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At the University of Louisville, a large, urban institution in the south-east United States, undergraduate engineering students take their mathematics courses from the school of engineering. In the fall of their freshman year, engineering students take Engineering Analysis I, a calculus-based engineering analysis course. After the first two weeks of the semester, many students end up leaving Engineering Analysis I and moving to a mathematics intervention course. In an effort to retain more students in Engineering Analysis I, the department collaborated with university academic support services to create a summer intervention programme. Students were targeted for the summer programme based on their score on an algebra readiness exam (ARE). In a previous study, the ARE scores were found to be a significant predictor of retention and performance in Engineering Analysis I. This study continues that work, analysing data from students who entered the engineering school in the fall of 2012. The predictive validity of the ARE was verified, and a hierarchical linear regression model was created using math American College Testing (ACT) scores, ARE scores, summer intervention participation, and several metacognitive and motivational factors as measured by subscales of the Motivated Strategies for Learning Questionnaire. In the regression model, ARE score explained an additional 5.1% of the variation in exam performance in Engineering Analysis I beyond math ACT score. Students took the ARE before and after the summer interventions and scores were significantly higher following the intervention. However, intervention participants nonetheless had lower exam scores in Engineering Analysis I. The following factors related to motivation and learning strategies were found to significantly predict exam scores in Engineering Analysis I: time and study environment management, internal goal orientation, and test anxiety. The adjusted R 2 for the full model was 0.42, meaning that the model could explain 42% of the variation in Engineering Analysis I exam scores.

Distributed control systems (DCSs) play an essential role in the operation of critical infrastructures. Perimeter field devices are important DCS components that measure physical process parameters and perform control actions. Modern field devices are vulnerable to cyber attacks due to their increased adoption of commodity technologies and that fact that control networks are no longer isolated. This paper describes an approach for creating security-hardened field devices using operating system microkernels that isolate vital field device operations from untrusted network-accessible applications. The approach, which is influenced by the MILS and Nizza architectures, is implemented in a prototype field device. Whereas, previous microkernel-based implementations have been plagued by poor inter-process communication (IPC) performance, the prototype exhibits an average IPC overhead for protected device calls of 64.59 µs. The overall performance of field devices is influenced by several factors; nevertheless, the observed IPC overhead is low enough to encourage the continued development of the prototype.

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