Abstract: Described is a light emitting diode (LED) that uses hybrid nanostructures to enhance light emission intensity and light extraction efficiency. In at least one embodiment, nanostructures comprise a combination of metal and metal oxide layers that simultaneously provide optical property for emission enhancement and support electrical property for charge transport to support electroluminescence. In at least one embodiment, LED comprises multiple layers including a hybrid metal-metal oxide layer, light-emissive layer, and a light outcoupling layer. In at least one embodiment, metal-metal oxide layer can be formed by evaporation or sputtering. In at least one embodiment, light-emissive layer can be formed by emissive layer spin coating. In at least one embodiment, light outcoupling layer is formed by imprinting or by particle lithography.

Abstract: A bearing unit includes a radially inner ring, a radially outer ring comprising a lubricant inlet, a plurality of rolling bodies interposed between the inner ring and the outer ring, and a first seal. The first seal includes an axially outer sealing ring, an axially inner sealing ring, and a first sealing lip formed on an axially inner surface of the axially inner sealing ring and extending axially inward to make sealing contact with an axially outer surface of the axially inner sealing ring. The first sealing lip prevents lubricant from flowing from an interior of the bearing unit to an exterior of the bearing unit at a first axial end of the bearing unit, and the first seal is disposed axially outside the inlet of the radially outer ring.

Abstract: The present disclosure relates to a device and a system for testing flatness. The device for testing flatness includes a base, a testing platform, and a ranging sensor. The testing platform is assembled on the base. The testing platform includes a supporting structure. The supporting structure is disposed on the side of the testing platform away from the base and is used to support a to-be-tested board. The structure matches the structure of the to-be-tested board. The ranging sensor is disposed on the side of the testing platform away from the base. After the to-be-tested board is placed on the testing platform, the ranging sensor is used to test distances between a number N of to-be-tested positions on the to-be-tested board and the ranging sensor, to obtain N pieces of distance information, and the N pieces of distance information are used to determine the flatness of the to-be-tested board, where N is an integer greater than 2.

Abstract: Systems and methods of the present disclosure relate to calibration of resistivity logging tool. A method to calibrate a resistivity logging tool comprises disposing the resistivity logging tool into a formation; acquiring a signal at each logging point with the resistivity logging tool; assuming a formation model for a first set of continuous logging points in the formation; inverting all of the signals for unknown model parameters of the formation model, wherein the formation model is the same for all of the continuous logging points in the first set; assigning at least one calibration coefficient to each type of signal, wherein the calibration coefficients are the same for the first set; and building an unknown vector that includes the unknown model parameters and the calibration coefficients, to calibrate the resistivity logging tool.

Abstract: A chip package and method for fabricating the same are provided that includes embedded off-die inductors coupled in series. One of the off-die inductors is disposed in a redistribution layer formed on a bottom surface of an integrated circuit (IC) die. The other of the series connected off-die inductors is disposed in a substrate of the chip package. The substrate may be either an interposer or a package substrate.

Abstract: A method includes: providing input information in an electronic format; converting the electronic input information into an optical input vector; optically transforming the optical input vector into an optical output vector based on an optical matrix multiplication; converting the optical output vector into an electronic format; and electronically applying a non-linear transformation to the electronically converted optical output vector to provide output information in an electronic format. For example, a set of input values are encoded on respective optical signals. For each of at least two subsets of optical signals, a copying module splits the subset into multiple copies of the optical signals. For each copy of a first subset of optical signals, a corresponding multiplication module multiplies the optical signals of the first subset by matrix element values using optical amplitude modulation. A summation module produces an electrical signal representing a sum of the results of the multiplication modules.

Abstract: Disclosed herein is an electrochemical biosensor comprising at least one working electrode on an electrically insulative substrate. At least one working electrode incorporates an electrocatalytic film, comprising a molecularly imprinted polymer (MIP) layer. In at least one embodiment, MIP layer comprises a plurality of shape-selective cavities and a plurality of non-biological electrocatalytic centers.

Abstract: Systems and methods that include: providing input information in an electronic format; converting at least a part of the electronic input information into an optical input vector; optically transforming the optical input vector into an optical output vector based on an optical matrix multiplication; converting the optical output vector into an electronic format; and electronically applying a non-linear transformation to the electronically converted optical output vector to provide output information in an electronic format. In some examples, a set of multiple input values are encoded on respective optical signals carried by optical waveguides. For each of at least two subsets of one or more optical signals, a corresponding set of one or more copying modules splits the subset of one or more optical signals into two or more copies of the optical signals.

Abstract: Systems and methods that include: providing input information in an electronic format; converting at least a part of the electronic input information into an optical input vector; optically transforming the optical input vector into an optical output vector based on an optical matrix multiplication; converting the optical output vector into an electronic format; and electronically applying a non-linear transformation to the electronically converted optical output vector to provide output information in an electronic format. In some examples, a set of multiple input values are encoded on respective optical signals carried by optical waveguides. For each of at least two subsets of one or more optical signals, a corresponding set of one or more copying modules splits the subset of one or more optical signals into two or more copies of the optical signals.

Abstract: Systems and methods that include: providing input information in an electronic format; converting at least a part of the electronic input information into an optical input vector; optically transforming the optical input vector into an optical output vector based on an optical matrix multiplication; converting the optical output vector into an electronic format; and electronically applying a non-linear transformation to the electronically converted optical output vector to provide output information in an electronic format. In some examples, a set of multiple input values are encoded on respective optical signals carried by optical waveguides. For each of at least two subsets of one or more optical signals, a corresponding set of one or more copying modules splits the subset of one or more optical signals into two or more copies of the optical signals.

Abstract: Data to be processed includes vector element values of an input vector and matrix element values of a model matrix associated with a neural network model. A vector-matrix multiplication module receives a set of matrix element values for performing a vector-matrix multiplication operation. Processing the data includes computing a plurality of intermediate vectors based on element-wise vector multiplication between different subsets of the vector element values and different respective pre-processing vectors. The vector-matrix multiplication module is loaded with a core matrix, and the input vector is multiplied by the model matrix based on separately multiplying each of the intermediate vectors by the loaded core matrix.

Abstract: Data to be processed includes vector element values of an input vector and matrix element values of a model matrix associated with a neural network model. A vector-matrix multiplication module receives a set of matrix element values for performing a vector-matrix multiplication operation. Processing the data includes computing a plurality of intermediate vectors based on element-wise vector multiplication between different subsets of the vector element values and different respective pre-processing vectors. The vector-matrix multiplication module is loaded with a core matrix, and the input vector is multiplied by the model matrix based on separately multiplying each of the intermediate vectors by the loaded core matrix.

Abstract: Data to be processed includes vector element values of an input vector and matrix element values of a model matrix associated with a neural network model. A vector-matrix multiplication module receives a set of matrix element values for performing a vector-matrix multiplication operation. Processing the data includes computing a plurality of intermediate vectors based on element-wise vector multiplication between different subsets of the vector element values and different respective pre-processing vectors. The vector-matrix multiplication module is loaded with a core matrix, and the input vector is multiplied by the model matrix based on separately multiplying each of the intermediate vectors by the loaded core matrix.

Abstract: Disclosed probes comprise metal nanoparticle cores associated with magnetic particles that allow probes associated with targets to be concentrated by an applied magnetic field to increase detection sensitivity and provide sufficient spacing between concentrated probes to avoid signal quenching. The probe may comprise at least one recognition receptor, and may further comprise at least one reporter molecule, such as a fluorescent tag, a Raman reporter, or combinations thereof. Concentrating probe-target composites substantially enhances a sensing signal, such as from 5 to 10 times, compared to detection without concentrating the probes. The method may be used to detect, for example, interleukins at concentrations at least as low as 25 pg/ml in sputum or blood from a subject for early and precise profiling of viral infections, such as SARS-CoV-2 infections.

Abstract: Disclosed herein are materials and a micro-LED display with carbon-based light-emitting materials, carbon quantum dots, that are made by a solvothermal synthesis of a mixture of aromatic amino acid, 3,4-dihydroxy-L-phenylalanine (LDOPA), and urea in dimethylformamide (DMF). The mixture is heated in a sealed pressure reactor at a temperature, ranging from 120 degrees Celsius to 350 degrees Celsius, for 4-24 hours. The product is then purified to collect the solid powder. The purified CDs can be dissolved in an acrylate monomer solution or a polymer solution for material delivery and curing process on a target substrate for the applications, including light-emitting devices or sensors.

Abstract: Systems and methods that include: providing input information in an electronic format; converting at least a part of the electronic input information into an optical input vector; optically transforming the optical input vector into an optical output vector based on an optical matrix multiplication; converting the optical output vector into an electronic format; and electronically applying a non-linear transformation to the electronically converted optical output vector to provide output information in an electronic format. In some examples, a set of multiple input values are encoded on respective optical signals carried by optical waveguides. For each of at least two subsets of one or more optical signals, a corresponding set of one or more copying modules splits the subset of one or more optical signals into two or more copies of the optical signals.

Abstract: The invention discloses a brand-new PCSK9 antibody. The PCSK9 antibody is sieved through a phage display technology, then a full-length gene sequence is obtained through a gene engineering technology, and applied to preparation of the antibody, and the prepared antibody is high in yield and short in period and is a humanized antibody. The invention further discloses an anti-tumor effect of the antihuman PCSK9 antibody, which expands the way of thinking for research on occurrence and development of tumors in the future, and lays foundations for preparing the antihuman PCSK9 antibody into monoclonal antibody medicines used for treating tumors in the later period.

Abstract: Systems and methods that include: providing input information in an electronic format; converting at least a part of the electronic input information into an optical input vector; optically transforming the optical input vector into an optical output vector based on an optical matrix multiplication; converting the optical output vector into an electronic format; and electronically applying a non-linear transformation to the electronically converted optical output vector to provide output information in an electronic format. In some examples, a set of multiple input values are encoded on respective optical signals carried by optical waveguides. For each of at least two subsets of one or more optical signals, a corresponding set of one or more copying modules splits the subset of one or more optical signals into two or more copies of the optical signals.

Abstract: A photonic parallel network can be used to sample combinatorially hard distributions of Ising problems. The photonic parallel network, also called a photonic processor, finds the ground state of a general Ising problem and can probe critical behaviors of universality classes and their critical exponents. In addition to the attractive features of photonic networks—passivity, parallelization, high-speed and low-power—the photonic processor exploits dynamic noise that occurs during the detection process to find ground states more efficiently.

Abstract: A photonic parallel network can be used to sample combinatorially hard distributions of Ising problems. The photonic parallel network, also called a photonic processor, finds the ground state of a general Ising problem and can probe critical behaviors of universality classes and their critical exponents. In addition to the attractive features of photonic networks—passivity, parallelization, high-speed and low-power—the photonic processor exploits dynamic noise that occurs during the detection process to find ground states more efficiently.