Estimation theory and Neural Networks revisited: REKF and RSVSF as optimization techniques for Deep-Learning.

Deep-Learning has become a leading strategy for artificial intelligence and is being applied in many fields due to its excellent performance that has surpassed human cognitive abilities in a number of classification and control problems (Ciregan, Meier, & Schmidhuber, 2012; Mnih et al., 2015). However, the training process of Deep-Learning is usually slow and requires high-performance computing, capable of handling large datasets.

Pullout strength of pedicle screws augmented with particulate calcium phosphate: an experimental study.

Background Context: Pressure-injected and in situ curing bone cements have been studied as alternatives in augmenting lumbar pedicle screw fixation but are frequently found to leak outside the confines of the target vertebra.

Purpose: The objective is set to determine the mechanical efficacy of a porous granular/particulate calcium phosphate (CP) bone augmentation product (Skelite) applied manually without pressurized injection in this application.

Study Design/setting: The biomechanical analysis compared the axial pullout strength and insertional torque of augmented and nonaugmented pedicle screws in cellular polyurethane foams.

Biaxial flexural modulus of antibiotic-impregnated orthopedic bone cement.

Previously reported antibiotic-impregnated cement strengths have been based on uniaxial and fatigue testing methodologies. These methods may not provide an accurate characterization of bone cement's true load-bearing capacity in total joint replacement (TJR). The present study utilized biaxial testing to report on the properties of antibiotic-impregnated cement.