psort

psort \ue8 stato il pi\uf9 veloce software di ordinamento per macchine di classe PC dal 2008 al 2011 (benchmark Pennysort, http://sortbenchmark.org) e un suo adattamento per cluster ha migliorato il record per il benchmark datamation di quasi un ordine di grandezza nel 2011. Il rapporto tecnico ufficiale si trova sul sito sortbenchmark.org (che cataloga i pi\uf9 efficienti software di ordinamento per varie categorie di task/hardware - originariamente mantenuto dal premio Turing Jim Gray) all'URL http://sortbenchmark.org/psort_2011.pdf -- Ulteriori dettagli si possono trovare nelle pubblicazioni: P. Bertasi, M. Bressan, E. Peserico. psort, yet another fast stable sorting software, ACM Journal of Experimental Algorithmics, vol. 16, 2011 -- P. Bertasi, M. Bonazza, M. Bressan, E. Peserico. Datamation: a quarter of a century and four orders of magnitude later. Proc. of IEEE CLUSTER 201

Growth potential of Listeria monocytogenes in sliced turkey bresaola packed in modified atmosphere

According to EC Regulation No 2073/2005, for Food Business Operators (FBOs) that produce Ready To Eat (RTE) product, is crucial to be able to demonstrate if the product supports the growth of Listeria monocytogenes. The objective of the study was therefore to evaluate the behaviour of L. monocytogenes in sliced RTE turkey bresaola (made by cured turkey breast 4.5% NaCl, 1% sodium lactate, sodium nitrite 150 ppm and flavouring) during the shelf life of the product, simulating a contamination during the slicing operation. Considering a shelf life of 90 days, as defined by manufacturer, the packages of sliced bresaola were stored at 5\ub0C for 7 days and at 8\ub0C for the remaining storage time (83 days). L. monocytogenes count decreased during storage test from 1.43/1.98 log cfu/g in the three batches tested to 1.03 log cfu/g in one batch and to undetectable levels in the other two batches. The results show that the investigated product is unable to support the growth of L. monocytogenes

Quantitative risk assessment of human salmonellosis and listeriosis related to the consumption of raw milk in Italy.

Two quantitative risk assessment (RA) models were developed to describe the risk of salmonellosis and listeriosis linked to consumption of raw milk sold in vending machines in Italy. Exposure assessment considered the official microbiological records monitoring raw milk samples from vending machines performed by the regional veterinary authorities from 2008 to 2011, microbial growth during storage, destruction experiments, consumption frequency of raw milk, serving size, and consumption preference. Two separate RA models were developed: one for the consumption of boiled milk and the other for the consumption of raw milk. The RA models predicted no human listeriosis cases per year either in the best or worst storage conditions and with or without boiling raw milk, whereas the annual estimated cases of salmonellosis depend on the dose-response relationships used in the model, the milk storage conditions, and consumer behavior in relation to boiling raw milk or not. For example, the estimated salmonellosis cases ranged from no expected cases, assuming that the entire population boiled milk before consumption, to a maximum of 980,128 cases, assuming that the entire population drank raw milk without boiling, in the worst milk storage conditions, and with the lowest dose-response model. The findings of this study clearly show how consumer behavior could affect the probability and number of salmonellosis cases and in general, the risk of illness. Hence, the proposed RA models emphasize yet again that boiling milk before drinking is a simple yet effective tool to protect consumers against the risk of illness inherent in the consumption of raw milk. The models may also offer risk managers a useful tool to identify or implement appropriate measures to control the risk of acquiring foodborne pathogens. Quantification of the risks associated with raw milk consumption is necessary from a public health perspective

The rapid spread of SARS-COV-2 Omicron variant in Italy reflected early through wastewater surveillance

The SARS-CoV-2 Omicron variant emerged in South Africa in November 2021, and has later been identified worldwide, raising serious concerns. A real-time RT-PCR assay was designed for the rapid screening of the Omicron variant, targeting characteristic mutations of the spike gene. The assay was used to test 737 sewage samples collected throughout Italy (19/21 Regions) between 11 November and 25 December 2021, with the aim of assessing the spread of the Omicron variant in the country. Positive samples were also tested with a real-time RT-PCR developed by the European Commission, Joint Research Centre (JRC), and through nested RT-PCR followed by Sanger sequencing. Overall, 115 samples tested positive for Omicron SARS-CoV-2 variant. The first occurrence was detected on 7 December, in Veneto, North Italy. Later on, the variant spread extremely fast in three weeks, with prevalence of positive wastewater samples rising from 1.0% (1/104 samples) in the week 5-11 December, to 17.5% (25/143 samples) in the week 12-18, to 65.9% (89/135 samples) in the week 19-25, in line with the increase in cases of infection with the Omicron variant observed during December in Italy. Similarly, the number of Regions/Autonomous Provinces in which the variant was detected increased from one in the first week, to 11 in the second, and to 17 in the last one. The presence of the Omicron variant was confirmed by the JRC real-time RT-PCR in 79.1% (91/115) of the positive samples, and by Sanger sequencing in 66% (64/97) of PCR amplicons. In conclusion, we designed an RT-qPCR assay capable to detect the Omicron variant, which can be successfully used for the purpose of wastewater-based epidemiology. We also described the history of the introduction and diffusion of the Omicron variant in the Italian population and territory, confirming the effectiveness of sewage monitoring as a powerful surveillance tool

PariPari: Design and Implementation of a Resilient Multi-Purpose Peer-to-Peer Network

Recent years have seen a considerable and constant growth in peer-to-peer (P2P) traffic over Internet. Internet Service Providers (ISPs) and software houses have begun to change their attitude towards P2P applications, no longer seen as bandwidth-eager enemies, but rather as interesting profit opportunities. PariPari is a P2P platform under development at Department of Information Engineering Engineering of the University of Padova. It differs from traditional P2P applications like eMule, Skype or Azureus in that it provides a multifunctional, extensible platform on which multiple services - from filesharing to VoIP to mail/web/IRC services --- can run simultaneously and cooperatively. PariPari offers a collection of APIs allowing third party developers to write their own applications; but unlike similar P2P development frameworks such as JXTA, PariPari already works ``out of the box'' for the end user offering a large number of applications. The number and the heterogeneity of services offered by PariPari together with the possibility of extending this platform with future, not currently defined, applications offer a number of challenges: effective use of Java, coordination of multiple services, design of a powerful but easy to use GUI, efficient and robust algorithms for clock synchronization and search etc. Effective group management was the key to successful development of PariPari. Over the past three years more than one hundred students have cooperated developing PariPari. To coordinate such a large number of people we have adopted software engineering techniques such as eXtreme Programming and Test Driven Development. However, these paradigms had to be adapted to a group of non-professional, although motivated, developers. This coordination process was difficult, but extremely rewarding, and taught us a number of lessons about software engineering that might be useful in other software projects involving large numbers of relatively inexperienced, part-time developers with high turnover.Negli ultimi anni il traffico dovuto al peer-to-peer (P2P) è aumentato costantemente. Gli Internet Service Providers (ISPs) e le software house hanno iniziato a considerare le applicazioni P2P non come programmi avidi di banda ma come interessanti opportunità. PariPari è una piattaforma P2P in sviluppo al Dipartimento di Ingegneria dell'Informazione. È molto diversa da altre ben note applicazioni P2P come eMule, Skype o Azureus. dato che fornisce una piattaforma multifunzionale e estensibile sulla quale diversi servizi --- dal filesharing al VoIP all'email --- possono funzionare simultaneamente. Inoltre, PariPari fornisce un insieme di API utili agli sviluppatori terzi per scrivere le loro applicazioni, ma diversamente dagli altri framework P2P come JXTA, PariPari offre già da subito un gran numero di applicazioni fruibili dall'utente finale. PariPari offre, quindi, un gran numero di servizi eterogenei e la possibilità di estendere la piattaforma, in futuro, con applicazioni non ancora definite. Per produrre questi due risultati, la progettazione di PariPari ha dovuto affrontare diverse interessanti sfide tra cui un uso efficiente di Java, la possibilità di coordinare diversi servizi e la studio di nuovi algoritmi per la sincronizzazione e la ricerca. La chiave del successo dello sviluppo di PariPari è sicuramente la gestione del gruppo. Negli ultimi tre anni, più di un centinaio di studenti hanno lavorato allo sviluppo di PariPari. Per coordinare tanti contributi abbiamo adottato tecniche tipiche dell'ingegneria del software come l'eXtreme Programming e il Test Driven Development. Questi paradigmi, tuttavia, hanno subito pesanti modifiche per essere adattati al nostro gruppo di sviluppatori dalle peculiari caratteristiche: gli studenti, sebbene motivati, non hanno nè il rendimento nè la preparazione di un professionista. La gestione è stata complessa ma estremamente appagante ed ha prodotto molti interessanti spunti che possono essere studiati ed applicati ad altri progetti che coinvolgono molti sviluppatori non professionisti con alto turn-over

psort, yet another fast stable sorting software

psort was the fastest sorting software in 2008 according to the Pennysort benchmark, sorting 181GB of data for 0.01 of computer time. This paper details its internals, and the careful fitting of its architecture to the structure of modern PCs-class platforms, allowing it to outperform state-of-the-art sorting software such as GNUsort or STXXL

Psort, yet another fast stable sorting software

psort is the fastest sorting software according to the PennySort benchmark, sorting 181GB of data in 2008 and 224GB in 2009 for $0.01 of computer time. This article details its internals, and the careful fitting of its architecture to the structure of modern PC-class platforms, allowing it to outperform state-of-the-art sorting software such as STXXL sort. © 2011 ACM 1084-6654/2011/08-ART1.6

Obtaining Performance Measures through Microbenchmarking in a Peer-to-Peer Overlay Computer ∗

Abstract- The availability of enormous amounts of unused computing power and data storage over the Internet makes the development of a globally distributed computing platform, called Overlay Computer (OC), attractive for the industry, the scientific community, and also the general user. In order to be effective, an OC must be equipped with tools for estimating and optimizing the performance of applications. The tools must rely upon performance metrics of the key factors impacting performance of a distributed application, such as interconnection latency and bandwidth, and available computing power at the nodes. This paper first reviews the stateof-the-art on performance benchmarking approaches in globally distributed systems, including performanceconscious P2P systems. Later, it addresses the problem of developing a suite of microbenchmarking experiments aimed at providing the basic functionalities of a measurement tool for a P2P-based OC platform. Results show that such a measuring system should take into account the communication patterns generated by the applications in order to provide useful performance insights.