Abstract: The present invention relates generally to the manufacture of conductive scaffolds of micro and/or nanofibers with the help of different printing techniques (e.g., near-field electrostatic printing, inkjet printing), such scaffolds enabling the formation of two-dimensional (2D) or three-dimensional (3D) neural networks to mimic the native counterparts. Applications of such patterned conductive scaffolds include, but are not limited to, an engineered conduit for guiding the differentiation and outgrowth of neural cells in peripheral nerve damage or in large-volume spinal cord injury under the electrical stimulation. Meanwhile, the scaffolds could also locally deliver various biomolecules in conjunction with electrical stimulation for facilitated nervous system regeneration.

Abstract: Embodiments of the present disclosure provide a method and apparatus for database query, an electronic device, and a medium. The method includes: determining, in response to receiving a structured query language (SQL) query for a database, a query condition and a target column of the SQL query that relates to a number of distinct values (NDV) estimation; selecting, from sampled data of the database, data of the target column that meets the query condition; generating feature data of the selected data of the target column; and determining an estimated NDV of the target column based on the feature data using a model for NDV estimation.

Abstract: Embodiments of the present disclosure provide a method and apparatus for estimating a number of distinct values in a database system, a device, and a medium. The method includes: reading and loading, from a distributed file system, a model for NDV estimation into a kernel of the database system; generating feature data of sample data of a target column in the database system; determining, using the model that has been loaded into the kernel, an estimated NDV for the target column based on the feature data; and storing the estimated NDV of the target column for use by the kernel of the database system.

Abstract: A composition that may be used, but is not limited to, in a range of cosmetic products, personal care products, health care products or topical products, in the form of a serum, lotion, cream, gel, a spray, a foam, a solid stick, a shampoo, a hair conditioner, a lacquer, a make-up or a sunscreen. The composition including three plant-derived hydrocolloids: Carrageenan; Guar extract; and Tamarind seed extract.

Abstract: A method for cultivating a Tung tree seedling through hypocotyl grafting, includes selection of a sand storage time of a rootstock (Vernicia montana Lour.) seed, seedbed construction, scion collection, scion cutting, rootstock cutting, bandaging, transplantation and management, and the like.

Abstract: Embodiments of the present disclosure provide a method and apparatus for estimating a number of distinct values in a database system, a device, and a medium. The method includes: reading and loading, from a distributed file system, a model for NDV estimation into a kernel of the database system; generating feature data of sample data of a target column in the database system; determining, using the model that has been loaded into the kernel, an estimated NDV for the target column based on the feature data; and storing the estimated NDV of the target column for use by the kernel of the database system.

Abstract: Systems and methods for an agent-based carbon emission reduction system. A carbon product of a supply chain system can be limited below a carbon product threshold by performing a corrective action to monitored entities based on a calculated carbon emission. The carbon emission can be calculated based on carbon-relevant data and a calculation route by utilizing an agent-based simulation model that simulates a learned relationship between a supply chain system and the carbon-relevant data. The calculation route can be determined based on the carbon-relevant data based on a relevance of a carbon product contribution of monitored entities to a goal of the monitored entities. Carbon-relevant data can be extracted from the monitored entities.

Abstract: Systems and methods for optimizing key performance indicators (KPIs) using adversarial imitation deep learning include processing sensor data received from sensors to remove irrelevant data based on correlation to a final KPI and generating, using a policy generator network with a transformer-based architecture, an optimal sequence of actions based on the processed sensor data. A discriminator network is employed to differentiate between the generated action sequences and real-world high performance sequences employing. Final KPI results are estimated based on the generated action sequences using a performance prediction network. The generated action sequences are applied to the process to optimize the KPI in real-time.

Abstract: Systems and methods are provided for classifying components include monitoring sensors to collect sensor data related to a state of a plurality of components; processing, by a computing system, the sensor data to generate an action sequence using a transformer-based policy network for each of the components. A risk score is generated for the action sequence using a Generative Adversarial Network (GAN), wherein the GAN includes a generator for generating action sequences and a discriminator to distinguish low-risk action sequences in accordance with a threshold. The low-risk action sequences are associated with components in the plurality of components based on the risk score. A status of the low-risk action sequences is communicated to the components.

Abstract: Systems and methods train a transformer-based policy network and Generative Adversarial Network (GAN) by initializing a transformer-based policy network to model action sequences by encoding temporal dependencies within sensor data. Multi-head self-attention mechanisms process sequential sensor inputs by being pre-trained on a labeled dataset having sensor data from known low-risk action sequences. A generator within the GAN is trained to produce generated action sequences, which mimic behavior of low-risk action sequences. A discriminator within the GAN is concurrently trained to differentiate between action sequences derived from the labeled dataset and synthetic action sequences produced by the generator. A feedback loop is employed to adjust parameters to produce sequences indistinguishable from real low-risk action sequences.

Abstract: A distributed optimization control method for an aircraft energy system considering loss includes the following steps: S1, based on characteristics of the actual near-space aircraft energy system and the near-space environment, the multi-bus DC microgrid system model is established for the near-space aircraft energy system according to Kirchhoff's law; S2, according to the energy scheduling requirement of the near-space aircraft energy system, the system control target is established; S3, according to the system control target, the optimal scheduling problem of the aircraft energy system considering operating loss is established; and S4, based on projection and penalty, the distributed optimization control algorithm is designed. The distributed optimization control method realizes bus voltage regulation and current distribution.

Abstract: Systems and methods for a demonstration uncertainty-based artificial intelligence model for open information extraction. A large language model (LLM) can generate initial structured sentences using an initial prompt for a domain-specific instruction extracted from an unstructured text input. Structural similarities between the initial structured sentences and sentences from a training dataset can be determined to obtain structurally similar sentences. The LLM can identify relational triplets from combinations of tokens from generated sentences using and the structurally similar sentences. The relational triplets can be filtered based on a calculated demonstration uncertainty to obtain a filtered triplet list. A domain-specific task can be performed using the filtered triplet list to assist the decision-making process of a decision-making entity.

Abstract: Systems and methods for correlation-aware explainable online change point detection. Collected data metrics from the cloud system can be transformed to correlation matrices. Correlation shifts from the correlation matrices can be captured as differences of correlation between batches of collected data metrics through determined statistics of the batches of collected data metrics across timesteps. Change points in the cloud system can be detected based on the correlation shifts to obtain detected change points. System maintenance can be performed autonomously based on the detected change points from identified system entities to optimize the cloud system with an updated configuration.

Abstract: A method for determining an optimal sensor set includes identifying a set of possible faults of a thermal hydraulic system and a set of diagnostic objectives. The method also includes obtaining descriptions of sensor sets, which includes: receiving a first description of a first sensor set, and generating, based on the first description, a second description of a second sensor set. The method further includes, for each sensor set: determining a diagnostic capability of the sensor set, and calculating a score of the particular sensor set based on the particular description and the diagnostic capability, wherein a score of a sensor set is increased by a monetary cost of the sensor set and decreased by the sensor set meeting a diagnostic objective. The method also includes identifying the optimal sensor set that has a lowest score of the sensor sets, and displaying an indication of the optimal sensor set.

Abstract: A steering wheel instrument assembly includes a button assembly having a microprocessor and a push-button switch. A function table has a unified format code associated with the push-button switch and indicates a desired vehicle operation. The assembly includes a vehicle protocol adapter module having another microprocessor with a second function table having the unified format code associated with an OEM format code. The microprocessor of the button assembly retrieves the unified format code when the push-button switch is activated and sends the unified format code to the vehicle protocol adapter module. The vehicle protocol adapter module retrieves the OEM format code from the second function table in response to receiving the unified format code and sends the OEM format code to an engine control unit.

Abstract: This disclosure proposes systems and methods for entity resolution using speech recognition data. The system can receive audio data representing an utterance and perform automatic speech recognition (ASR) processing on the audio data to generate at least a first ASR data and a second ASR data. The system can perform natural language understanding (NLU) processing on the first ASR data to determine intent data and an indication of an entity. The system can determine a first portion of the first ASR data that corresponds to the indication of the entity. The system can determine a second portion of the second ASR data that corresponds to the indication of the entity without performing NLU on the second ASR data. The system can perform entity resolution (ER) on the second portion to identify a first entity.

Abstract: Methods and systems for information extraction include configuring a language model with an information extraction instruction prompt and at least one labeled example prompt. Configuration of the language model is validated using at least one validation prompt. Errors made by the language model in response to the at least one validation prompt are corrected using a correction prompt. Information extraction is performed on an unlabeled sentence using the language model to identify a relation from the unlabeled sentence. An action is performed responsive to the identified relation.

Abstract: A handheld power tool includes a motor, a handle having a first switch for starting and stopping the motor, an output assembly, and an adjustment device including a telescopic rod assembly. The length of the adjustment device changes so that the handheld power tool has at least first and second working states. The distance between the frontmost side of the handheld power tool and the rearmost side of the first switch is an effective length of the handheld power tool. When the handheld power tool is in the first working state, the handheld power tool has a first effective length T1. When the handheld power tool is in the second working state, the handheld power tool has a second effective length T2. The first effective length T1 is less than or equal to 1 m, and the second effective length T2 is greater than the first effective length T1.

Abstract: The present disclosure relates to a PointEFF method for urban object classification with LiDAR point cloud data, and belongs to the field of LiDAR point cloud classification. The method comprises: point cloud data segmentation; End-to-end feature extraction layer construction; External feature fusion layer construction; and precision evaluation.

Abstract: The present disclosure provides an information processing method, an apparatus, an electronic device, and a storage medium. By establishing a session group including a first identity user and a second identity user in response to the first identity user satisfying a preset first condition, pushing a first session message for the first identity user within the session group according to a preset process node, and displaying a first information editing interface for the first identity user, in response to a first preset operation of the first identity user for the first session message, the present disclosure can achieve automatic group creation, automatic reminder through group session, and entry into a relevant information editing interface through group session, thereby achieving efficient distribution and processing of information.