Bibliometric analysis of nicotine research in China during the period of 1991 to 2007

With the widespread exposure of people to nicotine through recreational use of tobacco products, research into nicotine has attracted increasing attention. Tobacco smoking is by far the most important cause of lung cancer. As the world's largest producer and consumer of tobacco products, China bears a large proportion of the global burden of smoking-related disease; therefore, information on nicotine publications should be collected to formulate future research policy. In the present study, we investigated nicotine-related research articles published by Chinese authors that were indexed in the Science Citation Index (SCI) from 1991 to 2007. An indicator "citations per publication" (CPP) was used in the study to evaluate the impact of journals, articles, and institutes. The quantity of publications has increased at a quicker pace than the worldwide trend. Article visibility, measured as the frequency of being cited, also increased during the period. However, the overall quality of articles, based on the impact factor of journals publishing those articles, dropped behind the worldwide average level. There has been an increase in international collaboration, mainly with researchers in the USA. The average CPP of international co-authorship articles was higher than that of single country publications. Besides the USA, nicotine research in China will benefit from more collaboration with Taiwan, England, and Germany. Some 110 of 264 articles were published by a single institute, and the top six institutes were compared from various angles. Seventy-two subject categories were covered, and trends (in terms of both quantity and quality) of nicotine research in China were compared with worldwide trends. In addition, analysis of keywords in both nicotine and lung cancer research fields was applied to indicate research interests. Mutual cooperation among multiple disciplines needs further strengthening.

Research on multi-degree-of-freedom neurons with weighted graphs

In this paper, we redefine the sample points set in the feature space from the point of view of weighted graph and propose a new covering model - Multi-Degree-of-Freedorn Neurons (MDFN). Base on this model, we describe a geometric learning algorithm with 3-degree-of-freedom neurons. It identifies the sample points secs topological character in the feature space, which is different from the traditional "separation" method. Experiment results demonstrates the general superiority of this algorithm over the traditional PCA+NN algorithm in terms of efficiency and accuracy.

Continuous speech research based on HyperSausage Neuron

In this paper, we presents HyperSausage Neuron based on the High-Dimension Space(HDS), and proposes a new algorithm for speaker independent continuous digit speech recognition. At last, compared to HMM-based method, the recognition rate of HyperSausage Neuron method is higher than that of in HMM-based method.

Shallow gas in the muddy sediments of Eckernförde Bay, Germany

The seafloor of central Eckernförde Bay is characterised by soft muddy sediments that contain free methane gas. Bubbles of free gas cause acoustic turbidity which is observed with acoustic remote sensing systems. Repeated surveys with subbottom profiler and side scan sonar revealed an annual period both of depth of the acoustic turbidity and backscatter strength. The effects are delayed by 3–4 months relative to the atmospheric temperature cycle. In addition, prominent pockmarks, partly related to gas seepage, were detected with the acoustic systems. In a direct approach gas concentrations were measured from cores using the gas chromatography technique. From different tests it is concluded that subsampling of a core should start at its base and should be completed as soon as possible, at least within 35 min after core recovery. Comparison of methane concentrations of summer and winter cores revealed no significant seasonal variation. Thus, it is concluded that the temperature and pressure influences upon solubility control the depth variability of acoustic turbidity which is observed with acoustic remote sensing systems. The delay relative to the atmospheric temperature cycle is caused by slow heat transfer through the water column. The atmospheric temperature cycle as ‘exiting function’ for variable gas solubility offers an opportunity for modelling and predicting the depth of the acoustic turbidity. In practice, however, small-scale variations of, e.g., salinity, or gas concentration profile in the sediment impose limits to predictions. In addition, oceanographic influences as mixing in the water column, variable water inflow, etc. are further complications that reduce the reliability of predictions.