A new approach for computing a flood vulnerability index using cluster analysis

A Flood Vulnerability Index (FloodVI) was developed using Principal Component Analysis (PCA) and a new aggregation method based on Cluster Analysis (CA). PCA simplifies a large number of variables into a few uncorrelated factors representing the social, economic, physical and environmental dimensions of vulnerability. CA groups areas that have the same characteristics in terms of vulnerability into vulnerability classes. The grouping of the areas determines their classification contrary to other aggregation methods in which the areas' classification determines their grouping. While other aggregation methods distribute the areas into classes, in an artificial manner, by imposing a certain probability for an area to belong to a certain class, as determined by the assumption that the aggregation measure used is normally distributed, CA does not constrain the distribution of the areas by the classes. FloodVI was designed at the neighbourhood level and was applied to the Portuguese municipality of Vila Nova de Gaia where several flood events have taken place in the recent past. The FloodVI sensitivity was assessed using three different aggregation methods: the sum of component scores, the first component score and the weighted sum of component scores. The results highlight the sensitivity of the FloodVI to different aggregation methods. Both sum of component scores and weighted sum of component scores have shown similar results. The first component score aggregation method classifies almost all areas as having medium vulnerability and finally the results obtained using the CA show a distinct differentiation of the vulnerability where hot spots can be clearly identified. The information provided by records of previous flood events corroborate the results obtained with CA, because the inundated areas with greater damages are those that are identified as high and very high vulnerability areas by CA. This supports the fact that CA provides a reliable FloodVI.

Las tecnologías de información y comunicación (TIC) desde una perspectiva social

The intention of the article is to announce the needs of a curriculum Identified in the NicaraguanThis article analyses the potential of information and communication technologies from a rural social context approach. The author approaches the advantages of the social incorporation of this resource, its menaces and risks as well as the aspects that have to be valued when an impact on social development is expected. This perspective allows tackling the relation between ICTs and social concerns such as social inequality, gender equity, and community participation, among others. The article ends up presenting a reflection about some challenges in this matter.

Interoperability Challenges in Internet of Things Systems: a Service-Oriented Computing Approach

Internet of Things systems are pervasive systems evolved from cyber-physical to large-scale systems. Due to the number of technologies involved, software development involves several integration challenges. Among them, the ones preventing proper integration are those related to the system heterogeneity, and thus addressing interoperability issues. From a software engineering perspective, developers mostly experience the lack of interoperability in the two phases of software development: programming and deployment. On the one hand, modern software tends to be distributed in several components, each adopting its most-appropriate technology stack, pushing programmers to code in a protocol- and data-agnostic way. On the other hand, each software component should run in the most appropriate execution environment and, as a result, system architects strive to automate the deployment in distributed infrastructures. This dissertation aims to improve the development process by introducing proper tools to handle certain aspects of the system heterogeneity. Our effort focuses on three of these aspects and, for each one of those, we propose a tool addressing the underlying challenge. The first tool aims to handle heterogeneity at the transport and application protocol level, the second to manage different data formats, while the third to obtain optimal deployment. To realize the tools, we adopted a linguistic approach, i.e.\ we provided specific linguistic abstractions that help developers to increase the expressive power of the programming language they use, writing better solutions in more straightforward ways. To validate the approach, we implemented use cases to show that the tools can be used in practice and that they help to achieve the expected level of interoperability. In conclusion, to move a step towards the realization of an integrated Internet of Things ecosystem, we target programmers and architects and propose them to use the presented tools to ease the software development process.

Enabling Human Centric Smart Campuses via Edge Computing and Connected Objects

Early definitions of Smart Building focused almost entirely on the technology aspect and did not suggest user interaction at all. Indeed, today we would attribute it more to the concept of the automated building. In this sense, control of comfort conditions inside buildings is a problem that is being well investigated, since it has a direct effect on users’ productivity and an indirect effect on energy saving. Therefore, from the users’ perspective, a typical environment can be considered comfortable, if it’s capable of providing adequate thermal comfort, visual comfort and indoor air quality conditions and acoustic comfort. In the last years, the scientific community has dealt with many challenges, especially from a technological point of view. For instance, smart sensing devices, the internet, and communication technologies have enabled a new paradigm called Edge computing that brings computation and data storage closer to the location where it is needed, to improve response times and save bandwidth. This has allowed us to improve services, sustainability and decision making. Many solutions have been implemented such as smart classrooms, controlling the thermal condition of the building, monitoring HVAC data for energy-efficient of the campus and so forth. Though these projects provide to the realization of smart campus, a framework for smart campus is yet to be determined. These new technologies have also introduced new research challenges: within this thesis work, some of the principal open challenges will be faced, proposing a new conceptual framework, technologies and tools to move forward the actual implementation of smart campuses. Keeping in mind, several problems known in the literature have been investigated: the occupancy detection, noise monitoring for acoustic comfort, context awareness inside the building, wayfinding indoor, strategic deployment for air quality and books preserving.

Detecting social patterns within 20th century documentary photos: a deep learning based approach

The job of a historian is to understand what happened in the past, resorting in many cases to written documents as a firsthand source of information. Text, however, does not amount to the only source of knowledge. Pictorial representations, in fact, have also accompanied the main events of the historical timeline. In particular, the opportunity of visually representing circumstances has bloomed since the invention of photography, with the possibility of capturing in real-time the occurrence of a specific events. Thanks to the widespread use of digital technologies (e.g. smartphones and digital cameras), networking capabilities and consequent availability of multimedia content, the academic and industrial research communities have developed artificial intelligence (AI) paradigms with the aim of inferring, transferring and creating new layers of information from images, videos, etc. Now, while AI communities are devoting much of their attention to analyze digital images, from an historical research standpoint more interesting results may be obtained analyzing analog images representing the pre-digital era. Within the aforementioned scenario, the aim of this work is to analyze a collection of analog documentary photographs, building upon state-of-the-art deep learning techniques. In particular, the analysis carried out in this thesis aims at producing two following results: (a) produce the date of an image, and, (b) recognizing its background socio-cultural context,as defined by a group of historical-sociological researchers. Given these premises, the contribution of this work amounts to: (i) the introduction of an historical dataset including images of “Family Album” among all the twentieth century, (ii) the introduction of a new classification task regarding the identification of the socio-cultural context of an image, (iii) the exploitation of different deep learning architectures to perform the image dating and the image socio-cultural context classification.

Sustainability analysis in the mining sector: a case study on new recycling technologies for sulphidic mine residues valorisation

Research has demonstrated that mining activities can cause serious impacts on the environment, as well as the surrounding communities, mainly due to the unsafe storage of mine tailings. This research focuses on the sustainability assessment of new technologies for the recovery of metals from mine residues. The assessment consists in the evaluation of the environmental, economic, and social impacts through the Life Cycle based methods: Life Cycle Assessment (LCA), Life Cycle Costing (LCC), and Social Life Cycle Assessment (SLCA). The analyses are performed on the Mondo Minerals bioleaching project, which aim is to recover nickel and cobalt from the Sotkamo and Vuonos mine tailings. The LCA demonstrates that the project contributes to the avoided production of nickel and cobalt concentrates from new resources, hence reducing several environmental impacts. The LCC analysis shows that the company’s main costs are linked to the bioleaching process, caused by electricity consumption and the chemicals used. The SLCA analyses the impacts on three main stakeholder categories: workers, local community, and society. The results demonstrated that a fair salary (or the absence of it) impacts the workers the most, while the local community stakeholder category impacts are related to the access to material resources. The health and safety category is the most impacted category for the society stakeholder. The environmental and economic analyses demonstrate that the recovery of mine tailings may represents a good opportunity for mine companies both to reduce the environmental impacts linked to mine tailings and to increase the profitability. In particular, the project helps reduce the amounts of metals extracted from new resources and demonstrates that the use of the bioleaching technology for the extraction of metals can be economically profitable.

Appropriate technologies and natural systems for wastewater treatment in low- and middle-income countries

Wastewater management is an environmental and social burden that primarily affects populations in Low- and Middle-Income Countries and the global environment. Wastewater collection, treatment, and reuse have become urgent, especially considering that 80% of the world's wastewater is untreated or improperly treated and discharged directly into water bodies. In recent years, the role of wastewater treatment plants in a sustainable water cycle has become even more critical, as they are the final destination of the collected wastewater. Indeed, the management of wastewater treatment plants should play an essential role in achieving SDG target 6.3 of the United Nations 2030 Agenda for SD. In this context, water reuse, especially wastewater reuse, plays a key role. This research focuses on investigating the valorization of wastewater resources applying Appropriate Technologies and Natural Systems for wastewater treatment in two different Low- and Middle-Income Countries, the Palestinian Territories and Sub-Saharan Africa. The research objectives are: (1) Determine the characteristics and quality of wastewater in the two case studies analysed. (2) Identify Appropriate Technology to be used in the Palestinian Territories to treat wastewater for reuse in agriculture. (3) Assess the environmental, economic, and social impacts of this project. (4) Assess the feasibility of using natural wetlands for household wastewater treatment in Sub-Saharan region. The first study, conducted in Rafah, Gaza Strip, showed that implementing existing primary treatment plant with a natural secondary treatment plant properly optimized the wastewater quality for reuse in agriculture and was suitable for the study area. The second case study was conducted in Cape Coast, Ghana. It shows that the natural wetland studied is currently overly polluted and threatened by various anthropogenic factors that cannot remove pollutants from the incoming domestic wastewater. Therefore, some recommendations were made in order to improve the efficiency of this natural wetland.

Applying machine learning: a multi-role perspective

Machine (and deep) learning technologies are more and more present in several fields. It is undeniable that many aspects of our society are empowered by such technologies: web searches, content filtering on social networks, recommendations on e-commerce websites, mobile applications, etc., in addition to academic research. Moreover, mobile devices and internet sites, e.g., social networks, support the collection and sharing of information in real time. The pervasive deployment of the aforementioned technological instruments, both hardware and software, has led to the production of huge amounts of data. Such data has become more and more unmanageable, posing challenges to conventional computing platforms, and paving the way to the development and widespread use of the machine and deep learning. Nevertheless, machine learning is not only a technology. Given a task, machine learning is a way of proceeding (a way of thinking), and as such can be approached from different perspectives (points of view). This, in particular, will be the focus of this research. The entire work concentrates on machine learning, starting from different sources of data, e.g., signals and images, applied to different domains, e.g., Sport Science and Social History, and analyzed from different perspectives: from a non-data scientist point of view through tools and platforms; setting a problem stage from scratch; implementing an effective application for classification tasks; improving user interface experience through Data Visualization and eXtended Reality. In essence, not only in a quantitative task, not only in a scientific environment, and not only from a data-scientist perspective, machine (and deep) learning can do the difference.

QoS-aware architectures, technologies, and middleware for the cloud continuum

The recent trend of moving Cloud Computing capabilities to the Edge of the network is reshaping how applications and their middleware supports are designed, deployed, and operated. This new model envisions a continuum of virtual resources between the traditional cloud and the network edge, which is potentially more suitable to meet the heterogeneous Quality of Service (QoS) requirements of diverse application domains and next-generation applications. Several classes of advanced Internet of Things (IoT) applications, e.g., in the industrial manufacturing domain, are expected to serve a wide range of applications with heterogeneous QoS requirements and call for QoS management systems to guarantee/control performance indicators, even in the presence of real-world factors such as limited bandwidth and concurrent virtual resource utilization. The present dissertation proposes a comprehensive QoS-aware architecture that addresses the challenges of integrating cloud infrastructure with edge nodes in IoT applications. The architecture provides end-to-end QoS support by incorporating several components for managing physical and virtual resources. The proposed architecture features: i) a multilevel middleware for resolving the convergence between Operational Technology (OT) and Information Technology (IT), ii) an end-to-end QoS management approach compliant with the Time-Sensitive Networking (TSN) standard, iii) new approaches for virtualized network environments, such as running TSN-based applications under Ultra-low Latency (ULL) constraints in virtual and 5G environments, and iv) an accelerated and deterministic container overlay network architecture. Additionally, the QoS-aware architecture includes two novel middlewares: i) a middleware that transparently integrates multiple acceleration technologies in heterogeneous Edge contexts and ii) a QoS-aware middleware for Serverless platforms that leverages coordination of various QoS mechanisms and virtualized Function-as-a-Service (FaaS) invocation stack to manage end-to-end QoS metrics. Finally, all architecture components were tested and evaluated by leveraging realistic testbeds, demonstrating the efficacy of the proposed solutions.

On making web accessibility more accessible: strategy and tools for social good

The world currently faces a paradox in terms of accessibility for people with disabilities. While digital technologies hold immense potential to improve their quality of life, the majority of web content still exhibits critical accessibility issues. This PhD thesis addresses this challenge by proposing two interconnected research branches. The first introduces a groundbreaking approach to improving web accessibility by rethinking how it is approached, making it more accessible itself. It involves the development of: 1. AX, a declarative framework of web components that enforces the generation of accessible markup by means of static analysis. 2. An innovative accessibility testing and evaluation methodology, which communicates test results by exploiting concepts that developers are already familiar with (visual rendering and mouse operability) to convey the accessibility of a page. This methodology is implemented through the SAHARIAN browser extension. 3. A11A, a categorized and structured collection of curated accessibility resources aimed at facilitating their intended audiences discover and use them. The second branch focuses on unleashing the full potential of digital technologies to improve accessibility in the physical world. The thesis proposes the SCAMP methodology to make scientific artifacts accessible to blind, visually impaired individuals, and the general public. It enhances the natural characteristics of objects, making them more accessible through interactive, multimodal, and multisensory experiences. Additionally, the prototype of \gls{a11yvt}, a system supporting accessible virtual tours, is presented. It provides blind and visually impaired individuals with features necessary to explore unfamiliar indoor environments, while maintaining universal design principles that makes it suitable for usage by the general public. The thesis extensively discusses the theoretical foundations, design, development, and unique characteristics of these innovative tools. Usability tests with the intended target audiences demonstrate the effectiveness of the proposed artifacts, suggesting their potential to significantly improve the current state of accessibility.

[the Teaching Of Social Sciences In Health: Between Practice And Theory].

The models of teaching social sciences and clinical practice are insufficient for the needs of practical-reflective teaching of social sciences applied to health. The scope of this article is to reflect on the challenges and perspectives of social science education for health professionals. In the 1950s the important movement bringing together social sciences and the field of health began, however weak credentials still prevail. This is due to the low professional status of social scientists in health and the ill-defined position of the social sciences professionals in the health field. It is also due to the scant importance attributed by students to the social sciences, the small number of professionals and the colonization of the social sciences by the biomedical culture in the health field. Thus, the professionals of social sciences applied to health are also faced with the need to build an identity, even after six decades of their presence in the field of health. This is because their ambivalent status has established them as a partial, incomplete and virtual presence, requiring a complex survival strategy in the nebulous area between social sciences and health.

[almost An Autobiography: A Study Of Social Scientists In Health Based On The Lattes Curriculum].

Among the various ways of adopting the biographical approach, we used the curriculum vitaes (CVs) of Brazilian researchers who work as social scientists in health as our research material. These CVs are part of the Lattes Platform of CNPq - the National Council for Scientific and Technological Development, which includes Research and Institutional Directories. We analyzed 238 CVs for this study. The CVs contain, among other things, the following information: professional qualifications, activities and projects, academic production, participation in panels for the evaluation of theses and dissertations, research centers and laboratories and a summarized autobiography. In this work there is a brief review of the importance of autobiography for the social sciences, emphasizing the CV as a form of autobiographical practice. We highlight some results, such as it being a group consisting predominantly of women, graduates in social sciences, anthropology, sociology or political science, with postgraduate degrees. The highest concentration of social scientists is located in Brazil's southern and southeastern regions. In some institutions the main activities of social scientists are as teachers and researchers with great thematic diversity in research.

Infertility Care And The Introduction Of New Reproductive Technologies In Poor Resource Settings.

The overall prevalence of infertility was estimated to be 3.5-16.7% in developing countries and 6.9-9.3% in developed countries. Furthermore, according to reports from some regions of sub-Saharan Africa, the prevalence rate is 30-40%. The consequences of infertility and how it affects the lives of women in poor-resource settings, particularly in developing countries, has become an important issue to be discussed in reproductive health. In some societies, the inability to fulfill the desire to have children makes life difficult for the infertile couple. In many regions, infertility is considered a tragedy that affects not only the infertile couple or woman, but the entire family. This is a position paper which encompasses a review of the needs of low-income infertile couples, mainly those living in developing countries, regarding access to infertility care, including ART and initiatives to provide ART at low or affordable cost. Information was gathered from the databases MEDLINE, CENTRAL, POPLINE, EMBASE, LILACS, and ICTRP with the key words: infertility, low income, assisted reproductive technologies, affordable cost, low cost. There are few initiatives geared toward implementing ART procedures at low cost or at least at affordable cost in low-income populations. Nevertheless, from recent studies, possibilities have emerged for new low-cost initiatives that can help millions of couples to achieve the desire of having a biological child. It is necessary for healthcare professionals and policymakers to take into account these new initiatives in order to implement ART in resource-constrained settings.

Juan César García: Social Medicine As Project And Endeavor.

This paper analyses some aspects of the trajectory of the Argentinian physician and sociologist Juan César García (1932-1984) in the field of Latin American Social Medicine. Three dimensions constituting his basic orientations are highlighted: the elaboration of systematic and reflective social thought; a critical attitude in questioning teaching and professional practices; a commitment to the institutionalization and dissemination of health knowledge.

Brazilian multicentre study of common mental disorders in primary care: rates and related social and demographic factors

Mental health problems are common in primary health care, particularly anxiety and depression. This study aims to estimate the prevalence of common mental disorders and their associations with socio-demographic characteristics in primary care in Brazil (Family Health Strategy). It involved a multicenter cross-sectional study with patients from Rio de Janeiro, São Paulo, Fortaleza (Ceará State) and Porto Alegre (Rio Grande do Sul State), assessed using the General Health Questionnaire (GHQ-12) and the Hospital Anxiety and Depression Scale (HAD). The rate of mental disorders in patients from Rio de Janeiro, São Paulo, Fortaleza and Porto Alegre were found to be, respectively, 51.9%, 53.3%, 64.3% and 57.7% with significant differences between Porto Alegre and Fortaleza compared to Rio de Janeiro after adjusting for confounders. Prevalence proportions of mental problems were especially common for females, the unemployed, those with less education and those with lower incomes. In the context of the Brazilian government's moves towards developing primary health care and reorganizing mental health policies it is relevant to consider common mental disorders as a priority alongside other chronic health conditions.