Tight-Binding Approximations in 1D and 2D Coupled-Cavity Photonic Crystal Structures

Light confinement and controlling an optical field has numerous applications in the field of telecommunications for optical signals processing. When the wavelength of the electromagnetic field is on the order of the period of a photonic microstructure, the field undergoes reflection, refraction, and coherent scattering. This produces photonic bandgaps, forbidden frequency regions or spectral stop bands where light cannot exist. Dielectric perturbations that break the perfect periodicity of these structures produce what is analogous to an impurity state in the bandgap of a semiconductor. The defect modes that exist at discrete frequencies within the photonic bandgap are spatially localized about the cavity-defects in the photonic crystal. In this thesis the properties of two tight-binding approximations (TBAs) are investigated in one-dimensional and two-dimensional coupled-cavity photonic crystal structures We require an efficient and simple approach that ensures the continuity of the electromagnetic field across dielectric interfaces in complex structures. In this thesis we develop \textrm{E} -- and \textrm{D} --TBAs to calculate the modes in finite 1D and 2D two-defect coupled-cavity photonic crystal structures. In the \textrm{E} -- and \textrm{D} --TBAs we expand the coupled-cavity \overrightarrow{E} --modes in terms of the individual \overrightarrow{E} -- and \overrightarrow{D} --modes, respectively. We investigate the dependence of the defect modes, their frequencies and quality factors on the relative placement of the defects in the photonic crystal structures. We then elucidate the differences between the two TBA formulations, and describe the conditions under which these formulations may be more robust when encountering a dielectric perturbation. Our 1D analysis showed that the 1D modes were sensitive to the structure geometry. The antisymmetric \textrm{D} mode amplitudes show that the \textrm{D} --TBA did not capture the correct (tangential \overrightarrow{E} --field) boundary conditions. However, the \textrm{D} --TBA did not yield significantly poorer results compared to the \textrm{E} --TBA. Our 2D analysis reveals that the \textrm{E} -- and \textrm{D} --TBAs produced nearly identical mode profiles for every structure. Plots of the relative difference between the \textrm{E} and \textrm{D} mode amplitudes show that the \textrm{D} --TBA did capture the correct (normal \overrightarrow{E} --field) boundary conditions. We found that the 2D TBA CC mode calculations were 125-150 times faster than an FDTD calculation for the same two-defect PCS. Notwithstanding this efficiency, the appropriateness of either TBA was found to depend on the geometry of the structure and the mode(s), i.e. whether or not the mode has a large normal or tangential component.

Academic Integrity and Senior Nursing Undergraduate Clinical Practice

Background: Academic integrity (AI) has been defined as the commitment to the values of honesty, trust, fairness, respect, and responsibility with courage in all academic endeavours. The senior years of nursing studies provide an intersection for students to transition to professional roles through student clinical practice. It is essential to understand what predicts senior nursing students’ intention to behave with AI so that efforts can be directed to initiatives focused on strengthening their commitment to behaving with AI. Research Questions: To what extent do students differ on Theory of Planned Behaviour (TPB) variables? What predicts intention to behave with academic integrity among senior nursing students in clinical practice across three different Canadian Schools of Nursing? Method: The TPB framework, an elicitation (n=30) and two pilot studies (n=59, n=29) resulted in the development of a 38 question (41-item) self-report survey (Miron Academic Integrity Nursing Survey—MAINS: α>0.70) that was administered to Year 3 and 4 students (N=339). Three predictor variables (attitude, subjective norm, perceived behavioural control) were measured with students’ intention to behave with AI in clinical. Age, sex, year of study, program stream, students’ understanding of AI policies, and locations where students accessed AI information were also measured. Results: Hierarchical multiple regression analyses revealed that background, site, and TPB variables explained 32.6% of the variance in intention to behave with academic integrity. The TPB variables explained 26.8% of the variance in intention after controlling for background and site variables. In the final model, only the TPB predictor variables were statistically significant with Attitude having the highest beta value (beta=0.35, p<0.001), followed by Subjective Norm (beta=0.21, p<0.001) and Perceived Behavioural Control (beta=0.12, p<0.02). Conclusion: Student attitude is the strongest predictor to intention to behave with AI in clinical practice and efforts to positively influence students’ attitudes need to be a focus for schools, curricula, and clinical educators. Opportunities for future research should include replicating the current study with students enrolled in other professional programs and intervention studies that examine the effectiveness of specific endeavours to promote AI in practice.

An exploration of students’ perceptions and attitudes towards creativity in engineering education

This study used a mixed methods approach to develop a broad and deep understanding of students’ perceptions towards creativity in engineering education. Studies have shown that students’ attitudes can have an impact on their motivation to engage in creative behavior. Using an ex-post facto independent factorial design, attitudes of value towards creativity, time for creativity, and creativity stereotypes were measured and compared across gender, year of study, engineering discipline, preference for open-ended problem solving, and confidence in creative abilities. Participants were undergraduate engineering students at Queen’s University from all years of study. A qualitative phenomenological methodology was adopted to study students’ understandings and experiences with engineering creativity. Eleven students participated in oneon- one interviews that provided depth and insight into how students experience and define engineering creativity, and the survey included open-ended items developed using the 10 Maxims of Creativity in Education as a guiding framework. The findings from the survey suggested that students had high value for creativity, however students in fourth year or higher had less value than those in other years. Those with preference for open-ended problem solving and high confidence valued creative more than their counterparts. Students who preferred open-ended problem solving and students with high confidence reported that time was less of a hindrance to their creativity. Males identified more with creativity stereotypes than females, however overall they were both low. Open-ended survey and interview results indicated that students felt they experienced creativity in engineering design activities. Engineering creativity definitions had two elements: creative action and creative characteristic. Creative actions were associated with designing, and creative characteristics were predominantly associated with novelty. Other barriers that emerged from the qualitative analysis were lack of opportunity, lack of assessment, and discomfort with creativity. It was concluded that a universal definition is required to establish clear and aligned understandings of engineering creativity. Instructors may want to consider demonstrating value by assessing creativity and establishing clear criteria in design projects. It is recommended that students be given more opportunities for practice through design activities and that they be introduced to design and creative thinking concepts early in their engineering education.