The need for improvement of societal disaster resilience and response efforts was evident after the destruction caused by the 2017 Atlantic hurricane season. We present a novel conceptual framework for improving disaster resilience through the combination of serious games, geographic information systems (GIS), spatial thinking, and disaster resilience. Our framework is implemented via Project Lily Pad, a serious geogame based on our conceptual framework, serious game case studies, interviews and real-life experiences from 2017 Hurricane Harvey survivors in Dickinson, TX, and an immersive hurricane-induced flooding scenario. The game teaches a four-fold set of skills relevant to spatial thinking and disaster resilience, including reading a map, navigating an environment, coding verbal instructions, and determining best practices in a disaster situation. Results of evaluation of the four skills via Project Lily Pad through a “think aloud” study conducted by both emergency management novices and professionals revealed that the game encouraged players to think spatially, can help build awareness for disaster response scenarios, and has potential for real-life use by emergency management professionals. It can be concluded from our results that the combination of serious games, geographic information systems (GIS), spatial thinking, and disaster resilience, as implemented via Project Lily Pad and our evaluation results, demonstrated the wide range of possibilities for using serious geogames to improve disaster resilience spatial thinking and potentially save lives when disasters occur.
A significant amount of research on the intersection of sentiment analysis and social media platforms has been published in the past few years. While previous studies have focused on methods to identify the polarity of online posts, little has been done in terms of using the impact of such posts to enhance the discovery and description of trends in real time. Here, we present a tool for the retrieval and analysis of microblogging posts in real time. We have gathered a large sample of tweets related to the 2017 UK General Election. We introduce a novel classification of the polarity of sentiments, considering the correlation between words, events and sentiments.
While successful retrieval typically strengthens memories, errors made during retrieval attempts can become encoded and bias subsequent remembering. It is not clear however whether such updating occurs during recognition of faces based on their visual properties. We investigated recognition-induced updating of face memories across three experiments, by comparing the effects of active recognition attempts against two control tasks that also exposed participants to erroneous face information while they were not trying to remember. Importantly, we used computer generated facial images drawn from locations in a multidimensional “face space” to match the degree of error that was introduced by the different tasks, enabling us to isolate the role of active retrieval processes in face memory updating. Participants first learned a series of target faces. Next, target faces were shown mixed with similar distractor faces and participants either actively tried to recognize the targets, or tried to encode one of the faces, or selected the face they thought was most distinctive. We then tested participants’ recognition memory for targets in a surprise final test, and measured to what extent their recognition errors on the final test were biased by their responses on the prior task. Across the three experiments, final recognition bias was significantly enhanced after active recognition attempts and was larger following recognition attempts compared to either control task. The findings extend on prior demonstrations that retrieval-induced updating occurs for semantically rich, complex memories by showing that engagement of active retrieval processes during visually-based face recognition can also induce updating.
An electron image projector has been designed and built which is currently exposing 4 in. wafers and has the capability of going to 5 in. Linewidth control of ±0.04 μm for 1 μm lines has been measured over the whole surface of 4 in. wafers. Machine alignment accuracy of ±0.03 μm has been achieved and nonrepeatable image distortion is shown to be less than 0.1 μm. Examples of resolution capability and step coverage are also shown.
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