We report on progress at the University of Hawaii on the integration and testing setups for the adaptive secondary mirror (ASM) for the University of Hawaii 2.2-meter telescope on Maunakea, Hawaii. We report on the development of the handling fixtures and alignment tools we will use along with progress on the optical metrology tools we will use for the lab and on-sky testing of the system.
Curtis, H.; Gartner, J.; Dahlke, K.; Adesope, O.; Dutta, P.; Van Wie, B.; Thiessen, D.
(, ASEE annual conference proceedings)
Our team has developed Low-Cost Desktop Learning Modules (LCDLMS) as tools to study transport phenomena aimed at providing hands-on learning experiences. With an implementation design embedded in the community of inquiry framework, we disseminate units to professors across the country and train them on how to facilitate teacher presence in the classroom with the LC-DLMs. Professors are briefed on how create a homogenous learning environment for students based on best-practices using the LC-DLMs. By collecting student cognitive gain data using pre/posttests before and after students encounter the LC-DLMs, we aim to isolate the variable of the professor on the implementation with LC-DLMs. Because of the onset of COVID-19, we have modalities for both hands-on and virtual implementation data. An ANOVA whereby modality was grouped and professor effect was the independent variable had significance on the score difference in pre/posttest scores (p<0.0001) and on posttest score only (p=0.0004). When we divide out modality between hands-on and virtual, an ANOVA with an F- test using modality as the independent variable and professor effect as the nesting variable also show significance on the score difference between pre and posttests (p-value=0.0236 for hands- on, and p-value=0.0004 for virtual) and on the posttest score only (p-value=0.0314 for hands-on, and p-value<0.0001 for virtual). These results indicate that in all modalities professor had an effect on student cognitive gains with respect to differences in pre/posttest score and posttest score only. Future will focus on qualitative analysis of features of classrooms yield high cognitive gains in undergraduate engineering students.
Liu, Richard; McCormick, Callan; Bayram, Can
(, Gallium Nitride Materials and Devices XIV)
Optical properties of InGaN/GaN multi-quantum-well (MQWs) grown on sapphire and on Si(111) are reported. The tensile strain in the MQW on Si is shown to be beneficial for indium incorporation and Quantum-confined Stark Effect reduction in the multi-quantum wells. Raman spectroscopy reveals compressive strains of -0.107% in MQW on sapphire and tensile strain of +0.088% in MQW on Si. Temperature-dependent photoluminescence shows in MQW on sapphire a strong (30 meV peak-to-peak) S-shaped wavelength shift with decreasing temperature (6 K to 300K), whereas MQW on Si luminescence wavelength is stable and red-shifts monotonically. Micro-photoluminescence mapping over 200 by 200 μm2 shows the emission wavelength spatial uniformity of MQW on Si is 2.6 times higher than MQW on sapphire, possibly due to a more uniform indium incorporation in the multi-quantum-wells as a result of the tensile strain in MQW on Si. A positive correlation between emission energy and intensity is observed in MQW on sapphire but not in those on Si. Despite the lower crystal quality of MQW on Si revealed by atomic force microscopy, it exhibits a higher internal quantum efficiency (IQE) than MQW on sapphire from 6 K to 250 K, and equalizes at 300 K. Overall, MQW on Si exhibits a high IQE, higher wavelength spatial uniformity and temperature stability, while providing a much more scalable platform than MQW on sapphire for next generation integrated photonics.
Bobkov, S. G.; Ledoux, M.
(, Journal of fourier analysis applications)
null
(Ed.)
We explore upper bounds on Kantorovich transport distances between probability measures on the Euclidean spaces in terms of their Fourier-Stieltjes transforms, with focus on non-Euclidean metrics. The results are illustrated on empirical measures in the optimal matching problem on the real line.
Our team has developed Low-Cost Desktop Learning Modules (LCDLMS) as tools to study transport phenomena aimed at providing hands-on learning experiences. With an implementation design embedded in the community of inquiry framework, we disseminate units to professors across the country and train them on how to facilitate teacher presence in the classroom with the LC-DLMs. Professors are briefed on how create a homogenous learning environment for students based on best-practices using the LC-DLMs. By collecting student cognitive gain data using pre/posttests before and after students encounter the LC-DLMs, we aim to isolate the variable of the professor on the implementation with LC-DLMs. Because of the onset of COVID-19, we have modalities for both hands-on and virtual implementation data. An ANOVA whereby modality was grouped and professor effect was the independent variable had significance on the score difference in pre/posttest scores (p<0.0001) and on posttest score only (p=0.0004). When we divide out modality between hands-on and virtual, an ANOVA with an Ftest using modality as the independent variable and professor effect as the nesting variable also show significance on the score difference between pre and posttests (p-value=0.0236 for handson, and p-value=0.0004 for virtual) and on the posttest score only (p-value=0.0314 for hands-on, and p-value<0.0001 for virtual). These results indicate that in all modalities professor had an effect on student cognitive gains with respect to differences in pre/posttest score and posttest score only. Future will focus on qualitative analysis of features of classrooms yield high cognitive gains in undergraduate engineering students.
Wu, Z., and Khardon, R. Approximate Inference for Stochastic Planning in Factored Spaces. Retrieved from https://par.nsf.gov/biblio/10337212. Proceedings of Machine Learning Research .
Wu, Z., & Khardon, R. Approximate Inference for Stochastic Planning in Factored Spaces. Proceedings of Machine Learning Research, (). Retrieved from https://par.nsf.gov/biblio/10337212.
Wu, Z., and Khardon, R.
"Approximate Inference for Stochastic Planning in Factored Spaces". Proceedings of Machine Learning Research (). Country unknown/Code not available. https://par.nsf.gov/biblio/10337212.
@article{osti_10337212,
place = {Country unknown/Code not available},
title = {Approximate Inference for Stochastic Planning in Factored Spaces},
url = {https://par.nsf.gov/biblio/10337212},
abstractNote = {In the Proceedings on the 11th International Conference on Probabilistic Graphical Models (PGM), published as part of the PMLR series.},
journal = {Proceedings of Machine Learning Research},
author = {Wu, Z. and Khardon, R.},
}
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