Abstract: One or more Bluetooth® low energy (BLE) beacons in communication with a remote server that provides check in capabilities and payment capabilities may be installed at a location. The BLE beacons may connect with a user's mobile device when the user enters the location and allow the user to check in to the location and authorize payments to be made at the location. Once the user is checked in to the location, the user may be provided with additional functionality, benefits, offers, and applications related to the location and facilitated by the check in. Further, the user may be pre-checked in into a next location when the user is at a current location.

Abstract: A product count of a number of physical products within a physical grouping of a plurality of the physical products is determined based on an image of the physical grouping. A cycle counter generates image coordinates for visible product surfaces within the image. The cycle counter generates three-dimensional virtual base coordinates for the expected locations of the plurality of the physical products within the physical grouping. The cycle counter determines the actual locations of the visible product surfaces within three-dimensional space based on a comparison of the image coordinates and the virtual base coordinates. The cycle counter determines the product count of the number of physical products within the physical grouping based on the actual locations.

Abstract: An inline transducer meter body can include a channel that can accept a separate core to enable formation of a port to accept a sensor assembly and a notch to help stabilize the sensor assembly in alignment with fluid flow. The inline transducer meter body can include a circular shelf adapted to receive a transducer assembly that interfaces in a complimentary manner with a circular ledge of a circular head section of the transducer assembly, a portal formed between the circular shelf and an inner surface of the housing adapted to interface in a complimentary manner with an alignment section of the transducer, and a notch formed in the inner surface of the flow tube opposite the portal to interface with a positioner formed on and extending from a transducer holder.

Abstract: A system, method, apparatus, and computer program product that provides geographic event monitoring, detection, assessment, and alert notifications. The system utilizes an extract, transfer and load data pipeline (named ETheL); an asset risk calculator (ARC) data pipeline for evaluating proximity of an asset to a geographic event; a Disaster Intelligence Communication Environment (DICE) which acts as the front-end of the platform; a COMS node providing a near-real time event alert system and a postGIS database used for persistence of events, locations, and calculations). Geographical indexing of the geographical description of event data with a designated location of interest (LOI) determines a cell intersection inhabited by each of an evant and the LOI. Notifications may be programmed for each cell intersection. Mapping displays may be generated for showing the cell intersections with the LOIs.

Abstract: Pathways between reference locations in a physical system are generated based on a layout table. Nodes and edges of the directed graph are associated with cell locations of the layout table. The cell locations define features of the reference locations. Parameters of the nodes and edges are defined based on descriptors recalled from the cells associated with the nodes and edges. The nodes and edges are configured based on the descriptors. Path data regarding potential pathways is generated based on the defined nodes and edges.

Abstract: A method of adjusting mental state includes acquiring video data of an individual, extracting image data and audio data from the video data, extracting semantic text data from the audio data, identifying a first set of features, predicting a baseline mental state, identifying a target mental state, and simulating a predicted path from the baseline mental state to the target mental state. The baseline mental state is predicted based on the first set of features. The predicted path is simulated using a multidimensional mental state model, a plurality of actions, and a first computer-implemented machine learning model. The predicted path comprises one or more actions of the plurality of actions and corresponding changes to at least one of first and second dimensions of the multidimensional mental state model. An indication of the one or more actions of the predicted path is output to the individual.

Abstract: A method of adjusting mental state includes acquiring video data of an individual, extracting image data and audio data from the video data, extracting semantic text data from the audio data, identifying a first set of features, predicting a baseline mental state, identifying a target mental state, and simulating a predicted path from the baseline mental state to the target mental state. The baseline mental state is predicted based on the first set of features. The predicted path is simulated using a multidimensional mental state model, a plurality of actions, and a first computer-implemented machine learning model. The predicted path comprises one or more actions of the plurality of actions and corresponding changes to at least one of first and second dimensions of the multidimensional mental state model. An indication of the one or more actions of the predicted path is output to the individual.

Abstract: Pathways between reference locations in a physical system are generated based on a layout table. Nodes and edges of the directed graph are associated with cell locations of the layout table. The cell locations define features of the reference locations. Parameters of the nodes and edges are defined based on descriptors recalled from the cells associated with the nodes and edges. The nodes and edges are configured based on the descriptors. Path data regarding potential pathways is generated based on the defined nodes and edges.

Abstract: Systems and methods for contact imaging of stands with illumination are disclosed herein. A contact imaging system can include a contact imager. The contact imager can include a housing having a base and a lid. The lid can have a closed position against the base and an open position. The contact imager can include a contact area image sensor. The lid can shield the contact area image sensor from ambient light when the lid is in the closed position. The contact imager can include an illuminator that can illuminate at least a portion of a blot on the contact area image sensor when the lid is in the closed position.

Abstract: A method for determining the efficacy of drug combinations at treating a subject morbidity and providing personalized treatment proposals for treating the subject morbidity includes training a machine learning model based on sets of electronic medical records to determine the efficacy of the various drug combinations at treating the subject morbidity, generating pertinent health information regarding a subject patient, and generating patient-specific treatment proposals based on the pertinent health information.

Abstract: A method includes acquiring digital video data that portrays an interacting event, identifying a plurality of video features in the digital video data, analyzing the plurality of video features to create a relationship graph, determining a relationship score based on the relationship graph using a first computer-implemented machine learning model, and outputting the relationship score with a user interface. The interacting event comprises a plurality of interactions between a first individual and a second individual and each video feature of the plurality of video features corresponds to an interaction of the plurality of interactions. The relationship graph comprises a first node, a second node, and a first edge extending from the first node to the second node. The first node represents the first individual, the second node represents the second individual, and a weight of the first edge represents a relationship strength between the first individual and the second individual.

Abstract: A method includes acquiring digital video data that portrays an interacting event, identifying a plurality of features in the digital video data with a first computer-implemented machine learning model, analyzing the plurality of features to create a baseline relationship graph, determining a target relationship graph, generating one or more actions for increasing similarity between the baseline relationship graph and the target relationship graph, and outputting the one or more actions by a user interface. The one or more actions are generated using a simulator, a second computer-implemented machine learning model, and a plurality of actions. The second computer-implemented machine learning model is configured to relate actions of the plurality of actions to changes to relationship graphs, the simulator is configured to simulate changes to the baseline relationship graph using the second computer-implemented machine learning model and the plurality of actions.

Abstract: A method of adjusting mental state includes acquiring video data of a first individual, identifying a first set of features based on image, audio, and text data extracted from the video data, predicting a baseline mental state for the first individual based on the first set of features, identifying a target mental state, and simulating a predicted path from the baseline mental state to the target mental state. The predicted path is simulated using a multidimensional mental state model, a plurality of actions, and a computer-implemented machine learning model. The predicted path comprises one or more actions of the plurality of actions and corresponding changes to at least one of first and second dimensions of the multidimensional mental state model. The one or more actions of the predicted path are output to a second individual and are performable to adjust the baseline mental state of the first individual.

Abstract: A method of adjusting mental state includes acquiring video data of a first individual and a second individual, extracting image data and audio data from the video data, extracting first image data of the first individual from the video data, extracting first audio data of the first individual from the video data, extracting second image data of the second individual from the video data, extracting first semantic text data from the first audio data, and extracting second semantic text data from the second audio data. The method further includes identifying first and second sets of features, predicting a first baseline mental state, predicting a second baseline mental state, predicting an average baseline mental state, identifying a target mental state, simulating a predicted path from the average baseline mental state toward the target mental state, and outputting an indication of one or more actions for adjusting the baseline mental state.

Abstract: A method includes acquiring digital video data that portrays an interacting event, extracting image data, audio data, and semantic text data from the video data, analyzing the extracted data to identify a plurality of video features, and analyzing the plurality of video features to create a relationship graph. The interacting event comprises a plurality of interactions between plurality of individuals and the relationship graph comprises a plurality of nodes and a plurality of edges. Each node of the plurality of nodes represents an individual of the plurality of individuals, and each edge of the plurality of edges extends between two nodes of the plurality of nodes, and the plurality of edges represents the plurality of interactions. The method further comprises determining whether a first key feature is present in the relationship graph, wherein presence of the first key feature is predictive of a positive outcome of the interacting event.

Abstract: A method of predicting an group mental state according to the present disclosure includes acquiring video data of a first individual and a second individual, extracting first image data of the first individual from the video data, extracting first audio data of the first individual from the video data, extracting second image data of the first individual from the video data, extracting second audio data of the first individual from the video data, extracting first semantic text data from the first audio data, and extracting second semantic text data from the second audio data. The method further includes identifying a first set of features, identifying a second set of features, predicting a first mental state value, predicting a second mental state value, predicting a third mental state value, predicting a fourth mental state value, and generating first and second average mental state values.

Abstract: A method of adjusting task performance includes extracting image data and audio data from video data and extracting semantic text data from the audio data. The method further includes analyzing at least one of the image data, the audio data, and the semantic text data to identify a first set of features, generating an adjustment recommendation based on the first set of features and a relational feature model, and outputting the adjustment recommendation. The video data portrays a first individual performing a first iteration of a task. The at least one of the image data, audio data, and semantic text data is analyzed by a first computer-implemented machine learning model. The adjustment recommendation is generated by a second computer-implemented machine learning model and comprises instructions that can be performed by the first individual to adjust task performance. The relational feature model relates features and task performance.

Abstract: A method includes acquiring digital video data that portrays an interacting event, identifying a plurality of features in the digital video data, and analyzing the plurality of features to create a relationship graph. The relationship graph comprises a plurality of nodes and a plurality of edges, each node of the plurality of nodes represents an individual of the plurality of individuals, and each edge of the plurality of edges extends between two nodes of the plurality of nodes, and the plurality of edges represents a plurality of interactions of the interacting event. The method further includes identifying an edge of the plurality of edges as an anomalous edge, creating an output representative of the anomalous edge, and outputting the output representative of the anomalous edge. The anomalous edge is identified by a computer-implemented machine learning model configured to identify anomalous edges in relationship graphs.

Abstract: A method of predicting mental state includes extracting image data, audio data, and semantic text data from video data, where the video data portrays a first individual. The method further includes analyzing the image data to identify a first feature set, analyzing the audio data to identify a second feature set, analyzing the semantic text data to identify a third feature set, and predicting a mental state for the individual based on the first feature set, the second feature set, the third feature set, and a multidimensional mental state model. The predicted mental state is output. The multidimensional mental state model includes a first dimension, a second dimension, and a third dimension. The first dimension corresponds to a first aspect of mental state, the second dimension corresponds to a second aspect of mental state, and the third dimension corresponds to a third aspect of mental state.

Abstract: A method for identifying drug interactions that occur between various drugs utilized to treat various morbidities. Each drug combination used to treat a morbidity has an efficacy that can be measured relative to a baseline real world efficacy. Differences between effective and ineffective drug combinations are identified and drug interactions are identified based on overlapping drugs in the effective and ineffective combinations.