
The mobile application for the Community-Focused Microclimate-Informed Indoor Heat Emergency Alert (CommHEAT) system forecasts indoor temperatures and heat indices for the next seven days for users and their “community” friends. The primary challenge in the mobile application development was creating a system that connected external weather data sources, an indoor temperature simulation (EnergyPlus) that requires High Performance Computing (HPC), and a web-based database. Python scripts were used to retrieve input data from external weather websites (e.g., Mesonet and the National Weather Service) and empirical data for dwelling “archetypes” from spreadsheets, run simulations, and export the results to a MySQL database created for the system. The mobile application was created with Unity and forecast information was extracted from the database using C# and web-based PHP scripts for security. Interdisciplinary rhetoric challenges and mid-development requests for additional application features caused numerous modifications and replacements of methods to create the required mobile application features. Despite these challenges, the development team successfully connected each component of the CommHEAT system and delivered a fully functional application that was deployed to iOS and Android mobile phones for a user study conducted in the summer of 2025.

In this paper, we introduce FastPoints, a state-of-the-art point cloud renderer for the Unity game development platform. Our program supports standard unprocessed point cloud formats with non-programmatic, drag-and-drop support, and creates an out-of-core data structure for large clouds without requiring an explicit preprocessing step; instead, the software renders a decimated point cloud immediately and constructs a shallow octree online, during which time the Unity editor remains fully interactive.

Eye tracking is used by psychologists, neurologists, vision researchers, and many others to understand the nuances of the human visual system, and to provide insight into a person’s allocation of attention across the visual environment. When tracking the gaze behavior of an observer immersed in a virtual environment displayed on a head-mounted display, estimated gaze direction is encoded as a three-dimensional vector extending from the estimated location of the eyes into the 3D virtual environment. Additional computation is required to detect the target object at which gaze was directed. These methods must be robust to calibration error or eye tracker noise, which may cause the gaze vector to miss the target object and hit an incorrect object at a different distance. Thus, the straightforward solution involving a single vector-to-object collision could be inaccurate in indicating object gaze. More involved metrics that rely upon an estimation of the angular distance from the ray to the center of the object must account for an object’s angular size based on distance, or irregularly shaped edges - information that is not made readily available by popular game engines (e.g. Unity© /Unreal© ) or rendering pipelines (OpenGL). The approach presented here avoids this limitation by projecting many rays distributed across an angular space that is centered upon the estimated gaze direction.