Abstract: Techniques that facilitate optimization of prototype and machine design within a three-dimensional fluid modeling environment are presented. For example, a system includes a modeling component, a machine learning component, and a graphical user interface component. The modeling component generates three-dimensional model of a mechanical device based on a library of stored data elements. The machine learning component predicts one or more characteristics of the mechanical device based on a first machine learning process associated with the three-dimensional model. The machine learning component also generates physics modeling data of the mechanical device based on the one or more characteristics of the mechanical device. The graphical user interface component provides, via a graphical user interface, a three-dimensional design environment associated with the three-dimensional model and a probabilistic simulation environment associated with optimization of the three-dimensional model.

Abstract: Techniques that facilitate optimization of prototype and machine design within a three-dimensional fluid modeling environment are presented. For example, a system includes a modeling component, a machine learning component, and a graphical user interface component. The modeling component generates three-dimensional model of a mechanical device based on a library of stored data elements. The machine learning component predicts one or more characteristics of the mechanical device based on a first machine learning process associated with the three-dimensional model. The machine learning component also generates physics modeling data of the mechanical device based on the one or more characteristics of the mechanical device. The graphical user interface component provides, via a graphical user interface, a three-dimensional design environment associated with the three-dimensional model and a probabilistic simulation environment associated with optimization of the three-dimensional model.

Abstract: This application relates to various crystalline forms of (S)—N-(4-((5-(1,6-dimethyl-1H-pyrazolo[3,4-b]pyridin-4-yl)-3-methyl-4,5,6,7-tetrahydro-1H-pyrazolo[4,3-c]pyridin-1-yl)methyl)bicyclo[2.2.2]octan-1-yl)morpholine-3-carboxamide in its free-form, as well as compositions, method of making and methods of using the same. In some embodiments the crystalline forms also contain water (“hydrates”). These materials are useful in the treatment of various autoimmune diseases, including systemic lupus erythematosus, cutaneous lupus, discoid lupus, mixed connective tissue disease, primary biliary cirrhosis, immune thrombocytopenia purpura, hidradenitis suppurativa, dermatomyositis, polymyositis, Sjögren's syndrome, arthritis, rheumatoid arthritis and psoriasis.

Abstract: Techniques that facilitate optimization of prototype and machine design within a three-dimensional fluid modeling environment are presented. For example, a system includes a modeling component, a machine learning component, and a graphical user interface component. The modeling component generates three-dimensional model of a mechanical device based on a library of stored data elements. The machine learning component predicts one or more characteristics of the mechanical device based on a first machine learning process associated with the three-dimensional model. The machine learning component also generates physics modeling data of the mechanical device based on the one or more characteristics of the mechanical device. The graphical user interface component provides, via a graphical user interface, a three-dimensional design environment associated with the three-dimensional model and a probabilistic simulation environment associated with optimization of the three-dimensional model.

Abstract: The invention provides new solid forms, salts and polymorphs of 1-(((Z)-(1-(2-aminothiazol-4-yl)-2-oxo-2-(((3S,4R)-2-oxo-4-((2-oxooxazolidin-3-yl)methyl)-1-sulfoazetidin-3-yl)amino)ethylidene)amino)oxy)cyclopropanecarboxylic acid (referred to herein as Compound X), pharmaceutical compositions containing them, and processes for their manufacture and use in therapy.

Abstract: The invention provides new solid forms, salts and polymorphs of 1-(((Z)-(1-(2-aminothiazol-4-yl)-2-oxo-2-(((3S,4R)-2-oxo-4-((2-oxooxazolidin-3-yl)methyl)-1-sulfoazetidin-3-yl)amino)ethylidene)amino)oxy)cyclopropanecarboxylic acid (referred to herein as Compound X), pharmaceutical compositions containing them, and processes for their manufacture and use in therapy.

Abstract: Techniques that facilitate optimization of prototype and machine design within a three-dimensional fluid modeling environment are presented. For example, a system includes a modeling component, a machine learning component, and a graphical user interface component. The modeling component generates three-dimensional model of a mechanical device based on a library of stored data elements. The machine learning component predicts one or more characteristics of the mechanical device based on a first machine learning process associated with the three-dimensional model. The machine learning component also generates physics modeling data of the mechanical device based on the one or more characteristics of the mechanical device. The graphical user interface component provides, via a graphical user interface, a three-dimensional design environment associated with the three-dimensional model and a probabilistic simulation environment associated with optimization of the three-dimensional model.