Agent-based modeling is one appealing new methodology that has the potential to overcome some shortcomings of traditional methods.
Robustness and versatility are some of the most important properties of self-assembling natural systems and this makes self-assembly an even more appealing engineering methodology.
It is an appealing control methodology, but it is difficult to implement and its solution is not so performing since it unavoidably means to solve a usually large-scale, constrained, and multidimensional optimization.
The Hadoop MR framework has an appealing programming methodology in which programmers mainly need to implement two functions: map (mapper) and reduce (reducer).
Recently, the Hadoop platform has emerged as an interesting option to address the challenges of increasingly large datasets with distributed storage, distributed processing, built-in data locality, fault tolerance, and an appealing programming methodology.
The proposed methodology is appealing for its computational features and can easily be applicable to design fair clinical protocols or rationalize prescription decisions.
Although the idea of electronic self-monitoring seems appealing, studies using rigorous methodology investigating beneficial and possible harmful effects of electronic self-monitoring are needed.
This methodology is appealing in that it can incorporate complex relationships between covariates and survival more easily than standard approaches such as Cox regression, which may be too simplistic.
The advocated methodology seems appealing when compared with the more standard approach of estimating ambient exposure separately in individual metropolitan cities (Dockery et al. 1993; Jerrett et al. 2005; Miller et al. 2007; Pope et al. 1995) because it simplifies exposure assessment for multicity studies and allows inclusion of subjects far from monitoring sites.
The integration of fuzzy logic for decision-making purposes represents a reliable methodology that could be appealing for managers, practitioners and analysts (Malagoli, Magni & Mastroleo, 2007).
