Selective Laser Melting of High-strength, Low-modulus Ti–35Nb–7Zr–5Ta Alloy

The state-of-the-art alloys for load-bearing implant applications lack the necessary functional attributes and are largely a compromise between biocompatibility and mechanical properties. While commercial alloys pose long-term toxicity and detrimental stress shielding effects, the newly developed alloys are closing in on the gaps, however, falling short of the desired elastic modulus necessary to rule out stress shielding. In this work, we report the fabrication of a low modulus β-Ti alloy, Ti–35Nb–7Zr–5Ta (TNZT), by selective laser melting (SLM) with optimized laser parameters. The as-prepared SLM TNZT shows a high ultimate tensile strength (~630 MPa), excellent ductility (~15%) and a lower elastic modulus (~81 GPa) when compared to the state-of-the-art cp-Ti and Ti-based alloys. The mechanical performance of the as-printed TNZT alloy has been examined and is correlated to the microstructure (grain structure, phase constitution and dislocation density). It is proposed that a high density of GND (geometrically necessary dislocations), resulting from rapid cooling, in the as-prepared condition strengthens the alloy, whereas the single phase β-bcc crystal structure results in lowering the elastic modulus. High grain boundary area and a preferred crystal orientation of {200} planes within the bcc crystal lattices contribute to an additional drop in the elastic modulus of the alloy. It is shown that the TNZT alloy, processed by SLM, demonstrates the best combination of strength and modulus, illustrating its potential as a promising biomaterial of the future. © 2020.This work was supported by the European Regional Development Fund (ASTRA6-6, ASTRA35-6 and MOBERC15). The authors would like to thank Dr. Vitali Podgurski, Mr. Andrei Bogatov, Mr. Asad Alamgir Shaikh, Dr. Mart Viljus, Dr. Märt Kolnes, Mr. Rainer Traksmaa, Mr. Endel Esinurm and Ms. Laivi Väljaots for extending research facilities and helping to improve the research outcome with stimulating discussions

Regional integration of long-term national dense GNSS network solutions

The EUREF Permanent Network Densification is a collaborative effort of 26 European GNSS analysis centers providing series of daily or weekly station position estimates of dense national and regional GNSS networks, in order to combine them into one homogenized set of station positions and velocities. During the combination, the station meta-data, including station names, DOMES numbers, and position offset definitions were carefully homogenized, position outliers were efficiently eliminated, and the results were cross-checked for any remaining inconsistencies. The results cover the period from March 1999 to January 2017 (GPS week 1000-1933) and include 31 networks with positions and velocities for 3192 stations, well covering Europe. The positions and velocities are expressed in ITRF2014 and ETRF2014 reference frames based on the Minimum Constraint approach using a selected set of ITRF2014 reference stations. The position alignment with the ITRF2014 is at the level of 1.5, 1.2, and 3.2 mm RMS for the East, North, Up components, respectively, while the velocity RMS values are 0.17, 0.14, and 0.38 mm/year for the East, North, and Up components, respectively. The high quality of the combined solution is also reflected by the 1.1, 1.1, and 3.5 mm weighted RMS values for the East, North, and Up components, respectively

Regional integration of long-term national dense GNSS network solutions

The EUREF Permanent Network Densification is a collaborative effort of 26 European GNSS analysis centers providing series of daily or weekly station position estimates of dense national and regional GNSS networks, in order to combine them into one homogenized set of station positions and velocities. During the combination, the station meta-data, including station names, DOMES numbers, and position offset definitions were carefully homogenized, position outliers were efficiently eliminated, and the results were cross-checked for any remaining inconsistencies. The results cover the period from March 1999 to January 2017 (GPS week 1000-1933) and include 31 networks with positions and velocities for 3192 stations, well covering Europe. The positions and velocities are expressed in ITRF2014 and ETRF2014 reference frames based on the Minimum Constraint approach using a selected set of ITRF2014 reference stations. The position alignment with the ITRF2014 is at the level of 1.5, 1.2, and 3.2\ua0mm RMS for the East, North, Up components, respectively, while the velocity RMS values are 0.17, 0.14, and 0.38\ua0mm/year for the East, North, and Up components, respectively. The high quality of the combined solution is also reflected by the 1.1, 1.1, and 3.5\ua0mm weighted RMS values for the East, North, and Up components, respectively

Stochastic Ion Channel Gating in Dendritic Neurons: Morphology Dependence and Probabilistic Synaptic Activation of Dendritic Spikes

Neuronal activity is mediated through changes in the probability of stochastic transitions between open and closed states of ion channels. While differences in morphology define neuronal cell types and may underlie neurological disorders, very little is known about influences of stochastic ion channel gating in neurons with complex morphology. We introduce and validate new computational tools that enable efficient generation and simulation of models containing stochastic ion channels distributed across dendritic and axonal membranes. Comparison of five morphologically distinct neuronal cell types reveals that when all simulated neurons contain identical densities of stochastic ion channels, the amplitude of stochastic membrane potential fluctuations differs between cell types and depends on sub-cellular location. For typical neurons, the amplitude of membrane potential fluctuations depends on channel kinetics as well as open probability. Using a detailed model of a hippocampal CA1 pyramidal neuron, we show that when intrinsic ion channels gate stochastically, the probability of initiation of dendritic or somatic spikes by dendritic synaptic input varies continuously between zero and one, whereas when ion channels gate deterministically, the probability is either zero or one. At physiological firing rates, stochastic gating of dendritic ion channels almost completely accounts for probabilistic somatic and dendritic spikes generated by the fully stochastic model. These results suggest that the consequences of stochastic ion channel gating differ globally between neuronal cell-types and locally between neuronal compartments. Whereas dendritic neurons are often assumed to behave deterministically, our simulations suggest that a direct consequence of stochastic gating of intrinsic ion channels is that spike output may instead be a probabilistic function of patterns of synaptic input to dendrites

Interpretative and predictive modelling of Joint European Torus collisionality scans

Transport modelling of Joint European Torus (JET) dimensionless collisionality scaling experiments in various operational scenarios is presented. Interpretative simulations at a fixed radial position are combined with predictive JETTO simulations of temperatures and densities, using the TGLF transport model. The model includes electromagnetic effects and collisions as well as □(→┬E ) X □(→┬B ) shear in Miller geometry. Focus is on particle transport and the role of the neutral beam injection (NBI) particle source for the density peaking. The experimental 3-point collisionality scans include L-mode, and H-mode (D and H and higher beta D plasma) plasmas in a total of 12 discharges. Experimental results presented in (Tala et al 2017 44th EPS Conf.) indicate that for the H-mode scans, the NBI particle source plays an important role for the density peaking, whereas for the L-mode scan, the influence of the particle source is small. In general, both the interpretative and predictive transport simulations support the experimental conclusions on the role of the NBI particle source for the 12 JET discharges

On the joint distribution of a linear and a quadratic form in skew normal variables

Let z be distributed as multivariate skew normal vector. We derive the joint moment generating function (m.g.f.) of a linear form and a quadratic form in z, and the conditions for their independence. The first two multivariate cumulants of the two forms are derived and applied in special cases. Finally a simulation example is presented