Sensitivity of Nd isotopic composition in seawater to changes in Nd sources and paleoceanographic implications

It has been argued that past changes in the sources of Nd could hamper the use of the Nd isotopic composition (ϵNd) as a proxy for past changes in the overturning of deep water masses. Here we reconsider uncertainties associated with ϵNd in seawater due to potential regional to global scale changes in the sources of Nd by applying a modeling approach. For illustrative purposes we describe rather extreme changes in the magnitude of source fluxes, their isotopic composition or both. We find that the largest effects on ϵNd result from changes in the boundary source. Considerable changes also result from variations in the magnitude or ϵNd of dust and rivers but are largely constrained to depths shallower than 1 km, except if they occur in or upstream of regions where deep water masses are formed. From these results we conclude that changes in Nd sources have the potential to affect ϵNd. However, substantial changes are required to generate large-scale changes inϵNd in deep water that are similar in magnitude to those that have been reconstructed from sediment cores or result from changes in meridional overturning circulation in model experiments. Hence, it appears that a shift in ϵNdcomparable to glacial-interglacial variations is difficult to obtain by changes in Nd sources alone, but that more subtle variations can be caused by such changes and must be interpreted with caution.

From transient to steady state deformation and grain size: A thermodynamic approach using elasto-visco-plastic numerical modeling

Numerical simulation experiments give insight into the evolving energy partitioning during high-strain torsion experiments of calcite. Our numerical experiments are designed to derive a generic macroscopic grain size sensitive flow law capable of describing the full evolution from the transient regime to steady state. The transient regime is crucial for understanding the importance of micro structural processes that may lead to strain localization phenomena in deforming materials. This is particularly important in geological and geodynamic applications where the phenomenon of strain localization happens outside the time frame that can be observed under controlled laboratory conditions. Ourmethod is based on an extension of the paleowattmeter approach to the transient regime. We add an empirical hardening law using the Ramberg-Osgood approximation and assess the experiments by an evolution test function of stored over dissipated energy (lambda factor). Parameter studies of, strain hardening, dislocation creep parameter, strain rates, temperature, and lambda factor as well asmesh sensitivity are presented to explore the sensitivity of the newly derived transient/steady state flow law. Our analysis can be seen as one of the first steps in a hybrid computational-laboratory-field modeling workflow. The analysis could be improved through independent verifications by thermographic analysis in physical laboratory experiments to independently assess lambda factor evolution under laboratory conditions.

Fast Prediction of Femoral Biomechanics Using Supervised Machine Learning and Statistical Shape Modeling

Finite element (FE) analysis is an important computational tool in biomechanics. However, its adoption into clinical practice has been hampered by its computational complexity and required high technical competences for clinicians. In this paper we propose a supervised learning approach to predict the outcome of the FE analysis. We demonstrate our approach on clinical CT and X-ray femur images for FE predictions ( FEP), with features extracted, respectively, from a statistical shape model and from 2D-based morphometric and density information. Using leave-one-out experiments and sensitivity analysis, comprising a database of 89 clinical cases, our method is capable of predicting the distribution of stress values for a walking loading condition with an average correlation coefficient of 0.984 and 0.976, for CT and X-ray images, respectively. These findings suggest that supervised learning approaches have the potential to leverage the clinical integration of mechanical simulations for the treatment of musculoskeletal conditions.