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Citation patterns are important to understanding the spread of technological ideas as science is essentially a cumulative activity. One feature only now being appreciated is the obsolescence or ageing patterns in citations and the insights their study can bring. There have been a number of studies examining, predicting, modeling and plotting citation delays, ageing and the publication cycle. These normally apply lognormal, log-logistic and Weibull distributions to scientific papers. This paper adds to this body of work by examining a set of 18 other distributions, and tests their predictive power on a new data set based on 10 years of ISI Citation data for 10 innovation centered journals. The resulting grouping of journals appears to be a useful proxy for academic-practitioner involvement and warrants further investigation. The finding that the three-parameter Inverse Gaussian provides the best fit to the data extends the understanding of this process. As well as allowing the classification of literature, this improved representation of citation obsolescence will allow us to predict earlier and more precisely those scientific ideas which are generating noteworthy attention or may be suitable for early exploitation.

Renewable energy costs are now below those of fossil fuels. Five years ago, fossil fuels were the cheapest baseload. The collapse in renewable costs means that for 85% of the world, renewable electricity is the cheapest source of new baseload. By the early 2020s it will be every major country. Because of the rise of cheap renewables, the fossil fuel system is ripe for disruption. This disruption will be having profound financial implications for investors as a quarter of equity markets and half of corporate bond markets are “carbon entangled”…

In this paper, taking the 2015-2030 development plan of logistics park in Hunchun as an example, through the number and distribution of logistics enterprises in the location area, using the MATLAB software to program cellular genetic algorithm, according to the actual situation to set parameters, so that a reasonable logistics park location model can be built. In this model, the strong, weak logistics enterprises and the surrounding environment can be seen as a natural ecosystem, following the expansion-destruction strategy, and longterm dynamic development of logistics enterprise can be achieved. At the end, the result is accord with the S-Curve, which is follow the nature laws, combining enterprise cluster effect and actual development plan, and choose a reasonable position, for providing theoretical basis for the actual logistics park location problem.