Abstract: The present invention is directed to a wound dressing, surgical tape or plaster comprising an absorbent pad and an adhesive part in which the adhesive part is an adhesive dissolvable paper. Another aspect of the invention is directed to a bandage of elongate form in which sections of the bandage can be of a woven or non-woven fabric, comprising multiple sections, wherein the sections are held together via water-soluble fiber material.

Abstract: The invention relates to a system for sensor protection in electron imaging applications comprising a beam control device configured to provide a beam signal based on an incoming beam signal, wherein the beam signal comprises an altered beam intensity, wherein the beam control device is further configured to receive a control signal and to activate based on the control signal. The system further comprises a sensor configured to capture the beam signal and to provide a capture signal based on the beam signal, and a control module configured to provide the control signal to the beam control device, to generate an exposure value based on the capture signal and to modify the control signal based on the exposure value.

Abstract: Systems and methods for using smart sample containers to manage complex sample evaluation workflows, are disclosed. An example method for using a smart sample container configured to manage a sample evaluation workflow according to the present invention comprises, obtaining a sample evaluation workflow for the one or more samples, receiving interactions with external devices, and based on the sample evaluation workflow, and causing the external devices to perform actions to advance the sample evaluation workflow. The smart sample container may further modify the sample evaluation workflow based on results of actions performed by the sample evaluation workflow and/or store information relating to the results of such actions. In this way, the smart sample containers are able to dynamically drive the evaluation of a sample through its sample evaluation workflow.

Abstract: A cotton bud, in which a shaft is made from rolled dissolvable paper and the buds at the ends comprise entangled dissolvable cotton wool type fibres.

Abstract: A moist or wet wipe comprising or consisting of a non-woven wet laid material which contains or consists of soluble material and is coated or impregnated with cleaning or moisturising substances.

Abstract: A natural language processing system that includes an artificial intelligence (AI) engine and a tagging engine. The AI engine is configured to receive a set of audio files and to identify concepts within the set of audio files. The AI engine is further configured to determine a usage frequency for each of the identified concepts and to generate an AI-defined tag for concepts with a usage frequency that is greater than a usage frequency threshold. The tagging engine is configured to receive an audio file and to identify observed concepts within the audio file. The tagging engine is further configured to compare the observed concepts to the first set of concepts, to determine one or more observed concepts matches concepts linked with AI-defined tags, and to modify metadata for the audio file to include AI-defined tags.

Abstract: The present invention is directed to a wound dressing, surgical tape or plaster 10 comprising an absorbent pad 14 and an adhesive part in which the adhesive part is an adhesive dissolvable paper 12. Another aspect of the invention is directed to a bandage 30 of elongate form in which sections 32 of the bandage 30 can be of a woven or non-woven fabric, comprising multiple sections 32, wherein the sections 32 are held together via water-soluble fibre material.

Abstract: A natural language processing system that includes an artificial intelligence (AI) engine and a tag management engine. The AI engine is configured to receive a set of audio files and to identify concepts within the set of audio files. The AI engine is further configured to determine a usage frequency for each of the identified concepts and to generate an AI-defined tag for concepts with a usage frequency that is greater than a usage frequency threshold. The tag management engine is configured to receive an audio file, identify tags linked with the audio file, to determine an access frequency for the audio file within a predetermined time period, and to adjust the activity level of the tags based on the access frequency. The tag management engine is further configured to remove tags from the set of tags with an activity level that is less than a purge threshold.

Abstract: A natural language processing system that includes an artificial intelligence (AI) engine, a tagging engine, and a resource allocation engine. The AI engine is configured to receive a set of audio files and to identify concepts within the set of audio files. The AI engine is further configured to determine a usage frequency for each of the identified concepts and to generate an AI-defined tag for concepts with a usage frequency that is greater than a usage frequency threshold. The tagging engine is configured to receive an audio file and to modify metadata for the audio file to include AI-defined tags. The resource allocation engine is configured to identify a storage location from among the plurality of storage devices based on tags associated with the audio file and send the audio file to the identified storage location.

Abstract: In one or more embodiments, one or more systems, processes, and/or methods may receive first task sets that include respective first tasks and one or more of respective first priorities, respective first minimum computing resource allocations, and respective first maximum processing times; receive first satisfaction information associated with processing the first task sets; receive first execution metric information associated with processing the first task sets; determine a first pattern based at least on the first satisfaction information and based at least on the first execution metric information; receive second task sets that include respective second tasks and one or more of respective second priorities, respective second minimum computing resource allocations, and respective second maximum processing times; determine, based at least on the first pattern, computing resources allocations for the second task sets; and determine, based at least on the first pattern, a processing order for the second task

Abstract: A natural language processing system that includes an artificial intelligence (AI) engine and a tagging engine. The AI engine is configured to receive a set of audio files and to identify concepts within the set of audio files. The AI engine is further configured to determine a usage frequency for each of the identified concepts and to generate an AI-defined tag for concepts with a usage frequency that is greater than a usage frequency threshold. The tagging engine is configured to receive an audio file and to identify observed concepts within the audio file. The tagging engine is further configured to compare the observed concepts to the first set of concepts, to determine one or more observed concepts matches concepts linked with AI-defined tags, and to modify metadata for the audio file to include AI-defined tags.

Abstract: A natural language processing system that includes an artificial intelligence (AI) engine and a tag management engine. The AI engine is configured to receive a set of audio files and to identify concepts within the set of audio files. The AI engine is further configured to determine a usage frequency for each of the identified concepts and to generate an AI-defined tag for concepts with a usage frequency that is greater than a usage frequency threshold. The tag management engine is configured to receive an audio file, identify tags linked with the audio file, to determine an access frequency for the audio file within a predetermined time period, and to adjust the activity level of the tags based on the access frequency. The tag management engine is further configured to remove tags from the set of tags with an activity level that is less than a purge threshold.

Abstract: A natural language processing system that includes an artificial intelligence (AI) engine, a tagging engine, and a resource allocation engine. The AI engine is configured to receive a set of audio files and to identify concepts within the set of audio files. The AI engine is further configured to determine a usage frequency for each of the identified concepts and to generate an AI-defined tag for concepts with a usage frequency that is greater than a usage frequency threshold. The tagging engine is configured to receive an audio file and to modify metadata for the audio file to include AI-defined tags. The resource allocation engine is configured to identify a storage location from among the plurality of storage devices based on tags associated with the audio file and send the audio file to the identified storage location.

Abstract: In one or more embodiments, one or more systems, processes, and/or methods may receive a first data stream and determine a pattern from the first data stream. At least one rule set based at least on the pattern may be determined. A second data stream, different from the first data stream may be received and entities may be determined, where each of the entities may be associated with respective data of the second data stream that satisfies the at least one rule set. At least one data object of the second data stream may be tagged, in response to determining the entities. In one or more embodiments, tagging the at least one data object may associate the at least one data object with at least one of the entities.

Abstract: In one or more embodiments, one or more systems, processes, and/or methods may receive first task sets that include respective first tasks and one or more of respective first priorities, respective first minimum computing resource allocations, and respective first maximum processing times; receive first satisfaction information associated with processing the first task sets; receive first execution metric information associated with processing the first task sets; determine a first pattern based at least on the first satisfaction information and based at least on the first execution metric information; receive second task sets that include respective second tasks and one or more of respective second priorities, respective second minimum computing resource allocations, and respective second maximum processing times; determine, based at least on the first pattern, computing resources allocations for the second task sets; and determine, based at least on the first pattern, a processing order for the second task

Abstract: In one or more embodiments, one or more systems, processes, and/or methods may receive a first data stream and determine a pattern from the first data stream. At least one rule set based at least on the pattern may be determined. A second data stream, different from the first data stream may be received and entities may be determined, where each of the entities may be associated with respective data of the second data stream that satisfies the at least one rule set. At least one data object of the second data stream may be tagged, in response to determining the entities. In one or more embodiments, tagging the at least one data object may associate the at least one data object with at least one of the entities.

Abstract: A stool collection device (10) comprising a rigid dissolvable fibre sheet adapted to fit above the water in a toilet pan.

Abstract: A texture processing pipeline can be configured to service memory access requests that represent texture data access operations or generic data access operations. When the texture processing pipeline receives a memory access request that represents a texture data access operation, the texture processing pipeline may retrieve texture data based on texture coordinates. When the memory access request represents a generic data access operation, the texture pipeline extracts a virtual address from the memory access request and then retrieves data based on the virtual address. The texture processing pipeline is also configured to cache generic data retrieved on behalf of a group of threads and to then invalidate that generic data when the group of threads exits.

Abstract: A texture processing pipeline can be configured to service memory access requests that represent texture data access operations or generic data access operations. When the texture processing pipeline receives a memory access request that represents a texture data access operation, the texture processing pipeline may retrieve texture data based on texture coordinates. When the memory access request represents a generic data access operation, the texture pipeline extracts a virtual address from the memory access request and then retrieves data based on the virtual address. The texture processing pipeline is also configured to cache generic data retrieved on behalf of a group of threads and to then invalidate that generic data when the group of threads exits.

Abstract: A tag unit configured to manage a cache unit includes a coalescer that implements a set hashing function. The set hashing function maps a virtual address to a particular content-addressable memory unit (CAM). The coalescer implements the set hashing function by splitting the virtual address into upper, middle, and lower portions. The upper portion is further divided into even-indexed bits and odd-indexed bits. The even-indexed bits are reduced to a single bit using a XOR tree, and the odd-indexed are reduced in like fashion. Those single bits are combined with the middle portion of the virtual address to provide a CAM number that identifies a particular CAM. The identified CAM is queried to determine the presence of a tag portion of the virtual address, indicating a cache hit or cache miss.