Erscheinungsjahr 2022

Communication technologies play an important role in maintaining the grandparent-grandchild (GP-GC) relationship. Based on Media Richness Theory, this study investigates the frequency of use (RQ1) and perceived quality (RQ2) of established media as well as the potential use of selected innovative media (RQ3) in GP-GC relationships with a particular focus on digital media. A cross-sectional online survey and vignette experiment were conducted in February 2021 among N = 286 university students in Germany (mean age 23 years, 57% female) who reported on the direct and mediated communication with their grandparents. In addition to face-to-face interactions, non-digital and digital established media (such as telephone, texting, video conferencing) and innovative digital media, namely augmented reality (AR)-based and social robot-based communication technologies, were covered. Face-to-face and phone communication occurred most frequently in GP-GC relationships: 85% of participants reported them taking place at least a few times per year (RQ1). Non-digital established media were associated with higher perceived communication quality than digital established media (RQ2). Innovative digital media received less favorable quality evaluations than established media. Participants expressed doubts regarding the technology competence of their grandparents, but still met innovative media with high expectations regarding improved communication quality (RQ3). Richer media, such as video conferencing or AR, do not automatically lead to better perceived communication quality, while leaner media, such as letters or text messages, can provide rich communication experiences. More research is needed to fully understand and systematically improve the utility, usability, and joy of use of different digital communication technologies employed in GP-GC relationships.

Plasmonic nanostructures have attracted tremendous interest due to their special capability to trap light, which is of great significance for many applications such as solar steam generation and desalination, electric power generation, photodetection, sensing, catalysis, cancer therapy, and photoacoustic imaging. However, the noble metal-based (Au, Ag, Pd) plasmonic nanostructures with expensive costs and limitations to large-scale fabrication restrict their practical applications. Here, a novel and noble-metal-free Al/AlN plasmonic nanostructure fabricated by a reactive magnetron sputtering at the elevated temperature of 200 ˚C is presented. The unique 3D Al/AlN plasmonic nanostructures show a highly efficient (96.8%) and broadband (full solar spectrum) absorption and a strong photothermal conversion effect on its surface, demonstrating the potential in applications in light-induced thermal imaging and photo-thermoelectric power generation. This simple fabrication method and the developed Al/AlN plasmonic nanostructure combine excellent light trapping performance, abundant and low-cost Al and N elements, good heat localization effect, and scalable fabrication method, suggesting a promising alternative to noble-metal plasmonic nanostructures for photonic applications.

There is substantial intersubject variability of behavioral and neurophysiological responses to transcranial electrical stimulation (tES), which represents one of the most important limitations of tES. Many tES protocols utilize a fixed experimental parameter set disregarding individual anatomical and physiological properties. This one-size-fits-all approach might be one reason for the observed interindividual response variability. Simulation of current flow applying head models based on available anatomical data can help to individualize stimulation parameters and contribute to the understanding of the causes of this response variability. Current flow modeling can be used to retrospectively investigate the characteristics of tES effectivity. Previous studies examined, for example, the impact of skull defects and lesions on the modulation of current flow and demonstrated effective stimulation intensities in different age groups. Furthermore, uncertainty analysis of electrical conductivities in current flow modeling indicated the most influential tissue compartments. Current flow modeling, when used in prospective study planning, can potentially guide stimulation configurations resulting in individually effective tES. Specifically, current flow modeling using individual or matched head models can be employed by clinicians and scientists to, for example, plan dosage in tES protocols for individuals or groups of participants. We review studies that show a relationship between the presence of behavioral/neurophysiological responses and features derived from individualized current flow models. We highlight the potential benefits of individualized current flow modeling.

Ongoing developments in nanotechnology demand higher spatial resolution and thus, higher amplitude sensitivity in Atomic Force Microscopy (AFM). In this work, active cantilevers with integrated sensor and actuator systems are parametrically excited using a novel, analog feedback circuit. With that it is possible to adapt the strength and sign of a cubic nonlinearity which provides a bound to the amplitudes in resonance operation. The system response shows steeper resonance curves and therefore higher amplitude sensitivities compared to forced excited cantilevers. Theoretical findings are validated experimentally.

We study Hamiltonicity in graphs obtained as the union of a deterministic n-vertex graph H with linear degrees and a d-dimensional random geometric graph G d (n, r) for any d ≥ 1. We obtain an asymptotically optimal bound on the minimum r for which a.a.s. H ∪ G d (n, r) is Hamiltonian. Our proof provides a linear time algorithm to find a Hamilton cycle in such graphs.

Sensitivity analysis in general deals with the question of how changes in input data of a model affect its output data. In the context of optimization problems, such an analysis could, for instance, address how changes in capacity constraints affect the optimal solution value. Although well established in the domain of linear programming, sensitivity analysis approaches for combinatorial optimization problems are model-specific, limited in scope and not applicable to practical optimization problems. To overcome these limitations, Schulte et al. developed the concept of bilevel innovization. By using evolutionary bilevel optimization in combination with data mining and visualization techniques, bilevel innovization provides decision-makers with deeper insights into the behavior of the optimization model and supports decision-making related to model building and configuration. Originally introduced in the field of evolutionary computation, most recently bilevel innovization has been proposed as an approach to sensitivity analysis for combinatorial problems in general. Based on previous work on bilevel innovization, our paper illustrates this concept as a tool for sensitivity analysis by providing a comprehensive analysis of the generalized assignment problem. Furthermore, it is investigated how different algorithms for solving the combinatorial problem affect the insights gained by the sensitivity analysis, thus evaluating the robustness and reliability of the sensitivity analysis results.

Friction stir welding (FSW) is a solid-state joining process with a wide range of applications in the E-mobility, automotive, aerospace and energy industries. However, FSW is subjected to specific challenges including comparatively high process forces and high requirements on the clamping technique as well as tool wear resulting from the tool-workpiece interaction and thermo-mechanical stresses. Geometric-related tool wear can cause premature tool failure, process instabilities or weld seam irregularities. Therefore, tool wear in general, wear limits and tool life are essential factors for the efficient and sustainable implementation of friction stir welding. Against this background, this study analysed areas of significant tool wear on the shoulder and probe as a function of process temperature, weld seam length and weld seam quality. This provided functional correlations for determining limiting conditions on maximum tolerable tool wear. Geometrical deviations of the tool, induced by wear, were detected experimentally at different measuring points on the probe and shoulder and varying weld seam length. The investigations were carried out using a force-controlled robotized welding setup in which AA-6060-T66 sheets with a thickness of 5 mm were joined by weld seams up to 500 m in length. To identify the maximum tolerable tool wear, the weld seam properties were determined by visual and metallographic inspections and by tensile tests at 50-m intervals on the weld seam. It was shown that a 50% reduction in rotational speed (lower temperatures) resulted in less wear and thus in an increase of tool life of up to 150%. In addition, it was shown that the shoulder, like the probe, was also subject to significant wear. These results can be incorporated into FSW maintenance schedules to maximize tool life and minimize scrap rates.

Time-resolved photoluminescence (TRPL) measurements and the extraction of meaningful parameters involve four key ingredients: a suitable sample such as a semiconductor double heterostructure, a state-of-the-art measurement setup, a kinetic model appropriate for the description of the sample behavior, and a general analysis method to extract the model parameters of interest from the measured TRPL transients. Until now, the last ingredient is limited to single curve fits, which are mostly based on simple models and least-squares fits. These are often insufficient for the parameter extraction in real-world applications. The goal of this article is to give the community a universal method for the analysis of TRPL measurements, which accounts for the Poisson distribution of photon counting events. The method can be used to fit multiple TRPL transients simultaneously using general kinematic models, but should also be used for single transient fits. To demonstrate this approach, multiple TRPL transients of a GaAs/AlGaAs heterostructure are fitted simultaneously using coupled rate equations. It is shown that the simultaneous fits of several TRPL traces supplemented by systematic error estimations allow for a more meaningful and more robust parameter determination. The statistical methods also quantify the quality of the description by the underlying physical model.

This paper considers the structure consisting of the set of all words over a given alphabet together with the subword relation, regular predicates, and constants for every word. We are interested in the counting extension of first-order logic by threshold counting quantifiers. The main result shows that the two-variable fragment of this logic can be decided in twofold exponential alternating time with linearly many alternations (and therefore in particular in twofold exponential space as announced in the conference version (Kuske and Schwarz, in: MFCS’20, Leibniz International Proceedings in Informatics (LIPIcs) vol. 170, pp 56:1-56:13. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020) of this paper) provided the regular predicates are restricted to piecewise testable ones. This result improves prior insights by Karandikar and Schnoebelen by extending the logic and saving one exponent in the space bound. Its proof consists of two main parts: First, we provide a quantifier elimination procedure that results in a formula with constants of bounded length (this generalises the procedure by Karandikar and Schnoebelen for first-order logic). From this, it follows that quantification in formulas can be restricted to words of bounded length, i.e., the second part of the proof is an adaptation of the method by Ferrante and Rackoff to counting logic and deviates significantly from the path of reasoning by Karandikar and Schnoebelen.

Muscle fatigue as a defense body mechanism against overload is a result of the products of incomplete oxygen oxidation such as reactive oxygen species. Hence, C60 fullerene as a powerful nanoantioxidant can be used to speed up the muscle recovery process after fatigue. Here, the residual effect of C60 fullerene on the biomechanical and biochemical markers of the development of muscle soleus fatigue in rats for 2 days after 5 days of its application was studied. The known antioxidant N-acetylcysteine (NAC) was used as a comparison drug. The atomic force microscopy to determine the size distribution of C60 fullerenes in an aqueous solution, the tensiometry of skeletal muscles, and the biochemical analysis of their tissues and rat blood were used in this study. It was found that after the cessation of NAC injections, the value of the integrated muscle power is already slightly different from the control (5%-7%) on the first day, and on the second day, it does not significantly differ from the control. At the same time, after the cessation of C60 fullerene injections, its residual effect was 45%-50% on the first day, and 17%-23% of the control on the second one. A significant difference (more than 25%) between the pro- and antioxidant balance in the studied muscles and blood of rats after the application of C60 fullerene and NAС plays a key role in the long-term residual effect of C60 fullerene. This indicates prolonged kinetics of C60 fullerenes elimination from the body, which contributes to their long-term (at least 2 days) compensatory activation of the endogenous antioxidant system in response to muscle stimulation, which should be considered when developing new therapeutic agents based on these nanoparticles.

We present a highly efficient simulation method for the calculation of three-dimensional quasi-static interface shapes under the influence of electric fields. The method is especially useful for the simulation of microfluidic systems driven by electrowetting on dielectrics because it accounts automatically and inherently for the highly non-trivial interface shape in the vicinity of the triple-phase contact. In particular, the voltage independence of the local contact angle predicted based on analytical considerations is correctly reproduced in all our simulations. For the calculation of the shape of the interface, the geometry is triangulated and the mesh nodes are shifted until the system energy becomes minimal. The same mesh is also used to calculate the electric field using the boundary-element method. Therefore, only the surface of the geometry needs to be meshed, and no volume meshes are involved. The method can be used for the simulation of closed systems with a constant volume (e.g., droplet-based microfluidics) while preserving the volume very precisely as well as open systems (e.g., the liquid-air interface within micro-cavities or capillaries). Additional effects, such as the influence of gravitational forces, can easily be taken into account. In contrast to other efficient simulations, such as the volume-of-fluid, level-set, or phase-field methods, ideally, sharp interfaces are obtained. We calculate interface shapes for exemplary systems and compare with analytical as well as experimental results.

During the impregnation of reinforcement fabrics in liquid composite molding processes, the flow within fiber bundles and the channels between the fiber bundles usually advances at different velocities. This so-called “dual-scale flow” results in void formation inside the composite material and has a negative effect on its mechanical properties. Semi-empirical models can be applied to calculate the extent of the dual-scale flow. In this study, a methodology is presented that stops the impregnation of reinforcement fabrics at different filling levels by using a photo-reactive resin system. By means of optical evaluation, the theoretical calculation models of the dual-scale flow are validated metrologically. The results show increasingly distinct dual-scale flow effects with increasing pressure gradients. The methodology enables the measurability of microscopic differences in flow front progression to validate renowned theoretical models and compare simulations to measurements of applied injection processes.

Magnetorheological fluid (MR) based dampers have been established as an alternative to classical hydraulic dampers with proportional electromagnetic valves under vibration processes which demand adaptive damping forces. Almost all MR-dampers are spatially 1-Degree-of-Freedom (DOF) dampers, having only one axis or direction of damping force generation. In many technical applications there exist movements in more than one spatial DOF, eventually necessitating more than one damper. Because of this, the damping is required not only in one but in more spatial directions, yet adjustable. In this work, a new design of a spatial 3DOF MR damper is proposed to allow damping in three directions within one damping device. The underlying motivation is to spatially integrate three damping directions in one device to potentially reduce installation space compared to three separate 1 DOF dampers. The basic idea of the construction is to use one fluid chamber with several spatially distributed control elements at different positions of the fluid chamber. The control elements are electromagnets, generating the magnetic field in the fluid at different positions so that in total 3 spatial DOFs can be damped individually. Experiments and investigation are made, where the damper's behavior are analyzed not only in one single DOF but also in more than one DOF. It is shown, that the damping concept can generate damping in all three spatial DOFs, both individually or together. Moreover, the damping can be generated to be dominant in one specific direction, meanwhile minimum in the other direction orthogonal to it.

The prediction of turbulent flow by the application of machine learning (ML) algorithms to big data is a concept currently in its infancy which requires further development. It is of special importance if the aim is a prediction that is good in a statistical sense or if the vector fields should be predicted as good as possible. For this purpose, the statistical and deterministic prediction of the unsteady but periodic flow of the von Kármán Vortex Street (KVS) was examined using an Echo State Network (ESN) which is well suited for learning from time series due to its recurrent connections. The experimental data of the velocity field of the KVS were collected by Particle Image Velocimetry (PIV). Then, the data were reduced by Proper Orthogonal Decomposition (POD) and the flow was reconstructed by the first hundred most energetic modes. An ESN with 3000 neurons was optimized with respect to its three main hyperparameters to predict the time coefficients of the POD modes. For the deterministic prediction, the aim was to maximize the correct direction of the vertical velocities. The results indicate that the ESN can mimic the periodicity and the unsteadiness of the flow. It is also able to predict the sequence of the upward and downward directed velocities for longer time spans. For the statistical prediction, the similarity of the probability density functions of the vertical velocity fields between the predicted and actual flow was achieved. The leaking rate of the ESN played a key role in the transition from deterministic to statistical predictions.

In recent years, various depth sensors that are small enough to be used with mobile hardware have been introduced. They provide important information for use cases like 3D reconstruction or in the context of augmented reality where tracking and camera data alone would be insufficient. However, depth sensors may not always be available due to hardware limitations or when simulating augmented reality applications for prototyping purposes. In these cases, different approaches like stereo matching or depth estimation using neural networks may provide a viable alternative. In this paper, we therefore explore the imitation of depth sensors using deep neural networks. For this, we use a state-of-the-art network for depth estimation and adapt it in order to mimic a Structure Sensor as well as an iPad LiDAR sensor. We evaluate the network which was pre-trained on NYU V2 directly as well as several variations where transfer learning is applied in order to adapt the network to different depth sensors while using various data preprocessing and augmentation techniques. We show that a transfer learning approach together with appropriate data processing can enable an accurate modeling of the respective depth sensors.

Redox-based memristive devices have shown great potential for application in neuromorphic computing systems. However, the demands on the device characteristics depend on the implemented computational scheme and unifying the desired properties in one stable device is still challenging. Understanding how and to what extend the device characteristics can be tuned and stabilized is crucial for developing application specific designs. Here, we present memristive devices with a functional trilayer of HfOx/Al2O3/TiO2 tailored by the stoichiometry of HfOx (x = 1.8, 2) and the operating conditions. The device properties are experimentally analyzed, and a physics-based device model is developed to provide a microscopic interpretation and explain the role of the Al2O3 layer for a stable performance. Our results demonstrate that the resistive switching mechanism can be tuned from area type to filament type in the same device, which is well explained by the model: the Al2O3 layer stabilizes the area-type switching mechanism by controlling the formation of oxygen vacancies at the Al2O3/HfOx interface with an estimated formation energy of ≈ 1.65 ± 0.05 eV. Such stabilized area-type devices combine multi-level analog switching, linear resistance change, and long retention times (≈ 107-108 s) without external current compliance and initial electroforming cycles. This combination is a significant improvement compared to previous bilayer devices and makes the devices potentially interesting for future integration into memristive circuits for neuromorphic applications.

Well-defined hetero-interfaces with controlled properties are crucial for any high-performance, semiconductor-based, (opto-)electronic device. They are particularly important for device structures on the nanoscale with increased interfacial areas. Utilizing a ultrahigh-vacuum based multi-tip scanning tunneling microscope, this work reveals inadvertent conductivity channels between the nanowire (NW) base and the substrate, when measuring individual vertical core-shell III-V-semiconductor NWs. For that, four-terminal probing is applied on freestanding, epitaxially grown coaxial p-GaAs/i-GaInP/n-GaInP NWs without the need of nanoscale lithography or deposition of electrical contacts. This advanced analysis, carried out after composition-selective wet chemical etching, reveals a substantially degraded electrical performance of the freestanding NWs compared to detached ones. In an electron beam induced current mode of the nanosensor, charge separation at the substrate-to-NW base junction is demonstrated. An energy dispersive X-ray spectroscopic linescan shows an unintended compositional change of the epitaxially grown NW toward the planar layers caused by different incorporation mechanisms of Ga and In at the NW base. This approach provides direct insight into the NW-substrate transition area and leads to a model of the conductivity channels at the NW base, which should, in principle, be considered in the fabrication of all NW heterostructures grown bottom-up on heterogeneous substrate materials.

Range-resolved interferometry (RRI) allows the simultaneous demodulation of multiple interferometric signal sources and provides a tomographic view of all constituent interferometers that may be present in a setup. Through comparison with a reference distance of known length, absolute distance measurements can be performed. RRI is tailored to the use of laser frequency modulation through injection-current modulation of regular, monolithic laser diodes that are both cost-effective and highly coherent and therefore this approach promises broad applicability. In this paper, two methods for absolute distance measurement, one based on the direct evaluation of the signal peak positions and one based on the phase demodulation of an additional lock-in modulation signal, are experimentally demonstrated. Using an external verification displacement interferometer, both techniques are shown to achieve in-situ absolute distance measurements with systematic errors below over a 50 mm travel range. The aim of this paper is to establish the general suitability of RRI for absolute distance measurements and in-situ tomographic interferometer characterisation for precision engineering. In future, this approach could be used to diagnose interferometric setups for parasitic signal contributions, multiple reflections or to determine the dead path length for accurate environmental compensation, either for use during initial setup of, or for continuous operation alongside, a regular displacement measuring interferometer.

The ASi-Sii defect model as one possible explanation for light-induced degradation (LID) in typically boron-doped silicon solar cells, detectors, and related systems is discussed and reviewed. Starting from the basic experiments which led to the ASi-Sii defect model, the ASi-Sii defect model (A: boron, or indium) is explained and contrasted to the assumption of a fast-diffusing so-called “boron interstitial.” An LID cycle of illumination and annealing is discussed within the conceptual frame of the ASi-Sii defect model. The dependence of the LID defect density on the interstitial oxygen concentration is explained within the ASi-Sii defect picture. By comparison of electron paramagnetic resonance data and minority carrier lifetime data related to the assumed fast diffusion of the “boron interstitial” and the annihilation of the fast LID component, respectively, the characteristic EPR signal Si-G28 in boron-doped silicon is related to a specific ASi-Sii defect state. Several other LID-related experiments are found to be consistent with an interpretation by an ASi-Sii defect.

Solar energy is a green, sustainable, and de facto inexhaustible energy source for mankind. The conversion of solar energy into other forms of energy has attracted extensive research interest due to climate change and the energy crisis. Among all the solar energy conversion technologies, photothermal conversion of solar energy exhibits unique advantages when applied for water purification, desalination, high-temperature heterogeneous catalysis, anti-bacterial treatments, and deicing. In this review, the various photothermal conversion mechanisms based on different forms of heat release are summarized and some of the latest examples are presented. In addition, the necessary prerequisites for solar-driven photothermal materials toward their practical applications are also discussed. Further, the latest advances in photothermal conversion of solar energy are discussed, focusing on different types of photothermal applications. Finally, a summary is given and the challenges and opportunities in the photothermal conversion of solar energy are presented. This review aims to give a comprehensive understanding of emerging solar energy conversion technologies based on the photothermal effect, especially by using nanomaterials and nanostructures.

Processing continuous data streams is one of the hot topics of our time. A major challenge is the formulation of a suitable and efficient stream processing pipeline. This process is complicated by long restart times after pipeline modifications and tight dependencies on the actual data to process. To approach these issues, we have developed StreamVizzard - an interactive and explorative stream processing editor to simplify the pipeline engineering process. Our system allows to visually configure, execute, and completely modify a pipeline during runtime without any delay. Furthermore, an adaptive visualizer automatically displays the operator's processed data and statistics in a comprehensible way and allows the user to explore the data and support his design decisions. After the pipeline has been finalized our system automatically optimizes the pipeline based on collected statistics and generates standalone runtime code for productive use at a targeted stream processing engine.

A multistage optimization method is developed yielding Tesla valves that are efficient even at low flow rates, characteristic, e.g., for almost all microfluidic systems, where passive valves have intrinsic advantages over active ones. We report on optimized structures that show a diodicity of up to 1.8 already at flow rates of 20 μl s^-1 corresponding to a Reynolds number of 36. Centerpiece of the design is a topological optimization based on the finite element method. It is set-up to yield easy-to-fabricate valve structures with a small footprint that can be directly used in microfluidic systems. Our numerical two-dimensional optimization takes into account the finite height of the channel approximately by means of a so-called shallow-channel approximation. Based on the three-dimensionally extruded optimized designs, various test structures were fabricated using standard, widely available microsystem manufacturing techniques. The manufacturing process is described in detail since it can be used for the production of similar cost-effective microfluidic systems. For the experimentally fabricated chips, the efficiency of the different valve designs, i.e., the diodicity defined as the ratio of the measured pressure drops in backward and forward flow directions, respectively, is measured and compared to theoretical predictions obtained from full 3D calculations of the Tesla valves. Good agreement is found. In addition to the direct measurement of the diodicities, the flow profiles in the fabricated test structures are determined using a two-dimensional microscopic particle image velocimetry (μPIV) method. Again, a reasonable good agreement of the measured flow profiles with simulated predictions is observed.

Low-gain avalanche detectors (LGAD) suffer from an acceptor removal phenomenon due to irradiation. This acceptor removal phenomenon is investigated in boron, gallium, and indium implanted samples by 4-point-probe (4pp) measurements, low-temperature photoluminescence spectroscopy (LTPL), and secondary ion mass spectrometry (SIMS) before and after irradiation with electrons and protons. Different co-implantation species are evaluated with respect to their ability to reduce the acceptor removal phenomenon. In case of boron, the beneficial effect is found to be most pronounced for the low-dose fluorine and high-dose nitrogen co-implantation. In case of gallium, the low-dose implantations of carbon and oxygen are found to be beneficial. For indium, the different co-implantation species have no beneficial effect. SIMS boron concentration depth profiles measured before and after irradiation show no indication of a fast movement of boron at room temperature. Hence, the discussed BSi-Sii-defect explanation approach of the acceptor removal phenomenon seems to be more likely than the other discussed Bi-Oi-defect explanation approach.

This metagenomics investigation of three closely adjacent sampling sites from an archaeological excavation of a pre-industrial coal mining exploration shaft provides detailed information on the composition of the local soil bacterial communities. The observed significant differences between the samples, reflected in the 16S r-RNA analyses, were consistent with the archaeologically observed situation distinguishing the coal seam, the rapidly deposited bright sediment inside an exploration shaft, and the topsoil sediment. In general, the soils were characterized by a dominance of Proteobacteria, Actinobacteria, Acidobacteria, and Archaea, whereas the coal seam was characterized by the highest proportion of Proteobacteria; the topsoil was characterized by very high proportions of Archaea - in particular, Nitrosotaleaceae - and Acidobacteria, mainly of Subgroup 2. Interestingly, the samples of the fast-deposited bright sediment showed a rank function of OTU abundances with disproportional values in the lower abundance range. This could be interpreted as a reflection of the rapid redeposition of soil material during the refilling of the exploration shaft in the composition of the soil bacterial community. This interpretation is supported by the observation of a comparatively high proportion of reads relating to bacteria known to be alkaliphilic in this soil material. In summary, these investigations confirm that metagenomic analyses of soil material from archaeological excavations can provide valuable information about the local soil bacterial communities and the historical human impacts on them.

We simulate the nonlinear chaotic dynamics of Lorenz-type models for a classical two-dimensional thermal convection flow with three and eight degrees of freedom by a hybrid quantum-classical reservoir computing model. The high-dimensional quantum reservoir dynamics are established by universal quantum gates that rotate and entangle the individual qubits of the tensor product quantum state. A comparison of the quantum reservoir computing model with its classical counterpart shows that the same prediction and reconstruction capabilities of classical reservoirs with thousands of perceptrons can be obtained by a few strongly entangled qubits. We demonstrate that the mean squared error between model output and ground truth in the test phase of the quantum reservoir computing algorithm increases when the reservoir is decomposed into separable subsets of qubits. Furthermore, the quantum reservoir computing model is implemented on a real noisy IBM quantum computer for up to seven qubits. Our work thus opens the door to model the dynamics of classical complex systems in a high-dimensional phase space effectively with an algorithm that requires a small number of qubits.

Fluorescence lifetime imaging ophthalmoscopy (FLIO), a technique for investigating metabolic changes in the eye ground, can reveal the first signs of diseases related to metabolism. The fluorescence of the natural lens overlies the fundus fluorescence. Although the influence of natural lens fluorescence can be somewhat decreased with mathematical models, excluding this influence during the measurement by using hardware enables more exact estimation of the fundus fluorescence. Here, we analyze four 1-photon excitation hardware solutions to suppress the influence of natural lens fluorescence: aperture stop separation, confocal scanning laser ophthalmoscopy, combined confocal scanning laser ophthalmoscopy and aperture stop separation, and dual point confocal scanning laser ophthalmoscopy. The effect of each principle is demonstrated in examples. The best suppression is provided by the dual point principle, realized with a confocal scanning laser ophthalmoscope. In this case, in addition to the fluorescence of the whole eye, the fluorescence of the anterior part of the eye is detected from a non-excited spot of the fundus. The intensity and time-resolved fluorescence spectral data of the fundus are derived through the subtraction of the simultaneously measured fluorescence of the excited and non-excited spots. Advantages of future 2-photon fluorescence excitation are also discussed. This study provides the first quantitative evaluation of hardware principles to suppress the fluorescence of the natural lens during measurements of fundus autofluorescence.

The paper presents AVQBits, a versatile, bitstream-based video quality model. It can be applied in several contexts such as video service monitoring, evaluation of video encoding quality, of gaming video QoE, and even of omnidirectional video quality. In the paper, it is shown that AVQBits predictions closely match video quality ratings obained in various subjective tests with human viewers, for videos up to 4K-UHD resolution (Ultra-High Definition, 3840 x 2180 pixels) and framerates up 120 fps. With the different variants of AVQBits presented in the paper, video quality can be monitored either at the client side, in the network or directly after encoding. The no-reference AVQBits model was developed for different video services and types of input data, reflecting the increasing popularity of Video-on-Demand services and widespread use of HTTP-based adaptive streaming. At its core, AVQBits encompasses the standardized ITU-T P.1204.3 model, with further model instances that can either have restricted or extended input information, depending on the application context. Four different instances of AVQBits are presented, that is, a Mode 3 model with full access to the bitstream, a Mode 0 variant using only metadata such as codec type, framerate, resoution and bitrate as input, a Mode 1 model using Mode 0 information and frame-type and -size information, and a Hybrid Mode 0 model that is based on Mode 0 metadata and the decoded video pixel information. The models are trained on the authors’ own AVT-PNATS-UHD-1 dataset described in the paper. All models show a highly competitive performance by using AVT-VQDB-UHD-1 as validation dataset, e.g., with the Mode 0 variant yielding a value of 0.890 Pearson Correlation, the Mode 1 model of 0.901, the hybrid no-reference mode 0 model of 0.928 and the model with full bitstream access of 0.942. In addition, all four AVQBits variants are evaluated when applying them out-of-the-box to different media formats such as 360˚ video, high framerate (HFR) content, or gaming videos. The analysis shows that the ITU-T P.1204.3 and Hybrid Mode 0 instances of AVQBits for the considered use-cases either perform on par with or better than even state-of-the-art full reference, pixel-based models. Furthermore, it is shown that the proposed Mode 0 and Mode 1 variants outperform commonly used no-reference models for the different application scopes. Also, a long-term integration model based on the standardized ITU-T P.1203.3 is presented to estimate ratings of overall audiovisual streaming Quality of Experience (QoE) for sessions of 30 s up to 5 min duration. In the paper, the AVQBits instances with their per-1-sec score output are evaluated as the video quality component of the proposed long-term integration model. All AVQBits variants as well as the long-term integration module are made publicly available for the community for further research.

Biomechatronics is an engineering subject in which biomimetics as a method is one of its two supporting pillars: biology for engineering, or Bio4Eng. This is contrasted with biocompatible design, or Eng4Bio, examples of which are human-serving systems, such as exoskeletons, and biomedical engineering. The paper aims to illustrate that the research fields of biomimetics, biomechatronics, and biomedical engineering are not in competition but mutually supportive. The current attempts to place biomechatronics under the umbrella of biomimetics or biomedical engineering are therefore not expedient; they deprive the subject of its strength of combining Bio4Eng and Eng4Bio at any time in a task-related manner. In addition to research and development, however, the training of the specialists supporting the subjects must not be disregarded and is therefore described based on a proven design.

In recent investigations of magnetoelectric sensors based on microelectromechanical cantilevers made of TiN/AlN/Ni, a complex eigenfrequency behavior arising from the anisotropic ΔE effect was demonstrated. Within this work, a FEM simulation model based on this material system is presented to allow an investigation of the vibrational properties of cantilever-based sensors derived from magnetocrystalline anisotropy while avoiding other anisotropic contributions. Using the magnetocrystalline ΔE effect, a magnetic hardening of Nickel is demonstrated for the (110) as well as the (111) orientation. The sensitivity is extracted from the field-dependent eigenfrequency curves. It is found, that the transitions of the individual magnetic domain states in the magnetization process are the dominant influencing factor on the sensitivity for all crystal orientations. It is shown, that Nickel layers in the sensor aligned along the medium or hard axis yield a higher sensitivity than layers along the easy axis. The peak sensitivity was determined to 41.3 T−1 for (110) in-plane-oriented Nickel at a magnetic bias flux of 1.78 mT. The results achieved by FEM simulations are compared to the results calculated by the Euler-Bernoulli theory.

The morphology evolution by thermal annealing induced dewetting of gold (Au) thin films on silicon (Si) substrates with a native oxide layer and its dependences on annealing temperature and atmosphere are investigated. Both dewetting degree of thin film and Au/Si interdiffusion extent are enhanced with the annealing temperature. Au/Si interdiffusion can be observed beyond 800 ˚C and Au-Si droplets form in both argon and oxygen (Ar + O2) and argon and hydrogen (Ar + H2) environments. In Ar + O2 case, the passive oxidation (Si + O2 &flech; SiO2) of diffused Si happens and thick silicon oxide (SiOx) covering layers are formed. A high temperature of 1050 ˚C can even activate the outward growth of free-standing SiOx nanowires from droplets. Similarly, annealing at 800 ˚C under Ar + H2 situation also enables the slight Si passive oxidation, resulting in the formation of stripe-like SiOx areas. However, higher temperatures of 950-1050 ˚C in Ar + H2 environment initiate both the SiOx decomposition and the Si active oxidation (2Si + O2 &flech; 2SiO(g)), and the formation of solid SiOx is absent, leading to the only formation of isolated Au-Si droplets at elevated temperatures and droplets evolve to particles presenting two contrasts due to the Au/Si phase separation upon cooling.

Droplet-based microfluidic screening techniques can benefit from interfacing established microtiter plate-based screening and sample management workflows. Interfacing tools are required both for loading preconfigured microtiter-plate (MTP)-based sample collections into droplets and for dispensing the used droplets samples back into MTPs for subsequent storage or further processing. Here, we present a collection of Digital Microfluidic Pipetting Tips (DMPTs) with integrated facilities for droplet generation and manipulation together with a robotic system for its operation. This combination serves as a bidirectional sampling interface for sample transfer from wells into droplets (w2d) and vice versa droplets into wells (d2w). The DMPT were designed to fit into 96-deep-well MTPs and prepared from glass by means of microsystems technology. The aspirated samples are converted into the channel-confined droplets’ sequences separated by an immiscible carrier medium. To comply with the demands of dose-response assays, up to three additional assay compound solutions can be added to the sample droplets. To enable different procedural assay protocols, four different DMPT variants were made. In this way, droplet series with gradually changing composition can be generated for, e.g., 2D screening purposes. The developed DMPT and their common fluidic connector are described here. To handle the opposite transfer d2w, a robotic transfer system was set up and is described briefly.

The vertically driven pendulum is one of the classical systems where parametric instability occurs. We study its behavior with an additional electromagnetic interaction caused by eddy currents in a nearby thick conducting plate that are induced when the bob is a magnetic dipole. The known analytical expressions of the induced electromagnetic force and torque acting on the dipole are valid in the quasistatic limit, i.e., when magnetic diffusivity of the plate is sufficiently high to ensure an equilibrium between magnetic field advection and diffusion. The equation of motion of the vertically driven pendulum is derived assuming that its magnetic dipole moment is aligned with the axis of rotation and that the conducting plate is horizontal. The vertical position of the pendulum remains an equilibrium with the electromagnetic interaction. Conditions for instability of this equilibrium are derived analytically by the harmonic balance method for the subharmonic and harmonic resonances in the limit of weak electromagnetic interaction. The analytical stability boundaries agree with the results of numerical Floquet analysis for these conditions but differ substantially when the electromagnetic interaction is strong. The numerical analysis demonstrates that the area of harmonic instability can become doubly connected. Bifurcation diagrams obtained numerically show the co-existence of stable periodic orbits in such conditions. For moderately strong driving, chaotic motions can be maintained for the subharmonic instability.

Distributed in-memory processing frameworks accelerate iterative workloads by caching suitable datasets in memory rather than recomputing them in each iteration. Selecting appropriate datasets to cache as well as allocating a suitable cluster configuration for caching these datasets play a crucial role in achieving optimal performance. In practice, both are tedious, time-consuming tasks and are often neglected by end users, who are typically not aware of workload semantics, sizes of intermediate data, and cluster specification. To address these problems, we present Juggler, an end-to-end framework, which autonomously selects appropriate datasets for caching and recommends a correspondingly suitable cluster configuration to end users, with the aim of achieving optimal execution time and cost. We evaluate Juggler on various iterative, real-world, machine learning applications. Compared with our baseline, Juggler reduces execution time to 25.1% and cost to 58.1%, on average, as a result of selecting suitable datasets for caching. It recommends optimal cluster configuration in 50% of cases and near-to-optimal configuration in the remaining cases. Moreover, Juggler achieves an average performance prediction accuracy of 90%.

Recent trends in 3D cell culturing has placed organotypic tissue models at another level. Now, not only is the microenvironment at the cynosure of this research, but rather, microscopic geometrical parameters are also decisive for mimicking a tissue model. Over the years, technologies such as micromachining, 3D printing, and hydrogels are making the foundation of this field. However, mimicking the topography of a particular tissue-relevant substrate can be achieved relatively simply with so-called template or morphology transfer techniques. Over the last 15 years, in one such research venture, we have been investigating a micro thermoforming technique as a facile tool for generating bioinspired topographies. We call them MatriGrid®s. In this research account, we summarize our learning outcome from this technique in terms of the influence of 3D micro morphologies on different cell cultures that we have tested in our laboratory. An integral part of this research is the evolution of unavoidable aspects such as possible label-free sensing and fluidic automatization. The development in the research field is also documented in this account.

Our long-term goal is the development of a wearable warning system that uses electrocutaneous stimulation. To find appropriate stimulation parameters and electrode configurations, we investigate perception amplitude thresholds and qualitative perceptions of electrocutaneous stimulation for varying pulse widths, electrode sizes, and electrode positions. The upper right arm was stimulated in 81 healthy volunteers with biphasic rectangular current pulses varying between 20 and 2000 μs. We determined perception, attention, and intolerance thresholds and the corresponding qualitative perceptions for 8 electrode pairs distributed around the upper arm. For a pulse width of 150 μs, we find median values of 3.5, 6.9, and 13.8 mA for perception, attention, and intolerance thresholds, respectively. All thresholds decrease with increasing pulse width. Lateral electrode positions have higher intolerance thresholds than medial electrode positions, but perception and attention threshold are not significantly different across electrode positions. Electrode size between 15 × 15 mm2 and 40 × 40 mm2 has no significant influence on the thresholds. Knocking is the prevailing perception for perception and attention thresholds while mostly muscle twitching, pinching, and stinging are reported at the intolerance threshold. Biphasic stimulation pulse widths between 150 μs and 250 μs are suitable for electric warning wearables. Within the given practical limits at the upper arm, electrode size, inter-electrode distance, and electrode position are flexible parameters of electric warning wearables. Our investigations provide the basis for electric warning wearables.

Binder- and conducting additive-free Si-O-C composite layers are deposited electrochemically under potentiostatic conditions from sulfolane-based organic electrolyte. Quartz crystal microbalance with damping monitoring is used for evaluation of the layer growth and its physical properties. The sodiation-desodiation performance of the material is afterward explored in Na-ion electrolyte. In terms of specific capacity, rate capability, and long-term electrochemical stability, the experiments confirm the advantages of applying the electrochemically formed Si-O-C structure as anode for Na-ion batteries. The material displays high (722 mAh g^-1) initial reversible capacity at j = 70 mA g^-1 and preserves stable long-term capacity of 540 mAh g^-1 for at least 400 galvanostatic cycles, measured at j = 150 mA g^-1. The observed high performance can be attributed to its improved mechanical stability and accelerated Na-ion transport in the porous anode structure. The origin of the material electroactivity is revealed based on X-Ray photoelectron spectroscopic analysis of pristine (as deposited), sodiated, and desodiated Si-O-C layers. The evaluation of the spectroscopic data indicates reversible activity of the material due to the complex contribution of carbon and silicon redox centers.

Real-time monitoring of bioanalytes in organotypic cell cultivation devices is a major research challenge in establishing stand-alone diagnostic systems. Presently, no general technical facility is available that offers a plug-in system for bioanalytics in diversely available organotypic culture models. Therefore, each analytical device has to be tuned according to the microfluidic and interface environment of the 3D in vitro system. Herein, we report the design and function of a 3D automated culture and analysis device (3D-ACAD) which actively perfuses a custom-made 3D microbioreactor, samples the culture medium and simultaneously performs capillary-based flow ELISA. A microstructured MatriGrid® has been explored as a 3D scaffold for culturing HepaRG cells, with albumin investigated as a bioanalytical marker using flow ELISA. We investigated the effect of acetaminophen (APAP) on the albumin secretion of HepaRG cells over 96 h and compared this with the albumin secretion of 2D monolayer HepaRG cultures. Automated on-line monitoring of albumin secretion in the 3D in vitro mode revealed that the application of hepatotoxic drug-like APAP results in decreased albumin secretion. Furthermore, a higher sensitivity of the HepaRG cell culture in the automated 3D-ACAD system to APAP was observed compared to HepaRG cells cultivated as a monolayer. The results support the use of the 3D-ACAD model as a stand-alone device, working in real time and capable of analyzing the condition of the cell culture by measuring a functional analyte. Information obtained from our system is compared with conventional cell culture and plate ELISA, the results of which are presented herein.

As uncertainty and sensitivity analysis of complex models grows ever more important, the difficulty of their timely realizations highlights a need for more efficient numerical operations. Non-intrusive Polynomial Chaos methods are highly efficient and accurate methods of mapping input-output relationships to investigate complex models. There is substantial potential to increase the efficacy of the method regarding the selected sampling scheme. We examine state-of-the-art sampling schemes categorized in space-filling-optimal designs such as Latin Hypercube sampling and L1-optimal sampling and compare their empirical performance against standard random sampling. The analysis was performed in the context of L1 minimization using the least-angle regression algorithm to fit the GPCE regression models. Due to the random nature of the sampling schemes, we compared different sampling approaches using statistical stability measures and evaluated the success rates to construct a surrogate model with relative errors of < 0.1 %, < 1 %, and < 10 %, respectively. The sampling schemes are thoroughly investigated by evaluating the y of surrogate models constructed for various distinct test cases, which represent different problem classes covering low, medium and high dimensional problems. Finally, the sampling schemes are tested on an application example to estimate the sensitivity of the self-impedance of a probe that is used to measure the impedance of biological tissues at different frequencies. We observed strong differences in the convergence properties of the methods between the analyzed test functions.

Well-defined nanostructuring over size, shape, spatial configuration, and multi-combination is a feasible concept to reach unique properties of nanostructure arrays, while satisfying such broad and stringent requirements with conventional techniques is challenging. Here, we report designable anodic aluminium oxide templates to address this challenge by achieving well-defined pore features within templates in terms of in-plane and out-of-plane shape, size, spatial configuration, and pore combination. The structural designability of template pores arises from designing of unequal aluminium anodization rates at different anodization voltages, and further relies on a systematic blueprint guiding pore diversification. Starting from the designable templates, we realize a series of nanostructures that inherit equal structural controllability relative to their template counterparts. Proof-of-concept applications based on such nanostructures demonstrate boosted performance. In light of the broad selectivity and high controllability, designable templates will provide a useful platform for well-defined nanostructuring.

Surface-enhanced Raman scattering (SERS) is a label-free and accurate analytical technique for the detection of a broad range of various analytes such as, biomolecules, pesticides, petrochemicals, as well as, cellular and other biological systems. A key component for the SERS analysis is the substrate which is required to be equipped with plasmonic features of metal nanostructures that directly interact with light and targeted analytes. Either metal nanoparticles can be deposited on the solid support (glass or silicon) which is suitable for stationary SERS analysis or dispersed in the solution (freely moving nanoparticles). Besides these routinely utilizing SERS substrates, polymer-metal composite particles are promising for sustained SERS analysis where metal nanoparticles act as plasmon-active (hence SERS-active) components and polymer particles act as support to the metal nanoparticles. Composite sensor particles provide 3D interaction possibilities for analytes, suitable for stationary, continuous, and sequential analysis, and they are reusable/regenerated. Therefore, this review is focused on the experimental procedures for the development of multiscale, uniform, and reproducible composite sensor particles together with their application for SERS analysis. The microfluidic reaction technique is highly versatile in the production of uniform and size-tunable composite particles, as well as, for conducting SERS analysis.

For the realization of auditory augmented reality (AAR), it is important that the room acoustical properties of the virtual elements are perceived in agreement with the acoustics of the actual environment. This perceptual matching of room acoustics is the subject reviewed in this paper. Realizations of AAR that fulfill the listeners? expectations were achieved based on pre-characterization of the room acoustics, for example, by measuring acoustic impulse responses or creating detailed room models for acoustic simulations. For future applications, the goal is to realize an online adaptation in (close to) real-time. Perfect physical matching is hard to achieve with these practical constraints. For this reason, an understanding of the essential psychoacoustic cues is of interest and will help to explore options for simplifications. This paper reviews a broad selection of previous studies and derives a theoretical framework to examine possibilities for psychoacoustical optimization of room acoustical matching.

Aligned large-scale deposition of nanowires grown in a bottom-up manner with high yield is a persisting challenge but required to assemble single-nanowire devices effectively. Contact printing is a powerful strategy in this regard but requires so far adequate adjustment of the tribological surface interactions between nanowires and target substrate, e.g. by microtechnological surface patterning, chemical modifications or lift-off strategies. To expand the technological possibilities, we explored two-directional pressure-controlled contact printing as an alternative approach to efficiently transfer nanowires with controlled density and alignment angle onto target substrates through vertical-force control. To better understand this technology and the mechanical behavior of nanowires during the contact printing process, the dynamic bending behavior of nanowires under varying printing conditions is modeled by using the finite element method. We show that the density and angular orientation of transferred nanowires can be controlled using this three-axis printing approach, which thus enables potentially a controlled nanowire device fabrication on a large scale.

Biomechanical and biochemical changes in the muscle soleus of rats during imitation of hind limbs unuse were studied in the model of the Achilles tendon rupture (Achillotenotomy). Oral administration of water-soluble C60 fullerene at a dose of 1 mg/kg was used as a therapeutic agent throughout the experiment. Changes in the force of contraction and the integrated power of the muscle, the time to reach the maximum force response, the mechanics of fatigue processes development, in particular, the transition from dentate to smooth tetanus, as well as the levels of pro- and antioxidant balance in the blood of rats on days 15, 30 and 45 after injury were described. The obtained results indicate a promising prospect for C60 fullerene use as a powerful antioxidant for reducing and correcting pathological conditions of the muscular system arising from skeletal muscle atrophy.

Scientific computing requires handling large linear models, which are often composed of structured matrices. With increasing model size, dense representations quickly become infeasible to compute or store. Matrix-free implementations are suited to mitigate this problem at the expense of additional implementation overhead, which complicates research and development effort by months, when applied to practical research problems. Fastmat is a framework for handling large structured matrices by offering an easy-to-use abstraction model. It allows for the expression of matrix-free linear operators in a mathematically intuitive way, while retaining their benefits in computation performance and memory efficiency. A built-in hierarchical unit-test system boosts debugging productivity and run-time execution path optimization improves the performance of highly-structured operators. The architecture is completed with an interface for abstractly describing algorithms that apply such matrix-free linear operators, while maintaining clear separation of their respective implementation levels. Fastmat achieves establishing a close relationship between implementation code and the actual mathematical notation of a given problem, promoting readable, portable and re-usable scientific code.