We study galaxies undergoing ram pressure stripping in the Virgo cluster to examine whether we can identify any discernible trend in their star formation activity. We first use 48 galaxies undergoing different stages of stripping based on Hi morphology, Hi deficiency, and relative extent to the stellar disk, from the VIVA survey. We then employ a new scheme for galaxy classification which combines Hi mass fractions and locations in projected phase space, resulting in a new sample of 365 galaxies. We utilize a variety of star formation tracers, which include g - r, WISE [3.4]-[12] colors, and starburstiness that are defined by stellar mass and star formation rates to compare the star formation activity of galaxies at different stripping stages. We find no clear evidence for enhancement in the integrated star formation activity of galaxies undergoing early to active stripping. We are instead able to capture the overall quenching of star formation activity with increasing degree of ram pressure stripping, in agreement with previous studies. Our results suggest that if there is any ram pressure stripping induced enhancement, it is at best locally modest, and galaxies undergoing enhancement make up a small fraction of the total sample. Our results also indicate that it is possible to trace galaxies at different stages of stripping with the combination of Hi gas content and location in projected phase space, which can be extended to other galaxy clusters that lack high-resolution Hi imaging.

Many small and medium-sized manufacturing companies process various product types to respond different customer orders in a single production line. To improve their productivity, they often apply batch processing while considering various product types, constraints on batch sizes and setups, and due date of each order. This study introduces a batch scheduling heuristic for a production line with multiple product types and different due dates of each order. As the process times vary due to the different batch sizes and product types, a recursive equation is developed based on a flow line model to obtain the upper bound on the completion times with less computational complexity than full computation. The batch scheduling algorithm combines and schedules the orders with same product types into a batch to improve productivity, but within the constraints to match the due dates of the orders. The algorithm incorporates simple and intuitive principles for the purpose of being applied to small and medium companies. To test the algorithm, two case studies are introduced; a high pressure coolant (HPC) manufacturing line and a press process at a plate-type heat exchanger manufacturer. From the case studies, the developed algorithm provides significant improvements in setup frequency and thus convenience of workers and productivity, without violating due dates of each order.

The present study aimed to analyze the metaproteome of the microbial community comprising harmful algal bloom (HAB) in the Daechung reservoir, Korea. HAB samples located at GPS coordinates of 36°29’N latitude and 127°28’E longitude were harvested in October 2013. Microscopic observation of the HAB samples revealed red signals that were presumably caused by the autofluorescence of chlorophyll and phycocyanin in viable cyanobacteria. Metaproteomic analysis was performed by a gelbased shotgun proteomic method. Protein identification was conducted through a two-step analysis including a forward search strategy (FSS) (random search with the National Center for Biotechnology Information (NCBI), Cyanobase, and Phytozome), and a subsequent reverse search strategy (RSS) (additional Cyanobase search with a decoy database). The total number of proteins identified by the two-step analysis (FSS and RSS) was 1.8-fold higher than that by one-step analysis (FSS only). A total of 194 proteins were assigned to 12 cyanobacterial species (99 mol%) and one green algae species (1 mol%). Among the species identified, the toxic microcystin-producing Microcystis aeruginosa NIES-843 (62.3%) species was the most dominant. The largest functional category was proteins belonging to the energy category (39%), followed by metabolism (15%), and translation (12%). This study will be a good reference for monitoring ecological variations at the meta-protein level of aquatic microalgae for understanding HAB.

The seasonal variation in the zooplankton community and hydrographic conditions were examined in three regions (inner, central, and outer regions) of Gamak Bay, Korea. Zooplankton samples were collected over a period of 12 months from January to December 2006. The hydrographical parameters of temperature, salinity, chlorophyll-a concentrations, dissolved oxygen, and chemical oxygen demand were measured. The total zooplankton density varied from 411 to 58,485 ind. m-3, with peaks in early summer. A total of 65 taxa accounted for approximately 86.9% of the annual mean zooplankton density: Noctiluca scintillans (30.9%) Paracalanus parvus s. l. (24.3%), Acartia omorii (11.9 %), Eurytemora pacifica (5.7%), cladocerans (4.1%), cirriped larvae (3.8%), Oithona similis (3.7%), and Pseudevedne tergestina (2.5%). Copepods dominated numerically throughout the year and comprised 54.3% of the total zooplankton. Most of the dominant copepods showed a well-defined seasonal pattern. The density and diversity of zooplankton in Gamak Bay were influenced by the hydrographic environment that was subject to significant spatial and temporal variations. Multivariate statistics showed that seasonal temperature was the most significant predictor of zooplankton taxa, density, and diversity, as well as the density of dominant taxa. Our results suggest that fluctuations in the zooplankton populations, particularly copepods, followed progressive increments in the temperature and COD concentrations.