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Another target prediction algorithm developed by Koutsoukas et al. [39], is able to predict structure activity relationships (SARs) for orphan compounds using either a Laplacian-modified Naïve Bayes classifier or a Parzen-Rosenblatt Window (PRW) learner.
Second, one can use physical principles to predict structure or simulate motion.
A probabilistic framework is proposed to predict structure component fatigue life.
Also it is the first model that can predict structure gradients caused by drying in sol gel silica films.
This study might help to predict structure and property changes at high temperatures for PET fiber and any other well oriented fiber that has a lamellar and fibrillar structure.
On the one hand, MCE algorithms often create a backtracking search tree that has a highly irregular and hard-or-impossible to predict structure; therefore, almost any static decomposition of the search tree by parallel processors results in highly unbalanced processor execution times.
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Moreover, the template-based structure prediction models cannot predict structures with "new" motifs.
A model of the structure, trackform and vehicle is developed to determine the structural vibration levels caused by wheel/rail interaction and to predict structure-radiated noise levels from the viaduct vibration.
These results allowed us to predict structure-stability relationships and degradation mechanisms that agreed with experimental stability tests.
However, studies that predict structure-borne noise tend to model only single-span bridges, thus ignoring the sound pressure radiating from adjacent spans.
Recently, Zhang et al. [51] presented a hybrid finite element and SEA (hybrid FE-SEA) procedure to predict structure-borne noise from concrete box girder bridges.
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