Here we present a detailed ab initio study of two experimentally synthesized bismuth niobate BiNbO ₄ (BNO) polymorphs within the framework of density functional theory (DFT).

Existing navigation applications such as Google Maps, Apple maps provide a core service to users to find the shortest path or the path that takes the least amount of time towards a user’s destination. The applications and other research efforts have been sought to include other features such as 3D maps, neighboring facilities, traffic information, and multi-modal alternate route suggestion based on user constraints. None of these, however, take into account an important factor: "user safety while commuting". Although the common perception of street crime is that it is primarily a problem in the third-world countries, current popular hashtags or topics (e.g. “blacklifematters”) indicate that it is now prevalent in other parts of the world as well. Even existing multi-modal alternative route recommender systems are incapable of adapting to a dynamic set of safety features, are unable to provide new safe path updates in response to real-time commuter responses, and take little or no account of historical on-road events when designing the safe algorithm. In light of the above background, we begin by developing a generic framework, namely On-road Risk Minimization Problem (ORMP).We then introduce a dynamic population-based algorithm, that we call Safe Path for Everyone (SPaFE), that solves ORMP using multi-modal historical and crowdsourced data. Finally, our extensive empirical results demonstrate that SPaFE markedly outperforms the state-of-the-art.

Navigation and traffic services such as Google Maps, Bing Maps, and Apple Maps have become increasingly popular for their ability to calculate the shortest path, provide real-time traffic updates, recommend nearby points of interest, and suggest multi-modal route options based on user constraints. However, while these services offer convenience and efficiency, they may not always prioritize user safety. In response to this concern, recent research have begun to address safety issues in navigation and traffic services. To the best of our knowledge, none of these are capable of adapting to dynamic, conflicting safety features and real-time user feedback. A recent algorithm called SPaFE has been introduced to incorporate crowd-sourced and historical data, but it does not prioritize the most recent feedback or consider updated crime reports. It also does not account for distance and performs poorly in areas with insignificant or zero feedback. In light of the preceding, we introduce CrowdSPaFE, a population-based algorithm that adapts to dynamic crime reports, the most recent feedback, navigation in locations with negligible feedback, and a tradeoff between distance and safety considerations. Lastly, our empirical results demonstrate that the CrowdSPaFE algorithm outperforms the state-of-the-art.