Abstract: Systems, methods, and apparatus for a multi-spectral X-ray target and source are disclosed. In one or more embodiments, a disclosed method comprises emitting, by a source of the X-ray generator, electrons towards a section of a multi-spectral X-ray target of the X-ray generator. In one or more embodiments, the multi-spectral X-ray target is rotatable and comprises a plurality of sections, which each comprise an X-ray generating material and at least two of the sections comprise a different X-ray generating material. The method further comprises generating a set of X-rays, when the electrons impinge on the section of the multi-spectral X-ray target. The method further comprises rotating the multi-spectral X-ray target such that the source is in position to project the electrons towards another section of the multi-spectral X-ray target. Further, the method comprises repeating the above method steps for all of the remaining sections of the multi-spectral X-ray target.

Abstract: Systems, methods, and apparatus for a multi-spectral X-ray target and source are disclosed. In one or more embodiments, a disclosed method comprises emitting, by a source of the X-ray generator, electrons towards a section of a multi-spectral X-ray target of the X-ray generator. In one or more embodiments, the multi-spectral X-ray target is rotatable and comprises a plurality of sections, which each comprise an X-ray generating material and at least two of the sections comprise a different X-ray generating material. The method further comprises generating a set of X-rays, when the electrons impinge on the section of the multi-spectral X-ray target. The method further comprises rotating the multi-spectral X-ray target such that the source is in position to project the electrons towards another section of the multi-spectral X-ray target. Further, the method comprises repeating the above method steps for all of the remaining sections of the multi-spectral X-ray target.

Abstract: Methods, systems, and computer program products sustain a fleet of configuration-controlled assets. The method involves integrating a plurality of previously separate systems with reusable business transactions associated with managing a fleet of vehicles. The method also involves receiving operational data associated with a vehicle of the fleet, integrating the operational data per customer requirements, packaging and distributing the operational data to the plurality of systems, and installing and distributing the operational data to one or more applications of the plurality of systems utilizing reusable business transactions. The system utilizes a plurality of applications and a plurality of logical subsystems, which are connected to each other through gateways. The logical subsystems use a common format defined by a set of messages. However, the applications can use a plurality of different formats.

Abstract: A system is provided for benchmarking a progressive combinatorial solver. The system may initialize a parametric model collector, and perform a plurality of parametric trials associated with respective numbers of sub-part iterations of the progressive combinatorial solver. For each of the plurality of parametric trials, the system may initialize a statistical data collector. The system may perform a plurality of randomized executions of the progressive combinatorial solver, and add data including metric values for respective solutions of the executions to the statistical data collector. The system may then determine statistical parameter(s) of data in the statistical data collector, and add data including an indication of the respective number of sub-part iterations, a cost of finding the metric values for the respective solutions, and the statistical parameters of the trial to the parametric data collector. And the system may determine a best-fit model from data in the parametric data collector.

Abstract: A system is provided for benchmarking a progressive combinatorial solver. The system may initialize a parametric model collector, and perform a plurality of parametric trials associated with respective numbers of sub-part iterations of the progressive combinatorial solver. For each of the plurality of parametric trials, the system may initialize a statistical data collector. The system may perform a plurality of randomized executions of the progressive combinatorial solver, and add data including metric values for respective solutions of the executions to the statistical data collector. The system may then determine statistical parameter(s) of data in the statistical data collector, and add data including an indication of the respective number of sub-part iterations, a cost of finding the metric values for the respective solutions, and the statistical parameters of the trial to the parametric data collector. And the system may determine a best-fit model from data in the parametric data collector.

Abstract: A system is provided for benchmarking a progressive combinatorial solver. The system may initialize a parametric model collector, and perform a plurality of parametric trials associated with respective numbers of sub-part iterations of the progressive combinatorial solver. For each of the plurality of parametric trials, the system may initialize a statistical data collector. The system may perform a plurality of randomized executions of the progressive combinatorial solver, and add data including metric values for respective solutions of the executions to the statistical data collector. The system may then determine statistical parameter(s) of data in the statistical data collector, and add data including an indication of the respective number of sub-part iterations, a cost of finding the metric values for the respective solutions, and the statistical parameters of the trial to the parametric data collector. And the system may determine a best-fit model from data in the parametric data collector.

Abstract: A system is provided for benchmarking a progressive combinatorial solver. The system may initialize a parametric model collector, and perform a plurality of parametric trials associated with respective numbers of sub-part iterations of the progressive combinatorial solver. For each of the plurality of parametric trials, the system may initialize a statistical data collector. The system may perform a plurality of randomized executions of the progressive combinatorial solver, and add data including metric values for respective solutions of the executions to the statistical data collector. The system may then determine statistical parameter(s) of data in the statistical data collector, and add data including an indication of the respective number of sub-part iterations, a cost of finding the metric values for the respective solutions, and the statistical parameters of the trial to the parametric data collector. And the system may determine a best-fit model from data in the parametric data collector.

Abstract: A tracking unit includes a register for storing initial sequences and an accumulator for effecting a statistical treatment of the initial sequences, and ensures the statistical treatment relates to no more than a number of best sequences based on an evaluating algorithm. A generating unit employs the statistical treatment to present a new sequence. A measuring unit evaluates the new sequence according to the algorithm and cooperates with the tracking unit to effect providing the new sequence to the tracking unit when the evaluating indicates the new sequence is appropriate for the storing and statistical treatment. The measuring unit provides the new sequence to a regulating unit with an indication of efficacy of the new sequence. The regulating unit employs the indication to store a best-sequence-yet-received and responds to a criterion to order the generating unit to present a new sequence or to present the best-sequence-yet-received at an output locus.

Abstract: A system optimizing a sequence according to an algorithm includes: an initiator; a tracker; a generator; a measurer; a regulator; and an output. The initiator provides initial sequences to the tracker. The tracker stores and effects a statistical treatment of the initial sequences. The generator employs the statistical treatment to present a new sequence. The measurer evaluates the new sequence according to the algorithm. The measurer provides the new sequence to the tracker when the evaluating indicates so, and provides the new sequence and a quality indicator to the regulator. The regulator employs the indicator to store at least a best-sequence-yet-received, and responds to a criterion to order the generator to present a new sequence or to present the best-sequence-yet-received at the output.

Abstract: A system for Service Oriented Architecture (SOA) communication includes a plurality of SOA nodes having a standardized hardware configuration, wherein the standardized hardware configuration includes an operating engine, an encryption module accessed by the operating engine, which provides security for message traffic, a compression module to compress and decompress the message traffic, a routing module accessed by the operating engine, to determine the routing of message types, incoming traffic routed to appropriate service clients, outbound traffic routed to appropriate SOA devices, a security module that authenticates and authorizes message traffic, and one or more network interfaces, and one or more networks over which the SOA nodes communicate with one another.