Incremental communication patterns in online social groups

In the last decades, temporal networks played a key role in modelling, understanding, and analysing the properties of dynamic systems where individuals and events vary in time. Of paramount importance is the representation and the analysis of Social Media, in particular Social Networks and Online Communities, through temporal networks, due to their intrinsic dynamism (social ties, online/offline status, users’ interactions, etc.). The identification of recurrent patterns in Online Communities, and in detail in Online Social Groups, is an important challenge which can reveal information concerning the structure of the social network, but also patterns of interactions, trending topics, and so on. Different works have already investigated the pattern detection in several scenarios by focusing mainly on identifying the occurrences of fixed and well known motifs (mostly, triads) or more flexible subgraphs. In this paper, we present the concept on the Incremental Communication Patterns, which is something in-between motifs, from which they inherit the meaningfulness of the identified structure, and subgraph, from which they inherit the possibility to be extended as needed. We formally define the Incremental Communication Patterns and exploit them to investigate the interaction patterns occurring in a real dataset consisting of 17 Online Social Groups taken from the list of Facebook groups. The results regarding our experimental analysis uncover interesting aspects of interactions patterns occurring in social groups and reveal that Incremental Communication Patterns are able to capture roles of the users within the groups

Thermal hydraulic analysis of Alfred bayonet tube steam generator

The paper analyzes the performance of ALFRED steam generator from the thermal-hydraulic point of view highlighting the effect of some design features. The parameters object of the study are the regenerative heat transfer, the dimension of the inner tube and the length of the bayonet. The system code RELAP5-3D/2.4.2 has been chosen for the analysis. Sensitivities analysis allowed the determination of the different design parameters influence, here briefly summarized. The increase of regenerative heat transfer affects the efficiency of the steam generator through a degradation of the outlet steam quality: the number of bayonet tubes required to remove the nominal power increases with the increase of the global heat transfer coefficient of the inner tube. A higher inner diameter results in a larger surface area for the regenerative heat transfer and in a higher heat transfer coefficient in the annular region because of the reduction of the cross section. The result is an improvement of the performances of the steam generator thanks to the dimension reduction of the annular gap. Finally, if the height of the bayonet tube is reduced by 1 meter, the number of bayonet tubes required to remove the nominal power increases up to 20%

Quality of Life in COVID-Related ARDS Patients One Year after Intensive Care Discharge (Odissea Study): A Multicenter Observational Study

Background: Investigating the health-related quality of life (HRQoL) after intensive care unit (ICU) discharge is necessary to identify possible modifiable risk factors. The primary aim of this study was to investigate the HRQoL in COVID-19 critically ill patients one year after ICU discharge. Methods: In this multicenter prospective observational study, COVID-19 patients admitted to nine ICUs from 1 March 2020 to 28 February 2021 in Italy were enrolled. One year after ICU discharge, patients were required to fill in short-form health survey 36 (SF-36) and impact of event-revised (IES-R) questionnaire. A multivariate linear or logistic regression analysis to search for factors associated with a lower HRQoL and post-traumatic stress disorded (PTSD) were carried out, respectively. Results: Among 1003 patients screened, 343 (median age 63 years [57–70]) were enrolled. Mechanical ventilation lasted for a median of 10 days [2–20]. Physical functioning (PF 85 [60–95]), physical role (PR 75 [0–100]), emotional role (RE 100 [33–100]), bodily pain (BP 77.5 [45–100]), social functioning (SF 75 [50–100]), general health (GH 55 [35–72]), vitality (VT 55 [40–70]), mental health (MH 68 [52–84]) and health change (HC 50 [25–75]) describe the SF-36 items. A median physical component summary (PCS) and mental component summary (MCS) scores were 45.9 (36.5–53.5) and 51.7 (48.8–54.3), respectively, considering 50 as the normal value of the healthy general population. In all, 109 patients (31.8%) tested positive for post-traumatic stress disorder, also reporting a significantly worse HRQoL in all SF-36 domains. The female gender, history of cardiovascular disease, liver disease and length of hospital stay negatively affected the HRQoL. Weight at follow-up was a risk factor for PTSD (OR 1.02, p = 0.03). Conclusions: The HRQoL in COVID-19 ARDS (C-ARDS) patients was reduced regarding the PCS, while the median MCS value was slightly above normal. Some risk factors for a lower HRQoL have been identified, the presence of PTSD is one of them. Further research is warranted to better identify the possible factors affecting the HRQoL in C-ARDS

Deep Underground Science and Engineering Laboratory - Preliminary Design Report

The DUSEL Project has produced the Preliminary Design of the Deep Underground Science and Engineering Laboratory (DUSEL) at the rehabilitated former Homestake mine in South Dakota. The Facility design calls for, on the surface, two new buildings - one a visitor and education center, the other an experiment assembly hall - and multiple repurposed existing buildings. To support underground research activities, the design includes two laboratory modules and additional spaces at a level 4,850 feet underground for physics, biology, engineering, and Earth science experiments. On the same level, the design includes a Department of Energy-shepherded Large Cavity supporting the Long Baseline Neutrino Experiment. At the 7,400-feet level, the design incorporates one laboratory module and additional spaces for physics and Earth science efforts. With input from some 25 science and engineering collaborations, the Project has designed critical experimental space and infrastructure needs, including space for a suite of multidisciplinary experiments in a laboratory whose projected life span is at least 30 years. From these experiments, a critical suite of experiments is outlined, whose construction will be funded along with the facility. The Facility design permits expansion and evolution, as may be driven by future science requirements, and enables participation by other agencies. The design leverages South Dakota's substantial investment in facility infrastructure, risk retirement, and operation of its Sanford Laboratory at Homestake. The Project is planning education and outreach programs, and has initiated efforts to establish regional partnerships with underserved populations - regional American Indian and rural populations

Exploiting community detection to recommend privacy policies in decentralized online social networks

The usage of Online Social Networks (OSNs) has become a daily activity for billions of people that share their contents and personal information with the other users. Regardless of the platform exploited to provide the OSNs\u2019 services, these contents\u2019 sharing could expose the OSNs\u2019 users to a number of privacy risks if proper privacy-preserving mechanisms are not provided. Indeed, users must be able to define its own privacy policies that are exploited by the OSN to regulate access to the shared contents. To reduce such users\u2019 privacy risks, we propose a Privacy Policies Recommended System (PPRS) that assists the users in defining their own privacy policies. Besides suggesting the most appropriate privacy policies to end users, the proposed system is able to exploits a certain set of properties (or attributes) of the users to define permissions on the shared contents. The evaluation results based on real OSN dataset show that our approach classifies users with a higher accuracy by recommending specific privacy policies for different communities of the users\u2019 friends

Community evaluation in Facebook groups

One of the main points of the Next Generation Internet is to have a user-centric approach where daily behavior and social life of the users are studied and analyzed in order to model networks and services. Indeed, social life represents the general overview of the behaviour of people, because it can provide information about hobby, relationships, but also similarity, etc. Today, the main channels to study the behaviour of people are Social Media. A great trend in current Social Media platforms is to offer the opportunity to establish and join groups of people, which represents one of the main characteristics of offline social network, where people are clustering, usually based on their interest (work, family, etc.). Despite human behaviour in current Online Social Media have been studied in depth, characteristics of online content-based social groups are still unknown. In this paper, we investigate whether communities can be recognized also in groups defined by users of Social Media platforms and we study how these communities evolve over time. For this purpose, we exploited a real Facebook dataset which consists of 18 Facebook groups of different categories and 3 different community detection algorithms. Our results provide important insights about the behaviour of users in the context of social groups and reveal that the majority of the groups present interactions-based communities, and in particular there is one massive core community which attracts other users and communities

Exploiting homophily to characterize communities in online social networks

Online social networks (OSNs) have become one of the most popular platforms where people communicate by sharing contents and personal information. The interactions performed by the users allow to identify the homophily between users and reveal the presence of several communities that could depend on several factors: such as the type of relationships (eg, colleagues and school mates) or to users' preferences (eg, users' interests or hobbies). A very important issue in this scenario is the necessary to characterize such communities by using known real properties or attributes about their members. In this article, we propose an approach that identifies the communities of users by exploiting several community detection algorithms. Afterward, for each user, we exploit decision trees to find a model that describes and distinguishes community affiliations based on known attributes of the members. The evaluation of our approach is derived from a real dataset which consists of the profile information, relationships, and interactions of 95 716 Facebook users. The experimental results show that the proposed approach is able to correctly recognize which attributes of the members properly characterize their corresponding community while ensuring a high level of accuracy (about 85%)