Abstract: Methods for designing a bio-climatically adapted affordable Zero-Energy prefabricated modular building such as for housing, in which a Layered disposition of Envelope elements—including Structural Insulated Panels and Thermal Bridge Breakage Capsules—in the wall, floor and roof sections, provide a highly energy-efficient Building Envelope, which together with a modular building Support Structure including Aeriated Frames, and an adequately sized renewable energy power generator system, inexpensively achieves the energetic independence of the building. The Thermal Bridge Breakage Capsules have a PVC spherical cap with a hole and a flat plaque around it and are filled with a high insulation material. When inserted at a distance “L”, they improve and make more efficient the thermal transmittance of building envelopes, limiting energy losses in thermal bridges to a maximum of 12%.

Abstract: A method of classifying job observations with metadata tags for retrieval from a database during the designing of a wellbore treatment. A machine learning process applies a set of metadata tags to the observation description and observation object based on a training set of job observations. The machine learning process validates the metadata tags based on a classification grade determined by the ranking of the job observation within a search result. A managing application can modify a job design comprising an inventory of wellbore treatment materials and pumping equipment based on job observations with metadata tags that match the metadata tags of the job design.

Abstract: Methods and apparatus provide for determining a reservoir fluid phase envelope from downhole fluid analysis data using machine learning techniques.

Abstract: In applications such as adaptive learning of physics-based and data-driven models associated with industrial plants, the models are corrected periodically by taking into consideration the dynamic changes occurring in plant conditions and related data. However, accuracy of adaptive learning depends on accuracy of ground truth data being used as reference data. The disclosure herein generally relates to data preprocessing, and, more particularly, to a method and system for ground truth profile correction and instance selection. The system performs a ground truth profile correction for ground truth profiles having a Profile Deviation Index (PDI) value exceeding a threshold of distortion, to reduce the PDI value, and in turn reduce the distortion in the ground truth profiles. Further, the system performs a data instance selection to identify and remove outliers, and the data that remains after the data instance selection may be then used for applications such as model generation or retuning.

Abstract: A model learning apparatus is configured to learn a model that shows a relationship between an input variable u input into a system and an output variable y output from the system. The model learning apparatus includes a storage that stores store a model used to learn a nonlinear equation of state for predicting the output variable y by using the input variable u; and a processor programmed to learn the equation of state by using the model and an input-output data set including a set of data of a steady-state value of the output variable y and data of the input variable u corresponding to the data of the steady-state value. The model is an equation of state including a bijective mapping ? that uses the output variable y as an input thereof.

Abstract: A method includes obtaining sensor data from a plurality of sensors disposed at a plurality of routing control locations of an environment, where the sensor data is indicative of a number of a plurality of pallets at the plurality of routing control locations. The method includes calculating a plurality of difference values based on the sensor data, calculating a transient production value based on the sensor data and a transient objective function, and calculating a steady state production value based on the sensor data and a steady state objective function. The method includes generating a state vector based on the plurality of difference values, the transient production value, and the steady state production value, and defining a set of routes for a set of pallets from among the plurality of pallets based on the state vector and a digital twin of the environment.