Researchers are working on manipulating the digestive systems of wax worms to create a scalable way of disposing of plastic. Plastics that support modern life are inexpensive, strong, and versatile, but are difficult to dispose of and have a serious impact when released into the environment. Polyethylene, in particular, is the most widely produced plastic in the world, with more than 100 million tons distributed annually. Since it can take decades to decompose--and along the way can harm wildlife and degrade into harmful microplastics --its disposal is an urgent issue for mankind. In 2017, European researchers discovered a potential solution.

In George Lucas's classic 1980 film'The Empire Strikes Back', hero Han Solo (Harrison Ford) is frozen in carbonite by the evil Darth Vader. The fictional metal hardened around the heroic space smuggler as it cooled – sealing him in a state of'perfect hibernation'. Carbonite is of course a fictional material, consigned to the realms of the Star Wars galaxy far, far away. But according to one scientist, this scene is not completely the stuff of science-fiction. Dr Alex Baker, a chemist at the University of Warwick, thinks humans could potentially be frozen like Solo with a real-life equivalent.

Creyon Bio, 3210 Merryfield Row San Diego, CA 92121 Encoding the electronic structure of molecules using 2-electron reduced density matrices (2RDMs) as opposed to many-body wave functions has been a decades-long quest as the 2RDM contains sufficient information to compute the exact molecular energy but requires only polynomial storage. We focus on linear polymers with varying conformations and numbers of monomers and show that we can use machine learning to predict both the 1-electron and the 2-electron reduced density matrices. Moreover, by applying the Hamiltonian operator to the predicted reduced density matrices we show that we can recover the molecular energy. Thus, we demonstrate the feasibility of a machine learning approach to predicting electronic structure that is generalizable both to new conformations as well as new molecules. At the same time our work circumvents the N-representability problem that has stymied the adaption of 2RDM methods, by directly machine-learning valid Reduced Density Matrices. Specifically, we show that all desired 1-and 2-theory (DFT) and coupled-clusters methods, are electron correlations can be predicted at any level of theory key to ab initio understanding of molecular properties. However, these methods are slow. Specifically, currently considered to be the gold standard of quantum the sequence of n-electron reduced density matrices (n-chemistry it still involves major approximations which RDMs) forms a hierarchy of complexity that encodes preclude it from describing strongly correlated systems correlations between more and more electrons as n increase. Nevertheless, For example the 2RDM, which is obtained by making use of the fact that quantum correlations are essentially tracing the full electronic reduced density matrix over local, i.e. the quantum nearsightedness principle all electron coordinates but 2, encodes correlations between [2, 3], the latest generation of quantum chemistry 2 electrons.

Target detection in hyperspectral imagery is the process of locating pixels from an image which are likely to contain target, typically done by comparing one or more spectra for the desired target material to each pixel in the image. Target identification is the process of target detection incorporating an additional process to identify more specifically the material that is present in each pixel that scored high in detection. Detection is generally a 2-class problem of target vs. background, and identification is a many class problem including target, background, and additional know materials. The identification process we present is probabilistic and hierarchical which provides transparency to the process and produces trustworthy output. In this paper we show that target identification has a much lower false alarm rate than detection alone, and provide a detailed explanation of a robust identification method using probabilistic hierarchical classification that handles the vague categories of materials that depend on users which are different than the specific physical categories of chemical constituents. Identification is often done by comparing mixtures of materials including the target spectra to mixtures of materials that do not include the target spectra, possibly with other steps. (band combinations, feature checking, background removal, etc.) Standard linear regression does not handle these problems well because the number of regressors (identification spectra) is greater than the number of feature variables (bands), and there are multiple correlated spectra. Our proposed method handles these challenges efficiently and provides additional important practical information in the form of hierarchical probabilities computed from Bayesian model averaging.

No matter how conscientious the consumer, by the time the material gets to the end of the conveyor belt at the recycling plant, most plastics end up mixed together. Due to the rather rudimentary sorting techniques in use, only a small percentage of the plastic we try to recycle ends up getting recycled. "The ordinary consumer, with the best intentions--and also the correct procedure--puts everything in the plastic bin. We get it all," said Mogens Hinge, an associate professor in the department of biological and chemical engineering and process and materials engineering at Denmark's Aarhus University, and co-author of the paper "Plastic classification via in-line hyperspectral camera analysis and unsupervised machine learning," which appeared in Vibrational Spectroscopy this year. "Now we have a problem: we can wash it, but we can't unmix it. And plastic is not just plastic."

AMP Robotics has extended its partnership with Evergreen, a producer of food-grade recycled polyethylene terephthalate (rPET). Evergreen now has 15 of AMP's robotic sorting systems installed or planned across three facilities. In addition to six robots in Clyde, Evergreen has added six in Riverside, California, and will soon add three in Albany, New York. AMP's technology identifies and sorts green and clear PET from post-consumer bales of plastic soft drink bottles at speeds up to three times faster and at a higher accuracy than manual sorters can achieve. Evergreen then recycles the material into reusable flakes or pellets, which it sells to end markets as feedstock for new containers and packaging.

Survivors of central nervous system injury commonly present with spastic hypertonia. The affected muscles are hyperexcitable and can display involuntary static muscle tone and an exaggerated stretch reflex. These symptoms affect posture and disrupt activities of daily living. Symptoms are typically measured using subjective manual tests such as the Modified Ashworth Scale; however, more quantitative measures are necessary to evaluate potential treatments. The hands are one of the most common targets for intervention, but few investigators attempt to quantify symptoms of spastic hypertonia affecting the fingers. We present the isometric force pillow (IFP) to quantify involuntary grip force. This lightweight, computerized tool provides a holistic measure of finger flexion force and can be used in various orientations for clinical testing and to measure the impact of assistive devices.

This research spans academia, militaries (though it can be difficult to suss out the actual breakthroughs from government propaganda), and private enterprise. Perhaps the most well known privately-owned robotics developer is Boston Dynamics, makers of the Atlas. You may remember this bipedal robot from September when it showed off its uncanny parkour abilities, which the robot can pull off 80 percent of the time. The Atlas is able to move so fluidly thanks to a novel optimization algorithm that breaks down complex movements into smaller reference motions for its arms, torso, and legs. However, while Boston Dynamics' Big Dog was developed as a quadrupedal cargo carrier for military operations, the Atlas is strictly for use as an emergency first responder.