A system for analysis and display of hydrocarbon play information according to some aspects determines a probability of source rock occurrence according to source rock age based on a proven play concept. The system can also determine a relative probability of migration for hydrocarbons from a source rock of a proposed petroleum play concept to a reservoir. A relative probability of wellbore success for the proposed play concept can be determined at least in part based on these probabilities. The system can display the relative probability of wellbore success for the proposed play concept, either alone as part of a displayed inventory of proposed hydrocarbon play concepts. The system can produce accurate results that facilitate rapid play concept investigations for hydrocarbon exploration.

A demand forecasting method and a demand forecasting apparatus are provided. A preliminary prediction amount corresponding to a part number is obtained based on historical demand data. A demand probability of the part number is calculated based on the preliminary prediction amount. A prediction demand amount corresponding to the part number is obtained based on the historical demand data, the preliminary prediction amount and the demand probability.

In an embodiment, a method includes acquiring a current condition indicator of a condition indicator set associated with an operating condition of a machine, the condition indicator set indicating sensor readings associated with an operating element of the machine under the operating condition. The method also includes determining, by a data server, a relative trend significance over a trend window of the condition indicator set based, at least in part, on an evaluation of the trend window in relation to a historical window of the condition indicator set. The method also includes determining, by the data server, whether trend criteria associated with the operating element is satisfied, where the trend criteria may include criteria related to the relative trend significance. The method also includes executing, by the data server, an alerting process in response to the determining that the trend criteria is satisfied.

Techniques for recommending side-by-side plantings of pairs of hybrids or seeds include a server computer receiving agricultural data records that represent crop seed data describing seed and yield properties of hybrid seeds and first data for agricultural fields where the hybrid seeds were planted. The server receives second data for available hybrids and seeds and automatically calculates a dataset of success probability scores that describe the probability of a successful yield on the target fields. Data is organized as pairs to facilitate comparison of actual plantings to optimized plantings that have a probability of success (POS), in terms of yield lift or increased yield season-over-season, for different yield values. Confidence values are generated and stored in association with the POS values and can be used as a basis of visual output to support planting and/or field management decisions as part of an automated intelligent agricultural decision support system.

A reliability engineering software tool and associated method for scheduling maintenance events of at least one industrial asset comprises at least one identified physical mechanism of failure for the at least one asset and at least one identified precise evidence for each identified physical mechanism of failure for the at least one asset; a monitor for each identified precise evidence for each identified physical mechanism of failure for the at least one asset over time to obtain multiple inputs for each identified precise evidence for each identified physical mechanism of failure for the at least one asset; a machine learning based tool dynamically plotting a P-F curve based upon the monitoring of each identified precise evidence for each identified physical mechanism of failure for the at least one asset over time; and a schedule of maintenance events created based upon the dynamically plotted P-F curve of at least one asset.

A reliability engineering software tool and associated method for scheduling maintenance events of at least one industrial asset comprises at least one identified physical mechanism of failure for the at least one asset and at least one identified precise evidence for each identified physical mechanism of failure for the at least one asset; a monitor for each identified precise evidence for each identified physical mechanism of failure for the at least one asset over time to obtain multiple inputs for each identified precise evidence for each identified physical mechanism of failure for the at least one asset; a machine learning based tool dynamically plotting a P-F curve based upon the monitoring of each identified precise evidence for each identified physical mechanism of failure for the at least one asset over time; and a schedule of maintenance events created based upon the dynamically plotted P-F curve of at least one asset.

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.

State of the art predictive maintenance systems that generate predictions with respect to maintenance of High Performance Computing (HPC) systems have the disadvantage that they either are reactive, or the predictions are affected due to quality issues associated with the data being collected from the HPC systems. The disclosure herein generally relates to predictive maintenance, and, more particularly, to a method and system for predictive maintenance of High Performance Computing (HPC) systems. The system performs abstraction and cleansing on performance data collected from the HPC systems, and generates a cleansed performance data, on which a Machine Leaning (ML) prediction is applied to generate predictions with respect to maintenance of the HPC systems.

A method and a system for optimizing problem-solving based on probabilistic bit circuits are provided. The method includes: performing a modeling transformation on an objective problem to obtain a corresponding Hamiltonian relationship; obtaining a column Hamiltonian of said probabilistic bit circuit based on said Hamiltonian relationship; and performing parallel annealing iterations on multicolumn Hamiltonian based on row-flipping operations on said probabilistic bit circuits to obtain an updated probabilistic bit configuration, so as to achieve optimization of said problem.

A stabilization method and mechanism for model structure learning is described. A model is built based on a full data set. The full data set is partitioned into cross validation (CV) folds. A set of model structures of the model are cross validated for each CV fold while penalizing structural deviations from the model to determine CV errors. A model structure is selected from the set of model structures based on a comparison of CV errors with an industrial data set.