It is common in shallow seismic studies for the compressional-wave velocity in unconsolidated sediments to increase by a factor of four or more at the transition from dry or partial water saturation to full saturation. Under these conditions, conventional NMO velocity analysis fails and leads to large depth and layer thickness estimates if the Dix equation is assumed valid. Prestack depth migra...

The expressive power of neural networks is important for understanding deep learning. Most existing works consider this problem from the view of the depth of a network. In this paper, we study how width affects the expressiveness of neural networks. Classical results state that depth-bounded (e.g. depth-2) networks with suitable activation functions are universal approximators. We show a univer...

bearing capacity prediction of shallow foundation is one of the most important problems in geotechnical engineering practices, with a wide variety range of methods which have been introduced to forecast it accurately. recently, soft computing methods such as artificial neural networks (anns) and support vector machines (svms) have been used for prediction of the ultimate bearing capacity of sha...

KOSTEL-HUGHES, F., T. P. YOUNG, AND J. D. WEHR. (Louis Calder Center—Biological Field Station and Department of Biological Sciences, Fordham University, P.O. Box 887, Armonk, NY 10504). Effects of leaf litter depth on the emergence and seedling growth of deciduous forest tree species in relation to seed size. J. Torrey Bot. Soc. 132: 50–61. 2005.—Leaf litter has a major impact on soil microenvi...

We generalize the non-linear one-dimensional equation of a fluid layer for any depth and length as an infinite order differential equation for the steady waves. This equation can be written as a q-differential one, with its general solution written as a power series expansion with coefficients satisfying a nonlinear recurrence relation. In the limit of long and shallow water (shallow channels) ...

Determining the ultimate bearing capacity (UBC) is vital for design of shallow foundations. Recently, soft computing methods (i.e. artificial neural networks and support vector machines) have been used for this purpose. In this paper, Random Forest (RF) is utilized as a tree-based ensemble classifier for predicting the UBC of shallow foundations on cohesionless soils. The inputs of model are wi...

Abstract Introduction Shallow-depth vaginoplasty (SDV) is an excellent genital gender-affirming surgery (gGAS) option for patients who are not interested in the factors associated with full-depth (FDV), which include ability to engage receptive vaginal intercourse (RVI), increased surgical risks, longer wound healing, and/or strict, lifelong postoperative maintenance requirements (i.e., regular...