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The quality protection and improvement of the aquatic ecosystems and achievement of "good status" for all water bodies until 2015, involved integration of key ecosystem principles into water policies and a series of new management elements. Thus, the "health" status of aquatic ecosystems is a new objective for European water policy which is reflected in the structure and functioning of aquatic ecosystems, being defined by the biological, morphological and physico-chemical quality components as well as the presence of specific pollutants (synthetic and non-synthetic). In order to achieve good ecological status / good ecological potential for all water bodies (rivers) of Romania, the paper presents an innovative approach regarding monitoring of hydromorphological quality component and its integration in the assessment of ecological status of water bodies. In this approach the river system is seen in the context of stream corridor as a complex of ecosystems which includes not only the river, but also the riparian zone with the species of plants and animals that inhabit this space. The river corridor is responsible for shaping the river bad, retaining the water and sediments, and also constitutes the support for creating a variety of habitats / microhabitats for communities of aquatic organisms underlying the assessment of ecological status of rivers. In this context, the paper presents hydromorphological indicators set identified in accordance with the requirements of the WFD and a hydromorphological classification system of rivers in five quality classes which should represent a scientific basis for the water monitoring and evaluation system and assists in a judicious way the decision makers to improve water quality in Romania.

In this paper, we study distributed inference and learning over networks which can be modeled by a directed graph. A subset of the nodes observes different features, which are all relevant/required for the inference task that needs to be performed at some distant end (fusion) node. We develop a learning algorithm and an architecture that can combine the information from the observed distributed features, using the processing units available across the networks. In particular, we employ information-theoretic tools to analyze how inference propagates and fuses across a network. Based on the insights gained from this analysis, we derive a loss function that effectively balances the model's performance with the amount of information transmitted across the network. We study the design criterion of our proposed architecture and its bandwidth requirements. Furthermore, we discuss implementation aspects using neural networks in typical wireless radio access and provide experiments that illustrate benefits over state-of-the-art techniques.

Conducting hydromorphological assessments for evaluating the ecological status of rivers is a key requirement of the Directive 2000/60/EC (Water Framework Directive - WFD) within European Union (EU) Member States. This paper aims at understanding how this requirement was implemented, through an original comparative review of methodologies for rivers' hydromorphological assessment in three EU Member States, which joined the EU at different times, and with many differences in terms of hydrographic features, socio-economic and water management systems: France, Romania, and Croatia. More precisely, the paper aims at identifying and understanding the main principles guiding the hydromorphological assessment methodologies, elements and data used, giving an overview of the results of hydromorphological river status assessment, and concluding on the stage of hydromorphological assessment implementation. France developed numerous methodologies for physical habitat survey since the 1990s and it is currently conducting a rigorous hydromorphological field survey, but it does not yet have any national methodology for rivers' hydromorphological status assessment, nevertheless foreseen for the next cycle of the WFD. Besides, Romania and Croatia have already started the assessment of the hydromorphological status of rivers within the two cycles of the River Basin Management Plans and are making efforts to improve the hydromorphological monitoring activity. The methods generally differ in indicators, data used, and spatial scale of analysis, which makes it difficult to compare the results of the assessments. Despite a common water policy, the methodological dissimilarities seem to be rather usual between EU Member States. Therefore, the standardization of methodologies appears to be necessary, but the current results could be useful for setting priorities for river restoration and for achieving a better status at a national scale., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Understanding how microbial diversity influences ecosystem properties is of paramount importance. Cellular traits-which determine responses to the abiotic and biotic environment-may help us rigorously link them. However, our capacity to measure traits in natural communities has thus far been limited. Here we compared the predictive power of trait richness (trait space coverage), evenness (regularity in trait distribution) and divergence (prevalence of extreme phenotypes) derived from individual-based measurements with two species-level metrics (taxonomic richness and evenness) when modelling the productivity of natural phytoplankton communities. Using phytoplankton data obtained from 28 lakes sampled at different spatial and temporal scales, we found that the diversity in individual-level morphophysiological traits strongly improved our ability to predict community resource-use and biomass yield. Trait evenness-the regularity in distribution of individual cells/colonies within the trait space-was the strongest predictor, exhibiting a robust negative relationship across scales. Our study suggests that quantifying individual microbial phenotypes in trait space may help us understand how to link physiology to ecosystem-scale processes. Elucidating the mechanisms scaling individual-level trait variation to microbial community dynamics could there improve our ability to forecast changes in ecosystem properties across environmental gradients.