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Despite the intuitive appeal of using emerging technologies for disaster preparedness, there is a lack of comprehensive research exploring their applications. This study employs a nationwide 2023 and 2024 survey on technology use for disaster preparedness by older adults and people with disabilities. The survey assessed respondents' frequency of use, willingness to use, use comfort, perceived usefulness, and attitude toward using technologies. Overall, there were 1696 responses from 2023 and 2024 surveys, with 85 respondents completing the survey in both years. Using the TAM model, PATH findings indicate comfort significantly influenced perceived usefulness but not attitude. Attitude did not significantly influence behavioral intention to use, however, perceived usefulness did influence behavioral intention. 

While disaster preparedness is known as a force multiplier to reduce impacts for individuals, there is limited research or practice to measure the influence of preparedness activities. Most metrics focus on how many people received preparedness information or how many pamphlets were distributed. This study outlines a project to use virtual reality to measure preparedness among the public. Focused initially on older adults, the goal is to create a game that can be used on multiple populations, which will collect data about preparedness activities. The tool will be used in an experimental design to assist in further refinement. The mixed method experiment includes surveys, field study with the VR headsets, and observational data. Once complete the game will be useful for researchers or practitioners needed actionable data regarding the preparedness practices of individuals and households. 

This study gauges the preparedness levels of individuals (younger and older) across hazards and investigates their willingness to use emerging technology for disaster preparedness. Older adults are among the most vulnerable during disasters and more likely to be displaced. As climate change contributes to the increased frequency, intensity, and scale of disasters, the number of areas impacted by multiple hazards has also increased. In December 2023, a nationwide survey with over 1,000 respondents was launched. The results indicate a variation in the perception of preparedness across hazards, at the individual level. Additionally, most respondents would use emerging technology to help them improve their disaster preparedness, including smart speakers, phones, mobile appliances, cars, wearable devices, robots, and virtual reality devices. Findings indicate that older adults may be willing to use emerging technology that they are uncomfortable with for disaster preparedness, necessitating training, exercises, and qualitative research to understand how and why. 

We present an algorithm for skill discovery from expert demonstrations. The algorithm first utilizes Large Language Models (LLMs) to propose an initial segmentation of the trajectories. Following that, a hierarchical variational inference framework incorporates the LLM-generated segmentation information to discover reusable skills by merging trajectory segments. To further control the trade-off between compression and reusability, we introduce a novel auxiliary objective based on the Minimum Description Length principle that helps guide this skill discovery process. Our results demonstrate that agents equipped with our method are able to discover skills that help accelerate learning and outperform baseline skill learning approaches on new long-horizon tasks in BabyAI, a grid world navigation environment, as well as ALFRED, a household simulation environment. 

Antarctic sea ice modeling has become essential due to the exacerbating effects of climate change on the region, with the aim of utilizing present and past data to predict the future. However, a setback lies in the grand scale of the data needed to make these predictions best, spanning both spatial and temporal axes. As a result, dimension reduction is necessary to capture the most important patterns of variability – a pre-processing step for future predictions. The utilization of Machine Learning tools, such as autoencoders, has been investigated as an alternative to linear dimension reduction methods, such as EOFs. Input data includes satellite observed gridded data in the Antarctic region from 1979 to 2022. Different versions of the autoencoder model are investigated, with varying components in its architecture, including activation function (linear and ReLU), bottleneck units (compressed dimensions), and added layers. It is found that the seven-layered and five-layered ReLU models outperform other configurations across all bottleneck units, including when compared with EOFs. These models also contain a higher explained variance ratio: at 11 compressed dimensions, the seven-layered autoencoder can capture 18.7% more variance than the 11 EOF modes explain. The ReLU activation function also allows the model to detect nonlinear patterns, providing an additional benefit to the improved RMSE and variance ratio. The findings demonstrate that the autoencoder model serves as a worthy alternative to EOFs, likely extracting more predictable variance in the sea ice field. The result is crucial for understanding sea ice spatiotemporal variability and its predictability in the Antarctic. 

The motivation of students to actively engage in course activities has significant impact on the outcome of academic courses. Prior studies have shown that innovative instructional interventions and course delivery methods have a vital role in boosting the motivation of students. Gamification tools aid course delivery by utilizing well established game design principles to enhance skill development, routine practice and self-testing. In this article, we present a study on how the use of a course gamification platform dubbed OneUp impacts the motivation of students in an online cyber security course. The study shows that more than 90% of the respondents agreed that OneUp has improved the effectiveness of the course delivery. In addition, 75% of the respondents want to use OneUp in their future courses. Furthermore, our analysis shows that OneUp has improved the median grade of students from B+ to A- compared to the same course delivered the previous year without using OneUp.