A device for providing charging information for a charging process is configured to determine a total set of N data tuples for N different times of a charging time period for the charging process. A data tuple includes values of one or more characteristic variables relating to electrical energy that can be provided in the charging process. Furthermore, the device is configured to reduce the total set of N data tuples to a reduced set of M data tuples, with M<N, and to provide the reduced set of M data tuples for the determination of a charging plan for the charging process.

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.

Systems and methods for timing recovery in DNA storage systems is described. In one embodiment, the present systems and methods include generating a unique pattern of DNA bases and use the unique pattern for a phase-locked loop (PLL) field of a data layout, generating a multidimensional mapping, configuring the multidimensional mapping to include one or more prohibited sequences of DNA bases, identifying a prohibited sequence from the multidimensional mapping and use the prohibited sequence for one or more synch-mark (SM) fields of the data layout, prohibiting a User Data field from using any of the prohibited sequences of DNA bases when converting binary data to DNA bases, identifying random insertion and/or deletion of DNA bases in the User Data field, and repairing the random insertion and/or deletion of DNA bases in the User Data field.

A molecular data storage system is presented for encoding data-block(s). The system includes one or more populations of molecular sequences, each population encoding a respective one of the data-blocks. Each molecular sequence comprises a data encoding section comprising a sequence of similar predetermined length N of short k-mers, whereby in each population the data encoding sections of all molecular sequences have the similar predetermined length N. The short k-mers serve as data encoding building blocks of the data encoding sections, whereby valid short k-mers serving as data encoding building blocks form a subset of a building-block-set consisting of a number Z of different preselected short k-mers each presenting a unique combination of a number k of bases of a preselected set of bases, characterized in that all the Z types of short k-mers in said building-block-set have a similar predetermined size k≥2 (plurality) of bases. The data encoding sections collectively encode a sequence of encoded alphabet letters S=(π.sup.1, π.sup.2, . . . , π.sup.n . . . , π.sup.N−1, π.sup.N). Each valid encoded alphabet letter π.sup.n at location n of the sequence S of alphabet letters is characterized by occurrence of a predetermined plurality of different types of short k-mers of the building-block-set in a corresponding location n along the data encoding sections of the plurality of molecular sequences of said population.

A system monitors an individual for conditions indicating a possibility of occurrence of irregular heart events. A database includes a plurality of combinations of at least a first signature and a second signature. A first portion of the plurality of combinations is associated with a normal heartbeat and a second portion of the plurality of combinations is associated with an irregular heart event. A wearable heart monitor that is worn on a body of the patient includes a heart sensor for generating a heart signal responsive to monitoring a beating of a heart of the individual. The monitor further includes a processor for receiving the heart signal from the heart sensor. The processor is configured to analyze the heart signal using a plurality of different processes. Each of the plurality of different processes generates at least one of the first signature and the second signature. The plurality of different processes provide a unique combination including at least the first signature and the second signature for the generated heart signal. The processor compares the unique combination with the plurality of combinations in the database, locates a combination of the plurality of combinations that substantially matches the unique combination and generates a first indication if the unique combination substantially matches one of the first portion of the plurality of combinations and a second indication if the unique combination substantially matches one of the second portion of the plurality of combinations.

Techniques are disclosed for using a genetic algorithm to identify a processing pipeline that transforms spectra into a form usable to generate predicted characteristics of corresponding samples. The genetic algorithm is used to generate and evaluate multiple candidate solutions specifying various pre-processing and machine-learning-processing configurations. The processing pipeline is defined based on the candidate solutions.

A method for visualizing microbiome data is described. Respective microbes and/or genes in microbiome data stored In in a database are identified. A network comprising nodes interconnected by edges is generated in a memory of a computer, each node representing one or more identified microbes or one or more microbial metabolites, and each edge of the network representing an association between a respective pair of the one or more identified microbes or a reaction mediated between two metabolites by an enzyme encoded in the one or more identified genes, with at least some nodes and edges of the network being each associated with a condition attribute identifying a groups and/or a timestamp associated with a sample in the database. The displayed network is dynamically updated in accordance with a filtering of the microbiome data based on the condition attributed and/or the timestamp attributed. Corresponding systems and computer-readable storages are also described.

This disclosure relates to an analysis method for analyzing a chemical reaction from one or more starting materials, comprising a preparation step S1 of preparing a reaction network diagram indicating the one or more starting materials, at least part of one or more intermediate products and one or more final products generated from the chemical reaction, and reaction pathways of the chemical reaction, and a prediction step S2 of predicting the reaction rate in each reaction pathway using an artificial intelligence algorithm.

A method includes receiving a data set including medical information of respective patients, respective types of a clinical procedure performed on the patients, and respective survival rates of the patients. A survival tree graph is generated by maximizing a cost function of differences in the survival rates between the types of the clinical treatment procedure. A type of the clinical procedure is selected for a given patient, based on the survival tree graph.

A genetic algorithm system generates a set of computer programs and executes a process for assessment and conditional modification of the set, repeating the process over a plurality of generations to mutate the population of solutions over time. At each generation, the system scores each program in the set to generate a respective primary score adjustment, a respective secondary score adjustment, and a respective current score. If a current score for a program is less than or equal to a first threshold, the system removes the computer program from the set. If the current score is greater than or equal to a second threshold, the system modifies the computer program to generate one or more offspring programs for use in subsequent generations. If a primary score adjustment for a program is greater than or equal to a third threshold, the system selects the computer program for performance of a task.