In the United States, the word "earthquake" is extensively used. This natural disaster has a year-round impact on numerous states across the country. Earthquakes are simply more than a natural calamity; they also have a negative psychological impact. Earthquake safety measures are essential for ensuring citizens' safety. This paper proposes, a technique for evaluating earthquake safety activities and instructing individuals in selecting appropriate precautions. Earthquake protection using Reach.love plus Amazon Alexa is special in that it uses cutting-edge virtual reality technology. The platform developed by Reach.love takes earthquake prevention to a new and innovative direction. The feeling of presence in a VR headset linked within Reach.love, allows the user to feel that an earthquake is occurring right now. Additionally, each location includes audio instructions that explain what to do in specific scenarios. The user can practice and mentally train to respond appropriately when a real earthquake happens, comparable to a 3D drill. Finally, the user will be able to utilize Amazon Alexa for help within the rooms in Reach.love to improve the experience of earthquake safety training. For example, if the user speaks to Alexa during the simulation and says, "Alexa, turn off the audio instructions," Alexa will do so, and the user will no longer hear them. Alexa would be the user's personal assistant during the training of earthquake protection.

Enantiomers are pairs of non-superimposable mirror-image molecules. One molecule in the pair is the clockwise version (+) while the other is the counterclockwise version (-). Some pairs have divergent odor qualities, e.g. L-carvone (“spearmint”) vs. D-carvone (“caraway”), while other pairs do not. Existing theory about the origin of such differences is largely qualitative (Friedman and Miller, 1971; Bentley, 2006; Brookes et al., 2008). While quantitative models based on intrinsic molecular features predict some structure–odor relationships (Keller et al., 2017), they cannot identify, e.g. the more intense enantiomer in a pair; the mathematical operations underlying such features are invariant under symmetry (Shadmany et al., 2018). Only the olfactory receptor (OR) can break this symmetry because each molecule within an enantiomeric pair will have a different binding configuration with a receptor. However, features that predict odor divergence within a pair may be identifiable; for example, six-membered ring flexibility has been offered as a candidate (Brookes et al., 2008). To address this problem, we collected detection threshold data for >400 molecules (organized into enantiomeric pairs) from a variety of public data sources and academic literature. From each pair, we computed the within-pair divergence in odor detection threshold, as well as Mordred descriptors (molecular features derived from the structure of a molecule) and Morgan fingerprints (mathematical representations of molecule structure). While these molecular features are identical within-pair (due to symmetry), they remain distinct across pairs. The resulting structure+perception dataset was used to build a predictive model of odor detection threshold divergence. It predicted a modest fraction of variance in odor detection threshold divergence (r 2 ~ 0.3 in cross-validation). We speculate that most of the remaining variance could be explained by a better understanding of the ligand-receptor binding process.