Functional data analysis encompasses many methodologies for statistical analysis of data coming from measurements concerning continuous phenomena, e.g., curves or surfaces [ 47].
There are many methodologies to help recognize the responsibilities of a system and assign them to classes, but all of them depend greatly on human judgment and decision-making.
To date many methodologies for testing the differential expression of gene sets (molecular pathways, biological processes) have been suggested and are collectively named gene set analysis (GSA) approaches (Ackermann and Strimmer, 2009; Dinu et al., 2009; Emmert-Streib and Glazko, 2011; Huang da et al., 2009).
Important features of many methodologies under the umbrella of SAs are, among others, the importance of considering both environmental and social aspects, of acknowledging values brought into the assessment, consideration of how sustainability is viewed, uncertainty and transparency handled and the importance of participatory approaches [6, 7, 8, 9].
While formal methods form a part of many methodologies, modern software development uses a rich mixture of methods and techniques.
However, as also shown by recently published results [ 10, 11], in spite of the many methodologies and instrumentation proposed, the accurate determination of the optical properties of diffusive media remains a difficult task.
Following the isolation of HESCs [ 60], many methodologies now exist for successful maintenance of HESCs, generation of neural precursor cells and subsequent differentiation to defined neuronal sub-types [ 61– 64], also comprising adherent and non-adherent neurosphere based systems.
However, starting with a problem or issue in mind, is also part of many methodologies described in the literature [29, 37].
In their efforts to model uncertainty, decision theorists have proposed many methodologies with the majority of them having been based on statistics and probability[ 1- 4], information theory and entropy[ 5], or possibilistic approaches such as fuzzy logic[ 6, 7].
Many methodologies have been developed for making of EIEs, some of them are fairly complex and unsuitable for EMS implementation in an organizational context, principally when small and medium size enterprises (SMEs) are involved.
