Explicit Characterization of Spatial Heterogeneity Based on Water Quality, Sediment Contamination, and Ichthyofauna in a Riverine-to-Coastal Zone.

Our study aims to identify the spatial characteristics of water quality and sediment conditions in relation to fisheries resources, since the productivity of fisheries resources is closely related to the ambient conditions of the resource areas. We collected water quality samples and sediment contaminants from twenty-one sites at Gwangyang Bay, Korea, in the summer of 2018. Our study sites covered the area from the Seomjin River estuary to the inner and outer bays. To spatially characterize physicochemical features of Gwangyang Bay, we used Self-Organizing Map (SOM), which is known as a robust and powerful tool of unsupervised neural networks for pattern recognition. The present environmental conditions of Gwangyang Bay were spatially characterized according to four different attributes of water quality and sediment contamination. From the results, we put emphasis on several interesting points: (i) the SOM manifests the dominant physicochemical attributes of each geographical zone associated with the patterns of water quality and sediment contamination; (ii) fish populations appear to be closely associated with their food sources (e.g., shrimps and crabs) as well as the ambient physicochemical conditions; and (iii) in the context of public health and ecosystem services, the SOM result can potentially offer guidance for fish consumption associated with sediment heavy metal contamination. The present study may have limitations in representing general features of Gwangyang Bay, given the inability of snapshot data to characterize a complex ecosystem. In this regard, consistent sampling and investigation are needed to capture spatial variation and to delineate the temporal dynamics of water quality, sediment contamination, and fish populations. However, the SOM application is helpful and useful as a first approximation of an environmental assessment for the effective management of fisheries resources.

Suppression of cooling by strong magnetic fields in white dwarf stars.

Isolated cool white dwarf stars more often have strong magnetic fields than young, hotter white dwarfs, which has been a puzzle because magnetic fields are expected to decay with time but a cool surface suggests that the star is old. In addition, some white dwarfs with strong fields vary in brightness as they rotate, which has been variously attributed to surface brightness inhomogeneities similar to sunspots, chemical inhomogeneities and other magneto-optical effects. Here we describe optical observations of the brightness and magnetic field of the cool white dwarf WD 1953-011 taken over about eight years, and the results of an analysis of its surface temperature and magnetic field distribution. We find that the magnetic field suppresses atmospheric convection, leading to dark spots in the most magnetized areas. We also find that strong fields are sufficient to suppress convection over the entire surface in cool magnetic white dwarfs, which inhibits their cooling evolution relative to weakly magnetic and non-magnetic white dwarfs, making them appear younger than they truly are. This explains the long-standing mystery of why magnetic fields are more common amongst cool white dwarfs, and implies that the currently accepted ages of strongly magnetic white dwarfs are systematically too young.

Evolutionary grinding model for nanometric control of surface roughness for aspheric optical surfaces.

A new evolutionary grinding process model has been developed for nanometric control of material removal from an aspheric surface of Zerodur substrate. The model incorporates novel control features such as i) a growing database; ii) an evolving, multi-variable regression equation; and iii) an adaptive correction factor for target surface roughness (Ra) for the next machine run. This process model demonstrated a unique evolutionary controllability of machining performance resulting in the final grinding accuracy (i.e. averaged difference between target and measured surface roughness) of -0.2+/-2.3(sigma) nm Ra over seven trial machine runs for the target surface roughness ranging from 115 nm to 64 nm Ra.