Abstract: A method for manufacturing a high electron mobility transistor (HEMT), which comprises the following steps: providing a substrate, wherein a semiconductor layer is formed on the substrate, and a source electrode and a drain electrode are formed on the semiconductor layer; forming a passivation layer on the source electrode and the drain electrode; etching the passivation layer to form a through hole between the source electrode and the drain electrode, wherein a region of the semiconductor layer is exposed through the through hole; forming a photoresist layer on the passivation layer, wherein a first sub-region of the region of the semiconductor layer is covered by the photoresist layer, and a second sub-region of the region of the semiconductor layer is not covered by the photoresist layer; forming a metal layer on the second sub-region to form a gate electrode; and removing the passivation layer.

Abstract: The present disclosure relates to a method for configuring a priority level, a cloud platform, a system, a computing device, and a medium. The method for configuring a priority level for data on a cloud platform comprises determining the data type of received data; determining, according to the data type, a priority level specification used for describing a priority level of the received data; and calculating the priority level of the received data on the basis of the priority level specification of the received data.

Abstract: The present disclosure provides a direct current (DC) transformer error detection apparatus for a pulsating harmonic signal, including a DC and pulsating harmonic current output module and an external detected input module, where the DC and pulsating harmonic current output module outputs a DC and a DC superimposed pulsating harmonic current to an internal sampling circuit and a self-calibrated standard resistor array; and the internal sampling circuit converts the input DC and the input DC superimposed pulsating harmonic current into a voltage signal, and sends the voltage signal to an analog-to-digital (AD) sampling and measurement component through a front-end conditioning circuit and a detected input channel. The DC transformer error detection apparatus can complete self-calibration for measurement of the DC and the pulsating harmonic signal on a test site.

Abstract: The present disclosure provides a language model based writing aid method, apparatus and system. The method includes: a server acquiring original text, where the original text may be writing text already generated and/or user input text; the server inputting the original text into a language model to generate a preset number of pieces of writing text, where the writing text and the original text have a correlation; the server sending the preset number of pieces of writing text to a frontend interface. The method of the present disclosure enables a computer to aide a user in text creating so that intelligence for writing aid is improved.

Abstract: The present disclosure provides a direct current (DC) transformer error detection apparatus for a pulsating harmonic signal, including a DC and pulsating harmonic current output module and an external detected input module, where the DC and pulsating harmonic current output module outputs a DC and a DC superimposed pulsating harmonic current to an internal sampling circuit and a self-calibrated standard resistor array; and the internal sampling circuit converts the input DC and the input DC superimposed pulsating harmonic current into a voltage signal, and sends the voltage signal to an analog-to-digital (AD) sampling and measurement component through a front-end conditioning circuit and a detected input channel. The DC transformer error detection apparatus can complete self-calibration for measurement of the DC and the pulsating harmonic signal on a test site.

Abstract: The present invention provides a supported metal catalyst with synergistic sites, a preparation method therefor and an application thereof. The preparation method of this catalyst is to utilize the unsaturated cubane-like structure, M cation with catalytic activity is introduced into the cluster core unit. By using the vertex vacancy as the capturing center, and adjusting the impregnation temperature to maximize the loading of the cluster precursor, as well as depending on the electrostatic adsorption of the support and the confinement of the cluster structural unit, the number of S vacancies and the distance between S vacancies and Miso sites are effectively controlled through liquid phase reduction and atmosphere treatment at room temperature to obtain supported X3MSx/Al2O3 catalyst with Miso-Vs synergistic sites.

Abstract: Various embodiments include a method for deploying field device into an Internet of Things (IoT). The method may include: acquiring information from a field device using an edge device; transmitting the acquired information to a cloud platform; wherein the information comprises data and an industrial IoT model; converting the industrial IoT model into a graph; performing similarity analysis based on the graph; classifying the industrial IoT model based on the similarity analysis; generating a first industrial IoT model comprising a type or an example; performing data mapping on the first industrial IoT model; and operating the field device as part of the IoT.

Abstract: Various embodiments described herein include an application program development and deployment method comprising: acquiring, from a code template library, a first code template matching a code structure of an application program to be developed; creating a template instance based on the first code template; filling a custom code input by a developer into the template instance to obtain a source program code matching said application program; compiling the source program code to obtain a target program code; acquiring a target operating environment mirror matching the target program code from an environment mirror library, wherein at least one operating environment mirror is stored in the environment mirror library, and different operating environment mirrors correspond to different operating environments; and deploying the target program code and the target operating environment mirror onto a target cloud platform.

Abstract: Various embodiments of the teachings herein include a method for generating a virtual Internet-of-things device on the basis of an Internet-of-things model. The method may include: obtaining a first number of Internet-of-things models for an Internet-of-things solution; subjecting a variable in the first number of Internet-of-things models to variable attribute expansion to add an extended variable attribute to the variable, wherein the variable subjected to variable attribute expansion is capable of data simulation; configuring variable link relations and variable link processing rules for the extended Internet-of-things models to obtain an Internet-of-things model configuration file; and obtaining a second number of virtual Internet-of-things devices on the basis of a virtual physical network device architecture by using the Internet-of-things model configuration file.

Abstract: Various embodiments include methods for determining a resource configuration of a cloud service system using a system simulation tool with a model library. This may include: obtaining a model required for each service; obtaining a resource limit setting of each required model; creating a service workflow among the models to obtain a system simulation model, indicating the interaction workflow among the models and the sequence of model execution in a single service; using training data to train a service performance model of each service, wherein the training data comprises test data collected from the cloud service system in a testing environment, and the service performance model defines a resource consumption of a single request running on a service in the system simulation model; performing system simulation with a given resource configuration set and the resource limit settings, to obtain a system performance KPI; and determining a resource configuration with the best KPI.

Abstract: A field data transmission method comprises: a cloud platform determining at least one first device operation index be obtained via data analysis. For each first device operation index, the cloud platform generates control information for the first device operation index. The control information is used to determine a primary edge controller from among at least one edge controller, wherein the primary edge controller is used to send first field data to the cloud platform, the first field data is used for data analysis by the cloud platform to obtain the first device operation index, and the first field data is obtained by the primary edge controller preprocessing second field data. The cloud platform sends each piece of control information to each edge controller, respectively. The cloud platform receives first field data from each primary edge controller, respectively.

Abstract: Various embodiments include methods for determining a resource configuration of a cloud service system using a system simulation tool with a model library. This may include: obtaining a model required for each service; obtaining a resource limit setting of each required model; creating a service workflow among the models to obtain a system simulation model, indicating the interaction workflow among the models and the sequence of model execution in a single service; using training data to train a service performance model of each service, wherein the training data comprises test data collected from the cloud service system in a testing environment, and the service performance model defines a resource consumption of a single request running on a service in the system simulation model; performing system simulation with a given resource configuration set and the resource limit settings, to obtain a system performance KPI; and determining a resource configuration with the best KPI.

Abstract: Various embodiments include a method for deploying field device into an Internet of Things (IoT). The method may include: acquiring information from a field device using an edge device; transmitting the acquired information to a cloud platform; wherein the information comprises data and an industrial IoT model; converting the industrial IoT model into a graph; performing similarity analysis based on the graph; classifying the industrial IoT model based on the similarity analysis; generating a first industrial IoT model comprising a type or an example; performing data mapping on the first industrial IoT model; and operating the field device as part of the IoT.

Abstract: Various embodiments of the teachings herein include a method for generating a virtual Internet-of-things device on the basis of an Internet-of-things model. The method may include: obtaining a first number of Internet-of-things models for an Internet-of-things solution; subjecting a variable in the first number of Internet-of-things models to variable attribute expansion to add an extended variable attribute to the variable, wherein the variable subjected to variable attribute expansion is capable of data simulation; configuring variable link relations and variable link processing rules for the extended Internet-of-things models to obtain an Internet-of-things model configuration file; and obtaining a second number of virtual Internet-of-things devices on the basis of a virtual physical network device architecture by using the Internet-of-things model configuration file.

Abstract: Various embodiments described herein include an application program development and deployment method comprising: acquiring, from a code template library, a first code template matching a code structure of an application program to be developed; creating a template instance based on the first code template; filling a custom code input by a developer into the template instance to obtain a source program code matching said application program; compiling the source program code to obtain a target program code; acquiring a target operating environment mirror matching the target program code from an environment mirror library, wherein at least one operating environment mirror is stored in the environment mirror library, and different operating environment mirrors correspond to different operating environments; and deploying the target program code and the target operating environment mirror onto a target cloud platform.

Abstract: The present disclosure provides a language model based writing aid method, apparatus and system. The method includes: a server acquiring original text, where the original text may be writing text already generated and/or user input text; the server inputting the original text into a language model to generate a preset number of pieces of writing text, where the writing text and the original text have a correlation; the server sending the preset number of pieces of writing text to a frontend interface. The method of the present disclosure enables a computer to aide a user in text creating so that intelligence for writing aid is improved.

Abstract: The present disclosure relates to a method for configuring a priority level, a cloud platform, a system, a computing device, and a medium. The method for configuring a priority level for data on a cloud platform comprises determining the data type of received data; determining, according to the data type, a priority level specification used for describing a priority level of the received data; and calculating the priority level of the received data on the basis of the priority level specification of the received data.

Abstract: A field data transmission method comprises: a cloud platform determining at least one first device operation index be obtained via data analysis. For each first device operation index, the cloud platform generates control information for the first device operation index. The control information is used to determine a primary edge controller from among at least one edge controller, wherein the primary edge controller is used to send first field data to the cloud platform, the first field data is used for data analysis by the cloud platform to obtain the first device operation index, and the first field data is obtained by the primary edge controller preprocessing second field data. The cloud platform sends each piece of control information to each edge controller, respectively. The cloud platform receives first field data from each primary edge controller, respectively.

Abstract: Among others, the present invention provides apparatus for detecting a disease, comprising a system delivery biological subject and a probing and detecting device, wherein the probing and detecting device includes a first micro-device and a first substrate supporting the first micro-device, the first micro-device contacts a biologic material to be detected and is capable of measuring at the microscopic level an electric, magnetic, electromagnetic, thermal, optical, acoustical, biological, chemical, physical, or mechanical property of the biologic material.

Abstract: Embodiments provide a data transmission method and apparatus. The method includes: after obtaining information of a target input port of a data receiving device and information of a target protocol used by a target Internet of Things device connected to the target input port, the data receiving device determines the target input port and the target protocol, and decapsulates, according to the target protocol, a first data packet transmitted by the target Internet of Things device via means of the target input port to obtain data. The data receiving device can adaptively determine a target protocol according to the information of the target protocol used by the target Internet of Things device, without configuring the target protocol for the target Internet of Things device in advance.