Where are tiny, nearly invisible particles called neutrinos coming from? Answering this question is easier said than done, but a new algorithm aims to answer this question. A University of Hawaiʻi at Mānoa student-led team has developed a new algorithm to help scientists determine direction in complex 2D data. The team found a formula that lets them match patterns in data and accurately pinpoint the direction of the source. The algorithm uses a mathematical tool called the Frobenius norm to measure differences between grids of numbers, effectively acting as a “distance formula” for large data tables. By rotating a reference dataset and comparing it to measured data, the algorithm identifies the rotation that produces the smallest difference, revealing the most likely direction of the signal. Simulations show the method works especially well with high-resolution data and large datasets. The project began with simulated neutrino data to locate nuclear reactors, and further studies are underway. Here is how this can help: · Reveal information about nuclear reactors, the sun, and faraway cosmic events. · A clear mathematical foundation for extracting direction. · Scale with technological improvements. Looking to the future, this formula could be applied in many fields such as astronomy, medical imaging, weather mapping, and more. It is ideal for systems that rely on pattern recognition. Certainly, it will be something to keep an eye on in the…

Energetic materials have been produced using manufacturing methods such as casting and milling, which emphasize efficiency and scalability. Although these approaches are well suited for large-scale batch production, they offer limited flexibility for customization—restricting innovation and potentially preventing performance optimization. This is where new additive manufacturing and 3D printing research enters the equation. Purdue University engineer Monique McClain is developing new methods to control materials’ behaviors throughout the manufacturing process. Professor McClain specializes in the early manufacturing stages such as selecting binders with unique properties to hold energetic particles together and determine how they are mixed. As an example, a study from Professor McClain looked at adhesion between two polymers with different mechanical properties—think a stiff thermoplastic and a soft elastomer—that have been combined into one structure. Here is how this can help in manufacturing: Enable the two materials to blend and hold together. Give more options for controlling behavior. Improve safety. Looking to the future, additive manufacturing will give researchers the freedom to experiment with complex geometries and tune specific properties such as burn rate and blast shape. This is simply one example of research being done in the…