Topology optimisation under uncertainties with neural networks

Topology optimisation is a mathematical approach relevant to different engineering problems where the distribution of material in a defined domain is distributed in some optimal way, subject to a predefined cost function representing desired (e.g., mechanical) properties and constraints. The computation of such an optimal distribution depends on the numerical solution of some physical model (in our case linear elasticity) and robustness is achieved by introducing uncertainties into the model data, namely the forces acting on the structure and variations of the material stiffness, rendering the task high-dimensional and computationally expensive. To alleviate this computational burden, we develop two neural network architectures (NN) that are capable of predicting the gradient step of the optimisation procedure. Since state-of-the-art methods use adaptive mesh refinement, the neural networks are designed to use a sufficiently fine reference mesh such that only one training phase of the neural network suffices. As a first architecture, a convolutional neural network is adapted to the task. To include sequential information of the optimisation process, a recurrent neural network is constructed as a second architecture. A common 2D bridge benchmark is used to illustrate the performance of the proposed architectures. It is observed that the NN prediction of the gradient step clearly outperforms the classical optimisation method, in particular since larger iteration steps become viable

Varicella Zoster Virus ORF25 Gene Product: An Essential Hub Protein Linking Encapsidation Proteins and the Nuclear Egress Complex

Varicella zoster virus (VZV) ORF25 is a 156 amino acid protein belonging to the approximately 40 core proteins that are conserved throughout the Herpesviridae. By analogy to its functional orthologue UL33 in Herpes simplex virus 1 (HSV-1), ORF25 is thought to be a component of the terminase complex. To investigate how cleavage and encapsidation of viral DNA links to the nuclear egress of mature capsids in VZV, we tested 10 VZV proteins that are predicted to be involved in either of the two processes for protein interactions against each other using three independent protein-protein interaction (PPI) detection systems: the yeast-two-hybrid (Y2H) system, a luminescence based MBP pull-down interaction screening assay (LuMPIS), and a bioluminescence resonance energy transfer (BRET) assay. A set of 20 interactions was consistently detected by at least 2 methods and resulted in a dense interaction network between proteins associated in encapsidation and nuclear egress. The results indicate that the terminase complex in VZV consists of ORF25, ORF30, and ORF45/42 and support a model in which both processes are closely linked to each other. Consistent with its role as a central hub for protein interactions, ORF25 is shown to be essential for VZV replication.Fil: Vizoso Pinto, María Guadalupe. Ludwig Maximilians Universitat. Max Von Pettenkofer Institute. Cátedra Virology; Alemania. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Investigaciones Biológicas. Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas; ArgentinaFil: Pothineni, Venkata R.. Ludwig Maximilians Universitat. Max Von Pettenkofer Institute. Cátedra Virology; AlemaniaFil: Haase, Rudolf. Ludwig Maximilians Universitat. Max Von Pettenkofer Institute. Cátedra Virology; AlemaniaFil: Woidy, Mathias. Ludwig Maximilians Universitat; AlemaniaFil: Lotz Havla, Amelie. Ludwig Maximilians Universitat; AlemaniaFil: Gersting, Soren W.. Ludwig Maximilians Universitat; AlemaniaFil: Muntau, Ania C.. Ludwig Maximilians Universitat; AlemaniaFil: Haas, Jurgen. Ludwig Maximilians Universitat. Max Von Pettenkofer Institute. Cátedra Virology; AlemaniaFil: Sommer, Marvin. University of Stanford; Estados UnidosFil: Arvin, Ann M.. University of Stanford; Estados UnidosFil: Baiker, Armin. Bavarian Health and Food Safety Authority; Alemani

Strategisches Management in Kommunen: Entstehung, Inhalte und Wirkungen von Digitalisierungs- und Nachhaltigkeitsstrategien

Der Beitrag untersucht Praxen des strategischen Managements in deutschen Kommunalverwaltungen am Beispiel von Digitalisierungs- und Nachhaltigkeitsstrategien. Es werden drei Forschungsfragen bearbeitet: 1. Was sind typische Inhalte und Strukturen solcher Strategien? 2. Wie und warum entstehen solche Strategien? 3. Welche Erfahrungen machen Kommunen mit diesen Strategien?

Architectures and biogenesis of non-flagellar protein appendages in Gram-negative bacteria

Bacteria commonly expose non-flagellar proteinaceous appendages on their outer surfaces. These extracellular structures, called pili or fimbriae, are employed in attachment and invasion, biofilm formation, cell motility or protein and DNA transport across membranes. Over the past 15 years, the power of molecular and structural techniques has revolutionalized our understanding of the biogenesis, structure, function and mode of action of these bacterial organelles. Here, we review the five known classes of Gram-negative non-flagellar appendages from a biosynthetic and structural point of view

Barbarians at the British Museum: Anglo-Saxon Art, Race and Religion

A critical historiographical overview of art historical approaches to early medieval material culture, with a focus on the British Museum collections and their connections to religion

Dissipation-enabled resonant adiabatic quantum state transfer: Entanglement generation and quantum cloning

Resonant dissipation-enabled adiabatic quantum state transfer processes between the polarization degrees of freedom of a single-photon wave packet and quantum emitters are discussed. These investigations generalize previous work [Trautmann and Alber, Phys. Rev. A 93, 053807 (2016)] by taking into account the properties of the spontaneously emitted photon wave packet and of nonadiabatic corrections. It is demonstrated that the photonic degrees of freedoms of these adiabatic one-photon quantum state transfer processes can be used for the passive, heralded, and deterministic preparation of Bell states of two material quantum emitters and for realizing a large family of symmetric and asymmetric quantum cloning processes. Although these theoretical investigations concentrate on waveguide scenarios they are expected to be relevant also for other scenarios as long as the processes involved are adiabatic so that the Fourier-limited bandwidth of the single-photon wave packet involved is small in comparison with the relevant dissipative rates

Topology Optimisation under Uncertainties with Neural Networks

Topology optimisation is a mathematical approach relevant to different engineering problems where the distribution of material in a defined domain is distributed in some optimal way, subject to a predefined cost function representing desired (e.g., mechanical) properties and constraints. The computation of such an optimal distribution depends on the numerical solution of some physical model (in our case linear elasticity) and robustness is achieved by introducing uncertainties into the model data, namely the forces acting on the structure and variations of the material stiffness, rendering the task high-dimensional and computationally expensive. To alleviate this computational burden, we develop two neural network architectures (NN) that are capable of predicting the gradient step of the optimisation procedure. Since state-of-the-art methods use adaptive mesh refinement, the neural networks are designed to use a sufficiently fine reference mesh such that only one training phase of the neural network suffices. As a first architecture, a convolutional neural network is adapted to the task. To include sequential information of the optimisation process, a recurrent neural network is constructed as a second architecture. A common 2D bridge benchmark is used to illustrate the performance of the proposed architectures. It is observed that the NN prediction of the gradient step clearly outperforms the classical optimisation method, in particular since larger iteration steps become viable