A huge reservoir of ionized gas around the Milky Way: Accounting for the Missing Mass?

Most of the baryons from galaxies have been "missing" and several studies have attempted to map the circumgalactic medium (CGM) of galaxies in their quest. Recent studies with the Hubble Space Telescope have shown that many galaxies contain a large reservoir of ionized gas with temperatures of about 10^5 K. Here we report on X-ray observations made with the Chandra X-ray Observatory probing an even hotter phase of the CGM of our Milky Way at about 10^6 K. We show that this phase of the CGM is massive, extending over a large region around the Milky Way, with a radius of over 100 kpc. The mass content of this phase is over ten billion solar masses, many times more than that in cooler gas phases and comparable to the total baryonic mass in the disk of the Galaxy. The missing mass of the Galaxy appears to be in this warm-hot gas phase.Comment: 15 pages, 3 figures; http://stacks.iop.org/2041-8205/756/L

Recenti tendenze nei flussi di investimento estero delle economie emergenti. Sovereign wealth funds, imprese globali ed effetti per i paesi sviluppati

L\u2019articolo analizza le caratteristiche principali dei recenti flussi di investimento diretto estero realizzati dalle economie emergenti. In primo luogo, si approfondiscono le determinanti macroeconomiche alla base della disponibilit\ue0 di capitali presso tali paesi, esaminando in particolare il ruolo cruciale e controverso svolto dai sovereign wealth funds. Vengono poi discusse le determinanti istituzionali e strutturali degli investimenti esteri dalla prospettiva delle imprese degli stessi emergenti. Infine, l\u2019analisi viene completata focalizzando l\u2019attenzione sul caso particolare della politica pubblica della Cina nei riguardi degli investimenti diretti verso l\u2019estero, in particolare verso l\u2019Europa e l\u2019Italia

A battery-operated, stabilized, high-energy pulsed electron gun for the production of rare gas excimers

We report on the design of a new type of electron gun to be used for experiments of infrared emission spectroscopy of rare gas excimers. It is based on a filament heated by means of a pack of rechargeable batteries floated atop the high-voltage power supply. The filament current is controlled by a feedback circuit including a superluminescent diode decoupled from the high voltage by means of an optical fiber. Our experiment requires that the charge injection is pulsed and constant and stable in time. This electron gun can deliver several tens of nC per pulse of electrons of energy up to $100\,$keV into the sample cell. This new design eliminates ripples in the emission current and ensures up to 12 hrs of stable performance.Comment: 1o pages, 8 figures, to be submitted to Review of Scientific Instrument

Application of a Predictive Maintenance Strategy Based on Machine Learning in a Used Oil Refinery

The Itelyum Regeneration used oil re-refining plant in Pieve Fissiraga currently employs a condition-based maintenance strategy for its thermodeasphalting (TDA) section, particularly focusing on the TDA T-401 column. This strategy involves monitoring the real-time pressure differential (ΔP) between the column's top and bottom, which increases in time due to fouling phenomena. Maintenance is scheduled when ΔP exceeds a predetermined empirical threshold, ensuring that the T-401 column operates within normal operations limits. However, this approach has limitations with non-conventional used oils. To address this, a data-driven machine learning algorithm, previously successful in predicting key performance indicators of the PH-401B furnace in the TDA section, was applied to the T-401 column datasets. This algorithm, based on Gaussian Process Regressions, effectively predicts the evolution of ΔP and reduces the time during which T-401 operates in suboptimal conditions. The implementation of this machine learning approach marks a significant improvement in the maintenance strategy, shifting from a static, condition-based approach to a dynamic, predictive one, thus ensuring more efficient and reliable operations, even with non-conventional used oil

Dynamical Mass Ejection from Binary Neutron Star Mergers

We present fully general-relativistic simulations of binary neutron star mergers with a temperature and composition dependent nuclear equation of state. We study the dynamical mass ejection from both quasi-circular and dynamical-capture eccentric mergers. We systematically vary the level of our treatment of the microphysics to isolate the effects of neutrino cooling and heating and we compute the nucleosynthetic yields of the ejecta. We find that eccentric binaries can eject significantly more material than quasi-circular binaries and generate bright infrared and radio emission. In all our simulations the outflow is composed of a combination of tidally- and shock-driven ejecta, mostly distributed over a broad $\sim 60^\circ$ angle from the orbital plane, and, to a lesser extent, by thermally driven winds at high latitudes. Ejecta from eccentric mergers are typically more neutron rich than those of quasi-circular mergers. We find neutrino cooling and heating to affect, quantitatively and qualitatively, composition, morphology, and total mass of the outflows. This is also reflected in the infrared and radio signatures of the binary. The final nucleosynthetic yields of the ejecta are robust and insensitive to input physics or merger type in the regions of the second and third r-process peaks. The yields for elements on the first peak vary between our simulations, but none of our models is able to explain the Solar abundances of first-peak elements without invoking additional first-peak contributions from either neutrino and viscously-driven winds operating on longer timescales after the mergers, or from core-collapse supernovae.Comment: 19 pages, 10 figures. We corrected a problem in the formulation of the neutrino heating scheme and re-ran all of the affected models. The main conclusions are unchanged. This version also contains one more figure and a number of improvements on the tex

Surrogate-Based Optimization of the OPEX of a Modular Plant for Biogas Conversion to Methanol Using the MADS Algorithm

The present work studies the potential of surrogate models for the global optimization of complex chemical processes. In particular, a modular plant for the conversion of biogas to methanol is considered. The Aspen HYSYS simulation of this plant was run 480 times, which ensured the even distribution of points in the input space. The evenness of this design of experiments was evaluated using a discrepancy measurement called the Mixture Discrepancy. With the simulation data, some of the most widely used surrogate models such as regression models and the Kriging Gaussian process were trained. The most accurate model for the prediction of each output variable was selected and used for the optimization of the OPEX. The optimization complemented the trained surrogate models with the Mesh Adaptive Direct Search (MADS) algorithm. For this purpose, the openaccess computational implementation of the MADS algorithm called NOMAD was used. With the surrogate-based optimization, the computational times were reduced an 88% with respect to the simulation-based optimization. In addition, the accuracy of the surrogate model was paramount, as an average 0.75% prediction error was found. Consequently, the models proved sufficient for optimizing the studied process, resulting in a 22.2% reduction in the OPEX

Implementation of a simplified approach to radiative transfer in general relativity

We describe in detail the implementation of a simplified approach to radiative transfer in general relativity by means of the well-known neutrino leakage scheme (NLS). In particular, we carry out an extensive investigation of the properties and limitations of the NLS for isolated relativistic stars to a level of detail that has not been discussed before in a general-relativistic context. Although the numerous tests considered here are rather idealized, they provide a well-controlled environment in which to understand the relationship between the matter dynamics and the neutrino emission, which is important in order to model the neutrino signals from more complicated scenarios, such as binary neutron-star mergers. When considering nonrotating hot neutron stars we confirm earlier results of one-dimensional simulations, but also present novel results about the equilibrium properties and on how the cooling affects the stability of these configurations. In our idealized but controlled setup, we can then show that deviations from the thermal and weak-interaction equilibrium affect the stability of these models to radial perturbations, leading models that are stable in the absence of radiative losses, to a gravitational collapse to a black hole when neutrinos are instead radiated

Comparing 2D pictures with 3D replicas for the digital preservation and analysis of tangible heritage

In this paper, we present two experiments designed to compare 2D digital pictures and 3D digital replicas of artefacts, to understand how differently these media facilitate the perception and understanding of our past. Archaeologists and museum experts have commonly used 2D digital pictures to preserve and study artefacts. Recently these scholars have also started to use 3D digital archives for their studies. Yet we still need to determine how these two formats (2D vs 3D) affect the perception of our past. Results to our experiments point to 3D digital replicas of artifacts as more effective means to digitally preserve tangible cultural heritage, since 3D multi-visualization augments the perception of physical characteristics of the artifacts allowing a more embodied experience with these objects. Our experiments also suggest that multi-visualization (i.e., point-cloud, mesh, and color information) helps the viewers to overcome their personal conceptualization of specific objects.This is the author accepted manuscript. The final version is available from Taylor & Francis via http://dx.doi.org/10.1080/09647775.2015.104251

Predicting the performance of an industrial furnace using Gaussian process and linear regression: A comparison

Maintenance is a crucial aspect of the process industry affecting economic and efficiency losses. Among different approaches, predictive maintenance allows for anticipating failure, thus reducing downtime. This work explores a data-driven approach to predictive maintenance by comparing the performance of two different statistical models in extrapolating the future performance of an industrial furnace. The models of interest are a polynomial regression model and a Gaussian process regression model, compared using rolling cross-validation. Moreover, three different machine learning techniques were compared during the training phase: cross-validation, ensemble method and train/test split. The models were trained on real-time series data collected from the distributed control system of a refinery plant. The best performance was obtained with the Gaussian process regression model trained with a train/test split approach. The resulting model can satisfactorily extrapolate the performance of a process furnace over a relatively short-term period