We introduce a novel method for the implementation of shape optimization for non-parameterized shapes in fluid dynamics applications, where we propose to use the shape derivative to determine ...deformation fields with the help of the
p
-
Laplacian for
p
>
2
. This approach is closely related to the computation of steepest descent directions of the shape functional in the
W
1
,
∞
-
topology and refers to the recent publication Deckelnick et al. (A novel
W
1
,
∞
approach to shape optimisation with Lipschitz domains, 2021), where this idea is proposed. Our approach is demonstrated for shape optimization related to drag-minimal free floating bodies. The method is validated against existing approaches with respect to convergence of the optimization algorithm, the obtained shape, and regarding the quality of the computational grid after large deformations. Our numerical results strongly indicate that shape optimization related to the
W
1
,
∞
-topology—though numerically more demanding—seems to be superior over the classical approaches invoking Hilbert space methods, concerning the convergence, the obtained shapes and the mesh quality after large deformations, in particular when the optimal shape features sharp corners.
The contribution is devoted to combined shape- and mesh-update strategies for parameter-free (CAD-free) shape optimization methods. Three different strategies to translate the shape sensitivities ...computed by adjoint shape optimization procedures into simultaneous updates of both the shape and the discretized domain are employed in combination with a mesh-morphing strategy. Considered methods involve a linear Steklov–Poincaré (Hilbert space) approach, a recently suggested highly non-linear p-Laplace (Banach space) method, and a hybrid variant which updates the shape in Hilbert space. The methods are scrutinized for optimizing the power loss of a two-dimensional bent duct flow using an unstructured, locally refined grid that initially displays favorable grid properties. Optimization results are compared with respect to the optimization convergence, the computational effort, and the preservation of the mesh quality during the optimization sequence. Results indicate that all methods reach, approximately, the same converged optimal solution, which reduces the objective function by about 18% for this classical benchmark example. However, as regards the preservation of the mesh quality, more advanced Banach space methods are advantageous in comparison to Hilbert space methods even when the shape update is performed in Hilbert space to save costs. In specific, while the computational cost of the Banach space method and the hybrid method is about 3.5 and 2.5 times the cost of the pure Hilbert space method, respectively, the grid quality metrics are 2 times and 1.7 times improved for the Banach space and hybrid method, respectively.
Machine learning has become a popular instrument for the search of undiscovered particles and mechanisms at particle collider experiments. It enables the investigation of large datasets and is ...therefore suitable to operate directly on minimally-processed data coming from the detector instead of reconstructed objects. Here, we study patterns of raw pixel hits recorded by the Belle II pixel detector, that is operational since 2019 and presently features 4 M pixels and trigger rates up to 5 kHz. In particular, we focus on unsupervised techniques that operate without the need for a theoretical model. These model-agnostic approaches allow for an unbiased exploration of data while filtering out anomalous detector signatures that could hint at new physics scenarios. We present the identification of hypothetical magnetic monopoles against Belle II beam background using self-organizing kohonen maps and autoencoders. These two unsupervised algorithms are compared to a Multilayer Perceptron and a superior signal efficiency of the Autoencoder is found at high background-rejection levels. Our results strengthen the case for using unsupervised machine learning techniques to complement traditional search strategies at particle colliders and pave the way to potential online applications of the algorithms in the near future.
Patients scheduled for cochlear implantation often retain residual hearing in the low frequencies. Unfortunately, some patients lose their residual hearing following implantation and the reasons for ...this are not well understood. Evidence suggests that electrotoxicity could be one of the factors responsible for this late adverse effect. Therefore, the aim of this study was to investigate the survival of spiral ganglion neurons (SGN) subjected to in vitro electrical stimulation (ES). A stimulation setup was developed to provide defined electrical fields at given points of the chamber. SGN isolated from Sprague Dawley rats (P3–4) were dissociated and cultured in the chamber for 24 h prior to biphasic, pulsed electrical field exposure for another 24 h. The current varied in the range of 0 to 2 mA and the pulse width from 10 to 400 μs. Neurite growth and survival were evaluated with respect to the charge density at the position of the cells. Non-exposed SGN cultures served as control. Charge densities below 2.2 μC·cm
−2
·phase
−1
appeared to have no effect on SGN survival and neurite outgrowth. Charge densities above 4.9 μC·cm
−2
·phase
−1
were detrimental to almost all cells in culture. After fitting results to a sigmoidal dose response curve, a LD
50
of 2.9 μC·cm
−2
·phase
−1
was calculated. This screening regarding survival and outgrowth of SGN provides parameters that could be used to further investigate the effect of ES on SGN and to develop possible protection strategies, which could potentially rescue residual hearing in the implanted patients.
The first multiplication sign (.) for unit μC cm¯
2
·phase¯
1
was not placed, which is part of the author’s correction. Furthermore, the unit appears anywhere in the article.
Cyanines are useful fluorophores for a myriad of biological labeling applications, but their interactions with biomolecules are unpredictable. Cyanine fluorescence intensity can be highly variable ...due to complex photoisomerization kinetics, which are exceedingly sensitive to the surrounding environment. This introduces large errors in Förster resonance energy transfer (FRET)-based experiments where fluorescence intensity is the output parameter. However, this environmental sensitivity is a strength from a biological sensing point of view if specific relationships between biomolecular structure and cyanine photophysics can be identified. We describe a set of DNA structures that modulate cyanine fluorescence intensity through the insertion of adenine or thymine bases. These structures simultaneously provide photophysical predictability and tunability. We characterize these structures using steady-state fluorescence measurements, fluorescence correlation spectroscopy (FCS), and time-resolved photoluminescence (TRPL). We find that the photoisomerization rate decreases over an order of magnitude across the adenine series, which is consistent with increasing immobilization of the cyanine moiety by the surrounding DNA structure.
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