Abstract: This invention discloses a pneumatic and cable-driven hybrid artificial muscle, comprising pneumatic actuator, pneumatic pressure regulating assembly, and cable drive assembly. The pneumatic pressure regulating assembly is connected to the pneumatic actuator to regulate air pressure in the pneumatic actuator for controlling the pneumatic actuator to extend or contract. The cable actuation assembly comprises a cable fixedly connected to the pneumatic actuator for controlling the pneumatic actuator to contract. In this invention, the artificial muscle employs a dual pneumatic and cable actuation mechanism. By leveraging the inherent stiffness of the pneumatic actuator, it can provide substantial actuation force for joint movement. Simultaneously, the cable actuation assembly can provide significant pulling force, effectively ensuring safety in human-machine interaction and offering sufficient bidirectional aiding force to individuals with disabilities who use the artificial muscle as an aiding actuation device.

Abstract: A method for scheduling services in a computing environment includes receiving a service scheduling request corresponding to the computing environment and identifying a resource pool and a set of compliance requirements corresponding to the computing environment. The method continues by identifying target resources within the resource pool, wherein target resources are resources which meet the set of compliance requirements, and subsequently identifying a set of available target resources, wherein available target resources are target resources with scheduling availability. The method further includes analyzing the set of available target resources to determine a risk score for each available target resource and selecting one or more of the set of available target resources according to the determined risk scores. The method continues by scheduling a service corresponding to the service scheduling request on the selected one or more available target resources.

Abstract: The apparatus may be provided to improve a PAPR in at least one of a single-carrier FDM signal or DFT-s-OFDM. The apparatus may be configured to encode a set of information bits with a channel encoder; generate, based on a set of encoded bits from the channel encoder, a set of amplitude symbols using a distribution matching function; generate a set of modulated symbols based on the set of amplitude symbols; and transmit, to a second device, a signal based on the set of modulated symbols. An apparatus may be configured to receive, from a first device, a signal based on a set of modulated symbols and derive a set of encoded bits based on the received set of modulated symbols using a distribution matching function.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive information indicating a condition for a conditional handover, and an indication to communicate on a source cell after transmitting a message on a target cell, associated with the conditional handover, and before receiving an additional message on the target cell. The UE may transmit the message on the target cell responsive to the condition for the conditional handover being satisfied. Numerous other aspects are described.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a wireless communication device may obtain, at an access stratum (AS) layer, an indication of an inter-satellite link (ISL) activation that is to take place at an activation time. The wireless communication device may receive communications. The wireless communication device may buffer the communications in a buffer at the AS layer. The wireless communication device may release the communications from the buffer to an upper protocol layer at a rate of release that is to change between a transition start time and the activation time. Numerous other aspects are described.

Abstract: A device for manufacturing a storage container by integrally winding multiple bundles of fibers in a double-layer spiral circumferential direction. The device includes a rotary drive unit and two radial slide drive units provided at two sides of the rotary drive unit, each of the two radial slide drive units is provided with several spiral winding guide wire tubes in a circumferential array and drives the spiral winding guide wire tubes to perform a radial telescopic movement, the rotary drive unit drives the spiral winding guide wire tubes on the two radial slide drive units to perform a rotation movement, the rotary drive unit is connected to the two radial slide drive units by means of brackets, and a rack plate is connected to one side of each of the two radial slide drive units away from the rotary drive unit.

Abstract: An anomalous behavior detector has been designed to detect novel behavioral changes of devices based on network traffic data that likely correlate to anomalous behaviors. The anomalous behavior detector uses the local outlier factor (LOF) algorithm with novelty detection. After initial semi-supervised training with a single class training dataset representing stable device behaviors, the obtained model continues learning frontiers that delimit subspaces of inlier observations with live network traffic data. Instead of traffic variables being used as features, the features that form feature vectors are similarities of network traffic variable values across time intervals. A feature vector for the anomalous behavior detector represents stability or similarity of network traffic variables that have been chosen as device identifiers and behavioral indicators.

Abstract: Methods, systems, and devices for wireless communication at a user equipment (UE) are described. A UE may operate by asynchronously transmitting code division multiple access (CDMA) uplink signals to a network entity. The UE may transmit the CDMA signals via an uplink channel that may be shared with orthogonal frequency multiplexing (OFDM) signaling transmitted by one or more other UEs. The UE may indicate to the network entity a capability for communicating with CDMA waveforms and may use CDMA waveforms based on transmitting the indication. The UE may also receive one or more parameters associated with using the CDMA waveforms, and may use CDMA waveforms based on the one or more parameters. The uplink channel may be a random access channel (RACH) and may support RACH transmissions, or may be a data channel and support data transmissions.

Abstract: A preparation method of a zeolite/polyimide composite membrane includes: synthesizing a zeolite-doped polyamic acid precursor casting solution by condensation polymerization synthesis; coating a substrate with the obtained casting solution, and obtaining a zeolite/polyamic acid composite porous membrane by non-solvent induced phase separation; and obtaining the zeolite/polyimide composite membrane by performing thermal imidization on the zeolite/polyamic acid composite porous membrane through gradient heating.

Abstract: A computer-implemented method, system and computer program product for maximizing system scalability while guaranteeing enforcement of service level objectives. A request is received to access a backend database in a hierarchy of backend databases that includes heterogenous computing resources with a dynamic range of performance. Upon receiving the request, a reinforcement learning based filter determines if the request's frequency of access exceeds a cutoff frequency. If the received request is not filtered, but instead, is passed through the filter, then one of the backend databases in the hierarchy is selected. Such a selection is made by a load balancer that is trained using reinforcement learning to select the optimal backend database taking into consideration the storage size and speed of the backend databases as well as taking into consideration the user-specified service level objective to be met by the request to guarantee enforcement of such a service level objective.

Abstract: Methods, systems, and devices for wireless communications are described. A user equipment (UE) may release a connection with a first network entity and determine a first virtual coverage area of the UE based on a geographic location of the UE associated with the release of the connection. The UE may obtain a message that includes area information. The area information may be indicative of one or more second virtual coverage areas, which the UE may use in determining whether to monitor for a paging early indication (PEI). Based on the first virtual coverage area and the one or more second virtual coverage areas may monitor for a paging message, such as the PEI or downlink control information (DCI) associated with a paging occasion.

Abstract: Inline package name based supply chain attack detection and prevention is disclosed. An indication that a client device has made a request to a remote server for a package is received. A data appliance then performs an action responsive to the received indication. In an example implementation, the data appliance makes a determination of whether the request for the package is associated with a nonexisting package.

Abstract: Aspects of the present disclosure relate to converting between structured and tabular data formats. Data can be received in a tabular format. An array can be built for each of a plurality of objects within the data in the tabular format, each object corresponding to at least one identified header of the identified headers. A data row can be parsed using at least one of the built arrays and data within the data row can be added to the structured format in a specific location based characteristics indicated in the at least one array. Data can also be converted from the structured format into the tabular format using the built arrays.

Abstract: A computer-implemented method for managing one or more operations of a workload includes selecting a resource type for workload management on a platform. One or more operations of the selected resource to be managed are identified. A reconciliation time for execution of each of the identified operations is determined. A reconciliation period between two consecutive reconciliations is determined for each of the identified operations. A minimum number of processes for workload management of a given set of the operations on resources is calculated, and the determined minimum number of processes is deployed to manage the workload.

Abstract: A machine learning assessment system is provided. The system identifies multiple datasets and multiple machine learning (ML) modeling algorithms based on the client profile. The system assesses a cost of data collection for each dataset of the multiple datasets. The system assesses a performance metric for each ML modeling algorithm of the multiple modeling algorithms. The system recommends a dataset from the multiple datasets and an ML modeling algorithm from the multiple ML modeling algorithm based on the assessed costs of data collection for the multiple datasets and the assessed performance metrics for the multiple ML modeling algorithms.

Abstract: A method for scheduling services in a computing environment includes receiving a service scheduling request corresponding to the computing environment and identifying a resource pool and a set of compliance requirements corresponding to the computing environment. The method continues by identifying target resources within the resource pool, wherein target resources are resources which meet the set of compliance requirements, and subsequently identifying a set of available target resources, wherein available target resources are target resources with scheduling availability. The method further includes analyzing the set of available target resources to determine a risk score for each available target resource and selecting one or more of the set of available target resources according to the determined risk scores. The method continues by scheduling a service corresponding to the service scheduling request on the selected one or more available target resources.

Abstract: An artificial intelligence (AI) platform to support workflow version process control. One or more workflows corresponding to one or more workflow engines are monitored. A neural network is employed to capture a relationship associated with a detected change in the monitored workflows. The neural network is leveraged to identify and assess an impact of the detected change to one or more additional workflows. Responsive to the assessment, the impacted workflow engines are optimized. The optimization includes automatically mapping and encoding changes corresponding to the impacted workflow. The one or more workflows containing the encoded changes are then executed.

Abstract: Aspects of the present disclosure relate to converting between structured and tabular data formats. Data can be received in a tabular format. An array can be built for each of a plurality of objects within the data in the tabular format, each object corresponding to at least one identified header of the identified headers. A data row can be parsed using at least one of the built arrays and data within the data row can be added to the structured format in a specific location based characteristics indicated in the at least one array. Data can also be converted from the structured format into the tabular format using the built arrays.

Abstract: A computer-implemented method for managing one or more operations of a workload includes selecting a resource type for workload management on a platform. One or more operations of the selected resource to be managed are identified. A reconciliation time for execution of each of the identified operations is determined. A reconciliation period between two consecutive reconciliations is determined for each of the identified operations. A minimum number of processes for workload management of a given set of the operations on resources is calculated, and the determined minimum number of processes is deployed to manage the workload.

Abstract: A machine learning assessment system is provided. The system identifies multiple datasets and multiple machine learning (ML) modeling algorithms based on the client profile. The system assesses a cost of data collection for each dataset of the multiple datasets. The system assesses a performance metric for each ML modeling algorithm of the multiple modeling algorithms. The system recommends a dataset from the multiple datasets and an ML modeling algorithm from the multiple ML modeling algorithm based on the assessed costs of data collection for the multiple datasets and the assessed performance metrics for the multiple ML modeling algorithms.