The Overlooked Potential of Generalized Linear Models in Astronomy - I: Binomial Regression

Revealing hidden patterns in astronomical data is often the path to fundamental scientific breakthroughs; meanwhile the complexity of scientific inquiry increases as more subtle relationships are sought. Contemporary data analysis problems often elude the capabilities of classical statistical techniques, suggesting the use of cutting edge statistical methods. In this light, astronomers have overlooked a whole family of statistical techniques for exploratory data analysis and robust regression, the so-called Generalized Linear Models (GLMs). In this paper -- the first in a series aimed at illustrating the power of these methods in astronomical applications -- we elucidate the potential of a particular class of GLMs for handling binary/binomial data, the so-called logit and probit regression techniques, from both a maximum likelihood and a Bayesian perspective. As a case in point, we present the use of these GLMs to explore the conditions of star formation activity and metal enrichment in primordial minihaloes from cosmological hydro-simulations including detailed chemistry, gas physics, and stellar feedback. We predict that for a dark mini-halo with metallicity $\approx 1.3 \times 10^{-4} Z_{\bigodot}$, an increase of $1.2 \times 10^{-2}$ in the gas molecular fraction, increases the probability of star formation occurrence by a factor of 75%. Finally, we highlight the use of receiver operating characteristic curves as a diagnostic for binary classifiers, and ultimately we use these to demonstrate the competitive predictive performance of GLMs against the popular technique of artificial neural networks.Comment: 20 pages, 10 figures, 3 tables, accepted for publication in Astronomy and Computin

Effect of sulphur poisoning on perovskite catalysts prepared by flame-pyrolysis

ABO(3) perovskite-like catalysts are known to be sensitive to sulphur-containing compounds. Possible solutions to increase resistance to sulphur are represented by either catalyst bed protection with basic guards or catalyst doping with different transition or noble metals. In the present work La((1-x))A(x)'CoO(3), La((1-x))A(x)'MnO(3) and La((1-x))A(x)'FeO(3), with A' = Ce, Sr and x = 0, 0.1, 0.2, either pure or doped with noble metals (0.5 wt% Pt or Pd), were prepared in nano-powder form by flame-pyrolysis. All the catalysts were tested for the catalytic flameless combustion of methane, monitoring the activity by on-line mass spectrometry. The catalysts were then progressively deactivated in operando with a new procedure, consisting of repeated injection of some doses of tetrahydrothiophene (THT), usually employed as odorant in the natural gas grid, with continuous analysis of the transient response of the catalyst. The activity tests were then repeated on the poisoned catalyst. Different regenerative treatments were also tried, either in oxidising or reducing atmosphere. Among the unsubstituted samples, higher activity and better resistance to poisoning have been observed in general with manganites with respect to the corresponding formulations containing Co or Fe at the B-site. The worst catalyst showed LaFeO(3), from both the points of view of activity and of resistance to sulphur poisoning. La(0.9)Sr(0.1)MnO(3) showed, the best results, exhibiting very high activity and good resistance even after the addition of up to 8.4 mg of THT/g of catalyst. Interesting results were attained also by adding Sr to Co-based perovskites. Sr showed a first action by forcing Mn or Co in their highest oxidation state, but, in addition, it could also act as a sulphur guard, likely forming stable sulphates due to its basicity. Among noble metals, Pt doping proved beneficial in improving the activity of both the fresh and the poisoned catalyst

Robust PCA and MIC statistics of baryons in early minihaloes

We present a novel approach, based on robust principal components analysis (RPCA) and maximal information coefficient (MIC), to study the redshift dependence of halo baryonic properties. Our data are composed of a set of different physical quantities for primordial minihaloes: dark matter mass (M-dm), gas mass (M-gas), stellar mass (M-star), molecular fraction (x(mol)), metallicity (Z), star formation rate (SFR) and temperature. We find that M-dm and M-gas are dominant factors for variance, particularly at high redshift. Nonetheless, with the emergence of the first stars and subsequent feedback mechanisms, x(mol), SFR and Z start to have a more dominant role. Standard PCA gives three principal components (PCs) capable to explain more than 97 per cent of the data variance at any redshift (two PCs usually accounting for no less than 92 per cent), whilst the first PC from the RPCA analysis explains no less than 84 per cent of the total variance in the entire redshift range (with two PCs explaining greater than or similar to 95 per cent anytime). Our analysis also suggests that all the gaseous properties have a stronger correlation with M-gas than with M-dm, while M-gas has a deeper correlation with x(mol) than with Z or SFR. This indicates the crucial role of gas molecular content to initiate star formation and consequent metal pollution from Population III and Population II/I regimes in primordial galaxies. Finally, a comparison between MIC and Spearman correlation coefficient shows that the former is a more reliable indicator when halo properties are weakly correlated

Investigation of TiCr Hydrogen Storage Alloy

A new reversible hydrogen storage material, based on TiCr metal alloy, is proposed. Cr and Ti were mixed and melted in a final atomic ratio of 1,78. Chemical-physical characterisations, in terms of XRD and SEM-EDX, were performed. The quantification of Laves phases was performed through Rietveld refinements. The atomic Cr/Ti ratio was determined by EDX analysis and 1,71 was obtained. The H2 sorption/desorption measurements by Sievert apparatus were carried out. After different tests varying temperature and pressure, a protocol measurement was established; and a H2 sorption value of 0,4 wt% at 200 °C/10 bar with a fast kinetic at 5 bar (Dwt% of about 0,3 wt%) were obtained. Hydrogen desorption measurements performed in the same conditions of T confirmed a totally reversible trend. A confirm of metal hydride formation was recorded by XRD, in fact, comparing X-Ray patterns before and after volumetric tests a notable difference was recorded

Hot isostatic pressing and heat treatments of LPBFed CoCuFeMnNiTi0.13 high-entropy alloy: microstructure and mechanical properties

The present work explores the possibility of processing a CoCuFeMnNiTi0.13 high-entropy alloy by laser powder bed fusion (LPBF). The alloy, produced under optimised processing conditions, presents good densification but also hot cracks, caused by the liquation of an inter-dendritic Cu-rich phase. Microstructure of the as-built alloy is characterised by face centred cubic (FCC) columnar grains, containing Cu-poor dendrites and Cu-rich inter-dendritic areas. The alloy, which was designed to be strengthened by spinodal decomposition and precipitation, was subjected to different thermo-mechanical treatments to try and improve its properties. Direct ageing and solution treatment and ageing produced a strong but brittle material (tensile strength of 683 MPa and elongation to failure of 1.3%), whereas hot isostatic pressing followed by controlled cooling was able to heal pores and cracks while triggering the desired microstructural transformations (spinodal decomposition and precipitation). This resulted into a balanced set of mechanical properties (tensile strength of 473 MPa and elongation to failure of 7.6%). This work shows that proper post-processing can mitigate the issues typically affecting LPBF fabricated HEAs, producing tailored microstructures with satisfactory mechanical performances

Deep learning cardiac motion analysis for human survival prediction

Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimised for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients the predictive accuracy (quantified by Harrell's C-index) was significantly higher (p < .0001) for our model C=0.73 (95$\%$ CI: 0.68 - 0.78) than the human benchmark of C=0.59 (95$\%$ CI: 0.53 - 0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival