A method of generating a chemical structure performed by a neural network device includes receiving a target property value and a target structure characteristic value; selecting first generation descriptors; generating second generation descriptors; determining, using a first neural network of the neural network device, property values of the second generation descriptors; determining, using a second neural network of the neural network device, structure characteristic values of the second generation descriptors; selecting, from the second generation descriptors, candidate descriptors that satisfy the target property value and the target structure characteristic value; and generating, using the second neural network of the neural network device, chemical structures for the selected candidate descriptors.

A method of generating a chemical structure performed by a neural network device includes receiving a target property value and a target structure characteristic value; selecting first generation descriptors; generating second generation descriptors; determining, using a first neural network of the neural network device, property values of the second generation descriptors; determining, using a second neural network of the neural network device, structure characteristic values of the second generation descriptors; selecting, from the second generation descriptors, candidate descriptors that satisfy the target property value and the target structure characteristic value; and generating, using the second neural network of the neural network device, chemical structures for the selected candidate descriptors.

A computer implemented method for training a neural network to capture a structural feature specific to a set of chemical compounds is disclosed. In the method, the computer system reads an expression describing a structure of the chemical compound for each chemical compound in the set and enumerates one or more combinations of a position and a type of a structural element appearing in the expression for each chemical compound in the set. The computer system also generates training data based on the one or more enumerated combinations for each chemical compound in the set. The training data includes one or more values with a length, each of which indicates whether or not a corresponding type of the structural element appears at a corresponding position for each combination. Furthermore, the computer system trains the neural network based on the training data for the set of the chemical compounds.

One aspect of the present disclosure relates to a method of quantifying soil carbon in a unit of land. The method generally comprises the steps of (i) obtaining an estimated spatial distribution of carbon content in the unit of land, (ii) stratifying the unit of land into a plurality of strata based at least partly on the spatial distribution of carbon content, (iii) selecting one or more locations from each of one or more of the plurality of strata, the one or more locations being selected with randomness, (iv) determining sample carbon content associated with the one or more first locations and (v) determining total carbon content in the unit of land based at least partly on the sample carbon content. In another aspect, this method may be used to quantify soil carbon sequestered in a unit of land by repeating steps (iv) and (v) at a second time and thereafter determining the amount of carbon sequestered. Furthermore, in quantifying the soil carbon sequestered, steps (ii) and (iii) may also be repeated at the second time after re-stratification of the unit of land based on sample carbon determined at the first time.

One aspect of the present disclosure relates to a method of quantifying soil carbon in a unit of land. The method generally comprises the steps of (i) obtaining an estimated spatial distribution of carbon content in the unit of land, (ii) stratifying the unit of land into a plurality of strata based at least partly on the spatial distribution of carbon content, (iii) selecting one or more locations from each of one or more of the plurality of strata, the one or more locations being selected with randomness, (iv) determining sample carbon content associated with the one or more first locations and (v) determining total carbon content in the unit of land based at least partly on the sample carbon content. In another aspect, this method may be used to quantify soil carbon sequestered in a unit of land by repeating steps (iv) and (v) at a second time and thereafter determining the amount of carbon sequestered. Furthermore, in quantifying the soil carbon sequestered, steps (ii) and (iii) may also be repeated at the second time after re-stratification of the unit of land based on sample carbon determined at the first time.

The invention includes methods and systems for identifying targets for therapeutic intervention for various diseases and conditions; and provides specific materials and methods for treatment of specific diseases and conditions.

The present invention provides a method of deterioration analysis that enables detailed analysis of the deterioration, especially of the surface, of a polymer material containing at least two diene polymers. The present invention relates to a method of deterioration analysis including: irradiating a polymer material containing at least two diene polymers with high intensity x-rays; and measuring x-ray absorption while varying the energy of the x-rays, to analyze the deterioration of each diene polymer.

In a method of preparing a liquid solution by mixing ingredients according to a predetermined recipe, wherein at least one pair of species of the liquid solution is derived from a weak electrolyte and corresponds to an acid-base pair, the conductivity of the liquid solution is predicted by: (i) for each pair of species derived from a weak electrolyte, solving a respective equilibrium equation to calculate the actual molar concentration of each such species at equilibrium in the liquid solution, (ii) calculating for each ionic species of said plurality of species the molar conductivity by the formula:
Λ=Λ.sub.0−K×Sqrt(c) wherein Λ is the molar conductivity, Λ.sub.0 is the molar conductivity at infinite dilution, c is the concentration, and K is the Kohlrausch coefficient, and wherein K and Λ.sub.0 are predetermined values for K and Λ.sub.0 for each ionic species, (iii) calculating the conductivity κ for each ionic species by the formula:
κ=c×Λ and (iv) adding up the conductivities determined in step (iii) for the different ionic species to obtain a predicted conductivity of the liquid solution. A computer program product comprises instructions for causing a computer to perform the method steps.

In a method of preparing a liquid solution by mixing ingredients according to a predetermined recipe, wherein at least one pair of species of the liquid solution is derived from a weak electrolyte and corresponds to an acid-base pair, the conductivity of the liquid solution is predicted by: (i) for each pair of species derived from a weak electrolyte, solving a respective equilibrium equation to calculate the actual molar concentration of each such species at equilibrium in the liquid solution, (ii) calculating for each ionic species of said plurality of species the molar conductivity by the formula:
Λ=Λ.sub.0−K×Sqrt(c) wherein Λ is the molar conductivity, Λ.sub.0 is the molar conductivity at infinite dilution, c is the concentration, and K is the Kohlrausch coefficient, and wherein K and Λ.sub.0 are predetermined values for K and Λ.sub.0 for each ionic species, (iii) calculating the conductivity κ for each ionic species by the formula:
κ=c×Λ and (iv) adding up the conductivities determined in step (iii) for the different ionic species to obtain a predicted conductivity of the liquid solution. A computer program product comprises instructions for causing a computer to perform the method steps.

Various embodiments of an intelligent augmented reality personal assistant apparatus integrated (or co-packaged) with an eye motion sensor, a microprocessor or an intelligent microprocessor and an intelligent rendering algorithm are disclosed. Such an augmented reality personal assistant apparatus can interpret/analyze/learn activities, communication or contextual information of a user and recommend relevant and useful information to the user.