Evolution of novel activation functions in neural network training for astronomy data: habitability classification of exoplanets

Screening the Survival of Cyanobacteria Under Perchlorate Stress. Potential Implications for Mars In Situ Resource Utilization

Cyanobacteria are good candidates for various martian applications as a potential source of food, fertilizer, oxygen, and biofuels. However, the increased levels of highly toxic perchlorates may be a significant obstacle to their growth on Mars. Therefore, in the present study, 17 cyanobacteria strains that belong to Chroococcales, Chroococcidiopsidales, Nostocales, Oscillatoriales, Pleurocapsales, and Synechococcales were exposed to 0.25-1.0% magnesium perchlorate concentrations (1.5-6.0 mM ClO₄^- ions) for 14 days. The exposure to perchlorate induced at least partial inhibition of growth in all tested strains, although five of them were able to grow at the highest perchlorate concentration: Chroococcidiopsis thermalis, Leptolyngbya foveolarum, Arthronema africanum, Geitlerinema cf. acuminatum, and Cephalothrix komarekiana. Chroococcidiopsis sp. Chroococcidiopsis cubana demonstrated growth up to 0.5%. Strains that maintained growth displayed significantly increased malondialdehyde content, indicating perchlorate-induced oxidative stress, whereas the chlorophyll a/carotenoids ratio tended to be decreased. The results show that selected cyanobacteria from different orders can tolerate perchlorate concentrations typical for the martian regolith, indicating that they may be useful in Mars exploration. Further studies are required to elucidate the biochemical and molecular basis for the perchlorate tolerance in selected cyanobacteria.

Weed Detection in Rice Fields Using Remote Sensing Technique: A Review

This paper reviewed the weed problems in agriculture and how remote sensing techniques can detect weeds in rice fields. The comparison of weed detection between traditional practices and automated detection using remote sensing platforms is discussed. The ideal stage for controlling weeds in rice fields was highlighted, and the types of weeds usually found in paddy fields were listed. This paper will discuss weed detection using remote sensing techniques, and algorithms commonly used to differentiate them from crops are deliberated. However, weed detection in rice fields using remote sensing platforms is still in its early stages; weed detection in other crops is also discussed. Results show that machine learning (ML) and deep learning (DL) remote sensing techniques have successfully produced a high accuracy map for detecting weeds in crops using RS platforms. Therefore, this technology positively impacts weed management in many aspects, especially in terms of the economic perspective. The implementation of this technology into agricultural development could be extended further.