No abstract

Microbial eukaryotes can play prominent roles in the Arctic marine ecosystem, but their diversity and variability is not well known in the ice-covered ecosystems. We determined the community composition of microbial eukaryotes in an Arctic under-ice spring bloom north of Svalbard using metabarcoding of DNA and RNA from the hypervariable V4 region of 18S nrDNA. At the two stations studied, the photosynthetic biomass was dominated by protists >3 μm and was concentrated in the upper 70–80 m, above the thermocline and halocline. Hierarchical cluster analyses as well as ordination analyses showed a distinct clustering of the microbial eukaryote communities according to a combination of water mass and local environmental characteristics. While samples collected in the surface mixed layer differed distinctly between the two sites, the deeper communities collected in Atlantic Water were fairly similar despite being geographically distant. The differentiation of the microbial eukaryote communities of the upper mixed water was probably driven by local development and advection, while the lack of such differentiation in the communities of Atlantic Water reflects the homogenizing effect of water currents on microbial communities.

This research aims to enhance the compressive strength prediction and optimization of one-part geopolymer concrete through the utilization of machine learning methods, particularly the Artificial Neural Network (ANN) model. Existing data from previous research and literature regarding precursors and solid activators in geopolymer concrete will be gathered and examined to identify key factors influencing compressive strength and optimize mixture proportions for achieving maximum strength.