Reduced calcification and lack of acclimatization by coral colonies growing in areas of persistent natural acidification

As the surface ocean equilibrates with rising atmospheric CO2, the pH of surface seawater is decreasing with potentially negative impacts on coral calcification. A critical question is whether corals will be able to adapt or acclimate to these changes in seawater chemistry. We use high precision CT scanning of skeletal cores of Porites astreoides, an important Caribbean reef-building coral, to show that calcification rates decrease significantly along a natural gradient in pH and aragonite saturation (Omega arag). This decrease is accompanied by an increase in skeletal erosion and predation by boring organisms. The degree of sensitivity to reduced ?arag measured on our field corals is consistent with that exhibited by the same species in laboratory CO2 manipulation experiments. We conclude that the Porites corals at our field site were not able to acclimatize enough to prevent the impacts of local ocean acidification on their skeletal growth and development, despite spending their entire lifespan in low pH, low Omega arag seawater.

Continuous measurements at Schwingbachtal Studienlandschaft in the growing seasons 2013-2014

The recent development of in-situ monitoring devices, such as UV-spectrometers, makes the study of short-term stream chemistry variation relevant, especially the study of diurnal cycles, which are not yet fully understood. Our study is based on high-frequency data from an agricultural catchment (Studienlandschaft Schwingbachtal, Germany). We propose a novel approach, i.e. the combination of cluster analysis and Linear Discriminant Analysis, to mine from these data nitrate behavior patterns. As a result, we observe a seasonality of nitrate diurnal cycles, that differs from the most common cycle seasonality described in the literature, i.e. pre-dawn peaks in spring. Our cycles appear in summer and the maximum and minimum shift to a later time in late summer/autumn. This is observed both for water- and energy-limited years, thus potentially stressing the role of evapotranspiration. This concluding hypothesis on the role of evapotranspiration on nitrate stream concentration, which was obtained through data mining, broadens the perspective on the diurnal cycling of stream nitrate concentrations.

A note on ethnic return migration policy in Kazakhstan : changing priorities and a growing dilemma

This paper offers a brief analysis of the legal aspects of the ethnic return migration policy of Kazakhstan, a post-Soviet Central Asian state that has been active in seeking ties with its diaspora since independence. This paper examines the definition of oralman (repatriates) and the establishment of a quota on the number of Kazakh immigrants who are eligible for government funds to show how the rationale and preferences in repatriation policy have changed over the years. By focusing on changes in migration-related legislation in the late 2000s and early 2010s, the paper notes that two key goals of Kazakhstan’s migration policy are not necessarily consistent with each other: the promotion of an ethnically based nation-building project by encouraging the "return" of co-ethnics living abroad, and building a workforce that is best suited for the development of the state’s economy.

A combined measure for quantifying and qualifying the topology preservation of growing self-organizing maps

The Self-OrganizingMap (SOM) is a neural network model that performs an ordered projection of a high dimensional input space in a low-dimensional topological structure. The process in which such mapping is formed is defined by the SOM algorithm, which is a competitive, unsupervised and nonparametric method, since it does not make any assumption about the input data distribution. The feature maps provided by this algorithm have been successfully applied for vector quantization, clustering and high dimensional data visualization processes. However, the initialization of the network topology and the selection of the SOM training parameters are two difficult tasks caused by the unknown distribution of the input signals. A misconfiguration of these parameters can generate a feature map of low-quality, so it is necessary to have some measure of the degree of adaptation of the SOM network to the input data model. The topologypreservation is the most common concept used to implement this measure. Several qualitative and quantitative methods have been proposed for measuring the degree of SOM topologypreservation, particularly using Kohonen's model. In this work, two methods for measuring the topologypreservation of the Growing Cell Structures (GCSs) model are proposed: the topographic function and the topology preserving map