A search for lines in the bright X-ray afterglow of GRB120711A

GRB120711A, discovered and rapidly localized by the INTEGRAL satellite, attracted particular interest due to its high gamma-ray fluence, very bright X-ray afterglow, and the detection of a prompt optical transient and of long-lasting emission at GeV energies. A follow-up observation carried out with the XMM-Newton satellite has provided an X-ray spectrum in the 0.3-10 keV with unprecedented statistics for a GRB afterglow 20 hours after the burst. The spectrum is well fit by a power-law with photon index 1.87+-0.01, modified by absorption in our Galaxy and in the GRB host at z=1.4. A Galactic absorption consistent with that estimated from neutral hydrogen observations is obtained only with host metallicity lower than 0.05 of the Solar value. We report the results of a sensitive search for emission and absorption lines using the matched filter smoothing method (Rutledge and Sako 2003). No statistically significant lines were found. The upper limits on the equivalent width of emission lines, derived through Monte Carlo simulations, are few tens of eV, a factor about 10 lower than that of the possible lines reported in the literature for other bursts.Comment: Accepted for publication in A&

WHY DO RESEARCHERS COLLABORATE WITH INDUSTRY? AN ANALYSIS OF THE WINE SECTOR IN CHILE, SOUTH AFRICA AND ITALY

This paper explores the determinants of the linkages between industry and research organizations – including universities. We present new evidence on three wine producing areas – Piedmont, a region of Italy, Chile, South Africa - that have successfully reacted to the recent structural changes experienced in the industry worldwide. Based on an original data-set, we carry out an econometric exercise to study the microeconomic determinants of researchers' collaborations with industry. The evidence reveals that individual researcher characteristics, such as embeddedness in the academic system, age and sex, matter more than their publishing record or formal degrees.University-Industry Linkages, Innovation System, Wine Sector, Emerging Economies

Clusters of firms in space and time

The use of the K-functions (Ripley, 1977) has become recently popular in the analysis of the spatial pattern of firms. It was first introduced in the economic literature by Arbia and Espa (1996) and then popularized by Marcon and Puech (2003), Quah and Simpson (2003), Duranton and Overman (2005) and Arbia et al. (2008). In particular in Arbia et al. (2008) we used Ripley’s K-functions as instruments to study the inter-sectoral co-agglomeration pattern of firms in a single moment of time. All this researches have followed a static approach, disregarding the time dimension. Temporal dynamics, on the other hand, play a crucial role in understanding the economic and social phenomena, particularly when referring to the analysis of the individual choices leading to the observed clusters of economic activities. With respect to the contributions previously appeared in the literature, this paper uncovers the process of firm demography by studying the dynamics of localization through space-time K-functions. The empirical part of the paper will focus on the study of the long run localization of firms in the area of Rome (Italy), by concentrating on the ICT sector data collected by the Italian Industrial Union in the period 1920- 2005.Agglomeration, Non-parametric measures; Space-time K-functions, Spatial clusters, Spatial econometrics.

Neutrinoless double-beta decay

This paper introduces the neutrinoless double-beta decay the rarest nuclear weak process and describes the status of the research for this transition, both from the point of view of theoretical nuclear physics and in terms of the present and future experimental scenarios. Implications of this phenomenon on crucial aspects of particle physics are briefly discussed. The calculations of the nuclear matrix elements in case of mass mechanisms are reviewed, and a range for these quantities is proposed for the most appealing candidates. After introducing general experimental concepts—such as the choice of the best candidates, the different proposed technological approaches, and the sensitivity—we make the point on the experimental situation. Searches running or in preparation are described, providing an organic presentation which picks up similarities and differences. A critical comparison of the adopted technologies and of their physics reach in terms of sensitivity to the effective Majorana neutrino mass is performed. As a conclusion, we try to envisage what we expect round the corner and at a longer time scaleThis work was partially supported by the MICINN (Spain) (FPA2011-29854) ; by the Comunidad de Madrid (Spain) (HEPHACOS S2009-ESP-1473); by the Spanish Consolider-Ingenio 2010 Program, CPAN (CSD2007-00042

The LHAASO PeVatron bright sky: what we learned

The recent detection of 12 gamma-ray Galactic sources well above E > 100 TeV by the LHAASO observatory has been a breakthrough in the context of Cosmic Ray (CR) origin search. Although most of these sources are unidentified, they are often spatially correlated with leptonic accelerators, like pulsar and pulsar wind nebulae (PWNe). This dramatically affects the paradigm for which a gamma-ray detection at E > 100 TeV implies the presence of a hadronic accelerator of PeV particles (PeVatron). Moreover, the LHAASO results support the idea that sources other than the standard candidates, Supernova Remnants, can accelerate Galactic CRs. In this context, the good angular resolution of future Cherenkov telescopes, such as the ASTRI Mini-Array and CTA, and the higher sensitivity of future neutrino detectors, such as KM3NeT and IceCube-Gen2, will be of crucial importance. In this brief review, we want to summarize the efforts done up to now, from both theoretical and experimental points of view, to fully understand the LHAASO results in the context of the CR acceleration issue.Comment: Accepted for the special Issue "High Energy Multi-Messenger Astrophysics: Latest Research and Reviews" of the journal "Applied Science

Analysis of heat kernel highlights the strongly modular and heat-preserving structure of proteins

In this paper, we study the structure and dynamical properties of protein contact networks with respect to other biological networks, together with simulated archetypal models acting as probes. We consider both classical topological descriptors, such as the modularity and statistics of the shortest paths, and different interpretations in terms of diffusion provided by the discrete heat kernel, which is elaborated from the normalized graph Laplacians. A principal component analysis shows high discrimination among the network types, either by considering the topological and heat kernel based vector characterizations. Furthermore, a canonical correlation analysis demonstrates the strong agreement among those two characterizations, providing thus an important justification in terms of interpretability for the heat kernel. Finally, and most importantly, the focused analysis of the heat kernel provides a way to yield insights on the fact that proteins have to satisfy specific structural design constraints that the other considered networks do not need to obey. Notably, the heat trace decay of an ensemble of varying-size proteins denotes subdiffusion, a peculiar property of proteins

Profiling residential water users’ routines by eigenbehavior modelling

Developing effective demand-side management strategies is essential to meet future residential water demands, pursue water conservation, and reduce the costs for water utilities. The effectiveness of water demand management strategies relies on our understanding of water consumers’ behavior and their consumption habits and routines, which can be monitored through the deployment of smart metering technologies and the adoption of data analytics and machine learning techniques. This work contributes a novel modeling procedure, based on a combination of clustering and principal component analysis, which allows performing water users’ segmentation on the basis of their eigenbehaviors (i.e., recurrent water consumption behaviors) automatically identified from smart metered consumption data. The approach is tested against a dataset of smart metered water consumption data from 175 households in the municipality of Tegna (CH). Numerical results demonstrate the potential of the method for identifying typical profiles of water consumption, which constitute essential information to support residential water demand management

A convex optimization approach for automated water and energy end use disaggregation

A detailed knowledge of water consumption at an end-use level is an essential requirement to design and evaluate the efficiency of water saving policies. In the last years, this has led to the development of automated tools to disaggregate high resolution water consumption data at the household level into end use categories. In this work, a new disaggregation algorithm is presented. The proposed algorithm is based on the assumption that the disaggregated signals to be identified are piecewise constant over the time and it exploits the information on the time-of-day probability in which a specific water use event might occur. The disaggregation problem is formulated as a convex optimization problem, whose solution can be efficiently computed through numerical solvers. Specifically, the disaggregation problem is treated as a least-square error minimization problem, with an additional (convex) penalty term aiming at enforcing the disaggregate signals to be piece-wise constant over the time. The proposed disaggregation algorithm has been initially tested against household electricity data available in the literature. The obtained results look promising and similar results are expected to be obtained for water data