Current fire models are designed to model the spread of a linear fire front in dead, small-diameter fuels. Fires in predominantly living vegetation account for a large proportion of annual burned area in the United States. Prescribed burning is used to manage living fuels; however, prescribed burning is currently conducted under conditions that result in marginal burning. We do not understand quantitatively the relative importance of the fuel and environmental variables that determine spread in live vegetation. To address these weaknesses, laboratory fires have been burned to determine the effects of wind, slope, moisture content and fuel characteristics on fire spread in fuel beds of common chaparral species. Four species (Adenostoma fasciculatum, Ceanothus crassifolius, Quercus berberidifolia, Arctostaphylos parryana), two wind velocities (0 and 2 m s −1 ) and two fuel bed depths (20 and 40 cm) were used. Oven-dry moisture content of fine fuels (<0.63 cm diameter) ranged from 0.09 to 1.06. Seventy of 125 fires successfully propagated the length (2.0 m) of the elevated fuel bed. A logistic model to predict the probability of successful fire spread was developed using stepwise logistic regression. The variables selected to predict propagation were wind velocity, slope percent, moisture content, fuel loading, species and air temperature. Air temperature and species terms were removed from the model for parsimony. The final model correctly classified 94% of the observations. Comparison of results with an empirical decision matrix for prescribed burning in chaparral suggested some agreement between the laboratory data and the empirical tool.

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This paper describes a design and implementation of a NSF sponsored project in 2015. This study will test the hypothesis that the use of cognitive frameworks in second language acquisition for the development of a blended learning of programming languages can improve engagement and the learning experience of engineering students. Using this approach will place greater emphasis on problem solving techniques that can be utilized in all courses. The online module consists of a series of short videos (10-20 minutes), online quizzes with tiered questions, and topic specified discussion board led by student researchers. Students' demographic data, course-related behaviors such as usage of the instructional videos and discussion board, student performance such as quizzes and exams, and attitude toward the class will be compared across students in the experimental groups, and control groups to determine if student performance, behavior and attitudes vary across classrooms employing different teaching strategies.

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