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    Methods of small group research

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    FAST : a fault detection and identification software tool

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    The aim of this work is to improve the reliability and safety of complex critical control systems by contributing to the systematic application of fault diagnosis. In order to ease the utilization of fault detection and isolation (FDI) tools in the industry, a systematic approach is required to allow the process engineers to analyze a system from this perspective. In this way, it should be possible to analyze this system to find if it provides the required fault diagnosis and redundancy according to the process criticality. In addition, it should be possible to evaluate what-if scenarios by slightly modifying the process (f.i. adding sensors or changing their placement) and evaluating the impact in terms of the fault diagnosis and redundancy possibilities. Hence, this work proposes an approach to analyze a process from the FDI perspective and for this purpose provides the tool FAST which covers from the analysis and design phase until the final FDI supervisor implementation in a real process. To synthesize the process information, a very simple format has been defined based on XML. This format provides the needed information to systematically perform the Structural Analysis of that process. Any process can be analyzed, the only restriction is that the models of the process components need to be available in the FAST tool. The processes are described in FAST in terms of process variables, components and relations and the tool performs the structural analysis of the process obtaining: (i) the structural matrix, (ii) the perfect matching, (iii) the analytical redundancy relations (if any) and (iv) the fault signature matrix. To aid in the analysis process, FAST can operate stand alone in simulation mode allowing the process engineer to evaluate the faults, its detectability and implement changes in the process components and topology to improve the diagnosis and redundancy capabilities. On the other hand, FAST can operate on-line connected to the process plant through an OPC interface. The OPC interface enables the possibility to connect to almost any process which features a SCADA system for supervisory control. When running in on-line mode, the process is monitored by a software agent known as the Supervisor Agent. FAST has also the capability of implementing distributed FDI using its multi-agent architecture. The tool is able to partition complex industrial processes into subsystems, identify which process variables need to be shared by each subsystem and instantiate a Supervision Agent for each of the partitioned subsystems. The Supervision Agents once instantiated will start diagnosing their local components and handle the requests to provide the variable values which FAST has identified as shared with other agents to support the distributed FDI process.Per tal de facilitar la utilitzaciĂł d'eines per la detecciĂł i identificaciĂł de fallades (FDI) en la indĂșstria, es requereix un enfocament sistemĂ tic per permetre als enginyers de processos analitzar un sistema des d'aquesta perspectiva. D'aquesta forma, hauria de ser possible analitzar aquest sistema per determinar si proporciona el diagnosi de fallades i la redundĂ ncia d'acord amb la seva criticitat. A mĂ©s, hauria de ser possible avaluar escenaris de casos modificant lleugerament el procĂ©s (per exemple afegint sensors o canviant la seva localitzaciĂł) i avaluant l'impacte en quant a les possibilitats de diagnosi de fallades i redundĂ ncia. Per tant, aquest projecte proposa un enfocament per analitzar un procĂ©s des de la perspectiva FDI i per tal d'implementar-ho proporciona l'eina FAST la qual cobreix des de la fase d'anĂ lisi i disseny fins a la implementaciĂł final d'un supervisor FDI en un procĂ©s real. Per sintetitzar la informaciĂł del procĂ©s s'ha definit un format simple basat en XML. Aquest format proporciona la informaciĂł necessĂ ria per realitzar de forma sistemĂ tica l'AnĂ lisi Estructural del procĂ©s. Qualsevol procĂ©s pot ser analitzat, nomĂ©s hi ha la restricciĂł de que els models dels components han d'estar disponibles en l'eina FAST. Els processos es descriuen en termes de variables de procĂ©s, components i relacions i l'eina realitza l'anĂ lisi estructural obtenint: (i) la matriu estructural, (ii) el Perfect Matching, (iii) les relacions de redundĂ ncia analĂ­tica, si n'hi ha, i (iv) la matriu signatura de fallades. Per ajudar durant el procĂ©s d'anĂ lisi, FAST pot operar aĂŻlladament en mode de simulaciĂł permetent a l'enginyer de procĂ©s avaluar fallades, la seva detectabilitat i implementar canvis en els components del procĂ©s i la topologia per tal de millorar les capacitats de diagnosi i redundĂ ncia. Per altra banda, FAST pot operar en lĂ­nia connectat al procĂ©s de la planta per mitjĂ  d'una interfĂ­cie OPC. La interfĂ­cie OPC permet la possibilitat de connectar gairebĂ© a qualsevol procĂ©s que inclogui un sistema SCADA per la seva supervisiĂł. Quan funciona en mode en lĂ­nia, el procĂ©s estĂ  monitoritzat per un agent software anomenat l'Agent Supervisor. Addicionalment, FAST tĂ© la capacitat d'implementar FDI de forma distribuĂŻda utilitzant la seva arquitectura multi-agent. L'eina permet dividir sistemes industrials complexes en subsistemes, identificar quines variables de procĂ©s han de ser compartides per cada subsistema i generar una instĂ ncia d'Agent Supervisor per cadascun dels subsistemes identificats. Els Agents Supervisor un cop activats, començaran diagnosticant els components locals i despatxant les peticions de valors per les variables que FAST ha identificat com compartides amb altres agents, per tal d'implementar el procĂ©s FDI de forma distribuĂŻda.Postprint (published version

    Estimating causal networks in biosphere–atmosphere interaction with the PCMCI approach

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    Local meteorological conditions and biospheric activity are tightly coupled. Understanding these links is an essential prerequisite for predicting the Earth system under climate change conditions. However, many empirical studies on the interaction between the biosphere and the atmosphere are based on correlative approaches that are not able to deduce causal paths, and only very few studies apply causal discovery methods. Here, we use a recently proposed causal graph discovery algorithm, which aims to reconstruct the causal dependency structure underlying a set of time series. We explore the potential of this method to infer temporal dependencies in biosphere-atmosphere interactions. Specifically we address the following questions: How do periodicity and heteroscedasticity influence causal detection rates, i.e. the detection of existing and non-existing links? How consistent are results for noise-contaminated data? Do results exhibit an increased information content that justifies the use of this causal-inference method? We explore the first question using artificial time series with well known dependencies that mimic real-world biosphere-atmosphere interactions. The two remaining questions are addressed jointly in two case studies utilizing observational data. Firstly, we analyse three replicated eddy covariance datasets from a Mediterranean ecosystem at half hourly time resolution allowing us to understand the impact of measurement uncertainties. Secondly, we analyse global NDVI time series (GIMMS 3g) along with gridded climate data to study large-scale climatic drivers of vegetation greenness. Overall, the results confirm the capacity of the causal discovery method to extract time-lagged linear dependencies under realistic settings. The violation of the method's assumptions increases the likelihood to detect false links. Nevertheless, we consistently identify interaction patterns in observational data. Our findings suggest that estimating a directed biosphere-atmosphere network at the ecosystem level can offer novel possibilities to unravel complex multi-directional interactions. Other than classical correlative approaches, our findings are constrained to a few meaningful set of relations which can be powerful insights for the evaluation of terrestrial ecosystem models

    Predicting the Cosmological Constant from the Causal Entropic Principle

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    We compute the expected value of the cosmological constant in our universe from the Causal Entropic Principle. Since observers must obey the laws of thermodynamics and causality, the principle asserts that physical parameters are most likely to be found in the range of values for which the total entropy production within a causally connected region is maximized. Despite the absence of more explicit anthropic criteria, the resulting probability distribution turns out to be in excellent agreement with observation. In particular, we find that dust heated by stars dominates the entropy production, demonstrating the remarkable power of this thermodynamic selection criterion. The alternative approach - weighting by the number of "observers per baryon" - is less well-defined, requires problematic assumptions about the nature of observers, and yet prefers values larger than present experimental bounds.Comment: 38 pages, 9 figures, minor correction in Figure

    Intersubjective meaning and collective action in'fragile'societies : theory, evidence and policy implications

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    The capacity to act collectively is not just a matter of groups sharing interests, incentives and values (or being sufficiently small), as standard economic theory predicts, but a prior and shared understanding of the constituent elements of problem(s) and possible solutions. From this standpoint, the failure to act collectively can stem at least in part from relevant groups failing to ascribe a common intersubjective meaning to situations, processes and events. Though this is a general phenomenon, it is particularly salient in countries characterized by societal fragility and endemic conflict. We develop a conceptual account of intersubjective meanings, explain its relevance to development practice and research, and examine its implications for development work related to building the rule of law and managing common pool resources.Corporate Law,Public Sector Corruption&Anticorruption Measures,Cultural Policy,Labor Policies,Population Policies

    A Roadmap for Promoting Women's Economic Empowerment

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    This document summarizes findings of 18 research studies commissioned across 4 categories (entrepreneurship, farming, wage employment, young women's employment) to find out what works to empower women, for whom (categories of women), and where (country scenarios). The Roadmap is designed to guide investments from private sector and public-private partnerships, and highlights 9 proven, 9 promising, and 6 high-potential interventions to increase women's productivity and earnings in developing countries
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