The EMSO-ERIC Pan-European Consortium: Data Benefits and Lessons Learned as the Legal Entity Forms

The European Multidisciplinary Seafloor and water-column Observatory (EMSO) European Research Infrastructure Consortium (ERIC) provides power, communications, sensors, and data infrastructure for continuous, high-resolution, (near-)real-time, interactive ocean observations across a multidisciplinary and interdisciplinary range of research areas including biology, geology, chemistry, physics, engineering, and computer science, from polar to subtropical environments, through the water column down to the abyss. Eleven deep-sea and four shallow nodes span from the Arctic through the Atlantic and Mediterranean, to the Black Sea. Coordination among the consortium nodes is being strengthened through the EMSOdev project (H2020), which will produce the EMSO Generic Instrument Module (EGIM). Early installations are now being upgraded, for example, at the Ligurian, Ionian, Azores, and Porcupine Abyssal Plain (PAP) nodes. Significant findings have been flowing in over the years; for example, high-frequency surface and subsurface water-column measurements of the PAP node show an increase in seawater pCO2 (from 339 μatm in 2003 to 353 μatm in 2011) with little variability in the mean air-sea CO2 flux. In the Central Eastern Atlantic, the Oceanic Platform of the Canary Islands open-ocean canary node (aka ESTOC station) has a long-standing time series on water column physical, biogeochemical, and acidification processes that have contributed to the assessment efforts of the Intergovernmental Panel on Climate Change (IPCC). EMSO not only brings together countries and disciplines but also allows the pooling of resources and coordination to assemble harmonized data into a comprehensive regional ocean picture, which will then be made available to researchers and stakeholders worldwide on an open and interoperable access basis.

The 17th Annual Eric E. Williams Memorial Lecture

This flyer promotes an event for The 17th Annual Eric E.Williams Memorial Lecture, featuring the Honorable Dr. Ralph Everard Gonsalves, Prime Minister of Saint Vincent and the Grenadines. The event was held on October 23, 2015.

The 17th Annual Eric E. Williams Memorial Lecture- The US-Cuba Accord: How the Caribbean Paved the Way

This flyer promotes an event for The 17th Annual Eric E.Williams Memorial Lecture, featuring the Honorable Dr. Ralph Everard Gonsalves, Prime Minister of Saint Vincent and the Grenadines. The event was held on October 23, 2015 at the Wertheim Performing Arts Center, Florida International University.

Caracterização de Fusarium decemcellulare isolado de guaranazeiro (Paullinia cupana var. sorbilis) por meio de ERIC-PCR.

A presente pesquisa visa analisar a diversidade genética De F. decemcellulare isolado de mudas e plantas adultas de guaranazeiro com sintomas de superbrotamento, hipertrofia floral ou galhas por meio do marcador molecular ERIC-PCR.

On Ordinal VC-Dimension and Some Notions of Complexity

We generalize the classical notion of Vapnik–Chernovenkis (VC) dimension to ordinal VC-dimension, in the context of logical learning paradigms. Logical learning paradigms encompass the numerical learning paradigms commonly studied in Inductive Inference. A logical learning paradigm is defined as a set W of structures over some vocabulary, and a set D of first-order formulas that represent data. The sets of models of ϕ in W, where ϕ varies over D, generate a natural topology W over W. We show that if D is closed under boolean operators, then the notion of ordinal VC-dimension offers a perfect characterization for the problem of predicting the truth of the members of D in a member of W, with an ordinal bound on the number of mistakes. This shows that the notion of VC-dimension has a natural interpretation in Inductive Inference, when cast into a logical setting. We also study the relationships between predictive complexity, selective complexity—a variation on predictive complexity—and mind change complexity. The assumptions that D is closed under boolean operators and that W is compact often play a crucial role to establish connections between these concepts. We then consider a computable setting with effective versions of the complexity measures, and show that the equivalence between ordinal VC-dimension and predictive complexity fails. More precisely, we prove that the effective ordinal VC-dimension of a paradigm can be defined when all other effective notions of complexity are undefined. On a better note, when W is compact, all effective notions of complexity are defined, though they are not related as in the noncomputable version of the framework.

Polymer nanocomposites based on P3OT, TPU and SWNT: preparation and characterization

Single walled carbon nanotubes (SWNTs) were incorporated in polymer nanocomposites based on poly(3-octylthiophene) (P3OT), thermoplastic polyurethane (TPU) or a blend of them. Thermogravimetry demonstrated the success of the purification procedure employed in the chemical treatment of SWNTs prior to composite preparation. Stable dispersions of SWNTs in chloroform were obtained by non-covalent interactions with the dissolved polymers. Composites exhibited glass transitions, melting temperatures and heat of fusion which changed in relation to pure polymers. This behavior is discussed as associated to interactions between nanotubes and polymers. The conductivity at room temperature of the blend (TPU-P3OT) with SWNT is higher than the P3OT/SWNT composite.

The science of assessment : identifying and predicting environmental impacts

The historical challenge of environmental impact assessment (EIA) has been to predict project-based impacts accurately. Both EIA legislation and the practice of EIA have evolved over the last three decades in Canada, and the development of the discipline and science of environmental assessment has improved how we apply environmental assessment to complex projects. The practice of environmental assessment integrates the social and natural sciences and relies on an eclectic knowledge base from a wide range of sources. EIA methods and tools provide a means to structure and integrate knowledge in order to evaluate and predict environmental impacts.----- This Chapter will provide a brief overview of how impacts are identified and predicted. How do we determine what aspect of the natural and social environment will be affected when a mine is excavated? How does the practitioner determine the range of potential impacts, assess whether they are significant, and predict the consequences? There are no standard answers to these questions, but there are established methods to provide a foundation for scoping and predicting the potential impacts of a project.----- Of course, the community and publics play an important role in this process, and this will be discussed in subsequent chapters. In the first part of this chapter, we will deal with impact identification, which involves appplying scoping to critical issues and determining impact significance, baseline ecosystem evaluation techniques, and how to communicate environmental impacts. In the second part of the chapter, we discuss the prediction of impacts in relation to the complexity of the environment, ecological risk assessment, and modelling.

Characterization of commercial double-walled carbon nanotube material : composition, structure, and heat capacity

A purified commercial double-walled carbon nanotube (DWCNT) sample was investigated by transmission electron microscopy (TEM), thermogravimetry (TG), and Raman spectroscopy. Moreover, the heat capacity of the DWCNT sample was determined by temperature-modulated differential scanning calorimetry in the range of temperature between -50 and 290 °C. The main thermo-oxidation characterized by TG occurred at 474 °C with the loss of 90 wt% of the sample. Thermo-oxidation of the sample was also investigated by high-resolution TG, which indicated that a fraction rich in carbon nanotube represents more than 80 wt% of the material. Other carbonaceous fractions rich in amorphous coating and graphitic particles were identified by the deconvolution procedure applied to the derivative of TG curve. Complementary structural data were provided by TEM and Raman studies. The information obtained allows the optimization of composites based on this nanomaterial with reliable characteristics.