Investigation of grain orientations of melt-textured HTSC with addition of uranium oxide, Y2O3 and Y2BaCuO5

Local grain orientations were studied in melt-textured YBCO samples processed with various amounts of depleted uranuim oxide (DU) and Y 2O3 by means of electron backscatter diffraction (EBSD) analysis. The addition of DU leads to the formation of Ucontaining nanoparticles (Y2Ba4CuUOx) with sizes of around 200 nm, embedded in the superconducting Y-123 matrix. The orientation of the Y 2BaCuO5 (Y-211) particles, which are also present in the YBCO bulk microstructure, is generally random as is the case in other melttextured Y-123 samples. The presence of Y-211 particles, however, also affects the orientation of the Y-123 matrix in these samples

Imaging and modeling the ionospheric airglow response over Hawaii to the tsunami generated by the Tohoku earthquake of 11 March 2011

Although only centimeters in amplitude over the open ocean, tsunamis can generate appreciable wave amplitudes in the upper atmosphere, including the naturally occurring chemiluminescent airglow layers, due to the exponential decrease in density with altitude. Here, we present the first observation of the airglow tsunami signature, resulting from the 11 March 2011 Tohoku earthquake off the eastern coast of Japan. These images are taken using a wide-angle camera system located at the top of the Haleakala Volcano on Maui, Hawaii. They are correlated with GPS measurements of the total electron content from Hawaii GPS stations and the Jason-1 satellite. We find waves propagating in the airglow layer from the direction of the earthquake epicenter with a velocity that matches that of the ocean tsunami. The first ionospheric signature precedes the modeled ocean tsunami generated by the main shock by approximately one hour. These results demonstrate the utility of monitoring the Earth's airglow layers for tsunami detection and early warning

Science and Management of Intermittent Rivers and Ephemeral Streams (SMIRES)

More than half of the global river network is composed of intermittent rivers and ephemeral streams (IRES), which are expanding in response to climate change and increasing water demands. After years of obscurity, the science of IRES has bloomed recently and it is being recognised that IRES support a unique and high biodiversity, provide essential ecosystem services and are functionally part of river networks and groundwater systems. However, they still lack protective and adequate management, thereby jeopardizing water resources at the global scale. This Action brings together hydrologists, biogeochemists, ecologists, modellers, environmental economists, social researchers and stakeholders from 14 different countries to develop a research network for synthesising the fragmented, recent knowledge on IRES, improving our understanding of IRES and translating this into a science-based, sustainable management of river networks. Deliverables will be provided through i) research workshops synthesising and addressing key challenges in IRES science, supporting research exchange and educating young researchers, and ii) researcher-stakeholder workshops translating improved knowledge into tangible tools and guidelines for protecting IRES and raising awareness of their importance and value in societal and decision-maker spheres. This Action is organized within six Working Groups to address: (i) the occurrence, distribution and hydrological trends of IRES; (ii) the effects of flow alterations on IRES functions and services; (iii) the interaction of aquatic and terrestrial biogeochemical processes at catchment scale; (iv) the biomonitoring of the ecological status of IRES; (v) synergies in IRES research at the European scale, data assemblage and sharing; (vi) IRES management and advocacy training

Using machine learning and Biogeochemical-Argo (BGC-Argo) floats to assess biogeochemical models and optimize observing system design

Numerical models of ocean biogeochemistry are becoming the major tools used to detect and predict the impact of climate change on marine resources and to monitor ocean health. However, with the continuous improvement of model structure and spatial resolution, incorporation of these additional degrees of freedom into fidelity assessment has become increasingly challenging. Here, we propose a new method to provide information on the model predictive skill in a concise way. The method is based on the conjoint use of a k-means clustering technique, assessment metrics, and Biogeochemical-Argo (BGC-Argo) observations. The k-means algorithm and the assessment metrics reduce the number of model data points to be evaluated. The metrics evaluate either the model state accuracy or the skill of the model with respect to capturing emergent properties, such as the deep chlorophyll maximums and oxygen minimum zones. The use of BGC-Argo observations as the sole evaluation data set ensures the accuracy of the data, as it is a homogenous data set with strict sampling methodologies and data quality control procedures. The method is applied to the Global Ocean Biogeochemistry Analysis and Forecast system of the Copernicus Marine Service. The model performance is evaluated using the model efficiency statistical score, which compares the model–observation misfit with the variability in the observations and, thus, objectively quantifies whether the model outperforms the BGC-Argo climatology. We show that, overall, the model surpasses the BGC-Argo climatology in predicting pH, dissolved inorganic carbon, alkalinity, oxygen, nitrate, and phosphate in the mesopelagic and the mixed layers as well as silicate in the mesopelagic layer. However, there are still areas for improvement with respect to reducing the model–data misfit for certain variables such as silicate, pH, and the partial pressure of CO2 in the mixed layer as well as chlorophyll-a-related, oxygen-minimum-zone-related, and particulate-organic-carbon-related metrics. The method proposed here can also aid in refining the design of the BGC-Argo network, in particular regarding the regions in which BGC-Argo observations should be enhanced to improve the model accuracy via the assimilation of BGC-Argo data or process-oriented assessment studies. We strongly recommend increasing the number of observations in the Arctic region while maintaining the existing high-density of observations in the Southern Oceans. The model error in these regions is only slightly less than the variability observed in BGC-Argo measurements. Our study illustrates how the synergic use of modeling and BGC-Argo data can both provide information about the performance of models and improve the design of observing systems.</p

Sustainability of Global Golden Inland Waterways

Sustainable inland waterways should meet the needs of navigation without compromising the health of riverine ecosystems. Here we propose a hierarchical model to describe sustainable development of the Golden Inland Waterways (GIWs) which are characterized by great bearing capacity and transport need. Based on datasets from 66 large rivers (basin area > 100,000 km2) worldwide, we identify 34 GIWs, mostly distributed in Asia, Europe, North America, and South America, typically following a three-stage development path from the initial, through to the developing and on to the developed stage. For most GIWs, the exploitation ratio, defined as the ratio of actual to idealized bearing capacity, should be less than 80% due to ecological considerations. Combined with the indices of regional development, GIWs exploitation, and riverine ecosystem, we reveal the global diversity and evolution of GIWs' sustainability from 2015 to 2050, which highlights the importance of river-specific strategies for waterway exploitation worldwide

Growth of few-wall carbon nanotubes with narrow diameter distribution over Fe-Mo-MgO catalyst by methane/acetylene catalytic decomposition

Few-wall carbon nanotubes were synthesized by methane/acetylene decomposition over bimetallic Fe-Mo catalyst with MgO (1:8:40) support at the temperature of 900°C. No calcinations and reduction pretreatments were applied to the catalytic powder. The transmission electron microscopy investigation showed that the synthesized carbon nanotubes [CNTs] have high purity and narrow diameter distribution. Raman spectrum showed that the ratio of G to D band line intensities of IG/ID is approximately 10, and the peaks in the low frequency range were attributed to the radial breathing mode corresponding to the nanotubes of small diameters. Thermogravimetric analysis data indicated no amorphous carbon phases. Experiments conducted at higher gas pressures showed the increase of CNT yield up to 83%. Mössbauer spectroscopy, magnetization measurements, X-ray diffraction, high-resolution transmission electron microscopy, and electron diffraction were employed to evaluate the nature of catalyst particles

Generalized additive and fuzzy models in environmental flow assessment: A comparison employing the West Balkan trout (Salmo farioides; Karaman, 1938)

Human activities have altered flow regimes resulting in increased pressures and threats on river biota. Physical habitat simulation has been established as a standard approach among the methods for Environmental Flow Assessment (EFA). Traditionally, in EFA, univariate habitat suitability curves have been used to evaluate the habitat suitability at the microhabitat scale whereas Generalized Additive Models (GAMs) and fuzzy logic are considered the most common multivariate approaches to do so. The assessment of the habitat suitability for three size classes of the West Balkan trout (Salmo farioides; Karaman, 1938) inferred with these multivariate approaches was compared at three different levels. First the modelled patterns of habitat selection were compared by developing partial dependence plots. Then, the habitat assessment was spatially explicitly compared by calculating the fuzzy kappa statistic and finally, the habitat quantity and quality was compared broadly and at relevant flows under a hypothetical flow regulation, based on the Weighted Usable Area (WUA) vs. flow curves. The GAMs were slightly more accurate and the WUA-flow curves demonstrated that they were more optimistic in the habitat assessment with larger areas assessed with low to intermediate suitability (0.2 0.6). Nevertheless, both approaches coincided in the habitat assessment (the optimal areas were spatially coincident) and in the modelled patterns of habitat selection; large trout selected microhabitats with low flow velocity, large depth, coarse substrate and abundant cover. Medium sized trout selected microhabitats with low flow velocity, middle-to-large depth, any kind of substrate but bedrock and some elements of cover. Finally small trout selected microhabitats with low flow velocity, small depth, and light cover only avoiding bedrock substrate. Furthermore, both approaches also rendered similar WUA-flow curves and coincided in the predicted increases and decreases of the WUA under the hypothetical flow regulation. Although on an equal footing, GAMs performed slightly better, they do not automatically account for variables interactions. Conversely, fuzzy models do so and can be easily modified by experts to include new insights or to cover a wider range of environmental conditions. Therefore, as a consequence of the agreement between both approaches, we would advocate for combinations of GAMs and fuzzy models in fish-based EFA.This study was supported by the ECOFLOW project funded by the Hellenic General Secretariat of Research and Technology in the framework of the NSRF 2007-2013. We are grateful for field assistance of Dimitris Kommatas, Orfeas Triantafillou and Martin Palt and to Alcibiades N. Economou for assistance in discussions on trout biology and ecology.Muñoz Mas, R.; Papadaki, C.; Martinez-Capel, F.; Zogaris, S.; Ntoanidis, L.; Dimitriou, E. (2016). Generalized additive and fuzzy models in environmental flow assessment: A comparison employing the West Balkan trout (Salmo farioides; Karaman, 1938). Ecological Engineering. 91:365-377. doi:10.1016/j.ecoleng.2016.03.009S3653779

A change in the name of the type of Chondria C. Agardh (Rhodomelaceae, Rhodophyta)

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149775/1/tax01150.pd