Fishing effort validation and substitution possibilities among components: the case study of the VIII division European anchovy fishery

Command and control regulation programs, particularly input constraints, typically fail to achieve stated objectives, because fishermen may substitute unregulated for regulated inputs. It is, thus, essential to have an understanding of the internal structure of production technology. A primal formulation is used to estimate a translog production function at the vessels level that includes fishing effort and fisherman’s skill. The flexibility of the selected functional permits the analysis of the substitution possibilities among inputs by estimating the elasticity of substitution with no prior constraints. Particular attention is paid to the empirical validation of fishing effort as an aggregate input, which implies either, the acceptation of the joint hypothesis that inputs making up effort are weakly separable from the inputs out of the subgroup or considering that effort is an intermediate input produced by a non-separable two stage technology. Cross sectional data from the Spanish purse seine fleet operating in the VIII Division European anchovy fishery provide evidence of limited input substitution possibilities among the inputs making up the empirically validated fishing effort translog micro-production function.

Preprocess and data analysis techniques for affymetrix DNA microarrays using bioconductor: a case study in Alzheimer disease

DNA microarray, or DNA chip, is a technology that allows us to obtain the expression level of many genes in a single experiment. The fact that numerical expression values can be easily obtained gives us the possibility to use multiple statistical techniques of data analysis. In this project microarray data is obtained from Gene Expression Omnibus, the repository of National Center for Biotechnology Information (NCBI). Then, the noise is removed and data is normalized, also we use hypothesis tests to find the most relevant genes that may be involved in a disease and use machine learning methods like KNN, Random Forest or Kmeans. For performing the analysis we use Bioconductor, packages in R for the analysis of biological data, and we conduct a case study in Alzheimer disease. The complete code can be found in https://github.com/alberto-poncelas/ bioc-alzheimer

Weighting social preferences in participatory multi-criteria evaluations: a case study on sustainable natural resource management

28 p.