<p>Our main results are that splay trees are faster for sorted insertion, where AVL trees are faster for random insertion. For searching, skip lists are faster than single class top-down splay trees, but two-class and multi-class top-down splay trees can behave better than skip lists.</p>

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Magister Scientiae - MSc / Our main results are that splay trees are faster for sorted insertion, where AVL trees are faster for random insertion. For searching, skip lists are faster than single class top-down splay trees, but two-class and multi-class top-down splay trees can behave better than skip lists. / South Africa

The Academic Vocabulary List (AVL) was produced by a rationale for word inclusion improved from the methodology used to generate Coxhead's AWL (Gardner & Davies, 2014). It offers a comprehensive view of high frequency academic vocabulary that could greatly benefit ELLs if implemented into ELL curricula (Gardner & Davies, 2014). However, because of the newness of the list, there are few learning resources currently utilizing the AVL. The major objective of this thesis project was to create digital flashcards for the first 500 words of the AVL to increase the list's accessibility to ELLs and teachers worldwide. These flashcards were made available through Quizlet's online interface. This paper describes the two types of flashcards developed: AVL words paired with simplified English (learner) definitions, and AVL words paired with L1 translations into seven languages. It further describes an evaluation of these flashcards by ESOL teachers working at a U.S. intensive English program, and concludes with suggestions for the future development of AVL learning resources.

For decades, teachers and language learners have been concerned about matching the difficulty level of texts to the proficiency level of learners in order to achieve comprehensible input, which leads to effective learning. Some leveling systems and research use word lists as part of their leveling processes, particularly the Academic Word List. The Academic Vocabulary List (AVL) has not been explored yet as a leveling tool, so this study aims to address this lack of research by examining how the AVL words vary in cumulative frequency bands and also in separate frequency bands with regard to level and topic. The AVL was divided into 5 frequency bands and compared against corpora of biology and U.S. history texts at the elementary, junior high, high school, and university levels. Results showed that the biology texts had a higher percentage of total AVL tokens than the history texts did, suggesting that the AVL may be more suitable for some disciplines than others. For the cumulative bands, Bands 1 through 3 proved to have the highest percent deltas, suggesting that words 1 to 800 are the most useful to learn. Looking at each separate band, Bands 1 and 2 had the highest percent of AVL tokens at the high school level, implying that the words of these bands are especially valuable for learners at this level. The university level had the highest percentage for Bands 3 through 5. There was no statistical significance for any band concerning the factor of the relationship between topic and level, but there was statistical significance for the factor of proficiency level at every level. For the factor of topic, there was significance for every band except Band 3. For each band, the elementary and junior high texts were generally similar to each other concerning the AVL tokens found in them. High school and university texts were similar to each other for Bands 1 and 2 but were not similar to each other for Bands 3 through 5.

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Automatic vehicle locator (AVL) systems, utilizing military's global positioning system, may impact response time to law enforcement calls for service. In order to evaluate the impacts of AVL on response time to calls for service at the Altamonte Springs Police Department (ASPD), computer aided dispatch (CAD) data from years 1999 to 2003 were analyzed. The analysis of each of the data sets consisted of an initial sequence chart, an analysis of variance (ANOVA), a means plot and a linear regression. Interviews of ASPD personnel were conducted to understand user perceptions of AVL. Based on the ANOVA results, trends indicate that weekly response time was significantly lower during the AVL partial implementation period than during the pre or post AVL stages across all categories of data analyzed. Based on the regression results, trends indicate that the overall impact of AVL on response time for all categories analyzed is flat and show AVL as having no overall impact on response time across all calls for service analyzed. An exception to this is the findings related to Priority 3 calls for service; however this exception can be attributed to performance during the pre AVL implementation stage. These results do not suggest a capability for AVL to reduce response time to calls for service in a meaningful comprehensive way. Thus, the study's hypotheses are not supported.

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The primary objective of this study is to develop an integrated public transportation planning framework to evaluate and plan Advanced Public Transportation Systems (APTS). With this purpose, a systems approach point of view is adopted to study the influence of new APTS technology in supply and demand transit variables. In this project the Systems Dynamics methodology is adopted to track the dynamic behavior of model variables and feedback loops forming among them. The proposed framework is illustrated in a case study involving automated vehicle location systems (AVL) applied to a small transit community. The proposed approach follows the same steps of the Systems Dynamics method; First, identify some key variables which are not only susceptive to AVL technology but also affect the supply-demand relationship of a bus transit environment. Second, trace and simplify the causal relationships of the variables considering impacts of facility supply changes to passenger demand responses and vice versa. To accomplish this, four detailed sub-models representing parts of the transit system are developed and combined under the Systems Dynamics methodology point of view. Theses Sub-models are: 1) demography, 2) urban transportation planning, 3) bus operations, and 4) evaluation. Finally, to validate the model procedure, the model is applied to a case study. This study attempts to encompass as many as possible factors around a bus transit system environment which can be impacted by new APTS technology to illustrate the use of the proposed framework. Some of these factors include: 1) Demographic characteristics; 2) urban or social activity of the study area and 3) changes to transportation facilities. The case study illustrates how the physical characteristics of the transit systems such as traffic demand, traffic conditions along the transit route, route layout, and bus performance can be affected by the new technology. Since APTS impacts are time dependent a continuous multi-loop simulation technique is adopted to track dynamic changes of all model variables. The analysis of the transit system is carried over a 20-year life cycle to illustrate the long term dynamics of the feedback structures inherent in the model. <i>[Vita removed Aug. 2, 2010. GMc]</i> / Ph. D.