VOLARE: Adaptive Web Service Discovery Middleware for Mobile Systems

With the recent advent and widespread use of smart mobile devices, the flexibility and versatility offered by Service Oriented Architecture's (SOA) makes it an ideal approach to use in the rapidly changing mobile environment. However, the mobile setting presents a set of new challenges that service discovery methods developed for nonmobile environments cannot address. The requirements a mobile client device will have from a Web service may change due to changes in the context or the resources of the client device. In a similar manner, a mobile device that acts as a Web service provider will have different capabilities depending on its status, which may also change dramatically during runtime. This paper introduces VOLARE, a middleware-based solution that will monitor the resources and context of the device, and adapt service requests accordingly. The same method will be used to adapt the Quality of Service (QoS) levels advertised by service providers, to realistically reflect each provider's capabilities at any given moment. This approach will allow for more resource-efficient and accurate service discovery in mobile systems and will enable more reliable provider functionality in mobile devices

"...when you’re a Stranger": Evaluating Safety Perceptions of (un)familiar Urban Places

What makes us feel safe when walking around our cities? Previous research has shown that our perception of safety strongly depends on characteristics of the built environment; separately, research has also shown that safety perceptions depend on the people we encounter on the streets. However, it is not clear how the two relate to one another. In this paper, we propose a quantitative method to investigate this relationship. Using an online crowd–sourcing approach, we collected 5452 safety ratings from over 500 users about images showing various combinations of built environment and people inhabiting it. We applied analysis of covariance (ANCOVA) to the collected data and found that familiarity of the scene is the single most important predictor of our sense of safety. Controlling for familiarity, we identified then what features of the urban environment increase or decrease our safety perception

Cluster of legionnaires’ disease in an Italian prison

Background: Legionella pneumophila (Lp) is the most common etiologic agent causing Legionnaires’ Disease (LD). Water systems offer the best growth conditions for Lp and support its spread by producing aerosols. From 2015 to 2017, the Regional Reference Laboratory of Clinical and Environmental Surveillance of Legionellosis of Palermo monitored the presence of Lp in nine prisons in Western Sicily. During this investigation, we compared Lp isolates from environmental samples in a prison located in Palermo with isolates from two prisoners in the same prison. Methods: We collected 93 water samples from nine Sicilian prisons and the bronchoalveolar lavages (BALs) of two prisoners considered cases of LD. These samples were processed following the procedures described in the Italian Guidelines for the Prevention and Control of Legionellosis of 2015. Then, genotyping was performed on 19 Lp colonies (17 from water samples and 2 from clinical samples) using the Sequence-Based Typing (SBT) method, according to European Study Group for Legionella Infections (ESGLI) protocols. Results: Lp serogroup (sg) 6 was the most prevalent serogroup isolated from the prisons analyzed (40%), followed by Lp sg 1 (16%). Most of all, in four penitentiary institutions, we detected a high concentration of Lp >104 Colony Forming Unit/Liter (CFU/L). The environmental molecular investigation found the following Sequence Types (STs) in Lp sg 6: ST 93, ST 292, ST 461, ST 728, ST 1317 and ST 1362, while most of the isolates in sg 1 belonged to ST 1. We also found a new ST that has since been assigned the number 2451 in the ESGLI-SBT database. From the several Lp sg 1 colonies isolated from the two BALs, we identified ST 2451. Conclusions: In this article, we described the results obtained from environmental and epidemiological investigations of Lp isolated from prisons in Western Sicily. Furthermore, we reported the first cluster of Legionnaires’ in an Italian prison and the molecular typing of Lp sg 1 from one prison’s water system and two BALs, identified the source of the contamination, and discovered a new ST

How do they pay as they go?: Learning payment patterns from solar home system users data in Rwanda and Kenya

Pay-as-you-go (PAYGo) financing models play a vital role in boosting the distribution of solar-home-systems (SHSs) to electrify rural Sub-Saharan Africa. This financing model improves the affordability of SHSs by supporting the payment flexibility required in these contexts. Such flexibility comes at a cost, and yet the assumptions that guide the PAYGo model design remain largely untested. To close the gap, this paper proposes a methodology based on unsupervised machine learning algorithms to analyse the payment records of over 32,000 Rwandan and 25,000 Kenyan SHS users from Bboxx Ltd., and in so doing gain detailed insights into users' payment behavioural patterns. More precisely, the method first applies three clustering algorithms to automatically learn the main payment behavioural groups in each country separately; it then determines the preferred customer segmentation through a validation procedure which combines quantitative and qualitative insights. The results highlight six behavioural groups in Rwanda and four in Kenya; however, several parallels can be made between the two country profiles. These groups highlight the diversity of payment patterns found in the PAYGo model. Further analysis of their payment performance suggests that a one-size-fits-all approach leads to inefficiencies and that tailored plans should be considered to effectively cater to all SHS users

Piezoelectricity measurements of hybrid films functionalized with ZnO nanostructures and cellulose nanocrystals

Piezoelectric energy scavengers for the conversion of mechanical energy (e.g., pressure, bending, stretching and vibrational motions) into electricity, have been manufactured using both polymeric and inorganic materials as well as a combination of those [1-3]. It is well known that inorganic materials possess larger piezoelectric coefficients than polymers, however they exhibit higher stiffness, which makes them less sensitive to small vibrations and more prone to stress failure. On the other hand, polymer-based generators represent a relatively small proportion of the total research due to the involvement of complicated material processing and device fabrication (using precise manipulators), which represent hurdles for scalability and cost. The aim of this work is to develop a novel coating of easy fabrication and low environmental impact that could lead to a real competition in the field of renewable/alternative energy technologies. In particular, we have utilized two different geometries of ZnO nanoparticles, synthesized on purpose and embedded into a UV-curable acrylic polymer matrix. The experimental set-up for assessing the piezoelectric behavior of the obtained UV-cured films has been assembled and preliminary results of this behavior are here presented

Influence of Different Dry-Mixing Techniques on the Mechanical, Thermal and Electrical Behavior of Ultra High Molecular Weight Polyethylene/Exhausted Tire Carbon Composites

The mechanical, thermal and electrical behavior of ultra high molecular weight (UHMWPE) composites containing different amount of pyrolyzed exhausted tire carbon (ETC) is investigated. Composites were obtained by dry-mixing the powders with a homogenizer and an impact mill. The results clearly indicate that, by changing the mixing method, it is possible to tune the rheological and morphological characteristics of the composites and in turn their mechanical, thermal and electrical properties. Better performances were observed for the composites obtained with the impact mill, which showed improved Young modulus, reduced electrical and thermal resistance with respect to those of homogenized counterparts. All the composites exhibited a relevant decrease of electrical resistivity

The Geant4-DNA project

The Geant4-DNA project proposes to develop an open-source simulation software based and fully included in the general-purpose Geant4 Monte Carlo simulation toolkit. The main objective of this software is to simulate biological damages induced by ionising radiation at the cellular and sub-cellular scale. This project was originally initiated by the European Space Agency for the prediction of deleterious effects of radiation that may affect astronauts during future long duration space exploration missions. In this paper, the Geant4-DNA collaboration presents an overview of the whole ongoing project, including its most recent developments already available in the last Geant4 public release (9.3 BETA), as well as an illustration example simulating the direct irradiation of a chromatin fibre. Expected extensions involving several research domains, such as particle physics, chemistry and cellular and molecular biology, within a fully interdiciplinary activity of the Geant4 collaboration are also discussed.Comment: presented by S. Incerti at the ASIA SIMULATION CONFERENCE 2009, October 7-9, 2009, Ritsumeikan University, Shiga, Japa