Geo-material provenance and technological properties investigation in Copper Age menhirs production at Allai (central-western Sardinia, Italy)

During the 2nd millennium BC anthropomorphic menhirs belonging to a 3rd millennium BC sanctuary were reused as building material in the Arasseda Nuraghe (Sardinia, Italy). To analyse the Arasseda menhirs and the local Monte Ironi geological samples (presenting similar visual features), chemical (pXRF, ICP-OES, ICP-MS), mineralogical-chemical (PXRD) and physical (Mohs hardness) measurements were performed. Through the experimental data, the menhirs source provenance and the technological properties (workability, durability) of the raw material chosen for sculptural purposes during Copper Age were investigated. To the authors’ knowledge this is the first archaeometric study on the Arasseda menhirs (the third on Sardinian menhirs) and one between the few recently developed on European megaliths

A dynamical model of genetic networks describes cell differentiation

Cell differentiation is a complex phenomenon whereby a stem cell becomes progressively more specialized and eventually gives rise to a specific cell type. Differentiation can be either stochastic or, when appropriate signals are present, it can be driven to take a specific route. Induced pluripotency has also been recently obtained by overexpressing some genes in a differentiated cell. Here we show that a stochastic dynamical model of genetic networks can satisfactorily describe all these important features of differentiation, and others. The model is based on the emergent properties of generic genetic networks, it does not refer to specific control circuits and it can therefore hold for a wide class of lineages. The model points to a peculiar role of cellular noise in differentiation, which has never been hypothesized so far, and leads to non trivial predictions which could be subject to experimental testing

Photocatalyst based on titanium or iron semiconductors for the generation of hydrogen from water upon solar irradiation

Tesis por compendioThe objective of present thesis is to prepare and evaluate photocatalyst for hydrogen generation from water methanol mixture using solar light. This general objective has been accomplished by applying different methodology in material preparation as well as exploring the photocatalytic activity of novel semiconductors. In this way after a general introduction to the feed showing the relevance of solar fuels and in particular hydrogen generation, the focus of chapter 3 is, on the other and, to optimize the nature of the cocatalyst based on noble metals. In this way AuPt alloy nanoparticles with different composition will be deposited on p25 and they activity correlated with the irradiation wavelength and nature of the alloy. In chapter 4 and 5 we evaluate the photocatalytic activity of materials derived from titanate nanotubes either by hydrogen annealing ad various temperature (chapter 4) or by forming heterojunction with a combination of titania nanoparticles (chapter 5). The two final chapter of this thesis report the semiconductor behaviour and the photocatalytic activity of framework phosphate either mixed valence titanium III/IV (chapter 6) or iron (chapter 7) doped with various metal. The overall results achieved show that is possible to increase the photocatalytic activity of titanium-based materials by apply concept like control of morphology, amorphization of surface of particles, formation of heterojunction and control of the cocatalyst. We have also shows behind oxide, framework phosphate can also be valuable as photocatalysts.Serra, M. (2015). Photocatalyst based on titanium or iron semiconductors for the generation of hydrogen from water upon solar irradiation [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/48542TESISCompendi

Analysis of attractor distances in Random Boolean Networks

We study the properties of the distance between attractors in Random Boolean Networks, a prominent model of genetic regulatory networks. We define three distance measures, upon which attractor distance matrices are constructed and their main statistic parameters are computed. The experimental analysis shows that ordered networks have a very clustered set of attractors, while chaotic networks' attractors are scattered; critical networks show, instead, a pattern with characteristics of both ordered and chaotic networks.Comment: 9 pages, 6 figures. Presented at WIRN 2010 - Italian workshop on neural networks, May 2010. To appear in a volume published by IOS Pres

Strong tW Scattering at the LHC

Deviations of the top electroweak couplings from their Standard Model values imply that certain amplitudes for the scattering of third generation fermions and longitudinally polarized vector bosons or Higgses diverge quadratically with momenta. This high-energy growth is a genuine signal of models where the top quark is strongly coupled to the sector responsible for electroweak symmetry breaking. We propose to profit from the high energies accessible at the LHC to enhance the sensitivity to non-standard top-$Z$ couplings, which are currently very weakly constrained. To demonstrate the effectiveness of the approach, we perform a detailed analysis of $tW \to tW$ scattering, which can be probed at the LHC via $pp\to t\bar{t}Wj$. By recasting a CMS analysis at 8 TeV, we derive the strongest direct bounds to date on the $Ztt$ couplings. We also design a dedicated search at 13 TeV that exploits the distinctive features of the $t\bar{t}Wj$ signal. Finally, we present other scattering processes in the same class that could provide further tests of the top-Higgs sector.Comment: 37 pages, 10 figures; v2: minor improvements in the discussion, references added. Matches version published in JHE

Identification of galaxy cluster substructures with the Caustic method

We investigate the power of the caustic technique for identifying substructures of galaxy clusters from optical redshift data alone. The caustic technique is designed to estimate the mass profile of galaxy clusters to radii well beyond the virial radius, where dynamical equilibrium does not hold. Two by-products of this technique are the identification of the cluster members and the identification of the cluster substructures. We test the caustic technique as a substructure detector on two samples of 150 mock redshift surveys of clusters; the clusters are extracted from a large cosmological $N$-body simulation of a $\Lambda$CDM model and have masses of $M_{200} \sim 10^{14} h^{-1} M_{\odot}$ and $M_{200} \sim 10^{15} h^{-1} M_{\odot}$ in the two samples. We limit our analysis to substructures identified in the simulation with masses larger than $10^{13} h^{-1} M_{\odot}$. With mock redshift surveys with 200 galaxies within $3R_{200}$, (1) the caustic technique recovers $\sim 30-50$\% of the real substructures, and (2) $\sim 15-20$\% of the substructures identified by the caustic technique correspond to real substructures of the central cluster, the remaining fraction being low-mass substructures, groups or substructures of clusters in the surrounding region, or chance alignments of unrelated galaxies. These encouraging results show that the caustic technique is a promising approach for investigating the complex dynamics of galaxy clusters.Comment: 13 pages, 15 figures. Accepted for publication in Ap

A Data-Driven Approach for Tag Refinement and Localization in Web Videos

Tagging of visual content is becoming more and more widespread as web-based services and social networks have popularized tagging functionalities among their users. These user-generated tags are used to ease browsing and exploration of media collections, e.g. using tag clouds, or to retrieve multimedia content. However, not all media are equally tagged by users. Using the current systems is easy to tag a single photo, and even tagging a part of a photo, like a face, has become common in sites like Flickr and Facebook. On the other hand, tagging a video sequence is more complicated and time consuming, so that users just tag the overall content of a video. In this paper we present a method for automatic video annotation that increases the number of tags originally provided by users, and localizes them temporally, associating tags to keyframes. Our approach exploits collective knowledge embedded in user-generated tags and web sources, and visual similarity of keyframes and images uploaded to social sites like YouTube and Flickr, as well as web sources like Google and Bing. Given a keyframe, our method is able to select on the fly from these visual sources the training exemplars that should be the most relevant for this test sample, and proceeds to transfer labels across similar images. Compared to existing video tagging approaches that require training classifiers for each tag, our system has few parameters, is easy to implement and can deal with an open vocabulary scenario. We demonstrate the approach on tag refinement and localization on DUT-WEBV, a large dataset of web videos, and show state-of-the-art results.Comment: Preprint submitted to Computer Vision and Image Understanding (CVIU

A model of protocell based on the introduction of a semi-permeable membrane in a stochastic model of catalytic reaction networks

In this work we introduce some preliminary analyses on the role of a semi-permeable membrane in the dynamics of a stochastic model of catalytic reaction sets (CRSs) of molecules. The results of the simulations performed on ensembles of randomly generated reaction schemes highlight remarkable differences between this very simple protocell description model and the classical case of the continuous stirred-tank reactor (CSTR). In particular, in the CSTR case, distinct simulations with the same reaction scheme reach the same dynamical equilibrium, whereas, in the protocell case, simulations with identical reaction schemes can reach very different dynamical states, despite starting from the same initial conditions.Comment: In Proceedings Wivace 2013, arXiv:1309.712