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

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

Survey data for assessing the socio-economic performance of End of Life options of a bio-based product based on expert knowledge

This data article aims at providing a data description about the manuscript entitled “A socio-economic indicator for EoL strategies for bio-based products” [1]. Data regarding the socio-economic assessment of End of Life (EoL) options for the specific case of PLA-based film for food packaging are presented, with a special emphasis on policy recommendations and actions for the EoL practices in the bioeconomy sectors. A new framework, based on data gathering and validation through experts involvement, is proposed in order to calculate a new indicator to measure the socio-economic performance of EoL practices (SEI-EoL) for bio-based products. Experts were identified from the Horizon 2020 LIFE-funded projects and/or Scopus databases. Two rounds of survey were carried out to determine the weights of socio-economic criteria for bio-based products and the values for the selected case study. The aggregation of these data enabled us to obtain a final ranking of different EoL alternatives. Finally, a third round of survey was conducted to further deepen our understanding of actions and recommendations needed to improve EoL practices in bio-based sectors. Resulting data have a mix of quantitative and qualitative characterization. A potential reuse of these data can allow future estimations, empirical analyses or a direct comparison with the use of experimental observations

A Combined method to model policy interventions for local communities based on people knowledge

Policy interventions to promote innovative industries in peripheral regions are often hampered by lack of information on the functioning of the local socio-economic systems, due to their complexity. This might result in mismatches between policy objectives and the actual needs and capability of local communities. To overcome this drawback, it is crucial to obtain appropriate knowledge on the local system, which nevertheless is typically embedded in local actors’ minds in uncodified and tacit form. Fuzzy Cognitive Maps (FCMs) have been employed to decode this kind of knowledge in a reproducible manner. However, some problems remain as to how to integrate the necessary vagueness of local actors’ heuristic with experts’ knowledge into a rational framework. The following methodology customization is proposed: • Combine the FCMs with the Discourse Analysis to obtain relevant narratives (i.e. concepts, visions, insights, etc.) needed to define system boundaries and variables. • Employ individual interviews – rather than a participatory approach – to define the causal relations among system variables. • Integrate tacit and uncodified knowledge embedded in local actors within experts’ scientific knowledge