Introducing innovation in social networks: A cost-benefit analysis of entry point selection

Social networks have been growing and evolving from mere means of communication into the biggest potential global market and access platform to hundreds of millions of customers ever built. However, although companies and organisations can have access to millions of potential customers almost in an instant, being able to identify the best initial entry points for introducing innovation (be it a service or product) is key to aiding its acceptance and enhancing its prospects of further diffusion into the market. In this paper, by using the economic model of return to scale, we investigate a mechanism for identifying these potential best initial entry points for introducing innovation in social networks in terms of its efficiency and a cost-benefits analysis. We present a set of experiments based on two real social network datasets and also a synthetic one that shows the effects of deploying our mechanism

A review of the role of sensors in mobile context-aware recommendation systems

Recommendation systems are specialized in offering suggestions about specific items of different types (e.g., books, movies, restaurants, and hotels) that could be interesting for the user. They have attracted considerable research attention due to their benefits and also their commercial interest. Particularly, in recent years, the concept of context-aware recommendation system has appeared to emphasize the importance of considering the context of the situations in which the user is involved in order to provide more accurate recommendations. The detection of the context requires the use of sensors of different types, which measure different context variables. Despite the relevant role played by sensors in the development of context-aware recommendation systems, sensors and recommendation approaches are two fields usually studied independently. In this paper, we provide a survey on the use of sensors for recommendation systems. Our contribution can be seen from a double perspective. On the one hand, we overview existing techniques used to detect context factors that could be relevant for recommendation. On the other hand, we illustrate the interest of sensors by considering different recommendation use cases and scenarios

Characterization of Adherent-Invasive Escherichia coli (AIEC) Outer Membrane Proteins Provides Potential Molecular Markers to Screen Putative AIEC Strains

Adherent-invasive E. coli (AIEC) is a pathotype associated with the etiopathogenesis of Crohn's disease (CD), albeit with an as-yet unclear role. The main pathogenic mechanisms described for AIEC are adherence to epithelial cells, invasion of epithelial cells, and survival and replication within macrophages. A few virulence factors have been described as participating directly in these phenotypes, most of which have been evaluated only in AIEC reference strains. To date, no molecular markers have been identified that can differentiate AIEC from other E. coli pathotypes, so these strains are currently identified based on the phenotypic characterization of their pathogenic mechanisms. The identification of putative AIEC molecular markers could be beneficial not only from the diagnostic point of view but could also help in better understanding the determinants of AIEC pathogenicity. The objective of this study was to identify molecular markers that contribute to the screening of AIEC strains. For this, we characterized outer membrane protein (OMP) profiles in a group of AIEC strains and compared them with the commensal E. coli HS strain. Notably, we found a set of OMPs that were present in the AIEC strains but absent in the HS strain. Moreover, we developed a PCR assay and performed phylogenomic analyses to determine the frequency and distribution of the genes coding for these OMPs in a larger collection of AIEC and other E. coli strains. As result, it was found that three genes (chuA, eefC, and fitA) are widely distributed and significantly correlated with AIEC strains, whereas they are infrequent in commensal and diarrheagenic E. coli strains (DEC). Additional studies are needed to validate these markers in diverse strain collections from different geographical regions, as well as investigate their possible role in AIEC pathogenicity

The evolution of the ventilatory ratio is a prognostic factor in mechanically ventilated COVID-19 ARDS patients

Background: Mortality due to COVID-19 is high, especially in patients requiring mechanical ventilation. The purpose of the study is to investigate associations between mortality and variables measured during the first three days of mechanical ventilation in patients with COVID-19 intubated at ICU admission. Methods: Multicenter, observational, cohort study includes consecutive patients with COVID-19 admitted to 44 Spanish ICUs between February 25 and July 31, 2020, who required intubation at ICU admission and mechanical ventilation for more than three days. We collected demographic and clinical data prior to admission; information about clinical evolution at days 1 and 3 of mechanical ventilation; and outcomes. Results: Of the 2,095 patients with COVID-19 admitted to the ICU, 1,118 (53.3%) were intubated at day 1 and remained under mechanical ventilation at day three. From days 1 to 3, PaO2/FiO2 increased from 115.6 [80.0-171.2] to 180.0 [135.4-227.9] mmHg and the ventilatory ratio from 1.73 [1.33-2.25] to 1.96 [1.61-2.40]. In-hospital mortality was 38.7%. A higher increase between ICU admission and day 3 in the ventilatory ratio (OR 1.04 [CI 1.01-1.07], p = 0.030) and creatinine levels (OR 1.05 [CI 1.01-1.09], p = 0.005) and a lower increase in platelet counts (OR 0.96 [CI 0.93-1.00], p = 0.037) were independently associated with a higher risk of death. No association between mortality and the PaO2/FiO2 variation was observed (OR 0.99 [CI 0.95 to 1.02], p = 0.47). Conclusions: Higher ventilatory ratio and its increase at day 3 is associated with mortality in patients with COVID-19 receiving mechanical ventilation at ICU admission. No association was found in the PaO2/FiO2 variation

Clustering COVID-19 ARDS patients through the first days of ICU admission. An analysis of the CIBERESUCICOVID Cohort

Background Acute respiratory distress syndrome (ARDS) can be classified into sub-phenotypes according to different inflammatory/clinical status. Prognostic enrichment was achieved by grouping patients into hypoinflammatory or hyperinflammatory sub-phenotypes, even though the time of analysis may change the classification according to treatment response or disease evolution. We aimed to evaluate when patients can be clustered in more than 1 group, and how they may change the clustering of patients using data of baseline or day 3, and the prognosis of patients according to their evolution by changing or not the cluster.Methods Multicenter, observational prospective, and retrospective study of patients admitted due to ARDS related to COVID-19 infection in Spain. Patients were grouped according to a clustering mixed-type data algorithm (k-prototypes) using continuous and categorical readily available variables at baseline and day 3.Results Of 6205 patients, 3743 (60%) were included in the study. According to silhouette analysis, patients were grouped in two clusters. At baseline, 1402 (37%) patients were included in cluster 1 and 2341(63%) in cluster 2. On day 3, 1557(42%) patients were included in cluster 1 and 2086 (57%) in cluster 2. The patients included in cluster 2 were older and more frequently hypertensive and had a higher prevalence of shock, organ dysfunction, inflammatory biomarkers, and worst respiratory indexes at both time points. The 90-day mortality was higher in cluster 2 at both clustering processes (43.8% [n = 1025] versus 27.3% [n = 383] at baseline, and 49% [n = 1023] versus 20.6% [n = 321] on day 3). Four hundred and fifty-eight (33%) patients clustered in the first group were clustered in the second group on day 3. In contrast, 638 (27%) patients clustered in the second group were clustered in the first group on day 3.Conclusions During the first days, patients can be clustered into two groups and the process of clustering patients may change as they continue to evolve. This means that despite a vast majority of patients remaining in the same cluster, a minority reaching 33% of patients analyzed may be re-categorized into different clusters based on their progress. Such changes can significantly impact their prognosis

Multivariate Data Envelopment Analysis to Measure Airline Efficiency in European Airspace: A Network-Based Approach

In this paper, data envelopment analysis (DEA) is applied to exhaustively examine the efficiency of the main airline companies in the European airspace by using novel input/output parameters: business management factors, network analysis metrics, as well as social media estimators. Furthermore, we also use network analysis to provide a better differentiation among efficiency values. Results indicate that user engagement, as well as the analysis of the position within the airspace-from an operative perspective, influence the efficiency of the airline companies, allowing a more comprehensive understanding of its functioning

Entry Point Matters - Effective Introduction of Innovation in Social Networks

Social networks have grown massively in the last few years and have become a lot more than mere message exchange platforms. Apart from serving purposes such as linking friends and family, job linking or news feeding, their nearly pervasive nature and presence in day-to-day activities make them the biggest potential market and access platform to hundreds of millions of customers ever built. Faced with such a complex and challenging environment, we claim that introducing innovation in an efficient way in such networks is of extreme importance. In this paper, we put forward a mechanism to select suitable entry points in the network to introduce the innovation, so fostering its acceptance and enhancing its diffusion. To do this, we use the underlying structure of the network as well as the influencing power some users exercise over others. We present results of testing our approach with both a Facebook dataset and different examples of random networks

A distributed architecture for real-time evacuation guidance in large smart buildings

In this paper, we consider the route coordination problem in emergency evacuation of large smart buildings. The building evacuation time is crucial in saving lives in emergency situations caused by imminent natural or man-made threats and disasters. Conventional approaches to evacuation route coordination are static and predefined. They rely on evacuation plans present only at a limited number of building locations and possibly a trained evacuation personnel to resolve unexpected contingencies. Smart buildings today are equipped with sensory infrastructure that can be used for an autonomous situation-aware evacuation guidance optimized in real time. A system providing such a guidance can help in avoiding additional evacuation casualties due to the flaws of the conventional evacuation approaches. Such a system should be robust and scalable to dynamically adapt to the number of evacuees and the size and safety conditions of a building. In this respect, we propose a distributed route recommender architecture for situation-aware evacuation guidance in smart buildings and describe its key modules in detail. We give an example of its functioning dynamics on a use case

Decision making matters: A better way to evaluate trust models

Trust models are mechanisms that predict behavior of potential interaction partners. They have been proposed in several domains and many advances in trust formation have been made recently. The question of comparing trust models, however, is still without a clear answer. Traditionally, authors set up ad hoc experiments and present evaluation results that are difficult to compare - sometimes even interpret - in the context of other trust models. As a solution, the community came up with common evaluation platforms, called trust testbeds. In this paper we expose shortcomings of evaluation models that existing testbeds use; they evaluate trust models by combining them with some ad hoc decision making mechanism and then evaluate the quality of trust-based decisions. They assume that if all trust models use the same decision making mechanism, the mechanism itself becomes irrelevant for the evaluation. We hypothesized that the choice of decision making mechanism is in fact relevant. To test our claim we built a testbed, called Alpha testbed, that can evaluate trust models either with or without decision making mechanism. With it we evaluated five well-known trust models using two different decision making mechanisms. The results confirm our hypothesis; the choice of decision making mechanisms influences the performance of trust models. Based on our findings, we recommend to evaluate trust models independently of the decision making mechanism - and we also provide a method (and a tool) to do so. © 2013 Elsevier B.V. All rights reserved.The authors have been supported by the following institutions: David Jelenc by the Slovenian Research Agency (Grant 1000-09-310289) and the Agreement Technologies COST Action IC0801 (STSM Grant COST-STSM-ECOST-STSM-IC0801-160511-003839); Ramón Hermoso by the Spanish Ministry of Science and Innovation (Projects OVAMAH, Grant TIN2009-13839-C03-02; co-funded by Plan E) and the Spanish Ministry of Economy and Competitiveness through the Project iHAS (Grant TIN2012-36586-C03-02); Jordi Sabater-Mir by the CBIT Project (TIN2010-16306), the Agreement Technologies Project (CONSOLIDER CSD2007-0022, INGENIO 2010), the SINTELNET coordinated action, and the Generalitat de Catalunya (grant 2009-SGR-1434); and Denis Trcek by the Slovenian Research Agency (research program Pervasive computing P2-0359).Peer Reviewe

Análisis estratégico sobre la migración del talento en el ámbito de la Unión Europea

Introducción: Este artículo se centra, en primera instancia, en poner de relieve la importancia que tiene la movilidad de los recursos humanos y en concreto de talento en la Unión Europea, bajo la premisa de que este fenómeno en la actualidad continúa intensificándose, debido a la profunda competencia que existe. Metodología: La parte central del trabajo consiste en la recopilación de datos desde dos perspectivas diferentes: económica y educativa. A continuación, se procede al análisis de dichos datos cuantitativos a través de correlaciones estadísticas (coeficiente de Pearson). Resultados: Se obtiene una serie de resultados que ponen de manifiesto la relación existente entre las variables objeto de estudio. En concreto, se hayan distintas correlaciones estadísticas significativas. Conclusión: La interrelación de las variables macroeconómicas y educativas seleccionadas en este estudio aportan información relevante que pone de manifiesto el panorama general de la movilidad del talento de la Unión Europea dejando ver los nuevos retos a los que se enfrenta de manera general y de manera individual los países miembros. Introduction: this article focuses primarily on highlighting the importance of the mobility of human resources, especially talent, within the European Union. This is approached from the premise that the phenomenon is becoming ever more prevalent in today''s world due to the presence of a deep-rooted sense of competition. Methodology: a large part of the article is devoted to the collection of data from two different perspectives: economics and education. The quantitative data is then analysed using statistical correlations (Pearson''s coefficient). Results: there are a number of findings that draw attention to the relationship between the variables forming the object of this study, particularly in the case where any significant statistical correlations emerge. Conclusion: the relationship between the macroeconomic and educational variables selected in this study throw up relevant information that reveals the general situation of the mobility of EU talent, providing a glimpse of the new challenges - both general and individual - that the member states need to tackle