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  1. Free, publicly-accessible full text available March 1, 2024
  2. The growing disparity between food supply and demand requires innovative Digital Agriculture (DA) systems to increase farm sustainability and profitability. However, current systems suffer from problems of complexity. To increase farm efficiency and understand the tradeoffs of these new DA innovations we developed ROAM, which is a decision support system developed to find a Pareto optimal architectural design to build DA systems. Based on data from five live deployments at Cornell University, each DA design can be analyzed based on user defined metrics and evaluated under uncertain farming environments with ROAM. Paired with this, we develop a web interface that allows users to define personalized decision spaces and to visualize decision tradeoffs. To help validate ROAM, it was deployed to a commercial farm where the user was recommended a method to increase farm efficiency. ROAM allows users to quickly make key decisions in designing their DA systems to increase farm profitability. 
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  3. Abstract

    Imaging through diffusers presents a challenging problem with various digital image reconstruction solutions demonstrated to date using computers. Here, we present a computer-free, all-optical image reconstruction method to see through random diffusers at the speed of light. Using deep learning, a set of transmissive diffractive surfaces are trained to all-optically reconstruct images of arbitrary objects that are completely covered by unknown, random phase diffusers. After the training stage, which is a one-time effort, the resulting diffractive surfaces are fabricated and form a passive optical network that is physically positioned between the unknown object and the image plane to all-optically reconstruct the object pattern through an unknown, new phase diffuser. We experimentally demonstrated this concept using coherent THz illumination and all-optically reconstructed objects distorted by unknown, random diffusers, never used during training. Unlike digital methods, all-optical diffractive reconstructions do not require power except for the illumination light. This diffractive solution to see through diffusers can be extended to other wavelengths, and might fuel various applications in biomedical imaging, astronomy, atmospheric sciences, oceanography, security, robotics, autonomous vehicles, among many others.

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  7. As lithium-ion batteries (LIBs) become vital energy source for daily life and industry applications, a large volume of spent LIBs will be produced after their lifespan. Recycling of LIBs has been considered as an effective closed-loop solution to mitigate both environmental and economic issues associated with spent LIBs. While reclaiming of transition metal elements from LIB cathodes has been well established, recycling of graphite anodes has been overlooked. Here, we show an effect upcycling method involving both healing and doping to directly regenerate spent graphite anodes. Specifically, using boric acid pretreatment and short annealing, our regeneration process not only heals the composition/structure defects of degraded graphite but also creates functional boron-doping on the surface of graphite particles, providing high electrochemical activity and excellent cycling stability. The efficient direct regeneration of spent graphite by using low cost, non-volatile and non-caustic boric acid with low annealing temperature provides a more promising direction for green and sustainable recycling of spent LIB anodes.

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