Classification and Ranking of Fermi LAT Gamma-ray Sources from the 3FGL Catalog using Machine Learning Techniques

We apply a number of statistical and machine learning techniques to classify and rank gamma-ray sources from the Third Fermi Large Area Telescope (LAT) Source Catalog (3FGL), according to their likelihood of falling into the two major classes of gamma-ray emitters: pulsars (PSR) or Active Galactic Nuclei (AGN). Using 1904 3FGL sources that have been identified/associated with AGN (1738) and PSR (166), we train (using 70% of our sample) and test (using 30%) our algorithms and find that the best overall accuracy (>96%) is obtained with the Random Forest (RF) technique, while using a logistic regression (LR) algorithm results in only marginally lower accuracy. We apply the same techniques on a sub-sample of 142 known gamma-ray pulsars to classify them into two major subcategories: young (YNG) and millisecond pulsars (MSP). Once more, the RF algorithm has the best overall accuracy (~90%), while a boosted LR analysis comes a close second. We apply our two best models (RF and LR) to the entire 3FGL catalog, providing predictions on the likely nature of {\it unassociated} sources, including the likely type of pulsar (YNG or MSP). We also use our predictions to shed light on the possible nature of some gamma-ray sources with known associations (e.g. binaries, SNR/PWN). Finally, we provide a list of plausible X-ray counterparts for some pulsar candidates, obtained using Swift, Chandra, and XMM. The results of our study will be of interest for both in-depth follow-up searches (e.g. pulsar) at various wavelengths, as well as for broader population studies.Comment: Accepted by Ap

Near-infrared spectroscopy study of tourniquet-induced forearm ischaemia in patients with coronary artery disease

Near-Infrared Spectroscopy (NIR) can be employed to monitor local changes in haemodynamics and oxygenation of human tissues. A preliminary study has been performed in order to evaluate the NIRS transmittance response to induced forearm ischaemia in patients with coronary artery disease (CAD). The population consists in 40 patients with cardiovascular risk factors and angiographically documented CAD, compared to a group of 13 normal subjects. By inflating and subsequently deflating a cuff placed around the patient arm, an ischaemia has been induced and released, and the patients have been observed until recovery of the basal conditions. A custom LAIRS spectrometer (IRIS) has been used to collect the backscattered light intensities from the patient forearm throughout the ischaemic and the recovery phase. The time dependence of the near-infrared transmittance on the control group is consistent with the available literature. On the contrary, the magnitude and dynamics of the NIRS signal on the CAD patients show deviations from the documented normal behavior, which can be tentatively attributed to abnormal vessel stiffness. These preliminary results, while validating the performance of the IRIS spectrometer, are strongly conducive towards the applicability of the NIRS technique to ischaemia analysis and to endothelial dysfunction characterization in CAD patients with cardiovascular risk factors.Publisher PD

A sustainable revolution: Let's go sustainable to get our globe cleaner

The concept of sustainability is a clear blue sea, a snowy mountain, a flowery meadow, in which there is resource sharing that allows us to satisfy human needs without damaging natural resources. The challenge is complex, and we hope to support the decarbonization of our society and mitigate climate changes. This Special Issue aims to outline different approaches in several sectors with a common point of view: seeing our world with a green perception and encouraging a sustainable revolution to provide a cleaner world

Role of multidrug-resistant pathogens in health-care-associated pneumonia.

Comment on: Rethinking the concepts of community-acquired and health-care-associated pneumonia. [Lancet Infect Dis. 2010

Optimal scheduling and control for constrained multi-agent networked control systems

In this paper, we study optimal control and communication schedule co-design for multi-agent networked control systems, with assuming shared parallel communication channels and uncertain constrained linear time-invariant discrete-time systems. To that end, we specify the communication demand for each system using an associated robust control invariant set and reachability analysis. We use these communication demands and invariant sets to formulate tube-based model predictive control and offline/online communication schedule co-design problems. Since the scheduling part includes an infinite dimension integer problem, we propose heuristics to find suboptimal solutions that guarantee robust constraints satisfaction and recursive feasibility. The effectiveness of our approach is illustrated through numerical simulations

The networking dynamics of the Italian biofuel industry in time of crisis: Finding an effective instrument mix for fostering a sustainable energy transition

This paper aims to design the effective instrument mixes for fostering a sustainable energy transition in times of crises. We focus on a sufficiently developed green niche – namely the Italian biofuel sector – implementing a two-step investigation: 1) a social network analysis, to study the effects of the crisis on the basic niche development mechanisms, with the aim of eliciting its development needs; 2) a fuzzy inference simulation based on a causal-effect map drawn from experts' knowledge to identify the most effective instrument mix for the development of the niche studied. The major needs emerged in the case investigated are for an increase of actors' expectations towards the further development of the sector, and a need for a tailored networking activity, devoted to attracting specific knowledgeable actors. The results indicate that, among others, effective policy instruments are, in this case, the cooperation that has the best outcome in terms of networking, and the public procurement, which remarkably increases the level of expectation. The analysis presented constitutes a model to evaluate single policy drivers and their combinations to find adequate policy actions to promote the green energy transition in times of crisis