Abstract: Provided are systems and methods configured to optimize processing of feedstock sources into renewable energy. Optimization over conventional approaches can begin with systematic functionality at the first steps of delivering feedstock to various digester locations. Optimizing transport of materials to the various locations can significantly impact production efficiency and resultant greenhouse gas emissions stemming from such processing. Various embodiments resolve the technical issues of building the most efficient system to account for greenhouse gas emissions as well optimization of renewable energy production from source material having varying quality, consistency, and location. Trained ML models can be used to predict efficient use of resources across groups of digesters, various feedstock streams, respective locations, and the resources required to bring the feedstock to the digesters.

Abstract: Provided are systems and methods configured to optimize processing of feedstock sources into renewable energy. Optimization over conventional approaches can begin with systematic functionality at the first steps of delivering feedstock to various digester locations. Optimizing transport of materials to the various locations can significantly impact production efficiency and resultant greenhouse gas emissions stemming from such processing. Various embodiments resolve the technical issues of building the most efficient system to account for greenhouse gas emissions as well optimization of renewable energy production from source material having varying quality, consistency, and location. Trained ML models can be used to predict efficient use of resources across groups of digesters, various feedstock streams, respective locations, and the resources required to bring the feedstock to the digesters.

Abstract: A method for determining the thickness of a thin sample is described. The method includes the step of exciting time-dependent acoustic waveguide modes in the sample with an excitation radiation field. The acoustic waveguide modes are detected by diffracting probe radiation off a ripple morphology induced on the sample's surface by the acoustic waveguide modes. The diffracted probe radiation is then analyzed to measure phase velocities or frequencies of the acoustic waveguide modes. A thickness of the thin sample is determined by comparing the measured phase velocities or frequencies to the phase velocities or frequencies calculated from a mathematical model.

Abstract: A method for determining the thickness of a thin sample is described. The method includes the step of exciting time-dependent acoustic waveguide modes in the sample with an excitation radiation field. The acoustic waveguide modes are detected by diffracting probe radiation off a ripple morphology induced on the sample's surface by the acoustic waveguide modes. The diffracted probe radiation is then analyzed to measure phase velocities or frequencies of the acoustic waveguide modes. A thickness of the thin sample is determined by comparing the measured phase velocities or frequencies to the phase velocities or frequencies calculated from a mathematical model.

Abstract: A method for determining the thickness of a thin sample is described. The method includes the step of exciting time-dependent acoustic waveguide modes in the sample with an excitation radiation field. The acoustic waveguide modes are detected by diffracting probe radiation off a ripple morphology induced on the sample's surface by the acoustic waveguide modes. The diffracted probe radiation is then analyzed to measure phase velocities or frequencies of the acoustic waveguide modes. A thickness of the thin sample is determined by comparing the measured phase velocities or frequencies to the phase velocities or frequencies calculated from a mathematical model.