Abstract: A display panel includes: a display substrate, including a display region and a peripheral region located outside the display region. The peripheral region includes a bonding region; the display substrate specifically includes a base substrate, a plurality of fanout lines located on one side of the base substrate in the peripheral region and extending to the bonding region, and a plurality of first bonding electrodes electrically connected with the fanout lines in one-to-one correspondence in the bonding region; a drive chip, including a drive chip body and a plurality of pins located on a side of the drive chip body facing the display substrate; wherein the plurality of pins include a plurality of output pins bonded with the first bonding electrodes in one-to-one correspondence.

Abstract: A battery apparatus includes batteries arranged in a first direction; an end plate disposed at an end of the battery apparatus and having adjacent third and fourth surfaces; a protective support disposed on the end plate and having adjacent first and second surfaces. The first and third surfaces are disposed opposite to each other, and the second and fourth surfaces face the batteries; the fourth surface has an adhesive layer; the protective support has a notch at a junction of the first and second surfaces, and the notch is a first notch, and/or the end plate is provided with a notch at a junction of the third and fourth surfaces, and the notch is a second notch; a first (first notch and third surface), second (second notch and first surface), or third (first and second notches) combination forms a groove, and an opening of the groove faces the batteries.

Abstract: A method and system for multi-factor authenticating users in Web3 services includes a multi-factor authentication (e.g., 2FA) provider configured to validate that an end-user owns a public key in a blockchain based on a cryptocurrency or a NFT transference, the public key used as a first factor for the authentication. The authentication provider interacts with a smart contract to perform the multi-factor authentication in an operation in a smart contract by using off-chain data as the other factor(s) required for the multi-factor authentication. The off-chain data are received in a user terminal, e.g., a smartphone via SMS or a mobile application, associated with the account created in the authentication provider. The authentication result is read by each node executing the smart contract and, when a consensus is reached among nodes, a new block representing the result is generated in the blockchain.

Abstract: A method and system for optimizing indoor wireless coverage are proposed. A user device and computer program are also proposed. The method comprises connecting, by a mobile device, to a wireless router via a first wireless network, the wireless router being located at a customer premises; computing a coverage metric for a plurality of subpoints within at least a first point and a second point of the customer premises, obtaining a plurality of coverage metrics as a result, each coverage metric comprising a position of the mobile device and a quality indicator of the wireless connection of the mobile device in that position; generating a wireless coverage map using the plurality of coverage metrics; determining whether a wireless signal repeater has to be installed based on the generated wireless coverage map; and showing a new wireless coverage map considering the installation of the wireless signal repeater.

Abstract: A method and system for multi-factor authenticating a user wearing a VR headset (120) comprising a multi-factor authentication application programming interface to interact with a multi-factor authentication provider server (110) and enable the user to create an account in the provider server (110) associated with a secondary device (130). Each one-time code generated by the provider server (110) is received in the secondary device (130) through a secondary communication channel. The one-time code is entered, through a graphical interface (1000) of the VR headset (120), while the user is wearing it, by hearing or seeing the code, received in the secondary device (130), through the VR headset (120). The entered code is sent from the VR headset (120) to the provider server (110) to verify it and determines whether the user is enabled to access the VR application, the access is disabled or the user is asked for another entry of code through the VR headset (120).

Abstract: Systems and methods for identifying clouds and cloud shadows in satellite imagery are described herein. In an embodiment, a system receives a plurality of images of agronomic fields produced using one or more frequency bands. The system also receives corresponding data identifying cloud and cloud shadow locations in the images. The system trains. a machine learning system to identify at least cloud locations using the images as inputs and at least data identifying pixels as cloud pixels or non-cloud pixels as outputs. When the system receives one or more particular images of a particular agronomic field produced using the one or more frequency bands, the system uses the one or more particular images as inputs into the machine learning system to identify a plurality of pixels in the one or more particular images as particular cloud locations.

Abstract: A conjugate for active targeted therapy of tumors is obtained by coupling a hydrophilic affibody with an active tumor targeting function and a hydrophobic anti-tumor cytotoxin through a small molecule linker. The affibody has a specific binding activity and a high affinity to receptors overexpressed on surfaces of various tumor cells. The cytotoxin is a highly effective anti-tumor drug. The affibody-cytotoxin conjugate can self-assemble in water to form the nanoparticle with the affinity body as a shell and the cytotoxin as an inner core. Since the affibody can specifically recognize and bind to receptors overexpressed on the surfaces of the tumor cells, the nanoparticle can be efficiently enriched at tumor sites and biodegraded through the linker to release the cytotoxin and reach effective therapeutic concentrations for inhibiting the growth of the tumors while reducing toxic and side effects to normal organs and tissues.

Abstract: Systems and methods for identifying clouds and cloud shadows in satellite imagery are described herein. In an embodiment, a system receives a plurality of images of agronomic fields produced using one or more frequency bands. The system also receives corresponding data identifying cloud and cloud shadow locations in the images. The system trains. a machine learning system to identify at least cloud locations using the images as inputs and at least data identifying pixels as cloud pixels or non-cloud pixels as outputs. When the system receives one or more particular images of a particular agronomic field produced using the one or more frequency bands, the system uses the one or more particular images as inputs into the machine learning system to identify a plurality of pixels in the one or more particular images as particular cloud locations.

Abstract: A computer-implemented technique is described herein for interpolating input data that includes image and/or audio content. The technique identifies plural sizes associated with different respective phenomena exhibited by the input data. The technique then interpolates the input data in a pipeline that includes plural passes. The plural passes are controlled using plural respective parameter values. The plural respective parameter values, in turn, are selected based on the plural respective sizes, arranged from largest to smallest. In other implementations, the technique chooses pass-specific algorithmic changes to be applied by the interpolation algorithms used by the different passes. In other implementations, the technique chooses its configurations without regard to sizes of phenomena that may be exhibited in the input data. The technique is advantageous because it reduces the presence of artifacts in output data produced by the computer-implemented technique.

Abstract: In an embodiment, a computer-implemented method for predicting subfield soil properties for an agricultural field comprises: receiving satellite remote sensing data that includes a plurality of images capturing imagery of an agricultural field in a plurality of optical domains; receiving a plurality of environmental characteristics for the agricultural field; generating a plurality of preprocessed images based on the plurality of satellite remote sensing data and the plurality of environmental characteristics; identifying, based on the plurality preprocessed images, a plurality of features of the agricultural field; generating a subfield soil property prediction for the agricultural field by executing one or more machine learning models on the plurality of features; transmitting the subfield soil property prediction to an agricultural computer system.

Abstract: Systems and methods for identifying clouds and cloud shadows in satellite imagery are described herein. In an embodiment, a system receives a plurality of images of agronomic fields produced using one or more frequency bands. The system also receives corresponding data identifying cloud and cloud shadow locations in the images. The system trains. a machine learning system to identify at least cloud locations using the images as inputs and at least data identifying pixels as cloud pixels or non-cloud pixels as outputs. When the system receives one or more particular images of a particular agronomic field produced using the one or more frequency bands, the system uses the one or more particular images as inputs into the machine learning system to identify a plurality of pixels in the one or more particular images as particular cloud locations.

Abstract: A method and an orchestration server for packet data network service slicing over a network infrastructure for real-time IP services are provided. The method comprises providing an orchestration server that has knowledge of a whole connectivity status of a network infrastructure and that is configured to manage service nodes. The server receives a session request for a real-time IP service from a UE device located in a given region and requests to one or more service nodes of said given region whether it has/they have network resources. Each of the one or more service nodes compute a QoS measurement thereof in terms of KPIs including latency, jitter and bandwidth, the QoS measurement being computed by a software module that is implemented on a virtual machine deployed on the service node; and tag a plurality of interfaces. The orchestration server selects a given service node in view of said QoS measurement.

Abstract: Systems and methods for identifying clouds and cloud shadows in satellite imagery are described herein. In an embodiment, a system receives a plurality of images of agronomic fields produced using one or more frequency bands. The system also receives corresponding data identifying cloud and cloud shadow locations in the images. The system trains a machine learning system to identify at least cloud locations using the images as inputs and at least data identifying pixels as cloud pixels or non-cloud pixels as outputs. When the system receives one or more particular images of a particular agronomic field produced using the one or more frequency bands, the system uses the one or more particular images as inputs into the machine learning system to identify a plurality of pixels in the one or more particular images as particular cloud locations.

Abstract: In some embodiments, a computer-implemented method for predicting agronomic field property data for one or more agronomic fields using a trained machine learning model is disclosed. The method comprises receiving, at an agricultural intelligence computer system, agronomic training data; training a machine learning model, at the agricultural intelligence computer system, using the agronomic training data; in response to receiving a request from a client computing device for agronomic field property data for one or more agronomic fields, automatically predicting the agronomic field property data for the one or more agronomic fields using the machine learning model configured to predict agronomic field property data; based on the agronomic field property data, automatically generating a first graphical representation; and causing to display the first graphical representation on the client computing device.

Abstract: A control function configuration method includes broadcasting a control function message in a control system through a narrow-band communication network, and receiving a function-configured notification message sent by a target secondary control device in the control system through the narrow-band communication network. The function-configured notification message indicates that a control function of the target secondary control device has been configured to be a target control function. The method further includes broadcasting a function-configured status message in the control system through the narrow-band communication network. The function-configured status message indicates that the control function of the target secondary control device has been configured to be the target control function.

Abstract: A communication method includes, in response to determining that target data needs to be sent to a secondary control terminal that has already established a communication connection with a primary control terminal, generating secondary control terminal data including an identifier of the secondary control terminal and the target data, and, in a process of broadcasting synchronization data including an identifier of the primary control terminal, broadcasting the secondary control terminal data.

Abstract: In an embodiment, a computer-implemented method for predicting subfield soil properties for an agricultural field comprises: receiving satellite remote sensing data that includes a plurality of images capturing imagery of an agricultural field in a plurality of optical domains; receiving a plurality of environmental characteristics for the agricultural field; generating a plurality of preprocessed images based on the plurality of satellite remote sensing data and the plurality of environmental characteristics; identifying, based on the plurality preprocessed images, a plurality of features of the agricultural field; generating a subfield soil property prediction for the agricultural field by executing one or more machine learning models on the plurality of features; transmitting the subfield soil property prediction to an agricultural computer system.

Abstract: Systems and methods for identifying clouds and cloud shadows in satellite imagery are described herein. In an embodiment, a system receives a plurality of images of agronomic fields produced using one or more frequency bands. The system also receives corresponding data identifying cloud and cloud shadow locations in the images. The system trains. a machine learning system to identify at least cloud locations using the images as inputs and at least data identifying pixels as cloud pixels or non-cloud pixels as outputs. When the system receives one or more particular images of a particular agronomic field produced using the one or more frequency bands, the system uses the one or more particular images as inputs into the machine learning system to identify a plurality of pixels in the one or more particular images as particular cloud locations.

Abstract: The distribution network comprising a plurality of caching nodes and wherein the web content had been statically identified as an uncacheable content and originated on an origin-server and wherein the method establishes a time to live, TTL, period value to said uncacheable content based on users requests and responds to said users requests by sending to at least to one CDN user said uncacheable content within said TTL period value. Each one of said plurality of caching nodes in said distribution network including a content caching manager and a pseudo-dynamic content TTL predictor and performing the following steps: a) contacting each one of said plurality of nodes with a centralized repository, in order to download the configuration file of a plurality of said CDN users.

Abstract: A computer implemented method and network device for tracking OPT-OUT user preferences in a global communication network and computer programs thereof, The method comprising performing, by at least one a user, at least one operation request for a service of an online system (500); tracking, by a network device (300), said operation request and detecting if it is linked to a tracking mechanism, wherein: a) if it is not linked to a tracking mechanism, said network device (300), bypasses the operation request to said online system (500) without performing any action; or if it is linked to a tracking mechanism, the network device (300), identifies whether an OPT-OUT or an Opt-In status related to the user is included in the operation request and sends or not the operation request to a tracking server (600) depending on the result of said identification.