MultiChannelStory: un modello per l’utilizzo della narrativa interattiva a fini didattici, con la televisione digitale

Il tema dell’utilizzo di tecnologie innovative nel campo della didattica, catalizza da tempo l’attenzione della comunità scientifica. L’obiettivo è sinteticamente quello di offrire ai docenti nuovi strumenti atti a potenziare l’apprendimento degli studenti attraverso l’uso delle tecnologie. Si tratta quindi di progettare e realizzare soluzioni innovative che catturino l’interesse e la partecipazione di studenti e docenti. In questo contesto si è recentemente introdotto un nuovo elemento: la televisione digitale interattiva. La crescita e la diffusione di questo nuovo media ha condotto a nuove sfide, come quella di esplorare nuovi scenari d’utilizzo del mezzo televisivo. La narrativa a fini didattici può facilmente sfruttare quest’opportunità, proprio perché l’interattività offre agli spettatori la possibilità di cambiare la trama e, allo stesso tempo, all’autore quella di proporre più punti di vista della stessa storia. In quest’articolo è presentato un modello per l’utilizzo della narrativa interattiva a fini didattici sviluppata per la televisione digitale su piattaforma DVB-MHP [DVB, 2003].The growth of digital television has driven new challenges for broadcaster, content producers and software developers; interactivity, in particular, represents a clear shift in the paradigm of television applications. The art of interactive narrative can easily take this opportunity because interactivity allows viewers to change the plot and, at the same time, allows authors to present multiple perspectives of the story. The combination of interactive narrative with iTV could represent a further chance in the transition process from analogue to digital TV, where one of the critical needs is the development and delivering of new services and applications, able to attract new viewers. In this paper we propose a viable approach to develop interactive narrative with iTV technology. We present an idea to deliver television events, usually dramas or cartoons, having multiple and selectable plots, using the DVB-MHP platform.249-25

Quantum paradigms in psychopathology: multiscale investigations from biomolecular qubits to the brain, and its pathological states

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Designing peer-to-peer systems for business-to-business environments

Conference held in Firenze, Italy, 30 November -2 December 2005This paper describes the design of a peer-to-peer system integrated in a larger framework for the automatic content production, formatting, distribution and delivery over multiple platforms called AXMEDIS (E.U. IST-2-511299). One of the goals of the project is the reduction of costs and, among the others, the adoption of a collaborative environment based on a virtual database as an abstraction of a multitude of objects shared in a large network of content producers/distributors/aggregators. The peculiar properties of this system are the automation of P2P related operations, the professional query user interface based on Dublin Core and available rights of target objects, and the preemptive exclusion of uncertified participants.219-22

Playing by the rules: co-designing interactive installations with pupils

During the last couple of decades our perception of what constitutes a good learning environment has changed. Thanks to the use of technology, education is evolving from a passive model towards a more productive model, where students generate knowledge, teach each other, and collaborate on activities that make learning fun and interesting. In some previous works we have adopted this attitude: creating interactive installations thought for learning in an amusing way. Design-based research has demonstrated its potential as a methodology suitable to both research and design of technology-enhanced learning environments, a further step consists in co-design: students directly involved in designing with researchers. This paper provides some comments on the evaluation of the learning experience using two interactive installations promoting eco-friendly behaviours, and describe our experience in codesigning with pupils. We also report the ethnographic research performed underlining the weaknesses and the strengths, the difficulties and findings during the whole work

Longitudinal assessment of brain-derived neurotrophic factor in Sardinian psychotic patients (LABSP): a protocol for a prospective observational study

Brain-derived neurotrophic factor (BDNF) plays a crucial role in neurodevelopment, synaptic plasticity and neuronal function and survival. Serum and plasma BDNF levels are moderately, but consistently, decreased in patients with schizophrenia (SCZ) compared with healthy controls. There is a lack of knowledge, however, on the temporal manifestation of this decline. Clinical, illness course and treatment factors might influence the variation of BDNF serum levels in patients with psychosis. In this context, we propose a longitudinal study of a cohort of SCZ and schizophrenic and schizoaffective disorder (SAD) Sardinian patients with the aim of disentangling the relationship between peripheral BDNF serum levels and changes of psychopathology, cognition and drug treatments

DART: the distributed agent based retrieval toolkit

The technology of search engines is evolving from indexing and classification of web resources based on keywords to more sophisticated techniques which take into account the meaning and the context of textual information and usage. Replying to query, commercial search engines face the user requests with a large amount of results, mostly useless or only partially related to the request; the subsequent refinement, operated downloading and examining as much pages as possible and simply ignoring whatever stays behind the first few pages, is left up to the user. Furthermore, architectures based on centralized indexes, allow commercial search engines to control the advertisement of online information, in contrast to P2P architectures that focus the attention on user requirements involving the end user in search engine maintenance and operation. To address such wishes, new search engines should focus on three key aspects: semantics, geo-referencing, collaboration/distribution. Semantic analysis lets to increase the results relevance. The geo-referencing of catalogued resources allows contextualisation based on user position. Collaboration distributes storage, processing, and trust on a world-wide network of nodes running on users’ computers, getting rid of bottlenecks and central points of failures. In this paper, we describe the studies, the concepts and the solutions developed in the DART project to introduce these three key features in a novel search engine architecture

A collaborative, semantic and context-aware search engine

Search engines help people to find information in the largest public knowledge system of the world: the Web. Unfortunately its size makes very complex to discover the right information. The users are faced lots of useless results forcing them to select one by one the most suitable. The new generation of search engines evolve from keyword-based indexing and classification to more sophisticated techniques considering the meaning, the context and the usage of information. We argue about the three key aspects: collaboration, geo-referencing and semantics. Collaboration distributes storage, processing and trust on a world-wide network of nodes running on users’ computers, getting rid of bottlenecks and central points of failures. The geo-referencing of catalogued resources allows contextualisation based on user position. Semantic analysis lets to increase the results relevance. In this paper, we expose the studies, the concepts and the solutions of a research project to introduce these three key features in a novel search engine architecture.213-21

A Machine Learning Approach for Mortality Prediction in COVID-19 Pneumonia: Development and Evaluation of the Piacenza Score

Background: Several models have been developed to predict mortality in patients with COVID-19 pneumonia, but only a few have demonstrated enough discriminatory capacity. Machine learning algorithms represent a novel approach for the data-driven prediction of clinical outcomes with advantages over statistical modeling.Objective: We aimed to develop a machine learning-based score-the Piacenza score-for 30-day mortality prediction in patients with COVID-19 pneumonia.Methods: The study comprised 852 patients with COVID-19 pneumonia, admitted to the Guglielmo da Saliceto Hospital in Italy from February to November 2020. Patients' medical history, demographics, and clinical data were collected using an electronic health record. The overall patient data set was randomly split into derivation and test cohorts. The score was obtained through the naive Bayes classifier and externally validated on 86 patients admitted to Centro Cardiologico Monzino (Italy) in February 2020. Using a forward-search algorithm, 6 features were identified: age, mean corpuscular hemoglobin concentration, PaO2/FiO(2) ratio, temperature, previous stroke, and gender. The Brier index was used to evaluate the ability of the machine learning model to stratify and predict the observed outcomes. A user-friendly website was designed and developed to enable fast and easy use of the tool by physicians. Regarding the customization properties of the Piacenza score, we added a tailored version of the algorithm to the website, which enables an optimized computation of the mortality risk score for a patient when some of the variables used by the Piacenza score are not available. In this case, the naive Bayes classifier is retrained over the same derivation cohort but using a different set of patient characteristics. We also compared the Piacenza score with the 4C score and with a naive Bayes algorithm with 14 features chosen a priori.Results: The Piacenza score exhibited an area under the receiver operating characteristic curve (AUC) of 0.78 (95% CI 0.74-0.84, Brier score=0.19) in the internal validation cohort and 0.79 (95% CI 0.68-0.89, Brier score=0.16) in the external validation cohort, showing a comparable accuracy with respect to the 4C score and to the naive Bayes model with a priori chosen features; this achieved an AUC of 0.78 (95% CI 0.73-0.83, Brier score=0.26) and 0.80 (95% CI 0.75-0.86, Brier score=0.17), respectively.Conclusions: Our findings demonstrated that a customizable machine learning-based score with a purely data-driven selection of features is feasible and effective for the prediction of mortality among patients with COVID-19 pneumonia

Cardiovascular Risk Prediction in Ankylosing Spondylitis: From Traditional Scores to Machine Learning Assessment

Abstract Introduction The performance of seven cardiovascular (CV) risk algorithms is evaluated in a multicentric cohort of ankylosing spondylitis (AS) patients. Performance and calibration of traditional CV predictors have been compared with the novel paradigm of machine learning (ML). Methods A retrospective analysis of prospectively collected data from an AS cohort has been performed. The primary outcome was the first CV event. The discriminatory ability of the algorithms was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), which is like the concordance-statistic (c-statistic). Three ML techniques were considered to calculate the CV risk: support vector machine (SVM), random forest (RF), and k-nearest neighbor (KNN). Results Of 133 AS patients enrolled, 18 had a CV event. c-statistic scores of 0.71, 0.61, 0.66, 0.68, 0.66, 0.72, and 0.67 were found, respectively, for SCORE, CUORE, FRS, QRISK2, QRISK3, RRS, and ASSIGN. AUC values for the ML algorithms were: 0.70 for SVM, 0.73 for RF, and 0.64 for KNN. Feature analysis showed that C-reactive protein (CRP) has the highest importance, while SBP and hypertension treatment have lower importance. Conclusions All of the evaluated CV risk algorithms exhibit a poor discriminative ability, except for RRS and SCORE, which showed a fair performance. For the first time, we demonstrated that AS patients do not show the traditional ones used by CV scores and that the most important variable is CRP. The present study contributes to a deeper understanding of CV risk in AS, allowing the development of innovative CV risk patient-specific models