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  1. Free, publicly-accessible full text available December 1, 2024
  2. Free, publicly-accessible full text available September 1, 2024
  3. Starting in the early 2000s, sophisticated technologies have been developed for the rational construction of synthetic genetic networks that implement specified logical functionalities. Despite impressive progress, however, the scaling necessary in order to achieve greater computational power has been hampered by many constraints, including repressor toxicity and the lack of large sets of mutually orthogonal repressors. As a consequence, a typical circuit contains no more than roughly seven repressor-based gates per cell. A possible way around this scalability problem is to distribute the computation among multiple cell types, each of which implements a small subcircuit, which communicate among themselves using diffusible small molecules (DSMs). Examples of DSMs are those employed by quorum sensing systems in bacteria. This paper focuses on systematic ways to implement this distributed approach, in the context of the evaluation of arbitrary Boolean functions. The unique characteristics of genetic circuits and the properties of DSMs require the development of new Boolean synthesis methods, distinct from those classically used in electronic circuit design. In this work, we propose a fast algorithm to synthesize distributed realizations for any Boolean function, under constraints on the number of gates per cell and the number of orthogonal DSMs. The method is based on an exact synthesis algorithm to find the minimal circuit per cell, which in turn allows us to build an extensive database of Boolean functions up to a given number of inputs. For concreteness, we will specifically focus on circuits of up to 4 inputs, which might represent, for example, two chemical inducers and two light inputs at different frequencies. Our method shows that, with a constraint of no more than seven gates per cell, the use of a single DSM increases the total number of realizable circuits by at least 7.58-fold compared to centralized computation. Moreover, when allowing two DSM’s, one can realize 99.995% of all possible 4-input Boolean functions, still with at most 7 gates per cell. The methodology introduced here can be readily adapted to complement recent genetic circuit design automation software. A toolbox that uses the proposed algorithm was created and made available at https://github. com/sontaglab/DBC/. 
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  4. Abstract

    Microwave absorbing materials (MAMs) are highly utilized in the defense and telecommunications industries, as means for reducing radar cross sections for stealth technology, and providing electromagnetic interference shielding for the information processing and transport capabilities in electronic devices. Polyaniline materials have attracted enormous attention in the field of MAM development due to their strong dielectric properties. In this manuscript, the strong dielectric action of polyaniline is utilized to demonstrate for the first time how to mathematically model the EM response of materials as a means for accurately simulating, predicting, and optimizing the microwave absorption performance. Using this process, the microwave absorbing properties of polyaniline are successfully optimized and record‐breaking performances are demonstrated for both the reflection loss and effective bandwidth, yielding experimentally‐derived responses ofRL= −88.5 dB at 7.0 GHz, 3.8 mm and Δf10= 5.5 GHz at 2.1 mm. The methods presented herein are generalizable and have the potential to be broadly applicable to MAM research and development.

     
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  5. Abstract

    Machine learning is receiving more attention in classical engineering fields, and in particular, recurrent neural networks (RNNs) coupled with ensemble regression tools have demonstrated the capability of modeling nonlinear dynamic processes. In Part I of this two‐article series, the Lyapunov‐based model predictive control (LMPC) method using a single RNN model and an ensemble of RNN models, respectively, was rigorously developed for a general class of nonlinear systems. In the present article, computational implementation issues of this new control method ranging from training of the RNN models, ensemble regression of the RNN models, and parallel computation for accelerating the real‐time model calculations are addressed. Furthermore, a chemical reactor example is used to demonstrate the implementation and effectiveness of these machine‐learning tools in LMPC as well as compare them with standard state‐space model identification tools.

     
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