11 resultados para Recycle and reuse

em BORIS: Bern Open Repository and Information System - Berna - Suiça


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In rapidly evolving domains such as Computer Assisted Orthopaedic Surgery (CAOS) emphasis is often put first on innovation and new functionality, rather than in developing the common infrastructure needed to support integration and reuse of these innovations. In fact, developing such an infrastructure is often considered to be a high-risk venture given the volatility of such a domain. We present CompAS, a method that exploits the very evolution of innovations in the domain to carry out the necessary quantitative and qualitative commonality and variability analysis, especially in the case of scarce system documentation. We show how our technique applies to the CAOS domain by using conference proceedings as a key source of information about the evolution of features in CAOS systems over a period of several years. We detect and classify evolution patterns to determine functional commonality and variability. We also identify non-functional requirements to help capture domain variability. We have validated our approach by evaluating the degree to which representative test systems can be covered by the common and variable features produced by our analysis.

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The reclamation, treatment and reuse of municipal wastewater can provide important environmental benefits. In this paper, 25 studies on this topic were reviewed and it was found that there are many (\textgreater150) different drivers acting for and against wastewater recycling. To deal with the challenge of comparing studies which entailed different research designs, a framework was developed which allowed the literature to be organized into comparable study contexts. Studies were categorized according to the level of analysis (wastewater recycling scheme, city, water utility, state, country, global) and outcome investigated (development/investment in new schemes, program implementation, percentage of wastewater recycled, percentage of water demand covered by recycled water, multiple outcomes). Findings across comparable case studies were then grouped according to the type (for or against recycling) and category of driver (social, natural, technical, economic, policy or business). The utility of the framework is demonstrated by summarizing the findings from four Australian studies at the city level. The framework offers a unique approach for disentangling the broad range of potential drivers for and against water recycling and to focus on those that seem relevant in specific study contexts. It may offer a valuable starting point for building hypotheses in future work.

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Grammars for programming languages are traditionally specified statically. They are hard to compose and reuse due to ambiguities that inevitably arise. PetitParser combines ideas from scannerless parsing, parser combinators, parsing expression grammars and packrat parsers to model grammars and parsers as objects that can be reconfigured dynamically. Through examples and benchmarks we demonstrate that dynamic grammars are not only flexible but highly practical.

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Integration of multiple languages into each other and into an existing development environment is a difficult task. As a consequence, developers often end up using only internal DSLs that strictly rely on the constraints imposed by the host language. Infrastructures do exist to mix languages, but they often do it at the price of losing the development tools of the host language. Instead of inventing a completely new infrastructure, our solution is to integrate new languages deeply into the existing host environment and reuse the infrastructure offered by it. In this paper we show why Smalltalk is the best practical choice for such a host language.

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Although the recycling of municipal wastewater can play an important role in water supply security and ecosystem protection, the percentage of wastewater recycled is generally low and strikingly variable. Previous research has employed detailed case studies to examine the factors that contribute to recycling success but usually lacks a comparative perspective across cases. In this study, 25 water utilities in New South Wales, Australia, were compared using fuzzy-set Qualitative Comparative Analysis (fsQCA). This research method applies binary logic and set theory to identify the minimal combinations of conditions that are necessary and/or sufficient for an outcome to occur within the set of cases analyzed. The influence of six factors (rainfall, population density, coastal or inland location, proximity to users; cost recovery and revenue for water supply services) was examined for two outcomes, agricultural use and "heavy" (i.e., commercial/municipal/industrial) use. Each outcome was explained by two different pathways, illustrating that different combinations of conditions are associated with the same outcome. Generally, while economic factors are crucial for heavy use, factors relating to water stress and geographical proximity matter most for agricultural reuse. These results suggest that policies to promote wastewater reuse may be most effective if they target uses that are most feasible for utilities and correspond to the local context. This work also makes a methodological contribution through illustrating the potential utility of fsQCA for understanding the complex drivers of performance in water recycling.

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The increasing amount of data available about software systems poses new challenges for re- and reverse engineering research, as the proposed approaches need to scale. In this context, concerns about meta-modeling and analysis techniques need to be augmented by technical concerns about how to reuse and how to build upon the efforts of previous research. Moose is an extensive infrastructure for reverse engineering evolved for over 10 years that promotes the reuse of engineering efforts in research. Moose accommodates various types of data modeled in the FAMIX family of meta-models. The goal of this half-day workshop is to strengthen the community of researchers and practitioners who are working in re- and reverse engineering, by providing a forum for building future research starting from Moose and FAMIX as shared infrastructure.

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The increasing amount of data available about software systems poses new challenges for re- and reverse engineering research, as the proposed approaches need to scale. In this context, concerns about meta-modeling and analysis techniques need to be augmented by technical concerns about how to reuse and how to build upon the efforts of previous research. MOOSE is an extensive infrastructure for reverse engineering evolved for over 10 years that promotes the reuse of engineering efforts in research. MOOSE accommodates various types of data modeled in the FAMIX family of meta-models. The goal of this half-day workshop is to strengthen the community of researchers and practitioners who are working in re- and reverse engineering, by providing a forum for building future research starting from MOOSE and FAMIX as shared infrastructure.

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Digital technologies and the Internet in particular have transformed the ways we create, distribute, use, reuse and consume cultural content; have impacted on the workings of the cultural industries, and more generally on the processes of making, experiencing and remembering culture in local and global spaces. Yet, few of these, often profound, transformations have found reflection in law and institutional design. Cultural policy toolkits, in particular at the international level, are still very much offline/analogue and conceive of culture as static property linked to national sovereignty and state boundaries. The article describes this state of affairs and asks the key question of whether there is a need to reform global cultural law and policy and if yes, what the essential elements of such a reform should be.

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BACKGROUND Record linkage of existing individual health care data is an efficient way to answer important epidemiological research questions. Reuse of individual health-related data faces several problems: Either a unique personal identifier, like social security number, is not available or non-unique person identifiable information, like names, are privacy protected and cannot be accessed. A solution to protect privacy in probabilistic record linkages is to encrypt these sensitive information. Unfortunately, encrypted hash codes of two names differ completely if the plain names differ only by a single character. Therefore, standard encryption methods cannot be applied. To overcome these challenges, we developed the Privacy Preserving Probabilistic Record Linkage (P3RL) method. METHODS In this Privacy Preserving Probabilistic Record Linkage method we apply a three-party protocol, with two sites collecting individual data and an independent trusted linkage center as the third partner. Our method consists of three main steps: pre-processing, encryption and probabilistic record linkage. Data pre-processing and encryption are done at the sites by local personnel. To guarantee similar quality and format of variables and identical encryption procedure at each site, the linkage center generates semi-automated pre-processing and encryption templates. To retrieve information (i.e. data structure) for the creation of templates without ever accessing plain person identifiable information, we introduced a novel method of data masking. Sensitive string variables are encrypted using Bloom filters, which enables calculation of similarity coefficients. For date variables, we developed special encryption procedures to handle the most common date errors. The linkage center performs probabilistic record linkage with encrypted person identifiable information and plain non-sensitive variables. RESULTS In this paper we describe step by step how to link existing health-related data using encryption methods to preserve privacy of persons in the study. CONCLUSION Privacy Preserving Probabilistic Record linkage expands record linkage facilities in settings where a unique identifier is unavailable and/or regulations restrict access to the non-unique person identifiable information needed to link existing health-related data sets. Automated pre-processing and encryption fully protect sensitive information ensuring participant confidentiality. This method is suitable not just for epidemiological research but also for any setting with similar challenges.