Hyperelastic analysis based on a polygonal finite element method

In this contribution, we present a novel polygonal finite element method applied to hyperelastic analysis. For generating polygonal meshes in a bounded period of time, we use the adaptive Delaunay tessellation (ADT) proposed by Constantinu et al. [1 A. Constantinu, P. Steinmann, T. Bobach, G. Farin, and G. Umlauf, The adaptive Delaunay tessellation: A neighborhood covering meshing technique., Comput. Mech., vol. 42, no. 5, pp. 655–669, 2008.[Crossref], [Web of Science ®] , [Google Scholar]]. ADT is an unstructured hybrid tessellation of a scattered point set that minimally covers the proximal space around each point. In this work, we have extended the ADT to nonconvex domains using concepts from constrained Delaunay triangulation (CDT). The proposed method is thus based on a constrained adaptive Delaunay tessellation (CADT) for the discretization of domains into polygonal regions. We involve the metric coordinate (Malsch) method for obtaining the interpolation over convex and nonconvex domains. For the numerical integration of the Galerkin weak form, we resort to classical Gaussian quadrature based on triangles. Numerical examples of two-dimensional hyperelasticity are considered to demonstrate the advantages of the polygonal finite element method.     

STED‐TEM Correlative Microscopy Leveraging Nanodiamonds as Intracellular Dual‐Contrast Markers

Development of fluorescent and electron dense markers is essential for the implementation of correlative light and electron microscopy, as dual‐contrast landmarks are required to match the details in the multimodal images. Here, a novel method for correlative microscopy that utilizes fluorescent nanodiamonds (FNDs) as dual‐contrast probes is reported. It is demonstrated how the FNDs can be used as dual‐contrast labels—and together with automatic image registration tool SuperTomo, for precise image correlation—in high‐resolution stimulated emission depletion (STED)/confocal and transmission electron microscopy (TEM) correlative microscopy experiments. It is shown how FNDs can be employed in experiments with both live and fixed cells as well as simple test samples. The fluorescence imaging can be performed either before TEM imaging or after, as the robust FNDs survive the TEM sample preparation and can be imaged with STED and other fluorescence microscopes directly on the TEM grids.     

A Multi-view Camera Model for Line-Scan Cameras with Telecentric Lenses

Journal of Mathematical Imaging and Vision - We propose a novel multi-view camera model for line-scan cameras with telecentric lenses. The camera model supports an arbitrary number of cameras and...     

Flexible and Ultrasoft Inorganic 1D Semiconductor and Heterostructure Systems Based on SnIP

Low dimensionality and high flexibility are key demands for flexible electronic semiconductor devices. SnIP, the first atomic‐scale double helical semiconductor combines structural anisotropy and robustness with exceptional electronic properties. The benefit of the double helix, combined with a diverse structure on the nanoscale, ranging from strong covalent bonding to weak van der Waals interactions, and the large structure and property anisotropy offer substantial potential for applications in energy conversion and water splitting. It represents the next logical step in downscaling the inorganic semiconductors from classical 3D systems, via 2D semiconductors like MXenes or transition metal dichalcogenides, to the first downsizeable, polymer‐like atomic‐scale 1D semiconductor SnIP. SnIP shows intriguing mechanical properties featuring a bulk modulus three times lower than any IV, III‐V, or II‐VI semiconductor. In situ bending tests substantiate that pure SnIP fibers can be bent without an effect on their bonding properties. Organic and inorganic hybrids are prepared illustrating that SnIP is a candidate to fabricate flexible 1D composites for energy conversion and water splitting applications. SnIP@C₃N₄ hybrid forms an unusual soft material core–shell topology with graphenic carbon nitride wrapping around SnIP. A 1D van der Waals heterostructure is formed capable of performing effective water splitting.     

Additive Manufacturing Approaches for Hydroxyapatite‐Reinforced Composites

Additive manufacturing (AM) techniques have gained interest in the tissue engineering field, thanks to their versatility and unique possibilities of producing constructs with complex macroscopic geometries and defined patterns. Recently, composite materials—namely, heterogeneous biomaterials identified as continuous phase (matrix) and reinforcement (filler)—have been proposed as inks that can be processed by AM to obtain scaffolds with improved biomimetic and bioactive properties. Significant efforts have been dedicated to hydroxyapatite (HA)‐reinforced composites, especially targeting bone tissue engineering, thanks to the chemical similarities of HA with respect to mineral components of native mineralized tissues. Herein, applications of AM techniques to process HA‐reinforced composites and biocomposites for the production of scaffolds with biological matrices, including cellular tissues, are reviewed. The primary outcomes of recent investigations in terms of morphological, structural, and in vitro and in vivo biological properties of the materials are discussed. The approaches based on the nature of the matrices employed to embed the HA reinforcements and produce the tissue substitutes are classified, and a critical discussion is provided on the presented state of the art as well as the future perspectives, to offer a comprehensive picture of the strategies investigated as well as challenges in this emerging field of materiomics.     

The Ambiguous Identifier Clustering Technique

Investigations of online transaction data often face the problem that entries for identical products cannot be identified as such. There is, for example, typically no unique product identifier in online auctions; retailers make their offers at price comparison sites hardly comparable and online stores often use different identifiers for virtually equal products. Existing studies typically use data sets that are restricted to one or only a few products in order to avoid product heterogeneity if a unique product identifier is not available. We propose the Ambiguous Identifier Clustering Technique (AICT) that identifies online transaction data that refer to virtually the same product. Based on a data set of eBay auctions, we demonstrate that AICT clusters online transactions for identical products with high accuracy. We further show how researchers benefit from AICT and the reduced product heterogeneity when analyzing data with econometric models.     

Preference representation using Gaussian functions on a hyperplane in evolutionary multi-objective optimization

Many-objective optimization has attracted much attention in evolutionary multi-objective optimization (EMO). This is because EMO algorithms developed so far often degrade their search ability for optimization problems with four or more objectives, which are frequently referred to as many-objective problems. One of promising approaches to handle many objectives is to incorporate the preference of a decision maker (DM) into EMO algorithms. With the preference, EMO algorithms can focus the search on regions preferred by the DM, resulting in solutions close to the Pareto front around the preferred regions. Although a number of preference-based EMO algorithms have been proposed, it is not trivial for the DM to reflect his/her actual preference in the search. We previously proposed to represent the preference of the DM using Gaussian functions on a hyperplane. The DM specifies the center and spread vectors of the Gaussian functions so as to represent his/her preference. The preference handling is integrated into the framework of NSGA-II. This paper extends our previous work so that obtained solutions follow the distribution of Gaussian functions specified. The performance of our proposed method is demonstrated mainly for benchmark problems and real-world applications with a few objectives in this paper. We also show the applicability of our method to many-objective problems.     

Deep learning for content-based video retrieval in film and television production

While digitization has changed the workflow of professional media production, the content-based labeling of image sequences and video footage, necessary for all subsequent stages of film and television production, archival or marketing is typically still performed manually and thus quite time-consuming. In this paper, we present deep learning approaches to support professional media production. In particular, novel algorithms for visual concept detection, similarity search, face detection, face recognition and face clustering are combined in a multimedia tool for effective video inspection and retrieval. The analysis algorithms for concept detection and similarity search are combined in a multi-task learning approach to share network weights, saving almost half of the computation time. Furthermore, a new visual concept lexicon tailored to fast video retrieval for media production and novel visualization components are introduced. Experimental results show the quality of the proposed approaches. For example, concept detection achieves a mean average precision of approximately 90% on the top-100 video shots, and face recognition clearly outperforms the baseline on the public Movie Trailers Face Dataset.     

Direct computation of solution spaces

In engineering, it is often desirable to find a subset of the set of feasible designs, a solution space, rather than a single solution. A feasible design is defined as a design which does not violate any constraints and has a performance value below a desired threshold. Performance measure, threshold value and constraints depend on the specific problem. For evaluation of a design with respect to feasibility, a model is required which maps the design parameters from the input space onto the performance measures in the output space. In state-of-the-art methodology, iterative sampling is used to generate an estimate of the frontier between feasible and infeasible regions in the input space. By evaluating each sample point with respect to feasibility, areas which contain a large fraction of feasible designs are identified and subsequently resampled. The largest hypercube containing only feasible designs is sought, because this results in independent intervals for each design parameter. Estimating this hypercube with sufficient precision may require a large number of model evaluations, depending on the dimensionality of the input space. In this paper, a novel approach is proposed for modeling the inequality constraints and an objective function in a way for which a linear formulation can be used, independently of the dimensionality of the problem. Thereby the exact solution for the largest feasible hypercube can be calculated at much lower cost than with stochastic sampling as described above, as the problem is reduced to solving a linear system of equations. The method is applied to structural design with respect to the US-NCAP frontal impact. The obtained solution is compared to numerical solutions of an identical system, which are computed using reduced order models and stochastic methods. By this example, the high potential of the new direct method for solution space computation is shown.     

Photoinduced Tetrazole‐Based Functionalization of Off‐Stoichiometric Clickable Microparticles

We report the preparation of tetrazole‐containing step‐growth microparticles and the subsequent use of photoinduced nitrile imine‐mediated tetrazole‐ene cycloaddition (NITEC) reactions on the particles with spatiotemporal control. Microparticles with an average diameter of 4.1 µm and with inherent tetrazole‐ene dual functionality are prepared by a one‐pot off‐stoichiometric thiol‐Michael addition dispersion polymerization. The NITEC reaction is performed efficiently in the solid phase by UV irradiation, leading to the formation of fluorescent pyrozoline adducts, with an estimated quantum yield of 0.7. Particle concentration‐independent reaction kinetics are observed and full conversion is reached within 10 min of UV exposure at an intensity of 8 mW cm^?2. Temporal control is demonstrated with either UV or rooftop sunlight irradiation of variable duration. By using two‐photon writing with a laser centered around 700 nm wavelength, spatial control is demonstrated with micrometer‐scale resolution via surface patterning of the microparticles. Further, microparticles with exclusive tetrazole functionality are prepared by a one‐pot, two‐step thiol‐Michael addition dispersion polymerization. The NITEC reaction between tetrazole‐functional particles and acrylates in solution is examined at various tetrazole/alkene molar ratios, and a 10:1 excess of alkenes in solution is found necessary for efficient functionalization.