Occurrence of perfluoroalkyl compounds in surface waters from the North Pacific to the Arctic Ocean

Perfluoroalkyl compounds (PFCs) were determined in 22 surface water samples (39-76°N) and three sea ice core and snow samples (77-87°N) collected from North Pacific to the Arctic Ocean during the fourth Chinese Arctic Expedition in 2010. Geographically, the average concentration of ∑PFC in surface water samples were 560 ± 170 pg L(-1) for the Northwest Pacific Ocean, 500 ± 170 pg L(-1) for the Arctic Ocean, and 340 ± 130 pg L(-1) for the Bering Sea, respectively. The perfluoroalkyl carboxylates (PFCAs) were the dominant PFC class in the water samples, however, the spatial pattern of PFCs varied. The C(5), C(7) and C(8) PFCAs (i.e., perfluoropentanoate (PFPA), perfluoroheptanoate (PFHpA), and perfluorooctanoate (PFOA)) were the dominant PFCs in the Northwest Pacific Ocean while in the Bering Sea the PFPA dominated. The changing in the pattern and concentrations in Pacific Ocean indicate that the PFCs in surface water were influenced by sources from the East-Asian (such as Japan and China) and North American coast, and dilution effect during their transport to the Arctic. The presence of PFCs in the snow and ice core samples indicates an atmospheric deposition of PFCs in the Arctic. The elevated PFC concentration in the Arctic Ocean shows that the ice melting had an impact on the PFC levels and distribution. In addition, the C(4) and C(5) PFCAs (i.e., perfluorobutanoate (PFBA), PFPA) became the dominant PFCs in the Arctic Ocean indicating that PFBA is a marker for sea ice melting as the source of exposure.     

Accounting for Lag Order Uncertainty in Autoregressions: the Endogenous Lag Order Bootstrap Algorithm

Conventional asymptotic and bootstrap methods for finite-order autoregressive models condition on the estimated lag order of the model as though it were known to be the true lag order. Even if the order is estimated correctly, this procedure ignores the sampling uncertainty about the lag order estimate and may result in spurious inferences. In this paper an appropriately modified bootstrap algorithm is introduced that reflects the true extent of sampling uncertainty in the regression estimates. This endogenous lag order bootstrap algorithm recognizes that lag order selection is an integral part of the sampling procedure by re-estimating the lag order in each bootstrap iteration. It is suggested that the endogenous lag order bootstrap algorithm should routinely replace the standard bootstrap algorithm in applications. Monte Carlo simulations show that ignoring lag order uncertainty may seriously undermine the coverage accuracy of bootstrap confidence intervals for vector autoregression impulse response estimates. Endogenizing the lag order choice is shown to improve coverage accuracy in small samples at negligible additional computational cost. As the lag order uncertainty declines in large samples, the performance of the endogenous lag order interval converges to that of the standard interval.     

Extraction of Distinguished Hyperbolic Trajectories for 2D Time-Dependent Vector Field Topology

This paper does two main contributions to 2D time-dependent vector field topology. First, we present a technique for robust, accurate, and efficient extraction of distinguished hyperbolic trajectories (DHT), the generative structures of 2D time-dependent vector field topology. It is based on refinement of initial candidate curves. In contrast to previous approaches, it is robust because the refinement converges for reasonably close initial candidates, it is accurate due to its adaptive scheme, and it is efficient due to its high convergence speed. Second, we provide a detailed evaluation and discussion of previous approaches for the extraction of DHTs and time-dependent vector field topology in general. We demonstrate the utility of our approach using analytical flows, as well as data from computational fluid dynamics.     

A new contact model for the discrete element method simulation of $$\hbox {TiO}_2$$ nanoparticle films under mechanical load

We develop a novel coarse-grained contact model for Discrete Element Method simulations of \(\hbox {TiO}_2\) nanoparticle films subjected to mechanical stress. All model elements and parameters are derived in a self-consistent and physically sound way from all-atom Molecular Dynamics simulations of interacting particles and surfaces. In particular, the nature of atomic-scale friction and dissipation effects is taken into account by explicit modelling of the surface features and water adsorbate layers that strongly mediate the particle-particle interactions. The quantitative accuracy of the coarse-grained model is validated against all-atom simulations of \(\hbox {TiO}_2\) nanoparticle agglomerates under tensile stress. Moreover, its predictive power is demonstrated with calculations of force-displacement curves of entire nanoparticle films probed with force spectroscopy. The simulation results are compared with Atomic Force Microscopy and Transmission Electron Microscopy experiments.     

Prediction of fracture healing under axial loading,shear loading and bending is possible using distortional and dilatational strains as determining mechanical stimuli

Numerical models of secondary fracture healing are based on mechanoregulatory algorithms that use distortional strain alone or in combination with either dilatational strain or fluid velocity as determining stimuli for tissue differentiation and development. Comparison of these algorithms has previously suggested that healing processes under torsional rotational loading can only be properly simulated by considering fluid velocity and deviatoric strain as the regulatory stimuli. We hypothesize that sufficient calibration on uncertain input parameters will enhance our existing model, which uses distortional and dilatational strains as determining stimuli, to properly simulate fracture healing under various loading conditions including also torsional rotation. Therefore, we minimized the difference between numerically simulated and experimentally measured courses of interfragmentary movements of two axial compressive cases and two shear load cases (torsional and translational) by varying several input parameter values within their predefined bounds. The calibrated model was then qualitatively evaluated on the ability to predict physiological changes of spatial and temporal tissue distributions, based on respective in vivo data. Finally, we corroborated the model on five additional axial compressive and one asymmetrical bending load case. We conclude that our model, using distortional and dilatational strains as determining stimuli, is able to simulate fracture-healing processes not only under axial compression and torsional rotation but also under translational shear and asymmetrical bending loading conditions.