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Incidental capture, or bycatch, of marine species is a global conservation concern. Interactions with fishing gear can cause mortality in air-breathing marine megafauna, including sea turtles. Despite this, interactions between sea turtles and fishing gear—from a behavior standpoint—are not sufficiently documented or described in the literature. Understanding sea turtle behavior in relation to fishing gear is key to discovering how they become entangled or entrapped in gear. This information can also be used to reduce fisheries interactions. However, recording and analyzing these behaviors is difficult and time intensive. In this study, we present a machine learning-based sea turtle behavior recognition scheme. The proposed method utilizes visual object tracking and orientation estimation tasks to extract important features that are used for recognizing behaviors of interest with green turtles ( Chelonia mydas ) as the study subject. Then, these features are combined in a color-coded feature image that represents the turtle behaviors occurring in a limited time frame. These spatiotemporal feature images are used along a deep convolutional neural network model to recognize the desired behaviors, specifically evasive behaviors which we have labeled “reversal” and “U-turn.” Experimental results show that the proposed method achieves an average F1 score of 85% in recognizing the target behavior patterns. This method is intended to be a tool for discovering why sea turtles become entangled in gillnet fishing gear. 

Ammonium nitrate and nitromethane are two of the most prevalent ingredients in improvised explosive devices (IED). Developing a detection system for IEDs in open public events where no specific check points are available requires many large scale, fine-grained simulations to estimate the explosive vapors. However, such large scale molecular simulations at the required granularity is very time consuming and in most cases not feasible. In this paper, we propose region-specific meshing to alleviate the computational cost. The proposed simulation methodology provides accurate results as compared with a baseline simulation of fine grained mesh (in a small area) while providing significant reduction in simulation time. Thus, large scale simulations at feasible computational burden can be achieved.