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IntroductionChagas disease is caused by theTrypanosoma cruziinfection. It is a neglected tropical disease with considerable impact on the physical, psychological, familiar, and social spheres. The Valle Alto of Cochabamba is a hyperendemic region of Bolivia where efforts to control the transmission of the disease have progressed over the years. However, many challenges remain, above all, timely detection and health-care access.MethodsFollowing the Science Shop process, this bottom-up research emerged with the participation of the civil society from Valle Alto and representatives of the Association ofCorazones Unidos por el Chagasfrom Cochabamba. The aim of this study is to explore the social determinants in the living realities of those affected by Chagas disease or the silent infection and how families in the Valle Alto of Cochabamba cope with it. An interdisciplinary research team conducted a case study of the life stories of three families using information from in-depth interviews and performed a descriptive qualitative content analysis and triangulation processes.FindingsFindings provide insights into social circumstances of the research subjects’ lives; particularly, on how exposure toTrypanosoma cruziinfection affects their daily lives in terms of seeking comprehensive health care. Research subjects revealed needs and shared their experiences, thus providing an understanding of the complexity of Chagas disease from the socioeconomic, sociocultural, political, and biomedical perspectives. Results enlighten on three dimensions: structural, psychosocial, and plural health system. The diverse perceptions and attitudes toward Chagas within families, including the denial of its existence, are remarkable as gender and ethnocultural aspects. Findings support recommendations to various stakeholders and translation materials.ConclusionsIntersectional disease management and community involvement are essential for deciding the most appropriate and effective actions. Education, detection, health care, and social programs engaging family units ought to be the pillars of a promising approach.

Cyber Resilience is an important property of complex systems and is important consideration in developing specific IoT applications. This work aims at introducing a novel approach to assess IoT resilience adopting the risk perception in network based epidemic spreading approach. In particular IoT has been considered a network of devices where the probability of infection and interactions (communication), needs to be balanced in order to reduce the malware outbreack while maintaining the network functionalities at an acceptable level. The mathematical model and the simulation results reveal the benefit of a shift from a risk-based to a resilience based approach to threat management in IoT.

International audience; Datacleaninganddatapreparationhavebeenlong-standingchallenges in data science to avoid incorrect results, biases, and misleading conclusions ob- tained from “dirty” data. For a given dataset and data analytics task, a plethora of data preprocessing techniques and alternative data cleaning strategies are avail- able, but they may lead to dramatically different outputs with unequal result quality performances. For adequate data preparation, the users generally do not know how to start with or which methods to use. Most current work can be classified into two categories: 1) they propose new data cleaning algorithms specific to certain types of data anomalies usually considered in isolation and without a “pipeline vision” of the entire data preprocessing strategy; 2) they develop automated machine learning approaches (AutoML) that can optimize the hyper- parameters of a considered ML model with a list of by-default preprocessing methods. We argue that more efforts should be devoted to proposing a principled and adaptive data preparation approach to help and learn from the user for selecting the optimal sequence of data preparation tasks to obtain the best quality performance of the final result. In this paper, we extend Learn2Clean, a method based on Q-Learning, a model-free reinforcement learning technique that selects, for a given data set, a given ML model, and a pre-selected quality performance metric, the optimal sequence of tasks for preprocessing the data such that the quality metric is maximized. We will discuss some new results of Learn2Clean for semi-automating data preparation with “the human in the loop” using active reward learning and Q-learning.

In the last years, a rapid increase of life expectancy has been observed and thus ageing is becoming a hot topic in different research fields. Ensuring the wellbeing of elderly people has a positive impact on healthcare, economy and social equality and thus developing a system that takes into account and integrates physiological parameters, cognitive and physical training, elderly-user experience and perception of the system itself, may represent an innovative multi-modal mean through which ameliorate older people wellbeing.

The article proposes a new vision for the smart city operation center combining blockchain and virtual reality. The objective is to achieve high synchronization among the different operators, an enhanced operator performance and the creation of a trustworthy communication layer to record all the decisions taken in the virtual environment and to share data among the operators securely.

In many cases, tainted information in a computer network can spread in a way similar to an epidemics in the human world. On the other had, information processing paths are often redundant, so a single infection occurrence can be easily "reabsorbed". Randomly checking the information with a central server is equivalent to lowering the infection probability but with a certain cost (for instance processing time), so it is important to quickly evaluate the epidemic threshold for each node. We present a method for getting such information without resorting to repeated simulations. As for human epidemics, the local information about the infection level (risk perception) can be an important factor, and we show that our method can be applied to this case, too. Finally, when the process to be monitored is more complex and includes "disruptive interference", one has to use actual simulations, which however can be carried out "in parallel" for many possible infection probabilities.

The Climate Challenge is an online application in the tradition of games with a purpose that combines practical steps to reduce carbon footprint with predictive tasks to estimate future climate-related conditions. As part of the Collective Awareness Platform, the application aims to increase environmental literacy and motivate users to adopt more sustainable lifestyles. It has been deployed in conjunction with the Media Watch on Climate Change, a publicly available knowledge aggregator and visual analytics system for exploring environmental content from multiple online sources. This paper presents the motivation and goals of the Climate Challenge from an interdisciplinary perspective, outlines the application design including the types of tasks built into the application, discusses incentive mechanisms, and analyses the pursued user engagement strategies.

In this paper, we propose that a smart approach to city development must seek a continuous and recursive interplay between two levels of codes. On the one hand, computer codes, which rule the development and the functioning of the ubiquitous ICTs infrastructure. On the other hand, normative codes, which govern the practices through which social actors perceive ICTs and decide to exploit them in order to improve their lives. We thus take an exploratory standpoint and investigate to what extent key players in the EU smart cities policy domain are framed according to such a sociotechnical perspective. To this purpose, we first map an online issue network on the topic of smart cities in Europe and then explore the frames that circulate within its core. Our results suggest that, although smart cities are framed sociotechnically, EU key players tend to better converge around technological aspects rather than social ones.

Aim: To assess whether a very high number of prenatal ultrasonographies affects birthweight. Population and methods: We studied 1203 consecutive women who delivered in Siena Hospital. Exclusion criteria were the following: twin pregnancy, maternal smoke or alcohol ingestion in pregnancy, gestational diabetes, placenta or umbilical cord defects, gestational age at birth < 37 weeks, and major malformations. We analysed birthweights in relation to the number of ultrasound examinations. 120 women had undergone a minimum number (three or less-base group) and 167 a maximum number (nine or more-intensive group) of fetal US scans. We compared the birthweight of the children born in these two groups and the correlation between number of Us scans and birthweight in the whole population. Results: Mean birthweights of the base and the intensive groups were 3389.5 ± 434 g and 3268 ± 438 g, respectively (p = 0.0206). Nevertheless, the regression study did not show a significant correlation between birthweight and number of US scans. The mean age of the base group was 30.1 ± 5.3 years and that of the intensive group was 32.09 ± 4.99 years (p = 0.0018). Eighteen women of base group underwent amniocenteses vs. 71 in the intensive group (p < 0.001). In the base group 57.5% of the mothers had low school level vs. 24.4% in the intensive group (p < 0.01). Conclusion: More studies are needed to confirm or exclude any relationship between an intensive use of prenatal ultrasounds and birthweight, and to exclude other effects of ultrasounds on children's health. Moreover, our study shows an excess of prenatal diagnostic procedures, the causes of which should be investigated © 2005 Elsevier Ireland Ltd. All rights reserved.

Percolation, in its most general interpretation, refers to the “flow„ of something (a physical agent, data or information) in a network, possibly accompanied by some nonlinear dynamical processes on the network nodes (sometimes denoted reaction⁻diffusion systems, voter or opinion formation models, etc.). Originated in the domain of theoretical and matter physics, it has many applications in epidemiology, sociology and, of course, computer and Internet sciences. In this review, we illustrate some aspects of percolation theory and its generalization, cellular automata and briefly discuss their relationship with equilibrium systems (Ising and Potts models). We present a model of opinion spreading, the role of the topology of the network to induce coherent oscillations and the influence (and advantages) of risk perception for stopping epidemics. The models and computational tools that are briefly presented here have an application to the filtering of tainted information in automatic trading. Finally, we introduce the open problem of controlling percolation and other processes on distributed systems.