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Second language (L2) vocabulary learning is one of the most important aspects of second language acquisition (SLA). Its enormous importance, paired with the rapid development of mobile-assisted language learning (MALL), has led to increasing use of mobile-based dictionaries (MBDs) by English as a Foreign Language (EFL) learners at Chinese universities, especially for self-directed informal language learning. Although many studies have explored the role of dictionaries in Chinese learners’ L2 vocabulary learning, few have considered mobile-based dictionaries (MBDs). This study examined three types of MBDs: monolingual, bilingualised and bilingual. It aimed to investigate: (1) whether and how MBDs facilitate Chinese university EFL learners’ incidental vocabulary learning, (2) how learners use MBDs and (3) their attitudes toward MBDs. Participants comprised 66 English majors and 59 non-English majors. This study used two questionnaires, pre- and post-tests as well as interviews to collect quantitative and qualitative data. The results of the self-report questionnaire show that Chinese EFL learners are selective when using MBDs for reading. They prefer to look up word definitions, especially first definitions and Chinese translations. The non-English majors use MBDs more often than the English majors, especially in searching spelling, Chinese translation, part of speech (POS), and pronunciation. During the interviews, the learners noted that their MBD use mainly depends on the current task and their motivation to acquire a specific word for future use. The vocabulary test results verify the effectiveness of all three types of MBDs for incidental vocabulary learning in the short and long term, though retention weakened significantly over time. More specifically, while the English majors performed better on the vocabulary tests, the non-English majors made more significant progress. The monolingual group performed better, while the bilingualised group made more progress. The attitude questionnaire and semi-structured interviews addressed five key themes: (1) learners’ motivation for reading and vocabulary learning when using MBDs, (2) learners’ attitudes toward the effectiveness of MBDs for reading, (3) learners’ attitudes toward the effectiveness of MBDs for vocabulary learning, (4) learners’ overall evaluation of MBDs and (5) learners’ satisfaction with MBDs. The bilingualised group was the most positive, especially compared to the bilingual group. This study refines three strategies of MBD use by Chinese EFL learners: (1) the “so called relevance” strategy, in which MBD use is driven by the task type, goals and difficulty; (2) the “choose the first definition” strategy, which highlights learners’ preference to seek a word’s first definition; and (3) the “availability principle” strategy, which emphasises learners’ tendency to read the L1 equivalents. This study also highlights the importance of (1) L2 authentic and contextualised information as well as (2) repetition and elaboration in learners’ incidental vocabulary learning. Furthermore, this study identifies discrepancies between learners’ MBD use and their attitudes, analysing both micro- and macro-level factors. An ecological model is proposed to provide researchers, teachers and learners with a holistic ecological view of MBDs, which contributes theoretically to understanding the complex, interactive, dynamic and developmental nature of incidental vocabulary learning in the digital era. As an interdisciplinary study, this research fills theoretical and empirical gaps in the study of MALL, offering language teachers ways to help learners increase awareness of their vocabulary learning problems and to provide guidance in MBD selection and use. In the future, designers and developers could consider making MBDs more personalised, adaptive and interactive.

Formally-based design and implementation techniques for complex safety-critical embedded systems are required not only to handle the complexity, but also to provide correctness guarantees. Traditional design approaches struggle to cope with complexity, and they generally require extensive testing to guarantee correctness. As the designs get larger and more complex, traditional approaches face many limitations. An alternate design approach is to adopt a "correct-by-construction" paradigm and synthesize the desired hardware and software from the high-level descriptions expressed using one of the many formal modeling languages. Since these languages are equipped with formal semantics, formally-based tools can be employed for various analysis. In this dissertation, we adopt one such formal modeling language - MRICDF (Multi-Rate Instantaneous Channel-connected Data Flow). MRICDF is a graphical, declarative, polychronous modeling language, with a formalism that allows the modeler to easily describe multi-clocked systems without the necessity of global clock. Unnecessary synchronizations among concurrent computation entities can be avoided using a polychronous language such as MRICDF. We have explored a Boolean theory-based techniques for synthesizing multi-threaded/concurrent code and extended the technique to improve the performance of synthesized multi-threaded code. We also explored synthesizing ASIPs (Application Specific Instruction Set Processors) from MRICDF models. Further, we have developed formal techniques to identify constructive causality in polychronous models. We have also developed SMT (Satisfiablity Modulo Theory)-based techniques to identify dimensional inconsistencies and to perform value-range analysis of polychronous models.

The United States is the world's largest oil consumer demanding about twenty five percent of the total world oil production. Whenever there are difficulties to supply the increasing quantities of oil demanded by the market, the price of oil escalates leading to what is known as oil price spikes or oil price shocks. The last oil price shock which was the longest sustained oil price run up in history, began its course in year 2004, and ended in 2008. This last oil price shock initiated recognizable changes in transportation dynamics: transit operators realized that commuters switched to transit as a way to save gasoline costs, consumers began to search the market for more efficient vehicles leading car manufactures to close 'gas guzzlers' plants, and the government enacted a new law entitled the Energy Independence Act of 2007, which called for the progressive improvement of the fuel efficiency indicator of the light vehicle fleet up to 35 miles per gallon in year 2020. The past trend of gasoline consumption will probably change; so in the context of the problem a gasoline consumption model was developed in this thesis to ascertain how some of the changes will impact future gasoline demand. Gasoline demand was expressed in oil equivalent million barrels per day, in a two steps Ordinary Least Square (OLS) explanatory variable model. In the first step, vehicle miles traveled expressed in trillion vehicle miles was regressed on the independent variables: vehicles expressed in million vehicles, and price of oil expressed in dollars per barrel. In the second step, the fuel consumption in million barrels per day was regressed on vehicle miles traveled, and on the fuel efficiency indicator expressed in miles per gallon. The explanatory model was run in EVIEWS that allows checking for normality, heteroskedasticty, and serial correlation. Serial correlation was addressed by inclusion of autoregressive or moving average error correction terms. Multicollinearity was solved by first differencing. The 36 year sample series set (1970-2006) was divided into a 30 years sub-period for calibration and a 6 year "hold-out" sub-period for validation. The Root Mean Square Error or RMSE criterion was adopted to select the "best model" among other possible choices, although other criteria were also recorded. Three scenarios for the size of the light vehicle fleet in a forecasting period up to 2020 were created. These scenarios were equivalent to growth rates of 2.1, 1.28, and about 1 per cent per year. The last or more optimistic vehicle growth scenario, from the gasoline consumption perspective, appeared consistent with the theory of vehicle saturation. One scenario for the average miles per gallon indicator was created for each one of the size of fleet indicators by distributing the fleet every year assuming a 7 percent replacement rate. Three scenarios for the price of oil were also created: the first one used the average price of oil in the sample since 1970, the second was obtained by extending the price trend by exponential smoothing, and the third one used a longtime forecast supplied by the Energy Information Administration. The three scenarios created for the price of oil covered a range between a low of about 42 dollars per barrel to highs in the low 100's. The 1970-2006 gasoline consumption trend was extended to year 2020 by ARIMA Box-Jenkins time series analysis, leading to a gasoline consumption value of about 10 millions barrels per day in year 2020. This trend line was taken as the reference or baseline of gasoline consumption. The savings that resulted by application of the explanatory variable OLS model were measured against such a baseline of gasoline consumption. Even on the most pessimistic scenario the savings obtained by the progressive improvement of the fuel efficiency indicator seem enough to offset the increase in consumption that otherwise would have occurred by extension of the trend, leaving consumption at the 2006 levels or about 9 million barrels per day. The most optimistic scenario led to savings up to about 2 million barrels per day below the 2006 level or about 3 millions barrels per day below the baseline in 2020. The "expected" or average consumption in 2020 is about 8 million barrels per day, 2 million barrels below the baseline or 1 million below the 2006 consumption level. More savings are possible if technologies such as plug-in hybrids that have been already implemented in other countries take over soon, are efficiently promoted, or are given incentives or subsidies such as tax credits. The savings in gasoline consumption may in the future contribute to stabilize the price of oil as worldwide demand is tamed by oil saving policy changes implemented in the United States.