Abstract: A catalyst system for olefin polymerization contains a main catalyst and a cocatalyst. The cocatalyst contains a twelve-membered ring compound represented by formula (M). The catalyst system is suitable for preparing polypropylene products having high stereoregularity and low ash, and can regulate the melt index of the products within a wide range by adjusting the amount of hydrogenation. It is also suitable for copolymerization systems to improve the copolymerization yield.

Abstract: A magnesium-based solid, by means of determination based on a nitrogen adsorption method, has a multimodal pore distribution and a specific surface area of not less than 50 m2/g, and the pore size distribution of the solid is in a range of 1 nm to 300 nm. There is at least one peak within a pore size range of less than 10 nm, and there is at least another peak within a pore size range of not less than 10 nm. A catalyst is formed using the solid catalyst component is used for propylene polymerization.

Abstract: A catalyst system for olefin polymerization contains a main catalyst and a cocatalyst. The cocatalyst contains a twelve-membered ring compound represented by formula (M). The catalyst system is suitable for preparing polypropylene products having high stereoregularity and low ash, and can regulate the melt index of the products within a wide range by adjusting the amount of hydrogenation. It is also suitable for copolymerization systems to improve the copolymerization yield.

Abstract: A process for preparing a solid particle-type catalyst component used for olefin polymerization which contains magnesium, titanium, a halogen and an electron donor as essential ingredients, a catalyst component obtained by said process, and a catalyst containing the catalyst component are disclosed.

Abstract: Embodiments of the present invention are directed to methods and systems for training a neural network having weighted connections for classification of data, as well as embodiments corresponding to the use of such a neural network for the classification of data, including, for example, prediction of an event (e.g., disease). The method may include inputting input training data into the neural network, processing, by the neural network, the input training data to produce an output, determining an error between the output and a desired output corresponding to the input training data, rating the performance neural network using an objective function, wherein the objective function comprises a function C substantially in accordance with an approximation of the concordance index and adapting the weighted connections of the neural network based upon results of the objective function.

Abstract: Embodiments of the present invention are directed to methods and systems for training a neural network having weighted connections for classification of data, as well as embodiments corresponding to the use of such a neural network for the classification of data, including, for example, prediction of an event (e.g., disease). The method may include inputting input training data into the neural network, processing, by the neural network, the input training data to produce an output, determining an error between the output and a desired output corresponding to the input training data, rating the performance neural network using an objective function, wherein the objective function comprises a function C substantially in accordance with an approximation of the concordance index and adapting the weighted connections of the neural network based upon results of the objective function.

Abstract: Embodiments of the present invention are directed to methods and systems for training a neural network having weighted connections for classification of data, as well as embodiments corresponding to the use of such a neural network for the classification of data, including, for example, prediction of an event (e.g., disease). The method may include inputting input training data into the neural network, processing, by the neural network, the input training data to produce an output, determining an error between the output and a desired output corresponding to the input training data, rating the performance neural network using an objective function, wherein the objective function comprises a function C substantially in accordance with an approximation of the concordance index and adapting the weighted connections of the neural network based upon results of the objective function.

Abstract: Embodiments of the present invention are directed to methods and systems for training a neural network having weighted connections for classification of data, as well as embodiments corresponding to the use of such a neural network for the classification of data, including, for example, prediction of an event (e.g., disease). The method may include inputting input training data into the neural network, processing, by the neural network, the input training data to produce an output, determining an error between the output and a desired output corresponding to the input training data, rating the performance neural network using an objective function, wherein the objective function comprises a function C substantially in accordance with an approximation of the concordance index and adapting the weighted connections of the neural network based upon results of the objective function.

Abstract: A silicon comprising film and its method of fabrication is described. The silicon comprising film is grown on a substrate. A hexachlorodisilane (HCD) source gas is one of the reactant species used to form the silicon comprising film. The silicon comprising film is formed under a pressure between 10 Torr and 350 Torr.

Abstract: A silicon comprising film and its method of fabrication is described. The silicon comprising film is grown on a substrate. A hexachlorodisilane (HCD) source gas is one of the reactant species used to form the silicon comprising film. The silicon comprising film is formed under a pressure between 10 Torr and 350 Torr.