Geology: North America's broken heart
On a bright October Saturday, the trees have reached their full autumn blaze at Interstate State Park on the border between Minnesota and Wisconsin in the US heartland. Crowds of people gawking at leaves thread their way along paths that wind through bulwarks of dark basalt, leading to views of the St Croix River. Along one of the walkways, a photographer directs a young couple in coordinating grey shirts to lean against the rocks as she snaps a romantic portrait. If the two sweethearts are looking to commemorate their ever lasting love, they should have picked a different backdrop. The fractured basalt that frames their faces is part of a great gash that opened up in the middle of North America and nearly split the continent 1.1 billion years ago — hardly a symbol of a happy union. The volcanic rocks are remnants of what is called the Midcontinent Rift, and it is an enormous geological puzzle. Rifts are wounds in Earth’s outer layer that can grow to eventually form new oceans. That is how the A billion years ago, a huge rift nearly cleaved North America down the middle. And then it failed. Researchers may be getting close to finding out why.
Nitrification during extended co-composting of extreme mixtures of green waste and solid fraction of cattle slurry to obtain growing media.
Next generation of waste management systems should apply product-oriented bioconversion processes that produce composts or biofertilisers of desired quality that can be sold in high priced markets such as horticulture. Natural acidification linked to nitrification can be promoted during composting. If nitrification is enhanced, suitable compost in terms of pH can be obtained for use in horticultural substrates. Green waste compost (GW) represents a potential suitable product for use in growing medium mixtures. However its low N provides very limited slow-release nitrogen fertilization for suitable plant growth; and GW should be composted with a complementary N-rich raw material such as the solid fraction of cattle slurry (SFCS). Therefore, it is important to determine how very different or extreme proportions of the two materials in the mixture can limit or otherwise affect the nitrification process. The objectives of this work were two-fold: (a) To assess the changes in chemical and physicochemical parameters during the prolonged composting of extreme mixtures of green waste (GW) and separated cattle slurry (SFCS) and the feasibility of using the composts as growing media. (b) To check for nitrification during composting in two different extreme mixtures of GW and SFCS and to describe the conditions under which this process can be maintained and its consequences. The physical and physicochemical properties of both composts obtained indicated that they were appropriate for use as ingredients in horticultural substrates. The nitrification process occurred in both mixtures in the medium-late thermophilic stage of the composting process. In particular, its feasibility has been demonstrated in the mixtures with a low N content. Nitrification led to the inversion of each mixture's initial pH.
Research on Fracture Recognition in Well Logging Images: Adversarial Learning with Attention
Semantic recognition of fractures in well logging images is vital for engineers to implement oil and gas exploration. An essential approach to accomplishing the object is to adopt deep learning based on convolutional neural networks. However, due to the lack of annotated labels in well logging images, it is scarcely available to directly train the semantic segmentation network. In this paper, we explore a domain shift model attempting to achieve domain adaptation from one annotated dataset to our target well logging images. This model's core is to utilize adversarial learning, including generator and discriminator, which can prompt the model to generate fracture segmentation similar to the source domain in the target domain. For enhancing the domain adaptive model further, an attention module is introduced, which can suppress redundant noise in semantic segmentation results. We demonstrate that our proposed model performs well by extensive tests and ablation experiments in the ultrasonic well logging images.