Abstract: Systems and techniques are disclosed to identify and monitor side effects and treatment outcomes relating to neurostimulation programming. An example technique to identify a side effect relating to neurostimulation programming includes: obtaining neurostimulation programming parameters to be used in a neurostimulation device of a patient; obtaining source data that defines effects of neurostimulation treatment in an anatomical area of the patient; modeling use of the neurostimulation programming parameters in the anatomical area of the neurostimulation treatment, based on the source data, to determine a predicted side effect; and outputting information that identifies characteristics of the predicted side effect.

Abstract: As a farming machine travels through a field of plants, the farming machine operates in a normal operational state to perform one or more farming operations. The farming machine detects an operational failure of a component of the farming machine using measurements obtained from one or more sensors coupled to and monitoring the farming machine. The operational failure of the component impacts performance of a first farming operation of the farming operations. The farming machine configures the farming machine to operate in a remedial operational state. In the remedial operational state, the farming machine diagnoses the operational failure of the component using the obtained measurements. In the remedial operational state, the farming machine selects a solution operation to address the operational failure of the component based on the diagnosis. The farming machine performs the determined solution operation.

Abstract: A smoking device has an inner cylinder receivable within an outer cylinder, the inner cylinder having a combustion chamber, an air chamber, a mouthpiece, and one or more air chamber slots and combustion chamber slots; the outer cylinder having a bore hole for receiving the inner cylinder, a smoke channel, and one or more bore slots. In some examples, the inner cylinder and outer cylinder have one or more magnets for securing the inner cylinder to the outer cylinder and for aligning the air and combustion chamber slots with the bore slots. A user may rotate the inner cylinder in relation to the outer cylinder, thereby changing which air and combustion slots are aligned with the bore slots and the smoke channel. As a result of the various alignments, a user is able to vary the temperature, taste, fuel-to-air ratio, and other factors.

Abstract: Systems and methods for modelling a spatial distribution of evoked responses (ERs) to electrostimulation and using the model to guide neuromodulation are disclosed. An exemplary system comprises at least one multi-electrode lead, an electrostimulator to provide electrostimulation to a neural target, a sensing circuit to sense ERs to electrostimulation, and a controller circuit. In response to electrostimulation delivered to the neural target in accordance with a stimulation setting via a stimulating electrode, the controller circuit can collect ERs from each of a group of sensing electrodes positioned at respective sensing locations and selected from the electrodes on the at least one lead, generate a model representing a spatial distribution of ER features of the sensed ERs, and based on a comparison of the model to acceptance criteria, provide a recommendation to reposition the lead or to adjust the stimulation setting.

Abstract: Historical information is accessed that describes a previous state of a field, previous environmental conditions for the field, and previous farming machine actions performed in the field. A tank management model is applied to the historical information to determine expected weed densities within field portions and an amount of treatment fluid required to treat plants within the field. While the farming machine is treating plants in the field, current information describing a current state of the field is accessed. The tank management model is applied to the current information to determine updated weed densities within field portions not yet treated. The farming machine performs a modified plant treatment action based on the updated weed densities and a comparison of a remaining amount of treatment fluid within a tank of the farming machine and an updated amount of treatment fluid for treating plants within field portions not yet treated.

Abstract: A system may include a stimulator configured to deliver a DBS therapy according to a first therapy program, and a processing system. The processing system may be configured for use to receive a logged event indicative of an acute reduction in therapy, automatically identify a second therapy program for the DBS therapy to address the acute reduction in therapy, deliver the DBS therapy according to the second therapy program, prompt the user to report whether the user likes or dislikes the DBS therapy delivered according to the second therapy program, and control the DBS therapy based on the report by choosing a third therapy program, reverting to the first therapy program, or continuing to deliver the DBS therapy according to the second therapy program.

Abstract: Historical information is accessed that describes a previous state of a field, previous environmental conditions for the field, and previous farming machine actions performed in the field. A tank management model is applied to the historical information to determine expected weed densities within field portions and an amount of treatment fluid required to treat plants within the field. While the farming machine is treating plants in the field, current information describing a current state of the field is accessed. The tank management model is applied to the current information to determine updated weed densities within field portions not yet treated. The farming machine performs a modified plant treatment action based on the updated weed densities and a comparison of a remaining amount of treatment fluid within a tank of the farming machine and an updated amount of treatment fluid for treating plants within field portions not yet treated.

Abstract: A fuel system for an internal combustion engine includes a pressurized fuel supply, and a fuel injector fluidly connected to the pressurized fuel supply. The fuel injector includes a valve seat plate forming a valve seat, an injection control valve, and a nozzle cavity. A filter, which can include a 2-dimensional filter, is positioned partially within the valve seat plate and partially within an injector body to filter an incoming flow of high-pressure fuel. The valve seat plate and filter are integrated in a valve seat plate and filter assembly.

Abstract: As a farming machine travels through a field of plants, the farming machine operates in a normal operational state to perform one or more farming operations. The farming machine detects an operational failure of a component of the farming machine using measurements obtained from one or more sensors coupled to and monitoring the farming machine. The operational failure of the component impacts performance of a first farming operation of the farming operations. The farming machine configures the farming machine to operate in a remedial operational state. In the remedial operational state, the farming machine diagnoses the operational failure of the component using the obtained measurements. In the remedial operational state, the farming machine selects a solution operation to address the operational failure of the component based on the diagnosis. The farming machine performs the determined solution operation.

Abstract: A farming machine is configured to autonomously operate in a field to accomplish a farming objective. While doing so the farming machine can dynamically modify treatment plans and/or generate experimental treatment plans. Dynamically modifying treatment plans includes modifying, delaying, accelerating, adding, and/or removing farming actions in a treatment plan. Generating an experimental treatment plan includes intentionally modifying a treatment plan to induce a variance in a result, measuring the result, and determining whether the result from the experimental treatment plan is better than the unmodified treatment plan. The farming machine is also configured to generate user interactable feedback, and receive user generated instruction, to dynamically modify treatment plans and/or generate experimental treatment plans.

Abstract: As a farming machine travels through a field of plants, the farming machine operates in a normal operational state to perform one or more farming operations. The farming machine detects an operational failure of a component of the farming machine using measurements obtained from one or more sensors coupled to and monitoring the farming machine. The operational failure of the component impacts performance of a first farming operation of the farming operations. The farming machine configures the farming machine to operate in a remedial operational state. In the remedial operational state, the farming machine diagnoses the operational failure of the component using the obtained measurements. In the remedial operational state, the farming machine selects a solution operation to address the operational failure of the component based on the diagnosis. The farming machine performs the determined solution operation.

Abstract: As a farming machine travels through a field of plants, the farming machine operates in a normal operational state to perform one or more farming operations. The farming machine detects an operational failure of a component of the farming machine using measurements obtained from one or more sensors coupled to and monitoring the farming machine. The operational failure of the component impacts performance of a first farming operation of the farming operations. The farming machine configures the farming machine to operate in a remedial operational state. In the remedial operational state, the farming machine diagnoses the operational failure of the component using the obtained measurements. In the remedial operational state, the farming machine selects a solution operation to address the operational failure of the component based on the diagnosis. The farming machine performs the determined solution operation.

Abstract: As a farming machine travels through a field of plants, the farming machine operates in a normal operational state to perform one or more farming operations. The farming machine detects an operational failure of a component of the farming machine using measurements obtained from one or more sensors coupled to and monitoring the farming machine. The operational failure of the component impacts performance of a first farming operation of the farming operations. The farming machine configures the farming machine to operate in a remedial operational state. In the remedial operational state, the farming machine diagnoses the operational failure of the component using the obtained measurements. In the remedial operational state, the farming machine selects a solution operation to address the operational failure of the component based on the diagnosis. The farming machine performs the determined solution operation.

Abstract: Historical information is accessed that describes a previous state of a field, previous environmental conditions for the field, and previous farming machine actions performed in the field. A tank management model is applied to the historical information to determine expected weed densities within field portions and an amount of treatment fluid required to treat plants within the field. While the farming machine is treating plants in the field, current information describing a current state of the field is accessed. The tank management model is applied to the current information to determine updated weed densities within field portions not yet treated. The farming machine performs a modified plant treatment action based on the updated weed densities and a comparison of a remaining amount of treatment fluid within a tank of the farming machine and an updated amount of treatment fluid for treating plants within field portions not yet treated.

Abstract: A vehicle moves through an environment (e.g., a farming, construction, mining, or forestry environment) and performs one or more actions in the environment. Portions of the environment may include moisture, such as puddles or mud patches. A control system associated with the vehicle may include a traversability model or a moisture model to help the vehicle operate in the environment with the moisture. In particular, the control system may employ the traversability model to reduce the likelihood of the vehicle attempting to traverse an untraversable portion of the environment, and the control system may employ the moisture model to reduce the likelihood of the vehicle performing an action that will damage a portion of the environment.

Abstract: Embodiments of a system as described herein may receive financial institution product information from a plurality of financial institutions distributed across a computing network. The system may also receive data from the plurality of financial institutions distributed across the computer network and create or update an ontology. A relevance score may be generated for a set of financial institution products which may, in conjunction with a campaign definition provided by a financial institution administrator, be used to associate users with a list of campaigns which may be stored as campaign data. An online banking application at a user device may request campaign data for a user. In response, the system may return campaign data for the user to the online banking application. Using the campaign data, the online banking application may select one or more products to recommend to the user and display content for the selected products on the user device.

Abstract: A system may generate a prescription file including a plurality of data fields, assign the prescription file to a first queue for processing, provide a first queue GUI that includes the plurality of data fields grouped in a plurality of information modules and indicates an approval status of each information module, and receive user input editing one or more data fields. The system may assign the prescription file to a second queue, provide a second queue GUI that includes the plurality of information modules and indicates the approval status of each information module, and receive user input setting the approval status of each information module. If an information module is not approved, the system may reassign the prescription file to the first queue. If all information modules are approved, the system may assign the prescription file to a third queue for further processing.

Abstract: Embodiments of a system as described herein may receive financial institution product information from a plurality of financial institutions distributed across a computing network. The system may also receive data from the plurality of financial institutions distributed across the computer network and create or update an ontology. A relevance score may be generated for a set of financial institution products which may, in conjunction with a campaign definition provided by a financial institution administrator, be used to associate users with a list of campaigns which may be stored as campaign data. An online banking application at a user device may request campaign data for a user. In response, the system may return campaign data for the user to the online banking application. Using the campaign data, the online banking application may select one or more products to recommend to the user and display content for the selected products on the user device.