International Standard ISO 9001–A Soft Computing View

In order to add value to ISO 9001, a Quality Management Systems that assess, measure, documents, improves, and certify processes to increase productivity, i.e., that transforms business at any level. On the one hand, this work focuses on the development of a decision support system, which will allow companies to be able to meet the needs of customers by fulfilling requirements that reflect either the effectiveness or the non-effectiveness of an organization. On the other hand, many approaches for knowledge representation and reasoning have been proposed using Logic Programming (LP), namely in the area of Model Theory or Proof Theory. In this work it is followed the proof theoretical approach in terms of an extension to the LP language to knowledge representation and reasoning. The computational framework is centered on Artificial Neural Networks to evaluate customer’s satisfaction and the degree of confidence that one has on such a happening

The importance of crop growth modeling to interpret the Δ14CO2 signature of annual plants

[1] The 14C/C abundance in CO2(¿14CO2) promises to provide useful constraints on regional fossil fuel emissions and atmospheric transport through the large gradients introduced by anthropogenic activity. The currently sparse atmospheric ¿14CO2 monitoring network can potentially be augmented by using plant biomass as an integrated sample of the atmospheric ¿14CO2. But the interpretation of such an integrated sample requires knowledge about the day¿to¿day CO2 uptake of the sampled plants. We investigate here the required detail in daily plant growth variations needed to accurately interpret regional fossil fuel emissions from annual plant samples. We use a crop growth model driven by daily meteorology to reproduce daily fixation of ¿14CO2 in maize and wheat plants in the Netherlands in 2008. When comparing the integrated ¿14CO2 simulated with this detailed model to the values obtained when using simpler proxies for daily plant growth (such as radiation and temperature), we find differences that can exceed the reported measurement precision of ¿14CO2(~2‰). Furthermore, we show that even in the absence of any spatial differences in fossil fuel emissions, differences in regional weather can induce plant growth variations that result in spatial gradients of up to 3.5‰ in plant samples. These gradients are even larger when interpreting separate plant organs (leaves, stems, roots, or fruits), as they each develop during different time periods. Not accounting for these growth¿induced differences in ¿14CO2 in plant samples would introduce a substantial bias (1.5–2¿ppm) when estimating the fraction of atmospheric CO2 variations resulting from nearby fossil fuel emission

Decision support tools for site-specific fertilizer recommendations and agricultural planning in selected countries in sub-Sahara Africa

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