The Borexino detector at the Laboratori Nazionali del Gran Sasso

Borexino, a large volume detector for low energy neutrino spectroscopy, is currently running underground at the Laboratori Nazionali del Gran Sasso, Italy. The main goal of the experiment is the real-time measurement of sub MeV solar neutrinos, and particularly of the mono energetic (862 keV) Be7 electron capture neutrinos, via neutrino-electron scattering in an ultra-pure liquid scintillator. This paper is mostly devoted to the description of the detector structure, the photomultipliers, the electronics, and the trigger and calibration systems. The real performance of the detector, which always meets, and sometimes exceeds, design expectations, is also shown. Some important aspects of the Borexino project, i.e. the fluid handling plants, the purification techniques and the filling procedures, are not covered in this paper and are, or will be, published elsewhere (see Introduction and Bibliography).Comment: 37 pages, 43 figures, to be submitted to NI

Drug use and nightlife: more than just dance music

<p>Abstract</p> <p>Background</p> <p>Research over the last decade has focused almost exclusively on the association between electronic music and MDMA (3,4-Methylenedioxymethamphetamine or "ecstasy") or other stimulant drug use in clubs. Less attention has been given to other nightlife venues and music preferences, such as rock music or southern/funky music. This study aims to examine a broader spectrum of nightlife, beyond dance music. It looks at whether certain factors influence the frequency of illegal drug and alcohol use: the frequency of going to certain nightlife venues in the previous month (such as, pubs, clubs or goa parties); listening to rock music, dance music or southern and funky music; or sampling venues (such as, clubs, dance events or rock festivals). The question of how these nightlife variables influence the use of popular drugs like alcohol, MDMA, cannabis, cocaine and amphetamines is addressed.</p> <p>Methods</p> <p>The study sample consisted of 775 visitors of dance events, clubs and rock festivals in Belgium. Study participants answered a survey on patterns of going out, music preferences and drug use. Odds ratios were used to determine whether the odds of being an illegal substance user are higher for certain nightlife-related variables. Furthermore, five separate ordinal regression analyses were used to investigate drug use in relation to music preference, venues visited during the last month and sampling venue.</p> <p>Results</p> <p>Respondents who used illegal drugs were 2.5 times more likely to report that they prefer dance music. Goa party visitors were nearly 5 times more likely to use illegal drugs. For those who reported visiting clubs, the odds of using illegal drugs were nearly 2 times higher. Having gone to a pub in the last month was associated with both more frequent alcohol use and more frequent illegal substance use. People who reported liking rock music and attendees of rock festivals used drugs less frequently.</p> <p>Conclusions</p> <p>It was concluded that a more extended recreational environment, beyond dance clubs, is associated with frequent drug use. This stresses the importance of targeted prevention in various recreational venues tailored to the specific needs of the setting and its visitors.</p

Drug use and nightlife: more than just dance music

<p>Abstract</p> <p>Background</p> <p>Research over the last decade has focused almost exclusively on the association between electronic music and MDMA (3,4-Methylenedioxymethamphetamine or "ecstasy") or other stimulant drug use in clubs. Less attention has been given to other nightlife venues and music preferences, such as rock music or southern/funky music. This study aims to examine a broader spectrum of nightlife, beyond dance music. It looks at whether certain factors influence the frequency of illegal drug and alcohol use: the frequency of going to certain nightlife venues in the previous month (such as, pubs, clubs or goa parties); listening to rock music, dance music or southern and funky music; or sampling venues (such as, clubs, dance events or rock festivals). The question of how these nightlife variables influence the use of popular drugs like alcohol, MDMA, cannabis, cocaine and amphetamines is addressed.</p> <p>Methods</p> <p>The study sample consisted of 775 visitors of dance events, clubs and rock festivals in Belgium. Study participants answered a survey on patterns of going out, music preferences and drug use. Odds ratios were used to determine whether the odds of being an illegal substance user are higher for certain nightlife-related variables. Furthermore, five separate ordinal regression analyses were used to investigate drug use in relation to music preference, venues visited during the last month and sampling venue.</p> <p>Results</p> <p>Respondents who used illegal drugs were 2.5 times more likely to report that they prefer dance music. Goa party visitors were nearly 5 times more likely to use illegal drugs. For those who reported visiting clubs, the odds of using illegal drugs were nearly 2 times higher. Having gone to a pub in the last month was associated with both more frequent alcohol use and more frequent illegal substance use. People who reported liking rock music and attendees of rock festivals used drugs less frequently.</p> <p>Conclusions</p> <p>It was concluded that a more extended recreational environment, beyond dance clubs, is associated with frequent drug use. This stresses the importance of targeted prevention in various recreational venues tailored to the specific needs of the setting and its visitors.</p

Translating Safe Petri Nets to Statecharts in a Structure-Preserving Way

Abstract. Statecharts and Petri nets are two popular visual formalisms for modelling complex systems that exhibit concurrency. Both formalisms are supported by various design tools. To enable the automated exchange of models between Petri net and statechart tools, we present a structural, polynomial algorithm that translates safe Petri nets into statecharts. The translation algorithm preserves both the structure and the behaviour of the input net. The algorithm can fail, since not every safe net has a statechart translation that preserves both its structure and behaviour. The algorithm is proven correct and the class of safe nets for which the algorithm succeeds is formally characterised. We show that the algorithm can also fail for some nets that do have a structure- and behaviour-preserving statechart translation, but this incompleteness does not appear to be a severe limitation in practice.