Toward Semantics-aware Representation of Digital Business Processes

An extended enterprise (EE) can be described by a set of models each representing a specific aspect of the EE. Aspects can for example be the process flow or the value description. However, different models are done by different people, which may use different terminology, which prevents relating the models. Therefore, we propose a framework consisting of process flow and value aspects and in addition a static domain model with structural and relational components. Further, we outline the usage of the static domain model to enable relating the different aspects

Vascular dysfunction and fibrosis in stroke-prone spontaneously hypertensive rats: the aldosterone-mineralocorticoid receptor-Nox1 Axis

Aims: We questioned whether aldosterone and oxidative stress play a role in vascular damage in severe hypertension and investigated the role of Nox1 in this process. Materials and methods: We studied mesenteric arteries, aortas and vascular smooth muscle cells (VSMC) from WKY and SHRSP rats. Vascular effects of eplerenone or canrenoic acid (CA) (mineralocorticoid receptor (MR) blockers), ML171 (Nox1 inhibitor) and EHT1864 (Rac1/2 inhibitor) were assessed. Nox1-knockout mice were also studied. Vessels and VSMCs were probed for Noxs, reactive oxygen species (ROS) and pro-fibrotic/inflammatory signaling. Key findings: Blood pressure and plasma levels of aldosterone and galectin-3 were increased in SHRSP versus WKY. Acetylcholine-induced vasorelaxation was decreased (61% vs 115%) and phenylephrine-induced contraction increased in SHRSP versus WKY (Emax 132.8% vs 96.9%, p < 0.05). Eplerenone, ML171 and EHT1864 attenuated hypercontractility in SHRSP. Vascular expression of collagen, fibronectin, TGFβ, MCP-1, RANTES, MMP2, MMP9 and p66Shc was increased in SHRSP versus WKY. These changes were associated with increased ROS generation, 3-nitrotyrosine expression and Nox1 upregulation. Activation of vascular p66Shc and increased expression of Nox1 and collagen I were prevented by CA in SHRSP. Nox1 expression was increased in aldosterone-stimulated WKY VSMCs, an effect that was amplified in SHRSP VSMCs (5.2vs9.9 fold-increase). ML171 prevented aldosterone-induced VSMC Nox1-ROS production. Aldosterone increased vascular expression of fibronectin and PAI-1 in wild-type mice but not in Nox1-knockout mice. Significance: Our findings suggest that aldosterone, which is increased in SHRSP, induces vascular damage through MR-Nox1-p66Shc-mediated processes that modulate pro-fibrotic and pro-inflammatory signaling pathways

Determinants of invasiveness and ability to cause invasive pneumococcal disease, pneumonia and acute otitis media of different serotypes of Streptococcus pneumoniae

No abstrac

Archivi digitali e valutazione della produzione scientifica

Negli ultimi anni lo sviluppo del World Wide Web ha sostenuto la diffusione di archivi digitali, accessibili via Internet, per materiale bibliografico di vario tipo ma in particolare per i prodotti della ricerca scientifica. Nell\u2019ambito della produzione scientifica la pubblicazione online garantisce un\u2019immediata riduzione dei costi e dei tempi di pubblicazione, nonch\ue9 dei tempi di accesso e interrogazione degli archivi. Inoltre gli archivi bibliografici online, facilitando l\u2019accesso alle risorse, producono un impatto positivo sul numero di citazioni che una pubblicazione pu\uf2 ottenere; come \ue8 stato dimostrato anche empiricamente da alcuni studi [10]. Tuttavia, a ben vedere, questi elementi non esauriscono affatto le innovazioni ai processi di fruizione dei prodotti della ricerca scientifica che l\u2019adozione di archivi digitali pu\uf2 determinare. Una revisione delle applicazioni del diritto d\u2019autore \ue8 ad esempio auspicata dal movimento Open Access [11]. Secondo questo punto di vista l\u2019accesso alle pubblicazioni scientifiche va sempre incentivato in quanto ne beneficiano sia l\u2019autore che la societ\ue0 in generale. Di conseguenza, \ue8 sbagliato usare il diritto d\u2019autore come strumento per limitare l\u2019accesso ed andrebbe invece utilizzato per garantire l\u2019accessibilit\ue0 delle opere. Per realizzare al meglio le potenzialit\ue0 degli archivi digitali andrebbe quindi ripensato l\u2019intero processo editoriale, derivando profitti non dalla vendita delle opere quanto dall\u2019offerta di spazio agli autori. Ma un altro valore molto significativo che pu\uf2 essere tratto dall\u2019adozione di archivi digitali \ue8 relativo al processo di valutazione dell\u2019impatto della produzione scientifica. Ne sottolinea peraltro l\u2019importanza anche un recente documento della divisione di ricerca della comunit\ue0 europea intitolato \u201cAssessing Europe\u2019s University based Research\u201d [9]. La valutazione della ricerca \ue8 un processo che coinvolge un insieme articolato di attori, quali i centri di ricerca, le universit\ue0, i governi, le imprese. Gli strumenti di valutazione possono essere di varia natura e riguardare aspetti diversi delle attivit\ue0 di ricerca. Tipicamente un giudizio di valutazione \ue8 composto da indicatori che insistono su aspetti differenti. Tuttavia, soprattutto nel settore pubblico, assume particolare rilevanza la valutazione dell\u2019impatto delle pubblicazioni scientifiche prodotte. I temi aperti in questo ambito sono molti. Le iniziative si moltiplicano. L\u2019obiettivo \ue8 quello di proporre processi e strumenti in grado di garantire la pi\uf9 elevata correttezza dei dati raccolti e di certificare le informazioni in modo che possano essere utilizzate per procedure di valutazione affidabili. In questo articolo saranno discusse alcune innovazioni che potrebbero essere introdotte negli archivi digitali per migliorare la correttezza dei dati e garantire un processo di certificazione della loro effettivit\ue0. Dall\u2019analisi emergono alcune linee guida per la realizzazione di un prototipo innovativo che il progetto EPICA, interamente finanziato con i fondi 5 per mille dell\u2019Universit\ue0 degli Studi di Milano, sta implementando, con l\u2019obiettivo di illustrare un tipo di infrastruttura in grado di adattarsi ai processi di interazione attualmente in uso

Testing social network metrics for measuring electoral success in the italian municipal campaign of 2011

It is often argued that the bias hidden in Social Media data prevent from using them for any statistical inference. In this paper, we investigate the practicability of a new method for predicting electoral outcomes that is less affected by demographics and self-selection bias. In particular, we put in place a first test to understand which social network analysis metrics can exhibit positive correlation with electoral success. Our analysis is not intended to use social media audience as a sample of the whole electorate but just as a sample of the supporters of a candidate. In conclusion, we speculate on the information we can extract measuring the social network of the groups of supporters. Essentially, we get an overview on the variety and extent of the segments of the population represented in these groups, and this probably correlates with the capacity to attract consensus

Building typological classification in Switzerland using deep learning methods for seismic assessment

Natural disasters, such as earthquakes, have always represented a danger to human life. Seismic risk assessment consists of the evaluation of existing buildings and their expected response in case of an earthquake; the exposure model of buildings plays a key role in risk calculations. With this respect, in recent years, advanced techniques have been developed to speed up and automatize the processes of data acquisition to data interpretation, although it is worth mentioning that the visual survey is essential to train and validate Machine Learning (ML) methods. In the present study, the identification of building types is conducted by exploiting the traditional visual survey to implement a Deep Learning (DL) classification model. As a first step, city mapping schemes are obtained by classifying buildings according to the main features (i.e., construction period and height classes). Then, Random Forest (RF), a supervised learning algorithm, is applied to classify different building types by exploiting all their attributes. The RF model is trained and tested on the cities of Neuchatel and Yverdon-Les-Bains. The decent accuracy of the results encourages the application of the method to different cities, with proper adjustments in datasets, features and algorithms

Design principles for competence-based recommender systems

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Big data analytics as-a-service: Issues and challenges

Big Data domain is one of the most promising ICT sectors with substantial expectations both on the side of market growing and design shift in the area of data storage managment and analytics. However, today, the level of complexity achieved and the lack of standardisation of Big Data management architectures represent a huge barrier towards the adoption and execution of analytics especially for those organizations and SMEs not including a sufficient amount of competences and knowledge. The full potential of Big Data Analytics (BDA) can be unleashed only through the definition of approaches that accomplish Big Data users' expectations and requirements, also when the latter are fuzzy and ambiguous. Under these premises, we propose Big Data Analytics-as-a-Service (BDAaaS) as the next-generation Big Data Analytics paradigm and we discuss issues and challenges from the BDAaaS design and development perspective

Trustworthiness-related uncertainty of semantic web-style metadata : a possibilistic approach

We discuss the specific type of uncertainty deriving from the non-uniform trustworthiness of Semantic Web style metadata sources, arguing toward the feasibility of modal possibilistic reasoning based on trust assertions expressing such uncertainty

Knowledge Driven Behavioural Analysis in Process Intelligence

InthispaperweillustratehowtheknowledgedrivenBehaviourAnal- ysis, which has been used in the KITE.it process management framework, can support the evolution of analytics from descriptive to predictive. We describe how the methodology uses an iterative three-step process: first the descriptive knowledge is collected, querying the knowledge base, then the prescriptive and predictive knowledge phases allow us to evaluate business rules and objectives, extract unexpected business patterns, and screen exceptions. The procedure is iterative since this novel knowledge drives the definition of new descriptive an- alytics that can be combined with business rules and objectives to increase our level of knowledge on the combination between process behaviour and contex- tual information