The last major earthquake in Bantul causing severe damage occurred on May 27 th , 2006. The damages in the flat area of Bantul had a certain pattern. The damages pattern controlled the subsurface characteristic below the flat area. Understanding earthquake damage pattern through geomorphological approach is important for earthquake hazard analysis. The techniques of remote sensing and Geographical Information Systems were applied to analyze earthquake damage pattern and geomorphological characteristics. Gravity analysis was used to identify the subsurface structure and the basement depth while geoelectric analysis was used to identify sediment depth. Moreover, spatial correlation analysis was used to identify the relationship between the earthquake damage, geomorphological characteristics, and subsurface characteristics. The results show that fluvial, marine, and aeolian landforms have low rock density value based on gravity analysis. These indicate that they were composed by thick unconsolidated material of quaternary alluvium. While denudational, structural, and solutional landforms composed by material of tertiary rocks have high rock density value. The severe damage occurred in the area that has a lower value of local gravity and deeper basement. In contrast, the slight damage occurred in the area that has higher values of local gravity and shallower basement. Moreover, the severe damage occurred in areas of thicker sediment that consist of unconsolidated material. Consequently, the area of unconsolidated material that has deeper basement and thicker sediment is prone to earthquake. They were located on fluvial, marine, and aeolian landforms.
Feature Model (FM) became an important role in Software Product Line Engineering (SPLE) field. Many approaches have been introduced since the original FM came up with Feature Oriented Domain Analysis (FODA) introduced by Kang in 1990. The main purpose of FM is used for commonality and variability analysis in domain engineering, to optimize the reusable aspect of software features or components. Cardinality-based Feature Model (CBFM) is one extension of original FM, which integrates several notations of other extensions. In CBFM, feature model defined as hierarchy of feature, with each of feature has a cardinality. The other notation to express variability within SPLE is Orthogonal Variability Model (OVM). At the other hand, OMG as standard organization makes an effort to build standard generic language to express the commonality and variability in SPL field, by initiate Common Variability Language (CVL). This paper reports the comparison and mapping of FODA, CBFM and OVM to CVL where need to be explored first to define meta model mapping of these several approaches. Furthermore, the comparison and mapping of those approaches are discussed in term of R3ST (read as "REST") software feature model as the case study.
During the process of revision of the original manuscript we were asked to do additional control experiments. Until we have concluded these experiments and we have a clearer picture of the use of polarization cues for target detection in goldfish we have decided to withdraw the original article.
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