Abstract: A container that manages access to protected resources using rules to intelligently manage them includes an environment having a set of software and configurations that are to be managed. A rule engine, which executes the rules, may be called reactively when software accesses protected resources. The engine uses a combination of embedded and configurable rules. It may be desirable to assign and manage rules per process, per resource (e.g., file, registry, etc.), and per user. Access rules may be altitude-specific access rules.

Abstract: Interaction output over a local computer-readable medium (CRM) generated based on user interaction with rendered content input representing a virtualized asset being received at a virtualized asset local provisioning server. A manner to exploit the virtualized asset is determined from interaction output. The virtualized asset is exploited based on the determined manner to exploit the virtualized asset. A request for a portion of the virtualized asset generated in response to the exploiting the virtualized asset is intercepted. If it is determined that the portion of the virtualized asset is absent from the local storage, a request for the portion of the virtualized asset is sent to a virtualized asset delivery system over a non-local CRM; the portion of the virtualized asset retrieved by the virtualized asset delivery system is received over the non-local CRM; and the received portion of the virtualized asset is used in exploiting the virtualized asset.

Abstract: Examples of the present disclosure describe systems and methods of providing real-time scanning of IP addresses. In aspects, input may be received by a real-time IP scanning system. The system may generate one or more work orders based on the input. A scanner associated with the system may access a work order and attempt to communicate with one or more devices identified by the work order. If the attempted communication with a device is successful, a protocol analyzer may be used to provide a predefined payload to the device. If the response from the device matches an expected string, the device may be determined to be a safe and/or legitimate device. If the response from the device does not match an expected string, the device may be determined to be a malicious device.

Abstract: The probabilistic accumulation method is an independent and comprehensive approach for validating third party catastrophe models. The method starts with a standard technique, namely a limited scenario accumulation analysis, and extends the approach via a novel sampling methodology to evaluate the third part models against a significantly larger dataset. The sampling approach includes a technique for extending the fixed frequency and severity assumption used by the vendor catastrophe models. The result is more complete set of loss estimates against which to evaluate the vendor model output.

Abstract: An endoluminal prostheses delivery system having an outer sheath with a distal end region, a proximal end region and a lumen extending along a longitudinal axis between the proximal end region and the distal end region. An inner member extends through the lumen of the outer sheath, at least a portion of the inner member configured to support an expandable device within the lumen near the distal end region of the outer sheath. A release mechanism is configured to move at least one of the outer sheath and the inner member a distance in a controlled manner to break any friction between the expandable device and the outer sheath prior to deployment of the expandable device from the lumen. Related systems, devices, and methods are provided.

Abstract: Aspects of the disclosure relate to dynamic model configuration and execution. A computing platform may receive first model data comprising first model execution configuration data and first model output configuration data. The computing platform may generate a first model based on the first model execution configuration data. The computing platform may distribute, to a plurality of computing platforms, the first model, the first model execution configuration data, and the first model output configuration data. The computing platform may receive a second request to execute one or more models from a third computing platform. The computing platform may receive, from the third computing platform, first model execution data. The computing platform may execute the first model based on the first model execution data and the first model execution configuration data to generate a first model output score.

Abstract: A method and system for anomaly detection using event sequence prediction include a conversation module applying a system topology to historical log data to generate first structured event sequences as training data. An event sequence generation engine then builds a machine learning model using the training data. The conversation module then applies a system topology to runtime log data to generate a second plurality of structured event sequences. The generation engine then runs the second structured event sequences through the machine learning model. The generation engine then calculates a probability for each of the second structured event sequences using the machine learning model. The generation engine then identifies probabilities for each of the second structured event sequences that are lower than a probability threshold of classified event sequences of the first structured event sequences are identified as an anomaly.

Abstract: An intravenous therapy system, may include a processor; a data storage device; and a handheld ultrasound probe to detect structures within a patient's body, the handheld ultrasound probe including a video display device physically and operatively coupled to the handheld to display the structures within the patient's body and a magnetic field detector to detect the presence of a vascular access device (VAD) and provide closed-loop feedback to guide the VAD into a blood vessel within the patient's body detected by the ultrasound probe.

Abstract: Provided herein is an ionizable, cationic amino lipid or a pharmaceutically acceptable salt thereof. The ionizable, cationic amino lipid has: a protonatable amino head group; two lipophilic chains, wherein the protonatable amino head group has a central carbon atom to which each of the two lipophilic chains are directly bonded; at least one of the two lipophilic chains has a structure of Formula C: wherein E is an ester in either orientation. The compounds may be formulated in a lipid nanoparticle for use in the delivery of charged cargo such as nucleic acid.

Abstract: The disclosed subject matter provides systems and methods for delivering a therapeutic agent for targeted treatments. The system can include at least one microcapsule and a magnetic system. The microcapsule can include the therapeutic agent and a layer of iron oxide nanoparticles (IONPs). The therapeutic agent can be encapsulated by the layer of the IONPs that include a first type of nanoparticles and a second type of nanoparticles. The magnetic system can be configured to generate a magnetic field to control the motion of the microcapsule as well as to assemble into multi-capsule structure and control its motion. Assemblies of multiple microcapsules can be controlled simultaneously and directed to targeted areas for delivery of larger therapeutic payload.

Abstract: Aspects of the disclosure relate to dynamic model configuration and execution. A computing platform may receive first model data comprising first model execution configuration data and first model output configuration data. The computing platform may generate a first model based on the first model execution configuration data. The computing platform may distribute, to a plurality of computing platforms, the first model, the first model execution configuration data, and the first model output configuration data. The computing platform may receive a second request to execute one or more models from a third computing platform. The computing platform may receive, from the third computing platform, first model execution data. The computing platform may execute the first model based on the first model execution data and the first model execution configuration data to generate a first model output score.

Abstract: Provided herein is an ionizable, cationic amino lipid or a pharmaceutically acceptable salt thereof. The ionizable, cationic amino lipid has: a protonatable amino head group; two lipophilic chains, wherein the protonatable amino head group has a central carbon atom to which each of the two lipophilic chains are directly bonded; at least one of the two lipophilic chains has a structure of Formula C: wherein E is an ester in either orientation. The compounds may be formulated in a lipid nanoparticle for use in the delivery of charged cargo such as nucleic acid.

Abstract: A system and method for generating industrial process in a computing environment is disclosed. The system receives multimedia input from users, analyzes multimedia input, to determine semantics associated with multimedia inputs, using at least one of natural language and symbolic processing techniques, and deep learning technique. Furthermore, the system determines process specifications and descriptions in industrial process, using at least one of a dataset and library comprising representations of industrial processes in plurality of configurations, using machine learning (ML) models. Additionally, the system combines a plurality of types of process specifications and descriptions. Further, the system generates industrial processes corresponding to the multimedia inputs, based on combining the plurality of types of process specifications and descriptions. Furthermore, the system outputs the generated industrial processes, on at least one of display of a user device, and external devices.

Abstract: Provided herein is a method for the preparation of ionizable, cationic amino lipids using a doubly alkylated nucleophilic intermediate to produce a ketone. The ketone, or a corresponding alcohol, is subjected to one or more synthesis steps to add an ionizable moiety thereto. The method can be advantageously employed for the synthesis of unsymmetrical analogues of the above lipids that would be considerably more difficult to make by alternative strategies. The method can also be used to prepare symmetrical ionizable, cationic amino lipids with fewer steps and/or with the use of fewer hazardous chemicals than known synthesis methods.

Abstract: A system and method for generating industrial process in a computing environment is disclosed. The system receives multimedia input from users, analyzes multimedia input, to determine semantics associated with multimedia inputs, using at least one of natural language and symbolic processing techniques, and deep learning technique. Furthermore, the system determines process specifications and descriptions in industrial process, using at least one of a dataset and library comprising representations of industrial processes in plurality of configurations, using machine learning (ML) models. Additionally, the system combines a plurality of types of process specifications and descriptions. Further, the system generates industrial processes corresponding to the multimedia inputs, based on combining the plurality of types of process specifications and descriptions. Furthermore, the system outputs the generated industrial processes, on at least one of display of a user device, and external devices.

Abstract: Provided herein is an ionizable, cationic amino lipid or a pharmaceutically acceptable salt thereof. The ionizable, cationic amino lipid has: a protonatable amino head group; two lipophilic chains, wherein the protonatable amino head group has a central carbon atom to which each of the two lipophilic chains are directly bonded; at least one of the two lipophilic chains has a structure of Formula C: wherein E is an ester in either orientation. The compounds may be formulated in a lipid nanoparticle for use in the delivery of charged cargo such as nucleic acid.

Abstract: An intravenous (IV) therapy selection system, may include a processor; a memory device to maintain an electronic medical record database comprising a plurality of medical records related to a plurality of patients; a clinician training module to provide training services and credentials to a plurality of clinicians; an IV therapy recommendation module to provide, via a display device, an IV device recommendation based on data descriptive of a first patient and a first clinician; and a placement location recommendation module to provide, via the display device, a recommendation on where to introduce the IV device into the first patient's body based on data descriptive of the first patient and the first clinician.

Abstract: The present disclosure relates to a pharmaceutical composition having a mixture containing curcuminoids as at least one of the active therapeutic agents. Generally, the mixture comprises 4-8% by weight of purified curcuminoids; 20-35% by weight of an oil phase; 25-35% by weight of a co-solvent; and 40-50% by weight of a surfactant. Preferably, the purified curcuminoids in the mixture has an average size of about 19 nm or at least half of the curcuminoids carried in the mixture has particle size not exceeding 19 nm.

Abstract: A system and method for optimizing industrial assembly process in an industrial environment is disclosed. A system operates on artificial intelligence (AI) based conversational/GUI platform, where it receives user commands related to industrial assembly process improvement queries. By analyzing received user commands, system identifies type of industrial assembly process mentioned by extracting relevant keywords or other attributes. Using trained AI-based classification table, system determines performance attributes associated with identified type of process. The system leverages various sources such as domain knowledge, organization-specific knowledge bases, data from tools/internet-based services, and statistical measurements from industrial environment.

Abstract: A system and method for learning human activities from video demonstrations using video augmentation is disclosed. The method includes receiving original videos from one or more data sources. The method includes processing the received original videos using one or more video augmentation techniques to generate a set of augmented videos. Further, the method includes generating a set of training videos by combining the received original videos with the generated set of augmented videos. Also, the method includes generating a deep learning model for the received original videos based on the generated set of training videos. Further, the method includes learning the one or more human activities performed in the received original videos by deploying the generated deep learning model. The method includes outputting the learnt one or more human activities performed in the original videos.