Authors:István Péntek, Ábel Garai, and Attila Adamkó
, Garai Á. , Péntek I.
Common open telemedicine hub and interface standard recommendation , The 10th Jubilee Conference of PhD Students in Computer Science , Szeged , Hungary , 27-29 June 2016 , pp. 24 − 25 .
Authors:P. Biella, J. Ollerton, M. Barcella, and S. Assini
Conservation of species is often focused either only on those that are endangered, or on maximising the number recorded on species lists. However, species share space and time with others, thus interacting and building frameworks of relationships that can be unravelled by community-level network analysis. It is these relationships that ultimately drive ecosystem function via the transfer of energy and nutrients. However interactions are rarely considered in conservation planning. Network analysis can be used to detect key species (“hubs”) that play an important role in cohesiveness of networks. We applied this approach to plant-pollinator communities on two montane Northern Apennine grasslands, paying special attention to the modules and the identity of hubs. We performed season-wide sampling and then focused the network analyses on time units consistent with plant phenology. After testing for significance of modules, only some modules were found to be significantly segregated from others. Thus, networks were organized around a structured core of modules with a set of companion species that were not organized into compartments. Using a network approach we obtained a list of important plant and pollinator species, including three Network Hubs of utmost importance, and other hubs of particular biogeographical interest. By having a lot of links and high partner diversity, hubs should convey stability to networks. Due to their role in the networks, taking into account such key species when considering the management of sites could help to preserve the greatest number of interactions and thus support many other species.
Visualization with the algorithm of BibTechMon provides the out-degree as well as the in-degree. The analysis shows that both
frequency and co-occurrences of objects (nodes in the network) support the idea of Kleinberg's algorithm. The analysis of
the algorithm shows clearly that strongly linked scores lead the iteration to a convergence and give the highest weights.
Therefore BibTechMon visualizes the results well.
We show that inventive productivity can be described by two variables, Frenquency and Lifetime. For several samples of inventors,
we show that the Exponential and Generalized Pareto distributions provide excellent goodness-of-fit to these variables. Furthermore,
good fits to these distributions arises naturally from the statistics of exceedance. Thus, a better theoretical foundation
and connection to environmental variables is shown for Frequency and Lifetime than has been shown for Lotka's Law.
This study on co-authorship networks in the area of nanostructured solar cells aims to contribute to a further understanding
of the use of research evaluation measures of science output, impact and structure in an emerging research field. The study
incorporates quantitative bibliometric methods of analysis and social network analysis in combination with a qualitative case
study research approach. Conclusions drawn from the results emphasise, firstly, the importance of distinguishing between early
and later phases of the evolution of a novel research field, and secondly, the application of a systemic view on learning
processes and knowledge diffusion in a science-based technology field.
The ion microanalyzer permits a localized mass spectrometric analysis, i.e. the qualitative and quantitative analysis of the
impurities contained in a small selected volume. This procedure makes possible the analysis of very thin epitaxial layers
(for example silicon and gallium arsenide). As regards qualitative analysis, the apparatus is designed for the selection of
ions. After the recording and analysis of the ion spectrum, a large number of the impurities present in the sample are determined
qualitatively. Quantitative analysis can be performed with the equipment, but this requires the analysis of a homogeneous
standard sample previously dosed by spark-source mass spectrometry. The quantitative analysis of bulk and epitaxial silicon
and gallium arsenide is described and the limits of detection of the principal impurities are given. It is also shown how
the possibility of localized analysis was exploited. A correlation was established between the existing impurities and the
chemically revealed crystal imperfections. A comparative analysis of the distribution of the impurities in the epitaxial layers
was also carried out. The periodic analysis of epitaxial layers makes it possible to follow the deterioration by contamination,
if any, under the epitaxial conditions, and to improve the sample quality.
A new ICS system has recently been constructed at the University of Vienna. Using low-energy planar detectors and improved electronic components, the detection limit could be lowered to about 20 ng/g for carbonate rocks. As standards, either synthetic iridium standards loaded on high-purity quartz powder, or well characterized geological reference materials, were used. In a series of test measurements we attempted to optimize external factors, such as sample size, irradiation time and flux, decay time, and measuring time, and to determine the optimal electronic configuration, including shaping time, coincidence time, range, and gain. Further measurements are geared towards the establishment of a simple and effective routine procedure for a variety of rock types containing iridium at concentrations within the 0.02–1 ppb range.
We show that scientific production can be described by two variables: rate of production (rateof publications) and career duration. For 19th century physicists, we show that the time pattern ofproduction is random and Poisson distributed, contrary to the theory of cumulative advantage. Weshow that the exponential distribution provides excellent goodness-of-fit to rate of production andcareer duration. The good fits to these distributions can be explained naturally from the statisticsof exceedances. Thus, more powerful statistical tests and a better theoretical foundation isobtained for rate of production and career duration than has been the case for Lotka's Law.