@article{ListekHoenowGossenetal.2020,
  author    = {Listek, Martin and H{\"o}now, Anja and Gossen, Manfred and Hanack, Katja},
  title     = {A novel selection strategy for antibody producing hybridoma cells based on a new transgenic fusion cell line},
  series = {Scientific Reports},
  volume    = {10},
  journal   = {Scientific Reports},
  publisher = {Macmillan Publishers Limited, part of Springer Nature},
  address   = {London},
  issn      = {2045-2322},
  doi       = {10.1038/s41598-020-58571-w},
  pages     = {12},
  year      = {2020},
  abstract  = {The use of monoclonal antibodies is ubiquitous in science and biomedicine but the generation and validation process of antibodies is nevertheless complicated and time-consuming. To address these issues we developed a novel selective technology based on an artificial cell surface construct by which secreted antibodies were connected to the corresponding hybridoma cell when they possess the desired antigen-specificity. Further the system enables the selection of desired isotypes and the screening for potential cross-reactivities in the same context. For the design of the construct we combined the transmembrane domain of the EGF-receptor with a hemagglutinin epitope and a biotin acceptor peptide and performed a transposon-mediated transfection of myeloma cell lines. The stably transfected myeloma cell line was used for the generation of hybridoma cells and an antigen- and isotype-specific screening method was established. The system has been validated for globular protein antigens as well as for haptens and enables a fast and early stage selection and validation of monoclonal antibodies in one step.},
  language  = {en}
}
@article{GiotopoulosKritikosTsakanikas2022,
  author    = {Giotopoulos, Ioannis and Kritikos, Alexander and Tsakanikas, Aggelos},
  title     = {A lasting crisis affects R\&D decisions of smaller firms},
  series = {The Journal of technology transfer},
  journal   = {The Journal of technology transfer},
  number    = {48},
  publisher = {Springer Science+Business Media},
  address   = {Dordrecht},
  issn      = {0892-9912},
  doi       = {10.1007/s10961-022-09957-7},
  pages     = {1161 -- 1175},
  year      = {2022},
  abstract  = {We use the prolonged Greek crisis as a case study to understand how a lasting economic shock affects the innovation strategies of firms in economies with moderate innovation activities. Adopting the 3-stage CDM model, we explore the link between R\&D, innovation, and productivity for different size groups of Greek manufacturing firms during the prolonged crisis. At the first stage, we find that the continuation of the crisis is harmful for the R\&D engagement of smaller firms while it increased the willingness for R\&D activities among the larger ones. At the second stage, among smaller firms the knowledge production remains unaffected by R\&D investments, while among larger firms the R\&D decision is positively correlated with the probability of producing innovation, albeit the relationship is weakened as the crisis continues. At the third stage, innovation output benefits only larger firms in terms of labor productivity, while the innovation-productivity nexus is insignificant for smaller firms during the lasting crisis.},
  language  = {en}
}
@misc{PanzerBenderGronau2023,
  author    = {Panzer, Marcel and Bender, Benedict and Gronau, Norbert},
  title     = {A deep reinforcement learning based hyper-heuristic for modular production control},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe},
  issn      = {1867-5808},
  doi       = {10.25932/publishup-60564},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-605642},
  pages     = {24},
  year      = {2023},
  abstract  = {In nowadays production, fluctuations in demand, shortening product life-cycles, and highly configurable products require an adaptive and robust control approach to maintain competitiveness. This approach must not only optimise desired production objectives but also cope with unforeseen machine failures, rush orders, and changes in short-term demand. Previous control approaches were often implemented using a single operations layer and a standalone deep learning approach, which may not adequately address the complex organisational demands of modern manufacturing systems. To address this challenge, we propose a hyper-heuristics control model within a semi-heterarchical production system, in which multiple manufacturing and distribution agents are spread across pre-defined modules. The agents employ a deep reinforcement learning algorithm to learn a policy for selecting low-level heuristics in a situation-specific manner, thereby leveraging system performance and adaptability. We tested our approach in simulation and transferred it to a hybrid production environment. By that, we were able to demonstrate its multi-objective optimisation capabilities compared to conventional approaches in terms of mean throughput time, tardiness, and processing of prioritised orders in a multi-layered production system. The modular design is promising in reducing the overall system complexity and facilitates a quick and seamless integration into other scenarios.},
  language  = {en}
}
@article{PanzerBenderGronau2023,
  author    = {Panzer, Marcel and Bender, Benedict and Gronau, Norbert},
  title     = {A deep reinforcement learning based hyper-heuristic for modular production control},
  series = {International journal of production research},
  journal   = {International journal of production research},
  publisher = {Taylor \& Francis},
  address   = {London},
  issn      = {0020-7543},
  doi       = {10.1080/00207543.2023.2233641},
  pages     = {1 -- 22},
  year      = {2023},
  abstract  = {In nowadays production, fluctuations in demand, shortening product life-cycles, and highly configurable products require an adaptive and robust control approach to maintain competitiveness. This approach must not only optimise desired production objectives but also cope with unforeseen machine failures, rush orders, and changes in short-term demand. Previous control approaches were often implemented using a single operations layer and a standalone deep learning approach, which may not adequately address the complex organisational demands of modern manufacturing systems. To address this challenge, we propose a hyper-heuristics control model within a semi-heterarchical production system, in which multiple manufacturing and distribution agents are spread across pre-defined modules. The agents employ a deep reinforcement learning algorithm to learn a policy for selecting low-level heuristics in a situation-specific manner, thereby leveraging system performance and adaptability. We tested our approach in simulation and transferred it to a hybrid production environment. By that, we were able to demonstrate its multi-objective optimisation capabilities compared to conventional approaches in terms of mean throughput time, tardiness, and processing of prioritised orders in a multi-layered production system. The modular design is promising in reducing the overall system complexity and facilitates a quick and seamless integration into other scenarios.},
  language  = {en}
}
@misc{SadovnichiiPanasyukAmelyushkinetal.2017,
  author    = {Sadovnichii, V. A. and Panasyuk, M. I. and Amelyushkin, A. M. and Benghin, V. V. and Garipov, G. K. and Kalegaev, V. V. and Klimov, P. A. and Khrenov, B. A. and Petrov, V. L. and Sharakin, S. A. and Shirokov, A. V. and Svertilov, S. I. and Zotov, M. Y. and Yashin, I. V. and Gorbovskoy, E. S. and Lipunov, V. M. and Park, I. H. and Lee, J. and Jeong, S. and Kim, M. B. and Jeong, H. M. and Shprits, Yuri Y. and Angelopoulos, V. and Russell, C. T. and Runov, A. and Turner, D. and Strangeway, R. J. and Caron, R. and Biktemerova, S. and Grinyuk, A. and Lavrova, M. and Tkachev, L. and Tkachenko, A. and Martinez, O. and Salazar, H. and Ponce, E.},
  title     = {"Lomonosov" satellite-space observatory to study extreme phenomena in space},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {959},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-42818},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-428185},
  pages     = {1705 -- 1738},
  year      = {2017},
  abstract  = {The "Lomonosov" space project is lead by Lomonosov Moscow State University in collaboration with the following key partners: Joint Institute for Nuclear Research, Russia, University of California, Los Angeles (USA), University of Pueblo (Mexico), Sungkyunkwan University (Republic of Korea) and with Russian space industry organi-zations to study some of extreme phenomena in space related to astrophysics, astroparticle physics, space physics, and space biology. The primary goals of this experiment are to study: -Ultra-high energy cosmic rays (UHECR) in the energy range of the Greizen-ZatsepinKuzmin (GZK) cutoff; -Ultraviolet (UV) transient luminous events in the upper atmosphere; -Multi-wavelength study of gamma-ray bursts in visible, UV, gamma, and X-rays; -Energetic trapped and precipitated radiation (electrons and protons) at low-Earth orbit (LEO) in connection with global geomagnetic disturbances; -Multicomponent radiation doses along the orbit of spacecraft under different geomagnetic conditions and testing of space segments of optical observations of space-debris and other space objects; -Instrumental vestibular-sensor conflict of zero-gravity phenomena during space flight. This paper is directed towards the general description of both scientific goals of the project and scientific equipment on board the satellite. The following papers of this issue are devoted to detailed descriptions of scientific instruments.},
  language  = {en}
}