Abstract: A VCN process may receive, by a computing device, information associated with a set of value chain network entities of a value chain network, the information generated by at least one of: a set of sensors of the set of value chain network entities, a set of IoT devices configured to collect data relating to the set of value chain network entities, or a set of APIs configured to publish data relating to the set of value chain network entities. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models. A VCN process may determine a procurement action to be taken in the value chain network based upon, at least in part, an output of the set of AI-based learning models. A VCN process may execute the procurement action.

Abstract: A computer-implemented method for interrogating a sample with a microscopy device is disclosed. The computer-implemented method comprises capturing, by a microscopy device, one or more images of a biological sample. The computer-implemented method also comprises inputting the one or more images into one or more machine learning models and identifying, in the one or more images of the biological sample, via the one or more machine learning models, a plurality of images of a cell type. The computer-implemented method further comprises selecting, by the one or more machine learning models, a subset of the plurality of images of the cell type for transmission.

Abstract: An example method for refining a diagnostic machine-learning model includes receiving, at a central computing device and from multiple point-of-care (POC) analyzers of a plurality of POC analyzers, respective signals indicative of user input approving use of the multiple POC analyzers as remote training nodes, sending an initial training model to the multiple POC analyzers, at least some of the multiple POC analyzers training the initial training model on respective remote data sets stored on the at least some of the multiple POC analyzers using respective processors of the at least some of the multiple POC analyzers to form revised training models, receiving the revised training models from the at least some of the multiple POC analyzers, averaging parameters of the revised training models based on configurable criteria to form an adjusted training model, and sending the adjusted training model to the plurality of POC analyzers.

Abstract: An apparatus includes: a sample chamber having at least one depth dimension configured to allow an object of interest to move, where the object of interest has a size of at least 100 micrometers; an imaging device configured to capture images of the sample chamber, where the imaging device includes a single objective lens and has a resolving power of approximately 25 micrometers or larger than 25 micrometers; at least one processor; and at least one memory storing instructions. The instructions, when executed, cause the apparatus to: access at least one image, captured by the imaging device, of at least a portion of the sample chamber; processing the at least one image to provide an indication of one of: an object of interest, no object of interest, or a location in the sample chamber of a potential object of interest; and providing an output based on the indication.

Abstract: A VCN process may receive, by a value chain network digital twin, information associated with a value chain network. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the set of AI-based learning models is trained to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of the value chain network and at least one member of the set of AI-based learning models is trained to determine a task to be completed for the value chain network. A VCN process may provide at least one of an instruction for executing the task in the value chain network digital twin and a recommendation for executing the task in the value chain network digital twin.

Abstract: A system and method for water purification by capture of contaminants in an aqueous mixture is described herein. A system and method for regenerating the capture system is also described. An integrated capture and regeneration system and method is also described including a separation vessel that houses a capture bed and optionally an electrode in electrical contact with the bed with a power source for applying a voltage to the electrode. The aqueous wash liquid may contain a counter ion that binds to the contaminant forming an aggregate contaminant phase that separates from the aqueous wash liquid. The capture bed may be treated with a hydroxide and/or a peroxide, e.g., prior to the counter ion binding the contaminant.

Abstract: The present invention relates to methods of treating carbon to improve its ability to capture and retain contaminants, and also relates to treated carbon produced by such methods. Treatment of the carbon includes contacting the carbon with (a) a hydroxide and/or a peroxide, and (b) one or more cations selected from Ca2+, Mg2+, Zn2+, Sr2+, Al3+, B3+, and Fe3+.

Abstract: Battery arrays are provided for traction battery packs. An exemplary battery array may include a first cell stack subassembly including a first split dividing wall member, and a second cell stack subassembly including a second split dividing wall member. The first split dividing wall member and the second split dividing wall member cooperate to establish a dividing wall assembly between the first cell stack subassembly and the second cell stack subassembly. The dividing wall assembly can function to both provide the necessary tensile structure for maintaining battery cell compression and thermally isolate the adjacent cell stack subassemblies from one another.

Abstract: A system and method for efficiently determining a diagnostic protocol used by a vehicle under service and instantiating service appropriately. The diagnostic protocol is checked for expected electrical behaviors exhibited at the pins of a diagnostic port of a vehicle. If the expected behaviors are observed, a first protocol is designated for use. If the expected behaviors are not observed, other protocols are test for appropriate designation.

Abstract: Placing an event into a particular cluster can allow various inferences about the event. A new payment transaction that looks similar to a previously identified cluster of mostly fraudulent payment transactions, for example, may be higher risk. The present disclosure includes structural data improvements to the way that online clustering of events (which may include web events and not just payment transactions) occurs. A new event can be classified into a particular segment very quickly using feature table searching, which can allow for better decision making when a short timeframe is required (e.g. transaction processing, online advertising, etc.).

Abstract: An example method includes capturing, via an x-ray machine, a plurality of x-ray images of a patient covering a number of different anatomy of the patient in any order, using a machine learning algorithm to process the plurality of x-ray images for identification of an anatomy in respective x-ray images of the plurality of x-ray images, associating a label with each of the plurality of x-ray images based on the identification of the anatomy, positioning each of the plurality of x-ray images upright based on a preset coordinate scheme for the anatomy, arranging the plurality of x-ray images into a predetermined order based on the species of the patient, and generating and outputting a data file including the plurality of x-ray images in the predetermined order, positioned upright, and labeled.

Abstract: Methods, systems, and media for secure authentication of users using one or more biometric recognition systems are provided. In some embodiments, the method comprises: receiving an indication that a biometric identifier is to be used to authenticate a user to a service; receiving (i) the biometric identifier of the user from a capture device and (ii) knowledge-based secondary information associated with the user from an input device; determining a Voronoi cell identifier that corresponds to the biometric identifier; calculating a hash of the Voronoi cell identifier and the knowledge-based secondary information; transmitting the hash to a server device for verification; in response to transmitting the hash to the server device, receiving a response indicating whether the hash matches a previously stored hash that was stored in the server device; and determining whether to automatically authenticate the user to the service based on the response from the server device.

Abstract: Methods and systems for performing diagnosis using patient sample data are described. The method comprises receiving patient sample data at a point of care, analyzing the received patient sample data at the point of care, and evaluating a first result of analyzing the received patient sample data at the point of care to determine whether a criterion for advanced analysis at a central reference laboratory is met. In a case where the criterion for advanced analysis is met, the method further comprises reflexing the received patient sample data to the central reference laboratory for the advanced analysis, receiving, at the point of care, a second result of the advanced analysis from the central reference laboratory, and displaying at least one of the first result or the second result on a display at the point of care.

Abstract: An example method includes capturing, via an x-ray machine, a plurality of x-ray images of a patient covering a number of different anatomy of the patient in any order, using a machine learning algorithm to process the plurality of x-ray images for identification of an anatomy in respective x-ray images of the plurality of x-ray images, associating a label with each of the plurality of x-ray images based on the identification of the anatomy, positioning each of the plurality of x-ray images upright based on a preset coordinate scheme for the anatomy, arranging the plurality of x-ray images into a predetermined order based on the species of the patient, and generating and outputting a data file including the plurality of x-ray images in the predetermined order, positioned upright, and labeled.

Abstract: Secure methods, systems, and media for generating and verifying user credentials are provided. In some embodiments, the method comprises: receiving, from a user device, a request for access to a service that requires valid user credentials; determining an aspect of the user credentials that is to be satisfied to grant access to the requested service; transmitting, to the user device, a request for information related to the aspect of the user credential; receiving, from the user device, information related to the aspect of the user credential, wherein the information has been signed using a key associated with the user device; verifying the key used to sign the information by the user device; in response to verifying the key used to sign the information, determining whether the aspect of the user credential has been satisfied based on the received information; and, in response to determining that the aspect of the user credential has been satisfied, granting access to the service.

Abstract: An analyzer system for in vitro diagnostics includes a sample handler module having a robot arm that delivers samples from drawers into carriers on a linear synchronous motor automation track. Samples are delivered via the automation track to individual track sections associated with individual analyzer modules. Analyzer modules aspirate sample portions directly from the sample carriers and perform analysis thereon.

Abstract: An example method includes capturing, via an x-ray machine, a plurality of x-ray images of a patient covering a number of different anatomy of the patient in any order, using a machine learning algorithm to process the plurality of x-ray images for identification of an anatomy in respective x-ray images of the plurality of x-ray images, associating a label with each of the plurality of x-ray images based on the identification of the anatomy, positioning each of the plurality of x-ray images upright based on a preset coordinate scheme for the anatomy, arranging the plurality of x-ray images into a predetermined order based on the species of the patient, and generating and outputting a data file including the plurality of x-ray images in the predetermined order, positioned upright, and labeled.

Abstract: Identity systems, methods, and media for auditing and notifying users concerning verifiable claims are provided.

Abstract: A VCN process may receive information associated with a value chain network. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the set of AI-based learning models is trained on a training data set of a set of value chain network entities operating data to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of at least one value chain entity of the set of value chain network entities. A VCN process may determine a task to be completed for the value chain network based upon, at least in part, on an output of the set of AI-based learning models. A VCN process may execute the task to facilitate an improvement in the value chain network.

Abstract: A VCN process may receive information associated with a set of value chain network entities. A VCN process may provide the information to a first set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the first set of AI-based learning models is trained to generate a prediction of future demand for an item. A VCN process may provide the information to a second set of AI-based learning models, wherein at least one member of the second set of AI-based learning models is trained to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of at least one value chain entity. A VCN process may determine a potential risk in the value chain network associated with the at least one value chain network entity based upon, at least in part, an output of the AI-based learning models.