Signature of a Cosmic String Wake at z=3z=3

In this paper, we describe the results of N-body simulation runs, which include a cosmic string wake of tension $G\mu= 4 \times 10^{-8}$ on top of the usual $\Lambda CDM$ fluctuations. To obtain a higher resolution of the wake in the simulations compared to previous work, we insert the effects of the string wake at a lower redshift and perform the simulations in a smaller volume. A curvelet analysis of the wake and no-wake maps is applied, indicating that the presence of a wake can be extracted at a three-sigma confidence level from maps of the two-dimensional dark matter projection down to a redshift of $z=3$.Comment: 8 pages, 6 figures; We have improved the analysis and results. The text now agrees with the published versio

Resolving transition metal chemical space: feature selection for machine learning and structure-property relationships

Machine learning (ML) of quantum mechanical properties shows promise for accelerating chemical discovery. For transition metal chemistry where accurate calculations are computationally costly and available training data sets are small, the molecular representation becomes a critical ingredient in ML model predictive accuracy. We introduce a series of revised autocorrelation functions (RACs) that encode relationships between the heuristic atomic properties (e.g., size, connectivity, and electronegativity) on a molecular graph. We alter the starting point, scope, and nature of the quantities evaluated in standard ACs to make these RACs amenable to inorganic chemistry. On an organic molecule set, we first demonstrate superior standard AC performance to other presently-available topological descriptors for ML model training, with mean unsigned errors (MUEs) for atomization energies on set-aside test molecules as low as 6 kcal/mol. For inorganic chemistry, our RACs yield 1 kcal/mol ML MUEs on set-aside test molecules in spin-state splitting in comparison to 15-20x higher errors from feature sets that encode whole-molecule structural information. Systematic feature selection methods including univariate filtering, recursive feature elimination, and direct optimization (e.g., random forest and LASSO) are compared. Random-forest- or LASSO-selected subsets 4-5x smaller than RAC-155 produce sub- to 1-kcal/mol spin-splitting MUEs, with good transferability to metal-ligand bond length prediction (0.004-5 {\AA} MUE) and redox potential on a smaller data set (0.2-0.3 eV MUE). Evaluation of feature selection results across property sets reveals the relative importance of local, electronic descriptors (e.g., electronegativity, atomic number) in spin-splitting and distal, steric effects in redox potential and bond lengths.Comment: 43 double spaced pages, 11 figures, 4 table

Integrated micro X-ray fluorescence and chemometric analysis for printed circuit boards recycling

A novel approach, based on micro X-ray fluorescence (μXRF), was developed to define an efficient and fast automatic recognition procedure finalized to detect and topologically assess the presence of the different elements in waste electrical and electronic equipment (WEEE). More specifically, selected end-of-life (EOL) iPhone printed circuit boards (PCB) were investigated, whose technological improvement during time, can dramatically influence the recycling strategies (i.e. presence of different electronic components, in terms of size, shape, disposition and related elemental content). The implemented μXRF-based techniques allow to preliminary set up simple and fast quality control strategies based on the full recognition and characterization of precious and rare earth elements as detected inside the electronic boards. Furthermore, the proposed approach allows to identify the presence and the physical-chemical attributes of the other materials (i.e. mainly polymers), influencing the further physical-mechanical processing steps addressed to realize a pre-concentration of the valuable elements inside the PCB milled fractions, before the final chemical recovery