Ultrasonic consolidation (UC) debulking of thermosetting prepreg for autoclave curing of composite laminates

Debulking of prepreg (pre-impregnated resin system) layers during hand lay-up manufacturing of carbon fibre reinforced polymers (CFRP) is a key-step to reduce air content and maximise the mechanical properties of the final product. Debulking is usually performed using vacuum-bag cycles of 10–15 min applied after the lay-up of every three or five prepreg layers, leading to a considerable time-consuming process. In this work, the use of ultrasonic stimulation during vacuum is studied to improve the efficiency of the debulking process and reduce the number of operations in order to decrease the overall manufacturing time. Three CFRP laminates were laid-up using the proposed ultrasonic consolidation (UC) with three different exposition times (5, 10 and 15 min) and cured in autoclave. The UC debulking process consists in a vacuum cycle with ultrasonic waves sent to the uncured material through an ultrasonic transducer. In order to evaluate the efficiency of this process interlaminar shear strength (ILSS) and in-plane compressive properties were tested. Experimental results show for 15 min compressive properties comparable with the ones obtained from reference samples manufactured using the traditional debulking technique, and high improvements in terms of ILSS (>20%). Therefore, UC debulking process can be used during hand lay-up of prepreg in order to improve the interlaminar properties of the final part and reduce the debulking time by over 85%

Enabling IoT stream management in multi-cloud environment by orchestration

(c) 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Every-Day lives are becoming increasingly instrumented by electronic devices and any kind of computer-based (distributed) service. As a result, organizations need to analyse an enormous amounts of data in order to increase their incomings or to improve their services. Anyway, setting-up a private infrastructure to execute analytics over Big Data is still expensive. The exploitation of Cloud infrastructure in IoT Stream management is appealing because of costs reductions and potentiality of storage, network and computing resources. The Cloud can consistently reduce the cost of analysis of data from different sources, opening analytics to big storages in a multi-cloud environment. Anyway, creating and executing this kind of service is very complex since different resources have to be provisioned and coordinated depending on users' needs. Orchestration is a solution to this problem, but it requires proper languages and methodologies for automatic composition and execution. In this work we propose a methodology for composition of services used for analyses of different IoT Stream and, in general, Big Data sources: in particular an Orchestration language is reported able to describe composite services and resources in a multi-cloud environment.Peer ReviewedPostprint (author's final draft

Generation of game contents by social media analysis and MAS planning

In the age of pervasive computing and social networks, it has become commonplace to retrieve opinions about digital contents in games. In the case of multi-player, open world gaming, in fact even in “old-school” single players games, it is evident the need for adding new features in a game depending on users comments and needs. However this is a challenging task that usually requires considerable design and programming efforts, and more and more patches to games, with the inevitable consequence of loosing interest in the game by players over years. This is particularly a hard problem for all games that do not intend to be designed as interactive novels. Process Content Generation (PCG) of new contents could be a solution to this problem, but usually such techniques are used to design new maps or graphical contents. Here we propose a novel PCG technique able to introduce new contents in games by means of new story-lines and quests. We introduce new intelligent agents and events in the world: their attitudes and behaviors will promote new actions in the game, leading to the involvement of players in new gaming content. The whole methodology is driven by Social Media Analysis contents about the game, and by the use of formal planning techniques based on Multi-Agents modelsPeer ReviewedPostprint (author's final draft

A Model Driven Approach to Water Resource Analysis based on Formal Methods and Model Transformation

AbstractSeveral frameworks have been proposed in literature in order to cope with critical infrastructure modelling issues, and almost all rely on simulation techniques. Anyway simulation is not enough for critical systems, where any problem may lead to consistent loss in money and even human lives. Formal methods are widely used in order to enact exhaustive analyses of these systems, but their complexity grows with system dimension and heterogeneity. In addition, experts in application domains could not be familiar with formal modelling techniques. A way to manage complexity of analysis is the use of Model Based Transformation techniques: analysts can express their models in the way they use to do and automatic algorithms translate original models into analysable ones, reducing analysis complexity in a completely transparent way.In this work we describe an automatic transformation algorithm generating hybrid automata for the analysis of a natural water supply system. We use real system located in the South of Italy as case study

Mechanisms Of Inhibition Of Cigarette Smoke Genotoxicity And Carcinogenicity

Epidemiological studies have demonstrated that it is possible to prevent lung cancer and other smoke-related diseases by avoiding exposures to tobacco smoke. A complementary strategy is chemoprevention, which is based on the administration of dietary and pharmacological agents, which is addressed to (a) addicted active smokers, who are unable to quit smoking, (b) ex-smokers, who are still at risk for several years, and (c) involuntary smokers, including passively exposed individuals as well as transplacentally exposed individuals. The biological effects of cigarette smoke (CS) as a complex mixture, either mainstream (MCS) or sidestream (SCS) or environmental (ECS), have been poorly explored. We showed that MCS and ECS induce a broad variety of alterations of intermediate biomarkers in animal models, including adducts to nuclear DNA and mtDNA, oxidatively generated DNA damage, proliferation, apoptosis, alterations of oncogenes and tumor suppressor genes, multigene expression, microRNA and proteome profiles as well as cytogenetic damage in the respiratory tract, bone marrow and peripheral blood. CS-altered end-points were variously modulated by chemopreventive agents of natural or pharmacological origin, such as N-acetyl-L-cysteine (NAC), 1,2-dithiole-3-thione, oltipraz, 5,6-benzoflavone, phenethyl isothiocyanate (PEITC), indole-3-carbinol, sulindac, and budesonide. Combinations of agents were also assayed. Since it is difficult to assess the efficacy of chemopreventives in clinical trials, it is essential to understand the mechanisms by which certain agents are expected to prevent smoke-related cancer. Preclinical studies are also useful to demonstrate the potential efficacy of chemopreventive agents. Unfortunately, until recently a suitable animal model for evaluating CS carcinogenicity and its chemoprevention was not available. We demonstrated that ECS and especially MCS become potently carcinogenic when exposure of mice starts at birth, as shown by very short latency times, high incidence and multiplicity of benign lung tumors, early occurrence of malignant lung tumors, and lesions in other organs. This mouse model was successfully used to demonstrate the ability of NAC, PEITC, and budesonide to prevent smoke-induced lung cancer, according to protocols mimicking the situation either in current smokers or in ex-smokers. Other dietary or pharmacological agents, including curcumin, anthocyanins, myo-inositol, SAHA, bexarotene and pioglitazone, are now under study. NAC was even successful to prevent lung cancer induced by MCS after birth when it was administered during the prenatal life. Therefore, it is now possible to investigate in vivo not only alterations of intermediate biomarkers but also the modulation of CS carcinogenesis by chemopreventive agents working with different mechanisms

Enhancing Random Forest Classification with NLP in DAMEH: A system for DAta Management in EHealth Domain

The use of pervasive IoT devices in Smart Cities, have increased the Volume of data produced in many and many field. Interesting and very useful applications grow up in number in E-health domain, where smart devices are used in order to manage huge amount of data, in highly distributed environments, in order to provide smart services able to collect data to fill medical records of patients. The problem here is to gather data, to produce records and to analyze medical records depending on their contents. Since data gathering involve very different devices (not only wearable medical sensors, but also environmental smart devices, like weather, pollution and other sensors) it is very difficult to classify data depending their contents, in order to enable better management of patients. Data from smart devices couple with medical records written in natural language: we describe here an architecture that is able to determine best features for classification, depending on existent medical records. The architecture is based on pre-filtering phase based on Natural Language Processing, that is able to enhance Machine learning classification based on Random Forests. We carried on experiments on about 5000 medical records from real (anonymized) case studies from various health-care organizations in Italy. We show accuracy of the presented approach in terms of Accuracy-Rejection curves

Finding unexplained human behaviors in Social Networks

Detection of human behavior in On-line Social Networks (OSNs) has become a very important challenge for a wide range of appli- cations, such as security, marketing, parent controls and so on, opening a wide range of novel research areas, which have not been fully addressed yet. In this paper, we present a two-stage method for finding unexplained (and potentially anomalous) behaviors in social networks. First, we use Markov chains to automatically learn from the social network graph a number of models of human behaviors (normal behaviors); the second stage applies an activity detection framework based on the concept of possible words to detect all unexplained activities with respect to the well-known behaviors. Some preliminary experiments using Facebook data show the approach efficiency and effectiveness. Copyright © (2014) by Universita Reggio Calabria & Centro di Competenza (ICT-SUD) All rights reserved

Enhancement of radiosensitivity by the novel anticancer quinolone derivative vosaroxin in preclinical glioblastoma models

Purpose: Glioblastoma multiforme (GBM) is the most aggressive brain tumor. The activity of vosaroxin, a first-in-class anticancer quinolone derivative that intercalates DNA and inhibits topoisomerase II, was investigated in GBM preclinical models as a single agent and combined with radiotherapy (RT). Results: Vosaroxin showed antitumor activity in clonogenic survival assays, with IC50 of 10-100 nM, and demonstrated radiosensitization. Combined treatments exhibited significantly higher γH2Ax levels compared with controls. In xenograft models, vosaroxin reduced tumor growth and showed enhanced activity with RT; vosaroxin/RT combined was more effective than temozolomide/RT. Vosaroxin/ RT triggered rapid and massive cell death with characteristics of necrosis. A minor proportion of treated cells underwent caspase-dependent apoptosis, in agreement with in vitro results. Vosaroxin/RT inhibited RT-induced autophagy, increasing necrosis. This was associated with increased recruitment of granulocytes, monocytes, and undifferentiated bone marrow-derived lymphoid cells. Pharmacokinetic analyses revealed adequate blood-brain penetration of vosaroxin. Vosaroxin/RT increased disease-free survival (DFS) and overall survival (OS) significantly compared with RT, vosaroxin alone, temozolomide, and temozolomide/RT in the U251-luciferase orthotopic model. Materials and Methods: Cellular, molecular, and antiproliferative effects of vosaroxin alone or combined with RT were evaluated in 13 GBM cell lines. Tumor growth delay was determined in U87MG, U251, and T98G xenograft mouse models. (DFS) and (OS) were assessed in orthotopic intrabrain models using luciferasetransfected U251 cells by bioluminescence and magnetic resonance imaging. Conclusions: Vosaroxin demonstrated significant activity in vitro and in vivo in GBM models, and showed additive/synergistic activity when combined with RT in O6- methylguanine methyltransferase-negative and -positive cell lines