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.

‘Big Bang’ 품종은 꽃이 크고 생육이 왕성하며 엽소 및 잎 마름병에 강한 백합 신품종을 육성하기 위하여 2010년 충남농 업기술원 태안백합시험장에서 교배한 OT(Oriental hybrid × Trumpet hybrid) 백합의 종간교잡종이다. 모본으로는 노랑색의 OT백합 ‘Biarits’ 품종을, 부본으로는 백색의 오리엔탈백합 ‘O11-149’ 계통을 화주절단수분법에 의해 교배한 후 2010년부터 2013년까지 기내 종자배양 및 증식 육묘하여 2014년 우수 계통(OT14-6)을 1차 선발하였다. 2016년부터 2018년도에 걸쳐 ‘Nymph’ 품종을 대조품종으로 하여 일반재배를 통한 특성 검정을 실시하였으며, 2016년 화훼연구소 백합 육성계통 평가 회에서 우수계통으로 선발되어 ‘Big Bang’로 명명한 후 충청 남도와 농촌진흥청 농작물직무육성 신품종선정위원회 심의를 거쳐 국립종자원에 품종보호출원 하였다. ‘Big Bang’ 품종의 자연개화는 중부지역에서 6월 16일로 ‘Nymph’ 품종과 비슷하였고 초장은 155.3cm이었다. 적색 반점에 복색(백색+적색)의 화색과 사발화형의 꽃모양을 나타내었다. 재배에 있어서는 진딧물과 잎마름병 등의 병해충 방제를 주기적으로 실시하고, 영양분 관리 등 일반적인 재배관리를 통해 안정적인 구근생산 및 고품질 절화가 가능하다.