Abstract: An organic nanocomposite dental crown includes an organic composition, a starch biopolymer, and at least one organic polymer-forming agent. The starch biopolymer includes date seed-based nanostarch, coconut shell-based nanostarch, fish bone, and camel bone powder. The organic composition includes one of fruit waste, placenta glue, seed-based gel, natural glue, glycerin, and organic dye. The nanostarch biocomposite includes particles with an average particle size of less than 25 nm, dispersed in a solid matrix. The dental crown has a molded structure with a height of 10 mm or less and a width of 9.5 mm or less.

Abstract: An apparatus includes: a first sensor configured to provide a first input associated with an environment outside a vehicle; a second sensor configured to provide a second input associated with an operation of the vehicle; and a processing unit having a first-stage processing system and a second-stage processing system; the first-stage processing system configured to receive the first input and the second input, process the first input to obtain a first time series of information, and process the second input to obtain a second time series of information; wherein the second-stage processing system comprises a neural network model configured to receive the first time series of information and a second time series of information in parallel; and wherein the neural network is configured to process the first time series and the second time series to determine a probability of a predicted event associated with an operation of the vehicle.

Abstract: An apparatus includes: a first sensor configured to provide a first input; a second sensor configured to provide a second input; and a processing unit configured to receive the first input from the first sensor, and a second input from the second sensor; wherein the processing unit is configured to determine a first probability of a first predicted event, and a second probability of a second predicted event, wherein the first predicted event and the second predicted event are associated with an operation of the vehicle; and wherein the processing unit is configured to calculate a risk score based on the first probability of the first predicted event, and based on the second probability of the second predicted event.

Abstract: An organic nanocomposite dental crown includes an organic composition, a starch biopolymer, and at least one organic polymer-forming agent. The starch biopolymer includes date seed-based nanostarch, coconut shell-based nanostarch, fish bone, and camel bone powder. The organic composition includes one of fruit waste, placenta glue, seed-based gel, natural glue, glycerin, and organic dye. The nanostarch biocomposite includes particles with an average particle size of less than 25 nm, dispersed in a solid matrix. The dental crown has a molded structure with a height of 10 mm or less and a width of 9.5 mm or less.

Abstract: An apparatus includes: a first sensor configured to provide a first input associated with an environment outside a vehicle; a second sensor configured to provide a second input associated with an operation of the vehicle; and a processing unit having a first-stage processing system and a second-stage processing system; the first-stage processing system configured to receive the first input and the second input, process the first input to obtain a first time series of information, and process the second input to obtain a second time series of information; wherein the second-stage processing system comprises a neural network model configured to receive the first time series of information and a second time series of information in parallel; and wherein the neural network is configured to process the first time series and the second time series to determine a probability of a predicted event associated with an operation of the vehicle.

Abstract: The present inventive provides for a method of enhancing photosynthesis using IoT. The method includes obtaining data related to at least one crop and at least one location. Respective crop features and location features are extracted from the obtained data. At least one of photosynthetic rate potential, photosynthetic rate limiting conditions, and potential loss for the at least one crop at the location is determined based on the extracted crop features and location features. Ameliorative actions are calculated based on the determined at least one of photosynthetic rate potential, photosynthetic rate limiting conditions, and potential loss for the at least one crop at the location.

Abstract: A method and a system for removing flow assurance chemicals from a produced water stream are provided. The method includes generating a plasma and treating the produced water stream with the plasma to form a treated water.

Abstract: A system and method for removing sulfur compounds from gas, including discharging tail gas having sulfur compounds from a sulfur recovery unit (SRU) to a non-thermal plasma (NTP) catalytic unit including an NTP reactor, providing oxidant to the NTP reactor and placing the oxidant in an NTP state in the NTP reactor to give an oxidative reactive species formed from the oxidant, converting (oxidizing) the sulfur compounds with the oxidative reactive species and catalyst in the NTP catalytic unit into sulfur oxides (SOx) to discharge the tail gas as treated having the formed SOx without the sulfur compounds that were converted. The SOx is absorbed into water in a quench tower to give the tail gas beneficially having only small amounts (e.g., less than 200 ppmv) of sulfur compounds. SOx may be degassed from water discharged from the quench tower and sent to the SRU furnace.

Abstract: A system and method for removing sulfur compounds from gas, including providing a feed gas having sulfur compounds including hydrogen sulfide (H2S) to a sulfur recovery unit (SRU) that has a furnace or a non-thermal plasma (NTP) reactor, discharging an SRU tail gas having H2S from the SRU to a tail gas treatment (TGT) unit having a hydrogenation reactor, a quench tower, and a TGT NTP reactor, converting sulfur compounds in the SRU tail gas in the hydrogenation reactor into H2S, discharging a process gas having H2S from the hydrogenation reactor to the quench tower and removing water vapor from the process gas in the quench tower, discharging an overhead gas having H2S from the quench tower to the TGT NTP reactor, and converting H2S in the TGT NTP reactor into hydrogen (H2) and elemental sulfur.

Abstract: This disclosure relates to implementing a configuration portfolio having a compact set of model configurations that are predicted to perform well with respect to a wide variety of input tasks. Systems described herein involve evaluating machine learning models with respect to a set of training tasks to generate a regret matrix based on accuracy of the machine learning models in connection with predicting outputs for the training tasks. The systems described herein can identify a subset of model configurations from a plurality of model configurations based on the subset of model configurations having lower associated metrics of regret with respect to the training tasks. This ensures that each model configuration within the configuration portfolio will perform reasonably well for a given input task and provides a mechanism for selecting an output model configuration using significantly fewer processing resources than conventional model selection systems.

Abstract: An apparatus includes: a first sensor configured to provide a first input associated with an environment outside a vehicle; a second sensor configured to provide a second input associated with an operation of the vehicle; and a processing unit having a first-stage processing system and a second-stage processing system; the first-stage processing system configured to receive the first input and the second input, process the first input to obtain a first time series of information, and process the second input to obtain a second time series of information; wherein the second-stage processing system comprises a neural network model configured to receive the first time series of information and a second time series of information in parallel; and wherein the neural network is configured to process the first time series and the second time series to determine a probability of a predicted event associated with an operation of the vehicle.

Abstract: An apparatus includes: a first sensor configured to provide a first input; a second sensor configured to provide a second input; and a processing unit configured to receive the first input from the first sensor, and a second input from the second sensor; wherein the processing unit is configured to determine a first probability of a first predicted event, and a second probability of a second predicted event, wherein the first predicted event and the second predicted event are associated with an operation of the vehicle; and wherein the processing unit is configured to calculate a risk score based on the first probability of the first predicted event, and based on the second probability of the second predicted event.

Abstract: An apparatus for anomaly detection in a network, using an autoencoder including an encoder and a decoder.

Abstract: A sensor calibration system for a vehicle includes one or more processors. The system also includes a memory communicably coupled to the one or more processors and storing a sensor calibration module including instructions that when executed by the one or more processors cause the one or more processors to, for an environment sensor of the vehicle, determine if the sensor can detect at least a predetermined number of viable reference features in an environment of the vehicle. The module also includes processor-executable instructions to, if the sensor cannot not detect at least a predetermined number of viable reference features, determine at least one suggested positional arrangement of viable reference features with respect to a current position the sensor. The module also includes processor-executable instructions to generate instructions to arrange viable reference features in the at least one suggested positional arrangement.

Abstract: A system for determining the rigid-body transformation between 2D image data and 3D point cloud data includes a first sensor configured to capture image data of an environment, a second sensor configured to capture point cloud data of the environment; and a computing device communicatively coupled to the first sensor and the second sensor. The computing device is configured to receive image data from the first sensor and point cloud data from the second sensor, parameterize one or more 2D lines from image data, parameterize one or more 3D lines from point cloud data, align the one or more 2D lines with the one or more 3D lines by solving a registration problem formulated as a mixed integer linear program to simultaneously solve for a projection transform vector and a data association set, and generate a data mesh comprising the image data aligned with the point cloud data.

Abstract: An apparatus for anomaly detection in a network, using an autoencoder including an encoder and a decoder.

Abstract: A method and system for reducing ion concentration of a solution and converting gas. The system comprising a multi-chamber unitary dialysis cell comprising a gas chamber, a product chamber, and an acid chamber. Ion exchange barriers separate the chambers of the dialysis cell. A first anion exchange barrier is positioned between the product chamber and the acid chamber and a first cation exchange barrier is positioned between the product chamber and the gas chamber. Anions from the solution being treated associate with cations from the acid chamber to form an acid solution in the acid chamber, and cations from the solution being treated associate with anions from the fluid comprising gas to form salt, thereby reducing the ion concentration of the solution being treated and converting at least a portion of the gas into salt.

Abstract: In an example embodiment, actionable flows are found from customer tickets by autonomously reading and understanding customer queries using neural networks. Specifically, in an example embodiment, a deep neural network is trained to be utilized at two separate stages in a flow generation process. In one stage, the neural network is used to identify a list of repetitive queries from a repository of customer tickets. In another stage, the neural network is used to identify actionable flows from query steps obtained from the list of repetitive queries.

Abstract: A sensor calibration system for a vehicle includes one or more processors. The system also includes a memory communicably coupled to the one or more processors and storing a sensor calibration module including instructions that when executed by the one or more processors cause the one or more processors to, for an environment sensor of the vehicle, determine if the sensor can detect at least a predetermined number of viable reference features in an environment of the vehicle. The module also includes processor-executable instructions to, if the sensor cannot not detect at least a predetermined number of viable reference features, determine at least one suggested positional arrangement of viable reference features with respect to a current position the sensor. The module also includes processor-executable instructions to generate instructions to arrange viable reference features in the at least one suggested positional arrangement.

Abstract: A computer-implemented method can receive a request from a robotic process automation engine to identify a target user interface control element in a webpage represented by a current master data frame. The current master data frame comprises a current document object model (DOM). The method can determine that a target user interface control element identifier associated with the target user interface control element is absent in the current DOM. The method can retrieve an archived version of the target user interface control element from an archived master data frame of the webpage. The method can find an equivalent user interface control element within the current master data frame based at least on the archived version of the target user interface control element, and output an equivalent user interface control element identifier associated with the equivalent user interface control element.