Abstract: Provided is a methodology to improve soil performance by dispersing stabilized low C:N non-highly polymerized porous lignocellulose catalytic that improves porosity, carbon capture, and microbial activities.

Abstract: Provided is a process that includes sharing information among two or more parties or systems for modeling and decision-making purposes, while limiting the exposure of details either too sensitive to share, or whose sharing is controlled by laws, regulations, or business needs.

Abstract: Provided is a process including: writing, with a computing system, a first plurality of classes using object-oriented modelling of modelling methods; writing, with the computing system, a second plurality of classes using object-oriented modelling of governance; scanning, with the computing system, a set of libraries collectively containing both modelling object classes among the first plurality of classes and governance classes among the second plurality of classes to determine class definition information; using, with the computing system, at least some of the class definition information to produce object manipulation functions, wherein the object manipulation functions allow a governance system to access methods and attributes of classes among first plurality of classes or the second plurality of classes to manipulate objects of at least some of the modelling object classes; and using at least some of the class definition information to effectuate access to the object manipulation functions.

Abstract: Provided is a process including: writing modelling-object classes using object-oriented modelling of the modelling methods, the modelling-object classes being members of a set of class libraries; writing quality-management classes using object-oriented modelling of quality management, the quality-management classes being members of the set of class libraries; scanning modelling-object classes in the set of class libraries to determine modelling-object class definition information; scanning quality-management classes in the set of class libraries to determine quality-management class definition information; using the modelling-object class definition information and the quality-management class definition information to produce object manipulation functions that allow a quality management system to access methods and attributes of modelling-object classes to manipulate objects of the modelling-object classes; and using the modelling-object class definition information and the quality-management class definitio

Abstract: Provided is a process, including: obtaining a first training dataset, training a first machine-learning model on the first training dataset, obtaining a set of candidate question sequences, forming virtual subject-entity records, forming a second training dataset, training a second machine-learning model, and storing the adjusted parameters of the second machine-learning model in memory.

Abstract: Provided is a process including: writing, with a computing system, a first plurality of classes using object-oriented modelling of modelling methods; writing, with the computing system, a second plurality of classes using object-oriented modelling of governance; scanning, with the computing system, a set of libraries collectively containing both modelling object classes among the first plurality of classes and governance classes among the second plurality of classes to determine class definition information; using, with the computing system, at least some of the class definition information to produce object manipulation functions, wherein the object manipulation functions allow a governance system to access methods and attributes of classes among first plurality of classes or the second plurality of classes to manipulate objects of at least some of the modelling object classes; and using at least some of the class definition information to effectuate access to the object manipulation functions.

Abstract: Provided is a class of functional soil amendment a C:N ratio that does require a cascading effect to produce solid organic matter and improve porosity. This results in superior carbon capture.

Abstract: Provided is a process and material including the operation of a reactor that applies kinetic energy, heat, and pressure are performed nearly concurrently to biomass feeding of biomass to create functional soil amendment with superior SOM creation and carbon capture.

Abstract: Provided is a methodology to improve soil performance by dispersing stabilized low C:N non highly polymerized porous soil amendment that improves porosity, carbon capture, and microbial activities.

Abstract: Provided is a process including: obtaining, for a plurality of entities, entity logs, wherein: the entity logs comprise events involving the entities, a first subset of the events are actions by the entities, at least some of the actions by the entities are targeted actions, and the events are labeled according to an ontology of events having a plurality of event types; training, with one or more processors, based on the entity logs, a predictive machine learning model to predict whether an entity characterized by a set of inputs to the model will engage in a targeted action in a given duration of time in the future; and storing the trained predictive machine learning model in memory.

Abstract: Provided is a process that includes sharing information among two or more parties or systems for modeling and decision-making purposes, while limiting the exposure of details either too sensitive to share, or whose sharing is controlled by laws, regulations, or business needs.

Abstract: A circuit for direct current (DC) offset estimation comprises a quantile value circuit and a signal processor. The quantile value circuit determines a plurality of quantile values of an input signal and includes a plurality of quantile filters. Each quantile filter includes a comparator, a level shifter, a monotonic transfer function component, and a latched integrator. The comparator compares the input signal and a quantile value. The level shifter shifts the output of the comparator. The monotonic transfer function component determines the magnitude of the shifted signal and provide a transfer function signal. The latched integrator suppresses transient characteristics of the transfer function signal and provide the quantile value. The signal processor is configured to calculate a weighted average of the quantile values to yield a DC offset estimate.

Abstract: Provided is a process including: obtaining, for a plurality of entities, entity logs, wherein: the entity logs comprise events involving the entities, a first subset of the events are actions by the entities, at least some of the actions by the entities are targeted actions, and the events are labeled according to an ontology of events having a plurality of event types; training, with one or more processors, based on the entity logs, a predictive machine learning model to predict whether an entity characterized by a set of inputs to the model will engage in a targeted action in a given geographic locale in the future; and storing the training the trained predictive machine learning model in memory.

Abstract: Provided is a process including: obtaining, for a plurality of entities, entity logs, wherein: the entity logs comprise events involving the entities, a first subset of the events are actions by the entities, at least some of the actions by the entities are targeted actions, and the events are labeled according to an ontology of events having a plurality of event types; training, with one or more processors, based on the entity logs, a predictive machine learning model to predict whether an entity characterized by a set of inputs to the model will engage in a targeted action in a given duration of time in the future; and storing the trained predictive machine learning model in memory.

Abstract: Provided is a process including: writing classes using object-oriented modelling of modeling topics; scanning the classes to determine class definition information; receiving from a subscribing modeling object a request for a subscription to a given modeling topic in a given modeling topic class, the subscription request including a modeling topic filter to select the given modeling topic from a plurality of modeling topics described by the given modeling topic class; registering a modeling topic accessor associated with the subscribing modeling object and a modeling topic mutator associated with the subscribing modeling object; processing, through the modeling topic filter a modeling topic that is accessed through an accessor and is described by the modeling topic class, the modeling topic being received from a modeling publisher object; notifying the subscribing object of the received modeling topic through a registered modeling topic listener; and mutating the received modeling topic.

Abstract: Provided is a process, including: obtaining a first training dataset, training a first machine-learning model on the first training dataset, obtaining a set of candidate question sequences, forming virtual subject-entity records, forming a second training dataset, training a second machine-learning model, and storing the adjusted parameters of the second machine-learning model in memory.

Abstract: Provided is a process of modeling methods organized in racks of a machine learning pipeline to facilitate optimization of performance using modelling methods for implementation of machine learning design in an object-oriented modeling (OOM) framework, the process including: writing classes using object-oriented modelling of optimization methods, modelling methods, and modelling racks; writing parameters and hyper-parameters of the modeling methods as attributes as the modeling methods; scanning modelling racks classes to determine first class definition information; selecting a collection of rack and selecting modeling method objects; scanning modelling method classes to determine second class definition information; assigning racks and locations within the racks to modeling method objects; and invoking the class definition information to produce object manipulation functions that allow access the methods and attributes of at least some of the modeling method objects, the manipulation functions being configur

Abstract: Provided is a process that includes sharing information among two or more parties or systems for modeling and decision-making purposes, while limiting the exposure of details either too sensitive to share, or whose sharing is controlled by laws, regulations, or business needs.

Abstract: Provided is a process including: writing modelling-object classes using object-oriented modelling of the modelling methods, the modelling-object classes being members of a set of class libraries; writing quality-management classes using object-oriented modelling of quality management, the quality-management classes being members of the set of class libraries; scanning modelling-object classes in the set of class libraries to determine modelling-object class definition information; scanning quality-management classes in the set of class libraries to determine quality-management class definition information; using the modelling-object class definition information and the quality-management class definition information to produce object manipulation functions that allow a quality management system to access methods and attributes of modelling-object classes to manipulate objects of the modelling-object classes; and using the modelling-object class definition information and the quality-management class definitio

Abstract: Provided is a process that includes sharing information among two or more parties or systems for modeling and decision-making purposes, while limiting the exposure of details either too sensitive to share, or whose sharing is controlled by laws, regulations, or business needs.