Abstract: The invention relates to a method for preparing a cellulose dope comprising mixing and dissolving the cellulosic raw material in dilute and concentrated aqueous organic solvent in a two-stage process to form a homogeneous slurry, followed by heating the homogeneous slurry to obtain a cellulose dope containing 11% to 20% cellulose by weight. The invention also relates to a cellulose dope comprising 11% to 20% cellulose by weight and 73% to 79% aqueous organic solvent wherein the concentration of the cellulosic and metallic impurities in the cellulose dope shows a percent reduction of 20% to 50% from the cellulosic raw material.

Abstract: The invention relates to a method for preparing a cellulose dope comprising mixing and dissolving the cellulosic raw material in dilute and concentrated aqueous organic solvent in a two-stage process to form a homogeneous slurry, followed by heating the homogeneous slurry to obtain a cellulose dope containing 11% to 20% cellulose by weight. The invention also relates to a cellulose dope comprising 11% to 20% cellulose by weight and 73% to 79% aqueous organic solvent wherein the concentration of the cellulosic and metallic impurities in the cellulose dope shows a percent reduction of 20% to 50% from the cellulosic raw material.

Abstract: The invention relates to a method for preparing a cellulose dope comprising mixing and dissolving the cellulosic raw material in dilute and concentrated aqueous organic solvent in a two-stage process to form a homogeneous slurry, followed by heating the homogeneous slurry to obtain a cellulose dope containing 11% to 20% cellulose by weight. The invention also relates to a cellulose dope comprising 11% to 20% cellulose by weight and 73% to 79% aqueous organic solvent wherein the concentration of the cellulosic and metallic impurities in the cellulose dope shows a percent reduction of 20% to 50% from the cellulosic raw material.

Abstract: A system for discovery and analysis of network data usage of users of a communication network may collect information related to data usage over a network. The system may determine network data usage patterns for users from the data usage information. The network usage data, usage patterns and additional information may be analyzed to create user segments, and to analyze network data usage for the user segments. Differentiated data services may be created and implemented based on the network data usage for the user segments.

Abstract: In an example, network node failures may be predicted by extracting performance metrics for the network nodes from a plurality of data sources. A fail condition may be defined for the network nodes and input variables related to the fail condition for the network nodes may then be derived from the extracted performance metrics. A plurality of models may then be trained to predict the fail condition for the network nodes using a training set from the extracted performance metrics with at least one of the identified input variables. Each of the plurality of trained models may be validated using a validation set from the extracted performance metrics and may be rated according to predefined criteria. As a result, a highest rated model of the validated models may be selected to predict the fail condition for the network nodes.

Abstract: A system for discovery and analysis of network data usage of users of a communication network may collect information related to data usage over a network. The system may determine network data usage patterns for users from the data usage information. The network usage data, usage patterns and additional information may be analyzed to create user segments, and to analyze network data usage for the user segments. Differentiated data services may be created and implemented based on the network data usage for the user segments.

Abstract: In an example, network node failures may be predicted by extracting performance metrics for the network nodes from a plurality of data sources. A fail condition may be defined for the network nodes and input variables related to the fail condition for the network nodes may then be derived from the extracted performance metrics. A plurality of models may then be trained to predict the fail condition for the network nodes using a training set from the extracted performance metrics with at least one of the identified input variables. Each of the plurality of trained models may be validated using a validation set from the extracted performance metrics and may be rated according to predefined criteria. As a result, a highest rated model of the validated models may be selected to predict the fail condition for the network nodes.