A method of chemical process simulation includes providing a Sequential-Modular process simulator having a simulation algorithm. Responsive to receiving a process flowsheet creating a directed graph (DG) which represents a topology of the process flowsheet with components interconnected as nodes and process streams including recycle streams represented as cycles, with dependencies between process streams adding cycles. Partitioning the components into a first portion including strongly-connected component groups (SCCGs) along with individual components. An initial location is provided for each cycles for the SCCGs to generate a directed acyclic graph (DAG). An initial calculation order is determined for the flowsheet from the DAG, including an order for calculation within the SCCGs themselves. The SCCGs and components as nodes and process streams as edges with a graphical indication representing each cycle for the SCCGs along with the initial calculation order are graphically displayed, wherein the initial calculation order is user modifiable.

A quantitative pooled-sample testing method and apparatus for chemical test items of consumer products are provided. The test method includes: calculating a maximum number of samples that can be pooled according to parameters such as regulatory limit requirements, measurement uncertainty, the number of pooled samples, a sample pooling ratio, and the like; establishing a searchable table according to a relationship between a qualified rate, the number of samples that can be pooled, and reduced workload; for various items to be tested, directly querying the table to acquire reduced workloads corresponding to different numbers of samples that can be pooled; and when the number of samples that can be pooled does not exceed a maximum value and corresponds to the most greatly reduced workload, determining that number of samples that can be pooled as an optimum number of samples that can be pooled, and performing pooled-sample testing.

In some implementations, the present solution can determine a first structural vector of a first chemical based on a chemical structure of the first chemical. The system can also determine first target vector of the first chemical based on at least one gene target for the first chemical. The system can use the structural vector and the target vector to generate a toxicity predictor score for the first chemical.

In some implementations, the present solution can determine a first structural vector of a first chemical based on a chemical structure of the first chemical. The system can also determine first target vector of the first chemical based on at least one gene target for the first chemical. The system can use the structural vector and the target vector to generate a toxicity predictor score for the first chemical.

Techniques are described for determining the strain on a cell wall using two models: 1) a short timescale model, describing the relationship between physical properties assumed to be fixed and 2) a long timescale model, describing the dynamic chemical composition of a cell wall. Short term modeling of the physical properties in a cell wall is used to properly understand how physical factors such as osmotic pressure affects the strain on the cell wall, which is in turn used to identify conditions under which a cell wall will cease to function properly or lyse entirely. Although temporally the physical properties which cause cell walls to underperform/lyse can be evaluated under a short time frame, the chemical properties that lead to the physical properties which cause that behavior themselves change over much longer timescales, in a relative sense.

In some implementations, the present solution can determine a first structural vector of a first chemical based on a chemical structure of the first chemical. The system can also determine first target vector of the first chemical based on at least one gene target for the first chemical. The system can use the structural vector and the target vector to generate a toxicity predictor score for the first chemical.

In some implementations, the present solution can determine a first structural vector of a first chemical based on a chemical structure of the first chemical. The system can also determine first target vector of the first chemical based on at least one gene target for the first chemical. The system can use the structural vector and the target vector to generate a toxicity predictor score for the first chemical.

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.

The computer-implemented fragrance or flavour composition method (300), comprises: a step (305) of defining, upon a computer interface, a value representative of a sustainability impact digital index or of an aggregate sustainability impact digital indicator validity threshold for at least one fragrance or flavour ingredient in the fragrance or flavour composition, said fragrance or flavour ingredient being associated to a fragrance or flavour ingredient digital identifier, or to a fragrance or flavour composition digital identifier, a step (310) of selecting, upon a computer interface, at least one fragrance or flavour ingredient digital identifier, a step (315) of retrieval, by a computing device, from a fragrance or flavour ingredient physical parameter database of a sustainability impact digital index or of an aggregate sustainability impact digital indicator as a function of at least one said fragrance or flavour ingredient digital identifier, a step (320) of comparing, by a computing device, as a function of at least one selected fragrance or flavour ingredient digital identifier, a sustainability impact digital index or of an aggregate sustainability impact digital indicator retrieved to the validity threshold defined and a step (325) of providing, upon a computer interface, an indicator representative of the result of the step of comparing.

Systems and methods for scoring and visualizing the effects of variants in biological sequences. Variants may include substitutions, insertions and deletions. The method comprises encoding biological sequences as vector sequences and then operating a neural network in the forward-propagation mode and possibly in the back-propagation mode to compute variant scores. Variant scores are determined by normalizing the gradients. Variant scores may be used to select a subset of variants, which are then used to produce modified vector sequences which are analyzed by the neural network operating in forward-propagation mode, to determine improved variant scores. The variant scores may be visualized using black and white, greyscale or colored elements that are arranged in blocks with dimensions corresponding to different possible symbols and the length of the sequence. These blocks are aligned with the biological sequence, which is illustrated by a symbol sequence arranged in a line.