Thinking Outside of the Cereal Box: Breeding Underutilized (Pseudo)Cereals for Improved Human Nutrition.

Cereal grains have historically played a critical role in sustaining the caloric needs of the human population. The major cereal crops, wheat, rice, and maize, are widely cultivated and have been subjected to biofortification to enhance the vitamin and mineral nutrient content of grains. In contrast, grains of several other cereals as well as non-grass pseudocereals are naturally rich in micronutrients, but have yet to be explored for broad-scale cultivation and consumption. This mini review focuses on the micronutrient and phytochemical profiles of a few emerging (pseudo)cereals and examines the current constraints of their integration into the global food system. Prospects of leveraging whole genome sequence information and modern breeding technologies to promote the breeding and accessibility of these crops are also discussed

Advanced rocket engine cryogenic turbopump bearing thermal model

A lumped node thermal model was developed representing the Space Shuttle Main Engine (SSME) liquid oxygen (LOX) turbopump turbine end bearings operating in a cryogenically cooled bearing tester. Bearing elements, shaft, carrier, housing, cryogen flow characteristics, friction heat, and fluid viscous energy are included in the model. Heat transfer characteristics for the regimes of forced convection boiling are modeled for liquid oxygen (LOX) and liquid nitrogen (LN2). Large temperature differences between the cryogenic fluid and baring contact surfaces require detailed nodal representation in these areas. Internal loads and friction heat are affected by temperature dependent operating clearances requiring iterations between bearing thermal and mechanical models. Analyses indicate a thermal-mechanical coupling resulting in reduced operating clearances, increased loading and heating which can contribute to premature bearing failure. Contact surfaces operate at temperatures above local saturation resulting in vapor rather than liquid in the contacts, precluding possible liquid film lubrication. Elevated temperatures can reduce lubrication, increase friction, and reduce surface hardness supporting a surface failure mode rather than subsurface fatigue

Correlated analyses of D- and 15N-rich carbon grains from CR2 chondrite EET 92042

Extract from introduction: Insoluble organic matter (IOM) and matrix from primitive carbonaceous chondrites carry isotope enrichments (?D?20000', ?15N?3200�) that are comparable to those in interplanetary dust particles [1, this work]. Hence, primitive organics that formed in the protosolar cloud (PSC) – or maybe in the cold outer regions of the protoplanetary disk – survived accretion and planetary processing on the asteroids, the parent bodies of the chondrites

Hybrid LSTM and Encoder-Decoder Architecture for Detection of Image Forgeries

With advanced image journaling tools, one can easily alter the semantic meaning of an image by exploiting certain manipulation techniques such as copy-clone, object splicing, and removal, which mislead the viewers. In contrast, the identification of these manipulations becomes a very challenging task as manipulated regions are not visually apparent. This paper proposes a high-confidence manipulation localization architecture which utilizes resampling features, Long-Short Term Memory (LSTM) cells, and encoder-decoder network to segment out manipulated regions from non-manipulated ones. Resampling features are used to capture artifacts like JPEG quality loss, upsampling, downsampling, rotation, and shearing. The proposed network exploits larger receptive fields (spatial maps) and frequency domain correlation to analyze the discriminative characteristics between manipulated and non-manipulated regions by incorporating encoder and LSTM network. Finally, decoder network learns the mapping from low-resolution feature maps to pixel-wise predictions for image tamper localization. With predicted mask provided by final layer (softmax) of the proposed architecture, end-to-end training is performed to learn the network parameters through back-propagation using ground-truth masks. Furthermore, a large image splicing dataset is introduced to guide the training process. The proposed method is capable of localizing image manipulations at pixel level with high precision, which is demonstrated through rigorous experimentation on three diverse datasets