Tracking the evolution of waste recycling research using overlay maps of science

Tracking the evolution of research in waste recycling science (WRS) can be valuable for environmental agencies, as well as for recycling businesses. Maps of science are visual, easily readable representations of the cognitive structure of a branch of science, a particular area of research or the global spectrum of scientific production. They are generally built upon evidence collected from reliable sources of information, such as patent and scientific publication databases. This study uses the methodology developed by Rafols et al. (2010) to make a “double overlay map” of WRS upon a basemap reflecting the cognitive structure of all journal-published science, for the years 2005 and 2010. The analysis has taken into account the cognitive areas where WRS articles are published and the areas from where it takes its intellectual nourishing, paying special attention to the growing trends of the key areas. Interpretation of results lead to the conclusion that extraction of energy from waste will probably be an important research topic in the future, along with developments in general chemistry and chemical engineering oriented to the recovery of valuable materials from waste. Agricultural and material sciences, together with the combined economics, politics and geography field, are areas with which WRS shows a relevant and ever increasing cognitive relationship.

An Extension of Self-Organizing Maps to Categorical Data

Self-organizing maps (SOM) have been recognized as a powerful tool in data exploratoration, especially for the tasks of clustering on high dimensional data. However, clustering on categorical data is still a challenge for SOM. This paper aims to extend standard SOM to handle feature values of categorical type. A batch SOM algorithm (NCSOM) is presented concerning the dissimilarity measure and update method of map evolution for both numeric and categorical features simultaneously.

Automated, objective texture segmentation of multibeam echosounder data - Seafloor survey and substrate maps from James Island to Ozette Lake, Washington Outer Coast

Without knowledge of basic seafloor characteristics, the ability to address any number of critical marine and/or coastal management issues is diminished. For example, management and conservation of essential fish habitat (EFH), a requirement mandated by federally guided fishery management plans (FMPs), requires among other things a description of habitats for federally managed species. Although the list of attributes important to habitat are numerous, the ability to efficiently and effectively describe many, and especially at the scales required, does not exist with the tools currently available. However, several characteristics of seafloor morphology are readily obtainable at multiple scales and can serve as useful descriptors of habitat. Recent advancements in acoustic technology, such as multibeam echosounding (MBES), can provide remote indication of surficial sediment properties such as texture, hardness, or roughness, and further permit highly detailed renderings of seafloor morphology. With acoustic-based surveys providing a relatively efficient method for data acquisition, there exists a need for efficient and reproducible automated segmentation routines to process the data. Using MBES data collected by the Olympic Coast National Marine Sanctuary (OCNMS), and through a contracted seafloor survey, we expanded on the techniques of Cutter et al. (2003) to describe an objective repeatable process that uses parameterized local Fourier histogram (LFH) texture features to automate segmentation of surficial sediments from acoustic imagery using a maximum likelihood decision rule. Sonar signatures and classification performance were evaluated using video imagery obtained from a towed camera sled. Segmented raster images were converted to polygon features and attributed using a hierarchical deep-water marine benthic classification scheme (Greene et al. 1999) for use in a geographical information system (GIS). (PDF contains 41 pages.)