Spin effects in strong-field laser-electron interactions

The electron spin degree of freedom can play a significant role in relativistic scattering processes involving intense laser fields. In this contribution we discuss the influence of the electron spin on (i) Kapitza-Dirac scattering in an x-ray laser field of high intensity, (ii) photo-induced electron-positron pair production in a strong laser wave and (iii) multiphoton electron-positron pair production on an atomic nucleus. We show that in all cases under consideration the electron spin can have a characteristic impact on the process properties and their total probabilities. To this end, spin-resolved calculations based on the Dirac equation in the presence of an intense laser field are performed. The predictions from Dirac theory are also compared with the corresponding results from the Klein-Gordon equation.Comment: 9 pages, 6 figure

Development of a methodology and validation of the Geopyörä breakage test

Abstract. Mining and metals industry extract, process and refine raw materials that are used in every aspect of modern society. It is also a priority sector to achieve a low carbon economy; commodities such as copper, cobalt, nickel and lithium, among others, are essential to developing clean energy technologies and electromobility plans. At the same time, the mining sector is energy-intensive and can have long-lasting impacts on the environment, depending on the exploitation method. Mining industry represents 7% of the worldwide energy consumption and contributes 10% energy-related greenhouse emission gases. In the latest reports, the actions took for the mining industry to achieve the Paris agreement goals were qualified as insufficient, a problematic scenario, considering that the targets are most likely increase during the next agreement. Comminution is the most power-demanding stage, using around 50% of the total consumption. In this context, optimisation in comminution processes is one of the biggest challenges in the industry. Geometallurgy is a discipline that aims to address the current challenges of the sector from an integrated mindset. Geometallurgical models from the perspective of comminution currently face a problem, the lack of a fast and reliable test to allow mapping the distribution of rock properties in ore deposits. The lack of information on comminution parameters contributes to inefficient comminution processes and consequently, higher energy consumption and emitted amounts of GHG (Greenhouse Gasses). This thesis work presents a methodology to perform breakage tests using a new device called Geopyörä. The research uses the parameters measured by the testing device to derive and validate comminution parameters such as JKDWT Axb, SMC Test® DWi and BWi. A methodology to achieve the objective of this test was created, allowing to have a procedure for a fast test, requiring approximately 10 minutes per sample, which ultimately results in a low-cost operation. This test uses less than a kilogram of a halve of a meter of drill core to obtain parameters of rock competence and hardness. The calculation and validation of parameters were carried out in comparison with tests widely used in the industry: JK Drop Weight Test, SMC Test® and Bond ball mill grindability test. The Geopyörä test could deliver reliable results for competence parameters, Axb and DWi (Drop Weight Index), within a margin of error of 7%. Additionally, a correlation between measured and BBMWi was also developed and validated. The results showed that the Geopyörä was also capable of measuring the Bond grindability parameter within an acceptable margin of error of 10%

Hypoxic pre-conditioning increases the infiltration of endothelial cells into scaffolds for dermal regeneration pre-seeded with mesenchymal stem cells.

Many therapies using mesenchymal stem cells (MSC) rely on their ability to produce and release paracrine signals with chemotactic and pro-angiogenic activity. These characteristics, however, are mostly studied under standard in vitro culture conditions. In contrast, various novel cell-based therapies imply pre-seeding MSC into bio-artificial scaffolds. Here we describe human bone marrow-derived MSC seeded in Integra matrices, a common type of scaffold for dermal regeneration (SDR). We show and measured the distribution of MSC within the SDR, where cells clearly establish physical interactions with the scaffold, exhibiting constant metabolic activity for at least 15 days. In the SDR, MSC secrete VEGF and SDF-1α and induce transwell migration of CD34(+) hematopoietic/endothelial progenitor cells, which is inhibited in the presence of a CXCR4/SDF-1α antagonist. MSC in SDR respond to hypoxia by altering levels of angiogenic signals such as Angiogenin, Serpin-1, uPA, and IL-8. Finally, we show that MSC-containing SDR that have been pre-incubated in hypoxia show higher infiltration of endothelial cells after implantation into immune deficient mice. Our data show that MSC are fully functional ex vivo when implanted into SDR. In addition, our results strongly support the notion of hypoxic pre-conditioning MSC-containing SDR, in order to promote angiogenesis in the wounds

Productivity and reallocation: evidence from ecuadorian firm-level data

Ecuador, a developing small open economy, serves as an important case study for aggregate productivity growth and input reallocation. Since little is known about the economic performance of Ecuador with its crisis and reforms between 1998 and 2007, this paper uses a comprehensive microdata set from Ecuador’s National Statistics and Census Institute to study Ecuadorian firm dynamics in that period. We find that the reallocation of factor inputs (2.6 percent) and technical efficiency growth (3.2 percent) on the intensive margin are the dominant sources of aggregate productivity growth. Net entry, as a channel of reallocation on the extensive margin, generally has minor effects (–0.1 percent) and contributes to productivity growth only in the later recovery period (2002–04)

crs: A package for nonparametric spline estimation in R

crs is a library for R written by Jeffrey S. Racine (Maintainer) and Zhenghua Nie. This add-on package provides a collection of functions for spline-based nonparametric estimation of regression functions with both continuous and categorical regressors. Currently, the crs package integrates data-driven methods for selecting the spline degree, the number of knots and the necessary bandwidths for nonparametric conditional mean, IV and quantile regression. A function for multivariate density spline estimation with mixed data is also currently in the works. As a bonus, the authors have also provided the first simple R interface to the NOMAD (‘nonsmooth mesh adaptive direct search’) optimization solver which can be applied to solve other mixed integer optimization problems that future users might find useful in other settings. Although the crs package shares some of the same functionalities as its kernel-based counterpart—the np package by the same author—it currently lacks some of the features the np package provides, such as hypothesis testing and semiparametric estimation. However, what it lacks in breadth, crs makes up in speed. A Monte Carlo experiment in this review uncovers sizable speed gains compared to its np counterpart, with a marginal loss in terms of goodness of fit. Therefore, the package will be extremely useful for applied econometricians interested in employing nonparametric techniques using large amounts of data with a small number of discrete covariates

Lifetimes of Confined Acoustic Phonons in Ultra-Thin Silicon Membranes

We study the relaxation of coherent acoustic phonon modes with frequencies up to 500 GHz in ultra-thin free-standing silicon membranes. Using an ultrafast pump-probe technique of asynchronous optical sampling, we observe that the decay time of the first-order dilatational mode decreases significantly from \sim 4.7 ns to 5 ps with decreasing membrane thickness from \sim 194 to 8 nm. The experimental results are compared with theories considering both intrinsic phonon-phonon interactions and extrinsic surface roughness scattering including a wavelength-dependent specularity. Our results provide insight to understand some of the limits of nanomechanical resonators and thermal transport in nanostructures

A possible origin of superconducting currents in cosmic strings

The scattering and capture of right-handed neutrinos by an Abelian cosmic string in the SO(10) grand unification model are considered. The scattering cross-section of neutrinos per unit length due to the interaction with the gauge and Higgs fields of the string is much larger in its scaling regime than in the friction one because of the larger infrared cutoff of the former.The probability of capture in a zero mode of the string accompanied by the emission of a gauge or Higgs boson shows a resonant peak for neutrino momentum of the order of its mass. Considering the decrease of number of strings per unit comoving volume in the scaling epoch the cosmological consequences of the superconducting strings formed in this regime will be much smaller than those which could be produced already in the friction one.Comment: 14 pages Latex, 4 figues/ep

Data Science in Stata 16: Frames, Lasso, and Python Integration

Stata is one of the most widely used software for data analysis, statistics, and model fitting by economists, public policy researchers, epidemiologists, among others. Stata's recent release of version 16 in June 2019 includes an up-to-date methodological library and a user-friendly version of various cutting edge techniques. In the newest release, Stata has implemented several changes and additions that include:• Lasso• Multiple data sets in memory• Meta-analysis• Choice models• Python integration• Bayes-multiple chains• Panel-data ERMs• Sample-size analysis for CIs• Panel-data mixed logit• Nonlinear DSGE models• Numerical integrationThis review covers the most salient innovations in Stata 16. It is the first release that brings along an implementation of machine-learning tools. The three innovations we considered are: (1) Multiple data sets in Memory, (2) Lasso for causal inference, and (3) Python integration