A Network Based Methodology to Reveal Patterns in Knowledge Transfer

This paper motivates, presents and demonstrates in use a methodology based in complex network analysis to support research aimed at identification of sources in the process of knowledge transfer at the interorganizational level. The importance of this methodology is that it states a unified model to reveal knowledge sharing patterns and to compare results from multiple researches on data from different periods of time and different sectors of the economy. This methodology does not address the underlying statistical processes. To do this, national statistics departments (NSD) provide documents and tools at their websites. But this proposal provides a guide to model information inferences gathered from data processing revealing links between sources and recipients of knowledge being transferred and that the recipient detects as main source to new knowledge creation. Some national statistics departments set as objective for these surveys the characterization of innovation dynamics in firms and to analyze the use of public support instruments. From this characterization scholars conduct different researches. Measures of dimensions of the network composed by manufacturing firms and other organizations conform the base to inquiry the structure that emerges from taking ideas from other organizations to incept innovations. These two sets of data are actors of a two- mode-network. The link between two actors (network nodes, one acting as the source of the idea. The second one acting as the destination) comes from organizations or events organized by organizations that “provide” ideas to other group of firms. The resulting demonstrated design satisfies the objective of being a methodological model to identify sources in knowledge transfer of knowledge effectively used in innovation

Uncertainty Model For Quantitative Precipitation Estimation Using Weather Radars

This paper introduces an uncertainty model for the quantitatively estimate precipitation using weather radars. The model considers various key aspects associated to radar calibration, attenuation, and the tradeoff between accuracy and radar coverage. An S-band-radar case study is presented to illustrate particular fractional-uncertainty calculations obtained to adjust various typical radar-calibration elements such as antenna, transmitter, receiver, and some other general elements included in the radar equation. This paper is based in “Guide to the expression of Uncertainty in measurement” [1] and the results show that the fractional uncertainty calculated by the model was 40 % for the reflectivity and 30% for the precipitation using the Marshall Palmer Z-R relationship