Abstract: An additive manufacturing quality inspection method includes the following. An industrial PC (IPC) acquires electrical signals corresponding to optical signals during a first period in a laser additive manufacturing process; the IPC constructs at least two trajectory amplitude maps respectively based on at least two electrical signals corresponding to the at least two optical signals; the IPC partitions the at least two trajectory amplitude maps separately to obtain multiple image block sets; the IPC determines, based on electrical signal amplitudes of image blocks in each image block set, whether a region corresponding to the image blocks in each image block set in a target slice layer has a defect.

Abstract: An optical module including a circuit board, a substrate, a heater and a laser chip mounted on the substrate. The circuit board is provided with a signal line and a ground pad. The substrate is provided with a high-frequency transmission line wire-bonded to the signal line and a ground metal region wire-bonded to the ground pad. The laser chip is mounted on the ground metal region and wire-bonded to the high-frequency transmission line. The heater is thermally isolated from the substrate and has a first power supply terminal electrically connected to the circuit board to transmit a driving current to the heater, a second power supply terminal electrically connected to the circuit board, and a ground terminal connected to the ground metal region. The heater is connected to the laser chip via a gold wire to transmit the driving current and conduct heat to the laser chip.

Abstract: An industrial PC (IPC) acquires a first electrical signal corresponding to a laser emission signal, a second electrical signal corresponding to a laser reflection signal, a third electrical signal corresponding to a visible light signal, and a fourth electrical signal corresponding to an infrared light signal during a first period in a laser additive manufacturing process. The IPC determines a layer electrical-signal amplitude corresponding to at least one of the laser emission signal, the laser reflection signal, the visible light signal, or the infrared light signal based on at least one of the first electrical signal, the second electrical signal, the third electrical signal, or the fourth electrical signal during the first period; and determines whether a target slice layer has a defect based on at least one of these four layer electrical-signal amplitudes.

Abstract: An energy storage all-in-one machine and an energy storage system are provided. The energy storage all-in-one machine includes a battery, a switch box, and at least two connectors. The switch box is electrically connected to the battery; and the connectors are used for achieving capacity expansion of the energy storage all-in-one machine.

Abstract: A system may include a set of data integration facilities for integrating content of at least one publication data set relating to the biological strain and at least one proprietary data set including a set of parameters of a synthetic biological process in which the biological strain produces a functional output, wherein the output of data integration facilities is configured as an input to a set of artificial intelligence (AI)-based learning models. The system may include at least one member of the set of AI-based learning models that is configured to generate a set of recommendations wherein the set of recommendations relates to modifications to a set of genes of the biological strain such that the set of recommendations enhance production of the functional output by the biological strain.

Abstract: A warning system for a battery pack of a vehicle and a method for operating the same includes melting a phase change material in a phase change housing above a temperature threshold. In response to melting, extending a push rod from the housing. The method further includes electrically coupling a static contactor and a dynamic contactor. In response to electrically coupling, an alarm is activated.

Abstract: The present disclosure provides a method and system for data buffering, a device, and a non-volatile readable storage medium. The method includes: configuring an on chip buffer on a RAID management chip; reading new data from a host DDR into the on chip buffer, and obtaining new parity data by calculation according to a type of a current RAID group and the new data; and sending the new data to a data hard disk corresponding to a RAID group, and sending the new parity data to a parity hard disk corresponding to the RAID group.

Abstract: A system and method for analyzing the size distribution and chemical composition of neutral clusters below 3 nm, which achieves efficient sampling based on a core sampling method. The system utilizes the high charging efficiency of the unipolar charging device to charge the nanoparticles and efficiently transport them to the inlet of the nanoparticle electrical mobility size classification device. Through a specialized aerodynamic design, the nanoparticles pass through the electric field between the electrode plates and are efficiently transmitted to both the particle number concentration detector and the chemical composition detector, demonstrating excellent application prospects.

Abstract: An integrated circuit (IC) design layout may be partitioned into a set of content areas. A content area in the set of content areas may be divided into a set of zones based on an image-based representation of the content area. A repetition unit may be identified in a zone in the set of zones, where the repetition unit is a portion of the zone, and where the zone includes multiple instances of the repetition unit. A skeleton layout may be generated corresponding to the IC design layout, where the skeleton layout specifies locations of the multiple instances of the repetition unit in the IC design layout.

Abstract: According to one aspect, there is provided a computer-implemented method for training artificial intelligence models with specialized biologic data in an AI-guided analytic platform for development of a biologic synthesis process, comprising: collecting multimodal biologic data including at least one of a gene expression level, mRNA, metabolic reaction fluxes, or intracellular metabolite concentrations from biologic systems; processing the collected biologic data through data normalization and quality assurance steps to create model-ready data; and generating at least one output predicting an effect of genetic modification on a metabolite level or a reaction flux.

Abstract: A system may include a plurality of sensors configured to measure fermentation parameters. The system may include a control system operatively coupled to the fermentation chamber and the plurality of sensors, the control system comprising: at least one processor; memory storing instructions that, when executed by the at least one processor, cause the control system to: receive sensor data from the plurality of sensors; process the sensor data using a set of AI-based learning models to determine a set of improved fermentation parameters; generate control signals based on the determined set of improved fermentation parameters; and adjust operating conditions of the fermentation chamber based on the control signals.

Abstract: A data deduplication method, including determining, in response to a data deduplication request, a plurality of duplicate blocks from data blocks stored in a persistent medium, determining a to-be-deduplicated block from the plurality of duplicate blocks based on deduplication priorities of the plurality of duplicate blocks, where the deduplication priority of the duplicate block is determined based on an attribute of the duplicate block, and performing deduplication on the to-be-deduplicated block, where a fragmentation degree of data in the persistent medium after the to-be-deduplicated block is deduplicated is less than a fragmentation degree of data in the persistent medium after a data block other than the to-be-deduplicated block in the plurality of duplicate blocks is deduplicated.

Abstract: Use of OsHIPP36 gene in the regulation of arsenic tolerance in rice is provided, which belongs to the field of genetic engineering. The present disclosure provides the use of OsHIPP36 gene in the regulation of arsenic tolerance in rice, where protein encoded by the OsHIPP36 gene has the amino acid sequence of SEQ ID NO: 1. The present disclosure obtains a oshipp36 mutant plant by constructing a transgenic vector. After treatment with the external addition of As(III), the mutant plant is found to have significantly increased tolerance to As(III). In particular, the oshipp36 mutant plant after As(III) treatment has significantly longer roots than a Nipponbare wild-type plant, suggesting that the OsHIPP36 gene plays an important role in regulating arsenic tolerance in rice.

Abstract: A system may receive data from a plurality of sources. The system may perform a data quality assurance on the received data to identify at least one anomalous data point. The system may apply a Bayesian statistical normalization model to the data to: model a batch-specific systemic variation, account for a technical factor contributing to a batch effect, separate a signal from the technical factor; and generate normalized data. The system may output the normalized data for processing by a machine learning model.

Abstract: According to one aspect, there is provided a computer-implemented system for evaluating and improving strain production using three integrated components: a prototype system for evaluating candidate strains, an optimization system for enhancing strain performance, and a scale-up system for adapting laboratory results to commercial-scale parameters. The system employs artificial intelligence models trained on data from all three components. The system continuously receives performance data from these systems to update its AI models. Using these updated models, the system generates modified operational parameters for the prototype, optimization, and scale-up systems, creating a feedback loop for continuous process improvement.

Abstract: A system may include data integration facilities for integrating content of publication data sets relating to strains and proprietary data sets including parameters of a process in which the strain produces functional outputs, wherein integrated data is input to machine learning models. The machine learning models generate recommendations relating to modifications of the strain.

Abstract: A semiconductor package comprises a lead frame, a first high-voltage field-effect transistor (FET), a source clip, a gate clip, a second high-voltage FET, a drain clip or a bond wire, and a molding encapsulation. A method comprises the steps of providing a lead frame; mounting a first high-voltage FET; attaching a source clip and a gate clip; mounting a second high-voltage FET; attaching a drain clip or a bond wire, forming a molding encapsulation; and applying a singulation process.

Abstract: The present disclosure relates to method and apparatus for processing media materials, a device and a product. The method comprises obtaining a first identification of each of a plurality of slots, wherein each of the plurality of slots is used for filling a media material and the first identification is used for indicating whether respective slots belong to a same freeze-frame effect. The method also comprises obtaining a second identification of each of the plurality of slots, wherein the second identification is used for indicating whether respective slots are filled with a same media material. In addition, the method further comprises determining, based on the first identification and the second identification, a freeze-frame combination for media materials, to play the media materials based on the freeze-frame combination, such that a freeze-frame media material in the media materials is continuously displayed for a period of time.