@article{DadfarCamozziDarguzyteetal.2020,
  author    = {Dadfar, Dryed Mohammadali and Camozzi, Denise and Darguzyte, Milita and Roemhild, Karolin and Varvar{\`a}, Paola and Metselaar, Josbert and Banala, Srinivas and Straub, Marcel and G{\"u}ver, Nihan and Engelmann, Ulrich M. and Slabu, Ioana and Buhl, Miriam and Leusen, Jan van and K{\"o}gerler, Paul and Hermanns-Sachweh, Benita and Schulz, Volkmar and Kiessling, Fabian and Lammers, Twan},
  title     = {Size-isolation of superparamagnetic iron oxide nanoparticles improves MRI, MPI and hyperthermia performance},
  series = {Journal of Nanobiotechnology},
  volume    = {18},
  journal   = {Journal of Nanobiotechnology},
  number    = {Article number 22},
  publisher = {Nature Portfolio},
  issn      = {1477-3155},
  doi       = {10.1186/s12951-020-0580-1},
  pages     = {1 -- 13},
  year      = {2020},
  abstract  = {Superparamagnetic iron oxide nanoparticles (SPION) are extensively used for magnetic resonance imaging (MRI) and magnetic particle imaging (MPI), as well as for magnetic fluid hyperthermia (MFH). We here describe a sequential centrifugation protocol to obtain SPION with well-defined sizes from a polydisperse SPION starting formulation, synthesized using the routinely employed co-precipitation technique. Transmission electron microscopy, dynamic light scattering and nanoparticle tracking analyses show that the SPION fractions obtained upon size-isolation are well-defined and almost monodisperse. MRI, MPI and MFH analyses demonstrate improved imaging and hyperthermia performance for size-isolated SPION as compared to the polydisperse starting mixture, as well as to commercial and clinically used iron oxide nanoparticle formulations, such as Resovist® and Sinerem®. The size-isolation protocol presented here may help to identify SPION with optimal properties for diagnostic, therapeutic and theranostic applications.},
  language  = {en}
}
@article{AbelKahmannMellonetal.2020,
  author    = {Abel, Alexander and Kahmann, Stephanie Lucina and Mellon, Stephen and Staat, Manfred and Jung, Alexander},
  title     = {An open-source tool for the validation of finite element models using three-dimensional full-field measurements},
  series = {Medical Engineering \& Physics},
  volume    = {77},
  journal   = {Medical Engineering \& Physics},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {1350-4533},
  doi       = {10.1016/j.medengphy.2019.10.015},
  pages     = {125 -- 129},
  year      = {2020},
  abstract  = {Three-dimensional (3D) full-field measurements provide a comprehensive and accurate validation of finite element (FE) models. For the validation, the result of the model and measurements are compared based on two respective point-sets and this requires the point-sets to be registered in one coordinate system. Point-set registration is a non-convex optimization problem that has widely been solved by the ordinary iterative closest point algorithm. However, this approach necessitates a good initialization without which it easily returns a local optimum, i.e. an erroneous registration. The globally optimal iterative closest point (Go-ICP) algorithm has overcome this drawback and forms the basis for the presented open-source tool that can be used for the validation of FE models using 3D full-field measurements. The capability of the tool is demonstrated using an application example from the field of biomechanics. Methodological problems that arise in real-world data and the respective implemented solution approaches are discussed.},
  language  = {en}
}
@article{GerhardsSanderZivkovicetal.2020,
  author    = {Gerhards, Michael and Sander, Volker and Zivkovic, Miroslav and Belloum, Adam and Bubak, Marian},
  title     = {New approach to allocation planning of many-task workflows on clouds},
  series = {Concurrency and Computation: Practice and Experience},
  volume    = {32},
  journal   = {Concurrency and Computation: Practice and Experience},
  number    = {2 Article e5404},
  publisher = {Wiley},
  address   = {Chichester},
  issn      = {1532-0634},
  doi       = {10.1002/cpe.5404},
  pages     = {1 -- 16},
  year      = {2020},
  abstract  = {Experience has shown that a priori created static resource allocation plans are vulnerable to runtime deviations and hence often become uneconomic or highly exceed a predefined soft deadline. The assumption of constant task execution times during allocation planning is even more unlikely in a cloud environment where virtualized resources vary in performance. Revising the initially created resource allocation plan at runtime allows the scheduler to react on deviations between planning and execution. Such an adaptive rescheduling of a many-task application workflow is only feasible, when the planning time can be handled efficiently at runtime. In this paper, we present the static low-complexity resource allocation planning algorithm (LCP) applicable to efficiently schedule many-task scientific application workflows on cloud resources of different capabilities. The benefits of the presented algorithm are benchmarked against alternative approaches. The benchmark results show that LCP is not only able to compete against higher complexity algorithms in terms of planned costs and planned makespan but also outperforms them significantly by magnitudes of 2 to 160 in terms of required planning time. Hence, LCP is superior in terms of practical usability where low planning time is essential such as in our targeted online rescheduling scenario.},
  language  = {en}
}
@article{TranStaat2020,
  author    = {Tran, Ngoc Trinh and Staat, Manfred},
  title     = {Direct plastic structural design under lognormally distributed strength by chance constrained programming},
  series = {Optimization and Engineering},
  volume    = {21},
  journal   = {Optimization and Engineering},
  number    = {1},
  publisher = {Springer Nature},
  address   = {Cham},
  issn      = {1573-2924},
  doi       = {10.1007/s11081-019-09437-2},
  pages     = {131 -- 157},
  year      = {2020},
  abstract  = {We propose the so-called chance constrained programming model of stochastic programming theory to analyze limit and shakedown loads of structures under random strength with a lognormal distribution. A dual chance constrained programming algorithm is developed to calculate simultaneously both the upper and lower bounds of the plastic collapse limit and the shakedown limit. The edge-based smoothed finite element method (ES-FEM) is used with three-node linear triangular elements.},
  language  = {en}
}