Abstract: The disclosure provides a battery pack and a vehicle. The battery pack includes: a housing including a housing bottom wall and a housing side wall which form an accommodating space; a battery module disposed in the accommodating space and including a plurality of batteries, where a case side wall of the battery includes two first side walls oppositely disposed along a first direction and two second side walls oppositely disposed along a second direction; and a frame structure disposed in the accommodating space, which includes a first beam and a second beam respectively located at two ends of the battery module along the first direction, the first beam and the second beam extend along the second direction and are disposed opposite to the first side wall along the first direction, the first beam is non-detachably connected to the housing, and the second beam is detachably connected to the housing.

Abstract: The present application provides a chassis, a vehicle, a device for manufacturing a vehicle and a method for manufacturing a vehicle. The chassis includes at least two movable bodies that are continuously arranged along a first preset direction, and by adjusting a position of at least one movable body in the first preset direction, a length of the chassis in the first preset direction can be changed so as to enable the chassis to adapt to the vehicle body of a different length. Since each movable body is provided at an adjacent movable body thereof with a position adjustable in a preset direction, the chassis is made into a telescopic structure that is adjustable in length. The length of the chassis can be adjusted according to the requirements to enable the chassis to adapt to the vehicle body of a different length.

Abstract: This disclosure relates to methods and apparatus for generating real-time alerts about a patient. In one arrangement, vital sign data representing vital sign information obtained from the patient at one or more input times within an assessment time window is received. A Gaussian process model of at least a portion of the vital sign information is used to generate a time series of synthetic vital sign data based on the received vital sign data, the synthetic vital sign data comprising at least a posterior mean for each of one or more components of the vital sign information at each of a plurality of regularly spaced time points in the assessment time window. The generated synthetic vital sign data is used as input to a trained recurrent neural network to generate an early warning score, the early warning score representing a probability of an adverse event occurring during a prediction time window of predetermined length after the assessment time window.

Abstract: This application provides a battery pack and a vehicle. The battery pack includes: a battery accommodation box, including a box body and a cover body, where the box body fits with the cover body to form an accommodation space; a partition beam, disposed in the accommodation space to partition the accommodation space into a plurality of accommodation units, where the partition beam includes a hitch beam that extends in a first direction parallel to a bottom wall of the box body, a bottom of the hitch beam is fixed to the bottom wall of the box body, and a top of the hitch beam is connected to the cover body; and a plurality of battery modules, where the battery modules include a plurality of battery cells, and each battery module is disposed in a corresponding accommodation unit.

Abstract: Methods and apparatus for monitoring a patient are provided. In one arrangement, a multi- dimensional patient data set is received at each of a plurality of different reference times. Each dimension of the patient data set stores a value representing a different type of information about the patient. A plurality of predictions of a health trajectory of the patient are generated. Each prediction is generated using a trained machine learning model receiving as input a different one of the patient data sets. The trained machine learning model may be dimensionally adaptive, such that predictions of the patient trajectories are provided using patient data sets having different respective dimensionalities for at least a sub-set of the reference times. The trained machine learning model may use machine learned predictions of accuracy to select trained machine learning units from an ensemble of trained machine learning units.

Abstract: Methods and apparatus for classifying subjects based on time series phenotypic data are disclosed. In one arrangement, a data receiving unit receives a set of first subject-data-units, each first subject-data-unit in the set comprising time series data representing phenotypic information about a different respective one of a plurality of subjects to be classified. A data processing unit processes the set of first subject-data-units to reduce a dimensionality of each first subject-data-unit, thereby obtaining a corresponding set of second subject-data-units having lower dimensionality than the first subject-data-units. The set of second subject-data-units is processed to cluster the second subject-data-units into a plurality of clusters. Each of one or more of the subjects is classified by determining to which cluster a second subject-data-unit corresponding to the subject belongs.

Abstract: The present application is provided with a battery box and a battery module. The battery box includes a plurality of end plates; a plurality of side plates, where one end plate is provided between adjacent side plates, and the end plates and the side plates enclose and form a cavity of the battery box. A plurality of welding members are formed by bending an end portion of each of the side plates and/or each of the end plates for enclosure for multiple times. Each of the welding members is configured to weld to a corresponding end plate or side plate.

Abstract: The present application provides a battery case and a battery module. The battery case includes a bottom plate; and a plurality of circumferentially spaced extensions formed by extending horizontally outward along the bottom plate, and each extension is bended at least once to form a case side arm, and a cavity of the battery case is enclosed by the case side arms and the bottom plate, and end faces for connection of the case side arms are bended to form connecting portions, and two adjacent connecting portions are snap-fitted to fix the adjacent case side arms to each other.

Abstract: The disclosure provides a battery pack and a vehicle. The battery pack includes: a housing including a housing bottom wall and a housing side wall which form an accommodating space; a battery module disposed in the accommodating space and including a plurality of batteries, where a case side wall of the battery includes two first side walls oppositely disposed along a first direction and two second side walls oppositely disposed along a second direction; and a frame structure disposed in the accommodating space, which includes a first beam and a second beam respectively located at two ends of the battery module along the first direction, the first beam and the second beam extend along the second direction and are disposed opposite to the first side wall along the first direction, the first beam is non-detachably connected to the housing, and the second beam is detachably connected to the housing.

Abstract: The present application provides a battery case and a battery module. The battery case includes a bottom plate; and a plurality of circumferentially spaced extensions formed by extending horizontally outward along the bottom plate, and each extension is bended at least once to form a case side arm, and a cavity of the battery case is enclosed by the case side arms and the bottom plate, and end faces for connection of the case side arms are bended to form connecting portions, and two adjacent connecting portions are snap-fitted to fix the adjacent case side arms to each other.

Abstract: Methods and apparatus for subtyping subjects based on phenotypic information are disclosed. In one arrangement, a data receiving unit receives a subject data unit for each of a plurality of subjects. Each subject data unit represents a plurality of different phenotypic information items about the subject. A data processing unit uses a deep learning algorithm to derive a lower dimensional representation of each subject data unit and a clustering algorithm to detect clusters of the resulting lower dimensional representations. The deep learning algorithm and clustering algorithm are implemented by a single mathematical model in which the derivation of the lower dimensional representations and the detection of the clusters are performed jointly.

Abstract: Methods and apparatus for monitoring a human or animal subject are disclosed. In one arrangement, test data representing a time-series of physiological measurements performed on a subject in a measurement session is received. A mean trajectory with error bounds is fitted to the test data. A state of the subject is determined by comparing the fitted mean trajectory with error bounds to a stored model of normality. The stored model of normality comprises a library of latent mean trajectories with error bounds. Each latent mean trajectory with error bounds is derived by fitting a hierarchical probabilistic model to a respective one of a plurality of sets of historical data. Each set of historical data comprises a plurality of session data units. Each session data unit representing a time-series of physiological measurements obtained during a different measurement session.

Abstract: A method for preparing a magic-sized nano-crystalline substance, wherein a component containing at least one metal element of groups IIB, IIIA and IVA in the periodic table, and a component containing at least one non-metal element of groups VIA and VA are used as raw materials. In a reaction system for preparing a conventional nano-crystalline substance and in an inert gas atmosphere, after heating the reaction, reactants are cooled to a temperature 50% lower than the actual heating temperature of the reaction thereof, and after standing, the target product of the magic-sized nano-crystalline substance is obtained. The required pure target product can be obtained by the preparation method.

Abstract: A method for preparing a magic-sized nano-crystalline substance, wherein a component containing at least one metal element of groups IIB, IIIA and IVA in the periodic table, and a component containing at least one non-metal element of groups VIA and VA are used as raw materials. In a reaction system for preparing a conventional nano-crystalline substance and in an inert gas atmosphere, after heating the reaction, reactants are cooled to a temperature 50% lower than the actual heating temperature of the reaction thereof, and after standing, the target product of the magic-sized nano-crystalline substance is obtained. The required pure target product can be obtained by the preparation method.