Automated estimation of offshore polymetallic nodule abundance based on seafloor imagery using deep learning.

The burgeoning demand for critical metals used in high-tech and green technology industries has turned attention toward the vast resources of polymetallic nodules on the ocean floor. Traditional methods for estimating the abundance of these nodules, such as direct sampling or acoustic imagery are time and labour-intensive or often insufficient for large-scale or accurate assessment. This paper advocates for the automatization of polymetallic nodules detection and abundance estimation using deep learning algorithms applied to seabed photographs.

Seabed Modelling by Means of Airborne Laser Bathymetry Data and Imbalanced Learning for Offshore Mapping.

An important problem associated with the aerial mapping of the seabed is the precise classification of point clouds characterizing the water surface, bottom, and bottom objects. This study aimed to improve the accuracy of classification by addressing the asymmetric amount of data representing these three groups. A total of 53 Synthetic Minority Oversampling Technique (SMOTE) algorithms were adjusted and evaluated to balance the amount of data.

Ionic liquid pretreatment of stinging nettle stems and giant miscanthus for bioethanol production.

Production of ethanol from lignocellulosic biomass is considered the most promising proposition for developing a sustainable and carbon-neutral energy system. The use of renewable raw materials and variability of lignocellulosic feedstock generating hexose and pentose sugars also brings advantages of the most abundant, sustainable and non-food competitive biomass. Great attention is now paid to agricultural wastes and overgrowing plants as an alternative to fast-growing energetic crops.