Enabling Personalized Composition and Adaptive Provisioning of Web Services

The proliferation of interconnected computing devices is fostering the emergence of environments where Web services made available to mobile users are a commodity. Unfortunately, inherent limitations of mobile devices still hinder the seamless access to Web services, and their use in supporting complex user activities. In this paper, we describe the design and implementation of a distributed, adaptive, and context-aware framework for personalized service composition and provisioning adapted to mobile users. Users specify their preferences by annotating existing process templates, leading to personalized service-based processes. To cater for the possibility of low bandwidth communication channels and frequent disconnections, an execution model is proposed whereby the responsibility of orchestrating personalized processes is spread across the participating services and user agents. In addition, the execution model is adaptive in the sense that the runtime environment is able to detect exceptions and react to them according to a set of rules

Early action on Paris Agreement allows for more time to change energy systems

The IMAGE integrated assessment model was used to develop a set of scenarios to evaluate the Nationally Determined Contributions (NDCs) submitted by Parties under the Paris Agreement. The scenarios project emissions and energy system changes under (i) current policies, (ii) implementation of the NDCs, and (iii) various trajectories to a radiative forcing level of 2.8 W/m2 in 2100, which gives a probability of about two thirds to limit warming to below 2 °C. The scenarios show that a cost-optimal pathway from 2020 onwards towards 2.8 W/m2 leads to a global greenhouse gas emission level of 38 gigatonne CO2 equivalent (GtCO2eq) by 2030, equal to a reduction of 20% compared to the 2010 level. The NDCs are projected to lead to 2030 emission levels of 50 GtCO2eq, which is still an increase compared to the 2010 level. A scenario that achieves the 2.8 W/m2 forcing level in 2100 from the 2030 NDC level requires more rapid transitions after 2030 to meet the forcing target. It shows an annual reduction rate in greenhouse gas emissions of 4.7% between 2030 and 2050, rapidly phasing out unabated coal-fired power plant capacity, more rapid scale-up of low-carbon energy, and higher mitigation costs. A bridge scenario shows that enhancing the ambition level of NDCs before 2030 allows for a smoother energy system transition, with average annual emission reduction rates of 4.5% between 2030 and 2050, and more time to phase out coal capacity

Method Families Concept: Application to Decision-Making Methods

International audienceThe role of variability in Software engineering grows increasingly as it allows developing solutions that can be easily adapted to a specific context and reusing existing knowledge. In order to deal with variability in the method engineering (ME) domain, we suggest applying the notion of method families. Method components are organized as a method family, which is configured in the given situation into a method line. In this paper, we motivate the concept of method families by comparing the existing approaches of ME. We detail then the concept of method families and illustrate it with a family of decision-making (DM) methods that we call MADISE

Data for long-term marginal abatement cost curves of non-CO2 greenhouse gases

This dataset represents long-term marginal abatement cost (MAC) curves of all major emission sources of non-CO2 greenhouse gases (GHGs); methane (CH4), nitrous oxide (N2O) and fluorinated gases (HFCs, PFCs and SF6). The work is based on existing short-term MAC curve datasets and recent literature on individual mitigation measures. The data represent a comprehensive set of MAC curves, covering all major non-CO2 emission sources for 26 aggregated world regions. They are suitable for long-term global mitigation scenario development, as dynamical elements (technological progress, removal of implementation barriers) are included. The data is related to the research article: "Long-term marginal abatement cost curves of non-CO 2 greenhouse gases

Perspectives on carbon materials as powerful catalysts in continuous anaerobic bioreactors

Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.watres.2016.06.004.The catalytic effect of commercial microporous activated carbon (AC) and macroporous carbon nanotubes (CNT) is investigated in reductive bioreactions in continuous high rate anaerobic reactors, using the azo dye Acid Orange 10 (AO10) as model compound as electron acceptor and a mixture of VFA as electron donor. Size and concentration of carbon materials (CM) and hydraulic retention time (HRT) are assessed. CM increased the biological reduction rate of AO10, resulting in significantly higher colour removal, as compared to the control reactors. The highest efficiency, 98%, was achieved with a CNT diameter (d) lower than 0.25 mm, at a CNT concentration of 0.12 g per g of volatile solids (VS), a HRT of 10 h and resulted in a chemical oxygen demand (COD) removal of 85%. Reducing the HRT to 5 h, colour and COD removal in CM-mediated bioreactors were above 90% and 80%, respectively. In the control reactor, thought similar COD removal was achieved, AO10 decolourisation was just approximately 20%, demonstrating the ability of CM to significantly accelerate the reduction reactions in continuous bioreactors. AO10 reduction to the correspondent aromatic amines was proved by high performance liquid chromatography (HPLC). Colour decrease in the reactor treating a real effluent with CNT was the double comparatively to the reactor operated without CNT. The presence of AC in the reactor did not affect the microbial diversity, as compared to the control reactor, evidencing that the efficient reduction of AO10 was mainly due to AC rather than attributed to changes in the composition of the microbial communities.This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01- 0145-FEDER-006684). Raquel Pereira had a fellowship (SFRH/BD/ 72388/2010) and Luciana Pereira has the fellowship (SFRH/BPD/ 110235/2015) from FCT. The authors thank the FCT exploratory EXPL/AAG-TEC/0898/2013 project