Abstract: The invention relates generally to hybrid amebocyte lysate compositions (including both native and recombinant components) and their use in detecting and/or quantifying endotoxin in a sample.

Abstract: A tray for a vaporization vessel that includes a tray having a side wall, a bottom plate, one or more apertures that extend through the bottom plate, and a duct that extends through and from the bottom plate. The tray configured to support a solid reagent to be vaporized. A method of assembling the tray that includes forming a first tray that has the side wall and the bottom plate. A vaporization vessel that includes one or more of the trays.

Abstract: Apparatus and methods for producing ultrashort optical pulses are described. A high-power, solid-state, passively mode-locked laser can be manufactured in a compact module that can be incorporated into a portable instrument. The mode-locked laser can produce sub-50-ps optical pulses at a repetition rates between 200 MHz and 50 MHz, rates suitable for massively parallel data-acquisition. The optical pulses can be used to generate a reference clock signal for synchronizing data-acquisition and signal-processing electronics of the portable instrument.

Abstract: A convertible golf cart seat assembly is disclosed. The convertible golf cart seat assembly includes a main seat frame attached to the existing golf cart structure, a plastic stationary cargo board supported by the main seat frame, a plastic flip board rotatably hinged to the main seat frame, and a seat with a plastic backer board attached to the flip board. The hinge between the flip board and the main seat frame includes flip board flanges on the flip board and cargo board flanges on the stationary cargo board that abut each other and provide strength to the cargo bed created by the stationary cargo board and the flip board.

Abstract: A convertible golf cart seat assembly is disclosed. The convertible golf cart seat assembly includes a main seat frame attached to the existing golf cart structure, a plastic stationary cargo board supported by the main seat frame, a plastic flip board rotatably hinged to the main seat frame, and a seat with a plastic backer board attached to the flip board. The hinge between the flip board and the main seat frame includes flip board flanges on the flip board and cargo board flanges on the stationary cargo board that abut each other and provide strength to the cargo bed created by the stationary cargo board and the flip board.

Abstract: A convertible golf cart seat assembly is disclosed. The convertible golf cart seat assembly includes a main seat frame attached to the existing golf cart structure, a plastic stationary cargo board supported by the main seat frame, a plastic flip board rotatably hinged to the main seat frame, and a seat with a plastic backer board attached to the flip board. The hinge between the flip board and the main seat frame includes flip board flanges on the flip board and cargo board flanges on the stationary cargo board that abut each other and provide strength to the cargo bed created by the stationary cargo board and the flip board.

Abstract: A defect detection method includes acquiring a reference image; selecting a target region of the reference image; identifying, based on a matching metric, one or more comparative regions of the reference image corresponding to the target region; acquiring a test image; masking the test image with the target region of the reference image and the one or more comparative regions of the reference image; defining a defect threshold for the target region in the test image based on the one or more comparative regions in the test image; and determining whether the target region of the test image contains a defect based on the defect threshold.

Abstract: Defect classification includes acquiring one or more images of a specimen, receiving a manual classification of one or more training defects based on one or more attributes of the one or more training defects, generating an ensemble learning classifier based on the received manual classification and the attributes of the one or more training defects, generating a confidence threshold for each defect type of the one or more training defects based on a received classification purity requirement, acquiring one or more images including one or more test defects, classifying the one or more test defects with the generated ensemble learning classifier, calculating a confidence level for each of the one or more test defects with the generated ensemble learning classifier and reporting one or more test defects having a confidence level below the generated confidence threshold via the user interface device for manual classification.

Abstract: Defect classification includes acquiring one or more images of a specimen, receiving a manual classification of one or more training defects based on one or more attributes of the one or more training defects, generating an ensemble learning classifier based on the received manual classification and the attributes of the one or more training defects, generating a confidence threshold for each defect type of the one or more training defects based on a received classification purity requirement, acquiring one or more images including one or more test defects, classifying the one or more test defects with the generated ensemble learning classifier, calculating a confidence level for each of the one or more test defects with the generated ensemble learning classifier and reporting one or more test defects having a confidence level below the generated confidence threshold via the user interface device for manual classification.

Abstract: Defect classification includes acquiring one or more images of a specimen including multiple defects, grouping the defects into groups of defect types based on the attributes of the defects, receiving a signal from a user interface device indicative of a first manual classification of a selected number of defects from the groups, generating a classifier based on the first manual classification and the attributes of the defects, classifying, with the classifier, one or more defects not manually classified by the manual classification, identifying the defects classified by the classifier having the lowest confidence level, receiving a signal from the user interface device indicative of an additional manual classification of the defects having the lowest confidence level, determining whether the additional manual classification identifies one or more additional defect types not identified in the first manual classification, and iterating the procedure until no new defect types are found.

Abstract: Defect classification includes acquiring one or more images of a specimen, receiving a manual classification of one or more training defects based on one or more attributes of the one or more training defects, generating an ensemble learning classifier based on the received manual classification and the attributes of the one or more training defects, generating a confidence threshold for each defect type of the one or more training defects based on a received classification purity requirement, acquiring one or more images including one or more test defects, classifying the one or more test defects with the generated ensemble learning classifier, calculating a confidence level for each of the one or more test defects with the generated ensemble learning classifier and reporting one or more test defects having a confidence level below the generated confidence threshold via the user interface device for manual classification.

Abstract: A defect detection method includes acquiring a reference image; selecting a target region of the reference image; identifying, based on a matching metric, one or more comparative regions of the reference image corresponding to the target region; acquiring a test image; masking the test image with the target region of the reference image and the one or more comparative regions of the reference image; defining a defect threshold for the target region in the test image based on the one or more comparative regions in the test image; and determining whether the target region of the test image contains a defect based on the defect threshold.

Abstract: A convertible golf cart seat assembly is disclosed. The convertible golf cart seat assembly includes a main seat frame attached to the existing golf cart structure, a plastic stationary cargo board supported by the main seat frame, a plastic flip board rotatably hinged to the main seat frame, and a seat with a plastic backer board attached to the flip board. The hinge between the flip board and the main seat frame includes flip board flanges on the flip board and cargo board flanges on the stationary cargo board that abut each other and provide strength to the cargo bed created by the stationary cargo board and the flip board.

Abstract: Defect classification includes acquiring one or more images of a specimen including multiple defects, grouping the defects into groups of defect types based on the attributes of the defects, receiving a signal from a user interface device indicative of a first manual classification of a selected number of defects from the groups, generating a classifier based on the first manual classification and the attributes of the defects, classifying, with the classifier, one or more defects not manually classified by the manual classification, identifying the defects classified by the classifier having the lowest confidence level, receiving a signal from the user interface device indicative of an additional manual classification of the defects having the lowest confidence level, determining whether the additional manual classification identifies one or more additional defect types not identified in the first manual classification, and iterating the procedure until no new defect types are found.

Abstract: Defect classification includes acquiring one or more images of a specimen, receiving a manual classification of one or more training defects based on one or more attributes of the one or more training defects, generating an ensemble learning classifier based on the received manual classification and the attributes of the one or more training defects, generating a confidence threshold for each defect type of the one or more training defects based on a received classification purity requirement, acquiring one or more images including one or more test defects, classifying the one or more test defects with the generated ensemble learning classifier, calculating a confidence level for each of the one or more test defects with the generated ensemble learning classifier and reporting one or more test defects having a confidence level below the generated confidence threshold via the user interface device for manual classification.

Abstract: A convertible golf cart seat assembly is disclosed. The convertible golf cart seat assembly includes a main seat frame attached to the existing golf cart structure, a plastic stationary cargo board supported by the main seat frame, a plastic flip board rotatably hinged to the main seat frame, and a seat with a plastic backer board attached to the flip board. The hinge between the flip board and the main seat frame includes flip board flanges on the flip board and cargo board flanges on the stationary cargo board that abut each other and provide strength to the cargo bed created by the stationary cargo board and the flip board.

Abstract: A convertible golf cart seat assembly is disclosed. The convertible golf cart seat assembly includes a main seat frame attached to the existing golf cart structure, a plastic stationary cargo board supported by the main seat frame, a plastic flip board rotatably hinged to the main seat frame, and a seat with a plastic backer board attached to the flip board. The hinge between the flip board and the main seat frame includes flip board flanges on the flip board and cargo board flanges on the stationary cargo board that abut each other and provide strength to the cargo bed created by the stationary cargo board and the flip board.

Abstract: A high throughput surface inspection system with enhanced detection sensitivity is described. The acquired data is processed in real time at a rate of below 50 MHz thereby reducing the cost for data processing. Anomalies are detected and verified by comparing adjacent repeating patterns and the height of the surface is monitored and corrected dynamically to reduce misregistration errors between adjacent repeating patterns. Local thresholds employing neighborhood information are used for detecting and verifying the presence of anomalies. The sampled point spread function of the combined illumination and collection system is exploited for anomaly detection and verification.

Abstract: A system for concurrent processing of queries and transactions against a shared database. The system includes multiple processors wherein a processor is available for processing queries and another processor is available for concurrently processing transactions. A query buffer is established for performing the query search while the data accessed by transactions is available in a database cache. Control logic in a database management system distinguishes between transactions and queries and initiates file-read control for reading the file containing the database. File-read control contains logic for logical sequential reading and logical non-sequential reading of the file. Control structures provide a means for control over the load that the query is allowed to place on the system.

Abstract: A multiprocessor data processing system is implemented with processors, each of which may request for a temporary time the exclusive lock on an object which is stored on a data base. To achieve this a lock processor synchronizes the locking and unlocking of the objects. The requesting processor directs the storage of the object from the data base into a selected high performance storage unit, where it has exclusive rights to modify or write into the object until the object is unlocked by the processor. An audit tape or disk records all modifications made to any object during a transaction. A non-volatile cache memory is inserted in the audit trail to store a before-look image of the object that resides in the high performance storage unit. Data compaction occurs by comparison of the before-look image with an after-look image to provide a difference image, which is supplied to an audit buffer that is coupled to the audit tape.