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Production and inventory control of auto parts based on predicted probabilistic distribution of inventory

Digital Communications and Networks 2015年第04期1卷 292-301页

作者： jisun shin Sungshin Kim Jang-Myung Lee Pusan National University

Bayesian networks are probabilistic models used for prediction and decision making under uncertainty. The delivery quantity, the production quantity, and the inventory are changing according to various unexpected events. Then the prediction of a production inventory is required to cope with such irregular fluctuations. This paper considers a production adjustment method for an automobile parts production process by using a dynamic Bayesian network. All factors that may influence the production quantity, the delivery quantity, and the inventory quantity will be handled. This study also provides a production schedule algorithm that sequentially adjusts the production schedule in order to guarantee that all deadlines are met. Furthermore, an adjusting rule for the production quantities is provided in order to maintain guaranteed delivery.

关键词： Graphical modeling Dynamic Bayesian Network Production Adjusting Method Probabilistic distribution

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Fluorescent nanoswitch for monitoring specific pluripotency-related microRNAs of induced pluripotent stem cells： Development of polyethyleneimine- oligonucleotide hybridization probes

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Nano Research 2017年第8期10卷 2545-2559页

作者： Seungmin Han Hye Young Son Byunghoon Kang Eunji Jang jisun Ki Na Geum Lee Jongjin Park Moo-Kwang shin Byeonggeol Mun Jeong-Ki Min Seungjoo Haam Department of Chemical and Biomolecular Engineering College of Engineering Yonsei University Seou1120-749 Republic of Korea Department of Radiology College of Medicine Yonsei University Seou1120-752 Republic of Korea Severance Biomedical Science Institute College of Medicine Yonsei University Seou1120-752 Republic of Korea Biotherapeutics Translational Research Center Korea Research Institute of Bioscience and Biotechnology Daejeon 34141 Republic of Korea Department of Biomolecular Science University of Science & Technology Daejeon 34113 Republic of Korea

The isolation of high-grade （i.e. high-pluripotency） human induced pluripotent stem cells （hiPSCs） is a decisive factor for enhancing the purity of hiPSC populations or differentiation efficiency. A non-invasive imaging system that can monitor microRNA （miRNA） expression provides a useful tool to identify and analyze specific cell populations. However, previous studies on the monitoring/isolation of hiPSCs by miRNA expression have limited hiPSCs＇ differentiation system owing to long-term incubation with miRNA imaging probe-nanocarriers. Therefore, we focused on monitoring high-grade hiPSCs without influencing the pluripotency of hiPSCs. We reduced nanoparticle transfection time, because hiPSCs are prone to spontaneous differentiation under external factors during incubation. The fluorescent nanoswitch （＂ON＂ with target miRNA）, which can be applied for either imaging or sorting specific cells by fluorescence signals, contains an miRNA imaging probe （miP） and a PEI-PEG nanoparticle （miP-P）. Consequently, this nanoswitch can sense various endogenous target miRNAs within 30 min in vitro, and demonstrates strong potential for not only imaging but also sorting pluripotent hiPSCs without affecting pluripotency. Moreover, miP-P-treated hiPSCs differentiate well into endothelial cells, indicating that miP-P does not alter the pluripotency of hiPSCs. We envisage that this miRNA imaging system could be valuable for identifying and sorting high-grade hiPSCs for improved practical applications.

关键词： induced pluripotent stem cells (iPSCs) pluripotency microRNA (miRNA) fluorescence imaging nanoparticle

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