Abstract: The present invention belongs to the field of pharmaceutical chemistry, and relates to an N-(benzoyl)-phenylalanine compound used as an ?4?7 integrin antagonist, including the pharmaceutical composition and the use, and in particular to a compound represented by general formula (1). The compound exhibits good ?4?7 integrin binding inhibitory activity, can be used as a high-efficiency ?4?7 integrin antagonist, and used for preventing and/or treating ?4?7 integrin-related diseases such as autoimmune diseases and inflammatory diseases.

Abstract: A method for an auxiliary device of an auxiliary resonant commutated pole inverter (ARCPI) to determine a boosting time of the ARCPI is provided. The auxiliary device includes an auxiliary switch path that uses a resonant inductor with at least one saturable inductor. The method includes: calculating, by a processor of the auxiliary device, a first boosting time in the auxiliary switch path based on a theoretical resonance inductance and a voltage that are applied to the resonant inductor; determining, by the processor, a second boosting time in the auxiliary switch path based on a look-up table; and determining, by the processor, the boosting time in the auxiliary switch path based on the first boosting time and the second boosting time. The method further includes controlling, by the processor, boosting current of the ARCPI based on the boosting time in the auxiliary switch path.

Abstract: A computerized simulation validating method for a full-scale distribution network single phase-to-ground fault test is implemented by simulating a full-scale test system with external quantities being controlled to be conformant and validating the full-scale distribution network single phase-to-ground fault test based on a conformance check result between the internal quantities of the field testing and the internal quantities of the simulation testing. The simulation validating method for a full-scale distribution network single phase-to-ground fault test improves normalization and conformance of the full-scale distribution network ground fault test. The computerized simulation validating system, apparatus, and medium for a full-scale distribution network single phase-to-ground fault test also achieve the benefits noted above.

Abstract: Sidelink based relay communications include communications through a relay user equipment (“UE”) between a basestation and a remote UE. Relay communications can be improved by addressing incompatibilities between different devices, which support different features/operations, such as the layers of communication. Access control and an establishment cause value can also be addressed by the relay communications. Paging identification and system information can be communicated by relay communications, such as through the relay UE to improve communications.

Abstract: Systems and methods for detecting an anomaly in a power semiconductor device are disclosed. A system includes a server computing device and one or more local components communicatively coupled to the server computing device. Each local component includes sensors positioned adjacent to the power semiconductor device for sensing properties thereof. Each local component receives data corresponding to one or more sensed properties of the power semiconductor device from the sensors and transmits the data to the server computing device. The server computing device utilizes the data, via a machine learning algorithm, to generate a set of eigenvalues and associated eigenvectors and select a selected set of eigenvalues and associated eigenvectors. Each local component conducts a statistical analysis of the selected set of eigenvalues and associated eigenvectors to determine that the data is indicative of the anomaly.

Abstract: The present invention provides devices that replicate tumor microenvironments in a microfluidic chip. The devices can be used to model certain disease states related to tumor microenvironments. The devices can be adapted to replicate tumor microenvironments from patient-specific cells such that treatment conditions can be modeled and tailored to individual patients. In some embodiments, the devices are suitable for evaluating cancer therapies on a patient-specific basis.

Abstract: Provided are anti-tetranectin domain-specific antibodies and fragments thereof, as well as methods of use employing such antibodies and/or fragments.

Abstract: To predict a failure condition in a power module of a vehicle, it is determined whether a discontinuity in statistical data characterizing physical measurements of the power module meets a threshold criterion. Responsive to the discontinuity meeting the threshold criterion, a data offset in the physical measurements is computed at the discontinuity. A shift correction is applied to the physical measurements in accordance with the computed data offset responsive to a determination that the discontinuity is attributable to a restart of the power module. Other statistical data characterizing the shift-corrected physical measurements are computed and the statistical data and the other statistical data are provided to a machine learning processor that predicts the failure condition in the power module.

Abstract: Systems and methods for detecting an anomaly in a power semiconductor device are disclosed. A system includes a server computing device and one or more local components communicatively coupled to the server computing device. Each local component includes sensors positioned adjacent to the power semiconductor device for sensing properties thereof. Each local component receives data corresponding to one or more sensed properties of the power semiconductor device from the sensors and transmits the data to the server computing device. The server computing device utilizes the data, via a machine learning algorithm, to generate a set of eigenvalues and associated eigenvectors and select a selected set of eigenvalues and associated eigenvectors. Each local component conducts a statistical analysis of the selected set of eigenvalues and associated eigenvectors to determine that the data is indicative of the anomaly.

Abstract: Systems and methods for detecting an anomaly in a power semiconductor device are disclosed. A system includes a server computing device and one or more local components communicatively coupled to the server computing device. Each local component includes sensors positioned adjacent to the power semiconductor device for sensing properties thereof. Each local component receives data corresponding to one or more sensed properties of the power semiconductor device from the sensors and transmits the data to the server computing device. The server computing device utilizes the data, via a machine learning algorithm, to generate a set of eigenvalues and associated eigenvectors and select a selected set of eigenvalues and associated eigenvectors. Each local component conducts a statistical analysis of the selected set of eigenvalues and associated eigenvectors to determine that the data is indicative of the anomaly.

Abstract: Systems and methods for detecting a fault or model mismatch are disclosed. A system includes a processor, a memory, and one or more sensors. The sensors may detect data associated with an electronic device. The memory may store processor executable instructions to: compute T2 and Q statistics, over a time period, and apply a model mismatch and fault detection logic based on the T2 and Q statistics. The model mismatch and fault detection logic may: count consecutive instances where a T2 statistic exceeds a T2 threshold via a T2 counter, update a probability of fault based on the T2 counter, count consecutive instances where a Q statistic exceeds a Q threshold via a Q counter, update a probability of model mismatch based on the Q counter, and detect one of a fault or a model mismatch based on a probability of fault threshold and a probability of model mismatch threshold.

Abstract: A hill descent system for a vehicle and a control method thereof comprising: wheels; wheel speed sensors used for detecting the speeds of the wheels; motors used for selectively driving or braking the wheels; a motor controllers, for controlling the working states of the motors; resolver sensors for detecting the rotational speeds of the motors; and a vehicle control unit for determining the actual downhill speed of the vehicle and adjusting the working states of the motors to control the descent of the vehicle.

Abstract: To predict a failure condition in a power module of a vehicle, it is determined whether a discontinuity in statistical data characterizing physical measurements of the power module meets a threshold criterion. Responsive to the discontinuity meeting the threshold criterion, a data offset in the physical measurements is computed at the discontinuity. A shift correction is applied to the physical measurements in accordance with the computed data offset responsive to a determination that the discontinuity is attributable to a restart of the power module. Other statistical data characterizing the shift-corrected physical measurements are computed and the statistical data and the other statistical data are provided to a machine learning processor that predicts the failure condition in the power module.

Abstract: Systems and methods for detecting a fault or model mismatch are disclosed. A system includes a processor, a memory, and one or more sensors. The sensors may detect data associated with an electronic device. The memory may store processor executable instructions to: compute T2 and Q statistics, over a time period, and apply a model mismatch and fault detection logic based on the T2 and Q statistics. The model mismatch and fault detection logic may: count consecutive instances where a T2 statistic exceeds a T2 threshold via a T2 counter, update a probability of fault based on the T2 counter, count consecutive instances where a Q statistic exceeds a Q threshold via a Q counter, update a probability of model mismatch based on the Q counter, and detect one of a fault or a model mismatch based on a probability of fault threshold and a probability of model mismatch threshold.

Abstract: A brake system (100) and a brake method for a four-wheel drive electric vehicle and a four-wheel drive electric vehicle, in the system, according to a brake mode of the electric vehicle, a state of charge of a battery pack (4) and a vehicle speed, a first brake control unit controls a motor (6) to brake a wheel (9) through a motor controller (2) and a second brake control unit controls a brake actuator (12) to brake the wheels (9). The first brake control unit further determines whether a brake torque of the brake actuator (12) on the wheels (9) fails. If yes, the first brake control unit controls the motor (6) to brake the corresponding wheel (9) through the motor controller (2).

Abstract: A system includes a vehicle having an electronic device, a sensor designed to detect sensor data corresponding to at least one property of the electronic device, an output device designed to output data, and a vehicle network access device designed to transmit the sensor data. The system also includes a machine learning server separate from the vehicle and having a machine learning processor designed to receive the sensor data, and generate, using a machine learning algorithm, a model of the electronic device. The machine learning processor is also designed to determine that a fault is likely to occur with the electronic device by conducting a T squared statistical analysis of the sensor data using the model, and generate a signal to be transmitted to the vehicle network access device when the fault is likely to occur and output information indicating that the fault is likely to occur.

Abstract: A hill descent system for a vehicle and a control method thereof comprising: wheels; wheel speed sensors used for detecting the speeds of the wheels; motors used for selectively driving or braking the wheels; a motor controllers, for controlling the working states of the motors; resolver sensors for detecting the rotational speeds of the motors; and a vehicle control unit for determining the actual downhill speed of the vehicle and adjusting the working states of the motors to control the descent of the vehicle.

Abstract: Provided is a nanocrystal comprising a core comprised in a shell, wherein the core comprises a first material of a perovskite structure comprising a first organic cation not exceeding a molar weight of about 45 g/mol, a first divalent metal and a first counter anion, and, wherein the shell comprises a second material of a perovskite structure comprising a second organic cation having a molar weight between about 74 g/mol and about 187 g/mol, optionally the first organic cation, a second divalent metal and a second counter anion. Provided is further a matrix having the nanocrystal as defined above encapsulated therein.

Abstract: A system includes a vehicle having an electronic device, a sensor designed to detect sensor data corresponding to at least one property of the electronic device, an output device designed to output data, and a vehicle network access device designed to transmit the sensor data. The system also includes a machine learning server separate from the vehicle and having a machine learning processor designed to receive the sensor data, and generate, using a machine learning algorithm, a model of the electronic device. The machine learning processor is also designed to determine that a fault is likely to occur with the electronic device by conducting a T squared statistical analysis of the sensor data using the model, and generate a signal to be transmitted to the vehicle network access device when the fault is likely to occur and output information indicating that the fault is likely to occur.

Abstract: Systems and methods for detecting an anomaly in a power semiconductor device are disclosed. A system includes a server computing device and one or more local components communicatively coupled to the server computing device. Each local component includes sensors positioned adjacent to the power semiconductor device for sensing properties thereof Each local component receives data corresponding to one or more sensed properties of the power semiconductor device from the sensors and transmits the data to the server computing device. The server computing device utilizes the data, via a machine learning algorithm, to generate a set of eigenvalues and associated eigenvectors and select a selected set of eigenvalues and associated eigenvectors. Each local component conducts a statistical analysis of the selected set of eigenvalues and associated eigenvectors to determine that the data is indicative of the anomaly.