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The recurrent neural network methods have applications in kinematics control, support vector machine learning, and related branches of engineering.
Our methods have applications to inclusion theorems for multiple summing multilinear mappings and to the product of p-Sidon sets.
The methods have applications in studies of the response of structures to random environmental loads, such as wind and ocean wave forces.
Our methods have applications in clinical diagnostics [ 15, 31], forensics [ 33] and DNA-based information storage system [ 34].
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The method has applications in the determination of the displacements of structures having arbitrary boundary conditions.
The proposed method has applications in reliability analysis of vibrating structural series systems, where different failure modes are mutually dependent.
This method has applications for establishing a science-based rationale for, and a design space in, pharmaceutical formulation development.
The developed method has applications in the production of closely related isomers of naturally occurring resolvins and protectins, demonstrating the versatility of 15-sLOX-1 as a biocatalyst.
The method has applications in the automatic assessment of the usability of various product designs via measuring the frequency of operation difficulties.
Thus, the proposed method has application importance.
This method has application to many civil engineering problems that involve random fields of material properties or loads.
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