Nebula Public Library

‘STARCOP’ is an initiative to use machine learning and multiple satellites with diverse detection capabilities to quickly detect methane leaks and send notifications in near real-time. In this project Trillium Technologies developed machine learning models to automatically detect methane in hyperspectral and multispectral imagery. Their hyperspectral model (HyperSTARCOP) is able to capture more than 90% of plumes in test data while reducing the false positive rate by 39% when compared to state-of-the-art models.

In the ocean, transport and mixing processes tend to disperse matter in suspension over wide spatial (~100 km) and long temporal (~month) scales. Fronts appear at the boundary between water masses with different properties and are caused by diverse oceanic features and processes, including bottom topography. Frontal structures include tidal mixing fronts, shelf-break fronts, upwelling fronts, estuarine fronts, plume fronts, fronts generated by convergence or divergence of water masses, and frontal eddies (Acha et al. 2015; Largier 1993).

In order to enable lunar exploration in a sustainable manner, it is necessary to develop technologies that allow the use of lunar resources in-situ. Additive manufacturing techniques are being studied for in-situ resources utilization on the Moon (e.g.: solar or laser sintering). The results of these studies, presented samples with low mechanical properties due to the formation of defects and cracks within the final product.

In most in vitro cell models, cultures are made adhering to flat culture slides and the expected activation of specific cell markers is measured on cells. However, in this simplified condition, cell response is not representative of the in vivo response, which is based on cell interactions that occur in three-dimensional (3D) non-flat environments and between several cell populations.