Geförderte OA-Publikationen

Pressure reducing valves (PRVs) are essentially used to reduce operational pressures in water distribution systems (WDSs) to minimize water leakage. However, water age in a WDS is an important variable describing the water quality and should be kept as low as possible. Therefore, the aim of this study is to investigate the possibility and potential of simultaneously minimizing both pressure and water age by using PRVs. To determine the optimal location and setting of PRVs, a mixed-integer nonlinear programming (MINLP) problem is formulated with minimization of the sum of the weighted total water age and pressure as the objective function, where the weighting factor can be defined by the user’s preference. The equality constraints consist of the hydraulic equations and water age functions to describe pressure and water age in the distribution network, while the inequality constraints ensure them in the defined operating ranges, respectively. Applying the proposed approach to two case studies, the results show that both water age and pressure can indeed be significantly reduced by the optimized position and setting of the PRVs.

Research on innovation failure has proliferated lately but with little theoretical attention given to the diversity of the concept. Using process theorizing, we present a model and propositions to understand how a firm's anticipation and value toward failure depends on the type of failure (task versus outcome) and the phase (divergent versus convergent) and point (early versus later) ‘within’ the process that the failure occurs. Using the anticipation-value stances, we then present a typology of four modes of innovation failure that can arise ‘from’ task and outcomes failure in the innovation process. The four modes (and associated learning response) are unsolicited failures (prevent-alert-eliminate); hazardous failures (predict-modify-mitigate); fortuitous failures (probe-expose-extrapolate); and excursive failures (facilitate-analyze-harness). To help explain the ideas in our process model and typology, we use the well-known IDEO shopping cart innovation project as an illustrative example. Together, these contributions provide contingency oriented insights on how failure varies and journeys within and from the innovation process, which helps researchers and managers to better understand the related causes, effects and learning responses.

Elastic, bendable and stretchable electronics establish a new and promising area of multi-physics engineering for a variety of applications, e.g. on wearables or in complex-shaped machine parts. While the area of metamorphic electronics has been investigated comprehensively, the behavior at radio frequencies (RFs), especially in the GHz range, is much less well studied. The mechanical deformation of the soft substrates, for instance, due to stretching, changes the geometrical dimensions and the electrical properties of RF transmission lines. This effect could be desirable in some cases, e.g. for smart devices with shape-dependent transmission or radiation characteristics, or undesirable in other cases, e.g. in feed and distribution networks due to the variable electrical lengths and thus phase variations. This contribution describes the results of a systematic study of the broadband RF properties of coplanar transmission lines on Ecoflex® substrates, based on numerical simulations and experimental data. Two types of stretchable transmission line structures were studied: Meander- and circular ring-segmented lines. Modeling and simulation were performed combining a 2D circuit simulation software with electromagnetic full-wave simulations. The experimental part of the work included the fabrication of metamorphic substrates metallized with thin copper layers and systematic measurements of the electrical lengths and phase constants of coplanar waveguides in the frequency range from 1 to 5 GHz based on vector network analysis for different stretching levels. With the given substrate technology, we succeeded in demonstrating stretchability up to a level of 21%, while the theoretical limit is expected at 57%. The meander- and circular-shaped line structures revealed markedly different sensitivities to the stretching level, which was lower for circular structures compared to the meander structures by approximately a factor of three.

Artificial intelligence (AI) has become a part of the mainstream public discourse beyond expert communities about its risks, benefits, and need for regulation. In particular, since 2014, the news media have intensified their coverage of this emerging technology and its potential impact on most domains of society. Although many studies have analyzed traditional media coverage of AI, analyses of social media, especially video-sharing platforms, are rare. In addition, research from a risk communication perspective remains scarce, despite the widely recognized potential threats to society from many AI applications. This study aims to detect recurring patterns of societal threat/efficacy in YouTube videos, analyze their main sources, and compare detected frames in terms of reach and response. Using a theoretical framework combining framing and risk communication, the study analyzed the societal threat/efficacy attributed to AI in easily accessible YouTube videos published in a year when public attention to AI temporarily peaked (2018). Four dominant AI frames were identified: the balanced frame, the high-efficacy frame, the high-threat frame, and the no-threat frame. The balanced and no-threat frames were the most prevalent, with predominantly positive and neutral AI narratives that neither adequately address the risks nor the necessary societal response from a normative risk communication perspective. The results revealed the specific risks and benefits of AI that are most frequently addressed. Video views and user engagement with AI videos were analyzed. Recommendations for effective AI risk communication and implications for risk governance were derived from the results.

Understanding turbulent thermal convection is essential for modeling many natural phenomena. This study investigates the spatiotemporal dynamics of the vortical structures in the mid-plane of turbulent Rayleigh-Bénard convection in SF6 via experiments. For this, a Rayleigh-Bénard cell of aspect ratio 10 is placed inside a pressure vessel and pressurized up to 1, 1.5, and 2.5 bar in order to reach Rayleigh numbers of Ra = 9.4 × 10^5, 2.0 × 10^6, and 5.5 × 10^6, respectively. For all three cases, the Prandtl number is Pr = 0.79 and Δ T ≈ 7 K. Then, stereoscopic particle image velocimetry is conducted to measure the three velocity components in the horizontal-mid-plane for 5.78 × 10^3 free fall times. For the given aspect ratio, the flow is no longer dominated by the side walls of the cell and turbulent superstructures that show a two-dimensional repetitive organization form. These superstructures show diverse shapes with faster dissipation rates as Ra increases. Out-of-plane vortices are the main feature of the flow. As Ra increases, the number of these vortices also increases, and their size shrinks. However, their total number is almost constant for each Ra through the measurement period. Furthermore, their occurrence is random and does not depend on whether the flow is upward-heated, downward-cooled, or horizontally directed. Vortex tracking was applied to measure lifetime, displacement, and traveled distance of these structures. The relation between lifetime and traveled distance is rather linear. Interestingly, in the vortex centers, the out-of-plane momentum transport is larger in comparison to the bulk flow. Therefore, these vortices will play a major role in the heat transport in such flows.

The tuning of the self-propagating reaction is studied theoretically by introducing a non-reactive material between two reactive material elements. For the study, the Ni/Al bilayer system was chosen. The Ni/Al elements were placed on a silicon wafer covered with a 1-µm-thick silicon dioxide. The spaces between the multilayer reactive material elements were filled with different non-reactive materials covering a wide range of thermal properties. On top of this heterogeneous layer, a 1-µm-thick sealing layer was placed consisting of the filler material. The carried out two-dimensional simulations demonstrated that embedding material allows to scale the ignition transfer time and the heat propagation velocity. For example, for a transfer length of 1 µm, the ignition time can be tuned from nano- to microseconds. Consequently, in contrast to previous results embedding materials allow scaling the properties of the self-propagating reaction in heterogeneous reactive material systems.

Evolving software is a highly complex and creative problem in which a number of different strategies are used to solve the tasks at hand. These strategies and reoccurring coding patterns can offer insights into the process. However, they can be highly project or even task-specific. We aim to identify code change patterns in order to draw conclusions about the software development process. For this, we propose a novel way to calculate high-level file overarching diffs, and a novel way to parallelize pattern mining. In a study of 1000 Java projects, we mined and analyzed a total of 45,000 patterns. We present 13 patterns, showing extreme points of the 7 pattern categories we identified. We found that a large number of high-level change patterns exist and occur frequently. The majority of mined patterns were associated with a specific project and contributor, where and by whom it was more likely to be used. While a large number of different code change patterns are used, only a few, mostly unsurprising ones, are common under all circumstances. The majority of code change patterns are highly specific to different context factors that we further explore.

The paper presents the comparison of two different continuous-time adaptive control strategies applied to the temperature stabilization of molten glass during conditioning. Both control methods include on-line linear continuous-time model parameter identification using a nonstandard procedure based on the modulating functions method. The related control task is of great practical importance because it directly affects the quality of manufactured glass containers. The molten glass temperature must be stabilized with accuracy of about 1C˚ which can be very difficult. At the core of this work, the synthesis of a nonstandard adaptive control procedure is described that consists of a linear quadratic regulator (LQR) being fed with process parameters and state estimates. These new state estimates are generated with a special transform and reconstructed by a special type of modulating function state observer consisting of two modulating function based soft-sensors which rely on a continuous-time model. However, an equally important issue of this investigation is the efficiency and accuracy of the algorithm. To this end, the described stabilization method will be compared with a standard continuous-time model predictive control (MPC) approach that was used in the authors’ previous research on the continuous molten glass temperature stabilization in a single glass forehearth zone. Simulation results based on experimental calibration data are presented and compared for these two approaches. It turns out that the first method with LQR is simpler than the MPC approach while maintaining the same level of accuracy and quality of control.

Objective: Photic driving in the human visual cortex evoked by intermittent photic stimulation is usually characterized in averaged data by an ongoing oscillation showing frequency entrainment and resonance phenomena during the course of stimulation. We challenge this view of an ongoing oscillation by analyzing unaveraged data. Methods: 64-channel EEGs were recorded during visual stimulation with light flashes at eight stimulation frequencies between 7.8 and 23 Hz for fourteen healthy volunteers. Time-frequency analyses were performed in averaged and unaveraged data. Results: While we find ongoing oscillations in the averaged data during intermittent photic stimulation, we find transient events (bursts) of activity in the unaveraged data. Both resonance and entrainment occur for the ongoing oscillations in the averaged data and the bursts in the unaveraged data. Conclusions: We argue that the continuous oscillations in the averaged signal may be composed of brief, transient bursts in single trials. Our results can also explain previously observed amplitude fluctuations in averaged photic driving data. Significance: Single-trial analyses might consequently improve our understanding of resonance and entrainment phenomena in the brain.

Smartphone users are beyond two billion worldwide. Heavy users of the texting application rely on input prediction to reduce typing effort. In languages based on the Roman alphabet, many techniques are available. However, Japanese text is based on multiple character sets such as Kanji (Chinese-like word symbols), Hiragana and Katakana syllable sets. For its time/labor intensive input, next word prediction is crucial. It is still an open challenge. To tackle this, a hybrid language model is proposed. It integrates a Recurrent Neural Network (RNN) with an n-gram model. RNNs are powerful models for learning long sequences for next word prediction. N-gram models are best at current word completion. Our RNN language model (RNN-LM) predicts the next words. According the “price” of the performance gain paid by a higher time complexity, our model best deploys on a client-server architecture. Heavily-loaded RNN-LM deploys on the server while the n-gram model on the client. Our RNN-LM consists of an input layer equipped with word embedding, an output layer, and hidden layers connected with LSTMs (Long Short-Term Memories). Training is done via BPTT (Back Propagation Through Time). For robust training, BPTT is elaborated by learning rate refinement and gradient norm scaling. To avoid overfitting, the dropout technique is applied except for LSTM. Our novel model is compact (2 LSTMs, 650 units per layer), indeed. Due to synergetic elaboration, it shows 10 % lower perplexity than Zaremba's excellent conventional models in our Japanese text prediction experiment. Our model has been incorporated into IME (Input Method Editor) we call Flick. On the Japanese text input experiment, Flick outperforms Mozc (Google Japanese Input) by 16 % in time and 34 % in the number of keystrokes.

Reactive multilayers produce exothermic reaction with definite velocity and maximum temperature after ignition, which are the fundamental properties of the reactive multilayer systems. The generated heat with certain velocity makes it widely used in joining, bonding in the packaging, thermal batteries and many more applications. In this work, a distinct approach for achieving a reaction transfer between the reactive multilayers and different materials is demonstrated which can affect the generated temperature and velocity from the self-propagating properties of the reaction. For these intensions, we fabricated the Al/Ni reactive elements with certain separations between elements which allow to observe the reaction front transfer and emitted temperature in the reaction chain. The created separation between reactive elements are periodical and ordered systems with different thermal conductive properties. The temperature and definite velocity were measured by time-resolved pyrometer and high-speed camera measurements. SEM analysis showed the characteristics of the reaction transfer between reactive multilayer elements. It is predicted that: (I) The reaction front stops at a space with critical length; (II) Reducing heat loss through the substrate supports reaction front propagation through spaces; (III) Thermal property design of the spaces between the reactive elements enables property modification of the self-propagating reaction.

There is evidence that humans can survive a direct lightning strike to the head. Our question is: could water (rain) on the skin contribute to an increase in the survival rate? We measure the influence of rain during high-energy direct lightning strikes on a realistic three-compartment human head phantom. We find a lower number of perforations and eroded areas near the lightning strike impact points on the head phantom when rain was applied compared to no rain. Current amplitudes in the brain were lower with rain compared to no rain before a fully formed flashover. We conclude that rain on the scalp potentially contributes to the survival rate of 70-90% due to: (1) lower current exposition in the brain before a fully formed flashover, and (2) reduced mechanical and thermal damage.

Turbulent flow over permeable interfaces is omnipresent featuring complex flow topology. In this work, a data-driven, end-to-end machine learning model has been developed to model the turbulent flow in porous media. For the same, we have derived a non-linear reduced order model (ROM) with a deep convolution autoencoder. This model can reduce highly resolved spatial dimensions, which is a prerequisite for direct numerical simulation, by 99%. A downstream recurrent neural network has been trained to capture the temporal trend of reduced modes; thus, it is able to provide future evolution of modes. We further evaluate the trained model's capability on a newer dataset with a different porosity. In such cases, fine-tuning could reduce the efforts (up to two-order of magnitude) to train a model with limited dataset (10%) and knowledge and still show a good agreement on the mean velocity profile. Especially, the fine-tuned model shows a better agreement in the porous domain than the channel and interface areas indicating the topological feature is less challenging for training than the multi-scale nature of the turbulent flows. Leveraging the current model, we find that even quick fine-tuning achieves an impressive order-of-magnitude reduction in training time by approximately O(102) and still results in effective flow predictions. This promising discovery encourages the fast development of a substantial amount of data-driven models tailored for various types of porous media. The diminished training time substantially lowers the computational cost when dealing with changing porous topologies, making it feasible to systematically explore interface engineering with different types of porous media.

Risk communication is a keystone in crisis prevention and mitigation. For that purpose, many institutions worldwide have the task of translating scientific risk information into actionable messages for public safety. As a collaboration among international risk and crisis communication scholars and practitioners, we sought to identify what risk communication practitioners at higher education organizations in the Global South and North identify as essential elements of effective risk communication, based on 32 interviews in 16 countries during the first wave of the COVID-19 pandemic (June-August, 2020). Results exemplify a shared vision for addressing the stickiest, most wicked challenges to effective risk communication globally. The interviews revealed globally shared best practices related to form, function, and process leading directly to what we consider the keystone of effective risk communication: saving lives (outcome).

Considering environmental changes and the demand for more sustainable energy sources, stricter requirements have been placed on electrode materials for sodium and potassium-ion batteries, which are expected to provide higher energy and power density while being affordable and sustainable. Vanadium sulfide-based materials have emerged as intriguing contenders for the next generation of anode materials due to their high theoretical capacity, abundant reserves, and cost-effectiveness. Despite these advantages, challenges such as limited cycle life and restricted ion diffusion coefficients continue to impede their effective application in sodium and potassium-ion batteries. To overcome the limitations associated with electrochemical performance and circumvent bottlenecks imposed by the inherent properties of materials at the bulk scale, this review comprehensively summarizes and analyzes the crystal structures, modification strategies, and energy storage processes of vanadium sulfide-based electrode materials for sodium and potassium-ion batteries. The objective is to guide the development of high-performance vanadium-based sulfide electrode materials with refined morphologies and/or structures, employing environmentally friendly and cost-efficient methods. Finally, future perspectives and research suggestions for vanadium sulfide-based materials are presented to propel practical applications forward.

Collaborative Model-based Systems Engineering between companies is becoming increasingly important. The utilization of the modeling possibilities of the standard language SysML v2 and the multilateral data exchange via Dataspaces open new possibilities for efficient collaboration. Based on systemic approaches, a modeling concept for decomposing the system into sub-systems is developed as a basis for the exchange. In addition, based on the analysis of collaboration processes in the context of Systems Engineering, an architectural approach with a SysML editor and Dataspace for the exchange is elaborated. The architecture is implemented on the basis of open-source solutions. The investigations are based on an application example from precision engineering. The potential and challenges are discussed.

The adaptation of developed metrology-grade ac current source (MCS) to the velocity mode of measurements of the Planck-Balance 2 as a means for generating ac mechanical oscillations is presented. The universality in operating with the MCS unit especially practical for the Planck-Balance setup for frequencies of 0.1 Hz-20 Hz (including but not limited to the negligence of a broader range of 0.01 Hz up to several hundred Hz) and for amplitudes of up to 10 mA with 16 (offset with 14)-bit effective resolution is demonstrated. MCS allows generating complex ac waveform signals as waveform synthesizers by adding to the original signal an extra five independent harmonic components, each of which with an adjustable resolution of 10 ns for phase and 16-bit for amplitude. Additionally, the MCS is supported by an external clock at 10 MHz frequency which serves also as a common reference time base for the comparison between the direct output signal of MCS, or of the induced voltage in the coil of the Planck-Balance resulting due to the applied current by MCS, with the ac quantum voltage standard at the required accuracy levels.

2D nanomaterials are proposed as promising photothermal materials for interfacial photothermal water evaporation. However, low evaporation efficiency, the use of hazardous hydrofluoric solution, and poor stability severely limit their practical applications. Here, a mixed solvent exfoliation surface deposition (MSESD) strategy for the preparation of NiPS3 nanosheets and NiPS3/polyvinyl alcohol (PVA) converter is successfully developed. The converter is obtained by drop-casting the NiPS3/PVA nanosheets onto a sponge. The PVA is mainly deposited on the edge of NiPS3 nanosheets, which not only improves the stability of NiPS3 nanosheets, but also adheres to the sponge to prepare a 3D photothermal converter, which shows an evaporation rate of 1.48 kg m−2 h−1 and the average photothermal conversion efficiency (PTCE) of 93.5% under a light intensity of 1 kW m−2. The photothermal conversion mechanism reveals that the energy of absorbed photons in NiPS3 nanosheets can be effectively converted into heat through non-radiative photon transitions as well as multiple optical interactions. To the best of the knowledge, this is the first report on the application of 2D metal-phosphorus-chalcogen (MPChx) for solar desalination, which provides new insights and guidance for the development of high-performance 2D photothermal materials.

The performance of tactile and optical surface sensors for nano and micro coordinate measuring machines is currently limited by the lack of precisely characterised micro spheres, since established strategies have mainly been developed for spheres in the range of millimetres or above. We have, therefore, recently focused our research efforts towards a novel strategy for the characterisation of spheres in the sub-millimetre range. It is based on a set of atomic force microscope (AFM) surface scans in conjunction with a stitching algorithm. To obtain an uncertainty statement, the uncertainty about the shape of the reference surface needs to be propagated via the shape of the AFM tip to the actual measurement object. However, the sampling process of an AFM is non-linear and the processing of AFM scans requires complex algorithms. We have, therefore, recently begun to model the characterisation of micro spheres through simulations. In this contribution, this model is extended by the influence of the tip and reference surface. The influence of the tip’s shape and reference surface is investigated through virtual and real experiments. The shape of the tip is varied by using tips with mean radii of 200 nm and 2 μm while sampling the same ruby sphere with a mean radius of 150 μm. In general, the simulation results imply that an uncertainty of less then 10 nm is achievable. However, an experimental validation of the model is still pending. The experimental investigations were limited by the lack of a suitable cleaning strategy for micro parts, which demonstrates the need for further investigations in this area. Although the characterisation of a full sphere has already been demonstrated, the investigations in this contribution are limited to equator measurements.

Hintergrund - Sexualaufklärung im Elternhaus soll laut Sozialisationstheorie zu mehr sexueller Handlungskompetenz bei Jugendlichen führen. Aktuelle Daten für Deutschland fehlen jedoch. Ziel der Arbeit - Vor diesem Hintergrund war es Ziel der vorliegenden Studie, erstmals das allgemeine Sprechen über Sexualität im Elternhaus (Forschungsfrage 1, F1) sowie speziell die Verhütungsberatung durch die Eltern (F2) mit dem sexuellen Risikoverhalten der Jugendlichen in Verbindung zu setzen. Material und Methoden - Datengrundlage ist die 9. Welle der Repräsentativbefragung „Jugendsexualität“ der Bundeszentrale für gesundheitliche Aufklärung (BZgA). Analysiert wurden Daten aller sexuell aktiven 14- bis 17-jährigen Jugendlichen im Sample, von denen eigene Angaben zum Sexualverhalten sowie Angaben ihrer Eltern zum Aufklärungsverhalten vorliegen (N= 357). Zur Beantwortung der beiden Forschungsfragen wurden logistische Regressionsanalysen mit 4 zentralen Merkmalen des jugendlichen Sexualverhaltens gerechnet. Ergebnisse - Es zeigte sich, dass das Sprechen über Sexualität im Elternhaus bei Mädchen und Jungen positiv korreliert mit 1. dem erreichten Konsensalter beim ersten Geschlechtsverkehr, 2. einem positiven Erleben des ersten Geschlechtsverkehrs, 3. einem zuverlässigen Verhütungsverhalten und 4. einer geringen Anzahl an Sexualpartner*innen (F1). Das gleiche Ergebnismuster ergab sich für die Verhütungsberatung durch die Eltern (F2). Diskussion - Die positiven Zusammenhänge zwischen elterlicher Sexualaufklärung und risikoärmerem jugendlichen Sexualverhalten gilt es hinsichtlich der zugrunde liegenden Kausalmechanismen genauer zu untersuchen.

We analyze the role of institutional founding conditions and institutional legacy for universities’ self-representation in terms of diversity. Based on 374 universities located in the Czech Republic, France, Germany, Hungary, Italy, and Poland, we can differentiate between a more idealistic understanding (logic of inclusion and equality) and a more market-oriented understanding (market logic) of diversity. Our findings show that the founding phase has no significant effect on the likelihood of a university focusing on a market-oriented understanding of diversity - however, we observe an imprinting effect with respect to the adoption of a diversity statement in general and an equity-oriented statement. Moreover, our findings show that there is a socialistic heritage for universities in Central and Eastern European (CEE) countries that is at work and still influences universities’ understandings of diversity today.

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.

This publication introduces a novel open-access 31P Nuclear Magnetic Resonance (NMR) shift database. With 14,250 entries encompassing 13,730 distinct molecules from 3,648 references, this database offers a comprehensive repository of organic and inorganic compounds. Emphasizing single-phosphorus atom compounds, the database facilitates data mining and machine learning endeavors, particularly in signal prediction and Computer-Assisted Structure Elucidation (CASE) systems. Additionally, the article compares different models for 31P NMR shift prediction, showcasing the database’s potential utility. Hierarchically Ordered Spherical Environment (HOSE) code-based models and Graph Neural Networks (GNNs) perform exceptionally well with a mean squared error of 11.9 and 11.4 ppm respectively, achieving accuracy comparable to quantum chemical calculations.

The activation step of Vaska-type Rh(I) complexes, such as the photocleavage of the Rh‑CO bond, plays an important role in the subsequent C-H activation. To elucidate the details of the photochemistry of Vaska-type Rh(I) complexes, such as trans-Rh(PMe3)2(CO)(Cl), we here present a computationally derived picture as obtained at the density functional level of theory (DFT) in combination with multireference wavefunction-based methods. We have identified that the photocleavage of CO proceeds via the metal-centered excited state, which is populated through intersystem crossing (ISC) from the dipole-allowed excited state S1. Moreover, the present study unraveled the reasons for the low C-H activation efficiency when using Rh featuring the bidentate ligand 1,2-bis(dimethylphosphino)ethane (dmpe), namely due to its unfavorable photochemical properties, i.e., the small driving force for light-induced CO loss and the fast deactivation of 3MC state back to the singlet ground state. In this study, we provide theoretical insight into mechanistic details underlying the light-induced CO dissociation process, for Rh complexes featuring PMe3 and dmpe ligands.

The importance of data cleaning systems has continuously grown in recent years. Especially for real-time streaming applications, it is crucial, to identify and possibly remove anomalies in the data on the fly before further processing. The main challenge however lies in the construction of an appropriate data cleaning pipeline, which is complicated by the dynamic nature of streaming applications. To simplify this process and help data scientists to explore and understand the incoming data, we propose an interactive data cleaning system for streaming applications. In this paper, we list requirements for such a system and present our implementation to overcome the stated issues. Our demonstration shows, how a data cleaning pipeline can be interactively created, executed, and monitored at runtime. We also present several different tools, such as the automated advisor and the adaptive visualizer, that engage the user in the data cleaning process and help them understand the behavior of the pipeline.

A novel multimodal experimental setup and dyadic study protocol were designed to investigate the neurophysiological underpinnings of joint action through the synchronous acquisition of EEG, ECG, EMG, respiration and kinematic data from two individuals engaged in ecologic and naturalistic cooperative and competitive joint actions involving face-to-face real-time and real-space coordinated full body movements. Such studies are still missing because of difficulties encountered in recording reliable neurophysiological signals during gross body movements, in synchronizing multiple devices, and in defining suitable study protocols. The multimodal experimental setup includes the synchronous recording of EEG, ECG, EMG, respiration and kinematic signals of both individuals via two EEG amplifiers and a motion capture system that are synchronized via a single-board microcomputer and custom Python scripts. EEG is recorded using new dry sports electrode caps. The novel study protocol is designed to best exploit the multimodal data acquisitions. Table tennis is the dyadic motor task: it allows naturalistic and face-to-face interpersonal interactions, free in-time and in-space full body movement coordination, cooperative and competitive joint actions, and two task difficulty levels to mimic changing external conditions. Recording conditions - including minimum table tennis rally duration, sampling rate of kinematic data, total duration of neurophysiological recordings - were defined according to the requirements of a multilevel analytical approach including a neural level (hyperbrain functional connectivity, Graph Theoretical measures and Microstate analysis), a cognitive-behavioral level (integrated analysis of neural and kinematic data), and a social level (extending Network Physiology to neurophysiological data recorded from two interacting individuals). Four practical tests for table tennis skills were defined to select the study population, permitting to skill-match the dyad members and to form two groups of higher and lower skilled dyads to explore the influence of skill level on joint action performance. Psychometric instruments are included to assess personality traits and support interpretation of results. Studying joint action with our proposed protocol can advance the understanding of the neurophysiological mechanisms sustaining daily life joint actions and could help defining systems to predict cooperative or competitive behaviors before being overtly expressed, particularly useful in real-life contexts where social behavior is a main feature.

Partial shielding by means of local gas supply has proven to be very effective in reducing spatter. Besides the effect of gas-induced dynamic pressure, the shielding of oxygen is also highly relevant for melt pool dynamics and spatter formation due to the growth of oxides and the influence on surface tension. Therefore, this paper addresses the effect of local supplied argon on oxide growth and seam topography during keyhole mode laser beam welding of high-alloy steel AISI 304. To determine the shielding quality, the results are compared to laser beam welding in a global argon atmosphere. The topography of the upper weld seams was analyzed by scanning electron microscopy (SEM). An X-ray microanalysis (EDX) in line scan modus was performed to determine and to locate the elements which are covering the specimen surface. The chemical state of the found elements was quantified by X-ray photoelectron spectroscopy (XPS). In a last step, high-speed synchrotron X-ray imaging was performed to separate the effect of the gas-induced pressure and the gas-induced shielding on keyhole geometry. The results show that a local supply of argon contributes to a significant difference in oxide growth, affecting melt pool convection and weld seam geometry. It was further shown that the effect of gas flows at low flow rates is primarily because of oxygen shielding, as no significant difference in keyhole geometry was found by high-speed synchrotron X-ray imaging.

Sodium-carbon dioxide (Na-CO2) batteries are regarded as promising energy storage technologies because of their impressive theoretical energy density and CO2 reutilization, but their practical applications are restricted by uncontrollable sodium dendrite growth and poor electrochemical kinetics of CO2 cathode. Constructing suitable multifunctional electrodes for dendrite-free anodes and kinetics-enhanced CO2 cathodes is considered one of the most important ways to advance the practical application of Na-CO2 batteries. Herein, RuO2 nanoparticles encapsulated in carbon paper (RuCP) are rationally designed and employed as both Na anode host and CO2 cathode in Na-CO2 batteries. The outstanding sodiophilicity and high catalytic activity of RuCP electrodes can simultaneously contribute to homogenous Na+ distribution and dendrite-free sodium structure at the anode, as well as strengthen discharge and charge kinetics at the cathode. The morphological evolution confirmed the uniform deposition of Na on RuCP anode with dense and flat interfaces, delivering enhanced Coulombic efficiency of 99.5% and cycling stability near 1500 cycles. Meanwhile, Na-CO2 batteries with RuCP cathode demonstrated excellent cycling stability (>350 cycles). Significantly, implementation of a dendrite-free RuCPNa anode and catalytic-site-rich RuCP cathode allowed for the construction of a symmetric Na-CO2 battery with long-duration cyclability, offering inspiration for extensive practical uses of Na-CO2 batteries.

Currently, 5G low-earth orbit satellite communications offer enhanced wireless coverage beyond the reach of 5G terrestrial networks, with important implications, particularly for automated and connected vehicles. Such wireless automotive mass-market applications demand well-designed compact user equipment antenna terminals offering non-terrestrial jointly with terrestrial communications. The antenna should be low-profile, conformal, and meet specific parameter values for gain and operational frequency bandwidth, tailored to the intended applications, in line with the aesthetic design requirements of passenger cars. This work presents an original concept for a dual-band nested circularly polarized automotive user terminal that operates at the S-band frequencies around 3.5 GHz and Ka-band frequencies around 28 GHz, namely within the 5G new-radio bands n78 and n257, respectively. The proposed terminal is designed to be integrated into the plastic components of a passenger vehicle. The arrays consist of 2 × 2 aperture-coupled corner-truncated microstrip slot patch antenna elements for the n78 band and of 4 × 4 single-layer edge-truncated microstrip circular slot patch antenna elements for the n257 band. The embedded arrays offer, across the two bands, respectively, 9.9 and 13.7 dBi measured realized gain and 3-dB axial ratio bandwidths of 100 and 1500 MHz for the n78 and n257 bands along the broadside direction. Detailed link budget calculations anticipate uplink data rates of 21 and 6 Mbit/s, respectively, deeming it suitable for various automotive mobility and Internet-of-Things applications.

Concentric tube continuum robots are a promising type of robot for various medical applications. Their application in neurosurgery poses challenging requirements for design and control that can be addressed by physics-informed data-based approaches. A prerequisite to data-based modeling is an informative, rich data set. However, limited access to experimental data raises interest in partially or entirely synthetic data sets. In this contribution, we study the application of generative adversarial networks (GANs) for data augmentation in a data-based design process of such robots. We propose a GAN framework suitable for curve-fitting to generate synthetic trajectories of robots along with their corresponding control parameters. Our evaluation shows that the GANs can efficiently produce meaningful synthetic trajectories and control parameter pairs that show a good agreement with simulated trajectories.

The active manipulation of particle and cell trajectories in fluids by high-frequency standing surface acoustic waves (sSAW) allows to separate particles and cells systematically depending on their size and acoustic contrast. However, process technologies and biomedical applications usually operate with non-spherical particles, for which the prediction of acoustic forces is highly challenging and remains a subject of ongoing research. In this study, the dynamical behavior of prolate spheroids exposed to a three-dimensional acoustic field with multiple pressure nodes along the channel width is examined. Optical measurements reveal an alignment of the particles orthogonal to the pressure nodes of the sSAW, which has not been reported in literature so far. The dynamical behavior of the particles is analyzed under controlled initial conditions for various motion patterns by imposing a phase shift on the sSAW. To gain detailed understanding of the particle dynamics, a three-dimensional numerical model is developed to predict the acoustic force and torque acting on a prolate spheroid. Considering the acoustically induced streaming around the particle, the numerical results are in excellent agreement with experimental findings. Using the proposed numerical model, a dependence of the acoustic force on the particle shape is found in relation to the acoustic impedance of the channel ceiling. Hence, the numerical model presented herein promises high progress for the design of separation devices utilizing sSAW, exploiting an additional separation criterion based on the particle shape.

Dry electroencephalography (EEG) electrodes provide rapid, gel-free, and easy EEG preparation, but with limited wearing comfort. We propose a novel dry electrode comprising multiple tilted pins in a flower-like arrangement. The novel Flower electrode increases wearing comfort and contact area while maintaining ease of use. In a study with 20 volunteers, we compare the performance of a novel 64-channel dry Flower electrode cap to a commercial dry Multipin electrode cap in sitting and supine positions. The wearing comfort of the Flower cap was rated as significantly improved both in sitting and supine positions. The channel reliability and average impedances of both electrode systems were comparable. Averaged VEP components showed no considerable differences in global field power amplitude and latency, as well as in signal-to-noise ratio and topography. No considerable differences were found in the power spectral density of the resting state EEGs between 1 and 40 Hz. Overall, our findings provide evidence for equivalent channel reliability and signal characteristics of the compared cap systems in the sitting and supine positions. The reliability, signal quality, and significantly improved wearing comfort of the Flower electrode allow new fields of applications for dry EEG in long-term monitoring, sensitive populations, and recording in supine position.

Threat intelligence sharing is a promising solution to enhance knowledge and situational awareness of the rapidly growing number of emerging cyber threats. Accordingly, there are a variety of platforms on the security solutions market that enable the efficient and targeted exchange of threat intelligence across organisations. Unfortunately, very little is known so far about the dissemination and use of these platforms from the end-user perspective. To address this issue, we conducted an exploratory study on the use of threat intelligence sharing platforms in Germany, Austria and Switzerland. For this purpose, we surveyed 69 security and IT experts from large companies, federal authorities and public universities in autumn 2022. Our findings show, among other things, a growing interest in threat intelligence sharing platforms and their value to information security processes.

In a non-professional environment, multi-camera recordings of theater performances or other stage shows are difficult to realize, because amateurs are usually untrained in camera work and in using a vision mixing desk that mixes multiple cameras. This can be remedied by a production process with high-resolution cameras where recordings of image sections from long shots or medium-long shots are manually or automatically cropped in post-production. For this purpose, Gandhi et al. presented a single-camera system (referred to as Gandhi Recording System in the paper) that obtains close-ups from a high-resolution recording from the central perspective. The proposed system in this paper referred to as “Proposed Recording System” extends the method to four perspectives based on a Reference Recording System from professional TV theater recordings from the Ohnsorg Theater. Rules for camera selection, image cropping, and montage are derived from the Reference Recording System in this paper. For this purpose, body and pose recognition software is used and the stage action is reconstructed from the recordings into the stage set. Speakers are recognized by detecting lip movements and speaker changes are identified using audio diarization software. The Proposed Recording System proposed in this paper is practically instantiated on a school theater recording made by laymen using four 4K cameras. An automatic editing script is generated that outputs a montage of a scene. The principles can also be adapted for other recording situations with an audience, such as lectures, interviews, discussions, talk shows, gala events, award ceremonies, and the like. More than 70 % of test persons confirm in an online study the added value of the perspective diversity of four cameras of the Proposed Recording System versus the single-camera method of Gandhi et al.

Angiography is a very informative method for physicians such as cardiologists, neurologists and neuroscientists. The current modalities experience some shortages, e.g., ultrasound is very operator dependent. The computerized tomography (CT) and magnetic resonance (MR) angiography are very expensive and near infrared spectroscopy cannot capture the deep arteries. Microwave technology has the potential to address some of these issues while compromising between operator dependency, cost, speed, penetration depth and resolution. This paper studies the feasibility of microwave signals for monitoring of arteries. To this aim, a homogenous phantom mimicking body tissue is built. Four elastic tubes simulate arteries and a mechanical system creates pulsations in these arteries. A multiple input multiple output (MIMO) array of ultra-wideband (UWB) transmitters and receivers illuminates the phantom and captures the reflected signals over the desired observation time period. Since we are only interested in the imaging of dynamic parts, i.e., arteries, the static clutters can be suppressed easily by background subtraction method. To obtain a fast image of arteries, which are pulsating with the heartbeat rate, we calculate the Fourier transform of each channel of the MIMO system over the observation time and apply delay and sum (DAS) beamforming method on the heartbeat rate aligned spectral component. The results show that the lateral and longitudinal images and motion mode (M-mode) time series of different points of phantom have the potential to be used for diagnosis.

Photoelectrochemical (PEC) water splitting requires stable, efficient, and cost-effective photoelectrodes to enable future large-scale solar hydrogen production. Ultrathin hematite-hercynite photoanodes that meet all these criteria in an excellent way is presented here. Hematite-hercynite photoelectrodes are synthesized in a self-forming manner by thermal oxidation of iron-aluminum alloy films and characterized with regard to water splitting applications. Photoanodes fabricated from 17 wt.% Al at 493 ˚C for 8 h and 685 ˚C for 5 min exhibit, for instance, a photocurrent density of 1.24 and 1.53 mA cm−2 at 1.23 V versus RHE, respectively, as well as superior light absorption in the visible range of the solar spectrum. The PEC performance improvement in comparison to pure hematite thin film electrodes is first achieved by adjusting the aluminum concentration with an optimum range of 12-17 wt.% and second by optimizing the annealing conditions. The resulting photocurrent densities are about a factor of three higher than those obtained from electrodes synthesized from pure iron thin films using the same synthesis conditions. Finally, it is shown that ultrathin hematite-hercynite photoelectrodes enable even unassisted solar water splitting in a NaOH (1 m) electrolyte with a maximum solar-to-hydrogen conversion efficiency of 0.78%.

The traditional paradigm for managing memory in database management systems (DBMS) treats memory used for caching table data and memory for temporary data as separate entities. This leads to inefficient utilization of the available memory capacity for mixed workloads. With memory being a significant factor in the costs of operating a DBMS, utilizing memory as efficiently as possible is highly desirable. As an alternative to the traditional paradigm, we propose managing the entire available memory in a cooperative manner to achieve better memory utilization and consequently higher cost-effectiveness for DBMSs. Initial experimental evaluation of cooperative memory management using a prototype implementation shows promising results and leads to several interesting further research directions.

We discuss the effects of nonhomogeneous magnetic fields in liquid metal flows in two different configurations. In the first configuration, we briefly report the impact of fringing magnetic fields in a turbulent Rayleigh-Bénard convection setup, where it was shown that the global heat transport decreases with an increase of fringe-width. The convective motion in regions of strong magnetic fields is confined near the sidewalls. In the second configuration, we numerically study the effects of an oscillating magnetic obstacle with different frequencies of oscillation on liquid metal flow in a duct. The Reynolds number is low such that the wake of the stationary magnetic obstacle is steady. The transverse oscillation of the magnet creates a sinusoidal time-dependent wake reminiscent of the vortex shedding behind solid obstacles. We examine the behavior of the streamwise and spanwise components of the Lorentz forces as well as the work done by the magnets on the fluid. The frequency of the oscillation of the streamwise component of Lorentz force is twice that of the spanwise component as in the case of lift and drag on solid cylindrical obstacles. The total drag force and the energy transferred from the magnets to the fluid show a nonmonotonic dependence on the frequency of oscillation of the magnetic obstacle indicative of a resonant excitation of the sinusoidal vortex shedding.

Lithium decoration of graphene on SiC(0001) is achieved in a surface science approach by intercalation and adsorption of the alkali metal. Spectroscopy of the differential conductance with a scanning tunneling microscope at the Li-decorated graphene surfaces does not give rise to a pairing gap at the Fermi energy, which may be expected because of the previously predicted superconducting phase [Profeta et al., Nat. Phys. 2012, 8, 131]. Rather, pronounced gaps in the spectroscopic data of intercalated samples reflect the excitation of graphene phonons. Rationales that possibly explain this discrepancy between experimental findings and theoretical predictions are suggested.

Intelligent devices and equipment have affected almost all aspects of our life and behavior. The type of connection and the manner of communication between this large volume of devices has caused the emergence of a vast field in the Internet called the Internet of Things, which significantly highlights the issue of energy management and increases the lifetime of networks. Complex communications, especially in mobile networks, have generated many challenges for network designers. To solve these challenges, the Software Defined Networking (SDN) paradigm has reduced the overhead in the devices caused by processing and computing by adding new capabilities to mobile IoT networks. This technique transfers energy-consuming tasks to the central controller, which manages continuous topological changes of the network in dynamic environments. This paper presents a new routing approach called Intelligent Energy-aware Routing Protocol in Mobile IoT Networks based on SDN (IERMIoT), which tries to manage the dynamic changes of topology due to the movement of mobile nodes to increase the network’s lifetime and prevent energy dissipation. For this purpose, it defines clusters of nodes and uses an intelligent evolutionary algorithm to determine the number of clusters required in the network and their balanced distribution in the dynamic environment. Also, this approach considers a mechanism to reduce the overhead of control packets and routing packets, which significantly affects the energy consumption of nodes. The simulation results indicate the proposed solution’s effectiveness compared to other simulated approaches with respect to packet delivery rate, average energy consumption, network lifetime, number of alive nodes, coverage, and routing overhead.

Selecting the right cathode material is a key component to achieving high-energy and long-lifespan aqueous zinc-ion batteries (AZIBs); however, the development of cathode materials still faces serious challenges due to the high polarization of Zn2+. In this work, MnV12O31&hahog;10H2O (MnVO) synthesized via a one-step hydrothermal method is proposed as a promising cathode material for AZIBs. Because the stable layered structure and hieratical morphology of MnVO provide a large layer space for rapid ion transports, this material exhibits high specific capacity (433 mAh g−1 at 0.1 A g−1), an outstanding long-term cyclability (5000 cycles at a current density of 3 A g−1), and an excellent energy density (454.65 Wh kg−1). To illustrate the intercalation mechanism, ex situ X-Ray diffraction, Fourier transform infrared spectroscopy, and X-ray photoelectron spectroscopy are adopted, uncovering an H+/Zn2+ dual-cation co-intercalation processes. In addition, density-functional theory calculation analysis shows that MnVO has a delocalized electron cloud and the diffusion energy barrier of Zn2+ in MnVO is low, which promotes the Zn2+ transport and consequently improves the reversibility of the battery upon deep cycling. The key and enlightening insights are provided in the results for designing high-performance vanadium-oxide-based cathode materials for AZIBs.

The projection of a point cloud onto a 2D camera image is relevant in the case of various image analysis and enhancement tasks, e.g., (i) in multimodal image processing for data fusion, (ii) in robotic applications and in scene analysis, and (iii) for deep neural networks to generate real datasets with ground truth. The challenges of the current single-shot projection methods, such as simple state-of-the-art projection, conventional, polygon, and deep learning-based upsampling methods or closed source SDK functions of low-cost depth cameras, have been identified. We developed a new way to project point clouds onto a dense, accurate 2D raster image, called Triangle-Mesh-Rasterization-Projection (TMRP). The only gaps that the 2D image still contains with our method are valid gaps that result from the physical limits of the capturing cameras. Dense accuracy is achieved by simultaneously using the 2D neighborhood information (rx,ry) of the 3D coordinates in addition to the points P(X,Y,V). In this way, a fast triangulation interpolation can be performed. The interpolation weights are determined using sub-triangles. Compared to single-shot methods, our algorithm is able to solve the following challenges. This means that: (1) no false gaps or false neighborhoods are generated, (2) the density is XYZ independent, and (3) ambiguities are eliminated. Our TMRP method is also open source, freely available on GitHub, and can be applied to almost any sensor or modality. We also demonstrate the usefulness of our method with four use cases by using the KITTI-2012 dataset or sensors with different modalities. Our goal is to improve recognition tasks and processing optimization in the perception of transparent objects for robotic manufacturing processes.

In multi-objective mixed-integer convex optimization, multiple convex objective functions need to be optimized simultaneously while some of the variables are restricted to take integer values. In this paper, we present a new algorithm to compute an enclosure of the nondominated set of such optimization problems. More precisely, we decompose the multi-objective mixed-integer convex optimization problem into several multi-objective continuous convex optimization problems, which we refer to as patches. We then dynamically compute and improve coverages of the nondominated sets of those patches to finally combine them to obtain an enclosure of the nondominated set of the multi-objective mixed-integer convex optimization problem. Additionally, we introduce a mechanism to reduce the number of patches that need to be considered in total. Our new algorithm is the first of its kind and guaranteed to return an enclosure of prescribed quality within a finite number of iterations. For selected numerical test instances we compare our new criterion space based approach to other algorithms from the literature and show that much larger instances can be solved with our new algorithm.

A fabrication strategy for high-efficiency passive three-dimensional microfluidic valves with no mechanical parts fabricated in silicon and glass substrates is presented. 3D diffuser-nozzle valve structures were produced and characterized in their added value in comparison to conventional diffuser-nozzle valve designs with rectangular cross sections. A grayscale lithography approach for 3D photoresist structuring combined with a proportional transfer by reactive ion etching allowed to transfer 3D resist valve designs with high precision into the targeted substrate material. The efficiency with respect to the rectification characteristics or so-called diodicity of the studied valve designs is defined as the ratio of the pressure drops in backward and forward flow directions. The studied valve designs were characterized experimentally as well as numerically based on finite element simulations with overall matching results that demonstrate a significantly improved flow rectification of the 3D valves compared to the corresponding conventional structure. Our novel 3D valve structures show, for instance, even without systematic optimization a measured diodicity of up to 1.5 at low flow rates of only about 10 μl/s.

The Processing-in-Memory (PIM) paradigm promises to accelerate data processing by pushing down computation to memory, reducing the amount of data transfer between memory and CPU, and - in this way - relieving the CPU from processing. Particularly, in in-memory databases memory access becomes a performance bottleneck. Thus, PIM seems to offer an interesting solution for database processing. In this work, we investigate how commercially available PIM technology can be leveraged to accelerate query processing by offloading (parts of) query operators to memory. Furthermore, we show how to address the problem of limited PIM storage capacity by interleaving transfer and computation and present a cost model for the data placement problem.

Evaluating robustness is an important goal in simulation-based analysis. Robustness is achieved when the controllable factors of a system are adjusted in such a way that any possible variance in uncontrollable factors (noise) has minimal impact on the variance of the desired output. The optimization of system robustness using simulation is a dedicated and well-established research direction. However, once a simulation model is available, there is a lot of potential to learn more about the inherent relationships in the system, especially regarding its robustness. Data farming offers the possibility to explore large design spaces using smart experiment design, high performance computing, automated analysis, and interactive visualization. Sophisticated machine learning methods excel at recognizing and modelling the relation between large amounts of simulation input and output data. However, investigating and analyzing this modelled relationship can be very difficult, since most modern machine learning methods like neural networks or random forests are opaque black boxes. Explainable Artificial Intelligence (XAI) can help to peak into this black box, helping us to explore and learn about relations between simulation input and output. In this paper, we introduce a concept for using Data Farming, machine learning and XAI to investigate and understand system robustness of a given simulation model.

Application problems can often not be solved adequately by numerical algorithms as several difficulties might arise at the same time. When developing and improving algorithms which hopefully allow to handle those difficulties in the future, good test instances are required. These can then be used to detect the strengths and weaknesses of different algorithmic approaches. In this paper we present a generator for test instances to evaluate solvers for multiobjective mixed-integer linear and nonlinear optimization problems. Based on test instances for purely continuous and purely integer problems with known efficient solutions and known nondominated points, suitable multiobjective mixed-integer test instances can be generated. The special structure allows to construct instances scalable in the number of variables and objective functions. Moreover, it allows to control the resulting efficient and nondominated sets as well as the number of efficient integer assignments.

The dynamics in a confined turbulent convection flow is dominated by multiple long-lived macroscopic circulation states that are visited subsequently by the system in a Markov-type hopping process. In the present work, we analyze the short transition paths between these subsequent macroscopic system states by a data-driven learning algorithm that extracts the low-dimensional transition manifold and the related new coordinates, which we term collective variables, in the state space of the complex turbulent flow. We therefore transfer and extend concepts for conformation transitions in stochastic microscopic systems, such as in the dynamics of macromolecules, to a deterministic macroscopic flow. Our analysis is based on long-term direct numerical simulation trajectories of turbulent convection in a closed cubic cell at a Prandtl number Pr=0.7 and Rayleigh numbers Ra=10^6 and 10^7 for a time lag of 10^5 convective free-fall time units. The simulations resolve vortices and plumes of all physically relevant scales, resulting in a state space spanned by more than 3.5 million degrees of freedom. The transition dynamics between the large-scale circulation states can be captured by the transition manifold analysis with only two collective variables, which implies a reduction of the data dimension by a factor of more than a million. Our method demonstrates that cessations and subsequent reversals of the large-scale flow are unlikely in the present setup, and thus it paves the way for the development of efficient reduced-order models of the macroscopic complex nonlinear dynamical system.

The block-term decompositions (BTD) represent tensors as a linear combination of low multilinear rank terms and can be explicitly related to the Canonical Polyadic decomposition (CPD). In this paper, we introduce the SECSI-BTD framework, which exploits the connection between two decompositions to estimate the block-terms of the rank-(Lr, Lr, 1) BTD. The proposed SECSI-BTD algorithm includes the initial calculation of the factor estimates using the SEmi-algebraic framework for approximate Canonical polyadic decompositions via SImultaneous Matrix Diagonalizations (SECSI), followed by clustering and refinement procedures that return the appropriate rank-(Lr, Lr, 1) BTD terms. Moreover, we introduce a new approach to estimate the multilinear rank structure of the tensor based on the HOSVD and $k$-means clustering. Since the proposed SECSI-BTD algorithm does not require a known rank structure but can still take advantage of the known ranks when available, it is more flexible than the existing techniques in the literature. Additionally, our algorithm does not require multiple initializations, and the simulation results show that it provides more accurate results and a better convergence behavior for an extensive range of SNRs.

Aliphatic polyketone is a new-age eco-friendly, high-performance engineering thermoplastic. However, its potential for replacing other polymers depends on its ability to be processed. Considering that the first aliphatic polyketone suitable for processing was developed relatively recently (2015), the material gained new research potential. In this paper screw extrusion-based process was developed for additive manufacturing of aliphatic polyketone. A detailed characterisation of the process and printed samples was done. It was shown that the extruder-base process can produce stable additive-manufactured parts depending on printing speed (process parameters). The interpass temperature has a significant influence on printing properties and it depends on printing speed (travel speed of building platform and extruder rotational speed). With the increase in the printing speed, the interpass temperature increases as well. If it is low causes insufficient heat for diffusion to occur causing delamination and if it is too high causes geometrical deviation of workpieces which leads to defects causing a reduction in inter-road strength. The tensile strength of specimens with raster angle 0˚ was 62.7 ± 1.4 MPa, which is slightly higher than the tensile strength of base material guaranteed by the supplier (60 MPa) while the elongation up to the first crack was 32.8 ± 4.6%. Iinter-road strength in specimens with a raster angle of 90˚ was 37.2 ± 0.8 MPa which is 62% of the base material while interpass temperature was 189 ± 3.3 ˚C.

The flow behind nine different arrays of cylinders is experimentally investigated via Particle Image Velocimetry (PIV) at a Reynolds number of Re ∼ 100 based on the diameter of the cylinders. Each array consists of a column of four cylinders in front and three in the rear. The horizontal distance between the two columns and the vertical distance between the cylinders within each column are varied for H/D = [2, 4, 8] and V/D = [2, 4, 6], resulting in nine different arrays denoted as mVnH, where m corresponds to V/D and n stands for H/D. The PIV measurements are conducted for 15 s at 200 Hz frequency, corresponding to 39 to 360 vortex shedding events for the wakes in this study. Then, proper orthogonal decomposition is applied to the velocity fields to analyze the flow dynamics. All arrays show unsteady flow, and based on their flow structures, they are classified in to three main categories of single bluff body (SBB), transitional (TR), and co-shedding (CS) flow. SBB characteristics can be seen for 2V2H and 2V4H arrays, but the latter has more steady vortex shedding as the H/D increases from 2 to 4. Then, 2V8H and 4V2H have an asymmetric flow with several vortex streets and act as an intermediary stage in the shift from SBB to CS flow structure when the distances are increased. The highest total kinetic energy values and widest probability density functions of the velocity components are observed for this group. The five remaining arrays in the CS group have symmetric flow, with three or five vortex streets present behind. However, based on the distances, the frequency and phase synchronization of the vortex streets change considerably, which might have an important effect on, for example, the heat transfer or the structural load of the cylinders.

This article reports a new approach toward fabrication and directed assembly of nanoparticulate reactive system (Nanofoils) on patterned substrates. Different from current state-of-the-art, gas phase electrodeposition uses nanoparticles instead of atoms to form densely packed multilayered thin films at room temperature-pressure. On ignition, the multilayer system undergoes an exothermic self-propagating reaction. The numerous contact points between two metallic nanoparticulate layers aid in high heat release. Sub-10-nm Platinum (Pt) and Aluminum (Al) particles are synthesized through cathode erosion of metal electrodes in a flow of pure nitrogen gas (spark ablation). Pt/Al bilayer stacks with total thickness of 3–8 µm undergo self-propagating reaction with a 10.3 mm s−1 wavefront velocity on local ignition. The reaction wavefront is captured using high speed videography. Calorimetry studies reveal two exothermic peaks suggesting Pt/Al alloy formation. The peak at 135 ˚C has a higher calorific value of 150 mW g−1 while the peak at 400 ˚C has a 12 mW g−1 exothermic peak. X-ray diffraction study shows reaction-products are cubic Al2Pt with small quantities of orthorhombic Al6Pt and orthorhombic AlPt2. Electron microscopy studies help draw a correlation between film morphology, bimetallic interface, nanoparticle oxidation, and self-propagating reaction kinetics that is significant in broadening our understanding towards nanoparticulate reactive systems.

Compiling database queries into compact and efficient machine code has proven to be a great technique to improve query performance and exploit characteristics of modern hardware. Particularly for graph database queries, which often execute the exact instructions for processing, this technique can lead to an improvement. Furthermore, compilation frameworks like LLVM provide powerful optimization techniques and support different backends. However, the time for generating and optimizing machine code becomes an issue for short-running queries or queries which could produce early results quickly. In this work, we present an adaptive approach integrating graph query interpretation and compilation. While query compilation and code generation are running in the background, the query execution starts using the interpreter. When the code generation is finished, the execution switches to the compiled code. Our evaluation of the approach using short-running and complex queries show that autonomously switching execution modes helps to improve the runtime of all types of queries and additionally to hide compilation times and the additional latencies of the underlying storage.

Light sheet fluorescence microscope with single light sheet illumination enables rapid 3D imaging of living cells. In this paper we show the design, fabrication and characterization of a diffractive optical element producing several light sheets along a 45˚ inclined tube. The element, which is based on a multi-focal diffractive lens and a linear grating, generates five thin light sheets with equal intensities when combined with a refractive cylindrical lens. The generated uniform light sheets can be applied for the scanning of samples in tubes enabling flow-driven 3-dimensional imaging.

Hintergrund: Jugendliche und Erwachsene beziehen Informationen über Verhütung zunehmend über soziale Medien. Ziel der Arbeit: Vor diesem Hintergrund ist es Ziel der vorliegenden Studie, erstmals Inhalte und Qualität deutschsprachiger Verhütungsbeiträge auf YouTube, Instagram und TikTok zu untersuchen. Beantwortet werden sollen Forschungsfragen zu Anbieter*innen (F1), Inhalten (F2) und Qualität der Verhütungsbeiträge (F3) sowie zu Publikumsreaktionen (F4). Material und Methoden: Es wurde eine Stichprobe von N= 1000 Verhütungsbeiträgen von YouTube (500), Instagram (250) und TikTok (250) gezogen. Pro Beitrag gingen maximal 20 verhütungsbezogene Kommentare in das Kommentar-Sample ein (N= 12.454). Die Beiträge und Kommentare wurden mittels reliabilitätsgeprüfter Codebücher analysiert. Die Datenanalyse erfolgte mit SPSS. Die Studie ist präregistriert und alle Daten, Materialien und Analyseskripte sind öffentlich verfügbar. Ergebnisse: Es zeigte sich, dass die Verhütungsbeiträge mehrheitlich von Gesundheitslaien stammten (52%), gefolgt von Medienprofis und Gesundheitsprofis (F1). Inhaltlich deckten die Verhütungsbeiträge alle verfügbaren Verhütungsmethoden ab, wobei Pille (69%) und Kondom (40%) dominierten (F2). Nach gängigen Qualitätskriterien zeigten sich deutliche Defizite, wobei im Vergleich YouTube-Videos am besten abschnitten (F3). TikTok-Videos dagegen waren Spitzenreiter bei den Publikumsreaktionen, sie verzeichneten die meisten Views, Likes und Kommentare. Die Kommentarspalten wurden vom Publikum oft genutzt, um eigene Verhütungserfahrungen zu teilen oder Nachfragen zu stellen (F4). Diskussion: Weitere Forschung sowie Praxismaßnahmen sind notwendig, um die Qualität von Verhütungsinformationen in sozialen Medien besser einschätzen und optimieren zu können.

Sufficient energy consumption for conventional information processing makes it necessary to look for new computational methods. One of the possible solutions to this problem is neuromorphic computations using memristive devices. Memristors based on molybdenum disulfide (MoS2) are a promising way to provide a sizeable amount of hysteresis at low energy costs. Herein, different configurations of MoS2 memristors as well as the mechanisms involved in hysteresis formation are shown. Bottom gated configuration is beneficial in terms of hysteresis area and energy efficiency. The impact of device channel dimensions on the hysteresis area and energy consumption is discussed. Different operation conditions with triangular, rectangular, sinusoidal, and sawtooth drain-to-source pulses are simulated, and rectangular pulses demonstrate the highest energy efficiency. The study shows the potential to realize low-power neuromorphic systems using MoS2 memristive devices.

Changes in organizational structure affect the intra-organizational communication network. However, despite an increasing relevance of less hierarchical and less bureaucratic forms of organizing, the question of how self-managing forms of organization influence employees’ communication behavior has received only scant attention yet. In our study, we analyze the case of a medium-sized firm that experiences organizational change from rigid bureaucratic structures to a more self-managing organization model and question how employees’ centrality in the communication network is affected by this reorganization. Following an explorative research approach informed by prior research on self-managing forms of organizations and communication networks, our findings show that after the change process communication generally is less dependent on formal hierarchy. However, while employees who held a higher rank before the change process loose in centrality, their centrality at the end of the change process is still higher than that of other employees. Moreover, employees who experience a promotion become more central in the communication network while demotions do not have an effect. By discussing these findings in the context of organizational change and the persistence of organizational structures, we contribute to a more comprehensive understanding of the implementation process of self-managing organizations.

Many speech processing systems struggle in conditions with low signal-to-noise ratios and in changing acoustic environments. Adaptation at the transduction level with integrated signal processing could help to address this; in human hearing, transduction and signal processing are integrated and can be adaptively tuned for noisy conditions. Here we report a microelectromechanical cochlea as a bio-inspired acoustic sensor with integrated signal processing functionality. Real-time feedback is used to tune the sensing and processing properties, and dynamic switching between linear and nonlinear characteristics improves the detection of signals in noisy conditions, increases the sensor dynamic range and enables adaptation to changing acoustic environments. The transition to nonlinear behaviour is attributed to a Hopf bifurcation and we experimentally validate its dependence on sensor and feedback parameters. We also show that output-signal coupling between two coupled sensors can increase the frequency coverage.

Reactive aluminum-nickel multilayer system shows exothermic energetic materials which act as a heat source for packaging and bonding of microsystems. The main challenge is controlling the self-propagation reaction velocity and temperature generated by thermal management through different thermal conductive substrate materials. The current work investigates the heat distribution of Al/Ni multilayer foils from different thermal conductive substrates which act as heat sink materials during the self-propagating reaction. A two-dimensional numerical model was developed to study thermal conductive heat loss and substrate thermal properties on the self-propagating reaction in Al/Ni multilayer foils. The self-propagating reaction was introduced on the surface of the foils by an electrical spark. Here we investigate the minimum critical thickness of Al/Ni multilayer foils which shows the self-propagation reaction on different substrates and verified from the two-dimensional numerical model. The outcomes of this investigation will facilitate the integration of Al/Ni multilayer foils on different substrates as intrinsic heat sources for different applications of micro/nanodevices.

In many research fields, human-annotated data plays an important role as it is used to accomplish a multitude of tasks. One such example is in the field of multimedia quality assessment where subjective annotations can be used to train or evaluate quality prediction models. Lab-based tests could be one approach to get such quality annotations. They are usually performed in well-defined and controlled environments to ensure high reliability. However, this high reliability comes at a cost of higher time consumption and costs incurred. To mitigate this, crowd or online tests could be used. Usually, online tests cover a wider range of end devices, environmental conditions, or participants, which may have an impact on the ratings. To verify whether such online tests can be used for visual quality assessment, we designed three online tests. These online tests are based on previously conducted lab tests as this enables comparison of the results of both test paradigms. Our focus is on the quality assessment of high-resolution images and videos. The online tests use AVrate Voyager, which is a publicly accessible framework for online tests. To transform the lab tests into online tests, dedicated adaptations in the test methodologies are required. The considered modifications are, for example, a patch-based or centre cropping of the images and videos, or a randomly sub-sampling of the to-be-rated stimuli. Based on the analysis of the test results in terms of correlation and SOS analysis it is shown that online tests can be used as a reliable replacement for lab tests albeit with some limitations. These limitations relate to, e.g., lack of appropriate display devices, limitation of web technologies, and modern browsers considering support for different video codecs and formats.

AI-generated images have gained in popularity in recent years due to improvements and developments in the field of artificial intelligence. This has led to several new AI generators, which may produce realistic, funny, and impressive images using a simple text prompt. DALL-E-2, Midjourney, and Craiyon are a few examples of the mentioned approaches. In general, it can be seen that the quality, realism, and appeal of the images vary depending on the used approach. Therefore, in this paper, we analyze to what extent such AI-generated images are realistic or of high appeal from a more photographic point of view and how users perceive them. To evaluate the appeal of several state-of-the-art AI generators, we develop a dataset consisting of 27 different text prompts, with some of them being based on the DrawBench prompts. Using these prompts we generated a total of 135 images with five different AI-Text-To-Image generators. These images in combination with real photos form the basis of our evaluation. The evaluation is based on an online subjective study and the results are compared with state-of-the-art image quality models and features. The results indicate that some of the included generators are able to produce realistic and highly appealing images. However, this depends on the approach and text prompt to a large extent. The dataset and evaluation of this paper are made publicly available for reproducibility, following an Open Science approach.

The concept of Industry 4.0 brings the change of industry manufacturing patterns that become more efficient and more flexible. In response to this tendency, an efficient robot teaching approach without complex programming has become a popular research direction. Therefore, we propose an interactive finger-touch based robot teaching schema using a multimodal 3D image (color (RGB), thermal (T) and point cloud (3D)) processing. Here, the resulting heat trace touching the object surface will be analyzed on multimodal data, in order to precisely identify the true hand/object contact points. These identified contact points are used to calculate the robot path directly. To optimize the identification of the contact points we propose a calculation scheme using a number of anchor points which are first predicted by hand/object point cloud segmentation. Subsequently a probability density function is defined to calculate the prior probability distribution of true finger trace. The temperature in the neighborhood of each anchor point is then dynamically analyzed to calculate the likelihood. Experiments show that the trajectories estimated by our multimodal method have significantly better accuracy and smoothness than only by analyzing point cloud and static temperature distribution.

Nanosponges are subject of intensive research due to their unique morphology, which leads among other effects to electrodynamic field localization generating a strongly nonlinear optical response at hot spots and thus enable a variety of applications. Accurate predictions of physical properties require detailed knowledge of the sponges’ chaotic nanometer-sized structure, posing a metrological challenge. A major goal is to obtain computer models with equivalent structural and optical properties. Here, to understand the sponges’ morphology, we present a procedure for their accurate 3D reconstruction using focused ion beam tomography. Additionally, we introduce a simulation method to create nanoporous sponge models with adjustable geometric properties. It is shown that if certain morphological parameters are similar for computer-generated and experimental sponges, their optical response, including magnitudes and hot spot locations, are also similar. Finally, we analyze the anisotropy of experimental sponges and present an easy-to-use method to reproduce arbitrary anisotropies in computer-generated sponges.

We present a multidisciplinary forest ecosystem 3D perception dataset. The dataset was collected in the Hainich-Dün region in central Germany, which includes two dedicated areas, which are part of the Biodiversity Exploratories - a long term research platform for comparative and experimental biodiversity and ecosystem research. The dataset combines several disciplines, including computer science and robotics, biology, bio-geochemistry, and forestry science. We present results for common 3D perception tasks, including classification, depth estimation, localization, and path planning. We combine the full suite of modern perception sensors, including high-resolution fisheye cameras, 3D dense LiDAR, differential GPS, and an inertial measurement unit, with ecological metadata of the area, including stand age, diameter, exact 3D position, and species. The dataset consists of three hand held measurement series taken from sensors mounted on a UAV during each of three seasons: winter, spring, and early summer. This enables new research opportunities and paves the way for testing forest environment 3D perception tasks and mission set automation for robotics.

Despite their variable valence and favorable sodiation/desodiation potential, vanadium sulfides (VSx) used as anode materials of sodium-ion batteries (SIBs) have been held back by their capacity decline and low cycling capability, associated with the structure distortion volume expansion and pulverization. This study reports an accessible process to tackle these challenges via fabricating a 3D-VSx anode for SIBs with ultrahigh-rate and ultralong-duration stable sodium storage. The sodiation-driven reactivation of micro-nano 3D-VSx activates the reconfiguration effect, effectively maintaining structural integrity. Interestingly, the mechanical degradation of 3D-VSx over the sodiation process can be controlled by fine-tuning the operating voltage. The self-reconfigured open nanostructures with large void space not only effectively withstand repetitive volume changes and mitigate the damaging mechanical stresses, but also in turn construct a self-optimized shortened ion diffusion pathway. Moreover, the sodiation-driven reconfiguration excites many active sites and optimizes a stable solid-electrolyte interface, thereby delivering a reversible capacity of 961.4 mA h g^-1 after 1500 cycles at a high rate of 2 A g^-1. This work provides new insight into the rational design of electrodes toward long-lived SIBs through sodiation-driven reconfiguration.

A deeper understanding of interfaces comes after the rapid development of nano-hybrids. Atomic interfaces with atomic-level thickness, intimate bonds, inferior charge-transport resistance, and robust stability have received escalating interest in the field of photocatalysis. Taking into account the fact that the carrier dynamics and spectrum response of candidate photocatalysts like chalcogenides remain suffering, sustained efforts are devoted. Hybridization, which is accompanied by interface designing, behaves as a supportive strategy to enlarge the photocatalytic output. Hence, the comprehensive survey for recent empirical studies on atomic interfaces in chalcogenides is highly desirable. Precisely, the fundamental of atomic interfaces, the devised approaches to design atomic interfaces in chalcogenides and their feasible roles for maneuvering photocatalysis, and the auxiliary advanced characterization are enumerated and summarized. The multifarious interaction of structure, chemical environment, optical and electric properties, and photocatalytic performance in chalcogenides with atomic interfaces is highlighted. Meanwhile, perspectives of atomic interfaces benefiting photocatalysis are given with a summary, and outlooks related to controllable architecture, nucleation mechanism, calculation, and the correlation between atomic interfaces and amended photocatalysis are presented discreetly. Herein, the review is meant to provide the first systematic account of designing atomic interfaces in chalcogenides served for ultimate photocatalytic applications.

Two-dimensional (2D) materials catalysts provide an atomic-scale view on a fascinating arena for understanding the mechanism of electrocatalytic carbon dioxide reduction (CO2 ECR). Here, we successfully exfoliated both layered and nonlayered ultra-thin metal phosphorous trichalcogenides (MPCh3) nanosheets via wet grinding exfoliation (WGE), and systematically investigated the mechanism of MPCh3 as catalysts for CO2 ECR. Unlike the layered CoPS3 and NiPS3 nanosheets, the active Sn atoms tend to be exposed on the surfaces of nonlayered SnPS3 nanosheets. Correspondingly, the nonlayered SnPS3 nanosheets exhibit clearly improved catalytic activity, showing formic acid selectivity up to 31.6 % with -7.51 mA cm^-2 at -0.65 V vs. RHE. The enhanced catalytic performance can be attributed to the formation of HCOO* via the first proton-electron pair addition on the SnPS3 surface. These results provide a new avenue to understand the novel CO2 ECR mechanism of Sn-based and MPCh3-based catalysts.

The cognitive performance of the crew has a major impact on mission safety and success in space flight. Monitoring of cognitive performance during long-duration space flight therefore is of paramount importance and can be performed using compact state-of-the-art mobile EEG. However, signal quality of EEG may be compromised due to the vicinity to various electronic devices and constant movements. We compare noise characteristics between in-flight extraterrestrial microgravity and ground-level terrestrial electroencephalography (EEG) recordings. EEG data recordings from either aboard International Space Station (ISS) or on earth’s surface, utilizing three EEG amplifiers and two electrode types, were compared. In-flight recordings showed noise level of an order of magnitude lower when compared to pre- and post-flight ground-level recordings with the same EEG system. Noise levels between ground-level recordings with actively shielded cables, and in-flight recordings without shielded cables, were similar. Furthermore, noise level characteristics of shielded ground-level EEG recordings, using wet and dry electrodes, and in-flight EEG recordings were similar. Actively shielded mobile dry EEG systems will support neuroscientific research and neurocognitive monitoring during spaceflight, especially during long-duration space missions.

Analyzing time series data like EEG or MEG is challenging due to noisy, high-dimensional, and patient-specific signals. Deep learning methods have been demonstrated to be superior in analyzing time series data compared to shallow learning methods which utilize handcrafted and often subjective features. Especially, recurrent deep neural networks (RNN) are considered suitable to analyze such continuous data. However, previous studies show that they are computationally expensive and difficult to train. In contrast, feed-forward networks (FFN) have previously mostly been considered in combination with hand-crafted and problem-specific feature extractions, such as short time Fourier and discrete wavelet transform. A sought-after are easily applicable methods that efficiently analyze raw data to remove the need for problem-specific adaptations. In this work, we systematically compare RNN and FFN topologies as well as advanced architectural concepts on multiple datasets with the same data preprocessing pipeline. We examine the behavior of those approaches to provide an update and guideline for researchers who deal with automated analysis of EEG time series data. To ensure that the results are meaningful, it is important to compare the presented approaches while keeping the same experimental setup, which to our knowledge was never done before. This paper is a first step toward a fairer comparison of different methodologies with EEG time series data. Our results indicate that a recurrent LSTM architecture with attention performs best on less complex tasks, while the temporal convolutional network (TCN) outperforms all the recurrent architectures on the most complex dataset yielding a 8.61% accuracy improvement. In general, we found the attention mechanism to substantially improve classification results of RNNs. Toward a light-weight and online learning-ready approach, we found extreme learning machines (ELM) to yield comparable results for the less complex tasks.

Brake-wear particle emissions are the result of the components of a friction brake being in tribological contact, and they are classified as non-exhaust emissions. Since most of the emitted particles belong to the size classes of particulate matter (≤10 μm) and differ significantly in terms of their physico-chemical properties from automotive exhaust emissions, this source is of particular relevance to human health and, therefore, the focus of scientific studies. Previous studies have shown that coated brake discs offer significant wear and emission reduction potential. Nevertheless, no studies are available that describe the specific particle formation process, the contact conditions, the structure of the friction layer and the differences compared to conventional grey cast iron discs. The aim of this study is to describe those differences. For this purpose, the tribological behaviour, the structure of the friction layer and the associated particle dynamics within the friction contact between a laser cladding coated disc and a conventional grey cast iron disc are compared. The required investigations are carried out both ex situ (stationary) and in situ (dynamic). Parallel to the tribological investigations, the particle emission behaviour is determined on an inertia dynamometer using a constant volume sampling system (CVS) and equipment for particle number and particle size distribution measurement. The results show that, for two different brake pads, the laser cladding brake disc has lower wear and less particulate emissions than the grey cast iron brake disc. The wear behaviour of the coating varies significantly for the two brake pads. By contrast, the grey cast iron brake disc shows a significantly lower influence.

The systematic manipulation of components of multimodal particle solutions is a key for the design of modern industrial products and pharmaceuticals with highly customized properties. In order to optimize innovative particle separation devices on microfluidic scales, a particle size recognition with simultaneous volumetric position determination is essential. In the present study, the astigmatism particle tracking velocimetry is extended by a deterministic algorithm and a deep neural network (DNN) to include size classification of particles of multimodal size distribution. Without any adaptation of the existing measurement setup, a reliable classification of bimodal particle solutions in the size range of 1.14 μm–5.03 μm is demonstrated with a precision of up to 99.9 %. Concurrently, the high detection rate of the particles, suspended in a laminar fluid flow, is quantified by a recall of 99.0 %. By extracting particle images from the experimentally acquired images and placing them on a synthetic background, semi-synthetic images with consistent ground truth are generated. These contain labeled overlapping particle images that are correctly detected and classified by the DNN. The study is complemented by employing the presented algorithms for simultaneous size recognition of up to four particle species with a particle diameter in between 1.14 μm and 5.03 μm. With the very high precision of up to 99.3 % at a recall of 94.8 %, the applicability to classify multimodal particle mixtures even in dense solutions is confirmed. The present contribution thus paves the way for quantitative evaluation of microfluidic separation and mixing processes.

Alkali metal-CO2 batteries, which combine CO2 recycling with energy conversion and storage, are a promising way to address the energy crisis and global warming. Unfortunately, the limited cycle life, poor reversibility, and low energy efficiency of these batteries have hindered their commercialization. Li-CO2 battery systems have been intensively researched in these aspects over the past few years, however, the exploration of Na-CO2 batteries is still in its infancy. To improve the development of Na-CO2 batteries, one must have a full picture of the chemistry and electrochemistry controlling the operation of Na-CO2 batteries and a full understanding of the correlation between cell configurations and functionality therein. Here, recent advances in CO2 chemical and electrochemical mechanisms on nonaqueous Na-CO2 batteries and hybrid Na-CO2 batteries (including O2-involved Na-O2/CO2 batteries) are reviewed in-depth and comprehensively. Following this, the primary issues and challenges in various battery components are identified, and the design strategies for the interfacial structure of Na anodes, electrolyte properties, and cathode materials are explored, along with the correlations between cell configurations, functional materials, and comprehensive performances are established. Finally, the prospects and directions for rationally constructing Na-CO2 battery materials are foreseen.

Void formation as a result of irregular resin flow at the flow front is discussed and a practical method for reducing void formation during resin transfer molding (RTM) is introduced. In this study, a sensor system is developed for in situ measurement of resin velocity inside a closed cavity. Assisted by the acquired data, a resin injection system is augmented to automatically adjust the injection pressure and achieve a uniform flow front velocity. It is proven, that the developed system is suited to monitor the resin flow front and is able to sufficiently control flow velocity of a linear flow front. Test specimen produced by this method show significantly reduced void contents in comparison to a common resin transfer molding process.

We improve known perturbation results for self-adjoint operators in Hilbert spaces and prove spectral enclosures for diagonally dominant J-self-adjoint operator matrices. These are used in the proof of the central result, a perturbation theorem for J-non-negative operators. The results are applied to singular indefinite Sturm-Liouville operators with Lp-potentials. Known bounds on the non-real eigenvalues of such operators are improved.

In Türkiye, communication science falls under the category of social sciences and is regarded as an interdisciplinary scientific field focused on empirical research. The field is influenced by communication science developments of the West in terms of developing theories and methodology, and it faces challenges when it comes to the systematisation of theories and research methods created by different research traditions; their critical evaluation, development, and the failure to put theories to practical use. In order to ascertain the present condition of the field, this article explores the institutionalisation of communication science at universities and its potential tendencies as an academic field of study, its research trends, and the characteristics of communication science studies.

We consider Gabor frames generated by a general lattice and a window function that belongs to one of the following spaces: the Sobolev space $$V_1 = H^1(\mathbb {R}^d)$$, the weighted $$L^2$$-space $$V_2 = L_{1 + |x|}^2(\mathbb {R}^d)$$, and the space $$V_3 = \mathbb {H}^1(\mathbb {R}^d) = V_1 \cap V_2$$consisting of all functions with finite uncertainty product; all these spaces can be described as modulation spaces with respect to suitable weighted $$L^2$$spaces. In all cases, we prove that the space of Bessel vectors in $$V_j$$is mapped bijectively onto itself by the Gabor frame operator. As a consequence, if the window function belongs to one of the three spaces, then the canonical dual window also belongs to the same space. In fact, the result not only applies to frames, but also to frame sequences.

Digital trace data and computational methods are increasingly being used by researchers to study mental health phenomena (i.e. psychopathology and well-being) in social media. Computer-assisted mental health research is not simply a continuation of previous studies, but rather raises ethical, conceptual and methodological issues that are critical to behavioural science but have not yet been systematically explored. Based on a systematic review of n = 147 studies, we reveal a multidisciplinary field of research that has grown immensely since 2010, spanning the humanities, social sciences, and engineering. We find that a substantial majority of studies in our sample lack a standardized form of ethical consideration, focus on specific constructs and have a rather narrow focus on specific social media platforms. Based on our findings, we discuss how computational elements have influenced mental health research, highlight academic gaps and suggest promising directions for future studies.

Potassium-ion hybrid capacitors (PIHCs) have attracted considerable attention as emerging electrochemical energy storage devices for simultaneously achieving high energy and power density, which the key to success is the development of compatible electrode materials for both battery-type anode and capacitive cathode. Among numerous electrode materials, transition metal compounds (including oxides, chalcogenide, carbides, and nitrides) show great potential owing to their high theoretical capacity to achieve high energy density, but their sluggish reaction kinetics restrict the attainable power density. Hence, in the last few years, different strategies are proposed to improve the performance of transition metal compounds as electrode materials for PIHCs, and significant progress is achieved. Herein, this review outlines recent advances of employing transition metal compounds as electrode materials for PIHCs. The performance and challenges of different transition metal compounds are discussed in detail. Finally, the future prospects of practical applications of transition metal compounds in PIHCs are briefly discussed.

This publication reports self-aligning metallic via microlensing gas phase electrodeposition formation. Key operational parameters to fabricate vertical ruthenium and rhodium interconnects (via) with a diameter of 100 nm are discussed. Moreover, airgaps are implemented during the deposition process, which utilizes spark discharge to generate a flux of charged nanoparticles. An inert gas flow transports the nanoparticles through a reactor chamber close to the target substrate. The substrate uses a pre-patterned resist with openings to a silicon/silicon dioxide/metal stack to direct the deposition of the nanoparticles to form localized self-aligning vertical interconnects. Five process parameters were identified, which impact the morphology and conductance of the resulting interconnects: spark discharge power, gas flow rate, microlens via dimensions, substrate surface potential, and in situ flash lamp power. This parameter set enables a controlled adjustment of the via interconnect morphology and its minimum feature size. Gas flow rate in combination with spark discharge power contribute significantly to the morphology of the interconnect. Spark power and microlens via dimensions have the largest influence on the surface potential of the insulating resist cover, which enables a localized microlensing gas phase electrodeposition of a via with a controlled ratio between conducting diameter and airgap.

Pores are intrinsic defects of ceramic composites and influence their functional properties significantly. Their characterization is therefore a pivotal task in material and process optimization. It is demonstrated that polished section analysis allows for obtaining precise information on pore size, shape, area fraction, and homogeneous distribution. It is proven that laser scanning microscopy provides accurate height maps and is thus an appropriate technique for assessing surface features. Such data is used to compare areas with good and poor polishing results, and various surface parameters are evaluated in terms of their informative value and data processing effort. The material under investigation is a low temperature co-fired ceramic composite. Through statistical analysis of the data, the inclination angle was identified as an appropriate parameter to describe the polishing result. By using masked data, direct conclusions can be drawn about the leveling of load-bearing surface areas, which are crucial in photolithographic processing steps and bonding technology. A broad discussion of different defects based on the results contributes to a critical analysis of the potentials and obstacles of micromachining of low temperature cofired ceramic substrates.

C60 fullerene (C60) as a nanocarbon particle, compatible with biological structures, capable of penetrating through cell membranes and effectively scavenging free radicals, is widely used in biomedicine. A protective effect of C60 on the biomechanics of fast (m. gastrocnemius) and slow (m. soleus) muscle contraction in rats and the pro- and antioxidant balance of muscle tissue during the development of muscle fatigue was studied compared to the same effect of the known antioxidant N-acetylcysteine (NAC). C60 and NAC were administered intraperitoneally at doses of 1 and 150 mg kg−1, respectively, daily for 5 days and 1 h before the start of the experiment. The following quantitative markers of muscle fatigue were used: the force of muscle contraction, the level of accumulation of secondary products of lipid peroxidation (TBARS) and the oxygen metabolite H2O2, the activity of first-line antioxidant defense enzymes (superoxide dismutase (SOD) and catalase (CAT)), and the condition of the glutathione system (reduced glutathione (GSH) content and the activity of the glutathione peroxidase (GPx) enzyme). The analysis of the muscle contraction force dynamics in rats against the background of induced muscle fatigue showed, that the effect of C60, 1 h after drug administration, was (15-17)% more effective on fast muscles than on slow muscles. A further slight increase in the effect of C60 was revealed after 2 h of drug injection, (7-9)% in the case of m. gastrocnemius and (5-6)% in the case of m. soleus. An increase in the effect of using C60 occurred within 4 days (the difference between 4 and 5 days did not exceed (3-5)%) and exceeded the effect of NAC by (32-34)%. The analysis of biochemical parameters in rat muscle tissues showed that long-term application of C60 contributed to their decrease by (10-30)% and (5-20)% in fast and slow muscles, respectively, on the 5th day of the experiment. At the same time, the protective effect of C60 was higher compared to NAC by (28-44)%. The obtained results indicate the prospect of using C60 as a potential protective nano agent to improve the efficiency of skeletal muscle function by modifying the reactive oxygen species-dependent mechanisms that play an important role in the processes of muscle fatigue development.

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.

Surface-enhanced Raman scattering (SERS) is one of the most powerful analytical techniques for the identification of molecules. The substrate, on which SERS is dependent, contains regions of nanoscale gaps (hotspots) that hold the ability to concentrate incident electromagnetic fields and effectively amplify vibrational scattering signals of adsorbed analytes. While surface plasmon resonance from metal nanostructures is a central focus for the SERS effect, the support of polymers can be significantly advantageous to provide larger exposure of structured metal surfaces for efficient interactions with analytes. Characteristics of the polymer particles such as softness, flexibility, swellability, porosity, optical transparency, metal-loading ability, and high surface area can allow diffusion of analytes and penetrating light deeply that can enormously amplify sensing outcomes. As polymer-supported plasmon-active sensor particles can emerge as versatile SERS substrates, the microfluidic platform is promising for the generation of sensor particles as well as for performing sequential SERS analysis of multiple analytes. Therefore, in this perspective article, the development of multifunctional polymer-metal composite particles, and their applications as potential sensors for SERS sensing through microfluidics are presented. A detailed background from the beginning of the SERS field and perspectives for the multifunctional sensor particles for efficient SERS sensing are provided.

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.

High-density electroencephalography (HD-EEG) is currently limited to laboratory environments since state-of-the-art electrode caps require skilled staff and extensive preparation. We propose and evaluate a 256-channel cap with dry multipin electrodes for HD-EEG. We describe the designs of the dry electrodes made from polyurethane and coated with Ag/AgCl. We compare in a study with 30 volunteers the novel dry HD-EEG cap to a conventional gel-based cap for electrode-skin impedances, resting state EEG, and visual evoked potentials (VEP). We perform wearing tests with eight electrodes mimicking cap applications on real human and artificial skin. Average impedances below 900 k[Ohm] for 252 out of 256 dry electrodes enables recording with state-of-the-art EEG amplifiers. For the dry EEG cap, we obtained a channel reliability of 84% and a reduction of the preparation time of 69%. After exclusion of an average of 16% (dry) and 3% (gel-based) bad channels, resting state EEG, alpha activity, and pattern reversal VEP can be recorded with less than 5% significant differences in all compared signal characteristics metrics. Volunteers reported wearing comfort of 3.6 ± 1.5 and 4.0 ± 1.8 for the dry and 2.5 ± 1.0 and 3.0 ± 1.1 for the gel-based cap prior and after the EEG recordings, respectively (scale 1-10). Wearing tests indicated that up to 3,200 applications are possible for the dry electrodes. The 256-channel HD-EEG dry electrode cap overcomes the principal limitations of HD-EEG regarding preparation complexity and allows rapid application by not medically trained persons, enabling new use cases for HD-EEG.

This paper describes the preparation and obtained magnetic properties of large single domain iron oxide nanoparticles. Such ferrimagnetic particles are particularly interesting for diagnostic and therapeutic applications in medicine or (bio)technology. The particles were prepared by a modified oxidation method of non-magnetic precursors following the green rust synthesis and characterized regarding their structural and magnetic properties. For increasing preparation temperatures (5 to 85 &ring;C), an increasing particle size in the range of 30 to 60 nm is observed. Magnetic measurements confirm a single domain ferrimagnetic behavior with a mean saturation magnetization of ca. 90 Am2/kg and a size-dependent coercivity in the range of 6 to 15 kA/m. The samples show a specific absorption rate (SAR) of up to 600 W/g, which is promising for magnetic hyperthermia application. For particle preparation temperatures above 45 &ring;C, a non-magnetic impurity phase occurs besides the magnetic iron oxides that results in a reduced net saturation magnetization.

Si sources involved in the growth of Au-SiOx nanostructures are investigated through the rapid thermal annealing of gold thin films on SiO2/Si substrates with various SiO2 layer thicknesses (3, 25, 100, 500 nm) in a reducing atmosphere. This method reveals three Si sources whose involvement depends on the thickness of the SiO2 layers, i.e., Si diffusion from the substrate, and SiO from SiO2 decomposition and from Si active oxidation. Increasing thicknesses of the SiO2 layer hampers the Si diffusion and the decomposition of regions of the SiO2 layer, which decreases the concentrations of discovered regions weakening the Si active oxidation. These discovered regions appear in systems with a SiO2 layer of 25 or 100 nm, while they are absent for a 500 nm layer. Furthermore, Au-SiOx nanostructures of different shapes form in each system. Both behaviors indicate that the influence and transport mechanisms of the different Si sources are largely dependent on the thicknesses of the SiO2 layers and that they control the evolution of the Au-SiOx nanostructures. A clear understanding of the relationship between these thicknesses and the possible Si sources and their roles in the evolution of the nanostructures makes the tailored fabrication of nanostructures possible.

For a continuous multi-objective optimization problem, it is usually not a practical approach to compute all its nondominated points because there are infinitely many of them. For this reason, a typical approach is to compute an approximation of the nondominated set. A common technique for this approach is to generate a polyhedron which contains the nondominated set. However, often these approximations are used for further evaluations. For those applications a polyhedron is a structure that is not easy to handle. In this paper, we introduce an approximation with a simpler structure respecting the natural ordering. In particular, we compute a box-coverage of the nondominated set. To do so, we use an approach that, in general, allows us to update not only one but several boxes whenever a new nondominated point is found. The algorithm is guaranteed to stop with a finite number of boxes, each being sufficiently thin.

When operating a conditionally automated vehicle, humans occasionally have to take over control. If the driver is out of the loop, a certain amount of time is necessary to gain situation awareness. This work evaluates the potential of stereoscopic 3D (S3D) dashboards for presenting smart S3D take-over-requests (TORs) to support situation assessment. In a driving simulator study with a 4 × 2 between-within design, we presented 3 smart TORs showing the current traffic situation and a baseline TOR in 2D and S3D to 52 participants doing the n-back task. We further investigate if non-standard locations affect the results. Take-over performance indicates that participants looked at and processed the TORs' visual information and by that, could perform more safe take-overs. S3D warnings in general, as well as warnings appearing at the participants’ focus of attention and warnings at the instrument cluster, performed best. We conclude that visual warnings, presented on an S3D dashboard, can be a valid option to support take-over while not increasing workload. We further discuss participants’ gaze behavior in the context of visual warnings for automotive user interfaces.

A two-dimensional numerical model for self-propagating reactions in Al/Ni multilayer foils was developed. It was used to study thermal properties, convective heat loss, and the effect of initial temperature on the self-propagating reaction in Al/Ni multilayer foils. For model adjustments by experimental results, these Al/Ni multilayer foils were fabricated by the magnetron sputtering technique with a 1:1 atomic ratio. Heat of reaction of the fabricated foils was determined employing Differential Scanning Calorimetry (DSC). Self-propagating reaction was initiated by an electrical spark on the surface of the foils. The movement of the reaction front was recorded with a high-speed camera. Activation energy is fitted with these velocity data from the high-speed camera to adjust the numerical model. Calculated reaction front temperature of the self-propagating reaction was compared with the temperature obtained by time-resolved pyrometer measurements. X-ray diffraction results confirmed that all reactants reacted and formed a B2 NiAl phase. Finally, it is predicted that (1) increasing thermal conductivity of the final product increases the reaction front velocity; (2) effect of heat convection losses on reaction characteristics is insignificant, e.g., the foils can maintain their characteristics in water; and (3) with increasing initial temperature of the foils, the reaction front velocity and the reaction temperature increased.

While optically pumped magnetometers (OPMs) can be attached to the head of a person and allow for highly sensitive recordings of the human magnetoencephalogram (MEG), they are mostly limited to an operational range of approximately 5 nT. Consequently, even inside a magnetically shielded room (MSR), movements in the remnant magnetic field disable the OPMs. Active suppression of the remnant field utilizing compensation coils is therefore essential. We propose 8 compensation coils on 5 sides of a cube with a side length of approximately 2 m which were optimized for operation inside an MSR. Compared to previously built bi-planar compensation coils, the coils proposed in this report are more complex in geometry and achieved smaller errors for simulated compensation fields. The proposed coils will allow for larger head movements or smaller movement artifacts in future MEG experiments compared to existing coils.

Question Answering based on Knowledge Graphs (KGQA) still faces difficult challenges when transforming natural language (NL) to SPARQL queries. Simple questions only referring to one triple are answerable by most QA systems, but more complex questions requiring complex queries containing subqueries or several functions are still a tough challenge within this field of research. Evaluation results of QA systems therefore also might depend on the benchmark dataset the system has been tested on. For the purpose to give an overview and reveal specific characteristics, we examined currently available KGQA datasets regarding several challenging aspects. This paper presents a detailed look into the datasets and compares them in terms of challenges a KGQA system is facing.

We study spectral problems for two-dimensional integral system with two given non-decreasing functions R, W on an interval [0, b) which is a generalization of the Krein string. Associated to this system are the maximal linear relation Tmax and the minimal linear relation Tmin in the space L2(dW) which are connected by Tmax=T*min. It is shown that the limit point condition at b for this system is equivalent to the strong limit point condition for the linear relation Tmax. In the limit circle case the Evans-Everitt condition is proved to hold on a subspace T*N of Tmax characterized by the Neumann boundary condition at b. The notion of the principal Titchmarsh-Weyl coefficient of this integral system is introduced. Boundary triple for the linear relation Tmax in the limit point case (and for T*N in the limit circle case) is constructed and it is shown that the corresponding Weyl function coincides with the principal Titchmarsh-Weyl coefficient of the integral system. The notion of the dual integral system is introduced by reversing the order of R and W and the formula relating the principal Titchmarsh-Weyl coefficients of the direct and the dual integral systems is proved. For every integral system with the principal Titchmarsh-Weyl coefficients q a canonical system is constructed so that its Titchmarsh-Weyl coefficient Q is the unwrapping transform of q: Q(z)=zq(z2).

Ocular current stimulation (oCS) with weak current intensities (a few mA) has shown positive effects on retinal nerve cells, which indicates that neurodegenerative ocular diseases could be treated with current stimulation of the eye. During oCS, a significant polarity-independent reduction in the characteristic P50 amplitude of a pattern-reversal electroretinogram was found, while no current stimulation effect was found for a full field electroretinogram (ffERG). The ffERG data indicated a trend for a polarity-dependent influence during oCS on the photopic negative response (PhNR) wave, which represents the sum activity of the retinal ganglion cells. Therefore, an ffERG with adjusted parameters for the standardized measurement of the PhNR wave was combined with simultaneous oCS to study the potential effects of direct oCS on cumulative ganglion cell activity. Compared with that measured before oCS, the PhNR amplitude in the cathodal group increased significantly during current stimulation, while in the anodal and sham groups, no effect was visible (α = 0.05, pcathodal = 0.006*). Furthermore, repeated-measures ANOVA revealed a significant difference in PhNR amplitude between the anodal and cathodal groups as well as between the cathodal and sham groups (p* ≤ 0.0167, pcathodal - anodal = 0.002*, pcathodal - sham = 0.011*).

Friction stir welding (FSW) has developed into a reliable and increasing used industrial joining technology. Various tool configurations can be used for FSW, each of which has advantages and challenges. State-of-the-art FSW employs various tool configurations, including the conventional, the stationary shoulder, and the dual-rotational configuration which is characterized by separate control of shoulder and probe. In this study, an innovative method to combine various tool configurations was developed by a novel FSW spindle stack construction. With an additional servomotor, existing FSW systems can be extended by separate control of shoulder and probe so that varying rotational speeds and rotational directions can be set. This allows enhanced possibilities (a) to adjust frictional heat generation and (b) to apply several tool configurations. The main advantages of this enhanced type of FSW are demonstrated in three ways: increased weld penetration depth, reduction of undesirable machine vibrations, and the combination of varying tool configurations such as stationary shoulder and conventional FSW. The investigations were carried out with 2-mm EN AA 5754 H22 sheets and performed on a robotized FSW setup.

The world of audiovisual online markets is rapidly changing. Not long ago, it was dominated by linear television, transmitted terrestrially, through cable networks or via satellite. Recently, streaming services such as Netflix, YouTube, Amazon Prime and others have emerged as new suppliers of audiovisual content. In this quickly changing industry, competition interrelations between such different formats such as traditional TV, videos on YouTube, and streaming via Netflix are subject to controversy. In particular, doubt is cast on services such as YouTube exerting competitive pressure on services such as Netflix and traditional TV. Based upon a survey with 2920 participants, we provide an empirical analysis of consumption behavior of audiovisual contents. Using descriptive and analytical statistics, including multiple equation models, we show that there are specific areas within audiovisual content markets, where YouTube exerts considerable competitive pressure on both Netflix and classic TV, for instance, through prime time video entertainment. However, our analysis yields differentiated results as we also identify areas, where competition intensity between different service types appear to be low, for instance, through daytime and regarding the intention to shorten waiting time.

We investigate genericity of various controllability and stabilizability concepts of linear, time-invariant differential-algebraic systems. Based on well-known algebraic characterizations of these concepts (see the survey article by Berger and Reis (in: Ilchmann A, Reis T (eds) Surveys in differential-algebraic equations I, Differential-Algebraic Equations Forum, Springer, Berlin, pp 1-61. https://doi.org/10.1007/978-3-642-34928-7_1)), we use tools from algebraic geometry to characterize genericity of controllability and stabilizability in terms of matrix formats.

Crowdsensing offers a cost-effective way to collect large amounts of environmental sensor data; however, the spatial distribution of crowdsensing sensors can hardly be influenced, as the participants carry the sensors, and, additionally, the quality of the crowdsensed data can vary significantly. Hybrid systems that use mobile users in conjunction with fixed sensors might help to overcome these limitations, as such systems allow assessing the quality of the submitted crowdsensed data and provide sensor values where no crowdsensing data are typically available. In this work, we first used a simulation study to analyze a simple crowdsensing system concerning the detection performance of spatial events to highlight the potential and limitations of a pure crowdsourcing system. The results indicate that even if only a small share of inhabitants participate in crowdsensing, events that have locations correlated with the population density can be easily and quickly detected using such a system. On the contrary, events with uniformly randomly distributed locations are much harder to detect using a simple crowdsensing-based approach. A second evaluation shows that hybrid systems improve the detection probability and time. Finally, we illustrate how to compute the minimum number of fixed sensors for the given detection time thresholds in our exemplary scenario.

Gravel pavement has lower construction costs but poorer performance than asphalt surfaces on roads. It also emits dust and deforms under the impact of vehicle loads and ambient air factors; the resulting ripples and ruts constantly deepen, and therefore increase vehicle vibrations and fuel consumption, and reduce safe driving speed and comfort. In this study, existing pavement quality evaluation indexes are analysed, and a methodology for adapting them for roads with gravel pavement is proposed. We report the measured wave depth and length of gravel pavement profile using the straightedge method on a 160 m long road section at three stages of road utilization. The measured pavement elevation was processed according to ISO 8608, and the frequency response of a vehicle was investigated using simulations in MATLAB/Simulink. The international roughness index (IRI) analysis showed that a speed of 30-45 km/h instead of 80 km/h provided the objective results of the IRI calculation on the flexible pavement due to the decreasing velocity of a vehicle’s unsprung mass on a more deteriorated road pavement state. The influence of the corrugation phenomenon of gravel pavement was explored, identifying specific driving safety and comfort cases. Finally, an increase in the dynamic load coefficient (DLC) at a low speed of 30 km/h on the most deteriorated pavement and a high speed of 90 km/h on the middle-quality pavement demonstrated the demand for timely gravel pavement maintenance and the complicated prediction of a safe driving speed for drivers. The main relevant objectives of this study are the adaptation of a road roughness indicator to gravel pavement, including the evaluation of vehicle dynamic responses at different speeds and pavement deterioration states.

Uniform molding and demolding of structures on highly curved surfaces through conformal contact is a crucial yet often-overlooked aspect of nanoimprint lithography (NIL). This study describes the development of a NIL tool and its integration into a nanopositioning and nanomeasuring machine to achieve high-precision orthogonal molding and demolding for soft ultraviolet-assisted NIL (soft UV-NIL). The process was implemented primarily on the edges of highly curved plano-convex substrates to demonstrate structure uniformity on the edges. High-resolution nanostructures of sub-200-nm lateral dimension and microstructures in the range of tens of microns were imprinted. However, the nanostructures on the edges of the large, curved substrates were difficult to characterize precisely. Therefore, microstructures were used to measure the structure fidelity and were characterized using profilometry, white light interferometry, and confocal laser scanning microscopy. Regardless of the restricted imaging capabilities at high inclinations for high-resolution nanostructures, the scanning electron microscope (SEM) imaging of the structures on top of the lens substrate and at an inclination of 45&ring; was performed. The micro and nanostructures were successfully imprinted on the edges of the plano-convex lens at angles of 45&ring;, 60&ring;,and 90&ring; from the center of rotation of the rotating NIL tool. The method enables precise imprinting at high inclinations, thereby presenting a different approach to soft UV-NIL on curved surfaces.

In the past few years Meldrum's acid furfural conjugate (MAFC) have been extensively explored as starting material for the synthesis of photo switchable donor acceptor stenhouse adducts (DASA). Hereby, we have explored the interaction of MAFC with various amino acids. To our surprise, nitrogen rich amino acids like lysine and arginine interact spontaneously with MAFC to give colored adduct immediately, whereas other amino acids, including nitrogen rich histidine, didn't show any coloration. Naked eye detection of lysine in benign solvent make this reagent an attractive new entry to the collection of chemosensors for the colorimetric detection of lysine and arginine. Intense coloration corresponds to the absorption at 514 nm under UV-Vis spectrometer. Lowest concentration of 100 m can be detected with UV-Vis spectrometer. NMR titrations reveals that the appearance of color is due to ring opening of a furfural that leads to the formation of conjugated triene species. Compared to previously reported chemosensors for lysine and arginine, MAFC offers advantages including simple synthesis, easy handling, high speed, low cost, good sensitivity/selectivity.

In this paper, we study a first-order solution method for a particular class of set optimization problems where the solution concept is given by the set approach. We consider the case in which the set-valued objective mapping is identified by a finite number of continuously differentiable selections. The corresponding set optimization problem is then equivalent to find optimistic solutions to vector optimization problems under uncertainty with a finite uncertainty set. We develop optimality conditions for these types of problems and introduce two concepts of critical points. Furthermore, we propose a descent method and provide a convergence result to points satisfying the optimality conditions previously derived. Some numerical examples illustrating the performance of the method are also discussed. This paper is a modified and polished version of Chapter 5 in the dissertation by Quintana (On set optimization with set relations: a scalarization approach to optimality conditions and algorithms, Martin-Luther-Universität Halle-Wittenberg, 2020).

We present a new methodological approach for the assessment of the susceptibility of Rhodococcus erythropolis strains from specific sampling sites in response to increasing heavy metal concentration (Cu2+, Ni2+, and Co2+) using the droplet-based microfluid technique. All isolates belong to the species R. erythropolis identified by Sanger sequencing of the 16S rRNA. The tiny step-wise variation of metal concentrations from zero to the lower mM range in 500 nL droplets not only provided accurate data for critical metal ion concentrations but also resulted in a detailed visualization of the concentration-dependent response of bacterial growth and autofluorescence activity. As a result, some of the isolates showed similar characteristics in heavy metal tolerance against Cu2+, Ni2+, and Co2+. However, significantly different heavy metal tolerances were found for other strains. Surprisingly, samples from the surface soil of ancient copper mining areas supplied mostly strains with a moderate sensitivity to Cu2+, Ni2+, and Co2+, but in contrast, a soil sample from an excavation site of a medieval city that had been covered for about eight centuries showed an extremely high tolerance against cobalt ion (up to 36 mM). The differences among the strains not only may be regarded as results of adaptation to the different environmental conditions faced by the strains in nature but also seem to be related to ancient human activities and temporal partial decoupling of soil elements from the surface. This investigation confirmed that microfluidic screening offers empirical characterization of properties from same species which has been isolated from sites known to have different human activities in the past.

In this paper, a smartphone application is presented that was developed to lower the barrier to introduce particle image velocimetry (PIV) in lab courses. The first benefit is that a PIV system using smartphones and a continuous wave (cw-) laser is much cheaper than a conventional system and thus much more affordable for universities. The second benefit is that the design of the menus follows that of modern camera apps, which are intuitively used. Thus, the system is much less complex and costly than typical systems, and our experience showed that students have much less reservations to work with the system and to try different parameters. Last but not least the app can be applied in the field. The relative uncertainty was shown to be less than 8%, which is reasonable for quick velocity estimates. An analysis of the computational time necessary for the data evaluation showed that with the current implementation the app is capable of providing smooth live display vector fields of the flow. This might further increase the use of modern measurement techniques in industry and education.

Hadwiger conjectured that every graph of chromatic number k admits a clique minor of order k. Here we prove for k ≤ 10, that every graph of chromatic number k with a unique k-coloring (up to the color names) admits a clique minor of order k. The proof does not rely on the Four Color Theorem.

This work reports the formation of circular cavities and Au-SiOx nanoflowers after annealing of thin Au film deposited on SiO2/Si substrates, and the transformation of nanoflowers to nanosprouts with increasing the annealing time. Two reference experiments indicate that both H2 and Si are indispensable for the above structures. The formation of cavities can be attributed to the SiO2 layer decomposition and the product, volatile SiO, provides a Si source for the formation of nanoflowers at the early stage. A model is proposed to indicate that SiO gas produced at the Si/SiO2 interface can diffuse to the surface assisted by the defects in the SiO2 layer before the decomposed cavities are exposed. Then the exposing of those cavities introduces another volatile SiO from the active oxidation of Si substrate, provoking a change in the direction of the main Si source, which in turn makes the one nanoparticle of the nanoflower split in two and finally form the nanosprout. The model about the escape of SiO further details SiO2 decomposition process, and the transformation mechanism from nanoflowers to nanosprout sheds light on a feasible nanofabrication method to design tunable size and shape of nanoparticles.

Being able to identify plant species is an important factor for understanding biodiversity and its change due to natural and anthropogenic drivers. We discuss the freely available Flora Incognita app for Android, iOS and Harmony OS devices that allows users to interactively identify plant species and capture their observations. Specifically developed deep learning algorithms, trained on an extensive repository of plant observations, classify plant images with yet unprecedented accuracy. By using this technology in a context-adaptive and interactive identification process, users are now able to reliably identify plants regardless of their botanical knowledge level. Users benefit from an intuitive interface and supplementary educational materials. The captured observations in combination with their metadata provide a rich resource for researching, monitoring and understanding plant diversity. Mobile applications such as Flora Incognita stimulate the successful interplay of citizen science, conservation and education.

The east and southeast rim of Harz mountains (Germany) are marked by a high density of former copper mining places dating back from the late 20th century to the middle age. A set of 18 soil samples from pre- and early industrial mining places and one sample from an industrial mine dump have been selected for investigation by 16S rRNA and compared with six samples from non-mining areas. Although most of the soil samples from the old mines show pH values around 7, RNA profiling reflects many operational taxonomical units (OTUs) belonging to acidophilic genera. For some of these OTUs, similarities were found with their abundances in the comparative samples, while others show significant differences. In addition to pH-dependent bacteria, thermophilic, psychrophilic, and halophilic types were observed. Among these OTUs, several DNA sequences are related to bacteria which are reported to show the ability to metabolize special substrates. Some OTUs absent in comparative samples from limestone substrates, among them Thaumarchaeota were present in the soil group from ancient mines with pH > 7. In contrast, acidophilic types have been found in a sample from a copper slag deposit, e.g., the polymer degrading bacterium Granulicella and Acidicaldus, which is thermophilic, too. Soil samples of the group of pre-industrial mines supplied some less abundant, interesting OTUs as the polymer-degrading Povalibacter and the halophilic Lewinella and Halobacteriovorax. A particularly high number of bacteria (OTUs) which had not been detected in other samples were found at an industrial copper mine dump, among them many halophilic and psychrophilic types. In summary, the results show that soil samples from the ancient copper mining places contain soil bacterial communities that could be a promising source in the search for microorganisms with valuable metabolic capabilities.

Nanomeasuring machines developed at the Technische Universität Ilmenau enable three-dimensional measurements and manufacturing processes with the lowest uncertainties. Due to the requirements for these processes, a highly reproducible and long-term stable tool changing system is needed. For this purpose, kinematically determined couplings are widely used. The state-of-the-art investigations on those are not sufficient for the highest demands on the reproducibility required for this application. A theoretical determination of the reproducibility based on analytical or numerical methods is possible, however not in the desired nanometer range. Due to this, a measurement setup for the determination of the reproducibility in five degrees of freedom with nanometer uncertainty was developed. First, potential measuring devices are systematically examined and measurement principles were developed out of this. A three-dimensional vector-based uncertainty analysis is performed to prove the feasibility of the measurement principle and provides a basis for further design. As a result, a translatory measurement uncertainty of 10 nm and a rotatory uncertainty of 11 nrad can be reached. Afterwards, the measurement setup is designed, focusing on the metrological frame and the lift-off device. The developed setup exceeds the uncertainties of the measurement setups presented in the state-of-the-art by an order of magnitude, allowing new in-depth investigations of the reproducibility of kinematic couplings.

Illumination with LEDs is of increasing interest in imaging and lithography. In particular, compared to lasers, LEDs are temporally and spatially incoherent, so that speckle effects can be avoided by the application of LEDs. Besides, LED arrays are qualified due to their high optical output power. However, LED arrays have not been widely used for investigating optical effects, e.g., the Lau effect. In this paper, we propose the application of an LED array for realizing the Lau effect by taking into account the influence of the coherence properties of illumination on the Lau effect. Using spatially incoherent illumination with the LED array or a single LED, triangular distributed Lau fringes can be obtained. We apply the obtained Lau fringes in the optical lithography to produce analog structures. Compared to a single LED, the Lau fringes using the LED array have significantly higher intensities. Hence, the exposure time in the lithography process is largely reduced.

Deep etching of glass and glass ceramics is far more challenging than silicon etching. For thermally insensitive microelectromechanical and microoptical systems, zero-expansion materials such as Zerodur or ultralow expansion (ULE) glass are intriguing. In contrast to Zerodur that exhibits a complex glass network composition, ULE glass consists of only two components, namely, TiO2 and SiO2. This fact is highly beneficial for plasma etching. Herein, a deep fluorine-based etching process for ULE 7972 glass is shown for the first time that yields an etch rate of up to 425 nm min^-1 while still achieving vertical sidewall angles of 87&ring;. The process offers a selectivity of almost 20 with respect to a nickel hard mask and is overall comparable with fused silica. The chemical surface composition is additionally investigated to elucidate the etching process and the impact of the tool configuration in comparison with previously published etching results achieved in Zerodur. Therefore, deep and narrow trenches can be etched in ULE glass with high anisotropy, which supports a prospective implementation of ULE glass microstructures, for instance, in metrology and miniaturized precision applications.

Sodium-ion battery (SIB) is significant for grid-scale energy storage. However, a large radius of Na ions raises the difficulties of ion intercalation, hindering the electrochemical performance during fast charge/discharge. Conventional strategies to promote rate performance focus on the optimization of ion diffusion. Improving interface capacitive-like storage by tuning the electrical conductivity of electrodes is also expected to combine the features of the high energy density of batteries and the high power density of capacitors. Inspired by this concept, an oxide-metal sandwich 3D-ordered macroporous architecture (3DOM) stands out as a superior anode candidate for high-rate SIBs. Taking Ni-TiO2 sandwich 3DOM as a proof-of-concept, anatase TiO2 delivers a reversible capacity of 233.3 mAh g^-1 in half-cells and 210.1 mAh g^-1 in full-cells after 100 cycles at 50 mA g^-1. At the high charge/discharge rate of 5000 mA g^-1, 104.4 mAh g^-1 in half-cells and 68 mAh g^-1 in full-cells can also be obtained with satisfying stability. In-depth analysis of electrochemical kinetics evidence that the dominated interface capacitive-like storage enables ultrafast uptaking and releasing of Na-ions. This understanding between electrical conductivity and rate performance of SIBs is expected to guild future design to realize effective energy storage.

Mit der Vervielfachung des Datenvolumens und der raschen Weiterentwicklung entsprechender Analysemethoden und Werkzeuge gibt es immer mehr Unternehmen, insbesondere aus dem kleineren Mittelstand, die aufgrund mangelnder Ressourcen und Fähigkeiten nicht in der Lage sind, ihre Datenschätze zu heben und daraus gute Entscheidungen im Hinblick auf ihre Produkte, Prozesse und Geschäftsmodelle abzuleiten. Vor diesem Hintergrund wird im vorliegenden Beitrag ein Data Facilitator Geschäftsmodell für Consultingunternehmen vorgeschlagen. Um nicht im Wettbewerb zurückzufallen, lagern hierin Unternehmen Aufgaben der Datenaufbereitung, Analyse und Interpretation an ein Beratungsunternehmen aus, das sich auf solche Themen spezialisiert hat. Berater erhalten dabei Zugriff auf kritische Datenströme des Kunden, die dann auf regelmäßiger Basis fachkundig, unter Einsatz moderner Analyseinstrumente untersucht werden. Es geht darum, frühzeitig "schwache Signale" für zukünftige kritische Entwicklungen beim Kunden zu erkennen oder Ideen für geschäftliche Verbesserungen zu entwickeln. Dabei werden die Ergebnisse dem Kunden nicht kommentarlos übermittelt, sondern Auffälligkeiten von den Beratern unter Einbringung ihres Fachwissens mit den Kunden diskutiert und interpretiert. Der Kunde trifft dann gegebenenfalls Entscheidungen auf Basis der vorliegenden Datenlage. Da diese Dienstleistung fortlaufend erbracht wird, sind hierfür neue Bezahlmodelle im Consulting sinnvoll, wie z.B. eine monatliche Pauschale. Für eine erfolgreiche Umsetzung des Geschäftsmodells sind in der Praxis eine Reihe von Problemen zu bewältigen, worauf im Beitrag ebenfalls eingegangen wird.

High entropy alloy films of AlCoCrFeNi B2-ordered structure are formed during an ultrafast heating process by reactive Ni/Al multilayers. The self-propagating high-temperature reaction occurring in reactive Ni/Al multilayers after ignition represents an ultrafast heat source which is used for the transformation of a thin films Al/CoFe/CrNi multilayer structure into a single-phase high entropy alloy film. The materials design of the combined multilayers thus determines the phase formation. Conventional rapid thermal annealing transforms the multilayer into a film with multiple equilibrium phases. Ultrafast combustion synthesis produces films with ultrafine-grained single-phase B2-ordered compound alloy. The heating rates during the combustion synthesis are in the order of one million K/s, much higher than those of the rapid thermal annealing, which is about 7 K/s. The results are compared with differential scanning calorimetry experiments with heating rates ranging from about 100 K/s up to 25000 K/s. It is shown that the heating rate clearly determines the phase formation in the multilayers. The rapid kinetics of the combustion prevents long-range diffusion and promotes the run-away transformation. Thus, multilayer combustion synthesis using reactive Ni/Al multilayers as heat source represents a new pathway for the fabrication of single phase high-entropy alloy films.

The knowledge of temperature distribution inside the tissue to be treated is essential for patient safety, workflow and clinical outcomes of thermal therapies. Microwave imaging represents a promising approach for non-invasive tissue temperature monitoring during hyperthermia treatment. In the present paper, a methodology for quantitative non-invasive tissue temperature estimation based on ultra-wideband (UWB) radar imaging in the microwave frequency range is described. The capabilities of the proposed method are demonstrated by experiments with liquid phantoms and three-dimensional (3D) Delay-and-Sum beamforming algorithms. The results of our investigation show that the methodology can be applied for detection and estimation of the temperature induced dielectric properties change.

Polarizing beamsplitters have numerous applications in optical systems, such as systems for freeform surface metrology. They are classically manufactured from birefringent materials or with stacks of dielectric coatings. We present a binary subwavelength-structured form-birefringent diffraction grating, which acts as a polarizing beamsplitter for a wide range of incidence angles -30&ring;…+30&ring;. We refine the general design method for such hybrid gratings. We furthermore demonstrate the manufacturing steps with Soft-UV-Nanoimprint-Lithography, as well as the experimental verification, that the structure reliably acts as a polarizing beamsplitter. The experimental results show a contrast in efficiency for TE- and TM-polarization of up to 1:18 in the first order, and 34:1 in the zeroth order. The grating potentially enables us to realize integrated compact optical measurement systems, such as common-path interferometers.

Large-scale mean patterns in Rayleigh-Bénard convection, also referred to as turbulent superstructures, have mainly been studied by means of numerical simulations so far, but experimental investigations are still rare. However, the analysis of turbulent superstructures, which are of great importance due to their effect on the local transport of heat and momentum, require both numerical and experimental data. Therefore, within the scope of this study measurements were performed in the horizontal mid plane and in a horizontal plane closer to the top of a Rayleigh-Bénard cell with an aspect ratio of [Gamma]=l/h=25, thereby showing the initial formation of turbulent superstructures and their long-time rearrangement. The turbulent superstructures are investigated experimentally by noninvasive simultaneous measurements of temperature and velocity fields, using the color signal of thermochromic liquid crystals (TLCs) for the evaluation of the temperature and their temporal displacement for the determination of all three velocity components in the measurement planes via stereoscopic particle image velocimetry (stereo-PIV). Applying this measuring technique it is demonstrated that the time-averaging of instantaneous temperature and velocity fields uncovers the turbulent superstructures in both fields. Furthermore, the combination of the temperature and velocity data is used to characterize the local heat flux quantified by the local Nusselt number, which confirms that the turbulent superstructures strongly enhance the heat transfer in Rayleigh-Bénard convection.

We elaborate on the deviation of the Jordan structures of two linear relations that are finite-dimensional perturbations of each other. We compare their number of Jordan chains of length at least n. In the operator case, it was recently proved that the difference of these numbers is independent of n and is at most the defect between the operators. One of the main results of this paper shows that in the case of linear relations this number has to be multiplied by n+1 and that this bound is sharp. The reason for this behavior is the existence of singular chains. We apply our results to one-dimensional perturbations of singular and regular matrix pencils. This is done by representing matrix pencils via linear relations. This technique allows for both proving known results for regular pencils as well as new results for singular ones.

Recently, large progress was made in the development towards low-cost PIV (Particle Image Velocimetry) for industrial and educational applications. This paper presents the use of two low-cost action cameras for stereoscopic planar PIV. A continuous wave laser or alternatively an LED was used for illumination and pulsed by a frequency generator. A slight detuning of the light pulsation and camera frame rate minimizes systematic errors by the rolling shutter effect and allows for the synchronization of both cameras by postprocessing without the need of hardware synchronization. The setup was successfully qualified on a rotating particle pattern in a planar and stereoscopic configuration as well as on the jet of an aquarium pump. Since action cameras are intended to be used at outdoor activities, they are small, very robust and work autarkic. In conjunction with the synchronization and image pre-processing scheme presented herein, those cameras enable stereoscopic PIV in harsh environments and even on moving experiments.

A pendulum with an attached permanent magnet moving near a conductor is a typical experiment for the demonstration of electromagnetic braking. When the conductor itself moves, it can transfer energy to the pendulum. We study a simple but exact analytical model where the conductor is a horizontally unbounded flat plate. For this geometry, eddy currents and induced Lorentz force due to the motion of a magnetic dipole are known analytically in the quasistatic limit. A vertical oscillation of such a horizontal plate located beneath the magnet is considered. In this setup, the vertical position of the pendulum is an equilibrium point when the magnetic moment of the magnet is perpendicular to its plane of motion. Depending on the strength of the magnetic dipole moment, the frequency and amplitude of the plate as well as the distance between plate and magnet, the plate oscillation can destabilize the equilibrium. The stability limits for weak electromagnetic coupling are computed analytically using the harmonic balancing method. For stronger coupling, the stability limits are obtained numerically using Floquet analysis. Chaotic motions with finite amplitudes are also found.

The increasing demand for energy-efficient vehicles requires suitable methods for cost and weight reduction. This can be achieved by the replacement of copper by aluminum, in particular for the on-board power systems. However, the complete substitution is restricted by the mechanical and physical material properties of aluminum as well as challenges in the aluminum copper interface. The challenges concern the corrosion vulnerability and the occurrence of brittle intermetallic compounds (IMC) which can negatively influence the mechanical properties and the electrical conductivity. Therefore, current investigations focus on the one hand on the realization of dissimilar aluminum copper joints by suitable joining technologies, like ultrasonic welding, and on the other hand on the assurance of a sufficient prevention against harmful corrosion effects. In cases where the joint cannot be protected against corrosion by sealing, nickel coatings can be used to protect the joint. In the present study, the influence of electroless, electroplated, and sulfamate nickel coatings was investigated regarding the long-term stability. The joints were performed as industry-related arrester connections, consisting of EN AW 1370 cables and EN CW 004A terminals. The samples were exposed to corrosive as well as electrical, thermal, and mechanical stress tests according to current standards and regulations.

This paper presents a novel trust-region method for the optimization of multiple expensive functions. We apply this method to a biobjective optimization problem in fluid mechanics, the optimal mixing of particles in a flow in a closed container. The three-dimensional time-dependent flows are driven by Lorentz forces that are generated by an oscillating permanent magnet located underneath the rectangular vessel. The rectangular magnet provides a spatially non-uniform magnetic field that is known analytically. The magnet oscillation creates a steady mean flow (steady streaming) similar to those observed from oscillating rigid bodies. In the optimization problem, randomly distributed mass-less particles are advected by the flow to achieve a homogeneous distribution (objective function 1) while keeping the work done to move the permanent magnet minimal (objective function 2). A single evaluation of these two objective functions may take more than two hours. For that reason, to save computational time, the proposed method uses interpolation models on trust-regions for finding descent directions. We show that, even for our significantly simplified model problem, the mixing patterns vary significantly with the control parameters, which justifies the use of improved optimization techniques and their further development.

Assessments of source reconstruction procedures in electroencephalography and computations of transcranial electrical stimulation profiles require verification and validation with the help of ground truth configurations as implemented by physical head phantoms. For these phantoms, synthetic materials are needed, which are mechanically and electrochemically stable and possess conductivity values similar to the modeled human head tissues. Three-compartment head models comprise a scalp layer with a conductivity range of 0.137 S/m to 2.1 S/m, a skull layer with conductivity values between 0.066 S/m and 0.00275 S/m, and an intracranial volume with an often-used average conductivity value of 0.33 S/m. To establish a realistically shaped physical head phantom with a well-defined volume conduction configuration, we here characterize the electrical conductivity of synthetic materials for modeling head compartments. We analyzed agarose hydrogel, gypsum, and sodium chloride (NaCl) solution as surrogate materials for scalp, skull, and intracranial volume. We measured the impedance of all materials when immersed in NaCl solution using a four-electrode setup. The measured impedance values were used to calculate the electrical conductivity values of each material. Further, the conductivities in the longitudinal and transverse directions of reed sticks immersed in NaCl solution were measured to test their suitability for mimicking the anisotropic conductivity of white matter tracts.

The problem of the haze caused by huge forest fires persists as an annual transboundary problem for Indonesia and the rest of Southeast Asia. In 2015, the problem was worse than ever before, affecting many countries in the Association of Southeast Asian Nations (ASEAN) and causing respiratory ailments for more than half a million Indonesians. This study explores the media framing of the haze problem in Indonesia from June to December 2015. Using Entman’s framing approach, it investigates how media outlets from Indonesia, Malaysia, and Singapore covered the crisis. Articles from six online media outlets published in these three countries were analysed. Using cluster analysis, this research identified three frames as follows: (1) crisis frame, (2) immediate action frame, and (3) regular problem frame. The first cluster/frame consists of articles giving high salience to all problems and causes of the forest fires provoking the haze. In contrast, the second frame mostly ignores causes and problems and focuses almost exclusively on the need for immediate action. The third frame, which represents more than 60% of the articles, covers the haze problem as a regular issue without emphasizing prominently either the different aspects of the problem itself or its causes and solutions. Further results show that the media in Singapore and Malaysia used the crisis frame more often than the media in Indonesia.

Vapor compression systems (VCS) cover a wide range of applications and consume large amounts of energy. In this context, previous research identified the optimization of the condenser fans speed as a promising measure to improve the energy efficiency of VCS. The present paper introduces a steady-state modeling approach of an air-cooled VCS to predict the ideal condenser fan speed. The model consists of a hybrid characterization of the main components of a VCS and the optimization problem is formulated as minimizing the total energy consumption by respectively adjusting the condenser fan and compressor speed. In contrast to optimization strategies found in the literature, the proposed model does not relay on algorithms, but provides a single optimization term to predict the ideal fan speed. A detailed experimental validation demonstrates the feasibility of the model approach and further suggests that the ideal condenser fan speed can be calculated with sufficient precision, assuming constant evaporating pressure, compressor efficiency, subcooling, and superheating, respectively. In addition, a control strategy based on the developed model is presented, which is able to drive the VCS to its optimal operation. Therefore, the study provides a crucial input for set-point optimization and steady-state modeling of air-cooled vapor compression systems.

Previous research identified that battery temperature control is critical to the safety, lifetime, and performance of electric vehicles. In this paper, the liquid-based battery temperature control of electric buses is investigated subject to heat transfer behavior and control strategy. Therefore, a new transient calculation method is proposed to simulate the thermal behavior of a coolant-cooled battery system. The method is based on the system identification technique and combines the advantage of low computational effort and high accuracy. In detail, four transfer functions are extracted by a thermo-hydraulic 3D simulation model comprising 12 prismatic lithium nickel manganese cobalt oxide (NMC) cells, housing, arrestors, and a cooling plate. The transfer functions describe the relationship between heat generation, cell temperature, and coolant temperature. A vehicle model calculates the power consumption of an electric bus and thus provides the input for the transient calculation. Furthermore, a cell temperature control strategy is developed with respect to the constraints of a refrigerant-based battery cooling unit. The data obtained from the simulation demonstrate the high thermal inertia of the system and suggest sufficient control of the battery temperature using a quasi-stationary cooling strategy. Thereby, the study reveals a crucial design input for battery cooling systems in terms of heat transfer behavior and control strategy.

String dictionaries constitute a large portion of the memory footprint of database applications. While strong string dictionary compression algorithms exist, these come with impractical access and compression times. Therefore, lightweight algorithms such as front coding (PFC) are favored in practice. This paper endeavors to make strong string dictionary compression practical. We focus on Re-Pair Front Coding (RPFC), a grammar-based compression algorithm, since it consistently offers better compression ratios than other algorithms in the literature. To accelerate compression times, we propose block-based RPFC (BRPFC) which consists in independently compressing small blocks of the dictionary. For further accelerated compression times especially on large string dictionaries, we also propose an alternative version of BRPFC that uses sampling to speed up compression. Moreover, to accelerate access times, we devise a vectorized access method, using Intel® Advanced Vector Extensions 512 (Intel® AVX-512). Our experimental evaluation shows that sampled BRPFC offers compression times up to 190 × faster than RPFC, and random string lookups 2.3 × faster than RPFC on average. These results move our modified RPFC into a practical range for use in database systems because the overhead of Re-Pair-based compression for access times can be reduced by 2 ×.

Der Deal der beiden größten deutschen Energielieferanten RWE und E.ON zum Tausch verschiedener Geschäftseinheiten, welcher Mitte September 2019 genehmigt wurde, wird den deutschen Energiemarkt wesentlich umstrukturieren und sowohl im Bereich Erzeugung als auch im Vertrieb zu jeweils einem dominanten Wettbewerber führen. E.ON wird dabei durch die Übernahme der innogy Geschäfte im Bereich des klassischen Energievertriebs und der Ladeinfrastruktur für Elektrofahrzeuge wesentliche Wettbewerbsvorteile erhalten. Dazu zählt unter anderem der Zugang zu einer Vielzahl an Messstellen und damit Datensätzen im Bereich des Haushalts- und Geschäftskundenvertriebs. Die Auswertung und Nutzung dieser Datensätze eröffnet dem zusammengeschlossenen Unternehmen neue Geschäftsfelder, aber auch Möglichkeiten die dominante Stellung auf dem Markt zu missbrauchen. Dieser Beitrag widmet sich den potenziellen Auswirkungen der innogy-Übernahme durch E.ON in den Bereichen klassischer Vertrieb und E‑Mobilität, in welchen die angesprochenen Aspekte der Datenökonomik eine wesentliche Rolle spielen. Des Weiteren werden die Auswirkungen der Marktumstrukturierung auf den Konzessionsmarkt betrachtet und die politökonomische Dimension des Zusammenschlusses erläutert. Wir schließen mit einer Kurzanalyse der Erlaubnisentscheidung und der damit verbundenen Auflagen und kommen zu dem Schluss, dass diese nicht geeignet sind, die erheblichen anti-kompetitiven Auswirkungen des Zusammenschlusses einzudämmen oder zu verhindern.

Poly (3,4-ethylenedioxythiophene), PEDOT, films were synthesized at room temperature by potentiodynamic and potentiostatic step deposition in aqueous solutions containing EDOT monomer and LiClO4. In some solutions, the effect of small amounts of sodium dodecylsulfate, SDS, on the polymerization rate of EDOT and on the stiffness of the obtained PEDOT film was studied. The obtained PEDOT films were transferred in aqueous solutions containing cations with different molar mass, such as H+, Li+, Na+, K+, and Cs+. The apparent molar masses of the exchanged species during potentiodynamic experiments were determined by in situ microgravimetry. These measurements underlined the importance of the electrolyte chosen for electropolymerization process. It is known that SDS anions can be trapped inside the polymer layer during electropolymerization, providing them with a cation exchange behavior. However, even if the PEDOT films were deposited from an electrolyte without SDS, they still acted as cation exchangers.

The possibilities and challenges of using fiber optical sensors to monitor the laser-assisted joining of metal-polymer joints have been described in this article. Fundamental investigation proves the basic suitability of the measuring method for this application and studies the effect of essential influencing variables of the joining process - e.g., the clamping force - on the resulting sensor signals. In addition, the strain state (because of the process temperature and shrinkage of the polymer) of the parts to be joined can be traced as a function of the joining partners, the process parameters, and the material thicknesses. It is shown that the fiber optical method is suitable for process monitoring directly in the joining zone of metal-polymer hybrids and providing a tool for detailed strain measurements in the joint zone during subsequent component testing.

This research studies the effect of multi-wall carbon nanotube (MWCNT) coated nickel to foaming time on the foam expansion and the distribution of pore sizes MWCNT reinforced AlMg4Si8 foam composite by powder metallurgy process. To control interface reactivity and wettability between MWCNT and the metal matrix, nickel coating is carried out on the MWCNT surface. Significantly, different foaming behavior of the MWCNT coated nickel reinforced AlMg4Si8 was studied with a foaming time variation of 8 and 9 min. Digital images generated by the imaging system are used with the MATLAB R2017a algorithm to determine the porosity of the surface and the pore area of aluminum foam efficiently. The results can have important implications for processing MWCNT coated nickel reinforced aluminum alloy composites.

This study presents an investigation regarding the applicability of neural networks for temperature measurements using thermochromic liquid crystals (TLCs) and discusses advantages as well as disadvantages of common calibration approaches. For the characterization of the measurement technique, the dependency of the color of the TLCs on the temperature as well as on the observation angle and, therefore, on the position within the field of view of a color camera is analyzed in detail. In order to consider the influence of the position within the field of view on the color, neural networks are applied for the calibration of the temperature measurements. In particular, the focus of this study is on analysis of the error of temperature measurement for different network configurations as well as training methods, yielding a mean absolute deviation and a mean standard deviation in the range of 0.1 K for instantaneous measurements. On the basis of a comparison of this standard deviation to that of two further calibration approaches, it is shown that neural networks are suited for temperature measurements via the color of TLCs. Finally, the applicability of this measurement technique is illustrated at an exemplary temperature measurement in a horizontal plane of a Rayleigh-Bénard cell with large aspect ratio, which clearly shows the emergence of convective flow patterns by means of the temperature field.

In nature, there are several examples of sophisticated sensory systems to sense flows, e.g., the vibrissae of mammals. Seals can detect the flow of their prey, and rats are able to perceive the flow of surrounding air. The vibrissae are arranged around muzzle of an animal. A vibrissa consists of two major components: a shaft (infector) and a follicle-sinus complex (receptor), whereby the base of the shaft is supported by the follicle-sinus complex. The vibrissa shaft collects and transmits stimuli, e.g., flows, while the follicle-sinus complex transduces them for further processing. Beside detecting flows, the animals can also recognize the size of an object or determine the surface texture. Here, the combination of these functionalities in a single sensory system serves as paragon for artificial tactile sensors. The detection of flows becomes important regarding the measurement of flow characteristics, e.g., velocity, as well as the influence of the sensor during the scanning of objects. These aspects are closely related to each other, but, how can the characteristics of flow be represented by the signals at the base of a vibrissa shaft or by an artificial vibrissa-like sensor respectively? In this work, the structure of a natural vibrissa shaft is simplified to a slender, cylindrical/tapered elastic beam. The model is analyzed in simulation and experiment in order to identify the necessary observables to evaluate flows based on the quasi-static large deflection of the sensor shaft inside a steady, non-uniform, laminar, in-compressible flow.

A novel computational design tool to calculate the elasto-kinematic flexure hinge properties is presented. Four hinge contours are implemented. It is shown, that FEM results correlate well with the analytical design tool results. For a given deflection angle of 10&ring; and a corner-filleted contour, the deviations of the bending stiffness are between 0.1 % and 9.4 %. The design tool can be beneficial for the accelerated and systematic synthesis of compliant mechanisms with optimized flexure hinges.

Corporate social responsibility (CSR) is of increasing importance for the long-term success of corporations. Extending existing literature this paper explores corporate identity as important determinant for CSR disclosure. The relationship was examined based on 498 German companies that provided English language CSR reports and responded to a company survey measuring CSR-oriented corporate identity. CSR disclosure has been analyzed with an automated content analysis technique using artificial intelligence. Results indicate that value chain and future-oriented dimensions, which were more pronounced in mature CSR concepts, foster CSR disclosure, while introversive corporate identity dimensions that were strong in low level CSR concepts hinder the release of CSR information. The paper shows that a tradition of social responsibility and values results into a low perceived need for legitimacy and outwards communication. The findings support the view that that a combination of voluntary disclosure theory and legitimacy theory is necessary to explain the drivers and constraints of CSR disclosure.

This paper posits that the concept of European identity is an important indicator of the legitimacy of the European Union (EU). It further assumes that the exposure to EU related media content can influence the feeling of European identity. In order to verify this assumption, we combined the mere-exposure-theory and the hostile media phenomenon. We assume that these theoretical concepts could help to understand the influence of media on peoples levels of attachment to the EU. Regression analyses are performed on secondary data that were collected in a Eurobarometer survey in 2013. Our findings revealed that media exposure affected the respondents' identification with Europe, as well as the modifications of this effect based on their assessments of EU media coverage. The results of the current study not only validate assumptions about the mere-exposure effects on identity but also confirm the theoretical assumption that perceived hostility reduces such effects, whereas exposure to information that is perceived as neutral promotes the effects of media exposure on the feeling of European identity.