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Questo articolo si concentra sugli effetti prodotti dalla pandemia/ sindemia sul setting dei gruppi psicoterapeutici. Dopo avere descritto il concetto di gruppo e di gruppalita' intesa come stato di coscienza modificato e come vissuto intenzionale e non intenzionale, si sofferma sulle condizioni di passaggio dalla situazione di aggruppamento in cui la gruppalita non è cosciente di se, al gruppo in cui la gruppalita' prende coscienza. In oltre si mostrano le variabili indipendenti che favoriscono questo passaggio cioè il compito, lo spazio, il tempo e i ruoli/ funzioni. In conclusione vengono descritti gli effetti di cambiamento del setting dovuti alla pandemia, soprattutto in relazione allo spazio, in tre gruppi. Il primo si svolge nel Centro Osservazione e Diagnosi della comunità di Vallecchio, in provincia di Rimini. Il secondo è un gruppo di apprendimento. Il terzo e' un gruppo di psicoterapia.

Dependability and performance analysis of modern systems is facing great challenges: their scale is growing, they are becoming massively distributed, interconnected, and evolving. Such complexity makes model-based assessment a difficult and time-consuming task. For the evaluation of large systems, reusable submodels are typically adopted as an effective way to address the complexity and to improve the maintainability of models. When using state-based models, a common approach is to define libraries of generic submodels, and then compose concrete instances by state sharing, following predefined “patterns” that depend on the class of systems being modeled. However, such composition patterns are rarely formalized, or not even documented at all. In this paper, we address this problem using a model-driven approach, which combines a language to specify reusable submodels and composition patterns, and an automated composition algorithm. Clearly defining libraries of reusable submodels, together with patterns for their composition, allows complex models to be automatically assembled, based on a high-level description of the scenario to be evaluated. This paper provides a solution to this problem focusing on: formally defining the concept of model templates, defining a specification language for model templates, defining an automated instantiation and composition algorithm, and applying the approach to a case study of a large-scale distributed system.

To contribute to multiconcern assurance, we focus on system design and present a high-level process that builds on top of the synergy between qualitative and quantitative dependability analysis techniques, which have been used for mono- as well as multiconcern analysis.

Multi-parameter monitoring systems in Intensive Care Units (ICUs) monitor the clinical condition of critical state patients. These Systems of Systems (SoS) comprise a set of Constituent Systems (CS) to measure parameters such as heart rate, respiratory frequency, and temperature. Due to the critical nature and relevance of ICUs, such SoS shall be as reliable as possible. That is especially true in emergencies, as the COVID-19 outbreak that resulted in the burden of health care systems. We developed a modular and parametric model to perform reliability analysis and to provide insights to assist the management of multi-parameter monitoring systems used in ICUs, also considering maintenance. First, we modeled a multi-parameter monitoring system for ICUs using the CHESS methodology and modeling language. Afterward, we performed a reliability analysis using the CHESS state-based analysis plugin for different scenarios. We identified that the main power supply and the battery are the CS that present the most negative impacts on reliability. In emergencies, reduced time ranges of planned maintenance, when applied during a short period, showed to be promising strategies.

Model-based evaluation is extensively used to estimate performance and reliability of dependable systems. Traditionally, those systems were small and self-contained, and the main challenge for model-based evaluation has been the efficiency of the solution process. Recently, the problem of specifying and maintaining complex models has increasingly gained attention, as modern systems are characterized by many components and complex interactions. Components share similarities, but also exhibit variations in their behavior due to different configurations or roles in the system. From the modeling perspective, variations lead to replicating and altering a small set of base models multiple times. Variability is taken into account only informally, by defining a sample model and explaining its possible variations. In this paper we address the problem of including variability in performability models, focusing on Stochastic Activity Networks (SANs). We introduce the formal definition of Stochastic Activity Networks Templates (SAN-T), a formalism based on SANs with the addition of variability aspects. Differently from other approaches, parameters can also affect the structure of the model, like the number of cases of activities. We apply the SAN-T formalism to the modeling of the backbone network of an environmental monitoring infrastructure. In particular, we show how existing SAN models from the literature can be generalized using the newly introduced formalism.

Knowledge-intensive Processes (KiPs) are processes characterized by high levels of unpredictability and dynamism. Their process structure may not be known before their execution. One way to cope with this uncertainty is to defer decisions regarding the process structure until run time. In this paper, we consider the definition of the process structure as a planning problem. Our approach uses automated planning techniques to generate plans that define process models according to the current context. The generated plan model relies on a metamodel called METAKIP that represents the basic elements of KiPs. Our solution explores Markov Decision Processes (MDP) to generate plan models. This technique allows uncertainty representation by defining state transition probabilities, which gives us more flexibility than traditional approaches. We construct an MDP model and solve it with the help of the PRISM model-checker. The solution is evaluated by means of a proof of concept in the medical domain which reveals the feasibility of our approach.