Abstract: Certain aspects of the present disclosure provide techniques for securely accessing a wallet on a blockchain. An example method generally includes receiving a request to access a wallet on a blockchain. The request generally includes an authorization code associated with the wallet and user credentials associated with an owner of the wallet. A first portion of a private key is decrypted based on the authorization code and a salt associated with the user credentials, and a second portion of the private key is decrypted based on credentials associated with an application through which the wallet is accessed. Access to the wallet is granted based on the decrypted first portion and the decrypted second portion of the private key.

Abstract: Certain aspects of the present disclosure provide techniques for securely accessing a wallet maintained by a centralized platform on a blockchain. An example method generally includes receiving a request to access one or more wallets on a blockchain. Generally, the request includes an authorization code associated with a controlling party associated with the one or more wallets and user credentials associated with the controlling party. A first portion of a private key is decrypted based on the authorization code and a salt associated with the user credentials associated with the controlling party, and a second portion of the private key is decrypted based on credentials associated with an application through which the wallet is accessed. Access to the one or more wallets is granted based on the decrypted first portion and the decrypted second portion of the private key.

Abstract: Certain aspects of the present disclosure provide techniques for securely accessing a wallet maintained by a centralized platform on a blockchain. An example method generally includes receiving a request to access one or more wallets on a blockchain. Generally, the request includes an authorization code associated with a controlling party associated with the one or more wallets and user credentials associated with the controlling party. A first portion of a private key is decrypted based on the authorization code and a salt associated with the user credentials associated with the controlling party, and a second portion of the private key is decrypted based on credentials associated with an application through which the wallet is accessed. Access to the one or more wallets is granted based on the decrypted first portion and the decrypted second portion of the private key.

Abstract: The present disclosure provides an early warning method, apparatus, and system for a nitrogen concentration in industrialized aquaponic circulating water. The early warning method includes: obtaining video data of a sensitive fish farming container, where the video data includes a monitored movement behavior of a sensitive fish in the sensitive fish farming container, and the sensitive fish farming container is in communication with a farming pond of an aquaponic system; processing the video data using an object detection model and obtaining a behavior activity of the sensitive fish according to a processing result; processing the behavior activity using a neural network model to obtain a nitrogen concentration grade of the farming pond of the aquaponic system; and determining whether to send nitrogen concentration early warning information based on the nitrogen concentration grade.

Abstract: Methods and systems for building and maintaining model(s) of a physical process are disclosed. One method includes receiving training data associated with a plurality of different data sources, and performing a clustering process to form one or more clusters. For each of the one or more clusters, the method includes building a data model based on the training data associated with the data sources in the cluster, automatically performing a data cleansing process on operational data based on the data model, and automatically updating the data model based on updated training data that is received as operational data. For data sources excluded from the clusters, automatic building, data cleansing, and updating of models can also be applied.

Abstract: Methods and systems for building and maintaining model(s) of a physical process are disclosed. One method includes receiving training data associated with a plurality of different data sources, and performing a clustering process to form one or more clusters. For each of the one or more clusters, the method includes building a data model based on the training data associated with the data sources in the cluster, automatically performing a data cleansing process on operational data based on the data model, and automatically updating the data model based on updated training data that is received as operational data. For data sources excluded from the clusters, automatic building, data cleansing, and updating of models can also be applied.

Abstract: Methods and systems for building and maintaining model(s) of a physical process are disclosed. One method includes receiving training data associated with a plurality of different data sources, and performing a clustering process to form one or more clusters. For each of the one or more clusters, the method includes building a data model based on the training data associated with the data sources in the cluster, automatically performing a data cleansing process on operational data based on the data model, and automatically updating the data model based on updated training data that is received as operational data. For data sources excluded from the clusters, automatic building, data cleansing, and updating of models can also be applied.

Abstract: Methods and systems for building and maintaining model(s) of a physical process are disclosed. One method includes receiving training data associated with a plurality of different data sources, and performing a clustering process to form one or more clusters. For each of the one or more clusters, the method includes building a data model based on the training data associated with the data sources in the cluster, automatically performing a data cleansing process on operational data based on the data model, and automatically updating the data model based on updated training data that is received as operational data. For data sources excluded from the clusters, automatic building, data cleansing, and updating of models can also be applied.

Abstract: A lactone hydrolase having improved activity towards ?-zearalenol is disclosed. The lactone hydrolase has a modified amino acid sequence of SEQ ID NO: 5, wherein the modification is a substitution of valine at position 167 with histidine. A method of degrading ?-zearalenol using such lactone hydrolase is also disclosed.

Abstract: A method for monitoring a control of a parameter of one or more devices or systems in an oil or gas production site includes receiving process data, the process data being a result of the control of the parameter of the one or more devices or systems in the production site; smoothing the process data using a polynomial filter while preserving features of the process data to obtain smoothed data; and applying a pattern recognition algorithm to the smoothed data to determine whether there is a malfunction condition in the one or more devices or systems.

Abstract: A lactone hydrolase having improved activity towards ?-zearalenol is disclosed. The lactone hydrolase has a modified amino acid sequence of SEQ ID NO: 5, wherein the modification is a substitution of valine at position 167 with histidine. A method of degrading ?-zearalenol using such lactone hydrolase is also disclosed.

Abstract: A computer-implemented method for reconstructing data includes receiving a selection of one or more input data streams at a data processing framework. The method can include determining existence of a fault in the input data stream(s). This determination can be based on receiving a definition of one or more analytics components at the data processing framework and applying a dynamic principal component analysis (DPCA) to the input data streams. Detection of the fault can be based at least in part on a prediction error and a variation in principal component subspace generated based on the DPCA. Detection of the fault can also be based on performing a wavelet transform to generate a set of coefficients defining the data stream, the set of coefficients including one or more coefficients representing a high frequency portion of data included in the data stream. The method can include reconstructing data at the fault.

Abstract: Methods and systems for reconstructing data are disclosed. One method includes receiving a selection of one or more input data streams at a data processing framework, and receiving a definition of one or more analytics components at the data processing framework. The method further includes applying a dynamic principal component analysis to the one or more input data streams, and detecting a fault in the one or more input data streams based at least in part on a prediction error and a variation in principal component subspace generated based on the dynamic principal component analysis. The method also includes reconstructing data at the fault within the one or more input data streams based on data collected prior to occurrence of the fault.