Modelling and Validating a multiple-configuration railway signalling system using SDL

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ERMHAN: A Context-Aware Service Platform to Support Continuous Care Networks for Home-Based Assistance

Continuous care models for chronic diseases pose several technology-oriented challenges for home-based continuous care, where assistance services rely on a close collaboration among different stakeholders such as health operators, patient relatives, and social community members. Here we describe Emilia Romagna Mobile Health Assistance Network (ERMHAN) a multichannel context-aware service platform designed to support care networks in cooperating and sharing information with the goal of improving patient quality of life. In order to meet extensibility and flexibility requirements, this platform has been developed through ontology-based context-aware computing and a service oriented approach. We also provide some preliminary results of performance analysis and user survey activity

Natural Language Processing of Patents and Technical Documentation

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A Content Analysis Technique for Inconsistency Detection in Software Requirements Documents

Abstract. This paper presents J-RAn (Java Requirement Analyzer), a tool that implements a novel Content Analysis technique to support the verification of consistency and completeness of a Software Requirement Specification. This technique exploits the extraction, from a requirement document, of the interactions between the entities described in the document as Subject-Action-Object (SAO) triples (obtainable using a suitable syntactic parser). SAO triples represent a concept in its most synthesizing form. Analyzing the distribution of such concepts in the requirement document helps to locate possible sources of inconsistency and incompleteness.

Tocilizumab for patients with COVID-19 pneumonia. The single-arm TOCIVID-19 prospective trial

BackgroundTocilizumab blocks pro-inflammatory activity of interleukin-6 (IL-6), involved in pathogenesis of pneumonia the most frequent cause of death in COVID-19 patients.MethodsA multicenter, single-arm, hypothesis-driven trial was planned, according to a phase 2 design, to study the effect of tocilizumab on lethality rates at 14 and 30 days (co-primary endpoints, a priori expected rates being 20 and 35%, respectively). A further prospective cohort of patients, consecutively enrolled after the first cohort was accomplished, was used as a secondary validation dataset. The two cohorts were evaluated jointly in an exploratory multivariable logistic regression model to assess prognostic variables on survival.ResultsIn the primary intention-to-treat (ITT) phase 2 population, 180/301 (59.8%) subjects received tocilizumab, and 67 deaths were observed overall. Lethality rates were equal to 18.4% (97.5% CI: 13.6-24.0, P=0.52) and 22.4% (97.5% CI: 17.2-28.3, P<0.001) at 14 and 30 days, respectively. Lethality rates were lower in the validation dataset, that included 920 patients. No signal of specific drug toxicity was reported. In the exploratory multivariable logistic regression analysis, older age and lower PaO2/FiO2 ratio negatively affected survival, while the concurrent use of steroids was associated with greater survival. A statistically significant interaction was found between tocilizumab and respiratory support, suggesting that tocilizumab might be more effective in patients not requiring mechanical respiratory support at baseline.ConclusionsTocilizumab reduced lethality rate at 30 days compared with null hypothesis, without significant toxicity. Possibly, this effect could be limited to patients not requiring mechanical respiratory support at baseline.Registration EudraCT (2020-001110-38); clinicaltrials.gov (NCT04317092)