In this thesis, the design of a scaling-friendly continuous-time closed-loop voltage controlled oscillator (VCO) based Delta-Sigma analog to digital converter (ADC) is introduced. It uses the VCO as both quantizer and integrator, and thus, obviates the need for power-hungry scaling-unfriendly operational transconductance amplifiers (OTAs) and precision comparators. It arranges two VCOs in a pseudo-differential manner, which cancels out even-order distortions. More importantly, it brings an intrinsic clocked averaging (CLA) capability that automatically addresses digital to analog converter (DAC) mismatches. The prototype ADC in 130 nm complementary metal-oxide-semiconductor (CMOS) occupies a small area of 0.03 mm² and achieves 66.5 dB signal to noise and distortion ratio (SNDR) over 2 MHz bandwidth (BW) while sampling at 300 MHz and consuming 1.8 mW under a 1.2 V power supply. It can also operate with a low analog supply of 0.7 V and achieves 65.8 dB SNDR while consuming 1.1 mW. The corresponding figure-of-merits (FOMs) for the two cases are 0.25 pJ/conversion-step and 0.17 pJ/conversion-step, respectively. / text

The purpose of this study is to explore crowdsourcing as a research paradigm for creating evidence-based practice research in the field of speech pathology. Using an Internet survey, respondents provided de-identified information about one patient with aphasia they had treated in the previous year. The respondents were then asked to rate the success of treatment. Analysis and grading of the responses was performed to identify which responses were usable for the purpose of planning a treatment for a patient with similar demographics and diagnostic make-up. Results showed that crowdsourcing is a viable research method; however, further refinements to the collection and analysis are required before it can be an effectively used. / text

The principal objective of this report is to conceptualize and articulate a product plan for EvoLesson, a hosted application for supporting the implementation of the standards-based lesson cycle. EvoLesson is a lesson planning application that supports the creation of efficient, effective and aligned assessments and daily lesson plans without restricting teacher autonomy. EvoLesson's lesson design process creates shared resources for objective driven, backwards-planned lesson development that facilitates the creation of teacher-generated daily objective student mastery data. EvoLesson's mission is to provide public school teachers and administrators with a platform to find, create, edit and share lesson planning resources and generate student data to improve teacher effectiveness and drive student performance. EvoLesson was created as a means of solving the daily challenge teachers face in attempting to implement standards-based lesson plans effectively. EvoLesson's teacher centered design creates values for both teachers and administrators by offering the following lesson planning benefits to impact student achievement. EvoLesson addresses the critical need of providing teachers and administrators with essential resources to support effective planning and drive student achievement. EvoLesson is positioned to compete in the rapidly growing education technology market, with specific emphasis on the content and instructional support segments. This report outlines a comprehensive strategy for marketing EvoLesson to both teachers and school districts. EvoLesson's future role in providing critical support for the implementation of standards-based learning has wide-ranging education policy implications, including increased student performance, improved teacher quality, and data support for improved teacher evaluation methods. At the state level, the ability to aggregate daily objective student mastery data can provide quality data feedback on standards alignment and testing. / text

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Automatic test case generation is a process that starts with text based functional requirements which are converted to a formal system requirements model. Once the formal system requirements model is created the automatic test case generation software creates a set of test scenarios that will verify that the requirements are all met. The automatic test case generation software accomplishes the conversion in a four step process: create base scenarios, identify unverified requirements, enhance scenarios to cover all requirements and allow black box testing, and then combine the scenarios into a single scenario tree. The automatic test case generation system outputs a set of scenarios by walking the final scenario tree. This dissertation expands on automatic test case generation for embedded systems in two major ways. The first is to extend functional automatic test case generation to allow for time based requirements as first class objects. The second is to use the automatic test case generation system to enable system partitioning decisions. The addition of time based requirements to the automatic test case generation system allows more complex systems to be developed. By providing a partitioning recommendation based on the test cases generated from the system requirements, the scope and capabilities of a single designer can be expanded to more complex systems. The resulting upgrades to the theory of automatic test case generation could be applied to the existing tools or incorporated in modern UML/SysML based design tools.

In 2001 the Institute of Medicine identified a significant gap between what is known about how we care for patients and the care that they receive. This identified gap renewed interest in the development and implementation of evidence-based practice (EBP). A number of research studies have evaluated barriers to EBP yet questions still arise as to why evidence is not routinely incorporated into practice. This led to a new field of inquiry called implementation science focused on methods for translating evidence into practice. The Promoting Action on Research Implementation in Health Services (PARiHS) framework purports that successful implementation of EBP is a function of the strength of the evidence to be implemented, the quality of the context into which it will be implemented and appropriate facilitation. There currently are levels of evidence and measures of context that can be utilized but no measures of appropriate facilitation. The purpose of this research was to develop an instrument (the Facilitation Assessment Index) to measure facilitation. This methodological study was conducted to determine the psychometric properties of an investigator-developed instrument to define and measure the concept of facilitation. The instrument was distributed to1025 Registered Nurses in an Academic Medical Center. The response rate was 28%. The majority of respondents were in a staff nurse role, over age 35 and had at least a Bachelor's degree. The Facilitation Assessment Index (FAI) demonstrated adequate psychometrics. Factor analysis delineated four subscales entitled Support, Leadership, Respect and Autonomy. The overall reliability of the scale was r = .93 and the range of reliability of the subscales was r = .85-.93. The test-retest correlation for the total scale was r = .85 (p<.001). Correlations for the subscales ranged from r = .61-.85 (p<.01).Relationships between demographic variables and facilitation were evaluated. Both unit and job title variables demonstrated relationships with scale scores but group sizes were not equal. Future research is needed to strengthen the psychometric properties of the FAI. Subsequent research could focus on use in strengthening the PARiHS framework and quantifying the amount of facilitation needed to implement EBP.

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