Unsupervised analysis of small animal dynamic Cerenkov luminescence imaging

Clustering analysis (CA) and principal component analysis (PCA) were applied to dynamic Cerenkov lumi- nescence images (dCLI). In order to investigate the per- formances of the proposed approaches, two distinct dy- namic data sets obtained by injecting mice with 32 P-ATP and 18 F-FDG were acquired using the IVIS 200 optical im- ager. The k-means clustering algorithm has been applied to dCLI and was implemented using interactive data lan- guage 8.1. We show that cluster analysis allows us to ob- tain good agreement between the clustered and the corre- sponding emission regions like the bladder, the liver, and the tumor. We also show a good correspondence between the time activity curves of the different regions obtained by using CA and manual region of interest analysis on dCLIT and PCA images. We conclude that CA provides an auto- matic unsupervised method for the analysis of preclinical dynamic Cerenkov luminescence image data. C 2011 Society of Photo-Optical Instrumentation Engineers (SPIE). (DOI: 10.1117/1.3663442

Photodynamic therapy using Cerenkov and radioluminescence light

In this short review the potential use of Cerenkov radiation and radioluminescence as internal sources for Photodynamic therapy (PDT) is discussed. PDT has been developed over the course of more than 100 years and is based on the induced photo conversion of a drug called photosensitizer (PS) that triggers the production of cytotoxic reactive oxygen species (ROS) leading to the killing of the cells. In order to overcome the problem of light penetration in the tissues, different solutions were proposed in the past. The use of radioisotopes like: F-18, Cu-64, Y-90, Lu-177 as internal light sources increase the light fluence at the PS compared to an external source, resulting in a larger cytotoxic effect

Technology Adoption and Innovation in Public Services.The Case of E-Government in Italy

Using data on 1,176 Italian municipalities in 2005, this paper discusses a number of factors associated with the development of a particular type of innovative activities, namely e-government services supplied by local public administrations (PAs). We find that municipalities which got involved into e-government are larger, carry out more in-house ICT activities and are more likely to have intra-net infrastructures, relative to PAs that do not offer front office digitalised services. They are also generally located in regions with relatively large shares of firms using or producing ICT, where many other municipalities offer digitalised services, and where concentration of inhabitants in metropolitan areas is not very high. The range and quality of e-government services supplied by local PAs tend to increase with their stock of ICT competencies, with their efforts to train workers, and with their ability to organise efficient interfaces with end-users. Moreover, there is a correlation between the range and quality of e-government services offered and the broadband infrastructure development of the geographic area in which local PAs are located. In more general terms, we show that the combination of internal competencies and context specific factors is different when explaining the decision to start e-government activities vs. the intensity of such activities. Regional factors, relating to both demand and supply of services, appear to affect only the decision to enter e-government activities. Competencies needed to expand and improve the quality of services are much more numerous and complex than the ones associated with the mere decision to start e-government activities.Innovation system, Dynamic capabilities, Technology adoption, Electronic government, Innovation in services, Two-part model.

Slow melting of a disordered quantum crystal

The melting of the corner of a crystal is a classical, real-world, non-equilibrium statistical mechanics problem which has shown several connections with other branches of physics and mathematics. For a perfect, classical crystal in two and three dimensions the solution is known: the crystal melts reaching a certain asymptotic shape, which keeps expanding ballistically. In this paper, we move onto the quantum realm and show that the presence of quenched disorder slows down severely the melting process. Nevertheless, we show that there is no many-body localization transition, which could impede the crystal to be completely eroded. We prove such claim both by a perturbative argument, using the forward approximation, and via numerical simulations. At the same time we show how, despite the lack of localization, the erosion dynamics is slowed from ballistic to logarithmic, therefore pushing the complete melting of the crystal to extremely long timescales.Comment: 15 pages, 10 figures. Comments are welcome

Social Network Analysis at the 2021 Annual Conference of the Italian Society for Economic Sociology (SISEC)

The Italian Society for Economic Sociology (SISEC) is a national association that aims to represent Italy-based scholars in economic sociology, the sociology of work and labour markets, and the sociology of organisations. Its last two conferences included rich and highly participated sessions on applications of Social Network Analysis (SNA). In its most basic form, SNA allows researchers to model social relations by means of graph theory as a set of nodes (or vertices) – representing individual or collective actors – and ties (or edges) – representing relations between nodes. Recent advances in SNA have provided scholars with more sophisticated modelling techniques which allow statistical inference, thereby overcoming many limits of standard descriptive, case-based SNA. After setting the stage, this note summarizes the works on SNA presented at the 5th SISEC annual conference, hosted by the University of Catania in 2021

Laboratory and on-site tests for rapid runway repair

The attention to rapid pavement repair has grown fast in recent decades: this topic is strategic for the airport management process for civil purposes and peacekeeping missions. This work presents the results of laboratory and on-site tests for rapid runway repair, in order to analyse and compare technical and mechanical performances of 12 different materials currently used in airport. The study focuses on site repairs, a technique adopted most frequently than repairs with modular elements. After describing mechanical and physical properties of the examined materials (2 bituminous emulsions, 5 cement mortars, 4 cold bituminous mixtures and 1 expanding resin), the study presents the results of carried out mechanical tests. The results demonstrate that the best performing material is a one-component fast setting and hardening cement mortar with graded aggregates. This material allows the runway reopening 6 h after the work. A cold bituminous mixture (bicomponent premixed cold asphalt with water as catalyst) and the ordinary cement concrete allow the reopening to traffic after 18 h, but both ensure a lower service life (1000 coverages) than the cement mortar (10,000 coverages). The obtained results include important information both laboratory level and field, and they could be used by airport management bodies and road agencies when scheduling and evaluating pavement repairs

Deep Neural Networks for Multivariate Prediction of Photovoltaic Power Time Series

The large-scale penetration of renewable energy sources is forcing the transition towards the future electricity networks modeled on the smart grid paradigm, where energy clusters call for new methodologies for the dynamic energy management of distributed energy resources and foster to form partnerships and overcome integration barriers. The prediction of energy production of renewable energy sources, in particular photovoltaic plants that suffer from being highly intermittent, is a fundamental tool in the modern management of electrical grids shifting from reactive to proactive, with also the help of advanced monitoring systems, data analytics and advanced demand side management programs. The gradual move towards a smart grid environment impacts not only the operating control/management of the grid, but also the electricity market. The focus of this article is on advanced methods for predicting photovoltaic energy output that prove, through their accuracy and robustness, to be useful tools for an efficient system management, even at prosumer's level and for improving the resilience of smart grids. Four different deep neural models for the multivariate prediction of energy time series are proposed; all of them are based on the Long Short-Term Memory network, which is a type of recurrent neural network able to deal with long-term dependencies. Additionally, two of these models also use Convolutional Neural Networks to obtain higher levels of abstraction, since they allow to combine and filter different time series considering all the available information. The proposed models are applied to real-world energy problems to assess their performance and they are compared with respect to the classic univariate approach that is used as a reference benchmark. The significance of this work is to show that, once trained, the proposed deep neural networks ensure their applicability in real online scenarios characterized by high variability of data, without requiring retraining and end-user's tricks

Monte Carlo simulations support non-Cerenkov radioluminescence production in tissue

There is experimental evidence for the production of non-Cerenkov radioluminescence in a variety of materials, including tissue. We constructed a Geant4 Monte Carlo simulation of the radiation from P32 and Tc99m interacting in chicken breast and used experimental imaging data to model a scintillation-like emission. The same radioluminescence spectrum is visible from both isotopes and cannot otherwise be explained through fluorescence or filter miscalibration. We conclude that chicken breast has a near-infrared scintillation-like response with a light yield three orders of magnitude smaller than BGO