Science, Technology, Engineering, and Mathematics
Scroll below to select research from the following scholars

Dr. George A. Noble
Title: Founder and CEO of GG Noble Tutoring
Research Topic: Isotope Shifts and Fine Structure Of Li D Lines and Determination of 6;7 Li Relative Nuclear Charge Radius
Institution: York University, Toronto, Ontario
Committee Chair: J. Darewych, Ph.D.
Abstract: The lithium D lines were studied using a dye laser that was frequency modulated by an electro-optic modulator to excite an atomic beam. The modulation frequency was specified by a frequency synthesizer to an accuracy of a few parts in 107. The transmission of part of the laser beam through an etalon was monitored to check for any nonlinearity of the laser scan. Fluorescence, produced by the radiative decay of the excited state, was detected by a photomultiplier and recorded as the laser frequency was scanned across the resonance. Hence, each transition was multiply excited allowing for convenient calibration of the laser frequency scan. The 6;7Li 2P fine structure intervals were found to be 10; 052:964 0:050 and 10; 053:119 0:058 MHz which is about 2 MHz higher than the most reliable results calculated using Hylleraas Variational theory. This has motivated efforts to take higher order effects into consideration to improve the theoretical estimates. The D1 and D2 isotope shifts were determined to be 10; 534:039 0:070 and 10; 534:194 0:104 MHz. These two results yielded consistent values for the relative nuclear charge radius squared Rc 2 of 6;7Li. The average result of 0:7550:023 fm2 for Rc 2 agreed very well with values found by other experiments that studied the Li+ 1s2s 3S ! 1s2p 3P and the Li 2S1=2 ! 3S1=2 transitions. The relative nuclear charge radius was found with an uncertainty of only several millifermi (1018 meter) which is more than an order of magnitude smaller than obtained using electron scattering.
Title: Founder and CEO of GG Noble Tutoring
Research Topic: Isotope Shifts and Fine Structure Of Li D Lines and Determination of 6;7 Li Relative Nuclear Charge Radius
Institution: York University, Toronto, Ontario
Committee Chair: J. Darewych, Ph.D.
Abstract: The lithium D lines were studied using a dye laser that was frequency modulated by an electro-optic modulator to excite an atomic beam. The modulation frequency was specified by a frequency synthesizer to an accuracy of a few parts in 107. The transmission of part of the laser beam through an etalon was monitored to check for any nonlinearity of the laser scan. Fluorescence, produced by the radiative decay of the excited state, was detected by a photomultiplier and recorded as the laser frequency was scanned across the resonance. Hence, each transition was multiply excited allowing for convenient calibration of the laser frequency scan. The 6;7Li 2P fine structure intervals were found to be 10; 052:964 0:050 and 10; 053:119 0:058 MHz which is about 2 MHz higher than the most reliable results calculated using Hylleraas Variational theory. This has motivated efforts to take higher order effects into consideration to improve the theoretical estimates. The D1 and D2 isotope shifts were determined to be 10; 534:039 0:070 and 10; 534:194 0:104 MHz. These two results yielded consistent values for the relative nuclear charge radius squared Rc 2 of 6;7Li. The average result of 0:7550:023 fm2 for Rc 2 agreed very well with values found by other experiments that studied the Li+ 1s2s 3S ! 1s2p 3P and the Li 2S1=2 ! 3S1=2 transitions. The relative nuclear charge radius was found with an uncertainty of only several millifermi (1018 meter) which is more than an order of magnitude smaller than obtained using electron scattering.

george_noble_thesis.pdf | |
File Size: | 1596 kb |
File Type: |

Dr. Elliott Heflin, Jr.
Title: Adjunct Professor of Education
Research Topic: Educational Leaders’ Perspective of First-Year College Students’ Computer Skills
Institution: University of Phoenix
Committee Chair: Sherrilyn Scott, Ed.D.
Abstract: The purpose of this qualitative phenomenological study was to examine the lived experiences from the perspectives that educational leaders in a college setting, in relations to first-year college student’s computer skills. The phenomenological design was use to gather data in the manner of face-to-face interviews. In addition, all data was entered into Nvivo10® software to identify themes. The information shared will be used to write an effective curriculum that will ensure that first-year college students have the necessary computer skills to matriculate through a higher educational setting.
Title: Adjunct Professor of Education
Research Topic: Educational Leaders’ Perspective of First-Year College Students’ Computer Skills
Institution: University of Phoenix
Committee Chair: Sherrilyn Scott, Ed.D.
Abstract: The purpose of this qualitative phenomenological study was to examine the lived experiences from the perspectives that educational leaders in a college setting, in relations to first-year college student’s computer skills. The phenomenological design was use to gather data in the manner of face-to-face interviews. In addition, all data was entered into Nvivo10® software to identify themes. The information shared will be used to write an effective curriculum that will ensure that first-year college students have the necessary computer skills to matriculate through a higher educational setting.

out.pdf | |
File Size: | 1019 kb |
File Type: |

Dr. Brandeis Marshall
Title: Associate Professor, Computer Science, Spelman College,
Research Topic: An Evaluation of a Statistical Framework and Algorithms for Adaptive Aggregation
Institution: Rensselaer Polytechnic Institute
Committee Chair: Sibel Adalı, Thesis Advisor Malik Magdon-Ismail, Co-Thesis Advisor
Abstract: The rank aggregation problem aims to combine several ranked lists to obtain a fi- nal “consensus” ranked list that gives better results than any one of the individual ranked lists. With the emergence of the World Wide Web, the meta-search community has studied the rank aggregation problem in order to aggregate search results from multiple search engines and increase the coverage of the Web by accessing more information. The existing aggregation methods address the problem of capturing the user feedback accurately while minimizing the impact of outliers or spam. However, the prior work does not provide guidelines about when to use the aggregation method in which problem setting as well as how to dynamically select an aggregation method for that problem setting. In this thesis, we address these shortcomings in the prior work. Since rank aggregation also appears in fields other than the Web, we need a more general platform to examine an optimal ranking. We define two factors that contribute to the performance of rank aggregation methods including noise, such as spam, and misinformation, such as trustworthiness. In meta-search, relevance of an object is difficult to decide since evaluation depends on subjective expert judgments. To address this concern, we propose a flexible statistical framework to model the possible different relationships between the rankers, such as search engines, and the ground truth. Our model contains a ground truth ranker, which corresponds to the correct ordering of objects, and the input rankers that serve as approximations of the ground truth ranker. We also develop several aggregation methods that capture different aspects of the rank information including precision optimal, iterative best flip and three algorithms that are approximations to the minimum feedback arc set problem. We show that there is a trade off between information and robustness when selecting the best aggregation method and that none of the well-known rankers perform well uniformly in all different noise and misinformation conditions. We develop classification methods and bayesian techniques to dynamically select the optimal aggregator based on classified noise and misinformation.
Title: Associate Professor, Computer Science, Spelman College,
Research Topic: An Evaluation of a Statistical Framework and Algorithms for Adaptive Aggregation
Institution: Rensselaer Polytechnic Institute
Committee Chair: Sibel Adalı, Thesis Advisor Malik Magdon-Ismail, Co-Thesis Advisor
Abstract: The rank aggregation problem aims to combine several ranked lists to obtain a fi- nal “consensus” ranked list that gives better results than any one of the individual ranked lists. With the emergence of the World Wide Web, the meta-search community has studied the rank aggregation problem in order to aggregate search results from multiple search engines and increase the coverage of the Web by accessing more information. The existing aggregation methods address the problem of capturing the user feedback accurately while minimizing the impact of outliers or spam. However, the prior work does not provide guidelines about when to use the aggregation method in which problem setting as well as how to dynamically select an aggregation method for that problem setting. In this thesis, we address these shortcomings in the prior work. Since rank aggregation also appears in fields other than the Web, we need a more general platform to examine an optimal ranking. We define two factors that contribute to the performance of rank aggregation methods including noise, such as spam, and misinformation, such as trustworthiness. In meta-search, relevance of an object is difficult to decide since evaluation depends on subjective expert judgments. To address this concern, we propose a flexible statistical framework to model the possible different relationships between the rankers, such as search engines, and the ground truth. Our model contains a ground truth ranker, which corresponds to the correct ordering of objects, and the input rankers that serve as approximations of the ground truth ranker. We also develop several aggregation methods that capture different aspects of the rank information including precision optimal, iterative best flip and three algorithms that are approximations to the minimum feedback arc set problem. We show that there is a trade off between information and robustness when selecting the best aggregation method and that none of the well-known rankers perform well uniformly in all different noise and misinformation conditions. We develop classification methods and bayesian techniques to dynamically select the optimal aggregator based on classified noise and misinformation.

an_evaluation_of_a_statistical_framework_and_algorithms_for_adaptive_aggregation.pdf | |
File Size: | 836 kb |
File Type: |