A method for determining absorption properties in clay deposits is provided that includes obtaining a clay sample, preparing the clay sample, analyzing the clay sample, and applying one or more correlative models to the clay sample. Additionally a system for use in determining absorption properties in clay deposits is provided that includes a plurality of inorganic particles, an analytical instrument configured to gather physical and/or chemical data about the inorganic particles, and a computer system configured to accept the physical and/or chemical data and/or generate correlations between the inorganic particles based on the data.

The technology described herein is directed to structural analysis, particularly of small proteins via cryo-EM.

The technology described herein is directed to structural analysis, particularly of small proteins via cryo-EM.

Disclosed is a sulfate corrosion-resistant concrete, a method for optimizing proportion and application thereof. The concrete is formed by mixing and stirring base stocks, aggregates, admixtures, external additives and water. The base stock of the concrete is 17.4-17.5 parts of Portland cement; the aggregates include 38.9 parts of basalt with aggregate size of 5-10 mm and 33.1-33.2 parts of basalt medium sand; the admixtures are 1.9-1.95 parts of silica fume or fly ash, and further including 0.23-0.24 part of polycarboxylate water reducer and 1.34-1.35 part of sulfate corrosion-resistant liquid preservative. Optimized proportion method: according to the corrosion characteristics of sulfate and corrosion environment parameters, determine the composition and proportion of basic samples and comparison samples, make and cure sample components, test the deep components of the samples, and obtain the optimal composition and proportion according to the test results.

Disclosed is a sulfate corrosion-resistant concrete, a method for optimizing proportion and application thereof. The concrete is formed by mixing and stirring base stocks, aggregates, admixtures, external additives and water. The base stock of the concrete is 17.4-17.5 parts of Portland cement; the aggregates include 38.9 parts of basalt with aggregate size of 5-10 mm and 33.1-33.2 parts of basalt medium sand; the admixtures are 1.9-1.95 parts of silica fume or fly ash, and further including 0.23-0.24 part of polycarboxylate water reducer and 1.34-1.35 part of sulfate corrosion-resistant liquid preservative. Optimized proportion method: according to the corrosion characteristics of sulfate and corrosion environment parameters, determine the composition and proportion of basic samples and comparison samples, make and cure sample components, test the deep components of the samples, and obtain the optimal composition and proportion according to the test results.

A permittivity sensor, for determining an image of a distribution of permittivity of a material of an object in a scene, comprising an input interface, a hardware processor, and an output interface is provided. The input interface is configured to accept phaseless measurements of propagation of a known incident field through the scene and scattered by the material of the object in the scene. The hardware processor is configured to solve a multi-variable minimization problem over unknown phases of the phaseless measurements and unknown image of the permittivity of the material of the object by minimizing a difference of a nonlinear function of the known incident field and the unknown image with a product of known magnitudes of the phaseless measurements and the unknown phases. Further, the output interface is configured to render the permittivity of the material of the object provided by the solution of the multi-variable minimization problem.

A permittivity sensor, for determining an image of a distribution of permittivity of a material of an object in a scene, comprising an input interface, a hardware processor, and an output interface is provided. The input interface is configured to accept phaseless measurements of propagation of a known incident field through the scene and scattered by the material of the object in the scene. The hardware processor is configured to solve a multi-variable minimization problem over unknown phases of the phaseless measurements and unknown image of the permittivity of the material of the object by minimizing a difference of a nonlinear function of the known incident field and the unknown image with a product of known magnitudes of the phaseless measurements and the unknown phases. Further, the output interface is configured to render the permittivity of the material of the object provided by the solution of the multi-variable minimization problem.

A system of collating a spectral data of an arbitrary material with spectral data of existing materials to identify the kind of the arbitrary material comprises a one-dimensional CNN processor calculating a characteristic value vector based on a spectral data of a material by a one-dimensional convolution neural network algorithm, and a metric learning processor computing a probability that the kind of the material is each kind of the existing materials from the characteristic value vector by a deep metric learning algorithm. The processors learn with the spectral data of existing materials to compute a probability for the kind of each material such that the probabilities for the kinds of the respective materials inputted for data for learning becomes maximum. When the data of the arbitrary material is inputted, the kind giving the maximum probability is identified as the kind of the arbitrary material with high precision.

A system of collating a spectral data of an arbitrary material with spectral data of existing materials to identify the kind of the arbitrary material comprises a one-dimensional CNN processor calculating a characteristic value vector based on a spectral data of a material by a one-dimensional convolution neural network algorithm, and a metric learning processor computing a probability that the kind of the material is each kind of the existing materials from the characteristic value vector by a deep metric learning algorithm. The processors learn with the spectral data of existing materials to compute a probability for the kind of each material such that the probabilities for the kinds of the respective materials inputted for data for learning becomes maximum. When the data of the arbitrary material is inputted, the kind giving the maximum probability is identified as the kind of the arbitrary material with high precision.

Embodiments described herein utilize Non-Destructive Inspection (NDI) scan data obtained during a process performed on a surface of a structure to update a location of an NDI scanner on the surface. A subsurface feature within the structure is detected based on the NDI scan data, which are correlated with pre-defined position data for the subsurface feature. A measured location of the NDI scanner on the surface is corrected based on the pre-defined position data for the subsurface feature.