Alles Klar? Curso de alemão para a comunidade

Este resumo expandido tem como objetivo apresentar o projeto Alles klar? – curso de alemão para a comunidade, sua fundamentação e resultados obtidos no ano de 2018. Primeiramente apresenta-se o projeto e a forma como o trabalho se desenvolve, de forma a situar o contexto e os objetivos a que se propõe. Posteriormente, aborda-se a metodologia no que diz respeito às aulas do curso e a capacitação dos discentes envolvidos, e também a sua fundamentação teórica. Por fim são divulgados dados que correspondem ao período de execução e é feita uma análise entre o que era esperado do projeto e os objetivos até então alcançados como forma de avaliar a situação em que este se encontra

Alles Klar? Curso de alemão para a comunidade

Este resumo expandido tem como objetivo apresentar o projeto Alles klar? – curso de alemão para a comunidade, sua fundamentação e resultados obtidos no ano de 2018. Primeiramente apresenta-se o projeto e a forma como o trabalho se desenvolve, de forma a situar o contexto e os objetivos a que se propõe. Posteriormente, aborda-se a metodologia no que diz respeito às aulas do curso e a capacitação dos discentes envolvidos, e também a sua fundamentação teórica. Por fim são divulgados dados que correspondem ao período de execução e é feita uma análise entre o que era esperado do projeto e os objetivos até então alcançados como forma de avaliar a situação em que este se encontra

Providing producer mobility support in NDN through proactive data replication

Email Print Request Permissions Named Data Networking (NDN) is a novel architecture expected to overcome limitations of the current Internet. User mobility is one of the most relevant limitations to be addressed. NDN supports consumer mobility by design but fails to offer the same level of support for producer mobility. Existing approaches to extend NDN are host-centric, which conflicts with NDN principles, and provide limited support for producer mobility. This paper proposes a content-centric strategy that replicates and pushes objects proactively, and unlike previous approaches, takes full advantage of NDN routing and caching features. We compare the proposed strategy with default NDN mechanisms regarding content availability, consumer performance, and network overhead. The evaluation results indicate that our strategy can increase the hit rate of objects by at least 46% and reduce their retrieval time by over 60%, while not adding significant overhead

A brief review of the occurrence of Eulophinae (Hymenoptera: Eulophidae) for the Rio Grande do Sul state, with a key to genera

The occurrence of genera and species of Eulophinae in Rio Grande do Sul is briefly analyzed, and a commented list of genera, as well as a dichotomous key, are provided. There is a new registry of species for the Brazilian and South American eulophine fauna, and for the state there is one new genus occurrence, as well as three new species

Data-Driven Sliding Bearing Temperature Model for Condition Monitoring in Internal Combustion Engines

Condition monitoring of components in internal combustion engines is an essential tool for increasing engine durability and avoiding critical engine operation. If lubrication at the crankshaft main bearings is insufficient, metal-to-metal contacts become likely and thus wear can occur. Bearing temperature measurements with thermocouples serve as a reliable, fast responding, individual bearing-oriented method that is comparatively simple to apply. In combination with a corresponding reference model, such measurements could serve to monitor the bearing condition. Based on experimental data from an MAN D2676 LF51 heavy-duty diesel engine, the derivation of a data-driven model for the crankshaft main bearing temperatures under steady-state engine operation is discussed. A total of 313 temperature measurements per bearing are available for this task. Readily accessible engine operating data that represent the corresponding engine operating points serve as model inputs. Different machine learning methods are thoroughly tested in terms of their prediction error with the help of a repeated nested cross-validation. The methods include different linear regression approaches (i.e., with and without lasso regularization), gradient boosting regression and support vector regression. As the results show, support vector regression is best suited for the problem. In the final evaluation on unseen test data, this method yields a prediction error of less than 0.4 °C (root mean squared error). Considering the temperature range from approximately 76 °C to 112 °C, the results demonstrate that it is possible to reliably predict the bearing temperatures with the chosen approach. Therefore, the combination of a data-driven bearing temperature model and thermocouple-based temperature measurements forms a powerful tool for monitoring the condition of sliding bearings in internal combustion engines

A systematic review of machine learning techniques related to local energy communities

In recent years, digitalisation has rendered machine learning a key tool for improving processes in several sectors, as in the case of electrical power systems. Machine learning algorithms are data-driven models based on statistical learning theory and employed as a tool to exploit the data generated by the power system and its users. Energy communities are emerging as novel organisations for consumers and prosumers in the distribution grid. These communities may operate differently depending on their objectives and the potential service the community wants to offer to the distribution system operator. This paper presents the conceptualisation of a local energy community on the basis of a review of 25 energy community projects. Furthermore, an extensive literature review of machine learning algorithms for local energy community applications was conducted, and these algorithms were categorised according to forecasting, storage optimisation, energy management systems, power stability and quality, security, and energy transactions. The main algorithms reported in the literature were analysed and classified as supervised, unsupervised, and reinforcement learning algorithms. The findings demonstrate the manner in which supervised learning can provide accurate models for forecasting tasks. Similarly, reinforcement learning presents interesting capabilities in terms of control-related applications.publishedVersio

Shibboleth Access Management Federations as an Organisational Model for SDI

Shibboleth is an open source implementation of the OASIS standard Security Assertion Markup Language (SAML). Shibboleth Access Management Federations (AMFs) are used daily around the globe by millions of users – mainly in the academic realm – in order to securely exchange the identity information necessary to make authorisation decisions concerning protected web resources. AMFs are typically comprised of a number of entities, eg, organisations working together to achieve a set of shared objectives while each member retains control over its own internal affairs. There are three main categories of entities: identity management is devolved to individual member organisations who act as Identity Providers, Service Providers are established by organisations wanting to make protected resources available, and finally, there is a small Coordinating Centre. Principally through the European Spatial Data Infrastructure Network (ESDIN) project and the OGC Web Service (OWS) Shibboleth Interoperability Experiment, it has been established that Shibboleth provides a production strength, standards based, open source, interoperable mainstream IT solution to the problem of how to implement AMFs around the OWS central to SDI’s. Furthermore, it has been demonstrated using a prototype federation of INSPIRE compliant services established under ESDIN that this can be done without modifications to either mainstream Shibboleth or OWS. However, non browser based clients require adaptation. Various options exist as to how the main actors within a European SDI/Federation may organise themselves in order to realise the objective of allowing authorised users from key organisations, eg, EU bodies concerned with environmental policy formation, seamless access to harmonised protected geospatial information through OWS. This paper proposes that a parallel security infrastructure is necessary to realise SDI where protected resources are involved and gives an account of work undertaken demonstrating how Shibboleth based AMF’s meet this need

Creative collaboration in citizen science and the evolution of ThinkCamps

This chapter discusses how to harness the potential of creative collaboration through ThinkCamp events – an ‘unconference’ style event with an open and creative environment designed to foster co-creation, co-design and collaborative thinking at key points in the citizen science research cycle. It draws on the authors’ experiences of running (and participating in) creative collaborative events and explores their potential to support inclusive, co-creational approaches to citizen science. Finally, it makes specific recommendations for project initiators, event organisers and policymakers