TY - JOUR A1 - Dadfar, Dryed Mohammadali A1 - Camozzi, Denise A1 - Darguzyte, Milita A1 - Roemhild, Karolin A1 - Varvarà, Paola A1 - Metselaar, Josbert A1 - Banala, Srinivas A1 - Straub, Marcel A1 - Güver, Nihan A1 - Engelmann, Ulrich M. A1 - Slabu, Ioana A1 - Buhl, Miriam A1 - Leusen, Jan van A1 - Kögerler, Paul A1 - Hermanns-Sachweh, Benita A1 - Schulz, Volkmar A1 - Kiessling, Fabian A1 - Lammers, Twan T1 - Size-isolation of superparamagnetic iron oxide nanoparticles improves MRI, MPI and hyperthermia performance JF - Journal of Nanobiotechnology N2 - 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. Y1 - 2020 U6 - http://dx.doi.org/10.1186/s12951-020-0580-1 SN - 1477-3155 VL - 18 IS - Article number 22 SP - 1 EP - 13 PB - Nature Portfolio ER - TY - JOUR A1 - Abel, Alexander A1 - Kahmann, Stephanie Lucina A1 - Mellon, Stephen A1 - Staat, Manfred A1 - Jung, Alexander T1 - An open-source tool for the validation of finite element models using three-dimensional full-field measurements JF - Medical Engineering & Physics N2 - 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. Y1 - 2020 U6 - http://dx.doi.org/10.1016/j.medengphy.2019.10.015 SN - 1350-4533 VL - 77 SP - 125 EP - 129 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Gerhards, Michael A1 - Sander, Volker A1 - Zivkovic, Miroslav A1 - Belloum, Adam A1 - Bubak, Marian T1 - New approach to allocation planning of many‐task workflows on clouds JF - Concurrency and Computation: Practice and Experience N2 - 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. Y1 - 2020 U6 - http://dx.doi.org/10.1002/cpe.5404 SN - 1532-0634 VL - 32 IS - 2 Article e5404 SP - 1 EP - 16 PB - Wiley CY - Chichester ER - TY - JOUR A1 - Tran, Ngoc Trinh A1 - Staat, Manfred T1 - Direct plastic structural design under lognormally distributed strength by chance constrained programming JF - Optimization and Engineering N2 - 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. Y1 - 2020 U6 - http://dx.doi.org/10.1007/s11081-019-09437-2 SN - 1573-2924 VL - 21 IS - 1 SP - 131 EP - 157 PB - Springer Nature CY - Cham ER -