstart analyses regarding curricular design patterns.
This paper will provide details on the preliminary analyses we have conducted using Curricular Analytics, an introduction to the R package, and updates from our systematic literature review
The Quest for Immunity: Exploring Human Herpesviruses as Vaccine Vectors
Get PDFHerpesviruses are large DNA viruses that have long been used as powerful gene therapy tools. In recent years, the ability of herpesviruses to stimulate both innate and adaptive immune responses has led to their transition to various applications as vaccine vectors. This vaccinology branch is growing at an unprecedented and accelerated rate. To date, human herpesvirus-based vectors have been used in vaccines to combat a variety of infectious agents, including the Ebola virus, foot and mouth disease virus, and human immunodeficiency viruses. Additionally, these vectors are being tested as potential vaccines for cancer-associated antigens. Thanks to advances in recombinant DNA technology, immunology, and genomics, numerous steps in vaccine development have been greatly improved. A better understanding of herpesvirus biology and the interactions between these viruses and the host cells will undoubtedly foster the use of herpesvirus-based vaccine vectors in clinical settings. To overcome the existing drawbacks of these vectors, ongoing research is needed to further advance our knowledge of herpesvirus biology and to develop safer and more effective vaccine vectors. Advanced molecular virology and cell biology techniques must be used to better understand the mechanisms by which herpesviruses manipulate host cells and how viral gene expression is regulated during infection. In this review, we cover the underlying molecular structure of herpesviruses and the strategies used to engineer their genomes to optimize capacity and efficacy as vaccine vectors. Also, we assess the available data on the successful application of herpesvirus-based vaccines for combating diseases such as viral infections and the potential drawbacks and alternative approaches to surmount them
Spin-acoustic control of silicon vacancies in 4H silicon carbide
Get PDFBulk acoustic resonators can be fabricated on the same substrate as other components and can operate at various frequencies with high quality factors. Mechanical dynamic metrology of these devices is challenging as the surface information available through laser Doppler vibrometry lacks information about the acoustic energy stored in the bulk of the resonator. Here we report the spin-acoustic control of naturally occurring negatively charged silicon monovacancies in a lateral overtone bulk acoustic resonator that is based on 4H silicon carbide. We show that acoustic driving can be used at room temperature to induce coherent population oscillations. Spin-acoustic resonance is shown to be useful as a frequency-tunable probe of bulk acoustic wave resonances, highlighting the dynamical strain distribution inside a bulk acoustic wave resonator at ambient operating conditions. Our approach could be applied to the characterization of other high-quality-factor microelectromechanical systems and has the potential to be used in mechanically addressable quantum memory
Exploring Barriers to the Adoption of Internet of Things-Based Precision Agriculture Practices
Get PDFThe production of row crops in the Midwestern (Indiana) region of the US has been facing environmental and economic sustainability issues. There has been an increase in trend for the application of fertilizers (nitrogen & phosphorus), farm machinery fuel costs and decreasing labor productivity leading to non-optimized usage of farm inputs. Literature describes how sustainable practices such as profitability (return on investments), operational cost reduction, hazardous waste reduction, delivery performance and overall productivity might be adopted in the context of precision agriculture technologies (variable rate irrigation, variable rate fertilization, cloud-based analytics, and telematics for farm machinery navigation). The literature review describes low adoption of Internet of Things (IoT)-based precision agriculture technologies, such as variable rate fertilizer (39%), variable rate pesticide (8%), variable rate irrigation (4%), cloud-based data analytics (21%) and telematics (10%) amongst Midwestern row crop producers. Barriers to the adoption of IoT-based precision agriculture technologies cited in the literature include cost effectiveness, power requirements, wireless communication range, data latency, data scalability, data storage, data processing and data interoperability. Therefore, this study focused on exploring and understanding decision-making variables related to barriers through three focus group interview sessions conducted with eighteen (n = 18) subject matter experts (SME) in IoT- based precision agriculture practices. Dependency relationships described between cost, data latency, data scalability, power consumption, communication range, type of wireless communication and precision agriculture application is one of the main findings. The results might inform precision agriculture practitioners, producers and other stakeholders about variables related to technical and operational barriers for the adoption of IoT-based precision agriculture practices
