Abstract: An orthopedic implant includes an upper portion, a central portion having a greater lateral dimension than that of the upper portion, and a lower portion having a greater lateral dimension than the central portion. The upper portion, the central portion and the lower portion each may have at least one fixation aperture. Further, the upper portion, the central portion and the lower portion may each be non-linear and/or curved relative to at least one plane. An instrument having a body that includes a housing. The body includes a platform, at least one retention mechanism for holding the implant on the platform, an adjustment mechanism to adjust the height of the platform relative to the top surface of the housing, and a lid coupled with the housing with the top surface of the housing and the lid defining a slot configured to receive a cutting instrument.

Abstract: A method of correcting mass spectral data comprises making calibration measurements at one or more calibration times using calibrants which have known mass to charge ratio (m/z) values or previously mass measured mass to charge ratio (m/z) values, making a list of intrinsic components which are present during more than one acquisition periods, wherein the components have mass to charge ratio (m/z) values that were not present or observed during or close to the one or more calibration times, and utilising the list to calculate a mass or mass to charge ratio (m/z) correction factor for one or more acquisition periods which are not close or adjacent in time to an acquisition period containing a directly calibrated mass to charge ratio (m/z) value.

Abstract: Systems and methods are provided to create training data, validate, deploy and test artificial intelligence (AI) systems in a virtual development environment, incorporating virtual spaces, objects, machinery, devices, subsystems, and actual human action and behavior.

Abstract: A system continuously monitors, by at least one inspector, an inspection work queue for a class of inspection operation request, detects, by the at least one inspector, the class of inspection operation request in the inspection work queue, removes, by the at least one inspector, the class of inspection operation request from the inspection work queue, determines, by the at least one inspector, one of a class of inspection tool and a specific level of inspection to perform for the class of inspection operation request that references a data object, and executes, by the at least one inspector, the one of the class of inspection tool and the specific level of inspection for the class of inspection operation request that references the data object at one of a certain time and a certain event during a data lifecycle of the data object.

Abstract: A remote sliding door locking system includes a locking plate, a remote controller, and a remote locking device. The remote locking device includes a housing, a logic board, a locking system, and a power source. The housing includes an interior compartment, a mounting plate, and an exterior surface with a release mechanism slot, at least one input button, and at least one status indicator. The logic board includes a processor, memory, and a communications module. The locking system includes a bolt, a locking mechanism, and a manual release mechanism. The manual release mechanism is operatively engaged to the locking mechanism to drive the movement of the bolt. The logic board communicates with the remote controller by way of the communications module. The logic board and the power source are operatively coupled to the locking system to engage and disengage bolt from the slot by way of the locking mechanism.

Abstract: A system continuously stores, as machine learning data, metadata results associated with a previous cyber-attack, a previous inspection class policy definition at a time of the previous cyber-attack, and a result of a previous data protection operation taken upon indication of the previous cyber-attack; continuously monitors for a new security condition or event; detects the new security condition or event; determines an appropriate inspection class policy from a plurality of inspection class policies based on the new security condition or event; based on the determined inspection class policy and the machine learning training data, determines a specific class of inspection tool from a plurality of classes of inspection tools or a specific level of inspection from a plurality of different levels of inspection for the new security condition or event; and executes the specific class of inspection tool or the specific level of inspection using the specific class of inspection tool on a particular data object to

Abstract: A training device including a hub, at least one elastic cord, and at least two arms. The hub including an upper side, a lower side, and a plurality of ports. The at least one elastic cord extending from the hub and is accessible via at least one of the plurality of ports. The at least two arms extending from the hub and are configured to move relative to the hub. The training device is configured to assume (i) a closed state wherein the at least two arms both extend downwardly from the lower side of the hub and (ii) an open state wherein the at least two arms extend laterally outwardly from the hub between the upper side and the lower side of the hub. The at least one elastic cord biases the at least two arms in the open state.

Abstract: Systems and methods are provided to create training data, validate, deploy and test artificial intelligence (AI) systems in a virtual development environment, incorporating virtual spaces, objects, machinery, devices, subsystems, and actual human action and behavior.

Abstract: A system includes at least one processor to continually monitor at least one resource associated with a computing network for a condition in the computing network that may trigger an authorization control modification, determine that the condition has occurred in the computing network, and dynamically and automatically modify a user authorization control for at least one particular user responsive to the condition.

Abstract: A system includes a memory and at least one processor to set a network throughput level setting to a default network traffic rate in a computer network, begin a data protection operation at the network throughput level setting in the computer network, continually monitor the computer network and determine that a condition has occurred in the computer network, dynamically adjust the network throughput level setting in response to the condition by one of decreasing the network throughput level setting by a network traffic rate increment and increasing the network throughput level setting by the network traffic rate increment, and dynamically shape network or storage traffic for the data protection operation using the network throughput level setting.

Abstract: A system continuously stores, as machine learning data, metadata results associated with a previous cyber-attack, a previous inspection class policy definition at a time of the previous cyber-attack, and a result of a previous data protection operation taken upon indication of the previous cyber-attack; continuously monitors for a new security condition or event; detects the new security condition or event; determines an appropriate inspection class policy from a plurality of inspection class policies based on the new security condition or event; based on the determined inspection class policy and the machine learning training data, determines a specific class of inspection tool from a plurality of classes of inspection tools or a specific level of inspection from a plurality of different levels of inspection for the new security condition or event; and executes the specific class of inspection tool or the specific level of inspection using the specific class of inspection tool on a particular data object to

Abstract: A system includes a memory and at least one processor to monitor for a condition associated with a first cloud computing resource performing a cloud computing operation, determine that the condition associated with the first cloud computing resource has occurred, analyze the condition associated with the first cloud computing resource and compare the first cloud computing resource with a second cloud computing resource that is capable of performing at least a subset of the cloud computing operation, automatically modify the cloud computing operation that uses the first cloud computing resource to use the second cloud computing resource, and send at least one notification to a computing device about the condition associated with the first cloud computing resource and information associated with a modification of the cloud computing operation from the first cloud computing resource to the second cloud computing resource.

Abstract: A microfluidic system may include a microfluidic chip having a non-conductive substrate and wells connected in common to a microfluidic channel within the non-conductive substrate. Each well may have a galvanic contact with a first portion at an upper surface of the sample well and a second portion that extends into the non-conductive substrate. A plurality of electrodes may be provided as part of an electrical interface, with each electrode configured to contact a respective galvanic contact of the microfluidic chip. The electrical interface may also include at least one shared power amplifier that is configured to generate a power signal (e.g., constant current, constant voltage, pulsed power signal). A selector may be configured to receive the generated power signal from the shared power amplifier and configured to select at least one of the plurality of electrodes and output the received power signal thereto.

Abstract: A system continuously stores metadata results associated with a plurality of ransomware attacks, a plurality of inspection class policy definitions, a plurality of data protection operations, and operational forensics data as machine learning training data, continuously monitors for one of a new security condition and event, detects one of the new security condition and event, determines an appropriate inspection class policy based on the one of the new security condition and event, based on the inspection class policy, determines one to implement of a class of inspection operation, a cyber security analysis, and a data protection operation, and executes one of the class of inspection operation, the cyber security analysis, and the data protection operation based on the machine learning training data.

Abstract: A system continuously monitors, by at least one inspector, an inspection work queue for a class of inspection operation request, detects, by the at least one inspector, the class of inspection operation request in the inspection work queue, removes, by the at least one inspector, the class of inspection operation request from the inspection work queue, determines, by the at least one inspector, one of a class of inspection tool and a specific level of inspection to perform for the class of inspection operation request that references a data object, and executes, by the at least one inspector, the one of the class of inspection tool and the specific level of inspection for the class of inspection operation request that references the data object at one of a certain time and a certain event during a data lifecycle of the data object.

Abstract: A system continuously monitors, by at least one inspector, an inspection work queue for a class of inspection operation request, detects, by the at least one inspector, the class of inspection operation request in the inspection work queue, removes, by the at least one inspector, the class of inspection operation request from the inspection work queue, determines, by the at least one inspector, one of a class of inspection tool and a specific level of inspection to perform for the class of inspection operation request that references a data object, and executes, by the at least one inspector, the one of the class of inspection tool and the specific level of inspection for the class of inspection operation request that references the data object at one of a certain time and a certain event during a data lifecycle of the data object.

Abstract: Systems and methods are provided to create training data, validate, deploy and test artificial intelligence (AI) systems in a virtual development environment, incorporating virtual spaces, objects, machinery, devices, subsystems, and actual human action and behavior.

Abstract: A system includes a memory and at least one processor to disable automatic prioritization of at least one data protection operation in a computer network, begin the at least one data protection operation in the computer network, continually monitor the computer network and determine that a condition has occurred in the computer network, activate automatic prioritization of the at least one data protection operation in the computer network, and instruct backup server tooling software to direct backup data for the at least one data protection operation to move from a first target storage repository to a second target storage repository.

Abstract: A system includes at least one processor to receive training data and generate at least one machine learning rule based on the training data to apply when a condition occurs, continually monitor at least one resource associated with a computing network for the condition in the computing network that may trigger an authorization control modification, the condition comprising one of an active project that uses the at least one resource, a security alert level change, a resource locality change, metadata associated with the condition, a skill assessment, and a business state analysis, determine that the condition has occurred in the computing network, and dynamically and automatically modify a user authorization control for at least one particular user responsive to the machine learning rule.