Abstract: Saccharide-substituted silk fibroin compositions as well as methods for making and using the same are provided. The compositions can include at least one saccharide coupled to silk fibroin. The coupling can be via various residues on the silk fibroin.

Abstract: An embodiment involves persistent storage and one or more processors. The persistent storage may contain a plurality of hardware models specifying types of computing devices, wherein the hardware models include attributes representing manufacturer names, product names, and model numbers of the computing devices. The processors may be configured to: obtain, from the persistent storage, a hardware model; calculate a hash value over attributes of the hardware model; compare the hash value to hash values in a curated list, the curated list including normalized hardware models that map the hash values to normalized manufacturer names, normalized product names, or normalized model numbers; determine that the hash value matches a particular hash value for a normalized hardware model; and update, in the persistent storage, the hardware model to include at least one of a normalized manufacturer name, a normalized product name, or a normalized model number associated with the normalized hardware model.

Abstract: An embodiment involves persistent storage and one or more processors. The persistent storage may contain a plurality of hardware models specifying types of computing devices, wherein the hardware models include attributes representing manufacturer names, product names, and model numbers of the computing devices. The processors may be configured to: obtain, from the persistent storage, a hardware model; calculate a hash value over attributes of the hardware model; compare the hash value to hash values in a curated list, the curated list including normalized hardware models that map the hash values to normalized manufacturer names, normalized product names, or normalized model numbers; determine that the hash value matches a particular hash value for a normalized hardware model; and update, in the persistent storage, the hardware model to include at least one of a normalized manufacturer name, a normalized product name, or a normalized model number associated with the normalized hardware model.

Abstract: An embodiment involves persistent storage and one or more processors. The persistent storage may contain a plurality of hardware models specifying types of computing devices, wherein the hardware models include attributes representing manufacturer names, product names, and model numbers of the computing devices. The processors may be configured to: obtain, from the persistent storage, a hardware model; calculate a hash value over attributes of the hardware model; compare the hash value to hash values in a curated list, the curated list including normalized hardware models that map the hash values to normalized manufacturer names, normalized product names, or normalized model numbers; determine that the hash value matches a particular hash value for a normalized hardware model; and update, in the persistent storage, the hardware model to include at least one of a normalized manufacturer name, a normalized product name, or a normalized model number associated with the normalized hardware model.

Abstract: An embodiment involves persistent storage and one or more processors. The persistent storage may contain a plurality of hardware models specifying types of computing devices, wherein the hardware models include attributes representing manufacturer names, product names, and model numbers of the computing devices. The processors may be configured to: obtain, from the persistent storage, a hardware model; calculate a hash value over attributes of the hardware model; compare the hash value to hash values in a curated list, the curated list including normalized hardware models that map the hash values to normalized manufacturer names, normalized product names, or normalized model numbers; determine that the hash value matches a particular hash value for a normalized hardware model; and update, in the persistent storage, the hardware model to include at least one of a normalized manufacturer name, a normalized product name, or a normalized model number associated with the normalized hardware model.

Abstract: A method and apparatus is provided for creating and recognizing a USN (Unique Signature Number) from an analysis of living entity's (e.g. person, animal or other multi-cellular being having neural activity) emitted electrical or electromagnetic signal in a given, normalized or calm state. This USN may then be used at a later date, in a comparison function of a number of stored USN's, in ascertaining the identity of an entity recently detected as being in proximity to an antenna of the apparatus. A further analysis process can also detect neural activity that is abnormal to that indicated by the USN of a given entity in a calm state and which neural activity may be indicative of excessive aggression, stress or even ill health said given entity.

Abstract: A method and apparatus is provided for sensing a given situation such as the presence of operational aircraft for deactivation, or other modification, of a device such as wireless communication circuitry signal transmission circuitry while in the presence of the operational aircraft. The sensing of a situation may be by the presence of a predetermined signal, the arrival at a given location, a measurable change in air pressure and so forth. The sensor circuitry, upon detecting conditions suggestive of operational aircraft, operates to activate, deactivate or otherwise modify the performance of a device while in the presence or immediate vicinity of aircraft or other predetermined situation.

Abstract: A method and apparatus is provided for creating and recognizing a USN (Unique Signature Number) from an analysis of living entity's (e.g. person, animal or other multi-cellular being having neural activity) emitted electrical or electromagnetic signal in a given, normalized or calm state. This USN may then be used at a later date, in a comparison function of a number of stored USN's, in ascertaining the identity of an entity recently detected as being in proximity to an antenna of the apparatus. A further analysis process can also detect neural activity that is abnormal to that indicated by the USN of a given entity in a calm state and which neural activity may be indicative of excessive aggression, stress or even ill health said given entity.